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.
Completed and spun off:
https://github.com/timestocome/StockMarketMovingAverage ( Moving average buy/sell under/over vs buy and hold )
https://github.com/timestocome/StockMarketData Moved data and cleaning programs over here
Spun off, not yet fully finished:
https://github.com/timestocome/StockMarketClustering ( Various clustering techniques )
https://github.com/timestocome/StockMarketLinearRegression ( Experiments in various regression techniques )
Looks promising enough to test on financial data:
https://github.com/openai/evolution-strategies-starter ( Try some GA trading bots with and without NN )
http://surface.syr.edu/cgi/viewcontent.cgi?article=1056&context=eecs_techreports (Predict sunspot cycles with RNN)
http://www.kdnuggets.com/2017/04/time-series-analysis-generalized-additive-models.html ( Time Series Analysis with Generalized Additive Models )
Anomaly Detection: A Survey 2009 ACM $$$$ ( excellent paper, I'm going to try several techniques it covers )
Misc 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://shop.oreilly.com/product/0636920032441.do (O'Reilly Python for Finance, meh mostly a plug for author's library )
To read:
http://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.cs.elte.hu/blobs/diplomamunkak/bsc_matelem/2014/fora_gyula_krisztian.pdf ( Predictive analysis of financial time series )
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 )
http://www.wiley.com/WileyCDA/WileyTitle/productCd-111909657X.html ( The ultimate algorithmic trading system toolbox )
http://quant-econ.net/_static/pdfs/py-quant-econ.pdf ( Quantitative economics with Python )
https://research.google.com/pubs/pub41854.html ( Inferring causal impact using bayesian structural time series models )
http://www.unofficialgoogledatascience.com/2017/03/attributing-deep-networks-prediction-to.html ( Attributing a deep network’s prediction to its input features )
https://www.r-bloggers.com/forecasting-markets-using-extreme-gradient-boosting-xgboost/amp/ (X-Gradient Boosting for series predictions )
https://research.fb.com/prophet-forecasting-at-scale/ ( FB open source Prophet for series prediction )
https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series ( finding anomalies in time series )
https://medium.com/@harvitronix/lets-evolve-a-neural-network-with-a-genetic-algorithm-code-included-8809bece164 (Let's evolve a neural network )
Data sources: