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>
This commit is contained in:
jackyu66git
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#!/usr/bin/env python3
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"""
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Maker Edge Report v0.1
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章节：
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1. Fill Quality
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2. Adverse Selection
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3. MAE/MFE (Price + Time)
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4. State Attribution
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5. Spread Capture / Quote Lifecycle
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假设：
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H1: P(ret_30s 有利) > 50%
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H2: restore exit 优于 all fills
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H3: 亏损集中在某类状态 → 应撤单而非止损
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用法：
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python user_data/Chan/strategies/analyze_maker_edge.py
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python user_data/Chan/strategies/analyze_maker_edge.py --report
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python user_data/Chan/strategies/analyze_maker_edge.py --min-fills 500
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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import pandas as pd
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def load_events(log_dir: Path) -> pd.DataFrame:
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rows = []
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files = sorted(log_dir.glob("*.jsonl"))
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if not files:
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raise FileNotFoundError(f"No jsonl in {log_dir}")
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for f in files:
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for line in f.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line:
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continue
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rows.append(json.loads(line))
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return pd.DataFrame(rows)
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def _fav_ret(side: pd.Series, fill: pd.Series, px: pd.Series) -> pd.Series:
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"""多头：价格涨为正；空头：价格跌为正。"""
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raw = (px - fill) / fill
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return np.where(side == "long", raw, -raw)
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def report(df: pd.DataFrame, min_fills: int = 500, out_path: Path | None = None) -> None:
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fills = df[df["event"] == "fill"].copy() if "event" in df.columns else pd.DataFrame()
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paths = df[df["event"] == "fill_path"].copy() if "event" in df.columns else pd.DataFrame()
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created = df[df["event"] == "quote_created"].copy() if "event" in df.columns else pd.DataFrame()
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canceled = df[df["event"] == "quote_canceled"].copy() if "event" in df.columns else pd.DataFrame()
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qfilled = df[df["event"] == "quote_filled"].copy() if "event" in df.columns else pd.DataFrame()
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exits = df[df["event"] == "fill_exit"].copy() if "event" in df.columns else pd.DataFrame()
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lines: list[str] = []
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def p(s: str = ""):
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lines.append(s)
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print(s)
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p("=" * 72)
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p("Maker Edge Report v0.1")
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p("=" * 72)
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p(f"quote_created : {len(created)}")
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p(f"quote_canceled: {len(canceled)}")
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p(f"quote_filled : {len(qfilled)}")
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p(f"fills : {len(fills)}")
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p(f"fill_paths : {len(paths)} (需成交后≥5m)")
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p(f"target fills : ≥{min_fills} [{'OK' if len(fills) >= min_fills else 'COLLECTING'}]")
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if fills.empty:
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p("\n尚无 fill。先跑 Dry-run 探针。")
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return
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# merge exit_reason onto paths
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if not exits.empty and not paths.empty and "fill_id" in exits.columns:
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er = exits.drop_duplicates("fill_id").set_index("fill_id")["exit_reason"]
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if "exit_reason" not in paths.columns or paths["exit_reason"].isna().all():
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paths = paths.merge(er.rename("exit_reason_x"), left_on="fill_id", right_index=True, how="left")
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if "exit_reason" not in paths.columns:
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paths["exit_reason"] = paths.get("exit_reason_x")
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else:
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paths["exit_reason"] = paths["exit_reason"].fillna(paths.get("exit_reason_x"))
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# merge fill meta into paths
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if not paths.empty:
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cols = [
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c
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for c in [
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"side",
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"fill_price",
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"fill_reason",
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"time_to_fill",
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"trend_state",
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"volatility_regime",
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"pre_5s_deteriorated",
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"obi",
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"trade_imbalance",
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"spread",
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"entry_tag",
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]
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if c in fills.columns
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]
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if cols and "fill_id" in fills.columns:
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meta = fills.drop_duplicates("fill_id")[["fill_id"] + cols]
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paths = paths.merge(meta, on="fill_id", how="left", suffixes=("", "_f"))
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# -------------------- 1. Fill Quality --------------------
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p("\n" + "-" * 72)
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p("1. Fill Quality")
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p("-" * 72)
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if "time_to_fill" in fills.columns:
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ttf = fills["time_to_fill"].dropna()
