Autocorrelation Price Forecasting [The Quant Science]Discover how to predict future price movements using autocorrelation and linear regression models to identify potential trading opportunities.
An advanced model to predict future price movements using autocorrelation and linear regression. This script helps identify recurring market cycles and calculates potential gains, with clear visual signals for quick and informed decisions.
Main function
This script leverages an autocorrelation model to estimate the future price of an asset based on historical price relationships. It also integrates linear regression on percentage returns to provide more accurate predictions of price movements.
Insights types
1) Red label on a green candle: Bearish forecast and swing trading opportunity.
2) Red label on a red candle: Bearish forecast and trend-following opportunity.
3) Green label on a red candle: Bullish forecast and swing trading opportunity.
4) Green label on a green candle: Bullish forecast and trend-following opportunity.
IMPORTANT!
The indicator displays a future price forecast. When negative, it estimates a future price drop.
When positive, it estimates a future price increase.
Key Features
Customizable inputs
Analysis Length: number of historical bars used for autocorrelation calculation. Adjustable between 1 and 200.
Forecast Colors: customize colors for bullish and bearish signals.
Visual insights
Labels: hypothetical gains or losses are displayed as labels above or below the bars.
Dynamic coloring: bullish (green) and bearish (red) signals are highlighted directly on the chart.
Forecast line: A continuous line is plotted to represent the estimated future price values.
Practical applications
Short-term Trading: identify repetitive market cycles to anticipate future movements.
Visual Decision-making: colored signals and labels make it easier to visualize potential profit or loss for each trade.
Advanced Customization: adjust the data length and colors to tailor the indicator to your strategies.
Limitations
Prediction price models have some limitations. Trading decisions should be made with caution, considering additional market factors and risk management strategies.
線性回歸(LR)
Segment RegressionAs an example of the descriptive power of Pine Script, this very short example traces a 'segment regression', a result not entirely obvious with so few lines of code, repositioning them when the previous inference moves away from the graph beyond the pre-set limit.
A trick used is to restart the new inference segment
- from the maximum reached in the previous trend, when positive (slope>0)
- from the minimum reached in the previous trend, when negative (slope<0)
The result can in my opinion be easily used to build strategies.
[Excalibur] Advanced Polynomial Regression Trend ChannelIt's been a long time coming... Regression channel enthusiasts, it's 'ultimately' here! Welcome to my Apophis page. But first, let me explain the origins of its attributed name blending both descriptive & engaging content with concise & technical topics...
EGYPTIAN ROOTED TALES:
Apophis (Greek) or Apep (Egyptian) was known by many cultures to be a mighty Egyptian archetype of chaos, darkness, and destruction. In ancient Egyptian mythology, Apophis was often depicted in the form of a fearsome menacing serpent, in those days, with an insatiable appetite for relentless malevolence. This dreaded entity was considered a formidable enemy and was also believed to appear as a giant serpent arising from the underworld.
Forever engaging in eternal battle, according to lore, Apophis' adversarial attributes represented the forces of disorder and anarchy clashing with the forces of order and harmony. This serpent's wickedly described figure was significantly symbolic of the disruptive, treacherous powers that Apophis embodied, those which threatened to plunge the perceivable archaic world into darkness. To the ancients, the legendary cyclical struggles against Apophis served as allegory reflecting on the macrocosm of the larger conflict between good and evil disparities that shaped early ancient civilization, much like the tree serpent.
One of Apophis’ mythological roots was immortally depicted on tomb stone. On one particular hieroglyphic wall tableau, in the second chamber of Inherkau’s tomb at Deir el-Medina, within the Theban Necropolis, portrays a mural of a serpent (Apep) under an edible fruit tree being slain in defeat. The species of snake depicted on various locations of tomb walls appears to me to bear a striking resemblance to the big eyed Echis pyramidum (Egyptian saw-scaled viper) native to regions of North Africa and the Middle East. It's a species of viper notoriously contributing to the most snake bite fatalities in the world still to this day; talk about a true harbinger of chaos incarnate. You do NOT want to cross paths with this asp in the dark of night, ever! Nor the other species of Echis found around Echid trees in the garden.
As we all know, fabled archaic storytelling can be misconstruing. Yet, these archaic serpent narratives still have echoes of significant notions and wisdom to learn from, especially in a modern technological society still rife with miscalculating deep snakes slithering about with intent to specifically plot disorder on national scales, and then profitably capitalize on it. Many deep black snakes are hiding in plain sight and under rocks. They do indeed speak and spell with forked tongues and malfeasance to the masses. I have great news. Tools now exist in the realms of AI combined with fractal programming circles to uncover these venomous viper mesh networks and investigatively monitor their subversive activities, so their days are surely numbered for... GAME OVER. Prepare to meet the doom you vain vipers have sought!
The arrival of the great and powerful international storm of the century has come, clothed in vindication. It's the only just way for the globe to clean house and move forward economically into the evolving herafter unobstructed by rampant evils and corruption. The foundations of future architectures are being established, and these nefarious obstacles MUST NOT hinder that path ahead.
With my former days of serpent wrangling being behind me, I now explore avenues of history, philosophy, programming, and mathematics, weaving them all into my daily routine. Now is the time to make some mathematical history unfold and get to the good and spicy stuff that you as the reader seek...
CALCULATING ON CHAOS:
Perhaps frightful characteristics of serpents (their maneuverability to adapt to any swervy situation) could be harnessed and channeled into a powerful tool for navigating the treacherous waters of data chaos. What if taming a monstrous beast of mayhem was not only possible, but fully achievable? Well, I think I have improved upon an approach to better tackle fractal chaos handling and observation within a modest PSv6 float environment without doubles. Finally, I've successfully turned my pet anaconda, Apophis, into a docile form of mathematical charting resilience beyond anything I have ever visually witnessed before. This novel work clearly deprecates ALL of my prior regression works by performing everything those delivered AND more, but it doesn't necessarily eliminate them into extinction.
