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Linear

Linear Regression ChannelsThese channels are generated from the current values of the linear regression channel indicator, the standard deviation is calculated based off of the RSI . This indicator gives an idea of when the linear regression model predicts a change in direction. You are able to change the length of the linear regression model, as well as the size of the zone. A negative zone size will make the zone stretch away from the center, and a positive zone size will make it stretch towards the centerline.
อินดิเคเตอร์ Pine Script™
โดย NotFuz
1
Polynomial Regression Extrapolation [LuxAlgo]This indicator fits a polynomial with a user set degree to the price using least squares and then extrapolates the result. Settings Length: Number of most recent price observations used to fit the model. Extrapolate: Extrapolation horizon Degree: Degree of the fitted polynomial Src: Input source Lock Fit: By default the fit and extrapolated result will readjust to any new price observation, enabling this setting allow the model to ignore new price observations, and extend the extrapolation to the most recent bar. Usage Polynomial regression is commonly used when a relationship between two variables can be described by a polynomial. In technical analysis polynomial regression is commonly used to estimate underlying trends in the price as well as obtaining support/resistances. One common example being the linear regression which can be described as polynomial regression of degree 1. Using polynomial regression for extrapolation can be considered when we assume that the underlying trend of a certain asset follows polynomial of a certain degree and that this assumption hold true for time t+1...,t+n . This is rarely the case but it can be of interest to certain users performing longer term analysis of assets such as Bitcoin. The selection of the polynomial degree can be done considering the underlying trend of the observations we are trying to fit. In practice, it is rare to go over a degree of 3, as higher degree would tend to highlight more noisy variations. Using a polynomial of degree 1 will return a line, and as such can be considered when the underlying trend is linear, but one could improve the fit by using an higher degree. The chart above fits a polynomial of degree 2, this can be used to model more parabolic observations. We can see in the chart above that this improves the fit. In the chart above a polynomial of degree 6 is used, we can see how more variations are highlighted. The extrapolation of higher degree polynomials can eventually highlight future turning points due to the nature of the polynomial, however there are no guarantee that these will reflect exact future reversals. Details A polynomial regression model y(t) of degree p is described by: y(t) = β(0) + β(1)x(t) + β(2)x(t)^2 + ... + β(p)x(t)^p The vector coefficients β are obtained such that the sum of squared error between the observations and y(t) is minimized. This can be achieved through specific iterative algorithms or directly by solving the system of equations: β(0) + β(1)x(0) + β(2)x(0)^2 + ... + β(p)x(0)^p = y(0) β(0) + β(1)x(1) + β(2)x(1)^2 + ... + β(p)x(1)^p = y(1) ... β(0) + β(1)x(t-1) + β(2)x(t-1)^2 + ... + β(p)x(t-1)^p = y(t-1) Note that solving this system of equations for higher degrees p with high x values can drastically affect the accuracy of the results. One method to circumvent this can be to subtract x by its mean.
