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

Data sources:

http://finance.yahoo.com/

https://fred.stlouisfed.org/

https://stooq.com

Data and the cleaning programs:

https://github.com/timestocome/StockMarketData

Recommended Reading:

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

On my reading list:

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

Useful Websites:

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

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 http://google.github.io/CausalImpact/ to go with this paper )

https://gbeced.github.io/pyalgotrade/ Python Algorithmic Trading Library

http://pybrain.org/ PyBrain Machine Learning Library

https://github.com/CodeReclaimers/neat-python Python NEAT Library for evolving neural networks

Podcasts:

http://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