Core Concepts
Understanding coppock curve requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Implementation
Implementation of coppock curve follows a modular architecture. Configuration files define parameters, strategy modules handle logic, and exchange adapters manage execution.
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 coppock curve uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# Example configuration for Coppock Curve
strategy:
name: coppock-curve
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
Risk Considerations
Risk management for coppock curve addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.