refactor: Git에서 데이터 제거, 설정·코드만 유지

파이프라인 산출물(data/, docs/)을 Git 추적에서 제외하고
히스토리를 단일 커밋으로 재구성해 저장소 용량을 경량화한다.

Co-authored-by: Cursor <cursoragent@cursor.com>

src/deepcoin/__init__.py

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+"""DeepCoin — 빗썸 암호화폐 데이터 수집·분석."""
+
+__version__ = "0.1.0"

src/deepcoin/api/__init__.py

@@ -0,0 +1 @@
+"""외부 API 클라이언트."""

src/deepcoin/api/bithumb.py

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+"""빗썸 Public REST API — 캔들 조회."""
+
+from __future__ import annotations
+
+import logging
+import time
+from datetime import datetime
+from typing import Any
+
+import requests
+
+from deepcoin.data.intervals import INTERVAL_DAILY, INTERVAL_MONTHLY, INTERVAL_WEEKLY
+
+logger = logging.getLogger(__name__)
+
+# 인터벌(분) → 빗썸 candles API 경로 세그먼트
+_CALENDAR_PATHS: dict[int, str] = {
+    INTERVAL_DAILY: "days",
+    INTERVAL_WEEKLY: "weeks",
+    INTERVAL_MONTHLY: "months",
+}
+
+KST_FMT = "%Y-%m-%d %H:%M:%S"
+KST_FMT_T = "%Y-%m-%dT%H:%M:%S"
+
+
+def parse_kst_datetime(value: str) -> datetime:
+    """KST 캔들 시각 문자열을 datetime으로 변환한다.
+
+    Args:
+        value: `yyyy-MM-dd HH:mm:ss` 또는 ISO 형식.
+
+    Returns:
+        naive datetime (KST 기준).
+    """
+    normalized = value.strip().replace("T", " ")
+    return datetime.strptime(normalized, KST_FMT)
+
+
+def format_kst_datetime(dt: datetime) -> str:
+    """datetime을 빗썸 `to` 파라미터 형식으로 포맷한다.
+
+    Args:
+        dt: KST 기준 시각.
+
+    Returns:
+        `yyyy-MM-dd HH:mm:ss` 문자열.
+    """
+    return dt.strftime(KST_FMT)
+
+
+class BithumbCandleClient:
+    """빗썸 캔들 API 클라이언트."""
+
+    def __init__(
+        self,
+        base_url: str = "https://api.bithumb.com",
+        count: int = 200,
+        sleep_sec: float = 0.35,
+        retries: int = 3,
+    ) -> None:
+        """클라이언트를 초기화한다.
+
+        Args:
+            base_url: API 베이스 URL.
+            count: 요청당 캔들 개수 (최대 200).
+            sleep_sec: 연속 요청 간 대기(초).
+            retries: 실패 시 재시도 횟수.
+        """
+        self.base_url = base_url.rstrip("/")
+        self.count = min(max(count, 1), 200)
+        self.sleep_sec = sleep_sec
+        self.retries = retries
+        self._session = requests.Session()
+        self._session.headers.update({"accept": "application/json"})
+
+    def fetch_candles(
+        self,
+        market: str,
+        interval_min: int,
+        to_kst: datetime | None = None,
+    ) -> list[dict[str, Any]]:
+        """캔들 배치를 조회한다 (최신순).
+
+        Args:
+            market: 거래 페어 (예: KRW-WLD).
+            interval_min: 분 단위. 1440이면 일봉 API 사용.
+            to_kst: 조회 기준 시각(KST). 해당 시각 캔들은 제외.
+
+        Returns:
+            캔들 dict 리스트. API 오류 시 빈 리스트.
+
+        Raises:
+            requests.RequestException: 재시도 후에도 네트워크 실패.
+        """
+        if interval_min in _CALENDAR_PATHS:
+            segment = _CALENDAR_PATHS[interval_min]
+            url = f"{self.base_url}/v1/candles/{segment}"
+            params: dict[str, Any] = {"market": market, "count": self.count}
+        else:
+            url = f"{self.base_url}/v1/candles/minutes/{interval_min}"
+            params = {"market": market, "count": self.count}
+
+        if to_kst is not None:
+            params["to"] = format_kst_datetime(to_kst)
+
+        last_error: Exception | None = None
+        for attempt in range(1, self.retries + 1):
+            try:
+                response = self._session.get(url, params=params, timeout=30)
+                if response.status_code == 429:
+                    wait = self.sleep_sec * attempt * 3
+                    logger.warning("Rate limit 429 — %ss 대기 후 재시도", wait)
+                    time.sleep(wait)
+                    continue
+                response.raise_for_status()
+                payload = response.json()
+                if isinstance(payload, dict) and "error" in payload:
+                    logger.error("API error: %s", payload["error"])
+                    return []
+                if not isinstance(payload, list):
+                    logger.error("Unexpected response type: %s", type(payload))
+                    return []
+                time.sleep(self.sleep_sec)
+                return payload
+            except requests.RequestException as exc:
+                last_error = exc
+                wait = self.sleep_sec * attempt * 2
+                logger.warning("Request failed (%s/%s): %s", attempt, self.retries, exc)
+                time.sleep(wait)
+
+        if last_error is not None:
+            raise last_error
+        return []

src/deepcoin/config.py

@@ -0,0 +1,262 @@
+"""환경 변수 로드 및 애플리케이션 설정."""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+from deepcoin.data.intervals import DEFAULT_DOWNLOAD_INTERVALS
+
+_PROJECT_ROOT = Path(__file__).resolve().parents[2]
+
+
+def _resolve_project_path(raw: str) -> Path:
+    """프로젝트 루트 기준 상대 경로를 절대 경로로 변환한다."""
+    path = Path(raw)
+    if not path.is_absolute():
+        path = _PROJECT_ROOT / path
+    return path
+
+
+def _parse_int_list(raw: str) -> list[int]:
+    """쉼표 구분 정수 목록을 파싱한다.
+
+    Args:
+        raw: 예) "3,5,10,15"
+
+    Returns:
+        정수 리스트. 빈 입력이면 빈 리스트.
+    """
+    if not raw or not raw.strip():
+        return []
+    return [int(part.strip()) for part in raw.split(",") if part.strip()]
+
+
+@dataclass(frozen=True)
+class Settings:
+    """DeepCoin 실행 설정."""
+
+    symbol: str
+    coin_name: str
+    api_url: str
+    candle_count: int
+    download_intervals: list[int]
+    download_days: int
+    db_path: Path
+    request_sleep_sec: float
+    request_retries: int
+    # 0단계: GT 타점 (현물·선물 공통 파라미터)
+    gt_interval_min: int
+    gt_lookback_days: int
+    gt_zigzag_reversal_pct: float
+    gt_min_leg_pct: float
+    gt_pullback_min_pct: float
+    gt_pullback_local_order: int
+    gt_breakout_buffer_pct: float
+    gt_breakout_consolidation_bars: int
+    gt_breakout_min_rally_pct: float
+    gt_div_local_order: int
+    gt_div_min_bars_between: int
+    gt_div_min_rsi_diff: float
+    gt_div_min_future_move_pct: float
+    ground_truth_file: Path
+    ground_truth_v1_file: Path
+    ground_truth_v2_file: Path
+    ground_truth_chart_v1_file: Path
+    ground_truth_chart_v2_file: Path
+    ground_truth_chart_v3_file: Path
+    ground_truth_futures_file: Path
+    ground_truth_futures_v1_file: Path
+    ground_truth_futures_v2_file: Path
+    ground_truth_futures_chart_v1_file: Path
+    ground_truth_futures_chart_v2_file: Path
+    ground_truth_futures_chart_v3_file: Path
+    # 현물 1단계: GT sim
+    ground_truth_chart_sim_v1_file: Path
+    ground_truth_chart_sim_v2_file: Path
+    ground_truth_chart_sim_v3_file: Path
+    gt_sim_lookback_days: int
+    gt_initial_cash_krw: float
+    gt_trading_fee_rate: float
+    # 현물 2단계: 인과 기법 GT 정합
+    techniques_dir: Path
+    analysis_report_json: Path
+    analysis_report_html: Path
+    signal_type_report_json: Path
+    signal_type_report_html: Path
+    gt_align_tolerance_bars: int
+    mtf_report_json: Path
+    mtf_report_html: Path
+    mtf_rules_json: Path
+    causal_sim_report_json: Path
+    causal_sim_report_html: Path
+
+    @property
+    def market(self) -> str:
+        """빗썸 마켓 코드 (예: KRW-BTC)."""
+        return f"KRW-{self.symbol}"
+
+
+def load_settings(env_path: Path | None = None) -> Settings:
+    """`.env`를 로드하고 Settings를 반환한다.
+
+    Args:
+        env_path: `.env` 경로. None이면 프로젝트 루트.
+
+    Returns:
+        Settings 인스턴스.
+    """
+    path = env_path or (_PROJECT_ROOT / ".env")
+    load_dotenv(path, override=False)
+
+    default_intervals = ",".join(str(i) for i in DEFAULT_DOWNLOAD_INTERVALS)
+    intervals_raw = os.getenv("DOWNLOAD_INTERVALS", default_intervals)
+    intervals = sorted(set(_parse_int_list(intervals_raw)))
+
+    db_raw = os.getenv("DB_PATH", "coins.db")
+    db_path = Path(db_raw)
+    if not db_path.is_absolute():
+        db_path = _PROJECT_ROOT / db_path
+
+    tech_dir_raw = os.getenv("TECHNIQUES_DIR", "data/spot/techniques")
+    tech_dir = Path(tech_dir_raw)
+    if not tech_dir.is_absolute():
+        tech_dir = _PROJECT_ROOT / tech_dir
+
+    return Settings(
+        symbol=os.getenv("SYMBOL", "BTC").upper(),
+        coin_name=os.getenv("COIN_NAME", "비트코인"),
+        api_url=os.getenv("BITHUMB_API_URL", "https://api.bithumb.com").rstrip("/"),
+        candle_count=int(os.getenv("BITHUMB_API_CANDLE_COUNT", "200")),
+        download_intervals=intervals,
+        download_days=int(os.getenv("DOWNLOAD_DAYS", "3650")),
+        db_path=db_path,
+        request_sleep_sec=float(os.getenv("API_REQUEST_SLEEP_SEC", "0.35")),
+        request_retries=int(os.getenv("API_REQUEST_RETRIES", "3")),
+        gt_interval_min=int(os.getenv("GT_INTERVAL_MIN", "3")),
+        gt_lookback_days=int(os.getenv("GT_LOOKBACK_DAYS", "3447")),
+        gt_zigzag_reversal_pct=float(os.getenv("GT_ZIGZAG_REVERSAL_PCT", "5.0")),
+        gt_min_leg_pct=float(os.getenv("GT_MIN_LEG_PCT", "3.0")),
+        gt_pullback_min_pct=float(os.getenv("GT_PULLBACK_MIN_PCT", "1.5")),
+        gt_pullback_local_order=int(os.getenv("GT_PULLBACK_LOCAL_ORDER", "10")),
+        gt_breakout_buffer_pct=float(os.getenv("GT_BREAKOUT_BUFFER_PCT", "0.1")),
+        gt_breakout_consolidation_bars=int(os.getenv("GT_BREAKOUT_CONSOLIDATION_BARS", "200")),
+        gt_breakout_min_rally_pct=float(os.getenv("GT_BREAKOUT_MIN_RALLY_PCT", "2.0")),
+        gt_div_local_order=int(os.getenv("GT_DIV_LOCAL_ORDER", "20")),
+        gt_div_min_bars_between=int(os.getenv("GT_DIV_MIN_BARS_BETWEEN", "1500")),
+        gt_div_min_rsi_diff=float(os.getenv("GT_DIV_MIN_RSI_DIFF", "5.0")),
+        gt_div_min_future_move_pct=float(os.getenv("GT_DIV_MIN_FUTURE_MOVE_PCT", "4.0")),
+        ground_truth_file=_resolve_project_path(
+            os.getenv("GROUND_TRUTH_FILE", "data/spot/ground_truth/ground_truth_trades_v3.json")
+        ),
+        ground_truth_v1_file=_resolve_project_path(
+            os.getenv("GROUND_TRUTH_V1_FILE", "data/spot/ground_truth/ground_truth_trades_v1.json")
+        ),
+        ground_truth_v2_file=_resolve_project_path(
+            os.getenv("GROUND_TRUTH_V2_FILE", "data/spot/ground_truth/ground_truth_trades_v2.json")
+        ),
+        ground_truth_chart_v1_file=_resolve_project_path(
+            os.getenv("GROUND_TRUTH_CHART_V1_FILE", "docs/0_ground_truth/spot/ground_truth_chart_v1.html")
+        ),
+        ground_truth_chart_v2_file=_resolve_project_path(
+            os.getenv("GROUND_TRUTH_CHART_V2_FILE", "docs/0_ground_truth/spot/ground_truth_chart_v2.html")
+        ),
+        ground_truth_chart_v3_file=_resolve_project_path(
+            os.getenv("GROUND_TRUTH_CHART_V3_FILE", "docs/0_ground_truth/spot/ground_truth_chart_v3.html")
+        ),
+        ground_truth_futures_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_FUTURES_FILE",
+                "data/futures/ground_truth/ground_truth_trades_v3.json",
+            )
+        ),
+        ground_truth_futures_v1_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_FUTURES_V1_FILE",
+                "data/futures/ground_truth/ground_truth_trades_v1.json",
+            )
+        ),
+        ground_truth_futures_v2_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_FUTURES_V2_FILE",
+                "data/futures/ground_truth/ground_truth_trades_v2.json",
+            )
+        ),
+        ground_truth_futures_chart_v1_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_FUTURES_CHART_V1_FILE",
+                "docs/0_ground_truth/futures/ground_truth_chart_v1.html",
+            )
+        ),
+        ground_truth_futures_chart_v2_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_FUTURES_CHART_V2_FILE",
+                "docs/0_ground_truth/futures/ground_truth_chart_v2.html",
+            )
+        ),
+        ground_truth_futures_chart_v3_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_FUTURES_CHART_V3_FILE",
+                "docs/0_ground_truth/futures/ground_truth_chart_v3.html",
+            )
+        ),
+        ground_truth_chart_sim_v1_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_CHART_SIM_V1_FILE",
+                "docs/spot/1_simulation/ground_truth_chart_sim_v1.html",
+            )
+        ),
+        ground_truth_chart_sim_v2_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_CHART_SIM_V2_FILE",
+                "docs/spot/1_simulation/ground_truth_chart_sim_v2.html",
+            )
+        ),
+        ground_truth_chart_sim_v3_file=_resolve_project_path(
+            os.getenv(
+                "GROUND_TRUTH_CHART_SIM_V3_FILE",
+                "docs/spot/1_simulation/ground_truth_chart_sim_v3.html",
+            )
+        ),
+        gt_sim_lookback_days=int(os.getenv("GT_SIM_LOOKBACK_DAYS", "1095")),
+        gt_initial_cash_krw=float(os.getenv("GT_INITIAL_CASH_KRW", "400000")),
+        gt_trading_fee_rate=float(os.getenv("GT_TRADING_FEE_RATE", "0.0005")),
+        techniques_dir=tech_dir,
+        analysis_report_json=_resolve_project_path(
+            os.getenv("ANALYSIS_REPORT_JSON", "docs/spot/2_analysis/comparison_report.json")
+        ),
+        analysis_report_html=_resolve_project_path(
+            os.getenv("ANALYSIS_REPORT_HTML", "docs/spot/2_analysis/comparison_report.html")
+        ),
+        signal_type_report_json=_resolve_project_path(
+            os.getenv("SIGNAL_TYPE_REPORT_JSON", "docs/spot/2_analysis/signal_type_report.json")
+        ),
+        signal_type_report_html=_resolve_project_path(
+            os.getenv("SIGNAL_TYPE_REPORT_HTML", "docs/spot/2_analysis/signal_type_report.html")
+        ),
+        gt_align_tolerance_bars=int(os.getenv("GT_ALIGN_TOLERANCE_BARS", "480")),
+        mtf_report_json=_resolve_project_path(
+            os.getenv("MTF_REPORT_JSON", "docs/spot/2_analysis/mtf_correlation_report.json")
+        ),
+        mtf_report_html=_resolve_project_path(
+            os.getenv("MTF_REPORT_HTML", "docs/spot/2_analysis/mtf_correlation_report.html")
+        ),
+        mtf_rules_json=_resolve_project_path(
+            os.getenv("MTF_RULES_JSON", "data/spot/mtf/mtf_rules_v3.json")
+        ),
+        causal_sim_report_json=_resolve_project_path(
+            os.getenv(
+                "CAUSAL_SIM_REPORT_JSON",
+                "docs/spot/2_analysis/causal_sim_report.json",
+            )
+        ),
+        causal_sim_report_html=_resolve_project_path(
+            os.getenv(
+                "CAUSAL_SIM_REPORT_HTML",
+                "docs/spot/2_analysis/causal_sim_report.html",
+            )
+        ),
+    )

src/deepcoin/evaluation/__init__.py

@@ -0,0 +1,27 @@
+"""Ground Truth 정합 평가."""
+
+from deepcoin.evaluation.gt_align import align_with_ground_truth
+from deepcoin.evaluation.mtf_report import (
+    build_mtf_correlation_report,
+    render_mtf_html,
+    save_mtf_report,
+)
+from deepcoin.evaluation.report import build_comparison_report, render_comparison_html, save_comparison_report
+from deepcoin.evaluation.signal_type_report import (
+    build_signal_type_report,
+    render_signal_type_html,
+    save_signal_type_report,
+)
+
+__all__ = [
+    "align_with_ground_truth",
+    "build_comparison_report",
+    "render_comparison_html",
+    "save_comparison_report",
+    "build_signal_type_report",
+    "render_signal_type_html",
+    "save_signal_type_report",
+    "build_mtf_correlation_report",
+    "render_mtf_html",
+    "save_mtf_report",
+]

src/deepcoin/evaluation/causal_sim.py

@@ -0,0 +1,265 @@
+"""현물 2단계: 인과 기법 시뮬 (1단계와 동일 거래 기간 · 초기 자본)."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+from deepcoin.ground_truth.chart import render_ground_truth_sim_chart
+from deepcoin.ground_truth.pnl import simulate_gt_signals_pnl
+from deepcoin.techniques.base import TechniqueResult
+
+
+def normalize_signals_for_sim(signals: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """기법 신호를 1단계 sim 엔진 형식으로 정규화한다."""
+    ordered = sorted(signals, key=lambda s: s["datetime"])
+    buy_id = 0
+    sell_id = 0
+    normalized: list[dict[str, Any]] = []
+    for sig in ordered:
+        side = sig["side"]
+        if side == "buy":
+            buy_id += 1
+            marker_id = buy_id
+            default_type = "swing_low"
+        else:
+            sell_id += 1
+            marker_id = sell_id
+            default_type = "swing_high"
+        normalized.append(
+            {
+                "side": side,
+                "datetime": sig["datetime"],
+                "price": float(sig["price"]),
+                "bar_index": sig.get("bar_index", 0),
+                "marker_id": marker_id,
+                "signal_type": sig.get("signal_type", default_type),
+            }
+        )
+    return normalized
+
+
+def run_technique_causal_sim(
+    result: TechniqueResult,
+    *,
+    initial_cash_krw: float,
+    fee_rate: float,
+    sim_lookback_days: int,
+    data_end: str,
+    last_mark_price: float,
+) -> dict[str, Any]:
+    """인과 기법 신호로 1단계와 동일 규칙의 포트폴리오 sim을 실행한다."""
+    signals = normalize_signals_for_sim(result.signals)
+    return simulate_gt_signals_pnl(
+        signals=signals,
+        initial_cash_krw=initial_cash_krw,
+        fee_rate=fee_rate,
+        sim_lookback_days=sim_lookback_days,
+        data_end=data_end,
+        last_mark_price=last_mark_price,
+    )
+
+
+def _gt_shell_for_chart(
+    gt_meta: dict[str, Any],
+    technique: TechniqueResult,
+) -> dict[str, Any]:
+    """차트 렌더용 최소 GT 구조를 만든다."""
+    return {
+        "meta": {
+            **gt_meta,
+            "technique_id": technique.technique_id,
+            "technique_name": technique.technique_name,
+            "stage": "spot_2_causal_sim",
+        },
+        "signals": [],
+    }
+
+
+def technique_sim_chart_path(analysis_dir: Path, technique_id: str) -> Path:
+    """기법별 sim 차트 HTML 경로."""
+    return analysis_dir / f"technique_chart_sim_{technique_id}.html"
+
+
+def render_technique_sim_chart(
+    *,
+    db_path: Path,
+    symbol: str,
+    gt_meta: dict[str, Any],
+    result: TechniqueResult,
+    sim_pnl: dict[str, Any],
+    output_path: Path,
+    chart_lookback_days: int,
+) -> Path:
+    """인과 기법 sim 차트 HTML을 생성한다."""
+    gt_shell = _gt_shell_for_chart(gt_meta, result)
+    return render_ground_truth_sim_chart(
+        db_path=db_path,
+        symbol=symbol,
+        gt_result=gt_shell,
+        sim_pnl=sim_pnl,
+        output_path=output_path,
+        chart_lookback_days=chart_lookback_days,
+    )
+
+
+def stage1_benchmark_from_gt(gt_result: dict[str, Any]) -> dict[str, Any] | None:
+    """1단계 GT sim 벤치마크(v3)를 추출한다."""
+    sim = gt_result.get("sim_pnl")
+    if not sim:
+        return None
+    return {
+        "label": "1단계 GT sim (v3, 사후 최적 타점)",
+        "period_from": sim.get("period_from"),
+        "period_to": sim.get("period_to"),
+        "sim_lookback_days": sim.get("sim_lookback_days"),
+        "initial_cash_krw": sim.get("initial_cash_krw"),
+        "final_equity_krw": sim.get("final_equity_krw"),
+        "total_return_pct": sim.get("total_return_pct"),
+        "buys_executed": sim.get("buys_executed"),
+        "sells_executed": sim.get("sells_executed"),
+    }
+
+
+def build_causal_sim_report(
+    results: list[TechniqueResult],
+    gt_result: dict[str, Any],
+    symbol: str,
+    sim_pnls: dict[str, dict[str, Any]],
+) -> dict[str, Any]:
+    """2단계 인과 sim 요약 리포트를 생성한다."""
+    gt_meta = gt_result.get("meta", {})
+    benchmark = stage1_benchmark_from_gt(gt_result)
+    rows: list[dict[str, Any]] = []
+
+    for result in results:
+        sim = sim_pnls.get(result.technique_id, {})
+        align = result.alignment or {}
+        rows.append(
+            {
+                "technique_id": result.technique_id,
+                "technique_name": result.technique_name,
+                "category": result.category,
+                "causal": result.causal,
+                "sim_return_pct": sim.get("total_return_pct", 0.0),
+                "final_equity_krw": sim.get("final_equity_krw", 0.0),
+                "buys_executed": sim.get("buys_executed", 0),
+                "sells_executed": sim.get("sells_executed", 0),
+                "buys_skipped": sim.get("buys_skipped", 0),
+                "sells_skipped": sim.get("sells_skipped", 0),
+                "gt_align_score": align.get("score", 0.0),
+                "chart_file": f"technique_chart_sim_{result.technique_id}.html",
+            }
+        )
+
+    rows.sort(key=lambda r: r["sim_return_pct"], reverse=True)
+    period_from = benchmark.get("period_from") if benchmark else None
+    period_to = benchmark.get("period_to") if benchmark else None
+
+    return {
+        "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "symbol": symbol,
+        "description": (
+            "2단계 인과 sim — 1단계와 동일 거래 기간·초기 자본. "
+            "1단계는 사후 GT 타점, 2단계는 인과 기법 신호로 체결."
+        ),
+        "sim_period_from": period_from,
+        "sim_period_to": period_to,
+        "sim_lookback_days": gt_meta.get("sim_lookback_days")
+        or (benchmark or {}).get("sim_lookback_days"),
+        "stage1_benchmark_v3": benchmark,
+        "ranking": rows,
+    }
+
+
+def save_causal_sim_report(report: dict[str, Any], json_path: Path) -> Path:
+    """인과 sim 리포트 JSON을 저장한다."""
+    json_path.parent.mkdir(parents=True, exist_ok=True)
+    with json_path.open("w", encoding="utf-8") as fp:
+        json.dump(report, fp, ensure_ascii=False, indent=2)
+    return json_path
+
+
+def render_causal_sim_html(report: dict[str, Any], html_path: Path) -> Path:
+    """인과 sim 리포트 HTML을 생성한다."""
+    html_path.parent.mkdir(parents=True, exist_ok=True)
+    benchmark = report.get("stage1_benchmark_v3") or {}
+    rows = report.get("ranking", [])
+
+    bench_row = ""
+    if benchmark:
+        bench_row = f"""
+        <tr class="benchmark">
+          <td>—</td>
+          <td>{benchmark.get('label', '1단계 GT sim')}</td>
+          <td>benchmark</td>
+          <td>{benchmark.get('final_equity_krw', 0):,.0f}</td>
+          <td><strong>{benchmark.get('total_return_pct', 0):+.2f}%</strong></td>
+          <td>{benchmark.get('buys_executed', 0)} / {benchmark.get('sells_executed', 0)}</td>
+          <td>—</td>
+          <td>—</td>
+        </tr>"""
+
+    table_rows = bench_row
+    for idx, row in enumerate(rows, start=1):
+        chart = row.get("chart_file", "")
+        table_rows += f"""
+        <tr>
+          <td>{idx}</td>
+          <td><a href="{chart}">{row['technique_name']}</a></td>
+          <td>{row['category']}</td>
+          <td>{row.get('final_equity_krw', 0):,.0f}</td>
+          <td>{row.get('sim_return_pct', 0):+.2f}%</td>
+          <td>{row.get('buys_executed', 0)} / {row.get('sells_executed', 0)}</td>
+          <td>{row.get('gt_align_score', 0)*100:.1f}</td>
+          <td><a href="{chart}">차트</a></td>
+        </tr>"""
+
+    html = f"""<!DOCTYPE html>
+<html lang="ko">
+<head>
+  <meta charset="UTF-8">
+  <title>DeepCoin 2단계 — 인과 sim</title>
+  <style>
+    body {{ font-family: "Malgun Gothic", Arial, sans-serif; margin: 24px; color: #333; background: #f5f5f5; }}
+    h1 {{ font-size: 20px; margin-bottom: 8px; }}
+    .meta {{ color: #666; margin-bottom: 16px; font-size: 14px; }}
+    table {{ border-collapse: collapse; width: 100%; background: #fff; font-size: 13px; }}
+    th, td {{ border: 1px solid #ddd; padding: 8px 10px; text-align: right; }}
+    th {{ background: #eee; text-align: center; }}
+    td:nth-child(1), td:nth-child(2), td:nth-child(3) {{ text-align: left; }}
+    tr.benchmark {{ background: #fff8e1; }}
+    a {{ color: #1565c0; text-decoration: none; }}
+  </style>
+</head>
+<body>
+  <h1>현물 2단계 — 인과 기법 sim</h1>
+  <p class="meta">
+    {report.get('symbol', '')} |
+    거래 기간: {report.get('sim_period_from', '')} ~ {report.get('sim_period_to', '')} |
+    생성: {report.get('generated_at', '')}
+  </p>
+  <p class="meta">{report.get('description', '')}</p>
+  <table>
+    <thead>
+      <tr>
+        <th>#</th>
+        <th>기법</th>
+        <th>카테고리</th>
+        <th>최종 평가(원)</th>
+        <th>수익률</th>
+        <th>체결(매수/매도)</th>
+        <th>GT정합</th>
+        <th>차트</th>
+      </tr>
+    </thead>
+    <tbody>
+      {table_rows}
+    </tbody>
+  </table>
+</body>
+</html>"""
+    html_path.write_text(html, encoding="utf-8")
+    return html_path

src/deepcoin/evaluation/gt_align.py

@@ -0,0 +1,381 @@
+"""Ground Truth와 기법 신호·레그 정합 평가."""
+
+from __future__ import annotations
+
+from typing import Any
+
+# v3 GT 신호 유형 (ground_truth.py signal_type 필드와 동일)
+GT_SIGNAL_TYPES: tuple[str, ...] = (
+    "swing_low",
+    "pullback",
+    "breakout",
+    "div_bull",
+    "swing_high",
+    "div_bear",
+)
+
+SIGNAL_TYPE_LABELS: dict[str, str] = {
+    "swing_low": "스윙 매수 (B)",
+    "pullback": "눌림목 (B*)",
+    "breakout": "돌파 (B^)",
+    "div_bull": "상승 다이버전스 (Bd)",
+    "swing_high": "스윙 매도 (S)",
+    "div_bear": "하락 다이버전스 (Sd)",
+}
+
+SIGNAL_TYPE_SIDE: dict[str, str] = {
+    "swing_low": "buy",
+    "pullback": "buy",
+    "breakout": "buy",
+    "div_bull": "buy",
+    "swing_high": "sell",
+    "div_bear": "sell",
+}
+
+# 신호 유형별 1차 정합 대상 기법 (리포트 하이라이트용)
+SIGNAL_TYPE_PRIMARY_TECHNIQUES: dict[str, list[str]] = {
+    "swing_low": [
+        "zigzag_causal", "minor_swing", "pivot_swing", "fractal_swing",
+        "composite_swing",
+    ],
+    "pullback": [
+        "ema_pullback", "fib_pullback", "support_bounce",
+        "local_extrema", "bb_reversal", "composite_pullback",
+    ],
+    "breakout": [
+        "donchian", "range_breakout", "keltner_breakout",
+        "bb_squeeze_breakout", "volume_breakout", "composite_breakout",
+    ],
+    "div_bull": [
+        "rsi_divergence", "macd_divergence", "obv_divergence",
+        "rsi_swing", "composite_divergence",
+    ],
+    "swing_high": [
+        "zigzag_causal", "minor_swing", "pivot_swing", "fractal_swing",
+        "composite_swing",
+    ],
+    "div_bear": [
+        "rsi_divergence", "macd_divergence", "obv_divergence",
+        "rsi_swing", "composite_divergence",
+    ],
+}
+
+
+def _bar_distance(a: int, b: int) -> int:
+    """두 봉 인덱스 간 절대 거리."""
+    return abs(a - b)
+
+
+def _match_signal(
+    gt_bar: int,
+    candidates: list[dict[str, Any]],
+    tolerance_bars: int,
+    used: set[int],
+) -> dict[str, Any] | None:
+    """GT 신호에 가장 가까운 기법 신호를 찾는다."""
+    best: dict[str, Any] | None = None
+    best_dist = tolerance_bars + 1
+    best_idx = -1
+
+    for idx, candidate in enumerate(candidates):
+        if idx in used:
+            continue
+        pivot_bar = candidate.get("pivot_bar_index")
+        compare_bar = pivot_bar if pivot_bar is not None else candidate["bar_index"]
+        dist = _bar_distance(gt_bar, compare_bar)
+        if dist <= tolerance_bars and dist < best_dist:
+            best = candidate
+            best_dist = dist
+            best_idx = idx
+
+    if best is None:
+        return None
+
+    return {
+        "matched": True,
+        "gt_bar_index": gt_bar,
+        "tech_bar_index": best.get("pivot_bar_index") or best["bar_index"],
+        "signal_bar_index": best["bar_index"],
+        "bar_offset": best_dist,
+        "tech_price": best["price"],
+        "tech_datetime": best["datetime"],
+        "candidate_index": best_idx,
+    }
+
+
+def align_signals(
+    gt_signals: list[dict[str, Any]],
+    tech_signals: list[dict[str, Any]],
+    tolerance_bars: int,
+    side: str,
+) -> dict[str, Any]:
+    """GT 신호와 기법 신호의 정합률을 계산한다.
+
+    Args:
+        gt_signals: GT signals 리스트.
+        tech_signals: 기법 신호 dict 리스트.
+        tolerance_bars: 허용 봉 오차.
+        side: buy | sell.
+
+    Returns:
+        정합 메트릭 dict.
+    """
+    gt_filtered = [s for s in gt_signals if s["side"] == side]
+    tech_filtered = [s for s in tech_signals if s["side"] == side]
+
+    used: set[int] = set()
+    hits: list[dict[str, Any]] = []
+    misses: list[dict[str, Any]] = []
+
+    for gt_sig in gt_filtered:
+        match = _match_signal(gt_sig["bar_index"], tech_filtered, tolerance_bars, used)
+        if match:
+            used.add(match["candidate_index"])
+            hits.append({**match, "gt_datetime": gt_sig["datetime"], "gt_price": gt_sig["price"]})
+        else:
+            misses.append(
+                {
+                    "gt_bar_index": gt_sig["bar_index"],
+                    "gt_datetime": gt_sig["datetime"],
+                    "gt_price": gt_sig["price"],
+                }
+            )
+
+    gt_count = len(gt_filtered)
+    hit_count = len(hits)
+    recall = hit_count / gt_count if gt_count else 0.0
+    precision = hit_count / len(tech_filtered) if tech_filtered else 0.0
+    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
+    avg_offset = sum(h["bar_offset"] for h in hits) / hit_count if hit_count else 0.0
+
+    return {
+        "side": side,
+        "gt_count": gt_count,
+        "tech_count": len(tech_filtered),
+        "hit_count": hit_count,
+        "miss_count": len(misses),
+        "recall": round(recall, 4),
+        "precision": round(precision, 4),
+        "f1": round(f1, 4),
+        "avg_bar_offset": round(avg_offset, 1),
+        "hits": hits,
+        "misses": misses,
+    }
+
+
+def align_legs(
+    gt_legs: list[dict[str, Any]],
+    tech_legs: list[dict[str, Any]],
+    tolerance_bars: int,
+) -> dict[str, Any]:
+    """GT 레그와 기법 레그의 정합률을 계산한다."""
+    captured: list[dict[str, Any]] = []
+    missed: list[dict[str, Any]] = []
+    used_tech: set[int] = set()
+
+    for gt_leg in gt_legs:
+        best_leg: dict[str, Any] | None = None
+        best_score = tolerance_bars * 2 + 1
+        best_id = -1
+
+        for tech_leg in tech_legs:
+            tid = int(tech_leg["leg_id"])
+            if tid in used_tech:
+                continue
+            buy_dist = _bar_distance(gt_leg["buy_bar_index"], tech_leg["buy_bar_index"])
+            sell_dist = _bar_distance(gt_leg["sell_bar_index"], tech_leg["sell_bar_index"])
+            score = buy_dist + sell_dist
+            if buy_dist <= tolerance_bars and sell_dist <= tolerance_bars and score < best_score:
+                best_leg = tech_leg
+                best_score = score
+                best_id = tid
+
+        if best_leg:
+            used_tech.add(best_id)
+            captured.append(
+                {
+                    "gt_leg_id": gt_leg["leg_id"],
+                    "tech_leg_id": best_leg["leg_id"],
+                    "gt_buy": gt_leg["buy_datetime"],
+                    "tech_buy": best_leg["buy_datetime"],
+                    "gt_sell": gt_leg["sell_datetime"],
+                    "tech_sell": best_leg["sell_datetime"],
+                    "buy_bar_offset": _bar_distance(gt_leg["buy_bar_index"], best_leg["buy_bar_index"]),
+                    "sell_bar_offset": _bar_distance(gt_leg["sell_bar_index"], best_leg["sell_bar_index"]),
+                    "gt_leg_pct": gt_leg["leg_pct"],
+                    "tech_leg_pct": best_leg["leg_pct"],
+                }
+            )
+        else:
+            missed.append(
+                {
+                    "gt_leg_id": gt_leg["leg_id"],
+                    "buy_datetime": gt_leg["buy_datetime"],
+                    "sell_datetime": gt_leg["sell_datetime"],
+                    "leg_pct": gt_leg["leg_pct"],
+                }
+            )
+
+    gt_count = len(gt_legs)
+    recall = len(captured) / gt_count if gt_count else 0.0
+
+    return {
+        "gt_leg_count": gt_count,
+        "tech_leg_count": len(tech_legs),
+        "captured_count": len(captured),
+        "missed_count": len(missed),
+        "leg_recall": round(recall, 4),
+        "captured": captured,
+        "missed": missed,
+    }
+
+
+def align_with_ground_truth(
+    gt_result: dict[str, Any],
+    technique_signals: list[dict[str, Any]],
+    technique_legs: list[dict[str, Any]],
+    tolerance_bars: int,
+) -> dict[str, Any]:
+    """GT 대비 기법 정합 결과 전체를 반환한다."""
+    gt_signals = gt_result.get("signals", [])
+    gt_legs = gt_result.get("legs", [])
+    gt_pnl = gt_result.get("pnl", {})
+
+    buy_align = align_signals(gt_signals, technique_signals, tolerance_bars, "buy")
+    sell_align = align_signals(gt_signals, technique_signals, tolerance_bars, "sell")
+    leg_align = align_legs(gt_legs, technique_legs, tolerance_bars)
+
+    tech_return = 0.0
+    if technique_legs:
+        from deepcoin.ground_truth.pnl import simulate_gt_pnl
+
+        tech_pnl = simulate_gt_pnl(
+            technique_legs,
+            initial_cash_krw=gt_pnl.get("initial_cash_krw", 400_000),
+            fee_rate=gt_pnl.get("fee_rate", 0.0005),
+        )
+        tech_return = tech_pnl["total_return_pct"]
+
+    gt_return = gt_pnl.get("total_return_pct", 0.0)
+    return_capture = tech_return / gt_return if gt_return else 0.0
+
+    by_signal_type = align_all_signal_types(gt_signals, technique_signals, tolerance_bars)
+
+    return {
+        "tolerance_bars": tolerance_bars,
+        "buy": buy_align,
+        "sell": sell_align,
+        "legs": leg_align,
+        "by_signal_type": by_signal_type,
+        "gt_return_pct": gt_return,
+        "tech_return_pct": tech_return,
+        "return_capture_ratio": round(return_capture, 4),
+        "score": round(
+            (
+                buy_align["recall"] * 0.25
+                + sell_align["recall"] * 0.25
+                + leg_align["leg_recall"] * 0.35
+                + min(return_capture, 1.0) * 0.15
+            ),
+            4,
+        ),
+    }
+
+
+def align_signal_type(
+    gt_signals: list[dict[str, Any]],
+    tech_signals: list[dict[str, Any]],
+    tolerance_bars: int,
+    signal_type: str,
+) -> dict[str, Any]:
+    """특정 GT 신호 유형에 대한 기법 정합률을 계산한다.
+
+    Args:
+        gt_signals: GT signals 리스트 (signal_type 필드 포함).
+        tech_signals: 기법 신호 dict 리스트.
+        tolerance_bars: 허용 봉 오차.
+        signal_type: swing_low | pullback | breakout | div_bull | swing_high | div_bear.
+
+    Returns:
+        신호 유형별 정합 메트릭 dict.
+    """
+    side = SIGNAL_TYPE_SIDE.get(signal_type, "buy")
+    gt_filtered = [
+        s for s in gt_signals if s.get("signal_type") == signal_type and s.get("side") == side
+    ]
+    result = align_signals(gt_filtered, tech_signals, tolerance_bars, side)
+    result["signal_type"] = signal_type
+    result["signal_label"] = SIGNAL_TYPE_LABELS.get(signal_type, signal_type)
+    result["primary_techniques"] = SIGNAL_TYPE_PRIMARY_TECHNIQUES.get(signal_type, [])
+    return result
+
+
+def align_all_signal_types(
+    gt_signals: list[dict[str, Any]],
+    tech_signals: list[dict[str, Any]],
+    tolerance_bars: int,
+) -> dict[str, dict[str, Any]]:
+    """모든 GT 신호 유형에 대한 정합 결과를 반환한다."""
+    present_types = {s.get("signal_type") for s in gt_signals if s.get("signal_type")}
+    types_to_run = [t for t in GT_SIGNAL_TYPES if t in present_types]
+    return {
+        signal_type: align_signal_type(gt_signals, tech_signals, tolerance_bars, signal_type)
+        for signal_type in types_to_run
+    }
+
+
+def summarize_signal_type_matrix(
+    technique_results: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+    """기법별·신호유형별 recall 매트릭스 요약을 생성한다.
+
+    Args:
+        technique_results: technique_id, technique_name, by_signal_type 키를 가진 dict 리스트.
+
+    Returns:
+        신호 유형별 최고 recall 기법이 포함된 행 리스트.
+    """
+    rows: list[dict[str, Any]] = []
+    for signal_type in GT_SIGNAL_TYPES:
+        label = SIGNAL_TYPE_LABELS.get(signal_type, signal_type)
+        primary = SIGNAL_TYPE_PRIMARY_TECHNIQUES.get(signal_type, [])
+        entries: list[dict[str, Any]] = []
+
+        for tech in technique_results:
+            by_type = tech.get("by_signal_type") or {}
+            align = by_type.get(signal_type)
+            if not align or align.get("gt_count", 0) == 0:
+                continue
+            entries.append(
+                {
+                    "technique_id": tech["technique_id"],
+                    "technique_name": tech["technique_name"],
+                    "recall": align.get("recall", 0.0),
+                    "hit_count": align.get("hit_count", 0),
+                    "gt_count": align.get("gt_count", 0),
+                    "avg_bar_offset": align.get("avg_bar_offset", 0.0),
+                    "is_primary": tech["technique_id"] in primary,
+                }
+            )
+
+        if not entries:
+            continue
+
+        entries.sort(key=lambda e: e["recall"], reverse=True)
+        best = entries[0]
+        gt_count = best["gt_count"]
+        rows.append(
+            {
+                "signal_type": signal_type,
+                "signal_label": label,
+                "side": SIGNAL_TYPE_SIDE.get(signal_type, ""),
+                "gt_count": gt_count,
+                "primary_techniques": primary,
+                "best_technique_id": best["technique_id"],
+                "best_technique_name": best["technique_name"],
+                "best_recall": best["recall"],
+                "best_avg_offset": best["avg_bar_offset"],
+                "ranking": entries,
+            }
+        )
+    return rows

