Project Title: Algorithmic Trading with Machine Learning
Group project for Baruch MTH 9899 - Big Data II: Machine Learning
- Weiyi Chen, [email protected]
- Yifan Cui, [email protected]
- Chengbin Wang, [email protected]
- Haotian Wu, [email protected]
Use different machine learning algorithms to modify traditional technical strategies, and backtest with research in cycle to find a good algorithm,
- Machine Learning algorithms
- Nearest Neighbors, http://scikit-learn.org/stable/modules/neighbors.html
- Random Forests, http://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees
- Support Vector Machine, http://scikit-learn.org/stable/modules/svm.html
- Technical Strategies
- Bollinger Band
- Momentum
- Rotated Relative Graph
We use
Introduction to machine learningas our guide to understand the algorithms andEvidence-based technical analysisto learn technical strategies.
Quantopian is our main research and backtesting platform, we research algorithmic trading ideas and explore curated financial data with the assistance of machine learning algorithm in this hosted research environment. Quantopian supports flexible data access, custom plotting, and post-hoc analysis on backtest.
For convenience to verify our machine learning algorithms by cheating ('cheat' is like using today's data to forecast today), we also support local access to data from Yahoo, with event study and backtesting tools from Python module QuantSoftware Toolkit. In concludes,
- Data input
- Quantopian, or Yahoo
- Equity Universe: adjusted close of S&P500 index equity component
- Frequency: daily
- Software
- Quantopian, a platform to build, test, and execute trading algorithms.
- QuantSoftware Toolkit, a modified Python-based open source software framework designed to support portfolio construction and management
Python. The requirement of this course suggests Matlab, but we request for using Python because our preferred platform Quantopian is using Python and the Python module QSTK we will be using is also in Python.
The main module we will be using is scipy-learn (http://scikit-learn.org/stable/supervised_learning.html#supervised-learning), which contains implemented Machine Learning algorithms.
- Alpaydin, Ethem. Introduction to machine learning. MIT press, 2014.
- Aronson, David. Evidence-based technical analysis: applying the scientific method and statistical inference to trading signals. Vol. 274. John Wiley & Sons, 2011.
Repository installation
Make sure your system meets these requirements:
- Operating system: MacOS 10.9 10.10 (it has been tested successfully on these)
- RAM: 2GB.
- Disk space: 2GB
- Open terminal, type “xcode-select --install” in terminal (without quotes)
- A pop-up windows will appear asking you about install tools, choose install tools, wait install to finish
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew tap samueljohn/python
brew tap homebrew/science
brew install gcc
brew install python
pip install virtualenv
pip install nose
pip install pyparsing
pip install python-dateutil
pip install numpy
pip install scipy
pip install matplotlib
pip install pandas
pip install scikits.statsmodels
pip install scikit-learn
pip install QSTK
brew install sublime
git clone https://github.com/int3h/SublimeFixMacPath.git ~/Library/Application\ Support/Sublime\ Text\ 2/Packages/FixMacPath
git clone https://github.com/weiyialanchen/AlgoTrading.git
cp -r AlgoTrading/* /usr/local/lib/python2.7/site-packages/QSTK/
rm -r AlgoTrading/
Note that your working directory is /usr/local/lib/python2.7/site-packages/QSTK/, to make alias
subl ~/.bash_profile
Append the following line to the file
alias AlgoTrading='cd /usr/local/lib/python2.7/site-packages/QSTK/'
Save, exit and restart the terminal. Next time when you want to go to the working folder and open it, just run
AlgoTrading
subl ./