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# Trading
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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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## 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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<!-- [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> |
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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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|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
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| <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>3599.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
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| <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> |
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| <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>2857.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
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| <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> |
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| <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>4/3/21 23:21</sub> | <sub>1807.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
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| <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> |
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| <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>1451.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
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| <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> |
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| <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>1304.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
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| <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> |
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| <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>1264.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
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| <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> |
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| <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>1142.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
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| <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> |
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| <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>[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>547.0</sub> | <sub>:heavy_multiplication_x:</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>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> |
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| <sub>[awesome-deep-trading](https://github.com/cbailes/awesome-deep-trading)</sub> | <sub>curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes</sub> | <sub>11/26/18 3:23</sub> | <sub>1/1/21 9:41</sub> | <sub>541.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |<!-- [PLACEHOLDER_END:deep_learning_and_reinforcement_learning] -->
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| <sub>[awesome-deep-trading](https://github.com/cbailes/awesome-deep-trading)</sub> | <sub>curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes</sub> | <sub>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] -->
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## Other Models ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/other_models))
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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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<!-- [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>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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|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
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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>[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>[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>922.0</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>[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>381.0</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>[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> |
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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>[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>2158.0</sub> | <sub>:heavy_multiplication_x:</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>[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> |<!-- [PLACEHOLDER_END:other_models] -->
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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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## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations))
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## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations))
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<!-- [PLACEHOLDER_START:data_processing_techniques_and_transformations] -->
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<!-- [PLACEHOLDER_START:data_processing_techniques_and_transformations] -->
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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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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|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------|:--------------------|
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| <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 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>4/25/18 17:22</sub> | <sub>1/16/20 17:25</sub> | <sub>964.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:data_processing_techniques_and_transformations] -->
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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] -->
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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>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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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <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>[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>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>[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>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>[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>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>[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>9/5/16 19:12</sub> | <sub>4/24/17 10:48</sub> | <sub>12.0</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> |
|
||||||
@@ -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>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>[Oxford Man](https://www.oxford-man.ox.ac.uk/)</sub> | <sub>Oxford-Man Institute of Quantitative Finance</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Stanford Advanced Financial Technologies](https://fintech.stanford.edu/)</sub> | <sub>Stanford Advanced Financial Technologies Laboratory</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Berkeley Lab CIFT](https://cs.lbl.gov/news-media/news/news-archive/2010/berkeley-lab-launches-new-center-for-innovative-financial-technology/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
|
||||||
| <sub>[NYU Courant](https://cims.nyu.edu/)</sub> | <sub>Courant Institute of Mathematical Sciences, New York University</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
|
||||||
| <sub>[Cornell University](https://www.cornell.edu/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
|
||||||
| <sub>[NYU FRE](https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering)</sub> | <sub>Finance and Risk Engineering (NYU Tandon)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[NYU FRE](https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering)</sub> | <sub>Finance and Risk Engineering (NYU Tandon)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Stanford Advanced Financial Technologies](https://fintech.stanford.edu/)</sub> | <sub>Stanford Advanced Financial Technologies Laboratory</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Cornell University](https://www.cornell.edu/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
|
| <sub>[Berkeley Lab CIFT](https://cs.lbl.gov/news-media/news/news-archive/2010/berkeley-lab-launches-new-center-for-innovative-financial-technology/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
|
| <sub>[Oxford Man](https://www.oxford-man.ox.ac.uk/)</sub> | <sub>Oxford-Man Institute of Quantitative Finance</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
|
| <sub>[NYU Courant](https://cims.nyu.edu/)</sub> | <sub>Courant Institute of Mathematical Sciences, New York University</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
+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>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>[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>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>[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>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>[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>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>[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>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>[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>5/9/18 12:33</sub> | <sub>3/19/21 14:10</sub> | <sub>3717.0</sub> | <sub>:heavy_check_mark:</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>1/24/19 2:55</sub> | <sub>1/3/20 21:54</sub> | <sub>33.0</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>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>[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>8/20/18 14:10</sub> | <sub>1/15/21 8:57</sub> | <sub>119.0</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>5/6/17 2:39</sub> | <sub>6/21/17 4:04</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</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> |
|
||||||
+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>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>[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>[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>[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>[Rating Industries](http://www.ratingshistory.info/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Non-financial Corporate](http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[https://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>[http://finance.yahoo.com/](http://finance.yahoo.com/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Financial Corporate](http://raw.rutgers.edu/Corporate%20Financial%20Data.html)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[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>[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>[Non-financial Corporate](http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[https://fred.stlouisfed.org/](https://fred.stlouisfed.org/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[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>[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>[Capital Markets Data](https://www.capitalmarketsdata.com/)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[SEC Parsing](https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb)</sub> | <sub>nan</sub> | <sub>6/16/18 14:30</sub> | <sub>6/16/18 17:23</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
| <sub>[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>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>[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>2/19/19 19:02</sub> | <sub>7/22/20 16:48</sub> | <sub>576.0</sub> | <sub>:heavy_check_mark:</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>5/7/18 15:32</sub> | <sub>5/15/19 8:32</sub> | <sub>167.0</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>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>[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>6/26/18 23:33</sub> | <sub>8/14/18 1:31</sub> | <sub>10.0</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> |
|
||||||
@@ -1,4 +1,4 @@
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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>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>[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 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>964.0</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> |
|
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@@ -1,36 +1,49 @@
|
