fix(web): 自动刷新保留 K 线视窗；威科夫与图表增量更新
自动刷新改用 tail update 与 scrollToPosition 恢复视窗，避免 setData 后跳到最右；拆分 chart_tv 模块并扩展 analyze/recent API。同步威科夫分析、pipeline 增量构建及相关策略与配置。 Co-authored-by: Cursor <cursoragent@cursor.com>
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jackyu66git
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#!/usr/bin/env python3
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"""
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Soft-score Gate — 窄实验（研究纪律）
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1) 仅在 pre_2023 比较少数 Gate 形式并选定阈值
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2) 锁定后评估 2023+ / full
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3) 禁止全样本扫参；判定不要求超过 baseline PF
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候选:
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- state_set
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- soft_sum: state_set & (accum+markup) >= q q ∈ {80,100,120,140}
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- soft_bad_cap: state_set & max(bad) <= q q ∈ {40,50,60}
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Fit 目标（pre_2023）: n>=5 前提下优先更低 DD，其次更高 PF（非收益最大化）
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OOS 通过:
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- 2023+ PF >= 1.2
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- full DD 明显低于 baseline（<= baseline_dd * 0.7 或绝对差 >= 5pp）
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- pre_2023 n >= 5（非极低样本偶然）
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- 标签不漂移：gated 入场中 state∈{accumulation,markup}|UTAD镜像 比例 >= 0.95
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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import sys
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from pathlib import Path
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from typing import Any, Optional
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ROOT = Path(__file__).resolve().parents[3]
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sys.path.insert(0, str(ROOT))
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from user_data.Chan.scripts.wyckoff_tf_grid import install_offline_markets # noqa: E402
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STRAT_PATH = ROOT / "user_data/Chan/strategies/Wyckoff_BTC_GATED.py"
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BASE_CFG = ROOT / "user_data/Chan/config/Wyckoff_BTC_V1_BASELINE.json"
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GATE_CFG = ROOT / "user_data/Chan/config/Wyckoff_BTC_GATED.json"
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OUT = ROOT / "user_data/Chan/scripts/wyckoff_soft_gate_oos_result.json"
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PAIR = "BTC/USDT:USDT"
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FIT_TR = "20190901-20230101"
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OOS_TR = "20230101-"
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FULL_TR = "20190901-"
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CANDIDATES: list[dict[str, Any]] = [
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{"mode": "state_set", "q_sum": 100.0, "q_bad": 55.0},
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{"mode": "soft_sum", "q_sum": 80.0, "q_bad": 55.0},
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{"mode": "soft_sum", "q_sum": 100.0, "q_bad": 55.0},
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{"mode": "soft_sum", "q_sum": 120.0, "q_bad": 55.0},
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{"mode": "soft_sum", "q_sum": 140.0, "q_bad": 55.0},
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{"mode": "soft_bad_cap", "q_sum": 100.0, "q_bad": 40.0},
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{"mode": "soft_bad_cap", "q_sum": 100.0, "q_bad": 50.0},
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{"mode": "soft_bad_cap", "q_sum": 100.0, "q_bad": 60.0},
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]
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def set_gate(mode: str, q_sum: float, q_bad: float) -> None:
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text = STRAT_PATH.read_text()
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text2, n1 = re.subn(
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r'^(\tgate_mode: str = )".*"',
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rf'\g<1>"{mode}"',
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text,
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count=1,
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flags=re.M,
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)
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text2, n2 = re.subn(
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r'^(\tgate_q_sum: float = )[0-9.]+',
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rf"\g<1>{float(q_sum)}",
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text2,
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count=1,
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flags=re.M,
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)
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text2, n3 = re.subn(
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r'^(\tgate_q_bad: float = )[0-9.]+',
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rf"\g<1>{float(q_bad)}",
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text2,
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count=1,
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flags=re.M,
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)
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if min(n1, n2, n3) < 1:
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raise RuntimeError(f"failed patching gate attrs n=({n1},{n2},{n3})")
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STRAT_PATH.write_text(text2)
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pyc = STRAT_PATH.parent / "__pycache__"
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if pyc.is_dir():
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for p in pyc.glob("Wyckoff_BTC_GATED*.pyc"):
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p.unlink(missing_ok=True)
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def run_bt(strategy: str, config: Path, timerange: str) -> dict[str, Any]:
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from freqtrade.configuration import Configuration
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from freqtrade.enums import RunMode
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from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.persistence import LocalTrade
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import freqtrade.optimize.optimize_reports.bt_output as bt_output
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bt_output.show_backtest_results = lambda *a, **k: None # type: ignore
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for mod in list(sys.modules):
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if "Wyckoff_BTC" in mod or "market_state" in mod:
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del sys.modules[mod]
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cfg = Configuration.from_files([str(config)])
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cfg.update(
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{
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"strategy": strategy,
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"strategy_path": str(ROOT / "user_data/Chan/strategies"),
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"timerange": timerange,
