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## 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>[Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading)</sub> | <sub>curated list of books/online courses/youtube videos/blogs/interviews/papers/code etc. Updates are pretty infrequent</sub> | <sub>11/5/18 21:09</sub> | <sub>10/8/20 16:48</sub> | <sub>1005.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
| <sub>[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy)</sub> | <sub>machine learning framework built on sklearn and pandas. Support pyfolio/xgboost/lightgmb/catboost(gradient boosting on decision tress) etc. Examples include financial market prediction/sports prediction/kaggle. Configurations are set though yaml file for all model process including feature selection/grid search on parameters and aggregate results for each model</sub> | <sub>2/14/16 0:47</sub> | <sub>2/8/21 21:35</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
| <sub>[MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes)</sub> | <sub>Collections of news/articles on various topics including quant trading and machine learning. Some articles are from [ycombinator message board](https://news.ycombinator.com/news) and [rediit algotrading forum](https://www.reddit.com/r/algotrading/)</sub> | <sub>3/11/16 19:13</sub> | <sub>12/21/20 3:54</sub> | <sub>460.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>Machine learning algo to detect anomaly in equities data. Uses sklearn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) model and price/volume based technical signals as features using [ta](https://github.com/bukosabino/ta) library. Opensourced by [tradytics](https://tradytics.com/). Code structures are less extensible</sub> | <sub>8/30/20 7:56</sub> | <sub>9/21/20 4:32</sub> | <sub>1189.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <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>[Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading)</sub> | <sub>NEW</sub> | <sub>3/26/15 2:22</sub> | <sub>3/26/15 2:33</sub> | <sub>173.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <sub>[botflow](https://github.com/kkyon/botflow)</sub> | <sub>python dataflow programming framework. Similar and probably replaceable by sklearn.pipeline module. Uses [graphviz](https://graphviz.org/) for visiualization though not maintained with last commit over 3 years ago</sub> | <sub>8/20/18 3:13</sub> | <sub>5/23/19 14:40</sub> | <sub>1165.0</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>[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> |<!-- [PLACEHOLDER_END: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>[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>[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>[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>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>Machine learning algo to detect anomaly in equities data. Uses sklearn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) model and price/volume based technical signals as features using [ta](https://github.com/bukosabino/ta) library. Opensourced by [tradytics](https://tradytics.com/). Code structures are less extensible</sub> | <sub>8/30/20 7:56</sub> | <sub>9/21/20 4:32</sub> | <sub>1189.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models)</sub> | <sub>Mixture models to predict market bottoms and regime changes based on a seminar given to quantinsti in 2017 and summary and video is [here](https://blog.quantinsti.com/webinar-can-we-use-mixture-models-to-predict-market-bottoms/). Gaussian mixture models are build on markov models and expectation maximization thoery to detect regimes and seminar reported positive results using features asset returns/tedrate/10y2ysptread/10y3m spread from fred which can be access here [fredapi](https://github.com/mortada/fredapi). Though most of the returns came from being long equities after 2009 </sub> | <sub>3/20/17 18:54</sub> | <sub>4/25/17 23:35</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <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>[Machine-Learning-For-Finance](https://github.com/anthonyng2/Machine-Learning-For-Finance)</sub> | <sub>accompanying materials for slide [here](https://github.com/anthonyng2/Machine-Learning-For-Finance/blob/master/Regression%20Based%20Machine%20Learning%20for%20Algorithmic%20Trading/Machine%20Learning%20-%20Linear%20Regression%20for%20Algo%20Trading%20v2017-07-13.pdf) covering more tradition quant trading topics like pair trading/kalman filter/trend following etc. Referecing interesting paper [characterization of financial time series](http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_01.pdf)</sub> | <sub>7/11/17 9:09</sub> | <sub>2/21/18 5:36</sub> | <sub>205.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <sub>[Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading)</sub> | <sub>NEW</sub> | <sub>3/26/15 2:22</sub> | <sub>3/26/15 2:33</sub> | <sub>173.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |<!-- [PLACEHOLDER_END:other_models] -->
## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations))
@@ -86,8 +86,8 @@ ___
<!-- [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>[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>[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> |
@@ -105,16 +105,16 @@ ___
<!-- [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>[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> |
| <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> |
| <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> |
| <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>[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>[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>[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>[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>[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> |
@@ -171,11 +171,15 @@ ___
## 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] -->
| <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] -->
## Alternative Finance ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/alternative_finance))
<!-- [PLACEHOLDER_START:alternative_finance] -->
