refactor: 精简仓库为 chanlun 核心与 web 分析，移除威科夫与遗留模块
删除根目录旧 Chan 模块、策略、配置、文档及 wyckoff 相关代码；更新缠论 pipeline 与笔中枢计算；补充 research 研究与 web 测试。 Co-authored-by: Cursor <cursoragent@cursor.com>
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jackyu66git
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"""Step 12：中枢突破深挖 —— Step 11 里唯一正期望的信号。
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Step 11 中 4h 中枢突破 21 笔 PF 1.90，但样本太小无法定论。
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本步把样本扩到多周期（15m/1h/4h，最长 2524 天），并区分：
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A 突破即入场
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B 突破 + 回抽不回中枢（三类买卖点的本质，但用突破确认而非等笔）
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C 假突破反向（突破后重回中枢 -> 反向做）
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同时统计假突破率，这决定了 B 相对 A 的价值。
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"""
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from __future__ import annotations
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import argparse
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import sys
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import warnings
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from pathlib import Path
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import numpy as np
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import pandas as pd
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warnings.filterwarnings("ignore")
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from lib.breakout import run_trades, summarize_trades
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from lib.data import fetch_ohlcv
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from lib.nested_level import build_htf_zones
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from chanlun import TF_DF
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pd.set_option("display.width", 260)
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def collect_breakouts(
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df: pd.DataFrame, zones: pd.DataFrame, scan: int = 300, pullback: int = 20
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) -> pd.DataFrame:
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"""找出每个中枢的首次有效突破，并记录后续是否假突破 / 是否回抽确认。"""
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if zones.empty:
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return pd.DataFrame()
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ts = df["timestamp"].to_numpy()
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close = df["close"].to_numpy(dtype=float)
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low = df["low"].to_numpy(dtype=float)
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high = df["high"].to_numpy(dtype=float)
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n = len(df)
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rows = []
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for _, z in zones.iterrows():
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zg, zd = float(z["zg"]), float(z["zd"])
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width = zg - zd
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if width <= 0:
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continue
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start = int(np.searchsorted(ts, z["available_ts"], side="left"))
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was_inside = False
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bo_idx = None
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d = 0
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for j in range(start, min(start + scan, n)):
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c = close[j]
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if zd <= c <= zg:
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was_inside = True
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continue
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if not was_inside:
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continue
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bo_idx, d = j, (1 if c > zg else -1)
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break
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if bo_idx is None:
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continue
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# 突破后是否重回中枢（假突破）
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fake_idx = None
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for j in range(bo_idx + 1, min(bo_idx + pullback + 1, n)):
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if zd <= close[j] <= zg:
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fake_idx = j
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break
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# 回抽确认：回踩到边界附近但收盘未回中枢，随后再度顺势
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edge = zg if d == 1 else zd
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conf_idx = None
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touched = False
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for j in range(bo_idx + 1, min(bo_idx + pullback + 1, n)):
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near = (low[j] <= edge * 1.002) if d == 1 else (high[j] >= edge * 0.998)
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inside = zd <= close[j] <= zg
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if inside:
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break
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if near:
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touched = True
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continue
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if touched and ((close[j] > close[j - 1]) if d == 1 else (close[j] < close[j - 1])):
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conf_idx = j
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break
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rows.append({
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"zs_start_ts": z["start_ts"], "zg": zg, "zd": zd,
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"width_pct": width / close[bo_idx],
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"bo_idx": bo_idx, "dir": d,
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"is_fake": fake_idx is not None,
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"conf_idx": conf_idx,
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})
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return pd.DataFrame(rows)
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--symbol", default="BTC/USDT:USDT")
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ap.add_argument("--tfs", default="15m,1h,4h")
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args = ap.parse_args()
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all_rows = []
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for tf in args.tfs.split(","):
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df = fetch_ohlcv(args.symbol, tf, 10**9)
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chan = TF_DF(df, 1, tf)
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cdf = chan.dataframe
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zones = build_htf_zones(df, tf)
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bo = collect_breakouts(cdf, zones)
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if bo.empty:
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continue
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days = (cdf["date"].iloc[-1] - cdf["date"].iloc[0]).days
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print(f"\n{'=' * 92}")
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print(f"周期 {tf} K线 {len(cdf)} 跨度 {days} 天 中枢 {len(zones)} "
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f"突破 {len(bo)} 假突破率 {bo['is_fake'].mean() * 100:.1f}%")
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print(f" 回抽确认成功 {bo['conf_idx'].notna().sum()} / {len(bo)}")
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entries_a = list(zip(bo["bo_idx"].astype(int), bo["dir"].astype(int)))
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real = bo[~bo["is_fake"]]
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entries_real = list(zip(real["bo_idx"].astype(int), real["dir"].astype(int)))
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cf = bo.dropna(subset=["conf_idx"])
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entries_b = list(zip(cf["conf_idx"].astype(int), cf["dir"].astype(int)))
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fake = bo[bo["is_fake"]]
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entries_c = list(zip(fake["bo_idx"].astype(int), -fake["dir"].astype(int)))
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rows = [
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summarize_trades(run_trades(cdf, entries_a, 1.5, 3.0, 48), "A 突破即入"),
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summarize_trades(run_trades(cdf, entries_b, 1.5, 3.0, 48), "B 回抽确认"),
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summarize_trades(run_trades(cdf, entries_c, 1.5, 3.0, 48), "C 假突破反向"),
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summarize_trades(run_trades(cdf, entries_real, 1.5, 3.0, 48), "D 事后真突破*"),
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]
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print(pd.DataFrame(rows).to_string(index=False))
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print(" * D 用了事后信息，仅作上界参考，不可交易。")
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for _, r in bo.iterrows():
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all_rows.append({**r.to_dict(), "tf": tf})
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# 中枢宽度分层：窄中枢突破是否更有效
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bo2 = bo.copy()
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bo2["w_bin"] = pd.qcut(bo2["width_pct"], 3, labels=["窄", "中", "宽"], duplicates="drop")
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rows = []
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for name, g in bo2.groupby("w_bin", observed=True):
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e = list(zip(g["bo_idx"].astype(int), g["dir"].astype(int)))
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if len(e) >= 20:
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rows.append(summarize_trades(run_trades(cdf, e, 1.5, 3.0, 48), f"宽度-{name}"))
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if rows:
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print(pd.DataFrame(rows).to_string(index=False))
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# 多空拆分
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rows = []
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for d, nm in [(1, "向上突破"), (-1, "向下突破")]:
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e = [(i, dd) for i, dd in entries_a if dd == d]
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if len(e) >= 20:
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rows.append(summarize_trades(run_trades(cdf, e, 1.5, 3.0, 48), nm))
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if rows:
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print(pd.DataFrame(rows).to_string(index=False))
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if all_rows:
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out = Path(__file__).parent / "out" / "step12_zs_breakouts.csv"
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out.parent.mkdir(exist_ok=True)
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pd.DataFrame(all_rows).to_csv(out, index=False)
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print(f"\n明细已写入 {out}")
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if __name__ == "__main__":
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main()
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