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PulseWave + DivergenceOverview PulseWave + Divergence is a momentum oscillator designed to optimize the classic RSI. Unlike traditional RSI, which can produce delayed or noisy signals, PulseWave offers a smoother and faster oscillator line that better responds to changes in market dynamics. By using a formula based on the difference between RSI and its moving average, the indicator generates fewer false signals, making it a suitable tool for day traders and swing traders in stock, forex, and cryptocurrency markets. How It Works Generating the Oscillator Line The PulseWave oscillator line is calculated as follows: RSI is calculated based on the selected data source (default: close price) and RSI length (default: 20 periods). RSI is smoothed using a simple moving average (MA) with a selected length (default: 20 periods). The oscillator value is the difference between the current RSI and its moving average: oscillator = RSI - MA(RSI). This approach ensures high responsiveness to short-term momentum changes while reducing market noise. Unlike other oscillators, such as standard RSI or MACD, which rely on direct price values or more complex formulas, PulseWave focuses on the dynamics of the difference between RSI and its moving average. This allows it to better capture short-term trend changes while minimizing the impact of random price fluctuations. The oscillator line fluctuates around zero, making it easy to identify bullish trends (positive values) and bearish trends (negative values). Divergences The indicator optionally detects bullish and bearish divergences by comparing price extremes (swing highs/lows) with oscillator extremes within a defined pivot window (default: 5 candles left and right). Divergences are marked with "Bull" (bullish) and "Bear" (bearish) labels on the oscillator chart. Signals Depending on the selected signal type, PulseWave generates buy and sell signals based on: Crosses of the overbought and oversold levels. Crosses of the oscillator’s zero line. A combination of both (option "Both"). Signals are displayed as triangles above or below the oscillator, making them easy to identify. Input Parameters RSI Length: Length of the RSI used in calculations (default: 20). RSI MA Length: Length of the RSI moving average (default: 20). Overbought/Oversold Level: Oscillator overbought and oversold levels (default: 12.0 and -12.0). Pivot Length: Number of candles used to detect extremes for divergences (default: 5). Signal Type: Type of signals to display ("Overbought/Oversold", "Zero Line", "Both", or "None"). Colors and Gradients: Full customization of line, gradient, and label colors. How to Use Adjust Parameters: Increase RSI Length (e.g., to 30) for high-volatility markets to reduce noise. Decrease Pivot Length (e.g., to 3) for faster divergence detection on short timeframes. Interpret Signals: Buy Signal: The oscillator crosses above the oversold level or zero line, especially with a bullish divergence. Sell Signal: The oscillator crosses below the overbought level or zero line, especially with a bearish divergence. Combine with Other Tools: Use PulseWave alongside moving averages or support/resistance levels to confirm signals. Monitor Divergences: "Bull" and "Bear" labels indicate potential trend reversals. Set up alerts to receive notifications for divergences.
Pine Script® göstergesi
Uncle_the_shooter tarafından
3
Check OAS of EMAsThis script checks the Optimal Alignment and Slope of the EMA's and prints a label if it finds one. 🔍 1. Optimal Alignment This refers to the order of EMAs on the chart, which should reflect the trend. In an uptrend, the alignment might be: 10 EMA above 20 EMA above 50 EMA In a downtrend: 10 EMA below 20 EMA below 50 EMA This "stacked" alignment confirms trend strength and direction. 📈 2. Slope The angle or slope of the EMAs shows momentum. A steep upward slope = strong bullish momentum. A steep downward slope = strong bearish momentum. Flat or sideways slope = weak or no trend (ranging market).
Pine Script® göstergesi
helloram12 tarafından
Gann Support and Resistance LevelsThis indicator plots dynamic Gann Degree Levels as potential support and resistance zones around the current market price. You can fully customize the Gann degree step (e.g., 45°, 30°, 90°), the number of levels above and below the price, and the price movement per degree to fine-tune the levels to your strategy. Key Features: ✅ Dynamic levels update automatically with the live price ✅ Adjustable degree intervals (Gann steps) ✅ User control over how many levels to display above and below ✅ Fully customizable label size, label color, and text color for mobile-friendly visibility ✅ Clean visual design for easy chart analysis How to Use: Gann levels can act as potential support and resistance zones. Watch for price reactions at major degrees like 0°, 90°, 180°, and 270°. Can be combined with other technical tools like price action, trendlines, or Gann fans for deeper analysis. 📌 This tool is perfect for traders using Gann theory, grid-based strategies, or those looking to enhance their visual trading setups with structured levels.
Pine Script® göstergesi
signaltriangle tarafından
1
Step Channel Momentum Trend [ChartPrime]OVERVIEW Step Channel Momentum Trend is a momentum-based price filtering system that adapts to market structure using pivot levels and ATR volatility. It builds a dynamic channel around a stepwise midline derived from swing highs and lows. The system colors price candles based on whether price remains inside this channel (low momentum) or breaks out (strong directional flow). This allows traders to clearly distinguish ranging conditions from trending ones and take action accordingly. ⯁ STRUCTURAL MIDLNE (STEP CHANNEL CORE) The midline acts as the backbone of the trend system and is based on structure rather than smoothing. Calculated as the average of the most recent confirmed Pivot High and Pivot Low. The result is a step-like horizontal line that only updates when new pivot points are confirmed. This design avoids lag and makes the line "snap" to recent structural shifts. It reflects the equilibrium level between recent bullish and bearish control. This unique step logic creates clear regime shifts and prevents noise from distorting trend interpretation. ⯁ DYNAMIC VOLATILITY BANDS (ATR FILTERING) To detect momentum strength, the script constructs upper and lower bands using the ATR (Average True Range): The distance from the midline is determined by ATR × multiplier (default: 200-period ATR × 0.6). These bands adjust dynamically to volatility, expanding in high-ATR environments and contracting in calm markets. The area between upper and lower bands represents a neutral or ranging market state. Breakouts outside the bands are treated as significant momentum shifts. This filtering approach ensures that only meaningful breakouts are visually emphasized — not every candle fluctuation. ⯁ MOMENTUM-BASED CANDLE COLORING The system visually transforms price candles into momentum indicators: When price (hl2) is above the upper band, candles are green → bullish momentum. When price is below the lower band, candles are red → bearish momentum. When price is between the bands, candles are orange → low or no momentum (range). The candle body, wick, and border are all colored uniformly for visual clarity. This gives traders instant feedback on when momentum is expanding or fading — ideal for breakout, pullback, or trend-following strategies. ⯁ PIVOT-BASED SWING ANCHORS Each confirmed pivot is plotted as a label ⬥ directly on the chart: They also serve as potential manual entry zones, SL/TP anchors, or confirmation points. ⯁ MOMENTUM STATE LABEL To reinforce the current market mode, a live label is displayed at the most recent candle: Displays either: “ Momentum Up ” when price breaks above the upper band. “ Momentum Down ” when price breaks below the lower band. “ Range ” when price remains between the bands. Label color matches the candle color for quick identification. Automatically updates on each bar close. This helps discretionary traders filter trades based on market phase. USAGE Use the green/red zones to enter with momentum and ride trending moves. Use the orange zone to stay out or fade ranges. The step midline can act as a breakout base, pullback anchor, or bias reference. Combine with other indicators (e.g., order blocks, divergences, or volume) to build high-confluence systems. CONCLUSION Step Channel Momentum Trend gives traders a clean, adaptive framework for identifying trend direction, volatility-based breakouts, and ranging environments — all from structural logic and ATR responsiveness. Its stepwise midline provides clarity, while its dynamic color-coded candles make momentum shifts impossible to miss. Whether you’re scalping intraday momentum or managing swing entries, this tool helps you trade with the market’s rhythm — not against it.
Pine Script® göstergesi
ChartPrime tarafından
6
Z Score Overlay [BigBeluga]🔵 OVERVIEW A clean and effective Z-score overlay that visually tracks how far price deviates from its moving average. By standardizing price movements, this tool helps traders understand when price is statistically extended or compressed—up to ±4 standard deviations. The built-in scale and real-time bin markers offer immediate context on where price stands in relation to its recent mean. 🔵 CONCEPTS Z Score Calculation:   Z = (Close − SMA) ÷ Standard Deviation   This formula shows how many standard deviations the current price is from its mean. Statistical Extremes:   • Z > +2 or Z < −2 suggests statistically significant deviation.   • Z near 0 implies price is close to its average. Standardization of Price Behavior: Makes it easier to compare volatility and overextension across timeframes and assets. 🔵 FEATURES Colored Z Line: Gradient coloring based on how far price deviates—   • Red = oversold (−4),   • Green = overbought (+4),   • Yellow = neutral (~0). Deviation Scale Bar: A vertical scale from −4 to +4 standard deviations plotted to the right of price. Active Z Score Bin: Highlights the current Z-score bin with a “◀” arrow Context Labels: Clear numeric labels for each Z-level from −4 to +4 along the side. Live Value Display: Shows exact Z-score on the active level. Non-intrusive Overlay: Can be applied directly to price chart without changing scaling behavior. 🔵 HOW TO USE Identify overbought/oversold areas based on +2 / −2 thresholds. Spot potential mean reversion trades when Z returns from extreme levels. Confirm strong trends when price remains consistently outside ±2. Use in multi-timeframe setups to compare strength across contexts. 🔵 CONCLUSION Z Score Overlay transforms raw price action into a normalized statistical view, allowing traders to easily assess deviation strength and mean-reversion potential. The intuitive scale and color-coded display make it ideal for traders seeking objective, volatility-aware entries and exits.
