Multi-Timeline 1.0Multi-TimeLines 1.0 - Comprehensive Description
WHAT IT DOES:
This indicator creates dynamic horizontal support/resistance lines based on opening prices captured at user-defined New York times. Unlike static horizontal lines, these levels automatically appear and disappear based on sophisticated session logic, providing traders with time-sensitive reference levels that adapt to market sessions.
HOW IT WORKS - TECHNICAL IMPLEMENTATION:
1.
Timezone Conversion Engine:
The script uses Pine Script's "America/New_York" timezone functions to ensure all time calculations are based on NY time, regardless of the user's chart timezone. This eliminates confusion and provides consistent behavior across global markets.
2.
Dual-Category Time Classification System:
The indicator employs a unique two-category classification system:
Category A (16:00-23:59 NY): Evening times that extend overnight until next day 15:59 NY
Category B (00:00-15:59 NY): Day times that extend until same day 15:59 NY
This classification handles the complex logic of overnight sessions and prevents lines from incorrectly resetting at midnight for evening times.
3. Price Capture Mechanism:
Uses precise time-hit detection with backup systems for edge cases (especially midnight 00:00). When a specified time occurs, the script captures the bar's opening price and stores it in persistent variables using Pine Script's var declarations.
4. Session-Aware Display Logic:
Lines only appear during their designated "display windows" - periods when the captured price level is relevant. The script uses conditional plotting with plot.style_linebr to create clean breaks when lines are inactive.
5. Smart Reset System:
Different reset behaviors based on time classification:
Category A times persist across midnight (for overnight analysis)
Category B times reset on day changes (except 00:00 which captures AT day change)
Automatic cleanup when display windows close
ORIGINALITY & UNIQUE FEATURES:
1. Overnight Session Handling:
Unlike basic horizontal line tools, this script properly handles overnight spans for evening times, making it invaluable for analyzing gaps and overnight price action.
2. Automatic Session Management:
No manual line drawing required - the script automatically manages when lines appear/disappear based on NY market sessions (15:59 close, 18:00 after-hours start).
3. Time-Window Display Logic:
Lines only show during relevant periods, reducing chart clutter and focusing attention on currently active levels.
TRADING CONCEPTS & APPLICATIONS:
1. Session-Based Analysis:
Capture opening prices at key session times:
00:00 NY: Sydney/Asian session start
03:00 NY: London pre-market
08:00 NY: London session open
09:30 NY: NYSE opening bell
18:00 NY: After-hours start
2. Gap Analysis:
Evening times (20:00-23:59) that extend overnight are particularly useful for:
Identifying potential gap-fill levels
Tracking overnight high/low breaks
Setting reference points for next-day trading
3. Support/Resistance Framework:
Opening prices at significant times often act as:
Intraday support/resistance levels
Reference points for breakout/breakdown analysis
Pivot levels for mean reversion strategies
HOW TO USE:
1. Time Input:
Enter times in "HH:MM" format using 24-hour NY time:
"09:30" for NYSE open
"15:30" for late-day reference
"20:00" for evening level (extends overnight)
2. Line Behavior:
Blue/Green/Cyan/Red lines: Your custom times
Yellow line: After-hours day open (18:00 NY start)
Lines appear with breaks during inactive periods
3. Strategic Setup:
Use 2-3 key session times for your trading style
Combine morning times (immediate reference) with evening times (overnight analysis)
Toggle after-hours line based on your market focus
CALCULATION METHOD:
The script uses direct opening price capture (no smoothing or averaging) at precise time hits, ensuring the most accurate representation of actual market levels at specified times. This raw price approach maintains the integrity of actual market opening prices rather than manipulated or calculated values.
This method is particularly effective because opening prices at significant times often represent institutional order flow and can act as magnetic levels throughout subsequent sessions.
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AWR Pearsons R & LR Oscillator MTF1. Overview
This indicator is designed to analyze the correlation between a price series (or any custom indicator) and the bar index using Pearson’s correlation coefficient. It performs multiple linear regressions over shifted periods and then aggregates these results to create an oscillator. In addition, it integrates a multi-timeframe (MTF) analysis by retrieving the same calculations on 3 different time intervals, providing a more comprehensive view of the trend evolution.
2. User Parameters
The indicator offers several configurable parameters that allow the user to adjust both the calculations and the display:
Source (Linear Regression): The data source on which the regressions are applied (by default, the closing price).
Number of Linear Regressions (numOfLinReg): Allows choosing the number of correlation calculations (up to 10) to be carried out on different shifted periods.
Start Period (startPeriod) and Period Increment (periodIncrement): These parameters define the reference window for each regression. The calculation starts with a base period and then increases with each regression by a fixed increment, creating several time windows to assess the relationship between price evolution and time progression.
Deviation (def_deviation): Although defined, this parameter is intended to control the sensitivity of the calculations. It can be used in further developments of the indicator.
For Multi Time Frames analysis, three additional timeframes are provided through inputs in addition of the current period:
Sum up :
Timeframe 1 = current
Timeframe 2 = 30-minute (default settings)
Timeframe 3 = 1-hour (default settings)
Timeframe 4 = 4-hour (default settings)
These different timeframes allow you to obtain consistent or divergent signals over multiple resolutions, thereby enhancing the confidence of trading decisions.
3. Calculation Logic
At the core of the indicator is the f_calcConditions() function, which performs several essential tasks:
Calculating Pearson's Coefficients For each linear regression, the script uses ta.correlation() to measure the correlation between the chosen source (for example, the closing price) and the chronological index (bar_index). Up to 10 coefficients are computed over shifted windows, providing an evolving view of the linear relationship over different intervals.
Averaging the Results Once the coefficients are calculated, they are stored in an array and averaged to produce a global correlation value called avgPR_local.
Applying Moving Averages
The resulting average is then smoothed using several moving averages (SMA):
A short-term SMA (period of 14),
An intermediate SMA (period of 100),
A long-term SMA (period of 400).
These moving averages help to highlight the underlying trend of the oscillator by indicating the direction in which the correlation is moving.
Defining Trading Conditions Based on avgPR_local and its associated SMAs, multiple conditions are set to generate buy or sell signals:
Simple SMA Conditions :
Small signal :
Light blue below bar signal :
When the averaged coefficients lie between -1 and -0.63, are above the short-term SMA (14 periods), and are increasing, it may indicate a bullish dynamic (buy signal).
Orange above bar signal :
Conversely, when the value is higher (between 0.63 and 1) and below its SMA (14 periods), and are decreasing the trend is considered bearish (sell signal).
Medium signal :
Dark green signal
When the averaged coefficients lie between -1 and -0.45, are above the short-term SMA (14 periods), and are increasing, and also the average 100 is increasing. It may indicate a bullish dynamic (buy signal).
Light red signal :
Conversely, when the value is higher (between 0.45 and 1) and below its SMA (14 periods), the trend and are decreasing, and also the average 100 is decreasing. It may indicate a bearish dynamic(sell signal).
Light green signal :
When the averaged coefficients lie between -1 and -0.15, are above the short-term SMA (14 periods), and are increasing, and also the average 100 & 400 is increasing . It may indicate a bullish dynamic (buy signal).
Dark red signal :
Conversely, when the value is higher (between 0.45 and 1) and below its SMA (14 periods), the trend and are decreasing, and also the average 100 & 400 is decreasing. It may indicate a bearish dynamic(sell signal).
These additional conditions further refine the signals by verifying the consistency of the movement over longer periods. They check that the trends from the respective averages (intermediate and long-term) are in line with the direction indicated by the initial moving average.
These conditions are designed to capture moments when the oscillator's dynamics change, which can be interpreted as opportunities to enter or exit a trade.
4. Multi-Timeframes and Display
One of the main strengths of this indicator is its multi-timeframe approach.
This offers several advantages:
Comparative Analysis: Compare short-term dynamics with broader trends.
Enhanced Signal Reliability: A signal confirmed across multiple timeframes has a higher probability of success.
To visually highlight these signals on the chart, the indicator uses the plotchar() function with distinct symbols for each timeframe:
Current Timeframe: Signals are represented by the character "1"
30-Minute Timeframe: Displayed with the character "2".
1-Hour Timeframe: Displayed with the character "3".
4-Hour Timeframe: Displayed with the character "4".
The colors used are various shades of green for buy signals and shades of red/orange for sell signals, making it easy to distinguish between the different alerts.
5. Integrated Alerts
To avoid missing any trading opportunities, the indicator includes an alert condition via the alertcondition() function. This alert is triggered if any buy or sell signal is generated on any of the analyzed timeframes. The message "MTF valide" indicates that multiple timeframes are confirming the signal, enabling more informed decision-making.
6. How to Use This Indicator
Installation and Configuration: Copy the script into the TradingView Pine Script editor and add it to your chart. The default parameters can be tuned according to market behavior or personal preferences regarding sensitivity and responsiveness.
Interpreting the Signals:
Watch for the symbols on the chart corresponding to each timeframe.
A buy signal appears as a specific symbol below the bar (indicating a bullish condition based on a rising or less negative correlation), while a sell signal appears above the bar.
Multi-Timeframe Analysis: By comparing signals across timeframes, you can filter out false signals. For example, if the short-term timeframe shows a buy signal but the 4-hour timeframe indicates a bearish trend, you may need to reassess your position.
Adjusting the Settings: Depending on the asset type or market volatility, you might need to tweak the periods (startPeriod, periodIncrement) or the number of linear regressions to generate signals that better align with the price dynamics.
Using Alerts: Activate the built-in alert feature so that TradingView notifies you as soon as a multi-timeframe signal is detected. This ensures you stay informed even if you are not continuously monitoring the chart.
In Conclusion
The AWR Pearsons R & LR Oscillator MTF is a powerful tool for traders seeking a detailed understanding of market trends by combining statistical rigor (via Pearson's correlation coefficient) with a multi-timeframe approach. It is capable of generating clear entry and exit signals, visualized with specific symbols and colors depending on the timeframe. By adjusting the parameters to match your trading strategy and leveraging the alert system, you now have a robust instrument for making well-informed market decisions.
Feel free to dive deeper into each component and experiment with different configurations to see how the oscillator integrates with your overall technical analysis strategy. Enjoy exploring its potential and refining your trading approach!
ICT TIME ELEMENTS [KaninFX]## Overview
The ICT Time Elements indicator is a comprehensive trading tool designed to visualize the most critical market sessions and timeframes according to Inner Circle Trader (ICT) methodology. This indicator helps traders identify high-probability trading opportunities by highlighting key market sessions, killzones, and liquidity periods throughout the trading day.
