research: add strategy evaluation and exploration scripts

Add 28 research scripts covering DCA simulation, momentum evaluation,
Sharpe optimization, trend rider analysis, and US fundamentals exploration.

research/dca_simulation.py

@@ -0,0 +1,114 @@
+"""
+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()