Gahow Wang
541f7bcf5b
Add 28 research scripts covering DCA simulation, momentum evaluation, Sharpe optimization, trend rider analysis, and US fundamentals exploration.
251 lines
9.3 KiB
Python
251 lines
9.3 KiB
Python
"""
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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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