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# Financial Machine Learning and Data Science
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A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.
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A listed repository should be deprecated if:
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- Repository's owner explicitly say that "this library is not maintained".
- Not committed for long time (2~3 years).
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**This repo is officially under revamp as of 3/29/2021!!**
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- TODOs and roadmap is under the github project [here ](https://github.com/firmai/financial-machine-learning/projects/1 )
- If you would like to contribute to this repo, please send us a pull request or contact [@dereknow ](https://twitter.com/dereknow ) or [@bin-yang-algotune ](https://twitter.com/b3yang )
- Join us in the gitter chat [here ](https://gitter.im/financial-machine-learning/community )
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___
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- All repos/links status including last commit date is updated daily
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- Only 15 Highest ranked repos/links for each section are displayed on main README.md and full list is available within the wiki page
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- Both Wikis/README.md is updated in realtime as soon as new information are pushed to the repo
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___
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# Trading
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## Deep Learning & Reinforcement Learning ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/deep_learning_and_reinforcement_learning))
<!-- [PLACEHOLDER_START:deep_learning_and_reinforcement_learning] -->
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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> |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[Stock-Prediction-Models ](https://github.com/huseinzol05/Stock-Prediction-Models )</sub> | <sub>very good curated list of notebooks showing deep learning + reinforcement learning models. Also contain topics on outlier detections/overbought oversold study/monte carlo simulartions/sentiment analysis from text (text storage/parsing is not detailed but it mentioned using [BERT ](https://github.com/google-research/bert ))</sub> | <sub>12/18/17 10:49</sub> | <sub>1/5/21 10:31</sub> | <sub>3655.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
| <sub>[AI Trading ](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md )</sub> | <sub>AI to predict stock market movements.</sub> | <sub>1/9/19 8:02</sub> | <sub>2/11/19 16:32</sub> | <sub>2876.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
| <sub>[FinRL-Library ](https://github.com/AI4Finance-LLC/FinRL-Library )</sub> | <sub>started by Columbia university engineering students and designed as an end to end deep reinforcement learning library for automated trading platform. Implementation of DQN DDQN DDPG etc using PyTorch and [gym ](https://gym.openai.com/ ) use [pyfolio ](https://github.com/quantopian/pyfolio ) for showing backtesting stats. Big contributions on Proximal Policy Optimization (PPO) advantage actor critic (A2C) and Deep Deterministic Policy Gradient (DDPG) agents for trading</sub> | <sub>7/26/20 13:18</sub> | <sub>4/11/21 22:02</sub> | <sub>1857.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
| <sub>[Deep Learning IV ](https://github.com/achillesrasquinha/bulbea )</sub> | <sub>Bulbea: Deep Learning based Python Library.</sub> | <sub>3/9/17 6:11</sub> | <sub>3/19/17 7:42</sub> | <sub>1467.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
| <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>1312.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
| <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>1266.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
| <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>1144.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
| <sub>[RL Trading ](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW )</sub> | <sub>A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
| <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>560.0</sub> | <sub>:heavy_check_mark:</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>551.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
| <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>1207.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
| <sub>[RL III ](https://github.com/samre12/deep-trading-agent )</sub> | <sub>Github -Deep Reinforcement Learning based Trading Agent for Bitcoin.</sub> | <sub>9/21/17 17:05</sub> | <sub>4/13/18 16:33</sub> | <sub>576.0</sub> | <sub>:heavy_multiplication_x:</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>347.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |<!-- [PLACEHOLDER_END:deep_learning_and_reinforcement_learning] -->