อินดิเคเตอร์ Pine Script™
โดย LuxAlgo
33
Standard deviation channel of linear regression distance [AbAh]The indicator calculates the distance between linear regression line and the data point (price) as a percentage , then calculates the standard deviation for the linear regression distance , then draw the channel of two lines depending on the values of standard deviation . ///////// How to use //////////////// 1 - for Best result , indicator should be used on 2H frame Time of less : like 1H or 30 min 2 - The upper line and the lower line, both play a role as a support and resistance area, when the price bounces from the upper zone or lower zone, there is a high probability that it will move to the other line. 3 - The price breakout of one of the lower or upper lines may indicate a major price movement coming in the direction of the breakout /////////////////////////////////////
อินดิเคเตอร์ Pine Script™
โดย AbdullahAhmedEgypt
2
Relative slopeRelative slope metric Description: I was in need to create a simple, naive and elegant metric that was able to tell how strong is the trend in a given rolling window. While abstaining from using more complicated and arguably more precise approaches, I’ve decided to use Linearly Weighted Linear Regression slope for this goal. Outright values are useful, but the problem was that I wasn’t able to use it in comparative analysis, i.e between different assets & different resolutions & different window sizes, because obviously the outputs are scale-variant. Here is the asset-agnostic, resolution-agnostic and window size agnostic version of the metric. I made it asset agnostic & resolution agnostic by including spread information to the formula. In our case it's weighted stdev over differenced data (otherwise we contaminate the spread with the trend info). And I made it window size agnostic by adding a non-linear relation of length to the output, so finally it will be aprox in (-1, 1) interval, by taking square root of length, nothing fancy. All these / 2 and * 2 in unexpected places all around the formula help us to return the data to it’s natural scale while keeping the transformations in place. Peace TV
อินดิเคเตอร์ Pine Script™
โดย gorx1
5
StrengthA mathematically elegant, native & modern way how to measure velocity/ strength/ momentum. As you can see it looks like MACD, but !suddenly! has N times shorter code (disregard the functions), and only 1 parameter instead of 3. OMG HOW DID HE DO IT?!? MACD: "Let's take one filter (1 parameter), than another filter (2 parameters), then let's take dem difference, then let's place another filter over the difference (3rd parameter + introduction of a nested calculation), and let's write a whole book about it, make thousands of multi-hours YouTube videos about it, and let's never mention about the amount of uncertainty being introduced by multiple parameters & introduction of the nested calculation." Strength: "let's get real, let's drop a weighted linear regression & usual linear regression over the data of the same length, take dem slopes, then make the difference over these slopes, all good. And then share it with people w/o putting an ® sign". Fyi, regressions were introduced centuries ago, maybe decades idk, the point is long time ago, and computational power enough to calculate what I'm saying is slightly more than required for macd. Rationale. Linearly weighted linear regression has steeper slope (W) than the usual linear regression slope (S) due to the fact that the recent datapoints got more weight. This alone is enough of a metric to measure velocity. But still I've recalled macd and decided to make smth like it cuz I knew it'll might make you happy. I realized that S can be used instead of smoothing the W, thus eliminating the nested calculation and keeping entropy & info loss in place. And see, what we get is natural, simple, makes sense and brings flex. I also wanna remind you that by applying regression we maximize the info gain by using all the data in the window, instead of taking difference between the first and the last datapoints. This script is dedicated to my friend Fabien. Man, you were the light in the darkness in that company. You'll get your alien green Lambo if you'll really want it, no doubts on my side bout that. Good hunting
อินดิเคเตอร์ Pine Script™
โดย gorx1
14
Linear Channel - Scalp Strategy 15MSimple way how to use Linear Regression for trading. What we use: • Linear Regression • HMA as a trend filter Logic: Firstly we make simple linear regression moving. It is the white line which appears on the chart. Then we make second line (named: band2) on the chart by multiplying linreg and value difference. The third step is to ad HMA as a trend filter. The trade open when price is below band2, but still upper than Hullma. The trade close when price again upper than linreg.
กลยุทธ์ Pine Script™
โดย TradingAmmo
2
regressLibrary "regress" produces the slope (beta), y-intercept (alpha) and coefficient of determination for a linear regression regress(x, y, len) regress: computes alpha, beta, and r^2 for a linear regression of y on x   Parameters:      x : the explaining (independent) variable      y : the dependent variable      len : use the most recent "len" values of x and y   Returns: : alpha is the x-intercept, beta is the slope, an r2 is the coefficient of determination Note: the chart does not show anything, use the return values to compute model values in your own application, if you wish.
ไลบรารี Pine Script™
โดย taylor_o
Linear trendSimple way how to use Linear Regression for trading. What we use: • Linear Regression • EMA 200 as a trend filter Logic: Firstly we make two different linear regression movings as oscillator. For this we need to subtract slow moving from fast moving, so we get the single moving around zero. This is the green/red line which appears on the chart. The trade open when LR cross over the threshold. The trade close when LR cross under the threshold below. Crossing over the threshold is the same as faster moving cross over slower moving. Also we use EMA as a filter. The trades would be only when the price is over than EMA 200.
