Dynamic Trade Signal Validator (DTSV)The Dynamic Trade Signal Validator (DTSV) is designed to filter false trade signals while generating reliable, frequent trade opportunities. False signals, which lead to unprofitable trades, often occur in choppy or low-momentum markets. The DTSV combines Hull Moving Average (HMA) crossovers, Average True Range (ATR) breakout confirmation, and MACD histogram momentum filtering to ensure signals align with trend, volatility, and momentum, making it ideal for day trading or swing trading across assets like stocks, forex, or cryptocurrencies.
How It Works
The DTSV uses three components to validate trade signals, balancing frequency and reliability:
HMA Crossover for Trend Direction:
Two HMAs (default: 9-period fast, 21-period slow) detect trend changes. A buy signal triggers when the fast HMA crosses above the slow HMA (bullish), and a sell signal when it crosses below (bearish). HMAs reduce lag compared to traditional MAs, enabling more responsive trend detection.
ATR Breakout Confirmation:
The 14-period ATR ensures significant price movement by requiring the bar’s range (high minus low) to exceed the ATR multiplied by 1.0 (adjustable). This confirms volatility, reducing false signals in stagnant markets.
MACD Histogram Momentum Filter:
The MACD (default: 12, 26, 9) histogram confirms momentum. Buy signals require a positive histogram (bullish momentum), and sell signals need a negative histogram (bearish momentum), ensuring directional strength.
Signal Generation
Buy signals (green triangles below bars) occur when a bullish HMA crossover, ATR breakout, and positive MACD histogram align. Sell signals (red triangles above bars) require a bearish crossover, ATR breakout, and negative histogram. This triple confirmation minimizes false trades while maintaining frequent signals.
Indicatori e strategie
Smart Trend Lines [The_lurker]
Smart Trend Lines
A multi-level trend classifier that detects bullish and bearish conditions using a methodology based on drawing trend lines—main, intermediate, and short-term—by identifying peaks and troughs. The tool highlights trend strength by applying filters such as the Average Directional Index (ADX) (A), Relative Strength Index (RSI) (R), and Volume (V), making it easier to interpret trend strength. The filter markers (V, A, R) in the Smart Trend Lines indicator are powerful tools for assessing the reliability of breakouts. Breakouts containing are the most reliable, as they indicate strong volume support, trend strength, and favorable momentum. Breakouts with partial filters (such as or ) require additional confirmation, while breakouts without filters ( ) should be avoided unless supported by other strong signals. By understanding the meaning of each filter and the market context.
Core Functionality
1. Trend Line Types
The indicator generates three distinct trend line categories, each serving a specific analytical purpose:
Main Trend Lines: These are long-term trend lines designed to capture significant market trends. They are calculated based on pivot points over a user-defined period (default: 50 bars). Main trend lines are ideal for identifying macro-level support and resistance zones.
Mid Trend Lines: These are medium-term trend lines (default: 21 bars) that focus on intermediate price movements. They provide a balance between short-term fluctuations and long-term trends, suitable for swing trading strategies.
Short Trend Lines: These are short-term trend lines (default: 9 bars) that track rapid price changes. They are particularly useful for scalping or day trading, highlighting immediate support and resistance levels.
Each trend line type can be independently enabled or disabled, allowing traders to tailor the indicator to their preferred timeframes.
2. Breakout Detection
The indicator employs a robust breakout detection system that identifies when the price crosses a trend line, signaling a potential trend reversal or continuation. Breakouts are validated using the following filters:
ADX Filter: The Average Directional Index (ADX) measures trend strength. A user-defined threshold (default: 20) ensures that breakouts occur during strong trends, reducing false signals in range-bound markets.
RSI Filter: The Relative Strength Index (RSI) identifies overbought or oversold conditions. Breakouts are filtered based on RSI thresholds (default: 65 for overbought, 35 for oversold) to avoid signals in extreme market conditions.
Volume Filter: Breakouts are confirmed only when trading volume exceeds a moving average (default: 20 bars) and aligns with the breakout direction (e.g., higher volume on bullish breakouts when the candle closes higher).
Breakout events are marked with labels on the chart, indicating the type of trend line broken (Main, Mid, or Short) and the filters satisfied (Volume, ADX, RSI). Alerts are triggered for each breakout, providing real-time notifications.
3. Customization Options
The indicator offers extensive customization through input settings, organized into logical groups for ease of use:
Main Trend Line Settings
Length: Defines the number of bars used to calculate pivot points (default: 50).
Bullish Color: Color for upward-sloping (bullish) main trend lines (default: green).
Bearish Color: Color for downward-sloping (bearish) main trend lines (default: red).
Style: Line style options include solid, dashed, or dotted (default: solid).
Mid Trend Line Settings
Length: Number of bars for mid-term pivot points (default: 21).
Show/Hide: Toggle visibility of mid trend lines (default: enabled).
Bullish Color: Color for bullish mid trend lines (default: lime).
Bearish Color: Color for bearish mid trend lines (default: maroon).
Style: Line style (default: dashed).
Short Trend Line Settings
Length: Number of bars for short-term pivot points (default: 9).
Show/Hide: Toggle visibility of short trend lines (default: enabled).
Bullish Color: Color for bullish short trend lines (default: teal).
Bearish Color: Color for bearish short trend lines (default: purple).
Style: Line style (default: dotted).
General Display Settings
Break Check Price: Selects the price type for breakout detection (Close, High, or Low; default: Close).
Show Previous Trendlines: Option to display historical main trend lines (default: disabled).
Label Size: Size of breakout labels (Tiny, Small, Normal, Large, Huge; default: Small).
Filter Settings
ADX Threshold: Minimum ADX value for trend strength confirmation (default: 25).
Volume MA Period: Period for the volume moving average (default: 20).
RSI Filter: Enable/disable RSI filtering (default: enabled).
RSI Upper Threshold: Upper RSI limit for overbought conditions (default: 65).
RSI Lower Threshold: Lower RSI limit for oversold conditions (default: 35).
4. Technical Calculations
The indicator relies on several technical calculations to ensure accuracy:
Pivot Points: Pivot highs and lows are detected using the ta.pivothigh and ta.pivotlow functions, with separate lengths for Main, Mid, and Short trend lines.
Slope Calculation: The slope of each trend line is calculated as the change in price divided by the change in bar index between two pivot points.
ADX Calculation: ADX is computed using a 14-period Directional Movement Index (DMI), with smoothing over 14 bars.
RSI Calculation: RSI is calculated over a 14-period lookback using the ta.rsi function.
Volume Moving Average: A simple moving average (SMA) of volume is used to determine if current volume exceeds the average.
5. Strict Mode Validation
To ensure the reliability of trend lines, the indicator employs a strict mode check:
For bearish trend lines, all prices between pivot points must remain below the projected trend line.
For bullish trend lines, all prices must remain above the projected trend line.
Post-pivot break checks ensure that no breakouts occur between pivot points, enhancing the validity of the trend line.
6. Trend Line Extension
Trend lines are dynamically extended forward until a breakout occurs. The extension logic:
Projects the trend line using the calculated slope.
Continuously validates the extension using strict mode checks.
Stops extension upon a breakout, fixing the trend line at the breakout point.
7. Alerts and Labels
Labels: Breakout labels are placed above (for bearish breakouts) or below (for bullish breakouts) the price bar. Labels include:
A prefix indicating the trend line type (B for Main, M for Mid, S for Short).
A suffix showing satisfied filters (e.g., for Volume, ADX, and RSI).
Alerts: Each breakout triggers a one-time alert per bar close, with a descriptive message indicating the trend line type and filters met.
Detailed Code Breakdown
1. Initialization and Inputs
The script begins by defining the indicator with indicator('Smart Trend Lines ', overlay = true), ensuring it overlays on the price chart. Input settings are grouped into categories (Main, Mid, Short, General Display, Filters) for user convenience. Each input includes a tooltip in both English and Arabic, enhancing accessibility.
2. Technical Indicator Calculations
Volume MA: Calculated using ta.sma(volume, volPeriod) to compare current volume against the average.
ADX: Computed using custom dirmov and adx functions, which calculate the Directional Movement Index and smooth it over 14 periods.
RSI: Calculated with ta.rsi(close, rsiPeriod) over 14 periods.
Price Selection: The priceToCheck function selects the price type (Close, High, or Low) for breakout detection.
3. Pivot Detection
Pivot points are detected using ta.pivothigh and ta.pivotlow for each trend line type. The lookback period is set to the respective trend line length (e.g., 50 for Main, 21 for Mid, 9 for Short).
4. Trend Line Logic
For each trend line type (Main, Mid, Short):
Bearish Trend Lines: Identified when two consecutive pivot highs form a downward slope. The script validates the trend line using strict mode and post-pivot break checks.
Bullish Trend Lines: Identified when two consecutive pivot lows form an upward slope, with similar validation.
Trend lines are drawn using line.new, with separate lines for the initial segment (between pivots) and the extended segment (from the second pivot forward).
5. Breakout Detection and Labeling
Breakouts are detected when the selected price crosses the trend line level. The script checks:
Volume conditions (above average and aligned with candle direction).
ADX condition (above threshold).
RSI condition (within thresholds if enabled). Labels are created with label.new, and alerts are triggered with alert.
6. Trend Line Extension
The extendTrendline function dynamically updates the trend line’s endpoint unless a breakout occurs. It uses strict mode checks to ensure the trend line remains valid.
7. Previous Trend Lines
If enabled, previous main trend lines are stored in arrays (previousBearishStartLines, previousBullishTrendLines, etc.) and displayed on the chart, providing historical context.
Disclaimer:
The information and publications are not intended to be, nor do they constitute, financial, investment, trading, or other types of advice or recommendations provided or endorsed by TradingView.
Current Fractal High/Low (Dynamic)
This indicator dynamically tracks the most recent confirmed Fractal High and Fractal Low across any timeframe using custom left/right bar configurations.
🔍 Key Features:
Detects Fractal Highs and Lows based on user-defined pivot settings.
Draws a green line and label ("FH") at the most recent Fractal High.
Draws a red line and label ("FL") at the most recent Fractal Low.
All lines extend from the confirmation bar to the current candle.
Automatically removes old lines and labels for a clean, uncluttered chart.
🛠️ Customizable Inputs:
Left & Right bars for pivot sensitivity
Line width for visibility
📌 Use Cases:
Identifying structure shifts
Recognizing key swing points
Supporting liquidity and breakout strategies
💡 Fractals are confirmed only after the full formation of the pattern (left and right bars). This ensures reliability over reactivity.
This script is designed for intraday to swing traders who want a reliable way to visualize market turning points with minimal noise.
pymath█ OVERVIEW
This library ➕ enhances Pine Script's built-in types (`float`, `int`, `array`, `array`) with mathematical methods, mirroring 🪞 many functions from Python's `math` module. Import this library to overload or add to built-in capabilities, enabling calls like `myFloat.sin()` or `myIntArray.gcd()`.
█ CONCEPTS
This library wraps Pine's built-in `math.*` functions and implements others where necessary, expanding the mathematical toolkit available within Pine Script. It provides a more object-oriented approach to mathematical operations on core data types.
█ HOW TO USE
• Import the library: i mport kaigouthro/pymath/1
• Call methods directly on variables: myFloat.sin() , myIntArray.gcd()
• For raw integer literals, you MUST use parentheses: `(1234).factorial()`.
█ FEATURES
• **Infinity Handling:** Includes `isinf()` and `isfinite()` for robust checks. Uses `POS_INF_PROXY` to represent infinity.
• **Comprehensive Math Functions:** Implements a wide range of methods, including trigonometric, logarithmic, hyperbolic, and array operations.
• **Object-Oriented Approach:** Allows direct method calls on `int`, `float`, and arrays for cleaner code.
• **Improved Accuracy:** Some functions (e.g., `remainder()`) offer improved accuracy compared to default Pine behavior.
• **Helper Functions:** Internal helper functions optimize calculations and handle edge cases.
█ NOTES
This library improves upon Pine Script's built-in `math` functions by adding new ones and refining existing implementations. It handles edge cases such as infinity, NaN, and zero values, enhancing the reliability of your Pine scripts. For Speed, it wraps and uses built-ins, as thy are fastest.
█ EXAMPLES
//@version=6
indicator("My Indicator")
// Import the library
import kaigouthro/pymath/1
// Create some Vars
float myFloat = 3.14159
int myInt = 10
array myIntArray = array.from(1, 2, 3, 4, 5)
// Now you can...
plot( myFloat.sin() ) // Use sin() method on a float, using built in wrapper
plot( (myInt).factorial() ) // Factorial of an integer (note parentheses)
plot( myIntArray.gcd() ) // GCD of an integer array
method isinf(self)
isinf: Checks if this float is positive or negative infinity using a proxy value.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) value to check.
Returns: (bool) `true` if the absolute value of `self` is greater than or equal to the infinity proxy, `false` otherwise.
method isfinite(self)
isfinite: Checks if this float is finite (not NaN and not infinity).
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The value to check.
Returns: (bool) `true` if `self` is not `na` and not infinity (as defined by `isinf()`), `false` otherwise.
method fmod(self, divisor)
fmod: Returns the C-library style floating-point remainder of `self / divisor` (result has the sign of `self`).
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) Dividend `x`.
divisor (float) : (float) Divisor `y`. Cannot be zero or `na`.
Returns: (float) The remainder `x - n*y` where n is `trunc(x/y)`, or `na` if divisor is 0, `na`, or inputs are infinite in a way that prevents calculation.
method factorial(self)
factorial: Calculates the factorial of this non-negative integer.
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : (int) The integer `n`. Must be non-negative.
Returns: (float) `n!` as a float, or `na` if `n` is negative or overflow occurs (based on `isinf`).
method isqrt(self)
isqrt: Calculates the integer square root of this non-negative integer (floor of the exact square root).
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : (int) The non-negative integer `n`.
Returns: (int) The greatest integer `a` such that a² <= n, or `na` if `n` is negative.
method comb(self, k)
comb: Calculates the number of ways to choose `k` items from `self` items without repetition and without order (Binomial Coefficient).
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : (int) Total number of items `n`. Must be non-negative.
k (int) : (int) Number of items to choose. Must be non-negative.
Returns: (float) The binomial coefficient nCk, or `na` if inputs are invalid (n<0 or k<0), `k > n`, or overflow occurs.
method perm(self, k)
perm: Calculates the number of ways to choose `k` items from `self` items without repetition and with order (Permutations).
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : (int) Total number of items `n`. Must be non-negative.
k (simple int) : (simple int = na) Number of items to choose. Must be non-negative. Defaults to `n` if `na`.
Returns: (float) The number of permutations nPk, or `na` if inputs are invalid (n<0 or k<0), `k > n`, or overflow occurs.
method log2(self)
log2: Returns the base-2 logarithm of this float. Input must be positive. Wraps `math.log(self) / math.log(2.0)`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be positive.
Returns: (float) The base-2 logarithm, or `na` if input <= 0.
method trunc(self)
trunc: Returns this float with the fractional part removed (truncates towards zero).
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (int) The integer part, or `na` if input is `na` or infinite.
method abs(self)
abs: Returns the absolute value of this float. Wraps `math.abs()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (float) The absolute value, or `na` if input is `na`.
method acos(self)
acos: Returns the arccosine of this float, in radians. Wraps `math.acos()`. Input must be between -1 and 1.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be between -1 and 1.
Returns: (float) Angle in radians , or `na` if input is outside or `na`.
method asin(self)
asin: Returns the arcsine of this float, in radians. Wraps `math.asin()`. Input must be between -1 and 1.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be between -1 and 1.
Returns: (float) Angle in radians , or `na` if input is outside or `na`.
method atan(self)
atan: Returns the arctangent of this float, in radians. Wraps `math.atan()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (float) Angle in radians , or `na` if input is `na`.
method ceil(self)
ceil: Returns the ceiling of this float (smallest integer >= self). Wraps `math.ceil()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (int) The ceiling value, or `na` if input is `na` or infinite.
method cos(self)
cos: Returns the cosine of this float (angle in radians). Wraps `math.cos()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The angle in radians.
Returns: (float) The cosine, or `na` if input is `na`.
method degrees(self)
degrees: Converts this float from radians to degrees. Wraps `math.todegrees()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The angle in radians.
Returns: (float) The angle in degrees, or `na` if input is `na`.
method exp(self)
exp: Returns e raised to the power of this float. Wraps `math.exp()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The exponent.
Returns: (float) `e**self`, or `na` if input is `na`.
method floor(self)
floor: Returns the floor of this float (largest integer <= self). Wraps `math.floor()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (int) The floor value, or `na` if input is `na` or infinite.
method log(self)
log: Returns the natural logarithm (base e) of this float. Wraps `math.log()`. Input must be positive.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be positive.
Returns: (float) The natural logarithm, or `na` if input <= 0 or `na`.
method log10(self)
log10: Returns the base-10 logarithm of this float. Wraps `math.log10()`. Input must be positive.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be positive.
Returns: (float) The base-10 logarithm, or `na` if input <= 0 or `na`.
method pow(self, exponent)
pow: Returns this float raised to the power of `exponent`. Wraps `math.pow()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The base.
exponent (float) : (float) The exponent.
Returns: (float) `self**exponent`, or `na` if inputs are `na` or lead to undefined results.
method radians(self)
radians: Converts this float from degrees to radians. Wraps `math.toradians()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The angle in degrees.
Returns: (float) The angle in radians, or `na` if input is `na`.
method round(self)
round: Returns the nearest integer to this float. Wraps `math.round()`. Ties are rounded away from zero.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (int) The rounded integer, or `na` if input is `na` or infinite.
method sign(self)
sign: Returns the sign of this float (-1, 0, or 1). Wraps `math.sign()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (int) -1 if negative, 0 if zero, 1 if positive, `na` if input is `na`.
method sin(self)
sin: Returns the sine of this float (angle in radians). Wraps `math.sin()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The angle in radians.
Returns: (float) The sine, or `na` if input is `na`.
method sqrt(self)
sqrt: Returns the square root of this float. Wraps `math.sqrt()`. Input must be non-negative.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be non-negative.
Returns: (float) The square root, or `na` if input < 0 or `na`.
method tan(self)
tan: Returns the tangent of this float (angle in radians). Wraps `math.tan()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The angle in radians.
Returns: (float) The tangent, or `na` if input is `na`.
method acosh(self)
acosh: Returns the inverse hyperbolic cosine of this float. Input must be >= 1.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be >= 1.
Returns: (float) The inverse hyperbolic cosine, or `na` if input < 1 or `na`.
method asinh(self)
asinh: Returns the inverse hyperbolic sine of this float.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (float) The inverse hyperbolic sine, or `na` if input is `na`.
method atanh(self)
atanh: Returns the inverse hyperbolic tangent of this float. Input must be between -1 and 1 (exclusive).
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number. Must be between -1 and 1 (exclusive).
