Gahow Wang
541f7bcf5b
Add 28 research scripts covering DCA simulation, momentum evaluation, Sharpe optimization, trend rider analysis, and US fundamentals exploration.
115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
"""
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DCA simulation: $10,000 initial + $5,000 every Feb & Aug from 2017.
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Uses SharpeBoostedEnsembleStrategy daily returns.
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"""
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from __future__ import annotations
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import os, sys
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import numpy as np
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import pandas as pd
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from strategies.ensemble_alpha import SharpeBoostedEnsembleStrategy
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import data_manager
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from universe import get_sp500
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def main():
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# Load data and generate daily returns
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tickers = get_sp500()
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data_manager.update("us", tickers)
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data = data_manager.load("us")
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strat = SharpeBoostedEnsembleStrategy()
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weights = strat.generate_signals(data)
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daily_rets = (weights * data.pct_change().fillna(0.0)).sum(axis=1)
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# Also compute SPY buy-and-hold for comparison
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spy_rets = data["SPY"].pct_change().fillna(0.0)
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# Trim to evaluation period
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start = "2016-04-01"
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end = "2026-05-13"
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daily_rets = daily_rets.loc[start:end]
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spy_rets = spy_rets.loc[start:end]
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# --- DCA simulation ---
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# Initial: $10,000 at start
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# Contributions: $5,000 on first trading day of Feb and Aug, starting 2017
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# Find contribution dates (first trading day of each Feb and Aug from 2017)
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contrib_dates = []
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for year in range(2017, 2027):
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for month in [2, 8]:
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target = pd.Timestamp(f"{year}-{month:02d}-01")
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# Find first trading day on or after target
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mask = daily_rets.index >= target
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if mask.any():
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contrib_dates.append(daily_rets.index[mask][0])
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# Filter to only dates within our data range
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contrib_dates = [d for d in contrib_dates if d <= daily_rets.index[-1]]
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print("=" * 70)
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print("DCA SIMULATION: SharpeBoostedEnsembleStrategy")
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print("=" * 70)
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print(f"Initial investment: $10,000 on {daily_rets.index[0].strftime('%Y-%m-%d')}")
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print(f"Contributions: $5,000 on first trading day of Feb & Aug (from 2017)")
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print(f"End date: {daily_rets.index[-1].strftime('%Y-%m-%d')}")
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print(f"Total contribution dates: {len(contrib_dates)}")
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print()
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# Simulate for both strategy and SPY
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for label, rets in [("Strategy", daily_rets), ("SPY (Buy & Hold)", spy_rets)]:
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portfolio_value = 10000.0
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total_contributed = 10000.0
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contrib_idx = 0
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# Track milestones
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yearly_values = {}
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for i, date in enumerate(rets.index):
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# Apply daily return
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portfolio_value *= (1 + rets.iloc[i])
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# Check if today is a contribution date
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if contrib_idx < len(contrib_dates) and date >= contrib_dates[contrib_idx]:
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portfolio_value += 5000.0
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total_contributed += 5000.0
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contrib_idx += 1
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# Record year-end values
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if i == len(rets.index) - 1 or rets.index[i].year != rets.index[i + 1].year if i < len(rets.index) - 1 else True:
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yearly_values[date.year] = portfolio_value
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profit = portfolio_value - total_contributed
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roi = profit / total_contributed * 100
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print(f"--- {label} ---")
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print(f" Total contributed: ${total_contributed:,.0f}")
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print(f" Final portfolio: ${portfolio_value:,.0f}")
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print(f" Total profit: ${profit:,.0f}")
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print(f" ROI on contributions: {roi:.1f}%")
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print(f" Multiple on capital: {portfolio_value/total_contributed:.2f}x")
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print()
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# Year-end snapshots
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print(f" Year-end portfolio values:")
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for year, val in sorted(yearly_values.items()):
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# How much contributed by that year
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contribs_by_year = 10000 + 5000 * len([d for d in contrib_dates if d.year <= year])
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print(f" {year}: ${val:>12,.0f} (contributed: ${contribs_by_year:>8,.0f}, "
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f"gain: ${val - contribs_by_year:>+10,.0f})")
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print()
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# --- Monthly detail of contributions ---
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print("--- Contribution schedule ---")
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for i, d in enumerate(contrib_dates):
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print(f" {i+1:2d}. {d.strftime('%Y-%m-%d')} (${5000:,})")
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print(f" Total contributions (excl. initial): ${5000 * len(contrib_dates):,}")
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print(f" Total capital deployed: ${10000 + 5000 * len(contrib_dates):,}")
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if __name__ == "__main__":
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main()
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