กลยุทธ์ Pine Script™
โดย TradingAmmo
Linear Regression & RSI Multi-Function Screener with Table-LabelHi fellow traders.. Happy to share a Linear Regression & RSI Multi-Function Custom Screener with Table-Labels... The Screener scans for Linear Regression 2-SD Breakouts and RSI OB/OS levels for the coded tickers and gives Summary alerts Uses Tables (dynamica resizing) for the scanner output instead of standard labels! This Screener cum indicator collection has two distinct objectives.. 1. Attempt re-entry into trending trades. 2. Attempt Counter trend trades using linear regression , RSI and Zigzag. Briefly about the Screener functions.. a. It uses TABLES as Labels a FIRST for any Screener on TV. b. Tables dynamically resize based on criteria.. c. Alerts for breakouts of the UPPER and the LOWER regression channels.(2 SD) d. In addition to LinReg it also Screens RSI for OB/OS levels so a multifunction Screener. e. Of course has the standard summary Alerts and programmable format for Custom functions. f. Uses only the inbuilt Auto Fib and Lin Reg code for the screener.(No proprietary stuff) g. The auto Zigzag code is derived(Auto fib). Question what are all these doing in a single screener ?? ZigZag is very useful in determining Trend Up or Down from one Pivot to another. So Once you have a firm view of the Current Trend for your chosen timeframe and ticker… We can consider few possible trading scenarios.. a. Re-entry in an Up Trend - Combination of OS Rsi And a Lower Channel breach followed by a re-entry back into the regression channel CAN be used as an effective re-entry. b. Similarily one can join a Down Trend on OB Rsi and Upper Channel line breach followed by re-entry into the regression channel. If ZigZag signals a range-bound market, bound within channel lines then the Upper breakout can be used to Sell and vice-versa! In short many possibilities for using these functions together with Scanner and Alerts. This facilitates timely PROFITABLE Trending and Counter trend opportunities across multiple tickers. You must give a thorough READ to the various available tutorials on ZigZag / Regression and Fib retracements before attempting counter trend trades using these tools!! A small TIP – Markets are sideways or consolidating 70% of the time!! Acknowledgements: - Thanks a lot DGTRD for the Auto ZigZag code and also for the eagerness to help wherever possible..Respect!! Disclaimer: The Alerts and Screener are just few tools among many and not any kind of Buy/Sell recommendations. Unless you have sufficient trading experience please consult a Financial advisor before investing real money. *The alerts are set for crossovers however for viewing tickers trading above or below the channel use code in line 343 and 344 after setting up the Alerts! ** RSI alerts are disabled by default to avoid clutter, but if needed one can activate code lines 441,442,444 and 445 Wish you all, Happy Profitable Trading!
อินดิเคเตอร์ Pine Script™
โดย sharaaU7
19
Linear Regression ChannelHello Traders, There are several nice Linear Regression Channel scripts in the Public Library. and I tried to make one with some extra features too. This one can check if the Price breaks the channel and it shows where is was broken. Also it checks the momentum of the channel and shows it's increasing/decreasing/equal in a label, shape of the label also changes. The line colors change according to direction. using the options, you can; - Set the Source (Close, HL2 etc) - Set the Channel length - Set Deviation - Change Up/Down Line colors - Show/hide broken channels - Change line width meaning of arrows: ⇑ : Uptrend and moment incresing ⇗ : Uptrend and moment decreasing ⇓ : Downtrend and moment incresing ⇘ : Downtrend and moment decreasing ⇒ : No trend An example for how color of lines, arrow direction and shape of label change. Enjoy!
