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Feature

Advanced Backtesting

Prove your strategy before risking a single dollar.

Test your strategies against years of tick-level historical data before committing a single dollar to live trading.

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In-sample Out-of-sample Win 68% PF 2.1 SR 1.8

Overview

What is Advanced Backtesting?

Backtesting is the process of applying a trading strategy to historical market data to evaluate how it would have performed in the past. While past performance is never a guarantee of future results, rigorous backtesting is an indispensable step in validating that a strategy has genuine statistical edge — rather than being a lucky pattern that happened to fit one particular market period.

auto-Trading's backtesting engine is built for speed, accuracy, and depth. Our historical data library covers tick-level OHLCV (open, high, low, close, volume) data for thousands of trading pairs across all supported exchanges, with some datasets stretching back more than ten years. Tick-level granularity means your backtest accurately models slippage, spread, and partial fills — the details that matter most when you transition from a test environment to live trading.

Running a backtest is as simple as selecting your strategy, specifying the instrument and date range, and clicking run. Results are available within seconds for most strategies, even over multi-year windows. The results view shows the equity curve, maximum drawdown, Sharpe ratio, Sortino ratio, win rate, average win/loss, profit factor, and a full trade-by-trade breakdown. You can export results to CSV for further analysis in your preferred analytics tool.

For users who want to avoid overfitting, our walk-forward analysis tool automatically divides the historical window into in-sample (optimisation) and out-of-sample (validation) periods and reports performance across multiple out-of-sample segments. Monte Carlo simulation is also available: it randomly shuffles the order of historical trades to estimate the distribution of possible outcomes and the probability of ruin.

Strategy optimisation lets you sweep a range of parameter values (e.g., moving average periods or RSI thresholds) and identify configurations that maximise a chosen objective function — such as the Sharpe ratio or profit factor — while controlling for overfitting through parameter sensitivity analysis.

Resources: TradingView Strategy Tester · Investopedia — Backtesting Definition
Explore also: No-Code Strategy Builder · Risk Management · AI-Powered Signals

How It Works

Under the Hood

When you launch a backtest, the engine loads the requested historical data from our distributed time-series database and feeds it bar-by-bar through your strategy logic. Each bar triggers the same signal generation, order routing, and risk management code that runs in live trading — ensuring simulation fidelity. The engine tracks open positions, unrealised P&L, and margin usage at every tick, producing a precise equity curve that reflects realistic trading conditions including commissions, slippage estimates, and funding rates for perpetual contracts.

Resources: TradingView Strategy Tester · Investopedia — Backtesting Definition
Explore also: No-Code Strategy Builder · Risk Management · AI-Powered Signals

Key Benefits

Why traders love Advanced Backtesting

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Years of tick-level history

Data stretching back 10+ years at tick granularity for realistic simulation.

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Full performance analytics

Sharpe, Sortino, max drawdown, win rate, profit factor, and more — all in one view.

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Walk-forward validation

Automatically test on out-of-sample periods to guard against overfitting.

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Monte Carlo simulation

Estimate outcome distributions and probability of ruin from your trade history.

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Parameter optimisation

Sweep parameter ranges and identify robust configurations efficiently.

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CSV export

Export full trade logs and equity curves for external analysis.

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