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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> |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
| <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>[Microservices-Based-Algorithmic-Trading-System ](https://github.com/saeed349/Microservices-Based-Algorithmic-Trading-System )</sub> | <sub>docker based platfrom for developing algo trading strategies. Very interesting combinations of open source components were used including [backtrader ](https://www.backtrader.com/ ) for backtest strategies / [mlflow ](https://mlflow.org/ ) for managing the machine learning model life cycle (i.e. training and developing machine learning models) / [airflow ](https://airflow.apache.org/ ) used as workflow management including schedule data download etc. / [superset ](https://superset.apache.org/ ) web data visualization tool similar to tableau / [minio ](https://min.io/ ) for fast object storage (i.e. storing saved models and model artifacts) / postgresql used to store security master and daily and minute data. Also contains some details on deployment on cloud</sub> | <sub>1/6/20 0:21</sub> | <sub>3/31/20 13:02</sub> | <sub>104.0</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>[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> |
| <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> |
| <sub>[awesome-ai-in-finance ](https://github.com/georgezouq/awesome-ai-in-finance )</sub> | <sub>curated list of books/online courses/papers on AI and finance. Topics include crypto trading strategies/ta/backter etc.</sub> | <sub>8/29/18 2:07</sub> | <sub>11/27/20 9:43</sub> | <sub>941.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <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> |
| <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>[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> |
| <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> |
| <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> |
| <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> |
| <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> |
| <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> |<!-- [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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<!-- [PLACEHOLDER_START:data_processing_techniques_and_transformations] -->
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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> |
|:----------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[Advanced ML II ](https://github.com/hudson-and-thames/research )</sub> | <sub>More implementations of Financial Machine Learning (De Prado).</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Advanced ML ](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises )</sub> | <sub>Exercises too Financial Machine Learning (De Prado).</sub> | <sub>4/25/18 17:22</sub> | <sub>1/16/20 17:25</sub> | <sub>973.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[finserv-application-blueprint ](https://github.com/mapr-demos/finserv-application-blueprint )</sub> | <sub>NEW</sub> | <sub>9/26/16 19:42</sub> | <sub>1/20/21 23:07</sub> | <sub>72.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Google-Finance-Stock-Data-Analysis ](https://github.com/hpnhxxwn/Google-Finance-Stock-Data-Analysis )</sub> | <sub>NEW</sub> | <sub>7/23/17 2:59</sub> | <sub>7/23/17 3:10</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Twitter-Trends ](https://github.com/Medha11/Twitter-Trends )</sub> | <sub>NEW</sub> | <sub>5/22/17 17:07</sub> | <sub>5/23/17 8:06</sub> | <sub>66.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[cointrader ](https://github.com/timolson/cointrader )</sub> | <sub>NEW</sub> | <sub>6/1/14 1:14</sub> | <sub>10/22/20 0:24</sub> | <sub>339.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[CryptoNets ](https://github.com/microsoft/CryptoNets )</sub> | <sub>NEW</sub> | <sub>6/2/19 5:48</sub> | <sub>9/12/19 13:03</sub> | <sub>154.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:data_processing_techniques_and_transformations] -->
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# Portfolio Management
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## Portfolio Selection and Optimisation ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/portfolio_selection_and_optimisation))
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<!-- [PLACEHOLDER_START:portfolio_selection_and_optimisation] -->
