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# Financial Machine Learning and Data Science
A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.
A listed repository should be deprecated if:
- Repository's owner explicitly say that "this library is not maintained".
- Not committed for long time (2~3 years).
**This repo is officially under revamp as of 3/29/2021!!**
- 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)
___
- All repos/links status including last commit date is updated daily
- Only 15 Highest ranked repos/links for each section are displayed on main README.md and full list is available within the wiki page
- Both Wikis/README.md is updated in realtime as soon as new information are pushed to the repo
___
# Trading
## 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] -->
| <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> |
| <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] -->
## Other Models ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/other_models))
<!-- [PLACEHOLDER_START:other_models] -->
| <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>[CryptoBot](https://github.com/AdeelMufti/CryptoBot)</sub> | <sub>Hard fork of [bitpredit](https://github.com/cbyn/bitpredict) and form the trading strategy as a classification problem with -1 (sell) 0 (hold) 1 (buy). Models used are XGBClassifier/RandomForest/GradientBoosting. Not mentained</sub> | <sub>1/17/17 12:44</sub> | <sub>1/17/17 12:48</sub> | <sub>234.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <sub>[Trend Following](http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html)</sub> | <sub>A futures trend following portfolio investment strategy.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[awesome-ai-in-finance](https://github.com/georgezouq/awesome-ai-in-finance)</sub> | <sub>NEW</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></sub> |
| <sub>[Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks)</sub> | <sub>Using python and scikit-learn to make stock predictions.</sub> | <sub>2/12/17 4:50</sub> | <sub>2/4/21 3:48</sub> | <sub>931.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy)</sub> | <sub>NEW</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></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></sub> |
| <sub>[Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps)</sub> | <sub>Research in investment finance for long term forecasts.</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></sub> |
| <sub>[stock-trading-ml](https://github.com/yacoubb/stock-trading-ml)</sub> | <sub>NEW</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></sub> |
| <sub>[Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models)</sub> | <sub>Mixture models to predict market bottoms.</sub> | <sub>3/20/17 18:54</sub> | <sub>4/25/17 23:35</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[finance_ml](https://github.com/jjakimoto/finance_ml)</sub> | <sub>NEW</sub> | <sub>6/29/18 21:21</sub> | <sub>2/18/19 12:34</sub> | <sub>282.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original)</sub> | <sub>NEW</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></sub> |
| <sub>[Machine-Learning-and-AI-in-Trading](https://github.com/PyPatel/Machine-Learning-and-AI-in-Trading)</sub> | <sub>NEW</sub> | <sub>8/30/17 6:14</sub> | <sub>10/29/19 8:14</sub> | <sub>261.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes)</sub> | <sub>NEW</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></sub> |
| <sub>[mlfinlab](https://github.com/hudson-and-thames/mlfinlab)</sub> | <sub>NEW</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></sub> |<!-- [PLACEHOLDER_END:other_models] -->
## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations))
<!-- [PLACEHOLDER_START:data_processing_techniques_and_transformations] -->
| <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>2016-09-26 19:42:54</sub> | <sub>2021-01-20 23:07:40</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>2017-07-23 02:59:59</sub> | <sub>2017-07-23 03:10:35</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>2017-05-22 17:07:45</sub> | <sub>2017-05-23 08:06:27</sub> | <sub>66.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[cointrader](https://github.com/timolson/cointrader)</sub> | <sub>NEW</sub> | <sub>2014-06-01 01:14:12</sub> | <sub>2020-10-22 00:24:50</sub> | <sub>339.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[CryptoNets](https://github.com/microsoft/CryptoNets)</sub> | <sub>NEW</sub> | <sub>2019-06-02 05:48:39</sub> | <sub>2019-09-12 13:03:05</sub> | <sub>154.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:data_processing_techniques_and_transformations] -->
# Portfolio Management
## Portfolio Selection and Optimisation ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/portfolio_selection_and_optimisation))
<!-- [PLACEHOLDER_START:portfolio_selection_and_optimisation] -->
| <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>[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> |
| <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> |
| <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> |
| <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] -->
## Factor and Risk Analysis ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/factor_and_risk_analysis))
<!-- [PLACEHOLDER_START:factor_and_risk_analysis] -->
| <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>[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>[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> |
| <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> |
| <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>[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> |
