ObjectHelpersLibrary "ObjectHelpers"
Line | Box | Label | Linefill -- Maker, Setter, Getter Library
TODO: add table functionality
set(object)
set all params for `line`, `box`, `label`, `linefill` objects with 1 function
***
## Overloaded
***
```
method set(line Line, int x1=na, float y1=na, int x2=na, float y2=na,string xloc=na,string extend=na,color color=na,string style=na,int width=na,bool update=na) => line
```
### Params
- **Line** `line` - line object | `required`
- **x1** `int` - value to set x1
- **y1** `float` - value to set y1
- **x2** `int` - value to set x2
- **y2** `float` - value to set y2
- **xloc** `int` - value to set xloc
- **yloc** `int` - value to set yloc
- **extend** `string` - value to set extend
- **color** `color` - value to set color
- **style** `string` - value to set style
- **width** `int` - value to set width
- **update** `bool` - value to set update
***
```
method set(box Box,int left=na,float top=na,int right=na, float bottom=na,color bgcolor=na,color border_color=na,string border_style=na,int border_width=na,string extend=na,string txt=na,color text_color=na,string text_font_family=na,string text_halign=na,string text_valign=na,string text_wrap=na,bool update=false) => box
```
### Params
- **Box** `box` - box object
- **left** `int` - value to set left
- **top** `float` - value to set top
- **right** `int` - value to set right
- **bottom** `float` - value to set bottom
- **bgcolor** `color` - value to set bgcolor
- **border_color** `color` - value to set border_color
- **border_style** `string` - value to set border_style
- **border_width** `int` - value to set border_width
- **extend** `string` - value to set extend
- **txt** `string` - value to set _text
- **text_color** `color` - value to set text_color
- **text_font_family** `string` - value to set text_font_family
- **text_halign** `string` - value to set text_halign
- **text_valign** `string` - value to set text_valign
- **text_wrap** `string` - value to set text_wrap
- **update** `bool` - value to set update
***
```
method set(label Label,int x=na,float y=na, string txt=na,string xloc=na,color color=na,color textcolor=na,string size=na,string style=na,string textalign=na,string tooltip=na,string text_font_family=na,bool update=false) => label
```
### Paramas
- **Label** `label` - label object
- **x** `int` - value to set x
- **y** `float` - value to set y
- **txt** `string` - value to set text add`"+++"` to the _text striing to have the current label text concatenated to the location of the "+++")
- **textcolor** `color` - value to set textcolor
- **size** `string` - value to set size
- **style** `string` - value to set style (use "flip" ,as the style to have label flip to top or bottom of bar depending on if open > close and vice versa)
- **text_font_family** `string` - value to set text_font_family
- **textalign** `string` - value to set textalign
- **tooltip** `string` - value to set tooltip
- **update** `bool` - update label to next bar
***
```
method set(linefill Linefill=na,line line1=na,line line2=na,color color=na) => linefill
```
### Params
- **linefill** `linefill` - linefill object
- **line1** `line` - line object
- **line2** `line` - line object
- **color** `color` - color
Parameters:
object (obj)
Returns: `line`, `box`, `label`, `linefill`
method set(Line, x1, y1, x2, y2, xloc, extend, color, style, width, update)
set the location params of a line with 1 function auto detects time or bar_index for xloc param
Namespace types: series line
Parameters:
Line (line) : `line` - line object | `required`
x1 (int) : `int` - value to set x1
y1 (float) : `float` - value to set y1
x2 (int) : `int` - value to set x2
y2 (float) : `float` - value to set y2
xloc (string) : `int` - value to set xloc
extend (string) : `string` - value to set extend
color (color) : `color` - value to set color
style (string) : `string` - value to set style
width (int) : `int` - value to set width
update (bool) : `bool` - value to set update
Returns: `line`
method set(Box, left, top, right, bottom, bgcolor, border_color, border_style, border_width, extend, txt, text_color, text_font_family, text_halign, text_valign, text_wrap, update)
set the location params of a box with 1 function
Namespace types: series box
Parameters:
Box (box) : `box` - box object | `required`
left (int) : `int` - value to set left
top (float) : `float` - value to set top
right (int) : `int` - value to set right
bottom (float) : `float` - value to set bottom
bgcolor (color) : `color` - value to set bgcolor
border_color (color) : `color` - value to set border_color
border_style (string) : `string` - value to set border_style
border_width (int) : `int` - value to set border_width
extend (string) : `string` - value to set extend
txt (string) : `string` - value to set _text
text_color (color) : `color` - value to set text_color
text_font_family (string) : `string` - value to set text_font_family
text_halign (string) : `string` - value to set text_halign
text_valign (string) : `string` - value to set text_valign
text_wrap (string) : `string` - value to set text_wrap
update (bool) : `bool` - value to set update
Returns: `box`
method set(Label, x, y, txt, xloc, color, textcolor, size, style, textalign, tooltip, text_font_family, update)
set the location params of a label with 1 function auto detects time or bar_index for xloc param
Namespace types: series label
Parameters:
Label (label) : `label` | `required`
x (int) : `int` - value to set x
y (float) : `float` - value to set y
txt (string) : `string` - value to set text add`"+++"` to the _text striing to have the current label text concatenated to the location of the "+++")
xloc (string)
color (color)
textcolor (color) : `color` - value to set textcolor
size (string) : `string` - value to set size
style (string) : `string` - value to set style (use "flip" ,as the style to have label flip to top or bottom of bar depending on if open > close and vice versa)
textalign (string) : `string` - value to set textalign
tooltip (string) : `string` - value to set tooltip
text_font_family (string) : `string` - value to set text_font_family
update (bool) : `bool` - update label to next bar
Returns: `label`
method set(Linefill, line1, line2, color)
change the 1 or 2 of the lines in a linefill object
Namespace types: series linefill
Parameters:
Linefill (linefill)
line1 (line) : `line` - line object
line2 (line) : `line` - line object
color (color) : `color` - color
Returns: `linefill`
get(object)
get all of the location variables for `line`, `box`, `label` objects or the line objects from a `linefill`
***
## Overloaded
***
```
method get(line Line) =>
```
### Params
- **Line** `line` - line object | `required`
***
```
method get(box Box) =>
```
### Params
- **Box** `box` - box object | `required`
***
```
method get(label Label) =>
```
### Paramas
- **Label** `label` - label object | `required`
***
```
method get(linefill Linefill) =>
```
### Params
- **Linefill** `linefill` - linefill object | `required`
Parameters:
object (obj)
Returns: ` `
method get(Line)
Gets the location paramaters of a Line
Namespace types: series line
Parameters:
Line (line) : `line` - line object
Returns:
method get(Box)
Gets the location paramaters of a Box
Namespace types: series box
Parameters:
Box (box) : `box` - box object
Returns:
method get(Label)
Gets the `x`, `y`, `text` of a Label
Namespace types: series label
Parameters:
Label (label) : `label` - label object
Returns:
method get(Linefill)
Gets `line 1`, `line 2` from a Linefill
Namespace types: series linefill
Parameters:
Linefill (linefill) : `linefill` - linefill object
Returns:
method set_x(Line, x1, x2)
set the `x1`, `x2` of a line
***
### Params
- **Line** `line` - line object | `required`
- **x1** `int` - value to set x1 | `required`
- **x2** `int` - value to set x2 | `required`
Namespace types: series line
Parameters:
Line (line) : `line` - line object
x1 (int) : `int` - value to set x1
x2 (int) : `int` - value to set x2
Returns: `line`
method set_y(Line, y1, y2)
set `y1`, `y2` of a line
***
### Params
- **Line** `line` - line object | `required`
- **y1** `float` - value to set y1 | `required`
- **y2** `float` - value to set y2 | `required`
Namespace types: series line
Parameters:
Line (line) : `line` - line object
y1 (float) : `float` - value to set y1
y2 (float) : `float` - value to set y2
Returns: `line`
method Line(x1, y1, x2, y2, extend, color, style, width)
Similar to `line.new()` but can detect time or bar_index for xloc param and has defaults for all params but `x1`, `y1`, `x2`, `y2`
***
### Params
- **x1** `int` - value to set
- **y1** `float` - value to set
- **x2** `int` - value to set
- **y2** `float` - value to set
- **extend** `string` - extend value to set line
- **color** `color` - color to set line
- **style** `string` - style to set line
- **width** `int` - width to set line
Namespace types: series int, simple int, input int, const int
Parameters:
x1 (int) : `int` - value to set
y1 (float) : `float` - value to set
x2 (int) : `int` - value to set
y2 (float) : `float` - value to set
extend (string) : `string` - extend value to set line
color (color) : `color` - color to set line
style (string) : `string` - style to set line
width (int) : `int` - width to set line
Returns: `line`
method Box(left, top, right, bottom, extend, border_color, bgcolor, text_color, border_width, border_style, txt, text_halign, text_valign, text_size, text_wrap)
similar to box.new() with the but can detect xloc param and has defaults for everything but location params
***
### Params
- **left** `int` - value to set
- **top** `float` - value to set
- **right** `int` - value to set
- **bottom** `float` - value to set
- **extend** `string` - extend value to set box
- **border_color** `color` - color to set border
- **bgcolor** `color` - color to set background
- **text_color** `color` - color to set text
- **border_width** `int` - width to set border
- **border_style** `string` - style to set border
- **txt** `string` - text to set
- **text_halign** `string` - horizontal alignment to set text
- **text_valign** `string` - vertical alignment to set text
- **text_size** `string` - size to set text
- **text_wrap** `string` - wrap to set text
Namespace types: series int, simple int, input int, const int
Parameters:
left (int) : `int` - value to set
top (float) : `float` - value to set
right (int) : `int` - value to set
bottom (float) : `float` - value to set
extend (string) : `string` - extend value to set box
border_color (color) : `color` - color to set border
bgcolor (color) : `color` - color to set background
text_color (color) : `color` - color to set text
border_width (int) : `int` - width to set border
border_style (string) : `string` - style to set border
txt (string) : `string` - text to set
text_halign (string) : `string` - horizontal alignment to set text
text_valign (string) : `string` - vertical alignment to set text
text_size (string) : `string` - size to set text
text_wrap (string) : `string` - wrap to set text
Returns: `box`
method Label(txt, x, y, yloc, color, textcolor, style, size, textalign, text_font_family, tooltip)
Similar to label.new() but can detect time or bar_index for xloc param and has defaults for all params but x, y, txt, tooltip \n
***
### Params
- **txt** `string` - string to set
- **x** `int` - value to set
- **y** `float` - value to set
- **yloc** `string` - y location to set
- **color** `color` - label color to set
- **textcolor** `color` - text color to set
- **style** `string` - style to set
- **size** `string` - size to set
- **textalign** `string` - text alignment to set
- **text_font_family** `string` - font family to set
- **tooltip** `string` - tooltip to set
Namespace types: series string, simple string, input string, const string
Parameters:
txt (string) : `string` - string to set
x (int) : `int` - value to set
y (float) : `float` - value to set
yloc (string) : `string` - y location to set
color (color) : `color` - label color to set
textcolor (color) : `color` - text color to set
style (string) : `string` - style to set
size (string) : `string` - size to set
textalign (string) : `string` - text alignment to set
text_font_family (string) : `string` - font family to set
tooltip (string) : `string` - tooltip to set
Returns: `label`
obj
Fields:
obj (series__string)
Search in scripts for "top"
Order Blocks Volume Delta 3D | Flux ChartsGENERAL OVERVIEW:
Order Blocks Volume Delta 3D by Flux Charts is a rule-based order block and volume delta visualization tool. It detects bullish and bearish order blocks using a profile-of-price approach: the indicator finds the most actively traded price area (Point of Control, or POC) between a swing high/low and the Break of Structure (BOS), then anchors the order block to the earliest still-valid candle that traded through that POC band. From there, it tracks all candles that continue to interact with that zone and overlays both 2D and 3D volume delta views directly inside the order block.
Unlike traditional order block tools that simply use candle bodies or wicks, this indicator is volume-aware. It lets you optionally pull volume from a lower timeframe feed (for example, using 1-minute data while watching a 5-minute chart) to build a much more accurate picture of how buyers and sellers actually traded inside the zone. This makes every block not just a price box, but a volume story: which side dominated, where, and by how much.
All order blocks printed by this indicator are confirmed: BOS and retests are evaluated strictly on closed candles. Nothing is drawn or alerted on partially formed bars, which helps avoid repaint-style flicker and keeps the signals clean and stable.
What is the theory behind the indicator?:
The core idea behind Order Blocks Volume Delta 3D is that not all price levels inside an order block are equal. Some prices are barely touched, while others act like magnets where candles repeatedly trade and heavy volume passes through.
The indicator first finds a swing high or swing low, waits for a clear Break of Structure (BOS), then scans the candles between the swing point and the BOS to find the price level that was touched the most. That level is treated as the POC.
From all candles in the swing-to-BOS range that interact with this POC band, the indicator looks for the earliest candle that is not already mitigated and uses that as the anchor candle for the order block:
The top of the block equals the anchor candle’s high (for a bearish OB) or the top of its wick zone.
The bottom equals the anchor candle’s low (for a bullish OB) or the bottom of its wick zone.
This “earliest valid POC-touching candle” rule makes it easier to visualize how price and volume developed from the very start of a meaningful zone, while ignoring POC touches that are already fully mitigated by the time the structure is confirmed. On top of that, each candle is split into bullish and bearish volume. If you choose a lower timeframe volume input, the tool aggregates lower timeframe candles into your chart timeframe, giving a more granular bull-versus-bear breakdown for each bar. The result is
an order block that not only shows where price moved but also which side pushed it, how aggressively, and how that balance shifted over time.
ORDER BLOCKS VOLUME DELTA 3D FEATURES:
The Order Blocks Volume Delta 3D indicator includes 4 main features:
1. Order Blocks
2. Volume Delta
3. 3D Visualization
4. Alerts
ORDER BLOCKS:
🔹What is an Order Block
An order block is a price zone where a clear displacement move began after liquidity was taken. It usually forms around the last consolidation or cluster of candles before price breaks structure with a strong move.
In this indicator, order blocks are defined as structured zones that:
Begin at the earliest unmitigated candle that interacted with the most-touched price level (POC) between swing and BOS.
Extend through the full wick range of that anchor candle.
Stretch forward in time, tracking how price continues to trade through, respect, retest, or invalidate the zone.
Are only printed once the BOS is fully confirmed on closed candles (confirmed order blocks only).
Example of bullish and bearish order blocks anchored at the earliest unmitigated candle in the POC zone:
🔹How are Order Blocks detected
The indicator uses a step-by-step, rules-based process to detect bullish and bearish order blocks. The logic is designed to match discretionary Smart Money concepts but with strict, repeatable rules.
Step 1: Detect swing highs and swing lows
Swing High: a candle whose high is higher than the highs of surrounding candles.
Swing Low: a candle whose low is lower than the lows of surrounding candles.
The Swing Length input controls how many candles are checked to the left and right.
Example of swing high and swing low detection:
Step 2: Confirm Break of Structure (BOS)
Once a swing is confirmed, the indicator waits for price to break past that swing:
Bullish BOS: price closes above a previous swing high.
Bearish BOS: price closes below a previous swing low.
To avoid “live” flicker, BOS logic is evaluated based on the previous closed candle. The order block is only confirmed once the BOS candle has fully closed and the next bar has opened. This is one of the reasons the script only shows confirmed, non-repainting order blocks.
Example of bullish BOS and bearish BOS:
Step 3: Build the POC range between swing and BOS
Between the swing candle and the BOS candle, the indicator:
Scans all candles in that range.
Tracks every price level touched using binning (POC bins).
Counts how many times each price band was touched by candle wicks.
The bin with the highest touch count becomes the POC band. This is where price traded most often, not necessarily where volume was highest.
Example of the POC band between swing and BOS.
Step 4 – Anchor the order block to the earliest valid POC candle
From all candles in the swing-to-BOS range, the indicator finds the earliest candle whose high/low overlaps the POC band and whose zone is not already mitigated. That candle becomes the anchor candle for the order block:
For a bearish OB, the block spans the anchor candle’s full wick range, with its top at the high.
For a bullish OB, the block spans the anchor candle’s full wick range, with its bottom at the low.
By requiring the anchor to be the earliest unmitigated interaction with POC, the script avoids building blocks from price action that has already been fully traded through and is less relevant.
Step 5: Extend and manage the order block
Once created, the block:
Extends to the right by a configurable number of candles (Extend Zones).
Continues until it is invalidated by wick or close, depending on the chosen method.
Can show retest labels when price revisits the zone after creation.
Is included or excluded from display depending on the Show Nearest and Hide Invalidated Zones settings.
Example of active and invalidated OB.
🔹Order Block Settings
◇ Swing Length
Swing Length controls how sensitive swing highs and lows are.
Lower Swing Length: Swings form more frequently, which leads to more frequent BOS events and order block formations.
Higher Swing Length: Only larger, more meaningful swings are detected, which leads to less frequent BOS events and less order block formations.
◇ Invalidation
Invalidation determines how an order block is considered “mitigated” or no longer valid.
Wick: For bullish OBs, if price wicks completely through the bottom of the zone, the order block is invalidated. For bearish OBs, if price wicks completely through the top, the order block is invalidated.
Close: For bullish OBs, the block is invalidated only when a candle closes below the bottom. For bearish OBs, it is invalidated only when a candle closes above the top.
Example of wick invalidation:
Example of close invalidation:
◇ Show Nearest
Show Nearest limits how many active order blocks are displayed based on proximity to current price. For example, a value of 2 will display only the two nearest bullish order blocks and two nearest bearish order blocks.
Chart with Show Nearest set to 3:
◇ Extend Zones
Extend Zones define how many candles forward each order block should project beyond the right most candle on the chart.
Chart with Extend Zones set to 10:
◇ Retest Labels
When enabled, the indicator prints labels on every clean retest of an active order block, as long as that block remains valid. Key points:
A retest label is only printed once the retest candle has fully closed – you always see confirmed retests, not intrabar tests.
Retest labels are positioned on the actual retest candle so you can visually see which bar interacted with the zone.
In addition, if multiple retests occur in quick succession, the indicator applies a built-in three-candle buffer between retests. That means only the first valid retest within each three-bar window is labeled (and can trigger an alert), helping to reduce clutter while still highlighting meaningful interactions with the zone.
Example of retest labels on bullish and bearish order blocks.
◇ Hide Invalidated Zones
Hide Invalidated Zones controls whether mitigated/invalidated blocks stay drawn.
Enabled: Only currently valid, unmitigated order blocks are shown (subject to Show Nearest)
Disabled: Both active and invalidated order blocks are displayed.
VOLUME DELTA:
🔹What is Volume Delta
Volume delta measures the difference between buying and selling volume. Instead of only showing “how much volume traded”, it separates volume into bullish and bearish components.
In this indicator:
Bullish volume = volume from candles (or lower timeframe candles) that closed higher.
Bearish volume = volume from candles that closed lower.
Delta % shows how dominant one side was compared to the total.
Example of bullish and bearish order blocks with volume delta and total volume.
🔹How is Volume Delta calculated?
The indicator uses a flexible, timeframe-aware volume engine.
1. Choose a Volume Delta Timeframe.
If the selected timeframe is equal to or higher than the chart timeframe, the indicator simply uses chart-volume per candle.
If the selected timeframe is lower than the chart timeframe (for example, 1‑minute volume on a 5‑minute chart), the indicator pulls all lower timeframe candles for each chart bar and sums them.
2. Split each bar into bull and bear volume.
For each contributing candle:
If close > open → its volume is added to bullish volume.
If close < open → its volume is added to bearish volume.
If close == open → its volume is split evenly between bullish and bearish.
3. Aggregate for each order block.
For each order block:
The indicator loops once from the swing candle to the BOS candle.
It records every candle that touches the POC band.
For each touching candle, it adds its bull and bear volumes (either directly from chart candles or from aggregated lower timeframe candles).
Total volume = bullish volume + bearish volume
Delta % = (bullish volume or bearish volume / total volume ) * 100, depending on which side is dominant.
🔹Volume Delta Settings:
◇ Display Style
Display Style controls how the volume delta is drawn inside each order block:
Horizontal:
Bullish and bearish fills extend horizontally from left to right.
The filled strip sits along the base of the block, with a bull vs bear gradient.
Vertical:
Bullish and bearish fills stretch vertically inside the zone.
The bullish percentage controls how much of the block is filled with the “dominant” color.
Example of Horizontal display style.
Example of Vertical display style.
◇ Volume Delta Timeframe
Volume Delta Timeframe tells the indicator whether to use chart volume or lower timeframe volume. When set to a lower timeframe, the indicator aggregates all lower timeframe candles that fall inside each chart bar, splitting their volume into bullish and bearish components before summing.
Using a lower timeframe:
Increases precision for how volume truly behaved inside each bar.
Helps reveal hidden absorption and aggressive flows that a higher timeframe candle might hide.
Example of volume delta based on chart timeframe.
Example of volume delta based on lower timeframe than chart(same OB as above)
◇ Display Total Volume
When enabled, the indicator prints the total volume for each order block as a label positioned inside the zone, near the bottom-right corner. This total is the sum of bullish and bearish volume used in the delta calculation and gives you a quick sense of how “heavy” the trading was in that block compared to others.
Example of total volume label inside multiple order blocks.
◇ Show Delta %
Show Delta % draws a small text label on the strip of the block that displays the dominant side’s percentage. For example, a bullish block might show “72%” if 72% of all volume inside that POC band came from bullish volume.
Example of Delta %:
3D VISUALIZATION:
The 3D Visualization feature turns each order block into a 3D plot.
🔹What the 3D Visualization does:
Wraps the order block with side faces and a top face to create a 3D bar effect.
Uses delta percentages to tilt the top face toward the dominant side.
Projects blocks into the future using Extend Zones, making the 3D blocks visually stand out.
🔹How it works:
The front face of the OB shows the standard 2D zone.
The side face extends forward in time based on the 3D depth setting.
The top face is angled depending on the Display Style and bull vs bear delta, making strong bullish blocks “rise” and strong bearish blocks “sink”.
🔹How the 3D depth setting affects visuals
Lower 3D depth:
Shorter side faces.
Subtle 3D effect.
Higher 3D depth:
Longer side faces projecting further into the future.
Stronger 3D effect that visually highlights key zones.
Example of lower 3D depth:
Example of higher 3D depth:
ALERTS:
The indicator supports alert conditions through TradingView’s AnyAlert() engine, allowing you to set alerts for the following:
New Bullish Order Block formed
New Bearish Order Block formed
Bullish OB Retest
Bearish OB Retest
Important alert behavior:
Order block alerts only fire when a new block is confirmed (after BOS closes and the next bar opens).
Retest alerts only fire when a retest candle has completely finished, matching the behavior of the visual retest labels.
IMPORTANT NOTES:
3D faces for order blocks are built using polylines. In some situations, especially when an order block’s starting point (its left edge) is beyond the chart’s left-most visible bar, the top 3D face may appear slightly irregular, skewed, or incomplete. This is purely a drawing limitation related to how the chart engine handles off-screen polyline points. Once the starting point of that order block comes into view (by zooming out or scrolling back), the 3D top face corrects itself and the visual becomes fully consistent. This issue affects only the 3D top face drawing, not the actual order-block box itself. The underlying zone, prices, and volume calculations remain accurate at all times.
If all conditions are met to create a new order block but the resulting zone would overlap an existing active order block, the new block is intentionally not created. A built-in guard prevents overlapping active zones to keep the structure clean and easier to interpret.
3D face drawing is implemented using an adaptive polyline method, which can be relatively calculation-heavy on certain symbols, timeframes, or chart histories. In some cases this may lead to calculation timeout error from TradingView.
UNIQUENESS:
This indicator is unique because it:
Anchors each order block to the earliest unmitigated candle that traded through the most-touched POC band between swing and BOS, rather than a generic “last up/down candle” or a random volume spike.
Builds a dedicated volume engine that can pull either chart timeframe volume or aggregated lower timeframe volume, then splits it into bull and bear components.
Adds 3D visualization on top of standard zones, turning each OB into a visually weighted slab rather than a flat rectangle.
