Place of Contemplation: Machine Learning @ Quantopian
Place of Contemplation: Machine Learning @ Quantopian
Machine Learning on Quantopian Part 3: Building an Algorithm Thomas Wiecki : Nov 9, 2017
Machine Learning on Quantopian Part 2: ML as a Factor Thomas Wiecki : Jun 23, 2017
Multiple Model Machine Learning Eric Novinson : Aug 5, 2017
Machine Learning With Multiple Random Forest Models Eric Novinson : Aug 12, 2017
Market/Security Prediction using Machine Learning Classifier and Google Trend Luc Prieur : Aug 22, 2017
Uses fetch_csv to get the factors, computed using ScikitLearn in Notebook, into a scheduled function:
data.current(data.fetcher_assets, ['probability', 'predictiveness', 'prediction'])
Support Vector Machine in Pipeline Luc Prieur : Sep 29, 2017
Algorithm template: ScikitLearn SVM & GaussianNB in Pipeline to train ML model by stock to return predictions on stock movement for long/short positions.
Trading with Sentiment Machine Learning Hefei YU : Dec 7, 2017
Applies an NTU paper using ScikitLearn LogisticRegression, RandomForestClassifier & SVCs for Sentiment Analysis and Machine Learning to predict stock price movements.
Machine Learning Stock Selection + Mean Variance Portfolio Optimization Jun Ouyang : Dec 13, 2017
Predicts using ScikitLearn RandomForestClassifier & DecisionTreeClassifier with technical indicators: RSI MACD EMA SMA & ADX
Optimises using SciPy opt for Markowitz Mean Variance Optimization to construct a one-week portfolio.
Neural Network Tests for Mean Reversion or Momentum Trending Seong Lee : Oct 3, 2013
Uses Hurst Exponent and Sharpe Ratio as inputs to train for a sample of days before using stock data.
Alphalens Boilerplate to test ML Factor Luca : Mar 11, 2018
Text Mining Corporate Filings by Yin Luo (QuantCon NYC 2017) Phoebe Jordan : Mar 8, 2018
Applies web scraping, distributed cloud computing, NLP and machine learning techniques to systematically analyze corporate filings from the EDGAR database. Yin Luo's NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors.
Machine Learning Part 1 : Intro to Advances in Financial Machine Learning by Dr Marcos Lopez de Prado Anthony Cavallaro : Aug 23, 2018
Machine Learning Part 2 : Data Structures for Financial Machine Learning Anthony Cavallaro : Oct 1, 2018
Machine Learning Part 3 : Labeling Data for Financial Machine Learning Anthony Cavallaro : Jan 18, 2019
Wikipedia: Reinforcement Learning
Reinforcement Learning API Raphael Meudec : Aug 1, 2018
Deep Q-Learning with Keras and Gym keon : Feb 6, 2017
Reinforcement Learning Denny Britz : May 29, 2018
Reinforcement Learning for Stock Prediction Siraj Raval from Edward Lu : Sep 7, 2017
Deep Reinforcement Learning in Trading Saeed Rahman : May 11, 2018
Time Series Forecasting using Statistical and Machine Learning Models Jeffrey Yau : Dec 21, 2017
Deep Reinforcement Learning - MDP, Q-Value, Q-Learning, policy gradients, Actor-Critic Stanford University : Aug 11, 2017
Learn How to Build a Model in Python to Analyze Sentiment from Twitter Data (NLP & LSTM in Keras) Max Margenot : Sep 7, 2018
Use of Deep Learning in Tactical Multi-Asset Strategies Calvin Yu : Feb 22, 2019
The 10 Reasons Most Machine Learning Funds Fail Marcos Lopez de Prado : Jan 27, 2018
Deep Reinforcement Learning in Trading Ashwini Patil, Saeed Rahman, Padmanabha Guddeti : Jul 30, 2018
Deep Learning for Global Tactical Asset Allocation Gaurav Chakravorty, Ankit Awasthi, Brandon Da Silva : Oct 19, 2018
Reinforcement Learning - Q Learning Arthur Juliani : Aug 26, 2016 | Part 0 - Part 8
Deep Reinforcement Learning in Trading Saeed Rahman : Jul 30, 2018 | LinkedIn + Quantopian strategy workflow
Multivariate Time Series Forecasting with LSTMs in Keras Jason Brownlee : Aug 14, 2017
Linear Algebra for Machine Learning Jason Brownlee : Feb 21, 2018
Linear Algebra Cheat Sheet for Machine Learning Jason Brownlee : Feb 23, 2018
How to Develop Convolutional Neural Network Models for Time Series Forecasting Jason Brownlee : Nov 12, 2018
How to Develop LSTM Models for Time Series Forecasting Jason Brownlee : Nov 14, 2018
Using Machine Learning to Predict Stock Prices Vivek Palaniappan : Oct 30, 2018
Not sure that this is the place to talk about this...so can take it to another thread if not cool.
The current example of ML in the Q pipeline is @Wiecki 's above. I've tried using it quite a bit, yet have never gotten any compelling results out of it, even though it is embedded in all my contest algos. I'd like to discuss the underlying algorithm used for it with the goal of both trying to understand the fundamentals and the limitations of such a classifier, and how to move onto better solutions.
For the purposes of this discussion, I'll talk colloquially about the algorithm, in the hope that together we can get some traction of understanding on how to use AI/ML techniques in computational finance.
Let's outline how I use the ML method above, so that we can see if I'm understanding it correctly.
The main problem I see with the above method is that it is not taking any sequential information into account, so therefore one has to take potentially large unknown size time-windows and large unknown size number of assets and factors to fracture the search space to actually learn anything useful.
I liken it to taking the set of points [i, returns(i), f1(i), ..., fk(i), classifier(i)], where i is time, throwing them up in the air, and having them land in the data-array list totally randomly, and then learning the surfaces needed for the classification...with no regard for the fact that all the asset time series are stochastically-continuous traces that are not just random numbers, but have sequential and spatial cross relations that exhibit periodicity and continuity constraints.
AI models that take into account involve RNN and LSTM networks...still not sure that this works better in the environment we're talking about, but the current environment, without sequential information seems inefficient, and I can't seem to get it to be effective.
Here are some links, I believe, to some resources to move forward. I haven't read all of them, yet am willing to discuss them from a learning point of view.
Thanks @Karl for your detailed response!
Your CAPM comments cement what I've seen experimentally wrt ML, and in retrospect...what did I expect to see? ....
Do you consider Morningstar fundamentals as curated data? It's not ex-post trade data though...what is an example of that (showing my non-finance background) ?
I have yet to look at RNN for finance, yet expect to do that soon.
Have been working on a cloud platform to allow long and deep analysis offline from Q, which is what I feel is needed to make any progress using ML for trading.
Thanks for the links above.
I really appreciate the openness of the Q forum and, in particular this thread.
Machine learning and artificial intelligence both are related to each other. Machine learning applications are used almost everywhere. Leading companies like Facebook uses artificial intelligence for their advancement.