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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> |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[Modern Portfolio Theory ](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb )</sub> | <sub>Universal portfolios; modern portfolio theory.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Online Portfolio Selection ](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb )</sub> | <sub>****Comparing OLPS algorithms on a diversified set of ETFs.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[OLMAR Algorithm ](https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb )</sub> | <sub>Relative importance of each component of the OLMAR algorithm.</sub> | <sub>7/26/16 16:20</sub> | <sub>12/30/16 11:40</sub> | <sub>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Riskfolio-Lib ](https://github.com/dcajasn/Riskfolio-Lib )</sub> | <sub>NEW</sub> | <sub>3/2/20 19:49</sub> | <sub>4/1/21 3:50</sub> | <sub>371.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Reinforcement Learning ](https://github.com/filangel/qtrader )</sub> | <sub>Reinforcement Learning for Portfolio Management.</sub> | <sub>10/7/17 9:14</sub> | <sub>6/26/18 9:22</sub> | <sub>364.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[DeepDow ](https://github.com/jankrepl/deepdow )</sub> | <sub>Portfolio optimization with deep learning.</sub> | <sub>2/2/20 8:46</sub> | <sub>2/16/21 18:50</sub> | <sub>311.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Distribution Characteristic Optimisation ](https://github.com/VivekPa/OptimalPortfolio )</sub> | <sub>Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.</sub> | <sub>11/16/18 12:20</sub> | <sub>7/4/19 1:41</sub> | <sub>232.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[PyPortfolioOpt ](https://github.com/robertmartin8/PyPortfolioOpt )</sub> | <sub>Financial portfolio optimisation, including classical efficient frontier and advanced methods.</sub> | <sub>5/29/18 13:30</sub> | <sub>2/25/21 13:01</sub> | <sub>1895.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[401K Portfolio Optimisation ](https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb )</sub> | <sub>Portfolio analyses and optimisation for 401K.</sub> | <sub>8/1/18 19:48</sub> | <sub>9/5/19 11:18</sub> | <sub>14.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-12 21:23:25 +00:00
| <sub>[Policy Gradient Portfolio ](https://github.com/ZhengyaoJiang/PGPortfolio )</sub> | <sub>A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.</sub> | <sub>11/12/17 16:08</sub> | <sub>5/9/19 9:50</sub> | <sub>1281.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[riskparity.py ](https://github.com/dppalomar/riskparity.py )</sub> | <sub>NEW</sub> | <sub>7/13/19 21:30</sub> | <sub>1/30/21 1:53</sub> | <sub>124.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Deep Portfolio Theory ](https://github.com/tcloaa/Deep-Portfolio-Theory )</sub> | <sub>Autoencoder framework for portfolio selection.</sub> | <sub>2/10/17 9:03</sub> | <sub>3/8/18 16:47</sub> | <sub>105.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Efficient Frontier ](https://github.com/tthustla/efficient_frontier/blob/master/Efficient%20_Frontier_implementation.ipynb )</sub> | <sub>Modern Portfolio Theory.</sub> | <sub>2/17/18 8:19</sub> | <sub>2/27/18 13:16</sub> | <sub>104.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[node-finance ](https://github.com/albertosantini/node-finance )</sub> | <sub>NEW</sub> | <sub>9/17/11 17:49</sub> | <sub>4/5/21 8:01</sub> | <sub>101.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:portfolio_selection_and_optimisation] -->
2021-03-30 12:27:45 -04:00
2021-04-02 15:33:44 -04:00
## Factor and Risk Analysis ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/factor_and_risk_analysis))
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<!-- [PLACEHOLDER_START:factor_and_risk_analysis] -->
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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> |
|:-----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[stock-market-analysis-using-python-numpy-pandas ](https://github.com/Ashishsinha10/stock-market-analysis-using-python-numpy-pandas )</sub> | <sub>NEW</sub> | <sub>4/10/18 5:15</sub> | <sub>4/10/18 5:28</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-15 05:00:04 +00:00
| <sub>[Machine_learning_In_Finance ](https://github.com/chaudharigauravi/Machine_learning_In_Finance )</sub> | <sub>NEW</sub> | <sub>8/3/19 4:04</sub> | <sub>8/3/19 4:05</sub> | <sub>8.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-14 04:54:46 +00:00
| <sub>[-L- ](https://github.com/jettbrains/-L- )</sub> | <sub>NEW</sub> | <sub>10/28/19 21:50</sub> | <sub>10/28/19 21:51</sub> | <sub>6.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[TradeFinexLive ](https://github.com/XinFinOrg/TradeFinexLive )</sub> | <sub>NEW</sub> | <sub>3/21/18 10:05</sub> | <sub>3/18/21 9:48</sub> | <sub>5.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[VaR GaN ](https://github.com/hamaadshah/market_risk_gan_keras )</sub> | <sub>Estimate Value-at-Risk for market risk management using Keras and TensorFlow.</sub> | <sub>8/6/18 16:09</sub> | <sub>11/22/20 19:02</sub> | <sub>41.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Various Risk Measures ](https://github.com/Jorgencr/Alternative-and-Responsible-Investments/blob/master/Final_masterfile.ipynb )</sub> | <sub>Risk measures and factors for alternative and responsible investments.</sub> | <sub>8/7/17 14:44</sub> | <sub>8/8/17 22:52</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Pyfolio ](https://github.com/quantopian/pyfolio )</sub> | <sub>Portfolio and risk analytics in Python.</sub> | <sub>6/1/15 15:31</sub> | <sub>2/28/20 17:30</sub> | <sub>3673.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Quant Finance ](https://github.com/mrefermat/quant_finance )</sub> | <sub>General quant repository.</sub> | <sub>8/11/18 22:59</sub> | <sub>11/12/19 4:49</sub> | <sub>31.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-15 05:00:04 +00:00
