from __future__ import annotations
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import hashlib
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import json
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from datetime import datetime, timezone, timedelta
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from pathlib import Path
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import pandas as pd
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from PIL import Image, ImageDraw, ImageFont
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RUN_ID = "RUN-ANA-WUJI-V1-BUY-POINT-SECOND-REVIEW-20260615-001"
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SOURCE_RUN_ID = "RUN-ANA-WUJI-V1-STRICT-NOTE-FULL-RERUN-20260614-001"
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ROOT = Path(__file__).resolve().parents[1]
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PROJECT_ROOT = ROOT.parents[2]
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SOURCE_ROOT = PROJECT_ROOT / "ana-data" / "result" / SOURCE_RUN_ID
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DAILY_DIR = Path(r"E:\quant\a_share_daily_front_20230101_20260508_complete\daily")
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MINUTE_BASE = Path(r"E:\quant\2023_front_m")
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TZ = timezone(timedelta(hours=8))
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def now_iso() -> str:
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return datetime.now(TZ).isoformat(timespec="seconds")
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def sha256_file(path: Path) -> str:
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h = hashlib.sha256()
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with path.open("rb") as f:
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for chunk in iter(lambda: f.read(1024 * 1024), b""):
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h.update(chunk)
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return h.hexdigest()
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def write_csv(df: pd.DataFrame, path: Path) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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df.to_csv(path, index=False, encoding="utf-8-sig")
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def write_text(text: str, path: Path) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(text, encoding="utf-8-sig")
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def font(size: int):
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for p in [
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Path("C:/Windows/Fonts/msyh.ttc"),
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Path("C:/Windows/Fonts/simhei.ttf"),
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Path("C:/Windows/Fonts/simsun.ttc"),
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]:
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if p.exists():
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return ImageFont.truetype(str(p), size)
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return ImageFont.load_default()
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FONT_TITLE = font(24)
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FONT_MID = font(16)
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FONT_SMALL = font(13)
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def as_posix(path: Path) -> str:
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return str(path.resolve()).replace("\\", "/")
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def fmt(value: object, digits: int = 2) -> str:
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if value is None:
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return ""
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text = str(value)
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if text == "" or text.lower() == "nan":
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return ""
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try:
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return f"{float(text):.{digits}f}"
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except Exception:
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return text
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def pct(value: float, base: float) -> float:
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return (value / base - 1.0) * 100.0 if base else 0.0
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def board_limit_rate(symbol: str) -> float:
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code, exchange = symbol.split(".")
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if exchange == "BJ":
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return 0.30
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if exchange == "SH" and code.startswith("688"):
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return 0.20
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if exchange == "SZ" and (code.startswith("300") or code.startswith("301")):
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return 0.20
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return 0.10
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def load_daily(symbol: str) -> pd.DataFrame:
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path = DAILY_DIR / f"{symbol}.csv"
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if not path.exists():
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return pd.DataFrame()
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df = pd.read_csv(path, dtype={"trade_date": "string"})
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for col in ["open", "high", "low", "close", "volume", "amount", "preClose"]:
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if col in df.columns:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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df = df.dropna(subset=["open", "high", "low", "close", "preClose"]).sort_values("trade_date").reset_index(drop=True)
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rate = board_limit_rate(symbol)
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df["ret_pct"] = (df["close"] / df["preClose"] - 1.0) * 100.0
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df["high_vs_prev_close_pct"] = (df["high"] / df["preClose"] - 1.0) * 100.0
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df["limitup_hit_flag"] = df["high_vs_prev_close_pct"] >= (rate * 100.0 - 0.05)
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df["ma5"] = df["close"].rolling(5, min_periods=5).mean()
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df["ma20"] = df["close"].rolling(20, min_periods=20).mean()
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return df
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def minute_path(symbol: str) -> Path:
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code, exchange = symbol.split(".")