Pine Script® göstergesi
BigBeluga tarafından
6
Color█ OVERVIEW This library is a Pine Script® programming tool for advanced color processing. It provides a comprehensive set of functions for specifying and analyzing colors in various color spaces, mixing and manipulating colors, calculating custom gradients and schemes, detecting contrast, and converting colors to or from hexadecimal strings. █ CONCEPTS Color Color refers to how we interpret light of different wavelengths in the visible spectrum . The colors we see from an object represent the light wavelengths that it reflects, emits, or transmits toward our eyes. Some colors, such as blue and red, correspond directly to parts of the spectrum. Others, such as magenta, arise from a combination of wavelengths to which our minds assign a single color. The human interpretation of color lends itself to many uses in our world. In the context of financial data analysis, the effective use of color helps transform raw data into insights that users can understand at a glance. For example, colors can categorize series, signal market conditions and sessions, and emphasize patterns or relationships in data. Color models and spaces A color model is a general mathematical framework that describes colors using sets of numbers. A color space is an implementation of a specific color model that defines an exact range (gamut) of reproducible colors based on a set of primary colors , a reference white point , and sometimes additional parameters such as viewing conditions. There are numerous different color spaces — each describing the characteristics of color in unique ways. Different spaces carry different advantages, depending on the application. Below, we provide a brief overview of the concepts underlying the color spaces supported by this library. RGB RGB is one of the most well-known color models. It represents color as an additive mixture of three primary colors — red, green, and blue lights — with various intensities. Each cone cell in the human eye responds more strongly to one of the three primaries, and the average person interprets the combination of these lights as a distinct color (e.g., pure red + pure green = yellow). The sRGB color space is the most common RGB implementation. Developed by HP and Microsoft in the 1990s, sRGB provided a standardized baseline for representing color across CRT monitors of the era, which produced brightness levels that did not increase linearly with the input signal. To match displays and optimize brightness encoding for human sensitivity, sRGB applied a nonlinear transformation to linear RGB signals, often referred to as gamma correction . The result produced more visually pleasing outputs while maintaining a simple encoding. As such, sRGB quickly became a standard for digital color representation across devices and the web. To this day, it remains the default color space for most web-based content. TradingView charts and Pine Script `color.*` built-ins process color data in sRGB. The red, green, and blue channels range from 0 to 255, where 0 represents no intensity, and 255 represents maximum intensity. Each combination of red, green, and blue values represents a distinct color, resulting in a total of 16,777,216 displayable colors. CIE XYZ and xyY The XYZ color space, developed by the International Commission on Illumination (CIE) in 1931, aims to describe all color sensations that a typical human can perceive. It is a cornerstone of color science, forming the basis for many color spaces used today. XYZ, and the derived xyY space, provide a universal representation of color that is not tethered to a particular display. Many widely used color spaces, including sRGB, are defined relative to XYZ or derived from it. The CIE built the color space based on a series of experiments in which people matched colors they perceived from mixtures of lights. From these experiments, the CIE developed color-matching functions to calculate three components — X, Y, and Z — which together aim to describe a standard observer's response to visible light. X represents a weighted response to light across the color spectrum, with the highest contribution from long wavelengths (e.g., red). Y represents a weighted response to medium wavelengths (e.g., green), and it corresponds to a color's relative luminance (i.e., brightness). Z represents a weighted response to short wavelengths (e.g., blue). From the XYZ space, the CIE developed the xyY chromaticity space, which separates a color's chromaticity (hue and colorfulness) from luminance. The CIE used this space to define the CIE 1931 chromaticity diagram , which represents the full range of visible colors at a given luminance. In color science and lighting design, xyY is a common means for specifying colors and visualizing the supported ranges of other color spaces. CIELAB and Oklab The CIELAB (L*a*b*) color space, derived from XYZ by the CIE in 1976, expresses colors based on opponent process theory. The L* component represents perceived lightness, and the a* and b* components represent the balance between opposing unique colors. The a* value specifies the balance between green and red , and the b* value specifies the balance between blue and yellow . The primary intention of CIELAB was to provide a perceptually uniform color space, where fixed-size steps through the space correspond to uniform perceived changes in color. Although relatively uniform, the color space has been found to exhibit some non-uniformities, particularly in the blue part of the color spectrum. Regardless, modern applications often use CIELAB to estimate perceived color differences and calculate smooth color gradients. In 2020, a new LAB-oriented color space, Oklab , was introduced by Björn Ottosson as an attempt to rectify the non-uniformities of other perceptual color spaces. Similar to CIELAB, the L value in Oklab represents perceived lightness, and the a and b values represent the balance between opposing unique colors. Oklab has gained widespread adoption as a perceptual space for color processing, with support in the latest CSS Color specifications and many software applications. Cylindrical models A cylindrical-coordinate model transforms an underlying color model, such as RGB or LAB, into an alternative expression of color information that is often more intuitive for the average person to use and understand. Instead of a mixture of primary colors or opponent pairs, these models represent color as a hue angle on a color wheel , with additional parameters that describe other qualities such as lightness and colorfulness (a general term for concepts like chroma and saturation). In cylindrical-coordinate spaces, users can select a color and modify its lightness or other qualities without altering the hue. The three most common RGB-based models are HSL (Hue, Saturation, Lightness), HSV (Hue, Saturation, Value), and HWB (Hue, Whiteness, Blackness). All three define hue angles in the same way, but they define colorfulness and lightness differently. Although they are not perceptually uniform, HSL and HSV are commonplace in color pickers and gradients. For CIELAB and Oklab, the cylindrical-coordinate versions are CIELCh and Oklch , which express color in terms of perceived lightness, chroma, and hue. They offer perceptually uniform alternatives to RGB-based models. These spaces create unique color wheels, and they have more strict definitions of lightness and colorfulness. Oklch is particularly well-suited for generating smooth, perceptual color gradients. Alpha and transparency Many color encoding schemes include an alpha channel, representing opacity . Alpha does not help define a color in a color space; it determines how a color interacts with other colors in the display. Opaque colors appear with full intensity on the screen, whereas translucent (semi-opaque) colors blend into the background. Colors with zero opacity are invisible. In Pine Script, there are two ways to specify a color's alpha:  • Using the `transp` parameter of the built-in `color.*()` functions. The specified value represents transparency (the opposite of opacity), which the functions translate into an alpha value.  • Using eight-digit hexadecimal color codes. The last two digits in the code represent alpha directly. A process called alpha compositing simulates translucent colors in a display. It creates a single displayed color by mixing the RGB channels of two colors (foreground and background) based on alpha values, giving the illusion of a semi-opaque color placed over another color. For example, a red color with 80% transparency on a black background produces a dark shade of red. Hexadecimal color codes A hexadecimal color code (hex code) is a compact representation of an RGB color. It encodes a color's red, green, and blue values into a sequence of hexadecimal ( base-16 ) digits. The digits are numerals ranging from `0` to `9` or letters from `a` (for 10) to `f` (for 15). Each set of two digits represents an RGB channel ranging from `00` (for 0) to `ff` (for 255). Pine scripts can natively define colors using hex codes in the format `#rrggbbaa`. The first set of two digits represents red, the second represents green, and the third represents blue. The fourth set represents alpha . If unspecified, the value is `ff` (fully opaque). For example, `#ff8b00` and `#ff8b00ff` represent an opaque orange color. The code `#ff8b0033` represents the same color with 80% transparency. Gradients A color gradient maps colors to numbers over a given range. Most color gradients represent a continuous path in a specific color space, where each number corresponds to a mix between a starting color and a stopping color. In Pine, coders often use gradients to visualize value intensities in plots and heatmaps, or to add visual depth to fills. The behavior of a color gradient depends on the mixing method and the chosen color space. Gradients in sRGB usually mix along a straight line between the red, green, and blue coordinates of two colors. In cylindrical spaces such as HSL, a gradient often rotates the hue angle through the color wheel, resulting in more pronounced color transitions. Color schemes A color scheme refers to a set of colors for use in aesthetic or functional design. A color scheme usually consists of just a few distinct colors. However, depending on the purpose, a scheme can include many colors. A user might choose palettes for a color scheme arbitrarily, or generate them algorithmically. There are many techniques for calculating color schemes. A few simple, practical methods are:  • Sampling a set of distinct colors from a color gradient.  • Generating monochromatic variants of a color (i.e., tints, tones, or shades with matching hues).  • Computing color harmonies — such as complements, analogous colors, triads, and tetrads — from a base color. This library includes functions for all three of these techniques. See below for details. █ CALCULATIONS AND USE Hex string conversion The `getHexString()` function returns a string containing the eight-digit hexadecimal code corresponding to a "color" value or set of sRGB and transparency values. For example, `getHexString(255, 0, 0)` returns the string `"#ff0000ff"`, and `getHexString(color.new(color.red, 80))` returns `"#f2364533"`. The `hexStringToColor()` function returns the "color" value represented by a string containing a six- or eight-digit hex code. The `hexStringToRGB()` function returns a tuple containing the sRGB and transparency values. For example, `hexStringToColor("#f23645")` returns the same value as color.red . Programmers can use these functions to parse colors from "string" inputs, perform string-based color calculations, and inspect color data in text outputs such as Pine Logs and tables. Color space conversion All other `get*()` functions convert a "color" value or set of sRGB channels into coordinates in a specific color space, with transparency information included. For example, the tuple returned by `getHSL()` includes the color's hue, saturation, lightness, and transparency values. To convert data from a color space back to colors or sRGB and transparency values, use the corresponding `*toColor()` or `*toRGB()` functions for that space (e.g., `hslToColor()` and `hslToRGB()`). Programmers can use these conversion functions to process inputs that define colors in different ways, perform advanced color manipulation, design custom gradients, and more. The color spaces this library supports are:  • sRGB  • Linear RGB (RGB without gamma correction)  • HSL, HSV, and HWB  • CIE XYZ and xyY  • CIELAB and CIELCh  • Oklab and Oklch Contrast-based calculations Contrast refers to the difference in luminance or color that makes one color visible against another. This library features two functions for calculating luminance-based contrast and detecting themes. The `contrastRatio()` function calculates the contrast between two "color" values based on their relative luminance (the Y value from CIE XYZ) using the formula from version 2 of the Web Content Accessibility Guidelines (WCAG) . This function is useful for identifying colors that provide a sufficient brightness difference for legibility. The `isLightTheme()` function determines whether a specified background color represents a light theme based on its contrast with black and white. Programmers can use this function to define conditional logic that responds