Provides clean toggles (Show Nearest, Hide Invalidated Zones, Extend Zones, Display Style, Delta %, and total volume labels) so you can dial the indicator from extremely minimal to fully detailed, depending on your trading workflow.
Combined, these features make the indicator not just an order block plotter, but a complete volume‑informed structure tool tailored for traders who want to see where price actually traded and whether bulls or bears truly controlled the move inside each order block.
High/Low Location Frequency [LuxAlgo]The High/Low Location Frequency tool provides users with probabilities of tops and bottoms at user-defined periods, along with advanced filters that offer deep and objective market information about the likelihood of a top or bottom in the market.
🔶 USAGE
There are four different time periods that traders can select for analysis of probabilities:
HOUR OF DAY: Probability of occurrence of top and bottom prices for each hour of the day
DAY OF WEEK: Probability of occurrence of top and bottom prices for each day of the week
DAY OF MONTH: Probability of occurrence of top and bottom prices for each day of the month
MONTH OF YEAR: Probability of occurrence of top and bottom prices for each month
The data is displayed as a dashboard, which users can position according to their preferences. The dashboard includes useful information in the header, such as the number of periods and the date from which the data is gathered. Additionally, users can enable active filters to customize their view. The probabilities are displayed in one, two, or three columns, depending on the number of elements.
🔹 Advanced Filters
Advanced Filters allow traders to exclude specific data from the results. They can choose to use none or all filters simultaneously, inputting a list of numbers separated by spaces or commas. However, it is not possible to use both separators on the same filter.
The tool is equipped with five advanced filters:
HOURS OF DAY: The permitted range is from 0 to 23.
DAYS OF WEEK: The permitted range is from 1 to 7.
DAYS OF MONTH: The permitted range is from 1 to 31.
MONTHS: The permitted range is from 1 to 12.
YEARS: The permitted range is from 1000 to 2999.
It should be noted that the DAYS OF WEEK advanced filter has been designed for use with tickers that trade every day, such as those trading in the crypto market. In such cases, the numbers displayed will range from 1 (Sunday) to 7 (Saturday). Conversely, for tickers that do not trade over the weekend, the numbers will range from 1 (Monday) to 5 (Friday).
To illustrate the application of this filter, we will exclude results for Mondays and Tuesdays, the first five days of each month, January and February, and the years 2020, 2021, and 2022. Let us review the results:
DAYS OF WEEK: `2,3` or `2 3` (for crypto) or `1,2` or `1 2` (for the rest)
DAYS OF MONTH: `1,2,3,4,5` or `1 2 3 4 5`
MONTHS: `1,2` or `1 2`
YEARS: `2020,2021,2022` or `2020 2021 2022`
🔹 High Probability Lines
The tool enables traders to identify the next period with the highest probability of a top (red) and/or bottom (green) on the chart, marked with two horizontal lines indicating the location of these periods.
🔹 Top/Bottom Labels and Periods Highlight
The tool is capable of indicating on the chart the upper and lower limits of each selected period, as well as the commencement of each new period, thus providing traders with a convenient reference point.
🔶 SETTINGS
Period: Select how many bars (hours, days, or months) will be used to gather data from, max value as default.
Execution Window: Select how many bars (hours, days, or months) will be used to gather data from
🔹 Advanced Filters
Hours of day: Filter which hours of the day are excluded from the data, it accepts a list of hours from 0 to 23 separated by commas or spaces, users can not mix commas or spaces as a separator, must choose one
Days of week: Filter which days of the week are excluded from the data, it accepts a list of days from 1 to 5 for tickers not trading weekends, or from 1 to 7 for tickers trading all week, users can choose between commas or spaces as a separator, but can not mix them on the same filter.
Days of month: Filter which days of the month are excluded from the data, it accepts a list of days from 1 to 31, users can choose between commas or spaces as separator, but can not mix them on the same filter.
Months: Filter months to exclude from data. Accepts months from 1 to 12. Choose one separator: comma or space.
Years: Filter years to exclude from data. Accepts years from 1000 to 2999. Choose one separator: comma or space.
🔹 Dashboard
Dashboard Location: Select both the vertical and horizontal parameters for the desired location of the dashboard.
Dashboard Size: Select size for dashboard.
🔹 Style
High Probability Top Line: Enable/disable `High Probability Top` vertical line and choose color
High Probability Bottom Line: Enable/disable `High Probability Bottom` vertical line and choose color
Top Label: Enable/disable period top labels, choose color and size.
Bottom Label: Enable/disable period bottom labels, choose color and size.
Highlight Period Changes: Enable/disable vertical highlight at start of period
Williams Vix Fix BB + RVI & Squeeze (Keltner) filtered BBW + %BLegend:
- When line touches or crosses red band it is Top signal (Williams Vix Fix)
- When line touches or crosses blue band it is Bottom signal (Williams Vix Fix)
- Red dot at the top of indicator is a Top signal (Relative Volatility Index)
- Blue dot at the top of indicator is a Bottom signal (Relative Volatility Index)
- Gray dot at the bottom of indicator is a Keltner Squeeze signal (filtered by either BBW or %B)
- Silver dot at the bottom of indicator is a weaker Keltner Squeeze signal (Doesn't meet either BBW or %B filter)
- Purple is a 'Half Squeeze' only 1 Bollinger Band crossed the Keltner Channel
This is an attempt to make use of the main features of all 6 of these Volatility tools :
- Williams Vix Fix + Bollinger Bands
- Relative Volatility Index (RVI)
- The crossing of Keltner Channel by the Bollinger Bands (Squeeze)
Conditions to Help Filter Keltner Squeeze:
- When the Bollinger Bands Width (BBW) value is lower than the lowest value within a period plus a margin of error (percentage)
- When the %B value reaches the alert level detailed in LazyBears indicator. ()
If it meets one of these 2 filters and there is a Keltner Channel Squeeze than gray color or else if the squeeze doesn’t meet one of the 2 filters than silver color (weaker Squeeze).
The goal is to find the best tool to find bottoms and top relative to volatility and filter the squeeze.
The idea is that both Williams Vix Fix + Bollinger Bands and Relative Volatility Index both already give the main volatility bottom and top so combining them to compare and validate the signals makes sense. (Note: Bottom signal is more accurate than top). In addition, I added the squeeze to show the potential breakout pressure and to compliment bottom and top signals.
For ideas on how to continue this work :
I encourage ideas to combine the Williams Vix Fix and Relative Volatility Index for volatility top and bottom (with probability would be awesome)
And I encourage ideas to filter Keltner Channel Volatility Squeeze using both the BBW or %B or other volatility squeeze indicators or a combination of all of them.
Also, I encourage people to post their top parameters for the BBW and %B to filter the Keltner Squeeze in the comments or to send me them by chat relative to this indicator.
Half the battle is making the indicator, while the other half is tuning the parameters.
The current parameters are one of the least aggressive, and act as a mild filter.
Note: You can also change the threshold for RVI top and bottom.
And this work builds on my last indicator:
If you have ideas on this work or have ideas on potential combinations please message me, I always want to learn or get perspective on how it can be improved.
Sharing is how we get better (Parameter tuning, ideas, discussion)
I don’t reinvent the wheel, just trying to make the wheel better.
Momentum Day Trading ToolkitMomentum Day Trading Toolkit
Complete User Guide
Table of Contents
Overview
Quick Start
The Dashboard
Module 1: 5 Pillars Screener
Module 2: Gap & Go
Module 3: Bull Flag / Flat Top
Module 4: Float Rotation
Module 5: R2G / G2R
Module 6: Micro Pullback
Signal Reference
Quality Score
Settings Guide
Alerts Setup
Trading Workflows
Troubleshooting
Overview
The Momentum Day Trading Toolkit combines 6 powerful indicators into one unified system for day trading momentum stocks.
ModulePurpose① 5 PillarsConfirms stock is "in play"② Gap & GoPre-market levels & gap analysis③ Bull Flag / Flat TopClassic breakout patterns④ Float RotationMeasures true interest level⑤ R2G / G2RTracks prior close crosses⑥ Micro PullbackPrecision continuation entries
All modules work together - the dashboard shows you everything at a glance, and you can enable/disable any module you don't need.
Quick Start
Step 1: Add to Chart
Add the indicator to any stock chart
Recommended timeframes: 1-minute, 5-minute, or 15-minute
Step 2: Check the Dashboard (Top Right)
Look for:
Status = Current state (Scanning, Entry Signal, etc.)
Quality Score = Setup rating out of 10
Green checkmarks (✓) = Criteria passing
Step 3: Watch for Entry Signals
Triangles, circles, diamonds below bars = Entry signals
Arrows = R2G/G2R crosses
Step 4: Set Alerts
Right-click chart → Add Alert
Select "Momentum Day Trading Toolkit"
Choose your alert condition
The Dashboard
The dashboard in the top-right corner gives you instant analysis:
┌─────────────────────────────┐
│ MOMENTUM TOOLKIT │
├─────────────────────────────┤
│ Status │ 🎯 ENTRY SIGNAL │
│ Day │ 🟢 GREEN │
│ Gap │ +8.5% 🔥 │
│ RVol │ 3.2x ✓ │
│ Rotation │ 1.45x 🔥 │
│ Float │ 5.2M 🔥 │
│ Change │ +12.3% ✓ │
│ Pattern │ BULL FLAG! │
│ EMA 9/20 │ Above Both ✓ │
│ VWAP │ Above ✓ │
│ Prior Cl │ 5.91 │
│ PM High │ 9.11 ✓ │
│ Price │ 9.46 ✓ │
└─────────────────────────────┘
Dashboard Row Reference
RowWhat It ShowsGood ValuesStatusCurrent state🎯 ENTRY SIGNALDayGreen/Red vs prior close🟢 GREENGapGap % from prior close🔥 (5%+) or 🔥🔥 (10%+)RVolRelative volume✓ (2x+) or ✓✓ (5x+)RotationFloat rotation🔥 (1x) or 🔥🔥 (2x+)FloatFloat in millions🔥 (<5M) or Low (<10M)ChangeDaily % change✓ (meets minimum)PatternPattern statusBREAKOUT!EMA 9/20Trend positionAbove Both ✓VWAPVWAP positionAbove ✓Prior CloseKey R2G levelReference pricePM HighPre-market high✓ = Above itPriceCurrent price✓ = In range
Status Messages
StatusMeaningActionScanning...Looking for setupsWait✅ ALL PILLARSStock qualifiesWatch for pattern⏳ PATTERN FORMINGSetup developingGet ready🎯 ENTRY SIGNALSignal triggeredExecute trade
Module 1: 5 Pillars Screener
What It Does
Confirms the stock meets basic criteria to be worth trading.
The 5 Pillars
PillarDefaultWhy It MattersRelative Volume2x+ (5x for "strong")Confirms unusual interestDaily Change5%+Stock is movingPrice Range$1-$20Sweet spot for momentumFloat Size<20M sharesLower float = bigger moves
Visual Indicator
Green background appears when ALL pillars pass
Dashboard Shows
Individual pillar status with ✓ checkmarks
Quality score includes pillar factors
Settings
SettingDefaultDescriptionMin RVol2.0xMinimum relative volumeStrong RVol5.0xVolume for full qualificationMin Change5%Minimum daily moveMin Price$1Minimum stock priceMax Price$20Maximum stock priceMax Float20MMaximum float size
Module 2: Gap & Go
What It Does
Analyzes pre-market gaps and displays key price levels.
Key Levels Displayed
LevelColorDescriptionPrior CloseOrangeYesterday's close - THE key levelPM HighGreenPre-market high - breakout levelPM LowRedPre-market low - support
Gap Classification
Gap SizeRatingMeaning5-9.9%🔥 QualifyingWorth watching10%+🔥🔥 StrongHigh priority
Entry Signal
Small green triangle = PM High Breakout
How to Trade
Stock gaps up in pre-market
Wait for market open
Look for break above PM High
Enter on breakout with stop below PM Low
Settings
SettingDefaultDescriptionMin Gap %5%Qualifying gap thresholdStrong Gap %10%Strong gap thresholdShow PM LevelsONDisplay PM high/low lines
Module 3: Bull Flag / Flat Top
What It Does
Detects classic continuation patterns and signals breakouts.
Bull Flag Pattern
▲ BREAKOUT (Entry Signal)
│
┌────┴────┐
│ Pullback │ ← 2-5 red candles
│ (flag) │ Max 50% retrace
└─────────┘
│
┌────┴────┐
│ Pole │ ← 3+ green candles
│ (move) │ Strong momentum
└─────────┘
Flat Top Pattern
═══════════════ Resistance (2+ touches)
│
▲ BREAKOUT above resistance
Entry Signals
SignalShapeColorPatternBull Flag Breakout▲ TriangleLimeFlag breaks upFlat Top Breakout◆ DiamondAquaResistance breaks
How to Trade Bull Flag
See 3+ green candles (the pole)
Price pulls back 2-5 red candles
Pullback stays above 50% of move
Enter on break above pullback high
Stop below pullback low
Settings
SettingDefaultDescriptionMin Pole Candles3Green candles neededMax Pullback5Max red candles allowedMax Retrace50%Max pullback depthFT Touches2Resistance touches neededFT Lookback10Bars to check for resistance
Module 4: Float Rotation
What It Does
Tracks how many times the entire float has traded hands today.
The Formula
Rotation = Cumulative Day Volume ÷ Float
Rotation Levels
RotationEmojiMeaning0.5x—Half float traded1.0x🔥FULL rotation - significant!2.0x🔥🔥Double rotation - extreme3.0x+🔥🔥🔥Triple rotation - rare event
Why It Matters
High rotation = Extreme interest
Everyone who owns shares has likely traded
Often precedes explosive moves
Shows "real" demand beyond just volume
Dashboard Shows
Current rotation level
Fire emojis for milestones
Settings
SettingDefaultDescriptionFloat SourceAutoAuto-detect or manualManual Float10MIf auto fails, use thisAlert Level1.0xAlert when rotation hits this
Module 5: R2G / G2R
What It Does
Tracks when price crosses the prior day's close - a key psychological level.
Red to Green (R2G) 🟢
Prior Close ─────────────────
↗ CROSS TO GREEN
↗
(opened red)
Stock opened below prior close (red)
Crosses above prior close (green)
BULLISH signal
Green to Red (G2R) 🔴
(opened green)
↘
↘ CROSS TO RED
Prior Close ─────────────────
Stock opened above prior close (green)
Crosses below prior close (red)
BEARISH signal
Entry Signals
SignalShapeColorMeaningR2G↑ ArrowLimeCrossed to greenG2R↓ ArrowRedCrossed to red
Why R2G Matters
Bears who shorted get squeezed
Creates FOMO buying
Prior close becomes support
Momentum often continues
Dashboard Shows
Current day status (🟢 GREEN / 🔴 RED)
Whether R2G or G2R occurred (R2G ✓ or G2R ✓)
Settings
SettingDefaultDescriptionRequire Opposite OpenONR2G needs red openShow Prior CloseONDisplay the line
Module 6: Micro Pullback
What It Does
Finds precision entries on brief 1-3 candle pullbacks after strong moves.
The Pattern
▲ ENTRY (break pullback high)
│
┌──┴───┐
│ 1-3 │ ← Micro pullback (brief!)
│ red │ Stop = low of this
└──────┘
│
┌──┴───┐
│ 3+ │ ← Strong move
│green │ Momentum building
└──────┘
Why Micro Pullbacks Work
Tight stop = Pullback low is close
Momentum intact = Only paused briefly
Early entry = Catch continuation early
Clear trigger = Break of pullback high
Entry Signal
SignalShapeColorMicro Pullback Entry● CircleYellow
How to Trade
See 3+ green candles (strong move)
1-3 red candles (brief pause)
Pullback stays above 50% retrace
Enter when green candle breaks pullback high
Stop at pullback low
Settings
SettingDefaultDescriptionMin Green Candles3Candles before pullbackMax Pullback3Max red candlesMax Retrace50%Max pullback depth
Signal Reference
All Entry Signals (Below Bar)
ShapeColorSignalModule▲ Large TriangleLimeBull Flag BreakoutPatterns◆ DiamondAquaFlat Top BreakoutPatterns● CircleYellowMicro Pullback EntryMicro PB▲ Small TriangleGreenPM High BreakoutGap & Go↑ ArrowLimeRed to GreenR2G/G2R
Warning Signals (Above Bar)
ShapeColorSignalModule↓ ArrowRedGreen to RedR2G/G2R
Optional Forming Signals (Disabled by Default)
ShapeColorSignal🚩 FlagFaded LimeBull Flag Forming● CircleFaded YellowMicro PB Forming
Enable "Show 'Forming' Markers" in settings to see these
Quality Score
The quality score (0-10) rates the overall setup strength.
Scoring Breakdown
FactorPointsRVol 5x++2RVol 2x++1Daily change 5%++1Low float (<20M)+1Strong gap (10%+)+2Qualifying gap (5%+)+1Rotation 1x++2Rotation 0.5x++1Above EMA 20+1
Score Interpretation
ScoreGradeAction8-10A+Best setups - full position6-7AGood setups - standard size4-5BAverage - reduced size0-3CWeak - skip or paper trade
Settings Guide
Module Toggles
Turn each module ON/OFF:
SettingDefaultDescription① 5 Pillars ScreenerONStock qualification② Gap & Go AnalysisONGap & level analysis③ Bull Flag / Flat TopONPattern detection④ Float RotationONRotation tracking⑤ R2G / G2R TrackerONPrior close crosses⑥ Micro PullbackONPullback entries
Visual Settings
SettingDefaultDescriptionShow DashboardONDisplay info tableTable SizeNormalSmall/Normal/LargeShow Entry SignalsONDisplay entry shapesShow 'Forming' MarkersOFFShow pattern formingShow Key LevelsONPrior close, PM levelsShow EMA 9/20ONTrend EMAsShow VWAPONVWAP line
Recommended Presets
Minimal (Clean Chart)
Show Dashboard: ON
Show Entry Signals: ON
Show 'Forming' Markers: OFF
Show Key Levels: OFF
Show EMA: OFF
Show VWAP: OFF
Standard (Balanced)
All defaults
Full Analysis
All settings ON
Alerts Setup
Available Alerts
AlertTriggerAny Bullish EntryAny entry signal firesBull Flag BreakoutBull flag breaks outFlat Top BreakoutFlat top breaks outMicro Pullback EntryMicro PB triggersPM High BreakoutBreaks above PM highRed to GreenR2G crossGreen to RedG2R crossFloat RotationHits rotation level5 Pillars PassAll pillars qualifyPattern FormingPattern starts formingHigh Quality EntryEntry with score 7+/10
How to Set Alerts
Right-click on chart
Select "Add Alert"
Condition: "Momentum Day Trading Toolkit"
Select alert type from dropdown
Set expiration and notifications
Click "Create"
Recommended Alerts
For Active Trading:
Any Bullish Entry
High Quality Entry
For Watchlist Monitoring:
5 Pillars Pass
Float Rotation
Trading Workflows
Workflow 1: Full Qualification
Step 1: 5 PILLARS
└─→ Wait for "✅ ALL PILLARS" status
Step 2: CHECK SETUP
└─→ Quality score 6+?
└─→ Above EMA and VWAP?
Step 3: WAIT FOR ENTRY
└─→ Bull Flag, Flat Top, or Micro PB signal
Step 4: EXECUTE
└─→ Enter on signal
└─→ Stop below pattern low
└─→ Target 2:1 minimum
Workflow 2: Gap & Go
Step 1: PRE-MARKET
└─→ Stock gaps 5%+ (shows in Gap row)
Step 2: MARKET OPEN
└─→ Note PM High level (green line)
Step 3: WAIT FOR BREAK
└─→ PM High Breakout signal (small triangle)
Step 4: CONFIRM
└─→ R2G if opened red (double confirmation)
└─→ RVol 2x+
Step 5: EXECUTE
└─→ Enter on PM High break
└─→ Stop below PM Low
Workflow 3: Micro Pullback Scalp
Step 1: FIND MOMENTUM
└─→ Stock moving, 3+ green candles
Step 2: WAIT FOR PAUSE
└─→ 1-3 red candles (brief pullback)
Step 3: ENTRY
└─→ Yellow circle signal appears
Step 4: QUICK TRADE
└─→ Enter at signal
└─→ Tight stop at pullback low
└─→ Quick target (1:1 to 2:1)
Troubleshooting
Q: Lines are moving/jumping on real-time chart?
A: This was fixed in latest version. Make sure you have the newest code. Lines now lock in place at market open.
Q: Too many signals, chart is cluttered?
A:
Turn off "Show 'Forming' Markers"
Disable modules you don't need
Use "Minimal" visual preset
Q: No signals appearing?
A:
Check if "Show Entry Signals" is ON
Make sure relevant module is enabled
Stock may not meet pattern criteria
Q: Dashboard shows wrong float?
A:
TradingView float data isn't available for all stocks
Switch Float Source to "Manual"
Enter correct float in millions
Q: PM High/Low not showing?
A:
Only appears during market hours
Needs pre-market data to calculate
Check if "Show Key Levels" is ON
Q: Quality score seems wrong?
A:
Score updates in real-time
Check individual factors in dashboard
RVol and rotation change throughout day
Q: Alert not triggering?
A:
Make sure alert is set on correct symbol
Check alert hasn't expired
Verify condition is set correctly
Quick Reference Card
Entry Signals
▲ Lime Triangle = Bull Flag Breakout
◆ Aqua Diamond = Flat Top Breakout
● Yellow Circle = Micro Pullback
▲ Green Triangle = PM High Break
↑ Lime Arrow = R2G (bullish)
↓ Red Arrow = G2R (bearish)
Dashboard Quick Read
🎯 = Entry signal active
✅ = All pillars pass
🟢 = Day is green
🔥 = Strong (gap/rotation)
✓ = Criteria met
✗ = Criteria failed
Quality Score
8-10 = A+ (Best)
6-7 = A (Good)
4-5 = B (Average)
0-3 = C (Weak)
Key Levels
Orange Line = Prior Close (R2G level)
Green Line = PM High (breakout level)
Red Line = PM Low (support)
Purple Line = VWAP
Yellow/Orange = EMA 9/20
Happy Trading! 🎯📈
For questions or issues, use TradingView's comment section on the indicator page.
Smart Money Volume Matrix [Ata]Smart Money Volume Matrix
The Smart Money Volume Matrix (SMV Matrix) is an advanced volume-spread analysis (VSA) dashboard and charting tool designed to identify significant market anomalies by analyzing the relationship between price extremes and volume flow.
Unlike traditional indicators that rely solely on moving averages or oscillators, this tool performs a "Snapshot Analysis" of a defined lookback period (default: 100 bars) to rank price action based on Order Flow Dominance. It isolates the Top 10 Highest and Lowest Close prices and scrutinizes the volume behind them to categorize market sentiment into four distinct phases: Distribution, No Demand, Absorption, and Exhaustion.
Core Logic & Methodology
The script operates on a Zero-Lag Snapshot Engine. It does not print historical signals bar-by-bar; instead, it evaluates the current market structure relative to the recent history (Lookback Period).
1. Ranking Engine: The script scans the lookback period to find the Top 10 Highest Closes and Top 10 Lowest Closes.
2. Volume Classification: For each ranked bar, it calculates the "Intrabar Buy/Sell Volume" (or approximates it using candle geometry if Intrabar data is unavailable).
3. Dominance Detection: It compares Buying Volume vs. Selling Volume to determine who is in control at critical price levels.
Signal Classifications (VSA Logic)
The indicator generates labels on the chart and updates the dashboard table based on the following logic:
1. At Price Tops (Resistance Areas):
- Distribution (Supply): High Price + High Total Volume + Sellers Dominant.
Interpretation: Indicates heavy institutional selling into rising prices. Often precedes a reversal.
- Buy Climax: High Price + High Total Volume + Buyers Dominant.
Interpretation: Extreme buying frenzy. While bullish, it often marks a "trap" or temporary top due to exhaustion.
- No Demand: High Price + Low Volume.
Interpretation: Prices drifted higher but lack institutional participation. A sign of weakness.
2. At Price Bottoms (Support Areas):
- Absorption: Low Price + High Total Volume + Buyers Dominant.
Interpretation: Institutional money is absorbing selling pressure (passive buying). A strong sign of accumulation.
- Panic Sell: Low Price + High Total Volume + Sellers Dominant.
