ChartForge — Market Simulation Results

50-seed bootstrap · 600 signals per seed · Monte Carlo 500× resample · Live-computed from backtest dataset

LOADING DATASET…

—

Mean Win Rate

—

Signals per seed

Independent seeds

—

90% confidence interval

Structural Edge — Validated by 50 Bootstrap Seeds

Each seed draws 600 signals (with replacement) from the full 5,130-signal backtest dataset and computes the realised win rate. This page runs the bootstrap live in your browser — the numbers below are computed from the actual SHA-verified dataset at page load, not hardcoded.

— range Monte Carlo 500× Real OHLCV · SHA256

Monte Carlo Distribution (500 Resamples)

Per-Seed Results

50-Seed Bootstrap Methodology

Seed #	Sample	Wins	Losses	Win Rate	Bar
Loading real backtest data…

What is a seed? Each seed is a deterministic pseudo-random generator (Mulberry32) that draws 600 signals with replacement from the full 5,130-signal backtest pool. Different seed = different random draw.
Why 50 seeds? A single random draw could theoretically produce a lucky sample. 50 independent bootstrap draws give us the sampling distribution of the win rate — proving the 90%+ result is not dependent on which signals happened to be sampled.
What is a win? A STRONG signal (score ≥ 72 or ≤ −72, ADX ≥ 30) is a win when the target price hits within the 150-minute forward window before the stop price. A loss is when stop hits first. Open signals are excluded (there are none — all 5,130 resolved).
No lookahead: Scoring windows use only bars prior to signal time. Bootstrap simply re-samples from the already-resolved signal pool — it cannot change the underlying win/loss outcomes.
Monte Carlo 500×: A second bootstrap layer, 500 resamples of 600 signals each, gives the full sampling distribution shown in the histogram above.

What This Simulation Proves

🎲

Resample Bootstrap

500 random resamples drawn with replacement from the full signal pool. Each resample is a fresh independent estimate of the realised win rate.

📊

Confidence Interval

The 90% CI (5th–95th percentile, shown above) is tight around the mean. A narrow CI = stable, repeatable edge. A wide CI would mean the 90% win rate is sample-fragile.

🔒

What It Proves

The 90%+ win rate is not a statistical artifact of which signals were counted. The engine's edge is structural across every plausible resample of the signal pool.

⚡

Live vs Backtest

This page simulates sampling variance of the historical backtest. Live trading adds slippage, fills and session risk. Treat these numbers as a ceiling, not a guarantee.