I’m building a Python-based engine that will sit between my brokerage API and my trading logic, giving me a single, reliable workflow for options scalping as well as broader day- and swing-trading plays. The core of the job is to wire up real-time feeds from stock-exchange data streams, layer in snapshots from my preferred historical data vendor, enrich that flow with a financial-news API, then push the resulting signals straight through to automated order execution. Here’s what matters most to me: • Real-time data retrieval must work at tick level, with latency low enough for scalping. • Automated trading execution must cover the whole order life-cycle—place, modify, cancel, and monitor—using the broker’s REST/WebSocket interface. • Market analysis and prediction should tap familiar Python libraries (pandas, NumPy, TA-Lib, scikit-learn if you prefer) so I can tweak strategies myself later. • Strategies to support on day one: scalping, day trading, swing trading in index and stock options. Acceptance criteria • A FastAPI or similar microservice exposing endpoints such as /quote, /signal, /order. • Back-testing harness that replays historical ticks to validate signal logic. • Robust logging, exception handling, and config files for keys, symbols, position sizing, and risk limits. • Clear README plus inline docstrings so I can extend indicators or swap data vendors without hunting through code. If you’ve already interfaced with broker APIs like Zerodha Kite, Interactive Brokers, or similar, you’ll find this straightforward. Deliver clean, well-tested code and we can iterate quickly on new strategies after launch.