research: 因果回放推翻一类反手(step59)，线段顶点验证识别有效但不够(step60)
step59 逐根回放：init_stream 预热后逐根 append_bar，每根重算笔中枢与 bsp， 记录信号首现根并按它入场。信号身份用「类型+极值KLC时刻」而非 sure_time， 后者正是会被重画的字段。必须逐根，分段重建等于多给信息。 结果把 §3.395 推翻了：召回 100%、幻影 0，即存在性是因果的；但首现根比全量 sure_time 晚中位 15 根、P90 31 根，0% 能准时拿到。按真实首现根入场， B1 反手 PF 2.35→0.92、S1 2.75→1.30，t 0.20/0.64 完全不显著。 教训：sure_time 是引擎事后标注的确认时刻，不等于可执行时刻。任何拿它当 entry 的回测都要先过逐根回放。 step60 用线段终点当标准答案验证识别本身。对照组取所有同向笔端点——B1 按构造 就长在笔低点上，不设这个基准任何绝对命中率都无法解读。5m 上 B1 命中 30.3% 对基准 15.0%，提升 2.02 倍，S1 1.81 倍；B3/S3 恰为 0%，符合三类长在趋势 中段的预期，两者互为标签有效性旁证。 所以识别是对的，但精度只有 30%，且那 70% 噪声 PF 只有 0.08。更关键的是即使 用未来函数把精度提到 100%，命中组也只有 PF 0.70~0.83，仍不赚钱——因为入场价 已在结构底上方 2.90 ATR。一类线三层逐层否定后到此为止。 Co-authored-by: Cursor <cursoragent@cursor.com>
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"""因果性回放：一类反手的信号在当时真的发得出来吗？
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§3.395 的 PF 2.3~3.0 建立在全量数据一次算完的 `bsp_list` 上。但增量模块的
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文件头自己写着「笔必须整表重扫：**最后一笔 is_sure 允许收回**」，§5.41 也记了
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中枢右边缘会重画。若信号是事后才浮现的，那个 PF 就是幻觉。
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**做法**：用 `init_stream` 预热，随后逐根 `append_bar`，每根之后重算
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`cal_bi_zs_list_pure` + `find_all_bsp`，记录每个信号**第一次出现**在哪一根。
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入场用那一根（的次根开盘），而不是事后的 `sure_time` —— 实盘只能这样。
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必须逐根，不能分段重建：在第 t 根用 `data[0:t+S]` 重算等于多给了 S 根的信息，
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测出来的因果性是假的。
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**三个要看的量**
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召回 全量算出的信号，有多少在回放中真的出现过（没出现的是事后才浮现）
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幻影 回放中出现、但全量里没有的（当时发了、后来被重画掉）
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代价 回放首现根 vs 全量 sure_time 的滞后；以及按首现根入场的实际 PF
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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 concurrent.futures import ProcessPoolExecutor, as_completed
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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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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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sys.path.insert(0, str(HERE.parent))
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OUT = HERE / "out" / "step59_replay.feather"
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SL, SCALE_AT, RUNNER, RSTOP, MAXB = 2.0, 3.0, 8.0, 2.0, 48
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WANT = {"B1": 1, "S1": -1}
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def sig_key(b) -> tuple | None:
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"""信号身份用「类型 + 极值 KLC 的结束时刻」。
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不能用 sure_time 当身份：它正是会被重画的字段，用它做键会把同一个信号
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在不同根上算成两个。极值点稳定得多。
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"""
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t = getattr(b.type, "name", str(b.type))
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if t not in WANT:
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return None
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return (t, str(b.klc.end_time))
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def replay(sym: str, tf: str, rows: int, warm: int, steps: int) -> pd.DataFrame | None:
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from chanlun import TF_DF
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from lib.data import fetch_ohlcv
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df = fetch_ohlcv(f"{sym}/USDT:USDT", tf, rows)
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if df is None or len(df) < warm + steps + 100:
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print(f" {sym} {tf} 数据不足 {0 if df is None else len(df)}", flush=True)
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return None
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df = df.iloc[-(warm + steps):].reset_index(drop=True)
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# ---- 全量口径：一次算完，作为对照 ----
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full = TF_DF(df, 1, tf, lean=False)
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fz = full.cal_bi_zs_list_pure(full.bi_list)
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full_sig = {}
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for b in (full.find_all_bsp(full.bi_list, fz) or []):
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k = sig_key(b)
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if k and b.sure_time is not None:
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full_sig[k] = str(b.sure_time)
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# ---- 回放口径：逐根追加，记录首现根 ----
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chan = TF_DF(df.iloc[:warm].copy(), 1, tf, lean=False)
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chan.init_stream(df.iloc[:warm].copy(), 1, tf)
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first_seen: dict[tuple, int] = {}
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for i in range(warm, len(df)):
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chan.append_bar(df.iloc[i])
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try:
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zs = chan.cal_bi_zs_list_pure(chan.bi_list)
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bsp = chan.find_all_bsp(chan.bi_list, zs) if zs else []
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except Exception: # noqa: BLE001
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continue
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for b in (bsp or []):
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k = sig_key(b)
