loxxfftLibrary "loxxfft"
This code is a library for performing Fast Fourier Transform (FFT) operations. FFT is an algorithm that can quickly compute the discrete Fourier transform (DFT) of a sequence. The library includes functions for performing FFTs on both real and complex data. It also includes functions for fast correlation and convolution, which are operations that can be performed efficiently using FFTs. Additionally, the library includes functions for fast sine and cosine transforms.
Reference:
www.alglib.net
fastfouriertransform(a, nn, inversefft)
Returns Fast Fourier Transform
Parameters:
a (float ) : float , An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices.
nn (int) : int, The number of function values. It must be a power of two, but the algorithm does not validate this.
inversefft (bool) : bool, A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT.
Returns: float , Modifies the input array a in-place, which means that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. The transformed data will have real and imaginary parts interleaved, with the real parts at even indices and the imaginary parts at odd indices.
realfastfouriertransform(a, tnn, inversefft)
Returns Real Fast Fourier Transform
Parameters:
a (float ) : float , A float array containing the real-valued function samples.
tnn (int) : int, The number of function values (must be a power of 2, but the algorithm does not validate this condition).
inversefft (bool) : bool, A boolean flag that indicates the direction of the transformation (True for inverse, False for direct).
Returns: float , Modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution.
fastsinetransform(a, tnn, inversefst)
Returns Fast Discrete Sine Conversion
Parameters:
a (float ) : float , An array of real numbers representing the function values.
tnn (int) : int, Number of function values (must be a power of two, but the code doesn't validate this).
inversefst (bool) : bool, A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST.
Returns: float , The output is the transformed array 'a', which will contain the result of the transformation.
fastcosinetransform(a, tnn, inversefct)
Returns Fast Discrete Cosine Transform
Parameters:
a (float ) : float , This is a floating-point array representing the sequence of values (time-domain) that you want to transform. The function will perform the Fast Cosine Transform (FCT) or the inverse FCT on this input array, depending on the value of the inversefct parameter. The transformed result will also be stored in this same array, which means the function modifies the input array in-place.
tnn (int) : int, This is an integer value representing the number of data points in the input array a. It is used to determine the size of the input array and control the loops in the algorithm. Note that the size of the input array should be a power of 2 for the Fast Cosine Transform algorithm to work correctly.
inversefct (bool) : bool, This is a boolean value that controls whether the function performs the regular Fast Cosine Transform or the inverse FCT. If inversefct is set to true, the function will perform the inverse FCT, and if set to false, the regular FCT will be performed. The inverse FCT can be used to transform data back into its original form (time-domain) after the regular FCT has been applied.
Returns: float , The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array.
fastconvolution(signal, signallen, response, negativelen, positivelen)
Convolution using FFT
Parameters:
signal (float ) : float , This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1.
signallen (int) : int, This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array.
response (float ) : float , This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively.
negativelen (int) : int, This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero.
positivelen (int) : int, This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero.
Returns: float , The resulting convolved values are stored back in the input signal array.
fastcorrelation(signal, signallen, pattern, patternlen)
Returns Correlation using FFT
Parameters:
signal (float ) : float ,This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1.
signallen (int) : int, This is an integer representing the length of the input signal array.
pattern (float ) : float , This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1.
patternlen (int) : int, This is an integer representing the length of the pattern array.
Returns: float , The signal array containing the correlation values at points from 0 to SignalLen-1.
tworealffts(a1, a2, a, b, tn)
Returns Fast Fourier Transform of Two Real Functions
Parameters:
a1 (float ) : float , An array of real numbers, representing the values of the first function.
a2 (float ) : float , An array of real numbers, representing the values of the second function.
a (float ) : float , An output array to store the Fourier transform of the first function.
b (float ) : float , An output array to store the Fourier transform of the second function.
tn (int) : float , An integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition.
Returns: float , The a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b.
█ Detailed explaination of each function
Fast Fourier Transform
The fastfouriertransform() function takes three input parameters:
1. a: An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices.
2. nn: The number of function values. It must be a power of two, but the algorithm does not validate this.
3. inversefft: A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT.
The function performs the FFT using the Cooley-Tukey algorithm, which is an efficient algorithm for computing the discrete Fourier transform (DFT) and its inverse. The Cooley-Tukey algorithm recursively breaks down the DFT of a sequence into smaller DFTs of subsequences, leading to a significant reduction in computational complexity. The algorithm's time complexity is O(n log n), where n is the number of samples.
The fastfouriertransform() function first initializes variables and determines the direction of the transformation based on the inversefft parameter. If inversefft is True, the isign variable is set to -1; otherwise, it is set to 1.
Next, the function performs the bit-reversal operation. This is a necessary step before calculating the FFT, as it rearranges the input data in a specific order required by the Cooley-Tukey algorithm. The bit-reversal is performed using a loop that iterates through the nn samples, swapping the data elements according to their bit-reversed index.
After the bit-reversal operation, the function iteratively computes the FFT using the Cooley-Tukey algorithm. It performs calculations in a loop that goes through different stages, doubling the size of the sub-FFT at each stage. Within each stage, the Cooley-Tukey algorithm calculates the butterfly operations, which are mathematical operations that combine the results of smaller DFTs into the final DFT. The butterfly operations involve complex number multiplication and addition, updating the input array a with the computed values.
The loop also calculates the twiddle factors, which are complex exponential factors used in the butterfly operations. The twiddle factors are calculated using trigonometric functions, such as sine and cosine, based on the angle theta. The variables wpr, wpi, wr, and wi are used to store intermediate values of the twiddle factors, which are updated in each iteration of the loop.
Finally, if the inversefft parameter is True, the function divides the result by the number of samples nn to obtain the correct inverse FFT result. This normalization step is performed using a loop that iterates through the array a and divides each element by nn.
In summary, the fastfouriertransform() function is an implementation of the Cooley-Tukey FFT algorithm, which is an efficient algorithm for computing the DFT and its inverse. This FFT library can be used for a variety of applications, such as signal processing, image processing, audio processing, and more.
Feal Fast Fourier Transform
The realfastfouriertransform() function performs a fast Fourier transform (FFT) specifically for real-valued functions. The FFT is an efficient algorithm used to compute the discrete Fourier transform (DFT) and its inverse, which are fundamental tools in signal processing, image processing, and other related fields.
This function takes three input parameters:
1. a - A float array containing the real-valued function samples.
2. tnn - The number of function values (must be a power of 2, but the algorithm does not validate this condition).
3. inversefft - A boolean flag that indicates the direction of the transformation (True for inverse, False for direct).
The function modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution.
The algorithm uses a combination of complex-to-complex FFT and additional transformations specific to real-valued data to optimize the computation. It takes into account the symmetry properties of the real-valued input data to reduce the computational complexity.
Here's a detailed walkthrough of the algorithm:
1. Depending on the inversefft flag, the initial values for ttheta, c1, and c2 are determined. These values are used for the initial data preprocessing and post-processing steps specific to the real-valued FFT.
2. The preprocessing step computes the initial real and imaginary parts of the data using a combination of sine and cosine terms with the input data. This step effectively converts the real-valued input data into complex-valued data suitable for the complex-to-complex FFT.
3. The complex-to-complex FFT is then performed on the preprocessed complex data. This involves bit-reversal reordering, followed by the Cooley-Tukey radix-2 decimation-in-time algorithm. This part of the code is similar to the fastfouriertransform() function you provided earlier.
4. After the complex-to-complex FFT, a post-processing step is performed to obtain the final real-valued output data. This involves updating the real and imaginary parts of the transformed data using sine and cosine terms, as well as the values c1 and c2.
5. Finally, if the inversefft flag is True, the output data is divided by the number of samples (nn) to obtain the inverse DFT.
The function does not return a value explicitly. Instead, the transformed data is stored in the input array a. After the function execution, you can access the transformed data in the a array, which will have the real part at even indices and the imaginary part at odd indices.
Fast Sine Transform
This code defines a function called fastsinetransform that performs a Fast Discrete Sine Transform (FST) on an array of real numbers. The function takes three input parameters:
1. a (float array): An array of real numbers representing the function values.
2. tnn (int): Number of function values (must be a power of two, but the code doesn't validate this).
3. inversefst (bool): A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST.
The output is the transformed array 'a', which will contain the result of the transformation.
The code starts by initializing several variables, including trigonometric constants for the sine transform. It then sets the first value of the array 'a' to 0 and calculates the initial values of 'y1' and 'y2', which are used to update the input array 'a' in the following loop.
The first loop (with index 'jx') iterates from 2 to (tm + 1), where 'tm' is half of the number of input samples 'tnn'. This loop is responsible for calculating the initial sine transform of the input data.
The second loop (with index 'ii') is a bit-reversal loop. It reorders the elements in the array 'a' based on the bit-reversed indices of the original order.
The third loop (with index 'ii') iterates while 'n' is greater than 'mmax', which starts at 2 and doubles each iteration. This loop performs the actual Fast Discrete Sine Transform. It calculates the sine transform using the Danielson-Lanczos lemma, which is a divide-and-conquer strategy for calculating Discrete Fourier Transforms (DFTs) efficiently.
The fourth loop (with index 'ix') is responsible for the final phase adjustments needed for the sine transform, updating the array 'a' accordingly.
The fifth loop (with index 'jj') updates the array 'a' one more time by dividing each element by 2 and calculating the sum of the even-indexed elements.
Finally, if the 'inversefst' flag is True, the code scales the transformed data by a factor of 2/tnn to get the inverse Fast Sine Transform.
In summary, the code performs a Fast Discrete Sine Transform on an input array of real numbers, either in the direct or inverse direction, and returns the transformed array. The algorithm is based on the Danielson-Lanczos lemma and uses a divide-and-conquer strategy for efficient computation.
Fast Cosine Transform
This code defines a function called fastcosinetransform that takes three parameters: a floating-point array a, an integer tnn, and a boolean inversefct. The function calculates the Fast Cosine Transform (FCT) or the inverse FCT of the input array, depending on the value of the inversefct parameter.
The Fast Cosine Transform is an algorithm that converts a sequence of values (time-domain) into a frequency domain representation. It is closely related to the Fast Fourier Transform (FFT) and can be used in various applications, such as signal processing and image compression.
Here's a detailed explanation of the code:
1. The function starts by initializing a number of variables, including counters, intermediate values, and constants.
2. The initial steps of the algorithm are performed. This includes calculating some trigonometric values and updating the input array a with the help of intermediate variables.
3. The code then enters a loop (from jx = 2 to tnn / 2). Within this loop, the algorithm computes and updates the elements of the input array a.
4. After the loop, the function prepares some variables for the next stage of the algorithm.
5. The next part of the algorithm is a series of nested loops that perform the bit-reversal permutation and apply the FCT to the input array a.
6. The code then calculates some additional trigonometric values, which are used in the next loop.
7. The following loop (from ix = 2 to tnn / 4 + 1) computes and updates the elements of the input array a using the previously calculated trigonometric values.
8. The input array a is further updated with the final calculations.
9. In the last loop (from j = 4 to tnn), the algorithm computes and updates the sum of elements in the input array a.
10. Finally, if the inversefct parameter is set to true, the function scales the input array a to obtain the inverse FCT.
The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array.
Fast Convolution
This code defines a function called fastconvolution that performs the convolution of a given signal with a response function using the Fast Fourier Transform (FFT) technique. Convolution is a mathematical operation used in signal processing to combine two signals, producing a third signal representing how the shape of one signal is modified by the other.
The fastconvolution function takes the following input parameters:
1. float signal: This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1.
2. int signallen: This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array.
3. float response: This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively.
4. int negativelen: This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero.
5. int positivelen: This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero.
The function works by:
1. Calculating the length nl of the arrays used for FFT, ensuring it's a power of 2 and large enough to hold the signal and response.
2. Creating two new arrays, a1 and a2, of length nl and initializing them with the input signal and response function, respectively.
3. Applying the forward FFT (realfastfouriertransform) to both arrays, a1 and a2.
4. Performing element-wise multiplication of the FFT results in the frequency domain.
5. Applying the inverse FFT (realfastfouriertransform) to the multiplied results in a1.
6. Updating the original signal array with the convolution result, which is stored in the a1 array.
The result of the convolution is stored in the input signal array at the function exit.
Fast Correlation
This code defines a function called fastcorrelation that computes the correlation between a signal and a pattern using the Fast Fourier Transform (FFT) method. The function takes four input arguments and modifies the input signal array to store the correlation values.
Input arguments:
1. float signal: This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1.
2. int signallen: This is an integer representing the length of the input signal array.
3. float pattern: This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1.
4. int patternlen: This is an integer representing the length of the pattern array.
The function performs the following steps:
1. Calculate the required size nl for the FFT by finding the smallest power of 2 that is greater than or equal to the sum of the lengths of the signal and the pattern.
2. Create two new arrays a1 and a2 with the length nl and initialize them to 0.
3. Copy the signal array into a1 and pad it with zeros up to the length nl.
4. Copy the pattern array into a2 and pad it with zeros up to the length nl.
5. Compute the FFT of both a1 and a2.
6. Perform element-wise multiplication of the frequency-domain representation of a1 and the complex conjugate of the frequency-domain representation of a2.
7. Compute the inverse FFT of the result obtained in step 6.
8. Store the resulting correlation values in the original signal array.
At the end of the function, the signal array contains the correlation values at points from 0 to SignalLen-1.
Fast Fourier Transform of Two Real Functions
This code defines a function called tworealffts that computes the Fast Fourier Transform (FFT) of two real-valued functions (a1 and a2) using a Cooley-Tukey-based radix-2 Decimation in Time (DIT) algorithm. The FFT is a widely used algorithm for computing the discrete Fourier transform (DFT) and its inverse.
Input parameters:
1. float a1: an array of real numbers, representing the values of the first function.
2. float a2: an array of real numbers, representing the values of the second function.
3. float a: an output array to store the Fourier transform of the first function.
4. float b: an output array to store the Fourier transform of the second function.
5. int tn: an integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition.
The function performs the following steps:
1. Combine the two input arrays, a1 and a2, into a single array a by interleaving their elements.
2. Perform a 1D FFT on the combined array a using the radix-2 DIT algorithm.
3. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b.
Here is a detailed breakdown of the radix-2 DIT algorithm used in this code:
1. Bit-reverse the order of the elements in the combined array a.
2. Initialize the loop variables mmax, istep, and theta.
3. Enter the main loop that iterates through different stages of the FFT.
a. Compute the sine and cosine values for the current stage using the theta variable.
b. Initialize the loop variables wr and wi for the current stage.
c. Enter the inner loop that iterates through the butterfly operations within each stage.
i. Perform the butterfly operation on the elements of array a.
ii. Update the loop variables wr and wi for the next butterfly operation.
d. Update the loop variables mmax, istep, and theta for the next stage.
4. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b.
At the end of the function, the a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b.
█ Example scripts using functions contained in loxxfft
Real-Fast Fourier Transform of Price w/ Linear Regression
Real-Fast Fourier Transform of Price Oscillator
Normalized, Variety, Fast Fourier Transform Explorer
Variety RSI of Fast Discrete Cosine Transform
STD-Stepped Fast Cosine Transform Moving Average
Search in scripts for "pattern"
Koncorde PlusKONCORDE IS ONLY INTENDED TO BE APPLIED TO ASSETS WHERE VOLUME DATA IS PROVIDED.
This indicator is made up of 6 indicators: 4 trend (RSI, MFI, BB, Stochastic) and 2 volume. The 2's for volume are the PVI (positive volume index) and the NVI (negative volume index). These two indicators are the interesting ones as they are programmed to proportionally attribute the volume traded between the strong hands (sharks) and the weak hands (minnows).
As for what time period to use, the bigger the better, since after all what we are doing is data analysis and therefore the more data, the better.
When strong hands (blue histogram) are below zero, they are said to be selling while when they are above zero, they are said to be buying. The same goes for weak hands (green histogram).
Meaning of each zone:
Blue histogram: strong hand (sharks). If it is positive it indicates accumulation and if it is negative distribution.
Green histogram: weak hand (minnows). If it is positive it indicates buy and if it is negative it indicates sale.
Brown histogram: Indicates the trend and depends on previous values of weak hands and trend indicators (RSI, MFI, BB, Stochastic).
Red line: It is an average that smoothes the trend indicated by the brown histogram (default is the EMA).
Crossing Pattern
The pattern gives us a bullish entry signal when the trend (brown histogram) crosses above the average (red line) and is positioned bearish when the trend crosses below the average.
Zero Pattern
When the price trend (brown histogram) tends to zero, it means that there will be a change in its trend. This pattern is for trading in a bullish position.
Spring Pattern
When a cross between the average (red line) and the trend (brown histogram) has already occurred, and in addition the weak hands are above the price trend, that "spring on the mountain" is formed that gives us to understand that the upward trend will be more than evident.
Mirror Pattern
This pattern occurs when there is panic in the market and weak hands are selling (below zero). If at that moment the strong hands are buyers, the price tends to level off to begin the rise later.
This pattern is compatible with the Crossover Pattern, having more guarantees of success. If just after finishing the mirror pattern, the Crossover Pattern plus the Spring Pattern appears, then we have a good chance of winning.
Bear Hug Pattern
This pattern is for bearish positions only. It is the opposite figure to the mirror pattern. That is, we have strong hands clearly selling and weak hands clearly buying and above the price trend (brown histogram). It is the figure where you can see that the strong hands are distributing the assets to the weak hands.
Harpoon Pattern
If when the mirror pattern occurs, the red line crosses the blue histogram, a very strong bullish entry signal is produced.
Add an exit signal which occurs when we are in a spring pattern but the big hands start selling, mostly coinciding with the start of the bear hug pattern.
General rules for operating the Mirror Pattern:
a) Wait for the green histogram to start recovery, rise to positive values; if possible, until it crosses from bottom to top the brown line (brown histogram) and/or red average .
b) The blue histogram should be consistently positive. If it turns and goes towards negative values it can indicate a failed pattern at that same point.
c) Locate the low of the lower candle within the pattern and place the Stop Loss just below it for reference.
d) If we are not sure (we almost never will be) that there will be a turn or if it could finally be a bearish continuation we can use the SL to go short .
Additional:
A panel with performance statistics of the analyzed asset was added.
Added an indicator that shows the cumulative delta volume in the form of triangles at the top of the chart.
Added of user @DonovanWall
PS: Unofficial version, I was guided by the description of the BLAI5 author's website www.blai5.net
DISCLAIMER: For educational and entertainment purposes only. Nothing in this content should be interpreted as financial advice or a recommendation to buy or sell any sort of security or investment including all types of cryptos. DYOR, TYOB.
Rob Booker - ADX Breakout updated to pinescript V5Rob Booker - ADX Breakout. The strategy remains unchanged but the code has been updated to pinescript V5. This enables compatibility with all new Tradingview features. Additonally, indicators have been made more easily visible, default cash settings as well as input descriptions have been added.
Rob Booker - ADX Breakout: (Directly taken from the official Tradingview V1 version of the script)
Definition
Rob Booker’s Average Directional Index (ADX) Breakout is a trend strength indicator that affirms the belief that trading in the direction of a trend and continuing to follow its pull is more profitable for traders, while simultaneously reducing risk.
History
ADX was traditionally used and developed to determine a price’s trend strength. It is commonly known as a tool from the arsenal of Rob Booker, experienced entrepreneur and currency trader.
Calculations
Calculations for the ADX Breakout indicator are based on a moving average of price range expansion over a specific period of time. By default, the setting rests at 14 bars, this however is not mandatory, as other periods are routinely used for analysis as well.
Takeaways
The ADX line is used to measure and determine the strength of a trend, and so the direction of this line and its interpretation are crucial in a trader’s analysis. As the ADX line rises, a trend increases in strength and price moves in the trend’s direction. Similarly, if the ADX line is falling, a trend decreases in strength and price then enters a period of consolidation, or retracement.
Traditionally, the ADX is plotted on the chart as a single line that consists of values that range from 0-100. The line is non-directional, meaning that it always measures trend strength regardless of the position of a price’s trend (up or down). Essentially, ADX quantifies trend strength by presenting in both uptrends and downtrends of the line.
What to look for
The values associated with the ADX line help traders determine the most profitable trades and where risk lies in the current trend. It is important to know how to quantify trend strength and distinguish between the varying values in order to understand the differences in trending vs. non-trending conditions. Let’s take a look at ADX values and what they mean for trend strength.
ADX Value:
0-25: Signifies an absent of weak trend
25-50: Signifies a strong trend
50-75: Signifies a very strong trend
75-100: Signifies an extremely strong trend
To delve into this a bit further, let’s assess the meaning of ADX if it is valued below 25. If the ADX line remains below 25 for more than 30 or so bars, price then enters range conditions, making price patterns more distinguishable and visible to traders. Price will move up and down between resistance and support in order to determine selling and buying interest and may then eventually break out into a trend or pattern.
The way in which ADX peaks, ebs, and flows is also a signifier of its overall pattern and trend momentum. The line can clearly indicate to the trader when trend strength is strong versus when it is weak. When ADX peaks are pictured as higher, it points towards an increase in trend momentum. If ADX peaks are pictured as lower - you guessed it - it points towards a decrease in trend momentum. A trend of lower ADX peaks could be a warning for traders to watch prices and manage and assess risk before a trade gets out of hand. Similarly, whenever there is a sudden move that seems out of place or a change in trend character that goes against what you’ve seen before, this should be a clear sign to watch prices and assess risk.
Summary
The ADX Breakout indicator is a trend strength indicator that analyzes price movements relative to trend strength to signal a user when is best for a trade and when is best to manage risk and assess patterns. As long as a trader recognizes strong trends and assesses the risk of each trade properly, they should have no problem using this indicator and utilizing it to work in their favor. In addition, the ADX helps identify trending conditions, but while doing so, also aids traders in finding strong trends to trade. The indicator can even alert traders to specific changes in trend momentum, allowing them to be primed for risk management.
Candle Strength Analyzer by The Ultimate Bull Run# Candle Strength Analyzer
## 📊 Complete Beginner's Guide
---
### 🎯 What This Indicator Does
The **Candle Strength Analyzer** measures how "strong" or "weak" each candlestick is and displays a **score from 0 to 100** above or below every candle.
- **Green numbers** = Bullish (price went UP)
- **Red numbers** = Bearish (price went DOWN)
- **Gray numbers** = Doji (price barely moved)
**Higher score = Stronger candle = More reliable signal**
---
### 🕯️ Understanding Candlesticks (The Basics)
If you're new to trading, here's what a candlestick shows:
```
│ ← Upper Wick (prices that were rejected)
│
┌───┐
│ │ ← Body (the "real" price movement)
│ │ • Green/White body = Price went UP (Bullish)
│ │ • Red/Black body = Price went DOWN (Bearish)
└───┘
│
│ ← Lower Wick (prices that were rejected)
```
**Key Terms:**
- **Open**: The price when the candle started
- **Close**: The price when the candle ended
- **High**: The highest price during the candle
- **Low**: The lowest price during the candle
- **Body**: The rectangle between Open and Close
- **Wick/Shadow**: The thin lines above and below the body
---
## 📐 The 4 Components of Candle Strength
This indicator combines **4 measurements** to calculate the final strength score. Let's understand each one:
---
### 1️⃣ Body Ratio (30% of score)
**What it is:**
The percentage of the candle that is "body" versus "wicks."
**Formula:**
```
Body Ratio = Size of Body ÷ Total Candle Size × 100
```
**What it tells you:**
- **High Body Ratio (70-100%)**: Bulls or bears were in FULL control. The price moved in one direction and STAYED there. This is strong.
- **Low Body Ratio (0-30%)**: There was a fight. Price moved up AND down but ended up roughly where it started. This is weak/indecisive.
**Visual Example:**
```
Strong Candle (90% body): Weak Candle (20% body):
│ │
┌───┐ │
│ │ ┌─┴─┐
│ │ ← Mostly body │ │ ← Tiny body
│ │ └─┬─┘
└───┘ │
│ │
```
**How to interpret:**
| Body Ratio | Meaning |
|------------|---------|
| 90-100% | **Marubozu** - Extremely strong, full commitment |
| 70-90% | **Strong** - Clear winner (bulls or bears) |
| 40-70% | **Normal** - Typical market activity |
| 10-40% | **Weak** - Significant indecision |
| 0-10% | **Doji** - Complete indecision, no winner |
---
### 2️⃣ Close Position Score (25% of score)
**What it is:**
WHERE the candle closed within its range (high to low).
**What it tells you:**
- For a **bullish (green) candle**: Closing near the HIGH means buyers were still eager at the end = STRONG
- For a **bearish (red) candle**: Closing near the LOW means sellers were still eager at the end = STRONG
**Visual Example:**
```
Strong Bullish: Weak Bullish:
(closes near high) (closes near middle)
┌───┐ ← Close here │
│ │ ┌─┴─┐ ← Close here
│ │ │ │
│ │ │ │
└───┘ └───┘
│ │
```
**Why it matters:**
If price went UP but then sellers pushed it back down before the candle closed, that's a sign of weakness. The bulls couldn't hold their ground.
**How to interpret:**
| Close Position | For Bullish Candle | For Bearish Candle |
|----------------|-------------------|-------------------|
| 80-100% | Strong (near high) | Weak (near high) |
| 50-80% | Moderate | Moderate |
| 20-50% | Weak | Moderate |
| 0-20% | Very Weak (near low) | Strong (near low) |
---
### 3️⃣ Relative Volume - RVOL (25% of score)
**What is Volume?**
Volume is the NUMBER of shares/contracts traded during that candle. Think of it as "how many people participated."
**What is RVOL?**
RVOL compares TODAY'S volume to the AVERAGE volume.
**Formula:**
```
RVOL = Current Volume ÷ Average Volume (last 20 candles)
```
**What it tells you:**
- **RVOL = 1.0**: Normal activity (same as average)
- **RVOL = 2.0**: DOUBLE the normal activity (2x more traders involved)
- **RVOL = 0.5**: HALF the normal activity (fewer traders involved)
**Why it matters:**
A big price move with LOW volume is suspicious - it might not last.
A big price move with HIGH volume is confirmed - many traders agree.
**Think of it like voting:**
- High volume = Many people voted for this direction
- Low volume = Only a few people voted, decision might change
**How to interpret:**
| RVOL | Meaning | Signal Quality |
|------|---------|----------------|
| 2.0+ | Very High - Institutional activity likely | ⭐⭐⭐ Excellent |
| 1.5-2.0 | High - Significant interest | ⭐⭐ Good |
| 1.0-1.5 | Above Average | ⭐ Acceptable |
| 0.7-1.0 | Below Average | ⚠️ Caution |
| < 0.7 | Low - Lack of interest | ❌ Unreliable |
---
### 4️⃣ Size vs ATR (20% of score)
**What is ATR?**
ATR stands for "Average True Range." It measures how much the price TYPICALLY moves.
**What this component measures:**
How big is THIS candle compared to how big candles USUALLY are?
**Formula:**
```
ATR Ratio = This Candle's Size ÷ Average Candle Size (ATR)
```
**What it tells you:**
- **ATR Ratio = 2.0**: This candle is TWICE as big as normal = Significant move
- **ATR Ratio = 1.0**: This candle is normal sized
- **ATR Ratio = 0.5**: This candle is HALF the normal size = Minor move
**Why it matters:**
A 50-point move in a stock that normally moves 100 points is small.
A 50-point move in a stock that normally moves 20 points is HUGE.
Context matters!
**How to interpret:**
| ATR Ratio | Meaning |
|-----------|---------|
| 2.0+ | **Expansion** - Unusually large move, potential breakout |
| 1.5-2.0 | **Large** - Significant momentum |
| 1.0-1.5 | **Above Average** - Notable move |
| 0.5-1.0 | **Normal** - Typical movement |
| < 0.5 | **Small** - Insignificant, might be noise |
---
## 🧮 How the Final Score is Calculated
The indicator combines all 4 components with these weights:
```
Final Score = (Body Ratio × 30%) +
(Close Position × 25%) +
(RVOL Score × 25%) +
(Size Score × 20%)
```
**Result: A score from 0 to 100**
---
## 📊 Understanding the Strength Score
| Score | Classification | What It Means | Should You Trade It? |
|-------|---------------|---------------|---------------------|
| **70-100** | 🟢 STRONG | High conviction move, reliable signal | ✅ Yes - Good setup |
| **40-70** | 🟡 MODERATE | Average move, needs confirmation | ⚠️ Maybe - Add other indicators |
| **0-40** | 🔴 WEAK | Low conviction, unreliable | ❌ No - Wait for better setup |
---
## 🏷️ Special Pattern Markers
The indicator also detects special candlestick patterns:
### ⚡ Power Candle
**Requirements:**
- Body Ratio > 70% (strong body)
- RVOL > 1.5 (high volume)
- Close Position > 80% (closes near the extreme)
**What it means:** The BEST possible signal. Everything aligns perfectly.
### Ⓜ️ Marubozu
**Requirements:**
- Body Ratio > 90% (almost no wicks)
**What it means:** Complete dominance by bulls or bears. Very strong continuation signal.
### ◆ High Volume Doji
**Requirements:**
- Doji candle (tiny body)
- High volume
**What it means:** Many traders are fighting, but no one won. Often signals a REVERSAL is coming.
---
## ⚙️ Settings Explained
### Volume Settings
| Setting | Default | What It Does |
|---------|---------|--------------|
| Volume Lookback Period | 20 | How many candles to average for "normal" volume |
| RVOL Threshold | 1.5 | What counts as "high" volume (1.5 = 50% above average) |
### ATR Settings
| Setting | Default | What It Does |
|---------|---------|--------------|
| ATR Period | 14 | How many candles to calculate average movement |
| ATR Multiplier | 1.5 | What counts as a "large" candle |
### Strength Thresholds
| Setting | Default | What It Does |
|---------|---------|--------------|
| Strong Candle Threshold | 70 | Score needed to be "strong" |
| Weak Candle Threshold | 30 | Score below this is "weak" |
### Label Filter (Important!)
TradingView limits indicators to **500 labels maximum**. Use filters to see more history:
| Filter Mode | Shows | Best For |
|-------------|-------|----------|
| All Candles | Every single candle | Short-term charts (5min, 15min) |
| Strong Only (70+) | Only strong candles | Longer history, key signals only |
| Moderate+ (40+) | Moderate and strong | Balance of detail and history |
| Custom Minimum | Your choice | Full control |
**Tip:** On daily charts, use "Strong Only" to see months of history instead of just a few weeks.
### Label Settings
| Setting | What It Does |
|---------|--------------|
| Label Size | tiny / small / normal / large |
| Show Decimal Places | Show "72.5" instead of "73" |
| Label Style | With background bubble OR just text |
---
## 📖 How to Read the Info Table
The table in the corner shows details for the CURRENT (most recent) candle:
| Row | Meaning |
|-----|---------|
| **Candle Strength** | The final score (0-100) |
| **Direction** | BULLISH / BEARISH / DOJI |
| **Body Ratio** | Percentage of candle that is body |
| **Close Position** | Where it closed (0-100) |
| **Upper Wick** | Size of upper wick as % |
| **Lower Wick** | Size of lower wick as % |
| **RVOL** | Current volume vs average (1.5x = 50% above average) |
| **Size/ATR** | Candle size vs average size |
| **Classification** | STRONG / MODERATE / WEAK |
| **Vol Confirmed** | Is volume above threshold? |
| **Pattern** | Special pattern detected |
---
## 🎓 How to Use This Indicator
### Step 1: Add to Chart
1. Open Pine Editor in TradingView
2. Paste the code
3. Click "Add to Chart"
### Step 2: Adjust Filter (if needed)
- If you see "max labels reached," change filter to "Strong Only (70+)"
- This lets you see more candles in history
### Step 3: Look for Strong Signals
Focus on candles with:
- ✅ Score **70+** (bright green or red)
- ✅ **RVOL > 1.5** (confirmed by volume)
- ✅ Special markers (⚡, M, ◆)
### Step 4: Avoid Weak Signals
Be careful with candles that have:
- ❌ Score **below 40** (muted colors)
- ❌ **RVOL < 1.0** (no volume confirmation)
- ❌ Large wicks (rejection happened)
---
## 💡 Trading Tips for Beginners
### ✅ DO:
1. **Wait for strong candles (70+)** before entering trades
2. **Confirm with volume** - Look for RVOL > 1.5
3. **Use at support/resistance levels** - Strong candles at key levels are more meaningful
4. **Combine with other indicators** - RSI, MACD, or moving averages
5. **Practice on demo first** - Learn to recognize strong vs weak candles
### ❌ DON'T:
1. **Trade every candle** - Not all candles are worth trading
2. **Ignore volume** - A strong candle with low volume is suspicious
3. **Fight the trend** - Strong bearish candles in an uptrend might just be pullbacks
4. **Over-leverage** - Even strong signals can fail
---
## 📝 Quick Reference Cheat Sheet
```
STRONG CANDLE CHECKLIST:
□ Score 70+
□ RVOL > 1.5
□ Body Ratio > 60%
□ Close Position > 75% (bullish) or < 25% (bearish)
□ At key support/resistance level
WEAK CANDLE WARNING SIGNS:
□ Score < 40
□ RVOL < 0.7
□ Large wicks (> 30%)
□ Doji pattern
□ Small candle (ATR Ratio < 0.5)
```
---
## ⚠️ Important Disclaimers
1. **No indicator is 100% accurate** - Always use stop losses
2. **Past performance ≠ future results** - Markets change
3. **This is a tool, not a strategy** - Combine with other analysis
4. **Practice first** - Use paper trading before real money
---
## 🔔 Alerts Available
Set alerts for:
- Strong Bullish Candle (with volume confirmation)
- Strong Bearish Candle (with volume confirmation)
- Power Candle detected
- Marubozu detected
- High Volume Doji detected
---
## ❓ FAQ
**Q: Why are some candles missing labels?**
A: TradingView limits indicators to 500 labels. Use filters to see more history.
**Q: The label colors are hard to see. Can I change them?**
A: Yes! Go to Settings → Colors and customize all colors.
**Q: Should I only trade strong candles?**
A: Strong candles are MORE reliable, but not guaranteed. Always use proper risk management.
**Q: What timeframe works best?**
A: Works on all timeframes. Higher timeframes (4H, Daily) tend to have more reliable signals.
**Q: Can I use this for crypto/forex/stocks?**
A: Yes! This indicator works on any market with candlestick data and volume.
---
## 📚 Glossary
| Term | Definition |
|------|------------|
| **Bullish** | Price is going UP / Buyers are winning |
| **Bearish** | Price is going DOWN / Sellers are winning |
| **Doji** | Candle where open and close are nearly equal (indecision) |
| **Marubozu** | Candle with no wicks (full body) |
| **RVOL** | Relative Volume - current volume vs average |
| **ATR** | Average True Range - typical price movement |
| **Wick/Shadow** | The thin lines above/below the candle body |
| **Support** | Price level where buyers tend to step in |
| **Resistance** | Price level where sellers tend to step in |
| **Breakout** | When price moves beyond support/resistance |
---
**Happy Trading! 📈**
*Remember: The best traders are patient traders. Wait for strong setups.*
Unmitigated MTF High Low Pro - Cave Diving Bookmap Heatmap Plot
Unmitigated MTF High Low Pro - Cave Diving Bookmap Heatmap Plot
---
## 📖 Table of Contents
1. (#what-this-indicator-does)
2. (#core-concepts)
3. (#visual-components)
4. (#the-cave-diving-framework)
5. (#how-to-use-it-for-trading)
6. (#settings--customization)
7. (#best-practices)
8. (#common-scenarios)
---
## What This Indicator Does
The **Unmitigated MTF High Low v2.0** tracks unmitigated (untouch) high and low levels across multiple timeframes, helping you identify key support and resistance zones that the market hasn't revisited yet. Think of it as a sophisticated memory system for price action - it remembers where price has been, and more importantly, where it *hasn't been back to*.
### Why "Unmitigated" Matters
In futures trading, especially on instruments like NQ and ES, the market has a tendency to revisit levels where liquidity was left behind. An "unmitigated" level is one that hasn't been touched since it was formed. These levels often act as magnets for price, and understanding their age and proximity gives you a significant edge in:
- **Entry timing** - Waiting for price to approach tested levels
- **Exit planning** - Taking profits before ancient resistance/support
- **Risk management** - Avoiding entries when approaching multiple old levels
- **Liquidity mapping** - Visualizing where orders likely cluster
---
## Core Concepts
### 1. **Sessions & Age**
The indicator uses **New York trading sessions** (6:00 PM to 5:59 PM NY time) as the primary time measurement. This aligns with how futures markets naturally segment their activity.
**Age Categories:**
- 🟢 **New (0-1 sessions)** - Fresh levels, recently formed
- 🟡 **Medium (2-3 sessions)** - Tested by time, gaining significance
- 🔴 **Old (4-6 sessions)** - Highly significant, survived multiple days
- 🟣 **Ancient (7+ sessions)** - Extreme significance, major support/resistance
The longer a level remains unmitigated, the more significant it becomes. Think of it like compound interest - time adds weight to these zones.
### 2. **Multi-Timeframe Tracking**
You can set the indicator to track high/low levels from any timeframe (default is 15 minutes). This means you're watching for unmitigated 15-minute highs and lows while trading on, say, a 1-minute or 5-minute chart.
**Why this matters:**
- Higher timeframe levels have more weight
- You can see multiple timeframe structure simultaneously
- Helps you avoid fighting larger timeframe momentum
### 3. **Mitigation**
A level becomes "mitigated" (deactivated) when price touches it:
- **High levels** are mitigated when price reaches or exceeds them
- **Low levels** are mitigated when price reaches or goes below them
Once mitigated, the level disappears from view. The indicator only shows you the untouch levels that still matter.
---
## Visual Components
### 📊 The Dashboard Table
Located in the corner of your chart (configurable), the table shows:
```
┌─────────┬───────────┬────────┬─────┬───────┐
│ Level │ Price │ Points │ Age │ % │
├─────────┼───────────┼────────┼─────┼───────┤
│ ↑↑↑↑↑ │ 21,450.25 │ +45.50 │ 8 │ +0.21%│ ← 5th High (Ancient)
│ ↑↑↑↑ │ 21,430.00 │ +25.25 │ 5 │ +0.12%│ ← 4th High (Old)
│ ↑↑↑ │ 21,420.50 │ +15.75 │ 3 │ +0.07%│ ← 3rd High (Medium)
│ ↑↑ │ 21,412.00 │ +7.25 │ 1 │ +0.03%│ ← 2nd High (New)
│ ↑ ⚠️ │ 21,408.25 │ +3.50 │ 0 │ +0.02%│ ← 1st High (Proximity Alert!)
├─────────┼───────────┼────────┼─────┼───────┤
│ 15 mins │ 🟢 │ Δ 8.75 │ 2U │ │ ← Status Row
├─────────┼───────────┼────────┼─────┼───────┤
│ ↓ ⚠️ │ 21,399.50 │ -5.25 │ 0 │ -0.02%│ ← 1st Low (Proximity Alert!)
│ ↓↓ │ 21,395.00 │ -9.75 │ 2 │ -0.05%│ ← 2nd Low (Medium)
│ ↓↓↓ │ 21,385.25 │ -19.50 │ 4 │ -0.09%│ ← 3rd Low (Old)
│ ↓↓↓↓ │ 21,370.00 │ -34.75 │ 6 │ -0.16%│ ← 4th Low (Old)
│ ↓↓↓↓↓ │ 21,350.75 │ -54.00 │ 9 │ -0.25%│ ← 5th Low (Ancient)
├─────────┼───────────┼────────┼─────┼───────┤
│ 📊 15↑ / 12↓ │ ← Statistics (optional)
└─────────┴───────────┴────────┴─────┴───────┘
```
**Reading the Table:**
- **Level Column**: Number of arrows indicates position (1-5), color shows age
- **Price**: The actual price level
- **Points**: Distance from current price (+ for highs, - for lows)
- **Age**: Number of full sessions since creation
- **%**: Percentage distance from current price
- **⚠️**: Proximity alert - price is within threshold distance
- **Status Row**: Shows timeframe, direction (🟢 bullish/🔴 bearish), tunnel width (Δ), and Strat pattern
### 📈 Visual Elements on Chart
**1. Level Lines**
- Horizontal lines showing each unmitigated level
- **Color-coded by age**: Bright colors = new, darker = older, deep purple/teal = ancient
- **Line style**: Customizable (solid, dashed, dotted)
- Automatically turn **yellow** when price gets close (proximity alert)
**2. Price Labels**
- Show the exact price and age: "21,450.25 (8d)"
- Fixed at small size for clean readability
- Positioned with configurable offset from current bar
**3. Bands (Optional)**
- Shaded zones between pairs of unmitigated levels
- Default: Between 1st and 2nd levels (the "tunnel")
- Can switch to 1st-3rd, 2nd-3rd, or disable entirely
- **Upper band** (pink/maroon) - Between unmitigated highs
- **Lower band** (blue/teal) - Between unmitigated lows
- These represent the "no man's land" or consolidation zones
---
## The Cave Diving Framework
This indicator is designed around the **Cave Diving Trading Framework** - a psychological and technical approach that maps cave diving safety protocols to futures trading risk management.
### 🤿 The Core Metaphor
**Cave diving has clear danger zones based on depth and overhead environment. Your trading should too.**
#### Shallow Water (New Levels, 0-1 Sessions)
- **Light**: Bright colors (bright red highs, bright green lows)
- **Psychology**: Fresh territory, recently tested
- **Trading**: Be aware but not overly concerned
- **Cave Diving Parallel**: You can see the surface, easy exit
#### Penetration Depth (Medium Levels, 2-3 Sessions)
- **Light**: Medium intensity colors
- **Psychology**: Building significance, market memory forming
- **Trading**: Start respecting these levels for entries/exits
- **Cave Diving Parallel**: Deeper in, need to track your line back
#### Deep Dive Zone (Old Levels, 4-6 Sessions)
- **Light**: Dark colors (deep maroon, dark blue)
- **Psychology**: Highly tested support/resistance
- **Trading**: Major decision points, plan accordingly
- **Cave Diving Parallel**: Significant overhead, careful navigation required
#### Overhead Environment (Ancient Levels, 7+ Sessions)
- **Light**: Very dark, purple/deep teal
- **Psychology**: Extreme caution required, major liquidity zones
- **Trading**: These are your "turn back" signals - don't fight ancient levels
- **Cave Diving Parallel**: Maximum danger, no room for error
### 🎯 The Proximity Alert System
Just like a cave diver's depth gauge that warns at critical thresholds, the proximity alerts (⚠️) tell you when you're entering a danger zone. When price gets within your configured threshold (default 5 points), the indicator:
- Highlights the level in **yellow** on the chart
- Shows **⚠️** in the table
- Signals: "You're entering a high-significance zone - adjust your position accordingly"
This prevents the trading equivalent of going deeper into a cave without checking your air supply.
---
## How to Use It for Trading
### 🎯 Entry Strategies
**1. The "Bounce Setup" (Mean Reversion)**
- Wait for price to approach an old or ancient unmitigated level
- Look for confluence: multiple levels nearby, bands narrowing
- Enter when price shows rejection (reversal candle patterns)
- **Example**: Price drops to a 6-session-old low, shows bullish engulfing → Long entry
**2. The "Break and Retest" (Trend Following)**
- Wait for price to break through an unmitigated level (mitigates it)
- Enter on the retest of the newly broken level
- **Example**: Price breaks above 4-session-old high → Wait for pullback to that level → Long entry
**3. The "Tunnel Trade" (Range Trading)**
- When bands are active, trade the range between 1st-2nd levels
- Short near upper band resistance, long near lower band support
- Exit at opposite side or when bands break
### 🚨 Risk Management Rules
**The Ancient Level Rule**
> Never fight ancient levels (7+ sessions). If you're long and approaching an ancient high, take profits. If you're short and approaching an ancient low, take profits.
These levels have survived a full trading week without being touched - there's likely significant liquidity and institutional interest there.
**The Proximity Exit Rule**
> When you see ⚠️ proximity alerts on multiple levels above/below your position, tighten stops or scale out.
This is your "overhead environment" warning. You're in dangerous territory.
**The New Level Filter**
> Be cautious taking positions based solely on new levels (0-1 sessions). Wait for them to age or combine with other confluence.
Fresh levels haven't been tested by time. They're like unconfirmed support/resistance.
### 📊 Reading Market Structure
**Bullish Structure (🟢 in status row)**
- Unmitigated lows are aging and holding
- Price respecting the lower band
- Old lows below acting as strong support
- **Bias**: Look for long entries at lower levels
**Bearish Structure (🔴 in status row)**
- Unmitigated highs are aging and holding
- Price respecting the upper band
- Old highs above acting as strong resistance
- **Bias**: Look for short entries at higher levels
**The Tunnel Compression**
- When the Δ (delta) in the status row is small, levels are tight
- This often precedes a breakout
- **Trading**: Wait for breakout direction, then trade the break
### 🔄 Strat Integration
The indicator shows Strat patterns in the status row:
- **1** - Inside bar (consolidation)
- **2U** - Broke high only (bullish)
- **2D** - Broke low only (bearish)
- **3** - Broke both (wide range, volatility)
Use these with the unmitigated levels:
- **2U near old high** → Potential resistance, watch for rejection
- **2D near old low** → Potential support, watch for bounce
- **3 pattern** → High volatility, respect wider stops
---
## Settings & Customization
### 📅 Session & Timeframe Settings
**HL Interval** (Default: 15 minutes)
- The timeframe for high/low calculation
- **Lower (1m, 5m)**: More levels, more noise, good for scalping
- **Higher (30m, 1H, 4H)**: Fewer levels, stronger significance, good for swing trading
- **Recommendation for NQ/ES**: 15m or 30m for day trading, 1H for swing trading
**Session Age Threshold** (Default: 2)
- How many sessions before a level is considered "old"
- Lower = more levels classified as old
- Higher = stricter definition of significance
### 📊 Level Display Options
**Show Level Lines**
- Toggle: Display horizontal lines for each level
- **Turn off** if you prefer a cleaner chart and only want the table
**Show Level Labels**
- Toggle: Display price labels on the chart
- **Turn off** for minimal visual clutter
**Label Offset**
- Distance (in bars) from current price bar to place labels
- Increase if labels overlap with price action
**Level Line Width & Style**
- Customize visual appearance
- **Thin solid**: Minimal distraction
- **Thick dashed**: High visibility
### 🎨 Age-Based Color Coding
Customize colors for each age category (high and low separately):
- **New (0-1 sessions)**: Default bright red/green
- **Medium (2-3 sessions)**: Default medium intensity
- **Old (4+ sessions)**: Default dark red/blue
- **Ancient (7+ sessions)**: Default deep purple/teal
**Color Strategy Tips:**
- Keep ancient levels in highly contrasting colors
- Use opacity (transparency) if you want subtler lines
- Match your chart's color scheme for aesthetic coherence
### 🎯 Band Settings
**Band Mode**
- **1st-2nd** (Default): The primary "tunnel" between most recent levels
- **1st-3rd**: Wider band, more room for price action
- **2nd-3rd**: Band between less immediate levels
- **Disabled**: No bands, lines only
**Band Colors & Borders**
- Customize fill color and border separately
- **Tip**: Keep bands very transparent (90-95% transparency) to avoid obscuring price action
### ⚠️ Proximity Alert Settings
**Enable Proximity Alerts**
- Toggle: Turn on/off the warning system
- When enabled, levels within threshold distance show ⚠️ and turn yellow
**Alert Threshold** (Default: 5.0 points)
- Distance in points to trigger the alert
- **For NQ**: 5-10 points is reasonable
- **For ES**: 2-5 points is reasonable
- **For MES/MNQ**: Scale down proportionally
**Alert Highlight Color**
- The color lines/labels turn when proximity is triggered
- Default: Yellow (high visibility)
### 📋 Table Settings
**Show Table**
- Toggle: Display the dashboard table
**Table Location**
- Top Left, Top Right, Bottom Left, Bottom Right
- Choose based on your chart layout and other indicators
**Text Size**
- Tiny, Small, Normal, Large
- **Recommendation**: Normal for 1080p monitors, Small for 4K
**Show % Distance**
- Toggle: Add percentage distance column to table
- Useful for comparing relative distances across different price ranges
**Show Statistics Row**
- Toggle: Show total count of unmitigated highs/lows
- Format: "📊 15↑ / 12↓" (15 unmitigated highs, 12 unmitigated lows)
- Useful for gauging overall market structure
### ⚡ Performance Settings
**Enable Level Cleanup**
- Automatically remove very old levels to maintain performance
- **Keep on** unless you want unlimited history
**Max Lookback Levels** (Default: 10,000)
- Maximum number of levels to track
- 10,000 ≈ 6+ months of 15-minute bars
- **Increase** if you want more history
- **Decrease** if experiencing performance issues
**Max Boxes Per Band** (Default: 245)
- TradingView limit is 500 total boxes
- With 2 bands, 245 each = 490 total (safe maximum)
---
## Best Practices
### 🎯 Position Management
**1. Scaling In Near Old Levels**
```
Price approaching 5-session-old low:
- First position: 30% size at proximity alert (⚠️)
- Second position: 40% size at exact level
- Third position: 30% size if it shows strong rejection
```
**2. Scaling Out Near Ancient Levels**
```
Holding long position, approaching 8-session-old high:
- Exit 50% at proximity alert (⚠️)
- Exit 30% at exact level
- Trail stop on remaining 20%
```
### 🧠 Trading Psychology Integration
Drawing from principles in *The Mountain Is You*, this indicator helps you:
**1. Recognize Self-Sabotage Patterns**
- **The Premature Entry**: Entering before price reaches your planned level
- **Solution**: Set alerts at unmitigated levels, wait for proximity warnings
- **The Profit-Taking Problem**: Exiting too early from fear
- **Solution**: Identify the next unmitigated level and commit to holding until proximity alert
- **The Loss Holding**: Refusing to exit losing trades
- **Solution**: When price breaks through and mitigates your entry level, it's telling you the structure changed
**2. Building Better Habits**
The color-coded age system trains your brain to:
- Respect levels that have proven themselves over time
- Distinguish between noise (new levels) and structure (old levels)
- Make decisions based on objective data, not fear or greed
**3. Emotional Regulation**
The proximity alerts serve as:
- **Circuit breakers** - Forcing you to re-evaluate before dangerous zones
- **Permission to act** - Giving you objective signals to exit without second-guessing
- **Validation** - Confirming when you're in alignment with market structure
### 📝 Pre-Market Routine
**Daily Setup Checklist:**
1. ✅ Identify the 3 nearest unmitigated highs above current price
2. ✅ Identify the 3 nearest unmitigated lows below current price
3. ✅ Note which are ancient (7+) - these are your "no-go" zones
4. ✅ Check the tunnel width (Δ in status row) - tight or wide?
5. ✅ Set alerts at the 1st high and 1st low for proximity warnings
6. ✅ Plan: "If we go up, I exit at ___. If we go down, I enter at ___."
### 🔄 Timeframe Confluence
**Multi-Timeframe Strategy:**
Run the indicator on **three instances**:
- **15-minute** (short-term structure)
- **1-hour** (intermediate structure)
- **4-hour** (major structure)
**Strong Setup**: When all three timeframes show unmitigated levels converging at the same price zone.
**Example:**
- 15m: Old low at 21,400
- 1H: Ancient low at 21,398
- 4H: Ancient low at 21,395
- **Result**: 21,395-21,400 is a monster support zone
### ⚠️ What This Indicator Doesn't Do
**Not a Crystal Ball**
- It doesn't predict where price will go
- It shows you where price *hasn't been* and how long it's been avoided
- The trading decisions are still yours
**Not an Entry Signal Generator**
- It provides context and structure
- You need to combine it with your entry methodology (price action, indicators, order flow, etc.)
**Not Foolproof**
- Ancient levels get broken
- Proximity alerts can trigger early in strong trends
- The market doesn't "owe" you a reversal at any level
---
## Common Scenarios
### Scenario 1: "Level Cluster Ahead"
**Situation**: You're long at 21,400. The table shows:
- 1st High: 21,425 (2 sessions old)
- 2nd High: 21,428 (3 sessions old)
- 3rd High: 21,435 (6 sessions old)
**Interpretation**: There's a resistance cluster just 25-35 points away. The 6-session-old level is particularly significant.
**Action**:
- Set first profit target at 21,420 (before the cluster)
- Set second target at 21,426 (between 1st and 2nd)
- Trail remaining position, but be ready to exit on rejection at 21,435
**Cave Diving Analogy**: You're approaching an overhead section with limited clearance. Lighten your load (reduce position) before entering.
---
### Scenario 2: "Ancient Level Approaches"
**Situation**: The market is grinding higher. You see ⚠️ appear next to a 9-session-old high at 21,500.
**Interpretation**: This level has survived over a week without being touched. Massive potential liquidity zone.
**Action**:
- If long, this is your absolute exit zone. Take profits before or at level.
- If looking to short, wait for clear rejection (price taps and reverses)
- Don't try to buy the breakout until it clearly breaks and retests
**Cave Diving Analogy**: Your dive computer is beeping - you've reached your planned turn-back depth. No matter how interesting it looks ahead, honor your plan.
---
### Scenario 3: "Mitigated Levels Create New Structure"
**Situation**: Price breaks and mitigates the 1st High. The previous 2nd High becomes the new 1st High.
**Interpretation**: The structure just shifted. What was the 2nd level is now most relevant.
**Action**:
- Watch how price reacts to the newly-mitigated level
- If it holds below (acts as resistance), bearish
- If it reclaims and holds above (acts as support), bullish
- The NEW 1st High is your next target/resistance
**Cave Diving Analogy**: You've passed through a restriction - the cave layout ahead is different now. Update your mental map.
---
### Scenario 4: "Tight Tunnel, Upcoming Breakout"
**Situation**: The Δ in the status row shows 3.25 points (very tight). Bands are converging.
**Interpretation**: Price is consolidating between very close unmitigated levels. Breakout likely.
**Action**:
- Don't try to predict direction
- Set alerts above 1st High and below 1st Low
- When break occurs, trade the retest
- Expect volatility - use wider stops
**Cave Diving Analogy**: You're in a narrow passage. Movement will be sudden and directional once it starts.
---
### Scenario 5: "Imbalanced Structure"
**Situation**: The statistics row shows "📊 22↑ / 7↓"
**Interpretation**: There are many more unmitigated highs than lows. This suggests:
- Price has been declining (hitting lows, leaving highs behind)
- Potential bullish reversal zone (lots of overhead supply mitigated)
- Or continued bearish structure (resistance everywhere above)
**Action**:
- Look at the age of those 22 highs
- If mostly new (0-2 sessions): Just a recent downmove, not significant yet
- If many old/ancient: Strong overhead resistance, be cautious on longs
- Compare to price action: Is price respecting the remaining lows?
**Cave Diving Analogy**: You've swam deeper than your starting point - most of your markers are above you now. Are you planning the ascent or going deeper?
---
## Final Thoughts: The Philosophy
This indicator is built on a simple but powerful principle: **The market has memory, and that memory has weight.**
Every unmitigated level represents:
- Liquidity left behind
- Orders waiting to be filled
- Institutional interest potentially parked
- Psychological significance for participants
The longer a level remains unmitigated, the more "charged" it becomes. When price finally revisits it, something significant usually happens - either a strong reversal or a definitive break.
Your job as a trader isn't to predict which outcome will occur. Your job is to:
1. **Recognize** when you're approaching these charged zones
2. **Respect** them by adjusting position size and risk
3. **React** appropriately based on how price behaves at them
4. **Remember** that ancient levels (like ancient wisdom) deserve extra reverence
The Cave Diving Framework embedded in this indicator serves as a constant reminder: Trading, like cave diving, requires rigorous respect for environmental hazards, meticulous planning, and the discipline to turn back when your limits are reached.
**Every proximity alert is the market asking you**: *"Do you really want to go deeper?"*
Sometimes the answer is yes - when your setup, confluence, and risk management all align.
Often, the answer should be no - and that's the trader avoiding the accident that would have happened to the gambler.
---
### 🎯 Quick Reference Card
**Color System:**
- 🟢 Bright colors = New (0-1 sessions) = Shallow water
- 🟡 Medium colors = Medium (2-3 sessions) = Penetration depth
- 🔴 Dark colors = Old (4-6 sessions) = Deep dive zone
- 🟣 Deep dark colors = Ancient (7+ sessions) = Overhead environment
**Symbols:**
- ↑ ↑↑ ↑↑↑ ↑↑↑↑ ↑↑↑↑↑ = High levels (1st through 5th)
- ↓ ↓↓ ↓↓↓ ↓↓↓↓ ↓↓↓↓↓ = Low levels (1st through 5th)
- ⚠️ = Proximity alert (danger zone)
- 🟢 = Bullish structure
- 🔴 = Bearish structure
- Δ = Tunnel width (distance between 1st high and 1st low)
**Critical Rules:**
1. Never fight ancient levels (7+ sessions)
2. Respect proximity alerts (⚠️)
3. Scale out near old/ancient resistance
4. Wait for confluence when entering
5. Let mitigated levels prove their new role
---
**Remember**: The indicator gives you structure. The trading edge comes from your discipline in respecting that structure.
Trade safe, trade smart, and always know your exit before your entry. 🎯
---
*"You don't become your best self by denying your patterns. You become your best self by recognizing them, understanding them, and choosing differently." - Adapted from The Mountain Is You*
In trading: You don't become profitable by ignoring market structure. You become profitable by recognizing it, understanding it, and choosing your entries accordingly.
Advanced S&D Engine | ZikZak-Trader30About This Script
This is a fully custom-built Supply & Demand Zone detection engine for TradingView written by ZikZak-Trader30 (Kotdwar, UK). The script identifies potential key supply and demand zones based on market structure and pattern logic widely used by professional traders.
Detected Patterns:
RBR (Rally-Base-Rally, demand)
DBD (Drop-Base-Drop, supply)
RBD (Rally-Base-Drop, supply)
DBR (Drop-Base-Rally, demand)
Features Highlight
Detailed configurable zone filtering (freshness, gap detection, time spent, width, Fibonacci confluence, etc.)
Fair and adjustable scoring system for zone strength
Automatic management/removal of old or retested/violated zones
Optional Fibonacci level confluence and dynamic labeling
Transparency Statement
How It Works:
This script uses well-known price action concepts and compares candles’ movement, consolidation, and breakout patterns to mark S&D zones.
There are no repaints or future leaks: all logic is based entirely on historical and current bars.
Parameters and variables are fully described in the script inputs. The zone scoring and removal logic is also visible in the code for transparency.
IMPORTANT: Usage & Fair-Use Policy
This script is provided for educational and informational purposes only.
It should not be considered as financial advice or a trading signal.
Trading/investing involves risk—always do your own research or consult a financial advisor before making trading decisions.
Past performance or backtest results are not necessarily indicative of future results.
License & Fair Use
The code is original, written by ZikZak-Trader30.
All logic and comments are visible for users to study, adapt, or improve for personal, non-commercial use within TradingView.
You may NOT resell, repackage, or repost this script as your own.
If you fork or publicly remix/adapt the script, please credit "ZikZak-Trader30" and do not remove this disclosure section.
If you use ideas or snippets, kindly reference this script and author.
Absolutely NO plagiarized or resold code is permitted. This script is not for re-sale.
Acknowledgements
This indicator was inspired by years of price action study and usage of public S&D scripts. While the pattern logic is classic in nature, the version and scoring are original.
No proprietary datasets or paid logic from other sources are included.
Minor ideas on zone freshness and Fibonacci blending are common in the TradingView S&D community and have been custom-implemented here.
Strat Reversal MTF TableStrat Reversal MTF Table — Your Complete Multi-Timeframe Strat Command Center
Take your Strat trading to the next level with an indicator that shows every reversal, on every timeframe, in one powerful visual dashboard.
Designed for traders who demand speed, clarity, and full Strat alignment, the Strat Reversal MTF Table instantly identifies all major bullish and bearish reversal patterns:
Bullish Patterns
2-1-2
3-1-2
1-3-2
3-2-2
Bearish Patterns
2-1-2
3-1-2
1-3-2
3-2-2
Each signal is displayed with:
Clear pattern name (e.g., “2-1-2 Bull”)
Automatic trigger price
Timeframe label
Color-coded background (Bullish / Bearish / Neutral)
Whether you trade options, equities, futures, or crypto, this indicator makes it effortless to see what’s flipping — and where the strongest setups are emerging.
🔥 Key Features
📊 Multi-Timeframe Scanning (1 min → Daily)
Monitor 7 customizable timeframes at once.
From scalping to swing trading, you always know which timeframe is turning.
⚡ Real-Time OR Close-Confirmed Logic
Choose your style:
Realtime (Wick Mode) → Fast entries
Close-Confirmed → Stronger validation
Ideal for traders who want precision on any timeframe.
🎨 Clean & Customizable Dashboard
Move the table anywhere on the chart
Adjust text size
Choose your own colors
Lightweight and non-intrusive
A perfect blend of simplicity and power.
📩 Instant Alerts, Built In
Get notified instantly when:
Any timeframe reverses
A specific timeframe flips
Multiple reversals fire across the stack
The indicator works great with TradingView’s push notifications, email, and webhooks.
🎯 What This Helps You Do
✔ Catch Strat reversals as they happen
✔ Quickly spot full-timeframe alignment
✔ Improve your entries for options plays
✔ Avoid chop by reading higher-timeframe intent
✔ Trade more confidently with automated trigger levels
This indicator is built for Strat traders who want to trade smarter, faster, and cleaner.
✨ Perfect For
Strat Traders
Options Traders
Futures Scalpers
Intraday & Swing Traders
Quant/Algo-inspired traders
Anyone following Rob Smith’s methodology
Monthly Color Marker V4
## 📊 Monthly Color Marker - Historical Month Highlighting
### Overview
A unique indicator that allows rapid identification of all monthly candles from a specific month across multiple years. The indicator marks candles with different colors based on their direction (bullish/bearish), enabling quick analysis of seasonal patterns and cyclical behavior of stocks or assets.
### 🎯 Purpose
- **Identify Seasonal Patterns (Seasonality)** - Discover recurring trends in specific months
- **Quick Historical Analysis** - Visual representation of monthly performance over the years
- **Direction Recognition** - Instant understanding of whether a month tends to be bullish or bearish
- **Seasonal Trading Planning** - Build strategies based on cyclical patterns
### ⚙️ Adjustable Parameters
1. **Month to Mark (1-12)**
- Select the desired month for analysis
- 1 = January, 2 = February... 12 = December
- Default: 11 (November)
2. **Years Back (1-50)**
- Determines how many years back to scan
- Recommended: 10-25 years for statistically reliable data
- Default: 25 years
3. **Bullish Candle Color**
- Color for marking bullish candles (close > open)
- Default: Green
- Customizable to your personal color scheme
4. **Bearish Candle Color**
- Color for marking bearish candles (close < open)
- Default: Red
- Customizable to your personal color scheme
5. **Show Current Year**
- Whether to include the current month in the marking
- Useful when the month hasn't finished yet
- Default: Yes
### 📈 How to Use the Indicator
#### Step 1: Adding to Chart
1. Switch to **Monthly timeframe** - Required!
2. Add the indicator to your chart
3. Select the month you want to analyze
#### Step 2: Initial Analysis
- **Count green vs red candles** - What's the ratio?
- **Look for patterns** - Are there years where the month always rises/falls?
- **Identify outliers** - Years where behavior was different
#### Step 3: Making Decisions
- **Mostly green** → Statistically, the month tends to rise
- **Mostly red** → Statistically, the month tends to fall
- **Mixed** → No clear seasonal pattern
### 💡 Usage Examples
**Example 1: "Santa Claus Rally"**
- Select month 12 (December)
- Check if there are mostly green candles
- If yes, this confirms the well-known year-end rally effect
**Example 2: "September Effect"**
- Select month 9 (September)
- Historically, September is considered a weak month
- Do the data support this for this stock?
**Example 3: Quarterly Earnings**
- Identify which month earnings are released
- Check the historical response
- Plan entry/exit accordingly
### 🔍 Combining with Other Indicators
This indicator works excellently with:
- **Historical Monthly Levels** (the first indicator) - Identify nearby price levels
- **Volume Profile** - Check volume during those months
- **RSI/MACD** - Identify momentum strength in specific months
### ⚠️ Important Notes
1. **Must use Monthly timeframe!** The indicator won't work correctly on other timeframes
2. **Statistical Sample** - More years = more reliable analysis
3. **Not a Guarantee** - Past performance doesn't guarantee future results, use additional analysis
4. **Adjust Colors** - If hard to see, change colors in settings
### 🎨 Tips for Optimal Experience
- **Zoom Out** - See more years at a glance
- **Clean Chart** - Remove unnecessary indicators for clear analysis
- **Compare Stocks** - Check multiple stocks for the same month
- **Document Findings** - Take screenshots and save insights for future reference
### 📊 Recommended Statistics
After identifying an interesting month:
- Calculate success rate (green / total candles)
- Check average volatility
- Identify outlier years and investigate what happened
- Plan entry/exit strategy
### 🚀 Who Is This Indicator For?
✅ **Swing Traders** - Plan medium-term trades
✅ **Seasonal Investors** - Exploit cyclical patterns
✅ **Technical Analysts** - Understand historical behavior
✅ **Portfolio Managers** - Time entries and exits
---
### 📝 Summary
The Monthly Color Marker indicator is a powerful and easy-to-use tool for identifying seasonal patterns. The combination of clear visualization with flexible parameters makes it an essential tool for any trader seeking a statistical edge in the market.
**Recommendation:** Start with 25 years back, analyze 2-3 key months, and build a data-driven strategy.
---
**Version:** 4.0
**Compatibility:** Pine Script v5
**Timeframe:** Monthly only
**Author:** 954
## 📊 Monthly Color Marker - סימון חודשים היסטוריים
### תיאור כללי
אינדיקטור ייחודי המאפשר לזהות במהירות את כל הנרות החודשיים מחודש ספציפי לאורך השנים. האינדיקטור מסמן את הנרות בצבעים שונים בהתאם לכיוון התנועה (עלייה/ירידה), ומאפשר ניתוח מהיר של דפוסים עונתיים והתנהגות מחזורית של המניה או הנכס.
### 🎯 מטרת האינדיקטור
- **זיהוי דפוסים עונתיים (Seasonality)** - מציאת מגמות חוזרות בחודשים מסוימים
- **ניתוח היסטורי מהיר** - ראייה ויזואלית של ביצועי החודש לאורך השנים
- **זיהוי כיווניות** - הבנה מיידית האם החודש נוטה להיות שורי או דובי
- **תכנון מסחר עונתי** - בניית אסטרטגיות מבוססות מחזוריות
### ⚙️ פרמטרים מתכווננים
1. **חודש לסימון (1-12)**
- בחירת החודש הרצוי לניתוח
- 1 = ינואר, 2 = פברואר... 12 = דצמבר
- ברירת מחדל: 11 (נובמבר)
2. **שנים אחורה (1-50)**
- קובע כמה שנים אחורה לסרוק
- מומלץ: 10-25 שנים לקבלת תמונה סטטיסטית מהימנה
- ברירת מחדל: 25 שנים
3. **צבע נר עולה**
- צבע לסימון נרות שורים (close > open)
- ברירת מחדל: ירוק
- ניתן להתאים לסכמת הצבעים האישית
4. **צבע נר יורד**
- צבע לסימון נרות דוביים (close < open)
- ברירת מחדל: אדום
- ניתן להתאים לסכמת הצבעים האישית
5. **צבע את השנה הנוכחית**
- האם לכלול את החודש הנוכחי בסימון
- שימושי כאשר החודש טרם הסתיים
- ברירת מחדל: כן
### 📈 איך להשתמש באינדיקטור
#### שלב 1: הוספה לגרף
1. עבור לטיימפריים **חודשי (Monthly)** - חובה!
2. הוסף את האינדיקטור לגרף
3. בחר את החודש שאתה רוצה לנתח
#### שלב 2: ניתוח ראשוני
- **ספור נרות ירוקים מול אדומים** - מה היחס?
- **חפש דפוסים** - האם יש שנים שבהן החודש תמיד עולה/יורד?
- **זהה חריגים** - שנים שבהן ההתנהגות הייתה שונה
#### שלב 3: קבלת החלטות
- **רוב ירוקים** → סטטיסטית החודש נוטה לעלות
- **רוב אדומים** → סטטיסטית החודש נוטה לרדת
- **מעורב** → אין דפוס עונתי ברור
### 💡 דוגמאות שימוש
**דוגמה 1: "Santa Claus Rally"**
- בחר חודש 12 (דצמבר)
- בדוק אם יש רוב נרות ירוקים
- אם כן, זה מאשר את האפקט הידוע של עליות בסוף השנה
**דוגמה 2: "September Effect"**
- בחר חודש 9 (ספטמבר)
- היסטורית, ספטמבר נחשב לחודש חלש
- האם הנתונים תומכים בכך במניה זו?
**דוגמה 3: דיווחים רבעוניים**
- זהה בחודש אילו נפרסמים דיווחים
- בדוק את התגובה ההיסטורית
- תכנן כניסה/יציאה בהתאם
### 🔍 שילוב עם אינדיקטורים אחרים
האינדיקטור עובד מצוין בשילוב עם:
- **Historical Monthly Levels** (האינדיקטור הראשון) - זיהוי רמות מחיר קרובות
- **Volume Profile** - בדיקת ווליום באותם חודשים
- **RSI/MACD** - זיהוי כוח המומנטום בחודשים ספציפיים
### ⚠️ הערות חשובות
1. **חובה להשתמש בטיימפריים חודשי!** האינדיקטור לא יעבוד נכון בטיימפריים אחרים
2. **מדגם סטטיסטי** - ככל שיש יותר שנים, הניתוח מהימן יותר
3. **לא ערובה** - עבר לא מבטיח עתיד, השתמש בניתוח נוסף
4. **התאם צבעים** - אם קשה לראות, שנה את הצבעים בהגדרות
### 🎨 טיפים לחוויית שימוש מיטבית
- **זום אאוט** - ראה יותר שנים במבט אחד
- **נקה גרף** - הסר אינדיקטורים מיותרים לניתוח ברור
- **השווה מניות** - בדוק מספר מניות לאותו חודש
- **תעד ממצאים** - צלם מסך ושמור תובנות לעתיד
### 📊 סטטיסטיקה מומלצת
לאחר שזיהית חודש מעניין:
- חשב אחוז הצלחה (ירוקים / כל הנרות)
- בדוק תנודתיות ממוצעת
- זהה שנים חריגות ובדוק מה קרה אז
- תכנן אסטרטגיית כניסה/יציאה
### 🚀 למי מתאים האינדיקטור?
✅ **סווינג טריידרים** - תכנון עסקאות לטווח בינוני
✅ **משקיעים עונתיים** - ניצול דפוסים מחזוריים
✅ **אנליסטים טכניים** - הבנת התנהגות היסטורית
✅ **מנהלי תיקים** - תזמון כניסות ויציאות
---
### 📝 סיכום
אינדיקטור Monthly Color Marker הוא כלי חזק וקל לשימוש לזיהוי דפוסים עונתיים. השילוב של ויזואליזציה ברורה עם פרמטרים גמישים הופך אותו לכלי חיוני לכל טריידר המחפש יתרון סטטיסטי בשוק.
**המלצה:** התחל עם 25 שנים אחורה, נתח 2-3 חודשים מרכזיים, ובנה אסטרטגיה מבוססת נתונים.
---
**גרסה:** 4.0
**תאימות:** Pine Script v5
**טיימפריים:** חודשי בלבד
**מחבר:** [954
---
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
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Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
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Advanced ICT Theory - A-ICT📊 Advanced ICT Theory (A-ICT): The Institutional Manipulation Detector
Are you tired of being the liquidity? Stop chasing shadows and start tracking the architects of price movement.
This is not another lagging indicator. This is a complete framework for viewing the market through the lens of institutional traders. Advanced ICT Theory (A-ICT) is an all-in-one, military-grade analysis engine designed to decode the complex language of "Smart Money." It automates the core tenets of Inner Circle Trader (ICT) methodology, moving beyond simple patterns to build a dynamic, real-time narrative of market manipulation, liquidity engineering, and institutional order flow.
AIT provides a living blueprint of the market, identifying high-probability zones, tracking structural shifts, and scoring the quality of setups with a sophisticated, multi-factor algorithm. This is your X-ray into the market's true intentions.
🔬 THE CORE ENGINE: DECODING THE THEORY & FORMULAS
A-ICT is built upon a sophisticated, multi-layered logic system that interprets price action as a story of cause and effect. It does not guess; it confirms. Here is the foundational theory that drives the engine:
1. Market Structure: The Blueprint of Trend
The script first establishes a deep understanding of the market's skeleton through multi-level pivot analysis. It uses ta.pivothigh and ta.pivotlow to identify significant swing points.
Internal Structure (iBOS): Minor swings that show the short-term order flow. A break of internal structure is the first whisper of a potential shift.
External Structure (eBOS): Major swing points that define the primary trend. A confirmed break of external structure is a powerful statement of trend continuation. AIT validates this with optional Volume Confirmation (volume > volumeSMA * 1.2) and Candle Confirmation to ensure the break is driven by institutional force, not just a random spike.
Change of Character (CHoCH): This is the earthquake. A CHoCH occurs when a confirmed eBOS happens against the prevailing trend (e.g., a bearish eBOS in a clear uptrend). A-ICT flags this immediately, as it is the strongest signal that the primary trend is under threat of reversal.
2. Liquidity Engineering: The Fuel of the Market
Institutions don't buy into strength; they buy into weakness. They need liquidity. A-ICT maps these liquidity pools with forensic precision:
Buyside & Sellside Liquidity (BSL/SSL): Using ta.highest and ta.lowest, AIT identifies recent highs and lows where clusters of stop-loss orders (liquidity) are resting. These are institutional targets.
Liquidity Sweeps: This is the "manipulation" part of the detector. AIT has a specific formula to detect a sweep: high > bsl and close < bsl . This signifies that institutions pushed price just high enough to trigger buy-stops before aggressively selling—a classic "stop hunt." This event dramatically increases the quality score of subsequent patterns.
3. The Element Lifecycle: From Potential to Power
This is the revolutionary heart of A-ICT. Zones are not static; they have a lifecycle. AIT tracks this with its dynamic classification engine.
Phase 1: PENDING (Yellow): The script identifies a potential zone of interest based on a specific candle formation (a "displacement"). It is marked as "Pending" because its true nature is unknown. It is a question.
Phase 2: CLASSIFICATION: After the zone is created, AIT watches what happens next. The zone's identity is defined by its actions:
ORDER BLOCK (Blue): The highest-grade element. A zone is classified as an Order Block if it directly causes a Break of Structure (BOS) . This is the footprint of institutions entering the market with enough force to validate the new trend direction.
TRAP ZONE (Orange): A zone is classified as a Trap Zone if it is directly involved in a Liquidity Sweep . This indicates the zone was used to engineer liquidity, setting a "trap" for retail traders before a reversal.
REVERSAL / S&R ZONE (Green): If a zone is not powerful enough to cause a BOS or a major sweep, but still serves as a pivot point, it's classified as a general support/resistance or reversal zone.
4. Market Inefficiencies: Gaps in the Matrix
Fair Value Gaps (FVG): AIT detects FVGs—a 3-bar pattern indicating an imbalance—with a strict formula: low > high (for a bullish FVG) and gapSize > atr14 * 0.5. This ensures only significant, volatile gaps are shown. An FVG co-located with an Order Block is a high-confluence setup.
5. Premium & Discount: The Law of Value
Institutions buy at wholesale (Discount) and sell at retail (Premium). AIT uses a pdLookback to define the current dealing range and divides it into three zones: Premium (sell zone), Discount (buy zone), and Equilibrium. An element's quality score is massively boosted if it aligns with this principle (e.g., a bullish Order Block in a Discount zone).
⚙️ THE CONTROL PANEL: A COMPLETE GUIDE TO THE INPUTS MENU
Every setting is a lever, allowing you to tune the AIT engine to your exact specifications. Master these to unlock the script's full potential.
🎯 A-ICT Detection Engine
Min Displacement Candles: Controls the sensitivity of element detection. How it works: It defines the number of subsequent candles that must be "inside" a large parent candle. Best practice: Use 2-3 for a balanced view on most timeframes. A higher number (4-5) will find only major, more significant zones, ideal for swing trading. A lower number (1) is highly sensitive, suitable for scalping.
Mitigation Method: Defines when a zone is considered "used up" or mitigated. How it works: Cross triggers as soon as price touches the zone's boundary. Close requires a candle to fully close beyond it. Best practice: Cross is more responsive for fast-moving markets. Close is more conservative and helps filter out fake-outs caused by wicks, making it safer for confirmations.
Min Element Size (ATR): A crucial noise filter. How it works: It requires a detected zone to be at least this multiple of the Average True Range (ATR). Best practice: Keep this around 0.5. If you see too many tiny, irrelevant zones, increase this value to 0.8 or 1.0. If you feel the script is missing smaller but valid zones, decrease it to 0.3.
Age Threshold & Pending Timeout: These manage visual clutter. How they work: Age Threshold removes old, mitigated elements after a set number of bars. Pending Timeout removes a "Pending" element if it isn't classified within a certain window. Best practice: The default settings are optimized. If your chart feels cluttered, reduce the Age Threshold. If pending zones disappear too quickly, increase the Pending Timeout.
Min Quality Threshold: Your primary visual filter. How it works: It hides all elements (boxes, lines, labels) that do not meet this minimum quality score (0-100). Best practice: Start with the default 30. To see only A- or B-grade setups, increase this to 60 or 70 for an exceptionally clean, high-probability view.
🏗️ Market Structure
Lookbacks (Internal, External, Major): These define the sensitivity of the trend analysis. How they work: They set the number of bars to the left and right for pivot detection. Best practice: Use smaller values for Internal (e.g., 3) to see minor structure and larger values for External (e.g., 10-15) to map the main trend. For a macro, long-term view, increase the Major Swing Lookback.
Require Volume/Candle Confirmation: Toggles for quality control on BOS/CHoCH signals. Best practice: It is highly recommended to keep these enabled. Disabling them will result in more structure signals, but many will be false alarms. They are your filter against market noise.
... (Continue this detailed breakdown for every single input group: Display Configuration, Zones Style, Levels Appearance, Colors, Dashboards, MTF, Liquidity, Premium/Discount, Sessions, and IPDA).
📊 THE INTELLIGENCE DASHBOARDS: YOUR COMMAND CENTER
The dashboards synthesize all the complex analysis into a simple, actionable intelligence briefing.
Main Dashboard (Bottom Right)
ICT Metrics & Breakdown: This is your statistical overview. Total Elements shows how much structure the script is tracking. High Quality instantly tells you if there are any A/B grade setups nearby. Unmitigated vs. Mitigated shows the balance of fresh opportunities versus resolved price action. The breakdown by Order Blocks, Trap Zones, etc., gives you a quick read on the market's recent character.
Structure & Market Context: This is your core bias. Order Flow tells you the current script-determined trend. Last BOS shows you the most recent structural event. CHoCH Active is a critical warning. HTF Bias shows if you are aligned with the higher timeframe—the checkmark (✓) for alignment is one of the most important confluence factors.
Smart Money Flow: A volume-based sentiment gauge. Net Flow shows the raw buying vs. selling pressure, while the Bias provides an interpretation (e.g., "STRONG BULLISH FLOW").
Key Guide (Large Dashboard only): A built-in legend so you never have to guess. It defines every pattern, structure type, and special level visually.
📖 Narrative Dashboard (Bottom Left)
This is the "story" of the market, updated in real-time. It's designed to build your trading thesis.
Recent Elements Table: A live list of the most recent, high-quality setups. It displays the Type , its Narrative Role (e.g., "Bullish OB caused BOS"), its raw Quality percentage, and its final Trade Score grade. This is your at-a-glance opportunity scanner.
Market Narrative Section: This is the soul of A-ICT. It combines all data points into a human-readable story:
📍 Current Phase: Tells you if you are in a high-volatility Killzone or a consolidation phase like the Asian Range.
🎯 Bias & Alignment: Your primary direction, with a clear indicator of HTF alignment or conflict.
🔗 Events: A causal sequence of recent events, like "💧 Sell-side liquidity swept →
📊 Bullish BOS → 🎯 Active Order Block".
🎯 Next Expectation: The script's logical conclusion. It provides a specific, forward-looking hypothesis, such as "📉 Pullback expected to bullish OB at 1.2345 before continuation up."
🎨 READING THE BATTLEFIELD: A VISUAL INTERPRETATION GUIDE
Every color and line is a piece of information. Learn to read them together to see the full picture.
The Core Zones (Boxes):
Blue Box (Order Block): Highest probability zone for trend continuation. Look for entries here.
Orange Box (Trap Zone): A manipulation footprint. Expect a potential reversal after price interacts with this zone.
Green Box (Reversal/S&R): A standard pivot area. A good reference point but requires more confluence.
Purple Box (FVG): A market imbalance. Acts as a magnet for price. An FVG inside an Order Block is an A+ confluence.
The Structural Lines:
Green/Red Line (eBOS): Confirms the trend direction. A break above the green line is bullish; a break below the red line is bearish.
Thick Orange Line (CHoCH): WARNING. The previous trend is now in question. The market character has changed.
Blue/Red Lines (BSL/SSL): Liquidity targets. Expect price to gravitate towards these lines. A dotted line with a checkmark (✓) means the liquidity has been "swept" or "purged."
How to Synthesize: The magic is in the confluence. A perfect setup might look like this: Price sweeps below a red SSL line , enters a green Discount Zone during the NY Killzone , and forms a blue Order Block which then causes a green eBOS . This sequence, visible at a glance, is the story of a high-probability long setup.
🔧 THE ARCHITECT'S VISION: THE DEVELOPMENT JOURNEY
A-ICT was forged from the frustration of using lagging indicators in a market that is forward-looking. Traditional tools are reactive; they tell you what happened. The vision for A-ICT was to create a proactive engine that could anticipate institutional behavior by understanding their objectives: liquidity and efficiency. The development process was centered on creating a "lifecycle" for price patterns—the idea that a zone's true meaning is only revealed by its consequence. This led to the post-breakout classification system and the narrative-building engine. It's designed not just to show you patterns, but to tell you their story.
⚠️ RISK DISCLAIMER & BEST PRACTICES
Advanced ICT Theory (A-ICT) is a professional-grade analytical tool and does not provide financial advice or direct buy/sell signals. Its analysis is based on historical price action and probabilities. All forms of trading involve substantial risk. Past performance is not indicative of future results. Always use this tool as part of a comprehensive trading plan that includes your own analysis and a robust risk management strategy. Do not trade based on this indicator alone.
観の目つよく、見の目よわく
"Kan no me tsuyoku, ken no me yowaku"
— Miyamoto Musashi, The Book of Five Rings
English: "Perceive that which cannot be seen with the eye."
— Dskyz, Trade with insight. Trade with anticipation.
Price Reaction Analysis by Day of WeekOverview
The "Price Reaction Analysis by Day of Week" indicator is a tool that enables traders to analyze historical price reaction patterns to technical indicator signals on a selected day of the week. It examines price behavior on a chosen candle (from 1 to 30) in the next day or subsequent days after a signal, depending on the timeframe, and provides success rate statistics to support data-driven trading decisions. The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week comparisons. Lower timeframes generate more signals due to the higher number of candles per day.
Key Features
1. Flexible Technical Indicator Selection
Users can choose one of four technical indicators: RSI, SMI, MA, or Bollinger Bands. Each indicator has configurable parameters, such as:
RSI length, oversold/overbought levels.
SMI length, %K and %D smoothing, signal levels.
MA length.
Bollinger Bands length and multiplier.
2. Day-of-Week Analysis
The indicator allows users to select a day of the week (Monday, Tuesday, Wednesday, Thursday, Friday) for generating signals. It analyzes price reactions on a selected candle (from 1 to 30) in the next day or subsequent days after the signal. Examples:
On a daily timeframe, a signal on Monday can be analyzed for the first, fourth, or later candle (up to 30) in subsequent days (e.g., Tuesday, Wednesday).
On timeframes lower than 1 day (e.g., 12H, 8H, 6H, 4H, 1H, 15M), the analysis targets the selected candle in the next day or subsequent days. For example, on a 4H timeframe, you can analyze the second Tuesday candle following a Monday signal. The maximum timeframe is 1 day to ensure consistent day-of-week analysis.
3. Visual Signals
Signals for the analysis period are marked with background highlights in real-time when the indicator’s conditions are met. The last highlighted candle of the selected day is always analyzed. Arrows are displayed on the chart at the candle specified by the “Candles to Compare” setting (e.g., the first candle if set to 1):
Green upward triangles (below the candle) for successful buy signals (the closing price of the selected candle is higher than the signal candle’s close).
Red downward triangles (above the candle) for successful sell signals (the closing price of the selected candle is lower than the signal candle’s close).
Gray “x” marks for unsuccessful signals (no price reversal in the expected direction). Arrow positions are intuitive: buy signals below the candle, sell signals above. Highlights and arrows do not require waiting for future signals but are essential for calculating statistics.
Note: The first candle of the next day may appear shifted on the chart due to timezone differences, which can affect the timing of signal appearance.
4. Signal Conditions (Highlights) for Each Indicator
RSI: The oscillator is in oversold (buy) or overbought (sell) zones.
SMI: SMI returns from oversold (buy) or overbought (sell) zones.
MA: Price crosses the MA (upward for buy, downward for sell).
Bollinger Bands: Price returns inside the bands (from below for buy, from above for sell).
5. Success Rate Statistics
A table in the top-right corner of the chart displays:
The number of buy and sell signals for the selected day of the week.
The percentage of cases where the price of the selected candle in the next day or subsequent days reversed as expected (e.g., rising after a buy signal). Statistics are based on comparing the closing price of the signal candle with the closing price of the selected candle (e.g., first, fourth) in the next day or subsequent days.
Important: Statistics do not account for price movements within the candle or after its close. The price on the selected candle (e.g., fourth) may be lower than earlier candles but still higher than the signal candle, counting as a positive buy signal, though it does not guarantee profit.
6. Date Range
Users can specify the analysis date range, enabling strategy testing on historical data from a chosen period. Ensure the start and end dates are set correctly.
Applications
The indicator is designed for traders who want to leverage historical patterns for position planning. Examples:
On a 4-hour timeframe: If a sell signal highlight appears on Monday and statistics show an 80% chance that the fourth Tuesday candle is bearish, traders may consider playing a correction at the open of that candle.
On a daily timeframe: If a highlight indicates market overheating, traders may consider entering a position at the open of the first candle after the signal (e.g., Tuesday), provided statistics suggest an edge. Users can analyze the signal on the first candle and check later candles to validate results, increasing confidence in consistent patterns.
Key Settings
Indicator Type: Choose between RSI, SMI, MA, or Bollinger Bands.
Selected Day: Monday, Tuesday, Wednesday, Thursday, or Friday.
Candles to Compare: The number of the candle in the next day or subsequent days (from 1 to 30).
Indicator Parameters: Lengths, levels (e.g., oversold/overbought for RSI).
Background Colors: Configurable highlights for buy and sell signals.
Notes
Timeframes: The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week patterns. Timeframes lower than 1 day generate more signals due to the higher number of candles per day.
Candle Shift: The first candle of the next day may appear shifted on the chart due to timezone differences, affecting the timing of signals across markets or platforms.
Statistical Limitations: Results are based on the closing prices of the selected candle, ignoring fluctuations in earlier candles, within the candle, or subsequent price movements. Traders must assess whether entering at the open or after the close of the selected candle is profitable.
Testing: Effectiveness depends on historical data and parameter settings. Testing different configurations across markets and timeframes is recommended.
Who Is It For?
Swing and position traders who base decisions on technical analysis and historical patterns.
Market analysts seeking patterns in price behavior by day of the week.
TradingView users of all experience levels, thanks to an intuitive interface and flexible settings.
Pullback Candle (Bullish & Bearish, No EMA)🔍 Purpose
This indicator detects simple pullback reversal patterns based on price action and swing highs/lows — without any moving average or trend filters.
It highlights:
Bullish pullbacks (potential bounce/long setups)
Bearish pullbacks (potential rejection/short setups)
📈 Bullish Pullback Criteria
Three-bar pattern:
Bar 3: Highest close
Bar 2: Lower close
Bar 1: Even lower close
Current bar closes above previous bar (bullish reversal)
One of the last two candles is the lowest low of the past 6 bars (swing low)
📍 Result: A small green cross is plotted below the bar, and the bar is colored green.
📉 Bearish Pullback Criteria
Three-bar pattern:
Bar 3: Lowest close
Bar 2: Higher close
Bar 1: Even higher close
Current bar closes below previous bar (bearish reversal)
One of the last two candles is the highest high of the past 10 bars (swing high)
📍 Result: A small red cross is plotted above the bar, and the bar is colored red.
🔔 Alerts
One alert condition each for bullish and bearish pullback detection.
Can be used to trigger TradingView alerts.
🛠️ Customization
No inputs — fully automated logic
Clean, minimal, and fast
Can be extended with labels, alert sounds, or signals
Supply/Demand Zones + Engulfment-based ExecutionSupply/Demand Zones + Engulfment-Based Execution
Strategy Overview
This strategy combines institutional trading concepts—supply/demand zones and engulfing candle patterns—to generate high-probability long and short trade setups. The system uses aggregated price action to identify potential reversal zones and confirms entries with engulfing candle patterns, ensuring trades are only taken when market structure shows commitment in the direction of the trade.
Core Concepts
• Supply & Demand Zones: These are automatically detected by analyzing aggregated bullish and bearish candle structures over user-defined intervals. Supply zones are formed after bearish continuation patterns; demand zones appear after bullish continuation patterns.
• Engulfing Entries: Once price enters a zone, the strategy waits for a bullish engulfing pattern (in a demand zone) or a bearish engulfing pattern (in a supply zone) before executing a trade. This adds confirmation and reduces false signals.
• Risk Management: Stop-loss is placed at the low (for long trades) or high (for short trades) of the engulfed candle. Take-profit can be calculated using a fixed R-multiple (risk-to-reward ratio) or a user-defined target price.
Key Features
Fully customizable aggregation factor for zone detection
Visual zone boxes, entry/SL/TP boxes, and engulfing pattern labels
Optional removal of mitigated zones for cleaner charting
Configurable trade mode (Long only, Short only, or Both)
Support for trading sessions and date filtering
Alerts for price entering supply or demand zones
How to Use
Select Aggregation Factor: Choose how many candles to group together for identifying key zones (e.g., 4x timeframe).
Enable Zones: Turn on supply and/or demand zones as needed.
Set Execution Parameters:
– Choose R-multiple (e.g., 2:1 risk-reward)
– Or use a fixed take-profit price
Define Trade Time Window:
– Set the date and time ranges to restrict execution
– Use Start Hour and End Hour to limit trades to specific sessions (e.g., London/New York)
Run on Desired Timeframe: Typically used on 15m–4H charts, depending on your strategy and the asset’s volatility.
Ideal For
• Traders using Smart Money Concepts (SMC)
• Those who value high-confluence entries
• Intraday to swing traders looking for structure-based automation
⚠️ Important Notes
• The strategy requires engulfing confirmation within the zone to enter a position.
• This script does not repaint and executes trades on a bar close basis.
• Backtest results may vary based on session filters and aggregation factor.
© Attribution
This strategy was developed by The_Forex_Steward and is licensed under the Mozilla Public License 2.0.
You are free to use, modify, and distribute it under the terms of that license.
Three Candle Bullish Engulfing StrategyThe Three Candle Bullish Engulfing Strategy is a versatile, multi-mode trading system designed for TradingView, combining classic candlestick patterns with momentum confirmation and dynamic risk management. This script supports both swing trading and intraday approaches, as well as an optional RSI-based breakout mode for additional signal filtering.
Key Features:
Three Candle Pattern Detection:
The strategy identifies potential trend reversal points using a three-candle pattern:
The first candle is a strong bullish (or bearish) move.
The second candle is a doji or small-bodied candle, indicating indecision.
The third candle is a bullish (or bearish) engulfing candle that closes above (or below) the previous high (or low), confirming the reversal.
Flexible Trading Modes:
Swing Long Only: Enter long trades on bullish three-candle setups.
Intraday Long & Short: Trade both long and short based on bullish and bearish three-candle patterns, with automatic session-end exits.
RSI Breakout Mode: Enter long trades when the 1-hour RSI exceeds a user-defined threshold (default 80) and a bullish candle forms, with breakout confirmation and a fixed-percentage stop loss.
Visual Aids:
Plots the RSI breakout trigger price and stop loss on the chart for easy monitoring.
How It Works:
Three Candle Pattern Entries:
Long Entry: Triggered when a bullish candle is followed by a doji, then a bullish engulfing candle closes above the previous high.
Short Entry (Intraday only): Triggered by the inverse pattern—bearish candle, doji, then bearish engulfing candle closing below the previous low.
RSI Breakout Entries:
When the RSI on a higher timeframe (default 1 hour) exceeds the set threshold and a bullish candle forms, the script records a trigger price.
A long trade is entered if the price breaks above this trigger, with a stop loss set a fixed percentage below.
Exits:
Positions are closed if the trailing stop is hit, the session ends (for intraday mode), or the stop loss is triggered in RSI breakout mode.
In RSI breakout mode, positions are also closed if a new breakout trigger forms while in position.
FVG Premium [no1x]█ OVERVIEW
This indicator provides a comprehensive toolkit for identifying, visualizing, and tracking Fair Value Gaps (FVGs) across three distinct timeframes (current chart, a user-defined Medium Timeframe - MTF, and a user-defined High Timeframe - HTF). It is designed to offer traders enhanced insight into FVG dynamics through detailed state monitoring (formation, partial fill, full mitigation, midline touch), extensive visual customization for FVG representation, and a rich alert system for timely notifications on FVG-related events.
█ CONCEPTS
This indicator is built upon the core concept of Fair Value Gaps (FVGs) and their significance in price action analysis, offering a multi-layered approach to their detection and interpretation across different timeframes.
Fair Value Gaps (FVGs)
A Fair Value Gap (FVG), also known as an imbalance, represents a range in price delivery where one side of the market (buying or selling) was more aggressive, leaving an inefficiency or an "imbalance" in the price action. This concept is prominently featured within Smart Money Concepts (SMC) and Inner Circle Trader (ICT) methodologies, where such gaps are often interpreted as footprints left by "smart money" due to rapid, forceful price movements. These methodologies suggest that price may later revisit these FVG zones to rebalance a prior inefficiency or to seek liquidity before continuing its path. These gaps are typically identified by a three-bar pattern:
Bullish FVG : This is a three-candle formation where the second candle shows a strong upward move. The FVG is the space created between the high of the first candle (bottom of FVG) and the low of the third candle (top of FVG). This indicates a strong upward impulsive move.
Bearish FVG : This is a three-candle formation where the second candle shows a strong downward move. The FVG is the space created between the low of the first candle (top of FVG) and the high of the third candle (bottom of FVG). This indicates a strong downward impulsive move.
FVGs are often watched by traders as potential areas where price might return to "rebalance" or find support/resistance.
Multi-Timeframe (MTF) Analysis
The indicator extends FVG detection beyond the current chart's timeframe (Low Timeframe - LTF) to two higher user-defined timeframes: Medium Timeframe (MTF) and High Timeframe (HTF). This allows traders to:
Identify FVGs that might be significant on a broader market structure.
Observe how FVGs from different timeframes align or interact.
Gain a more comprehensive perspective on potential support and resistance zones.
FVG State and Lifecycle Management
The indicator actively tracks the lifecycle of each detected FVG:
Formation : The initial identification of an FVG.
Partial Fill (Entry) : When price enters but does not completely pass through the FVG. The indicator updates the "current" top/bottom of the FVG to reflect the filled portion.
Midline (Equilibrium) Touch : When price touches the 50% level of the FVG.
Full Mitigation : When price completely trades through the FVG, effectively "filling" or "rebalancing" the gap. The indicator records the mitigation time.
This state tracking is crucial for understanding how price interacts with these zones.
FVG Classification (Large FVG)
FVGs can be optionally classified as "Large FVGs" (LV) if their size (top to bottom range) exceeds a user-defined multiple of the Average True Range (ATR) for that FVG's timeframe. This helps distinguish FVGs that are significantly larger relative to recent volatility.
Visual Customization and Information Delivery
A key concept is providing extensive control over how FVGs are displayed. This control is achieved through a centralized set of visual parameters within the indicator, allowing users to configure numerous aspects (colors, line styles, visibility of boxes, midlines, mitigation lines, labels, etc.) for each timeframe. Additionally, an on-chart information panel summarizes the nearest unmitigated bullish and bearish FVG levels for each active timeframe, providing a quick glance at key price points.
█ FEATURES
This indicator offers a rich set of features designed to provide a highly customizable and comprehensive Fair Value Gap (FVG) analysis experience. Users can tailor the FVG detection, visual representation, and alerting mechanisms across three distinct timeframes: the current chart (Low Timeframe - LTF), a user-defined Medium Timeframe (MTF), and a user-defined High Timeframe (HTF).
Multi-Timeframe FVG Detection and Display
The core strength of this indicator lies in its ability to identify and display FVGs from not only the current chart's timeframe (LTF) but also from two higher, user-selectable timeframes (MTF and HTF).
Timeframe Selection: Users can specify the exact MTF (e.g., "60", "240") and HTF (e.g., "D", "W") through dedicated inputs in the "MTF (Medium Timeframe)" and "HTF (High Timeframe)" settings groups. The visibility of FVGs from these higher timeframes can be toggled independently using the "Show MTF FVGs" and "Show HTF FVGs" checkboxes.
Consistent Detection Logic: The FVG detection logic, based on the classic three-bar imbalance pattern detailed in the 'Concepts' section, is applied consistently across all selected timeframes (LTF, MTF, HTF)
Timeframe-Specific Visuals: Each timeframe's FVGs (LTF, MTF, HTF) can be customized with unique colors for bullish/bearish states and their mitigated counterparts. This allows for easy visual differentiation of FVGs originating from different market perspectives.
Comprehensive FVG Visualization Options
The indicator provides extensive control over how FVGs are visually represented on the chart for each timeframe (LTF, MTF, HTF).
FVG Boxes:
Visibility: Main FVG boxes can be shown or hidden per timeframe using the "Show FVG Boxes" (for LTF), "Show Boxes" (for MTF/HTF) inputs.
Color Customization: Colors for bullish, bearish, active, and mitigated FVG boxes (including Large FVGs, if classified) are fully customizable for each timeframe.
Box Extension & Length: FVG boxes can either be extended to the right indefinitely ("Extend Boxes Right") or set to a fixed length in bars ("Short Box Length" or "Box Length" equivalent inputs).
Box Labels: Optional labels can display the FVG's timeframe and fill percentage on the box. These labels are configurable for all timeframes (LTF, MTF, and HTF). Please note: If FVGs are positioned very close to each other on the chart, their respective labels may overlap. This can potentially lead to visual clutter, and it is a known behavior in the current version of the indicator.
Box Borders: Visibility, width, style (solid, dashed, dotted), and color of FVG box borders are customizable per timeframe.
Midlines (Equilibrium/EQ):
Visibility: The 50% level (midline or EQ) of FVGs can be shown or hidden for each timeframe.
Style Customization: Width, style, and color of the midline are customizable per timeframe. The indicator tracks if this midline has been touched by price.
Mitigation Lines:
Visibility: Mitigation lines (representing the FVG's opening level that needs to be breached for full mitigation) can be shown or hidden for each timeframe. If shown, these lines are always extended to the right.
Style Customization: Width, style, and color of the mitigation line are customizable per timeframe.
Mitigation Line Labels: Optional price labels can be displayed on mitigation lines, with a customizable horizontal bar offset for positioning. For optimal label placement, the following horizontal bar offsets are recommended: 4 for LTF, 8 for MTF, and 12 for HTF.
Persistence After Mitigation: Users can choose to keep mitigation lines visible even after an FVG is fully mitigated, with a distinct color for such lines. Importantly, this option is only effective if the general setting 'Hide Fully Mitigated FVGs' is disabled, as otherwise, the entire FVG and its lines will be removed upon mitigation.
FVG State Management and Behavior
The indicator tracks and visually responds to changes in FVG states.
Hide Fully Mitigated FVGs: This option, typically found in the indicator's general settings, allows users to automatically remove all visual elements of an FVG from the chart once price has fully mitigated it. This helps maintain chart clarity by focusing on active FVGs.
Partial Fill Visualization: When price enters an FVG, the indicator offers a dynamic visual representation: the portion of the FVG that has been filled is shown as a "mitigated box" (typically with a distinct color), while the original FVG box shrinks to clearly highlight the remaining, unfilled portion. This two-part display provides an immediate visual cue about how much of the FVG's imbalance has been addressed and what potential remains within the gap.
Visual Filtering by ATR Proximity: To help users focus on the most relevant price action, FVGs can be dynamically hidden if they are located further from the current price than a user-defined multiple of the Average True Range (ATR). This behavior is controlled by the "Filter Band Width (ATR Multiple)" input; setting this to zero disables the filter entirely, ensuring all detected FVGs remain visible regardless of their proximity to price.
Alternative Usage Example: Mitigation Lines as Key Support/Resistance Levels
For traders preferring a minimalist chart focused on key Fair Value Gap (FVG) levels, the indicator's visualization settings can be customized to display only FVG mitigation lines. This approach leverages these lines as potential support and resistance zones, reflecting areas where price might revisit to address imbalances.
To configure this view:
Disable FVG Boxes: Turn off "Show FVG Boxes" (for LTF) or "Show Boxes" (for MTF/HTF) for the desired timeframes.
Hide Midlines: Disable the visibility of the 50% FVG Midlines (Equilibrium/EQ).
Ensure Mitigation Lines are Visible: Keep "Mitigation Lines" enabled.
Retain All Mitigation Lines:
Disable the "Hide Fully Mitigated FVGs" option in the general settings.
Enable the feature to "keep mitigation lines visible even after an FVG is fully mitigated". This ensures lines from all FVGs (active or fully mitigated) remain on the chart, which is only effective if "Hide Fully Mitigated FVGs" is disabled.
This setup offers:
A Decluttered Chart: Focuses solely on the FVG opening levels.
Precise S/R Zones: Treats mitigation lines as specific points for potential price reactions.
Historical Level Analysis: Includes lines from past, fully mitigated FVGs for a comprehensive view of significant price levels.
For enhanced usability with this focused view, consider these optional additions:
The on-chart Information Panel can be activated to display a quick summary of the nearest unmitigated FVG levels.
Mitigation Line Labels can also be activated for clear price level identification. A customizable horizontal bar offset is available for positioning these labels; for example, offsets of 4 for LTF, 8 for MTF, and 12 for HTF can be effective.
FVG Classification (Large FVG)
This feature allows for distinguishing FVGs based on their size relative to market volatility.
Enable Classification: Users can enable "Classify FVG (Large FVG)" to identify FVGs that are significantly larger than average.
ATR-Based Threshold: An FVG is classified as "Large" if its height (price range) is greater than or equal to the Average True Range (ATR) of its timeframe multiplied by a user-defined "Large FVG Threshold (ATR Multiple)". The ATR period for this calculation is also configurable.
Dedicated Colors: Large FVGs (both bullish/bearish and active/mitigated) can be assigned unique colors, making them easily distinguishable on the chart.
Panel Icon: Large FVGs are marked with a special icon in the Info Panel.
Information Panel
An on-chart panel provides a quick summary of the nearest unmitigated FVG levels.
Visibility and Position: The panel can be shown/hidden and positioned in any of the nine standard locations on the chart (e.g., Top Right, Middle Center).
Content: It displays the price levels of the nearest unmitigated bullish and bearish FVGs for LTF, MTF (if active), and HTF (if active). It also indicates if these nearest FVGs are Large FVGs (if classification is enabled) using a selectable icon.
Styling: Text size, border color, header background/text colors, default text color, and "N/A" cell background color are customizable.
Highlighting: Background and text colors for the cells displaying the overall nearest bullish and bearish FVG levels (across all active timeframes) can be customized to draw attention to the most proximate FVG.
Comprehensive Alert System
The indicator offers a granular alert system for various FVG-related events, configurable for each timeframe (LTF, MTF, HTF) independently. Users can enable alerts for:
New FVG Formation: Separate alerts for new bullish and new bearish FVG formations.
FVG Entry/Partial Fill: Separate alerts for price entering a bullish FVG or a bearish FVG.
FVG Full Mitigation: Separate alerts for full mitigation of bullish and bearish FVGs.
FVG Midline (EQ) Touch: Separate alerts for price touching the midline of a bullish or bearish FVG.
Alert messages are detailed, providing information such as the timeframe, FVG type (bull/bear, Large FVG), relevant price levels, and timestamps.
█ NOTES
This section provides additional information regarding the indicator's usage, performance considerations, and potential interactions with the TradingView platform. Understanding these points can help users optimize their experience and troubleshoot effectively.
Performance and Resource Management
Maximum FVGs to Track : The "Max FVGs to Track" input (defaulting to 25) limits the number of FVG objects processed for each category (e.g., LTF Bullish, MTF Bearish). Increasing this value significantly can impact performance due to more objects being iterated over and potentially drawn, especially when multiple timeframes are active.
Drawing Object Limits : To manage performance, this script sets its own internal limits on the number of drawing objects it displays. While it allows for up to approximately 500 lines (max_lines_count=500) and 500 labels (max_labels_count=500), the number of FVG boxes is deliberately restricted to a maximum of 150 (max_boxes_count=150). This specific limit for boxes is a key performance consideration: displaying too many boxes can significantly slow down the indicator, and a very high number is often not essential for analysis. Enabling all visual elements for many FVGs across all three timeframes can cause the indicator to reach these internal limits, especially the stricter box limit
Optimization Strategies : To help you manage performance, reduce visual clutter, and avoid exceeding drawing limits when using this indicator, I recommend the following strategies:
Maintain or Lower FVG Tracking Count: The "Max FVGs to Track" input defaults to 25. I find this value generally sufficient for effective analysis and balanced performance. You can keep this default or consider reducing it further if you experience performance issues or prefer a less dense FVG display.
Utilize Proximity Filtering: I suggest activating the "Filter Band Width (ATR Multiple)" option (found under "General Settings") to display only those FVGs closer to the current price. From my experience, a value of 5 for the ATR multiple often provides a good starting point for balanced performance, but you should feel free to adjust this based on market volatility and your specific trading needs.
Hide Fully Mitigated FVGs: I strongly recommend enabling the "Hide Fully Mitigated FVGs" option. This setting automatically removes all visual elements of an FVG from the chart once it has been fully mitigated by price. Doing so significantly reduces the number of active drawing objects, lessens computational load, and helps maintain chart clarity by focusing only on active, relevant FVGs.
Disable FVG Display for Unused Timeframes: If you are not actively monitoring certain higher timeframes (MTF or HTF) for FVG analysis, I advise disabling their display by unchecking "Show MTF FVGs" or "Show HTF FVGs" respectively. This can provide a significant performance boost.
Simplify Visual Elements: For active FVGs, consider hiding less critical visual elements if they are not essential for your specific analysis. This could include box labels, borders, or even entire FVG boxes if, for example, only the mitigation lines are of interest for a particular timeframe.
Settings Changes and Platform Limits : This indicator is comprehensive and involves numerous calculations and drawings. When multiple settings are changed rapidly in quick succession, it is possible, on occasion, for TradingView to issue a "Runtime error: modify_study_limit_exceeding" or similar. This can cause the indicator to temporarily stop updating or display errors.
Recommended Approach : When adjusting settings, it is advisable to wait a brief moment (a few seconds) after each significant change. This allows the indicator to reprocess and update on the chart before another change is made
Error Recovery : Should such a runtime error occur, making a minor, different adjustment in the settings (e.g., toggling a checkbox off and then on again) and waiting briefly will typically allow the indicator to recover and resume correct operation. This behavior is related to platform limitations when handling complex scripts with many inputs and drawing objects.
Multi-Timeframe (MTF/HTF) Data and Behavior
HTF FVG Confirmation is Essential: : For an FVG from a higher timeframe (MTF or HTF) to be identified and displayed on your current chart (LTF), the three-bar pattern forming the FVG on that higher timeframe must consist of fully closed bars. The indicator does not draw speculative FVGs based on incomplete/forming bars from higher timeframes.
Data Retrieval and LTF Processing: The indicator may use techniques like lookahead = barmerge.lookahead_on for timely data retrieval from higher timeframes. However, the actual detection of an FVG occurs after all its constituent bars on the HTF have closed.
Appearance Timing on LTF (1 LTF Candle Delay): As a natural consequence of this, an FVG that is confirmed on an HTF (i.e., its third bar closes) will typically become visible on your LTF chart one LTF bar after its confirmation on the HTF.
Example: Assume an FVG forms on a 30-minute chart at 15:30 (i.e., with the close of the 30-minute bar that covers the 15:00-15:30 period). If you are monitoring this FVG on a 15-minute chart, the indicator will detect this newly formed 30-minute FVG while processing the data for the 15-minute bar that starts at 15:30 and closes at 15:45. Therefore, the 30-minute FVG will become visible on your 15-minute chart at the earliest by 15:45 (i.e., with the close of that relevant 15-minute LTF candle). This means the HTF FVG is reflected on the LTF chart with a delay equivalent to one LTF candle.
FVG Detection and Display Logic
Fair Value Gaps (FVGs) on the current chart timeframe (LTF) are detected based on barstate.isconfirmed. This means the three-bar pattern must be complete with closed bars before an FVG is identified. This confirmation method prevents FVGs from being prematurely identified on the forming bar.
Alerts
Alert Setup : To receive alerts from this indicator, you must first ensure you have enabled the specific alert conditions you are interested in within the indicator's own settings (see 'Comprehensive Alert System' under the 'FEATURES' section). Once configured, open TradingView's 'Create Alert' dialog. In the 'Condition' tab, select this indicator's name, and crucially, choose the 'Any alert() function call' option from the dropdown list. This setup allows the indicator to trigger alerts based on the precise event conditions you have activated in its settings
Alert Frequency : Alerts are designed to trigger once per bar close (alert.freq_once_per_bar_close) for the specific event.
User Interface (UI) Tips
Settings Group Icons: In the indicator settings menu, timeframe-specific groups are marked with star icons for easier navigation: 🌟 for LTF (Current Chart Timeframe), 🌟🌟 for MTF (Medium Timeframe), and 🌟🌟🌟 for HTF (High Timeframe).
Dependent Inputs: Some input settings are dependent on others being enabled. These dependencies are visually indicated in the settings menu using symbols like "↳" (dependent setting on the next line), "⟷" (mutually exclusive inline options), or "➜" (directly dependent inline option).
Settings Layout Overview: The indicator settings are organized into logical groups for ease of use. Key global display controls – such as toggles for MTF FVGs, HTF FVGs (along with their respective timeframe selectors), and the Information Panel – are conveniently located at the very top within the '⚙️ General Settings' group. This placement allows for quick access to frequently adjusted settings. Other sections provide detailed customization options for each timeframe (LTF, MTF, HTF), specific FVG components, and alert configurations.
█ FOR Pine Script® CODERS
This section provides a high-level overview of the FVG Premium indicator's internal architecture, data flow, and the interaction between its various library components. It is intended for Pine Script™ programmers who wish to understand the indicator's design, potentially extend its functionality, or learn from its structure.
System Architecture and Modular Design
The indicator is architected moduarly, leveraging several custom libraries to separate concerns and enhance code organization and reusability. Each library has a distinct responsibility:
FvgTypes: Serves as the foundational data definition layer. It defines core User-Defined Types (UDTs) like fvgObject (for storing all attributes of an FVG) and drawSettings (for visual configurations), along with enumerations like tfType.
CommonUtils: Provides utility functions for common tasks like mapping user string inputs (e.g., "Dashed" for line style) to their corresponding Pine Script™ constants (e.g., line.style_dashed) and formatting timeframe strings for display.
FvgCalculations: Contains the core logic for FVG detection (both LTF and MTF/HTF via requestMultiTFBarData), FVG classification (Large FVGs based on ATR), and checking FVG interactions with price (mitigation, partial fill).
FvgObject: Implements an object-oriented approach by attaching methods to the fvgObject UDT. These methods manage the entire visual lifecycle of an FVG on the chart, including drawing, updating based on state changes (e.g., mitigation), and deleting drawing objects. It's responsible for applying the visual configurations defined in drawSettings.
FvgPanel: Manages the creation and dynamic updates of the on-chart information panel, which displays key FVG levels.
The main indicator script acts as the orchestrator, initializing these libraries, managing user inputs, processing data flow between libraries, and handling the main event loop (bar updates) for FVG state management and alerts.
Core Data Flow and FVG Lifecycle Management
The general data flow and FVG lifecycle can be summarized as follows:
Input Processing: User inputs from the "Settings" dialog are read by the main indicator script. Visual style inputs (colors, line styles, etc.) are consolidated into a types.drawSettings object (defined in FvgTypes). Other inputs (timeframes, filter settings, alert toggles) control the behavior of different modules. CommonUtils assists in mapping some string inputs to Pine constants.
FVG Detection:
For the current chart timeframe (LTF), FvgCalculations.detectFvg() identifies potential FVGs based on bar patterns.
For MTF/HTF, the main indicator script calls FvgCalculations.requestMultiTFBarData() to fetch necessary bar data from higher timeframes, then FvgCalculations.detectMultiTFFvg() identifies FVGs.
Newly detected FVGs are instantiated as types.fvgObject and stored in arrays within the main script. These objects also undergo classification (e.g., Large FVG) by FvgCalculations.
State Update & Interaction: On each bar, the main indicator script iterates through active FVG objects to manage their state based on price interaction:
Initially, the main script calls FvgCalculations.fvgInteractionCheck() to efficiently determine if the current bar's price might be interacting with a given FVG.
If a potential interaction is flagged, the main script then invokes methods directly on the fvgObject instance (e.g., updateMitigation(), updatePartialFill(), checkMidlineTouch(), which are part of FvgObject).
These fvgObject methods are responsible for the detailed condition checking and the actual modification of the FVG's state. For instance, the updateMitigation() and updatePartialFill() methods internally utilize specific helper functions from FvgCalculations (like checkMitigation() and checkPartialMitigation()) to confirm the precise nature of the interaction before updating the fvgObject’s state fields (such as isMitigated, currentTop, currentBottom, or isMidlineTouched).
Visual Rendering:
The FvgObject.updateDrawings() method is called for each fvgObject. This method is central to drawing management; it creates, updates, or deletes chart drawings (boxes, lines, labels) based on the FVG's current state, its prev_* (previous bar state) fields for optimization, and the visual settings passed via the drawSettings object.
Information Panel Update: The main indicator script determines the nearest FVG levels, populates a panelData object (defined in FvgPanelLib), and calls FvgPanel.updatePanel() to refresh the on-chart display.
Alert Generation: Based on the updated FVG states and user-enabled alert settings, the main indicator script constructs and triggers alerts using Pine Script's alert() function."
Key Design Considerations
UDT-Centric Design: The fvgObject UDT is pivotal, acting as a stateful container for all information related to a single FVG. Most operations revolve around creating, updating, or querying these objects.
State Management: To optimize drawing updates and manage FVG lifecycles, fvgObject instances store their previous bar's state (e.g., prevIsVisible, prevCurrentTop). The FvgObject.updateDrawings() method uses this to determine if a redraw is necessary, minimizing redundant drawing calls.
Settings Object: A drawSettings object is populated once (or when inputs change) and passed to drawing functions. This avoids repeatedly reading numerous input() values on every bar or within loops, improving performance.
Dynamic Arrays for FVG Storage: Arrays are used to store collections of fvgObject instances, allowing for dynamic management (adding new FVGs, iterating for updates).
Candle/Keltner Channels BUY SELLWhy Use Candlesticks?
They help traders visualize price action
Used in technical analysis and price pattern recognition (e.g., Doji, Engulfing, Hammer)
Assist in determining entry and exit points
Why Traders Use Keltner Channels?
Keltner Channels are widely used by traders for identifying trends, detecting volatility, and spotting trade opportunities.
1. Trend Identification
The middle line (EMA) shows the general trend.
If price consistently stays above the middle line, it indicates a strong uptrend.
If price stays below, it signals a downtrend.
Use: Traders follow the trend direction to enter trades in line with momentum.
2. Volatility Measurement
The width of the channel expands and contracts based on Average True Range (ATR).
Wider channels = high volatility, tighter channels = low volatility.
Use: Helps traders decide when to expect breakouts or calm periods.
3. Breakout Signals
A break above the upper band can signal a bullish breakout.
A break below the lower band can signal a bearish breakout.
Use: Traders use this for momentum trading and breakout entries.
4. Overbought/Oversold Conditions
Price touching or crossing the upper band may suggest it's overbought.
Price touching or crossing the lower band may suggest it's oversold.
Use: Traders combine this with RSI or MACD to confirm reversal setups.
5. Trade Entry and Exit
When price pulls back to the middle EMA during a trend, it may present a buy/sell opportunity.
Exits can also be planned if price returns inside the bands after a breakout.
Use: Helps with precise entry and exit timing.
6. Combines Well With Other Indicators
Commonly used with:
RSI (for confirmation)
MACD (for momentum)
Candlestick patterns (for price action signals)
Combining Candlestick Patterns with Keltner Channels gives traders a powerful method to confirm entries, spot reversals, and improve accuracy. Here’s why this combination works so well:
1. Context for Candlestick Signals
Candlestick patterns (like doji, engulfing, or pin bars) show potential price reversals, but they need context to be reliable. Keltner Channels provide that context:
A bullish candlestick near the lower band suggests a stronger buy signal.
A bearish candlestick near the upper band strengthens a sell signal.
2. Filtering False Signals
Candlestick patterns occur frequently, and not all are meaningful.
The location within the Keltner Channel helps filter out weak or false patterns.
Example: A bullish engulfing candle outside the lower band = high-probability reversal.
3. Improved Entry Timing
Traders wait for a candlestick pattern confirmation when price touches or crosses a Keltner band.
This avoids premature entries and allows tighter stop-losses.
4. Better Risk-Reward Setup
Candlestick entry near channel extremes (upper/lower band) lets traders place stop-losses just beyond recent highs/lows.
The target can be the opposite side of the channel or the middle EMA.
5. Visual Simplicity
Keltner Channels + Candles are visually intuitive.
Even beginner traders can easily recognize:
Overextended candles near channel edges.
Confirmed breakouts or reversals.
This Timeframe 5 min : XAUUSD
Institutional Volume Profile# Institutional Volume Profile (IVP) - Advanced Volume Analysis Indicator
## Overview
The Institutional Volume Profile (IVP) is a sophisticated technical analysis tool that combines traditional volume profile analysis with institutional volume detection algorithms. This indicator helps traders identify key price levels where significant institutional activity has occurred, providing insights into market structure and potential support/resistance zones.
## Key Features
### 🎯 Volume Profile Analysis
- **Point of Control (POC)**: Identifies the price level with the highest volume activity
- **Value Area**: Highlights the price range containing a specified percentage (default 70%) of total volume
- **Multi-Row Distribution**: Displays volume distribution across 10-50 price levels for detailed analysis
- **Customizable Period**: Analyze volume profiles over 10-500 bars
### 🏛️ Institutional Volume Detection
- **Pocket Pivot Volume (PPV)**: Detects bullish institutional buying when up-volume exceeds recent down-volume peaks
- **Pivot Negative Volume (PNV)**: Identifies bearish institutional selling when down-volume exceeds recent up-volume peaks
- **Accumulation Detection**: Spots potential accumulation phases with high volume and narrow price ranges
- **Distribution Analysis**: Identifies distribution patterns with high volume but minimal price movement
### 🎨 Visual Customization Options
- **Multiple Color Schemes**: Heat Map, Institutional, Monochrome, and Rainbow themes
- **Bar Styles**: Solid, Gradient, Outlined, and 3D Effect rendering
- **Volume Intensity Display**: Visual intensity based on volume magnitude
- **Flexible Positioning**: Left or right side profile placement
- **Current Price Highlighting**: Real-time price level indication
### 📊 Advanced Visual Features
- **Volume Labels**: Display volume amounts at key price levels
- **Gradient Effects**: Multi-step gradient rendering for enhanced visibility
- **3D Styling**: Shadow effects for professional appearance
- **Opacity Control**: Adjustable transparency (10-100%)
- **Border Customization**: Configurable border width and styling
## How It Works
### Volume Distribution Algorithm
The indicator analyzes each bar within the specified period and distributes its volume proportionally across the price levels it touches. This creates an accurate representation of where trading activity has been concentrated.
### Institutional Detection Logic
- **PPV Trigger**: Current up-bar volume > highest down-volume in lookback period + above volume MA
- **PNV Trigger**: Current down-bar volume > highest up-volume in lookback period + above volume MA
- **Accumulation**: High volume + narrow range + bullish close
- **Distribution**: Very high volume + minimal price movement
### Value Area Calculation
Starting from the POC, the algorithm expands both upward and downward, adding volume until reaching the specified percentage of total volume (default 70%).
## Configuration Parameters
### Profile Settings
- **Profile Period**: 10-500 bars (default: 50)
- **Number of Rows**: 10-50 levels (default: 24)
- **Profile Width**: 10-100% of screen (default: 30%)
- **Value Area %**: 50-90% (default: 70%)
### Institutional Analysis
- **PPV Lookback Days**: 5-20 periods (default: 10)
- **Volume MA Length**: 10-200 periods (default: 50)
- **Institutional Threshold**: 1.0-2.0x multiplier (default: 1.2)
### Visual Controls
- **Bar Style**: Solid, Gradient, Outlined, 3D Effect
- **Color Scheme**: Heat Map, Institutional, Monochrome, Rainbow
- **Profile Position**: Left or Right side
- **Opacity**: 10-100%
- **Show Labels**: Volume amount display toggle
## Interpretation Guide
### Volume Profile Elements
- **Thick Horizontal Bars**: High volume nodes (strong support/resistance)
- **Thin Horizontal Bars**: Low volume nodes (weak levels)
- **White Line (POC)**: Strongest support/resistance level
- **Blue Highlighted Area**: Value Area (fair value zone)
### Institutional Signals
- **Blue Triangles (PPV)**: Bullish institutional buying detected
- **Orange Triangles (PNV)**: Bearish institutional selling detected
- **Color-Coded Bars**: Different colors indicate institutional activity types
### Color Scheme Meanings
- **Heat Map**: Red (high volume) → Orange → Yellow → Gray (low volume)
- **Institutional**: Blue (PPV), Orange (PNV), Aqua (Accumulation), Yellow (Distribution)
- **Monochrome**: Grayscale intensity based on volume
- **Rainbow**: Color-coded by price level position
## Trading Applications
### Support and Resistance
- POC acts as dynamic support/resistance
- High volume nodes indicate strong price levels
- Low volume areas suggest potential breakout zones
### Institutional Activity
- PPV above Value Area: Strong bullish signal
- PNV below Value Area: Strong bearish signal
- Accumulation patterns: Potential upward breakouts
- Distribution patterns: Potential downward pressure
### Market Structure Analysis
- Value Area defines fair value range
- Profile shape indicates market sentiment
- Volume gaps suggest potential price targets
## Alert Conditions
- PPV Detection at current price level
- PNV Detection at current price level
- PPV above Value Area (strong bullish)
- PNV below Value Area (strong bearish)
## Best Practices
1. Use multiple timeframes for confirmation
2. Combine with price action analysis
3. Pay attention to volume context (above/below average)
4. Monitor institutional signals near key levels
5. Consider overall market conditions
## Technical Notes
- Maximum 500 boxes and 100 labels for optimal performance
- Real-time calculations update on each bar close
- Historical analysis uses complete bar data
- Compatible with all TradingView chart types and timeframes
---
*This indicator is designed for educational and informational purposes. Always combine with other analysis methods and risk management strategies.*
Institutional Volume Footprint ProOVERVIEW
The Institutional Volume Footprint Pro is a comprehensive volume analysis indicator designed to identify institutional trading activity and significant volume patterns. Based on the proven Pocket Pivot Volume methodology by Chris Kacher and Gil Morales, this indicator has been enhanced with multiple additional volume analysis techniques to provide traders with a complete picture of smart money movements.
KEY FEATURES
1. Pocket Pivot Volume (PPV) Detection
- Identifies bullish volume patterns where current volume exceeds the highest down-day volume of the past 10 days
- Blue volume bars with "PPV" labels mark potential institutional accumulation
- Customizable lookback period (5-20 days)
2. Pivot Negative Volume (PNV) Detection
- Spots bearish volume patterns where selling volume exceeds recent up-day volumes
- Orange bars with "PNV" labels indicate potential institutional distribution
- Early warning system for trend reversals
3. Advanced Institutional Patterns
- Accumulation Detection (Aqua): High volume with narrow price range - classic stealth accumulation
- Churning/Distribution (Yellow): Heavy volume with minimal price progress - potential topping pattern
- Volume Dry-up (Purple): Extremely low volume periods that often precede significant moves
- Volume Climax (Fuchsia): Extreme volume spikes signaling potential exhaustion
4. Real-time Analytics Dashboard
- Relative Volume: Current volume compared to 10-day average
- Volume vs MA: Multiple of current volume to selected moving average
- Price Range Analysis: Narrow/Normal/Wide range classification
5. Accumulation/Distribution Trend
- Background coloring shows overall money flow direction
- Green tint: Net accumulation phase
- Red tint: Net distribution phase
HOW TO USE
Entry Signals:
- PPV (Blue): Consider long positions when price breaks above resistance with PPV confirmation
- Accumulation (Aqua): Watch for breakouts following multiple accumulation days
- Volume Dry-up (Purple): Prepare for potential explosive moves
Exit/Warning Signals:
- PNV (Orange): Consider taking profits or tightening stops
- Churning (Yellow): Distribution may be occurring despite stable prices
- Volume Climax (Fuchsia): Potential reversal point - extreme caution advised
CUSTOMIZATION OPTIONS
Analysis Parameters:
- PPV Lookback Period (5-20 days)
- Volume MA Length & Type (SMA/EMA/WMA)
- Relative Volume Threshold
- Climax Volume Multiplier
Visual Controls:
- Toggle Info Table display
- Enable/disable individual label types (PPV, PNV, ACC)
- Show/hide volume moving averages
- Control A/D trend background
- Customize threshold lines
BUILT-IN ALERTS
- Pocket Pivot Volume detected
- Pivot Negative Volume detected
- Institutional Accumulation pattern
- Volume Climax warning
- Volume Dry-up alert
PRO TIPS
1. Combine with Price Action: Volume confirms price - look for PPV at breakouts and PNV at breakdowns
2. Multiple Timeframes: Check daily and weekly charts for confluence
3. Relative Volume Matters: Patterns are stronger when relative volume > 1.5x
4. Watch for Divergences: Price up with decreasing volume = weakness
COLOR LEGEND
- Blue: Pocket Pivot Volume (Bullish)
- Orange: Pivot Negative Volume (Bearish)
- Aqua: Institutional Accumulation
- Yellow: Churning/Distribution
- Purple: Volume Dry-up
- Fuchsia: Volume Climax
- Green: Above-average up volume
- Red: Above-average down volume
- Gray: Below-average volume
EDUCATIONAL BACKGROUND
This indicator implements concepts from:
- "Trade Like an O'Neil Disciple" by Gil Morales & Chris Kacher
- William O'Neil's volume analysis principles
- Richard Wyckoff's accumulation/distribution methodology
Happy Trading! May the volume be with you!
Half Causal EstimatorOverview
The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output.
Core Concept
Traditional moving averages suffer from time lag, which can delay signals and reduce their effectiveness for real-time decision making. The Half Causal Estimator addresses this limitation by using a non-causal filtering method that incorporates recent historical data (the causal component) alongside expected future behavior based on time-of-day patterns (the non-causal component).
This dual approach allows the filter to respond more quickly to changing market conditions while maintaining smoothness. The name "Half Causal" refers to this hybrid methodology—half of the data window comes from actual historical observations, while the other half is derived from time-of-day patterns observed over multiple days. By incorporating these "future" values from past patterns, the estimator can reduce the inherent lag present in traditional moving averages.
How It Works
The indicator operates through several coordinated steps. First, it stores and organizes market data by specific times of day (minutes/hours). Then it builds a profile of typical behavior for each time period. For calculations, it creates a filtering window where half consists of recent actual data and half consists of expected future values based on historical time-of-day patterns. Finally, it applies a kernel-based smoothing function to weight the values in this composite window.
This approach is particularly effective because market variables like volume, true range, and price changes tend to follow recognizable intraday patterns (they are positive values without DC components). By leveraging these patterns, the indicator doesn't try to predict future values in the traditional sense, but rather incorporates the average historical behavior at those future times into the current estimate.
The benefit of using this "average future data" approach is that it counteracts the lag inherent in traditional moving averages. In a standard moving average, recent price action is underweighted because older data points hold equal influence. By incorporating time-of-day averages for future periods, the Half Causal Estimator essentially shifts the center of the filter window closer to the current bar, resulting in more timely outputs while maintaining smoothing benefits.
Understanding Kernel Smoothing
At the heart of the Half Causal Estimator is kernel smoothing, a statistical technique that creates weighted averages where points closer to the center receive higher weights. This approach offers several advantages over simple moving averages. Unlike simple moving averages that weight all points equally, kernel smoothing applies a mathematically defined weight distribution. The weighting function helps minimize the impact of outliers and random fluctuations. Additionally, by adjusting the kernel width parameter, users can fine-tune the balance between responsiveness and smoothness.
The indicator supports three kernel types. The Gaussian kernel uses a bell-shaped distribution that weights central points heavily while still considering distant points. The Epanechnikov kernel employs a parabolic function that provides efficient noise reduction with a finite support range. The Triangular kernel applies a linear weighting that decreases uniformly from center to edges. These kernel functions provide the mathematical foundation for how the filter processes the combined window of past and "future" data points.
Applicable Data Sources
The indicator can be applied to three different data sources: volume (the trading volume of the security), true range (expressed as a percentage, measuring volatility), and change (the absolute percentage change from one closing price to the next).
Each of these variables shares the characteristic of being consistently positive and exhibiting cyclical intraday patterns, making them ideal candidates for this filtering approach.
Practical Applications
The Half Causal Estimator excels in scenarios where timely information is crucial. It helps in identifying volume climaxes or diminishing volume trends earlier than conventional indicators. It can detect changes in volatility patterns with reduced lag. The indicator is also useful for recognizing shifts in price momentum before they become obvious in price action, and providing smoother data for algorithmic trading systems that require reduced noise without sacrificing timeliness.
When volatility or volume spikes occur, conventional moving averages typically lag behind, potentially causing missed opportunities or delayed responses. The Half Causal Estimator produces signals that align more closely with actual market turns.
Technical Implementation
The implementation of the Half Causal Estimator involves several technical components working together. Data collection and organization is the first step—the indicator maintains a data structure that organizes market data by specific times of day. This creates a historical record of how volume, true range, or price change typically behaves at each minute/hour of the trading day.
For each calculation, the indicator constructs a composite window consisting of recent actual data points from the current session (the causal half) and historical averages for upcoming time periods from previous sessions (the non-causal half). The selected kernel function is then applied to this composite window, creating a weighted average where points closer to the center receive higher weights according to the mathematical properties of the chosen kernel. Finally, the kernel weights are normalized to ensure the output maintains proper scaling regardless of the kernel type or width parameter.
This framework enables the indicator to leverage the predictable time-of-day components in market data without trying to predict specific future values. Instead, it uses average historical patterns to reduce lag while maintaining the statistical benefits of smoothing techniques.
Configuration Options
The indicator provides several customization options. The data period setting determines the number of days of observations to store (0 uses all available data). Filter length controls the number of historical data points for the filter (total window size is length × 2 - 1). Filter width adjusts the width of the kernel function. Users can also select between Gaussian, Epanechnikov, and Triangular kernel functions, and customize visual settings such as colors and line width.
These parameters allow for fine-tuning the balance between responsiveness and smoothness based on individual trading preferences and the specific characteristics of the traded instrument.
Limitations
The indicator requires minute-based intraday timeframes, securities with volume data (when using volume as the source), and sufficient historical data to establish time-of-day patterns.
Conclusion
The Half Causal Estimator represents an innovative approach to technical analysis that addresses one of the fundamental limitations of traditional indicators: time lag. By incorporating time-of-day patterns into its calculations, it provides a more timely representation of market variables while maintaining the noise-reduction benefits of smoothing. This makes it a valuable tool for traders who need to make decisions based on real-time information about volume, volatility, or price changes.
Hammer Detector### Hammer Pattern Detector
The Hammer Pattern Detector is a specialized technical analysis tool designed to identify high-probability hammer candlestick patterns. This indicator uses precise mathematical calculations to detect hammer patterns that meet specific criteria, helping traders identify potential market reversals.
#### Key Features
- Precise detection of hammer patterns based on shadow-to-body ratios
- Customizable parameters for fine-tuning pattern recognition
- Visual alerts with markers above qualifying candles
- Built-in alert functionality for real-time notifications
#### Parameters
1. **Shadow Length Multiplier (min)**: Controls how many times longer the lower shadow must be compared to the candle body (default: 2.0)
2. **Maximum Upper Shadow (%)**: Sets the maximum allowed length for the upper shadow as a percentage of total candle length (default: 10%)
3. **Minimum Body to High Distance (%)**: Defines how close the body must be to the high of the candle (default: 90%)
#### Detection Criteria
The indicator identifies hammer patterns based on three main conditions:
- Lower shadow must be at least twice the length of the body (adjustable)
- Upper shadow must be minimal (max 10% of total candle length by default)
- Candle body must be positioned near the high of the candle
#### Use Cases
- Identifying potential trend reversals
- Finding entry points in oversold conditions
- Confirming support levels
- Part of a broader reversal strategy
#### Tips for Best Results
- Use in conjunction with support/resistance levels
- Combine with volume analysis for confirmation
- Consider overall market context and trend
- Adjust parameters based on your trading timeframe
#### Installation
1. Add the indicator to your chart
2. Adjust the parameters according to your trading style
3. Optional: Set up alerts for real-time notifications
#### About
Created by WK
Version: 1.0
All rights reserved ©WK
For questions or suggestions, please reach out through TradingView.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
RegexLib█ OVERVIEW
This library contains regular expression (regex) search functions which are helpful, in particular, in reading configuration inputs.
feedRgx(): Searches for the first occurrence of `regex` pattern in the `src` and returns `src` split into parts as a tuple: ` `. If no match returns ` `
countRgx(): Counts `regex` occurrences in the `src`.
matchRgx(): Finds given `occurence` of `regex` pattern in `src` string.
NB! countRgx() and matchRgx() do not support `^` (beginning of the string placeholder), lookbehind some other complex patterns, because they works by cutting off the part of the string up to the first found occurence (inclusive) and then continuing the search on the remainder of the string. E.g. in a four line source `(?<=\n).+\n` should match the second and the third lines but matchRgx only matches the second line since after matching it continues to search in the remainder AFTER the match only.
█ FULL LIST OF FUNCTIONS AND PARAMETERS
method feedRgx(src, regex)
Searches for the first occurrence of ` regex ` pattern in the ` src ` and returns ` src ` split into parts as a tuple: ` `. If no match returns ` `
Namespace types: series string, simple string, input string, const string
Parameters:
src (string) : (string) String to search for the regex pattern
regex (string) : (string) RegEx pattern
Returns: A tuple ` ` where `begS` is the part of the `src` string from the beginning up to the
first occurence of the `regex` pattern (or up to the end if not found), `matchS` - the first occurrence of the
regex pattern and `endS` the part of the strinf following the first occurrence of the `regex` pattern.
method countRgx(src, regex)
Counts `regex` occurrences in the `src`. ___NB!___ _Does not support `^` (beginning of the string
placeholder), lookbehind some other complex patterns, because it works by cutting off the part of the string up to
the first found occurence (inclusive) and then continuing the search on the remainder of the string. E.g. in a four line source `(?<=\n).+\n` should match the second and the third lines but matchRgx only
matches the second line since after matching it continues to search in the remainder AFTER the match only. _
Namespace types: series string, simple string, input string, const string
Parameters:
src (string) : (string) String in which the regex pattern occurences are to be counted
regex (string) : (string) RegEx pattern
Returns: The number of occurrences of the `regex` pattern in the `src` string.
method matchRgx(src, regex, occurrence)
Finds given `occurence` of `regex` pattern in `src` string. ___NB!___ _Does not support `^` (beginning of the string placeholder), lookbehind and some other complex patterns, because it works by cutting off the part of the string up to the first found occurence (inclusive) and then continuing the search on the remainder of the string. E.g. in a four line source `(?<=\n).+\n` should match the second and the third lines but matchRgx only matches the second line since after matching it continues to search in the remainder AFTER the match only. _
Namespace types: series string, simple string, input string, const string
Parameters:
src (string) : (string) String to search for the regex pattern
regex (string) : (string) RegEx pattern
occurrence (int) : (int) (Default is 1) The number of the occurrence to search for. If this params exceeds the actual
number of occurrences of the pattern in the `src` string the following tuple is returned
Returns: A tuple, matchS - matched substring, matchPos - position of the match, matchL - match length
█ HOW TO USE
See DEMO section in the script.
You can test regex patterns by playing around with script input settings.
Another usage example can be found in my CSVParser and HTFMAs libraries.
Change in State of Delivery CISD ICT [TradingFinder] Liquidity 1🔵 Introduction
🟣 What is CISD ?
Change in State of Delivery (CISD) is a key concept in technical analysis, similar to Change of Character (ChoCh) and Market Structure Shift (MSS) in the ICT (Inner Circle Trader) and Smart Money trading styles. Like ChoCh and MSS, CISD helps traders identify critical changes in market structure and make timely entries into trades.
To determine the CISD Level, traders typically review the last 1 to 4 candles to identify the first positive or negative candle. The CISD Level is then set using the opening price of the next candle.
In this version of the indicator, support and resistance levels are defined based on liquidity, which includes patterns such as SFP (Swing Failure Pattern), fake breakout, and false breakout.
Bullish CISD :
Bearish CISD :
🔵 How to Use
🟣 Bullish CISD (Change in State of Delivery Upward)
In Bullish CISD, the trend shifts from bearish to bullish after the price hits a liquidity zone, typically indicated by patterns such as SFP, fake breakout, or false breakout.
The steps to identify Bullish CISD are as follow s:
Identify the liquidity zone (SFP, fake breakout).
Review the candles and find the first positive candle.
Set the CISD Level using the opening price of the next candle after the positive candle.
Confirm the change in state of delivery when the price closes above the CISD Level.
Enter the trade after CISD confirmation.
🟣 Bearish CISD (Change in State of Delivery Downward)
In Bearish CISD, the trader looks for a shift from a bullish to a bearish trend. This change typically occurs when the price hits a liquidity level, indicated by patterns such as SFP or false breakout.
The steps to identify Bearish CISD are :
Identify the liquidity zone.
Review the candles and find the first negative candle.
Set the CISD Level using the opening price of the next candle after the negative candle.
Confirm the change in state of delivery when the price closes below the CISD Level.
Enter a short trade after CISD confirmation.
🟣 CISD Compared to ChoCh and MSS (CISD Vs ChoCh/ MSS)
CISD, ChoCh, and MSS are all tools for identifying trend changes in the market, but they have some differences :
CISD: Focuses on a change in the state of delivery and uses liquidity patterns (SFP, fake breakout) and key candles to confirm trend reversals.
ChoCh: Identifies a change in the market’s character, often signaling rapid shifts in trend direction.
MSS: Focuses on changes in market structure and identifies the breaking of key levels as a signal of trend shifts.
🔵 Settings
🟣 CISD Logical settings
Bar Back Check : Determining the return of candles to identify the CISD level.
CISD Level Validity : CISD level validity period based on the number of candles.
🟣 SFP Logical settings
Swing period : You can set the swing detection period.
Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine.
Max Swing Back : You can set the number of swings that will go back for checking.
🟣 CISD Display settings
Displaying or not displaying swings and setting the color of labels and lines.
🟣 SFP Display settings
Displaying or not displaying swings and setting the color of labels and lines.
🔵 Conclusion
CISD is a powerful tool for identifying trend reversals using liquidity patterns and key candle analysis. Traders can use the CISD Level to detect trend changes and find optimal entry and exit points.
This concept is similar to ChoCh and MSS but stands out with its focus on confirming trend changes through liquidity and specific patterns. With the right approach, CISD helps traders capitalize on market movements more effectively.
