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_wave.py

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+"""
+general_analysis 파동·시장 구조 (다우, 엘리어트 라이트, 피보나치, Wyckoff 태그).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from deepcoin.analysis.general_analysis_core import find_pivots, ga_col
+
+
+def _fib_levels(low: float, high: float) -> dict[str, float]:
+    diff = high - low
+    return {
+        "fib_0": low,
+        "fib_382": low + diff * 0.382,
+        "fib_500": low + diff * 0.5,
+        "fib_618": low + diff * 0.618,
+        "fib_100": high,
+        "fib_1618": low + diff * 1.618,
+    }
+
+
+def general_analysis_wave_snapshot(win: pd.DataFrame) -> dict[str, object]:
+    """
+    lookback 윈도우 마지막 시점 파동·구조 스냅샷.
+
+    Args:
+        win: OHLCV.
+
+    Returns:
+        ga_wave_* / ga_struct_* / ga_fib_* dict.
+    """
+    res: dict[str, object] = {
+        "struct_trend": "range",
+        "struct_hh": 0,
+        "struct_hl": 0,
+        "struct_lh": 0,
+        "struct_ll": 0,
+        "struct_bos_bull": 0,
+        "struct_bos_bear": 0,
+        "struct_choch": 0,
+        "elliott_wave_count": 0,
+        "elliott_phase": "unknown",
+        "wyckoff_phase": "unknown",
+        "fib_near_level": "none",
+        "ichi_trend": "neutral",
+        "pitchfork_bias": "neutral",
+        "pitchfork_dist_pct": 0.0,
+        "wyckoff_spring": 0,
+        "wyckoff_utad": 0,
+    }
+    if win is None or len(win) < 15:
+        return {ga_col(k): v for k, v in res.items()}
+
+    h = win["High"].astype(float).values
+    l = win["Low"].astype(float).values
+    c = win["Close"].astype(float).values
+    peaks, troughs = find_pivots(h, l, order=2)
+
+    # Dow HH/HL/LH/LL
+    if len(peaks) >= 2 and len(troughs) >= 2:
+        hh = int(h[peaks[-1]] > h[peaks[-2]])
+        hl = int(l[troughs[-1]] > l[troughs[-2]])
+        lh = int(h[peaks[-1]] < h[peaks[-2]])
+        ll = int(l[troughs[-1]] < l[troughs[-2]])
+        res["struct_hh"] = hh
+        res["struct_hl"] = hl
+        res["struct_lh"] = lh
+        res["struct_ll"] = ll
+        if hh and hl:
+            res["struct_trend"] = "up"
+        elif lh and ll:
+            res["struct_trend"] = "down"
+        if hh and c[-1] > h[peaks[-2]]:
+            res["struct_bos_bull"] = 1
+        if ll and c[-1] < l[troughs[-2]]:
+            res["struct_bos_bear"] = 1
+        if (hh and ll) or (lh and hl):
+            res["struct_choch"] = 1
+
+    # Elliott lite: pivot count in window
+    swings = len(peaks) + len(troughs)
+    res["elliott_wave_count"] = swings
+    if swings >= 5:
+        res["elliott_phase"] = "impulse_late"
+    elif swings >= 3:
+        res["elliott_phase"] = "corrective"
+
+    # Wyckoff lite
+    vol = win["Volume"].astype(float).values if "Volume" in win.columns else np.ones(len(c))
+    vol_ma = vol[-20:].mean() if len(vol) >= 20 else vol.mean()
+    price_range = (h[-20:].max() - l[-20:].min()) / max(c[-1], 1e-9) * 100
+    if price_range < 6 and vol[-1] < vol_ma * 1.2:
+        res["wyckoff_phase"] = "accumulation"
+    elif price_range < 6 and vol[-1] > vol_ma * 1.5 and c[-1] > c[-5]:
+        res["wyckoff_phase"] = "distribution"
+
+    if price_range < 8 and l[-1] < l[-5] and c[-1] > c[-2] and vol[-1] > vol_ma * 1.3:
+        res["wyckoff_spring"] = 1
