quant-trading/main.py

import argparse

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import data_manager
import factor_attribution
import metrics
import universe_history as uh
from strategies.adaptive_momentum import AdaptiveMomentumStrategy
from strategies.base import BarStrategy
from signals import session_targets
from execution import simulate_book
from strategies.buy_and_hold import BuyAndHoldStrategy
from strategies.dual_momentum import DualMomentumStrategy
from strategies.inverse_vol import InverseVolatilityStrategy
from strategies.mean_reversion import MeanReversionStrategy
from strategies.momentum import MomentumStrategy
from strategies.momentum_quality import MomentumQualityStrategy
from strategies.multi_factor import MultiFactorStrategy
from strategies.recovery_momentum import RecoveryMomentumStrategy
from strategies.trend_following import TrendFollowingStrategy
from universe import UNIVERSES


# ---------------------------------------------------------------------------
# Backtest engine
# ---------------------------------------------------------------------------

def strategy_positions(strategy, data: pd.DataFrame, open_data: pd.DataFrame | None = None,
                       bars: dict[str, pd.DataFrame] | None = None,
                       eligible: pd.DataFrame | None = None) -> pd.DataFrame:
    """Weights held over (close t-1, close t], after a feasible prior close fill."""
    if eligible is None and bars is not None:
        eligible = bars.get("eligible")
    return session_targets(strategy, data, open_data, bars, eligible).shift(1).fillna(0.0)


def simulate_live_book(held: pd.DataFrame, data: pd.DataFrame, initial_capital: float,
                       transaction_cost: float = 0.001, fixed_fee: float = 0.0,
                       fee_base: float = 0.0, fee_per_share: float = 0.0,
                       min_trade_value: float = 0.0, resize_band: float = 0.0,
                       *, fresh_prices: pd.DataFrame | None = None,
                       integer_shares: bool = False, lot_size: int = 1):
    targets = held.reindex_like(data).shift(-1).fillna(0.0)
    # No decision after the last row is represented by `held`; preserve the book.
    result = simulate_book(targets.iloc[:-1], data.iloc[:-1], initial_capital,
                           fresh_prices=fresh_prices.iloc[:-1] if fresh_prices is not None else None,
                           transaction_cost=transaction_cost, fixed_fee=fixed_fee,
                           fee_base=fee_base, fee_per_share=fee_per_share,
                           min_trade_value=min_trade_value, resize_band=resize_band,
                           integer_shares=integer_shares, lot_size=lot_size)
    traded = pd.DataFrame(0.0, index=data.index, columns=data.columns)
    for trade in result["trade_log"]:
        i = data.index.get_loc(pd.Timestamp(trade["date"]))
        value = result["pretrade_equity"].iloc[i]
        traded.iloc[i + 1, data.columns.get_loc(trade["ticker"])] += (
            trade["value"] / value * (1 if trade["action"] == "BUY" else -1)) if value else 0.0
    marks = data.ffill().iloc[-1].dropna().to_dict()
    from execution import portfolio_value
    last = portfolio_value(result["holdings"], marks,
                           float(result["cash"].iloc[-1]) if len(result["cash"]) else initial_capital)
    equity = pd.concat([result["equity"], pd.Series([last], index=data.index[-1:])])
    return equity, traded


def backtest(
    strategy,
    data: pd.DataFrame,
    initial_capital: float = 100_000,
    transaction_cost: float = 0.001,
    fixed_fee: float = 0.0,
    fee_base: float = 0.0,
    fee_per_share: float = 0.0,
    open_data: pd.DataFrame | None = None,
    min_trade_value: float = 0.0,
    bars: dict[str, pd.DataFrame] | None = None,
    resize_band: float = 0.0,
) -> pd.Series:
    """
    Cash-funded backtest with next-session execution and drifting holdings.

    Parameters
    ----------
    strategy : Strategy
        Any class implementing generate_signals(data) → DataFrame of weights.
    data : pd.DataFrame
        Adjusted close prices, one column per asset.
    initial_capital : float
        Starting portfolio value.
    transaction_cost : float
        One-way cost per unit of turnover (e.g. 0.001 = 10 bps).
    fixed_fee : float
        Floor of the per-trade fee (e.g. 2.0 = $2 minimum per buy/sell).
        With fee_per_share=0 (default), this is also the actual per-trade fee.
    fee_base : float
        Fixed component of a per-share tiered fee schedule. The actual
        per-trade fee is ``max(fixed_fee, fee_base + fee_per_share * shares)``.
    fee_per_share : float
        Per-share variable component of the tiered fee (e.g. 0.009 = $0.009/share).
        With fee_base=1.88 + fee_per_share=0.009 + fixed_fee=2.0 you get an
        IBKR-style schedule: max(2, 1.88 + 0.009 * shares).
    open_data : pd.DataFrame, optional
        Open prices. When provided, the backtest keeps the book the way the
        live trader does: its signal timing (``strategy_positions``), drifting
        holdings, whole orders and ``resize_band`` (``simulate_live_book``).
    min_trade_value : float
        Per-ticker rebalances smaller than ``max(min_trade_value, 0.1% of
        equity)`` are not charged the per-trade fee, mirroring the live
        trader, which never places such orders. 0 (default) charges every
        weight change.
    bars : dict, optional
        ``{field: DataFrame}`` panels for a BarStrategy (close/open/high/low/volume).

