인과적 GT 신호·복리 배분 시뮬을 도입하고 운영 정합성을 맞춘다.
미래 데이터를 쓰지 않는 causal 신호/tier와 전기간 복리 포트폴리오 비교로 GT 대비 sim_sized 검증 경로를 정리하고, 일한도·매수 상한·live_buy 스케일을 제거한다. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
xavis
@@ -10,10 +10,14 @@ CHART_LOOKBACK_DAYS=365
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# 02 Ground Truth
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GT_MIN_ORDER_KRW=5000
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GT_MAX_BUY_ORDER_KRW=100000
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GT_BUY_PCT_LARGE_LEG=1.0
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GT_BUY_PCT_SMALL_LEG=0.05
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GT_LARGE_LEG_TOP_PCT=0.2
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# 시뮬·스캔: 1=인과적(운영 정합), 0=사후 ZigZag(정답 라벨용)
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GT_SIGNAL_CAUSAL=1
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SIM_CAUSAL_TIER=1
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GT_BUY_WEIGHT_RULE=inverse_price_normalized
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GT_SELL_SPLIT_WEIGHTS=0.65,0.35
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# 04 매칭
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MATCH_LABEL_MODE=leg_gt
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@@ -28,7 +32,7 @@ SIM_FEE_STRESS_MULT=2.0
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MONITOR_ALERT_COOLDOWN_MIN=180
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MONITOR_ALERT_KRW_AMOUNT=100000
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# 3 실거래 (오픈 시에만 1)
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# 3 실거래 (오픈 시에만 1) — 아래 한도는 06_live 전용, 시뮬(04)은 GT tier 복리 사용
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LIVE_TRADING_ENABLED=0
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LIVE_ORDER_KRW=100000
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LIVE_BUY_PCT_LARGE=1.0
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18
config.py
18
config.py
@@ -199,14 +199,30 @@ GT_BUY_MIN_BARS = _getenv_int("GT_BUY_MIN_BARS", "24")
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GT_MAX_BUYS_PER_LEG = _getenv_int("GT_MAX_BUYS_PER_LEG", "12")
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GT_MAX_SELLS_PER_LEG = _getenv_int("GT_MAX_SELLS_PER_LEG", "2")
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GT_SELL_SPLIT_GAP_PCT = _getenv_float("GT_SELL_SPLIT_GAP_PCT", "2.5")
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GT_SELL_SPLIT_WEIGHTS: tuple[float, ...] = tuple(
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float(x.strip())
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for x in _getenv("GT_SELL_SPLIT_WEIGHTS", "0.65,0.35").split(",")
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if x.strip()
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) or (0.65, 0.35)
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GT_BUY_WEIGHT_RULE = _getenv("GT_BUY_WEIGHT_RULE", "inverse_price_normalized")
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GT_MARKER_SIZE_MIN = _getenv_int("GT_MARKER_SIZE_MIN", "10")
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GT_MARKER_SIZE_MAX = _getenv_int("GT_MARKER_SIZE_MAX", "32")
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GT_INITIAL_CASH_KRW = _getenv_int("GT_INITIAL_CASH_KRW", "1000000")
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GT_MIN_ORDER_KRW = _getenv_int("GT_MIN_ORDER_KRW", "5000")
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GT_MAX_BUY_ORDER_KRW = _getenv_int("GT_MAX_BUY_ORDER_KRW", "100000")
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GT_BUY_PCT_LARGE_LEG = _getenv_float("GT_BUY_PCT_LARGE_LEG", "1.0")
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GT_BUY_PCT_SMALL_LEG = _getenv_float("GT_BUY_PCT_SMALL_LEG", "0.05")
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GT_LARGE_LEG_TOP_PCT = _getenv_float("GT_LARGE_LEG_TOP_PCT", "0.2")
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# 시뮬·스캔: 1=인과적 신호·tier (미래 데이터 미사용, 운영 정합)
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GT_SIGNAL_CAUSAL = _getenv("GT_SIGNAL_CAUSAL", "1").strip().lower() in (
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"1",
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"true",
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"yes",
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)
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SIM_CAUSAL_TIER = _getenv("SIM_CAUSAL_TIER", "1").strip().lower() in (
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"1",
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"true",
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"yes",
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)
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TRADING_FEE_RATE = _getenv_float("TRADING_FEE_RATE", "0.0005")
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# --- 모니터 / API 수집 ---
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@@ -16,7 +16,7 @@
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"pivot": "peak",
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"price": "High",
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"weight_rule": "fixed_split_or_full",
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"weights_two_sell": [
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"weights_split": [
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0.65,
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0.35
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],
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@@ -24,13 +24,13 @@
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"max_sells_per_leg": 2
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},
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"capital": {
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"buy": "min(total_asset * w_share * tier_scale, cash/(1+fee))",
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"buy": "target = total_asset * (weight/remaining_weights) * tier_scale; amount = min(target, available_cash/(1+fee))",
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"optimal_buy_rate": "weight / sum(remaining_buy_weights_in_leg)",
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"large_leg_top_pct": 0.2,
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"pct_large_leg": 1.0,
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"pct_small_leg": 0.05,
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"min_order_krw": 5000.0,
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"sell": "leg_qty * sell_weight * price (last sell = full leg_qty)"
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"sell": "sell_base_qty * sell_weight * price (last sell = full leg_qty)"
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},
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"execution": {
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"chrono": "amount_krw 배분·summary.pnl_pct = 시각순 체결(매도 후 현금 → 다음 매수 반영)",
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@@ -42,7 +42,7 @@
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"interval_min": 3,
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"lookback_days": 365,
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"period_start": "2025-06-04 03:57:00",
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"period_end": "2026-05-31 15:58:00",
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"period_end": "2026-05-31 19:36:00",
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"trend_at_end": "range",
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"params": {
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"min_swing_pct": 4.0,
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@@ -55,40 +55,39 @@
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"max_sells_per_leg": 2
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},
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"summary": {
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"pivot_candidates": 382,
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"pivot_candidates": 383,
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"sell_peaks": 75,
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"trade_count": 454,
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"buy_count": 304,
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"trade_count": 456,
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"buy_count": 306,
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"sell_count": 150,
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"round_trips": 76,
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"sum_sell_leg_return_pct": 1634.3,
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"sum_sell_leg_return_pct": 1631.75,
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"initial_cash_krw": 1000000,
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"final_asset_krw": 43963648.0,
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"pnl_krw": 42963648.0,
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"pnl_pct": 4296.36,
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"total_fees_krw": 238798.0,
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"cash_krw": 43963648.0,
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"final_asset_krw": 43913513.0,
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"pnl_krw": 42913513.0,
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"pnl_pct": 4291.35,
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"total_fees_krw": 240578.0,
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"cash_krw": 43913513.0,
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"holding_qty": 0.0,
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"holding_value_krw": 0.0,
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"mark_price": 503.0,
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"mark_price": 487.0,
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"fee_rate": 0.0005,
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"realized_final_asset_krw": 43963648.0,
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"realized_pnl_krw": 42963648.0,
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"realized_pnl_pct": 4296.36,
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"realized_final_asset_krw": 43913513.0,
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"realized_pnl_krw": 42913513.0,
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"realized_pnl_pct": 4291.35,
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"unrealized_pnl_krw": 0.0,
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"execution_order": "chronological",
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"order_amount_min_krw": 5000,
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"order_amount_max_buy_krw": 100000,
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"buy_pct_large_leg": 1.0,
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"buy_pct_small_leg": 0.05,
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"large_leg_top_pct": 0.2,
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"buy_executed": 295,
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"buy_executed": 297,
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"buy_skipped": 9,
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"sell_executed": 150,
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"sell_skipped": 0,
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"buy_total_krw": 217196361.0,
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"buy_total_krw": 219000974.0,
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"large_leg_count": 16,
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"buy_amount_avg_krw": 736259.0,
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"buy_amount_avg_krw": 737377.0,
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"buy_amount_min_krw": 5000,
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"buy_amount_max_krw": 14200590.0
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},
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@@ -5974,27 +5973,53 @@
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"dt": "2026-05-31 09:36:00",
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"action": "buy",
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"price": 495.0,
