Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
We study an idealized model of a wind-driven ocean, namely a 2-D lid-driven cavity with a linear temperature gradient along the side walls and constant hot and cold temperatures on the top and bottom boundaries respectively. In particular, we determine numerically the response on flow field and temperature stratification associated…
We study an idealized model of a wind-driven ocean, namely a 2-D lid-driven cavity with a linear temperature gradient along the side walls and constant hot and cold temperatures on the top and bottom boundaries respectively. In particular, we determine numerically the response on flow field and temperature stratification associated with the velocity of the lid driven by harmonic forcing using the Navier-Stokes equations with Boussinesq approximation in an attempt to gain an understanding of how variations of external forces (such as the wind over the ocean) transfer energy to a system by exciting internal modes through resonances. The time variation of the forcing, accounting for turbulence at the boundary is critical for allowing penetration of energy waves through the stratified medium in which the angles of the internal waves depend on these perturbation frequencies. Determining the results of the interaction of two 45 degree angle wave beams at the center of the cavity is of particular interest.
With the coming advances of computational power, algorithmic trading has become one of the primary strategies to trading on the stock market. To understand why and how these strategies have been effective, this project has taken a look at the complete process of creating tools and applications to analyze and…
With the coming advances of computational power, algorithmic trading has become one of the primary strategies to trading on the stock market. To understand why and how these strategies have been effective, this project has taken a look at the complete process of creating tools and applications to analyze and predict stock prices in order to perform low-frequency trading. The project is composed of three main components. The first component is integrating several public resources to acquire and process financial trading data and store it in order to complete the other components. Alpha Vantage API, a free open source application, provides an accurate and comprehensive dataset of features for each stock ticker requested. The second component is researching, prototyping, and implementing various trading algorithms in code. We began by focusing on the Mean Reversion algorithm as a proof of concept algorithm to develop meaningful trading strategies and identify patterns within our datasets. To augment our market prediction power (“alpha”), we implemented a Long Short-Term Memory recurrent neural network. Neural Networks are an incredibly effective but often complex tool used frequently in data science when traditional methods are found lacking. Following the implementation, the last component is to optimize, analyze, compare, and contrast all of the algorithms and identify key features to conclude the overall effectiveness of each algorithm. We were able to identify conclusively which aspects of each algorithm provided better alpha and create an entire pipeline to automate this process for live trading implementation. An additional reason for automation is to provide an educational framework such that any who may be interested in quantitative finance in the future can leverage this project to gain further insight.