+    if price_range < 8 and h[-1] > h[-5] and c[-1] < c[-2] and vol[-1] > vol_ma * 1.3:
+        res["wyckoff_utad"] = 1
+
+    # Andrews Pitchfork (3피벗 중앙선 대비 종가 위치)
+    pivots = sorted([(i, h[i]) for i in peaks] + [(i, l[i]) for i in troughs])
+    if len(pivots) >= 3:
+        p0, p1, p2 = pivots[-3], pivots[-2], pivots[-1]
+        y0 = p0[1]
+        y_mid = (p1[1] + p2[1]) / 2
+        x0, x2 = p0[0], p2[0]
+        if x2 != x0:
+            slope = (y_mid - y0) / (x2 - x0)
+            y_line = y0 + slope * (len(c) - 1 - x0)
+            dist_pct = (c[-1] - y_line) / max(c[-1], 1e-9) * 100
+            res["pitchfork_dist_pct"] = round(float(dist_pct), 3)
+            if dist_pct > 0.5:
+                res["pitchfork_bias"] = "above"
+            elif dist_pct < -0.5:
+                res["pitchfork_bias"] = "below"
+
+    # Fibonacci
+    hi, lo = float(h.max()), float(l.min())
+    levels = _fib_levels(lo, hi)
+    price = float(c[-1])
+    for name, lvl in levels.items():
+        if abs(price - lvl) / max(price, 1e-9) * 100 < 1.5:
+            res["fib_near_level"] = name.replace("fib_", "")
+            break
+
+    if "ichi_cloud_top" in win.columns:
+        row = win.iloc[-1]
+        ct = float(row.get("ichi_cloud_top", np.nan))
+        cb = float(row.get("ichi_cloud_bottom", np.nan))
+        if not np.isnan(ct) and price > ct:
+            res["ichi_trend"] = "above_cloud"
+        elif not np.isnan(cb) and price < cb:
+            res["ichi_trend"] = "below_cloud"
+        else:
+            res["ichi_trend"] = "in_cloud"
+
+    return {ga_col(k): v for k, v in res.items()}
+
+
+def general_analysis_wave_columns() -> list[str]:
+    return [
+        "struct_trend",
+        "struct_hh",
+        "struct_hl",
+        "struct_lh",
+        "struct_ll",
+        "struct_bos_bull",
+        "struct_bos_bear",
+        "struct_choch",
+        "elliott_wave_count",
+        "elliott_phase",
+        "wyckoff_phase",
+        "fib_near_level",
+        "ichi_trend",
+        "pitchfork_bias",
+        "pitchfork_dist_pct",
+        "wyckoff_spring",
+        "wyckoff_utad",
+    ]
+
+
+def general_analysis_apply_wave_to_bars(
+    df: pd.DataFrame,
+    interval: int,
+    tail_rows: int | None = None,
+) -> pd.DataFrame:
+    """파동·구조 스냅샷을 최근 봉에 롤링 적용."""
+    from deepcoin.analysis.general_analysis_config import CONTEXT_TAIL_ROWS, LOOKBACK_BARS
+
+    out = df.copy()
+    lb = LOOKBACK_BARS.get(interval, 80)
+    for k in general_analysis_wave_columns():
+        col = ga_col(k)
+        if k == "struct_trend":
+            out[col] = "range"
+        elif k in ("elliott_phase", "wyckoff_phase"):
+            out[col] = "unknown"
+        elif k == "fib_near_level":
+            out[col] = "none"
+        elif k in ("ichi_trend", "pitchfork_bias"):
+            out[col] = "neutral"
+        elif k == "pitchfork_dist_pct":
+            out[col] = 0.0
+        else:
+            out[col] = 0
+
+    n = len(out)
+    if n < lb + 1:
+        return out
+    if tail_rows is None:
+        tail_rows = CONTEXT_TAIL_ROWS.get(interval, 5000)
+    start = max(lb, n - tail_rows)
+
+    for i in range(start, n):
+        snap = general_analysis_wave_snapshot(out.iloc[i - lb : i])
+        idx = out.index[i]
+        for k, v in snap.items():
+            out.at[idx, k] = v
+
+    return out