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"memo": "저점 분할 매수 · 비중 100% · leg#75(기간말)",
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"weight": 1.0,
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"amount_krw": 2196518.0,
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"memo": "저점 분할 매수 · 비중 33% · leg#75(기간말)",
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"weight": 0.331,
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"amount_krw": 727048.0,
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"leg_id": 75,
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"bb_pos": 0.0,
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"rsi": 33.3,
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"pivot_kind": "trough",
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"forward_return_pct": 1.62
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"forward_return_pct": -1.62
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},
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{
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"dt": "2026-05-31 15:58:00",
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"dt": "2026-05-31 17:39:00",
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"action": "buy",
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"price": 498.0,
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"memo": "저점 분할 매수 · 비중 33% · leg#75(기간말)",
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"weight": 0.329,
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"amount_krw": 1080300.0,
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"leg_id": 75,
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"bb_pos": 0.028,
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"rsi": 38.5,
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"pivot_kind": "trough",
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"forward_return_pct": -2.21
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},
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{
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"dt": "2026-05-31 19:33:00",
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"action": "buy",
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"price": 482.0,
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"memo": "저점 분할 매수 · 비중 34% · leg#75(기간말)",
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"weight": 0.34,
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"amount_krw": 2193783.0,
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"leg_id": 75,
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"bb_pos": 0.0,
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"rsi": 16.7,
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"pivot_kind": "trough",
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"forward_return_pct": 1.04
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},
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{
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"dt": "2026-05-31 19:36:00",
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"action": "sell",
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"price": 503.0,
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"price": 487.0,
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"memo": "기간말 잔여 청산 · leg#75",
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"weight": 1.0,
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"amount_krw": 2232017.0,
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"amount_krw": 3988276.0,
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"leg_id": 75,
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"bb_pos": 0.091,
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"rsi": 35.7,
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"bb_pos": 0.042,
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"rsi": 16.7,
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"pivot_kind": "peak",
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"forward_return_pct": 1.62
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"forward_return_pct": -0.93
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}
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]
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}
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@@ -33,7 +33,6 @@ from config import (
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GT_INITIAL_CASH_KRW,
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GT_LARGE_LEG_TOP_PCT,
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GT_MIN_ORDER_KRW,
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GT_MAX_BUY_ORDER_KRW,
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GT_MAX_BUYS_PER_LEG,
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GT_MAX_ROUND_TRIPS,
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TRADING_FEE_RATE,
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@@ -49,6 +48,16 @@ from config import (
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from deepcoin.common.indicators import apply_bar_indicators, get_trend
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from deepcoin.data.mtf_bb import load_frames_from_db
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from deepcoin.ground_truth.gt_allocation import (
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allocate_order_amounts_chronological,
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resolve_sell_qty as _resolve_sell_qty,
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)
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from deepcoin.ground_truth.gt_model import (
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compute_entry_weights,
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leg_entry_weights,
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leg_exit_weights,
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sell_split_weights,
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)
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from deepcoin.paths import resolve_ground_truth_file
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DEFAULT_OUTPUT = resolve_ground_truth_file()
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@@ -476,15 +485,6 @@ def _row_at_ts(df: pd.DataFrame, ts: pd.Timestamp) -> pd.Series:
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return row
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def _normalize_weights(scores: list[float]) -> list[float]:
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"""비중 점수를 합 1로 정규화."""
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total = sum(scores)
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if total <= 0:
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n = len(scores)
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return [1.0 / n] * n if n else []
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return [s / total for s in scores]
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def _collect_buy_troughs(
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df: pd.DataFrame,
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buy_pivots: list[Pivot],
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@@ -525,7 +525,6 @@ def _collect_buy_troughs(
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filtered.append(p)
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if len(filtered) > max_buys:
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# 가격이 낮은(저점) 순으로 max_buys만 유지 후 시간순
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filtered.sort(key=lambda x: x.price)
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filtered = sorted(filtered[:max_buys], key=lambda x: x.ts)
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return filtered
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@@ -570,7 +569,8 @@ def _peak_sell_points(
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second = max(sub_peaks, key=lambda x: x.price)
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if second.ts == main.ts:
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return [(main, 1.0)]
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return [(main, 0.65), (second, 0.35)]
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w = sell_split_weights(2)
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return [(main, w[0]), (second, w[1])]
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def build_split_buy_peak_sell_trades(
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@@ -603,8 +603,11 @@ def build_split_buy_peak_sell_trades(
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for leg_id, peak in enumerate(sell_peaks):
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troughs = _collect_buy_troughs(df, buy_pivots, prev_sell_ts, peak.ts, buy_min_bars)
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if troughs:
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scores = [1.0 / max(t.price, 1e-9) for t in troughs]
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weights = _normalize_weights(scores)
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prices = [
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float(_row_at_ts(df, t.ts)["Low"]) if "Low" in _row_at_ts(df, t.ts) else t.price
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for t in troughs
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]
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weights = leg_entry_weights(prices)
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for t, w in zip(troughs, weights):
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row = _row_at_ts(df, t.ts)
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bb_pos, rsi, disp = _bb_context(row)
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@@ -672,7 +675,11 @@ def build_split_buy_peak_sell_trades(
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)
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leg_id = len(sell_peaks)
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if troughs:
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weights = _normalize_weights([1.0 / max(t.price, 1e-9) for t in troughs])
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prices = [
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float(_row_at_ts(df, t.ts)["Low"]) if "Low" in _row_at_ts(df, t.ts) else t.price
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for t in troughs
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]
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weights = leg_entry_weights(prices)
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leg_buys: list[TradePoint] = []
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for t, w in zip(troughs, weights):
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row = _row_at_ts(df, t.ts)
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@@ -916,7 +923,6 @@ def generate_ground_truth(
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else "leg_block"
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),
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"order_amount_min_krw": GT_MIN_ORDER_KRW,
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"order_amount_max_buy_krw": GT_MAX_BUY_ORDER_KRW,
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"buy_pct_large_leg": GT_BUY_PCT_LARGE_LEG,
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"buy_pct_small_leg": GT_BUY_PCT_SMALL_LEG,
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"large_leg_top_pct": GT_LARGE_LEG_TOP_PCT,
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@@ -963,13 +969,12 @@ def allocate_gt_order_amounts(
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trades: list[dict[str, Any]],
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initial_cash: float = GT_INITIAL_CASH_KRW,
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min_order_krw: float = GT_MIN_ORDER_KRW,
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max_buy_krw: float = GT_MAX_BUY_ORDER_KRW,
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fee_rate: float = TRADING_FEE_RATE,
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) -> tuple[list[dict[str, Any]], dict[str, Any]]:
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"""
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GT 각 타점에 amount_krw를 시각순·총자산·비중(최적 매수율)으로 배분합니다.