INTRODUCTION:
Allow me to introduce Apophis! What you see showcased above is also referred to as 'Advanced Polynomial Regression Trend Channel' (APRTC) for technical minds. I would describe it as an avant-garde trend channel obtaining accurate polynomial approximations on market data with Pine v6.0. APRTC is a fractal following demystifier that I can only describe as being a signal trajectory tracking stalker manifesting as a data devouring demon. My full-fledged 'Excalibur' version of poly-regression swiftly captures undulating patterns present in market data with ease and at warp speed faster than you can blink. Now unchained, this is my rendering of polynomial wrath employing the "Immense Power of Pine".
By pushing techniques of regression to extremes, I am able to trace the serpentine trajectory of chaos up to a 50th order with 100s or 1000s of samples via "advanced polynomial regression" (APR), aka Apophis. This uniquely reactive trend channel method is designed to enhance the way we engage with the complex challenge of observably interpreting chaotic price behavior. While this is the end of the road for my revolutionary trend channel technology, that doesn't imply that future polynomial regression upgrades won't/might occur... There are a number of other supplementary concepts I have in my mind that could potentially prove useful eventually, who knows. However, for the moment, I feel it's wisest to monitor how accommodating APRTC is towards servers for the present time.
HISTORICAL ENDEAVORS:
Having wrangled countless wild serpents in my youth by the handfuls, tackling this was one multi-headed regression challenge temptation I couldn't resist. Besides, serpents in reality are more than often scared of us in the wild, so I assumed this shouldn't be too terribly hard. Wrong! It's been a complex struggle indeed. APRTC gave me many stinging bites for a LONG time. I had unknowingly opened Pandora's box of polynomials unprepared for what was to follow.
Long have I wrestled with Apophis throughout many nights for years with adversity, at last having arrived at a current grand solution and ultimately emerging victorious. Now, does the significance of the entitled name Apophis become more apparent at this point of reading? What you can now witness above is a very powerful blend of precision combined with maneuverability, concluding my dreamy expectations of a maximal experience with polynomial regression in TV charts. With all of my wizardry components finally assembled, Apophis genuinely is the most phenomenal indicator I ever devised in my life... as of yet.
How was this accomplished? By unlocking a deep understanding of the mathematical principles that govern regression, combined with an arsenal of mathemagical trickeries through sheer determination. I also spent an incredible amount of time flexing the unbendable 64bit float numerics to obtain a feasible order/degree of up to 50 polynomials or up to 4000 bars of regression (never simultaneously) on a labyrinth of samples. Lastly, what was needed was a pinch of mathematical pixie dust with a pleasant dose of Pine upgrades (lots of line re-drawings) that millions of other members can also utilize. Thank you so much, Pine developers, for once again turning meager proposed visions into materialized reality by leveraging the "Power of Pine" for the many!
DESCRIBING POLYNOMIAL REGRESSION:
APRTC is a visual guide for navigating noisy markets, providing both trajectory and structure through the power of mathematical modeling. Polynomial regression, especially at higher orders, exhibits obvious sidewinder/serpentine like characteristics. Even the channel extremities, on swift one second charts, resemble scales in motion with a pair of dashed exterior lines. This poly version presently yields the best quality of fit, providing an extreme "visual analysis" of your price action in high noise environments. The greater the order of the polynomial, the more pronounced the meandering regression characteristics become, as the algorithm strives to visually capture the fundamental fractal patterns most effectively.
Polynomial Regression in Action:
The medial line displays the core polynomial regression approximation in similarity to spinal backbones of serpents when following the movements of market data. Encasing the central structure, the channel's skin consists of enveloping lines having upper and lower extremes. To further enhance visualization, background fill colors distinguish the breadth between positive and negative territories of potential movement.
Additional internal dotted variability lines are available with multiple customizable settings to adjust dynamic dispersion, color, etc. One other exciting feature I added is the the ability to see the polynomial values with up to 50 (adjustable) decimal places if available. Witnessing Xⁿ values tapering near to 0.0 may indicate overfitting. Linear regression is available at order=1 and quadratic regression is invoked using order=2.
Information Criterion:
A toggleable label provides a multitude of information such as Bayesian Information Criterion (BIC), order, period, etc. BIC serves as an polynomial regression fit metric, with lesser values indicating a better balance between polynomial order adjustments, reflecting a more accurate fit in relation to the channel's girth. One downside of BIC values is their often large numerical values, making visual comparisons challenging, and then also their rare occurrence as negative values.
Furthermore, I formulated my own "EXPERIMENTAL" Simpler Information Criterion (SIC) fit metric, which seems to offer better visual interpretability when adjusting order settings on a selected regression period, especially on minuscule price numerics. Positive valued SIC numerics with lesser digits also reflect a preferred better fit during order adjustment, same as applying BIC principles of the minimum having a superior calulation tendency. I'll let members be the judge of deciding whether my SIC is actually a superior information criterion compared to BIC.
TECHNICAL INTERPRETATION and APPLICATION:
The Apophis indicator utilizes high-order polynomial regression, up to a maximum 50th order ability to deliver a nuanced, visual representation of complex market dynamics. I would caution against using upwards toward a 50th order, because opting for a 50th order polynomial is categorically speaking "wildly unsane" in real-world practice. As the polynomial degree increases from lesser orders, the regression line exhibits more pronounced curvature and undulations.
Visually analyzing the regression curve can provide insights into prevailing trends, as well as volatility regimes. For example, a gently sloping line may signal a steady directional trend, while a tightly curled oscillating curve may indicate heightened volatility and range-bound trading. Settings are rather straight forward, and comparable to my former "Quadratic Regression Trend Channel" efforts, although one torturous feature from QRTC is omitted due too computational complexity concerns.
Notice: Trial invite only access will not be granted for this indicator. Those who are familiar with recognizing what APRTC is, you will either want it or not, to add to your arsenal of trading approaches.
When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members , I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
RISK DISCLAIMER:
My scripts and indicators are specifically intended for informational and educational use only. This script uses historical data points to perform calculations to derive real-time calculations. They do not infer, indicate, or guarantee future results or performance.
By utilizing this script/indicator or any portion of it, you agree to accept 100% responsibly and liability for your investment or financial decisions, and I will not be held liable for your subjective analytic interpretations incurring sustained monetary losses. The opinions and information visual or otherwise provided by this script/indicator is not investment advice, nor does it constitute recommendation.
Xmaster Formula Indicator [TradingFinder] No Repaint Strategies🔵 Introduction
The Xmaster Formula Indicator is a powerful tool for forex trading, combining multiple technical indicators to provide insights into market trends, support and resistance levels, and price reversals. Developed in the early 2010s, it is widely valued for generating reliable buy and sell signals.
Key components include Exponential Moving Averages (EMA) for identifying trends and price momentum, and MACD (Moving Average Convergence Divergence) for analyzing trend strength and direction.
The Stochastic Oscillator and RSI (Relative Strength Index) enhance accuracy by signaling potential price reversals. Additionally, the Parabolic SAR assists in identifying trend reversals and managing risk.
By integrating these tools, the Xmaster Formula Indicator provides a comprehensive view of market conditions, empowering traders to make informed decisions.
🔵 How to Use
The Xmaster Formula Indicator offers two distinct methods for generating signals: Standard Mode and Advance Mode. Each method caters to different trading styles and strategies.
Standard Mode :
In Standard Mode, the indicator uses normalized moving average data to generate buy and sell signals. The difference between the short-term (10-period) and long-term (38-period) EMAs is calculated and normalized to a 0-100 scale.
Buy Signal : When the normalized value crosses above 55, accompanied by the trend line turning green, a buy signal is generated.
Sell Signal : When the normalized value crosses below 45, and the trend line turns red, a sell signal is issued.
This mode is simple, making it ideal for traders looking for straightforward signals without the need for additional confirmations.
Advance Mode :
Advance Mode combines multiple technical indicators to provide more detailed and robust signals.
This method analyzes trends by incorporating :
🟣 MACD
Buy Signal : When the MACD histogram bars are positive.
Sell Signal : When the MACD histogram bars are negative.
🟣 RSI
Buy Signal : When RSI is below 30, indicating oversold conditions.
Sell Signal : When RSI is above 70, suggesting overbought conditions.
🟣 Stochastic Oscillator
Buy Signal : When Stochastic is below 20.
Sell Signal : When Stochastic is above 80.
🟣 Parabolic SAR
Buy Signal : When SAR is below the price.
Sell Signal : When SAR is above the price.
A signal is generated in Advance Mode only when all these indicators align :
Buy Signal : All conditions point to a bullish trend.
Sell Signal : All conditions indicate a bearish trend.
This mode is more comprehensive and suitable for traders who prefer deeper analysis and stronger confirmations before executing trades.
🔵 Settings
Method :
Choose between "Standard" and "Advance" modes to determine how signals are generated. In Standard Mode, signals are based on normalized moving average data, while in Advance Mode, signals rely on the combination of MACD, RSI, Stochastic Oscillator, and Parabolic SAR.
Moving Average Settings :
Short Length : The period for the short-term EMA (default is 10).
Mid Length : The period for the medium-term EMA (default is 20).
Long Length : The period for the long-term EMA (default is 38).
MACD Settings :
Fast Length : The period for the fast EMA in the MACD calculation (default is 12).
Slow Length : The period for the slow EMA in the MACD calculation (default is 26).
Signal Line : The signal line period for MACD (default is 9).
Stochastic Settings :
Length : The period for the Stochastic Oscillator (default is 14).
RSI Settings :
Length : The period for the Relative Strength Index (default is 14).
🔵 Conclusion
The Xmaster Formula Indicator is a versatile and reliable tool for forex traders, offering both simplicity and advanced analysis through its Standard and Advance modes. In Standard Mode, traders benefit from straightforward signals based on normalized moving average data, making it ideal for quick decision-making.
Advance Mode, on the other hand, provides a more detailed analysis by combining multiple indicators like MACD, RSI, Stochastic Oscillator, and Parabolic SAR, delivering stronger confirmations for critical market decisions.
While the Xmaster Formula Indicator offers valuable insights and reliable signals, it is important to use it alongside proper risk management and other analytical methods. By leveraging its capabilities effectively, traders can enhance their trading strategies and achieve better outcomes in the dynamic forex market.
NVOL Normalized Volume & VolatilityOVERVIEW
Plots a normalized volume (or volatility) relative to a given bar's typical value across all charted sessions. The concept is similar to Relative Volume (RVOL) and Average True Range (ATR), but rather than using a moving average, this script uses bar data from previous sessions to more accurately separate what's normal from what's anomalous. Compatible on all timeframes and symbols.
Having volume and volatility processed within a single indicator not only allows you to toggle between the two for a consistent data display, it also allows you to measure how correlated they are. These measurements are available in the data table.
DATA & MATH
The core formula used to normalize each bar is:
( Value / Basis ) × Scale
Value
The current bar's volume or volatility (see INPUTS section). When set to volume, it's exactly what you would expect (the volume of the bar). When set to volatility, it's the bar's range (high - low).
