Bullish and Bearish Harami DetectorHere’s a description of the script I built for you, designed for a **TradingView public indicator**:
### **Custom Bullish and Bearish Harami Detector with Timeframe Selection**
This custom Pine Script detects **Bullish Harami** and **Bearish Harami** candlestick patterns on the selected timeframe, with configurable settings for how many prior candles to consider for pattern detection.
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### **Features:**
1. **Timeframe Selection:**
- **Input Field for Timeframe**: The script allows users to choose the timeframe for detecting patterns. For instance, you can set it to 1 hour, 4 hours, or even daily candles, ensuring the detection works as per your chosen market view.
- This is controlled by the `input.timeframe` function, and the user is prompted to select the desired timeframe (e.g., "1h", "4h", "1d").
2. **Enable/Disable Pattern Detection:**
- The user has the flexibility to enable or disable the detection of **Bullish Harami** and **Bearish Harami** patterns.
- The two toggles `detectBullishHarami` and `detectBearishHarami` allow users to turn on/off the detection for each pattern type.
3. **Customizable Bearish Candle Count for Bullish Harami:**
- The user can define how many prior **bearish candles** should be present before a **Bullish Harami** can be detected.
- The input variable `bearishCandleCountBullish` lets you choose how many previous bearish candles to consider for detecting a **Bullish Harami** (for example, the last 3, 5, or 6 bearish candles).
4. **Customizable Bullish Candle Count for Bearish Harami:**
- Similar to the Bullish Harami, the script allows the user to define how many prior **bullish candles** should be present before a **Bearish Harami** pattern is detected.
- The input variable `bearishCandleCountBearish` lets you select how many previous bullish candles to check for **Bearish Harami**.
5. **Pattern Detection Logic:**
- **Bullish Harami**: Detected when a bearish candle (open > close) is followed by a smaller bullish candle (open < close) where the entire body of the second candle is contained within the body of the first candle.
- **Bearish Harami**: Detected when a bullish candle (open < close) is followed by a smaller bearish candle (open > close) where the entire body of the second candle is contained within the body of the first candle.
- Both patterns are subject to the user-defined conditions (number of previous bearish or bullish candles).
6. **Visual Indicators:**
- **Bullish Harami**: A green label is plotted **below the bar** to indicate a **Bullish Harami** pattern.
- **Bearish Harami**: A red label is plotted **above the bar** to indicate a **Bearish Harami** pattern.
- The labels are displayed using the `plotshape` function with custom colors and text.
7. **Additional Settings**:
- The script includes tooltips and descriptions for each input to make the settings clear for users, allowing even those unfamiliar with candlestick patterns to understand and use the indicator effectively.
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### **How It Works:**
- The script first checks the specified timeframe and identifies the current and previous candlesticks.
- It then applies the user-defined conditions for detecting the **Bullish Harami** and **Bearish Harami** patterns by checking the relative positions and sizes of the candlesticks over the selected number of previous candles.
- Once a pattern is detected, it plots a label on the chart (green for **Bullish Harami** and red for **Bearish Harami**) at the appropriate location (below or above the candle).
- The script updates dynamically as the price action unfolds.
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### **Use Cases:**
- **Traders**: This script is useful for traders who want to identify reversal patterns like **Bullish Harami** and **Bearish Harami** on their chosen timeframes and adjust the sensitivity by changing the number of prior candles for pattern detection.
- **Customization**: Users can fine-tune the script’s settings based on their specific trading strategy, adjusting both the timeframe and the number of candles for pattern detection.
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### **Conclusion:**
This indicator is an effective tool for detecting candlestick patterns, specifically **Bullish Harami** and **Bearish Harami**, on **TradingView**. By allowing customization in terms of timeframe and the number of prior candles to consider, users can tailor the script to fit their trading strategy and market conditions.
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Candlestick Structure [LuxAlgo]The Candlestick Structure indicator detects major market trends and displays various candlestick patterns aligning with the detected trend, filtering out potentially unwanted patterns as a result. Multiple trend detection methods are included and can be selected by the users.
A dashboard showing the alignment percentage of each individual pattern is also provided.
🔶 USAGE
By distinguishing major and minor trend detection, we can still detect patterns based on minor trends, yet filter out the patterns that do not align with the major trend.
By detecting candlestick patterns that align with a major trend, we can effectively detect the ending points of retracements, potentially providing various entry points of interest within a trend.
Users are able to track the alignment of each candlestick pattern in the dashboard to reveal which patterns typically align with the trend and which may not.
Note: Alignment % only checks if the pattern's direction is the same as the current trend direction. These are only raw readings and not any type of confidence score.
🔶 DETAILS
In this indicator, we are identifying and tracking 16 different Candlestick Patterns.
🔹 Bullish Patterns
Hammer: Identified by a small upper wick (or no upper wick) with a small body, and an elongated lower wick whose length is 2X greater than the candle body’s width.
Inverted Hammer: Identified by a small lower wick (or no lower wick) with a small body, and an elongated upper wick whose length is 2X greater than the candle body’s width.
Bullish Engulfing: A 2 bar pattern identified by a large bullish candle body fully encapsulating (opening lower and closing higher) the previous small (bearish) candle body.
Rising 3: A 5 bar pattern identified by an initial full-bodied bullish candle, followed by 3 bearish candles that trade within the high and low of the initial candle, followed by another full-bodied bullish candle closing above the high of the initial candle.
3 White Soldiers: Identified by 3 full-bodied bullish candles, each opening within the body and closing below the high, of the previous candle.
Morning Star: A 3 bar pattern identified by a full-bodied bearish candle, followed by a small-bodied bearish candle, followed by a full-bodied bullish candle that closes above the halfway point of the first candle.
Bullish Harami: A 2 bar pattern, identified by an initial bearish candle, followed by a small bullish candle whose range is entirely contained within the body of the initial candle.
Tweezer Bottom: A 2 bar pattern identified by an initial bearish candle, followed by a bullish candle, both having equal lows.
🔹 Bearish Patterns
Hanging Man: Identified by a small upper wick (or no upper wick) with a small body, and an elongated lower wick whose length is 2X greater than the candle body’s width.
Shooting Star: Identified by a small lower wick (or no lower wick) with a small body, and an elongated upper wick whose length is 2X greater than the candle body’s width.
Bearish Engulfing: A 2 bar pattern identified by a large bearish candle body fully encapsulating (opening higher and closing lower) the previous small (bullish) candle body.
Falling 3: A 5 bar pattern identified by an initial full-bodied bearish candle, followed by 3 bullish candles that trade within the high and low of the initial candle, followed by another full-bodied bearish candle closing below the low of the initial candle.
3 Black Crows: Identified by 3 full-bodied bearish candles, each open within the body and closing below the low, of the previous candle.
Evening Star: A 3 bar pattern identified by a full-bodied bullish candle, followed by a small-bodied bullish candle, followed by a full-bodied bearish candle that closes below the halfway point of the first candle.
Bearish Harami: A 2 bar pattern, identified by an initial bullish candle, followed by a small bearish candle whose range is entirely contained within the body of the initial candle.
Tweezer Top: A 2 bar pattern identified by an initial bullish candle, followed by a bearish candle, both having equal highs.
🔹 Trend Types
Major trend is displayed at all times, the display will change depending on the trend method selected.
The minor trend can also be visualized; to avoid confusion, the minor trend can optionally be displayed through the candle colors.
Supertrend: Displays Upper and Lower SuperTrend, When we break above the upper, it is considered an Uptrend. When we break below the lower, it is considered a Downtrend.
EMAs: Displays Fast and Slow EMAs, When Fast>Slow, it is considered an Uptrend. When Fast
ABC on Recursive Zigzag [Trendoscope]There are several implementations of ABC pattern in tradingview and pine script. However, we have made this indicator to provide users additional quantifiable information along with flexibility to experiment and develop their own strategy based on the patterns.
🎲 Highlights of this indicator over other ABC implementations are:
Implementation is based on recursive multi level zigzag allows bigger as well as smaller patterns to be identified
Allows users to set their trading rules with respect to entry, target and stop ratios, experiment and build their own strategy based on the ABC pattern.
Back test summary including win ratio and risk reward will help users understand the profitability based on different settings being used.
🎲 Concept of ABC Pattern
The ABC pattern, also known as the "Corrective Wave" or "Zigzag Pattern," is a fundamental concept in Elliott Wave Theory, which is widely used in technical analysis to identify and predict price movements in financial markets.
The ABC pattern is a three-wave corrective pattern that typically occurs within the context of a larger impulse or trending wave. It consists of two smaller waves in the opposite direction (A and C) separated by a corrective wave (B). These waves are labeled alphabetically and represent price movements.
Wave A (Impulse Wave): Wave A is the first leg of the ABC pattern and is characterized by a strong price move in the opposite direction of the prevailing trend. It is often driven by a fundamental or sentiment-driven event that temporarily disrupts the trend.
Wave B (Corrective Wave): Wave B is the corrective wave that follows Wave A. It represents a partial retracement of Wave A's price movement. Wave B can take various forms, such as a simple correction or a complex correction (e.g., a triangle or a flat correction). It typically doesn't retrace the entire length of Wave A.
Wave C (Impulse Wave): Wave C is the final leg of the ABC pattern and is characterized by a strong price move in the same direction as the prevailing trend. It often surpasses the starting point of Wave A and confirms the resumption of the larger trend.
🎲 Indicator Components
Upon loading the indicator on the chart, we can observe the following components on the chart.
Pattern Drawings is the graphical representation of present patterns. Please note that it is not necessary for patterns to be there on the chart all the time. Patterns will appear on the chart when price makes the patterns.
Trade Box is the box representing trade signals of the pattern. These trade levels are generated based on the user settings.
Summary Table is the back test summary containing details of historical pattern performance including Win Ratio and Risk Reward.
🎲 Indicator Settings
Details of each user settings are provided in the tooltips. Below is the snapshot of it.
🎲 Alerts
Basic level of alerts are built in the script using alert function to highlight the following conditions:
New ABC Pattern
Updates to existing Pattern
Both conditions will alert simple text messages. There is not much customization provided as part of this indicator. We will consider providing more options in future versions based on the interest and demand shown by users.
ReversalChartPatternLibraryLibrary "ReversalChartPatternLibrary"
User Defined Types and Methods for reversal chart patterns - Double Top, Double Bottom, Triple Top, Triple Bottom, Cup and Handle, Inverted Cup and Handle, Head and Shoulders, Inverse Head and Shoulders
method delete(this)
Deletes the drawing components of ReversalChartPatternDrawing object
Namespace types: ReversalChartPatternDrawing
Parameters:
this (ReversalChartPatternDrawing) : ReversalChartPatternDrawing object
Returns: current ReversalChartPatternDrawing object
method delete(this)
Deletes the drawing components of ReversalChartPattern object. In turn calls the delete of ReversalChartPatternDrawing
Namespace types: ReversalChartPattern
Parameters:
this (ReversalChartPattern) : ReversalChartPattern object
Returns: current ReversalChartPattern object
method lpush(this, obj, limit, deleteOld)
Array push with limited number of items in the array. Old items are deleted when new one comes and exceeds the limit
Namespace types: ReversalChartPattern
Parameters:
this (ReversalChartPattern ) : array object
obj (ReversalChartPattern) : ReversalChartPattern object which need to be pushed to the array
limit (int) : max items on the array. Default is 10
deleteOld (bool) : If set to true, also deletes the drawing objects. If not, the drawing objects are kept but the pattern object is removed from array. Default is false.
Returns: current ReversalChartPattern object
method draw(this)
Draws the components of ReversalChartPatternDrawing
Namespace types: ReversalChartPatternDrawing
Parameters:
this (ReversalChartPatternDrawing) : ReversalChartPatternDrawing object
Returns: current ReversalChartPatternDrawing object
method draw(this)
Draws the components of ReversalChartPatternDrawing within the ReversalChartPattern object.
Namespace types: ReversalChartPattern
Parameters:
this (ReversalChartPattern) : ReversalChartPattern object
Returns: current ReversalChartPattern object
method scan(zigzag, patterns, errorPercent, shoulderStart, shoulderEnd)
Scans zigzag for ReversalChartPattern occurences
Namespace types: zg.Zigzag
Parameters:
zigzag (Zigzag type from HeWhoMustNotBeNamed/ZigzagTypes/2) : ZigzagTypes.Zigzag object having array of zigzag pivots and other information on each pivots
patterns (ReversalChartPattern ) : Existing patterns array. Used for validating duplicates
errorPercent (float) : Error threshold for considering ratios. Default is 13
shoulderStart (float) : Starting range of shoulder ratio. Used for identifying shoulders, handles and necklines
shoulderEnd (float) : Ending range of shoulder ratio. Used for identifying shoulders, handles and necklines
Returns: int pattern type
method createPattern(zigzag, patternType, patternColor, riskAdjustment)
Create Pattern from ZigzagTypes.Zigzag object
Namespace types: zg.Zigzag
Parameters:
zigzag (Zigzag type from HeWhoMustNotBeNamed/ZigzagTypes/2) : ZigzagTypes.Zigzag object having array of zigzag pivots and other information on each pivots
patternType (int) : Type of pattern being created. 1 - Double Tap, 2 - Triple Tap, 3 - Cup and Handle, 4 - Head and Shoulders
patternColor (color) : Color in which the patterns are drawn
riskAdjustment (float) : Used for calculating stops
Returns: ReversalChartPattern object created
method getName(this)
get pattern name of ReversalChartPattern object
Namespace types: ReversalChartPattern
Parameters:
this (ReversalChartPattern) : ReversalChartPattern object
Returns: string name of the pattern
method getDescription(this)
get consolidated description of ReversalChartPattern object
Namespace types: ReversalChartPattern
Parameters:
this (ReversalChartPattern) : ReversalChartPattern object
Returns: string consolidated description
method init(this)
initializes the ReversalChartPattern object and creates sub object types
Namespace types: ReversalChartPattern
Parameters:
this (ReversalChartPattern) : ReversalChartPattern object
Returns: ReversalChartPattern current object
ReversalChartPatternDrawing
Type which holds the drawing objects for Reversal Chart Pattern Types
Fields:
patternLines (Line type from HeWhoMustNotBeNamed/DrawingTypes/1) : array of Line objects representing pattern
entry (Line type from HeWhoMustNotBeNamed/DrawingTypes/1) : Entry price Line
target (Line type from HeWhoMustNotBeNamed/DrawingTypes/1) : Target price Line
patternLabel (Label type from HeWhoMustNotBeNamed/DrawingTypes/1)
ReversalChartPattern
Reversal Chart Pattern master type which holds the pattern components, drawings and trade details
Fields:
pivots (Pivot type from HeWhoMustNotBeNamed/ZigzagTypes/2) : Array of Zigzag Pivots forming the pattern
patternType (series int) : Defines the main type of pattern 1 - Double Tap, 1 - Triple Tap, 3 - Cup and Handle, 4 - Head and Shoulders
patternColor (series color) : Color in which the pattern will be drawn on chart
riskAdjustment (series float) : Percentage adjustment of risk. Used for setting stops
drawing (ReversalChartPatternDrawing) : ReversalChartPatternDrawing object which holds the drawing components
trade (Trade type from HeWhoMustNotBeNamed/TradeTracker/1) : TradeTracker.Trade object holding trade components
RD Key Levels (Weekly, Daily, Previous vWAP)The RexDog Key Levels indicator plots the weekly open, daily open, and the previous day vWAP close.
These are all critical price levels (zones) to know when trading any market or instrument. These areas are also high probability reaction areas that you can trade using simple confirmation trading patterns.
First, I'll cover an overview of the indicator then I'll share general usage tips.
Weekly Open - default is white/orange. White is when price is above the weekly open. Orange is when price is below the weekly open.
Weekly High/Low - there are options to turn on the weekly high and lows. Default plot is circles. Green is the high. Red is the low.
Daily Open - default is green/red. Green is when price is above the daily open. Red is when price is below the daily open.
Previous vWAPs - aqua single lines. These are the closing price of the daily vWAPs.
Top Indicators - The triangles at the top of the chart signify is price is currently above or below the weekly open. This is helpful on lower timeframe charts (5m, 15m) to get a quick indication when price is far extended beyond the weekly open. Green triangle = above weekly open. Red triangle = below weekly open.
General Usage
Each one of these levels are important levels markets look use for continuation or failure of momentum and bias. I also find it extremely helpful to think of these levels as magnets, dual magnets. They both attract and repel price at the same time. Now you might say, how is that helpful to have opposing views at the same time? Be indifferent to direction, create your own rules on when these price zones repel or attract price, I have my own.
Here's the easiest way to use these price levels.
As price approaches one of these levels to expect a reaction. A reaction is price is going in one direction and price hits a price level zone and reacts in the opposite direction.
These are price zones, sometimes you will see a reaction right at the price but visualize these areas as zones of reaction.
A high percentage of the time when price approaches these level zones there will be a reaction. So trade the reaction .
How do you do that?
Simple. Trade patterns that repeat. I have 3 solid patterns I trade around these key levels:
The first pattern is early entry with precise scale in rules and a very effective protective stop loss placement.
The second pattern is wait for confirmation that the level holds. This requires more patience and for you to fully trust the chart. The benefit of this pattern is with confirmation you have even more precise stop placement.
There is a bonus third pattern I trade around these levels. I call this the confirmation and bluff entry. It's a combination of both of the patterns above. You wait for confirmation but on any pull back you call the bluff on the market and enter on key test. Trade management here is critical. In addition to the pattern you trade you should have a series of failure patterns that tell you to get out of the trade, I use 2 primary failure patterns.
I trade all markets, same system, same rules, so I'll show a few examples.
Usually I start with Bitcoin but let's start with equities:
BA - Boeing - 8 Trades
Here we see weekly low patterns, previous week low test, vwAP hold patterns, day magnets and day holding. Then 2 week failures and a double hold pattern.
These are all straightforward trades to execute following really simple patterns.
BTCUSD Previous vWAP and Day Open Trades
We see here on the circle areas both daily open and previous day vWAP zone tests. Within this chart are all 3 highly effective patterns I trade.
SPY - 7 High Probability Trades
Here we see a pDay vWAP mixed with a daily failure. Next a daily retest, then a pDay vWAP failure, then a vWAP capture and test. Then a double weekly failure test (great trade there) and finally a daily test.
I could provide more examples but most are just derivatives of the above examples.
Candlestick Channels [LuxAlgo]Candlestick Channels return channels whose extremities converge towards the price when a corresponding candlestick pattern is detected. This allows for us to obtain more reactive extremities in the presence of a cluster of candlestick patterns.
The detected candlestick patterns are also highlighted with labels on your chart automatically.
Settings
Trend Length: Period of the stochastic oscillator used to determine trend sentiment; this sentiment is used to detect certain candlestick patterns.
Convergence: Convergence percentage of the channel extremities used during the occurrence of a candlestick pattern. A lower value will return extremities converging more slowly toward the price.
Smooth: Determines the degree of smoothness of the channel extremities.
Patterns
This category determines which patterns are detected by the indicator. Patterns toggled off will not be detected and won't affect the channels.
Usage
Candlesticks patterns are commonly used by traders to detect potential reversals or continuation periods in the price. It can be of interest to use them as core elements in the calculation of more classical indicators, this can allow us to filter out potential false signals returned by candlestick patterns by shifting the source of interpretation towards the channel extremities instead.
In this indicator extremities converge towards the price when a corresponding pattern is detected. As such bullish patterns will make the upper extremity converge towards the price, facilitating a cross with price. Using a lower convergence percentage will require a greater number of patterns to make the extremity converge closer towards the price.
