Linear Regression Channel UltimateKey Features and Benefits
Logarithmic scale option for improved analysis of long-term trends and volatile markets
Activity-based profiling using either touch count or volume data
Customizable channel width and number of profile fills
Adjustable number of most active levels displayed
Highly configurable visual settings for optimal chart readability
Why Logarithmic Scale Matters
The logarithmic scale option is a game-changer for analyzing assets with exponential growth or high volatility. Unlike linear scales, log scales represent percentage changes consistently across the price range. This allows for:
Better visualization of long-term trends
More accurate comparison of price movements across different price levels
Improved analysis of volatile assets or markets experiencing rapid growth
How It Works
The indicator calculates a linear regression line based on the specified period
Upper and lower channel lines are drawn at a customizable distance from the regression line
The space between the channel lines is divided into a user-defined number of levels
For each level, the indicator tracks either:
- The number of times price touches the level (touch count method)
- The total volume traded when price is at the level (volume method)
The most active levels are highlighted based on this activity data
Understanding Touch Count vs Volume
Touch count method: Useful for identifying key support/resistance levels based on price action alone
Volume method: Provides insight into levels where the most trading activity occurs, potentially indicating stronger support/resistance
Practical Applications
Trend identification and strength assessment
Support and resistance level discovery
Entry and exit point optimization
Volume profile analysis for improved market structure understanding
This Linear Regression Channel indicator combines powerful statistical analysis with flexible visualization options, making it an invaluable tool for traders and analysts across various timeframes and markets. Its unique features, especially the logarithmic scale and activity profiling, provide deeper insights into market behavior and potential turning points.
Linear Regression
Mean Reversion Entry Signal
Mean Reversion Entry Signal Indicator
The Mean Reversion Entry Signal indicator is a trading tool designed for traders looking to capitalize on market corrections. This script leverages mean reversion principles, utilizing price levels and the Relative Strength Index (RSI) to generate potential entry signals for both long and short positions.
Key Features:
1. **Dynamic Price Levels**:
- The indicator calculates critical price levels over a user-defined lookback period, including:
- High (H)**: The highest price point over the lookback period.
- Low (L)**: The lowest price point over the lookback period.
- Midpoint (M)**: The average of the high and low.
- Midpoint High (Mh)** and **Midpoint Low (Ml)**: Additional reference levels derived from M for more nuanced trading signals.
2. User-Configurable Inputs:
- Lookback Period: Traders can specify the number of hours to look back for the calculations, allowing for tailored analysis that fits various trading strategies. By default the lookback is set for 24 hours, as i consider it the most adequate for day trading.
- Aggression Level: This input lets users choose their trading strategy's intensity, affecting the sensitivity of entry signals based on the percentage difference from the midpoint.
3. Entry Signal Generation:
The script evaluates market conditions to signal potential trades:
- Long Entries: Indicated when the price is below the Ml level and the price demonstrates a significant distance from the midpoint (M), coupled with RSI being near the oversold territory.
- Short Entries: Triggered when the price exceeds the Mh level, also indicating a significant distance from M, while the RSI indicates near overbought conditions.
4. Visual Indicators:
Clear visual signals are plotted directly on the chart:
- Long Signals are represented as upward triangles in green.
- Short Signals appear as downward triangles in red.
- Important price levels (M, H, L, Mh, and Ml) are displayed to provide traders with immediate context for potential trades.
5. No Entry Zone:
The area between Mh and Ml is shaded to indicate a "No Entry Zone," helping traders identify regions where conditions may not be favorable for taking new positions.
This can also be used as potencial profit taking area.
Conclusion
1. This indicator was built mainly for day trading, using timeframes between 1 minute and 1 hour. If you want to use it in 1D time frame, for instance, you should adjust the lookback period to 120 hours or so.
2. To use this as a strategy, you should not be afraid to "add to your losers" as the trade goes against you and the signals continue to appear.
Enjoy
Normalized Linear Regression (LSMA) OscillatorNormalized 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.
Trend Following Regression CloudTrend Following Regression Cloud Indicator
The Trend Following Regression Cloud is a versatile trading tool designed to help you effortlessly identify the market's prevailing trend. By analyzing price movements over multiple time frames, it provides a clear visual representation of whether the market is trending upwards or downwards.
How It Works:
- Adaptive Analysis: The indicator calculates linear regression lines over various periods ranging from short-term to long-term (e.g., 10, 20, 50, up to 500 periods). This means it adapts quickly to recent market changes, capturing new trends as they develop.
- Noise Reduction: By comparing and weighting the slopes of these regression lines, it filters out insignificant price fluctuations (market noise). This ensures that the signals you receive are more reliable and less prone to false alarms.
- Cloud Calculation: The cloud is generated by first calculating the slopes of multiple linear regression lines over different lengths. The differences between the slopes of shorter-term and longer-term regressions are then computed and weighted by their respective lengths. By summing up these weighted differences, the indicator produces a "total distance" value. This value is applied to a baseline (such as a 100-period simple moving average) to create the cloud line. The area between the baseline and the cloud line is filled, and its color changes based on whether the total distance is positive or negative, providing a visual cue of the market's trend direction.
- Visual Representation: The indicator plots two lines—a base line and a cloud line—creating a shaded area (the "cloud") between them. The color of this cloud changes based on market conditions:
- Green Cloud: Indicates that short-term trends are stronger than long-term trends, suggesting an upward market movement. This could be a good time to consider buying.
- Red Cloud: Signifies that the market may be trending downwards, as long-term trends overpower short-term ones. This could be an opportune moment to consider selling.
Periodic Linear Regressions [LuxAlgo]The Periodic Linear Regressions (PLR) indicator calculates linear regressions periodically (similar to the VWAP indicator) based on a user-set period (anchor).
This allows for estimating underlying trends in the price, as well as providing potential supports/resistances.
🔶 USAGE
The Periodic Linear Regressions indicator calculates a linear regression over a user-selected interval determined from the selected "Anchor Period".
The PLR can be visualized as a regular linear regression (Static), with a fit readjusting for new data points until the end of the selected period, or as a moving average (Rolling), with new values obtained from the last point of a linear regression fitted over the calculation interval. While the static method line is prone to repainting, it has value since it can further emphasize the linearity of an underlying trend, as well as suggest future trend directions by extrapolating the fit.
Extremities are included in the indicator, these are obtained from the root mean squared error (RMSE) between the price and calculated linear regression. The Multiple setting allows the users to control how far each extremity is from the other.
Periodic Linear Regressions can be helpful in finding support/resistance areas or even opportunities when ranging in a channel.
The anchor - where a new period starts - can be shown (in this case in the top right corner).
The shown bands can be visualized by enabling Show Extremities in settings ( Rolling or Static method).
The script includes a background gradient color option for the bands, which only applies when using the Rolling method.
The indicator colors can be suggestive of the detected trend and are determined as follows:
Method Rolling: a gradient color between red and green indicates the trend; more green if the output is rising, suggesting an uptrend, and more red if it is decreasing, suggesting a downtrend.
Method Static: green if the slope of the line is positive, suggesting an uptrend, red if negative, suggesting a downtrend.
🔶 DETAILS
🔹 Anchor Type
When the Anchor Type is set to Periodic , the indicator will be reset when the "Anchor Period" changes, after which calculations will start again.
An anchored rolling line set at First Bar won't reset at a new session; it will continue calculating the linear regression from the first bar to the last; in other words, every bar is included in the calculation. This can be useful to detect potential long-term tops/bottoms.
Note that a linear regression needs at least two values for its calculation, which explains why you won't see a static line at the first bar of the session. The rolling linear regression will only show from the 3rd bar of the session since it also needs a previous value.
🔹 Rolling/Static
When Anchor Type is set at Periodic , a linear regression is calculated between the first bar of the chosen session and the current bar, aiming to find the line that best fits the dataset.
The example above shows the lines drawn during the session. The offered script, though, shows the last calculated point connected to the previous point when the Rolling method is chosen, while the Static method shows the latest line.
Note that linear regression needs at least two values, which explains why you won't see a static line at the first bar of the session. The rolling line will only show from the 3rd bar of the session since it also needs a previous value.
🔶 SETTINGS
Method: Indicator method used, with options: "Static" (straight line) / "Rolling" (rolling linear regression).
Anchor Type: "Periodic / First Bar" (the latter works only when "Method" is set to "Rolling").
Anchor Period: Only applicable when "Anchor Type" is set at "Periodic".
Source: open, high, low, close, ...
Multiple: Alters the width of the bands when "Show Extremities" is enabled.
Show Extremities: Display one upper and one lower extremity.
