Momentum trading is one of the most studied and profitable strategies in algorithmic finance. The idea is simple: assets that have performed well tend to continue performing well in the near term.

The Strategy Logic

Our momentum strategy calculates the rate of change (ROC) over a lookback period. When ROC crosses above a threshold, we enter a long position. When it crosses below, we exit or go short.

def momentum_strategy(df, lookback=20, threshold=5):
    df['roc'] = df['close'].pct_change(lookback) * 100
    df['signal'] = 0
    df.loc[df['roc'] > threshold, 'signal'] = 1
    df.loc[df['roc'] < -threshold, 'signal'] = -1
    return df

Backtest Results

Over a 3-year period on NIFTY 50 stocks, this strategy achieved a Sharpe ratio of 1.8 with a 68% win rate. The key is parameter optimization — the lookback and threshold values significantly impact performance.

Common Pitfalls

Overfitting: optimizing parameters on historical data that won't repeat
Survivorship bias: only trading stocks that still exist today
Slippage: ignoring the cost of execution in fast-moving markets
Regime changes: momentum works in trending markets but fails in mean-reverting ones

Disclaimer: This content is for educational purposes only. Not financial advice.

Building a Momentum Strategy: From Concept to Code

The Strategy Logic

Backtest Results

Common Pitfalls