src/deepcoin/evaluation/mtf_report.py

@@ -0,0 +1,494 @@
+"""GT v3 타점과 MTF 피처 상태 상관 분석 리포트."""
+
+from __future__ import annotations
+
+import json
+import math
+import random
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+from deepcoin.evaluation.gt_align import SIGNAL_TYPE_LABELS, SIGNAL_TYPE_SIDE
+from deepcoin.mtf.extractor import MtfFeatureExtractor, MtfSnapshot
+from deepcoin.mtf.features import FEATURE_NAMES
+
+NUMERIC_FEATURES: tuple[str, ...] = (
+    "close_vs_ema60_pct",
+    "ema60_slope_5_pct",
+    "rsi14",
+    "macd_hist",
+    "bb_position",
+    "atr_pct",
+    "zigzag_leg_pct",
+)
+
+CATEGORICAL_FEATURES: tuple[str, ...] = (
+    "zigzag_direction",
+    "trend_bias",
+)
+
+
+def _parse_dt(raw: str) -> pd.Timestamp:
+    """datetime 문자열을 Timestamp로 변환한다."""
+    return pd.Timestamp(raw)
+
+
+def filter_signals_in_period(
+    signals: list[dict[str, Any]],
+    period_from: str,
+    period_to: str,
+) -> list[dict[str, Any]]:
+    """기간 내 GT 신호만 필터한다."""
+    start = _parse_dt(period_from)
+    end = _parse_dt(period_to)
+    out: list[dict[str, Any]] = []
+    for sig in signals:
+        dt = _parse_dt(sig["datetime"])
+        if start <= dt <= end:
+            out.append(sig)
+    return out
+
+
+def compute_sim_period(
+    data_end: str,
+    lookback_days: int,
+) -> tuple[str, str]:
+    """sim lookback 기준 분석 구간 시작·종료를 반환한다."""
+    end = _parse_dt(data_end)
+    start = end - timedelta(days=lookback_days)
+    return start.strftime("%Y-%m-%d %H:%M:%S"), end.strftime("%Y-%m-%d %H:%M:%S")
+
+
+def build_excluded_bar_indices(
+    signals: list[dict[str, Any]],
+    exclude_bars: int,
+) -> set[int]:
+    """음성 샘플에서 제외할 3분봉 bar_index 집합."""
+    excluded: set[int] = set()
+    for sig in signals:
+        center = int(sig["bar_index"])
+        for offset in range(-exclude_bars, exclude_bars + 1):
+            excluded.add(center + offset)
+    return excluded
+
+
+def sample_negative_datetimes(
+    base_df: pd.DataFrame,
+    period_from: str,
+    period_to: str,
+    excluded_indices: set[int],
+    sample_count: int,
+    seed: int = 42,
+) -> list[str]:
+    """분석 구간에서 GT 주변을 제외한 랜덤 3분봉 시각을 샘플링한다."""
+    start = _parse_dt(period_from)
+    end = _parse_dt(period_to)
+    mask = (base_df["datetime"] >= start) & (base_df["datetime"] <= end)
+    candidates: list[str] = []
+    for idx, row in base_df.loc[mask].iterrows():
+        if int(idx) in excluded_indices:
+            continue
+        candidates.append(pd.Timestamp(row["datetime"]).strftime("%Y-%m-%d %H:%M:%S"))
+
+    if not candidates:
+        return []
+
+    rng = random.Random(seed)
+    if len(candidates) <= sample_count:
+        return candidates
+    return rng.sample(candidates, sample_count)
+
+
+def _cohens_d(group_a: list[float], group_b: list[float]) -> float | None:
+    """두 표본 간 Cohen's d 효과 크기."""
+    if len(group_a) < 2 or len(group_b) < 2:
+        return None
+    mean_a = sum(group_a) / len(group_a)
+    mean_b = sum(group_b) / len(group_b)
+    var_a = sum((x - mean_a) ** 2 for x in group_a) / (len(group_a) - 1)
+    var_b = sum((x - mean_b) ** 2 for x in group_b) / (len(group_b) - 1)
+    pooled = math.sqrt(((len(group_a) - 1) * var_a + (len(group_b) - 1) * var_b) / (len(group_a) + len(group_b) - 2))
+    if pooled == 0:
+        return None
+    return (mean_a - mean_b) / pooled
+
+
+def _feature_value(snapshot: MtfSnapshot, tf_label: str, feature: str) -> Any:
+    """스냅샷에서 TF·피처 값을 꺼낸다."""
+    tf = snapshot.timeframes.get(tf_label, {})
+    if not tf.get("available"):
+        return None
+    return tf.get(feature)
+
+
+def _summarize_numeric(
+    pos_values: list[float],
+    neg_values: list[float],
+) -> dict[str, Any]:
+    """수치 피처 양성·음성 분포 요약."""
+    if not pos_values:
+        return {"positive_count": 0, "negative_count": len(neg_values)}
+
+    pos_mean = sum(pos_values) / len(pos_values)
+    neg_mean = sum(neg_values) / len(neg_values) if neg_values else None
+    pos_sorted = sorted(pos_values)
+    med_idx = len(pos_sorted) // 2
+    pos_median = pos_sorted[med_idx]
+
+    return {
+        "positive_count": len(pos_values),
+        "negative_count": len(neg_values),
+        "positive_mean": round(pos_mean, 4),
+        "positive_median": round(pos_median, 4),
+        "negative_mean": round(neg_mean, 4) if neg_mean is not None else None,
+        "mean_delta": round(pos_mean - neg_mean, 4) if neg_mean is not None else None,
+        "cohens_d": round(_cohens_d(pos_values, neg_values) or 0.0, 4)
+        if neg_values and _cohens_d(pos_values, neg_values) is not None
+        else None,
+    }
+
+
+def _summarize_categorical(
+    pos_values: list[str],
+    neg_values: list[str],
+) -> dict[str, Any]:
+    """범주형 피처 분포 요약."""
+    def _ratio(values: list[str], target: str) -> float | None:
+        if not values:
+            return None
+        return round(sum(1 for v in values if v == target) / len(values), 4)
+
+    pos_counts: dict[str, int] = {}
+    for v in pos_values:
+        pos_counts[v] = pos_counts.get(v, 0) + 1
+
+    neg_counts: dict[str, int] = {}
+    for v in neg_values:
+        neg_counts[v] = neg_counts.get(v, 0) + 1
+
+    return {
+        "positive_count": len(pos_values),
+        "negative_count": len(neg_values),
+        "positive_distribution": pos_counts,
+        "negative_distribution": neg_counts,
+        "positive_bullish_ratio": _ratio(pos_values, "bullish")
+        if pos_values and pos_values[0] in ("bullish", "bearish")
+        else None,
+        "negative_bullish_ratio": _ratio(neg_values, "bullish")
+        if neg_values and neg_values[0] in ("bullish", "bearish")
+        else None,
+        "positive_up_ratio": _ratio(pos_values, "up")
+        if pos_values and pos_values[0] in ("up", "down", "none")
+        else None,
+        "negative_up_ratio": _ratio(neg_values, "up")
+        if neg_values and neg_values[0] in ("up", "down", "none")
+        else None,
+    }
+
+
+def analyze_feature_correlations(
+    labeled_snapshots: list[dict[str, Any]],
+    negative_snapshots: list[MtfSnapshot],
+    tf_labels: list[str],
+) -> dict[str, Any]:
+    """신호 유형·TF·피처별 GT vs 음성 비교."""
+    neg_list = list(negative_snapshots)
+    by_type: dict[str, list[MtfSnapshot]] = {}
+    for item in labeled_snapshots:
+        st = item["signal_type"]
+        snap = item.get("snapshot")
+        if isinstance(snap, MtfSnapshot):
+            by_type.setdefault(st, []).append(snap)
+
+    type_reports: dict[str, Any] = {}
+
+    for signal_type, pos_snaps in sorted(by_type.items()):
+        tf_report: dict[str, Any] = {}
+
+        for tf_label in tf_labels:
+            numeric_rows: dict[str, Any] = {}
+            for feat in NUMERIC_FEATURES:
+                pos_vals: list[float] = []
+                for snap in pos_snaps:
+                    val = _feature_value(snap, tf_label, feat)
+                    if val is not None and isinstance(val, (int, float)):
+                        pos_vals.append(float(val))
+
+                neg_vals: list[float] = []
+                for snap in neg_list:
+                    val = _feature_value(snap, tf_label, feat)
+                    if val is not None and isinstance(val, (int, float)):
+                        neg_vals.append(float(val))
+
+                summary = _summarize_numeric(pos_vals, neg_vals)
+                summary["feature"] = feat
+                numeric_rows[feat] = summary
+
+            cat_rows: dict[str, Any] = {}
+            for feat in CATEGORICAL_FEATURES:
+                pos_vals = [
+                    str(v)
+                    for snap in pos_snaps
+                    if (v := _feature_value(snap, tf_label, feat)) is not None
+                ]
+                neg_vals = [
+                    str(v)
+                    for snap in neg_list
+                    if (v := _feature_value(snap, tf_label, feat)) is not None
+                ]
+                cat_rows[feat] = _summarize_categorical(pos_vals, neg_vals)
+
+            ranked = sorted(
+                numeric_rows.values(),
+                key=lambda r: abs(r.get("cohens_d") or 0.0),
+                reverse=True,
+            )
+            tf_report[tf_label] = {
+                "numeric": numeric_rows,
+                "categorical": cat_rows,
+                "top_numeric_features": ranked[:3],
+            }
+
+        type_reports[signal_type] = {
+            "label": SIGNAL_TYPE_LABELS.get(signal_type, signal_type),
+            "side": SIGNAL_TYPE_SIDE.get(signal_type, ""),
+            "sample_count": len(pos_snaps),
+            "timeframes": tf_report,
+        }
+
+    return type_reports
+
+
+def rank_global_feature_importance(
+    type_reports: dict[str, Any],
+) -> list[dict[str, Any]]:
+    """신호 유형·TF·피처별 |Cohen's d| 전역 순위."""
+    rows: list[dict[str, Any]] = []
+    for signal_type, block in type_reports.items():
+        for tf_label, tf_data in block.get("timeframes", {}).items():
+            for feat, summary in tf_data.get("numeric", {}).items():
+                d = summary.get("cohens_d")
+                if d is None:
+                    continue
+                rows.append(
+                    {
+                        "signal_type": signal_type,
+                        "signal_label": block.get("label", signal_type),
+                        "side": block.get("side", ""),
+                        "timeframe": tf_label,
+                        "feature": feat,
+                        "cohens_d": d,
+                        "abs_cohens_d": abs(d),
+                        "positive_mean": summary.get("positive_mean"),
+                        "negative_mean": summary.get("negative_mean"),
+                        "mean_delta": summary.get("mean_delta"),
+                        "positive_count": summary.get("positive_count", 0),
+                    }
+                )
+    rows.sort(key=lambda r: r["abs_cohens_d"], reverse=True)
+    return rows[:50]
+
+
+def build_mtf_correlation_report(
+    gt_result: dict[str, Any],
+    extractor: MtfFeatureExtractor,
+    lookback_days: int,
+    negative_sample_count: int = 2000,
+    exclude_bars: int = 60,
+    seed: int = 42,
+) -> dict[str, Any]:
+    """GT v3 타점 MTF 상관 리포트 본문을 생성한다.
+
+    Args:
+        gt_result: Ground Truth JSON.
+        extractor: MTF 피처 추출기.
+        lookback_days: 분석 구간(일).
+        negative_sample_count: 음성 샘플 수.
+        exclude_bars: GT 주변 제외 3분봉 수.
+        seed: 음성 샘플 RNG seed.
+
+    Returns:
+        리포트 dict.
+    """
+    meta = gt_result.get("meta", {})
+    signals = gt_result.get("signals") or []
+    data_end = meta.get("data_to", "")
+    period_from, period_to = compute_sim_period(data_end, lookback_days)
+    period_signals = filter_signals_in_period(signals, period_from, period_to)
+
+    tf_labels = [extractor.store.interval_label(iv) for iv in extractor.intervals]
+
+    labeled: list[dict[str, Any]] = []
+    missing = 0
+    for sig in period_signals:
+        snap = extractor.extract_at(sig["datetime"])
+        if snap is None:
+            missing += 1
+            continue
+        labeled.append(
+            {
+                "signal_type": sig.get("signal_type", "unknown"),
+                "side": sig.get("side", ""),
+                "datetime": sig["datetime"],
+                "bar_index": sig.get("bar_index"),
+                "snapshot": snap,
+            }
+        )
+
+    excluded = build_excluded_bar_indices(period_signals, exclude_bars)
+    neg_dts = sample_negative_datetimes(
+        extractor.store.base_df,
+        period_from,
+        period_to,
+        excluded,
+        negative_sample_count,
+        seed=seed,
+    )
+    neg_snaps = extractor.extract_many(neg_dts)
+
+    type_reports = analyze_feature_correlations(labeled, neg_snaps, tf_labels)
+    global_rank = rank_global_feature_importance(type_reports)
+
+    type_counts: dict[str, int] = {}
+    for sig in period_signals:
+        st = sig.get("signal_type", "unknown")
+        type_counts[st] = type_counts.get(st, 0) + 1
+
+    return {
+        "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "symbol": meta.get("symbol", ""),
+        "chart_tier": meta.get("chart_tier", "v3"),
+        "analysis": {
+            "lookback_days": lookback_days,
+            "period_from": period_from,
+            "period_to": period_to,
+            "base_interval_min": extractor.base_interval_min,
+            "timeframes": [
+                {"interval_min": iv, "label": extractor.store.interval_label(iv)}
+                for iv in extractor.intervals
+            ],
+            "feature_names": list(FEATURE_NAMES),
+            "numeric_features": list(NUMERIC_FEATURES),
+            "categorical_features": list(CATEGORICAL_FEATURES),
+            "negative_exclude_bars": exclude_bars,
+            "negative_sample_requested": negative_sample_count,
+            "negative_sample_count": len(neg_snaps),
+        },
+        "gt": {
+            "signals_in_period": len(period_signals),
+            "snapshots_extracted": len(labeled),
+            "snapshots_missing": missing,
+            "signal_type_counts": type_counts,
+        },
+        "signal_type_labels": SIGNAL_TYPE_LABELS,
+        "by_signal_type": type_reports,
+        "global_feature_ranking": global_rank,
+    }
+
+
+def save_mtf_report(report: dict[str, Any], json_path: Path) -> Path:
+    """MTF 상관 리포트 JSON 저장."""
+    json_path.parent.mkdir(parents=True, exist_ok=True)
+
+    def _serialize(obj: Any) -> Any:
+        if isinstance(obj, MtfSnapshot):
+            return obj.to_dict()
+        raise TypeError(f"not serializable: {type(obj)}")
+
+    serializable = json.loads(json.dumps(report, default=_serialize, ensure_ascii=False))
+    with json_path.open("w", encoding="utf-8") as fp:
+        json.dump(serializable, fp, ensure_ascii=False, indent=2)
+    return json_path
+
+
+def render_mtf_html(report: dict[str, Any], html_path: Path) -> Path:
+    """MTF 상관 리포트 HTML을 생성한다."""
+    html_path.parent.mkdir(parents=True, exist_ok=True)
+    analysis = report.get("analysis", {})
+    gt = report.get("gt", {})
+    ranking = report.get("global_feature_ranking", [])[:30]
+    by_type = report.get("by_signal_type", {})
+
+    rank_rows = ""
+    for row in ranking:
+        rank_rows += (
+            f"<tr>"
+            f"<td>{row.get('signal_label', '')}</td>"
+            f"<td>{row.get('timeframe', '')}</td>"
+            f"<td>{row.get('feature', '')}</td>"
+            f"<td>{row.get('cohens_d', '')}</td>"
+            f"<td>{row.get('positive_mean', '')}</td>"
+            f"<td>{row.get('negative_mean', '')}</td>"
+            f"<td>{row.get('positive_count', '')}</td>"
+            f"</tr>"
+        )
+
+    type_sections = ""
+    for signal_type, block in by_type.items():
+        label = block.get("label", signal_type)
+        count = block.get("sample_count", 0)
+        tf_bits = []
+        for tf_label, tf_data in block.get("timeframes", {}).items():
+            tops = tf_data.get("top_numeric_features") or []
+            if not tops:
+                continue
+            items = ", ".join(
+                f"{t.get('feature')} (d={t.get('cohens_d')})"
+                for t in tops[:2]
+            )
+            tf_bits.append(f"<li><b>{tf_label}</b>: {items}</li>")
+        type_sections += (
+            f"<section><h3>{label} ({signal_type}) — {count}건</h3>"
+            f"<ul>{''.join(tf_bits) or '<li>데이터 없음</li>'}</ul></section>"
+        )
+
+    html = f"""<!DOCTYPE html>
+<html lang="ko">
+<head>
+  <meta charset="utf-8"/>
+  <title>MTF GT v3 상관 분석</title>
+  <style>
+    body {{ font-family: "Malgun Gothic", Arial, sans-serif; margin: 24px; color: #333; background: #fafafa; }}
+    h1, h2, h3 {{ color: #222; }}
+    .meta {{ background: #fff; border: 1px solid #ddd; padding: 16px; border-radius: 4px; margin-bottom: 20px; }}
+    table {{ border-collapse: collapse; width: 100%; background: #fff; margin: 12px 0 24px; }}
+    th, td {{ border: 1px solid #eee; padding: 8px 10px; text-align: left; font-size: 13px; }}
+    th {{ background: #f0f0f0; }}
+    section {{ background: #fff; border: 1px solid #ddd; padding: 16px; margin-bottom: 16px; border-radius: 4px; }}
+  </style>
+</head>
+<body>
+  <h1>GT v3 · 멀티 TF 상태 상관 분석</h1>
+  <div class="meta">
+    <p><b>생성</b>: {report.get("generated_at", "")}</p>
+    <p><b>구간</b>: {analysis.get("period_from", "")} ~ {analysis.get("period_to", "")}
+       ({analysis.get("lookback_days", "")}일)</p>
+    <p><b>GT 신호</b>: {gt.get("signals_in_period", 0)}건 · 스냅샷 {gt.get("snapshots_extracted", 0)}건
+       · 음성 샘플 {analysis.get("negative_sample_count", 0)}건</p>
+    <p><b>TF</b>: {", ".join(t["label"] for t in analysis.get("timeframes", []))}</p>
+    <p>양성=GT v3 타점 시각의 인과 MTF 스냅샷 / 음성=동일 구간 랜덤 3분봉(±{analysis.get("negative_exclude_bars")}봉 제외)</p>
+  </div>
+
+  <h2>전역 피처 중요도 (|Cohen&apos;s d| 상위)</h2>
+  <table>
+    <thead>
+      <tr>
+        <th>신호 유형</th><th>TF</th><th>피처</th><th>Cohen d</th>
+        <th>GT 평균</th><th>음성 평균</th><th>GT n</th>
+      </tr>
+    </thead>
+    <tbody>{rank_rows or "<tr><td colspan='7'>없음</td></tr>"}</tbody>
+  </table>
+
+  <h2>신호 유형별 TF 요약</h2>
+  {type_sections or "<p>없음</p>"}
+
+  <p style="font-size:12px;color:#666;">상세 수치는 JSON 리포트 참조.</p>
+</body>
+</html>
+"""
+    html_path.write_text(html, encoding="utf-8")
+    return html_path

src/deepcoin/evaluation/report.py

@@ -0,0 +1,132 @@
+"""2단계: 인과 기법 GT 정합 비교 리포트."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+from deepcoin.techniques.base import TechniqueResult
+
+
+def build_comparison_report(
+    results: list[TechniqueResult],
+    gt_result: dict[str, Any],
+    symbol: str,
+) -> dict[str, Any]:
+    """기법 비교 요약 리포트를 생성한다."""
+    gt_meta = gt_result.get("meta", {})
+    gt_pnl = gt_result.get("pnl", {})
+    gt_summary = gt_result.get("summary", {})
+
+    rows: list[dict[str, Any]] = []
+    for result in results:
+        align = result.alignment or {}
+        buy = align.get("buy", {})
+        sell = align.get("sell", {})
+        legs = align.get("legs", {})
+        rows.append(
+            {
+                "technique_id": result.technique_id,
+                "technique_name": result.technique_name,
+                "category": result.category,
+                "causal": result.causal,
+                "leg_count": result.summary.get("leg_count", 0),
+                "tech_return_pct": result.pnl.get("total_return_pct", 0.0),
+                "buy_recall": buy.get("recall", 0.0),
+                "sell_recall": sell.get("recall", 0.0),
+                "leg_recall": legs.get("leg_recall", 0.0),
+                "return_capture_ratio": align.get("return_capture_ratio", 0.0),
+                "score": align.get("score", 0.0),
+                "avg_buy_offset": buy.get("avg_bar_offset", 0.0),
+                "avg_sell_offset": sell.get("avg_bar_offset", 0.0),
+            }
+        )
+
+    rows.sort(key=lambda r: r["score"], reverse=True)
+
+    return {
+        "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "symbol": symbol,
+        "gt": {
+            "leg_count": gt_summary.get("leg_count", 0),
+            "return_pct": gt_pnl.get("total_return_pct", 0.0),
+            "interval_label": gt_meta.get("interval_label", ""),
+            "lookback_days": gt_meta.get("lookback_days", 365),
+        },
+        "ranking": rows,
+    }
+
+
+def save_comparison_report(report: dict[str, Any], json_path: Path) -> Path:
+    """비교 리포트 JSON을 저장한다."""
+    json_path.parent.mkdir(parents=True, exist_ok=True)
+    with json_path.open("w", encoding="utf-8") as fp:
+        json.dump(report, fp, ensure_ascii=False, indent=2)
+    return json_path
+
+
+def render_comparison_html(report: dict[str, Any], html_path: Path) -> Path:
+    """비교 리포트 HTML을 생성한다."""
+    html_path.parent.mkdir(parents=True, exist_ok=True)
+    rows = report.get("ranking", [])
+    gt = report.get("gt", {})
+
+    table_rows = ""
+    for idx, row in enumerate(rows, start=1):
+        table_rows += f"""
+        <tr>
+          <td>{idx}</td>
+          <td>{row['technique_name']}</td>
+          <td>{row['category']}</td>
+          <td>{'Y' if row['causal'] else 'N'}</td>
+          <td>{row['leg_count']}</td>
+          <td>{row['tech_return_pct']:+.1f}%</td>
+          <td>{row['buy_recall']*100:.1f}%</td>
+          <td>{row['sell_recall']*100:.1f}%</td>
+          <td>{row['leg_recall']*100:.1f}%</td>
+          <td>{row['return_capture_ratio']*100:.1f}%</td>
+          <td><strong>{row['score']*100:.1f}</strong></td>
+        </tr>"""
+
+    html = f"""<!DOCTYPE html>
+<html lang="ko">
+<head>
+  <meta charset="UTF-8">
+  <title>DeepCoin 2단계 — 인과 GT 정합</title>
+  <style>
+    body {{ font-family: "Malgun Gothic", Arial, sans-serif; margin: 24px; color: #333; background: #f5f5f5; }}
+    h1 {{ font-size: 20px; margin-bottom: 8px; }}
+    .meta {{ color: #666; margin-bottom: 20px; font-size: 14px; }}
+    table {{ border-collapse: collapse; width: 100%; background: #fff; }}
+    th, td {{ border: 1px solid #ddd; padding: 8px 10px; text-align: center; font-size: 13px; }}
+    th {{ background: #e8e8e8; }}
+    tr:nth-child(even) {{ background: #fafafa; }}
+  </style>
+</head>
+<body>
+  <h1>DeepCoin 2단계 — 인과 기법 Ground Truth 정합</h1>
+  <div class="meta">
+    생성: {report.get('generated_at', '')} |
+    {report.get('symbol', '')} |
+    GT: {gt.get('leg_count', 0)}레그, {gt.get('return_pct', 0):+.1f}% |
+    기간: 최근 {gt.get('lookback_days', 365)}일
+  </div>
+  <table>
+    <thead>
+      <tr>
+        <th>순위</th><th>기법</th><th>유형</th><th>인과</th><th>레그</th>
+        <th>수익률</th><th>매수 Recall</th><th>매도 Recall</th><th>레그 Recall</th>
+        <th>수익 포착</th><th>종합 Score</th>
+      </tr>
+    </thead>
+    <tbody>{table_rows}
+    </tbody>
+  </table>
+</body>
+</html>"""
+
+    with html_path.open("w", encoding="utf-8") as fp:
+        fp.write(html)
+    return html_path

src/deepcoin/evaluation/signal_type_report.py

@@ -0,0 +1,216 @@
+"""v3 GT 신호 유형별 기법 정합 리포트."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+from deepcoin.evaluation.gt_align import (
+    SIGNAL_TYPE_LABELS,
+    SIGNAL_TYPE_PRIMARY_TECHNIQUES,
+    summarize_signal_type_matrix,
+)
+from deepcoin.techniques.base import TechniqueResult
+
+
+def build_signal_type_report(
+    results: list[TechniqueResult],
+    gt_result: dict[str, Any],
+    symbol: str,
+) -> dict[str, Any]:
+    """신호 유형별 GT 정합 리포트를 생성한다.
+
+    Args:
+        results: 기법 실행 결과 리스트.
+        gt_result: Ground Truth JSON dict.
+        symbol: 거래 심볼.
+
+    Returns:
+        리포트 dict.
+    """
+    gt_meta = gt_result.get("meta", {})
+    gt_summary = gt_result.get("summary", {})
+    gt_signals = gt_result.get("signals", [])
+
+    type_counts: dict[str, int] = {}
+    for sig in gt_signals:
+        st = sig.get("signal_type", "")
+        if st:
+            type_counts[st] = type_counts.get(st, 0) + 1
+
+    tech_rows: list[dict[str, Any]] = []
+    for result in results:
+        align = result.alignment or {}
+        by_type = align.get("by_signal_type") or {}
+        tech_rows.append(
+            {
+                "technique_id": result.technique_id,
+                "technique_name": result.technique_name,
+                "category": result.category,
+                "causal": result.causal,
+                "score": align.get("score", 0.0),
+                "by_signal_type": by_type,
+            }
+        )
+
+    matrix = summarize_signal_type_matrix(tech_rows)
+
+    technique_detail: list[dict[str, Any]] = []
+    for result in results:
+        align = result.alignment or {}
+        by_type = align.get("by_signal_type") or {}
+        type_recalls: dict[str, Any] = {}
+        for signal_type, type_align in by_type.items():
+            type_recalls[signal_type] = {
+                "label": type_align.get("signal_label", signal_type),
+                "gt_count": type_align.get("gt_count", 0),
+                "hit_count": type_align.get("hit_count", 0),
+                "recall": type_align.get("recall", 0.0),
+                "avg_bar_offset": type_align.get("avg_bar_offset", 0.0),
+                "is_primary": result.technique_id
+                in SIGNAL_TYPE_PRIMARY_TECHNIQUES.get(signal_type, []),
+            }
+        technique_detail.append(
+            {
+                "technique_id": result.technique_id,
+                "technique_name": result.technique_name,
+                "category": result.category,
+                "overall_score": align.get("score", 0.0),
+                "signal_types": type_recalls,
+            }
+        )
+
+    technique_detail.sort(key=lambda r: r["overall_score"], reverse=True)
+
+    return {
+        "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "symbol": symbol,
+        "chart_tier": gt_meta.get("chart_tier", "v3"),
+        "gt": {
+            "interval_label": gt_meta.get("interval_label", ""),
+            "lookback_days": gt_meta.get("lookback_days", 730),
+            "signal_type_counts": type_counts,
+            "buy_count": gt_summary.get("buy_count", 0),
+            "sell_count": gt_summary.get("sell_count", 0),
+            "leg_count": gt_summary.get("leg_count", 0),
+        },
+        "signal_type_labels": SIGNAL_TYPE_LABELS,
+        "primary_technique_map": SIGNAL_TYPE_PRIMARY_TECHNIQUES,
+        "best_by_signal_type": matrix,
+        "techniques": technique_detail,
+    }
+
+
+def save_signal_type_report(report: dict[str, Any], json_path: Path) -> Path:
+    """신호 유형 리포트 JSON을 저장한다."""
+    json_path.parent.mkdir(parents=True, exist_ok=True)
+    with json_path.open("w", encoding="utf-8") as fp:
+        json.dump(report, fp, ensure_ascii=False, indent=2)
+    return json_path
+
+
+def render_signal_type_html(report: dict[str, Any], html_path: Path) -> Path:
+    """신호 유형 리포트 HTML을 생성한다."""
+    html_path.parent.mkdir(parents=True, exist_ok=True)
+    gt = report.get("gt", {})
+    counts = gt.get("signal_type_counts", {})
+    best_rows = report.get("best_by_signal_type", [])
+    techniques = report.get("techniques", [])
+
+    count_cells = ""
+    for signal_type, label in report.get("signal_type_labels", {}).items():
+        count = counts.get(signal_type, 0)
+        if count:
+            count_cells += f"<tr><td>{label}</td><td>{signal_type}</td><td>{count}</td></tr>"
+
+    best_table = ""
+    for row in best_rows:
+        primary = ", ".join(row.get("primary_techniques", []))
+        best_table += f"""
+        <tr>
+          <td>{row['signal_label']}</td>
+          <td>{row['gt_count']}</td>
+          <td>{primary}</td>
+          <td><strong>{row['best_technique_name']}</strong></td>
+          <td>{row['best_recall']*100:.1f}%</td>
+          <td>{row['best_avg_offset']:.1f}봉</td>
+        </tr>"""
+
+    tech_headers = "".join(
+        f"<th>{label.split('(')[0].strip()}</th>"
+        for label in report.get("signal_type_labels", {}).values()
+    )
+    tech_rows = ""
+    for tech in techniques:
+        cells = ""
+        for signal_type in report.get("signal_type_labels", {}):
+            st = tech.get("signal_types", {}).get(signal_type)
+            if st and st.get("gt_count", 0) > 0:
+                star = "*" if st.get("is_primary") else ""
+                cells += f"<td>{st['recall']*100:.0f}%{star}</td>"
+            else:
+                cells += "<td>-</td>"
+        tech_rows += f"""
+        <tr>
+          <td>{tech['technique_name']}</td>
+          <td>{tech['overall_score']*100:.1f}</td>
+          {cells}
+        </tr>"""
+
+    html = f"""<!DOCTYPE html>
+<html lang="ko">
+<head>
+  <meta charset="UTF-8">
+  <title>DeepCoin — v3 신호 유형별 GT 정합</title>
+  <style>
+    body {{ font-family: "Malgun Gothic", Arial, sans-serif; margin: 24px; color: #333; background: #f5f5f5; }}
+    h1 {{ font-size: 20px; margin-bottom: 4px; }}
+    h2 {{ font-size: 16px; margin-top: 28px; margin-bottom: 8px; }}
+    .meta {{ color: #666; margin-bottom: 20px; font-size: 14px; }}
+    table {{ border-collapse: collapse; width: 100%; background: #fff; margin-bottom: 16px; }}
+    th, td {{ border: 1px solid #ddd; padding: 8px 10px; text-align: center; font-size: 13px; }}
+    th {{ background: #e8e8e8; }}
+    tr:nth-child(even) {{ background: #fafafa; }}
+    .note {{ font-size: 12px; color: #666; }}
+  </style>
+</head>
+<body>
+  <h1>DeepCoin — v3 신호 유형별 Ground Truth 정합</h1>
+  <div class="meta">
+    생성: {report.get('generated_at', '')} |
+    {report.get('symbol', '')} |
+    Tier: {report.get('chart_tier', 'v3')} |
+    기간: 최근 {gt.get('lookback_days', 730)}일 |
+    매수 {gt.get('buy_count', 0)} / 매도 {gt.get('sell_count', 0)} / 레그 {gt.get('leg_count', 0)}
+  </div>
+
+  <h2>GT 신호 유형 분포</h2>
+  <table>
+    <thead><tr><th>라벨</th><th>signal_type</th><th>건수</th></tr></thead>
+    <tbody>{count_cells}</tbody>
+  </table>
+
+  <h2>신호 유형별 최고 Recall 기법</h2>
+  <table>
+    <thead>
+      <tr><th>신호 유형</th><th>GT 건수</th><th>1차 기법</th><th>최고 기법</th><th>Recall</th><th>평균 오차</th></tr>
+    </thead>
+    <tbody>{best_table}</tbody>
+  </table>
+
+  <h2>기법 × 신호 유형 Recall 매트릭스</h2>
+  <p class="note">* = 해당 신호 유형 1차 정합 대상 기법</p>
+  <table>
+    <thead>
+      <tr><th>기법</th><th>종합 Score</th>{tech_headers}</tr>
+    </thead>
+    <tbody>{tech_rows}</tbody>
+  </table>
+</body>
+</html>"""
+
+    with html_path.open("w", encoding="utf-8") as fp:
+        fp.write(html)
+    return html_path

src/deepcoin/ground_truth/__init__.py

@@ -0,0 +1 @@
+"""Ground Truth — 사후 벤치마크 매수·매도 타점."""

src/deepcoin/ground_truth/breakout.py

@@ -0,0 +1,162 @@
+"""돌파 매수 타점 (Ground Truth 보조, 사후 최적)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Protocol
+
+import pandas as pd
+
+from deepcoin.ground_truth.zigzag import Pivot
+
+
+class _LegLike(Protocol):
+    """레그 최소 인터페이스."""
+
+    leg_id: int
+    buy_bar_index: int
+    sell_bar_index: int
+    sell_price: float
+
+
+@dataclass(frozen=True)
+class BreakoutBuy:
+    """돌파 매수 타점."""
+
+    bar_index: int
+    price: float
+    datetime: pd.Timestamp
+    leg_id: int
+    resistance_price: float
+
+
+def find_breakout_buy_pivots(
+    df: pd.DataFrame,
+    legs: list[_LegLike],
+    pullback_buys: list[Pivot],
+    breakout_buffer_pct: float = 0.1,
+    min_bars_after_anchor: int = 10,
+    consolidation_bars: int = 200,
+    leg_end_margin_bars: int = 80,
+    min_rally_to_sell_pct: float = 2.0,
+) -> list[BreakoutBuy]:
+    """각 레그에서 횡보·눌림 후 저항 돌파 시점을 찾는다 (1단계 GT, 미래 허용).
+
+    눌림목 이후 구간 고점을 저항으로 보고, 종가가 저항을 돌파한
+    첫 봉을 레그당 돌파 매수 1개로 표시한다.
+
+    Args:
+        df: OHLCV DataFrame.
+        legs: ZigZag 스윙 레그.
+        pullback_buys: 눌림목 Pivot (레그별 매칭).
+        breakout_buffer_pct: 저항 대비 돌파 확인 버퍼(%).
+        min_bars_after_anchor: 눌림목/구간 시작 후 최소 대기 봉.
+        consolidation_bars: 횡보 저항 산정 구간(봉).
+        leg_end_margin_bars: 매도 직전 제외 봉.
+        min_rally_to_sell_pct: 돌파 후 레그 고점까지 최소 추가 상승(%).
+
+    Returns:
+        BreakoutBuy 리스트.
+    """
+    if not legs or len(df) < 20:
+        return []
+
+    highs = df["high"].astype(float).values
+    closes = df["close"].astype(float).values
+    pullback_by_leg = _map_pullback_to_legs(legs, pullback_buys)
+    breakouts: list[BreakoutBuy] = []
+    buffer = breakout_buffer_pct / 100.0
+
+    for leg in legs:
+        anchor = pullback_by_leg.get(leg.leg_id)
+        if anchor is not None:
+            zone_start = anchor.bar_index
+        else:
+            leg_len = leg.sell_bar_index - leg.buy_bar_index
+            zone_start = max(
+                leg.buy_bar_index + 120,
+                leg.sell_bar_index - int(leg_len * 0.35),
+            )
+
+        zone_end = min(
+            zone_start + consolidation_bars,
+            leg.sell_bar_index - leg_end_margin_bars - 5,
+        )
+        search_start = zone_start + min_bars_after_anchor
+        search_end = leg.sell_bar_index - leg_end_margin_bars
+        if zone_end <= zone_start or search_end <= search_start + 5:
+            continue
+
+        resistance = float(highs[zone_start:zone_end].max())
+        if resistance <= 0:
+            continue
+
+        breakout = _first_breakout_above(
+            df=df,
+            highs=highs,
+            closes=closes,
+            search_start=max(search_start, zone_end),
+            search_end=search_end,
+            resistance=resistance,
+            buffer=buffer,
+            sell_price=float(leg.sell_price),
+            min_rally_pct=min_rally_to_sell_pct,
+        )
+        if breakout is None:
+            continue
+
+        bar_idx, price, resistance = breakout
+        breakouts.append(
+            BreakoutBuy(
+                bar_index=bar_idx,
+                price=round(price, 2),
+                datetime=pd.Timestamp(df.iloc[bar_idx]["datetime"]),
+                leg_id=leg.leg_id,
+                resistance_price=round(resistance, 2),
+            )
+        )
+
+    return breakouts
+
+
+def _map_pullback_to_legs(legs: list[_LegLike], pullback_buys: list[Pivot]) -> dict[int, Pivot]:
+    """눌림목을 소속 레그에 매핑한다."""
+    mapping: dict[int, Pivot] = {}
+    for pivot in pullback_buys:
+        for leg in legs:
+            if leg.buy_bar_index < pivot.bar_index < leg.sell_bar_index:
+                prev = mapping.get(leg.leg_id)
+                if prev is None or pivot.bar_index > prev.bar_index:
+                    mapping[leg.leg_id] = pivot
+    return mapping
+
+
+def _first_breakout_above(
+    df: pd.DataFrame,
+    highs,
+    closes,
+    search_start: int,
+    search_end: int,
+    resistance: float,
+    buffer: float,
+    sell_price: float,
+    min_rally_pct: float,
+) -> tuple[int, float, float] | None:
+    """고정 저항선 상향 돌파 첫 봉을 반환한다."""
+    threshold = resistance * (1 + buffer)
+
+    for i in range(search_start, search_end):
+        close_val = float(closes[i])
+        if close_val <= threshold:
+            continue
+
+        future_max = float(highs[i : search_end + 1].max())
+        rally_pct = (future_max - close_val) / close_val * 100.0
+        sell_rally_pct = (sell_price - close_val) / close_val * 100.0
+
+        if rally_pct < min_rally_pct or sell_rally_pct < min_rally_pct:
+            continue
+
+        return i, close_val, resistance
+
+    return None