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매수: 총보유자산 × (leg 비중 share × 티어 스케일), 상한=가용 현금.
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매수: 목표=총보유자산×(leg 비중 share×티어 스케일), 체결=min(목표, 보유현금/(1+fee)).
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leg 상위 GT_LARGE_LEG_TOP_PCT는 GT_BUY_PCT_LARGE_LEG, 그 외는 GT_BUY_PCT_SMALL_LEG.
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매도 후 현금 증가분은 다음 매수부터 자동 반영(시각순 복리).
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@@ -977,159 +982,18 @@ def allocate_gt_order_amounts(
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trades: trade dict 리스트(시각순 정렬 전).
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initial_cash: 초기 현금.
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min_order_krw: 매수·매도 최소 원화 금액.
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max_buy_krw: 매수 1회 상한(가용 현금·비중 배분 후 캡).
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fee_rate: 수수료율.
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Returns:
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(동일 dict 참조, amount_krw 채움), alloc_stats 요약.
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"""
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from deepcoin.matching.position_sizing import (
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compute_buy_amount_krw,
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leg_asset_pct_scale,
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top_leg_ids_by_forward_return,
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return allocate_order_amounts_chronological(
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trades,
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initial_cash=initial_cash,
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min_order_krw=min_order_krw,
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fee_rate=fee_rate,
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)
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chron = sorted(trades, key=lambda x: x["dt"])
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large_legs = top_leg_ids_by_forward_return(chron)
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leg_buy_idxs: dict[int, list[int]] = {}
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leg_sell_idxs: dict[int, list[int]] = {}
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for i, t in enumerate(chron):
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lid = int(t.get("leg_id", 0))
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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] = []
|
||||
|
||||
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":
|
||||
rem = [j for j in leg_buy_idxs.get(leg_id, []) if j >= i]
|
||||
w_sum = sum(float(chron[j].get("weight", 1.0)) for j in rem)
|
||||
w_share = (
|
||||
weight / w_sum if w_sum > 0 else 1.0 / max(len(rem), 1)
|
||||
)
|
||||
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,
|
||||
)
|
||||
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
|
||||
buy_executed += 1
|
||||
buy_amounts.append(amount)
|
||||
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
|
||||
if not is_last_leg_sell:
|
||||
sell_qty = gross / price
|
||||
else:
|
||||
sell_qty = leg_qty
|
||||
t["amount_krw"] = round(gross, 0)
|
||||
fee = gross * fee_rate
|
||||
cash += 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
|
||||
|
||||
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": len(large_legs),
|
||||
"large_leg_top_pct": GT_LARGE_LEG_TOP_PCT,
|
||||
}
|
||||
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 _resolve_sell_qty(
|
||||
t: dict[str, Any],
|
||||
qty: float,
|
||||
price: float,
|
||||
sell_base_qty: float,
|
||||
weight: float,
|
||||
) -> float:
|
||||
"""
|
||||
매도 수량: amount_krw가 보유 전량에 가깝으면 전량, 아니면 weight 비중.
|
||||
|
||||
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 _trade_buy_amount(
|
||||
t: dict[str, Any],
|
||||
|
||||
402
deepcoin/ground_truth/gt_allocation.py
Normal file
402
deepcoin/ground_truth/gt_allocation.py
Normal file
@@ -0,0 +1,402 @@
|
||||
"""
|
||||
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_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] = []
|
||||
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)
|
||||
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,
|
||||
)
|
||||
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)
|
||||
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 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": len(large_legs),
|
||||
}
|
||||
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,
|
||||
}
|
||||
150
deepcoin/ground_truth/gt_allocation_analysis.py
Normal file
150
deepcoin/ground_truth/gt_allocation_analysis.py
Normal file
@@ -0,0 +1,150 @@
|
||||
"""
|
||||
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)
|
||||
@@ -1,13 +1,14 @@
|
||||
"""
|
||||
Ground Truth 타점·비중·자본 배분 모델 (일반화 명세).
|
||||
|
||||
타점 생성(ground_truth.py)과 자본 배분(position_sizing.py)의 공통 언어.
|
||||
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
|
||||
from typing import Any, Callable
|
||||
|
||||
from config import (
|
||||
GT_BUY_BB_MAX,
|
||||
@@ -15,14 +16,130 @@ from config import (
|
||||
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:
|
||||
@@ -32,14 +149,14 @@ class GtEntrySpec:
|
||||
Attributes:
|
||||
pivot_kind: ZigZag 저점(trough).
|
||||
price_field: 체결가 = 봉 Low.
|
||||
weight_rule: 저가일수록 큰 비중 (1/price 정규화).
|
||||
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 = "inverse_price_normalized"
|
||||
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}"
|
||||
@@ -53,13 +170,13 @@ class GtExitSpec:
|
||||
Attributes:
|
||||
pivot_kind: major swing 고점(peak).
|
||||
price_field: 체결가 = 봉 High.
|
||||
split_weights: 2회 분할 시 (65%, 35%).
|
||||
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, float] = (0.65, 0.35)
|
||||
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
|
||||
|
||||
@@ -78,13 +195,16 @@ class GtCapitalSpec:
|
||||
min_order_krw: 최소 체결 원화.
|
||||
"""
|
||||
|
||||
buy_formula: str = "min(total_asset * w_share * tier_scale, cash/(1+fee))"
|
||||
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 = "leg_qty * sell_weight * price (last sell = full leg_qty)"
|
||||
sell_formula: str = "sell_base_qty * sell_weight * price (last sell = full leg_qty)"
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -121,37 +241,74 @@ def default_model() -> GroundTruthModel:
|
||||
return GroundTruthModel()
|
||||
|
||||
|
||||
def compute_buy_weights_inverse_price(prices: list[float]) -> list[float]:
|
||||
def sell_split_weights(
|
||||
n_sells: int,
|
||||
exit_spec: GtExitSpec | None = None,
|
||||
) -> list[float]:
|
||||
"""
|
||||
저점 매수 비중: score_i = 1/price_i → 합=1 정규화.
|
||||
leg 매도 비중 (1회=100%, N회=split_weights 정규화).
|
||||
|
||||
Args:
|
||||
prices: leg 내 매수 후보 가격.
|
||||
n_sells: 매도 횟수(1 이상).
|
||||
exit_spec: None이면 default.
|
||||
|
||||
Returns:
|
||||
weight 리스트 (합 ≈ 1).
|
||||
"""
|
||||
if not prices:
|
||||
return []
|
||||
scores = [1.0 / max(p, 1e-9) for p in prices]
|
||||
s = sum(scores)
|
||||
return [x / s for x in scores] if s > 0 else [1.0 / len(prices)] * len(prices)
|
||||
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 sell_split_weights(n_sells: int) -> list[float]:
|
||||
def pair_peak_sell_weights(
|
||||
n_peaks: int,
|
||||
exit_spec: GtExitSpec | None = None,
|
||||
) -> list[tuple[float, float]]:
|
||||
"""
|
||||
leg 매도 비중.
