Develop trading systems with MATLAB
Algorithmic trading uses algorithms to drive trading decisions, usually in electronic financial markets. Applied in buy-side and sell-side institutions, algorithmic trading forms the basis of high-frequency trading, FOREX trading, and associated risk and execution analytics.
Builders and users of algorithmic trading applications need to develop, backtest, and deploy mathematical models that detect and exploit market movements. An effective workflow involves:
- Developing trading strategies, using technical time-series, machine learning, and nonlinear time-series methods
- Applying parallel and GPU computing for time-efficient backtesting and parameter identification
- Calculating profit and loss and conducting risk analysis
- Performing execution analytics, such as market impact modeling and iceberg detection
- Incorporating strategies and analytics into production trading environments
For detail, see MATLAB and Trading Toolbox.
Examples and How To
See also: Financial Toolbox, Econometrics Toolbox, Parallel Computing Toolbox, Global Optimization Toolbox, Neural Network Toolbox, cointegration, commodities trading