| <sub>[Risk Basic ](https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%203%2C%20Risk.ipynb )</sub> | <sub>Active portfolio risk management .</sub> | <sub>5/10/16 11:03</sub> | <sub>5/17/16 3:44</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[CAPM ](https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%202%2C%20CAPM.ipynb )</sub> | <sub>Expected returns using CAPM.</sub> | <sub>5/10/16 11:03</sub> | <sub>5/17/16 3:44</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-14 04:54:46 +00:00
| <sub>[Factor Analysis ](https://github.com/garvit-kudesia91/factor_analysis/blob/master/Factor%20Analysis%20of%20Mutual%20Funds.ipynb )</sub> | <sub>Factor analysis for mutual funds.</sub> | <sub>3/13/18 7:39</sub> | <sub>3/13/18 7:42</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Statistical Finance ](https://github.com/mrefermat/FinancePhD/tree/master/FinancialExperiments )</sub> | <sub>Various financial experiments.</sub> | <sub>10/4/15 9:10</sub> | <sub>3/28/20 18:33</sub> | <sub>21.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Performance Analysis ](https://github.com/quantopian/alphalens )</sub> | <sub>Performance analysis of predictive (alpha) stock factors.</sub> | <sub>6/3/16 21:49</sub> | <sub>4/27/20 18:40</sub> | <sub>1847.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Convex Optimisation ](https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb )</sub> | <sub>Convex Optimization for Finance.</sub> | <sub>6/26/18 20:36</sub> | <sub>10/22/19 21:56</sub> | <sub>18.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Factor Analysis ](https://github.com/alpha-miner/alpha-mind/tree/master/notebooks )</sub> | <sub>Factor strategy notebooks.</sub> | <sub>5/1/17 7:36</sub> | <sub>4/7/21 15:25</sub> | <sub>172.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:factor_and_risk_analysis] -->
2019-03-22 04:24:28 +00:00
# Techniques
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## Unsupervised ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/unsupervised))
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<!-- [PLACEHOLDER_START:unsupervised] -->
2021-04-12 21:15:21 +00:00
| <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> |
|:-------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[PCA Pairs Trading ](https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading )</sub> | <sub>PCA, Factor Returns, and trading strategies.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Pairs Trading ](https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb )</sub> | <sub>Finding pairs with cluster analysis.</sub> | <sub>9/5/17 19:19</sub> | <sub>9/27/17 20:42</sub> | <sub>79.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Industry Clustering ](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries )</sub> | <sub>Clustering of industries.</sub> | <sub>7/21/17 2:12</sub> | <sub>7/23/17 2:53</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Fund Clusters ](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb )</sub> | <sub>Data exploration of fund clusters.</sub> | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-13 16:13:07 +00:00
| <sub>[Industry Clustering ](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries )</sub> | <sub>Project to cluster industries according to financial attributes.</sub> | <sub>7/21/17 2:12</sub> | <sub>7/23/17 2:53</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-12 21:15:21 +00:00
| <sub>[VRA Stock Embedding ](https://github.com/ml-hongkong/stock2vec )</sub> | <sub>Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.</sub> | <sub>6/21/17 4:47</sub> | <sub>6/21/17 4:51</sub> | <sub>32.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:unsupervised] -->
2019-03-22 04:24:28 +00:00
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## Textual ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/textual))
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<!-- [PLACEHOLDER_START:textual] -->
2021-04-12 21:15:21 +00:00
| <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> |
|:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[NLP ](https://github.com/toamitesh/NLPinFinance )</sub> | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Financial Statement Sentiment ](https://github.com/MAydogdu/TextualAnalysis )</sub> | <sub>Extracting sentiment from financial statements using neural networks.</sub> | <sub>6/4/18 20:54</sub> | <sub>6/4/18 20:56</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Extensive NLP ](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb )</sub> | <sub>Comprehensive NLP techniques for accounting research.