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This study investigates the application of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for predicting Bitcoin prices, with the objective of enhancing investment approaches. Given Bitcoin's significant volatility and inherent risk, accurate price forecasting is vital for making strategic decisions. The research evaluates model performance by analyzing Bitcoin price data spanning May 25, 2020, to May 25, 2024, employing quantitative methodologies like experimental design and predictive analysis. Results indicate the LSTM model achieves superior predictive accuracy over the RNN model, demonstrated by reduced Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) figures. Additionally, the research delves into how these models affect the Dollar Cost Averaging (DCA) investment strategy, offering perspectives on the advantages and drawbacks of using machine learning to optimize cryptocurrency investments. 

This study examines whether Large Language Models (LLMs) can support momentum-based breakout strategies for retail investors by analysing technical charts and validating statistical patterns. We analysed 3,621 price charts (2023–2025) to assess LLMs’ ability to identify trading setups and deviations from random walk behaviour. 13,624 tests were run on 1,204 trades using various statistical tests. While 70% of patterns conformed to random walk behaviour in the short term (7–15 days), momentum signals rose to 26.42% over longer periods (50–250 days). Complex prompting produced both higher random walk classification (72.41% vs. 69.29%) and better returns than single-shot prompting. Incorporating Gamma Exposure (GEX) improved caution but reduced return rates. Discrepancies of up to 20% between JavaScript and Python implementations underscored LLMs’ current limitations in statistical precision. Overall, LLMs may democratise technical analysis but still require human oversight, structured prompting, and validation through traditional methods.