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LaTeX template for theses published at Department of Mathematics of University of California San Diego (UCSD). Source: Graduate Student Resources—UCSD. This template was originally published on ShareLaTeX and subsequently moved to Overleaf in November 2019.

NSF-formatted biosketch, according to 2020 proposal preparation guide: https://www.nsf.gov/pubs/policydocs/pappg20_1/index.jsp

This is a presentation format for Thesis presentations in Vision Processing Laboratory, Information Technology University (ITU), Lahore. Anyone else or other Lab people can use this template.

Grant Morgan's First LaTeX Resume

Mall för verksamhetsberättelser

Clone from https://github.com/surabhibhargava/LaTex-Resume in order to use this template.

"ModernCV" CV and Cover Letter En español

Esta es una plantilla para hacer una tesis según los protocolos de la Biblioteca de la Universidad de Concepción. Sirve tanto para tesis de pregrado como de postgrado. Todos los comandos están comentados para su mejor comprensión y tiene instrucciones claras de cómo cambiar los textos necesarios para que se ajuste a los datos del usuario. Espero que te sirva y éxito en tu Tesis!

In the world of information technology where huge amount of useful information is available and easily accessible, we investigate an approach to utilize this information in Algorithmic Trading. Algorithmic trading involves implementation of a strategy using computer programs to automatically buy and sell financial instruments to generate profit at a speed and frequency that is impossible for a human trader. High Frequency Trading (HFT) is one type of algorithmic trading characterized by high turnover and high order-to-trade ratios. There are different strategies that can be applied to HFT. We propose a framework to utilize information available in the form of news articles, which can be used in stock trading at high frequency. We use semantic values of news articles for different stocks to generate buy/sell signals at a high frequency. We demonstrate the performance of our framework by simulating stock trade based on generated buy/sell signals for a small period of time.