The Hull Moving Average (HMA) – Is It Better than EMA?
In this article, we’ll explore and compare nine different moving averages, including traditional moving averages, to uncover the best moving average for trading.
With so many types of moving averages out there, it’s easy to feel overwhelmed, but we’ll simplify the complexity, focusing on how these averages can generate valuable trading signals over both longer and shorter periods.
Understanding moving averages is crucial for any trader. Essentially, moving averages plot the average price of a security over a specified number of periods, such as days, helping traders to identify the market trend by smoothing out past price data.
This smoothing process not only helps to clearly reveal the current trend but also filters out market noise, facilitating a clearer perspective on the market’s direction.
One popular application of moving averages is in identifying crossovers as trading signals. Successful trend followers have long relied on this strategy. For instance, when a shorter period moving average (like the 5-day MA) crosses over a longer period MA (such as the 20-day MA), it signals the beginning of a new uptrend, suggesting a bullish opportunity for traders.
Conversely, when the shorter moving average dips below the longer period moving average, it indicates the end of an uptrend and the start of a downtrend, serving as a bearish signal.
An innovative approach involves using the Hull Moving Average (HMA), which almost eliminates lag thanks to its unique calculation that incorporates the square root of the period.
This means both the shorter period HMA and the longer period HMA can provide more accurate and timely signals compared to traditional moving averages.
Whether you’re analyzing longer periods for a broad market outlook or shorter periods for quick trades, incorporating the HMA can significantly enhance your trading strategy.
What is the biggest problem with moving averages?
The main issue with moving averages, like all technical indicators, is their nature as lagging indicators. They calculate based on past price data, meaning they only reflect what has already happened, not predict the future.
The longer the period used for calculation, the more delayed the moving average becomes. For example, a 5-day moving average reacts faster to recent price changes than a 200-day one but also includes more noise, which can undermine its effectiveness.
Therefore, using moving averages involves balancing the trade-off between reducing noise and minimizing lag. Faster moving averages quickly pick up on new trends but are prone to noise and false signals, while slower moving averages smooth out noise more effectively but may lag in detecting new trends.
Various types of MAs
To address the trade-off between noise and lag, traders have sought to refine the calculation of the simple moving average (SMA). Calculating the SMA is straightforward, making it a common feature on nearly all trading platforms, where it can be easily added to a price chart with a simple click.
However, to create versions that are both faster and smoother for better trend tracking, developers have introduced more complex calculations.
One notable development is the Hull Moving Average (HMA), created by Alan Hull. The HMA stands out among various moving averages for its ability to be fast, responsive, and significantly reduce lag. Alan Hull aimed to design a moving average that not only minimized lag but also enhanced smoothing.
Despite its rarity on popular trading platforms, some consider the HMA an effective indicator for its innovative approach to combining speed and accuracy in trend analysis.
Exponential moving average and double exponential moving average
Now, let’s discuss the exponential moving average (EMA). The EMA seeks works in the same way as the simple moving average. However, it gives greater weight to more recent price moves.
The exponential moving average is, therefore, able to react faster to new trends but could consequently lead to more whipsaws. It is also very popular as well as available on nearly all trading as well as technical analysis platforms.
Notably, the double exponential moving average (DEMA) is a faster version of the EMA. Even though the calculation is actually based on both a simple MA and a double EMA.
The double exponential moving average was first introduced by Patrick Mulloy. The most important thing is that DEMA is a moving average that reacts quickly to new price moves.
Don’t forget about the TEMA. As in the case of DEMA, Patrick Mulloy developed the triple exponential moving average (TEMA). The TEMA became established from the composite of an EMA, a DEMA and a triple EMA. As such, the TEMA significantly reduces lag and reacts quickly to new price moves.
It can be so quick that the TEMA can also overshoot the market, which means TEMA, in some instances, goes too far and moves beyond the recent price action. This is another downside to using fast moving averages.
Wilders moving average and weighted moving average
Welles Wilder introduced the Wilders Moving Average (WILDERS) in the 1970s, modifying the traditional Exponential Moving Average (EMA) formula. By altering the calculation, Wilder made the WILDERS slightly slower than the EMA but quicker than the Simple Moving Average (SMA).
An interesting aspect of his formula is that a 27-day WMA matches the speed of a 14-day EMA.
The aim of the Wilders Moving Average is to identify trends more swiftly while avoiding false signals or whipsaws. It’s important to note that the WMA is calculated by applying different weights to each data point before summing them up. This method allows the WMA to outpace the standard EMA in speed.
Least squares moving average and Guppy multiple moving average
The least squares moving average is based on linear regression. Basically, the linear regression line projects forward. It indicated what would happen if the regression continued forward.
We should mention that the GUPPY multiple moving average (GMMA) differs from the other moving averages discussed here because GMMA is a combination of several experimental moving averages at once.
For the test, we will be using the following exponential moving average parameters: 3,5,7,10,12, 15, and 30,35,40,45,50,60. As shown in the chart below:
Which is the best moving average? Let’s find out
As an example, we can use old data. At this point, the purpose of these tests is to get a rough idea as to which MAs work best
Two different tests will be run, a long-only, moving average crossover comparison on the S&P 500 index and a portfolio test.
Let’s start with the S&P 500 crossover test. For example, we will buy the S&P 500 whenever the faster moving average crosses over the slowing moving average, indicating an upward trend. Next, we will sell our position when the fast moving average crosses back under.
It is worth noting that the starting capital starts at $10,000, and commissions will cost $0.01 per share. Moreover, the position size will be 100% with no leverage. Notably, the ticker used will be $SPX from Norgate Premium Data, as well as the test will be run from January 1st 2000 to January 1st 2015.
Besides, all MAs calculations will use close price, and entries/exits will be made on the next day open.
S&P 500 crossover results
The WILDERS Moving Average emerged as the top choice for a 5/20 day crossover, delivering a compounded annualized return of 2.11% and a maximum drawdown of -33%, resulting in a CAR/MDD ratio of 0.06. However, the HMA was the least effective average in this setup.
When examining the 50/200 day crossover, the EMA stood out as the best Moving Average, offering an annualized return of 5.96% with a maximum drawdown of -17%. The HMA and the least squares moving average both performed the worst in this scenario.
Test number two
For test number two, we’ll follow the same procedure as the first test but with a twist: we’ll run a 10-position long-only portfolio system.
Whenever the fast moving average crosses over the slow one for any stock in our selection, we’ll buy and add it to our portfolio. If it crosses back below, we sell the stock, removing it from the portfolio. Notably, we’ll execute entries and exits at the next day’s open. We’ll prioritize duplicate signals based on the RSI indicator, choosing the strongest stocks first. Also, the stock’s price must exceed $2.
We’ll incur a commission of $0.01 per share. The initial equity is divided equally among all positions, creating an equal-weighted portfolio.
S&P 100 portfolio test results:
The exponential moving average (EMA) topped the charts for a 5/20 day crossover, delivering a 3.6% compounded annualized return with a -34% maximum drawdown, leading to a CAR/MDD of 0.11. The least squares moving average lagged behind as the poorest performer.
For the 50/200 day crossover, the DEMA excelled as the best moving average, while the GMMA strategy fell short.
Analyzing the outcomes reveals two key insights. Firstly, long-term moving average crossovers generally outperform short-term ones. Secondly, the introduction of newer and more intricate moving averages doesn’t necessarily improve trend detection compared to traditional ones.
In conclusion, simple moving averages are just as effective at identifying trends as their complex counterparts, with the EMA standing out as the most effective.