Basis
A statistical threshold (Mean, Median, or Q3) plus a Sigma multiple (standard deviations). The default is set to the Mean + Sigma × 3 , which represents 99.7% of data in a normal distribution. The values are derived from the current bar's equivalent in other sessions. For example, if the current bar time is 9:30 AM, all previous 9:30 AM bars would be used to get the Mean and Sigma. Thus Mean + Sigma × 3 would represent the Normal Bar Vol at 9:30 AM.
Scale
Depends on the Normalize setting, where it is 1 when set to Ratio, and 100 when set to Percent. This simply determines the plot's scale (ie. 0 to 1 vs. 0 to 100).
INPUTS
While the default configuration is recommended for a majority of use cases (see BEST PRACTICES), settings should be adjusted so most of the Normalized Plot and Linear Regression are below the Signal Zone. Only the most extreme values should exceed this area.
Normalize
Allows you to specify what should be normalized (Volume or Volatility) and how it should be measured (as a Ratio or Percentage). This sets the value and scale in the core formula.
Basis
Specifies the statistical threshold (Mean, Median, or Q3) and how many standard deviations should be added to it (Sigma). This is the basis in the core formula.
Mean is the sum of values divided by the quantity of values. It's what most people think of when they say "average."
Median is the middle value, where 50% of the data will be lower and 50% will be higher.
Q3 is short for Third Quartile, where 75% of the data will be lower and 25% will be higher (think three quarters).
Sample
Determines the maximum sample size.
All Charted Bars is the default and recommended option, and ignores the adjacent lookback number.
Lookback is not recommended, but it is available for comparisons. It uses the adjacent lookback number and is likely to produce unreliable results outside a very specific context that is not suitable for most traders. Normalization is not a moving average. Unless you have a good reason to limit the sample size, do not use this option and instead use All Charted Bars .
Show Vol. name on plot
Overlays "VOLUME" or "VOLATILITY" on the plot (whichever you've selected).
Lin. Reg.
Polynomial regressions are great for capturing non-linear patterns in data. TradingView offers a "linear regression curve", which this script uses as a substitute. If you're unfamiliar with either term, think of this like a better moving average.
You're able to specify the color, length, and multiple (how much to amplify the value). The linear regression derives its value from the normalized values.
Norm. Val.
This is the color of the normalized value of the current bar (see DATA & MATH section). You're able to specify the default, within signal, and beyond signal colors. As well as the plot style.
Fade in colors between zero and the signal
Programmatically adjust the opacity of the primary plot color based on it's normalized value. When enabled, values equal to 0 will be fully transparent, become more opaque as they move away from 0, and be fully opaque at the signal. Adjusting opacity in this way helps make difference more obvious.
Plot relative to bar direction
If enabled, the normalized value will be multiplied by -1 when a bar's open is greater than the bar's close, mirroring price direction.
Technically volume and volatility are directionless. Meaning there's really no such thing as buy volume, sell volume, positive volatility, or negative volatility. There is just volume (1 buy = 1 sell = 1 volume) and volatility (high - low). Even so, visually reflecting the net effect of pricing pressure can still be useful. That's all this setting does.
Sig. Zone
Signal zones make identifying extremes easier. They do not signal if you should buy or sell, only that the current measurement is beyond what's normal. You are able to adjust the color and bounds of the zone.
Int. Levels
Interim levels can be useful when you want to visually bracket values into high / medium / low. These levels can have a value anywhere between 0 and 1. They will automatically be multiplied by 100 when the scale is set to Percent.
Zero Line
This setting allows you to specify the visibility of the zero line to best suit your trading style.
Volume & Volatility Stats
Displays a table of core values for both volume and volatility. Specifically the actual value, threshold (mean, median, or Q3), sigma (standard deviation), basis, normalized value, and linear regression.
Correlation Stats
Displays a table of correlation statistics for the current bar, as well as the data set average. Specifically the coefficient, R2, and P-Value.
Indices & Sample Size
Displays a table of mixed data. Specifically the current bar's index within the session, the current bar's index within the sample, and the sample size used to normalize the current bar's value.
BEST PRACTICES
NVOL can tell you what's normal for 9:30 AM. RVOL and ATR can only tell you if the current value is higher or lower than a moving average.
In a normal distribution (bell curve) 99.7% of data occurs within 3 standard deviations of the mean. This is why the default basis is set to "Mean, 3"; it includes the typical day-to-day fluctuations, better contextualizing what's actually normal, minimizing false positives.
This means a ratio value greater than 1 only occurs 0.3% of the time. A series of these values warrants your attention. Which is why the default signal zone is between 1 and 2. Ratios beyond 2 would be considered extreme with the default settings.
Inversely, ratio values less than 1 (the normal daily fluctuations) also tell a story. We should expect most values to occur around the middle 3rd, which is why interim levels default to 0.33 and 0.66, visually simplifying a given move's participation. These can be set to whatever you like and only serve as visual aids for your specific trading style.
It's worth noting that the linear regression oscillates when plotted directionally, which can help clarify short term move exhaustion and continuation. Akin to a relative strength index (RSI), it may be used to inform a trading decision, but it should not be the only factor.
Prediction Based on Linreg & Atr
We created this algorithm with the goal of predicting future prices 📊, specifically where the value of any asset will go in the next 20 periods ⏳. It uses linear regression based on past prices, calculating a slope and an intercept to forecast future behavior 🔮. This prediction is then adjusted according to market volatility, measured by the ATR 📉, and the direction of trend signals, which are based on the MACD and moving averages 📈.
How Does the Linreg & ATR Prediction Work?
1. Trend Calculation and Signals:
o Technical Indicators: We use short- and long-term exponential moving averages (EMA), RSI, MACD, and Bollinger Bands 📊 to assess market direction and sentiment (not visually presented in the script).
o Calculation Functions: These include functions to calculate slope, average, intercept, standard deviation, and Pearson's R, which are crucial for regression analysis 📉.
2. Predicting Future Prices:
o Linear Regression: The algorithm calculates the slope, average, and intercept of past prices to create a regression channel 📈, helping to predict the range of future prices 🔮.
o Standard Deviation and Pearson's R: These metrics determine the strength of the regression 🔍.
3. Adjusting the Prediction:
o The predicted value is adjusted by considering market volatility (ATR 📉) and the direction of trend signals 🔮, ensuring that the prediction is aligned with the current market environment 🌍.
4. Visualization:
o Prediction Lines and Bands: The algorithm plots lines that display the predicted future price along with a prediction range (upper and lower bounds) 📉📈.
5. EMA Cross Signals:
o EMA Conditions and Total Score: A bullish crossover signal is generated when the total score is positive and the short EMA crosses above the long EMA 📈. A bearish crossover signal is generated when the total score is negative and the short EMA crosses below the long EMA 📉.
6. Additional Considerations:
o Multi-Timeframe Regression Channel: The script calculates regression channels for different timeframes (5m, 15m, 30m, 4h) ⏳, helping determine the overall market direction 📊 (not visually presented).
Confidence Interpretation:
• High Confidence (close to 100%): Indicates strong alignment between timeframes with a clear trend (bullish or bearish) 🔥.
• Low Confidence (close to 0%): Shows disagreement or weak signals between timeframes ⚠️.
Confidence complements the interpretation of the prediction range and expected direction 🔮, aiding in decision-making for market entry or exit 🚀.
Español
Creamos este algoritmo con el objetivo de predecir los precios futuros 📊, específicamente hacia dónde irá el valor de cualquier activo en los próximos 20 períodos ⏳. Utiliza regresión lineal basada en los precios pasados, calculando una pendiente y una intersección para prever el comportamiento futuro 🔮. Esta predicción se ajusta según la volatilidad del mercado, medida por el ATR 📉, y la dirección de las señales de tendencia, que se basan en el MACD y las medias móviles 📈.
¿Cómo Funciona la Predicción con Linreg & ATR?
Cálculo de Tendencias y Señales:
Indicadores Técnicos: Usamos medias móviles exponenciales (EMA) a corto y largo plazo, RSI, MACD y Bandas de Bollinger 📊 para evaluar la dirección y el sentimiento del mercado (no presentados visualmente en el script).
Funciones de Cálculo: Incluye funciones para calcular pendiente, media, intersección, desviación estándar y el coeficiente de correlación de Pearson, esenciales para el análisis de regresión 📉.
Predicción de Precios Futuros:
Regresión Lineal: El algoritmo calcula la pendiente, la media y la intersección de los precios pasados para crear un canal de regresión 📈, ayudando a predecir el rango de precios futuros 🔮.
Desviación Estándar y Pearson's R: Estas métricas determinan la fuerza de la regresión 🔍.
Ajuste de la Predicción:
El valor predicho se ajusta considerando la volatilidad del mercado (ATR 📉) y la dirección de las señales de tendencia 🔮, asegurando que la predicción esté alineada con el entorno actual del mercado 🌍.
Visualización:
Líneas y Bandas de Predicción: El algoritmo traza líneas que muestran el precio futuro predicho, junto con un rango de predicción (límites superior e inferior) 📉📈.
Señales de Cruce de EMAs:
Condiciones de EMAs y Puntaje Total: Se genera una señal de cruce alcista cuando el puntaje total es positivo y la EMA corta cruza por encima de la EMA larga 📈. Se genera una señal de cruce bajista cuando el puntaje total es negativo y la EMA corta cruza por debajo de la EMA larga 📉.
Consideraciones Adicionales:
Canal de Regresión Multi-Timeframe: El script calcula canales de regresión para diferentes marcos de tiempo (5m, 15m, 30m, 4h) ⏳, ayudando a determinar la dirección general del mercado 📊 (no presentado visualmente).
Interpretación de la Confianza:
Alta Confianza (cerca del 100%): Indica una fuerte alineación entre los marcos temporales con una tendencia clara (alcista o bajista) 🔥.
Baja Confianza (cerca del 0%): Muestra desacuerdo o señales débiles entre los marcos temporales ⚠️.
La confianza complementa la interpretación del rango de predicción y la dirección esperada 🔮, ayudando en las decisiones de entrada o salida en el mercado 🚀.
Linear Regression Channel [TradingFinder] Existing Trend Line🔵 Introduction
The Linear Regression Channel indicator is one of the technical analysis tool, widely used to identify support, resistance, and analyze upward and downward trends.
The Linear Regression Channel comprises five main components : the midline, representing the linear regression line, and the support and resistance lines, which are calculated based on the distance from the midline using either standard deviation or ATR.
This indicator leverages linear regression to forecast price changes based on historical data and encapsulates price movements within a price channel.
The upper and lower lines of the channel, which define resistance and support levels, assist traders in pinpointing entry and exit points, ultimately aiding better trading decisions.
When prices approach these channel lines, the likelihood of interaction with support or resistance levels increases, and breaking through these lines may signal a price reversal or continuation.
Due to its precision in identifying price trends, analyzing trend reversals, and determining key price levels, the Linear Regression Channel indicator is widely regarded as a reliable tool across financial markets such as Forex, stocks, and cryptocurrencies.
🔵 How to Use
🟣 Identifying Entry Signals
One of the primary uses of this indicator is recognizing buy signals. The lower channel line acts as a support level, and when the price nears this line, the likelihood of an upward reversal increases.
In an uptrend : When the price approaches the lower channel line and signs of upward reversal (e.g., reversal candlesticks or high trading volume) are observed, it is considered a buy signal.
In a downtrend : If the price breaks the lower channel line and subsequently re-enters the channel, it may signal a trend change, offering a buying opportunity.
