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Description
This thesis explores the profitability of various technical trading strategies to explore their ability, generate returns in the American stock market. All strategies are based on five popular technical indicators, including the volume-weighted average price, moving average convergent-divergent index, Bollinger bands, support/resistance, and simple momentum trading. Most strategies were tested

This thesis explores the profitability of various technical trading strategies to explore their ability, generate returns in the American stock market. All strategies are based on five popular technical indicators, including the volume-weighted average price, moving average convergent-divergent index, Bollinger bands, support/resistance, and simple momentum trading. Most strategies were tested from 2019-2022 and tested for the SPY and QQQ stocks, representing the S&P 500 and NASDAQ. 3 of the 26 strategies had win rates of over 50%, but several were able to greatly outperform broader market returns. The best performing strategies were based on simple momentum trading, while the MACD and Bollinger Bands produced the worst results. Some strategies based on simple momentum trading or Bollinger bands found results greatly exceeding standard market returns in recent years, but most do not.
ContributorsEberle-Taylor, Nicholas (Author) / Boguth, Oliver (Thesis director) / Ikram, Atif (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor)
Created2022-05
Description
My project has been a long journey, one that I have learned a tremendous amount on. The final version of my project has come out to be a booklet teaching first time users of code and python the basic steps of getting started and some vital information that I learned

My project has been a long journey, one that I have learned a tremendous amount on. The final version of my project has come out to be a booklet teaching first time users of code and python the basic steps of getting started and some vital information that I learned while I was learning the language. I started my thesis with the idea of creating a portfolio of stock, bonds and commodities to determine the best allocation of your money over a 30-year period. To do this, I needed to learn how to code and become proficient quickly so I could create a program that would be powerful enough as well as spit out the correct output in the end. Unfortunately, I fell short of being able to build this portfolio out. I took on the challenge of learning Python on my own with no knowledge of any coding language to see if I could pull the whole project together. I failed, but I learned so much along the way and that I think is more valuable than anything. Since I was unable to complete my code, I shifted my attention to creating a small booklet on the basics of getting started in Python as if you have never looked at a coding language. Many of the tips I discuss in my booklet are problems I struggled with when I began. In the beginning I couldn’t even figure out how to get to a coding platform to begin my work, so I began to research and found many helpful tips that took me quite a while to understand.
ContributorsToumbs, Jason David (Author) / Boguth, Oliver (Thesis director) / Schreindorfer, David (Committee member) / Department of Finance (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05