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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Use deep learning, genetic programming and other methods to predict stock and market movements
| Date | Stars |
|---|---|
| 2026-07-31 | 434 |
| 2026-08-01 | 434 |
| 2026-08-06 | 434 |
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# StockPredictions 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 <b>Data sources:</b> http://finance.yahoo.com/ https://fred.stlouisfed.org/ https://stooq.com <b>Data and the cleaning programs:</b> https://github.com/timestocome/StockMarketData <b>Recommended Reading:</b> http://www.e-m-h.org/Fama70.pdf Efficient Market Hypothesis http://faculty.chicagobooth.edu/workshops/finance/pdf/Shleiferbff.pdf Bubbles for FAMA http://www.unofficialgoogledatascience.com/2017/04/our-quest-for-robust-time-series.html How Google does series predictions http://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 http://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 http://lib.ugent.be/fulltxt/RUG01/001/315/567/RUG01-001315567_2010_0001_AC.pdf An empirical analysis of algorithmic trading on financial markets http://www.radio.goldseek.com/bachelier-thesis-theory-of-speculation-en.pdf The Theory of Speculation, L. Bachelier http://dl.acm.org/citation.cfm?id=1541882 Anomaly Detection: A Survey 2009 ACM http://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 ) http://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 <b>On my reading list:</b> http://socserv.mcmaster.ca/racine/ECO0301.pdf Nonparametric Econometrics: A Primer http://natureofcode.com/ The Nature of Code http://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 <b>Useful Websites:</b> http://www.nber.org/ The National Bureau of Economic Research https://fred.stlouisfed.org/ FRED, Federal Reserve Bank of St Louis http://www.zerohedge.com/ ZeroHedge, mostly noise, occasionally something useful appears <b>Cool tools:</b> 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 http://google.github.io/CausalImpact/ to go with this paper ) https://gbeced.github.io/
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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