Short Sellingell signal when RSI < 40, MACD crosses zero or signal line downward in negative zone, close below 50 EMA, candle bearish.
Strong sell signal confirmed on 5-minute higher timeframe with same conditions.
Square off half/full signals as defined.
Target lines drawn bold based on previous swing lows and extended as described.
Blue candle color when RSI below 30.
One sell and one full square off per cycle, blocking repeated sells until full square off.
"信达股份40周年"に関するスクリプトを検索
Profit booking Indicatorell signal when RSI < 40, MACD crosses zero or signal line downward in negative zone, close below 50 EMA, candle bearish.
Strong sell signal confirmed on 5-minute higher timeframe with same conditions.
Square off half/full signals as defined.
Target lines drawn bold based on previous swing lows and extended as described.
Blue candle color when RSI below 30.
One sell and one full square off per cycle, blocking repeated sells until full square off.
KT_Global Bond Yields by CountryGlobal Bond Yields Indicator Summary
The Global Bond Yields by Country indicator, developed for Trading View (Pine Script v5), provides a comprehensive tool for visualizing and analyzing government bond yields across multiple countries and maturities. Below are its key features:
Features
Country Selection: Choose from 20 countries, including the United States, China, Japan, Germany, United Kingdom, and more, to display their respective bond yields.
Multiple Maturities: Supports 18 bond maturities ranging from 1 month to 40 years, allowing users to analyze short-term to long-term yield trends.
Customizable Display:
Toggle visibility for each maturity (1M, 3M, 6M, 1Y, 2Y, 3Y, 4Y, 5Y, 6Y, 7Y, 8Y, 9Y, 10Y, 15Y, 20Y, 25Y, 30Y, 40Y) individually.
Option to show or hide all maturities with a single toggle for streamlined analysis.
10Y-2Y Yield Spread: Plots the difference between 10-year and 2-year bond yields, a key indicator of yield curve dynamics, with an option to enable/disable.
Zero Line Reference: Displays a dashed grey horizontal line at zero for clear visual reference.
Color-Coded Plots: Each maturity is plotted with a distinct color, ranging from lighter shades (short-term) to darker shades (long-term), for easy differentiation.
Country Label: Displays the selected country's name as a large, prominent label on the chart for quick identification.
Error Handling: Alerts users if an invalid country is selected, ensuring robust operation.
Data Integration: Fetches bond yield data from Trading View's database (e.g., TVC:US10Y) with support for ignoring invalid symbols to prevent errors.
This indicator is ideal for traders and analysts monitoring global fixed-income markets, yield curve shapes, and cross-country comparisons.
Bar Index & TimeLibrary to convert a bar index to a timestamp and vice versa.
Utilizes runtime memory to store the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 values of every bar on the chart (and optional future bars), with the ability of storing additional custom values for every chart bar.
█ PREFACE
This library aims to tackle some problems that pine coders (from beginners to advanced) often come across, such as:
I'm trying to draw an object with a 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 that is more than 10,000 bars into the past, but this causes my script to fail. How can I convert the 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 to a UNIX time so that I can draw visuals using xloc.bar_time ?
I have a diagonal line drawing and I want to get the "y" value at a specific time, but line.get_price() only accepts a bar index value. How can I convert the timestamp into a bar index value so that I can still use this function?
I want to get a previous 𝚘𝚙𝚎𝚗 value that occurred at a specific timestamp. How can I convert the timestamp into a historical offset so that I can use 𝚘𝚙𝚎𝚗 ?
I want to reference a very old value for a variable. How can I access a previous value that is older than the maximum historical buffer size of 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎 ?
This library can solve the above problems (and many more) with the addition of a few lines of code, rather than requiring the coder to refactor their script to accommodate the limitations.
█ OVERVIEW
The core functionality provided is conversion between xloc.bar_index and xloc.bar_time values.
The main component of the library is the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object, created via the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function which basically stores the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 of every bar on the chart, and there are 3 more overloads to this function that allow collecting and storing additional data. Once a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object is created, use any of the exported methods:
Methods to convert a UNIX timestamp into a bar index or bar offset:
𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙽𝚞𝚖𝚋𝚎𝚛𝙾𝚏𝙱𝚊𝚛𝚜𝙱𝚊𝚌𝚔()
Methods to retrieve the stored data for a bar index:
𝚝𝚒𝚖𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚟𝚊𝚕𝚞𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙰𝚕𝚕𝚅𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡()
Methods to retrieve the stored data at a number of bars back (i.e., historical offset):
𝚝𝚒𝚖𝚎(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎(), 𝚟𝚊𝚕𝚞𝚎()
Methods to retrieve all the data points from the earliest bar (or latest bar) stored in memory, which can be useful for debugging purposes:
𝚐𝚎𝚝𝙴𝚊𝚛𝚕𝚒𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊(), 𝚐𝚎𝚝𝙻𝚊𝚝𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊()
Note: the library's strong suit is referencing data from very old bars in the past, which is especially useful for scripts that perform its necessary calculations only on the last bar.
█ USAGE
Step 1
Import the library. Replace with the latest available version number for this library.
//@version=6
indicator("Usage")
import n00btraders/ChartData/
Step 2
Create a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object to collect data on every bar. Do not declare as `var` or `varip`.
chartData = ChartData.collectChartData() // call on every bar to accumulate the necessary data
Step 3
Call any method(s) on the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object. Do not modify its fields directly.
if barstate.islast
int firstBarTime = chartData.timeAtBarIndex(0)
int lastBarTime = chartData.time(0)
log.info("First `time`: " + str.format_time(firstBarTime) + ", Last `time`: " + str.format_time(lastBarTime))
█ EXAMPLES
• Collect Future Times
The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument can additionally store time values for up to 500 bars into the future.
//@version=6
indicator("Example `collectChartData(barsForward)`")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData(barsForward = 500)
var rectangle = box.new(na, na, na, na, xloc = xloc.bar_time, force_overlay = true)
if barstate.islast
int futureTime = chartData.timeAtBarIndex(bar_index + 100)
int lastBarTime = time
box.set_lefttop(rectangle, lastBarTime, open)
box.set_rightbottom(rectangle, futureTime, close)
box.set_text(rectangle, "Extending box 100 bars to the right. Time: " + str.format_time(futureTime))
• Collect Custom Data
The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument can additionally store custom user-specified values for every bar on the chart.
//@version=6
indicator("Example `collectChartData(variables)`")
import n00btraders/ChartData/1
var map variables = map.new()
variables.put("open", open)
variables.put("close", close)
variables.put("open-close midpoint", (open + close) / 2)
variables.put("boolean", open > close ? 1 : 0)
chartData = ChartData.collectChartData(variables = variables)
var fgColor = chart.fg_color
var table1 = table.new(position.top_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true)
var table2 = table.new(position.bottom_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true)
if barstate.isfirst
table.cell(table1, 0, 0, "ChartData.value()", text_color = fgColor)
table.cell(table2, 0, 0, "open ", text_color = fgColor)
table.merge_cells(table1, 0, 0, 1, 0)
table.merge_cells(table2, 0, 0, 1, 0)
for i = 1 to 8
table.cell(table1, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace)
table.cell(table2, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace)
table.cell(table1, 1, i, text_color = fgColor)
table.cell(table2, 1, i, text_color = fgColor)
if barstate.islast
for i = 1 to 8
float open1 = chartData.value("open", 5000 * i)
float open2 = i < 3 ? open : -1
table.cell_set_text(table1, 0, i, "chartData.value(\"open\", " + str.tostring(5000 * i) + "): ")
table.cell_set_text(table2, 0, i, "open : ")
table.cell_set_text(table1, 1, i, str.tostring(open1))
table.cell_set_text(table2, 1, i, open2 >= 0 ? str.tostring(open2) : "Error")
• xloc.bar_index → xloc.bar_time
The 𝚝𝚒𝚖𝚎 value (or 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 value) can be retrieved for any bar index that is stored in memory by the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object.
//@version=6
indicator("Example `timeAtBarIndex()`")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData()
if barstate.islast
int start = bar_index - 15000
int end = bar_index - 100
// line.new(start, close, end, close) // !ERROR - `start` value is too far from current bar index
start := chartData.timeAtBarIndex(start)
end := chartData.timeAtBarIndex(end)
line.new(start, close, end, close, xloc.bar_time, width = 10)
• xloc.bar_time → xloc.bar_index
Use 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡() to find the bar that a timestamp belongs to.
If the timestamp falls in between the close of one bar and the open of the next bar,
the 𝚜𝚗𝚊𝚙 parameter can be used to determine which bar to choose:
𝚂𝚗𝚊𝚙.𝙻𝙴𝙵𝚃 - prefer to choose the leftmost bar (typically used for closing times)
𝚂𝚗𝚊𝚙.𝚁𝙸𝙶𝙷𝚃 - prefer to choose the rightmost bar (typically used for opening times)
𝚂𝚗𝚊𝚙.𝙳𝙴𝙵𝙰𝚄𝙻𝚃 (or 𝚗𝚊) - copies the same behavior as xloc.bar_time uses for drawing objects
//@version=6
indicator("Example `timestampToBarIndex()`")
import n00btraders/ChartData/1
startTimeInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Session Start Time")
endTimeInput = input.time(timestamp("01 Aug 2025 15:15 -0500"), "Session End Time")
chartData = ChartData.collectChartData()
if barstate.islastconfirmedhistory
int startBarIndex = chartData.timestampToBarIndex(startTimeInput, ChartData.Snap.RIGHT)
int endBarIndex = chartData.timestampToBarIndex(endTimeInput, ChartData.Snap.LEFT)
line1 = line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true)
line2 = line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true)
linefill.new(line1, line2, color.new(color.green, 90))
// using Snap.DEFAULT to show that it is equivalent to drawing lines using `xloc.bar_time` (i.e., it aligns to the same bars)
startBarIndex := chartData.timestampToBarIndex(startTimeInput)
endBarIndex := chartData.timestampToBarIndex(endTimeInput)
line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.yellow, width = 3)
line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.yellow, width = 3)
line.new(startTimeInput, 0, startTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11)
line.new(endTimeInput, 0, endTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11)
• Get Price of Line at Timestamp
The pine script built-in function line.get_price() requires working with bar index values. To get the price of a line in terms of a timestamp, convert the timestamp into a bar index or offset.
//@version=6
indicator("Example `line.get_price()` at timestamp")
import n00btraders/ChartData/1
lineStartInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Line Start")
chartData = ChartData.collectChartData()
var diagonal = line.new(na, na, na, na, force_overlay = true)
if time <= lineStartInput
line.set_xy1(diagonal, bar_index, open)
if barstate.islastconfirmedhistory
line.set_xy2(diagonal, bar_index, close)
if barstate.islast
int timeOneWeekAgo = timenow - (7 * timeframe.in_seconds("1D") * 1000)
// Note: could also use `timetampToBarIndex(timeOneWeekAgo, Snap.DEFAULT)` and pass the value directly to `line.get_price()`
int barsOneWeekAgo = chartData.getNumberOfBarsBack(timeOneWeekAgo)
float price = line.get_price(diagonal, bar_index - barsOneWeekAgo)
string formatString = "Time 1 week ago: {0,number,#}\n - Equivalent to {1} bars ago\n\n𝚕𝚒𝚗𝚎.𝚐𝚎𝚝_𝚙𝚛𝚒𝚌𝚎(): {2,number,#.##}"
string labelText = str.format(formatString, timeOneWeekAgo, barsOneWeekAgo, price)
label.new(timeOneWeekAgo, price, labelText, xloc.bar_time, style = label.style_label_lower_right, size = 16, textalign = text.align_left, force_overlay = true)
█ RUNTIME ERROR MESSAGES
This library's functions will generate a custom runtime error message in the following cases:
𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() is not called consecutively, or is called more than once on a single bar
Invalid 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function
Invalid 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function
Invalid 𝚕𝚎𝚗𝚐𝚝𝚑 argument in any of the functions that accept a number of bars back
Note: there is no runtime error generated for an invalid 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙 or 𝚋𝚊𝚛𝙸𝚗𝚍𝚎𝚡 argument in any of the functions. Instead, the functions will assign 𝚗𝚊 to the returned values.
