AI-Powered Investing Assistant - Revolutionizing Stock Analysis Part 5

Introduction

Welcome to the fifth installment of our series on building an AI investing assistant! By the end of this article, you will learn how to construct an investing assistant that is capable of analyzing and responding to natural language questions about any public company. Our goal is to harness AI for efficient analysis and to inform intelligent investment decisions.

Project Overview

In previous sections, we outlined our project and divided the application into three key modules:

Data Loading: In this module, we provided access to real-time data by gathering a company's complete financial data set from a financial API and loading it into a database.
Natural Language to SQL Query Conversion: This component gave our assistant the ability to access the database using simple natural language queries posed by the user.
Question Answering or Synthesis: In this final module, we generate well-formatted and comprehensible responses to the user's questions.

In this article, we will delve deeper into the third module, which synthesizes answers based on user inquiries.

How the Question Answering Works

When a user submits a question, we analyze it alongside the information extracted from our database. We then relay both pieces of information to a large language model (LLM) such as GPT. The model generates an articulate response using the extracted data. For instance, if a user asks about the trend in NVIDIA's operating profit margins over the past three trailing 12 months, the question is converted into a database query that retrieves the relevant margins. This data, along with the original question, is sent to an LLM which produces a coherent answer.

Utilizing real-time data ensures that our application provides relevant and current responses. This significantly reduces the risk of hallucinations from the model and enhances the reliability of the information, which is crucial for investment decision-making.

Coding Implementation

The function responsible for handling question answering takes a text input from the user. This text is passed into the natural language to SQL conversion function that we discussed in the previous video. The output is a dictionary containing:

The original question
A description of the data needed to answer the question
The SQL query created to fetch the data
The actual data retrieved from the database

Next, we format this data for submission to the large language model. The model we employed is GPT-4 Turbo, and we send a structured prompt outlining the task at hand, such as acting as a knowledgeable financial assistant to answer the user's question with the data provided.

Example Use Case

As an example, I queried, "What is the trend in operating profit margins over the last 5 years?" The output returned a succinct summary, indicating an upward trend in operating profit margins and providing specific figures for each year along with a clear interpretation of the data.

However, I noted that the initial textual output could be improved for clarity, especially with financial data.

Enhancing Output Quality

I modified the prompt to instruct the model to format the response in HTML, incorporating tables, lists, headings, and structured paragraphs to better organize the information. New queries, such as the aforementioned one, returned a nicely formatted table accompanied by a clear title and a summary laid out at the top.

This output method enhances readability and presentation, making information much easier to digest for users. Our system can be expanded to incorporate any type of data stored in a database, allowing seamless natural language access to a variety of data sets.

Future Directions

Looking ahead, several next steps are important for the evolution of this assistant:

Testing: Ensuring outputs are accurate and meet user expectations.
Prompt Engineering: Fine-tuning models to improve quality tailored to specific use cases.
User Interface Improvements: Enhancing visual outputs with charts and other graphics.
Incorporating Additional Data Sources: Integrating non-financial data for a more comprehensive analysis.
Utilizing Agents: Implementing AI systems that can manage complex analysis tasks with multiple steps.

As a proof of concept, this project shows promising progress, but there is always room for improvement.

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Keywords

AI Investing Assistant
Natural Language Processing
SQL Query
Financial Data
Large Language Model
Data Analysis
User Interface
HTML Formatting

FAQ

What is an AI investing assistant? An AI investing assistant is a tool that uses artificial intelligence to analyze financial data and help inform investment decisions.
How does the assistant understand natural language queries? The assistant uses a natural language to SQL query conversion module, which translates user questions into database queries.
What does the output look like? The output is formatted in HTML with tables and structured text to enhance readability and presentation.
What are some future improvements planned for the assistant? Future improvements include testing, prompt engineering, user interface enhancements, and integrating additional data sources.
Why is real-time data important for this application? Real-time data ensures that the answers provided are accurate and relevant, which is critical for making informed investment decisions.