I need a high-performance momentum-based trading system built inside QuantConnect’s Lean engine using Python. The strategy already has a defined logic it focuses on short-term intraday momentum signals with dynamic stacking and capital reallocation. The system should: • Run fully within QuantConnect, using its native data and order-handling framework. • Trade U.S. and high-volume global equities (Europe, Asia, Australia) universe should be modular. • Use technical indicators (EMA crossovers, RSI, MACD, ADX, ATR, volume spikes) for entry logic. • Implement dynamic stacking, add to winning positions every +1× ATR above last entry. • Use a shared trailing stop for each stacked group, updating dynamically as price rises. • Include a capital rotation system: pull capital from underperforming positions and reallocate to higher-momentum assets. • Run on short timeframes (e.g., 1,3,5,10, 15-minute candles) with adjustable parameters so i can test and decide myself. • Log all trades, capital changes, and performance metrics clearly. Deliverables I expect: A well-commented Python algorithm file ready to drop into my QuantConnect project (fully compatible with Lean). A backtest covering at least three years (preferably December 2022) with detailed performance metrics: total return, drawdown, win rate, and trade log. Clear in-code parameters for all tunable elements (EMA periods, RSI length, ATR multiplier, stacking distance, stop-loss aggressiveness, capital rotation intervals). A short read-me explaining how to adjust those parameters and rerun backtests. Additional Notes: • The system should not use margin or leverage only cash capital. • It should be designed to scale smoothly to higher capital levels. • Code must compile and run successfully in QuantConnect’s cloud environment before delivery. • Machine learning integration is a plus. NB: The price quoted is what will be agreed upon.