Getting started
These contents can help you to start with your quantitative trading (quant trading or QT) system: you can find some samples for the main steps.
There are
many platforms can give you historical data
a lot of program languages that you can use for implementing your strategies
many trading systems that you can use for backtesting your system
Read the documentation on readthedocs for
a sample of the main backtesting steps for a few of the main languages are used for statistical analysis, graphics representation and reporting
comparison among languages and methods for backtesting your strategies
the best practices for your quant trading system
Goals
These contents can be useful at the beginning, when you are newbie programmer.
And it can help you to evaluate which data, language or trading system you need.
Disclaimer
The strategies contained in this tutorial, are some simple samples for having an idea how to use the libraries: those strategies are for the educational purpose only. All investments and trading in the stock market involve risk: any decisions related to buying/selling of stocks or other financial instruments should only be made after a thorough research, backtesting, running in demo and seeking a professional assistance if required.
Contribution
The documentation for R and Python languages, it has been powered by Jupyter:
$ git clone https://github.com/bilardi/backtesting-tool-comparison
$ cd backtesting-tool-comparison/
$ pip install --upgrade -r requirements.txt
$ docker run --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -v "$PWD":/home/jovyan/ jupyter/datascience-notebook
The images are hosted on S3 and not in this repository:
use a PR for sharing a new version without images, only the new url
it will be our care to move them to S3 with all the others
For testing on your local client the documentation, see this README.md file.
License
These contents are released under the MIT license. See LICENSE for details.