EMA Crossover Signals [LummiCrypto]Handsome, just turn on the buy and sell alerts.
English Instruction
How to Use the EMA Crossover Signals Indicator
This TradingView Pine Script generates BUY and SELL signals based on the crossover of two Exponential Moving Averages (EMAs) with user-selectable lengths (9, 20, 50, 100, or 200). BUY signals occur when the first EMA crosses above the second EMA, and SELL signals occur when the first EMA crosses below the second EMA. Signals are visualized as tiny green triangles (BUY) below the bar and red triangles (SELL) above the bar. Alerts are set up using alertcondition() for compatibility with Telegram notifications.
1. Add the Script to TradingView
Open TradingView and go to the Pine Editor (bottom of the screen).
Copy and paste the Pine Script into the editor.
Click “Add to Chart” to apply the indicator to your chart.
2. Configure EMA Lengths
Double-click the indicator name (“EMA Crossover Signals”) on the chart to open its settings.
In the settings, select the “First EMA Length” and “Second EMA Length” from the dropdown menus. Options are 9, 20, 50, 100, or 200.
Note: Choose different lengths for the two EMAs (e.g., 9 and 20) to ensure crossovers can occur. Setting both to the same length will prevent signals.
Click “OK” to save the settings.
3. Set Up Alerts in TradingView
Click the “Alert” button (bell icon) at the top of TradingView.
In the alert creation window:
Select “EMA Crossover Signals” as the condition.
Choose either “BUY Signal” or “SELL Signal” from the condition dropdown.
Keep the default message (e.g., “EMA Crossover: BUY Signal at {{close}}”) or customize it if needed.
Set the frequency to “Once Per Bar Close” to avoid duplicate alerts.
Name the alert, e.g., “EMA Buy Alert” or “EMA Sell Alert”.
Repeat the process to create separate alerts for BUY and SELL signals if desired.
Click “Create” to activate the alert.
4. Connect Alerts to Telegram
To send BUY and SELL signals to Telegram, you need to link TradingView alerts to a Telegram bot or channel. Here are the options:
Option 1: Webhook Setup
Create a Telegram bot using BotFather in Telegram and obtain the bot token.
Set up a webhook server (e.g., using Python, Node.js, or a service like Heroku) to receive TradingView alerts and forward them to Telegram.
In the TradingView alert settings, enable “Webhook URL” and enter your server’s URL (e.g., your-server.com).
Configure your server to send the alert message to Telegram using the Telegram Bot API (e.g., api.telegram.org).
Option 2: Third-Party Service
Use a service like Zapier or IFTTT to connect TradingView alerts to Telegram.
Set up a zap with TradingView as the trigger (via webhook) and Telegram as the action to send messages to your chat or channel.
Follow the service’s instructions to link your Telegram bot and specify the destination chat or channel.
Option 3: TradingView’s Telegram Integration (if available)
If TradingView offers direct Telegram integration in your region, follow their official guide to link your Telegram account and configure alerts to send messages directly.
5. Test the Alerts
Create test alerts for both BUY and SELL signals to ensure they work correctly.
Verify that messages (e.g., “EMA Crossover: BUY Signal at 50000”) appear in your Telegram chat or channel.
Check that the signals (green triangles for BUY, red triangles for SELL) appear correctly on the chart.
Инструкция на русском
Как использовать индикатор EMA Crossover Signals
Этот Pine Script для TradingView генерирует сигналы на покупку (BUY) и продажу (SELL) на основе пересечения двух экспоненциальных скользящих средних (EMA) с длинами, выбираемыми пользователем (9, 20, 50, 100 или 200). Сигнал BUY появляется, когда первая EMA пересекает вторую EMA снизу вверх, а сигнал SELL — когда первая EMA пересекает вторую EMA сверху вниз. Сигналы отображаются в виде маленьких зеленых треугольников (BUY) под свечой и красных треугольников (SELL) над свечой. Для уведомлений в Telegram используются alertcondition().
1. Добавление скрипта в TradingView
Откройте TradingView и перейдите в редактор Pine (внизу экрана).
Скопируйте и вставьте Pine Script в редактор.
Нажмите «Добавить на график», чтобы применить индикатор.
2. Настройка длин EMA
Дважды щелкните по названию индикатора («EMA Crossover Signals») на графике, чтобы открыть настройки.
В настройках выберите «First EMA Length» (Длина первой EMA) и «Second EMA Length» (Длина второй EMA) из выпадающих меню. Варианты: 9, 20, 50, 100, 200.
Важно: Выберите разные длины для двух EMA (например, 9 и 20), чтобы сигналы пересечения были возможны. Если выбрать одинаковые длины, сигналы не будут генерироваться.
Нажмите «ОК», чтобы сохранить настройки.
3. Настройка алертов в TradingView
Нажмите на кнопку «Алерт» (значок колокольчика) вверху TradingView.
В окне создания алерта:
Выберите «EMA Crossover Signals» в качестве условия.
Выберите «BUY Signal» или «SELL Signal» из выпадающего списка условий.
Оставьте сообщение по умолчанию (например, «EMA Crossover: BUY Signal at {{close}}») или настройте его по желанию.
Установите частоту «Только при закрытии бара», чтобы избежать дублирования алертов.
Задайте имя алерта, например, «EMA Buy Alert» или «EMA Sell Alert».
Повторите процесс, чтобы создать отдельные алерты для сигналов BUY и SELL, если нужно.
Нажмите «Создать», чтобы активировать алерт.
4. Подключение алертов к Telegram
Чтобы отправлять сигналы BUY и SELL в Telegram, необходимо связать алерты TradingView с ботом или каналом Telegram. Варианты:
Вариант 1: Настройка вебхука
Создайте бота в Telegram с помощью BotFather и получите токен бота.
Настройте сервер вебхука (например, с использованием Python, Node.js или сервиса Heroku), чтобы принимать алерты от TradingView и отправлять их в Telegram.
В настройках алерта TradingView включите «Webhook URL» и укажите URL вашего сервера (например, your-server.com).
Настройте сервер для отправки сообщений в Telegram через Telegram Bot API (например, api.telegram.org).
Вариант 2: Сторонний сервис
Используйте сервисы, такие как Zapier или IFTTT, для связи алертов TradingView с Telegram.
Настройте задачу (zap), где TradingView будет триггером (через вебхук), а Telegram — действием для отправки сообщений в ваш чат или канал.
Следуйте инструкциям сервиса для подключения бота Telegram и указания чата или канала.
Вариант 3: Прямая интеграция TradingView с Telegram (если доступно)
Если TradingView поддерживает прямую интеграцию с Telegram в вашем регионе, следуйте их официальной инструкции для подключения вашего аккаунта Telegram и настройки отправки сообщений.
5. Тестирование алертов
Создайте тестовые алерты для сигналов BUY и SELL, чтобы убедиться, что они работают корректно.
Проверьте, что сообщения (например, «EMA Crossover: BUY Signal at 50000») появляются в вашем чате или канале Telegram.
Убедитесь, что сигналы (зеленые треугольники для BUY, красные треугольники для SELL) корректно отображаются на графике.
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Mean Reversion Oscillator [Alpha Extract]An advanced composite oscillator system specifically designed to identify extreme market conditions and high-probability mean reversion opportunities, combining five proven oscillators into a single, powerful analytical framework.
By integrating multiple momentum and volume-based indicators with sophisticated extreme level detection, this oscillator provides precise entry signals for contrarian trading strategies while filtering out false reversals through momentum confirmation.
🔶 Multi-Oscillator Composite Framework
Utilizes a comprehensive approach that combines Bollinger %B, RSI, Stochastic, Money Flow Index, and Williams %R into a unified composite score. This multi-dimensional analysis ensures robust signal generation by capturing different aspects of market extremes and momentum shifts.
// Weighted composite (equal weights)
normalized_bb = bb_percent
normalized_rsi = rsi
normalized_stoch = stoch_d_val
normalized_mfi = mfi
normalized_williams = williams_r
composite_raw = (normalized_bb + normalized_rsi + normalized_stoch + normalized_mfi + normalized_williams) / 5
composite = ta.sma(composite_raw, composite_smooth)
🔶 Advanced Extreme Level Detection
Features a sophisticated dual-threshold system that distinguishes between moderate and extreme market conditions. This hierarchical approach allows traders to identify varying degrees of mean reversion potential, from moderate oversold/overbought conditions to extreme levels that demand immediate attention.
