Momentum Strategies
Momentum strategies
Economic intuition
Crypto markets exhibit momentum due to:
- Narrative-driven capital flows: Retail FOMO creates self-reinforcing buying pressure
- Trend-following systematic funds: Algo-traders and quant funds add fuel to trends
- Liquidation cascades: Forced selling/buying creates trend persistence
- Positive feedback: Price increases → media coverage → more buyers → price increases
Signal calculation
\[Momentum_{t} = \frac{\text{EMA}_{fast}(t) - \text{EMA}_{slow}(t)}{\sigma_t}\]Where:
- \(EMA_{fast}(t)\)is fast exponential moving average (e.g., 5-day)
- \(EMA_{slow}(t)\)is slow exponential moving average (e.g., 60-day)
- \(\sigma_t\) is realized volatility at time t
Momentum variations
We will support two distinct variations of this signal to capture different timeframe effects:
Fast momentum (5-day vs 30-day):
- Purpose: Captures short-term trend continuation (several weeks moves)
- Best for: High-volatility tokens with strong directional moves (HYPE, small caps)
- Turnover: ~40 trades/year
- Risk: Whipsaws in choppy markets, high turnover costs
Slow momentum (10-day vs 60-day):
- Purpose: Core momentum signal for portfolio (monthly trends)
- Best for: BTC, ETH, large-cap tokens
- Turnover: ~20 trades/year
- Risk: Moderate - catches most major trends without excessive churn
Volatility calculation
\[\sigma_t = \sqrt{\text{EMA}_{20}\left[(P_t - P_{t-1})^2\right]}\]This exponentially-weighted volatility gives more weight to recent market conditions. Why 20 days? It balances responsiveness with stability. We use this volatility calculation throughout out entire framework.