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Full-Text Articles in Physical Sciences and Mathematics
Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii
Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii
Undergraduate Honors Theses
Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. …
Using Logical Specifications For Multi-Objective Reinforcement Learning, Kolby Nottingham
Using Logical Specifications For Multi-Objective Reinforcement Learning, Kolby Nottingham
Undergraduate Honors Theses
In the multi-objective reinforcement learning (MORL) paradigm, the relative importance of environment objectives is often unknown prior to training, so agents must learn to specialize their behavior to optimize different combinations of environment objectives that are specified post-training. These are typically linear combinations, so the agent is effectively parameterized by a weight vector that describes how to balance competing environment objectives. However, we show that behaviors can be successfully specified and learned by much more expressive non-linear logical specifications. We test our agent in several environments with various objectives and show that it can generalize to many never-before-seen specifications.
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Undergraduate Honors Theses
Parkinson’s Disease is a degenerative neurological condition that affects approximately 10 million people globally. Because there is currently no cure, there is a strong motivation for research into improved and automated diagnostic procedures. Using Random Forests, a computer can effectively learn to diagnose Parkinson’s disease in a patient with high accuracy (94%), precision (95%), and recall (91%) across the data of over 2800 patients. Using similar techniques, I further determine that the most predictive medical tests relate to tremors observed in patients.
Computational Regiospecific Analysis Of Brain Lipidomic Profiles, Austin Ahlstrom
Computational Regiospecific Analysis Of Brain Lipidomic Profiles, Austin Ahlstrom
Undergraduate Honors Theses
Mass spectrometry provides an extensive data set that can prove unwieldy for practical analytical purposes. Applying programming and machine learning methods to automate region analysis in DESI mass spectrometry of mouse brain tissue can help direct and refine such an otherwise unusable data set. The results carry promise of faster, more reliable analysis of this type, and yield interesting insights into molecular characteristics of regions of interest within these brain samples. These results have significant implications in continued investigation of molecular processes in the brain, along with other aspects of mass spectrometry, collective analysis of biological molecules (i.e. omics), and …