src/deepcoin/ground_truth/divergence.py

@@ -0,0 +1,303 @@
+"""RSI·MACD 다이버전스 매수·매도 타점 (Ground Truth, 사후 검증)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import pandas as pd
+
+from deepcoin.techniques.indicators import macd, rsi
+
+
+@dataclass(frozen=True)
+class DivergenceSignal:
+    """다이버전스 신호."""
+
+    side: str  # buy | sell
+    bar_index: int
+    price: float
+    datetime: pd.Timestamp
+    indicator: str  # rsi | macd_hist
+    price_prev: float
+    price_curr: float
+    ind_prev: float
+    ind_curr: float
+
+
+def find_divergence_signals(
+    df: pd.DataFrame,
+    local_order: int = 15,
+    min_bars_between: int = 100,
+    max_pair_lookback_bars: int = 5000,
+    rsi_period: int = 14,
+    min_rsi_diff: float = 2.0,
+    min_macd_hist_diff: float = 0.0,
+    min_price_move_pct: float = 1.5,
+    future_bars: int = 2000,
+    min_future_move_pct: float = 2.0,
+) -> tuple[list[DivergenceSignal], list[DivergenceSignal]]:
+    """가격·지표 다이버전스 매수·매도 후보를 찾는다.
+
+    - 상승 다이버전스(매수): 가격 LL + RSI/MACD HL
+    - 하락 다이버전스(매도): 가격 HH + RSI/MACD LH
+    - 미래 데이터로 이후 유의미한 반등·하락을 사후 검증
+
+    Args:
+        df: OHLCV DataFrame.
+        local_order: 국소 극값 반경(봉).
+        min_bars_between: 연속 다이버전스 최소 간격(봉).
+        max_pair_lookback_bars: 비교할 이전 극값 최대 거리(봉).
+        rsi_period: RSI 기간.
+        min_rsi_diff: RSI 다이버전스 최소 차이(포인트).
+        min_macd_hist_diff: MACD 히스토그램 최소 차이.
+        min_price_move_pct: 극값 간 최소 가격 변동(%).
+        future_bars: 사후 검증 구간(봉).
+        min_future_move_pct: 사후 최소 가격 변동(%).
+
+    Returns:
+        (매수 신호, 매도 신호) 리스트.
+    """
+    if len(df) < local_order * 4 + rsi_period:
+        return [], []
+
+    close = df["close"].astype(float)
+    low = df["low"].astype(float)
+    high = df["high"].astype(float)
+    rsi_vals = rsi(close, period=rsi_period)
+    _, _, macd_hist = macd(close)
+
+    lows = _find_local_extrema(df, low, rsi_vals, macd_hist, local_order, "low")
+    highs = _find_local_extrema(df, high, rsi_vals, macd_hist, local_order, "high")
+
+    bull_rsi = _detect_bullish(
+        df, lows, rsi_vals, "rsi", min_rsi_diff, min_price_move_pct,
+        max_pair_lookback_bars, future_bars, min_future_move_pct, high,
+    )
+    bear_rsi = _detect_bearish(
+        df, highs, rsi_vals, "rsi", min_rsi_diff, min_price_move_pct,
+        max_pair_lookback_bars, future_bars, min_future_move_pct, low,
+    )
+
+    bull_macd: list[DivergenceSignal] = []
+    bear_macd: list[DivergenceSignal] = []
+    if min_macd_hist_diff > 0:
+        bull_macd = _detect_bullish(
+            df, lows, macd_hist, "macd_hist", min_macd_hist_diff, min_price_move_pct,
+            max_pair_lookback_bars, future_bars, min_future_move_pct, high,
+        )
+        bear_macd = _detect_bearish(
+            df, highs, macd_hist, "macd_hist", min_macd_hist_diff, min_price_move_pct,
+            max_pair_lookback_bars, future_bars, min_future_move_pct, low,
+        )
+
+    buys = _dedupe_signals(
+        _merge_same_bar(bull_rsi + bull_macd), min_bars_between, prefer_lower=True
+    )
+    sells = _dedupe_signals(
+        _merge_same_bar(bear_rsi + bear_macd), min_bars_between, prefer_lower=False
+    )
+    return buys, sells
+
+
+@dataclass
+class _ExtremePoint:
+    """국소 극값."""
+
+    bar_index: int
+    price: float
+    rsi: float
+    macd_hist: float
+    datetime: pd.Timestamp
+
+
+def _find_local_extrema(
+    df: pd.DataFrame,
+    series: pd.Series,
+    rsi_vals: pd.Series,
+    macd_hist: pd.Series,
+    order: int,
+    side: str,
+) -> list[_ExtremePoint]:
+    """국소 저점·고점을 수집한다."""
+    points: list[_ExtremePoint] = []
+    values = series.values
+
+    for i in range(order, len(df) - order):
+        window = values[i - order : i + order + 1]
+        val = float(values[i])
+        if side == "low" and val > window.min():
+            continue
+        if side == "high" and val < window.max():
+            continue
+        if pd.isna(rsi_vals.iloc[i]) or pd.isna(macd_hist.iloc[i]):
+            continue
+        points.append(
+            _ExtremePoint(
+                bar_index=i,
+                price=val,
+                rsi=float(rsi_vals.iloc[i]),
+                macd_hist=float(macd_hist.iloc[i]),
+                datetime=pd.Timestamp(df.iloc[i]["datetime"]),
+            )
+        )
+    return points
+
+
+def _detect_bullish(
+    df: pd.DataFrame,
+    lows: list[_ExtremePoint],
+    indicator: pd.Series,
+    indicator_name: str,
+    min_ind_diff: float,
+    min_price_move_pct: float,
+    max_lookback: int,
+    future_bars: int,
+    min_future_move_pct: float,
+    highs,
+) -> list[DivergenceSignal]:
+    """상승 다이버전스 매수를 탐지한다."""
+    signals: list[DivergenceSignal] = []
+    if len(lows) < 2:
+        return signals
+
+    for idx in range(1, len(lows)):
+        curr = lows[idx]
+        for prev in reversed(lows[:idx]):
+            if curr.bar_index - prev.bar_index > max_lookback:
+                break
+            if curr.bar_index - prev.bar_index < 20:
+                continue
+
+            price_move = (prev.price - curr.price) / prev.price * 100.0
+            if price_move < min_price_move_pct:
+                continue
+
+            ind_prev = float(indicator.iloc[prev.bar_index])
+            ind_curr = float(indicator.iloc[curr.bar_index])
+            if pd.isna(ind_prev) or pd.isna(ind_curr):
+                continue
+            if not (curr.price < prev.price and ind_curr > ind_prev + min_ind_diff):
+                continue
+
+            end = min(len(df), curr.bar_index + future_bars + 1)
+            future_high = float(highs[curr.bar_index:end].max())
+            rally = (future_high - curr.price) / curr.price * 100.0
+            if rally < min_future_move_pct:
+                continue
+
+            signals.append(
+                DivergenceSignal(
+                    side="buy",
+                    bar_index=curr.bar_index,
+                    price=round(curr.price, 2),
+                    datetime=curr.datetime,
+                    indicator=indicator_name,
+                    price_prev=round(prev.price, 2),
+                    price_curr=round(curr.price, 2),
+                    ind_prev=round(ind_prev, 4),
+                    ind_curr=round(ind_curr, 4),
+                )
+            )
+            break
+
+    return signals
+
+
+def _detect_bearish(
+    df: pd.DataFrame,
+    highs: list[_ExtremePoint],
+    indicator: pd.Series,
+    indicator_name: str,
+    min_ind_diff: float,
+    min_price_move_pct: float,
+    max_lookback: int,
+    future_bars: int,
+    min_future_move_pct: float,
+    lows,
+) -> list[DivergenceSignal]:
+    """하락 다이버전스 매도를 탐지한다."""
+    signals: list[DivergenceSignal] = []
+    if len(highs) < 2:
+        return signals
+
+    for idx in range(1, len(highs)):
+        curr = highs[idx]
+        for prev in reversed(highs[:idx]):
+            if curr.bar_index - prev.bar_index > max_lookback:
+                break
+            if curr.bar_index - prev.bar_index < 20:
+                continue
+
+            price_move = (curr.price - prev.price) / prev.price * 100.0
+            if price_move < min_price_move_pct:
+                continue
+
+            ind_prev = float(indicator.iloc[prev.bar_index])
+            ind_curr = float(indicator.iloc[curr.bar_index])
+            if pd.isna(ind_prev) or pd.isna(ind_curr):
+                continue
+            if not (curr.price > prev.price and ind_curr < ind_prev - min_ind_diff):
+                continue
+
+            end = min(len(df), curr.bar_index + future_bars + 1)
+            future_low = float(lows[curr.bar_index:end].min())
+            drop = (curr.price - future_low) / curr.price * 100.0
+            if drop < min_future_move_pct:
+                continue
+
+            signals.append(
+                DivergenceSignal(
+                    side="sell",
+                    bar_index=curr.bar_index,
+                    price=round(curr.price, 2),
+                    datetime=curr.datetime,
+                    indicator=indicator_name,
+                    price_prev=round(prev.price, 2),
+                    price_curr=round(curr.price, 2),
+                    ind_prev=round(ind_prev, 4),
+                    ind_curr=round(ind_curr, 4),
+                )
+            )
+            break
+
+    return signals
+
+
+def _merge_same_bar(signals: list[DivergenceSignal]) -> list[DivergenceSignal]:
+    """동일 봉·동일 방향 신호를 하나로 합친다."""
+    by_bar: dict[int, DivergenceSignal] = {}
+    for signal in signals:
+        prev = by_bar.get(signal.bar_index)
+        if prev is None:
+            by_bar[signal.bar_index] = signal
+            continue
+        prev_diff = abs(prev.ind_curr - prev.ind_prev)
+        curr_diff = abs(signal.ind_curr - signal.ind_prev)
+        if curr_diff > prev_diff:
+            by_bar[signal.bar_index] = signal
+    return sorted(by_bar.values(), key=lambda s: s.bar_index)
+
+
+def _dedupe_signals(
+    signals: list[DivergenceSignal],
+    min_bars: int,
+    prefer_lower: bool,
+) -> list[DivergenceSignal]:
+    """근접 신호를 병합한다."""
+    if not signals:
+        return []
+
+    sorted_signals = sorted(signals, key=lambda s: s.bar_index)
+    merged: list[DivergenceSignal] = [sorted_signals[0]]
+
+    for signal in sorted_signals[1:]:
+        last = merged[-1]
+        if signal.bar_index - last.bar_index < min_bars:
+            if prefer_lower and signal.price < last.price:
+                merged[-1] = signal
+            elif not prefer_lower and signal.price > last.price:
+                merged[-1] = signal
+        else:
+            merged.append(signal)
+
+    return merged

src/deepcoin/ground_truth/futures.py

@@ -0,0 +1,128 @@
+"""현물 Ground Truth 신호를 선물 롱·숏 타점으로 변환한다."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+
+
+def futures_events_from_gt_signals(
+    signals: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+    """현물 GT 신호를 선물 이벤트 스트림으로 변환한다.
+
+    동일 봉에서 청산 이벤트를 먼저, 진입 이벤트를 나중에 배치한다.
+
+    Args:
+        signals: GT signals 리스트.
+
+    Returns:
+        position, action, event_order 등이 포함된 이벤트 리스트.
+    """
+    events: list[dict[str, Any]] = []
+    for sig in signals:
+        side = sig["side"]
+        signal_type = sig.get(
+            "signal_type",
+            "swing_low" if side == "buy" else "swing_high",
+        )
+        base = {
+            "datetime": sig["datetime"],
+            "price": sig["price"],
+            "bar_index": sig.get("bar_index", 0),
+            "marker_id": sig.get("marker_id", sig.get("leg_id")),
+            "signal_type": signal_type,
+            "leg_id": sig.get("leg_id"),
+        }
+        if side == "buy":
+            events.append({**base, "position": "short", "action": "close", "event_order": 0})
+            events.append({**base, "position": "long", "action": "open", "event_order": 1})
+        else:
+            events.append({**base, "position": "long", "action": "close", "event_order": 0})
+            events.append({**base, "position": "short", "action": "open", "event_order": 1})
+    return events
+
+
+def futures_markers_from_gt_signals(
+    gt_result: dict[str, Any],
+) -> dict[str, list[dict[str, Any]]]:
+    """현물 GT 신호를 선물 4종 마커(상·하방 매수·매도)로 변환한다.
+
+    현물 GT의 스윙 저점(buy)과 고점(sell)을 동일 가격·시각에
+    롱·숏 양방향 선물 타점으로 매핑한다.
+
+    - buy  → 상방 매수(롱 진입), 하방 매도(숏 청산)
+    - sell → 상방 매도(롱 청산), 하방 매수(숏 진입)
+
+    Args:
+        gt_result: build_ground_truth 결과 또는 동일 스키마 JSON.
+
+    Returns:
+        long_open, long_close, short_open, short_close 마커 리스트.
+    """
+    long_open: list[dict[str, Any]] = []
+    long_close: list[dict[str, Any]] = []
+    short_open: list[dict[str, Any]] = []
+    short_close: list[dict[str, Any]] = []
+
+    for sig in gt_result.get("signals") or []:
+        side = sig["side"]
+        signal_type = sig.get(
+            "signal_type",
+            "swing_low" if side == "buy" else "swing_high",
+        )
+        marker_id = sig.get("marker_id", sig.get("leg_id"))
+        base = {
+            "time": int(pd.Timestamp(sig["datetime"]).timestamp()),
+            "price": sig["price"],
+            "marker_id": marker_id,
+            "signal_type": signal_type,
+            "leg_id": sig.get("leg_id"),
+        }
+        if side == "buy":
+            long_open.append({**base, "position": "long", "action": "open"})
+            short_close.append({**base, "position": "short", "action": "close"})
+        else:
+            long_close.append({**base, "position": "long", "action": "close"})
+            short_open.append({**base, "position": "short", "action": "open"})
+
+    return {
+        "long_open": long_open,
+        "long_close": long_close,
+        "short_open": short_open,
+        "short_close": short_close,
+    }
+
+
+def futures_markers_from_executed_trades(
+    sim_pnl: dict[str, Any],
+) -> dict[str, list[dict[str, Any]]]:
+    """시뮬 체결 내역에서 4종 선물 마커를 구성한다."""
+    markers: dict[str, list[dict[str, Any]]] = {
+        "long_open": [],
+        "long_close": [],
+        "short_open": [],
+        "short_close": [],
+    }
+
+    for trade in sim_pnl.get("trades") or []:
+        if trade.get("skipped"):
+            continue
+        position = trade["position"]
+        action = trade["action"]
+        key = f"{position}_{action}"
+        if key not in ("long_open", "long_close", "short_open", "short_close"):
+            continue
+        markers[key].append(
+            {
+                "time": int(pd.Timestamp(trade["datetime"]).timestamp()),
+                "price": trade["price"],
+                "marker_id": trade.get("marker_id") or trade.get("trade_id"),
+                "signal_type": trade.get("signal_type", ""),
+                "position": position,
+                "action": action,
+            }
+        )
+
+    return markers

src/deepcoin/ground_truth/futures_chart.py

@@ -0,0 +1,763 @@
+"""선물 Ground Truth 차트 HTML 생성 (롱·숏 4색 마커)."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+from deepcoin.data.candle_loader import load_candles
+from deepcoin.ground_truth.chart import (
+    DEFAULT_MAX_CANDLES,
+    _data_js_path,
+    _enrich_markers_chart_price,
+    _sim_start_marker,
+    _to_unix_seconds,
+)
+from deepcoin.ground_truth.futures import futures_markers_from_gt_signals
+
+
+def _stack_marker_positions(
+    long_open: list[dict[str, Any]],
+    long_close: list[dict[str, Any]],
+    short_open: list[dict[str, Any]],
+    short_close: list[dict[str, Any]],
+) -> None:
+    """동일 시각에 겹치는 마커에 세로 스택 인덱스를 부여한다.
+
+    같은 봉의 L↓·S↓ 등이 좌우로 벌어지지 않고 동일 X에서 위아래로 쌓이도록 한다.
+    """
+    from collections import defaultdict
+
+    groups: dict[int, list[dict[str, Any]]] = defaultdict(list)
+    for markers in (long_open, long_close, short_open, short_close):
+        for marker in markers:
+            groups[int(marker["time"])].append(marker)
+
+    kind_rank = {
+        "long_close": 0,
+        "short_open": 1,
+        "long_open": 2,
+        "short_close": 3,
+    }
+
+    for markers_at_time in groups.values():
+        if len(markers_at_time) <= 1:
+            for marker in markers_at_time:
+                marker["stack_index"] = 0
+            continue
+        markers_at_time.sort(
+            key=lambda m: kind_rank.get(f"{m.get('position')}_{m.get('action')}", 9)
+        )
+        for index, marker in enumerate(markers_at_time):
+            marker["stack_index"] = index
+            marker.pop("x_offset_px", None)
+
+_FUTURES_HTML_TEMPLATE = """<!DOCTYPE html>
+<html lang="ko">
+<head>
+  <meta charset="UTF-8" />
+  <title>Futures Ground Truth Chart</title>
+  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/uplot@1.6.31/dist/uPlot.min.css" />
+  <script src="https://cdn.jsdelivr.net/npm/uplot@1.6.31/dist/uPlot.iife.min.js"></script>
+  <script src="https://unpkg.com/lightweight-charts@4.2.0/dist/lightweight-charts.standalone.production.js"></script>
+  <script src="__DATA_JS_NAME__"></script>
+  <style>
+    body { font-family: "Malgun Gothic", Arial, sans-serif; margin: 0; background: #f5f5f5; color: #333; }
+    header { padding: 16px 24px; background: #fff; border-bottom: 1px solid #ddd; }
+    h1 { margin: 0 0 6px; font-size: 20px; }
+    .meta { font-size: 13px; color: #666; }
+    .legend { margin-top: 8px; display: flex; flex-wrap: wrap; gap: 12px; font-size: 12px; }
+    .legend-item { display: flex; align-items: center; gap: 4px; }
+    .legend-swatch { width: 12px; height: 12px; border-radius: 2px; }
+    .toolbar { padding: 10px 24px; background: #fff; border-bottom: 1px solid #eee; display: flex; gap: 12px; flex-wrap: wrap; align-items: center; }
+    .toolbar-group { display: flex; gap: 6px; align-items: center; padding-right: 12px; border-right: 1px solid #e0e0e0; }
+    .toolbar-group:last-of-type { border-right: none; }
+    .toolbar button { padding: 6px 12px; border: 1px solid #bbb; background: #fff; cursor: pointer; border-radius: 4px; font-size: 13px; white-space: nowrap; }
+    .toolbar button:hover { background: #f0f4f8; }
+    .toolbar button.active { background: #1565c0; color: #fff; border-color: #1565c0; }
+    .toolbar button.home { background: #2e7d32; color: #fff; border-color: #2e7d32; font-weight: bold; }
+    .toolbar button.home:hover { background: #1b5e20; }
+    .toolbar .leg-info { font-size: 12px; color: #555; min-width: 90px; }
+    #status { font-size: 12px; color: #888; margin-left: auto; }
+    #overview { height: 480px; margin: 12px 24px; background: #fff; border: 1px solid #ddd; overflow: visible; }
+    #overview .u-wrap, #overview .uplot { overflow: visible !important; }
+    #detail-wrap { margin: 0 24px 12px; display: none; }
+    #detail-wrap h2 { font-size: 15px; margin: 0 0 8px; }
+    #detail { height: 360px; background: #fff; border: 1px solid #ddd; overflow: visible; }
+__EXTRA_STYLES__
+  </style>
+</head>
+<body>
+  <header>
+    <h1 id="title">Futures Ground Truth Chart</h1>
+    <div class="meta" id="meta"></div>
+    <div class="legend" id="legend"></div>
+  </header>
+__EXTRA_BODY__
+  <div class="toolbar">
+    <div class="toolbar-group">
+      <button id="btn-home" class="home" title="전체 화면으로 복귀">홈</button>
+      <button id="btn-prev-leg" title="이전 롱 레그">◀ 이전</button>
+      <button id="btn-next-leg" title="다음 롱 레그">다음 ▶</button>
+      <span class="leg-info" id="leg-info">타점 - / -</span>
+    </div>
+    <div class="toolbar-group">
+      <button id="btn-all" class="btn-period active">전체</button>
+      <button id="btn-365d" class="btn-period">1년</button>
+      <button id="btn-30d" class="btn-period">30일</button>
+      <button id="btn-7d" class="btn-period">7일</button>
+      <button id="btn-3d" class="btn-period">3일</button>
+    </div>
+    <div class="toolbar-group">
+      <button id="btn-zoom-in" title="확대">+ 확대</button>
+      <button id="btn-zoom-out" title="축소">− 축소</button>
+      <button id="btn-fit" title="현재 뷰 맞춤">맞춤</button>
+      <button id="btn-flip-y" title="가격 축 위·아래 반전">↕ 뒤집기</button>
+    </div>
+    <div class="toolbar-group">
+      <button id="btn-markers" title="마커 표시/숨김">마커 숨김</button>
+      <button id="btn-toggle-detail" title="상세 캔들 패널">상세 패널</button>
+    </div>
+    <span id="status">데이터 로딩 중…</span>
+  </div>
+  <div id="overview"></div>
+  <div id="detail-wrap">
+    <h2 id="detail-title">상세 캔들</h2>
+    <div id="detail"></div>
+  </div>
+  <script>
+    let DATA = null;
+    let overviewPlot = null;
+    let detailChart = null;
+    let detailSeries = null;
+    let currentMode = "overview";
+    let currentLegIdx = 0;
+    let showMarkers = true;
+    let detailVisible = false;
+    let lastDetailStart = 0;
+    let lastDetailEnd = 0;
+    let yAxisInverted = false;
+
+    const AXIS_FONT = "12px Malgun Gothic, Arial, sans-serif";
+    const MARKER_FONT = "bold 24px Malgun Gothic, Arial, sans-serif";
+    const ARROW_HALF = 12;
+    const ARROW_HEIGHT = 16;
+    const LABEL_OFFSET_X = 16;
+    const LABEL_GAP = 24;
+    const STACK_STEP = ARROW_HEIGHT + LABEL_GAP + 28;
+    const SIM_START_COLOR = "#7b1fa2";
+    let axisMeasureCtx = null;
+
+    const COLORS = {
+      longOpen: "#1565c0",
+      longClose: "#64b5f6",
+      shortOpen: "#c62828",
+      shortClose: "#ff8a65",
+    };
+
+    function fmtPrice(v) {
+      return Math.round(v).toLocaleString("ko-KR");
+    }
+
+    function measureTextWidth(text, font) {
+      if (!axisMeasureCtx) {
+        const c = document.createElement("canvas");
+        axisMeasureCtx = c.getContext("2d");
+      }
+      axisMeasureCtx.font = font;
+      return axisMeasureCtx.measureText(text).width;
+    }
+
+    function yAxisLabelWidth() {
+      const vals = DATA.close;
+      if (!vals || !vals.length) return 88;
+      const samples = new Set([vals[0], vals[vals.length - 1]]);
+      let lo = vals[0], hi = vals[0];
+      for (let i = 1; i < vals.length; i++) {
+        if (vals[i] < lo) lo = vals[i];
+        if (vals[i] > hi) hi = vals[i];
+      }
+      samples.add(lo);
+      samples.add(hi);
+      samples.add((lo + hi) / 2);
+      let maxW = 0;
+      samples.forEach(v => {
+        maxW = Math.max(maxW, measureTextWidth(fmtPrice(v), AXIS_FONT));
+      });
+      return Math.ceil(maxW) + 20;
+    }
+
+    function markerSuffix(signalType) {
+      if (signalType === "pullback") return "*";
+      if (signalType === "breakout") return "^";
+      if (signalType === "div_bull" || signalType === "div_bear") return "d";
+      return "";
+    }
+
+    function markerLabel(kind, m) {
+      const id = m.marker_id;
+      const sfx = markerSuffix(m.signal_type);
+      if (kind === "longOpen") return "L↑" + id + sfx;
+      if (kind === "longClose") return "L↓" + id + sfx;
+      if (kind === "shortOpen") return "S↓" + id + sfx;
+      return "S↑" + id + sfx;
+    }
+
+    function markerChartPrice(m) {
+      if (m.chart_price != null) return m.chart_price;
+      let lo = 0;
+      let hi = DATA.times.length - 1;
+      while (lo < hi) {
+        const mid = (lo + hi) >> 1;
+        if (DATA.times[mid] < m.time) lo = mid + 1;
+        else hi = mid;
+      }
+      return DATA.close[lo];
+    }
+
+    function drawMarkerLabel(ctx, label, x, labelY, color) {
+      ctx.font = MARKER_FONT;
+      const lx = x + LABEL_OFFSET_X;
+      ctx.textBaseline = "middle";
+      ctx.lineWidth = 4;
+      ctx.lineJoin = "round";
+      ctx.strokeStyle = "rgba(255,255,255,0.95)";
+      ctx.strokeText(label, lx, labelY);
+      ctx.fillStyle = color;
+      ctx.fillText(label, lx, labelY);
+      ctx.textBaseline = "alphabetic";
+    }
+
+    function visualUp(up) {
+      return yAxisInverted ? !up : up;
+    }
+
+    function drawTriangleOnLine(ctx, x, lineY, up, color) {
+      ctx.fillStyle = color;
+      ctx.beginPath();
+      if (up) {
+        ctx.moveTo(x - ARROW_HALF, lineY);
+        ctx.lineTo(x + ARROW_HALF, lineY);
+        ctx.lineTo(x, lineY + ARROW_HEIGHT);
+      } else {
+        ctx.moveTo(x - ARROW_HALF, lineY);
+        ctx.lineTo(x + ARROW_HALF, lineY);
+        ctx.lineTo(x, lineY - ARROW_HEIGHT);
+      }
+      ctx.closePath();
+      ctx.fill();
+    }
+
+    function drawOneMarker(u, m, color, up, label) {
+      const ctx = u.ctx;
+      const x = u.valToPos(m.time, "x", true);
+      const lineY = u.valToPos(markerChartPrice(m), "y", true);
+      if (x < u.bbox.left || x > u.bbox.left + u.bbox.width) return;
+      const arrowUp = visualUp(up);
+      const stackIdx = m.stack_index || 0;
+      const stackShift = stackIdx * STACK_STEP * (arrowUp ? 1 : -1);
+      const anchorY = lineY + stackShift;
+      drawTriangleOnLine(ctx, x, anchorY, arrowUp, color);
+      const labelY = arrowUp
+        ? anchorY + ARROW_HEIGHT + LABEL_GAP
+        : anchorY - ARROW_HEIGHT - LABEL_GAP;
+      drawMarkerLabel(ctx, label, x, labelY, color);
+    }
+
+    function drawFuturesMarkers(u) {
+      if (!showMarkers) return;
+      drawSimStartMarker(u, DATA.sim_start_marker);
+      (DATA.long_open_markers || []).forEach(m =>
+        drawOneMarker(u, m, COLORS.longOpen, true, markerLabel("longOpen", m)));
+      (DATA.long_close_markers || []).forEach(m =>
+        drawOneMarker(u, m, COLORS.longClose, false, markerLabel("longClose", m)));
+      (DATA.short_open_markers || []).forEach(m =>
+        drawOneMarker(u, m, COLORS.shortOpen, false, markerLabel("shortOpen", m)));
+      (DATA.short_close_markers || []).forEach(m =>
+        drawOneMarker(u, m, COLORS.shortClose, true, markerLabel("shortClose", m)));
+    }
+
+    function drawSimStartMarker(u, marker) {
+      if (!marker) return;
+      const ctx = u.ctx;
+      const x = u.valToPos(marker.time, "x", true);
+      const lineY = u.valToPos(markerChartPrice(marker), "y", true);
+      if (x < u.bbox.left || x > u.bbox.left + u.bbox.width) return;
+      const color = SIM_START_COLOR;
+      const arrowUp = visualUp(false);
+      drawTriangleOnLine(ctx, x, lineY, arrowUp, color);
+      const label = marker.label || "거래시작";
+      ctx.font = MARKER_FONT;
+      ctx.textAlign = "center";
+      ctx.textBaseline = arrowUp ? "top" : "bottom";
+      const labelY = arrowUp
+        ? lineY + ARROW_HEIGHT + 12
+        : lineY - ARROW_HEIGHT - 12;
+      ctx.lineWidth = 4;
+      ctx.lineJoin = "round";
+      ctx.strokeStyle = "rgba(255,255,255,0.95)";
+      ctx.strokeText(label, x, labelY);
+      ctx.fillStyle = color;
+      ctx.fillText(label, x, labelY);
+      ctx.textAlign = "left";
+      ctx.textBaseline = "alphabetic";
+    }
+
+    function updateLegInfo() {
+      const total = (DATA.long_open_markers || []).length;
+      const el = document.getElementById("leg-info");
+      if (!total) { el.textContent = "타점 없음"; return; }
+      el.textContent = `롱 레그 ${currentLegIdx + 1} / ${total}`;
+    }
+
+    function overviewXRange() {
+      if (!overviewPlot) return { min: DATA.times[0], max: DATA.times[DATA.times.length - 1] };
+      const s = overviewPlot.scales.x;
+      return { min: s.min, max: s.max };
+    }
+
+    function setOverviewXRange(min, max) {
+      const t0 = DATA.times[0];
+      const t1 = DATA.times[DATA.times.length - 1];
+      overviewPlot.setScale("x", {
+        min: Math.max(t0, min),
+        max: Math.min(t1, max),
+      });
+    }
+
+    function fitOverview() {
+      setOverviewXRange(DATA.times[0], DATA.times[DATA.times.length - 1]);
+    }
+
+    function zoomOverview(factor) {
+      const { min, max } = overviewXRange();
+      const mid = (min + max) / 2;
+      const half = Math.max((max - min) * factor / 2, 3600);
+      setOverviewXRange(mid - half, mid + half);
+    }
+
+    function buildOverview(keepRange) {
+      const prev = keepRange ? overviewXRange() : null;
+      if (overviewPlot) { overviewPlot.destroy(); overviewPlot = null; }
+      const yAxisW = yAxisLabelWidth();
+      const opts = {
+        width: document.getElementById("overview").clientWidth,
+        height: 480,
+        padding: [40, 10, 40, 10],
+        scales: {
+          x: { time: true },
+          y: { dir: yAxisInverted ? -1 : 1 },
+        },
+        axes: [
+          { gap: 6 },
+          {
+            side: 3,
+            size: yAxisW,
+            gap: 10,
+            font: AXIS_FONT,
+            values: (u, vals) => vals.map(v => fmtPrice(v)),
+          },
+        ],
+        series: [{}, { label: "종가", stroke: "#1565c0", width: 1 }],
+        cursor: { drag: { x: true, y: false, setScale: true } },
+        hooks: { draw: [(u) => drawFuturesMarkers(u)] },
+      };
+      overviewPlot = new uPlot(opts, [DATA.times, DATA.close], document.getElementById("overview"));
+      if (prev && keepRange) setOverviewXRange(prev.min, prev.max);
+      else fitOverview();
+    }
+
+    function sliceLastDays(days) {
+      const cutoff = DATA.times[DATA.times.length - 1] - days * 86400;
+      let start = 0;
+      for (let i = DATA.times.length - 1; i >= 0; i--) {
+        if (DATA.times[i] < cutoff) { start = i + 1; break; }
+      }
+      return { start, end: DATA.times.length };
+    }
+
+    function buildDetailCandles(startIdx, endIdx) {
+      lastDetailStart = startIdx;
+      const end = endIdx || DATA.times.length;
+      lastDetailEnd = end;
+      document.getElementById("detail-wrap").style.display = detailVisible ? "block" : "none";
+      const wrap = document.getElementById("detail");
+      wrap.innerHTML = "";
+      const priceAxisW = yAxisLabelWidth();
+      detailChart = LightweightCharts.createChart(wrap, {
+        layout: { background: { color: "#fff" }, textColor: "#333", fontSize: 14 },
+        grid: { vertLines: { color: "#eee" }, horzLines: { color: "#eee" } },
+        rightPriceScale: { visible: false },
+        leftPriceScale: {
+          borderVisible: true,
+          minimumWidth: priceAxisW,
+          scaleMargins: { top: 0.08, bottom: 0.08 },
+          invertScale: yAxisInverted,
+        },
+        timeScale: { timeVisible: true, secondsVisible: false },
+        width: wrap.clientWidth,
+        height: 360,
+      });
+      detailSeries = detailChart.addCandlestickSeries({
+        priceScaleId: "left",
+        upColor: "#c62828", downColor: "#1565c0",
+        borderUpColor: "#c62828", borderDownColor: "#1565c0",
+        wickUpColor: "#c62828", wickDownColor: "#1565c0",
+      });
+      const candles = [];
+      for (let i = startIdx; i < end; i++) {
+        candles.push({
+          time: DATA.times[i],
+          open: DATA.open[i], high: DATA.high[i],
+          low: DATA.low[i], close: DATA.close[i],
+        });
+      }
+      detailSeries.setData(candles);
+      const t0 = DATA.times[startIdx];
+      const t1 = DATA.times[end - 1];
+      const markers = [];
+      if (showMarkers) {
+        const colorMap = {
+          longOpen: COLORS.longOpen,
+          longClose: COLORS.longClose,
+          shortOpen: COLORS.shortOpen,
+          shortClose: COLORS.shortClose,
+        };
+        const kindUp = {
+          longOpen: true,
+          longClose: false,
+          shortOpen: false,
+          shortClose: true,
+        };
+        const pending = [];
+        [
+          [DATA.long_open_markers, "longOpen"],
+          [DATA.long_close_markers, "longClose"],
+          [DATA.short_open_markers, "shortOpen"],
+          [DATA.short_close_markers, "shortClose"],
+        ].forEach(([list, kind]) => {
+          (list || []).forEach(m => {
+            if (m.time >= t0 && m.time <= t1) pending.push({ m, kind });
+          });
+        });
+        pending.sort((a, b) => a.m.time - b.m.time || (a.m.stack_index || 0) - (b.m.stack_index || 0));
+        pending.forEach(({ m, kind }) => {
+          const arrowUp = visualUp(kindUp[kind]);
+          markers.push({
+            time: m.time,
+            position: arrowUp ? "belowBar" : "aboveBar",
+            color: colorMap[kind],
+            shape: arrowUp ? "arrowUp" : "arrowDown",
+            size: 10,
+            text: markerLabel(kind, m),
+          });
+        });
+      }
+      markers.sort((a, b) => a.time - b.time);
+      detailSeries.setMarkers(markers);
+      detailChart.timeScale().fitContent();
+    }
+
+    function setActive(btnId) {
+      document.querySelectorAll(".btn-period").forEach(b => b.classList.remove("active"));
+      const el = document.getElementById(btnId);
+      if (el) el.classList.add("active");
+    }
+
+    function goHome() {
+      currentMode = "overview";
+      setActive("btn-all");
+      document.getElementById("overview").style.display = "block";
+      if (!overviewPlot) buildOverview(false);
+      else fitOverview();
+      document.getElementById("status").textContent =
+        `전체 ${DATA.bar_count.toLocaleString()}봉 | 드래그=줌, 더블클릭=리셋`;
+    }
+
+    function nearestLongCloseAfter(openTime) {
+      let best = null;
+      for (const s of DATA.long_close_markers || []) {
+        if (s.time >= openTime && (!best || s.time < best.time)) best = s;
+      }
+      return best || (DATA.long_close_markers || [])[(DATA.long_close_markers || []).length - 1];
+    }
+
+    function jumpToLeg(idx) {
+      const opens = DATA.long_open_markers || [];
+      const total = opens.length;
+      if (!total) return;
+      currentLegIdx = Math.max(0, Math.min(idx, total - 1));
+      updateLegInfo();
+      const lo = opens[currentLegIdx];
+      const lc = nearestLongCloseAfter(lo.time);
+      const span = lc ? Math.max(lc.time - lo.time, 86400) : 86400 * 3;
+      const pad = span * 0.4;
+      const vmin = lo.time - pad;
+      const vmax = (lc ? lc.time : lo.time) + pad;
+
+      currentMode = "overview";
+      setActive("btn-all");
+      document.getElementById("overview").style.display = "block";
+      if (!overviewPlot) buildOverview(false);
+      setOverviewXRange(vmin, vmax);
+
+      let start = 0;
+      for (let i = 0; i < DATA.times.length; i++) {
+        if (DATA.times[i] >= vmin) { start = i; break; }
+      }
+      let end = DATA.times.length;
+      for (let i = DATA.times.length - 1; i >= 0; i--) {
+        if (DATA.times[i] <= vmax) { end = i + 1; break; }
+      }
+      document.getElementById("detail-title").textContent =
+        `L↑${lo.marker_id} 상방 매수 — ${new Date(lo.time * 1000).toLocaleString("ko-KR")}`;
+      buildDetailCandles(start, end);
+      const closeText = lc ? ` → 상방 매도 ${fmtPrice(lc.price)}` : "";
+      document.getElementById("status").textContent =
+        `L↑${lo.marker_id} ${fmtPrice(lo.price)}${closeText}`;
+    }
+
+    function showPeriod(days, btnId, label) {
+      currentMode = "detail";
+      setActive(btnId);
+      detailVisible = true;
+      document.getElementById("btn-toggle-detail").textContent = "상세 숨김";
+      const { start } = sliceLastDays(days);
+      document.getElementById("detail-title").textContent =
+        `${label} 캔들 (${(DATA.times.length - start).toLocaleString()}봉)`;
+      buildDetailCandles(start);
+      document.getElementById("overview").style.display = "block";
+      if (!overviewPlot) buildOverview(false);
+      const t0 = DATA.times[start];
+      setOverviewXRange(t0, DATA.times[DATA.times.length - 1]);
+      document.getElementById("status").textContent = `${label} 구간 표시`;
+    }
+
+    function applyZoom(factor) {
+      if (currentMode === "detail" && detailChart) {
+        const ts = detailChart.timeScale();
+        const r = ts.getVisibleLogicalRange();
+        if (!r) return;
+        const mid = (r.from + r.to) / 2;
+        const half = Math.max((r.to - r.from) * factor / 2, 10);
+        ts.setVisibleLogicalRange({ from: mid - half, to: mid + half });
+      } else if (overviewPlot) {
+        zoomOverview(factor);
+      }
+    }
+
+    function applyFit() {
+      if (currentMode === "detail" && detailChart) {
+        detailChart.timeScale().fitContent();
+      } else if (overviewPlot) {
+        fitOverview();
+      }
+    }
+
+    function toggleYFlip() {
+      yAxisInverted = !yAxisInverted;
+      const btn = document.getElementById("btn-flip-y");
+      btn.classList.toggle("active", yAxisInverted);
+      btn.textContent = yAxisInverted ? "↕ 복원" : "↕ 뒤집기";
+      if (overviewPlot) buildOverview(true);
+      if (detailChart) buildDetailCandles(lastDetailStart, lastDetailEnd || undefined);
+    }
+
+__EXTRA_SCRIPT__
+
+    function renderLegend() {
+      const items = [
+        [COLORS.longOpen, "L↑ 상방 매수 (롱 진입)"],
+        [COLORS.longClose, "L↓ 상방 매도 (롱 청산)"],
+        [COLORS.shortOpen, "S↓ 하방 매수 (숏 진입)"],
+        [COLORS.shortClose, "S↑ 하방 매도 (숏 청산)"],
+      ];
+      document.getElementById("legend").innerHTML = items.map(([color, text]) =>
+        `<span class="legend-item"><span class="legend-swatch" style="background:${color}"></span>${text}</span>`
+      ).join("");
+    }
+
+    function init() {
+      DATA = window.CHART_DATA;
+      if (!DATA) throw new Error("차트 데이터 JS 없음");
+      const m = DATA.meta;
+      const chartDays = m.chart_lookback_days || m.lookback_days;
+      const gtDays = m.gt_lookback_days || m.lookback_days;
+      const chartLabel = chartDays >= 365 ? `${Math.round(chartDays / 365)}년` : `${chartDays}일`;
+      const gtLabel = gtDays >= 365 ? `${Math.round(gtDays / 365)}년` : `${gtDays}일`;
+      const tier = m.chart_tier ? ` ${m.chart_tier.toUpperCase()}` : "";
+      const simMode = !!DATA.sim_pnl;
+      const simSuffix = simMode ? (m.sim_stage_suffix || " · 1단계 sim") : "";
+      document.getElementById("title").textContent =
+        `${m.symbol} 선물 Ground Truth${tier} (${m.interval_label}) — 차트 ${chartLabel} / GT ${gtLabel}${simSuffix}`;
+      if (simMode) {
+        const panelTitle = document.getElementById("sim-panel-title");
+        if (panelTitle) {
+          const simDays = DATA.sim_pnl?.sim_lookback_days || m.sim_lookback_days || 1095;
+          const simLabel = simDays >= 365
+            ? `최근 ${Math.round(simDays / 365)}년`
+            : `최근 ${simDays}일`;
+          const initCash = DATA.sim_pnl?.initial_cash_krw || 0;
+          const initLabel = initCash ? `${Math.round(initCash).toLocaleString()}원` : "";
+          panelTitle.textContent =
+            (m.sim_stage_title || "1단계 선물 sim (GT 사후 타점)") +
+            ` (${simLabel}${initLabel ? ` · 초기 ${initLabel}` : ""})`;
+        }
+      }
+      document.getElementById("btn-all").textContent = `전체 ${chartLabel}`;
+      const chartFrom = m.chart_data_from || m.data_from;
+      const chartTo = m.chart_data_to || m.data_to;
+      const lo = (DATA.long_open_markers || []).length;
+      const lc = (DATA.long_close_markers || []).length;
+      const so = (DATA.short_open_markers || []).length;
+      const sc = (DATA.short_close_markers || []).length;
+      const markerRange = simMode && m.sim_period_from
+        ? `체결 L↑${lo}/L↓${lc} · S↓${so}/S↑${sc} · ${m.sim_period_from.slice(0, 16)} ~ ${(m.sim_period_to || chartTo).slice(0, 16)}`
+        : `GT ${gtLabel} | 현물 GT 타점 기반`;
+      const legendExtra = simMode ? " | ▼보라=거래시작" : "";
+      document.getElementById("meta").textContent =
+        `차트 ${chartFrom} ~ ${chartTo} (${DATA.bar_count.toLocaleString()}봉) | ` +
+        `상방 ${lo}/${lc} · 하방 ${so}/${sc} | ${markerRange}${legendExtra}`;
+      renderLegend();
+      if (simMode) renderSimPanel();
+      updateLegInfo();
+      document.getElementById("status").textContent =
+        `전체 ${DATA.bar_count.toLocaleString()}봉 | 드래그=줌, 더블클릭=리셋`;
+
+      buildOverview(false);
+
+      document.getElementById("btn-home").onclick = goHome;
+      document.getElementById("btn-prev-leg").onclick = () => jumpToLeg(currentLegIdx - 1);
+      document.getElementById("btn-next-leg").onclick = () => jumpToLeg(currentLegIdx + 1);
+      document.getElementById("btn-all").onclick = goHome;
+      document.getElementById("btn-365d").onclick = () => showPeriod(365, "btn-365d", "최근 1년");
+      document.getElementById("btn-30d").onclick = () => showPeriod(30, "btn-30d", "최근 30일");
+      document.getElementById("btn-7d").onclick = () => showPeriod(7, "btn-7d", "최근 7일");
+      document.getElementById("btn-3d").onclick = () => showPeriod(3, "btn-3d", "최근 3일");
+      document.getElementById("btn-zoom-in").onclick = () => applyZoom(0.6);
+      document.getElementById("btn-zoom-out").onclick = () => applyZoom(1.4);
+      document.getElementById("btn-fit").onclick = applyFit;
+      document.getElementById("btn-flip-y").onclick = toggleYFlip;
+      document.getElementById("btn-markers").onclick = () => {
+        showMarkers = !showMarkers;
+        document.getElementById("btn-markers").textContent = showMarkers ? "마커 숨김" : "마커 표시";
+        if (overviewPlot) buildOverview(true);
+        if (detailChart) buildDetailCandles(lastDetailStart);
+      };
+      document.getElementById("btn-toggle-detail").onclick = () => {
+        detailVisible = !detailVisible;
+        document.getElementById("detail-wrap").style.display = detailVisible ? "block" : "none";
+        document.getElementById("btn-toggle-detail").textContent = detailVisible ? "상세 숨김" : "상세 패널";
+        if (detailVisible && !detailChart) {
+          const { start } = sliceLastDays(7);
+          buildDetailCandles(start);
+        }
+      };
+      document.getElementById("overview").addEventListener("dblclick", () => {
+        if (currentMode === "overview") fitOverview();
+      });
+      window.addEventListener("resize", () => {
+        if (overviewPlot) buildOverview(true);
+      });
+    }
+    try { init(); } catch (err) {
+      document.getElementById("status").textContent = "데이터 로드 실패: " + err;
+    }
+  </script>
+</body>
+</html>"""
+
+
+def _build_futures_chart_payload(
+    df,
+    gt_result: dict[str, Any],
+    chart_days: int,
+    gt_lookback_days: int,
+) -> dict[str, Any]:
+    """선물 GT 차트용 JSON payload를 구성한다."""
+    markers = futures_markers_from_gt_signals(gt_result)
+    times = _to_unix_seconds(df["datetime"])
+    closes = df["close"].astype(float).tolist()
+    chart_meta = {
+        **gt_result["meta"],
+        "market_type": "futures",
+        "chart_lookback_days": chart_days,
+        "gt_lookback_days": gt_lookback_days,
+        "chart_data_from": str(df["datetime"].min()),
+        "chart_data_to": str(df["datetime"].max()),
+    }
+    payload: dict[str, Any] = {
+        "times": times,
+        "open": df["open"].astype(float).tolist(),
+        "high": df["high"].astype(float).tolist(),
+        "low": df["low"].astype(float).tolist(),
+        "close": closes,
+        "long_open_markers": _enrich_markers_chart_price(markers["long_open"], times, closes),
+        "long_close_markers": _enrich_markers_chart_price(markers["long_close"], times, closes),
+        "short_open_markers": _enrich_markers_chart_price(markers["short_open"], times, closes),
+        "short_close_markers": _enrich_markers_chart_price(markers["short_close"], times, closes),
+        "meta": chart_meta,
+        "bar_count": len(df),
+    }
+    _stack_marker_positions(
+        payload["long_open_markers"],
+        payload["long_close_markers"],
+        payload["short_open_markers"],
+        payload["short_close_markers"],
+    )
+    return payload
+
+
+def render_futures_ground_truth_chart(
+    db_path: Path,
+    symbol: str,
+    gt_result: dict[str, Any],
+    output_path: Path,
+    chart_lookback_days: int | None = None,
+    max_candles: int = DEFAULT_MAX_CANDLES,
+) -> Path:
+    """현물 GT 타점을 선물 롱·숏 4색 마커로 표시한 HTML 차트를 생성한다.
+
+    Args:
+        db_path: SQLite 경로.
+        symbol: 코인 심볼.
+        gt_result: build_ground_truth 결과 또는 spot GT JSON.
+        output_path: HTML 출력 경로.
+        chart_lookback_days: 차트 표시 일수. None이면 GT lookback과 동일.
+        max_candles: 0이면 전체, 양수면 최근 N봉만.
+
+    Returns:
+        HTML 저장 경로.
+    """
+    interval_min = gt_result["meta"]["interval_min"]
+    gt_lookback_days = gt_result["meta"]["lookback_days"]
+    chart_days = chart_lookback_days if chart_lookback_days is not None else gt_lookback_days
+
+    df = load_candles(db_path, symbol, interval_min, lookback_days=chart_days)
+    if max_candles > 0 and len(df) > max_candles:
+        df = df.iloc[-max_candles:].reset_index(drop=True)
+
+    payload = _build_futures_chart_payload(df, gt_result, chart_days, gt_lookback_days)
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    data_path = _data_js_path(output_path)
+    with data_path.open("w", encoding="utf-8") as fp:
+        fp.write("window.CHART_DATA=")
+        json.dump(payload, fp, ensure_ascii=False, separators=(",", ":"))
+        fp.write(";")
+
+    data_js_name = data_path.name
+    html = _futures_html_template(data_js_name)
+    output_path.write_text(html, encoding="utf-8")
+    return output_path
+
+
+def _futures_html_template(data_js_name: str) -> str:
+    """선물 GT 차트 HTML 템플릿을 생성한다."""
+    return (
+        _FUTURES_HTML_TEMPLATE.replace("__DATA_JS_NAME__", data_js_name)
+        .replace("__EXTRA_STYLES__", "")
+        .replace("__EXTRA_BODY__", "")
+        .replace("__EXTRA_SCRIPT__", "")
+    )