differently to light and dark themes. Color manipulation and harmonies The `negative()` function calculates the negative (i.e., inverse) of a color by reversing the color's coordinates in either the sRGB or linear RGB color space. This function is useful for calculating high-contrast colors. The `grayscale()` function calculates a grayscale form of a specified color with the same relative luminance. The functions `complement()`, `splitComplements()`, `analogousColors()`, `triadicColors()`, `tetradicColors()`, `pentadicColors()`, and `hexadicColors()` calculate color harmonies from a specified source color within a given color space (HSL, CIELCh, or Oklch). The returned harmonious colors represent specific hue rotations around a color wheel formed by the chosen space, with the same defined lightness, saturation or chroma, and transparency. Color mixing and gradient creation The `add()` function simulates combining lights of two different colors by additively mixing their linear red, green, and blue components, ignoring transparency by default. Users can calculate a transparency-weighted mixture by setting the `transpWeight` argument to `true`. The `overlay()` function estimates the color displayed on a TradingView chart when a specific foreground color is over a background color. This function aids in simulating stacked colors and analyzing the effects of transparency. The `fromGradient()` and `fromMultiStepGradient()` functions calculate colors from gradients in any of the supported color spaces, providing flexible alternatives to the RGB-based color.from_gradient() function. The `fromGradient()` function calculates a color from a single gradient. The `fromMultiStepGradient()` function calculates a color from a piecewise gradient with multiple defined steps. Gradients are useful for heatmaps and for coloring plots or drawings based on value intensities. Scheme creation Three functions in this library calculate palettes for custom color schemes. Scripts can use these functions to create responsive color schemes that adjust to calculated values and user inputs. The `gradientPalette()` function creates an array of colors by sampling a specified number of colors along a gradient from a base color to a target color, in fixed-size steps. The `monoPalette()` function creates an array containing monochromatic variants (tints, tones, or shades) of a specified base color. Whether the function mixes the color toward white (for tints), a form of gray (for tones), or black (for shades) depends on the `grayLuminance` value. If unspecified, the function automatically chooses the mix behavior with the highest contrast. The `harmonyPalette()` function creates a matrix of colors. The first column contains the base color and specified harmonies, e.g., triadic colors. The columns that follow contain tints, tones, or shades of the harmonic colors for additional color choices, similar to `monoPalette()`. █ EXAMPLE CODE The example code at the end of the script generates and visualizes color schemes by processing user inputs. The code builds the scheme's palette based on the "Base color" input and the additional inputs in the "Settings/Inputs" tab:  • "Palette type" specifies whether the palette uses a custom gradient, monochromatic base color variants, or color harmonies with monochromatic variants.  • "Target color" sets the top color for the "Gradient" palette type.  • The "Gray luminance" inputs determine variation behavior for "Monochromatic" and "Harmony" palette types. If "Auto" is selected, the palette mixes the base color toward white or black based on its brightness. Otherwise, it mixes the color toward the grayscale color with the specified relative luminance (from 0 to 1).  • "Harmony type" specifies the color harmony used in the palette. Each row in the palette corresponds to one of the harmonious colors, starting with the base color. The code creates a table on the first bar to display the collection of calculated colors. Each cell in the table shows the color's `getHexString()` value in a tooltip for simple inspection. Look first. Then leap. █ EXPORTED FUNCTIONS Below is a complete list of the functions and overloads exported by this library. getRGB(source)   Retrieves the sRGB red, green, blue, and transparency components of a "color" value. getHexString(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channel values to a string representing the corresponding color's hexadecimal form. getHexString(source)   (Overload 2 of 2) Converts a "color" value to a string representing the sRGB color's hexadecimal form. hexStringToRGB(source)   Converts a string representing an sRGB color's hexadecimal form to a set of decimal channel values. hexStringToColor(source)   Converts a string representing an sRGB color's hexadecimal form to a "color" value. getLRGB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channel values to a set of linear RGB values with specified transparency information. getLRGB(source)   (Overload 2 of 2) Retrieves linear RGB channel values and transparency information from a "color" value. lrgbToRGB(lr, lg, lb, t)   Converts a set of linear RGB channel values to a set of sRGB values with specified transparency information. lrgbToColor(lr, lg, lb, t)   Converts a set of linear RGB channel values and transparency information to a "color" value. getHSL(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of HSL values with specified transparency information. getHSL(source)   (Overload 2 of 2) Retrieves HSL channel values and transparency information from a "color" value. hslToRGB(h, s, l, t)   Converts a set of HSL channel values to a set of sRGB values with specified transparency information. hslToColor(h, s, l, t)   Converts a set of HSL channel values and transparency information to a "color" value. getHSV(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of HSV values with specified transparency information. getHSV(source)   (Overload 2 of 2) Retrieves HSV channel values and transparency information from a "color" value. hsvToRGB(h, s, v, t)   Converts a set of HSV channel values to a set of sRGB values with specified transparency information. hsvToColor(h, s, v, t)   Converts a set of HSV channel values and transparency information to a "color" value. getHWB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of HWB values with specified transparency information. getHWB(source)   (Overload 2 of 2) Retrieves HWB channel values and transparency information from a "color" value. hwbToRGB(h, w, b, t)   Converts a set of HWB channel values to a set of sRGB values with specified transparency information. hwbToColor(h, w, b, t)   Converts a set of HWB channel values and transparency information to a "color" value. getXYZ(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of XYZ values with specified transparency information. getXYZ(source)   (Overload 2 of 2) Retrieves XYZ channel values and transparency information from a "color" value. xyzToRGB(x, y, z, t)   Converts a set of XYZ channel values to a set of sRGB values with specified transparency information xyzToColor(x, y, z, t)   Converts a set of XYZ channel values and transparency information to a "color" value. getXYY(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of xyY values with specified transparency information. getXYY(source)   (Overload 2 of 2) Retrieves xyY channel values and transparency information from a "color" value. xyyToRGB(xc, yc, y, t)   Converts a set of xyY channel values to a set of sRGB values with specified transparency information. xyyToColor(xc, yc, y, t)   Converts a set of xyY channel values and transparency information to a "color" value. getLAB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of CIELAB values with specified transparency information. getLAB(source)   (Overload 2 of 2) Retrieves CIELAB channel values and transparency information from a "color" value. labToRGB(l, a, b, t)   Converts a set of CIELAB channel values to a set of sRGB values with specified transparency information. labToColor(l, a, b, t)   Converts a set of CIELAB channel values and transparency information to a "color" value. getOKLAB(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of Oklab values with specified transparency information. getOKLAB(source)   (Overload 2 of 2) Retrieves Oklab channel values and transparency information from a "color" value. oklabToRGB(l, a, b, t)   Converts a set of Oklab channel values to a set of sRGB values with specified transparency information. oklabToColor(l, a, b, t)   Converts a set of Oklab channel values and transparency information to a "color" value. getLCH(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of CIELCh values with specified transparency information. getLCH(source)   (Overload 2 of 2) Retrieves CIELCh channel values and transparency information from a "color" value. lchToRGB(l, c, h, t)   Converts a set of CIELCh channel values to a set of sRGB values with specified transparency information. lchToColor(l, c, h, t)   Converts a set of CIELCh channel values and transparency information to a "color" value. getOKLCH(r, g, b, t)   (Overload 1 of 2) Converts a set of sRGB channels to a set of Oklch values with specified transparency information. getOKLCH(source)   (Overload 2 of 2) Retrieves Oklch channel values and transparency information from a "color" value. oklchToRGB(l, c, h, t)   Converts a set of Oklch channel values to a set of sRGB values with specified transparency information. oklchToColor(l, c, h, t)   Converts a set of Oklch channel values and transparency information to a "color" value. contrastRatio(value1, value2)   Calculates the contrast ratio between two colors values based on the formula from version 2 of the Web Content Accessibility Guidelines (WCAG). isLightTheme(source)   Detects whether a background color represents a light theme or dark theme, based on the amount of contrast between the color and the white and black points. grayscale(source)   Calculates the grayscale version of a color with the same relative luminance (i.e., brightness). negative(source, colorSpace)   Calculates the negative (i.e., inverted) form of a specified color. complement(source, colorSpace)   Calculates the complementary color for a `source` color using a cylindrical color space. analogousColors(source, colorSpace)   Calculates the analogous colors for a `source` color using a cylindrical color space. splitComplements(source, colorSpace)   Calculates the split-complementary colors for a `source` color using a cylindrical color space. triadicColors(source, colorSpace)   Calculates the two triadic colors for a `source` color using a cylindrical color space. tetradicColors(source, colorSpace, square)   Calculates the three square or rectangular tetradic colors for a `source` color using a cylindrical color space. pentadicColors(source, colorSpace)   Calculates the four pentadic colors for a `source` color using a cylindrical color space. hexadicColors(source, colorSpace)   Calculates the five hexadic colors for a `source` color using a cylindrical color space. add(value1, value2, transpWeight)   Additively mixes two "color" values, with optional transparency weighting. overlay(fg, bg)   Estimates the resulting color that appears on the chart when placing one color over another. fromGradient(value, bottomValue, topValue, bottomColor, topColor, colorSpace)   Calculates the gradient color that corresponds to a specific value based on a defined value range and color space. fromMultiStepGradient(value, steps, colors, colorSpace)   Calculates a multi-step gradient color that corresponds to a specific value based on an array of step points, an array of corresponding colors, and a color space. gradientPalette(baseColor, stopColor, steps, strength, model)   Generates a palette from a gradient between two base colors. monoPalette(baseColor, grayLuminance, variations, strength, colorSpace)   Generates a monochromatic palette from a specified base color. harmonyPalette(baseColor, harmonyType, grayLuminance, variations, strength, colorSpace)   Generates a palette consisting of harmonious base colors and their monochromatic variants.
Pine Script® kütüphanesi
TradingView tarafından
2
ORB Screener-Multiple IndicatorsThis custom screener is designed to identify high-probability intraday breakout opportunities across the top 40 NSE stocks by market capitalization. It is built on the proven Opening Range Breakout (ORB) concept and enhanced with a powerful combination of momentum and trend filters. ✅ Key Features: Opening Range (09:15–09:20) detection with automatic status: Abv High, Blw Low, BTW Real-time scanning of 40 pre-loaded NSE stocks (configurable) Composite scoring system (0–100) based on: RSI > 55 Price above VWAP Volume Surge (vs 20-period SMA) ADX > 20 MACD Histogram (positive and rising) ORB Breakout Direction Color-coded screener table to highlight top-scoring stocks Buy/Sell suggestions shown alongside score Manual sorting toggle for ranked display Fully customizable watchlist with checkboxes 🛠️ Best Use: Ideal for intraday traders looking for momentum trades. Focus on stocks with score ≥ 75 and green highlight for long trades. Designed to be lightweight despite scanning 40 instruments. ⚠️ Notes: This script does not plot on the chart; it only renders a dynamic screener table. No alerts are configured—manual review required. You can edit the top 40 symbols as needed.
