Monte Carlo Strategy & Liquidation Terminal
Generate one random path under a declared 3% independent-round model and compare staking rules. It is not a population ruin estimate.
Monte Carlo Strategy & Liquidation Terminal
Generate one random path under the declared independent-round model with a 3% edge. Compare funded staking rules; a single path does not estimate a probability of ruin.
USD. The dotted reference is initial bank minus 3% of turnover on this realized path; it is not the mean balance at this round across simulations. Stakes are capped at the available balance.
# Strategy properties under the declared 3% model
| Strategy / Staking Pattern | EV per unit wagered | Ruin probability | Tail Risk | Scientific Verdict |
|---|---|---|---|---|
| Martingale (Double on loss) | -3.0% / unit | Not estimated | Escalating exposure | Doubling can exceed available funds. A loss streak is a risk, not a guaranteed outcome in every finite session. |
| Low target1.08x | -3.0% / unit | Not estimated | Asymmetric returns | A low target raises the hit rate but retains negative expected return per unit wagered. |
| Fixed stake2x | -3.0% / unit | Not estimated | Fixed exposure | Fixed stakes keep exposure easier to track; no finite-horizon ruin percentage is estimated here. |
| Paroli | -3.0% / unit | Not estimated | Variable exposure | Increasing stakes after wins changes exposure without improving the per-unit expectation. |
| 1% sizing | -3.0% / unit | Not estimated | Variable exposure | This is recreational 1% sizing with a 1 USD floor, not Kelly-optimal betting. The constrained Kelly stake at negative EV is zero. |
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This is a synthetic path under your selected rules, not a prediction of the next session. Review game limits before deciding to play.
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One path and a negative expectation
The declared model costs 3% of expected turnover. Progressions change exposure, not the per-unit expectation. At 1.05x the ideal hit probability is 0.97/1.05, about 92.38%; one full loss offsets 20 wins of 0.05 units. This does not guarantee ruin within a selected finite horizon. The displayed path outcome is not a ruin-probability estimate.
P(L_k) = (1 - P)^k = (1 - 0.485)^k E(R) = 1.05 × ((1 - 0.03) / 1.05) - 1 = -0.03 (-3%)