What Is Algorithmic Trading?

Algorithmic trading is a cutting-edge trading strategy that leverages computational algorithms to make trading decisions in electronic financial markets. This approach is widely used in both buy-side and sell-side institutions and serves as the foundation for high-frequency trading, FOREX trading, and associated risk and execution analytics.

Key Components of Algorithmic Trading

Strategy Development: Utilize technical time-series, machine learning, and nonlinear time-series methods to create robust trading strategies.
Backtesting: Employ parallel and GPU computing to backtest strategies efficiently, identifying optimal parameters for algorithmic trading.
Risk Analysis: Calculate profit and loss metrics while conducting comprehensive risk assessments.
Execution Analytics: Perform market impact modeling through transaction cost analysis to optimize trade execution.
Production Deployment: Seamlessly integrate trading strategies and analytics into production trading environments.

For a deeper dive into algorithmic trading, see MATLAB^® and Datafeed Toolbox™.

Examples and How To

Software Reference

Datafeed Toolbox Functions - Documentation
Classification Learner App - Statistics and Machine Learning Toolbox App
movavg: Leading and lagging moving averages chart - Financial Toolbox Function
sharpe: Compute Sharpe ratio - Financial Toolbox Function
gaoptimset: Create genetic algorithm options structure - Global Optimization Toolbox Function
Cointegration Testing - Econometrics Toolbox Functions
Neural Network Time Series Tool - Deep Learning Toolbox Documentation

Machine Learning for Algorithmic Trading

Machine Learning and Big Data in Quantitative Investing

Read white paper