Algorithmic Risk Management

Algorithmic risk management is the set of controls, metrics, and processes used to keep automated trading within defined limits across market, model, execution, liquidity, and operational risk. Because algorithms can scale positions quickly, small errors can produce large losses. A robust risk framework limits damage and provides a clear response when conditions change.

Risk Categories

• Market risk from price moves, volatility spikes, and gaps.
• Model risk from incorrect assumptions, stale signals, or bad data.
• Execution risk from slippage, partial fills, and latency.
• Liquidity risk from thin markets and large market impact.
• Operational risk from outages, misconfiguration, and human error.
• Compliance risk from violating venue rules or internal policies.

Pre-Trade Controls

Pre-trade checks block unsafe orders before they hit the market. Common checks include price band limits, max order size, max notional exposure, and fat finger protections. Some systems also validate order types, verify instrument status, and enforce time-in-force rules.

Position and Portfolio Limits

Risk limits should exist at multiple levels. Typical limits include per-symbol exposure, sector concentration, gross and net exposure caps, leverage limits, and maximum drawdown thresholds. Limits can be static or adjusted based on volatility and liquidity conditions.

Real-Time Monitoring and Kill Switches

Live monitoring tracks positions, PnL, and risk metrics in real time. Alerting should be automatic and actionable. Kill switches and throttles provide a fast way to cut exposure when markets move sharply or when unexpected behavior is detected.

Stress Testing and Scenario Analysis

Stress tests examine how the strategy behaves during extreme conditions. Scenarios can include historical shocks, volatility spikes, liquidity droughts, and price gaps. The goal is to expose tail risks that are not visible in normal backtests.

Post-Trade Review

Post-trade analysis verifies that controls worked as intended. It examines limit breaches, execution quality, and model drift. Findings should feed back into parameter updates and operational procedures.

Conclusion

Effective algorithmic risk management combines strong limits, real-time monitoring, and disciplined review. It protects capital and makes strategy performance more stable across market regimes.