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Normalized Linear Regression (LSMA) Oscillator Normalized Linear Regression (LSMA) Oscillator By Nathan Farmer The Normalized LSMA Oscillator is a trend-following indicator that enhances the classic Linear Regression (LSMA) by applying a range of normalization techniques. This indicator allows traders to smooth out and normalize LSMA signals for better trend detection and dynamic market adaptation. Key Features: Configurable Normalization Methods: This indicator offers several normalization techniques, such as Z-Score, Min-Max, Mean Normalization, Robust Scaler, Logistic Function, and Quantile Transformation. Each method helps in refining LSMA outputs to improve clarity in both trending and ranging market conditions. Smoothing Options: Smoothing can be applied after normalization, helping to reduce noise in the signals, thus making trend-following strategies that use this indicator more effective. Recommended Settings: Logistic Function Normalization: Recommended length of around 12, based on my preferred signal frequency. Z-Score Normalization: Medium period (close to the default of 50), based on my preferred signal frequency. Min-Max Normalization: Medium period, based on my preferred signal frequency. Mean Normalization: Medium period, based on my preferred signal frequency. Robust Scaler: Medium period, based on my preferred signal frequency. Quantile Transformation: Medium period, based on my preferred signal frequency. Usage: Designed primarily for trend-following strategies, this indicator adapts well to varying market conditions. Traders can experiment with the various normalization and smoothing settings to match the indicator to their specific needs and market preferences. Recommendation before usage: Always backtest the indicator for yourself with respect to how you intend to use it. Modify the parameters to suit your needs, over your preferred time frame, on your preferred asset. My preferences are for the assets I happened to be looking at when I made this indicator. Odds are, you're looking at something else, over a different time frame, in a different market environment than what my settings are tailored for.

Leavitt Convolution Slope [CC]The Leavitt Convolution Slope indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to the Leavitt Convolution indicator but the big difference is that it is getting the slope instead of predicting the next Convolution value. I changed quite a few things from the original source code so let me know if you like these changes. I added a normalization function using code from a good friend @loxx that I recommend to leave on but feel free to experiment with it. Last but not least, the unsure levels are essentially acting as a buy or sell threshold. I personally recommend to buy or sell for zero crossovers but another option would be to buy or sell for crossovers using the unsure levels. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal. This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.

Leavitt Projection [CC]The Leavitt Projection indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very simple but is also the building block of many other indicators, so I'm starting with the publication of this one. Since this is the first in a series I will be publishing, keep in mind that the concepts introduced in this script will be the same across the entire series. The recommended strategy for how to trade with these indicators is to plot a fast version and a slow version and go long when the fast version crosses over the slow version or to go short when the fast version crosses under the slow version. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal. I know many of you have wondered where I have been, and my personal life has become super hectic. I was recently hired full-time by TradingView, and my wife is pregnant with twins, and she is due in a few months. I will do my absolute best to get back to posting scripts regularly, but I will post a bunch today in the meantime to fulfill a special request from one of my loyal followers (@ashok1961).

Many Moving Averages A smooth looking indicator created from a mix of ALMA and LRC curves. Includes alternative calculation for both which I came up with through trial and error so a variety of combinations work to varying degrees. Just something I was playing around with that looked pretty nice in the end.

Rollin' pseudo-Bollinger Bands 5 linear regression curves and new highs/lows mixed together from the basis for this indicator. Using slightly different logic an upper boundary and lower boundary are formed. Then the boundary's are built upon to show price channels within the band using variations of fib levels and the distance between the initial boundary's. Dots plotted show the inverse of the close price relative to either the upper or lower boundary depending on where the close is relative to the center of the band. This shows the market's tendency for symmetry which is useful when looking for reversals etc. If it's too cluttered feel free to turn off some things in the options and keep what you feel is helpful.

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 /////////////////////////////////////

Linear Regression Channel / Curve / Slope by DGT Tʜᴇ Lɪɴᴇᴀʀ Rᴇɢʀᴇꜱꜱɪᴏɴ Cʜᴀɴɴᴇʟꜱ Linear Regression Channels are useful measure for technical and quantitative analysis in financial markets that help identifying trends and trend direction. The use of standard deviation gives traders ideas as to when prices are becoming overbought or oversold relative to the long term trend The basis of a linear regression channel Linear Regression Line – is a line drawn according to the least-squares statistical technique which produces a best-fit line that cuts through the middle of price action, a line that best fits all the data points of interest. The resulting fitted model can be used to summarize the data, to predict unobserved values from the same system. Linear Regression Line then present basis for the channel calculations The linear regression channel 2. Upper Channel Line – A line that runs parallel to the Linear Regression Line and is usually one to two standard deviations above the Linear Regression Line. 3. Lower Channel Line – This line runs parallel to the Linear Regression Line and is usually one to two standard deviations below the Linear Regression Line. Unlike Fibonacci Channels and Andrew’s Pitchfork, Linear Regression Channels are calculated using statistical methods, both for the regression line (as expressed above) and deviation channels. Upper and Lower channel lines are presenting the idea of bell curve method, also known as a normal distribution and are calculated using standard deviation function. A standard deviation include 68% of the data points, two standard deviations include approximately 95% of the data points and any data point that appears outside two standard deviations is very rare. It is often assumed that the data points will move back toward the average, or regress and channels would allow us to see when a security is overbought or oversold and ready to revert to the mean please note : Over time, the price will move up and down, and the linear regression channel will experience changes as old prices fall off and new prices appear █ Linear Regression Study Features Linear Regression Channel - Linear regression line as basis - Customizable multiple channels based on Standard Deviation - ALERTs for the channel levels Linear Regression Curve - Linear regression curve as basis - Optional : Bands based on Standard Deviation or Volatility (ATR). Bands are applied with fixed levels 1, 2 and 3 times StdDev or ATR away from the curve Linear Regression Slope - Optional : Up/Down slope arrows for a used defined period █ Volume / Volatility Add-Ons High Volatile Bar Indication Volume Spike Bar Indication Volume Weighted Colored Bars