🔶 Momentum Confirmation System
Incorporates a specialized momentum histogram that confirms mean reversion signals by analyzing the rate of change in the composite oscillator. This prevents premature entries during strong trending conditions while highlighting genuine reversal opportunities.
// Oscillator momentum (rate of change)
osc_momentum = ta.mom(composite, 5)
histogram = osc_momentum
// Momentum confirmation
momentum_bullish = histogram > histogram
momentum_bearish = histogram < histogram
// Confirmed signals
confirmed_bullish = bullish_entry and momentum_bullish
confirmed_bearish = bearish_entry and momentum_bearish
🔶 Dynamic Visual Intelligence
The oscillator line adapts its color intensity based on proximity to extreme levels, providing instant visual feedback about market conditions. Background shading creates clear zones that highlight when markets enter moderate or extreme territories.
🔶 Intelligent Signal Generation
Generates precise entry signals only when the composite oscillator crosses extreme thresholds with momentum confirmation. This dual-confirmation approach significantly reduces false signals while maintaining sensitivity to genuine mean reversion opportunities.
How It Works
🔶 Composite Score Calculation
The indicator simultaneously tracks five different oscillators, each normalized to a 0-100 scale, then combines them into a smoothed composite score. This approach eliminates the noise inherent in single-oscillator analysis while capturing the consensus view of multiple momentum indicators.
// Mean reversion entry signals
bullish_entry = ta.crossover(composite, 100 - extreme_level) and composite < (100 - extreme_level)
bearish_entry = ta.crossunder(composite, extreme_level) and composite > extreme_level
// Bollinger %B calculation
bb_basis = ta.sma(src, bb_length)
bb_dev = bb_mult * ta.stdev(src, bb_length)
bb_percent = (src - bb_lower) / (bb_upper - bb_lower) * 100
🔶 Extreme Zone Identification
The system automatically identifies when markets reach statistically significant extreme levels, both moderate (65/35) and extreme (80/20). These zones represent areas where mean reversion has the highest probability of success based on historical market behavior.
🔶 Momentum Histogram Analysis
A specialized momentum histogram tracks the velocity of oscillator changes, helping traders distinguish between healthy corrections and potential trend reversals. The histogram's color-coded display makes momentum shifts immediately apparent.
🔶 Divergence Detection Framework
Built-in divergence analysis identifies situations where price and oscillator movements diverge, often signaling impending reversals. Diamond-shaped markers highlight these critical divergence patterns for enhanced pattern recognition.
🔶 Real-Time Information Dashboard
An integrated information table provides instant access to current oscillator readings, market status, and individual component values. This dashboard eliminates the need to manually check multiple indicators while trading.
🔶 Individual Component Display
Optional display of individual oscillator components allows traders to understand which specific indicators are driving the composite signal. This transparency enables more informed decision-making and deeper market analysis.
🔶 Adaptive Background Coloring
Intelligent background shading automatically adjusts based on market conditions, creating visual zones that correspond to different levels of mean reversion potential. The subtle color gradations make pattern recognition effortless.
1D
3D
🔶 Comprehensive Alert System
Multi-tier alert system covers confirmed entry signals, divergence patterns, and extreme level breaches. Each alert type provides specific context about the detected condition, enabling traders to respond appropriately to different signal strengths.
🔶 Customizable Threshold Management
Fully adjustable extreme and moderate levels allow traders to fine-tune the indicator's sensitivity to match different market volatilities and trading timeframes. This flexibility ensures optimal performance across various market conditions.
🔶 Why Choose AE - Mean Reversion Oscillator?
This indicator provides the most comprehensive approach to mean reversion trading by combining multiple proven oscillators with advanced confirmation mechanisms. By offering clear visual hierarchies for different extreme levels and requiring momentum confirmation for signals, it empowers traders to identify high-probability contrarian opportunities while avoiding false reversals. The sophisticated composite methodology ensures that signals are both statistically significant and practically actionable, making it an essential tool for traders focused on mean reversion strategies across all market conditions.
Volatility Channel Oscillator█ OVERVIEW
"Volatility Channel Oscillator" is a technical indicator that analyzes price volatility relative to dynamic price channels, displaying an oscillator, its moving average, and signals based on crossovers and divergences. The indicator offers customizable overbought and oversold levels, gradient visualization, and divergence detection, supported by alerts for key signals.
█ CONCEPTS
The VCO indicator creates dynamic price channels based on a moving average of the price (calculated as the arithmetic mean of the high and low prices: (high + low) / 2) and market volatility (measured as the average candle range and body size). These channels are not displayed on the chart but are used to calculate the oscillator value, which reflects the position of the closing price relative to the channel width, scaled to a range from -100 to +100, with the zero line as the central point. A moving average of the oscillator (SMA) smooths its values, enabling signals based on crossovers with the zero line or overbought/oversold levels. The indicator also detects divergences between price and the oscillator, which may indicate potential trend reversals. VCO is useful for identifying market momentum, reversal points, and trend confirmation, especially when combined with other technical analysis tools.
█ FEATURES
- Volatility Channels: Calculates invisible chart boundaries based on a simple moving average (SMA) of the price (high + low) / 2 and volatility (average candle range and body). The length parameter (default 30) sets the SMA length, and scale (default 200%) adjusts the channel width.
- Oscillator: Determines the oscillator value in the range of -100 to +100, indicating the closing price's position relative to the volatility channel. Displayed with dynamic coloring (green for positive values, red for negative).
- Oscillator Moving Average: A simple moving average (SMA) of the oscillator values, smoothing its movements. The signalLength parameter (default 20) defines the SMA length. Displayed in yellow with an optional gradient.
- Overbought/Oversold Levels: Configurable thresholds for the oscillator (overbought, default 50; oversold, default -50) and its moving average (maOverbought, default 30; maOversold, default -30), shown as horizontal lines with optional gradients. Band colors change dynamically (red for overbought, green for oversold, gray for neutral) based on the moving average's position relative to maOverbought/maOversold, reinforcing other signals.
- Divergences: Detects bullish (price forms a lower low, oscillator a higher low) and bearish (price forms a higher high, oscillator a lower high) divergences using pivots (pivotLength, default 2). Divergences are displayed with a delay equal to the pivot length; larger lengths increase reliability but delay signals. Use as additional confirmation.
Signals:
- Overbought/Oversold Crossovers: Green triangles (buy) when the oscillator crosses above the oversold level, red triangles (sell) when it crosses below the overbought level.
- Zero Line Crossovers: Buy/sell signals when the oscillator crosses the zero line upward (buy) or downward (sell).
- Moving Average Crossovers: Buy/sell signals when the oscillator's moving average crosses the zero line or the maOverbought/maOversold levels. Dynamic band color changes (red/green) at these crossovers reinforce other signals.
- Visualization: Gradient lines for the oscillator, its moving average, overbought/oversold levels, and zero line, with adjustable transparency. Gradient fill between the oscillator and zero line.
Divergence Labels: "Bull" (bullish) and "Bear" (bearish) labels with customizable color and transparency.
- Alerts: Built-in alerts for divergences, overbought/oversold crossovers, and zero line crossovers by the oscillator and its moving average.
█ HOW TO USE
Add to Chart: Apply the indicator via Pine Editor or the Indicators menu on TradingView.
Configure Settings:
- Channel and Oscillator Settings: Adjust the channel SMA length (length, default 30) and channel scaling (scale, default 200%). Increase scale for high-volatility markets.
- Threshold Levels: Set oscillator overbought (overbought, default 50) and oversold (oversold, default -50) levels, and moving average thresholds (maOverbought, default 30; maOversold, default -30).
- Divergence Settings: Enable/disable divergence detection (calculateDivergence) and set pivot length (pivotLength, default 2). Larger values increase reliability but delay signals.
- Signal Settings: Choose signal types (signalType): overbought/oversold, zero line, moving average, or all.
