Earnings Event Engine — Researching Stock and Options Reactions to Earnings

The Earnings Event Engine is a Python-based research framework I developed to analyze how stocks and options behave around corporate earnings announcements.

The system combines announcement timing, options-implied expectations, realized stock-price reactions, and post-earnings price behavior into a reproducible research workflow.

Key capabilities include:

• Event alignment: Correctly distinguishing before-market and after-market announcements.
• Implied versus realized moves: Comparing what the options market anticipated with what actually happened.
• Post-earnings analysis: Examining subsequent price behavior while preserving sample-size and evidence limitations.

The demonstration uses deterministic Apple and Microsoft examples to show the actual research pipeline.

Importantly, the framework does not turn limited observations into unsupported trading signals. It preserves uncertainty and identifies when the available evidence is insufficient for a defensible conclusion.

GitHub: https://github.com/rossautomatedsolutions/earnings_event_engine

Research and demonstration purposes only. Not investment advice.