Configuration Example
Configuration for machine learning integration uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# Example configuration for Machine Learning Integration
strategy:
name: machine-learning-integration
timeframe: 5m
indicators:
- rsi
- ema_fast
- ema_slow
entry_conditions:
- rsi_oversold
- ema_crossover_bullish
exit_conditions:
- rsi_overbought
- stop_loss_hit
risk:
max_position_size: 0.02
stop_loss_pct: 0.015
Common Issues
Common issues with machine learning integration include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.
Core Concepts
Understanding machine learning integration requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Implementation
Implementation of machine learning integration follows a modular architecture. Configuration files define parameters, strategy modules handle logic, and exchange adapters manage execution.
Risk Considerations
Risk management for machine learning integration addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.