TradingMachine is intend to bring optimization and machine techniques into finance algorithmic trading.
Discussion and Help
* Optimization on strategy parameters.
* PyBrain Integration (Reinforcement Learning, Neural Network ...).
* TA-Lib Integration (Most common technical analysis available.)
* Pandas (High speed time series data analysis)
# You will first need to install TALib. ta-lib is a python wrapper for that. Please refer [TA-Lib](http://ta-lib.org/hdr_doc.html)
TradingMachine can be installed via pip
pip install numpy
pip install matplotlib
pip install pandas
pip install ta-lib
pip install tradingmachine
If there are problems installing the dependencies, please consider install scipy stack.
For Windows, the [Enthought Python Distribution](http://www.enthought.com/products/epd.php)
includes most of the necessary dependencies.
On OSX, the [Scipy Superpack](http://fonnesbeck.github.com/ScipySuperpack/) works very well.
Other platforms, the [Scipy Stack](http://www.lfd.uci.edu/~gohlke/pythonlibs/) has binary available to install.
After installation, you will need to create a configuration file in home directory named ".tmconfig.ini".
2 HistoricalDataPath = /Users/chen/Repository/historicaldata
Configuration file is intended to point to the historical data folder.
A copy of historical data can be downloaded from: [historicalata](https://github.com/chinux23/historicaldata)
* Python (>= 3.3.1)
* numpy (>= 1.7.1)
* pandas (>= 0.11.0)
For other questions, please contact Chen Huang <email@example.com>.
TODO: Brief introduction on what you do with files - including link to relevant help section.