- Styling: Customize colors for the oscillator, moving average, horizontal levels, and divergence labels. Adjust gradient and fill transparency.
Interpreting Signals:
- Buy Signals: Green triangles below the bar when the oscillator or its moving average crosses above the oversold level or zero line.
- Sell Signals: Red triangles above the bar when the oscillator or its moving average crosses below the overbought level or zero line.
- Moving Average Signals: Green/red triangles when the moving average crosses maOverbought/maOversold levels, indicating potential reversals or trend continuation. Dynamic band color changes (red for overbought, green for oversold) at these crossovers reinforce other signals.
- Divergences: "Bull" (bullish) and "Bear" (bearish) labels indicate potential trend reversals with a delay based on pivot length. Use as confirmation.
- Overbought/Oversold Levels: Monitor price reactions in these zones as potential reversal points. Dynamic band color changes based on the moving average reinforce signals.
Signal Confirmation: Use VCO with other tools, such as pivot levels (for key turning points) or Fibonacci levels (for support/resistance zones).
█ APPLICATIONS
- Trend Trading: Zero line crossovers by the oscillator or its moving average identify momentum in uptrends or downtrends.
- Range Trading: Overbought/oversold levels help identify entry/exit points in sideways markets.
- Divergences: Use bullish/bearish divergences as additional confirmation of reversals, especially near key price levels.
- Trend Identification: To analyze trends over a longer perspective, increase the moving average length (signalLength) for more stable signals.
█ NOTES
- Test the indicator across different timeframes and markets to optimize parameters, such as length and scale, for your trading style.
- In strong trends, overbought/oversold levels may persist, requiring additional signal verification.
- Divergences are more reliable on higher timeframes (H4, D1), where market noise is reduced, but their delay requires caution.
- In low-liquidity markets, signals may be less effective, so use on high-liquidity assets is recommended.
Seasonality Heatmap [QuantAlgo]🟢 Overview
The Seasonality Heatmap analyzes years of historical data to reveal which months and weekdays have consistently produced gains or losses, displaying results through color-coded tables with statistical metrics like consistency scores (1-10 rating) and positive occurrence rates. By calculating average returns for each calendar month and day-of-week combination, it identifies recognizable seasonal patterns (such as which months or weekdays tend to rally versus decline) and synthesizes this into actionable buy low/sell high timing possibilities for strategic entries and exits. This helps traders and investors spot high-probability seasonal windows where assets have historically shown strength or weakness, enabling them to align positions with recurring bull and bear market patterns.
🟢 How It Works
1. Monthly Heatmap
How % Return is Calculated:
The indicator fetches monthly closing prices (or Open/High/Low based on user selection) and calculates the percentage change from the previous month:
(Current Month Price - Previous Month Price) / Previous Month Price × 100
Each cell in the heatmap represents one month's return in a specific year, creating a multi-year historical view
Colors indicate performance intensity: greener/brighter shades for higher positive returns, redder/brighter shades for larger negative returns
What Averages Mean:
The "Avg %" row displays the arithmetic mean of all historical returns for each calendar month (e.g., averaging all Januaries together, all Februaries together, etc.)
This metric identifies historically recurring patterns by showing which months have tended to rise or fall on average
Positive averages indicate months that have typically trended upward; negative averages indicate historically weaker months
Example: If April shows +18.56% average, it means April has averaged a 18.56% gain across all years analyzed
What Months Up % Mean:
Shows the percentage of historical occurrences where that month had a positive return (closed higher than the previous month)
Calculated as:
(Number of Months with Positive Returns / Total Months) × 100
Values above 50% indicate the month has been positive more often than negative; below 50% indicates more frequent negative months
Example: If October shows "64%", then 64% of all historical Octobers had positive returns
What Consistency Score Means:
A 1-10 rating that measures how predictable and stable a month's returns have been
Calculated using the coefficient of variation (standard deviation / mean) - lower variation = higher consistency
High scores (8-10, green): The month has shown relatively stable behavior with similar outcomes year-to-year
Medium scores (5-7, gray): Moderate consistency with some variability
Low scores (1-4, red): High variability with unpredictable behavior across different years
Example: A consistency score of 8/10 indicates the month has exhibited recognizable patterns with relatively low deviation
What Best Means:
Shows the highest percentage return achieved for that specific month, along with the year it occurred
Reveals the maximum observed upside and identifies outlier years with exceptional performance
Useful for understanding the range of possible outcomes beyond the average
Example: "Best: 2016: +131.90%" means the strongest January in the dataset was in 2016 with an 131.90% gain
What Worst Means:
Shows the most negative percentage return for that specific month, along with the year it occurred
Reveals maximum observed downside and helps understand the range of historical outcomes
Important for risk assessment even in months with positive averages
Example: "Worst: 2022: -26.86%" means the weakest January in the dataset was in 2022 with a 26.86% loss
2. Day-of-Week Heatmap
How % Return is Calculated:
Calculates the percentage change from the previous day's close to the current day's price (based on user's price source selection)
Returns are aggregated by day of the week within each calendar month (e.g., all Mondays in January, all Tuesdays in January, etc.)
Each cell shows the average performance for that specific day-month combination across all historical data
Formula:
(Current Day Price - Previous Day Close) / Previous Day Close × 100
What Averages Mean:
The "Avg %" row at the bottom aggregates all months together to show the overall average return for each weekday
Identifies broad weekly patterns across the entire dataset
Calculated by summing all daily returns for that weekday across all months and dividing by total observations
Example: If Monday shows +0.04%, Mondays have averaged a 0.04% change across all months in the dataset
What Days Up % Mean:
Shows the percentage of historical occurrences where that weekday had a positive return
Calculated as:
(Number of Positive Days / Total Days Observed) × 100
Values above 50% indicate the day has been positive more often than negative; below 50% indicates more frequent negative days
Example: If Fridays show "54%", then 54% of all Fridays in the dataset had positive returns
What Consistency Score Means:
A 1-10 rating measuring how stable that weekday's performance has been across different months
Based on the coefficient of variation of daily returns for that weekday across all 12 months
High scores (8-10, green): The weekday has shown relatively consistent behavior month-to-month
Medium scores (5-7, gray): Moderate consistency with some month-to-month variation
Low scores (1-4, red): High variability across months, with behavior differing significantly by calendar month
Example: A consistency score of 7/10 for Wednesdays means they have performed with moderate consistency throughout the year
What Best Means:
Shows which calendar month had the strongest average performance for that specific weekday
Identifies favorable day-month combinations based on historical data
Format shows the month abbreviation and the average return achieved
Example: "Best: Oct: +0.20%" means Mondays averaged +0.20% during October months in the dataset
What Worst Means:
Shows which calendar month had the weakest average performance for that specific weekday
Identifies historically challenging day-month combinations
Useful for understanding which month-weekday pairings have shown weaker performance
Example: "Worst: Sep: -0.35%" means Tuesdays averaged -0.35% during September months in the dataset
3. Optimal Timing Table/Summary Table
→ Best Month to BUY: Identifies the month with the lowest average return (most negative or least positive historically), representing periods where prices have historically been relatively lower
Based on the observation that buying during historically weaker months may position for subsequent recovery
Shows the month name, its average return, and color-coded performance
Example: If May shows -0.86% as "Best Month to BUY", it means May has historically averaged -0.86% in the analyzed period
→ Best Month to SELL: Identifies the month with the highest average return (most positive historically), representing periods where prices have historically been relatively higher
Based on historical strength patterns in that month
Example: If July shows +1.42% as "Best Month to SELL", it means July has historically averaged +1.42% gains
→ 2nd Best Month to BUY: The second-lowest performing month based on average returns
Provides an alternative timing option based on historical patterns
Offers flexibility for staged entries or when the primary month doesn't align with strategy
Example: Identifies the next-most favorable historical buying period
→ 2nd Best Month to SELL: The second-highest performing month based on average returns
Provides an alternative exit timing based on historical data
Useful for staged profit-taking or multiple exit opportunities
Identifies the secondary historical strength period
Note: The same logic applies to "Best Day to BUY/SELL" and "2nd Best Day to BUY/SELL" rows, which identify weekdays based on average daily performance across all months. Days with lowest averages are marked as buying opportunities (historically weaker days), while days with highest averages are marked for selling (historically stronger days).