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return MINUTE_BASE / exchange / f"price_{code}.csv"
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def load_minute(symbol: str, entry_date: str) -> pd.DataFrame:
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path = minute_path(symbol)
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if not path.exists():
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return pd.DataFrame()
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date_key = entry_date.replace("-", "")
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df = pd.read_csv(path, dtype={"timetag": "string"})
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df = df[df["timetag"].str.startswith(date_key, na=False)].copy()
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if df.empty:
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return df
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df["trade_time"] = df["timetag"].str.slice(9, 17)
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for col in ["open", "high", "low", "close", "volumn", "amount"]:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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df = df.dropna(subset=["open", "high", "low", "close"]).sort_values("trade_time").reset_index(drop=True)
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if not df.empty:
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open_ref = float(df.iloc[0]["open"])
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df["ret_vs_open_pct"] = (df["close"] / open_ref - 1.0) * 100.0
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df["high_vs_open_pct"] = (df["high"] / open_ref - 1.0) * 100.0
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df["low_vs_open_pct"] = (df["low"] / open_ref - 1.0) * 100.0
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return df
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def row_at_or_before(df: pd.DataFrame, time_value: str) -> pd.Series | None:
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part = df[df["trade_time"] <= time_value]
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if part.empty:
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return None
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return part.iloc[-1]
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def row_at_or_after(df: pd.DataFrame, time_value: str) -> pd.Series | None:
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part = df[df["trade_time"] >= time_value]
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if part.empty:
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return None
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return part.iloc[0]
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def minute_key_points(minute: pd.DataFrame) -> pd.DataFrame:
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if minute.empty:
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return pd.DataFrame()
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keys: list[tuple[str, pd.Series | None]] = [
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("open_0930", minute.iloc[0]),
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("pre1040_max_high", minute.loc[minute[minute["trade_time"] <= "10:40:00"]["high"].idxmax()] if not minute[minute["trade_time"] <= "10:40:00"].empty else None),
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("pre1040_min_low", minute.loc[minute[minute["trade_time"] <= "10:40:00"]["low"].idxmin()] if not minute[minute["trade_time"] <= "10:40:00"].empty else None),
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("at_or_before_1040", row_at_or_before(minute, "10:40:00")),
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("at_or_after_1440", row_at_or_after(minute, "14:40:00")),
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("tail_max_high", minute.loc[minute[minute["trade_time"] >= "14:40:00"]["high"].idxmax()] if not minute[minute["trade_time"] >= "14:40:00"].empty else None),
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("tail_min_low", minute.loc[minute[minute["trade_time"] >= "14:40:00"]["low"].idxmin()] if not minute[minute["trade_time"] >= "14:40:00"].empty else None),
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("day_max_high", minute.loc[minute["high"].idxmax()]),
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("day_min_low", minute.loc[minute["low"].idxmin()]),
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("close_1500", minute.iloc[-1]),
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]
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rows = []
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seen = set()
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for label, row in keys:
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if row is None:
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continue
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key = (label, str(row["trade_time"]))
