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# Trading
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## Deep Learning & Reinforcement Learning ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/deep_learning_and_reinforcement_learning))
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<!-- [PLACEHOLDER_START:deep_learning_and_reinforcement_learning] -->
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models)</sub> | <sub>very good curated list of notebooks showing deep learning + reinforcement learning models. Also contain topics on outlier detections/overbought oversold study/monte carlo simulartions/sentiment analysis from text (text storage/parsing is not detailed but it mentioned using [BERT](https://github.com/google-research/bert))</sub> | <sub>2017-12-18 10:49:59</sub> | <sub>2021-01-05 10:31:50</sub> | <sub>3655.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[AI Trading](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md)</sub> | <sub>AI to predict stock market movements.</sub> | <sub>2019-01-09 08:02:47</sub> | <sub>2019-02-11 16:32:47</sub> | <sub>2876.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[FinRL-Library](https://github.com/AI4Finance-LLC/FinRL-Library)</sub> | <sub>started by Columbia university engineering students and designed as an end to end deep reinforcement learning library for automated trading platform. Implementation of DQN DDQN DDPG etc using PyTorch and [gym](https://gym.openai.com/) use [pyfolio](https://github.com/quantopian/pyfolio) for showing backtesting stats. Big contributions on Proximal Policy Optimization (PPO) advantage actor critic (A2C) and Deep Deterministic Policy Gradient (DDPG) agents for trading</sub> | <sub>2020-07-26 13:18:16</sub> | <sub>2021-04-11 22:02:16</sub> | <sub>1857.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning IV](https://github.com/achillesrasquinha/bulbea)</sub> | <sub>Bulbea: Deep Learning based Python Library.</sub> | <sub>2017-03-09 06:11:06</sub> | <sub>2017-03-19 07:42:49</sub> | <sub>1467.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RLTrader](https://github.com/notadamking/RLTrader)</sub> | <sub>predecessor to [tensortrade](https://github.com/tensortrade-org/tensortrade) uses open api [gym](https://gym.openai.com/) and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using [Optuna](https://github.com/optuna/optuna) etc.</sub> | <sub>2019-04-27 18:35:15</sub> | <sub>2019-10-17 16:25:49</sub> | <sub>1312.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning III](https://github.com/Rachnog/Deep-Trading)</sub> | <sub>Algorithmic trading with deep learning experiments.</sub> | <sub>2016-06-18 18:23:06</sub> | <sub>2018-08-07 15:24:45</sub> | <sub>1266.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Personae](https://github.com/Ceruleanacg/Personae)</sub> | <sub>implementation of deep reinforcement learning and supervised learnings covering areas: deep deterministic policy gradient (DDPG) and DDQN etc. Data are being pulled from [rqalpha](https://github.com/ricequant/rqalpha) which is a python backtest engine and have a nice docker image to run training/testing</sub> | <sub>2018-03-10 11:22:00</sub> | <sub>2018-09-02 17:21:38</sub> | <sub>1144.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RL Trading](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW)</sub> | <sub>A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020)</sub> | <sub>Part of FinRL and provided code for paper [deep reinformacement learning for automated stock trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996) focuses on ensemble.</sub> | <sub>2020-07-26 13:12:53</sub> | <sub>2021-01-21 18:11:59</sub> | <sub>560.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[awesome-deep-trading](https://github.com/cbailes/awesome-deep-trading)</sub> | <sub>curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes</sub> | <sub>2018-11-26 03:23:04</sub> | <sub>2021-01-01 09:41:21</sub> | <sub>551.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |<!-- [PLACEHOLDER_END:deep_learning_and_reinforcement_learning] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models)</sub> | <sub>very good curated list of notebooks showing deep learning + reinforcement learning models. Also contain topics on outlier detections/overbought oversold study/monte carlo simulartions/sentiment analysis from text (text storage/parsing is not detailed but it mentioned using [BERT](https://github.com/google-research/bert))</sub> | <sub>12/18/17 10:49</sub> | <sub>1/5/21 10:31</sub> | <sub>3655.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[AI Trading](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md)</sub> | <sub>AI to predict stock market movements.</sub> | <sub>1/9/19 8:02</sub> | <sub>2/11/19 16:32</sub> | <sub>2876.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[FinRL-Library](https://github.com/AI4Finance-LLC/FinRL-Library)</sub> | <sub>started by Columbia university engineering students and designed as an end to end deep reinforcement learning library for automated trading platform. Implementation of DQN DDQN DDPG etc using PyTorch and [gym](https://gym.openai.com/) use [pyfolio](https://github.com/quantopian/pyfolio) for showing backtesting stats. Big contributions on Proximal Policy Optimization (PPO) advantage actor critic (A2C) and Deep Deterministic Policy Gradient (DDPG) agents for trading</sub> | <sub>7/26/20 13:18</sub> | <sub>4/11/21 22:02</sub> | <sub>1857.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning IV](https://github.com/achillesrasquinha/bulbea)</sub> | <sub>Bulbea: Deep Learning based Python Library.</sub> | <sub>3/9/17 6:11</sub> | <sub>3/19/17 7:42</sub> | <sub>1467.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RLTrader](https://github.com/notadamking/RLTrader)</sub> | <sub>predecessor to [tensortrade](https://github.com/tensortrade-org/tensortrade) uses open api [gym](https://gym.openai.com/) and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using [Optuna](https://github.com/optuna/optuna) etc.</sub> | <sub>4/27/19 18:35</sub> | <sub>10/17/19 16:25</sub> | <sub>1312.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning III](https://github.com/Rachnog/Deep-Trading)</sub> | <sub>Algorithmic trading with deep learning experiments.</sub> | <sub>6/18/16 18:23</sub> | <sub>8/7/18 15:24</sub> | <sub>1266.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Personae](https://github.com/Ceruleanacg/Personae)</sub> | <sub>implementation of deep reinforcement learning and supervised learnings covering areas: deep deterministic policy gradient (DDPG) and DDQN etc. Data are being pulled from [rqalpha](https://github.com/ricequant/rqalpha) which is a python backtest engine and have a nice docker image to run training/testing</sub> | <sub>3/10/18 11:22</sub> | <sub>9/2/18 17:21</sub> | <sub>1144.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RL Trading](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW)</sub> | <sub>A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
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| <sub>[Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020)</sub> | <sub>Part of FinRL and provided code for paper [deep reinformacement learning for automated stock trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996) focuses on ensemble.</sub> | <sub>7/26/20 13:12</sub> | <sub>1/21/21 18:11</sub> | <sub>560.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[awesome-deep-trading](https://github.com/cbailes/awesome-deep-trading)</sub> | <sub>curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes</sub> | <sub>11/26/18 3:23</sub> | <sub>1/1/21 9:41</sub> | <sub>551.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |<!-- [PLACEHOLDER_END:deep_learning_and_reinforcement_learning] -->
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## Other Models ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/other_models))
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<!-- [PLACEHOLDER_START:other_models] -->
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
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| <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> |
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| <sub>[awesome-ai-in-finance](https://github.com/georgezouq/awesome-ai-in-finance)</sub> | <sub>NEW</sub> | <sub>2018-08-29 02:07:02</sub> | <sub>2020-11-27 09:43:40</sub> | <sub>941.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks)</sub> | <sub>Using python and scikit-learn to make stock predictions.</sub> | <sub>2017-02-12 04:50:44</sub> | <sub>2021-02-04 03:48:33</sub> | <sub>931.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Hands-On-Machine-Learning-for-Algorithmic-Trading](https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading)</sub> | <sub>NEW</sub> | <sub>2019-05-07 11:04:25</sub> | <sub>2021-01-19 07:51:00</sub> | <sub>600.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy)</sub> | <sub>NEW</sub> | <sub>2016-02-14 00:47:32</sub> | <sub>2021-02-08 21:35:40</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes)</sub> | <sub>NEW</sub> | <sub>2016-03-11 19:13:00</sub> | <sub>2020-12-21 03:54:51</sub> | <sub>460.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps)</sub> | <sub>Research in investment finance for long term forecasts.</sub> | <sub>2018-07-22 08:14:46</sub> | <sub>2021-02-17 14:39:30</sub> | <sub>383.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[stock-trading-ml](https://github.com/yacoubb/stock-trading-ml)</sub> | <sub>NEW</sub> | <sub>2019-10-10 09:44:02</sub> | <sub>2019-10-12 11:38:49</sub> | <sub>340.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models)</sub> | <sub>Mixture models to predict market bottoms.</sub> | <sub>2017-03-20 18:54:24</sub> | <sub>2017-04-25 23:35:20</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[finance_ml](https://github.com/jjakimoto/finance_ml)</sub> | <sub>NEW</sub> | <sub>2018-06-29 21:21:17</sub> | <sub>2019-02-18 12:34:54</sub> | <sub>282.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:other_models] -->
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
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| <sub>[Hands-On-Machine-Learning-for-Algorithmic-Trading](https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading)</sub> | <sub>repo for book [hands-on-machine learning for algorithmic trading](https://www.packtpub.com/product/hands-on-machine-learning-for-algorithmic-trading/9781789346411) covering topic from data| unsupervised learning| NPL | RNN & CNN| reinforcement learning etc. Leverage zipline|alphalens|sklearn|openai-gym etc as well. Good references to have</sub> | <sub>5/7/19 11:04</sub> | <sub>1/19/21 7:51</sub> | <sub>600.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
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| <sub>[CryptoBot](https://github.com/AdeelMufti/CryptoBot)</sub> | <sub>Hard fork of [bitpredit](https://github.com/cbyn/bitpredict) and form the trading strategy as a classification problem with -1 (sell) 0 (hold) 1 (buy). Models used are XGBClassifier|RandomForest|GradientBoosting. Not mentained</sub> | <sub>1/17/17 12:44</sub> | <sub>1/17/17 12:48</sub> | <sub>234.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
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| <sub>[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> |
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| <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> |
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| <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> |
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| <sub>[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy)</sub> | <sub>NEW</sub> | <sub>2/14/16 0:47</sub> | <sub>2/8/21 21:35</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes)</sub> | <sub>Collections of news/articles on various topics including quant trading and machine learning. Some articles are from [ycombinator message board](https://news.ycombinator.com/news) and [rediit algotrading forum](https://www.reddit.com/r/algotrading/)</sub> | <sub>3/11/16 19:13</sub> | <sub>12/21/20 3:54</sub> | <sub>460.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
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| <sub>[Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps)</sub> | <sub>Research in investment finance for long term forecasts.</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> |
|
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| <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> |<!-- [PLACEHOLDER_END:other_models] -->
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## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations))
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<!-- [PLACEHOLDER_START:data_processing_techniques_and_transformations] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------|:--------------------|
|
||||
| <sub>[Advanced ML II](https://github.com/hudson-and-thames/research)</sub> | <sub>More implementations of Financial Machine Learning (De Prado).</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
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| <sub>[Advanced ML](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises)</sub> | <sub>Exercises too Financial Machine Learning (De Prado).</sub> | <sub>2018-04-25 17:22:40</sub> | <sub>2020-01-16 17:25:41</sub> | <sub>973.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:data_processing_techniques_and_transformations] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
|
||||
| <sub>[Advanced ML II](https://github.com/hudson-and-thames/research)</sub> | <sub>More implementations of Financial Machine Learning (De Prado).</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Advanced ML](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises)</sub> | <sub>Exercises too Financial Machine Learning (De Prado).</sub> | <sub>4/25/18 17:22</sub> | <sub>1/16/20 17:25</sub> | <sub>973.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:data_processing_techniques_and_transformations] -->
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# Portfolio Management
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## Portfolio Selection and Optimisation ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/portfolio_selection_and_optimisation))
