Building A Powerful Earnings Surprise Radar with Python

2026-05-15 · Source: AI Advances - Medium · Field: Finance & Economics — FinTech & Digital Financial Services, Capital Markets & Investment Management · Depth: Novice, long

Summary

This tutorial outlines building a Python-based "Earnings Surprise Radar" to prioritize S&P 500 companies with upcoming earnings reports. The script utilizes EODHD API data for S&P 500 components, earnings calendar events, and historical end-of-day prices. It calculates a radar score based on three factors: earnings proximity (40%), recent 30-day volatility (35%), and absolute 30-day price movement (25%). The process involves fetching S&P 500 constituents, filtering global earnings events to match these components, retrieving historical price data for each candidate, and computing price signals like 30-day return and volatility. Finally, these signals are normalized and combined into a weighted radar score, which is then used to rank and explain the top 10 stocks deserving immediate attention, without predicting earnings outcomes.

Key takeaway

For financial analysts or developers building market intelligence tools, this radar provides a practical framework for filtering the noisy earnings season. You can adapt this Python script to create a daily watchlist, power fintech product features, or inform editorial coverage by highlighting S&P 500 companies with high pre-earnings activity. This approach helps you focus research efforts on the most dynamic upcoming reports, rather than manually sifting through a vast earnings calendar.

Key insights

A Python script can prioritize S&P 500 earnings reports based on timing and recent price action.

Principles

• Prioritize earnings events by market activity.
• Combine multiple signals for robust ranking.
• Normalize disparate data for comparison.

Method

The method involves fetching S&P 500 components, upcoming earnings, and historical prices via EODHD APIs. It then calculates 30-day returns and volatility, normalizes these with earnings proximity, and combines them into a weighted radar score to rank the top 10 stocks.

In practice

• Use EODHD APIs for financial data.
• Implement min-max normalization for scores.
• Add explanatory labels to ranked outputs.

Topics

• Earnings Radar
• Python Financial Scripting
• EODHD API
• S&P 500 Earnings
• Stock Price Volatility

Best for: Software Engineer, Data Scientist, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.