Future of the Community

Two things:

We have decided to start a Slack group (invite) & a website ML-Quant see a screenshot of the website below. https://www.ml-quant.com/

Financial Machine Learning and Data Science

A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.

A listed repository should be deprecated if:

Repository's owner explicitly say that "this library is not maintained".
Not committed for long time (2~3 years).

This repo is officially under revamp as of 3/29/2021!!

TODOs and roadmap is under the github project here
If you would like to contribute to this repo, please send us a pull request or contact @dereknow or @bin-yang-algotune
Join us in the gitter chat here

All repos/links status including last commit date is updated daily
Only 15 Highest ranked repos/links for each section are displayed on main README.md and full list is available within the wiki page
Both Wikis/README.md is updated in realtime as soon as new information are pushed to the repo

Trading

Deep Learning & Reinforcement Learning (Wiki)

<sub>repo</sub>	<sub>comment</sub>	<sub>created_at</sub>	<sub>last_commit</sub>	<sub>star_count</sub>	<sub>repo_status</sub>	<sub>rating</sub>
<sub>Stock-Prediction-Models</sub>	<sub>very good curated list of notebooks showing deep learning + reinforcement learning models. Also contain topics on outlier detections/overbought oversold study/monte carlo simulartions/sentiment analysis from text (text storage/parsing is not detailed but it mentioned using BERT)</sub>	<sub>2017-12-18 10:49:59</sub>	<sub>2021-01-05 10:31:50</sub>	<sub>4635.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x5</sub>
<sub>AI Trading</sub>	<sub>AI to predict stock market movements.</sub>	<sub>2019-01-09 08:02:47</sub>	<sub>2019-02-11 16:32:47</sub>	<sub>3200.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x5</sub>
<sub>FinRL-Library</sub>	<sub>started by Columbia university engineering students and designed as an end to end deep reinforcement learning library for automated trading platform. Implementation of DQN DDQN DDPG etc using PyTorch and gym use pyfolio for showing backtesting stats. Big contributions on Proximal Policy Optimization (PPO) advantage actor critic (A2C) and Deep Deterministic Policy Gradient (DDPG) agents for trading</sub>	<sub>2020-07-26 13:18:16</sub>	<sub>2021-12-11 08:01:50</sub>	<sub>2982.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x5</sub>
<sub>Deep Learning IV</sub>	<sub>Bulbea: Deep Learning based Python Library.</sub>	<sub>2017-03-09 06:11:06</sub>	<sub>2017-03-19 07:42:49</sub>	<sub>1582.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x5</sub>
<sub>RLTrader</sub>	<sub>predecessor to tensortrade uses open api gym and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using Optuna etc.</sub>	<sub>2019-04-27 18:35:15</sub>	<sub>2019-10-17 16:25:49</sub>	<sub>1463.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x5</sub>
<sub>Deep Learning III</sub>	<sub>Algorithmic trading with deep learning experiments.</sub>	<sub>2016-06-18 18:23:06</sub>	<sub>2018-08-07 15:24:45</sub>	<sub>1307.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x5</sub>
<sub>Personae</sub>	<sub>implementation of deep reinforcement learning and supervised learnings covering areas: deep deterministic policy gradient (DDPG) and DDQN etc. Data are being pulled from rqalpha which is a python backtest engine and have a nice docker image to run training/testing</sub>	<sub>2018-03-10 11:22:00</sub>	<sub>2018-09-02 17:21:38</sub>	<sub>1179.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x5</sub>
<sub>RL Trading</sub>	<sub>A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.</sub>	<sub>nan</sub>	<sub>nan</sub>	<sub>nan</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x4</sub>
<sub>Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020</sub>	<sub>Part of FinRL and provided code for paper deep reinformacement learning for automated stock trading focuses on ensemble.</sub>	<sub>2020-07-26 13:12:53</sub>	<sub>2021-01-21 18:11:59</sub>	<sub>928.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x4</sub>
<sub>awesome-deep-trading</sub>	<sub>curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes</sub>	<sub>2018-11-26 03:23:04</sub>	<sub>2021-01-01 09:41:21</sub>	<sub>781.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x4</sub>
<sub>Neural Network</sub>	<sub>Neural networks to predict stock prices.</sub>	<sub>2018-09-10 06:34:53</sub>	<sub>2018-11-21 07:39:31</sub>	<sub>562.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x4</sub>
<sub>Deep Learning</sub>	<sub>Technical experimentations to beat the stock market using deep learning.</sub>	<sub>2016-12-12 02:15:12</sub>	<sub>2017-03-04 08:37:29</sub>	<sub>439.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x4</sub>
<sub>LTSM Recurrent</sub>	<sub>OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.</sub>	<sub>2018-10-07 03:58:26</sub>	<sub>2019-08-03 09:00:44</sub>	<sub>1336.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x4</sub>
<sub>RL III</sub>	<sub>Github -Deep Reinforcement Learning based Trading Agent for Bitcoin.</sub>	<sub>2017-09-21 17:05:19</sub>	<sub>2018-04-13 16:33:21</sub>	<sub>627.0</sub>	<sub>:heavy_multiplication_x:</sub>	<sub>:star:x3</sub>
<sub>crypto-rl</sub>	<sub>Retrieve limit order book level data from coinbase pro and bitfinex -> record in arctic timeseries database then implemented trend following strategies (market orders) and market making (limit orders). Uses reinforcement learning (DQN) keras-rl to create agents and uses openai gym to implement POMDP (partially observable markov decision process)</sub>	<sub>2018-06-21 01:06:01</sub>	<sub>2021-11-30 13:52:18</sub>	<sub>475.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x3</sub>