|
||||
고점 피벗 (피벗, weight) 쌍 — 1회 또는 분할.
|
||||
|
||||
Args:
|
||||
n_sells: 매도 횟수(1 또는 2).
|
||||
n_peaks: 인정된 고점 수 (1 또는 2+).
|
||||
exit_spec: 매도 명세.
|
||||
|
||||
Returns:
|
||||
weight 리스트.
|
||||
(weight,) 또는 (w1, w2) 리스트. 호출측에서 피벗과 zip.
|
||||
"""
|
||||
spec = GtExitSpec()
|
||||
if n_sells >= 2:
|
||||
return list(spec.split_weights)
|
||||
return [1.0]
|
||||
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]:
|
||||
@@ -165,6 +322,12 @@ def model_to_dict(model: GroundTruthModel | None = None) -> dict[str, Any]:
|
||||
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,
|
||||
@@ -172,7 +335,7 @@ def model_to_dict(model: GroundTruthModel | None = None) -> dict[str, Any]:
|
||||
"pivot": m.entry.pivot_kind,
|
||||
"price": m.entry.price_field,
|
||||
"weight_rule": m.entry.weight_rule,
|
||||
"weight_formula": "w_i = (1/price_i) / sum(1/price_j)",
|
||||
"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,
|
||||
@@ -181,7 +344,7 @@ def model_to_dict(model: GroundTruthModel | None = None) -> dict[str, Any]:
|
||||
"pivot": m.exit.pivot_kind,
|
||||
"price": m.exit.price_field,
|
||||
"weight_rule": "fixed_split_or_full",
|
||||
"weights_two_sell": list(m.exit.split_weights),
|
||||
"weights_split": list(m.exit.split_weights),
|
||||
"split_gap_pct": m.exit.split_gap_pct,
|
||||
"max_sells_per_leg": m.exit.max_per_leg,
|
||||
},
|
||||
@@ -209,7 +372,7 @@ def summarize_leg_weights(trades: list[dict[str, Any]]) -> dict[str, Any]:
|
||||
trades: GT trade dict.
|
||||
|
||||
Returns:
|
||||
leg_id → {buy_sum, sell_sum, n_buy, n_sell}.
|
||||
leg_id → {buy_sum, sell_sum, n_buy, n_sell, valid}.
|
||||
"""
|
||||
legs: dict[int, dict[str, Any]] = {}
|
||||
for t in trades:
|
||||
@@ -225,4 +388,30 @@ def summarize_leg_weights(trades: list[dict[str, Any]]) -> dict[str, Any]:
|
||||
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,
|
||||
}
|
||||
|
||||
181
deepcoin/ground_truth/gt_signal_causal.py
Normal file
181
deepcoin/ground_truth/gt_signal_causal.py
Normal file
@@ -0,0 +1,181 @@
|
||||
"""
|
||||
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,
|
||||
) -> 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 = GT_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"
|
||||
)
|
||||
peak_z = _zigzag_filter_causal(
|
||||
peak_conf, high, sell_swing_pct, "peak"
|
||||
)
|
||||
|
||||
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
|
||||
197
deepcoin/ground_truth/gt_signal_rules.py
Normal file
197
deepcoin/ground_truth/gt_signal_rules.py
Normal file
@@ -0,0 +1,197 @@
|
||||
"""
|
||||
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()}
|
||||
@@ -1,5 +1,5 @@
|
||||
"""
|
||||
규칙 발화 기반 고정 금액 체결 포트폴리오 시뮬 (GT HTML 카드·테이블용).
|
||||
규칙 발화 기반 GT 모델 복리 포트폴리오 시뮬.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -10,14 +10,13 @@ import pandas as pd
|
||||
|
||||
from config import (
|
||||
GT_INITIAL_CASH_KRW,
|
||||
LIVE_DAILY_KRW_MAX,
|
||||
LIVE_MAX_TRADES_PER_DAY,
|
||||
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_dynamic_buy_amounts,
|
||||
load_sizing_context_from_gt,
|
||||
attach_gt_model_amounts,
|
||||
)
|
||||
|
||||
|
||||
@@ -43,99 +42,95 @@ def _planned_order_krw(
|
||||
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:
|
||||
"""
|
||||
일한도·회수 제한으로 체결 가능한 발화만 남깁니다.
|
||||
시각순 발화 반환 (레거시명; 일·금액 한도 미적용).
|
||||
|
||||
Args:
|
||||
fires: fire_outcomes (dt, side, close, rule_id …).
|
||||
fires: fire_outcomes.
|
||||
use_dynamic_sizing: 미사용 (하위 호환).
|
||||
|
||||
Returns:
|
||||
체결된 발화 DataFrame.
|
||||
정렬된 발화 DataFrame.
|
||||
"""
|
||||
if fires.empty:
|
||||
return fires
|
||||
gt_trades, large_legs, approved = load_sizing_context_from_gt()
|
||||
df = fires.sort_values("dt").copy()
|
||||
df["ts"] = pd.to_datetime(df["dt"])
|
||||
df["day"] = df["ts"].dt.date.astype(str)
|
||||
cash = float(GT_INITIAL_CASH_KRW)
|
||||
qty = 0.0
|
||||
taken: list[pd.DataFrame] = []
|
||||
for _, day_grp in df.groupby("day", sort=True):
|
||||
spent = 0.0
|
||||
n_trades = 0
|
||||
idxs: list[Any] = []
|
||||
for idx, row in day_grp.iterrows():
|
||||
if n_trades >= LIVE_MAX_TRADES_PER_DAY:
|
||||
break
|
||||
side = row["side"]
|
||||
price = float(row["close"])
|
||||
if side == "buy" and use_dynamic_sizing:
|
||||
from deepcoin.matching.position_sizing import (
|
||||
compute_buy_amount_krw,
|
||||
live_buy_asset_pct_scale,
|
||||
)
|
||||
|
||||
scale = live_buy_asset_pct_scale(
|
||||
str(row["rule_id"]),
|
||||
str(row["dt"]),
|
||||
gt_trades,
|
||||
approved_rules=approved,
|
||||
large_legs=large_legs,
|
||||
)
|
||||
planned = compute_buy_amount_krw(
|
||||
cash,
|
||||
qty,
|
||||
price,
|
||||
1.0,
|
||||
1.0,
|
||||
asset_pct_scale=scale,
|
||||
)
|
||||
else:
|
||||
planned = float(LIVE_ORDER_KRW)
|
||||
if side == "buy":
|
||||
if spent + planned > LIVE_DAILY_KRW_MAX:
|
||||
break
|
||||
if planned <= 0:
|
||||
continue
|
||||
fee = planned * TRADING_FEE_RATE
|
||||
cash -= planned + fee
|
||||
qty += planned / price if price > 0 else 0.0
|
||||
spent += planned
|
||||
elif side == "sell" and qty > 0:
|
||||
gross = qty * price
|
||||
cash += gross * (1.0 - TRADING_FEE_RATE)
|
||||
qty = 0.0
|
||||
n_trades += 1
|
||||
idxs.append(idx)
|
||||
if idxs:
|
||||
taken.append(day_grp.loc[idxs])
|
||||
if not taken:
|
||||
return df.iloc[0:0]
|
||||
return pd.concat(taken, ignore_index=True)
|
||||
_ = 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 DataFrame을 포트폴리오 시뮬용 trade dict 리스트로 변환.
