NowTrade

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NowTrade is an algorithmic trading library with a focus on creating powerful strategies using easily-readable and simple Python code. With the help of NowTrade, full blown stock/currency trading strategies, harnessing the power of machine learning, can be implemented with few lines of code.

NowTrade strategies are not event driven like most other algorithmic trading libraries available. The strategies are implemented in a sequential manner (one line at a time) without worrying about events, callbacks, or object overloading.

The Basics

All strategies follow this basic pattern:

Check out some example strategies in the examples directory.

Features

Here is a list of reasons you may want to use NowTrade over alternatives:

Cons

List of reasons you may not want to choose NowTrade over alternatives:

Installation

NowTrade has only been tested on Ubuntu 14.04 and 16.04. It will most likely run just fine on any UNIX-based operating system provided all dependencies have been met.

Dependencies
apt-get install python-pip python-numpy python-pandas python-scipy cython
pip install pandas-datareader
TaLib for Technical Indicators
wget -O ta-lib-0.4.0-src.tar.gz http://sourceforge.net/projects/ta-lib/files/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz/download
tar xvzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
make install
pip install Ta-Lib
Matplotlib for pretty charts
apt-get install python-matplotlib
For MySQL Support
apt-get install python-mysqldb
For MongoDB Local Storage Support
apt-get install mongodb python-pymongo

Note: You may need to un-comment the line '#port 27017' in /etc/mongodb.conf for NowTrade to communicate with mongo

For ensemble (random forest) support
apt-get install python-sklearn
For neural network support
pip install pybrain
Finally, install nowtrade
python setup.py install
Tests

To run the tests you'll need nose:

apt-get install python-nose

Then you can run the tests by running the following command in the root of the repository:

sh tests/test_all.sh

Give it a Spin

You can try out some strategies in the examples directory.

I recommend taking a look at the following examples:

See Also

You may also be interested in these other python-based backtesting systems:

Author

Edouard Poitras edouardpoitras@gmail.com