Any other runtime errors are due to incorrect usage of the library.
█ NOTES
• Function Descriptions
The library source code uses Markdown for the exported functions. Hover over a function/method call in the Pine Editor to display formatted, detailed information about the function/method.
//@version=6
indicator("Demo Function Tooltip")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData()
int barIndex = chartData.timestampToBarIndex(timenow)
log.info(str.tostring(barIndex))
• Historical vs. Realtime Behavior
Under the hood, the data collector for this library is declared as `var`. Because of this, the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object will always reflect the latest available data on realtime updates. Any data that is recorded for historical bars will remain unchanged throughout the execution of a script.
//@version=6
indicator("Demo Realtime Behavior")
import n00btraders/ChartData/1
var map variables = map.new()
variables.put("open", open)
variables.put("close", close)
chartData = ChartData.collectChartData(variables)
if barstate.isrealtime
varip float initialOpen = open
varip float initialClose = close
varip int updateCount = 0
updateCount += 1
float latestOpen = open
float latestClose = close
float recordedOpen = chartData.valueAtBarIndex("open", bar_index)
float recordedClose = chartData.valueAtBarIndex("close", bar_index)
string formatString = "# of updates: {0}\n\n𝚘𝚙𝚎𝚗 at update #1: {1,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 at update #1: {2,number,#.##}\n\n"
+ "𝚘𝚙𝚎𝚗 at update #{0}: {3,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 at update #{0}: {4,number,#.##}\n\n"
+ "𝚘𝚙𝚎𝚗 stored in memory: {5,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 stored in memory: {6,number,#.##}"
string labelText = str.format(formatString, updateCount, initialOpen, initialClose, latestOpen, latestClose, recordedOpen, recordedClose)
label.new(bar_index, close, labelText, style = label.style_label_left, force_overlay = true)
• Collecting Chart Data for Other Contexts
If your use case requires collecting chart data from another context, avoid directly retrieving the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object as this may exceed memory limits .
//@version=6
indicator("Demo Return Calculated Results")
import n00btraders/ChartData/1
timeInput = input.time(timestamp("01 Sep 2025 08:30 -0500"), "Time")
var int oneMinuteBarsAgo = na
// !ERROR - Memory Limits Exceeded
// chartDataArray = request.security_lower_tf(syminfo.tickerid, "1", ChartData.collectChartData())
// oneMinuteBarsAgo := chartDataArray.last().getNumberOfBarsBack(timeInput)
// function that returns calculated results (a single integer value instead of an entire `ChartData` object)
getNumberOfBarsBack() =>
chartData = ChartData.collectChartData()
chartData.getNumberOfBarsBack(timeInput)
calculatedResultsArray = request.security_lower_tf(syminfo.tickerid, "1", getNumberOfBarsBack())
oneMinuteBarsAgo := calculatedResultsArray.size() > 0 ? calculatedResultsArray.last() : na
if barstate.islast
string labelText = str.format("The selected timestamp occurs 1-minute bars ago", oneMinuteBarsAgo)
label.new(bar_index, hl2, labelText, style = label.style_label_left, size = 16, force_overlay = true)
• Memory Usage
The library's convenience and ease of use comes at the cost of increased usage of computational resources. For simple scripts, using this library will likely not cause any issues with exceeding memory limits. But for large and complex scripts, you can reduce memory issues by specifying a lower 𝚌𝚊𝚕𝚌_𝚋𝚊𝚛𝚜_𝚌𝚘𝚞𝚗𝚝 amount in the indicator() or strategy() declaration statement.
//@version=6
// !ERROR - Memory Limits Exceeded using the default number of bars available (~20,000 bars for Premium plans)
//indicator("Demo `calc_bars_count` parameter")
// Reduce number of bars using `calc_bars_count` parameter
indicator("Demo `calc_bars_count` parameter", calc_bars_count = 15000)
import n00btraders/ChartData/1
map variables = map.new()
variables.put("open", open)
variables.put("close", close)
variables.put("weekofyear", weekofyear)
variables.put("dayofmonth", dayofmonth)
variables.put("hour", hour)
variables.put("minute", minute)
variables.put("second", second)
// simulate large memory usage
chartData0 = ChartData.collectChartData(variables)
chartData1 = ChartData.collectChartData(variables)
chartData2 = ChartData.collectChartData(variables)
chartData3 = ChartData.collectChartData(variables)
chartData4 = ChartData.collectChartData(variables)
chartData5 = ChartData.collectChartData(variables)
chartData6 = ChartData.collectChartData(variables)
chartData7 = ChartData.collectChartData(variables)
chartData8 = ChartData.collectChartData(variables)
chartData9 = ChartData.collectChartData(variables)
log.info(str.tostring(chartData0.time(0)))
log.info(str.tostring(chartData1.time(0)))
log.info(str.tostring(chartData2.time(0)))
log.info(str.tostring(chartData3.time(0)))
log.info(str.tostring(chartData4.time(0)))
log.info(str.tostring(chartData5.time(0)))
log.info(str.tostring(chartData6.time(0)))
log.info(str.tostring(chartData7.time(0)))
log.info(str.tostring(chartData8.time(0)))
log.info(str.tostring(chartData9.time(0)))
if barstate.islast
result = table.new(position.middle_right, 1, 1, force_overlay = true)
table.cell(result, 0, 0, "Script Execution Successful ✅", text_size = 40)
█ EXPORTED ENUMS
Snap
Behavior for determining the bar that a timestamp belongs to.
Fields:
LEFT : Snap to the leftmost bar.
RIGHT : Snap to the rightmost bar.
DEFAULT : Default `xloc.bar_time` behavior.
Note: this enum is used for the 𝚜𝚗𝚊𝚙 parameter of 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡().
█ EXPORTED TYPES
Note: users of the library do not need to worry about directly accessing the fields of these types; all computations are done through method calls on an object of the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 type.
Variable
Represents a user-specified variable that can be tracked on every chart bar.
Fields:
name (series string) : Unique identifier for the variable.
values (array) : The array of stored values (one value per chart bar).
ChartData
Represents data for all bars on a chart.
Fields:
bars (series int) : Current number of bars on the chart.
timeValues (array) : The `time` values of all chart (and future) bars.
timeCloseValues (array) : The `time_close` values of all chart (and future) bars.
variables (array) : Additional custom values to track on all chart bars.
█ EXPORTED FUNCTIONS
collectChartData()
Collects and tracks the `time` and `time_close` value of every bar on the chart.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(barsForward)
Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars.
Parameters:
barsForward (simple int) : Number of future bars to collect data for.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(variables)
Collects and tracks the `time` and `time_close` value of every bar on the chart. Additionally, tracks a custom set of variables for every chart bar.
Parameters:
variables (simple map) : Custom values to collect on every chart bar.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(barsForward, variables)
Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars. Additionally, tracks a custom set of variables for every chart bar.
Parameters:
barsForward (simple int) : Number of future bars to collect data for.
variables (simple map) : Custom values to collect on every chart bar.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
█ EXPORTED METHODS
method timestampToBarIndex(chartData, timestamp, snap)
Converts a UNIX timestamp to a bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
timestamp (series int) : A UNIX time.
snap (series Snap) : A `Snap` enum value.
Returns: A bar index, or `na` if unable to find the appropriate bar index.
method getNumberOfBarsBack(chartData, timestamp)
Converts a UNIX timestamp to a history-referencing length (i.e., number of bars back).
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
timestamp (series int) : A UNIX time.
Returns: A bar offset, or `na` if unable to find a valid number of bars back.
method timeAtBarIndex(chartData, barIndex)
Retrieves the `time` value for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (int) : The bar index.
Returns: The `time` value, or `na` if there is no `time` stored for the bar index.
method time(chartData, length)
Retrieves the `time` value of the bar that is `length` bars back relative to the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
length (series int) : Number of bars back.
Returns: The `time` value `length` bars ago, or `na` if there is no `time` stored for that bar.
method timeCloseAtBarIndex(chartData, barIndex)
Retrieves the `time_close` value for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (series int) : The bar index.
Returns: The `time_close` value, or `na` if there is no `time_close` stored for the bar index.
method timeClose(chartData, length)
Retrieves the `time_close` value of the bar that is `length` bars back from the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
length (series int) : Number of bars back.
Returns: The `time_close` value `length` bars ago, or `na` if there is none stored.
method valueAtBarIndex(chartData, name, barIndex)
Retrieves the value of a custom variable for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
name (series string) : The variable name.
barIndex (series int) : The bar index.
Returns: The value of the variable, or `na` if that variable is not stored for the bar index.
method value(chartData, name, length)
Retrieves a variable value of the bar that is `length` bars back relative to the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
name (series string) : The variable name.
length (series int) : Number of bars back.
Returns: The value `length` bars ago, or `na` if that variable is not stored for the bar index.
method getAllVariablesAtBarIndex(chartData, barIndex)
Retrieves all custom variables for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (series int) : The bar index.
Returns: Map of all custom variables that are stored for the specified bar index.
method getEarliestStoredData(chartData)
Gets all values from the earliest bar data that is currently stored in memory.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
Returns: A tuple:
method getLatestStoredData(chartData, futureData)
Gets all values from the latest bar data that is currently stored in memory.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
futureData (series bool) : Whether to include the future data that is stored in memory.
Returns: A tuple:
APC Companion – Volume Accumulation/DistributionIndicator Description (TradingView – Open Source)
APC Companion – Volume Accumulation/Distribution Filter
(Designed to work standalone or together with the APC Compass)
What this indicator does
The APC Companion measures whether markets are under Accumulation (buying pressure) or Distribution (selling pressure) by combining:
Chaikin A/D slope – volume flow into price moves
On-Balance Volume momentum – confirms trend strength
VWAP spread – price vs. fair value by traded volume
CLV × Volume Z-Score – detects intrabar absorption / selling pressure
VWMA vs. EMA100 – confirms whether weighted volume supports price action
The result is a single Acc/Dist Score (−5 … +5) and a Coherence % showing how many signals agree.
How to interpret
Score ≥ +3 & Coherence ≥ 60% → Accumulation (green) → market supported by buyers
Score ≤ −3 & Coherence ≥ 60% → Distribution (red) → market pressured by sellers
Anything in between = neutral (no strong bias)
Using with APC Compass
Long trades: Only take Compass Long signals when Companion shows Accumulation.
Short trades: Only take Compass Short signals when Companion shows Distribution.
Neutral Companion: Skip or reduce size if there is no confirmation.
This filter greatly reduces false signals and improves trade quality.
Best practice
Swing trading: 4H / 1D charts, lenZ 40–80, lenSlope 14–20
Intraday: 5m–30m charts, lenZ 20–30, lenSlope 10–14
Position sizing: Increase with higher Coherence %, reduce when below 60%
Exits: Reduce or close if Score drops back to neutral or flips opposite
Disclaimer
This script is published open source for educational purposes only.
It is not financial advice. Test thoroughly before using in live trading.
Retail Sentiment Indicator - Multi-Asset CFD & Fear/Greed IndexRetail Sentiment Indicator - Multi-Asset CFD & Fear/Greed Index
Overview
The Retail Sentiment Indicator provides real-time sentiment data for major financial instruments including stocks, forex, commodities, and cryptocurrencies. This indicator displays retail trader positioning and market sentiment using CFD data and fear/greed indices.
Methodology and Scale Calculation
This indicator operates on a **-50 to +50 scale** with zero representing perfect market equilibrium.
