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## Other Models ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/other_models))
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<!-- [PLACEHOLDER_START:other_models] -->
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| <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> |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
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| <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> |
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| <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> |
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| <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> |
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| <sub>[Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original)</sub> | <sub>official repo for [machine learning for algorithmic trading](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d) book. Covering topics including backtesting/boosting/nlp/deep&reinforcement learning. Leverage open source libraries including [backtrader](https://www.backtrader.com/) [zipline](https://github.com/quantopian/zipline) and [talib](https://github.com/mrjbq7/ta-lib)</sub> | <sub>11/15/19 8:51</sub> | <sub>1/21/21 7:56</sub> | <sub>279.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
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| <sub>[Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks)</sub> | <sub>using fundamental and pricing data to predict future stock returns. Sklearn's randomforest classifier is trainded and author claimed positive live trading results. Not actively mainained</sub> | <sub>2/12/17 4:50</sub> | <sub>2/4/21 3:48</sub> | <sub>931.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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| <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> |
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| <sub>[stock-trading-ml](https://github.com/yacoubb/stock-trading-ml)</sub> | <sub>lstm model using keras to predict msft prices. Data is from [alphavantage](https://www.alphavantage.co/) which provides some free data through web services. Showing how to use concatenation layer to join timeseries data with TA data. Might be abit of overfitting on the model though</sub> | <sub>10/10/19 9:44</sub> | <sub>10/12/19 11:38</sub> | <sub>340.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes)</sub> | <sub>accompanying materials for book [Machine Learning in Finance](https://www.springer.com/gp/book/9783030410674) covering probabilistic modeling/sequence modeling/neural networks/reinforcement learning etc.</sub> | <sub>9/27/19 16:13</sub> | <sub>6/13/20 21:20</sub> | <sub>250.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[mlfinlab](https://github.com/hudson-and-thames/mlfinlab)</sub> | <sub>open source library maintained by hudson and thames though much of the content has moved to a subscription model. Idea is to implement academic research in python code and aggregate it as a package. Sources from [Journal of financial data science](https://jfds.pm-research.com/) / [journal of portfolio management](https://jpm.pm-research.com/) / [journal of algorithmic finance](http://www.algorithmicfinance.org/) / [cambridge university press](https://www.cambridge.org/)</sub> | <sub>2/13/19 16:57</sub> | <sub>4/12/21 10:50</sub> | <sub>2295.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[mosquito](https://github.com/miro-ka/mosquito)</sub> | <sub>base framework trading bot for crypto. Stores data in local mongodb instance and supports backtest and live trading on [poloniex](https://poloniex.com/) and [bittrex](https://bittrex.com/) which are 12-15th ranked crypto exchanges by volume. Leverage [talib](https://github.com/mrjbq7/ta-lib) for ta data and [plotly](https://github.com/plotly/plotly.py) for visualization</sub> | <sub>6/18/17 19:57</sub> | <sub>3/14/21 22:22</sub> | <sub>220.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>Machine learning algo to detect anomaly in equities data. Uses sklearn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) model and price/volume based technical signals as features using [ta](https://github.com/bukosabino/ta) library. Opensourced by [tradytics](https://tradytics.com/). Code structures are less extensible</sub> | <sub>8/30/20 7:56</sub> | <sub>9/21/20 4:32</sub> | <sub>1189.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models)</sub> | <sub>Mixture models to predict market bottoms and regime changes based on a seminar given to quantinsti in 2017 and summary and video is [here](https://blog.quantinsti.com/webinar-can-we-use-mixture-models-to-predict-market-bottoms/). Gaussian mixture models are build on markov models and expectation maximization thoery to detect regimes and seminar reported positive results using features asset returns/tedrate/10y2ysptread/10y3m spread from fred which can be access here [fredapi](https://github.com/mortada/fredapi). Though most of the returns came from being long equities after 2009 </sub> | <sub>3/20/17 18:54</sub> | <sub>4/25/17 23:35</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
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| <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> |
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| <sub>[Machine-Learning-For-Finance](https://github.com/anthonyng2/Machine-Learning-For-Finance)</sub> | <sub>accompanying materials for slide [here](https://github.com/anthonyng2/Machine-Learning-For-Finance/blob/master/Regression%20Based%20Machine%20Learning%20for%20Algorithmic%20Trading/Machine%20Learning%20-%20Linear%20Regression%20for%20Algo%20Trading%20v2017-07-13.pdf) covering more tradition quant trading topics like pair trading/kalman filter/trend following etc. Referecing interesting paper [characterization of financial time series](http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_01.pdf)</sub> | <sub>7/11/17 9:09</sub> | <sub>2/21/18 5:36</sub> | <sub>205.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
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| <sub>[Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading)</sub> | <sub>NEW</sub> | <sub>3/26/15 2:22</sub> | <sub>3/26/15 2:33</sub> | <sub>173.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |<!-- [PLACEHOLDER_END:other_models] -->
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| <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> |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
