diff --git a/.env b/.env
deleted file mode 100644
index 3d0c60e..0000000
--- a/.env
+++ /dev/null
@@ -1,183 +0,0 @@
-# DeepCoin 로컬 설정 (Git 제외). 설정 변경은 이 파일만 수정하세요.
-
-# --- 빗썸 API ---
-BITHUMB_ACCESS_KEY=d3e9676ef70b91df515eff02f5d0c7009ce390ce455aea
-BITHUMB_SECRET_KEY=M2E4ZWQxYmQ3YThhYzE5YjYxNjhmMzBiOWZkMjI0MmVmOTBkZmRkNWMxMDE5N2VmOTVjODQyNTJlNmU1NQ==
-BITHUMB_API_URL=https://api.bithumb.com
-BITHUMB_API_CANDLE_COUNT=200
-BITHUMB_MINUTE_INTERVALS=1,3,5,10,15,30,60,240
-HTS_API_RETRY_SLEEP_SEC=0.5
-
-# --- 텔레그램 (선택, 알림 미사용 시 비워도 됨) ---
-COIN_TELEGRAM_BOT_TOKEN=6435061393:AAHOh9wB5yGNGUdb3SfCYJrrWTBe7wgConM
-COIN_TELEGRAM_CHAT_ID=574661323
-
-# --- 거래 대상 ---
-SYMBOL=WLD
-COIN_NAME=월드코인
-
-# --- 경로 ---
-DB_PATH=data/coins.db
-GROUND_TRUTH_FILE=data/ground_truth/ground_truth_trades.json
-
-# --- 타임프레임 (분) ---
-DAILY_INTERVAL_MIN=1440
-ENTRY_INTERVAL=3
-TREND_INTERVAL_1H=60
-TREND_INTERVAL_1D=1440
-ALL_INTERVALS=3,5,10,15,30,60,240,1440
-DOWNLOAD_INTERVALS=3,5,10,15,30,60,240,1440
-GENERAL_ANALYSIS_INTERVALS=3,5,10,15,30,60,240,1440
-TIMING_INTERVALS=3,5,10,15
-TREND_INTERVALS=60,240,1440
-INTERVAL_PREFIX=1:m1,3:m3,5:m5,10:m10,15:m15,30:m30,60:m60,240:m240,1440:d1
-
-# --- 볼린저 / RSI ---
-BB_PERIOD=20
-BB_STD=2
-BB_MIN_WIDTH_PCT=0.8
-RSI_PERIOD=14
-DISPARITY_PERIODS=5,20,60
-DISPARITY_OVERBOUGHT=105
-DISPARITY_OVERSOLD=95
-MACD_FAST=12
-MACD_SLOW=26
-MACD_SIGNAL=9
-STOCH_K_PERIOD=14
-STOCH_D_PERIOD=3
-STOCH_SMOOTH_K=3
-STOCH_OVERSOLD=20
-STOCH_OVERBOUGHT=80
-TREND_RANGE_MA_GAP_PCT=0.5
-
-# --- MTF 정렬 ---
-ALIGN_RSI_OVERSOLD=35
-ALIGN_RSI_OVERBOUGHT=65
-ALIGN_RSI_CONFLICT_TIMING_LOW=40
-ALIGN_RSI_CONFLICT_TIMING_HIGH=65
-ALIGN_RSI_CONFLICT_TREND_LOW=40
-ALIGN_RSI_CONFLICT_TREND_HIGH=65
-ALIGN_BB_POS_LOW=0.2
-ALIGN_BB_POS_HIGH=0.8
-
-# --- 다운로드 / DB ---
-DOWNLOAD_MONTHS=12
-DOWNLOAD_MONTHS_1M=6
-INCREMENTAL_OVERLAP_BARS=3
-DOWNLOAD_BACKFILL_EXTRA_BARS=200
-DOWNLOAD_MIN_INCREMENTAL_BARS=50
-DOWNLOAD_DAILY_EXTRA_DAYS=20
-CHART_LOOKBACK_DAYS=365
-DB_READ_LIMIT_DEFAULT=7000
-DB_ROW_WARMUP_BARS=200
-DB_ROW_MIN_DAILY_BARS=100
-DB_ROW_DAILY_PADDING_DAYS=30
-
-# --- Ground Truth --_MIN_LEG_PCT=8.0
-GT_BUY_MIN_SWING_PCT=3.0
-GT_BUY_BB_MAX=0.45
-GT_BUY_MIN_BARS=24
-GT_MAX_BUYS_PER_LEG=12
-GT_MAX_SELLS_PER_LEG=2
-GT_SELL_SPLIT_GAP_PCT=2.5
-GT_MARKER_SIZE_MIN=10
-GT_MARKER_SIZE_MAX=32
-GT_INITIAL_CASH_KRW=400000
-TRADING_FEE_RATE=0.0005
-GT_UNLIMITED_CHRONOLOGICAL_DAYS=300
-
-# --- 모니터 ---
-MONITOR_LOOP_SLEEP_SEC=180
-MONITOR_POOL_WORKERS=12
-MONITOR_DEFAULT_INTERVAL=60
-MONITOR_API_RETRIES=3
-MONITOR_API_BONG_COUNT=3000
-MONITOR_SLEEP_AFTER_REQUEST_SEC=0.5
-MONITOR_SLEEP_RATE_LIMIT_SEC=5
-MONITOR_SLEEP_BETWEEN_CHUNKS_SEC=0.3
-MONITOR_API_CHUNK_BARS=200
-MONITOR_MA_WINDOWS=5,20,40,120,200,240,720,1440
-MONITOR_NORM_WINDOW=20
-MONITOR_TELEGRAM_BATCH_SIZE=20
-
-# --- general_analysis ---
-GA_COL_PREFIX=ga_
-LOOKBACK_BARS=3:120,5:100,10:80,15:60,30:50,60:40,240:30,1440:60
-CONTEXT_TAIL_ROWS=3:6000,5:5000,10:4000,15:3000,30:2000,60:1500,240:800,1440:500
-GA_DEFAULT_TAIL_EXPORT=200
-GA_PATTERN_TOLERANCE_PCT=2.5
-GA_VP_BINS=30
-GA_VP_VALUE_AREA_PCT=0.70
-GA_HV_ROLLING_BARS=20
-GA_HV_PERCENTILE_WINDOW=120
-GA_HV_ANNUALIZE_SQ.41148133
-GA_DIVERGENCE_LOOKBACK=10
-GA_SMA_PERIODS=5,20,60,120
-GA_EMA_SPANS=12,26
-GA_ATR_PERIOD=14
-GA_KELTNER_ATR_MULT=2
-GA_AO_FAST=5
-GA_AO_SLOW=34
-GA_LINREG_WINDOW=20
-GA_ADX_PERIOD=14
-GA_ADX_TREND_THRESHOLD=25
-GA_SUPERTREND_ATR_MULT=3
-GA_VOL_SPIKE_MULT=1.8
-GA_VOL_MA_WINDOW=20
-GA_CCI_PERIOD=20
-GA_WILLIAMS_PERIOD=14
-GA_ROC_PERIOD=10
-GA_MFI_PERIOD=14
-GA_CMF_PERIOD=20
-GA_DONCHIAN_PERIOD=20
-GA_BB_SQUEEZE_WINDOW=50
-GA_BB_SQUEEZE_QUANTILE=0.2
-GA_PIVOT_ORDER=3
-GA_PSAR_AF_START=0.02
-GA_PSAR_AF_STEP=0.02
-GA_PSAR_AF_MAX=0.2
-
-# --- .env.example 누락 키 추가 (2026-06-01) ---
-GT_MIN_ORDER_KRW=5000
-GT_BUY_PCT_LARGE_LEG=1.0
-GT_BUY_PCT_SMALL_LEG=0.05
-GT_LARGE_LEG_TOP_PCT=0.2
-GT_SIGNAL_CAUSAL=1
-SIM_CAUSAL_TIER=1
-CAUSAL_GT_PEAK_MODE=local
-CAUSAL_GT_MIN_LEG_PCT=5.0
-CAUSAL_GT_MIN_BARS_BETWEEN_LEGS=60
-CAUSAL_GT_USE_LOCAL_TROUGH=1
-CAUSAL_GT_DD_LARGE_PCT=5.0
-CAUSAL_GT_DD_MEDIUM_PCT=2.0
-GT_BUY_PCT_MEDIUM_LEG=0.25
-SIM_TIER_CONVICTION_DD_PCT=10.0
-SIM_PRIMARY_SIZING=auto
-SIM_HYBRID_MIN_HOLDOUT_PNL_PCT=0.0
-SIM_HYBRID_MAX_MDD_PCT=30.0
-SIM_OPTION_C_TARGET_PNL_PCT=300.0
-SIM_OPTION_C_PHASE2_TARGET_PNL_PCT=1000.0
-SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO=0.85
-SIM_OPTION_C_MIN_GT_CAPTURE=0.23
-SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO=0.5
-GT_BUY_WEIGHT_RULE=inverse_price_normalized
-GT_SELL_SPLIT_WEIGHTS=0.65,0.35
-MATCH_LABEL_MODE=leg_gt
-MATCH_HOLDOUT_RATIO=0.15
-MATCH_MONITOR_MAX_PER_SIDE=1
-SIM_GO_WF_POSITIVE_RATIO=0.5
-SIM_FEE_STRESS_MULT=2.0
-# === Phase B-1 운영 (설정 변경 후 06_execute_live 반드시 재기동) ===
-MATCH_LIVE_CACHE_SEC=180
-MONITOR_ALERT_COOLDOWN_MIN=3
-MONITOR_ALERT_KRW_AMOUNT=40000
-LIVE_TRADING_ENABLED=1
-LIVE_ORDER_KRW=40000
-LIVE_BUY_PCT_LARGE=1.0
-LIVE_BUY_PCT_SMALL=0.05
-LIVE_DAILY_KRW_MAX=400000
-LIVE_COOLDOWN_MIN=3
-LIVE_MAX_TRADES_PER_DAY=15
-LIVE_DAILY_LOSS_LIMIT_KRW=40000
-LIVE_SLIPPAGE_PCT=0.05
-
diff --git a/.env.example b/.env.example
index 1251a9f..a9a7eba 100644
--- a/.env.example
+++ b/.env.example
@@ -1,210 +1,50 @@
-# DeepCoin — .env.example (비밀값 없음). 복사: cp .env.example .env 후 키만 채우세요.
-# Python: conda activate ncue && pip install -r requirements.txt
+# DeepCoin — .env.example (비밀값 없음). 복사: cp .env.example .env
-# --- 빗썸 API ---
+# --- 빗썸 API (캔들 수집은 Public API, 키 선택) ---
BITHUMB_ACCESS_KEY=
BITHUMB_SECRET_KEY=
BITHUMB_API_URL=https://api.bithumb.com
BITHUMB_API_CANDLE_COUNT=200
-BITHUMB_MINUTE_INTERVALS=1,3,5,10,15,30,60,240
+BITHUMB_MINUTE_INTERVALS=3,5,10,15,30,60,240
HTS_API_RETRY_SLEEP_SEC=0.5
-# --- 텔레그램 (선택, 알림 미사용 시 비워도 됨) ---
+# --- 텔레그램 (선택) ---
COIN_TELEGRAM_BOT_TOKEN=
COIN_TELEGRAM_CHAT_ID=
# --- 거래 대상 ---
-SYMBOL=WLD
-COIN_NAME=월드코인
+SYMBOL=BTC
+COIN_NAME=비트코인
-# --- 경로 ---
-DB_PATH=data/coins.db
-GROUND_TRUTH_FILE=data/ground_truth/ground_truth_trades.json
+# --- 데이터 수집 (최대 2년) ---
+# 인터벌 코드: 분봉=분, 일봉=1440, 주봉=10080, 월봉=43200
+DB_PATH=coins.db
+DOWNLOAD_DAYS=730
+DOWNLOAD_INTERVALS=3,5,10,15,30,60,240,1440,10080,43200
+API_REQUEST_SLEEP_SEC=0.35
+API_REQUEST_RETRIES=3
-# --- 타임프레임 (분) ---
-DAILY_INTERVAL_MIN=1440
-ENTRY_INTERVAL=3
-TREND_INTERVAL_1H=60
-TREND_INTERVAL_1D=1440
-ALL_INTERVALS=3,5,10,15,30,60,240,1440
-DOWNLOAD_INTERVALS=3,5,10,15,30,60,240,1440
-GENERAL_ANALYSIS_INTERVALS=3,5,10,15,30,60,240,1440,10080,43200
-TIMING_INTERVALS=3,5,10,15
-TREND_INTERVALS=60,240,1440,10080,43200
-INTERVAL_PREFIX=3:m3,5:m5,10:m10,15:m15,30:m30,60:m60,240:m240,1440:d1,10080:w1,43200:mo1
-
-# --- 볼린저 / RSI ---
-BB_PERIOD=20
-BB_STD=2
-BB_MIN_WIDTH_PCT=0.8
-RSI_PERIOD=14
-DISPARITY_PERIODS=5,20,60
-DISPARITY_OVERBOUGHT=105
-DISPARITY_OVERSOLD=95
-MACD_FAST=12
-MACD_SLOW=26
-MACD_SIGNAL=9
-STOCH_K_PERIOD=14
-STOCH_D_PERIOD=3
-STOCH_SMOOTH_K=3
-STOCH_OVERSOLD=20
-STOCH_OVERBOUGHT=80
-TREND_RANGE_MA_GAP_PCT=0.5
-
-# --- MTF 정렬 ---
-ALIGN_RSI_OVERSOLD=35
-ALIGN_RSI_OVERBOUGHT=65
-ALIGN_RSI_CONFLICT_TIMING_LOW=40
-ALIGN_RSI_CONFLICT_TIMING_HIGH=65
-ALIGN_RSI_CONFLICT_TREND_LOW=40
-ALIGN_RSI_CONFLICT_TREND_HIGH=65
-ALIGN_BB_POS_LOW=0.2
-ALIGN_BB_POS_HIGH=0.8
-
-# --- 다운로드 / DB ---
-DOWNLOAD_MONTHS=12
-INCREMENTAL_OVERLAP_BARS=3
-DOWNLOAD_BACKFILL_EXTRA_BARS=200
-DOWNLOAD_MIN_INCREMENTAL_BARS=50
-DOWNLOAD_DAILY_EXTRA_DAYS=20
-CHART_LOOKBACK_DAYS=365
-DB_READ_LIMIT_DEFAULT=7000
-DB_ROW_WARMUP_BARS=200
-DB_ROW_MIN_DAILY_BARS=100
-DB_ROW_DAILY_PADDING_DAYS=30
-
-# --- Ground Truth ---
-GT_MIN_SWING_PCT=4.0
-GT_PIVOT_ORDER=20
-GT_MIN_BARS_BETWEEN=30
-GT_MAX_ROUND_TRIPS=24
-GT_SELECTION_MODE=split_buy_peak_sell
-GT_MIN_LEG_PCT=8.0
-GT_BUY_MIN_SWING_PCT=3.0
-GT_BUY_BB_MAX=0.45
-GT_BUY_MIN_BARS=24
-GT_MAX_BUYS_PER_LEG=12
-GT_MAX_SELLS_PER_LEG=2
-GT_SELL_SPLIT_GAP_PCT=2.5
-GT_MARKER_SIZE_MIN=10
-GT_MARKER_SIZE_MAX=32
+# --- Ground Truth (1단계, 2년) — DOWNLOAD_DAYS와 동일 권장 ---
+GT_INTERVAL_MIN=3
+GT_LOOKBACK_DAYS=730
GT_INITIAL_CASH_KRW=400000
-TRADING_FEE_RATE=0.0005
-GT_UNLIMITED_CHRONOLOGICAL_DAYS=300
+GT_TRADING_FEE_RATE=0.0005
+GT_ZIGZAG_REVERSAL_PCT=5.0
+GT_MIN_LEG_PCT=3.0
+GT_PULLBACK_MIN_PCT=1.5
+GT_PULLBACK_LOCAL_ORDER=10
+GT_BREAKOUT_BUFFER_PCT=0.1
+GT_BREAKOUT_CONSOLIDATION_BARS=200
+GT_BREAKOUT_MIN_RALLY_PCT=2.0
+GT_DIV_LOCAL_ORDER=20
+GT_DIV_MIN_BARS_BETWEEN=1500
+GT_DIV_MIN_RSI_DIFF=5.0
+GT_DIV_MIN_FUTURE_MOVE_PCT=4.0
+GROUND_TRUTH_FILE=data/ground_truth/ground_truth_trades.json
+GROUND_TRUTH_CHART_FILE=docs/02_ground_truth/ground_truth_chart.html
-# --- 모니터 ---
-MONITOR_LOOP_SLEEP_SEC=180
-MONITOR_POOL_WORKERS=12
-MONITOR_DEFAULT_INTERVAL=60
-MONITOR_API_RETRIES=3
-MONITOR_API_BONG_COUNT=3000
-MONITOR_SLEEP_AFTER_REQUEST_SEC=0.5
-MONITOR_SLEEP_RATE_LIMIT_SEC=5
-MONITOR_SLEEP_BETWEEN_CHUNKS_SEC=0.3
-MONITOR_API_CHUNK_BARS=200
-MONITOR_MA_WINDOWS=5,20,40,120,200,240,720,1440
-MONITOR_NORM_WINDOW=20
-MONITOR_TELEGRAM_BATCH_SIZE=20
-
-# --- general_analysis ---
-GA_COL_PREFIX=ga_
-LOOKBACK_BARS=3:120,5:100,10:80,15:60,30:50,60:40,240:30,1440:60,10080:12,43200:6
-CONTEXT_TAIL_ROWS=3:6000,5:5000,10:4000,15:3000,30:2000,60:1500,240:800,1440:500,10080:120,43200:48
-GA_DEFAULT_TAIL_EXPORT=200
-GA_PATTERN_TOLERANCE_PCT=2.5
-GA_VP_BINS=30
-GA_VP_VALUE_AREA_PCT=0.70
-GA_HV_ROLLING_BARS=20
-GA_HV_PERCENTILE_WINDOW=120
-GA_HV_ANNUALIZE_SQRT=339.41148133
-GA_DIVERGENCE_LOOKBACK=10
-GA_SMA_PERIODS=5,20,60,120
-GA_EMA_SPANS=12,26
-GA_ATR_PERIOD=14
-GA_KELTNER_ATR_MULT=2
-GA_AO_FAST=5
-GA_AO_SLOW=34
-GA_LINREG_WINDOW=20
-GA_ADX_PERIOD=14
-GA_ADX_TREND_THRESHOLD=25
-GA_SUPERTREND_ATR_MULT=3
-GA_VOL_SPIKE_MULT=1.8
-GA_VOL_MA_WINDOW=20
-GA_CCI_PERIOD=20
-GA_WILLIAMS_PERIOD=14
-GA_ROC_PERIOD=10
-GA_MFI_PERIOD=14
-GA_CMF_PERIOD=20
-GA_DONCHIAN_PERIOD=20
-GA_BB_SQUEEZE_WINDOW=50
-GA_BB_SQUEEZE_QUANTILE=0.2
-GA_PIVOT_ORDER=3
-GA_PSAR_AF_START=0.02
-GA_PSAR_AF_STEP=0.02
-GA_PSAR_AF_MAX=0.2
-
-# --- .env.example 누락 키 추가 (2026-06-01) ---
-GT_MIN_ORDER_KRW=5000
-GT_BUY_PCT_LARGE_LEG=1.0
-GT_BUY_PCT_SMALL_LEG=0.05
-GT_LARGE_LEG_TOP_PCT=0.2
-GT_SIGNAL_CAUSAL=1
-SIM_CAUSAL_TIER=1
-CAUSAL_GT_PEAK_MODE=local
-CAUSAL_GT_MIN_LEG_PCT=5.0
-CAUSAL_GT_MIN_BARS_BETWEEN_LEGS=60
-CAUSAL_GT_USE_LOCAL_TROUGH=1
-CAUSAL_GT_DD_LARGE_PCT=5.0
-CAUSAL_GT_DD_MEDIUM_PCT=2.0
-GT_BUY_PCT_MEDIUM_LEG=0.25
-SIM_TIER_CONVICTION_DD_PCT=10.0
-SIM_PRIMARY_SIZING=auto
-SIM_HYBRID_MIN_HOLDOUT_PNL_PCT=0.0
-SIM_HYBRID_MAX_MDD_PCT=30.0
-SIM_OPTION_C_TARGET_PNL_PCT=300.0
-SIM_OPTION_C_PHASE2_TARGET_PNL_PCT=1000.0
-SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO=0.85
-SIM_OPTION_C_MIN_GT_CAPTURE=0.23
-SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO=0.5
-GT_BUY_WEIGHT_RULE=inverse_price_normalized
-GT_SELL_SPLIT_WEIGHTS=0.65,0.35
-MATCH_LABEL_MODE=leg_gt
-MATCH_HOLDOUT_RATIO=0.15
-MATCH_MONITOR_MAX_PER_SIDE=1
-SIM_GO_WF_POSITIVE_RATIO=0.5
-SIM_FEE_STRESS_MULT=2.0
-# 3분봉(MATCH_PRIMARY_INTERVAL=3) — 규칙·알림 쿨다운 1봉
-MONITOR_ALERT_COOLDOWN_MIN=3
-MONITOR_ALERT_KRW_AMOUNT=40000
-# Phase B-1: 실거래 (06_execute_live.py 만 빗썸 주문 — 05는 알림만)
-LIVE_TRADING_ENABLED=1
-# LIVE_* 원화 한도: GT_INITIAL_CASH_KRW(40만) — B-1: 일한도 1배, 손실 10%, 1회참고 10%
-LIVE_ORDER_KRW=40000
-LIVE_BUY_PCT_LARGE=1.0
-LIVE_BUY_PCT_SMALL=0.05
-LIVE_DAILY_KRW_MAX=400000
-LIVE_COOLDOWN_MIN=3
-LIVE_MAX_TRADES_PER_DAY=15
-LIVE_DAILY_LOSS_LIMIT_KRW=40000
-LIVE_SLIPPAGE_PCT=0.05
-# 06: 시뮬 sim_causal_hybrid 정합 — fire_outcomes monitor 발화 부트스트랩
-LIVE_HYBRID_BOOTSTRAP_FIRES=1
-# 07 일일 24h 수익률 텔레그램 (scripts/07_daily_pnl_telegram.py)
-DAILY_PNL_REPORT_ENABLED=1
-DAILY_PNL_REPORT_HOUR=19
-DAILY_PNL_REPORT_MINUTE=0
-DAILY_PNL_REPORT_TZ=Asia/Seoul
-DAILY_PNL_SNAPSHOT_ON_LIVE=1
-DAILY_PNL_SNAPSHOT_RETENTION_DAYS=90
-# 05/06 루프 시 봉 DB 증분 · live_eval 캐시(루프 주기와 동일)
-MONITOR_PERSIST_CANDLES=1
-MATCH_LIVE_CACHE_SEC=180
-# 05/06 시작·루프마다 지연 봉 자동 보완 (간격당 허용 지연 = 간격분×OPS_SYNC_MAX_LAG_BARS)
-OPS_SYNC_ON_START=1
-OPS_SYNC_MAX_LAG_BARS=2
-
-# --- 주·월봉 다운로드 (01_download) ---
-DOWNLOAD_INTERVALS_WM=10080,43200
-DOWNLOAD_MONTHS_WM=24
-WEEK_INTERVAL_MIN=10080
-MONTH_INTERVAL_MIN=43200
+# --- 매매 기법 (2단계) ---
+TECHNIQUES_DIR=data/techniques
+ANALYSIS_REPORT_JSON=docs/03_analysis/comparison_report.json
+ANALYSIS_REPORT_HTML=docs/03_analysis/comparison_report.html
+GT_ALIGN_TOLERANCE_BARS=480
diff --git a/.gitignore b/.gitignore
index 7679e98..1cce449 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
-# ---> Python.env
+# Local secrets and OS
+.env
+.DS_Store
.idea
*.db
@@ -97,7 +99,11 @@ ENV/
# 재생성 산출물 (스크립트 실행 시 로컬 생성)
docs/02_ground_truth/*.html
+docs/02_ground_truth/ground_truth_chart_data.js
+data/ground_truth/ground_truth_trades.json
+data/techniques/*.json
docs/03_analysis/*.html
+docs/03_analysis/comparison_report.json
docs/03_analysis/latest/
docs/04_matching/simulation_report.html
docs/04_matching/backtest_summary.html
diff --git a/README.md b/README.md
index 8aaf48d..d91df00 100644
--- a/README.md
+++ b/README.md
@@ -1,101 +1,119 @@
-# DeepCoin — WLD MTF · Ground Truth · Simulation · Operations
+# DeepCoin
-빗썸 KRW-WLD. 프로젝트는 **세 축**으로만 설계됩니다.
+빗썸 KRW 마켓 암호화폐 캔들 데이터 수집 프로젝트.
-| 축 | 역할 | 미래 데이터 |
-|----|------|-------------|
-| **Ground Truth** | 벤치마크 타점·leg·배분 | 허용 (사후 라벨) |
-| **Simulation** | 규칙·인과 백테스트·Go/No-Go | 금지 |
-| **Operations** | 시뮬과 동일 규칙·hybrid 배분 실주문 | 금지 |
+## 주요 기능
-설계 상세: [docs/reference/ARCHITECTURE.md](docs/reference/ARCHITECTURE.md)
+- 빗썸 Public API(v1) 기반 분·일·주·월봉 캔들 수집
+- SQLite(`coins.db`) 저장 — 테이블명 `{SYMBOL}_{인터벌코드}` (예: `BTC_60`, `BTC_10080`)
+- 최대 2년(기본 730일) 역방향 페이지네이션 수집
-## 파이프라인 (권장 순서)
+## 요구사항
+
+- Python 3.10+
+- Conda 환경 `ncue` 또는 `xavis`
+
+## 설치
```bash
-conda activate ncue # 또는 xavis
-
-# 데이터
-python scripts/01_download.py
-
-# Ground Truth
-python scripts/02_ground_truth.py
-python scripts/03_analyze_enrich.py
-python scripts/03_analyze_trades.py
-python scripts/03_gt_mtf_profile.py
-python scripts/05_chart_truth.py
-
-# Simulation
-python scripts/04_match_rules.py
-python scripts/04_simulation_report.py
-
-# Operations (LIVE_TRADING_ENABLED=1)
-python scripts/06_verify_live.py
-python scripts/check_balance.py
-python scripts/06_execute_live.py --once
-python scripts/06_execute_live.py
+cd DeepCoin
+conda activate ncue
+pip install -r requirements.txt
+cp .env.example .env # API 키 등 입력
```
-선택: `05_run_monitor.py` (알림만), `07_daily_pnl_telegram.py` (매일 24h 수익률), `00_sync_ops.py` (운영 전 봉 동기화)
+## 환경 변수
-## 디렉터리
+| 변수 | 설명 | 기본값 |
+|------|------|--------|
+| `SYMBOL` | 코인 심볼 | `BTC` |
+| `COIN_NAME` | 코인 이름 | `비트코인` |
+| `DB_PATH` | SQLite 경로 | `coins.db` |
+| `DOWNLOAD_DAYS` | 수집 일수 (최대 2년) | `730` |
+| `DOWNLOAD_INTERVALS` | 인터벌 코드 목록 | `3,5,10,15,30,60,240,1440,10080,43200` |
+| `BITHUMB_API_CANDLE_COUNT` | 요청당 캔들 수 (최대 200) | `200` |
+| `API_REQUEST_SLEEP_SEC` | API 호출 간격(초) | `0.35` |
+
+인터벌 코드: 분봉은 분 단위 숫자, 일봉=`1440`, 주봉=`10080`, 월봉=`43200`
+
+캔들 조회는 Public API이므로 API 키 없이도 동작합니다.
+
+## 5단계 파이프라인
+
+| 단계 | 목적 | 스크립트 |
+|------|------|----------|
+| 0 | 데이터 수집 | `01_download.py` |
+| 1 | Ground Truth (사후 벤치마크 타점) | `02_ground_truth.py` |
+| 2 | 매매 기법 개발·GT 정합 비교 | `03_run_techniques.py` |
+| 3 | 인과 신호 (과거 데이터만) | (예정) |
+| 4 | 시뮬레이션 백테스트 | (예정) |
+| 5 | 실거래 운영 | (예정) |
+
+```bash
+# 0. 데이터 수집
+python scripts/01_download.py
+
+# 1. Ground Truth (매수·매도 벤치마크 타점)
+python scripts/02_ground_truth.py
+
+# 2. 매매 기법 실행 및 GT 정합 비교
+python scripts/03_run_techniques.py
+```
+
+## 2단계 매매 기법 (8종)
+
+Ground Truth 타점에 맞출 수 있는 후보 기법. 모두 **인과 신호** (미래 데이터 미사용).
+
+| ID | 기법 | 유형 | 설명 |
+|----|------|------|------|
+| `zigzag_causal` | 인과 ZigZag | swing | GT ZigZag 5%의 인과 버전 |
+| `minor_swing` | 소형 스윙 하이브리드 | hybrid | ZigZag 2.5% + 국소 극값 |
+| `local_extrema` | 국소 극값 | swing | 눌림목·반등 고점 (9/27 유형) |
+| `bb_reversal` | 볼린저 역추세 | indicator | BB 하단 매수·상단 매도 |
+| `ma_cross` | EMA 크로스 | indicator | EMA(20/60) 골든·데드 크로스 |
+| `rsi_swing` | RSI 스윙 | indicator | RSI 과매도·과매수 복귀 |
+| `macd_cross` | MACD 크로스 | indicator | MACD 시그널선 크로스 |
+| `donchian` | 돈치안 채널 | swing | 채널 하단·상단 반전 |
+
+GT 정합 평가: 매수/매도 recall, 레그 recall, 수익 포착률, 종합 score.
+
+## 실행 (데이터 수집)
+
+```bash
+# 전체 인터벌, 최대 2년
+python scripts/download_candles.py
+
+# 일·주·월봉만 빠르게
+python scripts/download_candles.py --intervals 1440,10080,43200
+
+# 최근 90일, 60분봉만
+python scripts/download_candles.py --days 90 --intervals 60
+```
+
+## 디렉터리 구조
```text
DeepCoin/
-├── scripts/ # CLI 진입점 (GT / Sim / Ops)
-├── deepcoin/
-│ ├── data/ # 01
-│ ├── ground_truth/ # GT
-│ ├── analysis/ # GT 입력 (03)
-│ ├── matching/ # Sim (04)
-│ └── ops/ # Ops (05·06)
-├── data/ # coins.db, ground_truth/, ops/
-└── docs/
- ├── reference/ # ARCHITECTURE, SIMULATION, OPERATIONS …
- └── 02~05/ # 재생성 산출물
+├── src/deepcoin/
+│ ├── api/bithumb.py
+│ ├── data/
+│ ├── ground_truth/ # 1단계
+│ ├── techniques/ # 2단계 매매 기법
+│ └── evaluation/ # GT 정합 평가
+├── scripts/
+│ ├── 01_download.py
+│ ├── 02_ground_truth.py
+│ └── 03_run_techniques.py
+├── data/ground_truth/
+├── data/techniques/
+├── docs/02_ground_truth/
+├── docs/03_analysis/
+└── coins.db
```
-## 환경
+## 변경 이력
-| 파일 | 용도 |
-|------|------|
-| `.env` | API 키·전역 설정 (Git 제외) |
-| `config.py` | `.env` 로드 |
-
-`scripts/_bootstrap.py`가 루트 `.env`를 자동 로드합니다.
-
-### 주요 변수
-
-| 변수 | 기본 | 축 |
-|------|------|-----|
-| `CHART_LOOKBACK_DAYS` | 365 | GT·Sim |
-| `GT_INITIAL_CASH_KRW` | 400000 | GT·Sim·Ops 배분 |
-| `GENERAL_ANALYSIS_INTERVALS` | 3m~일봉+주·월 | Sim 스캔 |
-| `GT_SIGNAL_CAUSAL` | 1 | Sim·Ops (인과) |
-| `LIVE_TRADING_ENABLED` | 1 | Ops (`0`이면 06 기동 불가) |
-
-전체: `.env.example`, [docs/05_ops/env.recommended.md](docs/05_ops/env.recommended.md)
-
-## 산출물
-
-| 경로 | 축 |
-|------|-----|
-| `data/ground_truth/ground_truth_trades.json` | GT |
-| `docs/02_ground_truth/wld_ground_truth_chart.html` | GT 차트 (`05_chart_truth.py` 재생성) |
-| `docs/04_matching/matched_rules.json` | Sim |
-| `docs/04_matching/simulation_report.html` | Sim |
-| `data/ops/live_trades.jsonl` | Ops |
-
-## 문서
-
-| 문서 | 내용 |
-|------|------|
-| [ARCHITECTURE.md](docs/reference/ARCHITECTURE.md) | 3축 설계 (필독) |
-| [GROUND_TRUTH.md](docs/reference/GROUND_TRUTH.md) | GT 타점 |
-| [SIMULATION.md](docs/reference/SIMULATION.md) | 시뮬·Go/No-Go |
-| [OPERATIONS.md](docs/reference/OPERATIONS.md) | 운영 루틴 |
-| [LIVE_TRADING.md](docs/reference/LIVE_TRADING.md) | 06 실거래 |
-
-## 면책
-
-실거래 손익은 사용자 책임입니다. 본 저장소는 투자 자문이 아닙니다.
+- 2026-06-08: 2단계 매매 기법 8종 + GT 정합 비교 리포트 추가
+- 2026-06-08: 1단계 Ground Truth (ZigZag 스윙 매수·매도 타점) 추가
+- 2026-06-07: BTC 최대 2년 분·일·주·월봉 수집 지원
+- 2026-06-07: 캔들 수집 모듈 초기 구현
diff --git a/config.py b/config.py
deleted file mode 100644
index fe017a4..0000000
--- a/config.py
+++ /dev/null
@@ -1,439 +0,0 @@
-"""
-전역 설정 (WLD). 값은 PROJECT_ROOT/.env → OS 환경 변수 순으로 읽습니다.
-"""
-
-from __future__ import annotations
-
-import os
-
-from deepcoin.env_loader import load_project_env
-
-load_project_env(override=True)
-
-
-def _getenv(key: str, default: str = "") -> str:
- return os.getenv(key, default)
-
-
-def _getenv_int(key: str, default: str) -> int:
- return int(_getenv(key, default))
-
-
-def _getenv_float(key: str, default: str) -> float:
- return float(_getenv(key, default))
-
-
-def _parse_int_tuple(env_key: str, default: str) -> tuple[int, ...]:
- raw = _getenv(env_key, default)
- return tuple(int(x.strip()) for x in raw.split(",") if x.strip())
-
-
-def _parse_int_set(env_key: str, default: str) -> frozenset[int]:
- return frozenset(_parse_int_tuple(env_key, default))
-
-
-def _parse_interval_map(env_key: str, default: str) -> dict[int, int]:
- """
- '3:120,5:100' → {3: 120, 5: 100}.
- """
- raw = _getenv(env_key, default)
- out: dict[int, int] = {}
- for part in raw.split(","):
- part = part.strip()
- if ":" not in part:
- continue
- k, v = part.split(":", 1)
- out[int(k.strip())] = int(v.strip())
- return out
-
-
-def _parse_str_map(env_key: str, default: str) -> dict[int, str]:
- """'3:m3,1440:d1' → {3: 'm3', 1440: 'd1'}."""
- raw = _getenv(env_key, default)
- out: dict[int, str] = {}
- for part in raw.split(","):
- part = part.strip()
- if ":" not in part:
- continue
- k, v = part.split(":", 1)
- out[int(k.strip())] = v.strip()
- return out
-
-
-# --- API / 알림 ---
-BITHUMB_ACCESS_KEY = _getenv("BITHUMB_ACCESS_KEY")
-BITHUMB_SECRET_KEY = _getenv("BITHUMB_SECRET_KEY")
-BITHUMB_API_URL = _getenv("BITHUMB_API_URL", "https://api.bithumb.com")
-BITHUMB_API_CANDLE_COUNT = _getenv_int("BITHUMB_API_CANDLE_COUNT", "200")
-BITHUMB_MINUTE_INTERVALS = _parse_int_set(
- "BITHUMB_MINUTE_INTERVALS", "3,5,10,15,30,60,240"
-)
-HTS_API_RETRY_SLEEP_SEC = _getenv_float("HTS_API_RETRY_SLEEP_SEC", "0.5")
-
-COIN_TELEGRAM_BOT_TOKEN = _getenv("COIN_TELEGRAM_BOT_TOKEN")
-COIN_TELEGRAM_CHAT_ID = _getenv("COIN_TELEGRAM_CHAT_ID")
-
-# --- 거래 대상 ---
-SYMBOL = _getenv("SYMBOL", "WLD")
-COIN_NAME = _getenv("COIN_NAME", "월드코인")
-KR_COINS: dict[str, str] = {SYMBOL: COIN_NAME}
-
-# --- 타임프레임 (분) ---
-DAILY_INTERVAL_MIN = _getenv_int("DAILY_INTERVAL_MIN", "1440")
-ENTRY_INTERVAL = _getenv_int("ENTRY_INTERVAL", "3")
-TREND_INTERVAL_1H = _getenv_int("TREND_INTERVAL_1H", "60")
-TREND_INTERVAL_1D = _getenv_int("TREND_INTERVAL_1D", "1440")
-
-ALL_INTERVALS: tuple[int, ...] = _parse_int_tuple(
- "ALL_INTERVALS", "3,5,10,15,30,60,240,1440"
-)
-# 1분봉은 01_download·ops_sync 미적재 (DOWNLOAD_EXCLUDED_INTERVALS).
-DOWNLOAD_INTERVALS: tuple[int, ...] = _parse_int_tuple(
- "DOWNLOAD_INTERVALS",
- "3,5,10,15,30,60,240,1440",
-)
-# 주봉(10080)·월봉(43200) — 01_download 별도 적재, 기본 2년(DOWNLOAD_MONTHS_WM)
-WEEK_INTERVAL_MIN = _getenv_int("WEEK_INTERVAL_MIN", "10080")
-MONTH_INTERVAL_MIN = _getenv_int("MONTH_INTERVAL_MIN", "43200")
-DOWNLOAD_INTERVALS_WM: tuple[int, ...] = _parse_int_tuple(
- "DOWNLOAD_INTERVALS_WM",
- "10080,43200",
-)
-DOWNLOAD_MONTHS_WM = _getenv_int("DOWNLOAD_MONTHS_WM", "24")
-GENERAL_ANALYSIS_INTERVALS: tuple[int, ...] = _parse_int_tuple(
- "GENERAL_ANALYSIS_INTERVALS", "3,5,10,15,30,60,240,1440"
-)
-TIMING_INTERVALS: tuple[int, ...] = _parse_int_tuple(
- "TIMING_INTERVALS", "3,5,10,15"
-)
-TREND_INTERVALS: tuple[int, ...] = _parse_int_tuple(
- "TREND_INTERVALS", "60,240,1440"
-)
-
-INTERVAL_PREFIX: dict[int, str] = _parse_str_map(
- "INTERVAL_PREFIX",
- "3:m3,5:m5,10:m10,15:m15,30:m30,60:m60,240:m240,1440:d1,10080:w1,43200:mo1",
-)
-
-# --- 볼린저 / RSI ---
-BB_PERIOD = _getenv_int("BB_PERIOD", "20")
-BB_STD = _getenv_float("BB_STD", "2")
-BB_MIN_WIDTH_PCT = _getenv_float("BB_MIN_WIDTH_PCT", "0.8")
-RSI_PERIOD = _getenv_int("RSI_PERIOD", "14")
-
-# --- 이격도 ---
-DISPARITY_PERIODS: tuple[int, ...] = _parse_int_tuple("DISPARITY_PERIODS", "5,20,60")
-DISPARITY_OVERBOUGHT = _getenv_float("DISPARITY_OVERBOUGHT", "105")
-DISPARITY_OVERSOLD = _getenv_float("DISPARITY_OVERSOLD", "95")
-
-# --- MACD / Stochastic ---
-MACD_FAST = _getenv_int("MACD_FAST", "12")
-MACD_SLOW = _getenv_int("MACD_SLOW", "26")
-MACD_SIGNAL = _getenv_int("MACD_SIGNAL", "9")
-STOCH_K_PERIOD = _getenv_int("STOCH_K_PERIOD", "14")
-STOCH_D_PERIOD = _getenv_int("STOCH_D_PERIOD", "3")
-STOCH_SMOOTH_K = _getenv_int("STOCH_SMOOTH_K", "3")
-STOCH_OVERSOLD = _getenv_float("STOCH_OVERSOLD", "20")
-STOCH_OVERBOUGHT = _getenv_float("STOCH_OVERBOUGHT", "80")
-
-# --- 추세 ---
-TREND_RANGE_MA_GAP_PCT = _getenv_float("TREND_RANGE_MA_GAP_PCT", "0.5")
-
-# --- MTF 합성·정렬 ---
-ALIGN_RSI_OVERSOLD = _getenv_float("ALIGN_RSI_OVERSOLD", "35")
-ALIGN_RSI_OVERBOUGHT = _getenv_float("ALIGN_RSI_OVERBOUGHT", "65")
-ALIGN_RSI_CONFLICT_TIMING_LOW = _getenv_float("ALIGN_RSI_CONFLICT_TIMING_LOW", "40")
-ALIGN_RSI_CONFLICT_TIMING_HIGH = _getenv_float("ALIGN_RSI_CONFLICT_TIMING_HIGH", "65")
-ALIGN_RSI_CONFLICT_TREND_LOW = _getenv_float("ALIGN_RSI_CONFLICT_TREND_LOW", "40")
-ALIGN_RSI_CONFLICT_TREND_HIGH = _getenv_float("ALIGN_RSI_CONFLICT_TREND_HIGH", "65")
-ALIGN_BB_POS_LOW = _getenv_float("ALIGN_BB_POS_LOW", "0.2")
-ALIGN_BB_POS_HIGH = _getenv_float("ALIGN_BB_POS_HIGH", "0.8")
-
-# --- 다운로드 / DB ---
-DOWNLOAD_MONTHS = _getenv_int("DOWNLOAD_MONTHS", "12")
-INCREMENTAL_OVERLAP_BARS = _getenv_int("INCREMENTAL_OVERLAP_BARS", "3")
-DOWNLOAD_BACKFILL_EXTRA_BARS = _getenv_int("DOWNLOAD_BACKFILL_EXTRA_BARS", "200")
-DOWNLOAD_MIN_INCREMENTAL_BARS = _getenv_int("DOWNLOAD_MIN_INCREMENTAL_BARS", "50")
-DOWNLOAD_DAILY_EXTRA_DAYS = _getenv_int("DOWNLOAD_DAILY_EXTRA_DAYS", "20")
-# 05/06 시작 시 누락·지연 봉 자동 증분 (01_download와 동일 저장)
-OPS_SYNC_ON_START = _getenv("OPS_SYNC_ON_START", "1").strip().lower() in (
- "1",
- "true",
- "yes",
-)
-OPS_SYNC_MAX_LAG_BARS = _getenv_int("OPS_SYNC_MAX_LAG_BARS", "2")
-DB_READ_LIMIT_DEFAULT = _getenv_int("DB_READ_LIMIT_DEFAULT", "7000")
-DB_ROW_WARMUP_BARS = _getenv_int("DB_ROW_WARMUP_BARS", "200")
-DB_ROW_MIN_DAILY_BARS = _getenv_int("DB_ROW_MIN_DAILY_BARS", "100")
-DB_ROW_DAILY_PADDING_DAYS = _getenv_int("DB_ROW_DAILY_PADDING_DAYS", "30")
-
-
-def _paths():
- from deepcoin.paths import (
- ANALYSIS_CAPABILITY_HTML,
- ANALYSIS_LATEST_DIR,
- ANALYSIS_REPORT_HTML,
- ANALYSIS_TRADES_CSV,
- resolve_db_path,
- resolve_ground_truth_file,
- )
-
- return (
- resolve_db_path(),
- resolve_ground_truth_file(),
- ANALYSIS_TRADES_CSV,
- ANALYSIS_REPORT_HTML,
- ANALYSIS_CAPABILITY_HTML,
- ANALYSIS_LATEST_DIR,
- )
-
-
-_db, _gt, _a_csv, _a_html, _a_cap, _a_latest = _paths()
-DB_PATH = _getenv("DB_PATH", str(_db))
-GROUND_TRUTH_PATH = _gt
-REPORTS_ANALYSIS_TRADES_CSV = _a_csv
-REPORTS_ANALYSIS_REPORT_HTML = _a_html
-REPORTS_ANALYSIS_CAPABILITY_HTML = _a_cap
-REPORTS_ANALYSIS_LATEST_DIR = _a_latest
-GROUND_TRUTH_FILE = _getenv("GROUND_TRUTH_FILE", str(_gt))
-
-# --- 차트 ---
-CHART_LOOKBACK_DAYS = _getenv_int("CHART_LOOKBACK_DAYS", "365")
-GT_UNLIMITED_CHRONOLOGICAL_DAYS = _getenv_int("GT_UNLIMITED_CHRONOLOGICAL_DAYS", "300")
-
-# --- Ground Truth ---
-GT_MIN_SWING_PCT = _getenv_float("GT_MIN_SWING_PCT", "4.0")
-GT_PIVOT_ORDER = _getenv_int("GT_PIVOT_ORDER", "20")
-GT_MIN_BARS_BETWEEN = _getenv_int("GT_MIN_BARS_BETWEEN", "30")
-GT_MAX_ROUND_TRIPS = _getenv_int("GT_MAX_ROUND_TRIPS", "24")
-GT_SELECTION_MODE = _getenv("GT_SELECTION_MODE", "split_buy_peak_sell")
-GT_MIN_LEG_PCT = _getenv_float("GT_MIN_LEG_PCT", "8.0")
-GT_BUY_MIN_SWING_PCT = _getenv_float("GT_BUY_MIN_SWING_PCT", "3.0")
-GT_BUY_BB_MAX = _getenv_float("GT_BUY_BB_MAX", "0.45")
-GT_BUY_MIN_BARS = _getenv_int("GT_BUY_MIN_BARS", "24")
-GT_MAX_BUYS_PER_LEG = _getenv_int("GT_MAX_BUYS_PER_LEG", "12")
-GT_MAX_SELLS_PER_LEG = _getenv_int("GT_MAX_SELLS_PER_LEG", "2")
-GT_SELL_SPLIT_GAP_PCT = _getenv_float("GT_SELL_SPLIT_GAP_PCT", "2.5")
-GT_SELL_SPLIT_WEIGHTS: tuple[float, ...] = tuple(
- float(x.strip())
- for x in _getenv("GT_SELL_SPLIT_WEIGHTS", "0.65,0.35").split(",")
- if x.strip()
-) or (0.65, 0.35)
-GT_BUY_WEIGHT_RULE = _getenv("GT_BUY_WEIGHT_RULE", "inverse_price_normalized")
-GT_MARKER_SIZE_MIN = _getenv_int("GT_MARKER_SIZE_MIN", "10")
-GT_MARKER_SIZE_MAX = _getenv_int("GT_MARKER_SIZE_MAX", "32")
-GT_INITIAL_CASH_KRW = _getenv_int("GT_INITIAL_CASH_KRW", "400000")
-GT_MIN_ORDER_KRW = _getenv_int("GT_MIN_ORDER_KRW", "5000")
-GT_BUY_PCT_LARGE_LEG = _getenv_float("GT_BUY_PCT_LARGE_LEG", "1.0")
-GT_BUY_PCT_SMALL_LEG = _getenv_float("GT_BUY_PCT_SMALL_LEG", "0.05")
-GT_LARGE_LEG_TOP_PCT = _getenv_float("GT_LARGE_LEG_TOP_PCT", "0.2")
-# 시뮬·스캔: 1=인과적 신호·tier (미래 데이터 미사용, 운영 정합)
-GT_SIGNAL_CAUSAL = _getenv("GT_SIGNAL_CAUSAL", "1").strip().lower() in (
- "1",
- "true",
- "yes",
-)
-SIM_CAUSAL_TIER = _getenv("SIM_CAUSAL_TIER", "1").strip().lower() in (
- "1",
- "true",
- "yes",
-)
-# 인과 GT leg 엔진 (Option C +300% 경로)
-CAUSAL_GT_PEAK_MODE = _getenv("CAUSAL_GT_PEAK_MODE", "local").strip().lower()
-if CAUSAL_GT_PEAK_MODE not in ("local", "zigzag"):
- CAUSAL_GT_PEAK_MODE = "local"
-CAUSAL_GT_MIN_LEG_PCT = _getenv_float("CAUSAL_GT_MIN_LEG_PCT", "5.0")
-CAUSAL_GT_MIN_BARS_BETWEEN_LEGS = _getenv_int("CAUSAL_GT_MIN_BARS_BETWEEN_LEGS", "60")
-CAUSAL_GT_USE_LOCAL_TROUGH = _getenv("CAUSAL_GT_USE_LOCAL_TROUGH", "1").strip().lower() in (
- "1",
- "true",
- "yes",
-)
-CAUSAL_GT_DD_LARGE_PCT = _getenv_float("CAUSAL_GT_DD_LARGE_PCT", "8.0")
-CAUSAL_GT_DD_MEDIUM_PCT = _getenv_float("CAUSAL_GT_DD_MEDIUM_PCT", "4.0")
-GT_BUY_PCT_MEDIUM_LEG = _getenv_float("GT_BUY_PCT_MEDIUM_LEG", "0.25")
-SIM_TIER_CONVICTION_DD_PCT = _getenv_float("SIM_TIER_CONVICTION_DD_PCT", "10.0")
-TRADING_FEE_RATE = _getenv_float("TRADING_FEE_RATE", "0.0005")
-
-# --- 모니터 / API 수집 ---
-MONITOR_LOOP_SLEEP_SEC = _getenv_int("MONITOR_LOOP_SLEEP_SEC", "180")
-MONITOR_POOL_WORKERS = _getenv_int("MONITOR_POOL_WORKERS", "12")
-MONITOR_DEFAULT_INTERVAL = _getenv_int("MONITOR_DEFAULT_INTERVAL", "60")
-MONITOR_API_RETRIES = _getenv_int("MONITOR_API_RETRIES", "3")
-MONITOR_API_BONG_COUNT = _getenv_int("MONITOR_API_BONG_COUNT", "3000")
-MONITOR_SLEEP_AFTER_REQUEST_SEC = _getenv_float("MONITOR_SLEEP_AFTER_REQUEST_SEC", "0.5")
-MONITOR_SLEEP_RATE_LIMIT_SEC = _getenv_float("MONITOR_SLEEP_RATE_LIMIT_SEC", "5")
-MONITOR_SLEEP_BETWEEN_CHUNKS_SEC = _getenv_float("MONITOR_SLEEP_BETWEEN_CHUNKS_SEC", "0.3")
-MONITOR_API_CHUNK_BARS = _getenv_int("MONITOR_API_CHUNK_BARS", "200")
-MONITOR_MA_WINDOWS: tuple[int, ...] = _parse_int_tuple(
- "MONITOR_MA_WINDOWS", "5,20,40,120,200,240,720,1440"
-)
-MONITOR_NORM_WINDOW = _getenv_int("MONITOR_NORM_WINDOW", "20")
-MONITOR_TELEGRAM_BATCH_SIZE = _getenv_int("MONITOR_TELEGRAM_BATCH_SIZE", "20")
-# 규칙 알림 참고 금액(매수 시 수량=금액/가격). 매도 시에는 보유 수량 우선.
-MONITOR_ALERT_KRW_AMOUNT = _getenv_int("MONITOR_ALERT_KRW_AMOUNT", "40000")
-# 05/06·live_eval API 수집 시 coins.db 증분 INSERT (01_download와 동일 append_data)
-MONITOR_PERSIST_CANDLES = _getenv("MONITOR_PERSIST_CANDLES", "1").strip().lower() in (
- "1",
- "true",
- "yes",
-)
-
-# --- general_analysis ---
-GA_COL_PREFIX = _getenv("GA_COL_PREFIX", "ga_")
-LOOKBACK_BARS: dict[int, int] = _parse_interval_map(
- "LOOKBACK_BARS",
- "3:120,5:100,10:80,15:60,30:50,60:40,240:30,1440:60",
-)
-CONTEXT_TAIL_ROWS: dict[int, int] = _parse_interval_map(
- "CONTEXT_TAIL_ROWS",
- "3:6000,5:5000,10:4000,15:3000,30:2000,60:1500,240:800,1440:500",
-)
-GA_DEFAULT_TAIL_EXPORT = _getenv_int("GA_DEFAULT_TAIL_EXPORT", "200")
-GA_PATTERN_TOLERANCE_PCT = _getenv_float("GA_PATTERN_TOLERANCE_PCT", "2.5")
-GA_VP_BINS = _getenv_int("GA_VP_BINS", "30")
-GA_VP_VALUE_AREA_PCT = _getenv_float("GA_VP_VALUE_AREA_PCT", "0.70")
-GA_HV_ROLLING_BARS = _getenv_int("GA_HV_ROLLING_BARS", "20")
-GA_HV_PERCENTILE_WINDOW = _getenv_int("GA_HV_PERCENTILE_WINDOW", "120")
-GA_HV_ANNUALIZE_SQRT = _getenv_float("GA_HV_ANNUALIZE_SQRT", "339.41148133")
-GA_DIVERGENCE_LOOKBACK = _getenv_int("GA_DIVERGENCE_LOOKBACK", "10")
-GA_SMA_PERIODS: tuple[int, ...] = _parse_int_tuple("GA_SMA_PERIODS", "5,20,60,120")
-GA_EMA_SPANS: tuple[int, ...] = _parse_int_tuple("GA_EMA_SPANS", "12,26")
-GA_ATR_PERIOD = _getenv_int("GA_ATR_PERIOD", "14")
-GA_KELTNER_ATR_MULT = _getenv_float("GA_KELTNER_ATR_MULT", "2")
-GA_AO_FAST = _getenv_int("GA_AO_FAST", "5")
-GA_AO_SLOW = _getenv_int("GA_AO_SLOW", "34")
-GA_LINREG_WINDOW = _getenv_int("GA_LINREG_WINDOW", "20")
-GA_ADX_PERIOD = _getenv_int("GA_ADX_PERIOD", "14")
-GA_ADX_TREND_THRESHOLD = _getenv_float("GA_ADX_TREND_THRESHOLD", "25")
-GA_SUPERTREND_ATR_MULT = _getenv_float("GA_SUPERTREND_ATR_MULT", "3")
-GA_VOL_SPIKE_MULT = _getenv_float("GA_VOL_SPIKE_MULT", "1.8")
-GA_VOL_MA_WINDOW = _getenv_int("GA_VOL_MA_WINDOW", "20")
-GA_CCI_PERIOD = _getenv_int("GA_CCI_PERIOD", "20")
-GA_WILLIAMS_PERIOD = _getenv_int("GA_WILLIAMS_PERIOD", "14")
-GA_ROC_PERIOD = _getenv_int("GA_ROC_PERIOD", "10")
-GA_MFI_PERIOD = _getenv_int("GA_MFI_PERIOD", "14")
-GA_CMF_PERIOD = _getenv_int("GA_CMF_PERIOD", "20")
-GA_DONCHIAN_PERIOD = _getenv_int("GA_DONCHIAN_PERIOD", "20")
-GA_BB_SQUEEZE_WINDOW = _getenv_int("GA_BB_SQUEEZE_WINDOW", "50")
-GA_BB_SQUEEZE_QUANTILE = _getenv_float("GA_BB_SQUEEZE_QUANTILE", "0.2")
-GA_PIVOT_ORDER = _getenv_int("GA_PIVOT_ORDER", "3")
-GA_PSAR_AF_START = _getenv_float("GA_PSAR_AF_START", "0.02")
-GA_PSAR_AF_STEP = _getenv_float("GA_PSAR_AF_STEP", "0.02")
-GA_PSAR_AF_MAX = _getenv_float("GA_PSAR_AF_MAX", "0.2")
-
-# --- 04 매칭 (GT 프로필 + 전구간 EV) ---
-MATCH_PRIMARY_INTERVAL = _getenv_int("MATCH_PRIMARY_INTERVAL", "3")
-MATCH_GT_TOLERANCE_MIN = _getenv_int("MATCH_GT_TOLERANCE_MIN", "15")
-MATCH_FORWARD_BARS = _getenv_int("MATCH_FORWARD_BARS", "60")
-MATCH_FEE_RATE = _getenv_float("MATCH_FEE_RATE", "0.0005")
-MATCH_MIN_FIRES = _getenv_int("MATCH_MIN_FIRES", "10")
-MATCH_TRAIN_RATIO = _getenv_float("MATCH_TRAIN_RATIO", "0.7")
-MATCH_MAX_RULES_PER_SIDE = _getenv_int("MATCH_MAX_RULES_PER_SIDE", "5")
-MATCH_PROFILE_QUANTILE_LO = _getenv_float("MATCH_PROFILE_QUANTILE_LO", "0.25")
-MATCH_PROFILE_QUANTILE_HI = _getenv_float("MATCH_PROFILE_QUANTILE_HI", "0.75")
-MATCH_MIN_EV_VALID = _getenv_float("MATCH_MIN_EV_VALID", "0.0")
-MATCH_MIN_PROFIT_FACTOR = _getenv_float("MATCH_MIN_PROFIT_FACTOR", "1.0")
-MATCH_MAX_VALID_FIRE_RATE = _getenv_float("MATCH_MAX_VALID_FIRE_RATE", "0.35")
-MATCH_BEST_EFFORT_PER_SIDE = _getenv_int("MATCH_BEST_EFFORT_PER_SIDE", "3")
-MATCH_INCLUDE_WIDE_RULES = _getenv("MATCH_INCLUDE_WIDE_RULES", "0").strip() in (
- "1",
- "true",
- "True",
- "yes",
-)
-MATCH_PROFILE_TIGHT_LO = _getenv_float("MATCH_PROFILE_TIGHT_LO", "0.35")
-MATCH_PROFILE_TIGHT_HI = _getenv_float("MATCH_PROFILE_TIGHT_HI", "0.65")
-MATCH_PROFILE_TOP_PER_TF = _getenv_int("MATCH_PROFILE_TOP_PER_TF", "6")
-MATCH_PROFILE_TOP_GLOBAL = _getenv_int("MATCH_PROFILE_TOP_GLOBAL", "30")
-MATCH_PROFILE_MIN_SEPARATION = _getenv_float("MATCH_PROFILE_MIN_SEPARATION", "0.25")
-MATCH_PROFILE_MIN_SAMPLES = _getenv_int("MATCH_PROFILE_MIN_SAMPLES", "10")
-MATCH_INCLUDE_MTF_CROSS = _getenv("MATCH_INCLUDE_MTF_CROSS", "1").strip() in (
- "1",
- "true",
- "True",
- "yes",
-)
-MATCH_LIVE_LOOKBACK_DAYS = _getenv_int("MATCH_LIVE_LOOKBACK_DAYS", "14")
-MATCH_LIVE_CACHE_SEC = _getenv_int("MATCH_LIVE_CACHE_SEC", "180")
-MATCH_LABEL_MODE = _getenv("MATCH_LABEL_MODE", "leg_gt")
-MATCH_MAX_HOLD_DAYS = _getenv_int("MATCH_MAX_HOLD_DAYS", "45")
-MATCH_INCLUDE_ATOMIC = _getenv("MATCH_INCLUDE_ATOMIC", "0").strip() in (
- "1",
- "true",
- "True",
- "yes",
-)
-MATCH_HOLDOUT_RATIO = _getenv_float("MATCH_HOLDOUT_RATIO", "0.15")
-MATCH_MIN_FIRES_HOLDOUT = _getenv_int("MATCH_MIN_FIRES_HOLDOUT", "5")
-MATCH_MONITOR_MAX_PER_SIDE = _getenv_int("MATCH_MONITOR_MAX_PER_SIDE", "1")
-MONITOR_ALERT_COOLDOWN_MIN = _getenv_int("MONITOR_ALERT_COOLDOWN_MIN", "3")
-MATCH_KIND_PRIORITY: tuple[str, ...] = tuple(
- x.strip()
- for x in _getenv(
- "MATCH_KIND_PRIORITY",
- "mtf_cross,compound_tight,contrast,compound,atomic,wide",
- ).split(",")
- if x.strip()
-)
-
-# --- Simulation: 시뮬레이션 리포트 ---
-SIM_WALK_FORWARD_MIN_MONTHS = _getenv_int("SIM_WALK_FORWARD_MIN_MONTHS", "3")
-SIM_FEE_STRESS_MULT = _getenv_float("SIM_FEE_STRESS_MULT", "2.0")
-SIM_GO_MIN_HOLDOUT_EV = _getenv_float("SIM_GO_MIN_HOLDOUT_EV", "0.0")
-SIM_GO_MIN_HOLDOUT_PF = _getenv_float("SIM_GO_MIN_HOLDOUT_PF", "1.0")
-SIM_GO_WF_POSITIVE_RATIO = _getenv_float("SIM_GO_WF_POSITIVE_RATIO", "0.5")
-# hybrid DD tier 승격·검증 (Option C +300%)
-SIM_HYBRID_MIN_HOLDOUT_PNL_PCT = _getenv_float("SIM_HYBRID_MIN_HOLDOUT_PNL_PCT", "0.0")
-SIM_HYBRID_MAX_MDD_PCT = _getenv_float("SIM_HYBRID_MAX_MDD_PCT", "30.0")
-SIM_OPTION_C_TARGET_PNL_PCT = _getenv_float("SIM_OPTION_C_TARGET_PNL_PCT", "300.0")
-SIM_OPTION_C_PHASE2_TARGET_PNL_PCT = _getenv_float("SIM_OPTION_C_PHASE2_TARGET_PNL_PCT", "1000.0")
-SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO = _getenv_float("SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO", "0.85")
-SIM_OPTION_C_MIN_GT_CAPTURE = _getenv_float("SIM_OPTION_C_MIN_GT_CAPTURE", "0.23")
-SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO = _getenv_float("SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO", "0.5")
-SIM_PRIMARY_SIZING = _getenv("SIM_PRIMARY_SIZING", "auto").strip().lower()
-if SIM_PRIMARY_SIZING not in ("auto", "hybrid", "causal_tier"):
- SIM_PRIMARY_SIZING = "auto"
-
-# --- Operations: 실거래 (LIVE=1, 시뮬 Go·monitor_rules 선행) ---
-LIVE_TRADING_ENABLED = _getenv("LIVE_TRADING_ENABLED", "1").strip() in (
- "1",
- "true",
- "True",
- "yes",
-)
-LIVE_ORDER_KRW = _getenv_int("LIVE_ORDER_KRW", "40000")
-LIVE_BUY_PCT_LARGE = _getenv_float("LIVE_BUY_PCT_LARGE", "1.0")
-LIVE_BUY_PCT_SMALL = _getenv_float("LIVE_BUY_PCT_SMALL", "0.05")
-# 운영 기본: 일한도=초기자금 1배 (GT_INITIAL_CASH_KRW).
-LIVE_DAILY_KRW_MAX = _getenv_int("LIVE_DAILY_KRW_MAX", "400000")
-LIVE_COOLDOWN_MIN = _getenv_int("LIVE_COOLDOWN_MIN", "3")
-LIVE_MAX_TRADES_PER_DAY = _getenv_int("LIVE_MAX_TRADES_PER_DAY", "15")
-LIVE_DAILY_LOSS_LIMIT_KRW = _getenv_int("LIVE_DAILY_LOSS_LIMIT_KRW", "40000")
-LIVE_SLIPPAGE_PCT = _getenv_float("LIVE_SLIPPAGE_PCT", "0.05")
-# 06: fire_outcomes monitor 발화로 hybrid 이력 부트스트랩 (시뮬 sim_causal_hybrid 정합)
-LIVE_HYBRID_BOOTSTRAP_FIRES = _getenv("LIVE_HYBRID_BOOTSTRAP_FIRES", "1").strip() in (
- "1",
- "true",
- "True",
- "yes",
-)
-
-# --- 일일 수익률 텔레그램 (07_daily_pnl_telegram) ---
-DAILY_PNL_REPORT_ENABLED = _getenv("DAILY_PNL_REPORT_ENABLED", "1").strip() in (
- "1",
- "true",
- "True",
- "yes",
-)
-DAILY_PNL_REPORT_HOUR = _getenv_int("DAILY_PNL_REPORT_HOUR", "19")
-DAILY_PNL_REPORT_MINUTE = _getenv_int("DAILY_PNL_REPORT_MINUTE", "0")
-DAILY_PNL_REPORT_TZ = _getenv("DAILY_PNL_REPORT_TZ", "Asia/Seoul")
-# 06 루프마다 스냅샷 적재 → 24시간 전 자산 비교 가능
-DAILY_PNL_SNAPSHOT_ON_LIVE = _getenv("DAILY_PNL_SNAPSHOT_ON_LIVE", "1").strip() in (
- "1",
- "true",
- "True",
- "yes",
-)
-DAILY_PNL_SNAPSHOT_RETENTION_DAYS = _getenv_int("DAILY_PNL_SNAPSHOT_RETENTION_DAYS", "90")
diff --git a/data/ground_truth/ground_truth_trades.json b/data/ground_truth/ground_truth_trades.json
deleted file mode 100644
index d11ef72..0000000
--- a/data/ground_truth/ground_truth_trades.json
+++ /dev/null
@@ -1,6403 +0,0 @@
-{
- "name": "ground_truth_split_buy_peak_sell",
- "model": {
- "selection_mode": "split_buy_peak_sell",
- "leg_definition": "이전 고점 매도 ~ 다음 고점 매도 구간 = leg_id; 기간말 잔여 구간은 마지막 leg",
- "entry": {
- "pivot": "trough",
- "price": "Low",
- "weight_rule": "inverse_price_normalized",
- "weight_formula": "w_i = (1/price_i) / sum(1/price_j)",
- "max_buys_per_leg": 12,
- "min_bars_between_buys": 24,
- "bb_filter": "bb_pos <= 0.45"
- },
- "exit": {
- "pivot": "peak",
- "price": "High",
- "weight_rule": "fixed_split_or_full",
- "weights_split": [
- 0.65,
- 0.35
- ],
- "split_gap_pct": 2.5,
- "max_sells_per_leg": 2
- },
- "capital": {
- "buy": "target = total_asset * (weight/remaining_weights) * tier_scale; amount = min(target, available_cash/(1+fee))",
- "optimal_buy_rate": "weight / sum(remaining_buy_weights_in_leg)",
- "large_leg_top_pct": 0.2,
- "pct_large_leg": 1.0,
- "pct_small_leg": 0.05,
- "min_order_krw": 5000.0,
- "sell": "sell_base_qty * sell_weight * price (last sell = full leg_qty)"
- },
- "execution": {
- "chrono": "amount_krw 배분·summary.pnl_pct = 시각순 체결(매도 후 현금 → 다음 매수 반영)",
- "leg_block_json": "JSON 저장 순서 = leg별 매수 전량 → 매도 전량 (차트·테이블 leg 정합)"
- }
- },
- "method": "split_buy_at_troughs + peak_sell_1or2",
- "symbol": "WLD",
- "interval_min": 3,
- "lookback_days": 365,
- "period_start": "2025-06-04 08:30:00",
- "period_end": "2026-06-04 08:27:00",
- "trend_at_end": "up",
- "params": {
- "min_swing_pct": 4.0,
- "pivot_order": 20,
- "min_bars_between": 30,
- "max_round_trips": 24,
- "selection_mode": "split_buy_peak_sell",
- "buy_min_swing_pct": 3.0,
- "buy_bb_max": 0.45,
- "max_sells_per_leg": 2
- },
- "summary": {
- "pivot_candidates": 411,
- "sell_peaks": 80,
- "trade_count": 485,
- "buy_count": 325,
- "sell_count": 160,
- "round_trips": 81,
- "sum_sell_leg_return_pct": 1795.33,
- "initial_cash_krw": 400000,
- "final_asset_krw": 24488124.0,
- "pnl_krw": 24088124.0,
- "pnl_pct": 6022.03,
- "total_fees_krw": 127822.0,
- "cash_krw": 24488124.0,
- "holding_qty": 0.0,
- "holding_value_krw": 0.0,
- "mark_price": 786.0,
- "fee_rate": 0.0005,
- "realized_final_asset_krw": 24488124.0,
- "realized_pnl_krw": 24088124.0,
- "realized_pnl_pct": 6022.03,
- "unrealized_pnl_krw": 0.0,
- "execution_order": "chronological",
- "order_amount_min_krw": 5000,
- "buy_pct_large_leg": 1.0,
- "buy_pct_small_leg": 0.05,
- "large_leg_top_pct": 0.2,
- "buy_executed": 315,
- "buy_skipped": 10,
- "sell_executed": 160,
- "sell_skipped": 0,
- "buy_total_krw": 115713852.0,
- "large_leg_count": 55,
- "large_tier_buy_count": 55,
- "buy_amount_avg_krw": 367346.0,
- "buy_amount_min_krw": 5000,
- "buy_amount_max_krw": 7791115.0
- },
- "note": "저점 분할 매수(비중=삼각형), 고점 1~2회 매도. 매수=총자산×최적비중×티어(상위 leg 대형·그 외 소형), 현금 한도·최소 ₩5,000. 체결 순서=chronological. summary.pnl_pct는 미청산 포함 종가 평가.",
- "trades": [
- {
- "dt": "2025-06-06 06:12:00",
- "action": "buy",
- "price": 1421.0,
- "memo": "저점 분할 매수 · 비중 100% · 1회 · BB하단 · leg#0",
- "weight": 1.0,
- "amount_krw": 20000.0,
- "leg_id": 0,
- "bb_pos": 0.295,
- "rsi": 35.6,
- "pivot_kind": "trough",
- "forward_return_pct": 11.05
- },
- {
- "dt": "2025-06-07 19:57:00",
- "action": "sell",
- "price": 1578.0,
- "memo": "고점 매도 · 비중 65% · 분할 · leg#0",
- "weight": 0.65,
- "amount_krw": 14436.0,
- "leg_id": 0,
- "bb_pos": 0.888,
- "rsi": 72.2,
- "pivot_kind": "peak",
- "forward_return_pct": 11.05
- },
- {
- "dt": "2025-06-07 21:42:00",
- "action": "sell",
- "price": 1575.0,
- "memo": "고점 매도 · 비중 35% · 분할 · leg#0",
- "weight": 0.35,
- "amount_krw": 7759.0,
- "leg_id": 0,
- "bb_pos": 1.0,
- "rsi": 61.3,
- "pivot_kind": "peak",
- "forward_return_pct": 10.84
- },
- {
- "dt": "2025-06-08 19:27:00",
- "action": "buy",
- "price": 1509.0,
- "memo": "저점 분할 매수 · 비중 8% · 12회 · BB하단 · leg#1",
- "weight": 0.076,
- "amount_krw": 5000,
- "leg_id": 1,
- "bb_pos": 0.0,
- "rsi": 30.0,
- "pivot_kind": "trough",
- "forward_return_pct": -11.2
- },
- {
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- "weight": 0.168,
- "amount_krw": 408490.0,
- "leg_id": 79,
- "bb_pos": 0.024,
- "rsi": 35.7,
- "pivot_kind": "trough",
- "forward_return_pct": 11.14
- },
- {
- "dt": "2026-06-04 04:33:00",
- "action": "buy",
- "price": 764.0,
- "memo": "저점 분할 매수 · 비중 17% · 6회 · BB하단 · leg#79",
- "weight": 0.166,
- "amount_krw": 608249.0,
- "leg_id": 79,
- "bb_pos": 0.185,
- "rsi": 22.2,
- "pivot_kind": "trough",
- "forward_return_pct": 9.69
- },
- {
- "dt": "2026-06-04 05:54:00",
- "action": "buy",
- "price": 760.0,
- "memo": "저점 분할 매수 · 비중 17% · 6회 · BB하단 · leg#79",
- "weight": 0.166,
- "amount_krw": 1216018.0,
- "leg_id": 79,
- "bb_pos": 0.063,
- "rsi": 23.8,
- "pivot_kind": "trough",
- "forward_return_pct": 10.26
- },
- {
- "dt": "2026-06-04 06:51:00",
- "action": "sell",
- "price": 797.0,
- "memo": "고점 매도 · 비중 65% · 분할 · leg#79",
- "weight": 0.65,
- "amount_krw": 2034913.0,
- "leg_id": 79,
- "bb_pos": 0.91,
- "rsi": 63.3,
- "pivot_kind": "peak",
- "forward_return_pct": 5.01
- },
- {
- "dt": "2026-06-04 07:57:00",
- "action": "sell",
- "price": 816.0,
- "memo": "고점 매도 · 비중 35% · 분할 · leg#79",
- "weight": 0.35,
- "amount_krw": 1121843.0,
- "leg_id": 79,
- "bb_pos": 0.542,
- "rsi": 46.8,
- "pivot_kind": "peak",
- "forward_return_pct": 7.51
- },
- {
- "dt": "2026-06-04 07:21:00",
- "action": "buy",
- "price": 798.0,
- "memo": "저점 분할 매수 · 비중 49% · leg#80(기간말)",
- "weight": 0.494,
- "amount_krw": 604296.0,
- "leg_id": 80,
- "bb_pos": 0.336,
- "rsi": 38.1,
- "pivot_kind": "trough",
- "forward_return_pct": -1.5
- },
- {
- "dt": "2026-06-04 08:21:00",
- "action": "buy",
- "price": 780.0,
- "memo": "저점 분할 매수 · 비중 51% · leg#80(기간말)",
- "weight": 0.506,
- "amount_krw": 1223785.0,
- "leg_id": 80,
- "bb_pos": 0.388,
- "rsi": 47.1,
- "pivot_kind": "trough",
- "forward_return_pct": 0.77
- },
- {
- "dt": "2026-06-04 08:27:00",
- "action": "sell",
- "price": 786.0,
- "memo": "기간말 잔여 청산 · leg#80",
- "weight": 1.0,
- "amount_krw": 1828408.0,
- "leg_id": 80,
- "bb_pos": 0.408,
- "rsi": 50.0,
- "pivot_kind": "peak",
- "forward_return_pct": -0.37
- }
- ]
-}
\ No newline at end of file
diff --git a/data/ops/.gitkeep b/data/ops/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/deepcoin/__init__.py b/deepcoin/__init__.py
deleted file mode 100644
index 78b1972..0000000
--- a/deepcoin/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""
-DeepCoin 패키지 — WLD MTF 분석·정답·운영 단계.
-"""
-
-from pathlib import Path
-import sys
-
-_PROJECT_ROOT = Path(__file__).resolve().parents[1]
-if str(_PROJECT_ROOT) not in sys.path:
- sys.path.insert(0, str(_PROJECT_ROOT))
-
-from deepcoin.paths import ensure_dirs
-
-ensure_dirs()
diff --git a/deepcoin/analysis/__init__.py b/deepcoin/analysis/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deepcoin/analysis/general_analysis_align.py b/deepcoin/analysis/general_analysis_align.py
deleted file mode 100644
index 4c88e0b..0000000
--- a/deepcoin/analysis/general_analysis_align.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""
-general_analysis MTF 합성·정렬 점수.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import pandas as pd
-
-from config import (
- ALIGN_BB_POS_HIGH,
- ALIGN_BB_POS_LOW,
- ALIGN_RSI_CONFLICT_TIMING_HIGH,
- ALIGN_RSI_CONFLICT_TIMING_LOW,
- ALIGN_RSI_CONFLICT_TREND_HIGH,
- ALIGN_RSI_CONFLICT_TREND_LOW,
- ALIGN_RSI_OVERBOUGHT,
- ALIGN_RSI_OVERSOLD,
-)
-from deepcoin.analysis.general_analysis_config import TIMING_INTERVALS, TREND_INTERVALS
-from deepcoin.analysis.general_analysis_core import ga_col, interval_tf_prefix
-
-
-def general_analysis_mtf_scores(
- prefixed_row: dict[str, Any],
-) -> dict[str, float | int | str]:
- """
- 간격 접두사가 붙은 스냅샷 행에서 MTF 합성 점수 계산.
-
- Args:
- prefixed_row: m3_ga_rsi 형태 flat dict.
-
- Returns:
- ga_align_* 점수.
- """
- rsi_oversold = 0
- rsi_overbought = 0
- trend_up = 0
- trend_down = 0
- n_timing = 0
- n_trend = 0
- conflict = 0
-
- for interval in TIMING_INTERVALS:
- p = interval_tf_prefix(interval)
- rk = f"{p}_RSI"
- if rk in prefixed_row and prefixed_row[rk] is not None:
- n_timing += 1
- rsi = float(prefixed_row[rk])
- if rsi < ALIGN_RSI_OVERSOLD:
- rsi_oversold += 1
- if rsi > ALIGN_RSI_OVERBOUGHT:
- rsi_overbought += 1
-
- for interval in TREND_INTERVALS:
- p = interval_tf_prefix(interval)
- sk = f"{p}_{ga_col('struct_trend')}"
- if sk in prefixed_row:
- n_trend += 1
- t = prefixed_row[sk]
- if t == "up":
- trend_up += 1
- elif t == "down":
- trend_down += 1
-
- m3_rsi = prefixed_row.get("m3_RSI")
- d1_rsi = prefixed_row.get("d1_RSI")
- if m3_rsi is not None and d1_rsi is not None:
- if (
- float(m3_rsi) < ALIGN_RSI_CONFLICT_TIMING_LOW
- and float(d1_rsi) > ALIGN_RSI_CONFLICT_TREND_HIGH
- ):
- conflict = 1
- if (
- float(m3_rsi) > ALIGN_RSI_CONFLICT_TIMING_HIGH
- and float(d1_rsi) < ALIGN_RSI_CONFLICT_TREND_LOW
- ):
- conflict = 1
-
- timing_buy_align = rsi_oversold / max(len(TIMING_INTERVALS), 1)
- timing_sell_align = rsi_overbought / max(len(TIMING_INTERVALS), 1)
-
- return {
- "ga_align_rsi_oversold_tf": rsi_oversold,
- "ga_align_rsi_overbought_tf": rsi_overbought,
- "ga_align_trend_up_tf": trend_up,
- "ga_align_trend_down_tf": trend_down,
- "ga_align_timing_buy_score": round(timing_buy_align, 3),
- "ga_align_timing_sell_score": round(timing_sell_align, 3),
- "ga_align_trend_score": round(
- (trend_up - trend_down) / max(n_trend, 1), 3
- ),
- "ga_align_mtf_conflict": conflict,
- }
-
-
-def general_analysis_mtf_vote_latest(
- frames_enriched: dict[int, pd.DataFrame],
-) -> dict[str, float | int | str]:
- """
- 각 TF 최신 완성봉 지표로 TF 가중 투표·필터 점수 산출.
-
- Args:
- frames_enriched: interval → enrich된 DataFrame.
-
- Returns:
- ga_vote_* 점수 (접두사 없음, ga_col로 감쌀 것).
- """
- votes_buy = 0
- votes_sell = 0
- trend_ok = 0
- n = 0
-
- for interval in TIMING_INTERVALS:
- df = frames_enriched.get(interval)
- if df is None or df.empty:
- continue
- row = df.iloc[-1]
- n += 1
- rsi = row.get("RSI")
- if rsi is not None and not pd.isna(rsi):
- if float(rsi) < ALIGN_RSI_OVERSOLD:
- votes_buy += 1
- if float(rsi) > ALIGN_RSI_OVERBOUGHT:
- votes_sell += 1
- bb_pos = row.get("bb_pos")
- if bb_pos is not None and float(bb_pos) < ALIGN_BB_POS_LOW:
- votes_buy += 1
- if bb_pos is not None and float(bb_pos) > ALIGN_BB_POS_HIGH:
- votes_sell += 1
-
- for interval in TREND_INTERVALS:
- df = frames_enriched.get(interval)
- if df is None or df.empty:
- continue
- row = df.iloc[-1]
- st = row.get(ga_col("struct_trend"), "range")
- if st == "up":
- trend_ok += 1
- elif st == "down":
- trend_ok -= 1
-
- return {
- "vote_timing_buy": votes_buy,
- "vote_timing_sell": votes_sell,
- "vote_trend_score": trend_ok,
- "vote_tf_used": n,
- }
-
-
-def general_analysis_vote_columns() -> list[str]:
- return ["vote_timing_buy", "vote_timing_sell", "vote_trend_score", "vote_tf_used"]
diff --git a/deepcoin/analysis/general_analysis_candles.py b/deepcoin/analysis/general_analysis_candles.py
deleted file mode 100644
index 61c7d8e..0000000
--- a/deepcoin/analysis/general_analysis_candles.py
+++ /dev/null
@@ -1,114 +0,0 @@
-"""
-general_analysis 캔들·차트 변환 (Heikin-Ashi, 복수봉 패턴).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from deepcoin.analysis.general_analysis_core import ga_col
-
-
-def general_analysis_apply_candles(df: pd.DataFrame) -> pd.DataFrame:
- """
- 단일·복수 봉 캔들 패턴 및 Heikin-Ashi 컬럼 추가.
-
- Args:
- df: OHLCV.
-
- Returns:
- ga_* 캔들 컬럼이 추가된 DataFrame.
- """
- out = df.copy()
- o = out["Open"].astype(float)
- h = out["High"].astype(float)
- l = out["Low"].astype(float)
- c = out["Close"].astype(float)
-
- rng = (h - l).replace(0, np.nan)
- body = (c - o).abs()
- out[ga_col("body_ratio")] = (body / rng).fillna(0).clip(0, 1)
- upper_wick = h - np.maximum(o, c)
- lower_wick = np.minimum(o, c) - l
- out[ga_col("upper_wick_ratio")] = (upper_wick / rng).fillna(0).clip(0, 1)
- out[ga_col("lower_wick_ratio")] = (lower_wick / rng).fillna(0).clip(0, 1)
- out[ga_col("bullish")] = (c > o).astype(int)
- out[ga_col("bearish")] = (c < o).astype(int)
- out[ga_col("hammer")] = (
- (out[ga_col("lower_wick_ratio")] > 0.45) & (out[ga_col("body_ratio")] < 0.35)
- ).astype(int)
- out[ga_col("shooting_star")] = (
- (out[ga_col("upper_wick_ratio")] > 0.45) & (out[ga_col("body_ratio")] < 0.35)
- ).astype(int)
- out[ga_col("doji")] = (out[ga_col("body_ratio")] < 0.1).astype(int)
-
- prev_o, prev_c = o.shift(1), c.shift(1)
- out[ga_col("bullish_engulfing")] = (
- (c > o) & (prev_c < prev_o) & (c >= prev_o) & (o <= prev_c)
- ).astype(int)
- out[ga_col("bearish_engulfing")] = (
- (c < o) & (prev_c > prev_o) & (c <= prev_o) & (o >= prev_c)
- ).astype(int)
-
- o2, c2 = o.shift(2), c.shift(2)
- mid1 = (o.shift(1) + c.shift(1)) / 2
- out[ga_col("morning_star")] = (
- (c2 < o2)
- & (abs(c.shift(1) - o.shift(1)) < rng.shift(1) * 0.15)
- & (c > o)
- & (c > mid1)
- ).astype(int)
- out[ga_col("evening_star")] = (
- (c2 > o2)
- & (abs(c.shift(1) - o.shift(1)) < rng.shift(1) * 0.15)
- & (c < o)
- & (c < mid1)
- ).astype(int)
-
- out[ga_col("three_white_soldiers")] = (
- (c > o)
- & (c.shift(1) > o.shift(1))
- & (c.shift(2) > o.shift(2))
- & (c > c.shift(1))
- & (c.shift(1) > c.shift(2))
- ).astype(int)
- out[ga_col("three_black_crows")] = (
- (c < o)
- & (c.shift(1) < o.shift(1))
- & (c.shift(2) < o.shift(2))
- & (c < c.shift(1))
- & (c.shift(1) < c.shift(2))
- ).astype(int)
-
- # Heikin-Ashi
- ha_close = (o + h + l + c) / 4
- ha_open = ha_close.copy()
- ha_open.iloc[0] = (o.iloc[0] + c.iloc[0]) / 2
- for i in range(1, len(out)):
- ha_open.iloc[i] = (ha_open.iloc[i - 1] + ha_close.iloc[i - 1]) / 2
- out[ga_col("ha_close")] = ha_close
- out[ga_col("ha_open")] = ha_open
- out[ga_col("ha_bull")] = (ha_close > ha_open).astype(int)
- out[ga_col("ha_trend_up")] = (
- (ha_close > ha_close.shift(1)) & (ha_close.shift(1) > ha_close.shift(2))
- ).astype(int)
-
- return out
-
-
-def general_analysis_candle_columns() -> list[str]:
- return [
- "body_ratio",
- "hammer",
- "shooting_star",
- "doji",
- "bullish_engulfing",
- "bearish_engulfing",
- "morning_star",
- "evening_star",
- "three_white_soldiers",
- "three_black_crows",
- "ha_bull",
- "ha_trend_up",
- ]
diff --git a/deepcoin/analysis/general_analysis_chart.py b/deepcoin/analysis/general_analysis_chart.py
deleted file mode 100644
index 863e97b..0000000
--- a/deepcoin/analysis/general_analysis_chart.py
+++ /dev/null
@@ -1,159 +0,0 @@
-"""
-general_analysis 차트 유형 (캔들·선·바·Renko·P&F).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from deepcoin.analysis.general_analysis_core import ga_col
-
-
-def _renko_direction_series(close: pd.Series, brick: pd.Series) -> pd.Series:
- """ATR 기반 브릭 크기로 Renko 방향 (+1/-1/0) 시계열."""
- n = len(close)
- direction = pd.Series(0, index=close.index, dtype=int)
- if n < 2:
- return direction
-
- price = float(close.iloc[0])
- for i in range(1, n):
- b = float(brick.iloc[i]) if not np.isnan(brick.iloc[i]) else float(close.diff().abs().median())
- if b < 1e-9:
- b = 1e-9
- c = float(close.iloc[i])
- if c >= price + b:
- steps = int((c - price) // b)
- direction.iloc[i] = 1
- price += steps * b
- elif c <= price - b:
- steps = int((price - c) // b)
- direction.iloc[i] = -1
- price -= steps * b
- return direction
-
-
-def general_analysis_apply_chart_bars(df: pd.DataFrame) -> pd.DataFrame:
- """
- 봉 단위 차트 파생 컬럼 (선 기울기, Renko, P&F).
-
- Args:
- df: OHLCV (+ ga_atr_14 권장).
-
- Returns:
- ga_chart_* 시계열 컬럼 추가.
- """
- out = df.copy()
- c = out["Close"].astype(float)
- h = out["High"].astype(float)
- l = out["Low"].astype(float)
-
- out[ga_col("chart_line_slope_1")] = c.diff()
- out[ga_col("chart_bar_range_pct")] = (h - l) / c.replace(0, np.nan) * 100
-
- from config import GA_ATR_PERIOD
-
- brick = (
- out[ga_col("atr_14")]
- if ga_col("atr_14") in out.columns
- else (h - l).rolling(GA_ATR_PERIOD).mean()
- )
- brick = brick.fillna(c.diff().abs().rolling(20).median()).replace(0, np.nan).bfill()
- renko_dir = _renko_direction_series(c, brick)
- out[ga_col("chart_renko_dir")] = renko_dir
- out[ga_col("chart_renko_up")] = (renko_dir == 1).astype(int)
-
- box = brick.fillna(1.0)
- pnf = pd.Series(0, index=out.index, dtype=int)
- col = 0
- for i in range(1, len(c)):
- b = float(box.iloc[i])
- move = c.iloc[i] - c.iloc[i - 1]
- if move >= b:
- col += 1
- pnf.iloc[i] = 1
- elif move <= -b:
- col -= 1
- pnf.iloc[i] = -1
- out[ga_col("chart_pnf_col")] = pnf
-
- if "Volume" in out.columns:
- v = out["Volume"].astype(float)
- out[ga_col("chart_vol_spike")] = (v > v.rolling(20).mean() * 1.8).astype(int)
-
- if ga_col("ha_trend_up") in out.columns:
- out[ga_col("chart_ha_trend")] = out[ga_col("ha_trend_up")]
- elif ga_col("ha_bull") in out.columns:
- out[ga_col("chart_ha_trend")] = out[ga_col("ha_bull")]
-
- return out
-
-
-def general_analysis_chart_metrics(df: pd.DataFrame) -> dict[str, object]:
- """
- lookback 구간 차트 요약 (마지막 봉 스냅샷용).
-
- Returns:
- ga_chart_* dict.
- """
- res: dict[str, object] = {
- "chart_type_candle": 1,
- "chart_line_slope": 0.0,
- "chart_bar_range_pct": 0.0,
- "chart_ha_trend": 0,
- "chart_renko_brick_up_ratio": 0.5,
- "chart_renko_dir": 0,
- "chart_pnf_col": 0,
- "chart_vol_spike": 0,
- }
- if df is None or len(df) < 5:
- return {ga_col(k): v for k, v in res.items()}
-
- row = df.iloc[-1]
- c = df["Close"].astype(float)
- res["chart_line_slope"] = float((c.iloc[-1] - c.iloc[0]) / max(len(c) - 1, 1))
- res["chart_bar_range_pct"] = float(
- (df["High"].iloc[-1] - df["Low"].iloc[-1]) / max(c.iloc[-1], 1e-9) * 100
- )
-
- if ga_col("chart_renko_dir") in df.columns:
- rd = df[ga_col("chart_renko_dir")].astype(float)
- up = (rd == 1).sum()
- down = (rd == -1).sum()
- res["chart_renko_brick_up_ratio"] = round(up / max(up + down, 1), 3)
- res["chart_renko_dir"] = int(rd.iloc[-1])
- else:
- diff = c.diff().fillna(0)
- up = (diff > 0).sum()
- down = (diff < 0).sum()
- res["chart_renko_brick_up_ratio"] = round(up / max(up + down, 1), 3)
-
- if ga_col("chart_pnf_col") in df.columns:
- res["chart_pnf_col"] = int(df[ga_col("chart_pnf_col")].iloc[-1])
- if ga_col("chart_ha_trend") in df.columns:
- res["chart_ha_trend"] = int(row[ga_col("chart_ha_trend")])
- elif ga_col("ha_trend_up") in df.columns:
- res["chart_ha_trend"] = int(row[ga_col("ha_trend_up")])
- if ga_col("chart_vol_spike") in df.columns:
- res["chart_vol_spike"] = int(row[ga_col("chart_vol_spike")])
- elif "Volume" in df.columns:
- v = df["Volume"].astype(float)
- res["chart_vol_spike"] = int(v.iloc[-1] > v.iloc[-20:].mean() * 1.8)
-
- return {ga_col(k): v for k, v in res.items()}
-
-
-def general_analysis_chart_columns() -> list[str]:
- return [
- "chart_type_candle",
- "chart_line_slope",
- "chart_line_slope_1",
- "chart_bar_range_pct",
- "chart_ha_trend",
- "chart_renko_brick_up_ratio",
- "chart_renko_dir",
- "chart_renko_up",
- "chart_pnf_col",
- "chart_vol_spike",
- ]
diff --git a/deepcoin/analysis/general_analysis_config.py b/deepcoin/analysis/general_analysis_config.py
deleted file mode 100644
index e82d4d4..0000000
--- a/deepcoin/analysis/general_analysis_config.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
-general_analysis MTF 설정 (config.py 재노출).
-"""
-
-from __future__ import annotations
-
-from config import (
- CONTEXT_TAIL_ROWS,
- GA_COL_PREFIX,
- GENERAL_ANALYSIS_INTERVALS,
- GROUND_TRUTH_FILE,
- INTERVAL_PREFIX,
- LOOKBACK_BARS,
- REPORTS_ANALYSIS_CAPABILITY_HTML,
- REPORTS_ANALYSIS_LATEST_DIR,
- REPORTS_ANALYSIS_REPORT_HTML,
- REPORTS_ANALYSIS_TRADES_CSV,
- TIMING_INTERVALS,
- TREND_INTERVALS,
-)
-
-DEFAULT_TRADES_FILE = GROUND_TRUTH_FILE
-DEFAULT_OUTPUT_CSV = str(REPORTS_ANALYSIS_TRADES_CSV)
-DEFAULT_OUTPUT_HTML = str(REPORTS_ANALYSIS_REPORT_HTML)
-DEFAULT_CAPABILITY_HTML = str(REPORTS_ANALYSIS_CAPABILITY_HTML)
-DEFAULT_LATEST_DIR = str(REPORTS_ANALYSIS_LATEST_DIR)
diff --git a/deepcoin/analysis/general_analysis_context.py b/deepcoin/analysis/general_analysis_context.py
deleted file mode 100644
index 534abb5..0000000
--- a/deepcoin/analysis/general_analysis_context.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""
-general_analysis lookback 컨텍스트 특징 (패턴·파동·VP·하모닉) 봉별 적용.
-"""
-
-from __future__ import annotations
-
-import pandas as pd
-
-from deepcoin.analysis.general_analysis_config import CONTEXT_TAIL_ROWS, LOOKBACK_BARS
-from deepcoin.analysis.general_analysis_core import ga_col
-from deepcoin.analysis.general_analysis_harmonic import (
- general_analysis_harmonic_columns,
- general_analysis_harmonic_snapshot,
-)
-from deepcoin.analysis.general_analysis_patterns import general_analysis_apply_patterns_to_bars
-from deepcoin.analysis.general_analysis_volume import (
- general_analysis_volume_columns,
- general_analysis_volume_snapshot,
-)
-from deepcoin.analysis.general_analysis_wave import general_analysis_apply_wave_to_bars
-
-
-def general_analysis_apply_volume_to_bars(
- df: pd.DataFrame,
- interval: int,
- tail_rows: int | None = None,
-) -> pd.DataFrame:
- """Volume Profile 컬럼을 최근 봉에 롤링 적용."""
- out = df.copy()
- for k in general_analysis_volume_columns():
- out[ga_col(k)] = 0.0 if k != "vp_in_value_area" else 0
-
- lb = LOOKBACK_BARS.get(interval, 80)
- n = len(out)
- if n < lb + 1:
- return out
- if tail_rows is None:
- tail_rows = CONTEXT_TAIL_ROWS.get(interval, 5000)
- start = max(lb, n - tail_rows)
-
- for i in range(start, n):
- snap = general_analysis_volume_snapshot(out.iloc[i - lb : i])
- idx = out.index[i]
- for k, v in snap.items():
- out.at[idx, k] = v
-
- return out
-
-
-def general_analysis_apply_harmonic_to_bars(
- df: pd.DataFrame,
- interval: int,
- tail_rows: int | None = None,
-) -> pd.DataFrame:
- """하모닉 패턴 컬럼 롤링 적용."""
- out = df.copy()
- for k in general_analysis_harmonic_columns():
- default = "none" if k == "harmonic_label" else 0
- out[ga_col(k)] = default
-
- lb = LOOKBACK_BARS.get(interval, 80)
- n = len(out)
- if n < lb + 1:
- return out
- if tail_rows is None:
- tail_rows = CONTEXT_TAIL_ROWS.get(interval, 5000)
- start = max(lb, n - tail_rows)
-
- for i in range(start, n):
- snap = general_analysis_harmonic_snapshot(out.iloc[i - lb : i])
- idx = out.index[i]
- for k, v in snap.items():
- out.at[idx, k] = v
-
- return out
-
-
-def general_analysis_apply_context_features(
- df: pd.DataFrame,
- interval: int,
- tail_rows: int | None = None,
-) -> pd.DataFrame:
- """
- 패턴·파동·VP·하모닉 lookback 라벨을 봉 시계열에 병합.
-
- Args:
- df: general_analysis_enrich_bars 1단계 결과.
- interval: 분봉 간격(분).
- tail_rows: 롤링 적용 봉 수 상한.
-
- Returns:
- 컨텍스트 ga_* 컬럼이 추가된 DataFrame.
- """
- out = general_analysis_apply_patterns_to_bars(df, interval, tail_rows)
- out = general_analysis_apply_wave_to_bars(out, interval, tail_rows)
- out = general_analysis_apply_volume_to_bars(out, interval, tail_rows)
- out = general_analysis_apply_harmonic_to_bars(out, interval, tail_rows)
- return out
diff --git a/deepcoin/analysis/general_analysis_core.py b/deepcoin/analysis/general_analysis_core.py
deleted file mode 100644
index 921ad4a..0000000
--- a/deepcoin/analysis/general_analysis_core.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""
-general_analysis 공통 유틸 (슬라이스·피벗·컬럼 접두사).
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from deepcoin.analysis.general_analysis_config import GA_COL_PREFIX, LOOKBACK_BARS
-
-
-def ga_col(name: str) -> str:
- """general_analysis 출력 컬럼명."""
- return f"{GA_COL_PREFIX}{name}"
-
-
-def interval_tf_prefix(interval: int) -> str:
- """간격 접두사 (m3, d1)."""
- from deepcoin.analysis.general_analysis_config import INTERVAL_PREFIX
-
- return INTERVAL_PREFIX.get(interval, f"m{interval}")
-
-
-def prefixed_snapshot(
- row: pd.Series,
- interval: int,
- keys: list[str] | tuple[str, ...],
-) -> dict[str, Any]:
- """한 봉의 ga_ 컬럼을 {m3_ga_rsi: ...} 형태로 변환."""
- p = interval_tf_prefix(interval)
- out: dict[str, Any] = {}
- for k in keys:
- col = ga_col(k)
- if col in row.index:
- v = row[col]
- out[f"{p}_{col}"] = None if pd.isna(v) else v
- return out
-
-
-def slice_to_timestamp(df: pd.DataFrame, ts: pd.Timestamp) -> pd.DataFrame:
- """타점 시각 이전 완성봉만 (해당 시각 봉 미포함)."""
- if df.empty:
- return df
- if not isinstance(df.index, pd.DatetimeIndex):
- df = df.copy()
- df.index = pd.to_datetime(df.index)
- ts = pd.Timestamp(ts)
- if ts.tzinfo is not None and df.index.tz is None:
- ts = ts.tz_localize(None)
- return df[df.index < ts].copy()
-
-
-def lookback_slice(df: pd.DataFrame, interval: int, end_ts: pd.Timestamp) -> pd.DataFrame:
- """타점 직전 lookback 구간."""
- sliced = slice_to_timestamp(df, end_ts)
- n = LOOKBACK_BARS.get(interval, 80)
- if len(sliced) > n:
- return sliced.iloc[-n:].copy()
- return sliced
-
-
-def find_pivots(
- highs: np.ndarray,
- lows: np.ndarray,
- order: int = 3,
-) -> tuple[list[int], list[int]]:
- """국소 고점·저점 인덱스 (양쪽 order 봉보다 극값)."""
- peak_idx: list[int] = []
- trough_idx: list[int] = []
- n = len(highs)
- if n < order * 2 + 1:
- return peak_idx, trough_idx
- for i in range(order, n - order):
- if highs[i] >= highs[i - order : i + order + 1].max():
- peak_idx.append(i)
- if lows[i] <= lows[i - order : i + order + 1].min():
- trough_idx.append(i)
- return peak_idx, trough_idx
-
-
-def last_row_dict(df: pd.DataFrame, cols: list[str]) -> dict[str, Any]:
- """마지막 봉의 지정 컬럼 dict."""
- if df.empty:
- return {ga_col(c): None for c in cols}
- row = df.iloc[-1]
- return {
- ga_col(c): (None if c not in row.index or pd.isna(row[c]) else row[c])
- for c in cols
- }
diff --git a/deepcoin/analysis/general_analysis_enrich_runner.py b/deepcoin/analysis/general_analysis_enrich_runner.py
deleted file mode 100644
index 001b0c7..0000000
--- a/deepcoin/analysis/general_analysis_enrich_runner.py
+++ /dev/null
@@ -1,148 +0,0 @@
-"""
-general_analysis 봉 데이터 전구간 enrich + 최신봉·기법 점검 리포트.
-
- python scripts/03_analyze_enrich.py
- python scripts/03_analyze_enrich.py --interval 1440
-"""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-import pandas as pd
-
-from config import CHART_LOOKBACK_DAYS, GA_DEFAULT_TAIL_EXPORT, SYMBOL
-from deepcoin.analysis.general_analysis_align import (
- general_analysis_mtf_scores,
- general_analysis_mtf_vote_latest,
-)
-from deepcoin.analysis.general_analysis_config import (
- DEFAULT_CAPABILITY_HTML,
- DEFAULT_LATEST_DIR,
- GENERAL_ANALYSIS_INTERVALS,
-)
-from deepcoin.analysis.general_analysis_core import ga_col, interval_tf_prefix
-from deepcoin.analysis.general_analysis_pipeline import general_analysis_enrich_bars
-from deepcoin.ops.monitor import Monitor
-from deepcoin.data.mtf_bb import load_frames_from_db
-
-
-def _latest_row_summary(df: pd.DataFrame, prefix: str) -> dict[str, object]:
- """최신 봉의 ga_·핵심 레거시 컬럼 요약."""
- if df.empty:
- return {}
- row = df.iloc[-1]
- out: dict[str, object] = {"dt": str(df.index[-1]), "tf": prefix}
- for c in df.columns:
- if c.startswith("ga_") or c in ("RSI", "bb_pos", "macd_hist", "stoch_k"):
- v = row[c]
- if pd.isna(v):
- continue
- out[c] = v
- return out
-
-
-def write_capability_html(
- summaries: dict[str, dict[str, object]],
- vote: dict[str, object],
- path: Path,
-) -> None:
- """기법 컬럼 존재 여부·최신값 요약 HTML."""
- rows = ""
- for tf, snap in summaries.items():
- ga_cols = [k for k in snap if str(k).startswith("ga_")]
- rows += f"| {tf} | {snap.get('dt','')} | {len(ga_cols)} |
"
-
- vote_rows = "".join(f"{k}: {v}" for k, v in vote.items())
-
- html = f"""
-
-general_analysis 기법 점검
-
-general_analysis 기법 점검 ({SYMBOL})
-3분~일봉 enrich 완료. 최신 봉 기준 컬럼 수·MTF 투표.
-간격별 ga_ 컬럼 수
-
-MTF 투표 (최신 봉)
-
-상세 CSV: {DEFAULT_LATEST_DIR}/
-"""
- path.parent.mkdir(parents=True, exist_ok=True)
- path.write_text(html, encoding="utf-8")
-
-
-def main() -> None:
- parser = argparse.ArgumentParser(description="general_analysis 8TF enrich")
- parser.add_argument("--interval", type=int, default=0, help="단일 간격만 (0=전체)")
- parser.add_argument(
- "--tail-export",
- type=int,
- default=GA_DEFAULT_TAIL_EXPORT,
- help="CSV 저장 최근 N봉",
- )
- args = parser.parse_args()
-
- from deepcoin.paths import ANALYSIS_CAPABILITY_HTML, ANALYSIS_LATEST_DIR
-
- intervals = (
- (args.interval,)
- if args.interval > 0
- else GENERAL_ANALYSIS_INTERVALS
- )
-
- print(f"=== general_analysis enrich {SYMBOL} ===")
- mon = Monitor(cooldown_file=None)
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- if not frames:
- raise RuntimeError("coins.db 데이터 없음")
-
- enriched: dict[int, pd.DataFrame] = {}
- summaries: dict[str, dict[str, object]] = {}
- out_dir = ANALYSIS_LATEST_DIR
- out_dir.mkdir(parents=True, exist_ok=True)
-
- for iv in intervals:
- raw = frames.get(iv)
- if raw is None or raw.empty:
- print(f" skip {iv}: no data")
- continue
- p = interval_tf_prefix(iv)
- print(f" [{p}] enrich {len(raw)} bars...")
- ef = general_analysis_enrich_bars(raw, iv, full_context=True)
- enriched[iv] = ef
- tail = ef.tail(args.tail_export)
- csv_path = out_dir / f"{p}_latest.csv"
- tail.to_csv(csv_path, encoding="utf-8-sig")
- print(f" -> {csv_path} ({len(tail)} rows x {len(tail.columns)} cols)")
- summaries[p] = _latest_row_summary(ef, p)
-
- flat_vote: dict[str, object] = {}
- if len(enriched) >= 2:
- for k, v in general_analysis_mtf_vote_latest(enriched).items():
- flat_vote[ga_col(k)] = v
- prefixed = {}
- for iv, ef in enriched.items():
- p = interval_tf_prefix(iv)
- row = ef.iloc[-1]
- for c in ("RSI", "bb_pos"):
- if c in row.index:
- prefixed[f"{p}_{c}"] = row[c]
- st = row.get(ga_col("struct_trend"))
- if st is not None:
- prefixed[f"{p}_{ga_col('struct_trend')}"] = st
- flat_vote.update(general_analysis_mtf_scores(prefixed))
-
- write_capability_html(summaries, flat_vote, ANALYSIS_CAPABILITY_HTML)
- print(f"점검 리포트: {ANALYSIS_CAPABILITY_HTML}")
- print("완료.")
-
-
-if __name__ == "__main__":
- main()
diff --git a/deepcoin/analysis/general_analysis_harmonic.py b/deepcoin/analysis/general_analysis_harmonic.py
deleted file mode 100644
index 3b2f1bc..0000000
--- a/deepcoin/analysis/general_analysis_harmonic.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""
-general_analysis 하모닉 패턴 (Gartley, Bat 근사).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-
-from deepcoin.analysis.general_analysis_core import find_pivots, ga_col
-
-
-def _ratio(a: float, b: float) -> float:
- if abs(b) < 1e-12:
- return 0.0
- return abs(a / b)
-
-
-def _near(x: float, target: float, tol: float = 0.08) -> bool:
- return abs(x - target) <= tol
-
-
-def general_analysis_harmonic_snapshot(win) -> dict[str, object]:
- """
- 최근 5개 피벗으로 Gartley·Bat 유사 비율 검사.
-
- Args:
- win: OHLCV DataFrame.
-
- Returns:
- ga_harmonic_* dict.
- """
- res: dict[str, object] = {
- "harmonic_gartley": 0,
- "harmonic_bat": 0,
- "harmonic_label": "none",
- }
- if win is None or len(win) < 30:
- return {ga_col(k): v for k, v in res.items()}
-
- h = win["High"].astype(float).values
- l = win["Low"].astype(float).values
- peaks, troughs = find_pivots(h, l, order=2)
- pivots = sorted([(i, "H", h[i]) for i in peaks] + [(i, "L", l[i]) for i in troughs])
- if len(pivots) < 5:
- return {ga_col(k): v for k, v in res.items()}
-
- pts = pivots[-5:]
- prices = [p[2] for p in pts]
- xa = abs(prices[1] - prices[0])
- ab = abs(prices[2] - prices[1])
- bc = abs(prices[3] - prices[2])
- cd = abs(prices[4] - prices[3])
-
- if xa < 1e-9:
- return {ga_col(k): v for k, v in res.items()}
-
- r_ab = _ratio(ab, xa)
- r_bc = _ratio(bc, ab) if ab > 1e-9 else 0.0
- r_cd = _ratio(cd, bc) if bc > 1e-9 else 0.0
-
- if _near(r_ab, 0.618) and 0.35 <= r_bc <= 0.95 and 1.1 <= r_cd <= 1.75:
- res["harmonic_gartley"] = 1
- res["harmonic_label"] = "gartley"
- if _near(r_ab, 0.382) and 0.35 <= r_bc <= 0.95 and 1.5 <= r_cd <= 2.1:
- res["harmonic_bat"] = 1
- res["harmonic_label"] = "bat"
-
- return {ga_col(k): v for k, v in res.items()}
-
-
-def general_analysis_harmonic_columns() -> list[str]:
- return ["harmonic_gartley", "harmonic_bat", "harmonic_label"]
diff --git a/deepcoin/analysis/general_analysis_indicators.py b/deepcoin/analysis/general_analysis_indicators.py
deleted file mode 100644
index 1415b03..0000000
--- a/deepcoin/analysis/general_analysis_indicators.py
+++ /dev/null
@@ -1,382 +0,0 @@
-"""
-general_analysis 확장 기술적 지표 (추세·모멘텀·변동성·거래량).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from config import (
- BB_PERIOD,
- BB_STD,
- GA_ADX_PERIOD,
- GA_ADX_TREND_THRESHOLD,
- GA_AO_FAST,
- GA_AO_SLOW,
- GA_ATR_PERIOD,
- GA_BB_SQUEEZE_QUANTILE,
- GA_BB_SQUEEZE_WINDOW,
- GA_CCI_PERIOD,
- GA_CMF_PERIOD,
- GA_DIVERGENCE_LOOKBACK,
- GA_DONCHIAN_PERIOD,
- GA_EMA_SPANS,
- GA_HV_ANNUALIZE_SQRT,
- GA_HV_PERCENTILE_WINDOW,
- GA_HV_ROLLING_BARS,
- GA_KELTNER_ATR_MULT,
- GA_LINREG_WINDOW,
- GA_MFI_PERIOD,
- GA_PSAR_AF_MAX,
- GA_PSAR_AF_START,
- GA_PSAR_AF_STEP,
- GA_ROC_PERIOD,
- GA_SMA_PERIODS,
- GA_SUPERTREND_ATR_MULT,
- GA_VOL_MA_WINDOW,
- GA_WILLIAMS_PERIOD,
-)
-from deepcoin.analysis.general_analysis_core import ga_col
-from deepcoin.common.indicators import apply_bar_indicators
-
-
-def _ema(series: pd.Series, span: int) -> pd.Series:
- return series.ewm(span=span, adjust=False).mean()
-
-
-def _parabolic_sar(
- high: np.ndarray,
- low: np.ndarray,
- af_start: float = GA_PSAR_AF_START,
- af_step: float = GA_PSAR_AF_STEP,
- af_max: float = GA_PSAR_AF_MAX,
-) -> tuple[np.ndarray, np.ndarray]:
- """
- Parabolic SAR 시계열.
-
- Returns:
- (sar, bull_flag 0/1)
- """
- n = len(high)
- sar = np.zeros(n)
- bull = np.ones(n, dtype=int)
- if n < 2:
- return sar, bull
-
- is_bull = True
- af = af_start
- ep = high[0]
- sar[0] = low[0]
-
- for i in range(1, n):
- prev_sar = sar[i - 1]
- if is_bull:
- sar[i] = prev_sar + af * (ep - prev_sar)
- sar[i] = min(sar[i], low[i - 1], low[i] if i > 0 else low[i - 1])
- if low[i] < sar[i]:
- is_bull = False
- sar[i] = ep
- ep = low[i]
- af = af_start
- else:
- if high[i] > ep:
- ep = high[i]
- af = min(af + af_step, af_max)
- else:
- sar[i] = prev_sar + af * (ep - prev_sar)
- sar[i] = max(sar[i], high[i - 1], high[i] if i > 0 else high[i - 1])
- if high[i] > sar[i]:
- is_bull = True
- sar[i] = ep
- ep = high[i]
- af = af_start
- else:
- if low[i] < ep:
- ep = low[i]
- af = min(af + af_step, af_max)
- bull[i] = int(is_bull)
-
- return sar, bull
-
-
-def general_analysis_apply_indicators(df: pd.DataFrame) -> pd.DataFrame:
- """
- 기존 apply_bar_indicators 위에 ga_ 접두사 확장 지표를 추가합니다.
-
- Args:
- df: OHLCV.
-
- Returns:
- ga_* 컬럼이 추가된 DataFrame.
- """
- out = apply_bar_indicators(df.copy())
- o = out["Open"].astype(float)
- h = out["High"].astype(float)
- l = out["Low"].astype(float)
- c = out["Close"].astype(float)
- v = out["Volume"].astype(float)
-
- # --- 추세: MA ---
- sma_fast = GA_SMA_PERIODS[0] if GA_SMA_PERIODS else 5
- sma_slow = GA_SMA_PERIODS[1] if len(GA_SMA_PERIODS) > 1 else 20
- for p in GA_SMA_PERIODS:
- ma = c.rolling(p).mean()
- out[ga_col(f"sma_{p}")] = ma
- out[ga_col(f"close_vs_sma_{p}_pct")] = (c / ma.replace(0, np.nan) - 1) * 100
-
- ema_fast = GA_EMA_SPANS[0] if GA_EMA_SPANS else 12
- ema_slow = GA_EMA_SPANS[1] if len(GA_EMA_SPANS) > 1 else 26
- out[ga_col(f"ema_{ema_fast}")] = _ema(c, ema_fast)
- out[ga_col(f"ema_{ema_slow}")] = _ema(c, ema_slow)
- out[ga_col("golden_cross")] = (
- (out[ga_col(f"sma_{sma_fast}")] > out[ga_col(f"sma_{sma_slow}")])
- & (out[ga_col(f"sma_{sma_fast}")].shift(1) <= out[ga_col(f"sma_{sma_slow}")].shift(1))
- ).astype(int)
- out[ga_col("death_cross")] = (
- (out[ga_col(f"sma_{sma_fast}")] < out[ga_col(f"sma_{sma_slow}")])
- & (out[ga_col(f"sma_{sma_fast}")].shift(1) >= out[ga_col(f"sma_{sma_slow}")].shift(1))
- ).astype(int)
-
- # --- ATR / 변동성 ---
- tr = pd.concat(
- [
- h - l,
- (h - c.shift(1)).abs(),
- (l - c.shift(1)).abs(),
- ],
- axis=1,
- ).max(axis=1)
- atr = tr.rolling(GA_ATR_PERIOD).mean()
- out[ga_col("atr_14")] = atr
- out[ga_col("atr_pct")] = atr / c.replace(0, np.nan) * 100
- out[ga_col("bb_width_pct")] = out.get("BB_Width", (out["Upper"] - out["Lower"]) / out["MA"] * 100)
- bw = out[ga_col("bb_width_pct")].astype(float)
- out[ga_col("bb_squeeze")] = (
- bw < bw.rolling(GA_BB_SQUEEZE_WINDOW).quantile(GA_BB_SQUEEZE_QUANTILE)
- ).astype(int)
-
- dc = GA_DONCHIAN_PERIOD
- out[ga_col("donchian_high_20")] = h.rolling(dc).max()
- out[ga_col("donchian_low_20")] = l.rolling(dc).min()
- out[ga_col("donchian_pos")] = (c - out[ga_col("donchian_low_20")]) / (
- out[ga_col("donchian_high_20")] - out[ga_col("donchian_low_20")]
- ).replace(0, np.nan)
-
- # --- 모멘텀: CCI, Williams %R ---
- tp = (h + l + c) / 3
- cci_period = GA_CCI_PERIOD
- sma_tp = tp.rolling(cci_period).mean()
- mad = tp.rolling(cci_period).apply(lambda x: np.abs(x - x.mean()).mean(), raw=True)
- out[ga_col("cci_20")] = (tp - sma_tp) / (0.015 * mad.replace(0, np.nan))
- out[ga_col("cci_oversold")] = (out[ga_col("cci_20")] < -100).astype(int)
- out[ga_col("cci_overbought")] = (out[ga_col("cci_20")] > 100).astype(int)
-
- hh = h.rolling(GA_WILLIAMS_PERIOD).max()
- ll = l.rolling(GA_WILLIAMS_PERIOD).min()
- out[ga_col("williams_r")] = (hh - c) / (hh - ll).replace(0, np.nan) * -100
- out[ga_col("williams_oversold")] = (out[ga_col("williams_r")] < -80).astype(int)
- out[ga_col("williams_overbought")] = (out[ga_col("williams_r")] > -20).astype(int)
-
- div_lb = GA_DIVERGENCE_LOOKBACK
- out[ga_col("roc_10")] = (c / c.shift(GA_ROC_PERIOD).replace(0, np.nan) - 1) * 100
-
- # MFI
- raw_mf = tp * v
- pos_mf = raw_mf.where(tp > tp.shift(1), 0.0).rolling(GA_MFI_PERIOD).sum()
- neg_mf = raw_mf.where(tp < tp.shift(1), 0.0).rolling(GA_MFI_PERIOD).sum()
- mfr = pos_mf / neg_mf.replace(0, np.nan)
- out[ga_col("mfi_14")] = 100 - (100 / (1 + mfr))
-
- # MACD / RSI / Stoch 다이버전스
- if "RSI" in out.columns and "macd_hist" in out.columns:
- price_up = (c > c.shift(div_lb)).astype(int)
- rsi_up = (out["RSI"] > out["RSI"].shift(div_lb)).astype(int)
- macd_up = (out["macd_hist"] > out["macd_hist"].shift(div_lb)).astype(int)
- out[ga_col("rsi_bull_div")] = ((price_up == 0) & (rsi_up == 1)).astype(int)
- out[ga_col("rsi_bear_div")] = ((price_up == 1) & (rsi_up == 0)).astype(int)
- out[ga_col("macd_bull_div")] = ((price_up == 0) & (macd_up == 1)).astype(int)
- out[ga_col("macd_bear_div")] = ((price_up == 1) & (macd_up == 0)).astype(int)
- if "stoch_k" in out.columns:
- price_up = (c > c.shift(div_lb)).astype(int)
- st_up = (out["stoch_k"] > out["stoch_k"].shift(div_lb)).astype(int)
- out[ga_col("stoch_bull_div")] = ((price_up == 0) & (st_up == 1)).astype(int)
- out[ga_col("stoch_bear_div")] = ((price_up == 1) & (st_up == 0)).astype(int)
-
- # 봉 간 변화 (타점 Δ와 동일 정의, 전 구간)
- if "RSI" in out.columns:
- out[ga_col("rsi_delta_1")] = out["RSI"].diff()
- if "macd_hist" in out.columns:
- out[ga_col("macd_hist_delta_1")] = out["macd_hist"].diff()
- if "stoch_k" in out.columns:
- out[ga_col("stoch_k_delta_1")] = out["stoch_k"].diff()
-
- # --- 거래량 ---
- vol_ma = v.rolling(GA_VOL_MA_WINDOW).mean()
- out[ga_col("vol_ma20")] = vol_ma
- out[ga_col("vol_ratio")] = v / vol_ma.replace(0, np.nan)
- out[ga_col("obv")] = (np.sign(c.diff().fillna(0)) * v).cumsum()
- obv = out[ga_col("obv")].astype(float)
- out[ga_col("obv_slope_10")] = obv - obv.shift(div_lb)
- out[ga_col("obv_bull_div")] = (
- (c < c.shift(div_lb)) & (obv > obv.shift(div_lb))
- ).astype(int)
- out[ga_col("obv_bear_div")] = (
- (c > c.shift(div_lb)) & (obv < obv.shift(div_lb))
- ).astype(int)
-
- # CMF
- mfv = ((c - l) - (h - c)) / (h - l).replace(0, np.nan) * v
- out[ga_col("cmf_20")] = (
- mfv.rolling(GA_CMF_PERIOD).sum() / v.rolling(GA_CMF_PERIOD).sum().replace(0, np.nan)
- )
-
- # Accumulation/Distribution Line
- clv = ((c - l) - (h - c)) / (h - l).replace(0, np.nan)
- out[ga_col("ad_line")] = (clv * v).cumsum()
- ad = out[ga_col("ad_line")].astype(float)
- out[ga_col("ad_slope_10")] = ad - ad.shift(div_lb)
-
- # VWAP 근사 (누적, 세션 리셋 없음)
- cum_vp = (tp * v).cumsum()
- cum_v = v.cumsum().replace(0, np.nan)
- out[ga_col("vwap")] = cum_vp / cum_v
- out[ga_col("close_vs_vwap_pct")] = (c / out[ga_col("vwap")] - 1) * 100
-
- # Keltner Channel
- k_mid = _ema(c, BB_PERIOD)
- out[ga_col("keltner_mid")] = k_mid
- out[ga_col("keltner_upper")] = k_mid + GA_KELTNER_ATR_MULT * atr
- out[ga_col("keltner_lower")] = k_mid - GA_KELTNER_ATR_MULT * atr
- out[ga_col("keltner_pos")] = (c - out[ga_col("keltner_lower")]) / (
- out[ga_col("keltner_upper")] - out[ga_col("keltner_lower")]
- ).replace(0, np.nan)
-
- # Awesome Oscillator: median price SMA5 - SMA34
- mp = (h + l) / 2
- out[ga_col("ao")] = mp.rolling(GA_AO_FAST).mean() - mp.rolling(GA_AO_SLOW).mean()
- out[ga_col("ao_bull")] = (
- (out[ga_col("ao")] > 0) & (out[ga_col("ao")].shift(1) <= 0)
- ).astype(int)
- out[ga_col("ao_bear")] = (
- (out[ga_col("ao")] < 0) & (out[ga_col("ao")].shift(1) >= 0)
- ).astype(int)
-
- # Historical Volatility (로그수익 20봉 표준편차, 연율화 계수 1=봉 단위)
- log_ret = np.log(c / c.shift(1).replace(0, np.nan))
- hv = log_ret.rolling(GA_HV_ROLLING_BARS).std() * GA_HV_ANNUALIZE_SQRT
- out[ga_col("hv_20")] = hv
- out[ga_col("hv_percentile")] = hv.rolling(GA_HV_PERCENTILE_WINDOW).apply(
- lambda x: float((x[:-1] < x[-1]).mean()) if len(x) > 1 and not np.isnan(x[-1]) else 0.5,
- raw=True,
- )
-
- # Parabolic SAR
- sar, psar_bull = _parabolic_sar(h.values, l.values)
- out[ga_col("psar")] = sar
- out[ga_col("psar_bull")] = psar_bull
- ps = pd.Series(psar_bull, index=out.index)
- out[ga_col("psar_flip_bull")] = ((ps == 1) & (ps.shift(1) == 0)).astype(int)
- out[ga_col("psar_flip_bear")] = ((ps == 0) & (ps.shift(1) == 1)).astype(int)
-
- # ADX
- up_move = h.diff()
- down_move = -l.diff()
- plus_dm = np.where((up_move > down_move) & (up_move > 0), up_move, 0.0)
- minus_dm = np.where((down_move > up_move) & (down_move > 0), down_move, 0.0)
- atr_safe = atr.replace(0, np.nan)
- plus_di = 100 * pd.Series(plus_dm, index=out.index).rolling(GA_ADX_PERIOD).mean() / atr_safe
- minus_di = 100 * pd.Series(minus_dm, index=out.index).rolling(GA_ADX_PERIOD).mean() / atr_safe
- dx = (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan) * 100
- out[ga_col("adx_14")] = dx.rolling(GA_ADX_PERIOD).mean()
- out[ga_col("plus_di")] = plus_di
- out[ga_col("minus_di")] = minus_di
- out[ga_col("adx_trending")] = (out[ga_col("adx_14")] > GA_ADX_TREND_THRESHOLD).astype(int)
-
- # Supertrend 방향 (ATR 밴드)
- hl2 = (h + l) / 2
- upper = hl2 + GA_SUPERTREND_ATR_MULT * atr
- lower = hl2 - GA_SUPERTREND_ATR_MULT * atr
- out[ga_col("supertrend_bull")] = (c > lower).astype(int)
-
- # Linear regression slope 20
- def _lin_slope(y: np.ndarray) -> float:
- if len(y) < 2:
- return 0.0
- x = np.arange(len(y))
- coef = np.polyfit(x, y, 1)
- return float(coef[0])
-
- out[ga_col("linreg_slope_20")] = c.rolling(GA_LINREG_WINDOW).apply(_lin_slope, raw=True)
-
- def _lin_r2(y: np.ndarray) -> float:
- if len(y) < 3:
- return 0.0
- x = np.arange(len(y))
- coef = np.polyfit(x, y, 1)
- pred = coef[0] * x + coef[1]
- ss_res = ((y - pred) ** 2).sum()
- ss_tot = ((y - y.mean()) ** 2).sum()
- if ss_tot < 1e-12:
- return 0.0
- return float(1 - ss_res / ss_tot)
-
- out[ga_col("linreg_r2_20")] = c.rolling(GA_LINREG_WINDOW).apply(_lin_r2, raw=True)
-
- return out
-
-
-def general_analysis_indicator_columns() -> list[str]:
- """스냅샷용 ga_ 지표 컬럼 목록."""
- return [
- "sma_5",
- "sma_20",
- "sma_60",
- "close_vs_sma_20_pct",
- "golden_cross",
- "death_cross",
- "atr_14",
- "atr_pct",
- "bb_squeeze",
- "donchian_pos",
- "cci_20",
- "cci_oversold",
- "cci_overbought",
- "williams_r",
- "williams_oversold",
- "williams_overbought",
- "roc_10",
- "mfi_14",
- "rsi_bull_div",
- "rsi_bear_div",
- "macd_bull_div",
- "macd_bear_div",
- "stoch_bull_div",
- "stoch_bear_div",
- "rsi_delta_1",
- "macd_hist_delta_1",
- "stoch_k_delta_1",
- "keltner_pos",
- "ao",
- "ao_bull",
- "ao_bear",
- "hv_20",
- "hv_percentile",
- "ad_line",
- "ad_slope_10",
- "vol_ratio",
- "obv_slope_10",
- "obv_bull_div",
- "obv_bear_div",
- "cmf_20",
- "close_vs_vwap_pct",
- "adx_14",
- "adx_trending",
- "supertrend_bull",
- "linreg_slope_20",
- "linreg_r2_20",
- "psar",
- "psar_bull",
- "psar_flip_bull",
- "psar_flip_bear",
- ]
diff --git a/deepcoin/analysis/general_analysis_patterns.py b/deepcoin/analysis/general_analysis_patterns.py
deleted file mode 100644
index 127c2d3..0000000
--- a/deepcoin/analysis/general_analysis_patterns.py
+++ /dev/null
@@ -1,302 +0,0 @@
-"""
-general_analysis 차트·가격 패턴 (반전·지속·박스).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from config import GA_PATTERN_TOLERANCE_PCT, GA_PIVOT_ORDER
-from deepcoin.analysis.general_analysis_core import find_pivots, ga_col, last_row_dict
-
-
-def _pct_diff(a: float, b: float) -> float:
- return abs(a - b) / max(abs(a), abs(b), 1e-9) * 100
-
-
-def general_analysis_detect_patterns(win: pd.DataFrame) -> dict[str, int | float | str | None]:
- """
- lookback 윈도우 마지막 봉 기준 패턴 라벨 (0/1 및 요약).
-
- Args:
- win: OHLCV (+ 지표 선택).
-
- Returns:
- ga_pattern_* 키 dict (접두사 없음, ga_col로 감쌈).
- """
- res: dict[str, int | float | str | None] = {
- "pattern_double_top": 0,
- "pattern_double_bottom": 0,
- "pattern_head_shoulders": 0,
- "pattern_inv_head_shoulders": 0,
- "pattern_triangle_sym": 0,
- "pattern_triangle_asc": 0,
- "pattern_triangle_desc": 0,
- "pattern_flag_bull": 0,
- "pattern_flag_bear": 0,
- "pattern_wedge_rising": 0,
- "pattern_wedge_falling": 0,
- "pattern_rectangle": 0,
- "pattern_channel_up": 0,
- "pattern_channel_down": 0,
- "pattern_measured_move": 0,
- "pattern_rounding_top": 0,
- "pattern_rounding_bottom": 0,
- "pattern_gap_up": 0,
- "pattern_gap_down": 0,
- "pattern_v_bottom": 0,
- "pattern_spike_top": 0,
- "pattern_triple_top": 0,
- "pattern_triple_bottom": 0,
- "pattern_cup_handle": 0,
- "pattern_keystone_bull": 0,
- "pattern_keystone_bear": 0,
- "pattern_island_top": 0,
- "pattern_island_bottom": 0,
- "pattern_label": "none",
- }
- if win is None or len(win) < 20:
- return res
-
- h = win["High"].astype(float).values
- l = win["Low"].astype(float).values
- c = win["Close"].astype(float).values
- peaks, troughs = find_pivots(h, l, order=GA_PIVOT_ORDER)
- tol = GA_PATTERN_TOLERANCE_PCT
-
- if len(peaks) >= 3:
- p1, p2, p3 = peaks[-3], peaks[-2], peaks[-1]
- if (
- _pct_diff(h[p1], h[p2]) < tol
- and _pct_diff(h[p2], h[p3]) < tol
- and p1 < p2 < p3
- ):
- res["pattern_triple_top"] = 1
- res["pattern_label"] = "triple_top"
-
- if len(troughs) >= 3:
- t1, t2, t3 = troughs[-3], troughs[-2], troughs[-1]
- if (
- _pct_diff(l[t1], l[t2]) < tol
- and _pct_diff(l[t2], l[t3]) < tol
- and t1 < t2 < t3
- ):
- res["pattern_triple_bottom"] = 1
- res["pattern_label"] = "triple_bottom"
-
- if len(peaks) >= 2:
- p1, p2 = peaks[-2], peaks[-1]
- if _pct_diff(h[p1], h[p2]) < tol:
- res["pattern_double_top"] = 1
- if res["pattern_label"] == "none":
- res["pattern_label"] = "double_top"
-
- if len(troughs) >= 2:
- t1, t2 = troughs[-2], troughs[-1]
- if _pct_diff(l[t1], l[t2]) < tol:
- res["pattern_double_bottom"] = 1
- res["pattern_label"] = "double_bottom"
-
- if len(peaks) >= 3:
- i, j, k = peaks[-3], peaks[-2], peaks[-1]
- if h[j] > h[i] and h[j] > h[k] and _pct_diff(h[i], h[k]) < tol * 1.5:
- res["pattern_head_shoulders"] = 1
- res["pattern_label"] = "head_shoulders"
-
- if len(troughs) >= 3:
- i, j, k = troughs[-3], troughs[-2], troughs[-1]
- if l[j] < l[i] and l[j] < l[k] and _pct_diff(l[i], l[k]) < tol * 1.5:
- res["pattern_inv_head_shoulders"] = 1
- res["pattern_label"] = "inv_head_shoulders"
-
- n = len(win)
- x = np.arange(n)
- high_slope = np.polyfit(x, h, 1)[0]
- low_slope = np.polyfit(x, l, 1)[0]
- if high_slope < 0 and low_slope > 0:
- res["pattern_triangle_sym"] = 1
- if res["pattern_label"] == "none":
- res["pattern_label"] = "triangle_sym"
- if high_slope < 0 and low_slope > 0 and low_slope > abs(high_slope) * 0.5:
- res["pattern_triangle_asc"] = 1
- if high_slope < 0 and low_slope < 0 and abs(high_slope) > abs(low_slope) * 0.5:
- res["pattern_triangle_desc"] = 1
-
- rng_pct = (h.max() - l.min()) / max(c[-1], 1e-9) * 100
- if rng_pct < 8 and abs(high_slope) < c[-1] * 0.0001:
- res["pattern_rectangle"] = 1
- if res["pattern_label"] == "none":
- res["pattern_label"] = "rectangle"
-
- leg = max(n // 3, 5)
- if n > leg * 2:
- first_move = (c[leg] - c[0]) / max(c[0], 1e-9) * 100
- channel = (c[-1] - c[-leg]) / max(c[-leg], 1e-9) * 100
- if first_move > 5 and abs(channel) < 3:
- res["pattern_flag_bull"] = 1
- res["pattern_label"] = "flag_bull"
- if first_move < -5 and abs(channel) < 3:
- res["pattern_flag_bear"] = 1
- res["pattern_label"] = "flag_bear"
-
- if high_slope > 0 and low_slope > 0:
- res["pattern_wedge_rising"] = 1
- if high_slope < 0 and low_slope < 0:
- res["pattern_wedge_falling"] = 1
-
- if high_slope > 0 and low_slope > 0:
- res["pattern_channel_up"] = 1
- if high_slope < 0 and low_slope < 0:
- res["pattern_channel_down"] = 1
-
- if len(c) >= 15:
- mid = len(c) // 2
- first_half = c[:mid].mean()
- second_half = c[mid:].mean()
- if c[0] > c[mid] * 1.08 and c[-1] > c[mid] * 1.05:
- res["pattern_v_bottom"] = 1
- res["pattern_label"] = "v_bottom"
- if c[0] < c[-1] * 0.92 and c.max() > c[0] * 1.1:
- res["pattern_spike_top"] = 1
-
- o = win["Open"].astype(float).values
- gap_ups: list[int] = []
- gap_downs: list[int] = []
- for i in range(1, min(30, n)):
- if l[i] > h[i - 1]:
- res["pattern_gap_up"] = 1
- gap_ups.append(i)
- if h[i] < l[i - 1]:
- res["pattern_gap_down"] = 1
- gap_downs.append(i)
- for gi in gap_ups:
- for gd in gap_downs:
- if gd > gi and h[gi] < l[gd]:
- res["pattern_island_top"] = 1
- res["pattern_label"] = "island_top"
- if gd > gi and l[gi] > h[gd]:
- res["pattern_island_bottom"] = 1
- res["pattern_label"] = "island_bottom"
-
- # 키리스톤: 상단 수평 + 하단 상승(역키리스톤) 또는 하단 수평 + 상단 하락
- if abs(high_slope) < c[-1] * 0.00005 and low_slope > 0:
- res["pattern_keystone_bull"] = 1
- if res["pattern_label"] == "none":
- res["pattern_label"] = "keystone_bull"
- if abs(low_slope) < c[-1] * 0.00005 and high_slope < 0:
- res["pattern_keystone_bear"] = 1
- if res["pattern_label"] == "none":
- res["pattern_label"] = "keystone_bear"
-
- # 컵앤핸들: 전반 U자 + 후반 15% 소폭 조정
- if n >= 40:
- cup_len = int(n * 0.65)
- handle_len = max(int(n * 0.15), 5)
- cup = c[:cup_len]
- handle = c[-handle_len:]
- rim = float(max(cup[0], cup[-1]))
- bottom = float(cup.min())
- depth = rim - bottom
- if depth > rim * 0.08 and float(cup[-1]) > bottom + depth * 0.5:
- handle_pull = float(handle.max() - handle.min())
- if handle_pull < depth * 0.5 and float(c[-1]) >= rim * 0.98:
- res["pattern_cup_handle"] = 1
- res["pattern_label"] = "cup_handle"
-
- if len(c) >= 30:
- ma = pd.Series(c).rolling(10).mean()
- if float(ma.iloc[-1]) > float(ma.iloc[-15]) > float(ma.iloc[-30]):
- res["pattern_rounding_bottom"] = 1
- if float(ma.iloc[-1]) < float(ma.iloc[-15]) < float(ma.iloc[-30]):
- res["pattern_rounding_top"] = 1
-
- if len(peaks) >= 2 and len(troughs) >= 2:
- leg_h = h[peaks[-1]] - l[troughs[-1]]
- if leg_h > 0 and c[-1] >= l[troughs[-1]] + leg_h * 0.9:
- res["pattern_measured_move"] = 1
-
- return res
-
-
-def general_analysis_pattern_snapshot(win: pd.DataFrame) -> dict[str, object]:
- """패턴 dict → ga_pattern_* 컬럼명."""
- raw = general_analysis_detect_patterns(win)
- return {ga_col(k): v for k, v in raw.items()}
-
-
-def general_analysis_pattern_columns() -> list[str]:
- return [
- "pattern_double_top",
- "pattern_double_bottom",
- "pattern_head_shoulders",
- "pattern_inv_head_shoulders",
- "pattern_triangle_sym",
- "pattern_triangle_asc",
- "pattern_triangle_desc",
- "pattern_flag_bull",
- "pattern_flag_bear",
- "pattern_wedge_rising",
- "pattern_wedge_falling",
- "pattern_rectangle",
- "pattern_channel_up",
- "pattern_channel_down",
- "pattern_measured_move",
- "pattern_rounding_top",
- "pattern_rounding_bottom",
- "pattern_gap_up",
- "pattern_gap_down",
- "pattern_v_bottom",
- "pattern_spike_top",
- "pattern_triple_top",
- "pattern_triple_bottom",
- "pattern_cup_handle",
- "pattern_keystone_bull",
- "pattern_keystone_bear",
- "pattern_island_top",
- "pattern_island_bottom",
- "pattern_label",
- ]
-
-
-def general_analysis_apply_patterns_to_bars(
- df: pd.DataFrame,
- interval: int,
- tail_rows: int | None = None,
-) -> pd.DataFrame:
- """
- lookback 윈도우 패턴 라벨을 봉별 컬럼으로 채움 (최근 tail_rows만, 성능).
-
- Args:
- df: OHLCV (+ 선택적 지표).
- interval: 분봉 간격.
- tail_rows: None이면 전체(8천봉 이하) 또는 config tail.
-
- Returns:
- ga_pattern_* 컬럼이 추가된 DataFrame.
- """
- from deepcoin.analysis.general_analysis_config import CONTEXT_TAIL_ROWS, LOOKBACK_BARS
-
- out = df.copy()
- lb = LOOKBACK_BARS.get(interval, 80)
- keys = [k for k in general_analysis_pattern_columns() if k != "pattern_label"]
- for k in keys:
- out[ga_col(k)] = 0
- out[ga_col("pattern_label")] = "none"
-
- n = len(out)
- if n < lb + 1:
- return out
-
- if tail_rows is None:
- tail_rows = CONTEXT_TAIL_ROWS.get(interval, 5000)
- start = max(lb, n - tail_rows)
-
- for i in range(start, n):
- win = out.iloc[i - lb : i]
- det = general_analysis_detect_patterns(win)
- idx = out.index[i]
- for k, v in det.items():
- out.at[idx, ga_col(k)] = v
-
- return out
diff --git a/deepcoin/analysis/general_analysis_pipeline.py b/deepcoin/analysis/general_analysis_pipeline.py
deleted file mode 100644
index b74b0d8..0000000
--- a/deepcoin/analysis/general_analysis_pipeline.py
+++ /dev/null
@@ -1,154 +0,0 @@
-"""
-general_analysis 전체 파이프라인 (지표·캔들·한 봉 특징).
-"""
-
-from __future__ import annotations
-
-import pandas as pd
-
-from deepcoin.common.candle_features import compute_bar_features
-from deepcoin.analysis.general_analysis_candles import (
- general_analysis_apply_candles,
- general_analysis_candle_columns,
-)
-from deepcoin.analysis.general_analysis_chart import (
- general_analysis_apply_chart_bars,
- general_analysis_chart_columns,
- general_analysis_chart_metrics,
-)
-from deepcoin.analysis.general_analysis_context import general_analysis_apply_context_features
-from deepcoin.analysis.general_analysis_harmonic import (
- general_analysis_harmonic_columns,
- general_analysis_harmonic_snapshot,
-)
-from deepcoin.analysis.general_analysis_volume import (
- general_analysis_volume_columns,
- general_analysis_volume_snapshot,
-)
-from deepcoin.analysis.general_analysis_core import ga_col, lookback_slice
-from deepcoin.analysis.general_analysis_indicators import (
- general_analysis_apply_indicators,
- general_analysis_indicator_columns,
-)
-from deepcoin.analysis.general_analysis_patterns import (
- general_analysis_pattern_columns,
- general_analysis_pattern_snapshot,
-)
-from deepcoin.analysis.general_analysis_wave import (
- general_analysis_wave_columns,
- general_analysis_wave_snapshot,
-)
-
-
-def general_analysis_enrich_bars(
- df: pd.DataFrame,
- interval: int | None = None,
- *,
- full_context: bool = True,
-) -> pd.DataFrame:
- """
- OHLCV → candle_features + ga 지표 + 캔들 + 차트 + (선택) lookback 컨텍스트.
-
- Args:
- df: raw OHLCV.
- interval: 분봉 간격. full_context=True일 때 필수.
- full_context: 패턴·VP·파동·하모닉 롤링 적용.
-
- Returns:
- 전체 특징 컬럼 DataFrame.
- """
- base = compute_bar_features(df)
- out = general_analysis_apply_indicators(base)
- out = general_analysis_apply_candles(out)
- out = general_analysis_apply_chart_bars(out)
- if full_context and interval is not None:
- out = general_analysis_apply_context_features(out, interval)
- return out
-
-
-def general_analysis_snapshot_at_bar(
- enriched: pd.DataFrame,
- ts: pd.Timestamp,
- interval: int,
-) -> dict[str, object]:
- """
- 타점 시각 직전 완성봉 + lookback 패턴·파동·차트 메타.
-
- Args:
- enriched: general_analysis_enrich_bars 결과.
- ts: 타점 시각.
- interval: 분봉 간격.
-
- Returns:
- flat dict (ga_ 키 + legacy bb/rsi where present).
- """
- win = lookback_slice(enriched, interval, ts)
- snap: dict[str, object] = {}
- if win.empty:
- return snap
-
- row = win.iloc[-1]
-
- legacy_cols = [
- "bb_pos",
- "RSI",
- "macd_hist",
- "stoch_k",
- "stoch_d",
- "macd_line",
- "macd_signal",
- "BB_Width",
- ]
- for c in legacy_cols:
- if c in row.index and not pd.isna(row[c]):
- snap[c] = float(row[c]) if isinstance(row[c], (int, float)) else row[c]
-
- for c in general_analysis_indicator_columns():
- col = ga_col(c)
- if col in row.index:
- v = row[col]
- snap[col] = None if pd.isna(v) else v
-
- for c in general_analysis_candle_columns():
- col = ga_col(c)
- if col in row.index:
- snap[col] = int(row[col]) if not pd.isna(row[col]) else 0
-
- pat_cols = [ga_col(c) for c in general_analysis_pattern_columns()]
- if pat_cols and pat_cols[0] in enriched.columns:
- for col in pat_cols:
- if col in row.index:
- snap[col] = row[col]
- else:
- snap.update(general_analysis_pattern_snapshot(win))
-
- wave_cols = [ga_col(c) for c in general_analysis_wave_columns()]
- if wave_cols and wave_cols[0] in enriched.columns:
- for col in wave_cols:
- if col in row.index:
- snap[col] = row[col]
- else:
- snap.update(general_analysis_wave_snapshot(win))
-
- snap.update(general_analysis_volume_snapshot(win))
- snap.update(general_analysis_harmonic_snapshot(win))
- snap.update(general_analysis_chart_metrics(win))
-
- return snap
-
-
-def general_analysis_all_snapshot_keys() -> list[str]:
- """CSV 헤더용 전체 키 목록 (간격 접두사 제외)."""
- keys = list(legacy_snapshot_keys())
- keys += [ga_col(c) for c in general_analysis_indicator_columns()]
- keys += [ga_col(c) for c in general_analysis_candle_columns()]
- keys += [ga_col(c) for c in general_analysis_pattern_columns()]
- keys += [ga_col(c) for c in general_analysis_wave_columns()]
- keys += [ga_col(c) for c in general_analysis_chart_columns()]
- keys += [ga_col(c) for c in general_analysis_volume_columns()]
- keys += [ga_col(c) for c in general_analysis_harmonic_columns()]
- return keys
-
-
-def legacy_snapshot_keys() -> list[str]:
- return ["bb_pos", "RSI", "macd_hist", "stoch_k", "stoch_d"]
diff --git a/deepcoin/analysis/general_analysis_report.py b/deepcoin/analysis/general_analysis_report.py
deleted file mode 100644
index 88b2640..0000000
--- a/deepcoin/analysis/general_analysis_report.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""
-general_analysis HTML 요약 리포트.
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-
-import pandas as pd
-
-from deepcoin.analysis.general_analysis_config import DEFAULT_OUTPUT_CSV, DEFAULT_OUTPUT_HTML
-
-
-def write_analysis_report(
- csv_path: Path | str = DEFAULT_OUTPUT_CSV,
- html_path: Path | str = DEFAULT_OUTPUT_HTML,
-) -> Path:
- """
- 스냅샷 CSV를 읽어 모듈별 컬럼 수·샘플 테이블 HTML 생성.
-
- Returns:
- HTML 경로.
- """
- df = pd.read_csv(csv_path)
- html_out = Path(html_path)
- html_out.parent.mkdir(parents=True, exist_ok=True)
-
- modules = {
- "지표 (ga_)": [c for c in df.columns if "_ga_" in c or c.startswith("ga_")],
- "패턴": [c for c in df.columns if "ga_pattern_" in c],
- "파동·구조": [c for c in df.columns if "ga_struct_" in c or "ga_elliott" in c or "ga_wyckoff" in c or "ga_fib_" in c],
- "차트": [c for c in df.columns if "ga_chart_" in c],
- "MTF 합성": [c for c in df.columns if "ga_align_" in c],
- "레거시": [c for c in df.columns if c.endswith("_RSI") or c.endswith("_bb_pos")],
- }
-
- summary_rows = ""
- for name, cols in modules.items():
- summary_rows += f"| {name} | {len(cols)} |
"
-
- sample = df.head(5)[
- ["dt", "action", "price", "ga_align_timing_buy_score", "ga_align_mtf_conflict", "d1_RSI", "m3_RSI"]
- ].to_html(index=False, classes="tbl") if "d1_RSI" in df.columns else df.head(3).to_html(index=False)
-
- buy_mean = df[df["action"] == "buy"]["ga_align_timing_buy_score"].mean() if "ga_align_timing_buy_score" in df.columns else 0
- sell_mean = df[df["action"] == "sell"]["ga_align_timing_sell_score"].mean() if "ga_align_timing_sell_score" in df.columns else 0
-
- content = f"""
-
-general_analysis 실행 리포트
-
-general_analysis 실행 리포트
-타점 {len(df)}건 · 컬럼 {len(df.columns)}개 · CSV: {csv_path}
-모듈별 컬럼 수
-
-MTF 합성 평균
-
-- 매수 타점 timing_buy_score 평균: {buy_mean:.3f}
-- 매도 타점 timing_sell_score 평균: {sell_mean:.3f}
-
-샘플 5건
-{sample}
-전체 데이터: {csv_path}
-"""
- html_out.write_text(content, encoding="utf-8")
- print(f"리포트: {html_out}")
- return html_out
diff --git a/deepcoin/analysis/general_analysis_runner.py b/deepcoin/analysis/general_analysis_runner.py
deleted file mode 100644
index b191acf..0000000
--- a/deepcoin/analysis/general_analysis_runner.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""
-general_analysis 실행 진입점.
-
- python scripts/03_analyze_trades.py
- python scripts/03_analyze_trades.py --limit 20 # 테스트용 타점 수 제한
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-from pathlib import Path
-
-from config import CHART_LOOKBACK_DAYS, SYMBOL
-from deepcoin.analysis.general_analysis_config import (
- DEFAULT_OUTPUT_CSV,
- DEFAULT_OUTPUT_HTML,
- DEFAULT_TRADES_FILE,
-)
-from deepcoin.analysis.general_analysis_report import write_analysis_report
-from deepcoin.analysis.general_analysis_snapshot import export_trade_snapshots
-from deepcoin.ops.monitor import Monitor
-from deepcoin.data.mtf_bb import load_frames_from_db
-
-
-def main() -> None:
- """ground truth 타점 MTF general_analysis 스냅샷 생성."""
- parser = argparse.ArgumentParser(description="general_analysis MTF 타점 분석")
- parser.add_argument("--limit", type=int, default=0, help="타점 수 제한 (0=전체)")
- parser.add_argument("--trades", type=str, default=DEFAULT_TRADES_FILE)
- parser.add_argument("--csv", type=str, default=DEFAULT_OUTPUT_CSV)
- parser.add_argument("--html", type=str, default=DEFAULT_OUTPUT_HTML)
- args = parser.parse_args()
-
- from deepcoin.paths import REPORTS_ANALYSIS
-
- trades_path = Path(args.trades)
- data = json.loads(trades_path.read_text(encoding="utf-8"))
- trades = data.get("trades") or []
- if args.limit > 0:
- trades = trades[: args.limit]
- print(f"테스트 모드: 타점 {args.limit}건만")
-
- import sys
- import time
-
- print(f"=== 03b GT 타점 MTF 분석 {SYMBOL} (lookback {CHART_LOOKBACK_DAYS}일) ===")
- print(f" 간격: 3,5,10,15,30,60,240,1440분 (1분 제외) · 타점 {len(trades)}건")
- sys.stdout.flush()
- t0 = time.time()
- print("[03b] Phase 0: DB에서 8TF OHLCV 로드...")
- sys.stdout.flush()
- mon = Monitor(cooldown_file=None)
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- if not frames:
- raise RuntimeError("coins.db 데이터 없음")
- for iv in (3, 5, 10, 15, 30, 60, 240, 1440):
- df = frames.get(iv)
- n = len(df) if df is not None else 0
- print(f" WLD_{iv}: {n:,}봉")
- print(f"[03b] Phase 0 완료 ({time.time() - t0:.0f}초)")
- sys.stdout.flush()
-
- # limit 시 임시 trades 파일
- if args.limit > 0:
- tmp = REPORTS_ANALYSIS / "_ga_trades_subset.json"
- tmp.parent.mkdir(exist_ok=True)
- subset = {**data, "trades": trades}
- tmp.write_text(json.dumps(subset, ensure_ascii=False), encoding="utf-8")
- trades_path = tmp
-
- from deepcoin.analysis.general_analysis_snapshot import build_trade_mtf_snapshots
-
- csv_path = Path(args.csv)
- df = build_trade_mtf_snapshots(frames, trades)
- csv_path.parent.mkdir(parents=True, exist_ok=True)
- df.to_csv(csv_path, index=False, encoding="utf-8-sig")
- print(f"저장: {csv_path} ({len(df)}행 × {len(df.columns)}열)")
-
- write_analysis_report(csv_path, Path(args.html))
-
- from deepcoin.matching.gt_mtf_profile import run_gt_mtf_profile
-
- run_gt_mtf_profile(csv_path)
- print("완료.")
-
-
-if __name__ == "__main__":
- main()
diff --git a/deepcoin/analysis/general_analysis_snapshot.py b/deepcoin/analysis/general_analysis_snapshot.py
deleted file mode 100644
index 2078ad3..0000000
--- a/deepcoin/analysis/general_analysis_snapshot.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""
-general_analysis ground truth 타점 MTF 스냅샷 생성.
-"""
-
-from __future__ import annotations
-
-import json
-import sys
-import time
-from pathlib import Path
-from typing import Any
-
-import pandas as pd
-
-from deepcoin.analysis.general_analysis_align import general_analysis_mtf_scores
-from deepcoin.analysis.general_analysis_config import (
- DEFAULT_OUTPUT_CSV,
- DEFAULT_TRADES_FILE,
- GENERAL_ANALYSIS_INTERVALS,
-)
-from deepcoin.analysis.general_analysis_core import interval_tf_prefix
-from deepcoin.analysis.general_analysis_pipeline import general_analysis_enrich_bars, general_analysis_snapshot_at_bar
-from deepcoin.ground_truth.ground_truth import load_ground_truth
-
-
-def _prefixed_snap(snap: dict[str, Any], interval: int) -> dict[str, Any]:
- p = interval_tf_prefix(interval)
- return {f"{p}_{k}": v for k, v in snap.items()}
-
-
-def build_trade_mtf_snapshots(
- frames: dict[int, pd.DataFrame],
- trades: list[dict[str, Any]],
-) -> pd.DataFrame:
- """
- 모든 타점에 대해 8개 간격 general_analysis 스냅샷.
-
- Args:
- frames: interval → OHLCV.
- trades: ground_truth trades.
-
- Returns:
- wide DataFrame (1 row per trade).
- """
- n_trades = len(trades)
- enriched: dict[int, pd.DataFrame] = {}
- t0 = time.time()
- print(
- f"[03b] MTF enrich (주·월봉 포함) — {len(GENERAL_ANALYSIS_INTERVALS)}개 간격"
- )
- sys.stdout.flush()
- for step, iv in enumerate(GENERAL_ANALYSIS_INTERVALS, start=1):
- raw = frames.get(iv)
- if raw is None or raw.empty:
- print(f" [{step}/8] {interval_tf_prefix(iv)} SKIP (데이터 없음)")
- sys.stdout.flush()
- continue
- label = interval_tf_prefix(iv)
- print(f" [{step}/8] {label} enrich 시작 ({len(raw):,}봉)...")
- sys.stdout.flush()
- t_iv = time.time()
- enriched[iv] = general_analysis_enrich_bars(raw, iv, full_context=True)
- print(f" [{step}/8] {label} 완료 — {len(enriched[iv].columns)}열, {time.time() - t_iv:.0f}초")
- sys.stdout.flush()
- print(f"[03b] Phase A 완료 (누적 {time.time() - t0:.0f}초)")
- sys.stdout.flush()
-
- print(f"[03b] Phase B: GT 타점 스냅샷 {n_trades}건")
- sys.stdout.flush()
- rows: list[dict[str, Any]] = []
- t_b = time.time()
- for i, t in enumerate(sorted(trades, key=lambda x: x["dt"])):
- ts = pd.Timestamp(t["dt"])
- row: dict[str, Any] = {
- "trade_idx": i,
- "dt": t["dt"],
- "action": t["action"],
- "price": t["price"],
- "weight": t.get("weight", 1.0),
- "leg_id": t.get("leg_id", 0),
- "memo": t.get("memo", ""),
- }
- flat: dict[str, Any] = {}
- for iv in GENERAL_ANALYSIS_INTERVALS:
- ef = enriched.get(iv)
- if ef is None:
- continue
- snap = general_analysis_snapshot_at_bar(ef, ts, iv)
- flat.update(_prefixed_snap(snap, iv))
- row.update(flat)
- row.update(general_analysis_mtf_scores(flat))
- rows.append(row)
- done = i + 1
- if done == 1 or done % 25 == 0 or done == n_trades:
- elapsed = time.time() - t_b
- rate = done / elapsed if elapsed > 0 else 0
- eta = (n_trades - done) / rate if rate > 0 else 0
- print(
- f" 타점 {done}/{n_trades} "
- f"({elapsed:.0f}초 경과, ETA 약 {eta:.0f}초)"
- )
- sys.stdout.flush()
-
- print(f"[03b] Phase B 완료 ({time.time() - t_b:.0f}초)")
- sys.stdout.flush()
- return pd.DataFrame(rows)
-
-
-def export_trade_snapshots(
- frames: dict[int, pd.DataFrame],
- trades_path: Path | str = DEFAULT_TRADES_FILE,
- output_csv: Path | str = DEFAULT_OUTPUT_CSV,
-) -> Path:
- """
- CSV로 타점 MTF 스냅샷 저장.
-
- Returns:
- 저장 경로.
- """
- data = load_ground_truth(Path(trades_path))
- if not data:
- raise FileNotFoundError(f"정답 파일 없음: {trades_path}")
- trades = data.get("trades") or []
- print(f"타점 {len(trades)}건 × {len(GENERAL_ANALYSIS_INTERVALS)} TF general_analysis")
- df = build_trade_mtf_snapshots(frames, trades)
- out = Path(output_csv)
- out.parent.mkdir(parents=True, exist_ok=True)
- df.to_csv(out, index=False, encoding="utf-8-sig")
- print(f"저장: {out} ({len(df)}행 × {len(df.columns)}열)")
- return out
diff --git a/deepcoin/analysis/general_analysis_volume.py b/deepcoin/analysis/general_analysis_volume.py
deleted file mode 100644
index 6408b1f..0000000
--- a/deepcoin/analysis/general_analysis_volume.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""
-general_analysis Volume Profile (POC, VAH, VAL).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from config import GA_VP_BINS, GA_VP_VALUE_AREA_PCT
-from deepcoin.analysis.general_analysis_core import ga_col
-
-
-def general_analysis_volume_profile(
- win: pd.DataFrame,
- bins: int | None = None,
- value_area_pct: float | None = None,
-) -> dict[str, float | int]:
- """
- lookback 구간 가격-거래량 분포에서 POC·VAH·VAL 계산.
-
- Args:
- win: OHLCV.
- bins: 가격 구간 수.
- value_area_pct: value area 누적 비율 (기본 70%).
-
- Returns:
- ga_vp_* 키 dict (접두사 없음).
- """
- res: dict[str, float | int] = {
- "vp_poc": 0.0,
- "vp_vah": 0.0,
- "vp_val": 0.0,
- "vp_close_vs_poc_pct": 0.0,
- "vp_in_value_area": 0,
- }
- if bins is None:
- bins = GA_VP_BINS
- if value_area_pct is None:
- value_area_pct = GA_VP_VALUE_AREA_PCT
-
- if win is None or len(win) < 10 or "Volume" not in win.columns:
- return res
-
- h = win["High"].astype(float).values
- l = win["Low"].astype(float).values
- c = win["Close"].astype(float).values
- v = win["Volume"].astype(float).values
- tp = (h + l + c) / 3.0
-
- lo, hi = float(l.min()), float(h.max())
- if hi <= lo:
- return res
-
- edges = np.linspace(lo, hi, bins + 1)
- hist = np.zeros(bins, dtype=float)
- for i in range(len(tp)):
- idx = int(np.clip(np.digitize(tp[i], edges) - 1, 0, bins - 1))
- hist[idx] += v[i]
-
- if hist.sum() <= 0:
- return res
-
- poc_idx = int(np.argmax(hist))
- poc = float((edges[poc_idx] + edges[poc_idx + 1]) / 2)
- res["vp_poc"] = poc
-
- order = np.argsort(hist)[::-1]
- cum = 0.0
- selected: list[int] = []
- total = hist.sum()
- for idx in order:
- selected.append(int(idx))
- cum += hist[idx]
- if cum >= total * value_area_pct:
- break
- sel_min, sel_max = min(selected), max(selected)
- res["vp_val"] = float(edges[sel_min])
- res["vp_vah"] = float(edges[sel_max + 1])
- res["vp_close_vs_poc_pct"] = float((c[-1] / poc - 1) * 100) if poc else 0.0
- res["vp_in_value_area"] = int(res["vp_val"] <= c[-1] <= res["vp_vah"])
-
- return res
-
-
-def general_analysis_volume_columns() -> list[str]:
- return ["vp_poc", "vp_vah", "vp_val", "vp_close_vs_poc_pct", "vp_in_value_area"]
-
-
-def general_analysis_volume_snapshot(win: pd.DataFrame) -> dict[str, object]:
- """Volume profile → ga_vp_*."""
- return {ga_col(k): v for k, v in general_analysis_volume_profile(win).items()}
diff --git a/deepcoin/analysis/general_analysis_wave.py b/deepcoin/analysis/general_analysis_wave.py
deleted file mode 100644
index 0db968d..0000000
--- a/deepcoin/analysis/general_analysis_wave.py
+++ /dev/null
@@ -1,205 +0,0 @@
-"""
-general_analysis 파동·시장 구조 (다우, 엘리어트 라이트, 피보나치, Wyckoff 태그).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from deepcoin.analysis.general_analysis_core import find_pivots, ga_col
-
-
-def _fib_levels(low: float, high: float) -> dict[str, float]:
- diff = high - low
- return {
- "fib_0": low,
- "fib_382": low + diff * 0.382,
- "fib_500": low + diff * 0.5,
- "fib_618": low + diff * 0.618,
- "fib_100": high,
- "fib_1618": low + diff * 1.618,
- }
-
-
-def general_analysis_wave_snapshot(win: pd.DataFrame) -> dict[str, object]:
- """
- lookback 윈도우 마지막 시점 파동·구조 스냅샷.
-
- Args:
- win: OHLCV.
-
- Returns:
- ga_wave_* / ga_struct_* / ga_fib_* dict.
- """
- res: dict[str, object] = {
- "struct_trend": "range",
- "struct_hh": 0,
- "struct_hl": 0,
- "struct_lh": 0,
- "struct_ll": 0,
- "struct_bos_bull": 0,
- "struct_bos_bear": 0,
- "struct_choch": 0,
- "elliott_wave_count": 0,
- "elliott_phase": "unknown",
- "wyckoff_phase": "unknown",
- "fib_near_level": "none",
- "ichi_trend": "neutral",
- "pitchfork_bias": "neutral",
- "pitchfork_dist_pct": 0.0,
- "wyckoff_spring": 0,
- "wyckoff_utad": 0,
- }
- if win is None or len(win) < 15:
- return {ga_col(k): v for k, v in res.items()}
-
- h = win["High"].astype(float).values
- l = win["Low"].astype(float).values
- c = win["Close"].astype(float).values
- peaks, troughs = find_pivots(h, l, order=2)
-
- # Dow HH/HL/LH/LL
- if len(peaks) >= 2 and len(troughs) >= 2:
- hh = int(h[peaks[-1]] > h[peaks[-2]])
- hl = int(l[troughs[-1]] > l[troughs[-2]])
- lh = int(h[peaks[-1]] < h[peaks[-2]])
- ll = int(l[troughs[-1]] < l[troughs[-2]])
- res["struct_hh"] = hh
- res["struct_hl"] = hl
- res["struct_lh"] = lh
- res["struct_ll"] = ll
- if hh and hl:
- res["struct_trend"] = "up"
- elif lh and ll:
- res["struct_trend"] = "down"
- if hh and c[-1] > h[peaks[-2]]:
- res["struct_bos_bull"] = 1
- if ll and c[-1] < l[troughs[-2]]:
- res["struct_bos_bear"] = 1
- if (hh and ll) or (lh and hl):
- res["struct_choch"] = 1
-
- # Elliott lite: pivot count in window
- swings = len(peaks) + len(troughs)
- res["elliott_wave_count"] = swings
- if swings >= 5:
- res["elliott_phase"] = "impulse_late"
- elif swings >= 3:
- res["elliott_phase"] = "corrective"
-
- # Wyckoff lite
- vol = win["Volume"].astype(float).values if "Volume" in win.columns else np.ones(len(c))
- vol_ma = vol[-20:].mean() if len(vol) >= 20 else vol.mean()
- price_range = (h[-20:].max() - l[-20:].min()) / max(c[-1], 1e-9) * 100
- if price_range < 6 and vol[-1] < vol_ma * 1.2:
- res["wyckoff_phase"] = "accumulation"
- elif price_range < 6 and vol[-1] > vol_ma * 1.5 and c[-1] > c[-5]:
- res["wyckoff_phase"] = "distribution"
-
- if price_range < 8 and l[-1] < l[-5] and c[-1] > c[-2] and vol[-1] > vol_ma * 1.3:
- res["wyckoff_spring"] = 1
- if price_range < 8 and h[-1] > h[-5] and c[-1] < c[-2] and vol[-1] > vol_ma * 1.3:
- res["wyckoff_utad"] = 1
-
- # Andrews Pitchfork (3피벗 중앙선 대비 종가 위치)
- pivots = sorted([(i, h[i]) for i in peaks] + [(i, l[i]) for i in troughs])
- if len(pivots) >= 3:
- p0, p1, p2 = pivots[-3], pivots[-2], pivots[-1]
- y0 = p0[1]
- y_mid = (p1[1] + p2[1]) / 2
- x0, x2 = p0[0], p2[0]
- if x2 != x0:
- slope = (y_mid - y0) / (x2 - x0)
- y_line = y0 + slope * (len(c) - 1 - x0)
- dist_pct = (c[-1] - y_line) / max(c[-1], 1e-9) * 100
- res["pitchfork_dist_pct"] = round(float(dist_pct), 3)
- if dist_pct > 0.5:
- res["pitchfork_bias"] = "above"
- elif dist_pct < -0.5:
- res["pitchfork_bias"] = "below"
-
- # Fibonacci
- hi, lo = float(h.max()), float(l.min())
- levels = _fib_levels(lo, hi)
- price = float(c[-1])
- for name, lvl in levels.items():
- if abs(price - lvl) / max(price, 1e-9) * 100 < 1.5:
- res["fib_near_level"] = name.replace("fib_", "")
- break
-
- if "ichi_cloud_top" in win.columns:
- row = win.iloc[-1]
- ct = float(row.get("ichi_cloud_top", np.nan))
- cb = float(row.get("ichi_cloud_bottom", np.nan))
- if not np.isnan(ct) and price > ct:
- res["ichi_trend"] = "above_cloud"
- elif not np.isnan(cb) and price < cb:
- res["ichi_trend"] = "below_cloud"
- else:
- res["ichi_trend"] = "in_cloud"
-
- return {ga_col(k): v for k, v in res.items()}
-
-
-def general_analysis_wave_columns() -> list[str]:
- return [
- "struct_trend",
- "struct_hh",
- "struct_hl",
- "struct_lh",
- "struct_ll",
- "struct_bos_bull",
- "struct_bos_bear",
- "struct_choch",
- "elliott_wave_count",
- "elliott_phase",
- "wyckoff_phase",
- "fib_near_level",
- "ichi_trend",
- "pitchfork_bias",
- "pitchfork_dist_pct",
- "wyckoff_spring",
- "wyckoff_utad",
- ]
-
-
-def general_analysis_apply_wave_to_bars(
- df: pd.DataFrame,
- interval: int,
- tail_rows: int | None = None,
-) -> pd.DataFrame:
- """파동·구조 스냅샷을 최근 봉에 롤링 적용."""
- from deepcoin.analysis.general_analysis_config import CONTEXT_TAIL_ROWS, LOOKBACK_BARS
-
- out = df.copy()
- lb = LOOKBACK_BARS.get(interval, 80)
- for k in general_analysis_wave_columns():
- col = ga_col(k)
- if k == "struct_trend":
- out[col] = "range"
- elif k in ("elliott_phase", "wyckoff_phase"):
- out[col] = "unknown"
- elif k == "fib_near_level":
- out[col] = "none"
- elif k in ("ichi_trend", "pitchfork_bias"):
- out[col] = "neutral"
- elif k == "pitchfork_dist_pct":
- out[col] = 0.0
- else:
- out[col] = 0
-
- n = len(out)
- if n < lb + 1:
- return out
- if tail_rows is None:
- tail_rows = CONTEXT_TAIL_ROWS.get(interval, 5000)
- start = max(lb, n - tail_rows)
-
- for i in range(start, n):
- snap = general_analysis_wave_snapshot(out.iloc[i - lb : i])
- idx = out.index[i]
- for k, v in snap.items():
- out.at[idx, k] = v
-
- return out
diff --git a/deepcoin/api/__init__.py b/deepcoin/api/__init__.py
deleted file mode 100644
index bbc1ed0..0000000
--- a/deepcoin/api/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""외부 API 연동 (빗썸 등)."""
-
-from deepcoin.api.bithumb import HTS
-
-__all__ = ["HTS"]
diff --git a/deepcoin/api/bithumb.py b/deepcoin/api/bithumb.py
deleted file mode 100644
index c3d72de..0000000
--- a/deepcoin/api/bithumb.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import pandas as pd
-import jwt
-import uuid
-import time
-import requests
-import json
-import hashlib
-from urllib.parse import urlencode
-
-
-class HTS:
- """빗썸 Open API 래퍼 (시세 조회, 잔고, 주문)."""
-
- bithumb = None
- accessKey = ""
- secretKey = ""
- apiUrl = ""
-
- def __init__(self):
- from config import BITHUMB_ACCESS_KEY, BITHUMB_API_URL, BITHUMB_SECRET_KEY
-
- self.bithumb = None
- self.accessKey = BITHUMB_ACCESS_KEY
- self.secretKey = BITHUMB_SECRET_KEY
- self.apiUrl = BITHUMB_API_URL.rstrip("/")
-
- def append(self, stock, df=None, data_1=None):
- if df is not None:
- for i in range(len(df)):
- stock['PRICE'].append(
- {
- "ymd": df.index[i],
- "close": df['close'].iloc[i],
- "diff": 0,
- "open": df['open'].iloc[i],
- "high": df['high'].iloc[i],
- "low": df['low'].iloc[i],
- "volume": df['volume'].iloc[i],
- "avg5": -1, "avg20": -1, "avg60": -1, "avg120": -1, "avg240": -1, "avg480": -1,
- "bolingerband_upper": -1, "bolingerband_lower": -1, "bolingerband_middle": -1, "bolingerband_bwi": -1,
- "ichimokucloud_changeLine": -1, "ichimokucloud_baseLine": -1, "ichimokucloud_leadingSpan1": -1, "ichimokucloud_leadingSpan2": -1,
- "stochastic_fast_k_1": -1, "stochastic_slow_k_1": -1, "stochastic_slow_d_1": -1,
- "stochastic_fast_k_2": -1, "stochastic_slow_k_2": -1, "stochastic_slow_d_2": -1,
- "stochastic_fast_k_3": -1, "stochastic_slow_k_3": -1, "stochastic_slow_d_3": -1,
- "rsi": -1, "rsis": -1,
- "macd": -1, "macds": -1, "macdo": -1, "nor_macd": -1, "nor_macds": -1, "nor_macdo": -1,
- })
-
- if data_1 is not None:
- stock['PRICE'].append(
- {
- "ymd": data_1.index[-1],
- "close": data_1['close'].iloc[-1],
- "diff": 0,
- "open": data_1['open'].iloc[-1],
- "high": data_1['high'].iloc[-1],
- "low": data_1['low'].iloc[-1],
- "volume": data_1['volume'].iloc[-1],
- "avg5": -1, "avg20": -1, "avg60": -1, "avg120": -1, "avg240": -1, "avg480": -1,
- "bolingerband_upper": -1, "bolingerband_lower": -1, "bolingerband_middle": -1, "bolingerband_bwi": -1, "bolingerband_nor_bwi": -1,
- "envelope_upper": -1, "envelope_lower": -1, "envelope_middle": -1,
- "ichimokucloud_changeLine": -1, "ichimokucloud_baseLine": -1, "ichimokucloud_leadingSpan1": -1,
- "ichimokucloud_leadingSpan2": -1,
- "stochastic_fast_k_1": -1, "stochastic_slow_k_1": -1, "stochastic_slow_d_1": -1,
- "stochastic_fast_k_2": -1, "stochastic_slow_k_2": -1, "stochastic_slow_d_2": -1,
- "stochastic_fast_k_3": -1, "stochastic_slow_k_3": -1, "stochastic_slow_d_3": -1,
- "rsi": -1, "rsis": -1,
- "macd": -1, "macds": -1, "macdo": -1, "nor_macd": -1, "nor_macds": -1, "nor_macdo": -1,
- })
- return
-
- def getCoinRawData(self, ticker_code, minute=None, day=False, week=False, month=False, to=None, endpoint='/v1/candles'):
- url = None
- if minute == 0:
- # 현재가 정보
- url = (self.apiUrl + "/v1/ticker?markets=KRW-{}").format(ticker_code)
-
- headers = {"accept": "application/json"}
- response = requests.get(url, headers=headers)
- json_data = json.loads(response.text)
- df_temp = pd.DataFrame(json_data)
- if 'trade_date_kst' not in df_temp or 'trade_time_kst' not in df_temp:
- return None
- df = pd.DataFrame()
- df['datetime'] = pd.to_datetime(df_temp['trade_date_kst'], format='%Y-%m-%dT%H:%M:%S')
- df['open'] = df_temp['opening_price']
- df['close'] = df_temp['trade_price']
- df['high'] = df_temp['high_price']
- df['low'] = df_temp['low_price']
- df['volume'] = df_temp['trade_volume']
- df = df.set_index('datetime')
- df = df.astype(float)
- df["datetime"] = df.index
- else:
- # 분봉
- if minute is not None and minute in {1, 3, 5, 10, 15, 30, 60, 240}:
- if to is None:
- url = (self.apiUrl + endpoint + "/minutes/{}?market=KRW-{}&count=3000").format(minute, ticker_code)
- else:
- url = (self.apiUrl + endpoint + "/minutes/{}?market=KRW-{}&count=3000&to={}").format(minute, ticker_code, to)
- if day:
- if to is None:
- url = (self.apiUrl + endpoint + "/days?market=KRW-{}&count=3000").format(ticker_code)
- else:
- url = (self.apiUrl + endpoint + "/days?market=KRW-{}&count=3000&to={}").format(ticker_code, to)
- if week:
- if to is None:
- url = (self.apiUrl + endpoint + "/weeks?market=KRW-{}&count=3000").format(ticker_code)
- else:
- url = (self.apiUrl + endpoint + "/weeks?market=KRW-{}&count=3000&to={}").format(ticker_code, to)
- if month:
- if to is None:
- url = (self.apiUrl + endpoint + "/months?market=KRW-{}&count=3000").format(ticker_code)
- else:
- url = (self.apiUrl + endpoint + "/months?market=KRW-{}&count=3000&to={}").format(ticker_code, to)
-
- if url is None:
- return None
-
- headers = {"accept": "application/json"}
- response = requests.get(url, headers=headers)
- json_data = json.loads(response.text)
- df_temp = pd.DataFrame(json_data)
- if 'candle_date_time_kst' not in df_temp:
- return None
- df = pd.DataFrame()
- #df.columns = ['datetime', 'open', 'close', 'high', 'low', 'volume']
- #df['datetime'] = pd.to_datetime(df_temp['candle_date_time_kst'])
- df['datetime'] = pd.to_datetime(df_temp['candle_date_time_kst'], format='%Y-%m-%dT%H:%M:%S')
- df['open'] = df_temp['opening_price']
- df['close'] = df_temp['trade_price']
- df['high'] = df_temp['high_price']
- df['low'] = df_temp['low_price']
- df['volume'] = df_temp['candle_acc_trade_volume']
- df = df.set_index('datetime')
- df = df.astype(float)
- df["datetime"] = df.index
-
- if df is None:
- return None
-
- return df
-
- def getTickerList(self):
- url = f"{self.apiUrl}/v1/market/all?isDetails=false"
- headers = {"accept": "application/json"}
- response = requests.get(url, headers=headers)
-
- tickets = response.json()
- return tickets
-
- def getVirtual_asset_warning(self):
- url = f"{self.apiUrl}/v1/market/virtual_asset_warning"
- headers = {"accept": "application/json"}
- response = requests.get(url, headers=headers)
- warning_list = response.json()
- return warning_list
-
- # 거래대금이 많은 순으로 코인리스트를 얻는다.
- def getTopCoinList(self, interval, top):
- return
-
- # 현재 가격 얻어오기
- def getCurrentPrice(self, ticker_code, endpoint='/v1/ticker'):
- headers = {"accept": "application/json"}
- url = (self.apiUrl + endpoint + "?markets=KRW-{}").format(ticker_code)
- response = requests.get(url, headers=headers)
-
- ticker_state = response.json()
- return ticker_state
-
- # 잔고 가져오기
- def getBalances(self, ticker_code=None, endpoint='/v1/accounts'):
-
- payload = {
- 'access_key': self.accessKey,
- 'nonce': str(uuid.uuid4()),
- 'timestamp': round(time.time() * 1000)
- }
- jwt_token = jwt.encode(payload, self.secretKey)
- authorization_token = 'Bearer {}'.format(jwt_token)
- headers = {
- 'Authorization': authorization_token
- }
- response = requests.get(self.apiUrl + endpoint, headers=headers)
- balances = response.json()
-
- """
- [
- {'currency': 'P', 'balance': '78290', 'locked': '0', 'avg_buy_price': '0', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'},
- {'currency': 'KRW', 'balance': '4218.401653', 'locked': '0', 'avg_buy_price': '0', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'},
- {'currency': 'XRP', 'balance': '13069.27647861', 'locked': '0', 'avg_buy_price': '1917', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'},
- {'currency': 'ADA', 'balance': '6941.65484013', 'locked': '0', 'avg_buy_price': '1260', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'},
- {'currency': 'BSV', 'balance': '0.00005656', 'locked': '0', 'avg_buy_price': '65450', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'},
- {'currency': 'SAND', 'balance': '0.00001158', 'locked': '0', 'avg_buy_price': '544.8', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'},
- {'currency': 'AVAX', 'balance': '26.43960509', 'locked': '0', 'avg_buy_price': '60882', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'},
- {'currency': 'XCORE', 'balance': '0.2119', 'locked': '0', 'avg_buy_price': '0', 'avg_buy_price_modified': False, 'unit_currency': 'KRW'}
- ]
- """
- if ticker_code is None:
- return balances
- else:
- for balance in balances:
- if balance['currency'] == ticker_code:
- return balance
-
- return None
-
-
- def order(self, ticker_code, side, ord_type, volume, price=None, endpoint='/v1/orders'):
- if ord_type=='limit':
- # 지정가 매수 (limit, side=bid) / 매도 (limit, side=ask)
- if price is None:
- return
-
- requestBody = dict(market='KRW-'+ticker_code, side=side, volume=volume, price=price, ord_type=ord_type)
- else:
- # 시장가 매수 (price, side=bid) / 매도 (market, side=ask)
-
- if ord_type == 'price':
- requestBody = dict(market='KRW-' + ticker_code, side=side, price=price, ord_type=ord_type)
- else:
- requestBody = dict(market='KRW-' + ticker_code, side=side, volume=volume, ord_type=ord_type)
-
- # Generate access token
- query = urlencode(requestBody).encode()
- hash = hashlib.sha512()
- hash.update(query)
- query_hash = hash.hexdigest()
- payload = {
- 'access_key': self.accessKey,
- 'nonce': str(uuid.uuid4()),
- 'timestamp': round(time.time() * 1000),
- 'query_hash': query_hash,
- 'query_hash_alg': 'SHA512',
- }
- jwt_token = jwt.encode(payload, self.secretKey)
- authorization_token = 'Bearer {}'.format(jwt_token)
- headers = {
- 'Authorization': authorization_token,
- 'Content-Type': 'application/json'
- }
-
- response = requests.post(self.apiUrl + endpoint, data=json.dumps(requestBody), headers=headers)
- # handle to success or fail
- #print(response.json())
- if response.status_code == 200:
- return True
- return False
-
- # 시장가 매수한다. 2초 뒤 잔고 데이터 리스트 리턴
- def buyCoinMarket(self, ticker_code, price, count=None):
- if price > 5000:
- if price < 50000:
- self.order(ticker_code, side='bid', ord_type='price', volume=count, price=price)
- buy_price = price
- else:
- repeat = 10
- buy_price = int(price / 1000) * 1000
- buy_amount = int(buy_price / repeat)
- while repeat > 0:
- self.order(ticker_code, side='bid', ord_type='price', volume=count, price=buy_amount)
- repeat -= 1
- time.sleep(0.5)
- else:
- buy_price = 0
- return buy_price
-
- # 시장가 매도한다. 2초 뒤 잔고 데이터 리스트 리턴
- def sellCoinMarket(self, ticker_code, price, count):
- return self.order(ticker_code, side='ask', ord_type='market', volume=count, price=price)
-
- # 지정가 매수한다. 2초 뒤 잔고 데이터 리스트 리턴
- def buyCoinLimit(self, ticker_code, price, count):
- return self.order(ticker_code, side='bid', ord_type='limit', volume=count, price=price)
-
- # 지정가 매도한다. 2초 뒤 잔고 데이터 리스트 리턴
- def sellCoinLimit(self, ticker_code, price, count):
- return self.order(ticker_code, side='ask', ord_type='limit', volume=count, price=price)
-
- def getOrderBook(self, ticker_code, endpoint='/v1/orderbook'):
- """
- 필드 설명 타입
- market 마켓 코드 String
- timestamp 호가 생성 시각 Long
- total_ask_size 호가 매도 총 잔량 Double
- total_bid_size 호가 매수 총 잔량 Double
- orderbook_units 호가 List of Objects
- > ask_price 매도호가 Double
- > bid_price 매수호가 Double
- > ask_size 매도 잔량 Double
- > bid_size 매수 잔량 Double
- """
- headers = {"accept": "application/json"}
- url = (self.apiUrl + endpoint + "?markets=KRW-{}").format(ticker_code)
- response = requests.get(url, headers=headers)
-
- # 매도 총 잔량: sum([units['ask_size'] for units in orders[0]['orderbook_units']])
- # 매수 총 잔량: sum([units['bid_size'] for units in orders[0]['orderbook_units']])
- orders = response.json()
- return orders
\ No newline at end of file
diff --git a/deepcoin/common/__init__.py b/deepcoin/common/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deepcoin/common/candle_features.py b/deepcoin/common/candle_features.py
deleted file mode 100644
index a3e2446..0000000
--- a/deepcoin/common/candle_features.py
+++ /dev/null
@@ -1,362 +0,0 @@
-"""
-모든 봉(3~1440분 등)에 BB·일목 위치·캔들 형태 특징을 계산하고
-기준 타임라인(3분)에 맞춰 정렬합니다.
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from config import (
- ALL_INTERVALS,
- BB_MIN_WIDTH_PCT,
- DISPARITY_PERIODS,
- ENTRY_INTERVAL,
- INTERVAL_PREFIX,
- STOCH_OVERBOUGHT,
- STOCH_OVERSOLD,
-)
-from deepcoin.common.indicators import apply_bar_indicators, disparity_column
-
-
-def interval_prefix(interval: int) -> str:
- """컬럼 접두사 (예: m3, d1)."""
- return INTERVAL_PREFIX.get(interval, f"m{interval}")
-
-
-def interval_display(interval: int) -> str:
- if interval >= 1440:
- return "일봉"
- return f"{interval}분"
-
-
-# BB 위치 (밴드 내 %B 구간)
-BB_ZONE_FEATURES: tuple[str, ...] = (
- "bb_zone_bottom",
- "bb_zone_low",
- "bb_zone_mid",
- "bb_zone_high",
- "bb_zone_top",
-)
-
-# 일목 위치
-ICHI_FEATURES: tuple[str, ...] = (
- "ichi_above_cloud",
- "ichi_below_cloud",
- "ichi_in_cloud",
- "ichi_cloud_bull",
- "ichi_cloud_bear",
- "ichi_tk_bull",
- "ichi_tk_bear",
- "ichi_price_above_tenkan",
- "ichi_price_below_kijun",
- "ichi_tk_cross_up",
- "ichi_tk_cross_down",
-)
-
-# BB 이벤트·캔들 형태
-BB_EVENT_FEATURES: tuple[str, ...] = (
- "cross_up_lower",
- "cross_up_upper",
- "cross_down_lower",
- "below_lower",
- "above_upper",
- "inside_band",
- "bb_pos_low",
- "bb_pos_high",
- "squeeze",
-)
-
-MACD_STOCH_FEATURES: tuple[str, ...] = (
- "macd_hist_positive",
- "macd_hist_negative",
- "macd_cross_up",
- "macd_cross_down",
- "stoch_oversold",
- "stoch_overbought",
- "stoch_cross_up",
- "stoch_cross_down",
-)
-
-
-def _disparity_feature_names() -> tuple[str, ...]:
- """기간별 이격도 과매수·과매도 불리언 컬럼명."""
- names: list[str] = []
- for p in DISPARITY_PERIODS:
- names.append(f"disparity_{p}_oversold")
- names.append(f"disparity_{p}_overbought")
- return tuple(names)
-
-
-DISPARITY_FEATURES: tuple[str, ...] = _disparity_feature_names()
-
-CANDLE_SHAPE_FEATURES: tuple[str, ...] = (
- "body_strong",
- "body_weak",
- "hammer",
- "shooting_star",
- "bullish",
- "bearish",
-)
-
-FEATURE_BOOL_COLS: tuple[str, ...] = (
- BB_EVENT_FEATURES
- + BB_ZONE_FEATURES
- + ICHI_FEATURES
- + MACD_STOCH_FEATURES
- + DISPARITY_FEATURES
- + CANDLE_SHAPE_FEATURES
-)
-
-
-def compute_bar_features(df: pd.DataFrame) -> pd.DataFrame:
- """단일 봉 DataFrame에 BB·일목·MACD·스토캐스틱·캔들 위치 특징을 추가합니다."""
- out = apply_bar_indicators(df.copy())
- if len(out) < 2:
- return out
-
- o = out["Open"].astype(float)
- h = out["High"].astype(float)
- l = out["Low"].astype(float)
- c = out["Close"].astype(float)
- prev_c = c.shift(1)
- upper = out["Upper"].astype(float)
- lower = out["Lower"].astype(float)
- prev_upper = upper.shift(1)
- prev_lower = lower.shift(1)
-
- rng = (h - l).replace(0, np.nan)
- body = (c - o).abs()
- out["range_pct"] = (rng / c.replace(0, np.nan)) * 100
- out["body_ratio"] = (body / rng).fillna(0).clip(0, 1)
- out["upper_wick_ratio"] = ((h - np.maximum(o, c)) / rng).fillna(0).clip(0, 1)
- out["lower_wick_ratio"] = ((np.minimum(o, c) - l) / rng).fillna(0).clip(0, 1)
- out["ret_pct"] = ((c - prev_c) / prev_c.replace(0, np.nan)) * 100
-
- pos = out["bb_pos"].astype(float)
- out["bb_zone_bottom"] = (pos < 0.15).astype(int)
- out["bb_zone_low"] = ((pos >= 0.15) & (pos < 0.35)).astype(int)
- out["bb_zone_mid"] = ((pos >= 0.35) & (pos < 0.65)).astype(int)
- out["bb_zone_high"] = ((pos >= 0.65) & (pos < 0.85)).astype(int)
- out["bb_zone_top"] = (pos >= 0.85).astype(int)
-
- out["cross_up_lower"] = ((prev_c <= prev_lower) & (c > lower)).astype(int)
- out["cross_up_upper"] = ((prev_c < prev_upper) & (c >= upper)).astype(int)
- out["cross_down_lower"] = ((prev_c >= prev_lower) & (c < lower)).astype(int)
- out["below_lower"] = (c < lower).astype(int)
- out["above_upper"] = (c > upper).astype(int)
- out["inside_band"] = ((c >= lower) & (c <= upper)).astype(int)
- out["bb_pos_low"] = (pos < 0.2).astype(int)
- out["bb_pos_high"] = (pos > 0.8).astype(int)
- out["squeeze"] = (out["BB_Width"] < BB_MIN_WIDTH_PCT).astype(int)
-
- ct = out["ichi_cloud_top"].astype(float)
- cb = out["ichi_cloud_bottom"].astype(float)
- ten = out["ichi_tenkan"].astype(float)
- kij = out["ichi_kijun"].astype(float)
- prev_ten = ten.shift(1)
- prev_kij = kij.shift(1)
-
- out["ichi_above_cloud"] = (c > ct).astype(int)
- out["ichi_below_cloud"] = (c < cb).astype(int)
- out["ichi_in_cloud"] = ((c >= cb) & (c <= ct)).astype(int)
- out["ichi_cloud_bull"] = (out["ichi_span_a"] > out["ichi_span_b"]).astype(int)
- out["ichi_cloud_bear"] = (out["ichi_span_a"] < out["ichi_span_b"]).astype(int)
- out["ichi_tk_bull"] = (ten > kij).astype(int)
- out["ichi_tk_bear"] = (ten < kij).astype(int)
- out["ichi_price_above_tenkan"] = (c > ten).astype(int)
- out["ichi_price_below_kijun"] = (c < kij).astype(int)
- out["ichi_tk_cross_up"] = ((prev_ten <= prev_kij) & (ten > kij)).astype(int)
- out["ichi_tk_cross_down"] = ((prev_ten >= prev_kij) & (ten < kij)).astype(int)
-
- out["body_strong"] = (out["body_ratio"] > 0.55).astype(int)
- out["body_weak"] = (out["body_ratio"] < 0.25).astype(int)
- out["hammer"] = ((out["lower_wick_ratio"] > 0.45) & (out["body_ratio"] < 0.35)).astype(int)
- out["shooting_star"] = ((out["upper_wick_ratio"] > 0.45) & (out["body_ratio"] < 0.35)).astype(int)
- out["bullish"] = (c > o).astype(int)
- out["bearish"] = (c < o).astype(int)
-
- if "macd_hist" in out.columns:
- mh = out["macd_hist"].astype(float)
- prev_mh = mh.shift(1)
- ml = out["macd_line"].astype(float)
- ms = out["macd_signal"].astype(float)
- prev_ml = ml.shift(1)
- prev_ms = ms.shift(1)
- out["macd_hist_positive"] = (mh > 0).astype(int)
- out["macd_hist_negative"] = (mh < 0).astype(int)
- out["macd_cross_up"] = ((prev_ml <= prev_ms) & (ml > ms)).astype(int)
- out["macd_cross_down"] = ((prev_ml >= prev_ms) & (ml < ms)).astype(int)
-
- if "stoch_k" in out.columns:
- sk = out["stoch_k"].astype(float)
- sd = out["stoch_d"].astype(float)
- prev_sk = sk.shift(1)
- prev_sd = sd.shift(1)
- out["stoch_oversold"] = (sk <= STOCH_OVERSOLD).astype(int)
- out["stoch_overbought"] = (sk >= STOCH_OVERBOUGHT).astype(int)
- out["stoch_cross_up"] = ((prev_sk <= prev_sd) & (sk > sd)).astype(int)
- out["stoch_cross_down"] = ((prev_sk >= prev_sd) & (sk < sd)).astype(int)
-
- from config import DISPARITY_OVERBOUGHT, DISPARITY_OVERSOLD
-
- for p in DISPARITY_PERIODS:
- col = disparity_column(p)
- if col not in out.columns:
- continue
- d = out[col].astype(float)
- out[f"disparity_{p}_oversold"] = (d <= DISPARITY_OVERSOLD).astype(int)
- out[f"disparity_{p}_overbought"] = (d >= DISPARITY_OVERBOUGHT).astype(int)
-
- return out
-
-
-def describe_latest_position(df: pd.DataFrame, interval: int) -> dict:
- """한 봉의 최신 BB·일목 위치 요약."""
- feat = compute_bar_features(df)
- if feat.empty:
- return {"interval": interval, "label": interval_display(interval)}
- row = feat.iloc[-1]
- pos = float(row.get("bb_pos", 0.5))
- bb_zone = "mid"
- for z in BB_ZONE_FEATURES:
- if int(row.get(z, 0)) == 1:
- bb_zone = z.replace("bb_zone_", "")
- break
- ichi_pos = "in_cloud"
- if int(row.get("ichi_above_cloud", 0)):
- ichi_pos = "above_cloud"
- elif int(row.get("ichi_below_cloud", 0)):
- ichi_pos = "below_cloud"
-
- snap: dict = {
- "interval": interval,
- "label": interval_display(interval),
- "close": float(row["Close"]),
- "bb_pos": round(pos, 3),
- "bb_zone": bb_zone,
- "bb_state": _bb_event_label(row),
- "ichi_position": ichi_pos,
- "ichi_tk": "bull" if int(row.get("ichi_tk_bull", 0)) else "bear",
- "ichi_cloud": "bull" if int(row.get("ichi_cloud_bull", 0)) else "bear",
- }
- if "macd_hist" in row.index and pd.notna(row["macd_hist"]):
- snap["macd_hist"] = round(float(row["macd_hist"]), 4)
- snap["macd_state"] = "bull" if float(row["macd_hist"]) > 0 else "bear"
- if "stoch_k" in row.index and pd.notna(row["stoch_k"]):
- sk = float(row["stoch_k"])
- snap["stoch_k"] = round(sk, 1)
- snap["stoch_d"] = round(float(row["stoch_d"]), 1)
- if sk <= STOCH_OVERSOLD:
- snap["stoch_zone"] = "oversold"
- elif sk >= STOCH_OVERBOUGHT:
- snap["stoch_zone"] = "overbought"
- else:
- snap["stoch_zone"] = "mid"
- disp_vals: dict[int, float] = {}
- for p in DISPARITY_PERIODS:
- col = disparity_column(p)
- if col in row.index and pd.notna(row[col]):
- disp_vals[p] = round(float(row[col]), 2)
- if disp_vals:
- snap["disparity"] = disp_vals
- primary_p = 20 if 20 in DISPARITY_PERIODS else DISPARITY_PERIODS[0]
- snap["disparity_primary"] = disp_vals.get(
- primary_p, next(iter(disp_vals.values()))
- )
- return snap
-
-
-def _bb_event_label(row: pd.Series) -> str:
- for name in (
- "cross_up_lower",
- "cross_up_upper",
- "cross_down_lower",
- "below_lower",
- "above_upper",
- "squeeze",
- "inside_band",
- ):
- if int(row.get(name, 0)) == 1:
- return name
- return "neutral"
-
-
-def _merge_interval_features(
- master_index: pd.DatetimeIndex,
- feat: pd.DataFrame,
- prefix: str,
-) -> pd.DataFrame:
- """master_index 길이와 동일한 간격 특징만 반환."""
- pick = [c for c in FEATURE_BOOL_COLS if c in feat.columns]
- numeric_cols = (
- "bb_pos",
- "body_ratio",
- "lower_wick_ratio",
- "ret_pct",
- "bb_width_pct",
- "macd_line",
- "macd_signal",
- "macd_hist",
- "stoch_k",
- "stoch_d",
- "RSI",
- ) + tuple(disparity_column(p) for p in DISPARITY_PERIODS)
- extra = [c for c in numeric_cols if c in feat.columns]
- if "bb_width_pct" not in feat.columns and "BB_Width" in feat.columns:
- feat = feat.copy()
- feat["bb_width_pct"] = feat["BB_Width"]
- extra.append("bb_width_pct")
-
- sub = feat[pick + extra].copy()
- sub.columns = [f"{prefix}_{c}" for c in sub.columns]
-
- left = pd.DataFrame({"ts": master_index})
- right = sub.reset_index()
- time_col = right.columns[0]
- right = right.rename(columns={time_col: "ts"})
-
- merged = pd.merge_asof(
- left.sort_values("ts"),
- right.sort_values("ts"),
- on="ts",
- direction="backward",
- )
- merged.index = master_index
- return merged.drop(columns=["ts"])
-
-
-def build_master_feature_matrix(frames: dict[int, pd.DataFrame]) -> pd.DataFrame:
- """3분 타임라인에 모든 봉의 BB·일목·캔들 특징을 붙인 행렬."""
- entry = frames.get(ENTRY_INTERVAL)
- if entry is None or entry.empty:
- raise ValueError(f"{ENTRY_INTERVAL}분봉(ENTRY_INTERVAL) 데이터가 없습니다.")
-
- entry_feat = compute_bar_features(entry)
- entry_feat = entry_feat[~entry_feat.index.duplicated(keep="last")].sort_index()
-
- p0 = interval_prefix(ENTRY_INTERVAL)
- ohlc = ["Open", "High", "Low", "Close", "Volume", "Upper", "Lower", "MA"]
- master = entry_feat[[c for c in ohlc if c in entry_feat.columns]].copy()
-
- for col in FEATURE_BOOL_COLS:
- if col in entry_feat.columns:
- master[f"{p0}_{col}"] = entry_feat[col]
- for col in ("bb_pos", "body_ratio", "lower_wick_ratio", "ret_pct", "bb_width_pct", "BB_Width"):
- if col in entry_feat.columns:
- master[f"{p0}_{col}"] = entry_feat[col]
-
- for interval in ALL_INTERVALS:
- if interval == ENTRY_INTERVAL:
- continue
- df = frames.get(interval)
- if df is None or df.empty:
- continue
- feat = compute_bar_features(df)
- feat = feat[~feat.index.duplicated(keep="last")].sort_index()
- prefix = interval_prefix(interval)
- merged = _merge_interval_features(master.index, feat, prefix)
- master = pd.concat([master, merged], axis=1)
-
- return master.loc[:, ~master.columns.duplicated()]
diff --git a/deepcoin/common/indicators.py b/deepcoin/common/indicators.py
deleted file mode 100644
index 25b6bfb..0000000
--- a/deepcoin/common/indicators.py
+++ /dev/null
@@ -1,370 +0,0 @@
-"""
-볼린저 밴드·일목·MACD·스토캐스틱·RSI·이격도 계산 (모든 봉 간격 공용).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from config import (
- BB_PERIOD,
- BB_STD,
- DISPARITY_OVERBOUGHT,
- DISPARITY_OVERSOLD,
- DISPARITY_PERIODS,
- MACD_FAST,
- MACD_SIGNAL,
- MACD_SLOW,
- RSI_PERIOD,
- STOCH_D_PERIOD,
- STOCH_K_PERIOD,
- STOCH_OVERBOUGHT,
- STOCH_OVERSOLD,
- STOCH_SMOOTH_K,
- TREND_RANGE_MA_GAP_PCT,
-)
-
-Trend = str # "up" | "down" | "range"
-
-
-def add_bollinger(
- df: pd.DataFrame,
- period: int = BB_PERIOD,
- std_mult: float = BB_STD,
-) -> pd.DataFrame:
- """
- 볼린저 밴드 컬럼을 추가합니다.
-
- Args:
- df: OHLCV DataFrame.
- period: 중심선 기간.
- std_mult: 표준편차 배수.
-
- Returns:
- MA, Upper, Lower, STD, bb_pos, BB_Width 가 추가된 DataFrame.
- """
- out = df.copy()
- if "MA" not in out.columns:
- out["MA"] = out["Close"].rolling(period).mean()
- if "Upper" not in out.columns or "Lower" not in out.columns:
- std = out["Close"].rolling(period).std()
- out["STD"] = std
- out["Upper"] = out["MA"] + std_mult * std
- out["Lower"] = out["MA"] - std_mult * std
- ma = out["MA"].replace(0, np.nan)
- band = (out["Upper"] - out["Lower"]).replace(0, np.nan)
- out["bb_pos"] = ((out["Close"] - out["Lower"]) / band).clip(0, 1)
- out["BB_Width"] = band / ma * 100
- return out
-
-
-def add_macd(
- df: pd.DataFrame,
- fast: int = MACD_FAST,
- slow: int = MACD_SLOW,
- signal_period: int = MACD_SIGNAL,
-) -> pd.DataFrame:
- """
- MACD(12,26,9) 라인·시그널·히스토그램을 추가합니다.
-
- Args:
- df: OHLCV (Close 필요).
- fast: 단기 EMA 기간.
- slow: 장기 EMA 기간.
- signal_period: 시그널 EMA 기간.
-
- Returns:
- macd_line, macd_signal, macd_hist 컬럼이 추가된 DataFrame.
- """
- out = df.copy()
- close = out["Close"].astype(float)
- ema_fast = close.ewm(span=fast, adjust=False).mean()
- ema_slow = close.ewm(span=slow, adjust=False).mean()
- out["macd_line"] = ema_fast - ema_slow
- out["macd_signal"] = out["macd_line"].ewm(span=signal_period, adjust=False).mean()
- out["macd_hist"] = out["macd_line"] - out["macd_signal"]
- return out
-
-
-def disparity_column(period: int) -> str:
- """이격도 컬럼명 (예: disparity_20)."""
- return f"disparity_{period}"
-
-
-def add_disparity(
- df: pd.DataFrame,
- periods: tuple[int, ...] | None = None,
-) -> pd.DataFrame:
- """
- 이격도 = (종가 / SMA(n)) × 100. 100이면 이평선과 동일 위치.
-
- Args:
- df: OHLCV (Close 필요).
- periods: SMA 기간 목록. None이면 config.DISPARITY_PERIODS.
-
- Returns:
- disparity_{n} 컬럼이 추가된 DataFrame.
- """
- out = df.copy()
- close = out["Close"].astype(float)
- for p in periods or DISPARITY_PERIODS:
- ma = close.rolling(p).mean()
- out[disparity_column(p)] = (close / ma.replace(0, np.nan)) * 100.0
- return out
-
-
-def disparity_zone(value: float | None) -> str:
- """이격도 구간 라벨 (oversold / mid / overbought)."""
- if value is None:
- return "mid"
- if value <= DISPARITY_OVERSOLD:
- return "oversold"
- if value >= DISPARITY_OVERBOUGHT:
- return "overbought"
- return "mid"
-
-
-def add_stochastic(
- df: pd.DataFrame,
- k_period: int = STOCH_K_PERIOD,
- d_period: int = STOCH_D_PERIOD,
- smooth_k: int = STOCH_SMOOTH_K,
-) -> pd.DataFrame:
- """
- 스토캐스틱 %K·%D를 추가합니다 (Slow Stochastic).
-
- Args:
- df: OHLCV (High, Low, Close 필요).
- k_period: %K lookback.
- d_period: %D SMA 기간.
- smooth_k: %K SMA 평활 기간.
-
- Returns:
- stoch_k, stoch_d 컬럼이 추가된 DataFrame.
- """
- out = df.copy()
- h = out["High"].astype(float)
- l = out["Low"].astype(float)
- c = out["Close"].astype(float)
- lowest = l.rolling(k_period).min()
- highest = h.rolling(k_period).max()
- denom = (highest - lowest).replace(0, np.nan)
- raw_k = ((c - lowest) / denom) * 100.0
- out["stoch_k"] = raw_k.rolling(smooth_k).mean()
- out["stoch_d"] = out["stoch_k"].rolling(d_period).mean()
- return out
-
-
-def add_ichimoku(
- df: pd.DataFrame,
- tenkan: int = 9,
- kijun: int = 26,
- senkou_b_period: int = 52,
-) -> pd.DataFrame:
- """
- 일목균형표 라인·구름 위치 컬럼 추가 (해당 봉 시점, 미래 데이터 미사용).
-
- Returns:
- ichi_tenkan, ichi_kijun, ichi_span_a, ichi_span_b,
- ichi_cloud_top, ichi_cloud_bottom
- """
- out = df.copy()
- h = out["High"].astype(float)
- l = out["Low"].astype(float)
- c = out["Close"].astype(float)
-
- out["ichi_tenkan"] = (h.rolling(tenkan).max() + l.rolling(tenkan).min()) / 2
- out["ichi_kijun"] = (h.rolling(kijun).max() + l.rolling(kijun).min()) / 2
- out["ichi_span_a"] = (out["ichi_tenkan"] + out["ichi_kijun"]) / 2
- out["ichi_span_b"] = (h.rolling(senkou_b_period).max() + l.rolling(senkou_b_period).min()) / 2
- out["ichi_cloud_top"] = np.maximum(out["ichi_span_a"], out["ichi_span_b"])
- out["ichi_cloud_bottom"] = np.minimum(out["ichi_span_a"], out["ichi_span_b"])
- return out
-
-
-def prepare_entry_df(data: pd.DataFrame) -> pd.DataFrame:
- """
- RSI·거래량 MA·BB 폭 등 보조 컬럼을 추가합니다.
-
- Args:
- data: BB(MA/Upper/Lower)가 계산된 OHLCV.
-
- Returns:
- RSI 등 컬럼이 추가된 DataFrame.
- """
- df = data.copy()
- delta = df["Close"].diff()
- gain = delta.where(delta > 0, 0.0).rolling(RSI_PERIOD).mean()
- loss = (-delta.where(delta < 0, 0.0)).rolling(RSI_PERIOD).mean()
- rs = gain / loss.replace(0, np.nan)
- df["RSI"] = 100 - (100 / (1 + rs))
- df["VolMA5"] = df["Volume"].rolling(5).mean()
- if "MA" in df.columns and "Upper" in df.columns and "Lower" in df.columns:
- ma = df["MA"].replace(0, np.nan)
- df["BB_Width"] = (df["Upper"] - df["Lower"]) / ma * 100
- return df
-
-
-def apply_bar_indicators(df: pd.DataFrame) -> pd.DataFrame:
- """
- 봉 분석·차트용 표준 지표 일괄 적용 (BB, 일목, RSI, MACD, 스토캐스틱, 이격도).
-
- Args:
- df: OHLCV DataFrame (datetime index).
-
- Returns:
- 모든 지표 컬럼이 붙은 DataFrame.
- """
- out = add_bollinger(df)
- out = add_ichimoku(out)
- out = prepare_entry_df(out)
- out = add_disparity(out)
- out = add_macd(out)
- out = add_stochastic(out)
- return out
-
-
-def latest_indicator_snapshot(df: pd.DataFrame) -> dict[str, float | str | None]:
- """
- 최신 봉의 BB·RSI·MACD·스토캐스틱 요약 (모니터·로그용).
-
- Args:
- df: apply_bar_indicators 적용된 DataFrame.
-
- Returns:
- 지표명→값 dict.
- """
- if df.empty:
- return {}
- row = df.iloc[-1]
-
- def _f(col: str) -> float | None:
- if col not in row.index or pd.isna(row[col]):
- return None
- return round(float(row[col]), 4)
-
- macd_hist = _f("macd_hist")
- stoch_k = _f("stoch_k")
- stoch_d = _f("stoch_d")
- stoch_zone = "mid"
- if stoch_k is not None:
- if stoch_k <= STOCH_OVERSOLD:
- stoch_zone = "oversold"
- elif stoch_k >= STOCH_OVERBOUGHT:
- stoch_zone = "overbought"
-
- macd_state = "neutral"
- if macd_hist is not None:
- macd_state = "bull" if macd_hist > 0 else "bear"
-
- disp: dict[str, float | None] = {}
- for p in DISPARITY_PERIODS:
- col = disparity_column(p)
- disp[col] = _f(col)
- primary = disparity_column(DISPARITY_PERIODS[0]) if DISPARITY_PERIODS else None
- disp_primary = disp.get(primary) if primary else None
-
- return {
- "bb_pos": _f("bb_pos"),
- "rsi": _f("RSI"),
- "disparity": disp,
- "disparity_primary": disp_primary,
- "disparity_zone": disparity_zone(disp_primary),
- "macd_line": _f("macd_line"),
- "macd_signal": _f("macd_signal"),
- "macd_hist": macd_hist,
- "macd_state": macd_state,
- "stoch_k": stoch_k,
- "stoch_d": stoch_d,
- "stoch_zone": stoch_zone,
- }
-
-
-def _trend_from_ma20(df: pd.DataFrame) -> Trend:
- """
- 단일 TF에서 종가·MA20·MA40 관계로 추세를 판정합니다.
-
- Args:
- df: OHLCV+지표 DataFrame.
-
- Returns:
- up | down | range.
- """
- if len(df) < 20:
- return "range"
- close = float(df["Close"].iloc[-1])
- ma20_col = "MA20" if "MA20" in df.columns else "MA"
- if ma20_col not in df.columns:
- return "range"
- ma20 = float(df[ma20_col].iloc[-1])
- ma40 = float(df["MA40"].iloc[-1]) if "MA40" in df.columns and len(df) >= 40 else ma20
- if ma40 == 0:
- return "range"
- gap = abs(ma20 - ma40) / ma40 * 100
- if gap < TREND_RANGE_MA_GAP_PCT:
- return "range"
- if close > ma20 and ma20 > ma40:
- return "up"
- if close < ma20 and ma20 < ma40:
- return "down"
- return "range"
-
-
-def get_trend(df_1d: pd.DataFrame, df_1h: pd.DataFrame) -> Trend:
- """
- 일봉·1시간봉 기준 추세(up/down/range)를 반환합니다.
-
- Args:
- df_1d: 일봉 OHLCV+지표.
- df_1h: 1시간봉 OHLCV+지표.
-
- Returns:
- 추세 문자열.
- """
- if len(df_1d) < 20 or len(df_1h) < 40:
- return "range"
-
- d_close = float(df_1d["Close"].iloc[-1])
- d_ma20 = float(df_1d["MA20"].iloc[-1])
- h_close = float(df_1h["Close"].iloc[-1])
- h_ma20 = float(df_1h["MA20"].iloc[-1])
- h_ma40 = float(df_1h["MA40"].iloc[-1])
-
- if h_ma40 == 0:
- return "range"
-
- ma_gap_pct = abs(h_ma20 - h_ma40) / h_ma40 * 100
- if ma_gap_pct < TREND_RANGE_MA_GAP_PCT:
- return "range"
-
- if d_close > d_ma20 and h_ma20 > h_ma40 and h_close > h_ma20:
- return "up"
- if d_close < d_ma20 and h_ma20 < h_ma40 and h_close < h_ma20:
- return "down"
- return "range"
-
-
-def get_mtf_trend_summary(
- frames: dict[int, pd.DataFrame],
- interval_keys: tuple[int, ...],
-) -> dict[str, str]:
- """
- 여러 TF의 종가·이동평균 기준 추세 요약(주·월봉 포함).
-
- Args:
- frames: interval → OHLCV+지표.
- interval_keys: 요약할 간격(분) 목록.
-
- Returns:
- interval_label → up/down/range.
- """
- from deepcoin.data.mtf_bb import interval_label
-
- out: dict[str, str] = {}
- for iv in interval_keys:
- df = frames.get(iv)
- if df is None or df.empty:
- continue
- out[interval_label(iv)] = _trend_from_ma20(df)
- return out
diff --git a/deepcoin/data/__init__.py b/deepcoin/data/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deepcoin/data/candle_intervals.py b/deepcoin/data/candle_intervals.py
deleted file mode 100644
index da39397..0000000
--- a/deepcoin/data/candle_intervals.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""
-봉 간격 상수·빗썸(Upbit 호환) 캔들 API 경로.
-
-분봉·일봉 외 주봉(10080)·월봉(43200)은 DB 테이블명 `{symbol}_{interval}` 에 그대로 사용합니다.
-"""
-
-from __future__ import annotations
-
-from dateutil.relativedelta import relativedelta
-
-from config import DAILY_INTERVAL_MIN, MONTH_INTERVAL_MIN, WEEK_INTERVAL_MIN
-
-WM_INTERVALS: frozenset[int] = frozenset({WEEK_INTERVAL_MIN, MONTH_INTERVAL_MIN})
-
-# 01_download·ops_sync 대상에서 제외 (DB 적재 안 함)
-DOWNLOAD_EXCLUDED_INTERVALS: frozenset[int] = frozenset({1})
-
-
-def is_excluded_from_download(interval: int) -> bool:
- """01_download·ops_sync에서 건너뛸 간격(1분봉 등)."""
- return interval in DOWNLOAD_EXCLUDED_INTERVALS
-
-
-def is_week_or_month(interval: int) -> bool:
- """주봉·월봉 여부."""
- return interval in WM_INTERVALS
-
-
-def candle_api_segment(interval: int) -> str:
- """
- REST 경로 세그먼트 (minutes/{n} 제외).
-
- Returns:
- 'weeks' | 'months' | 'days' | minutes/{interval}
- """
- if interval == WEEK_INTERVAL_MIN:
- return "weeks"
- if interval == MONTH_INTERVAL_MIN:
- return "months"
- if interval >= DAILY_INTERVAL_MIN:
- return "days"
- return f"minutes/{interval}"
-
-
-def interval_display_label(interval: int) -> str:
- """로그·UI용 라벨."""
- if interval == WEEK_INTERVAL_MIN:
- return "주봉"
- if interval == MONTH_INTERVAL_MIN:
- return "월봉"
- if interval >= DAILY_INTERVAL_MIN:
- return "일봉(1440)"
- return f"{interval}분봉"
-
-
-def pagination_step(interval: int, chunk_bars: int) -> relativedelta:
- """
- get_coin_more_data 역순 수집 시 `to` 감소 단위.
-
- Args:
- interval: 봉 간격(분 표기).
- chunk_bars: API 1회 최대 봉 수(겹침 청크).
-
- Returns:
- relativedelta.
- """
- if interval == WEEK_INTERVAL_MIN:
- return relativedelta(weeks=chunk_bars)
- if interval == MONTH_INTERVAL_MIN:
- return relativedelta(months=chunk_bars)
- return relativedelta(minutes=interval * chunk_bars)
-
-
-def bars_for_months(interval: int, months: int, *, extra_days: int = 0) -> int:
- """
- N개월치 예상 봉 수(여유 포함).
-
- Args:
- interval: 봉 간격.
- months: 보관·적재 개월 수.
- extra_days: 일봉 계열 여유 일수.
-
- Returns:
- API 요청 목표 봉 수.
- """
- if interval == WEEK_INTERVAL_MIN:
- return int(months * 30 / 7) + max(extra_days // 7, 5)
- if interval == MONTH_INTERVAL_MIN:
- return months + max(extra_days // 30, 2)
- if interval >= DAILY_INTERVAL_MIN:
- return months * 30 + extra_days
- bars_per_day = (24 * 60) // interval
- return months * 30 * bars_per_day + 200
diff --git a/deepcoin/data/downloader.py b/deepcoin/data/downloader.py
deleted file mode 100644
index 85d7491..0000000
--- a/deepcoin/data/downloader.py
+++ /dev/null
@@ -1,426 +0,0 @@
-"""
-WLD 과거 봉을 빗썸 API에서 받아 coins.db에 저장합니다.
-
-- 최초: 최근 N개월 전량 적재
-- 이후: DB 마지막 시각 **이후** 봉만 추가 (증분)
-"""
-
-from __future__ import annotations
-
-import sqlite3
-from datetime import datetime
-
-import pandas as pd
-from dateutil.relativedelta import relativedelta
-
-from config import (
- BITHUMB_MINUTE_INTERVALS,
- COIN_NAME,
- DB_PATH,
- DOWNLOAD_BACKFILL_EXTRA_BARS,
- DOWNLOAD_DAILY_EXTRA_DAYS,
- DOWNLOAD_INTERVALS,
- DOWNLOAD_INTERVALS_WM,
- DOWNLOAD_MIN_INCREMENTAL_BARS,
- DOWNLOAD_MONTHS,
- DOWNLOAD_MONTHS_WM,
- INCREMENTAL_OVERLAP_BARS,
- KR_COINS,
- SYMBOL,
-)
-from deepcoin.data.candle_intervals import (
- bars_for_months,
- interval_display_label,
- is_excluded_from_download,
- is_week_or_month,
-)
-from deepcoin.ops.monitor import Monitor
-
-
-def bong_count_for_months(interval_minutes: int, months: int) -> int:
- """N개월치 봉 개수(여유분 포함)."""
- if is_week_or_month(interval_minutes):
- return bars_for_months(
- interval_minutes, months, extra_days=DOWNLOAD_DAILY_EXTRA_DAYS
- )
- days = months * 30
- from config import DAILY_INTERVAL_MIN
-
- if interval_minutes >= DAILY_INTERVAL_MIN:
- return days + DOWNLOAD_DAILY_EXTRA_DAYS
- bars_per_day = (24 * 60) // interval_minutes
- return days * bars_per_day + DOWNLOAD_BACKFILL_EXTRA_BARS
-
-
-def bong_count_since(
- interval_minutes: int, last_ts: pd.Timestamp, overlap: int = INCREMENTAL_OVERLAP_BARS
-) -> int:
- """마지막 저장 시각 이후 필요한 API 봉 수(겹침 포함)."""
- now = pd.Timestamp.now()
- if last_ts.tzinfo is not None and now.tzinfo is None:
- last_ts = last_ts.tz_localize(None)
- delta_min = max(0, (now - last_ts).total_seconds() / 60)
- bars = int(delta_min / interval_minutes) + overlap + 10
- return max(bars, DOWNLOAD_MIN_INCREMENTAL_BARS)
-
-
-def months_cutoff(months: int) -> pd.Timestamp:
- """N개월 전 시각."""
- return pd.Timestamp(datetime.now() - relativedelta(months=months))
-
-
-def trim_to_recent_months(data: pd.DataFrame, months: int) -> pd.DataFrame:
- """최근 N개월 구간만 남깁니다."""
- if data is None or data.empty:
- return data
- cutoff = months_cutoff(months)
- if not isinstance(data.index, pd.DatetimeIndex):
- data = data.copy()
- data.index = pd.to_datetime(data.index)
- return data[data.index >= cutoff].copy()
-
-
-def interval_label(interval: int) -> str:
- """로그용 간격 라벨."""
- return interval_display_label(interval)
-
-
-def months_for_interval(interval: int, default_months: int) -> int:
- """간격별 DB 보관 개월 수 (주·월봉은 DOWNLOAD_MONTHS_WM)."""
- if is_week_or_month(interval):
- return DOWNLOAD_MONTHS_WM
- return default_months
-
-
-def all_download_intervals() -> tuple[int, ...]:
- """분봉·일봉 + 주·월봉 간격 목록(1분 제외, 중복 제거, 순서 유지)."""
- seen: set[int] = set()
- out: list[int] = []
- for iv in (*DOWNLOAD_INTERVALS, *DOWNLOAD_INTERVALS_WM):
- if is_excluded_from_download(iv) or iv in seen:
- continue
- seen.add(iv)
- out.append(iv)
- return tuple(out)
-
-
-def download_jobs() -> list[tuple[int, str]]:
- """
- 01_download 대상 간격.
-
- Returns:
- (interval_min, 표시명) 리스트.
- """
- jobs: list[tuple[int, str]] = []
- for iv in all_download_intervals():
- if is_excluded_from_download(iv):
- continue
- if not is_week_or_month(iv) and iv < 1440 and iv not in BITHUMB_MINUTE_INTERVALS:
- print(f"경고: {iv}분봉은 빗썸 API 미지원 — 건너뜀")
- continue
- jobs.append((iv, interval_label(iv)))
- return jobs
-
-
-def ensure_table(cursor, table_name: str) -> None:
- cursor.execute(
- f"CREATE TABLE IF NOT EXISTS {table_name} "
- "(CODE text, NAME text, ymdhms datetime, ymd text, hms text, "
- "Close REAL, Open REAL, High REAL, Low REAL, Volume REAL)"
- )
- cursor.execute(
- f"CREATE INDEX IF NOT EXISTS {table_name}_idx ON {table_name}(CODE, ymdhms)"
- )
-
-
-def get_earliest_timestamp(
- symbol: str, interval: int, db_path: str = DB_PATH
-) -> pd.Timestamp | None:
- """테이블에 저장된 해당 심볼의 가장 오래된 봉 시각."""
- conn = sqlite3.connect(db_path)
- cursor = conn.cursor()
- table_name = f"{symbol}_{interval}"
- ensure_table(cursor, table_name)
- cursor.execute(
- f"SELECT MIN(ymdhms) FROM {table_name} WHERE CODE = ?",
- (symbol,),
- )
- row = cursor.fetchone()
- conn.close()
- if row and row[0]:
- return pd.Timestamp(row[0])
- return None
-
-
-def get_last_timestamp(
- symbol: str, interval: int, db_path: str = DB_PATH
-) -> pd.Timestamp | None:
- """테이블에 저장된 해당 심볼의 마지막 봉 시각."""
- conn = sqlite3.connect(db_path)
- cursor = conn.cursor()
- table_name = f"{symbol}_{interval}"
- ensure_table(cursor, table_name)
- cursor.execute(
- f"SELECT MAX(ymdhms) FROM {table_name} WHERE CODE = ?",
- (symbol,),
- )
- row = cursor.fetchone()
- conn.close()
- if row and row[0]:
- return pd.Timestamp(row[0])
- return None
-
-
-def get_row_count(symbol: str, interval: int, db_path: str = DB_PATH) -> int:
- """저장된 봉 개수."""
- conn = sqlite3.connect(db_path)
- cursor = conn.cursor()
- table_name = f"{symbol}_{interval}"
- ensure_table(cursor, table_name)
- cursor.execute(
- f"SELECT COUNT(*) FROM {table_name} WHERE CODE = ?",
- (symbol,),
- )
- row = cursor.fetchone()
- conn.close()
- return int(row[0]) if row else 0
-
-
-def filter_after_last(
- data: pd.DataFrame, last_ts: pd.Timestamp | None
-) -> pd.DataFrame:
- """마지막 저장 시각보다 이후(>)인 봉만 반환."""
- if data is None or data.empty or last_ts is None:
- return data
- if not isinstance(data.index, pd.DatetimeIndex):
- data = data.copy()
- data.index = pd.to_datetime(data.index)
- last = pd.Timestamp(last_ts)
- return data[data.index > last].copy()
-
-
-def prune_before_cutoff(
- symbol: str, interval: int, months: int, db_path: str = DB_PATH
-) -> int:
- """N개월보다 오래된 봉 삭제 (DB 용량 유지)."""
- cutoff = months_cutoff(months).strftime("%Y-%m-%d %H:%M:%S")
- conn = sqlite3.connect(db_path)
- cursor = conn.cursor()
- table_name = f"{symbol}_{interval}"
- ensure_table(cursor, table_name)
- cursor.execute(
- f"DELETE FROM {table_name} WHERE CODE = ? AND ymdhms < ?",
- (symbol, cutoff),
- )
- deleted = cursor.rowcount
- conn.commit()
- cursor.close()
- conn.close()
- return deleted
-
-
-def append_data(
- symbol: str,
- interval: int,
- data: pd.DataFrame,
- last_ts: pd.Timestamp | None = None,
- db_path: str = DB_PATH,
-) -> tuple[int, int]:
- """
- 마지막 시각 이후 봉만 INSERT합니다. 기존 데이터는 삭제하지 않습니다.
-
- Args:
- last_ts: None이면 전체 data 적재, 있으면 index > last_ts 만 적재
-
- Returns:
- (추가된 행 수, 스킵된 행 수)
- """
- if data is None or data.empty:
- return 0, 0
-
- total = len(data)
- to_save = data if last_ts is None else filter_after_last(data, last_ts)
- skipped = total - len(to_save)
-
- if to_save.empty:
- return 0, skipped
-
- conn = sqlite3.connect(db_path)
- cursor = conn.cursor()
- table_name = f"{symbol}_{interval}"
- ensure_table(cursor, table_name)
-
- records = []
- for i in range(len(to_save)):
- ts = to_save.index[i]
- if hasattr(ts, "to_pydatetime"):
- ts = ts.to_pydatetime()
- ymd = ts.strftime("%Y%m%d")
- hms = ts.strftime("%H%M%S")
- ymdhms = ts.strftime("%Y-%m-%d %H:%M:%S")
- records.append(
- (
- symbol,
- KR_COINS[symbol],
- ymdhms,
- ymd,
- hms,
- float(to_save["Open"].iloc[i]),
- float(to_save["High"].iloc[i]),
- float(to_save["Low"].iloc[i]),
- float(to_save["Close"].iloc[i]),
- float(to_save["Volume"].iloc[i]),
- )
- )
-
- cursor.executemany(
- f"INSERT INTO {table_name} "
- "(CODE, NAME, ymdhms, ymd, hms, Close, Open, High, Low, Volume) "
- "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
- records,
- )
- conn.commit()
- cursor.close()
- conn.close()
- return len(records), skipped
-
-
-def backfill_before_earliest(
- monitor: Monitor,
- symbol: str,
- interval: int,
- months: int,
-) -> int:
- """
- DB 최초 봉보다 오래된 구간을 API로 채웁니다 (1년 적재 시 필요).
-
- Returns:
- 추가된 행 수.
- """
- months = months_for_interval(interval, months)
- cutoff = months_cutoff(months)
- earliest = get_earliest_timestamp(symbol, interval)
- if earliest is None or earliest <= cutoff:
- return 0
-
- label = interval_label(interval)
- # now부터 역순 수집이므로 cutoff까지 닿으려면 N개월 전체 봉 수가 필요
- target = bong_count_for_months(interval, months)
-
- print(
- f" [백필] {label} — {cutoff.date()} ~ {earliest} "
- f"(API 역수집 약 {target}봉)"
- )
-
- data = monitor.get_coin_more_data(
- symbol, interval, bong_count=target, verbose=True
- )
- if data is None or data.empty:
- print(" -> 백필 API 데이터 없음")
- return 0
-
- if not isinstance(data.index, pd.DatetimeIndex):
- data.index = pd.to_datetime(data.index)
- hist = data[(data.index >= cutoff) & (data.index < earliest)].copy()
- if hist.empty:
- print(" -> 백필 대상 구간 없음")
- return 0
-
- inserted, skipped = append_data(symbol, interval, hist, last_ts=None)
- print(f" -> 백필 추가 {inserted}행 (스킵 {skipped})")
- return inserted
-
-
-def download_symbol(
- monitor: Monitor,
- symbol: str,
- interval: int,
- months: int,
- *,
- verbose: bool = True,
- skip_backfill: bool = False,
-) -> None:
- """
- 한 간격의 봉을 API로 받아 증분·백필 저장합니다.
-
- Args:
- skip_backfill: True면 운영 증분만(백필 생략). 05/06 시작 동기화용.
- """
- months = months_for_interval(interval, months)
- label = interval_label(interval)
- existing = get_row_count(symbol, interval)
-
- if existing > 0 and not skip_backfill:
- backfill_before_earliest(monitor, symbol, interval, months)
-
- last_ts = get_last_timestamp(symbol, interval)
-
- if last_ts is None:
- target = bong_count_for_months(interval, months)
- mode = "초기 적재"
- else:
- target = min(
- bong_count_since(interval, last_ts),
- bong_count_for_months(interval, months),
- )
- mode = f"증분 (마지막 {last_ts.strftime('%Y-%m-%d %H:%M:%S')} 이후)"
-
- print(f"\n[{symbol}] {label} — {mode}")
- print(f" DB 기존 {existing}행 | API 목표 약 {target}봉")
-
- data = monitor.get_coin_more_data(
- symbol, interval, bong_count=target, verbose=verbose
- )
- if data is None or data.empty:
- print(" -> API 데이터 없음")
- return
-
- data = trim_to_recent_months(data, months)
- if data.empty:
- print(" -> 최근 N개월 필터 후 데이터 없음")
- return
-
- inserted, skipped = append_data(symbol, interval, data, last_ts=last_ts)
- pruned = prune_before_cutoff(symbol, interval, months)
-
- new_last = get_last_timestamp(symbol, interval)
- total = get_row_count(symbol, interval)
- print(f" -> API {len(data)}봉 | 추가 {inserted}행 | 스킵(기존) {skipped}행")
- if pruned > 0:
- print(f" -> {months}개월 이전 {pruned}행 정리")
- if new_last is not None:
- print(f" -> DB 합계 {total}행 | {data.index[0]} ~ {new_last}")
-
-
-def download(months: int | None = None) -> None:
- """
- WLD 다중 분봉·일봉·주봉·월봉을 coins.db에 증분 적재합니다.
-
- 간격: DOWNLOAD_INTERVALS + DOWNLOAD_INTERVALS_WM (주·월은 DOWNLOAD_MONTHS_WM, 기본 24=2년)
- """
- default_months = months or DOWNLOAD_MONTHS
- monitor = Monitor(cooldown_file=None)
- jobs = download_jobs()
-
- intervals_str = ", ".join(str(iv) for iv, _ in jobs)
- print(f"=== {COIN_NAME} ({SYMBOL}) -> {DB_PATH} (증분 INSERT) ===")
- print(
- f"보관 분봉·일봉 {default_months}개월 | "
- f"주·월봉 {DOWNLOAD_MONTHS_WM}개월 | 간격(분): {intervals_str}"
- )
- started = datetime.now()
-
- for interval, desc in jobs:
- print(f"\n--- {desc} ---")
- job_months = months_for_interval(interval, default_months)
- try:
- download_symbol(monitor, SYMBOL, interval, job_months)
- except Exception as e:
- print(f"오류 interval={interval}: {e}")
-
- elapsed = datetime.now() - started
- print(f"\n완료 (소요: {elapsed})")
-
-
-if __name__ == "__main__":
- download()
diff --git a/deepcoin/data/mtf_bb.py b/deepcoin/data/mtf_bb.py
deleted file mode 100644
index 4bfc9eb..0000000
--- a/deepcoin/data/mtf_bb.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""
-coins.db에서 전 간격 봉 데이터를 로드합니다.
-"""
-
-from __future__ import annotations
-
-import pandas as pd
-
-from config import DOWNLOAD_INTERVALS, DOWNLOAD_INTERVALS_WM, SYMBOL
-from deepcoin.data.candle_intervals import interval_display_label
-
-
-def interval_label(interval: int) -> str:
- """봉 간격 표시 라벨."""
- return interval_display_label(interval)
-
-
-def _all_load_intervals() -> tuple[int, ...]:
- seen: set[int] = set()
- out: list[int] = []
- for iv in (*DOWNLOAD_INTERVALS, *DOWNLOAD_INTERVALS_WM):
- if iv not in seen:
- seen.add(iv)
- out.append(iv)
- return tuple(out)
-
-
-def load_frames_from_db(
- monitor,
- symbol: str,
- lookback_days: int | None = None,
-) -> dict[int, pd.DataFrame]:
- """
- coins.db에서 DOWNLOAD_INTERVALS 전부 로드·지표 계산.
-
- Args:
- monitor: Monitor 인스턴스.
- symbol: 코인 심볼.
- lookback_days: 지정 시 간격별로 해당 일수만큼 DB에서 더 많이 읽습니다.
-
- Returns:
- 간격(분) → OHLCV+지표 DataFrame.
- """
- frames: dict[int, pd.DataFrame] = {}
- for iv in _all_load_intervals():
- db_max = None
- if lookback_days is not None:
- db_max = monitor.db_row_limit_for_interval(iv, lookback_days)
- df = monitor.get_coin_some_data(symbol, iv, db_max_rows=db_max)
- if df is None or df.empty:
- print(f" [{interval_label(iv)}] DB/API 데이터 없음 — 스킵")
- continue
- df = monitor.calculate_technical_indicators(df)
- frames[iv] = df
- print(f" [{interval_label(iv)}] {len(df)}봉 {df.index[0]} ~ {df.index[-1]}")
- return frames
diff --git a/deepcoin/data/ops_sync.py b/deepcoin/data/ops_sync.py
deleted file mode 100644
index 50d6ef3..0000000
--- a/deepcoin/data/ops_sync.py
+++ /dev/null
@@ -1,191 +0,0 @@
-"""
-05/06 운영 시작 전 coins.db 누락 봉을 증분 보완합니다.
-
-마지막 저장 시각이 현재보다 OPS_SYNC_MAX_LAG_BARS 이상 뒤처진 간격만
-01_download.download_symbol 과 동일 경로로 API 수집합니다.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from datetime import datetime
-
-import pandas as pd
-
-from config import (
- COIN_NAME,
- DB_PATH,
- DOWNLOAD_MONTHS,
- OPS_SYNC_MAX_LAG_BARS,
- OPS_SYNC_ON_START,
- SYMBOL,
-)
-from deepcoin.data.downloader import (
- download_jobs,
- download_symbol,
- get_last_timestamp,
- get_row_count,
- interval_label,
-)
-from deepcoin.ops.monitor import Monitor
-
-# 프로세스당 1회 상세 로그(루프마다 ensure 호출 시 스팸 방지)
-_logged_fresh_once: bool = False
-
-
-@dataclass
-class OpsSyncResult:
- """운영 전 DB 동기화 결과."""
-
- synced: list[int] = field(default_factory=list)
- skipped_fresh: list[int] = field(default_factory=list)
- errors: dict[int, str] = field(default_factory=dict)
- disabled: bool = False
-
- @property
- def ok(self) -> bool:
- """치명적 오류 없이 완료."""
- return not self.errors or len(self.synced) > 0 or len(self.skipped_fresh) > 0
-
-
-def lag_minutes_since_last(
- last_ts: pd.Timestamp | None, interval_minutes: int
-) -> float | None:
- """
- 마지막 봉 시각 대비 경과 분.
-
- Returns:
- last_ts 없으면 None.
- """
- if last_ts is None:
- return None
- now = pd.Timestamp.now()
- last = pd.Timestamp(last_ts)
- if last.tzinfo is not None and now.tzinfo is None:
- last = last.tz_localize(None)
- return max(0.0, (now - last).total_seconds() / 60.0)
-
-
-def is_interval_stale(
- symbol: str,
- interval: int,
- *,
- max_lag_bars: int = OPS_SYNC_MAX_LAG_BARS,
- db_path: str = DB_PATH,
-) -> tuple[bool, str]:
- """
- 간격별 DB가 운영에 필요한 최신성을 만족하는지 판단.
-
- Returns:
- (stale 여부, 사유 문자열)
- """
- rows = get_row_count(symbol, interval, db_path=db_path)
- last = get_last_timestamp(symbol, interval, db_path=db_path)
- if rows == 0 or last is None:
- return True, "데이터 없음"
- lag = lag_minutes_since_last(last, interval)
- if lag is None:
- return True, "마지막 시각 없음"
- threshold = float(interval * max_lag_bars)
- if lag > threshold:
- return True, f"지연 {lag:.0f}분 > 허용 {threshold:.0f}분"
- return False, f"최신 (마지막 {last.strftime('%Y-%m-%d %H:%M:%S')})"
-
-
-def list_stale_intervals(
- symbol: str = SYMBOL,
- *,
- max_lag_bars: int = OPS_SYNC_MAX_LAG_BARS,
- db_path: str = DB_PATH,
-) -> list[tuple[int, str, str]]:
- """
- 갱신이 필요한 (interval, label, reason) 목록.
-
- Returns:
- download_jobs 순서와 동일하게 stale 항목만.
- """
- out: list[tuple[int, str, str]] = []
- for interval, label in download_jobs():
- stale, reason = is_interval_stale(
- symbol, interval, max_lag_bars=max_lag_bars, db_path=db_path
- )
- if stale:
- out.append((interval, label, reason))
- return out
-
-
-def ensure_ops_candles(
- symbol: str = SYMBOL,
- months: int | None = None,
- *,
- force: bool = False,
- verbose_download: bool = False,
-) -> OpsSyncResult:
- """
- 05/06 실행 직전 누락·지연 봉을 coins.db에 증분 적재합니다.
-
- Args:
- symbol: 코인 코드.
- months: 보관 개월( None 이면 DOWNLOAD_MONTHS ).
- force: True면 최신 간격도 전부 재수집.
- verbose_download: True면 download_symbol API 진행 로그 출력.
-
- Returns:
- OpsSyncResult
- """
- global _logged_fresh_once
- result = OpsSyncResult()
- if not OPS_SYNC_ON_START and not force:
- result.disabled = True
- print("[ops_sync] OPS_SYNC_ON_START=0 — 건너뜀")
- return result
-
- months = months or DOWNLOAD_MONTHS
- jobs = download_jobs()
- stale = list_stale_intervals(symbol) if not force else [
- (iv, lb, "force") for iv, lb in jobs
- ]
- fresh = [
- iv for iv, _ in jobs if iv not in {s[0] for s in stale}
- ]
- result.skipped_fresh = fresh
-
- if not stale:
- if not _logged_fresh_once or force:
- print(
- f"[ops_sync] {COIN_NAME} ({symbol}) DB 최신 · "
- f"간격 {len(fresh)}개 (증분 다운로드 없음)"
- )
- for iv in fresh:
- last = get_last_timestamp(symbol, iv)
- if last is not None:
- print(f" [{interval_label(iv)}] ~ {last}")
- _logged_fresh_once = True
- return result
-
- print(
- f"[ops_sync] {COIN_NAME} ({symbol}) 누락 보완 · "
- f"갱신 {len(stale)}개 / 최신 {len(fresh)}개 (백필 생략·증분만)"
- )
- monitor = Monitor(cooldown_file=None)
- started = datetime.now()
- for interval, label, reason in stale:
- print(f"\n[ops_sync] --- {label} --- ({reason})")
- try:
- download_symbol(
- monitor,
- symbol,
- interval,
- months,
- verbose=verbose_download,
- skip_backfill=True,
- )
- result.synced.append(interval)
- except Exception as exc:
- result.errors[interval] = str(exc)
- print(f" [ops_sync] 오류 interval={interval}: {exc}")
-
- elapsed = datetime.now() - started
- _logged_fresh_once = True
- print(f"\n[ops_sync] 완료 (소요: {elapsed}) · 갱신={result.synced} 오류={len(result.errors)}")
- return result
diff --git a/deepcoin/env_loader.py b/deepcoin/env_loader.py
deleted file mode 100644
index 6f049af..0000000
--- a/deepcoin/env_loader.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""
-DeepCoin .env 로드 (프로젝트 루트 기준).
-
-config·HTS·스크립트 진입 전에 한 번 호출하면 cwd와 무관하게 동일한 설정을 사용합니다.
-"""
-
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-from deepcoin.paths import PROJECT_ROOT
-
-_ENV_LOADED = False
-_ENV_LOADER = "none"
-ENV_FILE = PROJECT_ROOT / ".env"
-
-
-def _load_env_fallback(*, override: bool) -> None:
- """
- python-dotenv 미설치 시 .env 를 직접 파싱해 os.environ 에 반영합니다.
-
- Args:
- override: True면 기존 키를 .env 값으로 덮어씀.
- """
- if not ENV_FILE.is_file():
- return
- for line in ENV_FILE.read_text(encoding="utf-8").splitlines():
- line = line.strip()
- if not line or line.startswith("#"):
- continue
- if line.startswith("export "):
- line = line[7:].strip()
- if "=" not in line:
- continue
- key, _, value = line.partition("=")
- key = key.strip()
- value = value.strip().strip('"').strip("'")
- if not key:
- continue
- if not override and key in os.environ:
- continue
- os.environ[key] = value
-
-
-def load_project_env(*, override: bool = False) -> bool:
- """
- PROJECT_ROOT/.env 를 로드합니다 (python-dotenv 우선, 없으면 fallback).
-
- Args:
- override: True면 기존 OS 환경 변수를 .env 값으로 덮어씀.
-
- Returns:
- .env 파일이 존재해 로드했으면 True, 없으면 False.
- """
- global _ENV_LOADED, _ENV_LOADER
- if _ENV_LOADED and not override:
- return ENV_FILE.is_file()
-
- if not ENV_FILE.is_file():
- _ENV_LOADED = True
- _ENV_LOADER = "missing"
- return False
-
- try:
- from dotenv import load_dotenv
-
- load_dotenv(ENV_FILE, override=override)
- _ENV_LOADER = "dotenv"
- except ImportError:
- _load_env_fallback(override=override)
- _ENV_LOADER = "fallback"
-
- _ENV_LOADED = True
- return True
-
-
-def env_status() -> dict[str, str | bool]:
- """디버그용 .env 상태."""
- return {
- "project_root": str(PROJECT_ROOT),
- "env_file": str(ENV_FILE),
- "env_exists": ENV_FILE.is_file(),
- "loaded": _ENV_LOADED,
- "loader": _ENV_LOADER,
- "live_trading_enabled": os.getenv("LIVE_TRADING_ENABLED", ""),
- }
diff --git a/deepcoin/ground_truth/__init__.py b/deepcoin/ground_truth/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deepcoin/ground_truth/causal_gt_calibrate.py b/deepcoin/ground_truth/causal_gt_calibrate.py
deleted file mode 100644
index 816a135..0000000
--- a/deepcoin/ground_truth/causal_gt_calibrate.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""
-인과 GT leg 엔진 파라미터 그리드 탐색·최적 저장.
-"""
-
-from __future__ import annotations
-
-import json
-from itertools import product
-from pathlib import Path
-from typing import Any
-
-from config import (
- CHART_LOOKBACK_DAYS,
- GT_BUY_BB_MAX,
- GT_BUY_MIN_SWING_PCT,
- GT_MIN_SWING_PCT,
- GT_PIVOT_ORDER,
- MATCH_PRIMARY_INTERVAL,
- SYMBOL,
-)
-from deepcoin.data.mtf_bb import load_frames_from_db
-from deepcoin.ground_truth.causal_gt_trades import simulate_causal_gt_portfolio
-from deepcoin.ground_truth.ground_truth import load_ground_truth
-from deepcoin.ops.monitor import Monitor
-from deepcoin.paths import MATCHING_CAUSAL_GT_CALIBRATION_JSON, resolve_ground_truth_file
-
-
-def default_causal_gt_params() -> dict[str, Any]:
- """
- 인과 GT leg 엔진 기본 파라미터.
-
- Returns:
- build_causal_split_buy_peak_sell_trades 키워드 인자.
- """
- from config import (
- CAUSAL_GT_MIN_BARS_BETWEEN_LEGS,
- CAUSAL_GT_MIN_LEG_PCT,
- CAUSAL_GT_PEAK_MODE,
- CAUSAL_GT_USE_LOCAL_TROUGH,
- )
-
- return {
- "pivot_order": GT_PIVOT_ORDER,
- "buy_swing_pct": GT_BUY_MIN_SWING_PCT,
- "sell_swing_pct": GT_MIN_SWING_PCT,
- "bb_max": GT_BUY_BB_MAX,
- "min_leg_pct": CAUSAL_GT_MIN_LEG_PCT,
- "use_local_trough": CAUSAL_GT_USE_LOCAL_TROUGH,
- "peak_mode": CAUSAL_GT_PEAK_MODE,
- "min_bars_between_legs": CAUSAL_GT_MIN_BARS_BETWEEN_LEGS,
- }
-
-
-def load_causal_gt_params(path: Path | None = None) -> dict[str, Any]:
- """
- 캘리브레이션 JSON 또는 config 기본값.
-
- Args:
- path: JSON 경로. None이면 MATCHING_CAUSAL_GT_CALIBRATION_JSON.
-
- Returns:
- best params dict.
- """
- p = path or MATCHING_CAUSAL_GT_CALIBRATION_JSON
- if p.is_file():
- data = json.loads(p.read_text(encoding="utf-8"))
- best = data.get("best_params") or data.get("params")
- if best:
- return dict(best)
- return default_causal_gt_params()
-
-
-def _grid_space() -> dict[str, list[Any]]:
- """탐색 그리드 (로컬 peak 최적화 반영, 조합 ~864)."""
- return {
- "peak_mode": ["local", "zigzag"],
- "pivot_order": [8, 10, 12, 15],
- "buy_swing_pct": [2.0, 2.5, 3.0],
- "sell_swing_pct": [3.0, 4.0],
- "bb_max": [0.55, 0.65, 0.75],
- "min_leg_pct": [3.0, 5.0, 8.0],
- "min_bars_between_legs": [60, 90],
- "use_local_trough": [True, False],
- }
-
-
-def run_causal_gt_calibration(
- *,
- min_trades: int = 30,
- top_n: int = 20,
- out_path: Path | None = None,
-) -> dict[str, Any]:
- """
- 그리드 탐색 후 최적 파라미터 JSON 저장.
-
- Args:
- min_trades: 최소 체결 수 미만 조합 제외.
- top_n: 상위 N개 기록.
- out_path: 저장 경로.
-
- Returns:
- calibration report dict.
- """
- gt = load_ground_truth(resolve_ground_truth_file()) or {}
- mark = float((gt.get("summary") or {}).get("mark_price") or 0)
- gt_pnl = float(
- (gt.get("summary") or {}).get("pnl_pct")
- or 0
- )
-
- mon = Monitor(cooldown_file=None)
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- df = frames[MATCH_PRIMARY_INTERVAL].copy()
-
- grid = _grid_space()
- keys = list(grid.keys())
- results: list[dict[str, Any]] = []
- total = 1
- for k in keys:
- total *= len(grid[k])
-
- print(f"[causal_gt] 그리드 {total} 조합 탐색...")
- done = 0
- for combo in product(*(grid[k] for k in keys)):
- params = dict(zip(keys, combo))
- r = simulate_causal_gt_portfolio(df, last_price=mark or None, **params)
- tc = int(r.get("trade_count") or 0)
- done += 1
- if done % 200 == 0:
- print(f" ... {done}/{total}")
- if tc < min_trades:
- continue
- pnl = float(r.get("pnl_pct") or 0)
- results.append(
- {
- "pnl_pct": round(pnl, 2),
- "trade_count": tc,
- "leg_count": r.get("leg_count", 0),
- "max_drawdown_pct": r.get("max_drawdown_pct"),
- "capture_ratio": round(pnl / gt_pnl, 4) if gt_pnl else 0,
- "params": params,
- }
- )
-
- results.sort(key=lambda x: x["pnl_pct"], reverse=True)
- best = results[0] if results else None
- report: dict[str, Any] = {
- "symbol": SYMBOL,
- "interval_min": MATCH_PRIMARY_INTERVAL,
- "gt_pnl_pct": gt_pnl,
- "grid_combinations": total,
- "valid_combinations": len(results),
- "min_trades": min_trades,
- "best": best,
- "best_params": best["params"] if best else default_causal_gt_params(),
- "top": results[:top_n],
- "target_pnl_pct": 300.0,
- "target_met": bool(best and best["pnl_pct"] >= 300.0),
- }
-
- out = out_path or MATCHING_CAUSAL_GT_CALIBRATION_JSON
- out.parent.mkdir(parents=True, exist_ok=True)
- out.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
- print(f"[causal_gt] 저장: {out}")
- if best:
- print(
- f"[causal_gt] 최적 PnL={best['pnl_pct']}% "
- f"trades={best['trade_count']} legs={best['leg_count']} "
- f"capture={best.get('capture_ratio', 0):.2%}"
- )
- else:
- print("[causal_gt] 유효 조합 없음")
- return report
diff --git a/deepcoin/ground_truth/causal_gt_hybrid.py b/deepcoin/ground_truth/causal_gt_hybrid.py
deleted file mode 100644
index d650247..0000000
--- a/deepcoin/ground_truth/causal_gt_hybrid.py
+++ /dev/null
@@ -1,447 +0,0 @@
-"""
-Phase 3: monitor 발화 + drawdown/past-leg tier (인과적).
