Awesome
Future of the Community
Two things:
- We have decided to start a Slack group (invite) & a website ML-Quant see a screenshot of the website below. https://www.ml-quant.com/
Financial Machine Learning and Data Science
A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.
A listed repository should be deprecated if:
- Repository's owner explicitly say that "this library is not maintained".
- Not committed for long time (2~3 years).
This repo is officially under revamp as of 3/29/2021!!
- TODOs and roadmap is under the github project here
- If you would like to contribute to this repo, please send us a pull request or contact @dereknow or @bin-yang-algotune
- Join us in the gitter chat here
- All repos/links status including last commit date is updated daily
- Only 15 Highest ranked repos/links for each section are displayed on main README.md and full list is available within the wiki page
- Both Wikis/README.md is updated in realtime as soon as new information are pushed to the repo
Trading
Deep Learning & Reinforcement Learning (Wiki)
<!-- [PLACEHOLDER_START:deep_learning_and_reinforcement_learning] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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<sub>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)</sub> | <sub>2017-12-18 10:49:59</sub> | <sub>2021-01-05 10:31:50</sub> | <sub>4635.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
<sub>AI Trading</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>3200.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
<sub>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 use 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>2021-12-11 08:01:50</sub> | <sub>2982.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
<sub>Deep Learning IV</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>1582.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
<sub>RLTrader</sub> | <sub>predecessor to tensortrade uses open api gym and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using Optuna etc.</sub> | <sub>2019-04-27 18:35:15</sub> | <sub>2019-10-17 16:25:49</sub> | <sub>1463.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
<sub>Deep Learning III</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>1307.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
<sub>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 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>1179.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
<sub>RL Trading</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</sub> | <sub>Part of FinRL and provided code for paper deep reinformacement learning for automated stock trading focuses on ensemble.</sub> | <sub>2020-07-26 13:12:53</sub> | <sub>2021-01-21 18:11:59</sub> | <sub>928.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
<sub>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>781.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
<sub>Neural Network</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>562.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
<sub>Deep Learning</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>439.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
<sub>LTSM Recurrent</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>1336.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
<sub>RL III</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>627.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
<sub>crypto-rl</sub> | <sub>Retrieve limit order book level data from coinbase pro and bitfinex -> record in arctic timeseries database then implemented trend following strategies (market orders) and market making (limit orders). Uses reinforcement learning (DQN) keras-rl to create agents and uses openai gym to implement POMDP (partially observable markov decision process)</sub> | <sub>2018-06-21 01:06:01</sub> | <sub>2021-11-30 13:52:18</sub> | <sub>475.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
Other Models (Wiki)
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<sub>Hands-On-Machine-Learning-for-Algorithmic-Trading</sub> | <sub>repo for book hands-on-machine learning for algorithmic trading covering topic from data/unsupervised learning/NPL/RNN & CNN/reinforcement learning etc. Leverage zipline/alphalens/sklearn/openai-gym etc as well. Good references to have</sub> | <sub>2019-05-07 11:04:25</sub> | <sub>2021-01-19 07:51:00</sub> | <sub>760.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
<sub>Microservices-Based-Algorithmic-Trading-System</sub> | <sub>docker based platfrom for developing algo trading strategies. Very interesting combinations of open source components were used including backtrader for backtest strategies / mlflow for managing the machine learning model life cycle (i.e. training and developing machine learning models) / airflow used as workflow management including schedule data download etc. / superset web data visualization tool similar to tableau / minio for fast object storage (i.e. storing saved models and model artifacts) / postgresql used to store security master and daily and minute data. Also contains some details on deployment on cloud</sub> | <sub>2020-01-06 00:21:58</sub> | <sub>2021-05-29 18:07:29</sub> | <sub>180.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
