Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements.
Both successful and unsuccessful experiments will be posted. This section is things that are currently being explored. Completed projects will be wrapped up and moved to another repository to keep things simple.
The main goal of this project is to learn more about time series analysis and prediction. The stock market just happens to have lots of complicated time series and available data
The first evolving neural net does the best job of predicting daily changes. It's impressive. That'll be my first go to tool
The NASDAQ Evolved Network is a good simple example that should be easy to apply to any index
Data sources:
Data and the cleaning programs:
https://github.com/timestocome/StockMarketData
Recommended Reading:
https://www.e-m-h.org/Fama70.pdf Efficient Market Hypothesis
https://faculty.chicagobooth.edu/workshops/finance/pdf/Shleiferbff.pdf Bubbles for FAMA
https://www.unofficialgoogledatascience.com/2017/04/our-quest-for-robust-time-series.html How Google does series predictions
https://www.econ.ucla.edu/workingpapers/wp239.pdf Let's Take the Con Out of Economics
https://www.manning.com/books/machine-learning-with-tensorflow Meap Machine Learning with TensorFlow
https://www.amazon.com/gp/product/B01AFXZ2F4/ Everybody Lies, Big Data, New Data, and What the Internet can tell us about who we really are
https://www.amazon.com/gp/product/B06XDWV2Z2 The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets
https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series Finding anomalies in time series
https://www.wired.com/2009/02/wp-quant/ Wired: The Formula that Killed Wall St
https://onlinelibrary.wiley.com/doi/10.1111/j.1467-6419.2007.00519.x/abstract What do we know about the profitability of technical analysis
https://eng.uber.com/neural-networks/ Engineering extreme event forecasting at Uber with RNNs
https://lib.ugent.be/fulltxt/RUG01/001/315/567/RUG01-001315567_2010_0001_AC.pdf An empirical analysis of algorithmic trading on financial markets
https://www.radio.goldseek.com/bachelier-thesis-theory-of-speculation-en.pdf The Theory of Speculation, L. Bachelier
https://dl.acm.org/citation.cfm?id=1541882 Anomaly Detection: A Survey 2009 ACM
https://www.mrao.cam.ac.uk/~mph/Technical_Analysis.pdf Technical Analysis
https://is.muni.cz/th/422802/fi_b/bakalarka_final.pdf Prediction of Financial Markets Using Deep Learning ( see: https://github.com/timestocome/FullyConnectedForwardFeedNets for an example fully connected deep learning network )
https://www.doc.ic.ac.uk/teaching/distinguished-projects/2015/j.cumming.pdf An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain
On my reading list:
https://socserv.mcmaster.ca/racine/ECO0301.pdf Nonparametric Econometrics: A Primer
https://natureofcode.com/ The Nature of Code
https://www.penguinrandomhouse.com/books/314049/scale-by-geoffrey-west/9781594205583/ Scale: The universal laws of growth...
https://en.wikipedia.org/wiki/The_Drunkard%27s_Walk The Drunkard's Walk
Useful Websites:
https://www.nber.org/ The National Bureau of Economic Research
https://fred.stlouisfed.org/ FRED, Federal Reserve Bank of St Louis
https://www.zerohedge.com/ ZeroHedge, mostly noise, occasionally something useful appears
Cool tools:
https://facebookincubator.github.io/prophet/docs/quick_start.html Facebook Prophet - Python and R time series prediction library
https://research.google.com/pubs/pub41854.html Inferring causal impact using bayesian structural time series models ( Google has an R package https://google.github.io/CausalImpact/ to go with this paper )
https://gbeced.github.io/pyalgotrade/ Python Algorithmic Trading Library
https://pybrain.org/ PyBrain Machine Learning Library
https://github.com/CodeReclaimers/neat-python Python NEAT Library for evolving neural networks
Podcasts:
https://www.podcastchart.com/podcasts/berkshire-hathaway-2017-annual-shareholders-meeting/episodes/berkshire-hathaway-vice-chairman-charlie-munger-speaks-with-yahoo-finance-editor-in-chief-andy-serwer 2017 Berkshire Hathaway Shareholder's Meeting