Linear Regression

Linear Regression MTF + Bands Multiple Time Frames (MTFs): The indicator allows you to view linear regression trends over three different time frames (TF1, TF2, TF3) simultaneously. This means a trader can observe short, medium, and long-term trends on a single chart, which is valuable for understanding overall market direction and making cross-timeframe comparisons. Linear Regression Bands: For each time frame, the indicator calculates linear regression bands. These bands represent the expected price range based on past prices. The middle line is the linear regression line, and the upper and lower lines are set at a specified deviation from this line. Traders can use these bands to spot potential overbought or oversold conditions, or to anticipate future price movements. History Bands: Looking at linear regression channels can be deceiving if the user does not understand the calculation. In order to see where the channel was at in history the user can display the history bands to see where price actual was in a non-repainting fashion. Customization Options: Traders can customize various aspects of the indicator, such as whether to display each time frame, the length of the linear regression (how many past data points it considers), and the deviation for the bands. This flexibility allows traders to adapt the indicator to their specific trading style and the asset they are analyzing. Alerts: The script includes functionality to set alerts based on the price crossing the upper or lower bands of any time frame. This feature helps traders to be notified of potential trading opportunities or risks without constantly monitoring the chart. Examples The 15minute linear regression is overlayed onto a 5 minute chart. We are able to see higher timeframe average and extremes. The average is the middle of the channel and the extremes are the outer edges of the bands. The bands are non-repainting meaning that is the actual value of the channel at that place in time. Here multiple channels are shown at once. We have a linear regression for the 5, 15, and 60 minute charts. If your strategy uses those timeframes you can see the average and overbought/oversold areas without having to flip through charts. In this example we show just the history bands. The bands could be thought of as a "don't diddle in the middle" area if your strategy is looking for reversals You can extend the channel into the future via the various input settings.

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TTP Pair Slope/Hedge Pair slope/hedge uses linear regression to calculate the hedge ratio (slope) between the two assets within a period. It allows you to specify a "from" and a "to" candle. Example: "A regression from 1000 candles back in time and ignore the last 100 candles. This would result in making a regression of 900 candles in total." The formula used to perform the regression with the assts X and Y is: Hedge = mean( (X-mean(X))^2 ) —————————————————— mean( (X-mean(X)) * (Y-mean(Y)) ) You can later use the hedge in a chart of X - Hedge * Y (Confirm with 1 / hedge ) If the plot is stationary the period tested should look like stationary. If you cross an imaginary horizontal line across all the values in the period used it should look like a flat channel with values crossing above and below the line. The purpose of this indicator is to help finding the linear regression test used for conintegration analysis. Conintegration assets is one of the requirements to consider assets for pair and hedge trading.

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by TheTradingParrot

Triple Moving Averages + RSI Divergence + Trade Creator [CSJ7]This indicator uses triple moving averages to identify the prevailing trend, and calculates the linear regression of the closing price, and of the RSI, to either confirm the current trend direction, or to identify a potential trade reversal. Additionally, it includes a trade management tool that allows you to rate your trade setup according to your selected entry minimums and preferences, plus you obtain an estimated P&L with profitability metrics of your trade. The key features are: Dashboard : Includes entry/exit amounts, prices, quantities, estimated Profit & Loss, ROI, ROE, RRR, insights into market trends, entry conditions, and operational logs. Trade Setup : Allows you to design your trade in detail. Select entry/exit levels, and let the tool suggest optimal target levels based on your ROI and RRR preferences. Specify your desired stop-loss type, and the tool will present the corresponding price. Entry Conditions Management : Customize your trade entry prerequisites within the settings. The system evaluates these, offering a Trade Rating and displaying current values and entry statuses in the Entry Conditions table. Trade Box : Visualize your trade strategy with a trade box that shows in alongside your chart, highlighting potential profit/loss zones and entry price points. RSI & Close Price Linear Regressions : Calculates the linear regression of RSI and the close prices, since the beginning of the current trend, and presents them directly in the chart and alongside the active trend, to allow you to spot a potential trend continuation or reversal. Adaptive price levels : The tool calculates the viability, trade rating and P&L based on contextual levels, like moving averages and highest or lowest prices, instead of using fixed prices; this allows for the results to adapt dynamically to market fluctuations, eliminating the need for manual recalibrations and adjustments. Automatic Trade Side Detection : While manual input is available, the tool can intuitively determine the optimal trade side based on current data. Market Outlook Events : By using the crossings of the three averages, the tool keeps track of the evolution of the current trend, providing points of interest like when the initial momentum is observed, when the trend initiates, when a potential entry zone starts, when a buy or sell opportunity arises and when the trend ends. Alerts : You can set up two distinct alerts – one notifies on trend milestones and another for trade initiation conditions. Note: Manual activation is required in the Tradingview dashboard. Logs : The tool provides a log section where you can find relevant information regarding the operation and any encountered errors via the dashboard's log section. Usage Choose your desired ticker and timeframe. If a tradable trend is detected and levels are set correctly, the trade box appears. Incorrect levels will trigger a warning in the error logs. The tool will suggest the logical trade side, but manual adjustments are possible. Customize ROI, maximum loss, and RRR in the settings. When in 'Auto', the tool will calculate the target price accordingly. Adjust leverage to align with your risk and reward parameters. View linear regressions for trend analysis and spotting RSI divergences. Manage position sizing and risk in the settings, accounting for broker/exchange fees. Activate alerts for trade notifications. Enable 'Show Trade Levels' in settings to get the details of the necessary limit orders for the trade. In the image below, you can view the expanded Trade Creator Dashboard, the Trade Box, and the Linear Regression Lines: The linear regression lines are colored red when trending downward and green when trending upward. The labels displaying information related to the entry and exit prices can be hidden, as demonstrated in the image above.