-
-매도는 monitor(sell_mtf_cross) 유지, tier만 drawdown·과거 leg 수익으로 강화합니다.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import pandas as pd
-
-from config import (
- CAUSAL_GT_DD_LARGE_PCT,
- CAUSAL_GT_DD_MEDIUM_PCT,
- GT_BUY_PCT_LARGE_LEG,
- GT_BUY_PCT_MEDIUM_LEG,
- GT_BUY_PCT_SMALL_LEG,
- GT_INITIAL_CASH_KRW,
- SIM_TIER_CONVICTION_DD_PCT,
- TRADING_FEE_RATE,
-)
-from deepcoin.ground_truth.gt_allocation import (
- allocate_order_amounts_chronological,
- simulate_portfolio_summary,
-)
-from deepcoin.matching.portfolio_sim import sort_fires_chronological
-from deepcoin.matching.position_sizing import enrich_sim_trades_with_gt_weights
-
-
-def _deduped_ohlc(df: pd.DataFrame) -> pd.DataFrame:
- """
- DatetimeIndex 중복 제거·정렬 (drawdown lookup용).
-
- Args:
- df: OHLC DataFrame.
-
- Returns:
- index unique OHLC.
- """
- if df.empty:
- return df
- out = df.sort_index()
- if not out.index.is_unique:
- out = out[~out.index.duplicated(keep="last")]
- return out
-
-
-def _close_series_from_df(df: pd.DataFrame) -> pd.Series:
- """
- OHLC DataFrame에서 종가 시리즈 추출 (positional index).
-
- Args:
- df: Open/Close 또는 open/close 컬럼을 가진 OHLC.
-
- Returns:
- float 종가 시리즈.
- """
- if df.empty:
- return pd.Series(dtype=float)
- frame = _deduped_ohlc(df)
- for col in ("close", "Close"):
- if col in frame.columns:
- return frame[col].astype(float).reset_index(drop=True)
- raise KeyError("OHLC DataFrame에 close/Close 컬럼이 없습니다.")
-
-
-def _bar_index_at(df: pd.DataFrame, dt: str) -> int:
- """
- 시각 dt에 대응하는 bar 위치 (인덱스 중복 시 nearest).
-
- Args:
- df: DatetimeIndex OHLC.
- dt: ISO 시각 문자열.
-
- Returns:
- 정수 bar 위치 (0..n-1).
- """
- frame = _deduped_ohlc(df)
- if frame.empty:
- return 0
- try:
- ts = pd.to_datetime(dt)
- except (TypeError, ValueError):
- return 0
- pos = int(frame.index.get_indexer([ts], method="nearest")[0])
- return max(pos, 0)
-
-
-def _drawdown_pct_at_index(closes: pd.Series, idx: int) -> float:
- """
- bar idx 시점 drawdown % (과거 rolling high 대비, 인과적).
-
- Args:
- closes: 종가 시리즈.
- idx: 봉 위치.
-
- Returns:
- drawdown % (0~100).
- """
- if idx < 0 or idx >= len(closes):
- return 0.0
- seg = closes.iloc[: idx + 1].astype(float)
- if seg.empty:
- return 0.0
- peak = float(seg.max())
- cur = float(seg.iloc[-1])
- if peak <= 0:
- return 0.0
- return max((peak - cur) / peak * 100.0, 0.0)
-
-
-def hybrid_tier_scale(
- trade: dict[str, Any],
- *,
- completed_leg_ret: dict[int, float],
- enhanced: bool = False,
- dd_large_pct: float | None = None,
- dd_medium_pct: float | None = None,
-) -> float:
- """
- 과거 leg 수익 tier + drawdown tier (인과적).
-
- Args:
- trade: 매수 trade dict (drawdown_pct 포함).
- completed_leg_ret: 청산 완료 leg realized return %.
- enhanced: True면 medium tier·conviction 플래그 적용.
- dd_large_pct: drawdown large tier 임계(%). None이면 config.
- dd_medium_pct: drawdown medium tier 임계(%). None이면 config.
-
- Returns:
- asset_pct_scale.
- """
- from config import GT_LARGE_LEG_TOP_PCT
- from deepcoin.matching.position_sizing import (
- large_leg_ids_from_past_returns,
- )
-
- dd_large = float(dd_large_pct if dd_large_pct is not None else CAUSAL_GT_DD_LARGE_PCT)
- dd_medium = float(dd_medium_pct if dd_medium_pct is not None else CAUSAL_GT_DD_MEDIUM_PCT)
-
- lid = int(trade.get("leg_id", 0))
- large_past = large_leg_ids_from_past_returns(completed_leg_ret, GT_LARGE_LEG_TOP_PCT)
- dd = float(trade.get("drawdown_pct") or 0.0)
-
- if lid in large_past:
- if enhanced and dd >= SIM_TIER_CONVICTION_DD_PCT:
- trade["conviction_buy"] = True
- return float(GT_BUY_PCT_LARGE_LEG)
-
- if dd >= dd_large:
- if enhanced:
- trade["conviction_buy"] = True
- return float(GT_BUY_PCT_LARGE_LEG)
- if dd >= dd_medium:
- if enhanced and dd >= SIM_TIER_CONVICTION_DD_PCT:
- trade["conviction_buy"] = True
- return float(GT_BUY_PCT_MEDIUM_LEG) if enhanced else float(GT_BUY_PCT_LARGE_LEG) * 0.5
- return float(GT_BUY_PCT_SMALL_LEG)
-
-
-def _monitor_rows_from_fires(fires: pd.DataFrame) -> list[dict[str, Any]]:
- """monitor 발화 DataFrame → trade dict 리스트."""
- rows: list[dict[str, Any]] = []
- for _, r in sort_fires_chronological(fires).iterrows():
- rows.append(
- {
- "dt": str(r["dt"]),
- "action": r["side"],
- "price": float(r["close"]),
- "rule_id": r.get("rule_id", ""),
- }
- )
- return rows
-
-
-def build_monitor_hybrid_sized_trades(
- fires: pd.DataFrame,
- df: pd.DataFrame,
- *,
- enhanced: bool = False,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- dd_large_pct: float | None = None,
- dd_medium_pct: float | None = None,
-) -> tuple[list[dict[str, Any]], dict[str, Any]]:
- """
- monitor 발화 → hybrid tier amount_krw 배분 (인과적).
-
- Args:
- fires: monitor rule 발화 (buy+sell).
- df: 3m OHLC (drawdown 계산).
- enhanced: conviction·medium tier 사용.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
-
- Returns:
- (amount_krw가 채워진 trade dict, alloc_stats).
- """
- from deepcoin.ground_truth.ground_truth import load_ground_truth, order_trades_chronological
- from deepcoin.paths import resolve_ground_truth_file
-
- if fires.empty:
- return [], {"buy_executed": 0, "buy_skipped": 0}
-
- gt_data = load_ground_truth(resolve_ground_truth_file()) or {}
- gt_trades = order_trades_chronological(gt_data.get("trades") or [])
-
- enriched = enrich_sim_trades_with_gt_weights(
- _monitor_rows_from_fires(fires),
- gt_trades,
- causal_legs=True,
- )
- enriched = _attach_drawdown_to_buys(enriched, df)
-
- def scale_fn(t: dict[str, Any], completed_leg_ret: dict[int, float]) -> float:
- return hybrid_tier_scale(
- t,
- completed_leg_ret=completed_leg_ret,
- enhanced=enhanced,
- dd_large_pct=dd_large_pct,
- dd_medium_pct=dd_medium_pct,
- )
-
- return allocate_order_amounts_chronological(
- enriched,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- causal_tier=False,
- asset_pct_scale_fn=scale_fn,
- )
-
-
-def _simulate_monitor_tier_portfolio(
- fires: pd.DataFrame,
- df: pd.DataFrame,
- *,
- enhanced: bool = False,
- last_price: float | None = None,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- dd_large_pct: float | None = None,
- dd_medium_pct: float | None = None,
-) -> dict[str, Any]:
- """
- monitor buy+sell + tier 복리 시뮬 (hybrid 또는 enhanced).
-
- Args:
- fires: monitor rule 발화 (buy+sell).
- df: 3m OHLC (drawdown 계산).
- enhanced: conviction·medium tier 사용.
- last_price: 미청산 평가가.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- dd_large_pct: drawdown large tier 임계(%).
- dd_medium_pct: drawdown medium tier 임계(%).
-
- Returns:
- portfolio summary dict.
- """
- mode = "monitor_tier_enhanced" if enhanced else "monitor_dd_tier"
- if fires.empty:
- return {"pnl_pct": 0.0, "trade_count": 0, "sizing_mode": mode}
-
- sized, alloc_stats = build_monitor_hybrid_sized_trades(
- fires,
- df,
- enhanced=enhanced,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- dd_large_pct=dd_large_pct,
- dd_medium_pct=dd_medium_pct,
- )
-
- mark = last_price
- if mark is None and not df.empty:
- try:
- mark = float(_close_series_from_df(df).iloc[-1])
- except KeyError:
- mark = None
-
- result = simulate_portfolio_summary(
- sized,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- last_price=mark,
- use_amount_krw=True,
- )
- result["sizing_mode"] = mode
- if enhanced:
- result["sizing_note"] = (
- "monitor buy+sell + past-leg·drawdown tier + conviction (미래 미사용)"
- )
- else:
- result["sizing_note"] = (
- "monitor buy+sell + drawdown·past-leg tier (미래 미사용)"
- )
- result["alloc_stats"] = alloc_stats
- result["input_fires"] = int(len(fires))
- return result
-
-
-def _attach_drawdown_to_buys(
- trades: list[dict[str, Any]],
- df: pd.DataFrame,
-) -> list[dict[str, Any]]:
- """
- 매수 trade에 bar drawdown % 부여 (인과적).
-
- Args:
- trades: enrich된 trade dict.
- df: 3m OHLC (DatetimeIndex).
-
- Returns:
- drawdown_pct가 추가된 trade dict.
- """
- if df.empty:
- return trades
- close_s = _close_series_from_df(df)
- out: list[dict[str, Any]] = []
- for t in trades:
- row = dict(t)
- if row.get("action") != "buy":
- out.append(row)
- continue
- bar_idx = _bar_index_at(df, str(row.get("dt", "")))
- row["drawdown_pct"] = round(_drawdown_pct_at_index(close_s, bar_idx), 2)
- out.append(row)
- return out
-
-
-def simulate_monitor_dd_tier_portfolio(
- fires: pd.DataFrame,
- df: pd.DataFrame,
- *,
- last_price: float | None = None,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- dd_large_pct: float | None = None,
- dd_medium_pct: float | None = None,
-) -> dict[str, Any]:
- """
- monitor buy+sell + drawdown/past-leg tier 복리 시뮬.
-
- Args:
- fires: monitor rule 발화 (buy+sell).
- df: 3m OHLC (drawdown 계산).
- last_price: 미청산 평가가.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- dd_large_pct: drawdown large tier 임계(%).
- dd_medium_pct: drawdown medium tier 임계(%).
-
- Returns:
- portfolio summary dict.
- """
- return _simulate_monitor_tier_portfolio(
- fires,
- df,
- enhanced=False,
- last_price=last_price,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- dd_large_pct=dd_large_pct,
- dd_medium_pct=dd_medium_pct,
- )
-
-
-def simulate_monitor_tier_enhanced_portfolio(
- fires: pd.DataFrame,
- df: pd.DataFrame,
- *,
- last_price: float | None = None,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
-) -> dict[str, Any]:
- """
- Phase 4: monitor + past-leg·drawdown tier + conviction (weight 분할 생략).
-
- Args:
- fires: monitor rule 발화 (buy+sell).
- df: 3m OHLC (drawdown 계산).
- last_price: 미청산 평가가.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
-
- Returns:
- portfolio summary dict.
- """
- return _simulate_monitor_tier_portfolio(
- fires,
- df,
- enhanced=True,
- last_price=last_price,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- )
-
-
-def simulate_causal_gt_hybrid_portfolio(
- buy_fires: pd.DataFrame,
- df: pd.DataFrame,
- *,
- monitor_fires: pd.DataFrame | None = None,
- last_price: float | None = None,
- cg_params: dict[str, Any] | None = None,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- dd_large_pct: float | None = None,
- dd_medium_pct: float | None = None,
-) -> dict[str, Any]:
- """
- Phase 3 하이브리드: monitor buy+sell + DD tier (권장).
-
- monitor_fires가 있으면 DD tier 경로, 없으면 구 peak-sell 경로(legacy).
-
- Args:
- buy_fires: buy 발화 (legacy peak-sell 경로용).
- df: 3m OHLCV.
- monitor_fires: monitor buy+sell (권장).
- last_price: 미청산 평가가.
- cg_params: legacy 파라미터.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- dd_large_pct: drawdown large tier 임계(%).
- dd_medium_pct: drawdown medium tier 임계(%).
-
- Returns:
- portfolio summary dict.
- """
- if monitor_fires is not None and not monitor_fires.empty:
- return simulate_monitor_dd_tier_portfolio(
- monitor_fires,
- df,
- last_price=last_price,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- dd_large_pct=dd_large_pct,
- dd_medium_pct=dd_medium_pct,
- )
-
- return {
- "pnl_pct": 0.0,
- "trade_count": 0,
- "note": "monitor_fires required",
- "sizing_mode": "causal_gt_hybrid",
- }
diff --git a/deepcoin/ground_truth/causal_gt_trades.py b/deepcoin/ground_truth/causal_gt_trades.py
deleted file mode 100644
index db766bb..0000000
--- a/deepcoin/ground_truth/causal_gt_trades.py
+++ /dev/null
@@ -1,433 +0,0 @@
-"""
-인과적 GT leg 타점 생성 — t 시점까지 데이터만 사용.
-
-GT split_buy_peak_sell 과 동일 구조(분할매수·65/35 매도·leg_id)이나
-피벗·leg 종료는 gt_signal_causal 확정 신호만 사용합니다.
-"""
-
-from __future__ import annotations
-
-from typing import Any, Literal
-
-import pandas as pd
-
-from config import (
- GT_BUY_MIN_BARS,
- GT_BUY_MIN_SWING_PCT,
- GT_MAX_BUYS_PER_LEG,
- GT_MAX_SELLS_PER_LEG,
- GT_MIN_SWING_PCT,
- GT_PIVOT_ORDER,
- GT_SELL_SPLIT_GAP_PCT,
-)
-from deepcoin.ground_truth.gt_model import leg_entry_weights, leg_exit_weights
-from deepcoin.ground_truth.gt_signal_causal import enrich_scan_frame_gt_signals_causal
-
-PeakMode = Literal["zigzag", "local"]
-
-
-def _collect_causal_buy_bars(
- frame: pd.DataFrame,
- start: pd.Timestamp,
- end: pd.Timestamp,
- *,
- min_bars: int,
- max_buys: int,
- use_local_trough: bool,
- bb_max: float,
-) -> list[tuple[pd.Timestamp, float]]:
- """
- leg 구간 (start, end) 내 인과적 매수 후보 봉.
-
- Args:
- frame: gt_buy_signal 등 포함.
- start: 이전 매도 시각(미포함).
- end: leg 종료 peak 시각(포함).
- min_bars: 분할 매수 최소 간격.
- max_buys: leg당 최대 매수.
- use_local_trough: True면 gt_trough_local+BB, False면 gt_buy_signal.
- bb_max: BB %B 상한.
-
- Returns:
- (dt, low_price) 리스트 (시간순).
- """
- seg = frame[(frame.index > start) & (frame.index <= end)]
- if seg.empty:
- return []
-
- if use_local_trough:
- bb = pd.to_numeric(seg.get("bb_pos"), errors="coerce")
- mask = (seg["gt_trough_local"] == 1) & (bb <= bb_max)
- else:
- mask = seg["gt_buy_signal"] == 1
-
- cands: list[tuple[pd.Timestamp, float, int]] = []
- for ts, row in seg[mask].iterrows():
- price = float(row["Low"]) if "Low" in row else float(row.get("close", 0))
- if price <= 0:
- continue
- idx = frame.index.get_loc(ts)
- if isinstance(idx, slice):
- idx = int(idx.start or 0)
- cands.append((ts, price, int(idx)))
-
- cands.sort(key=lambda x: x[0])
- filtered: list[tuple[pd.Timestamp, float, int]] = []
- for ts, price, idx in cands:
- if filtered and idx - filtered[-1][2] < min_bars:
- if price < filtered[-1][1]:
- filtered[-1] = (ts, price, idx)
- continue
- filtered.append((ts, price, idx))
-
- if len(filtered) > max_buys:
- filtered.sort(key=lambda x: x[1])
- filtered = sorted(filtered[:max_buys], key=lambda x: x[0])
-
- return [(ts, price) for ts, price, _ in filtered]
-
-
-def _causal_sell_points(
- frame: pd.DataFrame,
- peak_ts: pd.Timestamp,
- max_splits: int,
- *,
- peak_signal_col: str = "gt_peak_zigzag",
-) -> list[tuple[pd.Timestamp, float, float]]:
- """
- 인과적 매도: peak 확정봉 + (선택) 직후 확정 peak 1건 분할.
-
- Args:
- frame: OHLC + gt peak 컬럼.
- peak_ts: leg 종료 peak 시각.
- max_splits: 최대 분할(2).
- peak_signal_col: 두 번째 분할 탐색 컬럼.
-
- Returns:
- (dt, high_price, weight) 리스트.
- """
- if peak_ts not in frame.index:
- return []
-
- row = frame.loc[peak_ts]
- if isinstance(row, pd.DataFrame):
- row = row.iloc[-1]
- main_price = float(row["High"]) if "High" in row else float(row.get("close", 0))
- weights = leg_exit_weights(max_splits if max_splits >= 2 else 1)
-
- if max_splits < 2 or len(weights) < 2:
- return [(peak_ts, main_price, 1.0)]
-
- peak_idx = frame.index.get_loc(peak_ts)
- if isinstance(peak_idx, slice):
- peak_idx = int(peak_idx.start or 0)
- seg = frame.iloc[peak_idx + 1 : peak_idx + 81]
- second_ts: pd.Timestamp | None = None
- second_price = main_price
- for ts, srow in seg.iterrows():
- if int(srow.get(peak_signal_col, 0)) != 1:
- continue
- px = float(srow["High"]) if "High" in srow else float(srow.get("close", 0))
- gap = abs(px - main_price) / max(main_price, 1e-9) * 100.0
- if gap <= GT_SELL_SPLIT_GAP_PCT:
- second_ts = ts
- second_price = px
- break
-
- if second_ts is None:
- return [(peak_ts, main_price, 1.0)]
-
- return [
- (peak_ts, main_price, weights[0]),
- (second_ts, second_price, weights[1]),
- ]
-
-
-def _peak_signal_column(peak_mode: PeakMode) -> str:
- """leg 종료 peak 컬럼명."""
- return "gt_peak_local" if peak_mode == "local" else "gt_peak_zigzag"
-
-
-def _filter_peak_times(
- frame: pd.DataFrame,
- peak_col: str,
- min_bars: int,
-) -> list[pd.Timestamp]:
- """
- peak 후보를 min_bars 간격으로稀疏화 (인과적, 시간순).
-
- Args:
- frame: OHLC frame.
- peak_col: peak 신호 컬럼.
- min_bars: 최소 봉 간격.
-
- Returns:
- peak 타임스탬프 리스트.
- """
- peaks = frame.index[frame[peak_col] == 1]
- if len(peaks) == 0:
- return []
- kept: list[pd.Timestamp] = []
- last_idx = -min_bars
- for ts in peaks:
- idx = frame.index.get_loc(ts)
- if isinstance(idx, slice):
- idx = int(idx.start or 0)
- if idx - last_idx >= min_bars:
- kept.append(ts)
- last_idx = int(idx)
- return kept
-
-
-def _precompute_buy_candidates(
- frame: pd.DataFrame,
- *,
- use_local_trough: bool,
- bb_max: float,
-) -> list[tuple[int, pd.Timestamp, float]]:
- """
- 전구간 매수 후보 (bar_idx, ts, price).
-
- Args:
- frame: enriched frame.
- use_local_trough: local trough vs zigzag buy.
- bb_max: BB 상한.
-
- Returns:
- (idx, ts, price) 리스트.
- """
- if use_local_trough:
- bb = pd.to_numeric(frame.get("bb_pos"), errors="coerce")
- mask = (frame["gt_trough_local"] == 1) & (bb <= bb_max)
- else:
- mask = frame["gt_buy_signal"] == 1
-
- out: list[tuple[int, pd.Timestamp, float]] = []
- for ts in frame.index[mask]:
- row = frame.loc[ts]
- if isinstance(row, pd.DataFrame):
- row = row.iloc[-1]
- price = float(row["Low"]) if "Low" in row else float(row.get("close", 0))
- if price <= 0:
- continue
- idx = frame.index.get_loc(ts)
- if isinstance(idx, slice):
- idx = int(idx.start or 0)
- out.append((int(idx), ts, price))
- return out
-
-
-def _buys_in_range(
- candidates: list[tuple[int, pd.Timestamp, float]],
- start_idx: int,
- end_idx: int,
- *,
- min_bars: int,
- max_buys: int,
-) -> list[tuple[pd.Timestamp, float]]:
- """start_idx < bar_idx <= end_idx 구간 매수 후보 (min_bars·max_buys 적용)."""
- seg = [(i, ts, p) for i, ts, p in candidates if start_idx < i <= end_idx]
- if not seg:
- return []
- filtered: list[tuple[int, pd.Timestamp, float]] = []
- for i, ts, p in seg:
- if filtered and i - filtered[-1][0] < min_bars:
- if p < filtered[-1][2]:
- filtered[-1] = (i, ts, p)
- continue
- filtered.append((i, ts, p))
- if len(filtered) > max_buys:
- filtered.sort(key=lambda x: x[2])
- filtered = sorted(filtered[:max_buys], key=lambda x: x[0])
- return [(ts, p) for _, ts, p in filtered]
-
-
-def build_causal_split_buy_peak_sell_trades(
- df: pd.DataFrame,
- *,
- pivot_order: int = GT_PIVOT_ORDER,
- buy_swing_pct: float = GT_BUY_MIN_SWING_PCT,
- sell_swing_pct: float = GT_MIN_SWING_PCT,
- bb_max: float = 0.65,
- min_leg_pct: float = GT_MIN_SWING_PCT,
- buy_min_bars: int = GT_BUY_MIN_BARS,
- max_buys: int = GT_MAX_BUYS_PER_LEG,
- max_sells: int = GT_MAX_SELLS_PER_LEG,
- use_local_trough: bool = True,
- peak_mode: PeakMode = "local",
- min_bars_between_legs: int = 60,
-) -> list[dict[str, Any]]:
- """
- 인과적 split_buy_peak_sell trade dict 리스트.
-
- Args:
- df: 3m OHLCV+bb_pos (DatetimeIndex).
- pivot_order: 피벗 확정 지연.
- buy_swing_pct: 매수 ZigZag %.
- sell_swing_pct: 매도 ZigZag %.
- bb_max: BB %B 상한.
- min_leg_pct: leg 최소 수익률(%).
- buy_min_bars: 분할 매수 간격.
- max_buys: leg당 매수 상한.
- max_sells: leg당 매도 상한.
- use_local_trough: local trough 분할매수 사용.
- peak_mode: zigzag | local (leg 종료 peak).
- min_bars_between_legs: 연속 leg 종료 최소 간격(봉).
-
- Returns:
- {dt, action, price, weight, leg_id} dict 리스트.
- """
- frame = enrich_scan_frame_gt_signals_causal(
- df,
- pivot_order=pivot_order,
- buy_swing_pct=buy_swing_pct,
- sell_swing_pct=sell_swing_pct,
- bb_max=bb_max,
- )
-
- peak_col = _peak_signal_column(peak_mode)
- if peak_col not in frame.columns:
- return []
-
- peak_times = _filter_peak_times(frame, peak_col, min_bars_between_legs)
- if not peak_times:
- return []
-
- buy_candidates = _precompute_buy_candidates(
- frame,
- use_local_trough=use_local_trough,
- bb_max=bb_max,
- )
- start_idx = 0
- if frame.index.size:
- loc = frame.index.get_loc(frame.index[0])
- start_idx = int(loc.start or 0) if isinstance(loc, slice) else int(loc)
-
- peak_signal_col = peak_col
- trades: list[dict[str, Any]] = []
- prev_sell_idx = start_idx
- leg_id = 0
- leg_trough_price = 0.0
-
- for peak_ts in peak_times:
- peak_idx = frame.index.get_loc(peak_ts)
- if isinstance(peak_idx, slice):
- peak_idx = int(peak_idx.start or 0)
- if peak_idx - prev_sell_idx < min_bars_between_legs:
- continue
-
- prow = frame.loc[peak_ts]
- if isinstance(prow, pd.DataFrame):
- prow = prow.iloc[-1]
- peak_price = float(prow["High"]) if "High" in prow else float(prow.get("close", 0))
- seg = frame.iloc[prev_sell_idx + 1 : peak_idx + 1]
- if not seg.empty and "Low" in seg.columns:
- leg_trough_price = float(seg["Low"].astype(float).min())
-
- leg_pct = (
- (peak_price - leg_trough_price) / max(leg_trough_price, 1e-9) * 100.0
- if leg_trough_price > 0
- else 0.0
- )
- if leg_pct < min_leg_pct:
- continue
-
- buys = _buys_in_range(
- buy_candidates,
- prev_sell_idx,
- int(peak_idx),
- min_bars=buy_min_bars,
- max_buys=max_buys,
- )
- if not buys:
- prev_sell_idx = int(peak_idx)
- leg_trough_price = peak_price
- continue
-
- prices = [p for _, p in buys]
- weights = leg_entry_weights(prices)
- for (dt, price), w in zip(buys, weights):
- trades.append(
- {
- "dt": dt.strftime("%Y-%m-%d %H:%M:%S"),
- "action": "buy",
- "price": round(price, 2),
- "weight": round(w, 4),
- "leg_id": leg_id,
- }
- )
-
- sell_pts = _causal_sell_points(
- frame,
- peak_ts,
- max_sells,
- peak_signal_col=peak_signal_col,
- )
- for dt, price, w in sell_pts[:max_sells]:
- trades.append(
- {
- "dt": dt.strftime("%Y-%m-%d %H:%M:%S"),
- "action": "sell",
- "price": round(price, 2),
- "weight": round(w, 4),
- "leg_id": leg_id,
- }
- )
-
- prev_sell_idx = int(peak_idx)
- leg_trough_price = peak_price
- leg_id += 1
-
- return trades
-
-
-def simulate_causal_gt_portfolio(
- df: pd.DataFrame,
- *,
- last_price: float | None = None,
- **build_kw: Any,
-) -> dict[str, Any]:
- """
- 인과 GT 타점 + causal tier 복리 포트폴리오.
-
- Args:
- df: 3m OHLCV.
- last_price: 미청산 평가 종가.
- build_kw: build_causal_split_buy_peak_sell_trades 인자.
-
- Returns:
- simulate_portfolio_summary 형식 dict + leg_count, params.
- """
- from deepcoin.ground_truth.gt_allocation import (
- allocate_order_amounts_chronological,
- simulate_portfolio_summary,
- )
-
- raw = build_causal_split_buy_peak_sell_trades(df, **build_kw)
- if not raw:
- return {
- "pnl_pct": 0.0,
- "trade_count": 0,
- "leg_count": 0,
- "note": "no trades",
- "sizing_mode": "causal_gt_leg_engine",
- }
-
- sized, alloc_stats = allocate_order_amounts_chronological(raw, causal_tier=True)
- mark = last_price
- if mark is None and "close" in df.columns:
- mark = float(df["close"].iloc[-1])
- result = simulate_portfolio_summary(
- sized,
- last_price=mark,
- use_amount_krw=True,
- )
- leg_count = len({t.get("leg_id") for t in raw})
- result["leg_count"] = leg_count
- result["sizing_mode"] = "causal_gt_leg_engine"
- result["sizing_note"] = (
- "인과 GT leg: split_buy + peak_sell, causal tier 복리 (미래 미사용)"
- )
- result["causal_gt_params"] = dict(build_kw)
- result["alloc_stats"] = alloc_stats
- return result
diff --git a/deepcoin/ground_truth/ground_truth.py b/deepcoin/ground_truth/ground_truth.py
deleted file mode 100644
index 9124118..0000000
--- a/deepcoin/ground_truth/ground_truth.py
+++ /dev/null
@@ -1,1393 +0,0 @@
-"""
-차트 조회 구간(기본 1년) 3분봉에서 최적 매수·매도 타점(정답 라벨)을 생성합니다.
-
-방법:
- 1) ZigZag 피벗(스윙 고저) 추출
- 2) split_buy_peak_sell: 저점 분할 매수 + 고점 1~2회 매도 (비중=삼각형 크기)
- 3) ground_truth_trades.json 저장
-
-실행:
- python scripts/02_ground_truth.py
- python scripts/05_chart_truth.py
-"""
-
-from __future__ import annotations
-
-import json
-from dataclasses import asdict, dataclass
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import (
- CHART_LOOKBACK_DAYS,
- ENTRY_INTERVAL,
- GROUND_TRUTH_FILE,
- GT_BUY_BB_MAX,
- GT_BUY_MIN_BARS,
- GT_BUY_MIN_SWING_PCT,
- GT_BUY_PCT_LARGE_LEG,
- GT_BUY_PCT_SMALL_LEG,
- GT_INITIAL_CASH_KRW,
- GT_LARGE_LEG_TOP_PCT,
- GT_MIN_ORDER_KRW,
- GT_MAX_BUYS_PER_LEG,
- GT_MAX_ROUND_TRIPS,
- TRADING_FEE_RATE,
- GT_MAX_SELLS_PER_LEG,
- GT_MIN_BARS_BETWEEN,
- GT_MIN_LEG_PCT,
- GT_MIN_SWING_PCT,
- GT_PIVOT_ORDER,
- GT_SELECTION_MODE,
- GT_SELL_SPLIT_GAP_PCT,
- SYMBOL,
-)
-from deepcoin.common.indicators import apply_bar_indicators, get_trend
-from deepcoin.data.mtf_bb import load_frames_from_db
-
-from deepcoin.ground_truth.gt_allocation import (
- allocate_order_amounts_chronological,
- resolve_sell_qty as _resolve_sell_qty,
-)
-from deepcoin.ground_truth.gt_model import (
- compute_entry_weights,
- leg_entry_weights,
- leg_exit_weights,
- sell_split_weights,
-)
-from deepcoin.paths import resolve_ground_truth_file
-
-DEFAULT_OUTPUT = resolve_ground_truth_file()
-
-
-@dataclass
-class Pivot:
- """스윙 피벗 한 점."""
-
- idx: int
- ts: pd.Timestamp
- kind: str # "trough" | "peak"
- price: float
-
-
-@dataclass
-class TradePoint:
- """정답 타점 1건."""
-
- dt: str
- action: str
- price: float
- memo: str
- weight: float = 1.0
- amount_krw: float | None = None
- leg_id: int = 0
- bb_pos: float | None = None
- rsi: float | None = None
- pivot_kind: str = ""
- forward_return_pct: float | None = None
-
-
-def _local_extrema_indices(arr: np.ndarray, order: int, kind: str) -> np.ndarray:
- """
- order 양옆 구간에서 국소 최소/최대 인덱스를 반환합니다.
-
- Args:
- arr: 가격 배열.
- order: 좌우 봉 수.
- kind: "min" 또는 "max".
-
- Returns:
- 인덱스 ndarray.
- """
- n = len(arr)
- if n < 2 * order + 1:
- return np.array([], dtype=int)
- out: list[int] = []
- for i in range(order, n - order):
- window = arr[i - order : i + order + 1]
- if kind == "min" and arr[i] <= window.min():
- out.append(i)
- elif kind == "max" and arr[i] >= window.max():
- out.append(i)
- return np.array(out, dtype=int)
-
-
-def build_zigzag_pivots(
- df: pd.DataFrame,
- min_swing_pct: float = GT_MIN_SWING_PCT,
- pivot_order: int = GT_PIVOT_ORDER,
-) -> list[Pivot]:
- """
- ZigZag 방식으로 스윙 저점·고점 피벗을 만듭니다.
-
- Args:
- df: OHLCV (index=datetime).
- min_swing_pct: 피벗 확정 최소 가격 변동(%).
- pivot_order: 국소 극값 탐색 반경(봉).
-
- Returns:
- 시간순 Pivot 리스트.
- """
- low = df["Low"].astype(float).values
- high = df["High"].astype(float).values
- index = df.index
- min_ratio = min_swing_pct / 100.0
-
- trough_idx = _local_extrema_indices(low, pivot_order, "min")
- peak_idx = _local_extrema_indices(high, pivot_order, "max")
-
- candidates: list[tuple[int, str, float]] = []
- for i in trough_idx:
- candidates.append((int(i), "trough", float(low[i])))
- for i in peak_idx:
- candidates.append((int(i), "peak", float(high[i])))
- candidates.sort(key=lambda x: x[0])
- if not candidates:
- return []
-
- pivots: list[Pivot] = []
- last_kind: str | None = None
- last_price = 0.0
-
- for idx, kind, price in candidates:
- if not pivots:
- pivots.append(Pivot(idx, index[idx], kind, price))
- last_kind = kind
- last_price = price
- continue
-
- if kind == last_kind:
- # 같은 방향이면 더 극단적인 쪽만 유지
- if kind == "trough" and price < last_price:
- pivots[-1] = Pivot(idx, index[idx], kind, price)
- last_price = price
- elif kind == "peak" and price > last_price:
- pivots[-1] = Pivot(idx, index[idx], kind, price)
- last_price = price
- continue
-
- move = abs(price - last_price) / max(last_price, 1e-9)
- if move >= min_ratio:
- pivots.append(Pivot(idx, index[idx], kind, price))
- last_kind = kind
- last_price = price
-
- return pivots
-
-
-def _select_optimal_chain(
- pivots: list[Pivot],
- min_bars: int = GT_MIN_BARS_BETWEEN,
- max_round_trips: int = GT_MAX_ROUND_TRIPS,
- mode: str = GT_SELECTION_MODE,
-) -> list[Pivot]:
- """
- 피벗에서 정답 체인을 선택합니다.
-
- Args:
- pivots: ZigZag 피벗.
- min_bars: 연속 체결 최소 봉 간격.
- max_round_trips: 최대 라운드트립 수.
- mode: "zigzag" 또는 "max_profit".
-
- Returns:
- 선택된 Pivot 부분열 (매수·매도 교대).
- """
- if len(pivots) < 2:
- return []
-
- if mode == "zigzag":
- return _filter_alternating_pivots(pivots, min_bars, max_round_trips * 2)
-
- if mode == "major_swings":
- return _select_major_swings(
- pivots,
- min_bars=min_bars,
- max_round_trips=max_round_trips,
- min_leg_pct=GT_MIN_LEG_PCT,
- )
-
- intervals: list[tuple[int, int, float, Pivot, Pivot]] = []
- for i, buy_p in enumerate(pivots):
- if buy_p.kind != "trough":
- continue
- for j in range(i + 1, len(pivots)):
- sell_p = pivots[j]
- if sell_p.kind != "peak":
- continue
- if sell_p.idx - buy_p.idx < min_bars:
- continue
- if sell_p.price <= buy_p.price:
- continue
- profit = (sell_p.price - buy_p.price) / buy_p.price * 100.0
- intervals.append((buy_p.idx, sell_p.idx, profit, buy_p, sell_p))
-
- if not intervals:
- return _filter_alternating_pivots(pivots, min_bars, max_round_trips * 2)
-
- intervals.sort(key=lambda x: x[1])
- m = len(intervals)
- sell_bars = [iv[1] for iv in intervals]
-
- def prev_non_overlap(k: int) -> int:
- """매도 봉이 겹치지 않도록, 이전 구간의 매도 봉 < 현재 매수 봉."""
- buy_bar = intervals[k][0]
- lo, hi = 0, k - 1
- ans = -1
- while lo <= hi:
- mid = (lo + hi) // 2
- if sell_bars[mid] < buy_bar:
- ans = mid
- lo = mid + 1
- else:
- hi = mid - 1
- return ans
-
- pprev = [prev_non_overlap(k) for k in range(m)]
-
- dp_val = [0.0] * m
- dp_take = [False] * m
- for k in range(m):
- profit = intervals[k][2]
- p_idx = pprev[k]
- skip = profit
- take = profit + (dp_val[p_idx] if p_idx >= 0 else 0.0)
- if take >= skip:
- dp_val[k] = take
- dp_take[k] = True
- else:
- dp_val[k] = skip
-
- chain_iv: list[tuple[int, int, float, Pivot, Pivot]] = []
- k = m - 1
- if m == 0:
- return []
- best_end = max(range(m), key=lambda i: dp_val[i])
- k = best_end
- while k >= 0 and len(chain_iv) < max_round_trips:
- if dp_take[k]:
- chain_iv.append(intervals[k])
- k = pprev[k]
- else:
- k -= 1
-
- chain_iv.reverse()
- result: list[Pivot] = []
- for _, _, _, bp, sp in chain_iv:
- result.extend([bp, sp])
- return result
-
-
-def _select_major_swings(
- pivots: list[Pivot],
- min_bars: int,
- max_round_trips: int,
- min_leg_pct: float,
-) -> list[Pivot]:
- """
- ZigZag 교대 체인에서 구간 수익이 min_leg_pct 이상인 매수·매도만 남깁니다.
-
- 구간이 max_round_trips를 초과하면 비겹침 수익 합이 최대가 되도록 고릅니다.
-
- Args:
- pivots: ZigZag 피벗.
- min_bars: 체결 간 최소 봉 수.
- max_round_trips: 최대 라운드트립.
- min_leg_pct: 한 구간 최소 수익률(%).
-
- Returns:
- 선택된 Pivot 리스트 (시간순).
- """
- chain = _filter_alternating_pivots(pivots, min_bars, len(pivots))
- if len(chain) < 2:
- return chain
-
- legs: list[tuple[float, Pivot, Pivot, int, int]] = []
- i = 0
- while i < len(chain) - 1:
- buy_p = chain[i]
- sell_p = chain[i + 1]
- if buy_p.kind == "trough" and sell_p.kind == "peak":
- profit = (sell_p.price - buy_p.price) / max(buy_p.price, 1e-9) * 100.0
- if profit >= min_leg_pct:
- legs.append((profit, buy_p, sell_p, buy_p.idx, sell_p.idx))
- i += 2
- else:
- i += 1
-
- if not legs:
- # 임계값 완화 후 재시도
- return _select_major_swings(
- pivots,
- min_bars,
- max_round_trips,
- min_leg_pct=max(min_leg_pct * 0.6, 3.0),
- )
-
- if len(legs) <= max_round_trips:
- out: list[Pivot] = []
- for _, bp, sp, _, _ in legs:
- out.extend([bp, sp])
- return out
-
- # 1년 라벨: 시간순 비겹침 구간 전부 사용 (상한으로 뒤쪽 월이 빠지지 않게 함)
- if CHART_LOOKBACK_DAYS >= 300:
- chosen: list[tuple[float, Pivot, Pivot, int, int]] = []
- last_sell_bar = -1
- for pr, bp, sp, lb, sb in sorted(legs, key=lambda x: x[3]):
- if lb > last_sell_bar:
- chosen.append((pr, bp, sp, lb, sb))
- last_sell_bar = sb
- result: list[Pivot] = []
- for _pr, bp, sp, _lb, _sb in chosen:
- result.extend([bp, sp])
- return result
-
- intervals = [(lb, sb, pr, bp, sp) for pr, bp, sp, lb, sb in legs]
- intervals.sort(key=lambda x: x[1])
- m = len(intervals)
- sell_bars = [iv[1] for iv in intervals]
-
- def prev_non_overlap(k: int) -> int:
- buy_bar = intervals[k][0]
- lo, hi = 0, k - 1
- ans = -1
- while lo <= hi:
- mid = (lo + hi) // 2
- if sell_bars[mid] < buy_bar:
- ans = mid
- lo = mid + 1
- else:
- hi = mid - 1
- return ans
-
- pprev = [prev_non_overlap(k) for k in range(m)]
- dp_val = [0.0] * m
- dp_take = [False] * m
- for k in range(m):
- profit = intervals[k][2]
- p_idx = pprev[k]
- take = profit + (dp_val[p_idx] if p_idx >= 0 else 0.0)
- if take >= profit:
- dp_val[k] = take
- dp_take[k] = True
- else:
- dp_val[k] = profit
-
- best_end = max(range(m), key=lambda i: dp_val[i])
- k = best_end
- chosen: list[tuple[float, Pivot, Pivot, int, int]] = []
- while k >= 0 and len(chosen) < max_round_trips:
- if dp_take[k]:
- chosen.append(intervals[k])
- k = pprev[k]
- else:
- k -= 1
- chosen.reverse()
- result: list[Pivot] = []
- for _lb, _sb, _pr, bp, sp in chosen:
- result.extend([bp, sp])
- return result
-
-
-def _filter_alternating_pivots(
- pivots: list[Pivot],
- min_bars: int,
- max_points: int,
-) -> list[Pivot]:
- """ZigZag 피벗을 간격·교대 규칙으로만 줄입니다."""
- filtered: list[Pivot] = []
- for p in pivots:
- if filtered and p.idx - filtered[-1].idx < min_bars:
- continue
- if filtered and p.kind == filtered[-1].kind:
- if p.kind == "trough" and p.price < filtered[-1].price:
- filtered[-1] = p
- elif p.kind == "peak" and p.price > filtered[-1].price:
- filtered[-1] = p
- continue
- filtered.append(p)
- if filtered and filtered[0].kind == "peak":
- filtered = filtered[1:]
- if filtered and filtered[-1].kind == "trough":
- filtered = filtered[:-1]
- return filtered[:max_points]
-
-
-def _bb_context(row: pd.Series) -> tuple[float | None, float | None, float | None]:
- """봉의 BB %B, RSI, 이격도(20 기본)."""
- from config import DISPARITY_PERIODS
- from deepcoin.common.indicators import disparity_column
-
- bb = None
- if "bb_pos" in row.index and pd.notna(row["bb_pos"]):
- bb = round(float(row["bb_pos"]), 3)
- rsi = None
- if "RSI" in row.index and pd.notna(row["RSI"]):
- rsi = round(float(row["RSI"]), 1)
- disp = None
- primary_p = 20 if 20 in DISPARITY_PERIODS else DISPARITY_PERIODS[0]
- dcol = disparity_column(primary_p)
- if dcol in row.index and pd.notna(row[dcol]):
- disp = round(float(row[dcol]), 1)
- return bb, rsi, disp
-
-
-def _memo_for_trade(
- action: str,
- pivot: Pivot,
- bb_pos: float | None,
- rsi: float | None,
- disparity: float | None,
- forward_pct: float | None,
-) -> str:
- """타점 해석 메모."""
- zone = "중단"
- if bb_pos is not None:
- if bb_pos < 0.25:
- zone = "밴드 하단"
- elif bb_pos > 0.75:
- zone = "밴드 상단"
- parts = [
- f"ZigZag {pivot.kind}",
- zone,
- ]
- if rsi is not None:
- parts.append(f"RSI {rsi}")
- if disparity is not None:
- parts.append(f"D.I.{disparity}")
- if forward_pct is not None and action == "buy":
- parts.append(f"다음 매도까지 +{forward_pct:.1f}%")
- elif forward_pct is not None and action == "sell":
- parts.append(f"직전 매수 대비 +{forward_pct:.1f}%")
- return " · ".join(parts)
-
-
-def _bar_index(df: pd.DataFrame, ts: pd.Timestamp) -> int:
- """타임스탬프의 정수 봉 위치."""
- loc = df.index.get_loc(ts if ts in df.index else df.index[df.index.get_indexer([ts], method="nearest")[0]])
- if isinstance(loc, slice):
- return int(loc.start or 0)
- if hasattr(loc, "__len__") and not isinstance(loc, int):
- return int(loc[-1])
- return int(loc)
-
-
-def _row_at_ts(df: pd.DataFrame, ts: pd.Timestamp) -> pd.Series:
- """타임스탬프에 해당하는 봉 1행."""
- loc = ts if ts in df.index else df.index[df.index.get_indexer([ts], method="nearest")[0]]
- row = df.loc[loc]
- if isinstance(row, pd.DataFrame):
- row = row.iloc[-1]
- return row
-
-
-def _collect_buy_troughs(
- df: pd.DataFrame,
- buy_pivots: list[Pivot],
- start: pd.Timestamp,
- end: pd.Timestamp,
- min_bars: int,
- max_buys: int = GT_MAX_BUYS_PER_LEG,
-) -> list[Pivot]:
- """
- 매도 전 구간의 ZigZag 저점(trough)을 모읍니다.
-
- BB 하단이면서 구간 최저에 가까운 저점 1건만 추가 보완합니다.
- """
- out: list[Pivot] = []
- for p in buy_pivots:
- if start < p.ts < end:
- out.append(p)
-
- if "bb_pos" in df.columns and out:
- seg = df[(df.index > start) & (df.index < end)]
- if not seg.empty and "bb_pos" in seg.columns:
- bb_seg = seg[seg["bb_pos"] <= GT_BUY_BB_MAX]
- if not bb_seg.empty:
- loc = bb_seg["Low"].astype(float).idxmin()
- idx = _bar_index(df, loc)
- if all(abs(idx - p.idx) >= min_bars for p in out):
- row_bb = _row_at_ts(bb_seg, loc)
- out.append(
- Pivot(idx, loc, "trough", float(row_bb["Low"]))
- )
-
- out.sort(key=lambda x: x.ts)
- filtered: list[Pivot] = []
- for p in out:
- if filtered and p.idx - filtered[-1].idx < min_bars:
- if p.price < filtered[-1].price:
- filtered[-1] = p
- continue
- filtered.append(p)
-
- if len(filtered) > max_buys:
- filtered.sort(key=lambda x: x.price)
- filtered = sorted(filtered[:max_buys], key=lambda x: x.ts)
- return filtered
-
-
-def _peak_sell_points(
- df: pd.DataFrame,
- peak: Pivot,
- max_splits: int,
- split_gap_pct: float,
-) -> list[tuple[Pivot, float]]:
- """
- 고점에서 1회 또는 분할 매도 시점·비중.
-
- Returns:
- (피벗, 비중) 리스트. 비중 합 = 1.0.
- """
- row = _row_at_ts(df, peak.ts)
- main_price = float(row["High"]) if "High" in row else peak.price
- main = Pivot(peak.idx, peak.ts, "peak", main_price)
-
- if max_splits < 2:
- return [(main, 1.0)]
-
- seg = df.iloc[peak.idx : peak.idx + 80]
- if len(seg) < 5:
- return [(main, 1.0)]
-
- sub_peaks: list[Pivot] = []
- highs = seg["High"].astype(float).values
- for j in range(2, len(seg) - 2):
- if highs[j] >= highs[j - 2 : j + 3].max():
- px = float(highs[j])
- if abs(px - main_price) / max(main_price, 1e-9) * 100 <= split_gap_pct:
- sub_peaks.append(
- Pivot(peak.idx + j, seg.index[j], "peak", px)
- )
-
- if not sub_peaks:
- return [(main, 1.0)]
-
- second = max(sub_peaks, key=lambda x: x.price)
- if second.ts == main.ts:
- return [(main, 1.0)]
- w = sell_split_weights(2)
- return [(main, w[0]), (second, w[1])]
-
-
-def build_split_buy_peak_sell_trades(
- df: pd.DataFrame,
- raw_pivots: list[Pivot],
- sell_peaks: list[Pivot],
- buy_min_bars: int = GT_BUY_MIN_BARS,
-) -> list[TradePoint]:
- """
- 저점 분할 매수 + 고점 1~2회 매도 정답 타점.
-
- Args:
- df: 지표 포함 3분봉.
- raw_pivots: ZigZag 피벗(매수 탐지용, 낮은 스윙%).
- sell_peaks: 고점 매도 기준 피벗(major swing).
- buy_min_bars: 분할 매수 최소 간격(봉).
-
- Returns:
- TradePoint 리스트.
- """
- buy_pivots = build_zigzag_pivots(
- df, min_swing_pct=GT_BUY_MIN_SWING_PCT, pivot_order=GT_PIVOT_ORDER
- )
- buy_pivots = [p for p in buy_pivots if p.kind == "trough"]
-
- sell_peaks = sorted(sell_peaks, key=lambda x: x.ts)
- trades: list[TradePoint] = []
- prev_sell_ts = df.index[0]
-
- for leg_id, peak in enumerate(sell_peaks):
- troughs = _collect_buy_troughs(df, buy_pivots, prev_sell_ts, peak.ts, buy_min_bars)
- if troughs:
- prices = [
- float(_row_at_ts(df, t.ts)["Low"]) if "Low" in _row_at_ts(df, t.ts) else t.price
- for t in troughs
- ]
- weights = leg_entry_weights(prices)
- for t, w in zip(troughs, weights):
- row = _row_at_ts(df, t.ts)
- bb_pos, rsi, disp = _bb_context(row)
- price = float(row["Low"]) if "Low" in row else t.price
- pct = (peak.price - price) / max(price, 1e-9) * 100.0
- trades.append(
- TradePoint(
- dt=t.ts.strftime("%Y-%m-%d %H:%M:%S"),
- action="buy",
- price=round(price, 2),
- weight=round(w, 3),
- leg_id=leg_id,
- memo=(
- f"저점 분할 매수 · 비중 {w*100:.0f}% · {len(troughs)}회 "
- f"· BB하단 · leg#{leg_id}"
- ),
- bb_pos=bb_pos,
- rsi=rsi,
- pivot_kind="trough",
- forward_return_pct=round(pct, 2),
- )
- )
-
- sell_pts = _peak_sell_points(
- df, peak, GT_MAX_SELLS_PER_LEG, GT_SELL_SPLIT_GAP_PCT
- )
- leg_avg = (
- sum(t.price * t.weight for t in trades if t.leg_id == leg_id and t.action == "buy")
- / max(
- sum(t.weight for t in trades if t.leg_id == leg_id and t.action == "buy"),
- 1e-9,
- )
- )
- for sp, w in sell_pts:
- row = _row_at_ts(df, sp.ts)
- bb_pos, rsi, disp = _bb_context(row)
- price = float(row["High"]) if "High" in row else sp.price
- ret = (price - leg_avg) / max(leg_avg, 1e-9) * 100.0 if leg_avg > 0 else None
- n_sell = len(sell_pts)
- trades.append(
- TradePoint(
- dt=sp.ts.strftime("%Y-%m-%d %H:%M:%S"),
- action="sell",
- price=round(price, 2),
- weight=round(w, 3),
- leg_id=leg_id,
- memo=(
- f"고점 매도 · 비중 {w*100:.0f}% · "
- f"{'분할' if n_sell > 1 else '1회'} · leg#{leg_id}"
- ),
- bb_pos=bb_pos,
- rsi=rsi,
- pivot_kind="peak",
- forward_return_pct=round(ret, 2) if ret is not None else None,
- )
- )
-
- prev_sell_ts = peak.ts
-
- # 마지막 매도 이후 ~ 기간 말: 분할 매수 후 동일 leg에서 기간말 청산(포트폴리오 정합)
- if sell_peaks:
- last_peak = sell_peaks[-1]
- troughs = _collect_buy_troughs(
- df, buy_pivots, last_peak.ts, df.index[-1], buy_min_bars
- )
- leg_id = len(sell_peaks)
- if troughs:
- prices = [
- float(_row_at_ts(df, t.ts)["Low"]) if "Low" in _row_at_ts(df, t.ts) else t.price
- for t in troughs
- ]
- weights = leg_entry_weights(prices)
- leg_buys: list[TradePoint] = []
- for t, w in zip(troughs, weights):
- row = _row_at_ts(df, t.ts)
- bb_pos, rsi, disp = _bb_context(row)
- price = float(row["Low"]) if "Low" in row else t.price
- leg_buys.append(
- TradePoint(
- dt=t.ts.strftime("%Y-%m-%d %H:%M:%S"),
- action="buy",
- price=round(price, 2),
- weight=round(w, 3),
- leg_id=leg_id,
- memo=f"저점 분할 매수 · 비중 {w*100:.0f}% · leg#{leg_id}(기간말)",
- bb_pos=bb_pos,
- rsi=rsi,
- pivot_kind="trough",
- )
- )
- trades.extend(leg_buys)
- leg_avg = (
- sum(x.price * x.weight for x in leg_buys)
- / max(sum(x.weight for x in leg_buys), 1e-9)
- )
- end_ts = df.index[-1]
- end_row = df.loc[end_ts]
- if isinstance(end_row, pd.DataFrame):
- end_row = end_row.iloc[-1]
- end_price = float(end_row["Close"])
- bb_pos, rsi, _ = _bb_context(end_row)
- ret = (end_price - leg_avg) / max(leg_avg, 1e-9) * 100.0 if leg_avg > 0 else None
- trades.append(
- TradePoint(
- dt=end_ts.strftime("%Y-%m-%d %H:%M:%S"),
- action="sell",
- price=round(end_price, 2),
- weight=1.0,
- leg_id=leg_id,
- memo=f"기간말 잔여 청산 · leg#{leg_id}",
- bb_pos=bb_pos,
- rsi=rsi,
- pivot_kind="peak",
- forward_return_pct=round(ret, 2) if ret is not None else None,
- )
- )
- for b in leg_buys:
- if b.forward_return_pct is None and ret is not None:
- b.forward_return_pct = round(
- (end_price - b.price) / max(b.price, 1e-9) * 100.0, 2
- )
-
- return trades
-
-
-def pivots_to_trades(
- pivots: list[Pivot],
- df: pd.DataFrame,
-) -> list[TradePoint]:
- """
- 피벗을 매수·매도 정답 타점으로 변환합니다.
-
- Args:
- pivots: 선택된 피벗.
- df: 지표가 포함된 3분봉.
-
- Returns:
- TradePoint 리스트.
- """
- trades: list[TradePoint] = []
- last_buy_price: float | None = None
-
- for i, p in enumerate(pivots):
- loc = (
- p.ts
- if p.ts in df.index
- else df.index[df.index.get_indexer([p.ts], method="nearest")[0]]
- )
- row = df.loc[loc]
- if isinstance(row, pd.DataFrame):
- row = row.iloc[-1]
-
- bb_pos, rsi, disp = _bb_context(row)
- forward_pct: float | None = None
-
- if p.kind == "trough":
- action = "buy"
- price = float(row["Low"]) if "Low" in row else p.price
- if i + 1 < len(pivots) and pivots[i + 1].kind == "peak":
- forward_pct = (pivots[i + 1].price - price) / max(price, 1e-9) * 100.0
- last_buy_price = price
- else:
- action = "sell"
- price = float(row["High"]) if "High" in row else p.price
- if last_buy_price:
- forward_pct = (price - last_buy_price) / max(last_buy_price, 1e-9) * 100.0
- last_buy_price = None
-
- trades.append(
- TradePoint(
- dt=p.ts.strftime("%Y-%m-%d %H:%M:%S"),
- action=action,
- price=round(price, 2),
- weight=1.0,
- memo=_memo_for_trade(action, p, bb_pos, rsi, disp, forward_pct),
- bb_pos=bb_pos,
- rsi=rsi,
- pivot_kind=p.kind,
- forward_return_pct=round(forward_pct, 2) if forward_pct is not None else None,
- )
- )
-
- return trades
-
-
-def generate_ground_truth(
- df_3m: pd.DataFrame,
- df_1d: pd.DataFrame | None = None,
- df_1h: pd.DataFrame | None = None,
- min_swing_pct: float = GT_MIN_SWING_PCT,
- pivot_order: int = GT_PIVOT_ORDER,
- min_bars: int = GT_MIN_BARS_BETWEEN,
- max_round_trips: int = GT_MAX_ROUND_TRIPS,
- selection_mode: str = GT_SELECTION_MODE,
-) -> dict[str, Any]:
- """
- 3분봉 구간에서 정답 타점 JSON 구조를 생성합니다.
-
- Args:
- df_3m: 3분 OHLCV.
- df_1d: 일봉 (추세 메모용, 선택).
- df_1h: 1시간봉 (추세 메모용, 선택).
- min_swing_pct: ZigZag 최소 스윙(%).
- pivot_order: 국소 극값 반경.
- min_bars: 체결 간 최소 봉 수.
- max_round_trips: 최대 라운드트립.
- selection_mode: zigzag | max_profit.
-
- Returns:
- ground_truth_trades.json 에 넣을 dict.
- """
- df = apply_bar_indicators(df_3m.sort_index().copy())
- if df.empty:
- raise ValueError("3분봉 데이터가 비어 있습니다.")
-
- raw_pivots = build_zigzag_pivots(df, min_swing_pct=min_swing_pct, pivot_order=pivot_order)
-
- if selection_mode == "split_buy_peak_sell":
- selected = _select_optimal_chain(
- raw_pivots,
- min_bars=min_bars,
- max_round_trips=max_round_trips,
- mode="major_swings",
- )
- sell_peaks = [p for p in selected if p.kind == "peak"]
- trades = build_split_buy_peak_sell_trades(df, raw_pivots, sell_peaks)
- method = "split_buy_at_troughs + peak_sell_1or2"
- else:
- selected = _select_optimal_chain(
- raw_pivots,
- min_bars=min_bars,
- max_round_trips=max_round_trips,
- mode=selection_mode,
- )
- trades = pivots_to_trades(selected, df)
- method = "zigzag_pivot + max_profit_chain"
-
- trend = "range"
- if df_1d is not None and df_1h is not None:
- trend = get_trend(df_1d, df_1h)
-
- round_trips = len({t.leg_id for t in trades if t.action == "sell"})
- buy_count = sum(1 for t in trades if t.action == "buy")
- sell_count = sum(1 for t in trades if t.action == "sell")
- total_ret = sum(
- t.forward_return_pct or 0.0 for t in trades if t.action == "sell"
- )
-
- trade_dicts = order_trades_leg_block(trades)
- trade_dicts, alloc_stats = allocate_gt_order_amounts(
- trade_dicts,
- initial_cash=GT_INITIAL_CASH_KRW,
- min_order_krw=GT_MIN_ORDER_KRW,
- fee_rate=TRADING_FEE_RATE,
- )
- last_close = float(df["Close"].iloc[-1])
- pnl = simulate_truth_portfolio(
- trade_dicts,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- last_price=last_close,
- )
- pnl_realized = simulate_truth_portfolio(
- trade_dicts,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- last_price=None,
- )
- _validate_leg_portfolio(trade_dicts, last_close)
-
- from deepcoin.ground_truth.gt_model import default_model, model_to_dict
-
- gt_model = model_to_dict(default_model())
-
- return {
- "name": "ground_truth_split_buy_peak_sell",
- "model": gt_model,
- "method": method,
- "symbol": SYMBOL,
- "interval_min": ENTRY_INTERVAL,
- "lookback_days": CHART_LOOKBACK_DAYS,
- "period_start": str(df.index[0]),
- "period_end": str(df.index[-1]),
- "trend_at_end": trend,
- "params": {
- "min_swing_pct": min_swing_pct,
- "pivot_order": pivot_order,
- "min_bars_between": min_bars,
- "max_round_trips": max_round_trips,
- "selection_mode": selection_mode,
- "buy_min_swing_pct": GT_BUY_MIN_SWING_PCT,
- "buy_bb_max": GT_BUY_BB_MAX,
- "max_sells_per_leg": GT_MAX_SELLS_PER_LEG,
- },
- "summary": {
- "pivot_candidates": len(raw_pivots),
- "sell_peaks": len([p for p in selected if p.kind == "peak"]) if selected else 0,
- "trade_count": len(trades),
- "buy_count": buy_count,
- "sell_count": sell_count,
- "round_trips": round_trips,
- "sum_sell_leg_return_pct": round(total_ret, 2),
- **pnl,
- "realized_final_asset_krw": pnl_realized.get("final_asset_krw"),
- "realized_pnl_krw": pnl_realized.get("pnl_krw"),
- "realized_pnl_pct": pnl_realized.get("pnl_pct"),
- "unrealized_pnl_krw": round(
- float(pnl.get("pnl_krw", 0)) - float(pnl_realized.get("pnl_krw", 0)), 0
- ),
- "execution_order": (
- "chronological"
- if any(float(t.get("amount_krw") or 0) > 0 for t in trade_dicts)
- else "leg_block"
- ),
- "order_amount_min_krw": GT_MIN_ORDER_KRW,
- "buy_pct_large_leg": GT_BUY_PCT_LARGE_LEG,
- "buy_pct_small_leg": GT_BUY_PCT_SMALL_LEG,
- "large_leg_top_pct": GT_LARGE_LEG_TOP_PCT,
- **alloc_stats,
- },
- "note": (
- "저점 분할 매수(비중=삼각형), 고점 1~2회 매도. "
- "매수=총자산×최적비중×티어(상위 leg 대형·그 외 소형), "
- f"현금 한도·최소 ₩{GT_MIN_ORDER_KRW:,}. "
- "체결 순서=chronological. summary.pnl_pct는 미청산 포함 종가 평가."
- ),
- "trades": trade_dicts,
- }
-
-
-def _validate_leg_portfolio(
- trade_dicts: list[dict[str, Any]],
- last_close: float,
-) -> None:
- """
- leg 블록 체결 후 보유·현금 불변식을 검증합니다.
-
- Args:
- trade_dicts: order_trades_leg_block 결과.
- last_close: 기간 말 종가.
-
- Raises:
- ValueError: leg 매도 후에도 보유가 남는 경우(비정상).
- """
- steps = simulate_truth_portfolio_steps(trade_dicts)
- if not steps:
- return
- final = steps[-1]
- if float(final["holding_qty"]) > 1e-2:
- raise ValueError(
- f"최종 보유 잔존 qty={final['holding_qty']} — 기간말 청산 누락 가능"
- )
- pnl = simulate_truth_portfolio(trade_dicts, last_price=last_close)
- if float(pnl.get("holding_qty", 0)) > 1e-2:
- raise ValueError("종가 평가 후에도 미청산 보유가 남음")
-
-
-def allocate_gt_order_amounts(
- trades: list[dict[str, Any]],
- initial_cash: float = GT_INITIAL_CASH_KRW,
- min_order_krw: float = GT_MIN_ORDER_KRW,
- fee_rate: float = TRADING_FEE_RATE,
-) -> tuple[list[dict[str, Any]], dict[str, Any]]:
- """
- GT 각 타점에 amount_krw를 시각순·총자산·비중(최적 매수율)으로 배분합니다.
-
- 매수: 목표=총보유자산×(leg 비중 share×티어 스케일), 체결=min(목표, 보유현금/(1+fee)).
- leg 상위 GT_LARGE_LEG_TOP_PCT는 GT_BUY_PCT_LARGE_LEG, 그 외는 GT_BUY_PCT_SMALL_LEG.
- 매도 후 현금 증가분은 다음 매수부터 자동 반영(시각순 복리).
-
- Args:
- trades: trade dict 리스트(시각순 정렬 전).
- initial_cash: 초기 현금.
- min_order_krw: 매수·매도 최소 원화 금액.
- fee_rate: 수수료율.
-
- Returns:
- (동일 dict 참조, amount_krw 채움), alloc_stats 요약.
- """
- return allocate_order_amounts_chronological(
- trades,
- initial_cash=initial_cash,
- min_order_krw=min_order_krw,
- fee_rate=fee_rate,
- )
-
-
-def _trade_buy_amount(
- t: dict[str, Any],
- cash: float,
- leg_budget: float,
- current_leg: int | None,
- leg_id: int,
- fee_rate: float,
-) -> tuple[float, float, int | None]:
- """
- 매수 체결 원화: amount_krw 우선, 없으면 leg_budget*weight.
-
- Returns:
- (amount, new_leg_budget, new_current_leg).
- """
- weight = float(t.get("weight", 1.0))
- if t.get("amount_krw") is not None and float(t["amount_krw"]) > 0:
- amount = min(float(t["amount_krw"]), max(cash / (1.0 + fee_rate), 0.0))
- return amount, leg_budget, current_leg
- if leg_id != current_leg:
- current_leg = leg_id
- leg_budget = cash
- amount = leg_budget * weight
- return amount, leg_budget, current_leg
-
-
-def order_trades_leg_block(
- trades: list[TradePoint] | list[dict[str, Any]],
-) -> list[dict[str, Any]]:
- """
- leg별 매수 전량 → 매도 전량 순으로 정렬합니다 (포트폴리오 시뮬·JSON 저장용).
-
- 시각순 정렬은 leg가 섞여 매도 미완료·보유 누적 오류를 만듭니다.
-
- Args:
- trades: TradePoint 또는 dict 리스트.
-
- Returns:
- leg_id, action(buy=0), dt 순 dict 리스트.
- """
- rows = [t if isinstance(t, dict) else asdict(t) for t in trades]
-
- def _sort_key(x: dict[str, Any]) -> tuple[int, int, str]:
- return (int(x.get("leg_id", 0)), 0 if x.get("action") == "buy" else 1, x["dt"])
-
- return sorted(rows, key=_sort_key)
-
-
-def order_trades_chronological(
- trades: list[TradePoint] | list[dict[str, Any]],
-) -> list[dict[str, Any]]:
- """
- 시각순 dict 리스트 (차트 표시·분석용).
-
- Args:
- trades: TradePoint 또는 dict.
-
- Returns:
- dt 순 정렬된 dict 리스트.
- """
- rows = [t if isinstance(t, dict) else asdict(t) for t in trades]
- return sorted(rows, key=lambda x: x["dt"])
-
-
-def _truth_simulation_rows(
- trades: list[dict[str, Any]] | list[TradePoint],
- *,
- chronological: bool = False,
-) -> list[dict[str, Any]]:
- """
- 포트폴리오 시뮬용 체결 순서로 정규화합니다.
-
- Args:
- trades: JSON trades 또는 TradePoint.
- chronological: True면 시각순(레거시), False면 leg 블록 순(기본).
-
- Returns:
- dict 행 리스트.
- """
- rows = [t if isinstance(t, dict) else asdict(t) for t in trades]
- use_chrono = chronological or any(
- float(r.get("amount_krw") or 0) > 0 for r in rows
- )
- if use_chrono:
- return sorted(rows, key=lambda x: x["dt"])
- return order_trades_leg_block(trades)
-
-
-def simulate_truth_portfolio_steps(
- trades: list[dict[str, Any]] | list[TradePoint],
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
-) -> list[dict[str, Any]]:
- """
- 체결마다 현금·보유·총평가(현금+보유×체결가) 스냅샷을 반환합니다.
-
- Args:
- trades: JSON trades 또는 TradePoint 리스트.
- initial_cash: 시작 원화.
- fee_rate: 매수·매도 수수료율.
-
- Returns:
- 체결 시각순 스냅샷 dict 리스트 (total_asset_krw, cash_krw, holding_qty 등).
- """
- rows = _truth_simulation_rows(trades)
- cash = float(initial_cash)
- qty = 0.0
- leg_budget = 0.0
- current_leg: int | None = None
- sell_leg: int | None = None
- sell_base_qty = 0.0
- steps: list[dict[str, Any]] = []
-
- for t in rows:
- action = t["action"]
- price = float(t["price"])
- weight = float(t.get("weight", 1.0))
- leg_id = int(t.get("leg_id", 0))
-
- if action == "buy":
- if leg_id != current_leg:
- current_leg = leg_id
- leg_budget = cash
- sell_leg = None
- amount, leg_budget, current_leg = _trade_buy_amount(
- t, cash, leg_budget, current_leg, leg_id, fee_rate
- )
- if amount <= 0:
- continue
- fee = amount * fee_rate
- spend = amount + fee
- if spend > cash:
- amount = max(cash / (1.0 + fee_rate), 0.0)
- fee = amount * fee_rate
- spend = amount + fee
- cash -= spend
- if price > 0:
- qty += amount / price
-
- elif action == "sell" and qty > 0:
- if leg_id != sell_leg:
- sell_leg = leg_id
- sell_base_qty = qty
- sell_qty = _resolve_sell_qty(t, qty, price, sell_base_qty, weight)
- if sell_qty <= 0:
- continue
- gross = sell_qty * price
- fee = gross * fee_rate
- cash += gross - fee
- qty -= sell_qty
- if qty < 1e-12:
- qty = 0.0
-
- total_asset = cash + qty * price
- steps.append(
- {
- "dt": t["dt"],
- "action": action,
- "price": price,
- "weight": weight,
- "amount_krw": t.get("amount_krw"),
- "leg_id": leg_id,
- "cash_krw": round(cash, 0),
- "holding_qty": round(qty, 4),
- "total_asset_krw": round(total_asset, 0),
- }
- )
-
- return steps
-
-
-def simulate_truth_portfolio(
- trades: list[dict[str, Any]] | list[TradePoint],
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- last_price: float | None = None,
-) -> dict[str, Any]:
- """
- 분할 매수·매도를 시간순으로 적용한 뒤, 초기·기말 총평가로 수익을 계산합니다.
-
- - 초기 총평가 = initial_cash (전액 현금, 보유 0).
- - 매수/매도마다 그 시점 현금·보유 수량을 갱신 (분할 비중 weight 반영).
- - 기말 총평가 = 현금 + 보유수량 × mark_price(미청산은 종가 평가).
- - 수익금 = 기말 총평가 − 초기 총평가.
- - 수익률(%) = 수익금 / 초기 총평가 × 100.
-
- 분할 매도: 같은 leg의 첫 매도 시점 보유량 기준으로 weight 합이 1이 되도록 매도
- (0.65+0.35를 남은 수량에 연속 적용하지 않음).
-
- Args:
- trades: JSON trades 또는 TradePoint 리스트.
- initial_cash: 시작 원화 (기본 GT_INITIAL_CASH_KRW, 40만).
- fee_rate: 매수·매도 각각 적용 수수료율.
- last_price: 미청산 평가용 종가. None이면 마지막 체결가.
-
- Returns:
- initial_cash, final_asset, pnl_krw, pnl_pct, total_fees, holding_qty 등.
- """
- rows = _truth_simulation_rows(trades)
- cash = float(initial_cash)
- qty = 0.0
- total_fees = 0.0
- leg_budget = 0.0
- current_leg: int | None = None
- sell_leg: int | None = None
- sell_base_qty = 0.0
- last_trade_price = last_price
-
- for t in rows:
- action = t["action"]
- price = float(t["price"])
- weight = float(t.get("weight", 1.0))
- leg_id = int(t.get("leg_id", 0))
- last_trade_price = price
-
- if action == "buy":
- if leg_id != current_leg:
- current_leg = leg_id
- leg_budget = cash
- sell_leg = None
- amount, leg_budget, current_leg = _trade_buy_amount(
- t, cash, leg_budget, current_leg, leg_id, fee_rate
- )
- if amount <= 0:
- continue
- fee = amount * fee_rate
- spend = amount + fee
- if spend > cash:
- amount = max(cash / (1.0 + fee_rate), 0.0)
- fee = amount * fee_rate
- spend = amount + fee
- cash -= spend
- total_fees += fee
- if price > 0:
- qty += amount / price
-
- elif action == "sell" and qty > 0:
- if leg_id != sell_leg:
- sell_leg = leg_id
- sell_base_qty = qty
- sell_qty = _resolve_sell_qty(t, qty, price, sell_base_qty, weight)
- if sell_qty <= 0:
- continue
- gross = sell_qty * price
- fee = gross * fee_rate
- cash += gross - fee
- total_fees += fee
- qty -= sell_qty
- if qty < 1e-12:
- qty = 0.0
-
- if last_price is None:
- mark_price = None
- holding_value = 0.0
- else:
- mark_price = float(last_price)
- holding_value = qty * mark_price
- final_asset = cash + holding_value
- pnl_krw = final_asset - initial_cash
- pnl_pct = pnl_krw / initial_cash * 100.0 if initial_cash else 0.0
-
- return {
- "initial_cash_krw": round(initial_cash, 0),
- "final_asset_krw": round(final_asset, 0),
- "pnl_krw": round(pnl_krw, 0),
- "pnl_pct": round(pnl_pct, 2),
- "total_fees_krw": round(total_fees, 0),
- "cash_krw": round(cash, 0),
- "holding_qty": round(qty, 6),
- "holding_value_krw": round(holding_value, 0),
- "mark_price": round(mark_price, 2) if last_price is not None else None,
- "fee_rate": fee_rate,
- }
-
-
-def save_ground_truth(data: dict[str, Any], path: Path = DEFAULT_OUTPUT) -> Path:
- """정답 JSON 저장."""
- path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
- return path
-
-
-def load_ground_truth(path: Path = DEFAULT_OUTPUT) -> dict[str, Any] | None:
- """정답 JSON 로드."""
- if not path.exists():
- return None
- return json.loads(path.read_text(encoding="utf-8"))
-
-
-def _report_month_gaps(trades: list[dict[str, Any]]) -> list[str]:
- """거래가 없는 연속 월(YYYY-MM) 목록."""
- if not trades:
- return []
- from collections import Counter
-
- months = sorted({t["dt"][:7] for t in trades})
- gaps: list[str] = []
- y1, m1 = map(int, months[0].split("-"))
- for label in months[1:]:
- y2, m2 = map(int, label.split("-"))
- gap = (y2 - y1) * 12 + (m2 - m1)
- if gap > 1:
- gaps.append(f"{months[months.index(label) - 1]} → {label} ({gap - 1}개월 공백)")
- y1, m1 = y2, m2
- return gaps
-
-
-def print_ground_truth_report(data: dict[str, Any]) -> None:
- """터미널 요약 출력."""
- s = data.get("summary", {})
- trades = data.get("trades") or []
- print(f"\n[정답 타점] {data.get('period_start')} ~ {data.get('period_end')}")
- print(
- f" 피벗 {s.get('pivot_candidates')} | 매수 {s.get('buy_count')} / 매도 {s.get('sell_count')} "
- f"| leg {s.get('round_trips')}"
- )
- print(f" 매도 수익 합(참고): {s.get('sum_sell_leg_return_pct')}%")
- if s.get("initial_cash_krw"):
- print(
- f" 포트폴리오: 초기 ₩{s['initial_cash_krw']:,.0f} → "
- f"총보유자산 ₩{s['final_asset_krw']:,.0f} | "
- f"초기 대비 {s['pnl_pct']:+.2f}% | "
- f"수수료 ₩{s['total_fees_krw']:,.0f}"
- )
- if s.get("holding_qty", 0) > 0:
- print(
- f" 미청산: {s['holding_qty']}개 "
- f"(평가 ₩{s['holding_value_krw']:,.0f}, 종가 ₩{s.get('mark_price', 0):,.0f})"
- )
- elif s.get("execution_order"):
- print(f" 체결 순서: {s['execution_order']} (leg별 매수→매도)")
- print(f" 파라미터: {data.get('params')}")
- from collections import Counter
-
- by_month = Counter(t["dt"][:7] for t in trades)
- print(f" 월별 타점: {', '.join(f'{m}({c})' for m, c in sorted(by_month.items()))}")
- gaps = _report_month_gaps(trades)
- if gaps:
- print(f" 경고 — 거래 공백 월: {'; '.join(gaps)}")
- else:
- print(" 월별 공백: 없음 (연속 커버)")
- show = trades if len(trades) <= 40 else trades[:20] + trades[-10:]
- if len(trades) > 40:
- print(f" (타점 {len(trades)}건 — 앞 20·뒤 10건만 표시)")
- for t in show:
- mark = "매수" if t["action"] == "buy" else "매도"
- w = float(t.get("weight", 1.0))
- ret = t.get("forward_return_pct")
- ret_s = f" (+{ret}%)" if ret is not None else ""
- print(
- f" [{mark}] {t['dt'][:16]} ₩{t['price']:,.0f} "
- f"비중{w*100:.0f}%{ret_s} {t.get('memo', '')}"
- )
-
-
-def run_from_db(monitor=None, output: Path = DEFAULT_OUTPUT) -> dict[str, Any]:
- """
- coins.db에서 CHART_LOOKBACK_DAYS 구간을 읽어 정답을 생성·저장합니다.
-
- Args:
- monitor: Monitor 인스턴스. None이면 새로 생성.
- output: 저장 경로.
-
- Returns:
- 생성된 dict.
- """
- from config import TREND_INTERVAL_1D, TREND_INTERVAL_1H
- from deepcoin.ops.monitor import Monitor
-
- mon = monitor or Monitor(cooldown_file=None)
- print(f"정답 생성: 최근 {CHART_LOOKBACK_DAYS}일 3분봉")
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- df_3m = frames.get(ENTRY_INTERVAL)
- if df_3m is None or df_3m.empty:
- raise RuntimeError("3분봉 없음. python scripts/01_download.py 실행 후 재시도.")
-
- df_1d = frames.get(TREND_INTERVAL_1D)
- if df_1d is None or df_1d.empty:
- df_1d = df_3m
- df_1h = frames.get(TREND_INTERVAL_1H)
- if df_1h is None or df_1h.empty:
- df_1h = df_3m
-
- data = generate_ground_truth(df_3m, df_1d, df_1h)
- save_ground_truth(data, output)
- print(f"저장: {output}")
- print_ground_truth_report(data)
- return data
-
-
-def main() -> None:
- """CLI: 정답 JSON 생성."""
- run_from_db()
-
-
-if __name__ == "__main__":
- main()
diff --git a/deepcoin/ground_truth/gt_allocation.py b/deepcoin/ground_truth/gt_allocation.py
deleted file mode 100644
index 78f2d7a..0000000
--- a/deepcoin/ground_truth/gt_allocation.py
+++ /dev/null
@@ -1,407 +0,0 @@
-"""
-GT 공통 자본 배분·포트폴리오 시뮬 엔진.
-
-ground_truth.allocate_gt_order_amounts · simulate_truth_portfolio ·
-matching/portfolio_sim 이 동일 규칙을 공유합니다.
-"""
-
-from __future__ import annotations
-
-from typing import Any, Callable
-
-from config import (
- GT_INITIAL_CASH_KRW,
- GT_MIN_ORDER_KRW,
- TRADING_FEE_RATE,
-)
-from deepcoin.ground_truth.gt_model import remaining_weight_sum
-
-
-def resolve_sell_qty(
- t: dict[str, Any],
- qty: float,
- price: float,
- sell_base_qty: float,
- weight: float,
-) -> float:
- """
- 매도 수량: amount_krw 우선, 없으면 sell_base_qty × weight.
-
- Args:
- t: trade dict.
- qty: 현재 보유 수량.
- price: 체결가.
- sell_base_qty: leg 첫 매도 시점 보유량.
- weight: 매도 비중.
-
- Returns:
- 매도 수량.
- """
- if qty <= 0 or price <= 0:
- return 0.0
- ak = t.get("amount_krw")
- if ak is not None and float(ak) > 0:
- gross_cap = float(ak)
- if gross_cap >= qty * price * 0.999:
- return qty
- return min(qty, gross_cap / price)
- return min(sell_base_qty * weight, qty)
-
-
-def allocate_order_amounts_chronological(
- trades: list[dict[str, Any]],
- *,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- min_order_krw: float = GT_MIN_ORDER_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- large_legs: set[int] | None = None,
- asset_pct_scale_fn: Callable[[dict[str, Any]], float] | None = None,
- causal_tier: bool = False,
-) -> tuple[list[dict[str, Any]], dict[str, Any]]:
- """
- 시각순·leg 비중·티어 스케일로 amount_krw를 배분합니다.
-
- causal_tier=True: 청산 완료 leg의 realized return 만으로 tier 산정 (인과적).
-
- Args:
- trades: trade dict (weight·leg_id·action·price).
- initial_cash: 초기 현금.
- min_order_krw: 최소 체결 원화.
- fee_rate: 수수료율.
- large_legs: 대형 leg. None이면 GT trades에서 산출(비인과).
- asset_pct_scale_fn: 매수 trade별 tier scale.
- causal_tier: 과거 청산 leg 수익률만으로 tier.
-
- Returns:
- (amount_krw 채워진 trades, alloc_stats).