🟣 Identifying Exit Signals
The Linear Regression Channel is also used to identify sell signals. The upper channel line generally acts as a resistance level, and when the price approaches this line, the likelihood of a price decrease increases.
In an uptrend : Approaching the upper channel line and observing weakness in the uptrend (e.g., declining volume or reversal patterns) indicates a sell signal.
In a downtrend : When the price reaches the upper channel line and reverses downward, this is considered a signal to exit trades.
🟣 Analyzing Channel Breakouts
The Linear Regression Channel allows traders to identify price breakouts as strong signals of potential trend changes.
Breaking the upper channel line : Indicates buyer strength and the likelihood of a continued uptrend, often accompanied by increased trading volume.
Breaking the lower channel line : Suggests seller dominance and the possibility of a continued downtrend, providing a strong sell signal.
🟣 Mean Reversion Analysis
A key concept in using the Linear Regression Channel is the tendency for prices to revert to the midline of the channel, which acts as a dynamic moving average, reflecting the price's equilibrium over time.
In uptrends : Significant deviations from the midline increase the likelihood of a price retracement toward the midline.
In downtrends : When prices deviate considerably from the midline, a return toward the midline can be used to identify potential reversal points.
🔵 Settings
🟣 Time Frame
The time frame setting enables users to view higher time frame data on a lower time frame chart. This feature is especially useful for traders employing multi-time frame analysis.
🟣 Regression Type
Standard : Utilizes classical linear regression to draw the midline and channel lines.
Advanced : Produces similar results to the standard method but may provide slightly different alignment on the chart.
🟣 Scaling Type
Standard Deviation : Suitable for markets with stable volatility.
ATR (Average True Range) : Ideal for markets with higher volatility.
🟣 Scaling Coefficients
Larger coefficients create broader channels for broader trend analysis.
Smaller coefficients produce tighter channels for precision analysis.
🟣 Channel Extension
None : No extension.
Left: Extends lines to the left to analyze historical trends.
Right : Extends lines to the right for future predictions.
Both : Extends lines in both directions.
🔵 Conclusion
The Linear Regression Channel indicator is a versatile and powerful tool in technical analysis, providing traders with support, resistance, and midline insights to better understand price behavior. Its advanced settings, including time frame selection, regression type, scaling options, and customizable coefficients, allow for tailored and precise analysis.
One of its standout advantages is its ability to support multi-time frame analysis, enabling traders to view higher time frame data within a lower time frame context. The option to use scaling methods like ATR or standard deviation further enhances its adaptability to markets with varying volatility.
Designed to identify entry and exit signals, analyze mean reversion, and assess channel breakouts, this indicator is suitable for a wide range of markets, including Forex, stocks, and cryptocurrencies. By incorporating this tool into your trading strategy, you can make more informed decisions and improve the accuracy of your market predictions.
Linear Regression Channel Screener [Daveatt]Hello traders
First and foremost, I want to extend a huge thank you to @LonesomeTheBlue for his exceptional Linear Regression Channel indicator that served as the foundation for this screener.
Original work can be found here:
Overview
This project demonstrates how to transform any open-source indicator into a powerful multi-asset screener.
The principles shown here can be applied to virtually any indicator you find interesting.
How to Transform an Indicator into a Screener
Step 1: Identify the Core Logic
First, identify the main calculations of the indicator.
In our case, it's the Linear Regression
Channel calculation:
get_channel(src, len) =>
mid = math.sum(src, len) / len
slope = ta.linreg(src, len, 0) - ta.linreg(src, len, 1)
intercept = mid - slope * math.floor(len / 2) + (1 - len % 2) / 2 * slope
endy = intercept + slope * (len - 1)
dev = 0.0
for x = 0 to len - 1 by 1
dev := dev + math.pow(src - (slope * (len - x) + intercept), 2)
dev
dev := math.sqrt(dev / len)
Step 2: Use request.security()
Pass the function to request.security() to analyze multiple assets:
= request.security(sym, timeframe.period, get_channel(src, len))
Step 3: Scale to Multiple Assets
PineScript allows up to 40 request.security() calls, letting you monitor up to 40 assets simultaneously.
Features of This Screener
The screener provides real-time trend detection for each monitored asset, giving you instant insights into market movements.
It displays each asset's position relative to its middle regression line, helping you understand price momentum.
The data is presented in a clean, organized table with color-coded trends for easy interpretation.
At its core, the screener performs trend detection based on regression slope calculations, clearly indicating whether an asset is in a bullish or bearish trend.
Each asset's price is tracked relative to its middle regression line, providing additional context about trend strength.
The color-coded visual feedback makes it easy to spot changes at a glance.
Built-in alerts notify you instantly when any asset experiences a trend change, ensuring you never miss important market moves.
Customization Tips
You can easily expand the screener by adding more symbols to the symbols array, adapting it to your watchlist.
The regression parameters can be adjusted to match your preferred trading timeframes and sensitivity.
The alert system is already configured to notify you of trend changes, but you can customize the alert messages and conditions to your needs.
Limitations
While powerful, the screener is bound by PineScript's limitation of 40 security calls, capping the maximum number of monitored assets.
Using AI to Help With Conversion
An interesting tip:
You can use AI tools to help convert single-asset indicators to screeners.
Simply provide the original code and ask for assistance in transforming it into a screener format. While the AI output might need some syntax adjustments, it can handle much of the heavy lifting in the conversion process.
Prompt (example) : " Please make a pinescript version 5 screener out of this indicator below or in attachment to scan 20 instruments "
I prefer Claude AI (Opus model) over ChatGPT for pinescript.
Conclusion
This screener transformation technique opens up endless possibilities for market analysis.
