EarningsPulse

Inspiration

Navigating financial markets and keeping up with fast-moving corporate earnings reports often feels overwhelming for retail investors. Sifting through dense SEC filings, lengthy earnings call transcripts, and scattered news feeds just to find actionable insights requires an unreasonable amount of time and effort. We wanted to build a centralized, intelligent platform that bridges this gap—automating financial data ingestion and providing real-time AI-powered advisory tools to democratize deep market research.

What it does

Earnings Pulse is a full-stack, AI-driven financial intelligence platform designed to streamline stock market analysis and portfolio tracking. Key features include:

Interactive Financial Dashboards & Charts: Real-time stock price charting and portfolio management tabs to track holdings seamlessly.
Automated Earnings Calendars & News Scans: Aggregates upcoming earnings, SEC disclosures, and financial market news in one unified place.
AI Financial Advisor: An intelligent advisory engine backed by multiple LLM routing options (including Gemini and Nemotron services) to answer complex financial queries and analyze portfolio risks.
Brokerage Integration: Secure account connectivity via SnapTrade to sync real user portfolios and asset data.

How we built it

Earnings Pulse is built on a robust, modern technology stack:

Frontend: Developed using React, Vite, and Tailwind CSS, featuring a responsive component architecture with dedicated tabs for portfolio tracking, earnings updates, market radar, and AI advisory.
Backend: Powered by a FastAPI Python application structured with modular services for SEC Edgar integration, price tracking, market news, and multi-model AI inference.
Data & Cloud Layers: Integrates external financial APIs, custom CSV parsers for holding data, and cloud-ready deployment configurations.

Challenges we ran into

Multi-Source Data Synchronization: Unifying asynchronous data streams from SEC filings, live pricing feeds, and brokerage APIs (SnapTrade) required writing robust error handling and asynchronous backend endpoints.
AI Response Optimization: Balancing speed and depth when querying large financial texts necessitated fine-tuning our prompt pipelines and setting up clean fallback routing between different LLM services.

Accomplishments that we're proud of

Successfully integrating real-world financial data feeds and secure broker account syncing via SnapTrade into a clean, intuitive UI.
Building a modular FastAPI backend that cleanly separates concerns between SEC filings, market news, and AI advisory logic.
Implementing multi-model AI routing to ensure fast and reliable financial insights.

What we learned

We gained deep technical experience in building production-ready financial tech stacks, handling complex asynchronous state on the frontend, and architecting modular FastAPI backends capable of processing text-heavy financial data feeds efficiently.

What's next for Earnings Pulse

Incorporating Real Data: Automatically connecting the user's stock portfolio from stock brokers like Robinhood and Fidelity.
Enhanced Portfolio Simulations: Adding risk-modeling tools and scenario stress-testing for different market conditions.
Mobile Optimization: Expanding the platform into a responsive mobile application for on-the-go portfolio tracking and market alerts.