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| <sub>[RLTrader](https://github.com/notadamking/RLTrader)</sub> | <sub>predecessor to [tensortrade](https://github.com/tensortrade-org/tensortrade) uses open api [gym](https://gym.openai.com/) and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using [Optuna](https://github.com/optuna/optuna) etc.</sub> | <sub>4/27/19 18:35</sub> | <sub>10/17/19 16:25</sub> | <sub>1304.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
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| <sub>[Deep Learning III](https://github.com/Rachnog/Deep-Trading)</sub> | <sub>Algorithmic trading with deep learning experiments.</sub> | <sub>6/18/16 18:23</sub> | <sub>8/7/18 15:24</sub> | <sub>1264.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
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| <sub>[Personae](https://github.com/Ceruleanacg/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](https://github.com/ricequant/rqalpha) which is a python backtest engine and have a nice docker image to run training/testing</sub> | <sub>3/10/18 11:22</sub> | <sub>9/2/18 17:21</sub> | <sub>1142.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
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| <sub>[Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020](https://github.com/AI4Finance-LLC/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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996) focuses on ensemble.</sub> | <sub>7/26/20 13:12</sub> | <sub>1/21/21 18:11</sub> | <sub>547.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
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| <sub>[Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020](https://github.com/AI4Finance-LLC/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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996) focuses on ensemble.</sub> | <sub>7/26/20 13:12</sub> | <sub>1/21/21 18:11</sub> | <sub>547.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
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| <sub>[awesome-deep-trading](https://github.com/cbailes/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>11/26/18 3:23</sub> | <sub>1/1/21 9:41</sub> | <sub>541.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
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| <sub>[Neural Network](https://github.com/VivekPa/IntroNeuralNetworks)</sub> | <sub>Neural networks to predict stock prices.</sub> | <sub>9/10/18 6:34</sub> | <sub>11/21/18 7:39</sub> | <sub>489.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
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| <sub>[Deep Learning](https://github.com/keon/deepstock)</sub> | <sub>Technical experimentations to beat the stock market using deep learning.</sub> | <sub>12/12/16 2:15</sub> | <sub>3/4/17 8:37</sub> | <sub>427.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
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| <sub>[Deep-Learning-Machine-Learning-Stock](https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock)</sub> | <sub>curated list of notebooks for machine learning models. Start with very simple linear models to more advanced reinforcement learning type of models. Problem with this repo is that the library version numbers may be changing over time and there's no specific way to track and upgrade</sub> | <sub>9/29/18 23:38</sub> | <sub>3/18/21 3:16</sub> | <sub>264.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[deep-RL-trading](https://github.com/golsun/deep-RL-trading)</sub> | <sub>trading game comparing RNN vs CNN vs MLP based on [paper](https://arxiv.org/abs/1803.03916)</sub> | <sub>2/25/18 17:41</sub> | <sub>12/1/20 22:06</sub> | <sub>233.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[ARIMA-LTSM Hybrid](https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid)</sub> | <sub>Hybrid model to predict future price correlation coefficients of two assets.</sub> | <sub>8/5/18 2:13</sub> | <sub>10/1/18 11:25</sub> | <sub>219.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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| <sub>[trading-rl](https://github.com/Kostis-S-Z/trading-rl)</sub> | <sub>Deep reinforcement learning for financial trading using [gym](https://gym.openai.com/) and [keras-rl](https://github.com/keras-rl/keras-rl) on FX dataset (EURUSD) not actively maintained</sub> | <sub>4/22/19 10:03</sub> | <sub>9/28/20 9:07</sub> | <sub>179.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[trading-rl](https://github.com/Kostis-S-Z/trading-rl)</sub> | <sub>Deep reinforcement learning for financial trading using [gym](https://gym.openai.com/) and [keras-rl](https://github.com/keras-rl/keras-rl) on FX dataset (EURUSD) not actively maintained</sub> | <sub>4/22/19 10:03</sub> | <sub>9/28/20 9:07</sub> | <sub>179.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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| <sub>[Deep Learning II](https://github.com/LiamConnell/deep-algotrading/tree/master/notebooks)</sub> | <sub>Tensorflow Regression.</sub> | <sub>7/12/16 12:56</sub> | <sub>2/16/18 2:43</sub> | <sub>174.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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| <sub>[Deep-Reinforcement-Stock-Trading](https://github.com/Albert-Z-Guo/Deep-Reinforcement-Stock-Trading)</sub> | <sub>inspired by Q-trader a deep reinforcement learning repo for trading. Only 3 actions allowed (buy/hold/sell) and no transaction cost is implemented yet. Uses [empyrical](https://github.com/quantopian/empyrical) for portfolio stats</sub> | <sub>5/19/19 22:20</sub> | <sub>9/27/20 19:22</sub> | <sub>141.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[Deep-Reinforcement-Learning-in-Trading](https://github.com/saeed349/Deep-Reinforcement-Learning-in-Trading)</sub> | <sub>Deep reinforcement learning for trading leveraging [openai gym](https://gym.openai.com/) framework. Keras implementation of DQN DDQN (double deep Q network) and DDDQN (dueling double dqn) trained/tested on s&p 500 daily data from 2013 to 2018. approach is described in an article [here](https://www.linkedin.com/pulse/deep-reinforcement-learning-trading-saeed-rahman)</sub> | <sub>5/11/18 0:52</sub> | <sub>10/26/19 14:22</sub> | <sub>137.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[DQN-DDPG_Stock_Trading](https://github.com/AI4Finance-LLC/DQN-DDPG_Stock_Trading)</sub> | <sub>merged into FinRL library and uses [gym](https://gym.openai.com/) and implementation of DQN</sub> | <sub>9/19/18 3:17</sub> | <sub>11/26/20 16:58</sub> | <sub>135.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[DQN-DDPG_Stock_Trading](https://github.com/AI4Finance-LLC/DQN-DDPG_Stock_Trading)</sub> | <sub>merged into FinRL library and uses [gym](https://gym.openai.com/) and implementation of DQN</sub> | <sub>9/19/18 3:17</sub> | <sub>11/26/20 16:58</sub> | <sub>135.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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| <sub>[AutomatedStockTrading-DeepQ-Learning](https://github.com/sachink2010/AutomatedStockTrading-DeepQ-Learning)</sub> | <sub>cornerstone project repo for Udacity nanodegree program [Become a machine learning engineer](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t) and focus on trading using deep q learning. Good explanation on design choices in the report</sub> | <sub>2/23/19 12:01</sub> | <sub>2/25/20 18:16</sub> | <sub>134.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[LTSM GRU](https://github.com/RajatHanda/Finance-Forecasting)</sub> | <sub>Stock Market Forecasting using LSTM\GRU.</sub> | <sub>5/13/18 2:39</sub> | <sub>2/25/19 0:26</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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