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 31：引擎 B3/S3 的方向是不是反的。
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引擎原版在完全相同条件下跑出 27.4% 胜率、t=-18.76，这不是滞后能解释的幅度，
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稳定做反才会有这种数字。本步只做一件事：把引擎信号的方向翻转再跑一遍。
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若翻转后由显著负转为显著正，说明 Chan_BSP_DIR 与实际交易方向的映射存在问题，
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而不是三类买卖点本身无效——这会影响引擎所有下游使用者，不只是本次研究。
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同时打印几个样本的价格上下文，用价格自己来判定哪个方向才是对的。
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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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def run_one(sym: str) -> 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 chanlun.core.ChanEnum import Chan_BSP_DIR
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from lib.breakout import run_trades
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from lib.data import fetch_ohlcv
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ltf = "30m"
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try:
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df_l = fetch_ohlcv(f"{sym}/USDT:USDT", ltf, 10**9)
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if df_l is None:
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return None
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chan = TF_DF(df_l, 1, ltf)
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cdf = chan.dataframe
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zs_list = chan.cal_bi_zs_list_pure(chan.bi_list)
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if not zs_list:
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return None
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bsp_list = chan.find_all_bsp(chan.bi_list, zs_list) or []
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idx_map = {k: i for i, k in
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enumerate(cdf["date"].dt.strftime("%Y-%m-%d %H:%M:%S"))}
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close = cdf["close"].to_numpy(dtype=float)
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n = len(cdf)
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rows = []
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for b in bsp_list:
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t = str(b.type).replace("Chan_BSP_TYPE.", "")
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if t not in ("B3", "S3") or not b.is_sure or b.sure_time is None:
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continue
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ek = str(b.sure_time)
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if ek not in idx_map:
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continue
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i = idx_map[ek]
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zs = getattr(b, "zs", None)
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rows.append({
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"entry_idx": i,
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"type": t,
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"dir_enum": "BUY" if b.dir == Chan_BSP_DIR.BUY else "SELL",
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"dir_mapped": 1 if b.dir == Chan_BSP_DIR.BUY else -1,
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"zg": float(zs.zg) if zs is not None else np.nan,
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"zd": float(zs.zd) if zs is not None else np.nan,
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"entry_px": close[i],
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# 入场后 12 根的实际涨跌，让价格自己说话
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"fwd12": (close[min(i + 12, n - 1)] - close[i]) / close[i],
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})
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sig = pd.DataFrame(rows).drop_duplicates("entry_idx")
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if len(sig) < 30:
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return None
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out = []
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for name, flip in (("原方向", 1), ("反方向", -1)):
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entries = [(int(i), int(d) * flip)
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for i, d in zip(sig["entry_idx"], sig["dir_mapped"])]
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tr = run_trades(cdf, entries, SL, TP, MAXB, fee=0.0, entry_delay=1)
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if tr.empty:
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continue
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tr["mode"], tr["symbol"] = name, sym
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m = sig.set_index("entry_idx")
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for c in ("type", "dir_enum", "fwd12", "zg", "zd", "entry_px"):
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tr[c] = tr["entry_idx"].map(m[c])
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out.append(tr)
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return {"sym": sym, "trades": pd.concat(out, ignore_index=True),
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"sig": sig.assign(symbol=sym)}
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except Exception as e:
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return {"sym": sym, "error": repr(e)[:250]}
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def stat(g: pd.DataFrame, label: str) -> dict:
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r = g["gross"].to_numpy() - FEE - SLIP
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if len(r) < 15:
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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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return {"口径": label, "笔数": len(r),
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"胜率": f"{(r > 0).mean() * 100:.1f}%",
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"均收益": f"{r.mean() * 100:+.3f}%",
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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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def main() -> None:
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syms = ["BTC", "ETH", "SOL"]
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res = []
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with ProcessPoolExecutor(max_workers=3) as ex:
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futs = {ex.submit(run_one, s): s for s in syms}
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for f in as_completed(futs):
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r = f.result()
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if r is None or "error" in (r or {}):
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print(f" {futs[f]} 跳过 {(r or {}).get('error', '')}", flush=True)
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continue
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res.append(r)
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print(f" {r['sym']} ok", flush=True)
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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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sig = pd.concat([r["sig"] for r in res], ignore_index=True)
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print("\n" + "=" * 100)
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print("########## 1. 原方向 vs 反方向（30m，引擎 B3/S3）##########")
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print(pd.DataFrame([r for r in
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[stat(g, m) for m, g in allt.groupby("mode")] if r]
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).to_string(index=False))
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print("\n########## 2. 分类型看 ##########")
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rows = []
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for (m, t), g in allt.groupby(["mode", "type"]):
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rows.append(stat(g, f"{m} {t}"))
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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. 让价格自己说话：入场后12根的实际涨跌 ##########")
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print(" B3 若真是买点，其后价格应偏涨（fwd12 均值>0）")
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rows = []
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for t, g in sig.groupby("type"):
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f = g["fwd12"].to_numpy()
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sd = f.std(ddof=1)
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rows.append({"类型": t, "枚举方向": g["dir_enum"].iloc[0], "笔数": len(f),
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"后12根均涨跌": f"{f.mean() * 100:+.3f}%",
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"上涨占比": f"{(f > 0).mean() * 100:.1f}%",
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"t值": f"{f.mean() / (sd / np.sqrt(len(f))):+.2f}"})
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print(pd.DataFrame(rows).to_string(index=False))
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print("\n########## 4. 入场价相对中枢的位置（B3 应在中枢上方）##########")
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rows = []
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for t, g in sig.groupby("type"):
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above = (g["entry_px"] > g["zg"]).mean() * 100
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below = (g["entry_px"] < g["zd"]).mean() * 100
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rows.append({"类型": t, "笔数": len(g),
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"入场价>中枢上沿zg": f"{above:.1f}%",
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"入场价<中枢下沿zd": f"{below:.1f}%",
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"在中枢内": f"{100 - above - below:.1f}%"})
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print(pd.DataFrame(rows).to_string(index=False))
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print("\n 若 B3 大量落在中枢下方、S3 落在上方，则标签与几何位置矛盾。")
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if __name__ == "__main__":
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main()
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