<sub>Awesome-Quant-Machine-Learning-Trading</sub> | <sub>curated list of books/online courses/youtube videos/blogs/interviews/papers/code etc. Updates are pretty infrequent</sub> | <sub>2018-11-05 21:09:06</sub> | <sub>2020-10-08 16:48:18</sub> | <sub>1278.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
<sub>AlphaPy</sub> | <sub>machine learning framework built on sklearn and pandas. Support pyfolio/xgboost/lightgmb/catboost(gradient boosting on decision tress) etc. Examples include financial market prediction/sports prediction/kaggle. Configurations are set though yaml file for all model process including feature selection/grid search on parameters and aggregate results for each model</sub> | <sub>2016-02-14 00:47:32</sub> | <sub>2021-10-23 07:17:16</sub> | <sub>672.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
<sub>Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original</sub> | <sub>official repo for machine learning for algorithmic trading book. Covering topics including backtesting/boosting/nlp/deep&reinforcement learning. Leverage open source libraries including backtrader zipline and talib</sub> | <sub>2019-11-15 08:51:40</sub> | <sub>2021-01-21 07:56:08</sub> | <sub>443.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
<sub>fin-ml</sub> | <sub>accompanying materials for book Machine Learning and Data Science Blueprints for Finance on top of basic machine learning models i.e. nlp/reinforcement learning/supervised & unsupervised learning it covers wider topics including robo-advisors/fraud detection/loan default/derivative pricing/yield curve construction.</sub> | <sub>2020-05-10 00:25:56</sub> | <sub>2021-01-23 17:15:07</sub> | <sub>207.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
<sub>Fundamental LT Forecasts</sub> | <sub>Research in investment finance for long term forecasts and a curated list of notebooks. Each topic contains a youtube video explaining in details. Interesting topics including using price per book ratio and other multiples for future return prediction and portfolio optimization. data sourced form simfin yahoo finance and s&p 500 earnings and estimate report etc.</sub> | <sub>2018-07-22 08:14:46</sub> | <sub>2021-11-05 10:58:02</sub> | <sub>507.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
<sub>MathAndScienceNotes</sub> | <sub>Collections of news/articles on various topics including quant trading and machine learning. Some articles are from ycombinator message board and rediit algotrading forum</sub> | <sub>2016-03-11 19:13:00</sub> | <sub>2020-12-21 03:54:51</sub> | <sub>467.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
<sub>stock-trading-ml</sub> | <sub>lstm model using keras to predict msft prices. Data is from alphavantage which provides some free data through web services. Showing how to use concatenation layer to join timeseries data with TA data. Might be abit of overfitting on the model though</sub> | <sub>2019-10-10 09:44:02</sub> | <sub>2019-10-12 11:38:49</sub> | <sub>422.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
<sub>Stock.Indicators</sub> | <sub>list of technical indicators implemented in c#. Full list and explanation available here. This list contains several indicators that ta-lib does not cover</sub> | <sub>2019-12-29 05:18:07</sub> | <sub>2021-12-03 04:33:54</sub> | <sub>349.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
<sub>ML_Finance_Codes</sub> | <sub>accompanying materials for book Machine Learning in Finance covering probabilistic modeling/sequence modeling/neural networks/reinforcement learning etc.</sub> | <sub>2019-09-27 16:13:50</sub> | <sub>2020-06-13 21:20:26</sub> | <sub>342.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
<sub>mlfinlab</sub> | <sub>open source library maintained by hudson and thames though much of the content has moved to a subscription model. Idea is to implement academic research in python code and aggregate it as a package. Sources from Journal of financial data science / journal of portfolio management / journal of algorithmic finance / cambridge university press</sub> | <sub>2019-02-13 16:57:25</sub> | <sub>2021-12-01 08:04:50</sub> | <sub>2664.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
<sub>mosquito</sub> | <sub>base framework trading bot for crypto. Stores data in local mongodb instance and supports backtest and live trading on poloniex and bittrex which are 12-15th ranked crypto exchanges by volume. Leverage talib for ta data and plotly for visualization</sub> | <sub>2017-06-18 19:57:17</sub> | <sub>2021-10-03 22:11:01</sub> | <sub>245.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
