Algorithmic Trading
CalPERS
If your trading model gives you an edge of even just 2% over 50/50, that is enough to make a substantial profit if you trade frequently enough. Using MathWorks tools, we developed and backtested some strategies that provide that statistical edge.
- Financial Services Overview
- Investment Management
- Risk Management
- Insurance and Actuarial Science
- Econometrics and Economics
- Algorithmic Trading
- Pricing and Valuation
Algorithmic trading represents a significant portion of trades in many major markets, including more than a third of EU and US equity trades. Algorithmic traders worldwide use MATLAB to develop, backtest, and deploy mathematical models that detect and exploit market movements.
Develop and Test Trading Strategies
Hedge funds, proprietary trading desks, brokerages, and exchanges use MATLAB to:
- Create trading rules, including technical, nonlinear time-series, or arbitrage
- Estimate parameters
- Import data from Thomson Reuters, specialist data providers, and databases
- Perform out-of-sample testing
By leveraging their multicore computers, servers, and clusters trading teams can backtest with more data and accelerate data analysis to quickly implement strategies in the market.
Deploy Strategies to the Market
With MATLAB and related deployment products, researchers gain flexibility in how and when trades are implemented by exporting trading parameters or deploying their strategies as C++, .NET, or Java callable components to integrate with in-house or third-party trading systems.
Free Financial Services Interactive Kit
Experience the Power of MATLAB for financial services.
Get free kit