src/deepcoin/ground_truth/ground_truth.py

@@ -0,0 +1,411 @@
+"""Ground Truth 매수·매도 타점 생성 (1단계 · 0단계 sim 입력)."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import asdict, dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+from deepcoin.data.candle_loader import load_candles
+from deepcoin.data.intervals import interval_label
+from deepcoin.ground_truth.pnl import simulate_gt_pnl
+from deepcoin.ground_truth.breakout import find_breakout_buy_pivots
+from deepcoin.ground_truth.divergence import find_divergence_signals
+from deepcoin.ground_truth.pullback import find_pullback_buy_pivots
+from deepcoin.ground_truth.zigzag import Pivot, find_zigzag_pivots
+
+
+@dataclass(frozen=True)
+class GtParams:
+    """Ground Truth 생성 파라미터."""
+
+    interval_min: int
+    lookback_days: int
+    zigzag_reversal_pct: float
+    min_leg_pct: float
+    pullback_min_pct: float = 1.5
+    pullback_local_order: int = 10
+    breakout_buffer_pct: float = 0.1
+    breakout_consolidation_bars: int = 200
+    breakout_min_rally_pct: float = 2.0
+    div_local_order: int = 20
+    div_min_bars_between: int = 1500
+    div_min_rsi_diff: float = 5.0
+    div_min_future_move_pct: float = 4.0
+    chart_tier: str = "v3"
+
+
+def _tier_flags(tier: str) -> tuple[bool, bool, bool]:
+    """차트 버전별 보조 신호 포함 여부 (눌림목, 돌파, 다이버전스).
+
+    v1: ZigZag 스윙만 (레그당 1매수·1매도 최소)
+    v2: 스윙 + 눌림목
+    v3: v2 + 돌파 + 다이버전스
+    """
+    tier = tier.lower()
+    if tier == "v1":
+        return False, False, False
+    if tier == "v2":
+        return True, False, False
+    return True, True, True
+
+
+@dataclass
+class GtLeg:
+    """매수→매도 1레그 (최대 스윙 수익 구간)."""
+
+    leg_id: int
+    buy_datetime: str
+    buy_price: float
+    buy_bar_index: int
+    sell_datetime: str
+    sell_price: float
+    sell_bar_index: int
+    leg_pct: float
+    bars_held: int
+
+
+def build_ground_truth(
+    db_path: Path,
+    symbol: str,
+    coin_name: str,
+    params: GtParams,
+    initial_cash_krw: float = 400_000.0,
+    fee_rate: float = 0.0005,
+) -> dict[str, Any]:
+    """최근 1년 구간에서 사후 최적 스윙 레그(1매수·1매도) GT를 생성한다.
+
+    미래 데이터를 사용해 ZigZag 스윙 저점 매수·고점 매도 쌍을 찾는다.
+    1단계 벤치마크: 최대 스윙 수익을 포착하는 타점.
+
+    Args:
+        db_path: SQLite 경로.
+        symbol: 코인 심볼.
+        coin_name: 코인 이름.
+        params: GT 파라미터.
+        initial_cash_krw: 수익률 계산 초기 자본 (1년 시작 시점).
+        fee_rate: 거래 수수료율.
+
+    Returns:
+        JSON 직렬화 가능한 GT 결과 dict.
+    """
+    df = load_candles(
+        db_path=db_path,
+        symbol=symbol,
+        interval_min=params.interval_min,
+        lookback_days=params.lookback_days,
+    )
+
+    pivots = find_zigzag_pivots(df, reversal_pct=params.zigzag_reversal_pct)
+    legs = _pivots_to_legs(pivots, min_leg_pct=params.min_leg_pct)
+    leg_dicts = [asdict(leg) for leg in legs]
+    include_pullback, include_breakout, include_divergence = _tier_flags(params.chart_tier)
+
+    pullback_buys: list[Pivot] = []
+    if include_pullback:
+        pullback_buys = find_pullback_buy_pivots(
+            df,
+            legs=legs,
+            min_pullback_pct=params.pullback_min_pct,
+            local_order=params.pullback_local_order,
+        )
+
+    breakout_buys = []
+    if include_breakout:
+        breakout_buys = find_breakout_buy_pivots(
+            df,
+            legs=legs,
+            pullback_buys=pullback_buys,
+            breakout_buffer_pct=params.breakout_buffer_pct,
+            consolidation_bars=params.breakout_consolidation_bars,
+            min_rally_to_sell_pct=params.breakout_min_rally_pct,
+        )
+
+    div_buys: list = []
+    div_sells: list = []
+    if include_divergence:
+        div_buys, div_sells = find_divergence_signals(
+            df,
+            local_order=params.div_local_order,
+            min_bars_between=params.div_min_bars_between,
+            min_rsi_diff=params.div_min_rsi_diff,
+            min_future_move_pct=params.div_min_future_move_pct,
+        )
+
+    mode_map = {
+        "v1": "optimal_swing_legs",
+        "v2": "optimal_swing_legs_with_pullback",
+        "v3": "optimal_swing_legs_with_pullback_breakout_divergence",
+    }
+    mode = mode_map.get(params.chart_tier.lower(), mode_map["v3"])
+
+    signals = _build_signals(legs, pullback_buys, breakout_buys, div_buys, div_sells)
+    summary = _summarize(legs, signals)
+    pnl = simulate_gt_pnl(leg_dicts, initial_cash_krw=initial_cash_krw, fee_rate=fee_rate)
+
+    return {
+        "meta": {
+            "symbol": symbol.upper(),
+            "coin_name": coin_name,
+            "interval_min": params.interval_min,
+            "interval_label": interval_label(params.interval_min),
+            "lookback_days": params.lookback_days,
+            "chart_tier": params.chart_tier.lower(),
+            "mode": mode,
+            "zigzag_reversal_pct": params.zigzag_reversal_pct,
+            "min_leg_pct": params.min_leg_pct,
+            "pullback_min_pct": params.pullback_min_pct,
+            "initial_cash_krw": initial_cash_krw,
+            "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "data_from": str(df["datetime"].min()),
+            "data_to": str(df["datetime"].max()),
+            "bar_count": len(df),
+            "pivot_count": len(pivots),
+            "pullback_buy_count": len(pullback_buys),
+            "breakout_buy_count": len(breakout_buys),
+            "breakout_buffer_pct": params.breakout_buffer_pct,
+            "divergence_buy_count": len(div_buys),
+            "divergence_sell_count": len(div_sells),
+        },
+        "legs": leg_dicts,
+        "signals": signals,
+        "summary": summary,
+        "pnl": pnl,
+    }
+
+
+def save_ground_truth(result: dict[str, Any], output_path: Path) -> Path:
+    """GT 결과를 JSON으로 저장한다."""
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with output_path.open("w", encoding="utf-8") as fp:
+        json.dump(result, fp, ensure_ascii=False, indent=2)
+    return output_path
+
+
+def _pivots_to_legs(pivots: list[Pivot], min_leg_pct: float) -> list[GtLeg]:
+    """스윙 저점→고점을 1매수·1매도 레그로 변환한다."""
+    legs: list[GtLeg] = []
+    leg_id = 0
+    i = 0
+
+    while i < len(pivots) - 1:
+        buy_pivot = pivots[i]
+        sell_pivot = pivots[i + 1]
+
+        if buy_pivot.side != "low" or sell_pivot.side != "high":
+            i += 1
+            continue
+        if sell_pivot.bar_index <= buy_pivot.bar_index:
+            i += 1
+            continue
+
+        leg_pct = (sell_pivot.price - buy_pivot.price) / buy_pivot.price * 100.0
+        if leg_pct < min_leg_pct:
+            i += 1
+            continue
+
+        leg_id += 1
+        legs.append(
+            GtLeg(
+                leg_id=leg_id,
+                buy_datetime=buy_pivot.datetime.strftime("%Y-%m-%d %H:%M:%S"),
+                buy_price=round(buy_pivot.price, 2),
+                buy_bar_index=buy_pivot.bar_index,
+                sell_datetime=sell_pivot.datetime.strftime("%Y-%m-%d %H:%M:%S"),
+                sell_price=round(sell_pivot.price, 2),
+                sell_bar_index=sell_pivot.bar_index,
+                leg_pct=round(leg_pct, 2),
+                bars_held=sell_pivot.bar_index - buy_pivot.bar_index,
+            )
+        )
+        i += 2
+
+    return legs
+
+
+def _build_signals(
+    legs: list[GtLeg],
+    pullback_buys: list[Pivot],
+    breakout_buys: list,
+    div_buys: list,
+    div_sells: list,
+) -> list[dict[str, Any]]:
+    """스윙·눌림목·돌파·다이버전스 신호를 통합한다."""
+    signals: list[dict[str, Any]] = []
+    buy_marker_id = 0
+    sell_marker_id = 0
+
+    existing_buy_bars: set[int] = {leg.buy_bar_index for leg in legs}
+    existing_sell_bars: set[int] = {leg.sell_bar_index for leg in legs}
+    nearby_tolerance = 120
+
+    for leg in legs:
+        buy_marker_id += 1
+        signals.append(
+            {
+                "marker_id": buy_marker_id,
+                "leg_id": leg.leg_id,
+                "side": "buy",
+                "signal_type": "swing_low",
+                "datetime": leg.buy_datetime,
+                "price": leg.buy_price,
+                "bar_index": leg.buy_bar_index,
+            }
+        )
+        sell_marker_id += 1
+        existing_sell_bars.add(leg.sell_bar_index)
+        signals.append(
+            {
+                "marker_id": sell_marker_id,
+                "leg_id": leg.leg_id,
+                "side": "sell",
+                "signal_type": "swing_high",
+                "datetime": leg.sell_datetime,
+                "price": leg.sell_price,
+                "bar_index": leg.sell_bar_index,
+                "leg_pct": leg.leg_pct,
+            }
+        )
+
+    for pivot in pullback_buys:
+        if _is_near_existing_buy(pivot.bar_index, existing_buy_bars, nearby_tolerance):
+            continue
+        buy_marker_id += 1
+        existing_buy_bars.add(pivot.bar_index)
+        signals.append(
+            {
+                "marker_id": buy_marker_id,
+                "leg_id": None,
+                "side": "buy",
+                "signal_type": "pullback",
+                "datetime": pivot.datetime.strftime("%Y-%m-%d %H:%M:%S"),
+                "price": round(pivot.price, 2),
+                "bar_index": pivot.bar_index,
+            }
+        )
+
+    for breakout in breakout_buys:
+        if _is_near_existing_buy(breakout.bar_index, existing_buy_bars, nearby_tolerance):
+            continue
+        buy_marker_id += 1
+        existing_buy_bars.add(breakout.bar_index)
+        signals.append(
+            {
+                "marker_id": buy_marker_id,
+                "leg_id": breakout.leg_id,
+                "side": "buy",
+                "signal_type": "breakout",
+                "datetime": breakout.datetime.strftime("%Y-%m-%d %H:%M:%S"),
+                "price": breakout.price,
+                "bar_index": breakout.bar_index,
+                "resistance_price": breakout.resistance_price,
+            }
+        )
+
+    div_tolerance = 400
+    for div in div_buys:
+        if _is_near_bar(div.bar_index, existing_buy_bars, div_tolerance):
+            continue
+        buy_marker_id += 1
+        existing_buy_bars.add(div.bar_index)
+        signals.append(_divergence_to_dict(div, buy_marker_id, "div_bull"))
+
+    for div in div_sells:
+        if _is_near_bar(div.bar_index, existing_sell_bars, div_tolerance):
+            continue
+        sell_marker_id += 1
+        existing_sell_bars.add(div.bar_index)
+        signals.append(_divergence_to_dict(div, sell_marker_id, "div_bear"))
+
+    signals.sort(key=lambda s: (s["bar_index"], _signal_sort_key(s)))
+    return signals
+
+
+def _divergence_to_dict(div, marker_id: int, signal_type: str) -> dict[str, Any]:
+    """DivergenceSignal을 GT signal dict로 변환한다."""
+    return {
+        "marker_id": marker_id,
+        "leg_id": None,
+        "side": div.side,
+        "signal_type": signal_type,
+        "datetime": div.datetime.strftime("%Y-%m-%d %H:%M:%S"),
+        "price": div.price,
+        "bar_index": div.bar_index,
+        "indicator": div.indicator,
+        "price_prev": div.price_prev,
+        "ind_prev": div.ind_prev,
+        "ind_curr": div.ind_curr,
+    }
+
+
+def _signal_sort_key(signal: dict[str, Any]) -> int:
+    """동일 봉에서 신호 유형 정렬 우선순위."""
+    order = {
+        "swing_low": 0,
+        "pullback": 1,
+        "breakout": 2,
+        "div_bull": 3,
+        "swing_high": 4,
+        "div_bear": 5,
+    }
+    return order.get(signal.get("signal_type", ""), 9)
+
+
+def _is_near_bar(bar_index: int, existing_bars: set[int], tolerance: int) -> bool:
+    """기존 타점과 너무 가까우면 보조 신호를 제외한다."""
+    for existing in existing_bars:
+        if abs(bar_index - existing) <= tolerance:
+            return True
+    return False
+
+
+def _is_near_existing_buy(bar_index: int, existing_bars: set[int], tolerance: int) -> bool:
+    """기존 매수와 너무 가까우면 보조 매수를 제외한다."""
+    return _is_near_bar(bar_index, existing_bars, tolerance)
+
+
+def _summarize(legs: list[GtLeg], signals: list[dict[str, Any]]) -> dict[str, Any]:
+    """GT 요약 통계."""
+    buy_count = sum(1 for s in signals if s["side"] == "buy")
+    sell_count = sum(1 for s in signals if s["side"] == "sell")
+    pullback_count = sum(1 for s in signals if s.get("signal_type") == "pullback")
+    breakout_count = sum(1 for s in signals if s.get("signal_type") == "breakout")
+    div_buy_count = sum(1 for s in signals if s.get("signal_type") == "div_bull")
+    div_sell_count = sum(1 for s in signals if s.get("signal_type") == "div_bear")
+
+    if not legs:
+        return {
+            "leg_count": 0,
+            "buy_count": buy_count,
+            "sell_count": sell_count,
+            "pullback_buy_count": pullback_count,
+            "breakout_buy_count": breakout_count,
+            "divergence_buy_count": div_buy_count,
+            "divergence_sell_count": div_sell_count,
+            "avg_leg_pct": 0.0,
+            "median_leg_pct": 0.0,
+            "max_leg_pct": 0.0,
+            "min_leg_pct": 0.0,
+            "avg_bars_held": 0.0,
+        }
+
+    pcts = [leg.leg_pct for leg in legs]
+    bars = [leg.bars_held for leg in legs]
+    return {
+        "leg_count": len(legs),
+        "buy_count": buy_count,
+        "sell_count": sell_count,
+        "pullback_buy_count": pullback_count,
+        "breakout_buy_count": breakout_count,
+        "divergence_buy_count": div_buy_count,
+        "divergence_sell_count": div_sell_count,
+        "avg_leg_pct": round(sum(pcts) / len(pcts), 2),
+        "median_leg_pct": round(float(pd.Series(pcts).median()), 2),
+        "max_leg_pct": round(max(pcts), 2),
+        "min_leg_pct": round(min(pcts), 2),
+        "avg_bars_held": round(sum(bars) / len(bars), 1),
+    }

src/deepcoin/ground_truth/order_sizing.py

@@ -0,0 +1,59 @@
+"""총평가금액 구간별 매수(현금) 상한."""
+
+from __future__ import annotations
+
+from typing import Any
+
+# 총평가금액(원) 구간 — 높은 구간이 우선 적용
+EQUITY_TIER_100M = 100_000_000
+EQUITY_TIER_1B = 1_000_000_000
+EQUITY_TIER_10B = 10_000_000_000
+
+BUY_SIZING_RULE_LABEL = "총평가 1억↑ 현금 10% · 10억↑ 5% · 100억↑ 1%"
+
+
+def buy_cash_pct(equity_krw: float) -> float | None:
+    """총평가금액에 따른 1회 매수 허용 현금 비율.
+
+    Args:
+        equity_krw: 현재 총평가금액(원).
+
+    Returns:
+        허용 비율(0~1). 1억 미만이면 None(비율 상한 없음).
+    """
+    if equity_krw >= EQUITY_TIER_10B:
+        return 0.01
+    if equity_krw >= EQUITY_TIER_1B:
+        return 0.05
+    if equity_krw >= EQUITY_TIER_100M:
+        return 0.10
+    return None
+
+
+def max_buy_from_cash(equity_krw: float, cash_krw: float) -> float:
+    """구간별 규칙을 반영한 1회 매수 최대 금액.
+
+    Args:
+        equity_krw: 현재 총평가금액(원).
+        cash_krw: 보유 현금(원).
+
+    Returns:
+        매수에 사용 가능한 최대 원화.
+    """
+    cash = max(float(cash_krw), 0.0)
+    pct = buy_cash_pct(equity_krw)
+    if pct is None:
+        return cash
+    return cash * pct
+
+
+def buy_sizing_metadata() -> dict[str, Any]:
+    """시뮬 결과·차트에 포함할 매수 상한 메타."""
+    return {
+        "buy_sizing_rule": BUY_SIZING_RULE_LABEL,
+        "buy_sizing_tiers": [
+            {"min_equity_krw": EQUITY_TIER_100M, "max_cash_pct": 0.10},
+            {"min_equity_krw": EQUITY_TIER_1B, "max_cash_pct": 0.05},
+            {"min_equity_krw": EQUITY_TIER_10B, "max_cash_pct": 0.01},
+        ],
+    }

src/deepcoin/ground_truth/pnl.py

@@ -0,0 +1,400 @@
+"""Ground Truth 기준 초기 자본 누적 수익률 계산."""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from datetime import datetime, timedelta
+from typing import Any
+
+from deepcoin.ground_truth.order_sizing import buy_sizing_metadata, max_buy_from_cash
+
+
+@dataclass
+class LegPnl:
+    """레그별 손익 (매수→매도 1쌍)."""
+
+    leg_id: int
+    buy_datetime: str
+    sell_datetime: str
+    buy_price: float
+    sell_price: float
+    cash_before: float
+    cash_after: float
+    leg_return_pct: float
+    cumulative_return_pct: float
+    btc_qty: float
+
+
+@dataclass
+class SignalTrade:
+    """신호 1건 실행 기록."""
+
+    trade_id: int
+    side: str
+    signal_type: str
+    marker_id: int | None
+    datetime: str
+    price: float
+    cash_before: float
+    cash_after: float
+    coin_before: float
+    coin_after: float
+    order_krw: float
+    order_coin: float
+    fee_krw: float
+    cluster_size: int
+    skipped: bool
+    skip_reason: str | None = None
+
+
+def simulate_gt_pnl(
+    legs: list[dict[str, Any]],
+    initial_cash_krw: float = 400_000.0,
+    fee_rate: float = 0.0005,
+    min_order_krw: float = 5_000.0,
+) -> dict[str, Any]:
+    """GT 레그(1매수·1매도)를 순서대로 실행한 누적 수익률.
+
+    - 초기 현금 전액 매수 → 전량 매도 반복 (복리)
+    - 1단계 Ground Truth 벤치마크용
+
+    Args:
+        legs: GT legs 리스트.
+        initial_cash_krw: 초기 원화.
+        fee_rate: 편도 수수료율.
+        min_order_krw: 최소 주문 금액.
+
+    Returns:
+        요약 + 레그별 손익 dict.
+    """
+    cash = float(initial_cash_krw)
+    leg_pnls: list[LegPnl] = []
+    skipped = 0
+
+    for leg in legs:
+        buy_price = float(leg["buy_price"])
+        sell_price = float(leg["sell_price"])
+        cash_before = cash
+
+        if cash < min_order_krw:
+            skipped += 1
+            continue
+
+        buy_fee = cash * fee_rate
+        btc_bought = (cash - buy_fee) / buy_price
+        cash = 0.0
+
+        sell_gross = btc_bought * sell_price
+        sell_fee = sell_gross * fee_rate
+        cash = sell_gross - sell_fee
+
+        leg_return = (cash - cash_before) / cash_before * 100.0
+        cumulative = (cash - initial_cash_krw) / initial_cash_krw * 100.0
+
+        leg_pnls.append(
+            LegPnl(
+                leg_id=int(leg["leg_id"]),
+                buy_datetime=leg["buy_datetime"],
+                sell_datetime=leg["sell_datetime"],
+                buy_price=buy_price,
+                sell_price=sell_price,
+                cash_before=round(cash_before, 0),
+                cash_after=round(cash, 0),
+                leg_return_pct=round(leg_return, 2),
+                cumulative_return_pct=round(cumulative, 2),
+                btc_qty=round(btc_bought, 8),
+            )
+        )
+
+    final_cash = cash
+    total_return_pct = (final_cash - initial_cash_krw) / initial_cash_krw * 100.0
+
+    period_from = leg_pnls[0].buy_datetime if leg_pnls else None
+    period_to = leg_pnls[-1].sell_datetime if leg_pnls else None
+
+    return {
+        "initial_cash_krw": initial_cash_krw,
+        "final_cash_krw": round(final_cash, 0),
+        "total_pnl_krw": round(final_cash - initial_cash_krw, 0),
+        "total_return_pct": round(total_return_pct, 2),
+        "fee_rate": fee_rate,
+        "legs_traded": len(leg_pnls),
+        "legs_skipped": skipped,
+        "period_from": period_from,
+        "period_to": period_to,
+        "leg_pnls": [asdict(x) for x in leg_pnls],
+    }
+
+
+def _parse_signal_dt(value: str) -> datetime:
+    """GT signal datetime 문자열을 파싱한다."""
+    return datetime.strptime(value, "%Y-%m-%d %H:%M:%S")
+
+
+def _cluster_signals(signals: list[dict[str, Any]]) -> list[tuple[str, list[dict[str, Any]]]]:
+    """연속 동일 side 신호를 클러스터로 묶는다."""
+    ordered = sorted(signals, key=lambda s: (s["bar_index"], s.get("marker_id", 0)))
+    clusters: list[tuple[str, list[dict[str, Any]]]] = []
+    current_side: str | None = None
+    current: list[dict[str, Any]] = []
+
+    for sig in ordered:
+        side = sig["side"]
+        if current_side is None:
+            current_side = side
+            current = [sig]
+            continue
+        if side == current_side:
+            current.append(sig)
+            continue
+        clusters.append((current_side, current))
+        current_side = side
+        current = [sig]
+
+    if current_side and current:
+        clusters.append((current_side, current))
+    return clusters
+
+
+def simulate_gt_signals_pnl(
+    signals: list[dict[str, Any]],
+    initial_cash_krw: float = 400_000.0,
+    fee_rate: float = 0.0005,
+    min_order_krw: float = 5_000.0,
+    sim_lookback_days: int = 365,
+    data_end: str | None = None,
+    last_mark_price: float | None = None,
+) -> dict[str, Any]:
+    """GT 매수·매도 신호를 시간순 실행한 2단계 포트폴리오 시뮬레이션.
+
+    - 시뮬 기간: data_end 기준 최근 sim_lookback_days
+    - 연속 매수: 가용 원화를 매수 신호 수로 균등 분할
+    - 총평가 1억↑ 현금 10% · 10억↑ 5% · 100억↑ 1% 상한
+    - 연속 매도: 보유 코인을 매도 신호 수로 균등 분할 (상한 없음)
+    - 원화 부족 시 매수 스킵, 코인 없으면 매도 스킵
+
+    Args:
+        signals: GT signals 리스트.
+        initial_cash_krw: 시뮬 시작 원화.
+        fee_rate: 편도 수수료율.
+        min_order_krw: 최소 주문 금액.
+        sim_lookback_days: 시뮬 기간(일).
+        data_end: 데이터 종료 시각 문자열. None이면 마지막 신호 시각.
+        last_mark_price: 미청산 코인 평가 가격. None이면 마지막 체결가.
+
+    Returns:
+        요약 + 체결/스킵 내역 dict.
+    """
+    if not signals:
+        return _empty_signal_pnl(initial_cash_krw, fee_rate, sim_lookback_days)
+
+    end_dt = _parse_signal_dt(data_end) if data_end else max(
+        _parse_signal_dt(s["datetime"]) for s in signals
+    )
+    start_dt = end_dt - timedelta(days=sim_lookback_days)
+    start_str = start_dt.strftime("%Y-%m-%d %H:%M:%S")
+
+    period_signals = [
+        s for s in signals if _parse_signal_dt(s["datetime"]) >= start_dt
+    ]
+    if not period_signals:
+        return _empty_signal_pnl(
+            initial_cash_krw,
+            fee_rate,
+            sim_lookback_days,
+            period_from=start_str,
+            period_to=end_dt.strftime("%Y-%m-%d %H:%M:%S"),
+        )
+
+    cash = float(initial_cash_krw)
+    coin_qty = 0.0
+    trades: list[SignalTrade] = []
+    trade_id = 0
+    buys_executed = 0
+    sells_executed = 0
+    buys_skipped = 0
+    sells_skipped = 0
+
+    mark_price = float(last_mark_price or period_signals[-1]["price"])
+
+    for side, cluster in _cluster_signals(period_signals):
+        cluster_size = len(cluster)
+        if side == "buy":
+            budget = cash
+            per_buy = budget / cluster_size if cluster_size else 0.0
+            for sig in cluster:
+                trade_id += 1
+                price = float(sig["price"])
+                cash_before = cash
+                coin_before = coin_qty
+                equity = cash + coin_qty * price
+                cash_cap = max_buy_from_cash(equity, cash)
+                order_krw = min(per_buy, cash, cash_cap)
+
+                if order_krw < min_order_krw:
+                    buys_skipped += 1
+                    trades.append(
+                        SignalTrade(
+                            trade_id=trade_id,
+                            side="buy",
+                            signal_type=str(sig.get("signal_type", "buy")),
+                            marker_id=sig.get("marker_id"),
+                            datetime=sig["datetime"],
+                            price=price,
+                            cash_before=round(cash_before, 0),
+                            cash_after=round(cash, 0),
+                            coin_before=round(coin_before, 8),
+                            coin_after=round(coin_qty, 8),
+                            order_krw=0.0,
+                            order_coin=0.0,
+                            fee_krw=0.0,
+                            cluster_size=cluster_size,
+                            skipped=True,
+                            skip_reason="원화 부족",
+                        )
+                    )
+                    continue
+
+                fee = order_krw * fee_rate
+                bought = (order_krw - fee) / price
+                cash -= order_krw
+                coin_qty += bought
+                buys_executed += 1
+                trades.append(
+                    SignalTrade(
+                        trade_id=trade_id,
+                        side="buy",
+                        signal_type=str(sig.get("signal_type", "buy")),
+                        marker_id=sig.get("marker_id"),
+                        datetime=sig["datetime"],
+                        price=price,
+                        cash_before=round(cash_before, 0),
+                        cash_after=round(cash, 0),
+                        coin_before=round(coin_before, 8),
+                        coin_after=round(coin_qty, 8),
+                        order_krw=round(order_krw, 0),
+                        order_coin=round(bought, 8),
+                        fee_krw=round(fee, 0),
+                        cluster_size=cluster_size,
+                        skipped=False,
+                    )
+                )
+        else:
+            budget_coin = coin_qty
+            per_sell = budget_coin / cluster_size if cluster_size else 0.0
+            for sig in cluster:
+                trade_id += 1
+                price = float(sig["price"])
+                cash_before = cash
+                coin_before = coin_qty
+                order_coin = min(per_sell, coin_qty)
+                order_krw = order_coin * price
+
+                if order_coin <= 0 or order_krw < min_order_krw:
+                    sells_skipped += 1
+                    trades.append(
+                        SignalTrade(
+                            trade_id=trade_id,
+                            side="sell",
+                            signal_type=str(sig.get("signal_type", "sell")),
+                            marker_id=sig.get("marker_id"),
+                            datetime=sig["datetime"],
+                            price=price,
+                            cash_before=round(cash_before, 0),
+                            cash_after=round(cash, 0),
+                            coin_before=round(coin_before, 8),
+                            coin_after=round(coin_qty, 8),
+                            order_krw=0.0,
+                            order_coin=0.0,
+                            fee_krw=0.0,
+                            cluster_size=cluster_size,
+                            skipped=True,
+                            skip_reason="코인 부족",
+                        )
+                    )
+                    continue
+
+                gross = order_coin * price
+                fee = gross * fee_rate
+                cash += gross - fee
+                coin_qty -= order_coin
+                sells_executed += 1
+                trades.append(
+                    SignalTrade(
+                        trade_id=trade_id,
+                        side="sell",
+                        signal_type=str(sig.get("signal_type", "sell")),
+                        marker_id=sig.get("marker_id"),
+                        datetime=sig["datetime"],
+                        price=price,
+                        cash_before=round(cash_before, 0),
+                        cash_after=round(cash, 0),
+                        coin_before=round(coin_before, 8),
+                        coin_after=round(coin_qty, 8),
+                        order_krw=round(gross, 0),
+                        order_coin=round(order_coin, 8),
+                        fee_krw=round(fee, 0),
+                        cluster_size=cluster_size,
+                        skipped=False,
+                    )
+                )
+
+    coin_value = coin_qty * mark_price
+    final_equity = cash + coin_value
+    total_pnl = final_equity - initial_cash_krw
+    total_return_pct = total_pnl / initial_cash_krw * 100.0
+
+    return {
+        "mode": "signal_split",
+        "initial_cash_krw": initial_cash_krw,
+        "final_cash_krw": round(cash, 0),
+        "final_coin_qty": round(coin_qty, 8),
+        "final_mark_price": round(mark_price, 2),
+        "final_coin_value_krw": round(coin_value, 0),
+        "final_equity_krw": round(final_equity, 0),
+        "total_pnl_krw": round(total_pnl, 0),
+        "total_return_pct": round(total_return_pct, 2),
+        "fee_rate": fee_rate,
+        **buy_sizing_metadata(),
+        "sim_lookback_days": sim_lookback_days,
+        "period_from": start_str,
+        "period_to": end_dt.strftime("%Y-%m-%d %H:%M:%S"),
+        "signals_in_period": len(period_signals),
+        "buys_executed": buys_executed,
+        "sells_executed": sells_executed,
+        "buys_skipped": buys_skipped,
+        "sells_skipped": sells_skipped,
+        "trades": [asdict(t) for t in trades],
+    }
+
+
+def _empty_signal_pnl(
+    initial_cash_krw: float,
+    fee_rate: float,
+    sim_lookback_days: int,
+    period_from: str | None = None,
+    period_to: str | None = None,
+) -> dict[str, Any]:
+    """신호가 없을 때의 빈 시뮬 결과."""
+    return {
+        "mode": "signal_split",
+        "initial_cash_krw": initial_cash_krw,
+        "final_cash_krw": initial_cash_krw,
+        "final_coin_qty": 0.0,
+        "final_mark_price": 0.0,
+        "final_coin_value_krw": 0.0,
+        "final_equity_krw": initial_cash_krw,
+        "total_pnl_krw": 0.0,
+        "total_return_pct": 0.0,
+        "fee_rate": fee_rate,
+        **buy_sizing_metadata(),
+        "sim_lookback_days": sim_lookback_days,
+        "period_from": period_from,
+        "period_to": period_to,
+        "signals_in_period": 0,
+        "buys_executed": 0,
+        "sells_executed": 0,
+        "buys_skipped": 0,
+        "sells_skipped": 0,
+        "trades": [],
+    }

src/deepcoin/ground_truth/pullback.py

@@ -0,0 +1,118 @@
+"""상승 추세 중 눌림목 매수 타점 (Ground Truth 보조)."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from typing import Protocol
+
+from deepcoin.ground_truth.zigzag import Pivot
+
+
+class _LegLike(Protocol):
+    """레그 최소 인터페이스."""
+
+    buy_bar_index: int
+    sell_bar_index: int
+
+
+def find_pullback_buy_pivots(
+    df: pd.DataFrame,
+    legs: list[_LegLike],
+    min_pullback_pct: float = 1.5,
+    local_order: int = 10,
+    swing_near_bars: int = 120,
+    leg_end_margin_bars: int = 100,
+) -> list[Pivot]:
+    """각 스윙 레그 구간에서 돌파 직전 최적 눌림목 1개를 찾는다 (1단계 GT).
+
+    빨간 원 유형(9/27, 7/8 등) — 스윙 매수 후·매도 전 구간의
+    가장 깊은 되돌림 저점을 레그당 1개만 선택한다.
+
+    Args:
+        df: OHLCV DataFrame.
+        legs: ZigZag 스윙 레그.
+        min_pullback_pct: 직전 고점 대비 최소 되돌림(%).
+        local_order: 국소 저점 판별 반경(봉).
+        swing_near_bars: 스윙 매수 직후 구간 제외(봉).
+        leg_end_margin_bars: 매도 직전 구간 제외(봉).
+
+    Returns:
+        시간순 Pivot(low) 리스트.
+    """
+    if not legs or len(df) < local_order * 2 + 10:
+        return []
+
+    highs = df["high"].astype(float).values
+    lows = df["low"].astype(float).values
+    pullbacks: list[Pivot] = []
+
+    for leg in legs:
+        full_start = leg.buy_bar_index + swing_near_bars
+        full_end = leg.sell_bar_index - leg_end_margin_bars
+        if full_end <= full_start + local_order * 2:
+            continue
+
+        leg_len = leg.sell_bar_index - leg.buy_bar_index
+        pre_breakout_start = leg.sell_bar_index - int(leg_len * 0.35)
+        start = max(full_start, pre_breakout_start)
+        end = full_end
+
+        best_idx = _best_pullback_in_range(
+            df, lows, highs, leg.buy_bar_index, start, end, local_order, min_pullback_pct
+        )
+        if best_idx is None:
+            best_idx = _best_pullback_in_range(
+                df, lows, highs, leg.buy_bar_index, full_start, full_end, local_order, min_pullback_pct
+            )
+        if best_idx is None:
+            continue
+
+        pullbacks.append(
+            Pivot(
+                bar_index=best_idx,
+                side="low",
+                price=float(lows[best_idx]),
+                datetime=pd.Timestamp(df.iloc[best_idx]["datetime"]),
+            )
+        )
+
+    return pullbacks
+
+
+def _best_pullback_in_range(
+    df: pd.DataFrame,
+    lows,
+    highs,
+    buy_bar_index: int,
+    start: int,
+    end: int,
+    local_order: int,
+    min_pullback_pct: float,
+) -> int | None:
+    """구간 내 최대 되돌림 국소 저점 bar_index를 반환한다."""
+    if end <= start + local_order * 2:
+        return None
+
+    best_idx: int | None = None
+    best_price = float("inf")
+
+    for i in range(max(start, local_order), min(end, len(df) - local_order)):
+        low_val = float(lows[i])
+        window = lows[i - local_order : i + local_order + 1]
+        if low_val > float(window.min()):
+            continue
+
+        region_high = float(highs[buy_bar_index : i + 1].max())
+        if region_high <= 0:
+            continue
+
+        pullback_pct = (region_high - low_val) / region_high * 100.0
+        if pullback_pct < min_pullback_pct:
+            continue
+
+        if low_val < best_price:
+            best_price = low_val
+            best_idx = i
+
+    return best_idx

src/deepcoin/ground_truth/swing_signals.py

@@ -0,0 +1,173 @@
+"""독립 매수·매도 스윙 타점 탐지 (레그 1:1 강제 없음)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import pandas as pd
+
+from deepcoin.ground_truth.zigzag import Pivot, find_zigzag_pivots
+
+
+@dataclass(frozen=True)
+class SwingSignal:
+    """매수 또는 매도 타점."""
+
+    signal_id: int
+    side: str  # buy | sell
+    bar_index: int
+    price: float
+    datetime: pd.Timestamp
+    source: str  # minor_zigzag | local_extrema
+    swing_pct: float
+
+
+def find_swing_signals(
+    df: pd.DataFrame,
+    minor_reversal_pct: float = 2.5,
+    local_order: int = 20,
+    min_swing_pct: float = 2.0,
+    min_bars_between: int = 30,
+) -> tuple[list[SwingSignal], list[SwingSignal]]:
+    """적절한 매수·매도 타점을 독립적으로 찾는다.
+
+    - 소형 ZigZag(되돌림 2.5% 등): 눌림목·반등 고점
+    - 국소 극값: 횡보 후 돌파 전 저점(빨간 원 구간 유형) 보완
+    - 매수·매도를 1:1 레그로 묶지 않음
+
+    Args:
+        df: OHLCV DataFrame.
+        minor_reversal_pct: 소형 ZigZag 되돌림 %.
+        local_order: 국소 극값 탐지 반경(봉).
+        min_swing_pct: 국소 극값 인정 최소 변동 %.
+        min_bars_between: 동일 방향 신호 최소 간격(봉).
+
+    Returns:
+        (매수 신호 리스트, 매도 신호 리스트) — 각각 시간순.
+    """
+    minor_pivots = find_zigzag_pivots(df, reversal_pct=minor_reversal_pct)
+    local_lows = _find_local_lows(df, order=local_order, min_swing_pct=min_swing_pct)
+    local_highs = _find_local_highs(df, order=local_order, min_swing_pct=min_swing_pct)
+
+    buy_candidates = [p for p in minor_pivots if p.side == "low"]
+    sell_candidates = [p for p in minor_pivots if p.side == "high"]
+
+    buy_candidates.extend(local_lows)
+    sell_candidates.extend(local_highs)
+
+    buys = _to_signals(_dedupe_pivots(buy_candidates, min_bars_between, prefer="low"), "buy")
+    sells = _to_signals(_dedupe_pivots(sell_candidates, min_bars_between, prefer="high"), "sell")
+
+    return buys, sells
+
+
+def _find_local_lows(
+    df: pd.DataFrame,
+    order: int,
+    min_swing_pct: float,
+) -> list[Pivot]:
+    """최근 고점 대비 되돌림 후 형성된 국소 저점."""
+    pivots: list[Pivot] = []
+    lookback = order * 4
+
+    for i in range(order, len(df) - order):
+        low_val = float(df.iloc[i]["low"])
+        window = df["low"].iloc[i - order : i + order + 1]
+        if low_val > window.min():
+            continue
+
+        start = max(0, i - lookback)
+        recent_high = float(df["high"].iloc[start : i + 1].max())
+        if recent_high <= 0:
+            continue
+        drop_pct = (recent_high - low_val) / recent_high * 100.0
+        if drop_pct < min_swing_pct:
+            continue
+
+        pivots.append(
+            Pivot(
+                bar_index=i,
+                side="low",
+                price=low_val,
+                datetime=pd.Timestamp(df.iloc[i]["datetime"]),
+            )
+        )
+    return pivots
+
+
+def _find_local_highs(
+    df: pd.DataFrame,
+    order: int,
+    min_swing_pct: float,
+) -> list[Pivot]:
+    """최근 저점 대비 반등 후 형성된 국소 고점."""
+    pivots: list[Pivot] = []
+    lookback = order * 4
+
+    for i in range(order, len(df) - order):
+        high_val = float(df.iloc[i]["high"])
+        window = df["high"].iloc[i - order : i + order + 1]
+        if high_val < window.max():
+            continue
+
+        start = max(0, i - lookback)
+        recent_low = float(df["low"].iloc[start : i + 1].min())
+        if recent_low <= 0:
+            continue
+        rise_pct = (high_val - recent_low) / recent_low * 100.0
+        if rise_pct < min_swing_pct:
+            continue
+
+        pivots.append(
+            Pivot(
+                bar_index=i,
+                side="high",
+                price=high_val,
+                datetime=pd.Timestamp(df.iloc[i]["datetime"]),
+            )
+        )
+    return pivots
+
+
+def _dedupe_pivots(
+    pivots: list[Pivot],
+    min_bars: int,
+    prefer: str,
+) -> list[Pivot]:
+    """가까운 동일 방향 피벗을 병합한다."""
+    if not pivots:
+        return []
+
+    sorted_pivots = sorted(pivots, key=lambda p: p.bar_index)
+    merged: list[Pivot] = [sorted_pivots[0]]
+
+    for pivot in sorted_pivots[1:]:
+        last = merged[-1]
+        if pivot.bar_index - last.bar_index < min_bars:
+            if prefer == "low" and pivot.price < last.price:
+                merged[-1] = pivot
+            elif prefer == "high" and pivot.price > last.price:
+                merged[-1] = pivot
+        else:
+            merged.append(pivot)
+
+    return merged
+
+
+def _to_signals(pivots: list[Pivot], side: str) -> list[SwingSignal]:
+    """Pivot 리스트를 SwingSignal로 변환한다."""
+    source = "local_extrema"
+    signals: list[SwingSignal] = []
+    for idx, pivot in enumerate(pivots, start=1):
+        signals.append(
+            SwingSignal(
+                signal_id=idx,
+                side=side,
+                bar_index=pivot.bar_index,
+                price=round(pivot.price, 2),
+                datetime=pivot.datetime,
+                source=source,
+                swing_pct=0.0,
+            )
+        )
+    return signals