อินดิเคเตอร์ Pine Script™
โดย LonesomeTheBlue
124
TF Segmented Linear RegressionFit a line at successive intervals, where the interval period is determined by a user-selected time frame, this allows the user to have an estimate of the intrinsic trend within various intervals. Settings Timeframe : Determine the period of the interval, if the timeframe is weekly then a new line will be fit at the start each weeks, by default "Daily" Mult : Multiplication factor for the RMSE, determine the distance between the upper and lower extremities Src : Input data for the indicator Plot Extremities : Logical value, if true then the extremities of the channel are plotted, if false only the midline is plotted, true by default. Usage The timeframe setting should be higher than the current chart timeframe, note however that too large values of timeframe might return an error. Since the maximum number of lines that can be plotted is 54, using the extremities will only return 18 channels. The indicator can be compared to the "regression trend" drawing tool Main tf = 5 min with the indicator using a daily timeframe, the filled area is produced by the regression trend drawing tool using the same interval as the indicator, and coincide with it. Main tf = 15 min with the indicator using a weekly timeframe, wider channel indicate that the values tend to be farther away from the fitted line. A line with a significant slope indicates a strong trend, in that case, the width of the channel is determined by the amplitude of the retracements in the trend, with a narrower channel indicating a cleaner trend. When the fitted line has a low slope value and the channel is wide, it means that there were two or more variations of opposite directions with large amplitudes within the interval, this also indicates that a linear model is not appropriate. A slope approximately equal to 0 with a low channel width indicates a trendless market with cyclical variations of low amplitude in it. Refrences Determining the starting and ending points of the fitted line was done using a linear combination between the wma and sma The wma and sma functions both use a series as period by making use of the Wma and Sum functions in the following script
อินดิเคเตอร์ Pine Script™
โดย alexgrover
27
LWMA: Linear Weighted Moving AverageCouldn't find searching for Linearly Weighted Moving Average (LWMA) in tradingview. Found one with the LWMA title, but it uses plain WMA calculation without the linearity which more heavily weights recent price data, which I need, so I try to made one. LWMAs are also quicker to react to price changes than SMA and EMA. If you want a moving average with less lag than an SMA, try a LWMA. It kind of also have a more clarity in defining the price trend and reversals. Trade signals usually based on crossovers, they can also indicate areas of potential support or resistance. But beware though, multiple false signals may also occur before a significant trend develops. Use a filter, some decent volatility oscillator might do the job. -- The formula for this Linearly Weighted Moving Average is: LWMA = (( P n∗ W 1)+( P n−1∗ W 2)+( P n−2∗ W 3)...) / ∑W ​ ​P = Price for the period n = The most recent period, n-1 is the prior period, and n-2 is two periods prior W = The assigned weight to each period, with the highest weight going first and then descending linearly based on the number of periods being used. I hope I'm doing right translating it to Pine Script 4. Let me know if I miss something. ​
อินดิเคเตอร์ Pine Script™
โดย io72signals
29
Price-Line Channel - A Friendly Support And Resistance IndicatorIntroduction Lines are the most widely used figures in technical analysis, this is due to the linear trends that some securities posses (daily log SP500 for example), support and resistances are also responsible for the uses of lines, basically linear support and resistances are made with the assumption that the line connecting two local maximas or minimas will help the user detect a new local maxima or minima when the price will cross the line. Technical indicators attempting to output lines have always been a concern in technical analysis, the mostly know certainly being the linear regression, however any linear models would fit in this category. In general those indicators always reevaluate their outputs values (repainting), others non repainting indicators returning lines are sometimes to impractical to set-up. This is what has encouraged me to make a simpler indicator based on the framework used in the recursive bands indicator that i published. The proposed indicator aim to be extremely flexible and easy to use while returning linear support and resistances, an option that allow readjustment is also introduced, thus allowing for a "smarter" indicator. The Indicator The indicator return two extremities, the upper one aim to detect resistance points while the lower one aim to detect support points. The length setting control the steepness of the line, with higher values of length involving a lower slope, this make the indicator less reactive and interact with the price less often. The name "price-line" comes from the fact that the channel is dependent on its own interaction with the price, therefore a breakout methodology can also be used, where price is up-trending when crossing with the upper extremity and down trending when crossing with the lower one. Readjusted Option The line steepness can be readjusted based on the market volatility, it make more sense for the line to be more steep when the market is more volatile, thus making it converge faster toward the price, this of course is done at the cost of some linearity. This is achieved by checking the "readjustment" option. The effects can be shown on BTCUSD, below the indicator without the readjusted option : when the "readjustment" option is checked we have the following results : The volatile down movement on BTCUSd make the upper extremity converge faster toward the price, this option can be great for volatile markets. Conclusion The recursive bands indicator prove to be an excellent framework that allow for the creation of lots of indicators, the proposed indicator is extremely efficient and provide an easy solution for returning linear support and resistances without much drawbacks, the readjusted option allow the indicator to adapt to the market volatility at the cost of linearity. The performance of the indicator is relative to the motion of the price, however the indicator show signs of returning accurate support and resistances points. I hope the indicator find its use in the community. Thanks for reading ! Note Respect the house rules, always request permission before publishing open source code. This is an original work, requesting permission is the least you can do.