<sub>Machine-Learning-for-Algorithmic-Trading-Bots-with-Python</sub> | <sub>code repo for machine learning for algorithmic trading bots video series. Contains notebooks and deep dive using zipline</sub> | <sub>2018-12-06 11:35:08</sub> | <sub>2021-01-18 06:40:53</sub> | <sub>233.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
<sub>Short-Term Movement Cues</sub> | <sub>Identify social/historical cues for short term stock movement. Sklearn SVM model is used and good visualization coded in matplotlib</sub> | <sub>2016-09-12 18:38:17</sub> | <sub>2021-06-24 15:43:54</sub> | <sub>2243.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x3</sub> |
Data Processing Techniques and Transformations (Wiki)
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<sub>Advanced ML</sub> | <sub>Exercises to book advances in financial machine learning. Relevant topics include data cleaning and outlier detection (using MAD)</sub> | <sub>2018-04-25 17:22:40</sub> | <sub>2020-01-16 17:25:41</sub> | <sub>1124.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
<sub>Twitter-Trends</sub> | <sub>sentiment analysis baed on twitter data. Relevant topics include data cleaning/tokenization/data aggregation using mangodb etc.</sub> | <sub>2017-05-22 17:07:45</sub> | <sub>2017-05-23 08:06:27</sub> | <sub>74.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
<sub>Google-Finance-Stock-Data-Analysis</sub> | <sub>data processing platform which stream data from kafka. The example shows two incoming data stream stock vs tweets and two spark streams are created to consume the kafka data then end results are stored in cassandra. Older tech stacks were used and not actively maintained.</sub> | <sub>2017-07-23 02:59:59</sub> | <sub>2017-07-23 03:10:35</sub> | <sub>71.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x3</sub> |
<sub>finserv-application-blueprint</sub> | <sub>generate streamable data using mapr converged data platfrom built mostly in java. Uses apache zepplin for web visualization </sub> | <sub>2016-09-26 19:42:54</sub> | <sub>2021-06-07 17:38:13</sub> | <sub>76.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x2</sub> |
<sub>cointrader</sub> | <sub>java based platform for trading crypto. Relevant sections including using esper event queries to transform data and place orders</sub> | <sub>2014-06-01 01:14:12</sub> | <sub>2021-10-05 18:44:36</sub> | <sub>381.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x2</sub> |
<sub>CryptoNets</sub> | <sub>CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted.</sub> | <sub>2019-06-02 05:48:39</sub> | <sub>2019-09-12 13:03:05</sub> | <sub>179.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x2</sub> |
Portfolio Management
Portfolio Selection and Optimisation (Wiki)
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<sub>Modern Portfolio Theory</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</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</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>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Riskfolio-Lib</sub> | <sub>NEW</sub> | <sub>2020-03-02 19:49:06</sub> | <sub>2021-10-11 04:31:03</sub> | <sub>791.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>DeepDow</sub> | <sub>Portfolio optimization with deep learning.</sub> | <sub>2020-02-02 08:46:33</sub> | <sub>2021-07-09 14:59:21</sub> | <sub>446.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Reinforcement Learning</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>384.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Distribution Characteristic Optimisation</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>2021-10-10 11:03:23</sub> | <sub>273.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>PyPortfolioOpt</sub> | <sub>Financial portfolio optimisation, including classical efficient frontier and advanced methods.</sub> | <sub>2018-05-29 13:30:30</sub> | <sub>2021-10-19 20:54:46</sub> | <sub>2492.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>riskparity.py</sub> | <sub>NEW</sub> | <sub>2019-07-13 21:30:55</sub> | <sub>2021-06-10 12:25:08</sub> | <sub>167.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>401K Portfolio Optimisation</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>15.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Policy Gradient Portfolio</sub> | <sub>A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.</sub> | <sub>2017-11-12 16:08:44</sub> | <sub>2021-07-30 15:03:59</sub> | <sub>1419.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Efficient Frontier</sub> | <sub>Modern Portfolio Theory.</sub> | <sub>2018-02-17 08:19:46</sub> | <sub>2018-02-27 13:16:57</sub> | <sub>121.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>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>111.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>node-finance</sub> | <sub>NEW</sub> | <sub>2011-09-17 17:49:56</sub> | <sub>2021-04-05 08:01:12</sub> | <sub>106.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
Factor and Risk Analysis (Wiki)
