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>
2026-08-25 22:57:43 +08:00
parent 1e60ab3bfa
commit 8ee11317d3
104 changed files with 21452 additions and 4988 deletions
@@ -0,0 +1,246 @@
+#!/usr/bin/env python3
+"""
+BTC Maker Micro Scalper — 回测结果统计
+
+重点指标：Net Expectancy（不是胜率）
+  E = 胜率×平均盈利 - 失败率×平均亏损 - 手续费 - 滑点
+
+用法：
+  python user_data/Chan/strategies/mms_stats.py
+  python user_data/Chan/strategies/mms_stats.py --file user_data/backtest_results/xxx.zip
+  python user_data/Chan/strategies/mms_stats.py --slippage 0.00005
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import zipfile
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+
+def _latest_backtest(results_dir: Path) -> Path | None:
+    zips = sorted(results_dir.glob("backtest-result-*.zip"), key=lambda p: p.stat().st_mtime)
+    return zips[-1] if zips else None
+
+
+def _load_trades(path: Path) -> tuple[pd.DataFrame, dict[str, Any]]:
+    meta: dict[str, Any] = {}
+    if path.suffix == ".zip":
+        with zipfile.ZipFile(path, "r") as zf:
+            names = zf.namelist()
+            # prefer meta + trades json inside zip
+            trade_name = next((n for n in names if n.endswith(".json") and "meta" not in n), None)
+            meta_name = next((n for n in names if n.endswith(".meta.json")), None)
+            if meta_name:
+                meta = json.loads(zf.read(meta_name))
+            if not trade_name:
+                raise FileNotFoundError(f"No trades json in {path}")
+            payload = json.loads(zf.read(trade_name))
+    else:
+        payload = json.loads(path.read_text())
+
+    # Freqtrade formats: {"strategy": {"BTC_...": {"trades": [...]}}}
+    # or flat list / {"trades": [...]}
+    trades = None
+    if isinstance(payload, list):
+        trades = payload
+    elif isinstance(payload, dict):
+        if "trades" in payload:
+            trades = payload["trades"]
+        elif isinstance(payload.get("strategy"), dict):
+            # freqtrade zip: {"strategy": {"BTC_Maker_Micro_Scalper": {"trades": [...]}}}
+            for name, v in payload["strategy"].items():
+                if isinstance(v, dict) and "trades" in v:
+                    trades = v["trades"]
+                    meta.setdefault("strategy", name)
+                    break
+        if trades is None:
+            for _k, v in payload.items():
+                if isinstance(v, dict) and "trades" in v:
+                    trades = v["trades"]
+                    meta.setdefault("strategy", _k)
+                    break
+    if trades is None:
+        raise ValueError(f"Cannot parse trades from {path}")
+
+    df = pd.DataFrame(trades)
+    return df, meta
+
+
+def summarize(df: pd.DataFrame, fee_rate: float = 0.00016, slippage: float = 0.0) -> dict[str, Any]:
+    if df.empty:
+        return {"error": "no trades"}
+
+    # profit_ratio is net of fees in freqtrade; also keep absolute
+    profit_col = "profit_ratio" if "profit_ratio" in df.columns else "close_profit"
+    profits = df[profit_col].astype(float)
+
+    wins = profits[profits > 0]
+    losses = profits[profits <= 0]
+    n = len(profits)
+    win_rate = len(wins) / n if n else 0.0
+    loss_rate = 1.0 - win_rate
+    avg_win = float(wins.mean()) if len(wins) else 0.0
+    avg_loss = float(losses.mean()) if len(losses) else 0.0  # negative or 0
+    avg_loss_abs = abs(avg_loss)
+
+    gross_profit = float(wins.sum()) if len(wins) else 0.0
+    gross_loss = float((-losses).sum()) if len(losses) else 0.0
+    profit_factor = (gross_profit / gross_loss) if gross_loss > 0 else float("inf")
+
+    # 手续费：freqtrade 的 profit 已扣费；这里单独估算双边 maker 占比
+    # 每笔双边 fee ≈ 2 * fee_rate（相对名义）
+    fee_per_trade = 2.0 * fee_rate
+    total_fee_est = n * fee_per_trade
+    # 滑点假设（每边）
+    slip_per_trade = 2.0 * slippage
+    total_slip_est = n * slip_per_trade
+
+    # Net Expectancy（每笔期望，比率）
+    # E = WR*avg_win - LR*avg_loss_abs - fee - slip
+    expectancy = win_rate * avg_win - loss_rate * avg_loss_abs - fee_per_trade - slip_per_trade
+
+    # 注意：若 profit_ratio 已含手续费，上式 fee 会双重扣除。
+    # 提供两个版本：
+    # 1) E_raw：用毛期望再减 fee/slip（假设 profit 含 fee → 用 E_from_net）
+    # 2) E_from_net：直接用已实现平均利润（已含 fee）再减额外滑点假设
+    e_from_net = float(profits.mean()) - slip_per_trade
+
+    # 最大回撤（权益曲线，相对）
+    equity = (1.0 + profits).cumprod()
+    peak = equity.cummax()
+    dd = (equity - peak) / peak
+    max_dd = float(dd.min()) if len(dd) else 0.0
+
+    # 持仓时间
+    hold_min = None
+    if "open_date" in df.columns and "close_date" in df.columns:
+        od = pd.to_datetime(df["open_date"], utc=True)
+        cd = pd.to_datetime(df["close_date"], utc=True)
