jackyu66gitandCursor
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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>
123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
"""Step 16：快速三买 + 大级别分型定位。
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Step 15 证明两件事：
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- 大级别分型定位有判别力（同向 PF 0.55 vs 反向 0.29）
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- 但引擎三买基线太差（PF 0.50），因为要等回拉笔确认，滞后 9~10 根
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本步把三买的确认从「等笔确认」换成「回抽当根实时判定」，
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在同样的区间套结构下重测。
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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.fast_bsp3 import find_fast_bsp3
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from lib.fx_signal import extract_fx_signals, signals_to_frame
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from lib.nested_bsp import attach_htf_context, htf_fx_timeline
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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 show(rows: list[dict]) -> None:
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rows = [r for r in rows if r.get("笔数", 0) > 0]
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print(pd.DataFrame(rows).to_string(index=False) if rows else " (样本不足)")
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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("--ltf", default="15m")
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ap.add_argument("--htf1", default="1h")
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ap.add_argument("--htf2", default="4h")
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ap.add_argument("--sl", type=float, default=1.5)
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ap.add_argument("--tp", type=float, default=3.0)
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ap.add_argument("--max-bars", type=int, default=48)
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args = ap.parse_args()
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df_l = fetch_ohlcv(args.symbol, args.ltf, 10**9)
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chan_l = TF_DF(df_l, 1, args.ltf)
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cdf_l = chan_l.dataframe
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zones = build_htf_zones(df_l, args.ltf)
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sig = find_fast_bsp3(cdf_l, zones)
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print(f"[小级别 {args.ltf}] {len(cdf_l)} 根K线 中枢 {len(zones)} "
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f"快速三类信号 {len(sig)}")
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if sig.empty:
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print("无信号")
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return
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print(f" 方向：三买 {(sig.direction == 1).sum()} / 三卖 {(sig.direction == -1).sum()}")
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print(f" 突破到入场滞后：中位 {sig['lag'].median():.0f} 根 "
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f"均值 {sig['lag'].mean():.1f} 根 —— 对照引擎三买 9~10 根")
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ctx = sig
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for tf, pref in ((args.htf1, "h1"), (args.htf2, "h2")):
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df_h = fetch_ohlcv(args.symbol, tf, 10**9)
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chan_h = TF_DF(df_h, 1, tf)
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sig_h = signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe))
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ctx = attach_htf_context(ctx, cdf_l, htf_fx_timeline(sig_h, chan_h.dataframe), pref)
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a1 = ctx["h1_agree"] == 1
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a2 = ctx["h2_agree"] == 1
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print(f"\n[方向一致率] {args.htf1} {a1.mean() * 100:.0f}% "
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f"{args.htf2} {a2.mean() * 100:.0f}% 两者同时 {(a1 & a2).mean() * 100:.0f}%")
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E = lambda g: list(zip(g["entry_idx"].astype(int), g["direction"].astype(int)))
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T = lambda g, nm: summarize_trades(
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run_trades(cdf_l, E(g), args.sl, args.tp, args.max_bars), nm)
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print("\n########## 逐层过滤 ##########")
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rows = [
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T(ctx, "A 全部快速三类"),
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T(ctx[a1], f"B +{args.htf1}同向"),
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T(ctx[a2], f"C +{args.htf2}同向"),
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T(ctx[a1 & a2], "D 双大级别同向"),
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T(ctx[~a1], f"F {args.htf1}反向(对照)"),
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]
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show(rows)
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print("\n########## 回抽深度分层（越大表示回抽越浅、未插入中枢）##########")
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rows = []
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q = ctx["depth"].quantile([0.33, 0.66]).to_numpy()
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for lo, hi, nm in [(-9, q[0], "深回抽"), (q[0], q[1], "中等"), (q[1], 9, "浅回抽")]:
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g = ctx[(ctx.depth >= lo) & (ctx.depth < hi)]
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if len(g) >= 20:
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rows.append(T(g, nm))
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show(rows)
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print("\n########## 多空拆分（双大级别同向）##########")
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both = ctx[a1 & a2]
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rows = [T(both[both.direction == d], nm)
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for d, nm in ((1, "三买做多"), (-1, "三卖做空"))
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if len(both[both.direction == d]) >= 10]
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show(rows)
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print("\n########## 与 Step 15 引擎三买的直接对照 ##########")
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print(f" 引擎三买（等笔确认，滞后 9~10 根）：675 笔 PF 0.50 t -7.87")
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a = T(ctx, "快速三买（实时判定）")
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print(f" 快速三买（回抽当根，滞后 {sig['lag'].median():.0f} 根）："
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f"{a['笔数']} 笔 PF {a['盈亏比PF']} t {a['t值']}")
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out = Path(__file__).parent / "out" / f"step16_fast_{args.symbol.split('/')[0]}_{args.ltf}.csv"
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out.parent.mkdir(exist_ok=True)
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ctx.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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