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<sub>Long-Term-Stock-Price-Growth-Prediction-using-NLP-on-10-K-Financial-Reports</sub> | <sub>NEW</sub> | <sub>2019-12-21 07:14:30</sub> | <sub>2020-07-06 11:21:13</sub> | <sub>9.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>stock-market-analysis-using-python-numpy-pandas</sub> | <sub>NEW</sub> | <sub>2018-04-10 05:15:49</sub> | <sub>2018-04-10 05:28:54</sub> | <sub>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>TradeFinexLive</sub> | <sub>NEW</sub> | <sub>2018-03-21 10:05:22</sub> | <sub>2021-08-16 11:40:03</sub> | <sub>7.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>SafetyAndTrade</sub> | <sub>NEW</sub> | <sub>2020-04-11 20:18:03</sub> | <sub>2020-04-12 17:00:36</sub> | <sub>6.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>VaR GaN</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>50.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Empirical-Method-in-Finance</sub> | <sub>NEW</sub> | <sub>2021-01-13 23:48:30</sub> | <sub>2021-01-13 23:57:32</sub> | <sub>5.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Various Risk Measures</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>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>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>4153.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Factor Analysis</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>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>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>34.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Quantropy</sub> | <sub>NEW</sub> | <sub>2020-06-13 15:34:25</sub> | <sub>2021-03-15 01:49:23</sub> | <sub>34.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>CAPM</sub> | <sub>Expected returns using CAPM.</sub> | <sub>2016-05-10 11:03:48</sub> | <sub>2016-05-17 03:44:56</sub> | <sub>33.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Risk Basic</sub> | <sub>Active portfolio risk management .</sub> | <sub>2016-05-10 11:03:48</sub> | <sub>2016-05-17 03:44:56</sub> | <sub>33.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Statistical Finance</sub> | <sub>Various financial experiments.</sub> | <sub>2015-10-04 09:10:54</sub> | <sub>2020-03-28 18:33:58</sub> | <sub>23.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Performance Analysis</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>2119.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
Techniques
Unsupervised (Wiki)
<!-- [PLACEHOLDER_START:unsupervised] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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<sub>PCA Pairs 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</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>95.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Fund Clusters</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>6.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Industry Clustering</sub> | <sub>Clustering of industries.</sub> | <sub>2017-07-21 02:12:51</sub> | <sub>2017-07-23 02:53:37</sub> | <sub>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Industry Clustering</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>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>all-classification-templetes-for-ML</sub> | <sub>NEW</sub> | <sub>2020-05-05 10:28:52</sub> | <sub>2020-05-05 10:30:32</sub> | <sub>44.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Eigen-Portfolio</sub> | <sub>NEW</sub> | <sub>2018-09-05 05:29:18</sub> | <sub>2020-04-09 21:40:04</sub> | <sub>41.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Stock_Support_Resistance_ML</sub> | <sub>NEW</sub> | <sub>2019-12-22 20:25:48</sub> | <sub>2021-05-02 04:25:21</sub> | <sub>37.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>VRA Stock Embedding</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>34.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>hmm_market_behavior</sub> | <sub>NEW</sub> | <sub>2019-09-08 17:37:39</sub> | <sub>2020-05-10 14:36:03</sub> | <sub>28.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>AnomalyDetectionOnRisk</sub> | <sub>NEW</sub> | <sub>2018-05-31 15:53:02</sub> | <sub>2018-05-31 16:18:28</sub> | <sub>12.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Credit-Card-Fraud-Detection</sub> | <sub>NEW</sub> | <sub>2019-03-31 05:33:17</sub> | <sub>2019-03-31 05:38:43</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
Textual (Wiki)
