Seven-Module Quant Trading Platform -- 2

I’m assembling a production-ready quantitative trading platform built around seven tightly-integrated modules: 1. Data Pipeline • Pull equities, options and futures data from Alpaca, Interactive Brokers and Yahoo Finance. • Prioritise data accuracy over raw ingestion speed, with validation, deduplication and time-zone normalisation baked in. • This pipeline and the Strategy Library are the only components that need to touch external APIs directly. 2. Backtesting Engine • A hybrid architecture: vectorised processing for speed, event-driven logic for realism. • Swappable execution models (market, limit, slippage) and portfolio-level handling. 3. Strategy Library • Five starter strategies: pairs trading, momentum, statistical arbitrage, regime detection and volatility trading. • Each strategy must expose clear long/short signals and accept risk parameters. • It should also be API-ready so I can bolt on new data sources later. 4. Risk Management Layer • Real-time VaR, CVaR and configurable circuit breakers. • Position sizing and capital allocation rules that slot seamlessly into both backtests and live trading. 5. Performance Analytics & PDF Reports • Tear-sheets with common metrics (Sharpe, Sortino, drawdown, hit ratio, turnover, etc.). • Auto-generated PDF reports with Matplotlib/Plotly visuals. 6. Walk-Forward Optimisation • Robust anti-overfitting workflow: rolling train/test splits, parameter stability checks and equity-curve validation. 7. Streamlit Dashboard • One-page overview plus drill-downs for data, strategy metrics, risk and live positions. • Simple authentication so I can share the link with collaborators. Acceptance criteria • End-to-end demo on sample equities universe. • All modules callable from a single orchestrator script (Python 3.11 preferred). • Clean, commented code with README and environment.yml. • Unit tests for core functions; ≥80 % coverage. If you have experience stitching together Alpaca or IB data feeds, writing vectorised backtests and deploying Streamlit apps, let’s talk about timelines and milestones—my goal is a maintainable, extensible codebase rather than a quick prototype.

Project ID: 40537652