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Volatility-Scaled Momentum
Keep the trend signal, but scale exposure down when realized volatility expands.
Strategy briefing
Understand what this strategy is actually betting on before you touch the parameter panel.
Use category and difficulty as context
Compare before optimizing
Map the strategy to a regime thesis
Read the math as a constraint system
Use parameters to test fragility, not creativity
Learning linkup
Open Learning Hub
Read the model brief like a skeptic
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Strategy families & selection
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Validation & skepticism
Pro Access
Volatility-Scaled Momentum is part of the expanded Pro strategy library. The guide stays public, but running it in backtest, compare, paper tracking, and source-code view is a Pro capability. Pro currently unlocks 8 additional pre-built strategies.
The Intuition
Volatility-scaled momentum keeps the core momentum idea but changes the position sizing logic. The direction still comes from medium-horizon trend persistence. What changes is exposure: when realized volatility rises, the strategy automatically cuts size; when volatility falls, it allows larger exposure up to a cap.
This matters because momentum crashes often arrive after volatility spikes. A raw trend rule can stay directionally correct on average but still suffer large drawdowns because it carries too much size into turbulent periods. Volatility scaling tries to make the same signal more survivable.
The approach is common in institutional systematic trading because it separates alpha from risk budgeting. Momentum decides the sign. Realized volatility decides how much balance-sheet usage the position deserves. That usually produces a smoother path than fixed one-unit exposure.
The cost is that sizing can cut exposure just before a strong move resumes, especially after sharp but temporary volatility spikes. Volatility targeting improves robustness more reliably than raw return, not necessarily headline return maximization in every sample.
The Math
Read this as a compact model summary: what the signal sees, what it ignores, and where fragility can creep in.
Momentum(t) = Close(t) / Close(t-h) - 1
sigma_real(t) = std(log returns[t-v : t]) × sqrt(252)
scale(t) = min(target_vol / sigma_real(t), max_leverage)
Position(t) = +scale(t) if Momentum(t) > 0
= -scale(t) if Momentum(t) < 0
= 0 otherwise
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| lookback | int | 126 | Momentum lookback window in trading days |
| vol_window | int | 20 | Realized-volatility estimation window |
| target_vol | float | 0.15 | Annualized volatility target used for position scaling |
| max_leverage | float | 1.5 | Maximum absolute exposure allowed after scaling |
Source Code
Source access for this built-in strategy is included with Pro.
Further Reading
- Barroso, P. & Santa-Clara, P. (2015). Momentum Has Its Moments. Journal of Financial Economics, 116(1), 111-120.
- Moskowitz, T., Ooi, Y. & Pedersen, L. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
- Moreira, A. & Muir, T. (2017). Volatility-Managed Portfolios. Journal of Finance, 72(4), 1611-1644.
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