QSTrader is an open-source event-driven backtesting and live trading platform for use in the equities markets, currently in an early "alpha" state.

It has been created as part of the Advanced Trading Infrastructure article series on QuantStart.com to provide the systematic trading community with a robust trading engine that allows straightforward equities/ETF strategy implementation and testing.

The software is provided under a permissive "MIT" license (see below).

• Open-Source - QSTrader has been released under an extremely permissive open-source MIT License, which allows full usage in both research and commercial applications, without restriction, but with no warranty of any kind whatsoever.

• Free - QSTrader is completely free and costs nothing to download or use.

• Collaboration - As QSTrader is open-source many developers collaborate to improve the software. New features are added frequently. Any bugs are quickly determined and fixed.

• Software Development - QSTrader is written in the Python programming language for straightforward cross-platform support. QSTrader contains a suite of unit tests for the majority of its calculation code and tests are constantly added for new features.

• Event-Driven Architecture - QSTrader is completely event-driven both for backtesting and live trading, which leads to straightforward transitioning of strategies from a research/testing phase to a live trading implementation.

• Backtesting - QSTrader supports intraday tick-resolution (top of order book bid/ask) datasets.

• Private Components - QSTrader allows you to include a repository of your own private strategies or components. Simply checkout your own repository within the root of QSTrader and rename the directory to private_files. This will ensure the QSTrader repository can be easily kept up to date without interfering with your private repository.

• Transaction Costs - Fees/commission, slippage and market impact will all be simulated using realistic assumptions. This means the costs predicted in backtests will be similar to those encountered in live trading.

• Trading - QSTrader will support live intraday trading using the Interactive Brokers API across a set of equities/ETFs.

• Performance Metrics - QSTrader will support extensive strategy/portfolio performance measurement and equity curve visualisation via the Matplotlib and Seaborn visualisation libraries.

QSTrader is in an extremely early alpha state at the moment and should only be used for exploratory backtesting research.

1. Clone this git repository into a suitable location on your machine using the following command in your terminal: git clone https://github.com/mhallsmoore/qstrader.git.

2. Create a virtual environment ("virtualenv") for the QSTrader code and utilise pip to install the requirements. For instance in a Unix-based system (Mac or Linux) you might create such a directory as follows by entering the following commands in the terminal:

mkdir -p ~/venv/qstrader
cd ~/venv/qstrader
virtualenv .

This will create a new virtual environment to install the packages into. Assuming you downloaded the QSTrader git repository into an example directory such as ~/projects/qstrader/ (change this directory below to wherever you installed QSTrader), then in order to install the packages you will need to run the following commands:

source ~/venv/qstrader/bin/activate
pip install -r ~/projects/qstrader/requirements.txt

This will take some time as NumPy, SciPy and Pandas must be compiled. There are many packages required for this to work, so please take a look at these two articles for more information:

• https://www.quantstart.com/articles/Quick-Start-Python-Quantitative-Research-Environment-on-Ubuntu-14-04
• https://www.quantstart.com/articles/Easy-Multi-Platform-Installation-of-a-Scientific-Python-Stack-Using-Anaconda

3. You will also need to create a symbolic link from your site-packages directory to your QSTrader installation directory in order to be able to call import qstrader within the code. To do this you will need a command similar to the following (make sure to change python3.5 to your specific Python version):

ln -s ~/projects/qstrader/ ~/venv/qstrader/lib/python3.5/site-packages/qstrader

Make sure to change ~/projects/qstrader to your installation directory and ~/venv/qstrader/lib/python3.5/site-packages/ to your virtualenv site packages directory.

You will now be able to run the python programs correctly.

At this stage there is only one simple backtest example in examples/test_strategy_backtest.py. This test itself requires some equities testing data. In the future the codebase will come with some sophisticated equity data simulators, so that any strategies can be tested without having to fetch external data, prior to a realistic test.

Future versions will also make use of a settings.py file to specify local input and output directories, as well as any authentication information required by a brokerage to authenticate against their API for live trading.

The project is constantly being developed, so unfortunately it is likely that the current API will experience backwards incompatibility until a mature beta version has been produced.

If you have any questions about the installation then please feel free to email feedback@quantstart.com.

If you notice any bugs or other issues that you think may be due to the codebase specifically, feel free to open a Github issue here: https://github.com/mhallsmoore/qstrader/issues

Copyright (c) 2016 Michael Halls-Moore

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Trading equities on margin carries a high level of risk, and may not be suitable for all investors. Past performance is not indicative of future results. The high degree of leverage can work against you as well as for you. Before deciding to invest in equities you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. You should be aware of all the risks associated with equities trading, and seek advice from an independent financial advisor if you have any doubts.