</sub> | <sub>10/25/17 7:10</sub> | <sub>6/5/20 3:28</sub> | <sub>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[NLP Event ](https://github.com/yuriak/DLQuant )</sub> | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub> | <sub>7/2/18 23:50</sub> | <sub>1/31/19 14:08</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Financial Sentiment Analysis ](https://github.com/EricHe98/Financial-Statements-Text-Analysis )</sub> | <sub>Sentiment, distance and proportion analysis for trading signals.</sub> | <sub>6/23/17 0:05</sub> | <sub>1/26/19 3:35</sub> | <sub>48.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Fund classification ](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb )</sub> | <sub>Fund classification using text mining and NLP.</sub> | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Earning call transcripts ](https://github.com/lin882/WebAnalyticsProject )</sub> | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>12/30/17 8:56</sub> | <sub>1/11/18 2:11</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Accounting Anomalies ](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb )</sub> | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub> | <sub>5/24/17 12:36</sub> | <sub>8/7/19 21:47</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Buzzwords ](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds )</sub> | <sub>Return performance and mutual fund selection.</sub> | <sub>2/4/18 21:51</sub> | <sub>2/4/18 21:57</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:textual] -->
2019-03-22 04:24:28 +00:00
# Other Assets
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## Derivatives and Hedging ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/derivatives_and_hedging))
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<!-- [PLACEHOLDER_START:derivatives_and_hedging] -->
2021-04-14 04:54:46 +00:00
| <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> |
|:----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:--------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[Options ](https://github.com/PHBS/2018.M1.ASP/tree/master/py )</sub> | <sub>Black Scholes and Copula.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Derivative Markets ](https://github.com/broughtj/Fin6470/tree/master/Notebooks )</sub> | <sub>The economics of futures, futures, options, and swaps.</sub> | <sub>2/9/16 5:30</sub> | <sub>4/6/21 20:49</sub> | <sub>8.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Volatility and Variance Derivatives ](https://github.com/yhilpisch/lvvd/tree/master/lvvd )</sub> | <sub>Volatility derivatives analytics.</sub> | <sub>10/21/16 4:12</sub> | <sub>2/22/21 13:32</sub> | <sub>79.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[tda-api ](https://github.com/alexgolec/tda-api )</sub> | <sub>NEW</sub> | <sub>4/3/20 21:19</sub> | <sub>4/12/21 3:12</sub> | <sub>649.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Strata ](https://github.com/OpenGamma/Strata )</sub> | <sub>NEW</sub> | <sub>6/16/14 11:45</sub> | <sub>4/12/21 6:32</sub> | <sub>607.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[FinanceDatabase ](https://github.com/JerBouma/FinanceDatabase )</sub> | <sub>NEW</sub> | <sub>1/28/21 18:36</sub> | <sub>4/8/21 11:10</sub> | <sub>595.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[gs-quant ](https://github.com/goldmansachs/gs-quant )</sub> | <sub>NEW</sub> | <sub>12/14/18 21:10</sub> | <sub>3/25/21 15:33</sub> | <sub>584.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[wallstreet ](https://github.com/mcdallas/wallstreet )</sub> | <sub>NEW</sub> | <sub>1/20/16 22:03</sub> | <sub>8/9/19 23:14</sub> | <sub>570.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[algotrader ](https://github.com/torreyleonard/algotrader )</sub> | <sub>NEW</sub> | <sub>4/10/18 2:31</sub> | <sub>8/27/20 8:16</sub> | <sub>477.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[FinancePy ](https://github.com/domokane/FinancePy )</sub> | <sub>NEW</sub> | <sub>10/27/19 15:04</sub> | <sub>3/11/21 21:40</sub> | <sub>441.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Hull White ](https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb )</sub> | <sub>Callable Bond, Hull White.</sub> | <sub>6/6/18 22:06</sub> | <sub>6/6/18 22:27</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[optopsy ](https://github.com/michaelchu/optopsy )</sub> | <sub>NEW</sub> | <sub>9/17/17 1:49</sub> | <sub>12/7/20 17:00</sub> | <sub>393.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[StockSharp ](https://github.com/StockSharp/StockSharp )</sub> | <sub>NEW</sub> | <sub>12/8/14 7:53</sub> | <sub>4/7/21 10:55</sub> | <sub>3924.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Derivatives Python ](https://github.com/yhilpisch/dawp/tree/master/python36 )</sub> | <sub>Derivative analytics with Python.</sub> | <sub>7/9/15 12:27</sub> | <sub>2/22/21 13:29</sub> | <sub>388.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[akshare ](https://github.com/jindaxiang/akshare )</sub> | <sub>NEW</sub> | <sub>10/1/19 7:34</sub> | <sub>4/13/21 11:18</sub> | <sub>3374.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:derivatives_and_hedging] -->
2021-03-30 12:27:45 -04:00
2021-04-02 15:33:44 -04:00
## Fixed Income ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/fixed_income))
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<!-- [PLACEHOLDER_START:fixed_income] -->