|
||||
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 키를 가진 dict 리스트.
|
||||
dt, action, price, amount_krw 키 dict 리스트.
|
||||
"""
|
||||
if fires.empty:
|
||||
return []
|
||||
rows: list[dict[str, Any]] = []
|
||||
for _, r in fires.sort_values("dt").iterrows():
|
||||
for _, r in sort_fires_chronological(fires).iterrows():
|
||||
rows.append(
|
||||
{
|
||||
"dt": str(r["dt"]),
|
||||
@@ -145,13 +140,11 @@ def fires_to_trade_list(
|
||||
"forward_ret_pct": float(r.get("forward_ret_pct", 0)),
|
||||
}
|
||||
)
|
||||
if apply_dynamic_sizing and rows:
|
||||
gt_trades, large_legs, approved = load_sizing_context_from_gt()
|
||||
attach_dynamic_buy_amounts(
|
||||
if apply_dynamic_sizing:
|
||||
attach_gt_model_amounts(
|
||||
rows,
|
||||
gt_trades=gt_trades,
|
||||
approved_rules=approved,
|
||||
large_legs=large_legs,
|
||||
initial_cash=initial_cash,
|
||||
fee_rate=fee_rate,
|
||||
)
|
||||
return rows
|
||||
|
||||
@@ -164,7 +157,7 @@ def simulate_sized_portfolio(
|
||||
fallback_order_krw: float = LIVE_ORDER_KRW,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
trade.amount_krw(총자산 비율 배분) 기준 포트폴리오 시뮬.
|
||||
trade.amount_krw(GT 모델·복리 배분) 기준 포트폴리오 시뮬 + MDD.
|
||||
|
||||
Args:
|
||||
trades: 시간순 trade dict (amount_krw 권장).
|
||||
@@ -174,16 +167,25 @@ def simulate_sized_portfolio(
|
||||
fallback_order_krw: amount_krw 없을 때 1회 금액.
|
||||
|
||||
Returns:
|
||||
simulate_truth_portfolio와 동일 키 구조.
|
||||
simulate_truth_portfolio와 동일 키 + max_drawdown_pct.
|
||||
"""
|
||||
return simulate_fixed_order_portfolio(
|
||||
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,
|
||||
order_krw=fallback_order_krw,
|
||||
initial_cash=initial_cash,
|
||||
fee_rate=fee_rate,
|
||||
last_price=last_price,
|
||||
sizing_mode="amount_krw",
|
||||
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(
|
||||
@@ -203,7 +205,7 @@ def simulate_fixed_order_portfolio(
|
||||
initial_cash: 시작 현금.
|
||||
fee_rate: 수수료율.
|
||||
last_price: 미청산 평가 종가.
|
||||
sizing_mode: 'fixed' | 'amount_krw' (없으면 order_krw).
|
||||
sizing_mode: 'fixed' | 'amount_krw'.
|
||||
|
||||
Returns:
|
||||
simulate_truth_portfolio와 동일 키 구조.
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
"""
|
||||
총자산 대비 최적 매수율(비중) · 현금 한도 · leg 상위·EV/WF 통과 대형 매수.
|
||||
총자산 대비 GT 모델 매수율(비중) · 보유 현금 한도 · leg tier 배분.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -15,19 +15,14 @@ from config import (
|
||||
GT_INITIAL_CASH_KRW,
|
||||
GT_LARGE_LEG_TOP_PCT,
|
||||
GT_MIN_ORDER_KRW,
|
||||
LIVE_BUY_PCT_LARGE,
|
||||
LIVE_BUY_PCT_SMALL,
|
||||
MATCH_GT_TOLERANCE_MIN,
|
||||
SIM_FEE_STRESS_MULT,
|
||||
SIM_GO_MIN_HOLDOUT_EV,
|
||||
SIM_GO_MIN_HOLDOUT_PF,
|
||||
SIM_GO_WF_POSITIVE_RATIO,
|
||||
SIM_WALK_FORWARD_MIN_MONTHS,
|
||||
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,
|
||||
@@ -78,10 +73,12 @@ def compute_buy_amount_krw(
|
||||
fee_rate: float = TRADING_FEE_RATE,
|
||||
) -> float:
|
||||
"""
|
||||
총자산 × (최적 매수율 × scale)을 목표로, 가용 현금을 넘지 않게 매수 원화를 산출합니다.
|
||||
목표=총보유자산×(최적 매수율×scale), 체결=min(목표, 보유현금/(1+fee)) 로 매수 원화를 산출합니다.
|
||||
|
||||
보유 현금 = 총보유자산 − 코인평가액(cash 인자).
|
||||
|
||||
Args:
|
||||
cash: 현금.
|
||||
cash: 보유 현금(가용 원화).
|
||||
qty: 보유 수량.
|
||||
price: 체결가.
|
||||
weight: 타점 비중.
|
||||
@@ -95,8 +92,8 @@ def compute_buy_amount_krw(
|
||||
"""
|
||||
if price <= 0:
|
||||
return 0.0
|
||||
total_asset, _, _ = portfolio_totals(cash, qty, price)
|
||||
budget = max(cash / (1.0 + fee_rate), 0.0)
|
||||
total_asset, _, available_cash = portfolio_totals(cash, qty, price)
|
||||
budget = max(available_cash / (1.0 + fee_rate), 0.0)
|
||||
opt_rate = optimal_weight_share(weight, weight_sum_remaining) * asset_pct_scale
|
||||
target = total_asset * opt_rate
|
||||
amount = min(target, budget)
|
||||
@@ -105,6 +102,27 @@ def compute_buy_amount_krw(
|
||||
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,
|
||||
@@ -215,6 +233,8 @@ def load_ev_wf_approved_rule_ids(
|
||||
if _APPROVED_RULES_CACHE is not None:
|
||||
return set(_APPROVED_RULES_CACHE)
|
||||
|
||||
from config import SIM_FEE_STRESS_MULT
|
||||
|
||||
from deepcoin.matching.select_rules import _rule_metrics, _split_train_valid_holdout
|
||||
from deepcoin.matching.simulation import (
|
||||
evaluate_go_no_go,
|
||||
@@ -258,6 +278,72 @@ def load_ev_wf_approved_rule_ids(
|
||||
return fallback
|
||||
|
||||
|
||||
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).
|
||||
|
||||
시뮬은 live_buy_asset_pct_scale 대신 GT와 동일 tier 정책을 사용합니다.
|
||||
|
||||
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 live_buy_asset_pct_scale(
|
||||
rule_id: str,
|
||||
dt: str,
|
||||
@@ -267,7 +353,7 @@ def live_buy_asset_pct_scale(
|
||||
large_legs: set[int],
|
||||
) -> float:
|
||||
"""
|
||||
실거래·시뮬 매수: EV/WF 통과 규칙 + leg 상위만 대형 비율.