Scale Interpretation:
- **Zero (0)**: Market balance - exactly 50% of investors buying, 50% selling
- **Positive values**: Majority buying pressure
- Example: If 63% of investors are buying, the indicator shows +13 (63 - 50 = +13)
- **Negative values**: Majority selling pressure
- Example: If 92% of investors are selling, the indicator shows -42 (50 - 92 = -42)
BTC Fear & Greed Index Scaling:
The original `BTC FEAR&GREED` index is natively scaled from 0-100 by its creator. In our indicator, this data has been rescaled to also fit the -50 to +50 range for consistency with other sentiment data sources.
This unified scaling approach allows for direct comparison across all instruments and data sources within the indicator.
-Important Data Source Selection-
Bitcoin (BTC) Data Sources
When viewing Bitcoin charts, the indicator offers **two different data sources**:
1. **Default Auto-Mode**: `BTCUSD Retail CFD` - Retail CFD traders sentiment data (automatically loaded).
2. **Manual Selection**: `BTC FEAR&GREED` - Fear & Greed Index from website: alternative dot me
**To access BTC Fear & Greed Index**: Input settings -> disable checkbox "Auto-load Sentiment Data" -> manually select "BTC FEAR&GREED" from the dropdown menu.
US Stock Market Data Sources
For US stocks and indices (S&P 500, NASDAQ, Dow Jones), there are **two data source options**:
1. **Default Auto-Mode**: Individual retail CFD sentiment data for each instrument
2. **Manual Selection**: `SNN FEAR&GREED` - SNN's Fear & Greed Index covering the overall US market sentiment. SNN was used as the name to avoid any potential trademark infringement.
**To access SNN Fear & Greed Index**: When viewing US market charts, disable in input settings checkbox "Auto-load Sentiment Data" and manually select "SNN FEAR&GREED" from the dropdown menu.
This distinction allows traders to choose between **instrument-specific retail sentiment** (auto-mode) or **broader market sentiment indices** (manual selection).
Features
- **Auto-Detection**: Automatically loads sentiment data based on the current chart symbol
- **Manual Selection**: Choose from 40+ supported instruments when auto-detection is unavailable
- **Multiple Data Sources**: Combines retail CFD sentiment with Fear & Greed indices
- **Visual Zones**: Clear greed/fear zones with color-coded backgrounds
- **Real-time Updates**: Live sentiment data from merged data sources
Supported Instruments
Major Indices
- S&P 500, NASDAQ, Dow Jones 30, DAX
Forex Pairs
- Major pairs: EURUSD, GBPUSD, USDJPY, USDCHF, USDCAD
- Cross pairs: EURJPY, GBPJPY, AUDUSD, NZDUSD, and 20+ others
Commodities
- Precious metals: Gold (XAUUSD), Silver (XAGUSD)
- Energy: WTI Oil
- Agricultural: Wheat, Coffee
- Industrial: Copper
Cryptocurrencies
- Bitcoin (BTC) sentiment data
- BTC & SNN Fear & Greed indices
How to Use
1. **Auto Mode** (Default): Enable "Auto-load Sentiment Data" to automatically display sentiment for the current chart symbol
2. **Manual Mode**: Disable auto-load and select from the dropdown menu for specific instruments
3. **Interpretation**:
- Values above 0 (green) indicate retail greed/bullish sentiment
- Values below 0 (red) indicate retail fear/bearish sentiment
- Fear & Greed indices use 0-100 scale (50 is neutral)
Data Sources
This indicator uses curated sentiment data from retail CFD providers and established fear/greed indices. Data is updated regularly and sourced from reputable financial data providers.
Trading Strategy & Market Philosophy
Contrarian Trading Approach
The primary purpose of this indicator is based on the fundamental market principle that **the majority of retail investors are often wrong**, and markets typically move opposite to the positions held by the majority of market participants.
Key Strategy Guidelines:
- **Contrarian Signal**: When the majority of users are positioned on one side of the market, there is statistically greater market advantage in taking positions in the opposite direction
- **Trend Exhaustion Signal**: An interesting observed phenomenon occurs when, during a long-lasting trend where the majority of investors have consistently been on the wrong side, the Sentiment indicator suddenly shows that the majority has flipped and opened positions in the direction of that long-running trend. This is often a signal of fuel exhaustion for further movement in that direction
Interpretation Examples
- High greed readings (majority bullish) → Consider bearish opportunities
- High fear readings (majority bearish) → Consider bullish opportunities
- Sudden sentiment flip during established trends → Potential trend reversal signal
Technical Notes
- Built with PineScript v6
- Dynamic symbol detection with fallback options
- Optimized for performance with minimal resource usage
- Color-coded visualization with customizable zones
Data Sources & Expansion
Acknowledgments
We extend our gratitude to **TradingView** for enabling the use of custom data feeds based on GitHub repositories, making this comprehensive sentiment analysis possible.
Data Expansion Opportunities
As the operator of this indicator, I am **open to suggestions for new data sources** that could be integrated and published. If you have ideas for additional instruments or sentiment data:
How to Submit Suggestions:
1. Send a **private message** with your proposal
2. Include: **instrument/data type**, **source**, and **brief description**
3. If technically feasible, we will work to import and publish the data
Data Infrastructure Status
Current Data Upload Process:
Please note that sentiment data uploads may occasionally experience minor interruptions. However, this should not pose significant issues as sentiment data typically changes gradually rather than rapidly.
Infrastructure Development:
We are actively working on establishing permanent cloud-based infrastructure to ensure continuous, automated data collection and upload processes. This will provide more reliable and consistent data availability in the future.
Disclaimer
This indicator is for educational and informational purposes only. Sentiment data should be used as part of a comprehensive trading strategy and not as the sole basis for trading decisions. Past performance does not guarantee future results. The contrarian approach described is a market theory and may not always produce profitable results.
Dow Theory Indicator## 🎯 Key Features of the Indicator
### 📈 Complete Implementation of Dow Theory
- Three-tier trend structure: primary trend (50 periods), secondary trend (20 periods), and minor trend (10 periods).
- Swing point analysis: automatically detects critical swing highs and lows.
- Trend confirmation mechanism: strict confirmation logic based on consecutive higher highs/higher lows or lower highs/lower lows.
- Volume confirmation: ensures price moves are supported by trading volume.
### 🕐 Flexible Timeframe Parameters
All key parameters are adjustable, making it especially suitable for U.S. equities:
Trend analysis parameters:
- Primary trend period: 20–200 (default 50; recommended 50–100 for U.S. stocks).
- Secondary trend period: 10–100 (default 20; recommended 15–30 for U.S. stocks).
- Minor trend period: 5–50 (default 10; recommended 5–15 for U.S. stocks).
Dow Theory parameters:
- Swing high/low lookback: 5–50 (default 10).
- Trend confirmation bar count: 1–10 (default 3).
- Volume confirmation period: 10–100 (default 20).
### 🇺🇸 U.S. Market Optimizations
- Session awareness: distinguishes Regular Trading Hours (9:30–16:00 EST) from pre-market and after-hours.
- Pre/post-market weighting: adjustable weighting factor for signals during extended hours.
- Earnings season filter: automatically adjusts sensitivity during earnings periods.
- U.S.-optimized default parameters.
## 🎨 Visualization
1. Trend lines: three differently colored trend lines.
2. Background fill: green (uptrend) / red (downtrend) / gray (neutral).
3. Signal markers: arrows, labels, and warning icons.
4. Swing point markers: small triangles at key turning points.
5. Info panel: real-time display of eight key metrics.
## 🚨 Alert System
- Trend turning to up/down.
- Strong bullish/bearish signals (dual confirmation).
- Volume divergence warning.
- New swing high/low formed.
## 📋 How to Use
1. Open the Pine Editor in TradingView.
2. Copy the contents of dow_theory_indicator.pine.
3. Paste and click “Add to chart.”
4. Adjust parameters based on trading style:
- Long-term investing: increase all period parameters.
- Swing trading: use the default parameters.
- Short-term trading: decrease all period parameters.
## 💡 Parameter Tips for U.S. Stocks
- Large-cap blue chips (AAPL, MSFT): primary 60–80, secondary 25–30.
- Mid-cap growth stocks: primary 40–60, secondary 18–25.
- Small-cap high-volatility stocks: primary 30–50, secondary 15–20.
Smart Breadth [smartcanvas]Overview
This indicator is a market breadth analysis tool focused on the S&P 500 index. It visualizes the percentage of S&P 500 constituents trading above their 50-day and 200-day moving averages, integrates the McClellan Oscillator for advance-decline analysis, and detects various breadth-based signals such as thrusts, divergences, and trend changes. The indicator is displayed in a separate pane and provides visual cues, a summary label with tooltip, and alert conditions to highlight potential market conditions.
The tool uses data symbols like S5FI (percentage above 50-day MA), S5TH (percentage above 200-day MA), ADVN/DECN (S&P advances/declines), and optionally NYSE advances/declines for certain calculations. If primary data is unavailable, it falls back to calculated breadth from advance-decline ratios.
This indicator is intended for educational and analytical purposes to help users observe market internals. My intention was to pack in one indicator things you will only find in a few. It does not provide trading signals as financial advice, and users are encouraged to use it in conjunction with their own research and risk management strategies. No performance guarantees are implied, and historical patterns may not predict future market behavior.
Key Components and Visuals
Plotted Lines:
Aqua line: Percentage of S&P 500 stocks above their 50-day MA.
Purple line: Percentage of S&P 500 stocks above their 200-day MA.
Optional orange line (enabled via "Show Momentum Line"): 10-day momentum of the 50-day MA breadth, shifted by +50 for scaling.
Optional line plot (enabled via "Show McClellan Oscillator"): McClellan Oscillator, colored green when positive and red when negative. Can use actual scale or normalized to fit breadth percentages (0-100).
Horizontal Levels:
Dotted green at 70%: "Strong" level.
Dashed green at user-defined green threshold (default 60%): "Buy Zone".
Dashed yellow at user-defined yellow threshold (default 50%): "Neutral".
Dotted red at 30%: "Oversold" level.
Optional dotted lines for McClellan (when shown and not using actual scale): Overbought (red), Oversold (green), and Zero (gray), scaled to fit.
Background Coloring:
Green shades for bullish/strong bullish states.
Yellow for neutral.
Orange for caution.
Red for bearish.
Signal Shapes:
Rocket emoji (🚀) at bottom for Zweig Breadth Thrust trigger.
Green circle at bottom for recovery signal.
Red triangle down at top for negative divergence warning.
Green triangle up at bottom for positive divergence.
Light green triangle up at bottom for McClellan oversold bounce.
Green diamond at bottom for capitulation signal.
Summary Label (Right Side):
Displays current action (e.g., "BUY", "HOLD") with emoji, breadth percentages with colored circles, McClellan value with emoji, market state, risk/reward stars, and active signals.
Hover tooltip provides detailed breakdown: action priority, breadth metrics, McClellan status, momentum/trend, market state, active signals, data quality, thresholds, recent changes, and a general recommendation category.
Calculations and Logic
Breadth Percentages: Derived from S5FI/S5TH or calculated from advances/(advances + declines) * 100, with fallback adjustments.
McClellan Oscillator: Difference between fast (default 19) and slow (default 39) EMAs of net advances (advances - declines).
Momentum: 10-day change in 50-day MA breadth percentage.
Trend Analysis: Counts consecutive rising days in breadth to detect upward trends.
Breadth Thrust (Zweig): 10-day EMA of advances/total issues crossing from below a bottom level (default 40) to above a top level (default 61.5). Can use S&P or NYSE data.
Divergences: Compares S&P 500 price highs/lows with breadth or McClellan over a lookback period (default 20) to detect positive (bullish) or negative (bearish) divergences.
Market States: Determined by breadth levels relative to thresholds, trend direction, and McClellan conditions (e.g., strong bullish if above green threshold, rising, and McClellan supportive).
Actions: Prioritized logic (0-10) selects an action like "BUY" or "AVOID LONGS" based on signals, states, and conditions. Higher priority (e.g., capitulation at 10) overrides lower ones.
Alerts: Triggered on new occurrences of key conditions, such as breadth thrust, divergences, state changes, etc.
Input Parameters
The indicator offers customization through grouped inputs, but the use of defaults is encouraged.
Usage Notes
Add the indicator to a chart of any symbol (though designed around S&P 500 data; works best on daily or higher timeframes). Monitor the label and tooltip for a consolidated view of conditions. Set up alerts for specific events.
This script relies on external security requests, which may have data availability issues on certain exchanges or timeframes. The fallback mechanism ensures continuity but may differ slightly from primary sources.
Disclaimer
This indicator is provided for informational and educational purposes only. It does not constitute investment advice, financial recommendations, or an endorsement of any trading strategy. Market conditions can change rapidly, and users should not rely solely on this tool for decision-making. Always perform your own due diligence, consult with qualified professionals if needed, and be aware of the risks involved in trading. The author and TradingView are not responsible for any losses incurred from using this script.
Intrabar Volume Delta — RealTime + History (Stocks/Crypto/Forex)Intrabar Volume Delta Grid — RealTime + History (Stocks/Crypto/Forex)
# Short Description
Shows intrabar Up/Down volume, Delta (absolute/relative) and UpShare% in a compact grid for both real-time and historical bars. Includes an MTF (M1…D1) dashboard, contextual coloring, density controls, and alerts on Δ and UpShare%. Smart historical splitting (“History Mode”) for Crypto/Futures/FX.
---
# What it does (Quick)
* **UpVol / DownVol / Δ / UpShare%** — visualizes order-flow inside each candle.
* **Real-time** — accumulates intrabar volume live by tick-direction.
* **History Mode** — splits Up/Down on closed bars via simple or range-aware logic.
* **MTF Dashboard** — one table view across M1, M5, M15, M30, H1, H4, D1 (Vol, Up/Down, Δ%, Share, Trend).
* **Contextual opacity** — stronger signals appear bolder.
* **Label density** — draw every N-th bar and limit to last X bars for performance.
* **Alerts** — thresholds for |Δ|, Δ%, and UpShare%.
---
# How it works (Real-Time vs History)
* **Real-time (open bar):** volume increments into **UpVolRT** or **DownVolRT** depending on last price move (↑ goes to Up, ↓ to Down). This approximates live order-flow even when full tick history isn’t available.
* **History (closed bars):**
* **None** — no split (Up/Down = 0/0). Safest for equities/indices with unreliable tick history.
* **Approx (Close vs Open)** — all volume goes to candle direction (green → Up 100%, red → Down 100%). Fast but yields many 0/100% bars.
* **Price Action Based** — splits by Close position within High-Low range; strength = |Close−mid|/(High−Low). Above mid → more Up; below mid → more Down. Falls back to direction if High==Low.
* **Auto** — **Stocks/Index → None**, **Crypto/Futures/FX → Approx**. If you see too many 0/100 bars, switch to **Price Action Based**.
---
# Rows & Meaning
* **Volume** — total bar volume (no split).
* **UpVol / DownVol** — directional intrabar volume.
* **Delta (Δ)** — UpVol − DownVol.
* **Absolute**: raw units
* **Relative (Δ%)**: Δ / (Up+Down) × 100
* **Both**: shows both formats
* **UpShare%** — UpVol / (Up+Down) × 100. >50% bullish, <50% bearish.
* Helpful icons: ▲ (>65%), ▼ (<35%).
---
# MTF Dashboard (🔧 Enable Dashboard)
A single table with **Vol, Up, Down, Δ%, Share, Trend (🔼/🔽/⏭️)** for selected timeframes (M1…D1). Great for a fast “panorama” read of flow alignment across horizons.
---
# Inputs (Grouped)
## Display
* Toggle rows: **Volume / Up / Down / Delta / UpShare**
* **Delta Display**: Absolute / Relative / Both
## Realtime & History
* **History Mode**: Auto / None / Approx / Price Action Based
* **Compact Numbers**: 1.2k, 1.25M, 3.4B…
## Theme & UI
* **Theme Mode**: Auto / Light / Dark
* **Row Spacing**: vertical spacing between rows
* **Top Row Y**: moves the whole grid vertically
* **Draw Guide Lines**: faint dotted guides
* **Text Size**: Tiny / Small / Normal / Large
## 🔧 Dashboard Settings
* **Enable Dashboard**
* **📏 Table Text Size**: Tiny…Huge
* **🦓 Zebra Rows**
* **🔲 Table Border**
## ⏰ Timeframes (for Dashboard)
* **M1…D1** toggles
## Contextual Coloring
* **Enable Contextual Coloring**: opacity by signal strength
* **Δ% cap / Share offset cap**: saturation caps
* **Min/Max transparency**: solid vs faint extremes
## Label Density & Size
* **Show every N-th bar**: draw labels only every Nth bar
* **Limit to last X bars**: keep labels only in the most recent X bars
## Colors
* Up / Down / Text / Guide
## Alerts
* **Delta Threshold (abs)** — |Δ| in volume units
* **UpShare > / <** — bullish/bearish thresholds
* **Enable Δ% Alert**, **Δ% > +**, **Δ% < −** — relative delta levels
---
# How to use (Quick Start)
1. Add the indicator to your chart (overlay=false → separate pane).
2. **History Mode**:
* Crypto/Futures/FX → keep **Auto** or switch to **Price Action Based** for richer history.
* Stocks/Index → prefer **None** or **Price Action Based** for safer splits.
3. **Label Density**: start with **Limit to last X bars = 30–150** and **Show every N-th bar = 2–4**.
4. **Contextual Coloring**: keep on to emphasize strong Δ% / Share moves.
5. **Dashboard**: enable and pick only the TFs you actually use.
6. **Alerts**: set thresholds (ideas below).
---
# Alerts (in TradingView)
Add alert → pick this indicator → choose any of:
* **Delta exceeds threshold** (|Δ| > X)
* **UpShare above threshold** (UpShare% > X)
* **UpShare below threshold** (UpShare% < X)
* **Relative Delta above +X%**
* **Relative Delta below −X%**
**Starter thresholds (tune per symbol & TF):**
* **Crypto M1/M5**: Δ% > +25…35 (bullish), Δ% < −25…−35 (bearish)
* **FX (tick volume)**: UpShare > 60–65% or < 40–35%
* **Stocks (liquid)**: set **Absolute Δ** by typical volume scale (e.g., 50k / 100k / 500k)
---
# Notes by Market Type
* **Crypto/Futures**: 24/7 and high liquidity — **Price Action Based** often gives nicer history splits than Approx.
* **Forex (FX)**: TradingView volume is typically **tick volume** (not true exchange volume). Treat Δ/Share as tick-based flow, still very useful intraday.
* **Stocks/Index**: historical tick detail can be limited. **None** or **Price Action Based** is a safer default. If you see too many 0/100% shares, switch away from Approx.
---
# “All Timeframes” accuracy
* Works on **any TF** (M1 → D1/W1).
* **Real-time accuracy** is strong for the open bar (live accumulation).
* **Historical accuracy** depends on your **History Mode** (None = safest, Approx = fastest/simplest, Price Action Based = more nuanced).
* The MTF dashboard uses `request.security` and therefore follows the same logic per TF.
---
# Trade Ideas (Use-Cases)
* **Scalping (M1–M5)**: a spike in Δ% + UpShare>65% + rising total Vol → momentum entries.
* **Intraday (M5–M30–H1)**: when multiple TFs show aligned Δ%/Share (e.g., M5 & M15 bullish), join the trend.
* **Swing (H4–D1)**: persistent Δ% > 0 and UpShare > 55–60% → structural accumulation bias.
---
# Advantages
* **True-feeling live flow** on the open bar.
* **Adaptable history** (three modes) to match data quality.
* **Clean visual layout** with guides, compact numbers, contextual opacity.
* **MTF snapshot** for quick bias read.
* **Performance controls** (last X bars, every N-th bar).
---
# Limitations & Care
* **FX uses tick volume** — interpret Δ/Share accordingly.
* **History Mode is an approximation** — confirm with trend/structure/liquidity context.
* **Illiquid symbols** can produce noisy or contradictory signals.
* **Too many labels** can slow charts → raise N, lower X, or disable guides.
---
# Best Practices (Checklist)
* Crypto/Futures: prefer **Price Action Based** for history.
* Stocks: **None** or **Price Action Based**; be cautious with **Approx**.
* FX: pair Δ% & UpShare% with session context (London/NY) and volatility.
* If labels overlap: tweak **Row Spacing** and **Text Size**.
* In the dashboard, keep only the TFs you actually act on.
* Alerts: start around **Δ% 25–35** for “punchy” moves, then refine per asset.
---
# FAQ
**1) Why do some closed bars show 0%/100% UpShare?**
You’re on **Approx** history mode. Switch to **Price Action Based** for smoother splits.
**2) Δ% looks strong but price doesn’t move — why?**
Δ% is an **order-flow** measure. Price also depends on liquidity pockets, sessions, news, higher-timeframe structure. Use confirmations.
**3) Performance slowdown — what to do?**
Lower **Limit to last X bars** (e.g., 30–100), increase **Show every N-th bar** (2–6), or disable **Draw Guide Lines**.
**4) Dashboard values don’t “match” the grid exactly?**
Dashboard is multi-TF via `request.security` and follows the history logic per TF. Differences are normal.
---
# Short “Store” Marketing Blurb
Intrabar Volume Delta Grid reveals the order-flow inside every candle (Up/Down, Δ, UpShare%) — live and on history. With smart history splitting, an MTF dashboard, contextual emphasis, and flexible alerts, it helps you spot momentum and bias across Crypto, Forex (tick volume), and Stocks. Tidy labels and compact numbers keep the panel readable and fast.
Radial Basis Kernel RSI for LoopRadial Basis Kernel RSI for Loop
What it is
An RSI-style oscillator that uses a radial basis function (RBF) kernel to compute a similarity-weighted average of gains and losses across many lookback lengths and kernel widths (γ). By averaging dozens of RSI estimates—each built with different parameters—it aims to deliver a smoother, more robust momentum signal that adapts to changing market conditions.
How it works
The script measures up/down price changes from your chosen Source (default: close).
For each combination of RSI length and Gamma (γ) in your ranges, it builds an RSI where recent bars that look most similar (by price behavior) get more weight via an RBF kernel.
It averages all those RSIs into a single value, then smooths it with your selected Moving Average type (SMA, EMA, WMA, HMA, DEMA) and a light regression-based filter for stability.
Inputs you can tune
Min/Max RSI Kernel Length & Step: Range of RSI lookbacks to include in the ensemble (e.g., 20→40 by 1) or (e.g., 30→50 by 1).
Min/Max Gamma & Step: Controls the RBF “width.” Lower γ = broader similarity (smoother); higher γ = more selective (snappier).
Source: Price series to analyze.
Overbought / Oversold levels: Defaults 70 / 30, with a midline at 50. Shaded regions help visualize extremes.
MA Type & Period (Confluence): Final smoothing on the averaged RSI line (e.g., DEMA(44) by default).
Red “OB” labels when the line crosses down from extreme highs (~80) → potential overbought fade/exit areas.
Green “OS” labels when the line crosses up from extreme lows (~20) → potential oversold bounce/entry areas.
How to use it
Treat it like RSI, but expect fewer whipsaws thanks to the ensemble and kernel weighting.
Common approaches:
Look for crosses back inside the bands (e.g., down from >70 or up from <30).
Use the 50 midline for directional bias (above = bullish momentum tilt; below = bearish).
Combine with trend filters (e.g., your chart MA) for higher-probability signals.
Performance note: This is really heavy and depending on how much time your subscription allows you could experience this timing out. Increasing the step size is the easiest way to reduce the load time.
Works on any symbol or timeframe. Like any oscillator, best used alongside price action and risk management rather than in isolation.
ADX with Custom Limit LineSimple ADX with Custom Threshold
A clean, educational ADX indicator that allows traders to set their own trend strength threshold.
