Backtest edge looks real, but execution is everything

Built an event-driven options statistical vol-arb strategy and tested it walk-forward OOS with fixed-dollar sizing and Monte Carlo simulations (bootstrapped different fill assumptions and post-event paths). At midpoint fills, annualized Sharpe is \~3.7 (0.50 partway slippage), and at \~0.575 partway slippage the strategy starts to be around break-even. At \~0.25 partway slippage Sharpe goes above 9, which is clearly unrealistic to achieve in the real market. A few live tests have filled around mid or slightly better, but the sample is still too small. There is very little room for error. At this point the backtest has hit its limit. I need real fill data to determine whether the edge is actually tradable, but I think it is. The strategy is currently running on small capital with IBKR, fully automated and a suite of risk parameters set up. My background is in math, data science, I've worked at 3 different HFT quant shops in Chicago, and I’ve spent roughly two years building the research, data pipelines, and execution stack. At this point, additional backtesting almost certainly has diminishing value. The main unknown is the actual distribution of live fills. The strategy does not have significant tail risk on any single trade, since we're trading defined-risk partially hedged option spreads (betting on IV). I've attached some charts from the backtests. TLDR; at fills worse than \~0.575 partway slippage, the strategy EV is around breakeven (negative after commissions). At around mid, the Sharpe is above 3. I'll deploy small capital and capture real fill data over the next few weeks and slowly scale up if i can consistently get fills at or better than mid.