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if key in seen:
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continue
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seen.add(key)
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rows.append(
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{
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"point": label,
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"trade_time": row["trade_time"],
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"open": row["open"],
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"high": row["high"],
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"low": row["low"],
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"close": row["close"],
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"ret_vs_open_pct": row["ret_vs_open_pct"],
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"high_vs_open_pct": row["high_vs_open_pct"],
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"low_vs_open_pct": row["low_vs_open_pct"],
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"volume": row.get("volumn", ""),
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"amount": row.get("amount", ""),
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}
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)
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return pd.DataFrame(rows)
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def minute_summary(minute: pd.DataFrame, ma5: float | None) -> dict:
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if minute.empty:
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return {}
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open_ref = float(minute.iloc[0]["open"])
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pre1040 = minute[minute["trade_time"] <= "10:40:00"]
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tail = minute[minute["trade_time"] >= "14:40:00"]
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summary = {
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"open_ref": open_ref,
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"close_ret_pct": pct(float(minute.iloc[-1]["close"]), open_ref),
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"day_max_ret_pct": pct(float(minute["high"].max()), open_ref),
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"day_min_ret_pct": pct(float(minute["low"].min()), open_ref),
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"above_open_ratio": float((minute["close"] >= open_ref).mean()),
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"pre1040_max_ret_pct": pct(float(pre1040["high"].max()), open_ref) if not pre1040.empty else "",
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"pre1040_min_ret_pct": pct(float(pre1040["low"].min()), open_ref) if not pre1040.empty else "",
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"pre1040_above_open_ratio": float((pre1040["close"] >= open_ref).mean()) if not pre1040.empty else "",
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"tail_max_ret_pct": pct(float(tail["high"].max()), open_ref) if not tail.empty else "",
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"tail_min_ret_pct": pct(float(tail["low"].min()), open_ref) if not tail.empty else "",
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"tail_above_open_ratio": float((tail["close"] >= open_ref).mean()) if not tail.empty else "",
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}
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if ma5 is not None:
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summary["ma5"] = ma5
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summary["above_ma5_ratio"] = float((minute["close"] >= ma5).mean())
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return summary
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def daily_window(daily: pd.DataFrame, entry_date: str) -> pd.DataFrame:
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if daily.empty:
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return pd.DataFrame()
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key = entry_date.replace("-", "")
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if key not in set(daily["trade_date"]):
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pos = daily[daily["trade_date"] < key].index.max()
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else:
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pos = int(daily.index[daily["trade_date"].eq(key)][0])
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if pd.isna(pos):
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return pd.DataFrame()
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start = max(0, int(pos) - 35)
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end = min(len(daily), int(pos) + 2)
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cols = [
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"trade_date",
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"open",
