26 September, 2020

Financial Machine Learning

Tài nguyên về Tài chính lượng tử và Giao dịch thuật toán sử dụng Trí tuệ nhân tạo.
Mình đã lượt bỏ các nguồn và các kiến thức có chất lượng thấp

BOOKs

  • ⭐Marcos López de Prado – Advances in Financial Machine Learning [Link].
  • ⭐Dr Howard B Bandy – Quantitative Technical Analysis: An integrated approach to trading system development and trading management [Link]
  • Tony Guida – Big Data and Machine Learning in Quantitative Investment [Link]
  • ⭐Michael Halls-Moore – Advanced Algorithmic Trading [Link]
  • Jannes Klaas – Machine Learning for Finance: Data algorithms for the markets and deep learning from the ground up for financial experts and economics [Link]
  • Stefan Jansen – Hands-On Machine Learning for Algorithmic Trading: Design and implement smart investment strategies to analyze market behavior using the Python ecosystem [Link]
  • Ali N. Akansu et al. – Financial Signal Processing and Machine Learning [Link]
  • David Aronson – Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading [Link]
  • David Aronson – Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments [Link]
  • Ernest P. Chan – Machine Trading: Deploying Computer Algorithms to Conquer the Markets [Link]

Các khoá học và Series Online

Các khoá học online về Meaching learning về algorithmic trading khá là nghèo nàn.

  • Udacity, Georgia Tech – Machine Learning for Trading [Link]
  • Udacity, WorldQuant – Artificial Intelligence for Trading [Link]
  • Coursera, NYU – Machine Learning and Reinforcement Learning in Finance Specialization (Weakly related to trading)
    • Coursera, NYU – Guided Tour of Machine Learning in Finance [Link]
    • Coursera, NYU – Fundamentals of Machine Learning in Finance [Link]
    • Coursera, NYU – Reinforcement Learning in Finance [Link]
    • Coursera, NYU – Overview of Advanced Methods for Reinforcement Learning in Finance [Link]

YOUTUBE Channels Videos

  • ⭐Siraj Raval – Videos about stock market prediction using Deep Learning [Link]
  • QuantInsti Youtube – webinars about Machine Learning for trading [Link]
  • ⭐ Quantopian – Webinars about Machine Learning for trading [Link]
  • Sentdex – Machine Learning for Forex and Stock analysis and algorithmic trading [Link]
  • Sentdex – Python programming for Finance (a few videos including Machine Learning) [Link]
  • QuantNews – Machine Learning for Algorithmic Trading 3 part series [Link]
  • ⭐ Howard Bandy – Machine Learning Trading System Development Webinar [Link]
  • Ernie Chan – Machine Learning for Quantitative Trading Webinar [Link]
  • Hitoshi Harada, CTO at Alpaca – Deep Learning in Finance Talk [Link]
  • Prediction Machines – Deep Learning with Python in Finance Talk [Link]
  • Master Thesis presentation, Uni of Essex – Analyzing the Limit Order Book, A Deep Learning Approach [Link]
  • Tucker Balch – Applying Deep Reinforcement Learning to Trading [Link]

Blogs and content websites

  • ⭐ Quantstart – Machine Learning for Trading articles [Link]
  • ⭐ Quantopian – Lecture notebooks on ML-related statistics [Link]
  • ⭐ Quantopian – Tutorials and notebooks tagged with Machine Learning [Link]
  • AAA Quants, Tom Starke Blog [Link]
  • RobotWealth, Kris Longmore Blog [Link]
  • Quantsportal, Jacques Joubert’s Blog [Link]
  • Blackarbs blog [Link]
  • Hardikp, Hardik Patel blog [Link]

Interviews

  • ⭐ Chat with Traders EP042 – Machine learning for algorithmic trading with Bert Mouler [Link]
  • ⭐ Chat with Traders EP142 – Algo trader using automation to bypass human flaws with Bert Mouler [Link]
  • Chat with Traders EP147 – Detective work leading to viable trading strategies with Tom Starke [Link]
  • ⭐ Chat with Traders Quantopian 5 – Good Uses of Machine Learning in Finance with Max Margenot [Link]
  • Chat With Traders EP131 – Trading strategies, powered by machine learning with Morgan Slade [Link]
  • Better System Trader EP023 – Portfolio manager Michael Himmel talks AI and machine learning in trading [Link]
  • ⭐ Better System Trader EP028 – David Aronson shares research into indicators that identify Bull and Bear markets. [Link]
  • Better System Trader EP082 – Machine Learning With Kris Longmore [Link]
  • ⭐ Better System Trader EP064 – Cryptocurrencies and Machine Learning with Bert Mouler [Link]
  • Better System Trader EP090 – This quants’ approach to designing algo strategies with Michael Halls-Moore [Link]

