Complex Event Processing

Overview

This guide covers complex event processing implementation in automated trading systems. The approach combines established patterns with operational considerations drawn from production deployments.

Common Issues

Common issues with complex event processing include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.

Configuration Example

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

# Example configuration for Complex Event Processing
strategy:
  name: complex-event-processing
  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

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 Complex Event Processing
strategy:
  name: complex-event-processing
  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

Core Concepts

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