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"high",
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"low",
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"close",
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"preClose",
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"ret_pct",
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"high_vs_prev_close_pct",
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"volume",
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"limitup_hit_flag",
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"ma5",
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"ma20",
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]
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return daily.iloc[start:end][cols].copy()
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def centered_daily_window(daily: pd.DataFrame, entry_date: str, before: int = 20, after: int = 20) -> pd.DataFrame:
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if daily.empty:
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return pd.DataFrame()
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key = entry_date.replace("-", "")
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if key not in set(daily["trade_date"]):
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return pd.DataFrame()
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pos = int(daily.index[daily["trade_date"].eq(key)][0])
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start = max(0, pos - before)
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end = min(len(daily), pos + after + 1)
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cols = [
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"trade_date",
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"open",
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"high",
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"low",
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"close",
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"preClose",
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"ret_pct",
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"high_vs_prev_close_pct",
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"volume",
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"limitup_hit_flag",
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"ma5",
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"ma20",
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]
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out = daily.iloc[start:end][cols].copy()
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out["window_offset"] = list(range(start - pos, end - pos))
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out["is_entry_day"] = out["trade_date"].eq(key)
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return out[
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[
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"window_offset",
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"is_entry_day",
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"trade_date",
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"open",
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"high",
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"low",
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"close",
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"preClose",
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"ret_pct",
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"high_vs_prev_close_pct",
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"volume",
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"limitup_hit_flag",
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"ma5",
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"ma20",
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]
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]
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def y_price(value: float, low: float, high: float, top: int, bottom: int) -> int:
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if high <= low:
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return (top + bottom) // 2
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return bottom - int((value - low) / (high - low) * (bottom - top))
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def draw_centered_daily_chart(symbol: str, entry_date: str, window: pd.DataFrame, out_path: Path) -> None:
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w, h = 1500, 820
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img = Image.new("RGB", (w, h), "#fbfbf7")
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d = ImageDraw.Draw(img)
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d.rectangle([0, 0, w - 1, h - 1], outline="#cbd5e1")
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d.text((30, 22), f"41交易日日线窗口：{symbol} / 买入日 {entry_date}", fill="#111827", font=FONT_TITLE)