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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>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>[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>3599.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</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>1/9/19 8:02</sub> | <sub>2/11/19 16:32</sub> | <sub>2857.0</sub> | <sub>:heavy_multiplication_x:</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>7/26/20 13:18</sub> | <sub>4/3/21 23:21</sub> | <sub>1807.0</sub> | <sub>:heavy_check_mark:</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>3/9/17 6:11</sub> | <sub>3/19/17 7:42</sub> | <sub>1451.0</sub> | <sub>:heavy_multiplication_x:</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>4/27/19 18:35</sub> | <sub>10/17/19 16:25</sub> | <sub>1304.0</sub> | <sub>:heavy_check_mark:</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>6/18/16 18:23</sub> | <sub>8/7/18 15:24</sub> | <sub>1264.0</sub> | <sub>:heavy_multiplication_x:</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>3/10/18 11:22</sub> | <sub>9/2/18 17:21</sub> | <sub>1142.0</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>[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>547.0</sub> | <sub>:heavy_multiplication_x:</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>11/26/18 3:23</sub> | <sub>1/1/21 9:41</sub> | <sub>541.0</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>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>[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>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>[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>10/7/18 3:58</sub> | <sub>8/3/19 9:00</sub> | <sub>1199.0</sub> | <sub>:heavy_check_mark:</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>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>[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>6/21/18 1:06</sub> | <sub>11/5/20 11:08</sub> | <sub>345.0</sub> | <sub>:heavy_check_mark:</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>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>[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>[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>288.0</sub> | <sub>:heavy_multiplication_x:</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>[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>286.0</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>[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>[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>[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>264.0</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>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>[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>2/25/18 17:41</sub> | <sub>12/1/20 22:06</sub> | <sub>233.0</sub> | <sub>:heavy_check_mark:</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>8/5/18 2:13</sub> | <sub>10/1/18 11:25</sub> | <sub>219.0</sub> | <sub>:heavy_multiplication_x:</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>4/22/19 10:03</sub> | <sub>9/28/20 9:07</sub> | <sub>179.0</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>7/12/16 12:56</sub> | <sub>2/16/18 2:43</sub> | <sub>174.0</sub> | <sub>:heavy_multiplication_x:</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>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-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>5/11/18 0:52</sub> | <sub>10/26/19 14:22</sub> | <sub>137.0</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>9/19/18 3:17</sub> | <sub>11/26/20 16:58</sub> | <sub>135.0</sub> | <sub>:heavy_multiplication_x:</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>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>[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>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>[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>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>[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>10/4/16 14:42</sub> | <sub>12/23/16 7:34</sub> | <sub>713.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</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>1/16/19 0:43</sub> | <sub>3/19/20 20:28</sub> | <sub>32.0</sub> | <sub>:heavy_check_mark:</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>[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>140.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></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> |
|
||||||
@@ -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>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>[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>3/18/21 3:47</sub> | <sub>8.0</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>10/21/16 4:12</sub> | <sub>2/22/21 13:32</sub> | <sub>78.0</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>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>[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>7/9/15 12:27</sub> | <sub>2/22/21 13:29</sub> | <sub>387.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Derivatives Python](https://github.com/yhilpisch/dawp/tree/master/python36)</sub> | <sub>Derivative analytics with Python.</sub> | <sub>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>7/28/17 15:48</sub> | <sub>3/17/21 17:17</sub> | <sub>332.0</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>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>[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>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>[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>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 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>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>[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>[Derman](https://github.com/rstreppa/valuation-convertibles-Goldman1994/blob/master/ConvertibleBond_Goldman1994_Derman.ipynb)</sub> | <sub>Binomial tree for American call.</sub> | <sub>5/18/18 18:08</sub> | <sub>9/21/18 19:59</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
| <sub>[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>[Options Risk Measures](https://github.com/wanglouis49/risk_estimation)</sub> | <sub>Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).</sub> | <sub>4/29/16 3:51</sub> | <sub>1/16/18 1:24</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
| <sub>[Options Risk Measures](https://github.com/wanglouis49/risk_estimation)</sub> | <sub>Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).</sub> | <sub>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>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>[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> |
|
||||||
@@ -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>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||||
|:-----------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
|
|:-----------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||||
| <sub>[Real Estate Property Fraud](https://github.com/aviroop1/Real_Estate_Property_Fraud)</sub> | <sub>Unsupervised fraud detection model that can identify likely candidates of fraud.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Commodity](https://github.com/felipessalvatore/fin2vec/blob/master/src/Commodity2BR.ipynb)</sub> | <sub>Commodity influence over Brazilian stocks.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[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>[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>[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>[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>[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>[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>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>[NLP Finance Papers](https://github.com/chenbowen184/Research_Documents_Curation_with_NLP)</sub> | <sub>Curating quantitative finance papers using machine learning.</sub> | <sub>10/11/18 20:32</sub> | <sub>12/24/18 23:27</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
| <sub>[Applied Corporate Finance](https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance)</sub> | <sub>Studies the empirical behaviours in stock market.</sub> | <sub>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>[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>743.0</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>11/9/14 4:49</sub> | <sub>12/3/18 16:30</sub> | <sub>709.0</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>1/21/17 11:24</sub> | <sub>8/1/20 17:03</sub> | <sub>662.0</sub> | <sub>:heavy_check_mark:</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>9/16/18 20:00</sub> | <sub>9/5/20 13:01</sub> | <sub>615.0</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>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>[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>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>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>[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>[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>3/12/19 21:11</sub> | <sub>3/12/19 22:09</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
| <sub>[Life-cycle](https://github.com/atulram/Finance-and-Stocks/blob/master/CompanyLifeCycle.ipynb)</sub> | <sub>Company life cycle.</sub> | <sub>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>[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>[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>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>[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>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>[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>8/28/18 14:45</sub> | <sub>8/6/20 22:03</sub> | <sub>231.0</sub> | <sub>:heavy_check_mark:</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>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>[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>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>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>[Simulation](https://github.com/chenbowen184/Computational_Finance)</sub> | <sub>Investigating simulations as part of computational finance.</sub> | <sub>1/29/18 5:01</sub> | <sub>8/2/18 5:56</sub> | <sub>17.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
|
| <sub>[Finance Graph Theory](https://github.com/AvijitGhosh82/Finance_Graph_Theory)</sub> | <sub>Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.</sub> | <sub>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>[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>[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>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>[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>1/24/19 13:37</sub> | <sub>2/13/19 16:48</sub> | <sub>1.0</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> |
|
||||||
@@ -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>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>[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>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>[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>6/1/15 15:31</sub> | <sub>2/28/20 17:30</sub> | <sub>3647.0</sub> | <sub>:heavy_check_mark:</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>[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>[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>[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>[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>[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>[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>[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>[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>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>[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>6/3/16 21:49</sub> | <sub>4/27/20 18:40</sub> | <sub>1839.0</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>[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>2/9/21 9:36</sub> | <sub>171.0</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>[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>17.0</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>[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>139.0</sub> | <sub>:heavy_check_mark:</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>12/15/14 11:23</sub> | <sub>7/10/18 6:38</sub> | <sub>1297.0</sub> | <sub>:heavy_multiplication_x:</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>[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> |
|
| <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> |
|
||||||
@@ -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>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>[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>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>[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>2/2/19 8:44</sub> | <sub>5/3/19 17:16</sub> | <sub>1.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[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> |
|
||||||
@@ -1,8 +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>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>[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>[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>922.0</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>[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>381.0</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>[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>[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>[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>2158.0</sub> | <sub>:heavy_multiplication_x:</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>[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>[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> |
|
||||||
@@ -1,6 +1,6 @@
|
|||||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------|:------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------|:------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
|
||||||
|
| <sub>[Machine Learning in Asset Management](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Financial Event Prediction using Machine Learning](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3481555)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Financial Event Prediction using Machine Learning](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3481555)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization](https://jfds.pm-research.com/content/2/2/17)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization](https://jfds.pm-research.com/content/2/2/17)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies](https://jfds.pm-research.com/content/2/1/10)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies](https://jfds.pm-research.com/content/2/1/10)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Machine Learning in Asset Management](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952)</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
|
||||||
@@ -1,13 +1,13 @@
|
|||||||
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
|
||||||
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
|
||||||
| <sub>[Modern Portfolio Theory](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb)</sub> | <sub>Universal portfolios; modern portfolio theory.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Modern Portfolio Theory](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb)</sub> | <sub>Universal portfolios; modern portfolio theory.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[Online Portfolio Selection](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb)</sub> | <sub>****Comparing OLPS algorithms on a diversified set of ETFs.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
| <sub>[Online Portfolio Selection](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb)</sub> | <sub>****Comparing OLPS algorithms on a diversified set of ETFs.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
|
||||||