🔹 Color Settings
Mono Color: color when "Bicolor" is disabled
Bicolor: Toggle on/off + Colors
Gradient: Background color when "Show extremities" is enabled + level of gradient
🔹 Dashboard
Show Dashboard
Location of dashboard
Text size
[DarkTrader] Intersection Level & PredictionLinear Regression Function Reference by @RicardoSantos :
The Intersection Level Calculation process identifies critical price levels where significant market reactions are expected. It starts by analyzing historical price action and technical indicators to pinpoint key support and resistance levels.
Price Forecast Min represents the predicted lowest price level that the asset might reach, while Price Forecast Max indicates the anticipated highest price level. These projections are calculated using statistical methods and historical price patterns, allowing traders to anticipate potential support and resistance zones. By providing these forecasts, traders can better manage their risk and set more informed entry and exit points based on projected price movements.
Example Of Prediction (Before & After)
Predicting Future Price Movements :
Once the intersection levels are identified, the indicator uses various predictive models to forecast what price might do next when it approaches these levels. Here’s a breakdown of how it achieves this :
Price Reaction Analysis: The indicator assesses how price has historically reacted to similar intersection levels. For instance, if price has reversed from a certain support level multiple times, the indicator can predict a potential reversal or bounce when price approaches that level again.
Trend Continuation or Reversal: It examines the strength of the current trend by analyzing momentum indicators, volume, and the angle or direction of trendlines. Based on this, it can predict whether price is likely to break through an intersection level, signaling trend continuation, or bounce off it, indicating a potential reversal.
Confluence of Factors: The prediction mechanism becomes more accurate when multiple factors converge at the same intersection level. For example, if a trendline, moving average, and support zone all intersect at the same price point, the indicator predicts a stronger likelihood of significant price movement.
Market Volatility and Momentum: The indicator also considers current market volatility and momentum in its prediction. For example, if price approaches an intersection level with high momentum, it might predict a breakout, whereas low momentum might suggest consolidation or a weaker price reaction.
In this indicator, I utilize Linear Regression to forecast price movements by analyzing historical data trends. Linear Regression involves fitting a straight line to past price data, enabling me to model and project future price levels based on identified trends. This method calculates a trend line that best represents the historical price behavior, providing a foundation for predicting future price points. By extending this trend line, I can estimate where prices might move, incorporating a range to account for potential deviations. This approach helps in identifying both minimum and maximum forecasted prices, offering valuable insights into potential market directions.
[DarkTrader] Liquidity Regression MapLinear Regression Function Reference by @RicardoSantos :
Liquidity Regression Map is an advanced indicator designed to assist traders in identifying key liquidity zones, reversals, and potential breakout areas within the market. By visualizing liquidity shifts and regression patterns, this tool provides a powerful visual guide to price movements that often go unnoticed by conventional indicators. The indicator's dynamic and adaptive approach helps traders better navigate complex market environments.
Purpose :
This indicator focuses on analyzing the behavior of liquidity in the market and mapping it out in a visual format on your TradingView charts. It provides a deeper understanding of where large clusters of liquidity exist, helping traders pinpoint potential areas where price is likely to react. It aims to highlight key liquidity zones and assess when price is likely to reverse or continue its trend, providing a comprehensive view of the market's internal structure.
Liquidity Regression Map supports multiple timeframes and multiple assets, providing traders with flexibility to analyze different market conditions. Whether you're analyzing short-term charts for scalping or higher timeframes for swing trades, the indicator adjusts its liquidity and regression calculations accordingly, ensuring accurate insights across all timeframes. Additionally, it is compatible with various asset classes, including stocks, forex, cryptocurrencies, and commodities, allowing you to apply the same powerful liquidity analysis across multiple markets for a unified trading strategy.
How It Works :
The indicator identifies liquidity zones by looking at the highs and lows of recent price action within a user-defined period, known as the lookback period. These zones represent areas where market participants are likely to have placed a significant number of stop orders or large positions, creating pockets of liquidity. The zones are visualized as levels on the chart, showing where the market is likely to react.
Next, the indicator performs a linear regression analysis on the price data. Linear regression helps smooth out the price action and gives an indication of the overall trend within the defined liquidity zone. This analysis is critical for determining the slope and direction of price movement, which provides insights into the market's momentum and strength in these liquidity areas.
A key feature of this indicator is its ability to detect liquidity swipes—sharp moves in price that sweep liquidity levels. When price approaches a liquidity zone and crosses it aggressively, the indicator highlights this as a swipe. Swipes often signal significant price reversals or trend continuation because they indicate that liquidity has been absorbed. The Akastra Liquidity Regression Map highlights these areas, helping traders anticipate where a reversal or continuation may occur.
As new price data comes in, the liquidity zones and regression lines dynamically adjust. This real-time update ensures that traders are always working with the most relevant and up-to-date liquidity information. The indicator recalculates the liquidity levels based on the recent highs and lows and repositions the regression lines accordingly. This makes it adaptive to both short-term volatility and long-term trends.
To make the analysis intuitive and easy to interpret, the liquidity levels are color-coded based on their strength and importance. Liquidity zones are shown using a gradient of colors, from weak liquidity (indicating potential minor reactions) to strong liquidity (where a significant price reaction is more likely). The heatmap visually communicates how liquidity is distributed across different levels and timeframes.
Liquidity Condition Filtering :
Another important aspect of the mechanism is the liquidity condition filtering, which only highlights significant liquidity shifts. The indicator evaluates if price movement meets certain thresholds, such as exceeding a 1.618 threshold for liquidity absorption or rejection . This filtering ensures that only the most relevant and impactful liquidity conditions are displayed, minimizing noise and false signals on the chart.
Finally, the indicator calculates and displays liquidity levels across multiple timeframes simultaneously, providing a more comprehensive view. For example, liquidity from a higher timeframe may interact with liquidity from a lower timeframe, providing traders with an overlapping view of where significant liquidity is concentrated. This multi-layer analysis helps to confirm trading setups and increases the probability of successful trades.
Adaptive LSMA Regression OscillatorOverview:
The Adaptive LSMA Regression Oscillator is an open-source technical analysis tool designed to reflect market price deviations from an adaptive least squares moving average (LSMA). The adaptive length of the LSMA changes dynamically based on the volatility of the market, making the indicator responsive to different market conditions.
Key Features:
Adaptive Length Adjustment : The base length of the LSMA is adjusted based on market volatility, measured by the Average True Range (ATR). The more volatile the market, the longer the adaptive length, and vice versa.
Oscillator : The indicator calculates the difference between the closing price and the adaptive LSMA. This difference is plotted as a histogram, showing whether prices are above or below the LSMA.
Color-Coded Histogram:
Positive values (where price is above the LSMA) are colored green.
Negative values (where price is below the LSMA) are colored red.
Debugging Information: The adaptive length is plotted for transparency, allowing users to see how the length changes based on the multiplier and ATR.
How It Works:
Inputs:
Base Length : This defines the starting length of the LSMA. It is adjusted based on market conditions.
Multiplier : A customizable multiplier is used to control how much the adaptive length responds to changes in volatility.
ATR Period : This determines the lookback period for the Average True Range calculation, a measure of market volatility.
Dynamic Adjustment:
The length of the LSMA is dynamically adjusted by multiplying the base length by a factor derived from ATR and the average close price.
This helps the indicator adapt to different market conditions, staying shorter during low volatility and longer during high volatility.
Example Use Cases:
Trend Analysis: By observing the oscillator, traders can see when prices deviate from a dynamically adjusted LSMA. This can be used to evaluate potential trend direction or changes in market behavior.
Volatility-Responsive Indicator: The adaptive length ensures that the indicator responds appropriately in both high and low volatility environments.
Magic Linear Regression Channel [MW]Introduction
The Magic Linear Regression Channel indicator provides users with a way to quickly include a linear regression channel ANYWHERE on their chart, in order to find channel breakouts and bounces within any time period. It uses a novel method that allows users to adjust the start and end period of the regression channel in order to quickly make adjustments faster, with fewer steps, and with more precision than with any other linear regression channel tool. It includes Fibonacci bands AND a horizontal mode in order for users to quickly define significant price levels based on the high, low, open, and close prices defined by the start period.
Settings
Start Time: This is initially MANUALLY SELECTED ON THE CHART when the indicator is first loaded.
End time: This is also initially MANUALLY SELECTED ON THE CHART when the indicator is first loaded.
Horizontal Line: This forces the baseline to be horizontal. The band distance is defined by the maximum price distance from the band.
