Adaptive Laguerre Filter – Strategy And Rules
In the fast-paced financial trading world, using the most responsive indicators for your technical analysis is key to catching the right moves in time, and this is where the Adaptive Laguerre Filter comes in. What do you know about this indicator?
The Adaptive Laguerre Filter is an improvement on the simple Laguerre filter developed by John Ehlers. It applies a variable gamma factor depending on how well the filter tracks the previous lookback price bars. Just like other adaptive price average-based indicators, the Adaptive Laguerre tracks the market closely when it is trending and less closely when it is in a range or consolidating.
In this post, we will take a look at most of the questions you may have about this indicator: what it is, how it works, and how you can improve your trading strategies with it. Let’s dive in!
Key takeaways
- The Adaptive Laguerre Filter uses the median price of the last four price bars and an alpha coefficient to filter the price data and achieve a balance between a triangular weighted moving average’s smoothing capability and its lagging.
- The Adaptive Laguerre tracks the market closely when it is trending and less closely when it is in a range or consolidating.
- We cover all the existing trading indicators.
What is an Adaptive Laguerre Filter?
The Adaptive Laguerre Filter is an improvement on the simple Laguerre filter developed by John Ehlers, which uses the median price of the last four price bars and an alpha coefficient to filter the price data and achieve a balance between a triangular weighted moving average’s smoothing capability and its lagging. In the Adaptive Laguerre Filter, a variable gamma factor is applied based on how well the filter tracks the previous lookback price bars.
Just like other adaptive price average-based indicators, the Adaptive Laguerre tracks the market closely when it is trending and less closely when it is in a range or consolidating. That is, when the price is moving away from the indicator line, the indicator becomes faster, but when the price is moving sideways, the indicator gets slower. Although similar to the Volatility Index Dynamic Average (VIDYA) developed by Tushar Chande, the Adaptive Laguerre Filter tends to be smoother than the VIDYA and adjusts more slowly to price action after consolidations.
According to John Ehlers, the reason for developing the filter is the dilemma that arises in technical analysis between trying to avoid whipsaw trades with moving average smoothing and the resulting lag that often renders signals too late to be effective. So, he introduced this new tool to address the smoothing vs. lag problem more effectively.
How does an Adaptive Laguerre Filter work?
The Adaptive Laguerre Filter works by using a variable gamma — based on how well the filter is tracking the previous lookback price bars — to filter price data and achieve a balance between a triangular weighted moving average’s smoothing capability and its lagging. It is an improvement on the simple Laguerre filter, which uses a fixed gamma or alpha coefficient to filter the data.
Unlike the simple Laguerre filter, the Adaptive Laguerre Filter tracks the market closely when it is trending and less closely when it is in a range or consolidating. Thus, when the price is moving away from the indicator line, the filter becomes faster, but when the price is moving sideways, it gets slower.
Why use an Adaptive Laguerre Filter in trading?
You use an Adaptive Laguerre Filter in trading because it helps to solve the smoothing vs. lag problem in technical analysis. As you may have found out, in technical analysis, a dilemma arises when using an indicator — especially moving average-based indicators — to track price movements and create a trading signal. You are faced with either trying to avoid whipsaws with much smoothing and thereby enduring too much lag in the indicator or avoiding lag and enduring too many whipsaws.
The Adaptive Laguerre Filter helps to solve this problem by using a variable gamma to filter price data based on how well the indicator is tracking the past lookback price bars. This helps to achieve a balance between the indicator’s smoothing capability and its lagging.
Adaptive Laguerre filter trading strategy – rules, backtest, returns, and settings
We make the following trading rules:
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We backtest SPY – the ETF that tracks the S&P 500, and we get the following results from inception until today:
Key performance metrics and statistics:
- No. of trades: 370
- Average gain per trade: 0.72%
- CAGR: 8.3%
- Time spent invested in the market: 17%
- Risk-adjusted return: 49%
- Win rate: 72 %
- Max drawdown: 23%
What are the key features of Adaptive Laguerre Filters?
The key features of Adaptive Laguerre Filters include:
- A triangular weighted moving average: This is basically a double-smoothed simple moving average. So, it gives more weight to the data in the middle of the period used. On many platforms, you will have the option to set how long you want the periods to be. This is the main part of the indicator that smoothens the price, filtering out market noise.
- A variable/adaptive Gamma Factor: This is a factor that is used to determine how much feedback is added to the result of the triangular weighted smoothing. In the Laguerre Filter, it uses a fixed value you set — so, the smoothing and the lag can be reduced by reducing this factor. In the Adaptive Laguerre Filter, however, this factor varies, adapting to the indicator reading based on how well the indicator is tracking the past lookback price bars.
How to implement an Adaptive Laguerre Filter in a trading strategy?
