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 […]

“cool” as information flow

“Coolness” is a measure of creative information’s degree of flow through a social network. Individuals merely channel and contribute to this flow as it progresses through such networks, they do not contain or retain coolness within themselves. However some individuals prove more receptive to the incoming information and more effective at transmitting it forward than […]

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 […]

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?”). […]

Bayesian method for filtering out mRNA turnover rate bias from siRNA knockdown measurements

Abstract siRNA performance prediction calculations for a given siRNA may be divided into two broad categories: functions of the siRNA’s sequence, hereafter referred to as “intrinsic” properties of the siRNA, and functions of the target mRNA, hereafter referred to as “extrinsic” properties of the siRNA. When training a statistical or machine learning model to select […]