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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Market State Gate OOS — Baseline vs Gated（Spring 冻结）
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比较:
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A) Wyckoff_BTC_V1_BASELINE — Spring always (within trend regime)
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B) Wyckoff_BTC_GATED — Spring only when causal state gate opens
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阈值先验固定，不对 2023+ 做网格搜索。
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指标: net PF / DD / n / worst year / max consecutive losses
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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 sys
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from pathlib import Path
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from typing import Any
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import numpy as np
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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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OUT = ROOT / "user_data/Chan/scripts/wyckoff_gate_oos_result.json"
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PAIR = "BTC/USDT:USDT"
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WINDOWS = [
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("define_pre2023", "20190901-20230101"), # 观察区（不调参）
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("oos_2023plus", "20230101-"),
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("full", "20190901-"),
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("y2020", "20200101-20210101"),
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("y2021", "20210101-20220101"),
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("y2022", "20220101-20230101"),
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("y2023", "20230101-20240101"),
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("y2024", "20240101-20250101"),
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("y2025", "20250101-20260101"),
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]
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STRATS = [
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{
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"name": "baseline",
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"strategy": "Wyckoff_BTC_V1_BASELINE",
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"config": ROOT / "user_data/Chan/config/Wyckoff_BTC_V1_BASELINE.json",
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},
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{
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"name": "gated",
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"strategy": "Wyckoff_BTC_GATED",
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"config": ROOT / "user_data/Chan/config/Wyckoff_BTC_GATED.json",
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},
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]
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def _max_consecutive_losses(profits: list[float]) -> int:
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best = cur = 0
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for p in profits:
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if p <= 0:
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cur += 1
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best = max(best, cur)
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else:
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cur = 0
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return best
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def _worst_year(trades: list[dict]) -> dict[str, Any]:
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by_y: dict[str, float] = {}
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for t in trades:
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ed = t.get("open_date") or t.get("entry_date") or ""
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y = str(ed)[:4]
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if len(y) < 4:
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continue
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by_y[y] = by_y.get(y, 0.0) + float(t.get("profit_ratio") or 0.0) * 100
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if not by_y:
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return {"year": None, "sum_pct": 0.0}
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y, v = min(by_y.items(), key=lambda x: x[1])
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return {"year": y, "sum_pct": round(v, 2)}
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def run_one(strategy: str, config_path: 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:
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del sys.modules[mod]
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config = Configuration.from_files([str(config_path)])
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config.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, # 5bps fee + 5bps slip
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"exchange": {
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**config.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(config)
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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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trade_rows = []
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profits = []
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for t in LocalTrade.bt_trades:
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pr = float(t.close_profit or 0.0)
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profits.append(pr)
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trade_rows.append(
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{
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"open_date": t.open_date_utc.isoformat() if t.open_date_utc else "",
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"enter_tag": t.enter_tag or "",
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"profit_ratio": pr,
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}
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)
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return {
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"timerange": timerange,
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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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"max_consec_loss": _max_consecutive_losses(profits),
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"worst_year": _worst_year(trade_rows),
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}
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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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results: dict[str, Any] = {
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"pair": PAIR,
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"fee_model": "fee 5bps + slip 5bps",
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"gate": {
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"version": "v1.1_state_set",
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"spring": "market_state ∈ {accumulation, markup}",
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"utad": "market_state ∈ {distribution, markdown}",
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"note": "Causal 8h EMA/slope rules (= attribution labels). Scores kept for observability. Not grid-searched on 2023+.",
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"v1_score_threshold": "FAILED OOS (destroyed 2023+ PF 1.45→0.67); archived as too misaligned",
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},
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"windows": {},
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"verdict": {},
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}
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print("===== Market State Gate OOS (BTC) =====", flush=True)
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for wname, tr in WINDOWS:
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print(f"\n--- {wname} {tr} ---", flush=True)
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block = {}
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for s in STRATS:
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r = run_one(s["strategy"], s["config"], tr)
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block[s["name"]] = r
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print(
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f" {s['name']:<9} profit={r['profit_pct']:>7.2f}% n={r['trades']:<3} "
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f"dd={r['dd_pct']:.1f}% pf={r['pf']:.2f} "
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f"mcl={r['max_consec_loss']} worst={r['worst_year']}",
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flush=True,
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)
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# delta gated - baseline
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b, g = block["baseline"], block["gated"]
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block["delta_gated_minus_baseline"] = {
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"pf": round(g["pf"] - b["pf"], 3),
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"dd_pct": round(g["dd_pct"] - b["dd_pct"], 3),
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"trades": g["trades"] - b["trades"],
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"profit_pct": round(g["profit_pct"] - b["profit_pct"], 3),
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"max_consec_loss": g["max_consec_loss"] - b["max_consec_loss"],
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}
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results["windows"][wname] = block
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oos_b = results["windows"]["oos_2023plus"]["baseline"]
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oos_g = results["windows"]["oos_2023plus"]["gated"]
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full_b = results["windows"]["full"]["baseline"]
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full_g = results["windows"]["full"]["gated"]
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pre_b = results["windows"]["define_pre2023"]["baseline"]
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pre_g = results["windows"]["define_pre2023"]["gated"]
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results["verdict"] = {
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"oos_gated_pf_ge_baseline": oos_g["pf"] >= oos_b["pf"] - 1e-9,
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"oos_gated_pf_ge_1_2": oos_g["pf"] >= 1.2,
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"oos_gated_dd_le_baseline": oos_g["dd_pct"] <= oos_b["dd_pct"] + 1e-9,
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"full_gated_pf_gt_baseline": full_g["pf"] > full_b["pf"],
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"pre2023_not_catastrophically_worse": pre_g["pf"] >= pre_b["pf"] - 0.15,
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"status": (
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"PASS"
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if (
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oos_g["pf"] >= 1.2
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and oos_g["dd_pct"] <= oos_b["dd_pct"] + 0.5
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and full_g["pf"] > full_b["pf"]
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)
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else "PARTIAL"
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if (oos_g["pf"] >= oos_b["pf"] and full_g["pf"] >= full_b["pf"])
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else "FAIL"
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),
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"note": "Gate must not destroy 2023+ edge; should improve or stabilize full-sample robustness.",
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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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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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