Indicator

by algoTraderGTP

Regression Line (Log)This indicator is based on the "Linear Regression Channel (Log)," which, in turn, is derived from PulseWire's "Linear Regression Channel." The "Regression Line (Log)" indicator is a valuable tool for traders and investors seeking to gain insights into long-term market trends. This indicator is personally favored for its ability to provide a comprehensive view of price movements over extended periods. It offers a unique perspective compared to traditional linear regression lines and moving averages, making it a valuable addition to the toolkit of experienced traders and investors. Indicator Parameters: Before delving into the details, it's worth noting that the chosen number of periods (2870) is a personal preference. This specific value is utilized for the S&P 500 index due to its alignment with various theories regarding the beginning of the modern economic era in the stock market. Different analysts propose different starting points, such as the 1950s, 1970s, or 1980s. However, users are encouraged to adjust this parameter to suit their specific needs and trading strategies. How It Works: The "Regression Line (Log)" indicator operates by transforming the closing price data into a logarithmic scale. This transformation can make the linear regression more suitable for data with exponential trends or rapid growth. Here's a breakdown of its functioning and why it can be advantageous for long-term trend analysis: 1. Logarithmic Transformation : The indicator begins by applying a logarithmic transformation to the closing price. This transformation helps capture price movements proportionally, making it especially useful for assets that exhibit exponential or rapid growth. This transformation can render linear regression more suitable for data with exponential or fast-paced trends. 2. Linear Regression on Log Scale : After the logarithmic transformation, the indicator calculates a linear regression line (lrc) on this log-transformed data. This step provides a smoother representation of long-term trends compared to a linear regression line on a linear scale. 3. Exponential Reversion : To present the results in a more familiar format, the indicator reverts the log-transformed regression line back to a linear scale using the math.exp function. This final output is the "Linear Regression Curve," which can be easily interpreted on standard price charts. Advantages: - Long-Term Trend Clarity : The logarithmic scale better highlights long-term trends and exponential price movements, making it a valuable tool for investors seeking to identify extended trends. - Smoothing Effect : The logarithmic transformation and linear regression on a log scale smooth out price data, reducing noise and providing a clearer view of underlying trends. - Adaptability : The indicator allows traders and investors to customize the number of periods (length) to align with their preferred historical perspective or trading strategy. - Complementary to Other Tools : While not meant to replace other technical indicators, the "Regression Line (Log)" indicator complements traditional linear regression lines and moving averages, offering an alternative perspective for more comprehensive analysis. Conclusion: In summary, the "Regression Line (Log)" indicator is a versatile tool that can enhance your ability to analyze long-term market trends. Its logarithmic transformation provides a unique perspective on price data, particularly suited for assets with exponential growth patterns. While the choice of the number of periods is a personal one, it can be adapted to fit various historical viewpoints. This indicator is best utilized as part of a well-rounded trading strategy, in conjunction with other technical tools, to aid in informed decision-making.