Pine Script® göstergesi
swarajchhallani tarafından
6
OBV ATR Strategy (OBV Breakout Channel) bas20230503ผมแก้ไขจาก OBV+SMA อันเดิม ของเดิม ดูที่เส้น SMA สองเส้นตัดกันมั่นห่วยแตกสำหรับที่ผมลองเทรดจริง และหลักการเบรค ได้แรงบันดาลใจ ATR จาก เทพคอย ที่ใช้กับราคา แต่นี้ใช้กับ OBV แทน และผมใช้เจมินี้ เพื่อแก้ ให้ เป็น strategy เพื่อเช็คย้อนหลังได้ง่ายกว่าเดิม หลักการง่ายคือถ้ามันขึ้น มันจะขึ้นเรื่อยๆ เขียน แบบสุภาพ (น่าจะอ่านได้ง่ายกว่าผมเขียน) สคริปต์นี้ได้รับการพัฒนาต่อยอดจากแนวคิด OBV+SMA Crossover แบบดั้งเดิม ซึ่งจากการทดสอบส่วนตัวพบว่าประสิทธิภาพยังไม่น่าพอใจ กลยุทธ์ใหม่นี้จึงเปลี่ยนมาใช้หลักการ "Breakout" ซึ่งได้รับแรงบันดาลใจมาจากการใช้ ATR สร้างกรอบของราคา แต่เราได้นำมาประยุกต์ใช้กับ On-Balance Volume (OBV) แทน นอกจากนี้ สคริปต์ได้ถูกแปลงเป็น Strategy เต็มรูปแบบ (โดยความช่วยเหลือจาก Gemini AI) เพื่อให้สามารถทดสอบย้อนหลัง (Backtest) และประเมินประสิทธิภาพได้อย่างแม่นยำ หลักการของกลยุทธ์: กลยุทธ์นี้ทำงานบนแนวคิดโมเมนตัมที่ว่า "เมื่อแนวโน้มได้เกิดขึ้นแล้ว มีโอกาสที่มันจะดำเนินต่อไป" โดยจะมองหาการทะลุของพลังซื้อ-ขาย (OBV) ที่แข็งแกร่งเป็นพิเศษเป็นสัญญาณเข้าเทร ---- สคริปต์นี้เป็นกลยุทธ์ (Strategy) ที่ใช้ On-Balance Volume (OBV) ซึ่งเป็นอินดิเคเตอร์ที่วัดแรงซื้อและแรงขายสะสม แทนที่จะใช้การตัดกันของเส้นค่าเฉลี่ย (SMA Crossover) ที่เป็นแบบพื้นฐาน กลยุทธ์นี้จะมองหาการ "ทะลุ" (Breakout) ของพลัง OBV ออกจากกรอบสูงสุด-ต่ำสุดของตัวเองในรอบที่ผ่านมา สัญญาณกระทิง (Bull Signal): เกิดขึ้นเมื่อพลังการซื้อ (OBV) แข็งแกร่งจนสามารถทะลุจุดสูงสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาขึ้น สัญญาณหมี (Bear Signal): เกิดขึ้นเมื่อพลังการขาย (OBV) รุนแรงจนสามารถกดดันให้ OBV ทะลุจุดต่ำสุดของตัวเองในอดีตได้ บ่งบอกถึงโอกาสที่แนวโน้มจะเปลี่ยนเป็นขาลง ส่วนประกอบบนกราฟ (Indicator Components) เส้น OBV เส้นหลัก ที่เปลี่ยนเขียวเป็นแดง เป็นทั้งแนวรับและแนวต้าน และ จุด stop loss เส้นนี้คือหัวใจของอินดิเคเตอร์ ที่แสดงถึงพลังสะสมของ Volume เมื่อเส้นเป็นสีเขียว (แนวรับ): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดกระทิง" เส้นนี้คือระดับต่ำสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวรับไดนามิก เมื่อเส้นกลายเป็นสีแดงสีแดง (แนวต้าน): จะปรากฏขึ้นเมื่อกลยุทธ์เข้าสู่ "โหมดหมี" เส้นนี้คือระดับสูงสุดของ OBV ในอดีต และทำหน้าที่เป็นแนวต้านไดนามิก สัญลักษณ์สัญญาณ (Signal Markers): Bull 🔼 (สามเหลี่ยมขึ้นสีเขียว): คือสัญญาณ "เข้าซื้อ" (Long) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุขึ้นไปเหนือกรอบด้านบนเป็นครั้งแรก Bear 🔽 (สามเหลี่ยมลงสีแดง): คือสัญญาณ "เข้าขาย" (Short) จะปรากฏขึ้น ณ จุดที่ OBV ทะลุลงไปต่ำกว่ากรอบด้านล่างเป็นครั้งแรก วิธีการใช้งาน (How to Use) เพิ่มสคริปต์นี้ลงบนกราฟราคาที่คุณสนใจ ไปที่แท็บ "Strategy Tester" ด้านล่างของ TradingView เพื่อดูผลการทดสอบย้อนหลัง (Backtest) ของกลยุทธ์บนสินทรัพย์และไทม์เฟรมต่างๆ ใช้สัญลักษณ์ "Bull" และ "Bear" เป็นตัวช่วยในการตัดสินใจเข้าเทรด ข้อควรจำ: ไม่มีกลยุทธ์ใดที่สมบูรณ์แบบ 100% ควรใช้สคริปต์นี้ร่วมกับการวิเคราะห์ปัจจัยอื่นๆ เช่น โครงสร้างราคา, แนวรับ-แนวต้านของราคา และการบริหารความเสี่ยง (Risk Management) ของตัวคุณเองเสมอ การตั้งค่า (Inputs) SMA Length 1 / SMA Length 2: ใช้สำหรับพล็อตเส้นค่าเฉลี่ยของ OBV เพื่อดูเป็นภาพอ้างอิง ไม่มีผลต่อตรรกะการเข้า-ออกของ Strategy อันใหม่ แต่มันเป็นของเก่า ถ้าชอบ ก็ใช้ได้ เมื่อ SMA สองเส้นตัดกัน หรือตัดกับเส้น OBV High/Low Lookback Length: (ค่าพื้นฐาน30/แก้ตรงนี้ให้เหมาะสมกับ coin หรือหุ้น ตามความผันผวน ) คือระยะเวลาที่ใช้ในการคำนวณกรอบสูงสุด-ต่ำสุดของ OBV ค่าน้อย: ทำให้กรอบแคบลง สัญญาณจะเกิดไวและบ่อยขึ้น แต่อาจมีสัญญาณหลอก (False Signal) เยอะขึ้น ค่ามาก: ทำให้กรอบกว้างขึ้น สัญญาณจะเกิดช้าลงและน้อยลง แต่มีแนวโน้มที่จะเป็นสัญญาณที่แข็งแกร่งกว่า แน่นอนครับ นี่คือคำแปลฉบับภาษาอังกฤษที่สรุปใจความสำคัญ กระชับ และสุภาพ เหมาะสำหรับนำไปใช้ในคำอธิบายสคริปต์ (Description) ของ TradingView ครับ ---Translate to English--- OBV Breakout Channel Strategy This script is an evolution of a traditional OBV+SMA Crossover concept. Through personal testing, the original crossover method was found to have unsatisfactory performance. This new strategy, therefore, uses a "Breakout" principle. The inspiration comes from using ATR to create price channels, but this concept has been adapted and applied to On-Balance Volume (OBV) instead. Furthermore, the script has been converted into a full Strategy (with assistance from Gemini AI) to enable precise backtesting and performance evaluation. The strategy's core principle is momentum-based: "once a trend is established, it is likely to continue." It seeks to enter trades on exceptionally strong breakouts of buying or selling pressure as measured by OBV. Core Concept This is a Strategy that uses On-Balance Volume (OBV), an indicator that measures cumulative buying and selling pressure. Instead of relying on a basic Simple Moving Average (SMA) Crossover, this strategy identifies a "Breakout" of the OBV from its own highest-high and lowest-low channel over a recent period. Bull Signal: Occurs when the buying pressure (OBV) is strong enough to break above its own recent highest high, indicating a potential shift to an upward trend. Bear Signal: Occurs when the selling pressure (OBV) is intense enough to push the OBV below its own recent lowest low, indicating a potential shift to a downward trend. On-Screen Components 1. OBV Line This is the main indicator line, representing the cumulative volume. Its color changes to green when OBV is rising and red when it is falling. 2. Dynamic Support & Resistance Line This is the thick Green or Red line that appears based on the strategy's current "mode." This line serves as a dynamic support/resistance level and can be used as a reference for stop-loss placement. Green Line (Support): Appears when the strategy enters "Bull Mode." This line represents the lowest low of the OBV in the recent past and acts as dynamic support. Red Line (Resistance): Appears when the strategy enters "Bear Mode." This line represents the highest high of the OBV in the recent past and acts as dynamic resistance. 3. Signal Markers Bull 🔼 (Green Up Triangle): This is the "Long Entry" signal. It appears at the moment the OBV first breaks out above its high-low channel. Bear 🔽 (Red Down Triangle): This is the "Short Entry" signal. It appears at the moment the OBV first breaks down below its high-low channel. How to Use Add this script to the price chart of your choice. Navigate to the "Strategy Tester" panel at the bottom of TradingView to view the backtesting results for the strategy on different assets and timeframes. Use the "Bull" and "Bear" signals as aids in your trading decisions. Disclaimer: No strategy is 100% perfect. This script should always be used in conjunction with other forms of analysis, such as price structure, key price-based support/resistance levels, and your own personal risk management rules. Inputs SMA Length 1 / SMA Length 2: These are used to plot moving averages on the OBV for visual reference. They are part of the legacy logic and do not affect the new breakout strategy. However, they are kept for traders who may wish to observe their crossovers for additional confirmation. High/Low Lookback Length: (Most Important Setting) This determines the period used to calculate the highest-high and lowest-low OBV channel. (Default is 30; adjust this to suit the asset's volatility). A smaller value: Creates a narrower channel, leading to more frequent and faster signals, but potentially more false signals. A larger value: Creates a wider channel, leading to fewer and slower signals, which are likely to be more significant.
Pine Script® stratejisi
bas20230503 tarafından
Daily Trading Barometer (DTB) with DJIA OverlayThe "Daily Trading Barometer (DTB) with DJIA Overlay" is a custom technical indicator designed to identify intermediate-term overbought and oversold conditions in the stock market, inspired by Edson Gould's original DTB methodology. This indicator combines three key components: A 7-day advance-decline oscillator, a 20-day volume oscillator, and a 28-day DJIA price ratio, normalized into a composite index scaled around 110–135. Values below 110 signal potential oversold conditions, while values above 135 indicate overbought territory, aiding in timing market reversals. The overlay of a normalized DJIA plot allows for visual correlation with the broader market trend. Use this tool to anticipate turning points in oscillating markets, though it’s best combined with other indicators for confirmation. Ideal for traders seeking probabilistic insights into bear or bull market transitions. How to use - If the DTB line (blue) and normalized DJIA (orange) are under the green dashed line, high probability for a long and reversal. Use with the symbol SPX/QQQ Dow Jones Industrial Average - DJIA
Pine Script® göstergesi
ZoharCho tarafından
ALMA Trend-boxALMA Trend-box — an innovative indicator for detecting trend and consolidation based on the ALMA moving average This indicator combines the Adaptive Laguerre Moving Average (ALMA) with unique visual representations of trend and consolidation zones, providing traders with clearer and deeper insight into current market conditions. Originality and Usefulness Unlike classic indicators based on simple moving averages, ALMA uses a Gaussian weighting function and an offset parameter to reduce lag, resulting in smoother and more accurate trend signals. This indicator not only plots the ALMA but also analyzes the slope angle of the ALMA line, combining it with the price’s position relative to the moving average to identify three key market states: Uptrend (bullish): when the ALMA slope angle is above a defined threshold and the price is above ALMA, Downtrend (bearish): when the slope angle is below a negative threshold and the price is below ALMA, Consolidation or sideways trend: when neither of the above conditions is met. A special contribution is the automatic identification of consolidation zones (periods of weak trend or transition between bullish and bearish phases), visually represented by blue-colored candlesticks on the chart. This feature can help traders better recognize moments when the market is indecisive and adjust their strategies accordingly. How the Indicator Works ALMA is calculated using user-defined parameters — length, offset, and sigma — which can be adjusted for different timeframes and instruments. The slope angle of the ALMA line is calculated based on the difference between the current and previous ALMA values, converted into degrees. Based on the slope angle and the relative price position to ALMA, the indicator determines the trend type and changes the candle colors accordingly: Green for bullish (uptrend), Red for bearish (downtrend), Blue for sideways trend (consolidation). When the slope angle falls within a certain range and the price behavior contradicts the trend, the indicator detects consolidation and displays it graphically through semi-transparent boxes and background color. How to Use This Indicator Use candle colors for quick identification of the current trend and potential trend reversals. Pay attention to consolidation zones marked by boxes (blue candles), as these are potential signals for trend breaks or preparation for stronger price moves. ALMA parameters can be adjusted depending on the timeframe and market volatility, providing flexibility in analysis. The indicator is useful for both short-term scalping strategies and longer-term trend monitoring and position management. Why This Indicator is Useful Many existing trend indicators do not consider the slope angle of the moving average as a quantitative measure of trend strength, nor do they automatically detect consolidations as separate zones. ALMA Trend-box fills this gap by combining sophisticated mathematical processing with simple and intuitive visual representation. This way, users get a tool that helps make decisions based on more objective criteria of trend and consolidation rather than just price location relative to averages.
Pine Script® göstergesi
Aleksin_Aleksandar tarafından
3
Gap % Distribution Table (2% Bins)Description This indicator displays a Gap % Distribution Table categorized in 2% bins ranging from `< -20%` to `> +20%`. It calculates the gap between today’s open and the previous day’s close, and groups occurrences into defined bins. The table includes: Gap range, count, and percentage for each bin A total row summarizing all entries Customizable appearance including: Font color, cell background fill (with transparency), and table border color Column headers and full outer border Date filtering using selectable start and end dates Position control for placing the table on the chart area Ideal for analyzing the historical behavior of opening gaps for any instrument.
Pine Script® göstergesi
AMalayaliTrader tarafından
Strategic LevelsIntroduction The Strategic Levels indicator plots key high and low price levels for monthly, weekly, daily, and Monday (current week) timeframes. It draws horizontal lines with consolidated labels to highlight significant support and resistance zones. How to use it ? Identify critical price levels for trade entries, exits, and risk management. These prices levels (monthly, weekly, daily open/close) are significant inflection points during short term price movements. Perfect for swing traders, day traders, or anyone using support/resistance strategies. Best used for trades lasting no more than a few days.