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<!-- [PLACEHOLDER_START:portfolio_selection_and_optimisation] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Online Portfolio Selection](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb)</sub> | <sub>****Comparing OLPS algorithms on a diversified set of ETFs.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Modern Portfolio Theory](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb)</sub> | <sub>Universal portfolios; modern portfolio theory.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[OLMAR Algorithm](https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb)</sub> | <sub>Relative importance of each component of the OLMAR algorithm.</sub> | <sub>2016-07-26 16:20:10</sub> | <sub>2016-12-30 11:40:53</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>2020-03-02 19:49:06</sub> | <sub>2021-04-01 03:50:59</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>2017-10-07 09:14:33</sub> | <sub>2018-06-26 09:22:27</sub> | <sub>364.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[DeepDow](https://github.com/jankrepl/deepdow)</sub> | <sub>Portfolio optimization with deep learning.</sub> | <sub>2020-02-02 08:46:33</sub> | <sub>2021-02-16 18:50:53</sub> | <sub>311.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Distribution Characteristic Optimisation](https://github.com/VivekPa/OptimalPortfolio)</sub> | <sub>Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.</sub> | <sub>2018-11-16 12:20:25</sub> | <sub>2019-07-04 01:41:46</sub> | <sub>232.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt)</sub> | <sub>Financial portfolio optimisation, including classical efficient frontier and advanced methods.</sub> | <sub>2018-05-29 13:30:30</sub> | <sub>2021-02-25 13:01:56</sub> | <sub>1895.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[401K Portfolio Optimisation](https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb)</sub> | <sub>Portfolio analyses and optimisation for 401K.</sub> | <sub>2018-08-01 19:48:24</sub> | <sub>2019-09-05 11:18:56</sub> | <sub>14.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Policy Gradient Portfolio](https://github.com/ZhengyaoJiang/PGPortfolio)</sub> | <sub>A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.</sub> | <sub>2017-11-12 16:08:44</sub> | <sub>2019-05-09 09:50:18</sub> | <sub>1281.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:portfolio_selection_and_optimisation] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Online Portfolio Selection](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb)</sub> | <sub>****Comparing OLPS algorithms on a diversified set of ETFs.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Modern Portfolio Theory](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb)</sub> | <sub>Universal portfolios; modern portfolio theory.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[OLMAR Algorithm](https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb)</sub> | <sub>Relative importance of each component of the OLMAR algorithm.</sub> | <sub>7/26/16 16:20</sub> | <sub>12/30/16 11:40</sub> | <sub>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib)</sub> | <sub>NEW</sub> | <sub>3/2/20 19:49</sub> | <sub>4/1/21 3:50</sub> | <sub>371.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Reinforcement Learning](https://github.com/filangel/qtrader)</sub> | <sub>Reinforcement Learning for Portfolio Management.</sub> | <sub>10/7/17 9:14</sub> | <sub>6/26/18 9:22</sub> | <sub>364.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[DeepDow](https://github.com/jankrepl/deepdow)</sub> | <sub>Portfolio optimization with deep learning.</sub> | <sub>2/2/20 8:46</sub> | <sub>2/16/21 18:50</sub> | <sub>311.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Distribution Characteristic Optimisation](https://github.com/VivekPa/OptimalPortfolio)</sub> | <sub>Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.</sub> | <sub>11/16/18 12:20</sub> | <sub>7/4/19 1:41</sub> | <sub>232.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt)</sub> | <sub>Financial portfolio optimisation, including classical efficient frontier and advanced methods.</sub> | <sub>5/29/18 13:30</sub> | <sub>2/25/21 13:01</sub> | <sub>1895.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[401K Portfolio Optimisation](https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb)</sub> | <sub>Portfolio analyses and optimisation for 401K.</sub> | <sub>8/1/18 19:48</sub> | <sub>9/5/19 11:18</sub> | <sub>14.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Policy Gradient Portfolio](https://github.com/ZhengyaoJiang/PGPortfolio)</sub> | <sub>A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.</sub> | <sub>11/12/17 16:08</sub> | <sub>5/9/19 9:50</sub> | <sub>1281.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:portfolio_selection_and_optimisation] -->
|
||||
|
||||
## Factor and Risk Analysis ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/factor_and_risk_analysis))
|
||||
<!-- [PLACEHOLDER_START:factor_and_risk_analysis] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[VaR GaN](https://github.com/hamaadshah/market_risk_gan_keras)</sub> | <sub>Estimate Value-at-Risk for market risk management using Keras and TensorFlow.</sub> | <sub>2018-08-06 16:09:44</sub> | <sub>2020-11-22 19:02:07</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>2017-08-07 14:44:32</sub> | <sub>2017-08-08 22:52:11</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>2015-06-01 15:31:39</sub> | <sub>2020-02-28 17:30:19</sub> | <sub>3673.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Quant Finance](https://github.com/mrefermat/quant_finance)</sub> | <sub>General quant repository.</sub> | <sub>2018-08-11 22:59:53</sub> | <sub>2019-11-12 04:49:01</sub> | <sub>31.0</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>2016-05-10 11:03:48</sub> | <sub>2016-05-17 03:44:56</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>2016-05-10 11:03:48</sub> | <sub>2016-05-17 03:44:56</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>2018-03-13 07:39:20</sub> | <sub>2018-03-13 07:42:36</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>2015-10-04 09:10:54</sub> | <sub>2020-03-28 18:33:58</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>2016-06-03 21:49:15</sub> | <sub>2020-04-27 18:40:41</sub> | <sub>1847.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Convex Optimisation](https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb)</sub> | <sub>Convex Optimization for Finance.</sub> | <sub>2018-06-26 20:36:47</sub> | <sub>2019-10-22 21:56:46</sub> | <sub>18.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:factor_and_risk_analysis] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[VaR GaN](https://github.com/hamaadshah/market_risk_gan_keras)</sub> | <sub>Estimate Value-at-Risk for market risk management using Keras and TensorFlow.</sub> | <sub>8/6/18 16:09</sub> | <sub>11/22/20 19:02</sub> | <sub>41.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Various Risk Measures](https://github.com/Jorgencr/Alternative-and-Responsible-Investments/blob/master/Final_masterfile.ipynb)</sub> | <sub>Risk measures and factors for alternative and responsible investments.</sub> | <sub>8/7/17 14:44</sub> | <sub>8/8/17 22:52</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Pyfolio](https://github.com/quantopian/pyfolio)</sub> | <sub>Portfolio and risk analytics in Python.</sub> | <sub>6/1/15 15:31</sub> | <sub>2/28/20 17:30</sub> | <sub>3673.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[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>[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>[Factor Analysis](https://github.com/garvit-kudesia91/factor_analysis/blob/master/Factor%20Analysis%20of%20Mutual%20Funds.ipynb)</sub> | <sub>Factor analysis for mutual funds.</sub> | <sub>3/13/18 7:39</sub> | <sub>3/13/18 7:42</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Statistical Finance](https://github.com/mrefermat/FinancePhD/tree/master/FinancialExperiments)</sub> | <sub>Various financial experiments.</sub> | <sub>10/4/15 9:10</sub> | <sub>3/28/20 18:33</sub> | <sub>21.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Performance Analysis](https://github.com/quantopian/alphalens)</sub> | <sub>Performance analysis of predictive (alpha) stock factors.</sub> | <sub>6/3/16 21:49</sub> | <sub>4/27/20 18:40</sub> | <sub>1847.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Convex Optimisation](https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb)</sub> | <sub>Convex Optimization for Finance.</sub> | <sub>6/26/18 20:36</sub> | <sub>10/22/19 21:56</sub> | <sub>18.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:factor_and_risk_analysis] -->
|
||||
|
||||
# Techniques
|
||||
## Unsupervised ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/unsupervised))
|
||||
<!-- [PLACEHOLDER_START:unsupervised] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[PCA Pairs Trading](https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading)</sub> | <sub>PCA, Factor Returns, and trading strategies.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Pairs Trading](https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb)</sub> | <sub>Finding pairs with cluster analysis.</sub> | <sub>2017-09-05 19:19:19</sub> | <sub>2017-09-27 20:42:14</sub> | <sub>79.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Project to cluster industries according to financial attributes.</sub> | <sub>2017-07-21 02:12:51</sub> | <sub>2017-07-23 02:53:37</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Clustering of industries.</sub> | <sub>2017-07-21 02:12:51</sub> | <sub>2017-07-23 02:53:37</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Fund Clusters](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Data exploration of fund clusters.</sub> | <sub>2018-04-16 22:18:55</sub> | <sub>2018-06-07 22:01:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[VRA Stock Embedding](https://github.com/ml-hongkong/stock2vec)</sub> | <sub>Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.</sub> | <sub>2017-06-21 04:47:14</sub> | <sub>2017-06-21 04:51:13</sub> | <sub>32.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:unsupervised] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[PCA Pairs Trading](https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading)</sub> | <sub>PCA, Factor Returns, and trading strategies.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Pairs Trading](https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb)</sub> | <sub>Finding pairs with cluster analysis.</sub> | <sub>9/5/17 19:19</sub> | <sub>9/27/17 20:42</sub> | <sub>79.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Project to cluster industries according to financial attributes.</sub> | <sub>7/21/17 2:12</sub> | <sub>7/23/17 2:53</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Clustering of industries.</sub> | <sub>7/21/17 2:12</sub> | <sub>7/23/17 2:53</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Fund Clusters](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Data exploration of fund clusters.</sub> | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[VRA Stock Embedding](https://github.com/ml-hongkong/stock2vec)</sub> | <sub>Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.</sub> | <sub>6/21/17 4:47</sub> | <sub>6/21/17 4:51</sub> | <sub>32.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:unsupervised] -->
|
||||
|
||||
|
||||
## Textual ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/textual))
|
||||
<!-- [PLACEHOLDER_START:textual] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[NLP](https://github.com/toamitesh/NLPinFinance)</sub> | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis)</sub> | <sub>Extracting sentiment from financial statements using neural networks.</sub> | <sub>2018-06-04 20:54:14</sub> | <sub>2018-06-04 20:56:02</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb)</sub> | <sub>Comprehensive NLP techniques for accounting research.</sub> | <sub>2017-10-25 07:10:26</sub> | <sub>2020-06-05 03:28:46</sub> | <sub>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[NLP Event](https://github.com/yuriak/DLQuant)</sub> | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub> | <sub>2018-07-02 23:50:52</sub> | <sub>2019-01-31 14:08:20</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis)</sub> | <sub>Sentiment, distance and proportion analysis for trading signals.</sub> | <sub>2017-06-23 00:05:49</sub> | <sub>2019-01-26 03:35:55</sub> | <sub>48.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Fund classification using text mining and NLP.</sub> | <sub>2018-04-16 22:18:55</sub> | <sub>2018-06-07 22:01:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Earning call transcripts](https://github.com/lin882/WebAnalyticsProject)</sub> | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>2017-12-30 08:56:03</sub> | <sub>2018-01-11 02:11:11</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb)</sub> | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub> | <sub>2017-05-24 12:36:38</sub> | <sub>2019-08-07 21:47:08</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds)</sub> | <sub>Return performance and mutual fund selection.</sub> | <sub>2018-02-04 21:51:16</sub> | <sub>2018-02-04 21:57:09</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:textual] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[NLP](https://github.com/toamitesh/NLPinFinance)</sub> | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis)</sub> | <sub>Extracting sentiment from financial statements using neural networks.</sub> | <sub>6/4/18 20:54</sub> | <sub>6/4/18 20:56</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb)</sub> | <sub>Comprehensive NLP techniques for accounting research.</sub> | <sub>10/25/17 7:10</sub> | <sub>6/5/20 3:28</sub> | <sub>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[NLP Event](https://github.com/yuriak/DLQuant)</sub> | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub> | <sub>7/2/18 23:50</sub> | <sub>1/31/19 14:08</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis)</sub> | <sub>Sentiment, distance and proportion analysis for trading signals.</sub> | <sub>6/23/17 0:05</sub> | <sub>1/26/19 3:35</sub> | <sub>48.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Fund classification using text mining and NLP.</sub> | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Earning call transcripts](https://github.com/lin882/WebAnalyticsProject)</sub> | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>12/30/17 8:56</sub> | <sub>1/11/18 2:11</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb)</sub> | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub> | <sub>5/24/17 12:36</sub> | <sub>8/7/19 21:47</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds)</sub> | <sub>Return performance and mutual fund selection.</sub> | <sub>2/4/18 21:51</sub> | <sub>2/4/18 21:57</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:textual] -->
|
||||
|
||||
# Other Assets
|
||||
## Derivatives and Hedging ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/derivatives_and_hedging))
|
||||
<!-- [PLACEHOLDER_START:derivatives_and_hedging] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Options](https://github.com/PHBS/2018.M1.ASP/tree/master/py)</sub> | <sub>Black Scholes and Copula.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Derivative Markets](https://github.com/broughtj/Fin6470/tree/master/Notebooks)</sub> | <sub>The economics of futures, futures, options, and swaps.</sub> | <sub>2016-02-09 05:30:27</sub> | <sub>2021-04-06 20:49:41</sub> | <sub>8.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Volatility and Variance Derivatives](https://github.com/yhilpisch/lvvd/tree/master/lvvd)</sub> | <sub>Volatility derivatives analytics.</sub> | <sub>2016-10-21 04:12:50</sub> | <sub>2021-02-22 13:32:00</sub> | <sub>79.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Hull White](https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb)</sub> | <sub>Callable Bond, Hull White.</sub> | <sub>2018-06-06 22:06:06</sub> | <sub>2018-06-06 22:27:02</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Derivatives Python](https://github.com/yhilpisch/dawp/tree/master/python36)</sub> | <sub>Derivative analytics with Python.</sub> | <sub>2015-07-09 12:27:29</sub> | <sub>2021-02-22 13:29:18</sub> | <sub>388.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Options](https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D)</sub> | <sub>Introduction to options.</sub> | <sub>2017-07-28 15:48:29</sub> | <sub>2021-03-17 17:17:08</sub> | <sub>335.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Delta Hedging](https://github.com/RobinsonGarcia/delta-hedging)</sub> | <sub>Advanced derivatives.</sub> | <sub>2018-03-02 23:53:53</sub> | <sub>2018-07-17 23:32:23</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Option Strategies](https://github.com/rstreppa/valuation-OptionStrategies)</sub> | <sub>Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.</sub> | <sub>2018-05-22 18:27:26</sub> | <sub>2018-05-22 18:30:24</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Computational Derivatives](https://github.com/chenbowen184/Computational_Finance)</sub> | <sub>Projects focusing on investigating simulations and computational techniques applied in finance.</sub> | <sub>2018-01-29 05:01:52</sub> | <sub>2018-08-02 05:56:49</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Reinforcement Learning](https://github.com/FinTechies/HedgingRL)</sub> | <sub>Hedging portfolios with reinforcement learning.</sub> | <sub>2017-04-21 10:58:56</sub> | <sub>2017-08-02 21:41:06</sub> | <sub>16.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:derivatives_and_hedging] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Options](https://github.com/PHBS/2018.M1.ASP/tree/master/py)</sub> | <sub>Black Scholes and Copula.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Derivative Markets](https://github.com/broughtj/Fin6470/tree/master/Notebooks)</sub> | <sub>The economics of futures, futures, options, and swaps.</sub> | <sub>2/9/16 5:30</sub> | <sub>4/6/21 20:49</sub> | <sub>8.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Volatility and Variance Derivatives](https://github.com/yhilpisch/lvvd/tree/master/lvvd)</sub> | <sub>Volatility derivatives analytics.</sub> | <sub>10/21/16 4:12</sub> | <sub>2/22/21 13:32</sub> | <sub>79.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Hull White](https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb)</sub> | <sub>Callable Bond, Hull White.</sub> | <sub>6/6/18 22:06</sub> | <sub>6/6/18 22:27</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Derivatives Python](https://github.com/yhilpisch/dawp/tree/master/python36)</sub> | <sub>Derivative analytics with Python.</sub> | <sub>7/9/15 12:27</sub> | <sub>2/22/21 13:29</sub> | <sub>388.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Options](https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D)</sub> | <sub>Introduction to options.</sub> | <sub>7/28/17 15:48</sub> | <sub>3/17/21 17:17</sub> | <sub>335.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Delta Hedging](https://github.com/RobinsonGarcia/delta-hedging)</sub> | <sub>Advanced derivatives.</sub> | <sub>3/2/18 23:53</sub> | <sub>7/17/18 23:32</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Option Strategies](https://github.com/rstreppa/valuation-OptionStrategies)</sub> | <sub>Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.</sub> | <sub>5/22/18 18:27</sub> | <sub>5/22/18 18:30</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Computational Derivatives](https://github.com/chenbowen184/Computational_Finance)</sub> | <sub>Projects focusing on investigating simulations and computational techniques applied in finance.</sub> | <sub>1/29/18 5:01</sub> | <sub>8/2/18 5:56</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Reinforcement Learning](https://github.com/FinTechies/HedgingRL)</sub> | <sub>Hedging portfolios with reinforcement learning.</sub> | <sub>4/21/17 10:58</sub> | <sub>8/2/17 21:41</sub> | <sub>16.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:derivatives_and_hedging] -->
