The video focuses on using Monte Carlo simulations to predict stock price movements for Apple, utilizing historical data to test various investment strategies with a hypothetical bankroll, starting at $1,000. The results indicate a slight positive bias in predictions and modest returns on investment, while the creator emphasizes the importance of conducting personal research and understanding the underlying code used in the simulations.
In the second part of the AI stock predictions series, the focus shifts to a set of files that perform comprehensive Monte Carlo simulations for stock trading. These simulations utilize historical stock data, specifically for Apple, to predict future price movements based on varying lengths of prior data. The simulations are designed to test different investment strategies using a hypothetical bankroll, starting with $1,000, and have shown positive returns across various time frames, including 3, 20, and 60 days of historical data. The results indicate that the model can predict stock price movements with a degree of accuracy, which is somewhat surprising.
The process begins with a dataset containing daily stock prices, including open, high, low, close, and volume, starting from November 2023. The simulations take a specified range of historical closing prices to predict the next day’s price. By automatically selecting different segments of the dataset, the model generates predictions multiple times, accumulating data for performance analysis. The results are saved in detailed reports that include statistical data and trading simulation outcomes, allowing for a thorough evaluation of the model’s effectiveness.
The performance report generated from the simulations includes metrics such as direction accuracy and mean absolute error, providing insights into the model’s predictive capabilities. For instance, using a 3-day historical data range, the direction accuracy ranged from 48% to 57%, indicating a slight positive bias in predictions. The trading simulation aspect involves executing mock trades with the hypothetical bankroll, tracking wins and losses, and ultimately showing a modest return on investment. However, the creator emphasizes that this is not financial advice and encourages viewers to conduct their own research.
The video also outlines the structure of the code used for the simulations, which consists of several files that handle different aspects of the Monte Carlo simulations. These files allow users to specify various parameters, such as the length of historical data to analyze and the model to use for predictions. The complexity of the code is significant, with around a thousand lines dedicated to generating reports and managing data. The creator plans to provide a detailed code review in future segments, highlighting the importance of understanding the underlying logic behind the simulations.
Finally, the video promotes the creator’s Patreon, where viewers can access the project files and additional resources, including courses on coding and AI. The creator emphasizes the value of becoming a patron, as it provides access to a wealth of projects and educational content. The overall aim of the video is to showcase the potential of AI-driven stock predictions through Monte Carlo simulations, while also encouraging responsible trading practices and further exploration of the subject.