|
||||
실거래 전용 매수 tier (EV/WF·leg 상위). 시뮬은 gt_tier_scale_for_trade 사용.
|
||||
|
||||
Args:
|
||||
rule_id: 규칙 ID.
|
||||
@@ -279,6 +365,8 @@ def live_buy_asset_pct_scale(
|
||||
Returns:
|
||||
LIVE_BUY_PCT_LARGE 또는 LIVE_BUY_PCT_SMALL(또는 0에 가까운 소형).
|
||||
"""
|
||||
from config import LIVE_BUY_PCT_LARGE, LIVE_BUY_PCT_SMALL
|
||||
|
||||
if rule_id not in approved_rules:
|
||||
return float(LIVE_BUY_PCT_SMALL)
|
||||
lid = nearest_gt_leg_id(dt, gt_trades)
|
||||
@@ -287,10 +375,193 @@ def live_buy_asset_pct_scale(
|
||||
return float(LIVE_BUY_PCT_SMALL)
|
||||
|
||||
|
||||
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 plan_open_position_buy(
|
||||
open_buys: list[dict[str, Any]],
|
||||
candidate: dict[str, Any],
|
||||
cash: float,
|
||||
qty: float,
|
||||
gt_trades: list[dict[str, Any]] | None = None,
|
||||
*,
|
||||
large_legs: set[int],
|
||||
analysis: dict[str, Any] | None = None,
|
||||
fee_rate: float = TRADING_FEE_RATE,
|
||||
) -> float:
|
||||
"""
|
||||
미청산 포지션 내 다음 매수 원화 (GT tier·보유 현금 한도, 1회 상한 없음).
|
||||
|
||||
Args:
|
||||
open_buys: 현재 포지션에서 이미 체결된 매수 dict.
|
||||
candidate: 이번 매수 후보 {dt, price, rule_id, leg_id?, ...}.
|
||||
cash: 보유 현금.
|
||||
qty: 보유 수량.
|
||||
gt_trades: GT leg 매칭용.
|
||||
large_legs: 상위 leg.
|
||||
analysis: GT 배분 분석.
|
||||
fee_rate: 수수료율.
|
||||
|
||||
Returns:
|
||||
매수 계획 원화.
|
||||
"""
|
||||
from deepcoin.ground_truth.gt_model import leg_entry_weights
|
||||
|
||||
if gt_trades is None:
|
||||
gt_trades, _, _ = load_sizing_context_from_gt()
|
||||
if analysis is None:
|
||||
analysis = load_gt_allocation_analysis(gt_trades)
|
||||
|
||||
prices = [float(t["price"]) for t in open_buys] + [float(candidate["price"])]
|
||||
weights = leg_entry_weights(prices)
|
||||
idx = len(open_buys)
|
||||
w = weights[idx]
|
||||
w_sum = sum(weights[idx:])
|
||||
cand = dict(candidate)
|
||||
if "leg_id" not in cand:
|
||||
cand["leg_id"] = nearest_gt_leg_id(str(candidate["dt"]), gt_trades)
|
||||
scale = gt_tier_scale_for_trade(
|
||||
cand,
|
||||
gt_trades,
|
||||
large_legs,
|
||||
analysis=analysis,
|
||||
)
|
||||
return compute_buy_amount_krw(
|
||||
cash,
|
||||
qty,
|
||||
float(candidate["price"]),
|
||||
w,
|
||||
w_sum,
|
||||
asset_pct_scale=scale,
|
||||
fee_rate=fee_rate,
|
||||
)
|
||||
|
||||
|
||||
def attach_dynamic_buy_amounts(
|
||||
trades: list[dict[str, Any]],
|
||||
*,
|
||||
gt_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,
|
||||
@@ -298,60 +569,18 @@ def attach_dynamic_buy_amounts(
|
||||
fee_rate: float = TRADING_FEE_RATE,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
시뮬 발화 trade dict에 amount_krw(총자산 비율·현금 한도)를 채웁니다.
|
||||
시뮬 발화 trade dict에 amount_krw(GT 모델·보유 현금 한도)를 채웁니다.
|
||||
|
||||
Args:
|
||||
trades: 시간순 {dt, action, price, rule_id, …}.
|
||||
gt_trades: GT leg 매칭용.
|
||||
approved_rules: EV/WF 통과 rule. None이면 전 규칙 대형 허용 안 함.
|
||||
large_legs: 상위 leg. None이면 GT에서 산출.
|
||||
initial_cash: 초기 현금.
|
||||
default_weight: 매수 weight 기본값.
|
||||
fee_rate: 수수료율.
|
||||
|
||||
Returns:
|
||||
amount_krw가 채워진 동일 리스트.
|
||||
attach_gt_model_amounts 별칭.
|
||||
"""
|
||||
if large_legs is None:
|
||||
large_legs = top_leg_ids_by_forward_return(gt_trades)
|
||||
if approved_rules is None:
|
||||
approved_rules = set()
|
||||
|
||||
cash = float(initial_cash)
|
||||
qty = 0.0
|
||||
for t in sorted(trades, key=lambda x: x["dt"]):
|
||||
action = t.get("action", t.get("side", ""))
|
||||
price = float(t["price"])
|
||||
if price <= 0:
|
||||
continue
|
||||
if action == "buy":
|
||||
rid = str(t.get("rule_id", ""))
|
||||
scale = live_buy_asset_pct_scale(
|
||||
rid, t["dt"], gt_trades,
|
||||
approved_rules=approved_rules,
|
||||
large_legs=large_legs,
|
||||
)
|
||||
amount = compute_buy_amount_krw(
|
||||
cash,
|
||||
qty,
|
||||
price,
|
||||
float(t.get("weight", default_weight)),
|
||||
float(t.get("weight", default_weight)),
|
||||
asset_pct_scale=scale,
|
||||
fee_rate=fee_rate,
|
||||
)
|
||||
t["amount_krw"] = amount
|
||||
if amount > 0:
|
||||
fee = amount * fee_rate
|
||||
cash -= amount + fee
|
||||
qty += amount / price
|
||||
elif action == "sell" and qty > 1e-12:
|
||||
gross = qty * price
|
||||
t["amount_krw"] = round(gross, 0)
|
||||
fee = gross * fee_rate
|
||||
cash += gross - fee
|
||||
qty = 0.0
|
||||
return trades
|
||||
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(
|
||||
@@ -374,5 +603,4 @@ def load_sizing_context_from_gt(
|
||||
data = json.loads(p.read_text(encoding="utf-8"))
|
||||
trades = data.get("trades") or []
|
||||
large = top_leg_ids_by_forward_return(trades)
|
||||
approved = load_ev_wf_approved_rule_ids()
|
||||
return trades, large, approved
|
||||
return trades, large, set()
|
||||
|
||||
@@ -380,6 +380,15 @@ def build_rule_candidates(
|
||||
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),
|
||||
|
||||
@@ -188,6 +188,22 @@ def build_mtf_scan_frame(
|
||||
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")
|
||||
|
||||
@@ -13,8 +13,6 @@ import pandas as pd
|
||||
|
||||
from config import (
|
||||
GT_INITIAL_CASH_KRW,
|
||||
LIVE_DAILY_KRW_MAX,
|
||||
LIVE_MAX_TRADES_PER_DAY,
|
||||
LIVE_ORDER_KRW,
|
||||
LIVE_SLIPPAGE_PCT,
|
||||
MATCH_HOLDOUT_RATIO,
|
||||
@@ -32,13 +30,19 @@ from config import (
|
||||
from deepcoin.ground_truth.ground_truth import (
|
||||
load_ground_truth,
|
||||
order_trades_chronological,
|
||||
simulate_truth_portfolio,
|
||||
)
|
||||
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,
|
||||
select_capped_fires,
|
||||
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
|
||||
@@ -129,7 +133,7 @@ def simulate_live_order_cap(
|
||||
holdout_only: bool = True,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
1회·일 한도·슬리피지 가정으로 체결 가능한 발화만 집계.