|
||||
|
||||
## Fixed Income ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/fixed_income))
|
||||
<!-- [PLACEHOLDER_START:fixed_income] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Corporate Bonds](https://github.com/ishank011/gs-quantify-bond-prediction)</sub> | <sub>Predicting the buying and selling volume of the corporate bonds.</sub> | <sub>2017-09-27 19:57:13</sub> | <sub>2017-09-27 20:00:29</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>2018-07-18 19:26:54</sub> | <sub>2018-07-18 19:34:48</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>2019-02-02 08:44:14</sub> | <sub>2019-05-03 17:16:52</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>[Binomial Tree](https://github.com/hy-lei/math-finance-exercise)</sub> | <sub>Utility functions in fixed income securities.</sub> | <sub>2/2/19 8:44</sub> | <sub>5/3/19 17:16</sub> | <sub>1.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:fixed_income] -->
|
||||
|
||||
## Alternative Finance ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/alternative_finance))
|
||||
<!-- [PLACEHOLDER_START:alternative_finance] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Venture Capital NN](https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring)</sub> | <sub>Cox-PH neural network predictions for VC/innovations finance research.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Art Valuation](https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb)</sub> | <sub>Art evaluation analytics.</sub> | <sub>2014-12-11 00:25:39</sub> | <sub>2014-12-12 21:25:46</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Private Equity](https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb)</sub> | <sub>Valuation models.</sub> | <sub>2016-01-27 21:13:33</sub> | <sub>2016-03-14 20:03:52</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Kiva Crowdfunding](https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb)</sub> | <sub>Exploratory data analysis.</sub> | <sub>2018-02-27 16:46:02</sub> | <sub>2019-02-13 00:15:27</sub> | <sub>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Watch Valuation](https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb)</sub> | <sub>Analysis of luxury watch data to classify whether a certain model is likely to be over-or undervalued.</sub> | <sub>2017-02-08 18:39:29</sub> | <sub>2017-04-27 22:55:55</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Venture Capital](https://github.com/julian-chan/etothex)</sub> | <sub>Insight into a new founder to make data-driven investment decisions.</sub> | <sub>2017-12-04 08:59:44</sub> | <sub>2017-12-13 05:35:27</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[VC OLS](https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb)</sub> | <sub>VC regression.</sub> | <sub>2018-03-29 23:31:13</sub> | <sub>2018-03-29 23:33:19</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Blockchain](https://github.com/nud3l/dInvest)</sub> | <sub>Repository for distributed autonomous investment banking.</sub> | <sub>2016-09-05 19:12:40</sub> | <sub>2017-04-24 10:48:56</sub> | <sub>12.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:alternative_finance] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:-------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Venture Capital NN](https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring)</sub> | <sub>Cox-PH neural network predictions for VC/innovations finance research.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Art Valuation](https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb)</sub> | <sub>Art evaluation analytics.</sub> | <sub>12/11/14 0:25</sub> | <sub>12/12/14 21:25</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Private Equity](https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb)</sub> | <sub>Valuation models.</sub> | <sub>1/27/16 21:13</sub> | <sub>3/14/16 20:03</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Kiva Crowdfunding](https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb)</sub> | <sub>Exploratory data analysis.</sub> | <sub>2/27/18 16:46</sub> | <sub>2/13/19 0:15</sub> | <sub>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Watch Valuation](https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb)</sub> | <sub>Analysis of luxury watch data to classify whether a certain model is likely to be over-or undervalued.</sub> | <sub>2/8/17 18:39</sub> | <sub>4/27/17 22:55</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Venture Capital](https://github.com/julian-chan/etothex)</sub> | <sub>Insight into a new founder to make data-driven investment decisions.</sub> | <sub>12/4/17 8:59</sub> | <sub>12/13/17 5:35</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[VC OLS](https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb)</sub> | <sub>VC regression.</sub> | <sub>3/29/18 23:31</sub> | <sub>3/29/18 23:33</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Blockchain](https://github.com/nud3l/dInvest)</sub> | <sub>Repository for distributed autonomous investment banking.</sub> | <sub>9/5/16 19:12</sub> | <sub>4/24/17 10:48</sub> | <sub>12.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:alternative_finance] -->
|
||||
|
||||
# Extended Research ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/extended_research))
|
||||
<!-- [PLACEHOLDER_START:extended_research] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[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>[Behavioural Economics](https://github.com/pcmichaud/notebooks)</sub> | <sub>Behavioural Economics and Finance Python Notebooks.</sub> | <sub>2018-12-20 00:21:38</sub> | <sub>2019-03-26 11:51:46</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>2017-09-09 03:35:24</sub> | <sub>2017-09-09 23:04:48</sub> | <sub>9.0</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>2018-01-29 05:14:52</sub> | <sub>2018-07-19 06:25:36</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>2018-10-11 20:32:37</sub> | <sub>2018-12-24 23:27:55</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>2016-07-21 05:14:14</sub> | <sub>2017-02-14 16:47:25</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>2014-11-09 04:49:01</sub> | <sub>2018-12-03 16:30:28</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>2017-01-21 11:24:18</sub> | <sub>2020-08-01 17:03:32</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>2018-09-16 20:00:36</sub> | <sub>2020-09-05 13:01:05</sub> | <sub>620.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END: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>[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>[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> |<!-- [PLACEHOLDER_END:extended_research] -->
|
||||
|
||||
# Courses ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/courses))
|
||||
<!-- [PLACEHOLDER_START:courses] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Basic Investments](https://github.com/SeanMcOwen/FinanceAndPython.com-Investments)</sub> | <sub>Basic investment tools in python.</sub> | <sub>2017-08-02 21:52:19</sub> | <sub>2017-08-17 03:24:53</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Mathematical Finance](https://github.com/yadongli/nyumath2048)</sub> | <sub>NYU Math-GA 2048: Scientific Computing in Finance.</sub> | <sub>2015-01-25 21:10:37</sub> | <sub>2020-03-25 04:24:25</sub> | <sub>69.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Algo Trading](https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading)</sub> | <sub>Intro to algo trading.</sub> | <sub>2017-10-29 20:34:54</sub> | <sub>2019-01-22 06:56:08</sub> | <sub>64.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Risk Management](https://github.com/andrey-lukyanov/Risk-Management)</sub> | <sub>Finance risk engagement course resources.</sub> | <sub>2018-10-03 16:26:14</sub> | <sub>2018-12-13 08:04:15</sub> | <sub>6.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Derivatives](https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives)</sub> | <sub>Basic forward contracts and hedging.</sub> | <sub>2017-08-24 00:11:37</sub> | <sub>2017-10-13 01:32:23</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Machine Learning for Trading](https://github.com/stefan-jansen/machine-learning-for-trading)</sub> | <sub>Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.</sub> | <sub>2018-05-09 12:33:08</sub> | <sub>2021-04-10 22:21:29</sub> | <sub>3842.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[ML Specialisation](https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization)</sub> | <sub>Machine Learning in Finance.</sub> | <sub>2019-01-24 02:55:01</sub> | <sub>2020-01-03 21:54:16</sub> | <sub>34.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Python for Finance](https://github.com/siaen/python_finance_course)</sub> | <sub>CEU python for finance course material.</sub> | <sub>2017-12-12 11:54:46</sub> | <sub>2020-02-25 20:31:41</sub> | <sub>16.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Handson Python for Finance](https://github.com/PacktPublishing/Hands-on-Python-for-Finance)</sub> | <sub>Hands-on Python for Finance published by Packt.</sub> | <sub>2018-08-20 14:10:37</sub> | <sub>2021-01-15 08:57:06</sub> | <sub>121.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Finance](https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance)</sub> | <sub>Source code notebooks basic finance applications.</sub> | <sub>2017-05-06 02:39:05</sub> | <sub>2017-06-21 04:04:09</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:courses] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:--------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Basic Investments](https://github.com/SeanMcOwen/FinanceAndPython.com-Investments)</sub> | <sub>Basic investment tools in python.</sub> | <sub>8/2/17 21:52</sub> | <sub>8/17/17 3:24</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Mathematical Finance](https://github.com/yadongli/nyumath2048)</sub> | <sub>NYU Math-GA 2048: Scientific Computing in Finance.</sub> | <sub>1/25/15 21:10</sub> | <sub>3/25/20 4:24</sub> | <sub>69.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Algo Trading](https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading)</sub> | <sub>Intro to algo trading.</sub> | <sub>10/29/17 20:34</sub> | <sub>1/22/19 6:56</sub> | <sub>64.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Risk Management](https://github.com/andrey-lukyanov/Risk-Management)</sub> | <sub>Finance risk engagement course resources.</sub> | <sub>10/3/18 16:26</sub> | <sub>12/13/18 8:04</sub> | <sub>6.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Derivatives](https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives)</sub> | <sub>Basic forward contracts and hedging.</sub> | <sub>8/24/17 0:11</sub> | <sub>10/13/17 1:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Machine Learning for Trading](https://github.com/stefan-jansen/machine-learning-for-trading)</sub> | <sub>Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.</sub> | <sub>5/9/18 12:33</sub> | <sub>4/10/21 22:21</sub> | <sub>3842.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[ML Specialisation](https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization)</sub> | <sub>Machine Learning in Finance.</sub> | <sub>1/24/19 2:55</sub> | <sub>1/3/20 21:54</sub> | <sub>34.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Python for Finance](https://github.com/siaen/python_finance_course)</sub> | <sub>CEU python for finance course material.</sub> | <sub>12/12/17 11:54</sub> | <sub>2/25/20 20:31</sub> | <sub>16.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Handson Python for Finance](https://github.com/PacktPublishing/Hands-on-Python-for-Finance)</sub> | <sub>Hands-on Python for Finance published by Packt.</sub> | <sub>8/20/18 14:10</sub> | <sub>1/15/21 8:57</sub> | <sub>121.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Finance](https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance)</sub> | <sub>Source code notebooks basic finance applications.</sub> | <sub>5/6/17 2:39</sub> | <sub>6/21/17 4:04</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:courses] -->
|
||||
|
||||
# Data ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data))
|
||||
<!-- [PLACEHOLDER_START:data] -->
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[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://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>[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>[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>[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>[SEC Parsing](https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb)</sub> | <sub>nan</sub> | <sub>2018-06-16 14:30:06</sub> | <sub>2018-06-16 17:23:46</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>2017-05-10 21:49:45</sub> | <sub>2017-08-06 19:23:18</sub> | <sub>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END: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://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>[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>[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>[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>[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> |<!-- [PLACEHOLDER_END:data] -->
|
||||
|
||||
# Colleges, Centers and Departments ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/colleges_centers_and_departments))
|
||||
<!-- [PLACEHOLDER_START:colleges_centers_and_departments] -->
