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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> |
| <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> |
| <sub>[LTSM Recurrent](https://github.com/VivekPa/AIAlpha)</sub> | <sub>OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.</sub> | <sub>10/7/18 3:58</sub> | <sub>8/3/19 9:00</sub> | <sub>1199.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
| <sub>[crypto-rl](https://github.com/sadighian/crypto-rl)</sub> | <sub>Retrieve limit order book level data from coinbase pro and bitfinex -> record in [arctic](https://github.com/man-group/arctic) timeseries database then implemented trend following strategies (market orders) and market making (limit orders). Uses reinforcement learning (DQN) (keras-rl)[https://github.com/keras-rl/keras-rl] to create agents and uses (openai gym)[https://gym.openai.com/] to implement POMDP (partially observable markov decision process)</sub> | <sub>6/21/18 1:06</sub> | <sub>11/5/20 11:08</sub> | <sub>345.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[crypto-rl](https://github.com/sadighian/crypto-rl)</sub> | <sub>Retrieve limit order book level data from coinbase pro and bitfinex -> record in [arctic](https://github.com/man-group/arctic) timeseries database then implemented trend following strategies (market orders) and market making (limit orders). Uses reinforcement learning (DQN) [keras-rl](https://github.com/keras-rl/keras-rl) to create agents and uses [openai gym](https://gym.openai.com/) to implement POMDP (partially observable markov decision process)</sub> | <sub>6/21/18 1:06</sub> | <sub>11/5/20 11:08</sub> | <sub>345.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[Advanced-Deep-Trading](https://github.com/Rachnog/Advanced-Deep-Trading)</sub> | <sub>notebooks containing experiments based on Lopez de Prado book "Advances in financial machine learning". Mostly not deep learning related but rather sklearn regression models. Interesting libraries include [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) for calculating return stats and [shap](https://github.com/slundberg/shap) for explaining models. Examlpe of shap can be which features are pushing the value up and and which features are pushing the value down. Also contain functions for calculating geometric brownian motion and jump diffusion functions. </sub> | <sub>2/16/19 21:18</sub> | <sub>11/29/20 20:12</sub> | <sub>319.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[BitcoinForecast](https://github.com/PiSimo/BitcoinForecast)</sub> | <sub>NEW</sub> | <sub>3/10/17 10:52</sub> | <sub>6/11/18 8:07</sub> | <sub>288.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
| <sub>[trading-bot](https://github.com/pskrunner14/trading-bot)</sub> | <sub>Implementation of deep reinforcement learning using Deep Q Network (DQN). Only supports single security at the moment. Idea is roughly based [here](https://keon.github.io/deep-q-learning/) and uses tensorflow/keras. Interesting helper python libraries used here are [tqdm](https://tqdm.github.io/) for console based progress bar and [altair](https://altair-viz.github.io/) for declarative visualization in python </sub> | <sub>8/13/18 10:44</sub> | <sub>1/23/20 4:41</sub> | <sub>286.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |