DUCKQUANT / THE QUANT & AI DIRECTORY
1,177 resources, ranked by in-directory PageRank
Reinforcement learning generates diverse, synergistic formulaic alpha factor sets.
Benchmarks deep-learning models for limit-order-book forecasting across multiple assets.
Applying Reinforcement Learning in Quantitative Trading
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management.
Algorithmic trading with deep learning experiments
Public repository by valeman.
Companion webpage to the book "Mathematics For Machine Learning"
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Executable lectures for Autumn 2025
Up-to-date version of labs for ISLP
Very large collection of PDF versions for books published before 2010
Code examples in pyTorch and Tensorflow for CS230
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by…
Explore this resource at its source website for documentation and current information.
An excellent collection of open access books on Data Science and Machine Learning
version of above, no videos. Free Online version
Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and…
Carnegie Mellon University (2018)
If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?"…
Explore this resource at its source website for documentation and current information.
Explore this resource at its source website for documentation and current information.
Explore this resource at its source website for documentation and current information.
Explore this resource at its source website for documentation and current information.
Explore this resource at its source website for documentation and current information.
Understanding Deep Learning - Simon J.D. Prince
By By Andrew Ng (2018) This course provides a broad introduction to machine learning and statistical pattern recognition. You will learn about both supervised…
Explore this resource at its source website for documentation and current information.
Explore this resource at its source website for documentation and current information.
MIT (2008) This course teaches the art of guessing results and solving problems without doing a proof or an exact calculation. Techniques include extreme-cases…
Jupyter notebook collection that cover many topics you are interested in great detail, and some come with concrete examples using PyTorch
Associated Python and R scripts
Enhance Your Options Knowledge: Explore OCC Learning for self-guided courses on options trading. Learn at your pace with industry professionals.
Applies transformer architectures to limit-order-book data for short-term price forecasting.
Early work applying deep neural networks to limit-order-book representation and price prediction.
Deep convolutional architecture predicting price movements directly from limit-order-book data.
