Yuxuan Yan
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
#!/usr/bin/env python3
|
|
"""End-to-end pipeline: HS300 universe -> momentum signal -> cross-sectional IC
|
|
-> multi-stock backtest (AlphaStrategy + RankEqualWeightBuilder) -> reports."""
|
|
import logging
|
|
|
|
import pandas as pd
|
|
|
|
from analysis.report import generate_report
|
|
from backtest.config import BacktestConfig
|
|
from backtest.runner import BacktestRunner
|
|
from data.downloader import download_batch
|
|
from data.universe import SYMBOLS
|
|
from eval.metrics import evaluate_cross_sectional
|
|
from portfolio.builder import RankEqualWeightBuilder
|
|
from signals.momentum import MomentumSignal
|
|
from strategies.alpha_strategy import AlphaStrategy
|
|
|
|
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def main():
|
|
symbols = SYMBOLS[:30]
|
|
start, end = "2023-01-01", "2024-12-31"
|
|
initial_cash = 1_000_000
|
|
|
|
# 1-2. Download daily data for the universe.
|
|
data = download_batch(symbols, start, end)
|
|
data = {s: df for s, df in data.items() if df is not None and not df.empty}
|
|
logger.info(f"Downloaded {len(data)}/{len(symbols)} symbols")
|
|
|
|
# 3. Compute the momentum signal per stock.
|
|
signal = MomentumSignal(lookback=5)
|
|
signal_series: dict[str, pd.Series] = {}
|
|
forward_returns: dict[str, pd.Series] = {}
|
|
for sym, df in data.items():
|
|
idx = pd.to_datetime(df["date"])
|
|
sig = signal.compute(df)
|
|
sig.index = idx
|
|
signal_series[sym] = sig
|
|
fwd = df["close"].pct_change().shift(-1) # next-day return
|
|
fwd.index = idx
|
|
forward_returns[sym] = fwd
|
|
|
|
# 4. Cross-sectional IC evaluation.
|
|
signals_df = pd.DataFrame(signal_series)
|
|
returns_df = pd.DataFrame(forward_returns)
|
|
signal_eval = evaluate_cross_sectional(signals_df, returns_df)
|
|
|
|
# 5. Attach the signal column to each DataFrame and build feeds.
|
|
config = BacktestConfig(
|
|
symbols=list(data.keys()),
|
|
start_date=start,
|
|
end_date=end,
|
|
initial_cash=initial_cash,
|
|
sizer_percent=0.95,
|
|
)
|
|
runner = BacktestRunner(config)
|
|
builder = RankEqualWeightBuilder(top_n=5)
|
|
for sym, df in data.items():
|
|
df = df.copy()
|
|
df["signal"] = signal.compute(df).values
|
|
runner.add_signal_data(df, name=sym)
|
|
|
|
# 6. Run the multi-stock backtest.
|
|
results = runner.run_prepared(AlphaStrategy, {"builder": builder})
|
|
|
|
# 7. Reports.
|
|
artifacts = generate_report(
|
|
results, signal_eval, output_dir="reports/", initial_cash=initial_cash
|
|
)
|
|
|
|
# 8. Print summary.
|
|
print("\nSIGNAL IC")
|
|
print("=" * 50)
|
|
print(f"IC mean / std / IR: {signal_eval['ic_mean']:.4f} / "
|
|
f"{signal_eval['ic_std']:.4f} / {signal_eval['ir']:.4f}")
|
|
print(f"Rank IC mean / std / IR: {signal_eval['rank_ic_mean']:.4f} / "
|
|
f"{signal_eval['rank_ic_std']:.4f} / {signal_eval['rank_ir']:.4f}")
|
|
print(f"Hit rate: {signal_eval['hit_rate']:.2%}")
|
|
print(f"Periods: {signal_eval['n_periods']}")
|
|
print(f"\nReports written to: {artifacts}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|