| <sub>[OLMAR Algorithm](https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb)</sub> | <sub>Relative importance of each component of the OLMAR algorithm.</sub> | <sub>7/26/16 16:20</sub> | <sub>12/30/16 11:40</sub> | <sub>6.0</sub> | <sub>:heavy_multiplication_x:</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>[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>[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>2/2/20 8:46</sub> | <sub>2/16/21 18:50</sub> | <sub>306.0</sub> | <sub>:heavy_check_mark:</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>11/16/18 12:20</sub> | <sub>7/4/19 1:41</sub> | <sub>229.0</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>5/29/18 13:30</sub> | <sub>2/25/21 13:01</sub> | <sub>1875.0</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>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>[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>11/12/17 16:08</sub> | <sub>5/9/19 9:50</sub> | <sub>1276.0</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>[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>104.0</sub> | <sub>:heavy_multiplication_x:</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>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>[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> |
|
||||||
+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>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>[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>[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>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>[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>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>[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>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>[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>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>[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>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>[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>5/24/17 12:36</sub> | <sub>8/7/19 21:47</sub> | <sub>107.0</sub> | <sub>:heavy_check_mark:</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>2/4/18 21:51</sub> | <sub>2/4/18 21:57</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</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> |
|
||||||
@@ -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>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>[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>78.0</sub> | <sub>:heavy_multiplication_x:</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>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>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>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>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>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>[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>6/21/17 4:47</sub> | <sub>6/21/17 4:51</sub> | <sub>32.0</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> |
|
||||||
+201
-165
@@ -1,165 +1,201 @@
|
|||||||
name,url,comment,category,last_update,star_count,fork_count,contributors_count,created_at,last_commit,repo_path,repo_status,rating,finml_added_date
|
name,url,comment,category,last_update,star_count,fork_count,contributors_count,created_at,last_commit,repo_path,repo_status,rating,finml_added_date
|
||||||
Venture Capital NN,https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring,Cox-PH neural network predictions for VC/innovations finance research.,Alternative Finance,,,,,,,tr7200/National-Culture-and-Venture-Capital-Monitoring,,,
|
Venture Capital NN,https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring,Cox-PH neural network predictions for VC/innovations finance research.,Alternative Finance,,,,,,,tr7200/National-Culture-and-Venture-Capital-Monitoring,,,
|
||||||
Private Equity,https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb,Valuation models.,Alternative Finance,11/26/20 3:34,8,6,2,1/27/16 21:13,3/14/16 20:03,TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity,inactive,,
|
Private Equity,https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb,Valuation models.,Alternative Finance,2020-11-26 03:34:45,8.0,6.0,2.0,2016-01-27 21:13:33,2016-03-14 20:03:52,TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity,inactive,,
|
||||||
VC OLS,https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb,VC regression.,Alternative Finance,10/6/20 20:56,2,1,1,3/29/18 23:31,3/29/18 23:33,fionawhitefield/venture-capital-ols,inactive,,
|
VC OLS,https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb,VC regression.,Alternative Finance,2020-10-06 20:56:14,2.0,1.0,1.0,2018-03-29 23:31:13,2018-03-29 23:33:19,fionawhitefield/venture-capital-ols,inactive,,
|
||||||
Watch Valuation,https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb,Analysis of luxury watch data to classify whether a certain model is likely to be over-or undervalued.,Alternative Finance,1/14/21 22:41,4,2,1,2/8/17 18:39,4/27/17 22:55,alporter08/Luxury-Watch-Valuation,inactive,,
|
Watch Valuation,https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb,Analysis of luxury watch data to classify whether a certain model is likely to be over-or undervalued.,Alternative Finance,2021-01-14 22:41:08,4.0,2.0,1.0,2017-02-08 18:39:29,2017-04-27 22:55:55,alporter08/Luxury-Watch-Valuation,inactive,,
|
||||||
Art Valuation,https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb,Art evaluation analytics.,Alternative Finance,2/26/21 12:10,9,5,1,12/11/14 0:25,12/12/14 21:25,ahmedhosny/theGreenCanvas,inactive,,
|
Art Valuation,https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb,Art evaluation analytics.,Alternative Finance,2021-02-26 12:10:53,9.0,5.0,1.0,2014-12-11 00:25:39,2014-12-12 21:25:46,ahmedhosny/theGreenCanvas,inactive,,
|
||||||
Blockchain,https://github.com/nud3l/dInvest,Repository for distributed autonomous investment banking.,Alternative Finance,2/6/21 7:38,12,7,2,9/5/16 19:12,4/24/17 10:48,nud3l/dInvest,inactive,,
|
Blockchain,https://github.com/nud3l/dInvest,Repository for distributed autonomous investment banking.,Alternative Finance,2021-02-06 07:38:28,12.0,7.0,2.0,2016-09-05 19:12:40,2017-04-24 10:48:56,nud3l/dInvest,inactive,,
|
||||||
Venture Capital,https://github.com/julian-chan/etothex,Insight into a new founder to make data-driven investment decisions.,Alternative Finance,10/6/20 20:56,3,2,1,12/4/17 8:59,12/13/17 5:35,julian-chan/etothex,inactive,,
|
Venture Capital,https://github.com/julian-chan/etothex,Insight into a new founder to make data-driven investment decisions.,Alternative Finance,2020-10-06 20:56:08,3.0,2.0,1.0,2017-12-04 08:59:44,2017-12-13 05:35:27,julian-chan/etothex,inactive,,
|
||||||
Kiva Crowdfunding,https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb,Exploratory data analysis.,Alternative Finance,2/19/21 13:40,5,1,1,2/27/18 16:46,2/13/19 0:15,CJL89/Kiva-Crowdfunding,inactive,,
|
Kiva Crowdfunding,https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb,Exploratory data analysis.,Alternative Finance,2021-02-19 13:40:33,5.0,1.0,1.0,2018-02-27 16:46:02,2019-02-13 00:15:27,CJL89/Kiva-Crowdfunding,inactive,,
|
||||||
Oxford Man,https://www.oxford-man.ox.ac.uk/,Oxford-Man Institute of Quantitative Finance,Colleges Centers and Departments,,,,,,,,,,
|
Stanford Advanced Financial Technologies,https://fintech.stanford.edu/,Stanford Advanced Financial Technologies Laboratory,Colleges Centers and Departments,,,,,,,,,,
|
||||||
Berkeley Lab CIFT,https://cs.lbl.gov/news-media/news/news-archive/2010/berkeley-lab-launches-new-center-for-innovative-financial-technology/,,Colleges Centers and Departments,,,,,,,,,,
|
NYU FRE,https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering,Finance and Risk Engineering (NYU Tandon),Colleges Centers and Departments,,,,,,,,,,
|
||||||
NYU Courant,https://cims.nyu.edu/,"Courant Institute of Mathematical Sciences, New York University",Colleges Centers and Departments,,,,,,,,,,
|
Cornell University,https://www.cornell.edu/,,Colleges Centers and Departments,,,,,,,,,,
|
||||||
Cornell University,https://www.cornell.edu/,,Colleges Centers and Departments,,,,,,,,,,
|
Berkeley Lab CIFT,https://cs.lbl.gov/news-media/news/news-archive/2010/berkeley-lab-launches-new-center-for-innovative-financial-technology/,,Colleges Centers and Departments,,,,,,,,,,
|
||||||
NYU FRE,https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering,Finance and Risk Engineering (NYU Tandon),Colleges Centers and Departments,,,,,,,,,,
|
Oxford Man,https://www.oxford-man.ox.ac.uk/,Oxford-Man Institute of Quantitative Finance,Colleges Centers and Departments,,,,,,,,,,
|
||||||
Stanford Advanced Financial Technologies,https://fintech.stanford.edu/,Stanford Advanced Financial Technologies Laboratory,Colleges Centers and Departments,,,,,,,,,,
|
NYU Courant,https://cims.nyu.edu/,"Courant Institute of Mathematical Sciences, New York University",Colleges Centers and Departments,,,,,,,,,,
|
||||||
Handson Python for Finance,https://github.com/PacktPublishing/Hands-on-Python-for-Finance,Hands-on Python for Finance published by Packt.,Courses,4/1/21 5:48,119,107,3,8/20/18 14:10,1/15/21 8:57,PacktPublishing/Hands-on-Python-for-Finance,active,,
|
Mathematical Finance,https://github.com/yadongli/nyumath2048,NYU Math-GA 2048: Scientific Computing in Finance.,Courses,2021-01-14 18:01:08,69.0,63.0,6.0,2015-01-25 21:10:37,2020-03-25 04:24:25,yadongli/nyumath2048,active,,
|
||||||
Basic Finance,https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance,Source code notebooks basic finance applications.,Courses,3/31/21 2:09,10,8,1,5/6/17 2:39,6/21/17 4:04,SeanMcOwen/FinanceAndPython.com-BasicFinance,inactive,,
|
Python for Finance,https://github.com/siaen/python_finance_course,CEU python for finance course material.,Courses,2021-03-31 02:08:55,16.0,15.0,4.0,2017-12-12 11:54:46,2020-02-25 20:31:41,siaen/python_finance_course,active,,
|
||||||
ML Specialisation,https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization,Machine Learning in Finance.,Courses,3/31/21 2:08,33,32,1,1/24/19 2:55,1/3/20 21:54,Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization,active,,
|
Algo Trading,https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading,Intro to algo trading.,Courses,2021-03-12 11:02:04,64.0,25.0,1.0,2017-10-29 20:34:54,2019-01-22 06:56:08,JCreeks/Machine-Learning-in-Finance,inactive,,
|
||||||
Basic Investments,https://github.com/SeanMcOwen/FinanceAndPython.com-Investments,Basic investment tools in python.,Courses,3/23/21 6:32,9,5,1,8/2/17 21:52,8/17/17 3:24,SeanMcOwen/FinanceAndPython.com-Investments,inactive,,
|
Machine Learning for Trading,https://github.com/stefan-jansen/machine-learning-for-trading,"Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.",Courses,2021-04-12 16:18:26,3842.0,1225.0,8.0,2018-05-09 12:33:08,2021-04-10 22:21:29,stefan-jansen/machine-learning-for-trading,active,,
|
||||||
Risk Management,https://github.com/andrey-lukyanov/Risk-Management,Finance risk engagement course resources.,Courses,11/12/20 0:49,6,5,3,10/3/18 16:26,12/13/18 8:04,andrey-lukyanov/Risk-Management,inactive,,
|
Basic Derivatives,https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives,Basic forward contracts and hedging.,Courses,2021-03-31 02:08:39,4.0,4.0,1.0,2017-08-24 00:11:37,2017-10-13 01:32:23,SeanMcOwen/FinanceAndPython.com-Derivatives,inactive,,
|
||||||
Basic Derivatives,https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives,Basic forward contracts and hedging.,Courses,3/31/21 2:08,4,4,1,8/24/17 0:11,10/13/17 1:32,SeanMcOwen/FinanceAndPython.com-Derivatives,inactive,,
|
Handson Python for Finance,https://github.com/PacktPublishing/Hands-on-Python-for-Finance,Hands-on Python for Finance published by Packt.,Courses,2021-04-12 00:49:10,121.0,110.0,3.0,2018-08-20 14:10:37,2021-01-15 08:57:06,PacktPublishing/Hands-on-Python-for-Finance,active,,
|
||||||
Mathematical Finance,https://github.com/yadongli/nyumath2048,NYU Math-GA 2048: Scientific Computing in Finance.,Courses,1/14/21 18:01,69,63,6,1/25/15 21:10,3/25/20 4:24,yadongli/nyumath2048,active,,
|
Basic Investments,https://github.com/SeanMcOwen/FinanceAndPython.com-Investments,Basic investment tools in python.,Courses,2021-03-23 06:32:10,9.0,5.0,1.0,2017-08-02 21:52:19,2017-08-17 03:24:53,SeanMcOwen/FinanceAndPython.com-Investments,inactive,,
|
||||||
Machine Learning for Trading,https://github.com/stefan-jansen/machine-learning-for-trading,"Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.",Courses,4/4/21 1:48,3717,1165,7,5/9/18 12:33,3/19/21 14:10,stefan-jansen/machine-learning-for-trading,active,,
|
ML Specialisation,https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization,Machine Learning in Finance.,Courses,2021-04-05 13:37:33,34.0,32.0,1.0,2019-01-24 02:55:01,2020-01-03 21:54:16,Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization,active,,
|
||||||
Algo Trading,https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading,Intro to algo trading.,Courses,3/12/21 11:02,64,25,1,10/29/17 20:34,1/22/19 6:56,JCreeks/Machine-Learning-in-Finance,inactive,,
|
Basic Finance,https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance,Source code notebooks basic finance applications.,Courses,2021-03-31 02:09:16,10.0,8.0,1.0,2017-05-06 02:39:05,2017-06-21 04:04:09,SeanMcOwen/FinanceAndPython.com-BasicFinance,inactive,,
|
||||||
Python for Finance,https://github.com/siaen/python_finance_course,CEU python for finance course material.,Courses,3/31/21 2:08,16,15,4,12/12/17 11:54,2/25/20 20:31,siaen/python_finance_course,active,,
|
Risk Management,https://github.com/andrey-lukyanov/Risk-Management,Finance risk engagement course resources.,Courses,2020-11-12 00:49:51,6.0,5.0,3.0,2018-10-03 16:26:14,2018-12-13 08:04:15,andrey-lukyanov/Risk-Management,inactive,,
|
||||||
IRS,http://social-metrics.org/sox/,,Data,,,,,,,,,,
|
http://finance.yahoo.com/,http://finance.yahoo.com/,,Data,,,,,,,,,,
|
||||||
Employee Count SEC Filings,https://github.com/healthgradient/sec_employee_information_extraction,,Data,2/27/21 3:33,10,2,1,6/26/18 23:33,8/14/18 1:31,healthgradient/sec_employee_information_extraction,inactive,,
|
Rating Industries,http://www.ratingshistory.info/,,Data,,,,,,,,,,
|
||||||
EDGAR,https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb,,Data,1/23/21 19:22,11,10,1,6/11/18 22:51,7/10/18 18:03,TiesdeKok/UW_Python_Camp,inactive,,
|
https://fred.stlouisfed.org/,https://fred.stlouisfed.org/,,Data,,,,,,,,,,
|
||||||
Open Edgar,https://github.com/LexPredict/openedgar,,Data,4/2/21 16:21,167,61,6,5/7/18 15:32,5/15/19 8:32,LexPredict/openedgar,active,,
|
Financial Corporate,http://raw.rutgers.edu/Corporate%20Financial%20Data.html,,Data,,,,,,,,,,
|
||||||
SEC Parsing,https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb,,Data,2/27/21 6:34,9,6,1,6/16/18 14:30,6/16/18 17:23,healthgradient/sec-doc-info-extraction,inactive,,
|
https://stooq.com,https://stooq.com,,Data,,,,,,,,,,
|
||||||
Web Scraping (FirmAI),https://github.com/firmai/business-machine-learning/blob/master/www.firmai.org/data,,Data,3/28/21 12:43,576,183,2,2/19/19 19:02,7/22/20 16:48,firmai/business-machine-learning,active,,
|
Non-financial Corporate,http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html,,Data,,,,,,,,,,
|
||||||
Capital Markets Data,https://www.capitalmarketsdata.com/,,Data,,,,,,,,,,
|