Other Models (Wiki)

<sub>repo</sub>	<sub>comment</sub>	<sub>created_at</sub>	<sub>last_commit</sub>	<sub>star_count</sub>	<sub>repo_status</sub>	<sub>rating</sub>
<sub>Hands-On-Machine-Learning-for-Algorithmic-Trading</sub>	<sub>repo for book hands-on-machine learning for algorithmic trading covering topic from data/unsupervised learning/NPL/RNN & CNN/reinforcement learning etc. Leverage zipline/alphalens/sklearn/openai-gym etc as well. Good references to have</sub>	<sub>2019-05-07 11:04:25</sub>	<sub>2021-01-19 07:51:00</sub>	<sub>760.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x5</sub>
<sub>Microservices-Based-Algorithmic-Trading-System</sub>	<sub>docker based platfrom for developing algo trading strategies. Very interesting combinations of open source components were used including backtrader for backtest strategies / mlflow for managing the machine learning model life cycle (i.e. training and developing machine learning models) / airflow used as workflow management including schedule data download etc. / superset web data visualization tool similar to tableau / minio for fast object storage (i.e. storing saved models and model artifacts) / postgresql used to store security master and daily and minute data. Also contains some details on deployment on cloud</sub>	<sub>2020-01-06 00:21:58</sub>	<sub>2021-05-29 18:07:29</sub>	<sub>180.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x5</sub>
<sub>Awesome-Quant-Machine-Learning-Trading</sub>	<sub>curated list of books/online courses/youtube videos/blogs/interviews/papers/code etc. Updates are pretty infrequent</sub>	<sub>2018-11-05 21:09:06</sub>	<sub>2020-10-08 16:48:18</sub>	<sub>1278.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x5</sub>
<sub>AlphaPy</sub>	<sub>machine learning framework built on sklearn and pandas. Support pyfolio/xgboost/lightgmb/catboost(gradient boosting on decision tress) etc. Examples include financial market prediction/sports prediction/kaggle. Configurations are set though yaml file for all model process including feature selection/grid search on parameters and aggregate results for each model</sub>	<sub>2016-02-14 00:47:32</sub>	<sub>2021-10-23 07:17:16</sub>	<sub>672.0</sub>	<sub>:heavy_check_mark:</sub>	<sub>:star:x4</sub>