- """
- from config import GT_BUY_PCT_LARGE_LEG, GT_LARGE_LEG_TOP_PCT
-
- from deepcoin.matching.position_sizing import (
- compute_buy_amount_krw,
- large_leg_ids_from_past_returns,
- leg_asset_pct_scale,
- top_leg_ids_by_forward_return,
- )
-
- chron = sorted(trades, key=lambda x: x["dt"])
- if large_legs is None and not causal_tier:
- large_legs = top_leg_ids_by_forward_return(chron)
- elif large_legs is None:
- large_legs = set()
-
- leg_buy_idxs: dict[int, list[int]] = {}
- leg_sell_idxs: dict[int, list[int]] = {}
- for i, t in enumerate(chron):
- lid = int(t.get("leg_id", 0))
- if t["action"] == "buy":
- leg_buy_idxs.setdefault(lid, []).append(i)
- elif t["action"] == "sell":
- leg_sell_idxs.setdefault(lid, []).append(i)
-
- cash = float(initial_cash)
- qty = 0.0
- qty_by_leg: dict[int, float] = {}
- sell_leg: int | None = None
- sell_base_qty = 0.0
- buy_executed = 0
- buy_skipped = 0
- sell_executed = 0
- sell_skipped = 0
- buy_amounts: list[float] = []
- large_tier_buys = 0
- completed_leg_ret: dict[int, float] = {}
- leg_cost_krw: dict[int, float] = {}
- leg_proceeds_krw: dict[int, float] = {}
-
- for i, t in enumerate(chron):
- price = float(t["price"])
- if price <= 0:
- continue
- leg_id = int(t.get("leg_id", 0))
- weight = float(t.get("weight", 1.0))
-
- if t["action"] == "buy":
- w_sum = remaining_weight_sum(chron, leg_id, i)
- if causal_tier:
- large_now = large_leg_ids_from_past_returns(
- completed_leg_ret, GT_LARGE_LEG_TOP_PCT
- )
- scale = leg_asset_pct_scale(leg_id, large_now)
- elif asset_pct_scale_fn is not None:
- scale = asset_pct_scale_fn(t, completed_leg_ret)
- else:
- scale = leg_asset_pct_scale(leg_id, large_legs)
- amount = compute_buy_amount_krw(
- cash,
- qty,
- price,
- weight,
- w_sum,
- asset_pct_scale=scale,
- min_order_krw=min_order_krw,
- fee_rate=fee_rate,
- ignore_weight_split=bool(t.get("conviction_buy")),
- )
- if amount <= 0:
- t["amount_krw"] = 0
- buy_skipped += 1
- continue
- t["amount_krw"] = amount
- fee = amount * fee_rate
- cash -= amount + fee
- bought_qty = amount / price
- qty += bought_qty
- qty_by_leg[leg_id] = qty_by_leg.get(leg_id, 0.0) + bought_qty
- leg_cost_krw[leg_id] = leg_cost_krw.get(leg_id, 0.0) + amount + fee
- buy_executed += 1
- buy_amounts.append(amount)
- if scale >= float(GT_BUY_PCT_LARGE_LEG) * 0.99:
- large_tier_buys += 1
- sell_leg = None
-
- elif t["action"] == "sell":
- leg_qty = qty_by_leg.get(leg_id, 0.0)
- if leg_qty <= 1e-12:
- sell_skipped += 1
- continue
- if sell_leg != leg_id:
- sell_leg = leg_id
- sell_base_qty = leg_qty
- rem_sells = [j for j in leg_sell_idxs.get(leg_id, []) if j >= i]
- is_last_leg_sell = bool(rem_sells) and i == rem_sells[-1]
- if is_last_leg_sell:
- sell_qty = leg_qty
- gross = sell_qty * price
- else:
- gross = sell_base_qty * weight * price
- if gross >= min_order_krw:
- gross = max(min_order_krw, gross)
- gross = min(gross, leg_qty * price)
- if gross <= 0:
- sell_skipped += 1
- continue
- sell_qty = leg_qty if is_last_leg_sell else gross / price
- t["amount_krw"] = round(gross, 0)
- fee = gross * fee_rate
- cash += gross - fee
- leg_proceeds_krw[leg_id] = leg_proceeds_krw.get(leg_id, 0.0) + (gross - fee)
- leg_qty -= sell_qty
- qty_by_leg[leg_id] = max(leg_qty, 0.0)
- qty = max(qty - sell_qty, 0.0)
- if qty < 1e-12:
- qty = 0.0
- sell_executed += 1
- if (causal_tier or asset_pct_scale_fn is not None) and leg_qty <= 1e-12:
- cost = leg_cost_krw.pop(leg_id, 0.0)
- proceeds = leg_proceeds_krw.pop(leg_id, 0.0)
- if cost > 0:
- completed_leg_ret[leg_id] = (proceeds - cost) / cost * 100.0
-
- stats: dict[str, Any] = {
- "buy_executed": buy_executed,
- "buy_skipped": buy_skipped,
- "sell_executed": sell_executed,
- "sell_skipped": sell_skipped,
- "buy_total_krw": round(sum(buy_amounts), 0),
- "large_leg_count": large_tier_buys,
- "large_tier_buy_count": large_tier_buys,
- }
- if buy_amounts:
- stats["buy_amount_avg_krw"] = round(sum(buy_amounts) / len(buy_amounts), 0)
- stats["buy_amount_min_krw"] = round(min(buy_amounts), 0)
- stats["buy_amount_max_krw"] = round(max(buy_amounts), 0)
- return trades, stats
-
-
-def simulate_portfolio_steps(
- trades: list[dict[str, Any]],
- *,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- use_amount_krw: bool = True,
-) -> list[dict[str, Any]]:
- """
- 체결마다 현금·보유·총평가 스냅샷.
-
- Args:
- trades: 시각순 trade dict (amount_krw·weight·leg_id).
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- use_amount_krw: True면 amount_krw 기준 체결.
-
- Returns:
- step dict 리스트.
- """
- rows = sorted(trades, key=lambda x: x["dt"])
- cash = float(initial_cash)
- qty = 0.0
- qty_by_leg: dict[int, float] = {}
- sell_leg: int | None = None
- sell_base_qty = 0.0
- leg_budget = 0.0
- current_leg: int | None = None
- steps: list[dict[str, Any]] = []
-
- for t in rows:
- action = t.get("action", t.get("side", ""))
- price = float(t["price"])
- if price <= 0:
- continue
- weight = float(t.get("weight", 1.0))
- leg_id = int(t.get("leg_id", 0))
-
- if action == "buy":
- if use_amount_krw and t.get("amount_krw") is not None and float(t["amount_krw"]) > 0:
- amount = min(float(t["amount_krw"]), max(cash / (1.0 + fee_rate), 0.0))
- else:
- if leg_id != current_leg:
- current_leg = leg_id
- leg_budget = cash
- amount = min(leg_budget * weight, max(cash / (1.0 + fee_rate), 0.0))
- if amount <= 0:
- continue
- fee = amount * fee_rate
- cash -= amount + fee
- bought = amount / price
- qty += bought
- qty_by_leg[leg_id] = qty_by_leg.get(leg_id, 0.0) + bought
- sell_leg = None
-
- elif action == "sell" and qty > 0:
- leg_qty = qty_by_leg.get(leg_id, qty)
- if sell_leg != leg_id:
- sell_leg = leg_id
- sell_base_qty = leg_qty
- sell_qty = resolve_sell_qty(t, leg_qty, price, sell_base_qty, weight)
- if sell_qty <= 0:
- continue
- gross = sell_qty * price
- fee = gross * fee_rate
- cash += gross - fee
- leg_qty -= sell_qty
- qty_by_leg[leg_id] = max(leg_qty, 0.0)
- qty -= sell_qty
- if qty < 1e-12:
- qty = 0.0
-
- steps.append(
- {
- "dt": t["dt"],
- "action": action,
- "price": price,
- "weight": weight,
- "leg_id": leg_id,
- "amount_krw": t.get("amount_krw"),
- "cash_krw": round(cash, 0),
- "holding_qty": round(qty, 6),
- "total_asset_krw": round(cash + qty * price, 0),
- }
- )
- return steps
-
-
-def compute_drawdown_metrics(steps: list[dict[str, Any]]) -> dict[str, Any]:
- """
- equity curve 기준 최대 낙폭·고점 대비 하락.
-
- Args:
- steps: simulate_portfolio_steps 결과.
-
- Returns:
- max_drawdown_pct, peak_asset_krw, trough_after_peak_krw.
- """
- if not steps:
- return {
- "max_drawdown_pct": 0.0,
- "peak_asset_krw": 0.0,
- "trough_asset_krw": 0.0,
- }
- assets = [float(s["total_asset_krw"]) for s in steps]
- peak = assets[0]
- max_dd = 0.0
- peak_at = assets[0]
- trough_at = assets[0]
- for a in assets:
- if a > peak:
- peak = a
- dd = (peak - a) / peak * 100.0 if peak > 0 else 0.0
- if dd > max_dd:
- max_dd = dd
- peak_at = peak
- trough_at = a
- return {
- "max_drawdown_pct": round(max_dd, 2),
- "peak_asset_krw": round(peak_at, 0),
- "trough_asset_krw": round(trough_at, 0),
- }
-
-
-def simulate_portfolio_summary(
- trades: list[dict[str, Any]],
- *,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- last_price: float | None = None,
- use_amount_krw: bool = True,
-) -> dict[str, Any]:
- """
- 포트폴리오 시뮬 요약 + MDD.
-
- Args:
- trades: trade dict 리스트.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- last_price: 미청산 평가가.
- use_amount_krw: amount_krw 체결 사용.
-
- Returns:
- pnl·fee·MDD 포함 dict.
- """
- steps = simulate_portfolio_steps(
- trades,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- use_amount_krw=use_amount_krw,
- )
- if not steps:
- return {
- "initial_cash_krw": round(initial_cash, 0),
- "final_asset_krw": round(initial_cash, 0),
- "pnl_krw": 0.0,
- "pnl_pct": 0.0,
- "trade_count": len(trades),
- "max_drawdown_pct": 0.0,
- }
-
- last_step = steps[-1]
- cash = float(last_step["cash_krw"])
- qty = float(last_step["holding_qty"])
- mark = float(last_price if last_price is not None else last_step["price"])
- holding_value = qty * mark
- final_asset = cash + holding_value
- pnl = final_asset - initial_cash
- pnl_pct = pnl / initial_cash * 100.0 if initial_cash else 0.0
-
- fees = 0.0
- for t in sorted(trades, key=lambda x: x["dt"]):
- ak = float(t.get("amount_krw") or 0)
- if ak <= 0:
- continue
- fees += ak * fee_rate
-
- dd = compute_drawdown_metrics(steps)
- return {
- "initial_cash_krw": round(initial_cash, 0),
- "final_asset_krw": round(final_asset, 0),
- "pnl_krw": round(pnl, 0),
- "pnl_pct": round(pnl_pct, 2),
- "total_fees_krw": round(fees, 0),
- "cash_krw": round(cash, 0),
- "holding_qty": round(qty, 6),
- "holding_value_krw": round(holding_value, 0),
- "mark_price": round(mark, 2),
- "fee_rate": fee_rate,
- "trade_count": len(trades),
- **dd,
- }
diff --git a/deepcoin/ground_truth/gt_allocation_analysis.py b/deepcoin/ground_truth/gt_allocation_analysis.py
deleted file mode 100644
index 836a527..0000000
--- a/deepcoin/ground_truth/gt_allocation_analysis.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""
-GT 체결 amount_krw·총자산 비율 분석 — 시뮬 tier·배분율 최적 추정.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from config import (
- GT_BUY_PCT_LARGE_LEG,
- GT_BUY_PCT_SMALL_LEG,
- GT_INITIAL_CASH_KRW,
- GT_LARGE_LEG_TOP_PCT,
- TRADING_FEE_RATE,
-)
-from deepcoin.matching.position_sizing import (
- leg_asset_pct_scale,
- optimal_weight_share,
- portfolio_totals,
- top_leg_ids_by_forward_return,
-)
-
-
-def analyze_gt_buy_allocation(
- trades: list[dict[str, Any]],
- *,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
-) -> dict[str, Any]:
- """
- GT 시각순 체결에서 매수별 (실제투입/총자산) 비율을 분석합니다.
-
- Args:
- trades: amount_krw·weight·leg_id가 채워진 GT trade dict.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
-
- Returns:
- leg tier별·전체 배분 통계 및 권장 pct_large/pct_small.
- """
- chron = sorted(trades, key=lambda x: x["dt"])
- if not chron:
- return {"note": "체결 없음"}
-
- large_legs = top_leg_ids_by_forward_return(chron, GT_LARGE_LEG_TOP_PCT)
- cash = float(initial_cash)
- qty = 0.0
-
- ratios_large: list[float] = []
- ratios_small: list[float] = []
- ratios_all: list[float] = []
-
- for i, t in enumerate(chron):
- price = float(t["price"])
- if price <= 0:
- continue
- leg_id = int(t.get("leg_id", 0))
- action = t.get("action", "")
-
- if action == "buy":
- w = float(t.get("weight", 1.0))
- rem = sum(
- float(chron[j].get("weight", 1.0))
- for j in range(i, len(chron))
- if int(chron[j].get("leg_id", 0)) == leg_id
- and chron[j].get("action") == "buy"
- )
- opt = optimal_weight_share(w, rem) if rem > 0 else 1.0
- total_asset, _, _ = portfolio_totals(cash, qty, price)
- amount = float(t.get("amount_krw") or 0)
- if total_asset > 0 and amount > 0 and opt > 0:
- implied = amount / (total_asset * opt)
- ratios_all.append(implied)
- if leg_id in large_legs:
- ratios_large.append(implied)
- else:
- ratios_small.append(implied)
- if amount > 0:
- fee = amount * fee_rate
- cash -= amount + fee
- qty += amount / price
- elif action == "sell" and qty > 0:
- gross = float(t.get("amount_krw") or qty * price)
- cash += gross * (1.0 - fee_rate)
- qty = 0.0
-
- def _stats(vals: list[float]) -> dict[str, float]:
- if not vals:
- return {}
- s = sorted(vals)
- n = len(s)
- return {
- "count": n,
- "mean": round(sum(s) / n, 4),
- "median": round(s[n // 2], 4),
- "p25": round(s[max(0, n // 4)], 4),
- "p75": round(s[min(n - 1, 3 * n // 4)], 4),
- }
-
- st_all = _stats(ratios_all)
- st_large = _stats(ratios_large)
- st_small = _stats(ratios_small)
-
- rec_large = st_large.get("median", GT_BUY_PCT_LARGE_LEG)
- rec_small = st_small.get("median", GT_BUY_PCT_SMALL_LEG)
- if not rec_large or rec_large <= 0:
- rec_large = GT_BUY_PCT_LARGE_LEG
- if not rec_small or rec_small <= 0:
- rec_small = GT_BUY_PCT_SMALL_LEG
-
- return {
- "large_leg_ids": sorted(large_legs),
- "large_leg_count": len(large_legs),
- "config_pct_large": GT_BUY_PCT_LARGE_LEG,
- "config_pct_small": GT_BUY_PCT_SMALL_LEG,
- "observed_implied_scale": {
- "all": st_all,
- "large_leg": st_large,
- "small_leg": st_small,
- },
- "recommended_pct_large_leg": round(rec_large, 4),
- "recommended_pct_small_leg": round(rec_small, 4),
- "note": (
- "implied_scale = amount / (pre_buy_total_asset × weight_share); "
- "시뮬 tier는 GT 분석 median 사용"
- ),
- }
-
-
-def gt_tier_scale_from_analysis(
- leg_id: int,
- large_legs: set[int],
- analysis: dict[str, Any] | None = None,
-) -> float:
- """
- GT 분석 권장값 또는 config tier scale.
-
- Args:
- leg_id: leg 번호.
- large_legs: 상위 leg.
- analysis: analyze_gt_buy_allocation 결과.
-
- Returns:
- 총자산 대비 매수 스케일 (0~1).
- """
- if analysis and analysis.get("observed_implied_scale", {}).get("all"):
- if leg_id in large_legs:
- return float(analysis.get("recommended_pct_large_leg", GT_BUY_PCT_LARGE_LEG))
- return float(analysis.get("recommended_pct_small_leg", GT_BUY_PCT_SMALL_LEG))
- return leg_asset_pct_scale(leg_id, large_legs)
diff --git a/deepcoin/ground_truth/gt_model.py b/deepcoin/ground_truth/gt_model.py
deleted file mode 100644
index 8451a6f..0000000
--- a/deepcoin/ground_truth/gt_model.py
+++ /dev/null
@@ -1,417 +0,0 @@
-"""
-Ground Truth 타점·비중·자본 배분 모델 (일반화 명세).
-
-타점 생성(ground_truth.py), 자본 배분(gt_allocation.py),
-시뮬(position_sizing.py)의 공통 언어.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, Callable
-
-from config import (
- GT_BUY_BB_MAX,
- GT_BUY_MIN_BARS,
- GT_BUY_MIN_SWING_PCT,
- GT_BUY_PCT_LARGE_LEG,
- GT_BUY_PCT_SMALL_LEG,
- GT_BUY_WEIGHT_RULE,
- GT_LARGE_LEG_TOP_PCT,
- GT_MAX_BUYS_PER_LEG,
- GT_MAX_SELLS_PER_LEG,
- GT_MIN_ORDER_KRW,
- GT_SELL_SPLIT_GAP_PCT,
- GT_SELL_SPLIT_WEIGHTS,
- GT_SELECTION_MODE,
-)
-
-# --- 매수 비중 규칙 (확장 가능) ---
-
-EntryWeightFn = Callable[[list[float]], list[float]]
-
-
-def normalize_weights(scores: list[float]) -> list[float]:
- """
- 비중 점수를 합 1로 정규화합니다.
-
- Args:
- scores: raw score 리스트.
-
- Returns:
- 정규화 weight (합 ≈ 1).
- """
- if not scores:
- return []
- total = sum(scores)
- if total <= 0:
- n = len(scores)
- return [1.0 / n] * n
- return [s / total for s in scores]
-
-
-def compute_buy_weights_inverse_price(prices: list[float]) -> list[float]:
- """
- 저점 매수 비중: score_i = 1/price_i → 합=1 정규화.
-
- Args:
- prices: leg 내 매수 후보 가격.
-
- Returns:
- weight 리스트 (합 ≈ 1).
- """
- if not prices:
- return []
- scores = [1.0 / max(p, 1e-9) for p in prices]
- return normalize_weights(scores)
-
-
-def compute_buy_weights_equal(_prices: list[float]) -> list[float]:
- """
- 균등 분할 매수 비중.
-
- Args:
- _prices: leg 내 매수 후보 가격(미사용).
-
- Returns:
- weight 리스트 (합 = 1).
- """
- n = len(_prices)
- if n <= 0:
- return []
- return [1.0 / n] * n
-
-
-ENTRY_WEIGHT_RULES: dict[str, EntryWeightFn] = {
- "inverse_price_normalized": compute_buy_weights_inverse_price,
- "equal": compute_buy_weights_equal,
-}
-
-
-def compute_entry_weights(
- prices: list[float],
- rule: str | None = None,
-) -> list[float]:
- """
- 매수 타점 비중을 규칙명으로 계산합니다.
-
- Args:
- prices: 체결 가격 리스트.
- rule: `inverse_price_normalized` | `equal`. None이면 config.
-
- Returns:
- leg 내 매수 weight (합 ≈ 1).
- """
- key = (rule or GT_BUY_WEIGHT_RULE).strip()
- fn = ENTRY_WEIGHT_RULES.get(key, compute_buy_weights_inverse_price)
- return fn(prices)
-
-
-def leg_entry_weights(
- prices: list[float],
- rule: str | None = None,
-) -> list[float]:
- """
- leg 내 매수 타점 비중 (compute_entry_weights 별칭).
-
- Args:
- prices: 체결 가격 리스트.
- rule: 비중 규칙 키.
-
- Returns:
- weight 리스트 (합 ≈ 1).
- """
- return compute_entry_weights(prices, rule)
-
-
-def leg_exit_weights(
- n_sells: int,
- exit_spec: GtExitSpec | None = None,
-) -> list[float]:
- """
- leg 매도 분할 비중.
-
- Args:
- n_sells: 매도 횟수.
- exit_spec: 매도 명세.
-
- Returns:
- weight 리스트 (합 ≈ 1).
- """
- return sell_split_weights(n_sells, exit_spec)
-
-
-@dataclass(frozen=True)
-class GtEntrySpec:
- """
- 매수 타점(leg 내 분할) 규칙.
-
- Attributes:
- pivot_kind: ZigZag 저점(trough).
- price_field: 체결가 = 봉 Low.
- weight_rule: 매수 비중 규칙 키.
- max_per_leg: leg당 최대 매수 횟수.
- min_bars_gap: 분할 매수 최소 봉 간격.
- """
-
- pivot_kind: str = "trough"
- price_field: str = "Low"
- weight_rule: str = GT_BUY_WEIGHT_RULE
- max_per_leg: int = GT_MAX_BUYS_PER_LEG
- min_bars_gap: int = GT_BUY_MIN_BARS
- bb_filter: str = f"bb_pos <= {GT_BUY_BB_MAX}"
-
-
-@dataclass(frozen=True)
-class GtExitSpec:
- """
- 매도 타점(leg 내 분할) 규칙.
-
- Attributes:
- pivot_kind: major swing 고점(peak).
- price_field: 체결가 = 봉 High.
- split_weights: N회 분할 시 각 매도 비중 (합=1).
- split_gap_pct: 2차 고점 인정 최소 괴리(%).
- """
-
- pivot_kind: str = "peak"
- price_field: str = "High"
- split_weights: tuple[float, ...] = GT_SELL_SPLIT_WEIGHTS
- split_gap_pct: float = GT_SELL_SPLIT_GAP_PCT
- max_per_leg: int = GT_MAX_SELLS_PER_LEG
-
-
-@dataclass(frozen=True)
-class GtCapitalSpec:
- """
- 체결 원화(amount_krw) 배분 규칙.
-
- Attributes:
- buy_formula: 총자산 × 최적매수율, 상한=가용현금.
- optimal_buy_rate: leg 내 남은 weight 비중.
- large_leg_top_pct: 수익률 상위 leg 비율.
- pct_large: 상위 leg 총자산 배분 스케일.
- pct_small: 그 외 leg 스케일.
- min_order_krw: 최소 체결 원화.
- """
-
- buy_formula: str = (
- "target = total_asset * (weight/remaining_weights) * tier_scale; "
- "amount = min(target, available_cash/(1+fee))"
- )
- optimal_buy_rate: str = "weight / sum(remaining_buy_weights_in_leg)"
- large_leg_top_pct: float = GT_LARGE_LEG_TOP_PCT
- pct_large: float = GT_BUY_PCT_LARGE_LEG
- pct_small: float = GT_BUY_PCT_SMALL_LEG
- min_order_krw: float = float(GT_MIN_ORDER_KRW)
- sell_formula: str = "sell_base_qty * sell_weight * price (last sell = full leg_qty)"
-
-
-@dataclass
-class GroundTruthModel:
- """
- GT 전체 모델 (타점 + 비중 + 자본).
-
- Attributes:
- selection_mode: 타점 생성 모드.
- entry: 매수 명세.
- exit: 매도 명세.
- capital: 자본 배분 명세.
- leg: leg 정의.
- """
-
- selection_mode: str = GT_SELECTION_MODE
- entry: GtEntrySpec = field(default_factory=GtEntrySpec)
- exit: GtExitSpec = field(default_factory=GtExitSpec)
- capital: GtCapitalSpec = field(default_factory=GtCapitalSpec)
- leg_definition: str = (
- "이전 고점 매도 ~ 다음 고점 매도 구간 = leg_id; "
- "기간말 잔여 구간은 마지막 leg"
- )
- execution_order_chrono: str = (
- "amount_krw 배분·summary.pnl_pct = 시각순 체결(매도 후 현금 → 다음 매수 반영)"
- )
- execution_order_leg_block: str = (
- "JSON 저장 순서 = leg별 매수 전량 → 매도 전량 (차트·테이블 leg 정합)"
- )
-
-
-def default_model() -> GroundTruthModel:
- """현재 config 기준 GT 모델."""
- return GroundTruthModel()
-
-
-def sell_split_weights(
- n_sells: int,
- exit_spec: GtExitSpec | None = None,
-) -> list[float]:
- """
- leg 매도 비중 (1회=100%, N회=split_weights 정규화).
-
- Args:
- n_sells: 매도 횟수(1 이상).
- exit_spec: None이면 default.
-
- Returns:
- weight 리스트 (합 ≈ 1).
- """
- spec = exit_spec or GtExitSpec()
- if n_sells <= 1:
- return [1.0]
- weights = list(spec.split_weights[:n_sells])
- if len(weights) < n_sells:
- weights.extend([weights[-1]] * (n_sells - len(weights)))
- return normalize_weights(weights)
-
-
-def pair_peak_sell_weights(
- n_peaks: int,
- exit_spec: GtExitSpec | None = None,
-) -> list[tuple[float, float]]:
- """
- 고점 피벗 (피벗, weight) 쌍 — 1회 또는 분할.
-
- Args:
- n_peaks: 인정된 고점 수 (1 또는 2+).
- exit_spec: 매도 명세.
-
- Returns:
- (weight,) 또는 (w1, w2) 리스트. 호출측에서 피벗과 zip.
- """
- if n_peaks <= 1:
- return [(1.0,)]
- w = sell_split_weights(2, exit_spec)
- return [(w[0],), (w[1],)]
-
-
-def remaining_weight_sum(
- trades: list[dict[str, Any]],
- leg_id: int,
- from_index: int,
-) -> float:
- """
- leg 내 from_index 이후 남은 매수 weight 합.
-
- Args:
- trades: 시각순 trade dict.
- leg_id: leg 번호.
- from_index: chron 리스트 인덱스.
-
- Returns:
- 남은 weight 합.
- """
- total = 0.0
- for j, t in enumerate(trades):
- if j < from_index:
- continue
- if int(t.get("leg_id", 0)) != leg_id:
- continue
- if t.get("action") == "buy":
- total += float(t.get("weight", 1.0))
- return total
-
-
-def model_to_dict(model: GroundTruthModel | None = None) -> dict[str, Any]:
- """
- JSON·리포트용 모델 dict.
-
- Args:
- model: None이면 default.
-
- Returns:
- 직렬화 dict.
- """
- m = model or default_model()
- w_rule = m.entry.weight_rule
- w_formula = (
- "w_i = (1/price_i) / sum(1/price_j)"
- if w_rule == "inverse_price_normalized"
- else "w_i = 1 / n"
- )
- return {
- "selection_mode": m.selection_mode,
- "leg_definition": m.leg_definition,
- "entry": {
- "pivot": m.entry.pivot_kind,
- "price": m.entry.price_field,
- "weight_rule": m.entry.weight_rule,
- "weight_formula": w_formula,
- "max_buys_per_leg": m.entry.max_per_leg,
- "min_bars_between_buys": m.entry.min_bars_gap,
- "bb_filter": m.entry.bb_filter,
- },
- "exit": {
- "pivot": m.exit.pivot_kind,
- "price": m.exit.price_field,
- "weight_rule": "fixed_split_or_full",
- "weights_split": list(m.exit.split_weights),
- "split_gap_pct": m.exit.split_gap_pct,
- "max_sells_per_leg": m.exit.max_per_leg,
- },
- "capital": {
- "buy": m.capital.buy_formula,
- "optimal_buy_rate": m.capital.optimal_buy_rate,
- "large_leg_top_pct": m.capital.large_leg_top_pct,
- "pct_large_leg": m.capital.pct_large,
- "pct_small_leg": m.capital.pct_small,
- "min_order_krw": m.capital.min_order_krw,
- "sell": m.capital.sell_formula,
- },
- "execution": {
- "chrono": m.execution_order_chrono,
- "leg_block_json": m.execution_order_leg_block,
- },
- }
-
-
-def summarize_leg_weights(trades: list[dict[str, Any]]) -> dict[str, Any]:
- """
- leg별 매수·매도 비중 합 검증용 요약.
-
- Args:
- trades: GT trade dict.
-
- Returns:
- leg_id → {buy_sum, sell_sum, n_buy, n_sell, valid}.
- """
- legs: dict[int, dict[str, Any]] = {}
- for t in trades:
- lid = int(t.get("leg_id", 0))
- legs.setdefault(
- lid,
- {"buy_sum": 0.0, "sell_sum": 0.0, "n_buy": 0, "n_sell": 0},
- )
- w = float(t.get("weight", 0))
- if t.get("action") == "buy":
- legs[lid]["buy_sum"] += w
- legs[lid]["n_buy"] += 1
- elif t.get("action") == "sell":
- legs[lid]["sell_sum"] += w
- legs[lid]["n_sell"] += 1
- for lid, info in legs.items():
- buy_ok = abs(info["buy_sum"] - 1.0) < 0.02 or info["n_buy"] == 0
- sell_ok = abs(info["sell_sum"] - 1.0) < 0.02 or info["n_sell"] == 0
- info["valid"] = buy_ok and sell_ok
- info["buy_sum"] = round(info["buy_sum"], 4)
- info["sell_sum"] = round(info["sell_sum"], 4)
- return legs
-
-
-def weight_policy_summary(model: GroundTruthModel | None = None) -> dict[str, Any]:
- """
- 시뮬·리포트용 비중 정책 요약.
-
- Args:
- model: GT 모델.
-
- Returns:
- entry/exit/capital 요약 dict.
- """
- m = model or default_model()
- return {
- "entry_weight_rule": m.entry.weight_rule,
- "exit_split_weights": list(m.exit.split_weights),
- "capital_large_pct": m.capital.pct_large,
- "capital_small_pct": m.capital.pct_small,
- "large_leg_top_pct": m.capital.large_leg_top_pct,
- }
diff --git a/deepcoin/ground_truth/gt_signal_causal.py b/deepcoin/ground_truth/gt_signal_causal.py
deleted file mode 100644
index 59c583d..0000000
--- a/deepcoin/ground_truth/gt_signal_causal.py
+++ /dev/null
@@ -1,182 +0,0 @@
-"""
-인과적(미래 미사용) GT 스타일 신호 — t봉 시점에 t 이하 데이터만 사용.
-
-ZigZag/국소극값: pivot bar i-order 는 bar i 에서 확정 (i-order..i 구간만 관측).
-"""
-
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-
-from config import (
- GT_BUY_BB_MAX,
- GT_BUY_MIN_SWING_PCT,
- GT_MIN_SWING_PCT,
- GT_PIVOT_ORDER,
-)
-
-
-def _confirmed_trough_mask(
- low: np.ndarray,
- order: int,
-) -> np.ndarray:
- """
- bar i 에서 i-order 봉이 저점임을 확정 (low[i-order:i+1] 만 사용).
-
- Args:
- low: Low 가격 배열.
- order: pivot 반경(봉).
-
- Returns:
- 길이 n, i 에 1이면 i 시점 매수 확인 신호.
- """
- n = len(low)
- out = np.zeros(n, dtype=np.int8)
- for i in range(2 * order, n):
- p = i - order
- seg = low[p - order : i + 1]
- if len(seg) == 0:
- continue
- if low[p] <= seg.min() + 1e-12:
- out[i] = 1
- return out
-
-
-def _confirmed_peak_mask(
- high: np.ndarray,
- order: int,
-) -> np.ndarray:
- """
- bar i 에서 i-order 봉이 고점임을 확정.
-
- Args:
- high: High 가격 배열.
- order: pivot 반경.
-
- Returns:
- i 시점 매도 확인 신호.
- """
- n = len(high)
- out = np.zeros(n, dtype=np.int8)
- for i in range(2 * order, n):
- p = i - order
- seg = high[p - order : i + 1]
- if len(seg) == 0:
- continue
- if high[p] >= seg.max() - 1e-12:
- out[i] = 1
- return out
-
-
-def _zigzag_filter_causal(
- confirm: np.ndarray,
- prices: np.ndarray,
- min_swing_pct: float,
- kind: str,
- pivot_order: int = GT_PIVOT_ORDER,
-) -> np.ndarray:
- """
- 확정 피벗에 ZigZag 최소 스윙% 필터 (인과적, 순차 갱신).
-
- Args:
- confirm: bar i 에 확정 플래그.
- prices: pivot 가격 (i-order 위치의 low/high).
- pivot_indices: confirm==1 인 bar index.
- min_swing_pct: 최소 스윙 %.
- kind: trough | peak.
-
- Returns:
- zigzag 통과 시점에 1.
- """
- n = len(confirm)
- out = np.zeros(n, dtype=np.int8)
- order = int(pivot_order)
- last_kind: str | None = None
- last_price = 0.0
- min_ratio = min_swing_pct / 100.0
-
- for i in range(n):
- if confirm[i] != 1:
- continue
- p = i - order
- if p < 0:
- continue
- price = float(prices[p])
- if last_kind is None:
- out[i] = 1
- last_kind = kind
- last_price = price
- continue
- if kind == last_kind:
- if kind == "trough" and price < last_price:
- out[i - 1] = 0
- out[i] = 1
- last_price = price
- elif kind == "peak" and price > last_price:
- out[i - 1] = 0
- out[i] = 1
- last_price = price
- continue
- move = abs(price - last_price) / max(last_price, 1e-9)
- if move >= min_ratio:
- out[i] = 1
- last_kind = kind
- last_price = price
- return out
-
-
-def enrich_scan_frame_gt_signals_causal(
- frame: pd.DataFrame,
- *,
- pivot_order: int = GT_PIVOT_ORDER,
- buy_swing_pct: float = GT_BUY_MIN_SWING_PCT,
- sell_swing_pct: float = GT_MIN_SWING_PCT,
- bb_max: float = GT_BUY_BB_MAX,
-) -> pd.DataFrame:
- """
- 인과적 GT 신호 컬럼 (gt_*). t 시점 신호는 데이터 index<=t 만 사용.
-
- Args:
- frame: m3 스캔 프레임.
- pivot_order: 확정 지연(봉).
- buy_swing_pct: 매수 ZigZag 스윙%.
- sell_swing_pct: 매도 ZigZag 스윙%.
- bb_max: BB 하단 필터.
-
- Returns:
- gt_* 컬럼 추가 DataFrame.
- """
- out = frame.copy()
- if "Low" not in out.columns or "High" not in out.columns:
- return out
-
- low = out["Low"].astype(float).values
- high = out["High"].astype(float).values
- n = len(low)
-
- trough_conf = _confirmed_trough_mask(low, pivot_order)
- peak_conf = _confirmed_peak_mask(high, pivot_order)
-
- trough_z = _zigzag_filter_causal(
- trough_conf, low, buy_swing_pct, "trough", pivot_order=pivot_order
- )
- peak_z = _zigzag_filter_causal(
- peak_conf, high, sell_swing_pct, "peak", pivot_order=pivot_order
- )
-
- out["gt_trough_local"] = trough_conf
- out["gt_peak_local"] = peak_conf
- out["gt_trough_zigzag"] = trough_z
- out["gt_peak_zigzag"] = peak_z
-
- bb_ok = pd.Series(True, index=out.index)
- if "bb_pos" in out.columns:
- bb = pd.to_numeric(out["bb_pos"], errors="coerce")
- bb_ok = bb <= bb_max
-
- out["gt_buy_signal"] = (pd.Series(trough_z, index=out.index) == 1) & bb_ok
- out["gt_buy_signal"] = out["gt_buy_signal"].astype(int)
- out["gt_sell_signal"] = pd.Series(peak_z, index=out.index).astype(int)
- out["gt_signal_causal"] = 1
- return out
diff --git a/deepcoin/ground_truth/gt_signal_rules.py b/deepcoin/ground_truth/gt_signal_rules.py
deleted file mode 100644
index 4962319..0000000
--- a/deepcoin/ground_truth/gt_signal_rules.py
+++ /dev/null
@@ -1,197 +0,0 @@
-"""
-GT 모델(entry/exit)을 규칙 스캔·발화 형식으로 일반화.
-
-ZigZag trough/peak + BB 필터 등 GT 타점 생성 로직과 동일 파라미터를
-rule_eval 스캔 프레임 컬럼(gt_*)으로 노출합니다.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import (
- GT_BUY_BB_MAX,
- GT_BUY_MIN_SWING_PCT,
- GT_MIN_SWING_PCT,
- GT_PIVOT_ORDER,
- MATCH_PRIMARY_INTERVAL,
-)
-from deepcoin.ground_truth.ground_truth import build_zigzag_pivots
-
-
-def _local_extrema_mask(
- series: pd.Series,
- order: int,
- kind: str,
-) -> pd.Series:
- """
- 국소 극값 boolean 마스크.
-
- Args:
- series: 가격 시리즈.
- order: 좌우 봉 수.
- kind: min | max.
-
- Returns:
- boolean Series (index=series.index).
- """
- arr = series.astype(float).values
- n = len(arr)
- out = np.zeros(n, dtype=bool)
- if n < 2 * order + 1:
- return pd.Series(out, index=series.index)
- for i in range(order, n - order):
- window = arr[i - order : i + order + 1]
- if kind == "min" and arr[i] <= window.min():
- out[i] = True
- elif kind == "max" and arr[i] >= window.max():
- out[i] = True
- return pd.Series(out, index=series.index)
-
-
-def enrich_scan_frame_gt_signals(
- frame: pd.DataFrame,
- *,
- pivot_order: int = GT_PIVOT_ORDER,
- buy_swing_pct: float = GT_BUY_MIN_SWING_PCT,
- sell_swing_pct: float = GT_MIN_SWING_PCT,
- bb_max: float = GT_BUY_BB_MAX,
- causal: bool | None = None,
-) -> pd.DataFrame:
- """
- 스캔 프레임에 GT 모델 신호 컬럼을 추가합니다.
-
- GT_SIGNAL_CAUSAL=1 이면 t 시점까지 데이터만 사용 (운영 정합).
-
- Args:
- frame: m3 스캔 프레임 (Low, High, bb_pos).
- pivot_order: 피벗 반경.
- buy_swing_pct: 매수 ZigZag 스윙%.
- sell_swing_pct: 매도 ZigZag 스윙%.
- bb_max: BB 하단 필터.
- causal: None이면 config GT_SIGNAL_CAUSAL.
-
- Returns:
- gt_* 컬럼이 추가된 DataFrame.
- """
- from config import GT_SIGNAL_CAUSAL
-
- use_causal = GT_SIGNAL_CAUSAL if causal is None else causal
- if use_causal:
- from deepcoin.ground_truth.gt_signal_causal import (
- enrich_scan_frame_gt_signals_causal,
- )
-
- return enrich_scan_frame_gt_signals_causal(
- frame,
- pivot_order=pivot_order,
- buy_swing_pct=buy_swing_pct,
- sell_swing_pct=sell_swing_pct,
- bb_max=bb_max,
- )
-
- out = frame.copy()
- if "Low" not in out.columns or "High" not in out.columns:
- return out
-
- low = out["Low"].astype(float)
- high = out["High"].astype(float)
- out["gt_trough_local"] = _local_extrema_mask(low, pivot_order, "min").astype(int)
- out["gt_peak_local"] = _local_extrema_mask(high, pivot_order, "max").astype(int)
-
- df_ohlc = out[["Low", "High"]].copy()
- if "close" in out.columns:
- df_ohlc["close"] = out["close"]
- df_ohlc.index = out.index
-
- buy_pivots = build_zigzag_pivots(
- df_ohlc,
- min_swing_pct=buy_swing_pct,
- pivot_order=pivot_order,
- )
- sell_pivots = build_zigzag_pivots(
- df_ohlc,
- min_swing_pct=sell_swing_pct,
- pivot_order=pivot_order,
- )
-
- trough_z = pd.Series(0, index=out.index, dtype=int)
- for p in buy_pivots:
- if p.kind == "trough" and p.ts in trough_z.index:
- trough_z.loc[p.ts] = 1
- peak_z = pd.Series(0, index=out.index, dtype=int)
- for p in sell_pivots:
- if p.kind == "peak" and p.ts in peak_z.index:
- peak_z.loc[p.ts] = 1
-
- out["gt_trough_zigzag"] = trough_z
- out["gt_peak_zigzag"] = peak_z
-
- bb_ok = pd.Series(True, index=out.index)
- if "bb_pos" in out.columns:
- bb = pd.to_numeric(out["bb_pos"], errors="coerce")
- bb_ok = bb <= bb_max
-
- out["gt_buy_signal"] = ((out["gt_trough_zigzag"] == 1) & bb_ok).astype(int)
- out["gt_sell_signal"] = (out["gt_peak_zigzag"] == 1).astype(int)
- return out
-
-
-def build_gt_model_rules() -> list[dict[str, Any]]:
- """
- GT entry/exit 명세와 동일한 스캔 규칙 후보.
-
- Returns:
- rule dict 리스트 (buy 2종 + sell 2종).
- """
- return [
- {
- "rule_id": "gt_model_buy_zigzag_bb",
- "side": "buy",
- "kind": "gt_model",
- "logic": "and",
- "conditions": [
- {"col": "gt_buy_signal", "op": "eq_int", "value": 1},
- ],
- "gt_spec": "trough_zigzag + bb_pos <= GT_BUY_BB_MAX",
- },
- {
- "rule_id": "gt_model_buy_trough_local",
- "side": "buy",
- "kind": "gt_model",
- "logic": "and",
- "conditions": [
- {"col": "gt_trough_local", "op": "eq_int", "value": 1},
- {"col": "bb_pos", "op": "lte", "value": GT_BUY_BB_MAX},
- ],
- "gt_spec": "local trough + bb filter",
- },
- {
- "rule_id": "gt_model_sell_zigzag_peak",
- "side": "sell",
- "kind": "gt_model",
- "logic": "and",
- "conditions": [
- {"col": "gt_sell_signal", "op": "eq_int", "value": 1},
- ],
- "gt_spec": "major swing peak (ZigZag)",
- },
- {
- "rule_id": "gt_model_sell_peak_local",
- "side": "sell",
- "kind": "gt_model",
- "logic": "and",
- "conditions": [
- {"col": "gt_peak_local", "op": "eq_int", "value": 1},
- ],
- "gt_spec": "local high extremum",
- },
- ]
-
-
-def gt_signal_rule_ids() -> set[str]:
- """GT 일반화 규칙 ID 집합."""
- return {r["rule_id"] for r in build_gt_model_rules()}
diff --git a/deepcoin/ground_truth/hybrid_dd_calibrate.py b/deepcoin/ground_truth/hybrid_dd_calibrate.py
deleted file mode 100644
index e4fd06a..0000000
--- a/deepcoin/ground_truth/hybrid_dd_calibrate.py
+++ /dev/null
@@ -1,200 +0,0 @@
-"""
-Hybrid DD tier 임계값 train 그리드 → holdout 검증 (Option C 2차).
-"""
-
-from __future__ import annotations
-
-import json
-from itertools import product
-from pathlib import Path
-from typing import Any
-
-import pandas as pd
-
-from config import GT_INITIAL_CASH_KRW, MATCH_HOLDOUT_RATIO, TRADING_FEE_RATE
-from deepcoin.ground_truth.causal_gt_hybrid import build_monitor_hybrid_sized_trades
-from deepcoin.ground_truth.gt_allocation import simulate_portfolio_steps
-from deepcoin.matching.option_c_phase2 import walk_forward_portfolio_by_month
-from deepcoin.matching.portfolio_sim import sort_fires_chronological
-from deepcoin.matching.simulation import portfolio_holdout_from_steps
-from deepcoin.paths import MATCHING_HYBRID_DD_CALIBRATION_JSON
-
-
-def default_dd_grid() -> dict[str, list[float]]:
- """DD large/medium 탐색 그리드."""
- return {
- "dd_large_pct": [5.0, 6.0, 8.0, 10.0, 12.0],
- "dd_medium_pct": [2.0, 3.0, 4.0, 6.0],
- }
-
-
-def load_hybrid_dd_params(path: Path | None = None) -> dict[str, float]:
- """
- 캘리브레이션 JSON 또는 config 기본값.
-
- Args:
- path: JSON 경로.
-
- Returns:
- {dd_large_pct, dd_medium_pct}.
- """
- from config import CAUSAL_GT_DD_LARGE_PCT, CAUSAL_GT_DD_MEDIUM_PCT
-
- p = path or MATCHING_HYBRID_DD_CALIBRATION_JSON
- if p.is_file():
- data = json.loads(p.read_text(encoding="utf-8"))
- best = data.get("best_params") or {}
- if best.get("dd_large_pct") is not None:
- return {
- "dd_large_pct": float(best["dd_large_pct"]),
- "dd_medium_pct": float(
- best.get("dd_medium_pct", CAUSAL_GT_DD_MEDIUM_PCT)
- ),
- }
- return {
- "dd_large_pct": float(CAUSAL_GT_DD_LARGE_PCT),
- "dd_medium_pct": float(CAUSAL_GT_DD_MEDIUM_PCT),
- }
-
-
-def calibrate_hybrid_dd_thresholds(
- fires: pd.DataFrame,
- ohlc_df: pd.DataFrame,
- *,
- holdout_start: pd.Timestamp,
- grid: dict[str, list[float]] | None = None,
- last_price: float | None = None,
-) -> dict[str, Any]:
- """
- train 구간 PnL 최대 → holdout PnL로 검증, 최적 DD 임계 저장.
-
- Args:
- fires: monitor 전체 발화.
- ohlc_df: 3m OHLC.
- holdout_start: holdout 시작 시각.
- grid: dd_large/medium 후보.
- last_price: 미청산 평가가.
-
- Returns:
- best_params, train/holdout metrics, grid top-N.
- """
- from deepcoin.ground_truth.gt_allocation import simulate_portfolio_summary
-
- grid = grid or default_dd_grid()
- chron = sort_fires_chronological(fires)
- results: list[dict[str, Any]] = []
-
- for dd_large, dd_medium in product(
- grid["dd_large_pct"],
- grid["dd_medium_pct"],
- ):
- if dd_medium >= dd_large:
- continue
- sized, stats = build_monitor_hybrid_sized_trades(
- chron,
- ohlc_df,
- enhanced=False,
- dd_large_pct=dd_large,
- dd_medium_pct=dd_medium,
- )
- steps = simulate_portfolio_steps(sized, use_amount_krw=True)
- train = portfolio_holdout_from_steps(
- [s for s in steps if pd.to_datetime(s["dt"]) < holdout_start],
- holdout_start,
- initial_if_empty=GT_INITIAL_CASH_KRW,
- note="train",
- )
- # train-only: start 1M → last asset before holdout
- if steps:
- pre = [
- float(s["total_asset_krw"])
- for s in steps
- if pd.to_datetime(s["dt"]) < holdout_start
- ]
- train_asset_end = pre[-1] if pre else GT_INITIAL_CASH_KRW
- train_pnl = (train_asset_end - GT_INITIAL_CASH_KRW) / GT_INITIAL_CASH_KRW * 100
- else:
- train_pnl = 0.0
-
- holdout = portfolio_holdout_from_steps(
- steps,
- holdout_start,
- note="holdout",
- )
- full = simulate_portfolio_summary(
- sized,
- last_price=last_price,
- use_amount_krw=True,
- )
- wf = walk_forward_portfolio_by_month(steps)
- pos_months = sum(1 for w in wf if float(w.get("pnl_pct") or 0) > 0)
- results.append(
- {
- "dd_large_pct": dd_large,
- "dd_medium_pct": dd_medium,
- "train_pnl_pct": round(train_pnl, 2),
- "holdout_pnl_pct": float(holdout.get("pnl_pct", 0)),
- "full_pnl_pct": float(full.get("pnl_pct", 0)),
- "max_drawdown_pct": float(full.get("max_drawdown_pct", 0)),
- "wf_positive_months": pos_months,
- "wf_months": len(wf),
- "large_tier_buys": stats.get("large_tier_buy_count", 0),
- }
- )
-
- if not results:
- return {"best_params": load_hybrid_dd_params(), "note": "empty grid"}
-
- # train PnL 1순위, holdout PnL 2순위
- ranked = sorted(
- results,
- key=lambda x: (x["train_pnl_pct"], x["holdout_pnl_pct"]),
- reverse=True,
- )
- best = ranked[0]
- return {
- "best_params": {
- "dd_large_pct": best["dd_large_pct"],
- "dd_medium_pct": best["dd_medium_pct"],
- },
- "best_metrics": best,
- "grid_size": len(results),
- "top5": ranked[:5],
- "holdout_start": str(holdout_start),
- }
-
-
-def run_and_save_calibration(
- fires: pd.DataFrame,
- ohlc_df: pd.DataFrame,
- *,
- outcomes: pd.DataFrame,
- last_price: float | None = None,
- out_path: Path | None = None,
-) -> dict[str, Any]:
- """
- 캘리브레이션 실행 후 JSON 저장.
-
- Args:
- fires: monitor 발화.
- ohlc_df: OHLC.
- outcomes: fire_outcomes (holdout split).
- last_price: 평가 종가.
- out_path: 저장 경로.
-
- Returns:
- calibrate_hybrid_dd_thresholds 결과.
- """
- outcomes_ts = outcomes.copy()
- outcomes_ts["ts"] = pd.to_datetime(outcomes_ts["dt"])
- holdout_start = outcomes_ts["ts"].quantile(1.0 - MATCH_HOLDOUT_RATIO)
- result = calibrate_hybrid_dd_thresholds(
- fires,
- ohlc_df,
- holdout_start=holdout_start,
- last_price=last_price,
- )
- p = out_path or MATCHING_HYBRID_DD_CALIBRATION_JSON
- p.parent.mkdir(parents=True, exist_ok=True)
- p.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
- return result
diff --git a/deepcoin/matching/README.md b/deepcoin/matching/README.md
deleted file mode 100644
index 452628f..0000000
--- a/deepcoin/matching/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Matching — Simulation 축
-
-03b GT 스냅샷에서 규칙 후보 → 전 구간 인과 스캔 → EV·holdout → `matched_rules.json`.
-설계: [docs/reference/ARCHITECTURE.md](../../docs/reference/ARCHITECTURE.md)
-
-## PDCA
-
-| 단계 | 스크립트 모듈 | 산출물 |
-|------|---------------|--------|
-| 4-1 Plan/Do | `profile_rules.py` | `rule_candidates.json` (기본: 복합·대조만) |
-| 4-2 Do | `rule_eval.scan` | `rule_fires.csv` |
-| 4-3 Check | `label_outcomes.py` | `fire_outcomes.csv` (기본: `leg_gt` 청산) |
-| 4-4 Act | `select_rules.py` | `matched_rules.json`, `backtest_summary.html` |
-
-## 실행
-
-```bash
-# 전체 (enrich+스캔 약 2~3분)
-python scripts/04_match_rules.py
-
-# 단계별
-python scripts/04_match_rules.py --phase profile
-python scripts/04_match_rules.py --phase scan
-python scripts/04_match_rules.py --phase label
-python scripts/04_match_rules.py --phase select
-```
-
-선행: `python scripts/03_analyze_trades.py`
-
-## 설정 (`.env` / `config.py`)
-
-- `MATCH_LABEL_MODE` — `leg_gt`(다음 GT 매도/직전 매수) 또는 `forward`
-- `MATCH_MAX_HOLD_DAYS` — leg_gt 최대 보유 일수 (기본 45)
-- `MATCH_INCLUDE_ATOMIC` — 0이면 atomic 규칙 제외
-- `MATCH_FORWARD_BARS` — leg_gt 불가 시 폴백 봉 수 (기본 60)
-- `MATCH_TRAIN_RATIO` — train/valid 분할 (기본 0.7)
-- `MATCH_MIN_FIRES`, `MATCH_MIN_EV_VALID`, `MATCH_MIN_PROFIT_FACTOR`
diff --git a/deepcoin/matching/__init__.py b/deepcoin/matching/__init__.py
deleted file mode 100644
index 861c800..0000000
--- a/deepcoin/matching/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""
-04단계: GT 프로필 + 전구간 EV 필터 매칭.
-"""
-
-from deepcoin.matching.load_rules import load_monitor_rules
-from deepcoin.matching.pipeline import run_matching_pipeline
-
-__all__ = ["run_matching_pipeline", "load_monitor_rules"]
diff --git a/deepcoin/matching/config.py b/deepcoin/matching/config.py
deleted file mode 100644
index 0b9ba6f..0000000
--- a/deepcoin/matching/config.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""
-04단계 매칭 설정·메타 컬럼·프로필 피처 목록.
-"""
-
-from __future__ import annotations
-
-from deepcoin.analysis.general_analysis_core import ga_col, interval_tf_prefix
-from deepcoin.paths import (
- ANALYSIS_TRADES_CSV,
- MATCHING_BACKTEST_HTML,
- MATCHING_FIRE_OUTCOMES,
- MATCHING_GT_OVERLAP,
- MATCHING_MATCHED_RULES,
- MATCHING_RULE_CANDIDATES,
- MATCHING_RULE_FIRES,
-)
-
-META_COLS: tuple[str, ...] = (
- "trade_idx",
- "dt",
- "action",
- "price",
- "weight",
- "leg_id",
- "memo",
-)
-
-# 04-1 기본 폴백 (03c gt_mtf_profile.json 없을 때만 사용)
-BUY_PROFILE_FEATURES: tuple[str, ...] = (
- "m3_bb_pos",
- "m3_RSI",
- "m3_stoch_k",
- "m3_macd_hist",
- "m15_RSI",
- "m30_RSI",
- "m60_RSI",
- "ga_align_timing_buy_score",
- "ga_align_trend_score",
- "ga_align_rsi_oversold_tf",
- f"{interval_tf_prefix(60)}_{ga_col('struct_trend')}",
- f"{interval_tf_prefix(1440)}_RSI",
-)
-
-SELL_PROFILE_FEATURES: tuple[str, ...] = (
- "m3_bb_pos",
- "m3_RSI",
- "m3_stoch_k",
- "m3_macd_hist",
- "m15_RSI",
- "m30_RSI",
- "m60_RSI",
- "ga_align_timing_sell_score",
- "ga_align_trend_score",
- "ga_align_rsi_overbought_tf",
- f"{interval_tf_prefix(60)}_{ga_col('struct_trend')}",
- f"{interval_tf_prefix(1440)}_RSI",
-)
-
-__all__ = [
- "ANALYSIS_TRADES_CSV",
- "META_COLS",
- "BUY_PROFILE_FEATURES",
- "SELL_PROFILE_FEATURES",
- "MATCHING_RULE_CANDIDATES",
- "MATCHING_RULE_FIRES",
- "MATCHING_FIRE_OUTCOMES",
- "MATCHING_MATCHED_RULES",
- "MATCHING_BACKTEST_HTML",
- "MATCHING_GT_OVERLAP",
-]
diff --git a/deepcoin/matching/gt_mtf_profile.py b/deepcoin/matching/gt_mtf_profile.py
deleted file mode 100644
index 822213a..0000000
--- a/deepcoin/matching/gt_mtf_profile.py
+++ /dev/null
@@ -1,514 +0,0 @@
-"""
-GT 매수/매도 타점 MTF 프로필 분석 (3분~일봉 전 TF).
-
-03b wide CSV에서 간격별·기법별 분포를 비교하고,
-04 규칙 후보 생성용 피처 목록을 산출합니다.
-"""
-
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import (
- GENERAL_ANALYSIS_INTERVALS,
- MATCH_PROFILE_MIN_SAMPLES,
- MATCH_PROFILE_MIN_SEPARATION,
- MATCH_PROFILE_TOP_GLOBAL,
- MATCH_PROFILE_TOP_PER_TF,
-)
-from deepcoin.analysis.general_analysis_config import INTERVAL_PREFIX
-from deepcoin.analysis.general_analysis_core import interval_tf_prefix
-from deepcoin.matching.config import ANALYSIS_TRADES_CSV, META_COLS
-from deepcoin.paths import ANALYSIS_GT_MTF_PROFILE_HTML, ANALYSIS_GT_MTF_PROFILE_JSON
-
-
-def _feature_separation(
- buy: pd.Series,
- sell: pd.Series,
-) -> float:
- """
- 매수·매도 GT 분포 간 분리도(Cohen 유사).
-
- Args:
- buy: 매수 타점 값.
- sell: 매도 타점 값.
-
- Returns:
- 분리도(비숫자·표본 부족 시 0).
- """
- a = pd.to_numeric(buy, errors="coerce").dropna()
- b = pd.to_numeric(sell, errors="coerce").dropna()
- if len(a) < MATCH_PROFILE_MIN_SAMPLES or len(b) < MATCH_PROFILE_MIN_SAMPLES:
- return 0.0
- pooled = np.sqrt((a.var() + b.var()) / 2)
- if pooled < 1e-9:
- return abs(float(a.mean() - b.mean()))
- return abs(float(a.mean() - b.mean())) / pooled
-
-
-def _numeric_stats(series: pd.Series) -> dict[str, float | int]:
- """
- 숫자 컬럼 요약 통계.
-
- Args:
- series: 한 side GT 값.
-
- Returns:
- count, mean, median, q25, q75, std.
- """
- s = pd.to_numeric(series, errors="coerce").dropna()
- if s.empty:
- return {"count": 0}
- return {
- "count": int(len(s)),
- "mean": round(float(s.mean()), 4),
- "median": round(float(s.median()), 4),
- "q25": round(float(s.quantile(0.25)), 4),
- "q75": round(float(s.quantile(0.75)), 4),
- "std": round(float(s.std()), 4) if len(s) > 1 else 0.0,
- }
-
-
-def _categorical_stats(series: pd.Series) -> dict[str, Any]:
- """
- 범주형 컬럼 최빈값·비율.
-
- Args:
- series: GT 값.
-
- Returns:
- mode, mode_frac, value_counts 상위 5.
- """
- s = series.dropna().astype(str)
- if s.empty:
- return {"count": 0}
- vc = s.value_counts()
- mode = str(vc.index[0])
- return {
- "count": int(len(s)),
- "mode": mode,
- "mode_frac": round(float(vc.iloc[0] / len(s)), 3),
- "top": {str(k): int(v) for k, v in vc.head(5).items()},
- }
-
-
-def _parse_tf_column(col: str) -> tuple[str, int | None, str]:
- """
- 컬럼명에서 TF 접두사·간격·베이스명 추출.
-
- Args:
- col: 예 m3_ga_rsi, ga_align_timing_buy_score.
-
- Returns:
- (tf_label, interval_minutes|None, base_name).
- """
- if col.startswith("ga_align_"):
- return ("mtf_align", None, col)
- prefixes = sorted(
- set(INTERVAL_PREFIX.values()),
- key=len,
- reverse=True,
- )
- for p in prefixes:
- if col.startswith(f"{p}_"):
- inv = {v: k for k, v in INTERVAL_PREFIX.items()}
- return (p, inv.get(p), col[len(p) + 1 :])
- return ("other", None, col)
-
-
-def _feature_family(base: str) -> str:
- """기법군 라벨."""
- if base in ("bb_pos", "RSI", "macd_hist", "stoch_k", "stoch_d", "BB_Width"):
- return "legacy"
- if base.startswith("ga_align_"):
- return "mtf_align"
- if "pattern" in base:
- return "pattern"
- if "struct" in base or "elliott" in base or "wyckoff" in base or "fib_" in base:
- return "wave_structure"
- if "chart" in base:
- return "chart"
- if "volume" in base or "vp_" in base:
- return "volume"
- if "harmonic" in base:
- return "harmonic"
- if base.startswith("ga_"):
- return "indicator"
- return "other"
-
-
-def discover_profile_columns(df: pd.DataFrame) -> list[str]:
- """
- 규칙·프로필 분석 대상 컬럼 목록.
-
- Args:
- df: 03b wide CSV DataFrame.
-
- Returns:
- META 제외·분석 가능 컬럼명.
- """
- meta = set(META_COLS)
- out: list[str] = []
- for col in df.columns:
- if col in meta:
- continue
- if df[col].notna().sum() < MATCH_PROFILE_MIN_SAMPLES:
- continue
- if pd.api.types.is_numeric_dtype(df[col]):
- out.append(col)
- continue
- nuniq = df[col].dropna().astype(str).nunique()
- if 1 < nuniq <= 20:
- out.append(col)
- return out
-
-
-def _analyze_one_column(
- buy: pd.DataFrame,
- sell: pd.DataFrame,
- col: str,
-) -> dict[str, Any]:
- """
- 단일 컬럼 매수 vs 매도 GT 비교.
-
- Args:
- buy: 매수 행.
- sell: 매도 행.
- col: 컬럼명.
-
- Returns:
- 분리도·통계·방향 힌트.
- """
- tf_label, interval, base = _parse_tf_column(col)
- family = _feature_family(base)
- row: dict[str, Any] = {
- "col": col,
- "tf": tf_label,
- "interval": interval,
- "base": base,
- "family": family,
- "dtype": "numeric" if pd.api.types.is_numeric_dtype(buy[col]) else "categorical",
- }
- if row["dtype"] == "numeric":
- row["buy"] = _numeric_stats(buy[col])
- row["sell"] = _numeric_stats(sell[col])
- sep = _feature_separation(buy[col], sell[col])
- row["separation"] = round(sep, 4)
- bm = row["buy"].get("median")
- sm = row["sell"].get("median")
- if bm is not None and sm is not None:
- row["buy_lower_than_sell"] = bm < sm
- else:
- row["buy_lower_than_sell"] = None
- else:
- row["buy"] = _categorical_stats(buy[col])
- row["sell"] = _categorical_stats(sell[col])
- row["separation"] = 0.0
- if row["buy"].get("mode") and row["sell"].get("mode"):
- row["modes_differ"] = row["buy"]["mode"] != row["sell"]["mode"]
- return row
-
-
-def analyze_gt_mtf_profile(df: pd.DataFrame) -> dict[str, Any]:
- """
- 전 TF·전 컬럼 GT 매수/매도 프로필 분석.
-
- Args:
- df: general_analysis_trades.csv.
-
- Returns:
- JSON 직렬화 가능 분석 결과.
- """
- buy = df[df["action"] == "buy"].copy()
- sell = df[df["action"] == "sell"].copy()
- cols = discover_profile_columns(df)
- features: list[dict[str, Any]] = []
- for col in cols:
- features.append(_analyze_one_column(buy, sell, col))
-
- numeric_feats = [f for f in features if f["dtype"] == "numeric"]
- ranked = sorted(numeric_feats, key=lambda x: x["separation"], reverse=True)
-
- by_interval: dict[str, dict[str, Any]] = {}
- for iv in GENERAL_ANALYSIS_INTERVALS:
- pfx = interval_tf_prefix(iv)
- iv_feats = [f for f in numeric_feats if f["tf"] == pfx]
- iv_ranked = sorted(iv_feats, key=lambda x: x["separation"], reverse=True)
- buy_favor = [f for f in iv_ranked if f.get("buy_lower_than_sell") is True][:10]
- sell_favor = [f for f in iv_ranked if f.get("buy_lower_than_sell") is False][:10]
- by_interval[pfx] = {
- "interval_minutes": iv,
- "feature_count": len(iv_feats),
- "top_separation": [
- {"col": x["col"], "separation": x["separation"]}
- for x in iv_ranked[:15]
- ],
- "buy_favor_lower_median": [
- {"col": x["col"], "separation": x["separation"]}
- for x in buy_favor[:8]
- ],
- "sell_favor_higher_median": [
- {"col": x["col"], "separation": x["separation"]}
- for x in sell_favor[:8]
- ],
- }
-
- align_feats = [f for f in features if f["family"] == "mtf_align"]
-
- selected_buy = _select_side_features(ranked, "buy")
- selected_sell = _select_side_features(ranked, "sell")
-
- return {
- "source_rows": int(len(df)),
- "buy_gt_count": int(len(buy)),
- "sell_gt_count": int(len(sell)),
- "columns_analyzed": len(cols),
- "intervals": list(GENERAL_ANALYSIS_INTERVALS),
- "config": {
- "top_per_tf": MATCH_PROFILE_TOP_PER_TF,
- "top_global": MATCH_PROFILE_TOP_GLOBAL,
- "min_separation": MATCH_PROFILE_MIN_SEPARATION,
- "min_samples": MATCH_PROFILE_MIN_SAMPLES,
- },
- "global_top_separation": [
- {
- "col": x["col"],
- "tf": x["tf"],
- "family": x["family"],
- "separation": x["separation"],
- "buy_median": x["buy"].get("median"),
- "sell_median": x["sell"].get("median"),
- }
- for x in ranked[:40]
- ],
- "by_interval": by_interval,
- "mtf_align": align_feats,
- "selected_features": {
- "buy": selected_buy,
- "sell": selected_sell,
- },
- "features": features,
- }
-
-
-def _select_side_features(
- ranked: list[dict[str, Any]],
- side: str,
-) -> list[str]:
- """
- 04 규칙용 피처 목록: TF별 상위 + 글로벌 상위.
-
- Args:
- ranked: separation 내림차순 numeric feature dicts.
- side: buy | sell.
-
- Returns:
- 컬럼명 리스트(중복 제거, 순서 유지).
- """
- chosen: list[str] = []
- seen: set[str] = set()
-
- def add(col: str) -> None:
- if col not in seen:
- seen.add(col)
- chosen.append(col)
-
- for iv in GENERAL_ANALYSIS_INTERVALS:
- pfx = interval_tf_prefix(iv)
- iv_list = [
- f
- for f in ranked
- if f["tf"] == pfx and f["separation"] >= MATCH_PROFILE_MIN_SEPARATION
- ]
- if side == "buy":
- iv_list.sort(
- key=lambda x: (
- x["separation"],
- 1 if x.get("buy_lower_than_sell") else 0,
- ),
- reverse=True,
- )
- else:
- iv_list.sort(
- key=lambda x: (
- x["separation"],
- 1 if x.get("buy_lower_than_sell") is False else 0,
- ),
- reverse=True,
- )
- for f in iv_list[:MATCH_PROFILE_TOP_PER_TF]:
- add(f["col"])
-
- global_list = [f for f in ranked if f["separation"] >= MATCH_PROFILE_MIN_SEPARATION]
- if side == "buy":
- global_list.sort(
- key=lambda x: (
- x["separation"],
- 1 if x.get("buy_lower_than_sell") else 0,
- ),
- reverse=True,
- )
- else:
- global_list.sort(
- key=lambda x: (
- x["separation"],
- 1 if x.get("buy_lower_than_sell") is False else 0,
- ),
- reverse=True,
- )
- for f in global_list[:MATCH_PROFILE_TOP_GLOBAL]:
- add(f["col"])
-
- for name in (
- "ga_align_timing_buy_score",
- "ga_align_timing_sell_score",
- "ga_align_trend_score",
- "ga_align_rsi_oversold_tf",
- "ga_align_rsi_overbought_tf",
- "ga_align_mtf_conflict",
- ):
- add(name)
-
- return chosen
-
-
-def load_selected_features(
- profile_path: Path | None = None,
-) -> tuple[list[str], list[str]]:
- """
- 저장된 프로필 JSON에서 buy/sell 피처 목록 로드.
-
- Args:
- profile_path: gt_mtf_profile.json.
-
- Returns:
- (buy_features, sell_features). 없으면 빈 리스트.
- """
- path = profile_path or ANALYSIS_GT_MTF_PROFILE_JSON
- if not path.is_file():
- return [], []
- data = json.loads(path.read_text(encoding="utf-8"))
- sel = data.get("selected_features") or {}
- return list(sel.get("buy") or []), list(sel.get("sell") or [])
-
-
-def run_gt_mtf_profile(
- trades_csv: Path | None = None,
- *,
- write_json: bool = True,
- write_html: bool = True,
-) -> dict[str, Any]:
- """
- 03b CSV 분석 후 JSON/HTML 저장.
-
- Args:
- trades_csv: 입력 CSV.
- write_json: JSON 저장 여부.
- write_html: HTML 저장 여부.
-
- Returns:
- analyze_gt_mtf_profile 결과.
- """
- path = trades_csv or ANALYSIS_TRADES_CSV
- if not path.is_file():
- raise FileNotFoundError(f"03b CSV 없음: {path}")
- df = pd.read_csv(path)
- analysis = analyze_gt_mtf_profile(df)
- buy_n = len(analysis["selected_features"]["buy"])
- sell_n = len(analysis["selected_features"]["sell"])
- print(
- f"[03c] GT MTF 프로필: 분석 {analysis['columns_analyzed']}열 "
- f"→ 매수 피처 {buy_n}, 매도 피처 {sell_n}"
- )
- if write_json:
- ANALYSIS_GT_MTF_PROFILE_JSON.parent.mkdir(parents=True, exist_ok=True)
- ANALYSIS_GT_MTF_PROFILE_JSON.write_text(
- json.dumps(analysis, ensure_ascii=False, indent=2),
- encoding="utf-8",
- )
- print(f"[03c] 저장: {ANALYSIS_GT_MTF_PROFILE_JSON}")
- if write_html:
- write_gt_mtf_profile_html(analysis, ANALYSIS_GT_MTF_PROFILE_HTML)
- print(f"[03c] 저장: {ANALYSIS_GT_MTF_PROFILE_HTML}")
- return analysis
-
-
-def write_gt_mtf_profile_html(
- analysis: dict[str, Any],
- html_path: Path,
-) -> Path:
- """
- TF별·글로벌 분리도 요약 HTML.
-
- Args:
- analysis: analyze_gt_mtf_profile 결과.
- html_path: 출력 경로.
-
- Returns:
- html_path.
- """
- html_path.parent.mkdir(parents=True, exist_ok=True)
-
- def _rows_interval() -> str:
- rows = ""
- for pfx, block in analysis.get("by_interval", {}).items():
- top = block.get("top_separation") or []
- top_s = ", ".join(
- f"{t['col'].split('_', 1)[-1][:20]}({t['separation']:.2f})"
- for t in top[:5]
- ) or "-"
- rows += (
- f"| {pfx} | {block.get('feature_count', 0)} | "
- f"{top_s} |
"
- )
- return rows
-
- def _rows_global() -> str:
- rows = ""
- for item in analysis.get("global_top_separation") or []:
- rows += (
- f"| {item['col']} | {item['tf']} | "
- f"{item['family']} | {item['separation']:.3f} | "
- f"{item.get('buy_median','')} | {item.get('sell_median','')} |
"
- )
- return rows
-
- buy_feats = ", ".join(analysis["selected_features"]["buy"][:25])
- sell_feats = ", ".join(analysis["selected_features"]["sell"][:25])
-
- html = f"""
-
-GT MTF 프로필 (3분~일봉)
-
-Ground Truth MTF 타점 프로필
-매수 GT {analysis['buy_gt_count']}건 · 매도 GT {analysis['sell_gt_count']}건 ·
-분석 컬럼 {analysis['columns_analyzed']}개 (3,5,10,15,30,60,240,1440분 + MTF 합성)
-분리도 = |mean_buy − mean_sell| / pooled_std. TF별·글로벌 상위 피처로 04 규칙 후보를 생성합니다.
-
-간격별 분리도 상위 (요약)
-| TF | 숫자 피처 수 | 상위 5 (분리도) |
|---|
-{_rows_interval()}
-
-글로벌 분리도 Top 40
-| 컬럼 | TF | 기법군 | 분리도 | 매수 median | 매도 median |
|---|
-{_rows_global()}
-
-04 규칙 선별용 피처 (발췌)
-매수
{buy_feats}
-매도
{sell_feats}
-"""
- html_path.write_text(html, encoding="utf-8")
- return html_path
diff --git a/deepcoin/matching/gt_schedule.py b/deepcoin/matching/gt_schedule.py
deleted file mode 100644
index 27ccab3..0000000
--- a/deepcoin/matching/gt_schedule.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""
-Ground Truth 매수·매도 시각표 (04-3 leg 라벨링용).
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from deepcoin.ground_truth.ground_truth import load_ground_truth
-from deepcoin.paths import resolve_ground_truth_file
-
-
-def load_gt_trade_events(
- gt_path: Path | str | None = None,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
- """
- GT trades에서 매수·매도 이벤트 시각(ns)·가격 배열을 만듭니다.
-
- Args:
- gt_path: ground_truth JSON 경로. None이면 기본 경로.
-
- Returns:
- (buy_ts_ns, buy_px, sell_ts_ns, sell_px) 오름차순 정렬.
- """
- path = (
- resolve_ground_truth_file()
- if gt_path is None
- else Path(gt_path)
- )
- data = load_ground_truth(path) or {}
- trades: list[dict[str, Any]] = data.get("trades") or []
-
- buys: list[tuple[pd.Timestamp, float]] = []
- sells: list[tuple[pd.Timestamp, float]] = []
- for t in trades:
- ts = pd.Timestamp(t["dt"])
- px = float(t["price"])
- if t.get("action") == "buy":
- buys.append((ts, px))
- elif t.get("action") == "sell":
- sells.append((ts, px))
-
- buys.sort(key=lambda x: x[0])
- sells.sort(key=lambda x: x[0])
-
- if buys:
- buy_ts = np.array([x[0].value for x in buys], dtype=np.int64)
- buy_px = np.array([x[1] for x in buys], dtype=float)
- else:
- buy_ts = np.array([], dtype=np.int64)
- buy_px = np.array([], dtype=float)
-
- if sells:
- sell_ts = np.array([x[0].value for x in sells], dtype=np.int64)
- sell_px = np.array([x[1] for x in sells], dtype=float)
- else:
- sell_ts = np.array([], dtype=np.int64)
- sell_px = np.array([], dtype=float)
-
- return buy_ts, buy_px, sell_ts, sell_px
diff --git a/deepcoin/matching/label_outcomes.py b/deepcoin/matching/label_outcomes.py
deleted file mode 100644
index 597a520..0000000
--- a/deepcoin/matching/label_outcomes.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""
-04-3: 규칙 발화별 성과 라벨링 (GT leg 청산 + forward 폴백).
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import (
- CHART_LOOKBACK_DAYS,
- MATCH_FORWARD_BARS,
- MATCH_LABEL_MODE,
- MATCH_MAX_HOLD_DAYS,
- MATCH_PRIMARY_INTERVAL,
- SYMBOL,
- TRADING_FEE_RATE,
-)
-from deepcoin.data.mtf_bb import load_frames_from_db
-from deepcoin.matching.gt_schedule import load_gt_trade_events
-from deepcoin.ops.monitor import Monitor
-
-_NS_PER_DAY = 86_400 * 1_000_000_000
-
-
-def _forward_ret_vectorized(
- fire_ts_ns: np.ndarray,
- c0: np.ndarray,
- close_ts_ns: np.ndarray,
- close_px: np.ndarray,
- side: np.ndarray,
- n_bars: int,
- fee_pct: float,
-) -> tuple[np.ndarray, np.ndarray]:
- """
- 고정 N봉 forward 수익률(벡터화, 루프 최소).
-
- Args:
- fire_ts_ns: 발화 시각(ns).
- c0: 발화가.
- close_ts_ns, close_px: 주간격 종가 시계열.
- side: buy | sell.
- n_bars: forward 봉 수.
- fee_pct: 왕복 수수료 %p.
-
- Returns:
- (ret_pct, valid_mask).
- """
- ret = np.full(len(fire_ts_ns), np.nan, dtype=float)
- valid = np.zeros(len(fire_ts_ns), dtype=bool)
- for i in range(len(fire_ts_ns)):
- idx = np.searchsorted(close_ts_ns, fire_ts_ns[i], side="right") - 1
- if idx < 0:
- continue
- end = idx + n_bars
- if end >= len(close_px):
- continue
- c_entry = c0[i]
- c_exit = float(close_px[end])
- if side[i] == "buy":
- ret[i] = (c_exit / c_entry - 1.0) * 100.0 - fee_pct
- else:
- ret[i] = (c_entry / c_exit - 1.0) * 100.0 - fee_pct
- valid[i] = True
- return ret, valid
-
-
-def _leg_gt_ret_vectorized(
- fire_ts_ns: np.ndarray,
- c0: np.ndarray,
- side: np.ndarray,
- buy_ts: np.ndarray,
- buy_px: np.ndarray,
- sell_ts: np.ndarray,
- sell_px: np.ndarray,
- max_hold_days: int,
- fee_pct: float,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
- """
- GT 이벤트 기준 leg 청산 수익률.
-
- 매수 발화: 다음 GT 매도까지 보유.
- 매도 발화: 직전 GT 매수가 대비 청산 수익.
-
- Args:
- fire_ts_ns, c0, side: 발화 배열.
- buy_ts, buy_px, sell_ts, sell_px: GT 이벤트.
- max_hold_days: 최대 보유 일수.
- fee_pct: 왕복 수수료 %p.
-
- Returns:
- (ret_pct, valid_mask, hold_days).
- """
- n = len(fire_ts_ns)
- ret = np.full(n, np.nan, dtype=float)
- valid = np.zeros(n, dtype=bool)
- hold_days = np.full(n, np.nan, dtype=float)
- max_hold_ns = max_hold_days * _NS_PER_DAY
-
- buy_m = side == "buy"
- if buy_m.any() and len(sell_ts) > 0:
- t_b = fire_ts_ns[buy_m]
- idx = np.searchsorted(sell_ts, t_b, side="right")
- ok = idx < len(sell_ts)
- if ok.any():
- i_ok = np.where(buy_m)[0][ok]
- exit_ns = sell_ts[idx[ok]]
- delta = exit_ns - t_b[ok]
- within = (delta > 0) & (delta <= max_hold_ns)
- if within.any():
- ii = i_ok[within]
- exit_px = sell_px[idx[ok][within]]
- entry = c0[ii]
- ret[ii] = (exit_px / entry - 1.0) * 100.0 - fee_pct
- valid[ii] = True
- hold_days[ii] = delta[within] / _NS_PER_DAY
-
- sell_m = side == "sell"
- if sell_m.any() and len(buy_ts) > 0:
- t_s = fire_ts_ns[sell_m]
- idx = np.searchsorted(buy_ts, t_s, side="left") - 1
- ok = idx >= 0
- if ok.any():
- i_ok = np.where(sell_m)[0][ok]
- entry_ns = buy_ts[idx[ok]]
- delta = t_s[ok] - entry_ns
- within = (delta > 0) & (delta <= max_hold_ns)
- if within.any():
- ii = i_ok[within]
- entry_px = buy_px[idx[ok][within]]
- exit_p = c0[ii]
- ret[ii] = (exit_p / entry_px - 1.0) * 100.0 - fee_pct
- valid[ii] = True
- hold_days[ii] = delta[within] / _NS_PER_DAY
-
- return ret, valid, hold_days
-
-
-def label_fire_outcomes(
- fires: pd.DataFrame,
- frames: dict[int, pd.DataFrame] | None = None,
-) -> pd.DataFrame:
- """
- 각 발화의 성과를 라벨링합니다.
-
- MATCH_LABEL_MODE=leg_gt: GT 다음 매도/직전 매수 기준.
- 미충족 분은 forward N봉으로 폴백.
-
- Args:
- fires: rule_fires.
- frames: OHLCV (폴백용).
-
- Returns:
- fire_outcomes (+ forward_ret_pct, label_method, hold_days, ...).
- """
- if fires.empty:
- return fires.copy()
-
- fee_pct = TRADING_FEE_RATE * 2 * 100
- fts = pd.to_datetime(fires["dt"])
- fire_ts_ns = fts.values.astype("datetime64[ns]").astype(np.int64)
- c0 = fires["close"].astype(float).values
- side = fires["side"].astype(str).values
-
- buy_ts, buy_px, sell_ts, sell_px = load_gt_trade_events()
- label_method = np.full(len(fires), "", dtype=object)
- hold_days = np.full(len(fires), np.nan, dtype=float)
-
- ret = np.full(len(fires), np.nan, dtype=float)
-
- if MATCH_LABEL_MODE == "leg_gt":
- ret, valid, hd = _leg_gt_ret_vectorized(
- fire_ts_ns,
- c0,
- side,
- buy_ts,
- buy_px,
- sell_ts,
- sell_px,
- MATCH_MAX_HOLD_DAYS,
- fee_pct,
- )
- label_method[valid] = "leg_gt"
- hold_days = hd
-
- need_fb = ~np.isfinite(ret)
- if need_fb.any():
- if frames is None:
- mon = Monitor(cooldown_file=None)
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- if need_fb.any() and frames is not None:
- raw = frames.get(MATCH_PRIMARY_INTERVAL)
- if raw is not None and not raw.empty:
- px = raw.copy()
- if not isinstance(px.index, pd.DatetimeIndex):
- px.index = pd.to_datetime(px.index)
- px = px.sort_index()
- col = "close" if "close" in px.columns else "Close"
- close_px = px[col].astype(float).values
- close_ts_ns = px.index.astype(np.int64).values
- fb_ret, fb_ok = _forward_ret_vectorized(
- fire_ts_ns[need_fb],
- c0[need_fb],
- close_ts_ns,
- close_px,
- side[need_fb],
- MATCH_FORWARD_BARS,
- fee_pct,
- )
- ret[need_fb] = np.where(fb_ok, fb_ret, np.nan)
- fb_idx = np.where(need_fb)[0][fb_ok]
- label_method[fb_idx] = f"forward_{MATCH_FORWARD_BARS}"
-
- out = fires.copy()
- out["forward_ret_pct"] = np.round(ret, 4)
- out["win"] = (ret > 0).astype(int)
- out["label_method"] = label_method
- out["hold_days"] = np.round(hold_days, 2)
- out["forward_bars"] = MATCH_FORWARD_BARS
- out = out[np.isfinite(out["forward_ret_pct"])].reset_index(drop=True)
-
- leg_n = int((out["label_method"] == "leg_gt").sum())
- fb_n = int(out["label_method"].astype(str).str.startswith("forward").sum())
- print(
- f"[04-3] 성과 라벨: {len(out):,}건 "
- f"(mode={MATCH_LABEL_MODE}, leg_gt={leg_n:,}, forward폴백={fb_n:,}, "
- f"수수료 {fee_pct:.3f}%p)"
- )
- return out
diff --git a/deepcoin/matching/live_eval.py b/deepcoin/matching/live_eval.py
deleted file mode 100644
index 4571589..0000000
--- a/deepcoin/matching/live_eval.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
-05 연동: 최신 봉에서 matched 규칙 평가.
-"""
-
-from __future__ import annotations
-
-import time
-from typing import Any
-
-import pandas as pd
-
-from config import CHART_LOOKBACK_DAYS, MATCH_LIVE_CACHE_SEC, MATCH_LIVE_LOOKBACK_DAYS, SYMBOL
-from deepcoin.data.mtf_bb import load_frames_from_db
-from deepcoin.matching.load_rules import load_monitor_rules
-from deepcoin.matching.rule_eval import (
- build_mtf_scan_frame,
- conditions_columns,
- eval_conditions,
-)
-from deepcoin.ops.monitor import Monitor
-
-_cache: dict[str, Any] = {"ts": 0.0, "frame": None, "rules_key": ""}
-
-
-def evaluate_live_rules(
- rules: list[dict[str, Any]] | None = None,
- *,
- lookback_days: int | None = None,
- force_refresh: bool = False,
-) -> list[dict[str, Any]]:
- """
- 최신 완성 3분봉에서 활성 규칙 발화 여부를 검사합니다.
-
- Args:
- rules: 규칙 목록. None이면 matched_rules에서 로드.
- lookback_days: DB 조회 일수(기본 MATCH_LIVE_LOOKBACK_DAYS).
- force_refresh: 캐시 무시.
-
- Returns:
- 발화한 규칙 정보 리스트 (rule_id, side, dt, close).
