Linear Regression Indicator – Strategy, Rules, Returns
As traders, we rely on different tools to analyze the markets effectively and gain useful insights into the price trend, and only a few tools can do it as well as the linear regression indicator does. What do you know about this analysis tool?
The Linear Regression Indicator is a technical analysis tool that uses the statistical method of linear regression to identify price trends and the strength of trends. The indicator plots the ending value of a linear regression line for a chosen number of price bars to show, statistically, how the price is expected to move and where it could get to. Traders use it to identify the general direction of a price trend and predict future price movements.
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 Linear Regression Indicator is a technical analysis tool that uses the statistical method of linear regression to identify price trends and the strength of trends.
- In statistics, linear regression is a technique for modeling the relationship between two variables to find “a line of best fit.”
- Please click on the link for a full list of all trading indicators.
What is a Linear Regression Indicator?
The Linear Regression Indicator is a technical analysis tool that uses the statistical method of linear regression to identify price trends and the strength of trends. In statistics, linear regression is a technique to model the relationship between two variables — a dependent variable and an independent variable — to find “a line of best fit”, often known as the Linear Regression Line.
In the case of the financial markets, the two variables are the price and the time — with the price on the y-axis and the time on the x-axis — and the line of best fit shows the price trend. This line of best fit (aka the linear regression line) constitutes the Linear Regression Indicator.
The indicator plots the ending value of a linear regression line for a chosen number of price bars to show, statistically, how the price is expected to move and where it could get to. It is similar to the moving average indicator but it is less lagging and thus, more responsive to price movements. Traders use it to identify the general direction of a price trend and predict future price movements based on historical data.
How does a Linear Regression Indicator work?
The Linear Regression Indicator works based on the statistical method of linear regression, which models the relationship between a dependent variable (in this case, the price) and an independent variable (time) to find a line of best fit — the linear regression line.
The ending value of the regression line over a given period (price bars) is plotted as the Linear Regression Indicator. For instance, a 14-period Linear Regression Indicator will plot the ending value of a Linear Regression line that covers 14 bars. The indicator behaves like the moving average indicator but is less lagging, and therefore, it responds faster to price movements than the moving average.
Traders use it to identify the general direction of a price trend and predict future price movements. When the indicator has an upward slope, it indicates an uptrend, And, when it has a downward slope, it implies a downward trend — the steeper the slope, the stronger the trend.
Linear Regression strategy – trading rules, settings, backtest, returns, and performance
Let’s test the indicator. The only way to determine whether it has predictive power is to make quantified trading rules and backtest it.
We did, and it seems the indicator has little predictive use for stocks, bonds, and commoditites. We tried many different settings for each of the assets with no luck. Linear regression needs strong trends.
Thus, it works better for Bitcoin. We made the following trading rules and backtested Bitcoin (USD) from 2014 until today:
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We get the following table when we sell after N trading days:
The best result is when we sell after 10 trading days. We get the following equity line:
Trading performance metrics and statistics from Bitcoin’s inception until today (including 0.03% commissions per trade):
- Number of trades: 175
- Average gain per trade: 4.3%
- Annual returns: 88% (buy and hold 72%)
- Win rate: 61%
- Time spent in the market: 50%
- Risk-adjusted return: 177%
- Max drawdown: 45%
The code of the strategy reads like this (Amibroker):
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Why use a Linear Regression Indicator in trading?
You use a Linear Regression Indicator in trading because it helps you to identify the direction of the trend so you can trade along that direction. It helps to reduce the randomness in price movements to a clear trend that is easier to understand. The indicator sorts out the market noise so you can see the trend and be able to forecast future price movements. You can use it as an entry or exit signal when combined with other trading indicators or analysis tools — the indicator sloping up may be a signal to go long and sloping down may be a signal to go short.
What are the benefits of Linear Regression Indicators?
The benefits of Linear Regression Indicators include:
- Aside from the calculation, which the trading platform will take care of anyway, the indicator is relatively easy to apply in trading analysis, as it consists of one line only.
- The indicator can help you identify the direction and strength of market trends — at least, short-term trends — which enables you to trade in the right direction.
- It can be used as an entry and exit signal when combined with other analysis tools.
- It can function as an ascending support level in an uptrend and a descending resistance level in a downtrend.
How to set up a Linear Regression Indicator?