Horizontal Line Type: This snaps the horizontal line to the close, high, low, or open price. Or, it can also use a regression calculation for the selected time period to define the y-position of the line.
Extend Line N Bars: How many bars to the left in which to extend the baseline and bands.
Show Baseline ONLY!!: Removes all lines except the baseline and it’s extension.
Add Half Band: Includes a band that is half the distance between the baseline and the top and bottom bands
Add Outer Fibonacci Band: Includes a band that is 1.618 (phi) times the default band distance
Add Inner Fibonacci Band - Upper: Includes a band that is 0.618 (1/phi) times the default band distance
Add Inner Fibonacci Band - Lower: Includes a band that is 0.382 (1 - 1/phi) times the default band distance
Calculations
This indicator uses the least squares approach for generating a straight regression line, which can be reviewed at Wikipedia’s “Simple Linear Regression” page. It sums all of the x-values, and y-values, as well as the sum of the product of corresponding x and y values, and the sum of the squares of the x-values. These values are used to calculate the slope and intercept using the following equations:
slope = (n * sum_xy - sum_x * sum_y) / (n * sum_xx - sum_x * sum_x)
And
intercept = (sum_y - slope * sum_x) / n
The slope and intercept are then used to generate the baseline and the corresponding bands using the user-selected offsets.
How to Use
When the Magic Linear Regression Channel indicator is first added to the chart, there will be a blue prompt behind the “Indicators, Metrics & Strategies” window. Close the window, then select a START POINT by clicking at a desired location on the chart. Next, you will be prompted to select an END POINT. The end point MUST be placed after the START POINT. At this time a channel will be generated. Once you’ve selected the START POINT and END POINT, you can adjust them by dragging them anywhere on the chart. Each adjustment will generate a new channel making it easier for you to quickly visualize and recognize any channel exits and bounces.
The Magic Linear Regression Channel indicator works great at identifying wave patterns. Place the start line at a top or bottom pivot point. Place the end line at the next respective top or bottom pivot. This will give you a complete wave form to work with. When price reaches a band and rejects, it can be a strong indication that price may move back to one of the bands in the channel. If price exits the channel with volume that supports the exit, it may be an indication of a breakout.
You can also use the horizontal mode to identify key levels, then add Fibonacci bands based on regression calculations for the given time period to provide more meaningful areas of support and resistance.
Other Usage Notes and Limitations
Occasionally, off-by-1 errors appear which makes the extended lines protrude at a slightly incorrect angle. This is a known bug and will be addressed in the next release.
It's important for traders to be aware of the limitations of any indicator and to use them as part of a broader, well-rounded trading strategy that includes risk management, fundamental analysis, and other tools that can help with reducing false signals, determining trend direction, and providing additional confirmation for a trade decision. Diversifying strategies and not relying solely on one type of indicator or analysis can help mitigate some of these risks.
Fibonacci Linear Regression Bands[Pinescriptlabs]🎯 This script is designed to draw Fibonacci-based linear regression bands.
It calculates and draws a linear regression channel and its Fibonacci levels across different time frames (5m, 15m, 30m, and 4h).
📊 How to use it?
🔍 Multidimensional Analysis
This strategy allows you to view the market from a multidimensional perspective, integrating long-term trends with short-term price action. By doing so, you can dynamically adjust your trades based on market developments, moving between time frames as needed. This not only enables you to capture large movements within the primary trend but also to exploit smaller fluctuations.
⏳ Time Frame Interaction
4-Hour Time Frame with Regression Channel: By using a regression channel on a broader time frame (like 4 hours), you gain a perspective on the dominant trend. This provides you with a solid foundation to evaluate the general market direction. In this scenario, you might deactivate the Fibonacci levels to avoid cluttering the visualization, focusing solely on the regression channel that shows you the prevailing trend.
Lower Time Frames with Regression and Fibonacci: You can activate the regression lines and Fibonacci levels on lower time frames (like 5m, 15m, or 30m) to obtain more precise signals. Here, Fibonacci levels will help you identify potential entry and exit points within the broader time frame.
🚩 Reversal Zone Identification
If the price breaks the regression channel on a lower time frame and approaches a key Fibonacci level, this could indicate a potential reversal.
🎯 Multiple Scenarios
By using different combinations of regression channels and Fibonacci levels across various time frames, you can create trading scenarios. For example, you could be in a long position on the 4-hour time frame while simultaneously trading within a lower time frame, taking advantage of bounces at Fibonacci levels.
🎯 Confluence Zone Identification
Zones where regression lines and Fibonacci levels coincide become areas of confluence. These zones represent points where a strong price reaction is likely to occur. If a Fibonacci retracement aligns with the upper or lower edge of a regression channel, this point acts as a significant support or resistance level.
⚙️ Input Configuration?
Activate/Deactivate Regression Lines: Click on the squares under "Linear Settings" to activate or deactivate the regression line in different time frames. If a square is colored, the regression line for that time frame is activated.
Show/Hide Fibonacci: Check or uncheck the boxes under "Fibonacci Settings" to show or hide Fibonacci levels in the selected time frames.
Fibonacci Color: Click on the color box under "Fibonacci Color" to select a new color for the Fibonacci levels.
Español:
🎯 Este script está diseñado para dibujar bandas de regresión lineal basadas en Fibonacci.
Calcula y dibuja un canal de regresión lineal y sus niveles de Fibonacci en diferentes marcos de tiempo (5m, 15m, 30m y 4h).
📊 ¿Cómo usarlo?
🔍 Análisis Multidimensional
Esta estrategia te permite ver el mercado desde una perspectiva multidimensional, integrando las tendencias a largo plazo con la acción del precio a corto plazo. Al hacerlo, puedes ajustar dinámicamente tus operaciones según la evolución del mercado, moviéndote entre marcos de tiempo según sea necesario. Esto no solo te permite captar movimientos grandes dentro de la tendencia principal, sino también explotar fluctuaciones más pequeñas
⏳ Interacción entre Marcos Temporales
Marco de Tiempo de 4 Horas con Canal de Regresión: Al utilizar un canal de regresión en un marco temporal más amplio (como 4 horas), obtienes una perspectiva sobre la tendencia dominante. Esto te da una base sólida para evaluar la dirección general del mercado. En este escenario, podrías desactivar los niveles de Fibonacci para evitar sobrecargar la visualización, enfocándote solo en el canal de regresión que muestra la tendencia predominante.
Marcos Temporales Menores con Regresión y Fibonacci: Puedes activar las líneas de regresión y los niveles de Fibonacci en marcos temporales menores (como 5m, 15m o 30m) para obtener señales más precisas. Aquí, los niveles de Fibonacci te ayudarán a identificar posibles puntos de entrada y salida dentro del marco temporal más amplio.
🚩 Identificación de Zonas de Reversión
Si el precio rompe el canal de regresión en un marco de tiempo menor y se aproxima a un nivel clave de Fibonacci, esto podría indicar una posible reversión.
🎯 Multiplicidad de Escenarios
Al usar diferentes combinaciones de canales de regresión y niveles de Fibonacci en varios marcos de tiempo, puedes crear escenarios de trading. Por ejemplo, podrías estar en una posición larga en el marco temporal de 4 horas, mientras que simultáneamente operas en un marco temporal menor aprovechando los rebotes en los niveles de Fibonacci.
🎯 Identificación de Zonas de Confluencia
Las zonas donde las líneas de regresión y los niveles de Fibonacci coinciden se convierten en áreas de confluencia. Estas zonas representan puntos donde es probable que ocurra una fuerte reacción del precio. Si un retroceso de Fibonacci se alinea con el borde superior o inferior de un canal de regresión, este punto actúa como un soporte o resistencia significativo.
⚙️ ¿Configuración de Inputs?
Activar/Desactivar Líneas de Regresión: Haz clic en los cuadrados bajo "Linear Settings" para activar o desactivar la línea de regresión en diferentes marcos temporales. Si un cuadrado está coloreado, la línea de regresión para ese marco temporal está activada.
Mostrar/Ocultar Fibonacci: Marca o desmarca las casillas bajo "Fibonacci Settings" para mostrar u ocultar los niveles de Fibonacci en los marcos temporales seleccionados.
Color de Fibonacci: Haz clic en el cuadro de color bajo "Fibonacci Color" para seleccionar un nuevo color para los niveles de Fibonacci.
FVG Price & Volume Graph [LuxAlgo]The FVG Price & Volume Graph tool plot recently detected fair value gaps relative to the volume traded within their area during their formation. This allows us to effectively visualize significant fair value gaps caused by high liquidity.
The indicator also returns levels from the fair value gaps areas average with the highest associated volume.
