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bitcoin-trader-rl

A profitable cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

clean-text

🧹 Clean Your Text to Create Normalized Text Representations

conllu

A CoNLL-U parser that takes a CoNLL-U formatted string and turns it into a nested python dictionary.

displacy

Python port of https://github.com/explosion/displacy

e2e-coref

End-to-end Neural Coreference Resolution

flask-caching

Continuation of the Flask-Cache Extension.

gnidart

jsonschema-nlplab

An(other) implementation of JSON Schema for Python

keras-transformer

Transformer implemented in Keras

lexdecomp

LexDecomp is an implementation of the Answer Selection (AS) model proposed in the paper Sentence Similarity Learning by Lexical Decomposition and Composition, by (Wang et al., 2016).

nlp-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

oandapy

Python wrapper for the OANDA REST API

orenbaldinger.github.io

Oren Baldinger's professional about-me website

parser-v1

pudl

The Public Utility Data Liberation Project

pyfolio

Portfolio and risk analytics in Python

python-tutorial-for-ipython

iPython tutorials for NLP, ML, AI in Python

samplemod

Sample module for Python-Guide.org.

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.

tradier-options-plotter

Python CLI tool for plotting options price history. Powered by Tradier's Sandbox API.