Features:
- Customizable limit line for personalized trend analysis
- Color-coded ADX line based on trend strength
- Educational reference lines (15, 25, 40, 50)
- Background highlighting when above custom threshold
- Comprehensive alerts for trend changes
Perfect for traders who want a simple but effective tool to assess trend strength without complexity.
This indicator helps understand:
- When trends are strong enough to trade
- How to interpret ADX readings
- Optimal entry/exit timing based on trend strength
Educational and straightforward - ideal for both beginners and experienced traders.
⚠️ RISK DISCLAIMER:
This indicator is for educational purposes only and does not constitute financial advice.
Trading involves significant risk of loss. Always do your own research and consider
consulting with a qualified financial advisor before making trading decisions.
Smart Money Precision Structure [BullByte]Smart Money Precision Structure
Advanced Market Structure Analysis Using Institutional Order Flow Concepts
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OVERVIEW
Smart Money Precision Structure (SMPS) is a comprehensive market analysis indicator that combines six analytical frameworks to identify high-probability market structure patterns. The indicator uses multi-dimensional scoring algorithms to evaluate market conditions through institutional order flow concepts, providing traders with professional-grade market analysis.
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PURPOSE AND ORIGINALITY
Why This Indicator Was Developed
• Addresses the gap between retail and institutional analysis methods
• Consolidates multiple analysis techniques that professionals use separately
• Automates complex market structure evaluation into actionable insights
• Eliminates the need for multiple indicators by providing comprehensive analysis
What Makes SMPS Original
• Six-Layer Confluence System - Unique combination of market regime, structure, volume flow, momentum, price action, and adaptive filtering
• Institutional Pattern Recognition - Identifies smart money accumulation and distribution patterns
• Adaptive Intelligence - Parameters automatically adjust based on detected market conditions
• Real-Time Market Scoring - Proprietary algorithm rates market quality from 0-100%
• Structure Break Detection - Advanced pivot analysis identifies trend reversals early
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HOW IT WORKS - TECHNICAL METHODOLOGY
1. Market Regime Analysis Engine
The indicator evaluates five core market dimensions:
• Volatility Score - Measures current volatility against 50-period historical baseline
• Trend Score - Analyzes alignment between 8, 21, and 50-period EMAs
• Momentum Score - Combines RSI divergence with MACD signal alignment
• Structure Score - Evaluates pivot point formation clarity
• Efficiency Score - Calculates directional movement efficiency ratio
These scores combine to classify markets into five regimes:
• TRENDING - Strong directional movement with aligned indicators
• RANGING - Sideways movement with mixed directional signals
• VOLATILE - Elevated volatility with unpredictable price swings
• QUIET - Low volatility consolidation periods
• TRANSITIONAL - Market shifting between different regimes
2. Market Structure Analysis
Advanced pivot point analysis identifies:
• Higher Highs and Higher Lows for bullish structure
• Lower Highs and Lower Lows for bearish structure
• Structure breaks when established patterns fail
• Dynamic support and resistance from recent pivot points
• Key level proximity detection using ATR-based buffers
3. Volume Flow Decoding
Institutional activity detection through:
• Volume surge identification when volume exceeds 2x average
• Buy versus sell pressure analysis using price-volume correlation
• Flow strength measurement through directional volume consistency
• Divergence detection between volume and price movements
• Institutional threshold alerts when unusual volume patterns emerge
4. Multi-Period Momentum Synthesis
Weighted momentum calculation across four timeframes:
• 1-period momentum weighted at 40%
• 3-period momentum weighted at 30%
• 5-period momentum weighted at 20%
• 8-period momentum weighted at 10%
Result smoothed with 6-period EMA for noise reduction.
5. Price Action Quality Assessment
Each bar evaluated for:
• Range quality relative to 20-period average
• Body-to-range ratio for directional conviction
• Wick analysis for rejection pattern identification
• Pattern recognition including engulfing and hammer formations
• Sequential price movement analysis
6. Adaptive Parameter System
Parameters automatically adjust based on detected regime:
• Trending markets reduce sensitivity and confirmation requirements
• Volatile markets increase filtering and require additional confirmations
• Ranging markets maintain neutral settings
• Transitional markets use moderate adjustments
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COMPLETE SETTINGS GUIDE
Section 1: Core Analysis Settings
Analysis Sensitivity (0.3-2.0)
• Default: 1.0
• Lower values require stronger price movements
• Higher values detect more subtle patterns
• Scalpers use 0.8-1.2, swing traders use 1.5-2.0
Noise Reduction Level (2-7)
• Default: 4
• Controls filtering of false patterns
• Higher values reduce pattern frequency
• Increase in volatile markets
Minimum Move % (0.05-0.50)
• Default: 0.15%
• Sets minimum price movement threshold
• Adjust based on instrument volatility
• Forex: 0.05-0.10%, Stocks: 0.15-0.25%, Crypto: 0.20-0.50%
High Confirmation Mode
• Default: True (Enabled)
• Requires all technical conditions to align
• Reduces frequency but increases reliability
• Disable for more aggressive pattern detection
Section 2: Market Regime Detection
Enable Regime Analysis
• Default: True (Enabled)
• Activates market environment evaluation
• Essential for adaptive features
• Keep enabled for best results
Regime Analysis Period (20-100)
• Default: 50 bars
• Determines regime calculation lookback
• Shorter for responsive, longer for stable
• Scalping: 20-30, Swing: 75-100
Minimum Market Clarity (0.2-0.8)
• Default: 0.4
• Quality threshold for pattern generation
• Higher values require clearer conditions
• Lower for more patterns, higher for quality
Adaptive Parameter Adjustment
• Default: True (Enabled)
• Enables automatic parameter optimization
• Adjusts based on market regime
• Highly recommended to keep enabled
Section 3: Market Structure Analysis
Enable Structure Validation
• Default: True (Enabled)
• Validates patterns against support/resistance
• Confirms trend structure alignment
• Essential for reliability
Structure Analysis Period (15-50)
• Default: 30 bars
• Period for structure pattern analysis
• Affects support/resistance calculation
• Match to your trading timeframe
Minimum Structure Alignment (0.3-0.8)
• Default: 0.5
• Required structure score for valid patterns
• Higher values need stronger structure
• Balance with desired frequency
Section 4: Analysis Configuration
Minimum Strength Level (3-5)
• Default: 4
• Minimum confirmations for pattern display
• 5 = Maximum reliability, 3 = More patterns
• Beginners should use 4-5
Required Technical Confirmations (4-6)
• Default: 5
• Number of aligned technical factors
• Higher = fewer but better patterns
• Works with High Confirmation Mode
Pattern Separation (3-20 bars)
• Default: 8 bars
• Minimum bars between patterns
• Prevents clustering and overtrading
• Increase for cleaner charts
Section 5: Technical Filters
Momentum Validation
• Default: True (Enabled)
• Requires momentum alignment
• Filters counter-trend patterns
• Essential for trend following
Volume Confluence Analysis
• Default: True (Enabled)
• Requires volume confirmation
• Identifies institutional participation
• Critical for reliability
Trend Direction Filter
• Default: True (Enabled)
• Only shows patterns with trend
• Reduces counter-trend signals
• Disable for reversal hunting
Section 6: Volume Flow Analysis
Institutional Activity Threshold (1.2-3.5)
• Default: 2.0
• Multiplier for unusual volume detection
• Lower finds more institutional activity
• Stock: 2.0-2.5, Forex: 1.5-2.0, Crypto: 2.5-3.5
Volume Surge Multiplier (1.8-4.5)
• Default: 2.5
• Defines significant volume increases
• Adjust per instrument characteristics
• Higher for stocks, lower for forex
Volume Flow Period (12-35)
• Default: 18 bars
• Smoothing for volume analysis
• Shorter = responsive, longer = smooth
• Match to timeframe used
Section 7: Analysis Frequency Control
Maximum Analysis Points Per Hour (1-5)
• Default: 3
• Limits pattern frequency
• Prevents overtrading
• Scalpers: 4-5, Swing traders: 1-2
Section 8: Target Level Configuration
Target Calculation Method
• Default: Market Adaptive
• Three modes available:
- Fixed: Uses set point distances
- Dynamic: ATR-based calculations
- Market Adaptive: Structure-based levels
Minimum Target/Risk Ratio (1.0-3.0)
• Default: 1.5
• Minimum acceptable reward vs risk
• Higher filters lower probability setups
• Professional standard: 1.5-2.0
Fixed Mode Settings:
• Fixed Target Distance: 50 points default
• Fixed Invalidation Distance: 30 points default
• Use for consistent instruments
Dynamic Mode Settings:
• Dynamic Target Multiplier: 1.8x ATR default
• Dynamic Invalidation Multiplier: 1.0x ATR default
• Adapts to volatility automatically
Market Adaptive Settings:
• Use Structure Levels: True (default)
• Structure Level Buffer: 0.1% default
• Places levels at actual support/resistance
Section 9: Visual Display Settings
Color Theme Options
• Professional (Teal/Red)
- Bullish: Teal (#26a69a)
- Bearish: Red (#ef5350)
- Neutral: Gray (#78909c)
- Best for: Traditional traders, clean appearance
• Dark (Neon Green/Pink)
- Bullish: Neon Green (#00ff88)
- Bearish: Hot Pink (#ff0044)
- Neutral: Dark Gray (#333333)
- Best for: Dark theme users, high contrast
• Light (Green/Red Classic)
- Bullish: Green (#4caf50)
- Bearish: Red (#f44336)
- Neutral: Light Gray (#9e9e9e)
- Best for: Light backgrounds, traditional colors
• Vibrant (Cyan/Magenta)
- Bullish: Cyan (#00ffff)
- Bearish: Magenta (#ff00ff)
- Neutral: Medium Gray (#888888)
- Best for: High visibility, modern appearance
Dashboard Position
• Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Left, Middle Right
• Default: Top Right
• Choose based on chart layout preference
Dashboard Size
• Full: Complete information display (desktop)
• Mobile: Compact view for small screens
• Default: Full
Analysis Display Style
• Arrows : Simple directional markers
• Labels : Detailed text information
• Zones : Colored areas showing pattern regions
• Default: Labels (most informative)
Display Options:
• Display Analysis Strength: Shows star rating
• Display Target Levels: Shows target/invalidation lines
• Display Market Regime: Shows regime in pattern labels
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HOW TO USE SMPS - DETAILED GUIDE
Understanding the Dashboard
Top Row - Header
• SMPS Dashboard title
• VALUE column: Current readings
• STATUS column: Condition assessments
Market Regime Row
• Shows: TRENDING, RANGING, VOLATILE, QUIET, or TRANSITIONAL
• Color coding: Green = Favorable, Red = Caution
• Status: FAVORABLE or CAUTION trading conditions
Market Score Row
• Percentage from 0-100%
• Above 60% = Strong conditions
• 40-60% = Moderate conditions
• Below 40% = Weak conditions
Structure Row
• Direction: BULLISH, BEARISH, or NEUTRAL
• Status: INTACT or BREAK
• Orange BREAK indicates structure failure
Volume Flow Row
• Direction: BUYING or SELLING
• Intensity: STRONG or WEAK
• Color indicates dominant pressure
Momentum Row
• Numerical momentum value
• Positive = Upward pressure
• Negative = Downward pressure
Volume Status Row
• INST = Institutional activity detected
• HIGH = Above average volume
• NORM = Normal volume levels
Adaptive Mode Row
• ACTIVE = Parameters adjusting
• STATIC = Fixed parameters
• Shows required confirmations
Analysis Level Row
• Minimum strength level setting
• Pattern separation in bars
Market State Row
• Current analysis: BULLISH, BEARISH, NEUTRAL
• Shows analysis price level when active
T:R Ratio Row
• Current target to risk ratio
• GOOD = Meets minimum requirement
• LOW = Below minimum threshold
Strength Row
• BULL or BEAR dominance
• Numerical strength value 0-100
Price Row
• Current price
• Percentage change
Last Analysis Row
• Previous pattern direction
• Bars since last pattern
Reading Pattern Signals
Bullish Structure Pattern
• Upward triangle or "Bullish Structure" label
• Star rating shows strength (★★★★★ = strongest)
• Green line = potential target level
