NASDAQ Earnings Scraper & Swing Stock Analyzer

An Apify Actor that performs web scraping to collect upcoming earnings data from NASDAQ for companies with market capitalization of $1 billion or more. The Actor curates potential swing stocks by conducting extensive analysis on historical stock performance, company fundamentals, and forward-looking guidance to identify high-confidence trading opportunities.

Overview

This Actor scrapes earnings calendar data from NASDAQ and performs comprehensive analysis to identify potential swing trading opportunities. By analyzing historical stock prices, quarter-over-quarter company performance metrics, and forward guidance from company leadership, the Actor filters and ranks companies to help identify stocks with the highest potential for significant price movements around earnings announcements.

The analysis includes evaluation of revenue growth, profit margins, subscription metrics (for SaaS companies), and future guidance provided by CEOs, CFOs, and other company executives. Using applied statistics, numerical analysis, and transformer-based models, the Actor computes confidence scores that indicate the likelihood of positive stock performance.

Included Features

• Apify SDK for Python - a toolkit for building Apify Actors and scrapers in Python
• Input schema - define and easily validate a schema for your Actor's input
• Dataset - store structured earnings data where each object has the same attributes
• HTTPX - library for making asynchronous HTTP requests in Python
• Pandas - powerful data manipulation and analysis library

Analysis Methodology

The Actor performs extensive analysis to curate potential swing stocks by examining:

• Historical Stock Price Analysis - Evaluation of price trends and volatility patterns
• Quarter-over-Quarter Performance - Analysis of revenue growth, profit margins, and earnings trends
• SaaS Metrics - For software companies, analysis of subscription growth, churn rates, and recurring revenue
• Forward Guidance - Evaluation of future outlook provided by company leadership (CEO, CFO, and other executives)
• Confidence Scoring - Advanced statistical analysis and numerical modeling using transformers to compute confidence scores

Output Data

The Actor returns structured data with the following fields:

• Earning Report Date - Date of the earnings announcement
• Time - Time of day (Pre-Market, After Hours, or Not Specified)
• Symbol - Stock ticker symbol
• Company Name - Full company name
• Market Cap - Full market capitalization value
• Market Cap (Short) - Abbreviated format (e.g., "2T$", "500B$", "100M$")
• Fiscal Quarter Ending - End date of the fiscal quarter
• Consensus EPS Forecast - Expected earnings per share from analysts
• # of Ests - Number of analyst estimates
• Last Year's Report Date - Date of the previous year's report
• Last Year's EPS* - Earnings per share from the previous year
• Confidence Score - Computed score (0-100) using applied statistics, numerical analysis, and transformer models indicating confidence in potential stock performance

Data is sorted by report date (earliest first) and then by market cap (largest first).

How It Works

1. Actor.get_input() retrieves the input configuration (default: 30 days ahead)
2. The Actor performs web scraping to collect earnings calendar data for the specified date range
3. Data is filtered to include only companies with market cap ≥ $1 billion
4. Extensive analysis is performed on historical performance, quarterly metrics, and forward guidance
5. Applied statistics, numerical analysis, and transformer models are used to compute confidence scores
6. Data is cleaned, formatted, and enriched with calculated fields
7. Actor.push_data() stores the structured earnings data in the dataset

Input Configuration

The Actor accepts the following input parameter:

• days_ahead (integer, optional, default: 30) - Number of days ahead to scrape earnings data for (range: 1-365)

Getting Started

For complete information see this article. To run the Actor locally use the following command:

$apify run

Deploy to Apify

Connect Git Repository to Apify

If you've created a Git repository for the project, you can easily connect to Apify:

1. Go to Actor creation page
2. Click on Link Git Repository button

Push Project on Your Local Machine to Apify

You can also deploy the project on your local machine to Apify without the need for the Git repository.

1. Log in to Apify. You will need to provide your Apify API Token to complete this action.

   $apify login
   

2. Deploy your Actor. This command will deploy and build the Actor on the Apify Platform. You can find your newly created Actor under Actors -> My Actors.

   $apify push
   

Documentation Reference

To learn more about Apify and Actors, take a look at the following resources:

• Apify SDK for Python documentation
• Apify Platform documentation
• Join our developer community on Discord