AI/ML + Quant Developer Needed — Predictive S&P 500 Dashboard (20+ Filters, Autopilot, QuantConnect

I’m looking for a full-stack AI/ML + Quant developer to build a predictive S&P 500 analytics dashboard with 20+ proprietary filters, real-time institutional flow tracking, and machine-learning-driven ranking models. The platform should operate like a QuantConnect-style proprietary dashboard but focused on S&P 500 swing trading signals and institutional activity detection. The system must run on autopilot—continuously scanning, filtering, modeling, and scoring opportunities without manual input. Core Features1.Real-Time Market Data IntegrationLive S&P 500 price, bid/ask, volume, volatilityBlock trade + dark pool activityInstitutional flow aggregationOptional: plug in QuantConnect data feeds or LEAN pipelines2. 20+ Custom Filters (Provided)Filters include:Volume spikesMomentum + accelerationTrend regimeInstitutional clusteringTechnical indicator behaviorRelative strengthVolatility compression/expansionMany more (full set provided after hire)Filters must feed both the dashboard and the ML model.3. AI / Machine Learning LayerBuild a predictive model that produces:Real-time trade opportunity scoringPredictive ranking for swing tradingMulti-feature signals using:Block trade clustersVolume anomaliesTechnical indicatorsInstitutional footprintsAutocorrelation and volatility featuresAbility to retrain model on schedule or manuallyOptional: backtesting via QuantConnect’s LEAN engine4. Proprietary Quant Dashboard (QuantConnect-Style)A web dashboard with:Real-time scanning across all S&P tickersML scoring + composite rankingColor-coded opportunity tiersWatchlistsFilter presetsSignal heatmapsInstitutional flow visualizationsExport to CSV / ExcelAutopilot mode (continuous scanning + alerting)Should have the polished, responsive feel of a hedge-fund internal dashboard.5. Optional Integrations(Not required, but a major plus)QuantConnect (LEAN) backtestingBroker API integrations (IBKR, Alpaca, Tradier)Redis / Kafka for real-time stream handlingKubernetes or Dockerized deploymentTech Stack (Flexible)Frontend: React / Vue / AngularBackend: Python (FastAPI, Django) or Node.jsML/Quant: Python (scikit-learn, PyTorch/TensorFlow), NumPy, pandasReal-time: WebSockets, streaming APIsExperience with quant platforms or hedge-fund tooling strongly preferredDeliverablesFully functional predictive dashboard20+ filters implemented + integratedML scoring engineAutopilot scanning modeReal-time institutional/block trade trackingClean documentationTo ApplyPlease include:Examples of quant dashboards, trading systems, or ML analytics toolsYour ML approach (feature engineering + model selection)Whether you have experience with QuantConnect/LEANEstimated timeline + budget Apply tot his job