Quant Developer Needed for BTC Prop Evaluation Backtesting Engine

I am looking for an experienced quantitative developer to build a Python backtesting and research engine focused on finding BTC day-trading strategies capable of passing prop-firm evaluations. The objective is not simply to maximise profit. The engine must test strategies against realistic evaluation constraints, including: Profit targets Maximum daily loss Maximum overall drawdown Minimum trading days Consistency rules Evaluation time limits Trading fees and slippage Main Requirements The system should: Use historical BTCUSDT data Support intraday and multi-timeframe strategies Test long and short setups Run large parameter searches efficiently Support risk-based position sizing Prevent look-ahead bias and data leakage Simulate complete prop-evaluation attempts Calculate the probability of passing an evaluation Identify the most common reasons strategies fail Use Monte Carlo or trade-sequence simulations Include in-sample, out-of-sample and walk-forward testing Export trades, performance metrics and evaluation results The prop-firm rules must be configurable rather than hardcoded. Key Outputs For every strategy, I want to see: Evaluation pass rate Average days required to pass Daily-loss violation rate Maximum-drawdown violation rate Expected number of attempts needed to pass Profit factor Win rate Expectancy Maximum drawdown Number of trades Performance by session and weekday A simple local interface using Streamlit or a clear command-line workflow is acceptable.