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Deep learning framework for HFT algorithmic trading strategy development

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# PyStrategies: Deep Learning Framework for HFT Trading Strategy Development

PyStrategies is a collection of Python tools for implementing, testing, and optimizing algorithmic trading strategies. This framework is built to be used with depth-of-book US equities data. Tools are included for deep learning, parameter optimization, and high-fidelity single-stock backtesting.

This project relies on another one of my libraries: [PyLimitBook](https://github.com/danielktaylor/PyLimitBook)

The bottom of this README includes important setup instructions.

## Toolset

### Writing Strategies

Strategies are implemented by extending the [BaseStrategy](backtest/PyBridge/basestrategy.py) base class and adding logic for event callbacks. Refer to the [Sample Strategy](strategy/pyStrategy.py) for an (unprofitable) example.

### Backtesting a Strategy

A single-stock backtester is included that accurately simulates the limit order book.

To run a backtest:

./bin/backtest.sh quotes/XOM_BATS_2010-06-23.csv strategy/pyStrategy.py

### Graphing Results

A sample [Jupyter](http://jupyter.org/) notebook is included to analyze signals generated from your strategy.

To generate data and interactively graph it:

# Generate book snapshots - make sure CREATE_ON_NBBO_CHANGE_ONLY variable in the script is set appropriately
        python PyLimitBook/create_graphing_data.py ./quotes/XOM_BATS_2010-06-23.csv ./analyze/data/book.csv

# Move signals generated by backtest
        mv ./strategy/signals_log.csv ./analyze/data/signals.csv

jupyter notebook --notebook-dir=./analyze

### Strategy Parameter Optimization

[Optunity](https://github.com/claesenm/optunity) is used for multi-threaded parameter optimization. Parameter ranges must be defined in the [parameters.csv](optimize/parameters.csv) file.

To run the parameter optimizer for 1000 iterations:

# Specify parameter ranges in optimize/parameters.csv
        python optimize/optimize.py quotes/XOM_BATS_2010-06-23.csv strategy/pyStrategy.py 1000

### Machine Learning

Wrapper scripts are included to generate features and do deep learning using [Theano](https://github.com/Theano/Theano) and [Keras](https://keras.io/):

# Generate features
        python machine_learning/generate_features.py quotes/XOM_BATS_2010-06-23.csv

# Generate labels to predict
        python machine_learning/generate_labels.py quotes/XOM_BATS_2010-06-23.csv

# Train the model
        python machine_learning/train_nn.py quotes/XOM_BATS_2010-06-23.csv

# Test the model
        python machine_learning/test_nn.py quotes/XOM_BATS_2010-06-24.csv

## Setup Instructions

### Install Python libraries

1. Setup virtualenv: ``virtualenv --no-site-packages venv``
2. Activate virtualenv: ``source venv/bin/activate``
3. Install libraries: ``pip install -r requirements.txt``

### Install jpy for bridging Python and Java code

1. ``git clone https://github.com/bcdev/jpy.git``
2. ``cd jpy``
3. ``export JDK_HOME=`/usr/libexec/java_home` ``
4. ``export JAVA_HOME=$JDK_HOME``
5. ``python setup.py --maven build``
6. Copy lib/jpy-0.8.jar to the Java project's [lib directory](backtest/TradingFramework4j/lib)
7. Copy properties file from build/lib.*/jpyconfig.properties into the Java project's [strategy directory](strategy/jpyconfig.properties)

### Install CuDNN for Speeding up Deep Learning

If your GPU supports CUDA, the machine learning with be much, much faster if you install CuDNN from NVIDIA.

1. Download CuDNN: https://developer.nvidia.com/rdp/cudnn-download
  * The following configuration works on Mac OS X:
    * nvcc version: release 7.5, V7.5.26
    * cuda driver: 7.5.27
    * clang version: clang-703.0.29 (XCODE 7.3.0)
    * osx version: 10.11.4
2. Install libraries:
  *  cd to where you want the libraries to live
  *  Linux: ``export LD_LIBRARY_PATH=`pwd`:$LD_LIBRARY_PATH``
  *  Mac: ``export DYLD_LIBRARY_PATH=`pwd`:$DYLD_LIBRARY_PATH``
       * Copied *.h files to CUDA_ROOT/include and *.so* files to CUDA_ROOT/lib64
           * By default, CUDA_ROOT is /usr/local/cuda on Linux and /Developer/NVIDIA/CUDA-* on mac
       * Add the following to the end of ~/.profile:

export PATH="/Developer/NVIDIA/CUDA-7.5/bin:$PATH"
             export DYLD_LIBRARY_PATH="/Developer/NVIDIA/CUDA-7.5/lib:$DYLD_LIBRARY_PATH"

*  Add the install path to your build and link process by adding -I`installpath` to your compile line and -L`installpath` -lcudnn to your link line.
3. Create a ~/.theanorc file: (adjust [cnmem](http://deeplearning.net/software/theano/library/config.html#config.config.lib.cnmem) as necessary)

[global]
        device=gpu
        floatX=float32
        allow_gc=False
        warn_float64=warn

[lib]
        cnmem=0.50

[nvcc]
        fastmath=True

PyStrategies

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