## 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 […]

Sometimes circumstances require that you calculate your position using no information other than knowledge of your previous direction and distance traveled. Of course, this statement specifically refers to marine navigation, but it serves as a rather good metaphor for life and leadership. Two years ago I became “Emily”, drawing courage only from deep confidence in […]

## RNAfold’s and RNAcofold’s predicted dG correlates with sequence length

This seems rather obvious, but I decided to double check before building a machine learning model based on RNAfold’s and RNAcofold’s predictions involving sequences of varying length. Method I generated 30,000 random RNA sequences of random length between 15 and 30 bases. I ran RNAfold on this list; and RNAcofold on this same list where […]

## a fashionista’s astronomy calculations

Cheers to all my fellow chic women in STEM! I take a lot of photos and film of myself, for reasons that mostly have to do with fashion. However, I do not have control over my lighting considering I do this outside without any equipment other than a video camera. So I try to shoot […]

## on leadership: things I learned by managing an intern

Last summer I took on the responsibility of managing an intern. My supervisor retained the “official” management role, but let me do all the work so that I could gain management experience. At the same time, I mentored a younger friend working as an intern at another company, letting her share her struggles and triumphs […]

## how I make a living: what is bioinformatics? (part #1)

I’m constantly asked to explain what I do for a living. Here is an attempt to do so in laypersons’ terms. I’ll assume my readers are non-scientists and non-engineers, but that they’ve taken a high school biology class. “Bioinformatics” is the application of mathematics and computer science to biological data, particularly molecular biology data. By […]