Supercharge Your Financial Analysis with GPT-4's Cutting-Edge App

Table of Contents

1. Introduction
2. Building the Web Application: Financial Analyst
   1. Selecting Multiple Report Types
      1. Single Stock Outlook
      2. Competitor Analysis
   2. Inputting Ticker Symbols
   3. Searching and Synthesizing Financial News
   4. Using GPT4 and Lama Index
3. Creating the User Interface with Streamlit
   1. Installing Streamlit
   2. Importing Streamlit and Setting Up the Application
   3. Displaying the Header and Title
   4. Creating the Select Box for Report Type
   5. Conditional Display of Input Boxes
   6. Generating the Reports
4. Conclusion
5. Future Developments
6. FAQ

Article

Introduction

In this article, we will explore how to build a web application called "Financial Analyst" that allows users to select multiple types of financial reports and generate them Based on their input. We will discuss the components and functionality of the web application and how it utilizes GPT4 and Lama Index to synthesize financial news and generate comprehensive reports.

Building the Web Application: Financial Analyst

The Financial Analyst web application revolves around the generation of financial reports based on user input. It provides two types of reports: the Single Stock Outlook and Competitor Analysis.

Selecting Multiple Report Types

The first step in using the Financial Analyst web application is selecting the desired report type. Users can choose between the Single Stock Outlook and Competitor Analysis. Each report type provides unique insights and analysis based on the user's selection.

Single Stock Outlook

The Single Stock Outlook report focuses on providing an outlook for a specific stock. Users are prompted to input the ticker symbol of the stock they want to analyze. The web application then searches recent financial news and synthesizes it using GPT4 and Lama Index to generate a comprehensive report. The report discusses the growth prospects, potential risks, and headwinds for the specified stock. It utilizes up-to-date information to provide an outlook for the chosen stock.

Competitor Analysis

The Competitor Analysis report allows users to compare the performance and competition between two stocks. Users are prompted to input the ticker symbols for the two stocks they want to analyze. The web application analyzes the financial news related to both stocks and generates a report that highlights the competition between them. The report discusses key points such as emerging competitors, potential market dominance, and other factors influencing their competition.

Inputting Ticker Symbols

To generate the desired reports, users input the ticker symbols of the stocks they want to analyze. These symbols serve as identifiers for specific stocks and allow the web application to retrieve the Relevant financial news and data for analysis.

Searching and Synthesizing Financial News

The Financial Analyst web application utilizes GPT4 and Lama Index to search and synthesize financial news. It dynamically fetches up-to-date financial news from Interactive Brokers and indexes it using Lama Index. This indexed data is then used to generate reports based on user queries. The integration of GPT4 allows for more in-depth analysis and synthesis of financial information, enabling the creation of comprehensive reports.

Using GPT4 and Lama Index

GPT4 plays a crucial role in generating the reports provided by the Financial Analyst web application. With its larger Context window and token limit, GPT4 enables the synthesis of longer-form reports with more detailed analysis. The integration of GPT4 and Lama Index allows for seamless customization and swapping between different language models and databases. Users can choose the model that best suits their requirements, whether it's GPT4 from OpenAI or other open-source language models.

Creating the User Interface with Streamlit

To enhance the user experience and make the Financial Analyst web application more user-friendly, we utilize Streamlit to Create a rich and interactive user interface. Streamlit provides a straightforward way to build and share data applications. We will discuss the steps to set up the user interface and incorporate dynamic input and selection boxes.

Installing Streamlit

Before we dive into building the user interface, we need to install Streamlit. It can be easily installed using pip, the Python Package manager.

pip install streamlit

Importing Streamlit and Setting Up the Application

To begin building the user interface, we need to import the Streamlit package and set up the application. By convention, Streamlit is imported as st. We create an application where we can write and display the components and functionality of the Financial Analyst web application.

import streamlit as st

# Set up the application
st.title("Financial Analyst")

Displaying the Header and Title

A header is an important element of the user interface as it provides an overview or branding for the application. We can easily add a header using the st.header function.

# Display the header
st.header("Financial Reports")

Creating the Select Box for Report Type

To allow users to select the report type, we can utilize the st.selectbox function from Streamlit. This will create a select box where users can choose between the Single Stock Outlook and Competitor Analysis report types.

# Create the select box for report type
report_type = st.selectbox("Select Report Type", ["Single Stock Outlook", "Competitor Analysis"])

Conditional Display of Input Boxes

Conditional logic allows us to dynamically display input boxes based on the user's selection. If the user chooses the Single Stock Outlook report type, we can display a single text input box where they can enter the stock symbol. Similarly, if the user selects the Competitor Analysis report type, we can display two text input boxes for entering two stock symbols.

# Conditional display of input boxes
if report_type == "Single Stock Outlook":
    symbol = st.text_input("Enter Stock Symbol")
elif report_type == "Competitor Analysis":
    symbol1 = st.text_input("Enter Stock Symbol 1")
    symbol2 = st.text_input("Enter Stock Symbol 2")

Generating the Reports

Once the user has entered the necessary input, we can generate the reports based on their selection. We can utilize the indexed financial data and GPT4 to generate comprehensive reports. By utilizing a spinner, we can provide visual feedback to the user that the report is being generated.

# Generating the reports
if report_type == "Single Stock Outlook" and symbol:
    st.spinner("Generating Report for " + symbol)
    # Perform index.query and generate the report

elif report_type == "Competitor Analysis" and symbol1 and symbol2:
    st.spinner("Generating Report for " + symbol1 + " vs " + symbol2)
    # Perform index.query and generate the report

Conclusion

The Financial Analyst web application offers users the ability to generate financial reports quickly and efficiently. By utilizing GPT4 and Lama Index, the application can synthesize relevant financial news and provide detailed analysis on a range of stocks. The user-friendly interface created using Streamlit enables seamless interaction and customization.

Future Developments

In the future, the Financial Analyst web application can be further enhanced and developed. Some potential extensions and improvements include:

1. Integration with LangChain for even more advanced natural language processing and analysis.
2. Adding additional report types and customization options to cater to varying user needs.
3. Implementing automated daily or periodic report generation and email delivery.
4. Incorporating visualizations and charts to enhance the presentation of the generated reports.
5. Integration with other open-source language models and databases for increased flexibility and cost-efficiency.

FAQ

Q: How does the Financial Analyst web application retrieve financial news and data?

A: The application utilizes Interactive Brokers to dynamically fetch recent financial news. This data is then indexed using Lama Index for efficient querying and synthesis.

Q: Is GPT4 the only language model supported by the Financial Analyst web application?

A: No, the web application is designed to be flexible, allowing users to swap between different language models. GPT4 is used as an example, but other models, such as those provided by open-source tools, can be integrated based on the user's preference.

Q: Can the Financial Analyst web application generate reports for multiple stocks simultaneously?

A: Yes, the Competitor Analysis report type allows users to compare the performance and competition between two stocks by inputting the respective stock symbols.

Q: Is Streamlit the only framework that can be used to build the user interface of the Financial Analyst web application?

A: While Streamlit is used in this article, there are other frameworks available for building web application interfaces. Streamlit was chosen for its simplicity and ease of use.

Q: Can the Financial Analyst web application be customized to cater to specific industries or sectors?

A: Yes, the application can be customized to analyze stocks and generate reports specific to different industries or sectors. The Prompts and queries used can be tailored to extract sector-specific information from the indexed data.