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>
164 lines
6.5 KiB
Python
164 lines
6.5 KiB
Python
"""Step 15：正确的区间套 —— 1h/4h 分型定位 + 15m 三类买卖点入场。
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此前 step7~step14 一直把中枢放在大级别、并在大级别自己交易，级别关系搞反了。
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本步按缠论正统结构重做：
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大级别 1h / 4h 只出分型，负责方向与「预设转折点」（确认滞后 1 根）
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小级别 15m 出中枢、出突破、出三类买卖点，负责精确入场
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逐层验证：
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A 全部 15m 三买三卖（基线）
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B + 1h 分型方向一致
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C + 4h 分型方向一致
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D + 1h 与 4h 同时一致（完整区间套）
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E + 大级别分型带背驰
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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.bsp_eval import forward_returns, run_pipeline, to_events
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from lib.data import fetch_ohlcv
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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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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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HORIZONS = (3, 5, 10, 20, 40)
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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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if not rows:
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print(" (样本不足)")
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return
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print(pd.DataFrame(rows).to_string(index=False))
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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", help="小级别：出中枢与三类买卖点")
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ap.add_argument("--htf1", default="1h", help="大级别一：出分型")
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ap.add_argument("--htf2", default="4h", help="大级别二：出分型")
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ap.add_argument("--zs", default="pure", choices=["pure", "seg"])
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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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# ---- 小级别：三类买卖点 ----
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df_l = fetch_ohlcv(args.symbol, args.ltf, 10**9)
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chan_l, bsp_list = run_pipeline(df_l, args.ltf, args.zs)
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cdf_l = chan_l.dataframe
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ev = forward_returns(to_events(bsp_list, cdf_l), cdf_l, HORIZONS)
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if ev.empty:
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print("小级别无买卖点")
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return
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ev3 = ev[ev["bsp_type"].isin(["B3", "S3"])].reset_index(drop=True)
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print(f"[小级别 {args.ltf}] {len(cdf_l)} 根K线 "
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f"{cdf_l['date'].iloc[0]} -> {cdf_l['date'].iloc[-1]}")
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print(f" 买卖点合计 {len(ev)} 其中三类 {len(ev3)} "
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f"(B3 {(ev3.bsp_type == 'B3').sum()} / S3 {(ev3.bsp_type == 'S3').sum()})")
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if not ev3.empty:
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print(f" 三类买卖点确认滞后：中位 {ev3['lag_bars'].median():.0f} 根 "
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f"均值 {ev3['lag_bars'].mean():.1f} 根")
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# ---- 大级别：分型时间线 ----
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ctx = ev3
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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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tl = htf_fx_timeline(sig_h, chan_h.dataframe)
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print(f"[大级别 {tf}] 分型 {len(tl)} 个")
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ctx = attach_htf_context(ctx, cdf_l, tl, 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] 1h {a1.mean() * 100:.0f}% 4h {a2.mean() * 100:.0f}% "
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f"两者同时 {(a1 & a2).mean() * 100:.0f}%")
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entries = lambda g: list(zip(g["entry_idx"].astype(int), g["direction"].astype(int)))
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print("\n########## 逐层过滤（事件驱动，sl1.5ATR tp3ATR max48）##########")
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rows = [
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summarize_trades(run_trades(cdf_l, entries(ctx), args.sl, args.tp, args.max_bars),
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"A 全部三类"),
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summarize_trades(run_trades(cdf_l, entries(ctx[a1]), args.sl, args.tp, args.max_bars),
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f"B +{args.htf1}分型同向"),
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summarize_trades(run_trades(cdf_l, entries(ctx[a2]), args.sl, args.tp, args.max_bars),
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f"C +{args.htf2}分型同向"),
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summarize_trades(run_trades(cdf_l, entries(ctx[a1 & a2]), args.sl, args.tp, args.max_bars),
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"D 双大级别同向"),
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]
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div1 = ctx["h1_div"] == 1
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rows.append(summarize_trades(
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run_trades(cdf_l, entries(ctx[a1 & div1]), args.sl, args.tp, args.max_bars),
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f"E +{args.htf1}同向且背驰"))
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# 反向对照：大级别分型与信号相悖时应当更差
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rows.append(summarize_trades(
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run_trades(cdf_l, entries(ctx[~a1]), args.sl, args.tp, args.max_bars),
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f"F {args.htf1}分型反向(对照)"))
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show(rows)
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print("\n########## 大级别分型的新鲜度（距分型确认的15m根数）##########")
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rows = []
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for lo, hi, name in [(0, 8, "0-8根"), (8, 24, "8-24根"),
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(24, 96, "24-96根"), (96, 10**9, ">96根")]:
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g = ctx[a1 & (ctx["h1_age_bars"] >= lo) & (ctx["h1_age_bars"] < hi)]
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if len(g) >= 15:
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rows.append(summarize_trades(
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run_trades(cdf_l, entries(g), args.sl, args.tp, args.max_bars), name))
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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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rows = []
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both = ctx[a1 & a2]
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for d, nm in ((1, "做多 B3"), (-1, "做空 S3")):
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g = both[both["direction"] == d]
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if len(g) >= 10:
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rows.append(summarize_trades(
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run_trades(cdf_l, entries(g), args.sl, args.tp, args.max_bars), nm))
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show(rows)
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print("\n########## 固定持有期收益（不设止损，看原始 alpha）##########")
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rows = []
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for name, g in (("A 全部三类", ctx), (f"B +{args.htf1}同向", ctx[a1]),
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("D 双大级别同向", ctx[a1 & a2])):
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if len(g) < 15:
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continue
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r = {"分组": name, "n": len(g)}
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for h in HORIZONS:
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v = g[f"ret_{h}"].dropna().to_numpy()
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if len(v) < 10:
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continue
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sd = v.std(ddof=1)
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r[f"{h}根"] = f"{v.mean() * 100:+.2f}%"
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r[f"{h}根t"] = f"{v.mean() / (sd / np.sqrt(len(v))):+.1f}" if sd else "—"
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rows.append(r)
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if rows:
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print(pd.DataFrame(rows).to_string(index=False))
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out = Path(__file__).parent / "out" / f"step15_nested_{args.symbol.split('/')[0]}.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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