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if len(ttf):
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p(
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f"time_to_fill mean={ttf.mean():.1f}s median={ttf.median():.1f}s "
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f"p90={ttf.quantile(0.9):.1f}s"
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)
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fast = fills[fills["time_to_fill"].fillna(1e9) <= 10]
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slow = fills[fills["time_to_fill"].fillna(0) > 30]
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p(f"fast fills (≤10s): {len(fast)} slow fills (>30s): {len(slow)}")
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if "fill_reason" in fills.columns:
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p("fill_reason: " + str(fills["fill_reason"].value_counts().to_dict()))
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if "pre_5s_deteriorated" in fills.columns:
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det = fills["pre_5s_deteriorated"].fillna(False).astype(bool)
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p(f"pre_5s book deteriorated: {det.mean()*100:.1f}% of fills")
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n_created = max(len(created), 1)
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p(f"fill rate (filled/created): {len(qfilled)/n_created*100:.1f}%")
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if len(canceled):
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p(f"cancel rate: {len(canceled)/n_created*100:.1f}%")
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# -------------------- 2. Adverse Selection --------------------
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p("\n" + "-" * 72)
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p("2. Adverse Selection (fill 后收益分布)")
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p("-" * 72)
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if paths.empty:
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p("等待 fill_path 完成（成交后 ≥5 分钟）…")
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else:
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side = paths["side"] if "side" in paths.columns else paths.get("side_f")
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fp = paths["fill_price"]
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for label, col in [
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("10s", "after_10s_price"),
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("30s", "after_30s_price"),
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("1m", "after_1m_price"),
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("5m", "after_5m_price"),
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]:
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if col not in paths.columns:
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continue
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fav = pd.Series(_fav_ret(side, fp, paths[col]), index=paths.index)
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p(
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f" +{label:3s} mean={fav.mean()*100:+.4f}% "
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f"median={fav.median()*100:+.4f}% "
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f"P(fav)={ (fav>0).mean()*100:.1f}% n={fav.notna().sum()}"
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)
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# toxic: 10s 立刻不利
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if "after_10s_price" in paths.columns:
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fav10 = pd.Series(_fav_ret(side, fp, paths["after_10s_price"]), index=paths.index)
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p(f" toxic@10s (fav<0): { (fav10<0).mean()*100:.1f}% → 接毒比例")
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# -------------------- 3. MAE / MFE --------------------
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p("\n" + "-" * 72)
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p("3. MAE / MFE (Price + Time)")
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p("-" * 72)
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if not paths.empty:
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if "price_mae" in paths.columns:
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p(
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f"Price MAE mean={paths['price_mae'].mean():+.2f} "
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f"Price MFE mean={paths['price_mfe'].mean():+.2f}"
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)
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for h in ["10s", "30s", "1m", "5m"]:
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mae_c, mfe_c = f"mae_{h}", f"mfe_{h}"
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if mae_c in paths.columns and mfe_c in paths.columns:
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p(
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f" Time@{h:3s} MAE={paths[mae_c].mean()*100:+.4f}% "
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f"MFE={paths[mfe_c].mean()*100:+.4f}%"
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)
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if "mae_5m" in paths.columns and "mfe_5m" in paths.columns:
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ratio = paths["mfe_5m"].mean() / abs(paths["mae_5m"].mean()) if paths["mae_5m"].mean() != 0 else np.nan
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p(f" MFE/|MAE| @5m = {ratio:.2f}")
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# -------------------- 4. State Attribution --------------------
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p("\n" + "-" * 72)
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p("4. State Attribution (亏损集中在哪？)")
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p("-" * 72)
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if not paths.empty and "after_5m_price" in paths.columns:
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side = paths["side"] if "side" in paths.columns else paths.get("side_f")
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fav5 = pd.Series(_fav_ret(side, paths["fill_price"], paths["after_5m_price"]), index=paths.index)
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paths = paths.copy()
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paths["_fav5"] = fav5
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paths["_loss"] = fav5 < 0
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loss_rate = float(paths["_loss"].mean())
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p(f"overall loss@5m: {loss_rate*100:.1f}%")
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for col in ["trend_state", "volatility_regime", "fill_reason", "pre_5s_deteriorated"]:
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c = col if col in paths.columns else (col + "_f" if col + "_f" in paths.columns else None)
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if not c:
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continue
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p(f"\n by {c}:")
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g = paths.groupby(c).agg(
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n=("_fav5", "count"),
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loss_rate=("_loss", "mean"),
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mean_ret=("_fav5", "mean"),
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)
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for idx, row in g.iterrows():
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p(
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f" {idx}: n={int(row['n'])} loss={row['loss_rate']*100:.1f}% "
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f"E[ret]={row['mean_ret']*100:+.4f}%"
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)
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# -------------------- 5. Spread Capture / Lifecycle --------------------
