117 lines
4.7 KiB
Python
117 lines
4.7 KiB
Python
"""复刻指南针 0AMV（活跃市值）——可行性验证原型。
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模型：
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活跃筹码比例(t) = 1 - Π_{近N个交易日} (1 - 换手率) （换手衰减）
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0AMV(t) = Σ_个股 流通市值(t) × 活跃筹码比例(t)
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流通市值不额外拉接口：成交量(股) / (换手%/100) 反推流通股本，× close 即得，
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且随解禁/增发每日自适应。N 为指南针未公开的窗口参数，输出多组候选供对照 app 校准。
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用法（backend 目录）：
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env -u SSLKEYLOGFILE uv run python scripts/active_mv_probe.py
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"""
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from __future__ import annotations
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import asyncio
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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import numpy as np
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import pandas as pd
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from sqlalchemy import text
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from app.db import async_session
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WINDOWS = (20, 60, 120) # 活跃窗口候选（交易日）
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LOOKBACK_DAYS = 500 # 日历日回看，足够 120 交易日窗口
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SHOW_DAYS = 20 # 打印最近 N 个交易日
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async def _load() -> tuple[pd.DataFrame, pd.DataFrame]:
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async with async_session() as session:
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rows = (await session.execute(text("""
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SELECT ts, symbol, close, volume, amount, turnover
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FROM candles
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WHERE timeframe = '1d'
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AND turnover IS NOT NULL AND turnover > 0
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AND volume > 0
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AND ts >= now() - interval '500 days'
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"""))).fetchall()
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snap = (await session.execute(text("""
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SELECT ts_code, circ_mv, trade_date
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FROM daily_snapshot
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WHERE trade_date >= (SELECT max(trade_date) - interval '10 days' FROM daily_snapshot)
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AND circ_mv IS NOT NULL
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"""))).fetchall()
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df = pd.DataFrame(rows, columns=["ts", "symbol", "close", "volume", "amount", "turnover"])
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snap = pd.DataFrame(snap, columns=["ts_code", "circ_mv_wan", "snap_date"])
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return df, snap
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def _validate(df: pd.DataFrame, snap: pd.DataFrame) -> None:
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"""反推流通市值 vs daily_snapshot（万元→元）交叉验证。"""
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if snap.empty:
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print("校验：daily_snapshot 为空")
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return
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snap["symbol"] = snap["ts_code"].str.split(".").str[0]
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snap["d"] = snap["snap_date"].dt.date
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common = set(snap["d"]) & set(df["date"])
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if not common:
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print("校验：daily_snapshot 与 candles 无可对比日期")
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return
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snap_date = max(common)
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day = df[df["date"] == snap_date]
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m = day[["symbol", "circ_mv"]].merge(
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snap.loc[snap["d"] == snap_date, ["symbol", "circ_mv_wan"]],
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on="symbol", how="inner",
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)
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if m.empty:
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print("校验：无可对比个股")
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return
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ratio = m["circ_mv"] / (m["circ_mv_wan"] * 1e4)
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print(f"校验（{snap_date}，{len(m)} 只）：反推/官方流通市值中位数 = {ratio.median():.4f}，"
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f"P10={ratio.quantile(0.1):.3f}，P90={ratio.quantile(0.9):.3f}")
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big = m.nlargest(5, "circ_mv")
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for _, r in big.iterrows():
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print(f" {r['symbol']}: 反推 {r['circ_mv']/1e8:,.0f}亿 vs 官方 {r['circ_mv_wan']/1e4:,.0f}亿"
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f"（比值 {r['circ_mv']/(r['circ_mv_wan']*1e4):.3f}）")
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def main() -> None:
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df, snap = asyncio.run(_load())
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df["date"] = df["ts"].dt.date
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# 关键：分组滚动前必须按 (symbol, date) 排序，SQL 返回顺序不保证
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df = df.sort_values(["symbol", "date"], kind="stable").reset_index(drop=True)
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print(f"candles 载入 {len(df):,} 行，{df['symbol'].nunique():,} 只，"
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f"{df['date'].min()} ~ {df['date'].max()}")
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t = (df["turnover"] / 100.0).clip(upper=0.9999)
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df["float_shares"] = df["volume"] / t # 流通股本（股）；volume 单位=股
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df["circ_mv"] = df["float_shares"] * df["close"] # 流通市值（元）
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_validate(df, snap)
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df["log_inactive"] = np.log1p(-t)
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agg_cols = {"circ_mv": "sum", "amount": "sum"}
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for n in WINDOWS:
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decay = df.groupby("symbol")["log_inactive"].transform(
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lambda s: s.rolling(n, min_periods=1).sum()
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)
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df[f"amv_{n}"] = df["circ_mv"] * (1.0 - np.exp(decay.to_numpy()))
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agg_cols[f"amv_{n}"] = "sum"
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daily = df.groupby("date").agg(agg_cols).sort_index().tail(SHOW_DAYS)
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print("\n日期 全市场流通市值(万亿) 日成交额(万亿) " +
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" ".join(f"0AMV_{n}(万亿) 活跃占比_{n}" for n in WINDOWS))
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for d, r in daily.iterrows():
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print(f"{d} {r['circ_mv']/1e12:8.3f} {r['amount']/1e12:6.3f} " +
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" ".join(
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f"{r[f'amv_{n}']/1e12:7.3f} {r[f'amv_{n}']/r['circ_mv']*100:5.1f}%"
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for n in WINDOWS
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))
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if __name__ == "__main__":
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main()
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