Do note that the indicator can consider the chart's visible range when being computed, which will recalculate the indicator when the chart's visible range changes.
🔶 USAGE
Fair Value Gaps (FVG) are core price action concepts occurring when the disparity between supply and demand is significant. Price has a tendency to come back to those areas and mitigating them, that is filling them.
The provided tools allow for effective visualization of both FVG's area's height as well as the volume originating from their creation, which is defined by the total traded volume located within the FVG during its creation. FVG's with more associated volume are displayed to the rightmost of the chart.
Users can determine the amount of most recent FVG's to display from the "Display Amount" setting. Disabling the "Consider Mitigation" setting will return mitigated FVGs in the plot, which can be useful to know where most FVGs were located.
We can use the area average of the FVGs with the most associated volume as potential support/resistance levels. Users can extend more FVG's averages by increasing the "Highest Volume Averages" setting.
🔹 Visualizing Volume/Price Relationships of FVG's
A linear regression is fit between FVG's areas average and their associated volume, with this linear regression helping us see where FVG's with specific volume might be located in the future based on existing FVG's.
Note that FVG's do not tend to exhibit linear relationships with their associated volume, the provided linear regression can give a general sense of tendency, but nothing necessarily accurate.
🔶 DETAILS
🔹 Intrabar Data TF
Given a formation of three candles causing an FVG, the volume traded within that FVG area is obtained by looking at the lower timeframe intrabar candles located within the intermediary candle of the formation. The volume of the intrabar candles located within the FVG areas is added up to obtain the associated volume of the FVG.
Using a lower "Intrabar Data TF" allows obtaining more precise volume results, at the cost of computation time and data availability (if there is a high difference between the "Intrabar Data TF" and the chart TF then less FVG can have their associated volume calculated due to Tradingview limitations).
🔹 Display
Users have access to multiple graphical settings affecting how the indicator is displayed.
The "Graph Resolution" setting determines the length of the X axis, with higher values returning more precise results on the location of FVGs over the X axis. Users can also control the number of labels displayed on the X-axis using the numerical input to the right of "Show X-Axis Labels".
Additionally, users can color FVG areas using a gradient relative to the size of the area, or the volume associated with the FVG.
🔶 SETTINGS
Display Amount: Amount of most recent FVGs to display.
Highest Volume Averages: Amount of FVG averages levels with the highest volume to display and extend.
Consider Mitigation: Only display unmitigated FVGs.
Filter FVGs Outside Visible Range: Only display FVGs areas that are located within the user chart visible range.
Intrabar Data TF: Timeframe used to obtain intrabar data. Should be lower than the user chart timeframe.
Composite Z-Score with Linear Regression Bands [UAlgo]The Composite Z-Score with Linear Regression Bands is a technical indicator designed to provide traders with a comprehensive analysis of price momentum, volatility, and volume. By combining multiple moving averages with slope analysis, volume/volatility compression-expansion metrics, and Z-Score calculations, this indicator aims to highlight potential breakout and breakdown points with high accuracy. The inclusion of linear regression bands further enhances the analysis by providing dynamic support and resistance levels, which adapt to market conditions. This makes the indicator particularly useful in identifying overbought/oversold conditions, volume squeezes, and the overall direction of the trend.
🔶 Key Features
Multi-Length Slope Calculation: The indicator uses multiple Hull Moving Averages (HMA) across various lengths to calculate slope angles, which are then converted into Z-Scores. This helps in capturing both short-term and long-term price momentum.
Volume/Volatility Composite Analysis: By calculating a composite value derived from both volume and volatility, the indicator identifies periods of compression (squeezes) and expansion, which are crucial for detecting potential breakout opportunities.
Linear Regression Bands: The inclusion of dynamic linear regression bands provides traders with adaptive support and resistance levels. These bands are enhanced by the composite value, which adjusts the band width based on market conditions, offering a clearer view of possible price reversals.
Overbought/Oversold Detection: The indicator highlights overbought and oversold conditions by comparing Z-Scores against the upper and lower bounds of the regression bands, which can signal potential reversal points.
Customizable Inputs: Users can customize key parameters such as the lengths of the moving averages, the regression band period, and the number of deviations used for the bands, allowing for flexibility in adapting the indicator to different market environments.
🔶 Interpreting the Indicator
Z-Score Plots: The individual Z-Score plots represent the normalized slope of the Hull Moving Averages over different periods. Positive values indicate upward momentum, while negative values suggest downward momentum. The combined Z-Sum provides a broader view of the overall market momentum.
Composite Value: The composite value is a ratio of volume to volatility, which highlights periods of market compression and expansion. When the composite value rises, it suggests increasing market activity, often preceding a breakout.
Why are we calculating values for multiple lengths?
The Composite Z-Score with Linear Regression Bands indicator employs a multi-timeframe analysis by calculating Z-scores for various moving average lengths. This approach provides a more comprehensive view of market dynamics and helps to identify trends and potential reversals across different timeframes. By considering multiple lengths, we can:
Capture a broader range of market behaviors: Different moving average lengths capture different aspects of price movement. Shorter lengths are more sensitive to recent price changes, while longer lengths provide a smoother representation of the underlying trend.
Reduce the impact of noise: By combining Z-scores from multiple lengths, we can help to filter out some of the noise that can be present in shorter-term data and obtain a more robust signal.
Enhance the reliability of signals: When Z-scores from multiple lengths align, it can increase the confidence in the identified trend or potential reversal. This can help to reduce the likelihood of false signals.
In essence, calculating values for multiple lengths allows the indicator to provide a more nuanced and reliable assessment of market conditions, making it a valuable tool for traders and analysts.
Linear Regression Bands: The central line represents the linear regression of the Z-Sum, while the upper and lower bands represent the dynamic resistance and support levels, respectively. The deviation from the regression line indicates the strength of the current trend. When price moves beyond these bands, it may signal an overbought (above upper band) or oversold (below lower band) condition.
Volume/Volatility Squeeze: When the price moves between the regression bands and the volume/volatility-adjusted bands, the market is in a squeeze. Breakouts from this squeeze can lead to significant price moves, which are indicated by the filling of areas between the Z-Score plots and the bands.
Color Interpretation: The indicator uses color changes to make it easier to interpret the data. Teal colors generally indicate upward momentum or strong conditions, while red suggests downward momentum or weakening conditions. The intensity of the color reflects the strength of the signal.
Overbought/Oversold Signals: The indicator marks potential overbought and oversold conditions when Z-Scores cross above or below the upper and lower regression bands, respectively. These signals are crucial for identifying potential reversal points in the market.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Linear Regression ChannelLinear Regression Channel with Logarithmic Scale Option
This advanced Linear Regression Channel indicator offers traders a powerful tool for technical analysis, with unique features that set it apart from standard implementations.
Key Features:
Logarithmic Scale Option: One of the most distinctive aspects of this indicator is the ability to switch between classic and logarithmic scales. This feature is particularly valuable for long-term analysis, as it ensures that equal percentage changes are represented equally, regardless of the price level.
Flexible Start Date: Unlike many indicators that rely on a fixed number of periods, this tool allows users to set a specific start date and time. This feature provides precise control over the regression analysis timeframe, enhancing its adaptability to various trading strategies.
Customizable Channel Settings: Users can adjust the upper and lower deviation multipliers, allowing for fine-tuning of the channel width to suit different market conditions and trading styles.
Trend Strength Indicator: An optional feature that displays the strength of the trend based on the Pearson correlation coefficient, offering additional insight into the reliability of the current trend.
Comprehensive Visual Customization: The indicator offers extensive color and style options for the regression line, upper and lower channel lines, and fill areas, allowing traders to create a visually appealing and easy-to-read chart setup.
Extended Line Options: Users can choose to extend the regression lines to the left, right, or both, facilitating projection and analysis of future price movements.
Multiple Alert Conditions: The indicator includes four alert conditions for crossing the upper deviation, lower deviation, and the main regression line in both directions, enhancing its utility for active traders.
Why Choose This Indicator:
The combination of logarithmic scale option and flexible start date setting makes this Linear Regression Channel uniquely suited for both short-term and long-term analysis. The logarithmic scale is particularly beneficial for analyzing assets with significant price changes over time, as it normalizes percentage moves across different price levels. This feature, coupled with the ability to set a precise start date, allows traders to perform more accurate and relevant regression analyses, especially when studying specific market cycles or events.
Moreover, the trend strength indicator and customizable visual elements provide traders with a comprehensive tool that not only identifies potential support and resistance levels but also offers insight into the reliability and strength of the current trend.
