This project compares language model agents, random search and genetic search for financial factor discovery, isolating the contribution of experimental memory under a common evaluation budget.
This project investigates whether financial news sentiment extracted by large language models can improve trading signals, with an emphasis on Apple stock, news timing and rigorous backtesting.
Design and compare decision trees with multiple-variable conditions for interpretable prediction on trading or fraud data.
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