Risk Management

The risk management framework implemented within the algorithmic trading algorithm employs a hierarchical approach to systematically quantify, monitor, and mitigate potential risk factors across multiple dimensions of trading operations.

The framework implements a five-tier risk management model that provides multiple layers of protection against adverse market conditions, operational inefficiencies, and systemic vulnerabilities.

4.1 Confidence-Based Position Sizing

Position allocation is dynamically calibrated according to the following deterministic function:

The position sizing algorithm systematically scales exposure based on:

• Prediction confidence intervals

• Spread between predicted quantiles

• Consistency of signals across the ensemble

4.2 Net Exposure Constraints

A predefined upper-bound constraint limits total market exposure as a percentage of total capital to decrease liquidation risk.

4.3 Dynamic Position Exposure Management

The system implements several mechanisms to control position exposure:

• Positions are automatically liquidated after a 3-hour holding period in the absence of confirming signals from the underlying predictive models, thereby preventing unwarranted exposure to mean-reverting price action.

• Position sizes and leverage are reduced in the presence of new opposite signals, diminishing risk during periods of elevated market turbulence when model prediction accuracy typically deteriorates.

4.4 Dynamic Trailing Stop-Loss Mechanism

A stop-loss framework is implemented with:

• Trailing stop-loss thresholds dynamically recalibrated based on recent price and maximum position value.

• Automatic position reduction or liquidation triggered by predefined loss thresholds.

4.5 Traffic Light Risk Monitoring System

The algorithmic trading framework implements a sophisticated weighted-scoring methodology that quantifies system health through a continuous numerical scale. This approach transforms categorical risk assessments across multiple operational dimensions into a unified risk metric, enabling objective decision-making and standardized risk control protocols. Each monitored metric is assessed against predefined threshold values and assigned a discrete score corresponding to its risk state:

• 0 points for metrics in the red zone (indicating critical risk conditions)

• 1 point for metrics in the yellow zone (indicating cautionary conditions)

• 2 points for metrics in the green zone (indicating optimal operating conditions)

This tripartite classification system provides clear demarcation between risk states while maintaining computational simplicity.

The scoring framework employs differential weighting to reflect the relative importance of various metrics within the risk management hierarchy. Critical metrics are allocated a 70% weight in the aggregate scoring calculation, reflecting their fundamental importance to system stability and capital preservation. Secondary metrics receive a 30% weight allocation, recognizing their contributory but less critical role in overall system health assessment. This weighted average approach produces a final score ranging from 0.0 (worst case, all metrics in red state) to 2.0 (optimal case, all metrics in green state. The thresholds for overall system state determination are:

• <1.2 for red state:

  • Immediate action required

  • Consider reducing positions

  • Potential strategy pause

• 1.2-1.6 for yellow state:

  • Enhanced monitoring

  • Review yellow and red metrics

  • Prepare contingency plans

• >1.6 for green state:

  • Continue normal operations

  • Regular monitoring

  • Focus on optimization

The resulting continuous-scale scoring system provides quantitative rigor to risk assessment while maintaining interpretational clarity for operational decision-making.

4.5.1 Critical Metrics

The critical metrics include:

• Portfolio Sharpe Ratio

• Margin Utilization Ratio

• Value at Risk (VaR)

4.5.2 Important Metrics

The important metrics include:

• Execution Quality

• Trade Frequency

• Win Rate