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RSRS (Resistance Support Relative Strength)The Resistance Support Relative Strength (RSRS) indicator, published by Everbright Securities, is a technical analysis tool that enjoys immense popularity among Chinese quantitative traders, owing to its stellar performance in China's stock markets. 🟠 Principle The indicator treats daily highs and lows as resistance and support levels respectively. It measures market strength by comparing the magnitude of price changes in daily highs versus lows. Specifically, it fits a linear regression model to the (low, high) data points over the past N days (typically 18) and uses the slope (beta) as the RSRS value. A steeper slope indicates stronger market strength. 🟠 Algorithm 1. Collect the daily low and high prices over the past N days. 2. Apply Ordinary Least Squares to estimate the linear regression model: high = alpha + beta * low. The beta is the RSRS value. 3. Compute the z-score of the RSRS over the past M days (typically 600). 4. Compare the z-score to preset buy and sell thresholds (typically 0.7 and -0.7) to generate trading signals. If z-score > buy threshold, a buy signal is triggered. If z-score < sell threshold, a sell signal is triggered.

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Linear Regression This indicator can be used to determine the direction of the current trend. The indicator plots two different histograms based on the linear regression formula: - The colored ones represent the direction of the short-term trend - The gray one represents the direction of the long-term trend In the settings, you can change the length of the short-term value, which also influences the long-term as a basis that will be multiplied

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Linear Regression Indicator This tool can be used to determine the direction of the current trend. The indicator changes the color of the candles based on the direction of the linear regression formula. This is made settings the length of the short-term linear regression in the settings, the longer one is also based on that parameter but significantly larger. The indicator also plots the average between the two linear regression lines used in the candle coloring formula, and can be used both for support and resistance or as a trend line used to analyze breakouts.

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Linear Cross Trading Strategy Linear Cross Trading Strategy The Linear Cross trading strategy is a technical analysis strategy that uses linear regression to predict the future price of a stock. The strategy is based on the following principles: The price of a stock tends to follow a linear trend over time. The slope of the linear trend can be used to predict the future price of the stock. The strategy enters a long position when the predicted price crosses above the current price, and exits the position when the predicted price crosses below the current price. The Linear Cross trading strategy is implemented in the PulseWire Pine script below. The script first calculates the linear regression of the stock price over a specified period of time. The script then plots the predicted price and the current price on the chart. The script also defines two signals: Long signal: The long signal is triggered when the predicted price crosses above the current price. Short signal: The short signal is triggered when the predicted price crosses below the current price. The script enters a long position when the long signal is triggered and exits the position when the short signal is triggered. Here is a more detailed explanation of the steps involved in the Linear Cross trading strategy: Calculate the linear regression of the stock price over a specified period of time. Plot the predicted price and the current price on the chart. Define two signals: the long signal and the short signal. Enter a long position when the long signal is triggered. Exit the long position when the short signal is triggered. The Linear Cross trading strategy is a simple and effective way to trade stocks. However, it is important to note that no trading strategy is guaranteed to be profitable. It is always important to do your own research and backtest the strategy before using it to trade real money. Here are some additional things to keep in mind when using the Linear Cross trading strategy: The length of the linear regression period is a key parameter that affects the performance of the strategy. A longer period will smooth out the noise in the price data, but it will also make the strategy less responsive to changes in the price. The strategy is more likely to generate profitable trades when the stock price is trending. However, the strategy can also generate profitable trades in ranging markets. The strategy is not immune to losses. It is important to use risk management techniques to protect your capital when using the strategy. I hope this blog post helps you understand the Linear Cross trading strategy better. Booost and share with your friend, if you like.