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Venture Capital NN](https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring)</sub> | <sub>Cox-PH neural network predictions for VC/innovations finance research.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Art Valuation](https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb)</sub> | <sub>Art evaluation analytics.</sub> | <sub>2014-12-11 00:25:39</sub> | <sub>2014-12-12 21:25:46</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Private Equity](https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb)</sub> | <sub>Valuation models.</sub> | <sub>2016-01-27 21:13:33</sub> | <sub>2016-03-14 20:03:52</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Kiva Crowdfunding](https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb)</sub> | <sub>Exploratory data analysis.</sub> | <sub>2018-02-27 16:46:02</sub> | <sub>2019-02-13 00:15:27</sub> | <sub>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Watch Valuation](https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb)</sub> | <sub>Analysis of luxury watch data to classify whether a certain model is likely to be over-or undervalued.</sub> | <sub>2017-02-08 18:39:29</sub> | <sub>2017-04-27 22:55:55</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Venture Capital](https://github.com/julian-chan/etothex)</sub> | <sub>Insight into a new founder to make data-driven investment decisions.</sub> | <sub>2017-12-04 08:59:44</sub> | <sub>2017-12-13 05:35:27</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[VC OLS](https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb)</sub> | <sub>VC regression.</sub> | <sub>2018-03-29 23:31:13</sub> | <sub>2018-03-29 23:33:19</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Blockchain](https://github.com/nud3l/dInvest)</sub> | <sub>Repository for distributed autonomous investment banking.</sub> | <sub>2016-09-05 19:12:40</sub> | <sub>2017-04-24 10:48:56</sub> | <sub>12.0</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>[Venture Capital NN](https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring)</sub> | <sub>Cox-PH neural network predictions for VC/innovations finance research.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Art Valuation](https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb)</sub> | <sub>Art evaluation analytics.</sub> | <sub>12/11/14 0:25</sub> | <sub>12/12/14 21:25</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Private Equity](https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb)</sub> | <sub>Valuation models.</sub> | <sub>1/27/16 21:13</sub> | <sub>3/14/16 20:03</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Kiva Crowdfunding](https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb)</sub> | <sub>Exploratory data analysis.</sub> | <sub>2/27/18 16:46</sub> | <sub>2/13/19 0:15</sub> | <sub>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Watch Valuation](https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb)</sub> | <sub>Analysis of luxury watch data to classify whether a certain model is likely to be over-or undervalued.</sub> | <sub>2/8/17 18:39</sub> | <sub>4/27/17 22:55</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Venture Capital](https://github.com/julian-chan/etothex)</sub> | <sub>Insight into a new founder to make data-driven investment decisions.</sub> | <sub>12/4/17 8:59</sub> | <sub>12/13/17 5:35</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[VC OLS](https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb)</sub> | <sub>VC regression.</sub> | <sub>3/29/18 23:31</sub> | <sub>3/29/18 23:33</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Blockchain](https://github.com/nud3l/dInvest)</sub> | <sub>Repository for distributed autonomous investment banking.</sub> | <sub>9/5/16 19:12</sub> | <sub>4/24/17 10:48</sub> | <sub>12.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
+12
-12
@@ -1,12 +1,12 @@
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Basic Investments](https://github.com/SeanMcOwen/FinanceAndPython.com-Investments)</sub> | <sub>Basic investment tools in python.</sub> | <sub>2017-08-02 21:52:19</sub> | <sub>2017-08-17 03:24:53</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Mathematical Finance](https://github.com/yadongli/nyumath2048)</sub> | <sub>NYU Math-GA 2048: Scientific Computing in Finance.</sub> | <sub>2015-01-25 21:10:37</sub> | <sub>2020-03-25 04:24:25</sub> | <sub>69.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Algo Trading](https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading)</sub> | <sub>Intro to algo trading.</sub> | <sub>2017-10-29 20:34:54</sub> | <sub>2019-01-22 06:56:08</sub> | <sub>64.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Risk Management](https://github.com/andrey-lukyanov/Risk-Management)</sub> | <sub>Finance risk engagement course resources.</sub> | <sub>2018-10-03 16:26:14</sub> | <sub>2018-12-13 08:04:15</sub> | <sub>6.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Derivatives](https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives)</sub> | <sub>Basic forward contracts and hedging.</sub> | <sub>2017-08-24 00:11:37</sub> | <sub>2017-10-13 01:32:23</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Machine Learning for Trading](https://github.com/stefan-jansen/machine-learning-for-trading)</sub> | <sub>Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.</sub> | <sub>2018-05-09 12:33:08</sub> | <sub>2021-04-10 22:21:29</sub> | <sub>3842.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[ML Specialisation](https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization)</sub> | <sub>Machine Learning in Finance.</sub> | <sub>2019-01-24 02:55:01</sub> | <sub>2020-01-03 21:54:16</sub> | <sub>34.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Python for Finance](https://github.com/siaen/python_finance_course)</sub> | <sub>CEU python for finance course material.</sub> | <sub>2017-12-12 11:54:46</sub> | <sub>2020-02-25 20:31:41</sub> | <sub>16.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Handson Python for Finance](https://github.com/PacktPublishing/Hands-on-Python-for-Finance)</sub> | <sub>Hands-on Python for Finance published by Packt.</sub> | <sub>2018-08-20 14:10:37</sub> | <sub>2021-01-15 08:57:06</sub> | <sub>121.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Finance](https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance)</sub> | <sub>Source code notebooks basic finance applications.</sub> | <sub>2017-05-06 02:39:05</sub> | <sub>2017-06-21 04:04:09</sub> | <sub>10.0</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>[Basic Investments](https://github.com/SeanMcOwen/FinanceAndPython.com-Investments)</sub> | <sub>Basic investment tools in python.</sub> | <sub>8/2/17 21:52</sub> | <sub>8/17/17 3:24</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Mathematical Finance](https://github.com/yadongli/nyumath2048)</sub> | <sub>NYU Math-GA 2048: Scientific Computing in Finance.</sub> | <sub>1/25/15 21:10</sub> | <sub>3/25/20 4:24</sub> | <sub>69.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Algo Trading](https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading)</sub> | <sub>Intro to algo trading.</sub> | <sub>10/29/17 20:34</sub> | <sub>1/22/19 6:56</sub> | <sub>64.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Risk Management](https://github.com/andrey-lukyanov/Risk-Management)</sub> | <sub>Finance risk engagement course resources.</sub> | <sub>10/3/18 16:26</sub> | <sub>12/13/18 8:04</sub> | <sub>6.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Derivatives](https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives)</sub> | <sub>Basic forward contracts and hedging.</sub> | <sub>8/24/17 0:11</sub> | <sub>10/13/17 1:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Machine Learning for Trading](https://github.com/stefan-jansen/machine-learning-for-trading)</sub> | <sub>Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.</sub> | <sub>5/9/18 12:33</sub> | <sub>4/10/21 22:21</sub> | <sub>3842.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[ML Specialisation](https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization)</sub> | <sub>Machine Learning in Finance.</sub> | <sub>1/24/19 2:55</sub> | <sub>1/3/20 21:54</sub> | <sub>34.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Python for Finance](https://github.com/siaen/python_finance_course)</sub> | <sub>CEU python for finance course material.</sub> | <sub>12/12/17 11:54</sub> | <sub>2/25/20 20:31</sub> | <sub>16.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Handson Python for Finance](https://github.com/PacktPublishing/Hands-on-Python-for-Finance)</sub> | <sub>Hands-on Python for Finance published by Packt.</sub> | <sub>8/20/18 14:10</sub> | <sub>1/15/21 8:57</sub> | <sub>121.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Basic Finance](https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance)</sub> | <sub>Source code notebooks basic finance applications.</sub> | <sub>5/6/17 2:39</sub> | <sub>6/21/17 4:04</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
+16
-16
@@ -1,16 +1,16 @@
|
||||
| <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://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>[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>[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>[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>[SEC Parsing](https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb)</sub> | <sub>nan</sub> | <sub>2018-06-16 14:30:06</sub> | <sub>2018-06-16 17:23:46</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>2017-05-10 21:49:45</sub> | <sub>2017-08-06 19:23:18</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>2019-02-19 19:02:59</sub> | <sub>2020-07-22 16:48:21</sub> | <sub>577.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Open Edgar](https://github.com/LexPredict/openedgar)</sub> | <sub>nan</sub> | <sub>2018-05-07 15:32:31</sub> | <sub>2019-05-15 08:32:30</sub> | <sub>169.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[EDGAR](https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb)</sub> | <sub>nan</sub> | <sub>2018-06-11 22:51:57</sub> | <sub>2018-07-10 18:03:52</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Employee Count SEC Filings](https://github.com/healthgradient/sec_employee_information_extraction)</sub> | <sub>nan</sub> | <sub>2018-06-26 23:33:51</sub> | <sub>2018-08-14 01:31:13</sub> | <sub>10.0</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>[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://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>[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>[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>[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>[SEC Parsing](https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb)</sub> | <sub>nan</sub> | <sub>6/16/18 14:30</sub> | <sub>6/16/18 17:23</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[https://github.com/timestocome/StockMarketData](https://github.com/timestocome/StockMarketData)</sub> | <sub>nan</sub> | <sub>5/10/17 21:49</sub> | <sub>8/6/17 19:23</sub> | <sub>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Web Scraping (FirmAI)](https://github.com/firmai/business-machine-learning/blob/master/www.firmai.org/data)</sub> | <sub>nan</sub> | <sub>2/19/19 19:02</sub> | <sub>7/22/20 16:48</sub> | <sub>577.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Open Edgar](https://github.com/LexPredict/openedgar)</sub> | <sub>nan</sub> | <sub>5/7/18 15:32</sub> | <sub>5/15/19 8:32</sub> | <sub>169.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[EDGAR](https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb)</sub> | <sub>nan</sub> | <sub>6/11/18 22:51</sub> | <sub>7/10/18 18:03</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Employee Count SEC Filings](https://github.com/healthgradient/sec_employee_information_extraction)</sub> | <sub>nan</sub> | <sub>6/26/18 23:33</sub> | <sub>8/14/18 1:31</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
@@ -1,4 +1,4 @@
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------|:--------------------|
|
||||
| <sub>[Advanced ML II](https://github.com/hudson-and-thames/research)</sub> | <sub>More implementations of Financial Machine Learning (De Prado).</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Advanced ML](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises)</sub> | <sub>Exercises too Financial Machine Learning (De Prado).</sub> | <sub>2018-04-25 17:22:40</sub> | <sub>2020-01-16 17:25:41</sub> | <sub>973.0</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>[Advanced ML II](https://github.com/hudson-and-thames/research)</sub> | <sub>More implementations of Financial Machine Learning (De Prado).</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Advanced ML](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises)</sub> | <sub>Exercises too Financial Machine Learning (De Prado).</sub> | <sub>4/25/18 17:22</sub> | <sub>1/16/20 17:25</sub> | <sub>973.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
@@ -1,49 +1,49 @@
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models)</sub> | <sub>very good curated list of notebooks showing deep learning + reinforcement learning models. Also contain topics on outlier detections/overbought oversold study/monte carlo simulartions/sentiment analysis from text (text storage/parsing is not detailed but it mentioned using [BERT](https://github.com/google-research/bert))</sub> | <sub>2017-12-18 10:49:59</sub> | <sub>2021-01-05 10:31:50</sub> | <sub>3655.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[AI Trading](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md)</sub> | <sub>AI to predict stock market movements.</sub> | <sub>2019-01-09 08:02:47</sub> | <sub>2019-02-11 16:32:47</sub> | <sub>2876.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[FinRL-Library](https://github.com/AI4Finance-LLC/FinRL-Library)</sub> | <sub>started by Columbia university engineering students and designed as an end to end deep reinforcement learning library for automated trading platform. Implementation of DQN DDQN DDPG etc using PyTorch and [gym](https://gym.openai.com/) use [pyfolio](https://github.com/quantopian/pyfolio) for showing backtesting stats. Big contributions on Proximal Policy Optimization (PPO) advantage actor critic (A2C) and Deep Deterministic Policy Gradient (DDPG) agents for trading</sub> | <sub>2020-07-26 13:18:16</sub> | <sub>2021-04-11 22:02:16</sub> | <sub>1857.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning IV](https://github.com/achillesrasquinha/bulbea)</sub> | <sub>Bulbea: Deep Learning based Python Library.</sub> | <sub>2017-03-09 06:11:06</sub> | <sub>2017-03-19 07:42:49</sub> | <sub>1467.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RLTrader](https://github.com/notadamking/RLTrader)</sub> | <sub>predecessor to [tensortrade](https://github.com/tensortrade-org/tensortrade) uses open api [gym](https://gym.openai.com/) and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using [Optuna](https://github.com/optuna/optuna) etc.</sub> | <sub>2019-04-27 18:35:15</sub> | <sub>2019-10-17 16:25:49</sub> | <sub>1312.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning III](https://github.com/Rachnog/Deep-Trading)</sub> | <sub>Algorithmic trading with deep learning experiments.</sub> | <sub>2016-06-18 18:23:06</sub> | <sub>2018-08-07 15:24:45</sub> | <sub>1266.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Personae](https://github.com/Ceruleanacg/Personae)</sub> | <sub>implementation of deep reinforcement learning and supervised learnings covering areas: deep deterministic policy gradient (DDPG) and DDQN etc. Data are being pulled from [rqalpha](https://github.com/ricequant/rqalpha) which is a python backtest engine and have a nice docker image to run training/testing</sub> | <sub>2018-03-10 11:22:00</sub> | <sub>2018-09-02 17:21:38</sub> | <sub>1144.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RL Trading](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW)</sub> | <sub>A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020)</sub> | <sub>Part of FinRL and provided code for paper [deep reinformacement learning for automated stock trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996) focuses on ensemble.</sub> | <sub>2020-07-26 13:12:53</sub> | <sub>2021-01-21 18:11:59</sub> | <sub>560.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[awesome-deep-trading](https://github.com/cbailes/awesome-deep-trading)</sub> | <sub>curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes</sub> | <sub>2018-11-26 03:23:04</sub> | <sub>2021-01-01 09:41:21</sub> | <sub>551.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[Neural Network](https://github.com/VivekPa/IntroNeuralNetworks)</sub> | <sub>Neural networks to predict stock prices.