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A collection of open-source GPU accelerated Python tools and examples for quantitative analyst tasks and leverages RAPIDS AI project, Numba, cuDF, and Dask.

Shell 0.36% JavaScript 0.12% C++ 0.67% Python 6.76% C 0.01% R 0.06% TypeScript 0.69% CSS 0.70% Cuda 0.08% Makefile 0.01% HTML 0.01% CMake 0.01% Batchfile 0.01% Jupyter Notebook 90.48% Dockerfile 0.04%

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FSI Examples

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autoencoder_statarb - Train a TensorFlow-based deep autoencoder on log returns for the Dow Jones 30 securities in a technique is known as Statistical Arbitrage. Profit from temporary mispricings.
backtesting_equity_investment_strats - Compute Sharpe Ratio on entire set of NYSE/NASDAQ stocks and, through bootstrapping, examine a robust number of potential market scenarios for stock selection and PnL measurement against the S&P Index.
credit_default_risk - Use the widely available public U.S. Fannie Mae mortgage dataset to train classifers for predicting loan delinquencies and examine explanations.
gQuant - An external plugin with a set of nodes for quantitative analyst tasks, built on top of the RAPIDS AI project, Numba, and Dask.
greenflow - A graph computation toolkit that helps you to organize the workflows in graph computation.
greenflowlab - A JupyterLab plugin that provides the UI interface for greenflow.
nlp_demo_riva - a simple conversational AI demo that you can ask the question to AI about the provided context text and get the answer back in speech.

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