develop automated trading systems with matlab
automated trading is a trading strategy that uses computers to automatically drive trading decisions, usually in electronic financial markets. applied in buy-side and sell-side institutions, automated trading forms the basis of high-frequency trading, for example in equities trading, forex trading, or commodities trading.
builders and users of automated 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 pretrade and posttrade analytics, including market impact modeling and execution analysis
- incorporating strategies and analytics into production trading environments, such as bloomberg® emsx
for more information, see matlab® and datafeed toolbox™.
examples and how to
software reference
see also: financial toolbox™, econometrics toolbox™, parallel computing toolbox™, deep learning toolbox™, transaction cost analysis, cointegration, momentum trading, swing trading, ai in finance