Returns: (float) The inverse hyperbolic tangent, or `na` if input is outside (-1, 1) or `na`.
method cosh(self)
cosh: Returns the hyperbolic cosine of this float.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (float) The hyperbolic cosine, or `na` if input is `na`.
method sinh(self)
sinh: Returns the hyperbolic sine of this float.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (float) The hyperbolic sine, or `na` if input is `na`.
method tanh(self)
tanh: Returns the hyperbolic tangent of this float.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The input number.
Returns: (float) The hyperbolic tangent, or `na` if input is `na`.
method atan2(self, dx)
atan2: Returns the angle in radians between the positive x-axis and the point (dx, self). Wraps `math.atan2()`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The y-coordinate `y`.
dx (float) : (float) The x-coordinate `x`.
Returns: (float) The angle in radians , result of `math.atan2(self, dx)`. Returns `na` if inputs are `na`. Note: `math.atan2(0, 0)` returns 0 in Pine.
Optimization: Use built-in math.atan2()
method cbrt(self)
cbrt: Returns the cube root of this float.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The value to find the cube root of.
Returns: (float) The real cube root. Handles negative inputs correctly, or `na` if input is `na`.
method exp2(self)
exp2: Returns 2 raised to the power of this float. Calculated as `2.0.pow(self)`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The exponent.
Returns: (float) `2**self`, or `na` if input is `na` or results in non-finite value.
method expm1(self)
expm1: Returns `e**self - 1`. Calculated as `self.exp() - 1.0`. May offer better precision for small `self` in some environments, but Pine provides no guarantee over `self.exp() - 1.0`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The exponent.
Returns: (float) `e**self - 1`, or `na` if input is `na` or `self.exp()` is `na`.
method log1p(self)
log1p: Returns the natural logarithm of (1 + self). Calculated as `(1.0 + self).log()`. Pine provides no specific precision guarantee for self near zero.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) Value to add to 1. `1 + self` must be positive.
Returns: (float) Natural log of `1 + self`, or `na` if input is `na` or `1 + self <= 0`.
method modf(self)
modf: Returns the fractional and integer parts of this float as a tuple ` `. Both parts have the sign of `self`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The number `x` to split.
Returns: ( ) A tuple containing ` `, or ` ` if `x` is `na` or non-finite.
method remainder(self, divisor)
remainder: Returns the IEEE 754 style remainder of `self` with respect to `divisor`. Result `r` satisfies `abs(r) <= 0.5 * abs(divisor)`. Uses round-half-to-even.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) Dividend `x`.
divisor (float) : (float) Divisor `y`. Cannot be zero or `na`.
Returns: (float) The IEEE 754 remainder, or `na` if divisor is 0, `na`, or inputs are non-finite in a way that prevents calculation.
method copysign(self, signSource)
copysign: Returns a float with the magnitude (absolute value) of `self` but the sign of `signSource`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) Value providing the magnitude `x`.
signSource (float) : (float) Value providing the sign `y`.
Returns: (float) `abs(x)` with the sign of `y`, or `na` if either input is `na`.
method frexp(self)
frexp: Returns the mantissa (m) and exponent (e) of this float `x` as ` `, such that `x = m * 2^e` and `0.5 <= abs(m) < 1` (unless `x` is 0).
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) The number `x` to decompose.
Returns: ( ) A tuple ` `, or ` ` if `x` is 0, or ` ` if `x` is non-finite or `na`.
method isclose(self, other, rel_tol, abs_tol)
isclose: Checks if this float `a` and `other` float `b` are close within relative and absolute tolerances.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) First value `a`.
other (float) : (float) Second value `b`.
rel_tol (simple float) : (simple float = 1e-9) Relative tolerance. Must be non-negative and less than 1.0.
abs_tol (simple float) : (simple float = 0.0) Absolute tolerance. Must be non-negative.
Returns: (bool) `true` if `abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)`. Handles `na`/`inf` appropriately. Returns `na` if tolerances are invalid.
method ldexp(self, exponent)
ldexp: Returns `self * (2**exponent)`. Inverse of `frexp`.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : (float) Mantissa part `x`.
exponent (int) : (int) Exponent part `i`.
Returns: (float) The result of `x * pow(2, i)`, or `na` if inputs are `na` or result is non-finite.
method gcd(self)
gcd: Calculates the Greatest Common Divisor (GCD) of all integers in this array.
Namespace types: array
Parameters:
self (array) : (array) An array of integers.
Returns: (int) The largest positive integer that divides all non-zero elements, 0 if all elements are 0 or array is empty. Returns `na` if any element is `na`.
method lcm(self)
lcm: Calculates the Least Common Multiple (LCM) of all integers in this array.
Namespace types: array
Parameters:
self (array) : (array) An array of integers.
Returns: (int) The smallest positive integer that is a multiple of all non-zero elements, 0 if any element is 0, 1 if array is empty. Returns `na` on potential overflow or if any element is `na`.
method dist(self, other)
dist: Returns the Euclidean distance between this point `p` and another point `q` (given as arrays of coordinates).
Namespace types: array
Parameters:
self (array) : (array) Coordinates of the first point `p`.
other (array) : (array) Coordinates of the second point `q`. Must have the same size as `p`.
Returns: (float) The Euclidean distance, or `na` if arrays have different sizes, are empty, or contain `na`/non-finite values.
method fsum(self)
fsum: Returns an accurate floating-point sum of values in this array. Uses built-in `array.sum()`. Note: Pine Script does not guarantee the same level of precision tracking as Python's `math.fsum`.
Namespace types: array
Parameters:
self (array) : (array) The array of floats to sum.
Returns: (float) The sum of the array elements. Returns 0.0 for an empty array. Returns `na` if any element is `na`.
method hypot(self)
hypot: Returns the Euclidean norm (distance from origin) for this point given by coordinates in the array. `sqrt(sum(x*x for x in coordinates))`.
Namespace types: array
Parameters:
self (array) : (array) Array of coordinates defining the point.
Returns: (float) The Euclidean norm, or 0.0 if the array is empty. Returns `na` if any element is `na` or non-finite.
method prod(self, start)
prod: Calculates the product of all elements in this array.
Namespace types: array
Parameters:
self (array) : (array) The array of values to multiply.
start (simple float) : (simple float = 1.0) The starting value for the product (returned if the array is empty).
Returns: (float) The product of array elements * start. Returns `na` if any element is `na`.
method sumprod(self, other)
sumprod: Returns the sum of products of values from this array `p` and another array `q` (dot product).
Namespace types: array
Parameters:
self (array) : (array) First array of values `p`.
other (array) : (array) Second array of values `q`. Must have the same size as `p`.
Returns: (float) The sum of `p * q ` for all i, or `na` if arrays have different sizes or contain `na`/non-finite values. Returns 0.0 for empty arrays.
Pivot ATR Zones [v6]🟩 Pivot ATR Zones
Overview:
The Pivot ATR Zones indicator plots dynamic support and resistance zones based on pivot highs and lows, combined with ATR (Average True Range) volatility levels. It helps traders visually identify potential long and short trade areas, along with realistic target and stop loss zones based on market conditions.
Features:
Automatically detects pivot highs and lows
Draws ATR-based entry zones on the chart
Plots dynamic take-profit and stop-loss levels using ATR multipliers
Color-coded long (green) and short (red) zones
Entry arrow markers for clearer trade visualization
Real-time alerts when new zones form
Best For:
Scalpers, intraday traders, and swing traders who want a visual, volatility-aware way to mark potential trade areas based on key pivot structures.
How to Use:
Look for newly formed green zones for long opportunities and red zones for short setups.
Use the dashed lines as dynamic take-profit and stop levels, tuned to the current ATR value.
Combine with other confirmation tools or indicators for optimal results.
Zero Lag MTF Moving Average by CoffeeshopCryptoBased on Moving Average Types supplied by @TradingView www.tradingview.com
Ideas and code enhanced to show higher timeframe by @CoffeeShopCrypto
It’s time to take the guesswork out of moving averages and multiple timeframes when day trading. Moving averages are a cornerstone of many trading strategies, often viewed as dynamic support and resistance levels. Traders rely on these levels to anticipate price reactions, whether it’s a bounce in a trending market or a reversal in a ranging one. Additionally, the direction and alignment of multi timeframe moving averages—whether they’re moving in the same direction or diverging—provide critical clues about market momentum and potential reversals. However, the traditional higher timeframe moving average indicators force traders to wait for higher timeframe candles to close, creating lag and missed opportunities.
The Old Way
For example: If you are on a 5 minute chart and you want to observe the location and direction of a 30 minute chart Moving Average, you'll need to wait for a total of 6 candles to close, and again every 6 candles after that. This only creates more lag.
The New Way
Now there is no waiting for high timeframe session candles to close. No matter what timeframe Moving Average you want to know about, this indicator will show you its location on your current chart at any time in real time.
For those who prefer Bollinger Bands, this indicator adds a whole new dimension to your strategy. Traders often wait for price action to break outside the lower time frame Bollinger bands before considering a trade, while still seeking key support or resistance levels beyond them. But if you don't know the position of your higher time frame Bollinger, you could be trading into a trap. With Zero Lag Multi Timeframe Moving Average, you can view both your current and higher timeframe Bollinger Bands simultaneously with zero waiting. This lets you instantly see when price action is traveling between the bands of either timeframe or breaking through both—indicating a strong trend in that direction. Additionally, when both sets of Bollinger Bands overlap at the same price levels, it highlights areas of strong consolidation and ranging conditions, giving you a clear picture of market dynamics. This is a key element in price action that tells you there is currently no direction to the market and both the current and higher time frames are flat.
