"""实时版一类买卖点（fast B1/S1）能否翻正。 step55 已判定引擎的 B1/S1 不可用（5m PF 0.24/0.20、胜率 18.6%/14.8%、 t −17/−22），且病根是滞后 8~9 根带来的几何劣势，不是参数。B3 当年同病 （PF 0.66），靠重新定义成实时判据翻到 1.59（B4）。本脚本对一类做同样的尝试。 对照组三条，缺一不可： 引擎 B1/S1 step55 的数，说明「不改判据」是什么下场 fast B1/S1 本次 fast B3/S3 同一份数据、同一套出场跑 B4，确认管线本身能跑出正数 —— 少了它，fast B1 若为负就分不清是判据不行还是管线接错了 """ from __future__ import annotations import argparse import sys import warnings from concurrent.futures import ProcessPoolExecutor, as_completed from pathlib import Path import numpy as np import pandas as pd warnings.filterwarnings("ignore") HERE = Path(__file__).resolve().parent sys.path.insert(0, str(HERE)) sys.path.insert(0, str(HERE.parent)) OUT = HERE / "out" / "step56_fast_bsp1.feather" SL, SCALE_AT, RUNNER, RSTOP, MAXB = 2.0, 3.0, 8.0, 2.0, 48 def collect(sym: str, rows: int, tf: str) -> pd.DataFrame | None: from chanlun import TF_DF from chanlun.analysis.fast_bsp import ( ensure_timestamp, find_fast_bsp3, zones_from_zs_list, ) from lib.data import fetch_ohlcv from lib.exit_model import cfg_name, walk_exits from lib.fast_bsp1 import find_fast_bsp1, zones_with_enter_area try: df = fetch_ohlcv(f"{sym}/USDT:USDT", tf, rows) if df is None or len(df) < 5_000: return None chan = TF_DF(df, 1, tf) cdf = ensure_timestamp(chan.dataframe) zs_list = chan.cal_bi_zs_list_pure(chan.bi_list) if not zs_list: return None atr = cdf["atr"].to_numpy(float) cl = cdf["close"].to_numpy(float) cfg = cfg_name(SL, RUNNER, MAXB, RSTOP) parts = [] d1 = {} s1 = find_fast_bsp1(cdf, zones_with_enter_area(zs_list, cdf), diag=d1) if not s1.empty: s1["kind"] = "fastB1" parts.append(s1) # 同数据同出场跑一遍 B4，作为「管线能出正数」的存在性证明 s3 = find_fast_bsp3(cdf, zones_from_zs_list(zs_list, cdf)) if not s3.empty: s3["kind"] = "fastB3" parts.append(s3) if not parts: return None r = pd.concat(parts, ignore_index=True) r = r[(r.entry_idx < len(cdf) - 2) & np.isfinite(atr[r.entry_idx.values]) & (atr[r.entry_idx.values] > 0)].reset_index(drop=True) if r.empty: return None res = walk_exits(cdf, r[["entry_idx", "direction"]], [SL], [RUNNER], [MAXB], scale_at=SCALE_AT, runners=(RUNNER,), runner_stops=(RSTOP,)) if len(res) != len(r): return None for k in ("g", "r", "c", "b"): r[k] = res[f"{cfg}_{k}"].to_numpy() r["sym"], r["tf"] = sym, tf r["atr_pct"] = atr[r.entry_idx.values] / cl[r.entry_idx.values] r["date"] = cdf["date"].to_numpy()[r.entry_idx.values] r["diag"] = str(d1) return r except Exception as e: # noqa: BLE001 print(f" {sym} {tf} 失败: {type(e).