Interpretation: Extreme fear. High volume at lows typically indicates capitulation and potential hands-changing.
- Exhaustion: Low Price + Low Volume.
Interpretation: Selling pressure has dried up. The market may float upward due to lack of sellers.
Key Features
- Dashboard Matrix Table:
Displays the exact Close Price, Buy/Sell Volume, and Market State (Group) for the Top 10 ranking bars.
Smart Footer: Automatically detects the active "Resistance Zone" (derived from G1 Distribution levels) and "Support Zone" (derived from G3 Absorption levels) and reports the current price status relative to these zones (e.g., "Testing Resistance", "Breakout", "At Support").
- Smart Zones (Auto S/R):
Automatically draws Support and Resistance boxes extending into the future based on the most significant volume clusters found in the rankings. Includes logic to detect "Flips" (e.g., when Support breaks, it is labeled as a flip to Resistance).
- Average Trend Channels:
Calculates a Linear Regression trend line based specifically on the coordinates of the Top 10 Highs and Top 10 Lows, providing a "Best Fit" channel for the current market structure.
- Visual Clarity:
Labels utilize a "Smart Stacking" algorithm to prevent overlap on the chart. Guide lines connect labels to their respective candles for precise identification.
Settings & Configuration
- Matrix Settings: Lookback Period (default 100 bars) and Top Rank Count.
- Volume Engine: Choose between "Intrabar (Precise)" for accurate order flow or "Geometry (Approx)" for standard volume estimation.
- Visuals: Toggle Table, Labels, Lines, Zones, and Trend Lines. Adjust transparency and font sizes.
IMPORTANT NOTE ON SNAPSHOT LOGIC
This indicator is designed as a Real-Time Dashboard. It continuously updates the "Top 10" list as new candles form. Therefore, a label that appears on a candle may disappear if that candle falls out of the Top 10 ranking or leaves the lookback window. This is intended behavior to ensure the chart always reflects the current most critical levels, rather than a historical record of past signals. It is best used for live market analysis rather than historical back testing.
Disclaimer: This tool is for educational and analytical purposes only. Volume analysis is subjective and should be used in conjunction with other methods of technical analysis.
Puell Multiple Variants [OperationHeadLessChicken]Overview
This script contains three different, but related indicators to visualise Bitcoin miner revenue.
The classical Puell Multiple : historically, it has been good at signaling Bitcoin cycle tops and bottoms, but due to the diminishing rewards miners get after each halving, it is not clear how you determine overvalued and undervalued territories on it. Here is how the other two modified versions come into play:
Halving-Corrected Puell Multiple : The idea is to multiply the miner revenue after each halving with a correction factor, so overvalued levels are made comparable by a horizontal line across cycles. After experimentation, this correction factor turned out to be around 1.63. This brings cycle tops close to each other, but we lose the ability to see undervalued territories as a horizontal region. The third variant aims to fix this:
Miner Revenue Relative Strength Index (Miner Revenue RSI) : It uses RSI to map miner revenue into the 0-100 range, making it easy to visualise over/undervalued territories. With correct parameter settings, it eliminates the diminishing nature of the original Puell Multiple, and shows both over- and undervalued revenues correctly.
Example usage
The goal is to determine cycle tops and bottoms. I recommend using it on high timeframes, like monthly or weekly . Lower than that, you will see a lot of noise, but it could still be used. Here I use monthly as the example.
The classical Puell Multiple is included for reference. It is calculated as Miner Revenue divided by the 365-day Moving Average of the Miner Revenue . As you can see in the picture below, it has been good at signaling tops at 1,3,5,7.
The problems:
- I have to switch the Puell Multiple to a logarithmic scale
- Still, I cannot use a horizontal oversold territory
- 5 didn't touch the trendline, despite being a cycle top
- 9 touched the trendline despite not being a cycle top
Halving-Corrected Puell Multiple (yellow): Multiplies the Puell Multiple by 1.63 (a number determined via experimentation) after each halving. In the picture below, you can see how the Classical (white) and Corrected (yellow) Puell Multiples compare:
Advantages:
- Now you can set a constant overvalued level (12.49 in my case)
- 1,3,7 are signaled correctly as cycle tops
- 9 is correctly not signaled as a cycle top
Caveats:
- Now you don't have bottom signals anymore
- 5 is still not signaled as cycle top
Let's see if we can further improve this:
Miner Revenue RSI (blue):
On the monthly, you can see that an RSI period of 6, an overvalued threshold of 90, and an undervalued threshold of 35 have given historically pretty good signals.
Advantages:
- Uses two simple and clear horizontal levels for undervalued and overvalued levels
- Signaling 1,3,5,7 correctly as cycle tops
- Correctly does not signal 9 as a cycle top
- Signaling 4,6,8 correctly as cycle bottoms
Caveats:
- Misses two as a cycle bottom, although it was a long time ago when the Bitcoin market was much less mature
- In the past, gave some early overvalued signals
Usage
Using the example above, you can apply these indicators to any timeframe you like and tweak their parameters to obtain signals for overvalued/undervalued BTC prices
You can show or hide any of the three indicators individually
Set overvalued/undervalued thresholds for each => the background will highlight in green (undervalued) or red (overvalued)
Set special parameters for the given indicators: correction factor for the Corrected Puell and RSI period for Revenue RSI
Show or hide halving events on the indicator panel
All parameters and colours are adjustable
Sector Rotation & Money Flow Dashboard📊 Overview
The Sector Rotation & Money Flow Dashboard is a comprehensive market analysis tool that tracks 39 major sector ETFs in real-time, providing institutional-grade insights into sector rotation, momentum shifts, and money flow patterns. This indicator helps traders identify which sectors are attracting capital, which are losing favor, and where the next opportunities might emerge.
Perfect for swing traders, position traders, and investors who want to stay ahead of sector rotation and ride the strongest trends while avoiding weak sectors.
🎯 What This Indicator Does
Tracks 39 Major Sectors: From technology to utilities, cryptocurrencies to commodities
Calculates Multiple Timeframes: 1-week, 1-month, 3-month, and 6-month performance
Advanced Momentum Metrics: Proprietary momentum score and acceleration calculations
Relative Strength Analysis: Compare sector performance against any benchmark index
Money Flow Signals: Visual indicators showing where institutional money is moving
Smart Filtering: Pre-built strategy filters for different trading styles
Trend Detection: Emoji-based visual system for quick trend identification
💡 Key Features
1. Performance Metrics
Multiple timeframe analysis (1W, 1M, 3M, 6M)
Month-over-month change tracking
Relative strength vs benchmark index
2. Advanced Analytics
Momentum Score: Weighted composite of recent performance
Acceleration: Rate of change in momentum (second derivative)
Money Flow Signals: IN/OUT/TURN/WATCH indicators
3. Strategy Preset Filters
🎯 Swing Trade: High momentum opportunities
📈 Trend Follow: Established uptrends
🔄 Mean Reversion: Oversold bounce candidates
💎 Value Hunt: Deep value opportunities
🚀 Breakout: Emerging strength
⚠️ Risk Off: Sectors to avoid
4. Customization
All 39 sector ETFs can be customized
Adjustable benchmark index
Flexible display options
Multiple sorting methods
📋 Settings Documentation
Display Settings
Show Table (Default: On)
Toggles the entire dashboard display
Table Position (Default: Middle Center)
Choose from 9 positions on your chart
Options: Top/Middle/Bottom × Left/Center/Right
Rows to Show (Default: 15)
Number of sectors displayed (5-40)
Useful for focusing on top/bottom performers
Sort By (Default: Momentum)
1M/3M/6M: Sort by specific timeframe performance
Momentum: Weighted recent performance score
Acceleration: Rate of momentum change
1M Change: Month-over-month improvement
RS: Relative strength vs benchmark
Flow: IN First: Prioritize sectors with inflows
Flow: TURN First: Focus on reversal candidates
Recovery Plays: Oversold sectors recovering
Oversold Bounce: Deepest declines with positive signs
Top Gainers/Losers 3M: Best/worst quarterly performers
Best Acc + Mom: Combined strength score
Worst Acc (Topping): Sectors losing momentum
Filter Settings
Strategy Preset Filter (Default: All)
All: No filtering
🎯 Swing Trade: Mom >5, Acc >2, Money flowing in
📈 Trend Follow: Positive 1M & 3M, RS >0
🔄 Mean Reversion: Oversold but improving
💎 Value Hunt: Down >10% with recovery signs
🚀 Breakout: Rapid momentum surge
⚠️ Risk Off: Declining or topping sectors
Custom Flow Filter: Use manual flow filter
Custom Flow Signal Filter (Default: All)
Only active when Strategy Preset = "Custom Flow Filter"
IN Only: Strong inflows
TURN Only: Reversal signals
WATCH Only: Recovery candidates
OUT Only: Outflow sectors
Active Flows Only: Any non-neutral signal
Hide Low Volume ETFs (Default: Off)
Filters out illiquid sectors (future enhancement)
Visual Settings
Show Trend Emojis (Default: On)
🚀 Breakout (Strong 1M + High Acceleration)
🔥 Hot Recovery (From -10% to positive)
💪 Steady Uptrend (All timeframes positive)
➡️ Sideways/Ranging
⚠️ Warning/Topping (Up >15%, now slowing)
📉 Falling (Negative + declining)
🔄 Bottoming (Improving from lows)
Compact Mode (Default: Off)
Removes decimals for cleaner display
Useful when showing many rows
Min Data Points Required (Default: 3)
Minimum data points needed to display a sector
Prevents showing sectors with insufficient data
Relative Strength Settings
RS Benchmark Index (Default: AMEX:SPY)
Index to compare all sectors against
Can use SPY, QQQ, IWM, or any other index
RS Period (Days) (Default: 21)
Lookback period for RS calculation
21 days = 1 month, 63 days = 3 months, etc.
Sector ETF Settings (Groups 1-39)
Each sector has two inputs:
Symbol: The ticker (e.g., "AMEX:XLF")
Name: Display name (e.g., "Financials")
All 39 sectors can be customized to track different ETFs or markets.
📈 Column Explanations
Sector: ETF name/description
1M%: 1-month (21-day) performance
3M%: 3-month (63-day) performance
6M%: 6-month (126-day) performance
Mom: Momentum score (weighted average, recent-biased)
Acc: Acceleration (momentum rate of change)
Δ1M: Month-over-month change
RS: Relative strength vs benchmark
Flow: Money flow signal
↗️ IN: Strong inflows
🔄 TURN: Potential reversal
👀 WATCH: Recovery candidate
↘️ OUT: Outflows
—: Neutral
🎮 Usage Tips
For Swing Traders (3-14 days)
Use "🎯 Swing Trade" filter
Sort by "Acceleration" or "Momentum"
Look for Flow = "IN" and Mom >10
Confirm with positive RS
For Position Traders (2-8 weeks)
Use "📈 Trend Follow" filter
Sort by "RS" or "Best Acc + Mom"
Focus on consistent green across timeframes
Ensure RS >3 for market leaders
For Value Investors
Use "💎 Value Hunt" filter
Sort by "Recovery Plays" or "Top Losers 3M"
Look for improving Δ1M
Check for "WATCH" or "TURN" signals
For Risk Management
Regularly check "⚠️ Risk Off" filter
Sort by "Worst Acc (Topping)"
Review holdings for ⚠️ warning emojis
Exit sectors showing "OUT" flow
Market Regime Recognition
Bull Market: Many sectors showing "IN" flow, positive RS
Bear Market: Widespread "OUT" flows, negative RS
Rotation: Mixed flows, some "IN" while others "OUT"
Recovery: Multiple "TURN" and "WATCH" signals
🔧 Pro Tips
Combine Filters + Sorting: Filter first to narrow candidates, then sort to prioritize
Multi-Timeframe Confirmation: Best setups show alignment across 1M, 3M, and momentum
RS is Key: Sectors outperforming SPY (RS >0) tend to continue outperforming
Acceleration Matters: Positive acceleration often precedes price breakouts
Flow Transitions: "WATCH" → "TURN" → "IN" progression identifies new trends early
Regular Scans:
Daily: Check "Acceleration" sort
Weekly: Review "1M Change"
Monthly: Analyze "RS" shifts
Divergence Signals:
Price up but Acceleration down = Potential top
Price down but Acceleration up = Potential bottom
Sector Pairs Trading: Long sectors with "IN" flow, short sectors with "OUT" flow
⚠️ Important Notes
This indicator makes 40 security requests (maximum allowed)
Best used on Daily timeframe
Data updates in real-time during market hours
Some ETFs may show "—" if data is unavailable
🎯 Common Strategies
"Follow the Flow"
Only trade sectors showing "IN" flow with positive RS
"Rotation Catcher"
Focus on "TURN" signals in sectors down >15% from highs
"Momentum Rider"
Trade top 3 sectors by Momentum score, exit when Acceleration turns negative
"Mean Reversion"
Buy sectors in bottom 20% by 3M performance when Δ1M improves
"Relative Strength Leader"
Maintain positions only in sectors with RS >5
Not financial advice - always do additional research
Log Regression OscillatorThe Log Regression Oscillator transforms the logarithmic regression curves into an easy-to-interpret oscillator that displays potential cycle tops/bottoms.
🔶 USAGE
Calculating the logarithmic regression of long-term swings can help show future tops/bottoms. The relationship between previous swing points is calculated and projected further. The calculated levels are directly associated with swing points, which means every swing point will change the calculation. Importantly, all levels will be updated through all bars when a new swing is detected.
The "Log Regression Oscillator" transforms the calculated levels, where the top level is regarded as 100 and the bottom level as 0. The price values are displayed in between and calculated as a ratio between the top and bottom, resulting in a clear view of where the price is situated.
The main picture contains the Logarithmic Regression Alternative on the chart to compare with this published script.
Included are the levels 30 and 70. In the example of Bitcoin, previous cycles showed a similar pattern: the bullish parabolic was halfway when the oscillator passed the 30-level, and the top was very near when passing the 70-level.
🔹 Proactive
A "Proactive" option is included, which ensures immediate calculations of tentative unconfirmed swings.
Instead of waiting 300 bars for confirmation, the "Proactive" mode will display a gray-white dot (not confirmed swing) and add the unconfirmed Swing value to the calculation.
The above example shows that the "Calculated Values" of the potential future top and bottom are adjusted, including the provisional swing.
When the swing is confirmed, the calculations are again adjusted, showing a red dot (confirmed top swing) or a green dot (confirmed bottom swing).
🔹 Dashboard
When less than two swings are available (top/bottom), this will be shown in the dashboard.
The user can lower the "Threshold" value or switch to a lower timeframe.
🔹 Notes
Logarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time, meaning some symbols/tickers will fit better than others.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Users have to check the validity of swings; for example, if the direction of swings is downwards, then the dataset is not fitted for logarithmic regression.
In the example above, the "Threshold" is lowered. However, the calculated levels are unreliable due to the swings, which do not fit the model well.
Here, the combination of downward bottom swings and price accelerates slower at first and faster recently, resulting in a non-fit for the logarithmic regression model.
Note the price value (white line) is bound to a limit of 150 (upwards) and -150 (down)
In short, logarithmic regression is best used when there are enough tops/bottoms, and all tops are around 100, and all bottoms around 0.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 DETAILS
In mathematics, the dot product or scalar product is an algebraic operation that takes two equal-length sequences of numbers (arrays) and returns a single number, the sum of the products of the corresponding entries of the two sequences of numbers.
The usual way is to loop through both arrays and sum the products.
In this case, the two arrays are transformed into a matrix, wherein in one matrix, a single column is filled with the first array values, and in the second matrix, a single row is filled with the second array values.
After this, the function matrix.mult() returns a new matrix resulting from the product between the matrices m1 and m2.
Then, the matrix.eigenvalues() function transforms this matrix into an array, where the array.sum() function finally returns the sum of the array's elements, which is the dot product.
dot(x, y)=>
if x.size() > 1 and y.size() > 1
m1 = matrix.new()
m2 = matrix.new()
m1.add_col(m1.columns(), y)
m2.add_row(m2.rows (), x)
m1.mult (m2)
.eigenvalues()
.sum()
🔶 SETTINGS
Threshold: Period used for the swing detection, with higher values returning longer-term Swing Levels.
Proactive: Tentative Swings are included with this setting enabled.
Style: Color Settings
Dashboard: Toggle, "Location" and "Text Size"
VolumeSpreadAnalysisLibrary "VolumeSpreadAnalysis"
A library for Volume Spread Analysis (VSA).
spread(_barIndex)
Calculates the spread of a bar.
Parameters:
_barIndex (int) : (int) The index of the bar.
Returns: (float) The spread of the bar.
volume(_barIndex)
Retrieves the volume of a bar.
Parameters:
_barIndex (int) : (int) The index of the bar.
Returns: (float) The volume of the bar.
body(_barIndex)
Calculates the body of a bar.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (float) The body size of the bar.
wickUpper(_barIndex)
Calculates the upper wick of a bar (upper shadow).
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (float) The upper wick size of the bar.
wickLower(_barIndex)
Calculates the lower wick of a bar (lower shadow).
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (float) The lower wick size of the bar.
calcForecastedSMA(_source, _length, _forecastedLevel)
Calculates the forecasted Simple Moving Average (SMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the SMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted SMA value.
calcForecastedEMA(_source, _length, _forecastedLevel)
Calculates the forecasted Exponential Moving Average (EMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the EMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted EMA value.
calcForecastedRMA(_source, _length, _forecastedLevel)
Calculates the forecasted Relative Moving Average (RMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the RMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted RMA value.
calcForecastedWMA(_source, _length, _forecastedLevel)
Calculates the forecasted Weighted Moving Average (WMA).
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the WMA.
_forecastedLevel (float) : (float) The forecasted level to include in the calculation.
Returns: (float) The forecasted WMA value.
calcElapsedTimePercent()
Calculates the elapsed time percent of the current bar.
Returns: (float) The elapsed time percent.
calcForecastedSpread(multiplierAtMidpoints, multiplierAtPeaks)
Calculates the forecasted spread using elapsed time and dynamic multipliers, handling spread's non-linear nature.
Parameters:
multiplierAtMidpoints (float) : (float) The multiplier value at midpoints.
multiplierAtPeaks (float) : (float) The multiplier value at peaks.
Returns: (float) The forecasted spread value.
calcForecastedVolume()
Calculates the forecasted volume using elapsed time, satisfying volume's linear nature.
Returns: (float) The forecasted volume value.
calcForecastedMA(_source, _length, _forecastedSource, _type)
Calculates the forecasted Moving Average (MA) based on the specified type.
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the MA.
_forecastedSource (float) : (float) The forecasted level to include in the calculation.
_type (simple string) : (string) The type of the MA ("SMA", "EMA", "SMMA (RMA)", "WMA").
Returns: (float) The forecasted MA value.
calcMA(_source, _length, _type)
Calculates the Moving Average (MA) based on the specified type.
Parameters:
_source (float) : (series float) Source data for calculation.
_length (simple int) : (int) The length of the MA.
_type (simple string) : (string) The type of the MA ("SMA", "EMA", "SMMA (RMA)", "WMA").
Returns: (float) The MA value.
bullBar(_barIndex)
Determines if the bar is bullish.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if the bar is bullish, otherwise false.
bearBar(_barIndex)
Determines if the bar is bearish.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if the bar is bearish, otherwise false.
breakout(_barIndex)
Determines if there is a breakout above the previous bar.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if there is a breakout, otherwise false.
breakdown(_barIndex)
Determines if there is a breakdown below the previous bar.
Parameters:
_barIndex (simple int) : (int) The index of the bar.
Returns: (bool) True if there is a breakdown, otherwise false.
rejectionWickUpper(_rejectionWick)
Determines if the upper wick is a rejection wick.
Parameters:
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if the upper wick is a rejection wick, otherwise false.
rejectionWickLower(_rejectionWick)
Determines if the lower wick is a rejection wick.
Parameters:
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if the lower wick is a rejection wick, otherwise false.
setupDataVolume(_data, _mult_Low, _mult_High, _mult_Ultra, _maLengthVolume, _maTypeVolume)
Sets up data for volume levels.
Parameters:
_data (map) : (map) The map to store the levels.
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthVolume (simple int) : (int) The length for MA.
_maTypeVolume (simple string) : (string) The type for MA.
Returns: (void) Nothing.
setupDataSpread(_data, _mult_Low, _mult_High, _mult_Ultra, _maLengthSpread, _maTypeSpread)
Sets up data for spread levels.
Parameters:
_data (map) : (map) The map to store the levels.
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthSpread (simple int) : (int) The length for MA.
_maTypeSpread (simple string) : (string) The type for MA.
Returns: (void) Nothing.
setupDataForecastVolume(_dataForecast, _mult_Low, _mult_High, _mult_Ultra, _maLengthVolume, _predictedLevelVolume, _maTypeVolume)
Sets up data for volume and spread levels for forecast.
Parameters:
_dataForecast (map)
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthVolume (simple int) : (int) The length for MA.
_predictedLevelVolume (float) : (float) The predicted level for MA.
_maTypeVolume (simple string) : (string) The type for MA.
Returns: (void) Nothing.
setupDataForecastSpread(_dataForecast, _mult_Low, _mult_High, _mult_Ultra, _maLengthSpread, _predictedLevelSpread, _maTypeSpread)
Sets up data for spread levels for forecast.
Parameters:
_dataForecast (map)
_mult_Low (simple float) : (float) The multiplier for low level.
_mult_High (simple float) : (float) The multiplier for high level.
_mult_Ultra (simple float) : (float) The multiplier for ultra level.
_maLengthSpread (simple int) : (int) The length for MA.
_predictedLevelSpread (float) : (float) The predicted level for MA.
_maTypeSpread (simple string) : (string) The type for MA.
Returns: (void) Nothing.
isVolumeLow(_data, _barIndex)
Determines if the volume is low.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is low, otherwise false.
isVolumeNormal(_data, _barIndex)
Determines if the volume is normal.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is normal, otherwise false.
isVolumeHigh(_data, _barIndex)
Determines if the volume is high.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is high, otherwise false.
isVolumeUltra(_data, _barIndex)
Determines if the volume is ultra.
Parameters:
_data (map) : (map) The data map with volume levels.
_barIndex (int)
Returns: (bool) True if the volume is ultra, otherwise false.
isSpreadLow(_data, _barIndex)
Determines if the spread is low.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is low, otherwise false.
isSpreadNormal(_data, _barIndex)
Determines if the spread is normal.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is normal, otherwise false.
isSpreadHigh(_data, _barIndex)
Determines if the spread is high.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is high, otherwise false.
isSpreadUltra(_data, _barIndex)
Determines if the spread is ultra.
Parameters:
_data (map) : (map) The data map with spread levels.
_barIndex (int)
Returns: (bool) True if the spread is ultra, otherwise false.
isVolumeText(_data)
Determines text string representing the volume area level.
Parameters:
_data (map) : (map) The data map with volume levels.
Returns: (string) Text string of Low, Normal, High, or Ultra.
isSpreadText(_data)
Determines text string representing the spread area level.
Parameters:
_data (map) : (map) The data map with spread levels.
Returns: (string) Text string of Low, Normal, High, or Ultra.
calcBarColor(_value, _level)
Calculates the color based level.
Parameters:
_value (float) : (float) The value to check.
_level (float) : (float) The value level for comparison.
Returns: (color) The color for the bar.
bullPinBar(_maxBodyPercent, _minWickPercent)
Determines if the bar is a bull pin bar.
Parameters:
_maxBodyPercent (simple float) : (float) The maximum body percentage.
_minWickPercent (simple float) : (float) The minimum wick percentage.
Returns: (bool) True if the bar is a bull pin bar, otherwise false.
bearPinBar(_maxBodyPercent, _minWickPercent)
Determines if the bar is a bear pin bar.
Parameters:
_maxBodyPercent (simple float) : (float) The maximum body percentage.
_minWickPercent (simple float) : (float) The minimum wick percentage.
Returns: (bool) True if the bar is a bear pin bar, otherwise false.
dojiBar(_maxBodyPercent)
Determines if the bar is a doji.
Parameters:
_maxBodyPercent (simple float) : (float) The maximum body percentage.