In summary, this Linear Regression Channel indicator combines flexibility, precision, and insightful analytics, making it an invaluable tool for traders seeking to enhance their technical analysis capabilities on TradingView.
Quadratic Kernel with Quadratic Divergence [PinescriptLabs]This indicator combines a quadratic kernel regression with adaptive deviation bands to provide a unique view of market trends.
Key Features:
**Customizable Parameters:**
- Regression Period: Adjusts the sensitivity of the central line (default 50).
- Time Deformation: Modifies the weight of recent vs. older data (default 1.0). Increasing the "Time Deformation" makes more recent data more relevant, while decreasing it gives more weight to older data in the regression calculation.
- Confidence Band Width: Controls the width of the bands (default 3.0). Determines how many standard deviations are added to or subtracted from the central line to form the confidence bands. The standard deviations are calculated as the difference between the central line and the closing prices. A higher confidence value will result in wider bands, indicating a broader range of expected price variation, while a lower confidence value will result in narrower bands, indicating a narrower range of expected price variation.
**How to Use the Indicator Based on Price Crossings with the Kernel Divergence Line?**
Short: We need a candle to cross and close below the Kernel Divergence Line (bullish), and at the same time, the quadratic channels must be in a Bearish state for confirmation. Once the entry is executed, our exit will be when the Divergence Line changes its color by at least two confirmation points, or the price crosses above, which nullifies the entry.
Long: We need a candle to cross and close above the Kernel Divergence Line (bearish), and at the same time, the quadratic channels must be in a Bullish state for confirmation. Once the entry is executed, our exit will be when the Divergence Line changes its color by at least two confirmation points, or the price crosses below, which nullifies the entry.
**How to Use the Indicator Based Solely on Kernel Divergence??**
We observe the Kernel Divergence line, which indicates bullish momentum while the price is declining, and we are looking for the Reversal point.
**Confirmation of the Reversal Point:** When the Kernel Divergence changes from bullish (green color) to bearish (red color), we look for the price at its lowest point to be below the first lower Quadratic channel or even outside the Quadratic channel. This signals a potential strong reversal.
How to Use the Indicator Based Solely on Quadratic Channels?
Use only confirmations of changes from Bullish to Bearish or vice versa. It is recommended to have at least three confirmation points in the same direction.
Quadratic Kernel Regression: Provides a smoothed trend line that adapts to market movements.
Adaptive Deviation Bands: Dynamically calculated to show market volatility.
Buy/Sell Signals: Based on the price crossing the central line and the direction of the trend.
Quadratic Kernel Regression calculates a smoothed central line based on recent prices.
The deviation bands automatically adjust according to market volatility.
The trend is determined by comparing the current position of the central line with its previous position.
Buy signals are generated when the price crosses above the central line in an uptrend.
Sell signals are generated when the price crosses below the central line in a downtrend.
Español:
Este indicador combina una regresión de kernel cuadrático con bandas de desviación adaptativas para proporcionar una visión única de la tendencia del mercado.
Características principales:
**Parámetros personalizables:**
- Período de regresión: Ajusta la sensibilidad de la línea central (por defecto 50).
- Deformación del tiempo: Modifica el peso de los datos recientes vs. antiguos (por defecto 1.0). Aumentar la "Deformación del tiempo" hace que los datos más recientes sean más relevantes, mientras que disminuirla da más peso a los datos antiguos en el cálculo de la regresión.
- Ancho de bandas de confianza: Controla la amplitud de las bandas (por defecto 3.0). Determina cuántas desviaciones estándar se añaden o restan a la línea central para formar las bandas de confianza. Las desviaciones estándar se calculan como la diferencia entre la línea central y los precios de cierre. Un valor mayor de confianza resultará en bandas más anchas, indicando un rango más amplio de variación esperada en los precios, mientras que un valor menor de confianza resultará en bandas más estrechas, indicando un rango más estrecho de variación esperada.
* *Cómo usar el Indicador Basados en los Cruces de Precio con la Línea de Divergencia del Kernel?**
Short: Necesitamos que una vela cruce y cierre por debajo de la línea de Divergencia del Kernel (bullish) y al mismo tiempo los Canales cuadráticos deben estar en un momento Bearish para confirmación. Una vez ejecutada la entrada, nuestra salida será cuando la Línea de Divergencia haga su cambio de color al menos dos puntos de confirmación o el precio haga un cruce por arriba, lo que anula la entrada.
Long: Necesitamos que una vela cruce y cierre por Encima de la linea de Divergencia del Kernel( Bearish) y al mismo tiempo los Canales cuadráticos deben estar en un momento Bullish para confirmación, una vez ejecutada la entrada nuestra salida será cuando la Linea de Divergencia haga su cambio de color al menos dos puntos de confirmación o el precio haga un cruce por Debajo lo que anula la entrada:
Como usar el indicador Basado en solo en Divergencia del Kernel? : Observamos la linea de Divergencia del Kernel la cual nos indica un momentum bullish mientras que precio va a la baja y lo que buscamos es el punto de Reversion.
Confirmación de punto de reversion: cuando la Divergencia de Kernel pasa de bullish ( color verde) a bearish ( color rojo) buscamos que el precio en su punto mas bajo este por debajo del primer canal inferior Quadratico o fuera incluso del canal Quadratico lo que nos indica una posible reversion con fuerza.
Como usar el indicador basado solo en Canales Quadraticos?
Utilizar únicamente las confirmaciones de Cambio de Bullish a Bearish o visceversa, se recomienda al menos tres puntos de confirmación en la misma dirección.
Regresión de kernel cuadrático: Ofrece una línea de tendencia suavizada que se adapta a los movimientos del mercado.
Bandas de desviación adaptativas: Calculadas dinámicamente para mostrar la volatilidad del mercado.
Señales de compra/venta: Basadas en el cruce del precio con la línea central y la dirección de la tendencia.
La regresión de kernel cuadrático calcula una línea central suavizada basada en los precios recientes.
Las bandas de desviación se ajustan automáticamente según la volatilidad del mercado.
La tendencia se determina comparando la posición actual de la línea central con su posición anterior.
Las señales de compra se generan cuando el precio cruza por encima de la línea central en una tendencia alcista.
Las señales de venta se generan cuando el precio cruza por debajo de la línea central en una tendencia bajista.
TrendMaster ProTrendMaster Pro: A Comprehensive Trend Analysis Tool for Long-Term Investors
TrendMaster Pro is an advanced technical indicator designed to provide long-term investors with a robust and comprehensive analysis of market trends. This sophisticated tool operates exclusively on daily timeframes, making it ideal for those focused on long-term investment strategies. By combining multiple analytical approaches, TrendMaster Pro offers investors a powerful means to assess trend quality and make informed decisions.
Automatic Trend Detection
At the heart of TrendMaster Pro lies its ability to automatically identify the most statistically significant trend. The indicator analyzes various timeframes ranging from 1000 to 5000 days, selecting the one that exhibits the highest correlation. This feature ensures that investors are always working with the most relevant trend data, eliminating the subjectivity often associated with manual trend identification.
The trend detection algorithm employs a regression analysis approach, evaluating approximately 80,000 different trend alternatives each day. Each potential trend is assigned a score based on criteria such as trend density, deviation from regression, and the number of price points near the trend's floor and ceiling. The trend with the highest score is then selected and displayed on the chart.
Comprehensive Scoring System
TrendMaster Pro employs a multi-faceted scoring system that evaluates four key aspects of a trend, providing a holistic view of its quality and potential. Each aspect is scored on a scale of 0 to 10, with the overall trend quality score being a weighted average of these individual scores.
1. Length Score
The Length Score measures the duration of the detected trend. Longer trends receive higher scores, reflecting increased reliability and significance. This score is calculated by normalizing the auto-selected period (which ranges from 1000 to 5000 days) to a scale of 5 to 10.
For example, if the auto-selected period is 3000 days, it would receive a score of around 7.5. This emphasizes the importance of long-term trends in investment decision-making, as they tend to be more stable and indicative of underlying market forces.
2. Strength Score
The Strength Score utilizes Pearson's Correlation Coefficient to assess trend strength. This statistical measure gauges the linear relationship between price and trend projection. A value closer to 1 indicates a strong positive correlation, reinforcing confidence in the trend direction based on historical price movements.
The indicator translates the Pearson's Correlation Coefficient into a score from 0 to 10. For instance, a correlation coefficient of 0.95 might translate to a Strength Score of 8, indicating a strong and reliable trend.
3. Performance Score
The Performance Score compares the asset's Compound Annual Growth Rate (CAGR) to a chosen benchmark, typically a major index like the S&P 500. This score provides insight into how well the asset is performing relative to the broader market.