## Key Features
### 🕐 Complete ICT Time Framework
- **Asian Range**: 8:00 PM - 12:00 AM (NY Time) - Evening consolidation period
- **London Killzone**: 2:00 AM - 5:00 AM (NY Time) - European market opening liquidity
- **NY Killzone**: 7:00 AM - 10:00 AM (NY Time) - US market opening with high volatility
- **Silver Bullet Sessions**:
- London Silver Bullet: 3:00 AM - 4:00 AM
- AM Silver Bullet: 10:00 AM - 11:00 AM
- PM Silver Bullet: 2:00 PM - 3:00 PM
- **Lunch Hours**: 5:00 AM - 7:00 AM & 12:00 PM - 1:00 PM (Lower volatility periods)
- **News Embargo**: 8:30 AM - 9:30 AM (High impact news release window)
- **20-Minute Macros**: :50 to :10 minutes of each hour (Short-term reversal periods)
- **True Day Close**: 4:00 PM - 4:30 PM (Official market close)
### 🎨 Visual Customization
- **Multiple Themes**: Dark, Light, and Custom color schemes
- **Adjustable Opacity**: Control zone transparency (0-100%)
- **Font Customization**: Tiny, Small, Normal, Large text sizes
- **Custom Colors**: Personalize each zone with your preferred colors
- **Professional Display**: Clean histogram visualization with zone labels
### 🌍 Multi-Timezone Support
Built-in support for major trading centers:
- America/New_York (Default)
- America/Chicago
- America/Los_Angeles
- Europe/London
- Asia/Tokyo
- Asia/Shanghai
- Australia/Sydney
### 📊 Smart Information Display
- **Real-time Zone Detection**: Automatically identifies current active session
- **Zone Labels**: Clear labeling at the center of each time period
- **Current Zone Indicator**: Arrow pointer showing the active session
- **Comprehensive Info Table**: Quick reference for all time zones and their schedules
- **Flexible Table Positioning**: Place info table in any corner of your chart
### ⚡ Performance Optimized
- **Memory Management**: Automatic cleanup of old labels to maintain performance
- **Efficient Processing**: Optimized time calculations for smooth operation
- **Resource Control**: Limited label generation to prevent system overload
## How It Works
The indicator continuously monitors the current time against predefined ICT session schedules. When price action enters a recognized time zone, the indicator:
1. **Highlights the Period**: Colors the histogram bar according to the active session
2. **Labels the Zone**: Places descriptive text identifying the current market condition
3. **Updates Info Table**: Shows current session status and complete schedule
4. **Tracks Macro Periods**: Identifies 20-minute reversal windows within major sessions
### Special Features
- **Macro Detection**: Automatically identifies when current time falls within a 20-minute macro period
- **Session Overlap Handling**: Properly manages overlapping time zones with priority logic
- **Dynamic Color Adjustment**: Theme-aware color selection for optimal visibility
## Best Use Cases
### For ICT Traders
- Identify optimal entry times during killzone sessions
- Recognize silver bullet opportunities for quick scalps
- Avoid trading during lunch hour consolidations
- Prepare for news embargo volatility
### For Session Traders
- Track major market session transitions
- Plan trading strategy around high-liquidity periods
- Understand global market flow and timing
### For Swing Traders
- Identify macro trend continuation points
- Time position entries during optimal sessions
- Understand market structure changes across sessions
## Installation & Setup
1. Add the indicator to your TradingView chart
2. Select your preferred timezone from the dropdown
3. Choose theme (Dark/Light) or customize colors
4. Adjust font size and table position to your preference
5. Enable/disable features as needed for your trading style
## Pro Tips
- **Combine with Price Action**: Use time zones alongside support/resistance levels
- **Focus on Killzones**: Highest probability setups occur during London and NY killzones
- **Watch Silver Bullets**: These 1-hour windows often provide excellent reversal opportunities
- **Respect Lunch Hours**: Lower volatility periods - consider smaller position sizes
- **News Embargo Awareness**: Prepare for potential whipsaws during 8:30-9:30 AM
## Conclusion
The ICT Time Elements indicator transforms complex ICT timing concepts into an easy-to-read visual tool. Whether you're a beginner learning ICT methodology or an experienced trader looking to optimize your timing, this indicator provides the essential market session awareness needed for successful trading.
*Compatible with all TradingView plans and timeframes. Works best on 1-minute to 1-hour charts for optimal session visualization.*
Polarity-VoVix Fusion Index (PVFI) Polarity-VoVix Fusion Index (PVFI) - Order Flow and Volatility Regime Detector
The PVFI is a next-generation indicator that fuses the Order Flow Polarity Index (OFPI) with a proprietary VoVix Volume Delta (VVD) engine. This tool is designed for traders who want to see not just how much volume is trading, but who is in control and how volatility is shifting beneath the surface.
What Makes PVFI Standout from the rest?
- Dual Engine: PVFI combines two advanced signals:
* OFPI: Measures real-time buy/sell pressure using candle body position and volume, then smooths it with a T3 moving average for clarity and responsiveness.
* VVD: Captures the "volatility of volume delta" - a normalized, memory-boosted measure of aggressive buying/selling, with a custom non-linear clamp for organic, non-pegged signals.
- Visual Clarity: Neon-glow OFPI line and shadowed, color-gradient VVD area make regime shifts and momentum instantly visible.
- Adaptive Dashboard: Toggle between a full-featured dashboard (desktop) and a compact info line (mobile) for seamless use on any device.
- Universal: Works on any asset - crypto, stocks, futures, forex - and any timeframe.
- No Chart Clutter: Clean, modern visuals and toggles for a pro look.
Inputs:
OFPI Lookback Length (ofpi_len): Sets the window for order flow pressure calculation. Shorter = more sensitive, longer = smoother. For scalping, try 5-10. For swing trading, 15-30. Crypto often benefits from shorter windows due to volatility.
OFPI T3 Smoothing Length (t3_len): Controls the smoothness of the OFPI line. Lower = more responsive, higher = smoother. Use 3-7 for fast markets, 8-15 for slow or higher timeframes.
OFPI T3 Volume Factor (t3_vf): Adjusts the T3’s sensitivity. Higher = more responsive, lower = more stable. 0.6-0.8 is typical. Raise for more “snappy” signals, lower for less noise.
VVD Delta Lookback (delta_len): Sets the window for VVD’s volume delta calculation. 10-20 for most assets. Shorter for high-volatility, longer for slow markets.
VVD Volatility Normalization Length (vol_norm_len): Normalizes VVD by recent volume. 15-30 is typical. Use higher for assets with wild volume swings.
VVD Momentum Memory (momentum_mem): Adds a “memory” boost to VVD, amplifying persistent buying/selling. 2-5 is common. Lower for choppy markets, higher for trending.
Show Dashboard (showDash): Toggles the full dashboard table (best for desktop). Turn off for a minimalist or mobile setup.
Show Compact Info Line (showInfoLabel): Toggles a single-line info label (best for mobile). Turn on for mobile or minimalist setups.
How PVFI Works:
- OFPI Calculation: Splits each candle’s volume into buy/sell pressure based on where the close is within the range. Aggregates over your chosen lookback, then smooths with a T3 moving average for a neon, lag-minimized signal.
- VVD Calculation: Measures the “aggression” of volume (body-weighted), normalizes by recent volume, and applies a memory boost for persistent trends. Uses a custom tanh clamp for a natural, non-pegged range.
- Visuals: OFPI is plotted as a neon line (with glow). VVD is a color-gradient area with a soft shadow, instantly showing regime shifts.
- Dashboard/Info Line: Desktop: Full dashboard with all key stats, color-coded and branded. Mobile: Compact info line with arrows for quick reads.
How you'll use PVFI:
- Bullish OFPI (Teal Neon, Up Arrow): Buyers are dominating. Look for breakouts, trend continuations, or confirmation with your own system.
- Bearish OFPI (Green Neon, Down Arrow): Sellers are in control. Watch for breakdowns or short setups.
- VVD Positive (Teal Area): Aggressive buying is increasing. Confirm with price action.
- VVD Negative (Purple Area): Aggressive selling is increasing. Use for risk management or short bias.
- Neutral/Flat: Market is balanced or indecisive. Consider waiting for a clear regime shift.
- Dashboard/Info Line: Use the dashboard for full context, or the info line for a quick glance on mobile.
Tips:
- For scalping, use lower lookbacks and smoothing.
- For swing trading, increase lookbacks and smoothing for stability.
- Works on all assets and timeframes - tune to your style.
Why PVFI is Unique:
- Fusion of Order Flow and Volatility: No other indicator combines body-based order flow with a volatility-of-volume delta, both visualized with modern, pro-grade graphics.
- Adaptive, Not Static: PVFI adapts to market regime, not just price movement.
- Mobile-Ready: Dashboard and info line toggles for any device.
- No Chart Clutter: Clean, color-coded, and easy to read.
For Educational Use Only
PVFI is a research and educational tool, not financial advice. Always use proper risk management and combine with your own strategy.
Trade with clarity. Trade with edge.
— Dskyz , for DAFE Trading Systems
Casa_UtilsLibrary "Casa_Utils"
A collection of convenience and helper functions for indicator and library authors on TradingView
formatNumber(num)
My version of format number that doesn't have so many decimal places...
Parameters:
num (float) : The number to be formatted
Returns: The formatted number
getDateString(timestamp)
Convenience function returns timestamp in yyyy/MM/dd format.
Parameters:
timestamp (int) : The timestamp to stringify
Returns: The date string
getDateTimeString(timestamp)
Convenience function returns timestamp in yyyy/MM/dd hh:mm format.
Parameters:
timestamp (int) : The timestamp to stringify
Returns: The date string
getInsideBarCount()
Gets the number of inside bars for the current chart. Can also be passed to request.security to get the same for different timeframes.
Returns: The # of inside bars on the chart right now.
getLabelStyleFromString(styleString, acceptGivenIfNoMatch)
Tradingview doesn't give you a nice way to put the label styles into a dropdown for configuration settings. So, I specify them in the following format: "Center", "Left", "Lower Left", "Lower Right", "Right", "Up", "Upper Left", "Upper Right", "Plain Text", "No Labels". This function takes care of converting those custom strings back to the ones expected by tradingview scripts.
Parameters:
styleString (string)
acceptGivenIfNoMatch (bool) : If no match for styleString is found and this is true, the function will return styleString, otherwise it will return tradingview's preferred default
Returns: The string expected by tradingview functions
getTime(hourNumber, minuteNumber)
Given an hour number and minute number, adds them together and returns the sum. To be used by getLevelBetweenTimes when fetching specific price levels during a time window on the day.
Parameters:
hourNumber (int) : The hour number
minuteNumber (int) : The minute number
Returns: The sum of all the minutes
getHighAndLowBetweenTimes(start, end)
Given a start and end time, returns the high or low price during that time window.
Parameters:
start (int) : The timestamp to start with (# of seconds)
end (int) : The timestamp to end with (# of seconds)
Returns: The high or low value
getPremarketHighsAndLows()
Returns an expression that can be used by request.security to fetch the premarket high & low levels in a tuple.
Returns: (tuple)
getAfterHoursHighsAndLows()
Returns an expression that can be used by request.security to fetch the after hours high & low levels in a tuple.
Returns: (tuple)
getOvernightHighsAndLows()
Returns an expression that can be used by request.security to fetch the overnight high & low levels in a tuple.
Returns: (tuple)
getNonRthHighsAndLows()
Returns an expression that can be used by request.security to fetch the high & low levels for premarket, after hours and overnight in a tuple.
Returns: (tuple)
getLineStyleFromString(styleString, acceptGivenIfNoMatch)
Tradingview doesn't give you a nice way to put the line styles into a dropdown for configuration settings. So, I specify them in the following format: "Solid", "Dashed", "Dotted", "None/Hidden". This function takes care of converting those custom strings back to the ones expected by tradingview scripts.
Parameters:
styleString (string) : Plain english (or TV Standard) version of the style string
acceptGivenIfNoMatch (bool) : If no match for styleString is found and this is true, the function will return styleString, otherwise it will return tradingview's preferred default
Returns: The string expected by tradingview functions
getPercentFromPrice(price)
Get the % the current price is away from the given price.
Parameters:
price (float)
Returns: The % the current price is away from the given price.
getPositionFromString(position)
Tradingview doesn't give you a nice way to put the positions into a dropdown for configuration settings. So, I specify them in the following format: "Top Left", "Top Center", "Top Right", "Middle Left", "Middle Center", "Middle Right", "Bottom Left", "Bottom Center", "Bottom Right". This function takes care of converting those custom strings back to the ones expected by tradingview scripts.
Parameters:
position (string) : Plain english position string
Returns: The string expected by tradingview functions
getRsiAvgsExpression(rsiLength)
Call request.security with this as the expression to get the average up/down values that can be used with getRsiPrice (below) to calculate the price level where the supplied RSI level would be reached.
Parameters:
rsiLength (simple int) : The length of the RSI requested.
Returns: A tuple containing the avgUp and avgDown values required by the getRsiPrice function.
getRsiPrice(rsiLevel, rsiLength, avgUp, avgDown)
use the values returned by getRsiAvgsExpression() to calculate the price level when the provided RSI level would be reached.
Parameters:
rsiLevel (float) : The RSI level to find price at.
rsiLength (int) : The length of the RSI to calculate.
avgUp (float) : The average move up of RSI.
avgDown (float) : The average move down of RSI.
Returns: The price level where the provided RSI level would be met.
getSizeFromString(sizeString)
Tradingview doesn't give you a nice way to put the sizes into a dropdown for configuration settings. So, I specify them in the following format: "Auto", "Huge", "Large", "Normal", "Small", "Tiny". This function takes care of converting those custom strings back to the ones expected by tradingview scripts.
Parameters:
sizeString (string) : Plain english size string
Returns: The string expected by tradingview functions
getTimeframeOfChart()
Get the timeframe of the current chart for display
Returns: The string of the current chart timeframe
getTimeNowPlusOffset(candleOffset)
Helper function for drawings that use xloc.bar_time to help you know the time offset if you want to place the end of the drawing out into the future. This determines the time-size of one candle and then returns a time n candleOffsets into the future.
Parameters:
candleOffset (int) : The number of items to find singular/plural for.
Returns: The future time
getVolumeBetweenTimes(start, end)
Given a start and end time, returns the sum of all volume across bars during that time window.
Parameters:
start (int) : The timestamp to start with (# of seconds)
end (int) : The timestamp to end with (# of seconds)
Returns: The volume
isToday()
Returns true if the current bar occurs on today's date.
Returns: True if current bar is today
padLabelString(labelText, labelStyle)
Pads a label string so that it appears properly in or not in a label. When label.style_none is used, this will make sure it is left-aligned instead of center-aligned. When any other type is used, it adds a single space to the right so there is padding against the right end of the label.
Parameters:
labelText (string) : The string to be padded
labelStyle (string) : The style of the label being padded for.
Returns: The padded string
plural(num, singular, plural)
Helps format a string for plural/singular. By default, if you only provide num, it will just return "s" for plural and nothing for singular (eg. plural(numberOfCats)). But you can optionally specify the full singular/plural words for more complicated nomenclature (eg. plural(numberOfBenches, 'bench', 'benches'))
Parameters:
num (int) : The number of items to find singular/plural for.
singular (string) : The string to return if num is singular. Defaults to an empty string.
plural (string) : The string to return if num is plural. Defaults to 's' so you can just add 's' to the end of a word.
Returns: The singular or plural provided strings depending on the num provided.
timeframeInSeconds(timeframe)
Get the # of seconds in a given timeframe. Tradingview's timeframe.in_seconds() expects a simple string, and we often need to use series string, so this is an alternative to get you the value you need.
Parameters:
timeframe (string)
Returns: The number of secondsof that timeframe
timeframeOfChart()
Convert a timeframe string to a consistent standard.
Returns: The standard format for the string, or the unchanged value if it is unknown.
timeframeToString(timeframe)
Convert a timeframe string to a consistent standard.
Parameters:
timeframe (string)
Returns: (string) The standard format for the string, or the unchanged value if it is unknown.
stringToTimeframe(strTimeframe)
Convert an english-friendly timeframe string to a value that can be used by request.security. Specifically, this corrects hour strings (eg. 4h) to their numeric "minute" equivalent (eg. 240)
Parameters:
strTimeframe (string)
Returns: (string) The standard format for the string, or the unchanged value if it is unknown.
getPriceLabel(price, labelOffset, labelStyle, labelSize, labelColor, textColor)
Defines a label for the end of a price level line.
Parameters:
price (float) : The price level to render the label at.
labelOffset (int) : The number of candles to place the label to the right of price.
labelStyle (string) : A plain english string as defined in getLabelStyleFromString.
labelSize (string) : The size of the label.
labelColor (color) : The color of the label.
textColor (color) : The color of the label text (defaults to #ffffff)
Returns: The label that was created.
setPriceLabel(label, labelName, price, labelOffset, labelTemplate, labelStyle, labelColor, textColor)
Updates the label position & text based on price changes.
Parameters:
label (label) : The label to update.
labelName (string) : The name of the price level to be placed on the label.
price (float) : The price level to render the label at.
labelOffset (int) : The number of candles to place the label to the right of price.
labelTemplate (string) : The str.format template to use for the label. Defaults to: '{0}: {1} {2}{3,number,#.##}%' which means '{price}: {labelName} {+/-}{percentFromPrice}%'
labelStyle (string)
labelColor (color)
textColor (color)
getPriceLabelLine(price, labelOffset, labelColor, lineWidth)
Defines a line that will stretch from the plot line to the label.
Parameters:
price (float) : The price level to render the label at.
labelOffset (int) : The number of candles to place the label to the right of price.
labelColor (color)
lineWidth (int) : The width of the line. Defaults to 1.
setPriceLabelLine(line, price, labelOffset, lastTime, lineColor)
Updates the price label line based on price changes.
Parameters:
line (line) : The line to update.
price (float) : The price level to render the label at.
labelOffset (int) : The number of candles to place the label to the right of price.
lastTime (int) : The last time that the line should stretch from. Defaults to time.
lineColor (color)
SYMPL Reversal BandsThis is an expansion of the Hybrid moving average. It uses the same hybrid moving code from the hybrid moving average script with an additional layer using the ta.hma function for some slight additional smoothing. Colors of the bands change dynamically based of the long and short hybrid moving averages running in the background. This can be really helpful in identifying periods to short bounces or long dips.
Below is the explanation of the hybrid moving average
Hybrid Moving Average Market Trend System - , designed to visualize market trends using a combination of three moving averages: FRAMA (Fractal Adaptive Moving Average), VIDYA (Variable Index Dynamic Average), and a Hamming windowed Volume-Weighted Moving Average (VWMA).
Key Features:
FRAMA Calculation:
FRAMA adapts to market volatility by dynamically adjusting its smoothing factor based on the fractal dimension of price movement. This allows it to be more responsive during trending periods while filtering out noise in sideways markets. The FRAMA is calculated for both short and long periods
VIDYA with CMO:
The VIDYA (Variable Index Dynamic Average) is based on a Chande Momentum Oscillator (CMO), which adjusts the smoothing factor dynamically depending on the momentum of the market. Higher momentum periods result in more responsive averages, while low momentum periods lead to smoother averages. Like FRAMA, VIDYA is calculated for both short and long periods.
Hamming Windowed VWMA:
This VWMA variation applies a Hamming window to smooth the weighting of volume across the calculation period. This method emphasizes central data points and reduces noise, making the VWMA more adaptive to volume fluctuations. The Hamming VWMA is calculated for short and long periods, offering another layer of adaptability to the hybrid moving average.
Hybrid Moving Averages:
Dynamic Coloring and Filling:
The script uses dynamic color transitions to visually distinguish between bullish and bearish conditions:
Hybrid Moving Average - Market TrendHybrid Moving Average Market Trend System - , designed to visualize market trends using a combination of three moving averages: FRAMA (Fractal Adaptive Moving Average), VIDYA (Variable Index Dynamic Average), and a Hamming windowed Volume-Weighted Moving Average (VWMA).
Key Features:
FRAMA Calculation:
FRAMA adapts to market volatility by dynamically adjusting its smoothing factor based on the fractal dimension of price movement. This allows it to be more responsive during trending periods while filtering out noise in sideways markets. The FRAMA is calculated for both short and long periods
VIDYA with CMO:
The VIDYA (Variable Index Dynamic Average) is based on a Chande Momentum Oscillator (CMO), which adjusts the smoothing factor dynamically depending on the momentum of the market. Higher momentum periods result in more responsive averages, while low momentum periods lead to smoother averages. Like FRAMA, VIDYA is calculated for both short and long periods.
Hamming Windowed VWMA:
This VWMA variation applies a Hamming window to smooth the weighting of volume across the calculation period. This method emphasizes central data points and reduces noise, making the VWMA more adaptive to volume fluctuations. The Hamming VWMA is calculated for short and long periods, offering another layer of adaptability to the hybrid moving average.
Hybrid Moving Averages:
Dynamic Coloring and Filling:
The script uses dynamic color transitions to visually distinguish between bullish and bearish conditions:
Overnight vs Intra-day Performance█ STRATEGY OVERVIEW
The "Overnight vs Intra-day Performance" indicator quantifies price behaviour differences between trading hours and overnight periods. It calculates cumulative returns, compound growth rates, and visualizes performance components across user-defined time windows. Designed for analytical use, it helps identify whether returns are primarily generated during market hours or overnight sessions.
█ USAGE
Use this indicator on Stocks and ETFs to visualise and compare intra-day vs overnight performance
█ KEY FEATURES
Return Segmentation : Separates total returns into overnight (close-to-open) and intraday (open-to-close) components
Growth Tracking : Shows simple cumulative returns and compound annual growth rates (CAGR)
█ VISUALIZATION SYSTEM
1. Time-Series
Overnight Returns (Red)
Intraday Returns (Blue)
Total Returns (White)
2. Summary Table
Displays CAGR
3. Price Chart Labels
Floating annotations showing absolute returns and CAGR
Color-coded to match plot series
█ PURPOSE
Quantify market behaviour disparities between active trading sessions and overnight positioning
Provide institutional-grade attribution analysis for returns generation
Enable tactical adjustment of trading schedules based on historical performance patterns
Serve as foundational research for session-specific trading strategies
█ IDEAL USERS
1. Portfolio Managers
Analyse overnight risk exposure across holdings
Optimize execution timing based on return distributions
2. Quantitative Researchers
Study market microstructure through time-segmented returns
Develop alpha models leveraging session-specific anomalies
3. Market Microstructure Analysts
Identify liquidity patterns in overnight vs daytime sessions
Research ETF premium/discount mechanics
4. Day Traders
Align trading hours with highest probability return windows
Avoid overnight gaps through informed position sizing
Regime Classifier Oscillator (AiBitcoinTrend)The Regime Classifier Oscillator (AiBitcoinTrend) is an advanced tool for understanding market structure and detecting dynamic price regimes. By combining filtered price trends, clustering algorithms, and an adaptive oscillator, it provides traders with detailed insights into market phases, including accumulation, distribution, advancement, and decline.
This innovative tool simplifies market regime classification, enabling traders to align their strategies with evolving market conditions effectively.
👽 What is a Regime Classifier, and Why is it Useful?
A Regime Classifier is a concept in financial analysis that identifies distinct market conditions or "regimes" based on price behavior and volatility. These regimes often correspond to specific phases of the market, such as trends, consolidations, or periods of high or low volatility. By classifying these regimes, traders and analysts can better understand the underlying market dynamics, allowing them to adapt their strategies to suit prevailing conditions.
👽 Common Uses in Finance
Risk Management: Identifying high-volatility regimes helps traders adjust position sizes or hedge risks.
Strategy Optimization: Traders tailor their approaches—trend-following strategies in trending regimes, mean-reversion strategies in consolidations.
Forecasting: Understanding the current regime aids in predicting potential transitions, such as a shift from accumulation to an upward breakout.
Portfolio Allocation: Investors allocate assets differently based on market regimes, such as increasing cash positions in high-volatility environments.
👽 Why It’s Important
Markets behave differently under varying conditions. A regime classifier provides a structured way to analyze these changes, offering a systematic approach to decision-making. This improves both accuracy and confidence in navigating diverse market scenarios.
👽 How We Implemented the Regime Classifier in This Indicator
The Regime Classifier Oscillator takes the foundational concept of market regime classification and enhances it with advanced computational techniques, making it highly adaptive.
👾 Median Filtering: We smooth price data using a custom median filter to identify significant trends while eliminating noise. This establishes a baseline for price movement analysis.
👾 Clustering Model: Using clustering techniques, the indicator classifies volatility and price trends into distinct regimes:
Advance: Strong upward trends with low volatility.
Decline: Downward trends marked by high volatility.
Accumulation: Consolidation phases with subdued volatility.
Distribution: Topping or bottoming patterns with elevated volatility.
This classification leverages historical price data to refine cluster boundaries dynamically, ensuring adaptive and accurate detection of market states.
Volatility Classification: Price volatility is analyzed through rolling windows, separating data into high and low volatility clusters using distance-based assignments.
Price Trends: The interaction of price levels with the filtered trendline and volatility clusters determines whether the market is advancing, declining, accumulating, or distributing.
👽 Dynamic Cycle Oscillator (DCO):
Captures cyclic behavior and overlays it with smoothed oscillations, providing real-time feedback on price momentum and potential reversals.
Regime Visualization:
Regimes are displayed with intuitive labels and background colors, offering clear, actionable insights directly on the chart.
👽 Why This Implementation Stands Out
Dynamic and Adaptive: The clustering and refit mechanisms adapt to changing market conditions, ensuring relevance across different asset classes and timeframes.
Comprehensive Insights: By combining price trends, volatility, and cyclic behaviors, the indicator provides a holistic view of the market.
This implementation bridges the gap between theoretical regime classification and practical trading needs, making it a powerful tool for both novice and experienced traders.
👽 Applications
👾 Regime-Based Trading Strategies
Traders can use the regime classifications to adapt their strategies effectively:
Advance & Accumulation: Favorable for entering or holding long positions.
Decline & Distribution: Opportunities for short positions or risk management.
👾 Oscillator Insights for Trend Analysis
Overbought/oversold conditions: Early warning of potential reversals.
Dynamic trends: Highlights the strength of price momentum.
👽 Indicator Settings
👾 Filter and Classification Settings
Filter Window Size: Controls trend detection sensitivity.
ATR Lookback: Adjusts the threshold for regime classification.
Clustering Window & Refit Interval: Fine-tunes regime accuracy.
👾 Oscillator Settings
Dynamic Cycle Oscillator Lookback: Defines the sensitivity of cycle detection.
Smoothing Factor: Balances responsiveness and stability.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
mathLibrary "math"
It's a library of discrete aproximations of a price or Series float it uses Fourier Discrete transform, Laplace Discrete Original and Modified transform and Euler's Theoreum for Homogenus White noice operations. Calling functions without source value it automatically take close as the default source value.
Here is a picture of Laplace and Fourier approximated close prices from this library:
Copy this indicator and try it yourself:
import AutomatedTradingAlgorithms/math/1 as math
//@version=5
indicator("Close Price with Aproximations", shorttitle="Close and Aproximations", overlay=false)
// Sample input data (replace this with your own data)
inputData = close
// Plot Close Price
plot(inputData, color=color.blue, title="Close Price")
ltf32_result = math.LTF32(a=0.01)
plot(ltf32_result, color=color.green, title="LTF32 Aproximation")
fft_result = math.FFT()
plot(fft_result, color=color.red, title="Fourier Aproximation")
wavelet_result = math.Wavelet()
plot(wavelet_result, color=color.orange, title="Wavelet Aproximation")
wavelet_std_result = math.Wavelet_std()
plot(wavelet_std_result, color=color.yellow, title="Wavelet_std Aproximation")
DFT3(xval, _dir)
Discrete Fourier Transform with last 3 points
Parameters:
xval (float) : Source series
_dir (int) : Direction parameter
Returns: Aproxiated source value
DFT2(xval, _dir)
Discrete Fourier Transform with last 2 points
Parameters:
xval (float) : Source series
_dir (int) : Direction parameter
Returns: Aproxiated source value
FFT(xval)
Fast Fourier Transform once. It aproximates usig last 3 points.
Parameters:
xval (float) : Source series
Returns: Aproxiated source value
DFT32(xval)
Combined Discrete Fourier Transforms of DFT3 and DTF2 it aproximates last point by first
aproximating last 3 ponts and than using last 2 points of the previus.
Parameters:
xval (float) : Source series
Returns: Aproxiated source value
DTF32(xval)
Combined Discrete Fourier Transforms of DFT3 and DTF2 it aproximates last point by first
aproximating last 3 ponts and than using last 2 points of the previus.
Parameters:
xval (float) : Source series
Returns: Aproxiated source value
LFT3(xval, _dir, a)
Discrete Laplace Transform with last 3 points
Parameters:
xval (float) : Source series
_dir (int) : Direction parameter
a (float) : laplace coeficient
Returns: Aproxiated source value
LFT2(xval, _dir, a)
Discrete Laplace Transform with last 2 points
Parameters:
xval (float) : Source series
_dir (int) : Direction parameter
a (float) : laplace coeficient
Returns: Aproxiated source value
LFT(xval, a)
Fast Laplace Transform once. It aproximates usig last 3 points.
Parameters:
xval (float) : Source series
a (float) : laplace coeficient
Returns: Aproxiated source value
LFT32(xval, a)
Combined Discrete Laplace Transforms of LFT3 and LTF2 it aproximates last point by first
aproximating last 3 ponts and than using last 2 points of the previus.
Parameters:
xval (float) : Source series
a (float) : laplace coeficient
Returns: Aproxiated source value
LTF32(xval, a)
Combined Discrete Laplace Transforms of LFT3 and LTF2 it aproximates last point by first
aproximating last 3 ponts and than using last 2 points of the previus.
Parameters:
xval (float) : Source series
a (float) : laplace coeficient
Returns: Aproxiated source value
whitenoise(indic_, _devided, minEmaLength, maxEmaLength, src)
Ehler's Universal Oscillator with White Noise, without extra aproximated src.
It uses dinamic EMA to aproximate indicator and thus reducing noise.
Parameters:
indic_ (float) : Input series for the indicator values to be smoothed
_devided (int) : Divisor for oscillator calculations
minEmaLength (int) : Minimum EMA length
maxEmaLength (int) : Maximum EMA length
src (float) : Source series
Returns: Smoothed indicator value
whitenoise(indic_, dft1, _devided, minEmaLength, maxEmaLength, src)
Ehler's Universal Oscillator with White Noise and DFT1.
It uses src and sproxiated src (dft1) to clearly define white noice.
It uses dinamic EMA to aproximate indicator and thus reducing noise.
Parameters:
indic_ (float) : Input series for the indicator values to be smoothed
dft1 (float) : Aproximated src value for white noice calculation
_devided (int) : Divisor for oscillator calculations
minEmaLength (int) : Minimum EMA length
maxEmaLength (int) : Maximum EMA length
src (float) : Source series
Returns: Smoothed indicator value
smooth(dft1, indic__, _devided, minEmaLength, maxEmaLength, src)
Smoothing source value with help of indicator series and aproximated source value
It uses src and sproxiated src (dft1) to clearly define white noice.
It uses dinamic EMA to aproximate src and thus reducing noise.
