I need a fully coded, back-tested trading algorithm for the BTCUSDT spot market that relies on a Simple Moving Average (SMA) crossover of a long-term band (100–200 days). All work must be done in QuantConnect’s LEAN framework, pulling historical and live data through the Binance API. Here is the flow I expect: • Build the SMA-crossover logic exactly as described, keeping parameters clearly exposed so I can tweak them later. • Run a complete backtest from 1 Jan 2019 to today. The results must show at least a 60 % win rate, a 50 % compound annual growth rate, and no more than 20 % drawdown. If the first parameter set misses the mark, iterate until the goals are met. • Once results are validated, enable QuantConnect’s live Paper Trade mode. The strategy should: – Push every trade signal to a private Telegram channel via bot API. – Place the corresponding spot order on Binance through QuantConnect’s built-in brokerage integration. • Hand over the fully commented source code, research notebook (if used), and a brief README explaining deployment steps inside a QC project. I will consider the job complete when I can: 1. Open the QC project, run the backtest, and reproduce the required metrics. 2. Switch to live paper mode and see real-time signals arriving on Telegram while orders hit the Binance paper account automatically. Python is preferred, though C# is acceptable if you document setup thoroughly. Please keep external dependencies light—anything beyond the standard LEAN libraries and python-telegram-bot (or equivalent) should be justified. Let me know your estimated timeline to hit the targets and whether you need any additional API keys or channel tokens from my side.