https://github.com/timestocome/StockMarketData,https://github.com/timestocome/StockMarketData,,Data,2021-03-26 22:35:04,7.0,5.0,1.0,2017-05-10 21:49:45,2017-08-06 19:23:18,timestocome/StockMarketData,inactive,,
|
||||||
https://github.com/timestocome/StockMarketData,https://github.com/timestocome/StockMarketData,,Data,3/26/21 22:35,7,6,1,5/10/17 21:49,8/6/17 19:23,timestocome/StockMarketData,inactive,,
|
Web Scraping (FirmAI),https://github.com/firmai/business-machine-learning/blob/master/www.firmai.org/data,,Data,2021-04-10 17:19:48,577.0,184.0,2.0,2019-02-19 19:02:59,2020-07-22 16:48:21,firmai/business-machine-learning,active,,
|
||||||
Non-financial Corporate,http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html,,Data,,,,,,,,,,
|
SEC Parsing,https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb,,Data,2021-02-27 06:34:55,9.0,6.0,1.0,2018-06-16 14:30:06,2018-06-16 17:23:46,healthgradient/sec-doc-info-extraction,inactive,,
|
||||||
http://finance.yahoo.com/,http://finance.yahoo.com/,,Data,,,,,,,,,,
|
Open Edgar,https://github.com/LexPredict/openedgar,,Data,2021-04-09 12:15:03,169.0,61.0,6.0,2018-05-07 15:32:31,2019-05-15 08:32:30,LexPredict/openedgar,active,,
|
||||||
https://stooq.com,https://stooq.com,,Data,,,,,,,,,,
|
EDGAR,https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb,,Data,2021-01-23 19:22:59,11.0,10.0,1.0,2018-06-11 22:51:57,2018-07-10 18:03:52,TiesdeKok/UW_Python_Camp,inactive,,
|
||||||
Financial Corporate,http://raw.rutgers.edu/Corporate%20Financial%20Data.html,,Data,,,,,,,,,,
|
Employee Count SEC Filings,https://github.com/healthgradient/sec_employee_information_extraction,,Data,2021-02-27 03:33:31,10.0,2.0,1.0,2018-06-26 23:33:51,2018-08-14 01:31:13,healthgradient/sec_employee_information_extraction,inactive,,
|
||||||
https://fred.stlouisfed.org/,https://fred.stlouisfed.org/,,Data,,,,,,,,,,
|
IRS,http://social-metrics.org/sox/,,Data,,,,,,,,,,
|
||||||
Rating Industries,http://www.ratingshistory.info/,,Data,,,,,,,,,,
|
Capital Markets Data,https://www.capitalmarketsdata.com/,,Data,,,,,,,,,,
|
||||||
Advanced ML II,https://github.com/hudson-and-thames/research,More implementations of Financial Machine Learning (De Prado).,Data Processing Techniques and Transformations,,,,,,,hudson-and-thames/research,,,
|
Advanced ML II,https://github.com/hudson-and-thames/research,More implementations of Financial Machine Learning (De Prado).,Data Processing Techniques and Transformations,,,,,,,hudson-and-thames/research,,,
|
||||||
Advanced ML,https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises,Exercises too Financial Machine Learning (De Prado).,Data Processing Techniques and Transformations,4/3/21 6:12,964,435,4,4/25/18 17:22,1/16/20 17:25,BlackArbsCEO/Adv_Fin_ML_Exercises,active,,
|
Advanced ML,https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises,Exercises too Financial Machine Learning (De Prado).,Data Processing Techniques and Transformations,2021-04-12 02:20:21,973.0,435.0,4.0,2018-04-25 17:22:40,2020-01-16 17:25:41,BlackArbsCEO/Adv_Fin_ML_Exercises,active,,
|
||||||
crypto-rl,https://github.com/sadighian/crypto-rl,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),Deep Learning And Reinforcement Learning,4/3/21 21:08,345,112,1,6/21/18 1:06,11/5/20 11:08,sadighian/crypto-rl,active,3,3/31/21 8:00
|
BitcoinForecast,https://github.com/PiSimo/BitcoinForecast,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 ,Deep Learning And Reinforcement Learning,2021-04-06 02:06:05,289.0,128.0,3.0,2017-03-10 10:52:02,2018-06-11 08:07:02,PiSimo/BitcoinForecast,inactive,3.0,3/31/21 8:00
|
||||||
DeepLearningInFinance,https://github.com/sonaam1234/DeepLearningInFinance,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. ,Deep Learning And Reinforcement Learning,3/8/21 13:09,266,145,1,8/21/17 16:00,8/21/17 17:23,sonaam1234/DeepLearningInFinance,inactive,3,3/31/21 8:00
|
Pair-Trading-Reinforcement-Learning,https://github.com/wai-i/Pair-Trading-Reinforcement-Learning,NEW,Deep Learning And Reinforcement Learning,2021-04-10 04:53:28,136.0,56.0,1.0,2019-06-09 22:50:37,2020-01-03 15:36:22,wai-i/Pair-Trading-Reinforcement-Learning,active,,2021-04-12 16:39:11.055181
|
||||||
LTSM GRU,https://github.com/RajatHanda/Finance-Forecasting,Stock Market Forecasting using LSTM\GRU.,Deep Learning And Reinforcement Learning,3/29/21 23:59,11,6,1,5/13/18 2:39,2/25/19 0:26,RajatHanda/Finance-Forecasting,inactive,3,
|
rl_trading,https://github.com/ucaiado/rl_trading,NEW,Deep Learning And Reinforcement Learning,2021-04-08 15:34:33,207.0,89.0,1.0,2017-05-29 22:19:00,2017-08-29 14:54:50,ucaiado/rl_trading,inactive,,2021-04-12 16:39:11.055181
|
||||||
Deep Learning,https://github.com/keon/deepstock,Technical experimentations to beat the stock market using deep learning.,Deep Learning And Reinforcement Learning,3/24/21 14:45,427,154,2,12/12/16 2:15,3/4/17 8:37,keon/deepstock,inactive,4,
|
Trading-Gym,https://github.com/thedimlebowski/Trading-Gym,NEW,Deep Learning And Reinforcement Learning,2021-04-10 08:00:08,507.0,147.0,3.0,2017-06-13 13:14:48,2017-07-10 08:09:30,thedimlebowski/Trading-Gym,inactive,,2021-04-12 16:39:11.055181
|
||||||
Deep Learning II,https://github.com/LiamConnell/deep-algotrading/tree/master/notebooks,Tensorflow Regression.,Deep Learning And Reinforcement Learning,3/21/21 6:53,174,67,1,7/12/16 12:56,2/16/18 2:43,LiamConnell/deep-algotrading,inactive,3,
|
DQN-DDPG_Stock_Trading,https://github.com/AI4Finance-LLC/DQN-DDPG_Stock_Trading,merged into FinRL library and uses [gym](https://gym.openai.com/) and implementation of DQN,Deep Learning And Reinforcement Learning,2021-04-07 12:42:22,136.0,49.0,4.0,2018-09-19 03:17:06,2020-11-26 16:58:00,AI4Finance-LLC/DQN-DDPG_Stock_Trading,active,3.0,3/31/21 8:00
|
||||||
Neural Network,https://github.com/VivekPa/IntroNeuralNetworks,Neural networks to predict stock prices.,Deep Learning And Reinforcement Learning,4/3/21 11:59,489,177,2,9/10/18 6:34,11/21/18 7:39,VivekPa/IntroNeuralNetworks,inactive,4,
|
pairstrade-fyp-2019,https://github.com/wywongbd/pairstrade-fyp-2019,NEW,Deep Learning And Reinforcement Learning,2021-04-04 23:47:56,110.0,41.0,2.0,2018-09-07 07:51:06,2020-05-13 05:06:51,wywongbd/pairstrade-fyp-2019,active,,2021-04-12 16:39:11.055181
|
||||||
Deep Learning IV,https://github.com/achillesrasquinha/bulbea,Bulbea: Deep Learning based Python Library.,Deep Learning And Reinforcement Learning,4/2/21 1:36,1451,416,1,3/9/17 6:11,3/19/17 7:42,achillesrasquinha/bulbea,inactive,5,
|
Machine-Learning-and-Reinforcement-Learning-in-Finance,https://github.com/joelowj/Machine-Learning-and-Reinforcement-Learning-in-Finance,NEW,Deep Learning And Reinforcement Learning,2021-03-30 09:11:21,175.0,98.0,1.0,2018-06-26 04:30:08,2018-09-23 16:50:33,joelowj/Machine-Learning-and-Reinforcement-Learning-in-Finance,inactive,,2021-04-12 16:39:11.055181
|
||||||
AI Trading,https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md,AI to predict stock market movements.,Deep Learning And Reinforcement Learning,4/3/21 21:14,2857,1378,1,1/9/19 8:02,2/11/19 16:32,borisbanushev/stockpredictionai,inactive,5,
|
maro,https://github.com/microsoft/maro,NEW,Deep Learning And Reinforcement Learning,2021-04-12 02:22:41,386.0,66.0,17.0,2019-12-27 06:48:27,2021-04-07 15:49:38,microsoft/maro,active,,2021-04-12 16:39:11.055181
|
||||||
ARIMA-LTSM Hybrid,https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid,Hybrid model to predict future price correlation coefficients of two assets.,Deep Learning And Reinforcement Learning,4/2/21 15:32,219,83,1,8/5/18 2:13,10/1/18 11:25,imhgchoi/ARIMA-LSTM-hybrid-corrcoef-predict,inactive,3,
|
RLQuant,https://github.com/yuriak/RLQuant,NEW,Deep Learning And Reinforcement Learning,2021-04-09 05:01:03,277.0,92.0,1.0,2018-04-05 05:42:44,2018-08-13 04:18:29,yuriak/RLQuant,inactive,,2021-04-12 16:39:11.055181
|
||||||
LTSM Recurrent,https://github.com/VivekPa/AIAlpha,OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.,Deep Learning And Reinforcement Learning,4/3/21 10:48,1199,371,2,10/7/18 3:58,8/3/19 9:00,VivekPa/AIAlpha,active,4,
|
TradingGym,https://github.com/cove9988/TradingGym,NEW,Deep Learning And Reinforcement Learning,2021-03-28 05:37:33,112.0,39.0,3.0,2017-11-06 00:50:01,2017-11-15 23:55:32,cove9988/TradingGym,inactive,,2021-04-12 16:39:11.055181
|
||||||
Deep-Reinforcement-Learning-in-Trading,https://github.com/saeed349/Deep-Reinforcement-Learning-in-Trading,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),Deep Learning And Reinforcement Learning,3/31/21 10:40,137,66,1,5/11/18 0:52,10/26/19 14:22,saeed349/Deep-Reinforcement-Learning-in-Trading,active,3,3/31/21 8:00
|
a3c_trading,https://github.com/evgps/a3c_trading,NEW,Deep Learning And Reinforcement Learning,2021-04-10 12:49:10,311.0,98.0,1.0,2018-06-04 15:30:16,2020-05-23 14:47:54,evgps/a3c_trading,active,,2021-04-12 16:39:11.055181
|
||||||
Deep Learning III,https://github.com/Rachnog/Deep-Trading,Algorithmic trading with deep learning experiments.,Deep Learning And Reinforcement Learning,4/3/21 5:26,1264,675,1,6/18/16 18:23,8/7/18 15:24,Rachnog/Deep-Trading,inactive,5,
|
RL III,https://github.com/samre12/deep-trading-agent,Github -Deep Reinforcement Learning based Trading Agent for Bitcoin.,Deep Learning And Reinforcement Learning,2021-04-03 20:48:36,576.0,203.0,1.0,2017-09-21 17:05:19,2018-04-13 16:33:21,samre12/deep-trading-agent,inactive,3.0,
|
||||||
Stock-Prediction-Models,https://github.com/huseinzol05/Stock-Prediction-Models,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)),Deep Learning And Reinforcement Learning,4/3/21 20:09,3599,1521,2,12/18/17 10:49,1/5/21 10:31,huseinzol05/Stock-Prediction-Models,active,5,3/31/21 8:00
|
TradingGym,https://github.com/Yvictor/TradingGym,NEW,Deep Learning And Reinforcement Learning,2021-04-11 20:20:33,841.0,237.0,2.0,2017-05-01 13:53:32,2018-02-14 13:58:18,Yvictor/TradingGym,inactive,,2021-04-12 16:39:11.055181
|
||||||
RLTrader,https://github.com/notadamking/RLTrader,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.,Deep Learning And Reinforcement Learning,4/3/21 20:09,1304,449,15,4/27/19 18:35,10/17/19 16:25,notadamking/RLTrader,active,5,3/31/21 8:00
|
QLearning_Trading,https://github.com/ucaiado/QLearning_Trading,NEW,Deep Learning And Reinforcement Learning,2021-04-06 22:09:31,433.0,168.0,1.0,2016-08-10 06:02:23,2016-10-15 02:36:09,ucaiado/QLearning_Trading,inactive,,2021-04-12 16:39:11.055181
|
||||||
trading-rl,https://github.com/Kostis-S-Z/trading-rl,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,Deep Learning And Reinforcement Learning,3/31/21 16:01,179,38,2,4/22/19 10:03,9/28/20 9:07,Kostis-S-Z/trading-rl,inactive,3,3/31/21 8:00
|
gym-trading,https://github.com/hackthemarket/gym-trading,NEW,Deep Learning And Reinforcement Learning,2021-04-12 09:06:22,581.0,195.0,2.0,2016-12-09 20:46:19,2017-12-24 15:34:37,hackthemarket/gym-trading,inactive,,2021-04-12 16:39:11.055181
|
||||||
awesome-deep-trading,https://github.com/cbailes/awesome-deep-trading,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,Deep Learning And Reinforcement Learning,4/3/21 19:52,541,137,1,11/26/18 3:23,1/1/21 9:41,cbailes/awesome-deep-trading,active,4,3/31/21 8:00
|
Pair Trading RL,https://github.com/shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading,Using deep actor-critic model to learn best strategies in pair trading.,Deep Learning And Reinforcement Learning,2021-03-27 02:19:29,241.0,113.0,1.0,2017-05-18 16:47:11,2017-05-18 16:56:38,shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading,inactive,3.0,
|
||||||
trading-bot,https://github.com/pskrunner14/trading-bot,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 ,Deep Learning And Reinforcement Learning,4/2/21 18:45,286,139,1,8/13/18 10:44,1/23/20 4:41,pskrunner14/trading-bot,active,3,3/31/21 8:00
|
RL II,https://github.com/deependersingla/deep_trader,reinforcement learning on stock market and agent tries to learn trading.,Deep Learning And Reinforcement Learning,2021-04-11 20:21:25,1340.0,489.0,3.0,2016-06-11 07:27:10,2018-01-22 14:35:50,deependersingla/deep_trader,inactive,3.0,
|
||||||
Advanced-Deep-Trading,https://github.com/Rachnog/Advanced-Deep-Trading,"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. ",Deep Learning And Reinforcement Learning,3/30/21 7:29,319,158,2,2/16/19 21:18,11/29/20 20:12,Rachnog/Advanced-Deep-Trading,active,3,3/31/21 8:00
|
RL,https://github.com/kh-kim/stock_market_reinforcement_learning,OpenGym with Deep Q-learning and Policy Gradient.,Deep Learning And Reinforcement Learning,2021-04-11 12:27:18,715.0,298.0,1.0,2016-10-04 14:42:19,2016-12-23 07:34:08,kh-kim/stock_market_reinforcement_learning,inactive,2.0,
|
||||||
FinRL-Library,https://github.com/AI4Finance-LLC/FinRL-Library,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,Deep Learning And Reinforcement Learning,4/4/21 1:53,1807,433,22,7/26/20 13:18,4/3/21 23:21,AI4Finance-LLC/FinRL-Library,active,5,3/31/21 8:00
|
RL V,https://github.com/gstenger98/rl-finance,Building an Agent to Trade with Reinforcement Learning.,Deep Learning And Reinforcement Learning,2021-04-08 18:57:53,33.0,8.0,5.0,2019-01-16 00:43:36,2020-03-19 20:28:08,gstenger98/rl-finance,active,2.0,
|
||||||
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,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.,Deep Learning And Reinforcement Learning,4/3/21 10:08,547,240,6,7/26/20 13:12,1/21/21 18:11,AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020,inactive,4,3/31/21 8:00
|
RL Trading,https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW,A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.,Deep Learning And Reinforcement Learning,,,,,,,,,4.0,
|
||||||
deep-RL-trading,https://github.com/golsun/deep-RL-trading,trading game comparing RNN vs CNN vs MLP based on [paper](https://arxiv.org/abs/1803.03916),Deep Learning And Reinforcement Learning,4/1/21 12:51,233,109,1,2/25/18 17:41,12/1/20 22:06,golsun/deep-RL-trading,active,3,3/31/21 8:00
|
RL IV,https://github.com/jjakimoto/DQN,Reinforcement Learning for finance.,Deep Learning And Reinforcement Learning,2021-04-05 11:42:45,142.0,55.0,1.0,2016-10-21 02:47:17,2017-04-07 08:11:57,jjakimoto/DQN,inactive,,
|
||||||
AutomatedStockTrading-DeepQ-Learning,https://github.com/sachink2010/AutomatedStockTrading-DeepQ-Learning,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,Deep Learning And Reinforcement Learning,3/24/21 1:11,134,51,2,2/23/19 12:01,2/25/20 18:16,sachink2010/AutomatedStockTrading-DeepQ-Learning,active,3,3/31/21 8:00
|
tensortrade,https://github.com/tensortrade-org/tensortrade,NEW,Deep Learning And Reinforcement Learning,2021-04-12 16:05:19,3101.0,715.0,39.0,2019-07-30 21:28:32,2021-03-24 16:25:28,tensortrade-org/tensortrade,active,,2021-04-12 16:39:11.055181
|
||||||
Personae,https://github.com/Ceruleanacg/Personae,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,Deep Learning And Reinforcement Learning,3/31/21 15:38,1142,332,2,3/10/18 11:22,9/2/18 17:21,Ceruleanacg/Personae,inactive,5,3/31/21 8:00