Parameters:
dft1 (float) : Value to be smoothed.
indic__ (float) : Optional input for indicator to help smooth dft1 (default is FFT)
_devided (int) : Divisor for smoothing calculations
minEmaLength (int) : Minimum EMA length
maxEmaLength (int) : Maximum EMA length
src (float) : Source series
Returns: Smoothed source (src) series
smooth(indic__, _devided, minEmaLength, maxEmaLength, src)
Smoothing source value with help of indicator series
It uses dinamic EMA to aproximate src and thus reducing noise.
Parameters:
indic__ (float) : Optional input for indicator to help smooth dft1 (default is FFT)
_devided (int) : Divisor for smoothing calculations
minEmaLength (int) : Minimum EMA length
maxEmaLength (int) : Maximum EMA length
src (float) : Source series
Returns: Smoothed src series
vzo_ema(src, len)
Volume Zone Oscillator with EMA smoothing
Parameters:
src (float) : Source series
len (simple int) : Length parameter for EMA
Returns: VZO value
vzo_sma(src, len)
Volume Zone Oscillator with SMA smoothing
Parameters:
src (float) : Source series
len (int) : Length parameter for SMA
Returns: VZO value
vzo_wma(src, len)
Volume Zone Oscillator with WMA smoothing
Parameters:
src (float) : Source series
len (int) : Length parameter for WMA
Returns: VZO value
alma2(series, windowsize, offset, sigma)
Arnaud Legoux Moving Average 2 accepts sigma as series float
Parameters:
series (float) : Input series
windowsize (int) : Size of the moving average window
offset (float) : Offset parameter
sigma (float) : Sigma parameter
Returns: ALMA value
Wavelet(src, len, offset, sigma)
Aproxiates srt using Discrete wavelet transform.
Parameters:
src (float) : Source series
len (int) : Length parameter for ALMA
offset (simple float)
sigma (simple float)
Returns: Wavelet-transformed series
Wavelet_std(src, len, offset, mag)
Aproxiates srt using Discrete wavelet transform with standard deviation as a magnitude.
Parameters:
src (float) : Source series
len (int) : Length parameter for ALMA
offset (float) : Offset parameter for ALMA
mag (int) : Magnitude parameter for standard deviation
Returns: Wavelet-transformed series
LaplaceTransform(xval, N, a)
Original Laplace Transform over N set of close prices
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
Returns: Aproxiated source value
NLaplaceTransform(xval, N, a, repeat)
Y repetirions on Original Laplace Transform over N set of close prices, each time N-k set of close prices
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
repeat (int) : number of repetitions
Returns: Aproxiated source value
LaplaceTransformsum(xval, N, a, b)
Sum of 2 exponent coeficient of Laplace Transform over N set of close prices
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
b (float) : second laplace coeficient
Returns: Aproxiated source value
NLaplaceTransformdiff(xval, N, a, b, repeat)
Difference of 2 exponent coeficient of Laplace Transform over N set of close prices
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
b (float) : second laplace coeficient
repeat (int) : number of repetitions
Returns: Aproxiated source value
N_divLaplaceTransformdiff(xval, N, a, b, repeat)
N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, with dynamic rotation
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
b (float) : second laplace coeficient
repeat (int) : number of repetitions
Returns: Aproxiated source value
LaplaceTransformdiff(xval, N, a, b)
Difference of 2 exponent coeficient of Laplace Transform over N set of close prices
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
b (float) : second laplace coeficient
Returns: Aproxiated source value
NLaplaceTransformdiffFrom2(xval, N, a, b, repeat)
N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
b (float) : second laplace coeficient
repeat (int) : number of repetitions
Returns: Aproxiated source value
N_divLaplaceTransformdiffFrom2(xval, N, a, b, repeat)
N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor, dynamic rotation
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
b (float) : second laplace coeficient
repeat (int) : number of repetitions
Returns: Aproxiated source value
LaplaceTransformdiffFrom2(xval, N, a, b)
Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor
Parameters:
xval (float) : series to aproximate
N (int) : number of close prices in calculations
a (float) : laplace coeficient
b (float) : second laplace coeficient
Returns: Aproxiated source value
Deep Volume [ChartPrime]Deep Volume is an indicator designed to give you high fidelity volume information. It does this by utilizing real time data provided by Tradingview to generate a wide range of metrics. We have included a convenient column chart to visualize the polarity of the volume, and a table to see the real time data. This works by utilizing pine script's varip feature to get information within candles. This is convenient as it allows users to get lower time frame information without the use of ltf functions. The result is seconds level data with out the need to be on a lower time frame chart. As a result, as you increase the time frame of the chart the updates will become slower. This is because Tradingview doesn't update the chart information as frequently on higher time frames as there isn't as much of a need.
This indicator works on real time data so to compensate for this we generate a simulated history based on candle structure. This helps in estimating the state of the moving average before the real time data starts. As a result the estimated history isn't as accurate and should be treated as such. That being said it is nice to have an estimation when the indicator is first loaded onto the chart.
Finally we have included a cumulative volume comparison that shows you how much volume there is compared to the average cumulative volume for the day. This metric utilizes a gradient to help you interpret the information at a glance. Low daily volume is represented with grays by default, while normal volume and greater is represented with a green color by default.
The table is partitioned into two sections; tick data, and average data. On the left you will see color coded information based on the direction of the move. On the left, the information is color coded based on the average movement direction. You can control how much information is displayed in the table within the indicators settings. This is defaulted to 20 but it can be as long or short as you like. Every new candle open the far left of the table you will see a 🗘 symbol and at the start of a new session you will see a 🗓 symbol.
The included metrics are as follows:
Time: This displays the time of the real time data update.
Time Delta: This displays the elapsed time between updates.
Order Size: This is the volume times the price change between updates.
Volume: This is the volume change for the update.
Price Change: This is the change in price since the last update.
Price: This is the price of the asset at the time of the update.
Speed of Tape: This is the average time delta. Use this to see how quickly the market is moving.
Average Order Size: This is the average order size.
Average Volume: This is the average volume
Volume Ratio: This the the ratio of bullish to bearish volume as expressed by a percent. 100% is all bullish within the window and -100% is all bearish within the window.
Average Price Change: This is the average price change within the window.
Sensitivity: This is a volatility metric designed to show you the price change per 1 volume unit.
Relative Sensitivity: This is a volatility metric designed to show you the average price change per average volume.
Enjoy
UtilsLibrary "Utils"
A collection of convenience and helper functions for indicator and library authors on TradingView
formatNumber(num)
My version of format number that doesn't have so many decimal places...
Parameters:
num (float) : (float) the number to be formatted
Returns: (string) The formatted number
getDateString(timestamp)
Convenience function returns timestamp in yyyy/MM/dd format.
Parameters:
timestamp (int) : (int) The timestamp to stringify
Returns: (int) The date string
getDateTimeString(timestamp)
Convenience function returns timestamp in yyyy/MM/dd hh:mm format.
Parameters:
timestamp (int) : (int) The timestamp to stringify
Returns: (int) The date string
getInsideBarCount()
Gets the number of inside bars for the current chart. Can also be passed to request.security to get the same for different timeframes.
Returns: (int) The # of inside bars on the chart right now.
getLabelStyleFromString(styleString, acceptGivenIfNoMatch)
Tradingview doesn't give you a nice way to put the label styles into a dropdown for configuration settings. So, I specify them in the following format: . This function takes care of converting those custom strings back to the ones expected by tradingview scripts.
Parameters:
styleString (string)
acceptGivenIfNoMatch (bool) : (bool) If no match for styleString is found and this is true, the function will return styleString, otherwise it will return tradingview's preferred default
Returns: (string) The string expected by tradingview functions
getTime(hourNumber, minuteNumber)
Given an hour number and minute number, adds them together and returns the sum. To be used by getLevelBetweenTimes when fetching specific price levels during a time window on the day.
Parameters:
hourNumber (int) : (int) The hour number
minuteNumber (int) : (int) The minute number
Returns: (int) The sum of all the minutes
getHighAndLowBetweenTimes(start, end)
Given a start and end time, returns the high or low price during that time window.
Parameters:
start (int) : The timestamp to start with (# of seconds)
end (int) : The timestamp to end with (# of seconds)
Returns: (float) The high or low value
getPremarketHighsAndLows()
Returns an expression that can be used by request.security to fetch the premarket high & low levels in a tuple.
Returns: (tuple)
getAfterHoursHighsAndLows()
Returns an expression that can be used by request.security to fetch the after hours high & low levels in a tuple.
Returns: (tuple)
getOvernightHighsAndLows()
Returns an expression that can be used by request.security to fetch the overnight high & low levels in a tuple.
Returns: (tuple)
getNonRthHighsAndLows()
Returns an expression that can be used by request.security to fetch the high & low levels for premarket, after hours and overnight in a tuple.
Returns: (tuple)
getLineStyleFromString(styleString, acceptGivenIfNoMatch)
Tradingview doesn't give you a nice way to put the line styles into a dropdown for configuration settings. So, I specify them in the following format: . This function takes care of converting those custom strings back to the ones expected by tradingview scripts.
Parameters:
styleString (string) : (string) Plain english (or TV Standard) version of the style string
acceptGivenIfNoMatch (bool) : (bool) If no match for styleString is found and this is true, the function will return styleString, otherwise it will return tradingview's preferred default
Returns: (string) The string expected by tradingview functions
getPercentFromPrice(price)
Get the % the current price is away from the given price.
Parameters:
price (float)
Returns: (float) The % the current price is away from the given price.
getPositionFromString(position)
Tradingview doesn't give you a nice way to put the positions into a dropdown for configuration settings. So, I specify them in the following format: . This function takes care of converting those custom strings back to the ones expected by tradingview scripts.
Parameters:
position (string) : (string) Plain english position string
Returns: (string) The string expected by tradingview functions
getTimeframeOfChart()
Get the timeframe of the current chart for display
Returns: (string) The string of the current chart timeframe
getTimeNowPlusOffset(candleOffset)
Helper function for drawings that use xloc.bar_time to help you know the time offset if you want to place the end of the drawing out into the future. This determines the time-size of one candle and then returns a time n candleOffsets into the future.
Parameters:
candleOffset (int) : (int) The number of items to find singular/plural for.
Returns: (int) The future time
getVolumeBetweenTimes(start, end)
Given a start and end time, returns the sum of all volume across bars during that time window.
Parameters:
start (int) : The timestamp to start with (# of seconds)
end (int) : The timestamp to end with (# of seconds)
Returns: (float) The volume
isToday()
Returns true if the current bar occurs on today's date.