2021-04-15 05:00:04 +00:00
| <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> |
|:-------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[Corporate Bonds ](https://github.com/ishank011/gs-quantify-bond-prediction )</sub> | <sub>Predicting the buying and selling volume of the corporate bonds.</sub> | <sub>9/27/17 19:57</sub> | <sub>9/27/17 20:00</sub> | <sub>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Vasicek ](https://github.com/RobinsonGarcia/fixed-income/blob/master/2.0%20Vasicek%20-%20example.ipynb )</sub> | <sub>Bootstrapping and interpolation.</sub> | <sub>7/18/18 19:26</sub> | <sub>7/18/18 19:34</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[rating_history ](https://github.com/govwiki/rating_history )</sub> | <sub>NEW</sub> | <sub>11/23/17 22:52</sub> | <sub>12/3/17 20:42</sub> | <sub>26.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[market-data ](https://github.com/kriasoft/market-data )</sub> | <sub>NEW</sub> | <sub>12/7/12 13:42</sub> | <sub>12/15/12 12:10</sub> | <sub>25.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[woe ](https://github.com/boredbird/woe )</sub> | <sub>NEW</sub> | <sub>9/11/17 7:15</sub> | <sub>3/1/18 10:45</sub> | <sub>224.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[MagentoExtensions ](https://github.com/5mehulhelp5/MagentoExtensions )</sub> | <sub>NEW</sub> | <sub>7/3/14 5:45</sub> | <sub>11/24/17 16:15</sub> | <sub>106.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Binomial Tree ](https://github.com/hy-lei/math-finance-exercise )</sub> | <sub>Utility functions in fixed income securities.</sub> | <sub>2/2/19 8:44</sub> | <sub>5/3/19 17:16</sub> | <sub>1.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:fixed_income] -->
2021-03-30 12:27:45 -04:00
2021-04-02 15:33:44 -04:00
## Alternative Finance ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/alternative_finance))
2021-04-12 17:46:08 +00:00
<!-- [PLACEHOLDER_START:alternative_finance] -->
2021-04-12 21:15:21 +00:00
| <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> |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[Venture Capital NN ](https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring )</sub> | <sub>Cox-PH neural network predictions for VC/innovations finance research.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Art Valuation ](https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb )</sub> | <sub>Art evaluation analytics.</sub> | <sub>12/11/14 0:25</sub> | <sub>12/12/14 21:25</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Private Equity ](https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb )</sub> | <sub>Valuation models.</sub> | <sub>1/27/16 21:13</sub> | <sub>3/14/16 20:03</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Kiva Crowdfunding ](https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb )</sub> | <sub>Exploratory data analysis.</sub> | <sub>2/27/18 16:46</sub> | <sub>2/13/19 0:15</sub> | <sub>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Watch Valuation ](https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb )</sub> | <sub>Analysis of luxury watch data to classify whether a certain model is likely to be over-or undervalued.</sub> | <sub>2/8/17 18:39</sub> | <sub>4/27/17 22:55</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Venture Capital ](https://github.com/julian-chan/etothex )</sub> | <sub>Insight into a new founder to make data-driven investment decisions.</sub> | <sub>12/4/17 8:59</sub> | <sub>12/13/17 5:35</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[VC OLS ](https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb )</sub> | <sub>VC regression.</sub> | <sub>3/29/18 23:31</sub> | <sub>3/29/18 23:33</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Blockchain ](https://github.com/nud3l/dInvest )</sub> | <sub>Repository for distributed autonomous investment banking.</sub> | <sub>9/5/16 19:12</sub> | <sub>4/24/17 10:48</sub> | <sub>12.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:alternative_finance] -->
2021-03-30 12:27:45 -04:00
2021-04-02 15:33:44 -04:00
# Extended Research ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/extended_research))
2021-04-12 17:46:08 +00:00
<!-- [PLACEHOLDER_START:extended_research] -->
2021-04-12 21:15:21 +00:00
| <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> |
|:------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
2021-04-14 03:14:10 +00:00
| <sub>[Real Estate Property Fraud ](https://github.com/aviroop1/Real_Estate_Property_Fraud )</sub> | <sub>Unsupervised fraud detection model that can identify likely candidates of fraud.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-15 05:00:04 +00:00
| <sub>[Commodity ](https://github.com/felipessalvatore/fin2vec/blob/master/src/Commodity2BR.ipynb )</sub> | <sub>Commodity influence over Brazilian stocks.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-12 21:15:21 +00:00
