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 |
Configuration Example
Configuration for slippage modeling uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# 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
Core Concepts
Understanding slippage modeling requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Implementation
Implementation of slippage modeling follows a modular architecture. Configuration files define parameters, strategy modules handle logic, and exchange adapters manage execution.
Testing Approach
Testing slippage modeling includes unit tests for signal logic, integration tests for exchange interaction, and backtests against historical data with realistic slippage modeling.