Expectancy Calculation

Overview

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

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 Expectancy Calculation
strategy:
  name: expectancy-calculation
  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, expectancy calculation requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.

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.

Testing Approach

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