Backtesting is the process of evaluating the performance of your trading strategy using past data. The development of successful trading systems requires the use of backtesting. It is done by reenacting trades that would have happened in the past under the conditions specified by a specific strategy using historical data. The outcome provides statistics to evaluate the strategy’s efficacy.

According to the underlying idea, any approach that did well in the past is likely to do so in the future. In contrast, any method that performed poorly in the past is likely to perform poorly going forward. The applications utilized for backtesting, the types of data obtained, and the uses for that data are examined in this article.

Backtesting is regarded as a crucial weapon in a trader’s arsenal. Traders wouldn’t even consider putting money at risk in the financial markets without backtesting. Backtesting can offer a wealth of insightful statistical information about a particular system.

However, you must have a trading strategy in place before you can backtest it as a set of rules that guides your trading decisions. You don’t want to look at a chart and think, Should I enter a trade now or later? How do I place my stop loss? How do I sell my profitable trades? Your backtest will be invalidated, and your findings will be incorrect.

Prerequisites for backtesting

You should take into account the following criteria before you begin backtesting a trading strategy:

• Data
• Trading logic
• Market segment
• Programming language

Metrics for backtesting

Here are a few typical backtesting metrics:

• Net percentage: How much gained or lost
• Measures of volatility: Maximum percentages of upside and downside
• Averages: Average gain and loss in percentage, average bars held
• Exposure: The proportion of invested funds or exposed to the market
• Ratios: Wins-to-losses ratio
• Return: Expressed as a percentage over a year, or annualized
• Return adjusted for risk: Percentage return adjusted for risk

Data for backtesting

You should backtest your trading technique after you’ve narrowed down your selection of assets. The next step is to select the asset’s historical data. You can obtain the data from your broker or the data vendor. It is crucial to choose high-quality data, or data that is error-free. The resulting analysis from backtesting will be inaccurate and deceptive if you use low quality data.

How to backtest a trading strategy – Example of backtesting trading strategies

Moving average is the foundation of all strategies that you will backtest. The average value for the given data field over a predetermined number of subsequent periods, like the price, is what is known as a moving average.

The average of the data is calculated by subtracting the oldest value and adding the most recent when new data becomes available.

The trading logic is very simple.

• We purchase the asset when the short-term moving average (50-day crossover) passes above the long-term moving average (200-day crossover). Another name for this is a golden crossover.
• We sell when the short-term moving average dips beneath the long-term average. The death cross is what it is called.

Trading backtesting software and tools

• TradingView
  A completely free cloud-based charting tool that enables manual back- and forward-testing.
• MT4
  A free charting tool that enables manual forward and reverses testing.
• Simple Forex Tester
  This is a paid add-on that enables you to perform backtesting within the MT4 program for those of you who wish to do so.
• Forex Tester
  You may easily perform manual backtesting with paid trading software.
• Amibroker
  Even if you don’t know how to code, you can perform automated backtesting using premium trading tools.

Now that you have all the information, you can always rely on this as your go to guide if you want to learn how to backtest a trading strategy.

The Bottom Line

One of the most crucial steps in creating a trading system is backtesting. If designed and evaluated correctly, it can assist traders in fine-tuning and improving their tactics, identifying any technical or theoretical shortcomings, and gaining confidence in their plan before implementing it in actual market conditions.