Pine Script® göstergesi
bricevalentin tarafından
Contrarian with 5 Levels5 Levels application was inspired and adapted from Predictive Ranges indicator developed by Lux Algo. So much credit to their work. Indicator Description: Contrarian with 5 Levels Overview The "Contrarian with 5 Levels" indicator is a powerful tool designed for traders seeking to identify potential reversal points in the market by combining contrarian trading principles with dynamic support and resistance levels. This indicator overlays a Simple Moving Average (SMA) shadow and five adaptive price levels, integrating Institutional Concepts of Structure (ICT) such as Break of Structure (BOS) and Market Structure Shift (MSS) to provide clear buy and sell signals. It is ideal for traders looking to capitalize on overextended price movements, particularly on the daily timeframe, though it is adaptable to other timeframes with proper testing. How It Works The indicator operates on two core components: Contrarian SMA Shadow: A shaded region between the SMA of highs and lows (default length: 100) acts as a dynamic zone to identify overbought or oversold conditions. When the price moves significantly outside this shadow, it signals potential exhaustion, aligning with contrarian trading principles. Five Adaptive Levels: Using a modified ATR-based calculation, the indicator plots five key levels (two resistance, one average, and two support) that adjust dynamically to market volatility. These levels serve as critical zones for potential reversals. ICT Structure Analysis: The indicator incorporates BOS and MSS logic to detect shifts in market structure, plotting bullish and bearish breaks with customizable colors for clarity. Buy and sell signals are generated when the price crosses key levels while outside the SMA shadow, indicating potential reversal opportunities. The signals are visualized as small circles above (sell) or below (buy) the price bars, making them easy to interpret. Mathematical Concepts SMA Shadow: The indicator calculates the SMA of the highest highs and lowest lows over a user-defined period (default: 100). This creates a dynamic range that highlights extreme price movements, which contrarian traders often target for reversals. Five Levels Calculation: The five levels are derived using a volatility-adjusted formula based on the Average True Range (ATR). The average level (central pivot) is calculated as a smoothed price, with two upper (resistance) and two lower (support) levels offset by a multiple of the ATR (default multiplier: 6.0). This adaptive approach ensures the levels remain relevant across varying market conditions. ICT BOS/MSS Logic: The indicator identifies pivot highs and lows on a user-defined timeframe (default: daily) to detect structural breaks. A BOS occurs when the price breaks a prior pivot high (bullish) or low (bearish), while an MSS signals a shift in market direction, providing context for potential reversals. Entry and Exit Rules Buy Signal (Blue Dot Below Bar): Triggered when the closing price is below both the SMA shadow (smaLow) and the average level (avg), and the price crosses under either the first or second support level (prS1 or prS2). This suggests the market may be oversold, indicating a potential reversal upward. Sell Signal (White Dot Above Bar): Triggered when the closing price is above both the SMA shadow (smaHigh) and the average level (avg), and the price crosses over either the first or second resistance level (prR1 or prR2). This suggests the market may be overbought, indicating a potential reversal downward. Recommended Usage This indicator is optimized for the daily timeframe, where it has been designed to capture significant reversal opportunities in trending or ranging markets. However, it can be adapted to other timeframes (e.g., 1H, 4H, 15M) with proper testing of settings such as SMA length, ATR multiplier, and structure timeframe. Users are encouraged to backtest and optimize parameters to suit their trading style and asset class. Customization Options SMA Length: Adjust the SMA period (default: 100) to control the sensitivity of the shadow. Five Levels Length and Multiplier: Modify the length (default: 200) and ATR multiplier (default: 6.0) to fine-tune the support/resistance levels. Timeframe Settings: Set separate timeframes for structure analysis and five levels to align with your trading strategy. Color and Signal Display: Customize colors for BOS/MSS lines and toggle buy/sell signals on or off for a cleaner chart. Why Use This Indicator? The "Contrarian with 5 Levels" indicator combines the power of contrarian trading with dynamic levels and market structure analysis, offering a unique perspective for identifying high-probability reversal setups. Its intuitive design, customizable settings, and clear signal visualization make it suitable for both novice and experienced traders. Whether you're trading forex, stocks, or cryptocurrencies, this indicator provides a robust framework for spotting potential turning points in the market. We hope you find the "Contrarian with 5 Levels" indicator a valuable addition to your trading toolkit! Happy trading! Please leave feedback in the comments section.
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chuckaschultz tarafından
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE) ## Advanced Multi-Dimensional Mathematical Analysis System *Where Quantum Mathematics Meets Market Structure* --- ## 🎓 THEORETICAL FOUNDATION The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry. ### 🌊 Multi-Dimensional Volatility with Jump Detection **Hawkes Process Implementation:** The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects. **Mathematical Foundation:** ``` Intensity λ(t) = μ + Σ α(t - Tᵢ) ``` Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99). **Mahalanobis Distance Calculation:** The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions: - **Dimension 1:** Price volatility (standard deviation of returns) - **Dimension 2:** Volume volatility (normalized volume fluctuations) - **Dimension 3:** Range volatility (high-low spread variations) - **Dimension 4:** Correlation volatility (price-volume relationship changes) - **Dimension 5:** Microstructure volatility (intrabar positioning analysis) This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches. ### 📐 Hurst Exponent Regime Detection **Fractal Market Hypothesis Integration:** The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification: - **H > 0.6:** Trending (persistent) markets - momentum strategies optimal - **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal - **H ≈ 0.5:** Random walk markets - breakout strategies preferred **Adaptive R/S Analysis:** Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions: ``` H = log(R/S) / log(n) ``` Where R is the range of cumulative deviations and S is the standard deviation over period n. **Dynamic Regime Classification:** The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods. ### 🔄 Transfer Entropy Analysis **Information Flow Quantification:** Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements: ``` TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log ``` **Causality Detection:** - **Volume → Price:** Indicates accumulation/distribution phases - **Price → Volume:** Suggests retail participation or momentum chasing - **Balanced Flow:** Market equilibrium or transition periods The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows. --- ## 🔧 COMPREHENSIVE INPUT SYSTEM ### Core Parameters Group **Primary Analysis Window (10-100, Default: 50)** The fundamental lookback period affecting all calculations. Optimization by timeframe: - **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements) - **15 minute-1 hour:** 30-50 (balanced responsiveness and stability) - **4 hour-daily:** 50-100 (smooth signals, reduced noise) - **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60 **Signal Sensitivity (0.1-2.0, Default: 0.7)** Master control affecting all threshold calculations: - **Conservative (0.3-0.6):** High-quality signals only, fewer false positives - **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles - **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering **Signal Generation Mode:** - **Aggressive:** Any component signals (highest frequency) - **Confluence:** 2+ components agree (balanced approach) - **Conservative:** All 3 components align (highest quality) ### Volatility Jump Detection Group **Volatility Dimensions (2-5, Default: 3)** Determines the mathematical space complexity: - **2D:** Price + Volume volatility (suitable for clean markets) - **3D:** + Range volatility (optimal for most conditions) - **4D:** + Correlation volatility (advanced multi-asset analysis) - **5D:** + Microstructure volatility (maximum sensitivity) **Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)** Standard deviations required for volatility jump classification: - **Cryptocurrency:** 2.0-2.5σ (naturally volatile) - **Stock Indices:** 2.5-3.0σ (moderate volatility) - **Forex Major Pairs:** 3.0-3.5σ (typically stable) - **Commodities:** 2.0-3.0σ (varies by commodity) **Jump Clustering Decay (0.5-0.99, Default: 0.85)** Hawkes process memory parameter: - **0.5-0.7:** Fast decay (jumps treated as independent) - **0.8-0.9:** Moderate clustering (realistic market behavior) - **0.95-0.99:** Strong clustering (crisis/event-driven markets) ### Hurst Exponent Analysis Group **Calculation Method Options:** - **Classic R/S:** Original Rescaled Range (fast, simple) - **Adaptive R/S:** Dynamic windowing (recommended for trading) - **DFA:** Detrended Fluctuation Analysis (best for noisy data) **Trending Threshold (0.55-0.8, Default: 0.60)** Hurst value defining persistent market behavior: - **0.55-0.60:** Weak trend persistence - **0.65-0.70:** Clear trending behavior - **0.75-0.80:** Strong momentum regimes **Mean Reversion Threshold (0.2-0.45, Default: 0.40)** Hurst value defining anti-persistent behavior: - **0.35-0.45:** Weak mean reversion - **0.25-0.35:** Clear ranging behavior - **0.15-0.25:** Strong reversion tendency ### Transfer Entropy Parameters Group **Information Flow Analysis:** - **Price-Volume:** Classic flow analysis for accumulation/distribution - **Price-Volatility:** Risk flow analysis for sentiment shifts - **Multi-Timeframe:** Cross-timeframe causality detection **Maximum Lag (2-20, Default: 5)** Causality detection window: - **2-5 bars:** Immediate causality (scalping) - **5-10 bars:** Short-term flow (day trading) - **10-20 bars:** Structural flow (swing trading) **Significance Threshold (0.05-0.3, Default: 0.15)** Minimum entropy for signal generation: - **0.05-0.10:** Detect subtle information flows - **0.10-0.20:** Clear causality only - **0.20-0.30:** Very strong flows only --- ## 🎨 ADVANCED VISUAL SYSTEM ### Tensor Volatility Field Visualization **Five-Layer Resonance Bands:** The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength: - **Core Layer (Purple):** Primary tensor field with highest intensity - **Layer 2 (Neutral):** Secondary mathematical resonance - **Layer 3 (Info Blue):** Tertiary harmonic frequencies - **Layer 4 (Warning Gold):** Outer field boundaries - **Layer 5 (Success Green):** Maximum field extension **Field Strength Calculation:** ``` Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity) ``` The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility. **Radiation Line Network:** During active tensor states, the system projects directional radiation lines showing field energy distribution: - **8 Directional Rays:** Complete angular coverage - **Tapering Segments:** Progressive transparency for natural visual flow - **Pulse Effects:** Enhanced visualization during volatility jumps ### Dimensional Portal System **Portal Mathematics:** Dimensional portals visualize regime transitions using category theory principles: - **Green Portals (◉):** Trending regime detection (appear below price for support) - **Red Portals (◎):** Mean-reverting regime (appear above price for resistance) - **Yellow Portals (○):** Random walk regime (neutral positioning) **Tensor Trail Effects:** Each portal generates 8 trailing particles showing mathematical momentum: - **Large Particles (●):** Strong mathematical signal - **Medium Particles (◦):** Moderate signal strength - **Small Particles (·):** Weak signal continuation - **Micro Particles (˙):** Signal dissipation ### Information Flow Streams **Particle Stream Visualization:** Transfer entropy creates flowing particle streams indicating information direction: - **Upward Streams:** Volume leading price (accumulation phases) - **Downward Streams:** Price leading volume (distribution phases) - **Stream Density:** Proportional to information flow strength **15-Particle Evolution:** Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent. ### Fractal Matrix Grid System **Multi-Timeframe Fractal Levels:** The system calculates and displays fractal highs/lows across five Fibonacci periods: - **8-Period:** Short-term fractal structure - **13-Period:** Intermediate-term patterns - **21-Period:** Primary swing levels - **34-Period:** Major structural levels - **55-Period:** Long-term fractal boundaries **Triple-Layer Visualization:** Each fractal level uses three-layer rendering: - **Shadow Layer:** Widest, darkest foundation (width 5) - **Glow Layer:** Medium white core line (width 3) - **Tensor Layer:** Dotted mathematical overlay (width 1) **Intelligent Labeling System:** Smart spacing prevents label overlap using ATR-based minimum distances. Labels include: - **Fractal Period:** Time-based identification - **Topological Class:** Mathematical complexity rating (0, I, II, III) - **Price Level:** Exact fractal price - **Mahalanobis Distance:** Current mathematical field strength - **Hurst Exponent:** Current regime classification - **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡) ### Wick Pressure Analysis **Rejection Level Mathematics:** The system analyzes candle wick patterns to project future pressure zones: - **Upper Wick Analysis:** Identifies selling pressure and resistance zones - **Lower Wick Analysis:** Identifies buying pressure and support zones - **Pressure Projection:** Extends lines forward based on mathematical probability **Multi-Layer Glow Effects:** Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart. ### Enhanced Regime Background **Dynamic Intensity Mapping:** Background colors reflect mathematical regime strength: - **Deep Transparency (98% alpha):** Subtle regime indication - **Pulse Intensity:** Based on regime strength calculation - **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random) **Smoothing Integration:** Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts. ### Color Scheme System **Six Professional Themes:** - **Dark (Default):** Professional trading environment optimization - **Light:** High ambient light conditions - **Classic:** Traditional technical analysis appearance - **Neon:** High-contrast visibility for active trading - **Neutral:** Minimal distraction focus - **Bright:** Maximum visibility for complex setups Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences. --- ## 📊 INSTITUTIONAL-GRADE DASHBOARD ### Tensor Field Status Section **Field Strength Display:** Real-time Mahalanobis distance calculation with dynamic emoji indicators: - **⚡ (Lightning):** Extreme field strength (>1.5× threshold) - **● (Solid Circle):** Strong field activity (>1.0× threshold) - **○ (Open Circle):** Normal field state **Signal Quality Rating:** Democratic algorithm assessment: - **ELITE:** All 3 components aligned (highest probability) - **STRONG:** 2 components aligned (good probability) - **GOOD:** 1 component active (moderate probability) - **WEAK:** No clear component signals **Threshold and Anomaly Monitoring:** - **Threshold Display:** Current mathematical threshold setting - **Anomaly Level (0-100%):** Combined volatility and volume spike measurement - **>70%:** High anomaly (red warning) - **30-70%:** Moderate anomaly (orange caution) - **<30%:** Normal conditions (green confirmation) ### Tensor State Analysis Section **Mathematical State Classification:** - **↑ BULL (Tensor State +1):** Trending regime with bullish bias - **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias - **◈ SUPER (Tensor State 0):** Random walk regime (neutral) **Visual State Gauge:** Five-circle progression showing tensor field polarity: - **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment - **⚪⚪🟡⚪⚪:** Neutral/transitional state - **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment **Trend Direction and Phase Analysis:** - **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification - **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength) - **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength) - **😴 CALM:** Low information flow (<1.0 flow strength) ### Trading Signals Section **Real-Time Signal Status:** - **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability - **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability - **Components (X/3):** Active algorithmic components - **Mode Display:** Current signal generation mode **Signal Strength Visualization:** Color-coded component count: - **Green:** 3/3 components (maximum confidence) - **Aqua:** 2/3 components (good confidence) - **Orange:** 1/3 components (moderate confidence) - **Gray:** 0/3 components (no signals) ### Performance Metrics Section **Win Rate Monitoring:** Estimated win rates based on signal quality with emoji indicators: - **🔥 (Fire):** ≥60% estimated win rate - **👍 (Thumbs Up):** 45-59% estimated win rate - **⚠️ (Warning):** <45% estimated win rate **Mathematical Metrics:** - **Hurst Exponent:** Real-time fractal dimension (0.000-1.000) - **Information Flow:** Volume/price leading indicators - **📊 VOL:** Volume leading price (accumulation/distribution) - **💰 PRICE:** Price leading volume (momentum/speculation) - **➖ NONE:** Balanced information flow - **Volatility Classification:** - **🔥 HIGH:** Above 1.5× jump threshold - **📊 NORM:** Normal volatility range - **😴 LOW:** Below 0.5× jump threshold ### Market Structure Section (Large Dashboard) **Regime Classification:** - **📈 TREND:** Hurst >0.6, momentum strategies optimal - **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal - **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred **Mathematical Field Analysis:** - **Dimensions:** Current volatility space complexity (2D-5D) - **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00) - **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal) ### Settings Summary Section (Large Dashboard) **Active Configuration Display:** - **Sensitivity:** Current master sensitivity setting - **Lookback:** Primary analysis window - **Theme:** Active color scheme - **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA) **Dashboard Sizing Options:** - **Small:** Essential metrics only (mobile/small screens) - **Normal:** Balanced information density (standard desktop) - **Large:** Maximum detail (multi-monitor setups) **Position Options:** - **Top Right:** Standard placement (avoids price action) - **Top Left:** Wide chart optimization - **Bottom Right:** Recent price focus (scalping) - **Bottom Left:** Maximum price visibility (swing trading) --- ## 🎯 SIGNAL GENERATION LOGIC ### Multi-Component Convergence System **Component Signal Architecture:** The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing: **Volatility Component:** - **Jump Detection:** Mahalanobis distance threshold breach - **Hawkes Intensity:** Self-exciting process activation (>0.2) - **Multi-dimensional:** Considers all volatility dimensions simultaneously **Hurst Regime Component:** - **Trending Markets:** Price above SMA-20 with positive momentum - **Mean-Reverting Markets:** Price at Bollinger Band extremes - **Random Markets:** Bollinger squeeze breakouts with directional confirmation **Transfer Entropy Component:** - **Volume Leadership:** Information flow from volume to price - **Volume Spike:** Volume 110%+ above 20-period average - **Flow Significance:** Above entropy threshold with directional bias ### Democratic Signal Weighting **Signal Mode Implementation:** - **Aggressive Mode:** Any single component triggers signal - **Confluence Mode:** Minimum 2 components must agree - **Conservative Mode:** All 3 components must align **Momentum Confirmation:** All signals require momentum confirmation: - **Long Signals:** RSI >50 AND price >EMA-9 - **Short Signals:** RSI <50 AND price 0.6):** - **Increase Sensitivity:** Catch momentum continuation - **Lower Mean Reversion Threshold:** Avoid counter-trend signals - **Emphasize Volume Leadership:** Institutional accumulation/distribution - **Tensor Field Focus:** Use expansion for trend continuation - **Signal Mode:** Aggressive or Confluence for trend following **Range-Bound Markets (Hurst <0.4):** - **Decrease Sensitivity:** Avoid false breakouts - **Lower Trending Threshold:** Quick regime recognition - **Focus on Price Leadership:** Retail sentiment extremes - **Fractal Grid Emphasis:** Support/resistance trading - **Signal Mode:** Conservative for high-probability reversals **Volatile Markets (High Jump Frequency):** - **Increase Hawkes Decay:** Recognize event clustering - **Higher Jump Threshold:** Avoid noise signals - **Maximum Dimensions:** Capture full volatility complexity - **Reduce Position Sizing:** Risk management adaptation - **Enhanced Visuals:** Maximum information for rapid decisions **Low Volatility Markets (Low Jump Frequency):** - **Decrease Jump Threshold:** Capture subtle movements - **Lower Hawkes Decay:** Treat moves as independent - **Reduce Dimensions:** Simplify analysis - **Increase Position Sizing:** Capitalize on compressed volatility - **Minimal Visuals:** Reduce distraction in quiet markets --- ## 🚀 ADVANCED TRADING STRATEGIES ### The Mathematical Convergence Method **Entry Protocol:** 1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels 2. **Tensor Field Confirmation:** Verify field expansion supporting direction 3. **Portal Signal:** Wait for dimensional portal appearance 4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals 5. **Component Consensus:** Confirm 2+ components agree in Confluence mode **Example Implementation:** - Price approaching 21-period fractal high - Tensor field expanding upward (bullish mathematical alignment) - Green portal appears below price (trending regime confirmation) - ELITE quality signal with 3/3 components active - Enter long position with stop below fractal level **Risk Management:** - **Stop Placement:** Below/above fractal level that generated signal - **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size) - **Profit Targets:** Next fractal level or tensor field resistance ### The Regime Transition Strategy **Regime Change Detection:** 1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds 2. **Portal Color Change:** Regime transitions show different portal colors 3. **Background Intensity:** Increasing regime background intensity 4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis) **Trading Implementation:** - **Trending Transitions:** Trade momentum breakouts, follow trend - **Mean Reversion Transitions:** Trade range boundaries, fade extremes - **Random Transitions:** Trade breakouts with tight stops **Advanced Techniques:** - **Multi-Timeframe:** Confirm regime on higher timeframe - **Early Entry:** Enter on regime transition rather than confirmation - **Regime Strength:** Larger positions during strong regime signals ### The Information Flow Momentum Strategy **Flow Detection Protocol:** 1. **Monitor Transfer Entropy:** Watch for significant information flow shifts 2. **Volume Leadership:** Strong edge when volume leads price 3. **Flow Acceleration:** Increasing flow strength indicates momentum 4. **Directional Confirmation:** Ensure flow aligns with intended trade direction **Entry Signals:** - **Volume → Price Flow:** Enter during accumulation/distribution phases - **Price → Volume Flow:** Enter on momentum confirmation breaks - **Flow Reversal:** Counter-trend entries when flow reverses **Optimization:** - **Scalping:** Use immediate flow detection (2-5 bar lag) - **Swing Trading:** Use structural flow (10-20 bar lag) - **Multi-Asset:** Compare flow between correlated assets ### The Tensor Field Expansion Strategy **Field Mathematics:** The tensor field expansion indicates mathematical pressure building in market structure: **Expansion Phases:** 1. **Compression:** Field contracts, volatility decreases 2. **Tension Building:** Mathematical pressure accumulates 3. **Expansion:** Field expands rapidly with directional movement 4. **Resolution:** Field stabilizes at new equilibrium **Trading Applications:** - **Compression Trading:** Prepare for breakout during field contraction - **Expansion Following:** Trade direction of field expansion - **Reversion Trading:** Fade extreme field expansion - **Multi-Dimensional:** Consider all field layers for confirmation ### The Hawkes Process Event Strategy **Self-Exciting Jump Trading:** Understanding that market shocks cluster and create follow-on opportunities: **Jump Sequence Analysis:** 1. **Initial Jump:** First volatility jump detected 2. **Clustering Phase:** Hawkes intensity remains elevated 3. **Follow-On Opportunities:** Additional jumps more likely 4. **Decay Period:** Intensity gradually decreases **Implementation:** - **Jump Confirmation:** Wait for mathematical jump confirmation - **Direction Assessment:** Use other components for direction - **Clustering Trades:** Trade subsequent moves during high intensity - **Decay Exit:** Exit positions as Hawkes intensity decays ### The Fractal Confluence System **Multi-Timeframe Fractal Analysis:** Combining fractal levels across different periods for high-probability zones: **Confluence Zones:** - **Double Confluence:** 2 fractal levels align - **Triple Confluence:** 3+ fractal levels cluster - **Mathematical Confirmation:** Tensor field supports the level - **Information Flow:** Transfer entropy confirms direction **Trading Protocol:** 1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR 2. **Mathematical Support:** Verify tensor field alignment 3. **Signal Quality:** Wait for STRONG or ELITE signal 4. **Risk Definition:** Use fractal level for stop placement 5. **Profit Targeting:** Next major fractal confluence zone --- ## ⚠️ COMPREHENSIVE RISK MANAGEMENT ### Mathematical Position Sizing **Mahalanobis Distance Integration:** Position size should inversely correlate with mathematical field strength: ``` Position Size = Base Size × (Threshold / Mahalanobis Distance) ``` **Risk Scaling Matrix:** - **Low Field Strength (<2.0):** Standard position sizing - **Moderate Field Strength (2.0-3.0):** 75% position sizing - **High Field Strength (3.0-4.0):** 50% position sizing - **Extreme Field Strength (>4.0):** 25% position sizing or no trade ### Signal Quality Risk Adjustment **Quality-Based Position Sizing:** - **ELITE Signals:** 100% of planned position size - **STRONG Signals:** 75% of planned position size - **GOOD Signals:** 50% of planned position size - **WEAK Signals:** No position or paper trading only **Component Agreement Scaling:** - **3/3 Components:** Full position size - **2/3 Components:** 75% position size - **1/3 Components:** 50% position size or skip trade ### Regime-Adaptive Risk Management **Trending Market Risk:** - **Wider Stops:** Allow for