Returns: (bool) True if current bar is today
padLabelString(labelText, labelStyle)
Pads a label string so that it appears properly in or not in a label. When label.style_none is used, this will make sure it is left-aligned instead of center-aligned. When any other type is used, it adds a single space to the right so there is padding against the right end of the label.
Parameters:
labelText (string) : (string) The string to be padded
labelStyle (string) : (string) The style of the label being padded for.
Returns: (string) The padded string
plural(num, singular, plural)
Helps format a string for plural/singular. By default, if you only provide num, it will just return "s" for plural and nothing for singular (eg. plural(numberOfCats)). But you can optionally specify the full singular/plural words for more complicated nomenclature (eg. plural(numberOfBenches, 'bench', 'benches'))
Parameters:
num (int) : (int) The number of items to find singular/plural for.
singular (string) : (string) The string to return if num is singular. Defaults to an empty string.
plural (string) : (string) The string to return if num is plural. Defaults to 's' so you can just add 's' to the end of a word.
Returns: (string) The singular or plural provided strings depending on the num provided.
timeframeInSeconds(timeframe)
Get the # of seconds in a given timeframe. Tradingview's timeframe.in_seconds() expects a simple string, and we often need to use series string, so this is an alternative to get you the value you need.
Parameters:
timeframe (string)
Returns: (int) The number of secondsof that timeframe
timeframeToString(tf)
Convert a timeframe string to a consistent standard.
Parameters:
tf (string) : (string) The timeframe string to convert
Returns: (string) The standard format for the string, or the unchanged value if it is unknown.
buyer_seller_scalping_indicatorThis code is a custom script designed for analyzing trading volume within a specific time window on the TradingView platform. It offers a comprehensive analysis of buying and selling activity during a defined period and provides visual aids and data summaries for traders to make informed decisions. Here's a detailed breakdown of its functionality and how to use it:
1. Custom Time Period: The script starts by allowing you to specify a custom time period for analysis. In this example, it's set from 04:00 to 09:29. You can modify these time values to suit your specific trading needs.
2. Volume Calculation: The script calculates buying and selling volume based on price levels. It takes into account the open, high, low, and close prices to determine whether buying or selling pressure is dominant during the specified time frame.
3. Total Volume Calculation: It calculates the total volume within the custom time period. This can help you gauge the overall activity and liquidity during the chosen time window.
4. Visualizations: The script then plots visual elements on the chart:
- A volume histogram, which provides a graphical representation of the total volume during the time period.
- Buying and selling volume indicators, which are shown as circles on the chart, highlighting the relative strength of buyers and sellers.
- An average volume line, represented in gray, which helps you identify the average trading volume over a 50-period moving average.
5. Volume Type Determination: The script determines whether buyers or sellers dominate the market during the specified time period. It labels this as "Buyers Volume > Sellers Volume," "Sellers Volume > Buyers Volume," or "Buyers Volume = Sellers Volume." This information can be crucial for assessing market sentiment.
6. Percentage Breakdown: The script calculates the percentage of buying and selling volume in relation to the total volume, helping you understand the distribution of market participants. These percentages are displayed in a table.
7. Table Display: Finally, the script creates a table that displays the following information:
- The current volume type (buyers, sellers, or balanced), with corresponding text colors.
- The percentage of buyers and sellers in the market.
How to Use:
1. Copy the script and add it as a custom script on TradingView.
2. Apply the script to your desired financial chart.
3. Adjust the custom time period if needed.
4. Interpret the visual elements and table to gain insights into market sentiment and volume distribution during the specified time frame.
5. Use this information to inform your trading decisions and strategies, especially when trading within the chosen time window.
This script is a valuable tool for traders seeking to understand market dynamics and volume behavior during specific trading hours, ultimately aiding in more informed trading decisions.
Disclaimer:
The indicator provided herein is experimental and has not undergone comprehensive testing. Its usage is solely at your own risk.
The publisher assumes no responsibility for any trading decisions made based on the utilization of this indicator.
ICT Silver Bullet [LuxAlgo]The ICT Silver Bullet indicator is inspired from the lectures of "The Inner Circle Trader" (ICT) and highlights the Silver Bullet (SB) window which is a specific 1-hour interval where a Fair Value Gap (FVG) pattern can be formed.
When a FVG is formed during the Silver Bullet window, Support & Resistance lines will be drawn at the end of the SB session.
There are 3 different Silver Bullet windows (New York local time):
The London Open Silver Bullet (3 AM — 4 AM ~ 03:00 — 04:00)
The AM Session Silver Bullet (10 AM — 11 AM ~ 10:00 — 11:00)
The PM Session Silver Bullet (2 PM — 3 PM ~ 14:00 — 15:00)
🔶 USAGE
The ICT Silver Bullet indicator aims to provide users a comprehensive display as similar as possible to how anyone would manually draw the concept on their charts.
It's important to use anything below the 15-minute timeframe to ensure proper setups can display. In this section, we are purely using the 3-minute timeframe.
In the image below, we can see a bullish setup whereas a FVG was successfully retested during the Silver Bullet session. This was then followed by a move upwards to liquidity as our target.
Alternatively, you can also see below a bearish setup utilizing the ICT Silver Bullet indicator outlined.
At this moment, the indicator has removed all other FVGs within the Silver Bullet session & has confirmed this FVG as the retested one.
There is also a support level marked below to be used as a liquidity target as per the ICT Silver Bullet concept suggests.
In the below chart we can see 4 separate consecutive examples of bullish & bearish setups on the 3-minute chart.
🔶 CONCEPTS
This technique can visualize potential support/resistance lines, which can be used as targets.
The script contains 2 main components:
• forming of a Fair Value Gap (FVG)
• drawing support/resistance (S/R) lines
🔹 Forming of FVG
1 basic principle: when a FVG at the end of the SB session is not retraced, it will be made invisible.
Dependable on the settings, different FVG's will be shown.
• 'All FVG': all FVG's are shown, regardless the trend
• 'Only FVG's in the same direction of trend': Only FVG's are shown that are similar to the trend at that moment (trend can be visualized by enabling ' Show ' -> ' Trend ')
-> only bearish FVG when the trend is bearish vs. bullish FVG when trend is bullish
• 'strict': Besides being similar to the trend, only FVG's are shown when the closing price at the end of the SB session is:
– below the top of the FVG box (bearish FVG)
– above bottom of the FVG box (bullish FVG)
• 'super-strict': Besides being similar to the trend, only FVG's are shown when the FVG box is NOT broken
in the opposite direction AND the closing price at the end of the SB session is:
– below bottom of the FVG box (bearish FVG)
– above the top of the FVG box (bullish FVG)
' Super-Strict ' mode resembles ICT lectures the most.
🔹 Drawing support/resistance lines
When the SB session has ended, the script draws potential support/resistance lines, again, dependable on the settings.
• Previous session (any): S/R lines are fetched between current and previous session.
For example, when current session is ' AM SB Session (10 AM — 11 AM) ', then previous session is
' London Open SB (3 AM — 4 AM) ', S/R lines between these 2 sessions alone will be included.
• Previous session (similar): S/R lines are fetched between current and previous - similar - session.
For example, when current session is ' London Open SB (3 AM — 4 AM)' , only S/R lines between
current session and previous ' London Open SB (3 AM — 4 AM) ' session are included.
When a new session starts, S/R lines will be removed, except when enabling ' Keep lines (only in strict mode) '
This is not possible in ' All FVG ' or ' Only FVG's in the same direction of trend ' mode, since the chart would be cluttered.
Note that in ' All FVG ' or ' Only FVG's in the same direction of trend ' mode, both, Support/Resistance lines will be shown,
while in Strict/Super-Strict mode:
• only Support lines will be shown if a bearish FVG appears
• only Resistance lines if a bullish FVG is shown
The lines will still be drawn the the end of the SB session, when a valid FVG appears,
but the S/R lines will remain visible and keep being updated until price reaches that line.
This publication contains a "Minimum Trade Framework (mTFW)", which represents the best-case expected price delivery, this is not your actual trade entry - exit range.
• 40 ticks for index futures or indices
• 15 pips for Forex pairs.
When on ' Strict/Super-Strict ' mode, only S/R lines will be shown which are:
• higher than the lowest FVG bottom + mTFW, in a bullish scenario
• lower than the highest FVG bottom - mTFW, in a bearish scenario
When on ' All FVG/Only FVG's in the same direction of trend ' mode, or on non-Forex/Futures/Indices symbols, S/R needs to be higher/lower than SB session high/low.
🔶 SETTINGS
(Check CONCEPTS for deeper insights and explanation)
🔹 Swing settings (left): Sets the length, which will set the lookback period/sensitivity of the Zigzag patterns (which directs the trend)
🔹 Silver Bullet Session; Show SB session: show lines and labels of SB session
Labels can be disabled separately in the ' Style ' section, color is set at the ' Inputs ' section.
🔹 FVG
– Mode
• All FVG
• Only FVG's in the same direction of trend
• Strict
• Super-Strict
– Colors
– Extend: extend till last bar of SB session
🔹 Targets – support/resistance lines
– Previous session (any): S/R lines fetched between current and previous SB session
– Previous session (similar): S/R lines fetched between current and previous similar SB session
– Colors
– Keep lines (only in strict mode)
🔹 Show
– MSS ~ Session: Show Market Structure Shift , only when this happens during a SB session
– Trend: Show trend (Zigzag, colored ~ trend)
90cycle @joshuuu90 minute cycle is a concept about certain time windows of the day.
This indicator has two different options. One uses the 90 minute cycle times mentioned by traderdaye, the other uses the cls operational times split up into 90 minutes session.
e.g. we can often see a fake move happening in the 90 minute window between 2.30am and 4am ny time.
The indicator draws vertical lines at the start/end of each session and the user is able to only display certain sessions (asia, london, new york am and pm)
For the traderdayes option, the indicator also counts the windows from 1 to 4 and calls them q1,q2,q3,q4 (q-quarter)
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
Nadaraya-Watson CombineThis is a combination of the Lux Algo Nadaraya-Watson Estimator and Envelope. Please note the repainting issue.
In addition, I've added a plot of the actual values of the current barstate of
the Nadaraya-Watson windows as they are computed (lines 92-95). It only plots values for the current data at
each time update. It is interesting to compare the trajectory of the end points of the Estimator and
Envelope to the smoothing function at each time update. Due to the kernel smoothing at each update the
history is lost at each update (repaint).
I've added a feature to allow adjustment to the kernel smoothing algorithm as suggested by thomsonraja (line 59).
The settings and usage are repeated from Lux Algo below.
Settings
Window Size: Determines the number of recent price observations to be used to fit the Nadaraya-Watson Estimator.
Bandwidth: Controls the degree of smoothness of the envelopes , with higher values returning smoother results.