To set up the linear regression indicator on your trading platform, you have to first check whether your platform has a built-in indicator, which is unlikely because most platforms don’t have this type of linear regression indicator — they only come with the linear regression channel tool.
If your platform doesn’t have the indicator, you will have to source a custom one for the platform and install it. After that, you can go to the indicator section of the platform to pick the indicator and attach it to your chart — you can double-click on it, grab it, and drag it to your chart. When you do that, a box opens up for you to input your preferred settings.
What are the key components of Linear Regression Indicators?
The key components of Linear Regression Indicators are the linear regression line and the number of price bars used. The linear regression line is obtained by calculating the line of best fit using the sum of least squares method. The line shows the trend in the price data.
The number of price bars indicates the lookback period or how far behind in data it looks to estimate the best-fit line. The line’s end point is plotted for each succeeding period to get the linear regression indicator.
How to interpret Linear Regression Indicator signals?
To interpret Linear Regression Indicator signals, you have to understand how the indicator works. The Linear Regression Indicator works like the moving average indicator in that its slope indicates the direction of the trend and can also serve as a dynamic support or resistance level.
When the indicator is sloping upward, the trend is bullish, and when it is sloping downward, the trend is bearish. The steepness of the slope indicates the strength of the trend. When the price is in an uptrend and trading above the indicator, the indicator can serve as an ascending support level. Likewise, when the price is in a downtrend and below the indicator, the indicator serves as a descending resistance level.
What are the common uses of Linear Regression Indicators?
The common uses of linear regression indicators include:
- Identifying the trend: Linear regression indicators can be used to spot a trend and identify its direction.
- Gauging the strength of the trend: The steepness of the slope of the indicators can give some insights into the strength of the trend.
- Entry and exit signals: The indicators can be used to create entry and exit signals when combined with other indicators.
- Trailing stop: While acting as dynamic support or resistance, the indicators can be used to guide trailing stop orders.
How does the Linear Regression Indicator compare to moving averages?
When compared to moving averages, the Linear Regression Indicator is similar to MAs in many respects. The slope of both indicators shows the direction of the trend, and the steepness of the slope estimates the strength of the trend. Both also act as dynamic support or resistance levels.
However, the Linear Regression Indicator has less lag than the moving average, and as such, responds faster to changes in price direction. But on the flip side, the Linear Regression Indicator is more prone to whipsaws than moving averages.
What is the formula for Linear Regression Indicators?
The formula for Linear Regression Indicators is very complex and involves different steps. The first step is to find the line of best fit for the chosen period using the sum of least squares method. This gives the linear regression trendline.
The next step is to find each point in the Linear Regression Indicator, which is simply the end-point of an n-period Linear Regression trendline. This is given as follows:
LRI = (Ending Value of Linear Regression Line) / (Number of Price Bars)
How to customize Linear Regression Indicator settings?
To customize Linear Regression Indicator settings, you have to find out what works best for your strategy and the market you are trading. The easiest way to find out is to backtest your strategy using different settings of the indicator. Generally, the default setting of the indicator is 50 periods with 21 for the filter. But you can test any settings you want and keep testing until you find the settings that work best for the market you want to trade. You may also consider the timeframe you trade when backtesting.
What is the history of Linear Regression Indicators?
The history of Linear Regression Indicators can be traced to the early 19th century when linear regression was popularized as a statistical model for the relationship between an independent variable and a dependent variable. The model was later adapted for analyzing the movement of the price of an asset in the financial market to help traders and investors make sense of trends. However, it was not until the 1990s that Gilbert Raff introduced the first Linear Regression Indicator for trading analysis. So, the active use of linear regression indicators in financial analysis was quite recent compared to other indicators that were introduced in the 1970s.
How to integrate Linear Regression Indicators into trading strategies?
To integrate Linear Regression Indicators into trading strategies, you have to first understand how linear regression indicators work so you can combine them with other indicators or analysis tools that complement them.
This way, you get the best out of the indicator. Since the indicators work like moving averages and show the trend, you can combine them with tools that show short-term changes in price momentum. For instance, you can use a long-period linear regression indicator to identify the trend and use candlestick patterns or a momentum oscillator like the RSI or stochastic to find entry after a pullback.
What are the limitations of Linear Regression Indicators?
The limitations of Linear Regression Indicators include the following:
- The indicators only show a linear relationship between two variables and can only work if the price is trending in one direction, but we know the price often swings about randomly.