|
||||
GT 복리 배분·슬리피지 가정으로 체결 가능한 발화 집계 (일·금액 한도 없음).
|
||||
|
||||
Args:
|
||||
outcomes: fire_outcomes (split 컬럼 있으면 holdout 필터 가능).
|
||||
@@ -142,71 +146,23 @@ def simulate_live_order_cap(
|
||||
if outcomes.empty:
|
||||
return {"rules": {}, "note": "발화 없음"}
|
||||
|
||||
df = outcomes
|
||||
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)]
|
||||
df = df.sort_values("dt").copy()
|
||||
df["ts"] = pd.to_datetime(df["dt"])
|
||||
df["day"] = df["ts"].dt.date.astype(str)
|
||||
slip = LIVE_SLIPPAGE_PCT
|
||||
taken_rows: list[pd.DataFrame] = []
|
||||
|
||||
from deepcoin.matching.position_sizing import (
|
||||
compute_buy_amount_krw,
|
||||
live_buy_asset_pct_scale,
|
||||
load_sizing_context_from_gt,
|
||||
)
|
||||
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))}
|
||||
|
||||
gt_trades, large_legs, approved = load_sizing_context_from_gt()
|
||||
cash = float(GT_INITIAL_CASH_KRW)
|
||||
qty = 0.0
|
||||
|
||||
for day, day_grp in df.groupby("day", sort=True):
|
||||
spent = 0.0
|
||||
n_trades = 0
|
||||
taken_idx: list[int] = []
|
||||
for idx, row in day_grp.iterrows():
|
||||
if n_trades >= LIVE_MAX_TRADES_PER_DAY:
|
||||
break
|
||||
side = row["side"]
|
||||
price = float(row["close"])
|
||||
if side == "buy":
|
||||
scale = live_buy_asset_pct_scale(
|
||||
str(row["rule_id"]),
|
||||
str(row["dt"]),
|
||||
gt_trades,
|
||||
approved_rules=approved,
|
||||
large_legs=large_legs,
|
||||
)
|
||||
planned = compute_buy_amount_krw(
|
||||
cash, qty, price, 1.0, 1.0, asset_pct_scale=scale
|
||||
)
|
||||
else:
|
||||
planned = float(LIVE_ORDER_KRW)
|
||||
if side == "buy":
|
||||
if planned <= 0:
|
||||
continue
|
||||
if spent + planned > LIVE_DAILY_KRW_MAX:
|
||||
break
|
||||
fee = planned * TRADING_FEE_RATE
|
||||
cash -= planned + fee
|
||||
qty += planned / price if price > 0 else 0.0
|
||||
spent += planned
|
||||
elif side == "sell" and qty > 0:
|
||||
gross = qty * price
|
||||
cash += gross * (1.0 - TRADING_FEE_RATE)
|
||||
qty = 0.0
|
||||
n_trades += 1
|
||||
taken_idx.append(idx)
|
||||
if taken_idx:
|
||||
taken_rows.append(day_grp.loc[taken_idx])
|
||||
|
||||
if not taken_rows:
|
||||
return {"rules": {}, "taken_count": 0}
|
||||
|
||||
taken = pd.concat(taken_rows, ignore_index=True)
|
||||
taken = df[df["dt"].astype(str).isin(executed_dts)].copy()
|
||||
taken["adj_ret_pct"] = taken["forward_ret_pct"] - slip
|
||||
|
||||
by_rule: dict[str, Any] = {}
|
||||
@@ -221,10 +177,8 @@ def simulate_live_order_cap(
|
||||
|
||||
return {
|
||||
"assumptions": {
|
||||
"order_krw": LIVE_ORDER_KRW,
|
||||
"daily_krw_max": LIVE_DAILY_KRW_MAX,
|
||||
"slippage_pct": slip,
|
||||
"sizing": "total_asset_pct_ev_wf_large_leg",
|
||||
"sizing": "gt_model_compound_no_daily_cap",
|
||||
},
|
||||
"taken_count": int(len(taken)),
|
||||
"total_count": int(len(df)),
|
||||
@@ -338,45 +292,126 @@ def build_simulation_report(
|
||||
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")
|
||||
if gt_trades:
|
||||
portfolio_compare["ground_truth_chrono"] = simulate_truth_portfolio(
|
||||
order_trades_chronological(gt_trades),
|
||||
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,
|
||||
)
|
||||
holdout = outcomes[
|
||||
outcomes["rule_id"].isin(monitor_ids) & (outcomes["split"] == "holdout")
|
||||
]
|
||||
capped = select_capped_fires(holdout)
|
||||
if not capped.empty:
|
||||
|
||||
# 전기간 monitor 규칙 — 100만원에서 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(
|
||||
fires_to_trade_list(capped, apply_dynamic_sizing=True),
|
||||
sim_trades_full,
|
||||
last_price=float(mark) if mark else None,
|
||||
)
|
||||
portfolio_compare["sim_fixed_order"] = simulate_fixed_order_portfolio(
|
||||
fires_to_trade_list(capped, apply_dynamic_sizing=False),
|
||||
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,
|
||||
)
|
||||
|
||||
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)
|
||||
assets = [(s["dt"], float(s["total_asset_krw"])) for s in steps]
|
||||
pre = [a for d, a in assets if pd.to_datetime(d) < h0]
|
||||
in_h = [a for d, a in assets if pd.to_datetime(d) >= h0]
|
||||
asset_start = pre[-1] if pre else float(GT_INITIAL_CASH_KRW)
|
||||
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
|
||||
)
|
||||
portfolio_compare["sim_sized_holdout"] = {
|
||||
"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),
|
||||
"note": "전기간 복리 후 holdout 구간 자산 증감 (1M 재시작 아님)",
|
||||
"trade_count": int(len(holdout)),
|
||||
}
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
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", {})
|
||||
else:
|
||||
from deepcoin.ground_truth.ground_truth import load_ground_truth
|
||||
from deepcoin.matching.gt_asset_calibration import (
|
||||
portfolio_asset_ratio,
|
||||
)
|
||||
|
||||
gt_data = load_ground_truth(resolve_ground_truth_file()) or {}
|
||||
trades = gt_data.get("trades") or []
|
||||
mark = (gt_data.get("summary") or {}).get("mark_price")
|
||||
gt_data_cal = load_ground_truth(resolve_ground_truth_file()) or {}
|
||||