The CAGR is calculated using the formula: CAGR = (Ending Value / Beginning Value)^(1/n) - 1, where n is the number of years. The Performance Score is then determined by comparing this CAGR to the benchmark's CAGR over the same period. A higher score indicates outperformance relative to the benchmark.
4. Level Score
The Level Score evaluates the current price position within the trend channel. Lower prices within the channel receive higher scores, suggesting potential value or buying opportunities. This score helps identify possible entry points based on historical trend behavior.
For example, if the current price is near the lower boundary of the trend channel, it might receive a Level Score of 9, indicating a potentially attractive entry point.
Visual Representation
TrendMaster Pro provides a clear visual representation of the detected trend by displaying a regression channel on the chart. This channel consists of three lines: a middle line representing the main trend, and upper and lower lines representing standard deviations from the main trend.
The channel offers a quick visual reference for support and resistance levels, helping investors identify potential entry and exit points. The color and style of these lines can be customized to suit individual preferences.
Detailed Information Table
A comprehensive table presents all scores and relevant data, allowing for quick and easy interpretation of the trend analysis. This table includes:
The auto-selected trend length
The Pearson's Correlation Coefficient
The asset's CAGR and the benchmark's CAGR
Individual scores for Length, Strength, Performance, and Level
The overall Trend Quality Score
This table provides investors with a clear, at-a-glance summary of the trend's key characteristics and quality.
Practical Application
To use TrendMaster Pro effectively, investors should consider the following:
Focus on the overall Trend Quality Score as a primary indicator of trend strength and reliability.
Use the Length Score to gauge the trend's longevity and potential stability.
Pay attention to the Strength Score to assess how well the price action aligns with the identified trend.
Utilize the Performance Score to compare the asset's performance against the broader market.
Consider the Level Score when timing entries, looking for opportunities when prices are relatively low within the trend channel.
Use the visual trend channel as a guide for potential support and resistance levels.
Limitations and Considerations
While TrendMaster Pro offers powerful insights, it's important to remember that no indicator can predict future market movements with certainty. The tool should be used in conjunction with fundamental analysis and other market information.
Additionally, as the indicator is designed for daily charts and long-term analysis, it may not be suitable for short-term trading strategies. Users should also be aware that past performance does not guarantee future results, even with strong trend indications.
Conclusion
TrendMaster Pro represents a significant advancement in trend analysis for long-term investors. By combining automatic trend detection, comprehensive scoring, and benchmark comparison, it offers a powerful tool for those seeking to make informed, data-driven investment decisions. Its ability to objectively assess trend quality across multiple dimensions provides investors with a valuable edge in navigating complex market conditions.
For investors looking to deepen their understanding of market trends and enhance their long-term investment strategies, TrendMaster Pro offers a sophisticated yet accessible solution. As with any investment tool, users are encouraged to thoroughly familiarize themselves with its features and interpret its outputs in the context of their overall investment approach.
Multiple Non-Linear Regression [ChartPrime]This indicator is designed to perform multiple non-linear regression analysis using four independent variables: close, open, high, and low prices. Here's a breakdown of its components and functionalities:
Inputs:
Users can adjust several parameters:
Normalization Data Length: Length of data used for normalization.
Learning Rate: Rate at which the algorithm learns from errors.
Smooth?: Option to smooth the output.
Smooth Length: Length of smoothing if enabled.
Define start coefficients: Initial coefficients for the regression equation.
Data Normalization:
The script normalizes input data to a range between 0 and 1 using the highest and lowest values within a specified length.
Non-linear Regression:
It calculates the regression equation using the input coefficients and normalized data. The equation used is a weighted sum of the independent variables, with coefficients adjusted iteratively using gradient descent to minimize errors.
Error Calculation:
The script computes the error between the actual and predicted values.
Gradient Descent: The coefficients are updated iteratively using gradient descent to minimize the error.
// Compute the predicted values using the non-linear regression function
predictedValues = nonLinearRegression(x_1, x_2, x_3, x_4, b1, b2, b3, b4)
// Compute the error
error = errorModule(initial_val, predictedValues)
// Update the coefficients using gradient descent
b1 := b1 - (learningRate * (error * x_1))
b2 := b2 - (learningRate * (error * x_2))
b3 := b3 - (learningRate * (error * x_3))
b4 := b4 - (learningRate * (error * x_4))
Visualization:
Plotting of normalized input data (close, open, high, low).
The indicator provides visualization of normalized data values (close, open, high, low) in the form of circular markers on the chart, allowing users to easily observe the relative positions of these values in relation to each other and the regression line.
Plotting of the regression line.
Color gradient on the regression line based on its value and bar colors.
Display of normalized input data and predicted value in a table.
Signals for crossovers with a midline (0.5).
Interpretation:
Users can interpret the regression line and its crossovers with the midline (0.5) as signals for potential buy or sell opportunities.
This indicator helps users analyze the relationship between multiple variables and make trading decisions based on the regression analysis. Adjusting the coefficients and parameters can fine-tune the model's performance according to specific market conditions.
Multi-Timeframe Linear Regression Channel (Pinescriptlabs)This script combines multiple timeframes for visualizing linear regression channels in a single chart, allowing us to obtain a holistic view of price behavior across different timeframes (5m, 15m, 30m, and 4h). It facilitates the identification of trends and support/resistance levels across various time horizons. This multi-timeframe approach is useful because it helps confirm signals and detect potential divergences.
Components and Their Interaction
Linear Regression: Calculates the regression line and standard deviations for different timeframes. These lines show the direction and strength of the trend.
Deviation Bands: The upper and lower bands act as dynamic support and resistance levels, based on the standard deviation or maximum deviation.
Colors and Labels: Different colors for each timeframe allow for quick and clear identification of the regression lines and their bands. The labels help identify the timeframe of each channel.
Justification for the Mashup
Combining linear regressions across different timeframes allows us to observe short, medium, and long-term trends in a single chart. This multi-timeframe approach provides a more comprehensive market perspective compared to using a single timeframe.
Default Properties
The default properties of the strategy are configured to provide a clear view of the regression channels across different timeframes. These properties include:
Channel Length: Default of 50 periods, adjustable between 1 and 5000.
Data Source: Closing price by default.
Deviations: Optional use of upper and lower deviations with adjustable multipliers.
Line Extension: Option to extend lines to the right for better visualization.
Underlying Concepts
Calculating linear regression involves determining the slope, mean, and intercept of a line that best fits the price data. Standard deviations are used to create bands around this line, providing a measure of volatility. Implementing this in different timeframes allows us to observe how the trend changes over time and helps identify more precise entry and exit points.
This script is particularly useful for traders looking for an integrated tool that allows them to observe price behavior across multiple timeframes without needing to switch between different charts.
1.- For example, in the main image of the script, we observe that we are in a 1-hour timeframe, where the 4-hour linear regression channel indicates an uptrend with a length of 60 periods. Meanwhile, the 15-minute and 30-minute channels identify a convergence in the same trend. However, in the 5-minute linear regression, we have a completely lateral channel. These channels, shown from different timeframes in a single chart, give us a clear idea of exactly where the price is heading in each timeframe. Each channel serves as support or resistance for a lower or higher timeframe, depending on which timeframe we are looking at. Next, we will go to each timeframe to observe how the regression channels are displayed
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Español:
Este script combina múltiples marcos de tiempo para la visualización de canales de regresión lineal en un solo gráfico, nos permitirá obtener una visión holística del comportamiento del precio en diferentes marcos temporales (5m, 15m, 30m y 4h) permite la identificación de tendencias y niveles de soporte/resistencia en diferentes horizontes de tiempo. Este enfoque multi-temporal es útil porque permite confirmar señales y detectar posibles divergencias.
Componentes y su Interacción
Regresión Lineal: Calcula la línea de regresión y las desviaciones estándar para diferentes marcos temporales. Estas líneas muestran la dirección y la fuerza de la tendencia.
Bandas de Desviación: Las bandas superior e inferior actúan como niveles dinámicos de soporte y resistencia, basados en la desviación estándar o la desviación máxima.
Colores y Etiquetas: Diferentes colores para cada marco temporal permiten una identificación rápida y clara de las líneas de regresión y sus bandas. Las etiquetas ayudan a identificar el marco temporal de cada canal.
Justificación del Mashup
La combinación de regresiones lineales en diferentes marcos temporales nos permite observar la tendencia a corto, medio y largo plazo en un solo gráfico. Este enfoque multi-temporal proporciona una perspectiva más completa del mercado en comparación con el uso de un solo marco temporal.
