Direct Answer
The linear regression curve plots, at each bar, the end-point value of the least-squares line fitted to the last N closes. It is a trend-following average that lags less than a simple moving average of the same length. Price above the curve and a rising curve indicate an upward fitted trend.
Linear Regression Curve: Formula, Meaning, Signals, Examples and How to Use It
How Is Linear Regression Curve Calculated?
The linear regression curve plots, at each bar, the end-point value of the least-squares line fitted to the last N closes. It is a trend-following average that lags less than a simple moving average of the same length.
Formula: Value_t = a + b x (N - 1), where a and b are the intercept and slope of the least-squares line over the last N closes (first bar = 0)
Inputs:
- Closing prices
- Lookback length N
- Least-squares slope and intercept
Worked example. A fitted line with intercept 100 at the first bar and slope 0.5 per bar gives an end-point value of 109.50 after 20 bars. The inputs are illustrative numbers, not market data.
Reading it. Price above the curve and a rising curve indicate an upward fitted trend. Because it extrapolates the line to the latest bar, it reacts faster than an average that weights all bars equally.
Limits. The end-point is the most sensitive part of a regression, so the curve can overshoot at turning points. Each value is a fit to past data and not a forecast.
What is Linear Regression Curve?
The linear regression curve plots, at each bar, the end-point value of the least-squares line fitted to the last N closes. It is a trend-following average that lags less than a simple moving average of the same length.
How to use Linear Regression Curve
Price above the curve and a rising curve indicate an upward fitted trend. Because it extrapolates the line to the latest bar, it reacts faster than an average that weights all bars equally. A reading alone is never a buy or sell signal; it needs context from price structure, volume, volatility and the broader market regime.
Strengths
- Evaluating the fitted line at the newest bar rather than the window center makes the output track recent closes more closely than a same-length simple average.
- Because it is a least-squares end-point, its gap from a simple average of the same length also reflects the slope inside the window.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- A least-squares fit weights all N closes equally, so one extreme close shifts the end-point more than a robust fit would.
- Short lengths make the end-point extrapolate recent noise, while long lengths approach a slow average, so N sets the whole character.
- Correlated indicators can create false confidence by repeating the same underlying information.
Editorial and risk note
This page is educational content, not individualized investment advice. It should not imply guaranteed prediction, accuracy or outperformance.
Frequently Asked Questions
Is Linear Regression Curve a buy or sell signal?
No. Price above the curve and a rising curve indicate an upward fitted trend. The end-point is the most sensitive part of a regression, so the curve can overshoot at turning points. A trade built on it still needs a trigger, an invalidation rule, a position size and realistic execution assumptions.
What is the best setting for Linear Regression Curve?
Linear Regression Curve takes these inputs: closing prices; lookback length N; least-squares slope and intercept. There is no universal best setting for the tunable ones, so test them across instruments, regimes and out-of-sample periods and favor stable parameter regions over one historical winner.
Can Linear Regression Curve be used by itself?
The end-point is the most sensitive part of a regression, so the curve can overshoot at turning points. Each value is a fit to past data and not a forecast. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does Linear Regression Curve work on every timeframe?
The calculation (Value_t = a + b x (N - 1), where a and b are the intercept and slope of the least-squares line over the last N closes (first bar = 0)) can be run on any bar size, but behavior is not portable: a reading on a five-minute chart describes a very different market window from the same reading on a daily chart. Validate the exact timeframe before relying on it.
Why does Linear Regression Curve give false signals?
A least-squares fit weights all N closes equally, so one extreme close shifts the end-point more than a robust fit would. Short lengths make the end-point extrapolate recent noise, while long lengths approach a slow average, so N sets the whole character. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should Linear Regression Curve be backtested?
Rebuild Linear Regression Curve from closing prices using point-in-time data, write the rules before testing, include delisted securities where relevant, model realistic fills and costs, reserve a validation sample and compare with a simpler baseline.