Overview
This guide covers support resistance bot 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 |
Production Notes
In production, support resistance bot requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.
Configuration Example
Configuration for support resistance bot uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# Example configuration for Support Resistance Bot
strategy:
name: support-resistance-bot
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 support resistance bot addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.