<!-- [PLACEHOLDER_START:textual] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>NLP</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>Extensive NLP</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>86.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>NLP Event</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>77.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Fund classification</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>6.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Financial Sentiment 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>54.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Earning call transcripts</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>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>awesome-financial-nlp</sub> | <sub>NEW</sub> | <sub>2019-10-03 03:53:20</sub> | <sub>2020-02-01 08:28:16</sub> | <sub>264.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>news-emotion</sub> | <sub>NEW</sub> | <sub>2017-09-14 02:59:03</sub> | <sub>2018-06-11 13:47:51</sub> | <sub>262.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>FinNLP-Progress</sub> | <sub>NEW</sub> | <sub>2020-05-21 09:59:56</sub> | <sub>2021-09-30 08:26:25</sub> | <sub>222.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Buzzwords</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>2.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>financial-news-dataset</sub> | <sub>NEW</sub> | <sub>2016-08-23 13:29:07</sub> | <sub>2021-03-04 06:34:24</sub> | <sub>160.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>FinBERT</sub> | <sub>NEW</sub> | <sub>2019-07-09 16:34:27</sub> | <sub>2020-05-19 02:02:20</sub> | <sub>156.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Accounting Anomalies</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>130.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Financial Statement Sentiment</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>13.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>BDCI2019-Negative_Finance_Info_Judge</sub> | <sub>NEW</sub> | <sub>2019-12-27 03:49:31</sub> | <sub>2020-12-04 03:38:57</sub> | <sub>115.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
Other Assets
Derivatives and Hedging (Wiki)
<!-- [PLACEHOLDER_START:derivatives_and_hedging] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>Options</sub> | <sub>Black Scholes and Copula.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Volatility and Variance Derivatives</sub> | <sub>Volatility derivatives analytics.</sub> | <sub>2016-10-21 04:12:50</sub> | <sub>2021-05-15 10:12:38</sub> | <sub>91.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>FinanceDatabase</sub> | <sub>NEW</sub> | <sub>2021-01-28 18:36:09</sub> | <sub>2021-12-03 12:10:52</sub> | <sub>900.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Derivative Markets</sub> | <sub>The economics of futures, futures, options, and swaps.</sub> | <sub>2016-02-09 05:30:27</sub> | <sub>2021-04-15 16:02:59</sub> | <sub>9.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>gs-quant</sub> | <sub>NEW</sub> | <sub>2018-12-14 21:10:40</sub> | <sub>2021-12-02 14:00:47</sub> | <sub>890.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>tda-api</sub> | <sub>NEW</sub> | <sub>2020-04-03 21:19:12</sub> | <sub>2021-12-05 03:01:16</sub> | <sub>881.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>wallstreet</sub> | <sub>NEW</sub> | <sub>2016-01-20 22:03:39</sub> | <sub>2021-07-09 21:03:50</sub> | <sub>723.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>FinancePy</sub> | <sub>NEW</sub> | <sub>2019-10-27 15:04:56</sub> | <sub>2021-12-03 18:58:50</sub> | <sub>689.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Strata</sub> | <sub>NEW</sub> | <sub>2014-06-16 11:45:55</sub> | <sub>2021-12-09 18:16:26</sub> | <sub>650.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>algotrader</sub> | <sub>NEW</sub> | <sub>2018-04-10 02:31:26</sub> | <sub>2020-08-27 08:16:44</sub> | <sub>521.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Hull White</sub> | <sub>Callable Bond, Hull White.</sub> | <sub>2018-06-06 22:06:06</sub> | <sub>2018-06-06 22:27:02</sub> | <sub>5.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>optopsy</sub> | <sub>NEW</sub> | <sub>2017-09-17 01:49:54</sub> | <sub>2021-06-04 16:13:34</sub> | <sub>496.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>StockSharp</sub> | <sub>NEW</sub> | <sub>2014-12-08 07:53:44</sub> | <sub>2021-12-10 19:03:27</sub> | <sub>4584.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Derivatives Python</sub> | <sub>Derivative analytics with Python.</sub> | <sub>2015-07-09 12:27:29</sub> | <sub>2021-02-22 13:29:18</sub> | <sub>431.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>akshare</sub> | <sub>NEW</sub> | <sub>2019-10-01 07:34:12</sub> | <sub>2021-12-11 16:29:11</sub> | <sub>4309.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
Fixed Income (Wiki)
<!-- [PLACEHOLDER_START:fixed_income] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>Vasicek</sub> | <sub>Bootstrapping and interpolation.</sub> | <sub>2018-07-18 19:26:54</sub> | <sub>2018-07-18 19:34:48</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Binomial Tree</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>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>rating_history</sub> | <sub>NEW</sub> | <sub>2017-11-23 22:52:14</sub> | <sub>2017-12-03 20:42:49</sub> | <sub>29.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>market-data</sub> | <sub>NEW</sub> | <sub>2012-12-07 13:42:48</sub> | <sub>2012-12-15 12:10:06</sub> | <sub>26.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>woe</sub> | <sub>NEW</sub> | <sub>2017-09-11 07:15:04</sub> | <sub>2018-03-01 10:45:40</sub> | <sub>235.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>punk.protocol</sub> | <sub>NEW</sub> | <sub>2021-04-29 08:39:42</sub> | <sub>2021-08-13 11:53:11</sub> | <sub>21.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>DROP-Fixed-Income</sub> | <sub>NEW</sub> | <sub>2017-08-10 20:58:18</sub> | <sub>2018-09-26 19:21:02</sub> | <sub>21.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Corporate Bonds</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>12.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>MagentoExtensions</sub> | <sub>NEW</sub> | <sub>2014-07-03 05:45:54</sub> | <sub>2017-11-24 16:15:49</sub> | <sub>112.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