🟢 Examples
Example 1: NVIDIA NASDAQ:NVDA - Strong May Pattern with High Consistency
Analyzing NVIDIA from 2015 onwards, the Monthly Heatmap reveals May averaging +15.84% with 82% of months being positive and a consistency score of 8/10 (green). December shows -1.69% average with only 40% of months positive and a low 1/10 consistency score (red). The Optimal Timing table identifies December as "Best Month to BUY" and May as "Best Month to SELL." A trader recognizes this high-probability May strength pattern and considers entering positions in late December when prices have historically been weaker, then taking profits in May when the seasonal tailwind typically peaks. The high consistency score in May (8/10) provides additional confidence that this pattern has been relatively stable year-over-year.
Example 2: Crypto Market Cap CRYPTOCAP:TOTALES - October Rally Pattern
An investor examining total crypto market capitalization notices September averaging -2.42% with 45% of months positive and 5/10 consistency, while October shows a dramatic shift with +16.69% average, 90% of months positive, and an exceptional 9/10 consistency score (blue). The Day-of-Week heatmap reveals Mondays averaging +0.40% with 54% positive days and 9/10 consistency (blue), while Thursdays show only +0.08% with 1/10 consistency (yellow). The investor uses this multi-layered analysis to develop a strategy: enter crypto positions on Thursdays during late September (combining the historically weak month with the less consistent weekday), then hold through October's historically strong period, considering exits on Mondays when intraweek strength has been most consistent.
Example 3: Solana BINANCE:SOLUSDT - Extreme January Seasonality
A cryptocurrency trader analyzing Solana observes an extraordinary January pattern: +59.57% average return with 60% of months positive and 8/10 consistency (teal), while May shows -9.75% average with only 33% of months positive and 6/10 consistency. August also displays strength at +59.50% average with 7/10 consistency. The Optimal Timing table confirms May as "Best Month to BUY" and January as "Best Month to SELL." The Day-of-Week data shows Sundays averaging +0.77% with 8/10 consistency (teal). The trader develops a seasonal rotation strategy: accumulate SOL positions during May weakness, hold through the historically strong January period (which has shown this extreme pattern with reasonable consistency), and specifically target Sunday exits when the weekday data shows the most recognizable strength pattern.
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced
📊 ORIGINALITY & INNOVATION
The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles.
Key Improvements:
Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre)
Maintains backward compatibility with traditional Stochastic calculations through SMA default setting
Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics
Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition
Comprehensive alert system covering crossovers and zone entries for systematic trade management
Differentiation from Traditional Stochastic:
Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs.
📐 MATHEMATICAL FOUNDATION
Core Stochastic Calculation:
The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period:
Step 1: Raw %K Calculation
%K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low)
Where:
Close = Current closing price
Lowest Low = Lowest low over the %K Length period
Highest High = Highest high over the %K Length period
Result ranges from 0 (close at period low) to 100 (close at period high)
Step 2: Smoothed %K Calculation
%K = MA(%K_raw, K Smoothing Period, MA Type)
Where:
MA = Selected moving average algorithm (SMA, EMA, etc.)
K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic
Traditional Fast Stochastic uses %K_raw directly without smoothing
Step 3: Signal Line %D Calculation
%D = MA(%K, D Smoothing Period, MA Type)
Where:
%D acts as a signal line and moving average of %K
D Smoothing typically set to 3 periods in traditional implementations
Both %K and %D use the same MA algorithm for consistent behavior
Available Smoothing Algorithms (21 Options):
Standard Moving Averages:
SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics
EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting
RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA
WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness
VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity
Advanced Moving Averages:
HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period
ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction
LSMA (Least Squares): Linear regression based, fits trend line to data points
DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique
TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation
ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive
TMA (Triangular): Double-smoothed SMA, very smooth but slower response
Adaptive & Intelligent Filters:
T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness
FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges
KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes
McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws
Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering
Advanced Digital Filters:
Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag
Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics
Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis
Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively
📊 COMPREHENSIVE SIGNAL ANALYSIS
Absolute Level Interpretation (%K Line):
%K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries
%K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing
%K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend
%K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend
%K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing
%K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries
Crossover Signal Analysis:
%K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level
%K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level
Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis
Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move
Advanced Divergence Patterns (%K Line vs Price):
Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone
Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone
Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation
Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation
Momentum Strength Analysis (%K Line Slope):
Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk
Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals
Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing
%K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following
🎯 STRATEGIC APPLICATIONS
Mean Reversion Strategy (Range-Bound Markets):
Identify ranging market conditions using price action or Bollinger Bands
Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold)
Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20)
Set profit targets near opposite extreme or midline (50 level)
Use tight stop-loss above recent swing high/low to protect against breakout scenarios
Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction
Trend Following with Momentum Confirmation:
Identify primary trend direction using higher timeframe analysis or moving averages
Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend
Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend)
Use wider stops to accommodate normal trend volatility
Add to position on subsequent pullbacks showing similar Stochastic pattern
Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence
Divergence-Based Reversal Strategy:
Scan for divergence between price and Stochastic at swing highs/lows
Confirm divergence with at least two price pivots showing divergent Stochastic readings
Wait for %K to cross %D in direction of anticipated reversal as entry trigger
Enter position in divergence direction with stop beyond recent swing extreme
Target profit at key support/resistance levels or Fibonacci retracements
Scale out as Stochastic reaches opposite extreme zone
Multi-Timeframe Momentum Alignment:
Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias
Switch to lower timeframe (1H or 15M) for precise entry timing
Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction
Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe
Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe
Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses
Zone Transition Strategy:
Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.)
Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish
Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish
Use zone midpoint (50) as dynamic support/resistance for position management
Trail stops as Stochastic advances through favorable zones
Exit when Stochastic fails to maintain momentum and reverses back into prior zone
📋 DETAILED PARAMETER CONFIGURATION
%K Length (Default: 14):
Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping
Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies
Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction
Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis
%K Smoothing (Default: 3):
Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise
Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard
Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing
Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+
%D Smoothing (Default: 3):
Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering
Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications
Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading
Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only
Smoothing Type Algorithm Selection:
For Trending Markets:
ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves
HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise
EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability
For Ranging/Choppy Markets:
Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points
Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges
T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification
For Adaptive Market Conditions:
KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions
FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty
McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges
For Conservative Long-Term Analysis:
SMA: Traditional choice, predictable behavior, widely understood characteristics
RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions
Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely
Source Selection:
Close (Default): Standard choice using closing prices, most common and widely tested
HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior
HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges
Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
Traditional SMA-Based Stochastic:
Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods
Equal treatment of trending and ranging markets, no adaptation to volatility changes
Predictable behavior but suboptimal in varying market regimes
Enhanced Version with Adaptive Algorithms:
KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges
ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels
Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA
Performance improvements vary by algorithm selection and market conditions
Signal Quality Improvements:
Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically
Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies
Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing
Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals
Comparison with Standard Implementations:
Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles
Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification
Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets
Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments
Flexibility Advantages:
Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants
Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends)
Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture
Backtesting capability with algorithm as optimization parameter for strategy development
Limitations and Considerations:
Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data
Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable
Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions
Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation
Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios
Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends
Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm
Divergence patterns require confirmation from price action or additional indicators before trading
Past indicator characteristics and backtested results do not guarantee future performance
Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation
Test selected algorithm on historical data of specific instrument and timeframe before live trading
Market regime changes may require algorithm adjustment for optimal performance
The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
Natural Gas Intraday Strategy [15m] with Partial Profit & TrailBuy when:
1. Close > EMA 100 and EMA 20 > EMA 100
2. MACD (8,21,5) > Signal and histogram rising
3. RSI > 60
4. ATR > threshold (avoid flat market)
Sell when:
1. Close < EMA 100 and EMA 20 < EMA 100
2. MACD (8,21,5) < Signal and histogram falling
3. RSI < 40
4. ATR > threshold
Exit:
• SL = recent swing ± 0.5 ATR
• TP1 = 1 ATR, trail rest with EMA 20
Multi-Timeframe Trend Table - EMA Based Trend Analysis📊 Stay Aligned with Higher Timeframe Trends While Scalping
This powerful indicator displays real-time trend direction for 1-hour and 4-hour timeframes in a clean, easy-to-read table format. Perfect for traders who want to align their short-term trades with higher timeframe momentum.