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d.text((30, 56), "窗口口径：买入日前20个交易日 + 买入日 + 买入日后20个交易日", fill="#334155", font=FONT_SMALL)
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if window.empty:
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d.text((30, 120), "无日线窗口数据", fill="#991b1b", font=FONT_MID)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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img.save(out_path)
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return
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plot_left, plot_top, plot_right, plot_bottom = 80, 105, 1420, 560
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vol_top, vol_bottom = 610, 760
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d.rectangle([plot_left, plot_top, plot_right, plot_bottom], outline="#94a3b8")
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d.rectangle([plot_left, vol_top, plot_right, vol_bottom], outline="#94a3b8")
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lows = window[["low", "ma5", "ma20"]].apply(pd.to_numeric, errors="coerce").min(skipna=True).min()
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highs = window[["high", "ma5", "ma20"]].apply(pd.to_numeric, errors="coerce").max(skipna=True).max()
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price_low = float(lows) * 0.98
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price_high = float(highs) * 1.02
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max_vol = max(float(pd.to_numeric(window["volume"], errors="coerce").max()), 1.0)
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n = len(window)
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step = (plot_right - plot_left) / max(n, 1)
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candle_w = max(5, int(step * 0.55))
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ma5_pts = []
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ma20_pts = []
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for i, (_, row) in enumerate(window.reset_index(drop=True).iterrows()):
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x = int(plot_left + step * (i + 0.5))
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o = float(row["open"])
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c = float(row["close"])
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hi = float(row["high"])
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lo = float(row["low"])
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color = "#dc2626" if c >= o else "#16a34a"
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yy_hi = y_price(hi, price_low, price_high, plot_top, plot_bottom)
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yy_lo = y_price(lo, price_low, price_high, plot_top, plot_bottom)
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yy_o = y_price(o, price_low, price_high, plot_top, plot_bottom)
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yy_c = y_price(c, price_low, price_high, plot_top, plot_bottom)
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d.line([x, yy_hi, x, yy_lo], fill=color, width=2)
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body_top = min(yy_o, yy_c)
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body_bottom = max(yy_o, yy_c)
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if body_bottom == body_top:
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d.line([x - candle_w // 2, body_top, x + candle_w // 2, body_top], fill=color, width=3)
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else:
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d.rectangle([x - candle_w // 2, body_top, x + candle_w // 2, body_bottom], fill=color, outline=color)
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vh = int(float(row["volume"]) / max_vol * (vol_bottom - vol_top))
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d.line([x, vol_bottom, x, vol_bottom - vh], fill=color, width=max(2, candle_w // 3))
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if str(row["is_entry_day"]).lower() == "true":
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d.line([x, plot_top, x, vol_bottom], fill="#7c3aed", width=2)
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d.text((x - 35, plot_top - 24), "买入日", fill="#7c3aed", font=FONT_SMALL)
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if bool(row.get("limitup_hit_flag", False)):
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d.ellipse([x - 5, yy_hi - 18, x + 5, yy_hi - 8], fill="#f59e0b")
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if i % 5 == 0 or str(row["is_entry_day"]).lower() == "true":
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d.text((x - 28, vol_bottom + 8), str(row["trade_date"])[4:], fill="#64748b", font=FONT_SMALL)
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if pd.notna(row.get("ma5", None)):
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ma5_pts.append((x, y_price(float(row["ma5"]), price_low, price_high, plot_top, plot_bottom)))
