Advanced SL/TP Optimisation in Python

I already have a Python strategy running on real-time market data streamed through the TopstepX Tradovate API. Basic stops and targets are wired in, yet they ignore the realities of slippage, partial fills, and intrabar volatility. I need a robust module—callable from my existing codebase—that will:

• Estimate and incorporate realistic slippage/fill distributions from live order book snapshots.
• Back-test and forward-test a range of stop-loss and take-profit distances under these assumptions.
• Surface the optimal combination of Stop Loss, Take Profit, and overall Risk-Reward Ratio, expressed in both ticks and currency.

Please work in pure Python (pandas, numpy, or vectorised libraries are fine; I am not tied to a specific back-testing framework) and make sure the logic is flexible enough to plug into live execution later. Clear inline comments plus a short notebook or script demonstrating how to call the optimiser with my live data feed will be the acceptance criteria.