Uptime Monitoring

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.

Risk Considerations

Risk management for uptime monitoring addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.

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 Uptime Monitoring
strategy:
  name: uptime-monitoring
  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

Configuration Example

Configuration for uptime monitoring uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.

# Example configuration for Uptime Monitoring
strategy:
  name: uptime-monitoring
  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

Production Notes

In production, uptime monitoring requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.