Mult: Controls the envelope width.
Src: Input source of the indicator.
Kernel power: See line 59, adjusts the exponential power (powh) as suggested by thomsonraja
Kernel denominator: See line 59, adjusts the denominator (den) as suggested by thomsonraja
Usage
This tool outlines extremes made by the prices within the selected window size.
This is achieved by estimating the underlying trend in the price using kernel smoothing,
calculating the mean absolute deviations from it, and adding/subtracting it
from the estimated underlying trend.
I repeat Lux Algo's caution: 'we do not recommend this tool to be used alone
or solely for real time applications.'
z_scoreStand-alone Z-score indicator for volatile currency pairs, showing STRONG BUY, BUY, SELL, STRONG SELL zones.
The use can define their own "window" or moving average length, which will affect the frequency and magnitude of trades.
Higher windows reduce trade size but increase frequency and vice versa.
The suggested window values are intended for the daily time-frame. They are selected to maximise returns.
ETHBTC . 64 days.
SOLBTC . 40 days.
Trading decisions must be confirmed by multiple indicators and other factors.
getSeries█ OVERVIEW
This library is a Pine programmer’s tool containing functions that build an array of values meeting specific conditions. Its functions use concepts from our ConditionalAverages library , but instead of returning a single value, they return an array containing all the values meeting the conditions, which can then be processed as needed. This provides more flexibility to the programmer than a single value.
The "getSeries" name of the library stems from the fact that is uses arrays to build the equivalent of custom series which can then be operated on using array-specific functions in the `array.*` namespace, looped through using a for...in structure to implement custom logic, or sent to functions designed to process arrays such as those in these libraries: ArrayStatistics , ArrayOperations , arrayutils or Averages .
The eight examples illustrated in the library's code showcase the diversity of scenarios where the functions can be used.
Look first. Then leap.
█ FUNCTIONS
The library contains the following functions:
whenSince(src, whenCond, sinceCond, length)
Creates an array containing the `length` last `src` values where `whenCond` is true, since the last occurence of `sinceCond`.
Parameters:
src : (series int/float) The source of the values to be included.
whenCond : (series bool) The condition determining which values are included. Optional. The default is `true`.
sinceCond : (series bool) The condition determining when the accumulated series resets. Optional. The default is false, which will not reset.
length : (simple int) The number of last values to return. Optional. The default is all values.
Returns: (float ) The array ID of the accumulated `src` values.
rollOnTimeWhen(src, timeWindow, cond, minBars)
Creates an array of `src` values where `cond` is true, over a moving window of length `timeWindow` milliseconds.
Parameters:
src : (series int/float) The source of the values to be included.
timeWindow : (simple int) The time duration in milliseconds defining the size of the moving window.
cond : (series bool) The condition determining which values are included. Optional. The default is `true`.
minBars : (simple int) The minimum number of values to maintain in the moving window. Optional. The default is 1.
Returns: (float ) The array ID of the accumulated `src` values.
Note that the functions must be called on each bar to work correctly. They must thus be pre-evaluated before using their results in conditional branches.
Simple scalping strategy for SOLThis is a simple scalping strategy for trading SOL made on top of the Rebalance Oscillator and the Rebalance Bear/Bull market indicators.
It is intended mostly as proof that these two indicators have their benefits even in simpler strategies.
This strategy in particular works well for the Bear months (check Nov. in the data window) but considerably underperforms Buy&Hold for the Bull months.
It tries to do a market operation per candle whenever the candle happens in a buy/sell trading window of the
Rebalance Bear/Bull indicator ().
It always buys/sells the same amount by default (you can set it in the cog menu in the option "Base Crypto Amount To Trade"),
for SOL this is set to 1.0SOL.
This is my first attempt at scalping, it differs slightly from the standards because it does not require fast
response candles or immediate market operations (it can work well with limit trading) and on top of this it also
does not require a stop loss since it uses an indicator that provides the trading windows (surprises can still happen though).
The profit that this strategy tries to take for each buy/sell pair is not configurable and is set at 12% each way when it happens.
KLemurs DeviationMarket: Stocks and ETF's
This overlay shows the deviation of the exponential moving average of the mid candle price of the currently loaded chart, away from the exponential moving average of the S&P and DOW combined and averaged mid candle price. The top and bottom lines also give a visual perspective of what a certain percentage (default 1%) looks like on the current charts window. This may help with making quick decisions for things like setting trailing stop trades with a percentage. This can be used for stocks, ETF's, and index's and It may be useful in finding potential stocks or ETF's if you are interested in these kinds of deviations. Defaults are set for a dark screen but can be edited to your taste. It's optimized to be an overlay on the current chart window as opposed to being a separate window.
Percentage Lines (editable)
This is three lines. The upper line (default green) plots the set percentage (default 1%) above the current chart’s ema. The middle line (default white) plots the current chart’s ema. The lower line (default red) plots the set percentage (default 1%) below the current chart’s ema.
Deviation Band (editable)
This is the colored band on the overlay between the upper and lower percentage lines. The band’s fill color indicates the deviation of the current charts ema from the ema of the combined S&P and DOW’s ema as follows:
- Red (default) = Current Chart’s ema is descending and the S&P/DOW ema is descending OR the Current Chart’s ema is below (underperforming) the S&P/DOW ema.
- Orange (default) = The Current Chart and S&P/DOW ema’s are both either ascending or descending together.
- Green (default) = The Current Chart’s ema is ascending but the S&P/DOW ema is descending.
To Set Line Colors
BY default, the upper line color uses the same colors as the ascending band color and the lower line uses the same color as the descending band color. To set the line colors, see "plotColor", "plotColorUp", or" plotColorDown" in variable settings within the script or use the “Central Plot Line”, “Upper Plot Line, or “Lower Plot Line” in the input dialogue to change this.
To Set Band Colors
To set the band colors, see "plotColor", "plotColorUp", or "plotColorDown" in variable settings within the script or use the “Color0”, “Color1", or “Color2” in the input dialogue to change this.
To Set EMA Lookback Period
The ema lookback period defaults to 5. This is the number of candles back that the script will use to determine the ema. See “CCemaN” in variable settings within the script or use the “EMA Period” in the input dialogue to change this.
To Set Percentage
To set the percentage that plots the upper and lower lines, see "CCP" in variable settings within the script or use “Upper/Lower Bands Percentage” in the input dialogue to change this. The default is .01 (or 1%).
Order Blocks + Order-Flow ProxiesOrder Blocks + Order-Flow Proxies
This indicator combines structural analysis of order blocks with lightweight order-flow style proxies, providing a tool for chart annotation and contextual study. It is designed to help users visualize where significant structural shifts occur and how simple volume-based signals behave around those areas. The script does not guarantee profitable outcomes, nor does it issue financial advice. It is intended purely for research, learning, and discretionary use.
Conceptual Background
Order Blocks
An “order block” is a term often used to describe a zone on the chart where price left behind a significant reversal or imbalance before continuing strongly in the opposite direction. In practice, this can mean the last bullish or bearish candle before a strong breakout. Traders sometimes study these regions because they believe that unfilled resting orders may exist there, or simply because they mark important pivots in price structure. This indicator detects such moments by scanning for breaks of structure (BOS). When price pushes above or below recent swing levels with sufficient displacement, the script identifies the prior opposite candle as the potential order block.
Break of Structure
A break of structure in this context is defined when the closing price moves beyond the highest high or lowest low of a short lookback window. The script compares the magnitude of this break to an ATR-based displacement filter. This helps ensure that only meaningful moves are marked rather than small, random fluctuations.
Order-Flow Proxies
Traditional order flow analysis may use bid/ask data, footprint charts, or volume profiles. Because TradingView scripts cannot access true order-book data, this indicator instead uses proxy signals derived from standard chart data:
Delta (proxy): Estimated imbalance of buying vs. selling pressure, approximated using bar direction and volume.
Imbalance ratio: Normalizes delta by total volume, ranging between -1 and +1 in theory.
Cumulative Delta (CVD): Running sum of delta over time.
Effort vs. Result (EvR): A comparison between volume and actual bar movement, highlighting cases where large effort produced little result (or vice versa).
These are not real order-flow measurements, but rather simple mathematical constructs that mimic some of its logic.
How the Script Works
Detecting Break of Structure
The user specifies a swing length. When price closes above the recent high (for bullish BOS) or below the recent low (for bearish BOS), a potential shift is recorded.
To qualify, the breakout must exceed a displacement filter proportional to the ATR. This helps filter out weak moves.
Locating the Order Block Candle
Once a BOS is confirmed, the script looks back within a short window to find the last opposite-colored candle.
The high/low or open/close of that candle (depending on user settings) is marked as the potential order block zone.
Drawing and Maintaining Zones
Each order block is represented as a colored rectangle extending forward in time.
Bullish zones are teal by default, bearish zones are red.
Zones extend until invalidated (price closing or wicking beyond them, depending on user preference) or until a user-defined lifespan expires.
A pruning mechanism ensures that only the most recent set number of zones remain, preventing chart overload.
Monitoring Touches
The script checks whether the current bar’s range overlaps any existing order block.
If so, the “closest” zone is considered touched, and a label may appear on the chart.
Confirmation Filters
Touches can optionally be confirmed by order-flow proxies.
For a bullish confirmation, the following must align:
Imbalance ratio above threshold,
Delta EMA positive,
Effort vs. Result positive.
For a bearish confirmation, the opposite holds true.
Optionally, a higher-timeframe EMA slope filter can gate these confirmations. For example, a bullish confirmation may only be accepted if the higher-timeframe EMA is sloping upward.
Alerts
Users may create alerts based on conditions such as “bullish touch confirmed” or “bearish touch confirmed.”
Alerts can be gated to only fire after bar close, reducing intrabar noise.
Standard alertcondition calls are provided, and optional inline alert() calls can be enabled.
Inputs and Customization
Structure & OB
Swing length: Defines how many bars back to check for BOS.
ATR length & displacement factor: Adjust sensitivity for structural breaks.
Body vs. wick reference: Choose whether zones are based on candle bodies or full ranges.
Invalidation rule: Pick between wick breach or close beyond the level.
Lifespan (bars): Limit how long a zone remains active.
Max keep: Cap the number of zones stored to reduce clutter.
Order-Flow Proxies
Delta mode: Choose between “Close vs Previous Close” or “Body” for delta calculation.
EMA length: Smooths the delta/imbalance series.
Z-score lookback: Defines the averaging window for EvR.
Confirmation thresholds: Adjust the imbalance levels required for long/short confirmation.
Higher Timeframe Filter
Enable HTF gate: Optional filter requiring higher-timeframe EMA slope alignment.