</sub> | <sub>2018-09-10 06:34:53</sub> | <sub>2018-11-21 07:39:31</sub> | <sub>489.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[Deep Learning](https://github.com/keon/deepstock)</sub> | <sub>Technical experimentations to beat the stock market using deep learning.</sub> | <sub>2016-12-12 02:15:12</sub> | <sub>2017-03-04 08:37:29</sub> | <sub>427.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[LTSM Recurrent](https://github.com/VivekPa/AIAlpha)</sub> | <sub>OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.</sub> | <sub>2018-10-07 03:58:26</sub> | <sub>2019-08-03 09:00:44</sub> | <sub>1207.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[RL III](https://github.com/samre12/deep-trading-agent)</sub> | <sub>Github -Deep Reinforcement Learning based Trading Agent for Bitcoin.</sub> | <sub>2017-09-21 17:05:19</sub> | <sub>2018-04-13 16:33:21</sub> | <sub>576.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[crypto-rl](https://github.com/sadighian/crypto-rl)</sub> | <sub>Retrieve limit order book level data from coinbase pro and bitfinex -> record in [arctic](https://github.com/man-group/arctic) timeseries database then implemented trend following strategies (market orders) and market making (limit orders). Uses reinforcement learning (DQN) [keras-rl](https://github.com/keras-rl/keras-rl) to create agents and uses [openai gym](https://gym.openai.com/) to implement POMDP (partially observable markov decision process)</sub> | <sub>2018-06-21 01:06:01</sub> | <sub>2020-11-05 11:08:19</sub> | <sub>347.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Advanced-Deep-Trading](https://github.com/Rachnog/Advanced-Deep-Trading)</sub> | <sub>notebooks containing experiments based on Lopez de Prado book "Advances in financial machine learning". Mostly not deep learning related but rather sklearn regression models. Interesting libraries include [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) for calculating return stats and [shap](https://github.com/slundberg/shap) for explaining models. Examlpe of shap can be which features are pushing the value up and and which features are pushing the value down. Also contain functions for calculating geometric brownian motion and jump diffusion functions. </sub> | <sub>2019-02-16 21:18:00</sub> | <sub>2020-11-29 20:12:59</sub> | <sub>319.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[trading-bot](https://github.com/pskrunner14/trading-bot)</sub> | <sub>Implementation of deep reinforcement learning using Deep Q Network (DQN). Only supports single security at the moment. Idea is roughly based [here](https://keon.github.io/deep-q-learning/) and uses tensorflow/keras. Interesting helper python libraries used here are [tqdm](https://tqdm.github.io/) for console based progress bar and [altair](https://altair-viz.github.io/) for declarative visualization in python </sub> | <sub>2018-08-13 10:44:08</sub> | <sub>2020-01-23 04:41:20</sub> | <sub>292.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[BitcoinForecast](https://github.com/PiSimo/BitcoinForecast)</sub> | <sub>RNN model to predict short term price movement (in this case BTC for the next 9 minutes) [deepchart](https://pisimo.github.io/DeepChart/) is used to visualize the model </sub> | <sub>2017-03-10 10:52:02</sub> | <sub>2018-06-11 08:07:02</sub> | <sub>289.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep-Learning-Machine-Learning-Stock](https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock)</sub> | <sub>curated list of notebooks for machine learning models. Start with very simple linear models to more advanced reinforcement learning type of models. Problem with this repo is that the library version numbers may be changing over time and there's no specific way to track and upgrade</sub> | <sub>2018-09-29 23:38:06</sub> | <sub>2021-03-18 03:16:36</sub> | <sub>275.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[DeepLearningInFinance](https://github.com/sonaam1234/DeepLearningInFinance)</sub> | <sub>Based on a [talk](https://towardsdatascience.com/deep-learning-in-finance-9e088cb17c03) Sonam Srivastava gave and there are two studies: 1. single timeseries return prediction using ARIMA/VAR/SVR/Deep Regression/CNN/LSTM 2. indexed portfolio construction using autoencoders i.e. replicate a index using handful of stocks. </sub> | <sub>2017-08-21 16:00:42</sub> | <sub>2017-08-21 17:23:48</sub> | <sub>266.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Pair Trading RL](https://github.com/shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading)</sub> | <sub>Using deep actor-critic model to learn best strategies in pair trading.</sub> | <sub>2017-05-18 16:47:11</sub> | <sub>2017-05-18 16:56:38</sub> | <sub>241.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[deep-RL-trading](https://github.com/golsun/deep-RL-trading)</sub> | <sub>trading game comparing RNN vs CNN vs MLP based on [paper](https://arxiv.org/abs/1803.03916)</sub> | <sub>2018-02-25 17:41:42</sub> | <sub>2020-12-01 22:06:39</sub> | <sub>235.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[ARIMA-LTSM Hybrid](https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid)</sub> | <sub>Hybrid model to predict future price correlation coefficients of two assets.</sub> | <sub>2018-08-05 02:13:21</sub> | <sub>2018-10-01 11:25:53</sub> | <sub>222.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[trading-rl](https://github.com/Kostis-S-Z/trading-rl)</sub> | <sub>Deep reinforcement learning for financial trading using [gym](https://gym.openai.com/) and [keras-rl](https://github.com/keras-rl/keras-rl) on FX dataset (EURUSD) not actively maintained</sub> | <sub>2019-04-22 10:03:21</sub> | <sub>2020-09-28 09:07:18</sub> | <sub>180.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep Learning II](https://github.com/LiamConnell/deep-algotrading/tree/master/notebooks)</sub> | <sub>Tensorflow Regression.</sub> | <sub>2016-07-12 12:56:10</sub> | <sub>2018-02-16 02:43:36</sub> | <sub>175.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep-Reinforcement-Stock-Trading](https://github.com/Albert-Z-Guo/Deep-Reinforcement-Stock-Trading)</sub> | <sub>inspired by Q-trader a deep reinforcement learning repo for trading. Only 3 actions allowed (buy/hold/sell) and no transaction cost is implemented yet. Uses [empyrical](https://github.com/quantopian/empyrical) for portfolio stats</sub> | <sub>2019-05-19 22:20:05</sub> | <sub>2020-09-27 19:22:28</sub> | <sub>141.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep-Reinforcement-Learning-in-Trading](https://github.com/saeed349/Deep-Reinforcement-Learning-in-Trading)</sub> | <sub>Deep reinforcement learning for trading leveraging [openai gym](https://gym.openai.com/) framework. Keras implementation of DQN DDQN (double deep Q network) and DDDQN (dueling double dqn) trained/tested on s&p 500 daily data from 2013 to 2018. approach is described in an article [here](https://www.linkedin.com/pulse/deep-reinforcement-learning-trading-saeed-rahman)</sub> | <sub>2018-05-11 00:52:14</sub> | <sub>2019-10-26 14:22:44</sub> | <sub>138.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[DQN-DDPG_Stock_Trading](https://github.com/AI4Finance-LLC/DQN-DDPG_Stock_Trading)</sub> | <sub>merged into FinRL library and uses [gym](https://gym.openai.com/) and implementation of DQN</sub> | <sub>2018-09-19 03:17:06</sub> | <sub>2020-11-26 16:58:00</sub> | <sub>136.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[RL II](https://github.com/deependersingla/deep_trader)</sub> | <sub>reinforcement learning on stock market and agent tries to learn trading.</sub> | <sub>2016-06-11 07:27:10</sub> | <sub>2018-01-22 14:35:50</sub> | <sub>1340.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[AutomatedStockTrading-DeepQ-Learning](https://github.com/sachink2010/AutomatedStockTrading-DeepQ-Learning)</sub> | <sub>cornerstone project repo for Udacity nanodegree program [Become a machine learning engineer](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t) and focus on trading using deep q learning. Good explanation on design choices in the report</sub> | <sub>2019-02-23 12:01:21</sub> | <sub>2020-02-25 18:16:34</sub> | <sub>134.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[LTSM GRU](https://github.com/RajatHanda/Finance-Forecasting)</sub> | <sub>Stock Market Forecasting using LSTM\GRU.</sub> | <sub>2018-05-13 02:39:32</sub> | <sub>2019-02-25 00:26:42</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[RL](https://github.com/kh-kim/stock_market_reinforcement_learning)</sub> | <sub>OpenGym with Deep Q-learning and Policy Gradient.</sub> | <sub>2016-10-04 14:42:19</sub> | <sub>2016-12-23 07:34:08</sub> | <sub>715.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
|
||||
| <sub>[RL V](https://github.com/gstenger98/rl-finance)</sub> | <sub>Building an Agent to Trade with Reinforcement Learning.</sub> | <sub>2019-01-16 00:43:36</sub> | <sub>2020-03-19 20:28:08</sub> | <sub>33.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x2</sub> |
|
||||
| <sub>[TradingGym](https://github.com/Yvictor/TradingGym)</sub> | <sub>NEW</sub> | <sub>2017-05-01 13:53:32</sub> | <sub>2018-02-14 13:58:18</sub> | <sub>841.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[gym-trading](https://github.com/hackthemarket/gym-trading)</sub> | <sub>NEW</sub> | <sub>2016-12-09 20:46:19</sub> | <sub>2017-12-24 15:34:37</sub> | <sub>581.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Trading-Gym](https://github.com/thedimlebowski/Trading-Gym)</sub> | <sub>NEW</sub> | <sub>2017-06-13 13:14:48</sub> | <sub>2017-07-10 08:09:30</sub> | <sub>507.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[QLearning_Trading](https://github.com/ucaiado/QLearning_Trading)</sub> | <sub>NEW</sub> | <sub>2016-08-10 06:02:23</sub> | <sub>2016-10-15 02:36:09</sub> | <sub>433.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[maro](https://github.com/microsoft/maro)</sub> | <sub>NEW</sub> | <sub>2019-12-27 06:48:27</sub> | <sub>2021-04-07 15:49:38</sub> | <sub>386.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[a3c_trading](https://github.com/evgps/a3c_trading)</sub> | <sub>NEW</sub> | <sub>2018-06-04 15:30:16</sub> | <sub>2020-05-23 14:47:54</sub> | <sub>311.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[tensortrade](https://github.com/tensortrade-org/tensortrade)</sub> | <sub>NEW</sub> | <sub>2019-07-30 21:28:32</sub> | <sub>2021-03-24 16:25:28</sub> | <sub>3101.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[RLQuant](https://github.com/yuriak/RLQuant)</sub> | <sub>NEW</sub> | <sub>2018-04-05 05:42:44</sub> | <sub>2018-08-13 04:18:29</sub> | <sub>277.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[rl_trading](https://github.com/ucaiado/rl_trading)</sub> | <sub>NEW</sub> | <sub>2017-05-29 22:19:00</sub> | <sub>2017-08-29 14:54:50</sub> | <sub>207.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Machine-Learning-and-Reinforcement-Learning-in-Finance](https://github.com/joelowj/Machine-Learning-and-Reinforcement-Learning-in-Finance)</sub> | <sub>NEW</sub> | <sub>2018-06-26 04:30:08</sub> | <sub>2018-09-23 16:50:33</sub> | <sub>175.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[RL IV](https://github.com/jjakimoto/DQN)</sub> | <sub>Reinforcement Learning for finance.</sub> | <sub>2016-10-21 02:47:17</sub> | <sub>2017-04-07 08:11:57</sub> | <sub>142.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Pair-Trading-Reinforcement-Learning](https://github.com/wai-i/Pair-Trading-Reinforcement-Learning)</sub> | <sub>NEW</sub> | <sub>2019-06-09 22:50:37</sub> | <sub>2020-01-03 15:36:22</sub> | <sub>136.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[TradingGym](https://github.com/cove9988/TradingGym)</sub> | <sub>NEW</sub> | <sub>2017-11-06 00:50:01</sub> | <sub>2017-11-15 23:55:32</sub> | <sub>112.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[pairstrade-fyp-2019](https://github.com/wywongbd/pairstrade-fyp-2019)</sub> | <sub>NEW</sub> | <sub>2018-09-07 07:51:06</sub> | <sub>2020-05-13 05:06:51</sub> | <sub>110.0</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>[Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models)</sub> | <sub>very good curated list of notebooks showing deep learning + reinforcement learning models. Also contain topics on outlier detections/overbought oversold study/monte carlo simulartions/sentiment analysis from text (text storage/parsing is not detailed but it mentioned using [BERT](https://github.com/google-research/bert))</sub> | <sub>12/18/17 10:49</sub> | <sub>1/5/21 10:31</sub> | <sub>3655.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[AI Trading](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md)</sub> | <sub>AI to predict stock market movements.</sub> | <sub>1/9/19 8:02</sub> | <sub>2/11/19 16:32</sub> | <sub>2876.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[FinRL-Library](https://github.com/AI4Finance-LLC/FinRL-Library)</sub> | <sub>started by Columbia university engineering students and designed as an end to end deep reinforcement learning library for automated trading platform. Implementation of DQN DDQN DDPG etc using PyTorch and [gym](https://gym.openai.com/) use [pyfolio](https://github.com/quantopian/pyfolio) for showing backtesting stats. Big contributions on Proximal Policy Optimization (PPO) advantage actor critic (A2C) and Deep Deterministic Policy Gradient (DDPG) agents for trading</sub> | <sub>7/26/20 13:18</sub> | <sub>4/11/21 22:02</sub> | <sub>1857.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning IV](https://github.com/achillesrasquinha/bulbea)</sub> | <sub>Bulbea: Deep Learning based Python Library.</sub> | <sub>3/9/17 6:11</sub> | <sub>3/19/17 7:42</sub> | <sub>1467.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RLTrader](https://github.com/notadamking/RLTrader)</sub> | <sub>predecessor to [tensortrade](https://github.com/tensortrade-org/tensortrade) uses open api [gym](https://gym.openai.com/) and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using [Optuna](https://github.com/optuna/optuna) etc.</sub> | <sub>4/27/19 18:35</sub> | <sub>10/17/19 16:25</sub> | <sub>1312.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Deep Learning III](https://github.com/Rachnog/Deep-Trading)</sub> | <sub>Algorithmic trading with deep learning experiments.</sub> | <sub>6/18/16 18:23</sub> | <sub>8/7/18 15:24</sub> | <sub>1266.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[Personae](https://github.com/Ceruleanacg/Personae)</sub> | <sub>implementation of deep reinforcement learning and supervised learnings covering areas: deep deterministic policy gradient (DDPG) and DDQN etc. Data are being pulled from [rqalpha](https://github.com/ricequant/rqalpha) which is a python backtest engine and have a nice docker image to run training/testing</sub> | <sub>3/10/18 11:22</sub> | <sub>9/2/18 17:21</sub> | <sub>1144.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[RL Trading](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW)</sub> | <sub>A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020](https://github.com/AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020)</sub> | <sub>Part of FinRL and provided code for paper [deep reinformacement learning for automated stock trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996) focuses on ensemble.</sub> | <sub>7/26/20 13:12</sub> | <sub>1/21/21 18:11</sub> | <sub>560.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[awesome-deep-trading](https://github.com/cbailes/awesome-deep-trading)</sub> | <sub>curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes</sub> | <sub>11/26/18 3:23</sub> | <sub>1/1/21 9:41</sub> | <sub>551.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[Neural Network](https://github.com/VivekPa/IntroNeuralNetworks)</sub> | <sub>Neural networks to predict stock prices.</sub> | <sub>9/10/18 6:34</sub> | <sub>11/21/18 7:39</sub> | <sub>489.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[Deep Learning](https://github.com/keon/deepstock)</sub> | <sub>Technical experimentations to beat the stock market using deep learning.</sub> | <sub>12/12/16 2:15</sub> | <sub>3/4/17 8:37</sub> | <sub>427.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[LTSM Recurrent](https://github.com/VivekPa/AIAlpha)</sub> | <sub>OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.</sub> | <sub>10/7/18 3:58</sub> | <sub>8/3/19 9:00</sub> | <sub>1207.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