🎯 Key Features
Multi-Timeframe Analysis: Monitor 1H and 4H trends while trading on any timeframe (3min, 5min, 15min, etc.)
EMA-Based Logic: Uses proven EMA 50 and EMA 100 crossover methodology
Visual Clarity: Color-coded table with green (uptrend) and red (downtrend) indicators
Customizable Display: Toggle EMA values and adjust table position
Real-Time Updates: Automatically refreshes with each bar close
Lightweight: Minimal resource usage with efficient data requests
📈 How It Works
The indicator determines trend direction using a simple but effective rule:
UPTREND: Price is above both EMA 50 AND EMA 100
DOWNTREND: Price is below either EMA 50 OR EMA 100
🔧 Settings
Show EMA Values: Display actual EMA 50/100 values in the table
Table Position: Choose from 4 corner positions (Top Right, Top Left, Bottom Right, Bottom Left)
Plot Current EMAs: Optional display of EMA lines on your current chart
💡 Trading Applications
✅ Trend Confirmation: Ensure your trades align with higher timeframe direction
✅ Risk Management: Avoid counter-trend trades in strong directional markets
✅ Entry Timing: Use lower timeframe for entries while respecting higher timeframe bias
✅ Scalping Enhancement: Perfect for 1-5 minute scalping with higher timeframe context
🎨 Visual Design
Clean, professional table design
Intuitive color coding (Green = Up, Red = Down)
Compact size that doesn't obstruct your chart
Clear typography for quick reading
📋 Perfect For
Day traders and scalpers
Swing traders seeking trend confirmation
Multi-timeframe analysis enthusiasts
Traders who want simple, effective trend identification
🚀 Easy Setup
Add to any chart (works on all timeframes)
Customize table position and settings
Start trading with higher timeframe awareness
Watch the table update automatically
No complex configurations needed - just add and trade!
This indicator is designed for educational and informational purposes. Always combine with proper risk management and your own analysis.
BOCS Channel Scalper Indicator - Mean Reversion Alert System# BOCS Channel Scalper Indicator - Mean Reversion Alert System
## WHAT THIS INDICATOR DOES:
This is a mean reversion trading indicator that identifies consolidation channels through volatility analysis and generates alert signals when price enters entry zones near channel boundaries. **This indicator version is designed for manual trading with comprehensive alert functionality.** Unlike automated strategies, this tool sends notifications (via popup, email, SMS, or webhook) when trading opportunities occur, allowing you to manually review and execute trades. The system assumes price will revert to the channel mean, identifying scalp opportunities as price reaches extremes and preparing to bounce back toward center.
## INDICATOR VS STRATEGY - KEY DISTINCTION:
**This is an INDICATOR with alerts, not an automated strategy.** It does not execute trades automatically. Instead, it:
- Displays visual signals on your chart when entry conditions are met
- Sends customizable alerts to your device/email when opportunities arise
- Shows TP/SL levels for reference but does not place orders
- Requires you to manually enter and exit positions based on signals
- Works with all TradingView subscription levels (alerts included on all plans)
**For automated trading with backtesting**, use the strategy version. For manual control with notifications, use this indicator version.
## ALERT CAPABILITIES:
This indicator includes four distinct alert conditions that can be configured independently:
**1. New Channel Formation Alert**
- Triggers when a fresh BOCS channel is identified
- Message: "New BOCS channel formed - potential scalp setup ready"
- Use this to prepare for upcoming trading opportunities
**2. Long Scalp Entry Alert**
- Fires when price touches the long entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "LONG scalp signal at 24731.75 | TP: 24743.2 | SL: 24716.5"
**3. Short Scalp Entry Alert**
- Fires when price touches the short entry zone
- Message includes current price, calculated TP, and SL levels
- Notification example: "SHORT scalp signal at 24747.50 | TP: 24735.0 | SL: 24762.75"
**4. Any Entry Signal Alert**
- Combined alert for both long and short entries
- Use this if you want a single alert stream for all opportunities
- Message: "BOCS Scalp Entry: at "
**Setting Up Alerts:**
1. Add indicator to chart and configure settings
2. Click the Alert (⏰) button in TradingView toolbar
3. Select "BOCS Channel Scalper" from condition dropdown
4. Choose desired alert type (Long, Short, Any, or Channel Formation)
5. Set "Once Per Bar Close" to avoid false signals during bar formation
6. Configure delivery method (popup, email, webhook for automation platforms)
7. Save alert - it will fire automatically when conditions are met
**Alert Message Placeholders:**
Alerts use TradingView's dynamic placeholder system:
- {{ticker}} = Symbol name (e.g., NQ1!)
- {{close}} = Current price at signal
- {{plot_1}} = Calculated take profit level
- {{plot_2}} = Calculated stop loss level
These placeholders populate automatically, creating detailed notification messages without manual configuration.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This indicator is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Indicator**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the indicator ideal for active day traders who want continuous alert opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased signal frequency also means higher potential commission costs and requires disciplined trade selection when acting on alerts.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The indicator normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The indicator uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The indicator tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The indicator uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. Visual markers (arrows and labels) appear on chart, and configured alerts fire immediately.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents alert spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long alert will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The indicator includes a multi-timeframe ATR filter to avoid alerts during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while viewing 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Alerts enabled
- If ATR < threshold: No alerts fire
This prevents notifications during dead zones where mean reversion is unreliable due to insufficient price movement. The ATR status is displayed in the info table with visual confirmation (✓ or ✗).
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. These levels are displayed as visual lines with labels and included in alert messages for reference when manually placing orders.
### Stop Loss Placement:
Stop losses are calculated just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. SL levels are displayed on chart and included in alert notifications as suggested stop placement.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
## INPUT PARAMETERS:
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long alert generation on/off
- **Enable Short Scalps**: Toggle short alert generation on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between alerts (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for alert enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time indicator status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Long Color**: Customize long signal color (default: darker green for readability)
- **Short Color**: Customize short signal color (default: red)
- **TP/SL Colors**: Customize take profit and stop loss line colors
- **Line Length**: Visual length of TP/SL reference lines (5-200 bars)
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short alerts
- **TP/SL reference lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing channel status, last signal, entry/TP/SL prices, risk/reward ratio, and ATR filter status
- **Visual confirmation** when alerts fire via on-chart markers synchronized with notifications
## HOW TO USE:
### For 1-3 Minute Scalping with Alerts (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars to reduce alert spam
- **Alert Setup**: Configure "Any Entry Signal" for combined long/short notifications
- **Execution**: When alert fires, verify chart visuals, then manually place limit order at entry zone with provided TP/SL levels
### For 5-15 Minute Day Trading with Alerts:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- **Alert Setup**: Configure separate "Long Scalp Entry" and "Short Scalp Entry" alerts if you trade directionally based on bias
- **Execution**: Review channel structure on alert, confirm ATR filter shows ✓, then enter manually
### For 30-60 Minute Swing Scalping with Alerts:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- **Alert Setup**: Use "New Channel Formation" to prepare for setups, then "Any Entry Signal" for execution alerts
- **Execution**: Larger timeframes allow more analysis time between alert and entry
### Webhook Integration for Semi-Automation:
- Configure alert webhook URL to connect with platforms like TradersPost, TradingView Paper Trading, or custom automation
- Alert message includes all necessary order parameters (direction, entry, TP, SL)
- Webhook receives structured data when signal fires
- External platform can auto-execute based on alert payload
- Still maintains manual oversight vs full strategy automation
## USAGE CONSIDERATIONS:
- **Manual Discipline Required**: Alerts provide opportunities but execution requires judgment. Not all alerts should be taken - consider market context, trend, and channel quality
- **Alert Timing**: Alerts fire on bar close by default. Ensure "Once Per Bar Close" is selected to avoid false signals during bar formation
- **Notification Delivery**: Mobile/email alerts may have 1-3 second delay. For immediate execution, use desktop popups or webhook automation
- **Cooldown Necessity**: Without cooldown, rapidly touching price action can generate excessive alerts. Start with 3-bar cooldown and adjust based on alert volume
- **ATR Filter Impact**: Enabling ATR filter dramatically reduces alert count but improves quality. Track filter status in info table to understand when you're receiving fewer alerts
- **Commission Awareness**: High alert frequency means high potential trade count. Calculate if your commission structure supports frequent scalping before acting on all alerts
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features are not included in this indicator version. Multi-timeframe ATR requires higher-tier TradingView subscription for request.security() functionality on timeframes below chart timeframe.