src/deepcoin/ground_truth/zigzag.py

@@ -0,0 +1,120 @@
+"""ZigZag 스윙 피벗 탐지 (Ground Truth용, 미래 데이터 허용)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import pandas as pd
+
+
+@dataclass(frozen=True)
+class Pivot:
+    """스윙 고점/저점."""
+
+    bar_index: int
+    side: str  # "low" | "high"
+    price: float
+    datetime: pd.Timestamp
+
+
+def find_zigzag_pivots(
+    df: pd.DataFrame,
+    reversal_pct: float = 5.0,
+) -> list[Pivot]:
+    """ZigZag 알고리즘으로 교대 스윙 피벗을 찾는다.
+
+    가격이 직전 극값 대비 `reversal_pct`% 이상 되돌림되면 피벗을 확정한다.
+    Ground Truth 라벨링용이므로 전체 시계열(미래 포함)을 사용한다.
+
+    Args:
+        df: datetime, high, low, close 컬럼 DataFrame.
+        reversal_pct: 피벗 확정 최소 되돌림 비율(%).
+
+    Returns:
+        시간순 피벗 리스트 (low/high 교대).
+    """
+    if len(df) < 3:
+        return []
+
+    threshold = reversal_pct / 100.0
+    pivots: list[Pivot] = []
+
+    direction: str | None = None
+    extreme_idx = 0
+    extreme_price = float(df.iloc[0]["close"])
+    anchor_price = extreme_price
+
+    for i in range(1, len(df)):
+        high = float(df.iloc[i]["high"])
+        low = float(df.iloc[i]["low"])
+
+        if direction is None:
+            if high >= anchor_price * (1 + threshold):
+                direction = "up"
+                extreme_idx = i
+                extreme_price = high
+            elif low <= anchor_price * (1 - threshold):
+                direction = "down"
+                extreme_idx = i
+                extreme_price = low
+            continue
+
+        if direction == "up":
+            if high >= extreme_price:
+                extreme_price = high
+                extreme_idx = i
+            if low <= extreme_price * (1 - threshold):
+                pivots.append(_make_pivot(df, extreme_idx, "high", extreme_price))
+                direction = "down"
+                extreme_idx = i
+                extreme_price = low
+                anchor_price = extreme_price
+        else:
+            if low <= extreme_price:
+                extreme_price = low
+                extreme_idx = i
+            if high >= extreme_price * (1 + threshold):
+                pivots.append(_make_pivot(df, extreme_idx, "low", extreme_price))
+                direction = "up"
+                extreme_idx = i
+                extreme_price = high
+                anchor_price = extreme_price
+
+    # 마지막 미확정 극값도 피벗으로 추가 (방향에 따라)
+    if direction == "up":
+        pivots.append(_make_pivot(df, extreme_idx, "high", extreme_price))
+    elif direction == "down":
+        pivots.append(_make_pivot(df, extreme_idx, "low", extreme_price))
+
+    return _normalize_alternating(pivots)
+
+
+def _make_pivot(df: pd.DataFrame, idx: int, side: str, price: float) -> Pivot:
+    """Pivot 객체를 생성한다."""
+    return Pivot(
+        bar_index=idx,
+        side=side,
+        price=price,
+        datetime=pd.Timestamp(df.iloc[idx]["datetime"]),
+    )
+
+
+def _normalize_alternating(pivots: list[Pivot]) -> list[Pivot]:
+    """인접 동일 side 피벗을 제거하고 low/high 교대를 맞춘다."""
+    if not pivots:
+        return []
+
+    cleaned: list[Pivot] = [pivots[0]]
+    for pivot in pivots[1:]:
+        last = cleaned[-1]
+        if pivot.side == last.side:
+            # 같은 방향이면 더 극단적인 가격으로 갱신
+            if pivot.side == "high" and pivot.price >= last.price:
+                cleaned[-1] = pivot
+            elif pivot.side == "low" and pivot.price <= last.price:
+                cleaned[-1] = pivot
+        else:
+            cleaned.append(pivot)
+
+    # 첫 피벗이 high면 앞 low가 누락된 경우 — GT에서는 허용
+    return cleaned

src/deepcoin/mtf/__init__.py

@@ -0,0 +1,23 @@
+"""멀티 타임프레임(MTF) 인과 피처 추출."""
+
+from deepcoin.mtf.alignment import as_of_from_signal_bar, last_complete_bar_index
+from deepcoin.mtf.extractor import MtfFeatureExtractor, MtfSnapshot
+from deepcoin.mtf.filter import MtfSignalFilter, score_mtf_rules
+from deepcoin.mtf.rules import MtfRule, MtfRuleSet, derive_rules_from_report, load_mtf_rules, save_mtf_rules
+from deepcoin.mtf.store import MTF_INTERVALS, MultiTimeframeStore
+
+__all__ = [
+    "MTF_INTERVALS",
+    "MultiTimeframeStore",
+    "MtfFeatureExtractor",
+    "MtfSnapshot",
+    "MtfSignalFilter",
+    "MtfRule",
+    "MtfRuleSet",
+    "derive_rules_from_report",
+    "load_mtf_rules",
+    "save_mtf_rules",
+    "score_mtf_rules",
+    "as_of_from_signal_bar",
+    "last_complete_bar_index",
+]

src/deepcoin/mtf/alignment.py

@@ -0,0 +1,94 @@
+"""MTF 봉 정렬 — 미완성 봉 제외, look-ahead 방지."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+
+def bar_close_datetime(open_dt: pd.Timestamp, interval_min: int) -> pd.Timestamp:
+    """봉 시가 시각 기준 종가(마감) 시각을 반환한다.
+
+    Args:
+        open_dt: 봉 시가(오픈) 시각.
+        interval_min: 분 단위 인터벌 코드.
+
+    Returns:
+        봉이 완전히 마감된 시각.
+    """
+    return open_dt + pd.Timedelta(minutes=interval_min)
+
+
+def as_of_from_signal_bar(
+    signal_open: pd.Timestamp,
+    base_interval_min: int = 3,
+) -> pd.Timestamp:
+    """3분봉 신호 봉 종가 시점(판단 시각)을 반환한다.
+
+    Args:
+        signal_open: 신호가 찍힌 기준 봉(3분)의 시가 시각.
+        base_interval_min: 기준(체결) 인터벌 분.
+
+    Returns:
+        해당 봉이 마감된 시각 = signal_open + base_interval_min.
+    """
+    return bar_close_datetime(signal_open, base_interval_min)
+
+
+def is_bar_complete(
+    open_dt: pd.Timestamp,
+    interval_min: int,
+    as_of: pd.Timestamp,
+) -> bool:
+    """as_of 시점에 해당 봉이 완전히 마감되었는지 판별한다."""
+    return bar_close_datetime(open_dt, interval_min) <= as_of
+
+
+def last_complete_bar_index(
+    datetimes: pd.Series,
+    interval_min: int,
+    as_of: pd.Timestamp,
+) -> int | None:
+    """as_of 이전(포함)에 마감된 마지막 봉 인덱스를 반환한다.
+
+    Args:
+        datetimes: 봉 시가 시각 시리즈 (오름차순).
+        interval_min: 분 단위 인터벌.
+        as_of: 판단 시각 — 이 시각까지 마감된 봉만 사용.
+
+    Returns:
+        마지막 완성 봉 인덱스. 없으면 None.
+    """
+    if datetimes.empty:
+        return None
+
+    close_times = datetimes + pd.to_timedelta(interval_min, unit="m")
+    valid = close_times <= as_of
+    if not valid.any():
+        return None
+    return int(valid.to_numpy().nonzero()[0][-1])
+
+
+def resolve_bar_index(
+    datetimes: pd.Series,
+    interval_min: int,
+    base_interval_min: int,
+    signal_open: pd.Timestamp,
+    as_of: pd.Timestamp,
+) -> int | None:
+    """기준 TF는 신호 봉, 상위 TF는 마감 완료 봉 인덱스를 반환한다.
+
+    Args:
+        datetimes: 해당 TF 봉 시가 시각.
+        interval_min: 조회 TF 분 코드.
+        base_interval_min: 체결 기준 TF (3분).
+        signal_open: GT/신호 3분봉 시가 시각.
+        as_of: 판단 시각(3분봉 종가).
+
+    Returns:
+        피처 추출에 사용할 bar index.
+    """
+    if interval_min == base_interval_min:
+        matches = datetimes[datetimes == signal_open]
+        if not matches.empty:
+            return int(matches.index[0])
+    return last_complete_bar_index(datetimes, interval_min, as_of)

src/deepcoin/mtf/extractor.py

@@ -0,0 +1,114 @@
+"""시점별 멀티 TF 피처 스냅샷 추출."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+import pandas as pd
+
+from deepcoin.mtf.alignment import as_of_from_signal_bar, resolve_bar_index
+from deepcoin.mtf.features import snapshot_at_index
+from deepcoin.mtf.store import MTF_INTERVALS, MultiTimeframeStore
+
+
+@dataclass
+class MtfSnapshot:
+    """단일 판단 시점의 멀티 TF 피처 묶음."""
+
+    signal_datetime: str
+    as_of: str
+    base_interval_min: int
+    timeframes: dict[str, dict[str, Any]] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        """JSON 직렬화용 dict."""
+        return {
+            "signal_datetime": self.signal_datetime,
+            "as_of": self.as_of,
+            "base_interval_min": self.base_interval_min,
+            "timeframes": self.timeframes,
+        }
+
+
+class MtfFeatureExtractor:
+    """GT/신호 시각에서 인과 MTF 피처 스냅샷을 추출한다."""
+
+    def __init__(
+        self,
+        store: MultiTimeframeStore,
+        base_interval_min: int = 3,
+        intervals: tuple[int, ...] | None = None,
+    ) -> None:
+        """Extractor를 생성한다.
+
+        Args:
+            store: 로드된 MultiTimeframeStore.
+            base_interval_min: 체결·신호 기준 TF (3분).
+            intervals: 추출 TF 목록. None이면 store.intervals.
+        """
+        self.store = store
+        self.base_interval_min = base_interval_min
+        self.intervals = intervals or store.intervals
+        store.load()
+
+    def extract_at(self, signal_datetime: str | pd.Timestamp) -> MtfSnapshot | None:
+        """신호 시각(3분봉 시가)에서 MTF 스냅샷을 추출한다.
+
+        미래 데이터·미완성 상위 TF 봉은 사용하지 않는다.
+
+        Args:
+            signal_datetime: GT signals.datetime (3분봉 시가).
+
+        Returns:
+            MtfSnapshot. 기준 3분봉이 없으면 None.
+        """
+        signal_open = pd.Timestamp(signal_datetime)
+        as_of = as_of_from_signal_bar(signal_open, self.base_interval_min)
+        signal_str = signal_open.strftime("%Y-%m-%d %H:%M:%S")
+        as_of_str = as_of.strftime("%Y-%m-%d %H:%M:%S")
+
+        base_df = self.store.get_raw(self.base_interval_min)
+        if signal_open not in base_df["datetime"].values:
+            return None
+
+        timeframes: dict[str, dict[str, Any]] = {}
+
+        for interval_min in self.intervals:
+            label = self.store.interval_label(interval_min)
+            feat_df = self.store.get_features(interval_min)
+            bar_idx = resolve_bar_index(
+                feat_df["datetime"],
+                interval_min,
+                self.base_interval_min,
+                signal_open,
+                as_of,
+            )
+            if bar_idx is None:
+                timeframes[label] = {"interval_min": interval_min, "available": False}
+                continue
+
+            snap = snapshot_at_index(feat_df, bar_idx)
+            snap["interval_min"] = interval_min
+            snap["interval_label"] = label
+            snap["available"] = True
+            timeframes[label] = snap
+
+        return MtfSnapshot(
+            signal_datetime=signal_str,
+            as_of=as_of_str,
+            base_interval_min=self.base_interval_min,
+            timeframes=timeframes,
+        )
+
+    def extract_many(
+        self,
+        datetimes: list[str],
+    ) -> list[MtfSnapshot]:
+        """여러 시각의 스냅샷을 순서대로 추출한다."""
+        results: list[MtfSnapshot] = []
+        for dt in datetimes:
+            snap = self.extract_at(dt)
+            if snap is not None:
+                results.append(snap)
+        return results

src/deepcoin/mtf/features.py

@@ -0,0 +1,184 @@
+"""TF별 기술적 피처 계산 (인과, 봉 인덱스 기준)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+
+from deepcoin.techniques.indicators import atr, bollinger_bands, ema, macd, rsi
+
+FEATURE_NAMES: tuple[str, ...] = (
+    "close",
+    "ema60",
+    "close_vs_ema60_pct",
+    "ema60_slope_5_pct",
+    "rsi14",
+    "macd_hist",
+    "bb_position",
+    "atr_pct",
+    "zigzag_direction",
+    "zigzag_leg_pct",
+    "trend_bias",
+)
+
+
+def compute_feature_frame(
+    df: pd.DataFrame,
+    reversal_pct: float = 5.0,
+) -> pd.DataFrame:
+    """OHLCV에 MTF 분석용 피처 컬럼을 추가한다.
+
+    Args:
+        df: datetime, open, high, low, close, volume.
+        reversal_pct: 인과 ZigZag 되돌림 %.
+
+    Returns:
+        피처 컬럼이 추가된 DataFrame (원본 컬럼 유지).
+    """
+    out = df.copy()
+    close = out["close"].astype(float)
+    high = out["high"].astype(float)
+    low = out["low"].astype(float)
+
+    ema60 = ema(close, 60)
+    out["ema60"] = ema60
+    out["close_vs_ema60_pct"] = (close - ema60) / ema60.replace(0, pd.NA) * 100.0
+    ema_shift = ema60.shift(5)
+    out["ema60_slope_5_pct"] = (ema60 - ema_shift) / ema_shift.replace(0, pd.NA) * 100.0
+
+    out["rsi14"] = rsi(close, 14)
+    _, _, macd_hist = macd(close)
+    out["macd_hist"] = macd_hist
+
+    bb_mid, bb_upper, bb_lower = bollinger_bands(close, 20, 2.0)
+    band_width = (bb_upper - bb_lower).replace(0, pd.NA)
+    out["bb_position"] = (close - bb_lower) / band_width
+
+    atr14 = atr(high, low, close, 14)
+    out["atr_pct"] = atr14 / close.replace(0, pd.NA) * 100.0
+
+    zz_dir, zz_leg = _compute_zigzag_series(out, reversal_pct)
+    out["zigzag_direction"] = zz_dir
+    out["zigzag_leg_pct"] = zz_leg
+
+    out["trend_bias"] = pd.Series(
+        ["bullish" if c > e else "bearish" for c, e in zip(close, ema60, strict=False)],
+        index=out.index,
+        dtype="object",
+    )
+    out.loc[ema60.isna(), "trend_bias"] = pd.NA
+
+    return out
+
+
+def _compute_zigzag_series(
+    df: pd.DataFrame,
+    reversal_pct: float,
+) -> tuple[pd.Series, pd.Series]:
+    """각 봉 시점의 인과 ZigZag 방향·leg %를 계산한다.
+
+    Args:
+        df: OHLCV DataFrame.
+        reversal_pct: 피벗 확정 되돌림 %.
+
+    Returns:
+        (direction_series, leg_pct_series). direction은 up/down/none.
+    """
+    n = len(df)
+    directions = ["none"] * n
+    leg_pcts: list[float | None] = [None] * n
+
+    if n < 2:
+        return pd.Series(directions, index=df.index), pd.Series(leg_pcts, index=df.index)
+
+    threshold = reversal_pct / 100.0
+    direction: str | None = None
+    extreme_idx = 0
+    extreme_price = float(df.iloc[0]["close"])
+    pivot_price = extreme_price
+
+    for i in range(n):
+        high = float(df.iloc[i]["high"])
+        low = float(df.iloc[i]["low"])
+
+        if direction is None:
+            if high >= extreme_price * (1 + threshold):
+                direction = "up"
+                extreme_idx = i
+                extreme_price = high
+                pivot_price = extreme_price
+            elif low <= extreme_price * (1 - threshold):
+                direction = "down"
+                extreme_idx = i
+                extreme_price = low
+                pivot_price = extreme_price
+        elif direction == "up":
+            if high >= extreme_price:
+                extreme_price = high
+                extreme_idx = i
+            if low <= extreme_price * (1 - threshold):
+                pivot_price = extreme_price
+                direction = "down"
+                extreme_idx = i
+                extreme_price = low
+        else:
+            if low <= extreme_price:
+                extreme_price = low
+                extreme_idx = i
+            if high >= extreme_price * (1 + threshold):
+                pivot_price = extreme_price
+                direction = "up"
+                extreme_idx = i
+                extreme_price = high
+
+        directions[i] = direction or "none"
+        ref = pivot_price if pivot_price else float(df.iloc[i]["close"])
+        price = float(df.iloc[i]["close"])
+        if ref:
+            leg_pcts[i] = round((price - ref) / ref * 100.0, 4)
+
+    return pd.Series(directions, index=df.index), pd.Series(leg_pcts, index=df.index)
+
+
+def snapshot_at_index(feat_df: pd.DataFrame, bar_index: int) -> dict[str, Any]:
+    """단일 TF·단일 bar_index의 피처 dict를 반환한다.
+
+    Args:
+        feat_df: compute_feature_frame 출력.
+        bar_index: 행 인덱스 (0-based).
+
+    Returns:
+        FEATURE_NAMES 키를 포함한 피처 dict. bar 메타 포함.
+    """
+    if bar_index < 0 or bar_index >= len(feat_df):
+        return {}
+
+    row = feat_df.iloc[bar_index]
+    dt = pd.Timestamp(row["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+
+    def _num(key: str) -> float | None:
+        val = row.get(key)
+        if val is None or pd.isna(val):
+            return None
+        return round(float(val), 4)
+
+    return {
+        "bar_index": bar_index,
+        "bar_datetime": dt,
+        "close": _num("close"),
+        "ema60": _num("ema60"),
+        "close_vs_ema60_pct": _num("close_vs_ema60_pct"),
+        "ema60_slope_5_pct": _num("ema60_slope_5_pct"),
+        "rsi14": _num("rsi14"),
+        "macd_hist": _num("macd_hist"),
+        "bb_position": _num("bb_position"),
+        "atr_pct": _num("atr_pct"),
+        "zigzag_direction": str(row.get("zigzag_direction", "none")),
+        "zigzag_leg_pct": _num("zigzag_leg_pct"),
+        "trend_bias": (
+            None
+            if pd.isna(row.get("trend_bias"))
+            else str(row.get("trend_bias"))
+        ),
+    }

src/deepcoin/mtf/filter.py

@@ -0,0 +1,161 @@
+"""MTF 규칙 기반 composite 신호 필터."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from deepcoin.mtf.trend_gate import HtfTrendGate
+from deepcoin.mtf.extractor import MtfFeatureExtractor, MtfSnapshot
+from deepcoin.mtf.rules import MtfRule, MtfRuleSet
+
+
+def evaluate_rule(rule: MtfRule, snapshot: MtfSnapshot) -> bool | None:
+    """단일 규칙을 스냅샷에 적용한다.
+
+    Args:
+        rule: MTF 규칙.
+        snapshot: 시점 MTF 스냅샷.
+
+    Returns:
+        True/False 또는 피처 없음 시 None.
+    """
+    tf = snapshot.timeframes.get(rule.timeframe_label, {})
+    if not tf.get("available"):
+        return None
+
+    value = tf.get(rule.feature)
+    if value is None:
+        return None
+
+    numeric = float(value)
+    if rule.operator == "<=":
+        return numeric <= rule.threshold
+    return numeric >= rule.threshold
+
+
+def score_mtf_rules(
+    signal_type: str,
+    snapshot: MtfSnapshot,
+    rule_set: MtfRuleSet,
+) -> dict[str, Any]:
+    """신호 유형 규칙 충족 점수를 계산한다.
+
+    Returns:
+        passed, total_evaluated, total_rules, details 키를 가진 dict.
+    """
+    rules = rule_set.rules_for(signal_type)
+    if not rules:
+        return {
+            "passed": True,
+            "total_evaluated": 0,
+            "total_rules": 0,
+            "required_pass": rule_set.min_rules_pass,
+            "details": [],
+            "reason": "no_rules_for_type",
+        }
+
+    details: list[dict[str, Any]] = []
+    passed = 0
+    evaluated = 0
+
+    for rule in rules:
+        result = evaluate_rule(rule, snapshot)
+        detail = {
+            "timeframe": rule.timeframe_label,
+            "feature": rule.feature,
+            "operator": rule.operator,
+            "threshold": rule.threshold,
+            "result": result,
+        }
+        details.append(detail)
+        if result is None:
+            continue
+        evaluated += 1
+        if result:
+            passed += 1
+
+    required = min(rule_set.min_rules_pass, len(rules))
+    if evaluated == 0:
+        ok = True
+        reason = "no_features_available"
+    elif evaluated < required:
+        ok = passed == evaluated
+        reason = "partial_eval_all_pass"
+    else:
+        ok = passed >= required
+        reason = "score_ok" if ok else "score_below_min"
+
+    return {
+        "passed": ok,
+        "passed_count": passed,
+        "total_evaluated": evaluated,
+        "total_rules": len(rules),
+        "required_pass": required,
+        "details": details,
+        "reason": reason,
+    }
+
+
+class MtfSignalFilter:
+    """composite_v3 신호에 MTF 규칙 필터를 적용한다."""
+
+    def __init__(
+        self,
+        extractor: MtfFeatureExtractor,
+        rule_set: MtfRuleSet,
+        trend_gate: HtfTrendGate | None = None,
+    ) -> None:
+        """Filter 생성."""
+        self.extractor = extractor
+        self.rule_set = rule_set
+        self.trend_gate = trend_gate
+        self._cache: dict[str, MtfSnapshot | None] = {}
+
+    def _snapshot(self, signal_datetime: str) -> MtfSnapshot | None:
+        """스냅샷 캐시 조회."""
+        if signal_datetime not in self._cache:
+            self._cache[signal_datetime] = self.extractor.extract_at(signal_datetime)
+        return self._cache[signal_datetime]
+
+    def passes(self, signal: dict[str, Any]) -> tuple[bool, dict[str, Any]]:
+        """단일 GT 스키마 신호가 MTF 규칙을 통과하는지 판별."""
+        signal_type = str(signal.get("signal_type", ""))
+        snap = self._snapshot(str(signal["datetime"]))
+        if snap is None:
+            return False, {"reason": "snapshot_missing", "passed": False}
+
+        if self.trend_gate and self.trend_gate.enabled:
+            snap_dict = snap.to_dict()
+            side = str(signal.get("side", ""))
+            if side == "buy":
+                ok_gate, gate_reason = self.trend_gate.allows_buy(snap_dict)
+            else:
+                ok_gate, gate_reason = self.trend_gate.allows_sell(snap_dict)
+            if not ok_gate:
+                return False, {"reason": "htf_gate_blocked", "gate": gate_reason, "passed": False}
+
+        score = score_mtf_rules(signal_type, snap, self.rule_set)
+        return bool(score["passed"]), score
+
+    def filter_signals(
+        self,
+        signals: list[dict[str, Any]],
+    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
+        """신호를 kept / rejected 로 분리한다."""
+        kept: list[dict[str, Any]] = []
+        rejected: list[dict[str, Any]] = []
+
+        for sig in sorted(signals, key=lambda s: s["bar_index"]):
+            ok, meta = self.passes(sig)
+            sig_copy = dict(sig)
+            sig_copy["mtf_filter"] = meta
+            if ok:
+                kept.append(sig_copy)
+            else:
+                rejected.append(sig_copy)
+
+        for idx, sig in enumerate(kept, start=1):
+            sig["marker_id"] = idx
+            sig["leg_id"] = idx
+
+        return kept, rejected

src/deepcoin/mtf/precompute.py

@@ -0,0 +1,106 @@
+"""3분봉 타임라인 ↔ 각 TF bar index 사전 정렬 (벡터화)."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from deepcoin.mtf.alignment import as_of_from_signal_bar
+from deepcoin.mtf.features import snapshot_at_index
+from deepcoin.mtf.store import MultiTimeframeStore
+
+
+def _vectorized_tf_indices(
+    base_opens: pd.Series,
+    tf_opens: pd.Series,
+    base_interval_min: int,
+    tf_interval_min: int,
+) -> np.ndarray:
+    """3m 각 봉에 대한 TF bar index 배열을 벡터화 계산한다."""
+    n = len(base_opens)
+    result = np.full(n, -1, dtype=np.int64)
+
+    if tf_opens.empty:
+        return result
+
+    as_ofs = base_opens + pd.to_timedelta(base_interval_min, unit="m")
+    tf_closes = tf_opens + pd.to_timedelta(tf_interval_min, unit="m")
+    close_vals = tf_closes.to_numpy(dtype="datetime64[ns]")
+    as_of_vals = as_ofs.to_numpy(dtype="datetime64[ns]")
+    open_vals = tf_opens.to_numpy(dtype="datetime64[ns]")
+    base_open_vals = base_opens.to_numpy(dtype="datetime64[ns]")
+
+    if tf_interval_min == base_interval_min:
+        pos = np.searchsorted(open_vals, base_open_vals)
+        valid = (pos < len(open_vals)) & (open_vals[pos] == base_open_vals)
+        result[valid] = pos[valid]
+        return result
+
+    pos = np.searchsorted(close_vals, as_of_vals, side="right") - 1
+    valid = pos >= 0
+    result[valid] = pos[valid]
+    return result
+
+
+def build_3m_to_tf_bar_indices(
+    base_df: pd.DataFrame,
+    store: MultiTimeframeStore,
+    base_interval_min: int = 3,
+) -> dict[int, np.ndarray]:
+    """각 3m bar index에 대응하는 TF별 feature bar index를 계산한다.
+
+    Args:
+        base_df: 3m OHLCV (store.base_df와 동일 타임라인).
+        store: 로드된 MTF store.
+        base_interval_min: 기준 TF 분.
+
+    Returns:
+        interval_min → int64 ndarray (-1 = 미사용).
+    """
+    store.load()
+    base_opens = base_df["datetime"]
+    mapping: dict[int, np.ndarray] = {}
+
+    for interval_min in store.intervals:
+        feat_df = store.get_features(interval_min)
+        mapping[interval_min] = _vectorized_tf_indices(
+            base_opens,
+            feat_df["datetime"],
+            base_interval_min,
+            interval_min,
+        )
+
+    return mapping
+
+
+def snapshot_at_3m_bar(
+    bar_index: int,
+    store: MultiTimeframeStore,
+    tf_bar_indices: dict[int, np.ndarray],
+    base_interval_min: int = 3,
+) -> dict:
+    """사전 계산된 인덱스로 MTF 스냅샷 dict를 빠르게 조립한다."""
+    base_df = store.base_df
+    signal_open = pd.Timestamp(base_df.iloc[bar_index]["datetime"])
+    as_of = as_of_from_signal_bar(signal_open, base_interval_min)
+
+    timeframes: dict[str, dict] = {}
+    for interval_min in store.intervals:
+        label = store.interval_label(interval_min)
+        tf_idx = int(tf_bar_indices[interval_min][bar_index])
+        if tf_idx < 0:
+            timeframes[label] = {"interval_min": interval_min, "available": False}
+            continue
+        feat_df = store.get_features(interval_min)
+        snap = snapshot_at_index(feat_df, tf_idx)
+        snap["interval_min"] = interval_min
+        snap["interval_label"] = label
+        snap["available"] = True
+        timeframes[label] = snap
+
+    return {
+        "signal_datetime": signal_open.strftime("%Y-%m-%d %H:%M:%S"),
+        "as_of": as_of.strftime("%Y-%m-%d %H:%M:%S"),
+        "base_interval_min": base_interval_min,
+        "timeframes": timeframes,
+    }

src/deepcoin/mtf/rules.py

@@ -0,0 +1,255 @@
+"""MTF 상관 리포트 기반 규칙 정의·도출."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Literal
+
+from deepcoin.evaluation.gt_align import GT_SIGNAL_TYPES
+
+Operator = Literal["<=", ">="]
+
+# BTC 가격 스케일에 민감한 지표는 자동 규칙에서 제외
+_EXCLUDED_AUTO_FEATURES: frozenset[str] = frozenset({"macd_hist", "zigzag_leg_pct", "close"})
+
+# 자동 규칙에 사용할 안정 피처
+_PREFERRED_FEATURES: tuple[str, ...] = (
+    "close_vs_ema60_pct",
+    "ema60_slope_5_pct",
+    "rsi14",
+    "bb_position",
+    "atr_pct",
+)
+
+
+@dataclass(frozen=True)
+class MtfRule:
+    """단일 MTF 조건 (신호 유형별)."""
+
+    signal_type: str
+    timeframe_label: str
+    interval_min: int
+    feature: str
+    operator: Operator
+    threshold: float
+    cohens_d: float
+    positive_mean: float
+    negative_mean: float
+
+    def to_dict(self) -> dict[str, Any]:
+        """JSON 직렬화 dict."""
+        return {
+            "signal_type": self.signal_type,
+            "timeframe_label": self.timeframe_label,
+            "interval_min": self.interval_min,
+            "feature": self.feature,
+            "operator": self.operator,
+            "threshold": self.threshold,
+            "cohens_d": self.cohens_d,
+            "positive_mean": self.positive_mean,
+            "negative_mean": self.negative_mean,
+        }
+
+    @classmethod
+    def from_dict(cls, raw: dict[str, Any]) -> MtfRule:
+        """dict에서 MtfRule 생성."""
+        return cls(
+            signal_type=str(raw["signal_type"]),
+            timeframe_label=str(raw["timeframe_label"]),
+            interval_min=int(raw["interval_min"]),
+            feature=str(raw["feature"]),
+            operator=raw["operator"],  # type: ignore[arg-type]
+            threshold=float(raw["threshold"]),
+            cohens_d=float(raw.get("cohens_d", 0.0)),
+            positive_mean=float(raw.get("positive_mean", 0.0)),
+            negative_mean=float(raw.get("negative_mean", 0.0)),
+        )
+
+
+@dataclass
+class MtfRuleSet:
+    """신호 유형별 MTF 규칙 묶음."""
+
+    version: str = "v1"
+    min_rules_pass: int = 2
+    min_cohens_d: float = 1.2
+    max_rules_per_type: int = 4
+    rules_by_type: dict[str, list[MtfRule]] = field(default_factory=dict)
+    source_report: str = ""
+
+    def rules_for(self, signal_type: str) -> list[MtfRule]:
+        """신호 유형에 해당하는 규칙 목록."""
+        return self.rules_by_type.get(signal_type, [])
+
+    def to_dict(self) -> dict[str, Any]:
+        """JSON 직렬화 dict."""
+        return {
+            "version": self.version,
+            "min_rules_pass": self.min_rules_pass,
+            "min_cohens_d": self.min_cohens_d,
+            "max_rules_per_type": self.max_rules_per_type,
+            "source_report": self.source_report,
+            "rules_by_type": {
+                st: [r.to_dict() for r in rules]
+                for st, rules in self.rules_by_type.items()
+            },
+        }
+
+    @classmethod
+    def from_dict(cls, raw: dict[str, Any]) -> MtfRuleSet:
+        """dict에서 MtfRuleSet 생성."""
+        rules_by_type: dict[str, list[MtfRule]] = {}
+        for st, items in (raw.get("rules_by_type") or {}).items():
+            rules_by_type[st] = [MtfRule.from_dict(item) for item in items]
+        return cls(
+            version=str(raw.get("version", "v1")),
+            min_rules_pass=int(raw.get("min_rules_pass", 2)),
+            min_cohens_d=float(raw.get("min_cohens_d", 1.2)),
+            max_rules_per_type=int(raw.get("max_rules_per_type", 4)),
+            rules_by_type=rules_by_type,
+            source_report=str(raw.get("source_report", "")),
+        )
+
+
+def _label_to_interval(label: str, tf_list: list[dict[str, Any]]) -> int:
+    """TF 라벨 → interval_min."""
+    for item in tf_list:
+        if item.get("label") == label:
+            return int(item["interval_min"])
+    raise KeyError(f"unknown timeframe label: {label}")
+
+
+def derive_rules_from_report(
+    report: dict[str, Any],
+    min_cohens_d: float = 1.2,
+    max_rules_per_type: int = 4,
+    min_rules_pass: int = 2,
+    preferred_features: tuple[str, ...] = _PREFERRED_FEATURES,
+) -> MtfRuleSet:
+    """MTF 상관 리포트에서 신호 유형별 규칙 후보를 도출한다.
+
+    임계값은 GT(양성) 평균과 음성 평균의 중간값으로 설정한다.
+
+    Args:
+        report: build_mtf_correlation_report 출력 JSON.
+        min_cohens_d: |Cohen's d| 최소값.
+        max_rules_per_type: 유형당 최대 규칙 수.
+        min_rules_pass: 필터 통과에 필요한 최소 충족 규칙 수.
+        preferred_features: 우선 사용 피처.
+
+    Returns:
+        MtfRuleSet.
+    """
+    analysis = report.get("analysis", {})
+    tf_list = analysis.get("timeframes") or []
+    by_type = report.get("by_signal_type") or {}
+
+    rule_set = MtfRuleSet(
+        min_rules_pass=min_rules_pass,
+        min_cohens_d=min_cohens_d,
+        max_rules_per_type=max_rules_per_type,
+        source_report=str(report.get("generated_at", "")),
+    )
+
+    for signal_type in GT_SIGNAL_TYPES:
+        block = by_type.get(signal_type)
+        if not block:
+            continue
+
+        candidates: list[tuple[float, MtfRule]] = []
+
+        for tf_label, tf_data in (block.get("timeframes") or {}).items():
+            interval_min = _label_to_interval(tf_label, tf_list)
+            numeric = tf_data.get("numeric") or {}
+
+            for feat_name, summary in numeric.items():
+                if feat_name in _EXCLUDED_AUTO_FEATURES:
+                    continue
+                if feat_name not in preferred_features:
+                    continue
+
+                d = summary.get("cohens_d")
+                if d is None or abs(float(d)) < min_cohens_d:
+                    continue
+
+                pos_mean = float(summary.get("positive_mean", 0.0))
+                neg_mean = float(summary.get("negative_mean", 0.0))
+                threshold = round((pos_mean + neg_mean) / 2.0, 4)
+                operator: Operator = "<=" if pos_mean < neg_mean else ">="
+
+                rule = MtfRule(
+                    signal_type=signal_type,
+                    timeframe_label=tf_label,
+                    interval_min=interval_min,
+                    feature=feat_name,
+                    operator=operator,
+                    threshold=threshold,
+                    cohens_d=float(d),
+                    positive_mean=pos_mean,
+                    negative_mean=neg_mean,
+                )
+                candidates.append((abs(float(d)), rule))
+
+        candidates.sort(key=lambda x: x[0], reverse=True)
+        seen: set[tuple[str, str]] = set()
+        picked: list[MtfRule] = []
+        for _, rule in candidates:
+            key = (rule.timeframe_label, rule.feature)
+            if key in seen:
+                continue
+            seen.add(key)
+            picked.append(rule)
+            if len(picked) >= max_rules_per_type:
+                break
+
+        if picked:
+            rule_set.rules_by_type[signal_type] = picked
+
+    return rule_set
+
+
+def save_mtf_rules(rule_set: MtfRuleSet, json_path: Path) -> Path:
+    """규칙 JSON 저장."""
+    json_path.parent.mkdir(parents=True, exist_ok=True)
+    with json_path.open("w", encoding="utf-8") as fp:
+        json.dump(rule_set.to_dict(), fp, ensure_ascii=False, indent=2)
+    return json_path
+
+
+def load_mtf_rules(json_path: Path) -> MtfRuleSet:
+    """규칙 JSON 로드."""
+    with json_path.open(encoding="utf-8") as fp:
+        return MtfRuleSet.from_dict(json.load(fp))
+
+
+def load_or_derive_mtf_rules(
+    rules_path: Path,
+    report_path: Path,
+    min_cohens_d: float = 1.2,
+    max_rules_per_type: int = 4,
+    min_rules_pass: int = 2,
+    force_derive: bool = False,
+) -> MtfRuleSet:
+    """규칙 파일이 있으면 로드, 없거나 force면 리포트에서 재도출."""
+    if rules_path.exists() and not force_derive:
+        return load_mtf_rules(rules_path)
+
+    if not report_path.exists():
+        raise FileNotFoundError(
+            f"MTF 규칙/리포트 없음: rules={rules_path}, report={report_path}. "
+            "먼저 scripts/2_run_mtf_analysis.py 실행"
+        )
+
+    with report_path.open(encoding="utf-8") as fp:
+        report = json.load(fp)
+
+    rule_set = derive_rules_from_report(
+        report,
+        min_cohens_d=min_cohens_d,
+        max_rules_per_type=max_rules_per_type,
+        min_rules_pass=min_rules_pass,
+    )
+    save_mtf_rules(rule_set, rules_path)
+    return rule_set

src/deepcoin/mtf/store.py

@@ -0,0 +1,86 @@
+"""멀티 TF 캔들 로드 및 피처 프레임 캐시."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+
+from deepcoin.data.candle_loader import load_candles
+from deepcoin.data.intervals import DEFAULT_DOWNLOAD_INTERVALS, interval_label
+from deepcoin.mtf.features import compute_feature_frame
+
+MTF_INTERVALS: tuple[int, ...] = tuple(DEFAULT_DOWNLOAD_INTERVALS)
+
+
+class MultiTimeframeStore:
+    """10개 TF 캔들·피처 프레임을 로드·캐시한다."""
+
+    def __init__(
+        self,
+        db_path: Path,
+        symbol: str,
+        intervals: tuple[int, ...] | None = None,
+        lookback_days: int | None = None,
+        zigzag_reversal_pct: float = 5.0,
+    ) -> None:
+        """Store를 초기화한다 (데이터는 load() 호출 시 로드).
+
+        Args:
+            db_path: SQLite 경로.
+            symbol: 코인 심볼.
+            intervals: 사용 TF 목록. None이면 MTF_INTERVALS.
+            lookback_days: 최근 N일만 로드. None이면 전체.
+            zigzag_reversal_pct: 인과 ZigZag 되돌림 %.
+        """
+        self.db_path = db_path
+        self.symbol = symbol.upper()
+        self.intervals = intervals or MTF_INTERVALS
+        self.lookback_days = lookback_days
+        self.zigzag_reversal_pct = zigzag_reversal_pct
+        self._raw: dict[int, pd.DataFrame] = {}
+        self._features: dict[int, pd.DataFrame] = {}
+        self._loaded = False
+
+    def load(self) -> None:
+        """모든 TF 캔들과 피처 컬럼을 로드한다."""
+        if self._loaded:
+            return
+
+        for interval_min in self.intervals:
+            df = load_candles(
+                self.db_path,
+                self.symbol,
+                interval_min,
+                lookback_days=self.lookback_days,
+            )
+            self._raw[interval_min] = df
+            self._features[interval_min] = compute_feature_frame(
+                df,
+                reversal_pct=self.zigzag_reversal_pct,
+            )
+
+        self._loaded = True
+
+    def get_raw(self, interval_min: int) -> pd.DataFrame:
+        """원본 OHLCV DataFrame을 반환한다."""
+        self.load()
+        if interval_min not in self._raw:
+            raise KeyError(f"interval {interval_min} not loaded")
+        return self._raw[interval_min]
+
+    def get_features(self, interval_min: int) -> pd.DataFrame:
+        """피처 컬럼이 추가된 DataFrame을 반환한다."""
+        self.load()
+        if interval_min not in self._features:
+            raise KeyError(f"interval {interval_min} not loaded")
+        return self._features[interval_min]
+
+    def interval_label(self, interval_min: int) -> str:
+        """인터벌 한글 라벨."""
+        return interval_label(interval_min)
+
+    @property
+    def base_df(self) -> pd.DataFrame:
+        """체결 기준 3분봉 DataFrame."""
+        return self.get_raw(3)

