feat: dump alpha values + daily PnL as parquet (pyarrow)

analysis/report.py

@@ -122,6 +122,51 @@ def plot_ic(signal_eval: dict, output_path: str = "reports/ic.png") -> str:
     return output_path
 
 
+def dump_signals(signals_df: pd.DataFrame, output_dir: str = "results/") -> str:
+    """Save the signal matrix (date x stock) as a parquet file.
+
+    Args:
+        signals_df: Date-indexed DataFrame of per-stock signal values.
+        output_dir: Directory to write the parquet file into.
+
+    Returns:
+        The path the parquet file was written to.
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    path = os.path.join(output_dir, "signals.parquet")
+    signals_df.to_parquet(path)
+    return path
+
+
+def dump_daily_pnl(
+    results: list, output_dir: str = "results/", initial_cash: float = 1_000_000.0
+) -> str:
+    """Extract the daily portfolio value from a backtest run and save as parquet.
+
+    Compounds the per-day TimeReturn analyzer into an equity curve.
+
+    Args:
+        results: The list returned by ``cerebro.run()``.
+        output_dir: Directory to write the parquet file into.
+        initial_cash: Starting portfolio value for scaling the curve.
+
+    Returns:
+        The path the parquet file was written to.
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    series = pd.Series(dtype=float)
+    if results:
+        tr = results[0].analyzers.timereturn.get_analysis()
+        series = pd.Series(tr).sort_index()
+
+    equity = (1.0 + series).cumprod() * initial_cash
+    pnl_df = pd.DataFrame({"date": equity.index, "value": equity.values})
+
+    path = os.path.join(output_dir, "daily_pnl.parquet")
+    pnl_df.to_parquet(path)
+    return path
+
+
 def generate_report(
     results: list,
     signal_eval: dict,

run_example.py

@@ -11,7 +11,7 @@ import logging
 
 import pandas as pd
 
-from analysis.report import generate_report
+from analysis.report import dump_daily_pnl, dump_signals, generate_report
 from backtest.config import BacktestConfig
 from backtest.runner import BacktestRunner
 from data.downloader import download_batch
@@ -36,7 +36,8 @@ def _forward_returns(data: dict[str, pd.DataFrame], horizon: int) -> pd.DataFram
     return pd.DataFrame(forward_returns)
 
 
-def main(forward_horizon: int = 5, universe: str = "csi500", signal_name: str = "reversal_vol"):
+def main(forward_horizon: int = 5, universe: str = "csi500", signal_name: str = "reversal_vol",
+         dump_dir: str = "results/"):
     universes = {"hs300": SYMBOLS, "csi500": CSI500_SYMBOLS}
     symbols = universes.get(universe, CSI500_SYMBOLS)[:30]
 
@@ -97,6 +98,10 @@ def main(forward_horizon: int = 5, universe: str = "csi500", signal_name: str =
         results, signal_eval, output_dir="reports/", initial_cash=initial_cash
     )
 
+    # 7b. Dump signals and daily PnL.
+    dump_signals(signals_df, dump_dir)
+    dump_daily_pnl(results, dump_dir, initial_cash=initial_cash)
+
     # 8. Print summary.
     print("\nSIGNAL IC")
     print("=" * 50)
@@ -122,5 +127,6 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Chinese equity quant backtest")
     parser.add_argument("--universe", default="csi500", choices=["hs300", "csi500"])
     parser.add_argument("--signal", default="reversal_vol", choices=["reversal", "reversal_vol"])
+    parser.add_argument("--dump-dir", default="results/")
     args = parser.parse_args()
-    main(universe=args.universe, signal_name=args.signal)
+    main(universe=args.universe, signal_name=args.signal, dump_dir=args.dump_dir)