HTF timeframe & EMA length: Configurable for context alignment.
Style
Colors and transparency for bullish and bearish zones.
Border color customization.
Alerts
Enable inline alerts: Optional direct calls to alert().
Alerts on bar close only: Helps avoid multiple firings during bar formation.
Practical Use
This tool is best seen as a way to annotate charts and to study how simple volume-derived signals behave near important structural levels. Some users may:
Observe whether order blocks line up with later price reactions.
Study how imbalance or cumulative delta conditions align with these zones.
Use it in a discretionary workflow to highlight areas of interest for deeper analysis.
Because the proxies are based only on candle OHLCV data, they are approximations. They cannot replace true depth-of-market analysis. Similarly, order block detection here is one specific algorithmic interpretation; other traders may define order blocks differently.
Limitations and Disclaimers
This indicator does not predict future price movement.
It does not access real order book or tick-by-tick data. All signals are derived from bar OHLCV.
Past performance of signals or zones does not guarantee future results.
The script is for educational and informational purposes only. It is not financial advice.
Users should test thoroughly, adjust parameters to their own instruments and timeframes, and use it in combination with broader analysis.
Summary
The Order Blocks + Order-Flow Proxies script is an experimental study tool that:
Detects potential order blocks using a displacement-filtered break of structure.
Marks these zones as boxes that persist until invalidation or expiry.
Provides lightweight order-flow-style proxies such as delta, imbalance, CVD, and effort vs. result.
Allows confirmation of zone touches through these proxies and optional higher-timeframe context.
Offers flexible customization, alerting, and chart-style options.
It is not a trading system by itself but rather a framework for studying price/volume behavior around structurally significant areas. With careful exploration, it can give users new ways to visualize market structure and to understand how simple flow-like measures behave in those contexts.
Order Blocks + Order-Flow ProxiesOrder Blocks + Order-Flow Proxies
This indicator combines structural analysis of order blocks with lightweight order-flow style proxies, providing a tool for chart annotation and contextual study. It is designed to help users visualize where significant structural shifts occur and how simple volume-based signals behave around those areas. The script does not guarantee profitable outcomes, nor does it issue financial advice. It is intended purely for research, learning, and discretionary use.
Conceptual Background
Order Blocks
An “order block” is a term often used to describe a zone on the chart where price left behind a significant reversal or imbalance before continuing strongly in the opposite direction. In practice, this can mean the last bullish or bearish candle before a strong breakout. Traders sometimes study these regions because they believe that unfilled resting orders may exist there, or simply because they mark important pivots in price structure. This indicator detects such moments by scanning for breaks of structure (BOS). When price pushes above or below recent swing levels with sufficient displacement, the script identifies the prior opposite candle as the potential order block.
Break of Structure
A break of structure in this context is defined when the closing price moves beyond the highest high or lowest low of a short lookback window. The script compares the magnitude of this break to an ATR-based displacement filter. This helps ensure that only meaningful moves are marked rather than small, random fluctuations.
Order-Flow Proxies
Traditional order flow analysis may use bid/ask data, footprint charts, or volume profiles. Because TradingView scripts cannot access true order-book data, this indicator instead uses proxy signals derived from standard chart data:
Delta (proxy): Estimated imbalance of buying vs. selling pressure, approximated using bar direction and volume.
Imbalance ratio: Normalizes delta by total volume, ranging between -1 and +1 in theory.
Cumulative Delta (CVD): Running sum of delta over time.
Effort vs. Result (EvR): A comparison between volume and actual bar movement, highlighting cases where large effort produced little result (or vice versa).
These are not real order-flow measurements, but rather simple mathematical constructs that mimic some of its logic.
How the Script Works
Detecting Break of Structure
The user specifies a swing length. When price closes above the recent high (for bullish BOS) or below the recent low (for bearish BOS), a potential shift is recorded.
To qualify, the breakout must exceed a displacement filter proportional to the ATR. This helps filter out weak moves.
Locating the Order Block Candle
Once a BOS is confirmed, the script looks back within a short window to find the last opposite-colored candle.
The high/low or open/close of that candle (depending on user settings) is marked as the potential order block zone.
Drawing and Maintaining Zones
Each order block is represented as a colored rectangle extending forward in time.
Bullish zones are teal by default, bearish zones are red.
Zones extend until invalidated (price closing or wicking beyond them, depending on user preference) or until a user-defined lifespan expires.
A pruning mechanism ensures that only the most recent set number of zones remain, preventing chart overload.
Monitoring Touches
The script checks whether the current bar’s range overlaps any existing order block.
If so, the “closest” zone is considered touched, and a label may appear on the chart.
Confirmation Filters
Touches can optionally be confirmed by order-flow proxies.
For a bullish confirmation, the following must align:
Imbalance ratio above threshold,
Delta EMA positive,
Effort vs. Result positive.
For a bearish confirmation, the opposite holds true.
Optionally, a higher-timeframe EMA slope filter can gate these confirmations. For example, a bullish confirmation may only be accepted if the higher-timeframe EMA is sloping upward.
Alerts
Users may create alerts based on conditions such as “bullish touch confirmed” or “bearish touch confirmed.”
Alerts can be gated to only fire after bar close, reducing intrabar noise.
Standard alertcondition calls are provided, and optional inline alert() calls can be enabled.
Inputs and Customization
Structure & OB
Swing length: Defines how many bars back to check for BOS.
ATR length & displacement factor: Adjust sensitivity for structural breaks.
Body vs. wick reference: Choose whether zones are based on candle bodies or full ranges.
Invalidation rule: Pick between wick breach or close beyond the level.
Lifespan (bars): Limit how long a zone remains active.
Max keep: Cap the number of zones stored to reduce clutter.
Order-Flow Proxies
Delta mode: Choose between “Close vs Previous Close” or “Body” for delta calculation.
EMA length: Smooths the delta/imbalance series.
Z-score lookback: Defines the averaging window for EvR.
Confirmation thresholds: Adjust the imbalance levels required for long/short confirmation.
Higher Timeframe Filter
Enable HTF gate: Optional filter requiring higher-timeframe EMA slope alignment.
HTF timeframe & EMA length: Configurable for context alignment.
Style
Colors and transparency for bullish and bearish zones.
Border color customization.
Alerts
Enable inline alerts: Optional direct calls to alert().
Alerts on bar close only: Helps avoid multiple firings during bar formation.
Practical Use
This tool is best seen as a way to annotate charts and to study how simple volume-derived signals behave near important structural levels. Some users may:
Observe whether order blocks line up with later price reactions.
Study how imbalance or cumulative delta conditions align with these zones.
Use it in a discretionary workflow to highlight areas of interest for deeper analysis.
Because the proxies are based only on candle OHLCV data, they are approximations. They cannot replace true depth-of-market analysis. Similarly, order block detection here is one specific algorithmic interpretation; other traders may define order blocks differently.
Limitations and Disclaimers
This indicator does not predict future price movement.
It does not access real order book or tick-by-tick data. All signals are derived from bar OHLCV.
Past performance of signals or zones does not guarantee future results.
The script is for educational and informational purposes only. It is not financial advice.
Users should test thoroughly, adjust parameters to their own instruments and timeframes, and use it in combination with broader analysis.
Summary
The Order Blocks + Order-Flow Proxies script is an experimental study tool that:
Detects potential order blocks using a displacement-filtered break of structure.
Marks these zones as boxes that persist until invalidation or expiry.
Provides lightweight order-flow-style proxies such as delta, imbalance, CVD, and effort vs. result.
Allows confirmation of zone touches through these proxies and optional higher-timeframe context.
Offers flexible customization, alerting, and chart-style options.
It is not a trading system by itself but rather a framework for studying price/volume behavior around structurally significant areas. With careful exploration, it can give users new ways to visualize market structure and to understand how simple flow-like measures behave in those contexts.
Relative Volatility Mass [SciQua]The ⚖️ Relative Volatility Mass (RVM) is a volatility-based tool inspired by the Relative Volatility Index (RVI) .
While the RVI measures the ratio of upward to downward volatility over a period, RVM takes a different approach:
It sums the standard deviation of price changes over a rolling window, separating upward volatility from downward volatility .
The result is a measure of the total “volatility mass” over a user-defined period, rather than an average or normalized ratio.
This makes RVM particularly useful for identifying sustained high-volatility conditions without being diluted by averaging.
────────────────────────────────────────────────────────────
╭────────────╮
How It Works
╰────────────╯
1. Standard Deviation Calculation
• Computes the standard deviation of the chosen `Source` over a `Standard Deviation Length` (`stdDevLen`).
2. Directional Separation
• Volatility on up bars (`chg > 0`) is treated as upward volatility .
• Volatility on down bars (`chg < 0`) is treated as downward volatility .
3. Rolling Sum
• Over a `Sum Length` (`sumLen`), the upward and downward volatilities are summed separately using `math.sum()`.
4. Relative Volatility Mass
• The two sums are added together to get the total volatility mass for the rolling window.
Formula:
RVM = Σ(σ up) + Σ(σ down)
where σ is the standard deviation over `stdDevLen`.
╭────────────╮
Key Features
╰────────────╯
Directional Volatility Tracking – Differentiates between volatility during price advances vs. declines.
Rolling Volatility Mass – Shows the total standard deviation accumulation over a given period.
Optional Smoothing – Multiple MA types, including SMA, EMA, SMMA (RMA), WMA, VWMA.
Bollinger Band Overlay – Available when SMA is selected, with adjustable standard deviation multiplier.
Configurable Source – Apply RVM to `close`, `open`, `hl2`, or any custom source.
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Usage
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Trend Confirmation: High RVM values can confirm strong trending conditions.
Breakout Detection: Spikes in RVM often precede or accompany price breakouts.
Volatility Cycle Analysis: Compare periods of contraction and expansion.
RVM is not bounded like the RVI, so absolute values depend on market volatility and chosen parameters.
Consider normalizing or using smoothing for easier visual comparison.
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Example Settings
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Short-term volatility detection: `stdDevLen = 5`, `sumLen = 10`
Medium-term trend volatility: `stdDevLen = 14`, `sumLen = 20`
Enable `SMA + Bollinger Bands` to visualize when volatility is unusually high or low relative to recent history.
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Notes & Limitations
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Not a directional signal by itself — use alongside price structure, volume, or other indicators.
Higher `sumLen` will smooth short-term fluctuations but reduce responsiveness.
Because it sums, not averages, values will scale with both volatility and chosen window size.
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Credits
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Based on the Relative Volatility Index concept by Donald Dorsey (1993).
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SciQua - Joshua Danford
