How to backtest stocks succsessfully – Insidertipps
- Von Thomas
Backtesting is important if you would like to use a strategy that is based on a solid foundation. Backtesting is a method that uses historical data to derive rules whose purpose is to generate profits on the stock market in the future.
In this post, we will go into more detail about stock backtesting, what it is, how it works, and how it can help traders make better decisions. Let’s see further.
What is backtesting?
A backtest is a “what if” calculation. A back calculation. It uses historical data to simulate how an investment strategy would have worked in the past.
The goal is to identify the strengths and weaknesses of the investment strategy and to find out whether it could be successful in the future. It is therefore – correctly – about probabilities.
Backtesting is also often used by professional investors to evaluate and improve the performance of their investment strategies. Because, as we all know, you can only improve what you can measure. And experts know that.
But backtesting can also be a valuable tool for private investors to make better decisions when selecting stocks. And we will now explain how this works.
Video - what is a backtest?
Watch what Thomas tells us about backtesting.
How does backtesting of stocks work?
Before you start backtesting stocks, you need the following: backtesting software and historical stock price data. And of course the knowledge how to set up the testing process correctly.
Software
There are several free and paid software solutions on the market that are used by private investors and professional investors alike.
One of them is Wealth Lab, which is particularly suitable for trading and backtesting stocks in the form of large portfolios.
The software allows you to apply a certain investment strategy (rules) to historical data to see how it would have worked in the past.
In doing so, one can set various parameters, such as the number of stocks in the portfolio, the duration of the holding period, the entry and exit price based on various indicators, and much more.
Once you have set all the parameters, you can start backtesting and analyze the results. One should pay attention to various ratios, such as the Sharpe ratio, volatility and various drawdown times, although there are dozens of other ratios.
These help the trader to evaluate the quality of the systems and compare them with other strategies.
Data
The trader is faced with different providers of data. Especially with regard to stocks, it makes a big difference if you want to work with daily closing prices or with intraday data.
While the former are often offered free of charge in good quality, the latter are sometimes subject to high fees.
Why is backtesting stocks important?
By using historical data, one can simulate the performance of an investment strategy in the past and thus find out whether it could be successful in the future.
After all, I want to know whether my strategy makes sense or not. So with backtesting, we replace the principle of try and error with hard and, above all, resilient facts.
Backtesting can further help to remove emotions from the investment decision. Often investors make decisions based on emotions rather than facts and data. Backtesting can help remove these emotions by following your plan and learning to put “thinking” to the back of your mind when executing your plan.
And last but not least, the trader saves time in daily trading. This is because the backtesting software can be used as a signal generator for ready-made strategies. So instead of thinking about which trades to make today, this decision – based on the developed set of rules – is taken from the trader, in which the trading signals are generated from the software. Simple, isn’t it?
While there are many benefits to backtesting stocks, there are also some challenges to watch out for.
Data quality
The first challenge is that historical data is not always reliable. It can happen that historical data is incorrect or incomplete, which can lead to incorrect results.
It is therefore important to check the quality of the data and make sure it is accurate and complete before backtesting. By the way, the already mentioned Wealth Lab offers the data of many blue chips in high quality for free.
Curve Fitting
Another challenge is that an investment strategy that was successful in the past will not necessarily be successful in the future. This occurs mainly when the trader wanted too much and over-optimized the strategy (curve fitting).
Note Over Optimization – Curve Fitting
Based on in sample and out of sample data as well as with modern extensions of the backtesting software Wealth Lab, even the private trader can detect overoptimization, reduce it step by step and thus develop trading systems that are very close to reality and generate a realistic expected value.
Untaugliche Strategie
Markets and the economy continue to be in a constant state of flux, and historical patterns can change in the future. It is therefore important to actively backtest and constantly monitor whether the strategy is doing what it is supposed to do according to the backtest.
Even a backtest is no guarantee of future success; the developer must always keep that in mind.
Over-engineering (as distinct from over-optimization).
Finally, backtesting can also lead to traders spending too much time analyzing and optimizing historical data instead of focusing on the future.
In other words, there is a risk that the trader forgets or neglects real live trading and can no longer see the proverbial forest for the trees.
Conclusion
Stock backtesting is a method that helps traders make better stock selection decisions. By using historical data, one can simulate the performance of an investment strategy in the past and find out if it could be successful in the future.
Backtesting can likewise help remove emotion from investment decision-making.
However, there are challenges to backtesting stocks, such as the reliability of historical data and uncertainty about the future performance of an investment strategy.
Of course, backtesting is not an absolutely perfect solution. There is no such thing as a 100% perfect solution in the stock market, because all the zooming around of prices is far too random for that. The “noise” is very large, although patterns exist that can be used.
