| <sub>[Hands-On-Machine-Learning-for-Algorithmic-Trading](https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading)</sub> | <sub>repo for book [hands-on-machine learning for algorithmic trading](https://www.packtpub.com/product/hands-on-machine-learning-for-algorithmic-trading/9781789346411) 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>5/7/19 11:04</sub> | <sub>1/19/21 7:51</sub> | <sub>600.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
| <sub>[Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/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>11/5/18 21:09</sub> | <sub>10/8/20 16:48</sub> | <sub>1005.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
| <sub>[AlphaPy](https://github.com/ScottfreeLLC/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>2/14/16 0:47</sub> | <sub>2/8/21 21:35</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
| <sub>[MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes)</sub> | <sub>Collections of news/articles on various topics including quant trading and machine learning. Some articles are from [ycombinator message board](https://news.ycombinator.com/news) and [rediit algotrading forum](https://www.reddit.com/r/algotrading/)</sub> | <sub>3/11/16 19:13</sub> | <sub>12/21/20 3:54</sub> | <sub>460.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[CryptoBot](https://github.com/AdeelMufti/CryptoBot)</sub> | <sub>Hard fork of [bitpredit](https://github.com/cbyn/bitpredict) and form the trading strategy as a classification problem with -1 (sell) 0 (hold) 1 (buy). Models used are XGBClassifier/RandomForest/GradientBoosting. Not mentained</sub> | <sub>1/17/17 12:44</sub> | <sub>1/17/17 12:48</sub> | <sub>234.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |