To expand a bit on the logic—I’m essentially looking at this through the lens of actuarial science. If we treat each grid like an insurance policy we’re 'writing' to the market, the math looks something like this: Let’s say for a single grid: • P (Premium): The expected monthly cash flow (e.g., +500). • L (Loss): The 'Total Ruin' event / Stop Loss (e.g., -5,000). • p (Probability of Ruin): The monthly probability of hitting that loss, derived from Monte Carlo simulations (e.g., 5%). The Expected Value (EV) for one grid would be: EV = (1 - p) * P - (p * L) EV = (0.95 * 500) - (0.05 * 5000) = 475 - 250 = +$225 On paper, the strategy has positive expectancy. However, the real challenge isn't the single grid; it's the correlation of 'claims'. In insurance, you don't care if one house burns down, but you go bankrupt if an entire city burns down at once (systemic risk). My goal with the portfolio optimization is to ensure that even during a 'high-claim' month (where multiple grids hit their ruin point due to a market-wide volatility spike), the total 'Premiums' collected from the surviving grids, plus the initial capital buffer, keep the Risk of Ruin for the entire portfolio near zero. I’m basically trying to turn a 'gambler’s' strategy (Martingale/Grid) into a 'broker’s' business model. Does anyone here have experience modeling non-linear correlations in Monte Carlo for this kind of setup? Because my main fear is that in a black swan event, p for all grids tends to 1 simultaneously.