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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Phase3 — Evidence Expansion（不改 Spring 规则）
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目标: 将样本从 N=20 推向 N>=50
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手段:
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- 多品种外部验证（本地有数据的 pair）
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- 分开统计 SPRING_LONG / UTAD_SHORT
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- 同一净成本模型（fee+slip）
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- 不引入 LPS、不扫参
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用法:
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.venv/bin/python user_data/Chan/scripts/wyckoff_phase3_evidence.py
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缺 4h/8h 时从 1h resample（离线，不依赖 API）。
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BTC 若无 2019 更早数据，脚本会标明 gap，不伪造历史。
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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, Optional
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import pandas as pd
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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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DATADIR = ROOT / "user_data/data/binance/futures"
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STRAT = "Wyckoff_BTC_V1_BASELINE"
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CONFIG = ROOT / "user_data/Chan/config/Wyckoff_BTC_V1_BASELINE.json"
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OUT = ROOT / "user_data/Chan/scripts/wyckoff_phase3_evidence_result.json"
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# 候选外部验证（规则冻结；用本地最长可用历史）
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CANDIDATES = [
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{"pair": "BTC/USDT:USDT", "file": "BTC_USDT_USDT", "timerange": "20190901-"},
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{"pair": "ETH/USDT:USDT", "file": "ETH_USDT_USDT", "timerange": "20191101-"},
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{"pair": "SOL/USDT:USDT", "file": "SOL_USDT_USDT", "timerange": "20200901-"},
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]
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MIN_1H_BARS = 4000 # ~ema200@8h 需要足够历史；过短 skip
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def ensure_tf(file_stub: str, tf: str, source_tf: str = "1h") -> bool:
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"""从更细周期 resample 生成 tf feather；已存在则跳过。"""
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out = DATADIR / f"{file_stub}-{tf}-futures.feather"
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src = DATADIR / f"{file_stub}-{source_tf}-futures.feather"
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if out.exists():
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return True
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if not src.exists():
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return False
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df = pd.read_feather(src)
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df["date"] = pd.to_datetime(df["date"], utc=True)
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df = df.set_index("date").sort_index()
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rule = tf.replace("m", "min") if tf.endswith("m") else tf
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ohlc = df.resample(rule).agg(
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{"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}
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).dropna(subset=["open", "close"])
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ohlc = ohlc.reset_index()
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ohlc.to_feather(out)
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print(f" resampled {out.name} n={len(ohlc)}", flush=True)
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return True
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def pair_ready(file_stub: str) -> tuple[bool, str]:
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p1 = DATADIR / f"{file_stub}-1h-futures.feather"
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if not p1.exists():
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return False, "missing 1h"
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df = pd.read_feather(p1)
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n = len(df)
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if n < MIN_1H_BARS:
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return False, f"1h bars={n} < {MIN_1H_BARS} (insufficient for 8h ema200)"
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ok4 = ensure_tf(file_stub, "4h")
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ok8 = ensure_tf(file_stub, "8h")
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if not (ok4 and ok8):
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return False, "cannot build 4h/8h"
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return True, f"1h={n}"
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def run_bt(pair: str, timerange: str, fee: float = 0.0005, extra: float = 0.0) -> 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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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)])
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config.update(
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{
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"strategy": STRAT,
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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": fee + extra,
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"exchange": {
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**config.get("exchange", {}),
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"pair_whitelist": [pair],
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"name": config.get("exchange", {}).get("name", "binance"),
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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(STRAT) 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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# 按 enter_tag 拆分（freqtrade 可能是 dict 或 list[dict]）
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by_tag: dict[str, dict[str, Any]] = {}
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trades = st.get("trades") or []
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tag_stats = st.get("results_per_enter_tag") or {}
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items = []
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if isinstance(tag_stats, dict):
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items = list(tag_stats.items())
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elif isinstance(tag_stats, list):
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items = [
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(x.get("key") or x.get("enter_tag") or x.get("tag") or "unknown", x)
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for x in tag_stats
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if isinstance(x, dict)
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]
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if items:
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for tag, info in items:
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if not isinstance(info, dict):
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continue
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by_tag[str(tag)] = {
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"trades": int(info.get("trades") or info.get("total_trades") or 0),
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"profit_pct": float(
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info.get("profit_total_pct")
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if info.get("profit_total_pct") is not None
