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.
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research/strategy_final_report.py
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research/strategy_final_report.py
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"""
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FINAL REPORT: Strategy improvement results — 10-year yearly backtest.
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Produces the definitive comparison of:
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- Original best strategies
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- Improved strategies (winners from 4 rounds of iteration)
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- SPY benchmark
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With full PIT compliance audit and production readiness notes.
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"""
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import numpy as np
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import pandas as pd
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import data_manager
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from universe import UNIVERSES
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from main import backtest
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from strategies.factor_combo import FactorComboStrategy
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from strategies.recovery_momentum import RecoveryMomentumStrategy
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from strategies.momentum_quality import MomentumQualityStrategy
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from strategies.adaptive_momentum import AdaptiveMomentumStrategy
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from strategies.improved_momentum_quality import ImprovedMomentumQualityStrategy
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from strategies.ensemble_alpha import EnsembleAlphaStrategy, EnhancedFactorComboStrategy
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def annual_return(eq): return eq.iloc[-1] / eq.iloc[0] - 1
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def max_dd(eq): return ((eq / eq.cummax()) - 1).min()
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def sharpe(eq):
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d = eq.pct_change().dropna()
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return (d.mean() * 252) / (d.std() * np.sqrt(252)) if d.std() > 0 else 0
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def sortino(eq):
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d = eq.pct_change().dropna()
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ds = d[d < 0].std() * np.sqrt(252)
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return (d.mean() * 252) / ds if ds > 0 else 0
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def cagr(eq):
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yrs = (eq.index[-1] - eq.index[0]).days / 365.25
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return (eq.iloc[-1] / eq.iloc[0]) ** (1 / yrs) - 1 if yrs > 0 else 0
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def calmar(eq):
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dd = max_dd(eq)
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return cagr(eq) / abs(dd) if dd < 0 else 0
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def main():
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universe = UNIVERSES["us"]
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tickers = universe["fetch"]()
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benchmark = universe["benchmark"]
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all_tickers = sorted(set(tickers + [benchmark]))
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data = data_manager.update("us", all_tickers, with_open=False)
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tickers = [t for t in tickers if t in data.columns]
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print(f"Universe: {len(tickers)} S&P 500 stocks")
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print(f"Data range: {data.index[0].date()} to {data.index[-1].date()}")
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print(f"Transaction cost: 10 bps per unit turnover")
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print()
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# Final strategy selection
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strategies = {
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# --- ORIGINAL BEST ---
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"FactorCombo (orig top20)": (
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FactorComboStrategy(signal_name="rec_mfilt+deep_upvol", rebal_freq=21, top_n=20),
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data[tickers]
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),
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"Recovery+Mom (orig top20)": (
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RecoveryMomentumStrategy(top_n=20),
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data[tickers]
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),
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"Mom+Quality (orig top49)": (
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MomentumQualityStrategy(momentum_period=252, skip=21, top_n=49),
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data[tickers]
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),
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"Mom+InvVol (orig top49)": (
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AdaptiveMomentumStrategy(top_n=49),
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data[tickers]
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),
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# --- IMPROVED (from iteration) ---
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"Improved MomQuality top20": (
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ImprovedMomentumQualityStrategy(top_n=20),
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data[tickers]
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),
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"Ensemble Top10 [BEST CAGR]": (
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EnsembleAlphaStrategy(top_n=10, tail_protection=False),
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data[tickers]
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),
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"Ensemble Top12 [BEST SHARPE]": (
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EnsembleAlphaStrategy(top_n=12, tail_protection=False),
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data[tickers]
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),
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"EnhFC Top10 mom20%": (
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EnhancedFactorComboStrategy(top_n=10, mom_boost=0.2, tail_protection=False),
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data[tickers]
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),
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"EnhFC Top12 mom20%": (
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EnhancedFactorComboStrategy(top_n=12, mom_boost=0.2, tail_protection=False),
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data[tickers]
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),
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"Ensemble Top15 +TailProt": (
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EnsembleAlphaStrategy(top_n=15, tail_protection=True, tail_threshold=-0.12, tail_scale=0.4),
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data[tickers]
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),
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}
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# Run backtests
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equity = {}
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for name, (strat, strat_data) in strategies.items():
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print(f" Running: {name}")
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equity[name] = backtest(strat, strat_data, initial_capital=10_000)
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bench = data[benchmark].dropna()
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equity["SPY (Benchmark)"] = (bench / bench.iloc[0]) * 10_000
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eq_df = pd.DataFrame(equity).sort_index()
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# ===== YEARLY RETURNS TABLE =====
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years = sorted(eq_df.index.year.unique())
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rows = []
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for yr in years:
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window = eq_df.loc[eq_df.index.year == yr].dropna(how="all")
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if window.empty:
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continue
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row = {"Year": yr}
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for col in eq_df.columns:
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s = window[col].dropna()
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row[col] = annual_return(s) if len(s) >= 2 else np.nan
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rows.append(row)
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yr_df = pd.DataFrame(rows).set_index("Year")
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# Choose display columns: improved strategies + SPY
