QuantSonar

🚀 About the Project: QuantSonar 🎯 Inspiration Institutional investors operate in a world flooded with data but starved for clarity. From dense earnings calls to real-time social sentiment, making sense of noisy, fragmented sources can be overwhelming—and costly.

I wanted to build something that not only synthesized this chaos but offered trustworthy, actionable insights that could move real portfolios. The idea for QuantSonar was born:

An AI-powered quantitative research assistant that delivers alpha, manages risk, and ensures compliance—faster and smarter than ever before.

🧠 What I Learned AI is only as good as its reasoning: Transparent, chain-of-thought explanations were essential to earn trust from financial users.

Institutional needs are deep and specific: Surface-level summaries weren’t enough—our tools had to be data-rich, citation-backed, and audit-friendly.

UX matters for trust: Visualizing insights clearly, and allowing data traceability, significantly improved credibility and usability.

🛠️ How I Built It Frontend: A responsive, React-based web dashboard that displays research briefs, trade signals, backtest results, and risk visualizations.

Backend + AI:

Integrated the Sonar API for deep research queries across real-time (X posts, news) and static sources (SEC filings, historical data).

Built NLP pipelines to flag contradictions, extract sentiment, and generate chain-of-thought narratives.

Used predictive modeling to forecast risks and alpha signals.

Data Sources: SEC filings, earnings call transcripts, social media sentiment, ETF flows, commodity prices, central bank policy updates, and more.

⚔️ Challenges I Faced Data Overload: Designing a system that filters signal from noise while remaining explainable was tough—especially with volatile real-time inputs.

Latency vs. Depth: Balancing fast performance with deep, citation-heavy insights required careful API chaining and caching.

Contextual Accuracy: Financial language is nuanced. Ensuring that the AI understood not just what was said, but what it implied, involved multiple iterations on our LLM prompt design.

Backtesting at Scale: Running statistical tests on alternative data sources (e.g., CEO LinkedIn activity) while ensuring significance and reproducibility pushed the limits of our modeling pipeline.

Built With

• firebase
• next.js
• sonarapi