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_volume.py

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+"""
+general_analysis Volume Profile (POC, VAH, VAL).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from config import GA_VP_BINS, GA_VP_VALUE_AREA_PCT
+from deepcoin.analysis.general_analysis_core import ga_col
+
+
+def general_analysis_volume_profile(
+    win: pd.DataFrame,
+    bins: int | None = None,
+    value_area_pct: float | None = None,
+) -> dict[str, float | int]:
+    """
+    lookback 구간 가격-거래량 분포에서 POC·VAH·VAL 계산.
+
+    Args:
+        win: OHLCV.
+        bins: 가격 구간 수.
+        value_area_pct: value area 누적 비율 (기본 70%).
+
+    Returns:
+        ga_vp_* 키 dict (접두사 없음).
+    """
+    res: dict[str, float | int] = {
+        "vp_poc": 0.0,
+        "vp_vah": 0.0,
+        "vp_val": 0.0,
+        "vp_close_vs_poc_pct": 0.0,
+        "vp_in_value_area": 0,
+    }
+    if bins is None:
+        bins = GA_VP_BINS
+    if value_area_pct is None:
+        value_area_pct = GA_VP_VALUE_AREA_PCT
+
+    if win is None or len(win) < 10 or "Volume" not in win.columns:
+        return res
+
+    h = win["High"].astype(float).values
+    l = win["Low"].astype(float).values
+    c = win["Close"].astype(float).values
+    v = win["Volume"].astype(float).values
+    tp = (h + l + c) / 3.0
+
+    lo, hi = float(l.min()), float(h.max())
+    if hi <= lo:
+        return res
+
+    edges = np.linspace(lo, hi, bins + 1)
+    hist = np.zeros(bins, dtype=float)
+    for i in range(len(tp)):
+        idx = int(np.clip(np.digitize(tp[i], edges) - 1, 0, bins - 1))
+        hist[idx] += v[i]
+
+    if hist.sum() <= 0:
+        return res
+
+    poc_idx = int(np.argmax(hist))
+    poc = float((edges[poc_idx] + edges[poc_idx + 1]) / 2)
+    res["vp_poc"] = poc
+
+    order = np.argsort(hist)[::-1]
+    cum = 0.0
+    selected: list[int] = []
+    total = hist.sum()
+    for idx in order:
+        selected.append(int(idx))
+        cum += hist[idx]
+        if cum >= total * value_area_pct:
+            break
+    sel_min, sel_max = min(selected), max(selected)
+    res["vp_val"] = float(edges[sel_min])
+    res["vp_vah"] = float(edges[sel_max + 1])
+    res["vp_close_vs_poc_pct"] = float((c[-1] / poc - 1) * 100) if poc else 0.0
+    res["vp_in_value_area"] = int(res["vp_val"] <= c[-1] <= res["vp_vah"])
+
+    return res
+
+
+def general_analysis_volume_columns() -> list[str]:
+    return ["vp_poc", "vp_vah", "vp_val", "vp_close_vs_poc_pct", "vp_in_value_area"]
+
+
+def general_analysis_volume_snapshot(win: pd.DataFrame) -> dict[str, object]:
+    """Volume profile → ga_vp_*."""
+    return {ga_col(k): v for k, v in general_analysis_volume_profile(win).items()}