AI-Powered Investing Assistant - Revolutionizing Stock Analysis Part 2

Introduction

Have you ever wished for a highly capable assistant who could process data at the speed of light, perform expert analysis, and help you make informed decisions? Welcome to the second installment of our series on designing and building an AI-powered investing assistant. This assistant is engineered to analyze financial data and provide accurate and insightful answers to inquiries regarding various companies.

In the previous video, we discussed the motivations behind building this AI assistant, which aims to simplify complex financial analysis and support intelligent investment decisions. We also demonstrated the assistant's current capabilities and showcased how it can provide valuable answers to intricate financial questions.

One of the major points from the last video was the limitations of GPT and other large language models when it comes to handling tasks like this independently. The primary reasons include:

Risk of Inaccuracies: There exists a significant risk of receiving false or hallucinated answers from GPT.
Lack of Real-Time Data: Models like GPT do not have access to up-to-date financial information.

To overcome these challenges, we need to integrate real-time financial data with GPT's analytical and natural language processing abilities. The solution involves fetching current, valid data independently from GPT and then supplying this data, along with the user's question, to GPT for delivering accurate and insightful responses.

Breakdown of the Process

The process can be described in several key steps:

Data Acquisition: We start by utilizing a financial API to retrieve comprehensive financial data about a company. This data is subsequently stored in a SQL database.
Natural Language Querying: When a user poses a question in natural language, it is converted into a SQL query that retrieves the precise data necessary to answer the user's inquiry.
Answer Generation: Finally, the relevant data and the original question are fed into GPT, which generates a coherent and accurate answer.

This system effectively harnesses GPT's exceptional natural language understanding while maintaining control over the data, ensuring that it is accurate and up-to-date.

The Heart of the System

At the core of this system is the transformation of everyday natural language into SQL queries. Historically, one would have needed to master complex query statements to access a database. However, with powerful language models like GPT, it is now possible to decode natural language and convert it into data-friendly queries. This is akin to having a conversation with the database.

By designing a system that seamlessly converts natural language to SQL, we have simplified the process of fetching precise data. Subsequently, GPT can craft coherent and accurate answers based on this data.

The application can be divided into three main segments:

Data Loading: Acquiring and storing financial data.
Natural Language to SQL Conversion: Transforming user inquiries into SQL queries.
Answer Generation: Utilizing GPT to formulate answers based on the retrieved data.

In the upcoming three videos, we will explore each segment in greater detail, including how the system operates and a closer look at the underlying code. Additionally, we will investigate ways to enhance the investing assistant's performance and analytical capabilities.

Thank you for taking the time to watch, and I look forward to seeing you in the next video.

Keywords

AI Assistant
Financial Data
Real-Time Analysis
SQL Query
Natural Language Processing
Data Accuracy
Investment Decisions

FAQ

1. What is the purpose of the AI-powered investing assistant?
The assistant aims to simplify complex financial analysis and help users make informed investment decisions by providing accurate and insightful answers to financial queries.

2. Why can't GPT handle financial analysis independently?
GPT lacks real-time financial data access and has a risk of generating false or hallucinated answers, which makes independent financial analysis unreliable.

3. How does the system convert natural language to SQL?
The assistant uses advanced language models, like GPT, to transform user inquiries expressed in natural language into SQL queries that can retrieve the necessary data from a database.

4. What are the three main segments of the application?
The application is divided into data loading, natural language to SQL conversion, and answer generation.

5. What can we expect in the upcoming videos?
In the next videos, each segment will be explored in detail, along with insights into the code, and ways to enhance the assistant's performance.