Returns: (bool) True if the bar is a doji, otherwise false.
spinningTopBar(_minWicksPercent, _emaLength)
Determines if the bar is a spinning top.
Parameters:
_minWicksPercent (simple float) : (float) The minimum wicks percentage.
_emaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if the bar is a spinning top, otherwise false.
highWaveBar(_minBodyPercent, _minWickPercent, _bars)
Determines if the bar is a high wave bar.
Parameters:
_minBodyPercent (simple float) : (float) The minimum body percentage.
_minWickPercent (simple float) : (float) The minimum wick percentage.
_bars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if the bar is a high wave bar, otherwise false.
consolidationBar(_data, _spread, _bars)
Determines if the bars are in consolidation.
Parameters:
_data (map) : (map) The data map with spread levels.
_spread (simple float) : (float) The spread percentage for comparison.
_bars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if the bars are in consolidation, otherwise false.
S_DownThrust(_data, _bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of strength (DownThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of strength (DownThrust), otherwise false.
S_SellingClimax(_data, _rejectionWick)
Determines if there is a sign of strength (Selling Climax).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if there is a sign of strength (Selling Climax), otherwise false.
S_NoEffortBearishResult()
Determines if there is a sign of strength (No Effort Bearish Result).
Returns: (bool) True if there is a sign of strength (No Effort Bearish Result), otherwise false.
S_BearishEffortNoResult()
Determines if there is a sign of strength (Bearish Effort No Result).
Returns: (bool) True if there is a sign of strength (Bearish Effort No Result), otherwise false.
S_InverseDownThrust(_data, _bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of strength (Inverse DownThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of strength (Inverse DownThrust), otherwise false.
S_FailedSellingClimax()
Determines if there is a sign of strength (Failed Selling Climax).
Returns: (bool) True if there is a sign of strength (Failed Selling Climax), otherwise false.
S_BullOutsideReversal(_data)
Determines if there is a sign of strength (Bull Outside Reversal).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of strength (Bull Outside Reversal), otherwise false.
S_EndOfFallingMarket(_data)
Determines if there is a sign of strength (End of Falling Market).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of strength (End of Falling Market), otherwise false.
S_PseudoDownThrust(_bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of strength (Pseudo DownThrust).
Parameters:
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of strength (Pseudo DownThrust), otherwise false.
S_NoSupply(_bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of strength (No Supply).
Parameters:
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of strength (No Supply), otherwise false.
W_UpThrust(_data, _bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of weakness (UpThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of weakness (UpThrust), otherwise false.
W_BuyingClimax(_data, _rejectionWick)
Determines if there is a sign of weakness (Buying Climax).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_rejectionWick (simple float) : (float) The rejection wick percentage.
Returns: (bool) True if there is a sign of weakness (Buying Climax), otherwise false.
W_NoEffortBullishResult()
Determines if there is a sign of weakness (No Effort Bullish Result).
Returns: (bool) True if there is a sign of weakness (No Effort Bullish Result), otherwise false.
W_BullishEffortNoResult()
Determines if there is a sign of weakness (Bullish Effort No Result).
Returns: (bool) True if there is a sign of weakness (Bullish Effort No Result), otherwise false.
W_InverseUpThrust(_data, _bullPinBarMaxBody, _bullPinBarMinWick)
Determines if there is a sign of weakness (Inverse UpThrust).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_bullPinBarMaxBody (simple float) : (float) The maximum body percentage for bull pin bar.
_bullPinBarMinWick (simple float) : (float) The minimum wick percentage for bull pin bar.
Returns: (bool) True if there is a sign of weakness (Inverse UpThrust), otherwise false.
W_FailedBuyingClimax()
Determines if there is a sign of weakness (Failed Buying Climax).
Returns: (bool) True if there is a sign of weakness (Failed Buying Climax), otherwise false.
W_BearOutsideReversal(_data)
Determines if there is a sign of weakness (Bear Outside Reversal).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of weakness (Bear Outside Reversal), otherwise false.
W_EndOfRisingMarket(_data)
Determines if there is a sign of weakness (End of Rising Market).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
Returns: (bool) True if there is a sign of weakness (End of Rising Market), otherwise false.
W_PseudoUpThrust(_bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of weakness (Pseudo UpThrust).
Parameters:
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of weakness (Pseudo UpThrust), otherwise false.
W_NoDemand(_bearPinBarMaxBody, _bearPinBarMinWick)
Determines if there is a sign of weakness (No Demand).
Parameters:
_bearPinBarMaxBody (simple float) : (float) The maximum body percentage for bear pin bar.
_bearPinBarMinWick (simple float) : (float) The minimum wick percentage for bear pin bar.
Returns: (bool) True if there is a sign of weakness (No Demand), otherwise false.
N_QuietDoji(_dojiBarMaxBody)
Determines if there is a neutral signal (Quiet Doji).
Parameters:
_dojiBarMaxBody (simple float) : (float) The maximum body percentage for doji bar.
Returns: (bool) True if there is a neutral signal (Quiet Doji), otherwise false.
N_BalancedDoji(_data, _dojiBarMaxBody)
Determines if there is a neutral signal (Balanced Doji).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_dojiBarMaxBody (simple float) : (float) The maximum body percentage for doji bar.
Returns: (bool) True if there is a neutral signal (Balanced Doji), otherwise false.
N_StrongDoji(_dojiBarMaxBody)
Determines if there is a neutral signal (Strong Doji).
Parameters:
_dojiBarMaxBody (simple float) : (float) The maximum body percentage for doji bar.
Returns: (bool) True if there is a neutral signal (Strong Doji), otherwise false.
N_QuietSpinningTop(_spinningTopBarMinWicks, _spinningTopBarEmaLength)
Determines if there is a neutral signal (Quiet Spinning Top).
Parameters:
_spinningTopBarMinWicks (simple float) : (float) The minimum wicks percentage for spinning top bar.
_spinningTopBarEmaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if there is a neutral signal (Quiet Spinning Top), otherwise false.
N_BalancedSpinningTop(_data, _spinningTopBarMinWicks, _spinningTopBarEmaLength)
Determines if there is a neutral signal (Balanced Spinning Top).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_spinningTopBarMinWicks (simple float) : (float) The minimum wicks percentage for spinning top bar.
_spinningTopBarEmaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if there is a neutral signal (Balanced Spinning Top), otherwise false.
N_StrongSpinningTop(_spinningTopBarMinWicks, _spinningTopBarEmaLength)
Determines if there is a neutral signal (Strong Spinning Top).
Parameters:
_spinningTopBarMinWicks (simple float) : (float) The minimum wicks percentage for spinning top bar.
_spinningTopBarEmaLength (simple int) : (int) The length for EMA calculation.
Returns: (bool) True if there is a neutral signal (Strong Spinning Top), otherwise false.
N_QuietHighWave(_highWaveBarMinBody, _highWaveBarMinWick, _highWaveBarBars)
Determines if there is a neutral signal (Quiet High Wave).
Parameters:
_highWaveBarMinBody (simple float) : (float) The minimum body percentage for high wave bar.
_highWaveBarMinWick (simple float) : (float) The minimum wick percentage for high wave bar.
_highWaveBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Quiet High Wave), otherwise false.
N_BalancedHighWave(_data, _highWaveBarMinBody, _highWaveBarMinWick, _highWaveBarBars)
Determines if there is a neutral signal (Balanced High Wave).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_highWaveBarMinBody (simple float) : (float) The minimum body percentage for high wave bar.
_highWaveBarMinWick (simple float) : (float) The minimum wick percentage for high wave bar.
_highWaveBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Balanced High Wave), otherwise false.
N_StrongHighWave(_highWaveBarMinBody, _highWaveBarMinWick, _highWaveBarBars)
Determines if there is a neutral signal (Strong High Wave).
Parameters:
_highWaveBarMinBody (simple float) : (float) The minimum body percentage for high wave bar.
_highWaveBarMinWick (simple float) : (float) The minimum wick percentage for high wave bar.
_highWaveBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Strong High Wave), otherwise false.
N_Consolidation(_data, _consolidationBarSpread, _consolidationBarBars)
Determines if there is a neutral signal (Consolidation).
Parameters:
_data (map) : (map) The data map with volume and spread levels.
_consolidationBarSpread (simple float) : (float) The spread percentage for consolidation bar.
_consolidationBarBars (simple int) : (int) The number of bars for comparison.
Returns: (bool) True if there is a neutral signal (Consolidation), otherwise false.
Gann Square of 144This indicator will create lines on the chart based on W.D. Gann's Square of 144. All the inputs will be detailed below
Why create this indicator?
I didn't find it on Tradingview (at least with open source). But the main reason is to study the strategy and be able to draw it fast. Manually drawing the square is not hard, but moving all together to the right spots and scale was time-consuming.
It has a lot of inputs...
Yes, each square point divisible by 6 has information with some options, so the user can create any configuration he wants. Also, it has the advantage of having the square built in seconds and adjusting itself on each new calculation.
About the inputs
Starting Date
This input will be used when the "Set Upper/Lower Prices and Start Bar Automatically" checkbox is not selected. The indicator will calculate all the line locations on the chart using the selected start date. When selecting this input, change the Manual Max and Min Prices to the better calculation
Manual Max/Min Price
This input will be used when the "Set Upper/Lower Prices and Start Bar Automatically" checkbox is not selected. The indicator will calculate all the line's locations on the chart using these prices
Set Upper/Lower Prices and Start Bar Automatically
Selects if the starting date will be automatically selected by the system or based on the input data. When it's set, the indicator will use the most recent bar as the middle point of the square, using the higher price as the Upper Price and the lowest price as the Lower Price in the latest 72 bars (or more based on the Candles Per Division parameter)
Update at a new bar
When this option is market, the indicator will update all created lines to match the new bar position, together with all the possible new Upper/Lower prices. Let it unchecked to watch the progression of the price while the square remains fixed in the chart.
Top X-Axis
When checked, it will display the labels on the Top of the square
Bottom X-Axis
When checked, it will display the labels on the Bottom of the square
Left X-Axis
When checked, it will display the labels on the left of the square
Right X-Axis
When checked, it will display the labels on the right of the square
Show Prices on the Right Y-Axis
When checked, it will display the prices together with the labels on the right of the square
Show Vertical Divisions
Show the lines that will divide the square into 9 equal parts
Show Extra Lines
Show unique lines that will come from the Top and bottom middle of the square, connecting the center to the 36 and 108 levels
Show Grid
When selected, it will display a grid in the square
Line Patterns
A selector with some options of built-in lines configuration. When any option besides None is selected, it will override the lines inputs below
Numbers Color
Select the color of each number on the Axis
Vertical Lines Color
Select the color of the vertical lines
Grid Color
Select the grid line color
Connections from corners to N
Each corner is represented by 2 characters, so they all fit in a single line
It will indicate where the line starts and where it ends
┏ ↓ = Top Left to Bottom
┏ → = Top Left to Right
┗ ↑ = Bottom Left to Top
┗ → = Bottom Left to Right
┓ ← = Top Right to Left
┓ ↓ = Top Right to Bottom
┛ ← = Bottom Right to Left
┛ ↑ = Bottom Right to Top
Besides selecting what line will be created, it's possible to select the color, the style, and the extension
How to use this indicator
When you dig into Gann's books for more information about the square of 144, you find that it was part of his setup with multiple indicators (technical and fundamental, and astrological). It is not a "one indicator" setup, so it's hard to say that you will find entries, exits, stop loss, and take profit in this. Still, it will help see trendiness, support, and resistance levels.
Mixing this with other indicators is probably a good idea, but some may find this indicator the only one needed.
Some aspects of the square
The end of the square is important, so where it starts is crucial. The end is important because it is where the price and time expire. The other parts of the square are defined based on their start and end, so placing them right is essential.
So, where to set the start of the square?
The last major low is the most indicated. The minimum price will be the lowest, and the max price will be the last major Top. Note that the indicator uses 1 candle on each point.
After finding the start, the minimum, and the maximum prices for the square, it will draw all lines. Another essential part of the square is The Midpoint.
The midpoint is the most crucial part of the square and is the best way to see if you positioned the square correctly. When the price is inside the square, using the starting candle as the start, a second higher low or a lower high occurs in that spot. When using the Vertical lines in the indicator, it's the middle square inside Gann's square.
The other divisions will be opposing each other most of the time. So if the price is rising in the 1/3 of the square, it's common to see the price fall in the 3/3 of the square.
More information about these aspects here
Considerations
This indicator was meant for price targets and a time calculator for possible support/resistances in the chart. It was created by William Delbert Gann and was part of his setup for trading almost a century ago. The lines will form geometric figures, which Gann used with high accuracy to predict tops/bottoms and when they would occur.
Murrey Math Lines with extreme compartor and pivot pointsI did combine 3 scripts into one that really shows a lot. This is to bring together other good ideas to show improve on others work. Special thanks to pipcharlie for the murrey math lines Mage3 for the pivot points and morpheous747 for the murrey extreme compartor.
// this script shows sveral things.
//1. Breakouts - multiple diamonds and price staying in the outer murrey bands is a strong trend. This will lead to a double top or bottom. You could also buy/sell
// the pullbacks and expext another diamond.
//2. Tops - 2 pivots in the top murrey bands first top must have a diamond second must be in the top murrey zone or a higher murrey outer band without a diamond.
// There also must have a dip between tops outside the top murrey zone.
//3. bottoms bounces - a quick pull back if there are 2 pivots close together and the second pivot is not a diamond.
//4. double bottoms - 2 pivots with the first pivot a diamond. The second is after a pullback out of the bottom murrey zone and a diamond.
//5. pullbacks - red and green lines. These are a little more benificial for tops/bottoms
//6. Overall trends - the triangles on top or bottoms show overall general trend. Also the murrey bands act like bollinger bands, once trend changes, expect targets
// to hit the opposite band.
//7. Pivots - adding pivots and extentions help determine trends, resistance levels and tops/bottoms\
//8. Volitility - the wider the murry bands the more movement is likely and ususally in trend with stronger pullbacks. The smaller the bands smaller moves and more
// up and down movement
Bitcoin Macro Trend IndicatorBitcoin Macro Trend Indicator: A Multi-Timeframe Confirmation System for Strategic Positioning
Introduction
The Bitcoin Macro Trend Indicator is a comprehensive technical analysis tool designed for cryptocurrency traders and investors seeking to navigate Bitcoin's volatile cycles. It integrates multiple exponential moving averages (EMAs) and MACD variations into a unified system that identifies long-term accumulation zones, bull market tops, and strategic re-entry points. This document outlines the logical foundation of this integration, explains the synergistic operation of its components, and provides practical guidance for its application.
Rationale for Multi-Indicator Integration
Bitcoin's market behavior exhibits distinct cyclical patterns characterized by extended accumulation periods, parabolic advances, and sharp corrections. Single indicators often generate false signals during volatile conditions. This system employs a layered confirmation approach where:
Ultra-long-term EMAs establish the primary trend context
Medium-term EMA crossovers identify trend transitions
Multiple MACD configurations detect momentum shifts across different time horizons
This multi-timeframe methodology reduces noise and increases signal reliability by requiring convergence across independent but complementary technical elements.
Component Synergy and Operational Mechanism
1. EMA Framework: The Trend Foundation
700-period EMA: Serves as the primary trend baseline. Prices below this line suggest long-term undervaluation (accumulation territory), while sustained positions above indicate established bull markets.
18/63-period EMA Pair: Functions as the core trend transition system. The golden cross (18 above 63) confirms bullish momentum, while the death cross signals potential trend exhaustion.
12/52-period EMA Pair: Specialized for identifying renewed momentum after corrections within ongoing trends, reducing premature re-entry during false recoveries.
12-period EMA (Auxiliary): Provides early warning of short-term trend deterioration that may precede larger corrections.
2. MACD Ensemble: Momentum Verification
Bottom MACD (168/364/6): With exceptionally slow parameters, this configuration filters out short-term noise to identify genuine long-term momentum shifts characteristic of market bottoms.
Top MACD (63/133/1): Optimized for detecting momentum divergence at potential market tops, where traditional MACD settings often lag.
Local Top Warning MACD (30/65/4): Balanced to capture intermediate-term momentum deterioration that frequently precedes significant pullbacks.
Early Bull MACD (9/19/6): Sensitive to initial momentum surges following accumulation periods, providing early confirmation of trend initiation.
3. Signal Hierarchy and Progressive Confirmation
The indicator employs a cascading confirmation logic:
Stage 1 (Accumulation): Requires both long-term MACD improvement AND price position below the 700-period EMA. Strong accumulation signals add Early Bull MACD confirmation.
Stage 2 (Warning): Local top warnings activate only when multiple conditions align: medium-term trend remains bullish, ultra-long-term trend confirms strength, AND specialized MACDs show momentum deterioration.
Stage 3 (Re-entry): Requires both EMA crossover confirmation AND momentum recovery in the warning MACD, reducing false continuation signals.
Stage 4 (Top Confirmation): The most stringent criteria, demanding convergence across long-term, medium-term, and momentum indicators before signaling major trend reversal.
Practical Application and Interpretation
Signal Classification System
Accumulation Zones (Green): Areas where long-term indicators suggest undervaluation. These represent strategic buying opportunities for patient investors.
Strong Accumulation Signals (Dark Green): Enhanced accumulation zones with additional momentum confirmation, suggesting higher-probability entry points.
Local Top Warnings (Orange/Red): Progressive warnings of increasing risk, with red zones indicating conditions historically associated with more severe corrections.
Re-entry Opportunities (Blue): Post-correction zones where momentum recovery aligns with trend resumption signals.
Bull Market Termination (Purple): Signals suggesting completion of major advance cycles, prompting defensive positioning.
Top Confirmation (Dark Red): High-confidence reversal signals with multi-timeframe confirmation.
Usage Guidelines
Timeframe Recommendation: Designed primarily for daily and weekly charts where macroeconomic trends are most evident.
Position Sizing: Accumulation signals support gradual position building, while warning signals suggest reducing exposure rather than immediate liquidation.
Corroboration: Although self-contained, the indicator performs best when combined with volume analysis and fundamental considerations.
Historical Validation: Users should review signal performance across multiple market cycles to understand characteristic behaviors.
Limitations and Considerations
No technical indicator predicts market movements with absolute certainty. This tool provides probabilistic assessments based on historical patterns.
Extraordinary market events or fundamental shifts may override technical signals.
The indicator's parameters, while optimized for Bitcoin's historical behavior, may require adjustment for unprecedented market conditions.
Signals should be interpreted in context of overall market structure and trader/investor time horizon.
Originality and Differentiation
This system represents a novel synthesis of established technical concepts through:
Parameter Optimization: Specific EMA and MACD periods calibrated to Bitcoin's characteristic volatility and cycle duration.
Conditional Layering: Unlike single-criterion systems, signals require convergence across independent technical dimensions.
Progressive Warning System: Multi-stage alerts that distinguish between routine corrections and potential trend reversals.
Macro-Micro Integration: Simultaneous analysis of ultra-long-term trends and short-term momentum shifts.
Empirical Foundation
The indicator's design incorporates observations from Bitcoin's market behavior since 2010, particularly:
The tendency for major bottoms to form during extended periods below long-term moving averages
Characteristic momentum patterns preceding significant tops
The predictive value of specific EMA relationships during different market phases
Historical performance of multi-timeframe confirmation versus single indicators
Conclusion
The Bitcoin Macro Trend Indicator provides a structured framework for identifying high-probability turning points in Bitcoin's market cycles. By integrating multiple technical perspectives into a confirmation hierarchy, it reduces reaction to market noise while maintaining sensitivity to genuine trend changes. Users should employ this tool as part of a comprehensive trading plan that includes risk management protocols and consideration of external market factors. The system's greatest utility emerges when understood not as a predictive oracle but as a systematic method for identifying favorable risk-reward scenarios based on multi-timeframe technical convergence.
BUZARA// © Buzzara
// =================================
// PLEASE SUPPORT THE TEAM
// =================================
//
// Telegram: t.me
// =================================
//@version=5
VERSION = ' Buzzara2.0'
strategy('ALGOX V6_1_24', shorttitle = '🚀〄 Buzzara2.0 〄🚀'+ VERSION, overlay = true, explicit_plot_zorder = true, pyramiding = 0, default_qty_type = strategy.percent_of_equity, initial_capital = 1000, default_qty_value = 1, calc_on_every_tick = false, process_orders_on_close = true)
G_SCRIPT01 = '■ ' + 'SAIYAN OCC'
//#region ———— <↓↓↓ G_SCRIPT01 ↓↓↓> {
// === INPUTS ===
res = input.timeframe('15', 'TIMEFRAME', group ="NON REPAINT")
useRes = input(true, 'Use Alternate Signals')
intRes = input(10, 'Multiplier for Alernate Signals')
basisType = input.string('ALMA', 'MA Type: ', options= )
basisLen = input.int(50, 'MA Period', minval=1)
offsetSigma = input.int(5, 'Offset for LSMA / Sigma for ALMA', minval=0)
offsetALMA = input.float(2, 'Offset for ALMA', minval=0, step=0.01)
scolor = input(false, 'Show coloured Bars to indicate Trend?')
