scripts/validate_with_moneyflow.py

#!/usr/bin/env python3
"""
校验：将推断的主力仓位信号与 Tushare moneyflow_dc 的主力净流入做相关性检查。

这不是拟合目标——只是 sanity check，确认推断方向不与外部数据严重矛盾。

用法:
  python scripts/validate_with_moneyflow.py --signal outputs/signals/position_signal_daily.parquet
"""

from __future__ import annotations

import argparse
import os
import sys
from pathlib import Path

import numpy as np
import pandas as pd
from scipy.stats import pearsonr, spearmanr

sys.path.insert(0, str(Path(__file__).resolve().parent.parent))

from src.data.loader import load_tushare_table
from huggingface_hub import snapshot_download


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--signal", required=True, help="position_signal_daily.parquet 路径")
    parser.add_argument("--output-dir", default="./outputs/reports")
    args = parser.parse_args()

    # 加载信号
    signal = pd.read_parquet(args.signal)
    signal["date"] = signal["date"].astype(int)

    # 下载并加载 moneyflow_dc
    tushare_root = snapshot_download(
        "kangkangchen/a-share-tushare-context-600809",
        repo_type="dataset",
        allow_patterns=["data/moneyflow_dc_daily/**/*.parquet"],
    )
    mf = load_tushare_table(tushare_root, "moneyflow_dc_daily")
    if mf.empty:
        print("ERROR: moneyflow_dc_daily is empty")
        return

    mf["trade_date"] = mf["trade_date"].astype(int)

    # Merge
    merged = signal.merge(mf, left_on="date", right_on="trade_date", how="inner")
    print(f"Merged days: {len(merged)} / {len(signal)} signal days")

    if len(merged) < 10:
        print("Too few overlapping days for correlation")
        return

    # 相关性检查
    # moneyflow_dc 的主力净流入字段通常叫 buy_elg_amount - sell_elg_amount 或 net_mf_amount
    net_col = None
    for col in ["net_mf_amount", "buy_elg_amount", "net_mainforce_amount"]:
        if col in merged.columns:
            net_col = col
            break
    # fallback: compute from buy/sell
    if net_col is None and "buy_lg_amount" in merged.columns:
        merged["_net_mf"] = merged["buy_lg_amount"] - merged["sell_lg_amount"]
        net_col = "_net_mf"

    if net_col is None:
        print(f"Available moneyflow columns: {list(merged.columns)}")
        print("Cannot find net flow column, printing sample:")
        print(merged.head())
        return

    # Normalize
    merged["mf_norm"] = merged[net_col] / merged[net_col].abs().mean()

    for score_col in ["score_z", "score"]:
        if score_col not in merged.columns:
            continue
        clean = merged.dropna(subset=[score_col, net_col])
        r_pearson, p_pearson = pearsonr(clean[score_col], clean[net_col])
        r_spearman, p_spearman = spearmanr(clean[score_col], clean[net_col])
        print(f"\n{score_col} vs {net_col}:")
        print(f"  Pearson: r={r_pearson:.4f}, p={p_pearson:.4f}")
        print(f"  Spearman: r={r_spearman:.4f}, p={p_spearman:.4f}")
        print(
            f"  Interpretation: {'同向 ✓' if r_pearson > 0 else '反向 ✗' if r_pearson < -0.1 else '弱相关'}"
        )

    # 保存合并结果
    os.makedirs(args.output_dir, exist_ok=True)
    out_path = os.path.join(args.output_dir, "signal_vs_moneyflow.parquet")
    merged.to_parquet(out_path)
    print(f"\nMerged data saved to {out_path}")


if __name__ == "__main__":
    main()