@@ -194,20 +198,20 @@ ___
<!-- [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>[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>[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>[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>[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>[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>[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>[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>[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))
@@ -229,14 +233,14 @@ ___
<!-- [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>[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>[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>[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://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>[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://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>[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>[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>[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> |
@@ -248,9 +252,9 @@ ___
<!-- [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>[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>[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>[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>[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>[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>[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> |<!-- [PLACEHOLDER_END:colleges_centers_and_departments] -->
| <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] -->
generated_wiki/colleges_centers_and_departments.md
+5 -5
View File
@@ -1,8 +1,8 @@
| <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>[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>[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>[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>[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>[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>[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>[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> |
generated_wiki/data.md
+6 -6
View File
@@ -1,13 +1,13 @@
| <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>[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>[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>[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://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>[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://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>[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>[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>[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> |
generated_wiki/derivatives_and_hedging.md
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@@ -35,6 +35,6 @@
| <sub>[huobi_futures_Python](https://github.com/hbdmapi/huobi_futures_Python)</sub> | <sub>NEW</sub> | <sub>1/14/20 7:21</sub> | <sub>3/2/21 7:30</sub> | <sub>137.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[fast_arrow](https://github.com/westonplatter/fast_arrow)</sub> | <sub>NEW</sub> | <sub>7/19/18 23:15</sub> | <sub>4/18/20 18:05</sub> | <sub>127.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[robin_stocks](https://github.com/jmfernandes/robin_stocks)</sub> | <sub>NEW</sub> | <sub>2/23/18 0:49</sub> | <sub>2/27/21 17:48</sub> | <sub>1012.0</sub> | <sub>:heavy_check_mark:</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>[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> |
| <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>[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>[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> |
generated_wiki/extended_research.md
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@@ -1,26 +1,26 @@
| <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>[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>[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>[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>[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>[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>[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>[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>[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> |
| <sub>[Bayesian Finance I](https://github.com/AlexIoannides/pymc-stochastic-process/blob/master/bayes_stoch_proc_calib.ipynb)</sub> | <sub>Stochastic Process Calibration using Bayesian Inference & Probabilistic Programs.</sub> | <sub>1/4/19 12:30</sub> | <sub>2/18/19 9:55</sub> | <sub>25.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[High Frequency](https://github.com/cswaney/prickle)</sub> | <sub>A Python toolkit for high-frequency trade research.</sub> | <sub>7/6/16 20:32</sub> | <sub>6/9/18 10:53</sub> | <sub>24.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Bayesian Finance](https://github.com/marketneutral/alphatools/blob/master/notebooks/pymc3-minimal.ipynb)</sub> | <sub>Notebook PyMC3 implementation.</sub> | <sub>8/28/18 14:45</sub> | <sub>8/6/20 22:03</sub> | <sub>233.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Economic Foundations](https://github.com/SeanMcOwen/FinanceAndPython.com-EconomicFoundations)</sub> | <sub>Basic economic models.</sub> | <sub>5/25/17 2:27</sub> | <sub>6/30/17 3:53</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Finance Graph Theory](https://github.com/AvijitGhosh82/Finance_Graph_Theory)</sub> | <sub>Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.</sub> | <sub>8/2/18 2:48</sub> | <sub>3/16/19 18:39</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Simulation](https://github.com/chenbowen184/Computational_Finance)</sub> | <sub>Investigating simulations as part of computational 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>[Finance Graph Theory](https://github.com/AvijitGhosh82/Finance_Graph_Theory)</sub> | <sub>Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.</sub> | <sub>8/2/18 2:48</sub> | <sub>3/16/19 18:39</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Computational Finance](https://github.com/lnsongxf/Applied_Computational_Economics_and_Finance)</sub> | <sub>Applied Computational Economics and Finance.</sub> | <sub>8/27/17 3:46</sub> | <sub>8/26/17 4:26</sub> | <sub>12.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Critical Transitions](https://github.com/ryanholbrook/critical-transitions)</sub> | <sub>Detecting critical transitions in financial networks with topological data analysis.</sub> | <sub>1/22/19 10:59</sub> | <sub>3/12/19 18:35</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Market Crash Prediction](https://github.com/sarachmax/MarketCrashes_Prediction/blob/master/LPPL_Comparasion.ipynb)</sub> | <sub>Predicting market crashes using an LPPL model.</sub> | <sub>1/24/19 13:37</sub> | <sub>2/13/19 16:48</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