delayOffset = input.int(0, 'Delay Open/Close MA', minval=0, step=1,
tooltip = 'Forces Non-Repainting')
tradeType = input.string('BOTH', 'What trades should be taken : ',
options = )
//=== /INPUTS ===
h = input(false, 'Signals for Heikin Ashi Candles')
//INDICATOR SETTINGS
swing_length = input.int(10, 'Swing High/Low Length', group = 'Settings', minval = 1, maxval = 50)
history_of_demand_to_keep = input.int(20, 'History To Keep', minval = 5, maxval = 50)
box_width = input.float(2.5, 'Supply/Demand Box Width', group = 'Settings', minval = 1, maxval = 10, step = 0.5)
//INDICATOR VISUAL SETTINGS
show_zigzag = input.bool(false, 'Show Zig Zag', group = 'Visual Settings', inline = '1')
show_price_action_labels = input.bool(false, 'Show Price Action Labels', group = 'Visual Settings', inline = '2')
supply_color = input.color(#00000000, 'Supply', group = 'Visual Settings', inline = '3')
supply_outline_color = input.color(#00000000, 'Outline', group = 'Visual Settings', inline = '3')
demand_color = input.color(#00000000, 'Demand', group = 'Visual Settings', inline = '4')
demand_outline_color = input.color(#00000000, 'Outline', group = 'Visual Settings', inline = '4')
bos_label_color = input.color(#00000000, 'BOS Label', group = 'Visual Settings', inline = '5')
poi_label_color = input.color(#00000000, 'POI Label', group = 'Visual Settings', inline = '7')
poi_border_color = input.color(#00000000, 'POI border', group = 'Visual Settings', inline = '7')
swing_type_color = input.color(#00000000, 'Price Action Label', group = 'Visual Settings', inline = '8')
zigzag_color = input.color(#00000000, 'Zig Zag', group = 'Visual Settings', inline = '9')
//END SETTINGS
// FUNCTION TO ADD NEW AND REMOVE LAST IN ARRAY
f_array_add_pop(array, new_value_to_add) =>
array.unshift(array, new_value_to_add)
array.pop(array)
// FUNCTION SWING H & L LABELS
f_sh_sl_labels(array, swing_type) =>
var string label_text = na
if swing_type == 1
if array.get(array, 0) >= array.get(array, 1)
label_text := 'HH'
else
label_text := 'LH'
label.new(
bar_index - swing_length,
array.get(array,0),
text = label_text,
style = label.style_label_down,
textcolor = swing_type_color,
color = swing_type_color,
size = size.tiny)
else if swing_type == -1
if array.get(array, 0) >= array.get(array, 1)
label_text := 'HL'
else
label_text := 'LL'
label.new(
bar_index - swing_length,
array.get(array,0),
text = label_text,
style = label.style_label_up,
textcolor = swing_type_color,
color = swing_type_color,
size = size.tiny)
// FUNCTION MAKE SURE SUPPLY ISNT OVERLAPPING
f_check_overlapping(new_poi, box_array, atrValue) =>
atr_threshold = atrValue * 2
okay_to_draw = true
for i = 0 to array.size(box_array) - 1
top = box.get_top(array.get(box_array, i))
bottom = box.get_bottom(array.get(box_array, i))
poi = (top + bottom) / 2
upper_boundary = poi + atr_threshold
lower_boundary = poi - atr_threshold
if new_poi >= lower_boundary and new_poi <= upper_boundary
okay_to_draw := false
break
else
okay_to_draw := true
okay_to_draw
// FUNCTION TO DRAW SUPPLY OR DEMAND ZONE
f_supply_demand(value_array, bn_array, box_array, label_array, box_type, atrValue) =>
atr_buffer = atrValue * (box_width / 10)
box_left = array.get(bn_array, 0)
box_right = bar_index
var float box_top = 0.00
var float box_bottom = 0.00
var float poi = 0.00
if box_type == 1
box_top := array.get(value_array, 0)
box_bottom := box_top - atr_buffer
poi := (box_top + box_bottom) / 2
else if box_type == -1
box_bottom := array.get(value_array, 0)
box_top := box_bottom + atr_buffer
poi := (box_top + box_bottom) / 2
okay_to_draw = f_check_overlapping(poi, box_array, atrValue)
// okay_to_draw = true
//delete oldest box, and then create a new box and add it to the array
if box_type == 1 and okay_to_draw
box.delete( array.get(box_array, array.size(box_array) - 1) )
f_array_add_pop(box_array, box.new( left = box_left, top = box_top, right = box_right, bottom = box_bottom, border_color = supply_outline_color,
bgcolor = supply_color, extend = extend.right, text = 'SUPPLY', text_halign = text.align_center, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
box.delete( array.get(label_array, array.size(label_array) - 1) )
f_array_add_pop(label_array, box.new( left = box_left, top = poi, right = box_right, bottom = poi, border_color = poi_border_color,
bgcolor = poi_border_color, extend = extend.right, text = 'POI', text_halign = text.align_left, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
else if box_type == -1 and okay_to_draw
box.delete( array.get(box_array, array.size(box_array) - 1) )
f_array_add_pop(box_array, box.new( left = box_left, top = box_top, right = box_right, bottom = box_bottom, border_color = demand_outline_color,
bgcolor = demand_color, extend = extend.right, text = 'DEMAND', text_halign = text.align_center, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
box.delete( array.get(label_array, array.size(label_array) - 1) )
f_array_add_pop(label_array, box.new( left = box_left, top = poi, right = box_right, bottom = poi, border_color = poi_border_color,
bgcolor = poi_border_color, extend = extend.right, text = 'POI', text_halign = text.align_left, text_valign = text.align_center, text_color = poi_label_color, text_size = size.small, xloc = xloc.bar_index))
// FUNCTION TO CHANGE SUPPLY/DEMAND TO A BOS IF BROKEN
f_sd_to_bos(box_array, bos_array, label_array, zone_type) =>
if zone_type == 1
for i = 0 to array.size(box_array) - 1
level_to_break = box.get_top(array.get(box_array,i))
// if ta.crossover(close, level_to_break)
if close >= level_to_break
copied_box = box.copy(array.get(box_array,i))
f_array_add_pop(bos_array, copied_box)
mid = (box.get_top(array.get(box_array,i)) + box.get_bottom(array.get(box_array,i))) / 2
box.set_top(array.get(bos_array,0), mid)
box.set_bottom(array.get(bos_array,0), mid)
box.set_extend( array.get(bos_array,0), extend.none)
box.set_right( array.get(bos_array,0), bar_index)
box.set_text( array.get(bos_array,0), 'BOS' )
box.set_text_color( array.get(bos_array,0), bos_label_color)
box.set_text_size( array.get(bos_array,0), size.small)
box.set_text_halign( array.get(bos_array,0), text.align_center)
box.set_text_valign( array.get(bos_array,0), text.align_center)
box.delete(array.get(box_array, i))
box.delete(array.get(label_array, i))
if zone_type == -1
for i = 0 to array.size(box_array) - 1
level_to_break = box.get_bottom(array.get(box_array,i))
// if ta.crossunder(close, level_to_break)
if close <= level_to_break
copied_box = box.copy(array.get(box_array,i))
f_array_add_pop(bos_array, copied_box)
mid = (box.get_top(array.get(box_array,i)) + box.get_bottom(array.get(box_array,i))) / 2
box.set_top(array.get(bos_array,0), mid)
box.set_bottom(array.get(bos_array,0), mid)
box.set_extend( array.get(bos_array,0), extend.none)
box.set_right( array.get(bos_array,0), bar_index)
box.set_text( array.get(bos_array,0), 'BOS' )
box.set_text_color( array.get(bos_array,0), bos_label_color)
box.set_text_size( array.get(bos_array,0), size.small)
box.set_text_halign( array.get(bos_array,0), text.align_center)
box.set_text_valign( array.get(bos_array,0), text.align_center)
box.delete(array.get(box_array, i))
box.delete(array.get(label_array, i))
// FUNCTION MANAGE CURRENT BOXES BY CHANGING ENDPOINT
f_extend_box_endpoint(box_array) =>
for i = 0 to array.size(box_array) - 1
box.set_right(array.get(box_array, i), bar_index + 100)
//
stratRes = timeframe.ismonthly ? str.tostring(timeframe.multiplier * intRes, '###M') :
timeframe.isweekly ? str.tostring(timeframe.multiplier * intRes, '###W') :
timeframe.isdaily ? str.tostring(timeframe.multiplier * intRes, '###D') :
timeframe.isintraday ? str.tostring(timeframe.multiplier * intRes, '####') :
'60'
src = h ? request.security(ticker.heikinashi(syminfo.tickerid),
timeframe.period, close, lookahead = barmerge.lookahead_off) : close
// CALCULATE ATR
atrValue = ta.atr(50)
// CALCULATE SWING HIGHS & SWING LOWS
swing_high = ta.pivothigh(high, swing_length, swing_length)
swing_low = ta.pivotlow(low, swing_length, swing_length)
// ARRAYS FOR SWING H/L & BN
var swing_high_values = array.new_float(5,0.00)
var swing_low_values = array.new_float(5,0.00)
var swing_high_bns = array.new_int(5,0)
var swing_low_bns = array.new_int(5,0)
// ARRAYS FOR SUPPLY / DEMAND
var current_supply_box = array.new_box(history_of_demand_to_keep, na)
var current_demand_box = array.new_box(history_of_demand_to_keep, na)
// ARRAYS FOR SUPPLY / DEMAND POI LABELS
var current_supply_poi = array.new_box(history_of_demand_to_keep, na)
var current_demand_poi = array.new_box(history_of_demand_to_keep, na)
// ARRAYS FOR BOS
var supply_bos = array.new_box(5, na)
var demand_bos = array.new_box(5, na)
//END CALCULATIONS
// NEW SWING HIGH
if not na(swing_high)
//MANAGE SWING HIGH VALUES
f_array_add_pop(swing_high_values, swing_high)
f_array_add_pop(swing_high_bns, bar_index )
if show_price_action_labels
f_sh_sl_labels(swing_high_values, 1)
f_supply_demand(swing_high_values, swing_high_bns, current_supply_box, current_supply_poi, 1, atrValue)
// NEW SWING LOW
else if not na(swing_low)
//MANAGE SWING LOW VALUES
f_array_add_pop(swing_low_values, swing_low)
f_array_add_pop(swing_low_bns, bar_index )
if show_price_action_labels
f_sh_sl_labels(swing_low_values, -1)
f_supply_demand(swing_low_values, swing_low_bns, current_demand_box, current_demand_poi, -1, atrValue)
f_sd_to_bos(current_supply_box, supply_bos, current_supply_poi, 1)
f_sd_to_bos(current_demand_box, demand_bos, current_demand_poi, -1)
f_extend_box_endpoint(current_supply_box)
f_extend_box_endpoint(current_demand_box)
channelBal = input.bool(false, "Channel Balance", group = "CHART")
lr_slope(_src, _len) =>
x = 0.0, y = 0.0, x2 = 0.0, xy = 0.0
for i = 0 to _len - 1
val = _src
per = i + 1
x += per
y += val
x2 += per * per
xy += val * per
_slp = (_len * xy - x * y) / (_len * x2 - x * x)
_avg = y / _len
_int = _avg - _slp * x / _len + _slp
lr_dev(_src, _len, _slp, _avg, _int) =>
upDev = 0.0, dnDev = 0.0
val = _int
for j = 0 to _len - 1
price = high - val
if price > upDev
upDev := price
price := val - low
if price > dnDev
dnDev := price
price := _src
val += _slp
//
= ta.kc(close, 80, 10.5)
= ta.kc(close, 80, 9.5)
= ta.kc(close, 80, 8)
= ta.kc(close, 80, 3)
barsL = 10
barsR = 10
pivotHigh = fixnan(ta.pivothigh(barsL, barsR) )
pivotLow = fixnan(ta.pivotlow(barsL, barsR) )
source = close, period = 150
= lr_slope(source, period)
= lr_dev(source, period, s, a, i)
y1 = low - (ta.atr(30) * 2), y1B = low - ta.atr(30)
y2 = high + (ta.atr(30) * 2), y2B = high + ta.atr(30)
x1 = bar_index - period + 1, _y1 = i + s * (period - 1), x2 = bar_index, _y2 = i
//Functions
//Line Style function
get_line_style(style) =>
out = switch style
'???' => line.style_solid
'----' => line.style_dashed
' ' => line.style_dotted
//Function to get order block coordinates
get_coordinates(condition, top, btm, ob_val)=>
var ob_top = array.new_float(0)
var ob_btm = array.new_float(0)
var ob_avg = array.new_float(0)
var ob_left = array.new_int(0)
float ob = na
//Append coordinates to arrays
if condition
avg = math.avg(top, btm)
array.unshift(ob_top, top)
array.unshift(ob_btm, btm)
array.unshift(ob_avg, avg)
ob := ob_val
//Function to remove mitigated order blocks from coordinate arrays
remove_mitigated(ob_top, ob_btm, ob_left, ob_avg, target, bull)=>
mitigated = false
target_array = bull ? ob_btm : ob_top
for element in target_array
idx = array.indexof(target_array, element)
if (bull ? target < element : target > element)
mitigated := true
array.remove(ob_top, idx)
array.remove(ob_btm, idx)
array.remove(ob_avg, idx)
array.remove(ob_left, idx)
mitigated
//Function to set order blocks
set_order_blocks(ob_top, ob_btm, ob_left, ob_avg, ext_last, bg_css, border_css, lvl_css)=>
var ob_box = array.new_box(0)
var ob_lvl = array.new_line(0)
//Global elements
var os = 0
var target_bull = 0.
var target_bear = 0.
// Create non-repainting security function
rp_security(_symbol, _res, _src) =>
request.security(_symbol, _res, _src )
htfHigh = rp_security(syminfo.tickerid, res, high)
htfLow = rp_security(syminfo.tickerid, res, low)
// Main Indicator
// Functions
smoothrng(x, t, m) =>
wper = t * 2 - 1
avrng = ta.ema(math.abs(x - x ), t)
smoothrng = ta.ema(avrng, wper) * m
rngfilt(x, r) =>
rngfilt = x
rngfilt := x > nz(rngfilt ) ? x - r < nz(rngfilt ) ? nz(rngfilt ) : x - r : x + r > nz(rngfilt ) ? nz(rngfilt ) : x + r
percWidth(len, perc) => (ta.highest(len) - ta.lowest(len)) * perc / 100
securityNoRep(sym, res, src) => request.security(sym, res, src, barmerge.gaps_off, barmerge.lookahead_on)
swingPoints(prd) =>
pivHi = ta.pivothigh(prd, prd)
pivLo = ta.pivotlow (prd, prd)
last_pivHi = ta.valuewhen(pivHi, pivHi, 1)
last_pivLo = ta.valuewhen(pivLo, pivLo, 1)
hh = pivHi and pivHi > last_pivHi ? pivHi : na
lh = pivHi and pivHi < last_pivHi ? pivHi : na
hl = pivLo and pivLo > last_pivLo ? pivLo : na
ll = pivLo and pivLo < last_pivLo ? pivLo : na
f_chartTfInMinutes() =>
float _resInMinutes = timeframe.multiplier * (
timeframe.isseconds ? 1 :
timeframe.isminutes ? 1. :
timeframe.isdaily ? 60. * 24 :
timeframe.isweekly ? 60. * 24 * 7 :
timeframe.ismonthly ? 60. * 24 * 30.4375 : na)
f_kc(src, len, sensitivity) =>
basis = ta.sma(src, len)
span = ta.atr(len)
wavetrend(src, chlLen, avgLen) =>
esa = ta.ema(src, chlLen)
d = ta.ema(math.abs(src - esa), chlLen)
ci = (src - esa) / (0.015 * d)
wt1 = ta.ema(ci, avgLen)
wt2 = ta.sma(wt1, 3)
f_top_fractal(_src) => _src < _src and _src < _src and _src > _src and _src > _src
f_bot_fractal(_src) => _src > _src and _src > _src and _src < _src and _src < _src
top_fractal = f_top_fractal(src)
bot_fractal = f_bot_fractal(src)
f_fractalize (_src) => top_fractal ? 1 : bot_fractal ? -1 : 0
f_findDivs(src, topLimit, botLimit) =>
fractalTop = f_fractalize(src) > 0 and src >= topLimit ? src : na
fractalBot = f_fractalize(src) < 0 and src <= botLimit ? src : na
highPrev = ta.valuewhen(fractalTop, src , 0)
highPrice = ta.valuewhen(fractalTop, high , 0)
lowPrev = ta.valuewhen(fractalBot, src , 0)
lowPrice = ta.valuewhen(fractalBot, low , 0)
bearSignal = fractalTop and high > highPrice and src < highPrev
bullSignal = fractalBot and low < lowPrice and src > lowPrev
// Get user input
enableSR = input(false , "SR On/Off", group="SR")
colorSup = input(#00000000 , "Support Color", group="SR")
colorRes = input(#00000000 , "Resistance Color", group="SR")
strengthSR = input.int(2 , "S/R Strength", 1, group="SR")
lineStyle = input.string("Dotted", "Line Style", , group="SR")
lineWidth = input.int(2 , "S/R Line Width", 1, group="SR")
useZones = input(true , "Zones On/Off", group="SR")
useHLZones = input(true , "High Low Zones On/Off", group="SR")
zoneWidth = input.int(2 , "Zone Width %", 0,
tooltip = "it's calculated using % of the distance between highest/lowest in last 300 bars", group="SR")
expandSR = input(true , "Expand SR")
// Get components
rb = 10
prd = 284
ChannelW = 10
label_loc = 55
style = lineStyle == "Solid" ? line.style_solid :
lineStyle == "Dotted" ? line.style_dotted : line.style_dashed
ph = ta.pivothigh(rb, rb)
pl = ta.pivotlow (rb, rb)
sr_levels = array.new_float(21, na)
prdhighest = ta.highest(prd)
prdlowest = ta.lowest(prd)
cwidth = percWidth(prd, ChannelW)
zonePerc = percWidth(300, zoneWidth)
aas = array.new_bool(41, true)
u1 = 0.0, u1 := nz(u1 )
d1 = 0.0, d1 := nz(d1 )
highestph = 0.0, highestph := highestph
lowestpl = 0.0, lowestpl := lowestpl
var sr_levs = array.new_float(21, na)
label hlabel = na, label.delete(hlabel )
label llabel = na, label.delete(llabel )
var sr_lines = array.new_line(21, na)
var sr_linesH = array.new_line(21, na)
var sr_linesL = array.new_line(21, na)
var sr_linesF = array.new_linefill(21, na)
var sr_labels = array.new_label(21, na)
if (not na(ph) or not na(pl))
for x = 0 to array.size(sr_levels) - 1
array.set(sr_levels, x, na)
highestph := prdlowest
lowestpl := prdhighest
countpp = 0
for x = 0 to prd
if na(close )
break
if not na(ph ) or not na(pl )
highestph := math.max(highestph, nz(ph , prdlowest), nz(pl , prdlowest))
lowestpl := math.min(lowestpl, nz(ph , prdhighest), nz(pl , prdhighest))
countpp += 1
if countpp > 40
break
if array.get(aas, countpp)
upl = (not na(ph ) and (ph != 0) ? high : low ) + cwidth
dnl = (not na(ph ) and (ph != 0) ? high : low ) - cwidth
u1 := countpp == 1 ? upl : u1
d1 := countpp == 1 ? dnl : d1
tmp = array.new_bool(41, true)
cnt = 0
tpoint = 0
for xx = 0 to prd
if na(close )
break
if not na(ph ) or not na(pl )
chg = false
cnt += 1
if cnt > 40
break
if array.get(aas, cnt)
if not na(ph )
if high <= upl and high >= dnl
tpoint += 1
chg := true
if not na(pl )
if low <= upl and low >= dnl
tpoint += 1
chg := true
if chg and cnt < 41
array.set(tmp, cnt, false)
if tpoint >= strengthSR
for g = 0 to 40 by 1
if not array.get(tmp, g)
array.set(aas, g, false)
if (not na(ph ) and countpp < 21)
array.set(sr_levels, countpp, high )
if (not na(pl ) and countpp < 21)
array.set(sr_levels, countpp, low )
// Plot
var line highest_ = na, line.delete(highest_)
var line lowest_ = na, line.delete(lowest_)
var line highest_fill1 = na, line.delete(highest_fill1)
var line highest_fill2 = na, line.delete(highest_fill2)
var line lowest_fill1 = na, line.delete(lowest_fill1)
var line lowest_fill2 = na, line.delete(lowest_fill2)
hi_col = close >= highestph ? colorSup : colorRes
lo_col = close >= lowestpl ? colorSup : colorRes
if enableSR
highest_ := line.new(bar_index - 311, highestph, bar_index, highestph, xloc.bar_index, expandSR ? extend.both : extend.right, hi_col, style, lineWidth)
lowest_ := line.new(bar_index - 311, lowestpl , bar_index, lowestpl , xloc.bar_index, expandSR ? extend.both : extend.right, lo_col, style, lineWidth)
if useHLZones
highest_fill1 := line.new(bar_index - 311, highestph + zonePerc, bar_index, highestph + zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na)
highest_fill2 := line.new(bar_index - 311, highestph - zonePerc, bar_index, highestph - zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na)
lowest_fill1 := line.new(bar_index - 311, lowestpl + zonePerc , bar_index, lowestpl + zonePerc , xloc.bar_index, expandSR ? extend.both : extend.right, na)
lowest_fill2 := line.new(bar_index - 311, lowestpl - zonePerc , bar_index, lowestpl - zonePerc , xloc.bar_index, expandSR ? extend.both : extend.right, na)
linefill.new(highest_fill1, highest_fill2, hi_col)
linefill.new(lowest_fill1 , lowest_fill2 , lo_col)
if (not na(ph) or not na(pl))
for x = 0 to array.size(sr_lines) - 1
array.set(sr_levs, x, array.get(sr_levels, x))
for x = 0 to array.size(sr_lines) - 1
line.delete(array.get(sr_lines, x))
line.delete(array.get(sr_linesH, x))
line.delete(array.get(sr_linesL, x))
linefill.delete(array.get(sr_linesF, x))
if (not na(array.get(sr_levs, x)) and enableSR)
line_col = close >= array.get(sr_levs, x) ? colorSup : colorRes
array.set(sr_lines, x, line.new(bar_index - 355, array.get(sr_levs, x), bar_index, array.get(sr_levs, x), xloc.bar_index, expandSR ? extend.both : extend.right, line_col, style, lineWidth))
if useZones
array.set(sr_linesH, x, line.new(bar_index - 355, array.get(sr_levs, x) + zonePerc, bar_index, array.get(sr_levs, x) + zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na))
array.set(sr_linesL, x, line.new(bar_index - 355, array.get(sr_levs, x) - zonePerc, bar_index, array.get(sr_levs, x) - zonePerc, xloc.bar_index, expandSR ? extend.both : extend.right, na))
array.set(sr_linesF, x, linefill.new(array.get(sr_linesH, x), array.get(sr_linesL, x), line_col))
for x = 0 to array.size(sr_labels) - 1
label.delete(array.get(sr_labels, x))
if (not na(array.get(sr_levs, x)) and enableSR)
lab_loc = close >= array.get(sr_levs, x) ? label.style_label_up : label.style_label_down
lab_col = close >= array.get(sr_levs, x) ? colorSup : colorRes
array.set(sr_labels, x, label.new(bar_index + label_loc, array.get(sr_levs, x), str.tostring(math.round_to_mintick(array.get(sr_levs, x))), color=lab_col , textcolor=#000000, style=lab_loc))
hlabel := enableSR ? label.new(bar_index + label_loc + math.round(math.sign(label_loc)) * 20, highestph, "High Level : " + str.tostring(highestph), color=hi_col, textcolor=#000000, style=label.style_label_down) : na
llabel := enableSR ? label.new(bar_index + label_loc + math.round(math.sign(label_loc)) * 20, lowestpl , "Low Level : " + str.tostring(lowestpl) , color=lo_col, textcolor=#000000, style=label.style_label_up ) : na
// Get components
rsi = ta.rsi(close, 28)
//rsiOb = rsi > 78 and rsi > ta.ema(rsi, 10)
//rsiOs = rsi < 27 and rsi < ta.ema(rsi, 10)
rsiOb = rsi > 65 and rsi > ta.ema(rsi, 10)
rsiOs = rsi < 35 and rsi < ta.ema(rsi, 10)
dHigh = securityNoRep(syminfo.tickerid, "D", high )
dLow = securityNoRep(syminfo.tickerid, "D", low )
dClose = securityNoRep(syminfo.tickerid, "D", close )
ema = ta.ema(close, 144)
emaBull = close > ema
equal_tf(res) => str.tonumber(res) == f_chartTfInMinutes() and not timeframe.isseconds
higher_tf(res) => str.tonumber(res) > f_chartTfInMinutes() or timeframe.isseconds
too_small_tf(res) => (timeframe.isweekly and res=="1") or (timeframe.ismonthly and str.tonumber(res) < 10)
securityNoRep1(sym, res, src) =>
bool bull_ = na
bull_ := equal_tf(res) ? src : bull_
bull_ := higher_tf(res) ? request.security(sym, res, src, barmerge.gaps_off, barmerge.lookahead_on) : bull_
bull_array = request.security_lower_tf(syminfo.tickerid, higher_tf(res) ? str.tostring(f_chartTfInMinutes()) + (timeframe.isseconds ? "S" : "") : too_small_tf(res) ? (timeframe.isweekly ? "3" : "10") : res, src)
if array.size(bull_array) > 1 and not equal_tf(res) and not higher_tf(res)
bull_ := array.pop(bull_array)
array.clear(bull_array)
bull_
// === BASE FUNCTIONS ===
// Returns MA input selection variant, default to SMA if blank or typo.
variant(type, src, len, offSig, offALMA) =>
v1 = ta.sma(src, len) // Simple
v2 = ta.ema(src, len) // Exponential
v3 = 2 * v2 - ta.ema(v2, len) // Double Exponential
v4 = 3 * (v2 - ta.ema(v2, len)) + ta.ema(ta.ema(v2, len), len) // Triple Exponential
v5 = ta.wma(src, len) // Weighted
v6 = ta.vwma(src, len) // Volume Weighted
v7 = 0.0
sma_1 = ta.sma(src, len) // Smoothed
v7 := na(v7 ) ? sma_1 : (v7 * (len - 1) + src) / len
v8 = ta.wma(2 * ta.wma(src, len / 2) - ta.wma(src, len), math.round(math.sqrt(len))) // Hull
v9 = ta.linreg(src, len, offSig) // Least Squares
v10 = ta.alma(src, len, offALMA, offSig) // Arnaud Legoux
v11 = ta.sma(v1, len) // Triangular (extreme smooth)
// SuperSmoother filter
// 2013 John F. Ehlers
a1 = math.exp(-1.414 * 3.14159 / len)
b1 = 2 * a1 * math.cos(1.414 * 3.14159 / len)
c2 = b1
c3 = -a1 * a1
c1 = 1 - c2 - c3
v12 = 0.0
v12 := c1 * (src + nz(src )) / 2 + c2 * nz(v12 ) + c3 * nz(v12 )
type == 'EMA' ? v2 : type == 'DEMA' ? v3 : type == 'TEMA' ? v4 : type == 'WMA' ? v5 : type == 'VWMA' ? v6 : type == 'SMMA' ? v7 : type == 'HullMA' ? v8 : type == 'LSMA' ? v9 : type == 'ALMA' ? v10 : type == 'TMA' ? v11 : type == 'SSMA' ? v12 : v1
// security wrapper for repeat calls
reso(exp, use, res) =>
security_1 = request.security(syminfo.tickerid, res, exp, gaps = barmerge.gaps_off, lookahead = barmerge.lookahead_on)
use ? security_1 : exp
// === /BASE FUNCTIONS ===
// === SERIES SETUP ===
closeSeries = variant(basisType, close , basisLen, offsetSigma, offsetALMA)
openSeries = variant(basisType, open , basisLen, offsetSigma, offsetALMA)
// === /SERIES ===
// Get Alternate resolution Series if selected.
