전 봉 BB·일목 조합 분석 및 simulation 단일 실행으로 통합

9개 간격(1~1440분) BB·일목 위치 특징을 3분 타임라인에 맞춰 분석하고, discover로 매수·매도 규칙을 찾은 뒤 HTML 차트에 해당 체결만 표시한다. simulation_1h.py를 simulation.py로 변경했으며, 파라미터 없이 실행하면 analyze→discover→차트가 한 번에 수행된다. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-05-29 01:20:36 +09:00
parent 7d53090034
commit e218a8ea32
15 changed files with 1510 additions and 803 deletions
--- a/candle_features.py
+++ b/candle_features.py
@@ -1,5 +1,5 @@
 """
-모든 봉 간격에 대해 BB 위치·캔들 형태(몸통/꼬리/높이) 특징을 계산하고
+모든 봉(1~1440분)에 BB·일목 위치·캔들 형태 특징을 계산하고
 기준 타임라인(3분)에 맞춰 정렬합니다.
 """

@@ -8,11 +8,14 @@ from __future__ import annotations
 import numpy as np
 import pandas as pd

-from config import ENTRY_INTERVAL
+from config import ALL_INTERVALS, ENTRY_INTERVAL
+from indicators import add_bollinger, add_ichimoku
 from strategy import prepare_entry_df

 INTERVAL_LABELS: dict[int, str] = {
+    1: "m1",
    3: "m3",
+    5: "m5",
    10: "m10",
    15: "m15",
    30: "m30",
@@ -27,9 +30,69 @@ def interval_prefix(interval: int) -> str:
    return INTERVAL_LABELS.get(interval, f"m{interval}")


+def interval_display(interval: int) -> str:
+    if interval >= 1440:
+        return "일봉"
+    return f"{interval}분"
+
+
+# BB 위치 (밴드 내 %B 구간)
+BB_ZONE_FEATURES: tuple[str, ...] = (
+    "bb_zone_bottom",
+    "bb_zone_low",
+    "bb_zone_mid",
+    "bb_zone_high",
+    "bb_zone_top",
+)
+
+# 일목 위치
+ICHI_FEATURES: tuple[str, ...] = (
+    "ichi_above_cloud",
+    "ichi_below_cloud",
+    "ichi_in_cloud",
+    "ichi_cloud_bull",
+    "ichi_cloud_bear",
+    "ichi_tk_bull",
+    "ichi_tk_bear",
+    "ichi_price_above_tenkan",
+    "ichi_price_below_kijun",
+    "ichi_tk_cross_up",
+    "ichi_tk_cross_down",
+)
+
+# BB 이벤트·캔들 형태
+BB_EVENT_FEATURES: tuple[str, ...] = (
+    "cross_up_lower",
+    "cross_up_upper",
+    "cross_down_lower",
+    "below_lower",
+    "above_upper",
+    "inside_band",
+    "bb_pos_low",
+    "bb_pos_high",
+    "squeeze",
+)
+
+CANDLE_SHAPE_FEATURES: tuple[str, ...] = (
+    "body_strong",
+    "body_weak",
+    "hammer",
+    "shooting_star",
+    "bullish",
+    "bearish",
+)
+
+FEATURE_BOOL_COLS: tuple[str, ...] = (
+    BB_EVENT_FEATURES
+    + BB_ZONE_FEATURES
+    + ICHI_FEATURES
+    + CANDLE_SHAPE_FEATURES
+)
+
+
 def compute_bar_features(df: pd.DataFrame) -> pd.DataFrame:
-    """단일 봉 DataFrame에 위치·캔들 높이 특징을 추가합니다."""
-    out = prepare_entry_df(df.copy())
+    """단일 봉 DataFrame에 BB·일목·캔들 위치 특징을 추가합니다."""
+    out = add_bollinger(add_ichimoku(prepare_entry_df(df.copy())))
    if len(out) < 2:
        return out

@@ -42,13 +105,6 @@ def compute_bar_features(df: pd.DataFrame) -> pd.DataFrame:
    lower = out["Lower"].astype(float)
    prev_upper = upper.shift(1)
    prev_lower = lower.shift(1)
-    ma = out["MA"].astype(float)
-
-    band = (upper - lower).replace(0, np.nan)
-    out["bb_pos"] = ((c - lower) / band).clip(0, 1)
-    out["bb_width_pct"] = (
-        out["BB_Width"] if "BB_Width" in out.columns else (band / ma.replace(0, np.nan) * 100)
-    )