|
Deep-Learning-Machine-Learning-Stock,https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock,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,Deep Learning And Reinforcement Learning,2021-04-12 02:58:32,275.0,99.0,1.0,2018-09-29 23:38:06,2021-03-18 03:16:36,LastAncientOne/Deep-Learning-Machine-Learning-Stock,active,3.0,3/31/21 8:00
|
||||||
Deep-Reinforcement-Stock-Trading,https://github.com/Albert-Z-Guo/Deep-Reinforcement-Stock-Trading,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,Deep Learning And Reinforcement Learning,4/3/21 22:50,141,42,2,5/19/19 22:20,9/27/20 19:22,Albert-Z-Guo/Deep-Reinforcement-Stock-Trading,active,3,3/31/21 8:00
|
Deep-Reinforcement-Learning-in-Trading,https://github.com/saeed349/Deep-Reinforcement-Learning-in-Trading,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),Deep Learning And Reinforcement Learning,2021-04-10 13:17:25,138.0,66.0,1.0,2018-05-11 00:52:14,2019-10-26 14:22:44,saeed349/Deep-Reinforcement-Learning-in-Trading,active,3.0,3/31/21 8:00
|
||||||
Deep-Learning-Machine-Learning-Stock,https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock,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,Deep Learning And Reinforcement Learning,4/4/21 1:27,264,94,1,9/29/18 23:38,3/18/21 3:16,LastAncientOne/Deep-Learning-Machine-Learning-Stock,active,3,3/31/21 8:00
|
Personae,https://github.com/Ceruleanacg/Personae,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,Deep Learning And Reinforcement Learning,2021-04-11 20:20:48,1144.0,330.0,2.0,2018-03-10 11:22:00,2018-09-02 17:21:38,Ceruleanacg/Personae,inactive,5.0,3/31/21 8:00
|
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BitcoinForecast,https://github.com/PiSimo/BitcoinForecast,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 ,Deep Learning And Reinforcement Learning,4/3/21 8:03,288,127,3,3/10/17 10:52,6/11/18 8:07,PiSimo/BitcoinForecast,inactive,3,3/31/21 8:00
|
awesome-deep-trading,https://github.com/cbailes/awesome-deep-trading,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,Deep Learning And Reinforcement Learning,2021-04-11 09:02:46,551.0,140.0,1.0,2018-11-26 03:23:04,2021-01-01 09:41:21,cbailes/awesome-deep-trading,active,4.0,3/31/21 8:00
|
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DQN-DDPG_Stock_Trading,https://github.com/AI4Finance-LLC/DQN-DDPG_Stock_Trading,merged into FinRL library and uses [gym](https://gym.openai.com/) and implementation of DQN,Deep Learning And Reinforcement Learning,4/3/21 21:48,135,49,4,9/19/18 3:17,11/26/20 16:58,AI4Finance-LLC/DQN-DDPG_Stock_Trading,inactive,3,3/31/21 8:00
|
trading-rl,https://github.com/Kostis-S-Z/trading-rl,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,Deep Learning And Reinforcement Learning,2021-04-10 04:59:14,180.0,38.0,2.0,2019-04-22 10:03:21,2020-09-28 09:07:18,Kostis-S-Z/trading-rl,active,3.0,3/31/21 8:00
|
||||||
Derman,https://github.com/rstreppa/valuation-convertibles-Goldman1994/blob/master/ConvertibleBond_Goldman1994_Derman.ipynb,Binomial tree for American call.,Derivatives and Hedging,10/6/20 20:37,1,3,1,5/18/18 18:08,9/21/18 19:59,rstreppa/valuation-convertibles-Goldman1994,inactive,,
|
RLTrader,https://github.com/notadamking/RLTrader,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.,Deep Learning And Reinforcement Learning,2021-04-12 02:50:22,1312.0,451.0,15.0,2019-04-27 18:35:15,2019-10-17 16:25:49,notadamking/RLTrader,active,5.0,3/31/21 8:00
|
||||||
Hull White,https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb,"Callable Bond, Hull White.",Derivatives and Hedging,10/6/20 20:37,4,6,1,6/6/18 22:06,6/6/18 22:27,rstreppa/valuation-callables-HullWhite,inactive,,
|
Stock-Prediction-Models,https://github.com/huseinzol05/Stock-Prediction-Models,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)),Deep Learning And Reinforcement Learning,2021-04-12 13:54:15,3655.0,1542.0,2.0,2017-12-18 10:49:59,2021-01-05 10:31:50,huseinzol05/Stock-Prediction-Models,active,5.0,3/31/21 8:00
|
||||||
Derivative Markets,https://github.com/broughtj/Fin6470/tree/master/Notebooks,"The economics of futures, futures, options, and swaps.",Derivatives and Hedging,3/18/21 3:47,8,8,1,2/9/16 5:30,3/18/21 3:47,broughtj/Fin6470,active,,
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Deep Learning III,https://github.com/Rachnog/Deep-Trading,Algorithmic trading with deep learning experiments.,Deep Learning And Reinforcement Learning,2021-04-09 10:39:54,1266.0,675.0,1.0,2016-06-18 18:23:06,2018-08-07 15:24:45,Rachnog/Deep-Trading,inactive,5.0,
|
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Options,https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D,Introduction to options.,Derivatives and Hedging,4/4/21 0:48,332,165,36,7/28/17 15:48,3/17/21 17:17,QuantConnect/Tutorials,active,,
|
LTSM Recurrent,https://github.com/VivekPa/AIAlpha,OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.,Deep Learning And Reinforcement Learning,2021-04-12 02:39:50,1207.0,370.0,2.0,2018-10-07 03:58:26,2019-08-03 09:00:44,VivekPa/AIAlpha,active,4.0,
|
||||||
Volatility and Variance Derivatives,https://github.com/yhilpisch/lvvd/tree/master/lvvd,Volatility derivatives analytics.,Derivatives and Hedging,3/23/21 12:10,78,77,1,10/21/16 4:12,2/22/21 13:32,yhilpisch/lvvd,active,,
|
ARIMA-LTSM Hybrid,https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid,Hybrid model to predict future price correlation coefficients of two assets.,Deep Learning And Reinforcement Learning,2021-04-11 04:12:38,222.0,86.0,1.0,2018-08-05 02:13:21,2018-10-01 11:25:53,imhgchoi/ARIMA-LSTM-hybrid-corrcoef-predict,inactive,3.0,
|
||||||
Reinforcement Learning,https://github.com/FinTechies/HedgingRL,Hedging portfolios with reinforcement learning.,Derivatives and Hedging,1/20/21 8:12,16,9,1,4/21/17 10:58,8/2/17 21:41,FinTechies/HedgingRL,inactive,,
|
AI Trading,https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md,AI to predict stock market movements.,Deep Learning And Reinforcement Learning,2021-04-12 15:42:20,2876.0,1384.0,1.0,2019-01-09 08:02:47,2019-02-11 16:32:47,borisbanushev/stockpredictionai,inactive,5.0,
|
||||||
Option Strategies,https://github.com/rstreppa/valuation-OptionStrategies,"Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.",Derivatives and Hedging,2/27/21 8:50,2,3,1,5/22/18 18:27,5/22/18 18:30,rstreppa/valuation-OptionStrategies,inactive,,
|
Deep Learning IV,https://github.com/achillesrasquinha/bulbea,Bulbea: Deep Learning based Python Library.,Deep Learning And Reinforcement Learning,2021-04-09 20:38:16,1467.0,416.0,1.0,2017-03-09 06:11:06,2017-03-19 07:42:49,achillesrasquinha/bulbea,inactive,5.0,
|
||||||
Options,https://github.com/PHBS/2018.M1.ASP/tree/master/py,Black Scholes and Copula.,Derivatives and Hedging,,,,,,,PHBS/2018.M1.ASP,,,
|
Neural Network,https://github.com/VivekPa/IntroNeuralNetworks,Neural networks to predict stock prices.,Deep Learning And Reinforcement Learning,2021-04-03 11:59:40,489.0,176.0,2.0,2018-09-10 06:34:53,2018-11-21 07:39:31,VivekPa/IntroNeuralNetworks,inactive,4.0,
|
||||||
Derivatives Python,https://github.com/yhilpisch/dawp/tree/master/python36,Derivative analytics with Python.,Derivatives and Hedging,3/23/21 15:35,387,297,1,7/9/15 12:27,2/22/21 13:29,yhilpisch/dawp,active,,
|
Deep Learning II,https://github.com/LiamConnell/deep-algotrading/tree/master/notebooks,Tensorflow Regression.,Deep Learning And Reinforcement Learning,2021-04-10 06:06:01,175.0,67.0,1.0,2016-07-12 12:56:10,2018-02-16 02:43:36,LiamConnell/deep-algotrading,inactive,3.0,
|
||||||
Options Risk Measures,https://github.com/wanglouis49/risk_estimation,Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).,Derivatives and Hedging,10/6/20 20:37,1,2,1,4/29/16 3:51,1/16/18 1:24,wanglouis49/risk_estimation,inactive,,
|
Deep Learning,https://github.com/keon/deepstock,Technical experimentations to beat the stock market using deep learning.,Deep Learning And Reinforcement Learning,2021-03-24 14:45:00,427.0,154.0,2.0,2016-12-12 02:15:12,2017-03-04 08:37:29,keon/deepstock,inactive,4.0,
|
||||||
Delta Hedging,https://github.com/RobinsonGarcia/delta-hedging,Advanced derivatives.,Derivatives and Hedging,2/27/21 8:48,3,2,1,3/2/18 23:53,7/17/18 23:32,RobinsonGarcia/delta-hedging,inactive,,
|
LTSM GRU,https://github.com/RajatHanda/Finance-Forecasting,Stock Market Forecasting using LSTM\GRU.,Deep Learning And Reinforcement Learning,2021-03-29 23:59:32,11.0,6.0,1.0,2018-05-13 02:39:32,2019-02-25 00:26:42,RajatHanda/Finance-Forecasting,inactive,3.0,
|
||||||
Computational Derivatives,https://github.com/chenbowen184/Computational_Finance,Projects focusing on investigating simulations and computational techniques applied in finance.,Derivatives and Hedging,1/12/21 12:22,17,12,1,1/29/18 5:01,8/2/18 5:56,chen-bowen/Computational_Finance,inactive,,
|
DeepLearningInFinance,https://github.com/sonaam1234/DeepLearningInFinance,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. ,Deep Learning And Reinforcement Learning,2021-03-08 13:09:27,266.0,145.0,1.0,2017-08-21 16:00:42,2017-08-21 17:23:48,sonaam1234/DeepLearningInFinance,inactive,3.0,3/31/21 8:00
|
||||||
Black Scholes,https://github.com/irajwani/numerical_methods_python/blob/master/black_scholes.ipynb,Options pricing.,Derivatives and Hedging,10/6/20 20:36,1,2,0,12/9/17 18:50,7/9/18 9:48,irajwani/numerical_methods_python,inactive,,
|
crypto-rl,https://github.com/sadighian/crypto-rl,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),Deep Learning And Reinforcement Learning,2021-04-12 10:24:43,347.0,111.0,1.0,2018-06-21 01:06:01,2020-11-05 11:08:19,sadighian/crypto-rl,active,3.0,3/31/21 8:00
|
||||||
Life-cycle,https://github.com/atulram/Finance-and-Stocks/blob/master/CompanyLifeCycle.ipynb,Company life cycle.,Extended Research,12/21/20 14:42,3,3,1,1/19/19 18:16,2/18/19 16:57,atulram/Finance-and-Stocks,inactive,,
|
trading-bot,https://github.com/pskrunner14/trading-bot,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 ,Deep Learning And Reinforcement Learning,2021-04-11 05:10:55,292.0,143.0,1.0,2018-08-13 10:44:08,2020-01-23 04:41:20,pskrunner14/trading-bot,active,3.0,3/31/21 8:00
|
||||||
Computational Finance,https://github.com/lnsongxf/Applied_Computational_Economics_and_Finance,Applied Computational Economics and Finance.,Extended Research,3/7/21 17:47,12,13,1,8/27/17 3:46,8/26/17 4:26,lnsongxf/Applied_Computational_Economics_and_Finance,inactive,,
|
Deep-Reinforcement-Stock-Trading,https://github.com/Albert-Z-Guo/Deep-Reinforcement-Stock-Trading,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,Deep Learning And Reinforcement Learning,2021-04-03 22:50:43,141.0,42.0,2.0,2019-05-19 22:20:05,2020-09-27 19:22:28,Albert-Z-Guo/Deep-Reinforcement-Stock-Trading,active,3.0,3/31/21 8:00
|
||||||
Bayesian Finance,https://github.com/marketneutral/alphatools/blob/master/notebooks/pymc3-minimal.ipynb,Notebook PyMC3 implementation.,Extended Research,4/3/21 15:17,231,55,1,8/28/18 14:45,8/6/20 22:03,marketneutral/alphatools,active,,
|
Advanced-Deep-Trading,https://github.com/Rachnog/Advanced-Deep-Trading,"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. ",Deep Learning And Reinforcement Learning,2021-03-30 07:29:06,319.0,158.0,2.0,2019-02-16 21:18:00,2020-11-29 20:12:59,Rachnog/Advanced-Deep-Trading,active,3.0,3/31/21 8:00
|
||||||
Corporate Finance,https://github.com/SeanMcOwen/FinanceAndPython.com-CorporateFinance,Basic corporate finance.,Extended Research,1/16/21 19:01,9,4,1,9/9/17 3:35,9/9/17 23:04,SeanMcOwen/FinanceAndPython.com-CorporateFinance,inactive,,
|
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,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.,Deep Learning And Reinforcement Learning,2021-04-12 16:24:32,560.0,249.0,6.0,2020-07-26 13:12:53,2021-01-21 18:11:59,AI4Finance-LLC/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020,active,4.0,3/31/21 8:00
|
||||||
Mathematical Finance,https://github.com/Auquan/Tutorials,Notebooks for math and financial tutorials.,Extended Research,3/31/21 18:05,662,426,9,1/21/17 11:24,8/1/20 17:03,Auquan/Tutorials,active,,
|
AutomatedStockTrading-DeepQ-Learning,https://github.com/sachink2010/AutomatedStockTrading-DeepQ-Learning,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,Deep Learning And Reinforcement Learning,2021-03-24 01:11:36,134.0,51.0,2.0,2019-02-23 12:01:21,2020-02-25 18:16:34,sachink2010/AutomatedStockTrading-DeepQ-Learning,active,3.0,3/31/21 8:00
|
||||||
Economic Foundations,https://github.com/SeanMcOwen/FinanceAndPython.com-EconomicFoundations,Basic economic models.,Extended Research,10/6/20 21:01,2,3,1,5/25/17 2:27,6/30/17 3:53,SeanMcOwen/FinanceAndPython.com-EconomicFoundations,inactive,,
|
deep-RL-trading,https://github.com/golsun/deep-RL-trading,trading game comparing RNN vs CNN vs MLP based on [paper](https://arxiv.org/abs/1803.03916),Deep Learning And Reinforcement Learning,2021-04-10 07:09:48,235.0,108.0,1.0,2018-02-25 17:41:42,2020-12-01 22:06:39,golsun/deep-RL-trading,active,3.0,3/31/21 8:00
|
||||||
Financial Economics,https://github.com/rsvp/fecon235/tree/master/nb,Financial Economics Models.,Extended Research,3/29/21 8:31,709,273,2,11/9/14 4:49,12/3/18 16:30,rsvp/fecon235,inactive,,
|
FinRL-Library,https://github.com/AI4Finance-LLC/FinRL-Library,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,Deep Learning And Reinforcement Learning,2021-04-12 12:45:15,1857.0,447.0,22.0,2020-07-26 13:18:16,2021-04-11 22:02:16,AI4Finance-LLC/FinRL-Library,active,5.0,3/31/21 8:00
|
||||||
Finance Graph Theory,https://github.com/AvijitGhosh82/Finance_Graph_Theory,Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.,Extended Research,3/28/21 2:22,17,7,3,8/2/18 2:48,3/16/19 18:39,evijit/Finance_Graph_Theory,inactive,,
|
Derman,https://github.com/rstreppa/valuation-convertibles-Goldman1994/blob/master/ConvertibleBond_Goldman1994_Derman.ipynb,Binomial tree for American call.,Derivatives and Hedging,2020-10-06 20:37:15,1.0,3.0,1.0,2018-05-18 18:08:16,2018-09-21 19:59:01,rstreppa/valuation-convertibles-Goldman1994,inactive,,
|
||||||
Market Crash Prediction,https://github.com/sarachmax/MarketCrashes_Prediction/blob/master/LPPL_Comparasion.ipynb,Predicting market crashes using an LPPL model.,Extended Research,10/6/20 21:01,1,3,1,1/24/19 13:37,2/13/19 16:48,sarachmax/MarketCrashes_Prediction,inactive,,
|
Hull White,https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb,"Callable Bond, Hull White.",Derivatives and Hedging,2020-10-06 20:37:16,4.0,6.0,1.0,2018-06-06 22:06:06,2018-06-06 22:27:02,rstreppa/valuation-callables-HullWhite,inactive,,
|
||||||
M&A,https://github.com/atulram/Finance-and-Stocks,Mergers and Acquisitions.,Extended Research,12/21/20 14:42,3,3,1,1/19/19 18:16,2/18/19 16:57,atulram/Finance-and-Stocks,inactive,,
|
Derivative Markets,https://github.com/broughtj/Fin6470/tree/master/Notebooks,"The economics of futures, futures, options, and swaps.",Derivatives and Hedging,2021-04-06 20:49:50,8.0,8.0,1.0,2016-02-09 05:30:27,2021-04-06 20:49:41,broughtj/Fin6470,active,,