closeSeriesAlt = reso(closeSeries, useRes, stratRes)
openSeriesAlt = reso(openSeries, useRes, stratRes)
//
lxTrigger = false
sxTrigger = false
leTrigger = ta.crossover (closeSeriesAlt, openSeriesAlt)
seTrigger = ta.crossunder(closeSeriesAlt, openSeriesAlt)
G_RISK = '■ ' + 'Risk Management'
//#region ———— <↓↓↓ G_RISK ↓↓↓> {
// ———————————
//Tooltip
T_LVL = '(%) Exit Level'
T_QTY = '(%) Adjust trade exit volume'
T_MSG = 'Paste JSON message for your bot'
//Webhook Message
O_LEMSG = 'Long Entry'
O_LXMSGSL = 'Long SL'
O_LXMSGTP1 = 'Long TP1'
O_LXMSGTP2 = 'Long TP2'
O_LXMSGTP3 = 'Long TP3'
O_LXMSG = 'Long Exit'
O_SEMSG = 'Short Entry'
O_SXMSGSL = 'Short SL'
O_SXMSGA = 'Short TP1'
O_SXMSGB = 'Short TP2'
O_SXMSGC = 'Short TP3'
O_SXMSGX = 'Short Exit'
// ——————————— | | | Line length guide |
i_lxLvlTP1 = input.float (0.2, 'Level TP1' , group = G_RISK,
tooltip = T_LVL)
i_lxQtyTP1 = input.float (80.0, 'Qty TP1' , group = G_RISK,
tooltip = T_QTY)
i_lxLvlTP2 = input.float (0.5, 'Level TP2' , group = G_RISK,
tooltip = T_LVL)
i_lxQtyTP2 = input.float (10.0, 'Qty TP2' , group = G_RISK,
tooltip = T_QTY)
i_lxLvlTP3 = input.float (7.0, 'Level TP3' , group = G_RISK,
tooltip = T_LVL)
i_lxQtyTP3 = input.float (2, 'Qty TP3' , group = G_RISK,
tooltip = T_QTY)
i_lxLvlSL = input.float (0.5, 'Stop Loss' , group = G_RISK,
tooltip = T_LVL)
i_sxLvlTP1 = i_lxLvlTP1
i_sxQtyTP1 = i_lxQtyTP1
i_sxLvlTP2 = i_lxLvlTP2
i_sxQtyTP2 = i_lxQtyTP2
i_sxLvlTP3 = i_lxLvlTP3
i_sxQtyTP3 = i_lxQtyTP3
i_sxLvlSL = i_lxLvlSL
G_MSG = '■ ' + 'Webhook Message'
i_leMsg = input.string (O_LEMSG ,'Long Entry' , group = G_MSG, tooltip = T_MSG)
i_lxMsgSL = input.string (O_LXMSGSL ,'Long SL' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP1 = input.string (O_LXMSGTP1,'Long TP1' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP2 = input.string (O_LXMSGTP2,'Long TP2' , group = G_MSG, tooltip = T_MSG)
i_lxMsgTP3 = input.string (O_LXMSGTP3,'Long TP3' , group = G_MSG, tooltip = T_MSG)
i_lxMsg = input.string (O_LXMSG ,'Long Exit' , group = G_MSG, tooltip = T_MSG)
i_seMsg = input.string (O_SEMSG ,'Short Entry' , group = G_MSG, tooltip = T_MSG)
i_sxMsgSL = input.string (O_SXMSGSL ,'Short SL' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP1 = input.string (O_SXMSGA ,'Short TP1' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP2 = input.string (O_SXMSGB ,'Short TP2' , group = G_MSG, tooltip = T_MSG)
i_sxMsgTP3 = input.string (O_SXMSGC ,'Short TP3' , group = G_MSG, tooltip = T_MSG)
i_sxMsg = input.string (O_SXMSGX ,'Short Exit' , group = G_MSG, tooltip = T_MSG)
i_src = close
G_DISPLAY = 'Display'
//
i_alertOn = input.bool (true, 'Alert Labels On/Off' , group = G_DISPLAY)
i_barColOn = input.bool (true, 'Bar Color On/Off' , group = G_DISPLAY)
// ———————————
// @function Calculate the Take Profit line, and the crossover or crossunder
f_tp(_condition, _conditionValue, _leTrigger, _seTrigger, _src, _lxLvlTP, _sxLvlTP)=>
var float _tpLine = 0.0
_topLvl = _src + (_src * (_lxLvlTP / 100))
_botLvl = _src - (_src * (_sxLvlTP / 100))
_tpLine := _condition != _conditionValue and _leTrigger ? _topLvl :
_condition != -_conditionValue and _seTrigger ? _botLvl :
nz(_tpLine )
// @function Similar to "ta.crossover" or "ta.crossunder"
f_cross(_scr1, _scr2, _over)=>
_cross = _over ? _scr1 > _scr2 and _scr1 < _scr2 :
_scr1 < _scr2 and _scr1 > _scr2
// ———————————
//
var float condition = 0.0
var float slLine = 0.0
var float entryLine = 0.0
//
entryLine := leTrigger and condition <= 0.0 ? close :
seTrigger and condition >= 0.0 ? close : nz(entryLine )
//
slTopLvl = i_src + (i_src * (i_lxLvlSL / 100))
slBotLvl = i_src - (i_src * (i_sxLvlSL / 100))
slLine := condition <= 0.0 and leTrigger ? slBotLvl :
condition >= 0.0 and seTrigger ? slTopLvl : nz(slLine )
slLong = f_cross(low, slLine, false)
slShort = f_cross(high, slLine, true )
//
= f_tp(condition, 1.2,leTrigger, seTrigger, i_src, i_lxLvlTP3, i_sxLvlTP3)
= f_tp(condition, 1.1,leTrigger, seTrigger, i_src, i_lxLvlTP2, i_sxLvlTP2)
= f_tp(condition, 1.0,leTrigger, seTrigger, i_src, i_lxLvlTP1, i_sxLvlTP1)
tp3Long = f_cross(high, tp3Line, true )
tp3Short = f_cross(low, tp3Line, false)
tp2Long = f_cross(high, tp2Line, true )
tp2Short = f_cross(low, tp2Line, false)
tp1Long = f_cross(high, tp1Line, true )
tp1Short = f_cross(low, tp1Line, false)
switch
leTrigger and condition <= 0.0 => condition := 1.0
seTrigger and condition >= 0.0 => condition := -1.0
tp3Long and condition == 1.2 => condition := 1.3
tp3Short and condition == -1.2 => condition := -1.3
tp2Long and condition == 1.1 => condition := 1.2
tp2Short and condition == -1.1 => condition := -1.2
tp1Long and condition == 1.0 => condition := 1.1
tp1Short and condition == -1.0 => condition := -1.1
slLong and condition >= 1.0 => condition := 0.0
slShort and condition <= -1.0 => condition := 0.0
lxTrigger and condition >= 1.0 => condition := 0.0
sxTrigger and condition <= -1.0 => condition := 0.0
longE = leTrigger and condition <= 0.0 and condition == 1.0
shortE = seTrigger and condition >= 0.0 and condition == -1.0
longX = lxTrigger and condition >= 1.0 and condition == 0.0
shortX = sxTrigger and condition <= -1.0 and condition == 0.0
longSL = slLong and condition >= 1.0 and condition == 0.0
shortSL = slShort and condition <= -1.0 and condition == 0.0
longTP3 = tp3Long and condition == 1.2 and condition == 1.3
shortTP3 = tp3Short and condition == -1.2 and condition == -1.3
longTP2 = tp2Long and condition == 1.1 and condition == 1.2
shortTP2 = tp2Short and condition == -1.1 and condition == -1.2
longTP1 = tp1Long and condition == 1.0 and condition == 1.1
shortTP1 = tp1Short and condition == -1.0 and condition == -1.1
// ——————————— {
//
if strategy.position_size <= 0 and longE and barstate.isconfirmed
strategy.entry(
'Long',
strategy.long,
alert_message = i_leMsg,
comment = 'LE')
if strategy.position_size > 0 and condition == 1.0
strategy.exit(
id = 'LXTP1',
from_entry = 'Long',
qty_percent = i_lxQtyTP1,
limit = tp1Line,
stop = slLine,
comment_profit = 'LXTP1',
comment_loss = 'SL',
alert_profit = i_lxMsgTP1,
alert_loss = i_lxMsgSL)
if strategy.position_size > 0 and condition == 1.1
strategy.exit(
id = 'LXTP2',
from_entry = 'Long',
qty_percent = i_lxQtyTP2,
limit = tp2Line,
stop = slLine,
comment_profit = 'LXTP2',
comment_loss = 'SL',
alert_profit = i_lxMsgTP2,
alert_loss = i_lxMsgSL)
if strategy.position_size > 0 and condition == 1.2
strategy.exit(
id = 'LXTP3',
from_entry = 'Long',
qty_percent = i_lxQtyTP3,
limit = tp3Line,
stop = slLine,
comment_profit = 'LXTP3',
comment_loss = 'SL',
alert_profit = i_lxMsgTP3,
alert_loss = i_lxMsgSL)
if longX
strategy.close(
'Long',
alert_message = i_lxMsg,
comment = 'LX')
//
if strategy.position_size >= 0 and shortE and barstate.isconfirmed
strategy.entry(
'Short',
strategy.short,
alert_message = i_leMsg,
comment = 'SE')
if strategy.position_size < 0 and condition == -1.0
strategy.exit(
id = 'SXTP1',
from_entry = 'Short',
qty_percent = i_sxQtyTP1,
limit = tp1Line,
stop = slLine,
comment_profit = 'SXTP1',
comment_loss = 'SL',
alert_profit = i_sxMsgTP1,
alert_loss = i_sxMsgSL)
if strategy.position_size < 0 and condition == -1.1
strategy.exit(
id = 'SXTP2',
from_entry = 'Short',
qty_percent = i_sxQtyTP2,
limit = tp2Line,
stop = slLine,
comment_profit = 'SXTP2',
comment_loss = 'SL',
alert_profit = i_sxMsgTP2,
alert_loss = i_sxMsgSL)
if strategy.position_size < 0 and condition == -1.2
strategy.exit(
id = 'SXTP3',
from_entry = 'Short',
qty_percent = i_sxQtyTP3,
limit = tp3Line,
stop = slLine,
comment_profit = 'SXTP3',
comment_loss = 'SL',
alert_profit = i_sxMsgTP3,
alert_loss = i_sxMsgSL)
if shortX
strategy.close(
'Short',
alert_message = i_sxMsg,
comment = 'SX')
// ———————————
c_tp = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.green
c_entry = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.blue
c_sl = leTrigger or seTrigger ? na :
condition == 0.0 ? na : color.red
p_tp1Line = plot (
condition == 1.0 or
condition == -1.0 ? tp1Line : na,
title = "TP Line 1",
color = c_tp,
linewidth = 1,
style = plot.style_linebr)
p_tp2Line = plot (
condition == 1.0 or
condition == -1.0 or
condition == 1.1 or
condition == -1.1 ? tp2Line : na,
title = "TP Line 2",
color = c_tp,
linewidth = 1,
style = plot.style_linebr)
p_tp3Line = plot (
condition == 1.0 or
condition == -1.0 or
condition == 1.1 or
condition == -1.1 or
condition == 1.2 or
condition == -1.2 ? tp3Line : na,
title = "TP Line 3",
color = c_tp,
linewidth = 1,
style = plot.style_linebr)
p_entryLine = plot (
condition >= 1.0 or
condition <= -1.0 ? entryLine : na,
title = "Entry Line",
color = c_entry,
linewidth = 1,
style = plot.style_linebr)
p_slLine = plot (
condition == 1.0 or
condition == -1.0 or
condition == 1.1 or
condition == -1.1 or
condition == 1.2 or
condition == -1.2 ? slLine : na,
title = "SL Line",
color = c_sl,
linewidth = 1,
style = plot.style_linebr)
fill(
p_tp3Line, p_entryLine,
color = leTrigger or seTrigger ? na :color.new(color.green, 90))
fill(
p_entryLine, p_slLine,
color = leTrigger or seTrigger ? na :color.new(color.red, 90))
//
plotshape(
i_alertOn and longE,
title = 'Long',
text = 'Long',
textcolor = color.white,
color = color.green,
style = shape.labelup,
size = size.tiny,
location = location.belowbar)
plotshape(
i_alertOn and shortE,
title = 'Short',
text = 'Short',
textcolor = color.white,
color = color.red,
style = shape.labeldown,
size = size.tiny,
location = location.abovebar)
plotshape(
i_alertOn and (longX or shortX) ? close : na,
title = 'Close',
text = 'Close',
textcolor = color.white,
color = color.gray,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
l_tp = i_alertOn and (longTP1 or shortTP1) ? close : na
plotshape(
l_tp,
title = "TP1 Cross",
text = "TP1",
textcolor = color.white,
color = color.olive,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
plotshape(
i_alertOn and (longTP2 or shortTP2) ? close : na,
title = "TP2 Cross",
text = "TP2",
textcolor = color.white,
color = color.olive,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
plotshape(
i_alertOn and (longTP3 or shortTP3) ? close : na,
title = "TP3 Cross",
text = "TP3",
textcolor = color.white,
color = color.olive,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
plotshape(
i_alertOn and (longSL or shortSL) ? close : na,
title = "SL Cross",
text = "SL",
textcolor = color.white,
color = color.maroon,
style = shape.labelup,
size = size.tiny,
location = location.absolute)
//
plot(
na,
title = "─── ───",
editable = false,
display = display.data_window)
plot(
condition,
title = "condition",
editable = false,
display = display.data_window)
plot(
strategy.position_size * 100,
title = ".position_size",
editable = false,
display = display.data_window)
//#endregion }
// ——————————— <↑↑↑ G_RISK ↑↑↑>
//#region ———— <↓↓↓ G_SCRIPT02 ↓↓↓> {
// @function Queues a new element in an array and de-queues its first element.
f_qDq(_array, _val) =>
array.push(_array, _val)
_return = array.shift(_array)
_return
var line a_slLine = array.new_line(1)
var line a_entryLine = array.new_line(1)
var line a_tp3Line = array.new_line(1)
var line a_tp2Line = array.new_line(1)
var line a_tp1Line = array.new_line(1)
var label a_slLabel = array.new_label(1)
var label a_tp3label = array.new_label(1)
var label a_tp2label = array.new_label(1)
var label a_tp1label = array.new_label(1)
var label a_entryLabel = array.new_label(1)
newEntry = longE or shortE
entryIndex = 1
entryIndex := newEntry ? bar_index : nz(entryIndex )
lasTrade = bar_index >= entryIndex
l_right = 10
line.delete(
f_qDq(a_slLine,
line.new(
entryIndex,
slLine,
last_bar_index + l_right,
slLine,
style = line.style_solid,
color = c_sl)))
line.delete(
f_qDq(a_entryLine,
line.new(
entryIndex,
entryLine,
last_bar_index + l_right,
entryLine,
style = line.style_solid,
color = color.blue)))
line.delete(
f_qDq(a_tp3Line,
line.new(
entryIndex,
tp3Line,
last_bar_index + l_right,
tp3Line,
style = line.style_solid,
color = c_tp)))
line.delete(
f_qDq(a_tp2Line,
line.new(
entryIndex,
tp2Line,
last_bar_index + l_right,
tp2Line,
style = line.style_solid,
color = c_tp)))
line.delete(
f_qDq(a_tp1Line,
line.new(
entryIndex,
tp1Line,
last_bar_index + l_right,
tp1Line,
style = line.style_solid,
color = c_tp)))
label.delete(
f_qDq(a_slLabel,
label.new(
last_bar_index + l_right,
slLine,
'SL: ' + str.tostring(slLine, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_sl)))
label.delete(
f_qDq(a_entryLabel,
label.new(
last_bar_index + l_right,
entryLine,
'Entry: ' + str.tostring(entryLine, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = color.blue)))
label.delete(
f_qDq(a_tp3label,
label.new(
last_bar_index + l_right,
tp3Line,
'TP3: ' + str.tostring(tp3Line, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_tp)))
label.delete(
f_qDq(a_tp2label,
label.new(
last_bar_index + l_right,
tp2Line,
'TP2: ' + str.tostring(tp2Line, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_tp)))
label.delete(
f_qDq(a_tp1label,
label.new(
last_bar_index + l_right,
tp1Line,
'TP1: ' + str.tostring(tp1Line, '##.###'),
style = label.style_label_left,
textcolor = color.white,
color = c_tp)))
// ———————————
//
if longE or shortE or longX or shortX
alert(message = 'Any Alert', freq = alert.freq_once_per_bar_close)
if longE
alert(message = 'Long Entry', freq = alert.freq_once_per_bar_close)
if shortE
alert(message = 'Short Entry', freq = alert.freq_once_per_bar_close)
if longX
alert(message = 'Long Exit', freq = alert.freq_once_per_bar_close)
if shortX
alert(message = 'Short Exit', freq = alert.freq_once_per_bar_close)
//#endregion }
// ——————————— <↑↑↑ G_SCRIPT03 ↑↑↑>
USD Session 8FX - LDN & NY (TF-invariant, Live + Table)What it is
A USD strength/weakness meter for the London (08:00–08:45) or New York (15:30–16:00/16:15) session. It blends the movement of 8 markets—EURUSD, GBPUSD, AUDUSD, NZDUSD, USDCHF, USDCAD, USDJPY, XAUUSD—into one Score that is timeframe-invariant (it uses a 1-minute “boundary TF” under the hood so changing chart TF doesn’t change the math).
Core logic (simple)
During the chosen session window, it records each symbol’s start and live end prices, computes returns, optionally normalizes by ATR (volatility), applies your weights, and averages anti-USD (EUR/GBP/AUD/NZD/XAU) vs USD-base (CHF/CAD/JPY) groups.
The final Score is the normalized sum of weighted contributions:
Score > 0 → “USD Strong”
Score < 0 → “USD Weak”
At the session close it freezes (“Locked”) the results so you can review them later.
What you see
Main plot: the USD Score line (with a 0 baseline).
Optional lines: Anti-USD average vs USD-base average (post-normalization, pre-weights).
Session background shading (London silver, New York aqua).
Live table with:
Each symbol’s % change, its weight, and its contribution to the Score.
TOP badges for the two biggest drivers (by absolute contribution).
A Side column (only for the two TOPs) showing BUY/SELL aligned with the USD verdict (e.g., if USD Strong → SELL anti-USD pairs like EURUSD, BUY USD-base like USDCHF).
Verdict row with USD Strong/Weak, the Score value, the window text, and whether you’re LIVE / CLOSED / FROZEN.
Trade Gate panel:
Shows Verdict (USD Strong/Weak), Bias OK/weak (|Score| vs your threshold), Top-1/Top-2 VWAP checks, an overall GATE: OK/NO, and an Entry hint string (e.g., “SELL EURUSD, BUY USDCHF”) when conditions align.
VWAP “Trade Gate”
It confirms alignment between the USD bias and price vs VWAP for the top movers:
If USD Strong: anti-USD symbols should be below VWAP (short bias), USD-base symbols above VWAP (long bias).
If USD Weak: the opposite.
Gate = OK only if |Score| ≥ minAbsScore and at least one of the two TOP symbols is on the correct side of VWAP.
Tip: set vwapTF to an intraday value (“1”, “5”, “15”) for reliable VWAP on higher-TF charts.
Alerts
At session close: “USD Strong/Weak – session close”.
Live threshold: alerts when |Score| crosses your intraday threshold up/down.
Entry hint (Gate OK): triggers when the Gate flips from NO → OK inside the window.
If you create an alert of type “Any alert() function call”, you also get a dynamic message like:
ENTRY HINT • Hint: SELL EURUSD, BUY USDCHF
Key inputs you can tweak
Session: London vs New York; NY end time 16:00 or 16:15.
Timezone: default Europe/Tirane.
Boundary TF: default “1” (keeps the indicator TF-invariant).
minAbsScore: sensitivity threshold for “Bias OK”.
ATR normalization (len): stabilizes comparisons across different volatility regimes.
VWAP settings: toggle panel and set vwapTF.
How to use (playbook)
Choose the session (e.g., New York 15:30–16:15), keep Boundary TF = 1.
If you’re on a higher-TF chart, set vwapTF = "1" or "5".
Watch Score and Verdict; when |Score| ≥ minAbsScore, bias is meaningful.
Check Top-1/Top-2 and the Trade Gate:
If Gate = OK, use the Entry hint (e.g., “SELL EURUSD, BUY USDCHF”) as the aligned idea.
Use your own execution rules (e.g., structure, risk, stops) on the suggested symbols.
After close, review the Frozen table to validate behavior and refine thresholds/weights.
Notes & edge cases
If some markets are illiquid/holiday, a few returns may be na; the script handles that gracefully.
If ta.vwap is na on high TFs, the Gate will simply not confirm—set vwapTF intraday.
You can customize weights (e.g., reduce XAUUSD to -0.3 or similar) to suit your basket philosophy.
If you want, I can add toggles to show Side for all 8 symbols, or print a one-line summary (e.g., “USD Strong • Score 0.23 • Gate OK • SELL EURUSD, BUY USDCHF”) in the top-left of the pane.
Measured Pattern Move (Bulkowski) [SS]Hey everyone,
This is the Measured Pattern Move using Bulkowski's process for measured move calculation.
What the indicator does:
The indicator has the associated measured move across 20 of the most common and frequent Bulkowski patterns, including:
Double Bottom / Adam Eve Bottom
Double Top / Adam Eve Top
Inverse Head and Shoulders
Bear Flag
Bull Flag
Horn Bottom
Horon Top
Broadening Top
Descending Broadening Wedge
Broadening Bottoms
Broadening Tops
Cup and Handle
Inverted cup and handle
Diamond Bottom
Diamond Top
Falling Wedge
Rising Wedge
Pipe Bottom
Pipe Top
Head and Shoulders
It will calculate the measured move according to the Bulkowski process.
What is the Bulkowski Process?
Each move has an associated continuation percentage, which Bulkowski has studied, analyzed and concluded statistically.
For example, Double tops have a continuation percent of 54%. Bear flags, 47%. These are "constants" that are associated with the pattern.
Bulkowski applies them to the daily, but how I have formulated this, it can be used on all timeframes, and with the constant, it will correctly calculate the measured move of the pattern.
What this indicator DOES NOT DO
This indicator will not identify the pattern for you.
I tried this using Dynamic Time Warping (DTW) using my own pre-trained Bulkowski model in R. I was successfully able to get Pinescript to calculate DTW which was amazing! But applying it to all these patterns actually went over the execution time limit, which is understandable.
As such, you will need to identify the pattern yourself, then use this indicator to hilight the pattern and it will calculate the measured move based on the constant and the pattern range.
Let's look at some examples:
Use examples
Double bottom / adam eve bottom on SPY on the 1-Minute chart
Adam and Eve Double Bottom QQQ 1-Hour Chart
Adam Eve Double Bottom MSFT Daily Chart
Bearish Head and Shoulders Pattern MSFT Daily
You get the point.
How to use the indicator
To use the indicator, identify the pattern of interest to you.
Then, highlight the pattern using the indicator (it will ask you to select start time of the pattern and end time of the pattern). The indicator will then highlight the pattern and calculate the measured move, as seen in the examples above.
Best approaches
To make the most of the indicator, its best to draw out your pattern and wait for an actual break, the point of the break is usually the end of the pattern formation.
