goldenwalden's repositories
AA228-CS238-Student
Starter code and data files for AA228/CS238 at Stanford University
adversarial-policies
Find best-response to a fixed policy in multi-agent RL
alpaca-backtrader-api
Alpaca Trading API integrated with backtrader
awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
BayesianOptimization
A Python implementation of global optimization with gaussian processes.
CrossSection
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"
cs228-notes
Course notes for CS228: Probabilistic Graphical Models.
DecisiveML
Exploration for machine learning
Deep-Learning-in-Asset-Pricing
https://arxiv.org/abs/1805.01104
eiten
Statistical and Algorithmic Investing Strategies for Everyone
gym-continuousDoubleAuction
A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray (RLLib) is used for training.
High-Frequency-Trading-Model-with-IB
A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python
homework
Assignments for CS294-112.
mlfinlab
Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning
multiagent-particle-envs
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Paper-Implementation-DSTP-RNN-For-Stock-Prediction-Based-On-DA-RNN
基於DA-RNN之DSTP-RNN論文試做(Ver1.0)
pgmpy_notebook
Short Tutorial to Probabilistic Graphical Models(PGM) and pgmpy
planet
Learning Latent Dynamics for Planning from Pixels
reinforcement-learning-1
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
rmm.arl
Robust Market Making via Adversarial Reinforcement Learning
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.
trading-rl
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
VineCopula
Statistical inference of vine copulas