Proprietary Trading

Discover new proprietary trading strategies with MATLAB

Proprietary trading is the trading activity of financial institutions or independent investment firms that use their own capital to seek profit instead of trading using clients’ funds. Financial regulators require that proprietary trading activities are separated from client-related trading activities and information.

To generate profitability for the firm, proprietary traders employ trading strategies such as momentum trading, statistical arbitrage, pair trading, news sentiment, fundamental analysis, long-short investment, and high-frequency trading. As a best practice, the firm usually implements a robust risk management policy to monitor and control such trading activities.

Proprietary trading desks rely on analytical tools like MATLAB® that let them define trading strategies, integrate with financial data feeds, and are able to be deployed into a production environment. Common tasks for developing and implementing proprietary trading strategies include:

  • Importing various data types (e.g., real time, current, historical, intraday tick data, time-series, and machine-readable news)
  • Executing orders through platforms  such as Bloomberg® EMSX, CQG® Integrated Client, Interactive Brokers® TWS, FIX Flyer™, and Trading Technologies® X_TRADER®
  • Plotting financial charts and calculating technical indicators
  • Hypothesis testing, machine learning, and pattern recognition
  • Conducting trading cost analysis and market impact modeling
  • Analyzing financial time series to generate trading signals
  • High-performance parallel computing using GPUs, clusters, grids, and clouds

For more on tools for proprietary trading, see MATLAB, Datafeed Toolbox™Financial Toolbox™, and Trading Toolbox™.

See also: high-frequency trading, algorithmic trading, statistical arbitrage, momentum trading