mirror of
https://github.com/firmai/financial-machine-learning.git
synced 2026-08-02 05:37:46 +00:00
combine deep learning and reinforcement learning since new repos pulled covers both
This commit is contained in:
Bin Yang
@@ -25,8 +25,8 @@ ___
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___
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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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<!-- [PLACEHOLDER_START:deep_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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| <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> |
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@@ -38,21 +38,7 @@ ___
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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>[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>[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> |
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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>489.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |<!-- [PLACEHOLDER_END:deep_learning] -->
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## Reinforcement Learning ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/reinforcement_learning))
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<!-- [PLACEHOLDER_START: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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|:------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
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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>713.0</sub> | <sub>:heavy_multiplication_x:</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>576.0</sub> | <sub>:heavy_multiplication_x:</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_multiplication_x:</sub> | <sub></sub> |
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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_multiplication_x:</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_multiplication_x:</sub> | <sub></sub> |<!-- [PLACEHOLDER_END:reinforcement_learning] -->
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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>489.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub>:star:x4</sub> |<!-- [PLACEHOLDER_END:deep_learning_and_reinforcement_learning] -->
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+70
-19
@@ -1,5 +1,5 @@
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import os
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from typing import Dict
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from typing import Dict, List
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import datetime
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from conf import PROJECT_ROOT_DIR
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import re
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@@ -33,6 +33,43 @@ def search_repo(search_term: str, qualifier_dict: Dict):
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return repo_result
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def search_repo_multiple_terms(term_list: List[str],
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category: str,
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min_stars_number: int = None,
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created_at: str = None,
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pushed_date: str = None,
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drop_duplicate: bool = True
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):
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"""
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:param term_list:
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:param category:
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:param min_stars_number:
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:param created_at:
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:param pushed_date:
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:param drop_duplicate:
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:return:
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usage:
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>>> term_list = ['deep learning trading', 'deep learning finance', 'reinforcement learning trading',
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'reinforcement learning finance']
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>>> category = 'Deep Learning And Reinforcement Learning'
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>>> min_stars_number = 100
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>>> created_at = None
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>>> pushed_date = None
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>>> drop_duplicate = True
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"""
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repo_df_list = []
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for search_term in term_list:
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repo_list = search_repo_simple(search_term, min_stars_number, created_at=created_at, pushed_date=pushed_date)
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repo_df = convert_repo_list_to_df(repo_list, category)
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repo_df_list.append(repo_df)
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combined_df = pd.concat(repo_df_list).reset_index(drop=True)
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if drop_duplicate:
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combined_df = combined_df.drop_duplicates()
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combined_df['finml_added_date'] = datetime.datetime.now()
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return combined_df
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def search_repo_simple(search_term: str = None,
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min_stars_number: int = None,
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created_at: str = None,
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@@ -45,7 +82,7 @@ def search_repo_simple(search_term: str = None,
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:param created_at:
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:param pushed_date:
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usage:
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>>> search_term = '(deep learning) AND trading'
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>>> search_term = 'machine learning trading'
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>>> min_stars_number = 100
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>>> created_at = None
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>>> pushed_date = None
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@@ -85,27 +122,41 @@ def convert_repo_list_to_df(repo_list, category):
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return result_df
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def search_new_repo_and_append(min_stars_number: int = 100):
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def search_new_repo_by_category(category: str,
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min_stars_number: int = 100,
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existing_repo_df: pd.DataFrame = None):
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combined_df = None
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if category == 'Deep Learning And Reinforcement Learning':
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combined_df = search_repo_multiple_terms(['deep learning trading',
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'deep learning finance',
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'reinforcement learning trading',
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'reinforcement learning finance'],
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category,
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min_stars_number=min_stars_number
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)
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# only find ones that need to be inserted
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if existing_repo_df is not None:
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combined_df = combined_df[
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~combined_df['repo_path'].str.lower().isin(existing_repo_df['repo_path'].str.lower())]
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return combined_df
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def search_new_repo_and_append(min_stars_number: int = 100, filter_list=None):
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"""
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:param min_stars_number:
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:param filter_list:
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"""
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repo_df = get_repo_list()
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category_list = repo_df['category'].unique().tolist()
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if filter_list is not None:
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category_list = [x for x in category_list if x in filter_list]
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new_repo_list = []
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for category in category_list:
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if category == 'Deep Learning':
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# github not yet support OR operator, issue here
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# https://github.com/isaacs/github/issues/660
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# hence run the search terms twice and combine
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search_term = 'deep learning trading'