Propiedades por Defecto
Las propiedades por defecto de la estrategia están configuradas para proporcionar una visión clara de los canales de regresión en diferentes marcos temporales. Estas propiedades incluyen:
Longitud del Canal: 50 períodos por defecto, ajustable entre 1 y 5000.
Fuente de Datos: Precio de cierre por defecto.
Desviaciones: Uso opcional de desviaciones superiores e inferiores con multiplicadores ajustables.
Extensión de Líneas: Opción para extender las líneas hacia la derecha para una mejor visualización.
Conceptos Subyacentes
El cálculo de la regresión lineal implica determinar la pendiente, la media y la intersección de una línea que mejor se ajusta a los datos de precios. Las desviaciones estándar se utilizan para crear bandas alrededor de esta línea, proporcionando una medida de la volatilidad. La implementación en diferentes marcos temporales permite observar cómo cambia la tendencia a lo largo del tiempo y ayuda a identificar puntos de entrada y salida más precisos.
Este script es particularmente útil para traders que buscan una herramienta integrada que les permita observar el comportamiento del precio en múltiples marcos temporales sin necesidad de cambiar entre diferentes gráficos.
Por ejemplo en la imagen principal del script observamos que estamos en un timeframe de 1h, donde el canal de regresión lineal de 4h, nos indica en un length de 60 periodos una tendencia alcista, mientras que los canales de 15min y 30 min nos identifican una convergencia en la misma tendencia, sin embargo en la regresión lineal de 5 minutos tenemos un canal totalmente lateral, estos canales mostrados de diferentes marcos de tiempo en un solo grafico nos da una clara idea de exactamente de a donde esta dirigiendo el precio en cada marco de tiempo a la par que cada canal nos sirve como soporte o resistencia de un marco de tiempo ya sea inferior o mayor dependiendo en que time frame nos coloquemos, a continuación iremos a cada marco de tiempo para que observemos como se muestran los canales de regresión:
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Log Regression Channel [UAlgo]The "Log Regression Channel " channel is useful for analyzing price trends and volatility in a financial instrument over a specified period. By using logarithmic scaling, this indicator can more effectively handle the wide range of price movements seen in many financial markets, making it particularly valuable for assets with exponential growth characteristics.
The indicator plots the central regression line along with upper and lower deviation bands, providing a visual representation of potential support and resistance levels.
🔶 Key Features
Logarithmic Regression Line: The central line represents the logarithmic regression, which fits the price data over the specified length using a logarithmic scale. This helps in identifying the overall trend direction.
Deviation Bands: The upper and lower bands are plotted at a specified multiple of the standard deviation from the regression line, highlighting areas of potential overbought and oversold conditions.
Customizable Parameters: Users can adjust the length of the regression, the deviation multiplier, the color of the labels, and the size of the text labels to suit their preferences.
R-Squared Display: The R-squared value, which measures the goodness of fit of the regression model, is displayed on the chart. This helps traders assess the reliability of the regression line.
🔶 Calculations
The indicator performs several key calculations to plot the logarithmic regression channel:
Logarithmic Transformation: The prices and time indices are transformed using the natural logarithm to handle exponential growth in price data.
Regression Coefficients: The slope and intercept of the regression line are calculated using the least squares method on the transformed data.
Predicted Values: The regression equation is used to calculate predicted values for each data point.
Standard Deviation: The standard deviation of the residuals (differences between actual and predicted values) is computed to determine the width of the deviation bands.
Deviation Bands: Upper and lower bands are plotted at a specified multiple of the standard deviation above and below the regression line.
R-Squared Value: The R-squared value is calculated to measure how well the regression line fits the data. This value is displayed on the chart to inform the user of the model's reliability.
🔶 Disclaimer
The "Log Regression Channel " indicator is provided for educational and informational purposes only.
It is not intended as investment advice or a recommendation to buy or sell any financial instrument. Trading financial instruments involves substantial risk and may not be suitable for all investors.
Past performance is not indicative of future results. Users should conduct their own research.
Linear Regression Oscillator [ChartPrime]Linear Regression Oscillator Indicator
Overview:
The Linear Regression Oscillator is a custom TradingView indicator designed to provide insights into potential mean reversion and trend conditions. By calculating a linear regression on the closing prices over a user-defined period, this oscillator helps identify overbought and oversold levels and highlights trend changes. The indicator also offers visual cues and color-coded price bars to aid in quick decision-making.
Key Features:
◆ Customizable Look-Back Period:
Input: Length
Default: 20
Description: Determines the period over which the linear regression is calculated. A longer period smooths the oscillator but may lag, while a shorter period is more responsive but may be noisier.
◆ Overbought and Oversold Thresholds:
Inputs: Upper Threshold and Lower Threshold
Default: 1.5 and -1.5 respectively
Description: Define the upper and lower bounds for identifying overbought and oversold conditions. Values outside these thresholds suggest potential reversals.
◆ Candlestick Color Plotting:
Input: Plot Bar Color
Default: false
Description: Option to color the price bars based on the oscillator's value, providing a visual representation of market conditions. Bars turn cyan for positive oscillator values and blue for negative.
◆ Mean Reversion and Trend Signals:
Visual markers and labels indicate when the oscillator suggests mean reversion or trend changes, aiding in identifying key market turning points.
◆ Invalidation Levels:
Tracks the highest and lowest prices over a recent period to set levels where the current trend signal would be considered invalidated.
◆ Gradient Color Coding:
Utilizes gradient color coding to enhance the visualization of oscillator values, making it easier to interpret overbought and oversold conditions.
◆ Usage Notes:
Setting the Look-Back Period:
Adjust the "Length" input based on the timeframe and the type of trading you are conducting. Shorter periods are more suited for intraday trading, while longer periods can be used for swing trading.
Interpreting Thresholds:
Use the upper and lower threshold inputs to fine-tune the sensitivity of the overbought and oversold signals. Higher absolute values reduce the number of signals but increase their reliability.
Candlestick Coloring:
Enabling the "Plot Bar Color" option can help quickly identify the current state of the oscillator in relation to the zero line. This visual aid can be particularly useful in fast-moving markets.
Mean Reversion and Trend Signals:
Pay attention to the symbols and labels on the chart indicating mean reversion and trend changes. These signals are designed to highlight potential entry and exit points.
Invalidation Levels:
Use the plotted invalidation levels as stop-loss or signal invalidation points. If the price moves beyond these levels, the current trend signal is likely invalid.
This indicator helps traders identify overbought and oversold conditions, potential mean reversions, and trend changes based on the linear regression of the closing prices over a specified look-back period.
Linear Regression Trend ChannelThe "Linear Regression Trend Channel" is a technical indicator designed to illustrate price trends and their volatility using linear regression. This indicator calculates the main linear regression line based on the user-defined period length and computes the standard deviation to form a trend channel.
Key Features:
- Linear Regression Calculation: Computes the linear regression line based on the selected price data source and the defined period length.
- Slope and Intercept Calculation: Calculates the slope and intercept of the linear regression line using the calcSlopeIntercept function.
- Deviation Channels: Adds standard deviation channels to the linear regression line to highlight potential support and resistance areas.
Settings
- Linear Regression Length: Specifies the length of the period for the linear regression calculation (default: 100).
- Linear Regression Source: Defines the data source for the linear regression calculation, such as close price, open price, etc. (default: close).
- Linear Regression Color: Sets the color of the linear regression line (default: gray).
- Show Price Labels: Option to display price labels on the horizontal lines (default: true).
How to Use
- Set the Linear Regression Length to define the period for regression calculation.
- Choose the Linear Regression Source to specify the price data (e.g., close, open).
- Enable or disable Show Price Labels based on whether you want to see price labels on the horizontal lines.
This Indicator helps identify dynamic support and resistance levels and potential market turning points.
Trend Maestro - Linear Regression & Volatility BandsTrend Maestro - Linear Regression & Volatility Bands
Description:
The "Trend Maestro - Linear Regression & Volatility Bands" indicator is meticulously designed to provide traders with a clear understanding of market trends through the application of linear regression techniques and enhanced market data visualization. This tool is essential for traders looking to interpret long-term trends and market stability. Here's how the indicator functions and what makes it a unique addition to your trading toolkit:
1. Linear Regression Calculation:
At the heart of this indicator lies the linear regression calculation, which identifies the primary trend direction over a specified period. It does this by computing a line of best fit through the closing prices, helping to smooth out price fluctuations and highlight the prevailing trend direction. Users have the flexibility to adjust both the length of the regression and the offset period, enabling them to tailor the indicator's responsiveness to different market conditions.