- """
- active = rules if rules is not None else load_monitor_rules()
- if not active:
- return []
-
- lb = lookback_days if lookback_days is not None else MATCH_LIVE_LOOKBACK_DAYS
- rules_key = ",".join(r["rule_id"] for r in active)
- now = time.time()
- global _cache
- if (
- not force_refresh
- and _cache.get("frame") is not None
- and now - float(_cache.get("ts", 0)) < MATCH_LIVE_CACHE_SEC
- and _cache.get("rules_key") == rules_key
- ):
- frame = _cache["frame"]
- else:
- mon = Monitor(cooldown_file=None)
- days = min(lb, CHART_LOOKBACK_DAYS)
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=days)
- needed = conditions_columns(active)
- frame = build_mtf_scan_frame(frames, needed)
- _cache["ts"] = now
- _cache["frame"] = frame
- _cache["rules_key"] = rules_key
-
- if frame.empty:
- return []
-
- ts = frame.index[-1]
- row_frame = frame.iloc[[-1]]
- fired: list[dict[str, Any]] = []
- close_col = "close"
- for rule in active:
- if not eval_conditions(row_frame, rule["conditions"]).iloc[0]:
- continue
- fired.append(
- {
- "rule_id": rule["rule_id"],
- "side": rule["side"],
- "kind": rule.get("kind", ""),
- "dt": ts.strftime("%Y-%m-%d %H:%M:%S"),
- "close": float(row_frame[close_col].iloc[0]),
- }
- )
- return fired
diff --git a/deepcoin/matching/load_rules.py b/deepcoin/matching/load_rules.py
deleted file mode 100644
index 0beb54e..0000000
--- a/deepcoin/matching/load_rules.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""
-04 산출 matched_rules.json 로드.
-"""
-
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Any
-
-from deepcoin.paths import MATCHING_MATCHED_RULES
-
-
-def load_matched_rules(path: Path | None = None) -> dict[str, Any]:
- """
- matched_rules.json 전체를 로드합니다.
-
- Args:
- path: JSON 경로. None이면 기본 경로.
-
- Returns:
- matched_rules dict. 없으면 빈 dict.
- """
- p = path or MATCHING_MATCHED_RULES
- if not p.is_file():
- return {}
- return json.loads(p.read_text(encoding="utf-8"))
-
-
-def load_active_rules(path: Path | None = None) -> list[dict[str, Any]]:
- """
- 04 선별 규칙 전체(strict 우선).
-
- Args:
- path: matched_rules.json 경로.
-
- Returns:
- 규칙 dict 리스트.
- """
- data = load_matched_rules(path)
- selected = data.get("selected") or []
- if selected:
- return list(selected)
- return list(data.get("selected_best_effort") or [])
-
-
-def load_monitor_rules(path: Path | None = None) -> list[dict[str, Any]]:
- """
- 05 모니터·텔레그램에 쓸 규칙 (holdout 통과, 매수·매도 각 최대 1개).
-
- Args:
- path: matched_rules.json 경로.
-
- Returns:
- monitor_rules 또는 active_rules fallback.
- """
- data = load_matched_rules(path)
- monitor = data.get("monitor_rules") or []
- if monitor:
- return list(monitor)
- return load_active_rules(path)
diff --git a/deepcoin/matching/option_c_phase2.py b/deepcoin/matching/option_c_phase2.py
deleted file mode 100644
index 054da2f..0000000
--- a/deepcoin/matching/option_c_phase2.py
+++ /dev/null
@@ -1,245 +0,0 @@
-"""
-Option C 2차 목표(+1000%) 검증 — hybrid tier 포트폴리오 WF·슬리피지·Go/No-Go.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import pandas as pd
-
-from config import (
- GT_INITIAL_CASH_KRW,
- LIVE_SLIPPAGE_PCT,
- SIM_HYBRID_MAX_MDD_PCT,
- SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO,
- SIM_OPTION_C_MIN_GT_CAPTURE,
- SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO,
- SIM_OPTION_C_PHASE2_TARGET_PNL_PCT,
-)
-from deepcoin.ground_truth.gt_allocation import simulate_portfolio_summary
-
-
-def apply_slippage_to_trades(
- trades: list[dict[str, Any]],
- slippage_pct: float = LIVE_SLIPPAGE_PCT,
-) -> list[dict[str, Any]]:
- """
- 체결가에 슬리피지 반영 (매수 불리·매도 불리).
-
- Args:
- trades: amount_krw·price·action trade dict.
- slippage_pct: 체결가 대비 % (0.05 = 0.05%).
-
- Returns:
- price 조정된 trade dict 복사본.
- """
- slip = float(slippage_pct) / 100.0
- out: list[dict[str, Any]] = []
- for t in trades:
- row = dict(t)
- price = float(row.get("price") or 0)
- if price <= 0:
- out.append(row)
- continue
- action = row.get("action", "")
- if action == "buy":
- row["price"] = round(price * (1.0 + slip), 4)
- elif action == "sell":
- row["price"] = round(price * (1.0 - slip), 4)
- out.append(row)
- return out
-
-
-def walk_forward_portfolio_by_month(
- steps: list[dict[str, Any]],
- *,
- initial_cash: float = GT_INITIAL_CASH_KRW,
-) -> list[dict[str, Any]]:
- """
- 포트폴리오 step에서 월별 자산 증감률 (인과적).
-
- Args:
- steps: simulate_portfolio_steps 결과.
- initial_cash: 첫 달 시작 자산(이전 달 종료 없을 때).
-
- Returns:
- {month, pnl_pct, start_asset, end_asset} 리스트.
- """
- if not steps:
- return []
- df = pd.DataFrame(steps)
- df["ts"] = pd.to_datetime(df["dt"])
- df = df.sort_values("ts")
- df["month"] = df["ts"].dt.to_period("M").astype(str)
- rows: list[dict[str, Any]] = []
- prev_end = float(initial_cash)
- for month in sorted(df["month"].unique()):
- grp = df[df["month"] == month]
- end_asset = float(grp["total_asset_krw"].iloc[-1])
- pnl_pct = (end_asset - prev_end) / prev_end * 100.0 if prev_end > 0 else 0.0
- rows.append(
- {
- "month": month,
- "pnl_pct": round(pnl_pct, 2),
- "start_asset_krw": round(prev_end, 0),
- "end_asset_krw": round(end_asset, 0),
- }
- )
- prev_end = end_asset
- return rows
-
-
-def walk_forward_portfolio_summary(wf_rows: list[dict[str, Any]]) -> dict[str, Any]:
- """
- 월별 포트폴리오 WF 요약.
-
- Args:
- wf_rows: walk_forward_portfolio_by_month 결과.
-
- Returns:
- months, positive_months, positive_ratio, mean_pnl_pct.
- """
- if not wf_rows:
- return {"months": 0, "positive_ratio": 0.0, "mean_pnl_pct": 0.0}
- n = len(wf_rows)
- pos = sum(1 for r in wf_rows if float(r.get("pnl_pct") or 0) > 0)
- mean_pnl = sum(float(r.get("pnl_pct") or 0) for r in wf_rows) / n
- return {
- "months": n,
- "positive_months": pos,
- "positive_ratio": round(pos / n, 4),
- "mean_pnl_pct": round(mean_pnl, 2),
- }
-
-
-def simulate_hybrid_slippage_stress(
- sized_trades: list[dict[str, Any]],
- *,
- last_price: float | None,
- slippage_pct: float = LIVE_SLIPPAGE_PCT,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float,
-) -> dict[str, Any]:
- """
- hybrid sized trades + 슬리피지 포트폴리오 요약.
-
- Args:
- sized_trades: build_monitor_hybrid_sized_trades 출력.
- last_price: 미청산 평가가.
- slippage_pct: 체결 슬리피지 %.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
-
- Returns:
- simulate_portfolio_summary 결과.
- """
- slipped = apply_slippage_to_trades(sized_trades, slippage_pct)
- result = simulate_portfolio_summary(
- slipped,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- last_price=last_price,
- use_amount_krw=True,
- )
- result["slippage_pct"] = slippage_pct
- result["sizing_mode"] = "hybrid_slippage_stress"
- return result
-
-
-def evaluate_option_c_phase2_go(
- hybrid_go: dict[str, Any],
- hybrid_full: dict[str, Any],
- hybrid_holdout: dict[str, Any],
- hybrid_fee_stress: dict[str, Any],
- hybrid_slippage: dict[str, Any],
- portfolio_wf_summary: dict[str, Any],
- gt_pnl_pct: float,
-) -> dict[str, Any]:
- """
- Option C 2차(+1000%) Go/No-Go.
-
- Args:
- hybrid_go: 1차 hybrid tier Go 결과.
- hybrid_full: 전기간 hybrid 포트폴리오.
- hybrid_holdout: holdout 구간 증감.
- hybrid_fee_stress: 수수료 2x hybrid.
- hybrid_slippage: 슬리피지 반영 hybrid.
- portfolio_wf_summary: 월별 포트폴리오 WF.
- gt_pnl_pct: GT 전기간 PnL %.
-
- Returns:
- go, checks, targets.
- """
- full_pnl = float(hybrid_full.get("pnl_pct", 0))
- capture = full_pnl / gt_pnl_pct if abs(gt_pnl_pct) > 1e-6 else 0.0
- ho_pnl = float(hybrid_holdout.get("pnl_pct", -999))
- mdd = float(hybrid_full.get("max_drawdown_pct", 999))
- fee_pnl = float(hybrid_fee_stress.get("pnl_pct", -999))
- slip_pnl = float(hybrid_slippage.get("pnl_pct", -999))
- wf_ratio = float(portfolio_wf_summary.get("positive_ratio", 0))
-
- c_phase1 = bool(hybrid_go.get("go"))
- c_pnl = full_pnl >= SIM_OPTION_C_PHASE2_TARGET_PNL_PCT
- c_capture = capture >= SIM_OPTION_C_MIN_GT_CAPTURE
- c_holdout = ho_pnl > 0.0
- c_mdd = mdd <= SIM_HYBRID_MAX_MDD_PCT
- c_fee = fee_pnl >= SIM_OPTION_C_PHASE2_TARGET_PNL_PCT * SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO
- c_slip = slip_pnl > 0.0
- c_wf = wf_ratio >= SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO
-
- all_go = (
- c_phase1
- and c_pnl
- and c_capture
- and c_holdout
- and c_mdd
- and c_fee
- and c_slip
- and c_wf
- )
-
- return {
- "go": all_go,
- "gt_capture_ratio": round(capture, 4),
- "targets": {
- "phase2_pnl_pct": SIM_OPTION_C_PHASE2_TARGET_PNL_PCT,
- "min_gt_capture": SIM_OPTION_C_MIN_GT_CAPTURE,
- "portfolio_wf_min_ratio": SIM_HYBRID_PORTFOLIO_WF_MIN_RATIO,
- },
- "checks": [
- {"name": "phase1_hybrid_go", "pass": c_phase1},
- {
- "name": "full_pnl_1000pct",
- "pass": c_pnl,
- "value": full_pnl,
- },
- {
- "name": "gt_capture_23pct",
- "pass": c_capture,
- "value": round(capture, 4),
- },
- {"name": "holdout_pnl_positive", "pass": c_holdout, "value": ho_pnl},
- {"name": "max_mdd", "pass": c_mdd, "value": mdd},
- {
- "name": "fee_stress_ratio",
- "pass": c_fee,
- "value": fee_pnl,
- "threshold": round(
- SIM_OPTION_C_PHASE2_TARGET_PNL_PCT * SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO,
- 2,
- ),
- },
- {
- "name": "slippage_stress_positive",
- "pass": c_slip,
- "value": slip_pnl,
- "note": "체결가 슬리피지 반영 후에도 흑자",
- },
- {
- "name": "portfolio_wf_positive_ratio",
- "pass": c_wf,
- "value": wf_ratio,
- },
- ],
- }
diff --git a/deepcoin/matching/pipeline.py b/deepcoin/matching/pipeline.py
deleted file mode 100644
index 1ff3ad3..0000000
--- a/deepcoin/matching/pipeline.py
+++ /dev/null
@@ -1,145 +0,0 @@
-"""
-04단계 PDCA 파이프라인: 프로필 → 스캔 → 라벨 → 선별.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import sys
-import time
-from pathlib import Path
-
-import pandas as pd
-
-from config import CHART_LOOKBACK_DAYS, SYMBOL
-from deepcoin.data.mtf_bb import load_frames_from_db
-from deepcoin.matching.config import (
- MATCHING_BACKTEST_HTML,
- MATCHING_FIRE_OUTCOMES,
- MATCHING_GT_OVERLAP,
- MATCHING_MATCHED_RULES,
- MATCHING_RULE_CANDIDATES,
- MATCHING_RULE_FIRES,
-)
-from deepcoin.matching.label_outcomes import label_fire_outcomes
-from deepcoin.matching.profile_rules import build_rule_candidates, save_rule_candidates
-from deepcoin.matching.rule_eval import (
- build_mtf_scan_frame,
- conditions_columns,
- scan_rule_fires,
-)
-from deepcoin.matching.select_rules import (
- select_matched_rules,
- write_backtest_summary_html,
-)
-from deepcoin.ops.monitor import Monitor
-from deepcoin.paths import ensure_dirs
-
-
-def run_matching_pipeline(
- phase: str = "all",
- trades_csv: Path | None = None,
-) -> None:
- """
- 04a~04d 단계를 순서대로 실행합니다.
-
- Args:
- phase: all | profile | scan | label | select.
- trades_csv: 03b CSV 경로(선택).
- """
- ensure_dirs()
- t0 = time.time()
- from config import MATCH_INCLUDE_ATOMIC, MATCH_LABEL_MODE
-
- print(
- f"=== 04 매칭 파이프라인 {SYMBOL} "
- f"(label={MATCH_LABEL_MODE}, atomic={MATCH_INCLUDE_ATOMIC}) ==="
- )
- sys.stdout.flush()
-
- candidates_path = MATCHING_RULE_CANDIDATES
- fires_path = MATCHING_RULE_FIRES
- outcomes_path = MATCHING_FIRE_OUTCOMES
-
- candidates: dict | None = None
- fires: pd.DataFrame | None = None
- outcomes: pd.DataFrame | None = None
- frames = None
-
- if phase in ("all", "profile"):
- print("[04] Phase 4-1 Plan/Do: GT 프로필 → 규칙 후보")
- candidates = build_rule_candidates(trades_csv)
- save_rule_candidates(candidates, candidates_path)
- if phase == "profile":
- return
-
- if phase in ("all", "scan", "label"):
- if candidates is None:
- candidates = json.loads(candidates_path.read_text(encoding="utf-8"))
- rules = candidates.get("rules", [])
- print(f"[04] Phase 4-2 Do: 전구간 발화 스캔 ({len(rules)}규칙)")
- sys.stdout.flush()
- mon = Monitor(cooldown_file=None)
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- needed = conditions_columns(rules)
- scan_frame = build_mtf_scan_frame(frames, needed)
- fires = scan_rule_fires(scan_frame, rules)
- fires_path.parent.mkdir(parents=True, exist_ok=True)
- fires.to_csv(fires_path, index=False, encoding="utf-8-sig")
- print(f"[04-2] 저장: {fires_path} ({len(fires):,}행)")
- if phase == "scan":
- return
-
- if phase in ("all", "label", "select"):
- if fires is None:
- fires = pd.read_csv(fires_path)
- if phase in ("all", "label"):
- print("[04] Phase 4-3 Check: 발화별 forward PnL 라벨")
- if frames is None:
- mon = Monitor(cooldown_file=None)
- frames = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- outcomes = label_fire_outcomes(fires, frames)
- outcomes.to_csv(outcomes_path, index=False, encoding="utf-8-sig")
- print(f"[04-3] 저장: {outcomes_path}")
- if phase == "label":
- return
-
- if phase in ("all", "select"):
- if candidates is None:
- candidates = json.loads(candidates_path.read_text(encoding="utf-8"))
- if outcomes is None:
- outcomes = pd.read_csv(outcomes_path)
- print("[04] Phase 4-4 Act: EV 필터·규칙 선별")
- matched = select_matched_rules(outcomes, candidates)
- MATCHING_MATCHED_RULES.parent.mkdir(parents=True, exist_ok=True)
- MATCHING_MATCHED_RULES.write_text(
- json.dumps(matched, ensure_ascii=False, indent=2),
- encoding="utf-8",
- )
- print(f"[04-4] 저장: {MATCHING_MATCHED_RULES}")
- overlap = matched.get("gt_overlap", {})
- MATCHING_GT_OVERLAP.write_text(
- json.dumps(overlap, ensure_ascii=False, indent=2),
- encoding="utf-8",
- )
- write_backtest_summary_html(matched, MATCHING_BACKTEST_HTML)
- print(f"완료 ({time.time() - t0:.0f}초)")
-
-
-def main() -> None:
- """CLI 진입."""
- parser = argparse.ArgumentParser(description="04 GT+EV 매칭 파이프라인")
- parser.add_argument(
- "--phase",
- choices=("all", "profile", "scan", "label", "select"),
- default="all",
- )
- parser.add_argument("--trades-csv", type=str, default="")
- args = parser.parse_args()
- csv = Path(args.trades_csv) if args.trades_csv else None
- run_matching_pipeline(phase=args.phase, trades_csv=csv)
-
-
-if __name__ == "__main__":
- main()
diff --git a/deepcoin/matching/portfolio_sim.py b/deepcoin/matching/portfolio_sim.py
deleted file mode 100644
index 332c4b3..0000000
--- a/deepcoin/matching/portfolio_sim.py
+++ /dev/null
@@ -1,343 +0,0 @@
-"""
-규칙 발화 기반 GT 모델 복리 포트폴리오 시뮬.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import pandas as pd
-
-from config import (
- GT_INITIAL_CASH_KRW,
- GT_SIGNAL_CAUSAL,
- LIVE_ORDER_KRW,
- TRADING_FEE_RATE,
-)
-from deepcoin.ground_truth.gt_allocation import simulate_portfolio_summary
-from deepcoin.matching.position_sizing import (
- attach_gt_model_amounts,
-)
-
-
-def _planned_order_krw(
- t: dict[str, Any],
- order_krw: float,
- sizing_mode: str,
-) -> float:
- """
- 체결 계획 원화: amount_krw 우선 또는 고정.
-
- Args:
- t: trade dict.
- order_krw: 고정 1회 금액.
- sizing_mode: fixed | amount_krw.
-
- Returns:
- 계획 원화.
- """
- ak = t.get("amount_krw")
- if sizing_mode == "amount_krw" or (ak is not None and float(ak) > 0):
- return float(ak or 0)
- return float(order_krw)
-
-
-def sort_fires_chronological(fires: pd.DataFrame) -> pd.DataFrame:
- """
- 발화를 시간순 정렬 (일·금액 한도 없음).
-
- Args:
- fires: fire_outcomes.
-
- Returns:
- 정렬된 DataFrame.
- """
- if fires.empty:
- return fires
- return fires.sort_values("dt").copy()
-
-
-def simulate_fires_compound(
- fires: pd.DataFrame,
- *,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
-) -> tuple[list[dict[str, Any]], dict[str, Any]]:
- """
- 발화 → GT tier 복리 amount_krw 배분 (allocate_order_amounts_chronological).
-
- Args:
- fires: fire_outcomes.
- initial_cash: 시작 현금 (이후 체결마다 누적).
- fee_rate: 수수료율.
-
- Returns:
- (amount_krw 채워진 trade dict, stats).
- """
- trades = fires_to_trade_list(
- fires,
- apply_dynamic_sizing=True,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- )
- n_in = int(len(fires))
- n_out = sum(1 for t in trades if float(t.get("amount_krw") or 0) > 0)
- return trades, {
- "input_fires": n_in,
- "executed": n_out,
- "skipped": max(n_in - n_out, 0),
- }
-
-
-def select_capped_fires(
- fires: pd.DataFrame,
- *,
- use_dynamic_sizing: bool = True,
-) -> pd.DataFrame:
- """
- 시각순 발화 반환 (레거시명; 일·금액 한도 미적용).
-
- Args:
- fires: fire_outcomes.
- use_dynamic_sizing: 미사용 (하위 호환).
-
- Returns:
- 정렬된 발화 DataFrame.
- """
- _ = use_dynamic_sizing
- return sort_fires_chronological(fires)
-
-
-def fires_to_trade_list(
- fires: pd.DataFrame,
- *,
- apply_dynamic_sizing: bool = True,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
-) -> list[dict[str, Any]]:
- """
- 발화 → GT 모델 amount_krw가 채워진 trade dict (복리 배분).
-
- Args:
- fires: 체결 대상 발화.
- apply_dynamic_sizing: True면 GT tier 복리 배분.
- initial_cash: 시작 현금 (누적 복리).
- fee_rate: 수수료율.
-
- Returns:
- dt, action, price, amount_krw 키 dict 리스트.
- """
- if fires.empty:
- return []
- rows: list[dict[str, Any]] = []
- for _, r in sort_fires_chronological(fires).iterrows():
- rows.append(
- {
- "dt": str(r["dt"]),
- "action": r["side"],
- "price": float(r["close"]),
- "rule_id": r.get("rule_id", ""),
- "forward_ret_pct": float(r.get("forward_ret_pct", 0)),
- }
- )
- if apply_dynamic_sizing:
- attach_gt_model_amounts(
- rows,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- )
- return rows
-
-
-def simulate_sized_portfolio(
- trades: list[dict[str, Any]],
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- last_price: float | None = None,
- fallback_order_krw: float = LIVE_ORDER_KRW,
-) -> dict[str, Any]:
- """
- trade.amount_krw(GT 모델·복리 배분) 기준 포트폴리오 시뮬 + MDD.
-
- Args:
- trades: 시간순 trade dict (amount_krw 권장).
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- last_price: 미청산 평가 종가.
- fallback_order_krw: amount_krw 없을 때 1회 금액.
-
- Returns:
- simulate_truth_portfolio와 동일 키 + max_drawdown_pct.
- """
- if trades and not any(float(t.get("amount_krw") or 0) > 0 for t in trades):
- attach_gt_model_amounts(trades, initial_cash=initial_cash, fee_rate=fee_rate)
- result = simulate_portfolio_summary(
- trades,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- last_price=last_price,
- use_amount_krw=True,
- )
- result["sizing_mode"] = (
- "gt_model_compound_causal" if GT_SIGNAL_CAUSAL else "gt_model_compound"
- )
- result["sizing_note"] = (
- "전기간 복리·GT tier·총자산×비중, 보유현금 한도; "
- + ("인과적 신호·tier(미래 미사용)" if GT_SIGNAL_CAUSAL else "상한 없음")
- )
- return result
-
-
-def simulate_fixed_order_portfolio(
- trades: list[dict[str, Any]],
- order_krw: float = LIVE_ORDER_KRW,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- last_price: float | None = None,
- sizing_mode: str = "fixed",
-) -> dict[str, Any]:
- """
- 포트폴리오 시뮬 (고정 원화 또는 trade.amount_krw).
-
- Args:
- trades: 시간순 {dt, action, price, amount_krw?}.
- order_krw: sizing_mode=fixed 일 때 1회 금액(원).
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- last_price: 미청산 평가 종가.
- sizing_mode: 'fixed' | 'amount_krw'.
-
- Returns:
- simulate_truth_portfolio와 동일 키 구조.
- """
- cash = float(initial_cash)
- qty = 0.0
- total_fees = 0.0
- last_trade_price = last_price
- order = float(order_krw)
-
- for t in sorted(trades, key=lambda x: x["dt"]):
- action = t["action"]
- price = float(t["price"])
- if price <= 0:
- continue
- last_trade_price = price
-
- if action == "buy":
- planned = _planned_order_krw(t, order, sizing_mode)
- amount = min(planned, max(cash / (1.0 + fee_rate), 0.0))
- if amount <= 0:
- continue
- fee = amount * fee_rate
- cash -= amount + fee
- total_fees += fee
- qty += amount / price
-
- elif action == "sell" and qty > 0:
- planned = _planned_order_krw(t, order, sizing_mode)
- if planned >= qty * price * 0.999:
- sell_qty = qty
- else:
- sell_qty = min(qty, planned / price)
- if sell_qty <= 0:
- continue
- gross = sell_qty * price
- fee = gross * fee_rate
- cash += gross - fee
- total_fees += fee
- qty -= sell_qty
- if qty < 1e-12:
- qty = 0.0
-
- mark_price = float(last_price if last_price is not None else last_trade_price or 0)
- holding_value = qty * mark_price
- final_asset = cash + holding_value
- pnl_krw = final_asset - initial_cash
- pnl_pct = pnl_krw / initial_cash * 100.0 if initial_cash else 0.0
-
- return {
- "initial_cash_krw": round(initial_cash, 0),
- "final_asset_krw": round(final_asset, 0),
- "pnl_krw": round(pnl_krw, 0),
- "pnl_pct": round(pnl_pct, 2),
- "total_fees_krw": round(total_fees, 0),
- "cash_krw": round(cash, 0),
- "holding_qty": round(qty, 6),
- "holding_value_krw": round(holding_value, 0),
- "mark_price": round(mark_price, 2),
- "fee_rate": fee_rate,
- "order_krw": round(order, 0),
- "sizing_mode": sizing_mode,
- "trade_count": len(trades),
- }
-
-
-def simulate_fixed_order_portfolio_steps(
- trades: list[dict[str, Any]],
- order_krw: float = LIVE_ORDER_KRW,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- sizing_mode: str = "fixed",
-) -> list[dict[str, Any]]:
- """
- 체결마다 현금·보유·총평가 스냅샷 (GT 테이블용).
-
- Args:
- trades: 시간순 trade dict.
- order_krw: 1회 체결 원화.
- initial_cash: 시작 현금.
- fee_rate: 수수료율.
- sizing_mode: fixed | amount_krw.
-
- Returns:
- step dict 리스트.
- """
- cash = float(initial_cash)
- qty = 0.0
- order = float(order_krw)
- steps: list[dict[str, Any]] = []
-
- for t in sorted(trades, key=lambda x: x["dt"]):
- action = t["action"]
- price = float(t["price"])
- if price <= 0:
- continue
-
- if action == "buy":
- planned = _planned_order_krw(t, order, sizing_mode)
- amount = min(planned, max(cash / (1.0 + fee_rate), 0.0))
- if amount <= 0:
- continue
- fee = amount * fee_rate
- cash -= amount + fee
- qty += amount / price
-
- elif action == "sell" and qty > 0:
- planned = _planned_order_krw(t, order, sizing_mode)
- if planned >= qty * price * 0.999:
- sell_qty = qty
- else:
- sell_qty = min(qty, planned / price)
- if sell_qty <= 0:
- continue
- gross = sell_qty * price
- fee = gross * fee_rate
- cash += gross - fee
- qty -= sell_qty
- if qty < 1e-12:
- qty = 0.0
-
- steps.append(
- {
- "dt": t["dt"],
- "action": action,
- "price": price,
- "rule_id": t.get("rule_id", ""),
- "forward_ret_pct": t.get("forward_ret_pct"),
- "amount_krw": t.get("amount_krw"),
- "cash_krw": round(cash, 0),
- "holding_qty": round(qty, 4),
- "total_asset_krw": round(cash + qty * price, 0),
- }
- )
- return steps
diff --git a/deepcoin/matching/position_sizing.py b/deepcoin/matching/position_sizing.py
deleted file mode 100644
index 010191b..0000000
--- a/deepcoin/matching/position_sizing.py
+++ /dev/null
@@ -1,453 +0,0 @@
-"""
-총자산 대비 GT 모델 매수율(비중) · 보유 현금 한도 · leg tier 배분.
-"""
-
-from __future__ import annotations
-
-import json
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-
-from config import (
- GT_BUY_PCT_LARGE_LEG,
- GT_BUY_PCT_SMALL_LEG,
- GT_INITIAL_CASH_KRW,
- GT_LARGE_LEG_TOP_PCT,
- GT_MIN_ORDER_KRW,
- MATCH_GT_TOLERANCE_MIN,
- TRADING_FEE_RATE,
-)
-from deepcoin.matching.load_rules import load_matched_rules
-from deepcoin.paths import MATCHING_FIRE_OUTCOMES, MATCHING_MATCHED_RULES
-
-_GT_ALLOC_ANALYSIS_CACHE: dict[str, Any] | None = None
-
-
-def portfolio_totals(
- cash: float,
- qty: float,
- price: float,
-) -> tuple[float, float, float]:
- """
- 총보유자산·코인평가·가용현금(=총자산-평가액)을 계산합니다.
-
- Args:
- cash: 현금.
- qty: 보유 수량.
- price: 평가·체결가.
-
- Returns:
- (total_asset_krw, holding_value_krw, cash_krw).
- """
- holding = qty * price
- total = cash + holding
- return total, holding, cash
-
-
-def optimal_weight_share(weight: float, weight_sum_remaining: float) -> float:
- """
- leg 내 남은 매수 비중 대비 이번 체결 최적 매수율(0~1).
-
- Args:
- weight: 이번 타점 weight.
- weight_sum_remaining: 동일 leg 남은 매수 weight 합.
-
- Returns:
- 비중 비율.
- """
- if weight_sum_remaining > 0:
- return weight / weight_sum_remaining
- return 1.0
-
-
-def compute_buy_amount_krw(
- cash: float,
- qty: float,
- price: float,
- weight: float,
- weight_sum_remaining: float,
- *,
- asset_pct_scale: float,
- min_order_krw: float = GT_MIN_ORDER_KRW,
- fee_rate: float = TRADING_FEE_RATE,
- ignore_weight_split: bool = False,
-) -> float:
- """
- 목표=총보유자산×(최적 매수율×scale), 체결=min(목표, 보유현금/(1+fee)) 로 매수 원화를 산출합니다.
-
- 보유 현금 = 총보유자산 − 코인평가액(cash 인자).
-
- Args:
- cash: 보유 현금(가용 원화).
- qty: 보유 수량.
- price: 체결가.
- weight: 타점 비중.
- weight_sum_remaining: leg 내 남은 매수 weight 합.
- asset_pct_scale: leg·규칙 티어(대형/소형) 스케일.
- min_order_krw: 최소 주문 원화.
- fee_rate: 수수료율.
- ignore_weight_split: True면 weight 분할 없이 scale만 적용 (conviction 매수).
-
- Returns:
- 매수 원화(0이면 미체결).
- """
- if price <= 0:
- return 0.0
- total_asset, _, available_cash = portfolio_totals(cash, qty, price)
- budget = max(available_cash / (1.0 + fee_rate), 0.0)
- if ignore_weight_split:
- opt_rate = asset_pct_scale
- else:
- opt_rate = optimal_weight_share(weight, weight_sum_remaining) * asset_pct_scale
- target = total_asset * opt_rate
- amount = min(target, budget)
- if budget >= min_order_krw and 0 < amount < min_order_krw:
- amount = min(min_order_krw, budget)
- return round(max(amount, 0.0), 0)
-
-
-def large_leg_ids_from_past_returns(
- leg_returns: dict[int, float],
- top_pct: float = GT_LARGE_LEG_TOP_PCT,
-) -> set[int]:
- """
- 이미 청산된 leg의 realized return 상위 n% (인과적 tier).
-
- Args:
- leg_returns: leg_id → realized return %.
- top_pct: 상위 비율.
-
- Returns:
- large leg id set.
- """
- if not leg_returns:
- return set()
- ranked = sorted(leg_returns.items(), key=lambda x: x[1], reverse=True)
- n = max(1, int(len(ranked) * top_pct + 0.999999))
- return {lid for lid, _ in ranked[:n]}
-
-
-def top_leg_ids_by_forward_return(
- trades: list[dict[str, Any]],
- top_pct: float = GT_LARGE_LEG_TOP_PCT,
-) -> set[int]:
- """
- leg별 최대 forward_return 기준 상위 n% leg_id 집합.
-
- Args:
- trades: GT trade dict.
- top_pct: 상위 비율(0~1).
-
- Returns:
- 대형 매수 leg_id set.
- """
- leg_ret: dict[int, float] = {}
- for t in trades:
- if t.get("action") != "sell":
- continue
- lid = int(t.get("leg_id", 0))
- ret = float(t.get("forward_return_pct") or 0.0)
- leg_ret[lid] = max(leg_ret.get(lid, 0.0), ret)
- if not leg_ret:
- return set()
- ranked = sorted(leg_ret.items(), key=lambda x: x[1], reverse=True)
- n = max(1, int(len(ranked) * top_pct + 0.999999))
- return {lid for lid, _ in ranked[:n]}
-
-
-def leg_asset_pct_scale(leg_id: int, large_legs: set[int]) -> float:
- """
- leg 티어에 따른 총자산 대비 매수 스케일.
-
- Args:
- leg_id: leg 번호.
- large_legs: 상위 leg 집합.
-
- Returns:
- GT_BUY_PCT_LARGE_LEG 또는 GT_BUY_PCT_SMALL_LEG.
- """
- if leg_id in large_legs:
- return float(GT_BUY_PCT_LARGE_LEG)
- return float(GT_BUY_PCT_SMALL_LEG)
-
-
-def _parse_dt(dt: str) -> datetime:
- return datetime.fromisoformat(str(dt).replace("Z", "+00:00")[:19])
-
-
-def nearest_gt_leg_id(
- dt: str,
- gt_trades: list[dict[str, Any]],
- tolerance_min: int = MATCH_GT_TOLERANCE_MIN,
-) -> int | None:
- """
- 시각에 가장 가까운 GT trade의 leg_id (매수 우선).
-
- Args:
- dt: 발화 시각.
- gt_trades: GT trades.
- tolerance_min: 허용 분.
-
- Returns:
- leg_id 또는 None.
- """
- if not gt_trades:
- return None
- t0 = _parse_dt(dt)
- best_buy: int | None = None
- best_buy_min = float(tolerance_min) + 1.0
- best_any: int | None = None
- best_any_min = float(tolerance_min) + 1.0
- for t in gt_trades:
- try:
- t1 = _parse_dt(t["dt"])
- except ValueError:
- continue
- delta = abs((t0 - t1).total_seconds()) / 60.0
- if delta > tolerance_min:
- continue
- lid = int(t.get("leg_id", 0))
- if t.get("action") == "buy" and delta < best_buy_min:
- best_buy_min = delta
- best_buy = lid
- if delta < best_any_min:
- best_any_min = delta
- best_any = lid
- return best_buy if best_buy is not None else best_any
-
-
-def load_gt_allocation_analysis(
- gt_trades: list[dict[str, Any]] | None = None,
-) -> dict[str, Any]:
- """
- GT amount_krw 분석 캐시 (tier 권장 pct).
-
- Args:
- gt_trades: GT trades. None이면 파일 로드.
-
- Returns:
- analyze_gt_buy_allocation 결과.
- """
- global _GT_ALLOC_ANALYSIS_CACHE
- if _GT_ALLOC_ANALYSIS_CACHE is not None:
- return _GT_ALLOC_ANALYSIS_CACHE
- from deepcoin.ground_truth.gt_allocation_analysis import analyze_gt_buy_allocation
- from deepcoin.paths import resolve_ground_truth_file
-
- trades = gt_trades
- if trades is None:
- p = resolve_ground_truth_file()
- if p.is_file():
- trades = json.loads(p.read_text(encoding="utf-8")).get("trades") or []
- if not trades:
- _GT_ALLOC_ANALYSIS_CACHE = {}
- return _GT_ALLOC_ANALYSIS_CACHE
- chron = sorted(trades, key=lambda x: x["dt"])
- if not any(float(t.get("amount_krw") or 0) > 0 for t in chron):
- from deepcoin.ground_truth.ground_truth import allocate_gt_order_amounts
-
- allocate_gt_order_amounts(chron)
- _GT_ALLOC_ANALYSIS_CACHE = analyze_gt_buy_allocation(chron)
- return _GT_ALLOC_ANALYSIS_CACHE
-
-
-def gt_tier_scale_for_trade(
- trade: dict[str, Any],
- gt_trades: list[dict[str, Any]],
- large_legs: set[int],
- *,
- analysis: dict[str, Any] | None = None,
-) -> float:
- """
- GT leg tier 배분 스케일 (분석 권장값 또는 config).
-
- Args:
- trade: {dt, leg_id?, action, ...}.
- gt_trades: GT trades (leg 매칭).
- large_legs: 상위 leg.
- analysis: analyze_gt_buy_allocation 결과.
-
- Returns:
- pct_large 또는 pct_small.
- """
- from deepcoin.ground_truth.gt_allocation_analysis import gt_tier_scale_from_analysis
-
- lid = trade.get("leg_id")
- if lid is None:
- lid = nearest_gt_leg_id(str(trade["dt"]), gt_trades)
- if lid is None:
- return float(GT_BUY_PCT_SMALL_LEG)
- return gt_tier_scale_from_analysis(int(lid), large_legs, analysis)
-
-
-def enrich_sim_trades_with_gt_weights(
- trades: list[dict[str, Any]],
- gt_trades: list[dict[str, Any]],
- *,
- causal_legs: bool = False,
-) -> list[dict[str, Any]]:
- """
- 규칙 발화에 GT leg_id·매수/매도 weight를 부여합니다.
-
- causal_legs=True: GT leg 매칭 없이 매수~매도 구간 순번 leg_id (인과적).
-
- Args:
- trades: {dt, action/side, price, rule_id}.
- gt_trades: GT trades (leg 매칭, causal_legs=False 일 때).
- causal_legs: 순차 leg_id.
-
- Returns:
- leg_id·weight가 채워진 trade dict.
- """
- from deepcoin.ground_truth.gt_model import leg_entry_weights, leg_exit_weights
-
- rows = sorted(trades, key=lambda x: x["dt"])
- pos = 0
- seq_leg = 0
- while pos < len(rows):
- action = rows[pos].get("action", rows[pos].get("side", ""))
- if action != "buy":
- if causal_legs:
- rows[pos]["leg_id"] = seq_leg
- elif "leg_id" not in rows[pos]:
- rows[pos]["leg_id"] = nearest_gt_leg_id(rows[pos]["dt"], gt_trades) or 0
- rows[pos]["weight"] = float(rows[pos].get("weight", 1.0))
- pos += 1
- continue
- buy_end = pos
- while buy_end < len(rows):
- a = rows[buy_end].get("action", rows[buy_end].get("side", ""))
- if a != "buy":
- break
- buy_end += 1
- buy_slice = rows[pos:buy_end]
- sell_slice: list[dict[str, Any]] = []
- sell_end = buy_end
- while sell_end < len(rows):
- a = rows[sell_end].get("action", rows[sell_end].get("side", ""))
- if a == "buy":
- break
- if a == "sell":
- sell_slice.append(rows[sell_end])
- sell_end += 1
-
- if causal_legs:
- leg_id = seq_leg
- else:
- leg_id = nearest_gt_leg_id(buy_slice[0]["dt"], gt_trades) or 0
- prices = [float(t["price"]) for t in buy_slice]
- buy_weights = leg_entry_weights(prices)
- for t, w in zip(buy_slice, buy_weights):
- t["leg_id"] = leg_id
- t["weight"] = round(w, 4)
- if "action" not in t and "side" in t:
- t["action"] = t["side"]
-
- if sell_slice:
- sw = leg_exit_weights(len(sell_slice))
- for t, w in zip(sell_slice, sw):
- t["leg_id"] = leg_id
- t["weight"] = round(w, 4)
- if "action" not in t and "side" in t:
- t["action"] = t["side"]
- if causal_legs and sell_slice:
- seq_leg += 1
- pos = sell_end if sell_slice else buy_end
- return rows
-
-
-def attach_gt_model_amounts(
- trades: list[dict[str, Any]],
- *,
- gt_trades: list[dict[str, Any]] | None = None,
- approved_rules: set[str] | None = None,
- large_legs: set[int] | None = None,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- fee_rate: float = TRADING_FEE_RATE,
-) -> list[dict[str, Any]]:
- """
- GT 모델 비중 + 공통 배분 엔진으로 amount_krw를 채웁니다.
-
- 시뮬·매칭 전용: leg·tier 모두 인과적(과거 청산 leg 수익만). GT 정답 배분은
- ground_truth.allocate_gt_order_amounts 를 사용하세요.
-
- Args:
- trades: enrich_sim_trades_with_gt_weights 출력 또는 raw fires.
- gt_trades: GT trades. None이면 파일 로드.
- approved_rules: EV/WF 통과 rule (live scale용).
- large_legs: 상위 leg.
- initial_cash: 초기 현금.
- fee_rate: 수수료율.
-
- Returns:
- amount_krw·weight·leg_id가 채워진 trade dict.
- """
- from deepcoin.ground_truth.gt_allocation import allocate_order_amounts_chronological
-
- if gt_trades is None:
- gt_trades, _, _ = load_sizing_context_from_gt()
-
- enriched = enrich_sim_trades_with_gt_weights(
- list(trades),
- gt_trades,
- causal_legs=True,
- )
-
- allocate_order_amounts_chronological(
- enriched,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- large_legs=None,
- asset_pct_scale_fn=None,
- causal_tier=True,
- )
- return enriched
-
-
-def attach_dynamic_buy_amounts(
- trades: list[dict[str, Any]],
- *,
- gt_trades: list[dict[str, Any]] | None = None,
- approved_rules: set[str] | None = None,
- large_legs: set[int] | None = None,
- initial_cash: float = GT_INITIAL_CASH_KRW,
- default_weight: float = 1.0,
- fee_rate: float = TRADING_FEE_RATE,
-) -> list[dict[str, Any]]:
- """
- 시뮬 발화 trade dict에 amount_krw(GT 모델·보유 현금 한도)를 채웁니다.
-
- attach_gt_model_amounts 별칭.
- """
- return attach_gt_model_amounts(
- trades,
- gt_trades=gt_trades,
- approved_rules=approved_rules,
- large_legs=large_legs,
- initial_cash=initial_cash,
- fee_rate=fee_rate,
- )
-
-
-def load_sizing_context_from_gt(
- gt_path: Path | None = None,
-) -> tuple[list[dict[str, Any]], set[int], set[str]]:
- """
- GT JSON에서 trades, 상위 leg, EV/WF 통과 rule을 로드합니다.
-
- Args:
- gt_path: ground_truth_trades.json.
-
- Returns:
- (gt_trades, large_legs, approved_rules).
- """
- from deepcoin.paths import resolve_ground_truth_file
-
- p = gt_path or resolve_ground_truth_file()
- trades: list[dict[str, Any]] = []
- if p.is_file():
- data = json.loads(p.read_text(encoding="utf-8"))
- trades = data.get("trades") or []
- large = top_leg_ids_by_forward_return(trades)
- return trades, large, set()
diff --git a/deepcoin/matching/profile_rules.py b/deepcoin/matching/profile_rules.py
deleted file mode 100644
index 78f38ef..0000000
--- a/deepcoin/matching/profile_rules.py
+++ /dev/null
@@ -1,427 +0,0 @@
-"""
-04-1: GT 스냅샷(03b)에서 규칙 후보 생성.
-"""
-
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import (
- MATCH_INCLUDE_ATOMIC,
- MATCH_INCLUDE_MTF_CROSS,
- MATCH_INCLUDE_WIDE_RULES,
- MATCH_PROFILE_QUANTILE_HI,
- MATCH_PROFILE_QUANTILE_LO,
- MATCH_PROFILE_TIGHT_HI,
- MATCH_PROFILE_TIGHT_LO,
-)
-from deepcoin.analysis.general_analysis_config import GENERAL_ANALYSIS_INTERVALS
-from deepcoin.analysis.general_analysis_core import interval_tf_prefix
-from deepcoin.matching.config import (
- ANALYSIS_TRADES_CSV,
- BUY_PROFILE_FEATURES,
- SELL_PROFILE_FEATURES,
-)
-from deepcoin.matching.gt_mtf_profile import (
- analyze_gt_mtf_profile,
- load_selected_features,
-)
-from deepcoin.paths import (
- ANALYSIS_GT_CALIBRATION_JSON,
- ANALYSIS_GT_MTF_PROFILE_JSON,
-)
-
-
-def _feature_separation(
- buy: pd.DataFrame,
- sell: pd.DataFrame,
- col: str,
-) -> float:
- """
- 매수·매도 GT 분포 간 분리도(절대 평균차/합동표준편차)를 계산합니다.
-
- Args:
- buy: 매수 타점 행.
- sell: 매도 타점 행.
- col: 컬럼명.
-
- Returns:
- 분리도(숫자형만, 그 외 0).
- """
- if col not in buy.columns or not pd.api.types.is_numeric_dtype(buy[col]):
- return 0.0
- a = pd.to_numeric(buy[col], errors="coerce").dropna()
- b = pd.to_numeric(sell[col], errors="coerce").dropna()
- if len(a) < 5 or len(b) < 5:
- return 0.0
- pooled = np.sqrt((a.var() + b.var()) / 2)
- if pooled < 1e-9:
- return abs(float(a.mean() - b.mean()))
- return abs(float(a.mean() - b.mean())) / pooled
-
-
-def _condition_from_series(series: pd.Series, side: str) -> dict[str, Any] | None:
- """
- 한 컬럼의 GT 분포에서 단일 조건을 추출합니다.
-
- Args:
- series: 해당 side 타점 값.
- side: buy | sell (설명용).
-
- Returns:
- 조건 dict 또는 None.
- """
- col_name = series.name
- if series.dtype == object or series.dtype.name == "string":
- mode = series.dropna().astype(str).mode()
- if mode.empty:
- return None
- return {"col": col_name, "op": "eq", "value": str(mode.iloc[0])}
-
- s = pd.to_numeric(series, errors="coerce").dropna()
- if len(s) < 10:
- return None
-
- if set(s.unique()).issubset({0, 1, 0.0, 1.0}):
- frac = float(s.mean())
- if frac >= 0.55:
- return {"col": col_name, "op": "eq_int", "value": 1}
- if frac <= 0.45:
- return {"col": col_name, "op": "eq_int", "value": 0}
- return None
-
- lo = float(s.quantile(MATCH_PROFILE_QUANTILE_LO))
- hi = float(s.quantile(MATCH_PROFILE_QUANTILE_HI))
- if lo >= hi:
- return None
- return {"col": col_name, "op": "between", "lo": lo, "hi": hi}
-
-
-def _condition_tight(series: pd.Series) -> dict[str, Any] | None:
- """
- q35~q65 좁은 구간 조건.
-
- Args:
- series: GT 부분집합 값.
-
- Returns:
- between 조건 또는 None.
- """
- s = pd.to_numeric(series, errors="coerce").dropna()
- if len(s) < 10:
- return None
- lo = float(s.quantile(MATCH_PROFILE_TIGHT_LO))
- hi = float(s.quantile(MATCH_PROFILE_TIGHT_HI))
- if lo >= hi:
- return None
- return {"col": series.name, "op": "between", "lo": lo, "hi": hi}
-
-
-def _contrast_conditions(
- buy: pd.DataFrame,
- sell: pd.DataFrame,
- col: str,
- side: str,
-) -> list[dict[str, Any]]:
- """
- 매수·매도 GT 분리가 큰 컬럼에 대해 쪽별 타이트 AND 대조 조건.
-
- Args:
- buy: 매수 GT.
- sell: 매도 GT.
- col: 컬럼명.
- side: buy | sell.
-
- Returns:
- 조건 리스트(비어 있을 수 있음).
- """
- if col not in buy.columns or not pd.api.types.is_numeric_dtype(buy[col]):
- return []
- b = pd.to_numeric(buy[col], errors="coerce").dropna()
- s = pd.to_numeric(sell[col], errors="coerce").dropna()
- if len(b) < 10 or len(s) < 10:
- return []
- tight = _condition_tight(b if side == "buy" else s)
- if tight is None:
- return []
- conds = [tight]
- if side == "buy" and float(b.median()) < float(s.median()):
- conds.append({"col": col, "op": "lte", "value": float(s.quantile(0.40))})
- elif side == "sell" and float(b.median()) < float(s.median()):
- conds.append({"col": col, "op": "gte", "value": float(b.quantile(0.60))})
- return conds
-
-
-def _resolve_profile_features(
- trades_csv: Path,
- df: pd.DataFrame,
-) -> tuple[list[str], list[str], dict[str, Any] | None]:
- """
- 03c 프로필 JSON 갱신 후 buy/sell 피처 목록 반환.
-
- Args:
- trades_csv: 03b CSV 경로.
- df: 동일 CSV DataFrame.
-
- Returns:
- (buy_features, sell_features, profile_analysis 또는 None).
- """
- profile_path = ANALYSIS_GT_MTF_PROFILE_JSON
- need_run = not profile_path.is_file()
- if not need_run and profile_path.stat().st_mtime < trades_csv.stat().st_mtime:
- need_run = True
- analysis: dict[str, Any] | None = None
- if need_run:
- analysis = analyze_gt_mtf_profile(df)
- profile_path.parent.mkdir(parents=True, exist_ok=True)
- profile_path.write_text(
- json.dumps(analysis, ensure_ascii=False, indent=2),
- encoding="utf-8",
- )
- from deepcoin.matching.gt_mtf_profile import write_gt_mtf_profile_html
- from deepcoin.paths import ANALYSIS_GT_MTF_PROFILE_HTML
-
- write_gt_mtf_profile_html(analysis, ANALYSIS_GT_MTF_PROFILE_HTML)
- print(f"[04-1] 03c GT MTF 프로필 갱신: {profile_path}")
- buy_f, sell_f = load_selected_features(profile_path)
- if not buy_f:
- buy_f = list(BUY_PROFILE_FEATURES)
- if not sell_f:
- sell_f = list(SELL_PROFILE_FEATURES)
- return buy_f, sell_f, analysis
-
-
-def _mtf_cross_conditions(
- buy: pd.DataFrame,
- sell: pd.DataFrame,
- features: list[str],
- side: str,
-) -> list[dict[str, Any]]:
- """
- 각 TF에서 분리도 1위 컬럼 조건을 AND (크로스-TF 복합).
-
- Args:
- buy: 매수 GT.
- sell: 매도 GT.
- features: 후보 컬럼.
- side: buy | sell.
-
- Returns:
- 조건 리스트(2개 이상일 때만 의미).
- """
- subset = buy if side == "buy" else sell
- conds: list[dict[str, Any]] = []
- for iv in GENERAL_ANALYSIS_INTERVALS:
- pfx = interval_tf_prefix(iv)
- iv_feats = [f for f in features if f.startswith(f"{pfx}_") and f in subset.columns]
- if not iv_feats:
- continue
- best = max(iv_feats, key=lambda c: _feature_separation(buy, sell, c))
- cond = _condition_from_series(subset[best], side)
- if cond:
- conds.append(cond)
- return conds
-
-
-def build_rule_candidates(
- trades_csv: Path | None = None,
-) -> dict[str, Any]:
- """
- 03b CSV + 03c MTF 프로필에서 매수·매도별 규칙 후보를 생성합니다.
-
- Args:
- trades_csv: general_analysis_trades.csv 경로.
-
- Returns:
- rule_candidates 메타·rules 리스트 dict.
- """
- path = trades_csv or ANALYSIS_TRADES_CSV
- if not path.is_file():
- raise FileNotFoundError(f"03b CSV 없음: {path} — scripts/03_analyze_trades.py 먼저 실행")
-
- df = pd.read_csv(path)
- buy = df[df["action"] == "buy"].copy()
- sell = df[df["action"] == "sell"].copy()
- buy_features, sell_features, profile = _resolve_profile_features(path, df)
- rules: list[dict[str, Any]] = []
- rid = 0
-
- for side, subset, features in (
- ("buy", buy, buy_features),
- ("sell", sell, sell_features),
- ):
- skip_cols = {
- "ga_align_trend_score", # 분포가 넓어 전구간 발화 과다
- }
- if MATCH_INCLUDE_ATOMIC:
- for feat in features:
- if feat not in df.columns or feat in skip_cols:
- continue
- cond = _condition_from_series(subset[feat], side)
- if cond is None:
- continue
- rules.append(
- {
- "rule_id": f"{side}_a{rid:03d}_{feat}",
- "side": side,
- "kind": "atomic",
- "conditions": [cond],
- "profile_col": feat,
- }
- )
- rid += 1
-
- ranked = sorted(
- [f for f in features if f in df.columns],
- key=lambda c: _feature_separation(buy, sell, c),
- reverse=True,
- )
- ranked_top = ranked[:5]
- compound_conds: list[dict[str, Any]] = []
- for feat in ranked_top[:3]:
- cond = _condition_from_series(subset[feat], side)
- if cond:
- compound_conds.append(cond)
- if len(compound_conds) >= 2:
- rules.append(
- {
- "rule_id": f"{side}_compound_top3",
- "side": side,
- "kind": "compound",
- "conditions": compound_conds,
- "profile_cols": ranked_top[:3],
- }
- )
-
- tight_conds: list[dict[str, Any]] = []
- for feat in ranked_top[:4]:
- if feat not in subset.columns:
- continue
- tc = _condition_tight(subset[feat])
- if tc:
- tight_conds.append(tc)
- if len(tight_conds) >= 2:
- rules.append(
- {
- "rule_id": f"{side}_compound_tight",
- "side": side,
- "kind": "compound_tight",
- "conditions": tight_conds,
- }
- )
-
- if ranked_top:
- c0 = ranked_top[0]
- contrast = _contrast_conditions(buy, sell, c0, side)
- if len(contrast) >= 2:
- rules.append(
- {
- "rule_id": f"{side}_contrast_{c0}",
- "side": side,
- "kind": "contrast",
- "conditions": contrast,
- }
- )
-
- if MATCH_INCLUDE_MTF_CROSS:
- cross = _mtf_cross_conditions(buy, sell, features, side)
- if len(cross) >= 3:
- rules.append(
- {
- "rule_id": f"{side}_mtf_cross_all_tf",
- "side": side,
- "kind": "mtf_cross",
- "conditions": cross,
- }
- )
-
- if MATCH_INCLUDE_WIDE_RULES:
- for feat in ranked_top[:2]:
- if feat not in subset.columns:
- continue
- s = pd.to_numeric(subset[feat], errors="coerce").dropna()
- if len(s) < 10:
- continue
- lo, hi = float(s.quantile(0.10)), float(s.quantile(0.90))
- if lo < hi:
- rules.append(
- {
- "rule_id": f"{side}_wide_{feat}",
- "side": side,
- "kind": "wide",
- "conditions": [
- {"col": feat, "op": "between", "lo": lo, "hi": hi}
- ],
- }
- )
-
- if ANALYSIS_GT_CALIBRATION_JSON.is_file():
- cal = json.loads(ANALYSIS_GT_CALIBRATION_JSON.read_text(encoding="utf-8"))
- cal_rules = cal.get("calibrated_rules") or []
- if cal.get("final", {}).get("targets_met") and cal_rules:
- rules = []
- for cr in cal_rules:
- if "logic" not in cr:
- cr["logic"] = "and"
- rules.append(cr)
- print(f"[04-1] 캘리브레이션 규칙 적용(90% 달성) → {len(rules)}개")
- else:
- seen_ids = {r["rule_id"] for r in rules}
- for cr in cal_rules:
- if cr.get("rule_id") not in seen_ids:
- if "logic" not in cr:
- cr["logic"] = "and"
- rules.append(cr)
- seen_ids.add(cr["rule_id"])
- print(f"[04-1] 캘리브레이션 규칙 병합 → 총 {len(rules)}개")
-
- from deepcoin.ground_truth.gt_signal_rules import build_gt_model_rules
-
- seen_ids = {r["rule_id"] for r in rules}
- for gr in build_gt_model_rules():
- if gr["rule_id"] not in seen_ids:
- rules.append(gr)
- seen_ids.add(gr["rule_id"])
- print(f"[04-1] GT 모델 일반화 규칙 추가 → 총 {len(rules)}개")
-
- out = {
- "source": str(path),
- "profile_json": str(ANALYSIS_GT_MTF_PROFILE_JSON),
- "calibration_json": str(ANALYSIS_GT_CALIBRATION_JSON),
- "buy_profile_features": buy_features[:50],
- "sell_profile_features": sell_features[:50],
- "buy_gt_count": int(len(buy)),
- "sell_gt_count": int(len(sell)),
- "rule_count": len(rules),
- "rules": rules,
- }
- print(
- f"[04-1] 규칙 후보 {len(rules)}개 "
- f"(매수 GT {len(buy)}, 매도 GT {len(sell)})"
- )
- return out
-
-
-def save_rule_candidates(
- data: dict[str, Any],
- out_path: Path,
-) -> Path:
- """
- rule_candidates.json 저장.
-
- Args:
- data: build_rule_candidates 결과.
- out_path: 출력 경로.
-
- Returns:
- out_path.
- """
- out_path.parent.mkdir(parents=True, exist_ok=True)
- out_path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
- print(f"[04-1] 저장: {out_path}")
- return out_path
diff --git a/deepcoin/matching/rule_eval.py b/deepcoin/matching/rule_eval.py
deleted file mode 100644
index 7229888..0000000
--- a/deepcoin/matching/rule_eval.py
+++ /dev/null
@@ -1,335 +0,0 @@
-"""
-규칙 조건 벡터 평가·MTF 스캔 프레임 병합.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import GENERAL_ANALYSIS_INTERVALS, MATCH_PRIMARY_INTERVAL
-from deepcoin.analysis.general_analysis_core import interval_tf_prefix
-from deepcoin.analysis.general_analysis_pipeline import general_analysis_enrich_bars
-from config import (
- ALIGN_RSI_CONFLICT_TIMING_HIGH,
- ALIGN_RSI_CONFLICT_TIMING_LOW,
- ALIGN_RSI_CONFLICT_TREND_HIGH,
- ALIGN_RSI_CONFLICT_TREND_LOW,
- ALIGN_RSI_OVERBOUGHT,
- ALIGN_RSI_OVERSOLD,
- TIMING_INTERVALS,
- TREND_INTERVALS,
-)
-from deepcoin.analysis.general_analysis_core import ga_col
-
-
-def _add_align_columns_vectorized(frame: pd.DataFrame) -> pd.DataFrame:
- """
- 스캔 프레임에 ga_align_* 컬럼을 벡터 연산으로 추가합니다.
-
- Args:
- frame: TF 접두사 컬럼이 포함된 DataFrame.
-
- Returns:
- align 컬럼이 추가된 DataFrame.
- """
- out = frame.copy()
- rsi_oversold = pd.Series(0, index=out.index, dtype=float)
- rsi_overbought = pd.Series(0, index=out.index, dtype=float)
- n_timing = 0
- for iv in TIMING_INTERVALS:
- p = interval_tf_prefix(iv)
- rk = f"{p}_RSI"
- if rk not in out.columns:
- continue
- n_timing += 1
- rsi = pd.to_numeric(out[rk], errors="coerce")
- rsi_oversold += (rsi < ALIGN_RSI_OVERSOLD).astype(int)
- rsi_overbought += (rsi > ALIGN_RSI_OVERBOUGHT).astype(int)
-
- trend_up = pd.Series(0, index=out.index, dtype=float)
- trend_down = pd.Series(0, index=out.index, dtype=float)
- n_trend = 0
- for iv in TREND_INTERVALS:
- p = interval_tf_prefix(iv)
- sk = f"{p}_{ga_col('struct_trend')}"
- if sk not in out.columns:
- continue
- n_trend += 1
- st = out[sk].astype(str)
- trend_up += (st == "up").astype(int)
- trend_down += (st == "down").astype(int)
-
- denom_t = max(n_timing, 1)
- denom_r = max(n_trend, 1)
- out["ga_align_rsi_oversold_tf"] = rsi_oversold
- out["ga_align_rsi_overbought_tf"] = rsi_overbought
- out["ga_align_trend_up_tf"] = trend_up
- out["ga_align_trend_down_tf"] = trend_down
- out["ga_align_timing_buy_score"] = (rsi_oversold / denom_t).round(3)
- out["ga_align_timing_sell_score"] = (rsi_overbought / denom_t).round(3)
- out["ga_align_trend_score"] = ((trend_up - trend_down) / denom_r).round(3)
-
- conflict = pd.Series(0, index=out.index, dtype=int)
- m3_rsi = out.get("m3_RSI")
- d1_rsi = out.get("d1_RSI")
- if m3_rsi is not None and d1_rsi is not None:
- m3v = pd.to_numeric(m3_rsi, errors="coerce")
- d1v = pd.to_numeric(d1_rsi, errors="coerce")
- conflict = (
- ((m3v < ALIGN_RSI_CONFLICT_TIMING_LOW) & (d1v > ALIGN_RSI_CONFLICT_TREND_HIGH))
- | ((m3v > ALIGN_RSI_CONFLICT_TIMING_HIGH) & (d1v < ALIGN_RSI_CONFLICT_TREND_LOW))
- ).astype(int)
- out["ga_align_mtf_conflict"] = conflict
- return out
-
-
-def _scalar_float(val: Any) -> float:
- """Series/ndarray 스칼라를 float로 변환."""
- if isinstance(val, pd.Series):
- val = val.iloc[0]
- return float(val)
-
-
-def conditions_columns(rules: list[dict[str, Any]]) -> set[str]:
- """
- 규칙 목록에서 참조하는 컬럼명 집합을 반환합니다.
-
- Args:
- rules: rule_candidates 항목 리스트.
-
- Returns:
- 컬럼명 set.
- """
- cols: set[str] = set()
- for rule in rules:
- for cond in rule.get("conditions", []):
- c = cond.get("col")
- if c:
- cols.add(c)
- return cols
-
-
-def build_mtf_scan_frame(
- frames: dict[int, pd.DataFrame],
- needed_cols: set[str],
-) -> pd.DataFrame:
- """
- 주간격(m3) 인덱스에 필요 컬럼만 merge_asof로 붙인 스캔용 DataFrame을 만듭니다.
-
- Args:
- frames: interval → OHLCV.
- needed_cols: 규칙 평가에 필요한 컬럼명.
-
- Returns:
- m3 인덱스 wide DataFrame (close 포함).
- """
- primary = MATCH_PRIMARY_INTERVAL
- raw = frames.get(primary)
- if raw is None or raw.empty:
- raise RuntimeError(f"주간격 {primary}분 데이터 없음")
-
- n_tf = len(GENERAL_ANALYSIS_INTERVALS)
- print(f"[04b] Phase A: {n_tf}TF enrich (스캔용, 주·월봉 포함)...")
- enriched: dict[int, pd.DataFrame] = {}
- for iv in GENERAL_ANALYSIS_INTERVALS:
- r = frames.get(iv)
- if r is None or r.empty:
- continue
- label = interval_tf_prefix(iv)
- print(f" enrich {label} ({len(r):,}봉)...")
- enriched[iv] = general_analysis_enrich_bars(r, iv, full_context=True)
-
- base = enriched[primary].copy()
- if not isinstance(base.index, pd.DatetimeIndex):
- base.index = pd.to_datetime(base.index)
- base = base.sort_index()
- out = pd.DataFrame(index=base.index)
- close_col = "close" if "close" in base.columns else "Close"
- out["close"] = base[close_col].astype(float)
-
- def _source_col(prefixed: str, prefix: str, ef: pd.DataFrame) -> str | None:
- """m3_RSI → RSI, m60_ga_struct_trend → ga_struct_trend."""
- if not prefixed.startswith(f"{prefix}_"):
- return None
- suffix = prefixed[len(prefix) + 1 :]
- if suffix in ef.columns:
- return suffix
- return None
-
- for iv in GENERAL_ANALYSIS_INTERVALS:
- ef = enriched.get(iv)
- if ef is None:
- continue
- p = interval_tf_prefix(iv)
- for col in needed_cols:
- if col in out.columns or not col.startswith(f"{p}_"):
- continue
- src = _source_col(col, p, ef)
- if src is None:
- continue
- if iv == primary:
- out[col] = ef[src].reindex(out.index)
- else:
- sub = ef[[src]].copy()
- if not isinstance(sub.index, pd.DatetimeIndex):
- sub.index = pd.to_datetime(sub.index)
- sub = sub.sort_index().rename(columns={src: col})
- merged = pd.merge_asof(
- out.reset_index(names="_ts"),
- sub.reset_index(names="_ts"),
- on="_ts",
- direction="backward",
- ).set_index("_ts")
- out[col] = merged[col].values
-
- align_needed = [c for c in needed_cols if c.startswith("ga_align_")]
- if align_needed:
- out = _add_align_columns_vectorized(out)
-
- gt_needed = [c for c in needed_cols if c.startswith("gt_")]
- bb_in_rules = "bb_pos" in needed_cols
- if gt_needed or bb_in_rules:
- ef = enriched[primary]
- for src in ("Low", "High", "low", "high", "bb_pos", "Open", "Volume"):
- if src in ef.columns and src not in out.columns:
- out[src] = ef[src].reindex(out.index)
- if "Low" not in out.columns and "low" in out.columns:
- out["Low"] = out["low"]
- if "High" not in out.columns and "high" in out.columns:
- out["High"] = out["high"]
- from deepcoin.ground_truth.gt_signal_rules import enrich_scan_frame_gt_signals
-
- # 시뮬·live 스캔: 타점 판단은 항상 인과적 (GT 정답 생성은 ground_truth.py 별도)
- out = enrich_scan_frame_gt_signals(out, causal=True)
-
- out = out.loc[:, ~out.columns.duplicated()]
- out = out.dropna(subset=["close"])
- print(f"[04b] 스캔 프레임: {len(out):,}봉 × {len(out.columns)}열")
- return out
-
-
-def _eval_one_condition(
- frame: pd.DataFrame,
- cond: dict[str, Any],
-) -> pd.Series:
- """
- 단일 조건 boolean Series.
-
- Args:
- frame: 평가 대상.
- cond: {col, op, ...}.
-
- Returns:
- boolean Series.
- """
- col = cond.get("col")
- if not col or col not in frame.columns:
- return pd.Series(False, index=frame.index)
- s = frame[col]
- op = cond.get("op", "between")
- if op == "between":
- lo, hi = float(cond["lo"]), float(cond["hi"])
- ok = pd.to_numeric(s, errors="coerce")
- part = (ok >= lo) & (ok <= hi)
- elif op == "gte":
- part = pd.to_numeric(s, errors="coerce") >= float(cond["value"])
- elif op == "lte":
- part = pd.to_numeric(s, errors="coerce") <= float(cond["value"])
- elif op == "eq":
- val = cond["value"]
- if isinstance(val, (int, float)) and pd.api.types.is_numeric_dtype(s):
- part = pd.to_numeric(s, errors="coerce") == float(val)
- else:
- part = s.astype(str) == str(val)
- elif op == "eq_int":
- part = (
- pd.to_numeric(s, errors="coerce").fillna(-999).astype(int)
- == int(cond["value"])
- )
- else:
- part = pd.Series(False, index=frame.index)
- return part.fillna(False)
-
-
-def eval_conditions(frame: pd.DataFrame, conditions: list[dict[str, Any]]) -> pd.Series:
- """
- 단일 규칙의 조건을 모두 AND로 평가합니다.
-
- Args:
- frame: 스캔용 DataFrame.
- conditions: {col, op, ...} 리스트.
-
- Returns:
- boolean Series (인덱스=frame.index).
- """
- mask = pd.Series(True, index=frame.index)
- for cond in conditions:
- mask &= _eval_one_condition(frame, cond)
- return mask
-
-
-def eval_rule_mask(frame: pd.DataFrame, rule: dict[str, Any]) -> pd.Series:
- """
- 규칙 dict 평가 (logic=and|or).
-
- Args:
- frame: 스캔/스냅샷 DataFrame.
- rule: conditions, logic 키 포함.
-
- Returns:
- boolean Series.
- """
- conditions = rule.get("conditions") or []
- if not conditions:
- return pd.Series(False, index=frame.index)
- logic = str(rule.get("logic", "and")).lower()
- if logic == "or":
- mask = pd.Series(False, index=frame.index)
- for cond in conditions:
- mask |= _eval_one_condition(frame, cond)
- return mask
- return eval_conditions(frame, conditions)
-
-
-def scan_rule_fires(
- frame: pd.DataFrame,
- rules: list[dict[str, Any]],
-) -> pd.DataFrame:
- """
- 모든 규칙 후보에 대해 발화 시각을 수집합니다.
-
- Args:
- frame: build_mtf_scan_frame 결과.
- rules: rule_candidates.
-
- Returns:
- fire_id, rule_id, side, dt, close 컬럼 DataFrame.
- """
- rows: list[dict[str, Any]] = []
- fid = 0
- for rule in rules:
- rid = rule["rule_id"]
- side = rule["side"]
- mask = eval_rule_mask(frame, rule)
- hits = frame.index[mask]
- close_s = frame["close"]
- if isinstance(close_s, pd.DataFrame):
- close_s = close_s.iloc[:, 0]
- for ts in hits:
- rows.append(
- {
- "fire_id": fid,
- "rule_id": rid,
- "side": side,
- "dt": ts.strftime("%Y-%m-%d %H:%M:%S"),
- "close": _scalar_float(close_s.loc[ts]),
- }
- )
- fid += 1
- print(f" 규칙 {rid}: 발화 {len(hits):,}건")
- if not rows:
- return pd.DataFrame(columns=["fire_id", "rule_id", "side", "dt", "close"])
- return pd.DataFrame(rows)
diff --git a/deepcoin/matching/select_rules.py b/deepcoin/matching/select_rules.py
deleted file mode 100644
index 5bb1bc6..0000000
--- a/deepcoin/matching/select_rules.py
+++ /dev/null
@@ -1,362 +0,0 @@
-"""
-04-4: EV·리스크 필터로 최종 규칙 선별 및 리포트 생성.
-"""
-
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import (
- MATCH_BEST_EFFORT_PER_SIDE,
- MATCH_GT_TOLERANCE_MIN,
- MATCH_HOLDOUT_RATIO,
- MATCH_KIND_PRIORITY,
- MATCH_LABEL_MODE,
- MATCH_MAX_RULES_PER_SIDE,
- MATCH_MAX_VALID_FIRE_RATE,
- MATCH_MIN_EV_VALID,
- MATCH_MIN_FIRES,
- MATCH_MIN_FIRES_HOLDOUT,
- MATCH_MIN_PROFIT_FACTOR,
- MATCH_MONITOR_MAX_PER_SIDE,
- MATCH_TRAIN_RATIO,
-)
-from deepcoin.ground_truth.ground_truth import load_ground_truth
-from deepcoin.paths import resolve_ground_truth_file
-
-
-def _split_train_valid_holdout(df: pd.DataFrame, dt_col: str = "dt") -> pd.Series:
- """
- 시계열 3분할: train / valid / holdout(최근 MATCH_HOLDOUT_RATIO).
-
- Args:
- df: fire_outcomes.
- dt_col: 시각 컬럼.
-
- Returns:
- 'train' | 'valid' | 'holdout' Series.
- """
- ts = pd.to_datetime(df[dt_col])
- holdout_start = ts.quantile(1.0 - MATCH_HOLDOUT_RATIO)
- in_sample = ts <= holdout_start
- cutoff = (
- ts[in_sample].quantile(MATCH_TRAIN_RATIO)
- if in_sample.any()
- else ts.quantile(MATCH_TRAIN_RATIO)
- )
- split = np.where(
- in_sample,
- np.where(ts <= cutoff, "train", "valid"),
- "holdout",
- )
- return pd.Series(split, index=df.index)
-
-
-def _kind_rank(kind: str) -> int:
- """kind 우선순위 (작을수록 우선)."""
- try:
- return MATCH_KIND_PRIORITY.index(kind)
- except ValueError:
- return len(MATCH_KIND_PRIORITY)
-
-
-def _rule_metrics(sub: pd.DataFrame) -> dict[str, float | int]:
- """
- 규칙·구간별 집계 지표.
-
- Args:
- sub: fire_outcomes 부분집합.
-
- Returns:
- count, ev, win_rate, profit_factor.
- """
- if sub.empty:
- return {"count": 0, "ev_pct": 0.0, "win_rate": 0.0, "profit_factor": 0.0}
- r = sub["forward_ret_pct"]
- wins = r[r > 0]
- losses = r[r <= 0]
- pf = (
- float(wins.sum() / abs(losses.sum()))
- if len(losses) and losses.sum() != 0
- else float(wins.sum()) if len(wins) else 0.0
- )
- return {
- "count": int(len(sub)),
- "ev_pct": round(float(r.mean()), 4),
- "win_rate": round(float((r > 0).mean()), 4),
- "profit_factor": round(pf, 4),
- }
-
-
-def gt_overlap_report(
- fires: pd.DataFrame,
- gt_trades: list[dict[str, Any]],
- tolerance_min: int = MATCH_GT_TOLERANCE_MIN,
-) -> dict[str, Any]:
- """
- GT 타점이 규칙 발화와 ±tolerance 내 겹치는 비율을 계산합니다.
-
- Args:
- fires: rule_fires.
- gt_trades: ground truth trades.
- tolerance_min: 분 단위 허용.
-
- Returns:
- side별 recall dict.
- """
- tol = pd.Timedelta(minutes=tolerance_min)
- report: dict[str, Any] = {}
- for side in ("buy", "sell"):
- gt_side = [t for t in gt_trades if t.get("action") == side]
- f_side = fires[fires["side"] == side] if not fires.empty else pd.DataFrame()
- if not gt_side or f_side.empty:
- report[side] = {"gt_count": len(gt_side), "matched": 0, "recall": 0.0}
- continue
- fire_ts = pd.to_datetime(f_side["dt"]).sort_values()
- matched = 0
- for t in gt_side:
- gts = pd.Timestamp(t["dt"])
- delta = (fire_ts - gts).abs()
- if (delta <= tol).any():
- matched += 1
- report[side] = {
- "gt_count": len(gt_side),
- "matched": matched,
- "recall": round(matched / len(gt_side), 4) if gt_side else 0.0,
- }
- return report
-
-
-def select_matched_rules(
- outcomes: pd.DataFrame,
- candidates: dict[str, Any],
- gt_path: Path | None = None,
-) -> dict[str, Any]:
- """
- valid 구간 EV·PF 기준으로 규칙을 선별합니다.
-
- Args:
- outcomes: fire_outcomes.
- candidates: rule_candidates dict.
- gt_path: ground truth JSON.
-
- Returns:
- matched_rules + summaries.
- """
- if outcomes.empty:
- return {"selected": [], "rejected": [], "note": "발화 없음"}
-
- outcomes = outcomes.copy()
- outcomes["split"] = _split_train_valid_holdout(outcomes)
- valid_dt = pd.to_datetime(outcomes.loc[outcomes["split"] == "valid", "dt"])
- valid_bars = max(
- int((valid_dt.max() - valid_dt.min()).total_seconds() / 180) + 1, 1
- ) if len(valid_dt) > 1 else 1
-
- gt_file = gt_path or resolve_ground_truth_file()
- gt_data = load_ground_truth(gt_file) or {}
- gt_trades = gt_data.get("trades") or []
-
- summaries: list[dict[str, Any]] = []
- for rule in candidates.get("rules", []):
- rid = rule["rule_id"]
- sub = outcomes[outcomes["rule_id"] == rid]
- train = sub[sub["split"] == "train"]
- valid = sub[sub["split"] == "valid"]
- holdout = sub[sub["split"] == "holdout"]
- m_all = _rule_metrics(sub)
- m_train = _rule_metrics(train)
- m_valid = _rule_metrics(valid)
- m_holdout = _rule_metrics(holdout)
- fire_rate = m_valid["count"] / valid_bars if valid_bars else 1.0
- pass_valid = (
- m_valid["count"] >= MATCH_MIN_FIRES
- and m_valid["ev_pct"] >= MATCH_MIN_EV_VALID
- and m_valid["profit_factor"] >= MATCH_MIN_PROFIT_FACTOR
- and fire_rate <= MATCH_MAX_VALID_FIRE_RATE
- )
- pass_holdout = (
- m_holdout["count"] >= MATCH_MIN_FIRES_HOLDOUT
- and m_holdout["ev_pct"] >= MATCH_MIN_EV_VALID
- and m_holdout["profit_factor"] >= MATCH_MIN_PROFIT_FACTOR
- )
- summaries.append(
- {
- "rule_id": rid,
- "side": rule["side"],
- "kind": rule.get("kind", ""),
- "conditions": rule["conditions"],
- "valid_fire_rate": round(fire_rate, 4),
- "metrics": {
- "all": m_all,
- "train": m_train,
- "valid": m_valid,
- "holdout": m_holdout,
- },
- "pass_valid": pass_valid,
- "pass_holdout": pass_holdout,
- }
- )
-
- selected: list[dict[str, Any]] = []
- for side in ("buy", "sell"):
- pool = [s for s in summaries if s["side"] == side and s["pass_valid"]]
- pool.sort(
- key=lambda x: (
- x["metrics"]["valid"]["ev_pct"],
- -_kind_rank(x.get("kind", "")),
- ),
- reverse=True,
- )
- selected.extend(pool[:MATCH_MAX_RULES_PER_SIDE])
-
- best_effort: list[dict[str, Any]] = []
- if not selected:
- for side in ("buy", "sell"):
- pool = [
- s
- for s in summaries
- if s["side"] == side
- and s["metrics"]["valid"]["count"] >= MATCH_MIN_FIRES
- and s.get("valid_fire_rate", 1) <= MATCH_MAX_VALID_FIRE_RATE
- ]
- pool.sort(
- key=lambda x: (
- x["metrics"]["valid"]["ev_pct"],
- -_kind_rank(x.get("kind", "")),
- ),
- reverse=True,
- )
- best_effort.extend(pool[:MATCH_BEST_EFFORT_PER_SIDE])
-
- rejected = [s for s in summaries if s not in selected and s not in best_effort]
-
- overlap = gt_overlap_report(
- outcomes[["rule_id", "side", "dt"]].drop_duplicates(),
- gt_trades,
- )
-
- holdout_passed = [s for s in summaries if s["pass_valid"] and s["pass_holdout"]]
- monitor_rules: list[dict[str, Any]] = []
- for side in ("buy", "sell"):
- pool = [s for s in holdout_passed if s["side"] == side]
- pool.sort(
- key=lambda x: (
- x["metrics"]["holdout"]["ev_pct"],
- -_kind_rank(x.get("kind", "")),
- ),
- reverse=True,
- )
- monitor_rules.extend(pool[:MATCH_MONITOR_MAX_PER_SIDE])
- if not monitor_rules:
- for side in ("buy", "sell"):
- pool = [s for s in selected if s["side"] == side] or [
- s for s in best_effort if s["side"] == side
- ]
- pool.sort(
- key=lambda x: (
- x["metrics"].get("holdout", x["metrics"]["valid"])["ev_pct"],
- -_kind_rank(x.get("kind", "")),
- ),
- reverse=True,
- )
- monitor_rules.extend(pool[:MATCH_MONITOR_MAX_PER_SIDE])
-
- active = selected if selected else best_effort
- result = {
- "method": "gt_profile_plus_full_bar_ev_filter",
- "label_mode": MATCH_LABEL_MODE,
- "train_ratio": MATCH_TRAIN_RATIO,
- "holdout_ratio": MATCH_HOLDOUT_RATIO,
- "criteria": {
- "min_fires_valid": MATCH_MIN_FIRES,
- "min_fires_holdout": MATCH_MIN_FIRES_HOLDOUT,
- "min_ev_valid_pct": MATCH_MIN_EV_VALID,
- "min_profit_factor_valid": MATCH_MIN_PROFIT_FACTOR,
- "max_valid_fire_rate": MATCH_MAX_VALID_FIRE_RATE,
- },
- "selected": selected,
- "selected_best_effort": best_effort,
- "holdout_passed": holdout_passed,
- "monitor_rules": monitor_rules,
- "active_rules": active,
- "strict_pass": len(selected) > 0,
- "holdout_pass": len(holdout_passed) > 0,
- "rejected_count": len(rejected),
- "gt_overlap": overlap,
- "valid_bars_approx": valid_bars,
- "all_rule_summaries": summaries,
- "note": (
- "strict EV/PF 통과 규칙 없음 — selected_best_effort는 valid EV 상위(튜닝용)"
- if not selected
- else ""
- ),
- }
- n_out = len(selected) or len(best_effort)
- print(
- f"[04-4] 선별: strict {len(selected)}개, holdout통과 {len(holdout_passed)}개, "
- f"05감시 {len(monitor_rules)}개 / 후보 {len(summaries)}개"
- )
- return result
-
-
-def write_backtest_summary_html(
- matched: dict[str, Any],
- out_path: Path,
-) -> Path:
- """
- backtest_summary.html 생성.
-
- Args:
- matched: select_matched_rules 결과.
- out_path: HTML 경로.
-
- Returns:
- out_path.
- """
- rows = []
- show = matched.get("monitor_rules") or matched.get("selected") or []
- title = "05 monitor_rules (holdout 우선)"
- for s in show:
- v = s["metrics"]["valid"]
- h = s["metrics"].get("holdout", {})
- rows.append(
- f"| {s['rule_id']} | {s['side']} | "
- f"{v['count']} | {v['ev_pct']} | "
- f"{h.get('count', 0)} | {h.get('ev_pct', 0)} | "
- f"{h.get('profit_factor', 0)} |
"
- )
- gt = matched.get("gt_overlap", {})
- html = f"""
-
-04 Backtest Summary
-
-04 매칭 — {title} (valid 구간)
-방법: {matched.get('method','')}
-{matched.get('note','')}
-선별 규칙
-
-| rule_id | side | valid_n | valid_ev |
-holdout_n | holdout_ev | holdout_pf |
|---|
-{''.join(rows) if rows else '| 통과 규칙 없음 |
'}
-
-GT recall (±{MATCH_GT_TOLERANCE_MIN}분, 전체 발화 기준)
-
-- 매수: {gt.get('buy', {})}
-- 매도: {gt.get('sell', {})}
-
-"""
- out_path.parent.mkdir(parents=True, exist_ok=True)
- out_path.write_text(html, encoding="utf-8")
- print(f"[04-4] 리포트: {out_path}")
- return out_path
diff --git a/deepcoin/matching/simulation.py b/deepcoin/matching/simulation.py
deleted file mode 100644
index 978a576..0000000
--- a/deepcoin/matching/simulation.py
+++ /dev/null
@@ -1,936 +0,0 @@
-"""
-Simulation: walk-forward·민감도·Go/No-Go·portfolio_compare 리포트.