To implement an Adaptive Laguerre Filter in a trading strategy, you have to understand how the indicator works so you know the type of trading strategy you create with it. The indicator may work best in a trending market, so you should look to implement it in a trend-following strategy. Your strategy must have clear entry and exit criteria, as well as risk management parameters. After creating a strategy, ensure you backtest it to know its profitability and whether it has a positive expectancy.
What are the benefits of Adaptive Laguerre Filters?
The benefits of Adaptive Laguerre Filters include:
- The indicator smoothens the price data, filtering out the market noise, so you can see the direction of the trend.
- It does its smoothening while making sure its signals don’t lag behind the price action by a lot.
- It adapts to price movements such that when the price is trending, it tracks it closely, and when the price is in a range or consolidating, it becomes slow and tracks it less closely, thereby reducing the rate of whipsaws.
- It can be used to create trend-following strategies and even track trailing stop loss.
How to set parameters for an Adaptive Laguerre Filter?
To set parameters for an Adaptive Laguerre Filter, you have to first add the indicator on your chart. When you do that, a box will come up for you to set the parameters you want for the Adaptive Laguerre Filter.
You input the parameters you want and click ‘OK’. What determines the parameters you choose is the result of your backtesting in the market you want to trade. This is why you have to backtest with different parameters to know the ones that perform best in the market you’re trading.
Can Adaptive Laguerre Filters improve trading accuracy?
Yes, Adaptive Laguerre Filters can improve trading accuracy by smoothing the price data without making its signal lag so much behind the price action. In other words, the indicator can filter out market noise so you can trade the more significant market trends.
It gives fewer whipsaws, even when the market is moving sideways. It does this by slowing down how closely it tracks the price action. However, no matter how well the indicator adapts to the price action, the best way to ensure trading accuracy is to have a clear strategy with an edge in the market.
What is the mathematical basis of Adaptive Laguerre Filters?
The mathematical basis of Adaptive Laguerre Filters centers around the triangular weighted moving average and the adaptive gamma/alpha factor. The formula is complex, but it is based on multiple smoothing of a simple moving average of the data, with the gamma (alpha) factor used to add or subtract from the process based on how well the indicator is tracking the past lookback price bars. Here’s a snippet of the highly complex calculation in MotiveWave:
“l0 = alpha*price + (1-alpha)*prevL0;
l1 = -(1 – alpha)*l0 + prevL0 + (1 – alpha)*prevL1;
l2 = -(1 – alpha)*l1 + prevL1 + (1 – alpha)*prevL2;
l3 = -(1 – alpha)*l2 + prevL2 + (1 – alpha)*prevL3;
Plot: filt = (l0 + 2*l1 + 2*l2 + l3) / 6;”
How does an Adaptive Laguerre Filter differ from other filters?
An Adaptive Laguerre Filter differs from other filters in that it uses a variable gamma/alpha factor to adapt its filtering to the market condition such that when the price is trending, it tracks the price closely, and when the price is in a range or consolidating, it becomes slow and tracks it less closely, thereby reducing the rate of whipsaws.
The variable alpha works based on how well the filter is tracking the past lookback price bars. This is unlike the simple Laguerre filter that uses a set gamma/alpha value.
The Adaptive Laguerre Filter is similar to the Volatility Index Dynamic Average (VIDYA) developed by Tushar Chande, but it tends to be smoother than the VIDYA and adjusts more slowly to price action after consolidations.
What markets can benefit from Adaptive Laguerre Filters?
The markets that can benefit from Adaptive Laguerre Filters are markets that are trending very well. In such markets, the indicator tracks the price more closely and gives signals without much lag from the price action, enabling traders to get into their trades in time.
In non-trending markets, the indicator slows down and doesn’t track the price closely, thereby reducing the possibility of whipsaws, but at the same time, giving less and late signals.
How to optimize an Adaptive Laguerre Filter for different assets?
To optimize an Adaptive Laguerre Filter for different assets, you have to understand how the indicator works and the market conditions of those assets, as well as how the markets move. With that knowledge, you formulate the strategies for those markets.
Then, you backtest those strategies with different parameters of the indicator to know the ones that work best for each asset’s market.
What are common mistakes when using Adaptive Laguerre Filters?
Common mistakes when using Adaptive Laguerre Filters include:
- Starting to trade with the indicator without first understanding how it works.
- Trading with the Adaptive Laguerre Filter as a standalone strategy and not combining it with other analysis tools to formulate reliable trading strategies with clear entry and exit criteria.
- Not backtesting your strategies before putting money on the line.
- Not having a risk management plan.
How to backtest an Adaptive Laguerre Filter?
To backtest an Adaptive Laguerre Filter, you can follow these steps:
- Formulate the strategies you want to backtest and the parameters or settings you need to adjust for the various strategies.
- Choose the markets you want to backtest the strategy
- Get the data you need for the backtesting
- Divide them into in-sample and out-of-sample data.
