Testing Approach
Testing schaff trend cycle includes unit tests for signal logic, integration tests for exchange interaction, and backtests against historical data with realistic slippage modeling.
Overview
This guide covers schaff trend cycle implementation in automated trading systems. The approach combines established patterns with operational considerations drawn from production deployments.
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 schaff trend cycle uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# Example configuration for Schaff Trend Cycle
strategy:
name: schaff-trend-cycle
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 schaff trend cycle include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.