From here, you will then apply this indicator to calculate the expected up or down move.
Let me show you an example:
Here we see CME_MINI:ES1! has made an Adam bottom pattern. We know the Eve should be forming soon and it indeed does:
We mark the top of the pattern like so:
Then we use our Measured move indicator to calculate the measured move:
Measured move here for CME_MINI:ES1! is 6,510.
Now let's see....
Voila!
Selecting the Pattern
After you highlight the selected pattern, in the indicator settings, simply select the type of pattern it is, for example "head and shoulders" or "Broadening wedge", etc.
The indicator will then adjust its measurements to the appropriate constant and direction.
Concluding remarks
That is the indicator!
It is helpful for determining the actual projected move of a pattern on breakout.
Remember, it does not find the pattern for you , you are responsible for identifying the pattern. But this will calculate the actual TP of the pattern for you, without you having to do your own calculations.
I hope you find it useful, I actually use this indicator every day, especially on the lower timeframes!
And you will find, the more you use it, the better you get at recognizing significant patterns!
If you are not aware of these patterns, Bulkowski lists all of this information freely accessible on his website. I cannot link it here but you can just Google him and he has graciously made his information public and free!
That's it, I hope you enjoy and safe trades!
Disclaimer
This is not my intellectual property. The pattern calculations come from the work of Thomas Bulkowski and not myself. I simply coded this into an indicator using his publicly accessible information.
You can get more information from Bulkowski's official website about his work and patterns.
MestreDoFOMO Future Projection BoxMestreDoFOMO Future Projection Box - Description & How to Use
Description
The "MestreDoFOMO Future Projection Box" is a TradingView indicator tailored for crypto traders (e.g., BTC/USDT on 1H, 4H, or 1D timeframes). It visualizes current price ranges, projects future levels, and confirms trends using semi-transparent boxes. With labeled price levels and built-in alerts, it’s a simple yet powerful tool for identifying support, resistance, and potential price targets.
How It Works
Blue Box (Current Channel): Shows the recent price range over the last 10 bars (adjustable). The top is the highest high plus an ATR buffer, and the bottom is the lowest low minus the buffer. Labels display exact levels (e.g., "Top: 114000", "Bottom: 102600").
Green Box (Future Projection): Projects the price range 10 bars ahead (adjustable) based on the trend slope of the moving average. Labels show "Proj Top" and "Proj Bottom" for future targets.
Orange Box (Moving Average): Traces a 50-period EMA (adjustable) to confirm the trend. An upward slope signals a bullish trend; a downward slope signals a bearish trend. A label shows the current MA value (e.g., "MA: 105000").
Alerts: Triggers when the price nears the projected top or bottom, helping you catch breakouts or retracements.
How to Use
Add the Indicator: Apply "MestreDoFOMO Future Projection Box" to your chart in TradingView.
Interpret the Trend: Check the orange box’s slope—upward for bullish, downward for bearish.
Identify Key Levels: Use the blue box’s top as resistance and bottom as support. On a 4H chart, if the top is 114,000, expect resistance; if the bottom is 102,600, expect support.
Plan Targets: Use the green box for future targets—top for profit-taking (e.g., 114,000), bottom for stop-loss or buying (e.g., 102,600).
Set Alerts: Enable alerts for "Near Upper Projection" or "Near Lower Projection" to get notified when the price hits key levels.
Trade Examples:
Bullish: If the price breaks above the blue box top (e.g., 114,000), buy with a target at the green box top. Set a stop-loss below the green box bottom.
Bearish: If the price rejects at the blue box top and drops below the orange MA, short with a target at the blue box bottom.
Customize: Adjust the lookback period, projection bars, ATR multiplier, and MA length in the settings to fit your trading style.
Tips
Use on 1H for short-term trades, 4H for swing trades, or 1D for long-term trends.
Combine with volume or RSI to confirm signals.
Validate levels with market structure (e.g., candlestick patterns).
MVRVZ BTCMVRVZ BTC (Market Value to Realized Value Z-Score)
Description:
The MVRVZ BTC indicator provides insights into the relationship between the market value and realized value of Bitcoin, using the Market Value to Realized Value (MVRV) ratio, which is then adjusted using a Z-Score. This indicator highlights potential market extremes and helps in identifying overbought or oversold conditions, offering a unique perspective on Bitcoin's valuation.
How It Works:
MVRVZ is calculated by taking the difference between Bitcoin's Market Capitalization (MC) and Realized Capitalization (MCR), then dividing that by the Standard Deviation (Stdev) of the price over a specified period (usually 104 weeks).
The resulting value is plotted as the MVRVZ line, representing how far the market price deviates from its realized value.
Z-Score is then applied to the MVRVZ line, with the Z-Score bounded between +2 and -2, which allows it to be used within a consistent evaluation framework, regardless of how high or low the MVRVZ line goes. The Z-Score will reflect overbought or oversold conditions:
A Z-Score above +2 indicates the market is likely overbought (possible market top).
A Z-Score below -2 indicates the market is likely oversold (possible market bottom).
Values between -2 and +2 indicate more neutral market conditions.
How to Read the Indicator:
MVRVZ Line:
The MVRVZ line shows the relationship between market cap and realized cap. A higher value indicates the market is overvalued relative to the actual capital realized by holders.
The MVRVZ line can move above or below the top and bottom lines you define, which are adjustable according to your preferences. These lines act as trigger levels.
Top and Bottom Trigger Lines:
You can customize the Top Line and Bottom Line values to your preference.
When the MVRVZ line crosses the Top Line, the market might be considered overbought.
When the MVRVZ line crosses the Bottom Line, the market might be considered oversold.
SCDA Z-Score:
The Z-Score is displayed alongside the MVRVZ line and is bounded between -2 and +2. It scales proportionally based on the MVRVZ line's position relative to the top and bottom trigger lines.
The Z-Score ensures that even if the MVRVZ line moves beyond the trigger lines, the Z-Score will stay within the limits of -2 to +2, making it ideal for your custom evaluation system (SCDA).
Background Highlighting:
The background color changes when the MVRVZ line crosses key levels:
When the MVRVZ line exceeds the Top Trigger, the background turns red, indicating overbought conditions.
When the MVRVZ line falls below the Bottom Trigger, the background turns green, indicating oversold conditions.
Data Sources:
The data for the MVRVZ indicator is sourced from Glassnode and Coinmetrics, which provide the necessary values for:
BTC Market Cap (MC) – The total market capitalization of Bitcoin.
BTC Realized Market Cap (MCR) – The capitalization based on the price at which Bitcoin was last moved on the blockchain (realized value).
How to Use the Indicator:
Market Extremes:
Use the MVRVZ and Z-Score to spot potential market tops or bottoms.
A high Z-Score (above +2) suggests the market is overbought, while a low Z-Score (below -2) suggests the market is oversold.
Adjusting the Triggers:
Customize the Top and Bottom Trigger Lines to suit your trading strategy. These lines can act as dynamic reference points for when to take action based on the Z-Score or MVRVZ line crossing these levels.
Market Evaluation (SCDA Framework):
The bounded Z-Score (from -2 to +2) is tailored for your SCDA evaluation system, allowing you to assess market conditions based on consistent criteria, no matter how volatile the MVRVZ line becomes.
Conclusion:
The MVRVZ BTC indicator is a powerful tool for assessing the relative valuation of Bitcoin based on its market and realized capitalization. By combining it with the Z-Score, you get an easy-to-read, bounded evaluation system that highlights potential market extremes and helps you make informed decisions about Bitcoin's price behavior.
Drummond Geometry - Pldot and EnvelopeThis Pine Script will:
1.Calculate and display the PL Dot (Price Level Dot), a moving average that reflects short-term market trends.
2.Plot the Envelope Top and Bottom lines based on averages of previous highs and lows, which represent key areas of resistance and support.
Drummond Geometry Overview
Drummond Geometry is a method of market analysis focused on:
PL Dot : Captures market energy and trend direction. It reacts to price deviations and serves as a magnet for price returns, often referred to as a "PL Dot Refresh."
Envelope Theory : Considers price movements as cycles oscillating between the Envelope Top and Bottom. Prices breaking these boundaries often indicate trends, retracements, or exhaustion.
The geometry helps traders visualize energy flows in the market and anticipate directional changes using established support and resistance zones.
Understanding PL Dot and Envelope Top/Bottom
PL Dot:
Formula: Average(Average(H, L, C) of last three bars)
Usage: Indicates short-term trends:
Trend: PL Dot slopes upward or downward.
Congestion: PL Dot moves horizontally.
Envelope Top and Bottom:
Formula:
Top: (11 H1 + 11 H2 + 11 H3) / 3
Bottom: (11 L1 + 11 L2 + 11 L3) / 3
Usage: Acts as dynamic resistance and support:
Price above the top: Indicates strong bullish momentum.
Price below the bottom: Indicates strong bearish momentum.
Advantages of Drummond Geometry
Clarity of Market Flow: Highlights the relationship between price and key levels (PL Dot, Envelope Top/Bottom).
Predictive Power: Suggests possible reversals or continuation based on energy distribution.
Adaptability: Works across multiple time frames and market types (trending, congestion).
Trading Strategy
PL Dot Trades:
Buy: When price returns to the PL Dot in an uptrend.
Sell: When price returns to the PL Dot in a downtrend.
Envelope Trades:
Reversal: Trade counter to price if it breaks and retreats from the Envelope Top/Bottom.
Continuation: Trade in the direction of price if it sustains movement beyond the Envelope Top/Bottom.
Bitcoin Cycle Master [InvestorUnknown]The "Bitcoin Cycle Master" indicator is designed for in-depth, long-term analysis of Bitcoin's price cycles, using several key metrics to track market behavior and forecast potential price tops and bottoms. The indicator integrates multiple moving averages and on-chain metrics, offering a comprehensive view of Bitcoin’s historical and projected performance. Each of its components plays a crucial role in identifying critical cycle points:
Top Cap: This is a multiple of the Average Cap, which is calculated as the cumulative sum of Bitcoin’s price (price has a longer history than Market Cap) divided by its age in days. Top Cap serves as an upper boundary for speculative price peaks, multiplied by a factor of 35.
Time_dif() =>
date = ta.valuewhen(bar_index == 0, time, 0)
sec_r = math.floor(date / 1000)
min_r = math.floor(sec_r / 60)
h_r = math.floor(min_r / 60)
d_r = math.floor(h_r / 24)
// Launch of BTC
start = timestamp(2009, 1, 3, 00, 00)
sec_rb = math.floor(start / 1000)
min_rb = math.floor(sec_rb / 60)
h_rb = math.floor(min_rb / 60)
d_rb = math.floor(h_rb / 24)
difference = d_r - d_rb
AverageCap() =>
ta.cum(btc_price) / (Time_dif() + btc_age)
TopCap() =>
// To calculate Top Cap, it is first necessary to calculate Average Cap, which is the cumulative sum of Market Cap divided by the age of the market in days.
// This creates a constant time-based moving average of market cap.
// Once Average cap is calculated, those values are multiplied by 35. The result is Top Cap.
// For AverageCap the BTC price was used instead of the MC because it has more history
// (the result should have minimal if any deviation since MC would have to be divided by Supply)
AverageCap() * 35
Delta Top: Defined as the difference between the Realized Cap and the Average Cap, this metric is further multiplied by a factor of 7. Delta Top provides a historically reliable signal for Bitcoin market cycle tops.
DeltaTop() =>
// Delta Cap = Realized Cap - Average Cap
// Average Cap is explained in the Top Cap section above.
// Once Delta Cap is calculated, its values over time are then multiplied by 7. The result is Delta Top.
(RealizedPrice() - AverageCap()) * 7
Terminal Price: Derived from Coin Days Destroyed, Terminal Price normalizes Bitcoin’s historical price behavior by its finite supply (21 million bitcoins), offering an adjusted price forecast as all bitcoins approach being mined. The original formula for Terminal Price didn’t produce expected results, hence the calculation was adjusted slightly.
CVDD() =>
// CVDD stands for Cumulative Value Coin Days Destroyed.
// Coin Days Destroyed is a term used for bitcoin to identify a value of sorts to UTXO’s (unspent transaction outputs). They can be thought of as coins moving between wallets.
(MCR - TV) / 21000000
TerminalPrice() =>
// Theory:
// Before Terminal price is calculated, it is first necessary to calculate Transferred Price.
// Transferred price takes the sum of > Coin Days Destroyed and divides it by the existing supply of bitcoin and the time it has been in circulation.
// The value of Transferred Price is then multiplied by 21. Remember that there can only ever be 21 million bitcoin mined.
// This creates a 'terminal' value as the supply is all mined, a kind of reverse supply adjustment.
// Instead of heavily weighting later behavior, it normalizes historical behavior to today. By normalizing by 21, a terminal value is created
// Unfortunately the theoretical calculation didn't produce results it should, in pinescript.
// Therefore the calculation was slightly adjusted/improvised
TransferredPrice = CVDD() / (Supply * math.log(btc_age))
tp = TransferredPrice * 210000000 * 3
Realized Price: Calculated as the Market Cap Realized divided by the current supply of Bitcoin, this metric shows the average value of Bitcoin based on the price at which coins last moved, giving a market consensus price for long-term holders.
CVDD (Cumulative Value Coin Days Destroyed): This on-chain metric analyzes Bitcoin’s UTXOs (unspent transaction outputs) and the velocity of coins moving between wallets. It highlights key market dynamics during prolonged accumulation or distribution phases.
Balanced Price: The Balanced Price is the difference between the Realized Price and the Terminal Price, adjusted by Bitcoin's supply constraints. This metric provides a useful signal for identifying oversold market conditions during bear markets.
BalancedPrice() =>
// It is calculated by subtracting Transferred Price from Realized Price
RealizedPrice() - (TerminalPrice() / (21 * 3))
Each component can be toggled individually, allowing users to focus on specific aspects of Bitcoin’s price cycle and derive meaningful insights from its long-term behavior. The combination of these models provides a well-rounded view of both speculative peaks and long-term value trends.
Important consideration:
Top Cap did historically provide reliable signals for cycle peaks, however it may not be a relevant indication of peaks in the future.
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
█ Brief Overview of the Goertzel Browser
The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing.
5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used.
Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Browser Code
The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in matrices:
The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast and WindowSizeFuture:
The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for past and future waveforms:
Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively.
Calculating composite waveform for past bars (goertzel array):
The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Calculating composite waveform for future bars (goertzelFuture array):
The future composite waveform is calculated in a similar way as the past composite waveform.
Drawing past composite waveform (pvlines):
The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
Drawing future composite waveform (fvlines):
The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward).
Displaying cycle information in a table (table3):
A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
Filling the table with cycle information:
The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places.
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions.
Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period.
The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend.
The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series.
Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions.
This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time.
The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals.
█ Conclusion
The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
The first term represents the deviation of the data from the trend.
The second term represents the smoothness of the trend.
λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Williams Vix Fix BB + RVI + LinReg & Squeeze (Keltner) BBW + %BLegend:
Entery signal: When line color turns to lime (lighter green) after a blue dot appears
Exit signal: When line color turns to red (darker red) after a red dot appears
Note: it is more affective as an entry signal (Bottom is stronger)
- When line touches or crosses red band it is Top signal (Williams Vix Fix)
- When line touches or crosses blue band it is Bottom signal (Williams Vix Fix)
- Red dot at the top of indicator is a Top signal (Relative Volatility Index)
- Blue dot at the top of indicator is a Bottom signal (Relative Volatility Index)
- Gray dot at the bottom of indicator is a Keltner Squeeze signal (filtered by either BBW or %B)
- Silver dot at the bottom of indicator is a weaker Keltner Squeeze signal (Doesn't meet either BBW or %B filter)
- Purple is a 'Half Squeeze' only 1 Bollinger Band crossed the Keltner Channel
This is an attempt to make use of the main features of all 6 of these Volatility tools:
- Williams Vix Fix + Bollinger Bands
- Relative Volatility Index (RVI)
- Linear Regression (detects Vix Fix starts to rise or fall to a certain degree in order to help validate bottom/top)
Note : There is also added precision on Linear Regression entry by dividing WVF by square roots of basis.
- The crossing of Keltner Channel by the Bollinger Bands (Squeeze)
Conditions to Help Filter Keltner Squeeze:
- When the Bollinger Bands Width (BBW) value is lower than the lowest value within a period plus a margin of error (percentage)
- When the %B value goes up or down by the impulse value (threshold value in setting) detailed in LazyBears indicator. (www.tradingview.com)
If it meets one of these 2 filters and there is a Keltner Channel Squeeze than gray color or else if the squeeze doesn’t meet one of the 2 filters than silver color (weaker Squeeze).
The goal is to find the best tool to find bottoms and top relative to volatility and filter squeeze.
Note: You can also change the threshold for RVI top and bottom.
And this work builds on my last indicators:
- Williams Vix Fix + BB & RVI (Top/Bottom) & Squeeze ()
- Williams Vix Fix BB + RVI & Squeeze (Keltner) filtered BBW + %B ()
If you have ideas on this work or have ideas on potential combinations please message me, I always want to learn or get perspective on how it can be improved.
Sharing is how we get better (Parameter tuning, ideas, discussion)
I don’t reinvent the wheel, just trying to make the wheel better.
Penny Stock Short Signal Pro# Penny Stock Short Signal Pro (PSSP) v1.0
## Complete User Guide & Documentation
---
# 📋 TABLE OF CONTENTS
1. (#introduction)
2. (#why-short-penny-stocks)
3. (#the-7-core-detection-systems)
4. (#installation--setup)
5. (#understanding-the-dashboard)
6. (#input-settings-deep-dive)
7. (#visual-elements-explained)
8. (#alert-configuration)
9. (#trading-strategies)
10. (#risk-management)
11. (#best-practices)
12. (#troubleshooting)
13. (#changelog)
---
# Introduction
**Penny Stock Short Signal Pro (PSSP)** is a comprehensive Pine Script v6 indicator specifically engineered for identifying high-probability short-selling opportunities on low-priced, high-volatility stocks. Unlike generic indicators that apply broad technical analysis, PSSP is purpose-built for the unique characteristics of penny stock price action—where parabolic moves, retail FOMO, and violent reversals create predictable patterns for prepared traders.
## Key Features
- **7 Independent Detection Systems** working in concert to identify exhaustion points
- **Composite Signal Engine** that requires multiple confirmations before triggering
- **Real-Time Dashboard** displaying all signal states and market metrics
- **Automatic Risk Management** with dynamic stop-loss and profit target calculations
- **Customizable Sensitivity** for different trading styles (scalping vs. swing)
- **Built-in Alert System** for all major signal types
## Who Is This For?
- **Active Day Traders** looking to capitalize on intraday reversals
- **Short Sellers** who specialize in penny stocks and small caps
- **Momentum Traders** who want to identify when momentum is exhausting
- **Risk-Conscious Traders** who need clear entry/exit levels
---
# Why Short Penny Stocks?
## The Penny Stock Lifecycle
Penny stocks follow a remarkably predictable lifecycle that creates shorting opportunities:
```
PHASE 1: ACCUMULATION
└── Low volume, tight range
└── Smart money quietly building positions
PHASE 2: MARKUP / PROMOTION
└── News catalyst or promotional campaign
└── Volume increases, price begins rising
└── Early momentum traders enter
PHASE 3: DISTRIBUTION (YOUR OPPORTUNITY)
└── Parabolic move attracts retail FOMO buyers
└── Smart money selling into strength
└── Volume climax signals exhaustion
└── ⚠️ PSSP SIGNALS FIRE HERE ⚠️
PHASE 4: DECLINE
└── Support breaks, panic selling
└── Price returns toward origin
└── Short sellers profit
```
## Why Shorts Work on Penny Stocks
1. **No Fundamental Support**: Most penny stocks have no earnings, revenue, or assets to justify elevated prices
2. **Promotional Nature**: Many rallies are driven by promoters who will eventually stop
3. **Retail Exhaustion**: Retail buying power is finite—when it's exhausted, gravity takes over
4. **Float Dynamics**: Low float stocks move fast in both directions
5. **Technical Levels Matter**: VWAP, round numbers, and prior highs become self-fulfilling resistance
---
# The 7 Core Detection Systems
PSSP employs seven independent detection algorithms. Each identifies a specific type of exhaustion or reversal signal. When multiple systems fire simultaneously, the probability of a successful short dramatically increases.
---
## 1. PARABOLIC EXHAUSTION DETECTOR
### What It Detects
Identifies when price has moved too far, too fast and is likely to reverse. This system looks for the classic "blow-off top" pattern common in penny stock runners.
### Technical Logic
```
Parabolic Signal = TRUE when:
├── Consecutive green candles ≥ threshold (default: 3)
├── AND price extension from VWAP ≥ threshold ATRs (default: 1.5)
└── OR shooting star / upper wick rejection pattern forms
```
### Visual Representation
```
╱╲ ← Shooting star / upper wick
╱ ╲ (Parabolic exhaustion)
╱
╱
╱
══════════════ VWAP
╱
╱
```
### Why It Works on Penny Stocks
Penny stocks are notorious for parabolic moves driven by retail FOMO. When everyone who wants to buy has bought, there's no one left to push prices higher. The shooting star pattern shows that sellers are already stepping in at higher prices.
### Key Settings
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| Lookback Period | 10 | 3-30 | Bars to analyze for pattern |
| Extension Threshold | 1.5 ATR | 0.5-5.0 | How far above VWAP is "parabolic" |
| Consecutive Green Bars | 3 | 2-10 | Minimum green bars for exhaustion |
---
## 2. VWAP REJECTION SYSTEM
### What It Detects
Volume Weighted Average Price (VWAP) is the single most important level for institutional traders. This system identifies when price tests above VWAP and gets rejected back below—a powerful short signal.
### Technical Logic
```
VWAP Rejection = TRUE when:
├── Candle high pierces above VWAP
├── AND candle closes below VWAP
├── AND candle is bearish (close < open)
└── AND rejection distance is within sensitivity threshold
```
### Visual Representation
```
High ──→ ╱╲
╱ ╲
VWAP ════════╱════╲═══════════
Close ←── Rejection
```
### Extended VWAP Signals
The system also tracks VWAP standard deviation bands. Rejection from the upper band (2 standard deviations above VWAP) is an even stronger signal.
### Why It Works on Penny Stocks
- Algorithms and institutions use VWAP as their benchmark
- Failed attempts to reclaim VWAP often lead to waterfall selling
- VWAP acts as a "magnet" that price tends to revert toward
### Key Settings
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| Rejection Sensitivity | 0.5 ATR | 0.1-2.0 | How close to VWAP for valid rejection |
| Show VWAP Line | True | - | Display VWAP on chart |
| Show VWAP Bands | True | - | Display standard deviation bands |
| Band Multiplier | 2.0 | 0.5-4.0 | Standard deviations for bands |
---
## 3. VOLUME CLIMAX DETECTOR
### What It Detects
Identifies "blow-off tops" where extreme volume accompanies a price spike. This often marks the exact top as it represents maximum retail participation—after which buying power is exhausted.
### Technical Logic
```
Volume Climax = TRUE when:
├── Current volume ≥ (Average volume × Climax Multiple)
├── AND one of:
│ ├── Selling into the high (upper wick > lower wick on green bar)
│ └── OR post-climax weakness (red bar following climax bar)
```
### Visual Representation
```
Price: ╱╲
╱ ╲
╱ ╲
╱ ╲
╱
Volume:
▂▃▅▇██▇▅▃▂▁
↑
Volume Climax (3x+ average)
```
### Why It Works on Penny Stocks
- Retail traders pile in at the top, creating volume spikes
- Market makers and smart money use this liquidity to exit
- Once the volume spike passes, there's no fuel left for higher prices
- The "smart money selling into dumb money buying" creates the top
### Key Settings
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| Volume MA Length | 20 | 5-50 | Period for average volume calculation |
| Climax Volume Multiple | 3.0x | 1.5-10.0 | Multiple of average for "climax" |
| Show Volume Bars | True | - | Visual volume representation |
---
## 4. RSI DIVERGENCE ANALYZER
### What It Detects
Bearish divergence occurs when price makes higher highs but RSI (momentum) makes lower highs. This indicates that momentum is weakening even as price pushes higher—a warning of imminent reversal.