    Returns
    -------
    pd.Series
        Daily equity curve.
    """
    positions = strategy_positions(strategy, data, open_data=open_data, bars=bars)
    equity, _ = simulate_live_book(positions, data, initial_capital, transaction_cost,
                                   fixed_fee, fee_base, fee_per_share, min_trade_value, resize_band)
    return equity


# ---------------------------------------------------------------------------
# Visualization
# ---------------------------------------------------------------------------

def plot_results(results: pd.DataFrame) -> None:
    """Two-panel chart: equity curves (top) + drawdowns (bottom)."""
    # Compute drawdowns
    drawdowns = results.div(results.cummax()) - 1

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 9), sharex=True,
                                    gridspec_kw={"height_ratios": [3, 1]})

    for col in results.columns:
        ax1.plot(results.index, results[col], label=col, linewidth=1.5)
    ax1.set_title("Strategy Comparison — Equity Curves", fontsize=14)
    ax1.set_ylabel("Portfolio Value ($)")
    ax1.legend(loc="upper left")
    ax1.grid(True, alpha=0.3)

    for col in drawdowns.columns:
        ax2.plot(drawdowns.index, drawdowns[col] * 100, label=col, linewidth=1.0)
    ax2.set_title("Drawdowns")
    ax2.set_ylabel("Drawdown (%)")
    ax2.set_xlabel("Date")
    ax2.grid(True, alpha=0.3)

    plt.tight_layout()
    plt.show()


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------

def main() -> None:
    parser = argparse.ArgumentParser(description="Run strategy backtest")
    parser.add_argument(
        "--market", choices=UNIVERSES.keys(), default="us",
        help="Market universe to backtest (default: us)",
    )
    parser.add_argument(
        "--capital", type=float, default=None,
        help="Initial capital (default: 10000)",
    )
    parser.add_argument(
        "--top-n", type=int, default=None,
        help="Number of stocks for selective strategies (default: ~10%% of universe)",
    )
    parser.add_argument(
        "--years", type=int, default=None,
        help="Limit backtest to last N years of data",
    )
    parser.add_argument(
        "--no-plot", action="store_true",
        help="Skip plotting charts",
    )
    parser.add_argument(
        "--fixed-fee", type=float, default=0.0,
        help="Fixed dollar cost per trade, e.g. 2.0 means $2 per buy or sell",
    )
    parser.add_argument(
        "--execution", choices=["close", "open-close"], default="close",
        help="Execution mode: 'close' (default, signal & execute on close) or "
             "'open-close' (signal on morning open, execute at close)",
    )
    parser.add_argument(
        "--attribution", action="store_true",
        help="Run factor attribution after performance metrics",
    )
    parser.add_argument(
        "--attribution-model", choices=["capm", "ff5", "ff5plus", "all"], default="all",
        help="Factor model selection for attribution output",
    )
    parser.add_argument(
        "--attribution-export", default=None,
        help="Directory to export factor attribution CSVs",
    )
    parser.add_argument("--offline", action="store_true", help="Use local prices and membership without refreshing")
    args = parser.parse_args()
    initial_capital = args.capital if args.capital is not None else 10_000
    use_open = args.execution == "open-close"

    universe = UNIVERSES[args.market]
    tickers = [] if args.offline else universe["fetch"]()
    benchmark = universe["benchmark"]
    benchmark_label = universe["benchmark_label"]

    membership = uh.pit_membership(args.market, pd.DatetimeIndex([pd.Timestamp.today()]), refresh=not args.offline)
    all_tickers = sorted(set(tickers) | set(membership.columns) | {benchmark})
    fields = ["close", "open", "volume"]
    panels = (data_manager.load_fields(args.market, fields) if args.offline else
              data_manager.update(args.market, all_tickers, fields=fields))
    data = data_manager.load_raw(args.market).reindex_like(panels["close"])
    sessions = data.index[data[benchmark].notna()]
    data = data.loc[sessions]
    open_data = panels["open"].reindex_like(data) if use_open else None
    member = uh.pit_membership(args.market, data.index).reindex(columns=data.columns, fill_value=False)
    member[benchmark] = True
    tickers = sorted(t for t in member.columns if t != benchmark and member[t].any())
    if len(data) <= 315:
        raise ValueError("Need 315 pre-window trading sessions")
    requested = data.index[-1] - pd.DateOffset(years=args.years) if args.years else data.index[0]
    start = max(requested, data.index[315])
    days = data.index[data.index >= start]
    print(f"--- Scoring {days[0].date()} to {days[-1].date()}; prior history is retained ---")

