A Docker configuration for machine learning

I loved The Biggest Bluff: How I Learned to Pay Attention, Master Myself, and Win, by Maria Konnikova.

Reinforcement learning is a machine learning paradigm that can learn behavior to achieve maximum reward in complex dynamic environments, as simple as Tic-Tac-Toe, or as complex as Go, and options trading. In this post, we will try to explain what reinforcement learning is, share code to apply it, and references to learn more about it. First, we’ll learn a simple algorithm to play Tic-Tac-Toe, then learn to trade a non-random price series. Finally, we’ll talk about how reinforcement learning can master complex financial concepts like option pricing and optimal diversification.

Shiller’s CAPE ratio is a popular and useful metric for measuring whether stock prices are overvalued or undervalued relative to earnings. Recently, Vanguard analysts Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz, and Joseph H. Davis have written a very interesting paper on forecasting equity returns using Shiller’s CAPE and machine learning: “The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach“, which effectively applies machine learning to an import investing problem. Image source: Improving U.S. stock return forecasts: A “fair-value” CAPE approach, Joseph Davis, et al. (2017)

As a data scientist, you might say…“A blog post about thresholds? It’s not even a data science problem, it’s more of a business decision.” And you would not be wrong! Threshold selection lacks the appeal of say, generative adversarial networks.

Cryptocurrencies are useless. They’re only used by speculators looking for quick riches, people who don’t like government-backed currencies, and criminals who want a black-market way to exchange money. - Bruce Schneier

I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity. - Oliver Wendell Holmes

The StreetEYE news aggregator experiment came to an end on 3/31/2019. Many thanks for supporting StreetEYE over the years!

Many people are talking about ‘quantitative tightening’ and ‘balance sheet reduction’, and some people are blaming it for market volatility, discussed here, here, here. IMHO, blaming balance sheet reduction for market volatility is cargo cult mumbo jumbo.

It’s been more than a year since we posted our last list of people to follow on Twitter for financial news. Time for an update!

In this piece, we review machine learning methods for classification. Then we apply classification to the classic value/momentum factors (spoiler: the results are a bit too good).

This is a tutorial on running Jupyter Notebook on an Amazon EC2 instance. It is based on a tutorial by Chris Albon, which did not work for me immediately (itself based on a tutorial by Piyush Agarwal). But I tweaked a few things and got it working.

Wall Street never changes, the pockets change, the suckers change, the stocks change, but Wall Street never changes, because human nature never changes. _ Jesse Livermore

About once a year I’ll post the top Twitter accounts to follow. It’s a fun piece of social media analytics, and I’ll try to do it again later this year, after seeing if I can sidestep Twitter’s efforts to cut me off.

We explore the paper “Dynamic Return Dependencies Across Industries: A Machine Learning Approach”, by David Rapach, Jack Strauss, Jun Tu and Guofu Zhou, and then try to improve the results with more sophisticated machine learning approaches.