Design, develop, and maintain automated quantitative trading systems for cryptocurrency and financial markets using Python.
Develop and implement systematic trading strategies, including trend following, mean reversion, momentum, reversal, market regime, and statistical strategies.
Research, develop, and evaluate alpha factors, technical indicators, market microstructure signals, and alternative data to identify potential trading opportunities.
Build robust backtesting and forward-testing frameworks to evaluate strategy performance across different market conditions, timeframes, and parameter combinations.
Perform quantitative research, statistical analysis, feature engineering, and signal optimization to improve strategy robustness and reduce overfitting.
Implement market regime detection and adaptive strategy logic using volatility, volume, open interest, funding rates, on-chain metrics, and statistical or machine-learning models.
Develop risk management and position-sizing models, including volatility-adjusted sizing, exposure limits, stop-loss/take-profit logic, drawdown controls, and portfolio-level risk management.
Build real-time trading infrastructure integrating exchange APIs and WebSocket market-data feeds for automated order execution and position management.
Integrate multiple cryptocurrency exchanges and financial data providers using APIs and libraries such as CCXT, while handling market-data normalization and exchange-specific execution requirements.
Develop reliable order execution, trade management, and monitoring systems, including order-state tracking, error handling, reconnection mechanisms, and execution monitoring.
Design and maintain data pipelines for real-time and historical market data, including OHLCV, trades, order-book data, funding rates, open interest, liquidations, and alternative/on-chain metrics.
Work with databases such as PostgreSQL, TimescaleDB, and Redis for high-frequency market data, strategy state, caching, and trading-system persistence.
Develop scalable backend services and APIs using frameworks such as FastAPI and Flask to support trading engines, analytics platforms, dashboards, and internal tools.
Deploy and operate trading systems on AWS and Docker-based infrastructure, ensuring reliability, scalability, monitoring, and automated deployment.
Develop quantitative analytics and monitoring dashboards using Pandas, NumPy, Plotly, Dash, and Streamlit to monitor strategy performance, risk, positions, and trading activity.
Conduct performance attribution and statistical analysis using metrics such as Sharpe ratio, Sortino ratio, maximum drawdown, win rate, profit factor, volatility, and risk-adjusted returns.
Develop automated research workflows for parameter optimization, walk-forward analysis, sensitivity analysis, and regime-based performance evaluation.
Apply machine learning and statistical techniques where appropriate for signal generation, market-regime classification, feature selection, and adaptive trading models.
Implement software engineering best practices, including modular architecture, testing, logging, version control, CI/CD, and production monitoring.
Continuously monitor live strategies and investigate execution issues, abnormal market behavior, data-quality problems, and strategy performance degradation.
Collaborate across quantitative research, engineering, product, and operations functions to translate trading ideas into production-ready automated strategies.
Continuously research new market signals, trading methodologies, data sources, and quantitative techniques to improve the trading system and overall risk-adjusted performance.

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Senior Software Engineer