อินดิเคเตอร์ Pine Script™
โดย alexgrover
21
Forecasting - Drift MethodIntroduction Nothing fancy in terms of code, take this post as an educational post where i provide information rather than an useful tool. Time-Series Forecasting And The Drift Method In time-series analysis one can use many many forecasting methods, some share similarities but they can all by classified in groups and sub-groups, the drift method is a forecasting method that unlike averages/naive methods does not have a constant (flat) forecast, instead the drift method can increase or decrease over time, this is why its a great method when it comes to forecasting linear trends. Basically a drift forecast is like a linear extrapolation, first you take the first and last point of your data and draw a line between those points, extend this line into the future and you have a forecast, thats pretty much it. One of the advantage of this method is first its simplicity, everyone could do it by hand without any mathematical calculations, then its ability to be non-conservative, conservative methods involve methods that fit the data very well such as linear/non-linear regression that best fit a curve to the data using the method of least-squares, those methods take into consideration all the data points, however the drift method only care about the first and last point. Understanding Bias And Variance In order to follow with the ability of methods to be non-conservative i want to introduce the concept of bias and variance, which are essentials in time-series analysis and machine learning. First lets talk about training a model, when forecasting a time-series we can divide our data set in two, the first part being the training set and the second one the testing set. In the training set we fit a model to the training data, for example : We use 200 data points, we split this set in two sets, the first one is for training which is in blue, and the other one for testing which is in green. Basically the Bias is related to how well a forecasting model fit the training set, while the variance is related to how well the model fit the testing set. In our case we can see that the drift line does not fit the training set very well, it is then said to have high bias. If we check the testing set : We can see that it does not fit the testing set very well, so the model is said to have high variance. It can be better to talk of bias and variance when using regression, but i think you get it. This is an important concept in machine learning, you'll often see the term "overfitting" which relate to a model fitting the training set really well, those models have a low to no bias, however when it comes to testing they don't fit well at all, they have high variance. Conclusion On The Drift Method The drift method is good at forecasting linear trends, and thats all...you see, when forecasting financial data you need models that are able to capture the complexity of the price structure as well as being robust to noise and outliers, the drift method isn't able to capture such complexity, its not a super smart method, same goes for linear regression. This is why more peoples are switching to more advanced models such a neural networks that can sometimes capture such complexity and return decent results. So this method might not be the best but if you like lines then here you go.
อินดิเคเตอร์ Pine Script™
โดย alexgrover
18
Forecasting - Holt’s Linear Trend ForecastingHolt's Forecasting method Holt (1957) extended simple exponential smoothing to allow the forecasting of data with a trend. This method involves a forecast equation and two smoothing equations (one for the level and one for the trend): Forecast equation: ŷ = l + h * b Level equation: l = alpha * y + (1 - alpha) * (l + b) Trend equation: b = beta * (l - l) + (1 - beta) * b where: l (or l) denotes an estimate of the level of the series at time t, b (or b) denotes an estimate of the trend (slope) of the series at time t, alpha is the smoothing parameter for the level, 0 ≤ alpha ≤ 1, and beta is the smoothing parameter for the trend, 0 ≤ beta ≤ 1. As with simple exponential smoothing, the level equation here shows that l is a weighted average of observation y and the one-step-ahead training forecast for time t, here given by l+b. The trend equation shows that b is a weighted average of the estimated trend at time t based on l-l and b, the previous estimate of the trend. The forecast function is not flat but trending. The h-step-ahead forecast is equal to the last estimated level plus h times the last estimated trend value. Hence the forecasts are a linear function of h.