trades = gt_data_cal.get("trades") or []
|
||||
mark_cal = (gt_data_cal.get("summary") or {}).get("mark_price")
|
||||
if trades:
|
||||
gt_portfolio = {
|
||||
"portfolio": portfolio_asset_ratio(trades, set(), mark),
|
||||
"portfolio": portfolio_asset_ratio(trades, set(), mark_cal),
|
||||
"note": "캘리브레이션 미실행 — scripts/04_calibrate_gt_assets.py",
|
||||
}
|
||||
|
||||
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,
|
||||
@@ -389,7 +424,13 @@ def build_simulation_report(
|
||||
"live_order_cap_sim": live_cap,
|
||||
"go_no_go": go,
|
||||
"portfolio_compare": portfolio_compare,
|
||||
"gt_model": gt_data.get("model"),
|
||||
"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": {
|
||||
@@ -450,6 +491,24 @@ def run_simulation_report(
|
||||
)
|
||||
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_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(
|
||||
|
||||
@@ -26,10 +26,10 @@ from deepcoin.ground_truth.ground_truth import (
|
||||
)
|
||||
from deepcoin.matching.portfolio_sim import (
|
||||
fires_to_trade_list,
|
||||
select_capped_fires,
|
||||
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 (
|
||||
@@ -231,7 +231,7 @@ def _summary_cards_html(
|
||||
gt_pnl, "정답 GT", len(gt_trades)
|
||||
)
|
||||
sim_sized_title = (
|
||||
"시뮬·총자산% (EV/WF·leg상위) — "
|
||||
"시뮬·GT tier 복리 (전기간, 상한 없음) — "
|
||||
f'<span class="{go_cls}">{"GO" if go_flag else "NO-GO"}</span>'
|
||||
)
|
||||
sim_fixed_title = f"시뮬·고정 ₩{LIVE_ORDER_KRW:,}/회 (비교)"
|
||||
@@ -280,15 +280,17 @@ def build_simulation_page_html(
|
||||
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()
|
||||
|
||||
capped = select_capped_fires(holdout_fires)
|
||||
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 []
|
||||
@@ -316,8 +318,8 @@ def build_simulation_page_html(
|
||||
elif gt_summary.get("mark_price"):
|
||||
close_val = float(gt_summary["mark_price"])
|
||||
|
||||
sim_trades_sized = fires_to_trade_list(capped, apply_dynamic_sizing=True)
|
||||
sim_trades_fixed = fires_to_trade_list(capped, apply_dynamic_sizing=False)
|
||||
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:
|
||||
@@ -377,14 +379,14 @@ def build_simulation_page_html(
|
||||
return ""
|
||||
|
||||
sim_table = f"""
|
||||
<h2>시뮬 타점 (holdout {len(holdout_fires)}건 → 체결 가정 {len(capped)}건)</h2>
|
||||
<p class="meta">총자산×최적비중·현금한도·EV/WF통과·leg상위 대형 매수. 일한도·최대 거래수 적용.
|
||||
<h2>시뮬 타점 (전기간 {len(sim_fires)}건 → 복리 체결 {len(compound_fires)}건)</h2>
|
||||
<p class="meta">총자산×GT비중×leg tier·보유현금 한도·전기간 복리(일한도 없음).
|
||||
가격 열 (+/-) = <b>{label_mode}</b> 구간 수익%.{_mark_note(close_val)}</p>
|
||||
<div class="table-scroll">
|
||||
<table>
|
||||
<thead><tr><th>시각</th><th>구분</th><th>규칙</th><th>유형</th><th>가격</th>
|
||||
<th>총 평가금액</th><th>승/패</th><th>비고</th></tr></thead>
|
||||
<tbody>{_sim_fire_table_rows(capped, rules_by_id, sim_steps)}</tbody>
|
||||
<tbody>{_sim_fire_table_rows(compound_fires, rules_by_id, sim_steps)}</tbody>
|
||||
</table>
|
||||
</div>"""
|
||||
|
||||
@@ -409,12 +411,12 @@ def build_simulation_page_html(
|
||||
|
||||
note = (
|
||||
f"1단계 시뮬 · holdout {report.get('holdout_ratio', 0.15)} · "
|
||||
f"발화 {len(holdout_fires)}건 / 체결가정 {len(capped)}건. "
|
||||
f"전기간 발화 {len(sim_fires)}건 / holdout {len(holdout_fires)}건. "
|
||||
"상단 카드: 초기 금액·총보유자산·초기 대비 증감율·수수료."
|
||||
)
|
||||
legend = (
|
||||
"▲ <b>정답 매수</b> · ▼ <b>정답 매도</b> — 삼각형 = GT 체결 금액.<br>"
|
||||
"● <b>시뮬</b> — 원 = holdout 발화 (차트). 테이블 = 일한도 적용 체결 순서."
|
||||
"● <b>시뮬</b> — holdout 발화 (차트). 테이블 = 전기간 GT tier 복리 체결."
|
||||
)
|
||||
if frames is not None:
|
||||
meta_line = (
|
||||
@@ -434,7 +436,7 @@ def build_simulation_page_html(
|
||||
gt_pnl,
|
||||
sim_sized_pnl,
|
||||
sim_fixed_pnl,
|
||||
len(capped),
|
||||
len(compound_fires),
|
||||
go_flag,
|
||||
model_note=model_note,
|
||||
)
|
||||
@@ -446,7 +448,7 @@ def build_simulation_page_html(
|
||||
note=note,
|
||||
truth_trades=gt_trades,
|
||||
sim_trades=_fires_to_chart_trades(holdout_fires),
|
||||
# 차트 마커는 holdout 전체; 카드·테이블은 일한도 capped
|
||||
# 차트 마커는 holdout; 카드·테이블은 전기간 GT tier 복리
|
||||
title_suffix="1단계 시뮬레이션 (monitor · holdout)",
|
||||
legend_html=legend,
|
||||
footer_sections=sections,
|
||||
|
||||
@@ -28,7 +28,6 @@ from config import (
|
||||
GT_INITIAL_CASH_KRW,
|
||||
GT_MARKER_SIZE_MAX,
|
||||
GT_MARKER_SIZE_MIN,
|
||||
GT_MAX_BUY_ORDER_KRW,
|
||||
LIVE_ORDER_KRW,
|
||||
MACD_FAST,
|
||||
MACD_SIGNAL,
|
||||
@@ -108,7 +107,7 @@ def _marker_hover_text(
|
||||
|
||||
def _trade_amount_krw(t: dict) -> float:
|
||||
"""
|
||||
마커 크기·툴팁용 체결 원화. amount_krw 없으면 비중×상한으로 추정.
|
||||
마커 크기·툴팁용 체결 원화. amount_krw 없으면 비중×초기자본으로 상대 크기만 추정.
|
||||
|
||||
Args:
|
||||
t: trade dict.
|
||||
@@ -119,7 +118,7 @@ def _trade_amount_krw(t: dict) -> float:
|
||||
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_MAX_BUY_ORDER_KRW)
|
||||
return max(float(t.get("weight", 1.0)), 0.05) * float(GT_INITIAL_CASH_KRW)
|
||||
|
||||
|
||||
def _marker_sizes(pts: list[dict]) -> list[float]:
|
||||
|
||||
Reference in New Issue
Block a user