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Center of Gravity BF 🚀Thanks to HPotter for the code I based this strategy on. Center of Gravity calculation is based here on a linear regression function using the least squares method. We use this to calculate a channel consisting of 2 lines, green and red on the chart This strategy employs a dynamic stop loss function that measures stop loss placement based on recent ATR. How signals are generated: Price closes above green line = Go Long Price closes below red line = Go Short Yellow dotted line = stop loss based on long entry Orange dotted line = stop loss based on short entry INSTRUCTIONS Green background = Go Long, put your stop loss at the yellow dotted line Red background = Go Short, put your stop loss at the orange dotted line NB: The stop losses printed on the chart are calculated from the point of entry on a trade, if you make a different entry to what is indicated, the corresponding stop loss will be different to what the indicator displays.
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bennefの投稿
2
Regression Channel [DW]This is an experimental study which calculates a linear regression channel over a specified period or interval using custom moving average types for its calculations. Linear regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. The regression channel in this study is modeled using the least squares approach with four base average types to choose from: -> Arnaud Legoux Moving Average (ALMA) -> Exponential Moving Average (EMA) -> Simple Moving Average (SMA) -> Volume Weighted Moving Average (VWMA) When using VWMA, if no volume is present, the calculation will automatically switch to tick volume, making it compatible with any cryptocurrency, stock, currency pair, or index you want to analyze. There are two window types for calculation in this script as well: -> Continuous, which generates a regression model over a fixed number of bars continuously. -> Interval, which generates a regression model that only moves its starting point when a new interval starts. The number of bars for calculation cumulatively increases until the end of the interval. The channel is generated by calculating standard deviation multiplied by the channel width coefficient, adding it to and subtracting it from the regression line, then dividing it into quartiles. To observe the path of the regression, I've included a tracer line, which follows the current point of the regression line. This is also referred to as a Least Squares Moving Average (LSMA). For added predictive capability, there is an option to extend the channel lines into the future. A custom bar color scheme based on channel direction and price proximity to the current regression value is included. I don't necessarily recommend using this tool as a standalone, but rather as a supplement to your analysis systems. Regression analysis is far from an exact science. However, with the right combination of tools and strategies in place, it can greatly enhance your analysis and trading.
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DonovanWallの投稿
20
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!
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ImmortalFreedomの投稿
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 !
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alexgroverの投稿
24
[RD] LCS - line channels (basic) v3 - [republish]================================================================== July 18 2019 - LCS - line channels (basic) by RootDuk Version : v3 - read the notes ================================================================== Easy script to draw channels based on ie. GANN using the following input params - ibback : barsback to search for high and low - ffactor : factor to up/downscale the lines - oextend : extend lines, left, right, both, none - bshowline : show horizontal lines yes/no - bsshowdown : show diag down lines yes/no - bsshowup : show diag up lines yes/no - bshowindm : show index marker, where is you current ibback =================================================================== Notes =================================================================== When you use this code, pls let me know where and how you used it as iam always curious what some can do with it. Thanks! =================================================================== Updates =================================================================== v1 v2 - July 17 2019 - Removed support wallets from source code. I think the source - has been deleted because of this, find no other clue. v3 - July 18 2019 - Republish
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RootDukの投稿
9
Linear regression slope as SARThis model calculate the slope of the linear regression and give you the uptrend or the down trend of the slope red line are downtrend and green line are up trend using len 1 you can make it longer or shorter (I put it on length of 40 but you can change that to what ever you want ) Similar to SAR it can give us the trends of buy and sell only by doing it by the linear way try longer length to see if it smooth the signal 4 hour graph
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RafaelZioniの投稿
6
Linear regression % channelThis channel is based on Linear regression by its % difference from the price the high and the low are determine by the % from the close price . For the best fit you need to put the TF on the same min of the graph. I put the channel to be 3 % above and bellow the price .by the same way you can add to the script more % line to make it better this is good indicator for filtering (if you want your signal to be above or bellow the % channel) or if you want to enter at certain price and exit by the regular TV alerts so it more easy where your target for long or short exist
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RafaelZioniの投稿
Dorsey InertiaThis indicator was originally developed by Donald Dorsey (Stocks & Commodities, V.13:9 (September, 1995): "Refining the Relative Volatility Index"). Inertia is based on Relative Volatility Index (RVI) smoothed using linear regression. In physics, inertia is the tendency of an object to resist to acceleration. Dorsey chose this name because he believes that trend and inertia are related and that it takes more effort and energy to reverse the direction of a stock or market than to keep it in the same direction. He argues that the volatility is the simplest and most accurate measure of inertia. When the indicator is below 50, it signals bearish market sentiment and when the indicator is above 50 it signals a bullish trend. Good luck!
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evergetの投稿
5