⚠️Limitations

As helpful as the backtesting tool is, it has several limitations that can affect the accuracy and usefulness of the results. Some of the main limitations of backtesting include

Data quality and availability: The quality and availability of historical data can be a significant limitation to backtesting. If the data is incomplete or of poor quality, it can affect the accuracy and reliability of the backtest results. Additionally, it can be difficult to backtest trading modules accurately if certain data is unavailable or not recorded.

At Tradetomato, we get market data directly from Binance. As a result, the accuracy of our backtests is limited to the availability and quality of data provided by Binance.

Limited scope: Backtests are based on historical data and do not account for future market developments or changes in market conditions. The results of a backtest may not accurately reflect how a module would perform in real-world market conditions.

Limited to a specific time period: A backtest is only as good as the data used to conduct it. If the data used in the backtest is from a specific time period, the results may not be applicable to other time periods or market conditions. This can limit the usefulness and applicability of the backtest results.

Not accounting for transaction costs: In a backtest, transaction costs such as commissions and fees are sometimes not considered. This can lead to overly optimistic results, as these costs can significantly impact the profitability of a module in live market conditions. We recommend you include exchange fees in your backtests to get more accurate results.

Curve fitting: Backtesting can also be susceptible to curve fitting, where a trading strategy is designed to fit the characteristics of a specific historical data set. This can lead to a module that performs well on the data used in the backtest but may not perform as well in live market conditions.

Optimization bias: Backtesting can be susceptible to optimization bias, where a module is designed and tested in such a way as to produce the best possible results. This can lead to overly optimistic results that do not accurately reflect the performance of your module in live market conditions.

While backtesting can be a useful tool for evaluating your modules, it is important to be aware of its limitations and consider the results with a critical eye.