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repo_list = search_repo_simple(search_term, min_stars_number)
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top_df = convert_repo_list_to_df(repo_list, category)
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search_term = 'deep learning finance'
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repo_list = search_repo_simple(search_term, min_stars_number)
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bottom_df = convert_repo_list_to_df(repo_list, category)
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combined_df = pd.concat([top_df, bottom_df]).reset_index(drop=True)
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combined_df = combined_df.drop_duplicates()
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# only find ones that need to be inserted
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combined_df = combined_df[~combined_df['repo_path'].str.lower().isin(repo_df['repo_path'].str.lower())]
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combined_df['finml_added_date'] = datetime.datetime.now()
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new_repo_list.append(combined_df)
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combined_df = search_new_repo_by_category(category, min_stars_number, repo_df)
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new_repo_list.append(combined_df)
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new_repo_df = pd.concat(new_repo_list).reset_index(drop=True)
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final_df = pd.concat([repo_df, new_repo_df]).reset_index(drop=True)
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final_df = final_df.sort_values(by='category')
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+34
-34
@@ -39,33 +39,33 @@ https://fred.stlouisfed.org/,https://fred.stlouisfed.org/,,Data,,,,,,,,,,
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Rating Industries,http://www.ratingshistory.info/,,Data,,,,,,,,,,
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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,,,
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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,,
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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,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
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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,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
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LTSM GRU,https://github.com/RajatHanda/Finance-Forecasting,Stock Market Forecasting using LSTM\GRU.,Deep Learning,3/29/21 23:59,11,6,1,5/13/18 2:39,2/25/19 0:26,RajatHanda/Finance-Forecasting,inactive,3,
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Deep Learning,https://github.com/keon/deepstock,Technical experimentations to beat the stock market using deep learning.,Deep Learning,3/24/21 14:45,427,154,2,12/12/16 2:15,3/4/17 8:37,keon/deepstock,inactive,4,
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Deep Learning II,https://github.com/LiamConnell/deep-algotrading/tree/master/notebooks,Tensorflow Regression.,Deep Learning,3/21/21 6:53,174,67,1,7/12/16 12:56,2/16/18 2:43,LiamConnell/deep-algotrading,inactive,3,
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Neural Network,https://github.com/VivekPa/IntroNeuralNetworks,Neural networks to predict stock prices.,Deep Learning,4/3/21 11:59,489,177,2,9/10/18 6:34,11/21/18 7:39,VivekPa/IntroNeuralNetworks,inactive,4,
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Deep Learning IV,https://github.com/achillesrasquinha/bulbea,Bulbea: Deep Learning based Python Library.,Deep Learning,4/2/21 1:36,1451,416,1,3/9/17 6:11,3/19/17 7:42,achillesrasquinha/bulbea,inactive,5,
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AI Trading,https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md,AI to predict stock market movements.,Deep Learning,4/3/21 21:14,2857,1378,1,1/9/19 8:02,2/11/19 16:32,borisbanushev/stockpredictionai,inactive,5,
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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.,Deep 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,
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LTSM Recurrent,https://github.com/VivekPa/AIAlpha,OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.,Deep Learning,4/3/21 10:48,1199,371,2,10/7/18 3:58,8/3/19 9:00,VivekPa/AIAlpha,active,4,
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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,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
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Deep Learning III,https://github.com/Rachnog/Deep-Trading,Algorithmic trading with deep learning experiments.,Deep Learning,4/3/21 5:26,1264,675,1,6/18/16 18:23,8/7/18 15:24,Rachnog/Deep-Trading,inactive,5,
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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,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
|
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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,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
|
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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,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
|
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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,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
|
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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,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
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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,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
|
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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,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
|
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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,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
|
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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,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
|
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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,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
|
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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,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-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,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-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,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
|
||||
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,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
|
||||
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,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
|
||||
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
|
||||
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
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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
|
||||
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,
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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-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-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
|
||||
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
|
||||
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
|
||||
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,,
|
||||
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,,
|
||||
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,,
|
||||
@@ -141,13 +141,13 @@ Deep Portfolio Theory,https://github.com/tcloaa/Deep-Portfolio-Theory,Autoencode
|
||||
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,,
|
||||
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,,
|
||||
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,,
|
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RL IV,https://github.com/jjakimoto/DQN,Reinforcement Learning for finance.,Reinforcement Learning,3/25/21 19:14,140,55,1,10/21/16 2:47,4/7/17 8:11,jjakimoto/DQN,inactive,,
|
||||
RL Trading,https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW,A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab.,Reinforcement Learning,,,,,,,,,,
|
||||
RL V,https://github.com/gstenger98/rl-finance,Building an Agent to Trade with Reinforcement Learning.,Reinforcement Learning,1/3/21 4:36,32,7,5,1/16/19 0:43,3/19/20 20:28,gstenger98/rl-finance,active,,
|
||||
RL,https://github.com/kh-kim/stock_market_reinforcement_learning,OpenGym with Deep Q-learning and Policy Gradient.,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,,
|
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RL II,https://github.com/deependersingla/deep_trader,reinforcement learning on stock market and agent tries to learn trading.,Reinforcement Learning,3/29/21 11:10,1340,490,3,6/11/16 7:27,1/22/18 14:35,deependersingla/deep_trader,inactive,,
|
||||
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.,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,,
|
||||
RL III,https://github.com/samre12/deep-trading-agent,Github -Deep Reinforcement Learning based Trading Agent for Bitcoin.,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,,
|
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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,,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,
|
||||
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,,
|
||||
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,,
|
||||
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,,
|
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|
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Reference in New Issue
Block a user