"""Step 2：Walk-forward 重放，检验缠论买卖点在实时环境下是否真实可交易。 与 Step 1 的区别：入场时刻不再取引擎自报的 sure_time，而是取信号在滑动窗口重放中 「第一次真正可被观测到」的时刻。这才是没有未来函数的口径。 """ from __future__ import annotations import argparse import sys import time from pathlib import Path import numpy as np import pandas as pd sys.path.insert(0, str(Path(__file__).resolve().parent)) from lib.bsp_eval import DEFAULT_HORIZONS, baseline_stats, run_pipeline from lib.data import fetch_ohlcv from lib.walkforward import analyze_stability, replay pd.set_option("display.width", 220) from chanlun.core.ChanEnum import Chan_BSP_DIR def realtime_event_study(life: pd.DataFrame, df: pd.DataFrame, horizons=DEFAULT_HORIZONS) -> pd.DataFrame: """以 first_seen 为入场点做事件研究（无未来函数）。""" closes = df["close"].to_numpy(dtype=float) n = len(df) rows = [] for _, r in life.iterrows(): i = int(r["first_seen_idx"]) d = int(r["direction"]) entry = closes[i] row = {"bsp_type": r["bsp_type"], "direction": d, "side": "LONG" if d == 1 else "SHORT", "entry_idx": i} for h in horizons: j = i + h row[f"ret_{h}"] = d * (closes[j] - entry) / entry if j < n else np.nan rows.append(row) return pd.DataFrame(rows) def summarize_rt(fwd: pd.DataFrame, df: pd.DataFrame, horizons=DEFAULT_HORIZONS) -> pd.DataFrame: base = baseline_stats(df, horizons).set_index("horizon") groups = [("ALL", fwd), ("ALL_LONG", fwd[fwd.direction == 1]), ("ALL_SHORT", fwd[fwd.direction == -1])] groups += [(t, g) for t, g in fwd.groupby("bsp_type")] rows = [] for name, g in groups: if g.empty: continue for h in horizons: r = g[f"ret_{h}"].dropna().to_numpy() if len(r) == 0: continue dirs = g.loc[g[f"ret_{h}"].notna(), "direction"].to_numpy() base_mean = float(np.mean(dirs) * base.loc[h, "base_mean_long"]) sd = r.std(ddof=1) if len(r) > 1 else np.nan rows.append({ "group": name, "horizon": h, "n": len(r), "mean": r.mean(), "winrate": (r > 0).mean(), "excess": r.mean() - base_mean, "tstat": r.mean() / (sd / np.sqrt(len(r))) if sd else np.nan, }) return pd.DataFrame(rows) def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--symbol", default="BTC/USDT:USDT") ap.add_argument("--tf", default="1h") ap.add_argument("--limit", type=int, default=50000) ap.add_argument("--window", type=int, default=3000) ap.add_argument("--step", type=int, default=6) ap.add_argument("--tail", type=int, default=0, help="只用最后N根做快速验证，0=全量") args = ap.parse_args() df = fetch_ohlcv(args.symbol, args.tf, args.limit) if args.tail: df = df.tail(args.tail).reset_index(drop=True) print(f"[data] {args.symbol} {args.tf} rows={len(df)} {df['date'].iloc[0]} -> {df['date'].iloc[-1]}") _, final_bsp = run_pipeline(df, args.tf) final_keys = { (str(b.type).replace("Chan_BSP_TYPE.", ""), 1 if b.dir == Chan_BSP_DIR.BUY else -1, str(b.end_time)) for b in final_bsp if b.is_sure and b.sure_time is not None } print(f"[final] 全量口径买卖点 = {len(final_keys)}") n_windows = len(range(args.window, len(df) + 1, args.step)) print(f"[replay] window={args.window} step={args.step} 共 {n_windows} 个窗口") t0 = time.time() result = replay( df, args.tf, window=args.window, step=args.step, cache_key=f"{args.symbol.replace('/', '_').replace(':', '-')}_{args.tf}", ) print(f"[replay] 完成，用时 {time.time() - t0:.0f}s") life = analyze_stability(result, final_keys, df, window=args.window) out_dir = Path(__file__).parent / "out" out_dir.mkdir(exist_ok=True) tag = f"{args.symbol.split('/')[0]}_{args.tf}" + (f"_tail{args.tail}" if args.tail else "") life.to_csv(out_dir / f"step2_life_{tag}.csv", index=False) valid = life[~life["truncated"]].copy() mature = valid[~valid["immature"]].copy() print(f"\n[信号总数] 重放中出现 = {len(life)}；剔除左截断后 = {len(valid)}；其中已成熟 = {len(mature)}") if valid.empty: return print("\n=== 信号稳定性 ===") print(f"[幻影率] 实时报出但完整历史上不存在: {(~mature['in_final']).mean() * 100:.1f}%" f" ({(~mature['in_final']).sum()}/{len(mature)})") print(f"[撤销率] 窗口内报出后又被抹掉 : {(~mature['alive_at_scope_end']).mean() * 100:.1f}%" f" ({(~mature['alive_at_scope_end']).sum()}/{len(mature)})") print(f"[存活度] persist_ratio 均值 : {mature['persist_ratio'].mean():.3f}") print( mature.groupby("bsp_type") .agg(n=("in_final", "size"), phantom=("in_final", lambda s: 1 - s.mean()), revoked=("alive_at_scope_end", lambda s: 1 - s.mean()), persist=("persist_ratio", "mean")) .assign(phantom=lambda d: (d["phantom"] * 100).map(lambda v: f"{v:.1f}%"), revoked=lambda d: (d["revoked"] * 100).map(lambda v: f"{v:.1f}%"), persist=lambda d: d["persist"].map(lambda v: f"{v:.3f}")) .to_string() ) print("\n=== 真实滞后 ===") idx_of = {t: i for i, t in enumerate(df["date"].dt.strftime("%Y-%m-%d %H:%M:%S"))} sure_lag = {} for b in final_bsp: if b.is_sure and b.sure_time is not None and str(b.sure_time) in idx_of: k = str(b.end_time) if k in idx_of: sure_lag[k] = idx_of[str(b.sure_time)] - idx_of[k] valid["sure_lag"] = valid["fx_time"].map(sure_lag) print(f"[实时首见滞后] 中位数 = {valid['observed_lag'].median():.0f} 根" f" 均值 = {valid['observed_lag'].mean():.1f} 根") both = valid.dropna(subset=["sure_lag"]) if not both.empty: print(f"[引擎自报滞后] 中位数 = {both['sure_lag'].median():.0f} 根" f" 均值 = {both['sure_lag'].mean():.1f} 根") print(f"[被低估的滞后] 中位数 = {(both['observed_lag'] - both['sure_lag']).median():.0f} 根" f" 均值 = {(both['observed_lag'] - both['sure_lag']).mean():.1f} 根") print("\n=== 无未来函数事件研究（以实时首见时刻入场）===") fwd = realtime_event_study(valid, df) s = summarize_rt(fwd, df) out = s.copy() for c in ("mean", "excess"): out[c] = out[c].map(lambda v: f"{v * 100:+.2f}%") out["winrate"] = out["winrate"].map(lambda v: f"{v * 100:.0f}%") out["tstat"] = out["tstat"].map(lambda v: f"{v:+.2f}") print(out.to_string(index=False)) fwd.to_csv(out_dir / f"step2_rt_events_{tag}.csv", index=False) s.to_csv(out_dir / f"step2_rt_summary_{tag}.csv", index=False) print(f"\n明细已写入 {out_dir}/step2_*_{tag}.csv") if __name__ == "__main__": main()