mirror of
https://github.com/firmai/financial-machine-learning.git
synced 2026-08-03 22:27:46 +00:00
auto update readme
This commit is contained in:
Bin Yang
@@ -34,4 +34,9 @@ jobs:
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with:
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path: "generated_wiki"
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env:
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GH_PERSONAL_ACCESS_TOKEN: ${{ secrets.GIT_TOKEN }}
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GH_PERSONAL_ACCESS_TOKEN: ${{ secrets.GIT_TOKEN }}
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- name: Commit & Push changes
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uses: actions-js/push@master
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with:
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github_token: ${{ secrets.GIT_TOKEN }}
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@@ -22,39 +22,73 @@ ___
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# Trading
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## Deep Learning ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/deep_learning))
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- [Deep Learning](https://github.com/keon/deepstock) - Technical experimentations to beat the stock market using deep learning.
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- [Deep Learning II](https://github.com/LiamConnell/deep-algotrading/tree/master/notebooks) - Tensorflow Regression.
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- [Deep Learning III](https://github.com/Rachnog/Deep-Trading) - Algorithmic trading with deep learning experiments.
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- [Deep Learning IV](https://github.com/achillesrasquinha/bulbea) - Bulbea: Deep Learning based Python Library.
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- [LTSM GRU](https://github.com/RajatHanda/Finance-Forecasting) - Stock Market Forecasting using LSTM\GRU.
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- [LTSM Recurrent](https://github.com/VivekPa/AIAlpha) - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.
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- [ARIMA-LTSM Hybrid](https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid) - Hybrid model to predict future price correlation coefficients of two assets.
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- [Neural Network](https://github.com/VivekPa/IntroNeuralNetworks) - Neural networks to predict stock prices.
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- [AI Trading](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md) - AI to predict stock market movements.
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:---------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:--------------------------|:--------------------------|:------------------------|:------------------------------------|:--------------------|
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| <sub>[Stock-Prediction-Models](https://github.com/huseinzol05/Stock-Prediction-Models)</sub> | <sub>NEW</sub> | <sub>12/18/17 10:49</sub> | <sub>1/5/21 10:31</sub> | <sub>3584.0</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>2852.0</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>NEW</sub> | <sub>7/26/20 13:18</sub> | <sub>3/28/21 13:46</sub> | <sub>1780.0</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>1448.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
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| <sub>[RLTrader](https://github.com/notadamking/RLTrader)</sub> | <sub>NEW</sub> | <sub>4/27/19 18:35</sub> | <sub>10/17/19 16:25</sub> | <sub>1300.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x5</sub> |
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<details>
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<summary><sub>next 5</sub></summary>
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
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| <sub>[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>1262.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x5</sub> |
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| <sub>[Personae](https://github.com/Ceruleanacg/Personae)</sub> | <sub>NEW</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>[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>NEW</sub> | <sub>7/26/20 13:12</sub> | <sub>1/21/21 18:11</sub> | <sub>542.0</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>528.0</sub> | <sub>:heavy_check_mark:</sub> | <sub>:star:x4</sub> |
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| <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>488.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |
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</details>
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## Reinforcement Learning ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/reinforcement_learning))
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- [RL Trading](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW) - A collection of 25+ Reinforcement Learning Trading Strategies - Google Colab.
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- [RL](https://github.com/kh-kim/stock_market_reinforcement_learning) - OpenGym with Deep Q-learning and Policy Gradient.
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- [RL II](https://github.com/deependersingla/deep_trader) - reinforcement learning on stock market and agent tries to learn trading.
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- [RL III](https://github.com/samre12/deep-trading-agent) - Github - Deep Reinforcement Learning based Trading Agent for Bitcoin.
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- [RL IV](https://github.com/jjakimoto/DQN) - Reinforcement Learning for finance.
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- [RL V](https://github.com/gstenger98/rl-finance) - Building an Agent to Trade with Reinforcement Learning.
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- [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.
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
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| <sub>[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></sub> |
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| <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>712.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <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>575.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <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></sub> |
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| <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_check_mark:</sub> | <sub></sub> |
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<details>
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<summary><sub>next 5</sub></summary>
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:-------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
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| <sub>[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_check_mark:</sub> | <sub></sub> |
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| <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_check_mark:</sub> | <sub></sub> |
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</details>
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## Other Models ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/other_models))
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- [Mixture Models I](https://github.com/BlackArbsCEO/Mixture_Models) - Mixture models to predict market bottoms.
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- [Mixture Models II](https://github.com/BlackArbsCEO/mixture_model_trading_public) - Mixture models and stock trading.
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- [Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks) - Using python and scikit-learn to make stock predictions.
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- [Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps) - Research in investment finance for long term forecasts.
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- [Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant) - Identify social/historical cues for short term stock movement.
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- [Trend Following](http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html) - A futures trend following portfolio investment strategy.
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
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| <sub>[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>919.0</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>379.0</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_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>2157.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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<details>
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<summary><sub>next 5</sub></summary>
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:---------------------------------------------------------------------------------------------|:---------------------------------------------|:--------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
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| <sub>[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> |
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</details>
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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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- [Advanced ML](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises) - Exercises too Financial Machine Learning (De Prado).
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- [Advanced ML II](https://github.com/hudson-and-thames/research) - More implementations of Financial Machine Learning (De Prado).
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------|:--------------------|
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| <sub>[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>958.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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<details>
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<summary><sub>next 5</sub></summary>
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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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</details>
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# Portfolio Management
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## Portfolio Selection and Optimisation ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/portfolio_selection_and_optimisation))
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+26
-1
@@ -22,9 +22,10 @@ def get_wiki_rating(input_rating):
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return '<sub>{}</sub>'.format(result_text)
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def generate_wiki_per_category(output_path):
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def generate_wiki_per_category(output_path, update_readme: bool = True):
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"""
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:param update_readme:
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:param output_path:
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"""
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repo_df = get_repo_list()
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@@ -44,6 +45,7 @@ def generate_wiki_per_category(output_path):
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'rating': category_df['rating']
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})
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# add color for the status
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formatted_df = formatted_df.sort_values(by=['rating', 'star_count'], ascending=False).reset_index(drop=True)
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formatted_df['repo_status'] = formatted_df['repo_status'].apply(lambda x: get_wiki_status_color(x))
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formatted_df['rating'] = formatted_df['rating'].apply(lambda x: get_wiki_rating(x))
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formatted_df.columns = ['<sub>{}</sub>'.format(x) for x in formatted_df.columns]
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@@ -54,6 +56,29 @@ def generate_wiki_per_category(output_path):
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f.write(formatted_df.to_markdown(index=False))
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print('wiki generated in [{}]'.format(output_path_full))
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if update_readme:
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check_str = '[PLACEHOLDER:{}]'.format(clean_category_name)
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all_read_me = ''
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with open(os.path.join(PROJECT_ROOT_DIR, 'README.md')) as f:
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all_read_me = f.read()
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if check_str not in all_read_me:
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print(f'section {check_str} not found')
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continue
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# only display top 5, then expandable for extra 5
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with open(os.path.join(PROJECT_ROOT_DIR, 'README.md'), 'w') as f:
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table_str = formatted_df.iloc[:5].to_markdown(index=False)
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collapsible_str = """
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<details>
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<summary><sub>next 5</sub></summary>
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{}
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</details>
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""".format(formatted_df.iloc[5:10].to_markdown(index=False))
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new_str = table_str + collapsible_str
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s = all_read_me.replace(check_str, new_str)
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f.write(s)
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if __name__ == '__main__':
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local_path = os.path.join(PROJECT_ROOT_DIR, 'generated_wiki')
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