Enter Zero Lag Multi Timeframe Moving Average—the ultimate tool for real-time higher timeframe moving averages and Bollinger Bands. This innovative indicator eliminates the delay, delivering instant, precise values for higher timeframe averages and bands, even on open candles. Seamlessly combining current and higher timeframe data, it allows traders to identify key moments where moving averages or Bollinger Bands align or diverge, signaling market conditions. Whether you’re gauging the strength of a trend, pinpointing potential reversals, or identifying consolidation zones, Zero Lag Multi Timeframe Moving Average gives you the clarity needed to make better trading decisions according to market conditions.
Why is this "Mashup" of moving averages different and important?
Honestly its really about the calculation thats imported through the "import library" function.
Heres what it does:
The ZLMTF-MA is designed to help traders easily see where higher timeframe moving averages and Bollinger Bands are—without needing to switch chart timeframes or wait for those larger candles to close. It works by adjusting common moving average types like SMA, EMA, and VWMA to show what they would look like if they were based on a higher timeframe, right on your current chart. This helps users stay focused on their main timeframe while still having a clear view of the bigger picture, making it easier to spot trend direction, key support and resistance levels, and overall market structure. The goal is to keep things simple, fast, and more visually informative for everyday traders.
Bollinger Bands
When working with Bollinger Bands, a common strategy is to take the trades once price action has escaped through the top or bottom of your current Bollinger Band.
A false breakout occurs when both Bollinger Bands are not moving in the same direction as eachother or when they are overlapping.
Moving Averages as Support and Resistance:
Traders who use Moving Averages as support or resistance, looking for rejections or failures of these areas can now see multiple timeframe price action instantly and simultaneously.
Trading Setup Examples:
Price Action Scenario 1:
Higher Timeframe Ranging-
When price action breaks through a current moving average headed toward a higher timeframe moving average, trades are taken with caution if the moving averages are converging.
Price Action Scenario 2:
Strong Trending Market -
If the moving averages are in the same direction, and your price action is now leading the low timeframe moving average, you have re-entered a strong trend.
Price Action Scenario 3:
High Timeframe Rejections -
If you have a rejection of a higher timeframe moving average, and your both averages are still diverging, this is the end of a pullback as you re-enter a strong trend in the original direction
Price Action Scenario 4:
Trend Reversals -
If you close beyond both the low and high timeframe moving averages, you can consider that price action is strong enough to change direction here and you should prepare for trade setups in the opposite direction of the previous.
HTF MA Label Information:
Even if your high timeframe moving average is turned off, you can still see this label.
It gives you a quick reminder of what high timeframe settings you have used to see MA values.
SMPivot Gaussian Trend Strategy [Js.K]This open-source strategy combines a Gaussian-weighted moving average with “Smart Money” swing-pivot breaks (BoS = Break-of-Structure) to capture trend continuations and early reversals. It is intended for educational and research purposes only and must not be interpreted as financial advice.
How the logic works
-------------------
1. Gaussian Moving Average (GMA)
• A custom Gaussian kernel (length = 30 by default) smooths price while preserving turning points.
• A second pass (“Smoothed GMA”) further filters noise; only its direction is used for bias.
2. Swing-Pivot detection
• High/Low pivots are found with a symmetric look-back/forward window (Pivot Length = 20).
• The most recent confirmed pivot creates a dynamic structure level (UpdatedHigh / UpdatedLow).
3. Entry rules
Long
• Price closes above the most recent pivot high **and** above Smoothed GMA.
Short
• Price closes below the most recent pivot low **and** below Smoothed GMA.
4. Exit rules
• Fixed stop-loss and take-profit in percent of current price (user-defined).
• Separate parameters and on/off switches for longs and shorts.
5. Visuals
• GMA (dots) and Smoothed GMA (line).
• Structure break lines plus “BoS PH/PL” labels at the midpoint between pivot and break.
Inputs
------
Gaussian
• Gaussian Length (default 30) – smoothing window.
• Gaussian Scatterplot – toggle GMA dots.
Smart-Money Pivot
• Pivot Length (default 20).
• Bull / Bear colors.
Risk settings
• Long / Short enable.
• Individual SL % and TP % (default 1 % SL, 30 % TP).
• Strategy uses percent-of-equity sizing; initial capital defaults to 10 000 USD.
Adjust these to reflect your own account size, realistic commission and slippage.
Best practice & compliance notes
--------------------------------
• Test on a data sample that yields ≥ 100 trades to obtain statistically relevant results.
• Keep risk per trade below 5–10 % of equity; the default values comply with this guideline.
• Explain any custom settings you publish that differ from the defaults.
• Do **not** remove the code header or licence notice (MPL-2.0).
• Include realistic commission and slippage in your back-test before publishing.
• The script does **not** repaint; orders are processed on bar close.
Usage
-----
1. Add the script to any symbol / timeframe; intraday and swing timeframes both work—adjust lengths accordingly.
2. Configure SL/TP and position size to match your personal risk management.
3. Run “List of trades” and the performance summary to evaluate expectancy; forward-test before live use.
Disclaimer
----------
Trading involves substantial risk. Past performance based on back-testing is not necessarily indicative of future results. The author is **not** responsible for any financial losses arising from the use of this script.
Vietnamese Market Structure With CountersThis indicator is designed to track Market Structure with Swing-Low Breakdowns and Swing-High Breakups specifically tailored for the Vietnamese stock market, though it can be applied elsewhere too. By default, it uses a 10-period EMA to dynamically detect key turning points in price action and count significant breakdowns or breakups from previous swing levels.
As an open source, you can modify the source code to match your needs.
What it does:
Detects when price breaks below previous swing lows or above previous swing highs.
Plots swing levels for both highs and lows.
Displays labeled counters on the chart to show how many consecutive breakdowns or breakups have occurred.
Helps traders identify trend shifts and possible exhaustion in moves.
Why it's useful:
This tool is great for visually tracking market momentum and structure changes — especially in trending or volatile environments. It emphasizes structure over indicators, helping you understand price behavior in a simplified, intuitive way.
License:
This script is published under the Mozilla Public License 2.0. Feel free to use, modify, and contribute!
Created with care by @doqkhanh.
If you find it useful, consider leaving a comment or sharing it with others!
SMCDrawingLibrary "SMCDrawing"
drawSwingPointLabel(bar_index, price, swing_type, text_color, size)
Parameters:
bar_index (int)
price (float)
swing_type (string)
text_color (color)
size (string)
drawBOSLabel(start_bar, price, end_bar, bos_text, line_color, line_style, line_width, text_color, size)
Parameters:
start_bar (int)
price (float)
end_bar (int)
bos_text (string)
line_color (color)
line_style (string)
line_width (int)
text_color (color)
size (string)
drawRetracementLine(start_bar, price, end_bar, line_color, line_style, line_width)
Parameters:
start_bar (int)
price (float)
end_bar (int)
line_color (color)
line_style (string)
line_width (int)
drawFVG(high_time, low_time, high_price, low_price, is_bullish, line_color, line_width, bar_time)
Parameters:
high_time (int)
low_time (int)
high_price (float)
low_price (float)
is_bullish (bool)
line_color (color)
line_width (int)
bar_time (int)
drawBPRLabel(bar_time, price, is_bullish, text_color)
Parameters:
bar_time (int)
price (float)
is_bullish (bool)
text_color (color)
drawVolumeSpike(bar_time, price, percent_oi, normalized_volume, spike_color)
Parameters:
bar_time (int)
price (float)
percent_oi (float)
normalized_volume (float)
spike_color (color)
drawCandle(bar_index, open, high, low, close, up_color, down_color, wick_color, up_border_color, down_border_color)
Parameters:
bar_index (int)
open (float)
high (float)
low (float)
close (float)
up_color (color)
down_color (color)
wick_color (color)
up_border_color (color)
down_border_color (color)
Bitcoin Impact AnalyzerSummary of the "Bitcoin Impact Analyzer" script, the adjustments users can make, and an explanation of what the chart and table represent:
Script Summary:
The "Bitcoin Impact Analyzer" script is designed to help traders and analysts understand the relationship between a chosen altcoin and Bitcoin (BTC). It does this by:
Fetching price data for the specified altcoin and Bitcoin.
Calculating several key comparative metrics:
Normalized Prices: Shows the percentage performance of both assets from a common starting point.
Price Correlation: Measures how similarly the two assets' prices move over a defined period.
Beta: Indicates the altcoin's volatility relative to Bitcoin.
Altcoin/BTC Ratio: Shows the altcoin's value expressed in Bitcoin.
Fetching and displaying Bitcoin Dominance (BTC.D) data.
Visualizing these metrics on the chart as distinct plots.
Displaying the current values of these key metrics in a data table on the chart for quick reference.
The script aims to provide insights into whether an altcoin is outperforming or underperforming Bitcoin, how closely its price movements are tied to Bitcoin's, and its relative volatility.
User Adjustments:
Users can customize the script's behavior through several input settings:
Symbol Inputs:
Altcoin Symbol: Users can enter the ticker symbol for any altcoin they wish to analyze (e.g., BINANCE:ETHUSDT, KUCOIN:SOLUSDT).
Bitcoin Reference Symbol: Users can specify the Bitcoin pair to use as a reference, though BINANCE:BTCUSDT is a common default.
Lookback for Correlation/Beta:
Lookback Period: This integer value (default 50 periods) determines how many past candles are used to calculate the price correlation and beta.
A shorter lookback makes the metrics more sensitive to recent price action.
A longer lookback provides a smoother, more stable indication of the longer-term relationship.
Plot Visibility Options:
Users can toggle on or off the display of each individual plot on the chart:
Normalized BTC & Altcoin Prices
Altcoin/BTC Ratio
Correlation Plot
Bitcoin Dominance (BTC.D)
Beta Plot
This allows users to focus on specific metrics and reduce chart clutter.
