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Analytics and automation for LPs and funding rate arbitrage.
Problem
Create reliable, real-time analytics across multiple venues and protocols.
Approach
Built streaming pipelines, oracle integrations, and alerting for execution systems.
Impact
Supported automated trading strategies and improved decision latency.
What I Built
- Built streaming data pipelines aggregating real-time price feeds, funding rates, and liquidity data across 10+ DeFi protocols.
- Integrated Pyth Network oracle for low-latency on-chain price data with sub-second update frequency.
- Developed automated alerting system for funding rate divergence detection and arbitrage opportunity identification.
- Created execution-ready signals with risk-adjusted position sizing for LP and basis trade strategies.
- Implemented backtesting framework to validate strategy performance against historical on-chain data.
Technologies
PythonReal-time APIsPyth OracleData Pipelines