- Code your strategies into trading algorithms, and run your backtesting on the in-sample data
- Optimize the strategy with the out-of-sample data, adjusting your parameters as needed.
- Evaluate the results of your backtesting.
What tools can help implement Adaptive Laguerre Filters?
Tools that can help implement Adaptive Laguerre Filters include other indicators that complement it and technical analysis drawing tools, such as trendlines, pivot lines, support and resistance levels, and so on. There are other analysis tools like price action analysis, including chart patterns and candlestick patterns.
Indicators that can help in implementing the Adaptive Laguerre Filters include momentum oscillators like the RSI, stochastic, and CCI. These indicators can be used to pick the right entries after a pullback while the Adaptive Laguerre Filter shows the trend direction. Other tools like reversal candlestick patterns can also be good entry triggers after a pullback.
How to interpret signals from an Adaptive Laguerre Filter?
To interpret signals from an Adaptive Laguerre Filter, you have to watch the direction of the indicator’s slope and the position of the price action it tracks. If the indicator is sloping upward and the price is trading above it, the market is trending upward and may likely continue in that direction for a while.
On the other hand, if the indicator is sloping downward and the price is trading below it, the market is trending downward and may likely continue in that direction for a while.
What are the limitations of Adaptive Laguerre Filters?
The limitations of Adaptive Laguerre Filters are many. These are some of them:
- Despite the adaptive filtering, the indicator cannot reliably predict the direction of the trend — it can only show what the price has been doing but cannot predict whether it will keep doing the same in the future or change direction.
- The indicator cannot be used to anticipate price reversal — it only tracks what the price has done and probably doing.
- It does not directly give buy or sell signals, so it cannot be used alone.
How do Adaptive Laguerre Filters compare to moving averages?
Compared to moving averages, Adaptive Laguerre Filters provide a minimal-lag smoothing of the price data. One problem that has plagued moving averages is the fact that the more smoothing they provide, the more the indicators lag the price, and to reduce the lag, the indicator won’t smoothen the data enough, leading to too many whipsaws.
The Adaptive Laguerre Filters solve this problem by using an adaptive gamma factor to determine how much filtering to provide based on how well the filter is tracking the previous lookback price bars.
What historical data is needed for Adaptive Laguerre Filters?
The historical data needed for Adaptive Laguerre Filters is the price data. John Ehlers, who created the indicator, specifically chooses the median price, which is calculated for any price bar as follows:
Median price = Bar’s high – Bar’s low.
Most trading platforms provide this price data as one of the price types traders can apply their indicators. But if it is not provided, it can always be calculated for each price bar using the formula above.
How to combine Adaptive Laguerre Filters with other indicators?
To combine Adaptive Laguerre Filters with other indicators, you have to understand how the indicator works so you know the other indicators that can complement it. The indicator works best in a trending market, where it tracks the price action closely.
You can combine it with momentum oscillators to help you identify a pullback and when the pullback has reversed so that you trade in the trend direction. Together with momentum oscillators, you can use the indicator to formulate trend-following swing trade strategies.
What are the settings for short-term and long-term use?
The settings for short-term and long-term use will depend on the market you are trading, as there are no universal settings for short-term and long-term use for every market. The best way to know the settings for any type of trading in any market is from your backtesting result.
You have to backtest your strategy with different settings to know the ones that work best for each type of trading.
How often should you adjust Adaptive Laguerre Filter parameters?
How often you adjust your Adaptive Laguerre Filter parameters will depend on the strategy you are using and the markets you are trading. The indicator is created to adjust the way it tracks the price action by itself, but your specific parameters may not work so well in different markets, for different strategies, and in all market conditions.
You have to study your markets and strategies to know when changes need to be made. Your backtesting results and regular evaluations of trading performances will guide you.
What is the impact of market volatility on Adaptive Laguerre Filters?
The impact of market volatility on Adaptive Laguerre Filters will depend on the level of market volatility and whether the indicator is able to adapt to that level of volatility in the market.
Typically, the indicator is created in a way that enables it to adapt to volatile markets by slowing down how fast it tracks the price action when the market becomes volatile and choppy. However, the indicator’s settings can limit the level of volatility it can handle.
Can Adaptive Laguerre Filters be automated in trading systems?
Yes, Adaptive Laguerre Filters can be automated in trading systems if it is used to create reliable trading strategies that are coded into trading algorithms. With functional trading algorithms based on the indicator, you can have automated trading systems. But you must backtest them to be sure they are profitable.
How do professional traders use Adaptive Laguerre Filters?
Professional traders use Adaptive Laguerre Filters to create trend-following trading strategies that allow them to be in the trend early enough and stay with the trend for as long as the trend lasts. Some trade their strategies discretionarily, while others convert them to trading algos for automated trading systems.