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| <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> |
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| <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> |
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| <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> |
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| <sub>[Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original)</sub> | <sub>official repo for [machine learning for algorithmic trading](https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d) book. Covering topics including backtesting/boosting/nlp/deep&reinforcement learning. Leverage open source libraries including [backtrader](https://www.backtrader.com/) [zipline](https://github.com/quantopian/zipline) and [talib](https://github.com/mrjbq7/ta-lib)</sub> | <sub>11/15/19 8:51</sub> | <sub>1/21/21 7:56</sub> | <sub>279.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
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| <sub>[fin-ml](https://github.com/tatsath/fin-ml)</sub> | <sub>accompanying materials for book [Machine Learning and Data Science Blueprints for Finance](https://www.amazon.com/Machine-Learning-Science-Blueprints-Finance/dp/1492073059) on top of basic machine learning models i.e. nlp/reinforcement learning/supervised & unsupervised learning it covers wider topics including robo-advisors/fraud detection/loan default/derivative pricing/yield curve construction.</sub> | <sub>5/10/20 0:25</sub> | <sub>1/23/21 17:15</sub> | <sub>116.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
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| <sub>[Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks)</sub> | <sub>using fundamental and pricing data to predict future stock returns. Sklearn's randomforest classifier is trainded and author claimed positive live trading results. Not actively mainained</sub> | <sub>2/12/17 4:50</sub> | <sub>2/4/21 3:48</sub> | <sub>931.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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| <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> |
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| <sub>[Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps)</sub> | <sub>Research in investment finance for long term forecasts and a curated list of notebooks. Each topic contains a youtube video explaining in details. Interesting topics including using price per book ratio and other multiples for future return prediction and portfolio optimization. data sourced form [simfin](https://github.com/SimFin/simfin) yahoo finance and [s&p 500 earnings and estimate report](https://www.spglobal.com/spdji/en/documents/additional-material/sp-500-eps-est.xlsx) etc.</sub> | <sub>7/22/18 8:14</sub> | <sub>2/17/21 14:39</sub> | <sub>383.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[stock-trading-ml](https://github.com/yacoubb/stock-trading-ml)</sub> | <sub>lstm model using keras to predict msft prices. Data is from [alphavantage](https://www.alphavantage.co/) which provides some free data through web services. Showing how to use concatenation layer to join timeseries data with TA data. Might be abit of overfitting on the model though</sub> | <sub>10/10/19 9:44</sub> | <sub>10/12/19 11:38</sub> | <sub>340.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes)</sub> | <sub>accompanying materials for book [Machine Learning in Finance](https://www.springer.com/gp/book/9783030410674) covering probabilistic modeling/sequence modeling/neural networks/reinforcement learning etc.</sub> | <sub>9/27/19 16:13</sub> | <sub>6/13/20 21:20</sub> | <sub>250.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[mlfinlab](https://github.com/hudson-and-thames/mlfinlab)</sub> | <sub>open source library maintained by hudson and thames though much of the content has moved to a subscription model. Idea is to implement academic research in python code and aggregate it as a package. Sources from [Journal of financial data science](https://jfds.pm-research.com/) / [journal of portfolio management](https://jpm.pm-research.com/) / [journal of algorithmic finance](http://www.algorithmicfinance.org/) / [cambridge university press](https://www.cambridge.org/)</sub> | <sub>2/13/19 16:57</sub> | <sub>4/12/21 10:50</sub> | <sub>2295.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[mosquito](https://github.com/miro-ka/mosquito)</sub> | <sub>base framework trading bot for crypto. Stores data in local mongodb instance and supports backtest and live trading on [poloniex](https://poloniex.com/) and [bittrex](https://bittrex.com/) which are 12-15th ranked crypto exchanges by volume. Leverage [talib](https://github.com/mrjbq7/ta-lib) for ta data and [plotly](https://github.com/plotly/plotly.py) for visualization</sub> | <sub>6/18/17 19:57</sub> | <sub>3/14/21 22:22</sub> | <sub>220.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant)</sub> | <sub>Identify social/historical cues for short term stock movement. Sklearn SVM model is used and good visualization coded in matplotlib</sub> | <sub>9/12/16 18:38</sub> | <sub>8/29/18 20:27</sub> | <sub>2166.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
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| <sub>[Machine-Learning-for-Finance](https://github.com/PacktPublishing/Machine-Learning-for-Finance)</sub> | <sub>repo for book [machine learning for finance](https://www.packtpub.com/product/machine-learning-for-finance/9781789136364) with heavier focus on machine learning and less on finance. Topics covered including computer vision/time series/nlp/generative models (i.e. autoencoder)/reinforcement learning/debugging ml systems</sub> | <sub>3/15/18 6:28</sub> | <sub>1/14/21 15:58</sub> | <sub>180.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
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| <sub>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>Machine learning algo to detect anomaly in equities data. Uses sklearn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) model and price/volume based technical signals as features using [ta](https://github.com/bukosabino/ta) library. Opensourced by [tradytics](https://tradytics.com/). Code structures are less extensible</sub> | <sub>8/30/20 7:56</sub> | <sub>9/21/20 4:32</sub> | <sub>1189.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |<!-- [PLACEHOLDER_END:other_models] -->
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## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations))
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