Users can use the channel like most indicators returning extremities, with an uptrend being detected when price cross over the upper extremity and a downtrend being detected when price cross under the lower extremity.
An approach solely making use of crosses between the price and the average line can be used but the user should expect further whipsaws signals.
Users can eventually use the candlestick patterns as entries and use the extremities for confirmation. For example, users can follow a candlestick pattern return an indication in accordance with the detected trend by the channels.
This approach would lead to the following of bullish patterns when they occur in an uptrend, that is when the price is above the average line (in orange). The same logic applies to bearish patterns.
The chart above highlights the candlesticks patterns in accordance with a detected trend.
Notes
- Bullish/Bearish engulfing patterns are turned off by default due their more frequent appearance.
- Candlestick patterns relying on gaps were not included, since they would be more uncommon in cryptocurrencies, thus leading to a disparity between the indicator performance on the cryptocurrency and stock market.
harmonicpatterns1Library "harmonicpatterns1"
harmonicpatterns: methods required for calculation of harmonic patterns. Correction for library (missing export in line 303)
isGartleyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isGartleyPattern: Checks for harmonic pattern Gartley
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Gartley. False otherwise.
isBatPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isBatPattern: Checks for harmonic pattern Bat
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Bat. False otherwise.
isButterflyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isButterflyPattern: Checks for harmonic pattern Butterfly
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Butterfly. False otherwise.
isCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isCrabPattern: Checks for harmonic pattern Crab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Crab. False otherwise.
isDeepCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isDeepCrabPattern: Checks for harmonic pattern DeepCrab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is DeepCrab. False otherwise.
isCypherPattern(xabRatio, axcRatio, xadRatio, err_min, err_max) isCypherPattern: Checks for harmonic pattern Cypher
Parameters:
xabRatio : AB/XA
axcRatio : XC/AX
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Cypher. False otherwise.
isSharkPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isSharkPattern: Checks for harmonic pattern Shark
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Shark. False otherwise.
isNenStarPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isNenStarPattern: Checks for harmonic pattern Nenstar
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Nenstar. False otherwise.
isAntiNenStarPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiNenStarPattern: Checks for harmonic pattern Anti NenStar
Parameters:
xabRatio : - AB/XA
abcRatio : - BC/AB
bcdRatio : - CD/BC
xadRatio : - AD/XA
err_min : - Minumum error threshold
err_max : - Maximum error threshold
Returns: True if the pattern is Anti NenStar. False otherwise.
isAntiSharkPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiSharkPattern: Checks for harmonic pattern Anti Shark
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Shark. False otherwise.
isAntiCypherPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiCypherPattern: Checks for harmonic pattern Anti Cypher
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Cypher. False otherwise.
isAntiCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiCrabPattern: Checks for harmonic pattern Anti Crab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Crab. False otherwise.
isAntiButterflyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiButterflyPattern: Checks for harmonic pattern Anti Butterfly
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Butterfly. False otherwise.
isAntiBatPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiBatPattern: Checks for harmonic pattern Anti Bat
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Bat. False otherwise.
isAntiGartleyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiGartleyPattern: Checks for harmonic pattern Anti Gartley
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Gartley. False otherwise.
isNavarro200Pattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isNavarro200Pattern: Checks for harmonic pattern Navarro200
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Navarro200. False otherwise.
isHarmonicPattern(x, a, c, c, d, flags, errorPercent) isHarmonicPattern: Checks for harmonic patterns
Parameters:
x : X coordinate value
a : A coordinate value
c : B coordinate value
c : C coordinate value
d : D coordinate value
flags : flags to check patterns. Send empty array to enable all
errorPercent : Error threshold
Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names
harmonicpatternsLibrary "harmonicpatterns"
harmonicpatterns: methods required for calculation of harmonic patterns. These are customised to be used in my scripts. But, also simple enough for others to make use of :)
isGartleyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isGartleyPattern: Checks for harmonic pattern Gartley
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Gartley. False otherwise.
isBatPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isBatPattern: Checks for harmonic pattern Bat
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Bat. False otherwise.
isButterflyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isButterflyPattern: Checks for harmonic pattern Butterfly
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Butterfly. False otherwise.
isCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isCrabPattern: Checks for harmonic pattern Crab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Crab. False otherwise.
isDeepCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isDeepCrabPattern: Checks for harmonic pattern DeepCrab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is DeepCrab. False otherwise.
isCypherPattern(xabRatio, axcRatio, xadRatio, err_min, err_max) isCypherPattern: Checks for harmonic pattern Cypher
Parameters:
xabRatio : AB/XA
axcRatio : XC/AX
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Cypher. False otherwise.
isSharkPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isSharkPattern: Checks for harmonic pattern Shark
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Shark. False otherwise.
isNenStarPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isNenStarPattern: Checks for harmonic pattern Nenstar
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Nenstar. False otherwise.
isAntiNenStarPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiNenStarPattern: Checks for harmonic pattern Anti NenStar
Parameters:
xabRatio : - AB/XA
abcRatio : - BC/AB
bcdRatio : - CD/BC
xadRatio : - AD/XA
err_min : - Minumum error threshold
err_max : - Maximum error threshold
Returns: True if the pattern is Anti NenStar. False otherwise.
isAntiSharkPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiSharkPattern: Checks for harmonic pattern Anti Shark
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Shark. False otherwise.
isAntiCypherPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiCypherPattern: Checks for harmonic pattern Anti Cypher
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Cypher. False otherwise.
isAntiCrabPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiCrabPattern: Checks for harmonic pattern Anti Crab
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Crab. False otherwise.
isAntiBatPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiBatPattern: Checks for harmonic pattern Anti Bat
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Bat. False otherwise.
isAntiGartleyPattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isAntiGartleyPattern: Checks for harmonic pattern Anti Gartley
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Anti Gartley. False otherwise.
isNavarro200Pattern(xabRatio, abcRatio, bcdRatio, xadRatio, err_min, err_max) isNavarro200Pattern: Checks for harmonic pattern Navarro200
Parameters:
xabRatio : AB/XA
abcRatio : BC/AB
bcdRatio : CD/BC
xadRatio : AD/XA
err_min : Minumum error threshold
err_max : Maximum error threshold
Returns: True if the pattern is Navarro200. False otherwise.
isHarmonicPattern(x, a, c, c, d, flags, errorPercent) isHarmonicPattern: Checks for harmonic patterns
Parameters:
x : X coordinate value
a : A coordinate value
c : B coordinate value
c : C coordinate value
d : D coordinate value
flags : flags to check patterns. Send empty array to enable all
errorPercent : Error threshold
Returns: Array of boolean values which says whether valid pattern exist and array of corresponding pattern names
CSPDMost Advanced & Accurate CandleStick Pattern Detector
Looking All Over of All Markets for All Important Powerful Reversal | Corrective Patterns (25 type)
Filtering the Results with Optional Features like Oscillator, Bollinger Bands, Volume Confirmation, Prior and Following Candles Confirmation which are Fully Customizable.
With this you can detect:
Hammer | Shooting star
Inverted Hammer | Hanging
Long legged Doji | Dragonfly Doji | Gravestone Doji
Bullish tweezers | Bearish tweezers
Bullish inside bar | Bearish inside bar
Bullish three line strike | Bearish three line strike
Bullish engulfing | Bearish engulfing
Piercing line | Dark cloud cover
Bullish abandoned baby | Bearish abandoned baby
Morning star | Evening star
Three white soldiers | Three black crows
*Bullish kayo | *Bearish kayo
Features:
Prior and Following candles Confirmation
You can set prior and following candle as basic prerequisites for marking candles as pattern to make sure you are at the top or bottom of the trend.
Volume confirmation
You can active volume increasing confirmation for some of pattern with adjustable increase % relative to prior candle | candles.
Oscillator Confirmation
Active oscillator confirmation. Select your approved oscillator from list (Stochastic, RSI, StochRSI, MFI, StochMFI) then enter desired value for marking candles as bullish | bearish pattern.
Bollinger Bands Confirmation
Active BB confirmation. Configure your Bollinger Bands. Now only see the patterns which reached or crossed from Bands. Also you can enable BB clod to have your BB indicator at the same time.
Adjustable Flexibility and Precision
You can set tolerance% for Osc and BB Confirmation - each one separately - for more control.
Self-adaptability
The properties of markets change over time, for example the amplitude of fluctuations and the intensity of movements. The script is designed in such a way that you can examine the price history as a benchmark for changes in market properties to adopt determinants. Also you can control those manually.
Self-regulatory
The user has the ability to change input factors depending on their point of view.
Behind the pattern recognition algorithms, there are relationships and similarities in their appearance that cause them to be influenced by each other. Simultaneously with changing the determining factors of each pattern by you, script automatically adjusts other details in accordance with your changes.
Alerts
You can set for type of pattern or each one of pattern have been detected.
Fully Costumizable
All of these options can be change and adjust.
Note 1.
The patterns are based on extensive study of reference and famous sources and the experience of me and my friends in trading and analysis with price action methods.
Note 2.
Due to the Pine limitations in the number of outputs | plots for each script, our attempt has been made to select the best and most important patterns.
Note 3.
So far, many scripts have been written in this field, but our experience with all of them and even the build in version was not satisfactory, and this was the initial motivation for making the script.
We strive to improve progress and elimination of shortcomings and we will continue to make this better.
Note 4.
Through personal experience and based on the principles of market and candlestick psychology, we discovered a new type of pattern and named it as Kayo.
kayo is a two candle pattern which formed when:
1.We have a pivot point with prior candles as left bars and following candles as right bar. Pivot candle called as second candle.
2.In a bullish kayo, first candle must closed descending and second candle must closed as ascending. For bearish kayo vise versa.
3.In a bullish kayo, second candle's lower wick must be longer then upper wick.
4. A pattern marked as kayo when its conditions do not correspond to any of the other patterns(include the confirmation that user added), ie it has the last naming priority over the other patterns.
Note 5.
When you active confirmation options for patterns like hammer, engulf and etc which they conditions are like kayo in some extent, if they can't pass the confirmation steps, they can be marked as kayo pattern.
Note 6.
If you active volume confirmation for Three white soldiers | Three black crows, the confirmation get passed if the volume of forth or third candle increased as value as entered relative to volume EMA3 of 3 candles.
Note 7.
In a bullish pattern all highs of following candles MUST be higher than prior highs and all lows of following candles MUST be higher than prior lows. For bearish patterns vise versa.
This type of confirmation depends on High and Low NOT close!
Gift to price action lovers!
Support us with your like and comments. let us know your experience, points and idea to make this better together.
VMDM - Volume, Momentum & Divergence Master [BullByte]VMDM - Volume, Momentum and Divergence Master
Educational Multi-Layer Market Structure Analysis System
Multi-factor divergence engine that scores RSI momentum, volume pressure, and institutional footprints into one non-repainting confluence rating (0-100).
WHAT THIS INDICATOR IS
VMDM is an educational indicator designed to teach traders how to recognize high-probability reversal and continuation patterns by analyzing four independent market dimensions simultaneously. Instead of relying on a single indicator that may produce frequent false signals, VMDM creates a confluence-based scoring system that weights multiple confirmation factors, helping you understand which setups have stronger technical backing and which are lower quality.
This is NOT a trading system or signal generator. It is a learning tool that visualizes complex market structure concepts in an accessible format for both coders and non-coders.
THE PROBLEM IT SOLVES
Most traders face these common challenges:
Challenge 1 - Indicator Overload: Running RSI, volume analysis, and divergence detection separately creates chart clutter and conflicting signals. You waste time cross-referencing multiple windows trying to determine if all factors align.
Challenge 2 - False Divergences: Standard divergence indicators trigger on every minor pivot, creating noise. Many divergences fail because they lack supporting evidence from volume or market structure.
Challenge 3 - Missed Context: A bullish RSI divergence means nothing if it occurs during weak volume or in the middle of strong distribution. Context determines quality.
Challenge 4 - Repainting Confusion: Many divergence scripts repaint, showing perfect historical signals that never actually triggered in real-time, leading to false confidence.
Challenge 5 - Institutional Pattern Recognition: Absorption zones, stop hunts, and exhaustion patterns are taught in trading education but difficult to identify systematically without manual analysis.
VMDM addresses all five challenges by combining complementary analytical layers into one transparent, non-repainting, confluence-weighted system with visual clarity.
WHY THIS SPECIFIC COMBINATION - MASHUP JUSTIFICATION
This indicator is NOT a random mashup of popular indicators. Each of the four layers serves a specific analytical purpose and together they create a complete market structure assessment framework.
THE FOUR ANALYTICAL LAYERS
LAYER 1 - RSI MOMENTUM DIVERGENCE (Trend Exhaustion Detection)
Purpose: Identifies when price momentum is weakening before price itself reverses.
Why RSI: The Relative Strength Index measures momentum on a bounded 0-100 scale, making divergence detection mathematically consistent across all assets and timeframes. Unlike raw price oscillators, RSI normalizes momentum regardless of volatility regime.
How It Contributes: Divergence between price pivots and RSI pivots reveals early momentum exhaustion. A lower price low with a higher RSI low (bullish regular divergence) signals sellers are losing strength even as price makes new lows. This is the PRIMARY signal generator in VMDM.
Limitation If Used Alone: RSI divergence by itself produces many false signals because momentum can remain weak during continued trends. It needs confirmation from volume and structural evidence.
LAYER 2 - VOLUME PRESSURE ANALYSIS (Buying vs Selling Intensity)
Purpose: Quantifies whether the current bar's volume reflects buying pressure or selling pressure based on where price closed within the bar's range.
Methodology: Instead of just measuring volume size, VMDM calculates WHERE in the bar range the close occurred. A close near the high on high volume indicates strong buying absorption. A close near the low indicates selling pressure. The calculation accounts for wick size (wicks reduce pressure quality) and uses percentile ranking over a lookback period to normalize pressure strength on a 0-100 scale.
Formula Concept:
Buy Pressure = Volume × (Close - Low) / (High - Low) × Wick Quality Factor
Sell Pressure = Volume × (High - Close) / (High - Low) × Wick Quality Factor
Net Pressure = Buy Pressure - Sell Pressure
Pressure Strength = Percentile Rank of Net Pressure over lookback period
Why Percentile Ranking: Absolute volume varies by asset and session. Percentile ranking makes 85th percentile pressure on low-volume crypto comparable to 85th percentile pressure on high-volume forex.
How It Contributes: When a bullish divergence occurs at a pivot low AND pressure strength is above 60 (strong buying), this adds 25 confluence points. It confirms that the divergence is occurring during actual accumulation, not just weak selling.
Limitation If Used Alone: Pressure analysis shows current bar intensity but cannot identify trend exhaustion or reversal timing. High buying pressure can exist during a strong uptrend with no reversal imminent.
LAYER 3 - BEHAVIORAL FOOTPRINT PATTERNS (Volume Anomaly Detection)
CRITICAL DISCLAIMER: The terms "institutional footprint," "absorption," "stop hunt," and "exhaustion" used in this indicator are EDUCATIONAL LABELS for specific price and volume behavioral patterns. These patterns are detected through technical analysis of publicly available price, volume, and bar structure data. This indicator does NOT have access to actual institutional order flow, market maker data, broker stop-loss locations, or any non-public data source. These pattern names are used because they are common terminology in trading education to describe these technical behaviors. The analysis is interpretive and based on observable price action, not privileged information.
Purpose: Detect volume anomalies and price patterns that historically correlate with potential reversal zones or trend continuation failure.
Pattern Type 1 - Absorption (Labeled as "ACCUMULATION" or "DISTRIBUTION")
Detection Criteria: Volume is more than 2x the moving average AND bar range is less than 50 percent of the average bar range.
Interpretation: High volume compressed into a tight range suggests large participants are absorbing supply (accumulation) or distribution (distribution) without allowing price to move significantly. This often precedes directional moves once absorption completes.
Visual: Colored box zone highlighting the absorption area.
Pattern Type 2 - Stop Hunt (Labeled as "BULL HUNT" or "BEAR HUNT")
Detection Criteria: Price penetrates a recent 10-bar high or low by a small margin (0.2 percent), then closes back inside the range on above-average volume (1.5x+).
Interpretation: Price briefly spikes beyond recent structure (likely triggering stop losses placed just beyond obvious levels) then reverses. This is a classic false breakout pattern often seen before reversals.
Visual: Label at the wick extreme showing hunt direction.
Pattern Type 3 - Exhaustion (Labeled as "SELL EXHAUST" or "BUY EXHAUST")
Detection Criteria: Lower wick is more than 2.5x the body size with volume above 1.8x average and RSI below 35 (sell exhaustion), OR upper wick more than 2.5x body size with volume above 1.8x average and RSI above 65 (buy exhaustion).
Interpretation: Large wicks with high volume and extreme RSI suggest aggressive buying or selling was met with equally aggressive rejection. This exhaustion often marks short-term extremes.
Visual: Label showing exhaustion type.
How These Contribute: When a divergence forms at a pivot AND one of these behavioral patterns is active, the confluence score increases by 20 points. This confirms the divergence is occurring during structural anomaly activity, not just normal price flow.
Limitation If Used Alone: These patterns can occur mid-trend and do not indicate direction without momentum context. Absorption in a strong uptrend may just be continuation accumulation.
LAYER 4 - CONFLUENCE SCORING MATRIX (Quality Weighting System)
Purpose: Translate all detected conditions into a single 0-100 quality score so you can objectively compare setups.
Scoring Breakdown:
Divergence Present: +30 points (primary signal)
Pressure Confirmation: +25 points (volume supports direction)
Behavioral Footprint Active: +20 points (structural anomaly present)
RSI Extreme: +15 points (RSI below 30 or above 70 at pivot)
Volume Spike: +10 points (current volume above 1.5x average)
Maximum Possible Score: 100 points
Why These Weights: The weights reflect reliability hierarchy based on backtesting observation. Divergence is the core signal (30 points), but without volume confirmation (25 points) many fail. Behavioral patterns add meaningful context (20 points). RSI extremes and volume spikes are secondary confirmations (15 and 10 points).
Quality Tiers:
90-100: TEXTBOOK (all factors aligned)
75-89: HIGH QUALITY (strong confluence)
60-74: VALID (meets minimum threshold)
Below 60: DEVELOPING (not displayed unless threshold lowered)
How It Contributes: The confluence score allows you to filter noise. You can set your minimum quality threshold in settings. Higher thresholds (75+) show fewer but higher-quality patterns. Lower thresholds (50-60) show more patterns but include lower-confidence setups. This teaches you to distinguish strong setups from weak ones.
Limitation: Confluence scoring is historical observation-based, not predictive guarantee. A 95-point setup can still fail. The score represents technical alignment, not future certainty.
WHY THIS COMBINATION WORKS TOGETHER
Each layer addresses a limitation in the others:
RSI Divergence identifies WHEN momentum is exhausting (timing)
Volume Pressure confirms WHETHER the exhaustion is accompanied by opposite-side accumulation (confirmation)
Behavioral Footprint shows IF structural anomalies support the reversal hypothesis (context)
Confluence Scoring weights ALL factors into an objective quality metric (filtering)
Using only RSI divergence gives you timing without confirmation. Using only volume pressure gives you intensity without directional context. Using only pattern detection gives you anomalies without trend exhaustion context. Using all four together creates a complete analytical framework where each layer compensates for the others' weaknesses.
This is not a mashup for the sake of combining indicators. It is a structured analytical system where each component has a defined role in a multi-dimensional market assessment process.
HOW TO READ THE INDICATOR - VISUAL ELEMENTS GUIDE
VMDM displays up to five visual layer types. You can enable or disable each layer independently in settings under "Visual Layers."
VISUAL LAYER 1 - MARKET STRUCTURE (Pivot Points and Lines)
What You See:
Small labels at swing highs and lows marked "PH" (Pivot High) and "PL" (Pivot Low) with horizontal dashed lines extending right from each pivot.
What It Means:
These are CONFIRMED pivots, not real-time. A pivot low appears AFTER the required right-side confirmation bars pass (default 3 bars). This creates a delay but prevents repainting. The pivot only appears once it is mathematically confirmed.
The horizontal lines represent support (from pivot lows) and resistance (from pivot highs) levels where price previously found significant rejection.
Color Coding:
Green label and line: Pivot Low (potential support)
Red label and line: Pivot High (potential resistance)
How To Use:
These pivots are the foundation for divergence detection. Divergence is only calculated between confirmed pivots, ensuring all signals are non-repainting. The lines help you see historical structure levels.
VISUAL LAYER 2 - PRESSURE ZONES (Background Color)
What You See:
Subtle background color shading on bars - light green or light red tint.
What It Means:
This visualizes volume pressure strength in real-time.
Color Coding:
Light Green Background: Pressure Strength above 70 (strong buying pressure - price closing near highs on volume)
Light Red Background: Pressure Strength below 30 (strong selling pressure - price closing near lows on volume)
No Color: Neutral pressure (pressure between 30-70)
How To Use:
When a bullish divergence pattern appears during green pressure zones, it suggests the divergence is forming during accumulation. When a bearish divergence appears during red zones, distribution is occurring. Pressure zones help you filter divergences - those forming in supportive pressure environments have higher probability.
VISUAL LAYER 3 - DIVERGENCE LINES (Dotted Connectors)
What You See:
Dotted lines connecting two pivot points (either two pivot lows or two pivot highs).
What It Means:
A divergence has been detected between those two pivots. The line connects the price pivots where RSI showed opposite behavior.
Color Coding:
Bright Green Line: Bullish divergence (regular or hidden)
Bright Red Line: Bearish divergence (regular or hidden)
How To Use:
The divergence line appears ONLY after the second pivot is confirmed (delayed by right-side confirmation bars). This is intentional to prevent repainting. When you see the line appear, it means:
For Bullish Regular Divergence:
Price made a lower low (second pivot lower than first)
RSI made a higher low (RSI at second pivot higher than first)
Interpretation: Downtrend losing momentum
For Bullish Hidden Divergence:
Price made a higher low (second pivot higher than first)
RSI made a lower low (RSI at second pivot lower than first)
Interpretation: Uptrend continuation likely (pullback within uptrend)
For Bearish Regular Divergence:
Price made a higher high (second pivot higher than first)
RSI made a lower high (RSI at second pivot lower than first)
Interpretation: Uptrend losing momentum
For Bearish Hidden Divergence:
Price made a lower high (second pivot lower than first)
RSI made a higher high (RSI at second pivot higher than first)
Interpretation: Downtrend continuation likely (bounce within downtrend)
If "Show Consolidated Analysis Label" is disabled, a small label will appear on the divergence line showing the divergence type abbreviation.
VISUAL LAYER 4 - BEHAVIORAL FOOTPRINT MARKERS
What You See:
Boxes, labels, and markers at specific bars showing pattern detection.
ABSORPTION ZONES (Boxes):
Colored rectangular boxes spanning one or more bars.
Purple Box: Accumulation absorption zone (high volume, tight range, bullish close)
Red Box: Distribution absorption zone (high volume, tight range, bearish close)
If absorption continues for multiple consecutive bars, the box extends and a counter appears in the label showing how many bars the absorption lasted.
What It Means: Large volume is being absorbed without significant price movement. This often precedes directional breakouts once the absorption phase completes.
STOP HUNT MARKERS (Labels):
Small labels below or above wicks labeled "BULL HUNT" or "BEAR HUNT" (may show bar count if consecutive).
What It Means:
BULL HUNT : Price spiked below recent lows then reversed back up on volume - likely triggered sell stops before reversing
BEAR HUNT : Price spiked above recent highs then reversed back down on volume - likely triggered buy stops before reversing
EXHAUSTION MARKERS (Labels):
Labels showing "SELL EXHAUST" or "BUY EXHAUST."
What It Means:
SELL EXHAUST : Large lower wick with high volume and low RSI - aggressive selling met with strong rejection
BUY EXHAUST : Large upper wick with high volume and high RSI - aggressive buying met with strong rejection
How To Use:
These markers help you identify WHERE structural anomalies occurred. When a divergence signal appears AT THE SAME TIME as one of these patterns, the confluence score increases. You are looking for alignment - divergence + behavioral pattern + pressure confirmation = high-quality setup.
VISUAL LAYER 5 - CONSOLIDATED ANALYSIS LABEL (Main Pattern Signal)
What You See:
A large label appearing at pivot points (or in real-time mode, at current bar) containing full pattern analysis.
Label Appearance:
Depending on your "Use Compact Label Format" setting:
COMPACT MODE (Single Line):
Example: "BULLISH REGULAR | Q:HIGH QUALITY C:82"
Breakdown:
BULLISH REGULAR: Divergence type detected
Q:HIGH QUALITY: Pattern quality tier
C:82: Confluence score (82 out of 100)
FULL MODE (Multi-Line Detailed):
Example:
PATTERN DETECTED
-------------------
BULLISH REGULAR
Quality: HIGH QUALITY
Price: Lower Low
Momentum: Higher Low
Signal: Weakening Downtrend
CONFLUENCE: 82/100
-------------------
Divergence: 30
Pressure: 25
Institutional: 20
RSI Extreme: 0
Volume: 10
Breakdown:
Top section: Pattern type and quality
Middle section: Divergence explanation (what price did vs what RSI did)
Bottom section: Confluence score with itemized breakdown showing which factors contributed
Label Position:
In Confirmed modes: Label appears AT the pivot point (delayed by confirmation bars)
In Real-time mode: Label appears at current bar as conditions develop
Label Color:
Gold: Textbook quality (90+ confluence)
Green: High quality (75-89 confluence)
Blue: Valid quality (60-74 confluence)
How To Use:
This is your primary decision-making label. When it appears:
Check the divergence type (regular divergences are reversal signals, hidden divergences are continuation signals)
Review the quality tier (textbook and high quality have better historical win rates)
Examine the confluence breakdown to see which factors are present and which are missing
Look at the chart context (trend, support/resistance, timeframe)
Use this information to assess whether the setup aligns with your strategy
The label does NOT tell you to buy or sell. It tells you a technical pattern has formed and provides the quality assessment. Your trading decision must incorporate risk management, market context, and your strategy rules.
UNDERSTANDING THE THREE DETECTION MODES
VMDM offers three signal detection modes in settings to accommodate different trading styles and learning objectives.
MODE 1: "Confluence Only (Real-Time)"
How It Works: Displays signals AS THEY DEVELOP on the current bar without waiting for pivot confirmation. The system calculates confluence score from pressure, volume, RSI extremes, and behavioral patterns. Divergence signals are NOT required in this mode.
Delay: ZERO - signals appear immediately.
Use Case: Real-time scanning for high-confluence zones without divergence requirement. Useful for intraday traders who want immediate alerts when multiple factors align.
Tradeoff: More frequent signals but includes setups without confirmed divergence. Higher false signal rate. Signals can change as the bar develops (not repainting in historical bars, but current bar updates).
Visual Behavior: Labels appear at the current bar. No divergence lines unless divergence happens to be present.
MODE 2: "Divergence + Confluence (Confirmed)" - DEFAULT RECOMMENDED
How It Works: Full system engagement. Signals appear ONLY when:
A pivot is confirmed (requires right-side confirmation bars to pass)
Divergence is detected between current pivot and previous pivot
Total confluence score meets or exceeds your minimum threshold
Delay: Equal to your "Pivot Right Bars" setting (default 3 bars). This means signals appear 3 bars AFTER the actual pivot formed.
Use Case: Highest-quality, non-repainting signals for swing traders and learners who want to study confirmed pattern completion.
Tradeoff: Delayed signals. You will not receive the signal until confirmation occurs. In fast-moving markets, price may have already moved significantly by the time the signal appears.
Visual Behavior: Labels appear at the historical pivot location (in the past). Divergence lines connect the two pivots. This is the most educational mode because it shows completed, confirmed patterns.
Non-Repainting Guarantee: Yes. Once a signal appears, it never disappears or changes.
MODE 3: "Divergence + Confluence (Relaxed)"
How It Works: Same as Confirmed mode but with adaptive thresholds. If confluence is very high (10 points above threshold), the signal may appear even if some factors are weak. If divergence is present but confluence is slightly below threshold (within 10 points), it may still appear.
Delay: Same as Confirmed mode (right-side confirmation bars).
Use Case: Slightly more signals than Confirmed mode for traders willing to accept near-threshold setups.
Tradeoff: More signals but lower average quality than Confirmed mode.
Visual Behavior: Same as Confirmed mode.
DASHBOARD GUIDE - READING THE METRICS
The dashboard appears in the corner of your chart (position selectable in settings) and provides real-time market state analysis.
You can choose between four dashboard detail levels in settings: Off, Compact, Optimized (default), Full.
DASHBOARD ROW EXPLANATIONS
ROW 1 - Header Information
Left: Current symbol and timeframe
Center: "VMDM "
Right: Version number
ROW 2 - Mode and Delay
Shows which detection mode you are using and the signal delay.
Example: "CONFIRMED | Delay: 3 bars"
This reminds you that signals in confirmed mode appear 3 bars after the pivot forms.
ROW 3 - Market Regime
Format: "TREND UP HV" or "RANGING NV"
First Part - Trend State:
TREND UP: 20 EMA above 50 EMA with strong separation
TREND DOWN: 20 EMA below 50 EMA with strong separation
RANGING: EMAs close together, low trend strength
TRANSITION: Between trending and ranging states
Second Part - Volatility State:
HV: High Volatility (current ATR more than 1.3x the 50-bar average ATR)
NV: Normal Volatility (current ATR between 0.7x and 1.3x average)
LV: Low Volatility (current ATR less than 0.7x average)
Third Column: Volatility ratio (example: "1.45x" means current ATR is 1.45 times normal)
How To Use: Regime context helps you interpret signals. Reversal divergences are more reliable in ranging or transitional regimes. Continuation divergences (hidden) are more reliable in trending regimes. High volatility means wider stops may be needed.
ROW 4 - Pressure
Shows current volume pressure state.
Format: "BUYING | ██████████░░░░░░░░░"
States:
BUYING : Pressure strength above 60 (closes near highs)
SELLING : Pressure strength below 40 (closes near lows)
NEUTRAL : Pressure strength between 40-60
Bar Visualization: Each block represents 10 percentile points. A full bar (10 filled blocks) = 100th percentile pressure.
Color: Green for buying, red for selling, gray for neutral.
How To Use: When pressure aligns with divergence direction (bullish divergence during buying pressure), confluence is stronger.
ROW 5 - Volume and RSI
Format: "1.8x | RSI 68 | OB"
First Value: Current volume ratio (1.8x = volume is 1.8 times the moving average)
Second Value: Current RSI reading
Third Value: RSI state
OB: Overbought (RSI above 70)
OS: Oversold (RSI below 30)
Blank: Neutral RSI
How To Use: Volume spikes (above 1.5x) during divergence formation add confluence. RSI extremes at pivots add confluence.
ROW 6 - Behavioral Footprint
Format: "BULL HUNT | 2 bars"
Shows the most recent behavioral pattern detected and how long ago.
States:
ACCUMULATION / DISTRIBUTION: Absorption detected
BULL HUNT / BEAR HUNT: Stop hunt detected
SELL EXHAUST / BUY EXHAUST: Exhaustion detected
SCANNING: No recent pattern
NOW: Pattern is active on current bar
How To Use: When footprint activity is recent (within 50 bars) or active now, it adds context to divergence signals forming in that area.
ROW 7 - Current Pattern
Shows the divergence type currently detected (if any).
Examples: "BULLISH REGULAR", "BEARISH HIDDEN", "Scanning..."
Quality: Shows pattern quality (TEXTBOOK, HIGH QUALITY, VALID)
How To Use: This tells you what type of signal is active. Regular divergences are reversal setups. Hidden divergences are continuation setups.
ROW 8 - Session Summary
Format: "14 events | A3 H8 E3"
First Value: Total institutional events this session
Breakdown:
A: Absorption events
H: Stop hunt events
E: Exhaustion events
How To Use: High event counts suggest an active, volatile session with frequent structural anomalies. Low counts suggest quiet, orderly price action.
ROW 9 - Confluence Score (Optimized/Full mode only)
Format: "78/100 | ████████░░"
Shows current real-time confluence score even if no pattern is confirmed yet.
How To Use: Watch this in real-time to see how close you are to pattern formation. When it exceeds your threshold and divergence forms, a signal will appear (after confirmation delay).
ROW 10 - Patterns Studied (Optimized/Full mode only)
Format: "47 patterns | 12 bars ago"
First Value: Total confirmed patterns detected since chart loaded
Second Value: How many bars since the last confirmed pattern appeared
How To Use: Helps you understand pattern frequency on your selected symbol and timeframe. If many bars have passed since last pattern, market may be trending without reversal opportunities.
ROW 11 - Bull/Bear Ratio (Optimized/Full mode only)
Format: "28:19 | BULL"
Shows count of bullish vs bearish patterns detected.
Balance:
BULL: More bullish patterns detected (suggests market has had more bullish reversals/continuations)
BEAR: More bearish patterns detected
BAL: Equal counts
How To Use: Extreme imbalances can indicate directional bias in the studied period. A heavily bullish ratio in a downtrend might suggest frequent failed rallies (bearish continuation). Context matters.
ROW 12 - Volume Ratio Detail (Optimized/Full mode only)
Shows current volume vs average volume in absolute terms.
Example: "1.4x | 45230 / 32300"
How To Use: Confirms whether current activity is above or below normal.
ROW 13 - Last Institutional Event (Full mode only)
Shows the most recent institutional pattern type and how many bars ago it occurred.
Example: "DISTRIBUTION | 23 bars"
How To Use: Tracks recency of last anomaly for context.
SETTINGS GUIDE - EVERY PARAMETER EXPLAINED
PERFORMANCE SECTION
Enable All Visuals (Master Toggle)
Default: ON
What It Does: Master kill switch for ALL visual elements (labels, lines, boxes, background colors, dashboard). When OFF, only plot outputs remain (invisible unless you open data window).
When To Change: Turn OFF on mobile devices, 1-second charts, or slow computers to improve performance. You can still receive alerts even with visuals disabled.
Impact: Dramatic performance improvement when OFF, but you lose all visual feedback.
Maximum Object History
Default: 50 | Range: 10-100
What It Does: Limits how many of each object type (labels, lines, boxes) are kept in memory. Older objects beyond this limit are deleted.
When To Change: Lower to 20-30 on fast timeframes (1-minute charts) to prevent slowdown. Increase to 100 on daily charts if you want more historical pattern visibility.
Impact: Lower values = better performance but less historical visibility. Higher values = more history visible but potential slowdown on fast timeframes.
Alert Cooldown (Bars)
Default: 5 | Range: 1-50
What It Does: Minimum number of bars that must pass before another alert of the same type can fire. Prevents alert spam when multiple patterns form in quick succession.
When To Change: Increase to 20+ on 1-minute charts to reduce noise. Decrease to 1-2 on daily charts if you want every pattern alerted.
Impact: Higher cooldown = fewer alerts. Lower cooldown = more alerts.
USER EXPERIENCE SECTION
Show Enhanced Tooltips
Default: ON
What It Does: Enables detailed hover-over tooltips on labels and visual elements.
When To Change: Turn OFF if you encounter Pine Script compilation errors related to tooltip arguments (rare, platform-specific issue).
Impact: Minimal. Just adds helpful hover text.
MARKET STRUCTURE DETECTION SECTION
Pivot Left Bars
Default: 3 | Range: 2-10
What It Does: Number of bars to the LEFT of the center bar that must be higher (for pivot low) or lower (for pivot high) than the center bar for a pivot to be valid.
Example: With value 3, a pivot low requires the center bar's low to be lower than the 3 bars to its left.
When To Change:
Increase to 5-7 on noisy timeframes (1-minute charts) to filter insignificant pivots
Decrease to 2 on slow timeframes (daily charts) to catch more pivots
Impact: Higher values = fewer, more significant pivots = fewer signals. Lower values = more frequent pivots = more signals but more noise.
Pivot Right Bars
Default: 3 | Range: 2-10
What It Does: Number of bars to the RIGHT of the center bar that must pass for confirmation. This creates the non-repainting delay.
Example: With value 3, a pivot is confirmed 3 bars AFTER it forms.
When To Change:
Increase to 5-7 for slower, more confirmed signals (better for swing trading)
Decrease to 2 for faster signals (better for intraday, but still non-repainting)
Impact: Higher values = longer delay but more reliable confirmation. Lower values = faster signals but less confirmation. This setting directly controls your signal delay in Confirmed and Relaxed modes.
Minimum Confluence Score
Default: 60 | Range: 40-95
What It Does: The threshold score required for a pattern to be displayed. Patterns with confluence scores below this threshold are not shown.
When To Change:
Increase to 75+ if you only want high-quality textbook setups (fewer signals)
Decrease to 50-55 if you want to see more developing patterns (more signals, lower average quality)
Impact: This is your primary signal filter. Higher threshold = fewer, higher-quality signals. Lower threshold = more signals but includes weaker setups. Recommended starting point is 60-65.
TECHNICAL PERIODS SECTION
RSI Period
Default: 14 | Range: 5-50
What It Does: Lookback period for RSI calculation.
When To Change:
Decrease to 9-10 for faster, more sensitive RSI that detects shorter-term momentum changes
Increase to 21-28 for slower, smoother RSI that filters noise
Impact: Lower values make RSI more volatile (more frequent extremes and divergences). Higher values make RSI smoother (fewer but more significant divergences). 14 is industry standard.
Volume Moving Average Period
Default: 20 | Range: 10-200
What It Does: Lookback period for calculating average volume. Current volume is compared to this average to determine volume ratio.
When To Change:
Decrease to 10-14 for shorter-term volume comparison (more sensitive to recent volume changes)
Increase to 50-100 for longer-term volume comparison (smoother, less sensitive)
Impact: Lower values make volume ratio more volatile. Higher values make it more stable. 20 is standard.
ATR Period
Default: 14 | Range: 5-100
What It Does: Lookback period for Average True Range calculation used for volatility measurement and label positioning.
When To Change: Rarely needs adjustment. Use 7-10 for faster volatility response, 21-28 for slower.
Impact: Affects volatility ratio calculation and visual label spacing. Minimal impact on signals.
Pressure Percentile Lookback
Default: 50 | Range: 10-300
What It Does: Lookback period for calculating volume pressure percentile ranking. Your current pressure is ranked against the pressure of the last X bars.
When To Change:
Decrease to 20-30 for shorter-term pressure context (more responsive to recent changes)
Increase to 100-200 for longer-term pressure context (smoother rankings)
Impact: Lower values make pressure strength more sensitive to recent bars. Higher values provide more stable, long-term pressure assessment. Capped at 300 for performance reasons.
SIGNAL DETECTION SECTION
Signal Detection Mode
Default: "Divergence + Confluence (Confirmed)"
Options:
Confluence Only (Real-time)
Divergence + Confluence (Confirmed)
Divergence + Confluence (Relaxed)
What It Does: Selects which detection logic mode to use (see "Understanding The Three Detection Modes" section above).
When To Change: Use Confirmed for learning and non-repainting signals. Use Real-time for live scanning without divergence requirement. Use Relaxed for slightly more signals than Confirmed.
Impact: Fundamentally changes when and how signals appear.
VISUAL LAYERS SECTION
All toggles default to ON. Each controls visibility of one visual layer:
Show Market Structure: Pivot markers and support/resistance lines
Show Pressure Zones: Background color shading
Show Divergence Lines: Dotted lines connecting pivots
Show Institutional Footprint Markers: Absorption boxes, hunt labels, exhaustion labels
Show Consolidated Analysis Label: Main pattern detection label
Use Compact Label Format
Default: OFF
What It Does: Switches consolidated label between single-line compact format and multi-line detailed format.
When To Change: Turn ON if you find full labels too large or distracting.
Impact: Visual clarity vs. information density tradeoff.
DASHBOARD SECTION
Dashboard Mode
Default: "Optimized"
Options: Off, Compact, Optimized, Full
What It Does: Controls how much information the dashboard displays.
Off: No dashboard
Compact: 8 rows (essential metrics only)
Optimized: 12 rows (recommended balance)
Full: 13 rows (every available metric)
Dashboard Position
Default: "Top Right"
Options: Top Right, Top Left, Bottom Right, Bottom Left
What It Does: Screen corner where dashboard appears.
HOW TO USE VMDM - PRACTICAL WORKFLOW
STEP 1 - INITIAL SETUP
Add VMDM to your chart
Select your detection mode (Confirmed recommended for learning)
Set your minimum confluence score (start with 60-65)
Adjust pivot parameters if needed (default 3/3 is good for most timeframes)
Enable the visual layers you want to see
STEP 2 - CHART ANALYSIS
Let the indicator load and analyze historical data
Review the patterns that appear historically
Examine the confluence scores - notice which patterns had higher scores
Observe which patterns occurred during supportive pressure zones
Notice the divergence line connections - understand what price vs RSI did
STEP 3 - PATTERN RECOGNITION LEARNING
When a consolidated analysis label appears:
Read the divergence type (regular or hidden, bullish or bearish)
Check the quality tier (textbook, high quality, or valid)
Review the confluence breakdown - which factors contributed
Look at the chart context - where is price relative to structure, trend, etc.
Observe the behavioral footprint markers nearby - do they support the pattern
STEP 4 - REAL-TIME MONITORING
Watch the dashboard for real-time regime and pressure state
Monitor the current confluence score in the dashboard
When it approaches your threshold, be alert for potential pattern formation
When a new pattern appears (after confirmation delay), evaluate it using the workflow above
Use your trading strategy rules to decide if the setup aligns with your criteria
STEP 5 - POST-PATTERN OBSERVATION
After a pattern appears:
Mark the level on your chart
Observe what price does after the pattern completes
Did price respect the reversal/continuation signal
What was the confluence score of patterns that worked vs. those that failed
Learn which quality tiers and confluence levels produce better results on your specific symbol and timeframe
RECOMMENDED TIMEFRAMES AND ASSET CLASSES
VMDM is timeframe-agnostic and works on any asset with volume data. However, optimal performance varies:
BEST TIMEFRAMES
15-Minute to 1-Hour: Ideal balance of signal frequency and reliability. Pivot confirmation delay is acceptable. Sufficient volume data for pressure analysis.
4-Hour to Daily: Excellent for swing trading. Very high-quality signals. Lower frequency but higher significance. Recommended for learning because patterns are clearer.
1-Minute to 5-Minute: Works but requires adjustment. Increase pivot bars to 5-7 for filtering. Decrease max object history to 30 for performance. Expect more noise.
Weekly/Monthly: Works but very infrequent signals. Increase confluence threshold to 70+ to ensure only major patterns appear.
BEST ASSET CLASSES
Forex Majors: Excellent volume data and clear trends. Pressure analysis works well.
Crypto (Major Pairs): Good volume data. High volatility makes divergences more pronounced. Works very well.
Stock Indices (SPY, QQQ, etc.): Excellent. Clean price action and reliable volume.
Individual Stocks: Works well on high-volume stocks. Low-volume stocks may produce unreliable pressure readings.
Commodities (Gold, Oil, etc.): Works well. Clear trends and reactions.
WHAT THIS INDICATOR CANNOT DO - LIMITATIONS
LIMITATION 1 - It Does Not Predict The Future
VMDM identifies when technical conditions align historically associated with potential reversals or continuations. It does not predict what will happen next. A textbook 95-confluence pattern can still fail if fundamental events, news, or larger timeframe structure override the setup.
LIMITATION 2 - Confirmation Delay Means You Miss Early Entry
In Confirmed and Relaxed modes, the non-repainting design means you receive signals AFTER the pivot is confirmed. Price may have already moved significantly by the time you receive the signal. This is the tradeoff for non-repainting reliability. You can use Real-time mode for faster signals but sacrifice divergence confirmation.
LIMITATION 3 - It Does Not Tell You Position Sizing or Risk Management
VMDM provides technical pattern analysis. It does not calculate stop loss levels, take profit targets, or position sizing. You must apply your own risk management rules. Never risk more than you can afford to lose based on a technical signal.
LIMITATION 4 - Volume Pressure Analysis Requires Reliable Volume Data
On assets with thin volume or unreliable volume reporting, pressure analysis may be inaccurate. Stick to major liquid assets with consistent volume data.
LIMITATION 5 - It Cannot Detect Fundamental Events
VMDM is purely technical. It cannot predict earnings reports, central bank decisions, geopolitical events, or other fundamental catalysts that can override technical patterns.
LIMITATION 6 - Divergence Requires Two Pivots
The indicator cannot detect divergence until at least two pivots of the same type have formed. In strong trends without pullbacks, you may go long periods without signals.
LIMITATION 7 - Institutional Pattern Names Are Interpretive
The behavioral footprint patterns are named using common trading education terminology, but they are detected through technical analysis, not actual institutional data access. The patterns are interpretations based on price and volume behavior.
CONCEPT FOUNDATION - WHY THIS APPROACH WORKS
MARKET PRINCIPLE 1 - Momentum Divergence Precedes Price Reversal
Price is the final output of market forces, but momentum (the rate of change in those forces) shifts first. When price makes a new low but the momentum behind that move is weaker (higher RSI low), it signals that sellers are losing strength even though they temporarily pushed price lower. This precedes reversal. This is a fundamental principle in technical analysis taught by Charles Dow, widely observed in market behavior.
MARKET PRINCIPLE 2 - Volume Reveals Conviction
Price can move on low volume (low conviction) or high volume (high conviction). When price makes a new low on declining volume while RSI shows improving momentum, it suggests the new low is not confirmed by participant conviction. Adding volume pressure analysis to momentum divergence adds a confirmation layer that filters false divergences.
MARKET PRINCIPLE 3 - Anomalies Mark Structural Extremes
When volume spikes significantly but range contracts (absorption), or when price spikes beyond structure then reverses (stop hunt), or when aggressive moves are met with large-wick rejection (exhaustion), these anomalies often mark short-term extremes. Combining these structural observations with momentum analysis creates context.
MARKET PRINCIPLE 4 - Confluence Improves Probability
No single technical factor is reliable in isolation. RSI divergence alone fails frequently. Volume analysis alone cannot time entries. Combining multiple independent factors into a weighted system increases the probability that observed patterns have structural significance rather than random noise.
THE EDUCATIONAL VALUE
By visualizing all four layers simultaneously and breaking down the confluence scoring transparently, VMDM teaches you to think in terms of multi-dimensional analysis rather than single-indicator reliance. Over time, you will learn to recognize these patterns manually and understand which combinations produce better results on your traded assets.
INSTITUTIONAL TERMINOLOGY - IMPORTANT CLARIFICATION
This indicator uses the following terms that are common in trading education:
Institutional Footprint
Absorption (Accumulation / Distribution)
Stop Hunt
Exhaustion
CRITICAL DISCLAIMER:
These terms are EDUCATIONAL LABELS for specific price action and volume behavior patterns detected through technical analysis of publicly available chart data (open, high, low, close, volume). This indicator does NOT have access to:
Actual institutional order flow or order book data
Market maker positions or intentions
Broker stop-loss databases
Non-public trading data
Proprietary institutional information
The patterns labeled as "institutional footprint" are interpretations based on observable price and volume behavior that educational trading literature often associates with potential large-participant activity. The detection is algorithmic pattern recognition, not privileged data access.
When this indicator identifies "absorption," it means it detected high volume within a small range - a condition that MAY indicate large orders being filled but is not confirmation of actual institutional participation.
When it identifies a "stop hunt," it means price briefly penetrated a structural level then reversed - a pattern that MAY have triggered stop losses but is not confirmation that stops were specifically targeted.
When it identifies "exhaustion," it means high volume with large rejection wicks - a pattern that MAY indicate aggressive participation meeting strong opposition but is not confirmation of institutional involvement.
These are technical analysis interpretations, not factual statements about market participant identity or intent.
DISCLAIMER AND RISK WARNING
EDUCATIONAL PURPOSE ONLY
This indicator is designed as an educational tool to help traders learn to recognize technical patterns, understand multi-factor analysis, and practice systematic market observation. It is NOT a trading system, signal service, or financial advice.
NO PERFORMANCE GUARANTEE
Past pattern behavior does not guarantee future results. A pattern that historically preceded price movement in one direction may fail in the future due to changing market conditions, fundamental events, or random variance. Confluence scores reflect historical technical alignment, not future certainty.
TRADING INVOLVES SUBSTANTIAL RISK
Trading financial instruments involves substantial risk of loss. You can lose more than your initial investment. Never trade with money you cannot afford to lose. Always use proper risk management including stop losses, position sizing, and portfolio diversification.
NO PREDICTIVE CLAIMS
This indicator does NOT predict future price movement. It identifies when technical conditions align in patterns that historically have been associated with potential reversals or continuations. Market behavior is probabilistic, not deterministic.
BACKTESTING LIMITATIONS
If you backtest trading strategies using this indicator, ensure you account for:
Realistic commission costs
Realistic slippage (difference between signal price and actual fill price)
Sufficient sample size (minimum 100 trades for statistical relevance)
Reasonable position sizing (risking no more than 1-2 percent of account per trade)
The confirmation delay inherent in the indicator (you cannot enter at the exact pivot in Confirmed mode)
Backtests that do not account for these factors will produce unrealistic results.
AUTHOR LIABILITY
The author (BullByte) is not responsible for any trading losses incurred using this indicator. By using this indicator, you acknowledge that all trading decisions are your sole responsibility and that you understand the risks involved.
NOT FINANCIAL ADVICE
Nothing in this indicator, its code, its description, or its visual outputs constitutes financial, investment, or trading advice. Consult a licensed financial advisor before making investment decisions.
FREQUENTLY ASKED QUESTIONS
Q: Why do signals appear in the past, not at the current bar
A: In Confirmed and Relaxed modes, signals appear at confirmed pivots, which requires waiting for right-side confirmation bars (default 3). This creates a delay but prevents repainting. Use Real-time mode if you want current-bar signals without pivot confirmation.
Q: Can I use this for automated trading
A: You can create alert-based automation, but understand that Confirmed mode signals appear AFTER the pivot with delay, so your entry will not be at the pivot price. Real-time mode signals can change as the current bar develops. Automation requires careful consideration of these factors.
Q: How do I know which confluence score to use
A: Start with 60. Observe which patterns work on your symbol/timeframe. If too many false signals, increase to 70-75. If too few signals, decrease to 55. Quality vs. quantity tradeoff.
Q: Do regular divergences mean I should enter a reversal trade immediately
A: No. Regular divergences indicate momentum exhaustion, which is a WARNING sign that trend may reverse, not a confirmation that it will. Use confluence score, market context, support/resistance, and your strategy rules to make entry decisions. Many divergences fail.
Q: What's the difference between regular and hidden divergence
A: Regular divergence = price and momentum move in opposite directions at extremes = potential reversal signal. Hidden divergence = price and momentum move in opposite directions during pullbacks = potential continuation signal. Hidden divergence suggests the pullback is just a correction within the larger trend.
Q: Why does the pressure zone color sometimes conflict with the divergence direction
A: Pressure is real-time current bar analysis. Divergence is confirmed pivot analysis from the past. They measure different things at different times. A bullish divergence confirmed 3 bars ago might appear during current selling pressure. This is normal.
Q: Can I use this on stocks without volume data
A: No. Volume is required for pressure analysis and behavioral pattern detection. Use only on assets with reliable volume reporting.
Q: How often should I expect signals
A: Depends on timeframe and settings. Daily charts might produce 5-10 signals per month. 1-hour charts might produce 20-30. 15-minute charts might produce 50-100. Adjust confluence threshold to control frequency.
Q: Can I modify the code
A: Yes, this is open source. You can modify for personal use. If you publish a modified version, please credit the original and ensure your publication meets TradingView guidelines.
Q: What if I disagree with a pattern's confluence score
A: The scoring weights are based on general observations and may not suit your specific strategy or asset. You can modify the code to adjust weights if you have data-driven reasons to do so.
Final Notes
VMDM - Volume, Momentum and Divergence Master is an educational multi-layer market analysis system designed to teach systematic pattern recognition through transparent, confluence-weighted signal detection. By combining RSI momentum divergence, volume pressure quantification, behavioral footprint pattern recognition, and quality scoring into a unified framework, it provides a comprehensive learning environment for understanding market structure.
Use this tool to develop your analytical skills, understand how multiple technical factors interact, and learn to distinguish high-quality setups from noise. Remember that technical analysis is probabilistic, not predictive. No indicator replaces proper education, risk management, and trading discipline.
Trade responsibly. Learn continuously. Risk only what you can afford to lose.
-BullByte
light_logLight Log - A Defensive Programming Library for Pine Script
Overview
The Light Log library transforms Pine Script development by introducing structured logging and defensive programming patterns typically found in enterprise languages like C#. This library addresses a fundamental challenge in Pine Script: the lack of sophisticated error handling and debugging tools that developers expect when building complex trading systems.
At its core, Light Log provides three transformative capabilities that work together to create more reliable and maintainable code. First, it wraps all native Pine Script types in error-aware containers, allowing values to carry validation state alongside their data. Second, it offers a comprehensive logging system with severity levels and conditional rendering. Third, it includes defensive programming utilities that catch errors early and make code self-documenting.
The Philosophy of Errors as Values
Traditional Pine Script error handling relies on runtime errors that halt execution, making it difficult to build resilient systems that can gracefully handle edge cases. Light Log introduces a paradigm shift by treating errors as first-class values that flow through your program alongside regular data.
When you wrap a value using Light Log's type system, you're not just storing data – you're creating a container that can carry both the value and its validation state. For example, when you call myNumber.INT() , you receive an INT object that contains both the integer value and a Log object that can describe any issues with that value. This approach, inspired by functional programming languages, allows errors to propagate through calculations without causing immediate failures.
Consider how this changes error handling in practice. Instead of a calculation failing catastrophically when it encounters invalid input, it can produce a result object that contains both the computed value (which might be na) and a detailed log explaining what went wrong. Subsequent operations can check has_error() to decide whether to proceed or handle the error condition gracefully.
The Typed Wrapper System
Light Log provides typed wrappers for every native Pine Script type: INT, FLOAT, BOOL, STRING, COLOR, LINE, LABEL, BOX, TABLE, CHART_POINT, POLYLINE, and LINEFILL. These wrappers serve multiple purposes beyond simple value storage.
Each wrapper type contains two fields: the value field v holds the actual data, while the error field e contains a Log object that tracks the value's validation state. This dual nature enables powerful programming patterns. You can perform operations on wrapped values and accumulate error information along the way, creating an audit trail of how values were processed.
The wrapper system includes convenient methods for converting between wrapped and unwrapped values. The extension methods like INT() , FLOAT() , etc., make it easy to wrap existing values, while the from_INT() , from_FLOAT() methods extract the underlying values when needed. The has_error() method provides a consistent interface for checking whether any wrapped value has encountered issues during processing.
The Log Object: Your Debugging Companion
The Log object represents the heart of Light Log's debugging capabilities. Unlike simple string concatenation for error messages, the Log object provides a structured approach to building, modifying, and rendering diagnostic information.
Each Log object carries three essential pieces of information: an error type (info, warning, error, or runtime_error), a message string that can be built incrementally, and an active flag that controls conditional rendering. This structure enables sophisticated logging patterns where you can build up detailed diagnostic information throughout your script's execution and decide later whether and how to display it.
The Log object's methods support fluent chaining, allowing you to build complex messages in a readable way. The write() and write_line() methods append text to the log, while new_line() adds formatting. The clear() method resets the log for reuse, and the rendering methods ( render_now() , render_condition() , and the general render() ) control when and how messages appear.
Defensive Programming Made Easy
Light Log's argument validation functions transform how you write defensive code. Instead of cluttering your functions with verbose validation logic, you can use concise, self-documenting calls that make your intentions clear.
The argument_error() function provides strict validation that halts execution when conditions aren't met – perfect for catching programming errors early. For less critical issues, argument_log_warning() and argument_log_error() record problems without stopping execution, while argument_log_info() provides debug visibility into your function's behavior.
These functions follow a consistent pattern: they take a condition to check, the function name, the argument name, and a descriptive message. This consistency makes error messages predictable and helpful, automatically formatting them to show exactly where problems occurred.
Building Modular, Reusable Code
Light Log encourages a modular approach to Pine Script development by providing tools that make functions more self-contained and reliable. When functions validate their inputs and return wrapped values with error information, they become true black boxes that can be safely composed into larger systems.
The void_return() function addresses Pine Script's requirement that all code paths return a value, even in error handling branches. This utility function provides a clean way to satisfy the compiler while making it clear that a particular code path should never execute.
The static log pattern, initialized with init_static_log() , enables module-wide error tracking. You can create a persistent Log object that accumulates information across multiple function calls, building a comprehensive diagnostic report that helps you understand complex behaviors in your indicators and strategies.
Real-World Applications
In practice, Light Log shines when building sophisticated trading systems. Imagine developing a complex indicator that processes multiple data streams, performs statistical calculations, and generates trading signals. With Light Log, each processing stage can validate its inputs, perform calculations, and pass along both results and diagnostic information.
For example, a moving average calculation might check that the period is positive, that sufficient data exists, and that the input series contains valid values. Instead of failing silently or throwing runtime errors, it can return a FLOAT object that contains either the calculated average or a detailed explanation of why the calculation couldn't be performed.
Strategy developers benefit even more from Light Log's capabilities. Complex entry and exit logic often involves multiple conditions that must all be satisfied. With Light Log, each condition check can contribute to a comprehensive log that explains exactly why a trade was or wasn't taken, making strategy debugging and optimization much more straightforward.
Performance Considerations
While Light Log adds a layer of abstraction over raw Pine Script values, its design minimizes performance impact. The wrapper objects are lightweight, containing only two fields. The logging operations only consume resources when actually rendered, and the conditional rendering system ensures that production code can run with logging disabled for maximum performance.
The library follows Pine Script best practices for performance, using appropriate data structures and avoiding unnecessary operations. The var keyword in init_static_log() ensures that persistent logs don't create new objects on every bar, maintaining efficiency even in real-time calculations.
Getting Started
Adopting Light Log in your Pine Script projects is straightforward. Import the library, wrap your critical values, add validation to your functions, and use Log objects to track important events. Start small by adding logging to a single function, then expand as you see the benefits of better error visibility and code organization.
Remember that Light Log is designed to grow with your needs. You can use as much or as little of its functionality as makes sense for your project. Even simple uses, like adding argument validation to key functions, can significantly improve code reliability and debugging ease.
Transform your Pine Script development experience with Light Log – because professional trading systems deserve professional development tools.
Light Log Technical Deep Dive: Advanced Patterns and Architecture
Understanding Errors as Values
The concept of "errors as values" represents a fundamental shift in how we think about error handling in Pine Script. In traditional Pine Script development, errors are events – they happen at a specific moment in time and immediately interrupt program flow. Light Log transforms errors into data – they become information that flows through your program just like any other value.
This transformation has profound implications. When errors are values, they can be stored, passed between functions, accumulated, transformed, and inspected. They become part of your program's data flow rather than exceptions to it. This approach, popularized by languages like Rust with its Result type and Haskell with its Either monad, brings functional programming's elegance to Pine Script.
Consider a practical example. Traditional Pine Script might calculate a momentum indicator like this:
momentum = close - close
If period is invalid or if there isn't enough historical data, this calculation might produce na or cause subtle bugs. With Light Log's approach:
calculate_momentum(src, period)=>
result = src.FLOAT()
if period <= 0
result.e.write("Invalid period: must be positive", true, ErrorType.error)
result.v := na
else if bar_index < period
result.e.write("Insufficient data: need " + str.tostring(period) + " bars", true, ErrorType.warning)
result.v := na
else
result.v := src - src
result.e.write("Momentum calculated successfully", false, ErrorType.info)
result
Now the function returns not just a value but a complete computational result that includes diagnostic information. Calling code can make intelligent decisions based on both the value and its associated metadata.
The Monad Pattern in Pine Script
While Pine Script lacks the type system features to implement true monads, Light Log brings monadic thinking to Pine Script development. The wrapped types (INT, FLOAT, etc.) act as computational contexts that carry both values and metadata through a series of transformations.
The key insight of monadic programming is that you can chain operations while automatically propagating context. In Light Log, this context is the error state. When you have a FLOAT that contains an error, operations on that FLOAT can check the error state and decide whether to proceed or propagate the error.
This pattern enables what functional programmers call "railway-oriented programming" – your code follows a success track when all is well but can switch to an error track when problems occur. Both tracks lead to the same destination (a result with error information), but they take different paths based on the validity of intermediate values.
Composable Error Handling
Light Log's design encourages composition – building complex functionality from simpler, well-tested components. Each component can validate its inputs, perform its calculation, and return a result with appropriate error information. Higher-level functions can then combine these results intelligently.
Consider building a complex trading signal from multiple indicators:
generate_signal(src, fast_period, slow_period, signal_period) =>
log = init_static_log(ErrorType.info)
// Calculate components with error tracking
fast_ma = calculate_ma(src, fast_period)
slow_ma = calculate_ma(src, slow_period)
// Check for errors in components
if fast_ma.has_error()
log.write_line("Fast MA error: " + fast_ma.e.message, true)
if slow_ma.has_error()
log.write_line("Slow MA error: " + slow_ma.e.message, true)
// Proceed with calculation if no errors
signal = 0.0.FLOAT()
if not (fast_ma.has_error() or slow_ma.has_error())
macd_line = fast_ma.v - slow_ma.v
signal_line = calculate_ma(macd_line, signal_period)
if signal_line.has_error()
log.write_line("Signal line error: " + signal_line.e.message, true)
signal.e := log
else
signal.v := macd_line - signal_line.v
log.write("Signal generated successfully")
else
signal.e := log
signal.v := na
signal
This composable approach makes complex calculations more reliable and easier to debug. Each component is responsible for its own validation and error reporting, and the composite function orchestrates these components while maintaining comprehensive error tracking.
The Static Log Pattern
The init_static_log() function introduces a powerful pattern for maintaining state across function calls. In Pine Script, the var keyword creates variables that persist across bars but are initialized only once. Light Log leverages this to create logging objects that can accumulate information throughout a script's execution.
This pattern is particularly valuable for debugging complex strategies where you need to understand behavior across multiple bars. You can create module-level logs that track important events:
// Module-level diagnostic log
diagnostics = init_static_log(ErrorType.info)
// Track strategy decisions across bars
check_entry_conditions() =>
diagnostics.clear() // Start fresh each bar
diagnostics.write_line("Bar " + str.tostring(bar_index) + " analysis:")
if close > sma(close, 20)
diagnostics.write_line("Price above SMA20", false)
else
diagnostics.write_line("Price below SMA20 - no entry", true, ErrorType.warning)
if volume > sma(volume, 20) * 1.5
diagnostics.write_line("Volume surge detected", false)
else
diagnostics.write_line("Normal volume", false)
// Render diagnostics based on verbosity setting
if debug_mode
diagnostics.render_now()
Advanced Validation Patterns
Light Log's argument validation functions enable sophisticated precondition checking that goes beyond simple null checks. You can implement complex validation logic while keeping your code readable:
validate_price_data(open_val, high_val, low_val, close_val) =>
argument_error(na(open_val) or na(high_val) or na(low_val) or na(close_val),
"validate_price_data", "OHLC values", "contain na values")
argument_error(high_val < low_val,
"validate_price_data", "high/low", "high is less than low")
argument_error(close_val > high_val or close_val < low_val,
"validate_price_data", "close", "is outside high/low range")
argument_log_warning(high_val == low_val,
"validate_price_data", "high/low", "are equal (no range)")
This validation function documents its requirements clearly and fails fast with helpful error messages when assumptions are violated. The mix of errors (which halt execution) and warnings (which allow continuation) provides fine-grained control over how strict your validation should be.
Performance Optimization Strategies
While Light Log adds abstraction, careful design minimizes overhead. Understanding Pine Script's execution model helps you use Light Log efficiently.
Pine Script executes once per bar, so operations that seem expensive in traditional programming might have negligible impact. However, when building real-time systems, every optimization matters. Light Log provides several patterns for efficient use:
Lazy Evaluation: Log messages are only built when they'll be rendered. Use conditional logging to avoid string concatenation in production:
if debug_mode
log.write_line("Calculated value: " + str.tostring(complex_calculation))
Selective Wrapping: Not every value needs error tracking. Wrap values at API boundaries and critical calculation points, but use raw values for simple operations:
// Wrap at boundaries
input_price = close.FLOAT()
validated_period = validate_period(input_period).INT()
// Use raw values internally
sum = 0.0
for i = 0 to validated_period.v - 1
sum += close
Error Propagation: When errors occur early, avoid expensive calculations:
process_data(input) =>
validated = validate_input(input)
if validated.has_error()
validated // Return early with error
else
// Expensive processing only if valid
perform_complex_calculation(validated)
Integration Patterns
Light Log integrates smoothly with existing Pine Script code. You can adopt it incrementally, starting with critical functions and expanding coverage as needed.
Boundary Validation: Add Light Log at the boundaries of your system – where user input enters and where final outputs are produced. This catches most errors while minimizing changes to existing code.
Progressive Enhancement: Start by adding argument validation to existing functions. Then wrap return values. Finally, add comprehensive logging. Each step improves reliability without requiring a complete rewrite.
Testing and Debugging: Use Light Log's conditional rendering to create debug modes for your scripts. Production users see clean output while developers get detailed diagnostics:
// User input for debug mode
debug = input.bool(false, "Enable debug logging")
// Conditional diagnostic output
if debug
diagnostics.render_now()
else
diagnostics.render_condition() // Only shows errors/warnings
Future-Proofing Your Code
Light Log's patterns prepare your code for Pine Script's evolution. As Pine Script adds more sophisticated features, code that uses structured error handling and defensive programming will adapt more easily than code that relies on implicit assumptions.
The type wrapper system, in particular, positions your code to take advantage of potential future features or more sophisticated type inference. By thinking in terms of wrapped values and error propagation today, you're building code that will remain maintainable and extensible tomorrow.
Light Log doesn't just make your Pine Script better today – it prepares it for the trading systems you'll need to build tomorrow.
Library "light_log"
A lightweight logging and defensive programming library for Pine Script.
Designed for modular and extensible scripts, this utility provides structured runtime validation,
conditional logging, and reusable `Log` objects for centralized error propagation.
It also introduces a typed wrapping system for all native Pine values (e.g., `INT`, `FLOAT`, `LABEL`),
allowing values to carry errors alongside data. This enables functional-style flows with built-in
validation tracking, error detection (`has_error()`), and fluent chaining.
Inspired by structured logging patterns found in systems like C#, it reduces boilerplate,
enforces argument safety, and encourages clean, maintainable code architecture.
method INT(self, error_type)
Wraps an `int` value into an `INT` struct with an optional log severity.
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : The raw `int` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: An `INT` object containing the value and a default Log instance.
method FLOAT(self, error_type)
Wraps a `float` value into a `FLOAT` struct with an optional log severity.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : The raw `float` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `FLOAT` object containing the value and a default Log instance.
method BOOL(self, error_type)
Wraps a `bool` value into a `BOOL` struct with an optional log severity.
Namespace types: series bool, simple bool, input bool, const bool
Parameters:
self (bool) : The raw `bool` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOOL` object containing the value and a default Log instance.
method STRING(self, error_type)
Wraps a `string` value into a `STRING` struct with an optional log severity.
Namespace types: series string, simple string, input string, const string
Parameters:
self (string) : The raw `string` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `STRING` object containing the value and a default Log instance.
method COLOR(self, error_type)
Wraps a `color` value into a `COLOR` struct with an optional log severity.
Namespace types: series color, simple color, input color, const color
Parameters:
self (color) : The raw `color` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `COLOR` object containing the value and a default Log instance.
method LINE(self, error_type)
Wraps a `line` object into a `LINE` struct with an optional log severity.
Namespace types: series line
Parameters:
self (line) : The raw `line` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINE` object containing the value and a default Log instance.
method LABEL(self, error_type)
Wraps a `label` object into a `LABEL` struct with an optional log severity.
Namespace types: series label
Parameters:
self (label) : The raw `label` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LABEL` object containing the value and a default Log instance.
method BOX(self, error_type)
Wraps a `box` object into a `BOX` struct with an optional log severity.
Namespace types: series box
Parameters:
self (box) : The raw `box` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOX` object containing the value and a default Log instance.
method TABLE(self, error_type)
Wraps a `table` object into a `TABLE` struct with an optional log severity.
Namespace types: series table
Parameters:
self (table) : The raw `table` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `TABLE` object containing the value and a default Log instance.
method CHART_POINT(self, error_type)
Wraps a `chart.point` value into a `CHART_POINT` struct with an optional log severity.
Namespace types: chart.point
Parameters:
self (chart.point) : The raw `chart.point` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `CHART_POINT` object containing the value and a default Log instance.
method POLYLINE(self, error_type)
Wraps a `polyline` object into a `POLYLINE` struct with an optional log severity.
Namespace types: series polyline, series polyline, series polyline, series polyline
Parameters:
self (polyline) : The raw `polyline` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `POLYLINE` object containing the value and a default Log instance.
method LINEFILL(self, error_type)
Wraps a `linefill` object into a `LINEFILL` struct with an optional log severity.
Namespace types: series linefill
Parameters:
self (linefill) : The raw `linefill` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINEFILL` object containing the value and a default Log instance.
method from_INT(self)
Extracts the integer value from an INT wrapper.
Namespace types: INT
Parameters:
self (INT) : The wrapped INT instance.
Returns: The underlying `int` value.
method from_FLOAT(self)
Extracts the float value from a FLOAT wrapper.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The wrapped FLOAT instance.
Returns: The underlying `float` value.
method from_BOOL(self)
Extracts the boolean value from a BOOL wrapper.
Namespace types: BOOL
Parameters:
self (BOOL) : The wrapped BOOL instance.
Returns: The underlying `bool` value.
method from_STRING(self)
Extracts the string value from a STRING wrapper.
Namespace types: STRING
Parameters:
self (STRING) : The wrapped STRING instance.
Returns: The underlying `string` value.
method from_COLOR(self)
Extracts the color value from a COLOR wrapper.
Namespace types: COLOR
Parameters:
self (COLOR) : The wrapped COLOR instance.
Returns: The underlying `color` value.
method from_LINE(self)
Extracts the line object from a LINE wrapper.
Namespace types: LINE
Parameters:
self (LINE) : The wrapped LINE instance.
Returns: The underlying `line` object.
method from_LABEL(self)
Extracts the label object from a LABEL wrapper.
Namespace types: LABEL
Parameters:
self (LABEL) : The wrapped LABEL instance.
Returns: The underlying `label` object.
method from_BOX(self)
Extracts the box object from a BOX wrapper.
Namespace types: BOX
Parameters:
self (BOX) : The wrapped BOX instance.
Returns: The underlying `box` object.
method from_TABLE(self)
Extracts the table object from a TABLE wrapper.
Namespace types: TABLE
Parameters:
self (TABLE) : The wrapped TABLE instance.
Returns: The underlying `table` object.
method from_CHART_POINT(self)
Extracts the chart.point from a CHART_POINT wrapper.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The wrapped CHART_POINT instance.
Returns: The underlying `chart.point` value.
method from_POLYLINE(self)
Extracts the polyline object from a POLYLINE wrapper.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The wrapped POLYLINE instance.
Returns: The underlying `polyline` object.
method from_LINEFILL(self)
Extracts the linefill object from a LINEFILL wrapper.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The wrapped LINEFILL instance.
Returns: The underlying `linefill` object.
method has_error(self)
Returns true if the INT wrapper has an active log entry.
Namespace types: INT
Parameters:
self (INT) : The INT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the FLOAT wrapper has an active log entry.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The FLOAT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOOL wrapper has an active log entry.
Namespace types: BOOL
Parameters:
self (BOOL) : The BOOL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the STRING wrapper has an active log entry.
Namespace types: STRING
Parameters:
self (STRING) : The STRING instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the COLOR wrapper has an active log entry.
Namespace types: COLOR
Parameters:
self (COLOR) : The COLOR instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINE wrapper has an active log entry.
Namespace types: LINE
Parameters:
self (LINE) : The LINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LABEL wrapper has an active log entry.
Namespace types: LABEL
Parameters:
self (LABEL) : The LABEL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOX wrapper has an active log entry.
Namespace types: BOX
Parameters:
self (BOX) : The BOX instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the TABLE wrapper has an active log entry.
Namespace types: TABLE
Parameters:
self (TABLE) : The TABLE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the CHART_POINT wrapper has an active log entry.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The CHART_POINT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the POLYLINE wrapper has an active log entry.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The POLYLINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINEFILL wrapper has an active log entry.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The LINEFILL instance to check.
Returns: True if an error or message is active in the log.
void_return()
Utility function used when a return is syntactically required but functionally unnecessary.
Returns: Nothing. Function never executes its body.
argument_error(condition, function, argument, message)
Throws a runtime error when a condition is met. Used for strict argument validation.
Parameters:
condition (bool) : Boolean expression that triggers the runtime error.
function (string) : Name of the calling function (for formatting).
argument (string) : Name of the problematic argument.
message (string) : Description of the error cause.
Returns: Never returns. Halts execution if the condition is true.
argument_log_info(condition, function, argument, message)
Logs an informational message when a condition is met. Used for optional debug visibility.
Parameters:
condition (bool) : Boolean expression that triggers the log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Informational message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_warning(condition, function, argument, message)
Logs a warning when a condition is met. Non-fatal but highlights potential issues.
Parameters:
condition (bool) : Boolean expression that triggers the warning.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Warning message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_error(condition, function, argument, message)
Logs an error message when a condition is met. Does not halt execution.
Parameters:
condition (bool) : Boolean expression that triggers the error log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Error message to log.
Returns: Nothing. Logs if the condition is true.
init_static_log(error_type, message, active)
Initializes a persistent (var) Log object. Ideal for global logging in scripts or modules.
Parameters:
error_type (series ErrorType) : Initial severity level (required).
message (string) : Optional starting message string. Default value of ("").
active (bool) : Whether the log should be flagged active on initialization. Default value of (false).
Returns: A static Log object with the given parameters.
method new_line(self)
Appends a newline character to the Log message. Useful for separating entries during chained writes.
Namespace types: Log
Parameters:
self (Log) : The Log instance to modify.
Returns: The updated Log object with a newline appended.
method write(self, message, flag_active, error_type)
Appends a message to a Log object without a newline. Updates severity and active state if specified.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method write_line(self, message, flag_active, error_type)
Appends a message to a Log object, prefixed with a newline for clarity.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method clear(self, flag_active, error_type)
Clears a Log object’s message and optionally reactivates it. Can also update the error type.
Namespace types: Log
Parameters:
self (Log) : The Log instance being cleared.
flag_active (bool) : Whether to activate the log after clearing. Default value of (false).
error_type (series ErrorType) : Optional new error type to assign. If not provided, the previous type is retained. Default value of (na).
Returns: The cleared Log object.
method render_condition(self, flag_active, error_type)
Conditionally renders the log if it is active. Allows overriding error type and controlling active state afterward.
Namespace types: Log
Parameters:
self (Log) : The Log instance to evaluate and render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Useful for contextual formatting just before rendering. Default value of (na).
Returns: The updated Log object.
method render_now(self, flag_active, error_type)
Immediately renders the log regardless of `active` state. Allows overriding error type and active flag.
Namespace types: Log
Parameters:
self (Log) : The Log instance to render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Allows dynamic severity adjustment at render time. Default value of (na).
Returns: The updated Log object.
render(self, condition, flag_active, error_type)
Renders the log conditionally or unconditionally. Allows full control over render behavior.
Parameters:
self (Log) : The Log instance to render.
condition (bool) : If true, renders only if the log is active. If false, always renders. Default value of (false).
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override passed to the render methods. Default value of (na).
Returns: The updated Log object.
Log
A structured object used to store and render logging messages.
Fields:
error_type (series ErrorType) : The severity level of the message (from the ErrorType enum).
message (series string) : The text of the log message.
active (series bool) : Whether the log should trigger rendering when conditionally evaluated.
INT
A wrapped integer type with attached logging for validation or tracing.
Fields:
v (series int) : The underlying `int` value.
e (Log) : Optional log object describing validation status or error context.
FLOAT
A wrapped float type with attached logging for validation or tracing.
Fields:
v (series float) : The underlying `float` value.
e (Log) : Optional log object describing validation status or error context.
BOOL
A wrapped boolean type with attached logging for validation or tracing.
Fields:
v (series bool) : The underlying `bool` value.
e (Log) : Optional log object describing validation status or error context.
STRING
A wrapped string type with attached logging for validation or tracing.
Fields:
v (series string) : The underlying `string` value.
e (Log) : Optional log object describing validation status or error context.
COLOR
A wrapped color type with attached logging for validation or tracing.
Fields:
v (series color) : The underlying `color` value.
e (Log) : Optional log object describing validation status or error context.
LINE
A wrapped line object with attached logging for validation or tracing.
Fields:
v (series line) : The underlying `line` value.
e (Log) : Optional log object describing validation status or error context.
LABEL
A wrapped label object with attached logging for validation or tracing.
Fields:
v (series label) : The underlying `label` value.
e (Log) : Optional log object describing validation status or error context.
BOX
A wrapped box object with attached logging for validation or tracing.
Fields:
v (series box) : The underlying `box` value.
e (Log) : Optional log object describing validation status or error context.
TABLE
A wrapped table object with attached logging for validation or tracing.
Fields:
v (series table) : The underlying `table` value.
e (Log) : Optional log object describing validation status or error context.
CHART_POINT
A wrapped chart point with attached logging for validation or tracing.
Fields:
v (chart.point) : The underlying `chart.point` value.
e (Log) : Optional log object describing validation status or error context.
POLYLINE
A wrapped polyline object with attached logging for validation or tracing.
Fields:
v (series polyline) : The underlying `polyline` value.
e (Log) : Optional log object describing validation status or error context.
LINEFILL
A wrapped linefill object with attached logging for validation or tracing.
Fields:
v (series linefill) : The underlying `linefill` value.
e (Log) : Optional log object describing validation status or error context.
Artaking 2Components of the Indicator:
Moving Averages:
Short-Term Moving Average (MA): This is a 50-period Simple Moving Average (SMA) applied to the closing price. It is used to track the short-term trend of the market.
Long-Term Moving Average (MA): This is a 200-period SMA used to track the long-term trend.
Day Trading Moving Average: A 20-period SMA is used specifically for day trading signals, focusing on shorter-term price movements.
Purpose:
The crossing of these moving averages (short-term crossing above or below long-term) provides basic buy and sell signals, indicative of potential trend reversals or continuations.
ADX (Average Directional Index) for Trend Strength:
ADX Calculation: The ADX is calculated using a 14-period length with 14-period smoothing. The ADX value indicates the strength of a trend, regardless of direction.
Strong Trend Condition: The indicator considers a trend to be strong if the ADX value is above 25. This threshold helps filter out trades during weak or sideways markets.
Purpose:
To ensure that the strategy only generates signals when there is a strong trend, thus avoiding whipsaws in low volatility or range-bound conditions.
Support Levels:
Support Level Calculation: The indicator calculates the lowest close over the last 100 periods. This level is used to identify significant support zones where the price might find a floor.
Purpose:
Support levels are critical in identifying potential areas where the price might bounce, making them ideal for setting stop losses or identifying buy opportunities.
Volatility Spike (Proxy for News Trading):
ATR (Average True Range) Calculation: The indicator uses a 14-period ATR to measure market volatility. A volatility spike is identified when the ATR is greater than 1.5 times the 14-period SMA of the ATR.
Purpose:
This serves as a proxy for news events or other sudden market movements that could make the market unpredictable. The indicator avoids generating signals during these periods to reduce the risk of being caught in a volatile, potentially news-driven move.
Fibonacci Retracement Levels:
61.8% Fibonacci Level: Calculated from the highest high and lowest low over the long MA period, this retracement level is widely regarded as a significant support or resistance level.
Purpose:
Position traders often use Fibonacci levels to identify potential reversal points. The indicator incorporates the 61.8% level to fine-tune entries and exits.
Candlestick Patterns for Price Action Trading:
Bullish Engulfing Pattern: A bullish reversal pattern where a green candle fully engulfs the previous red candle.
Bearish Engulfing Pattern: A bearish reversal pattern where a red candle fully engulfs the previous green candle.
Purpose:
These patterns are classic signals used in price action trading to identify potential reversals at key levels, especially when they align with other conditions like support/resistance or Fibonacci levels.
Signal Generation:
The indicator generates buy and sell signals by combining the above elements:
Buy Signal:
A buy signal is triggered when:
The short-term MA crosses above the long-term MA (indicating a potential uptrend).
The trend is strong (ADX > 25).
The current price is near or below the 61.8% Fibonacci retracement level, suggesting a potential reversal.
No significant volatility spike is detected, ensuring the market isn’t reacting unpredictably to news.
Sell Signal:
A sell signal is triggered when:
The short-term MA crosses below the long-term MA (indicating a potential downtrend).
The trend is strong (ADX > 25).
The current price is near or above the 61.8% Fibonacci retracement level, suggesting potential resistance.
No significant volatility spike is detected.
Day Trading Signals:
Independent of the main trend signals, the indicator also generates intraday buy and sell signals when the price crosses above or below the 20-period day trading MA.
Price Action Signals:
The indicator can trigger buy or sell signals based purely on price action, such as the occurrence of bullish or bearish engulfing patterns. This is optional and can be enabled or disabled.
Alerts:
The indicator includes built-in alert conditions that notify the trader when a buy or sell signal is generated. This allows traders to act immediately without having to constantly monitor the charts.
Practical Application:
This indicator is versatile and can be used across various trading styles:
Position Trading: The long-term MA, Fibonacci retracement, and ADX provide a solid foundation for identifying long-term trends and potential entry/exit points.
Day Trading: The short-term MA and day trading MA offer quick signals for intraday trading.
Price Action: Candlestick pattern recognition allows for precise entry points based on market sentiment and behavior.
News Trading: The volatility spike filter helps avoid trading during periods of market instability, often driven by news events.
Conclusion:
The Comprehensive Trading Strategy Indicator is a robust tool designed to help traders navigate various market conditions by integrating multiple strategies into a single, coherent framework. It provides clear, actionable signals while filtering out potentially dangerous trades during volatile or weak market conditions. Whether you're a long-term trader, a day trader, or someone who relies on price action, this indicator can be a valuable addition to your trading toolkit.
ChartArt-Bankniftybuying5minName: ChartArt-BankNifty Buying Strategy (5-Minute)
Timeframe: 5-Minute Candles
Asset: BankNifty (Indian Stock Market Index)
Trading Hours: 9:30 AM - 2:45 PM IST (Indian Standard Time)
This strategy is designed for BankNifty intraday traders who want to capitalize on short-term price movements within a defined trading window. It combines technical indicators like Simple Moving Averages (SMA), Relative Strength Index (RSI), and candlestick patterns to identify potential buy signals during intraday downtrends. The strategy employs specific entry, stop-loss, and target conditions to manage trades effectively and minimize risk.
Technical Indicators Used
Simple Moving Averages (SMA):
EMA7: 7-period SMA on closing price.
EMA5: 5-period SMA on closing price.
Purpose: Used to identify the intraday trend by comparing short-term moving averages. The strategy focuses on situations where the market is in a minor downtrend, indicated by EMA5 being below EMA7.
Relative Strength Index (RSI):
RSI14: 14-period RSI, a momentum oscillator that measures the speed and change of price movements.
SMA14: 14-period SMA of the RSI.
Purpose: RSI is used to identify potential reversal points. The strategy looks for situations where the RSI is below its own moving average, suggesting weakening momentum in the downtrend.
Candlestick Patterns:
Relaxed Hammer or Doji (2nd Candle): A pattern where the second candle in a 3-candle sequence shows a potential reversal signal (Hammer or Doji), indicating indecision or a potential turning point.
Bearish 1st Candle: The first candle is bearish, setting up the context for a potential reversal.
Bullish 3rd Candle: The third candle must be bullish with specific characteristics (closing near the high, surpassing the previous high), confirming the reversal.
Strategy Conditions
Time Condition:
The strategy is only active during specific hours (9:30 AM to 2:45 PM IST). This ensures that trades are only taken during the most liquid hours of the trading day, avoiding potential volatility or lack of liquidity towards market close.
Intraday Downtrend Condition:
EMA5 < EMA7: Indicates that the market is in a minor downtrend. The strategy looks for reversal opportunities within this trend.
RSI Condition:
RSI14 <= SMA14: Indicates that the current RSI value is below its 14-period SMA, suggesting potential weakening momentum, which can precede a reversal.
Candlestick Patterns:
1st Candle: Must be bearish, setting up the context for a potential reversal.
2nd Candle: Must either be a Hammer or Doji, indicating a potential reversal pattern.
3rd Candle: Must be bullish, with specific characteristics (closing near the high, breaking the previous high, etc.), confirming the reversal.
RSI Crossover Condition:
A crossover of the RSI over its SMA in the last 5 periods is also checked, adding further confirmation to the reversal signal.
Entry and Exit Rules
Entry Signal:
A buy signal is generated when all the conditions (time, intraday downtrend, bearish 1st candle, hammer/doji 2nd candle, bullish 3rd candle, and RSI condition) are met. The trade is entered at the high of the bullish third candle.
Stop Loss:
The stop loss is calculated based on the difference between the entry price and the low of the second candle. If this difference is greater than 90 points, the stop loss is placed at the midpoint of the second candle's range (average of high and low). Otherwise, it is placed at the low of the second candle.
Target 1:
The first target is set at 1.8 times the difference between the entry price and the stop loss. When this target is hit, half of the position is exited to lock in partial profits.
Target 2:
The second target is set at 3 times the difference between the entry price and the stop loss. The remaining position is exited at this point, or if the price hits the stop loss.
Originality and Usefulness
This strategy is original in its combination of multiple technical indicators and candlestick patterns to identify potential reversals in a specific intraday timeframe. By focusing on minor downtrends and utilizing a 3-candle reversal pattern, the strategy seeks to capture quick price movements with a structured approach to risk management.
Key Benefits:
High Precision: The strategy’s multi-step filtering process (time condition, trend confirmation, candlestick pattern analysis, and momentum evaluation via RSI) increases the likelihood of accurate trade signals.
Risk Management: The use of a dynamic stop-loss based on candle characteristics, combined with partial profit-taking, allows traders to lock in profits while still giving the trade room to develop further.
Structured Approach: The strategy provides a clear, rule-based system for entering and exiting trades, which can help remove emotional decision-making from the trading process.
Charts and Signals
The strategy produces signals in the form of labels on the chart:
Buy Signal: A green label is plotted below the candle that meets all entry conditions, indicating a potential buy opportunity.
Stop Loss (SL): A red dashed line is drawn at the stop-loss level with a label indicating "SL".
Target 1 (1st TG): A blue dashed line is drawn at the first target level with a label indicating "1st TG".
Target 2 (2nd TG): Another blue dashed line is drawn at the second target level with a label indicating "2nd TG".
These visual aids help traders quickly identify entry points, stop loss levels, and target levels on the chart, making the strategy easy to follow and implement.
Backtesting and Optimization
Backtesting: The strategy can be backtested on TradingView using historical data to evaluate its performance. Traders should consider testing across different market conditions to ensure the strategy's robustness.
Optimization: Parameters such as the RSI period, moving averages, and target multipliers can be optimized based on backtesting results to refine the strategy further.
Conclusion
The ChartArt-BankNifty Buying Strategy offers a well-rounded approach to intraday trading, focusing on capturing reversals in minor downtrends. With a strong emphasis on technical analysis, precise entry and exit rules, and robust risk management, this strategy provides a solid framework for traders looking to engage in intraday trading on BankNifty.
Brooks Always In [KintsugiTrading]Brooks Always In
Overview:
The "Brooks Always In Indicator" by KintsugiTrading is a tool designed for traders who follow price action methodologies inspired by Al Brooks. This indicator identifies key bar patterns and breakouts, plots an Exponential Moving Average (EMA), and highlights consecutive bullish and bearish bars. It is intended to assist traders in making informed decisions based on price action dynamics.
Features:
Consecutive Bar Patterns:
Identifies and highlights consecutive bullish and bearish bars.
Differentiates between bars that are above/below the EMA and those that are not.
Customizable EMA:
Option to display an Exponential Moving Average (EMA) with user-defined length and offset.
The EMA can be smoothed using various methods such as SMA, EMA, SMMA (RMA), WMA, and VWMA.
Breakout Patterns:
Recognizes bullish and bearish breakout bars and outside bars.
Tracks inside bars and prior bar conditions to better understand the market context.
Customizable Display:
Users can display or hide the EMA, consecutive bar patterns, and consecutive bars relative to the moving average.
How to Use:
Customize Settings:
First, I like to navigate to the top right corner of the chart (bolt icon), and change both the bull and bear body color to match the background (white/black) - this helps the user visualize the indicator far better.
Next, Toggle to display EMA, consecutive bar patterns, and consecutive bars relative to the moving average using the provided input options.
Adjust the EMA length, source, and offset as per your trading strategy.
Select the smoothing method and length for the EMA if desired.
Analyze Key Patterns:
Observe the highlighted bars on the chart to identify consecutive bullish and bearish patterns.
Use the plotted EMA to gauge the general trend and analyze the relationship between price bars and the moving average.
Informed Decision Making:
Utilize the identified bar patterns and breakouts to make informed trading decisions, such as identifying potential entry and exit points based on price action dynamics.
Good luck with your trading!
ICT Unicorn Model [LuxAlgo]The ICT Unicorn Model indicator highlights the presence of "unicorn" patterns on the user's chart which is derived from the lectures of "The Inner Circle Trader" (ICT) .
Detected patterns are followed by targets with a distance controlled by the user.
🔶 USAGE
At its core, the ICT Unicorn Model relies on two popular concepts, Fair Value Gaps and Breaker Blocks. This combination highlights a future area of support/resistance.
A Bullish Unicorn Pattern consists out of:
A Lower Low (LL), followed by a Higher High (HH)
A Fair Value Gap (FVG), overlapping the established Breaker Block
A successful re-test of the FVG which confirms the pattern.
A Bearish Unicorn Pattern consists of:
A Higher High (HH), followed by a Lower Low (LL)
A Fair Value Gap (FVG), overlapping the established Breaker Block
A successful re-test of the FVG which confirms the pattern
The pattern detection depends on detected swings, which can be controlled by the Swing setting. Using higher values of this setting will return longer-term breaker blocks.
🔹 Using Risk/Reward Targets
A confirmed Unicorn pattern will show a blue ( Target ) / grey ( Stop Loss) "Risk/Reward" areas (RR).
When the Stop Loss or Target is hit, a white line is shown on the concerned side.
The Risk/Reward ratio can be adjusted in the "Targets" settings.
🔹 Trailing Stop
As seen in the previous snapshots, besides the RR areas, this indicator also includes an optional Trailing Stop .
This can be helpful to lower your risk, by exiting earlier than if you would wait until the Stop Loss is hit.
This example shows a successful bullish and bearish Unicorn Pattern . In this scenario, the Trailing Stop could be used for partial Take Profit.
The goal of this publication is to show confirmed Unicorn Patterns . To increase the chance of success, it is important to evaluate the bigger picture & use this in confluence with your price action analysis. For example, look for potential areas of liquidity, consider this pattern only during certain market sessions, avoid trading during heavy impact news, &/or incorporate other aspects of technical analysis rather than just following this pattern blindly.
🔶 DETAILS
🔹 Combine
When disabled, all potential Unicorn Patterns will delete previous unconfirmed patterns:
Enabling Combine ensures the last Unicorn Patterns in the opposite direction will remain.
While the latter bullish pattern became invalid, another one formed.
The combination of the previous bearish pattern, and looking at the big picture, the bullish pattern did not have much chance to be successful.
While disabling 'combine' helps minimize clutter, enabling this feature can give a pattern more chance to hit the SL/Target level.
🔹 Mitigated FVG
Users can determine if a pattern becomes invalid due to a mitigated FVG, causing the pattern to be deleted.
🔹 New pattern detected
When a new pattern is detected, the previous unconfirmed pattern in the same direction (bullish - bullish or bearish - bearish) will be deleted. This will always be the case, whether "Combine' is enabled or disabled.
When the previous pattern was confirmed but no SL or Target level was hit, this pattern will stop updating.
🔶 SETTINGS
🔹 Unicorn
Swings: This sets the length of swings, used for the underlying ZigZag and Unicorn Patterns detection.
Bull: Enable/disable Bullish patterns, and set the color of FVG box and Trailing Stop .
Bear: Enable/disable Bearish patterns, and set the color of FVG box and Trailing Stop .
Combine: When enabled, patterns in opposite directions (bullish/bearish) can exist at the same time. disabling this feature tends to give less clutter. See the "Usage" section for more information.
🔹 Targets
Risk/Reward: Sets the Risk/Reward ratio.
Trailing Stop: Set the length of small swings, which is used for the Trailing Stop .
Price Action in action
What?
Price Action in Action is an indicator to help Price Action learners and practitioners to get everything related for Price Action in one place.
Price Action is:
Price + Volume = Action
In this indicator, we have the following features available:
Support/Resistance
Using the RSI with different periods in a multiple of 7 (7, 14, 21, 28), we first determine the overbought (above 70, customizable) and oversold (below 30, customizable) regions. Then we pick up the highest point and lowest point in the RSI values in the overbought and oversold regions, respectively. These are the point, historically supply/demand emerged for surety to push down/up the RSI indicator and the corresponding price. So, these are the most accurate way, we believe, to draw support/resistance (or demand/supply) in the chart. By default, the Support is green color and Resistance is red color. To give a visual representation, we differentiate the different shades of green and red. For example, for Level-1 (i.e. 7 by default) we use the darkest shade (0 transparency) and Level-4 (i.e. 28 by default) we use lighter shade (60 transparency). Note please: you can customize the color of support and resistance lines (say if you want resistance as green and support as red). The respective shades (transparency) will be automatically adjusted accordingly. But those shade (transparency) levels are not customizable, they are fixed (please bear with it for version-1 at least).
Strength of Support/Resistance
In the chart above/below the Resistance / Support lines you can see the tiny labels with some numbers like 1, 2.
We found out how many times a particular support/resistance is appearing across multiple RSI periods. E.g. if price P1 appears 2 times among 4 different RSI periods, the number will be 2 for that calculation, and so on.
There can be multiple presence of these numbers in a support/resistance line (i.e. multiple tiny labels). Something like: 1, 1, 2 (into different candles). This means the same support/resistance is tested so many times in different occasion (means there is a RSI max/min coincides in this level over multiple occasions) at different candles.
This will help you to intuitionally gauge the “strength” of a support/resistance line.
The more the marrier, unworthy to mention.
Candle Stick Patterns
Well: we don’t need to tell anything about the Candlestick. All of you know it better than us. And it’s a time proven, zero-lag mechanism to judge the Price-Action is unfolding in the market. We do not know if there is anything better possible than this time tested patterns to judge the prevailing sentiments of market.
Price-Action does not complete without finding out the Candlestick Patterns correctly.
And in this indicator your will get all of these: Single Candle such as Doji (default off), Marubozu, Spinner, hammers, inverted-hammer etc. ; 2 candles like Tweezer, Inside Candle, Engulfing; 3 candles like morning star/evening star.
In the multi candle patterns (2/3 candles), we are grouping the candles with a dotted rectangle such that it is clear which 2/3 candles are part of the pattern. E.g. Morning Star: 3 candles are grouped in a dotted rectangle and the Morning Star label will come to the latest candle (3rd most – as the pattern is detected reliably only on the completion of the 3rd final candle).
Of course, any program can not eliminate your trained eyes and brain to capture the patterns. But we have provided sufficient knobs to adjust various parameters to tweak the candle-pattern detection. Such as Strict Inside Candle(Harami) Boolean knob where the whole current candle including wicks will be inside the body part of the previous big candle. For non-strict mode, the current candle just inside the previous candle, possibly by wicks.
To make it better usable, for every such knobs (which are not obvious) we have added user-friendly tooltip (just mouse hover the question mark (?) besides the control/switch). There are plenty of it.
Volume
Here we have a rudimentary (yet effective) way to judge the volumes.
We find out the Volume Weighted Moving Average (VMWA) of the 20-period (default, but customizable) and the latest volume. If the latest volume is more than the 20 period vwma, we just add a grey diamond on the top of the candle to denote it’s attracting volumes. Of course, we provide a Weight coefficient (default is set to 1). So if the current bar’s volume on bar’s completion is more than the 20 period volume vmwa times the weigh-cofficient, we mark it with a tiny grey diamond.
Points to be noted:
In all places we mark the indication only on the completion of the bar (technically speaking we have checks, as far as possible, with barstate.isconfirmed). However, if you wish, you can turn it off for Candlestick (as some experts may want to check candlestick on the real time, even before the closing of bars).
In case if you see the chart looks cluttered (because of many information, specially in smaller timeframes like 5 min), there are controls given in the settings to toggle each and every features.
By default, we turn off Doji candles (all 3 types of Doji’s – normal, Gravestone & Dragonfly) as they are mainly indecision. However, you can toggle it to turn it on.
It does not give you any Buy/Sell call. The interpretation it does not have.
Why?
What’s unique in it?
As we already mentioned our intention is to include Price (in forms of Support / Resistance), Volume and Action (sentiments in terms of Candlestick patterns) into a single place. And so far, to the best of our knowledge, we could not come across a single indicator provides all of these.
There were works available to determine the RSI based support / resistance zones. Those are great piece works at that time (lets say 3 years back when PineScript was in earlier versions). To the best of our knowledge those does not cover up finding out the lowest / highest point of RSI and the corresponding price to get the simplistic and distinct support/resistance lines.
We have the intuitive support/resistance strength included which we could not found out in current set of available indicators.
To the best of our knowledge, there seems no indicator can detect 3-candle patterns which are extremely popular to detect trend reversals (such as Morning Star or Evening Star). Moreover for the multi-candle patterns we are grouping the candles part of the pattens (2-candles or 3-candles) using a dotted rectangle such that it’s visually clearly (and a well educative material for Price-Action learners also).
Mentions:
There are many works which inspire us along the way. Honestly: we sometimes forgot which all indicators we experimented with. We are sincerely apologetic in case we forgot to mention. A few note-worthy:
There is an indicator from user “repo32” named as “Candlestick Patterns Identified (updated 3/11/15)”. (We could not be able to contact “repo32”). We are inspired from his work that it’s feasible to detect Candlestick patterns.
There is an awesome work done by “RSI Based Automatic Demand and Supply” by user “shtcoinr”. The idea of consulting multiple RSI levels to find out the demand/supply zone we inspired from him. (We did contact “shtcoinr” and got his kind permission to use the concept.)
We are greatly thankful to these abovementioned wizards for their pioneering a-prior work in this front.
And of course, this TradingView platform to provide this abstraction, facilitates and felicitates collaborative contributions.
Ultimately, what’s for you?
That’s the main question. What’s for you?
Price-action comprises of following 3 tasks (at least):
Draw support/resistance lines in the chart.
Once price reaches at the support/resistance line, you fervently look out the candles’ formation to mentally map to the candle patterns. Your aim is divine: You want to judge if the price-action will continue or take a rejection/reversal.
Then you double-confirm with the volume (in a non-overlaid chart below).
Finally take a trade.
For a price-action newbie or seasoned, expert practitioner, you must be doing all the above tasks regularly and manually, in a mechanical, mundane way. There come the humanly subjectivity & the inevitable emotions . This indicator, being a piece of program/code in PineScript latest version v5 , eliminates (or at least, reduces to a great extend) that subjectivity & emotions out of the way of decision making . Thus resulting better yield.
Of course, you can argue that you draw slanted trend lines also. We recommend an already existing indicator by user LuxAlgo named as “Trendlines with Breaks ”, if you wish so.
Disclaimer:
This piece of software does not come up with any warrantee or any rights of not changing it over the future course of time.
We are not responsible for any trading/investment decision you are taking out of the outcome of this indicator.
Happy trading.
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
🎯 Wyckoff Order Block Entry System🎯 Wyckoff Order Block Entry System
📝 INDICATOR DESCRIPTION
🎯 Wyckoff Order Block Entry System Short Description:
Professional institutional zone trading combined with Wyckoff methodology. Identifies high-probability entries where smart money meets classic price action patterns.
Full Description:
Wyckoff Order Block Entry System is a precision trading tool that combines two powerful concepts:
Order Blocks - Institutional zones where large players place their orders
Wyckoff Method - Classic price action patterns revealing smart money behavior
🎯 What Makes This Different?
Unlike traditional indicators that flood your chart with signals, this system only triggers entries when BOTH conditions are met:
Price enters an institutional Order Block zone (current timeframe OR higher timeframe)
A Wyckoff pattern occurs (Spring, SOS, Upthrust, or SOW)
This dual-confirmation approach ensures you're trading with institutional flow at optimal entry points.
📊 Key Features:
✅ Order Block Detection
Automatically identifies institutional buying/selling zones
Current timeframe order blocks (solid lines)
Higher timeframe order blocks (dashed lines) for stronger zones
Customizable strength and extension settings
✅ 4 Wyckoff Entry Patterns
SPRING (Bullish Reversal): Fake breakdown below support → Quick recovery
SOS (Sign of Strength): Strong bullish candle after accumulation
UPTHRUST (Bearish Reversal): Fake breakout above resistance → Quick rejection
SOW (Sign of Weakness): Strong bearish candle after distribution
✅ Clean Visual Design
Minimalist approach - only essential information
Color-coded zones (Green = Bullish, Red = Bearish, Cyan/Magenta = HTF)
Clear entry signals with pattern type labels
No chart clutter - focus on what matters
✅ Multi-Timeframe Analysis
Integrates higher timeframe order blocks
HTF signals marked with "+HTF" tag for extra confidence
Fully customizable HTF selection (H1, H4, Daily, etc.)
✅ Smart Alerts
Entry signal alerts (Long/Short)
Order block formation alerts
HTF order block alerts
Customizable alert messages
💡 How To Use:
Setup: Add indicator to your chart, configure HTF timeframe (default H1)
Wait: Let order blocks form (green/red boxes appear)
Watch: Price returns to order block zone
Entry: Signal appears when Wyckoff pattern confirms
Trade: Enter with the signal, stop below/above order block
📈 Best For:
Forex pairs (all majors and crosses)
Gold (XAUUSD)
Crypto (BTC, ETH, etc.)
Indices (SPX, NAS100, etc.)
Stocks
Commodities
⏱️ Recommended Timeframes:
M15 for scalping
M30 for day trading
H1 for swing trading
H4 for position trading
🎯 Win Rate Expectations:
Current TF signals: 60-70%
HTF signals (+HTF tag): 70-80%
Spring/Upthrust patterns: Highest probability
Works on ALL liquid markets
⚙️ Customizable Settings:
Order block detection parameters
HTF timeframe selection
Wyckoff sensitivity (swing length, volume threshold)
Zone extension duration
Color schemes
📚 Trading Strategy:
This indicator works best when:
Trading in the direction of higher timeframe trend
Using proper risk management (1-2% per trade)
Placing stops just outside order block zones
Taking profits at opposite order blocks
Focusing on HTF signals for higher quality
🔒 Risk Management:
Always use stop losses! Recommended placement:
LONG: 10-20 pips below order block
SHORT: 10-20 pips above order block
Target: Minimum 1:2 risk/reward ratio
💎 Why Traders Love This System:
"Finally, an indicator that doesn't spam my chart with useless signals!" - The quality-over-quantity approach means you only get high-probability setups.
"The HTF order blocks changed my trading!" - Multi-timeframe analysis built-in removes the need for manual higher timeframe checks.
"Wyckoff + Order Blocks = Perfect combination!" - Two proven concepts working together create powerful confluence.
📊 Universal Application:
This system works on ANY liquid market with sufficient volume:
✅ Forex (EUR/USD, GBP/USD, USD/JPY, etc.)
✅ Commodities (Gold, Silver, Oil, etc.)
✅ Indices (S&P 500, NASDAQ, DAX, etc.)
✅ Cryptocurrencies (Bitcoin, Ethereum, etc.)
✅ Stocks (Large cap with good liquidity)
🎓 Educational Value:
Beyond just signals, this indicator teaches you:
How institutional traders think
Where smart money places orders
Classic Wyckoff accumulation/distribution patterns
Multi-timeframe analysis techniques
⚡ Performance:
Lightning-fast calculations
No repainting
Real-time signal generation
Clean code, optimized for speed
🚀 Get Started:
Add to your favorite chart
Adjust HTF timeframe to match your trading style
Wait for high-quality signals
Trade with confidence
Remember: Quality beats quantity. This system prioritizes precision over frequency. You might see 2-5 signals per day on M30 - and that's exactly the point. Each signal is carefully filtered for maximum probability.
Ready to trade like institutions?
👉 Add this indicator to your chart now
👉 Configure your preferred HTF timeframe
👉 Start catching high-probability setups
👉 Trade smarter, not harder
Questions or feedback? Drop a comment below!
Found this useful? Hit that ⭐ button and share with fellow traders!
Happy Trading! 🚀📈
Absorption DetectorABSORPTION DETECTOR -
The Absorption Detector identifies institutional order flow by detecting "absorption" patterns where smart money quietly accumulates or distributes positions by absorbing retail order flow. This creates high-probability support and resistance zones for trading. This is an approximation only and does not read any footprint data.
WHAT IS ABSORPTION?
Absorption occurs when institutions take the opposite side of retail trades, creating specific candlestick patterns with high volume and significant wicks. The indicator identifies two main patterns:
SELLING ABSORPTION (P-Pattern): Red zones above candles where institutions sell into retail buying pressure, creating resistance levels. Look for high volume candles with large upper wicks that close in the lower half.
BUYING ABSORPTION (B-Pattern): Green zones below candles where institutions buy from retail selling pressure, creating support levels. Look for high volume candles with large lower wicks that close in the upper half.
KEY FEATURES
- Automatic detection of institutional absorption patterns
- Dynamic support and resistance zone creation
- Customizable styling for all visual elements
- Historic zone display for backtesting analysis
- Strength-based filtering to show only high-probability setups
- Real-time alerts for new absorption patterns
- Professional info panel with key statistics
- Multi-timeframe compatibility
MAIN SETTINGS
Volume Threshold (1.2): Minimum volume surge required compared to average. Higher values = fewer but stronger signals.
Minimum Volume (2500): Absolute volume floor to prevent signals during low-volume periods.
Min Wick Size (0.2): Minimum wick size as ATR multiple. Ensures significant rejection occurred.
Minimum Strength (1.5): Combined volume and wick strength filter. Higher values = higher quality signals.
Show Historic Zones (OFF): Enable to see all historical zones for backtesting. Disable for better performance.
Zone Extension (20): How many bars to project zones forward for anticipating future reactions.
TRADING APPROACH
ZONE REACTION STRATEGY: Wait for price to approach absorption zones and trade the bounce or rejection. Use the zones as dynamic support and resistance levels.
BREAKOUT STRATEGY: Trade decisive breaks of strong absorption zones with proper risk management. Failed zones often lead to strong moves.
CONFLUENCE TRADING: Combine absorption zones with other technical analysis for highest probability setups. Look for alignment with trend lines, Fibonacci levels, and key support/resistance.
RISK MANAGEMENT: Always use stop losses beyond the absorption zones. Target minimum 1:2 risk-reward ratios. Position size appropriately based on zone strength.
OPTIMIZATION GUIDE
For Conservative Trading (fewer, higher quality signals):
- Volume Threshold: 1.5
- Minimum Strength: 2.0
- Min Wick Size: 0.3
For Aggressive Trading (more signals, requires careful filtering):
- Volume Threshold: 1.1
- Minimum Strength: 1.0
- Min Wick Size: 0.15
BEST PRACTICES
Markets: Works best on liquid instruments with good volume - major forex pairs, popular stocks, liquid futures, and established cryptocurrencies.
Timeframes: Effective on all timeframes from 1-minute scalping to daily swing trading. Adjust settings based on your timeframe and trading style.
Confirmation: Never trade absorption signals in isolation. Always combine with trend analysis, market structure, and proper risk management.
Session Timing: Be aware of market sessions and avoid trading during low liquidity periods or major news events.
Backtesting: Use the historic zones feature to validate performance on your chosen market and timeframe before live trading.
CUSTOMIZATION
The indicator offers complete visual customization including zone colors, border styles, label appearances, and info panel positioning. All colors can be adapted to match your chart theme and personal preferences.
Alert system provides both basic and custom message alerts for real-time notifications of new absorption patterns.
PERFORMANCE NOTES
Default settings are optimized for most markets and timeframes. For best performance on older charts, keep "Show Historic Zones" disabled unless specifically backtesting.
The indicator maintains excellent performance even with extensive historical analysis enabled, handling up to 500 zones and 100 labels for comprehensive backtesting.
Multi-Confluence Swing Hunter V1# Multi-Confluence Swing Hunter V1 - Complete Description
Overview
The Multi-Confluence Swing Hunter V1 is a sophisticated low timeframe scalping strategy specifically optimized for MSTR (MicroStrategy) trading. This strategy employs a comprehensive point-based scoring system that combines optimized technical indicators, price action analysis, and reversal pattern recognition to generate precise trading signals on lower timeframes.
Performance Highlight:
In backtesting on MSTR 5-minute charts, this strategy has demonstrated over 200% profit performance, showcasing its effectiveness in capturing rapid price movements and volatility patterns unique to MicroStrategy's trading behavior.
The strategy's parameters have been fine-tuned for MSTR's unique volatility characteristics, though they can be optimized for other high-volatility instruments as well.
## Key Innovation & Originality
This strategy introduces a unique **dual scoring system** approach:
- **Entry Scoring**: Identifies swing bottoms using 13+ different technical criteria
- **Exit Scoring**: Identifies swing tops using inverse criteria for optimal exit timing
Unlike traditional strategies that rely on simple indicator crossovers, this system quantifies market conditions through a weighted scoring mechanism, providing objective, data-driven entry and exit decisions.
## Technical Foundation
### Optimized Indicator Parameters
The strategy utilizes extensively backtested parameters specifically optimized for MSTR's volatility patterns:
**MACD Configuration (3,10,3)**:
- Fast EMA: 3 periods (vs standard 12)
- Slow EMA: 10 periods (vs standard 26)
- Signal Line: 3 periods (vs standard 9)
- **Rationale**: These faster parameters provide earlier signal detection while maintaining reliability, particularly effective for MSTR's rapid price movements and high-frequency volatility
**RSI Configuration (21-period)**:
- Length: 21 periods (vs standard 14)
- Oversold: 30 level
- Extreme Oversold: 25 level
- **Rationale**: The 21-period RSI reduces false signals while still capturing oversold conditions effectively in MSTR's volatile environment
**Parameter Adaptability**: While optimized for MSTR, these parameters can be adjusted for other high-volatility instruments. Faster-moving stocks may benefit from even shorter MACD periods, while less volatile assets might require longer periods for optimal performance.
### Scoring System Methodology
**Entry Score Components (Minimum 13 points required)**:
1. **RSI Signals** (max 5 points):
- RSI < 30: +2 points
- RSI < 25: +2 points
- RSI turning up: +1 point
2. **MACD Signals** (max 8 points):
- MACD below zero: +1 point
- MACD turning up: +2 points
- MACD histogram improving: +2 points
- MACD bullish divergence: +3 points
3. **Price Action** (max 4 points):
- Long lower wick (>50%): +2 points
- Small body (<30%): +1 point
- Bullish close: +1 point
4. **Pattern Recognition** (max 8 points):
- RSI bullish divergence: +4 points
- Quick recovery pattern: +2 points
- Reversal confirmation: +4 points
**Exit Score Components (Minimum 13 points required)**:
Uses inverse criteria to identify swing tops with similar weighting system.
## Risk Management Features
### Position Sizing & Risk Control
- **Single Position Strategy**: 100% equity allocation per trade
- **No Overlapping Positions**: Ensures focused risk management
- **Configurable Risk/Reward**: Default 5:1 ratio optimized for volatile assets
### Stop Loss & Take Profit Logic
- **Dynamic Stop Loss**: Based on recent swing lows with configurable buffer
- **Risk-Based Take Profit**: Calculated using risk/reward ratio
- **Clean Exit Logic**: Prevents conflicting signals
## Default Settings Optimization
### Key Parameters (Optimized for MSTR/Bitcoin-style volatility):
- **Minimum Entry Score**: 13 (ensures high-conviction entries)
- **Minimum Exit Score**: 13 (prevents premature exits)
- **Risk/Reward Ratio**: 5.0 (accounts for volatility)
- **Lower Wick Threshold**: 50% (identifies true hammer patterns)
- **Divergence Lookback**: 8 bars (optimal for swing timeframes)
### Why These Defaults Work for MSTR:
1. **Higher Score Thresholds**: MSTR's volatility requires more confirmation
2. **5:1 Risk/Reward**: Compensates for wider stops needed in volatile markets
3. **Faster MACD**: Captures momentum shifts quickly in fast-moving stocks
4. **21-period RSI**: Reduces noise while maintaining sensitivity
## Visual Features
### Score Display System
- **Green Labels**: Entry scores ≥10 points (below bars)
- **Red Labels**: Exit scores ≥10 points (above bars)
- **Large Triangles**: Actual trade entries/exits
- **Small Triangles**: Reversal pattern confirmations
### Chart Cleanliness
- Indicators plotted in separate panes (MACD, RSI)
- TP/SL levels shown only during active positions
- Clear trade markers distinguish signals from actual trades
## Backtesting Specifications
### Realistic Trading Conditions
- **Commission**: 0.1% per trade
- **Slippage**: 3 points
- **Initial Capital**: $1,000
- **Account Type**: Cash (no margin)
### Sample Size Considerations
- Strategy designed for 100+ trade sample sizes
- Recommended timeframes: 4H, 1D for swing trading
- Optimal for trending/volatile markets
## Strategy Limitations & Considerations
### Market Conditions
- **Best Performance**: Trending markets with clear swings
- **Reduced Effectiveness**: Highly choppy, sideways markets
- **Volatility Dependency**: Optimized for moderate to high volatility assets
### Risk Warnings
- **High Allocation**: 100% position sizing increases risk
- **No Diversification**: Single position strategy
- **Backtesting Limitation**: Past performance doesn't guarantee future results
## Usage Guidelines
### Recommended Assets & Timeframes
- **Primary Target**: MSTR (MicroStrategy) - 5min to 15min timeframes
- **Secondary Targets**: High-volatility stocks (TSLA, NVDA, COIN, etc.)
- **Crypto Markets**: Bitcoin, Ethereum (with parameter adjustments)
- **Timeframe Optimization**: 1min-15min for scalping, 30min-1H for swing scalping
### Timeframe Recommendations
- **Primary Scalping**: 5-minute and 15-minute charts
- **Active Monitoring**: 1-minute for precise entries
- **Swing Scalping**: 30-minute to 1-hour timeframes
- **Avoid**: Sub-1-minute (excessive noise) and above 4-hour (reduces scalping opportunities)
## Technical Requirements
- **Pine Script Version**: v6
- **Overlay**: Yes (plots on price chart)
- **Additional Panes**: MACD and RSI indicators
- **Real-time Compatibility**: Confirmed bar signals only
## Customization Options
All parameters are fully customizable through inputs:
- Indicator lengths and levels
- Scoring thresholds
- Risk management settings
- Visual display preferences
- Date range filtering
## Conclusion
This scalping strategy represents a comprehensive approach to low timeframe trading that combines multiple technical analysis methods into a cohesive, quantified system specifically optimized for MSTR's unique volatility characteristics. The optimized parameters and scoring methodology provide a systematic way to identify high-probability scalping setups while managing risk effectively in fast-moving markets.
The strategy's strength lies in its objective, multi-criteria approach that removes emotional decision-making from scalping while maintaining the flexibility to adapt to different instruments through parameter optimization. While designed for MSTR, the underlying methodology can be fine-tuned for other high-volatility assets across various markets.
**Important Disclaimer**: This strategy is designed for experienced scalpers and is optimized for MSTR trading. The high-frequency nature of scalping involves significant risk. Past performance does not guarantee future results. Always conduct your own analysis, consider your risk tolerance, and be aware of commission/slippage costs that can significantly impact scalping profitability.