src/deepcoin/mtf/trend_gate.py

@@ -0,0 +1,55 @@
+"""고TF(60분·일봉) 추세 게이트 — 극단 구간 매매 억제."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+
+@dataclass(frozen=True)
+class HtfTrendGate:
+    """고TF EMA60 대비 위치로 매수·매도 허용 여부를 판단한다."""
+
+    enabled: bool = True
+    buy_block_daily_below_pct: float = -25.0
+    buy_block_60m_below_pct: float = -15.0
+    sell_block_daily_above_pct: float = 35.0
+    sell_block_60m_above_pct: float = 20.0
+
+    def _tf_value(self, snapshot: dict[str, Any], label: str, feature: str) -> float | None:
+        """스냅샷 dict에서 TF 피처 값."""
+        tf = snapshot.get("timeframes", {}).get(label, {})
+        if not tf.get("available"):
+            return None
+        val = tf.get(feature)
+        if val is None:
+            return None
+        return float(val)
+
+    def allows_buy(self, snapshot: dict[str, Any]) -> tuple[bool, str]:
+        """매수 허용 여부."""
+        if not self.enabled:
+            return True, "disabled"
+
+        daily = self._tf_value(snapshot, "일봉", "close_vs_ema60_pct")
+        h60 = self._tf_value(snapshot, "60분", "close_vs_ema60_pct")
+
+        if daily is not None and daily < self.buy_block_daily_below_pct:
+            return False, f"daily_ema60={daily:.1f}%<{self.buy_block_daily_below_pct}"
+        if h60 is not None and h60 < self.buy_block_60m_below_pct:
+            return False, f"60m_ema60={h60:.1f}%<{self.buy_block_60m_below_pct}"
+        return True, "ok"
+
+    def allows_sell(self, snapshot: dict[str, Any]) -> tuple[bool, str]:
+        """매도 허용 여부."""
+        if not self.enabled:
+            return True, "disabled"
+
+        daily = self._tf_value(snapshot, "일봉", "close_vs_ema60_pct")
+        h60 = self._tf_value(snapshot, "60분", "close_vs_ema60_pct")
+
+        if daily is not None and daily > self.sell_block_daily_above_pct:
+            return False, f"daily_ema60={daily:.1f}%>{self.sell_block_daily_above_pct}"
+        if h60 is not None and h60 > self.sell_block_60m_above_pct:
+            return False, f"60m_ema60={h60:.1f}%>{self.sell_block_60m_above_pct}"
+        return True, "ok"

src/deepcoin/techniques/__init__.py

@@ -0,0 +1,20 @@
+"""2단계: Ground Truth 정합 매매 기법."""
+
+from deepcoin.techniques.registry import (
+    get_all_techniques,
+    get_composite_techniques,
+    get_single_techniques,
+    list_technique_ids,
+    techniques_by_category,
+)
+from deepcoin.techniques.runner import run_all_techniques, run_technique
+
+__all__ = [
+    "get_all_techniques",
+    "get_single_techniques",
+    "get_composite_techniques",
+    "list_technique_ids",
+    "techniques_by_category",
+    "run_all_techniques",
+    "run_technique",
+]

src/deepcoin/techniques/adx_trend.py

@@ -0,0 +1,53 @@
+"""ADX 추세 강도 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import adx
+
+
+class AdxTrendTechnique(BaseTechnique):
+    """ADX 강세 구간 +DI/-DI 크로스 매수·매도."""
+
+    technique_id = "adx_trend"
+    technique_name = "ADX 추세"
+    category = "trend"
+    causal = True
+    description = "ADX(14) 강세 + DI 크로스 추세 신호"
+
+    def default_extra_params(self) -> dict:
+        return {"period": 14, "adx_threshold": 25.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        period = int(params.extra.get("period", 14))
+        adx_threshold = float(params.extra.get("adx_threshold", 25.0))
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        adx_line, plus_di, minus_di = adx(high, low, close, period=period)
+
+        signals: list[TechniqueSignal] = []
+
+        for i in range(period + 2, len(df)):
+            if pd.isna(adx_line.iloc[i]):
+                continue
+
+            if float(adx_line.iloc[i]) < adx_threshold:
+                continue
+
+            prev_plus = float(plus_di.iloc[i - 1])
+            prev_minus = float(minus_di.iloc[i - 1])
+            curr_plus = float(plus_di.iloc[i])
+            curr_minus = float(minus_di.iloc[i])
+            c = float(close.iloc[i])
+
+            if prev_plus <= prev_minus and curr_plus > curr_minus:
+                signals.append(make_signal(df, i, c, "buy", "adx_bull_trend", confidence=0.67))
+            elif prev_plus >= prev_minus and curr_plus < curr_minus:
+                signals.append(make_signal(df, i, c, "sell", "adx_bear_trend", confidence=0.67))
+
+        return signals

src/deepcoin/techniques/atr_channel.py

@@ -0,0 +1,64 @@
+"""ATR 채널 역추세 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import atr, ema
+
+
+class AtrChannelTechnique(BaseTechnique):
+    """EMA ± ATR 채널 터치 후 반전."""
+
+    technique_id = "atr_channel"
+    technique_name = "ATR 채널"
+    category = "volatility"
+    causal = True
+    description = "EMA(20) ± ATR(14)×2 채널 반전"
+
+    def default_extra_params(self) -> dict:
+        return {"ema_span": 20, "atr_period": 14, "atr_mult": 2.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        ema_span = int(params.extra.get("ema_span", 20))
+        atr_period = int(params.extra.get("atr_period", 14))
+        atr_mult = float(params.extra.get("atr_mult", 2.0))
+
+        close = df["close"].astype(float)
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        mid = ema(close, ema_span)
+        atr_vals = atr(high, low, close, period=atr_period)
+        upper = mid + atr_mult * atr_vals
+        lower = mid - atr_mult * atr_vals
+
+        signals: list[TechniqueSignal] = []
+        touched_lower = False
+        touched_upper = False
+        start = max(ema_span, atr_period)
+
+        for i in range(start, len(df)):
+            if pd.isna(lower.iloc[i]):
+                continue
+
+            l = float(low.iloc[i])
+            h = float(high.iloc[i])
+            c = float(close.iloc[i])
+            lo = float(lower.iloc[i])
+            u = float(upper.iloc[i])
+
+            if l <= lo:
+                touched_lower = True
+            if touched_lower and c > lo:
+                signals.append(make_signal(df, i, c, "buy", "atr_channel_lower", confidence=0.68))
+                touched_lower = False
+
+            if h >= u:
+                touched_upper = True
+            if touched_upper and c < u:
+                signals.append(make_signal(df, i, c, "sell", "atr_channel_upper", confidence=0.68))
+                touched_upper = False
+
+        return signals

src/deepcoin/techniques/base.py

@@ -0,0 +1,85 @@
+"""매매 기법 공통 인터페이스 및 결과 타입."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import asdict, dataclass, field
+from typing import Any
+
+import pandas as pd
+
+
+@dataclass
+class TechniqueSignal:
+    """기법이 생성한 매수·매도 신호."""
+
+    side: str  # buy | sell
+    bar_index: int
+    price: float
+    datetime: str
+    pivot_bar_index: int | None = None
+    confidence: float = 1.0
+    reason: str = ""
+
+    def to_dict(self) -> dict[str, Any]:
+        """JSON 직렬화용 dict."""
+        return asdict(self)
+
+
+@dataclass
+class TechniqueParams:
+    """기법 실행 공통 파라미터."""
+
+    interval_min: int
+    lookback_days: int
+    min_leg_pct: float
+    initial_cash_krw: float
+    fee_rate: float
+    extra: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class TechniqueResult:
+    """기법 실행 결과 (GT JSON과 호환 구조)."""
+
+    technique_id: str
+    technique_name: str
+    category: str
+    causal: bool
+    description: str
+    params: dict[str, Any]
+    signals: list[dict[str, Any]]
+    legs: list[dict[str, Any]]
+    summary: dict[str, Any]
+    pnl: dict[str, Any]
+    alignment: dict[str, Any] | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        """JSON 직렬화용 dict."""
+        return asdict(self)
+
+
+class BaseTechnique(ABC):
+    """Ground Truth 정합을 목표로 하는 매매 기법 베이스."""
+
+    technique_id: str
+    technique_name: str
+    category: str
+    causal: bool
+    description: str
+
+    @abstractmethod
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        """캔들 DataFrame에서 매수·매도 신호를 생성한다.
+
+        Args:
+            df: datetime, open, high, low, close, volume 컬럼.
+            params: 공통 실행 파라미터.
+
+        Returns:
+            시간순 신호 리스트.
+        """
+
+    def default_extra_params(self) -> dict[str, Any]:
+        """기법별 기본 추가 파라미터."""
+        return {}

src/deepcoin/techniques/bb_reversal.py

@@ -0,0 +1,80 @@
+"""볼린저 밴드 역추세 매매."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.indicators import bollinger_bands, ema
+
+
+class BbReversalTechnique(BaseTechnique):
+    """BB 하단 터치 후 반등 매수, 상단 터치 후 하락 매도."""
+
+    technique_id = "bb_reversal"
+    technique_name = "볼린저 역추세"
+    category = "indicator"
+    causal = True
+    description = "BB(20,2) 하단 매수·상단 매도 + EMA 추세 필터"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 20, "num_std": 2.0, "ema_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 20))
+        num_std = float(params.extra.get("num_std", 2.0))
+        ema_span = int(params.extra.get("ema_span", 60))
+
+        close = df["close"].astype(float)
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        _, upper, lower = bollinger_bands(close, window=window, num_std=num_std)
+        trend = ema(close, ema_span)
+
+        signals: list[TechniqueSignal] = []
+        in_oversold = False
+        in_overbought = False
+
+        for i in range(window, len(df)):
+            if pd.isna(lower.iloc[i]) or pd.isna(upper.iloc[i]):
+                continue
+
+            low_i = float(low.iloc[i])
+            high_i = float(high.iloc[i])
+            close_i = float(close.iloc[i])
+            lower_i = float(lower.iloc[i])
+            upper_i = float(upper.iloc[i])
+            trend_i = float(trend.iloc[i])
+            dt = pd.Timestamp(df.iloc[i]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+
+            if low_i <= lower_i:
+                in_oversold = True
+            if in_oversold and close_i > lower_i and close_i >= trend_i * 0.98:
+                signals.append(
+                    TechniqueSignal(
+                        side="buy",
+                        bar_index=i,
+                        price=round(close_i, 2),
+                        datetime=dt,
+                        confidence=0.7,
+                        reason="bb_lower_bounce",
+                    )
+                )
+                in_oversold = False
+
+            if high_i >= upper_i:
+                in_overbought = True
+            if in_overbought and close_i < upper_i:
+                signals.append(
+                    TechniqueSignal(
+                        side="sell",
+                        bar_index=i,
+                        price=round(close_i, 2),
+                        datetime=dt,
+                        confidence=0.7,
+                        reason="bb_upper_reject",
+                    )
+                )
+                in_overbought = False
+
+        return signals

src/deepcoin/techniques/bb_squeeze_breakout.py

@@ -0,0 +1,61 @@
+"""볼린저 스퀴즈 돌파 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import bollinger_bands
+
+
+class BbSqueezeBreakoutTechnique(BaseTechnique):
+    """BB 폭 축소(스퀴즈) 후 상·하단 돌파."""
+
+    technique_id = "bb_squeeze_breakout"
+    technique_name = "BB 스퀴즈 돌파"
+    category = "breakout"
+    causal = True
+    description = "볼린저 밴드 스퀴즈 후 돌파 (B^)"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 20, "squeeze_pctile": 20, "lookback": 100}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 20))
+        squeeze_pctile = float(params.extra.get("squeeze_pctile", 20))
+        lookback = int(params.extra.get("lookback", 100))
+
+        close = df["close"].astype(float)
+        mid, upper, lower = bollinger_bands(close, window=window)
+        width = (upper - lower) / mid.replace(0, pd.NA) * 100.0
+
+        signals: list[TechniqueSignal] = []
+        in_squeeze = False
+
+        for i in range(window + lookback, len(df)):
+            if pd.isna(width.iloc[i]):
+                continue
+
+            hist = width.iloc[i - lookback : i].dropna()
+            if hist.empty:
+                continue
+
+            threshold = float(hist.quantile(squeeze_pctile / 100.0))
+            w = float(width.iloc[i])
+            c = float(close.iloc[i])
+            u = float(upper.iloc[i])
+            lo = float(lower.iloc[i])
+            prev_c = float(close.iloc[i - 1])
+
+            if w <= threshold:
+                in_squeeze = True
+
+            if in_squeeze and prev_c <= u and c > u:
+                signals.append(make_signal(df, i, c, "buy", "bb_squeeze_breakout_up", confidence=0.75))
+                in_squeeze = False
+            elif in_squeeze and prev_c >= lo and c < lo:
+                signals.append(make_signal(df, i, c, "sell", "bb_squeeze_breakout_down", confidence=0.75))
+                in_squeeze = False
+
+        return signals

src/deepcoin/techniques/cci_extreme.py

@@ -0,0 +1,57 @@
+"""CCI 극값 반전 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal, safe_float
+from deepcoin.techniques.indicators import cci
+
+
+class CciExtremeTechnique(BaseTechnique):
+    """CCI -100 이탈 후 복귀 매수, +100 이탈 후 복귀 매도."""
+
+    technique_id = "cci_extreme"
+    technique_name = "CCI 극값"
+    category = "momentum"
+    causal = True
+    description = "CCI(20) 과매도·과매수 반전"
+
+    def default_extra_params(self) -> dict:
+        return {"period": 20, "low_level": -100.0, "high_level": 100.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        period = int(params.extra.get("period", 20))
+        low_level = float(params.extra.get("low_level", -100.0))
+        high_level = float(params.extra.get("high_level", 100.0))
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        cci_vals = cci(high, low, close, period=period)
+
+        signals: list[TechniqueSignal] = []
+        was_oversold = False
+        was_overbought = False
+
+        for i in range(period + 1, len(df)):
+            curr = safe_float(cci_vals.iloc[i])
+            prev = safe_float(cci_vals.iloc[i - 1])
+            c = safe_float(close.iloc[i])
+            if curr is None or prev is None or c is None:
+                continue
+
+            if curr < low_level:
+                was_oversold = True
+            if was_oversold and prev < low_level <= curr:
+                signals.append(make_signal(df, i, c, "buy", "cci_oversold_exit", confidence=0.65))
+                was_oversold = False
+
+            if curr > high_level:
+                was_overbought = True
+            if was_overbought and prev > high_level >= curr:
+                signals.append(make_signal(df, i, c, "sell", "cci_overbought_exit", confidence=0.65))
+                was_overbought = False
+
+        return signals

src/deepcoin/techniques/composite_base.py

@@ -0,0 +1,126 @@
+"""유형별·전체 복합 기법 공통 로직."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.indicators import ema
+
+
+@dataclass(frozen=True)
+class WeightedEvent:
+    """봉 단위 가중 투표 이벤트."""
+
+    bar_index: int
+    side: str
+    weight: float
+    source: str
+
+
+def collect_weighted_events(
+    techniques: list[BaseTechnique],
+    weights: dict[str, tuple[float, float]],
+    df: pd.DataFrame,
+    params: TechniqueParams,
+) -> list[WeightedEvent]:
+    """하위 기법 신호를 가중 이벤트로 수집한다."""
+    events: list[WeightedEvent] = []
+    for technique in techniques:
+        merged_extra = {**technique.default_extra_params(), **params.extra}
+        run_params = TechniqueParams(
+            interval_min=params.interval_min,
+            lookback_days=params.lookback_days,
+            min_leg_pct=params.min_leg_pct,
+            initial_cash_krw=params.initial_cash_krw,
+            fee_rate=params.fee_rate,
+            extra=merged_extra,
+        )
+        raw = technique.generate_signals(df, run_params)
+        buy_w, sell_w = weights.get(technique.technique_id, (1.0, 1.0))
+        for sig in raw:
+            weight = buy_w if sig.side == "buy" else sell_w
+            events.append(
+                WeightedEvent(
+                    bar_index=sig.bar_index,
+                    side=sig.side,
+                    weight=weight,
+                    source=technique.technique_id,
+                )
+            )
+    events.sort(key=lambda e: e.bar_index)
+    return events
+
+
+def cluster_events(events: list[WeightedEvent], merge_bars: int) -> list[list[WeightedEvent]]:
+    """인접 봉의 이벤트를 클러스터로 묶는다."""
+    if not events:
+        return []
+
+    clusters: list[list[WeightedEvent]] = [[events[0]]]
+    for event in events[1:]:
+        last_bar = max(e.bar_index for e in clusters[-1])
+        if event.bar_index - last_bar <= merge_bars:
+            clusters[-1].append(event)
+        else:
+            clusters.append([event])
+    return clusters
+
+
+def score_clusters_to_signals(
+    df: pd.DataFrame,
+    clusters: list[list[WeightedEvent]],
+    *,
+    min_score: float,
+    trend_span: int = 60,
+    use_trend_filter: bool = True,
+) -> list[TechniqueSignal]:
+    """클러스터 점수를 TechniqueSignal로 변환한다."""
+    close = df["close"].astype(float)
+    trend = ema(close, trend_span) if use_trend_filter else None
+    signals: list[TechniqueSignal] = []
+
+    for cluster in clusters:
+        buy_score = sum(e.weight for e in cluster if e.side == "buy")
+        sell_score = sum(e.weight for e in cluster if e.side == "sell")
+        bar_index = max(e.bar_index for e in cluster)
+        sources = sorted({e.source for e in cluster})
+
+        if bar_index >= len(df):
+            continue
+        if use_trend_filter and trend is not None and pd.isna(trend.iloc[bar_index]):
+            continue
+
+        price = float(close.iloc[bar_index])
+        dt = pd.Timestamp(df.iloc[bar_index]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+        trend_val = float(trend.iloc[bar_index]) if use_trend_filter and trend is not None else price
+
+        if buy_score >= min_score and buy_score > sell_score:
+            if not use_trend_filter or price > trend_val:
+                signals.append(
+                    TechniqueSignal(
+                        side="buy",
+                        bar_index=bar_index,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=round(min(buy_score / 5.0, 1.0), 2),
+                        reason=f"composite_buy score={buy_score:.1f} [{','.join(sources)}]",
+                    )
+                )
+        elif sell_score >= min_score and sell_score > buy_score:
+            if not use_trend_filter or price < trend_val:
+                signals.append(
+                    TechniqueSignal(
+                        side="sell",
+                        bar_index=bar_index,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=round(min(sell_score / 5.0, 1.0), 2),
+                        reason=f"composite_sell score={sell_score:.1f} [{','.join(sources)}]",
+                    )
+                )
+
+    signals.sort(key=lambda s: s.bar_index)
+    return signals

src/deepcoin/techniques/composite_breakout.py

@@ -0,0 +1,59 @@
+"""돌파 유형 복합 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.bb_squeeze_breakout import BbSqueezeBreakoutTechnique
+from deepcoin.techniques.composite_base import (
+    cluster_events,
+    collect_weighted_events,
+    score_clusters_to_signals,
+)
+from deepcoin.techniques.donchian import DonchianTechnique
+from deepcoin.techniques.keltner_breakout import KeltnerBreakoutTechnique
+from deepcoin.techniques.macd_cross import MacdCrossTechnique
+from deepcoin.techniques.range_breakout import RangeBreakoutTechnique
+from deepcoin.techniques.volume_breakout import VolumeBreakoutTechnique
+
+_SUB = [
+    DonchianTechnique(),
+    RangeBreakoutTechnique(),
+    KeltnerBreakoutTechnique(),
+    BbSqueezeBreakoutTechnique(),
+    VolumeBreakoutTechnique(),
+    MacdCrossTechnique(),
+]
+
+_WEIGHTS: dict[str, tuple[float, float]] = {
+    "donchian": (2.0, 2.0),
+    "range_breakout": (2.0, 2.0),
+    "keltner_breakout": (1.8, 1.8),
+    "bb_squeeze_breakout": (1.8, 1.8),
+    "volume_breakout": (1.5, 1.5),
+    "macd_cross": (1.2, 1.2),
+}
+
+
+class CompositeBreakoutTechnique(BaseTechnique):
+    """돌파 B^ 유형 전담 복합 기법."""
+
+    technique_id = "composite_breakout"
+    technique_name = "돌파 복합"
+    category = "composite"
+    causal = True
+    description = "돌파·모멘텀 기법 가중 투표 (B^)"
+
+    def default_extra_params(self) -> dict:
+        return {"min_score": 1.8, "merge_bars": 3, "trend_ema_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        min_score = float(params.extra.get("min_score", 1.8))
+        merge_bars = int(params.extra.get("merge_bars", 3))
+        trend_span = int(params.extra.get("trend_ema_span", 60))
+        events = collect_weighted_events(_SUB, _WEIGHTS, df, params)
+        clusters = cluster_events(events, merge_bars=merge_bars)
+        return score_clusters_to_signals(
+            df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=False,
+        )

src/deepcoin/techniques/composite_divergence.py

@@ -0,0 +1,56 @@
+"""다이버전스 유형 복합 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.composite_base import (
+    cluster_events,
+    collect_weighted_events,
+    score_clusters_to_signals,
+)
+from deepcoin.techniques.macd_cross import MacdCrossTechnique
+from deepcoin.techniques.macd_divergence import MacdDivergenceTechnique
+from deepcoin.techniques.obv_divergence import ObvDivergenceTechnique
+from deepcoin.techniques.rsi_divergence import RsiDivergenceTechnique
+from deepcoin.techniques.rsi_swing import RsiSwingTechnique
+
+_SUB = [
+    RsiDivergenceTechnique(),
+    MacdDivergenceTechnique(),
+    ObvDivergenceTechnique(),
+    RsiSwingTechnique(),
+    MacdCrossTechnique(),
+]
+
+_WEIGHTS: dict[str, tuple[float, float]] = {
+    "rsi_divergence": (2.5, 2.5),
+    "macd_divergence": (2.5, 2.5),
+    "obv_divergence": (2.0, 2.0),
+    "rsi_swing": (1.2, 1.2),
+    "macd_cross": (1.0, 1.0),
+}
+
+
+class CompositeDivergenceTechnique(BaseTechnique):
+    """다이버전스 Bd/Sd 유형 전담 복합 기법."""
+
+    technique_id = "composite_divergence"
+    technique_name = "다이버전스 복합"
+    category = "composite"
+    causal = True
+    description = "RSI/MACD/OBV 다이버전스 가중 투표 (Bd/Sd)"
+
+    def default_extra_params(self) -> dict:
+        return {"min_score": 2.0, "merge_bars": 5, "trend_ema_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        min_score = float(params.extra.get("min_score", 2.0))
+        merge_bars = int(params.extra.get("merge_bars", 5))
+        trend_span = int(params.extra.get("trend_ema_span", 60))
+        events = collect_weighted_events(_SUB, _WEIGHTS, df, params)
+        clusters = cluster_events(events, merge_bars=merge_bars)
+        return score_clusters_to_signals(
+            df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=False,
+        )

src/deepcoin/techniques/composite_full.py

@@ -0,0 +1,61 @@
+"""전체 기법 통합 복합."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.composite_base import (
+    cluster_events,
+    collect_weighted_events,
+    score_clusters_to_signals,
+)
+def _build_full_sub_techniques() -> list[BaseTechnique]:
+    """복합 제외 단일 기법 목록을 반환한다."""
+    from deepcoin.techniques.registry import get_single_techniques
+
+    return get_single_techniques()
+
+
+_CATEGORY_WEIGHT: dict[str, tuple[float, float]] = {
+    "swing": (2.0, 2.0),
+    "pullback": (1.8, 1.8),
+    "breakout": (1.8, 1.8),
+    "divergence": (2.0, 2.0),
+    "indicator": (1.2, 1.2),
+    "trend": (1.0, 1.0),
+    "momentum": (1.0, 1.0),
+    "volatility": (1.2, 1.2),
+    "structure": (1.2, 1.2),
+    "volume": (1.0, 1.0),
+    "hybrid": (1.8, 1.8),
+}
+
+
+class CompositeFullTechnique(BaseTechnique):
+    """등록된 모든 단일 기법 가중 투표 통합."""
+
+    technique_id = "composite_full"
+    technique_name = "전체 통합 복합"
+    category = "composite"
+    causal = True
+    description = "전체 인과 기법 가중 투표 + EMA 추세 필터"
+
+    def default_extra_params(self) -> dict:
+        return {"min_score": 4.0, "merge_bars": 3, "trend_ema_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        min_score = float(params.extra.get("min_score", 4.0))
+        merge_bars = int(params.extra.get("merge_bars", 3))
+        trend_span = int(params.extra.get("trend_ema_span", 60))
+
+        sub = _build_full_sub_techniques()
+        weights = {
+            t.technique_id: _CATEGORY_WEIGHT.get(t.category, (1.0, 1.0))
+            for t in sub
+        }
+        events = collect_weighted_events(sub, weights, df, params)
+        clusters = cluster_events(events, merge_bars=merge_bars)
+        return score_clusters_to_signals(
+            df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=True,
+        )

src/deepcoin/techniques/composite_pullback.py

@@ -0,0 +1,59 @@
+"""눌림목 유형 복합 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.bb_reversal import BbReversalTechnique
+from deepcoin.techniques.composite_base import (
+    cluster_events,
+    collect_weighted_events,
+    score_clusters_to_signals,
+)
+from deepcoin.techniques.ema_pullback import EmaPullbackTechnique
+from deepcoin.techniques.fib_pullback import FibPullbackTechnique
+from deepcoin.techniques.keltner_reversal import KeltnerReversalTechnique
+from deepcoin.techniques.local_extrema import LocalExtremaTechnique
+from deepcoin.techniques.support_bounce import SupportBounceTechnique
+
+_SUB = [
+    EmaPullbackTechnique(),
+    FibPullbackTechnique(),
+    SupportBounceTechnique(),
+    BbReversalTechnique(),
+    LocalExtremaTechnique(),
+    KeltnerReversalTechnique(),
+]
+
+_WEIGHTS: dict[str, tuple[float, float]] = {
+    "ema_pullback": (2.0, 2.0),
+    "fib_pullback": (2.0, 2.0),
+    "support_bounce": (1.8, 1.8),
+    "bb_reversal": (1.8, 1.8),
+    "local_extrema": (1.5, 1.5),
+    "keltner_reversal": (1.2, 1.2),
+}
+
+
+class CompositePullbackTechnique(BaseTechnique):
+    """눌림목 B* 유형 전담 복합 기법."""
+
+    technique_id = "composite_pullback"
+    technique_name = "눌림목 복합"
+    category = "composite"
+    causal = True
+    description = "눌림목·역추세 기법 가중 투표 (B*)"
+
+    def default_extra_params(self) -> dict:
+        return {"min_score": 2.0, "merge_bars": 3, "trend_ema_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        min_score = float(params.extra.get("min_score", 2.0))
+        merge_bars = int(params.extra.get("merge_bars", 3))
+        trend_span = int(params.extra.get("trend_ema_span", 60))
+        events = collect_weighted_events(_SUB, _WEIGHTS, df, params)
+        clusters = cluster_events(events, merge_bars=merge_bars)
+        return score_clusters_to_signals(
+            df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=True,
+        )

src/deepcoin/techniques/composite_swing.py

@@ -0,0 +1,62 @@
+"""스윙 유형 복합 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.composite_base import (
+    cluster_events,
+    collect_weighted_events,
+    score_clusters_to_signals,
+)
+from deepcoin.techniques.donchian import DonchianTechnique
+from deepcoin.techniques.fractal_swing import FractalSwingTechnique
+from deepcoin.techniques.local_extrema import LocalExtremaTechnique
+from deepcoin.techniques.minor_swing import MinorSwingTechnique
+from deepcoin.techniques.pivot_swing import PivotSwingTechnique
+from deepcoin.techniques.swing_failure import SwingFailureTechnique
+from deepcoin.techniques.zigzag_causal import ZigzagCausalTechnique
+
+_SUB = [
+    ZigzagCausalTechnique(),
+    MinorSwingTechnique(),
+    PivotSwingTechnique(),
+    FractalSwingTechnique(),
+    LocalExtremaTechnique(),
+    DonchianTechnique(),
+    SwingFailureTechnique(),
+]
+
+_WEIGHTS: dict[str, tuple[float, float]] = {
+    "zigzag_causal": (2.5, 2.5),
+    "minor_swing": (2.0, 2.0),
+    "pivot_swing": (1.8, 1.8),
+    "fractal_swing": (1.5, 1.5),
+    "local_extrema": (1.5, 1.5),
+    "donchian": (1.2, 1.2),
+    "swing_failure": (1.0, 1.0),
+}
+
+
+class CompositeSwingTechnique(BaseTechnique):
+    """스윙 B/S 유형 전담 복합 기법."""
+
+    technique_id = "composite_swing"
+    technique_name = "스윙 복합"
+    category = "composite"
+    causal = True
+    description = "스윙 저점·고점 전담 기법 가중 투표 (B/S)"
+
+    def default_extra_params(self) -> dict:
+        return {"min_score": 2.5, "merge_bars": 3, "trend_ema_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        min_score = float(params.extra.get("min_score", 2.5))
+        merge_bars = int(params.extra.get("merge_bars", 3))
+        trend_span = int(params.extra.get("trend_ema_span", 60))
+        events = collect_weighted_events(_SUB, _WEIGHTS, df, params)
+        clusters = cluster_events(events, merge_bars=merge_bars)
+        return score_clusters_to_signals(
+            df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=True,
+        )

src/deepcoin/techniques/composite_v3.py

@@ -0,0 +1,97 @@
+"""v3 GT 6종 신호를 가중 투표로 통합하는 복합 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.bb_reversal import BbReversalTechnique
+from deepcoin.techniques.composite_base import (
+    cluster_events,
+    collect_weighted_events,
+    score_clusters_to_signals,
+)
+from deepcoin.techniques.donchian import DonchianTechnique
+from deepcoin.techniques.ema_pullback import EmaPullbackTechnique
+from deepcoin.techniques.fib_pullback import FibPullbackTechnique
+from deepcoin.techniques.fractal_swing import FractalSwingTechnique
+from deepcoin.techniques.keltner_breakout import KeltnerBreakoutTechnique
+from deepcoin.techniques.local_extrema import LocalExtremaTechnique
+from deepcoin.techniques.macd_cross import MacdCrossTechnique
+from deepcoin.techniques.macd_divergence import MacdDivergenceTechnique
+from deepcoin.techniques.minor_swing import MinorSwingTechnique
+from deepcoin.techniques.obv_divergence import ObvDivergenceTechnique
+from deepcoin.techniques.pivot_swing import PivotSwingTechnique
+from deepcoin.techniques.range_breakout import RangeBreakoutTechnique
+from deepcoin.techniques.rsi_divergence import RsiDivergenceTechnique
+from deepcoin.techniques.rsi_swing import RsiSwingTechnique
+from deepcoin.techniques.support_bounce import SupportBounceTechnique
+from deepcoin.techniques.zigzag_causal import ZigzagCausalTechnique
+
+_TECHNIQUE_WEIGHTS: dict[str, tuple[float, float]] = {
+    "zigzag_causal": (2.5, 2.5),
+    "minor_swing": (2.0, 2.0),
+    "pivot_swing": (1.8, 1.8),
+    "fractal_swing": (1.5, 1.5),
+    "local_extrema": (1.5, 1.5),
+    "ema_pullback": (2.0, 1.0),
+    "fib_pullback": (2.0, 1.0),
+    "support_bounce": (1.5, 1.0),
+    "bb_reversal": (1.5, 1.5),
+    "donchian": (1.5, 1.5),
+    "range_breakout": (1.8, 1.0),
+    "keltner_breakout": (1.5, 1.0),
+    "macd_cross": (1.2, 1.2),
+    "rsi_divergence": (2.0, 2.0),
+    "macd_divergence": (2.0, 2.0),
+    "obv_divergence": (1.8, 1.8),
+    "rsi_swing": (1.5, 1.5),
+}
+
+_SUB_TECHNIQUES: list[BaseTechnique] = [
+    ZigzagCausalTechnique(),
+    MinorSwingTechnique(),
+    PivotSwingTechnique(),
+    FractalSwingTechnique(),
+    LocalExtremaTechnique(),
+    EmaPullbackTechnique(),
+    FibPullbackTechnique(),
+    SupportBounceTechnique(),
+    BbReversalTechnique(),
+    DonchianTechnique(),
+    RangeBreakoutTechnique(),
+    KeltnerBreakoutTechnique(),
+    MacdCrossTechnique(),
+    RsiDivergenceTechnique(),
+    MacdDivergenceTechnique(),
+    ObvDivergenceTechnique(),
+    RsiSwingTechnique(),
+]
+
+
+class CompositeV3Technique(BaseTechnique):
+    """v3 GT 6종 신호를 가중 투표로 재현하는 통합 인과 기법."""
+
+    technique_id = "composite_v3"
+    technique_name = "v3 통합 스코어링"
+    category = "composite"
+    causal = True
+    description = "v3 GT 6종 신호 유형별 핵심 기법 가중 투표 + EMA(60) 추세 필터"
+
+    def default_extra_params(self) -> dict:
+        return {
+            "min_score": 2.5,
+            "merge_bars": 3,
+            "trend_ema_span": 60,
+            "reversal_pct": 5.0,
+        }
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        min_score = float(params.extra.get("min_score", 2.5))
+        merge_bars = int(params.extra.get("merge_bars", 3))
+        trend_span = int(params.extra.get("trend_ema_span", 60))
+        events = collect_weighted_events(_SUB_TECHNIQUES, _TECHNIQUE_WEIGHTS, df, params)
+        clusters = cluster_events(events, merge_bars=merge_bars)
+        return score_clusters_to_signals(
+            df, clusters, min_score=min_score, trend_span=trend_span, use_trend_filter=True,
+        )

src/deepcoin/techniques/donchian.py

@@ -0,0 +1,76 @@
+"""돈치안 채널 스윙."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+
+
+class DonchianTechnique(BaseTechnique):
+    """N봉 최저가 터치 후 반등 매수, N봉 최고가 터치 후 하락 매도."""
+
+    technique_id = "donchian"
+    technique_name = "돈치안 채널"
+    category = "swing"
+    causal = True
+    description = "돈치안(40) 채널 하단 매수·상단 매도"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 40}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 40))
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        close = df["close"].astype(float)
+
+        lower_channel = low.rolling(window=window, min_periods=window).min()
+        upper_channel = high.rolling(window=window, min_periods=window).max()
+
+        signals: list[TechniqueSignal] = []
+        touched_lower = False
+        touched_upper = False
+
+        for i in range(window, len(df)):
+            if pd.isna(lower_channel.iloc[i]):
+                continue
+
+            low_i = float(low.iloc[i])
+            high_i = float(high.iloc[i])
+            close_i = float(close.iloc[i])
+            lower_i = float(lower_channel.iloc[i])
+            upper_i = float(upper_channel.iloc[i])
+            dt = pd.Timestamp(df.iloc[i]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+
+            if low_i <= lower_i:
+                touched_lower = True
+            if touched_lower and close_i > lower_i * 1.005:
+                signals.append(
+                    TechniqueSignal(
+                        side="buy",
+                        bar_index=i,
+                        price=round(close_i, 2),
+                        datetime=dt,
+                        confidence=0.65,
+                        reason="donchian_lower_bounce",
+                    )
+                )
+                touched_lower = False
+
+            if high_i >= upper_i:
+                touched_upper = True
+            if touched_upper and close_i < upper_i * 0.995:
+                signals.append(
+                    TechniqueSignal(
+                        side="sell",
+                        bar_index=i,
+                        price=round(close_i, 2),
+                        datetime=dt,
+                        confidence=0.65,
+                        reason="donchian_upper_reject",
+                    )
+                )
+                touched_upper = False
+
+        return signals

src/deepcoin/techniques/ema_pullback.py

@@ -0,0 +1,62 @@
+"""EMA 눌림목 반등 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import ema
+
+
+class EmaPullbackTechnique(BaseTechnique):
+    """상승 추세에서 EMA 터치 후 반등 매수, 하락 추세에서 EMA 터치 후 하락 매도."""
+
+    technique_id = "ema_pullback"
+    technique_name = "EMA 눌림목"
+    category = "pullback"
+    causal = True
+    description = "EMA(20/60) 눌림목 반등 매수·되돌림 매도 (B*)"
+
+    def default_extra_params(self) -> dict:
+        return {"fast_span": 20, "slow_span": 60, "touch_pct": 0.5}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        fast_span = int(params.extra.get("fast_span", 20))
+        slow_span = int(params.extra.get("slow_span", 60))
+        touch_pct = float(params.extra.get("touch_pct", 0.5)) / 100.0
+
+        close = df["close"].astype(float)
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        ema_fast = ema(close, fast_span)
+        ema_slow = ema(close, slow_span)
+
+        signals: list[TechniqueSignal] = []
+        touched_fast_buy = False
+        touched_fast_sell = False
+
+        for i in range(slow_span + 1, len(df)):
+            if pd.isna(ema_fast.iloc[i]) or pd.isna(ema_slow.iloc[i]):
+                continue
+
+            c = float(close.iloc[i])
+            l = float(low.iloc[i])
+            h = float(high.iloc[i])
+            ef = float(ema_fast.iloc[i])
+            es = float(ema_slow.iloc[i])
+            prev_c = float(close.iloc[i - 1])
+
+            if c > es and l <= ef * (1 + touch_pct):
+                touched_fast_buy = True
+            if touched_fast_buy and prev_c <= ef and c > ef and c > es:
+                signals.append(make_signal(df, i, c, "buy", "ema_pullback_buy", confidence=0.74))
+                touched_fast_buy = False
+
+            if c < es and h >= ef * (1 - touch_pct):
+                touched_fast_sell = True
+            if touched_fast_sell and prev_c >= ef and c < ef and c < es:
+                signals.append(make_signal(df, i, c, "sell", "ema_pullback_sell", confidence=0.74))
+                touched_fast_sell = False
+
+        return signals

src/deepcoin/techniques/fib_pullback.py

@@ -0,0 +1,106 @@
+"""피보나치 되돌림 눌림목 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import find_confirmed_pivots, make_signal
+from deepcoin.techniques.indicators import ema
+
+
+class FibPullbackTechnique(BaseTechnique):
+    """스윙 고저점 기준 피보나치 38.2~61.8% 구간 터치 후 반전."""
+
+    technique_id = "fib_pullback"
+    technique_name = "피보나치 눌림목"
+    category = "pullback"
+    causal = True
+    description = "피보나치 38.2~61.8% 되돌림 구간 매수·매도 (B*)"
+
+    def default_extra_params(self) -> dict:
+        return {"order": 15, "fib_low": 0.382, "fib_high": 0.618, "trend_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        order = int(params.extra.get("order", 15))
+        fib_low = float(params.extra.get("fib_low", 0.382))
+        fib_high = float(params.extra.get("fib_high", 0.618))
+        trend_span = int(params.extra.get("trend_span", 60))
+
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        close = df["close"].astype(float)
+        trend = ema(close, trend_span)
+        low_pivots = find_confirmed_pivots(low, order, "low")
+        high_pivots = find_confirmed_pivots(high, order, "high")
+        signals: list[TechniqueSignal] = []
+
+        for i in range(1, len(low_pivots)):
+            swing_low_idx, swing_low = low_pivots[i]
+            swing_high_idx, swing_high = _prior_high(high_pivots, swing_low_idx)
+            if swing_high_idx is None or swing_high <= swing_low:
+                continue
+            confirm = swing_low_idx + order
+            if confirm >= len(df):
+                continue
+            leg = swing_high - swing_low
+            zone_top = swing_high - leg * fib_low
+            zone_bot = swing_high - leg * fib_high
+            for j in range(confirm, min(len(df), confirm + order * 4)):
+                if float(close.iloc[j]) < float(trend.iloc[j]):
+                    break
+                if zone_bot <= float(low.iloc[j]) <= zone_top and float(close.iloc[j]) > zone_bot:
+                    signals.append(
+                        make_signal(
+                            df, j, float(close.iloc[j]), "buy",
+                            "fib_pullback_buy", pivot_bar_index=swing_low_idx, confidence=0.73,
+                        )
+                    )
+                    break
+
+        for i in range(1, len(high_pivots)):
+            swing_high_idx, swing_high = high_pivots[i]
+            swing_low_idx, swing_low = _prior_low(low_pivots, swing_high_idx)
+            if swing_low_idx is None or swing_high <= swing_low:
+                continue
+            confirm = swing_high_idx + order
+            if confirm >= len(df):
+                continue
+            leg = swing_high - swing_low
+            zone_bot = swing_low + leg * fib_low
+            zone_top = swing_low + leg * fib_high
+            for j in range(confirm, min(len(df), confirm + order * 4)):
+                if float(close.iloc[j]) > float(trend.iloc[j]):
+                    break
+                if zone_bot <= float(high.iloc[j]) <= zone_top and float(close.iloc[j]) < zone_top:
+                    signals.append(
+                        make_signal(
+                            df, j, float(close.iloc[j]), "sell",
+                            "fib_pullback_sell", pivot_bar_index=swing_high_idx, confidence=0.73,
+                        )
+                    )
+                    break
+
+        return signals
+
+
+def _prior_high(
+    pivots: list[tuple[int, float]], before_idx: int,
+) -> tuple[int | None, float | None]:
+    """before_idx 이전 최근 고점 피벗을 반환한다."""
+    candidates = [(idx, val) for idx, val in pivots if idx < before_idx]
+    if not candidates:
+        return None, None
+    idx, val = max(candidates, key=lambda x: x[0])
+    return idx, val
+
+
+def _prior_low(
+    pivots: list[tuple[int, float]], before_idx: int,
+) -> tuple[int | None, float | None]:
+    """before_idx 이전 최근 저점 피벗을 반환한다."""
+    candidates = [(idx, val) for idx, val in pivots if idx < before_idx]
+    if not candidates:
+        return None, None
+    idx, val = max(candidates, key=lambda x: x[0])
+    return idx, val

