Before we begin this article offers no financial advice and is only an experiment carried out using ChatGPT and its Advanced Data Analysis feature. However the integration of OpenAI’s new Advanced Data Analysis previously known as Code Interpreter with stock market trading has become a focal point for many investors and traders.
A recent experiment carried out by All About AI provides an overview of how you can use ChatGPT’s advanced data analysis capabilities to create a technical stock report and a 30-day trading plan. The experiment was not designed to provide financial advice, but rather to offer a glimpse into the potential of combining artificial intelligence with financial market analysis.
The experiment began with the setting of custom instructions and the acquisition of fresh stock prices from Yahoo Finance. The stock chosen for this experiment was Apple and a CSV file was uploaded to ChatGPT containing information about Apple’s stock prices, which served as the foundation for the ensuing analysis.
ChatGPT stock market analysis experiment
The advanced data analysis capabilities of ChatGPT were then put to work. The system was tasked with creating a detailed technical trading report on Apple’s stock, along with a comprehensive 30-day trading plan. The data used for this analysis included open, high, low, close, adjust, and close, and volume for about one year, all sourced from Yahoo Finance.
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The technical trading report was a comprehensive document that included trade analysis, technical indicators like moving averages, RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence). It also incorporated risk management techniques, such as never risking more than 1-2% of trading capital on a single trade. The report concluded with an expert analysis on Apple for technical trading, including key findings, moving averages, RSI, MACD, volume, and conservative risk management.
30 day trading plan
The 30-day trading plan was equally detailed, including back testing, risk management, and a detailed plan for each week. The plan was conservative in nature, including preparation, monitoring, reevaluation, mid-month review, and final week actions. It also included notes on keeping an eye on news and quarterly reports, reconsidering position sizes and risk parameters if the trading capital falls below a certain amount, and adjusting the stop loss and take profit percentages.
The experiment also simulated the trading strategy for a single day, with the action being to hold the stock as the price did not hit the stop loss or take profit levels. This simulation provided a practical application of the trading plan, offering a glimpse into how it might perform in real-world conditions. The experiment is set to be revisited in 30 days to assess how the trading plan would have performed. This will provide valuable insights into the effectiveness of the plan and the potential for improvements.
While this experiment was intended for fun and learning, it underscores the potential of integrating advanced data analysis with stock market trading. It demonstrates how artificial intelligence can be leveraged to create detailed trading plans and technical reports, potentially offering traders and investors a new tool in their financial arsenal. However, it’s important to remember that this experiment is not intended as financial advice, but rather as a demonstration of the capabilities of advanced data analysis in the context of stock market trading.
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