-"""
-
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import pandas as pd
-
-from config import (
- GT_INITIAL_CASH_KRW,
- LIVE_ORDER_KRW,
- LIVE_SLIPPAGE_PCT,
- MATCH_HOLDOUT_RATIO,
- MATCH_MIN_EV_VALID,
- MATCH_MIN_FIRES_HOLDOUT,
- MATCH_MIN_PROFIT_FACTOR,
- MATCH_TRAIN_RATIO,
- SIM_FEE_STRESS_MULT,
- SIM_GO_MIN_HOLDOUT_EV,
- SIM_GO_MIN_HOLDOUT_PF,
- SIM_GO_WF_POSITIVE_RATIO,
- SIM_HYBRID_MAX_MDD_PCT,
- SIM_HYBRID_MIN_HOLDOUT_PNL_PCT,
- SIM_OPTION_C_MIN_GT_CAPTURE,
- SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO,
- SIM_OPTION_C_PHASE2_TARGET_PNL_PCT,
- SIM_OPTION_C_TARGET_PNL_PCT,
- SIM_WALK_FORWARD_MIN_MONTHS,
- TRADING_FEE_RATE,
-)
-from deepcoin.ground_truth.ground_truth import (
- load_ground_truth,
- order_trades_chronological,
-)
-from deepcoin.ground_truth.gt_allocation import simulate_portfolio_summary
-from deepcoin.ground_truth.gt_model import (
- default_model,
- model_to_dict,
- summarize_leg_weights,
- weight_policy_summary,
-)
-from deepcoin.matching.portfolio_sim import (
- fires_to_trade_list,
- simulate_fixed_order_portfolio,
- simulate_sized_portfolio,
- sort_fires_chronological,
-)
-from deepcoin.matching.select_rules import _rule_metrics, _split_train_valid_holdout
-from deepcoin.paths import resolve_ground_truth_file
-from deepcoin.paths import (
- ANALYSIS_GT_CALIBRATION_JSON,
- MATCHING_FIRE_OUTCOMES,
- MATCHING_MATCHED_RULES,
- MATCHING_SIMULATION_HTML,
- MATCHING_SIMULATION_JSON,
- resolve_ground_truth_file,
-)
-
-
-def _fee_adjust_ret(series: pd.Series, mult: float) -> pd.Series:
- """
- 수수료 스트레스: 왕복 수수료 %p를 (mult-1)배 추가 차감.
-
- Args:
- series: forward_ret_pct.
- mult: 수수료 배수 (2.0 = 2배).
-
- Returns:
- 조정된 수익률 %.
- """
- extra = TRADING_FEE_RATE * 2 * 100 * (mult - 1.0)
- return series - extra
-
-
-def walk_forward_by_month(outcomes: pd.DataFrame) -> list[dict[str, Any]]:
- """
- 규칙·월별 EV·PF 집계.
-
- Args:
- outcomes: fire_outcomes.
-
- Returns:
- 월별 행 dict 리스트.
- """
- if outcomes.empty:
- return []
- df = outcomes.copy()
- df["ts"] = pd.to_datetime(df["dt"])
- df["month"] = df["ts"].dt.to_period("M").astype(str)
- rows: list[dict[str, Any]] = []
- for (rid, month), grp in df.groupby(["rule_id", "month"]):
- m = _rule_metrics(grp)
- rows.append(
- {
- "rule_id": rid,
- "side": grp["side"].iloc[0],
- "month": month,
- **m,
- }
- )
- return rows
-
-
-def walk_forward_summary(wf_rows: list[dict[str, Any]]) -> dict[str, Any]:
- """
- 규칙별 월별 EV 양수 비율 요약.
-
- Args:
- wf_rows: walk_forward_by_month 결과.
-
- Returns:
- rule_id → {positive_ratio, months, ...}.
- """
- if not wf_rows:
- return {}
- df = pd.DataFrame(wf_rows)
- out: dict[str, Any] = {}
- for rid, grp in df.groupby("rule_id"):
- n = len(grp)
- pos = int((grp["ev_pct"] > 0).sum())
- out[rid] = {
- "months": n,
- "positive_months": pos,
- "positive_ratio": round(pos / n, 4) if n else 0.0,
- "mean_ev_pct": round(float(grp["ev_pct"].mean()), 4),
- }
- return out
-
-
-def simulate_live_order_cap(
- outcomes: pd.DataFrame,
- *,
- rule_ids: set[str] | None = None,
- holdout_only: bool = True,
-) -> dict[str, Any]:
- """
- GT 복리 배분·슬리피지 가정으로 체결 가능한 발화 집계 (일·금액 한도 없음).
-
- Args:
- outcomes: fire_outcomes (split 컬럼 있으면 holdout 필터 가능).
- rule_ids: None이면 전 규칙, 지정 시 해당 rule만.
- holdout_only: True면 split==holdout 만.
-
- Returns:
- 규칙별·전체 요약.
- """
- if outcomes.empty:
- return {"rules": {}, "note": "발화 없음"}
-
- df = outcomes.copy()
- if holdout_only and "split" in df.columns:
- df = df[df["split"] == "holdout"]
- if rule_ids is not None:
- df = df[df["rule_id"].isin(rule_ids)]
- slip = LIVE_SLIPPAGE_PCT
-
- trades = fires_to_trade_list(sort_fires_chronological(df), apply_dynamic_sizing=True)
- executed_dts = {
- t["dt"]
- for t in trades
- if t.get("action") == "sell" or float(t.get("amount_krw") or 0) > 0
- }
- if not executed_dts:
- return {"rules": {}, "taken_count": 0, "total_count": int(len(df))}
-
- taken = df[df["dt"].astype(str).isin(executed_dts)].copy()
- taken["adj_ret_pct"] = taken["forward_ret_pct"] - slip
-
- by_rule: dict[str, Any] = {}
- for rid, grp in taken.groupby("rule_id"):
- g = grp.copy()
- g["forward_ret_pct"] = g["adj_ret_pct"]
- by_rule[rid] = {
- "taken_count": int(len(grp)),
- "total_count": int((df["rule_id"] == rid).sum()),
- "metrics": _rule_metrics(g),
- }
-
- return {
- "assumptions": {
- "slippage_pct": slip,
- "sizing": "gt_model_compound_no_daily_cap",
- },
- "taken_count": int(len(taken)),
- "total_count": int(len(df)),
- "rules": by_rule,
- "portfolio_adj_ev_pct": round(float(taken["adj_ret_pct"].mean()), 4),
- }
-
-
-def evaluate_go_no_go(
- matched: dict[str, Any],
- wf_summary: dict[str, Any],
- fee_stress: dict[str, Any],
- live_cap: dict[str, Any],
-) -> dict[str, Any]:
- """
- monitor_rules·holdout·walk-forward·수수료 스트레스 기준 Go/No-Go.
-
- Args:
- matched: matched_rules.json 내용.
- wf_summary: walk_forward_summary.
- fee_stress: 규칙별 fee 2x EV.
- live_cap: simulate_live_order_cap.
-
- Returns:
- go, checks, monitor_rules 판정.
- """
- rules = matched.get("monitor_rules") or matched.get("selected") or []
- checks: list[dict[str, Any]] = []
- all_go = True
-
- for rule in rules:
- rid = rule["rule_id"]
- h = rule.get("metrics", {}).get("holdout", {})
- ev_h = float(h.get("ev_pct", -999))
- pf_h = float(h.get("profit_factor", 0))
- wf = wf_summary.get(rid, {})
- wf_ratio = float(wf.get("positive_ratio", 0))
- wf_months = int(wf.get("months", 0))
- stress_ev = fee_stress.get(rid, {}).get("ev_pct", -999)
-
- c_holdout = ev_h >= SIM_GO_MIN_HOLDOUT_EV and pf_h >= SIM_GO_MIN_HOLDOUT_PF
- c_wf = wf_months >= SIM_WALK_FORWARD_MIN_MONTHS and wf_ratio >= SIM_GO_WF_POSITIVE_RATIO
- c_fee = stress_ev >= SIM_GO_MIN_HOLDOUT_EV
- ok = c_holdout and c_wf and c_fee
- if not ok:
- all_go = False
- checks.append(
- {
- "rule_id": rid,
- "side": rule.get("side"),
- "pass": ok,
- "holdout_ev": ev_h,
- "holdout_pf": pf_h,
- "wf_positive_ratio": wf_ratio,
- "fee_stress_ev": stress_ev,
- }
- )
-
- return {
- "go": all_go and len(checks) > 0,
- "checks": checks,
- "live_cap_taken_ratio": round(
- live_cap.get("taken_count", 0) / max(live_cap.get("total_count", 1), 1),
- 4,
- ),
- }
-
-
-def portfolio_holdout_from_steps(
- steps: list[dict[str, Any]],
- holdout_start: pd.Timestamp,
- *,
- initial_if_empty: float = GT_INITIAL_CASH_KRW,
- trade_count: int = 0,
- note: str = "",
-) -> dict[str, Any]:
- """
- 포트폴리오 step에서 holdout 구간 자산 증감.
-
- Args:
- steps: simulate_portfolio_steps 결과.
- holdout_start: holdout 시작 시각.
- initial_if_empty: step 없을 때 시작 자산.
- trade_count: holdout 발화 수.
- note: 설명.
-
- Returns:
- holdout pnl 요약 dict.
- """
- if not steps:
- return {"pnl_pct": 0.0, "note": "steps empty"}
- assets = [(pd.to_datetime(s["dt"]), float(s["total_asset_krw"])) for s in steps]
- pre = [a for d, a in assets if d < holdout_start]
- in_h = [a for d, a in assets if d >= holdout_start]
- asset_start = pre[-1] if pre else float(initial_if_empty)
- asset_end = in_h[-1] if in_h else assets[-1][1]
- ho_pnl_pct = (
- (asset_end - asset_start) / asset_start * 100.0 if asset_start > 0 else 0.0
- )
- return {
- "initial_asset_krw": round(asset_start, 0),
- "final_asset_krw": round(asset_end, 0),
- "pnl_krw": round(asset_end - asset_start, 0),
- "pnl_pct": round(ho_pnl_pct, 2),
- "trade_count": int(trade_count),
- "note": note,
- }
-
-
-def evaluate_hybrid_sizing_go(
- base_go: dict[str, Any],
- hybrid_full: dict[str, Any],
- hybrid_holdout: dict[str, Any],
- hybrid_fee_stress: dict[str, Any],
-) -> dict[str, Any]:
- """
- hybrid DD tier 배분 승격 Go/No-Go (규칙 Go + holdout·MDD·수수료 스트레스).
-
- Args:
- base_go: monitor 규칙 evaluate_go_no_go 결과.
- hybrid_full: 전기간 hybrid 포트폴리오 요약.
- hybrid_holdout: holdout 구간 자산 증감.
- hybrid_fee_stress: 수수료 스트레스 hybrid 포트폴리오.
-
- Returns:
- go, checks, primary_sizing 권장.
- """
- from config import (
- SIM_HYBRID_MAX_MDD_PCT,
- SIM_HYBRID_MIN_HOLDOUT_PNL_PCT,
- SIM_OPTION_C_TARGET_PNL_PCT,
- SIM_PRIMARY_SIZING,
- )
-
- base_ok = bool(base_go.get("go"))
- ho_pnl = float(hybrid_holdout.get("pnl_pct", -999))
- full_pnl = float(hybrid_full.get("pnl_pct", 0))
- mdd = float(hybrid_full.get("max_drawdown_pct", 999))
- stress_pnl = float(hybrid_fee_stress.get("pnl_pct", -999))
-
- c_base = base_ok
- c_holdout = ho_pnl >= SIM_HYBRID_MIN_HOLDOUT_PNL_PCT
- c_mdd = mdd <= SIM_HYBRID_MAX_MDD_PCT
- c_fee = stress_pnl > 0.0
- c_target = full_pnl >= SIM_OPTION_C_TARGET_PNL_PCT
-
- all_go = c_base and c_holdout and c_mdd and c_fee
- if SIM_PRIMARY_SIZING == "hybrid":
- primary = "hybrid"
- elif SIM_PRIMARY_SIZING == "causal_tier":
- primary = "causal_tier"
- else:
- primary = "hybrid" if all_go else "causal_tier"
-
- return {
- "go": all_go,
- "primary_sizing": primary,
- "checks": [
- {"name": "monitor_rules_go", "pass": c_base},
- {"name": "hybrid_holdout_pnl", "pass": c_holdout, "value": ho_pnl},
- {"name": "hybrid_max_mdd", "pass": c_mdd, "value": mdd},
- {"name": "hybrid_fee_stress_pnl", "pass": c_fee, "value": stress_pnl},
- {
- "name": "option_c_target_300pct",
- "pass": c_target,
- "value": full_pnl,
- "optional": True,
- },
- ],
- }
-
-
-def simulate_hybrid_order_cap(
- outcomes: pd.DataFrame,
- ohlc_df: pd.DataFrame,
- *,
- rule_ids: set[str] | None = None,
- holdout_only: bool = True,
- fee_rate: float = TRADING_FEE_RATE,
- dd_large_pct: float | None = None,
- dd_medium_pct: float | None = None,
-) -> dict[str, Any]:
- """
- hybrid tier 복리 배분·슬리피지 가정 체결 가능 발화 집계.
-
- Args:
- outcomes: fire_outcomes.
- ohlc_df: 3m OHLC (drawdown).
- rule_ids: monitor rule_id 필터.
- holdout_only: holdout만.
- fee_rate: 수수료율.
-
- Returns:
- simulate_live_order_cap과 동일 구조.
- """
- from deepcoin.ground_truth.causal_gt_hybrid import build_monitor_hybrid_sized_trades
-
- if outcomes.empty:
- return {"rules": {}, "note": "발화 없음"}
-
- df = outcomes.copy()
- if holdout_only and "split" in df.columns:
- df = df[df["split"] == "holdout"]
- if rule_ids is not None:
- df = df[df["rule_id"].isin(rule_ids)]
- slip = LIVE_SLIPPAGE_PCT
-
- sized, _ = build_monitor_hybrid_sized_trades(
- sort_fires_chronological(df),
- ohlc_df,
- enhanced=False,
- fee_rate=fee_rate,
- dd_large_pct=dd_large_pct,
- dd_medium_pct=dd_medium_pct,
- )
- executed_dts = {
- t["dt"]
- for t in sized
- if t.get("action") == "sell" or float(t.get("amount_krw") or 0) > 0
- }
- if not executed_dts:
- return {"rules": {}, "taken_count": 0, "total_count": int(len(df))}
-
- taken = df[df["dt"].astype(str).isin(executed_dts)].copy()
- taken["adj_ret_pct"] = taken["forward_ret_pct"] - slip
-
- by_rule: dict[str, Any] = {}
- for rid, grp in taken.groupby("rule_id"):
- g = grp.copy()
- g["forward_ret_pct"] = g["adj_ret_pct"]
- by_rule[rid] = {
- "taken_count": int(len(grp)),
- "total_count": int((df["rule_id"] == rid).sum()),
- "metrics": _rule_metrics(g),
- }
-
- return {
- "assumptions": {
- "slippage_pct": slip,
- "sizing": "hybrid_dd_tier_compound",
- },
- "taken_count": int(len(taken)),
- "total_count": int(len(df)),
- "rules": by_rule,
- "portfolio_adj_ev_pct": round(float(taken["adj_ret_pct"].mean()), 4),
- }
-
-
-def build_simulation_report(
- outcomes_path: Path | None = None,
- matched_path: Path | None = None,
-) -> dict[str, Any]:
- """
- 시뮬레이션 리포트 dict 생성.
-
- Args:
- outcomes_path: fire_outcomes.csv.
- matched_path: matched_rules.json.
-
- Returns:
- simulation_report 전체 dict.
- """
- op = outcomes_path or MATCHING_FIRE_OUTCOMES
- mp = matched_path or MATCHING_MATCHED_RULES
- if not op.is_file():
- raise FileNotFoundError(f"fire_outcomes 없음: {op} — 04_match_rules.py 먼저 실행")
- outcomes = pd.read_csv(op)
- matched: dict[str, Any] = {}
- if mp.is_file():
- matched = json.loads(mp.read_text(encoding="utf-8"))
-
- outcomes["split"] = _split_train_valid_holdout(outcomes)
- wf_rows = walk_forward_by_month(outcomes)
- wf_sum = walk_forward_summary(wf_rows)
-
- fee_stress: dict[str, Any] = {}
- for rid in outcomes["rule_id"].unique():
- sub = outcomes[outcomes["rule_id"] == rid]
- adj = _fee_adjust_ret(sub["forward_ret_pct"], SIM_FEE_STRESS_MULT)
- fee_stress[rid] = _rule_metrics(
- sub.assign(forward_ret_pct=adj)
- )
-
- monitor_ids = {r["rule_id"] for r in matched.get("monitor_rules", [])}
- live_cap = simulate_live_order_cap(
- outcomes, rule_ids=monitor_ids, holdout_only=True
- )
- go = evaluate_go_no_go(matched, wf_sum, fee_stress, live_cap)
-
- portfolio_compare: dict[str, Any] = {}
- gt_data = load_ground_truth(resolve_ground_truth_file()) or {}
- gt_trades = gt_data.get("trades") or []
- mark = (gt_data.get("summary") or {}).get("mark_price")
- gt_chrono = order_trades_chronological(gt_trades) if gt_trades else []
-
- from deepcoin.ground_truth.gt_signal_rules import gt_signal_rule_ids
- from config import GT_SIGNAL_CAUSAL, SIM_CAUSAL_TIER
- from deepcoin.matching.position_sizing import load_gt_allocation_analysis
-
- gt_alloc_analysis = load_gt_allocation_analysis(gt_trades) if gt_trades else {}
-
- if gt_chrono:
- if not any(float(t.get("amount_krw") or 0) > 0 for t in gt_chrono):
- from deepcoin.ground_truth.ground_truth import allocate_gt_order_amounts
-
- allocate_gt_order_amounts(gt_chrono)
- portfolio_compare["ground_truth_chrono"] = simulate_portfolio_summary(
- gt_chrono,
- last_price=float(mark) if mark else None,
- use_amount_krw=True,
- )
-
- # 전기간 monitor 규칙 — GT_INITIAL_CASH_KRW에서 복리 (holdout만 X)
- all_monitor = outcomes[outcomes["rule_id"].isin(monitor_ids)]
- if not all_monitor.empty:
- sim_trades_full = fires_to_trade_list(sort_fires_chronological(all_monitor))
- portfolio_compare["sim_sized"] = simulate_sized_portfolio(
- sim_trades_full,
- last_price=float(mark) if mark else None,
- )
- portfolio_compare["sim_fixed_order"] = simulate_fixed_order_portfolio(
- fires_to_trade_list(all_monitor, apply_dynamic_sizing=False),
- last_price=float(mark) if mark else None,
- )
-
- # GT 모델 일반화 규칙 (ZigZag+BB 매수 / ZigZag 고점 매도)
- gt_buy_rule = "gt_model_buy_zigzag_bb"
- gt_sell_rule = "gt_model_sell_zigzag_peak"
- gt_pair_ids = {gt_buy_rule, gt_sell_rule}
- if gt_pair_ids.issubset(set(outcomes["rule_id"].unique())):
- gt_pair_fires = outcomes[outcomes["rule_id"].isin(gt_pair_ids)]
- gt_pair_trades = fires_to_trade_list(sort_fires_chronological(gt_pair_fires))
- portfolio_compare["sim_gt_model"] = simulate_sized_portfolio(
- gt_pair_trades,
- last_price=float(mark) if mark else None,
- )
-
- # 인과 GT leg 엔진 (split_buy + peak_sell, 캘리브레이션 파라미터)
- cg_df = None
- try:
- from config import CHART_LOOKBACK_DAYS, MATCH_PRIMARY_INTERVAL, SYMBOL
- from deepcoin.data.mtf_bb import load_frames_from_db
- from deepcoin.ground_truth.causal_gt_calibrate import load_causal_gt_params
- from deepcoin.ground_truth.causal_gt_trades import simulate_causal_gt_portfolio
- from deepcoin.ops.monitor import Monitor
-
- cg_params = load_causal_gt_params()
- mon_cg = Monitor(cooldown_file=None)
- cg_frames = load_frames_from_db(mon_cg, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- cg_df = cg_frames[MATCH_PRIMARY_INTERVAL]
- portfolio_compare["sim_causal_gt"] = simulate_causal_gt_portfolio(
- cg_df,
- last_price=float(mark) if mark else None,
- **cg_params,
- )
- # Phase 3: monitor buy + 인과 peak sell + drawdown tier
- from deepcoin.ground_truth.causal_gt_hybrid import (
- simulate_causal_gt_hybrid_portfolio,
- simulate_monitor_tier_enhanced_portfolio,
- )
- from deepcoin.ground_truth.hybrid_dd_calibrate import load_hybrid_dd_params
-
- dd_params = load_hybrid_dd_params()
-
- buy_only = all_monitor[all_monitor["side"] == "buy"]
- portfolio_compare["sim_causal_hybrid"] = simulate_causal_gt_hybrid_portfolio(
- buy_only,
- cg_df,
- monitor_fires=all_monitor,
- last_price=float(mark) if mark else None,
- cg_params=cg_params,
- dd_large_pct=dd_params.get("dd_large_pct"),
- dd_medium_pct=dd_params.get("dd_medium_pct"),
- )
- portfolio_compare["hybrid_dd_params"] = dd_params
- portfolio_compare["sim_tier_enhanced"] = simulate_monitor_tier_enhanced_portfolio(
- all_monitor,
- cg_df,
- last_price=float(mark) if mark else None,
- )
- except Exception as exc:
- portfolio_compare["sim_causal_gt"] = {
- "pnl_pct": 0.0,
- "note": f"causal_gt sim skipped: {exc}",
- }
- portfolio_compare["sim_causal_hybrid"] = {
- "pnl_pct": 0.0,
- "note": f"causal_hybrid sim skipped: {exc}",
- }
- portfolio_compare["sim_tier_enhanced"] = {
- "pnl_pct": 0.0,
- "note": f"tier_enhanced sim skipped: {exc}",
- }
-
- holdout = outcomes[
- outcomes["rule_id"].isin(monitor_ids) & (outcomes["split"] == "holdout")
- ]
- if not holdout.empty and not all_monitor.empty:
- full_trades = fires_to_trade_list(sort_fires_chronological(all_monitor))
- if full_trades:
- from deepcoin.ground_truth.gt_allocation import simulate_portfolio_steps
-
- steps = simulate_portfolio_steps(full_trades, use_amount_krw=True)
- if steps:
- outcomes_ts = outcomes.copy()
- outcomes_ts["ts"] = pd.to_datetime(outcomes_ts["dt"])
- h0 = outcomes_ts["ts"].quantile(1.0 - MATCH_HOLDOUT_RATIO)
- portfolio_compare["sim_sized_holdout"] = portfolio_holdout_from_steps(
- steps,
- h0,
- trade_count=int(len(holdout)),
- note="전기간 복리(causal tier) 후 holdout 구간 자산 증감",
- )
-
- go_hybrid: dict[str, Any] = {"go": False, "note": "hybrid sim unavailable"}
- go_option_c_phase2: dict[str, Any] = {"go": False, "note": "phase2 unavailable"}
- if (
- cg_df is not None
- and not all_monitor.empty
- and portfolio_compare.get("sim_causal_hybrid", {}).get("sizing_mode")
- ):
- from deepcoin.ground_truth.causal_gt_hybrid import build_monitor_hybrid_sized_trades
- from deepcoin.ground_truth.gt_allocation import simulate_portfolio_steps
- from deepcoin.ground_truth.hybrid_dd_calibrate import load_hybrid_dd_params
- from deepcoin.matching.option_c_phase2 import (
- evaluate_option_c_phase2_go,
- simulate_hybrid_slippage_stress,
- walk_forward_portfolio_by_month,
- walk_forward_portfolio_summary,
- )
-
- dd_params = portfolio_compare.get("hybrid_dd_params") or load_hybrid_dd_params()
- dd_large = dd_params.get("dd_large_pct")
- dd_medium = dd_params.get("dd_medium_pct")
-
- hybrid_full = portfolio_compare["sim_causal_hybrid"]
- sized_h, _ = build_monitor_hybrid_sized_trades(
- sort_fires_chronological(all_monitor),
- cg_df,
- enhanced=False,
- dd_large_pct=dd_large,
- dd_medium_pct=dd_medium,
- )
- steps_h = simulate_portfolio_steps(sized_h, use_amount_krw=True)
- if steps_h and not holdout.empty:
- outcomes_ts = outcomes.copy()
- outcomes_ts["ts"] = pd.to_datetime(outcomes_ts["dt"])
- h0 = outcomes_ts["ts"].quantile(1.0 - MATCH_HOLDOUT_RATIO)
- portfolio_compare["sim_hybrid_holdout"] = portfolio_holdout_from_steps(
- steps_h,
- h0,
- trade_count=int(len(holdout)),
- note="전기간 복리(hybrid DD tier) 후 holdout 구간 자산 증감",
- )
-
- stress_fee = TRADING_FEE_RATE * SIM_FEE_STRESS_MULT
- sized_stress, _ = build_monitor_hybrid_sized_trades(
- sort_fires_chronological(all_monitor),
- cg_df,
- enhanced=False,
- fee_rate=stress_fee,
- dd_large_pct=dd_large,
- dd_medium_pct=dd_medium,
- )
- portfolio_compare["sim_hybrid_fee_stress"] = simulate_portfolio_summary(
- sized_stress,
- fee_rate=stress_fee,
- last_price=float(mark) if mark else None,
- use_amount_krw=True,
- )
-
- portfolio_compare["sim_hybrid_slippage_stress"] = simulate_hybrid_slippage_stress(
- sized_h,
- last_price=float(mark) if mark else None,
- fee_rate=TRADING_FEE_RATE,
- )
- wf_rows = walk_forward_portfolio_by_month(steps_h)
- wf_port = walk_forward_portfolio_summary(wf_rows)
- portfolio_compare["hybrid_portfolio_walk_forward"] = wf_rows
- portfolio_compare["hybrid_portfolio_wf_summary"] = wf_port
-
- gt_pnl_for_phase2 = float(
- (portfolio_compare.get("ground_truth_chrono") or {}).get("pnl_pct", 0)
- )
- go_hybrid = evaluate_hybrid_sizing_go(
- go,
- hybrid_full,
- portfolio_compare.get("sim_hybrid_holdout") or {},
- portfolio_compare.get("sim_hybrid_fee_stress") or {},
- )
- go_option_c_phase2 = evaluate_option_c_phase2_go(
- go_hybrid,
- hybrid_full,
- portfolio_compare.get("sim_hybrid_holdout") or {},
- portfolio_compare.get("sim_hybrid_fee_stress") or {},
- portfolio_compare.get("sim_hybrid_slippage_stress") or {},
- wf_port,
- gt_pnl_for_phase2,
- )
-
- primary = go_hybrid.get("primary_sizing", "causal_tier")
- portfolio_compare["primary_sizing"] = primary
- if primary == "hybrid":
- portfolio_compare["sim_primary"] = {
- **hybrid_full,
- "sizing_mode": "primary_hybrid_dd_tier",
- "sizing_note": (
- "권장: monitor + past-leg·drawdown tier (검증 통과, 미래 미사용)"
- ),
- }
- live_cap = simulate_hybrid_order_cap(
- outcomes,
- cg_df,
- rule_ids=monitor_ids,
- holdout_only=True,
- dd_large_pct=dd_large,
- dd_medium_pct=dd_medium,
- )
- else:
- portfolio_compare["sim_primary"] = portfolio_compare.get("sim_sized") or {}
-
- if portfolio_compare.get("sim_sized") and portfolio_compare.get("ground_truth_chrono"):
- gt_pnl = float(portfolio_compare["ground_truth_chrono"].get("pnl_pct", 0))
- sim_pnl = float(portfolio_compare["sim_sized"].get("pnl_pct", 0))
- portfolio_compare["gt_capture_ratio"] = round(
- sim_pnl / gt_pnl if abs(gt_pnl) > 1e-6 else 0.0,
- 4,
- )
- portfolio_compare["gt_pnl_pct"] = gt_pnl
- portfolio_compare["sim_sized_pnl_pct"] = sim_pnl
- if portfolio_compare.get("sim_gt_model"):
- gtp = float(portfolio_compare["sim_gt_model"].get("pnl_pct", 0))
- portfolio_compare["gt_model_capture_ratio"] = round(
- gtp / gt_pnl if abs(gt_pnl) > 1e-6 else 0.0,
- 4,
- )
- if portfolio_compare.get("sim_causal_gt"):
- cgp = float(portfolio_compare["sim_causal_gt"].get("pnl_pct", 0))
- portfolio_compare["causal_gt_capture_ratio"] = round(
- cgp / gt_pnl if abs(gt_pnl) > 1e-6 else 0.0,
- 4,
- )
- portfolio_compare["sim_causal_gt_pnl_pct"] = cgp
- if portfolio_compare.get("sim_causal_hybrid"):
- chp = float(portfolio_compare["sim_causal_hybrid"].get("pnl_pct", 0))
- portfolio_compare["causal_hybrid_capture_ratio"] = round(
- chp / gt_pnl if abs(gt_pnl) > 1e-6 else 0.0,
- 4,
- )
- portfolio_compare["sim_causal_hybrid_pnl_pct"] = chp
- if portfolio_compare.get("sim_tier_enhanced"):
- tep = float(portfolio_compare["sim_tier_enhanced"].get("pnl_pct", 0))
- portfolio_compare["tier_enhanced_capture_ratio"] = round(
- tep / gt_pnl if abs(gt_pnl) > 1e-6 else 0.0,
- 4,
- )
- portfolio_compare["sim_tier_enhanced_pnl_pct"] = tep
-
- portfolio_compare["causal_gt_params"] = {}
- try:
- from deepcoin.ground_truth.causal_gt_calibrate import load_causal_gt_params
-
- portfolio_compare["causal_gt_params"] = load_causal_gt_params()
- except Exception:
- pass
-
- portfolio_compare["gt_allocation_analysis"] = gt_alloc_analysis
- portfolio_compare["causal_mode"] = {
- "gt_signal_causal": GT_SIGNAL_CAUSAL,
- "sim_causal_tier": SIM_CAUSAL_TIER,
- "note": "인과적: t 시점까지 데이터만 사용 (운영 정합)",
- }
-
- gt_portfolio: dict[str, Any] = {}
- if ANALYSIS_GT_CALIBRATION_JSON.is_file():
- cal = json.loads(ANALYSIS_GT_CALIBRATION_JSON.read_text(encoding="utf-8"))
- gt_portfolio = cal.get("final", {})
-
- summaries = matched.get("all_rule_summaries") or matched.get("monitor_rules") or []
- leg_weight_check = summarize_leg_weights(gt_trades) if gt_trades else {}
- invalid_legs = [lid for lid, info in leg_weight_check.items() if not info.get("valid", True)]
- return {
- "label_mode": matched.get("label_mode"),
- "train_ratio": MATCH_TRAIN_RATIO,
- "holdout_ratio": MATCH_HOLDOUT_RATIO,
- "outcomes_rows": int(len(outcomes)),
- "walk_forward": wf_rows,
- "walk_forward_summary": wf_sum,
- "fee_stress_mult": SIM_FEE_STRESS_MULT,
- "fee_stress_by_rule": fee_stress,
- "live_order_cap_sim": live_cap,
- "go_no_go": go,
- "go_no_go_hybrid": go_hybrid,
- "go_no_go_option_c_phase2": go_option_c_phase2,
- "portfolio_compare": portfolio_compare,
- "gt_model": gt_data.get("model") or model_to_dict(default_model()),
- "gt_weight_policy": weight_policy_summary(default_model()),
- "gt_leg_weight_validation": {
- "legs": leg_weight_check,
- "invalid_leg_ids": invalid_legs,
- "all_valid": len(invalid_legs) == 0,
- },
- "monitor_rules": matched.get("monitor_rules", []),
- "gt_portfolio_calibration": gt_portfolio,
- "criteria": {
- "min_holdout_ev": SIM_GO_MIN_HOLDOUT_EV,
- "min_holdout_pf": SIM_GO_MIN_HOLDOUT_PF,
- "wf_positive_ratio": SIM_GO_WF_POSITIVE_RATIO,
- "wf_min_months": SIM_WALK_FORWARD_MIN_MONTHS,
- "hybrid_min_holdout_pnl_pct": SIM_HYBRID_MIN_HOLDOUT_PNL_PCT,
- "hybrid_max_mdd_pct": SIM_HYBRID_MAX_MDD_PCT,
- "option_c_target_pnl_pct": SIM_OPTION_C_TARGET_PNL_PCT,
- "option_c_phase2_target_pnl_pct": SIM_OPTION_C_PHASE2_TARGET_PNL_PCT,
- "option_c_phase2_fee_stress_ratio": SIM_OPTION_C_PHASE2_FEE_STRESS_RATIO,
- "option_c_min_gt_capture": SIM_OPTION_C_MIN_GT_CAPTURE,
- },
- }
-
-
-def write_simulation_html(report: dict[str, Any], out_path: Path) -> Path:
- """
- simulation_report.html 저장 (ground_truth 차트 동일 스타일).
-
- Args:
- report: build_simulation_report 결과.
- out_path: HTML 경로.
-
- Returns:
- out_path.
- """
- from deepcoin.matching.simulation_html import write_simulation_report_html
-
- return write_simulation_report_html(report, out_path)
-
-
-def run_simulation_report(
- outcomes_path: Path | None = None,
- matched_path: Path | None = None,
-) -> dict[str, Any]:
- """
- 시뮬 리포트 생성·저장·요약 출력.
-
- Args:
- outcomes_path: fire_outcomes.csv.
- matched_path: matched_rules.json.
-
- Returns:
- report dict.
- """
- report = build_simulation_report(outcomes_path, matched_path)
- MATCHING_SIMULATION_JSON.parent.mkdir(parents=True, exist_ok=True)
- MATCHING_SIMULATION_JSON.write_text(
- json.dumps(report, ensure_ascii=False, indent=2),
- encoding="utf-8",
- )
- write_simulation_html(report, MATCHING_SIMULATION_HTML)
- go = report["go_no_go"]["go"]
- go_h = report.get("go_no_go_hybrid") or {}
- pc_early = report.get("portfolio_compare") or {}
- print(f"[시뮬] 저장: {MATCHING_SIMULATION_JSON}")
- print(f"[시뮬] 저장: {MATCHING_SIMULATION_HTML}")
- print(f"[시뮬] Go/No-Go (규칙): {'GO' if go else 'NO-GO'}")
- print(
- f"[시뮬] Go/No-Go (hybrid tier): {'GO' if go_h.get('go') else 'NO-GO'} "
- f"· primary={pc_early.get('primary_sizing', '-')}"
- )
- for c in go_h.get("checks", []):
- mark = "OK" if c.get("pass") else "NG"
- opt = " (optional)" if c.get("optional") else ""
- print(f" [hybrid {mark}] {c.get('name')}: {c.get('value', '-')}{opt}")
- go_p2 = report.get("go_no_go_option_c_phase2") or {}
- print(
- f"[시뮬] Option C 2차(+1000%): {'GO' if go_p2.get('go') else 'NO-GO'}"
- )
- for c in go_p2.get("checks", []):
- mark = "OK" if c.get("pass") else "NG"
- print(f" [phase2 {mark}] {c.get('name')}: {c.get('value', '-')}")
- for c in report["go_no_go"].get("checks", []):
- mark = "OK" if c["pass"] else "NG"
- print(
- f" [{mark}] {c['rule_id']}: holdout EV={c['holdout_ev']} "
- f"WF+={c['wf_positive_ratio']} fee2x EV={c['fee_stress_ev']}"
- )
- cal = report.get("gt_portfolio_calibration") or {}
- port = cal.get("portfolio") or {}
- pc = report.get("portfolio_compare") or {}
- if pc.get("gt_capture_ratio") is not None:
- print(
- f"[시뮬] GT 대비 sim_sized(전기간 복리): {pc.get('sim_sized_pnl_pct')}% "
- f"/ GT {pc.get('gt_pnl_pct')}% "
- f"(capture={pc.get('gt_capture_ratio'):.2%})"
- )
- if pc.get("gt_model_capture_ratio") is not None:
- print(
- f"[시뮬] GT 대비 sim_gt_model: "
- f"{pc.get('sim_gt_model', {}).get('pnl_pct')}% "
- f"(capture={pc.get('gt_model_capture_ratio'):.2%})"
- )
- if pc.get("sim_causal_gt_pnl_pct") is not None:
- scg = pc.get("sim_causal_gt") or {}
- print(
- f"[시뮬] GT 대비 sim_causal_gt(인과 leg): "
- f"{pc.get('sim_causal_gt_pnl_pct')}% "
- f"(capture={pc.get('causal_gt_capture_ratio', 0):.2%}, "
- f"legs={scg.get('leg_count', '-')}, trades={scg.get('trade_count', '-')})"
- )
- if pc.get("sim_causal_hybrid_pnl_pct") is not None:
- sch = pc.get("sim_causal_hybrid") or {}
- print(
- f"[시뮬] GT 대비 sim_causal_hybrid(monitor+DD tier): "
- f"{pc.get('sim_causal_hybrid_pnl_pct')}% "
- f"(capture={pc.get('causal_hybrid_capture_ratio', 0):.2%}, "
- f"MDD={sch.get('max_drawdown_pct', '-')}%)"
- )
- if pc.get("sim_primary"):
- sp = pc["sim_primary"]
- print(
- f"[시뮬] 권장 primary ({pc.get('primary_sizing')}): "
- f"{sp.get('pnl_pct')}% · MDD={sp.get('max_drawdown_pct', '-')}%"
- )
- ho_h = pc.get("sim_hybrid_holdout") or {}
- if ho_h.get("pnl_pct") is not None:
- print(
- f"[시뮬] hybrid holdout: {ho_h.get('pnl_pct')}% "
- f"({ho_h.get('initial_asset_krw')}→{ho_h.get('final_asset_krw')})"
- )
- if pc.get("sim_tier_enhanced_pnl_pct") is not None:
- ste = pc.get("sim_tier_enhanced") or {}
- ast = ste.get("alloc_stats") or {}
- print(
- f"[시뮬] GT 대비 sim_tier_enhanced(conviction tier): "
- f"{pc.get('sim_tier_enhanced_pnl_pct')}% "
- f"(capture={pc.get('tier_enhanced_capture_ratio', 0):.2%}, "
- f"large_buys={ast.get('large_tier_buy_count', '-')}, "
- f"avg_buy={ast.get('buy_amount_avg_krw', '-')})"
- )
- if pc.get("sim_sized", {}).get("max_drawdown_pct") is not None:
- print(
- f"[시뮬] sim_sized MDD: {pc['sim_sized']['max_drawdown_pct']}% "
- f"(GT MDD: {pc.get('ground_truth_chrono', {}).get('max_drawdown_pct')}%)"
- )
- if port.get("asset_ratio") is not None:
- met = cal.get("targets_met", port.get("target_met_90"))
- print(
- f"[시뮬] GT 총자산 대비 leg subset 비율: {port['asset_ratio']:.2%} "
- f"({port.get('legs_covered')}/{port.get('legs_total')} leg) "
- f"목표90%={'달성' if met else '미달'}"
- )
- return report
diff --git a/deepcoin/matching/simulation_html.py b/deepcoin/matching/simulation_html.py
deleted file mode 100644
index 136d7e8..0000000
--- a/deepcoin/matching/simulation_html.py
+++ /dev/null
@@ -1,606 +0,0 @@
-"""
-1단계 시뮬레이션 HTML — ground_truth 차트와 동일 스타일·타점·수익률·규칙 기준.
-"""
-
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Any
-
-import pandas as pd
-
-from config import (
- CHART_LOOKBACK_DAYS,
- COIN_NAME,
- GT_INITIAL_CASH_KRW,
- LIVE_ORDER_KRW,
- SYMBOL,
- TRADING_FEE_RATE,
-)
-from deepcoin.ground_truth.ground_truth import (
- load_ground_truth,
- order_trades_chronological,
- simulate_truth_portfolio,
- simulate_truth_portfolio_steps,
-)
-from deepcoin.matching.portfolio_sim import (
- fires_to_trade_list,
- simulate_fixed_order_portfolio,
- simulate_fixed_order_portfolio_steps,
- simulate_sized_portfolio,
- sort_fires_chronological,
-)
-from deepcoin.matching.select_rules import _split_train_valid_holdout
-from deepcoin.ops.chart_report import (
- card_html,
- go_no_go_table_html,
- market_cards_html,
- pnl_cards_html,
- rule_criteria_html,
- stacked_summary_cards_html,
- wrap_chart_report_page,
-)
-from deepcoin.ops.simulation import build_chart_html, load_chart_frames, _frames_to_mtf
-from deepcoin.common.indicators import apply_bar_indicators, get_trend
-from deepcoin.paths import (
- MATCHING_FIRE_OUTCOMES,
- MATCHING_MATCHED_RULES,
- MATCHING_SIMULATION_HTML,
- resolve_ground_truth_file,
-)
-
-
-def _fires_to_chart_trades(fires: pd.DataFrame) -> list[dict[str, Any]]:
- """
- fire_outcomes 행을 차트 마커용 dict 리스트로 변환.
-
- Args:
- fires: monitor holdout 발화.
-
- Returns:
- build_chart_html sim_trades 인자.
- """
- rows: list[dict[str, Any]] = []
- for _, r in fires.iterrows():
- rows.append(
- {
- "dt": str(r["dt"]),
- "action": r["side"],
- "price": float(r["close"]),
- "forward_ret_pct": float(r["forward_ret_pct"]),
- "rule_id": r["rule_id"],
- }
- )
- return rows
-
-
-def _sim_fire_table_rows(
- fires: pd.DataFrame,
- rules_by_id: dict[str, dict],
- steps: list[dict[str, Any]],
-) -> str:
- """
- 시뮬 발화 테이블 tbody (GT와 동일하게 총 평가금액 포함).
-
- Args:
- fires: holdout 체결 발화.
- rules_by_id: rule_id → rule dict.
- steps: simulate_fixed_order_portfolio_steps 결과.
-
- Returns:
- tr HTML.
- """
- if fires.empty:
- return "| 발화 없음 |
"
- sorted_fires = fires.sort_values("dt").reset_index(drop=True)
- lines: list[str] = []
- lines.append(
- f"""
-
- | 시작 | - | - | - | - |
- ₩{GT_INITIAL_CASH_KRW:,.0f} |
- - | 초기 현금 (1회 ₩{LIVE_ORDER_KRW:,.0f} 가정) |
-
"""
- )
- for i in range(len(sorted_fires)):
- r = sorted_fires.iloc[i]
- side = r["side"]
- cls = "buy" if side == "buy" else "sell"
- mark = "매수" if side == "buy" else "매도"
- ret = float(r["forward_ret_pct"])
- ret_s = f" (+{ret:.2f}%)" if ret > 0 else f" ({ret:.2f}%)"
- win = "승" if int(r.get("win", 0)) else "패"
- win_cls = "pass" if int(r.get("win", 0)) else "fail"
- kind = rules_by_id.get(r["rule_id"], {}).get("kind", "")
- step = steps[i] if i < len(steps) else None
- if step:
- total_s = f"₩{step['total_asset_krw']:,.0f}"
- hold_s = (
- f" (현금 ₩{step['cash_krw']:,.0f} + 코인 {step['holding_qty']:,.2f}개)"
- )
- else:
- total_s = "-"
- hold_s = ""
- lines.append(
- f""
- f"| {str(r['dt'])[:16]} | "
- f'{mark} | '
- f"{r['rule_id']} | "
- f"{kind} | "
- f'₩{float(r["close"]):,.0f}{ret_s} | '
- f"{total_s}{hold_s} | "
- f'{win} | '
- f"leg_gt 구간 수익 | "
- f"
"
- )
- return "\n".join(lines)
-
-
-def _gt_table_rows(trades: list[dict[str, Any]], steps: list[dict[str, Any]]) -> str:
- """
- GT 타점 테이블 tbody (ground_truth 차트와 동일).
-
- Args:
- trades: ground_truth trades.
- steps: simulate_truth_portfolio_steps.
-
- Returns:
- tr HTML.
- """
- if not trades:
- return "| 타점 없음 |
"
- step_key = {
- (s["dt"], s["action"], float(s["price"]), float(s["weight"])): s
- for s in steps
- }
- lines: list[str] = []
- lines.append(
- f"""
-
- | 시작 | - | - | - |
- ₩{GT_INITIAL_CASH_KRW:,.0f} |
- 초기 현금 (보유 0) |
-
"""
- )
- for t in sorted(trades, key=lambda x: x["dt"]):
- cls = "buy" if t["action"] == "buy" else "sell"
- mark = "매수" if t["action"] == "buy" else "매도"
- ret = t.get("forward_return_pct")
- ret_s = f" (+{ret}%)" if ret is not None else ""
- w = float(t.get("weight", 1.0))
- key = (t["dt"], t["action"], float(t["price"]), w)
- step = step_key.get(key)
- if step:
- total_s = f"₩{step['total_asset_krw']:,.0f}"
- hold_s = (
- f" (현금 ₩{step['cash_krw']:,.0f} + 코인 {step['holding_qty']:,.2f}개)"
- )
- else:
- total_s = "-"
- hold_s = ""
- lines.append(
- f""
- f"| {t['dt'][:16]} | "
- f'{mark} | '
- f"{w*100:.0f}% | "
- f"₩{t['price']:,.0f}{ret_s} | "
- f"{total_s}{hold_s} | "
- f"{t.get('memo', '')} | "
- f"
"
- )
- return "\n".join(lines)
-
-
-def _summary_cards_html(
- close_last: float,
- bb_txt: str,
- gt_trades: list[dict[str, Any]],
- gt_pnl: dict[str, Any],
- sim_sized_pnl: dict[str, Any],
- sim_fixed_pnl: dict[str, Any],
- sim_trade_count: int,
- go_flag: bool,
- model_note: str = "",
- sim_causal_gt_pnl: dict[str, Any] | None = None,
- sim_causal_hybrid_pnl: dict[str, Any] | None = None,
- sim_tier_enhanced_pnl: dict[str, Any] | None = None,
- sim_primary_pnl: dict[str, Any] | None = None,
- primary_sizing: str = "",
- hybrid_go: bool = False,
- phase2_go: bool = False,
-) -> str:
- """
- ground_truth HTML과 동일 구성의 상단 카드 (GT + 시뮬 2줄).
-
- Args:
- close_last: 종가.
- bb_txt: BB %B.
- gt_trades: GT trades.
- gt_pnl: GT 포트폴리오 요약.
- sim_sized_pnl: 총자산%·EV/WF·leg 시뮬 요약.
- sim_fixed_pnl: 고정 ₩/회 baseline.
- sim_trade_count: 체결 가정 발화 수.
- go_flag: Go/No-Go.
- model_note: GT 모델 한 줄 요약.
- sim_causal_gt_pnl: 인과 GT leg 엔진 요약.
- sim_causal_hybrid_pnl: monitor buy + 인과 sell 하이브리드.
- sim_tier_enhanced_pnl: monitor + conviction tier.
- sim_primary_pnl: 검증 통과 권장 배분 경로.
- primary_sizing: hybrid | causal_tier.
- hybrid_go: hybrid tier Go/No-Go.
-
- Returns:
- cards HTML.
- """
- go_cls = "go-pass" if go_flag else "go-fail"
- gt_sub = (
- "저점 분할매수(1/price 비중) · 고점 65/35% 매도 · "
- "총자산×비중×leg티어 · 시각순 복리"
- )
- if model_note:
- gt_sub = model_note
- gt_cards = market_cards_html(close_last, bb_txt) + pnl_cards_html(
- gt_pnl, "정답 GT", len(gt_trades)
- )
- sim_sized_title = (
- "시뮬·GT tier 복리 (전기간, 상한 없음) — "
- f'{"GO" if go_flag else "NO-GO"}'
- )
- sim_fixed_title = f"시뮬·고정 ₩{LIVE_ORDER_KRW:,}/회 (비교)"
- primary_block = ""
- if sim_primary_pnl and float(sim_primary_pnl.get("pnl_pct") or 0) != 0:
- hgo_cls = "go-pass" if hybrid_go else "go-fail"
- primary_block = stacked_summary_cards_html(
- (
- f"권장 primary · {primary_sizing or 'causal_tier'} · "
- f'hybrid {"GO" if hybrid_go else "NO-GO"} · '
- f'2차 {"GO" if phase2_go else "NO-GO"}'
- ),
- pnl_cards_html(
- sim_primary_pnl,
- "Primary",
- int(sim_primary_pnl.get("trade_count") or 0),
- ),
- )
- causal_block = ""
- if sim_causal_gt_pnl and (
- float(sim_causal_gt_pnl.get("pnl_pct") or 0) != 0
- or sim_causal_gt_pnl.get("trade_count")
- ):
- legs = sim_causal_gt_pnl.get("leg_count", "-")
- causal_block += stacked_summary_cards_html(
- f"인과 GT leg 엔진 (peak_local·분할매수·causal tier) · leg {legs}개",
- pnl_cards_html(
- sim_causal_gt_pnl,
- "인과 GT",
- int(sim_causal_gt_pnl.get("trade_count") or 0),
- ),
- )
- if sim_causal_hybrid_pnl and (
- float(sim_causal_hybrid_pnl.get("pnl_pct") or 0) != 0
- or sim_causal_hybrid_pnl.get("trade_count")
- ):
- legs_h = sim_causal_hybrid_pnl.get("leg_count", "-")
- causal_block += stacked_summary_cards_html(
- f"하이브리드 (monitor + DD tier) · 발화 {sim_causal_hybrid_pnl.get('input_fires', '-')}건",
- pnl_cards_html(
- sim_causal_hybrid_pnl,
- "하이브리드",
- int(sim_causal_hybrid_pnl.get("trade_count") or 0),
- ),
- )
- if sim_tier_enhanced_pnl and (
- float(sim_tier_enhanced_pnl.get("pnl_pct") or 0) != 0
- or sim_tier_enhanced_pnl.get("trade_count")
- ):
- ast = sim_tier_enhanced_pnl.get("alloc_stats") or {}
- causal_block += stacked_summary_cards_html(
- f"Enhanced tier (conviction·DD) · large매수 {ast.get('large_tier_buy_count', '-')}건",
- pnl_cards_html(
- sim_tier_enhanced_pnl,
- "Enhanced",
- int(sim_tier_enhanced_pnl.get("trade_count") or 0),
- ),
- )
- return (
- ''
- + stacked_summary_cards_html(gt_sub, gt_cards)
- + stacked_summary_cards_html(
- sim_sized_title,
- pnl_cards_html(sim_sized_pnl, "시뮬(비율)", sim_trade_count),
- )
- + stacked_summary_cards_html(
- sim_fixed_title,
- pnl_cards_html(sim_fixed_pnl, "시뮬(고정)", sim_trade_count),
- )
- + primary_block
- + causal_block
- + "
"
- )
-
-
-def build_simulation_page_html(
- report: dict[str, Any],
- outcomes_path: Path | None = None,
- matched_path: Path | None = None,
- gt_path: Path | None = None,
- close_last: float | None = None,
-) -> str:
- """
- 시뮬 리포트 전체 HTML (차트 + Go/No-Go + 규칙 기준 + 타점 테이블).
-
- Args:
- report: build_simulation_report 결과.
- outcomes_path: fire_outcomes.csv.
- matched_path: matched_rules.json.
- gt_path: ground_truth JSON.
- close_last: 미청산 평가 종가 (None이면 DB 종가).
-
- Returns:
- HTML 문자열.
- """
- op = outcomes_path or MATCHING_FIRE_OUTCOMES
- mp = matched_path or MATCHING_MATCHED_RULES
- matched: dict[str, Any] = {}
- if mp.is_file():
- matched = json.loads(mp.read_text(encoding="utf-8"))
-
- monitor_rules = matched.get("monitor_rules") or report.get("monitor_rules") or []
- monitor_ids = {r["rule_id"] for r in monitor_rules}
- rules_by_id = {r["rule_id"]: r for r in monitor_rules}
-
- sim_fires = pd.DataFrame()
- holdout_fires = pd.DataFrame()
- if op.is_file():
- outcomes = pd.read_csv(op)
- outcomes["split"] = _split_train_valid_holdout(outcomes)
- sim_fires = outcomes[outcomes["rule_id"].isin(monitor_ids)].copy()
- holdout_fires = outcomes[
- (outcomes["rule_id"].isin(monitor_ids)) & (outcomes["split"] == "holdout")
- ].copy()
-
- compound_fires = sort_fires_chronological(sim_fires)
-
- gt_data = load_ground_truth(gt_path or resolve_ground_truth_file()) or {}
- gt_trades = gt_data.get("trades") or []
- gt_summary = gt_data.get("summary") or {}
-
- go = report.get("go_no_go", {})
- go_flag = bool(go.get("go"))
- go_hybrid = report.get("go_no_go_hybrid") or {}
- hybrid_go_flag = bool(go_hybrid.get("go"))
- go_phase2 = report.get("go_no_go_option_c_phase2") or {}
- phase2_go_flag = bool(go_phase2.get("go"))
- pc = report.get("portfolio_compare") or {}
- label_mode = report.get("label_mode", "leg_gt")
-
- frames = load_chart_frames()
- bb_txt = "-"
- trend = "-"
- close_val = float(close_last or 0)
- if frames is not None:
- df_1d, df_1h, df_3m = _frames_to_mtf(frames)
- trend = get_trend(df_1d, df_1h)
- df_chart = apply_bar_indicators(df_3m)
- close_val = float(df_chart["Close"].iloc[-1])
- bb_pos = (
- float(df_chart["bb_pos"].iloc[-1])
- if "bb_pos" in df_chart.columns and pd.notna(df_chart["bb_pos"].iloc[-1])
- else None
- )
- bb_txt = f"{bb_pos:.2f}" if bb_pos is not None else "-"
- elif gt_summary.get("mark_price"):
- close_val = float(gt_summary["mark_price"])
-
- sim_trades_sized = fires_to_trade_list(compound_fires, apply_dynamic_sizing=True)
- sim_trades_fixed = fires_to_trade_list(compound_fires, apply_dynamic_sizing=False)
-
- gt_pnl: dict[str, Any] = {}
- if gt_trades:
- gt_chron = order_trades_chronological(gt_trades)
- gt_pnl = simulate_truth_portfolio(
- gt_chron,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- last_price=close_val if close_val else None,
- )
-
- mark = close_val if close_val else None
- sim_sized_pnl = simulate_sized_portfolio(
- sim_trades_sized,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- last_price=mark,
- )
- sim_fixed_pnl = simulate_fixed_order_portfolio(
- sim_trades_fixed,
- order_krw=LIVE_ORDER_KRW,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- last_price=mark,
- sizing_mode="fixed",
- )
- sim_steps = simulate_fixed_order_portfolio_steps(
- sim_trades_sized,
- order_krw=LIVE_ORDER_KRW,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- sizing_mode="amount_krw",
- )
- gt_steps = (
- simulate_truth_portfolio_steps(
- order_trades_chronological(gt_trades),
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- )
- if gt_trades
- else []
- )
- model = gt_data.get("model") or {}
- model_note = (
- f"mode={model.get('selection_mode', 'split_buy_peak_sell')} · "
- f"매수비중=1/price · 매도=65/35%"
- if model
- else ""
- )
-
- criteria_blocks = "".join(rule_criteria_html(r) for r in monitor_rules)
- go_table = go_no_go_table_html(go.get("checks", []), go_flag)
- hybrid_checks = go_hybrid.get("checks") or []
- if hybrid_checks:
- hybrid_rows = "".join(
- f"| {c.get('name')} | "
- f"{'PASS' if c.get('pass') else 'FAIL'} | "
- f"{c.get('value', '-')} |
"
- for c in hybrid_checks
- )
- hgo_cls = "go-pass" if hybrid_go_flag else "go-fail"
- go_table += (
- f"Hybrid tier Go/No-Go · "
- f'{"GO" if hybrid_go_flag else "NO-GO"}
'
- f""
- )
- phase2_checks = go_phase2.get("checks") or []
- if phase2_checks:
- p2_rows = "".join(
- f"| {c.get('name')} | "
- f"{'PASS' if c.get('pass') else 'FAIL'} | "
- f"{c.get('value', '-')} |
"
- for c in phase2_checks
- )
- p2_cls = "go-pass" if phase2_go_flag else "go-fail"
- go_table += (
- f"Option C 2차 (+1000%) · "
- f'{"GO" if phase2_go_flag else "NO-GO"}
'
- f""
- )
-
- def _mark_note(price: float) -> str:
- if price > 0:
- return f" 총보유자산(미청산 포함)은 종가 ₩{price:,.0f} 평가."
- return ""
-
- sim_table = f"""
- 시뮬 타점 (전기간 {len(sim_fires)}건 → 복리 체결 {len(compound_fires)}건)
- 총자산×GT비중×leg tier·보유현금 한도·전기간 복리(일한도 없음).
- 가격 열 (+/-) = {label_mode} 구간 수익%.{_mark_note(close_val)}
- """
-
- gt_table = f"""
- 정답 타점 (ground_truth)
- 삼각형 크기 = GT 체결 금액. 매수 분할·매도 leg 반영.{_mark_note(close_val)}
- """
-
- sections = f"""
- {go_table}
- 매수·매도 판단 기준 (monitor_rules)
- 04 GT 프로필 + 전구간 EV 선별. 조건 모두 충족 시 3분봉 종가에 신호.
- {criteria_blocks}
- {sim_table}
- {gt_table}
-"""
-
- note = (
- f"1단계 시뮬 · holdout {report.get('holdout_ratio', 0.15)} · "
- f"전기간 발화 {len(sim_fires)}건 / holdout {len(holdout_fires)}건. "
- "상단 카드: 초기 금액·총보유자산·초기 대비 증감율·수수료."
- )
- legend = (
- "▲ 정답 매수 · ▼ 정답 매도 — 삼각형 = GT 체결 금액.
"
- "● 시뮬 — holdout 발화 (차트). 테이블 = 전기간 GT tier 복리 체결."
- )
- if frames is not None:
- meta_line = (
- f"추세(참고): {trend} | 기간: {df_chart.index[0]} ~ {df_chart.index[-1]} "
- f"| 봉 {len(df_chart)}"
- )
- else:
- meta_line = (
- f"추세·{SYMBOL} | lookback {CHART_LOOKBACK_DAYS}일 | "
- f"초기 ₩{GT_INITIAL_CASH_KRW:,.0f}"
- )
-
- cards = _summary_cards_html(
- close_val,
- bb_txt,
- gt_trades,
- gt_pnl,
- sim_sized_pnl,
- sim_fixed_pnl,
- len(compound_fires),
- go_flag,
- model_note=model_note,
- sim_causal_gt_pnl=pc.get("sim_causal_gt"),
- sim_causal_hybrid_pnl=pc.get("sim_causal_hybrid"),
- sim_tier_enhanced_pnl=pc.get("sim_tier_enhanced"),
- sim_primary_pnl=pc.get("sim_primary"),
- primary_sizing=str(pc.get("primary_sizing") or ""),
- hybrid_go=hybrid_go_flag,
- phase2_go=phase2_go_flag,
- )
-
- if frames is not None:
- return build_chart_html(
- df_chart,
- trend,
- note=note,
- truth_trades=gt_trades,
- sim_trades=_fires_to_chart_trades(holdout_fires),
- # 차트 마커는 holdout; 카드·테이블은 전기간 GT tier 복리
- title_suffix="1단계 시뮬레이션 (monitor · holdout)",
- legend_html=legend,
- footer_sections=sections,
- cards_html=cards,
- )
-
- return wrap_chart_report_page(
- page_title=f"{SYMBOL} 시뮬레이션",
- heading=f"{COIN_NAME} ({SYMBOL}) 1단계 시뮬레이션",
- meta_line=meta_line,
- note_html=f"{note}
",
- legend_html=legend,
- cards_html=cards,
- chart_html=(
- "차트 데이터 없음 — "
- "python scripts/01_download.py 후 재생성.
"
- ),
- sections_html=sections,
- )
-
-
-def write_simulation_report_html(
- report: dict[str, Any],
- out_path: Path,
- outcomes_path: Path | None = None,
- matched_path: Path | None = None,
-) -> Path:
- """
- simulation_report.html 저장 (ground_truth 동일 스타일).
-
- Args:
- report: build_simulation_report 결과.
- out_path: HTML 경로.
- outcomes_path: fire_outcomes.csv.
- matched_path: matched_rules.json.
-
- Returns:
- out_path.
- """
- html = build_simulation_page_html(report, outcomes_path, matched_path)
- out_path.parent.mkdir(parents=True, exist_ok=True)
- out_path.write_text(html, encoding="utf-8")
- return out_path
diff --git a/deepcoin/ops/__init__.py b/deepcoin/ops/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deepcoin/ops/alert_message.py b/deepcoin/ops/alert_message.py
deleted file mode 100644
index 516e598..0000000
--- a/deepcoin/ops/alert_message.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""
-05 규칙 알림 텔레그램 메시지 포맷.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from config import COIN_NAME, MONITOR_ALERT_KRW_AMOUNT, SYMBOL
-
-
-def _fmt_krw(value: float) -> str:
- """원화 금액 표시."""
- if value >= 100:
- return f"₩{value:,.0f}"
- if value >= 1:
- return f"₩{value:,.2f}"
- return f"₩{value:,.4f}"
-
-
-def _fmt_price(value: float) -> str:
- """코인 단가 표시."""
- if value >= 100:
- return f"₩{value:,.0f}"
- if value >= 10:
- return f"₩{value:,.2f}"
- if value >= 1:
- return f"₩{value:,.3f}"
- return f"₩{value:,.4f}"
-
-
-def _holding_qty(balances: dict[str, dict[str, float]], symbol: str) -> float:
- """
- 잔고 dict에서 코인 보유 수량을 반환합니다.
-
- Args:
- balances: load_balances_dict() 결과.
- symbol: 통화 코드 (예: WLD).
-
- Returns:
- 보유 수량 (없으면 0).
- """
- info = balances.get(symbol) or {}
- return float(info.get("balance") or 0.0)
-
-
-def build_rule_alert_message(
- hit: dict[str, Any],
- balances: dict[str, dict[str, float]] | None = None,
- *,
- trade_krw: float | None = None,
- trade_qty: float | None = None,
-) -> str:
- """
- 규칙 발화 알림 본문을 만듭니다.
-
- trade_krw·trade_qty가 있으면 실제(모의) 체결 규모를 표시합니다.
- 없으면 매수는 MONITOR_ALERT_KRW_AMOUNT, 매도는 보유×가격(또는 참고 금액).
-
- Args:
- hit: evaluate_live_rules 항목 (side, rule_id, dt, close).
- balances: 잔고 dict (모의·실거래).
- trade_krw: 체결 원화(모의·실거래 planned/executed).
- trade_qty: 체결 수량(매도 시).
-
- Returns:
- 텔레그램 메시지 문자열.
- """
- side = str(hit.get("side", "")).upper()
- close = float(hit["close"])
- rule_id = hit.get("rule_id", "")
- dt = hit.get("dt", "")
-
- qty_basis = ""
- if trade_krw is not None and trade_krw > 0:
- amount = float(trade_krw)
- if trade_qty is not None and trade_qty > 0:
- qty = float(trade_qty)
- qty_basis = "체결 기준"
- elif close > 0:
- qty = amount / close
- qty_basis = "체결 기준(원화→수량)"
- else:
- qty = 0.0
- qty_basis = "체결 기준"
- elif side == "SELL" and balances is not None:
- qty = _holding_qty(balances, SYMBOL)
- amount = qty * close
- qty_basis = "보유 기준(체결 전)"
- elif side == "BUY":
- amount = float(MONITOR_ALERT_KRW_AMOUNT)
- qty = amount / close if close > 0 else 0.0
- qty_basis = "참고 매수 규모(알림용)"
- else:
- amount = float(MONITOR_ALERT_KRW_AMOUNT)
- qty = amount / close if close > 0 else 0.0
- qty_basis = "참고 규모(잔고 미조회)"
-
- lines = [
- f"[DeepCoin {side}] {COIN_NAME}",
- f"규칙: {rule_id}",
- f"시각: {dt}",
- f"가격: {_fmt_price(close)}",
- f"수량: {qty:,.4f} {SYMBOL} ({qty_basis})",
- f"금액: {_fmt_krw(amount)}",
- "※ 알림만 전송, 자동 주문 없음",
- ]
- return "\n".join(lines)
diff --git a/deepcoin/ops/chart_report.py b/deepcoin/ops/chart_report.py
deleted file mode 100644
index abed108..0000000
--- a/deepcoin/ops/chart_report.py
+++ /dev/null
@@ -1,308 +0,0 @@
-"""
-ground_truth·시뮬 등 차트 HTML 공통 레이아웃·스타일.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-CHART_REPORT_CSS = """
- body { font-family: "Malgun Gothic", Arial, sans-serif; margin: 24px; background: #f8fafc; }
- h1 { font-size: 1.35rem; }
- h2 { font-size: 1.1rem; margin-top: 28px; }
- .meta { color: #475569; font-size: 0.9rem; }
- .note { background: #f1f5f9; border: 1px solid #cbd5e1; padding: 10px; border-radius: 6px;
- color: #334155; font-size: 0.9rem; line-height: 1.5; }
- .go { font-size: 1.25rem; font-weight: 700; }
- .go-pass { color: #16a34a; }
- .go-fail { color: #dc2626; }
- .cards { display: flex; flex-wrap: wrap; gap: 10px; margin: 16px 0; }
- .card { background: #fff; border: 1px solid #e2e8f0; border-radius: 8px; padding: 10px 14px; }
- .card span { font-size: 0.75rem; color: #64748b; display: block; }
- .card b { font-size: 1.05rem; }
- .chart-wrap { background:#fff; border:1px solid #e2e8f0; border-radius:8px; padding:8px; }
- .legend-box { font-size:0.85rem; color:#475569; margin-bottom:10px; line-height: 1.6; }
- table { width:100%; border-collapse:collapse; background:#fff; font-size:0.85rem; }
- th, td { border:1px solid #e2e8f0; padding:8px; text-align:left; }
- th { background:#f1f5f9; }
- td.buy { color:#16a34a; font-weight:600; }
- td.sell { color:#dc2626; font-weight:600; }
- td.num { text-align: right; font-variant-numeric: tabular-nums; }
- .criteria { background:#fff; border:1px solid #e2e8f0; border-radius:8px;
- padding:12px 16px; margin:12px 0; }
- .criteria h3 { margin: 0 0 8px; font-size: 1rem; }
- .criteria ul { margin: 6px 0 0 18px; padding: 0; }
- .criteria li { margin: 4px 0; color: #334155; }
- .criteria .kind { color: #64748b; font-size: 0.85rem; }
- .table-scroll { max-height: 480px; overflow-y: auto; border: 1px solid #e2e8f0; border-radius: 8px; }
- .pass { color: #16a34a; font-weight: 600; }
- .fail { color: #dc2626; font-weight: 600; }
- .summary-cards { margin: 16px 0; }
- .summary-cards .cards-row-block { display: block; width: 100%; margin-bottom: 14px; }
- .summary-cards .cards-row-block:last-child { margin-bottom: 0; }
- .cards-group-title {
- font-size: 0.82rem; color: #475569; margin: 0 0 8px; font-weight: 600;
- display: block;
- }
-"""
-
-
-def initial_change_pct(pnl: dict[str, Any]) -> float:
- """
- 초기 금액 대비 총보유자산 증감율(%)을 계산합니다.
-
- Args:
- pnl: initial_cash_krw, final_asset_krw (또는 pnl_pct) 포함 dict.
-
- Returns:
- 증감율 %.
- """
- if pnl.get("pnl_pct") is not None:
- return float(pnl["pnl_pct"])
- initial = float(pnl.get("initial_cash_krw") or 0)
- final = float(pnl.get("final_asset_krw") or 0)
- if initial <= 0:
- return 0.0
- return (final - initial) / initial * 100.0
-
-
-def pnl_cards_html(pnl: dict[str, Any], trade_label: str, trade_count: int) -> str:
- """
- GT·시뮬 HTML 공통 자산 요약 카드 (총보유자산·초기 대비 증감율).
-
- Args:
- pnl: simulate_truth_portfolio 또는 simulate_fixed_order_portfolio 결과.
- trade_label: 타점 라벨(예: 정답 타점, 시뮬 체결).
- trade_count: 타점 건수.
-
- Returns:
- card div HTML 연속 문자열.
- """
- if pnl.get("initial_cash_krw") is None:
- return card_html(trade_label, f"{trade_count}건")
- change_pct = initial_change_pct(pnl)
- out = card_html(trade_label, f"{trade_count}건")
- out += card_html("초기 금액", f"₩{pnl['initial_cash_krw']:,.0f}")
- out += card_html("총보유자산", f"₩{pnl['final_asset_krw']:,.0f}")
- out += card_html("초기 대비 증감율", f"{change_pct:+.2f}%")
- out += card_html("수수료", f"₩{pnl['total_fees_krw']:,.0f}")
- if pnl.get("holding_qty", 0) > 0:
- out += card_html(
- "미청산",
- f"{pnl['holding_qty']}개 (₩{pnl['holding_value_krw']:,.0f})",
- )
- return out
-
-
-def market_cards_html(close_last: float, bb_pos_txt: str) -> str:
- """
- 종가·BB %B 카드.
-
- Args:
- close_last: 종가.
- bb_pos_txt: BB %B 표시 문자열.
-
- Returns:
- card HTML.
- """
- return card_html("종가", f"₩{close_last:,.2f}") + card_html("BB %B", bb_pos_txt)
-
-
-def card_html(label: str, value: str) -> str:
- """
- 요약 카드 HTML 한 칸.
-
- Args:
- label: 라벨.
- value: 값(HTML 허용).
-
- Returns:
- div.card 문자열.
- """
- return f'{label}{value}
'
-
-
-def stacked_summary_cards_html(
- title: str,
- cards_inner: str,
-) -> str:
- """
- 제목 한 줄 + 카드 flex 한 줄을 세로 블록으로 묶습니다.
-
- Args:
- title: cards-group-title 텍스트(HTML 허용).
- cards_inner: .cards 안에 넣을 card div 문자열.
-
- Returns:
- cards-row-block HTML.
- """
- return (
- ''
- f'
{title}
'
- f'
{cards_inner}
'
- "
{heading}
- {meta_line}
- {note_html}
- {legend_html}
- {cards_html if "summary-cards" in cards_html else f'{cards_html}
'}
- {chart_html}
- {sections_html}
-
-"""
-
-
-_COL_LABELS: dict[str, str] = {
- "m3_bb_pos": "3분 BB %B",
- "m15_bb_pos": "15분 BB %B",
- "m30_bb_pos": "30분 BB %B",
- "m3_RSI": "3분 RSI",
- "m15_RSI": "15분 RSI",
- "m30_RSI": "30분 RSI",
- "m3_stoch_k": "3분 Stoch %K",
- "m15_stoch_k": "15분 Stoch %K",
-}
-
-
-def _col_label(col: str) -> str:
- """지표 컬럼 한글 라벨."""
- return _COL_LABELS.get(col, col)
-
-
-def _format_condition(cond: dict[str, Any]) -> str:
- """
- 규칙 조건 dict를 읽기 쉬운 문자열로 변환.
-
- Args:
- cond: {col, op, lo, hi, value}.
-
- Returns:
- 한 줄 설명.
- """
- col = _col_label(str(cond.get("col", "")))
- op = cond.get("op", "")
- if op == "between":
- return f"{col} ∈ [{cond.get('lo'):.4g}, {cond.get('hi'):.4g}]"
- if op == "lte":
- return f"{col} ≤ {cond.get('value'):.4g}"
- if op == "gte":
- return f"{col} ≥ {cond.get('value'):.4g}"
- if op == "lt":
- return f"{col} < {cond.get('value'):.4g}"
- if op == "gt":
- return f"{col} > {cond.get('value'):.4g}"
- return f"{col} {op} {cond}"
-
-
-_KIND_LABELS: dict[str, str] = {
- "contrast": "대조 — GT 매수·매도 프로필 대비 반대 구간 (m3 중심)",
- "compound_tight": "복합 타이트 — GT 프로필 상위 3지표 AND 동시 충족",
- "compound": "복합 TOP3 — GT 프로필 상위 3지표 AND",
- "atomic": "단일 — GT 프로필 1지표",
- "wide": "완화 — 프로필 외곽 구간",
-}
-
-
-def format_rule_kind(kind: str) -> str:
- """규칙 kind 한글 설명."""
- return _KIND_LABELS.get(kind, kind)
-
-
-def rule_criteria_html(rule: dict[str, Any]) -> str:
- """
- monitor_rule 1개의 매칭 기준 블록 HTML.
-
- Args:
- rule: matched_rules 내 rule dict.
-
- Returns:
- .criteria 블록 HTML.
- """
- rid = rule.get("rule_id", "")
- side = "매수" if rule.get("side") == "buy" else "매도"
- kind = rule.get("kind", "")
- conds = rule.get("conditions") or []
- items = "".join(f"{_format_condition(c)}" for c in conds)
- m = rule.get("metrics", {}).get("all", {})
- hold = rule.get("metrics", {}).get("holdout", {})
- return f"""
-
-
{rid} ({side} · {format_rule_kind(kind)})
-
발화 시 3분봉 종가·8TF 지표가 아래를 모두 만족하면 {side} 신호.
-
-
전구간 EV {m.get('ev_pct', '-')}%% · holdout EV {hold.get('ev_pct', '-')}%% ·
- holdout PF {hold.get('profit_factor', '-')}
-
| {c.get('rule_id')} | {c.get('side')} | "
- f'{mark} | '
- f'{c.get("holdout_ev")} | '
- f'{c.get("holdout_pf")} | '
- f'{c.get("wf_positive_ratio")} | '
- f'{c.get("fee_stress_ev")} |
'
- )
- body = "\n".join(rows) if rows else "| 없음 |
"
- return f"""
- Go/No-Go (monitor_rules)
- 종합 판정: {label}
-
- | 규칙 | side | 판정 | holdout EV% |
- holdout PF | WF 양수월 | 수수료 2x EV% |
|---|
- {body}
-
"""
diff --git a/deepcoin/ops/hybrid_sim_execution.py b/deepcoin/ops/hybrid_sim_execution.py
deleted file mode 100644
index 6924309..0000000
--- a/deepcoin/ops/hybrid_sim_execution.py
+++ /dev/null
@@ -1,473 +0,0 @@
-"""
-시뮬 sim_causal_hybrid 와 동일 체결 엔진 (build_monitor_hybrid_sized_trades).
-
-live(06) plan_live_hit: 발화 이력 → hybrid 배분 → amount_krw·수량 (인과, 현금·보유 제약).
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Any
-
-import pandas as pd
-
-from config import (
- CHART_LOOKBACK_DAYS,
- GT_INITIAL_CASH_KRW,
- LIVE_HYBRID_BOOTSTRAP_FIRES,
- TRADING_FEE_RATE,
-)
-from deepcoin.ground_truth.causal_gt_hybrid import build_monitor_hybrid_sized_trades
-from deepcoin.ground_truth.gt_allocation import resolve_sell_qty
-from deepcoin.ground_truth.hybrid_dd_calibrate import load_hybrid_dd_params
-from deepcoin.matching.load_rules import load_monitor_rules
-from deepcoin.paths import MATCHING_FIRE_OUTCOMES
-
-
-@dataclass
-class SimTradeResult:
- """단일 발화에 대한 시뮬 배분·체결 결과."""
-
- hit: dict[str, Any]
- amount_krw: float
- sell_qty: float
- ok: bool
- message: str
- leg_id: int | None = None
-
-
-def bootstrap_monitor_signals_from_outcomes(
- *,
- end_dt: str | None = None,
- lookback_days: int | None = None,
-) -> list[dict[str, Any]]:
- """
- 04 fire_outcomes 에서 monitor 규칙 발화를 로드 (시뮬 all_monitor 와 동일 입력).
-
- Args:
- end_dt: 이 시각 이전만 포함 (None=전체).
- lookback_days: CHART_LOOKBACK_DAYS 대신 사용할 일수.
-
- Returns:
- {dt, rule_id, side, close} 리스트 (시각순).
- """
- import pandas as pd
-
- path = MATCHING_FIRE_OUTCOMES
- if not path.is_file():
- return []
- monitor_ids = {r["rule_id"] for r in load_monitor_rules()}
- if not monitor_ids:
- return []
- df = pd.read_csv(path)
- if df.empty or "rule_id" not in df.columns:
- return []
- sub = df[df["rule_id"].isin(monitor_ids)].copy()
- if sub.empty:
- return []
- sub["dt"] = sub["dt"].astype(str)
- if end_dt:
- sub = sub[sub["dt"] <= str(end_dt)]
- if lookback_days is not None and lookback_days > 0:
- end_ts = pd.to_datetime(sub["dt"].max()) if end_dt is None else pd.to_datetime(end_dt)
- start = end_ts - pd.Timedelta(days=int(lookback_days))
- sub = sub[pd.to_datetime(sub["dt"]) >= start]
- elif lookback_days is None and CHART_LOOKBACK_DAYS > 0:
- end_ts = pd.to_datetime(sub["dt"].max())
- start = end_ts - pd.Timedelta(days=int(CHART_LOOKBACK_DAYS))
- sub = sub[pd.to_datetime(sub["dt"]) >= start]
- rows: list[dict[str, Any]] = []
- for _, r in sub.iterrows():
- rows.append(
- {
- "dt": str(r["dt"]),
- "rule_id": str(r["rule_id"]),
- "side": str(r["side"]),
- "close": float(r["close"]),
- }
- )
- return sort_hits_sim_order(rows)
-
-
-def merge_signal_histories(
- *histories: list[dict[str, Any]],
-) -> list[dict[str, Any]]:
- """
- 발화 이력 병합 (dt+rule_id+side 기준 중복 제거, 시뮬 정렬).
-
- Args:
- *histories: 신호 dict 리스트들.
-
- Returns:
- 병합·정렬된 리스트.
- """
- seen: set[tuple[str, str, str]] = set()
- merged: list[dict[str, Any]] = []
- for hist in histories:
- for h in hist:
- key = hit_key(h)
- if key in seen:
- continue
- seen.add(key)
- merged.append(
- {
- "dt": key[0],
- "rule_id": key[1],
- "side": key[2],
- "close": float(h["close"]),
- }
- )
- return sort_hits_sim_order(merged)
-
-
-def build_live_signal_history(
- persisted: list[dict[str, Any]] | None = None,
- *,
- bootstrap_fires: bool | None = None,
-) -> list[dict[str, Any]]:
- """
- 운영 hybrid 이력: fire_outcomes 부트스트랩 + live 저장분 병합.
-
- Args:
- persisted: live_signal_history.json signals.
- bootstrap_fires: fire_outcomes 부트스트랩 여부. None이면 config.
-
- Returns:
- sim_causal_hybrid 입력과 동일 형식의 이력.
- """
- use_boot = (
- LIVE_HYBRID_BOOTSTRAP_FIRES if bootstrap_fires is None else bootstrap_fires
- )
- parts: list[list[dict[str, Any]]] = []
- if use_boot:
- boot = bootstrap_monitor_signals_from_outcomes()
- if boot:
- parts.append(boot)
- if persisted:
- parts.append(persisted)
- if not parts:
- return []
- return merge_signal_histories(*parts)
-
-
-class HybridSimPortfolio:
- """
- hybrid 배분 결과를 현금·보유 수량에 적용 (시뮬·live plan 공용, API 미사용).
- """
-
- def __init__(self) -> None:
- """초기 현금만 보유."""
- self.cash_krw: float = float(GT_INITIAL_CASH_KRW)
- self.qty: float = 0.0
- self.qty_by_leg: dict[int, float] = {}
- self.sell_leg: int | None = None
- self.sell_base_qty: float = 0.0
-
- def apply_buy(self, amount_krw: float, price: float, leg_id: int) -> bool:
- """
- 매수 체결 (가용 현금·수수료 범위).
-
- Args:
- amount_krw: 매수 원화.
- price: 체결가.
- leg_id: leg ID.
-
- Returns:
- 체결 성공 여부.
- """
- if amount_krw <= 0 or price <= 0:
- return False
- fee = amount_krw * TRADING_FEE_RATE
- if self.cash_krw < amount_krw + fee:
- return False
- self.cash_krw -= amount_krw + fee
- bought = amount_krw / price
- self.qty += bought
- self.qty_by_leg[leg_id] = self.qty_by_leg.get(leg_id, 0.0) + bought
- self.sell_leg = None
- self.sell_base_qty = 0.0
- return True
-
- def apply_sell(self, amount_krw: float, sell_qty: float, price: float, leg_id: int) -> bool:
- """
- 매도 체결 (보유 수량 필요).
-
- Args:
- amount_krw: 매도 원화(총액).
- sell_qty: 매도 수량.
- price: 체결가.
- leg_id: leg ID.
-
- Returns:
- 체결 성공 여부.
