All Work
LLM Time Series Python Quantitative Finance
Nexus Analytics
Client
Nexus Capital
Industry
Fintech
Year
2024
$2.4B
Trading Volume
50K
Signals/Second
18%
Alpha Improvement
3ms
Latency
Summary
Designed an AI market intelligence engine that processes 50,000 financial signals per second, powering $2.4B in algorithmic trading volume.
The Challenge
Nexus Capital's quant team was drowning in unstructured data — earnings calls, SEC filings, social sentiment, and satellite imagery — with no unified way to extract actionable signals at scale.
Our Solution
We built a multi-modal AI pipeline combining specialized LLMs for financial document analysis, computer vision for satellite/imagery signals, and a custom time-series model for pattern detection across 8 years of market data.
Results
- → $2.4B in trading volume processed through the system
- → 50,000 signals analyzed per second
- → 18% improvement in alpha generation vs. baseline
- → 3ms average signal-to-execution latency
◈ Ready to build