generated_wiki/factor_and_risk_analysis.md
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@@ -1,15 +1,15 @@
| <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>[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> |
| <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> |
| <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> |
| <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>[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>[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>[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>[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>[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> |
generated_wiki/fixed_income.md
+9 -5
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@@ -1,5 +1,9 @@
| <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> |
| <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> |
generated_wiki/other_models.md
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@@ -1,31 +1,31 @@
| <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>[Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading)</sub> | <sub>curated list of books/online courses/youtube videos/blogs/interviews/papers/code etc. Updates are pretty infrequent</sub> | <sub>11/5/18 21:09</sub> | <sub>10/8/20 16:48</sub> | <sub>1005.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
| <sub>[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy)</sub> | <sub>machine learning framework built on sklearn and pandas. Support pyfolio/xgboost/lightgmb/catboost(gradient boosting on decision tress) etc. Examples include financial market prediction/sports prediction/kaggle. Configurations are set though yaml file for all model process including feature selection/grid search on parameters and aggregate results for each model</sub> | <sub>2/14/16 0:47</sub> | <sub>2/8/21 21:35</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
| <sub>[MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes)</sub> | <sub>Collections of news/articles on various topics including quant trading and machine learning. Some articles are from [ycombinator message board](https://news.ycombinator.com/news) and [rediit algotrading forum](https://www.reddit.com/r/algotrading/)</sub> | <sub>3/11/16 19:13</sub> | <sub>12/21/20 3:54</sub> | <sub>460.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>Machine learning algo to detect anomaly in equities data. Uses sklearn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) model and price/volume based technical signals as features using [ta](https://github.com/bukosabino/ta) library. Opensourced by [tradytics](https://tradytics.com/). Code structures are less extensible</sub> | <sub>8/30/20 7:56</sub> | <sub>9/21/20 4:32</sub> | <sub>1189.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <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>[Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading)</sub> | <sub>NEW</sub> | <sub>3/26/15 2:22</sub> | <sub>3/26/15 2:33</sub> | <sub>173.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <sub>[botflow](https://github.com/kkyon/botflow)</sub> | <sub>python dataflow programming framework. Similar and probably replaceable by sklearn.pipeline module. Uses [graphviz](https://graphviz.org/) for visiualization though not maintained with last commit over 3 years ago</sub> | <sub>8/20/18 3:13</sub> | <sub>5/23/19 14:40</sub> | <sub>1165.0</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>[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> |
| <sub>[mosquito](https://github.com/miro-ka/mosquito)</sub> | <sub>NEW</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></sub> |
| <sub>[Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant)</sub> | <sub>Identify social/historical cues for short term stock movement.</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></sub> |
| <sub>[Machine-Learning-For-Finance](https://github.com/anthonyng2/Machine-Learning-For-Finance)</sub> | <sub>NEW</sub> | <sub>7/11/17 9:09</sub> | <sub>2/21/18 5:36</sub> | <sub>205.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <sub>[Machine-Learning-for-Finance](https://github.com/PacktPublishing/Machine-Learning-for-Finance)</sub> | <sub>NEW</sub> | <sub>3/15/18 6:28</sub> | <sub>1/14/21 15:58</sub> | <sub>180.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Stock.Indicators](https://github.com/DaveSkender/Stock.Indicators)</sub> | <sub>NEW</sub> | <sub>12/29/19 5:18</sub> | <sub>4/11/21 19:17</sub> | <sub>175.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Machine-Learning-for-Algorithmic-Trading-Bots-with-Python](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python)</sub> | <sub>NEW</sub> | <sub>12/6/18 11:35</sub> | <sub>1/18/21 6:40</sub> | <sub>172.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Mixture Models II](https://github.com/BlackArbsCEO/mixture_model_trading_public)</sub> | <sub>Mixture models and stock trading.</sub> | <sub>12/11/17 17:05</sub> | <sub>5/13/20 23:50</sub> | <sub>166.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[fin-ml](https://github.com/tatsath/fin-ml)</sub> | <sub>NEW</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></sub> |
| <sub>[Microservices-Based-Algorithmic-Trading-System](https://github.com/saeed349/Microservices-Based-Algorithmic-Trading-System)</sub> | <sub>NEW</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></sub> |
