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What are some common pitfalls and challenges of backtesting trend following strategies?

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Trend following is a popular trading strategy that involves identifying and following the direction of market movements over time. It can be applied to various asset classes, such as stocks, commodities, currencies, and futures. However, backtesting trend following strategies is not as simple as it may seem. Backtesting is the process of testing how a strategy would have performed in the past, using historical data. It can help traders evaluate the profitability, risk, and robustness of their strategy, as well as optimize their parameters and rules. However, backtesting also comes with some common pitfalls and challenges that can affect the accuracy and reliability of the results. In this article, we will discuss some of these issues and how to avoid or overcome them.

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Greg Peters

Private Equity Trader / Day Trader

1 Data quality

One of the most important factors in backtesting is the quality of the data used. Data quality refers to the accuracy, completeness, consistency, and relevance of the data. Poor data quality can lead to false signals, unrealistic assumptions, and misleading outcomes. For example, if the data contains errors, gaps, outliers, or duplicates, it can distort the trend signals and the performance metrics. Therefore, it is essential to use reliable and clean data sources, as well as check and correct any data issues before backtesting.

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Neville Hornsey

Director at Macrobriefing Ltd.
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When it comes to developing and testing trading strategies, the quality of the data used is of utmost importance. Good quality data can help traders test their strategies accurately and reliably. Using third-party data is a common practice in the trading industry as it can be provided cleaned and ready to import into popular trading platforms such as MT4/MT5. One popular broker offering good-quality data is Dukascopy. They provide 1-minute data that is highly accurate and comprehensive. To access this data set, traders can use https://tickstory.com/ which allows them to download the data from Dukascopy onto their local machine. Once the data is downloaded, traders can then point their data feed for testing strategies to this data set.

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2 Survivorship bias

Another common pitfall in backtesting is survivorship bias. Survivorship bias is the tendency to exclude or ignore the data of assets that have stopped trading or have been delisted over time. This can create a false impression that the strategy is more profitable or less risky than it actually is, because it only considers the winners and ignores the losers. For example, if a trend following strategy is backtested on a stock index that only includes the current constituents, it may overlook the fact that some stocks have dropped out of the index due to poor performance or bankruptcy. Therefore, it is important to use data that includes both the survivors and the non-survivors, or adjust the results for survivorship bias.

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3 Look-ahead bias

A third challenge in backtesting is look-ahead bias. Look-ahead bias is the error of using information that was not available at the time of the trade, such as future prices, events, or indicators. This can result in overestimating the profitability or underestimating the risk of the strategy, because it assumes perfect foresight and ignores uncertainty. For example, if a trend following strategy is backtested using moving averages that are calculated based on future data, it may generate more accurate and timely signals than it would in real time. Therefore, it is crucial to use data that is only available at or before the time of the trade, or lag the indicators accordingly.

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4 Curve fitting

A fourth pitfall in backtesting is curve fitting. Curve fitting is the process of optimizing the parameters or rules of a strategy to fit the historical data, without considering its generalizability or validity. This can lead to overfitting, which is the problem of creating a strategy that performs well on the past data, but fails to perform well on new or unseen data. For example, if a trend following strategy is backtested using too many parameters or too complex rules, it may capture the noise and randomness of the data, rather than the true underlying trend. Therefore, it is advisable to use simple and logical parameters and rules, as well as test the strategy on out-of-sample data or different time periods.

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Neville Hornsey

Director at Macrobriefing Ltd.
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Curve fitting a trading strategy for the last 10 years is of no use due to the changes in the interconnectivity of currencies. It is better to first grasp an understanding of why a certain asset was trending. What was the driver of the move? Currencies are highly sensitive to a wide range of factors, including national GDP, geopolitics, central bank policies, and governmental policies. Moreover, the relationship between currencies is highly complex and non-linear. Any strategy that is based solely on past data is likely to be irrelevant in the current market conditions and may lead to significant losses.

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5 Transaction costs

A fifth challenge in backtesting is transaction costs. Transaction costs are the expenses incurred by executing a trade, such as commissions, fees, spreads, slippage, and taxes. Transaction costs can have a significant impact on the profitability and risk of a strategy, especially for trend following strategies that tend to generate frequent trades and rely on small price movements. However, transaction costs are often ignored or underestimated in backtesting, which can inflate the returns and understate the drawdowns. Therefore, it is important to account for realistic and updated transaction costs in backtesting, as well as evaluate the net returns after deducting them.

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Neville Hornsey

Director at Macrobriefing Ltd.
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In MT5 using the Strategy Tester there is a way to add broker commissions to your backtest for Expert Advisors. After you have chosen your time period and an asset to backtest, underneath these options you'll find a button labeled 'select a delay to emulate slippage and requotes during trade execution'. Clicking this button opens up a dialogue box where you are able to input the Commission for the assets you trade.

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6 Market conditions

A final pitfall in backtesting is market conditions. Market conditions are the factors that influence the behavior and dynamics of the market, such as volatility, liquidity, trends, cycles, and events. Market conditions can vary significantly over time and across different asset classes and regions. However, backtesting often assumes that market conditions are stable and consistent, which can lead to false assumptions and expectations. For example, if a trend following strategy is backtested on a period that is characterized by strong and persistent trends, it may perform well on that period, but poorly on other periods that are more choppy or sideways. Therefore, it is essential to backtest the strategy on different market conditions, as well as adapt the strategy to changing market conditions.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Greg Peters

Private Equity Trader / Day Trader
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Why ramble on with All the rhetoric? Maybe to come across as educated? Strict Risk Mitigation is Keystone!! Learning how to ride the Cyclical markets is Keystone, & your P&L tells your story. Happy Trading, Cheers, GP

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Aakash Sukheja

I help companies with content marketing and brand building. I document my journey on my - Business Nerd Podcast (Stories on Business, Stock Markets, Marketing) (Link in bio)
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Generation bias. As the markets evolve with time, new people entering the markets are much more mature than what they were in the previous generation. The information that is available for free on the internet is astounding. Therefore, new people have a different mindset - resulting in an evolved market behavior which may not be similar to the past data that is used for backtesting. This creates a friction in hypothesis and data that is fed into the system for backtesting.

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What are some common pitfalls and challenges of backtesting trend following strategies?

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2

3

4

5

6

7

1 Data quality

2 Survivorship bias

3 Look-ahead bias

4 Curve fitting

5 Transaction costs

6 Market conditions

7 Here’s what else to consider

Technical Analysis

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What are some common pitfalls and challenges of backtesting trend following strategies?

1

2

3

4

5

6

7

1 Data quality

2 Survivorship bias

3 Look-ahead bias

4 Curve fitting

5 Transaction costs

6 Market conditions

7 Here’s what else to consider

Technical Analysis

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