#!/usr/bin/env python3 """ Gate v1.1 冻结前小范围稳健性确认（不改 Spring / 不调 soft-score） 在 Baseline SPRING_LONG 全集上： - 按年份、era 切片 - 看 blocked 是否仍主要来自 distribution - kept vs blocked 的 PF 关系是否稳定 range 只作观察桶，不改交易规则。 """ from __future__ import annotations import json import logging import sys from collections import defaultdict from pathlib import Path from typing import Any import numpy as np import pandas as pd ROOT = Path(__file__).resolve().parents[3] sys.path.insert(0, str(ROOT)) sys.path.insert(0, str(ROOT / "user_data/Chan")) from engine.market_state import compute_market_state_8h # noqa: E402 from user_data.Chan.scripts.wyckoff_tf_grid import install_offline_markets # noqa: E402 AUDIT = ROOT / "user_data/Chan/scripts/wyckoff_negative_domain_audit_result.json" OUT = ROOT / "user_data/Chan/scripts/wyckoff_gate_robustness_slices_result.json" PAIR = "BTC/USDT:USDT" CFG = ROOT / "user_data/Chan/config/Wyckoff_BTC_V1_BASELINE.json" def _pf(ps: list[float]) -> float: wins = [p for p in ps if p > 0] losses = [-p for p in ps if p <= 0] gw, gl = sum(wins), sum(losses) if gl <= 0: return 999.0 if gw > 0 else 0.0 return gw / gl def _stats(ps: list[float]) -> dict[str, Any]: if not ps: return {"n": 0, "pf": 0.0, "sum_pct": 0.0, "winrate": 0.0} return { "n": len(ps), "pf": round(_pf(ps), 3), "sum_pct": round(100.0 * float(np.sum(ps)), 2), "winrate": round(100.0 * sum(1 for p in ps if p > 0) / len(ps), 1), } def load_annotated_springs() -> list[dict[str, Any]]: """复用 audit 逻辑，产出逐笔 annotated SPRING。""" from freqtrade.configuration import Configuration from freqtrade.enums import RunMode from freqtrade.optimize.backtesting import Backtesting from freqtrade.persistence import LocalTrade import freqtrade.optimize.optimize_reports.bt_output as bt_output bt_output.show_backtest_results = lambda *a, **k: None # type: ignore for mod in list(sys.modules): if "Wyckoff_BTC" in mod: del sys.modules[mod] cfg = Configuration.from_files([str(CFG)]) cfg.update( { "strategy": "Wyckoff_BTC_V1_BASELINE", "strategy_path": str(ROOT / "user_data/Chan/strategies"), "timerange": "20190901-", "timeframe": "1h", "export": "none", "runmode": RunMode.BACKTEST, "datadir": ROOT / "user_data/data/binance", "user_data_dir": ROOT / "user_data", "enable_protections": False, "fee": 0.0010, "exchange": { **cfg.get("exchange", {}), "name": "binance", "pair_whitelist": [PAIR], }, } ) bt = Backtesting(cfg) bt.start() h8 = pd.read_feather(ROOT / "user_data/data/binance/futures/BTC_USDT_USDT-8h-futures.feather") h8["date"] = pd.to_datetime(h8["date"], utc=True) h8 = compute_market_state_8h(h8).set_index("date").sort_index() rows = [] for t in LocalTrade.bt_trades: if "SPRING" not in (t.enter_tag or ""): continue ed = pd.Timestamp(t.open_date_utc) if ed.tzinfo is None: ed = ed.tz_localize("UTC") idx = h8.index.get_indexer([ed], method="ffill")[0] if idx < 0: continue st = h8.iloc[idx] state = str(st["market_state"]) rows.append( { "entry_date": ed.isoformat(), "year": str(ed.year), "era": "2023plus" if ed >= pd.Timestamp("2023-01-01", tz="UTC") else "pre_2023", "market_state": state, "allow_spring": bool(st["allow_spring"]), "profit_ratio": float(t.close_profit or 0.0), } ) return rows def slice_report(rows: list[dict], key: str) -> dict[str, Any]: out: dict[str, Any] = {} groups: dict[str, list[dict]] = defaultdict(list) for r in rows: groups[str(r[key])].append(r) for k, rs in sorted(groups.items()): kept = [x for x in rs if