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 […]

demonstrating a simple expert system with cascading effects (Clojure version)

We originally implemented this demonstration in CLIPS (see this post) but decided Clojure would provide a better platform due to advantages discussed below. “An expert system is a program capable of pairing up a set of facts with a set of rules to those facts, and execute some actions based on the matching rules. [1]” […]

encoding fashion rules into mathematical data structures (part one)

As we build our fashion recommendation engine, we seek rules to populate it with. With few exceptions (e.g. [1]), we find these rules encoded in prose or infographic form, rather than a semantic web form suitable for computation. For example, [2] provides written advice on dressing fabulously for a “rectangular” women’s body type. The writers […]

demonstrating a simple expert system with cascading effects (CLIPS version)

We demonstrate a more modern implementation of this system in Clojure here. “An expert system is a program capable of pairing up a set of facts with a set of rules to those facts, and execute some actions based on the matching rules. [1]” At 180 centimeters, Emily Williams stands tall. She carries a “rectangular” […]

summary of our FOREX experiments and next steps

We started by building a support vector machine model based on features used in harmonic trading, with the idea that ideal “harmonic” ratios can be learned rather than explicitly specified. This worked on testing sets but not when we started trading with it. We abandoned the model before we realized that we need to manage […]

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. […]

rapidly identifying potential CRISPR/Cas9 off-target sites (part one)

Before we can score segments in the genome having a small number of mismatches to a CRISPR for their off-target risk, we must first find these segments. Searching for every possible mismatch permutation proves computationally expensive, so we apply the following heuristic: We only search for mismatches in the top positions relevant to CRISPR efficiency. […]

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 […]