Automated stock market analysis
TEXT_INPUT:Ask the user to input the stock symbol or company name.;;HTTP_REQUEST:Fetch the latest stock price data for the given stock symbol or company name.;;CHATGPT:Prompt ChatGPT with the retrieved stock price data and ask for analysis or prediction.;;HTTP_REQUEST:Fetch relevant news articles related to the given stock symbol or company name.;;DISPLAY_OUTPUT:Display the stock price data, analysis result, and relevant news articles to the user.
Generative AI models could revolutionize automated stock market analysis. Language model like GPT-3 could parse news articles, press releases, and financial reports, reformatting them into structured data for faster insights. For instance, GPT-3 could preprocess recent statements from the Federal Reserve, identifying key takeaways related to economic outlook and subsequent effects on certain stock sectors, assisting traders in making speedy decisions.
Furthermore, AI image generators could analyse visual data: chart patterns, candlestick graphs for instance. The AI could identify patterns indicating bullish or bearish market signals, greatly enhancing technical analysis. Also, such AI models could generate simulated data for back-testing. For instance, the AI could create a set of plausible price charts for the next month, based on historical data, allowing traders to evaluate potential strategies in varying future scenarios, thus enhancing decision-making capabilities.
How to build with Clevis
This example application was created using Clevis, a versatile tool that enables users to build a wide variety of AI applications. The sample app denotes how an automated stock market analysis could be executed.
In the first step, the user is requested to provide the stock symbol or company name through a Text Input. The app then fetches the latest stock market data linked to the provided user input by executing an Http Request.
Once the data is retrieved, it leverages OpenAI's powerful language model, ChatGPT. The app prompts ChatGPT with the obtained stock data to solicit an in-depth analysis or prediction, which combines AI processing power with up-to-date stock information. This is indicated by the ChatGPT step.
Subsequently, the app makes another Http Request to fetch pertinent news articles that are relevant to the given stock symbol or company name. This provides contextual information alongside the technical stock analysis.
Finally, the culmination of these processes is the Display Output stage. This step presents to the user the original stock price data, the analysis result interpreted by ChatGPT, and the relevant news articles fetched earlier. Hence, it provides a comprehensive, AI-powered stock market overview.
Apps of this nature and others within the same data analysis area can be effectively and efficiently built using Clevis.