What the Chart Represents:
The chart visually displays the historical trends and relationships of the selected metrics:
Normalized Prices Plot: Two lines (typically orange for BTC, blue for the altcoin) show the percentage growth of each asset from the start of the loaded chart data (or the first available data point for each symbol). This makes it easy to see which asset has performed better over time on a relative basis.
Correlation Plot: A single line (purple) oscillates between -1 and +1.
Values near +1 indicate a strong positive correlation (altcoin and BTC prices tend to move in the same direction).
Values near -1 indicate a strong negative correlation (they tend to move in opposite directions).
Values near 0 indicate little to no linear relationship.
Lines at +0.7 and -0.7 are often plotted as thresholds for "strong" correlation.
Beta Plot (if enabled): A single line (teal) shows the altcoin's volatility relative to BTC.
A Beta of 1 (often marked by a dashed line) means the altcoin has, on average, the same volatility as BTC.
Beta > 1 suggests the altcoin is more volatile than BTC (moves by a larger percentage for a given BTC move).
Beta < 1 suggests the altcoin is less volatile than BTC.
Bitcoin Dominance Plot: An area plot (gray) shows the percentage of the total cryptocurrency market capitalization that Bitcoin holds. This helps understand broader market sentiment and capital flows.
Altcoin/BTC Ratio Plot: A line (fuchsia) shows the price of the altcoin denominated in BTC.
An upward trend means the altcoin is gaining value against Bitcoin (outperforming).
A downward trend means the altcoin is losing value against Bitcoin (underperforming).
What the Table Represents:
The data table, typically located in the bottom-right corner of the chart, provides a snapshot of the current values for the most important calculated metrics. It includes:
Altcoin: The ticker symbol of the analyzed altcoin.
Bitcoin Ref: The ticker symbol of the Bitcoin reference.
Correlation (lookback): The current correlation coefficient between the altcoin and BTC, based on the specified lookback period. The value is color-coded (e.g., green for strong positive, red for strong negative).
Beta (lookback): The current beta value of the altcoin relative to BTC, based on the specified lookback period. The value may be color-coded to highlight significantly high or low volatility.
BTC.D Current: The current Bitcoin Dominance percentage.
ALT/BTC Ratio: The current price of the altcoin expressed in Bitcoin.
The table offers a quick, at-a-glance summary of the present market dynamics between the two assets without needing to interpret the lines on the chart for their exact current values.
On Balance Volume W DivergenceOBV With Divergence Indicator
A comprehensive On Balance Volume (OBV) indicator enhanced with divergence detection capabilities.
Core Features:
Classic OBV calculation with volume-based price movement tracking
Advanced divergence detection system
Multiple smoothing options for OBV
Bollinger Bands integration
Technical Components:
Volume-based price movement analysis
Pivot point detection for divergence
Customizable lookback periods
Adjustable divergence range parameters
Customization Options:
Multiple Moving Average types (SMA, EMA, SMMA, WMA, VWMA)
Bollinger Bands with adjustable standard deviation
Divergence sensitivity settings
Visual customization for signals and alerts
The indicator combines traditional OBV analysis with modern divergence detection, offering traders a powerful tool for identifying potential trend reversals and market momentum shifts.
Key Parameters:
- Pivot Lookback Right/Left: 5 (default)
- Divergence Range: 5-60 bars
- MA Length: 14 (default)
- BB StdDev: 2.0 (default)
Alert System:
- Bullish divergence alerts
- Bearish divergence alerts
- Customizable alert messages
Note: The indicator requires volume data to function properly and will display an error if volume data is not available.
Adaptive Momentum Oscillator [LuxAlgo]The Adaptive Momentum Oscillator tool allows traders to measure the current relative momentum over a given period using the maximum delta in price.
It features a histogram with gradient color, divergences, and an adaptive moving average that allows traders to clearly see the smoothed trend direction.
🔶 USAGE
This unbounded oscillator has positive momentum when values are above 0 and negative momentum when values are below 0. The adaptive moving average is used as a minimum lag smoothing tool over the momentum histogram.
🔹 Signal Line
There are two main uses for the signal line drawn on the chart above.
Momentum crosses above or below the signal line: acceleration in momentum.
Signal line crosses the 0 value: positive or negative momentum.
🔹 Data Length
On the chart above, we can compare different length sizes and how the tool values change, allowing traders to get a shorter or longer-term view of current market strength.
🔹 Smoothing Length
In the previous figure, we can compare how different Smoothing Length values affect the oscillator output.
🔹 Divergences
The divergence detector is disabled by default. Traders can enable it and adjust the divergence length from the settings panel.
As we can see in the chart above, by changing the length of the divergences, traders can fine-tune their detection, a small number will detect smaller divergences, and use a larger number for larger divergences.
🔶 SETTINGS
Data: Select data source, close price by default
Data Length: Select the length for data gathering
Smoothing Length: Select the length for data smoothing
Divergences: Enable/Disable divergences detection and length
Quarterly Theory ICT 05 [TradingFinder] Doubling Theory Signals🔵 Introduction
Doubling Theory is an advanced approach to price action and market structure analysis that uniquely combines time-based analysis with key Smart Money concepts such as SMT (Smart Money Technique), SSMT (Sequential SMT), Liquidity Sweep, and the Quarterly Theory ICT.
By leveraging fractal time structures and precisely identifying liquidity zones, this method aims to reveal institutional activity specifically smart money entry and exit points hidden within price movements.
At its core, the market is divided into two structural phases: Doubling 1 and Doubling 2. Each phase contains four quarters (Q1 through Q4), which follow the logic of the Quarterly Theory: Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal.
These segments are anchored by the True Open, allowing for precise alignment with cyclical market behavior and providing a deeper structural interpretation of price action.
During Doubling 1, a Sequential SMT (SSMT) Divergence typically forms between two correlated assets. This time-structured divergence occurs between two swing points positioned in separate quarters (e.g., Q1 and Q2), where one asset breaks a significant low or high, while the second asset fails to confirm it. This lack of confirmation—especially when aligned with the Manipulation and Accumulation phases—often signals early smart money involvement.
Following this, the highest and lowest price points from Doubling 1 are designated as liquidity zones. As the market transitions into Doubling 2, it commonly returns to these zones in a calculated move known as a Liquidity Sweep—a sharp, engineered spike intended to trigger stop orders and pending positions. This sweep, often orchestrated by institutional players, facilitates entry into large positions with minimal slippage.
Bullish :
Bearish :
🔵 How to Use
Applying Doubling Theory requires a simultaneous understanding of temporal structure and inter-asset behavioral divergence. The method unfolds over two main phases—Doubling 1 and Doubling 2—each divided into four quarters (Q1 to Q4).
The first phase focuses on identifying a Sequential SMT (SSMT) divergence, which forms when two correlated assets (e.g., EURUSD and GBPUSD, or NQ and ES) react differently to key price levels across distinct quarters. For example, one asset may break a previous low while the other maintains structure. This misalignment—especially in Q2, the Manipulation phase—often indicates early smart money accumulation or distribution.
Once this divergence is observed, the extreme highs and lows of Doubling 1 are marked as liquidity zones. In Doubling 2, the market gravitates back toward these zones, executing a Liquidity Sweep.
This move is deliberate—designed to activate clustered stop-loss and pending orders and to exploit pockets of resting liquidity. These sweeps are typically driven by institutional forces looking to absorb liquidity and position themselves ahead of the next major price move.
The key to execution lies in the fact that, during the sweep in Doubling 2, a classic SMT divergence should also appear between the two assets. This indicates a weakening of the previous trend and adds an extra layer of confirmation.
🟣 Bullish Doubling Theory
In the bullish scenario, Doubling 1 begins with a bullish SSMT divergence, where one asset forms a lower low while the other maintains its structure. This divergence signals weakening bearish momentum and possible smart money accumulation. In Doubling 2, the market returns to the previous low and sweeps the liquidity zone—breaking below it on one asset, while the second fails to confirm, forming a bullish SMT divergence.
f this move is followed by a bullish PSP and a clear market structure break (MSB), a long entry is triggered. The stop-loss is placed just below the swept liquidity zone, while the target is set in the premium zone, anticipating a move driven by institutional buyers.
🟣 Bearish Doubling Theory
The bearish scenario follows the same structure in reverse. In Doubling 1, a bearish SSMT divergence occurs when one asset prints a higher high while the other fails to do so. This suggests distribution and weakening buying pressure. Then, in Doubling 2, the market returns to the previous high and executes a liquidity sweep, targeting trapped buyers.
A bearish SMT divergence appears, confirming the move, followed by a bearish PSP on the lower timeframe. A short position is initiated after a confirmed MSB, with the stop-loss placed
🔵 Settings
⚙️ Logical Settings
Quarterly Cycles Type : Select the time segmentation method for SMT analysis.
Available modes include : Yearly, Monthly, Weekly, Daily, 90 Minute, and Micro.
These define how the indicator divides market time into Q1–Q4 cycles.
Symbol : Choose the secondary asset to compare with the main chart asset (e.g., XAUUSD, US100, GBPUSD).
Pivot Period : Sets the sensitivity of the pivot detection algorithm. A smaller value increases responsiveness to price swings.
Pivot Sync Threshold : The maximum allowed difference (in bars) between pivots of the two assets for them to be compared.
Validity Pivot Length : Defines the time window (in bars) during which a divergence remains valid before it's considered outdated.
🎨 Display Settings
Show Cycle :Toggles the visual display of the current Quarter (Q1 to Q4) based on the selected time segmentation
Show Cycle Label : Shows the name (e.g., "Q2") of each detected Quarter on the chart.
