I’m building a fully-automated, quantitative trading system and need a Python developer who can take my raw ideas and translate them into production-ready algorithms. The core of the job is end-to-end algorithm development: ingesting historical market data, crafting and refining signal logic, running robust backtests, and preparing the strategy for live execution through a broker API. You should be comfortable working with common quant tools—pandas, NumPy, SciPy, ta-lib, or similar—as well as a backtesting framework such as Zipline, Backtrader, or your preferred equivalent. Clean, modular code with thorough docstrings and unit tests is essential because I plan to iterate quickly once the initial version proves itself. I will supply any proprietary data or specific parameter ideas; you’ll advise on data cleaning, feature engineering, position sizing, and risk controls to keep the strategy market-ready. Once the model passes agreed performance metrics in walk-forward testing, the final step is deploying it into a live trading environment with logging, error handling, and basic monitoring dashboards. Deliverables: • Python source code for the strategy, utilities, and configuration files • Backtest reports (performance summary, equity curve, drawdown analysis) • Brief implementation guide explaining setup, dependencies, and how to extend the model Acceptance criteria: reproducible backtest results, Sharpe ratio above the benchmark I’ll provide, and execution latency within the limits we discuss. If this aligns with your expertise, let’s talk through the details and timeline.