research: 低滞后信号口径定稿与实盘前偏差审计
fast_bsp3 改用 tol=-1 + require_touch=False，信号滞后从 5.8 根降到 2.2 根。 滞后与收益严格单调（年化 370% -> 906%，同一份数据同一套成本）， 这是本轮提升的主因，也意味着实盘延迟会直接侵蚀收益。 新增 step31~39 验证策略能否落地： - 跨品种样本外——8 个未参与调参的币，PF 2.73 / t 28.5，无一为负 - 时点重建——只喂到信号那一根重算，同根命中 100%，确认无未来函数； 1m 在 2000 根窗口即饱和，计算耗时 0.20s - 偏差审计——多空对称、中枢生效时刻零回退、滑点稳健至 30bp、持仓几乎不重叠 - 消融——alpha 来自缠论中枢的上下文定位，而非「收盘转强」这个触发动作 补 research/HANDOFF.md：记录确切口径与参数、已排除的偏差、 已验证无效因而不必重做的方向，以及下一步用影子交易器实测执行滑点的方案。 清理 step1~20 的输出：早期方法论已被推翻（存在未来函数偏差）， 其结论不再被引用；脚本保留，需要时可重跑。 Co-authored-by: Cursor <cursoragent@cursor.com>
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jackyu66git
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"""Step 37：跨品种样本外——在完全没参与过调参的币种上原样重跑。
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到此为止所有参数（级别对、tol、SL/TP、过滤条件）都是在 BTC/ETH/SOL 上挑的，
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即便 step36 的时间切分也仍是这三个币。本步换 8 个全新品种，
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一个参数都不动，直接套用最终配置，看结论是否还在。
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固定配置（不做任何搜索）：
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信号 find_fast_bsp3 默认（require_touch=False, tol=-1）
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过滤 大级别分型同向 + 中枢阶梯顺向推进
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出场 1.5 ATR 止损 / 3.0 ATR 止盈 / 最多 48 根
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成交 信号次根开盘，4bp 手续费 + 1bp 滑点
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"""
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from __future__ import annotations
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import os
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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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for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
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os.environ.setdefault(v, "1")
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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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pd.set_option("display.width", 320)
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SL, TP, MAXB = 1.5, 3.0, 48
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FEE, SLIP = 0.0004, 0.0001
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PAIRS = [("5m", "30m"), ("15m", "1h"), ("30m", "2h")]
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IS_SYMS = ["BTC", "ETH", "SOL"]
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OOS_SYMS = ["BNB", "XRP", "DOGE", "ADA", "AVAX", "LINK", "LTC", "TRX"]
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def run_one(task: tuple) -> dict | None:
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import warnings as _w
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_w.filterwarnings("ignore")
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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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from chanlun import TF_DF
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from lib.breakout import run_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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sym, ltf, htf, group = task
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pair = f"{sym}/USDT:USDT"
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try:
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df_l = fetch_ohlcv(pair, ltf, 10**9)
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if df_l is None or len(df_l) < 3000:
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return {"task": f"{sym} {ltf}", "error": "小级别数据不足"}
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chan_l = TF_DF(df_l, 1, ltf)
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cdf = chan_l.dataframe
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zones = build_htf_zones(cdf, ltf, chan=chan_l).reset_index(drop=True)
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if zones.empty:
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return {"task": f"{sym} {ltf}", "error": "无中枢"}
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z = zones.copy()
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pg, pdn = z["zg"].shift(), z["zd"].shift()
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z["z_above"], z["z_below"] = z["zd"] > pg, z["zg"] < pdn
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z["zone_i"] = np.arange(len(z))
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df_h = fetch_ohlcv(pair, htf, 10**9)
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if df_h is None or len(df_h) < 300:
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return {"task": f"{sym} {ltf}", "error": "大级别数据不足"}
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chan_h = TF_DF(df_h, 1, htf)
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s = signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe))
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tl = htf_fx_timeline(s, chan_h.dataframe)
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sig = find_fast_bsp3(cdf, zones)
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if sig.empty:
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return {"task": f"{sym} {ltf}", "error": "无信号"}
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sig = sig.merge(z[["zone_i", "z_above", "z_below"]], on="zone_i", how="left")
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sig = attach_htf_context(sig, cdf, tl, "h1")
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entries = list(zip(sig["entry_idx"].astype(int), sig["direction"].astype(int)))
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tr = run_trades(cdf, entries, SL, TP, MAXB, fee=FEE,
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entry_delay=1, slippage=SLIP)
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if tr.empty:
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return {"task": f"{sym} {ltf}", "error": "无成交"}
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m = sig.set_index("entry_idx")
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tr["symbol"], tr["ltf"], tr["group"] = sym, ltf, group
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tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()]
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for c in ("h1_agree", "z_above", "z_below", "direction"):
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tr[c if c != "direction" else "dir_sig"] = tr["entry_idx"].map(m[c])
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return {"task": f"{sym} {ltf}", "trades": tr}
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except Exception as e:
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return {"task": f"{sym} {ltf}", "error": repr(e)[:200]}
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def stat(g: pd.DataFrame, label: str, minn: int = 25) -> dict:
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r = g["ret"].to_numpy()
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if len(r) < minn:
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return {}
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w, o = r[r > 0], r[r <= 0]
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sd = r.std(ddof=1)
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t10 = r[r <= np.quantile(r, 0.90)]
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yrs = max((g["date"].max() - g["date"].min()).days / 365.25, 0.25)
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return {"分组": label, "笔数": len(r), "年笔数": round(len(r) / yrs),
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"胜率": f"{(r > 0).mean() * 100:.1f}%",
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"中位": f"{np.median(r) * 100:+.3f}%",
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"PF": f"{w.sum() / abs(o.sum()):.2f}" if len(o) else "inf",
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"t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}",
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"剔10%PF": f"{t10[t10 > 0].sum() / abs(t10[t10 <= 0].sum()):.2f}"}
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def main() -> None:
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tasks = ([(s, l, h, "样本内") for s in IS_SYMS for l, h in PAIRS]
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+ [(s, l, h, "样本外") for s in OOS_SYMS for l, h in PAIRS])
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print(f"[跨品种样本外] {len(tasks)} 个任务\n", flush=True)
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res, skip = [], []
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with ProcessPoolExecutor(max_workers=5) as ex:
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futs = {ex.submit(run_one, t): t for t in tasks}
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for i, f in enumerate(as_completed(futs), 1):
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r = f.result()
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if r is None or "error" in (r or {}):
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skip.append(f"{(r or {}).get('task', '?')}: {(r or {}).get('error', '')}")
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continue
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res.append(r)
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print(f" [{i}/{len(tasks)}] {r['task']} — {len(r['trades'])} 笔", flush=True)
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if skip:
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print(f"\n 跳过 {len(skip)} 个：")
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for s in skip:
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print(f" {s}")
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if not res:
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return
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allt = pd.concat([r["trades"] for r in res], ignore_index=True)
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allt["date"] = pd.to_datetime(allt["date"])
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allt["push"] = np.where(allt["dir_sig"] == 1, allt["z_above"], allt["z_below"])
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allt["push"] = allt["push"].fillna(False).astype(bool)
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fin = allt[(allt["h1_agree"] == 1) & allt["push"]].copy()
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fin.to_csv(HERE / "out" / "step37_oos.csv", index=False)
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print("\n" + "=" * 116)
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print("########## 1. 样本内（调参用的三个币）vs 样本外（八个全新币）##########")
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rows = [stat(g, k) for k, g in fin.groupby("group")]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print(" 参数一个没动。若样本外仍显著为正，说明不是在三个币上过拟合。")
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print("\n########## 2. 样本外逐个品种 ##########")
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oos = fin[fin["group"] == "样本外"]
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rows = [stat(g, s, minn=20) for s, g in oos.groupby("symbol")]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 3. 样本外分级别 ##########")
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rows = [stat(g, f"样本外 {k}", minn=20) for k, g in oos.groupby("ltf")]
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rows += [stat(g, f"样本内 {k}", minn=20)
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for k, g in fin[fin["group"] == "样本内"].groupby("ltf")]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 4. 样本外多空 ##########")
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rows = [stat(oos[oos["dir_sig"] == d], n, minn=20)
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for d, n in ((1, "做多"), (-1, "做空"))]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 5. 样本外分年 ##########")
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o = oos.copy()
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o["y"] = o["date"].dt.year
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rows = [stat(g, str(y), minn=20) for y, g in o.groupby("y")]
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print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
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print("\n########## 6. 资金曲线（固定名义1倍，不复利，已扣 5bp）##########")
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for k, g in fin.groupby("group"):
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g = g.sort_values("date")
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r = g["ret"].to_numpy()
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yrs = (g["date"].max() - g["date"].min()).days / 365.25
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eq = 1 + np.cumsum(r)
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dd = (np.maximum.accumulate(eq) - eq).max()
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print(f" {k}: n={len(r):>4} 年{len(r) / yrs:>3.0f}笔 "
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f"年化 {(eq[-1] - 1) / yrs * 100:+6.1f}% 回撤 {dd * 100:5.1f}% "
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f"Sharpe {r.mean() / r.std(ddof=1) * np.sqrt(len(r) / yrs):5.2f}")
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if __name__ == "__main__":
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main()
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