|
||||
| <sub>[RL III](https://github.com/samre12/deep-trading-agent)</sub> | <sub>Github -Deep Reinforcement Learning based Trading Agent for Bitcoin.</sub> | <sub>9/21/17 17:05</sub> | <sub>4/13/18 16:33</sub> | <sub>576.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[crypto-rl](https://github.com/sadighian/crypto-rl)</sub> | <sub>Retrieve limit order book level data from coinbase pro and bitfinex -> record in [arctic](https://github.com/man-group/arctic) timeseries database then implemented trend following strategies (market orders) and market making (limit orders). Uses reinforcement learning (DQN) [keras-rl](https://github.com/keras-rl/keras-rl) to create agents and uses [openai gym](https://gym.openai.com/) to implement POMDP (partially observable markov decision process)</sub> | <sub>6/21/18 1:06</sub> | <sub>11/5/20 11:08</sub> | <sub>347.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Advanced-Deep-Trading](https://github.com/Rachnog/Advanced-Deep-Trading)</sub> | <sub>notebooks containing experiments based on Lopez de Prado book "Advances in financial machine learning". Mostly not deep learning related but rather sklearn regression models. Interesting libraries include [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) for calculating return stats and [shap](https://github.com/slundberg/shap) for explaining models. Examlpe of shap can be which features are pushing the value up and and which features are pushing the value down. Also contain functions for calculating geometric brownian motion and jump diffusion functions. </sub> | <sub>2/16/19 21:18</sub> | <sub>11/29/20 20:12</sub> | <sub>319.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[trading-bot](https://github.com/pskrunner14/trading-bot)</sub> | <sub>Implementation of deep reinforcement learning using Deep Q Network (DQN). Only supports single security at the moment. Idea is roughly based [here](https://keon.github.io/deep-q-learning/) and uses tensorflow/keras. Interesting helper python libraries used here are [tqdm](https://tqdm.github.io/) for console based progress bar and [altair](https://altair-viz.github.io/) for declarative visualization in python </sub> | <sub>8/13/18 10:44</sub> | <sub>1/23/20 4:41</sub> | <sub>292.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[BitcoinForecast](https://github.com/PiSimo/BitcoinForecast)</sub> | <sub>RNN model to predict short term price movement (in this case BTC for the next 9 minutes) [deepchart](https://pisimo.github.io/DeepChart/) is used to visualize the model </sub> | <sub>3/10/17 10:52</sub> | <sub>6/11/18 8:07</sub> | <sub>289.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep-Learning-Machine-Learning-Stock](https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock)</sub> | <sub>curated list of notebooks for machine learning models. Start with very simple linear models to more advanced reinforcement learning type of models. Problem with this repo is that the library version numbers may be changing over time and there's no specific way to track and upgrade</sub> | <sub>9/29/18 23:38</sub> | <sub>3/18/21 3:16</sub> | <sub>275.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[DeepLearningInFinance](https://github.com/sonaam1234/DeepLearningInFinance)</sub> | <sub>Based on a [talk](https://towardsdatascience.com/deep-learning-in-finance-9e088cb17c03) Sonam Srivastava gave and there are two studies: 1. single timeseries return prediction using ARIMA/VAR/SVR/Deep Regression/CNN/LSTM 2. indexed portfolio construction using autoencoders i.e. replicate a index using handful of stocks. </sub> | <sub>8/21/17 16:00</sub> | <sub>8/21/17 17:23</sub> | <sub>266.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Pair Trading RL](https://github.com/shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading)</sub> | <sub>Using deep actor-critic model to learn best strategies in pair trading.</sub> | <sub>5/18/17 16:47</sub> | <sub>5/18/17 16:56</sub> | <sub>241.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[deep-RL-trading](https://github.com/golsun/deep-RL-trading)</sub> | <sub>trading game comparing RNN vs CNN vs MLP based on [paper](https://arxiv.org/abs/1803.03916)</sub> | <sub>2/25/18 17:41</sub> | <sub>12/1/20 22:06</sub> | <sub>235.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[ARIMA-LTSM Hybrid](https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid)</sub> | <sub>Hybrid model to predict future price correlation coefficients of two assets.</sub> | <sub>8/5/18 2:13</sub> | <sub>10/1/18 11:25</sub> | <sub>222.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[trading-rl](https://github.com/Kostis-S-Z/trading-rl)</sub> | <sub>Deep reinforcement learning for financial trading using [gym](https://gym.openai.com/) and [keras-rl](https://github.com/keras-rl/keras-rl) on FX dataset (EURUSD) not actively maintained</sub> | <sub>4/22/19 10:03</sub> | <sub>9/28/20 9:07</sub> | <sub>180.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep Learning II](https://github.com/LiamConnell/deep-algotrading/tree/master/notebooks)</sub> | <sub>Tensorflow Regression.</sub> | <sub>7/12/16 12:56</sub> | <sub>2/16/18 2:43</sub> | <sub>175.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep-Reinforcement-Stock-Trading](https://github.com/Albert-Z-Guo/Deep-Reinforcement-Stock-Trading)</sub> | <sub>inspired by Q-trader a deep reinforcement learning repo for trading. Only 3 actions allowed (buy/hold/sell) and no transaction cost is implemented yet. Uses [empyrical](https://github.com/quantopian/empyrical) for portfolio stats</sub> | <sub>5/19/19 22:20</sub> | <sub>9/27/20 19:22</sub> | <sub>141.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[Deep-Reinforcement-Learning-in-Trading](https://github.com/saeed349/Deep-Reinforcement-Learning-in-Trading)</sub> | <sub>Deep reinforcement learning for trading leveraging [openai gym](https://gym.openai.com/) framework. Keras implementation of DQN DDQN (double deep Q network) and DDDQN (dueling double dqn) trained/tested on s&p 500 daily data from 2013 to 2018. approach is described in an article [here](https://www.linkedin.com/pulse/deep-reinforcement-learning-trading-saeed-rahman)</sub> | <sub>5/11/18 0:52</sub> | <sub>10/26/19 14:22</sub> | <sub>138.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[DQN-DDPG_Stock_Trading](https://github.com/AI4Finance-LLC/DQN-DDPG_Stock_Trading)</sub> | <sub>merged into FinRL library and uses [gym](https://gym.openai.com/) and implementation of DQN</sub> | <sub>9/19/18 3:17</sub> | <sub>11/26/20 16:58</sub> | <sub>136.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[RL II](https://github.com/deependersingla/deep_trader)</sub> | <sub>reinforcement learning on stock market and agent tries to learn trading.</sub> | <sub>6/11/16 7:27</sub> | <sub>1/22/18 14:35</sub> | <sub>1340.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[AutomatedStockTrading-DeepQ-Learning](https://github.com/sachink2010/AutomatedStockTrading-DeepQ-Learning)</sub> | <sub>cornerstone project repo for Udacity nanodegree program [Become a machine learning engineer](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t) and focus on trading using deep q learning. Good explanation on design choices in the report</sub> | <sub>2/23/19 12:01</sub> | <sub>2/25/20 18:16</sub> | <sub>134.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[LTSM GRU](https://github.com/RajatHanda/Finance-Forecasting)</sub> | <sub>Stock Market Forecasting using LSTM\GRU.</sub> | <sub>5/13/18 2:39</sub> | <sub>2/25/19 0:26</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
|
||||
| <sub>[RL](https://github.com/kh-kim/stock_market_reinforcement_learning)</sub> | <sub>OpenGym with Deep Q-learning and Policy Gradient.</sub> | <sub>10/4/16 14:42</sub> | <sub>12/23/16 7:34</sub> | <sub>715.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
|
||||
| <sub>[RL V](https://github.com/gstenger98/rl-finance)</sub> | <sub>Building an Agent to Trade with Reinforcement Learning.</sub> | <sub>1/16/19 0:43</sub> | <sub>3/19/20 20:28</sub> | <sub>33.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x2</sub> |
|
||||
| <sub>[TradingGym](https://github.com/Yvictor/TradingGym)</sub> | <sub>NEW</sub> | <sub>5/1/17 13:53</sub> | <sub>2/14/18 13:58</sub> | <sub>841.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[gym-trading](https://github.com/hackthemarket/gym-trading)</sub> | <sub>NEW</sub> | <sub>12/9/16 20:46</sub> | <sub>12/24/17 15:34</sub> | <sub>581.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Trading-Gym](https://github.com/thedimlebowski/Trading-Gym)</sub> | <sub>NEW</sub> | <sub>6/13/17 13:14</sub> | <sub>7/10/17 8:09</sub> | <sub>507.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[QLearning_Trading](https://github.com/ucaiado/QLearning_Trading)</sub> | <sub>NEW</sub> | <sub>8/10/16 6:02</sub> | <sub>10/15/16 2:36</sub> | <sub>433.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[maro](https://github.com/microsoft/maro)</sub> | <sub>NEW</sub> | <sub>12/27/19 6:48</sub> | <sub>4/7/21 15:49</sub> | <sub>386.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[a3c_trading](https://github.com/evgps/a3c_trading)</sub> | <sub>NEW</sub> | <sub>6/4/18 15:30</sub> | <sub>5/23/20 14:47</sub> | <sub>311.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[tensortrade](https://github.com/tensortrade-org/tensortrade)</sub> | <sub>NEW</sub> | <sub>7/30/19 21:28</sub> | <sub>3/24/21 16:25</sub> | <sub>3101.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[RLQuant](https://github.com/yuriak/RLQuant)</sub> | <sub>NEW</sub> | <sub>4/5/18 5:42</sub> | <sub>8/13/18 4:18</sub> | <sub>277.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[rl_trading](https://github.com/ucaiado/rl_trading)</sub> | <sub>NEW</sub> | <sub>5/29/17 22:19</sub> | <sub>8/29/17 14:54</sub> | <sub>207.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Machine-Learning-and-Reinforcement-Learning-in-Finance](https://github.com/joelowj/Machine-Learning-and-Reinforcement-Learning-in-Finance)</sub> | <sub>NEW</sub> | <sub>6/26/18 4:30</sub> | <sub>9/23/18 16:50</sub> | <sub>175.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[RL IV](https://github.com/jjakimoto/DQN)</sub> | <sub>Reinforcement Learning for finance.</sub> | <sub>10/21/16 2:47</sub> | <sub>4/7/17 8:11</sub> | <sub>142.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Pair-Trading-Reinforcement-Learning](https://github.com/wai-i/Pair-Trading-Reinforcement-Learning)</sub> | <sub>NEW</sub> | <sub>6/9/19 22:50</sub> | <sub>1/3/20 15:36</sub> | <sub>136.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[TradingGym](https://github.com/cove9988/TradingGym)</sub> | <sub>NEW</sub> | <sub>11/6/17 0:50</sub> | <sub>11/15/17 23:55</sub> | <sub>112.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[pairstrade-fyp-2019](https://github.com/wywongbd/pairstrade-fyp-2019)</sub> | <sub>NEW</sub> | <sub>9/7/18 7:51</sub> | <sub>5/13/20 5:06</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
@@ -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>[Options](https://github.com/PHBS/2018.M1.ASP/tree/master/py)</sub> | <sub>Black Scholes and Copula.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Derivative Markets](https://github.com/broughtj/Fin6470/tree/master/Notebooks)</sub> | <sub>The economics of futures, futures, options, and swaps.</sub> | <sub>2016-02-09 05:30:27</sub> | <sub>2021-04-06 20:49:41</sub> | <sub>8.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Volatility and Variance Derivatives](https://github.com/yhilpisch/lvvd/tree/master/lvvd)</sub> | <sub>Volatility derivatives analytics.</sub> | <sub>2016-10-21 04:12:50</sub> | <sub>2021-02-22 13:32:00</sub> | <sub>79.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Hull White](https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb)</sub> | <sub>Callable Bond, Hull White.</sub> | <sub>2018-06-06 22:06:06</sub> | <sub>2018-06-06 22:27:02</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Derivatives Python](https://github.com/yhilpisch/dawp/tree/master/python36)</sub> | <sub>Derivative analytics with Python.</sub> | <sub>2015-07-09 12:27:29</sub> | <sub>2021-02-22 13:29:18</sub> | <sub>388.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Options](https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D)</sub> | <sub>Introduction to options.</sub> | <sub>2017-07-28 15:48:29</sub> | <sub>2021-03-17 17:17:08</sub> | <sub>335.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Delta Hedging](https://github.com/RobinsonGarcia/delta-hedging)</sub> | <sub>Advanced derivatives.</sub> | <sub>2018-03-02 23:53:53</sub> | <sub>2018-07-17 23:32:23</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Option Strategies](https://github.com/rstreppa/valuation-OptionStrategies)</sub> | <sub>Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.</sub> | <sub>2018-05-22 18:27:26</sub> | <sub>2018-05-22 18:30:24</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Computational Derivatives](https://github.com/chenbowen184/Computational_Finance)</sub> | <sub>Projects focusing on investigating simulations and computational techniques applied in finance.</sub> | <sub>2018-01-29 05:01:52</sub> | <sub>2018-08-02 05:56:49</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Reinforcement Learning](https://github.com/FinTechies/HedgingRL)</sub> | <sub>Hedging portfolios with reinforcement learning.</sub> | <sub>2017-04-21 10:58:56</sub> | <sub>2017-08-02 21:41:06</sub> | <sub>16.0</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>2016-04-29 03:51:25</sub> | <sub>2018-01-16 01:24:07</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>2017-12-09 18:50:20</sub> | <sub>2018-07-09 09:48:36</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>2018-05-18 18:08:16</sub> | <sub>2018-09-21 19:59:01</sub> | <sub>1.0</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>[Options](https://github.com/PHBS/2018.M1.ASP/tree/master/py)</sub> | <sub>Black Scholes and Copula.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Derivative Markets](https://github.com/broughtj/Fin6470/tree/master/Notebooks)</sub> | <sub>The economics of futures, futures, options, and swaps.</sub> | <sub>2/9/16 5:30</sub> | <sub>4/6/21 20:49</sub> | <sub>8.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Volatility and Variance Derivatives](https://github.com/yhilpisch/lvvd/tree/master/lvvd)</sub> | <sub>Volatility derivatives analytics.</sub> | <sub>10/21/16 4:12</sub> | <sub>2/22/21 13:32</sub> | <sub>79.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Hull White](https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb)</sub> | <sub>Callable Bond, Hull White.</sub> | <sub>6/6/18 22:06</sub> | <sub>6/6/18 22:27</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Derivatives Python](https://github.com/yhilpisch/dawp/tree/master/python36)</sub> | <sub>Derivative analytics with Python.</sub> | <sub>7/9/15 12:27</sub> | <sub>2/22/21 13:29</sub> | <sub>388.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Options](https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D)</sub> | <sub>Introduction to options.</sub> | <sub>7/28/17 15:48</sub> | <sub>3/17/21 17:17</sub> | <sub>335.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Delta Hedging](https://github.com/RobinsonGarcia/delta-hedging)</sub> | <sub>Advanced derivatives.</sub> | <sub>3/2/18 23:53</sub> | <sub>7/17/18 23:32</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Option Strategies](https://github.com/rstreppa/valuation-OptionStrategies)</sub> | <sub>Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.</sub> | <sub>5/22/18 18:27</sub> | <sub>5/22/18 18:30</sub> | <sub>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Computational Derivatives](https://github.com/chenbowen184/Computational_Finance)</sub> | <sub>Projects focusing on investigating simulations and computational techniques applied in finance.</sub> | <sub>1/29/18 5:01</sub> | <sub>8/2/18 5:56</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Reinforcement Learning](https://github.com/FinTechies/HedgingRL)</sub> | <sub>Hedging portfolios with reinforcement learning.</sub> | <sub>4/21/17 10:58</sub> | <sub>8/2/17 21:41</sub> | <sub>16.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[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> |