## KNOWN LIMITATIONS:
- **Indicator does not execute trades** - alerts are informational only; you must manually place all orders
- **Alert delivery depends on TradingView infrastructure** - delays or failures possible during platform issues
- **No position tracking** - indicator doesn't know if you're in a trade; you must manage open positions independently
- **TP/SL levels are reference only** - you must manually set these on your broker platform; they are not live orders
- **Immediate touch entry can generate many alerts** in choppy zones without adequate cooldown
- **Channel deletion at 10-tick breaks** may be too aggressive or lenient depending on instrument tick size
- **ATR filter from lower timeframes** requires TradingView Premium/Pro+ for request.security()
- **Mean reversion logic fails** in strong breakout scenarios - alerts will fire but trades may hit stops
- **No partial closing capability** - full position management is manual; you determine scaling out
- **Alerts do not account for gaps** or overnight price changes; morning alerts may be stale
## RISK DISCLOSURE:
Trading involves substantial risk of loss. This indicator provides signals for educational and informational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Mean reversion strategies can experience extended drawdowns during trending markets. Alerts are not guaranteed to be profitable and should be combined with your own analysis. Stop losses may not fill at intended levels during extreme volatility or gaps. Never trade with capital you cannot afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Always verify alerts against current market conditions before executing trades manually.
## ACKNOWLEDGMENT & CREDITS:
This indicator is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based alert generation, comprehensive alert condition system with customizable notifications, multi-timeframe ATR volatility filtering, cooldown period for alert management, dual TP methods (fixed points vs channel percentage), visual TP/SL reference lines, and real-time status monitoring table. This indicator version is specifically designed for manual traders who prefer alert-based decision making over automated execution.
BOCS AdaptiveBOCS Adaptive Strategy - Automated Volatility Breakout System
WHAT THIS STRATEGY DOES:
This is an automated trading strategy that detects consolidation patterns through volatility analysis and executes trades when price breaks out of these channels. Take-profit and stop-loss levels are calculated dynamically using Average True Range (ATR) to adapt to current market volatility. The strategy closes positions partially at the first profit target and exits the remainder at the second target or stop loss.
TECHNICAL METHODOLOGY:
Price Normalization Process:
The strategy begins by normalizing price to create a consistent measurement scale. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). The current close price is then normalized using the formula: (close - lowest_low) / (highest_high - lowest_low). This produces values between 0 and 1, allowing volatility analysis to work consistently across different instruments and price levels.
Volatility Detection:
A 14-period standard deviation is applied to the normalized price series. Standard deviation measures how much prices deviate from their average - higher values indicate volatility expansion, lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() functions to track when volatility reaches peaks and troughs over the detection length period (default 14 bars).
Channel Formation Logic:
When volatility crosses from a high level to a low level, this signals the beginning of a consolidation phase. The strategy records this moment using ta.crossover(upper, lower) and begins tracking the highest and lowest prices during the consolidation. These become the channel boundaries. The duration between the crossover and current bar must exceed 10 bars minimum to avoid false channels from brief volatility spikes. Channels are drawn using box objects with the recorded high/low boundaries.
Breakout Signal Generation:
Two detection modes are available:
Strong Closes Mode (default): Breakout occurs when the candle body midpoint math.avg(close, open) exceeds the channel boundary. This filters out wick-only breaks.
Any Touch Mode: Breakout occurs when the close price exceeds the boundary.
When price closes above the upper channel boundary, a bullish breakout signal generates. When price closes below the lower boundary, a bearish breakout signal generates. The channel is then removed from the chart.
ATR-Based Risk Management:
The strategy uses request.security() to fetch ATR values from a specified timeframe, which can differ from the chart timeframe. For example, on a 5-minute chart, you can use 1-minute ATR for more responsive calculations. The ATR is calculated using ta.atr(length) with a user-defined period (default 14).
Exit levels are calculated at the moment of breakout:
Long Entry Price = Upper channel boundary
Long TP1 = Entry + (ATR × TP1 Multiplier)
Long TP2 = Entry + (ATR × TP2 Multiplier)
Long SL = Entry - (ATR × SL Multiplier)
For short trades, the calculation inverts:
Short Entry Price = Lower channel boundary
Short TP1 = Entry - (ATR × TP1 Multiplier)
Short TP2 = Entry - (ATR × TP2 Multiplier)
Short SL = Entry + (ATR × SL Multiplier)
Trade Execution Logic:
When a breakout occurs, the strategy checks if trading hours filter is satisfied (if enabled) and if position size equals zero (no existing position). If volume confirmation is enabled, it also verifies that current volume exceeds 1.2 times the 20-period simple moving average.
If all conditions are met:
strategy.entry() opens a position using the user-defined number of contracts
strategy.exit() immediately places a stop loss order
The code monitors price against TP1 and TP2 levels on each bar
When price reaches TP1, strategy.close() closes the specified number of contracts (e.g., if you enter with 3 contracts and set TP1 close to 1, it closes 1 contract). When price reaches TP2, it closes all remaining contracts. If stop loss is hit first, the entire position exits via the strategy.exit() order.
Volume Analysis System:
The strategy uses ta.requestUpAndDownVolume(timeframe) to fetch up volume, down volume, and volume delta from a specified timeframe. Three display modes are available:
Volume Mode: Shows total volume as bars scaled relative to the 20-period average
Comparison Mode: Shows up volume and down volume as separate bars above/below the channel midline
Delta Mode: Shows net volume delta (up volume - down volume) as bars, positive values above midline, negative below
The volume confirmation logic compares breakout bar volume to the 20-period SMA. If volume ÷ average > 1.2, the breakout is classified as "confirmed." When volume confirmation is enabled in settings, only confirmed breakouts generate trades.
INPUT PARAMETERS:
Strategy Settings:
Number of Contracts: Fixed quantity to trade per signal (1-1000)
Require Volume Confirmation: Toggle to only trade signals with volume >120% of average
TP1 Close Contracts: Exact number of contracts to close at first target (1-1000)
Use Trading Hours Filter: Toggle to restrict trading to specified session
Trading Hours: Session input in HHMM-HHMM format (e.g., "0930-1600")
Main Settings:
Normalization Length: Lookback bars for high/low calculation (1-500, default 100)
Box Detection Length: Period for volatility peak/trough detection (1-100, default 14)
Strong Closes Only: Toggle between body midpoint vs close price for breakout detection
Nested Channels: Allow multiple overlapping channels vs single channel at a time
ATR TP/SL Settings:
ATR Timeframe: Source timeframe for ATR calculation (1, 5, 15, 60, etc.)
ATR Length: Smoothing period for ATR (1-100, default 14)
Take Profit 1 Multiplier: Distance from entry as multiple of ATR (0.1-10.0, default 2.0)
Take Profit 2 Multiplier: Distance from entry as multiple of ATR (0.1-10.0, default 3.0)
Stop Loss Multiplier: Distance from entry as multiple of ATR (0.1-10.0, default 1.0)
Enable Take Profit 2: Toggle second profit target on/off
VISUAL INDICATORS:
Channel boxes with semi-transparent fill showing consolidation zones
Green/red colored zones at channel boundaries indicating breakout areas
Volume bars displayed within channels using selected mode
TP/SL lines with labels showing both price level and distance in points
Entry signals marked with up/down triangles at breakout price
Strategy status table showing position, contracts, P&L, ATR values, and volume confirmation status
HOW TO USE:
For 2-Minute Scalping:
Set ATR Timeframe to "1" (1-minute), ATR Length to 12, TP1 Multiplier to 2.0, TP2 Multiplier to 3.0, SL Multiplier to 1.5. Enable volume confirmation and strong closes only. Use trading hours filter to avoid low-volume periods.
