Overview
This guide covers slippage modeling implementation in automated trading systems. The approach combines established patterns with operational considerations drawn from production deployments.
Core Concepts
Understanding slippage modeling requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Production Notes
In production, slippage modeling requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.
Common Issues
Common issues with slippage modeling include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.
Code Structure
The code structure separates concerns: data ingestion, signal generation, risk checks, and order submission. This modularity enables testing and reuse across strategies.
# Example configuration for Slippage Modeling
strategy:
name: slippage-modeling
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