Show Labels : Displays dynamic labels (e.g., “Q2”, “Bullish SMT”, “Sweep”) at relevant points.
Show Lines : Draws connection lines between key pivot or divergence points.
Color Settings : Allows customization of colors for bullish and bearish elements (lines, labels, and shapes)
🔔 Alert Settings
Alert Name : Custom name for the alert messages (used in TradingView’s alert system).
Message Frequenc y:
All : Every signal triggers an alert.
Once Per Bar : Alerts once per bar regardless of how many signals occur.
Per Bar Close : Only triggers when the bar closes and the signal still exists.
Time Zone Display : Choose the time zone in which alert timestamps are displayed (e.g., UTC).
Bullish SMT Divergence Alert : Enable/disable alerts specifically for bullish signals.
Bearish SMT Divergence Alert : Enable/disable alerts specifically for bearish signals
🔵 Conclusion
Doubling Theory is a powerful and structured framework within the realm of Smart Money Concepts and ICT methodology, enabling traders to detect high-probability reversal points with precision. By integrating SSMT, SMT, Liquidity Sweeps, and the Quarterly Theory into a unified system, this approach shifts the focus from reactive trading to anticipatory analysis—anchored in time, structure, and liquidity.
What makes Doubling Theory stand out is its logical synergy of time cycles, behavioral divergence, liquidity targeting, and institutional confirmation. In both bullish and bearish scenarios, it provides clearly defined entry and exit strategies, allowing traders to engage the market with confidence, controlled risk, and deeper insight into the mechanics of price manipulation and smart money footprints.
Candle Rating (1–5)This “Candle Rating (1–5)” indicator measures where each bar’s close sits within its own high-low range and assigns a simple strength score:
Range Calculation
It computes the candle’s total range (high − low) and finds the close’s position as a percentage of that range (0 = close at low, 1 = close at high).
Five-Point Rating
1 (Strong Buy): Close in the top 20% of the range
2 (Moderate Buy): 60–80%
3 (Neutral): 40–60%
4 (Moderate Sell): 20–40%
5 (Strong Sell): Bottom 20%
Visual Feedback
It plots the numeric rating above each bar (colored green → red), giving you an at-a-glance read of candle momentum and potential reversal strength across any timeframe.
Dealing rangeHi all!
This indicator will show you the current dealing range. The concept of dealing range comes from the inner circle trader (ICT) and gives you a range between an established swing high and an established swing low (the length of these pivots can be changed in settings parameter Length and defaults to 5/2 (left/right)). These swing points must have taken out liquidity to be considered "established". The liquidity that must be grabbed by the swing point has to be a pivot of left length of 1 and a right length of 1.
The dealing range that's created should be used in conjunction with market structure. This could be done through scripts (maybe the Market structure script that I published ()) or manually. It's a common approach to look for long opportunities when the trend is bullish and price is currently in the discount zone of the dealing range. If the trend is bearish then short opportunities are presented when the price is currently in the premium zone of the dealing range.
The zones within the dealing range are premium and discount that are split on the 50% level of the dealing range. These zones can be split into 3 zone with a Fair price (also called Fair value ) zone in between premium and discount. This makes the premium zone to be in the upper third of the dealing range, fair price in the middle third and discount in the lower third. This can be enabled in the settings through the Fair price parameter.
Enabled:
You can choose to enable/disable the visualisation of liquidity grabs and the External liquidity available above and below the swing points that created the dealing range.
Enabled:
Disabled:
Enabled on a higher timeframe (will display a box of the liquidity grab price instead of a label):
This dealing range is configurable to be created by a higher timeframe then the visible charts. Use the setting Higher timeframe to change this.
You can force candles to be closed (for liquidity and swing points). Please note that if you use a higher timeframe then the visible charts the candles must be closed on this timeframe.
Lastly you can also change the transparency of liquidity grabs and external liquidity outside of the dealing range. Use the Transparency setting to change this (a lower value will lead to stronger visuals).
If you have any input or suggestions on future features or bugs, don't hesitate to let me know!
Best of trading luck!
Parsifal.Swing.TrendScoreThe Parsifal.Swing.TrendScore indicator is a module within the Parsifal Swing Suite, which includes a set of swing indicators such as:
• Parsifal Swing TrendScore
• Parsifal Swing Composite
• Parsifal Swing RSI
• Parsifal Swing Flow
Each module serves as an indicator facilitating judgment of the current swing state in the underlying market.
________________________________________
Background
Market movements typically follow a time-varying trend channel within which prices oscillate. These oscillations—or swings—within the trend are inherently tradable.
They can be approached:
• One-sidedly, aligning with the trend (generally safer), or
• Two-sidedly, aiming to profit from mean reversions as well.
Note: Mean reversions in strong trends often manifest as sideways consolidations, making one-sided trades more stable.
________________________________________
The Parsifal Swing Suite
The modules aim to provide additional insights into the swing state within a trend and offer various trigger points to assist with entry decisions.
All modules in the suite act as weak oscillators, meaning they fluctuate within a range but are not bounded like true oscillators (e.g., RSI, which is constrained between 0% and 100%).
________________________________________
The Parsifal.Swing.TrendScore – Specifics
The Parsifal.Swing.TrendScore module combines short-term trend data with information about the current swing state, derived from raw price data and classical technical indicators. It provides an indication of how well the short-term trend aligns with the prevailing swing, based on recent market behavior.
________________________________________
How Swing.TrendScore Works
The Swing.TrendScore calculates a swing score by collecting data within a bin (i.e., a single candle or time bucket) that signals an upside or downside swing. These signals are then aggregated together with insights from classical swing indicators.
Additionally, it calculates a short-term trend score using core technical signals, including:
• The Z-score of the price's distance from various EMAs
• The slope of EMAs
• Other trend-strength signals from additional technical indicators
These two components—the swing score and the trend score—are then combined to form the Swing.TrendScore indicator, which evaluates the short-term trend in context with swing behavior.
________________________________________
How to Interpret Swing.TrendScore
The trend component enhances Swing.TrendScore’s ability to provide stronger signals when the short-term trend and swing state align.
It can also override the swing score; for example, even if a mean reversion appears to be forming, a dominant short-term trend may still control the market behavior.
This makes Swing.TrendScore particularly valuable for:
• Short-term trend-following strategies
• Medium-term swing trading
Unlike typical swing indicators, Swing.TrendScore is designed to respond more to medium-term swings rather than short-lived fluctuations.
________________________________________
Behavior and Chart Representation
The Swing.TrendScore indicator fluctuates within a range, as most of its components are range-bound (though Z-score components may technically extend beyond).
• Historically high or low values may suggest overbought or oversold conditions
• The chart displays:
o A fast curve (orange)
o A slow curve (white)
o A shaded background representing the market state
• Extreme values followed by curve reversals may signal a developing mean reversion
________________________________________
TrendScore Background Value
The Background Value reflects the combined state of the short-term trend and swing:
• > 0 (shaded green) → Bullish mode: swing and short-term trend both upward
• < 0 (shaded red) → Bearish mode: swing and short-term trend both downward
• The absolute value represents the confidence level in the market mode
Notably, the Background Value can remain positive during short downswings if the short-term trend remains bullish—and vice versa.
________________________________________
How to Use the Parsifal.Swing.TrendScore
Several change points can act as entry triggers or aids:
• Fast Trigger: change in slope of the fast signal curve
• Trigger: fast line crosses slow line or the slope of the slow signal changes
• Slow Trigger: change in sign of the Background Value
Examples of these trigger points are illustrated in the accompanying chart.
Additionally, market highs and lows aligning with the swing indicator values may serve as pivot points in the evolving price process.
________________________________________
As always, this indicator should be used in conjunction with other tools and market context in live trading.
While it provides valuable insight and potential entry points, it does not predict future price action.
Instead, it reflects recent tendencies and should be used judiciously.
________________________________________
Extensions
The aggregation of information—whether derived from bins or technical indicators—is currently performed via simple averaging. However, this can be modified using alternative weighting schemes, based on:
• Historical performance
• Relevance of the data
• Specific market conditions
Smoothing periods used in calculations are also modifiable. In general, the EMAs applied for smoothing can be extended to reflect expectations based on relevance-weighted probability measures.
Since EMAs inherently give more weight to recent data, this allows for adaptive smoothing.
Additionally, EMAs may be further extended to incorporate negative weights, akin to wavelet transform techniques.
Camarilla Pivot Plays█ OVERVIEW
This indicator implements the Camarilla Pivot Points levels and a system for suggesting particular plays. It only calculates and shows the 3rd, 4th, and 6th levels, as these are the only ones used by the system. In total, there are 12 possible plays, grouped into two groups of six. The algorithm constantly evaluates conditions for entering and exiting the plays and indicates them in real time, also triggering user-configurable alerts.
█ CREDITS
The Camarilla pivot plays are defined in a strategy developed by Thor Young, and the whole system is explained in his book "A Complete Day Trading System" . The indicator is published with his permission, and he is a user of it. The book is not necessary in order to understand and use the indicator; this description contains sufficient information to use it effectively.