By following these steps, you can convert any indicator into a powerful multi-asset scanner, enhancing your trading toolkit significantly.
Remember: The power of a screener lies not just in monitoring multiple assets, but in applying consistent analysis across your entire watchlist in real-time.
Feel free to fork and modify this screener for your own needs.
Happy trading! 🚀📈
Daveatt
Pearson's R TrendPearson's R Trend Indicator
Overview
The Pearson's R Trend Indicator is an advanced technical analysis tool that measures the strength and direction of price trends using statistical correlation. By comparing fast and slow-period Pearson correlation coefficients, this indicator helps identify trend momentum, potential reversals, and overbought/oversold conditions.
Key Features
Dual timeframe correlation analysis (Fast and Slow periods)
Signal line with crossover alerts
Dynamic histogram for trend visualization
Configurable overbought/oversold levels
Multiple visual components with customizable colors
Comprehensive alert system
Technical Details
Core Calculations
Fast R: Calculates Pearson's correlation coefficient over the faster period (default: 21 periods)
Slow R: Calculates Pearson's correlation coefficient over the slower period (default: 34 periods)
R-Diff: Normalized difference between Fast and Slow R values (range: -1 to 1)
Signal Line: Smoothed average of R-Diff (default: 3-period SMA)
Histogram: Difference between R-Diff and Signal Line
Signal Generation
Zero Line Crosses
• Bullish: R-Diff crosses above zero
• Bearish: R-Diff crosses below zero
Signal Line Crosses
• Bullish: R-Diff crosses above Signal Line
• Bearish: R-Diff crosses below Signal Line
Overbought/Oversold Conditions
• Overbought: R-Diff above 0.45 (default)
• Oversold: R-Diff below -0.45 (default)
Customization Options
Calculation Settings
Fast Length: Period for fast correlation calculation (default: 21)
Slow Length: Period for slow correlation calculation (default: 34)
Signal Smoothing: Moving average period for signal line (default: 3)
Price Source: Configurable input source (default: close)
Visual Settings
Histogram display toggle
Signal line display toggle
Crossing markers toggle
Line width adjustments
Custom color schemes for bullish/bearish signals
Configurable overbought/oversold levels
Interpretation
Bullish Signals
R-Diff crossing above zero
R-Diff crossing above signal line
R-Diff emerging from oversold territory
Positive histogram expansion
Bearish Signals
R-Diff crossing below zero
R-Diff crossing below signal line
R-Diff dropping from overbought territory
Negative histogram expansion
Alert Conditions
Zero line crosses (both directions)
Signal line crosses (both directions)
Overbought zone entry
Oversold zone entry
Best Practices
Use multiple timeframe analysis for confirmation
Combine with volume and price action analysis
Watch for divergences between R-Diff and price
Pay attention to histogram momentum changes
Use overbought/oversold levels as dynamic support/resistance zones
Notes
The indicator uses statistical correlation to measure trend strength
Normalized calculations ensure consistent -1 to 1 range
Built-in alert system for all major signal types
Customizable visual elements for optimal chart analysis
Disclaimer
This technical indicator is designed to supplement your trading analysis and should not be used as the sole decision-making tool. Always combine with other forms of analysis and proper risk management techniques.
Adaptive Linear Regression ChannelOverview
The Adaptive Linear Regression Channel Script is an advanced, multi-functional trading tool crafted to help traders pinpoint market trends, identify potential reversals, assess volatility, and establish dynamic levels for profit-taking and position exits. By incorporating key concepts such as linear regression , standard deviation , and other volatility measures like the ATR , the script offers a comprehensive view of market behavior beyond traditional deviation metrics.
This dynamic model continuously adapts to changing market conditions, adjusting in real-time to provide clear visualizations of trends, channels, and volatility levels. This adaptability makes the script invaluable for both trend-following and counter-trend strategies, giving traders the flexibility to respond effectively to different market environments.
Background
What is Linear Regression?
Definition : Linear regression is a statistical technique used to model the relationship between a dependent variable (target) and one or more independent variables (predictors).
In its simplest form (simple linear regression), the relationship between two variables is represented by a straight line (the regression line).
y = mx + b
where :
- y is the target variable (price)
- m is the slope
- x is the independent variable (time)
- b is the intercept
Slope of the Regression Line
Definition: The slope (m) measures the rate at which the dependent variable (y) changes as the independent variable (x) changes.
Interpretation:
- A positive slope indicates an uptrend.
- A negative slope indicates a downtrend.
Uses in Trading:
- Identifying the strength and direction of market trends.
- Assessing the momentum of price movements.
R-squared (Coefficient of Determination)
Definition: A measure of how well the regression line fits the data, ranging from 0 to 1.
Calculation :
R2 = 1− (SS tot/SS res)
where:
- SSres is the sum of squared residuals.
- SStot is the total sum of squares.
Interpretation:
- Higher R2 indicates a better fit, meaning the model explains a larger proportion of the variance in the data.
Uses in Trading:
- Higher R-squared values give traders confidence in trend-based signals.
- Low R-squared values may suggest that the market is more random or volatile.
Standard Deviation
Definition: Standard Deviation quantifies the dispersion of data points in a dataset relative to the mean. A low standard deviation indicates that data points tend to be close to the mean, while a high standard deviation indicates that the data points are spread out over a larger range of values.
Calculation
σ=√∑(xi−μ)2/N
Where
- σ is the standard deviation.
- ∑ is the summation symbol, indicating that the expression that follows should be summed over all data points.
- xi, this represents the i-th data point in the dataset.
- μ\mu, this represents the mean(average) of all the data points in the dataset.
- (xi−μ)2, this is the squared difference between each data point and the mean.
- N is the total number of data points in the dataset.
- **Interpretation**
- A higher standard deviation indicates greater volatility.