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"timeframe": "1h",
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"export": "none",
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"runmode": RunMode.BACKTEST,
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"datadir": ROOT / "user_data/data/binance",
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"user_data_dir": ROOT / "user_data",
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"enable_protections": False,
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"fee": 0.0010,
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"exchange": {
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**cfg.get("exchange", {}),
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"name": "binance",
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"pair_whitelist": [PAIR],
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},
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}
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)
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bt = Backtesting(cfg)
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bt.start()
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st = bt.results["strategy"].get(strategy) or list(bt.results["strategy"].values())[0]
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profit = st.get("profit_total_pct")
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if profit is None:
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profit = float(st.get("profit_total") or 0) * 100
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profits = [float(t.close_profit or 0.0) for t in LocalTrade.bt_trades]
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wins = [p for p in profits if p > 0]
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losses = [p for p in profits if p <= 0]
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avg_win = float(sum(wins) / len(wins)) if wins else 0.0
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avg_loss = float(sum(losses) / len(losses)) if losses else 0.0
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expectancy = float(sum(profits) / len(profits)) if profits else 0.0
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# 标签漂移：用原生 8h 因果状态（不依赖 analyzed 缓存窗口）
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label_ok_rate = None
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try:
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import pandas as pd
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from engine.market_state import compute_market_state_8h
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h8 = pd.read_feather(ROOT / "user_data/data/binance/futures/BTC_USDT_USDT-8h-futures.feather")
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h8["date"] = pd.to_datetime(h8["date"], utc=True)
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h8 = compute_market_state_8h(h8).set_index("date").sort_index()
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ok = tot = 0
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for t in LocalTrade.bt_trades:
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ed = pd.Timestamp(t.open_date_utc)
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if ed.tzinfo is None:
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ed = ed.tz_localize("UTC")
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idx = h8.index.get_indexer([ed], method="ffill")[0]
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if idx < 0:
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continue
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stt = str(h8.iloc[idx]["market_state"])
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tag = t.enter_tag or ""
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if "SPRING" in tag:
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ok += int(stt in ("accumulation", "markup"))
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elif "UTAD" in tag:
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ok += int(stt in ("distribution", "markdown"))
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else:
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ok += 1
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tot += 1
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label_ok_rate = (ok / tot) if tot else None
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except Exception:
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label_ok_rate = None
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return {
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"profit_pct": float(profit),
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"trades": int(st.get("total_trades") or 0),
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"dd_pct": float(st.get("max_drawdown_account") or 0) * 100,
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"pf": float(st.get("profit_factor") or 0),
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"winrate": float(st.get("winrate") or 0) * 100,
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"expectancy": expectancy,
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"avg_win": avg_win,
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"avg_loss": avg_loss,
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"label_ok_rate": label_ok_rate,
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}
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def fit_score(m: dict[str, Any]) -> tuple:
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"""pre_2023 选择：n>=5；DD 越低越好；PF 次之；n 再之。"""
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n = m["trades"]
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if n < 5:
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return (0, 999.0, 0.0, 0) # invalid
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return (1, m["dd_pct"], -m["pf"], -n)
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def main() -> None:
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logging.getLogger("freqtrade").setLevel(logging.ERROR)
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install_offline_markets([PAIR])
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orig = STRAT_PATH.read_text()
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results: dict[str, Any] = {
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"discipline": "fit on pre_2023 only; lock; test 2023+/full; no full-sample sweep",
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"baseline": {},
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"candidates_fit_pre2023": [],
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"locked": None,
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"oos": {},
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"verdict": {},
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}
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try:
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print("===== Baseline (reference) =====", flush=True)
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for name, tr in [("pre_2023", FIT_TR), ("oos_2023plus", OOS_TR), ("full", FULL_TR)]:
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r = run_bt("Wyckoff_BTC_V1_BASELINE", BASE_CFG, tr)
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results["baseline"][name] = r
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print(
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f" baseline {name:<12} n={r['trades']:<3} pf={r['pf']:.2f} "
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f"dd={r['dd_pct']:.1f}% exp={r['expectancy']*100:.2f}%",
