"""
DCA simulation: $10,000 initial + $5,000 every Feb & Aug from 2017.
Uses SharpeBoostedEnsembleStrategy daily returns.
"""
from __future__ import annotations
import os, sys
import numpy as np
import pandas as pd

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from strategies.ensemble_alpha import SharpeBoostedEnsembleStrategy
import data_manager
from universe import get_sp500


def main():
    # Load data and generate daily returns
    tickers = get_sp500()
    data_manager.update("us", tickers)
    data = data_manager.load("us")

    strat = SharpeBoostedEnsembleStrategy()
    weights = strat.generate_signals(data)
    daily_rets = (weights * data.pct_change().fillna(0.0)).sum(axis=1)

    # Also compute SPY buy-and-hold for comparison
    spy_rets = data["SPY"].pct_change().fillna(0.0)

    # Trim to evaluation period
    start = "2016-04-01"
    end = "2026-05-13"
    daily_rets = daily_rets.loc[start:end]
    spy_rets = spy_rets.loc[start:end]

    # --- DCA simulation ---
    # Initial: $10,000 at start
    # Contributions: $5,000 on first trading day of Feb and Aug, starting 2017

    # Find contribution dates (first trading day of each Feb and Aug from 2017)
    contrib_dates = []
    for year in range(2017, 2027):
        for month in [2, 8]:
            target = pd.Timestamp(f"{year}-{month:02d}-01")
            # Find first trading day on or after target
            mask = daily_rets.index >= target
            if mask.any():
                contrib_dates.append(daily_rets.index[mask][0])

    # Filter to only dates within our data range
    contrib_dates = [d for d in contrib_dates if d <= daily_rets.index[-1]]

    print("=" * 70)
    print("DCA SIMULATION: SharpeBoostedEnsembleStrategy")
    print("=" * 70)
    print(f"Initial investment: $10,000 on {daily_rets.index[0].strftime('%Y-%m-%d')}")
    print(f"Contributions: $5,000 on first trading day of Feb & Aug (from 2017)")
    print(f"End date: {daily_rets.index[-1].strftime('%Y-%m-%d')}")
    print(f"Total contribution dates: {len(contrib_dates)}")
    print()

    # Simulate for both strategy and SPY
    for label, rets in [("Strategy", daily_rets), ("SPY (Buy & Hold)", spy_rets)]:
        portfolio_value = 10000.0
        total_contributed = 10000.0
        contrib_idx = 0

        # Track milestones
        yearly_values = {}

        for i, date in enumerate(rets.index):
            # Apply daily return
            portfolio_value *= (1 + rets.iloc[i])

            # Check if today is a contribution date
            if contrib_idx < len(contrib_dates) and date >= contrib_dates[contrib_idx]:
                portfolio_value += 5000.0
                total_contributed += 5000.0
                contrib_idx += 1

            # Record year-end values
            if i == len(rets.index) - 1 or rets.index[i].year != rets.index[i + 1].year if i < len(rets.index) - 1 else True:
                yearly_values[date.year] = portfolio_value

        profit = portfolio_value - total_contributed
        roi = profit / total_contributed * 100

        print(f"--- {label} ---")
        print(f"  Total contributed: ${total_contributed:,.0f}")
        print(f"  Final portfolio:   ${portfolio_value:,.0f}")
        print(f"  Total profit:      ${profit:,.0f}")
        print(f"  ROI on contributions: {roi:.1f}%")
        print(f"  Multiple on capital:  {portfolio_value/total_contributed:.2f}x")
        print()

        # Year-end snapshots
        print(f"  Year-end portfolio values:")
        for year, val in sorted(yearly_values.items()):
            # How much contributed by that year
            contribs_by_year = 10000 + 5000 * len([d for d in contrib_dates if d.year <= year])
            print(f"    {year}: ${val:>12,.0f}  (contributed: ${contribs_by_year:>8,.0f}, "
                  f"gain: ${val - contribs_by_year:>+10,.0f})")
        print()

    # --- Monthly detail of contributions ---
    print("--- Contribution schedule ---")
    for i, d in enumerate(contrib_dates):
        print(f"  {i+1:2d}. {d.strftime('%Y-%m-%d')} (${5000:,})")
    print(f"  Total contributions (excl. initial): ${5000 * len(contrib_dates):,}")
    print(f"  Total capital deployed: ${10000 + 5000 * len(contrib_dates):,}")


if __name__ == "__main__":
    main()