• Red dashed line = invalidation level
• Appears below price bars
Bearish Structure Pattern
• Downward triangle or "Bearish Structure" label
• Star rating indicates reliability
• Green line = potential target level
• Red dashed line = invalidation level
• Appears above price bars
Pattern Strength Interpretation
• ★★★★★ = 6 confirmations (exceptional)
• ★★★★☆ = 5 confirmations (strong)
• ★★★☆☆ = 4 confirmations (moderate)
• ★★☆☆☆ = 3 confirmations (minimum)
• Below minimum = filtered out
Visual Elements on Chart
Lines and Levels:
• Gray Line = 21 EMA trend reference
• Green Stepline = Dynamic support level
• Red Stepline = Dynamic resistance level
• Green Solid Line = Active target level
• Red Dashed Line = Active invalidation level
Pattern Markers:
• Triangles = Arrow display mode
• Text Labels = Label display mode
• Colored Boxes = Zone display mode
Target Completion Labels:
• "Target" = Price reached target level
• "Invalid" = Pattern invalidated by price
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RECOMMENDED USAGE BY TIMEFRAME
1-Minute Charts (Scalping)
• Sensitivity: 0.8-1.2
• Noise Reduction: 3-4
• Pattern Separation: 3-5 bars
• High Confirmation: Optional
• Best for: Quick intraday moves
5-Minute Charts (Precision Intraday)
• Sensitivity: 1.0 (default)
• Noise Reduction: 4 (default)
• Pattern Separation: 8 bars
• High Confirmation: Enabled
• Best for: Day trading
15-Minute Charts (Short Swing)
• Sensitivity: 1.0-1.5
• Noise Reduction: 4-5
• Pattern Separation: 10-12 bars
• High Confirmation: Enabled
• Best for: Intraday swings
30-Minute to 1-Hour (Position Trading)
• Sensitivity: 1.5-2.0
• Noise Reduction: 5-7
• Pattern Separation: 15-20 bars
• Regime Period: 75-100
• Best for: Multi-day positions
Daily Charts (Swing Trading)
• Sensitivity: 1.8-2.0
• Noise Reduction: 6-7
• Pattern Separation: 20 bars
• All filters enabled
• Best for: Long-term analysis
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MARKET-SPECIFIC SETTINGS
Forex Pairs
• Minimum Move: 0.05-0.10%
• Institutional Threshold: 1.5-2.0
• Volume Surge: 1.8-2.2
• Target Mode: Dynamic or Market Adaptive
Stock Indices (ES, NQ, YM)
• Minimum Move: 0.10-0.15%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.0
• Target Mode: Market Adaptive
Individual Stocks
• Minimum Move: 0.15-0.25%
• Institutional Threshold: 2.0-2.5
• Volume Surge: 2.5-3.5
• Target Mode: Dynamic
Cryptocurrency
• Minimum Move: 0.20-0.50%
• Institutional Threshold: 2.5-3.5
• Volume Surge: 3.0-4.5
• Target Mode: Dynamic
• Increase noise reduction
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PRACTICAL APPLICATION EXAMPLES
Example 1: Strong Trending Market
Dashboard Reading:
• Market Regime: TRENDING
• Market Score: 75%
• Structure: BULLISH, INTACT
• Volume Flow: BUYING, STRONG
• Momentum: +0.45
Interpretation:
• Strong uptrend environment
• Institutional buying present
• Look for bullish patterns as continuation
• Higher probability of success
• Consider using lower sensitivity
Example 2: Range-Bound Conditions
Dashboard Reading:
• Market Regime: RANGING
• Market Score: 35%
• Structure: NEUTRAL
• Volume Flow: SELLING, WEAK
• Momentum: -0.05
Interpretation:
• No clear direction
• Low opportunity environment
• Patterns are less reliable
• Consider waiting for regime change
• Or switch to a range-trading approach
Example 3: Structure Break Alert
Dashboard Reading:
• Previous: BULLISH structure
• Current: Structure BREAK
• Volume: INST flag active
• Momentum: Shifting negative
Interpretation:
• Trend reversal potentially beginning
• Institutional participation detected
• Watch for bearish pattern confirmation
• Adjust bias accordingly
• Increase caution on long positions
Example 4: Volatile Market
Dashboard Reading:
• Market Regime: VOLATILE
• Market Score: 45%
• Adaptive Mode: ACTIVE
• Confirmations: Increased to 6
Interpretation:
• Choppy conditions
• Parameters auto-adjusted
• Fewer but higher quality patterns
• Wider stops may be needed
• Consider reducing position size
Below are a few chart examples of the Smart Money Precision Structure (SMPS) indicator in action.
• Example 1 – Bullish Structure Detection on SOLUSD 5m
• Example 2 – Bearish Structure Detected with Strong Confluence on SOLUSD 5m
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TROUBLESHOOTING GUIDE
No Patterns Appearing
Check these settings:
• High Confirmation Mode may be too restrictive
• Minimum Strength Level may be too high
• Market Clarity threshold may be too high
• Regime filter may be blocking patterns
• Try increasing sensitivity
Too Many Patterns
Adjust these settings:
• Enable High Confirmation Mode
• Increase Minimum Strength Level to 5
• Increase Pattern Separation
• Reduce Sensitivity below 1.0
• Enable all technical filters
Dashboard Shows "CAUTION"
This indicates:
• Market conditions are unfavorable
• Regime is RANGING or QUIET
• Market score is low
• Consider waiting for better conditions
• Or adjust expectations accordingly
Patterns Not Reaching Targets
Consider:
• Market may be choppy
• Volatility may have changed
• Try Dynamic target mode
• Reduce target/risk ratio requirement
• Check if regime is VOLATILE
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ALERTS CONFIGURATION
Alert Message Format
Alerts include:
• Pattern type (Bullish/Bearish)
• Strength rating
• Market regime
• Analysis price level
• Target and invalidation levels
• Strength percentage
• Target/Risk ratio
• Educational disclaimer
Setting Up Alerts
• Click Alert button on TradingView
• Select SMPS indicator
• Choose alert frequency
• Customize message if desired
• Alerts fire on pattern detection
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DATA WINDOW INFORMATION
The Data Window displays:
• Market Regime Score (0-100)
• Market Structure Bias (-1 to +1)
• Bullish Strength (0-100)
• Bearish Strength (0-100)
• Bull Target/Risk Ratio
• Bear Target/Risk Ratio
• Relative Volume
• Momentum Value
• Volume Flow Strength
• Bull Confirmations Count
• Bear Confirmations Count
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BEST PRACTICES AND TIPS
For Beginners
• Start with default settings
• Use High Confirmation Mode
• Focus on TRENDING regime only
• Paper trade first
• Learn one timeframe thoroughly
For Intermediate Users
• Experiment with sensitivity settings
• Try different target modes
• Use multiple timeframes
• Combine with price action analysis
• Track pattern success rate
For Advanced Users
• Customize per instrument
• Create setting templates
• Use regime information for bias
• Combine with other indicators
• Develop systematic rules
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IMPORTANT DISCLAIMERS
• This indicator is for educational and informational purposes only
• Not financial advice or a trading system
• Past performance does not guarantee future results
• Trading involves substantial risk of loss
• Always use appropriate risk management
• Verify patterns with additional analysis
• The author is not a registered investment advisor
• No liability accepted for trading losses
---
VERSION NOTES
Version 1.0.0 - Initial Release
• Six-layer confluence system
• Adaptive parameter technology
• Institutional volume detection
• Market regime classification
• Structure break identification
• Real-time dashboard
• Multiple display modes
• Comprehensive settings
## My Final Thoughts
Smart Money Precision Structure represents an advanced approach to market analysis, bringing institutional-grade techniques to retail traders through intelligent automation and multi-dimensional evaluation. By combining six analytical frameworks with adaptive parameter adjustment, SMPS provides comprehensive market intelligence that single indicators cannot achieve.
The indicator serves as an educational tool for understanding how professional traders analyze markets, while providing practical pattern detection for those seeking to improve their technical analysis. Remember that all trading involves risk, and this tool should be used as part of a complete analysis approach, not as a standalone trading system.
- BullByte
Market Cap Landscape 3DHello, traders and creators! 👋
Market Cap Landscape 3D. This project is more than just a typical technical analysis tool; it's an exploration into what's possible when code meets artistry on the financial charts. It's a demonstration of how we can transcend flat, two-dimensional lines and step into a vibrant, three-dimensional world of data.
This project continues a journey that began with a previous 3D experiment, the T-Virus Sentiment, which you can explore here:
The Market Cap Landscape 3D builds on that foundation, visualizing market data—particularly crypto market caps—as a dynamic 3D mountain range. The entire landscape is procedurally generated and rendered in real-time using the powerful drawing capabilities of polyline.new() and line.new() , pushed to their creative limits.
This work is intended as a guide and a design example for all developers, born from the spirit of learning and a deep love for understanding the Pine Script™ language.
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🧐 Core Concept: How It Works
The indicator synthesizes multiple layers of information into a single, cohesive 3D scene:
The Surface: The mountain range itself is a procedurally generated 3D mesh. Its peaks and valleys create a rich, textured landscape that serves as the canvas for our data.
Crypto Data Integration: The core feature is its ability to fetch market cap data for a list of cryptocurrencies you provide. It then sorts them in descending order and strategically places them onto the 3D surface.
The Summit: The highest point on the mountain is reserved for the asset with the #1 market cap in your list, visually represented by a flag and a custom emblem.
The Mountain Labels: The other assets are distributed across the mountainside, with their rank determining their general elevation. This creates an intuitive visual hierarchy.
The Leaderboard Pole: For clarity, a dedicated pole in the back-right corner provides a clean, ranked list of the symbols and their market caps, ensuring the data is always easy to read.
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🧐 Example of adjusting the view
To evoke the feeling of flying over mountains
To evoke the feeling of looking at a mountain peak on a low plain
🧐 Example of predefined colors
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🚀 How to Use
Getting started with the Market Cap Landscape 3D:
Add to Chart: Apply the "Market Cap Landscape 3D" indicator to your active chart.
Open Settings: Double-click anywhere on the 3D landscape or click the "Settings" icon next to the indicator's name.
Customize Your Crypto List: The most important setting is in the Crypto Data tab. In the "Symbols" text area, enter a comma-separated list of the crypto tickers you want to visualize (e.g., BTC,ETH,SOL,XRP ). The indicator supports up to 40 unique symbols.
> Important Note: This indicator exclusively uses TradingView's `CRYPTOCAP` data source. To find valid symbols, use the main symbol search bar on your chart. Type `CRYPTOCAP:` (including the colon) and you will see a list of available options. For example, typing `CRYPTOCAP:BTC` will confirm that `BTC` is a valid ticker for the indicator's settings. Using symbols that do not exist in the `CRYPTOCAP` index will result in a script error. or, to display other symbols, simply type CRYPTOCAP: (including the colon) and you will see a list of available options.
Adjust Your View: Use the settings in the Camera & Projection tab to rotate ( Yaw ), tilt ( Pitch ), and scale the landscape until you find a view you love.
Explore & Customize: Play with the color palettes, flag design, and other settings to make the landscape truly your own!
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⚙️ Settings & Customization
This indicator is highly customizable. Here’s a breakdown of what each setting does:
#### 🪙 Crypto Data
Symbols: Enter the crypto tickers you want to track, separated by commas. The script automatically handles duplicates and case-insensitivity.
Show Market Cap on Mountain: When checked, it displays the full market cap value next to the symbol on the mountain. When unchecked, it shows a cleaner look with just the symbol and a colored circle background.
#### 📷 Camera & Projection
Yaw (°): Rotates the camera view horizontally (side to side).
Pitch (°): Tilts the camera view vertically (up and down).
Scale X, Y, Z: Stretches or compresses the landscape in width, depth, and height, respectively. Fine-tune these to get the perfect perspective.