For 5-15 Minute Day Trading:
Set ATR Timeframe to match chart or use 5-minute, ATR Length to 14, TP1 Multiplier to 2.0, TP2 Multiplier to 3.5, SL Multiplier to 1.2. Volume confirmation recommended but optional.
For Hourly+ Swing Trading:
Set ATR Timeframe to 15-30 minute, ATR Length to 14-21, TP1 Multiplier to 2.5, TP2 Multiplier to 4.0, SL Multiplier to 1.5. Volume confirmation optional, nested channels can be enabled for multiple setups.
BACKTEST CONSIDERATIONS:
Strategy performs best during trending or volatility expansion phases
Consolidation-heavy or choppy markets produce more false signals
Shorter timeframes require wider stop loss multipliers due to noise
Commission and slippage significantly impact performance on sub-5-minute charts
Volume confirmation generally improves win rate but reduces trade frequency
ATR multipliers should be optimized for specific instrument characteristics
COMPATIBLE MARKETS:
Works on any instrument with price and volume data including forex pairs, stock indices, individual stocks, cryptocurrency, commodities, and futures contracts. Requires TradingView data feed that includes volume for volume confirmation features to function.
KNOWN LIMITATIONS:
Stop losses execute via strategy.exit() and may not fill at exact levels during gaps or extreme volatility
request.security() on lower timeframes requires higher-tier TradingView subscription
False breakouts inherent to breakout strategies cannot be completely eliminated
Performance varies significantly based on market regime (trending vs ranging)
Partial closing logic requires sufficient position size relative to TP1 close contracts setting
RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance of this or any strategy does not guarantee future results. This strategy is provided for educational purposes and automated backtesting. Thoroughly test on historical data and paper trade before risking real capital. Market conditions change and strategies that worked historically may fail in the future. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions.
ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by AlgoAlpha in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns and sharing this innovative technique with the TradingView community. The enhancements added to the original concept include automated trade execution, multi-timeframe ATR-based risk management, partial position closing by contract count, volume confirmation filtering, and real-time position monitoring.
Market Pressure Oscillator█ OVERVIEW
The Market Pressure Oscillator is an advanced technical indicator for TradingView, enabling traders to identify potential trend reversals and momentum shifts through candle-based pressure analysis and divergence detection. It combines a smoothed oscillator with moving average signals, overbought/oversold levels, and divergence visualization, enhanced by customizable gradients, dynamic band colors, and alerts for quick decision-making.
█ CONCEPT
The indicator measures buying or selling pressure based on candle body size (open-to-close difference) and direction, with optional smoothing for clarity and divergence detection between price action and the oscillator. It relies solely on candle data, offering insights into trend strength, overbought/oversold conditions, and potential reversals with a customizable visual presentation.
█ WHY USE IT?
- Divergence Detection: Identifies bullish and bearish divergences to reinforce signals, especially near overbought/oversold zones.
- Candle Pressure Analysis: Measures pressure based on candle body size, normalized to a ±100 scale.
- Signal Generation: Provides buy/sell signals via overbought/oversold crossovers, zero-line crossovers, moving average zero-line crossovers, and dynamic band color changes.
- Visual Clarity: Uses dynamic colors, gradients, and fill layers for intuitive chart analysis.
Flexibility: Extensive settings allow customization to individual trading preferences.
█ HOW IT WORKS?
- Candle Pressure Calculation: Computes candle body size as math.abs(close - open), normalized against the average body size over a lookback period (avgBody = ta.sma(body, len)). - Candle direction (bullish: +1, bearish: -1, neutral: 0) is multiplied by body weight to derive pressure.
- Cumulative Pressure: Sums pressure values over the lookback period (Lookback Length) and normalizes to ±100 relative to the maximum possible value.
- Smoothing: Optionally applies EMA (Smoothing Length) to normalized pressure.
- Moving Average: Calculates SMA (Moving Average Length) for trend confirmation (Moving Average (SMA)).
- Divergence Detection: Identifies bullish/bearish divergences by comparing price and oscillator pivot highs/lows within a specified range (Pivot Length). Divergence signals appear with a delay equal to the Pivot Length.
- Signals: Generates signals for:
Crossing oversold upward (buy) or overbought downward (sell).
Crossing the zero line by the oscillator or moving average (buy/sell).
Bullish/bearish divergences, marked with labels, enhancing signals, especially near overbought/oversold zones.
Dynamic band color changes when the moving average crosses MA overbought/oversold thresholds (green for oversold, red for overbought).
- Visualization: Plots the oscillator and moving average with dynamic colors, gradient fills, transparent bands, and labels, with customizable overbought/oversold levels.
Alerts: Built-in alerts for divergences, overbought/oversold crossovers, and zero-line crossovers (oscillator and moving average).
█ SETTINGS AND CUSTOMIZATION
- Lookback Length: Period for aggregating candle pressure (default: 14).
- Smoothing Length (EMA): EMA length for smoothing the oscillator (default: 1). Higher values smooth the signal but may reduce signal frequency; adjust overbought/oversold levels accordingly.
- Moving Average Length (SMA): SMA length for the moving average (default: 14, minval=1). Higher values make SMA a trend indicator, requiring adjusted MA overbought/oversold levels.
- Pivot Length (Left/Right): Candles for detecting pivot highs/lows in divergence calculations (default: 2, minval=1). Higher values reduce noise but add delay equal to the set value.
- Enable Divergence Detection: Enables divergence detection (default: true).
- Overbought/Oversold Levels: Thresholds for the oscillator (default: 30/-30) and moving average (default: 10/-10). For the moving average, no arrows appear; bands change color from gray to green (oversold) or red (overbought), reinforcing entry signals.
- Signal Type: Select signals to display: "None", "Overbought/Oversold", "Zero Line", "MA Zero Line", "All" (default: "Overbought/Oversold").
- Colors and Gradients: Customize colors for bullish/bearish oscillator, moving average, zero line, overbought/oversold levels, and divergence labels.
- Transparency: Adjust gradient fill transparency (default: 70, minval=0, maxval=100) and band/label transparency (default: 40, minval=0, maxval=100) for consistent visuals.
- Visualizations: Enable/disable moving average, gradients for zero/overbought/oversold levels, and gradient fills.
█ USAGE EXAMPLES
- Momentum Analysis: Observe the MPO Oscillator above 0 for bullish momentum or below 0 for bearish momentum. The SMA, being smoother, reacts slower and can confirm trend direction as a noise filter.
- Reversal Signals: Look for buy triangles when the oscillator crosses oversold upward, especially when the SMA is below the MA oversold threshold and the band turns green. Similarly, seek sell triangles when crossing overbought downward, with the SMA above the MA overbought threshold and the band turning red.
- Using Divergences: Treat bullish (green labels) and bearish (red labels) divergences as reinforcement for other signals, especially near overbought/oversold zones, indicating stronger potential trend reversals.
- Customization: Adjust lookback length, smoothing, and moving average length to specific instruments and timeframes to minimize false signals.
█ USER NOTES
Combine the indicator with tools like Fibonacci levels or pivot points to enhance accuracy.
Test different settings for lookback length, smoothing, and moving average length on your chosen instrument and timeframe to find optimal values.
Algorithmic Value Oscillator [CRYPTIK1]Algorithmic Value Oscillator
Introduction: What is the AVO? Welcome to the Algorithmic Value Oscillator (AVO), a powerful, modern momentum indicator that reframes the classic "overbought" and "oversold" concept. Instead of relying on a fixed lookback period like a standard RSI, the AVO measures the current price relative to a significant, higher-timeframe Value Zone .
This gives you a more contextual and structural understanding of price. The core question it answers is not just "Is the price moving up or down quickly?" but rather, " Where is the current price in relation to its recently established area of value? "
This allows traders to identify true "premium" (overbought) and "discount" (oversold) levels with greater accuracy, all presented with a clean, futuristic aesthetic designed for the modern trader.
