blockchain is punk

Wide distribution of power lies at the heart of anarchistic thinking. While “punk” and “anarchy” do not necessarily imply one another, they often overlap. Punks tend to balk at centralized authority, as do anarchists. A short leap of logic concludes that we therefore dislike centralized technology. Concentration in technology long parallels concentration in political power. […]

machine learning in FOREX (part one: establishing a performance baseline)

Introduction We’ve been applying machine learning to FOREX price prediction. The performance of our models varies widely, so to establish a baseline we created a simple linear regression model with which we can compare performance of more sophisticated models against. What We Are Trying To Do Given a time-series of 26 four-hour price samples, we […]

autocorrelation in FOREX

To inform the construction of a machine learning-based price prediction algorithm, we want to understand how many lags prove statistically significant with regard to autocorrelation in the seven major FOREX pairs. So we first choose 10,000 random time points between January 1, 2000 and January 1, 2017 for each of the seven pairs. Then we […]

artificial intelligence in fashion (part two: a first step)

In my last post, “artificial intelligence in fashion (part one: brainstorming)“, I produced a list of big ideas on how machine learning and artificial intelligence may be applied to the fashion industry. I addressed sizing, marketing, and design activities when brainstorming this list. This post doesn’t specifically cover an artificial intelligence solution, but it lays […]

artificial intelligence in fashion (part one: brainstorming)

Brainstorming as usual: Fashion dictums involve many IF-THEN-ELSE rules. One can convert this into a decision engine (inference engine). User specifies their body shape, and a recommendation engine selects suitable clothing for them, taking into account the user’s tastes. Upload an image of a dress you want to buy, and specify the dress’s given size. […]

applying market basket analysis to the stock market

I’ve started learning market basket analysis and decided to test drive my knowledge against the stock market: I own a (proprietary) database of predicted stock causality relationships. An export to tabular form looks something like this: I won’t tell you what the “causality” is, as that is the proprietary part, and the example data shown […]

pseudo-harmonic FOREX prediction with machine learning (part one)

“Harmonic” trading methods seek patterns in the relationships between neighboring peaks and valleys in the time series. Particularly, harmonic traders seek pre-specified ratios in the price differences among a series of peaks and valleys. For example, a trader might observe the following pattern: Let A, B, C, D, and E be the points in the […]

picking stocks by graph database (part 2: machine learning)

In our last post, we demonstrated a graph database created to enable study of the stock market, particularly the study of causality relationships. So how to proceed from there? At this stage we want to pick winning stocks, not write an academic paper, so our focus turns toward practical machine learning. Source Data We start […]

picking stocks by graph database (part one)

Historical stock price data comes readily available at daily resolution. So we calculated the Granger causality for each pair of stocks we hold data for, at one and two day lags (testing the question “does daily percent change in volume for stock X Granger cause daily percent change in adjusted close price for stock Y?”). […]