| <sub>[Behavioural Economics ](https://github.com/pcmichaud/notebooks )</sub> | <sub>Behavioural Economics and Finance Python Notebooks.</sub> | <sub>12/20/18 0:21</sub> | <sub>3/26/19 11:51</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Corporate Finance ](https://github.com/SeanMcOwen/FinanceAndPython.com-CorporateFinance )</sub> | <sub>Basic corporate finance.</sub> | <sub>9/9/17 3:35</sub> | <sub>9/9/17 23:04</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-13 16:13:07 +00:00
| <sub>[Applied Corporate Finance ](https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance )</sub> | <sub>Studies the empirical behaviours in stock market.</sub> | <sub>1/29/18 5:14</sub> | <sub>7/19/18 6:25</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-15 05:00:04 +00:00
| <sub>[NLP Finance Papers ](https://github.com/chenbowen184/Research_Documents_Curation_with_NLP )</sub> | <sub>Curating quantitative finance papers using machine learning.</sub> | <sub>10/11/18 20:32</sub> | <sub>12/24/18 23:27</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-12 21:15:21 +00:00
| <sub>[HFT ](https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy )</sub> | <sub>High frequency trading.</sub> | <sub>7/21/16 5:14</sub> | <sub>2/14/17 16:47</sub> | <sub>748.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Financial Economics ](https://github.com/rsvp/fecon235/tree/master/nb )</sub> | <sub>Financial Economics Models.</sub> | <sub>11/9/14 4:49</sub> | <sub>12/3/18 16:30</sub> | <sub>713.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Mathematical Finance ](https://github.com/Auquan/Tutorials )</sub> | <sub>Notebooks for math and financial tutorials.</sub> | <sub>1/21/17 11:24</sub> | <sub>8/1/20 17:03</sub> | <sub>664.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-12 21:23:25 +00:00
| <sub>[Backtests ](https://github.com/AlgoTraders/stock-analysis-engine )</sub> | <sub>Trading data and algorithms.</sub> | <sub>9/16/18 20:00</sub> | <sub>9/5/20 13:01</sub> | <sub>620.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Liquidity and Momentum ](https://github.com/mrefermat/quant_finance )</sub> | <sub>Various factors and portfolio constructions.</sub> | <sub>8/11/18 22:59</sub> | <sub>11/12/19 4:49</sub> | <sub>31.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-15 05:00:04 +00:00
| <sub>[Currency PCA ](https://github.com/shanemulqueen/python-finance-pca/blob/master/FX_spots_w_PCA.ipynb )</sub> | <sub>Forex spots PCA.</sub> | <sub>3/12/19 21:11</sub> | <sub>3/12/19 22:09</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-12 21:23:25 +00:00
| <sub>[M&A ](https://github.com/atulram/Finance-and-Stocks )</sub> | <sub>Mergers and Acquisitions.</sub> | <sub>1/19/19 18:16</sub> | <sub>2/18/19 16:57</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-14 03:14:10 +00:00
| <sub>[Life-cycle ](https://github.com/atulram/Finance-and-Stocks/blob/master/CompanyLifeCycle.ipynb )</sub> | <sub>Company life cycle.</sub> | <sub>1/19/19 18:16</sub> | <sub>2/18/19 16:57</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-12 21:23:25 +00:00
| <sub>[Deep Portfolio ](https://github.com/DLColumbia/DL_forFinance )</sub> | <sub>Deep learning for finance Predict volume of bonds.</sub> | <sub>5/8/18 19:34</sub> | <sub>5/9/18 15:39</sub> | <sub>27.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:extended_research] -->
2019-03-22 04:24:28 +00:00
2021-04-02 15:33:44 -04:00
# Courses ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/courses))
2021-04-12 17:46:08 +00:00
<!-- [PLACEHOLDER_START:courses] -->
2021-04-12 21:15:21 +00:00
| <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> |
|:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:--------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
| <sub>[Basic Investments ](https://github.com/SeanMcOwen/FinanceAndPython.com-Investments )</sub> | <sub>Basic investment tools in python.</sub> | <sub>8/2/17 21:52</sub> | <sub>8/17/17 3:24</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Mathematical Finance ](https://github.com/yadongli/nyumath2048 )</sub> | <sub>NYU Math-GA 2048: Scientific Computing in Finance.</sub> | <sub>1/25/15 21:10</sub> | <sub>3/25/20 4:24</sub> | <sub>69.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Algo Trading ](https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading )</sub> | <sub>Intro to algo trading.</sub> | <sub>10/29/17 20:34</sub> | <sub>1/22/19 6:56</sub> | <sub>64.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Risk Management ](https://github.com/andrey-lukyanov/Risk-Management )</sub> | <sub>Finance risk engagement course resources.</sub> | <sub>10/3/18 16:26</sub> | <sub>12/13/18 8:04</sub> | <sub>6.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Basic Derivatives ](https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives )</sub> | <sub>Basic forward contracts and hedging.</sub> | <sub>8/24/17 0:11</sub> | <sub>10/13/17 1:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Machine Learning for Trading ](https://github.com/stefan-jansen/machine-learning-for-trading )</sub> | <sub>Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.</sub> | <sub>5/9/18 12:33</sub> | <sub>4/10/21 22:21</sub> | <sub>3842.