Strategy

by stocktechbot

Smart Trend Envelope The "Smart Trend Envelope" indicator is a powerful tool that combines the "Nadaraya-Watson Envelope " indicator by LuxAlgo and the "Strongest Trendline" indicator by Julien_Eche. This indicator provides valuable insights into price trends and projection confidence levels in financial markets. However, it's important to note that the indicator may repaint, meaning that the displayed results can change after the fact. The "Strongest Trendline" indicator by Julien_Eche focuses on identifying the strongest trendlines using logarithmic transformations of price data. It calculates the slope, average, and intercept of each trendline over user-defined lengths. The indicator also provides standard deviation, Pearson's R correlation coefficient, and upper/lower deviation values to assess the strength and reliability of the trendlines. In addition, the "Nadaraya-Watson Envelope " indicator developed by LuxAlgo utilizes the Nadaraya-Watson kernel regression technique. It applies a kernel function to smooth the price data and estimate future price movements. The indicator allows adjustment of the bandwidth parameter and multiplier to control the width of the envelope lines around the smoothed line. Combining these two indicators, the "Smart Trend Envelope" indicator offers traders and investors a comprehensive analysis of price trends and projection confidence levels. It automatically selects the strongest trendline length based on the highest Pearson's R correlation coefficient. Traders can observe the trendlines on the price chart, along with upper and lower envelope lines generated by the Nadaraya-Watson smoothing technique. The "Smart Trend Envelope" indicator has several qualities that make it a valuable tool for technical analysis: 1. Automatic Length Selection: The indicator dynamically selects the optimal trendline length based on the highest Pearson's R correlation coefficient, ensuring accurate trend analysis. 2. Projection Confidence Level: The indicator provides a projection confidence level ranging from "Ultra Weak" to "Ultra Strong." This allows traders to assess the reliability of the projected trend and make informed trading decisions. 3. Color-Coded Visualization: The indicator uses color schemes, such as teal and red, to highlight the direction of the trend and the corresponding envelope lines. This visual representation makes it easier to interpret the market trends at a glance. 4. Customizable Settings: Traders can adjust parameters such as bandwidth, multiplier, line color, and line width to tailor the indicator to their specific trading strategies and preferences. The "Smart Trend Envelope" indicator has been specifically designed and coded to be used in logarithmic scale. It takes advantage of the logarithmic scale's ability to represent exponential price movements accurately. Therefore, it is highly recommended to use this indicator with the chart set to logarithmic scale for optimal performance and reliable trend analysis, especially on higher timeframes. It's important to remember that the "Smart Trend Envelope" indicator may repaint, meaning that the displayed results can change after the fact. Traders should use this indicator as a tool for generating trade ideas and confirmation, rather than relying solely on its historical values. Combining the indicator with other technical analysis tools and considering fundamental factors can lead to more robust trading strategies.

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Trend Finder++ (by Alex L.)This indicator seeks for a short term trend within a bigger long term trend and displays both in a channel with an extension lines (optional). Use of this indicator is quite simple: when the stock is near the trend line bottom (default RED) it can be a good time to buy and when the stock is near the trend line top (default GREEN) it can be a good time to sell. What new ideas and cool stuff this indicator offers: - 'Trend (Months)' - Trend channels will always be displayed over the period: last 'X' months (regardless of the 'Time Interval' set in your chart) This allows you to go into a larger or smaller resolution and still see the same trend lines! - ' Trend (Bars)' - Optional. You can choose to display the Trend channel based on bars instead of months. This can be useful for advanced traders, or in case a security is new and there isn't even 1 month of data. - 'Show long-term trend' - Optional. Displays a larger 3rd (even more long-term) trend in addition to the two current trends. This is for advanced traders who want to see an even more bigger picture. It is best viewed on a weekly time interval. - Customizable channel size, channel colors and channel style. - 'Extend lines' - Optional (default: yes). Trend channels' can be displayed with extension or without using this option. - Internal Feature - When trend channel goes below zero (can happen if stock's price falls sharply) - its below-zero portion will be drawn as 'extension' instead. This is useful if such occurs, and we're in an auto-scaled chart - the lines will take less space on screen (for cleaner view). Based on an idea/indicator by @ DevLucem called "Linear Regression ++" Open Source. Enjoy!

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Pearson's R Convergence Divergence This script calculates the convergence divergence and breakouts from the deviations for a fast and slow linear regression slope. This can be used to predict major market moves before they happen. For users familiar with MacD, the blue line is similar to the MacD line and the orange line the signal. The difference is this is not a moving average comparison but a comparison between Pearson's R values. -0.1 (positive direction) 0.1 (negative direction) This is why the colors look inverse for a typical MacD. How to use this: The idea is that when both trends converge in the 0.8 or -0.8 range and you see a breakout cross occur on either line then the price has a high likelihood of reversing its current trend. If you see a green cross it means the top of the linear regression for the 'fast' or 'slow' linear regression deviation was broken by the current price. This can signify that upward movement is coming soon. On the flip side a red cross means the bottom of the linear regression for the 'fast' or 'slow' linear regression deviation was broken by the current price. This can signify that downward movement is coming soon. These crosses mean a lot more if the pearson's R value is already maxed out near 0.8 or -0.8. This indicator works because the more sure a trend becomes the more likely it is to break as more traders see the pattern. The histogram colors do not mean much being 'red' or 'green', what you want to look for is when the histogram starts to approach the 0 mark. This signifies that both linear regression trends are about to reach their peak before reversing trend. So don't confuse this with how you might read the MacD even though it looks very similar. The histogram sloping towards the 0 line will give you a clue how long it might take before the reversal occurs . Please PM me if you have any questions, and enjoy!

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by Gentleman-Goat

Ultimate Trend Channel The "Ultimate Trend Channel" indicator is a comprehensive trend analysis tool that calculates and displays a series of upper and lower bands based on user-defined input lengths. It uses linear regression and standard deviation to determine these bands for each of the 21 different group lengths. The indicator then computes the averages of these upper and lower bands, as well as the average of all the bands combined. The visualization on the chart includes the plotting of the average upper and lower bands, with the space between these bands shaded for easy visualization of the overall trend. Additionally, the average of all the bands, referred to as the "Ultimate Trend Line," is also plotted on the chart. This indicator provides a robust way of assessing market trends and volatility over varying periods, which can be extremely useful for both short-term and long-term trading strategies.

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Nonlinear Regression, Zero-lag Moving Average [Loxx]Nonlinear Regression and Zero-lag Moving Average Technical indicators are widely used in financial markets to analyze price data and make informed trading decisions. This indicator presents an implementation of two popular indicators: Nonlinear Regression and Zero-lag Moving Average (ZLMA). Let's explore the functioning of these indicators and discuss their significance in technical analysis. Nonlinear Regression The Nonlinear Regression indicator aims to fit a nonlinear curve to a given set of data points. It calculates the best-fit curve by minimizing the sum of squared errors between the actual data points and the predicted values on the curve. The curve is determined by solving a system of equations derived from the data points. We define a function "nonLinearRegression" that takes two parameters: "src" (the input data series) and "per" (the period over which the regression is calculated). It calculates the coefficients of the nonlinear curve using the least squares method and returns the predicted value for the current period. The nonlinear regression curve provides insights into the overall trend and potential reversals in the price data. Zero-lag Moving Average (ZLMA) Moving averages are widely used to smoothen price data and identify trend directions. However, traditional moving averages introduce a lag due to the inclusion of past data. The Zero-lag Moving Average (ZLMA) overcomes this lag by dynamically adjusting the weights of past values, resulting in a more responsive moving average. We create a function named "zlma" that calculates the ZLMA. It takes two parameters: "src" (the input data series) and "per" (the period over which the ZLMA is calculated). The ZLMA is computed by first calculating a weighted moving average (LWMA) using a linearly decreasing weight scheme. The LWMA is then used to calculate the ZLMA by applying the same weight scheme again. The ZLMA provides a smoother representation of the price data while reducing lag. Combining Nonlinear Regression and ZLMA The ZLMA is applied to the input data series using the function "zlma(src, zlmaper)". The ZLMA values are then passed as input to the "nonLinearRegression" function, along with the specified period for nonlinear regression. The output of the nonlinear regression is stored in the variable "out". To enhance the visual representation of the indicator, colors are assigned based on the relationship between the nonlinear regression value and a signal value (sig) calculated from the previous period's nonlinear regression value. If the current "out" value is greater than the previous "sig" value, the color is set to green; otherwise, it is set to red. The indicator also includes optional features such as coloring the bars based on the indicator's values and displaying signals for potential long and short positions. The signals are generated based on the crossover and crossunder of the "out" and "sig" values. Wrapping Up This indicator combines two important concepts: Nonlinear Regression and Zero-lag Moving Average indicators, which are valuable tools for technical analysis in financial markets. These indicators help traders identify trends, potential reversals, and generate trading signals. By combining the nonlinear regression curve with the zero-lag moving average, this indicator provides a comprehensive view of the price dynamics. Traders can customize the indicator's settings and use it in conjunction with other analysis techniques to make well-informed trading decisions.

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Linear Regression Channel (Log)The Linear Regression Channel (Log) indicator is a modified version of the Linear Regression channel available on PulseWire. It is designed to be used on a logarithmic scale, providing a different perspective on price movements. The indicator utilizes the concept of linear regression to visualize the overall price trend in a specific section of the chart. The central line represents the linear regression calculation, while the upper and lower lines indicate a certain number of standard deviations away from the central line. These bands serve as support and resistance levels, and when prices remain outside the channel for an extended period, a potential reversal may be anticipated. I have replaced the Pearson values with trend strength levels to enhance understanding for individuals unfamiliar with Pearson correlation.