█ FEATURES
Automatically draws plays, suggesting an entry, stop-loss, and maximum target
User can set alerts on chosen ticker to call these plays, even when not currently viewing them
Highly configurable via many options
Works for US/European stocks and US futures (at least)
Works correctly on both RTH and ETH charts
Automatically switches between RTH and ETH data
Optionally also shows the "other" set of pivots (RTH vs ETH data)
Configurable behaviour in the pre-market, not active in the post-market
Configurable sensitivity of the play detection algorithm
Can also show weekly and monthly Camarilla pivots
Well-documented options tooltips
Sensible defaults which are suitable for immediate use
Well-documented and high-quality open-source code for those who are interested
█ HOW TO USE
The defaults work well; at a minimum, just add the indicator and watch the plays being called. To avoid having to watch securities, by selecting the three dots next to the indicator name, you can set an alert on the indicator and choose to be alerted on play entry or exit events—or both. The following diagram shows several plays activated in the past (with the "Show past plays" option selected).
By default, the indicator draws plays 5 days back; this can be changed up to 20 days. The labels can be shifted left/right using the "label offset" option to avoid overlapping with other labels in this indicator or those of another indicator.
An information box at the top-right of the chart shows:
The data currently in use for the main pivots. This can switch in the pre-market if the H/L range exceeds the previous day's H/L, and if it does, you will see that switch at the time that it happens
Whether the current day's pivots are in a higher or lower range compared to the previous day's. This is based on the RTH close, so large moves in the post-market won't be reflected (there is an advanced option to change this)
The width of the value relationship in the current day compared to the previous day
The currently active play. If multiple plays are active in parallel, only the last activated one is shown
The resistance pivots are all drawn in the same colour (red by default), as are the support pivots (green by default). You can change the resistance and support colours, but it is not possible to have different colours for different levels of the same kind. Plays will always use the correct colour, drawing over the pivots. For example, R4 is red by default, but if a play treats R4 as a support, then the play will draw a green line (by default) over the red R4 line, thereby hiding it while the play is active.
There are a few advanced parameters; leave these as default unless you really know what they do. Please note the script is complicated—it does a lot. You might need to wait a few seconds while it (re)calculates on new tickers or when changing options. Give it time when first loading or changing options!
█ CONCEPTS
The indicator is focused around daily Camarilla pivots and implements 12 possible plays: 6 when in a higher range, 6 when in a lower range. The plays are labelled by two letters—the first indicates the range, the second indicates the play—as shown in this diagram:
The pivots can be calculated using only RTH (Regular Trading Hours) data, or ETH (Extended Trading Hours) data, which includes the pre-market and post-market. The indicator implements logic to automatically choose the correct data, based on the rules defined by the strategy. This is user-overridable. With the default options, ETH will be used when the H/L range in the previous day's post-market or current day's pre-market exceeds that of the previous day's regular market. In auto mode, the chosen pivots are considered the main pivots for that day and are the ones used for play evaluation. The "other" pivots can also be shown—"other" here meaning using ETH data when the main pivots use RTH data, and vice versa.
When displaying plays in the pre-market, since the RTH open is not yet known (and that value is needed to evaluate play pre-conditions), the pre-market open is used as a proxy for the RTH open. After the regular market opens, the correct RTH open is used to evaluate play conditions.
█ NOTE FOR FUTURES
Futures always use full ETH data in auto mode. Users may, however, wish to use the option "Always use RTH close," which uses the 3 p.m. Central Time (CME/Chicago) as a basis for the close in the pivot calculations (instead of the 4 p.m. actual close).
Futures don't officially have a pre-market or post-market like equities. Let's take ES on CME as an example (CME is in Chicago, so all times are Central Time, i.e., 1 hour behind Eastern Time). It trades from 17:00 Sunday to 16:00 Friday, with a daily pause between 16:00 and 17:00. However, most of the trading activity is done between 08:30 and 15:00 (Central), which you can tell from the volume spikes at those times, and this coincides with NYSE/NASDAQ regular hours (09:30–16:00 Eastern). So we define a pseudo-pre-market from 17:00 the previous day to 08:30 on the current day, then a pseudo-regular market from 08:30 to 15:00, then a pseudo-post-market from 15:00 to 16:00.
The indicator then works exactly the same as with equities—all the options behave the same, just with different session times defined for the pre-, regular, and post-market, with "RTH" meaning just the regular market and "ETH" meaning all three. The only difference from equities is that the auto calculation mode always uses ETH instead of switching based on ETH range compared to RTH range. This is so users who just leave all the defaults are not confused by auto-switching of the calculation mode; normally you'll want the pivots based on all the (ETH) data. However, both "Force RTH" and "Use RTH close with ETH data" work the same as with equities—so if, in the calculations, you really want to only use RTH data, or use all ETH H/L data but use the RTH close (at 15:00), you can.
█ LIMITATIONS
The pivots are very close to those shown in DAS Trader Pro. They are not to-the-cent exact, but within a few cents. The reasons are:
TradingView uses real-time data from CBOE One, so doesn't have access to full exchange data (unless you pay for it in TradingView), and
the close/high/low are taken from the intraday timeframe you are currently viewing, not daily data—which are very close, but often not exactly the same. For example, the high on the daily timeframe may differ slightly from the daily high you'll see on an intraday timeframe.
I have occasionally seen larger than a few cents differences in the pivots between these and DAS Trader Pro—this is always due to differences in data, for example a big spike in the data in TradingView but not in DAS Trader Pro, or vice versa. The more traded the stock is, the less the difference tends to be. Highly traded stocks are usually within a few cents. Less traded stocks may be more (for example, 30¢ difference in R4 is the highest I've seen). If it bothers you, official NYSE/NASDAQ data in TradingView is quite inexpensive (but even that doesn't make the 8am candle identical).
The 6th Camarilla level does not have a standard definition and may not match the level shown on other platforms. It does match the definition used by DAS Trader Pro.
The indicator is an intraday indicator (despite also being able to show weekly and monthly pivots on an intraday chart). It deactivates on a daily timeframe and higher. It is untested on sub-minute timeframes; you may encounter runtime errors on these due to various historical data referencing issues. Also, the play detection algorithm would likely be unpredictable on sub-minute timeframes. Therefore, sub-minute timeframes are formally unsupported.
The indicator was developed and tested for US/European stocks and US futures. It may or may not work as intended for stocks and futures in different locations. It does not work for other security types (e.g., crypto), where I have no evidence that the strategy has any relevance.
Volumetric Pivot Echo🔮 Volumetric Pivot Echo (VPE)
Future Price Projection Zones with Confidence Scoring
📘 Overview
The Volumetric Pivot Echo (VPE) is a next-generation leading indicator that identifies high-volume reversal points and echoes their price + time behavior into the future — giving you a visual forecast box that includes a confidence score, price range, and duration estimate.
It’s designed for swing and options traders who want forward guidance based on real structure, not just reactive signals.
⚙️ How It Works
Pivot Detection – Finds pivot highs/lows based on configurable bar structure.
Volume Confirmation – Only confirms pivots backed by strong volume (e.g., 1.5× average).
Echo Logic – Measures the price move and time it took to reach the pivot.
ATR Scaling – Adjusts projections based on current market volatility.
Confidence Score – Rates each projection (0–100%) based on structure match, volatility, and direction alignment.
📦 What Appears on Chart
Projection Box:
A forward-drawn rectangle from the current bar to the estimated future zone. The box's size and duration mirror the last valid momentum leg.
Box Label Text:
🔹 Range (projected move size)
⏱️ Duration (bars expected)
✅ Confidence %
VPH/VPL Markers:
Pivot highs and lows confirmed by volume, marked with “VPH” or “VPL”.
🎯 How to Trade with It
Use the box as a target zone for directional trades.
If price enters a box with >85% confidence, consider it a high-quality path projection.
Use with support/resistance confluence or entry systems.
Works especially well for swing trading, breakout setups, or options targeting.
🛠️ Recommended Settings
Box Transparency: Set Projection Up/Down Color to 90 (10% visible).
Text Color: Set to white for readability.
Volume Multiplier: Default 1.5x, increase in choppy markets.
Projection Duration: Start with 1.0x echo multiplier and fine-tune.
⏳ Timeframes & Accuracy
Timeframe Confidence Zones Most Reliable
15m – 1h Use 70–85% confidence scores
1h – 4h Sweet spot for balanced signals
1D – 1W Strongest historical echo tracking (>85% ideal)
✅ Key Features
Forward-looking, non-repainting logic
Clear visual projections — no guesswork
Confidence scoring built-in
ATR-adjusted — adapts to volatility
Works on any asset (stocks, crypto, FX)
🧠 Why It’s Unique
This is not a lagging oscillator or classic trend-following tool.
It’s a leading structure projection model — combining pivot behavior, volume intensity, and market volatility to sketch forward “echo zones” based on the past.
Ceres Trader Simple Trend & Momentum SignalsCeres Trader – Simple Trend & Momentum Signals
Description:
Cut through chart noise with a lightweight, two-factor signal system that combines a classic trend filter (200 EMA) with momentum confirmation (smoothed RSI as a QQE proxy). This indicator plots clean entry arrows—no background shading, no clutter—so you can trade only in the high-probability regime:
Trend Filter: 200-period exponential moving average
Momentum Filter: RSI(14) smoothed over N bars, offset by 50 to create a zero-line
Long Entry: Price above the 200 EMA and the smoothed RSI crosses up through zero → green up-arrow below bar
Short Entry: Price below the 200 EMA and the smoothed RSI crosses down through zero → red down-arrow above bar
Key Features:
Minimalist display: only the 200 EMA and entry arrows
Customizable inputs: EMA length, RSI length, RSI smoothing period
Ultra-low CPU load: suitable for lower timeframes (e.g. 1 min gold futures)
Yellow label text: for optimal visibility on dark or light chart backgrounds
How to Use:
Add the script to your TradingView chart.
Choose your timeframe and adjust inputs as needed.
Take only the long signals when price is above the EMA, and only the short signals when price is below.
Place stops just beyond the EMA; targets can be measured swings or fixed R-multiples.