    rng = (h - l).replace(0, np.nan)
    body = (c - o).abs()
@@ -57,8 +113,13 @@ def compute_bar_features(df: pd.DataFrame) -> pd.DataFrame:
    out["upper_wick_ratio"] = ((h - np.maximum(o, c)) / rng).fillna(0).clip(0, 1)
    out["lower_wick_ratio"] = ((np.minimum(o, c) - l) / rng).fillna(0).clip(0, 1)
    out["ret_pct"] = ((c - prev_c) / prev_c.replace(0, np.nan)) * 100
-    out["bullish"] = (c > o).astype(int)
-    out["bearish"] = (c < o).astype(int)
+
+    pos = out["bb_pos"].astype(float)
+    out["bb_zone_bottom"] = (pos < 0.15).astype(int)
+    out["bb_zone_low"] = ((pos >= 0.15) & (pos < 0.35)).astype(int)
+    out["bb_zone_mid"] = ((pos >= 0.35) & (pos < 0.65)).astype(int)
+    out["bb_zone_high"] = ((pos >= 0.65) & (pos < 0.85)).astype(int)
+    out["bb_zone_top"] = (pos >= 0.85).astype(int)

    out["cross_up_lower"] = ((prev_c <= prev_lower) & (c > lower)).astype(int)
    out["cross_up_upper"] = ((prev_c < prev_upper) & (c >= upper)).astype(int)
@@ -66,35 +127,83 @@ def compute_bar_features(df: pd.DataFrame) -> pd.DataFrame:
    out["below_lower"] = (c < lower).astype(int)
    out["above_upper"] = (c > upper).astype(int)
    out["inside_band"] = ((c >= lower) & (c <= upper)).astype(int)
+    out["bb_pos_low"] = (pos < 0.2).astype(int)
+    out["bb_pos_high"] = (pos > 0.8).astype(int)
+    out["squeeze"] = (out["BB_Width"] < 0.8).astype(int)
+
+    ct = out["ichi_cloud_top"].astype(float)
+    cb = out["ichi_cloud_bottom"].astype(float)
+    ten = out["ichi_tenkan"].astype(float)
+    kij = out["ichi_kijun"].astype(float)
+    prev_ten = ten.shift(1)
+    prev_kij = kij.shift(1)
+
+    out["ichi_above_cloud"] = (c > ct).astype(int)
+    out["ichi_below_cloud"] = (c < cb).astype(int)
+    out["ichi_in_cloud"] = ((c >= cb) & (c <= ct)).astype(int)
+    out["ichi_cloud_bull"] = (out["ichi_span_a"] > out["ichi_span_b"]).astype(int)
+    out["ichi_cloud_bear"] = (out["ichi_span_a"] < out["ichi_span_b"]).astype(int)
+    out["ichi_tk_bull"] = (ten > kij).astype(int)
+    out["ichi_tk_bear"] = (ten < kij).astype(int)
+    out["ichi_price_above_tenkan"] = (c > ten).astype(int)
+    out["ichi_price_below_kijun"] = (c < kij).astype(int)
+    out["ichi_tk_cross_up"] = ((prev_ten <= prev_kij) & (ten > kij)).astype(int)
+    out["ichi_tk_cross_down"] = ((prev_ten >= prev_kij) & (ten < kij)).astype(int)

-    out["bb_pos_low"] = (out["bb_pos"] < 0.2).astype(int)
-    out["bb_pos_high"] = (out["bb_pos"] > 0.8).astype(int)
    out["body_strong"] = (out["body_ratio"] > 0.55).astype(int)
    out["body_weak"] = (out["body_ratio"] < 0.25).astype(int)
    out["hammer"] = ((out["lower_wick_ratio"] > 0.45) & (out["body_ratio"] < 0.35)).astype(int)
    out["shooting_star"] = ((out["upper_wick_ratio"] > 0.45) & (out["body_ratio"] < 0.35)).astype(int)
-    out["squeeze"] = (out["bb_width_pct"] < 0.8).astype(int)
+    out["bullish"] = (c > o).astype(int)
+    out["bearish"] = (c < o).astype(int)