|
||||
| <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> |
|
||||
@@ -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>[Behavioural Economics](https://github.com/pcmichaud/notebooks)</sub> | <sub>Behavioural Economics and Finance Python Notebooks.</sub> | <sub>2018-12-20 00:21:38</sub> | <sub>2019-03-26 11:51:46</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>2017-09-09 03:35:24</sub> | <sub>2017-09-09 23:04:48</sub> | <sub>9.0</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>2018-01-29 05:14:52</sub> | <sub>2018-07-19 06:25:36</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>2018-10-11 20:32:37</sub> | <sub>2018-12-24 23:27:55</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>2016-07-21 05:14:14</sub> | <sub>2017-02-14 16:47:25</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>2014-11-09 04:49:01</sub> | <sub>2018-12-03 16:30:28</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>2017-01-21 11:24:18</sub> | <sub>2020-08-01 17:03:32</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>2018-09-16 20:00:36</sub> | <sub>2020-09-05 13:01:05</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>2018-08-11 22:59:53</sub> | <sub>2019-11-12 04:49:01</sub> | <sub>31.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Life-cycle](https://github.com/atulram/Finance-and-Stocks/blob/master/CompanyLifeCycle.ipynb)</sub> | <sub>Company life cycle.</sub> | <sub>2019-01-19 18:16:47</sub> | <sub>2019-02-18 16:57:19</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>2019-01-19 18:16:47</sub> | <sub>2019-02-18 16:57:19</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>2019-03-12 21:11:29</sub> | <sub>2019-03-12 22:09:10</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>2018-05-08 19:34:17</sub> | <sub>2018-05-09 15:39:25</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>2019-01-04 12:30:41</sub> | <sub>2019-02-18 09:55:21</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>2016-07-06 20:32:21</sub> | <sub>2018-06-09 10:53:51</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>2018-08-28 14:45:00</sub> | <sub>2020-08-06 22:03:47</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>2017-05-25 02:27:36</sub> | <sub>2017-06-30 03:53:59</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>2018-08-02 02:48:24</sub> | <sub>2019-03-16 18:39:38</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>2018-01-29 05:01:52</sub> | <sub>2018-08-02 05:56:49</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>2017-08-27 03:46:33</sub> | <sub>2017-08-26 04:26:04</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>2019-01-22 10:59:50</sub> | <sub>2019-03-12 18:35:02</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>2019-01-24 13:37:45</sub> | <sub>2019-02-13 16:48:00</sub> | <sub>1.0</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>[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>[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>[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>[Life-cycle](https://github.com/atulram/Finance-and-Stocks/blob/master/CompanyLifeCycle.ipynb)</sub> | <sub>Company life cycle.</sub> | <sub>1/19/19 18:16</sub> | <sub>2/18/19 16:57</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[M&A](https://github.com/atulram/Finance-and-Stocks)</sub> | <sub>Mergers and Acquisitions.</sub> | <sub>1/19/19 18:16</sub> | <sub>2/18/19 16:57</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[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>[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> |
|
||||
@@ -1,18 +1,18 @@
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[VaR GaN](https://github.com/hamaadshah/market_risk_gan_keras)</sub> | <sub>Estimate Value-at-Risk for market risk management using Keras and TensorFlow.</sub> | <sub>2018-08-06 16:09:44</sub> | <sub>2020-11-22 19:02:07</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>2017-08-07 14:44:32</sub> | <sub>2017-08-08 22:52:11</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>2015-06-01 15:31:39</sub> | <sub>2020-02-28 17:30:19</sub> | <sub>3673.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Quant Finance](https://github.com/mrefermat/quant_finance)</sub> | <sub>General quant repository.</sub> | <sub>2018-08-11 22:59:53</sub> | <sub>2019-11-12 04:49:01</sub> | <sub>31.0</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>2016-05-10 11:03:48</sub> | <sub>2016-05-17 03:44:56</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>2016-05-10 11:03:48</sub> | <sub>2016-05-17 03:44:56</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>2018-03-13 07:39:20</sub> | <sub>2018-03-13 07:42:36</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>2015-10-04 09:10:54</sub> | <sub>2020-03-28 18:33:58</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>2016-06-03 21:49:15</sub> | <sub>2020-04-27 18:40:41</sub> | <sub>1847.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Convex Optimisation](https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb)</sub> | <sub>Convex Optimization for Finance.</sub> | <sub>2018-06-26 20:36:47</sub> | <sub>2019-10-22 21:56:46</sub> | <sub>18.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Factor Analysis](https://github.com/alpha-miner/alpha-mind/tree/master/notebooks)</sub> | <sub>Factor strategy notebooks.</sub> | <sub>2017-05-01 07:36:54</sub> | <sub>2021-04-07 15:25:27</sub> | <sub>172.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[AlphaTrading](https://github.com/jerryxyx/AlphaTrading)</sub> | <sub>NEW</sub> | <sub>2018-05-18 22:09:52</sub> | <sub>2018-08-07 18:05:37</sub> | <sub>149.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Risk and Return](https://github.com/PyDataBlog/Python-for-Data-Science/tree/master/Tutorials)</sub> | <sub>Riskiness of portfolios and assets.</sub> | <sub>2017-09-12 13:35:09</sub> | <sub>2020-08-06 12:35:44</sub> | <sub>140.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Python for Finance](https://github.com/yhilpisch/py4fi/tree/master/jupyter36)</sub> | <sub>Various financial notebooks.</sub> | <sub>2014-12-15 11:23:34</sub> | <sub>2018-07-10 06:38:12</sub> | <sub>1298.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Stock-Prediction](https://github.com/Ronak-59/Stock-Prediction)</sub> | <sub>NEW</sub> | <sub>2018-03-18 04:54:45</sub> | <sub>2020-02-28 11:43:07</sub> | <sub>129.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[VaR](https://github.com/willb/var-notebook/blob/master/var-notebook/var-pdfs.ipynb)</sub> | <sub>Value-at-risk calculations.</sub> | <sub>2016-11-15 19:24:17</sub> | <sub>2017-01-14 21:19:30</sub> | <sub>10.0</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>[VaR GaN](https://github.com/hamaadshah/market_risk_gan_keras)</sub> | <sub>Estimate Value-at-Risk for market risk management using Keras and TensorFlow.</sub> | <sub>8/6/18 16:09</sub> | <sub>11/22/20 19:02</sub> | <sub>41.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Various Risk Measures](https://github.com/Jorgencr/Alternative-and-Responsible-Investments/blob/master/Final_masterfile.ipynb)</sub> | <sub>Risk measures and factors for alternative and responsible investments.</sub> | <sub>8/7/17 14:44</sub> | <sub>8/8/17 22:52</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Pyfolio](https://github.com/quantopian/pyfolio)</sub> | <sub>Portfolio and risk analytics in Python.</sub> | <sub>6/1/15 15:31</sub> | <sub>2/28/20 17:30</sub> | <sub>3673.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Quant Finance](https://github.com/mrefermat/quant_finance)</sub> | <sub>General quant repository.</sub> | <sub>8/11/18 22:59</sub> | <sub>11/12/19 4:49</sub> | <sub>31.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <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>[Factor Analysis](https://github.com/garvit-kudesia91/factor_analysis/blob/master/Factor%20Analysis%20of%20Mutual%20Funds.ipynb)</sub> | <sub>Factor analysis for mutual funds.</sub> | <sub>3/13/18 7:39</sub> | <sub>3/13/18 7:42</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Statistical Finance](https://github.com/mrefermat/FinancePhD/tree/master/FinancialExperiments)</sub> | <sub>Various financial experiments.</sub> | <sub>10/4/15 9:10</sub> | <sub>3/28/20 18:33</sub> | <sub>21.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Performance Analysis](https://github.com/quantopian/alphalens)</sub> | <sub>Performance analysis of predictive (alpha) stock factors.</sub> | <sub>6/3/16 21:49</sub> | <sub>4/27/20 18:40</sub> | <sub>1847.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Convex Optimisation](https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb)</sub> | <sub>Convex Optimization for Finance.</sub> | <sub>6/26/18 20:36</sub> | <sub>10/22/19 21:56</sub> | <sub>18.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Factor Analysis](https://github.com/alpha-miner/alpha-mind/tree/master/notebooks)</sub> | <sub>Factor strategy notebooks.</sub> | <sub>5/1/17 7:36</sub> | <sub>4/7/21 15:25</sub> | <sub>172.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[AlphaTrading](https://github.com/jerryxyx/AlphaTrading)</sub> | <sub>NEW</sub> | <sub>5/18/18 22:09</sub> | <sub>8/7/18 18:05</sub> | <sub>149.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Risk and Return](https://github.com/PyDataBlog/Python-for-Data-Science/tree/master/Tutorials)</sub> | <sub>Riskiness of portfolios and assets.</sub> | <sub>9/12/17 13:35</sub> | <sub>8/6/20 12:35</sub> | <sub>140.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Python for Finance](https://github.com/yhilpisch/py4fi/tree/master/jupyter36)</sub> | <sub>Various financial notebooks.</sub> | <sub>12/15/14 11:23</sub> | <sub>7/10/18 6:38</sub> | <sub>1298.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Stock-Prediction](https://github.com/Ronak-59/Stock-Prediction)</sub> | <sub>NEW</sub> | <sub>3/18/18 4:54</sub> | <sub>2/28/20 11:43</sub> | <sub>129.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[VaR](https://github.com/willb/var-notebook/blob/master/var-notebook/var-pdfs.ipynb)</sub> | <sub>Value-at-risk calculations.</sub> | <sub>11/15/16 19:24</sub> | <sub>1/14/17 21:19</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
@@ -1,5 +1,5 @@
|
||||
| <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>2017-09-27 19:57:13</sub> | <sub>2017-09-27 20:00:29</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>2018-07-18 19:26:54</sub> | <sub>2018-07-18 19:34:48</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>2019-02-02 08:44:14</sub> | <sub>2019-05-03 17:16:52</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>[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> |
|
||||
@@ -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>[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>2018-08-29 02:07:02</sub> | <sub>2020-11-27 09:43:40</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>2017-02-12 04:50:44</sub> | <sub>2021-02-04 03:48:33</sub> | <sub>931.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Hands-On-Machine-Learning-for-Algorithmic-Trading](https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading)</sub> | <sub>NEW</sub> | <sub>2019-05-07 11:04:25</sub> | <sub>2021-01-19 07:51:00</sub> | <sub>600.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy)</sub> | <sub>NEW</sub> | <sub>2016-02-14 00:47:32</sub> | <sub>2021-02-08 21:35:40</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes)</sub> | <sub>NEW</sub> | <sub>2016-03-11 19:13:00</sub> | <sub>2020-12-21 03:54:51</sub> | <sub>460.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps)</sub> | <sub>Research in investment finance for long term forecasts.</sub> | <sub>2018-07-22 08:14:46</sub> | <sub>2021-02-17 14:39:30</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>2019-10-10 09:44:02</sub> | <sub>2019-10-12 11:38:49</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>2017-03-20 18:54:24</sub> | <sub>2017-04-25 23:35:20</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>2018-06-29 21:21:17</sub> | <sub>2019-02-18 12:34:54</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>2019-11-15 08:51:40</sub> | <sub>2021-01-21 07:56:08</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>2017-08-30 06:14:59</sub> | <sub>2019-10-29 08:14:39</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>2019-09-27 16:13:50</sub> | <sub>2020-06-13 21:20:26</sub> | <sub>250.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[CryptoBot](https://github.com/AdeelMufti/CryptoBot)</sub> | <sub>NEW</sub> | <sub>2017-01-17 12:44:52</sub> | <sub>2017-01-17 12:48:17</sub> | <sub>234.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[mlfinlab](https://github.com/hudson-and-thames/mlfinlab)</sub> | <sub>NEW</sub> | <sub>2019-02-13 16:57:25</sub> | <sub>2021-04-12 10:50:54</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>2017-06-18 19:57:17</sub> | <sub>2021-03-14 22:22:00</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>2016-09-12 18:38:17</sub> | <sub>2018-08-29 20:27:19</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>2017-07-11 09:09:15</sub> | <sub>2018-02-21 05:36:35</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>2018-03-15 06:28:00</sub> | <sub>2021-01-14 15:58:03</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>2019-12-29 05:18:07</sub> | <sub>2021-04-11 19:17:17</sub> | <sub>175.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Pattern-Recognition-for-Forex-Trading](https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading)</sub> | <sub>NEW</sub> | <sub>2015-03-26 02:22:03</sub> | <sub>2015-03-26 02:33:51</sub> | <sub>173.0</sub> | <sub>:heavy_multiplication_x:</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>2018-12-06 11:35:08</sub> | <sub>2021-01-18 06:40:53</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>2017-12-11 17:05:38</sub> | <sub>2020-05-13 23:50:47</sub> | <sub>166.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>NEW</sub> | <sub>2020-08-30 07:56:22</sub> | <sub>2020-09-21 04:32:05</sub> | <sub>1189.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[botflow](https://github.com/kkyon/botflow)</sub> | <sub>NEW</sub> | <sub>2018-08-20 03:13:31</sub> | <sub>2019-05-23 14:40:50</sub> | <sub>1165.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[fin-ml](https://github.com/tatsath/fin-ml)</sub> | <sub>NEW</sub> | <sub>2020-05-10 00:25:56</sub> | <sub>2021-01-23 17:15:07</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>2020-01-06 00:21:58</sub> | <sub>2020-03-31 13:02:26</sub> | <sub>104.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Speculator](https://github.com/amicks/Speculator)</sub> | <sub>NEW</sub> | <sub>2017-09-03 17:43:03</sub> | <sub>2018-09-12 18:58:38</sub> | <sub>101.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading)</sub> | <sub>NEW</sub> | <sub>2018-11-05 21:09:06</sub> | <sub>2020-10-08 16:48:18</sub> | <sub>1005.0</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>[Hands-On-Machine-Learning-for-Algorithmic-Trading](https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading)</sub> | <sub>repo for book [hands-on-machine learning for algorithmic trading](https://www.packtpub.com/product/hands-on-machine-learning-for-algorithmic-trading/9781789346411) covering topic from data| unsupervised learning| NPL | RNN & CNN| reinforcement learning etc. Leverage zipline|alphalens|sklearn|openai-gym etc as well. Good references to have</sub> | <sub>5/7/19 11:04</sub> | <sub>1/19/21 7:51</sub> | <sub>600.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
|
||||
| <sub>[CryptoBot](https://github.com/AdeelMufti/CryptoBot)</sub> | <sub>Hard fork of [bitpredit](https://github.com/cbyn/bitpredict) and form the trading strategy as a classification problem with -1 (sell) 0 (hold) 1 (buy). Models used are XGBClassifier|RandomForest|GradientBoosting. Not mentained</sub> | <sub>1/17/17 12:44</sub> | <sub>1/17/17 12:48</sub> | <sub>234.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
|
||||
