"""Step 27：小级别三买入场后，大级别也给出三买，该不该改止盈。 现在的出场是入场瞬间就锁死的 1.5/3.0 ATR，持仓期间无论出现什么新证据都不动。 但按区间套的逻辑，小级别三买只是「可能转折」，等大级别同向三买落地， 趋势的证据强度完全变了，此时仍用原止盈就把最大的一段行情让掉了。 对照四种出场： A 固定 TP 3.0 ATR B 放大止盈 等到大级别同向确认 -> TP 6.0 ATR C 放大+保本 同上，并把止损收到成本价 D 更大止盈 TP 9.0 ATR 另附 E 移动止损 作为「让利润奔跑」的另一种实现。 """ from __future__ import annotations import argparse import os 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") for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"): os.environ.setdefault(v, "1") HERE = Path(__file__).resolve().parent sys.path.insert(0, str(HERE)) sys.path.insert(0, str(HERE.parent)) pd.set_option("display.width", 300) SL, TP, MAXB = 1.5, 3.0, 48 FEE, SLIP = 0.0004, 0.0001 def run_one(task: tuple) -> dict | None: import warnings as _w _w.filterwarnings("ignore") sys.path.insert(0, str(HERE)) sys.path.insert(0, str(HERE.parent)) from chanlun import TF_DF from lib.breakout import run_trades from lib.data import fetch_ohlcv from lib.fast_bsp3 import find_fast_bsp3 from lib.fx_signal import extract_fx_signals, signals_to_frame from lib.nested_bsp import attach_htf_context, htf_fx_timeline from lib.nested_level import build_htf_zones sym, ltf, h1, h2 = task try: df_l = fetch_ohlcv(f"{sym}/USDT:USDT", ltf, 10**9) if df_l is None or len(df_l) < 3000: return None chan_l = TF_DF(df_l, 1, ltf) cdf = chan_l.dataframe sig = find_fast_bsp3(cdf, build_htf_zones(cdf, ltf, chan=chan_l)) if sig.empty or len(sig) < 10: return None ts_l = cdf["timestamp"].to_numpy() boost_bsp = np.zeros(len(cdf)) # 大级别三买 boost_fx = np.zeros(len(cdf)) # 大级别分型 for tf, pref in ((h1, "h1"), (h2, "h2")): df_h = fetch_ohlcv(f"{sym}/USDT:USDT", tf, 10**9) if df_h is None or len(df_h) < 300: continue chan_h = TF_DF(df_h, 1, tf) cdf_h = chan_h.dataframe s = signals_to_frame(extract_fx_signals(chan_h, cdf_h)) tl = htf_fx_timeline(s, cdf_h) sig = attach_htf_context(sig, cdf, tl, pref) if pref == "h1": ts_h = cdf_h["timestamp"].to_numpy() period = int(np.median(np.diff(ts_h))) if len(ts_h) > 1 else 0 # 大级别三买：同样跑一遍中枢+快速三买，收盘后才可用 sh = find_fast_bsp3(cdf_h, build_htf_zones(cdf_h, tf, chan=chan_h)) if not sh.empty: bts = ts_h[sh["entry_idx"].to_numpy().astype(int)] + period pos = np.searchsorted(ts_l, bts, side="left") for p, d in zip(pos, sh["direction"].to_numpy()): if 0 <= p < len(boost_bsp): boost_bsp[p] = d # 大级别分型确认（更频繁的弱证据） pos = np.searchsorted(ts_l, tl["confirm_ts"].to_numpy(), side="left") for p, d in zip(pos, tl["direction"].to_numpy()): if 0 <= p < len(boost_fx): boost_fx[p] = d entries = list(zip(sig["entry_idx"].astype(int), sig["direction"].astype(int))) m = sig.drop_duplicates("entry_idx").set_index("entry_idx") variants = { "A 固定TP3": dict(), "B 大级别三买->TP6": dict(boost_dir=boost_bsp, tp_boost=2.0), "C 大级别三买->TP6+保本": dict(boost_dir=boost_bsp, tp_boost=2.0, boost_breakeven=True), "D 大级别三买->TP9": dict(boost_dir=boost_bsp, tp_boost=3.0), "E 大级别分型->TP6": dict(boost_dir=boost_fx, tp_boost=2.0), "F 移动止损": dict(trail=True), } frames = [] for name, kw in variants.items(): tr = run_trades(cdf, entries, SL, TP, MAXB, fee=0.0, entry_delay=1, **kw) if tr.empty: continue tr["variant"], tr["symbol"], tr["ltf"] = name, sym, ltf tr["date"] = cdf["date"].to_numpy()[tr["entry_idx"].to_numpy()] for c in ("h1_agree", "h2_agree"): tr[c] = tr["entry_idx"].map(m[c]) if c in m.columns else np.nan frames.append(tr) return {"task": f"{sym} {ltf}", "trades": pd.concat(frames, ignore_index=True)} except Exception as e: return {"task": f"{sym} {ltf}", "error": repr(e)[:200]} def desc(g: pd.DataFrame, label: str) -> dict: r = g["gross"].to_numpy() - FEE - SLIP if len(r) < 20: return {} w, o = r[r > 0], r[r <= 0] sd = r.std(ddof=1) return { "出场方式": label, "笔数": len(r), "胜率": f"{(r > 0).mean() * 100:.1f}%", "均收益": f"{r.mean() * 100:+.3f}%", "中位": f"{np.median(r) * 100:+.3f}%", "PF": f"{w.sum() / abs(o.sum()):.2f}" if len(o) else "inf", "偏度": f"{pd.Series(r).skew():.2f}", "t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}", "均持有": f"{g['bars_held'].mean():.0f}", } def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--pairs", default="15m:1h:4h,30m:2h:4h") ap.add_argument("--symbols", default="BTC,ETH,SOL") ap.add_argument("--workers", type=int, default=6) ap.add_argument("--reuse", action="store_true") args = ap.parse_args() cache = HERE / "out" / "step27_dynexit.csv" if args.reuse and cache.exists(): allt = pd.read_csv(cache, parse_dates=["date"]) print(f"[复用] {len(allt)} 笔\n") else: tasks = [(s, *tuple(p.split(":"))) for p in args.pairs.split(",") if p for s in args.symbols.split(",")] print(f"[动态出场] {len(tasks)} 个任务\n", flush=True) res = [] with ProcessPoolExecutor(max_workers=args.workers) as ex: futs = {ex.submit(run_one, t): t for t in tasks} for i, f in enumerate(as_completed(futs), 1): r = f.result() if r is None or "error" in (r or {}): print(f" [{i}] 跳过 {(r or {}).get('error', '')}", flush=True) continue res.append(r) print(f" [{i}/{len(tasks)}] {r['task']}", flush=True) if not res: return allt = pd.concat([r["trades"] for r in res], ignore_index=True) allt.to_csv(cache, index=False) allt["date"] = pd.to_datetime(allt["date"]) a = allt[allt["h1_agree"] == 1] print("=" * 115) print("########## 1. 六种出场方式（大级别同向，全级别合并）##########") print(pd.DataFrame([r for r in [desc(g, v) for v, g in a.groupby("variant")] if r] ).to_string(index=False)) print("\n########## 2. 分级别 ##########") for tf, g in a.groupby("ltf"): rows = [desc(x, f"{tf} {v}") for v, x in g.groupby("variant")] print(pd.DataFrame([r for r in rows if r]).to_string(index=False)) print("\n########## 3. 触发率：多少笔真的等到了大级别确认 ##########") rows = [] for v in ("B 大级别三买->TP6", "E 大级别分型->TP6"): g = a[a.variant == v] if g.empty or "boosted" not in g.columns: continue b = g["boosted"].astype(bool) rows.append({"变体": v, "总笔数": len(g), "触发数": int(b.sum()), "触发率": f"{b.mean() * 100:.1f}%"}) if rows: print(pd.DataFrame(rows).to_string(index=False)) print("\n########## 4. 只看触发了的那批：改单到底有没有用 ##########") base = a[a.variant == "A 固定TP3"].set_index(["symbol", "ltf", "entry_idx"]) for v in ("B 大级别三买->TP6", "C 大级别三买->TP6+保本", "E 大级别分型->TP6"): g = a[a.variant == v] if g.empty or "boosted" not in g.columns: continue gb = g[g["boosted"].astype(bool)].set_index(["symbol", "ltf", "entry_idx"]) if len(gb) < 20: continue common = gb.index.intersection(base.index) if len(common) < 20: continue r_new = gb.loc[common, "gross"].to_numpy() - FEE - SLIP r_old = base.loc[common, "gross"].to_numpy() - FEE - SLIP d = r_new - r_old sd = d.std(ddof=1) print(f" {v}: n={len(d)} 改单后均收益 {r_new.mean() * 100:+.3f}% vs " f"原 {r_old.mean() * 100:+.3f}% 差 {d.mean() * 100:+.3f}% " f"t={d.mean() / (sd / np.sqrt(len(d))):+.2f}" if sd else "") print("\n########## 5. 资金曲线 ##########") for v, g in a.groupby("variant"): g = g.sort_values("date") r = g["gross"].to_numpy() - FEE - SLIP lev = np.clip(0.01 / np.clip(g["risk_pct"].to_numpy(), 0.002, None), 0, 20) pnl = r * lev eq = np.cumprod(1 + pnl) yrs = (g["date"].max() - g["date"].min()).days / 365.25 dd = (1 - eq / np.maximum.accumulate(eq)).max() print(f" {v:>22}: 年化 {(eq[-1] ** (1 / yrs) - 1) * 100:+6.1f}% " f"回撤 {dd * 100:4.1f}% Sharpe " f"{pnl.mean() / pnl.std(ddof=1) * np.sqrt(len(pnl) / yrs):4.2f}") if __name__ == "__main__": main()