    top_n = args.top_n if args.top_n else max(5, len(tickers) // 10)
    print(f"--- Selective strategies will pick top {top_n} stocks ---")
    if args.fixed_fee > 0:
        print(f"--- Fixed fee: ${args.fixed_fee:.2f} per trade ---")
    if use_open:
        print(f"--- Execution: open-close (signal on open, execute at close) ---")

    # Build strategy tuples: (strategy, close_data, open_data_or_None)
    open_tickers = open_data[tickers] if open_data is not None else None
    open_all = open_data if open_data is not None else None

    strategies = {
        # --- Original strategies ---
        "Buy & Hold":         (BuyAndHoldStrategy(asset=benchmark),                             data[[benchmark]], open_data[[benchmark]] if open_data is not None else None),
        "Momentum":           (MomentumStrategy(lookback=252, skip=21, top_n=top_n),           data[tickers], open_tickers),
        "Inverse Volatility": (InverseVolatilityStrategy(vol_window=20),                       data[tickers], open_tickers),
        "Multi-Factor":       (MultiFactorStrategy(tickers=tickers, benchmark=benchmark, top_n=top_n), data, open_all),
        # --- New strategies ---
        "Mean Reversion":     (MeanReversionStrategy(top_n=top_n),                                data[tickers], open_tickers),
        "Trend Following":    (TrendFollowingStrategy(ma_window=150, momentum_period=126, top_n=top_n), data[tickers], open_tickers),
        "Dual Momentum":      (DualMomentumStrategy(top_n=top_n),                               data[tickers], open_tickers),
        "Momentum+Quality":   (MomentumQualityStrategy(momentum_period=252, skip=21, top_n=top_n), data[tickers], open_tickers),
        "Mom+InvVol":         (AdaptiveMomentumStrategy(top_n=top_n),                          data[tickers], open_tickers),
        "Recovery+Mom Top20": (RecoveryMomentumStrategy(top_n=min(20, top_n)),                  data[tickers], open_tickers),
        "Recovery+Mom Top10": (RecoveryMomentumStrategy(top_n=10),                              data[tickers], open_tickers),
    }

    results: dict[str, pd.Series] = {}
    for name, (strategy, strat_data, strat_open) in strategies.items():
        print(f"\nRunning: {name}")
        targets = session_targets(strategy, strat_data, strat_open,
                                  eligible=member.reindex_like(strat_data))
        run = simulate_book(targets.loc[days], strat_data.loc[days], initial_capital,
                            previous_prices=strat_data.ffill().shift(1).loc[days],
                            volume=panels["volume"].reindex(index=days, columns=strat_data.columns),
                            member=member.reindex(index=days, columns=strat_data.columns, fill_value=True),
                            market=args.market, integer_shares=True,
                            lot_size=100 if args.market == "cn" else 1,
                            fixed_fee=args.fixed_fee, min_trade_value=50, resize_band=.10)
        anchor = pd.Series([initial_capital], index=[days[0] - pd.Timedelta(days=1)])
        results[name] = pd.concat([anchor, run["equity"]])

    bench = data[benchmark].reindex(days).ffill().dropna()
    results[benchmark_label] = pd.concat([anchor, bench / bench.iloc[0] * initial_capital])

    results_df = pd.DataFrame(results)

    # --- Performance metrics ---
    for name, equity in results_df.items():
        eq = equity.dropna()
        if len(eq) < 2:
            print(f"\n  {name}: insufficient data, skipping")
            continue
        metrics.summary(eq, name=name)

    if args.attribution:
        summary_df, loadings_df = factor_attribution.attribute_strategies(
            results_df=results_df,
            benchmark_label=benchmark_label,
            benchmark=benchmark,
            price_data=data,
            market=args.market,
            model_selection=args.attribution_model,
        )
        factor_attribution.print_attribution_summary(summary_df)
        if args.attribution_export:
            factor_attribution.export_attribution(summary_df, loadings_df, args.attribution_export)
            print(f"Attribution CSVs written to {args.attribution_export}")

    # --- Visualization ---
    if not args.no_plot:
        plot_results(results_df.dropna())


if __name__ == "__main__":
    main()