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.
Displaying 1 - 2 of 2
Filtering by
- All Subjects: Automation
- Creators: Economics Program in CLAS
Description
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.
ContributorsYurowkin, Alexander (Co-author) / Kumar, Rohit (Co-author) / Welfert, Bruno (Thesis director) / Li, Baoxin (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Apple’s HomeKit framework centralizes control of smart home devices and allows users to create home automations based on predefined rules. For example, a user can add a rule to turn off all the lights in their house whenever they leave. Currently, these rules must be added through a graphical user interface provided by Apple or a third-party app on iOS. This thesis describes how a text-based language provides users with a more expressive means of creating complex home automations and successfully implements such a language. Rules created using this text-based format are parsed and interpreted into rules that can be added directly into HomeKit. This thesis also explores how security features should be implemented with this text-based approach. Since automations are run by the system without user interaction, it is important to consider how the system itself can provide functionality to address the unintended consequences that may result from running an automation. This is especially important for the text-based approach since its increase in expressiveness makes it easier for a user to make a mistake in programming that leads to a security concern. The proposed method for preventing unintended side effects is using a simulation to run every automation prior to actually running the automation on real-world devices. This approach allows users to code some conditions that must be satisfied in order for the automation to run on devices in the home. This thesis describes the creation of such a program that successfully simulates every device in the home. There were limitations, however, with Apple's HomeKit framework, which made it impractical to match the state of simulated devices to real devices in the home. Without being able to match the current state of the home to the current state of the simulation, this method cannot satisfy the goal of ensuring that certain adverse effects will not occur as a result of automations. Other smart home control platforms that provide more extensibility could be used to create this simulation-based security approach. Perhaps as Apple continues to open up their HomeKit platform to developers, this approach may be feasible within Apple's ecosystem at some point in the future.
ContributorsSharp, Trevor Ryan (Co-author) / Sharp, Trevor (Co-author) / Bazzi, Rida (Thesis director) / Doupe, Adam (Committee member) / Economics Program in CLAS (Contributor) / Department of Management and Entrepreneurship (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05