| <sub>[Trend Following](http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html)</sub> | <sub>A futures trend following portfolio investment strategy.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[awesome-ai-in-finance](https://github.com/georgezouq/awesome-ai-in-finance)</sub> | <sub>NEW</sub> | <sub>8/29/18 2:07</sub> | <sub>11/27/20 9:43</sub> | <sub>941.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks)</sub> | <sub>Using python and scikit-learn to make stock predictions.</sub> | <sub>2/12/17 4:50</sub> | <sub>2/4/21 3:48</sub> | <sub>931.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[AlphaPy](https://github.com/ScottfreeLLC/AlphaPy)</sub> | <sub>NEW</sub> | <sub>2/14/16 0:47</sub> | <sub>2/8/21 21:35</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[MathAndScienceNotes](https://github.com/melling/MathAndScienceNotes)</sub> | <sub>Collections of news/articles on various topics including quant trading and machine learning. Some articles are from [ycombinator message board](https://news.ycombinator.com/news) and [rediit algotrading forum](https://www.reddit.com/r/algotrading/)</sub> | <sub>3/11/16 19:13</sub> | <sub>12/21/20 3:54</sub> | <sub>460.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps)</sub> | <sub>Research in investment finance for long term forecasts.</sub> | <sub>7/22/18 8:14</sub> | <sub>2/17/21 14:39</sub> | <sub>383.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[stock-trading-ml](https://github.com/yacoubb/stock-trading-ml)</sub> | <sub>NEW</sub> | <sub>10/10/19 9:44</sub> | <sub>10/12/19 11:38</sub> | <sub>340.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models)</sub> | <sub>Mixture models to predict market bottoms.</sub> | <sub>3/20/17 18:54</sub> | <sub>4/25/17 23:35</sub> | <sub>31.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[finance_ml](https://github.com/jjakimoto/finance_ml)</sub> | <sub>NEW</sub> | <sub>6/29/18 21:21</sub> | <sub>2/18/19 12:34</sub> | <sub>282.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original](https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original)</sub> | <sub>NEW</sub> | <sub>11/15/19 8:51</sub> | <sub>1/21/21 7:56</sub> | <sub>279.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Machine-Learning-and-AI-in-Trading](https://github.com/PyPatel/Machine-Learning-and-AI-in-Trading)</sub> | <sub>NEW</sub> | <sub>8/30/17 6:14</sub> | <sub>10/29/19 8:14</sub> | <sub>261.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[ML_Finance_Codes](https://github.com/mfrdixon/ML_Finance_Codes)</sub> | <sub>NEW</sub> | <sub>9/27/19 16:13</sub> | <sub>6/13/20 21:20</sub> | <sub>250.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[mlfinlab](https://github.com/hudson-and-thames/mlfinlab)</sub> | <sub>NEW</sub> | <sub>2/13/19 16:57</sub> | <sub>4/12/21 10:50</sub> | <sub>2295.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <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>[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></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>[surpriver](https://github.com/tradytics/surpriver)</sub> | <sub>NEW</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></sub> |
|
||||
| <sub>[botflow](https://github.com/kkyon/botflow)</sub> | <sub>NEW</sub> | <sub>8/20/18 3:13</sub> | <sub>5/23/19 14:40</sub> | <sub>1165.0</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>[Awesome-Quant-Machine-Learning-Trading](https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading)</sub> | <sub>NEW</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></sub> |
|
||||
@@ -1,16 +1,16 @@
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[Online Portfolio Selection](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb)</sub> | <sub>****Comparing OLPS algorithms on a diversified set of ETFs.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Modern Portfolio Theory](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb)</sub> | <sub>Universal portfolios; modern portfolio theory.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[OLMAR Algorithm](https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb)</sub> | <sub>Relative importance of each component of the OLMAR algorithm.</sub> | <sub>2016-07-26 16:20:10</sub> | <sub>2016-12-30 11:40:53</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>2020-03-02 19:49:06</sub> | <sub>2021-04-01 03:50:59</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>2017-10-07 09:14:33</sub> | <sub>2018-06-26 09:22:27</sub> | <sub>364.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[DeepDow](https://github.com/jankrepl/deepdow)</sub> | <sub>Portfolio optimization with deep learning.</sub> | <sub>2020-02-02 08:46:33</sub> | <sub>2021-02-16 18:50:53</sub> | <sub>311.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Distribution Characteristic Optimisation](https://github.com/VivekPa/OptimalPortfolio)</sub> | <sub>Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.</sub> | <sub>2018-11-16 12:20:25</sub> | <sub>2019-07-04 01:41:46</sub> | <sub>232.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt)</sub> | <sub>Financial portfolio optimisation, including classical efficient frontier and advanced methods.</sub> | <sub>2018-05-29 13:30:30</sub> | <sub>2021-02-25 13:01:56</sub> | <sub>1895.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[401K Portfolio Optimisation](https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb)</sub> | <sub>Portfolio analyses and optimisation for 401K.</sub> | <sub>2018-08-01 19:48:24</sub> | <sub>2019-09-05 11:18:56</sub> | <sub>14.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Policy Gradient Portfolio](https://github.com/ZhengyaoJiang/PGPortfolio)</sub> | <sub>A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.</sub> | <sub>2017-11-12 16:08:44</sub> | <sub>2019-05-09 09:50:18</sub> | <sub>1281.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[riskparity.py](https://github.com/dppalomar/riskparity.py)</sub> | <sub>NEW</sub> | <sub>2019-07-13 21:30:55</sub> | <sub>2021-01-30 01:53:09</sub> | <sub>124.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Deep Portfolio Theory](https://github.com/tcloaa/Deep-Portfolio-Theory)</sub> | <sub>Autoencoder framework for portfolio selection.</sub> | <sub>2017-02-10 09:03:08</sub> | <sub>2018-03-08 16:47:00</sub> | <sub>105.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Efficient Frontier](https://github.com/tthustla/efficient_frontier/blob/master/Efficient%20_Frontier_implementation.ipynb)</sub> | <sub>Modern Portfolio Theory.</sub> | <sub>2018-02-17 08:19:46</sub> | <sub>2018-02-27 13:16:57</sub> | <sub>104.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[node-finance](https://github.com/albertosantini/node-finance)</sub> | <sub>NEW</sub> | <sub>2011-09-17 17:49:56</sub> | <sub>2021-04-05 08:01:12</sub> | <sub>101.0</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>[Online Portfolio Selection](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb)</sub> | <sub>****Comparing OLPS algorithms on a diversified set of ETFs.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Modern Portfolio Theory](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb)</sub> | <sub>Universal portfolios; modern portfolio theory.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[OLMAR Algorithm](https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb)</sub> | <sub>Relative importance of each component of the OLMAR algorithm.</sub> | <sub>7/26/16 16:20</sub> | <sub>12/30/16 11:40</sub> | <sub>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib)</sub> | <sub>NEW</sub> | <sub>3/2/20 19:49</sub> | <sub>4/1/21 3:50</sub> | <sub>371.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Reinforcement Learning](https://github.com/filangel/qtrader)</sub> | <sub>Reinforcement Learning for Portfolio Management.</sub> | <sub>10/7/17 9:14</sub> | <sub>6/26/18 9:22</sub> | <sub>364.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[DeepDow](https://github.com/jankrepl/deepdow)</sub> | <sub>Portfolio optimization with deep learning.</sub> | <sub>2/2/20 8:46</sub> | <sub>2/16/21 18:50</sub> | <sub>311.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Distribution Characteristic Optimisation](https://github.com/VivekPa/OptimalPortfolio)</sub> | <sub>Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.</sub> | <sub>11/16/18 12:20</sub> | <sub>7/4/19 1:41</sub> | <sub>232.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt)</sub> | <sub>Financial portfolio optimisation, including classical efficient frontier and advanced methods.</sub> | <sub>5/29/18 13:30</sub> | <sub>2/25/21 13:01</sub> | <sub>1895.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[401K Portfolio Optimisation](https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb)</sub> | <sub>Portfolio analyses and optimisation for 401K.</sub> | <sub>8/1/18 19:48</sub> | <sub>9/5/19 11:18</sub> | <sub>14.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Policy Gradient Portfolio](https://github.com/ZhengyaoJiang/PGPortfolio)</sub> | <sub>A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.</sub> | <sub>11/12/17 16:08</sub> | <sub>5/9/19 9:50</sub> | <sub>1281.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[riskparity.py](https://github.com/dppalomar/riskparity.py)</sub> | <sub>NEW</sub> | <sub>7/13/19 21:30</sub> | <sub>1/30/21 1:53</sub> | <sub>124.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Deep Portfolio Theory](https://github.com/tcloaa/Deep-Portfolio-Theory)</sub> | <sub>Autoencoder framework for portfolio selection.</sub> | <sub>2/10/17 9:03</sub> | <sub>3/8/18 16:47</sub> | <sub>105.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Efficient Frontier](https://github.com/tthustla/efficient_frontier/blob/master/Efficient%20_Frontier_implementation.ipynb)</sub> | <sub>Modern Portfolio Theory.</sub> | <sub>2/17/18 8:19</sub> | <sub>2/27/18 13:16</sub> | <sub>104.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[node-finance](https://github.com/albertosantini/node-finance)</sub> | <sub>NEW</sub> | <sub>9/17/11 17:49</sub> | <sub>4/5/21 8:01</sub> | <sub>101.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
+11
-11
@@ -1,11 +1,11 @@
|
||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||
|:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||
| <sub>[NLP](https://github.com/toamitesh/NLPinFinance)</sub> | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis)</sub> | <sub>Extracting sentiment from financial statements using neural networks.</sub> | <sub>2018-06-04 20:54:14</sub> | <sub>2018-06-04 20:56:02</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb)</sub> | <sub>Comprehensive NLP techniques for accounting research.</sub> | <sub>2017-10-25 07:10:26</sub> | <sub>2020-06-05 03:28:46</sub> | <sub>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[NLP Event](https://github.com/yuriak/DLQuant)</sub> | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub> | <sub>2018-07-02 23:50:52</sub> | <sub>2019-01-31 14:08:20</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis)</sub> | <sub>Sentiment, distance and proportion analysis for trading signals.</sub> | <sub>2017-06-23 00:05:49</sub> | <sub>2019-01-26 03:35:55</sub> | <sub>48.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Fund classification using text mining and NLP.</sub> | <sub>2018-04-16 22:18:55</sub> | <sub>2018-06-07 22:01:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Earning call transcripts](https://github.com/lin882/WebAnalyticsProject)</sub> | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>2017-12-30 08:56:03</sub> | <sub>2018-01-11 02:11:11</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb)</sub> | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub> | <sub>2017-05-24 12:36:38</sub> | <sub>2019-08-07 21:47:08</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds)</sub> | <sub>Return performance and mutual fund selection.</sub> | <sub>2018-02-04 21:51:16</sub> | <sub>2018-02-04 21:57:09</sub> | <sub>1.0</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>[NLP](https://github.com/toamitesh/NLPinFinance)</sub> | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis)</sub> | <sub>Extracting sentiment from financial statements using neural networks.</sub> | <sub>6/4/18 20:54</sub> | <sub>6/4/18 20:56</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb)</sub> | <sub>Comprehensive NLP techniques for accounting research.</sub> | <sub>10/25/17 7:10</sub> | <sub>6/5/20 3:28</sub> | <sub>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[NLP Event](https://github.com/yuriak/DLQuant)</sub> | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub> | <sub>7/2/18 23:50</sub> | <sub>1/31/19 14:08</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis)</sub> | <sub>Sentiment, distance and proportion analysis for trading signals.</sub> | <sub>6/23/17 0:05</sub> | <sub>1/26/19 3:35</sub> | <sub>48.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Fund classification using text mining and NLP.</sub> | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Earning call transcripts](https://github.com/lin882/WebAnalyticsProject)</sub> | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>12/30/17 8:56</sub> | <sub>1/11/18 2:11</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb)</sub> | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub> | <sub>5/24/17 12:36</sub> | <sub>8/7/19 21:47</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds)</sub> | <sub>Return performance and mutual fund selection.</sub> | <sub>2/4/18 21:51</sub> | <sub>2/4/18 21:57</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
@@ -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>[PCA Pairs Trading](https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading)</sub> | <sub>PCA, Factor Returns, and trading strategies.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Pairs Trading](https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb)</sub> | <sub>Finding pairs with cluster analysis.</sub> | <sub>2017-09-05 19:19:19</sub> | <sub>2017-09-27 20:42:14</sub> | <sub>79.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Project to cluster industries according to financial attributes.</sub> | <sub>2017-07-21 02:12:51</sub> | <sub>2017-07-23 02:53:37</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Clustering of industries.</sub> | <sub>2017-07-21 02:12:51</sub> | <sub>2017-07-23 02:53:37</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[Fund Clusters](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Data exploration of fund clusters.</sub> | <sub>2018-04-16 22:18:55</sub> | <sub>2018-06-07 22:01:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
||||
| <sub>[VRA Stock Embedding](https://github.com/ml-hongkong/stock2vec)</sub> | <sub>Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.</sub> | <sub>2017-06-21 04:47:14</sub> | <sub>2017-06-21 04:51:13</sub> | <sub>32.0</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>[PCA Pairs Trading](https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading)</sub> | <sub>PCA, Factor Returns, and trading strategies.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||
| <sub>[Pairs Trading](https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb)</sub> | <sub>Finding pairs with cluster analysis.</sub> | <sub>9/5/17 19:19</sub> | <sub>9/27/17 20:42</sub> | <sub>79.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Project to cluster industries according to financial attributes.</sub> | <sub>7/21/17 2:12</sub> | <sub>7/23/17 2:53</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries)</sub> | <sub>Clustering of industries.</sub> | <sub>7/21/17 2:12</sub> | <sub>7/23/17 2:53</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Fund Clusters](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Data exploration of fund clusters.</sub> | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[VRA Stock Embedding](https://github.com/ml-hongkong/stock2vec)</sub> | <sub>Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.</sub> | <sub>6/21/17 4:47</sub> | <sub>6/21/17 4:51</sub> | <sub>32.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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Reference in New Issue
Block a user