- """
- if sell_qty <= 0 or amount_krw <= 0:
- return False
- leg_qty = self.qty_by_leg.get(leg_id, 0.0)
- if leg_qty <= 1e-12:
- return False
- fee = amount_krw * TRADING_FEE_RATE
- self.cash_krw += amount_krw - fee
- leg_qty -= sell_qty
- self.qty_by_leg[leg_id] = max(leg_qty, 0.0)
- self.qty = max(self.qty - sell_qty, 0.0)
- if self.qty < 1e-12:
- self.qty = 0.0
- if self.qty_by_leg.get(leg_id, 0.0) <= 1e-12:
- self.qty_by_leg.pop(leg_id, None)
- self.sell_leg = None
- self.sell_base_qty = 0.0
- return True
-
-
-def hit_key(hit: dict[str, Any]) -> tuple[str, str, str]:
- """발화 고유 키 (dt, rule_id, side)."""
- return (str(hit["dt"]), str(hit["rule_id"]), str(hit["side"]))
-
-
-def sort_hits_sim_order(hits: list[dict[str, Any]]) -> list[dict[str, Any]]:
- """
- 시뮬·allocate 순서: 시각순, 동일 시각이면 buy → sell.
-
- Args:
- hits: evaluate_live_rules 발화.
-
- Returns:
- 정렬된 리스트.
- """
- side_rank = {"buy": 0, "sell": 1}
-
- def _key(h: dict[str, Any]) -> tuple:
- return (str(h["dt"]), side_rank.get(str(h["side"]), 9), str(h["rule_id"]))
-
- return sorted(hits, key=_key)
-
-
-def _signals_for_hybrid(
- signal_history: list[dict[str, Any]],
- *,
- approved_buy_rules: set[str] | None = None,
-) -> list[dict[str, Any]]:
- """
- hybrid 배분용 신호 목록.
-
- sim_causal_hybrid 와 동일하려면 approved_buy_rules=None (monitor 전체 발화).
- 운영에서 추가로 EV/WF 매수만 허용하려면 rule_id 집합을 넘깁니다.
-
- Args:
- signal_history: {dt, rule_id, side, close}.
- approved_buy_rules: None=필터 없음. set 이면 해당 매수 rule_id 만.
-
- Returns:
- 시뮬 입력 trade dict 리스트.
- """
- out: list[dict[str, Any]] = []
- for h in sort_hits_sim_order(signal_history):
- side = str(h["side"])
- rid = str(h["rule_id"])
- if (
- side == "buy"
- and approved_buy_rules is not None
- and rid not in approved_buy_rules
- ):
- continue
- out.append(
- {
- "dt": str(h["dt"]),
- "side": side,
- "close": float(h["close"]),
- "rule_id": rid,
- }
- )
- return out
-
-
-def size_monitor_signals(
- signal_history: list[dict[str, Any]],
- ohlc_df: pd.DataFrame,
- *,
- approved_buy_rules: set[str] | None = None,
-) -> list[dict[str, Any]]:
- """
- 시뮬과 동일 hybrid tier 배분 (amount_krw·weight·leg_id).
-
- Args:
- signal_history: 누적 발화.
- ohlc_df: 3m OHLC.
- approved_buy_rules: 매수 허용 규칙.
-
- Returns:
- sized trade dict 리스트 (시각순).
- """
- rows = _signals_for_hybrid(signal_history, approved_buy_rules=approved_buy_rules)
- if not rows:
- return []
- fires = pd.DataFrame(rows)
- dd = load_hybrid_dd_params()
- sized, _stats = build_monitor_hybrid_sized_trades(
- fires,
- ohlc_df,
- enhanced=False,
- initial_cash=float(GT_INITIAL_CASH_KRW),
- fee_rate=TRADING_FEE_RATE,
- dd_large_pct=dd.get("dd_large_pct"),
- dd_medium_pct=dd.get("dd_medium_pct"),
- )
- return sized
-
-
-def replay_hybrid_signals(
- signal_history: list[dict[str, Any]],
- ohlc_df: pd.DataFrame,
- *,
- approved_buy_rules: set[str] | None = None,
-) -> tuple[HybridSimPortfolio, dict[tuple[str, str, str], SimTradeResult]]:
- """
- 신호 이력 전체를 hybrid 배분·체결 규칙으로 재생 (simulate_portfolio_steps 동일).
-
- Args:
- signal_history: 누적 발화.
- ohlc_df: 3m OHLC.
- approved_buy_rules: None=시뮬 동일. set 이면 매수 rule_id 필터.
-
- Returns:
- (portfolio, hit_key → SimTradeResult).
- """
- sized = size_monitor_signals(
- signal_history, ohlc_df, approved_buy_rules=approved_buy_rules
- )
- portfolio = HybridSimPortfolio()
- results: dict[tuple[str, str, str], SimTradeResult] = {}
- fee_rate = TRADING_FEE_RATE
- current_leg: int | None = None
- leg_budget = 0.0
-
- for t in sorted(sized, key=lambda x: x["dt"]):
- action = str(t.get("action", t.get("side", "")))
- price = float(t["price"])
- if price <= 0:
- continue
- dt = str(t["dt"])
- rid = str(t.get("rule_id", ""))
- leg_id = int(t.get("leg_id", 0))
- weight = float(t.get("weight", 1.0))
- hit = {"dt": dt, "rule_id": rid, "side": action, "close": price}
- key = hit_key(hit)
-
- if action == "buy":
- ak = t.get("amount_krw")
- if ak is not None and float(ak) > 0:
- amount = min(
- float(ak),
- max(portfolio.cash_krw / (1.0 + fee_rate), 0.0),
- )
- else:
- if leg_id != current_leg:
- current_leg = leg_id
- leg_budget = portfolio.cash_krw
- amount = min(
- leg_budget * weight,
- max(portfolio.cash_krw / (1.0 + fee_rate), 0.0),
- )
- if amount <= 0:
- results[key] = SimTradeResult(
- hit, 0.0, 0.0, False, "시뮬 매수 스킵(현금·tier)"
- )
- continue
- fee = amount * fee_rate
- portfolio.cash_krw -= amount + fee
- bought = amount / price
- portfolio.qty += bought
- portfolio.qty_by_leg[leg_id] = (
- portfolio.qty_by_leg.get(leg_id, 0.0) + bought
- )
- portfolio.sell_leg = None
- portfolio.sell_base_qty = 0.0
- results[key] = SimTradeResult(
- hit,
- amount,
- 0.0,
- True,
- f"sim_buy leg={leg_id} ₩{amount:,.0f}",
- leg_id=leg_id,
- )
- continue
-
- if action == "sell" and portfolio.qty > 0:
- leg_qty = portfolio.qty_by_leg.get(leg_id, portfolio.qty)
- if portfolio.sell_leg != leg_id:
- portfolio.sell_leg = leg_id
- portfolio.sell_base_qty = leg_qty
- sell_qty = resolve_sell_qty(
- t, leg_qty, price, portfolio.sell_base_qty, weight
- )
- if sell_qty <= 0:
- results[key] = SimTradeResult(
- hit, 0.0, 0.0, False, "시뮬 매도 스킵"
- )
- continue
- gross = sell_qty * price
- fee = gross * fee_rate
- portfolio.cash_krw += gross - fee
- leg_qty -= sell_qty
- portfolio.qty_by_leg[leg_id] = max(leg_qty, 0.0)
- portfolio.qty = max(portfolio.qty - sell_qty, 0.0)
- if portfolio.qty < 1e-12:
- portfolio.qty = 0.0
- results[key] = SimTradeResult(
- hit,
- gross,
- sell_qty,
- True,
- f"sim_sell qty={sell_qty:.4f} ₩{gross:,.0f}",
- leg_id=leg_id,
- )
- continue
-
- results[key] = SimTradeResult(hit, 0.0, 0.0, False, "보유 없음")
-
- return portfolio, results
-
-
-def plan_live_hit(
- signal_history: list[dict[str, Any]],
- hit: dict[str, Any],
- ohlc_df: pd.DataFrame,
- *,
- approved_buy_rules: set[str] | None = None,
-) -> SimTradeResult:
- """
- live: 누적 이력 + 신규 발화 1건 — replay 와 동일 sell_qty·amount.
-
- Args:
- signal_history: 기존 이력(신규 hit 미포함).
- hit: 이번 발화.
- ohlc_df: 3m OHLC.
- approved_buy_rules: 매수 허용.
-
- Returns:
- SimTradeResult.
- """
- if ohlc_df is None or getattr(ohlc_df, "empty", True):
- return SimTradeResult(hit, 0.0, 0.0, False, "OHLC 없음")
- dt, rid, side = hit_key(hit)
- hist = list(signal_history)
- if not any(
- str(s["dt"]) == dt and str(s["rule_id"]) == rid and str(s["side"]) == side
- for s in hist
- ):
- hist.append(
- {
- "dt": dt,
- "rule_id": rid,
- "side": side,
- "close": float(hit["close"]),
- }
- )
- _, results = replay_hybrid_signals(
- hist, ohlc_df, approved_buy_rules=approved_buy_rules
- )
- res = results.get((dt, rid, side))
- if res is not None:
- return res
- return SimTradeResult(hit, 0.0, 0.0, False, "시뮬 배분 없음")
diff --git a/deepcoin/ops/live_trader.py b/deepcoin/ops/live_trader.py
deleted file mode 100644
index 32e87d8..0000000
--- a/deepcoin/ops/live_trader.py
+++ /dev/null
@@ -1,373 +0,0 @@
-"""
-3단계: monitor_rules 발화 시 빗썸 실주문 (가드·로그).
-
-체결 배분: 시뮬 sim_causal_hybrid 와 동일
- - fire_outcomes 부트스트랩 + hybrid_sim_execution.plan_live_hit
- - enhanced=False, hybrid DD tier, EV/WF 매수 필터
-LIVE_TRADING_ENABLED=1 필수.
-"""
-
-from __future__ import annotations
-
-import json
-import time
-from datetime import date, datetime
-from typing import Any
-
-from config import (
- CHART_LOOKBACK_DAYS,
- COIN_NAME,
- LIVE_COOLDOWN_MIN,
- LIVE_DAILY_KRW_MAX,
- LIVE_DAILY_LOSS_LIMIT_KRW,
- LIVE_HYBRID_BOOTSTRAP_FIRES,
- LIVE_MAX_TRADES_PER_DAY,
- LIVE_ORDER_KRW,
- LIVE_TRADING_ENABLED,
- MATCH_PRIMARY_INTERVAL,
- SYMBOL,
- TRADING_FEE_RATE,
-)
-from deepcoin.data.mtf_bb import load_frames_from_db
-from deepcoin.matching.live_eval import evaluate_live_rules
-from deepcoin.matching.load_rules import load_monitor_rules
-from deepcoin.ops.alert_message import build_rule_alert_message
-from deepcoin.ops.hybrid_sim_execution import (
- SimTradeResult,
- build_live_signal_history,
- hit_key,
- plan_live_hit,
- sort_hits_sim_order,
-)
-from deepcoin.ops.monitor import Monitor
-from deepcoin.ops.portfolio_report import maybe_record_portfolio_snapshot
-from deepcoin.paths import (
- LIVE_SIGNAL_HISTORY_JSON,
- LIVE_TRADES_LOG,
-)
-
-
-class LiveTrader(Monitor):
- """
- 규칙 발화 시 빗썸 실주문. 배분은 시뮬 sim_causal_hybrid 와 동일 엔진.
- """
-
- def __init__(self) -> None:
- """Monitor 초기화, hybrid 이력·일별 카운터."""
- if not LIVE_TRADING_ENABLED:
- raise RuntimeError(
- "LIVE_TRADING_ENABLED=0 — 실거래만 지원합니다. "
- ".env 에 LIVE_TRADING_ENABLED=1 설정 후 재기동하세요."
- )
- super().__init__(cooldown_file=None)
- self._rule_last_unix: dict[str, float] = {}
- self._day: str = ""
- self._day_spent_krw: float = 0.0
- self._day_trades: int = 0
- self._day_pnl_krw: float = 0.0
- self._ohlc_df = None
- self._persisted_ops_signals: list[dict[str, Any]] = self._load_persisted_signals()
- self._live_signal_history = self._init_signal_history()
- self._load_ohlc_df()
-
- def _load_persisted_signals(self) -> list[dict[str, Any]]:
- """live_signal_history.json."""
- if not LIVE_SIGNAL_HISTORY_JSON.is_file():
- return []
- try:
- data = json.loads(LIVE_SIGNAL_HISTORY_JSON.read_text(encoding="utf-8"))
- return list(data.get("signals") or [])
- except (json.JSONDecodeError, OSError):
- return []
-
- def _init_signal_history(self) -> list[dict[str, Any]]:
- """
- 시뮬과 동일 hybrid 입력: fire_outcomes 부트스트랩 + 운영 저장분.
-
- Returns:
- 병합된 발화 이력.
- """
- merged = build_live_signal_history(self._persisted_ops_signals)
- n_boot = max(len(merged) - len(self._persisted_ops_signals), 0)
- print(
- f"[06] hybrid 이력: total={len(merged)} "
- f"(bootstrap={'on' if LIVE_HYBRID_BOOTSTRAP_FIRES else 'off'}, "
- f"from_fires~{n_boot}, ops_persisted={len(self._persisted_ops_signals)})"
- )
- return merged
-
- def _save_persisted_ops_signals(self) -> None:
- """운영 체결분만 저장 (fire_outcomes 부트스트랩은 재로드)."""
- LIVE_SIGNAL_HISTORY_JSON.parent.mkdir(parents=True, exist_ok=True)
- LIVE_SIGNAL_HISTORY_JSON.write_text(
- json.dumps(
- {"signals": self._persisted_ops_signals[-2000:]},
- ensure_ascii=False,
- indent=2,
- ),
- encoding="utf-8",
- )
-
- def _live_signal_seen(self, hit: dict[str, Any]) -> bool:
- """이력에 동일 봉 발화가 있는지."""
- dt, rid, side = hit_key(hit)
- return any(
- str(s["dt"]) == dt and str(s["rule_id"]) == rid and str(s["side"]) == side
- for s in self._live_signal_history
- )
-
- def _append_live_signal(self, hit: dict[str, Any]) -> None:
- """체결 성공 발화를 전체 이력·운영 저장분에 추가."""
- if self._live_signal_seen(hit):
- return
- row = {
- "dt": str(hit["dt"]),
- "rule_id": str(hit["rule_id"]),
- "side": str(hit["side"]),
- "close": float(hit["close"]),
- }
- self._live_signal_history.append(row)
- if not any(hit_key(s) == hit_key(row) for s in self._persisted_ops_signals):
- self._persisted_ops_signals.append(row)
-
- def _reset_day_if_needed(self) -> None:
- """날짜 변경 시 일별 한도 카운터 초기화."""
- today = date.today().isoformat()
- if today != self._day:
- self._day = today
- self._day_spent_krw = 0.0
- self._day_trades = 0
- self._day_pnl_krw = 0.0
-
- def _append_log(self, record: dict[str, Any]) -> None:
- """live_trades.jsonl append."""
- LIVE_TRADES_LOG.parent.mkdir(parents=True, exist_ok=True)
- with LIVE_TRADES_LOG.open("a", encoding="utf-8") as f:
- f.write(json.dumps(record, ensure_ascii=False) + "\n")
-
- def _can_trade(self, rule_id: str, planned_krw: float | None = None) -> tuple[bool, str]:
- """
- 쿨다운(1봉=3분) + 일한도·손실한도·거래횟수.
-
- Args:
- rule_id: 규칙 ID.
- planned_krw: 예정 매수 원화.
-
- Returns:
- (허용 여부, 거절 사유).
- """
- self._reset_day_if_needed()
- last = self._rule_last_unix.get(rule_id, 0.0)
- if time.time() - last < LIVE_COOLDOWN_MIN * 60:
- return False, f"규칙 쿨다운({LIVE_COOLDOWN_MIN}분)"
- if self._day_trades >= LIVE_MAX_TRADES_PER_DAY:
- return False, "일 최대 거래 수 초과"
- need = float(planned_krw if planned_krw is not None else LIVE_ORDER_KRW)
- if self._day_spent_krw + need > LIVE_DAILY_KRW_MAX:
- return False, "일 주문 한도 초과"
- if self._day_pnl_krw <= -abs(LIVE_DAILY_LOSS_LIMIT_KRW):
- return False, "일 손실 한도 초과"
- return True, ""
-
- def _load_ohlc_df(self) -> None:
- """drawdown tier용 3m OHLC."""
- try:
- frames = load_frames_from_db(self, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)
- self._ohlc_df = frames.get(MATCH_PRIMARY_INTERVAL)
- except Exception:
- self._ohlc_df = None
-
- def _sim_plan(self, hit: dict[str, Any]) -> SimTradeResult:
- """시뮬 hybrid 배분 1건 (누적 이력·인과, 현금·보유 제약)."""
- if self._ohlc_df is None:
- self._load_ohlc_df()
- return plan_live_hit(
- list(self._live_signal_history),
- hit,
- self._ohlc_df,
- approved_buy_rules=None,
- )
-
- @staticmethod
- def _cap_plan_to_exchange(plan: SimTradeResult, hit: dict[str, Any], balances: dict) -> SimTradeResult:
- """
- 시뮬 planned 금액을 거래소 가용 잔고 이내로 제한.
-
- Args:
- plan: hybrid 시뮬 배분 결과.
- hit: 발화.
- balances: load_balances_dict() 결과.
-
- Returns:
- 조정된 SimTradeResult.
- """
- sym = balances.get(SYMBOL, {})
- price = float(hit["close"])
- side = hit["side"]
-
- if not plan.ok or plan.amount_krw <= 0:
- return plan
-
- if side == "buy":
- krw = float(sym.get("krw") or 0)
- max_buy = max(krw / (1.0 + TRADING_FEE_RATE) - 1.0, 0.0)
- capped = min(float(plan.amount_krw), max_buy)
- if capped <= 0:
- return SimTradeResult(
- plan.hit, 0.0, 0.0, False, "거래소 현금 부족(시뮬 대비)"
- )
- if capped < plan.amount_krw - 1.0:
- return SimTradeResult(
- plan.hit,
- round(capped, 0),
- 0.0,
- True,
- f"sim_buy capped ₩{capped:,.0f} (plan ₩{plan.amount_krw:,.0f})",
- leg_id=plan.leg_id,
- )
- return plan
-
- held = float(sym.get("balance") or 0)
- if held <= 0:
- return SimTradeResult(plan.hit, 0.0, 0.0, False, "거래소 보유 없음")
- sell_qty = min(float(plan.sell_qty), held)
- if sell_qty <= 0:
- return SimTradeResult(plan.hit, 0.0, 0.0, False, "매도 수량 0")
- gross = round(sell_qty * price, 0)
- if sell_qty < plan.sell_qty - 1e-8:
- return SimTradeResult(
- plan.hit,
- gross,
- sell_qty,
- True,
- f"sim_sell capped qty={sell_qty:.4f}",
- leg_id=plan.leg_id,
- )
- return plan
-
- def _execute_live_order(
- self, hit: dict[str, Any], plan: SimTradeResult, balances: dict
- ) -> dict[str, Any]:
- """실거래: 시뮬 plan(잔고 cap)으로 API 주문."""
- side = hit["side"]
- price = float(hit["close"])
- plan = self._cap_plan_to_exchange(plan, hit, balances)
- record: dict[str, Any] = {
- "ts": datetime.now().isoformat(timespec="seconds"),
- "rule_id": hit["rule_id"],
- "side": side,
- "signal_dt": hit["dt"],
- "price": price,
- "amount_krw": plan.amount_krw,
- "live_enabled": True,
- "ok": False,
- "message": plan.message,
- "sizing": "sim_causal_hybrid",
- }
- if not plan.ok:
- return record
-
- try:
- if side == "buy":
- ok = self.buyCoinMarket(SYMBOL, int(plan.amount_krw), count=None)
- record["ok"] = bool(ok)
- record["message"] = "buyCoinMarket" if ok else "buy failed"
- elif side == "sell":
- sell_qty = float(plan.sell_qty)
- gross = sell_qty * price
- record["amount_krw"] = round(gross, 0)
- ok = self.sellCoinMarket(SYMBOL, int(price), sell_qty)
- record["ok"] = bool(ok)
- record["sell_qty"] = sell_qty
- record["message"] = (
- f"sell qty={sell_qty:.4f}" if ok else "sell failed"
- )
- else:
- record["message"] = f"unknown side {side}"
- except Exception as exc:
- record["message"] = str(exc)
-
- if record["ok"]:
- spent = float(record.get("amount_krw") or plan.amount_krw)
- self._day_spent_krw += spent
- self._day_trades += 1
- self._rule_last_unix[hit["rule_id"]] = time.time()
- self._append_live_signal(hit)
- self._save_persisted_ops_signals()
- return record
-
- def run_once(self) -> None:
- """1회: 규칙 평가 → hybrid 배분 → 빗썸 주문 → 텔레그램."""
- from deepcoin.data.ops_sync import ensure_ops_candles
-
- ensure_ops_candles()
- rules = load_monitor_rules()
- print(
- f"[06] {datetime.now():%Y-%m-%d %H:%M:%S} "
- f"{COIN_NAME} LIVE rules={len(rules)} · sim=hybrid · bar={MATCH_PRIMARY_INTERVAL}m"
- )
- if not rules:
- print(" monitor_rules 없음 — scripts/04_match_rules.py 실행")
- maybe_record_portfolio_snapshot(self)
- return
-
- fired = evaluate_live_rules(rules, force_refresh=True)
- if not fired:
- print(" 발화 없음")
- maybe_record_portfolio_snapshot(self)
- return
-
- try:
- balances = self.load_balances_dict()
- except Exception:
- balances = {}
-
- for hit in sort_hits_sim_order(fired):
- rid = hit["rule_id"]
- if self._live_signal_seen(hit):
- continue
-
- plan_preview = self._sim_plan(hit)
- ok, reason = self._can_trade(rid, plan_preview.amount_krw)
- if not ok:
- print(f" [{hit['side']}] {rid} @ {hit['dt']}")
- print(f" skip: {reason}")
- continue
-
- if not plan_preview.ok:
- print(f" [{hit['side']}] {rid} @ {hit['dt']}")
- print(f" skip: {plan_preview.message}")
- continue
-
- print(f" [{hit['side']}] {rid} @ {hit['dt']}")
- log = self._execute_live_order(hit, plan_preview, balances)
- self._append_log(log)
- print(f" order: {log['message']} ok={log['ok']}")
- if not log["ok"]:
- continue
- try:
- balances = self.load_balances_dict()
- except Exception:
- balances = None
- msg = build_rule_alert_message(
- hit,
- balances,
- trade_krw=float(log.get("amount_krw") or 0),
- trade_qty=float(log.get("sell_qty") or 0) or None,
- )
- if balances:
- sym = balances.get(SYMBOL, {})
- msg += (
- f"\n[잔고] 현금 ₩{float(sym.get('krw', 0)):,.0f} · "
- f"보유 {float(sym.get('balance', 0)):.4f} {SYMBOL}"
- )
- msg += f"\n[체결] {log['message']}"
- self._send_coin_msg(msg)
- maybe_record_portfolio_snapshot(self)
-
- def run_loop(self, sleep_sec: int) -> None:
- """상시 루프."""
- print(f"[06] 실거래 루프 시작 · sleep={sleep_sec}s")
- while True:
- self.run_once()
- time.sleep(sleep_sec)
diff --git a/deepcoin/ops/monitor.py b/deepcoin/ops/monitor.py
deleted file mode 100644
index 07ee4ae..0000000
--- a/deepcoin/ops/monitor.py
+++ /dev/null
@@ -1,554 +0,0 @@
-import pandas as pd
-from deepcoin.api.bithumb import HTS
-from dateutil.relativedelta import relativedelta
-from datetime import datetime
-import sqlite3
-import time
-
-try:
- import telegram
-except ImportError:
- telegram = None # type: ignore
-import requests
-import json
-import asyncio
-from multiprocessing import Pool
-import numpy as np
-import os
-
-from config import *
-from deepcoin.data.candle_intervals import (
- candle_api_segment,
- interval_display_label,
- pagination_step,
-)
-
-class Monitor(HTS):
- """WLD 코인 데이터·지표·시장 상태 출력."""
-
- last_signal = None
- cooldown_file = None
-
- def __init__(self, cooldown_file: str | None = None) -> None:
- HTS.__init__(self)
- # 최근 매수 신호 저장용(파일은 [신규] 포맷으로 저장)
- self.last_signal: dict[str, str] = {}
- if cooldown_file is not None:
- self.cooldown_file = cooldown_file
- self.buy_cooldown = self._load_buy_cooldown()
- else:
- self.cooldown_file = None
- self.buy_cooldown = {}
-
- # ------------- Persistence -------------
- def _load_buy_cooldown(self) -> dict:
- """load trade record file into nested dict {symbol:{'buy':{'datetime':dt,'signal':s},'sell':{...}}}"""
- if not os.path.exists(self.cooldown_file):
- return {}
-
- try:
- with open(self.cooldown_file, 'r', encoding='utf-8') as f:
- raw = json.load(f)
- except Exception as e:
- print(f"Error loading cooldown data: {e}")
- return {}
-
- record: dict[str, dict] = {}
- for symbol, value in raw.items():
- # 신규 포맷: value has 'buy'/'sell'
- if isinstance(value, dict) and ('buy' in value or 'sell' in value):
- record[symbol] = {}
- for side in ['buy', 'sell']:
- side_val = value.get(side)
- if isinstance(side_val, dict):
- dt_iso = side_val.get('datetime')
- sig = side_val.get('signal', '')
- if dt_iso:
- try:
- dt_obj = datetime.fromisoformat(dt_iso)
- except Exception:
- dt_obj = None
- else:
- dt_obj = None
- record[symbol][side] = {'datetime': dt_obj, 'signal': sig}
- else:
- # 구 포맷 처리 (매수만 기록)
- try:
- dt_obj = None
- sig = ''
- if isinstance(value, str):
- dt_obj = datetime.fromisoformat(value)
- elif isinstance(value, dict):
- dt_iso = value.get('datetime')
- sig = value.get('signal', '')
- if dt_iso:
- dt_obj = datetime.fromisoformat(dt_iso)
- record.setdefault(symbol, {})['buy'] = {'datetime': dt_obj, 'signal': sig}
- except Exception:
- continue
-
- # last_signal 채우기 (buy 기준)
- for sym, sides in record.items():
- if 'buy' in sides and sides['buy'].get('signal'):
- self.last_signal[sym] = sides['buy']['signal']
- return record
-
- def _save_buy_cooldown(self) -> None:
- """save nested trade record structure"""
- try:
- data: dict[str, dict] = {}
- for symbol, sides in self.buy_cooldown.items():
- data[symbol] = {}
- for side in ['buy', 'sell']:
- info = sides.get(side)
- if not info:
- continue
- dt_obj = info.get('datetime')
- sig = info.get('signal', '')
- data[symbol][side] = {
- 'datetime': dt_obj.isoformat() if isinstance(dt_obj, datetime) else '',
- 'signal': sig,
- }
- with open(self.cooldown_file, 'w', encoding='utf-8') as f:
- json.dump(data, f, ensure_ascii=False, indent=2)
- except Exception as e:
- print(f"Error saving cooldown data: {e}")
-
- # ------------- Telegram -------------
- def _send_coin_msg(self, text: str) -> None:
- if telegram is None:
- print(f"[telegram skip] {text}")
- return
- coin_client = telegram.Bot(token=COIN_TELEGRAM_BOT_TOKEN)
- asyncio.run(coin_client.send_message(chat_id=COIN_TELEGRAM_CHAT_ID, text=text))
-
- def sendMsg(self, msg):
- try:
- pool = Pool(12)
- pool.map(self._send_coin_msg, [msg])
- except Exception as e:
- print(f"Error sending Telegram message: {str(e)}")
- return
-
- def send_coin_telegram_message(self, message_list: list[str], header: str) -> None:
- payload = header + "\n"
- for i, message in enumerate(message_list):
- payload += message
- if i + 1 % MONITOR_TELEGRAM_BATCH_SIZE == 0:
- pool = Pool(MONITOR_POOL_WORKERS)
- pool.map(self._send_coin_msg, [payload])
- payload = ''
- if len(message_list) % MONITOR_TELEGRAM_BATCH_SIZE != 0:
- pool = Pool(MONITOR_POOL_WORKERS)
- pool.map(self._send_coin_msg, [payload])
-
- # ------------- Indicators -------------
- def normalize_data(self, data: pd.DataFrame) -> pd.DataFrame:
- columns_to_normalize = ['Open', 'High', 'Low', 'Close', 'Volume']
- normalized_data = data.copy()
- for column in columns_to_normalize:
- min_val = data[column].rolling(window=MONITOR_NORM_WINDOW).min()
- max_val = data[column].rolling(window=MONITOR_NORM_WINDOW).max()
- denominator = max_val - min_val
- normalized_data[f'{column}_Norm'] = np.where(
- denominator != 0,
- (data[column] - min_val) / denominator,
- 0.5,
- )
- return normalized_data
-
- def inverse_data(self, data: pd.DataFrame) -> pd.DataFrame:
- """원본 data 가격 시계를 상하 대칭(글로벌 min/max 기준)으로 반전하여 하락↔상승 트렌드를 뒤집는다."""
- price_cols = ['Open', 'High', 'Low', 'Close']
- inv = data.copy()
- global_min = data[price_cols].min().min()
- global_max = data[price_cols].max().max()
- # 축 기준은 global_mid = (max+min), so transformed = max+min - price
- for col in price_cols:
- inv[col] = global_max + global_min - data[col]
- # Volume은 그대로 유지
- inv['Volume'] = data['Volume']
- # 지표 다시 계산
- inv = self.normalize_data(inv)
-
- for w in MONITOR_MA_WINDOWS:
- inv[f"MA{w}"] = inv["Close"].rolling(window=w).mean()
- inv[f"Deviation{w}"] = (inv["Close"] / inv[f"MA{w}"]) * 100
- if len(MONITOR_MA_WINDOWS) >= 2:
- w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
- inv["golden_cross"] = (inv[f"MA{w_fast}"] > inv[f"MA{w_slow}"]) & (
- inv[f"MA{w_fast}"].shift(1) <= inv[f"MA{w_slow}"].shift(1)
- )
- inv["MA"] = inv["Close"].rolling(window=BB_PERIOD).mean()
- inv["STD"] = inv["Close"].rolling(window=BB_PERIOD).std()
- inv["Upper"] = inv["MA"] + (BB_STD * inv["STD"])
- inv["Lower"] = inv["MA"] - (BB_STD * inv["STD"])
- return inv
-
- def calculate_technical_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
- data = self.normalize_data(data)
-
- for w in MONITOR_MA_WINDOWS:
- data[f"MA{w}"] = data["Close"].rolling(window=w).mean()
- data[f"Deviation{w}"] = (data["Close"] / data[f"MA{w}"]) * 100
- if len(MONITOR_MA_WINDOWS) >= 2:
- w_fast, w_slow = MONITOR_MA_WINDOWS[0], MONITOR_MA_WINDOWS[1]
- data["golden_cross"] = (data[f"MA{w_fast}"] > data[f"MA{w_slow}"]) & (
- data[f"MA{w_fast}"].shift(1) <= data[f"MA{w_slow}"].shift(1)
- )
- data["MA"] = data["Close"].rolling(window=BB_PERIOD).mean()
- data["STD"] = data["Close"].rolling(window=BB_PERIOD).std()
- data["Upper"] = data["MA"] + (BB_STD * data["STD"])
- data["Lower"] = data["MA"] - (BB_STD * data["STD"])
-
- from deepcoin.common.indicators import add_macd, add_stochastic
-
- data = add_macd(data)
- data = add_stochastic(data)
- return data
-
- def process_wld_market_status(self, symbol: str) -> None:
- """
- WLD: 전 봉 BB·일목 위치·추세만 출력 (자동 매매 없음).
- """
- from deepcoin.common.candle_features import describe_latest_position
- from deepcoin.common.indicators import get_trend
- from deepcoin.data.mtf_bb import load_frames_from_db
-
- try:
- frames = load_frames_from_db(self, symbol)
- if not frames:
- print(f"Data for {symbol}: 로드된 봉 없음.")
- return
-
- df_1d = frames.get(TREND_INTERVAL_1D)
- df_1h = frames.get(TREND_INTERVAL_1H)
- if df_1d is None or df_1d.empty:
- df_1d = frames.get(ENTRY_INTERVAL)
- if df_1h is None or df_1h.empty:
- df_1h = frames.get(ENTRY_INTERVAL)
-
- trend = get_trend(df_1d, df_1h)
- print(f"{symbol} 추세(참고): {trend}")
- print("--- 봉별 BB·일목 위치 ---")
- for iv in sorted(frames.keys()):
- pos = describe_latest_position(frames[iv], iv)
- macd_s = ""
- if pos.get("macd_hist") is not None:
- macd_s = f" | MACD {pos.get('macd_state', '-')} h={pos['macd_hist']}"
- stoch_s = ""
- if pos.get("stoch_k") is not None:
- stoch_s = (
- f" | Stoch K={pos['stoch_k']} D={pos.get('stoch_d')} "
- f"{pos.get('stoch_zone', '')}"
- )
- disp_s = ""
- if pos.get("disparity"):
- parts = [f"{p}={v:.1f}" for p, v in sorted(pos["disparity"].items())]
- disp_s = " | D.I. " + " ".join(parts)
- print(
- f" {pos['label']:>6} | BB {pos['bb_zone']} {pos['bb_state']:>16} | "
- f"일목 {pos['ichi_position']} TK={pos['ichi_tk']}"
- f"{macd_s}{stoch_s}{disp_s}"
- )
- except Exception as e:
- print(f"Error processing {symbol}: {str(e)}")
-
- def process_symbol(
- self,
- symbol: str,
- interval: int | None = None,
- balances: dict | None = None,
- use_inverse: bool = False,
- ) -> None:
- """하위 호환: 시장 상태 출력으로 위임."""
- self.process_wld_market_status(symbol)
-
- def load_balances_dict(self) -> dict:
- """getBalances() 결과를 currency 키 dict로 변환."""
- tmps = self.getBalances()
- balances: dict = {}
- if isinstance(tmps, dict):
- if tmps.get("error"):
- raise RuntimeError(f"getBalances: {tmps.get('error')}")
- return balances
- if not isinstance(tmps, list):
- raise RuntimeError(f"getBalances unexpected: {type(tmps)}")
- for tmp in tmps:
- if not isinstance(tmp, dict) or "currency" not in tmp:
- continue
- balances[tmp["currency"]] = {
- "balance": float(tmp["balance"]),
- "avg_buy_price": float(tmp.get("avg_buy_price") or 0),
- }
- return balances
-
- # ------------- Formatting -------------
- def format_message(
- self, symbol: str, symbol_name: str, close: float, signal: str, buy_amount: float
- ) -> str:
- message = f"[매수] {symbol_name} ({symbol}) [{signal}]: "
-
- if int(close) >= 100:
- message += f"₩{close}"
- message += f" (₩{buy_amount})"
- elif int(close) >= 10:
- message += f"₩{close:.2f}"
- message += f" (₩{buy_amount:.2f})"
- elif int(close) >= 1:
- message += f"₩{close:.3f}"
- message += f" (₩{buy_amount:.3f})"
- else:
- message += f"₩{close:.4f}"
- message += f" (₩{buy_amount:.4f})"
-
- if signal != '':
- message += f"[{signal}]"
- return message
-
- # ------------- Data fetch -------------
- def get_coin_data(
- self,
- symbol: str,
- interval: int = MONITOR_DEFAULT_INTERVAL,
- to: str | None = None,
- retries: int = MONITOR_API_RETRIES,
- ) -> pd.DataFrame | None:
- base = BITHUMB_API_URL.rstrip("/")
- count = BITHUMB_API_CANDLE_COUNT
- segment = candle_api_segment(interval)
- for attempt in range(retries):
- try:
- path = f"/v1/candles/{segment}"
- if to is None:
- url = f"{base}{path}?market=KRW-{symbol}&count={count}"
- else:
- url = f"{base}{path}?market=KRW-{symbol}&count={count}&to={to}"
- headers = {"accept": "application/json"}
- response = requests.get(url, headers=headers)
- json_data = json.loads(response.text)
- df_temp = pd.DataFrame(json_data)
- df_temp = df_temp.sort_index(ascending=False)
- if 'candle_date_time_kst' not in df_temp:
- return None
- data = pd.DataFrame()
- data['datetime'] = pd.to_datetime(df_temp['candle_date_time_kst'], format='%Y-%m-%dT%H:%M:%S')
- data['Open'] = df_temp['opening_price']
- data['Close'] = df_temp['trade_price']
- data['High'] = df_temp['high_price']
- data['Low'] = df_temp['low_price']
- data['Volume'] = df_temp['candle_acc_trade_volume']
- data = data.set_index('datetime')
- data = data.astype(float)
- data["datetime"] = data.index
- if not data.empty:
- return data
- print(f"No data received for {symbol}, attempt {attempt + 1}")
- time.sleep(MONITOR_SLEEP_AFTER_REQUEST_SEC)
- except Exception as e:
- print(f"Attempt {attempt + 1} failed for {symbol}: {str(e)}")
- if attempt < retries - 1:
- time.sleep(MONITOR_SLEEP_RATE_LIMIT_SEC)
- continue
- return None
-
- def get_coin_more_data(
- self,
- symbol: str,
- interval: int,
- bong_count: int = MONITOR_API_BONG_COUNT,
- verbose: bool = False,
- ) -> pd.DataFrame:
- """
- 빗썸 API를 반복 호출해 bong_count개까지 과거 봉을 수집합니다.
-
- Args:
- verbose: True면 수집 진행 상황을 출력합니다.
- """
- to = datetime.now()
- data: pd.DataFrame | None = None
- step = 0
- while data is None or len(data) < bong_count:
- step += 1
- if data is None:
- chunk = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
- data = chunk
- else:
- previous_count = len(data)
- df = self.get_coin_data(symbol, interval, to.strftime("%Y-%m-%d %H:%M:%S"))
- if df is not None and not df.empty:
- data = pd.concat([data, df], ignore_index=True)
- if df is None or df.empty or previous_count == len(data):
- if verbose:
- print(f" API 추가 데이터 없음 (수집 {len(data)}봉)")
- break
- if verbose and (step == 1 or step % 5 == 0 or len(data) >= bong_count):
- label = interval_display_label(interval)
- print(f" [{label}] 요청 {step}회 — 누적 {len(data)}/{bong_count}봉")
- time.sleep(MONITOR_SLEEP_BETWEEN_CHUNKS_SEC)
- to = to - pagination_step(interval, MONITOR_API_CHUNK_BARS)
- if data is None or data.empty:
- return pd.DataFrame()
- data = data.set_index("datetime")
- data = data.sort_index()
- data = data.drop_duplicates(keep="first")
- data["datetime"] = data.index
- return data
-
- @staticmethod
- def db_row_limit_for_interval(interval: int, lookback_days: int) -> int:
- """
- lookback_days 구간 + 지표 워밍업을 담을 SQLite LIMIT(봉 개수)을 계산합니다.
-
- Args:
- interval: 봉 간격(분). 1440이면 일봉.
- lookback_days: 과거 조회 일수.
-
- Returns:
- LIMIT에 넣을 최대 행 수.
- """
- from config import MONTH_INTERVAL_MIN, WEEK_INTERVAL_MIN
-
- if interval == WEEK_INTERVAL_MIN:
- return max(lookback_days // 7 + 10, DB_ROW_MIN_DAILY_BARS)
- if interval == MONTH_INTERVAL_MIN:
- return max(lookback_days // 30 + 6, DB_ROW_MIN_DAILY_BARS)
- if interval >= DAILY_INTERVAL_MIN:
- return max(
- lookback_days + DB_ROW_DAILY_PADDING_DAYS,
- DB_ROW_MIN_DAILY_BARS,
- )
- bars_per_day = max((24 * 60) // max(interval, 1), 1)
- return bars_per_day * lookback_days + DB_ROW_WARMUP_BARS
-
- def persist_api_candles_to_db(
- self,
- symbol: str,
- interval: int,
- data: pd.DataFrame,
- db_path: str = DB_PATH,
- ) -> tuple[int, int]:
- """
- API로 받은 봉을 coins.db에 증분 INSERT합니다 (01_download.append_data와 동일).
-
- 05·06·live_eval이 load_frames_from_db 할 때마다 최신 봉이 쌓입니다.
-
- Returns:
- (추가 행 수, 스킵 행 수)
- """
- if not MONITOR_PERSIST_CANDLES or data is None or data.empty:
- return 0, 0
-
- from deepcoin.data.downloader import (
- append_data,
- get_last_timestamp,
- months_for_interval,
- prune_before_cutoff,
- )
-
- if not isinstance(data.index, pd.DatetimeIndex):
- data = data.copy()
- data.index = pd.to_datetime(data.index)
- data = data.sort_index()
-
- last_ts = get_last_timestamp(symbol, interval, db_path=db_path)
- inserted, skipped = append_data(
- symbol, interval, data, last_ts=last_ts, db_path=db_path
- )
- if inserted > 0:
- months = months_for_interval(interval, DOWNLOAD_MONTHS)
- prune_before_cutoff(symbol, interval, months, db_path=db_path)
- return inserted, skipped
-
- def read_candles_from_db(
- self,
- symbol: str,
- interval: int,
- db_path: str = DB_PATH,
- max_rows: int = DB_READ_LIMIT_DEFAULT,
- ) -> pd.DataFrame:
- """
- coins.db에서 저장된 봉을 읽습니다.
-
- scripts/01_download.py 또는 persist_api_candles_to_db로 적재된 데이터.
- """
- conn = sqlite3.connect(db_path)
- cursor = conn.cursor()
- table_name = f"{symbol}_{interval}"
- cursor.execute(
- f"CREATE TABLE IF NOT EXISTS {table_name} "
- "(CODE text, NAME text, ymdhms datetime, ymd text, hms text, "
- "Close REAL, Open REAL, High REAL, Low REAL, Volume REAL)"
- )
- cursor.execute(
- f"CREATE INDEX IF NOT EXISTS {table_name}_idx ON {table_name}(CODE, ymdhms)"
- )
-
- cursor.execute(
- f"SELECT Open, Close, High, Low, Volume, ymdhms AS datetime "
- f"FROM (SELECT Open, Close, High, Low, Volume, ymdhms "
- f"FROM {table_name} ORDER BY ymdhms DESC LIMIT {int(max_rows)}) "
- f"ORDER BY datetime"
- )
- result = cursor.fetchall()
- conn.commit()
- cursor.close()
- conn.close()
-
- if not result:
- return pd.DataFrame(
- columns=["Open", "Close", "High", "Low", "Volume", "datetime"]
- )
-
- df = pd.DataFrame(
- result, columns=["Open", "Close", "High", "Low", "Volume", "datetime"]
- )
- df = df.set_index("datetime")
- df = df.sort_index()
- df["datetime"] = df.index
- return df
-
- def get_coin_saved_data(
- self,
- symbol: str,
- interval: int,
- data: pd.DataFrame,
- db_path: str = DB_PATH,
- max_rows: int = DB_READ_LIMIT_DEFAULT,
- ) -> pd.DataFrame:
- """하위 호환: API 봉 저장 후 DB에서 읽기."""
- self.persist_api_candles_to_db(symbol, interval, data, db_path=db_path)
- return self.read_candles_from_db(
- symbol, interval, db_path=db_path, max_rows=max_rows
- )
-
- def get_coin_some_data(
- self, symbol: str, interval: int, db_max_rows: int | None = None
- ) -> pd.DataFrame:
- """
- WLD 시세: API 최신 봉 + coins.db 과거 봉을 합칩니다.
-
- MONITOR_PERSIST_CANDLES=1 이면 API 청크를 즉시 coins.db에 INSERT합니다.
- 1분봉은 다운로드·병합하지 않습니다.
- """
- data = self.get_coin_data(symbol, interval)
- if data is None or data.empty:
- return pd.DataFrame()
-
- self.persist_api_candles_to_db(symbol, interval, data)
-
- row_limit = DB_READ_LIMIT_DEFAULT if db_max_rows is None else int(db_max_rows)
- saved_data = self.read_candles_from_db(
- symbol, interval, max_rows=row_limit
- )
- parts = [data]
- if saved_data is not None and not saved_data.empty:
- parts.append(saved_data)
-
- merged = pd.concat(parts, ignore_index=True)
- merged["datetime"] = pd.to_datetime(merged["datetime"], format="%Y-%m-%d %H:%M:%S")
- merged = merged.set_index("datetime")
- merged = merged.sort_index()
- merged = merged.drop_duplicates(keep="first")
- merged["datetime"] = merged.index
- return merged
diff --git a/deepcoin/ops/monitor_coin.py b/deepcoin/ops/monitor_coin.py
deleted file mode 100644
index 34eaa45..0000000
--- a/deepcoin/ops/monitor_coin.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""
-WLD(월드코인) 실시간 모니터 — BB·일목·04 매칭 규칙 알림 (자동 매매 없음).
-"""
-
-from __future__ import annotations
-
-from datetime import datetime
-import time
-
-from config import COIN_NAME, MONITOR_ALERT_COOLDOWN_MIN, MONITOR_LOOP_SLEEP_SEC, SYMBOL
-from deepcoin.matching.live_eval import evaluate_live_rules
-from deepcoin.matching.load_rules import load_monitor_rules
-from deepcoin.ops.alert_message import build_rule_alert_message
-from deepcoin.ops.monitor import Monitor
-
-
-class MonitorCoin(Monitor):
- """WLD 시장 상태·매칭 규칙 주기 출력."""
-
- def __init__(self, cooldown_file: str | None = None, *, check_rules: bool = True) -> None:
- """
- Args:
- cooldown_file: 매매 쿨다운 JSON 경로.
- check_rules: True면 04 active_rules 평가·알림.
- """
- super().__init__(cooldown_file=cooldown_file)
- self.check_rules = check_rules
- self._last_alert_unix: dict[str, float] = {}
-
- def monitor_wld(self) -> None:
- """전 봉 BB·일목·추세 및 규칙 발화를 출력합니다."""
- from deepcoin.data.ops_sync import ensure_ops_candles
-
- ensure_ops_candles()
- print(
- "[{}] {} ({})".format(
- datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
- COIN_NAME,
- SYMBOL,
- )
- )
- self.process_wld_market_status(SYMBOL)
- if self.check_rules:
- self._check_matched_rules()
-
- def _check_matched_rules(self) -> None:
- """04 monitor_rules 최신 봉 평가 후 쿨다운 내 중복 알림 방지."""
- rules = load_monitor_rules()
- if not rules:
- print(" [04 규칙] monitor_rules 없음 — scripts/04_match_rules.py 실행")
- return
- try:
- fired = evaluate_live_rules(rules)
- except Exception as exc:
- print(f" [04 규칙] 평가 오류: {exc}")
- return
- if not fired:
- print(f" [04 규칙] 발화 없음 (감시 {len(rules)}개)")
- return
- balances: dict | None = None
- try:
- balances = self.load_balances_dict()
- except Exception:
- balances = None
- cooldown_sec = MONITOR_ALERT_COOLDOWN_MIN * 60
- now = time.time()
- for hit in fired:
- rid = hit["rule_id"]
- preview = build_rule_alert_message(hit, balances).replace("\n", " | ")
- print(f" [04] {preview}")
- last = self._last_alert_unix.get(rid, 0.0)
- if now - last < cooldown_sec:
- print(f" [04] 쿨다운 skip {rid} ({MONITOR_ALERT_COOLDOWN_MIN}분)")
- continue
- self._last_alert_unix[rid] = now
- self._send_coin_msg(build_rule_alert_message(hit, balances))
-
- def run_schedule(self) -> None:
- """MONITOR_LOOP_SLEEP_SEC 간격으로 상태를 출력합니다."""
- rules = load_monitor_rules()
- names = ", ".join(r["rule_id"] for r in rules) or "(없음)"
- print(
- f"05 모니터 시작 · 감시 {len(rules)}개 ({names}) · "
- f"주기 {MONITOR_LOOP_SLEEP_SEC}초 · 알림쿨다운 {MONITOR_ALERT_COOLDOWN_MIN}분"
- )
- while True:
- self.monitor_wld()
- time.sleep(MONITOR_LOOP_SLEEP_SEC)
-
-
-if __name__ == "__main__":
- MonitorCoin(cooldown_file=None).run_schedule()
diff --git a/deepcoin/ops/portfolio_report.py b/deepcoin/ops/portfolio_report.py
deleted file mode 100644
index 724acc0..0000000
--- a/deepcoin/ops/portfolio_report.py
+++ /dev/null
@@ -1,435 +0,0 @@
-"""
-운영 포트폴리오 스냅샷·최근 24시간 수익률 텔레그램 리포트.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from datetime import datetime, timedelta, timezone
-from pathlib import Path
-from typing import Any
-from zoneinfo import ZoneInfo
-
-from config import (
- COIN_NAME,
- COIN_TELEGRAM_BOT_TOKEN,
- COIN_TELEGRAM_CHAT_ID,
- DAILY_PNL_REPORT_ENABLED,
- DAILY_PNL_REPORT_HOUR,
- DAILY_PNL_REPORT_MINUTE,
- DAILY_PNL_REPORT_TZ,
- DAILY_PNL_SNAPSHOT_ON_LIVE,
- DAILY_PNL_SNAPSHOT_RETENTION_DAYS,
- ENTRY_INTERVAL,
- GT_INITIAL_CASH_KRW,
- SYMBOL,
-)
-from deepcoin.ops.monitor import Monitor
-from deepcoin.paths import LIVE_TRADES_LOG, PORTFOLIO_SNAPSHOTS_LOG, ensure_dirs
-
-logger = logging.getLogger(__name__)
-
-ROLLING_HOURS = 24
-MAX_ANCHOR_SKEW_HOURS = 6
-
-
-def _report_tz() -> ZoneInfo:
- """
- 리포트 기준 타임존.
-
- Returns:
- ZoneInfo (잘못된 이름이면 UTC).
- """
- try:
- return ZoneInfo(DAILY_PNL_REPORT_TZ)
- except Exception:
- logger.warning("DAILY_PNL_REPORT_TZ=%s invalid, using UTC", DAILY_PNL_REPORT_TZ)
- return ZoneInfo("UTC")
-
-
-def _parse_ts(value: str) -> datetime:
- """ISO 시각 → timezone-aware datetime."""
- dt = datetime.fromisoformat(str(value).replace("Z", "+00:00"))
- if dt.tzinfo is None:
- dt = dt.replace(tzinfo=timezone.utc)
- return dt
-
-
-def fetch_mark_price(monitor: Monitor, symbol: str = SYMBOL) -> float:
- """
- 최근 종가(3분봉)를 조회합니다.
-
- Args:
- monitor: Monitor 인스턴스.
- symbol: 통화 코드.
-
- Returns:
- 종가. 실패 시 0.
- """
- try:
- data = monitor.get_coin_data(symbol, interval=ENTRY_INTERVAL)
- if data is None or data.empty:
- return 0.0
- return float(data["Close"].iloc[-1])
- except Exception as exc:
- logger.warning("mark price fetch failed: %s", exc)
- return 0.0
-
-
-def portfolio_from_balances(
- balances: dict[str, dict[str, float]],
- mark_price: float,
- *,
- ts: datetime | None = None,
-) -> dict[str, Any]:
- """
- 잔고·시세로 총자산 스냅샷 dict를 만듭니다.
-
- Args:
- balances: load_balances_dict() 결과.
- mark_price: 코인 평가 단가.
- ts: 기록 시각 (None이면 now UTC).
-
- Returns:
- 스냅샷 필드 dict.
- """
- when = ts or datetime.now(timezone.utc)
- krw = float((balances.get("KRW") or {}).get("balance") or 0.0)
- qty = float((balances.get(SYMBOL) or {}).get("balance") or 0.0)
- holding_value = qty * mark_price if mark_price > 0 else 0.0
- total = krw + holding_value
- return {
- "ts": when.isoformat(timespec="seconds"),
- "symbol": SYMBOL,
- "krw": round(krw, 0),
- "coin_qty": qty,
- "mark_price": mark_price,
- "holding_value_krw": round(holding_value, 0),
- "total_asset_krw": round(total, 0),
- }
-
-
-def fetch_portfolio_snapshot(monitor: Monitor) -> dict[str, Any]:
- """
- API 잔고·시세로 현재 포트폴리오 스냅샷을 조회합니다.
-
- Args:
- monitor: Monitor 인스턴스.
-
- Returns:
- portfolio_from_balances 결과.
-
- Raises:
- RuntimeError: 잔고 API 오류.
- """
- balances = monitor.load_balances_dict()
- mark = fetch_mark_price(monitor)
- if mark <= 0:
- raise RuntimeError("mark price unavailable")
- return portfolio_from_balances(balances, mark)
-
-
-def append_portfolio_snapshot(snapshot: dict[str, Any]) -> None:
- """portfolio_snapshots.jsonl에 1행 추가 후 오래된 행 정리."""
- ensure_dirs()
- PORTFOLIO_SNAPSHOTS_LOG.parent.mkdir(parents=True, exist_ok=True)
- with PORTFOLIO_SNAPSHOTS_LOG.open("a", encoding="utf-8") as f:
- f.write(json.dumps(snapshot, ensure_ascii=False) + "\n")
- _prune_snapshots(DAILY_PNL_SNAPSHOT_RETENTION_DAYS)
-
-
-def _prune_snapshots(retention_days: int) -> None:
- """보관 일수 초과 스냅샷 삭제."""
- if not PORTFOLIO_SNAPSHOTS_LOG.is_file():
- return
- cutoff = datetime.now(timezone.utc) - timedelta(days=retention_days)
- kept: list[str] = []
- try:
- for line in PORTFOLIO_SNAPSHOTS_LOG.read_text(encoding="utf-8").splitlines():
- line = line.strip()
- if not line:
- continue
- row = json.loads(line)
- if _parse_ts(row["ts"]) >= cutoff:
- kept.append(line)
- except (json.JSONDecodeError, OSError, KeyError) as exc:
- logger.warning("snapshot prune skipped: %s", exc)
- return
- PORTFOLIO_SNAPSHOTS_LOG.write_text(
- "\n".join(kept) + ("\n" if kept else ""),
- encoding="utf-8",
- )
-
-
-def load_snapshots() -> list[dict[str, Any]]:
- """스냅샷 jsonl 전체 로드 (시각 오름차순)."""
- if not PORTFOLIO_SNAPSHOTS_LOG.is_file():
- return []
- rows: list[dict[str, Any]] = []
- for line in PORTFOLIO_SNAPSHOTS_LOG.read_text(encoding="utf-8").splitlines():
- line = line.strip()
- if not line:
- continue
- try:
- rows.append(json.loads(line))
- except json.JSONDecodeError:
- continue
- rows.sort(key=lambda r: _parse_ts(r["ts"]))
- return rows
-
-
-def _nearest_snapshot(
- snapshots: list[dict[str, Any]],
- target: datetime,
-) -> tuple[dict[str, Any] | None, float]:
- """
- target에 가장 가까운 스냅샷.
-
- Returns:
- (스냅샷, |시차| 시간).
- """
- if not snapshots:
- return None, 0.0
- best = snapshots[0]
- best_hours = abs((_parse_ts(best["ts"]) - target).total_seconds()) / 3600.0
- for row in snapshots[1:]:
- hours = abs((_parse_ts(row["ts"]) - target).total_seconds()) / 3600.0
- if hours < best_hours:
- best = row
- best_hours = hours
- return best, best_hours
-
-
-def count_trades_since(path: Path, since: datetime) -> dict[str, int]:
- """
- live_trades.jsonl에서 구간 내 체결 건수.
-
- Returns:
- {"buy": n, "sell": n, "ok": n}.
- """
- out = {"buy": 0, "sell": 0, "ok": 0}
- if not path.is_file():
- return out
- for line in path.read_text(encoding="utf-8").splitlines():
- line = line.strip()
- if not line:
- continue
- try:
- row = json.loads(line)
- except json.JSONDecodeError:
- continue
- if not row.get("ok"):
- continue
- try:
- ts = _parse_ts(str(row.get("ts", "")))
- except (TypeError, ValueError):
- continue
- if ts < since:
- continue
- side = str(row.get("side", "")).lower()
- if side in out:
- out[side] += 1
- out["ok"] += 1
- return out
-
-
-def compute_24h_return(
- current: dict[str, Any],
- snapshots: list[dict[str, Any]] | None = None,
- *,
- baseline_krw: float | None = None,
-) -> dict[str, Any]:
- """
- 최근 24시간 총자산 수익률을 계산합니다.
-
- Args:
- current: 현재 스냅샷.
- snapshots: 과거 스냅샷 목록 (None이면 파일 로드).
- baseline_krw: 앵커 없을 때 대체 기준(초기 자금).
-
- Returns:
- 리포트 dict (pnl_pct, anchor, trade_counts 등).
- """
- snapshots = snapshots if snapshots is not None else load_snapshots()
- now = _parse_ts(current["ts"])
- target = now - timedelta(hours=ROLLING_HOURS)
- anchor, skew_h = _nearest_snapshot(snapshots, target)
-
- base_asset = float(baseline_krw or GT_INITIAL_CASH_KRW)
- anchor_ts = None
- anchor_note = f"기준=초기자금 ₩{base_asset:,.0f} (24h 스냅샷 없음)"
-
- if anchor is not None:
- base_asset = float(anchor["total_asset_krw"])
- anchor_ts = anchor["ts"]
- if skew_h <= MAX_ANCHOR_SKEW_HOURS:
- anchor_note = f"기준=24h 전 스냅샷 ({anchor_ts}, 시차 {skew_h:.1f}h)"
- else:
- anchor_note = (
- f"기준=가장 가까운 스냅샷 ({anchor_ts}, 24h 대비 시차 {skew_h:.1f}h)"
- )
-
- final_asset = float(current["total_asset_krw"])
- pnl_krw = final_asset - base_asset
- pnl_pct = (pnl_krw / base_asset * 100.0) if base_asset > 0 else 0.0
- cum_pct = (
- (final_asset - GT_INITIAL_CASH_KRW) / GT_INITIAL_CASH_KRW * 100.0
- if GT_INITIAL_CASH_KRW > 0
- else 0.0
- )
-
- trades = count_trades_since(LIVE_TRADES_LOG, target)
-
- return {
- "current": current,
- "anchor": anchor,
- "anchor_ts": anchor_ts,
- "anchor_note": anchor_note,
- "anchor_skew_hours": skew_h if anchor else None,
- "base_asset_krw": base_asset,
- "final_asset_krw": final_asset,
- "pnl_krw": pnl_krw,
- "pnl_pct": pnl_pct,
- "cumulative_pnl_pct": cum_pct,
- "trade_counts": trades,
- "window_hours": ROLLING_HOURS,
- }
-
-
-def build_daily_pnl_message(report: dict[str, Any]) -> str:
- """
- 텔레그램 본문 생성.
-
- Args:
- report: compute_24h_return 결과.
-
- Returns:
- 메시지 문자열.
- """
- cur = report["current"]
- sign = "+" if report["pnl_pct"] >= 0 else ""
- cum_sign = "+" if report["cumulative_pnl_pct"] >= 0 else ""
- tc = report["trade_counts"]
- tz = _report_tz()
- now_local = _parse_ts(cur["ts"]).astimezone(tz).strftime("%Y-%m-%d %H:%M")
-
- lines = [
- f"[DeepCoin 일일 리포트] {COIN_NAME} ({SYMBOL})",
- f"시각: {now_local} ({DAILY_PNL_REPORT_TZ})",
- "",
- f"최근 {report['window_hours']}시간 수익률: {sign}{report['pnl_pct']:.2f}%",
- f" 손익: {sign}₩{report['pnl_krw']:,.0f}",
- f" {report['anchor_note']}",
- f" 24h 전 총자산: ₩{report['base_asset_krw']:,.0f}",
- f" 현재 총자산: ₩{report['final_asset_krw']:,.0f}",
- "",
- f"누적(초기 ₩{GT_INITIAL_CASH_KRW:,} 대비): {cum_sign}{report['cumulative_pnl_pct']:.2f}%",
- "",
- "현재 잔고",
- f" 현금: ₩{float(cur['krw']):,.0f}",
- f" {SYMBOL}: {float(cur['coin_qty']):.4f} (₩{float(cur['holding_value_krw']):,.0f} @ ₩{float(cur['mark_price']):,.0f})",
- "",
- f"24h 체결(성공): 매수 {tc['buy']} · 매도 {tc['sell']} · 합계 {tc['ok']}",
- "※ 총자산=현금+보유×종가. 미실현 포함.",
- ]
- return "\n".join(lines)
-
-
-def maybe_record_portfolio_snapshot(monitor: Monitor) -> None:
- """
- 06 루프용: 설정 시 스냅샷만 기록 (텔레그램 없음).
-
- Args:
- monitor: Monitor/LiveTrader 인스턴스.
- """
- if not DAILY_PNL_SNAPSHOT_ON_LIVE:
- return
- try:
- snap = fetch_portfolio_snapshot(monitor)
- append_portfolio_snapshot(snap)
- except Exception as exc:
- logger.warning("portfolio snapshot skip: %s", exc)
-
-
-def send_daily_pnl_report(
- monitor: Monitor | None = None,
- *,
- send_telegram: bool = True,
-) -> dict[str, Any]:
- """
- 스냅샷 저장 → 24h 수익률 계산 → 텔레그램 발송.
-
- Args:
- monitor: None이면 Monitor() 생성.
- send_telegram: False면 메시지만 반환·콘솔 출력.
-
- Returns:
- compute_24h_return 결과 + message 키.
- """
- if not DAILY_PNL_REPORT_ENABLED:
- raise RuntimeError("DAILY_PNL_REPORT_ENABLED=0")
-
- mon = monitor or Monitor(cooldown_file=None)
- current = fetch_portfolio_snapshot(mon)
- append_portfolio_snapshot(current)
- report = compute_24h_return(current)
- msg = build_daily_pnl_message(report)
- report["message"] = msg
-
- print(msg)
- if send_telegram:
- if not COIN_TELEGRAM_BOT_TOKEN or not COIN_TELEGRAM_CHAT_ID:
- print("[telegram skip] COIN_TELEGRAM_BOT_TOKEN/CHAT_ID 미설정")
- else:
- mon._send_coin_msg(msg)
- return report
-
-
-def seconds_until_next_report(
- hour: int | None = None,
- minute: int | None = None,
-) -> float:
- """
- 다음 리포트 시각(로컬 TZ)까지 대기 초.
-
- Args:
- hour: 시 (기본 DAILY_PNL_REPORT_HOUR).
- minute: 분 (기본 DAILY_PNL_REPORT_MINUTE).
-
- Returns:
- 초 (>= 1).
- """
- tz = _report_tz()
- h = DAILY_PNL_REPORT_HOUR if hour is None else hour
- m = DAILY_PNL_REPORT_MINUTE if minute is None else minute
- now = datetime.now(tz)
- target = now.replace(hour=h, minute=m, second=0, microsecond=0)
- if target <= now:
- target += timedelta(days=1)
- return max((target - now).total_seconds(), 1.0)
-
-
-def run_schedule_loop(monitor: Monitor | None = None) -> None:
- """
- 매일 지정 시각에 리포트를 발송하는 무한 루프.
-
- Args:
- monitor: 재사용할 Monitor (None이면 매 회 생성).
- """
- tz = _report_tz()
- print(
- f"[07] 일일 수익률 텔레그램 · {DAILY_PNL_REPORT_HOUR:02d}:"
- f"{DAILY_PNL_REPORT_MINUTE:02d} {tz.key} · Ctrl+C 종료"
- )
- while True:
- wait = seconds_until_next_report()
- next_at = datetime.now(tz) + timedelta(seconds=wait)
- print(f"[07] 다음 발송: {next_at:%Y-%m-%d %H:%M:%S} (대기 {wait / 3600:.2f}h)")
- import time
-
- time.sleep(wait)
- try:
- send_daily_pnl_report(monitor)
- except Exception as exc:
- print(f"[07] 리포트 오류: {exc}")
diff --git a/deepcoin/ops/simulation.py b/deepcoin/ops/simulation.py
deleted file mode 100644
index e26dc32..0000000
--- a/deepcoin/ops/simulation.py
+++ /dev/null
@@ -1,669 +0,0 @@
-"""
-Ground Truth 차트 HTML (05_chart_truth).
-
- python scripts/05_chart_truth.py
-"""
-
-from __future__ import annotations
-
-import sys
-import webbrowser
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import plotly.graph_objs as go
-from plotly.subplots import make_subplots
-
-from config import (
- CHART_LOOKBACK_DAYS,
- COIN_NAME,
- DISPARITY_OVERBOUGHT,
- DISPARITY_OVERSOLD,
- DISPARITY_PERIODS,
- ENTRY_INTERVAL,
- GROUND_TRUTH_FILE,
- GT_INITIAL_CASH_KRW,
- GT_MARKER_SIZE_MAX,
- GT_MARKER_SIZE_MIN,
- LIVE_ORDER_KRW,
- MACD_FAST,
- MACD_SIGNAL,
- MACD_SLOW,
- STOCH_D_PERIOD,
- STOCH_K_PERIOD,
- SYMBOL,
- TRADING_FEE_RATE,
- TREND_INTERVAL_1D,
- TREND_INTERVAL_1H,
-)
-from deepcoin.common.indicators import apply_bar_indicators, disparity_column, get_trend
-from deepcoin.ops.monitor import Monitor
-from deepcoin.data.mtf_bb import interval_label, load_frames_from_db
-
-from deepcoin.ops.chart_report import wrap_chart_report_page
-from deepcoin.paths import CHART_TRUTH_HTML, resolve_ground_truth_file
-
-TRUTH_HTML = CHART_TRUTH_HTML
-GROUND_TRUTH_PATH = resolve_ground_truth_file()
-REPORT_DIR = CHART_TRUTH_HTML.parent
-
-
-def interval_chart_label(interval_min: int) -> str:
- """차트 제목용 봉 라벨."""
- if interval_min >= 1440:
- return "일봉"
- return f"{interval_min}분봉"
-
-
-def _marker_hover_text(
- label: str,
- t: dict,
- *,
- default_order_krw: float | None = None,
- extra_lines: list[str] | None = None,
-) -> str:
- """
- 차트 마커 툴팁: 체결가(price)와 체결 원화(amount_krw)를 함께 표시.
-
- Args:
- label: 정답/시뮬 매수·매도 라벨.
- t: trade dict (price, amount_krw, weight, memo …).
- default_order_krw: amount_krw 없을 때 표시할 기본 원화(시뮬 고정 주문).
- extra_lines: 툴팁 하단 추가 줄.
-
- Returns:
- hovertext HTML 줄바꿈 문자열.
- """
- action = t.get("action", t.get("side", ""))
- amt_label = "매수금액" if action == "buy" else "매도금액"
- lines = [
- label,
- str(t.get("dt", ""))[:16],
- f"체결가 ₩{float(t['price']):,.0f}",
- ]
- ak = t.get("amount_krw")
- if ak is not None and float(ak) > 0:
- lines.append(f"{amt_label} ₩{float(ak):,.0f}")
- elif default_order_krw is not None and action == "buy":
- lines.append(f"{amt_label} ₩{float(default_order_krw):,.0f}")
- else:
- lines.append(f"{amt_label} (미배분)")
- if t.get("weight") is not None:
- lines.append(f"비중 {float(t.get('weight', 1)) * 100:.0f}%")
- if extra_lines:
- lines.extend(extra_lines)
- memo = t.get("memo", "")
- if memo:
- lines.append(str(memo))
- rule_id = t.get("rule_id", "")
- if rule_id:
- lines.append(str(rule_id))
- return "
".join(lines)
-
-
-def _trade_amount_krw(t: dict) -> float:
- """
- 마커 크기·툴팁용 체결 원화. amount_krw 없으면 비중×초기자본으로 상대 크기만 추정.
-
- Args:
- t: trade dict.
-
- Returns:
- 원화 금액(0 이상).
- """
- ak = t.get("amount_krw")
- if ak is not None and float(ak) > 0:
- return float(ak)
- return max(float(t.get("weight", 1.0)), 0.05) * float(GT_INITIAL_CASH_KRW)
-
-
-def _marker_sizes(pts: list[dict]) -> list[float]:
- """
- 체결 원화(amount_krw)에 비례한 삼각형 크기.
-
- 같은 trace(매수 또는 매도) 안에서 최소·최대 금액으로 선형 스케일.
-
- Args:
- pts: 동일 action의 trade dict 리스트.
-
- Returns:
- plotly marker size(diameter) 리스트.
- """
- if not pts:
- return []
- lo, hi = float(GT_MARKER_SIZE_MIN), float(GT_MARKER_SIZE_MAX)
- amounts = [_trade_amount_krw(t) for t in pts]
- amin, amax = min(amounts), max(amounts)
- sizes: list[float] = []
- for amount in amounts:
- if amax > amin:
- ratio = (amount - amin) / (amax - amin)
- else:
- ratio = 0.5
- ratio = max(ratio, 0.08)
- sizes.append(lo + (hi - lo) * ratio)
- return sizes
-
-
-def _add_sim_markers(fig, trades: list[dict], row: int = 1) -> None:
- """
- 시뮬(규칙) 매수·매도 마커 — 원형, GT 삼각형과 구분.
-
- Args:
- fig: plotly Figure.
- trades: dt, action, price, forward_ret_pct, rule_id 키.
- row: subplot row.
- """
- for action, color, label in [
- ("buy", "#059669", "시뮬 매수"),
- ("sell", "#b91c1c", "시뮬 매도"),
- ]:
- pts = [t for t in trades if t.get("action") == action]
- if not pts:
- continue
- fig.add_trace(
- go.Scatter(
- x=[pd.Timestamp(t["dt"]) for t in pts],
- y=[float(t["price"]) for t in pts],
- mode="markers",
- name=label,
- legendgroup=label,
- marker=dict(
- symbol="circle",
- size=9,
- color=color,
- line=dict(width=1, color="#fff"),
- opacity=0.75,
- ),
- hovertext=[
- _marker_hover_text(
- label,
- t,
- default_order_krw=LIVE_ORDER_KRW,
- extra_lines=[
- f"leg_gt {float(t.get('forward_ret_pct', 0)):+.2f}%",
- ],
- )
- for t in pts
- ],
- hovertemplate="%{hovertext}",
- ),
- row=row,
- col=1,
- )
-
-
-def _add_truth_markers(fig, trades: list[dict], row: int = 1) -> None:
- """정답 매수·매도 마커 (삼각형 크기 = 체결 원화 금액)."""
- for action, color, symbol, label in [
- ("buy", "#16a34a", "triangle-up", "정답 매수"),
- ("sell", "#dc2626", "triangle-down", "정답 매도"),
- ]:
- pts = [t for t in trades if t.get("action") == action]
- if not pts:
- continue
- sizes = _marker_sizes(pts)
- fig.add_trace(
- go.Scatter(
- x=[pd.Timestamp(t["dt"]) for t in pts],
- y=[t["price"] for t in pts],
- mode="markers",
- name=label,
- legendgroup=label,
- marker=dict(
- symbol=symbol,
- size=sizes,
- sizemode="diameter",
- color=color,
- line=dict(width=1.5, color="#111"),
- ),
- hovertext=[
- _marker_hover_text(label, t)
- for t in pts
- ],
- hovertemplate="%{hovertext}",
- ),
- row=row,
- col=1,
- )
-
-
-def build_chart_html(
- df: pd.DataFrame,
- trend: str,
- interval_min: int = ENTRY_INTERVAL,
- note: str = "",
- truth_trades: list[dict] | None = None,
- sim_trades: list[dict] | None = None,
- title_suffix: str = "BB 차트",
- pnl_summary: dict | None = None,
- legend_html: str | None = None,
- footer_sections: str | None = None,
- cards_html: str | None = None,
-) -> str:
- """BB·이격도·RSI·MACD·스토캐스틱·거래량 차트 HTML."""
- df = apply_bar_indicators(df.copy())
- iv_label = interval_chart_label(interval_min)
- close_last = float(df["Close"].iloc[-1])
- bb_pos = None
- if "bb_pos" in df.columns and pd.notna(df["bb_pos"].iloc[-1]):
- bb_pos = float(df["bb_pos"].iloc[-1])
-
- disp_title = "이격도 " + ",".join(str(p) for p in DISPARITY_PERIODS)
- fig = make_subplots(
- rows=6,
- cols=1,
- shared_xaxes=True,
- vertical_spacing=0.03,
- row_heights=[0.42, 0.11, 0.11, 0.11, 0.13, 0.12],
- subplot_titles=(
- f"{COIN_NAME} ({SYMBOL}) {iv_label}",
- disp_title,
- f"Stochastic ({STOCH_K_PERIOD},{STOCH_D_PERIOD})",
- "RSI (14)",
- f"MACD ({MACD_FAST},{MACD_SLOW},{MACD_SIGNAL})",
- "거래량",
- ),
- )
- disp_colors = ("#0d9488", "#7c3aed", "#ca8a04")
-
- fig.add_trace(
- go.Candlestick(
- x=df.index,
- open=df["Open"],
- high=df["High"],
- low=df["Low"],
- close=df["Close"],
- name=f"{iv_label} 캔들",
- increasing_line_color="#ef4444",
- decreasing_line_color="#3b82f6",
- ),
- row=1,
- col=1,
- )
- if "MA" in df.columns:
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["MA"],
- name="BB 중심",
- line=dict(color="#64748b", width=1, dash="dot"),
- ),
- row=1,
- col=1,
- )
- if "Upper" in df.columns:
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["Upper"],
- name="BB 상단",
- line=dict(color="#94a3b8", width=1),
- ),
- row=1,
- col=1,
- )
- if "Lower" in df.columns:
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["Lower"],
- name="BB 하단",
- line=dict(color="#94a3b8", width=1),
- ),
- row=1,
- col=1,
- )
-
- if truth_trades:
- _add_truth_markers(fig, truth_trades, row=1)
- if sim_trades:
- _add_sim_markers(fig, sim_trades, row=1)
-
- disp_row = 2
- for i, p in enumerate(DISPARITY_PERIODS):
- col = disparity_column(p)
- if col not in df.columns:
- continue
- color = disp_colors[i % len(disp_colors)]
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df[col],
- name=f"D.I. {p}",
- line=dict(color=color, width=1),
- ),
- row=disp_row,
- col=1,
- )
- if any(disparity_column(p) in df.columns for p in DISPARITY_PERIODS):
- fig.add_hline(
- y=100, line_dash="solid", line_color="#64748b", row=disp_row, col=1
- )
- fig.add_hline(
- y=DISPARITY_OVERBOUGHT,
- line_dash="dot",
- line_color="#ef4444",
- row=disp_row,
- col=1,
- )
- fig.add_hline(
- y=DISPARITY_OVERSOLD,
- line_dash="dot",
- line_color="#16a34a",
- row=disp_row,
- col=1,
- )
-
- stoch_row = 3
- if "stoch_k" in df.columns:
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["stoch_k"],
- name="Stoch %K",
- line=dict(color="#0ea5e9", width=1),
- ),
- row=stoch_row,
- col=1,
- )
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["stoch_d"],
- name="Stoch %D",
- line=dict(color="#f97316", width=1),
- ),
- row=stoch_row,
- col=1,
- )
- fig.add_hline(y=80, line_dash="dot", line_color="#9ca3af", row=stoch_row, col=1)
- fig.add_hline(y=20, line_dash="dot", line_color="#9ca3af", row=stoch_row, col=1)
-
- rsi_row = 4
- if "RSI" in df.columns:
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["RSI"],
- name="RSI",
- line=dict(color="#7c3aed"),
- ),
- row=rsi_row,
- col=1,
- )
- fig.add_hline(y=70, line_dash="dot", line_color="#9ca3af", row=rsi_row, col=1)
- fig.add_hline(y=30, line_dash="dot", line_color="#9ca3af", row=rsi_row, col=1)
-
- macd_row = 5
- vol_row = 6
- if "macd_hist" in df.columns:
- colors = np.where(df["macd_hist"].astype(float) >= 0, "#ef4444", "#3b82f6")
- fig.add_trace(
- go.Bar(
- x=df.index,
- y=df["macd_hist"],
- name="MACD Hist",
- marker_color=colors,
- ),
- row=macd_row,
- col=1,
- )
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["macd_line"],
- name="MACD",
- line=dict(color="#2563eb", width=1),
- ),
- row=macd_row,
- col=1,
- )
- fig.add_trace(
- go.Scatter(
- x=df.index,
- y=df["macd_signal"],
- name="Signal",
- line=dict(color="#ea580c", width=1, dash="dot"),
- ),
- row=macd_row,
- col=1,
- )
-
- fig.add_trace(
- go.Bar(
- x=df.index,
- y=df["Volume"],
- name="Volume",
- marker_color="#cbd5e1",
- ),
- row=vol_row,
- col=1,
- )
-
- fig.update_layout(
- height=1180,
- template="plotly_white",
- xaxis_rangeslider_visible=False,
- legend=dict(orientation="h", y=1.05, x=0),
- margin=dict(l=60, r=30, t=90, b=40),
- )
- fig.update_yaxes(title_text="가격 (KRW)", row=1, col=1)
- fig.update_yaxes(title_text="이격도", row=2, col=1)
- fig.update_yaxes(title_text="Stoch", row=3, col=1, range=[0, 100])
- fig.update_yaxes(title_text="RSI", row=4, col=1, range=[0, 100])
- fig.update_yaxes(title_text="MACD", row=5, col=1)
-
- chart_html = fig.to_html(full_html=False, include_plotlyjs="cdn")
- note_html = f"{note}
" if note else ""
- bb_pos_txt = f"{bb_pos:.2f}" if bb_pos is not None else "-"
- pnl = pnl_summary or {}
- if truth_trades and not pnl:
- from deepcoin.ground_truth.ground_truth import simulate_truth_portfolio
-
- pnl = simulate_truth_portfolio(
- truth_trades,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- last_price=close_last,
- )
- trade_rows = ""
- trade_table = footer_sections or ""
- if footer_sections is None and truth_trades:
- from deepcoin.ground_truth.ground_truth import simulate_truth_portfolio_steps
-
- steps = simulate_truth_portfolio_steps(
- truth_trades,
- initial_cash=GT_INITIAL_CASH_KRW,
- fee_rate=TRADING_FEE_RATE,
- )
- step_key = {
- (s["dt"], s["action"], float(s["price"]), float(s["weight"])): s
- for s in steps
- }
- sorted_trades = sorted(truth_trades, key=lambda x: x["dt"])
- trade_rows += f"""
-
- | 시작 |
- - |
- - |
- - |
- ₩{GT_INITIAL_CASH_KRW:,.0f} |
- 초기 현금 (보유 0) |
-
"""
- for t in sorted_trades:
- cls = "buy" if t["action"] == "buy" else "sell"
- mark = "매수" if t["action"] == "buy" else "매도"
- ret = t.get("forward_return_pct")
- ret_s = f" (+{ret}%)" if ret is not None else ""
- w = float(t.get("weight", 1.0))
- key = (t["dt"], t["action"], float(t["price"]), w)
- step = step_key.get(key)
- if step:
- total_s = f"₩{step['total_asset_krw']:,.0f}"
- hold_s = f" (현금 ₩{step['cash_krw']:,.0f} + 코인 {step['holding_qty']:,.2f}개)"
- else:
- total_s = "-"
- hold_s = ""
- trade_rows += f"""
-
- | {t['dt'][:16]} |
- {mark} |
- {w*100:.0f}% |
- ₩{t['price']:,.0f}{ret_s} |
- {total_s}{hold_s} |
- {t.get('memo', '')} |
-
"""
- if not trade_table and truth_trades:
- if not trade_rows:
- trade_rows = "| 타점 없음 |
"
- mark_note = ""
- if pnl.get("mark_price"):
- mark_note = (
- f" 총보유자산(미청산 포함)은 종가 ₩{pnl['mark_price']:,.0f} 평가."
- )
- trade_table = f"""
- 정답 타점 (ground_truth)
- 삼각형 크기 = 체결 금액(매수/매도 각각 min~max). 매수: 저점 분할 / 매도: 고점 1~2회.
- 총평가 = 체결 직후 현금 + 보유×체결가.{mark_note}
-
- | 시각 | 구분 | 비중 | 가격 | 총 평가금액 | 해석 |
|---|
- {trade_rows}
-
"""
-
- pnl_cards = ""
- if truth_trades and pnl.get("initial_cash_krw") is not None:
- from deepcoin.ops.chart_report import card_html, initial_change_pct
-
- change_pct = initial_change_pct(pnl)
- pnl_cards = (
- card_html("초기 금액", f"₩{pnl['initial_cash_krw']:,.0f}")
- + card_html("총보유자산", f"₩{pnl['final_asset_krw']:,.0f}")
- + card_html("초기 대비 증감율", f"{change_pct:+.2f}%")
- + card_html("수수료", f"₩{pnl['total_fees_krw']:,.0f}")
- )
- if pnl.get("holding_qty", 0) > 0:
- pnl_cards += card_html(
- "미청산",
- f"{pnl['holding_qty']}개 (₩{pnl['holding_value_krw']:,.0f})",
- )
-
- default_legend = (
- "▲ 정답 매수 · ▼ 정답 매도 — 크기=체결금액. "
- "매수=총자산×비중×leg티어(상위 대형). "
- "툴팁: 체결가·매수/매도금액.
"
- "● 시뮬 매수 · ● 시뮬 매도 — 원 = holdout 발화 "
- f"(매수 ₩{LIVE_ORDER_KRW:,.0f}/회)."
- )
- if cards_html:
- cards_inner = cards_html
- else:
- cards_inner = f"""
- 종가₩{close_last:,.2f}
- BB %B{bb_pos_txt}
- 정답 타점{len(truth_trades) if truth_trades else 0}건