Notes:
Designed as a regime-based entry filter—no exits or background fills included.
Feel free to combine with your own stop-loss, take-profit, and money-management rules.
Trade smarter, not harder—let the market tell you only when both trend and momentum align.
Atlas BBTlevelsAtlas BBTlevels is a custom Bollinger Bands-based indicator that measures the momentum and strength of price trends using the difference between short- and long-period Bollinger Bands. Inspired by John Bollinger’s official tools like BBTrend, %b, and Bandwidth, this script adds adjustable horizontal threshold levels so traders can mark important reaction zones on their charts.
It visualizes when markets may be entering overheated or exhausted conditions — either for trend continuation or potential reversals — and works across crypto, stocks, forex, spot, or perpetual charts.
How I personally use it:
I apply Atlas BBTlevels across three timeframes:
Low timeframe (LTF): 5m–15m
Mid timeframe (MTF): 1h–6h
High timeframe (HTF): 1d–2d
I review where the indicator historically spiked during major moves. For example, if the 4-hour chart shows repeated spikes to +10 or −10, I’ll set my positive and negative thresholds near those levels. This lets me anticipate zones where the market may reverse, cool off, or break out. I then compare LTF, MTF, and HTF levels to look for confluence. When multiple timeframes align near key levels, it gives me higher confidence to prepare for a trade — but I always combine this with price action and other confirmation tools.
How others can use it:
Identify overbought/oversold zones by adjusting the thresholds to match historical extremes on your chosen asset.
Use it as a trend strength gauge: when the histogram is near or above the top threshold, the trend is likely strong; when it fades back toward zero, momentum is weakening.
Watch for volatility expansions or contractions as the indicator accelerates away from or returns toward zero.
Combine it with price action (support/resistance, trendlines, chart patterns) or other momentum tools to reduce false signals.
Apply it across multiple timeframes to look for confluence — this increases reliability compared to using it on just one chart.
Important tips:
Positive spikes (above zero) usually indicate strength or overextension upward; negative spikes (below zero) show weakness or downward exhaustion.
You can reverse the color logic if you want (for example, highlight negative spikes as green for buy interest and positive spikes as red for sell interest) — this is just a visual preference.
This is not a standalone buy/sell system. Always combine it with other tools, market context, and risk management.
Kaito Box with RSI Div(Dynamic Adjustment + MA + Long)The script implements a dynamic trading strategy that combines box range detection, RSI divergence signals, and moving average trend analysis. It is designed for use on OKX Signal Bots and includes features for dynamic position scaling and partial position closing. Below is a summary of its key functionalities:
Key Features:
Box Range Detection:
The script identifies price ranges using the highest high and lowest low of a configurable boxLength period.
These levels are plotted on the chart to visualize the price range.
RSI Divergence Detection:
The script calculates RSI using a configurable rsiLength.
Detects bullish divergence when price makes a lower low, but RSI makes a higher low.
Detects bearish divergence when price makes a higher high, but RSI makes a lower high.
Includes separate left and right lookback periods (leftLookback, rightLookback) for precise local extrema detection.
Customizable Moving Averages:
Supports multiple types of Moving Averages (SMA, EMA, SMMA, WMA, VWMA).
Calculates and plots MA20, MA50, MA100, and MA200 on a user-defined timeframe (custom_timeframe).
Identifies uptrends and downtrends based on the alignment of the moving averages and price levels.
Dynamic Position Scaling:
Implements dynamic position sizing for long entries and partial position closing for exits.
The percentage of position size added or closed is based on the difference between the current price and the average position price (avgPrice), with configurable minimum thresholds (minEnterPercent, minExitPercent).
Signal Integration for OKX Bots:
Sends buy/sell signals to OKX Signal Bots using the configured signalToken.
Supports market or limit orders with configurable price offsets and investment types.
Trend-Based Signal Filtering:
Only triggers long signals during downtrends and short signals during uptrends, ensuring trades align with the overall market context.
Visual Annotations:
Plots bullish and bearish divergence signals on the chart.
Displays labels showing dynamic position size adjustments and current average price during trades.
How It Works:
Long Signals:
Triggered when the price breaches the lower box range, and a bullish RSI divergence is detected.
Additional filtering ensures long trades are executed only during downtrend conditions.
Dynamically adjusts the position size based on the price difference from the average entry price.
Short Signals:
Triggered when the price breaches the upper box range, and a bearish RSI divergence is detected.
Additional filtering ensures short trades are executed only during uptrend conditions.
Dynamically closes portions of the position based on price movement relative to the average entry price.
Alerts:
Generates actionable alerts formatted for OKX bots, including order type, signal token, and dynamically calculated position sizes.
Use Case:
This strategy is well-suited for automated trading on platforms like OKX, where it can:
Exploit price ranges and RSI divergences for precise entries and exits.
Dynamically manage position sizes to optimize risk-reward.
Adapt to different market conditions using configurable parameters like moving averages, divergence lookbacks, and trend filters.
This script provides a robust foundation for traders looking to automate their strategies while maintaining flexibility and control over their trading logic.
Q KAMA Clarity Trend Q KAMA Clarity Trend
A minimalistic yet versatile trend-following tool that combines **Kaufman’s Adaptive Moving Average (KAMA) with Gaussian smoothing and ATR-based breakout logic. Built for traders who value clarity, responsiveness, and visual simplicity.
🔧 Core Features
1. Adaptive KAMA Trend Line
• Dynamically adjusts to market volatility using Kaufman’s KAMA.
• Gaussian filter pre-smooths price to reduce noise before calculating KAMA.
2. Dual Trend Logic (toggle)
• Default: Trend shifts on price breakouts above/below KAMA ± ATR channel.
• Alternative: Faster signals based on price crossing KAMA directly.
3. Visual Feedback
• Auto-colored KAMA line based on trend direction (up/down/neutral).
• Arrows on trend reversals (up = green, down = red).
• Optional shadow fill below line for regime clarity.
• Optional dot marker ("⦿") on the KAMA line to show trend shifts.
4. Alerts
• Real-time alerts when a new uptrend or downtrend begins.
• Compatible with manual or automated strategies.
⚙️ Configurable Inputs
• Source: Price input (default: close)
• KAMA Length: Adjusts sensitivity (longer = smoother)
• ATR Length & Multiplier: Defines channel width for breakout detection
• Gaussian Filter (Length & Sigma): Controls smoothing strength
• Trend Logic Mode: ATR channel breakout vs. price-KAMA cross
• Style: Custom colors, background fill, marker visibility
📈 How to Use
• Follow trend arrows for directional confirmation
• Use ATR breakout mode for cleaner, filtered signals
• Switch to price-KAMA crossover mode for earlier entries
• Works well with structure, momentum, and volume confirmation
Thai Gold BahtIndicator Name: Thai Gold Baht
Short Title: Thai Gold Baht
Purpose
This indicator calculates and visualizes the real-time price of 1 Thai Gold Baht (15.244 grams) based on the global gold price ( XAU/USD ) and the USD/THB exchange rate .
Users can customize gold weight and purity to simulate the local Thai gold market price.
What it does
Retrieves live gold price per troy ounce in USD
Retrieves current USD to Thai Baht exchange rate
Converts the value using user-defined weight and purity
Displays result as a real-time chart
Shows calculation details in the Data Window
Ideal for
Traders tracking Thai gold based on international prices
Analysts comparing local and global bullion markets
Anyone needing a configurable, transparent gold price conversion
Pine Script Functionality
// Uses XAU/USD and USD/THB as inputs
// Calculates 1 Baht Gold (96.5% default purity)
// Outputs the value in THB as a chart line
ชื่ออินดิเคเตอร์: Thai Gold Baht
ชื่อย่อ: Thai Gold Baht
วัตถุประสงค์
อินดิเคเตอร์นี้ใช้คำนวณและแสดงราคาทองคำไทย 1 บาท (15.244 กรัม) แบบเรียลไทม์
โดยอ้างอิงจากราคาทองคำในตลาดโลก ( XAU/USD ) และอัตราแลกเปลี่ยน USD/THB
ผู้ใช้สามารถกำหนดน้ำหนักทองและความบริสุทธิ์เองได้ เพื่อจำลองราคาทองคำในประเทศไทยอย่างแม่นยำ
สิ่งที่อินดิเคเตอร์นี้ทำ
ดึงราคาทองคำแบบเรียลไทม์ต่อทรอยออนซ์ในสกุลเงิน USD
ดึงอัตราแลกเปลี่ยน USD → THB แบบเรียลไทม์
คำนวณราคาจากน้ำหนักและเปอร์เซ็นต์ความบริสุทธิ์ที่ผู้ใช้กำหนด
แสดงผลลัพธ์เป็นกราฟแบบเรียลไทม์ในหน่วยบาทไทย
แสดงรายละเอียดการคำนวณในหน้าต่าง Data Window ของ TradingView
เหมาะสำหรับ
นักเทรดที่ต้องการติดตามราคาทองคำไทยจากราคาทองคำตลาดโลก
นักวิเคราะห์ที่เปรียบเทียบราคาทองคำในประเทศและต่างประเทศ
ผู้ใช้งานที่ต้องการการแปลงราคาทองคำระหว่างประเทศให้โปร่งใสและปรับแต่งได้
การทำงานของ Pine Script
// ใช้ข้อมูล XAU/USD และ USD/THB เป็นอินพุต
// คำนวณราคาทองคำไทย 1 บาท (ความบริสุทธิ์เริ่มต้นที่ 96.5%)
// แสดงผลเป็นเส้นกราฟของราคาทองคำในหน่วยบาทไทย
