What does backtesting mean in trading?

Backtesting means testing a trading strategy on historical market data to evaluate how it would have performed before using it in live markets.

How do you backtest a trading strategy?

To backtest a trading strategy you simulate trades on historical price data using defined entry and exit rules, then analyze metrics like profit, drawdown, and win rate.

Is there a free trading backtesting engine?

Yes. This platform provides a free trading backtesting engine that allows traders to test strategies using historical crypto, stock, and ETF market data.

Backtest Engine

This free trading backtesting engine allows traders to backtest trading strategies using historical market data. Run crypto, stock, and ETF backtests, analyze trade performance, and evaluate strategies before risking real capital. The platform supports indicator-based systems, portfolio backtesting ideas, and Python based algorithmic trading logic.

database

Data

Data source

—

Limit (candles)

Window mode

Switch “Window mode” to Explicit range to set dates.

Start date

End date

Status

idle

Availability

—

tune

Strategy Builder

Preset

Side

Fee (bps / side)

Slippage (bps / side)

Stop loss %

Take profit %

Intrabar priority

Custom Strategy Builder

Entry conditions

Exit conditions

Tip: “cross” uses two indicators (LHS crosses RHS). “compare” compares an indicator to a value.

Force close open position on last candle

Request preview

analytics

Results

Final equity

—

Trades

—

Win rate

—

PnL (without compounding)

—

Max drawdown

—

Profit factor

—

Avg win / Avg loss

—

Equity curve

Drawdown

Open position

—

Errors

—

candlestick_chart

Candles

Overlay trades

Indicator panes (auto)

Pane controls

gesture

Candle Sequences

Click candles to build a sequence, save it, then click saved items to highlight matches.

Enable tolerance

Gap tolerance (candles)

Δ% tolerance

When OFF, new steps seed as exact observed values (min=max).

Per-step rules

Current selection

OFF

Preview

0 candles selected

Label

Turn on “Select sequence”, then click candles on the chart to append them.

Saved sequences

Click one to highlight all occurrences

No saved sequences yet.

receipt_long

Trades

#	Entry ts	Exit ts	Entry	Exit	Reason	Net	Equity after
No results yet.

Raw result JSON

What is Backtesting in Trading?

Backtesting is the process of testing a trading strategy on historical price data to see how it would have performed in the past. Instead of risking real capital, traders simulate trades using historical candles to evaluate profitability, drawdowns, and risk exposure.

Backtest Trading Strategies with Historical Data

This trading backtesting engine allows users to backtest trading strategies directly in the browser. Load datasets, configure indicators, simulate entries and exits, and evaluate performance metrics such as win rate, profit factor, and maximum drawdown.

Portfolio Backtesting

Portfolio backtesting evaluates how a portfolio of assets would perform historically. Many traders use tools like Portfolio Visualizer to analyze asset allocations. This platform allows similar analysis by testing strategies across multiple datasets and evaluating risk-adjusted returns.

ETF and Stock Backtesting

Backtesting is commonly used for stock and ETF strategies such as trading the S&P 500 ETF (SPY). By analyzing historical market data, traders can test momentum, mean-reversion, or indicator-based strategies to determine whether they would have worked in previous market cycles.

Python Backtesting Strategies

Many quantitative traders build backtesting systems in Python. This platform provides similar capabilities through a web interface, enabling traders to experiment with algorithmic strategies without writing code.

TradingView Backtesting Alternatives

Platforms like TradingView allow users to backtest strategies using Pine Script. This backtesting engine offers an alternative environment where traders can load custom datasets, configure rules, and analyze detailed trade logs.