|
||||||
Behavioural Economics,https://github.com/pcmichaud/notebooks,Behavioural Economics and Finance Python Notebooks.,Extended Research,2/3/21 7:22,9,4,1,12/20/18 0:21,3/26/19 11:51,pcmichaud/notebooks,inactive,,
|
Options,https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D,Introduction to options.,Derivatives and Hedging,2021-04-09 21:17:26,335.0,163.0,36.0,2017-07-28 15:48:29,2021-03-17 17:17:08,QuantConnect/Tutorials,active,,
|
||||||
Applied Corporate Finance,https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance,Studies the empirical behaviours in stock market.,Extended Research,2/19/21 13:40,8,9,1,1/29/18 5:14,7/19/18 6:25,chen-bowen/Data_Science_in_Applied_Corporate_Finance,inactive,,
|
Volatility and Variance Derivatives,https://github.com/yhilpisch/lvvd/tree/master/lvvd,Volatility derivatives analytics.,Derivatives and Hedging,2021-04-07 19:21:15,79.0,78.0,1.0,2016-10-21 04:12:50,2021-02-22 13:32:00,yhilpisch/lvvd,active,,
|
||||||
HFT,https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy,High frequency trading.,Extended Research,4/1/21 15:27,743,331,1,7/21/16 5:14,2/14/17 16:47,rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy,inactive,,
|
Reinforcement Learning,https://github.com/FinTechies/HedgingRL,Hedging portfolios with reinforcement learning.,Derivatives and Hedging,2021-01-20 08:12:13,16.0,9.0,1.0,2017-04-21 10:58:56,2017-08-02 21:41:06,FinTechies/HedgingRL,inactive,,
|
||||||
Critical Transitions,https://github.com/ryanholbrook/critical-transitions,Detecting critical transitions in financial networks with topological data analysis.,Extended Research,1/30/21 11:50,10,3,1,1/22/19 10:59,3/12/19 18:35,ryanholbrook/critical-transitions,inactive,,
|
Options,https://github.com/PHBS/2018.M1.ASP/tree/master/py,Black Scholes and Copula.,Derivatives and Hedging,,,,,,,PHBS/2018.M1.ASP,,,
|
||||||
Real Estate Property Fraud,https://github.com/aviroop1/Real_Estate_Property_Fraud,Unsupervised fraud detection model that can identify likely candidates of fraud.,Extended Research,,,,,,,aviroop1/Real_Estate_Property_Fraud,,,
|
Derivatives Python,https://github.com/yhilpisch/dawp/tree/master/python36,Derivative analytics with Python.,Derivatives and Hedging,2021-04-12 14:39:55,388.0,299.0,1.0,2015-07-09 12:27:29,2021-02-22 13:29:18,yhilpisch/dawp,active,,
|
||||||
Deep Portfolio,https://github.com/DLColumbia/DL_forFinance,Deep learning for finance Predict volume of bonds.,Extended Research,1/12/21 11:48,27,20,2,5/8/18 19:34,5/9/18 15:39,DLColumbia/DL_forFinance,inactive,,
|
Options Risk Measures,https://github.com/wanglouis49/risk_estimation,Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).,Derivatives and Hedging,2020-10-06 20:37:02,1.0,2.0,1.0,2016-04-29 03:51:25,2018-01-16 01:24:07,wanglouis49/risk_estimation,inactive,,
|
||||||
NLP Finance Papers,https://github.com/chenbowen184/Research_Documents_Curation_with_NLP,Curating quantitative finance papers using machine learning.,Extended Research,2/27/21 6:33,8,9,1,10/11/18 20:32,12/24/18 23:27,chen-bowen/Research_Documents_Curation_with_NLP,inactive,,
|
Delta Hedging,https://github.com/RobinsonGarcia/delta-hedging,Advanced derivatives.,Derivatives and Hedging,2021-02-27 08:48:27,3.0,2.0,1.0,2018-03-02 23:53:53,2018-07-17 23:32:23,RobinsonGarcia/delta-hedging,inactive,,
|
||||||
Simulation,https://github.com/chenbowen184/Computational_Finance,Investigating simulations as part of computational finance.,Extended Research,1/12/21 12:22,17,12,1,1/29/18 5:01,8/2/18 5:56,chen-bowen/Computational_Finance,inactive,,
|
Computational Derivatives,https://github.com/chenbowen184/Computational_Finance,Projects focusing on investigating simulations and computational techniques applied in finance.,Derivatives and Hedging,2021-01-12 12:22:31,17.0,12.0,1.0,2018-01-29 05:01:52,2018-08-02 05:56:49,chen-bowen/Computational_Finance,inactive,,
|
||||||
Liquidity and Momentum,https://github.com/mrefermat/quant_finance,Various factors and portfolio constructions.,Extended Research,3/30/21 0:09,31,15,1,8/11/18 22:59,11/12/19 4:49,mrefermat/quant_finance,active,,
|
Black Scholes,https://github.com/irajwani/numerical_methods_python/blob/master/black_scholes.ipynb,Options pricing.,Derivatives and Hedging,2020-10-06 20:36:29,1.0,2.0,0.0,2017-12-09 18:50:20,2018-07-09 09:48:36,irajwani/numerical_methods_python,inactive,,
|
||||||
Bayesian Finance I,https://github.com/AlexIoannides/pymc-stochastic-process/blob/master/bayes_stoch_proc_calib.ipynb,Stochastic Process Calibration using Bayesian Inference & Probabilistic Programs.,Extended Research,11/28/20 3:02,25,6,0,1/4/19 12:30,2/18/19 9:55,AlexIoannides/pymc-stochastic-process,inactive,,
|
Option Strategies,https://github.com/rstreppa/valuation-OptionStrategies,"Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.",Derivatives and Hedging,2021-02-27 08:50:16,2.0,3.0,1.0,2018-05-22 18:27:26,2018-05-22 18:30:24,rstreppa/valuation-OptionStrategies,inactive,,
|
||||||
Currency PCA,https://github.com/shanemulqueen/python-finance-pca/blob/master/FX_spots_w_PCA.ipynb,Forex spots PCA.,Extended Research,10/26/20 0:55,3,1,1,3/12/19 21:11,3/12/19 22:09,shanemulqueen/python-finance-pca,inactive,,
|
Commodity,https://github.com/felipessalvatore/fin2vec/blob/master/src/Commodity2BR.ipynb,Commodity influence over Brazilian stocks.,Extended Research,,,,,,,felipessalvatore/fin2vec,,,
|
||||||
Backtests,https://github.com/AlgoTraders/stock-analysis-engine,Trading data and algorithms.,Extended Research,4/3/21 21:19,615,163,3,9/16/18 20:00,9/5/20 13:01,AlgoTraders/stock-analysis-engine,active,,
|
High Frequency,https://github.com/cswaney/prickle,A Python toolkit for high-frequency trade research.,Extended Research,2021-03-22 02:19:15,24.0,17.0,2.0,2016-07-06 20:32:21,2018-06-09 10:53:51,cswaney/prickle,inactive,,
|
||||||
High Frequency,https://github.com/cswaney/prickle,A Python toolkit for high-frequency trade research.,Extended Research,3/22/21 2:19,24,17,2,7/6/16 20:32,6/9/18 10:53,cswaney/prickle,inactive,,
|
Bayesian Finance I,https://github.com/AlexIoannides/pymc-stochastic-process/blob/master/bayes_stoch_proc_calib.ipynb,Stochastic Process Calibration using Bayesian Inference & Probabilistic Programs.,Extended Research,2020-11-28 03:02:48,25.0,6.0,0.0,2019-01-04 12:30:41,2019-02-18 09:55:21,AlexIoannides/pymc-stochastic-process,inactive,,
|
||||||
Commodity,https://github.com/felipessalvatore/fin2vec/blob/master/src/Commodity2BR.ipynb,Commodity influence over Brazilian stocks.,Extended Research,,,,,,,felipessalvatore/fin2vec,,,
|
Currency PCA,https://github.com/shanemulqueen/python-finance-pca/blob/master/FX_spots_w_PCA.ipynb,Forex spots PCA.,Extended Research,2020-10-26 00:55:20,3.0,1.0,1.0,2019-03-12 21:11:29,2019-03-12 22:09:10,shanemulqueen/python-finance-pca,inactive,,
|
||||||
VaR GaN,https://github.com/hamaadshah/market_risk_gan_keras,Estimate Value-at-Risk for market risk management using Keras and TensorFlow.,Factor and Risk Analysis,3/20/21 21:53,41,28,1,8/6/18 16:09,11/22/20 19:02,hamaadshah/market_risk_gan_tensorflow,active,,
|
Liquidity and Momentum,https://github.com/mrefermat/quant_finance,Various factors and portfolio constructions.,Extended Research,2021-03-30 00:09:34,31.0,15.0,1.0,2018-08-11 22:59:53,2019-11-12 04:49:01,mrefermat/quant_finance,active,,
|
||||||
Pyfolio,https://github.com/quantopian/pyfolio,Portfolio and risk analytics in Python.,Factor and Risk Analysis,4/3/21 15:55,3647,1147,42,6/1/15 15:31,2/28/20 17:30,quantopian/pyfolio,active,,
|
Simulation,https://github.com/chenbowen184/Computational_Finance,Investigating simulations as part of computational finance.,Extended Research,2021-01-12 12:22:31,17.0,12.0,1.0,2018-01-29 05:01:52,2018-08-02 05:56:49,chen-bowen/Computational_Finance,inactive,,
|
||||||
Performance Analysis,https://github.com/quantopian/alphalens,Performance analysis of predictive (alpha) stock factors.,Factor and Risk Analysis,4/2/21 19:02,1839,693,17,6/3/16 21:49,4/27/20 18:40,quantopian/alphalens,active,,
|
NLP Finance Papers,https://github.com/chenbowen184/Research_Documents_Curation_with_NLP,Curating quantitative finance papers using machine learning.,Extended Research,2021-02-27 06:33:23,8.0,9.0,1.0,2018-10-11 20:32:37,2018-12-24 23:27:55,chen-bowen/Research_Documents_Curation_with_NLP,inactive,,
|
||||||
Python for Finance,https://github.com/yhilpisch/py4fi/tree/master/jupyter36,Various financial notebooks.,Factor and Risk Analysis,4/3/21 11:16,1297,792,1,12/15/14 11:23,7/10/18 6:38,yhilpisch/py4fi,inactive,,
|
Deep Portfolio,https://github.com/DLColumbia/DL_forFinance,Deep learning for finance Predict volume of bonds.,Extended Research,2021-01-12 11:48:27,27.0,19.0,2.0,2018-05-08 19:34:17,2018-05-09 15:39:25,DLColumbia/DL_forFinance,inactive,,
|
||||||
Convex Optimisation,https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb,Convex Optimization for Finance.,Factor and Risk Analysis,11/4/20 7:19,17,9,1,6/26/18 20:36,10/22/19 21:56,ssanderson/convex-optimization-for-finance,active,,
|
Real Estate Property Fraud,https://github.com/aviroop1/Real_Estate_Property_Fraud,Unsupervised fraud detection model that can identify likely candidates of fraud.,Extended Research,,,,,,,aviroop1/Real_Estate_Property_Fraud,,,
|
||||||
Statistical Finance,https://github.com/mrefermat/FinancePhD/tree/master/FinancialExperiments,Various financial experiments.,Factor and Risk Analysis,3/30/21 0:09,21,16,1,10/4/15 9:10,3/28/20 18:33,mrefermat/FinancePhD,active,,
|
Critical Transitions,https://github.com/ryanholbrook/critical-transitions,Detecting critical transitions in financial networks with topological data analysis.,Extended Research,2021-01-30 11:50:22,10.0,3.0,1.0,2019-01-22 10:59:50,2019-03-12 18:35:02,ryanholbrook/critical-transitions,inactive,,
|
||||||
VaR,https://github.com/willb/var-notebook/blob/master/var-notebook/var-pdfs.ipynb,Value-at-risk calculations.,Factor and Risk Analysis,3/31/21 2:06,10,9,1,11/15/16 19:24,1/14/17 21:19,willb/var-notebook,inactive,,
|
Computational Finance,https://github.com/lnsongxf/Applied_Computational_Economics_and_Finance,Applied Computational Economics and Finance.,Extended Research,2021-03-07 17:47:01,12.0,13.0,1.0,2017-08-27 03:46:33,2017-08-26 04:26:04,lnsongxf/Applied_Computational_Economics_and_Finance,inactive,,
|
||||||
Factor Analysis,https://github.com/garvit-kudesia91/factor_analysis/blob/master/Factor%20Analysis%20of%20Mutual%20Funds.ipynb,Factor analysis for mutual funds.,Factor and Risk Analysis,12/21/20 14:26,3,4,1,3/13/18 7:39,3/13/18 7:42,garvit-kudesia91/factor_analysis,inactive,,
|
Bayesian Finance,https://github.com/marketneutral/alphatools/blob/master/notebooks/pymc3-minimal.ipynb,Notebook PyMC3 implementation.,Extended Research,2021-04-10 19:50:47,233.0,55.0,1.0,2018-08-28 14:45:00,2020-08-06 22:03:47,marketneutral/alphatools,active,,
|
||||||
Risk and Return,https://github.com/PyDataBlog/Python-for-Data-Science/tree/master/Tutorials,Riskiness of portfolios and assets.,Factor and Risk Analysis,3/19/21 22:01,139,61,2,9/12/17 13:35,8/6/20 12:35,PyDataBlog/Python-for-Data-Science,active,,
|
Backtests,https://github.com/AlgoTraders/stock-analysis-engine,Trading data and algorithms.,Extended Research,2021-04-12 02:28:59,620.0,165.0,3.0,2018-09-16 20:00:36,2020-09-05 13:01:05,AlgoTraders/stock-analysis-engine,active,,
|
||||||
Risk Basic,https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%203%2C%20Risk.ipynb,Active portfolio risk management .,Factor and Risk Analysis,3/1/21 13:53,31,18,1,5/10/16 11:03,5/17/16 3:44,RJT1990/Active-Portfolio-Management-Notes,inactive,,
|
Corporate Finance,https://github.com/SeanMcOwen/FinanceAndPython.com-CorporateFinance,Basic corporate finance.,Extended Research,2021-01-16 19:01:31,9.0,4.0,1.0,2017-09-09 03:35:24,2017-09-09 23:04:48,SeanMcOwen/FinanceAndPython.com-CorporateFinance,inactive,,
|
||||||
Factor Analysis,https://github.com/alpha-miner/alpha-mind/tree/master/notebooks,Factor strategy notebooks.,Factor and Risk Analysis,3/12/21 22:42,171,59,3,5/1/17 7:36,2/9/21 9:36,alpha-miner/alpha-mind,active,,
|
Mathematical Finance,https://github.com/Auquan/Tutorials,Notebooks for math and financial tutorials.,Extended Research,2021-04-08 19:37:16,664.0,425.0,9.0,2017-01-21 11:24:18,2020-08-01 17:03:32,Auquan/Tutorials,active,,
|
||||||
Various Risk Measures,https://github.com/Jorgencr/Alternative-and-Responsible-Investments/blob/master/Final_masterfile.ipynb,Risk measures and factors for alternative and responsible investments.,Factor and Risk Analysis,11/4/20 7:04,4,5,1,8/7/17 14:44,8/8/17 22:52,Jorgencr/Alternative-and-Responsible-Investments,inactive,,
|
Economic Foundations,https://github.com/SeanMcOwen/FinanceAndPython.com-EconomicFoundations,Basic economic models.,Extended Research,2020-10-06 21:01:59,2.0,3.0,1.0,2017-05-25 02:27:36,2017-06-30 03:53:59,SeanMcOwen/FinanceAndPython.com-EconomicFoundations,inactive,,
|
||||||
CAPM,https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%202%2C%20CAPM.ipynb,Expected returns using CAPM.,Factor and Risk Analysis,3/1/21 13:53,31,18,1,5/10/16 11:03,5/17/16 3:44,RJT1990/Active-Portfolio-Management-Notes,inactive,,
|
Financial Economics,https://github.com/rsvp/fecon235/tree/master/nb,Financial Economics Models.,Extended Research,2021-04-10 17:02:20,713.0,275.0,2.0,2014-11-09 04:49:01,2018-12-03 16:30:28,rsvp/fecon235,inactive,,
|
||||||
Quant Finance,https://github.com/mrefermat/quant_finance,General quant repository.,Factor and Risk Analysis,3/30/21 0:09,31,15,1,8/11/18 22:59,11/12/19 4:49,mrefermat/quant_finance,active,,
|
Finance Graph Theory,https://github.com/AvijitGhosh82/Finance_Graph_Theory,Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.,Extended Research,2021-03-28 02:22:22,17.0,7.0,3.0,2018-08-02 02:48:24,2019-03-16 18:39:38,evijit/Finance_Graph_Theory,inactive,,
|
||||||
Vasicek,https://github.com/RobinsonGarcia/fixed-income/blob/master/2.0%20Vasicek%20-%20example.ipynb,Bootstrapping and interpolation.,Fixed Income,12/10/20 21:20,3,3,1,7/18/18 19:26,7/18/18 19:34,RobinsonGarcia/fixed-income,inactive,,
|
Market Crash Prediction,https://github.com/sarachmax/MarketCrashes_Prediction/blob/master/LPPL_Comparasion.ipynb,Predicting market crashes using an LPPL model.,Extended Research,2020-10-06 21:01:42,1.0,3.0,1.0,2019-01-24 13:37:45,2019-02-13 16:48:00,sarachmax/MarketCrashes_Prediction,inactive,,
|
||||||
Binomial Tree,https://github.com/hy-lei/math-finance-exercise,Utility functions in fixed income securities.,Fixed Income,10/6/20 20:55,1,2,1,2/2/19 8:44,5/3/19 17:16,hy-lei/math-finance-toolbox,active,,
|
M&A,https://github.com/atulram/Finance-and-Stocks,Mergers and Acquisitions.,Extended Research,2020-12-21 14:42:43,3.0,3.0,1.0,2019-01-19 18:16:47,2019-02-18 16:57:19,atulram/Finance-and-Stocks,inactive,,
|
||||||
Corporate Bonds,https://github.com/ishank011/gs-quantify-bond-prediction,Predicting the buying and selling volume of the corporate bonds.,Fixed Income,1/3/21 21:46,7,5,1,9/27/17 19:57,9/27/17 20:00,ishank011/gs-quantify-bond-prediction,inactive,,