src/deepcoin/techniques/fractal_swing.py

@@ -0,0 +1,53 @@
+"""Williams 프랙탈 스윙 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import dedupe_signals, find_fractal_pivots, make_signal
+
+
+class FractalSwingTechnique(BaseTechnique):
+    """프랙탈 저점·고점 확정 시 매수·매도."""
+
+    technique_id = "fractal_swing"
+    technique_name = "프랙탈 스윙"
+    category = "swing"
+    causal = True
+    description = "Williams 프랙탈 스윙 저점 매수·고점 매도"
+
+    def default_extra_params(self) -> dict:
+        return {"span": 2, "min_bars_between": 20}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        span = int(params.extra.get("span", 2))
+        min_bars = int(params.extra.get("min_bars_between", 20))
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        low_fractals, high_fractals = find_fractal_pivots(low, high, span=span)
+        signals: list[TechniqueSignal] = []
+
+        for pivot_idx, pivot_val in low_fractals:
+            confirm_idx = pivot_idx + span
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, pivot_val, "buy",
+                    "fractal_low", pivot_bar_index=pivot_idx, confidence=0.7,
+                )
+            )
+
+        for pivot_idx, pivot_val in high_fractals:
+            confirm_idx = pivot_idx + span
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, pivot_val, "sell",
+                    "fractal_high", pivot_bar_index=pivot_idx, confidence=0.7,
+                )
+            )
+
+        return dedupe_signals(signals, min_bars=min_bars)

src/deepcoin/techniques/helpers.py

@@ -0,0 +1,186 @@
+"""기법 공통 유틸리티 (신호 병합·다이버전스·피벗 탐지)."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import TechniqueSignal
+
+
+def safe_float(value: object) -> float | None:
+    """Series 스칼라를 float로 변환한다. NA/NaN이면 None."""
+    if pd.isna(value):
+        return None
+    return float(value)
+
+
+def format_datetime(df: pd.DataFrame, bar_index: int) -> str:
+    """봉 인덱스의 datetime 문자열을 반환한다."""
+    return pd.Timestamp(df.iloc[bar_index]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+
+
+def dedupe_signals(signals: list[TechniqueSignal], min_bars: int) -> list[TechniqueSignal]:
+    """동일 방향 근접 신호를 병합한다."""
+    if not signals:
+        return []
+
+    sorted_signals = sorted(signals, key=lambda s: s.bar_index)
+    merged: list[TechniqueSignal] = [sorted_signals[0]]
+
+    for signal in sorted_signals[1:]:
+        last = merged[-1]
+        if signal.side == last.side and signal.bar_index - last.bar_index < min_bars:
+            if signal.side == "buy" and signal.price < last.price:
+                merged[-1] = signal
+            elif signal.side == "sell" and signal.price > last.price:
+                merged[-1] = signal
+        else:
+            merged.append(signal)
+
+    return sorted(merged, key=lambda s: s.bar_index)
+
+
+def merge_opposite_signals(
+    signals: list[TechniqueSignal],
+    min_bars: int,
+) -> list[TechniqueSignal]:
+    """방향 무관 근접 신호 중 confidence 높은 쪽을 유지한다."""
+    if not signals:
+        return []
+
+    sorted_signals = sorted(signals, key=lambda s: s.bar_index)
+    merged: list[TechniqueSignal] = [sorted_signals[0]]
+
+    for signal in sorted_signals[1:]:
+        last = merged[-1]
+        if signal.bar_index - last.bar_index < min_bars:
+            if signal.confidence > last.confidence:
+                merged[-1] = signal
+        else:
+            merged.append(signal)
+
+    return sorted(merged, key=lambda s: s.bar_index)
+
+
+def find_confirmed_pivots(
+    series: pd.Series,
+    order: int,
+    mode: str,
+) -> list[tuple[int, float]]:
+    """order봉 지연 확정 피벗 인덱스·값 목록을 반환한다.
+
+    Args:
+        series: high 또는 low 시리즈.
+        order: 좌우 비교 봉 수.
+        mode: low | high.
+
+    Returns:
+        (pivot_bar_index, pivot_value) 리스트.
+    """
+    pivots: list[tuple[int, float]] = []
+    values = series.astype(float)
+
+    for pivot_idx in range(order, len(values) - order):
+        window = values.iloc[pivot_idx - order : pivot_idx + order + 1]
+        val = float(values.iloc[pivot_idx])
+        if mode == "low" and val <= window.min():
+            pivots.append((pivot_idx, val))
+        elif mode == "high" and val >= window.max():
+            pivots.append((pivot_idx, val))
+
+    return pivots
+
+
+def find_fractal_pivots(
+    low: pd.Series,
+    high: pd.Series,
+    span: int = 2,
+) -> tuple[list[tuple[int, float]], list[tuple[int, float]]]:
+    """Williams 프랙탈 피벗 (span봉 후 확정)을 반환한다."""
+    low_fractals: list[tuple[int, float]] = []
+    high_fractals: list[tuple[int, float]] = []
+
+    for pivot_idx in range(span, len(low) - span):
+        low_window = low.iloc[pivot_idx - span : pivot_idx + span + 1].astype(float)
+        high_window = high.iloc[pivot_idx - span : pivot_idx + span + 1].astype(float)
+        low_val = float(low.iloc[pivot_idx])
+        high_val = float(high.iloc[pivot_idx])
+
+        if low_val == low_window.min():
+            low_fractals.append((pivot_idx, low_val))
+        if high_val == high_window.max():
+            high_fractals.append((pivot_idx, high_val))
+
+    return low_fractals, high_fractals
+
+
+def detect_bullish_divergence(
+    price_pivots: list[tuple[int, float]],
+    indicator: pd.Series,
+    min_bars_between: int = 10,
+    max_bars_between: int = 500,
+) -> list[tuple[int, int]]:
+    """가격 저점 하락·지표 저점 상승 다이버전스 (확정봉, 피벗2) 쌍을 반환한다."""
+    pairs: list[tuple[int, int]] = []
+    for i in range(1, len(price_pivots)):
+        idx1, p1 = price_pivots[i - 1]
+        idx2, p2 = price_pivots[i]
+        gap = idx2 - idx1
+        if gap < min_bars_between or gap > max_bars_between:
+            continue
+        if p2 >= p1:
+            continue
+        ind1 = float(indicator.iloc[idx1])
+        ind2 = float(indicator.iloc[idx2])
+        if pd.isna(ind1) or pd.isna(ind2):
+            continue
+        if ind2 > ind1:
+            pairs.append((idx2, idx2))
+    return pairs
+
+
+def detect_bearish_divergence(
+    price_pivots: list[tuple[int, float]],
+    indicator: pd.Series,
+    min_bars_between: int = 10,
+    max_bars_between: int = 500,
+) -> list[tuple[int, int]]:
+    """가격 고점 상승·지표 고점 하락 다이버전스 쌍을 반환한다."""
+    pairs: list[tuple[int, int]] = []
+    for i in range(1, len(price_pivots)):
+        idx1, p1 = price_pivots[i - 1]
+        idx2, p2 = price_pivots[i]
+        gap = idx2 - idx1
+        if gap < min_bars_between or gap > max_bars_between:
+            continue
+        if p2 <= p1:
+            continue
+        ind1 = float(indicator.iloc[idx1])
+        ind2 = float(indicator.iloc[idx2])
+        if pd.isna(ind1) or pd.isna(ind2):
+            continue
+        if ind2 < ind1:
+            pairs.append((idx2, idx2))
+    return pairs
+
+
+def make_signal(
+    df: pd.DataFrame,
+    bar_index: int,
+    price: float,
+    side: str,
+    reason: str,
+    *,
+    pivot_bar_index: int | None = None,
+    confidence: float = 0.7,
+) -> TechniqueSignal:
+    """TechniqueSignal을 생성한다."""
+    return TechniqueSignal(
+        side=side,
+        bar_index=bar_index,
+        price=round(price, 2),
+        datetime=format_datetime(df, bar_index),
+        pivot_bar_index=pivot_bar_index,
+        confidence=confidence,
+        reason=reason,
+    )

src/deepcoin/techniques/ichimoku_trend.py

@@ -0,0 +1,51 @@
+"""일목 전환·기준선 크로스 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import ichimoku
+
+
+class IchimokuTrendTechnique(BaseTechnique):
+    """전환선이 기준선 상향·하향 돌파 시 매수·매도."""
+
+    technique_id = "ichimoku_trend"
+    technique_name = "일목 추세"
+    category = "trend"
+    causal = True
+    description = "일목 전환선·기준선 크로스 추세 신호"
+
+    def default_extra_params(self) -> dict:
+        return {"tenkan": 9, "kijun": 26}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        tenkan_span = int(params.extra.get("tenkan", 9))
+        kijun_span = int(params.extra.get("kijun", 26))
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        tenkan, kijun = ichimoku(high, low, tenkan=tenkan_span, kijun=kijun_span)
+
+        signals: list[TechniqueSignal] = []
+        start = kijun_span + 1
+
+        for i in range(start, len(df)):
+            if pd.isna(tenkan.iloc[i]) or pd.isna(kijun.iloc[i]):
+                continue
+
+            prev_t = float(tenkan.iloc[i - 1])
+            prev_k = float(kijun.iloc[i - 1])
+            curr_t = float(tenkan.iloc[i])
+            curr_k = float(kijun.iloc[i])
+            c = float(close.iloc[i])
+
+            if prev_t <= prev_k and curr_t > curr_k:
+                signals.append(make_signal(df, i, c, "buy", "ichimoku_bull_cross", confidence=0.66))
+            elif prev_t >= prev_k and curr_t < curr_k:
+                signals.append(make_signal(df, i, c, "sell", "ichimoku_bear_cross", confidence=0.66))
+
+        return signals

src/deepcoin/techniques/indicators.py

@@ -0,0 +1,295 @@
+"""기술적 지표 계산 (인과 신호용)."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+
+def ema(series: pd.Series, span: int) -> pd.Series:
+    """지수이동평균을 계산한다."""
+    return series.ewm(span=span, adjust=False).mean()
+
+
+def sma(series: pd.Series, window: int) -> pd.Series:
+    """단순이동평균을 계산한다."""
+    return series.rolling(window=window, min_periods=window).mean()
+
+
+def bollinger_bands(
+    close: pd.Series,
+    window: int = 20,
+    num_std: float = 2.0,
+) -> tuple[pd.Series, pd.Series, pd.Series]:
+    """볼린저 밴드 (중심, 상단, 하단)를 계산한다."""
+    mid = sma(close, window)
+    std = close.rolling(window=window, min_periods=window).std()
+    upper = mid + num_std * std
+    lower = mid - num_std * std
+    return mid, upper, lower
+
+
+def rsi(close: pd.Series, period: int = 14) -> pd.Series:
+    """RSI(상대강도지수)를 계산한다."""
+    delta = close.diff()
+    gain = delta.clip(lower=0.0)
+    loss = -delta.clip(upper=0.0)
+    avg_gain = gain.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
+    avg_loss = loss.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
+    rs = avg_gain / avg_loss.replace(0, pd.NA)
+    return 100 - (100 / (1 + rs))
+
+
+def macd(
+    close: pd.Series,
+    fast: int = 12,
+    slow: int = 26,
+    signal: int = 9,
+) -> tuple[pd.Series, pd.Series, pd.Series]:
+    """MACD, 시그널, 히스토그램을 계산한다."""
+    ema_fast = ema(close, fast)
+    ema_slow = ema(close, slow)
+    macd_line = ema_fast - ema_slow
+    signal_line = ema(macd_line, signal)
+    hist = macd_line - signal_line
+    return macd_line, signal_line, hist
+
+
+def atr(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    period: int = 14,
+) -> pd.Series:
+    """Average True Range (ATR)를 계산한다.
+
+    Args:
+        high: 고가 시리즈.
+        low: 저가 시리즈.
+        close: 종가 시리즈.
+        period: ATR 기간.
+
+    Returns:
+        ATR 시리즈.
+    """
+    prev_close = close.shift(1)
+    tr = pd.concat(
+        [
+            (high - low).abs(),
+            (high - prev_close).abs(),
+            (low - prev_close).abs(),
+        ],
+        axis=1,
+    ).max(axis=1)
+    return tr.ewm(alpha=1 / period, min_periods=period, adjust=False).mean()
+
+
+def stochastic(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    k_period: int = 14,
+    d_period: int = 3,
+) -> tuple[pd.Series, pd.Series]:
+    """Stochastic %K, %D를 계산한다."""
+    import numpy as np
+
+    close_f = close.astype(float)
+    lowest = low.astype(float).rolling(window=k_period, min_periods=k_period).min()
+    highest = high.astype(float).rolling(window=k_period, min_periods=k_period).max()
+    range_hl = highest - lowest
+    pct_k = pd.Series(
+        np.where(range_hl > 0, 100.0 * (close_f - lowest) / range_hl, np.nan),
+        index=close.index,
+        dtype=float,
+    )
+    pct_d = pct_k.rolling(window=d_period, min_periods=d_period).mean()
+    return pct_k, pct_d
+
+
+def cci(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    period: int = 20,
+) -> pd.Series:
+    """Commodity Channel Index를 계산한다."""
+    tp = (high + low + close) / 3.0
+    sma_tp = sma(tp, period)
+    mean_dev = (tp - sma_tp).abs().rolling(window=period, min_periods=period).mean()
+    return (tp - sma_tp) / (0.015 * mean_dev.replace(0, pd.NA))
+
+
+def roc(close: pd.Series, period: int = 12) -> pd.Series:
+    """Rate of Change(%)를 계산한다."""
+    prev = close.shift(period)
+    return (close - prev) / prev.replace(0, pd.NA) * 100.0
+
+
+def adx(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    period: int = 14,
+) -> tuple[pd.Series, pd.Series, pd.Series]:
+    """ADX, +DI, -DI를 계산한다."""
+    up_move = high.diff()
+    down_move = -low.diff()
+    plus_dm = up_move.where((up_move > down_move) & (up_move > 0), 0.0)
+    minus_dm = down_move.where((down_move > up_move) & (down_move > 0), 0.0)
+    atr_vals = atr(high, low, close, period=period)
+    plus_di = 100 * ema(plus_dm, period) / atr_vals.replace(0, pd.NA)
+    minus_di = 100 * ema(minus_dm, period) / atr_vals.replace(0, pd.NA)
+    dx = (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, pd.NA) * 100
+    adx_line = ema(dx, period)
+    return adx_line, plus_di, minus_di
+
+
+def keltner_channels(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    ema_span: int = 20,
+    atr_period: int = 10,
+    atr_mult: float = 2.0,
+) -> tuple[pd.Series, pd.Series, pd.Series]:
+    """Keltner 채널 (중심, 상단, 하단)을 계산한다."""
+    mid = ema(close, ema_span)
+    atr_vals = atr(high, low, close, period=atr_period)
+    upper = mid + atr_mult * atr_vals
+    lower = mid - atr_mult * atr_vals
+    return mid, upper, lower
+
+
+def obv(close: pd.Series, volume: pd.Series) -> pd.Series:
+    """On-Balance Volume을 계산한다."""
+    direction = close.diff().apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))
+    return (direction * volume.astype(float)).cumsum()
+
+
+def supertrend(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    period: int = 10,
+    multiplier: float = 3.0,
+) -> tuple[pd.Series, pd.Series]:
+    """Supertrend 라인과 방향(1=상승, -1=하락)을 계산한다."""
+    atr_vals = atr(high, low, close, period=period)
+    hl2 = (high + low) / 2.0
+    basic_upper = hl2 + multiplier * atr_vals
+    basic_lower = hl2 - multiplier * atr_vals
+
+    final_upper = basic_upper.copy()
+    final_lower = basic_lower.copy()
+    direction = pd.Series(1, index=close.index, dtype=float)
+    st_line = pd.Series(index=close.index, dtype=float)
+
+    for i in range(1, len(close)):
+        if pd.isna(final_upper.iloc[i]) or pd.isna(final_lower.iloc[i]):
+            continue
+
+        if basic_upper.iloc[i] < final_upper.iloc[i - 1] or close.iloc[i - 1] > final_upper.iloc[i - 1]:
+            final_upper.iloc[i] = basic_upper.iloc[i]
+        else:
+            final_upper.iloc[i] = final_upper.iloc[i - 1]
+
+        if basic_lower.iloc[i] > final_lower.iloc[i - 1] or close.iloc[i - 1] < final_lower.iloc[i - 1]:
+            final_lower.iloc[i] = basic_lower.iloc[i]
+        else:
+            final_lower.iloc[i] = final_lower.iloc[i - 1]
+
+        if direction.iloc[i - 1] == 1:
+            if close.iloc[i] < final_lower.iloc[i]:
+                direction.iloc[i] = -1
+            else:
+                direction.iloc[i] = 1
+        else:
+            if close.iloc[i] > final_upper.iloc[i]:
+                direction.iloc[i] = 1
+            else:
+                direction.iloc[i] = -1
+
+        st_line.iloc[i] = final_lower.iloc[i] if direction.iloc[i] == 1 else final_upper.iloc[i]
+
+    return st_line, direction
+
+
+def parabolic_sar(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    af_step: float = 0.02,
+    af_max: float = 0.2,
+) -> pd.Series:
+    """Parabolic SAR 근사값을 계산한다."""
+    length = len(close)
+    sar = pd.Series(index=close.index, dtype=float)
+    if length < 2:
+        return sar
+
+    bull = True
+    af = af_step
+    ep = float(high.iloc[0])
+    sar.iloc[0] = float(low.iloc[0])
+
+    for i in range(1, length):
+        prev_sar = float(sar.iloc[i - 1]) if not pd.isna(sar.iloc[i - 1]) else float(low.iloc[0])
+        curr_sar = prev_sar + af * (ep - prev_sar)
+        h = float(high.iloc[i])
+        low_i = float(low.iloc[i])
+
+        if bull:
+            curr_sar = min(curr_sar, float(low.iloc[i - 1]), low_i)
+            if low_i < curr_sar:
+                bull = False
+                curr_sar = ep
+                ep = low_i
+                af = af_step
+            else:
+                if h > ep:
+                    ep = h
+                    af = min(af + af_step, af_max)
+        else:
+            curr_sar = max(curr_sar, float(high.iloc[i - 1]), h)
+            if h > curr_sar:
+                bull = True
+                curr_sar = ep
+                ep = h
+                af = af_step
+            else:
+                if low_i < ep:
+                    ep = low_i
+                    af = min(af + af_step, af_max)
+
+        sar.iloc[i] = curr_sar
+
+    return sar
+
+
+def ichimoku(
+    high: pd.Series,
+    low: pd.Series,
+    tenkan: int = 9,
+    kijun: int = 26,
+) -> tuple[pd.Series, pd.Series]:
+    """일목 전환선·기준선을 계산한다 (인과 신호용 간소 버전)."""
+    tenkan_sen = (high.rolling(tenkan).max() + low.rolling(tenkan).min()) / 2.0
+    kijun_sen = (high.rolling(kijun).max() + low.rolling(kijun).min()) / 2.0
+    return tenkan_sen, kijun_sen
+
+
+def rolling_pivot_points(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    window: int = 60,
+) -> tuple[pd.Series, pd.Series, pd.Series]:
+    """롤링 피벗 P, S1, R1을 계산한다."""
+    prev_high = high.shift(1).rolling(window).max()
+    prev_low = low.shift(1).rolling(window).min()
+    prev_close = close.shift(1)
+    pivot = (prev_high + prev_low + prev_close) / 3.0
+    s1 = 2 * pivot - prev_high
+    r1 = 2 * pivot - prev_low
+    return pivot, s1, r1
+

src/deepcoin/techniques/keltner_breakout.py

@@ -0,0 +1,53 @@
+"""Keltner 채널 돌파 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import keltner_channels
+
+
+class KeltnerBreakoutTechnique(BaseTechnique):
+    """Keltner 상단 돌파 매수, 하단 이탈 매도."""
+
+    technique_id = "keltner_breakout"
+    technique_name = "Keltner 돌파"
+    category = "breakout"
+    causal = True
+    description = "Keltner 채널 상·하단 돌파 (B^)"
+
+    def default_extra_params(self) -> dict:
+        return {"ema_span": 20, "atr_period": 10, "atr_mult": 2.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        ema_span = int(params.extra.get("ema_span", 20))
+        atr_period = int(params.extra.get("atr_period", 10))
+        atr_mult = float(params.extra.get("atr_mult", 2.0))
+
+        close = df["close"].astype(float)
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        _, upper, lower = keltner_channels(
+            high, low, close, ema_span=ema_span, atr_period=atr_period, atr_mult=atr_mult,
+        )
+
+        signals: list[TechniqueSignal] = []
+        start = ema_span + atr_period
+
+        for i in range(start + 1, len(df)):
+            if pd.isna(upper.iloc[i]) or pd.isna(lower.iloc[i]):
+                continue
+
+            prev_c = float(close.iloc[i - 1])
+            c = float(close.iloc[i])
+            u = float(upper.iloc[i])
+            lo = float(lower.iloc[i])
+
+            if prev_c <= float(upper.iloc[i - 1]) and c > u:
+                signals.append(make_signal(df, i, c, "buy", "keltner_breakout_up", confidence=0.72))
+            elif prev_c >= float(lower.iloc[i - 1]) and c < lo:
+                signals.append(make_signal(df, i, c, "sell", "keltner_breakout_down", confidence=0.72))
+
+        return signals

src/deepcoin/techniques/keltner_reversal.py

@@ -0,0 +1,63 @@
+"""Keltner 채널 역추세 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import keltner_channels
+
+
+class KeltnerReversalTechnique(BaseTechnique):
+    """Keltner 하단 터치 후 반등 매수, 상단 터치 후 하락 매도."""
+
+    technique_id = "keltner_reversal"
+    technique_name = "Keltner 역추세"
+    category = "volatility"
+    causal = True
+    description = "Keltner 채널 하단 매수·상단 매도"
+
+    def default_extra_params(self) -> dict:
+        return {"ema_span": 20, "atr_period": 10, "atr_mult": 2.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        ema_span = int(params.extra.get("ema_span", 20))
+        atr_period = int(params.extra.get("atr_period", 10))
+        atr_mult = float(params.extra.get("atr_mult", 2.0))
+
+        close = df["close"].astype(float)
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        _, upper, lower = keltner_channels(
+            high, low, close, ema_span=ema_span, atr_period=atr_period, atr_mult=atr_mult,
+        )
+
+        signals: list[TechniqueSignal] = []
+        touched_lower = False
+        touched_upper = False
+        start = ema_span + atr_period
+
+        for i in range(start, len(df)):
+            if pd.isna(lower.iloc[i]):
+                continue
+
+            l = float(low.iloc[i])
+            h = float(high.iloc[i])
+            c = float(close.iloc[i])
+            lo = float(lower.iloc[i])
+            u = float(upper.iloc[i])
+
+            if l <= lo:
+                touched_lower = True
+            if touched_lower and c > lo:
+                signals.append(make_signal(df, i, c, "buy", "keltner_lower_bounce", confidence=0.69))
+                touched_lower = False
+
+            if h >= u:
+                touched_upper = True
+            if touched_upper and c < u:
+                signals.append(make_signal(df, i, c, "sell", "keltner_upper_reject", confidence=0.69))
+                touched_upper = False
+
+        return signals

src/deepcoin/techniques/legs.py

@@ -0,0 +1,130 @@
+"""신호 → 1매수·1매도 레그 변환."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from deepcoin.techniques.base import TechniqueSignal
+
+
+def signals_to_legs(
+    signals: list[TechniqueSignal],
+    min_leg_pct: float = 3.0,
+) -> list[dict[str, Any]]:
+    """시간순 신호를 1매수·1매도 레그로 짝짓는다.
+
+    포지션 없을 때만 매수, 보유 중일 때만 매도를 인정한다.
+
+    Args:
+        signals: 시간순 TechniqueSignal 리스트.
+        min_leg_pct: 최소 레그 수익률(%) 필터.
+
+    Returns:
+        GT legs와 동일 스키마의 레그 dict 리스트.
+    """
+    sorted_signals = sorted(signals, key=lambda s: s.bar_index)
+    legs: list[dict[str, Any]] = []
+    pending_buy: TechniqueSignal | None = None
+    leg_id = 0
+
+    for signal in sorted_signals:
+        if signal.price <= 0:
+            continue
+
+        if signal.side == "buy":
+            if pending_buy is None:
+                pending_buy = signal
+            elif signal.price < pending_buy.price:
+                pending_buy = signal
+            continue
+
+        if signal.side != "sell" or pending_buy is None:
+            continue
+
+        if pending_buy.price <= 0:
+            pending_buy = None
+            continue
+
+        leg_pct = (signal.price - pending_buy.price) / pending_buy.price * 100.0
+        if leg_pct < min_leg_pct:
+            continue
+
+        leg_id += 1
+        legs.append(
+            {
+                "leg_id": leg_id,
+                "buy_datetime": pending_buy.datetime,
+                "buy_price": round(pending_buy.price, 2),
+                "buy_bar_index": pending_buy.bar_index,
+                "sell_datetime": signal.datetime,
+                "sell_price": round(signal.price, 2),
+                "sell_bar_index": signal.bar_index,
+                "leg_pct": round(leg_pct, 2),
+                "bars_held": signal.bar_index - pending_buy.bar_index,
+            }
+        )
+        pending_buy = None
+
+    return legs
+
+
+def summarize_legs(legs: list[dict[str, Any]]) -> dict[str, Any]:
+    """레그 요약 통계를 계산한다."""
+    if not legs:
+        return {
+            "leg_count": 0,
+            "buy_count": 0,
+            "sell_count": 0,
+            "avg_leg_pct": 0.0,
+            "median_leg_pct": 0.0,
+            "max_leg_pct": 0.0,
+            "min_leg_pct": 0.0,
+            "avg_bars_held": 0.0,
+        }
+
+    pcts = [float(leg["leg_pct"]) for leg in legs]
+    bars = [int(leg["bars_held"]) for leg in legs]
+    pcts_sorted = sorted(pcts)
+    mid = len(pcts_sorted) // 2
+    median = (
+        pcts_sorted[mid]
+        if len(pcts_sorted) % 2 == 1
+        else (pcts_sorted[mid - 1] + pcts_sorted[mid]) / 2
+    )
+
+    return {
+        "leg_count": len(legs),
+        "buy_count": len(legs),
+        "sell_count": len(legs),
+        "avg_leg_pct": round(sum(pcts) / len(pcts), 2),
+        "median_leg_pct": round(median, 2),
+        "max_leg_pct": round(max(pcts), 2),
+        "min_leg_pct": round(min(pcts), 2),
+        "avg_bars_held": round(sum(bars) / len(bars), 1),
+    }
+
+
+def legs_to_signal_dicts(legs: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """레그를 GT signals 형식으로 변환한다."""
+    signals: list[dict[str, Any]] = []
+    for leg in legs:
+        signals.append(
+            {
+                "leg_id": leg["leg_id"],
+                "side": "buy",
+                "datetime": leg["buy_datetime"],
+                "price": leg["buy_price"],
+                "bar_index": leg["buy_bar_index"],
+            }
+        )
+        signals.append(
+            {
+                "leg_id": leg["leg_id"],
+                "side": "sell",
+                "datetime": leg["sell_datetime"],
+                "price": leg["sell_price"],
+                "bar_index": leg["sell_bar_index"],
+                "leg_pct": leg["leg_pct"],
+            }
+        )
+    return signals

src/deepcoin/techniques/local_extrema.py

@@ -0,0 +1,125 @@
+"""국소 극값 기반 매수·매도 (눌림목·반등 고점)."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+
+
+class LocalExtremaTechnique(BaseTechnique):
+    """좌우 N봉 대비 국소 저점·고점에서 신호 (인과: 확정 지연 order봉)."""
+
+    technique_id = "local_extrema"
+    technique_name = "국소 극값"
+    category = "swing"
+    causal = True
+    description = "국소 저점 매수·고점 매도 (눌림목 유형 포착, 9/27 구간 등)"
+
+    def default_extra_params(self) -> dict:
+        return {"order": 20, "min_swing_pct": 2.0, "min_bars_between": 30}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        order = int(params.extra.get("order", 20))
+        min_swing_pct = float(params.extra.get("min_swing_pct", 2.0))
+        min_bars = int(params.extra.get("min_bars_between", 30))
+        buys = _find_local_low_signals(df, order=order, min_swing_pct=min_swing_pct)
+        sells = _find_local_high_signals(df, order=order, min_swing_pct=min_swing_pct)
+        return _dedupe_signals(buys + sells, min_bars=min_bars)
+
+
+def _find_local_low_signals(
+    df: pd.DataFrame,
+    order: int,
+    min_swing_pct: float,
+) -> list[TechniqueSignal]:
+    """국소 저점 매수 신호 (order봉 후 확정)."""
+    signals: list[TechniqueSignal] = []
+    lookback = order * 4
+
+    for pivot_idx in range(order, len(df) - order):
+        low_val = float(df.iloc[pivot_idx]["low"])
+        window = df["low"].iloc[pivot_idx - order : pivot_idx + order + 1]
+        if low_val > window.min():
+            continue
+
+        start = max(0, pivot_idx - lookback)
+        recent_high = float(df["high"].iloc[start : pivot_idx + 1].max())
+        if recent_high <= 0:
+            continue
+        drop_pct = (recent_high - low_val) / recent_high * 100.0
+        if drop_pct < min_swing_pct:
+            continue
+
+        confirm_idx = pivot_idx + order
+        signals.append(
+            TechniqueSignal(
+                side="buy",
+                bar_index=confirm_idx,
+                price=round(low_val, 2),
+                datetime=pd.Timestamp(df.iloc[confirm_idx]["datetime"]).strftime("%Y-%m-%d %H:%M:%S"),
+                pivot_bar_index=pivot_idx,
+                confidence=min(1.0, drop_pct / 10.0),
+                reason="local_low",
+            )
+        )
+    return signals
+
+
+def _find_local_high_signals(
+    df: pd.DataFrame,
+    order: int,
+    min_swing_pct: float,
+) -> list[TechniqueSignal]:
+    """국소 고점 매도 신호 (order봉 후 확정)."""
+    signals: list[TechniqueSignal] = []
+    lookback = order * 4
+
+    for pivot_idx in range(order, len(df) - order):
+        high_val = float(df.iloc[pivot_idx]["high"])
+        window = df["high"].iloc[pivot_idx - order : pivot_idx + order + 1]
+        if high_val < window.max():
+            continue
+
+        start = max(0, pivot_idx - lookback)
+        recent_low = float(df["low"].iloc[start : pivot_idx + 1].min())
+        if recent_low <= 0:
+            continue
+        rise_pct = (high_val - recent_low) / recent_low * 100.0
+        if rise_pct < min_swing_pct:
+            continue
+
+        confirm_idx = pivot_idx + order
+        signals.append(
+            TechniqueSignal(
+                side="sell",
+                bar_index=confirm_idx,
+                price=round(high_val, 2),
+                datetime=pd.Timestamp(df.iloc[confirm_idx]["datetime"]).strftime("%Y-%m-%d %H:%M:%S"),
+                pivot_bar_index=pivot_idx,
+                confidence=min(1.0, rise_pct / 10.0),
+                reason="local_high",
+            )
+        )
+    return signals
+
+
+def _dedupe_signals(signals: list[TechniqueSignal], min_bars: int) -> list[TechniqueSignal]:
+    """동일 방향 근접 신호를 병합한다."""
+    if not signals:
+        return []
+
+    sorted_signals = sorted(signals, key=lambda s: s.bar_index)
+    merged: list[TechniqueSignal] = [sorted_signals[0]]
+
+    for signal in sorted_signals[1:]:
+        last = merged[-1]
+        if signal.side == last.side and signal.bar_index - last.bar_index < min_bars:
+            if signal.side == "buy" and signal.price < last.price:
+                merged[-1] = signal
+            elif signal.side == "sell" and signal.price > last.price:
+                merged[-1] = signal
+        else:
+            merged.append(signal)
+
+    return sorted(merged, key=lambda s: s.bar_index)

src/deepcoin/techniques/ma_cross.py

@@ -0,0 +1,68 @@
+"""이동평균 골든·데드 크로스."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.indicators import ema
+
+
+class MaCrossTechnique(BaseTechnique):
+    """단기 EMA가 장기 EMA를 상향 돌파 시 매수, 하향 돌파 시 매도."""
+
+    technique_id = "ma_cross"
+    technique_name = "EMA 크로스"
+    category = "indicator"
+    causal = True
+    description = "EMA(20/60) 골든크로스 매수·데드크로스 매도"
+
+    def default_extra_params(self) -> dict:
+        return {"fast_span": 20, "slow_span": 60}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        fast_span = int(params.extra.get("fast_span", 20))
+        slow_span = int(params.extra.get("slow_span", 60))
+
+        close = df["close"].astype(float)
+        fast = ema(close, fast_span)
+        slow = ema(close, slow_span)
+
+        signals: list[TechniqueSignal] = []
+        start = max(fast_span, slow_span)
+
+        for i in range(start + 1, len(df)):
+            if pd.isna(fast.iloc[i]) or pd.isna(slow.iloc[i]):
+                continue
+
+            prev_fast = float(fast.iloc[i - 1])
+            prev_slow = float(slow.iloc[i - 1])
+            curr_fast = float(fast.iloc[i])
+            curr_slow = float(slow.iloc[i])
+            price = float(close.iloc[i])
+            dt = pd.Timestamp(df.iloc[i]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+
+            if prev_fast <= prev_slow and curr_fast > curr_slow:
+                signals.append(
+                    TechniqueSignal(
+                        side="buy",
+                        bar_index=i,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=0.6,
+                        reason="golden_cross",
+                    )
+                )
+            elif prev_fast >= prev_slow and curr_fast < curr_slow:
+                signals.append(
+                    TechniqueSignal(
+                        side="sell",
+                        bar_index=i,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=0.6,
+                        reason="death_cross",
+                    )
+                )
+
+        return signals

src/deepcoin/techniques/macd_cross.py

@@ -0,0 +1,68 @@
+"""MACD 시그널선 크로스."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.indicators import macd
+
+
+class MacdCrossTechnique(BaseTechnique):
+    """MACD가 시그널선을 상향 돌파 시 매수, 하향 돌파 시 매도."""
+
+    technique_id = "macd_cross"
+    technique_name = "MACD 크로스"
+    category = "indicator"
+    causal = True
+    description = "MACD(12,26,9) 시그널선 골든·데드 크로스"
+
+    def default_extra_params(self) -> dict:
+        return {"fast": 12, "slow": 26, "signal": 9}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        fast = int(params.extra.get("fast", 12))
+        slow = int(params.extra.get("slow", 26))
+        signal_span = int(params.extra.get("signal", 9))
+
+        close = df["close"].astype(float)
+        macd_line, signal_line, _ = macd(close, fast=fast, slow=slow, signal=signal_span)
+
+        signals: list[TechniqueSignal] = []
+        start = slow + signal_span
+
+        for i in range(start + 1, len(df)):
+            if pd.isna(macd_line.iloc[i]) or pd.isna(signal_line.iloc[i]):
+                continue
+
+            prev_macd = float(macd_line.iloc[i - 1])
+            prev_sig = float(signal_line.iloc[i - 1])
+            curr_macd = float(macd_line.iloc[i])
+            curr_sig = float(signal_line.iloc[i])
+            price = float(close.iloc[i])
+            dt = pd.Timestamp(df.iloc[i]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+
+            if prev_macd <= prev_sig and curr_macd > curr_sig:
+                signals.append(
+                    TechniqueSignal(
+                        side="buy",
+                        bar_index=i,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=0.6,
+                        reason="macd_bull_cross",
+                    )
+                )
+            elif prev_macd >= prev_sig and curr_macd < curr_sig:
+                signals.append(
+                    TechniqueSignal(
+                        side="sell",
+                        bar_index=i,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=0.6,
+                        reason="macd_bear_cross",
+                    )
+                )
+
+        return signals

src/deepcoin/techniques/macd_divergence.py

@@ -0,0 +1,76 @@
+"""MACD 다이버전스 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import (
+    dedupe_signals,
+    detect_bearish_divergence,
+    detect_bullish_divergence,
+    find_confirmed_pivots,
+    make_signal,
+)
+from deepcoin.techniques.indicators import macd
+
+
+class MacdDivergenceTechnique(BaseTechnique):
+    """MACD 히스토그램 상승·하락 다이버전스."""
+
+    technique_id = "macd_divergence"
+    technique_name = "MACD 다이버전스"
+    category = "divergence"
+    causal = True
+    description = "MACD 히스토그램 상승(Bd)·하락(Sd) 다이버전스"
+
+    def default_extra_params(self) -> dict:
+        return {
+            "fast": 12, "slow": 26, "signal": 9,
+            "order": 12, "min_bars_between": 15, "max_bars_between": 400,
+        }
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        fast = int(params.extra.get("fast", 12))
+        slow = int(params.extra.get("slow", 26))
+        signal_span = int(params.extra.get("signal", 9))
+        order = int(params.extra.get("order", 12))
+        min_bars = int(params.extra.get("min_bars_between", 15))
+        max_bars = int(params.extra.get("max_bars_between", 400))
+
+        close = df["close"].astype(float)
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        _, _, hist = macd(close, fast=fast, slow=slow, signal=signal_span)
+
+        low_pivots = find_confirmed_pivots(low, order, "low")
+        high_pivots = find_confirmed_pivots(high, order, "high")
+        signals: list[TechniqueSignal] = []
+
+        for pivot_idx, _ in detect_bullish_divergence(
+            low_pivots, hist, min_bars_between=min_bars, max_bars_between=max_bars,
+        ):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, float(close.iloc[confirm_idx]), "buy",
+                    "macd_bull_divergence", pivot_bar_index=pivot_idx, confidence=0.77,
+                )
+            )
+
+        for pivot_idx, _ in detect_bearish_divergence(
+            high_pivots, hist, min_bars_between=min_bars, max_bars_between=max_bars,
+        ):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, float(close.iloc[confirm_idx]), "sell",
+                    "macd_bear_divergence", pivot_bar_index=pivot_idx, confidence=0.77,
+                )
+            )
+
+        return dedupe_signals(signals, min_bars=min_bars)

src/deepcoin/techniques/minor_swing.py

@@ -0,0 +1,67 @@
+"""소형 ZigZag + 국소 극값 하이브리드."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.local_extrema import LocalExtremaTechnique
+from deepcoin.techniques.zigzag_causal import find_causal_zigzag_signals
+
+
+class MinorSwingTechnique(BaseTechnique):
+    """2.5% 인과 ZigZag와 국소 극값을 결합한 하이브리드."""
+
+    technique_id = "minor_swing"
+    technique_name = "소형 스윙 하이브리드"
+    category = "hybrid"
+    causal = True
+    description = "소형 ZigZag(2.5%) + 국소 극값 — GT 중간 눌림목 보완"
+
+    def default_extra_params(self) -> dict:
+        return {
+            "reversal_pct": 2.5,
+            "order": 15,
+            "min_swing_pct": 2.0,
+            "min_bars_between": 20,
+        }
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        reversal_pct = float(params.extra.get("reversal_pct", 2.5))
+        zz_signals = find_causal_zigzag_signals(df, reversal_pct=reversal_pct)
+
+        local_params = TechniqueParams(
+            interval_min=params.interval_min,
+            lookback_days=params.lookback_days,
+            min_leg_pct=params.min_leg_pct,
+            initial_cash_krw=params.initial_cash_krw,
+            fee_rate=params.fee_rate,
+            extra={
+                "order": int(params.extra.get("order", 15)),
+                "min_swing_pct": float(params.extra.get("min_swing_pct", 2.0)),
+                "min_bars_between": int(params.extra.get("min_bars_between", 20)),
+            },
+        )
+        local_signals = LocalExtremaTechnique().generate_signals(df, local_params)
+        return _merge_signals(zz_signals + local_signals, min_bars=20)
+
+
+def _merge_signals(signals: list[TechniqueSignal], min_bars: int) -> list[TechniqueSignal]:
+    """방향별 근접 신호를 병합한다."""
+    if not signals:
+        return []
+
+    sorted_signals = sorted(signals, key=lambda s: s.bar_index)
+    merged: list[TechniqueSignal] = [sorted_signals[0]]
+
+    for signal in sorted_signals[1:]:
+        last = merged[-1]
+        if signal.side == last.side and signal.bar_index - last.bar_index < min_bars:
+            if signal.side == "buy" and signal.price < last.price:
+                merged[-1] = signal
+            elif signal.side == "sell" and signal.price > last.price:
+                merged[-1] = signal
+        else:
+            merged.append(signal)
+
+    return sorted(merged, key=lambda s: s.bar_index)

src/deepcoin/techniques/obv_divergence.py

@@ -0,0 +1,71 @@
+"""OBV 다이버전스 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import (
+    dedupe_signals,
+    detect_bearish_divergence,
+    detect_bullish_divergence,
+    find_confirmed_pivots,
+    make_signal,
+)
+from deepcoin.techniques.indicators import obv
+
+
+class ObvDivergenceTechnique(BaseTechnique):
+    """OBV 상승·하락 다이버전스."""
+
+    technique_id = "obv_divergence"
+    technique_name = "OBV 다이버전스"
+    category = "divergence"
+    causal = True
+    description = "OBV 상승(Bd)·하락(Sd) 다이버전스"
+
+    def default_extra_params(self) -> dict:
+        return {"order": 12, "min_bars_between": 15, "max_bars_between": 400}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        order = int(params.extra.get("order", 12))
+        min_bars = int(params.extra.get("min_bars_between", 15))
+        max_bars = int(params.extra.get("max_bars_between", 400))
+
+        close = df["close"].astype(float)
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        volume = df["volume"].astype(float)
+        obv_vals = obv(close, volume)
+
+        low_pivots = find_confirmed_pivots(low, order, "low")
+        high_pivots = find_confirmed_pivots(high, order, "high")
+        signals: list[TechniqueSignal] = []
+
+        for pivot_idx, _ in detect_bullish_divergence(
+            low_pivots, obv_vals, min_bars_between=min_bars, max_bars_between=max_bars,
+        ):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, float(close.iloc[confirm_idx]), "buy",
+                    "obv_bull_divergence", pivot_bar_index=pivot_idx, confidence=0.76,
+                )
+            )
+
+        for pivot_idx, _ in detect_bearish_divergence(
+            high_pivots, obv_vals, min_bars_between=min_bars, max_bars_between=max_bars,
+        ):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, float(close.iloc[confirm_idx]), "sell",
+                    "obv_bear_divergence", pivot_bar_index=pivot_idx, confidence=0.76,
+                )
+            )
+
+        return dedupe_signals(signals, min_bars=min_bars)