Papers

  • ⭐ James Cumming – An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain [Link]
  • ⭐ Marcos López de Prado – The 10 reasons most Machine Learning Funds fails [Link]
  • Zhuoran Xiong et al. – Practical Deep Reinforcement Learning Approach for Stock Trading [Link]
  • Gordon Ritter – Machine Learning for Trading [Link]
  • J.B. Heaton et al. – Deep Learning for Finance: Deep Portfolios [Link]
  • Justin Sirignano et al. – Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning [Link]
  • Marcial Messmer – Deep Learning and the Cross-Section of Expected Returns [Link]
  • ⭐ Marcos Lopez de Prado – Ten Financial Applications of Machine Learning (Presentation Slides) [Link]
  • ⭐ Marcos Lopez de Prado – The Myth and Reality of Financial Machine Learning (Presentation Slides) [Link]
  • Artur Sepp – Machine Learning for Volatility Trading (Presentation Slides) [Link]
  • Marcos Lopez de Prado – Market Microstructure in the Age of Machine Learning [Link]
  • Jonathan Brogaard – Machine Learning and the Stock Market [Link]
  • Xinyao Qian – Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods [Link]
  • Milan Fičura – Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks [Link]
  • Samuel Edet – Recurrent Neural Networks in Forecasting S&P 500 Index [Link] Amin Hedayati et al. – Stock Market Index Prediction Using Artificial Neural Network [Link]
  • Jaydip Sen et al. – A Robust Predictive Model for Stock Price Forecasting [Link]
  • O.B. Sezer et al. – An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework [Link]
  • Ritika Singh et al. – Stock prediction using deep learning [Link]
  • Thomas Fischera et al. – Deep learning with long short-term memory networks for financial market predictions [Link]
  • R.C.Cavalcante et al. – Computational Intelligence and Financial Markets: A Survey and Future Directions [Link]
  • E. Chong et al. – Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies [Link]
  • Chien Yi Huang – Financial Trading as a Game: A Deep Reinforcement Learning Approach [Link]
  • W. Bao et al. – A deep learning framework for financial time series using stacked autoencoders and longshort term memory [Link]
  • Xingyu Zhou et al. – Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets [Link]
  • Fuli Feng et al. – Improving Stock Movement Prediction with Adversarial Training [Link]
  • Z. Zhao et al. – Time-Weighted LSTM Model with Redefined Labeling for Stock Trend Prediction [Link]
  • Arthur le Calvez, Dave Cliff – Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market [Link]
  • Dang Lien Minh et al. – Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network [Link]
  • Yue Deng et al. – Deep Direct Reinforcement Learning for Financial Signal Representation and Trading [Link]
  • Xiao Zhong – A comprehensive cluster and classification mining procedure for daily stock market return forecasting [Link]
  • J. Zhang et al. – A novel data-driven stock price trend prediction system [Link]
  • Ehsan Hoseinzade et al. – CNNPred: CNN-based stock market prediction using several data sources [Link]
  • Hyejung Chung et al. – Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction [Link]
  • Yujin Baek et al. – ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module [Link]
  • Rajashree Dash et al. – A hybrid stock trading framework integrating technical analysis with machine learning techniques [Link]
  • E.A. Gerlein et al. – Evaluating machine learning classification for financial trading: an empirical approach [Link]
  • Justin Sirignano – Deep Learning for Limit Order Books [Link]

Events & Sentiment trading

  • Frank Z. Xing et al. – Natural language based financial forecasting: a survey [Link]
  • Ziniu Hu et al. – Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction [Link]
  • J.W. Leung, Master Thesis, MIT – Application of Machine Learning: Automated Trading Informed by Event Driven Data [Link]
  • Xiao Ding et al. – Deep Learning for Event-Driven Stock Prediction [Link]

Reinforcement Learning environments

Code

  • marketneutral – pairs trading with ML [Link]
  • BlackArbsCEO – Advances in Financial Machine Learning Exercises [Link]
  • mlfinlab – Package for Advances in Financial Machine Learning [Link]
  • MachineLearningStocks – Using python and scikit-learn to make stock predictions [Link]
  • AlphaAI – Use unsupervised and supervised learning to predict stocks [Link]
  • SGX-Full-OrderBook-Tick-Data-Trading-Strategy – Providing the solutions for high-frequency trading (HFT) strategies using ML [Link]
  • NeuralNetworkStocks – Using Python and keras to make stock predictions [Link]
  • Stock-Price-Prediction-LSTM – OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network [Link]
  • SravB – Algorithmic trading using machine learning [Link]
  • Flow – High frequency AI based algorithmic trading module [Link]
  • timestocome – Test-stock-prediction-algorithms [Link]
  • deepstock – Technical experimentations to beat the stock market using deep learning [Link]
  • qtrader – Reinforcement Learning for Portfolio Management [Link]
  • stockPredictor – Predict stock movement with Machine Learning and Deep Learning algorithms [Link]
  • stock_market_reinforcement_learning – Stock market environment using OpenGym with Deep Q-learning and Policy Gradient [Link]
  • deep-algotrading – deep learning techniques from regression to LSTM using financial data [Link]
  • deep_trader – Use reinforcement learning on stock market and agent tries to learn trading [Link]
  • Deep-Trading – Algorithmic trading with deep learning experiments [Link]
  • Deep-Trading – Algorithmic Trading using RNN [Link]
  • Multidimensional-LSTM-BitCoin-Time-Series – Using multidimensional LSTM neural networks to create a forecast for Bitcoin price [Link]
  • QLearning_Trading – Learning to trade under the reinforcement learning framework [Link]
  • Day-Trading-Application – Use deep learning to make accurate future stock return predictions [Link]
  • bulbea – Deep Learning based Python Library for Stock Market Prediction and Modelling [Link]
  • PGPortfolio – source code of “A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem” [Link]
  • Thesis – Reinforcement Learning for Automated Trading [Link]
  • DQN – Reinforcement Learning for finance [Link]
  • Deep-Trading-Agent – Deep Reinforcement Learning based Trading Agent for Bitcoin [Link]
  • deep_portfolio – Use Reinforcement Learning and Supervised learning to Optimize portfolio allocation [Link]
  • Deep-Reinforcement-Learning-in-Stock-Trading – Using deep actor-critic model to learn best strategies in pair trading [Link]
  • Stock-Price-Prediction-LSTM – OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network [Link]
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