Crypto arbitrage trader

     This real-time crypto arbitrage dashboard identifies profitable trading opportunities by comparing live cryptocurrency prices across multiple exchanges. The system simulates paper trades—executing a virtual $1000 trade whenever an opportunity appears—while accounting for trading fees, making the analysis realistic and practical. It tracks cumulative profits from these simulated trades and presents a live feed of current prices, trade logs, and net returns. The dashboard also includes interactive features such as customizable refresh intervals and a toggle to enable or disable trading fee simulations, giving users control over the conditions of their virtual strategy.
     I developed this project to explore algorithmic trading strategies while building my skills in real-time data analysis and financial modeling. It was built using Python, with the Streamlit framework for the interactive front-end and the CCXT library to fetch price data from exchanges like Kraken, Bitfinex, and Coinbase. I implemented custom logic to detect arbitrage opportunities based on net profit after fees, simulate realistic trades, and track performance over time—all within a responsive interface that updates live.
     Beyond technical implementation, this project demonstrates my understanding of real-world trading constraints such as taker/maker fees, execution latency, and exchange pricing discrepancies. I also gained experience managing financial data structures and designing user-facing tools that deliver actionable insights. Ultimately, the dashboard serves as both a testing ground for crypto strategy development and a proof of concept for building robust, data-driven applications in the fintech space.