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flush=True,
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)
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print("\n===== Fit soft gates on pre_2023 only =====", flush=True)
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fit_rows = []
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for c in CANDIDATES:
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set_gate(c["mode"], c["q_sum"], c["q_bad"])
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r = run_bt("Wyckoff_BTC_GATED", GATE_CFG, FIT_TR)
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row = {**c, **r, "valid_n": r["trades"] >= 5}
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fit_rows.append(row)
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print(
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f" {c['mode']:<12} q_sum={c['q_sum']:<5} q_bad={c['q_bad']:<5} "
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f"n={r['trades']:<3} pf={r['pf']:.2f} dd={r['dd_pct']:.1f}% "
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f"label_ok={r['label_ok_rate']}",
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flush=True,
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)
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results["candidates_fit_pre2023"] = fit_rows
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valid = [x for x in fit_rows if x["valid_n"]]
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if not valid:
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raise RuntimeError("no candidate with n>=5 on pre_2023")
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locked = sorted(valid, key=fit_score)[0]
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results["locked"] = {
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"mode": locked["mode"],
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"q_sum": locked["q_sum"],
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"q_bad": locked["q_bad"],
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"pre_2023": {
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k: locked[k]
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for k in (
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"trades", "pf", "dd_pct", "profit_pct", "expectancy",
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"avg_win", "avg_loss", "label_ok_rate",
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)
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},
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}
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print(
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f"\nLOCKED (pre_2023): mode={locked['mode']} q_sum={locked['q_sum']} "
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f"q_bad={locked['q_bad']} n={locked['trades']} pf={locked['pf']:.2f} "
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f"dd={locked['dd_pct']:.1f}%",
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flush=True,
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)
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set_gate(locked["mode"], locked["q_sum"], locked["q_bad"])
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print("\n===== Locked gate → OOS / full =====", flush=True)
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for name, tr in [("pre_2023", FIT_TR), ("oos_2023plus", OOS_TR), ("full", FULL_TR)]:
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r = run_bt("Wyckoff_BTC_GATED", GATE_CFG, tr)
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results["oos"][name] = r
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print(
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f" gated {name:<12} n={r['trades']:<3} pf={r['pf']:.2f} "
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f"dd={r['dd_pct']:.1f}% exp={r['expectancy']*100:.2f}% "
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f"avgW={r['avg_win']*100:.2f}% avgL={r['avg_loss']*100:.2f}% "
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f"label_ok={r['label_ok_rate']}",
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flush=True,
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)
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b_full = results["baseline"]["full"]
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b_oos = results["baseline"]["oos_2023plus"]
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g_pre = results["oos"]["pre_2023"]
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g_oos = results["oos"]["oos_2023plus"]
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g_full = results["oos"]["full"]
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dd_ok = (g_full["dd_pct"] <= b_full["dd_pct"] * 0.7) or (
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(b_full["dd_pct"] - g_full["dd_pct"]) >= 5.0
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)
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label_ok = (g_oos.get("label_ok_rate") is None) or (g_oos["label_ok_rate"] >= 0.95)
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results["verdict"] = {
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"oos_pf_ge_1_2": g_oos["pf"] >= 1.2,
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"full_dd_clearly_below_baseline": dd_ok,
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"pre2023_n_ge_5": g_pre["trades"] >= 5,
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"label_no_drift": label_ok,
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"oos_pf": g_oos["pf"],
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"oos_n": g_oos["trades"],
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"full_dd_gated": g_full["dd_pct"],
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"full_dd_baseline": b_full["dd_pct"],
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"baseline_oos_pf": b_oos["pf"],
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"status": (
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"PASS"
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if (
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g_oos["pf"] >= 1.2
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and dd_ok
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and g_pre["trades"] >= 5
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and label_ok
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)
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else "FAIL"
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),
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"note": "Success = domain control (PF floor + DD cut), not beating baseline PF.",
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}
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print("\n===== Verdict =====")
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print(json.dumps(results["verdict"], indent=2, ensure_ascii=False))
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finally:
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# 恢复默认 state_set，避免污染 live 默认
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STRAT_PATH.write_text(orig)
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print("\nRestored Wyckoff_BTC_GATED.py defaults", flush=True)
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OUT.write_text(json.dumps(results, indent=2, ensure_ascii=False))
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print(f"Saved {OUT}")
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if __name__ == "__main__":
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main()
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Block a user