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if k and k not in first_seen:
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first_seen[k] = i
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dser = pd.to_datetime(full.dataframe["date"])
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if dser.dt.tz is not None:
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dser = dser.dt.tz_localize(None)
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didx = pd.DatetimeIndex(dser)
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def to_i(ts) -> int:
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t = pd.Timestamp(ts)
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return int(didx.searchsorted(t.tz_localize(None) if t.tz else t))
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rec = []
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for k in set(full_sig) | set(first_seen):
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t, ext_t = k
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i_seen = first_seen.get(k)
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# 只统计回放窗口内的：预热段的信号本来就不在考察范围
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i_ext = to_i(ext_t)
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if i_ext < warm - 200:
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continue
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rec.append({
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"sym": sym, "tf": tf, "type": t, "dir": WANT[t],
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"in_full": k in full_sig, "in_replay": i_seen is not None,
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"i_ext": i_ext,
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"i_seen": -1 if i_seen is None else i_seen,
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"i_sure_full": to_i(full_sig[k]) if k in full_sig else -1,
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})
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r = pd.DataFrame(rec)
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if r.empty:
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return None
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# 按回放首现根入场，跑与实盘一致的出场
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from lib.exit_model import cfg_name, walk_exits
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cdf = full.dataframe
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atr = cdf["atr"].to_numpy(float)
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cl = cdf["close"].to_numpy(float)
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live = r[r.in_replay & (r.i_seen < len(cdf) - 2)].copy()
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live = live[np.isfinite(atr[live.i_seen.values])
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& (atr[live.i_seen.values] > 0)]
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if not live.empty:
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cfg = cfg_name(SL, RUNNER, MAXB, RSTOP)
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# 反手：§3.395 判定该反着做
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t_ = pd.DataFrame({"entry_idx": live.i_seen.values,
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"direction": -live.dir.values})
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res = walk_exits(cdf, t_, [SL], [RUNNER], [MAXB], scale_at=SCALE_AT,
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runners=(RUNNER,), runner_stops=(RSTOP,))
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if len(res) == len(live):
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for c in ("g", "r", "c"):
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live[c] = res[f"{cfg}_{c}"].to_numpy()
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live["atr_pct"] = atr[live.i_seen.values] / cl[live.i_seen.values]
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r = r.merge(live[["i_ext", "type", "g", "r", "c", "atr_pct"]],
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on=["i_ext", "type"], how="left")
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return r
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def report(d: pd.DataFrame) -> None:
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from lib.exit_model import fee_of, taker_notional
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print("\n" + "=" * 92)
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print("########## 一、召回与幻影 ##########")
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rows = []
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for (tf, t), x in d.groupby(["tf", "type"]):
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full = x[x.in_full]
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rep = x[x.in_replay]
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both = x[x.in_full & x.in_replay]
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rows.append({
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"tf": tf, "类型": t,
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"全量信号": len(full), "回放信号": len(rep),
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"召回": f"{len(both)/max(len(full),1)*100:.1f}%",
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"事后才浮现": len(full) - len(both),
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"幻影(被重画掉)": len(rep) - len(both),
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})
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print(pd.DataFrame(rows).to_string(index=False))
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print("\n召回 = 全量算出的信号里，回放中真的出现过的比例。"
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"\n幻影 = 回放中发过、全量里却没有的 —— 实盘会照做，回测却看不见它。")
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print("\n" + "=" * 92)
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print("########## 二、时点代价：回放首现 vs 全量 sure_time ##########")
