/fsi-samples

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

Primary LanguageJupyter Notebook

FSI Examples

Repo Index

  1. 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.
  2. 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.
  3. credit_default_risk - Use the widely available public U.S. Fannie Mae mortgage dataset to train classifers for predicting loan delinquencies and examine explanations.
  4. gQuant - An external plugin with a set of nodes for quantitative analyst tasks, built on top of the RAPIDS AI project, Numba, and Dask.
  5. greenflow - A graph computation toolkit that helps you to organize the workflows in graph computation.
  6. greenflowlab - A JupyterLab plugin that provides the UI interface for greenflow.
  7. 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.