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p("\n" + "-" * 72)
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p("5. Spread Capture / Quote Lifecycle")
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p("-" * 72)
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if "spread" in fills.columns and fills["spread"].notna().any():
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mid = (fills.get("bid_price", 0) + fills.get("ask_price", 0)) / 2
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# 简化：相对价差
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p(f"spread at fill mean={fills['spread'].mean():.4f} ({(fills['spread']/fills['fill_price']).mean()*100:.5f}%)")
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if not paths.empty and "after_30s_price" in paths.columns:
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side = paths["side"] if "side" in paths.columns else paths.get("side_f")
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fav30 = pd.Series(_fav_ret(side, paths["fill_price"], paths["after_30s_price"]), index=paths.index)
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p(f"mean edge@30s (proxy spread capture): {fav30.mean()*100:+.4f}%")
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# -------------------- Hypotheses --------------------
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p("\n" + "-" * 72)
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p("Hypotheses")
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p("-" * 72)
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# H1
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h1 = None
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if not paths.empty and "after_30s_price" in paths.columns:
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side = paths["side"] if "side" in paths.columns else paths.get("side_f")
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fav30 = pd.Series(_fav_ret(side, paths["fill_price"], paths["after_30s_price"]), index=paths.index)
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h1 = float((fav30 > 0).mean())
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p(f"H1 P(fav@30s)>50%: {h1*100:.1f}% [{'PASS' if h1>0.5 else 'FAIL'}]")
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else:
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p("H1: insufficient fill_path with after_30s")
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# H2 restore vs all
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if not paths.empty and "after_5m_price" in paths.columns:
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side = paths["side"] if "side" in paths.columns else paths.get("side_f")
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fav5 = pd.Series(_fav_ret(side, paths["fill_price"], paths["after_5m_price"]), index=paths.index)
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er_col = "exit_reason" if "exit_reason" in paths.columns else None
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if er_col and paths[er_col].notna().any():
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restore_mask = paths[er_col].astype(str).str.contains("restore", case=False, na=False)
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if restore_mask.any():
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r_all = float(fav5.mean())
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r_res = float(fav5[restore_mask].mean())
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verdict = (
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"PASS"
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if r_res > r_all + 1e-12
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else ("INCONCLUSIVE" if abs(r_res - r_all) < 1e-12 else "FAIL")
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)
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p(
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f"H2 restore vs all @5m: restore={r_res*100:+.4f}% all={r_all*100:+.4f}% "
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f"[{verdict}] n_restore={int(restore_mask.sum())}"
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)
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else:
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p("H2: no restore exits tagged yet")
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else:
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p("H2: exit_reason not linked yet (need closed trades)")
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else:
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p("H2: waiting for paths")
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# H3 concentrated losses
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if not paths.empty and "_loss" in paths.columns and paths["_loss"].any():
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losses = paths[paths["_loss"]]
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for col in ["trend_state", "volatility_regime", "fill_reason"]:
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c = col if col in losses.columns else None
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if c and losses[c].notna().any():
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top = losses[c].value_counts(normalize=True).head(1)
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if len(top):
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k, v = top.index[0], float(top.iloc[0])
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p(f"H3 loss concentration: {v*100:.1f}% of losses in {c}={k} "
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f"[{'ACTION: cancel in this state' if v>=0.5 else 'diffuse'}]")
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else:
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p("H3: need completed paths with losses")
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p("\n" + "=" * 72)
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p("Next: accumulate ≥500 fills (ideal 1000) before designing quote model / v1.2.")
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p("=" * 72)
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if out_path:
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
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print(f"\nReport saved: {out_path}")
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument(
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"--dir",
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type=str,
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default=str(Path(__file__).resolve().parents[2] / "logs" / "maker_edge"),
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)
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ap.add_argument("--min-fills", type=int, default=500)
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ap.add_argument("--report", action="store_true", help="also write markdown/txt report")
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args = ap.parse_args()
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log_dir = Path(args.dir)
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if not log_dir.exists():
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print(f"日志目录不存在: {log_dir}")
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return
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try:
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df = load_events(log_dir)
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except FileNotFoundError as e:
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print(e)
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return
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out = None
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if args.report:
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out = Path(__file__).resolve().parents[2] / "logs" / "maker_edge" / "Maker_Edge_Report_v0.1.txt"
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report(df, min_fills=args.min_fills, out_path=out)
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user