Core Technical Experience • Python Proficiency: Strong expertise in Python 3.x, including advanced features like decorators, context managers, async programming (e.g., for real-time data feeds), and performance profiling with tools like cProfile or line_profiler. They should be comfortable with idiomatic Python and adhering to standards like PEP 8. • Financial/Trading Domain Knowledge: Hands-on experience building or reviewing algorithmic trading systems, such as momentum, mean-reversion, or arbitrage strategies. Familiarity with concepts like backtesting, forward-testing, slippage, transaction costs, risk management (e.g., Sharpe ratio, drawdown), and handling market data (e.g., OHLCV, order books). • Data Handling and Analysis Libraries: Deep experience with libraries like pandas and NumPy for data manipulation, TA-Lib or pandas-ta for technical indicators, and backtesting frameworks like Backtrader, Zipline, or PyAlgoTrade. If your strategies involve machine learning, knowledge of scikit-learn or TensorFlow/PyTorch for predictive models. • API and Integration Skills: Experience integrating with financial APIs (e.g., Alpha Vantage, Yahoo Finance, or broker APIs like Alpaca, Interactive Brokers) for data fetching or execution. Understanding of RESTful APIs, WebSockets for live data, and handling rate limits or authentication (e.g., OAuth, API keys).