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Auto Trend Projection Auto Trend Projection is an indicator designed to automatically project the short-term trend based on historical price data. It utilizes a dynamic calculation method to determine the slope of the linear regression line, which represents the trend direction. The indicator takes into account multiple length inputs and calculates the deviation and Pearson's R values for each length. Using the highest Pearson's R value, Auto Trend Projection identifies the optimal length for the trend projection. This ensures that the projected trend aligns closely with the historical price data. The indicator visually displays the projected trend using trendlines. These trendlines extend into the future, providing a visual representation of the potential price movement in the short term. The color and style of the trendlines can be customized according to user preferences. Auto Trend Projection simplifies the process of trend analysis by automating the projection of short-term trends. Traders and investors can use this indicator to gain insights into potential price movements and make informed trading decisions. Please note that Auto Trend Projection is not a standalone trading strategy but a tool to assist in trend analysis. It is recommended to combine it with other technical analysis tools and indicators for comprehensive market analysis. Overall, Auto Trend Projection offers a convenient and automated approach to projecting short-term trends, empowering traders with valuable insights into the potential price direction.

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Strongest Trendline Unleashing the Power of Trendlines with the "Strongest Trendline" Indicator. Trendlines are an invaluable tool in technical analysis, providing traders with insights into price movements and market trends. The "Strongest Trendline" indicator offers a powerful approach to identifying robust trendlines based on various parameters and technical analysis metrics. When using the "Strongest Trendline" indicator, it is recommended to utilize a logarithmic scale . This scale accurately represents percentage changes in price, allowing for a more comprehensive visualization of trends. Logarithmic scales highlight the proportional relationship between prices, ensuring that both large and small price movements are given due consideration. One of the notable advantages of logarithmic scales is their ability to balance price movements on a chart. This prevents larger price changes from dominating the visual representation, providing a more balanced perspective on the overall trend. Logarithmic scales are particularly useful when analyzing assets with significant price fluctuations. In some cases, traders may need to scroll back on the chart to view the trendlines generated by the "Strongest Trendline" indicator. By scrolling back, traders ensure they have a sufficient historical context to accurately assess the strength and reliability of the trendline. This comprehensive analysis allows for the identification of trendline patterns and correlations between historical price movements and current market conditions. The "Strongest Trendline" indicator calculates trendlines based on historical data, requiring an adequate number of data points to identify the strongest trend. By scrolling back and considering historical patterns, traders can make more informed trading decisions and identify potential entry or exit points. When using the "Strongest Trendline" indicator, a higher Pearson's R value signifies a stronger trendline. The closer the Pearson's R value is to 1, the more reliable and robust the trendline is considered to be. In conclusion, the "Strongest Trendline" indicator offers traders a robust method for identifying trendlines with significant predictive power. By utilizing a logarithmic scale and considering historical data, traders can unleash the full potential of this indicator and gain valuable insights into price trends. Trendlines, when used in conjunction with other technical analysis tools, can help traders make more informed decisions in the dynamic world of financial markets.

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Volume Profile Regression Channel [LuxAlgo]The Volume Profile Regression Channel calculates a volume profile from an anchored linear regression channel. Users can choose the starting and ending points for the indicator calculation interval. Like a regular volume profile, a "line" of control (LOC), value area, and a developing LOC are displayed. 🔶 SETTINGS Sections: The number of sections the linear regression channel is divided into for the calculation of the volume profile. Width %: Determines the length of the profile within the channel relative to the channel length. Value Area %: Highlights the sections starting from the POC whose accumulated volume is equal to the user-defined percentage of the total profile sections volume. 🔶 USAGES Regular volume profiles are often constructed from a horizontal price area, this can allow highlighting price areas where most trading activity takes place. However, when price is strongly trending a classical volume profile can sometimes be more uniform. This is where using an angled volume profile can be useful. The line of control allows highlighting the section of the channel with the most accumulated volume, this line can be used as a potential future support/resistance. This is where an angled volume profile might be the most useful. The developing LOC highlights the LOC location at a specific time within the profile (from left to right) and can sometimes provide an estimate of the underlying trend in the price. 🔶 DETAILS To be computed the script requires a left and right chart time coordinates. When adding the script to their charts users can determine the left and right time coordinates by clicking on the chart. The linear regression channel width is determined so that the channel precisely encompasses the whole price. 🔶 LIMITATIONS Using a very large calculation interval can return timeouts. Users can reduce the calculation interval to fix that issue from occurring. The amount of drawing objects that can be used is limited, as such using a high calculation interval can display an incomplete profile. 🔶 ACKNOWLEDGEMENTS If you are interested in these types of scripts, @HeWhoMustNotBeNamed published a similar script where users can use a custom line angle. See his 'Angled Volume Profile' script from March 2023.

