Biases in Algorithmic Trading Strategy

Backtesting Biases in Algorithmic Trading Strategy

There are many 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 I wish to discuss: Optimisation Bias, Look-Ahead Bias, Survivorship Bias and Cognitive Bias.

1.) Optimisation Bias:

This is probably the most insidious of all backtest biases. It involves adjusting or introducing additional trading parameters until the strategy performance on the backtest data set is very attractive. However, once live the performance of the strategy can be markedly different. Another name for this bias is “curve fitting” or “data-snooping 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 quantity of data points in the training set. In fact, one must also be careful of the latter as older training points can be subject to a prior regime (such as a regulatory environment) and thus may not be relevant to your current strategy.

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 phenomena 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 fantastic backtest!

2.) 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. If we are running the backtest chronologically and we reach time point N, then look-ahead bias occurs if data is included for any point N + k, where k > 0. Look-ahead bias errors can be incredibly subtle. Here are three examples of how look-ahead bias can be introduced:

• Technical Bugs – Arrays/vectors in code often have iterators or index variables. Incorrect offsets of these indices can lead to a look-ahead bias by incorporating data at N + k for non-zero k.
• Parameter Calculation – Another common example of look-ahead bias occurs when calculating optimal strategy parameters, such as with linear regressions between two time series. If the whole data set (including future data) is used to calculate the regression coefficients, and thus retroactively applied to a trading strategy for optimisation purposes, then future data is being incorporated and a look-ahead bias exists.
• Maxima/Minima – Certain trading strategies make use of extreme values in any time period, such as incorporating the high or low prices in OHLC data. However, since these maximal/minimal values can only be calculated at the end of a time period, a look-ahead bias is introduced if these values are used -during- the current period. It is always necessary to lag high/low values by at least one period in any trading strategy making use of them.

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”.

3.) 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.

As an example, consider testing a strategy on a random selection of equities before and after the 2001 market crash. Some technology stocks went bankrupt, while others managed to stay afloat and even prospered. If we had restricted this strategy only to stocks which made it through the market drawdown period, we would be introducing a survivorship bias because they have already demonstrated their success to us. In fact, this is just another specific case of look-ahead bias, as future information is being incorporated into past analysis.

There are two main ways to mitigate survivorship bias in your strategy backtests:

• Survivorship Bias Free Datasets – In the case of equity data it is possible to purchase datasets that include delisted entities, although they are not cheap and only tend to be utilised by institutional firms. In particular, Yahoo Finance data is NOT survivorship bias free, and this is commonly used by many retail algo traders. One can also trade on asset classes that are not prone to survivorship bias, such as certain commodities (and their future derivatives).
• Use More Recent Data – In the case of equities, utilising a more recent data set mitigates the possibility that the stock selection chosen is weighted to “survivors”, simply as there is less likelihood of overall stock delisting in shorter time periods. One can also start building a personal survivorship-bias free dataset by collecting data from current point onward. After 3-4 years, you will have a solid survivorship-bias free set of equities data with which to backtest further strategies.

We will now consider certain psychological phenomena that can influence your trading performance.

4.) Cognitive Bias:

This particular phenomena is not often discussed in the context of quantitative trading. However, it is discussed extensively in regard to more discretionary trading methods. 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 rosy. However, in practice, it is far harder!

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. Thus, 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.

Read More; Successful Backtesting Algorithmic Trading Strategies