diff --git a/README.md b/README.md index bc50bf3..453dff2 100644 --- a/README.md +++ b/README.md @@ -49,23 +49,23 @@ ___ ## Other Models ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/other_models)) -| repo | comment | created_at | last_commit | star_count | repo_status | rating | -|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------| -| [Hands-On-Machine-Learning-for-Algorithmic-Trading](https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading) | 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 | 5/7/19 11:04 | 1/19/21 7:51 | 600.0 | :heavy_check_mark: | :star:x5 | -| [Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading) | curated list of books/online courses/youtube videos/blogs/interviews/papers/code etc. Updates are pretty infrequent | 11/5/18 21:09 | 10/8/20 16:48 | 1005.0 | :heavy_check_mark: | :star:x5 | -| [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy) | 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 | 2/14/16 0:47 | 2/8/21 21:35 | 576.0 | :heavy_check_mark: | :star:x4 | -| [Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original) | 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) | 11/15/19 8:51 | 1/21/21 7:56 | 279.0 | :heavy_check_mark: | :star:x4 | -| [Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks) | 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 | 2/12/17 4:50 | 2/4/21 3:48 | 931.0 | :heavy_multiplication_x: | :star:x3 | -| [MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes) | 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/) | 3/11/16 19:13 | 12/21/20 3:54 | 460.0 | :heavy_check_mark: | :star:x3 | -| [stock-trading-ml](https://github.com/yacoubb/stock-trading-ml) | 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 | 10/10/19 9:44 | 10/12/19 11:38 | 340.0 | :heavy_check_mark: | :star:x3 | -| [ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes) | 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. | 9/27/19 16:13 | 6/13/20 21:20 | 250.0 | :heavy_check_mark: | :star:x3 | -| [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) | 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/) | 2/13/19 16:57 | 4/12/21 10:50 | 2295.0 | :heavy_check_mark: | :star:x3 | -| [mosquito](https://github.com/miro-ka/mosquito) | 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 | 6/18/17 19:57 | 3/14/21 22:22 | 220.0 | :heavy_check_mark: | :star:x3 | -| [surpriver](https://github.com/tradytics/surpriver) | 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 | 8/30/20 7:56 | 9/21/20 4:32 | 1189.0 | :heavy_check_mark: | :star:x3 | -| [Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models) | 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 | 3/20/17 18:54 | 4/25/17 23:35 | 31.0 | :heavy_multiplication_x: | :star:x2 | -| [CryptoBot](https://github.com/AdeelMufti/CryptoBot) | 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 | 1/17/17 12:44 | 1/17/17 12:48 | 234.0 | :heavy_multiplication_x: | :star:x2 | -| [Machine-Learning-For-Finance](https://github.com/anthonyng2/Machine-Learning-For-Finance) | 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) | 7/11/17 9:09 | 2/21/18 5:36 | 205.0 | :heavy_multiplication_x: | :star:x2 | -| [Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading) | NEW | 3/26/15 2:22 | 3/26/15 2:33 | 173.0 | :heavy_multiplication_x: | :star:x2 | +| repo | comment | created_at | last_commit | star_count | repo_status | rating | +|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------| +| [Hands-On-Machine-Learning-for-Algorithmic-Trading](https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading) | 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 | 5/7/19 11:04 | 1/19/21 7:51 | 600.0 | :heavy_check_mark: | :star:x5 | +| [Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading) | curated list of books/online courses/youtube videos/blogs/interviews/papers/code etc. Updates are pretty infrequent | 11/5/18 21:09 | 10/8/20 16:48 | 1005.0 | :heavy_check_mark: | :star:x5 | +| [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy) | 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 | 2/14/16 0:47 | 2/8/21 21:35 | 576.0 | :heavy_check_mark: | :star:x4 | +| [Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original) | 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) | 11/15/19 8:51 | 1/21/21 7:56 | 279.0 | :heavy_check_mark: | :star:x4 | +| [fin-ml](https://github.com/tatsath/fin-ml) | 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. | 5/10/20 0:25 | 1/23/21 17:15 | 116.0 | :heavy_check_mark: | :star:x4 | +| [Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks) | 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 | 2/12/17 4:50 | 2/4/21 3:48 | 931.0 | :heavy_multiplication_x: | :star:x3 | +| [MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes) | 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/) | 3/11/16 19:13 | 12/21/20 3:54 | 460.0 | :heavy_check_mark: | :star:x3 | +| [Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps) | 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. | 7/22/18 8:14 | 2/17/21 14:39 | 383.0 | :heavy_check_mark: | :star:x3 | +| [stock-trading-ml](https://github.com/yacoubb/stock-trading-ml) | 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 | 10/10/19 9:44 | 10/12/19 11:38 | 340.0 | :heavy_check_mark: | :star:x3 | +| [ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes) | 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. | 9/27/19 16:13 | 6/13/20 21:20 | 250.0 | :heavy_check_mark: | :star:x3 | +| [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) | 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/) | 2/13/19 16:57 | 4/12/21 10:50 | 2295.0 | :heavy_check_mark: | :star:x3 | +| [mosquito](https://github.com/miro-ka/mosquito) | 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 | 6/18/17 19:57 | 3/14/21 22:22 | 220.0 | :heavy_check_mark: | :star:x3 | +| [Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant) | Identify social/historical cues for short term stock movement. Sklearn SVM model is used and good visualization coded in matplotlib | 9/12/16 18:38 | 8/29/18 20:27 | 2166.0 | :heavy_multiplication_x: | :star:x3 | +| [Machine-Learning-for-Finance](https://github.com/PacktPublishing/Machine-Learning-for-Finance) | 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 | 3/15/18 6:28 | 1/14/21 15:58 | 180.0 | :heavy_check_mark: | :star:x3 | +| [surpriver](https://github.com/tradytics/surpriver) | 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 | 8/30/20 7:56 | 9/21/20 4:32 | 1189.0 | :heavy_check_mark: | :star:x3 | ## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations)) diff --git a/generated_wiki/other_models.md b/generated_wiki/other_models.md index 6723256..520e041 100644 --- a/generated_wiki/other_models.md +++ b/generated_wiki/other_models.md @@ -4,28 +4,28 @@ | [Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading) | curated list of books/online courses/youtube videos/blogs/interviews/papers/code etc. Updates are pretty infrequent | 11/5/18 21:09 | 10/8/20 16:48 | 1005.0 | :heavy_check_mark: | :star:x5 | | [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy) | 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 | 2/14/16 0:47 | 2/8/21 21:35 | 576.0 | :heavy_check_mark: | :star:x4 | | [Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original) | 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) | 11/15/19 8:51 | 1/21/21 7:56 | 279.0 | :heavy_check_mark: | :star:x4 | +| [fin-ml](https://github.com/tatsath/fin-ml) | 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. | 5/10/20 0:25 | 1/23/21 17:15 | 116.0 | :heavy_check_mark: | :star:x4 | | [Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks) | 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 | 2/12/17 4:50 | 2/4/21 3:48 | 931.0 | :heavy_multiplication_x: | :star:x3 | | [MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes) | 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/) | 3/11/16 19:13 | 12/21/20 3:54 | 460.0 | :heavy_check_mark: | :star:x3 | +| [Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps) | 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. | 7/22/18 8:14 | 2/17/21 14:39 | 383.0 | :heavy_check_mark: | :star:x3 | | [stock-trading-ml](https://github.com/yacoubb/stock-trading-ml) | 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 | 10/10/19 9:44 | 10/12/19 11:38 | 340.0 | :heavy_check_mark: | :star:x3 | | [ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes) | 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. | 9/27/19 16:13 | 6/13/20 21:20 | 250.0 | :heavy_check_mark: | :star:x3 | | [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) | 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/) | 2/13/19 16:57 | 4/12/21 10:50 | 2295.0 | :heavy_check_mark: | :star:x3 | | [mosquito](https://github.com/miro-ka/mosquito) | 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 | 6/18/17 19:57 | 3/14/21 22:22 | 220.0 | :heavy_check_mark: | :star:x3 | +| [Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant) | Identify social/historical cues for short term stock movement. Sklearn SVM model is used and good visualization coded in matplotlib | 9/12/16 18:38 | 8/29/18 20:27 | 2166.0 | :heavy_multiplication_x: | :star:x3 | +| [Machine-Learning-for-Finance](https://github.com/PacktPublishing/Machine-Learning-for-Finance) | 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 | 3/15/18 6:28 | 1/14/21 15:58 | 180.0 | :heavy_check_mark: | :star:x3 | | [surpriver](https://github.com/tradytics/surpriver) | 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 | 8/30/20 7:56 | 9/21/20 4:32 | 1189.0 | :heavy_check_mark: | :star:x3 | +| [Speculator](https://github.com/amicks/Speculator) | python code to predict crypto prices using machine learning models. Two models used here tensorflow DNNClassifier and sklearn randomforest. Exposes the functionality as web services api using flask. Some idea are based on this paper [predicting the direction of stock market prices](https://arxiv.org/pdf/1605.00003.pdf) | 9/3/17 17:43 | 9/12/18 18:58 | 101.0 | :heavy_multiplication_x: | :star:x3 | | [Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models) | 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 | 3/20/17 18:54 | 4/25/17 23:35 | 31.0 | :heavy_multiplication_x: | :star:x2 | +| [Machine-Learning-and-AI-in-Trading](https://github.com/PyPatel/Machine-Learning-and-AI-in-Trading) | repo covering lstm and other sklearn model for trading. Code is not structured well and less extensible | 8/30/17 6:14 | 10/29/19 8:14 | 261.0 | :heavy_multiplication_x: | :star:x2 | | [CryptoBot](https://github.com/AdeelMufti/CryptoBot) | 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 | 1/17/17 12:44 | 1/17/17 12:48 | 234.0 | :heavy_multiplication_x: | :star:x2 | | [Machine-Learning-For-Finance](https://github.com/anthonyng2/Machine-Learning-For-Finance) | 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) | 7/11/17 9:09 | 2/21/18 5:36 | 205.0 | :heavy_multiplication_x: | :star:x2 | | [Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading) | NEW | 3/26/15 2:22 | 3/26/15 2:33 | 173.0 | :heavy_multiplication_x: | :star:x2 | | [botflow](https://github.com/kkyon/botflow) | python dataflow programming framework. Similar and probably replaceable by sklearn.pipeline module. Uses [graphviz](https://graphviz.org/) for visiualization though not maintained with last commit over 3 years ago | 8/20/18 3:13 | 5/23/19 14:40 | 1165.0 | :heavy_multiplication_x: | :star:x2 | | [Trend Following](http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html) | A futures trend following portfolio investment strategy. | nan | nan | nan | :heavy_check_mark: | | | [awesome-ai-in-finance](https://github.com/georgezouq/awesome-ai-in-finance) | NEW | 8/29/18 2:07 | 11/27/20 9:43 | 941.0 | :heavy_check_mark: | | -| [Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps) | Research in investment finance for long term forecasts. | 7/22/18 8:14 | 2/17/21 14:39 | 383.0 | :heavy_check_mark: | | | [finance_ml](https://github.com/jjakimoto/finance_ml) | NEW | 6/29/18 21:21 | 2/18/19 12:34 | 282.0 | :heavy_multiplication_x: | | -| [Machine-Learning-and-AI-in-Trading](https://github.com/PyPatel/Machine-Learning-and-AI-in-Trading) | NEW | 8/30/17 6:14 | 10/29/19 8:14 | 261.0 | :heavy_check_mark: | | -| [Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant) | Identify social/historical cues for short term stock movement. | 9/12/16 18:38 | 8/29/18 20:27 | 2166.0 | :heavy_multiplication_x: | | -| [Machine-Learning-for-Finance](https://github.com/PacktPublishing/Machine-Learning-for-Finance) | NEW | 3/15/18 6:28 | 1/14/21 15:58 | 180.0 | :heavy_check_mark: | | | [Stock.Indicators](https://github.com/DaveSkender/Stock.Indicators) | NEW | 12/29/19 5:18 | 4/11/21 19:17 | 175.0 | :heavy_check_mark: | | | [Machine-Learning-for-Algorithmic-Trading-Bots-with-Python](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python) | NEW | 12/6/18 11:35 | 1/18/21 6:40 | 172.0 | :heavy_check_mark: | | | [Mixture Models II](https://github.com/BlackArbsCEO/mixture_model_trading_public) | Mixture models and stock trading. | 12/11/17 17:05 | 5/13/20 23:50 | 166.0 | :heavy_check_mark: | | -| [fin-ml](https://github.com/tatsath/fin-ml) | NEW | 5/10/20 0:25 | 1/23/21 17:15 | 116.0 | :heavy_check_mark: | | -| [Microservices-Based-Algorithmic-Trading-System](https://github.com/saeed349/Microservices-Based-Algorithmic-Trading-System) | NEW | 1/6/20 0:21 | 3/31/20 13:02 | 104.0 | :heavy_check_mark: | | -| [Speculator](https://github.com/amicks/Speculator) | NEW | 9/3/17 17:43 | 9/12/18 18:58 | 101.0 | :heavy_multiplication_x: | | \ No newline at end of file +| [Microservices-Based-Algorithmic-Trading-System](https://github.com/saeed349/Microservices-Based-Algorithmic-Trading-System) | NEW | 1/6/20 0:21 | 3/31/20 13:02 | 104.0 | :heavy_check_mark: | | \ No newline at end of file