I need a complete trading algorithm built around the Smoothed Gaussian Trend Filter indicator that can flip seamlessly between long and short positions. My preferred home for the project is QuantConnect, with live execution routed through Interactive Brokers, so familiarity with QuantConnect’s Python or C# API, its research notebook, back-testing engine, and live deployment hooks is essential. Core requirements • Use the Smoothed Gaussian Trend Filter on DAILY bars as the primary signal generator. • Accept a user-supplied list of stocks and handle several symbols simultaneously. • Place, modify, and close orders autonomously—both long and short—while respecting margin and account constraints. • Persist data and model parameters so the strategy can be stopped and restarted without losing state information. • Include robust risk controls: position sizing, max drawdown rules, and circuit breakers. • Provide a simple configuration layer (JSON or in-code dictionaries are fine) so I can adjust indicator parameters, trading hours, and risk limits without redeploying. • Output clean performance metrics after each back-test and push trade logs in real time when live. Nice to have If you’re comfortable with machine learning, I’d like an optional optimisation module—perhaps Bayesian search or a lightweight reinforcement loop—that tunes indicator smoothing lengths and stop levels based on historical walk-forward tests. Deliverables 1. Well-documented QuantConnect project files ready to clone and run. 2. A short Loom or Zoom walkthrough showing deployment to a paper-trading IB account. 3. A README covering setup, parameter tweaks, and how to extend the code to additional timeframes later. I’m happy to answer clarifying questions quickly so we can keep momentum. Let me know how you would architect the solution and your estimated turnaround time.