|
Behavioural Economics,https://github.com/pcmichaud/notebooks,Behavioural Economics and Finance Python Notebooks.,Extended Research,2021-02-03 07:22:40,9.0,4.0,1.0,2018-12-20 00:21:38,2019-03-26 11:51:46,pcmichaud/notebooks,inactive,,
|
||||||
Trend Following,http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html,A futures trend following portfolio investment strategy.,Other Models,,,,,,,,,,
|
Life-cycle,https://github.com/atulram/Finance-and-Stocks/blob/master/CompanyLifeCycle.ipynb,Company life cycle.,Extended Research,2020-12-21 14:42:43,3.0,3.0,1.0,2019-01-19 18:16:47,2019-02-18 16:57:19,atulram/Finance-and-Stocks,inactive,,
|
||||||
Mixture Models I,https://github.com/BlackArbsCEO/Mixture_Models,Mixture models to predict market bottoms.,Other Models,3/2/21 19:44,31,31,1,3/20/17 18:54,4/25/17 23:35,BlackArbsCEO/Mixture_Models,inactive,,
|
Applied Corporate Finance,https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance,Studies the empirical behaviours in stock market.,Extended Research,2021-02-19 13:40:37,8.0,9.0,1.0,2018-01-29 05:14:52,2018-07-19 06:25:36,chen-bowen/Data_Science_in_Applied_Corporate_Finance,inactive,,
|
||||||
Mixture Models II,https://github.com/BlackArbsCEO/mixture_model_trading_public,Mixture models and stock trading.,Other Models,3/12/21 13:21,166,73,1,12/11/17 17:05,5/13/20 23:50,BlackArbsCEO/mixture_model_trading_public,active,,
|
HFT,https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy,High frequency trading.,Extended Research,2021-04-11 23:36:10,748.0,333.0,1.0,2016-07-21 05:14:14,2017-02-14 16:47:25,rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy,inactive,,
|
||||||
Fundamental LT Forecasts,https://github.com/Hvass-Labs/FinanceOps,Research in investment finance for long term forecasts.,Other Models,4/3/21 10:59,381,126,1,7/22/18 8:14,2/17/21 14:39,Hvass-Labs/FinanceOps,active,,
|
Factor Analysis,https://github.com/garvit-kudesia91/factor_analysis/blob/master/Factor%20Analysis%20of%20Mutual%20Funds.ipynb,Factor analysis for mutual funds.,Factor and Risk Analysis,2020-12-21 14:26:46,3.0,4.0,1.0,2018-03-13 07:39:20,2018-03-13 07:42:36,garvit-kudesia91/factor_analysis,inactive,,
|
||||||
Scikit-learn Stock Prediction,https://github.com/robertmartin8/MachineLearningStocks,Using python and scikit-learn to make stock predictions.,Other Models,4/3/21 11:22,922,344,2,2/12/17 4:50,2/4/21 3:48,robertmartin8/MachineLearningStocks,active,,
|
VaR GaN,https://github.com/hamaadshah/market_risk_gan_keras,Estimate Value-at-Risk for market risk management using Keras and TensorFlow.,Factor and Risk Analysis,2021-03-20 21:53:18,41.0,28.0,1.0,2018-08-06 16:09:44,2020-11-22 19:02:07,hamaadshah/market_risk_gan_tensorflow,active,,
|
||||||
Short-Term Movement Cues,https://github.com/anfederico/Clairvoyant,Identify social/historical cues for short term stock movement.,Other Models,3/31/21 20:19,2158,676,1,9/12/16 18:38,8/29/18 20:27,anfederico/clairvoyant,inactive,,
|
Pyfolio,https://github.com/quantopian/pyfolio,Portfolio and risk analytics in Python.,Factor and Risk Analysis,2021-04-12 11:55:41,3673.0,1157.0,42.0,2015-06-01 15:31:39,2020-02-28 17:30:19,quantopian/pyfolio,active,,
|
||||||
Financial Event Prediction using Machine Learning,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3481555,,Personal Papers,,,,,,,,,,
|
Performance Analysis,https://github.com/quantopian/alphalens,Performance analysis of predictive (alpha) stock factors.,Factor and Risk Analysis,2021-04-10 12:58:45,1847.0,700.0,17.0,2016-06-03 21:49:15,2020-04-27 18:40:41,quantopian/alphalens,active,,
|
||||||
Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization,https://jfds.pm-research.com/content/2/2/17,,Personal Papers,,,,,,,,,,
|
Python for Finance,https://github.com/yhilpisch/py4fi/tree/master/jupyter36,Various financial notebooks.,Factor and Risk Analysis,2021-04-09 08:12:10,1298.0,794.0,1.0,2014-12-15 11:23:34,2018-07-10 06:38:12,yhilpisch/py4fi,inactive,,
|
||||||
Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies,https://jfds.pm-research.com/content/2/1/10,,Personal Papers,,,,,,,,,,
|
Convex Optimisation,https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb,Convex Optimization for Finance.,Factor and Risk Analysis,2021-04-08 19:02:22,18.0,10.0,1.0,2018-06-26 20:36:47,2019-10-22 21:56:46,ssanderson/convex-optimization-for-finance,active,,
|
||||||
Machine Learning in Asset Management,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952,,Personal Papers,,,,,,,,,,
|
Statistical Finance,https://github.com/mrefermat/FinancePhD/tree/master/FinancialExperiments,Various financial experiments.,Factor and Risk Analysis,2021-03-30 00:09:28,21.0,16.0,1.0,2015-10-04 09:10:54,2020-03-28 18:33:58,mrefermat/FinancePhD,active,,
|
||||||
PyPortfolioOpt,https://github.com/robertmartin8/PyPortfolioOpt,"Financial portfolio optimisation, including classical efficient frontier and advanced methods.",Portfolio Selection and Optimisation,4/3/21 15:57,1875,478,16,5/29/18 13:30,2/25/21 13:01,robertmartin8/PyPortfolioOpt,active,,
|
VaR,https://github.com/willb/var-notebook/blob/master/var-notebook/var-pdfs.ipynb,Value-at-risk calculations.,Factor and Risk Analysis,2021-03-31 02:06:48,10.0,9.0,1.0,2016-11-15 19:24:17,2017-01-14 21:19:30,willb/var-notebook,inactive,,
|
||||||
DeepDow,https://github.com/jankrepl/deepdow,Portfolio optimization with deep learning.,Portfolio Selection and Optimisation,4/1/21 16:06,306,57,2,2/2/20 8:46,2/16/21 18:50,jankrepl/deepdow,active,,
|
Risk and Return,https://github.com/PyDataBlog/Python-for-Data-Science/tree/master/Tutorials,Riskiness of portfolios and assets.,Factor and Risk Analysis,2021-04-06 17:03:06,140.0,62.0,2.0,2017-09-12 13:35:09,2020-08-06 12:35:44,PyDataBlog/Python-for-Data-Science,active,,
|
||||||
Modern Portfolio Theory,https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb,Universal portfolios; modern portfolio theory.,Portfolio Selection and Optimisation,,,,,,,,,,
|
Risk Basic,https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%203%2C%20Risk.ipynb,Active portfolio risk management .,Factor and Risk Analysis,2021-03-01 13:53:42,31.0,18.0,1.0,2016-05-10 11:03:48,2016-05-17 03:44:56,RJT1990/Active-Portfolio-Management-Notes,inactive,,
|
||||||
OLMAR Algorithm,https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb,Relative importance of each component of the OLMAR algorithm.,Portfolio Selection and Optimisation,12/16/20 17:28,6,3,1,7/26/16 16:20,12/30/16 11:40,charlessutton/OLMAR,inactive,,
|
Factor Analysis,https://github.com/alpha-miner/alpha-mind/tree/master/notebooks,Factor strategy notebooks.,Factor and Risk Analysis,2021-04-08 19:02:25,172.0,60.0,3.0,2017-05-01 07:36:54,2021-04-07 15:25:27,alpha-miner/alpha-mind,active,,
|
||||||
Online Portfolio Selection,https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb,****Comparing OLPS algorithms on a diversified set of ETFs.,Portfolio Selection and Optimisation,,,,,,,,,,
|
Various Risk Measures,https://github.com/Jorgencr/Alternative-and-Responsible-Investments/blob/master/Final_masterfile.ipynb,Risk measures and factors for alternative and responsible investments.,Factor and Risk Analysis,2020-11-04 07:04:38,4.0,5.0,1.0,2017-08-07 14:44:32,2017-08-08 22:52:11,Jorgencr/Alternative-and-Responsible-Investments,inactive,,
|
||||||
401K Portfolio Optimisation,https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb,Portfolio analyses and optimisation for 401K.,Portfolio Selection and Optimisation,12/25/20 9:39,14,5,1,8/1/18 19:48,9/5/19 11:18,otosman/Python-for-Finance,active,,
|
CAPM,https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%202%2C%20CAPM.ipynb,Expected returns using CAPM.,Factor and Risk Analysis,2021-03-01 13:53:42,31.0,18.0,1.0,2016-05-10 11:03:48,2016-05-17 03:44:56,RJT1990/Active-Portfolio-Management-Notes,inactive,,
|
||||||
Policy Gradient Portfolio,https://github.com/ZhengyaoJiang/PGPortfolio,A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.,Portfolio Selection and Optimisation,3/31/21 9:42,1276,628,6,11/12/17 16:08,5/9/19 9:50,ZhengyaoJiang/PGPortfolio,active,,
|
Quant Finance,https://github.com/mrefermat/quant_finance,General quant repository.,Factor and Risk Analysis,2021-03-30 00:09:34,31.0,15.0,1.0,2018-08-11 22:59:53,2019-11-12 04:49:01,mrefermat/quant_finance,active,,
|
||||||
Deep Portfolio Theory,https://github.com/tcloaa/Deep-Portfolio-Theory,Autoencoder framework for portfolio selection.,Portfolio Selection and Optimisation,1/30/21 13:50,104,58,1,2/10/17 9:03,3/8/18 16:47,tcloaa/Deep-Portfolio-Theory,inactive,,
|
Vasicek,https://github.com/RobinsonGarcia/fixed-income/blob/master/2.0%20Vasicek%20-%20example.ipynb,Bootstrapping and interpolation.,Fixed Income,2020-12-10 21:20:03,3.0,3.0,1.0,2018-07-18 19:26:54,2018-07-18 19:34:48,RobinsonGarcia/fixed-income,inactive,,
|
||||||
Efficient Frontier,https://github.com/tthustla/efficient_frontier/blob/master/Efficient%20_Frontier_implementation.ipynb,Modern Portfolio Theory.,Portfolio Selection and Optimisation,3/30/21 0:01,104,57,1,2/17/18 8:19,2/27/18 13:16,tthustla/efficient_frontier,inactive,,
|
Corporate Bonds,https://github.com/ishank011/gs-quantify-bond-prediction,Predicting the buying and selling volume of the corporate bonds.,Fixed Income,2021-01-03 21:46:55,7.0,5.0,1.0,2017-09-27 19:57:13,2017-09-27 20:00:29,ishank011/gs-quantify-bond-prediction,inactive,,
|
||||||
Reinforcement Learning,https://github.com/filangel/qtrader,Reinforcement Learning for Portfolio Management.,Portfolio Selection and Optimisation,3/29/21 3:47,364,151,1,10/7/17 9:14,6/26/18 9:22,filangelos/qtrader,inactive,,
|
Binomial Tree,https://github.com/hy-lei/math-finance-exercise,Utility functions in fixed income securities.,Fixed Income,2020-10-06 20:55:18,1.0,2.0,1.0,2019-02-02 08:44:14,2019-05-03 17:16:52,hy-lei/math-finance-toolbox,active,,
|
||||||
Distribution Characteristic Optimisation,https://github.com/VivekPa/OptimalPortfolio,Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.,Portfolio Selection and Optimisation,3/18/21 22:35,229,82,3,11/16/18 12:20,7/4/19 1:41,VivekPa/OptimalPortfolio,active,,
|
Machine-Learning-for-Finance,https://github.com/PacktPublishing/Machine-Learning-for-Finance,NEW,Other Models,2021-04-08 16:54:52,180.0,122.0,4.0,2018-03-15 06:28:00,2021-01-14 15:58:03,PacktPublishing/Machine-Learning-for-Finance,active,,2021-04-12 16:39:24.593899
|
||||||
RL IV,https://github.com/jjakimoto/DQN,Reinforcement Learning for finance.,Deep Learning And Reinforcement Learning,3/25/21 19:14,140,55,1,10/21/16 2:47,4/7/17 8:11,jjakimoto/DQN,inactive,,
|
ML_Finance_Codes,https://github.com/mfrdixon/ML_Finance_Codes,NEW,Other Models,2021-04-11 08:30:21,250.0,104.0,3.0,2019-09-27 16:13:50,2020-06-13 21:20:26,mfrdixon/ML_Finance_Codes,active,,2021-04-12 16:39:24.593899
|
||||||
RL Trading,https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW,A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.,Deep Learning And Reinforcement Learning,,,,,,,,,4,
|
Awesome-Quant-Machine-Learning-Trading,https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading,NEW,Other Models,2021-04-10 13:38:56,1005.0,319.0,3.0,2018-11-05 21:09:06,2020-10-08 16:48:18,grananqvist/Awesome-Quant-Machine-Learning-Trading,active,,2021-04-12 16:39:24.593899
|
||||||
RL V,https://github.com/gstenger98/rl-finance,Building an Agent to Trade with Reinforcement Learning.,Deep Learning And Reinforcement Learning,1/3/21 4:36,32,7,5,1/16/19 0:43,3/19/20 20:28,gstenger98/rl-finance,active,2,
|
Machine-Learning-For-Finance,https://github.com/anthonyng2/Machine-Learning-For-Finance,NEW,Other Models,2021-04-01 20:11:59,205.0,119.0,1.0,2017-07-11 09:09:15,2018-02-21 05:36:35,anthonyng2/Machine-Learning-For-Finance,inactive,,2021-04-12 16:39:24.593899
|
||||||
RL,https://github.com/kh-kim/stock_market_reinforcement_learning,OpenGym with Deep Q-learning and Policy Gradient.,Deep Learning And Reinforcement Learning,4/1/21 14:04,713,299,1,10/4/16 14:42,12/23/16 7:34,kh-kim/stock_market_reinforcement_learning,inactive,2,
|
Stock.Indicators,https://github.com/DaveSkender/Stock.Indicators,NEW,Other Models,2021-04-12 10:47:32,175.0,64.0,9.0,2019-12-29 05:18:07,2021-04-11 19:17:17,DaveSkender/Stock.Indicators,active,,2021-04-12 16:39:24.593899
|
||||||
RL II,https://github.com/deependersingla/deep_trader,reinforcement learning on stock market and agent tries to learn trading.,Deep Learning And Reinforcement Learning,3/29/21 11:10,1340,490,3,6/11/16 7:27,1/22/18 14:35,deependersingla/deep_trader,inactive,3,
|
AlphaPy,https://github.com/ScottfreeLLC/AlphaPy,NEW,Other Models,2021-04-04 20:02:31,576.0,130.0,3.0,2016-02-14 00:47:32,2021-02-08 21:35:40,ScottfreeLLC/AlphaPy,active,,2021-04-12 16:39:24.593899
|
||||||
Pair Trading RL,https://github.com/shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading,Using deep actor-critic model to learn best strategies in pair trading.,Deep Learning And Reinforcement Learning,3/27/21 2:19,241,114,1,5/18/17 16:47,5/18/17 16:56,shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading,inactive,3,
|
mlfinlab,https://github.com/hudson-and-thames/mlfinlab,NEW,Other Models,2021-04-12 10:51:12,2295.0,709.0,3.0,2019-02-13 16:57:25,2021-04-12 10:50:54,hudson-and-thames/mlfinlab,active,,2021-04-12 16:39:24.593899
|
||||||
RL III,https://github.com/samre12/deep-trading-agent,Github -Deep Reinforcement Learning based Trading Agent for Bitcoin.,Deep Learning And Reinforcement Learning,4/3/21 20:48,576,204,1,9/21/17 17:05,4/13/18 16:33,samre12/deep-trading-agent,inactive,3,
|
Microservices-Based-Algorithmic-Trading-System,https://github.com/saeed349/Microservices-Based-Algorithmic-Trading-System,NEW,Other Models,2021-04-10 12:59:57,104.0,56.0,0.0,2020-01-06 00:21:58,2020-03-31 13:02:26,saeed349/Microservices-Based-Algorithmic-Trading-System,active,,2021-04-12 16:39:24.593899
|
||||||
Fund classification,https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb,Fund classification using text mining and NLP.,Textual,3/31/21 2:12,4,2,1,4/16/18 22:18,6/7/18 22:01,frechfrechfrech/Mutual-Fund-Market-Clusters,inactive,,
|
botflow,https://github.com/kkyon/botflow,NEW,Other Models,2021-03-31 10:56:44,1165.0,102.0,8.0,2018-08-20 03:13:31,2019-05-23 14:40:50,kkyon/botflow,active,,2021-04-12 16:39:24.593899
|
||||||
Financial Sentiment Analysis,https://github.com/EricHe98/Financial-Statements-Text-Analysis,"Sentiment, distance and proportion analysis for trading signals.",Textual,3/31/21 23:48,48,27,1,6/23/17 0:05,1/26/19 3:35,EricHe98/Financial-Statements-Text-Analysis,inactive,,
|
surpriver,https://github.com/tradytics/surpriver,NEW,Other Models,2021-04-12 12:27:29,1189.0,221.0,6.0,2020-08-30 07:56:22,2020-09-21 04:32:05,tradytics/surpriver,active,,2021-04-12 16:39:24.593899
|
||||||
