Top 5 Momentum Trading Strategies Backtested Over 10 Years (With Data)

1. The Empirical Basis for Price Momentum

Why assets that outperformed in the past 3 to 12 months continue to outperform

First formally documented by Jegadeesh and Titman in 1993, the momentum effect has survived dozens of out-of-sample replications across global asset classes. Behavioral finance explains momentum through investor underreaction to earnings surprises followed by herd-driven overreaction as media coverage peaks.

However, naive momentum strategies suffer from violent momentum crashes during sharp market turning points. In this study, we backtested five distinct momentum variations across a 10-year span (2014-2024) including zero-rate environments, inflation shocks, and pandemic selloffs.

2. 10-Year Backtest Comparison Matrix

Performance across 500 liquid equities, normalized for 10 bps slippage per side

The table below contrasts the 10-year annualized return, maximum peak-to-trough drawdown, Sharpe ratio, and overall win rate across all five systematic momentum models.

3. Deconstructing the Winning Strategy: Dual-Momentum

How Gary Antonacci's framework eliminates false breakouts

The highest risk-adjusted performer across the decade was Dual-Momentum. The architecture combines two distinct filters:

1. Relative Momentum: Rank the investment universe by 6-month and 12-month total return. Allocate capital to the top 10% percentile assets exhibiting strongest price velocity.

2. Absolute Momentum (Trend Filter): Verify that the selected assets are trading above their 200-day simple moving average AND that 12-month return exceeds the 3-month Treasury yield. If false, move to cash or short-term Treasuries.

# Dual-Momentum Alpha Filter
def evaluate_dual_momentum(asset_price_history, benchmark_history, cash_yield=0.04):
    # 1. Absolute Momentum: Must beat 12-month cash yield
    annual_return = (asset_price_history.iloc[-1] / asset_price_history.iloc[-252]) - 1
    if annual_return <= cash_yield:
        return "CASH"  # Capital preservation mode
        
    # 2. Trend Filter: Above 200 SMA
    sma200 = asset_price_history.rolling(200).mean().iloc[-1]
    if asset_price_history.iloc[-1] < sma200:
        return "CASH"
        
    # 3. Relative Momentum: Score against benchmark
    relative_strength = annual_return - ((benchmark_history.iloc[-1] / benchmark_history.iloc[-252]) - 1)
    return {
        "status": "INVESTED",
        "annual_return": annual_return,
        "relative_strength": relative_strength
    }