Due to coronavirus, this internship was shortened to five weeks and was fully remote. This summer I worked as an intern for J.P. I am also a Prep for Prep alum, which is a leadership development program that offers promising students of color access to private school education. My name is Lawrence Huang, and I’m a rising senior at Carnegie Mellon studying Physics. In the future, I would like to examine the multi-scale nature of the problem as well as expand the study to include more clusters in the unsupervised analysis. I also find that further filtering the data by time, sector, or profitability doesn’t add predictive power to the clusters.įurther exploration of the data is still needed. To my surprise, all methods unanimously agree that simple harmonic functions best characterize the data. Specifically, I used various unsupervised machine-learning methods to cluster the time-series data into separable classes. In this study, I used a data-driven approach based on objective machine-learning methods to identify distinct patterns that best characterize the data and enable examination of the patterns’ predictive power. The patterns and their interpretations, however, are subjective and may lead to inconsistent inference and biased interpretation. Analysts use chart patterns as indicators to predict future price movements. Chart patterns are a commonly-used tool in the analysis of financial data.
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