Features of the Quantiacs Toolbox in python and Matlab

Writing an Algorithmic Trading Strategy

Quantiacs provides a backtesting toolbox in Python 3.7 and Matlab to aid in the development of your trading algorithms. The toolbox is free and open source. It is a general tool through which you can write and evaluate trading algorithms.

  • Markets
    • Quantiacs provides data for US Futures Markets and Stocks.
  • Commissions and Slippage Image of toolbox
    • The toolbox allows you to simulate trading costs based on the previous day’s range. The default trading cost simulation is a conservative 5% of the previous day’s range.
  • Trading Logic
    • The toolbox allows you to define nearly any trading logic you wish. Using the toolbox’s API, you can implement trading logic ranging from simple moving averages to complex machine learning inference engines.


After Evaluation

After evaluation of a quantiative strategy, the toolbox provides details of the performance of the trading system in both raw data and in a GUI. The GUI provides and interface to explore several performance aspects of the system:

  • Equity Curve – the plot of the trading systems equity over time
  • Long vs. Short exposure – What percentage of the portfolio as long, what percentage was short.
  • Marketwise performance – how the strategy performed in individual markets

Furthermore the toolbox calculates several performance statistics: 

  • Annual Performance
  • Annual Volatility
  • Sharpe Ratio (Performance/Volatility)
  • Sortino Ratio (upside performance/downside volatility)
  • Maximum Drawdown
  • Max time off-peak
  • MAR Ratio

The toolbox is written in both matlab and python and supports many 3rd party packages supplied in both languages. Here are in-depth tutorials about the installation and how to use the toolbox.