Alternative Finance (Wiki)
<!-- [PLACEHOLDER_START:alternative_finance] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>Venture Capital NN</sub> | <sub>Cox-PH neural network predictions for VC/innovations finance research.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>EDMarketConnector</sub> | <sub>NEW</sub> | <sub>2015-06-02 19:17:34</sub> | <sub>2021-11-22 11:08:03</sub> | <sub>796.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Watch Valuation</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>7.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>yahoofinancials</sub> | <sub>NEW</sub> | <sub>2017-10-22 03:10:57</sub> | <sub>2020-10-18 23:43:29</sub> | <sub>618.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>botupdate</sub> | <sub>NEW</sub> | <sub>2019-07-01 20:22:44</sub> | <sub>2020-10-29 02:31:17</sub> | <sub>582.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Kiva Crowdfunding</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>Venture Capital</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>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>VC OLS</sub> | <sub>VC regression.</sub> | <sub>2018-03-29 23:31:13</sub> | <sub>2018-03-29 23:33:19</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>pitch_deck</sub> | <sub>NEW</sub> | <sub>2016-09-17 01:30:26</sub> | <sub>2021-12-10 23:53:23</sub> | <sub>177.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>pitch-deck</sub> | <sub>NEW</sub> | <sub>2016-09-17 01:30:26</sub> | <sub>2021-12-10 23:53:23</sub> | <sub>177.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Blockchain</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>13.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Art Valuation</sub> | <sub>Art evaluation analytics.</sub> | <sub>2014-12-11 00:25:39</sub> | <sub>2014-12-12 21:25:46</sub> | <sub>11.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Private Equity</sub> | <sub>Valuation models.</sub> | <sub>2016-01-27 21:13:33</sub> | <sub>2016-03-14 20:03:52</sub> | <sub>10.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
Extended Research (Wiki)
<!-- [PLACEHOLDER_START:extended_research] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>Commodity</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</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>ITCH</sub> | <sub>NEW</sub> | <sub>2019-03-09 18:20:12</sub> | <sub>2021-10-22 23:41:53</sub> | <sub>95.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>fraud-detection-handbook</sub> | <sub>NEW</sub> | <sub>2021-05-03 11:33:12</sub> | <sub>2021-12-03 09:35:43</sub> | <sub>94.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>NLP Finance Papers</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>9.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>HFT</sub> | <sub>High frequency trading.</sub> | <sub>2016-07-21 05:14:14</sub> | <sub>2017-02-14 16:47:25</sub> | <sub>886.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>FraudDetection-Microservices</sub> | <sub>NEW</sub> | <sub>2016-06-08 23:24:21</sub> | <sub>2017-01-18 17:52:01</sub> | <sub>86.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>system</sub> | <sub>NEW</sub> | <sub>2013-04-11 16:14:53</sub> | <sub>2021-11-13 22:34:09</sub> | <sub>86.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>fecon236</sub> | <sub>NEW</sub> | <sub>2018-04-05 19:34:51</sub> | <sub>2019-01-11 08:07:56</sub> | <sub>86.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>HFT-CNN</sub> | <sub>NEW</sub> | <sub>2018-08-18 06:39:32</sub> | <sub>2018-11-09 02:29:00</sub> | <sub>85.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>fx_systrade</sub> | <sub>NEW</sub> | <sub>2015-07-24 11:46:28</sub> | <sub>2020-10-23 10:17:01</sub> | <sub>82.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Trading-Bot</sub> | <sub>NEW</sub> | <sub>2017-11-27 21:20:40</sub> | <sub>2018-01-22 21:00:57</sub> | <sub>80.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>freqtrade_bot</sub> | <sub>NEW</sub> | <sub>2020-12-21 00:14:25</sub> | <sub>2021-01-07 19:52:54</sub> | <sub>80.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>go-quantcup</sub> | <sub>NEW</sub> | <sub>2015-02-04 10:33:12</sub> | <sub>2015-06-11 12:50:09</sub> | <sub>76.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>HFT_Bitcoin</sub> | <sub>NEW</sub> | <sub>2017-07-27 07:11:48</sub> | <sub>2017-08-21 14:50:35</sub> | <sub>76.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
