Statistical Arbitrage

Code Structure Overview Configuration Example Testing Approach Core Concepts

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 Statistical Arbitrage
strategy:
  name: statistical-arbitrage
  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

Overview

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

Configuration Example

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

# Example configuration for Statistical Arbitrage
strategy:
  name: statistical-arbitrage
  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

Testing Approach

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

Core Concepts

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