trend continuation - **Trend Following:** Trade with regime direction - **Higher Position Size:** Trend probability advantage - **Momentum Stops:** Trail stops based on momentum indicators **Mean-Reverting Market Risk:** - **Tighter Stops:** Quick exits on trend continuation - **Contrarian Positioning:** Trade against extremes - **Smaller Position Size:** Higher reversal failure rate - **Level-Based Stops:** Use fractal levels for stops **Random Market Risk:** - **Breakout Focus:** Trade only clear breakouts - **Tight Initial Stops:** Quick exit if breakout fails - **Reduced Frequency:** Skip marginal setups - **Range-Based Targets:** Profit targets at range boundaries ### Volatility-Adaptive Risk Controls **High Volatility Periods:** - **Reduced Position Size:** Account for wider price swings - **Wider Stops:** Avoid noise-based exits - **Lower Frequency:** Skip marginal setups - **Faster Exits:** Take profits more quickly **Low Volatility Periods:** - **Standard Position Size:** Normal risk parameters - **Tighter Stops:** Take advantage of compressed ranges - **Higher Frequency:** Trade more setups - **Extended Targets:** Allow for compressed volatility expansion ### Multi-Timeframe Risk Alignment **Higher Timeframe Trend:** - **With Trend:** Standard or increased position size - **Against Trend:** Reduced position size or skip - **Neutral Trend:** Standard position size with tight management **Risk Hierarchy:** 1. **Primary:** Current timeframe signal quality 2. **Secondary:** Higher timeframe trend alignment 3. **Tertiary:** Mathematical field strength 4. **Quaternary:** Market regime classification --- ## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS ### Advanced Mathematical Concepts **Tensor Analysis in Markets:** The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture. **Information Theory Applications:** Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior. **Fractal Geometry in Trading:** The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure. **Stochastic Process Theory:** The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects. ### Learning Progressive Complexity **Beginner Mathematical Concepts:** - **Volatility Dimensions:** Understanding multi-dimensional analysis - **Regime Classification:** Learning market personality types - **Signal Democracy:** Algorithmic consensus building - **Visual Mathematics:** Interpreting mathematical concepts visually **Intermediate Mathematical Applications:** - **Mahalanobis Distance:** Statistical distance in multi-dimensional space - **Rescaled Range Analysis:** Fractal dimension measurement - **Information Entropy:** Quantifying uncertainty and causality - **Field Theory:** Understanding mathematical fields in market context **Advanced Mathematical Integration:** - **Tensor Field Dynamics:** Multi-dimensional market force analysis - **Stochastic Self-Excitation:** Event clustering and memory effects - **Categorical Composition:** Mathematical signal combination theory - **Topological Market Analysis:** Understanding market shape and connectivity ### Practical Mathematical Intuition **Developing Market Mathematics Intuition:** The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of: - **How markets exhibit mathematical structure beneath apparent randomness** - **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches** - **How information flows through markets in measurable, predictable ways** - **Why mathematical models provide probabilistic edges rather than certainties** --- ## 🔬 IMPLEMENTATION AND OPTIMIZATION ### Getting Started Protocol **Phase 1: Observation (Week 1)** 1. **Apply with defaults:** Use standard settings on your primary trading timeframe 2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams 3. **Monitor dashboard:** Observe how metrics change with market conditions 4. **No trading:** Focus entirely on pattern recognition and understanding **Phase 2: Pattern Recognition (Week 2-3)** 1. **Identify signal patterns:** Note what market conditions produce different signal qualities 2. **Regime correlation:** Observe how Hurst regimes affect signal performance 3. **Visual confirmation:** Learn to read tensor field expansion and portal signals 4. **Component analysis:** Understand which components drive signals in different markets **Phase 3: Parameter Optimization (Week 4-5)** 1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument 2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe 3. **Sensitivity adjustment:** Balance signal frequency with quality 4. **Visual customization:** Optimize colors and intensity for your trading environment **Phase 4: Live Implementation (Week 6+)** 1. **Paper trading:** Test signals with hypothetical trades 2. **Small position sizing:** Begin with minimal risk during learning phase 3. **Performance tracking:** Monitor actual vs. expected signal performance 4. **Continuous optimization:** Refine settings based on real performance data ### Performance Monitoring System **Signal Quality Tracking:** - **ELITE Signal Win Rate:** Track highest quality signals separately - **Component Performance:** Monitor which components provide best signals - **Regime Performance:** Analyze performance across different market regimes - **Timeframe Analysis:** Compare performance across different session times **Mathematical Metric Correlation:** - **Field Strength vs. Performance:** Higher field strength should correlate with better performance - **Component Agreement vs. Win Rate:** More component agreement should improve win rates - **Regime Alignment vs. Success:** Trading with mathematical regime should outperform ### Continuous Optimization Process **Monthly Review Protocol:** 1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown 2. **Parameter Assessment:** Evaluate if current settings remain optimal 3. **Market Adaptation:** Adjust for changes in market character or volatility 4. **Component Weighting:** Consider if certain components should receive more/less emphasis **Quarterly Deep Analysis:** 1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid 2. **Regime Distribution:** Analyze time spent in different market regimes 3. **Signal Evolution:** Track how signal characteristics change over time 4. **Correlation Analysis:** Monitor correlations between different mathematical components --- ## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS ### Revolutionary Mathematical Integration **First-Ever Implementations:** 1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility 2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes 3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation 4. **Democratic Component Voting:** First algorithmic consensus system for signal generation 5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels ### Advanced Visualization Innovations **Mathematical Visualization Breakthroughs:** - **Tensor Field Radiation:** Visual representation of mathematical field energy - **Dimensional Portal System:** Category theory visualization for regime transitions - **Information Flow Streams:** Real-time visual display of market information transfer - **Multi-Layer Fractal Grid:** Intelligent spacing and projection system - **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength ### Practical Trading Innovations **Trading System Advances:** - **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence - **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality - **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering - **Component Performance Tracking:** Real-time monitoring of algorithmic component success - **Field-Strength Position Sizing:** Mathematical volatility integration for risk management --- ## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS ### Mathematical Model Limitations **Understanding Model Boundaries:** While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations: - **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements - **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely - **Model Risk:** Complex models can fail during unprecedented market conditions - **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions ### Proper Implementation Guidelines **Risk Management Requirements:** - **Never Risk More Than 2% Per Trade:** Regardless of signal quality - **Diversification Mandatory:** Don't rely solely on mathematical signals - **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence - **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters **Realistic Expectations:** - **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes - **Learning Curve Required:** Complex mathematical concepts require time to master - **Market Adaptation Necessary:** Parameters must evolve with changing market conditions - **Continuous Education Important:** Understanding underlying mathematics improves application ### Ethical Trading Considerations **Market Impact Awareness:** - **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants - **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure - **Sharing Knowledge:** Consider educational contributions to trading community - **Fair Market Participation:** Use mathematical advantages responsibly within market framework ### Professional Development Path **Skill Development Sequence:** 1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application 2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals 3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights 4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution --- ## 🔮 CONCLUSION The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability. ### Mathematical Innovation Legacy This indicator establishes new paradigms in technical analysis: - **Tensor analysis for market volatility understanding** - **Stochastic self-excitation for event clustering prediction** - **Information theory for causality-based trade generation** - **Democratic algorithmic consensus for signal quality enhancement** - **Mathematical field visualization for intuitive market understanding** ### Practical Trading Revolution Beyond mathematical innovation, the TMAE transforms practical trading: - **Quality-rated signals replace binary buy/sell decisions** - **Regime-adaptive strategies automatically optimize for market personality** - **Multi-dimensional risk management integrates mathematical volatility measures** - **Visual mathematical concepts make complex analysis immediately interpretable** - **Educational value creates lasting improvement in trading understanding** ### Future-Proof Design The mathematical foundations ensure lasting relevance: - **Universal mathematical principles transcend market evolution** - **Multi-dimensional analysis adapts to new market structures** - **Regime detection automatically adjusts to changing market personalities** - **Component democracy allows for future algorithmic additions** - **Mathematical visualization scales with increasing market complexity** ### Commitment to Excellence The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value. ### Trading with Mathematical Precision In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals. By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics. ### Final Perspective Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure. Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior. Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE. *"In mathematics, you don't understand things. You just get used to them." - John von Neumann* *With the TMAE, mathematical market understanding becomes not just possible, but intuitive.* — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® göstergesi
DskyzInvestments tarafından
10
80% Rule Indicator (ETH Session + SVP Prior Session)I created this script to show the 80% opportunity on chart if setting lines up. "80% rule: Open outside the vah or Val. Spend 30 mins outside there then break back inside spend 15 mins below or above depending which way u broke. Then come back and retest the vah/val and take it to the poc as a first target with the final target being the other Val/vah " 📌 Script Summary The "80% Rule Indicator (ETH Session + SVP Prior Session)" overlays your chart with prior session value area levels (VAH, VAL, and POC) calculated from extended-hours 30-minute data. It tracks when the price reenters the value area and confirms 80% Rule setups during your chosen trading session. You can optionally trigger alerts, show/hide market sessions, and fine-tune line appearance for a clean, modular workflow. ⚙️ Options & Settings Breakdown - Use 24-Hour Session (All Markets) When checked, the indicator ignores time zones and tracks signals during a full 24-hour period (0000-0000), helpful if you're outside U.S. trading hours or want consistent behavior globally. - Market Session Dropdown to select one of three key market zones: - New York (09:30–16:00 ET) - London (08:00–16:30 local) - Tokyo (09:00–15:00 local) Used to gate entry signals during relevant hours unless you choose the 24-hour option. - Show PD VAH/VAL/POC Lines Toggle to show or hide prior day’s levels (based on the 30-min extended session). Turning this off removes both the lines and their white text labels. - Extend Lines Right When enabled, the VAH/VAL/POC lines extend into the current day’s session. If disabled, they appear only at their anchor point. - Highlight Selected Session Adds a soft blue background to help visualize the active session you selected. - Enable Alert Conditions Allows TradingView alerts to be created for long/short 80% Rule entries. - Enable Audible Alerts Plays an in-chart sound with a popup message (“80% Rule LONG” or “SHORT”) when signals trigger. Requires the chart to be active and sounds enabled in TradingView.