Multiple (12) Strong Buy/Sell Signals + Momentum
Indicator Manual: "Multiple (12) Strong Buy/Sell Signals + Momentum"
This indicator is designed to identify strong buy and sell signals based on 12 configurable conditions, which include a variety of technical analysis methods such as trend-following indicators, pattern recognition, volume analysis, and momentum oscillators. It allows for customizable alerts and visual cues on the chart. The indicator helps traders spot potential entry and exit points by displaying buy and sell signals based on the selected conditions.
Key Observations:
• The script integrates multiple indicators and pattern recognition methods to provide comprehensive buy/sell signals.
• Trend-based indicators like EMAs and MACD are combined with pattern recognition (flags, triangles) and momentum-based signals (RSI, ADX, and volume analysis).
• User customization is a core feature, allowing adjustments to the conditions and thresholds for more tailored signals.
• The script is designed to be responsive to market conditions, with multiple conditions filtering out noise to generate reliable signals.
________________________________________
Key Features:
1. 12 Combined Buy/Sell Signal Conditions: This indicator incorporates a diverse set of conditions based on trend analysis, momentum, and price patterns.
2. Minimum Conditions Input: You can adjust the threshold of conditions that need to be met for the buy/sell signals to appear.
3. Alert Customization: Set alert thresholds for both buy and sell signals.
4. Dynamic Visualization: Buy and sell signals are shown as triangles on the chart, with momentum signals highlighted as circles.
________________________________________
Detailed Description of the 12 Conditions:
1. Exponential Moving Averages (EMA):
o Conditions: The indicator uses EMAs with periods 3, 8, and 13 for quick trend-following signals.
o Bullish Signal: EMA3 > EMA8 > EMA13 (Bullish stack).
o Bearish Signal: EMA3 < EMA8 < EMA13 (Bearish stack).
o Reversal Signal: The crossing over or under of these EMAs can signify trend reversals.
2. MACD (Moving Average Convergence Divergence):
o Fast MACD (2, 7, 3) is used to confirm trends quickly.
o Bullish Signal: When the MACD line crosses above the signal line.
o Bearish Signal: When the MACD line crosses below the signal line.
3. Donchian Channel:
o Tracks the highest high and lowest low over a given period (default 20).
o Breakout Signal: Price breaking above the upper band is bullish; breaking below the lower band is bearish.
4. VWAP (Volume-Weighted Average Price):
o Above VWAP: Bullish condition (price above VWAP).
o Below VWAP: Bearish condition (price below VWAP).
5. EMA Stacking & Reversal:
o Tracks the order of EMAs (3, 8, 13) to confirm strong trends and reversals.
o Bullish Reversal: EMA3 < EMA8 < EMA13 followed by a crossing to bullish.
o Bearish Reversal: EMA3 > EMA8 > EMA13 followed by a crossing to bearish.
6. Bull/Bear Flags:
o Bull Flag: Characterized by a strong price movement (flagpole) followed by a pullback and breakout.
o Bear Flag: Similar to Bull Flag but in the opposite direction.
7. Triangle Patterns (Ascending and Descending):
o Detects ascending and descending triangles using pivot highs and lows.
o Ascending Triangle: Higher lows and flat resistance.
o Descending Triangle: Lower highs and flat support.
8. Volume Sensitivity:
o Identifies price moves with significant volume increases.
o High Volume: When current volume is significantly above the moving average volume (set to 1.2x of the average).
9. Momentum Indicators:
o RSI (Relative Strength Index): Confirms overbought and oversold levels with thresholds set at 65 (overbought) and 35 (oversold).
o ADX (Average Directional Index): Confirms strong trends when ADX > 28.
o Momentum Up: Momentum is upward with strong volume and bullish RSI/ADX conditions.
o Momentum Down: Momentum is downward with strong volume and bearish RSI/ADX conditions.
10. Bollinger & Keltner Squeeze:
o Squeeze Condition: A contraction in both Bollinger Bands and Keltner Channels indicates low volatility, signaling a potential breakout.
o Squeeze Breakout: Price breaking above or below the squeeze bands.
11. 3 Consecutive Candles Condition:
o Bullish: Price rises for three consecutive candles with higher highs and lows.
o Bearish: Price falls for three consecutive candles with lower highs and lows.
12. Williams %R and Stochastic RSI:
o Williams %R: A momentum oscillator with signals when the line crosses certain levels.
o Stochastic RSI: Provides overbought/oversold levels with smoother signals.
o Combined Signals: You can choose whether to require both WPR and StochRSI to signal a buy/sell.
________________________________________
User Inputs (Inputs Tab):
1. Minimum Conditions for Buy/Sell:
o min_conditions: Number of conditions required to trigger a buy/sell signal on the chart (1 to 12).
o Alert_min_conditions: User-defined alert threshold (how many conditions must be met before an alert is triggered).
2. Donchian Channel Settings:
o Show Donchian: Toggle visibility of the Donchian channel.
o Donchian Length: The length of the Donchian Channel (default 20).
3. Bull/Bear Flag Settings:
o Bull Flag Flagpole Strength: ATR multiplier to define the strength of the flagpole.
o Bull Flag Pullback Length: Length of pullback for the bull flag pattern.
o Bull Flag EMA Length: EMA length used to confirm trend during bull flag pattern.
Similar settings exist for Bear Flag patterns.
4. Momentum Indicators:
o RSI Length: Period for calculating the RSI (default 9).
o RSI Overbought: Overbought threshold for the RSI (default 65).
o RSI Oversold: Oversold threshold for the RSI (default 35).
5. Bollinger/Keltner Squeeze Settings:
o Squeeze Width Threshold: The maximum width of the Bollinger and Keltner Bands for squeeze conditions.
6. Stochastic RSI Settings:
o Stochastic RSI Length: The period for calculating the Stochastic RSI.
7. WPR Settings:
o WPR Length: Period for calculating Williams %R (default 14).
________________________________________
User Inputs (Style Tab):
1. Signal Plotting:
o Control the display and colors of the buy/sell signals, momentum indicators, and pattern signals on the chart.
o Buy/Sell Signals: Can be customized with different colors and shapes (triangle up for buys, triangle down for sells).
o Momentum Signals: Custom circle placement for momentum-up or momentum-down signals.
2. Donchian Channel:
o Show Donchian: Toggle visibility of the Donchian upper, lower, and middle bands.
o Band Colors: Choose the color for each band (upper, lower, middle).
________________________________________
How to Use the Indicator:
1. Adjust Minimum Conditions: Set the minimum number of conditions that must be met for a signal to appear. For example, set it to 5 if you want only stronger signals.
2. Set Alert Threshold: Define the number of conditions needed to trigger an alert. This can be different from the minimum conditions for visual signals.
3. Customize Appearance: Modify the colors and styles of the signals to match your preferences.
________________________________________
Conclusion:
This comprehensive trading indicator uses a combination of trend-following, pattern recognition, and momentum-based conditions to help you spot potential buy and sell opportunities. By adjusting the input settings, you can fine-tune it to match your specific trading strategy, making it a versatile tool for different market conditions.
Signal Reliability Based on Condition Count
The reliability of the buy/sell signals increases as more conditions are met. Here's a breakdown of the probabilities:
1. 1-3 Conditions Met: Lower Probability
o Signals that meet only 1-3 conditions tend to have lower reliability and are considered less probable. These signals may represent false positives or weaker market movements, and traders should approach them with caution.
2. 4 Conditions Met: More Reliable Signal
o When 4 conditions are met, the signal becomes more reliable. This indicates that multiple indicators or market patterns are aligning, increasing the likelihood of a valid buy/sell opportunity. While not foolproof, it's a stronger indication that the market may be moving in a particular direction.
3. 5-6 Conditions Met: Strong Signal
o A signal meeting 5-6 conditions is considered a strong signal. This indicates a well-confirmed move, with several technical indicators and market factors aligning to suggest a higher probability of success. These are the signals that traders often prioritize.
4. 7+ Conditions Met: Rare and High-Confidence Signal
o Signals that meet 7 or more conditions are rare and should be considered high-confidence signals. These represent a significant alignment of multiple factors, and while they are less frequent, they are highly reliable when they do occur. Traders can be more confident in acting on these signals, but they should still monitor market conditions for confirmation.
________________________________________
You can adjust the number of conditions as needed, but this breakdown should give a clear structure on how the signal strength correlates with the number of conditions met!
Arrow-SimplyTrade vol1.5-FinalTitle: Arrow-SimplyTrade vol1.5-Final
Description:
This advanced trading indicator is designed to assist traders in analyzing market trends and identifying optimal entry signals. It combines several popular technical analysis tools and strategies, including EMA (Exponential Moving Average), MA (Simple Moving Averages), Bollinger Bands, and candlestick patterns. This indicator provides both trend-following and counter-trend signals, making it suitable for various trading styles, such as scalping and swing trading.
Main Features:
EMA (Exponential Moving Average):
EMA200 is the main trend line that helps determine the overall market direction. When the price is above EMA200, the trend is considered bullish, and when the price is below EMA200, the trend is considered bearish.
It helps filter out signals that go against the prevailing market trend.
Simple Moving Averages (MA5 and MA15):
This indicator uses two Simple Moving Averages: MA5 (Fast) and MA15 (Slow). Their crossovers create buy or sell signals:
Buy Signal: When MA5 crosses above MA15, signaling a potential upward trend.
Sell Signal: When MA5 crosses below MA15, signaling a potential downward trend.
Bollinger Bands:
Bollinger Bands measure market volatility and can identify periods of overbought or oversold conditions. The Upper and Lower Bands help detect potential breakout points, while the Middle Line (Basis) serves as dynamic support or resistance.
This tool is particularly useful for identifying volatile conditions and potential reversals.
Arrows:
The indicator plots arrows on the chart to signal entry opportunities:
Green Arrows signal buy opportunities (when MA5 crosses above MA15 and price is above EMA200).
Red Arrows signal sell opportunities (when MA5 crosses below MA15 and price is below EMA200).
Opposite Arrows: Optionally, the indicator can also display arrows for counter-trend signals, triggered by MA5 and MA15 crossovers, regardless of the price's position relative to EMA200.
Candlestick Patterns:
The indicator detects popular candlestick patterns such as Bullish Engulfing, Bearish Engulfing, Hammer, and Doji.
These patterns are important for confirming entry points or anticipating trend reversals.
How to Use:
EMA200: The main trend line. If the price is above EMA200, consider long positions. If the price is below EMA200, consider short positions.
MA5 and MA15: Short-term trend indicators. The crossover of these averages generates buy or sell signals.
Bollinger Bands: Use these bands to spot overbought/oversold conditions. Breakouts from the bands may signal potential entry points.
Arrows: Green arrows represent buy signals, and red arrows represent sell signals. Opposite direction arrows can be used for counter-trend strategies.
Candlestick Patterns: Patterns like Bullish Engulfing or Doji can help confirm the signals.
Customizable Settings:
Fully customizable colors, line styles, and display settings for EMA, MAs, Bollinger Bands, and arrows.
The Candlestick Patterns feature can be toggled on or off based on user preference.
Important Notes:
This indicator is intended to be used in conjunction with other analysis tools.
Past performance does not guarantee future results.
Polish:
Tytuł: Arrow-SimplyTrade vol1.5-Final
Opis:
Ten zaawansowany wskaźnik handlowy jest zaprojektowany, aby pomóc traderom w analizie trendów rynkowych oraz identyfikowaniu optymalnych sygnałów wejścia. Łączy w sobie kilka popularnych narzędzi analizy technicznej i strategii, w tym EMA (Wykładnicza Średnia Ruchoma), MA (Prosta Średnia Ruchoma), Bollinger Bands oraz formacje świecowe. Wskaźnik generuje zarówno sygnały podążające za trendem, jak i przeciwnym trendowi, co sprawia, że jest odpowiedni do różnych stylów handlu, takich jak scalping oraz swing trading.
Główne Funkcje:
EMA (Wykładnicza Średnia Ruchoma):
EMA200 to główna linia trendu, która pomaga określić ogólny kierunek rynku. Gdy cena znajduje się powyżej EMA200, trend jest uznawany za wzrostowy, a gdy poniżej EMA200, za spadkowy.
Pomaga to filtrować sygnały, które są niezgodne z głównym trendem rynkowym.
Proste Średnie Ruchome (MA5 i MA15):
Wskaźnik używa dwóch Prostych Średnich Ruchomych: MA5 (szybka) oraz MA15 (wolna). Ich przecięcia generują sygnały kupna lub sprzedaży:
Sygnał Kupna: Kiedy MA5 przecina MA15 od dołu, sygnalizując potencjalny wzrost.
Sygnał Sprzedaży: Kiedy MA5 przecina MA15 od góry, sygnalizując potencjalny spadek.
Bollinger Bands:
Bollinger Bands mierzą zmienność rynku i mogą pomóc w identyfikowaniu okresów wykupienia lub wyprzedania rynku. Górna i dolna linia pomagają wykrywać punkty wybicia, a Środkowa Linia (Basis) działa jako dynamiczny poziom wsparcia lub oporu.
Narzędzie to jest szczególnie przydatne w wykrywaniu warunków zmienności i potencjalnych odwróceń trendu.
Strzałki:
Wskaźnik wyświetla strzałki na wykresie, które wskazują sygnały kupna i sprzedaży:
Zielona strzałka wskazuje sygnał kupna (gdy MA5 przecina MA15 i cena jest powyżej EMA200).
Czerwona strzałka wskazuje sygnał sprzedaży (gdy MA5 przecina MA15 i cena jest poniżej EMA200).
Strzałki w przeciwnym kierunku: Opcjonalna funkcja, która pokazuje strzałki w przeciwnym kierunku, uruchamiane przez przecięcia MA5 i MA15, niezależnie od pozycji ceny względem EMA200.
Formacje Świecowe:
Wskaźnik wykrywa popularne formacje świecowe, takie jak Bullish Engulfing, Bearish Engulfing, Hammer oraz Doji.
Formacje te pomagają traderom potwierdzić punkty wejścia i przewidzieć możliwe odwrócenia trendu.
Jak Używać:
EMA200: Główna linia trendu. Jeśli cena jest powyżej EMA200, rozważaj pozycje długie. Jeśli cena jest poniżej EMA200, rozważaj pozycje krótkie.
MA5 i MA15: Śledzą krótkoterminowe zmiany trendu. Przecięcia tych średnich generują sygnały kupna lub sprzedaży.
Bollinger Bands: Używaj tych pasm do wykrywania wykupionych lub wyprzedanych warunków. Wybicia z pasm mogą wskazywać potencjalne punkty wejścia.
Strzałki: Zielona strzałka wskazuje sygnał kupna, a czerwona strzałka sygnał sprzedaży. Strzałki w przeciwnym kierunku mogą być używane do strategii przeciwtrendowych.
Formacje Świecowe: Formacje takie jak Bullish Engulfing czy Doji mogą pomóc w potwierdzaniu sygnałów.
Ustawienia Personalizacji:
W pełni personalizowalne kolory, style linii i ustawienia wyświetlania dla EMA, MAs, Bollinger Bands oraz strzałek.
Funkcja Formacji Świecowych może być włączana lub wyłączana według preferencji użytkownika.
Ważne Uwagi:
Ten wskaźnik powinien być używany w połączeniu z innymi narzędziami analizy rynku.
Wyniki z przeszłości nie gwarantują wyników w przyszłości.
Sri Yantra MTF - AynetSri Yantra MTF - Aynet Script Overview
This Pine Script generates a Sri Yantra-inspired geometric pattern overlay on price charts. The pattern is dynamically updated based on multi-timeframe (MTF) inputs, utilizing high and low price ranges, and adjusting its size relative to a chosen multiplier.
The Sri Yantra is a sacred geometric figure used in various spiritual and mathematical contexts, symbolizing the interconnectedness of the universe. Here, it is applied to visualize structured price levels.
Scientific and Technical Explanation
Multi-Timeframe Integration:
Base Timeframe (baseRes): This is the primary timeframe for the analysis. The opening price and ATR (Average True Range) are calculated from this timeframe.
Pattern Timeframe (patternRes): Defines the granularity of the pattern. It ensures synchronization with price movements on specific time intervals.
Geometric Construction:
ATR-Based Scaling: The script uses ATR as a volatility measure to dynamically size the geometric pattern. The sizeMult input scales the pattern relative to price volatility.
Pattern Width (barOffset): Defines the horizontal extent of the pattern in terms of bars. This ensures the pattern is aligned with price movements and scales appropriately.
Sri Yantra-Like Geometry:
Outer Square: A bounding box is drawn around the price level.
Triangles: Multiple layers of triangles (primary, secondary, and tertiary) are calculated and drawn to mimic the structure of the Sri Yantra. These triangles converge and diverge based on price levels.
Horizontal Lines: Added at key levels to provide additional structure and aesthetic alignment.
Dynamic Updates:
The pattern recalculates and redraws itself on the last bar of the selected timeframe, ensuring it adapts to real-time price data.
A built-in check identifies new bars in the chosen timeframe (patternRes), ensuring accurate updates.
Information Table:
Displays the selected base and pattern timeframes in a table format on the top-right corner of the chart.
Allows traders to see the active settings for quick adjustments.
Key Inputs
Style Settings:
Pattern Color: Customize the color of the geometric patterns.
Size Multiplier (sizeMult): Adjusts the size of the pattern relative to price movements.
Line Width: Controls the thickness of the geometric lines.
Timeframe Settings:
Base Resolution (baseRes): Timeframe for calculating the pattern's anchor (default: daily).
Pattern Resolution (patternRes): Timeframe granularity for the pattern’s formation.
Geometric Adjustments:
Pattern Width (barOffset): Horizontal width in bars.
ATR Multiplier (rangeSize): Vertical size adjustment based on price volatility.
Scientific Concepts
Volatility Representation:
ATR (Average True Range): A standard measure of market volatility, representing the average range of price movements over a defined period. Here, ATR adjusts the vertical height of the geometric figures.
Geometric Symmetry:
The script emulates symmetry similar to the Sri Yantra, aligning with the principles of sacred geometry, which often appear in nature and mathematical constructs. Symmetry in financial data visualizations can aid in intuitive interpretation of price movements.
Multi-Timeframe Fusion:
Synchronizing patterns with multiple timeframes enhances the relevance of overlays for different trading strategies. For example, daily trends combined with hourly patterns can help traders optimize entries and exits.
Visual Features
Outer Square:
Drawn to encapsulate the geometric structure.
Represents the broader context of price levels.
Triangles:
Three layers of interlocking triangles create a fractal pattern, providing a visual alignment to price dynamics.
Horizontal Lines:
Emphasize critical levels within the pattern, offering visual cues for potential support or resistance areas.
Information Table:
Displays the active timeframe settings, helping traders quickly verify configurations.
Applications
Trend Visualization:
Patterns overlay on price movements provide a clearer view of trend direction and potential reversals.
Volatility Mapping:
ATR-based scaling ensures the pattern adjusts to varying market conditions, making it suitable for different asset classes and trading strategies.
Multi-Timeframe Analysis:
Integrates higher and lower timeframes, enabling traders to spot confluences between short-term and long-term price levels.
Potential Enhancements
Add Fibonacci Levels: Overlay Fibonacci retracements within the pattern for deeper price level insights.
Dynamic Alerts: Include alert conditions when price intersects key geometric lines.
Custom Labels: Add text descriptions for critical intersections or triangle centers.
This script is a unique blend of technical analysis and sacred geometry, providing traders with an innovative way to visualize market dynamics.
