A backtest helps you test how a strategy would have performed in the past using historical market data. Backtesting is useful for reviewing strategy behavior, but it is not a guarantee of future trading results.
What is a backtest?
A backtest is a simulation that applies a strategy to historical market data.
In QuantPilot, backtesting can help you review:
how a strategy performed during the selected period
how often the strategy traded
how profit and loss changed over time
how much drawdown the strategy had
whether the strategy looks stable enough for further testing
Backtesting is part of the strategy workflow. You can create or edit a strategy, validate the QuantScript, and run a backtest to review the result.
What data does a backtest use?
Backtests use historical market data for the selected market and date range.
The selected market, timeframe, strategy logic, and backtest date range can affect the result. A backtest run only reflects the data and assumptions used for that specific run.
What do performance metrics mean?
Backtest metrics summarize how the strategy performed during the selected historical period.
Common backtest metrics may include:
Realized PnL: the simulated profit or loss from completed trades during the backtest.
Total Return: the overall percentage return shown by the backtest.
Sharpe Ratio: a risk-adjusted performance metric that compares return to overall volatility.
Sortino Ratio: a risk-adjusted performance metric that focuses more on downside volatility.
Max Drawdown: the largest drop from a previous high during the backtest.
Win Rate: the percentage of trades that closed with a positive result.
Total Trades: the total number of simulated trades created during the backtest.
Fill Rate: the percentage of simulated orders that were filled.
Filled Orders: the number of orders filled during the backtest.
Rejected Orders: the number of orders rejected during the backtest.
Canceled Orders: the number of orders canceled during the backtest.
Partially Filled Orders: the number of orders that were only partially filled.
Buy Orders: the number of simulated buy orders.
Sell Orders: the number of simulated sell orders.
Gross Volume: the total simulated trading volume during the backtest.
Fees: the fees included in the backtest result, if any.
Maker Trades: the number of simulated maker trades.
Taker Trades: the number of simulated taker trades.
Total Orders: the total number of simulated orders.
These metrics help compare strategy versions and backtest runs. They should not be read as expected future returns.
What is not guaranteed?
A backtest does not guarantee that a strategy will perform the same way in the future.
Backtest results can look strong because of the selected time period, market conditions, parameters, or assumptions. A strategy that performed well historically may perform poorly in live market conditions.
QuantPilot helps with strategy testing and analysis. It does not guarantee profits or trading performance.
What may not be fully reflected in a backtest?
A backtest is a simulation, not a live trading result.
Even when a backtest shows order, fee, fill, maker, or taker metrics, it is still a simulation. Depending on the strategy, market, and available data, a backtest may not fully reflect:
live execution speed
order book changes during execution
liquidity conditions
slippage
trading fees
funding rates
partial fills
rejected orders
sudden market gaps
exchange downtime or connectivity issues
manual changes made during live trading
Because of this, backtest results should be treated as research and testing data, not as a promise of what will happen in live trading.
How to backtest your strategy
After generating a QuantScript:
Run a backtest using QuantPilot tools.
Review performance metrics (such as P&L).
Adjust the strategy if needed.
You can iterate multiple times before saving.
Backtests and strategy versions
A saved QuantScript version can have multiple backtest runs. Each backtest run can use its own date range, status, result, and created time. This lets you compare how the same saved strategy version performed across different backtest runs.
A working-version backtest is different. It can be used to test unsaved QuantScript changes, but it is not saved as a versioned backtest history for a saved strategy version.
Arena backtests
Arena backtests are used for competition-related strategy evaluation.
The general idea is the same: a strategy is tested against historical market data and performance metrics are shown. However, Arena may use specific rules, ranking logic, dates, or submission requirements depending on the season.
If you are participating in Arena, check the Arena article or season rules for the exact backtest and ranking conditions.
How to use backtest results safely
Use backtests to compare ideas, review risk, and decide what needs more testing.
Before relying on a backtest result:
test different date ranges
compare multiple strategy versions
check drawdown, not only return
review the number of trades
look for signs of overfitting
validate the strategy logic
remember that past performance does not guarantee future results
Important: Backtests are historical simulations. They are not live trading results and should not be treated as financial advice or a guarantee of future performance.