Pine Script® göstergesi
dscottmuller tarafından
1
Percent Change IndicatorPercent Change Indicator Description Overview: The Percent Change Indicator is a Pine Script (version 6) indicator designed for TradingView to calculate and visualize the percentage change of the current close price relative to a user-selected reference price. It provides a customizable interface to display percentage changes as candlesticks or a line plot, with optional horizontal lines and labels for key levels. The indicator also includes visual signals and alerts for user-defined percentage thresholds, making it useful for identifying significant price movements. Key Features: 1. Percentage Change Calculation: - Computes the percentage change of the current close price compared to a reference price, scaled by a user-defined length parameter. - Formula: percentChange = (close - refPrice) / refPrice * len - The reference price is sourced from a user-selected timeframe (default: 1D) and price type (Open, High, Low, Close, HL2, HLC3, or HLCC4). 2. Visualization Options: - Candlestick Plot: Displays percentage change as candlesticks, colored green for rising values and red for falling values. - Line Plot: Plots the percentage change as a line, with the same color logic. - Horizontal Lines: Optional horizontal lines at key percentage levels (0%, ±0.2%, ±0.5%, ±0.8%, ±1%) for reference. - Labels: Optional labels for percentage levels (0, ±15%, ±35%, ±50%, ±65%, ±85%, ±100%) displayed at the chart's right edge. - All visualizations are toggleable via input settings. 3. Signal and Alert System: - Threshold-Based Signals: Plots green triangles below bars for long signals (percent change above a user-defined threshold) and red triangles above bars for short signals (percent change below the threshold). - Alerts: Configurable alerts for long and short conditions, triggered when the percentage change crosses the user-defined threshold (default: 2%). Alert messages include the threshold value for clarity. 4. Customizable Inputs: - Show Labels: Toggle visibility of percentage level labels (default: true). - Show Percentage Change: Toggle the line plot of percentage change (default: true). - Show HLines: Toggle visibility of horizontal reference lines (default: false). - Show Candle Plot: Toggle the candlestick plot (default: true). - Percent Change Length: Adjust the scaling factor for percentage change (default: 14). - Plot Timeframe: Select the timeframe for the reference price (default: 1D). - Price Type: Choose the reference price type (Open, High, Low, Close, HL2, HLC3, HLCC4; default: Open). - Percentage Threshold: Set the threshold for long/short signals and alerts (default: 0.02 or 2%). How It Works: - The indicator fetches the reference price using request.security() based on the selected timeframe and price type. - It calculates the percentage change and scales it by the user-defined length. - Visuals (candlesticks, lines, labels, horizontal lines) are plotted based on user preferences. - Long and short signals are generated when the percentage change exceeds or falls below the user-defined threshold, with corresponding triangles plotted and alerts triggered. Use Cases: - Trend Identification: Monitor significant price movements relative to a reference price. - Signal Generation: Identify potential entry/exit points based on percentage change thresholds. - Custom Analysis: Analyze price changes across different timeframes and price types for various trading strategies. - Alert Notifications: Receive alerts for significant price movements to stay informed without constant chart monitoring. Setup Instructions: 1. Add the indicator to a TradingView chart. 2. Adjust input settings (timeframe, price type, threshold, etc.) to suit your analysis. 3. Enable/disable visualization options (candlesticks, lines, labels, horizontal lines) as needed. 4. Set up alerts in TradingView: - Go to the "Alerts" tab and select "Percent Change Indicator." - Choose "Long Alert" or "Short Alert" to monitor threshold crossings. - Configure alert frequency and notification method (e.g., email, webhook). Notes: - The indicator is non-overlay, displayed in a separate pane below the main chart. - Alerts trigger on bar close by default; adjust TradingView alert settings for real-time notifications if needed. - The indicator is released under the Mozilla Public License 2.0. Author: Dshergill This indicator is ideal for traders seeking a flexible tool to track percentage-based price movements with customizable visuals and alerts.
Pine Script® göstergesi
Dshergill tarafından
2
Greer Value Yields Dashboard🧾 Greer Value Yields Dashboard – v1.0 Author: Sean Lee Greer Release Date: June 22, 2025 🧠 Overview The Greer Value Yields Dashboard visualizes and evaluates four powerful valuation metrics for any publicly traded company: 📘 Earnings per Share Yield 💵 Free Cash Flow Yield 💰 Revenue Yield 🏦 Book Value Yield Each yield is measured as a percentage of current stock price and compared against its historical average. The script assigns 1 point per metric when the current yield exceeds its long-term average. The total score (0 to 4) is displayed as a color-coded column chart, helping long-term investors quickly assess fundamental valuation strength. ✅ Key Features 📊 Real-time calculation of 4 yield-based valuation metrics ⚖ Historical average tracking for each yield 🎯 Visual scoring system: 🟥 0–1 = Weak 🟨 2 = Neutral 🟩 4 = Strong (all metrics above average) 🎛️ Toggle visibility of each yield independently 🧮 Fully compatible with other Greer Financial Toolkit indicators 🛠 Ideal For Long-term value investors Dividend and cash-flow-focused investors Analysts seeking clean yield visualizations Greer Toolkit users combining with Greer Value and BuyZone
Pine Script® göstergesi
controllinghand tarafından
Universal Sentiment Oscillator with Trade RecommendationsUniversal Sentiment Oscillator & Strategy Guide Summary This all-in-one indicator is designed to be a comprehensive co-pilot for your trading journey. It moves beyond simple buy/sell signals by analyzing the underlying market sentiment and providing a dynamic, risk-assessed guide of potential trading strategies. Whether you're a novice learning the ropes or an expert seeking confirmation, this tool provides a structured framework for making smarter, more informed decisions in stocks, options, and futures. How It Works The core of the indicator is the Sentiment Oscillator, which calculates a score from -5 (Extremely Bearish) to +5 (Extremely Bullish) on every bar. This isn't just a single measurement; it's a weighted aggregate of several key technical conditions: Trend Analysis: Price position relative to the 20, 50, and 200 EMAs. Momentum Analysis: The current RSI value. Hybrid Analysis: The state of the MACD and its signal line. These factors are intelligently combined and normalized to produce a single, intuitive sentiment score, giving you an at-a-glance understanding of the market's pulse. Core Features Dynamic Trade Recommendation Table: The informational heart of the indicator. This on-chart table provides a list of potential trades perfectly aligned with the current sentiment score. Risk-Ranked Strategies: All suggested trades are logically ordered by risk, helping you quickly identify strategies that match your comfort level. Adjusted Trade Suggestions: The indicator analyzes sentiment momentum (the score vs. its signal line) to provide proactive, forward-looking trade ideas based on where the market might be heading next. Customizable Trading Styles: Tell the indicator if you are a Conservative, Neutral, or Aggressive trader, and the "Adjusted Trade Suggestion" will automatically tailor its recommendations to your personal risk preference. Context-Aware Futures Mode: When viewing a futures contract, enable this mode to switch all recommendations from stock/options to futures-specific actions (e.g., "Cautious Long," "Monitor Range"). Predictive Sentiment Cone: Visualize the potential short-term path of sentiment based on current momentum, helping you anticipate future conditions. Fully Customizable: Every parameter—from EMA lengths to trade filters—can be adjusted, allowing you to fine-tune the indicator to your exact specifications. How to Use This Indicator This tool is flexible and can be integrated into many trading systems. Here is a powerful, professional approach: Top-Down Analysis (for Swing or Position Trading): Establish the Trend: Start on the higher timeframes (Monthly, Weekly, Daily). Use the oscillator's color and score to define the dominant, long-term market sentiment. You only want to look for trades that align with this macro trend. Refine the Entry: Drop down to the medium timeframes (4-Hour, 1-Hour). Wait for the sentiment on these charts to come into alignment with the higher-timeframe trend. This pullback or consolidation is your "zone of interest." Pinpoint the Execution: Move to a lower timeframe (e.g., 15-Minute). Use the Adjusted Trade Suggestion and Sentiment Momentum to find a precise entry as momentum begins to shift back in the direction of the primary trend. You can set alerts on the oscillator's zero-line for early warnings of a sentiment shift. As a Confirmation Tool: If you have an existing trade idea, use the indicator to validate it. Does the sentiment score align with your bullish or bearish thesis? Does the momentum confirm that now is a good time to enter? As an Idea Generation Tool: Unsure what to trade? Browse different assets and let the indicator's "Primary Trades" and "Adjusted Trade Suggestion" present you with a list of risk-assessed ideas that you can then investigate further. Disclaimer: This is an analysis tool and should not be considered financial advice. All forms of trading involve substantial risk. You should not trade with money you cannot afford to lose. Always perform your own due diligence and use this indicator as one component of a complete trading plan.
Pine Script® göstergesi
heliav8r tarafından
AdvancedOFPIAnalyzerLibrary "AdvancedOFPIAnalyzer" Advanced Order Flow Pressure Index Analyzer Library Implements sophisticated volume distribution analysis with candle microstructure Provides comprehensive order flow assessment for institutional activity detection analyzeAdvancedOrderFlow(priceOpen, priceHigh, priceLow, priceClose, volumeData, analysisWindow, institutionalSensitivity)   Performs comprehensive order flow analysis with advanced institutional detection   Parameters:      priceOpen (float) : float Opening price for analysis      priceHigh (float) : float High price for range calculation      priceLow (float) : float Low price for support detection      priceClose (float) : float Closing price for trend assessment      volumeData (float) : float Volume data for flow analysis      analysisWindow (int) : int Analysis window period      institutionalSensitivity (float) : float Institutional detection sensitivity   Returns: OFPI, momentum, institutional detected, strength, phase, overall strength, class, volume available, trend, efficiency, market structure calculateMicrostructurePressure(priceOpen, priceHigh, priceLow, priceClose, volumeData, microWindow)   Calculates sophisticated order flow pressure with comprehensive candle microstructure analysis   Parameters:      priceOpen (float) : float Opening price for pressure calculation      priceHigh (float) : float High price for range analysis      priceLow (float) : float Low price for support detection      priceClose (float) : float Closing price for trend assessment      volumeData (float) : float Volume data for pressure analysis      microWindow (int) : int Microstructure analysis window   Returns: Pressure index, buying pressure, selling pressure, body ratio, upper wick ratio, lower wick ratio, microstructure confidence, volume confirmation, institutional pressure, pressure velocity, microstructure quality generateInstitutionalAlerts(priceClose, volumeData, alertSensitivity, lookbackPeriod)   Generates sophisticated volume-weighted institutional activity alerts   Parameters:      priceClose (float) : float Close price for analysis      volumeData (float) : float Volume data for detection      alertSensitivity (float) : float Alert sensitivity threshold      lookbackPeriod (int) : int Analysis lookback period   Returns: Institutional detected, alert level, phase, strength, volume signature, pressure signature, time signature, absorption signature, impact signature, reliability, active methods, priority
Pine Script® kütüphanesi
Infinitefutures tarafından