METHODOLOGY
How We Test.
No Shortcuts.
Every number on PRUVIQ comes from a reproducible simulation. This page explains exactly how we backtest, what we measure, and what we leave out.
How We Backtest
2+ years of 1-hour OHLCV candles sourced from Binance Futures. All candles are complete (closed) — no partial or in-progress data is used to avoid look-ahead bias.
570+ USDT perpetual futures pairs listed on Binance. Stablecoins, delisted, and illiquid pairs are excluded. Daily rankings use the top 50 coins by market cap. The exact count varies by strategy version.
Entry is assumed at the candle close price. This is conservative — real fills may differ by a small amount due to timing and order book depth.
0.04% taker fee per side (0.08% round-trip). This is the Binance Futures default for VIP 0 tier.
Not modeled by default. Limit orders are assumed. In practice, entry slippage is typically under 0.05% for most trades on Binance Futures.
Fixed $60 per trade with 5x leverage ($300 notional). No compounding — each trade uses the same dollar amount regardless of account equity.
Strategy Evaluation Metrics
Every strategy is evaluated using the same set of metrics, calculated after fees:
- Win Rate — Percentage of trades that closed in profit.
- Profit Factor — Gross profit divided by gross loss. Above 1.0 means profitable.
- Total Return % — Net P&L as a percentage of starting capital.
- Max Drawdown — Largest peak-to-trough decline in equity.
- Trade Count — Total number of completed round-trip trades.
Risk-Adjusted Metrics
Risk-adjusted return relative to volatility. Higher is better.
Formula: (Return - Risk-free Rate) / Volatility
Like Sharpe, but only penalizes downside volatility.
Formula: (Return - Risk-free Rate) / Downside Deviation
Total return divided by max drawdown. Measures return per unit of risk.
Formula: Annualized Return / Max Drawdown
Advanced metrics (Sharpe, Sortino, Calmar) are calculated for every simulation and displayed in simulator results.
Robustness Validation
Numbers alone can be misleading. We apply two additional validation methods to test whether strategy performance is real or just noise:
Data is split into in-sample (training) and out-of-sample (testing) periods. A strategy must perform consistently on unseen data to be considered validated. We test across 2024, 2025, and 2026 independently.
Out-of-Sample = testing on data the strategy has never seen, to check if it really works or just memorized patterns.
We run 1,000+ randomized simulations per strategy — shuffling trade order and applying statistical noise. This reveals worst-case scenarios, confidence intervals, and whether returns depend on lucky sequencing.
Monte Carlo = running 1,000+ random simulations to check worst-case scenarios and confidence levels.
What We Don't Model
Transparency means showing the gaps. These factors are not included in our backtests:
Funding rates — Perpetual futures charge/pay funding every 8 hours. This can add or subtract from returns depending on direction and market conditions.
Market impact — Large orders can move the price. Our $60 position size minimizes this, but it exists.
Exchange outages — Binance has occasional downtime. Backtests assume 100% uptime.
Black swan events — Flash crashes, delistings, and regulatory shocks are not simulated.
Liquidity variations — Order book depth changes throughout the day and across coins.
Reproducibility
All strategy parameters are published openly — entry conditions, exit conditions, stop-loss, take-profit, time filters, and coin universe. Signal logic is documented so you can replicate or modify it. The interactive simulator lets you adjust SL/TP and see how results change in real time.
Disclaimer
Past performance does not guarantee future results. Backtests are simulations — not predictions. Real trading involves risks not captured in any model, including emotional decision-making, connectivity issues, and market regime changes. Always test with capital you can afford to lose.
See It in Action
Run a backtest yourself — adjust parameters and see real results on 570+ coins.
Open Simulator →