#### 🏞️ Grid / Surface
Grid X/Y resolution: Controls the detail level of the 3D mesh. Higher values create a smoother surface but may use more resources.
Fill surface strips: Toggles the beautiful color gradient on the surface.
Show wireframe lines: Toggles the visibility of the grid lines.
Show nodes (markers): Toggles the small dots at each grid intersection point.
#### 🏔️ Peaks / Mountains
Fill peaks volume: Draws vertical lines on high peaks, giving them a sense of volume.
Fill peaks surface: Draws a cross-hatch pattern on the surface of high peaks.
Peak height threshold: Defines the minimum height for a peak to receive the fill effect.
Peak fill color/density: Customizes the appearance of the fill lines.
#### 🚩 Flags (3D)
Show Flag on Summit: A master switch to show or hide the flag and emblem entirely.
Flag height, width, etc.: Provides full control over the dimensions and orientation of the flag on the highest peak.
#### 🎨 Color Palette
Base Gradient Palette: Choose from 13 stunning, pre-designed color themes for the landscape, from the classic SUNSET_WAVE to vibrant themes like NEON_DREAM and OCEANIC .
#### 🛡️ Emblem / Badge Controls
This section gives you granular control over every element of the custom emblem on the flag. Tweak rotation, offsets, and scale to design your unique logo.
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👨💻 Developer's Corner: Modifying the Core Logic
If you're a developer and wish to customize the indicator's core data source, this section is for you. The script is designed to be modular, making it easy to change what data is being ranked and visualized.
The heart of the data retrieval and ranking logic is within the f_getSortedCryptoData() function. Here’s how you can modify it:
1. Changing the Data Source (from Market Cap to something else):
The current logic uses request.security("CRYPTOCAP:" + syms.get(i), ...) to fetch market capitalization data. To change this, you need to modify this line.
Example: Ranking by RSI (14) on the Daily timeframe.
First, you'll need a function to calculate RSI. Add this function to the script:
f_getRSI(symbol, timeframe, length) =>
request.security(symbol, timeframe, ta.rsi(close, length))
Then, inside f_getSortedCryptoData() , find the `for` loop that populates the `caps` array and replace the `request.security` call:
// OLD LINE:
// caps.set(i, request.security("CRYPTOCAP:" + syms.get(i), timeframe.period, close))
// NEW LINE for RSI:
// Note: You'll need to decide how to format the symbol name (e.g., "BINANCE:" + syms.get(i) + "USDT")
caps.set(i, f_getRSI("BINANCE:" + syms.get(i) + "USDT", "D", 14))
2. Changing the Data Formatting:
The ranking values are formatted for display using the f_fmtCap() function, which currently formats large numbers into "M" (millions), "B" (billions), etc.
If you change the data source to something like RSI, you'll want to change the formatting. You can modify f_fmtCap() or create a new formatting function.
Example: Formatting for RSI.
// Modify f_fmtCap or create f_fmtRSI
f_fmtRSI(float v) =>
str.tostring(v, "#.##") // Simply format to two decimal places
Remember to update the calls to this function in the main drawing loop where the labels are created (e.g., str.format("{0}: {1}", crypto.symbol, f_fmtCap(crypto.cap)) ).
By modifying these key functions ( f_getSortedCryptoData and f_fmtCap ), you can adapt the Market Cap Landscape 3D to visualize and rank almost any dataset you can imagine, from technical indicators to fundamental data.
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We hope you enjoy using the Market Cap Landscape 3D as much as we enjoyed creating it. Happy charting! ✨
20/40/60Displays three consecutive, connected range boxes showing high/low price ranges for customizable periods. Boxes are positioned seamlessly with shared boundaries for continuous price action visualization.
Features
Three Connected Boxes: Red (most recent), Orange (middle), Green (earliest) periods
Customizable Positioning: Set range length and starting offset from current bar
Individual Styling: Custom colors, transparency, and border width for each box
Display Controls: Toggle borders, fills, and line visibility
Use Cases
Range Analysis: Compare volatility across time periods, spot breakouts
Support/Resistance: Use box boundaries as potential S/R levels
Market Structure: Visualize recent price development and trend patterns
Key Settings
Range Length: Bars per box (default: 20)
Starting Offset: Bars back from current to position boxes (default: 0)
Style Options: Colors, borders, and visibility controls for each box
Perfect for traders analyzing consecutive price ranges and comparing current conditions to recent historical periods.
RSI with Moving Averages[UO] EnhancedWhat This Indicator Does
Displays the RSI (Relative Strength Index) with two customizable moving averages to help identify trend direction and momentum shifts.
Key Features
RSI Line: Shows momentum (overbought above 70, oversold below 30)
Two Moving Averages: Smooth RSI signals and show trend direction
Color-Coded Fills: Visual areas between lines indicate bullish/bearish conditions
Support/Resistance Lines: Bull market support (40) and bear market resistance (60)
Customization Options
Moving Average Types: Choose SMA or EMA for each line
Periods: Adjust RSI (14), First MA (13), Second MA (33)
Visual Elements: Toggle background shading and fills on/off
Colors & Styles: Customize all line colors and widths in Style tab
How to Read It
Green Fill: Second MA below first MA (bullish momentum)
Red Fill: Second MA above first MA (bearish momentum)
RSI Above 70: Potentially overbought
RSI Below 30: Potentially oversold
Perfect for traders wanting enhanced RSI analysis with flexible moving average confirmation signals.
Constance Brown Composite Index EnhancedWhat This Indicator Does
Implements Constance Brown's copyrighted Composite Index formula (1996) from her Master's thesis - a breakthrough oscillator that solves the critical problem where RSI fails to show divergences in long-horizon trends, providing early warning signals for major market reversals.
The Problem It Solves
Traditional RSI frequently fails to display divergence signals in Global Equity Indexes and long-term charts, leaving asset managers without warning of major price reversals. Brown's research showed RSI failed to provide divergence signals 42 times across major markets - failures that would have been "extremely costly for asset managers."
Key Components
Composite Line: RSI Momentum (9-period) + Smoothed RSI Average - the core breakthrough formula
Fast/Slow Moving Averages: Trend direction confirmation (13/33 periods default)
Bollinger Bands: Volatility envelope around the composite signal
Enhanced Divergence Detection: Significantly improved trend reversal timing vs standard RSI
Research-Proven Performance
Based on Brown's extensive study across 6 major markets (1919-2015):
42 divergence signals triggered where RSI showed none
33 signals passed with meaningful reversals (78% success rate)
Only 5 failures - exceptional performance in monthly/2-month timeframes
Tested on: German DAX, French CAC 40, Shanghai Composite, Dow Jones, US/Japanese Government Bonds
New Customization Features
Moving Average Types: Choose SMA or EMA for fast/slow lines
Optional Fills: Toggle composite and Bollinger band fills on/off
All Periods Adjustable: RSI length, momentum, smoothing periods
Visual Styling: Customize colors and line widths in Style tab
Default Settings (Original Formula)
RSI Length: 14
RSI Momentum: 9 periods
RSI MA Length: 3
SMA Length: 3
Fast SMA: 13, Slow SMA: 33
Bollinger STD: 2.0
Applications
Long-term investing: Monthly/2-month charts for major trend changes
Elliott Wave analysis: Maximum displacement at 3rd-of-3rd waves, divergence at 5th waves
Multi-timeframe: Pairs well with MACD, works across all timeframes
Global markets: Proven effective on equities, bonds, currencies, commodities
Perfect for serious traders and asset managers seeking the proven mathematical edge that traditional RSI cannot provide.
Trend Continuation Filter - 🚀 Trend Continuation Filter — Multi-Factor Overlay
This overlay plots bullish / bearish continuation labels & arrows only when the market has enough confluence behind the move. Think of it as your “trend gatekeeper” — cutting out weak setups and highlighting only those with real momentum + structure.
🔍 Built-in Filters
✔ Ichimoku Cloud → trend bias + Tenkan/Kijun confirmation
✔ MACD (12/26/9) → acceleration via histogram slope
✔ RSI / MFI (14) → momentum quality (≥60 bullish / ≤40 bearish)
✔ ADX (14) → strength check (≥20 and rising)
➕ EMA Alignment (9/21/55/233) (optional)
➕ ATR Slope (14) (optional)
🎯 How it works
✅ Prints a Bull Continuation label/arrow when ≥4 filters align to the upside
✅ Prints a Bear Continuation label/arrow when ≥4 filters align to the downside
⚙️ minChecks input lets you adjust the strictness:
• Normal Days → set to 4 (more frequent, flexible)
• Trend Days → raise to 5–6 (fewer, high-conviction setups)
📈 Best Practices
⏰ Focus on London & New York sessions for clean expectancy
🧩 Pair with a HUD/Dashboard panel to see exactly which filters are active
EMA 20–55 + SMMA50 (20↔55 Band, Alerts, Color Controls)This script plots a customizable EMA ribbon and a smoothed moving average (SMMA).
Key features:
EMA 20 (blue) and EMA 55 (yellow) highlighted with a green/red background ribbon depending on trend.
Optional middle EMAs (25, 30, 35, 40, 45, 50) can be shown or hidden.
SMMA (50) plotted in white as a trend smoother.
Customizable line colors, band colors, and transparency.
Alerts for EMA20/EMA55 bullish or bearish crossovers.
Optional candle bar coloring based on trend.
This is useful for quickly spotting medium-term trend shifts when EMA20 crosses EMA55, while still tracking the broader EMA ribbon context.
DYNAMIC TRADING DASHBOARDStudy Material for the "Dynamic Trading Dashboard"
This Dynamic Trading Dashboard is designed as an educational tool within the TradingView environment. It compiles commonly used market indicators and analytical methods into one visual interface so that traders and learners can see relationships between indicators and price action. Understanding these indicators, step by step, can help traders develop discipline, improve technical analysis skills, and build strategies. Below is a detailed explanation of each module.
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1. Price and Daily Reference Points
The dashboard displays the current price, along with percentage change compared to the day’s opening price. It also highlights whether the price is moving upward or downward using directional symbols. Alongside, it tracks daily high, low, open, and daily range.
For traders, daily levels provide valuable reference points. The daily high and low are considered intraday support and resistance, while the median price of the day often acts as a pivot level for mean reversion traders. Monitoring these helps learners see how price oscillates within daily ranges.
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2. VWAP (Volume Weighted Average Price)
VWAP is calculated as a cumulative average price weighted by volume. The dashboard compares the current price with VWAP, showing whether the market is trading above or below it.
For traders, VWAP is often a guide for institutional order flow. Price trading above VWAP suggests bullish sentiment, while trading below VWAP indicates bearish sentiment. Learners can use VWAP as a training tool to recognize trend-following vs. mean reversion setups.
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3. Volume Analysis
The system distinguishes between buy volume (when the closing price is higher than the open) and sell volume (when the closing price is lower than the open). A progress bar highlights the ratio of buying vs. selling activity in percentage.
This is useful because volume confirms price action. For instance, if prices rise but sell volume dominates, it can signal weakness. New traders learning with this tool should focus on how volume often precedes price reversals and trends.
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4. RSI (Relative Strength Index)
RSI is a momentum oscillator that measures price strength on a scale from 0 to 100. The dashboard classifies RSI readings into overbought (>70), oversold (<30), or neutral zones and adds visual progress bars.
RSI helps learners understand momentum shifts. During training, one should notice how trending markets can keep RSI extended for longer periods (not immediate reversal signals), while range-bound markets react more sharply to RSI extremes. It is an excellent tool for practicing trend vs. range identification.
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5. MACD (Moving Average Convergence Divergence)
The MACD indicator involves a fast EMA, slow EMA, and signal line, with focus on crossovers. The dashboard shows whether a “bullish cross” (MACD above signal line) or “bearish cross” (MACD below signal line) has occurred.
MACD teaches traders to identify trend momentum shifts and divergence. During practice, traders can explore how MACD signals align with VWAP trends or RSI levels, which helps in building a structured multi-indicator analysis.