- Useful for identifying overbought/oversold conditions in markets.
Key Features
Dynamic Linear Regression Channels:
The script automatically generates adaptive regression channels that expand or contract based on the current market volatility. This real-time adjustment ensures that traders are always working with the most relevant data, making it easier to spot key support and resistance levels.
The channel width itself serves as an indicator of market volatility, expanding during periods of heightened uncertainty and contracting during more stable phases. Additionally, the channel width is trained on previous channel widths , allowing the script to adapt and provide a more accurate view of volatility trends of the asset. Traders can also customize the script to train on less historical data , enabling a more recent view of volatility , which is particularly useful in fast-moving or changing markets.
Dynamic Profits and Stops:
What is it?
Dynamic profit levels allow traders to adjust take-profit targets based on real-time market conditions. Unlike static levels, which remain fixed regardless of market changes, these adaptive levels leverage past volatility data to create more flexible profit-taking strategies.
How does it work?
The script determines these levels using previously stored deviation values. These deviations are categorized into quantiles (like Q1, Q2, Q3, etc.) to classify current market conditions. As new deviation data is recorded, the profit levels are adjusted dynamically to reflect changes in market volatility. This approach helps to refine profit targets, especially when using regression channels with standard deviation rather than traditional ATR bands.
Why is it valuable?
By utilizing adaptive profit levels, traders can optimize their exits based on the current volatility landscape. For instance, when volatility increases, the dynamic levels expand, allowing trades to capture larger price movements. Conversely, during low volatility, profit targets tighten to lock in gains sooner, reducing exposure to market reversals. This flexibility is especially beneficial when combined with adaptive regression channels that respond to changes in standard deviation.
Slope-Based Trend Analysis:
One of the core elements of this script is the slope of the regression line , which helps define the direction and strength of the trend. Positive slopes indicate bullish momentum, while negative slopes suggest bearish conditions. The slope's steepness gives traders insight into the market's momentum, allowing them to adjust their strategies based on the strength of the trend.
Additionally, the script uses the slope to create a color gradient , which visually represents the intensity of the market's momentum. The gradient peaks at one color to show the maximum bullish momentum experienced in the past, while another color represents the maximum bearish momentum experienced in the past. This color-coded visualization makes it easier for traders to quickly assess the market's strength and direction at a glance.
Volatility Heatmap:
The integrated heatmap provides an intuitive, color-coded visualization of market volatility. The heatmap highlights areas where price action is expanding or contracting, giving traders a clear view of where volatility is rising or falling. By mapping out deviations from the regression line, the heatmap makes it easier to spot periods of high volatility that could lead to major market moves or potential reversals.
Deviation Concepts:
The script tracks price deviations from the regression line when a new range is formed, providing valuable insights when the price significantly deviates from the expected trend. These deviations are key in identifying potential breakout points or trend shifts .
This helps traders understand when the market is overextended or when a pullback may be imminent, allowing them to make more informed trading decisions.
Adaptive Model Properties:
Unlike static indicators, this script adapts over time . As the market changes, it stores historical data related to channel widths , slope dynamics , and volatility levels , adjusting its analysis accordingly to stay relevant to current market conditions.
Traders have the ability to train the model on all available data or specify a set number of bars to focus on more recent market activity. This flexibility allows for more tailored analysis , ensuring that traders can work with data that best fits their trading style and time horizon.
This continuous learning approach ensures that traders always have the most up-to-date insight into the market's structure.
Table
The table displays key metrics in real time to provide deeper insights into market behavior:
1. Deviation & Slope : Shows the current deviation if set to standard deviation or atr if set to atr(values used to calculated the channel widths) and the trend slope, helping to gauge market volatility and trend direction.
2. Rate of Change : For both deviation/atr and slope, the table also calculates the rate of change of their rates—essentially capturing the acceleration or deceleration of trends and volatility. This helps identify shifts in market momentum early.
3. R-squared : Indicates the strength and reliability of the trend fit. A higher value means the regression line better explains the price movements.
4. Quantiles : Uses historical deviation data to categorize current market conditions into quartiles (e.g., Q1, Q2, Q3). This helps classify the market's current volatility level, allowing traders to adjust strategies dynamically.
By combining these metrics, the table offers a comprehensive, real-time snapshot of market conditions, enabling more informed and adaptive trading decisions.
Settings
Here’s a breakdown of the script's settings for easy reference:
Linear Regression Settings
Show Dynamic Levels :Toggle to display dynamic profit levels on the chart.
Deviation Type :Select the method for calculating deviation—options include ATR (Average True Range) or Standard Deviation.
Timeframe :Sets the specific timeframe for the regression analysis (default is the chart’s timeframe).
Period :Defines the number of bars used for calculating the regression line (e.g., 50 bars).
Deviation Multiplier :Multiplier used to adjust the width of the deviation channel around the regression line.
Rate of Change :Sets the period for calculating the rate of change of the slope (used for momentum analysis).
Max Bars Back :Limits the number of historical bars to analyze (0 means all available data).
Slope Lookback :Number of bars used to calculate the slope gradient for trend detection.
Slope Gradient Display :Toggle to enable gradient coloring based on slope direction.
Slope Gradient Colors :Set colors for positive and negative slopes, respectively.
Slope Fill :Adjusts the transparency of the slope gradient fill.
Volatility Gradient Display :Toggle to enable gradient coloring based on volatility levels.
Volatility Gradient Colors :Set colors for low and high volatility, respectively.
Volatility Fill :Adjusts the transparency of the volatility gradient fill.
Table Settings
Show Table :Toggle to display the metrics table on the chart.
Table Position :Choose where to position the table (e.g., top-right, middle-center, etc.).
Font Size :Set the size of the text in the table. Options include Tiny, Small, Normal, Large, and Huge.
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.
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.