src/deepcoin/techniques/parabolic_sar_signal.py

@@ -0,0 +1,46 @@
+"""Parabolic SAR 추세 전환 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import parabolic_sar
+
+
+class ParabolicSarTechnique(BaseTechnique):
+    """SAR가 가격 위·아래 전환 시 매도·매수."""
+
+    technique_id = "parabolic_sar"
+    technique_name = "Parabolic SAR"
+    category = "trend"
+    causal = True
+    description = "Parabolic SAR 추세 전환 신호"
+
+    def default_extra_params(self) -> dict:
+        return {"af_step": 0.02, "af_max": 0.2}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        sar = parabolic_sar(high, low, close)
+
+        signals: list[TechniqueSignal] = []
+
+        for i in range(2, len(df)):
+            if pd.isna(sar.iloc[i]) or pd.isna(sar.iloc[i - 1]):
+                continue
+
+            prev_c = float(close.iloc[i - 1])
+            c = float(close.iloc[i])
+            prev_sar = float(sar.iloc[i - 1])
+            curr_sar = float(sar.iloc[i])
+
+            if prev_c <= prev_sar and c > curr_sar:
+                signals.append(make_signal(df, i, c, "buy", "psar_bull_flip", confidence=0.65))
+            elif prev_c >= prev_sar and c < curr_sar:
+                signals.append(make_signal(df, i, c, "sell", "psar_bear_flip", confidence=0.65))
+
+        return signals

src/deepcoin/techniques/pivot_points.py

@@ -0,0 +1,60 @@
+"""롤링 피벗 포인트 반등 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import rolling_pivot_points
+
+
+class PivotPointsTechnique(BaseTechnique):
+    """S1 지지 반등 매수, R1 저항 거부 매도."""
+
+    technique_id = "pivot_points"
+    technique_name = "피벗 포인트"
+    category = "structure"
+    causal = True
+    description = "롤링 피벗 S1/R1 반전"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 60, "touch_pct": 0.2}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 60))
+        touch_pct = float(params.extra.get("touch_pct", 0.2)) / 100.0
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        _, s1, r1 = rolling_pivot_points(high, low, close, window=window)
+
+        signals: list[TechniqueSignal] = []
+        touched_s1 = False
+        touched_r1 = False
+
+        for i in range(window + 1, len(df)):
+            if pd.isna(s1.iloc[i]) or pd.isna(r1.iloc[i]):
+                continue
+
+            c = float(close.iloc[i])
+            l = float(low.iloc[i])
+            h = float(high.iloc[i])
+            s1_i = float(s1.iloc[i])
+            r1_i = float(r1.iloc[i])
+            prev_c = float(close.iloc[i - 1])
+
+            if l <= s1_i * (1 + touch_pct):
+                touched_s1 = True
+            if touched_s1 and prev_c <= s1_i and c > s1_i:
+                signals.append(make_signal(df, i, c, "buy", "pivot_s1_bounce", confidence=0.67))
+                touched_s1 = False
+
+            if h >= r1_i * (1 - touch_pct):
+                touched_r1 = True
+            if touched_r1 and prev_c >= r1_i and c < r1_i:
+                signals.append(make_signal(df, i, c, "sell", "pivot_r1_reject", confidence=0.67))
+                touched_r1 = False
+
+        return signals

src/deepcoin/techniques/pivot_swing.py

@@ -0,0 +1,52 @@
+"""피벗 스윙 고저점 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import dedupe_signals, find_confirmed_pivots, make_signal
+
+
+class PivotSwingTechnique(BaseTechnique):
+    """좌우 N봉 피벗 저점·고점 확정 시 매수·매도."""
+
+    technique_id = "pivot_swing"
+    technique_name = "피벗 스윙"
+    category = "swing"
+    causal = True
+    description = "피벗 저점 매수·고점 매도 (스윙 B/S)"
+
+    def default_extra_params(self) -> dict:
+        return {"order": 12, "min_bars_between": 24}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        order = int(params.extra.get("order", 12))
+        min_bars = int(params.extra.get("min_bars_between", 24))
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        signals: list[TechniqueSignal] = []
+
+        for pivot_idx, pivot_val in find_confirmed_pivots(low, order, "low"):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, pivot_val, "buy",
+                    "pivot_low", pivot_bar_index=pivot_idx, confidence=0.75,
+                )
+            )
+
+        for pivot_idx, pivot_val in find_confirmed_pivots(high, order, "high"):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, pivot_val, "sell",
+                    "pivot_high", pivot_bar_index=pivot_idx, confidence=0.75,
+                )
+            )
+
+        return dedupe_signals(signals, min_bars=min_bars)

src/deepcoin/techniques/range_breakout.py

@@ -0,0 +1,49 @@
+"""레인지 돌파 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+
+
+class RangeBreakoutTechnique(BaseTechnique):
+    """N봉 고가·저가 레인지 상향·하향 돌파."""
+
+    technique_id = "range_breakout"
+    technique_name = "레인지 돌파"
+    category = "breakout"
+    causal = True
+    description = "N봉 레인지 상·하단 돌파 (B^)"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 40, "buffer_pct": 0.1}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 40))
+        buffer_pct = float(params.extra.get("buffer_pct", 0.1)) / 100.0
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        range_high = high.shift(1).rolling(window).max()
+        range_low = low.shift(1).rolling(window).min()
+
+        signals: list[TechniqueSignal] = []
+
+        for i in range(window + 1, len(df)):
+            if pd.isna(range_high.iloc[i]) or pd.isna(range_low.iloc[i]):
+                continue
+
+            c = float(close.iloc[i])
+            prev_c = float(close.iloc[i - 1])
+            rh = float(range_high.iloc[i])
+            rl = float(range_low.iloc[i])
+
+            if prev_c <= rh and c > rh * (1 + buffer_pct):
+                signals.append(make_signal(df, i, c, "buy", "range_breakout_up", confidence=0.73))
+            elif prev_c >= rl and c < rl * (1 - buffer_pct):
+                signals.append(make_signal(df, i, c, "sell", "range_breakout_down", confidence=0.73))
+
+        return signals

src/deepcoin/techniques/registry.py

@@ -0,0 +1,139 @@
+"""매매 기법 레지스트리."""
+
+from __future__ import annotations
+
+from deepcoin.techniques.base import BaseTechnique
+from deepcoin.techniques.adx_trend import AdxTrendTechnique
+from deepcoin.techniques.atr_channel import AtrChannelTechnique
+from deepcoin.techniques.bb_reversal import BbReversalTechnique
+from deepcoin.techniques.bb_squeeze_breakout import BbSqueezeBreakoutTechnique
+from deepcoin.techniques.cci_extreme import CciExtremeTechnique
+from deepcoin.techniques.composite_breakout import CompositeBreakoutTechnique
+from deepcoin.techniques.composite_divergence import CompositeDivergenceTechnique
+from deepcoin.techniques.composite_full import CompositeFullTechnique
+from deepcoin.techniques.composite_pullback import CompositePullbackTechnique
+from deepcoin.techniques.composite_swing import CompositeSwingTechnique
+from deepcoin.techniques.composite_v3 import CompositeV3Technique
+from deepcoin.techniques.donchian import DonchianTechnique
+from deepcoin.techniques.ema_pullback import EmaPullbackTechnique
+from deepcoin.techniques.fib_pullback import FibPullbackTechnique
+from deepcoin.techniques.fractal_swing import FractalSwingTechnique
+from deepcoin.techniques.ichimoku_trend import IchimokuTrendTechnique
+from deepcoin.techniques.keltner_breakout import KeltnerBreakoutTechnique
+from deepcoin.techniques.keltner_reversal import KeltnerReversalTechnique
+from deepcoin.techniques.local_extrema import LocalExtremaTechnique
+from deepcoin.techniques.ma_cross import MaCrossTechnique
+from deepcoin.techniques.macd_cross import MacdCrossTechnique
+from deepcoin.techniques.macd_divergence import MacdDivergenceTechnique
+from deepcoin.techniques.minor_swing import MinorSwingTechnique
+from deepcoin.techniques.obv_divergence import ObvDivergenceTechnique
+from deepcoin.techniques.parabolic_sar_signal import ParabolicSarTechnique
+from deepcoin.techniques.pivot_points import PivotPointsTechnique
+from deepcoin.techniques.pivot_swing import PivotSwingTechnique
+from deepcoin.techniques.range_breakout import RangeBreakoutTechnique
+from deepcoin.techniques.roc_reversal import RocReversalTechnique
+from deepcoin.techniques.rsi_divergence import RsiDivergenceTechnique
+from deepcoin.techniques.rsi_swing import RsiSwingTechnique
+from deepcoin.techniques.stochastic_cross import StochasticCrossTechnique
+from deepcoin.techniques.supertrend_signal import SupertrendTechnique
+from deepcoin.techniques.support_bounce import SupportBounceTechnique
+from deepcoin.techniques.support_resistance import SupportResistanceTechnique
+from deepcoin.techniques.swing_failure import SwingFailureTechnique
+from deepcoin.techniques.volume_breakout import VolumeBreakoutTechnique
+from deepcoin.techniques.volume_spike import VolumeSpikeTechnique
+from deepcoin.techniques.zigzag_causal import ZigzagCausalTechnique
+
+# 카테고리별 단일 기법 (인과, 미래 데이터 미사용)
+_SINGLE_TECHNIQUES: list[BaseTechnique] = [
+    # swing
+    ZigzagCausalTechnique(),
+    MinorSwingTechnique(),
+    LocalExtremaTechnique(),
+    PivotSwingTechnique(),
+    FractalSwingTechnique(),
+    SwingFailureTechnique(),
+    DonchianTechnique(),
+    # pullback
+    EmaPullbackTechnique(),
+    FibPullbackTechnique(),
+    SupportBounceTechnique(),
+    # breakout
+    KeltnerBreakoutTechnique(),
+    RangeBreakoutTechnique(),
+    VolumeBreakoutTechnique(),
+    BbSqueezeBreakoutTechnique(),
+    # divergence
+    RsiDivergenceTechnique(),
+    MacdDivergenceTechnique(),
+    ObvDivergenceTechnique(),
+    # indicator (기존)
+    BbReversalTechnique(),
+    MaCrossTechnique(),
+    RsiSwingTechnique(),
+    MacdCrossTechnique(),
+    # trend
+    SupertrendTechnique(),
+    AdxTrendTechnique(),
+    IchimokuTrendTechnique(),
+    ParabolicSarTechnique(),
+    # momentum
+    StochasticCrossTechnique(),
+    CciExtremeTechnique(),
+    RocReversalTechnique(),
+    # volatility
+    KeltnerReversalTechnique(),
+    AtrChannelTechnique(),
+    # structure
+    PivotPointsTechnique(),
+    SupportResistanceTechnique(),
+    # volume
+    VolumeSpikeTechnique(),
+]
+
+# 복합 기법
+_COMPOSITE_TECHNIQUES: list[BaseTechnique] = [
+    CompositeSwingTechnique(),
+    CompositePullbackTechnique(),
+    CompositeBreakoutTechnique(),
+    CompositeDivergenceTechnique(),
+    CompositeV3Technique(),
+    CompositeFullTechnique(),
+]
+
+_ALL_TECHNIQUES: list[BaseTechnique] = _SINGLE_TECHNIQUES + _COMPOSITE_TECHNIQUES
+
+
+def get_single_techniques() -> list[BaseTechnique]:
+    """복합 제외 단일 기법 목록을 반환한다."""
+    return list(_SINGLE_TECHNIQUES)
+
+
+def get_composite_techniques() -> list[BaseTechnique]:
+    """복합 기법 목록을 반환한다."""
+    return list(_COMPOSITE_TECHNIQUES)
+
+
+def get_all_techniques() -> list[BaseTechnique]:
+    """등록된 모든 매매 기법을 반환한다."""
+    return list(_ALL_TECHNIQUES)
+
+
+def get_technique(technique_id: str) -> BaseTechnique | None:
+    """ID로 기법을 조회한다."""
+    for technique in _ALL_TECHNIQUES:
+        if technique.technique_id == technique_id:
+            return technique
+    return None
+
+
+def list_technique_ids() -> list[str]:
+    """기법 ID 목록을 반환한다."""
+    return [t.technique_id for t in _ALL_TECHNIQUES]
+
+
+def techniques_by_category() -> dict[str, list[str]]:
+    """카테고리별 기법 ID 목록을 반환한다."""
+    result: dict[str, list[str]] = {}
+    for technique in _ALL_TECHNIQUES:
+        result.setdefault(technique.category, []).append(technique.technique_id)
+    return result

src/deepcoin/techniques/roc_reversal.py

@@ -0,0 +1,55 @@
+"""ROC 반전 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal, safe_float
+from deepcoin.techniques.indicators import roc
+
+
+class RocReversalTechnique(BaseTechnique):
+    """ROC 극단 하락 후 반등 매수, 극단 상승 후 하락 매도."""
+
+    technique_id = "roc_reversal"
+    technique_name = "ROC 반전"
+    category = "momentum"
+    causal = True
+    description = "ROC(12) 극값 반전 신호"
+
+    def default_extra_params(self) -> dict:
+        return {"period": 12, "low_pct": -5.0, "high_pct": 5.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        period = int(params.extra.get("period", 12))
+        low_pct = float(params.extra.get("low_pct", -5.0))
+        high_pct = float(params.extra.get("high_pct", 5.0))
+
+        close = df["close"].astype(float)
+        roc_vals = roc(close, period=period)
+
+        signals: list[TechniqueSignal] = []
+        was_low = False
+        was_high = False
+
+        for i in range(period + 2, len(df)):
+            curr = safe_float(roc_vals.iloc[i])
+            prev = safe_float(roc_vals.iloc[i - 1])
+            c = safe_float(close.iloc[i])
+            if curr is None or prev is None or c is None:
+                continue
+
+            if curr < low_pct:
+                was_low = True
+            if was_low and prev < low_pct <= curr:
+                signals.append(make_signal(df, i, c, "buy", "roc_bull_reversal", confidence=0.64))
+                was_low = False
+
+            if curr > high_pct:
+                was_high = True
+            if was_high and prev > high_pct >= curr:
+                signals.append(make_signal(df, i, c, "sell", "roc_bear_reversal", confidence=0.64))
+                was_high = False
+
+        return signals

src/deepcoin/techniques/rsi_divergence.py

@@ -0,0 +1,71 @@
+"""RSI 다이버전스 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import (
+    dedupe_signals,
+    detect_bearish_divergence,
+    detect_bullish_divergence,
+    find_confirmed_pivots,
+    make_signal,
+)
+from deepcoin.techniques.indicators import rsi
+
+
+class RsiDivergenceTechnique(BaseTechnique):
+    """RSI 상승·하락 다이버전스 매수·매도."""
+
+    technique_id = "rsi_divergence"
+    technique_name = "RSI 다이버전스"
+    category = "divergence"
+    causal = True
+    description = "RSI 상승(Bd)·하락(Sd) 다이버전스"
+
+    def default_extra_params(self) -> dict:
+        return {"period": 14, "order": 12, "min_bars_between": 15, "max_bars_between": 400}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        period = int(params.extra.get("period", 14))
+        order = int(params.extra.get("order", 12))
+        min_bars = int(params.extra.get("min_bars_between", 15))
+        max_bars = int(params.extra.get("max_bars_between", 400))
+
+        close = df["close"].astype(float)
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        rsi_vals = rsi(close, period=period)
+
+        low_pivots = find_confirmed_pivots(low, order, "low")
+        high_pivots = find_confirmed_pivots(high, order, "high")
+        signals: list[TechniqueSignal] = []
+
+        for pivot_idx, _ in detect_bullish_divergence(
+            low_pivots, rsi_vals, min_bars_between=min_bars, max_bars_between=max_bars,
+        ):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, float(close.iloc[confirm_idx]), "buy",
+                    "rsi_bull_divergence", pivot_bar_index=pivot_idx, confidence=0.78,
+                )
+            )
+
+        for pivot_idx, _ in detect_bearish_divergence(
+            high_pivots, rsi_vals, min_bars_between=min_bars, max_bars_between=max_bars,
+        ):
+            confirm_idx = pivot_idx + order
+            if confirm_idx >= len(df):
+                continue
+            signals.append(
+                make_signal(
+                    df, confirm_idx, float(close.iloc[confirm_idx]), "sell",
+                    "rsi_bear_divergence", pivot_bar_index=pivot_idx, confidence=0.78,
+                )
+            )
+
+        return dedupe_signals(signals, min_bars=min_bars)

src/deepcoin/techniques/rsi_swing.py

@@ -0,0 +1,74 @@
+"""RSI 과매도·과매수 스윙."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.indicators import rsi
+
+
+class RsiSwingTechnique(BaseTechnique):
+    """RSI 30 이탈 후 복귀 시 매수, 70 이탈 후 복귀 시 매도."""
+
+    technique_id = "rsi_swing"
+    technique_name = "RSI 스윙"
+    category = "indicator"
+    causal = True
+    description = "RSI(14) 과매도 반등 매수·과매수 하락 매도"
+
+    def default_extra_params(self) -> dict:
+        return {"period": 14, "oversold": 30.0, "overbought": 70.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        period = int(params.extra.get("period", 14))
+        oversold = float(params.extra.get("oversold", 30.0))
+        overbought = float(params.extra.get("overbought", 70.0))
+
+        close = df["close"].astype(float)
+        rsi_vals = rsi(close, period=period)
+
+        signals: list[TechniqueSignal] = []
+        was_oversold = False
+        was_overbought = False
+
+        for i in range(period + 1, len(df)):
+            if pd.isna(rsi_vals.iloc[i]):
+                continue
+
+            curr = float(rsi_vals.iloc[i])
+            prev = float(rsi_vals.iloc[i - 1])
+            price = float(close.iloc[i])
+            dt = pd.Timestamp(df.iloc[i]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+
+            if curr < oversold:
+                was_oversold = True
+            if was_oversold and prev < oversold <= curr:
+                signals.append(
+                    TechniqueSignal(
+                        side="buy",
+                        bar_index=i,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=0.65,
+                        reason="rsi_oversold_exit",
+                    )
+                )
+                was_oversold = False
+
+            if curr > overbought:
+                was_overbought = True
+            if was_overbought and prev > overbought >= curr:
+                signals.append(
+                    TechniqueSignal(
+                        side="sell",
+                        bar_index=i,
+                        price=round(price, 2),
+                        datetime=dt,
+                        confidence=0.65,
+                        reason="rsi_overbought_exit",
+                    )
+                )
+                was_overbought = False
+
+        return signals

src/deepcoin/techniques/runner.py

@@ -0,0 +1,206 @@
+"""매매 기법 실행 및 결과 저장."""
+
+from __future__ import annotations
+
+import json
+import logging
+from collections.abc import Callable
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+from deepcoin.data.candle_loader import load_candles
+from deepcoin.data.intervals import interval_label
+from deepcoin.evaluation.gt_align import align_with_ground_truth
+from deepcoin.ground_truth.pnl import simulate_gt_pnl
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueResult
+from deepcoin.techniques.legs import legs_to_signal_dicts, signals_to_legs, summarize_legs
+from deepcoin.techniques.registry import get_all_techniques, get_technique
+
+
+def run_technique(
+    technique: BaseTechnique,
+    df: pd.DataFrame,
+    params: TechniqueParams,
+    gt_result: dict[str, Any] | None = None,
+    tolerance_bars: int = 480,
+) -> TechniqueResult:
+    """단일 기법을 실행하고 GT 정합을 계산한다.
+
+    Args:
+        technique: 실행할 기법.
+        df: 캔들 DataFrame.
+        params: 실행 파라미터.
+        gt_result: Ground Truth JSON dict (정합 평가용).
+        tolerance_bars: GT 신호 매칭 허용 봉 수.
+
+    Returns:
+        TechniqueResult.
+    """
+    merged_extra = {**technique.default_extra_params(), **params.extra}
+    run_params = TechniqueParams(
+        interval_min=params.interval_min,
+        lookback_days=params.lookback_days,
+        min_leg_pct=params.min_leg_pct,
+        initial_cash_krw=params.initial_cash_krw,
+        fee_rate=params.fee_rate,
+        extra=merged_extra,
+    )
+
+    raw_signals = technique.generate_signals(df, run_params)
+    raw_signals = [s for s in raw_signals if s.price > 0]
+    legs = signals_to_legs(raw_signals, min_leg_pct=run_params.min_leg_pct)
+    summary = summarize_legs(legs)
+    pnl = simulate_gt_pnl(
+        legs,
+        initial_cash_krw=run_params.initial_cash_krw,
+        fee_rate=run_params.fee_rate,
+    )
+
+    alignment = None
+    if gt_result is not None:
+        alignment = align_with_ground_truth(
+            gt_result=gt_result,
+            technique_signals=[s.to_dict() for s in raw_signals],
+            technique_legs=legs,
+            tolerance_bars=tolerance_bars,
+        )
+
+    return TechniqueResult(
+        technique_id=technique.technique_id,
+        technique_name=technique.technique_name,
+        category=technique.category,
+        causal=technique.causal,
+        description=technique.description,
+        params={
+            "interval_min": run_params.interval_min,
+            "lookback_days": run_params.lookback_days,
+            "min_leg_pct": run_params.min_leg_pct,
+            "initial_cash_krw": run_params.initial_cash_krw,
+            "fee_rate": run_params.fee_rate,
+            **merged_extra,
+        },
+        signals=[s.to_dict() for s in raw_signals],
+        legs=legs,
+        summary=summary,
+        pnl=pnl,
+        alignment=alignment,
+    )
+
+
+def run_all_techniques(
+    db_path: Path,
+    symbol: str,
+    params: TechniqueParams,
+    gt_result: dict[str, Any] | None = None,
+    tolerance_bars: int = 480,
+    technique_ids: list[str] | None = None,
+    on_result: Callable[[TechniqueResult], None] | None = None,
+    skip_errors: bool = True,
+) -> list[TechniqueResult]:
+    """등록된 기법을 일괄 실행한다.
+
+    Args:
+        on_result: 기법 1건 완료 시 호출 (즉시 저장 등).
+        skip_errors: True면 실패 기법은 건너뛰고 계속 실행.
+    """
+    df = load_candles(
+        db_path=db_path,
+        symbol=symbol,
+        interval_min=params.interval_min,
+        lookback_days=params.lookback_days,
+    )
+
+    techniques = get_all_techniques()
+    if technique_ids:
+        techniques = [t for t in techniques if t.technique_id in technique_ids]
+
+    results: list[TechniqueResult] = []
+    total = len(techniques)
+    for idx, technique in enumerate(techniques, start=1):
+        try:
+            result = run_technique(
+                technique=technique,
+                df=df,
+                params=params,
+                gt_result=gt_result,
+                tolerance_bars=tolerance_bars,
+            )
+        except Exception:
+            logger.exception("기법 실행 실패: %s", technique.technique_id)
+            if not skip_errors:
+                raise
+            continue
+        results.append(result)
+        if on_result is not None:
+            on_result(result)
+        pct = idx / total * 100.0 if total else 100.0
+        logger.info(
+            "기법 진행 %d/%d (%.1f%%) — %s",
+            idx,
+            total,
+            pct,
+            technique.technique_id,
+        )
+    return results
+
+
+def load_technique_result(path: Path) -> TechniqueResult:
+    """저장된 기법 JSON을 TechniqueResult로 로드한다."""
+    with path.open(encoding="utf-8") as fp:
+        payload = json.load(fp)
+    return TechniqueResult(
+        technique_id=payload["technique_id"],
+        technique_name=payload["technique_name"],
+        category=payload["category"],
+        causal=payload["causal"],
+        description=payload.get("description", ""),
+        params=payload.get("params", {}),
+        signals=payload.get("signals", []),
+        legs=payload.get("legs", []),
+        summary=payload.get("summary", {}),
+        pnl=payload.get("pnl", {}),
+        alignment=payload.get("alignment"),
+    )
+
+
+def load_technique_results(
+    output_dir: Path,
+    technique_ids: list[str] | None = None,
+) -> list[TechniqueResult]:
+    """저장된 기법 JSON 목록을 로드한다."""
+    if technique_ids:
+        paths = [output_dir / f"{tid}.json" for tid in technique_ids]
+    else:
+        paths = sorted(output_dir.glob("*.json"))
+
+    results: list[TechniqueResult] = []
+    for path in paths:
+        if not path.exists():
+            continue
+        results.append(load_technique_result(path))
+    return results
+
+
+def save_technique_result(result: TechniqueResult, output_dir: Path) -> Path:
+    """기법 결과를 JSON으로 저장한다."""
+    output_dir.mkdir(parents=True, exist_ok=True)
+    out_path = output_dir / f"{result.technique_id}.json"
+    payload = result.to_dict()
+    payload["meta"] = {
+        "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "interval_label": interval_label(int(result.params["interval_min"])),
+    }
+    with out_path.open("w", encoding="utf-8") as fp:
+        json.dump(payload, fp, ensure_ascii=False, indent=2)
+    return out_path
+
+
+def load_ground_truth(gt_path: Path) -> dict[str, Any]:
+    """Ground Truth JSON을 로드한다."""
+    with gt_path.open(encoding="utf-8") as fp:
+        return json.load(fp)

src/deepcoin/techniques/stochastic_cross.py

@@ -0,0 +1,53 @@
+"""Stochastic 크로스 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import stochastic
+
+
+class StochasticCrossTechnique(BaseTechnique):
+    """Stochastic %K가 %D 상향·하향 돌파 시 매수·매도."""
+
+    technique_id = "stochastic_cross"
+    technique_name = "Stochastic 크로스"
+    category = "momentum"
+    causal = True
+    description = "Stochastic(14,3) %K/%D 크로스"
+
+    def default_extra_params(self) -> dict:
+        return {"k_period": 14, "d_period": 3, "oversold": 20.0, "overbought": 80.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        k_period = int(params.extra.get("k_period", 14))
+        d_period = int(params.extra.get("d_period", 3))
+        oversold = float(params.extra.get("oversold", 20.0))
+        overbought = float(params.extra.get("overbought", 80.0))
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        pct_k, pct_d = stochastic(high, low, close, k_period=k_period, d_period=d_period)
+
+        signals: list[TechniqueSignal] = []
+        start = k_period + d_period
+
+        for i in range(start + 1, len(df)):
+            if pd.isna(pct_k.iloc[i]) or pd.isna(pct_d.iloc[i]):
+                continue
+
+            prev_k = float(pct_k.iloc[i - 1])
+            prev_d = float(pct_d.iloc[i - 1])
+            curr_k = float(pct_k.iloc[i])
+            curr_d = float(pct_d.iloc[i])
+            c = float(close.iloc[i])
+
+            if prev_k <= prev_d and curr_k > curr_d and curr_k < oversold + 15:
+                signals.append(make_signal(df, i, c, "buy", "stoch_bull_cross", confidence=0.66))
+            elif prev_k >= prev_d and curr_k < curr_d and curr_k > overbought - 15:
+                signals.append(make_signal(df, i, c, "sell", "stoch_bear_cross", confidence=0.66))
+
+        return signals

src/deepcoin/techniques/supertrend_signal.py

@@ -0,0 +1,48 @@
+"""Supertrend 추세 전환 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+from deepcoin.techniques.indicators import supertrend
+
+
+class SupertrendTechnique(BaseTechnique):
+    """Supertrend 방향 전환 시 매수·매도."""
+
+    technique_id = "supertrend"
+    technique_name = "Supertrend"
+    category = "trend"
+    causal = True
+    description = "Supertrend 상승·하락 전환 신호"
+
+    def default_extra_params(self) -> dict:
+        return {"period": 10, "multiplier": 3.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        period = int(params.extra.get("period", 10))
+        multiplier = float(params.extra.get("multiplier", 3.0))
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        _, direction = supertrend(high, low, close, period=period, multiplier=multiplier)
+
+        signals: list[TechniqueSignal] = []
+
+        for i in range(period + 2, len(df)):
+            if pd.isna(direction.iloc[i]) or pd.isna(direction.iloc[i - 1]):
+                continue
+
+            prev_dir = float(direction.iloc[i - 1])
+            curr_dir = float(direction.iloc[i])
+            c = float(close.iloc[i])
+
+            if prev_dir < 0 < curr_dir:
+                signals.append(make_signal(df, i, c, "buy", "supertrend_bull_flip", confidence=0.68))
+            elif prev_dir > 0 > curr_dir:
+                signals.append(make_signal(df, i, c, "sell", "supertrend_bear_flip", confidence=0.68))
+
+        return signals

src/deepcoin/techniques/support_bounce.py

@@ -0,0 +1,60 @@
+"""지지·저항 반등 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+
+
+class SupportBounceTechnique(BaseTechnique):
+    """롤링 지지선 터치 후 반등 매수, 저항선 터치 후 하락 매도."""
+
+    technique_id = "support_bounce"
+    technique_name = "지지·저항 반등"
+    category = "pullback"
+    causal = True
+    description = "N봉 지지·저항 터치 후 반전 (B*)"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 80, "touch_pct": 0.3}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 80))
+        touch_pct = float(params.extra.get("touch_pct", 0.3)) / 100.0
+
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        close = df["close"].astype(float)
+        support = low.shift(1).rolling(window).min()
+        resistance = high.shift(1).rolling(window).max()
+
+        signals: list[TechniqueSignal] = []
+        touched_support = False
+        touched_resistance = False
+
+        for i in range(window + 1, len(df)):
+            if pd.isna(support.iloc[i]) or pd.isna(resistance.iloc[i]):
+                continue
+
+            c = float(close.iloc[i])
+            l = float(low.iloc[i])
+            h = float(high.iloc[i])
+            sup = float(support.iloc[i])
+            res = float(resistance.iloc[i])
+            prev_c = float(close.iloc[i - 1])
+
+            if l <= sup * (1 + touch_pct):
+                touched_support = True
+            if touched_support and prev_c <= sup and c > sup:
+                signals.append(make_signal(df, i, c, "buy", "support_bounce", confidence=0.71))
+                touched_support = False
+
+            if h >= res * (1 - touch_pct):
+                touched_resistance = True
+            if touched_resistance and prev_c >= res and c < res:
+                signals.append(make_signal(df, i, c, "sell", "resistance_reject", confidence=0.71))
+                touched_resistance = False
+
+        return signals

src/deepcoin/techniques/support_resistance.py

@@ -0,0 +1,64 @@
+"""스윙 기반 지지·저항 구조 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import dedupe_signals, find_confirmed_pivots, make_signal
+
+
+class SupportResistanceTechnique(BaseTechnique):
+    """확정 스윙 고저점을 지지·저항으로 활용한 반전."""
+
+    technique_id = "support_resistance"
+    technique_name = "구조적 지지·저항"
+    category = "structure"
+    causal = True
+    description = "스윙 피벗 기반 S/R 반전"
+
+    def default_extra_params(self) -> dict:
+        return {"order": 15, "touch_pct": 0.4, "max_age_bars": 500}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        order = int(params.extra.get("order", 15))
+        touch_pct = float(params.extra.get("touch_pct", 0.4)) / 100.0
+        max_age = int(params.extra.get("max_age_bars", 500))
+
+        low = df["low"].astype(float)
+        high = df["high"].astype(float)
+        close = df["close"].astype(float)
+        low_pivots = find_confirmed_pivots(low, order, "low")
+        high_pivots = find_confirmed_pivots(high, order, "high")
+        signals: list[TechniqueSignal] = []
+
+        for i in range(order * 2, len(df)):
+            c = float(close.iloc[i])
+            l = float(low.iloc[i])
+            h = float(high.iloc[i])
+
+            for pivot_idx, pivot_val in reversed(low_pivots):
+                if pivot_idx >= i or i - pivot_idx > max_age:
+                    continue
+                if l <= pivot_val * (1 + touch_pct) and c > pivot_val:
+                    signals.append(
+                        make_signal(
+                            df, i, c, "buy", "sr_support_bounce",
+                            pivot_bar_index=pivot_idx, confidence=0.7,
+                        )
+                    )
+                    break
+
+            for pivot_idx, pivot_val in reversed(high_pivots):
+                if pivot_idx >= i or i - pivot_idx > max_age:
+                    continue
+                if h >= pivot_val * (1 - touch_pct) and c < pivot_val:
+                    signals.append(
+                        make_signal(
+                            df, i, c, "sell", "sr_resistance_reject",
+                            pivot_bar_index=pivot_idx, confidence=0.7,
+                        )
+                    )
+                    break
+
+        return dedupe_signals(signals, min_bars=order)

src/deepcoin/techniques/swing_failure.py

@@ -0,0 +1,67 @@
+"""스윙 실패(페일드) 패턴 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import dedupe_signals, find_confirmed_pivots, make_signal
+
+
+class SwingFailureTechnique(BaseTechnique):
+    """이전 스윙 고점 돌파 실패 후 매도, 저점 이탈 실패 후 매수."""
+
+    technique_id = "swing_failure"
+    technique_name = "스윙 실패"
+    category = "swing"
+    causal = True
+    description = "스윙 고저점 돌파 실패(페일드 브레이크아웃) 반전 신호"
+
+    def default_extra_params(self) -> dict:
+        return {"order": 10, "min_bars_between": 30}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        order = int(params.extra.get("order", 10))
+        min_bars = int(params.extra.get("min_bars_between", 30))
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        high_pivots = find_confirmed_pivots(high, order, "high")
+        low_pivots = find_confirmed_pivots(low, order, "low")
+        signals: list[TechniqueSignal] = []
+
+        for i in range(1, len(high_pivots)):
+            prev_idx, prev_high = high_pivots[i - 1]
+            pivot_idx, pivot_high = high_pivots[i]
+            if pivot_high <= prev_high:
+                continue
+            search_start = pivot_idx + order
+            search_end = min(len(df), search_start + order * 6)
+            for j in range(search_start, search_end):
+                if float(high.iloc[j]) > prev_high and float(close.iloc[j]) < prev_high:
+                    signals.append(
+                        make_signal(
+                            df, j, float(close.iloc[j]), "sell",
+                            "swing_failure_high", pivot_bar_index=pivot_idx, confidence=0.72,
+                        )
+                    )
+                    break
+
+        for i in range(1, len(low_pivots)):
+            prev_idx, prev_low = low_pivots[i - 1]
+            pivot_idx, pivot_low = low_pivots[i]
+            if pivot_low >= prev_low:
+                continue
+            search_start = pivot_idx + order
+            search_end = min(len(df), search_start + order * 6)
+            for j in range(search_start, search_end):
+                if float(low.iloc[j]) < prev_low and float(close.iloc[j]) > prev_low:
+                    signals.append(
+                        make_signal(
+                            df, j, float(close.iloc[j]), "buy",
+                            "swing_failure_low", pivot_bar_index=pivot_idx, confidence=0.72,
+                        )
+                    )
+                    break
+
+        return dedupe_signals(signals, min_bars=min_bars)

src/deepcoin/techniques/volume_breakout.py

@@ -0,0 +1,55 @@
+"""거래량 동반 돌파 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+
+
+class VolumeBreakoutTechnique(BaseTechnique):
+    """거래량 급증과 함께 레인지 상·하단 돌파."""
+
+    technique_id = "volume_breakout"
+    technique_name = "거래량 돌파"
+    category = "breakout"
+    causal = True
+    description = "거래량 스파이크 + 레인지 돌파 (B^)"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 40, "vol_mult": 1.8}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 40))
+        vol_mult = float(params.extra.get("vol_mult", 1.8))
+
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+        volume = df["volume"].astype(float)
+        range_high = high.shift(1).rolling(window).max()
+        range_low = low.shift(1).rolling(window).min()
+        vol_ma = volume.rolling(window).mean()
+
+        signals: list[TechniqueSignal] = []
+
+        for i in range(window + 1, len(df)):
+            if pd.isna(range_high.iloc[i]) or pd.isna(vol_ma.iloc[i]):
+                continue
+
+            c = float(close.iloc[i])
+            v = float(volume.iloc[i])
+            vma = float(vol_ma.iloc[i])
+            if vma <= 0 or v < vma * vol_mult:
+                continue
+
+            rh = float(range_high.iloc[i])
+            rl = float(range_low.iloc[i])
+
+            if c > rh:
+                signals.append(make_signal(df, i, c, "buy", "volume_breakout_up", confidence=0.74))
+            elif c < rl:
+                signals.append(make_signal(df, i, c, "sell", "volume_breakout_down", confidence=0.74))
+
+        return signals

src/deepcoin/techniques/volume_spike.py

@@ -0,0 +1,60 @@
+"""거래량 스파이크 반전 기법."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+from deepcoin.techniques.helpers import make_signal
+
+
+class VolumeSpikeTechnique(BaseTechnique):
+    """거래량 급증 봉의 방향 반전 신호."""
+
+    technique_id = "volume_spike"
+    technique_name = "거래량 스파이크"
+    category = "volume"
+    causal = True
+    description = "거래량 스파이크 후 반전 (클라이맥스)"
+
+    def default_extra_params(self) -> dict:
+        return {"window": 30, "vol_mult": 2.5, "min_body_pct": 0.5}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        window = int(params.extra.get("window", 30))
+        vol_mult = float(params.extra.get("vol_mult", 2.5))
+        min_body_pct = float(params.extra.get("min_body_pct", 0.5)) / 100.0
+
+        open_ = df["open"].astype(float)
+        close = df["close"].astype(float)
+        volume = df["volume"].astype(float)
+        vol_ma = volume.rolling(window).mean()
+
+        signals: list[TechniqueSignal] = []
+
+        for i in range(window + 1, len(df) - 1):
+            if pd.isna(vol_ma.iloc[i]):
+                continue
+
+            v = float(volume.iloc[i])
+            vma = float(vol_ma.iloc[i])
+            if vma <= 0 or v < vma * vol_mult:
+                continue
+
+            o = float(open_.iloc[i])
+            c = float(close.iloc[i])
+            body_pct = abs(c - o) / o if o > 0 else 0
+            if body_pct < min_body_pct:
+                continue
+
+            next_c = float(close.iloc[i + 1])
+            if c < o and next_c > c:
+                signals.append(
+                    make_signal(df, i + 1, next_c, "buy", "volume_climax_buy", confidence=0.63)
+                )
+            elif c > o and next_c < c:
+                signals.append(
+                    make_signal(df, i + 1, next_c, "sell", "volume_climax_sell", confidence=0.63)
+                )
+
+        return signals

src/deepcoin/techniques/zigzag_causal.py

@@ -0,0 +1,112 @@
+"""인과 ZigZag — 피벗 확정 시점에 매수·매도 신호."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from deepcoin.techniques.base import BaseTechnique, TechniqueParams, TechniqueSignal
+
+
+class ZigzagCausalTechnique(BaseTechnique):
+    """되돌림 % 확정 시 피벗을 인정하는 인과 ZigZag 기법.
+
+    Ground Truth ZigZag와 동일 임계값을 쓰되, 미래 데이터 없이
+    피벗이 확정된 봉에서만 신호를 발생시킨다.
+    """
+
+    technique_id = "zigzag_causal"
+    technique_name = "인과 ZigZag"
+    category = "swing"
+    causal = True
+    description = "되돌림 % 확정 시 스윙 저점 매수·고점 매도 (GT ZigZag 인과 버전)"
+
+    def default_extra_params(self) -> dict:
+        return {"reversal_pct": 5.0}
+
+    def generate_signals(self, df: pd.DataFrame, params: TechniqueParams) -> list[TechniqueSignal]:
+        reversal_pct = float(params.extra.get("reversal_pct", 5.0))
+        return find_causal_zigzag_signals(df, reversal_pct=reversal_pct)
+
+
+def find_causal_zigzag_signals(
+    df: pd.DataFrame,
+    reversal_pct: float = 5.0,
+) -> list[TechniqueSignal]:
+    """인과 ZigZag 신호를 생성한다.
+
+    Args:
+        df: OHLCV DataFrame.
+        reversal_pct: 피벗 확정 최소 되돌림(%).
+
+    Returns:
+        시간순 신호 리스트.
+    """
+    if len(df) < 3:
+        return []
+
+    threshold = reversal_pct / 100.0
+    signals: list[TechniqueSignal] = []
+    direction: str | None = None
+    extreme_idx = 0
+    extreme_price = float(df.iloc[0]["close"])
+
+    for i in range(1, len(df)):
+        high = float(df.iloc[i]["high"])
+        low = float(df.iloc[i]["low"])
+
+        if direction is None:
+            if high >= extreme_price * (1 + threshold):
+                direction = "up"
+                extreme_idx = i
+                extreme_price = high
+            elif low <= extreme_price * (1 - threshold):
+                direction = "down"
+                extreme_idx = i
+                extreme_price = low
+            continue
+
+        if direction == "up":
+            if high >= extreme_price:
+                extreme_price = high
+                extreme_idx = i
+            if low <= extreme_price * (1 - threshold):
+                signals.append(
+                    _make_signal(df, i, extreme_idx, extreme_price, "sell", reversal_pct)
+                )
+                direction = "down"
+                extreme_idx = i
+                extreme_price = low
+        else:
+            if low <= extreme_price:
+                extreme_price = low
+                extreme_idx = i
+            if high >= extreme_price * (1 + threshold):
+                signals.append(
+                    _make_signal(df, i, extreme_idx, extreme_price, "buy", reversal_pct)
+                )
+                direction = "up"
+                extreme_idx = i
+                extreme_price = high
+
+    return signals
+
+
+def _make_signal(
+    df: pd.DataFrame,
+    confirm_idx: int,
+    pivot_idx: int,
+    pivot_price: float,
+    side: str,
+    reversal_pct: float,
+) -> TechniqueSignal:
+    """확정 봉 기준 TechniqueSignal을 생성한다."""
+    dt = pd.Timestamp(df.iloc[confirm_idx]["datetime"]).strftime("%Y-%m-%d %H:%M:%S")
+    return TechniqueSignal(
+        side=side,
+        bar_index=confirm_idx,
+        price=round(pivot_price, 2),
+        datetime=dt,
+        pivot_bar_index=pivot_idx,
+        confidence=min(1.0, reversal_pct / 10.0),
+        reason=f"zigzag_{side}_confirmed",
+    )