WLD DeepCoin 단계별 구조 재편 및 설정·문서 통합

로고스/루트 레거시를 제거하고 deepcoin 패키지·scripts 01~05 CLI·docs/reference로
데이터·GT·분석·매칭·운영 단계를 정리했다. config와 .env 기반 설정, trade_anaysis.html 동기화 포함.

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

deepcoin/analysis/general_analysis_snapshot.py

@@ -0,0 +1,100 @@
+"""
+general_analysis ground truth 타점 MTF 스냅샷 생성.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+from deepcoin.analysis.general_analysis_align import general_analysis_mtf_scores
+from deepcoin.analysis.general_analysis_config import (
+    DEFAULT_OUTPUT_CSV,
+    DEFAULT_TRADES_FILE,
+    GENERAL_ANALYSIS_INTERVALS,
+)
+from deepcoin.analysis.general_analysis_core import interval_tf_prefix
+from deepcoin.analysis.general_analysis_pipeline import general_analysis_enrich_bars, general_analysis_snapshot_at_bar
+from deepcoin.ground_truth.ground_truth import load_ground_truth
+
+
+def _prefixed_snap(snap: dict[str, Any], interval: int) -> dict[str, Any]:
+    p = interval_tf_prefix(interval)
+    return {f"{p}_{k}": v for k, v in snap.items()}
+
+
+def build_trade_mtf_snapshots(
+    frames: dict[int, pd.DataFrame],
+    trades: list[dict[str, Any]],
+) -> pd.DataFrame:
+    """
+    모든 타점에 대해 8개 간격 general_analysis 스냅샷.
+
+    Args:
+        frames: interval → OHLCV.
+        trades: ground_truth trades.
+
+    Returns:
+        wide DataFrame (1 row per trade).
+    """
+    enriched: dict[int, pd.DataFrame] = {}
+    for iv in GENERAL_ANALYSIS_INTERVALS:
+        raw = frames.get(iv)
+        if raw is None or raw.empty:
+            continue
+        print(f"  [GA] {interval_tf_prefix(iv)} 봉 지표 계산 ({len(raw)}봉)...")
+        enriched[iv] = general_analysis_enrich_bars(raw, iv, full_context=True)
+
+    rows: list[dict[str, Any]] = []
+    for i, t in enumerate(sorted(trades, key=lambda x: x["dt"])):
+        ts = pd.Timestamp(t["dt"])
+        row: dict[str, Any] = {
+            "trade_idx": i,
+            "dt": t["dt"],
+            "action": t["action"],
+            "price": t["price"],
+            "weight": t.get("weight", 1.0),
+            "leg_id": t.get("leg_id", 0),
+            "memo": t.get("memo", ""),
+        }
+        flat: dict[str, Any] = {}
+        for iv in GENERAL_ANALYSIS_INTERVALS:
+            ef = enriched.get(iv)
+            if ef is None:
+                continue
+            snap = general_analysis_snapshot_at_bar(ef, ts, iv)
+            flat.update(_prefixed_snap(snap, iv))
+        row.update(flat)
+        row.update(general_analysis_mtf_scores(flat))
+        rows.append(row)
+        if (i + 1) % 50 == 0:
+            print(f"  타점 스냅샷 {i + 1}/{len(trades)}")
+
+    return pd.DataFrame(rows)
+
+
+def export_trade_snapshots(
+    frames: dict[int, pd.DataFrame],
+    trades_path: Path | str = DEFAULT_TRADES_FILE,
+    output_csv: Path | str = DEFAULT_OUTPUT_CSV,
+) -> Path:
+    """
+    CSV로 타점 MTF 스냅샷 저장.
+
+    Returns:
+        저장 경로.
+    """
+    data = load_ground_truth(Path(trades_path))
+    if not data:
+        raise FileNotFoundError(f"정답 파일 없음: {trades_path}")
+    trades = data.get("trades") or []
+    print(f"타점 {len(trades)}건 × {len(GENERAL_ANALYSIS_INTERVALS)} TF general_analysis")
+    df = build_trade_mtf_snapshots(frames, trades)
+    out = Path(output_csv)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    df.to_csv(out, index=False, encoding="utf-8-sig")
+    print(f"저장: {out} ({len(df)}행 × {len(df.columns)}열)")
+    return out