Pinned Repositories
anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
baselines-A2C
Advantage Actor Critic model in PyTorch inspired by OpenAI baselines TensorFlow implementation
django-admin-numeric-filter
Numeric filters for Django admin
predictions
py4fi
Python for Finance (O'Reilly)
pytorch-rl
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
pytorch-seq2seq
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. [IN PROGRESS]
RL-stocktrading
secretary
Take the power of Jinja2 templates to OpenOffice and LibreOffice.
urfinorg's Repositories
urfinorg/anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
urfinorg/baselines-A2C
Advantage Actor Critic model in PyTorch inspired by OpenAI baselines TensorFlow implementation
urfinorg/django-admin-numeric-filter
Numeric filters for Django admin
urfinorg/predictions
urfinorg/py4fi
Python for Finance (O'Reilly)
urfinorg/pytorch-rl
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
urfinorg/pytorch-seq2seq
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. [IN PROGRESS]
urfinorg/RL-stocktrading
urfinorg/secretary
Take the power of Jinja2 templates to OpenOffice and LibreOffice.
urfinorg/selenium
cbr_test
urfinorg/simple-A2C
A simple A2C made from scratch in PyTorch. Accompanying comic at https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752
urfinorg/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.
urfinorg/test_scoring