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Ultimate Trend Line The "Ultimate Trend Line" indicator, designed for overlay on financial charts, calculates and plots a global trend line. It works by first allowing users to input several parameters such as different lengths for up to 21 groups, a multiplier that defines the deviation from the linear regression line for calculating the upper and lower bands, and a color for the fill. Using these inputs, it calculates the upper and lower bands for each length group based on a multiple of the standard deviation from the linear regression line. It then averages these bands to define the global trend line, which is plotted on the graph. Although the code includes commented-out lines for plotting each individual upper and lower band, the indicator as it stands only displays the overall average trend line. The line's color and linewidth can be adjusted according to user preferences. This indicator can be effectively used on both logarithmic and linear scales. This versatility allows it to be adaptable to various types of financial charts and trading styles, providing a flexible tool for users to assess and visualize trend patterns across different market conditions and time frames. It maintains its accuracy and relevance, regardless of the scale used, thus making it a comprehensive solution for trend line analysis in diverse scenarios. It's important to note that the "Ultimate Trend Line" indicator requires a substantial amount of historical data to function properly. If insufficient historical data is available, the indicator may not display accurately or at all. This issue is particularly prevalent when using larger time units, such as weekly or monthly charts, where the available data may not stretch back far enough to satisfy the requirements of the indicator. As such, users should ensure they are operating on a time scale and data set that provides adequate historical depth for the reliable operation of this indicator.

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TrueLevel Bands TrueLevel Bands is a powerful trading indicator that employs linear regression and standard deviation to create dynamic, envelope-style bands around the price action of a financial instrument. These bands are designed to help traders identify potential support and resistance levels, trend direction, and volatility. The TrueLevel Bands indicator consists of multiple envelope bands, each constructed using different timeframes or lengths, and a multiple (mult) factor. The multiple factor determines the width of the bands by adjusting the number of standard deviations from the linear regression line. Key Features of TrueLevel Bands 1. Multi-Timeframe Analysis: Unlike traditional moving average-based indicators, TrueLevel Bands allow traders to incorporate multiple timeframes into their analysis. This helps traders capture both short-term and long-term market dynamics, offering a more comprehensive understanding of price behavior. 2. Customization: The TrueLevel Bands indicator offers a high level of customization, allowing traders to adjust the lengths and multiple factors to suit their trading style and preferences. This flexibility enables traders to fine-tune the indicator to work optimally with various instruments and market conditions. 3. Adaptive Volatility: By incorporating standard deviation, TrueLevel Bands can automatically adjust to changing market volatility. This feature enables the bands to expand during periods of high volatility and contract during periods of low volatility, providing traders with a more accurate representation of market dynamics. 4. Dynamic Support and Resistance Levels: TrueLevel Bands can help traders identify dynamic support and resistance levels, as the bands adjust in real-time according to price action. This can be particularly useful for traders looking to enter or exit positions based on support and resistance levels. 5. The "Global Trend Line" refers to the average of the bands used to indicate the overall trend. Why TrueLevel Bands are Different from Classic Moving Averages TrueLevel Bands differ from conventional moving averages in several ways: 1. Linear Regression: While moving averages are based on simple arithmetic means, TrueLevel Bands use linear regression to determine the centerline. This offers a more accurate representation of the trend and helps traders better assess potential entry and exit points. 2. Envelope Style Bands: Unlike moving averages, which are single lines, TrueLevel Bands form envelope-style bands around the price action. This provides traders with a visual representation of potential support and resistance levels, trend direction, and volatility. 3. Multi-Timeframe Analysis: Classic moving averages typically focus on a single timeframe. In contrast, TrueLevel Bands incorporate multiple timeframes, enabling traders to capture a broader understanding of market dynamics. 4. Adaptive Volatility: Traditional moving averages do not account for changing market volatility, whereas TrueLevel Bands automatically adjust to volatility shifts through the use of standard deviation. The TrueLevel Bands indicator is a powerful, versatile tool that offers traders a unique approach to technical analysis. With its ability to adapt to changing market conditions, provide multi-timeframe analysis, and dynamic support and resistance levels, TrueLevel Bands can serve as an invaluable asset to both novice and experienced traders looking to gain an edge in the markets.

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Advanced Trend Detection Strategy The Advanced Trend Detection Strategy is a sophisticated trading algorithm based on the indicator "Percent Levels From Previous Close". This strategy is based on calculating the Pearson's correlation coefficient of logarithmic-scale linear regression channels across a range of lengths from 50 to 1000. It then selects the highest value to determine the length for the channel used in the strategy, as well as for the computation of the Simple Moving Average (SMA) that is incorporated into the strategy. In this methodology, a script is applied to an equity in which multiple length inputs are taken into consideration. For each of these lengths, the slope, average, and intercept are calculated using logarithmic values. Deviation, the Pearson's correlation coefficient, and upper and lower deviations are also computed for each length. The strategy then selects the length with the highest Pearson's correlation coefficient. This selected length is used in the channel of the strategy and also for the calculation of the SMA. The chosen length is ultimately the one that best fits the logarithmic regression line, as indicated by the highest Pearson's correlation coefficient. In short, this strategy leverages the power of Pearson's correlation coefficient in a logarithmic scale linear regression framework to identify optimal trend channels across a broad range of lengths, assisting traders in making more informed decisions.