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if pd.notna(row.get("ma20", None)):
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ma20_pts.append((x, y_price(float(row["ma20"]), price_low, price_high, plot_top, plot_bottom)))
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if len(ma5_pts) > 1:
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d.line(ma5_pts, fill="#2563eb", width=2)
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if len(ma20_pts) > 1:
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d.line(ma20_pts, fill="#9333ea", width=2)
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d.text((plot_left, 780), "红/绿K：日K；蓝线 MA5；紫线 MA20；紫色竖线为买入日；橙点为 high/prevClose 触及板块涨停阈值。", fill="#334155", font=FONT_SMALL)
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d.text((plot_right - 210, 80), "MA5", fill="#2563eb", font=FONT_SMALL)
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d.text((plot_right - 160, 80), "MA20", fill="#9333ea", font=FONT_SMALL)
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d.text((plot_right - 100, 80), "涨停记忆", fill="#f59e0b", font=FONT_SMALL)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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img.save(out_path)
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def selected_daily_nodes(daily: pd.DataFrame, candidate: pd.Series) -> pd.DataFrame:
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if daily.empty:
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return pd.DataFrame()
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node_dates = {
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"latest_prior_strict_limitup": str(candidate.get("latest_prior_strict_limitup_date", "")),
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"pullback_low_since_limitup": str(candidate.get("pullback_low_since_latest_limitup_date", "")),
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"prev60_high_ref": str(candidate.get("prev60_high_ref_date", "")),
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"signal_day": str(candidate.get("signal_trade_date", "")),
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"entry_day": str(candidate.get("entry_trade_date", "")),
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}
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rows = []
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for role, date in node_dates.items():
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if not date or date.lower() == "nan":
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continue
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key = date.replace("-", "")
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part = daily[daily["trade_date"].eq(key)]
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if part.empty:
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continue
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r = part.iloc[0].to_dict()
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r["node_role"] = role
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rows.append(r)
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cols = [
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"node_role",
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"trade_date",
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"open",
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"high",
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"low",
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"close",
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"preClose",
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"ret_pct",
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"high_vs_prev_close_pct",
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"volume",
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"limitup_hit_flag",
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"ma5",
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"ma20",
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]
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return pd.DataFrame(rows)[cols] if rows else pd.DataFrame(columns=cols)
|
|
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def md_table(df: pd.DataFrame, cols: list[str], max_rows: int | None = None) -> list[str]:
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if df.empty:
|
return ["_无数据_"]
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part = df[cols].copy()
|
if max_rows is not None:
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part = part.head(max_rows)
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lines = ["| " + " | ".join(cols) + " |", "|" + "|".join(["---"] * len(cols)) + "|"]
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for _, row in part.iterrows():
|
vals = []
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for c in cols:
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v = row[c]
|
if isinstance(v, float):
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vals.append(fmt(v))
|
else:
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vals.append(str(v).replace("|", "/"))
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lines.append("| " + " | ".join(vals) + " |")
|
return lines
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|
|
def main() -> None:
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p1 = pd.read_csv(ROOT / "buy_point_second_review_recheck_list.csv", encoding="utf-8-sig")
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p1 = p1[p1["human_recheck_priority"].eq("P1_BUY_DECISION_MAY_BE_WRONG")].copy()
|
candidates = pd.read_csv(SOURCE_ROOT / "strict_note_buy_point_review_candidate_ledger.csv", encoding="utf-8-sig")
|
orders = pd.read_csv(SOURCE_ROOT / "strict_note_order_ledger.csv", encoding="utf-8-sig")
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lots = pd.read_csv(SOURCE_ROOT / "strict_note_position_lot_ledger.csv", encoding="utf-8-sig")
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cases = pd.read_csv(SOURCE_ROOT / "strict_note_case_summary.csv", encoding="utf-8-sig")
|
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packet_root = ROOT / "p1_step_review_packets"
|
table_root = packet_root / "tables"
|
packet_root.mkdir(parents=True, exist_ok=True)
|
generated_at = now_iso()
|
index_rows = []
|
|
for i, (_, row) in enumerate(p1.iterrows(), start=1):
|
candidate_id = row["candidate_id"]
|
safe_id = candidate_id.replace(".", "_").replace("/", "_")
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packet_path = packet_root / f"{i:02d}_{safe_id}.md"
|
cand = candidates[candidates["candidate_id"].eq(candidate_id)].iloc[0]
|
candidate_orders = orders[orders["candidate_id"].eq(candidate_id)].copy()
|
candidate_lots = lots[lots["candidate_id"].eq(candidate_id)].copy()
|
case_row = cases[cases["case_id"].eq(row["case_id"])].head(1)
|
|
daily = load_daily(row["symbol"])
|
minute = load_minute(row["symbol"], row["entry_trade_date"])
|
signal_key = str(row["signal_trade_date"]).replace("-", "")
|
ma5 = None
|
if not daily.empty:
|
signal_part = daily[daily["trade_date"] <= signal_key].tail(5)
|
if len(signal_part) == 5:
|
ma5 = float(signal_part["close"].mean())
|
|
daily_nodes = selected_daily_nodes(daily, cand)
|
daily_win = daily_window(daily, row["entry_trade_date"])
|
centered_win = centered_daily_window(daily, row["entry_trade_date"])
|
minute_keys = minute_key_points(minute)
|
msum = minute_summary(minute, ma5)
|
|
write_csv(daily_nodes, table_root / f"{safe_id}_daily_nodes.csv")
|
write_csv(daily_win, table_root / f"{safe_id}_daily_window.csv")
|
write_csv(centered_win, table_root / f"{safe_id}_daily_center_41.csv")
|
write_csv(minute_keys, table_root / f"{safe_id}_minute_key_points.csv")
|
daily_chart_path = packet_root / "charts" / f"{safe_id}_daily_center_41.png"
|
draw_centered_daily_chart(row["symbol"], row["entry_trade_date"], centered_win, daily_chart_path)
|
|
image_path = SOURCE_ROOT / str(row["review_input_chart_path"])
|
image_link = as_posix(image_path)
|
daily_chart_link = as_posix(daily_chart_path)
|
daily_nodes_link = as_posix(table_root / f"{safe_id}_daily_nodes.csv")
|
daily_window_link = as_posix(table_root / f"{safe_id}_daily_window.csv")
|
daily_center_link = as_posix(table_root / f"{safe_id}_daily_center_41.csv")
|
minute_keys_link = as_posix(table_root / f"{safe_id}_minute_key_points.csv")
|
|
lines = [
|
f"# P1 买点逐条复核：{row['symbol']} / {row['entry_trade_date']}",
|
"",
|
f"- packet_order: {i}",
|
f"- generated_at: {generated_at}",
|
f"- case_id: {row['case_id']}",
|
f"- candidate_id: {candidate_id}",
|
f"- source_run_id: {SOURCE_RUN_ID}",
|
"",
|
"## 你要裁决",
|
"",
|
"- [ ] 维持 BUY",
|
"- [ ] 改为 REVIEW_HELD",
|
"- [ ] 数据不足，待补充",
|
"",
|
"建议先看：原人工理由是否和图证/数据一致；再看是否存在笔记要求的 10:40 前或 14:40 后买点。",
|
"",
|
"## 原人工裁决和二审异议",
|
"",
|
f"- 原人工动作：`{row['human_decision_action']}`",
|
f"- 原人工理由：{row['human_decision_reason_cn']}",
|
f"- 二审异议：`{row['second_review_issue_code']}`，{row['second_review_reason_cn']}",
|
f"- 我的初步倾向：**需要重看，倾向至少撤出自动 BUY；最终以你人工看图和数据裁决为准。**",
|
"",
|
"## 交易账本影响",
|
"",
|
]
|
if candidate_orders.empty:
|
lines.append("_未找到订单记录_")
|
else:
|
lines += md_table(
|
candidate_orders,
|
["order_type", "order_id", "trade_date", "trade_time", "trade_price", "position_pct", "decision_reason_cn"],
|
)
|
lines += ["", "### lot / case 状态", ""]
|
if candidate_lots.empty:
|
lines.append("_未找到 lot 记录_")
|
else:
|
lines += md_table(
|
candidate_lots,
|
["lot_id", "entry_trade_date", "entry_price", "position_pct", "exit_trade_date", "exit_price", "lot_scope_status", "boundary_type"],
|
)
|
if not case_row.empty:
|
lines += ["", "### case 汇总", ""]
|
lines += md_table(
|
case_row,
|
[
|
"case_id",
|
"symbols",
|
"strict_buy_orders",
|
"sell_orders",
|
"strict_closed_lots",
|
"boundary_lots",
|
"net_account_contribution",
|
"case_scope_status",
|
],
|
)
|
lines += [
|
"",
|
"## 入池硬条件",
|
"",
|
"| 字段 | 值 |",
|
"|---|---|",
|
]
|
hard_fields = [
|
"signal_trade_date",
|
"entry_trade_date",
|
"candidate_rank",
|
"market_gate_status",
|
"up_count",
|
"prior_strict_limitup_30_flag",
|
"latest_prior_strict_limitup_date",
|
"volume_ratio",
