Core Concepts
Understanding api response caching requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Testing Approach
Testing api response caching includes unit tests for signal logic, integration tests for exchange interaction, and backtests against historical data with realistic slippage modeling.
Risk Considerations
Risk management for api response caching addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.
Performance Metrics
Key metrics include Sharpe ratio, maximum drawdown, win rate, profit factor, and average trade duration. Compare against baseline strategies and adjust parameters based on out-of-sample results.
| Metric | Value | Benchmark |
|---|---|---|
| Sharpe Ratio | 1.85 | 1.20 |
| Max Drawdown | -12.4% | -18.0% |
| Win Rate | 58.2% | 52.0% |
| Profit Factor | 1.72 | 1.45 |
Common Issues
Common issues with api response caching include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.