    return out


-FEATURE_BOOL_COLS: tuple[str, ...] = (
-    "cross_up_lower",
-    "cross_up_upper",
-    "cross_down_lower",
-    "below_lower",
-    "above_upper",
-    "inside_band",
-    "bb_pos_low",
-    "bb_pos_high",
-    "body_strong",
-    "body_weak",
-    "hammer",
-    "shooting_star",
-    "squeeze",
-    "bullish",
-    "bearish",
-)
+def describe_latest_position(df: pd.DataFrame, interval: int) -> dict:
+    """한 봉의 최신 BB·일목 위치 요약."""
+    feat = compute_bar_features(df)
+    if feat.empty:
+        return {"interval": interval, "label": interval_display(interval)}
+    row = feat.iloc[-1]
+    pos = float(row.get("bb_pos", 0.5))
+    bb_zone = "mid"
+    for z in BB_ZONE_FEATURES:
+        if int(row.get(z, 0)) == 1:
+            bb_zone = z.replace("bb_zone_", "")
+            break
+    ichi_pos = "in_cloud"
+    if int(row.get("ichi_above_cloud", 0)):
+        ichi_pos = "above_cloud"
+    elif int(row.get("ichi_below_cloud", 0)):
+        ichi_pos = "below_cloud"
+
+    return {
+        "interval": interval,
+        "label": interval_display(interval),
+        "close": float(row["Close"]),
+        "bb_pos": round(pos, 3),
+        "bb_zone": bb_zone,
+        "bb_state": _bb_event_label(row),
+        "ichi_position": ichi_pos,
+        "ichi_tk": "bull" if int(row.get("ichi_tk_bull", 0)) else "bear",
+        "ichi_cloud": "bull" if int(row.get("ichi_cloud_bull", 0)) else "bear",
+    }
+
+
+def _bb_event_label(row: pd.Series) -> str:
+    for name in (
+        "cross_up_lower",
+        "cross_up_upper",
+        "cross_down_lower",
+        "below_lower",
+        "above_upper",
+        "squeeze",
+        "inside_band",
+    ):
+        if int(row.get(name, 0)) == 1:
+            return name
+    return "neutral"


 def _merge_interval_features(
@@ -104,7 +213,16 @@ def _merge_interval_features(
 ) -> pd.DataFrame:
    """master_index 길이와 동일한 간격 특징만 반환."""
    pick = [c for c in FEATURE_BOOL_COLS if c in feat.columns]
-    extra = [c for c in ("bb_pos", "body_ratio", "lower_wick_ratio", "ret_pct") if c in feat.columns]
+    extra = [
+        c
+        for c in ("bb_pos", "body_ratio", "lower_wick_ratio", "ret_pct", "bb_width_pct")
+        if c in feat.columns
+    ]
+    if "bb_width_pct" not in feat.columns and "BB_Width" in feat.columns:
+        feat = feat.copy()
+        feat["bb_width_pct"] = feat["BB_Width"]
+        extra.append("bb_width_pct")
+
    sub = feat[pick + extra].copy()
    sub.columns = [f"{prefix}_{c}" for c in sub.columns]

@@ -124,33 +242,35 @@ def _merge_interval_features(


 def build_master_feature_matrix(frames: dict[int, pd.DataFrame]) -> pd.DataFrame:
-    """3분 타임라인에 모든 봉의 위치·캔들 특징을 붙인 행렬 (인덱스 유일)."""
+    """3분 타임라인에 모든 봉의 BB·일목·캔들 특징을 붙인 행렬."""
    entry = frames.get(ENTRY_INTERVAL)
    if entry is None or entry.empty:
-        raise ValueError("ENTRY_INTERVAL 데이터가 없습니다.")
+        raise ValueError(f"{ENTRY_INTERVAL}분봉(ENTRY_INTERVAL) 데이터가 없습니다.")

    entry_feat = compute_bar_features(entry)
    entry_feat = entry_feat[~entry_feat.index.duplicated(keep="last")].sort_index()

-    p3 = interval_prefix(ENTRY_INTERVAL)
+    p0 = interval_prefix(ENTRY_INTERVAL)
    ohlc = ["Open", "High", "Low", "Close", "Volume", "Upper", "Lower", "MA"]
    master = entry_feat[[c for c in ohlc if c in entry_feat.columns]].copy()

    for col in FEATURE_BOOL_COLS:
        if col in entry_feat.columns:
-            master[f"{p3}_{col}"] = entry_feat[col]
-    for col in ("bb_pos", "body_ratio", "lower_wick_ratio", "ret_pct", "bb_width_pct"):
+            master[f"{p0}_{col}"] = entry_feat[col]
+    for col in ("bb_pos", "body_ratio", "lower_wick_ratio", "ret_pct", "bb_width_pct", "BB_Width"):
        if col in entry_feat.columns:
-            master[f"{p3}_{col}"] = entry_feat[col]
+            master[f"{p0}_{col}"] = entry_feat[col]

-    parts = [master]
-    for interval, df in sorted(frames.items()):
-        if interval == ENTRY_INTERVAL or df is None or df.empty:
+    for interval in ALL_INTERVALS:
+        if interval == ENTRY_INTERVAL:
+            continue
+        df = frames.get(interval)
+        if df is None or df.empty:
            continue
        feat = compute_bar_features(df)
        feat = feat[~feat.index.duplicated(keep="last")].sort_index()
        prefix = interval_prefix(interval)
-        parts.append(_merge_interval_features(master.index, feat, prefix))
+        merged = _merge_interval_features(master.index, feat, prefix)
+        master = pd.concat([master, merged], axis=1)

-    out = pd.concat(parts, axis=1)
-    return out.loc[:, ~out.columns.duplicated()]
+    return master.loc[:, ~master.columns.duplicated()]