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else (float(info.get("profit_total") or 0) * 100)
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),
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"pf": float(info.get("profit_factor") or 0),
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}
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elif trades:
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from collections import defaultdict
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agg: dict[str, list] = defaultdict(list)
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for t in trades:
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tag = t.get("enter_tag") or "unknown"
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agg[tag].append(float(t.get("profit_ratio") or 0))
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for tag, profits in agg.items():
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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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gross_win = sum(wins)
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gross_loss = sum(losses)
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pf = (gross_win / gross_loss) if gross_loss > 0 else (999.0 if gross_win > 0 else 0.0)
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by_tag[tag] = {
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"trades": len(profits),
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"profit_pct": sum(profits) * 100,
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"pf": float(pf),
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}
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return {
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"pair": pair,
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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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"fee_used": config["fee"],
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"by_setup": by_tag,
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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([c["pair"] for c in CANDIDATES])
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results: dict[str, Any] = {
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"phase": "Phase3 Evidence Expansion",
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"strategy": STRAT,
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"rule": "frozen Spring-only; no LPS; no param change",
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"pairs": {},
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"skipped": {},
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"notes": [],
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}
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# BTC 历史缺口说明
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btc_1h = DATADIR / "BTC_USDT_USDT-1h-futures.feather"
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if btc_1h.exists():
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d0 = pd.read_feather(btc_1h)["date"].min()
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results["notes"].append(
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f"BTC local 1h starts {d0}; 2019-2022 not in datadir — download separately for deeper N"
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)
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print("===== Phase3: prepare TF data =====", flush=True)
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run_list = []
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for c in CANDIDATES:
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ok, msg = pair_ready(c["file"])
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if ok:
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print(f" READY {c['pair']}: {msg}", flush=True)
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run_list.append(c)
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else:
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print(f" SKIP {c['pair']}: {msg}", flush=True)
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results["skipped"][c["pair"]] = msg
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print("\n===== Phase3: backtests (fee 5bps, then fee+slip) =====", flush=True)
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total_n = 0
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spring_n = 0
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utad_n = 0
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for c in run_list:
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print(f"\n--- {c['pair']} ---", flush=True)
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base = run_bt(c["pair"], c["timerange"], fee=0.0005, extra=0.0)
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mid = run_bt(c["pair"], c["timerange"], fee=0.0005, extra=0.0005)
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block = {"base_fee": base, "net_mid": mid}
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results["pairs"][c["pair"]] = block
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total_n += base["trades"]
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for tag, info in base.get("by_setup", {}).items():
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if "SPRING" in tag:
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spring_n += info["trades"]
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if "UTAD" in tag:
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utad_n += info["trades"]
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print(
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f" fee5bps profit={base['profit_pct']:.2f}% n={base['trades']} "
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f"dd={base['dd_pct']:.1f}% pf={base['pf']:.2f}",
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flush=True,
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)
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print(
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f" net_mid profit={mid['profit_pct']:.2f}% n={mid['trades']} "
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f"pf={mid['pf']:.2f}",
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flush=True,
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)
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print(f" by_setup {base.get('by_setup')}", flush=True)
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results["aggregate"] = {
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"pairs_tested": len(run_list),
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"total_trades": total_n,
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"spring_long_trades": spring_n,
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"utad_short_trades": utad_n,
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"target_n": 50,
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"target_met": total_n >= 50,
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"next": (
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"目标 N>=50 已达成 — 再看跨品种 net PF 是否仍>1.3"
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if total_n >= 50
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else "继续补历史数据（BTC 2019+）或更多品种 1h/4h/8h"
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),
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}
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print("\n===== Aggregate =====")
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print(json.dumps(results["aggregate"], ensure_ascii=False, indent=2))
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OUT.write_text(json.dumps(results, indent=2, ensure_ascii=False))
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print(f"\nSaved {OUT}")
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if __name__ == "__main__":
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main()
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