| <sub>[Speculator](https://github.com/amicks/Speculator)</sub> | <sub>NEW</sub> | <sub>9/3/17 17:43</sub> | <sub>9/12/18 18:58</sub> | <sub>101.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
| <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>[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>[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>[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>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>Machine learning algo to detect anomaly in equities data. Uses sklearn [IsolationForest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) model and price/volume based technical signals as features using [ta](https://github.com/bukosabino/ta) library. Opensourced by [tradytics](https://tradytics.com/). Code structures are less extensible</sub> | <sub>8/30/20 7:56</sub> | <sub>9/21/20 4:32</sub> | <sub>1189.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
| <sub>[Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models)</sub> | <sub>Mixture models to predict market bottoms and regime changes based on a seminar given to quantinsti in 2017 and summary and video is [here](https://blog.quantinsti.com/webinar-can-we-use-mixture-models-to-predict-market-bottoms/). Gaussian mixture models are build on markov models and expectation maximization thoery to detect regimes and seminar reported positive results using features asset returns/tedrate/10y2ysptread/10y3m spread from fred which can be access here [fredapi](https://github.com/mortada/fredapi). Though most of the returns came from being long equities after 2009 </sub> | <sub>3/20/17 18:54</sub> | <sub>4/25/17 23:35</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <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>[Machine-Learning-For-Finance](https://github.com/anthonyng2/Machine-Learning-For-Finance)</sub> | <sub>accompanying materials for slide [here](https://github.com/anthonyng2/Machine-Learning-For-Finance/blob/master/Regression%20Based%20Machine%20Learning%20for%20Algorithmic%20Trading/Machine%20Learning%20-%20Linear%20Regression%20for%20Algo%20Trading%20v2017-07-13.pdf) covering more tradition quant trading topics like pair trading/kalman filter/trend following etc. Referecing interesting paper [characterization of financial time series](http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/RN_11_01.pdf)</sub> | <sub>7/11/17 9:09</sub> | <sub>2/21/18 5:36</sub> | <sub>205.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <sub>[Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading)</sub> | <sub>NEW</sub> | <sub>3/26/15 2:22</sub> | <sub>3/26/15 2:33</sub> | <sub>173.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
| <sub>[botflow](https://github.com/kkyon/botflow)</sub> | <sub>python dataflow programming framework. Similar and probably replaceable by sklearn.pipeline module. Uses [graphviz](https://graphviz.org/) for visiualization though not maintained with last commit over 3 years ago</sub> | <sub>8/20/18 3:13</sub> | <sub>5/23/19 14:40</sub> | <sub>1165.0</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>[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>[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-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>[Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant)</sub> | <sub>Identify social/historical cues for short term stock movement.</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></sub> |
| <sub>[Machine-Learning-for-Finance](https://github.com/PacktPublishing/Machine-Learning-for-Finance)</sub> | <sub>NEW</sub> | <sub>3/15/18 6:28</sub> | <sub>1/14/21 15:58</sub> | <sub>180.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Stock.Indicators](https://github.com/DaveSkender/Stock.Indicators)</sub> | <sub>NEW</sub> | <sub>12/29/19 5:18</sub> | <sub>4/11/21 19:17</sub> | <sub>175.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Machine-Learning-for-Algorithmic-Trading-Bots-with-Python](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python)</sub> | <sub>NEW</sub> | <sub>12/6/18 11:35</sub> | <sub>1/18/21 6:40</sub> | <sub>172.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Mixture Models II](https://github.com/BlackArbsCEO/mixture_model_trading_public)</sub> | <sub>Mixture models and stock trading.</sub> | <sub>12/11/17 17:05</sub> | <sub>5/13/20 23:50</sub> | <sub>166.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[fin-ml](https://github.com/tatsath/fin-ml)</sub> | <sub>NEW</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></sub> |
| <sub>[Microservices-Based-Algorithmic-Trading-System](https://github.com/saeed349/Microservices-Based-Algorithmic-Trading-System)</sub> | <sub>NEW</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></sub> |
| <sub>[Speculator](https://github.com/amicks/Speculator)</sub> | <sub>NEW</sub> | <sub>9/3/17 17:43</sub> | <sub>9/12/18 18:58</sub> | <sub>101.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
generated_wiki/personal_papers.md
+2 -2
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@@ -1,6 +1,6 @@
| <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>[Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies](https://jfds.pm-research.com/content/2/1/10)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Machine Learning in Asset Management](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Financial Event Prediction using Machine Learning](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3481555)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization](https://jfds.pm-research.com/content/2/2/17)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies](https://jfds.pm-research.com/content/2/1/10)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
| <sub>[Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization](https://jfds.pm-research.com/content/2/2/17)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
generated_wiki/portfolio_selection_and_optimisation.md
+1 -1
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@@ -1,7 +1,7 @@
| <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>[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>[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> |