Courses (Wiki)
<!-- [PLACEHOLDER_START:courses] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>zero-to-mastery-ml</sub> | <sub>NEW</sub> | <sub>2019-09-23 04:56:51</sub> | <sub>2021-10-18 02:23:51</sub> | <sub>980.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>datasci_course_materials</sub> | <sub>NEW</sub> | <sub>2013-04-12 05:54:36</sub> | <sub>2017-03-21 19:21:02</sub> | <sub>902.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>ciml</sub> | <sub>NEW</sub> | <sub>2015-08-12 19:26:00</sub> | <sub>2017-01-20 16:24:19</sub> | <sub>798.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>Octave</sub> | <sub>NEW</sub> | <sub>2011-10-24 23:50:52</sub> | <sub>2016-07-08 20:45:40</sub> | <sub>797.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>mlcourse.ai</sub> | <sub>NEW</sub> | <sub>2017-02-27 08:32:20</sub> | <sub>2021-12-09 15:20:32</sub> | <sub>7951.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>ml-course-msu</sub> | <sub>NEW</sub> | <sub>2015-09-11 08:51:24</sub> | <sub>2018-05-07 15:40:56</sub> | <sub>795.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>DAT4</sub> | <sub>NEW</sub> | <sub>2014-12-10 19:38:29</sub> | <sub>2021-02-15 23:26:27</sub> | <sub>764.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>ML_course</sub> | <sub>NEW</sub> | <sub>2016-07-13 15:37:38</sub> | <sub>2021-12-09 18:50:09</sub> | <sub>756.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>coursera-deep-learning-specialization</sub> | <sub>NEW</sub> | <sub>2020-06-24 05:59:01</sub> | <sub>2021-11-02 02:27:50</sub> | <sub>749.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>datascience-box</sub> | <sub>NEW</sub> | <sub>2017-12-29 22:16:17</sub> | <sub>2021-11-08 01:14:14</sub> | <sub>734.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Mathematical Finance</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>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>DataScienceSpCourseNotes</sub> | <sub>NEW</sub> | <sub>2015-03-09 00:51:32</sub> | <sub>2016-02-16 06:12:54</sub> | <sub>719.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
<sub>china-dictatorship</sub> | <sub>NEW</sub> | <sub>2015-04-02 20:51:50</sub> | <sub>2021-11-17 09:20:18</sub> | <sub>695.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>ml-mipt</sub> | <sub>NEW</sub> | <sub>2019-02-01 16:20:39</sub> | <sub>2021-11-22 16:23:09</sub> | <sub>690.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>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>68.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
Data (Wiki)
<!-- [PLACEHOLDER_START:data] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>Non-financial Corporate</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>IRS</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Financial Corporate</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/</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Capital Markets Data</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/</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Tools-termux</sub> | <sub>NEW</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Rating Industries</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>https://stooq.com</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>cs-fundamentals</sub> | <sub>NEW</sub> | <sub>2016-12-29 21:47:23</sub> | <sub>2020-06-09 00:04:00</sub> | <sub>948.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>element-china-area-data</sub> | <sub>NEW</sub> | <sub>2017-03-01 06:10:33</sub> | <sub>2020-09-29 02:55:21</sub> | <sub>925.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>hypercube</sub> | <sub>NEW</sub> | <sub>2021-09-08 06:47:07</sub> | <sub>2021-10-14 13:44:17</sub> | <sub>924.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>investpy</sub> | <sub>NEW</sub> | <sub>2018-11-27 14:51:47</sub> | <sub>2021-10-26 13:37:05</sub> | <sub>915.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>BitcoinExchangeFH</sub> | <sub>NEW</sub> | <sub>2016-10-24 13:30:31</sub> | <sub>2021-11-30 22:16:21</sub> | <sub>887.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>acra</sub> | <sub>NEW</sub> | <sub>2016-11-14 16:23:25</sub> | <sub>2021-12-07 18:12:20</sub> | <sub>880.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
Colleges, Centers and Departments (Wiki)
<!-- [PLACEHOLDER_START:colleges_centers_and_departments] --><sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
---|---|---|---|---|---|---|
<sub>Oxford Man</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>Berkeley Lab CIFT</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>NYU Courant</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</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>Stanford Advanced Financial Technologies</sub> | <sub>Stanford Advanced Financial Technologies Laboratory</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
<sub>NYU FRE</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> |