A 4-hour coding workshop to understand how LLMs are implemented and used

Python training for business analysts and traders

Well-documented demonstration workflows with the GeostatsPy package.

A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models on multiple stocks to generate Buy/Sell signals. This is a WI…

Crawlee—A web scraping and browser automation library for Python to build reliable crawlers. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. Wo…

Framework for algorithmic trading

This repository contains everything you need to become proficient in Data Science

42nd General Election Prediction for CSCI 6509 Natural Language Processing

🗳️+👀 A platform to protect elections in a disinformation world.

an election prediction system using tweets

A curated list of research in machine learning systems (MLSys). Paper notes are also provided.

We propose using Probabilistic Graphical Models such as Bayesian Networks and Hidden Markov Models to construct a global-macro trading strategy of the Crude Oil Markets.

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

Implements "Application of Probabilistic Graphical Models in Forecasting Crude Oil Price (Danish, 2018)"

WQU capstone project - short term currency trading strategy utilizing machine learning

WQU capstone project - short term currency trading strategy utilizing machine learning

A curated list of FREE courses available online from top universities of the world on CS-DS-ML!

Original transformer paper: Implementation of Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.

Transformer and MultiTransformer layers for stock volatility forecasting purposes

Implementation of a variety of Value-at-Risk backtests

FinRL: Financial Reinforcement Learning. 🔥

Reinforcement Learning for Automated Trading

My Personal Site

Portfolio and risk analytics in Python

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.

Code for Machine Learning for Algorithmic Trading, 2nd edition.