Momentum Trading Strategies for Crypto Algo Traders

Momentum is one of the most persistent anomalies in financial markets. Assets that have been going up tend to continue going up in the short term. This forms the basis of many successful algorithmic trading python systems.

Time-Series Momentum (TSMOM)

TSMOM looks at an asset's own past returns. If BTC's 30-day return is positive, go long. If negative, go short. Simple but surprisingly effective when applied to a diversified set of crypto assets.

def tsmom_signal(prices, lookback=30):\n    past_return = prices[-1] / prices[-lookback] - 1\n    return 1 if past_return > 0 else -1

Cross-Sectional Momentum

Instead of looking at one asset in isolation, rank all assets by their past returns and go long the top quartile and short the bottom quartile. This is a market-neutral algo trading strategy that hedges against broad market moves.

Practical Considerations

Momentum strategies are sensitive to transaction costs. In crypto algo trading, use limit orders to keep costs low, and avoid assets with wide spreads. Rebalance daily or weekly rather than intraday to minimize friction.