2. Visualization Through Volatility Bands:
The volatility bands, plotted at half, one, two, and three standard deviations around the linear regression line, serve primarily as a visualization tool rather than a basis for investment decisions.
These bands:
Measure the dispersion of price from the trend line, providing a graphical representation of volatility.
Help traders visually assess the market's stability and the reliability of the current trend, with broader bands indicating higher volatility and narrower bands suggesting more stability.
3. Customization Features:
The indicator offers customization options including toggle switches for bar color and the display of SD bands, enhancing visual clarity. These settings allow traders to personalize the display according to their visual preferences and analysis requirements.
By incorporating these elements, the "Trend Maestro - Linear Regression & Volatility Bands" indicator offers a framework for understanding market trends through both quantitative calculations and qualitative visual aids. This makes it a valuable tool for those looking to make informed decisions based on longer-term market observations.
Linear Regression Channel [GOODY]Linear Regression Channel
The Linear Regression Channel indicator is a versatile tool for traders, providing valuable insights into price trends and potential reversal points. It plots two linear regression channels on the chart, helping you visualize price dynamics and make informed trading decisions.
Indicator Features and Settings
General Settings:
• Source: The price source used for channel calculations. Typically, the close price is used.
1st Channel Settings:
• Length: The number of bars used to calculate the linear regression channel. Increasing this value widens the channel and makes it less responsive to recent price changes.
• Upper Deviation Multiplier: Multiplier for the upper deviation from the regression line. Higher values widen the upper boundary.
• Lower Deviation Multiplier: Multiplier for the lower deviation from the regression line. Higher values widen the lower boundary.
• Show Channel Lines: Toggle to show or hide the channel lines, useful for visualizing channel boundaries.
• Show Channel Background: Toggle to show or hide the background color between the channel lines, highlighting the area covered by the channel.
• Show Labels: Toggle to show or hide price level labels for the channel lines, helping to identify exact price levels at the boundaries.
• Upper Label Color: Color for the upper price level label.
• Lower Label Color: Color for the lower price level label.
• Label Offset: Offset for the price level labels, adjusting them horizontally.
1st Channel Display Settings:
• Extend Lines Left: Extend the regression channel lines to the left of the chart, visualizing historical performance.
• Extend Lines Right: Extend the regression channel lines to the right of the chart, anticipating future price movements.
1st Channel Style Settings:
• Upper 1st Channel Line Color: Color for the upper line of the first channel.
• Lower 1st Channel Line Color: Color for the lower line of the first channel.
• Upper Channel Color: Color for the upper channel area, filling the area between the upper channel line and the midline.
• Lower Channel Color: Color for the lower channel area, filling the area between the lower channel line and the midline.
• Baseline Color (DownTrend): Color of the baseline during a downtrend.
• Baseline Color (Up Trend): Color of the baseline during an uptrend.
2nd Channel Settings:
• Length for 2nd Channel: The number of bars used to calculate the second linear regression channel.
• Upper Deviation Multiplier for 2nd Channel: Multiplier for the upper deviation from the regression line in the second channel.
• Lower Deviation Multiplier for 2nd Channel: Multiplier for the lower deviation from the regression line in the second channel.
2nd Channel Display Settings:
• Show 2nd Channel Lines: Toggle to show or hide the second channel lines, useful for visualizing channel boundaries.
• Show 2nd Channel Background: Toggle to show or hide the background color between the second channel lines, highlighting the area covered by the second channel.
2nd Channel Style Settings:
• Upper 2nd Channel Color: Color for the upper line of the second channel.
• Lower 2nd Channel Color: Color for the lower line of the second channel.
• Baseline Color for 2nd Channel (Up Trend): Color of the baseline during an uptrend in the second channel.
• Baseline Color for 2nd Channel (Down Trend): Color of the baseline during a downtrend in the second channel.
• Upper 2nd Channel Background Color: Background color for the upper part of the second channel, filling the area between the upper channel line and the midline.
• Lower 2nd Channel Background Color: Background color for the lower part of the second channel, filling the area between the lower channel line and the midline.
• Line Style for 2nd Channel: Choose the style of the second channel lines (Solid, Dotted, Dashed, Arrow, Round).
2nd Channel Line Settings:
• Extend 2nd Channel Lines Left: Extend the second channel lines to the left of the chart, visualizing historical performance.
• Extend 2nd Channel Lines Right: Extend the second channel lines to the right of the chart, anticipating future price movements.
Other Settings:
• Show VWAP Detection: Toggle to enable or disable VWAP detection. VWAP (Volume Weighted Average Price) indicates the average price of the asset, weighted by volume.
• Show Doji Detection: Toggle to enable or disable Doji candle detection. Doji candles have small bodies, indicating market indecision.
• Doji Size Threshold: Threshold to determine a Doji candle. A smaller value indicates a stricter Doji definition.
How to Read the Indicator for Trading
Channel Lines and Colors:
• The upper line of the 1st channel (green) and the 2nd channel (blue) represents the upper boundary based on linear regression and deviation multipliers.
• The lower line of the 1st channel (red) and the 2nd channel (orange) represents the lower boundary.
• The midline changes color dynamically based on the trend direction:
• Pink during a downtrend for the 1st channel.
• Blue during an uptrend for the 1st channel.
• Gray during a consolidation for both channels.
• The 2nd channel uses similar color logic.
Channel Background:
• The background color between the channel lines highlights the area covered by the channel:
• Green for the upper area and red for the lower area in the 1st channel.
• Blue and orange for the upper and lower areas in the 2nd channel, respectively.
Labels:
• Price level labels at the channel boundaries provide exact price levels, displayed at the upper and lower lines if enabled.
VWAP and Doji Detection:
• VWAP is plotted as circles on the chart, showing the volume-weighted average price.
• Doji candles are highlighted with a background color if detected, indicating potential market indecision.
Alerts:
• Alerts are triggered when the trend direction of the channels changes. For example:
• An alert notifies you if the 1st channel is in an uptrend while the 2nd channel is in a downtrend.
• An alert notifies you if the 1st channel is in a downtrend while the 2nd channel is in an uptrend.
Trading with the Indicator
• Trend Identification: Use the color and direction of the midline and baseline to identify the current trend. An uptrend is indicated by a blue midline, while a downtrend is indicated by a pink midline.
• Reversal Points: Monitor when the price approaches the upper or lower boundaries of the channels, as these can act as support or resistance levels.
• Volume Insights: Use the VWAP and liquidity levels to understand the true average price based on volume and identify significant areas of trading activity.
• Market Indecision: Watch for Doji candles, which can signal potential reversals or periods of consolidation.
Linear Regression Trendline - Log, R-Squared, Dynamic RangeDescription:
This Pine Script tool is specifically crafted for in-depth technical analysis, integrating a logarithmic regression trendline with standard deviation (STDV) channel bands and the R-squared coefficient of determination. This sophisticated tool is tailored to provide a nuanced perspective on trend dynamics and volatility, particularly suitable for markets where changes are exponential rather than linear.
Key Features:
Logarithmic Regression Trendline: Uniquely employs a logarithmic approach to regression analysis, ideal for data that grows exponentially. This method emphasizes proportional changes and offers a more accurate fit for certain types of financial data.
STDV Channel Bands: Incorporates channel bands set at one or more standard deviations from the regression line. These bands offer insights into the volatility and relative price movements, aiding in the identification of potential buy and sell zones.
R-squared Coefficient: This tool differentiates itself by focusing on the R-squared coefficient of determination rather than Pearson's correlation coefficient. The R-squared value measures the proportion of variance in the dependent variable that is predictable from the independent variable, offering a more precise evaluation of the trendline’s effectiveness.
Flexible Period Settings: Unlike traditional tools, this script allows users to specify exact start and end points for the trendline analysis, either through direct date selection or by choosing specific bars. This flexibility facilitates precise modifications and adaptations to various analytical needs.
Interactive Usability: Features interactive capabilities allowing users to manually adjust the coordinates of the trendline’s start and end points during active sessions. This feature ensures that analysts can dynamically respond to market movements and adjust their analyses in real time.
Logarithmic Scaling: Specifically designed for logarithmic scaling, this tool is adept at handling data where growth rates are multiplicative, making it exceptionally useful in sectors like cryptocurrencies and rapidly growing stocks.
Usage:
This tool is ideal for traders and financial analysts who deal with high growth markets or any datasets where growth is exponential rather than linear. The focus on the R-squared coefficient enhances its utility by providing a critical assessment tool for evaluating the predictive strength and reliability of trends under logarithmic transformations.