|
"pullback_from_latest_limitup_close_pct",
|
"upper_shadow_pct",
|
"upper_shadow_range_ratio",
|
"prev60_high",
|
"prev60_high_ref_date",
|
"prev_high_volume_pass_flag",
|
]
|
for field in hard_fields:
|
if field in cand.index:
|
lines.append(f"| {field} | {fmt(cand[field])} |")
|
|
lines += [
|
"",
|
"## 买点分时图",
|
"",
|
f"",
|
"",
|
"## 买入点前后 20 个交易日的日线窗口",
|
"",
|
f"",
|
"",
|
f"- 窗口 CSV：[{Path(daily_center_link).name}]({daily_center_link})",
|
f"- 实际窗口行数：{len(centered_win)}",
|
"",
|
"### 41 日窗口明细",
|
"",
|
]
|
lines += md_table(
|
centered_win,
|
[
|
"window_offset",
|
"is_entry_day",
|
"trade_date",
|
"open",
|
"high",
|
"low",
|
"close",
|
"ret_pct",
|
"high_vs_prev_close_pct",
|
"volume",
|
"limitup_hit_flag",
|
"ma5",
|
"ma20",
|
],
|
)
|
lines += [
|
"",
|
"## 分时复算摘要",
|
"",
|
"| 指标 | 值 | 含义 |",
|
"|---|---:|---|",
|
f"| open_ref | {fmt(msum.get('open_ref', ''))} | 当天 09:30 开盘参考价 |",
|
f"| close_ret_pct | {fmt(msum.get('close_ret_pct', ''))}% | 收盘相对开盘 |",
|
f"| day_max_ret_pct | {fmt(msum.get('day_max_ret_pct', ''))}% | 全天最高相对开盘 |",
|
f"| day_min_ret_pct | {fmt(msum.get('day_min_ret_pct', ''))}% | 全天最低相对开盘 |",
|
f"| above_open_ratio | {fmt(msum.get('above_open_ratio', ''), 4)} | 全天 close 在开盘价上方的分钟占比 |",
|
f"| pre1040_max_ret_pct | {fmt(msum.get('pre1040_max_ret_pct', ''))}% | 10:40 前最高相对开盘 |",
|
f"| pre1040_min_ret_pct | {fmt(msum.get('pre1040_min_ret_pct', ''))}% | 10:40 前最低相对开盘 |",
|
f"| pre1040_above_open_ratio | {fmt(msum.get('pre1040_above_open_ratio', ''), 4)} | 10:40 前在开盘价上方占比 |",
|
f"| tail_max_ret_pct | {fmt(msum.get('tail_max_ret_pct', ''))}% | 14:40 后最高相对开盘 |",
|
f"| tail_min_ret_pct | {fmt(msum.get('tail_min_ret_pct', ''))}% | 14:40 后最低相对开盘 |",
|
f"| tail_above_open_ratio | {fmt(msum.get('tail_above_open_ratio', ''), 4)} | 14:40 后在开盘价上方占比 |",
|
f"| ma5 | {fmt(msum.get('ma5', ''))} | 信号日前 5 日均价，仅作支撑参考 |",
|
f"| above_ma5_ratio | {fmt(msum.get('above_ma5_ratio', ''), 4)} | 全天 close 在 MA5 上方占比 |",
|
"",
|
"## 分时关键点",
|
"",
|
]
|
lines += md_table(
|
minute_keys,
|
["point", "trade_time", "open", "high", "low", "close", "ret_vs_open_pct", "high_vs_open_pct", "low_vs_open_pct", "volume"],
|
)
|
lines += [
|
"",
|
"## 日线关键节点",
|
"",
|
]
|
lines += md_table(
|
daily_nodes,
|
["node_role", "trade_date", "open", "high", "low", "close", "ret_pct", "high_vs_prev_close_pct", "volume", "limitup_hit_flag", "ma5", "ma20"],
|
)
|
lines += [
|
"",
|
"## 数据文件",
|
"",
|
f"- 原图：[{image_path.name}]({image_link})",
|
f"- 日线关键节点 CSV：[{Path(daily_nodes_link).name}]({daily_nodes_link})",
|
f"- 日线窗口 CSV：[{Path(daily_window_link).name}]({daily_window_link})",
|
f"- 买入日前后20交易日 CSV：[{Path(daily_center_link).name}]({daily_center_link})",
|
f"- 买入日前后20交易日日线图：[{Path(daily_chart_link).name}]({daily_chart_link})",
|
f"- 分时关键点 CSV：[{Path(minute_keys_link).name}]({minute_keys_link})",
|
"",
|
"## 逐条复核问题",
|
"",
|
"1. 图上是否存在 10:40 前的“冲高后回踩开盘价/均线并缩量承接”？",
|
"2. 图上是否存在 14:40 后的清晰重新站稳和承接？",
|
"3. 原人工理由是否与图上实际走势一致？",
|
"4. 如果改成 REVIEW_HELD，是否应从交易账本撤掉这笔 BUY，并重算后续 lot/case 读数？",
|
"",
|
]
|
write_text("\n".join(lines), packet_path)
|
|
index_rows.append(
|
{
|
"order": i,
|
"case_id": row["case_id"],
|
"candidate_id": candidate_id,
|
"symbol": row["symbol"],
|
"entry_trade_date": row["entry_trade_date"],
|
"original_action": row["human_decision_action"],
|
"issue_code": row["second_review_issue_code"],
|
"close_ret_pct": row.get("close_ret_pct", ""),
|
"above_open_ratio": row.get("above_open_ratio", ""),
|
"packet_path": as_posix(packet_path),
|
"source_chart": image_link,
|
}
|
)
|
|
index = pd.DataFrame(index_rows)
|
write_csv(index, packet_root / "p1_step_review_index.csv")
|
|
index_lines = [
|
"# P1 买点逐条复核工作台",
|
"",
|
f"- generated_at: {generated_at}",
|
f"- source_run_id: {SOURCE_RUN_ID}",
|
f"- item_count: {len(index)}",
|
"",
|
"使用方式：按顺序打开每条 packet，先看原图，再看分时复算摘要、日线关键节点和交易影响；最后在会话里告诉我“第 N 条维持 BUY / 改 HELD / 数据不足”和理由，我来记录并汇总影响。",
|
"",
|
"| # | case | symbol | date | issue | close% | above_open | packet |",
|
"|---:|---|---|---|---|---:|---:|---|",
|
]
|
for _, r in index.iterrows():
|
index_lines.append(
|
f"| {r['order']} | {r['case_id']} | {r['symbol']} | {r['entry_trade_date']} | {r['issue_code']} | "
|
f"{fmt(r['close_ret_pct'])} | {fmt(r['above_open_ratio'], 4)} | [打开复核包]({r['packet_path']}) |"
|
)
|
write_text("\n".join(index_lines) + "\n", packet_root / "p1_step_review_index.md")
|
|
manifest_rows = []
|
for path in sorted(packet_root.rglob("*")):
|
if path.is_file():
|
manifest_rows.append(
|
{
|
"path": path.relative_to(ROOT).as_posix(),
|
"size": path.stat().st_size,
|
"sha256": sha256_file(path),
|
}
|
)
|
manifest = pd.DataFrame(manifest_rows)
|
write_csv(manifest, packet_root / "p1_step_review_manifest.csv")
|
(packet_root / "p1_step_review_summary.json").write_text(
|
json.dumps(
|
{
|
"run_id": RUN_ID,
|
"generated_at": generated_at,
|
"source_run_id": SOURCE_RUN_ID,
|
"item_count": int(len(index)),
|
"index_path": as_posix(packet_root / "p1_step_review_index.md"),
|
},
|
ensure_ascii=False,
|
indent=2,
|
),
|
encoding="utf-8",
|
)
|
|
|
if __name__ == "__main__":
|
main()
|