### Technical Logic
```
Bearish Divergence = TRUE when:
├── RSI is in overbought territory (> threshold)
├── AND RSI is declining (current < previous < prior)
└── Indicates momentum exhaustion before price catches up
```
### Visual Representation
```
Price: /\ /\
/ \ / \ ← Higher high
/ \/
/
/
RSI: /\
/ \ /\
/ \/ \ ← Lower high (DIVERGENCE)
/ \
════════════════════ Overbought (70)
```
### Why It Works on Penny Stocks
- Penny stocks often push to new highs on weaker and weaker momentum
- Divergence signals that fewer buyers are participating at each new high
- Eventually, the lack of buying pressure leads to collapse
### Key Settings
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| RSI Length | 14 | 5-30 | Standard RSI calculation period |
| Overbought Level | 70 | 60-90 | RSI level considered overbought |
| Divergence Lookback | 14 | 5-30 | Bars to look back for swing highs |
---
## 5. KEY LEVEL REJECTION TRACKER
### What It Detects
Identifies rejections from significant price levels where shorts are likely to be concentrated: High of Day (HOD), premarket highs, and psychological levels (whole and half dollars).
### Technical Logic
```
Level Rejection = TRUE when:
├── Price touches key level (within 0.2% tolerance)
├── AND candle is bearish (close < open)
├── AND close is in lower portion of candle range
│
├── Key Levels Tracked:
│ ├── High of Day (HOD)
│ ├── Premarket High
│ └── Psychological levels ($1.00, $1.50, $2.00, etc.)
```
### Visual Representation
```
HOD ─────────────────────────────────
╱╲ ← Rejection
╱ ╲
╱ ╲
╱
─────────────────────────────────
PM High ─────────────────────────────
```
### Why It Works on Penny Stocks
- **HOD**: The high of day is where the most traders are trapped long. Failure to break HOD often triggers stop-loss cascades
- **Premarket High**: Represents overnight enthusiasm; failure to exceed often means the "news" is priced in
- **Psychological Levels**: Round numbers ($1, $2, $5) attract orders and act as natural resistance
### Key Settings
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| Track HOD Rejection | True | - | Monitor high of day |
| Track Premarket High | True | - | Monitor premarket resistance |
| Track Psychological Levels | True | - | Monitor round numbers |
---
## 6. FAILED BREAKOUT DETECTOR
### What It Detects
Identifies "bull traps" where price breaks above resistance but immediately fails and closes back below. This traps breakout buyers and often leads to accelerated selling.
### Technical Logic
```
Failed Breakout = TRUE when:
├── Price breaks above recent high (lookback period)
├── AND one of:
│ ├── Same bar closes below the breakout level
│ └── OR following bars show consecutive red candles
```
### Visual Representation
```
╱╲
╱ ╲ ← False breakout
Recent High ══╱════╲════════════════
╱ ╲
╱ ╲
╱ ╲ ← Trapped longs panic sell
```
### Why It Works on Penny Stocks
- Breakout traders enter on the break, providing exit liquidity for smart money
- When the breakout fails, these traders become trapped and must exit
- Their forced selling accelerates the decline
- Penny stocks have thin order books, making failed breakouts especially violent
### Key Settings
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| Breakout Lookback | 5 | 2-15 | Bars to define "recent high" |
| Confirmation Bars | 2 | 1-5 | Bars to confirm failure |
---
## 7. MOVING AVERAGE BREAKDOWN SYSTEM
### What It Detects
Monitors exponential moving averages (EMAs) for bearish crossovers and price rejections. EMA crosses often signal trend changes, while rejections from EMAs indicate resistance.
### Technical Logic
```
MA Breakdown = TRUE when:
├── Bearish EMA cross (fast crosses below slow)
└── OR EMA rejection (price tests EMA from below and fails)
```
### Visual Representation
```
╱╲ ← Rejection from EMA
╱ ╲
EMA 9 ═══════════╱════╲═══════════
╲
EMA 20 ═══════════════════╲════════
╲
Bearish cross ↓
```
### Why It Works on Penny Stocks
- EMAs smooth out the noise and show underlying trend direction
- When fast EMA crosses below slow EMA, it signals momentum shift
- Rejected attempts to reclaim EMAs show sellers are in control
### Key Settings
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| Fast EMA | 9 | 3-20 | Short-term trend |
| Slow EMA | 20 | 10-50 | Medium-term trend |
| Show EMAs | True | - | Display on chart |
---
# Installation & Setup
## Step 1: Access Pine Editor
1. Open TradingView (tradingview.com)
2. Open any chart
3. Click "Pine Editor" at the bottom of the screen
## Step 2: Create New Indicator
1. Click "Open" → "New blank indicator"
2. Delete any existing code
3. Paste the entire PSSP code
## Step 3: Save and Add to Chart
1. Click "Save" (give it a name like "PSSP")
2. Click "Add to chart"
3. The indicator will appear with default settings
## Step 4: Configure Settings
1. Click the gear icon (⚙️) on the indicator
2. Adjust settings based on your trading style (see Settings section)
3. Click "OK" to apply
## Recommended Chart Setup
- **Timeframe**: 1-minute or 5-minute for scalping, 15-minute for swing shorts
- **Chart Type**: Candlestick
- **Extended Hours**: Enable if trading premarket/afterhours
- **Volume**: Can disable default volume since PSSP tracks it
---
# Understanding the Dashboard
The real-time dashboard provides at-a-glance status of all systems:
```
┌─────────────────────────────────────────┐
│ 📊 SHORT SIGNAL DASHBOARD │
├─────────────────────────────────────────┤
│ Signal Strength: 5/7 │
├─────────────────────────────────────────┤
│ ─── ACTIVE SIGNALS ─── │
│ │
│ Parabolic Exhaustion 🔴 2.1 ATR │
│ VWAP Rejection 🔴 Above │
│ Volume Climax 🔴 4.2x Avg │
│ RSI Divergence ⚪ RSI: 68 │
│ Level Rejection 🔴 @ HOD │
│ Failed Breakout 🔴 │
│ MA Breakdown ⚪ Bullish │
├─────────────────────────────────────────┤
│ ─── RISK LEVELS ─── │
│ Stop: $2.45 T1: $2.10 T2: $1.85 │
└─────────────────────────────────────────┘
```
## Dashboard Elements Explained
### Signal Strength Indicator
| Rating | Signals | Color | Interpretation |
|--------|---------|-------|----------------|
| STRONG | 5-7 | Red | High-confidence short opportunity |
| MODERATE | 3-4 | Orange | Decent setup, consider other factors |
| WEAK | 1-2 | Gray | Insufficient confirmation |
| NONE | 0 | Gray | No short signals active |
### Signal Status Icons
- 🔴 = Signal is ACTIVE (condition met)
- ⚪ = Signal is INACTIVE (condition not met)
### Contextual Metrics
Each signal row includes relevant metrics:
- **Parabolic**: Shows ATR extension from VWAP
- **VWAP**: Shows if price is Above/Below VWAP
- **Volume**: Shows current volume as multiple of average
- **RSI**: Shows current RSI value
- **Level**: Shows which level was touched (HOD, PM High, etc.)
- **MA**: Shows EMA relationship (Bullish/Bearish)
### Risk Levels
When a composite short signal fires:
- **Stop**: Suggested stop-loss level (high + ATR multiple)
- **T1**: First profit target (1:1 risk/reward)
- **T2**: Second profit target (user-defined R:R)
---
# Input Settings Deep Dive
## Group 1: Parabolic Exhaustion
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Enable | True | True | True | Turn system on/off |
| Lookback Period | 10 | 15 | 5 | Bars analyzed for pattern |
| Extension Threshold | 1.5 | 2.0 | 1.0 | ATRs above VWAP for "parabolic" |
| Consecutive Green Bars | 3 | 4 | 2 | Minimum green bars required |
**Tuning Tips:**
- Lower thresholds = more signals but more false positives
- Higher thresholds = fewer signals but higher quality
- For very volatile penny stocks, consider higher thresholds
## Group 2: VWAP Rejection
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Enable | True | True | True | Turn system on/off |
| Rejection Sensitivity | 0.5 | 0.3 | 0.8 | ATR distance for valid rejection |
| Show VWAP Line | True | True | True | Display VWAP |
| Show VWAP Bands | True | True | True | Display deviation bands |
| Band Multiplier | 2.0 | 2.5 | 1.5 | Standard deviations for bands |
**Tuning Tips:**
- Tighter sensitivity (lower number) = must reject very close to VWAP
- Wider bands = less frequent upper band rejections but more significant
## Group 3: Volume Climax
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Enable | True | True | True | Turn system on/off |
| Volume MA Length | 20 | 30 | 10 | Baseline volume period |
| Climax Volume Multiple | 3.0 | 4.0 | 2.0 | Multiple for "climax" status |
| Show Volume Profile | True | True | True | Visual volume bars |
**Tuning Tips:**
- Higher multiple = only extreme volume spikes trigger
- Shorter MA = more responsive to recent volume changes
- For highly liquid stocks, consider higher multiples
## Group 4: Momentum Divergence
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Enable | True | True | True | Turn system on/off |
| RSI Length | 14 | 21 | 7 | RSI calculation period |
| Overbought Level | 70 | 75 | 65 | Threshold for "overbought" |
| Divergence Lookback | 14 | 20 | 10 | Bars for swing high detection |
**Tuning Tips:**
- Lower overbought threshold = more frequent signals
- Shorter RSI length = more responsive but noisier
## Group 5: Key Level Rejection
| Setting | Default | Description |
|---------|---------|-------------|
| Enable | True | Master toggle for level system |
| Track Premarket High | True | Monitor premarket resistance |
| Track HOD Rejection | True | Monitor high of day |
| Track Psychological Levels | True | Monitor round numbers |
**Tuning Tips:**
- Disable premarket tracking if stock doesn't have significant premarket activity
- Psychological levels work best on stocks under $10
## Group 6: Failed Follow-Through
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Enable | True | True | True | Turn system on/off |
| Breakout Lookback | 5 | 8 | 3 | Bars defining "recent high" |
| Confirmation Bars | 2 | 3 | 1 | Bars to confirm failure |
**Tuning Tips:**
- Shorter lookback = more breakouts detected but smaller significance
- More confirmation bars = higher confidence but later entry
## Group 7: Moving Average Signals
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Enable | True | True | True | Turn system on/off |
| Fast EMA | 9 | 12 | 5 | Short-term trend |
| Slow EMA | 20 | 26 | 13 | Medium-term trend |
| Show EMAs | True | True | True | Display on chart |
**Tuning Tips:**
- Standard 9/20 works well for most penny stocks
- Faster EMAs (5/13) for scalping, slower (12/26) for swing trading
## Group 8: Composite Signal
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Minimum Signals | 3 | 4-5 | 2 | Signals needed for trigger |
| Show Dashboard | True | True | True | Display signal table |
| Dashboard Position | top_right | - | - | Screen location |
**Tuning Tips:**
- **Minimum Signals is the most important setting**
- Higher minimum = fewer trades but higher win rate
- Lower minimum = more trades but more false signals
## Group 9: Risk Management
| Setting | Default | Conservative | Aggressive | Description |
|---------|---------|--------------|------------|-------------|
| Show Stop Levels | True | True | True | Display stop loss |
| Stop ATR Multiple | 1.5 | 2.0 | 1.0 | Stop distance in ATRs |
| Show Targets | True | True | True | Display profit targets |
| Target R:R | 2.0 | 1.5 | 3.0 | Risk:Reward for Target 2 |
**Tuning Tips:**
- Tighter stops (lower ATR multiple) = less risk but more stop-outs
- Higher R:R targets = bigger winners but fewer targets hit
## Group 10: Visual Settings
| Setting | Default | Description |
|---------|---------|-------------|
| Bullish Color | Green | Color for bullish elements |
| Bearish Color | Red | Color for bearish/short signals |
| Warning Color | Orange | Color for caution signals |
| Neutral Color | Gray | Color for inactive elements |
---
# Visual Elements Explained
## Chart Overlays
### VWAP Line (Blue)
- **Solid blue line** = Volume Weighted Average Price
- Price above VWAP = bullish bias
- Price below VWAP = bearish bias
- **Use**: Short when price rejects from above VWAP
### VWAP Bands (Purple circles)
- Upper band = 2 standard deviations above VWAP
- Lower band = 2 standard deviations below VWAP
- **Use**: Extreme extension to upper band signals potential reversal
### EMAs (Orange and Red)
- **Orange line** = Fast EMA (9-period default)
- **Red line** = Slow EMA (20-period default)
- **Use**: Bearish cross or price rejection from EMAs confirms short
### HOD Line (Red, dashed)
- Shows the current day's high
- **Use**: Rejection from HOD is a key short signal
### Premarket High (Orange, dashed)
- Shows premarket session high
- **Use**: Failure to break PM high often signals weakness
## Signal Markers
### Individual Signal Markers (Small)
| Shape | Color | Signal |
|-------|-------|--------|
| ▼ Triangle | Purple | Parabolic Exhaustion |
| ✕ X-Cross | Blue | VWAP Rejection |
| ◆ Diamond | Yellow | Volume Climax |
| ● Circle | Orange | RSI Divergence |
| ■ Square | Red | Failed Breakout |
### Composite Short Signal (Large)
- **Large red triangle** with "SHORT" text
- Only appears when minimum signal threshold is met
- This is your primary trading signal
## Risk Level Lines
### Stop Loss (Red line)
- Calculated as: Entry + (ATR × Stop Multiple)
- Represents maximum acceptable loss
- **RESPECT THIS LEVEL**
### Target 1 (Light green line)
- First profit target at 1:1 risk/reward
- Consider taking partial profits here
### Target 2 (Dark green line)
- Second profit target at user-defined R:R
- Let winners run to this level
## Background Coloring
### Light Red Background
- Appears when composite short signal is active
- Indicates you should be looking for shorts, not longs
### Light Purple Background
- Appears during extreme parabolic extension
- Warning of potential imminent reversal
---
# Alert Configuration
## Available Alerts
### 1. Composite Short Signal
**Best for**: Primary trading signal
```
Condition: Composite short signal fires
Message: "PSSP: Short Signal Triggered - {ticker} at {close}"
```
### 2. Parabolic Exhaustion
**Best for**: Early warning of potential top
```
Condition: Parabolic exhaustion detected
Message: "PSSP: Parabolic exhaustion detected on {ticker}"
```
### 3. Volume Climax
**Best for**: Blow-off top identification
```
Condition: Volume climax occurs
Message: "PSSP: Volume climax / blow-off top on {ticker}"
```
### 4. Strong Short Setup (5+ Signals)
**Best for**: High-confidence opportunities only
```
Condition: 5 or more signals active
Message: "PSSP: STRONG short setup on {ticker}"
```
### 5. Very Strong Short Setup (6+ Signals)
**Best for**: Maximum confidence trades
```
Condition: 6 or more signals active
Message: "PSSP: VERY STRONG short setup on {ticker}"
```
### 6. Failed Breakout
**Best for**: Bull trap identification
```
Condition: Failed breakout detected
Message: "PSSP: Failed breakout detected on {ticker}"
```
### 7. Key Level Rejection
**Best for**: Resistance level plays
```
Condition: Key level rejection occurs
Message: "PSSP: Key level rejection on {ticker}"
```
## Setting Up Alerts in TradingView
1. Right-click on the chart
2. Select "Add Alert"
3. Set Condition to "Penny Stock Short Signal Pro"
4. Choose your desired alert condition
5. Configure notification method (popup, email, webhook, etc.)
6. Set expiration (or "Open-ended" for permanent)
7. Click "Create"
## Alert Strategy Recommendations
### For Active Day Traders
- Enable: Composite Short Signal, Volume Climax
- Set to: Popup + Sound
- Check frequently during market hours
### For Swing Traders
- Enable: Strong Short Setup (5+), Very Strong Short Setup (6+)
- Set to: Email + Mobile Push
- Review at key times (open, lunch, close)
### For Part-Time Traders
- Enable: Very Strong Short Setup (6+) only
- Set to: Email + SMS
- Only trade highest-conviction setups
---
# Trading Strategies
## Strategy 1: The Parabolic Fade
**Setup Requirements:**
- Parabolic Exhaustion signal ACTIVE
- Extension from VWAP ≥ 2.0 ATR
- Volume climax or declining volume on push
**Entry:**
- Short on first red candle after signal
- Or short on break below prior candle's low
**Stop Loss:**
- Above the high of the parabolic move
- Maximum: 1.5 ATR above entry
**Targets:**
- T1: VWAP (take 50% off)
- T2: Lower VWAP band or LOD
**Best Time:** 9:30-10:30 AM (morning runners)
---
## Strategy 2: VWAP Rejection Short
**Setup Requirements:**
- VWAP Rejection signal ACTIVE
- Price came from below VWAP
- Rejection candle has significant upper wick
**Entry:**
- Short on close below VWAP
- Or short on break below rejection candle low
**Stop Loss:**
- Above VWAP + 0.5 ATR
- Or above rejection candle high
**Targets:**
- T1: Lower VWAP band
- T2: Prior support or LOD
**Best Time:** Midday (11:00 AM - 2:00 PM)
---
## Strategy 3: HOD Failure Short
**Setup Requirements:**
- Level Rejection signal ACTIVE (HOD)
- Multiple tests of HOD without breakthrough
- Volume declining on each test
**Entry:**
- Short on confirmed HOD rejection
- Wait for close below the rejection candle
**Stop Loss:**
- Above HOD + 0.25 ATR (tight)
- Clear invalidation if HOD breaks
**Targets:**
- T1: VWAP
- T2: Morning support levels
**Best Time:** 10:30 AM - 12:00 PM
---
## Strategy 4: Volume Climax Fade
**Setup Requirements:**
- Volume Climax signal ACTIVE
- Volume ≥ 3x average on green candle
- Followed by bearish candle or upper wick
**Entry:**
- Short on first red candle after climax
- Or short on break below climax candle low
**Stop Loss:**
- Above climax candle high
- Give room for volatility spike
**Targets:**
- T1: 50% retracement of the run
- T2: VWAP or start of the run
**Best Time:** First hour of trading
---
## Strategy 5: The Full Composite (High Conviction)
**Setup Requirements:**
- Composite Short signal ACTIVE
- Minimum 4-5 individual signals
- Clear visual of signal markers clustering
**Entry:**
- Short immediately on composite signal
- Use market order for fast-moving stocks
**Stop Loss:**
- Use indicator's automatic stop level
- Do not deviate from system
**Targets:**
- T1: Indicator's T1 level (1:1)
- T2: Indicator's T2 level (2:1)
**Best Time:** Any time with sufficient signals
---
# Risk Management
## Position Sizing Formula
```
Position Size = (Account Risk %) / (Stop Loss %)
Example:
- Account: $25,000
- Risk per trade: 1% = $250
- Entry: $2.00
- Stop: $2.20 (10% stop)
- Position Size: $250 / 10% = $2,500 worth
- Shares: $2,500 / $2.00 = 1,250 shares
```
## Risk Rules
### The 1% Rule
Never risk more than 1% of your account on any single trade. For a $25,000 account, max risk = $250.
### The 2x Stop Rule
If your stop gets hit twice on the same stock, stop trading it for the day. The pattern isn't working.
### The Daily Loss Limit
Set a maximum daily loss (e.g., 3% of account). Stop trading if hit.
### The Size-Down Rule
After a losing trade, reduce your next position size by 50%. Rebuild after a winner.
## Short-Specific Risks
### The Short Squeeze
- Penny stocks can squeeze violently
- ALWAYS use stops
- Never "hope" a position comes back
- Size appropriately for volatility
### The Hard-to-Borrow
- Check borrow availability before trading
- High borrow fees eat into profits
- Some stocks become HTB mid-trade
### The Halt Risk
- Penny stocks can halt on volatility
- Position size for worst-case halt against you
- Halts can open significantly higher
---
# Best Practices
## DO's
✅ **Wait for multiple signals** - Single signals have lower accuracy
✅ **Trade with the trend** - Short when daily trend is down
✅ **Use the dashboard** - Check signal count before entering
✅ **Respect stops** - The indicator calculates them for a reason
✅ **Size appropriately** - Penny stocks are volatile; position small
✅ **Trade liquid stocks** - Volume ≥ 500K daily average
✅ **Know the catalyst** - Understand why the stock is moving
✅ **Take partial profits** - Secure gains at T1
✅ **Journal your trades** - Track what works and what doesn't
✅ **Time your entries** - Best shorts often come 10:30-11:30 AM
## DON'Ts
❌ **Don't short strong stocks** - If it won't go down, don't force it
❌ **Don't fight the tape** - A stock going up can keep going up
❌ **Don't average up on losers** - Adding to losing shorts is dangerous
❌ **Don't ignore the dashboard** - It exists to help you
❌ **Don't overtrade** - Quality over quantity
❌ **Don't short into news** - Wait for the reaction first
❌ **Don't trade the first 5 minutes** - Too chaotic for reliable signals
❌ **Don't hold overnight** - Penny stock gaps can destroy accounts
❌ **Don't trade without stops** - Ever.
❌ **Don't trade on tilt** - After losses, take a break
## Optimal Trading Windows
| Time (ET) | Quality | Notes |
|-----------|---------|-------|
| 9:30-9:35 | ⭐ | Too volatile, avoid |
| 9:35-10:30 | ⭐⭐⭐⭐⭐ | Best shorts, morning runners exhaust |
| 10:30-11:30 | ⭐⭐⭐⭐ | Secondary exhaustion, HOD rejections |
| 11:30-2:00 | ⭐⭐ | Midday lull, lower quality |
| 2:00-3:00 | ⭐⭐⭐ | Afternoon setups develop |
| 3:00-3:30 | ⭐⭐⭐⭐ | End of day momentum |
| 3:30-4:00 | ⭐⭐ | Closing volatility, risky |
---
# Troubleshooting
## Common Issues
### "Signals aren't appearing"
- Check that the relevant system is enabled in settings
- Ensure minimum signals threshold isn't too high
- Verify the stock has sufficient volume for calculations
### "Too many false signals"
- Increase minimum signals threshold
- Use more conservative settings (see Settings section)
- Focus on stocks with cleaner price action
### "Dashboard not showing"
- Ensure "Show Signal Dashboard" is enabled
- Check that your chart has enough space
- Try a different dashboard position
### "VWAP line is missing"
- VWAP requires intraday timeframes (1m, 5m, 15m, etc.)
- VWAP resets daily; won't show on daily+ charts
- Ensure "Show VWAP Line" is enabled
### "Stop loss seems too tight/wide"
- Adjust Stop ATR Multiple in Risk Management settings
- Lower multiple = tighter stop
- Higher multiple = wider stop
### "Alerts not triggering"
- Verify alert is set to the correct indicator
- Check that alert hasn't expired
- Ensure notification settings are configured in TradingView
## Performance Optimization
If the indicator is slow:
1. Reduce the number of visual elements shown
2. Disable unused signal systems
3. Use on fewer simultaneous charts
4. Close unused browser tabs
---
# Changelog
## Version 1.0 (Initial Release)
- 7 core detection systems implemented
- Real-time signal dashboard
- Automatic risk management calculations
- 7 alert conditions
- Full visual overlay system
- Comprehensive input settings
## Planned Features (Future Updates)
- Scanner integration for multi-stock screening
- Machine learning signal weighting
- Backtesting statistics panel
- Volume profile analysis
- Level 2 data integration (if available)
- Custom timeframe VWAP options
---
# Support & Feedback
## Reporting Issues
When reporting issues, please include:
1. TradingView username
2. Stock symbol and timeframe
3. Screenshot of the issue
4. Your indicator settings
5. Steps to reproduce
## Feature Requests
We welcome suggestions for improving PSSP. Consider:
- What specific pattern are you trying to catch?
- How would this help your trading?
- Any reference examples?
---
# Disclaimer
**IMPORTANT: This indicator is for educational and informational purposes only.**
- Past performance does not guarantee future results
- Short selling carries unlimited risk potential
- Always use proper position sizing and stop losses
- Paper trade before using real capital
- The creator assumes no liability for trading losses
- Consult a financial advisor before trading
**Trade at your own risk.**
---
*Penny Stock Short Signal Pro v1.0*
*Pine Script v6*
*© 2025*