__name__}: {e}", flush=True) return None def stats(g: pd.DataFrame) -> dict: from lib.exit_model import fee_of, taker_notional net = g.g.values - fee_of(g.r.values, g.c.values) gR = g.g.values / (SL * g.atr_pct.values) R = net / (SL * g.atr_pct.values) tn = taker_notional(g.r.values, g.c.values) w, o = net[net > 0].sum(), -net[net <= 0].sum() return { "笔数": len(g), "胜率": f"{(net > 0).mean()*100:.1f}%", "毛R": round(gR.mean(), 3), "净均R": round(R.mean(), 3), "PF": round(w / o, 2) if o > 0 else np.inf, "R夏普": round(R.mean() / R.std(ddof=1), 3), "余量bp": round(net.mean() / tn.mean() * 1e4, 2), "中位持仓": int(np.median(g.b.values)), "t值": round(gR.mean() / (gR.std(ddof=1) / np.sqrt(len(g))), 2), } def report(d: pd.DataFrame) -> None: for tf, x in d.groupby("tf"): span = (pd.to_datetime(x.date).max() - pd.to_datetime(x.date).min()).total_seconds() / 86400 print("\n" + "#" * 92) print(f"########## {tf} · {x.sym.nunique()} 币 · 跨 {span:.0f} 天" f" ##########") rows = [] for (kind, dirn), g in x.groupby(["kind", "direction"]): if len(g) < 40: continue nm = {"fastB1": ("一买", "一卖"), "fastB3": ("三买", "三卖")}[kind] rows.append({"信号": f"{kind} {nm[0 if dirn == 1 else 1]}", "每币每天": round(len(g) / span / x.sym.nunique(), 3), **stats(g)}) if rows: print(pd.DataFrame(rows).to_string(index=False)) print("\n对照 · step55 引擎原生（同周期同出场）：") print(" 5m B1 PF 0.24 胜率 18.6% t −17.3 ｜ S1 PF 0.20 胜率 14.8% " "t −22.3") print(" 15m B1 PF 0.21 t −18.9 ｜ S1 PF 0.23 t −15.9") f1 = x[x.kind == "fastB1"] if len(f1) >= 60: print("\n背驰强度分档（div = 离开段面积/进入段面积，越小背驰越强）：") q = pd.qcut(f1["div"], 4, labels=["Q1最强", "Q2", "Q3", "Q4最弱"], duplicates="drop") print(pd.DataFrame([{"档": k, **stats(v)} for k, v in f1.groupby(q, observed=True) if len(v) >= 20]).to_string(index=False)) print("\n滞后分档（lag = 入场根 − 离开段极值根）：") b = pd.cut(f1["lag"], [-1, 1, 3, 6, 12, 1e9], labels=["≤1根", "2-3根", "4-6根", "7-12根", ">12根"]) print(pd.DataFrame([{"档": k, **stats(v)} for k, v in f1.groupby(b, observed=True) if len(v) >= 20]).to_string(index=False)) dg = d[d.kind == "fastB1"].diag.dropna() if len(dg): print("\n" + "=" * 92) print("fast B1 漏斗（首个中枢样本）：", dg.iloc[0]) def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--symbols", default="BTC,ETH,SOL,LINK,DOGE") ap.add_argument("--tfs", default="5m,15m") ap.add_argument("--rows", type=int, default=200_000) ap.add_argument("--workers", type=int, default=3) ap.add_argument("--reuse", action="store_true") args = ap.parse_args() if args.reuse and OUT.exists(): d = pd.read_feather(OUT) else: syms = [s.strip() for s in args.symbols.split(",")] tfs = [t.strip() for t in args.tfs.split(",")] print(f"[实时版一类] {len(syms)} 币 × {tfs} × {args.rows} 根\n", flush=True) parts = [] with ProcessPoolExecutor(max_workers=args.workers) as ex: fut = {ex.submit(collect, s, args.rows, t): (s, t) for s in syms for t in tfs} for i, f in enumerate(as_completed(fut), 1): r = f.result() s, t = fut[f] print(f" [{i}/{len(fut)}] {s} {t} " f"{0 if r is None else len(r)}", flush=True) if r is not None: parts.append(r) if not parts: print("无结果") return d = pd.concat(parts, ignore_index=True) OUT.parent.mkdir(exist_ok=True) d.to_feather(OUT) report(d) if __name__ == "__main__": main()