Monitoring with Prometheus

Testing Approach

Testing monitoring with prometheus includes unit tests for signal logic, integration tests for exchange interaction, and backtests against historical data with realistic slippage modeling.

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 monitoring with prometheus addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.

Core Concepts

Understanding monitoring with prometheus requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.

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 Monitoring with Prometheus
strategy:
  name: monitoring-with-prometheus
  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