NLP Event,https://github.com/yuriak/DLQuant,Applying Deep Learning and NLP in Quantitative Trading.,Textual,4/1/21 2:16,70,31,1,7/2/18 23:50,1/31/19 14:08,yuriak/DLQuant,inactive,,
|
finance_ml,https://github.com/jjakimoto/finance_ml,NEW,Other Models,2021-04-08 15:28:17,282.0,117.0,1.0,2018-06-29 21:21:17,2019-02-18 12:34:54,jjakimoto/finance_ml,inactive,,2021-04-12 16:39:24.593899
|
||||||
Financial Statement Sentiment,https://github.com/MAydogdu/TextualAnalysis,Extracting sentiment from financial statements using neural networks.,Textual,3/31/21 2:10,8,7,1,6/4/18 20:54,6/4/18 20:56,MAydogdu/TextualAnalysis,inactive,,
|
awesome-ai-in-finance,https://github.com/georgezouq/awesome-ai-in-finance,NEW,Other Models,2021-04-11 07:43:25,941.0,162.0,8.0,2018-08-29 02:07:02,2020-11-27 09:43:40,georgezouq/awesome-ai-in-finance,active,,2021-04-12 16:39:24.593899
|
||||||
Extensive NLP,https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb,Comprehensive NLP techniques for accounting research.,Textual,3/21/21 7:39,73,42,1,10/25/17 7:10,6/5/20 3:28,TiesdeKok/Python_NLP_Tutorial,active,,
|
Pattern-Recognition-for-Forex-Trading,https://github.com/PythonProgramming/Pattern-Recognition-for-Forex-Trading,NEW,Other Models,2021-04-05 03:23:46,173.0,91.0,1.0,2015-03-26 02:22:03,2015-03-26 02:33:51,PythonProgramming/Pattern-Recognition-for-Forex-Trading,inactive,,2021-04-12 16:39:24.593899
|
||||||
Accounting Anomalies,https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb,Using deep-learning frameworks to identify accounting anomalies.,Textual,3/31/21 2:11,107,51,2,5/24/17 12:36,8/7/19 21:47,GitiHubi/deepAI,active,,
|
mosquito,https://github.com/miro-ka/mosquito,NEW,Other Models,2021-04-12 09:44:40,220.0,44.0,2.0,2017-06-18 19:57:17,2021-03-14 22:22:00,miro-ka/mosquito,active,,2021-04-12 16:39:24.593899
|
||||||
Buzzwords,https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds,Return performance and mutual fund selection.,Textual,10/6/20 18:54,1,4,1,2/4/18 21:51,2/4/18 21:57,swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds,inactive,,
|
Machine-Learning-for-Algorithmic-Trading-Bots-with-Python,https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python,NEW,Other Models,2021-04-11 06:02:53,172.0,94.0,5.0,2018-12-06 11:35:08,2021-01-18 06:40:53,PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python,active,,2021-04-12 16:39:24.593899
|
||||||
Earning call transcripts,https://github.com/lin882/WebAnalyticsProject,Correlation between mutual fund investment decision and earning call transcripts.,Textual,12/17/20 8:24,3,3,1,12/30/17 8:56,1/11/18 2:11,lin882/WebAnalyticsProject,inactive,,
|
stock-trading-ml,https://github.com/yacoubb/stock-trading-ml,NEW,Other Models,2021-04-11 14:46:52,340.0,186.0,1.0,2019-10-10 09:44:02,2019-10-12 11:38:49,yacoubb/stock-trading-ml,active,,2021-04-12 16:39:24.593899
|
||||||
NLP,https://github.com/toamitesh/NLPinFinance,This project assembles a lot of NLP operations needed for finance domain.,Textual,,,,,,,toamitesh/NLPinFinance,,,
|
Machine-Learning-and-AI-in-Trading,https://github.com/PyPatel/Machine-Learning-and-AI-in-Trading,NEW,Other Models,2021-04-08 11:31:22,261.0,101.0,1.0,2017-08-30 06:14:59,2019-10-29 08:14:39,PyPatel/Machine-Learning-and-AI-in-Trading,active,,2021-04-12 16:39:24.593899
|
||||||
Industry Clustering,https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,Project to cluster industries according to financial attributes.,Unsupervised,10/6/20 18:51,4,5,1,7/21/17 2:12,7/23/17 2:53,SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,inactive,,
|
Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original,https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original,NEW,Other Models,2021-04-08 20:01:06,279.0,126.0,4.0,2019-11-15 08:51:40,2021-01-21 07:56:08,PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original,active,,2021-04-12 16:39:24.593899
|
||||||
Pairs Trading,https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb,Finding pairs with cluster analysis.,Unsupervised,3/8/21 11:01,78,36,0,9/5/17 19:19,9/27/17 20:42,marketneutral/pairs-trading-with-ML,inactive,,
|
Hands-On-Machine-Learning-for-Algorithmic-Trading,https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading,NEW,Other Models,2021-04-12 15:41:16,600.0,386.0,2.0,2019-05-07 11:04:25,2021-01-19 07:51:00,PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading,active,,2021-04-12 16:39:24.593899
|
||||||
VRA Stock Embedding,https://github.com/ml-hongkong/stock2vec,Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.,Unsupervised,10/20/20 11:05,32,12,1,6/21/17 4:47,6/21/17 4:51,ml-hongkong/stock2vec,inactive,,
|
CryptoBot,https://github.com/AdeelMufti/CryptoBot,NEW,Other Models,2021-03-25 09:17:42,234.0,94.0,1.0,2017-01-17 12:44:52,2017-01-17 12:48:17,AdeelMufti/CryptoBot,inactive,,2021-04-12 16:39:24.593899
|
||||||
PCA Pairs Trading,https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading,"PCA, Factor Returns, and trading strategies.",Unsupervised,,,,,,,joelQF/quant-finance,,,
|
MathAndScienceNotes,https://github.com/melling/MathAndScienceNotes,NEW,Other Models,2021-04-12 00:49:50,460.0,54.0,1.0,2016-03-11 19:13:00,2020-12-21 03:54:51,melling/MathAndScienceNotes,active,,2021-04-12 16:39:24.593899
|
||||||
Industry Clustering,https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,Clustering of industries.,Unsupervised,10/6/20 18:51,4,5,1,7/21/17 2:12,7/23/17 2:53,SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,inactive,,
|
fin-ml,https://github.com/tatsath/fin-ml,NEW,Other Models,2021-04-11 03:29:15,116.0,66.0,2.0,2020-05-10 00:25:56,2021-01-23 17:15:07,tatsath/fin-ml,active,,2021-04-12 16:39:24.593899
|
||||||
Fund Clusters,https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb,Data exploration of fund clusters.,Unsupervised,3/31/21 2:12,4,2,1,4/16/18 22:18,6/7/18 22:01,frechfrechfrech/Mutual-Fund-Market-Clusters,inactive,,
|
Trend Following,http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html,A futures trend following portfolio investment strategy.,Other Models,,,,,,,,,,
|
||||||
|
Mixture Models I,https://github.com/BlackArbsCEO/Mixture_Models,Mixture models to predict market bottoms.,Other Models,2021-03-02 19:44:01,31.0,31.0,1.0,2017-03-20 18:54:24,2017-04-25 23:35:20,BlackArbsCEO/Mixture_Models,inactive,,
|
||||||
|
Mixture Models II,https://github.com/BlackArbsCEO/mixture_model_trading_public,Mixture models and stock trading.,Other Models,2021-03-12 13:21:17,166.0,73.0,1.0,2017-12-11 17:05:38,2020-05-13 23:50:47,BlackArbsCEO/mixture_model_trading_public,active,,
|
||||||
|
Fundamental LT Forecasts,https://github.com/Hvass-Labs/FinanceOps,Research in investment finance for long term forecasts.,Other Models,2021-04-05 23:36:04,383.0,127.0,1.0,2018-07-22 08:14:46,2021-02-17 14:39:30,Hvass-Labs/FinanceOps,active,,
|
||||||
|
Scikit-learn Stock Prediction,https://github.com/robertmartin8/MachineLearningStocks,Using python and scikit-learn to make stock predictions.,Other Models,2021-04-11 10:00:39,931.0,347.0,2.0,2017-02-12 04:50:44,2021-02-04 03:48:33,robertmartin8/MachineLearningStocks,active,,
|
||||||
|
Short-Term Movement Cues,https://github.com/anfederico/Clairvoyant,Identify social/historical cues for short term stock movement.,Other Models,2021-04-12 13:11:07,2166.0,678.0,1.0,2016-09-12 18:38:17,2018-08-29 20:27:19,anfederico/clairvoyant,inactive,,
|
||||||
|
Speculator,https://github.com/amicks/Speculator,NEW,Other Models,2021-03-15 16:27:16,101.0,31.0,2.0,2017-09-03 17:43:03,2018-09-12 18:58:38,amicks/Speculator,inactive,,2021-04-12 16:39:24.593899
|
||||||
|
Machine Learning in Asset Management,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952,,Personal Papers,,,,,,,,,,
|
||||||
|
Financial Event Prediction using Machine Learning,https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3481555,,Personal Papers,,,,,,,,,,
|
||||||
|
Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization,https://jfds.pm-research.com/content/2/2/17,,Personal Papers,,,,,,,,,,
|
||||||
|
Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies,https://jfds.pm-research.com/content/2/1/10,,Personal Papers,,,,,,,,,,
|
||||||
|
401K Portfolio Optimisation,https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb,Portfolio analyses and optimisation for 401K.,Portfolio Selection and Optimisation,2020-12-25 09:39:33,14.0,5.0,1.0,2018-08-01 19:48:24,2019-09-05 11:18:56,otosman/Python-for-Finance,active,,
|
||||||
|
Modern Portfolio Theory,https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb,Universal portfolios; modern portfolio theory.,Portfolio Selection and Optimisation,,,,,,,,,,
|
||||||
|
Deep Portfolio Theory,https://github.com/tcloaa/Deep-Portfolio-Theory,Autoencoder framework for portfolio selection.,Portfolio Selection and Optimisation,2021-04-06 11:47:13,105.0,57.0,1.0,2017-02-10 09:03:08,2018-03-08 16:47:00,tcloaa/Deep-Portfolio-Theory,inactive,,
|
||||||
|
Efficient Frontier,https://github.com/tthustla/efficient_frontier/blob/master/Efficient%20_Frontier_implementation.ipynb,Modern Portfolio Theory.,Portfolio Selection and Optimisation,2021-03-30 00:01:39,104.0,57.0,1.0,2018-02-17 08:19:46,2018-02-27 13:16:57,tthustla/efficient_frontier,inactive,,
|
||||||
|
Policy Gradient Portfolio,https://github.com/ZhengyaoJiang/PGPortfolio,A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.,Portfolio Selection and Optimisation,2021-04-09 10:41:40,1281.0,629.0,6.0,2017-11-12 16:08:44,2019-05-09 09:50:18,ZhengyaoJiang/PGPortfolio,active,,
|
||||||
|
Distribution Characteristic Optimisation,https://github.com/VivekPa/OptimalPortfolio,Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.,Portfolio Selection and Optimisation,2021-04-12 13:10:31,232.0,82.0,3.0,2018-11-16 12:20:25,2019-07-04 01:41:46,VivekPa/OptimalPortfolio,active,,
|
||||||
|
DeepDow,https://github.com/jankrepl/deepdow,Portfolio optimization with deep learning.,Portfolio Selection and Optimisation,2021-04-07 06:57:39,311.0,58.0,2.0,2020-02-02 08:46:33,2021-02-16 18:50:53,jankrepl/deepdow,active,,
|
||||||
|
PyPortfolioOpt,https://github.com/robertmartin8/PyPortfolioOpt,"Financial portfolio optimisation, including classical efficient frontier and advanced methods.",Portfolio Selection and Optimisation,2021-04-12 11:54:54,1895.0,479.0,16.0,2018-05-29 13:30:30,2021-02-25 13:01:56,robertmartin8/PyPortfolioOpt,active,,
|
||||||
|
Online Portfolio Selection,https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb,****Comparing OLPS algorithms on a diversified set of ETFs.,Portfolio Selection and Optimisation,,,,,,,,,,
|
||||||
|
Reinforcement Learning,https://github.com/filangel/qtrader,Reinforcement Learning for Portfolio Management.,Portfolio Selection and Optimisation,2021-03-29 03:47:07,364.0,150.0,1.0,2017-10-07 09:14:33,2018-06-26 09:22:27,filangelos/qtrader,inactive,,
|
||||||
|
OLMAR Algorithm,https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb,Relative importance of each component of the OLMAR algorithm.,Portfolio Selection and Optimisation,2021-04-08 19:07:41,7.0,4.0,1.0,2016-07-26 16:20:10,2016-12-30 11:40:53,charlessutton/OLMAR,inactive,,
|
||||||
|
NLP Event,https://github.com/yuriak/DLQuant,Applying Deep Learning and NLP in Quantitative Trading.,Textual,2021-04-01 02:16:38,70.0,31.0,1.0,2018-07-02 23:50:52,2019-01-31 14:08:20,yuriak/DLQuant,inactive,,
|
||||||
|
Fund classification,https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb,Fund classification using text mining and NLP.,Textual,2021-03-31 02:12:50,4.0,2.0,1.0,2018-04-16 22:18:55,2018-06-07 22:01:32,frechfrechfrech/Mutual-Fund-Market-Clusters,inactive,,
|
||||||
|
Financial Sentiment Analysis,https://github.com/EricHe98/Financial-Statements-Text-Analysis,"Sentiment, distance and proportion analysis for trading signals.",Textual,2021-03-31 23:48:48,48.0,27.0,1.0,2017-06-23 00:05:49,2019-01-26 03:35:55,EricHe98/Financial-Statements-Text-Analysis,inactive,,
|
||||||
|
Financial Statement Sentiment,https://github.com/MAydogdu/TextualAnalysis,Extracting sentiment from financial statements using neural networks.,Textual,2021-03-31 02:10:58,8.0,7.0,1.0,2018-06-04 20:54:14,2018-06-04 20:56:02,MAydogdu/TextualAnalysis,inactive,,
|
||||||
|
Extensive NLP,https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb,Comprehensive NLP techniques for accounting research.,Textual,2021-03-21 07:39:02,73.0,42.0,1.0,2017-10-25 07:10:26,2020-06-05 03:28:46,TiesdeKok/Python_NLP_Tutorial,active,,
|
||||||
|
Accounting Anomalies,https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb,Using deep-learning frameworks to identify accounting anomalies.,Textual,2021-04-12 07:47:50,110.0,51.0,2.0,2017-05-24 12:36:38,2019-08-07 21:47:08,GitiHubi/deepAI,active,,
|
||||||
|
Buzzwords,https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds,Return performance and mutual fund selection.,Textual,2020-10-06 18:54:58,1.0,4.0,1.0,2018-02-04 21:51:16,2018-02-04 21:57:09,swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds,inactive,,
|
||||||
|
Earning call transcripts,https://github.com/lin882/WebAnalyticsProject,Correlation between mutual fund investment decision and earning call transcripts.,Textual,2020-12-17 08:24:20,3.0,3.0,1.0,2017-12-30 08:56:03,2018-01-11 02:11:11,lin882/WebAnalyticsProject,inactive,,
|
||||||
|
NLP,https://github.com/toamitesh/NLPinFinance,This project assembles a lot of NLP operations needed for finance domain.,Textual,,,,,,,toamitesh/NLPinFinance,,,
|
||||||
|
Industry Clustering,https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,Project to cluster industries according to financial attributes.,Unsupervised,2020-10-06 18:51:22,4.0,5.0,1.0,2017-07-21 02:12:51,2017-07-23 02:53:37,SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,inactive,,
|
||||||
|
Pairs Trading,https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb,Finding pairs with cluster analysis.,Unsupervised,2021-04-04 17:55:35,79.0,36.0,0.0,2017-09-05 19:19:19,2017-09-27 20:42:14,marketneutral/pairs-trading-with-ML,inactive,,
|
||||||
|
VRA Stock Embedding,https://github.com/ml-hongkong/stock2vec,Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.,Unsupervised,2020-10-20 11:05:55,32.0,12.0,1.0,2017-06-21 04:47:14,2017-06-21 04:51:13,ml-hongkong/stock2vec,inactive,,
|
||||||
|
PCA Pairs Trading,https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading,"PCA, Factor Returns, and trading strategies.",Unsupervised,,,,,,,joelQF/quant-finance,,,
|
||||||
|
Industry Clustering,https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,Clustering of industries.,Unsupervised,2020-10-06 18:51:22,4.0,5.0,1.0,2017-07-21 02:12:51,2017-07-23 02:53:37,SeanMcOwen/FinanceAndPython.com-ClusteringIndustries,inactive,,
|
||||||
|
Fund Clusters,https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb,Data exploration of fund clusters.,Unsupervised,2021-03-31 02:12:50,4.0,2.0,1.0,2018-04-16 22:18:55,2018-06-07 22:01:32,frechfrechfrech/Mutual-Fund-Market-Clusters,inactive,,
|
||||||
|
|||||||
|
Reference in New Issue
Block a user