#!/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()