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6. Stochastic Oscillator
This indicator compares the current close relative to a range of highs and lows over a period. Displayed values oscillate between 0 and 100, marking zones of overbought (>80) and oversold (<20).
Stochastics are useful for students of trading to recognize short-term momentum changes. Unlike RSI, it reacts faster to price volatility, so false signals are common. Part of the training exercise can be to observe how stochastic “flips” can align with volume surges or daily range endpoints.
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7. Trend & Momentum Classification
The dashboard adds simple labels for trend (uptrend, downtrend, neutral) based on RSI thresholds. Additionally, it provides quick momentum classification (“bullish hold”, “bearish hold”, or neutral).
This is beneficial for beginners as it introduces structured thinking: differentiating long-term market bias (trend) from short-term directional momentum. By combining both, traders can practice filtering signals instead of trading randomly.
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8. Accumulation / Distribution Bias
Based on RSI levels, the script generates simplified tags such as “Accumulate Long”, “Accumulate Short”, or “Wait”.
This is purely an interpretive guide, helping learners think in terms of accumulation phases (when markets are low) and distribution phases (when markets are high). It reinforces the concept that trading is not only directional but also involves timing.
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9. Overall Market Status and Score
Finally, the dashboard compiles multiple indicators (VWAP position, RSI, MACD, Stochastics, and price vs. median levels) into a Market Score expressed as a percentage. It also labels the market as Overbought, Oversold, or Normal.
This scoring system isn’t a recommendation but a learning framework. Students can analyze how combining different indicators improves decision-making. The key training focus here is confluence: not depending on one indicator but observing when several conditions align.
Extended Study Material with Formulas
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1. Daily Reference Levels (High, Low, Open, Median, Range)
• Day High (H): Maximum price of the session.
DayHigh=max(Hightoday)DayHigh=max(Hightoday)
• Day Low (L): Minimum price of the session.
DayLow=min(Lowtoday)DayLow=min(Lowtoday)
• Day Open (O): Opening price of the session.
DayOpen=OpentodayDayOpen=Opentoday
• Day Range:
Range=DayHigh−DayLowRange=DayHigh−DayLow
• Median: Mid-point between high and low.
Median=DayHigh+DayLow2Median=2DayHigh+DayLow
These act as intraday guideposts for seeing how far the price has stretched from its key reference levels.
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2. VWAP (Volume Weighted Average Price)
VWAP considers both price and volume for a weighted average:
VWAPt=∑i=1t(Pricei×Volumei)∑i=1tVolumeiVWAPt=∑i=1tVolumei∑i=1t(Pricei×Volumei)
Here, Price_i can be the average price (High + Low + Close) ÷ 3, also known as hlc3.
• Interpretation: Price above VWAP = bullish bias; Price below = bearish bias.
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3. Volume Buy/Sell Analysis
The dashboard splits total volume into buy volume and sell volume based on candle type.
• Buy Volume:
BuyVol=Volumeif Close > Open, else 0BuyVol=Volumeif Close > Open, else 0
• Sell Volume:
SellVol=Volumeif Close < Open, else 0SellVol=Volumeif Close < Open, else 0
• Buy Ratio (%):
VolumeRatio=BuyVolBuyVol+SellVol×100VolumeRatio=BuyVol+SellVolBuyVol×100
This helps traders gauge who is in control during a session—buyers or sellers.
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4. RSI (Relative Strength Index)
RSI measures strength of momentum by comparing gains vs. losses.
Step 1: Compute average gains (AG) and losses (AL).
AG=Average of Upward Closes over N periodsAG=Average of Upward Closes over N periodsAL=Average of Downward Closes over N periodsAL=Average of Downward Closes over N periods
Step 2: Calculate relative strength (RS).
RS=AGALRS=ALAG
Step 3: RSI formula.
RSI=100−1001+RSRSI=100−1+RS100
• Used to detect overbought (>70), oversold (<30), or neutral momentum zones.
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5. MACD (Moving Average Convergence Divergence)
• Fast EMA:
EMAfast=EMA(Close,length=fast)EMAfast=EMA(Close,length=fast)
• Slow EMA:
EMAslow=EMA(Close,length=slow)EMAslow=EMA(Close,length=slow)
• MACD Line:
MACD=EMAfast−EMAslowMACD=EMAfast−EMAslow
• Signal Line:
Signal=EMA(MACD,length=signal)Signal=EMA(MACD,length=signal)
• Histogram:
Histogram=MACD−SignalHistogram=MACD−Signal
Crossovers between MACD and Signal are used in studying bullish/bearish phases.
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6. Stochastic Oscillator
Stochastic compares the current close against a range of highs and lows.
%K=Close−LowestLowHighestHigh−LowestLow×100%K=HighestHigh−LowestLowClose−LowestLow×100
Where LowestLow and HighestHigh are the lowest and highest values over N periods.
The %D line is a smooth version of %K (using a moving average).
%D=SMA(%K,smooth)%D=SMA(%K,smooth)
• Values above 80 = overbought; below 20 = oversold.
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7. Trend and Momentum Classification
This dashboard generates simplified trend/momentum logic using RSI.
• Trend:
• RSI < 40 → Downtrend
• RSI > 60 → Uptrend
• In Between → Neutral
• Momentum Bias:
• RSI > 70 → Bullish Hold
• RSI < 30 → Bearish Hold
• Otherwise Neutral
This is not predictive, only a classification framework for educational use.
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8. Accumulation/Distribution Bias
Based on extreme RSI values:
• RSI < 25 → Accumulate Long Bias
• RSI > 80 → Accumulate Short Bias
• Else → Wait/No Action
This helps learners understand the idea of accumulation at lows (strength building) and distribution at highs (profit booking).
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9. Overall Market Status and Score
The tool adds up 5 bullish conditions:
1. Price above VWAP
2. RSI > 50
3. MACD > Signal
4. Stochastic > 50
5. Price above Daily Median
BullishScore=ConditionsMet5×100BullishScore=5ConditionsMet×100
Then it categorizes the market:
• RSI > 70 or Stoch > 80 → Overbought
• RSI < 30 or Stoch < 20 → Oversold
• Else → Normal
This encourages learners to think in terms of probabilistic conditions instead of single-indicator signals.
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⚠️ Warning:
• Trading financial markets involves substantial risk.
• You can lose more money than you invest.
• Past performance of indicators does not guarantee future results.
• This script must not be copied, resold, or republished without authorization from aiTrendview.
By using this material or the code, you agree to take full responsibility for your trading decisions and acknowledge that this is not financial advice.
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⚠️ Disclaimer and Warning (From aiTrendview)
This Dynamic Trading Dashboard is created strictly for educational and research purposes on the TradingView platform. It does not provide financial advice, buy/sell recommendations, or guaranteed returns. Any use of this tool in live trading is completely at the user’s own risk. Markets are inherently risky; losses can exceed initial investment.
The intellectual property of this script and its methodology belongs to aiTrendview. Unauthorized reproduction, modification, or redistribution of this code is strictly prohibited. By using this study material or the script, you acknowledge personal responsibility for any trading outcomes. Always consult professional financial advisors before making investment decisions.
MTF RSI + ADX + ATR SL/TP vivekDescription:
This strategy combines the power of multi-timeframe RSI filtering with ADX trend confirmation and ATR-based risk management to capture strong directional moves.
🔑 Entry Rules:
• Daily RSI > 60
• 4H RSI > 60
• 1H RSI > 60
• 10m RSI > 40
• ADX (current timeframe) > 20
When all conditions align, a long entry is triggered.
🛡 Risk Management:
• ATR-based Stop-Loss (customizable multiplier)
• Take-Profit defined as a Risk-Reward multiple of the ATR stop
🎯 Why this Strategy?
• Ensures alignment across higher timeframes before entering a trade
• Uses ADX to avoid choppy/range-bound markets
• Built-in ATR stop-loss & take-profit for disciplined risk control
• Fully customizable parameters
This strategy is designed for trend-following swing entries. It works best on liquid instruments such as indices, forex pairs, and large-cap stocks. Always optimize the parameters based on your preferred asset and timeframe.
Daryl Guppy's Multiple Moving Averages - GMMAThe Guppy EMAs indicator (Daryl Guppy’s method) displays two groups of exponential moving averages (EMAs) on the chart:
Fast EMA group: 3, 5, 8, 10, 12, 15 periods (thinner, more responsive lines)
Slow EMA group: 30, 35, 40, 45, 50, 60 periods (thicker, smoother lines)
Color Logic:
Fast EMAs turn AQUA if all fast EMAs are in bullish alignment and slow EMAs are in bullish alignment.
Fast EMAs turn ORANGE if all fast EMAs are in bearish alignment and slow EMAs are in bearish alignment.
Otherwise, fast EMAs appear GRAY.
Slow EMAs turn LIME when in bullish order, RED when bearish, and remain GRAY otherwise.
The area between the outermost fast EMAs and slow EMAs is filled with a semi-transparent silver color for visual emphasis.
MTF RSI + ADX + ATR SL/TPThis strategy combines the power of multi-timeframe RSI filtering with ADX trend confirmation and ATR-based risk management to capture strong directional moves.
🔑 Entry Rules:
• Daily RSI > 60
• 4H RSI > 60
• 1H RSI > 60
• 10m RSI > 40
• ADX (current timeframe) > 20
When all conditions align, a long entry is triggered.
🛡 Risk Management:
• ATR-based Stop-Loss (customizable multiplier)
• Take-Profit defined as a Risk-Reward multiple of the ATR stop
🎯 Why this Strategy?
• Ensures alignment across higher timeframes before entering a trade
• Uses ADX to avoid choppy/range-bound markets
• Built-in ATR stop-loss & take-profit for disciplined risk control
• Fully customizable parameters
This strategy is designed for trend-following swing entries. It works best on liquid instruments such as indices, forex pairs, and large-cap stocks. Always optimize the parameters based on your preferred asset and timeframe.
Advanced RSI — Mark 4 RSI was introduced by J. Welles Wilder Jr. in 1978 in New Concepts in Technical Trading Systems. It measures the velocity of gains vs. losses on a bounded 0–100 scale and popularized the 14-period lookback with 70/30 guide rails for overbought/oversold. Over time, traders added variations (different lengths, thresholds, smoothing, adaptive levels), but the core idea stayed the same: momentum turns often precede price turns.
and i initially started to make minor adjustments for personal use like changing the default to 17 , and using Tradingviews official RSI which comes with a MA embedded. but it was not enough. especially the visuals.
so, for this public release Mark 4 i enhanced RSI by incorporating :
1. Dual-Length Fusion
Two RSI periods (default 17 + 21) blended then lightly smoothed (TEMA by default) → steadier
line without dulling turns.
2. Adaptive OB/OS (ATR-aware) for fewer whipsaws.
3. OB/OS alt solution:
Brief yellow segments appear only at local extremes (default: >72 tops, <32 bottoms) to
emphasize exhaustion without repainting the whole line.
4. Signals you can actually see
Triangle markers for:
Bullish: RSI crossing up through adaptive OS (and still <40 at the cross).
Bearish: RSI crossing down through adaptive OB (and still >60 at the cross).
“Strong Bull/Bear” background nudges appear when momentum is pushing beyond the
bands.
Optional Divergence Tags
and
Tiny diamonds to flag potential bullish/bearish divergences (look-back based).
Info Table (can be hidden)
my Fav feature i included 5 colorways with modern themes.(pls check under INPUTS)
and i made all that to make the indicator visualization look awesome on high end displays.
Credits & acknowledgment
Inspired by the original RSI by J. Welles Wilder Jr. (1978).
Built to be modern, focused, and comfortable for long sessions—especially on dark/OLED displays.
THIS INDICATOR IS MORE THAN ENOUGH BUT I DO HAVE PRIVATE INDICATORS WITH DIFFERENT LOGIC FUNCTIONS.
I'm open for feedback/collaboration.
drsamc.
