torchquantum

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

Contents

• Installation
• Features
• Contribution

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.