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.
Start for Free →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
Years of tick-level history
Data stretching back 10+ years at tick granularity for realistic simulation.
Full performance analytics
Sharpe, Sortino, max drawdown, win rate, profit factor, and more — all in one view.
Walk-forward validation
Automatically test on out-of-sample periods to guard against overfitting.
Monte Carlo simulation
Estimate outcome distributions and probability of ruin from your trade history.
Parameter optimisation
Sweep parameter ranges and identify robust configurations efficiently.
CSV export
Export full trade logs and equity curves for external analysis.
Resources
Learn more & explore related tools
Curated links to trusted platforms and educational resources that complement Advanced Backtesting.
TradingView's built-in backtesting documentation — great for understanding fundamentals.
Clear explanation of backtesting concepts, limitations, and best practices.
Algorithmic trading research platform — a great complement for advanced users.
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