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aai-site

NYU PSYCH-GA 3405.001 / DS-GA 3001.014 : Advancing AI through cognitive science

awesome-normalizing-flows

Awesome resources on normalizing flows.

bayesian_nonparametrics

Slides and code for Bayesian nonparametrics in R and Julia

chatgpt-prompt-engineering-for-developers

chatgpt-retrieval

crosssection

Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"

dataflowr

code for deep learning courses

day1-nn

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep_partial_least_squares

Source code for Deep Partial Least Squares for Empirical Asset Pricing.

finbert

Financial Sentiment Analysis with BERT

googlescraper

A Python module to scrape several search engines (like Google, Yandex, Bing, Duckduckgo, Baidu and others) by using proxies (socks4/5, http proxy) and with many different IP's, including asynchronous networking support (very fast).

gt-nlp-class

Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"

lxmls-toolkit

Machine Learning applied to Natural Language Processing Toolkit used in the Lisbon Machine Learning Summer School

mlfactor.github.io

Website dedicated to a book on machine learning for factor investing

mongo

nonparametricbayes

Dummy code for nonparametric bayes

nstrayer.github.io

Personal website

pml_2023

rebellion_research_nlp

In this repository you will find code for our capstone project "How to Predict Stock Movements Using NLP Techniques". The code has been adapted in the first 3 pairs to illustrate how to scrape the EDGAR webpage. However, the word2vec analysis and FinBERT analysis use as data the data uploaded in this repository. Get in touch with me if you have any questions or if you find any mistakes.

scraping-13f-filings

Tutorial on Scraping 13F Filings

statsmodels

Statsmodels: statistical modeling and econometrics in Python

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.