| <sub>[AlphaTrading](https://github.com/jerryxyx/AlphaTrading)</sub> | <sub>NEW</sub> | <sub>5/18/18 22:09</sub> | <sub>8/7/18 18:05</sub> | <sub>149.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Risk and Return](https://github.com/PyDataBlog/Python-for-Data-Science/tree/master/Tutorials)</sub> | <sub>Riskiness of portfolios and assets.</sub> | <sub>9/12/17 13:35</sub> | <sub>8/6/20 12:35</sub> | <sub>140.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Python for Finance](https://github.com/yhilpisch/py4fi/tree/master/jupyter36)</sub> | <sub>Various financial notebooks.</sub> | <sub>12/15/14 11:23</sub> | <sub>7/10/18 6:38</sub> | <sub>1298.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Stock-Prediction](https://github.com/Ronak-59/Stock-Prediction)</sub> | <sub>NEW</sub> | <sub>3/18/18 4:54</sub> | <sub>2/28/20 11:43</sub> | <sub>129.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:factor_and_risk_analysis] -->
# Techniques
## Unsupervised ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/unsupervised))
<!-- [PLACEHOLDER_START:unsupervised] -->
| <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> |
| <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> |
| <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] -->
## Textual ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/textual))
<!-- [PLACEHOLDER_START:textual] -->
| <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] -->
# Other Assets
## Derivatives and Hedging ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/derivatives_and_hedging))
<!-- [PLACEHOLDER_START:derivatives_and_hedging] -->
| <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>[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>[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>[Options](https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D)</sub> | <sub>Introduction to options.</sub> | <sub>7/28/17 15:48</sub> | <sub>3/17/21 17:17</sub> | <sub>335.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Delta Hedging](https://github.com/RobinsonGarcia/delta-hedging)</sub> | <sub>Advanced derivatives.</sub> | <sub>3/2/18 23:53</sub> | <sub>7/17/18 23:32</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Option Strategies](https://github.com/rstreppa/valuation-OptionStrategies)</sub> | <sub>Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.</sub> | <sub>5/22/18 18:27</sub> | <sub>5/22/18 18:30</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Computational Derivatives](https://github.com/chenbowen184/Computational_Finance)</sub> | <sub>Projects focusing on investigating simulations and computational techniques applied in finance.</sub> | <sub>1/29/18 5:01</sub> | <sub>8/2/18 5:56</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Reinforcement Learning](https://github.com/FinTechies/HedgingRL)</sub> | <sub>Hedging portfolios with reinforcement learning.</sub> | <sub>4/21/17 10:58</sub> | <sub>8/2/17 21:41</sub> | <sub>16.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Black Scholes](https://github.com/irajwani/numerical_methods_python/blob/master/black_scholes.ipynb)</sub> | <sub>Options pricing.</sub> | <sub>12/9/17 18:50</sub> | <sub>7/9/18 9:48</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Options Risk Measures](https://github.com/wanglouis49/risk_estimation)</sub> | <sub>Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).</sub> | <sub>4/29/16 3:51</sub> | <sub>1/16/18 1:24</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Derman](https://github.com/rstreppa/valuation-convertibles-Goldman1994/blob/master/ConvertibleBond_Goldman1994_Derman.ipynb)</sub> | <sub>Binomial tree for American call.</sub> | <sub>5/18/18 18:08</sub> | <sub>9/21/18 19:59</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:derivatives_and_hedging] -->
## Fixed Income ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/fixed_income))
<!-- [PLACEHOLDER_START:fixed_income] -->
| <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>[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] -->
## Alternative Finance ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/alternative_finance))
<!-- [PLACEHOLDER_START:alternative_finance] -->
| <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] -->
# Extended Research ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/extended_research))
<!-- [PLACEHOLDER_START:extended_research] -->
| <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>[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> |
| <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> |
| <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> |
| <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> |
| <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> |
| <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> |
| <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> |
| <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> |
| <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> |
| <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> |
| <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] -->
# Courses ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/courses))
<!-- [PLACEHOLDER_START:courses] -->
| <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] -->
# Data ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data))
<!-- [PLACEHOLDER_START:data] -->
| <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>[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> |
| <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>[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>[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> |
| <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> |
| <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] -->
# Colleges, Centers and Departments ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/colleges_centers_and_departments))
<!-- [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> |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
| <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> |
| <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 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> |
| <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> |
| <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> |<!-- [PLACEHOLDER_END:colleges_centers_and_departments] -->
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