Risk Management in Algorithmic Trading Strategies

Risk management is not a safety net — it is the strategy. Professional systems treat risk as a first-order input, not a post-processing step.

The Three Risk Pillars

1. Sizing — how much you bet
2. Stops — when you exit
3. Portfolio constraints — how exposures interact

Position Sizing: Practical Defaults

def fractional_kelly(win_prob: float, payoff: float, fraction: float = 0.25) -> float:
    return max(0.0, fraction * (win_prob - (1 - win_prob) / payoff))

If you do not have stable edge estimates, a fixed 1–2% risk cap is often better than “optimal” sizing.

Portfolio Limits That Actually Work

• Correlation caps: avoid stacking multiple trades on the same factor
• Sector ceilings: prevent hidden concentration
• Daily loss limits: force cooling-off periods during volatility spikes

Operational Risk

Markets do not care about your uptime. Build system guardrails:

• Redundant data feeds
• Failover execution paths
• Alerting on stale prices or bad fills

Conclusion

Risk is the compounding engine. When it is engineered with the same care as alpha, your strategy lasts long enough to matter.

For a practical framework, see how Provectus Quantus structures portfolio controls and execution safety.

References

• Expected Shortfall and VaR — Basel Committee
• Risk and Asset Allocation — Wiley
• CBOE VIX Methodology — CBOE