Proof — Kuro Capital

// Proof

Every other "trading mentor"
shows you one equity curve.

We're showing you the bootstrap distribution, the slippage curve, the ablation test, and the regime stress test. If a system can't survive being properly interrogated, you have no business trading it. You really have no business teaching it.

Exhibit 01 — Full Backtest

$50,000 → $498,022. Six years.

MNQ · 1m · 2020-03 → 2026-03

The flat first 1,000 trades? That's the strategy surviving 2020-2023 chop. Most retail traders quit here. The ones who didn't caught the 2024-2025 expansion. +150.8% then +211.0%.

Exhibit 02 — Funded Challenge

1,453 prop-firm simulations.

19.3% pass rate. Industry average sits around 7%. The math doesn't lie.

Exhibit 03 — Regime Segmentation

Profit factor by market condition.

Asia-session triggers print PF 3.22. Tuesdays print 1.36. We teach you the regimes.

Exhibit 04 — Slippage Degradation

What happens when fills go wrong.

We modeled fills worse than reality. Even with 2-tick adverse slippage, the system still nets +$33,820.

Exhibit 05 — Ablation Test

Which components actually matter.

Remove the VWAP filter and Sharpe collapses from 1.03 → 0.46. Every filter we teach has earned its place.

Exhibit 06 — Parameter Sensitivity

Profitable across the entire parameter grid.

A curve-fit strategy works at one set of parameters and dies everywhere else. Ours stays green across the entire SL × R:R grid. That's not luck. That's edge.

Exhibit 07 — Bootstrap Distribution

$440K mean across thousands of resamples.

Block bootstrap, 4 different block sizes. The distribution is centered around profitability. Statistically validated, not lucky.

Exhibit 08 — Rolling Performance

50-trade rolling expectancy.

Edge has expanded over time, not decayed. The strategy is more profitable today than it was in 2020.

Exhibit 09 — Conditional Expectancy

Where the money actually comes from.

Wide-stop trades print $518. Tight stops average $54. We teach you to size your stops to the setup, not the other way around.

Exhibit 10 — Fill Sensitivity

We model the worst case.

Pessimistic fills nets $448K. Optimistic nets $513K. The number you saw above is the conservative one.

Exhibit 11 — Entry Timing

Why precision is the whole game.

A single-bar delay costs $137,021. Three bars and it's a losing system. We teach you the exact trigger.

Exhibit 12 — Capacity

It scales.

5× scaling stays profitable. The strategy can support a real-sized account, not just a $1K demo.

Exhibit 13 — DD Permutation

Better than 93% of random shuffles.

Mean shuffle DD: -50.3%. Actual: -38.8%.

Exhibit 14 — Missing Trade Stress

Miss 40% of setups. Still print.

Even removing 40% randomly: +$260K mean. Forgiving system.

Exhibit 15 — Regime Transition

Know when to stand down.

Stable: PF 1.19. Transition: PF 0.92. The off-switch matters.

// receipts

15 charts. Zero edited. Generated by the same Python the desk audits with.

If you've ever wondered why your favorite "trading educator" only ever shows one zoomed-in equity curve, it's because this is what real validation looks like, and most of them have nothing to show.

$ python backtester.py --asset MNQ --tf 1m
✅  2,127,848 candles  (2020-03-03 → 2026-03-15)
🔍  Running backtest …
    234,506 structural highs | 232,813 structural lows
    358,805 long | 364,931 short trigger bars cached
    1746 trade(s) executed.

  Net P&L          $ +448,022.43
  Final Capital    $  498,022.43
  Return             +896.04%
  CAGR               +44.94%
  Sharpe Ratio          1.03
  Profit Factor         1.17
  Max Drawdown        -38.78%

Every other "trading mentor"shows you one equity curve.