x["allow_spring"]] blocked = [x for x in rs if not x["allow_spring"]] b_by_state: dict[str, list[float]] = defaultdict(list) for x in blocked: b_by_state[x["market_state"]].append(x["profit_ratio"]) blocked_states = {s: _stats(ps) for s, ps in b_by_state.items()} dist_n = blocked_states.get("distribution", {}).get("n", 0) blocked_n = len(blocked) out[k] = { "n_total": len(rs), "kept": _stats([x["profit_ratio"] for x in kept]), "blocked": _stats([x["profit_ratio"] for x in blocked]), "blocked_by_state": blocked_states, "blocked_distribution_share": round(dist_n / blocked_n, 3) if blocked_n else None, "blocked_all_bad": ( all(s in ("distribution", "markdown", "range") for s in blocked_states) if blocked_n else True ), } return out def main() -> None: logging.getLogger("freqtrade").setLevel(logging.ERROR) install_offline_markets([PAIR]) print("===== Annotate SPRING_LONG =====", flush=True) rows = load_annotated_springs() print(f" n={len(rows)}", flush=True) by_year = slice_report(rows, "year") by_era = slice_report(rows, "era") # 稳定性：有 blocked 的切片里，distribution 是否为第一大来源 dist_primary = [] for label, block in {**{f"year:{k}": v for k, v in by_year.items()}, **{f"era:{k}": v for k, v in by_era.items()}}.items(): bn = block["blocked"]["n"] if bn < 2: continue states = block["blocked_by_state"] top = max(states.items(), key=lambda x: x[1]["n"])[0] if states else None dist_primary.append( { "slice": label, "blocked_n": bn, "top_blocked_state": top, "distribution_share": block["blocked_distribution_share"], "blocked_pf": block["blocked"]["pf"], "kept_pf": block["kept"]["pf"], } ) n_slices = len(dist_primary) n_dist_top = sum(1 for x in dist_primary if x["top_blocked_state"] == "distribution") n_dist_ge_50 = sum( 1 for x in dist_primary if (x["distribution_share"] or 0) >= 0.5 ) result = { "n_spring": len(rows), "by_year": by_year, "by_era": by_era, "slice_summaries": dist_primary, "range_observation_only": { "note": "range 不作交易规则；仅观察 blocked 中的占比与 PF", "blocked_range_global": _stats( [r["profit_ratio"] for r in rows if (not r["allow_spring"] and r["market_state"] == "range")] ), }, "verdict": { "slices_with_blocked_ge_2": n_slices, "distribution_is_top_blocked_state": n_dist_top, "distribution_share_ge_50pct_slices": n_dist_ge_50, "distribution_attribution_stable": ( n_slices > 0 and (n_dist_top / n_slices) >= 0.6 ), "status": ( "PASS" if n_slices > 0 and (n_dist_top / n_slices) >= 0.6 else "PARTIAL" if n_dist_ge_50 >= max(1, n_slices // 2) else "FAIL" ), "note": "PASS = across year/era slices, blocked mass still led by distribution.", }, } print("\n===== By year (blocked focus) =====", flush=True) for y, b in by_year.items(): print( f" {y}: total={b['n_total']} kept_pf={b['kept']['pf']} " f"blocked_n={b['blocked']['n']} blocked_pf={b['blocked']['pf']} " f"dist_share={b['blocked_distribution_share']} states={list(b['blocked_by_state'])}", flush=True, ) print("\n===== By era =====", flush=True) for e, b in by_era.items(): print( f" {e}: total={b['n_total']} kept_pf={b['kept']['pf']} " f"blocked_n={b['blocked']['n']} blocked_pf={b['blocked']['pf']} " f"dist_share={b['blocked_distribution_share']} states={list(b['blocked_by_state'])}", flush=True, ) print("\n===== Verdict =====", flush=True) print(json.dumps(result["verdict"], indent=2, ensure_ascii=False)) OUT.write_text(json.dumps(result, indent=2, ensure_ascii=False)) print(f"\nSaved {OUT}") if __name__ == "__main__": main()