+        hold_min = float(((cd - od).dt.total_seconds() / 60.0).mean())
+
+    # Maker 成交率：若有 order_type / is_short 等字段无法直接得，默认限价策略按 100% 标注
+    maker_rate = 1.0
+    if "exit_reason" in df.columns:
+        # 无法精确时保持 1.0；实盘可从策略 _maker_fills 导出
+        pass
+
+    # 手续费占毛利
+    fee_share = None
+    if "fee_open" in df.columns and "fee_close" in df.columns:
+        fees = df["fee_open"].astype(float).fillna(0) + df["fee_close"].astype(float).fillna(0)
+        abs_pnl = df.get("profit_abs", profits).astype(float).abs().sum()
+        fee_share = float(fees.sum() / abs_pnl) if abs_pnl else None
+        total_fee_est = float(fees.sum())
+
+    return {
+        "total_trades": n,
+        "win_rate": win_rate,
+        "avg_win": avg_win,
+        "avg_loss": avg_loss,
+        "profit_factor": profit_factor,
+        "max_drawdown": max_dd,
+        "fee_est_total_ratio_units": total_fee_est,
+        "fee_share_of_abs_pnl": fee_share,
+        "maker_fill_rate_assumed": maker_rate,
+        "avg_hold_minutes": hold_min,
+        "net_expectancy_from_realized": e_from_net,
+        "net_expectancy_formula_rebuild": expectancy,
+        "total_profit_ratio_sum": float(profits.sum()),
+        "avg_profit": float(profits.mean()),
+        "slippage_assumed_per_side": slippage,
+        "note": (
+            "优先看 net_expectancy_from_realized（已含 freqtrade 手续费）。"
+            "net_expectancy_formula_rebuild 会再减一遍 fee，仅作分解参考。"
+        ),
+    }
+
+
+def print_report(stats: dict[str, Any], source: str) -> None:
+    print("=" * 60)
+    print("BTC Maker Micro Scalper — Backtest Stats")
+    print(f"source: {source}")
+    print("=" * 60)
+    if "error" in stats:
+        print(stats["error"])
+        return
+
+    def pct(x):
+        return f"{x * 100:.4f}%" if x is not None else "n/a"
+
+    print(f"总交易次数          : {stats['total_trades']}")
+    print(f"胜率                : {pct(stats['win_rate'])}  (勿作为主指标)")
+    print(f"平均盈利            : {pct(stats['avg_win'])}")
+    print(f"平均亏损            : {pct(stats['avg_loss'])}")
+    print(f"Profit Factor       : {stats['profit_factor']:.4f}")
+    print(f"最大回撤            : {pct(stats['max_drawdown'])}")
+    print(f"手续费占比(abs pnl) : {stats['fee_share_of_abs_pnl']}")
+    print(f"Maker成交率(假设)   : {pct(stats['maker_fill_rate_assumed'])}")
+    print(f"平均持仓时间(分钟)  : {stats['avg_hold_minutes']}")
+    print("-" * 60)
+    print(f"Net Expectancy/笔   : {pct(stats['net_expectancy_from_realized'])}  ★主指标")
+    print(f"公式重建 E(参考)    : {pct(stats['net_expectancy_formula_rebuild'])}")
+    print(f"累计收益(比率和)    : {pct(stats['total_profit_ratio_sum'])}")
+    print(f"平均单笔            : {pct(stats['avg_profit'])}")
+    print("-" * 60)
+    print(stats["note"])
+    print("=" * 60)
+
+
+def export_equity_csv(df: pd.DataFrame, out: Path) -> None:
+    if df.empty or "profit_ratio" not in df.columns:
+        return
+    profits = df["profit_ratio"].astype(float)
+    equity = (1.0 + profits).cumprod()
+    out_df = pd.DataFrame({
+        "close_date": df.get("close_date"),
+        "profit_ratio": profits,
+        "equity": equity,
+    })
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out_df.to_csv(out, index=False)
+    print(f"净收益曲线已导出: {out}")
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--file", type=str, default=None, help="backtest zip/json path")
+    ap.add_argument("--slippage", type=float, default=0.0, help="per-side slippage ratio")
+    ap.add_argument("--fee", type=float, default=0.00016, help="per-side maker fee ratio")
+    ap.add_argument(
+        "--equity-out",
+        type=str,
+        default="user_data/plot/mms_equity.csv",
+        help="equity curve csv",
+    )
+    args = ap.parse_args()
+
+    root = Path(__file__).resolve().parents[3]  # freqtrade root
+    results_dir = root / "user_data" / "backtest_results"
+
+    path = Path(args.file) if args.file else _latest_backtest(results_dir)
+    if path is None or not path.exists():
+        print("未找到回测结果。请先运行 backtesting，或用 --file 指定。")
+        print(
+            "示例:\n"
+            "  freqtrade backtesting -c ./user_data/Chan/config/BTC_Maker_Micro_Scalper.json \\\n"
+            "    --strategy BTC_Maker_Micro_Scalper --strategy-path ./user_data/Chan/strategies \\\n"
+            "    --timerange=20260101- --fee 0.00016 --enable-protections"
+        )
+        return
+
+    df, meta = _load_trades(path)
+    stats = summarize(df, fee_rate=args.fee, slippage=args.slippage)
+    print_report(stats, str(path))
+    if meta:
+        print(f"meta keys: {list(meta.keys())[:8]}")
+    export_equity_csv(df, root / args.equity_out)
+
+
+if __name__ == "__main__":
+    main()