อินดิเคเตอร์ Pine Script™
โดย capissimo
3
Linear Regression Trend ChannelThis is my first public release of indicator code and my PSv4.0 version of "Linear Regression Channel", as it is more commonly known. It replicates TV's built-in "Linear Regression" without the distraction of heavy red/blue fill bleeding into other indicators. We can't fill() line.new() at this time in Pine Script anyways. I entitled it Linear Regression Trend Channel, simply because it seems more accurate as a proper description. I nicely packaged this to the size of an ordinary napkin within 20 lines of compact code, simplifying the math to the most efficient script I could devise that fits in your pocket. This is commonly what my dense intricate code looks like behind the veil, and if you are wondering why there is no notes, that's because the notation is in the variable naming. I excluded Pearson correlation because it doesn't seem very useful to me, and it would comprise of additional lines of code I would rather avoid in this public release. Pearson correlation is included in my invite-only advanced version of "Enhanced Linear Regression Trend Channel", where I have taken Linear Regression Channeling to another level of fully featured novel attainability using this original source code. Features List Includes: "Period" adjustment "Deviation(s)" adjustment "Extend Method" option to extend or not extend the upper, medial, and lower channeling Showcased in the chart below is my free to use "Enhanced Schaff Trend Cycle Indicator", having a common appeal to TV users frequently. If you do have any questions or comments regarding this indicator, I will consider your inquiries, thoughts, and ideas presented below in the comments section, when time provides it. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
อินดิเคเตอร์ Pine Script™
โดย ImmortalFreedom
20
Time Series ForecastIntroduction Forecasting is a blurry science that deal with lot of uncertainty. Most of the time forecasting is made with the assumption that past values can be used to forecast a time series, the accuracy of the forecast depend on the type of time series, the pre-processing applied to it, the forecast model and the parameters of the model. In tradingview we don't have much forecasting models appart from the linear regression which is definitely not adapted to forecast financial markets, instead we mainly use it as support/resistance indicator. So i wanted to try making a forecasting tool based on the lsma that might provide something at least interesting, i hope you find an use to it. The Method Remember that the regression model and the lsma are closely related, both share the same equation ax + b but the lsma will use running parameters while a and b are constants in a linear regression, the last point of the lsma of period p is the last point of the linear regression that fit a line to the price at time p to 1, try to add a linear regression with count = 100 and an lsma of length = 100 and you will see, this is why the lsma is also called "end point moving average". The forecast of the linear regression is the linear extrapolation of the fitted line, however the proposed indicator forecast is the linear extrapolation between the value of the lsma at time length and the last value of the lsma when short term extrapolation is false, when short term extrapolation is checked the forecast is the linear extrapolation between the lsma value prior to the last point and the last lsma value. long term extrapolation, length = 1000 short term extrapolation, length = 1000 How To Use Intervals are create from the running mean absolute error between the price and the lsma. Those intervals can be interpreted as possible support and resistance levels when using long term extrapolation, make sure that the intervals have been priorly tested, this mean the intervals are more significants. The short term extrapolation is made with the assumption that the price will follow the last two lsma points direction, the forecast tend to become inaccurate during a trend change or when noise affect heavily the lsma. You can test both method accuracy with the replay mode. Comparison With The Linear Regression Both methods share similitudes, but they have different results, lets compare them. In blue the indicator and in red a linear regression of both period 200, the linear regression is always extremely conservative since she fit a line using the least squares method, at the contrary the indicator is less conservative which can be an advantage as well as a problem. Conclusion Linear models are good when what we want to forecast is approximately linear, thats not the case with market price and this is why other methods are used. But the use of the lsma to provide a forecast is still an interesting method that might require further studies. Thanks for reading !
อินดิเคเตอร์ Pine Script™
โดย alexgrover
24