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Project description

torchquantum

TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.

Contents

Installation

for Unix:

cd /path/to/your/directory
git clone git@github.com:nymath/torchquantum.git
cd ./torchquantum

Before running examples, you should compile the cython code.

python setup.py build_ext --inplace

Now you can run examples

python ./examples/main.py

If you are not downloading the dataset, then you should

cd ./examples
mkdir largedata
cd ./largedata
wget https://github.com/nymath/torchquantum/releases/download/V0.1/Stocks.pkl.zip
unzip Stocks.pkl.zip
rm Stocks.pkl.zip
cd ../
cd ../

Features

  • High-speed backtesting framework.
  • A revised gplearn library that is compatible with Alpha mining.
  • CNN and other state of the art models for mining alphas.
  • Event Driven backtesting framework will be available.

Contribution

For more information, we refer to Documentation.

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