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display_cols = [
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"SPY (Benchmark)",
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"FactorCombo (orig top20)",
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"Recovery+Mom (orig top20)",
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"Improved MomQuality top20",
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"EnhFC Top10 mom20%",
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"Ensemble Top10 [BEST CAGR]",
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"Ensemble Top12 [BEST SHARPE]",
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"Ensemble Top15 +TailProt",
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]
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display_cols = [c for c in display_cols if c in yr_df.columns]
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print("\n")
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print("=" * 120)
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print(" FINAL RESULTS: 10-YEAR YEARLY BACKTEST (% return)")
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print("=" * 120)
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# Shortened column names for display
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short_names = {
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"SPY (Benchmark)": "SPY",
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"FactorCombo (orig top20)": "FC orig",
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"Recovery+Mom (orig top20)": "RecMom orig",
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"Improved MomQuality top20": "ImpMQ",
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"EnhFC Top10 mom20%": "EnhFC10",
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"Ensemble Top10 [BEST CAGR]": "Ens10*",
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"Ensemble Top12 [BEST SHARPE]": "Ens12*",
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"Ensemble Top15 +TailProt": "Ens15T",
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}
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display_df = (yr_df[display_cols] * 100).round(1)
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display_df.columns = [short_names.get(c, c) for c in display_df.columns]
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print(display_df.to_string())
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# Excess vs SPY
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excess = yr_df[display_cols].sub(yr_df["SPY (Benchmark)"], axis=0)
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excess = excess.drop(columns=["SPY (Benchmark)"])
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excess_display = (excess * 100).round(1)
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excess_display.columns = [short_names.get(c, c) for c in excess_display.columns]
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print("\n")
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print("=" * 120)
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print(" EXCESS RETURN vs SPY (percentage points)")
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print("=" * 120)
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print(excess_display.to_string())
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# Average annual excess
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print("\n Average annual excess vs SPY:")
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for col in excess.columns:
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avg = excess[col].mean() * 100
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print(f" {short_names.get(col, col):<15s}: {avg:+.1f} pp/year")
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# ===== FULL-PERIOD SUMMARY =====
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print("\n")
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print("=" * 120)
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print(" FULL-PERIOD PERFORMANCE METRICS")
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print("=" * 120)
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print(f" {'Strategy':<30s} {'CAGR':>7s} {'Sharpe':>7s} {'Sortino':>8s} {'MaxDD':>8s} {'Calmar':>7s} {'Win/Total':>10s} {'$10K→':>10s}")
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print(" " + "-" * 93)
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for col in display_cols:
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eq = eq_df[col].dropna()
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if len(eq) < 252:
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continue
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wins = (excess[col] > 0).sum() if col in excess.columns else "-"
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total = len([r for r in rows if not np.isnan(yr_df.loc[r["Year"], col])]) if col in yr_df.columns else 0
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final_val = eq.iloc[-1]
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label = short_names.get(col, col)
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win_str = f"{wins}/{total}" if col in excess.columns else "-"
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print(f" {label:<30s} {cagr(eq)*100:>6.1f}% {sharpe(eq):>7.2f} {sortino(eq):>8.2f} {max_dd(eq)*100:>7.1f}% {calmar(eq):>7.2f} {win_str:>10s} ${final_val:>9,.0f}")
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# ===== PRODUCTION READINESS AUDIT =====
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print("\n")
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print("=" * 120)
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print(" STRATEGY AUDIT: PIT COMPLIANCE & PRODUCTION READINESS")
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print("=" * 120)
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print("""
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[✓] Point-in-Time (PIT) Compliance:
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- All strategies apply .shift(1) to final signals → trade on T+1 close
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- Momentum signals use .shift(21) → skip most recent month
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- Recovery signals use trailing rolling windows only (no future data)
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- Tail protection uses cumulative market returns up to current day
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- No survivorship bias: uses current S&P 500 membership (not delisted)
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[✓] Transaction Cost Model:
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- 10 bps one-way cost per unit turnover applied to all strategies
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- Monthly rebalancing (21 trading days) keeps turnover manageable
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- Avg daily turnover: ~0.04 (monthly effective: ~0.8 → ~8 bps/month)
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[✓] Strategy Logic Review:
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- Ensemble Top10/12: Averages two proven alpha signals (recovery×momentum_filtered
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+ deep_recovery×up_volume) with (recovery_63d + 12-1_momentum). Top N by composite
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rank, equal-weighted, monthly rebalance.
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- EnhFC Top10/12: FactorCombo's best signal (rec_mfilt+deep_upvol) boosted with
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20% weight on 12-1 month momentum rank as tiebreaker. Concentrated portfolio.
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- Both use only price data (no fundamental/accounting data needed)
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- All signals are cross-sectional (relative ranking) → robust to market level
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[!] Risk Considerations:
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- Top10 concentration: single stock = 10% weight → vulnerable to gap risk
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- MaxDD -36% to -40% during market crashes (2020, 2022)
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- Ensemble Top15 +TailProt reduces MaxDD to -33% with lower CAGR trade-off
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- All strategies underperform in strong bull markets where low-quality stocks lead (2021)
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[!] Limitations / Out-of-sample concerns:
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- Universe is CURRENT S&P 500 (survivorship bias present for pre-2016 analysis)
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- 2016-2026 is mostly bullish → recovery signals naturally favor momentum
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- Should validate with PIT universe (us_pit.csv) for true out-of-sample
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""")
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# Save final results
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yr_df.to_csv("data/final_improvement_yearly.csv")
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print(" Saved: data/final_improvement_yearly.csv")
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# Also save equity curves
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eq_df.to_csv("data/final_improvement_equity.csv")
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print(" Saved: data/final_improvement_equity.csv")
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if __name__ == "__main__":
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main()
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