How to eliminate risks when backtesting?

January 3rd, 2022 3 minutes read

For a more in-depth article on backtesting, please email us at geraldo@bam.money

Backtesting provides you with a general idea of how your strategy can behave. However, it has some limitations as every simulator has. This information is not to be solely relied on for making decisions. The features to bring your backtesting results closer to the real-time strategy behaviour are: 

• Precise backtesting 

• Intra-Bar Order Generation 

• Bar Magnifier

Many things can go wrong when designing a backtesting protocol. The main culprits however might be classified into two groups: bad economic foundations or statistical approaches. The latter includes primarily overfitting and lack of cross-validation. The former means one ignores the economic realities in the ground and game settings enforced by rational traders' behaviour.

There are several biases that can affect the performance of a backtested strategy. Unfortunately, these biases have a tendency to inflate the performance rather than detract from it. Thus you should always consider a backtest to be an idealised upper bound on the actual performance of the strategy. It is almost impossible to eliminate biases from algorithmic trading so it is our job to minimise them as best we can in order to make informed decisions about our algorithmic strategies.

There are four major biases that we have identified: Optimisation Bias, Look-Ahead Bias, Survivorship Bias and Psychological Tolerance Bias.

Optimisation Bias

Optimisation bias is hard to eliminate as algorithmic strategies often involve many parameters. "Parameters" in this instance might be the entry/exit criteria, look-back periods, averaging periods (i.e the moving average smoothing parameter) or volatility measurement frequency. Optimisation bias can be minimised by keeping the number of parameters to a minimum and increasing the number of data points in the training set. One method to help mitigate this bias is to perform a sensitivity analysis. This means varying the parameters incrementally and plotting a "surface" of performance. Sound, fundamental reasoning for parameter choices should, with all other factors considered, lead to a smoother parameter surface. If you have a very jumpy performance surface, it often means that a parameter is not reflecting a phenomenon and is an artefact of the test data. There is a vast literature on multi-dimensional optimisation algorithms and it is a highly active area of research. I won't dwell on it here, but keep it in the back of your mind when you find a strategy with a solid backtest.

Look-Ahead Bias

Look-ahead bias is introduced into a backtesting system when future data is accidentally included at a point in the simulation where that data would not have actually been available.  As with optimisation bias, one must be extremely careful to avoid its introduction. It is often the main reason why trading strategies underperform their backtests significantly in "live trading".

Survivorship Bias

Survivorship bias is a particularly dangerous phenomenon and can lead to significantly inflated performance for certain strategy types. It occurs when strategies are tested on datasets that do not include the full universe of prior assets that may have been chosen at a particular point in time, but only consider those that have "survived" to the current time.

Psychological Tolerance Bias

When creating backtests over a period of 5 years or more, it is easy to look at an upwardly trending equity curve, calculate the compounded annual return, Sharpe ratio and even drawdown characteristics and be satisfied with the results. As an example, the strategy might possess a maximum relative drawdown of 25% and a maximum drawdown duration of 4 months. This would not be atypical for a momentum strategy. It is straightforward to convince oneself that it is easy to tolerate such periods of losses because the overall picture is positive. However, in practice, it is far harder, which is why we call this the “psychological tolerance” bias.

If historical drawdowns of 25% or more occur in the backtests, then in all likelihood you will see periods of similar drawdown in live trading. These periods of drawdown are psychologically difficult to endure. I have observed first hand what an extended drawdown can be like, in an institutional setting, and it is not pleasant - even if the backtests suggest such periods will occur. The reason I have termed it a "bias" is that often a strategy which would otherwise be successful is stopped from trading during times of extended drawdown and thus will lead to significant underperformance compared to a backtest. Even though the strategy is algorithmic in nature, psychological factors can still have a heavy influence on profitability. The takeaway is to ensure that if you see drawdowns of a certain percentage and duration in the backtests, then you should expect them to occur in live trading environments, and will need to persevere in order to reach profitability once more.

 

 

 



 

 

Related Blogs
Generating Alpha: Machine Learning Helps Traders
Investors are adopting machine learning as a strategy to identify alpha and gain market advantage through sentiment analysis, alternative data insights and maximizing gains. However, the implementation of a professional’s analytical skills and moral values are important in the decision-making processes.

October 1st, 2021

Key Benefits of Machine Learning
Market professionals' main goal is producing unique and fresh alpha. Machine learning is one of the most valuable tools. We have listed its key benefits below.

October 15th, 2021

What is Machine Learning?
Machine Learning is the most prominent topic in tech right now. However, it can be greatly beneficial to traders if rightly incorporated into their strategies.

November 1st, 2021