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Advanced Trend Channel Detection (Log Scale)The Advanced Trend Channel Detection (Log Scale) indicator is designed to identify the strongest trend channels using logarithmic scaling. It does this by calculating the highest Pearson's R value among all length inputs and then determining which length input to use for the selected slope, average, and intercept. The script then draws the upper and lower deviation lines on the chart based on the selected slope, average, and intercept, and optionally displays the Pearson's R value. To use this indicator, you will need to switch to logarithmic scale. There are several advantages to using logarithmic scale over regular scale. Firstly, logarithmic scale provides a better visualization of data that spans multiple orders of magnitude by compressing large ranges of values into a smaller space. Secondly, logarithmic scale can help to minimize the impact of outliers, making it easier to identify patterns and trends in the data. Finally, logarithmic scale is often utilized in scientific contexts as it can reveal relationships between variables that may not be visible on a linear scale. If the trend channel does not appear on the chart, it may be necessary to scroll back to view historical data. The indicator uses past price data to calculate the trend channel, so if there is not enough historical data visible on the chart, the indicator may not be able to identify the trend channel. In this case, the user should adjust the chart's timeframe or zoom out to view more historical data. Additionally, the indicator may need to be recalibrated if there is a significant shift in market conditions or if the selected length input is no longer appropriate.

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MACD TrueLevel Strategy This strategy uses the MACD indicator to determine buy and sell signals. In addition, the strategy employs the use of "TrueLevel Bands," which are essentially envelope bands that are calculated based on the linear regression and standard deviation of the price data over various lengths. The TrueLevel Bands are calculated for 14 different lengths and are plotted on the chart as lines. The bands are filled with a specified color to make them more visible. The highest upper band and lowest lower band values are stored in variables for easy access. The user can input the lengths for the TrueLevel Bands and adjust the multiplier for the standard deviation. They can also select the bands they want to use for entry and exit, and enable long and short positions. The entry conditions for a long position are either a crossover of the MACD line over the signal line or a crossover of the price over the selected entry lower band. The entry conditions for a short position are either a crossunder of the MACD line under the signal line or a crossunder of the price under the selected exit upper band. The exit conditions for both long and short positions are not specified in the code and are left to the user to define. Overall, the strategy aims to capture trends by entering long or short positions based on the MACD and TrueLevel Bands, and exiting those positions when the trend reverses.

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Deming Linear Regression [wbburgin]Deming regression is a type of linear regression used to model the relationship between two variables when there is variability in both variables. Deming regression provides a solution by simultaneously accounting for the variability in both the independent and dependent variables, resulting in a more accurate estimation of the underlying relationship. In the hard-science fields, where measurements are critically important to judging the conclusions drawn from data, Deming regression can be used to account for measurement error. Tradingview's default linear regression indicator (the ta.linreg() function) uses least squares linear regression, which is similar but different than Deming regression. In least squares regression, the regression function minimizes the sum of the squared vertical distances between the data points and the fitted line. This method assumes that the errors or variability are only present in the y-values (dependent variable), and that the x-values (independent variable) are measured without error. In time series data used in trading, Deming regression can be more accurate than least squares regression because the ratio of the variances of the x and y variables is large. X is the bar index, which is an incrementally-increasing function that has little variance, while Y is the price data, which has extremely high variance when compared to the bar index. In such situations, least squares regression can be heavily influenced by outliers or extreme points in the data, whereas Deming regression is more resistant to such influence. Additionally, if your x-axis uses variable widths - such as renko blocks or other types of non-linear widths - Deming regression might be more effective than least-squares linear regression because it accounts for the variability in your x-values as well. Additionally, if you are creating a machine-learning model that uses linear regression to filter or extrapolate data, this regression method may be more accurate than least squares. In contrast to least squares regression, Deming regression takes into account the variability or errors in both the x- and y-values. It minimizes the sum of the squared perpendicular distances between the data points and the fitted line, accounting for both the x- and y-variability. This makes Deming regression more robust in both variables than least squares regression.

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