earnings-analyzer 1.0.2

A Python package to fetch, scrape, and analyze earnings call transcripts.

Earnings Call Sentiment Analyzer

This Python package provides a tool to fetch company profile data, scrape the latest earnings call transcript from The Motley Fool, perform sentiment analysis, and correlate sentiment with stock performance.

Features

• Flexible Transcript Scraping: Automatically finds and scrapes earnings call transcripts for a given stock ticker from fool.com, with options to specify a particular quarter and year or default to the latest.
• Company Data: Fetches company profile information (sector, industry) from the Financial Modeling Prep API.
• AI-Powered Sentiment Analysis: Uses the Google Gemini API to analyze the transcript and provide a sentiment score, confidence level, and key discussion themes.
• Data Persistence: Stores company profiles, earnings call details, sentiment analysis results, and stock performance data in a local SQLite database (earnings_analyzer.db).
• Structured Output: Returns a clean dictionary object containing all the fetched and analyzed data, including the earnings call date and quarter.
• Robust Error Handling: Includes comprehensive error handling and logging for API calls, data processing, and database operations.

Installation

You can install the package from PyPI:

pip install earnings-analyzer

Prerequisites

Before using the package, you must set up the required API keys.

Financial Modeling Prep (FMP) API Key

The Financial Modeling Prep (FMP) API is used to fetch company profile information (sector, industry). You can obtain a free API key by signing up on their website: https://financialmodelingprep.com/developer/docs/

Google Gemini API Key

The Google Gemini API is used for AI-powered sentiment analysis of the transcripts. You can obtain a Gemini API key from the Google AI Studio: https://aistudio.google.com/app/apikey

Setting Environment Variables

Create a file named .env in the directory where you will run the script (or set them as system-wide environment variables) and add the following keys:

GEMINI_API_KEY=your_gemini_api_key_here
FMP_API_KEY=your_financial_modeling_prep_api_key_here

Alternatively, you can set them directly in your shell:

Windows (Command Prompt):

set GEMINI_API_KEY=your_gemini_api_key_here
set FMP_API_KEY=your_financial_modeling_prep_api_key_here

Windows (PowerShell):

$env:GEMINI_API_KEY="your_gemini_api_key_here"
$env:FMP_API_KEY="your_financial_modeling_prep_api_key_here"

macOS/Linux:

export GEMINI_API_KEY=your_gemini_api_key_here
export FMP_API_KEY=your_financial_modeling_prep_api_key_here

Usage

The package provides an EarningsAnalyzer class that encapsulates the entire workflow. You can use it in your own Python script or via the command-line interface.

Data Usage Disclaimer

This tool is for educational and research purposes. Users are responsible for:

• Obtaining proper API keys and respecting rate limits
• Complying with data provider terms of service
• Not using for illegal market manipulation