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[ML Specialisation ](https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization )</sub> | <sub>Machine Learning in Finance.</sub> | <sub>1/24/19 2:55</sub> | <sub>1/3/20 21:54</sub> | <sub>34.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Python for Finance ](https://github.com/siaen/python_finance_course )</sub> | <sub>CEU python for finance course material.</sub> | <sub>12/12/17 11:54</sub> | <sub>2/25/20 20:31</sub> | <sub>16.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Handson Python for Finance ](https://github.com/PacktPublishing/Hands-on-Python-for-Finance )</sub> | <sub>Hands-on Python for Finance published by Packt.</sub> | <sub>8/20/18 14:10</sub> | <sub>1/15/21 8:57</sub> | <sub>121.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Basic Finance ](https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance )</sub> | <sub>Source code notebooks basic finance applications.</sub> | <sub>5/6/17 2:39</sub> | <sub>6/21/17 4:04</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:courses] -->
2019-03-22 04:24:28 +00:00
2021-04-02 15:33:44 -04:00
# Data ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data))
2021-04-12 17:46:08 +00:00
<!-- [PLACEHOLDER_START:data] -->
2021-04-12 21:15:21 +00:00
| <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> |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
2021-04-14 03:14:10 +00:00
| <sub>[https://fred.stlouisfed.org/ ](https://fred.stlouisfed.org/ )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Financial Corporate ](http://raw.rutgers.edu/Corporate%20Financial%20Data.html )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-15 05:00:04 +00:00
| <sub>[http://finance.yahoo.com/ ](http://finance.yahoo.com/ )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Rating Industries ](http://www.ratingshistory.info/ )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Non-financial Corporate ](http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[https://stooq.com ](https://stooq.com )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[IRS ](http://social-metrics.org/sox/ )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Capital Markets Data ](https://www.capitalmarketsdata.com/ )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-12 21:15:21 +00:00
| <sub>[SEC Parsing ](https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb )</sub> | <sub>nan</sub> | <sub>6/16/18 14:30</sub> | <sub>6/16/18 17:23</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
2021-04-12 21:23:25 +00:00
| <sub>[https://github.com/timestocome/StockMarketData ](https://github.com/timestocome/StockMarketData )</sub> | <sub>nan</sub> | <sub>5/10/17 21:49</sub> | <sub>8/6/17 19:23</sub> | <sub>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Web Scraping (FirmAI) ](https://github.com/firmai/business-machine-learning/blob/master/www.firmai.org/data )</sub> | <sub>nan</sub> | <sub>2/19/19 19:02</sub> | <sub>7/22/20 16:48</sub> | <sub>577.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Open Edgar ](https://github.com/LexPredict/openedgar )</sub> | <sub>nan</sub> | <sub>5/7/18 15:32</sub> | <sub>5/15/19 8:32</sub> | <sub>169.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[EDGAR ](https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb )</sub> | <sub>nan</sub> | <sub>6/11/18 22:51</sub> | <sub>7/10/18 18:03</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Employee Count SEC Filings ](https://github.com/healthgradient/sec_employee_information_extraction )</sub> | <sub>nan</sub> | <sub>6/26/18 23:33</sub> | <sub>8/14/18 1:31</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:data] -->
2019-07-17 00:36:28 +01:00
2021-04-02 15:33:44 -04:00
# Colleges, Centers and Departments ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/colleges_centers_and_departments))
2021-04-12 17:46:08 +00:00
<!-- [PLACEHOLDER_START:colleges_centers_and_departments] -->
| <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> |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
2021-04-15 05:00:04 +00:00
| <sub>[NYU FRE ](https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering )</sub> | <sub>Finance and Risk Engineering (NYU Tandon)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-14 03:14:10 +00:00
| <sub>[Cornell University ](https://www.cornell.edu/ )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
2021-04-15 05:00:04 +00:00
| <sub>[Stanford Advanced Financial Technologies ](https://fintech.stanford.edu/ )</sub> | <sub>Stanford Advanced Financial Technologies Laboratory</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[NYU Courant ](https://cims.nyu.edu/ )</sub> | <sub>Courant Institute of Mathematical Sciences, New York University</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Berkeley Lab CIFT ](https://cs.lbl.gov/news-media/news/news-archive/2010/berkeley-lab-launches-new-center-for-innovative-financial-technology/ )</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Oxford Man ](https://www.oxford-man.ox.ac.uk/ )</sub> | <sub>Oxford-Man Institute of Quantitative Finance</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:colleges_centers_and_departments] -->
