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_chart.py

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+"""
+general_analysis 차트 유형 (캔들·선·바·Renko·P&F).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from deepcoin.analysis.general_analysis_core import ga_col
+
+
+def _renko_direction_series(close: pd.Series, brick: pd.Series) -> pd.Series:
+    """ATR 기반 브릭 크기로 Renko 방향 (+1/-1/0) 시계열."""
+    n = len(close)
+    direction = pd.Series(0, index=close.index, dtype=int)
+    if n < 2:
+        return direction
+
+    price = float(close.iloc[0])
+    for i in range(1, n):
+        b = float(brick.iloc[i]) if not np.isnan(brick.iloc[i]) else float(close.diff().abs().median())
+        if b < 1e-9:
+            b = 1e-9
+        c = float(close.iloc[i])
+        if c >= price + b:
+            steps = int((c - price) // b)
+            direction.iloc[i] = 1
+            price += steps * b
+        elif c <= price - b:
+            steps = int((price - c) // b)
+            direction.iloc[i] = -1
+            price -= steps * b
+    return direction
+
+
+def general_analysis_apply_chart_bars(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    봉 단위 차트 파생 컬럼 (선 기울기, Renko, P&F).
+
+    Args:
+        df: OHLCV (+ ga_atr_14 권장).
+
+    Returns:
+        ga_chart_* 시계열 컬럼 추가.
+    """
+    out = df.copy()
+    c = out["Close"].astype(float)
+    h = out["High"].astype(float)
+    l = out["Low"].astype(float)
+
+    out[ga_col("chart_line_slope_1")] = c.diff()
+    out[ga_col("chart_bar_range_pct")] = (h - l) / c.replace(0, np.nan) * 100
+
+    from config import GA_ATR_PERIOD
+
+    brick = (
+        out[ga_col("atr_14")]
+        if ga_col("atr_14") in out.columns
+        else (h - l).rolling(GA_ATR_PERIOD).mean()
+    )
+    brick = brick.fillna(c.diff().abs().rolling(20).median()).replace(0, np.nan).bfill()
+    renko_dir = _renko_direction_series(c, brick)
+    out[ga_col("chart_renko_dir")] = renko_dir
+    out[ga_col("chart_renko_up")] = (renko_dir == 1).astype(int)
+
+    box = brick.fillna(1.0)
+    pnf = pd.Series(0, index=out.index, dtype=int)
+    col = 0
+    for i in range(1, len(c)):
+        b = float(box.iloc[i])
+        move = c.iloc[i] - c.iloc[i - 1]
+        if move >= b:
+            col += 1
+            pnf.iloc[i] = 1
+        elif move <= -b:
+            col -= 1
+            pnf.iloc[i] = -1
+    out[ga_col("chart_pnf_col")] = pnf
+
+    if "Volume" in out.columns:
+        v = out["Volume"].astype(float)
+        out[ga_col("chart_vol_spike")] = (v > v.rolling(20).mean() * 1.8).astype(int)
+
+    if ga_col("ha_trend_up") in out.columns:
+        out[ga_col("chart_ha_trend")] = out[ga_col("ha_trend_up")]
+    elif ga_col("ha_bull") in out.columns:
+        out[ga_col("chart_ha_trend")] = out[ga_col("ha_bull")]
+
+    return out
+
+
+def general_analysis_chart_metrics(df: pd.DataFrame) -> dict[str, object]:
+    """
+    lookback 구간 차트 요약 (마지막 봉 스냅샷용).
+
+    Returns:
+        ga_chart_* dict.
+    """
+    res: dict[str, object] = {
+        "chart_type_candle": 1,
+        "chart_line_slope": 0.0,
+        "chart_bar_range_pct": 0.0,
+        "chart_ha_trend": 0,
+        "chart_renko_brick_up_ratio": 0.5,
+        "chart_renko_dir": 0,
+        "chart_pnf_col": 0,
+        "chart_vol_spike": 0,
+    }
+    if df is None or len(df) < 5:
+        return {ga_col(k): v for k, v in res.items()}
+
+    row = df.iloc[-1]
+    c = df["Close"].astype(float)
+    res["chart_line_slope"] = float((c.iloc[-1] - c.iloc[0]) / max(len(c) - 1, 1))
+    res["chart_bar_range_pct"] = float(
+        (df["High"].iloc[-1] - df["Low"].iloc[-1]) / max(c.iloc[-1], 1e-9) * 100
+    )
+
+    if ga_col("chart_renko_dir") in df.columns:
+        rd = df[ga_col("chart_renko_dir")].astype(float)
+        up = (rd == 1).sum()
+        down = (rd == -1).sum()
+        res["chart_renko_brick_up_ratio"] = round(up / max(up + down, 1), 3)
+        res["chart_renko_dir"] = int(rd.iloc[-1])
+    else:
+        diff = c.diff().fillna(0)
+        up = (diff > 0).sum()
+        down = (diff < 0).sum()
+        res["chart_renko_brick_up_ratio"] = round(up / max(up + down, 1), 3)
+
+    if ga_col("chart_pnf_col") in df.columns:
+        res["chart_pnf_col"] = int(df[ga_col("chart_pnf_col")].iloc[-1])
+    if ga_col("chart_ha_trend") in df.columns:
+        res["chart_ha_trend"] = int(row[ga_col("chart_ha_trend")])
+    elif ga_col("ha_trend_up") in df.columns:
+        res["chart_ha_trend"] = int(row[ga_col("ha_trend_up")])
+    if ga_col("chart_vol_spike") in df.columns:
+        res["chart_vol_spike"] = int(row[ga_col("chart_vol_spike")])
+    elif "Volume" in df.columns:
+        v = df["Volume"].astype(float)
+        res["chart_vol_spike"] = int(v.iloc[-1] > v.iloc[-20:].mean() * 1.8)
+
+    return {ga_col(k): v for k, v in res.items()}
+
+
+def general_analysis_chart_columns() -> list[str]:
+    return [
+        "chart_type_candle",
+        "chart_line_slope",
+        "chart_line_slope_1",
+        "chart_bar_range_pct",
+        "chart_ha_trend",
+        "chart_renko_brick_up_ratio",
+        "chart_renko_dir",
+        "chart_renko_up",
+        "chart_pnf_col",
+        "chart_vol_spike",
+    ]