- They are less effective in highly volatile or ranging markets, as the price trend becomes less predictable in such situations.
- Since the indicators are based on historical data, they lag the current price action and may not reliably predict future price movements.
How to avoid false signals in Linear Regression Indicators?
To avoid false signals in Linear Regression Indicators, you should combine such indicators with other analysis tools or indicators that complement them so you can have a broader view of the market and make more precise trading decisions. Such combinations allow you to create a reliable trading strategy with clear entry and exit criteria and a risk management plan.
With such a strategy, you can only trade signals that fit your criteria so that even when some signals fail, they become part of the inevitable losing trades that make up a profitable system.
What are the best markets for Linear Regression Indicators?
The best markets for Linear Regression Indicators are markets that are in healthy trends. Since those indicators model linear relationships between the price and time, they work best in markets that have a clear trend in one direction — up or down — and not sideways markets or markets that are twisting and turning randomly without a direction.
However, you may need to backtest your strategy on several markets to see where it performs best.
How to combine Linear Regression Indicators with other tools?
To combine Linear Regression Indicators with other tools, you should first understand how the indicators work so you know the tools that can complement them and get the best out of them.
Since the indicators work like moving averages and show the trend, you can combine them with tools that show short-term changes in price momentum, such as reversal candlestick patterns, which can help you know when a pullback is reversing for the trend to continue.
What is the difference between the Linear Regression Slope and Indicator?
The difference between Linear Regression Slope and Indicator is that the linear regression indicator is displayed on the price chart and tracks the direction of the price trend on the chart while serving as a dynamic support or resistance level, whereas the linear regression slope is placed in the indicator box and functions like an oscillator, tracking price swings.
Thus, the slope doesn’t act as dynamic support or resistance.
How to backtest Linear Regression Indicator strategies?
To backtest Linear Regression Indicator strategies, you have to follow these steps:
- Identify the markets you want to backest the strategy and gather the data you need, dividing them into in-sample and out-of-sample data.
- Formulate the strategies you want to backtest and the parameters or settings you need to adjust
- Code the strategies into trading algorithms.
- Run your backtesting on the in-sample data and optimize with the out-of-sample data, adjusting your parameters as needed.
- Evaluate the results of your backtesting.
How to use Linear Regression Indicators for trend analysis?
To use Linear Regression Indicators for trend analysis, you should focus on the slope of the indicator, the steepness of the slope, and where the price is trading relative to the indicator. When the indicator is sloping upward and the price is trading above the indicator, there is a bullish trend, and when it is sloping downward and the price is trading below the indicator, there is a bearish trend.
The steepness of the indicator shows the strength of the trend.
What is the Linear Regression Channel?
The Linear Regression Channel is a tool commonly built into many trading platforms that uses a linear regression model to identify the market regression line (line of best fit) and plot two parallel lines — one above and one below — at a standard deviation away from the regression line.
It is similar to the normal price channel and the Bollinger Bands, but the middle line is a linear regression line.
How to adjust Linear Regression Indicator periods?
To adjust Linear Regression Indicator periods, you have to go to the indicator settings and change the period length to the number you want. On TradingView, the default period length is 50, but you can change it to whatever you want.
Generally, the most commonly used periods vary from 14 to 300, depending on whether the key focus is on identifying the short-term, medium-term, or long-term trend.
How to interpret Linear Regression Indicator divergences?
To interpret Linear Regression Indicator divergences, you must use the linear regression indicator that functions as an oscillator since it’s only oscillators that track price swings and give divergence signals.
This is to say that you have to use the Linear Regression Slope Oscillator: When the oscillator makes a higher low but the price makes a lower low, there is a bullish divergence, and the price could reverse to the upside. On the flip side, when the oscillator makes a lower high but the price makes a higher high, there is a bearish divergence, and the price could reverse to the downside.
What are common mistakes using Linear Regression Indicators?
The common mistakes when using Linear Regression Indicators include:
- Not combining the indicator with other analysis tools to have a better view of the market and make informed decisions.
- Not having a reliable trading strategy with clear entry and exit criteria
- Not following a risk management plan
How do professionals use Linear Regression Indicators?
Professionals use Linear Regression Indicators to get insights into market trends and get a good view of the market direction and price structure so they know how to position themselves in the markets.
Some create their trading strategies based on the indicator — they combine the linear regression indicator with other indicators and analysis tools to formulate reliable entry and exit signals, as well as risk management parameters.