AI-generated investment strategies
TEXT_INPUT:Prompt the user to enter their risk tolerance and investment horizon.;;CHATGPT:ChatGPT to provide investment options based on user's risk tolerance and investment horizon.;;HTTP_REQUEST:Fetch real-time market data for the recommended investment options.;;DISPLAY_OUTPUT:Display the investment options and related market data to the user.
Generative AI models such as Language Models (LLMs) and image generators can provide significant insights for AI-generated investment strategies. For instance, ChatGPT, another model by OpenAI, can analyze vast datasets of financial reports, blog posts, and news articles to predict future market trends and recommend investment opportunities. This model's capability to understand human language allows it to decipher complex economic reports, providing a competitive edge to investors.
Image generators can analyze graphical data such as charts, graphs, or heatmaps to identify patterns and predict future movements. They can evaluate technical indicators from these visual representations to guide investment decisions. For example, an image generator could interpret patterns from a stock's price chart to determine its future trajectory. Hence, AI models can fully automate the process of analyzing, predicting, and suggesting valuable investment strategies, providing an edge in the world of finance.
How to build with Clevis
This is an example of an application developed using Clevis - a tool that enables AI developers to leverage artificial intelligence within various application areas. This particular application is named 'AI-generated Investment Strategies'. Its functionality is to support users in making well-informed investment decisions by providing AI-generated investment strategies.
Four key steps comprise the operation of this app. The first step (Text Input) prompts the user to detail their risk tolerance and investment timeline. Then, in the second step, Clevis uses ChatGPT (a language model developed by OpenAI) to generate suitable investment options based on the user's inputted risk level and investment horizon. Next, an Http Request is made to fetch the most recent market data corresponding to the recommended investment options. This allows the application to provide the user with up-to-date financial information, ensuring more precise and timely investment advice. Lastly, in the Display Output step, the application presents the user with both the AI-recommended investment options and their associated real-time market data. Through this approach, users are equipped with strategic and data-driven guidance to support their investment decisions.
By using Clevis, similar applications focusing on offering AI-based investment advice can be readily developed.