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b = d[d.in_full & d.in_replay].copy()
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b["delay"] = b.i_seen - b.i_sure_full
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for tf, x in b.groupby("tf"):
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q = x.delay.quantile([.25, .5, .75, .9])
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print(f" {tf} 中位 {q[.5]:+.0f} 根 P25 {q[.25]:+.0f} "
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f"P75 {q[.75]:+.0f} P90 {q[.9]:+.0f} "
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f"｜ 早于或等于全量的占比 {(x.delay <= 0).mean()*100:.0f}%")
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print(" 正值 = 回放比全量晚知道，实盘要在更差的价位入场。")
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if "g" not in d.columns:
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return
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print("\n" + "=" * 92)
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print("########## 三、真正能落地的收益：按回放首现根入场（反手）##########")
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x = d.dropna(subset=["g"]).copy()
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rows = []
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for (tf, t), g in x.groupby(["tf", "type"]):
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if len(g) < 30:
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continue
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net = g.g.values - fee_of(g.r.values, g.c.values)
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gR = g.g.values / (SL * g.atr_pct.values)
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R = net / (SL * g.atr_pct.values)
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tn = taker_notional(g.r.values, g.c.values)
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w, o = net[net > 0].sum(), -net[net <= 0].sum()
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rows.append({
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"tf": tf, "类型(反手)": t, "笔数": len(g),
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"胜率": f"{(net > 0).mean()*100:.1f}%",
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"毛R": round(gR.mean(), 3), "净均R": round(R.mean(), 3),
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"PF": round(w / o, 2) if o > 0 else np.inf,
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"余量bp": round(net.mean() / tn.mean() * 1e4, 2),
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"t值": round(gR.mean() / (gR.std(ddof=1) / np.sqrt(len(g))), 2),
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})
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if rows:
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print(pd.DataFrame(rows).to_string(index=False))
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print("\n对照 · §3.395 全量口径 5m：B1反手 PF 2.35 / S1反手 2.75")
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print("若这里明显掉下来，说明那个 PF 吃了右边缘重画的红利，不可落地。")
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--symbols", default="BTC,ETH,SOL")
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ap.add_argument("--tf", default="5m")
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ap.add_argument("--rows", type=int, default=60_000)
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ap.add_argument("--warm", type=int, default=20_000)
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ap.add_argument("--steps", type=int, default=10_000)
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ap.add_argument("--workers", type=int, default=3)
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ap.add_argument("--reuse", action="store_true")
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args = ap.parse_args()
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if args.reuse and OUT.exists():
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report(pd.read_feather(OUT))
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return
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syms = [x.strip() for x in args.symbols.split(",")]
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print(f"[因果回放] {len(syms)} 币 × {args.steps} 根逐根重放"
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f"（每根都要重算笔中枢与 bsp，慢是必然的）\n", flush=True)
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parts = []
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with ProcessPoolExecutor(max_workers=args.workers) as ex:
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fut = {ex.submit(replay, s, args.tf, args.rows, args.warm,
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args.steps): s for s in syms}
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for i, f in enumerate(as_completed(fut), 1):
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r = f.result()
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print(f" [{i}/{len(syms)}] {fut[f]} "
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f"{0 if r is None else len(r)} 个信号", flush=True)
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if r is not None:
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parts.append(r)
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if not parts:
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print("无结果")
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return
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d = pd.concat(parts, ignore_index=True)
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OUT.parent.mkdir(exist_ok=True)
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d.to_feather(OUT)
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report(d)
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if __name__ == "__main__":
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main()
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