The Core Concept: Price vs. Value The market is constantly trying to find equilibrium. The AVO is built on the principle that the high and low of a significant prior period (like the previous day or week) create a powerful area of perceived value.
The Value Zone: The range between the high and low of the selected higher timeframe.
Premium Territory (Distribution Zone): When the oscillator moves into the glowing pink/purple zone above +100, it is trading at a premium.
Discount Territory (Accumulation Zone): When the oscillator moves into the glowing teal/blue zone below -100, it is trading at a discount.
Key Features
1. Glowing Gradient Oscillator: The main oscillator line is a dynamic visual guide to momentum.
The line changes color smoothly from light blue to neon teal as bullish momentum increases.
It shifts from hot pink to bright purple as bearish momentum increases.
Multiple transparent layers create a professional "glow" effect, making the trend easy to see at a glance.
2. Dynamic Volatility Histogram: This histogram at the bottom of the indicator is a custom volatility meter. It has been engineered to be adaptive, ensuring that the visual differences between high and low volatility are always clear and dramatic, no matter your zoom level. It uses a multi-color gradient to visualize the intensity of market volatility.
3. Volatility Regime Dashboard: This simple on-screen table analyzes the histogram and provides a clear, one-word summary of the current market state: Compressing, Stable, or Expanding.
How to Use the AVO: Trading Strategies
1. Reversion Trading This is the most direct way to use the indicator.
Look for Buys: When the AVO line drops into the teal "Accumulation Zone" (below -100), the price is trading at a discount. Watch for the oscillator to form a bottom and start turning up as a signal that buying pressure is returning.
Look for Sells: When the AVO line moves into the pink "Distribution Zone" (above +100), the price is trading at a premium. Watch for the oscillator to form a peak and start turning down as a signal that selling pressure is increasing.
2. Best Practices & Settings
Timeframe Synergy: The AVO is most effective when your chart timeframe is lower than your selected "Value Zone Source." For example, if you trade on the 1-hour chart, set your Value Zone to "Previous Day."
Confirmation is Key: This indicator provides powerful context, but it should not be used in isolation. Always combine its readings with your primary analysis, such as market structure and support/resistance levels.
Stiffness IndexStiffness Index Indicator
Overview
The Stiffness Index is a technical analysis indicator created by Markos Katsanos and first introduced in the November 2018 issue of Technical Analysis of Stocks & Commodities magazine. This indicator attempts to recognize strong price trends by counting the number of times price was above the 100-day moving average during the indicator period.
Core Philosophy
The premise is the fewer number of times price penetrates the MA, the stronger the trend. The philosophy behind this indicator is that traders should trade when the trend is at its strongest point - when the trend is at its "stiffest". Based on the observation that in strong long-lasting uptrends, price seldom penetrates the 100-bar simple moving average, this indicator helps assess the quality and strength of an uptrend.
How It Works
The Stiffness Index operates through several key components:
1. Moving Average Baseline: Uses a 100-period moving average as the primary reference level
2. Volatility Threshold: Includes a volatility threshold to eliminate minor movements - typically 0.2 standard deviations to reject minimal penetrations above the moving average
3. Counting Mechanism: Calculates the stiffness coefficient as the ratio of the number of times the price has closed above the moving average during the indicator period to the length of that period
4. Smoothing: Applies additional smoothing to the final result for cleaner signals
Key Components
Input Parameters
- Period 1 (100): The moving average period for the baseline calculation
- MA Method 1: Type of moving average for the baseline (SMA, EMA, SMMA, LWMA)
- Summation Period (60): The lookback period for counting closes above the moving average
- Period 2 (3): Smoothing period for the final signal line
- MA Method 2: Smoothing method for the signal line
- Threshold Level (80): Reference level for identifying strong trends
Visual Elements
- Blue Signal Line: The main stiffness reading showing trend strength
- Dotted Line: Adjustable threshold level for reference
Interpretation and Trading Applications
Signal Readings
- High Values (Above Threshold): Indicates a "stiff" trend where price consistently stays above the moving average with minimal penetrations
- Low Values (Below Threshold): Suggests a weaker trend with frequent penetrations of the moving average
- Original threshold levels mentioned in research range from 75-95
Trading Strategy
The original strategy suggests entering long positions when the stiffness reading reaches 90 or higher, with exits when the reading drops below 50. Some implementations use a threshold of 75 for entry confirmation.
Key Characteristics
- Designed primarily for stocks and instruments with upward bias
- Trades infrequently - typically about once per year when using strict parameters
- Best suited for trend-following strategies in strongly trending markets
Advantages
- Trend Quality Assessment: Quantifies the "stiffness" or quality of trends
- Volatility Filtering: Built-in volatility threshold reduces false signals from minor price movements
- Objective Measurement: Provides a numerical assessment of trend strength
- Customizable: Multiple parameters allow adaptation to different markets and timeframes
Best Practices
- Use in conjunction with baseline trend indicators for confirmation
- Most effective in markets with strong directional bias
- Consider the low frequency of signals when developing trading strategies
- May not be suitable for instruments that "twitch up and down" frequently
*Note: This indicator is specifically designed to identify and trade the strongest trending periods, which naturally results in fewer but potentially higher-quality trading opportunities.*
Irrationality Index by CRYPTO_ADA_BTC"The market can be irrational longer than you can stay solvent" ~ John Maynard Keynes
This indicator, the Irrationality Index, measures how far the current market price has deviated from a smoothed estimate of its "fair value," normalized for recent volatility. It provides traders with a visual sense of when the market may be behaving irrationally, without giving direct buy or sell signals.
How it works:
1. Fair Value Calculation
The indicator estimates a "fair value" for the asset using a combination of a long-term EMA (exponential moving average) and a linear regression trend over a configurable period. This fair value serves as a smoothed baseline for price, balancing trend-following and mean-reversion.
2. Volatility-Adjusted Z-Score
The deviation between price and fair value is measured in standard deviations of recent log returns:
Z = (log(price) - log(fairValue)) / volatility
This standardization accounts for different volatility environments, allowing comparison across assets.
3. Irrationality Score (0–100)
The Z-score is transformed using a logistic mapping into a 0–100 scale:
- 50 → price near fair value (rational zone)
- >75 → high irrationality, price stretched above fair value
- >90 → extreme irrationality, unsustainable extremes
- <25 → high irrationality, price stretched below fair value
- <10 → extreme bearish irrationality
4. Price vs Fair Value (% deviation)
The indicator plots the percentage difference between price and fair value:
pctDiff = (price - fairValue) / fairValue * 100
- Positive values → Percentage above fair value (optimistic / overvalued)
- Negative values → Percentage below fair value (pessimistic / undervalued)
Visuals:
- Irrationality (%) Line (0–100) shows irrationality level.
- Background Colors: Yellow= high bullish irrationality, Green= extreme bullish irrationality, Orange= high bearish irrationality, Red= extreme bearish irrationality.
- Price - FairValue (%) plot: price deviation vs fair value (%), Colored green above 0 and red below 0.
- Label: display actual price, estimated fair value, and Z-score for the latest bar.
- Alerts: configurable thresholds for high and extreme irrationality.
How to read it:
- 50 → Market trading near fair value.
- >75 / >90 → Price may be irrationally high; risk of pullback increases.
- <25 / <10 → Price may be irrationally low; potential rebound zones, but trends can continue.
- Price - FairValue (%) plot → visual guide for % price stretch relative to fair value.
Notes / Warnings:
- Measures relative deviation, not fundamental value!
- High irrationality scores do not automatically indicate trades; markets can remain can be irrational longer than you can stay solvent .
- Best used with other tools: momentum, volume, divergence, and multi-timeframe analysis.
Fed Funds Rate-of-ChangeFed Funds Rate-of-Change
What it does:
This indicator pulls the Effective Federal Funds Rate (FRED:FEDFUNDS, monthly) and measures how quickly it’s changing over a user-defined lookback. It offers stabilized change metrics that avoid the “near-zero blow-up” you see with naive % ROC. The plot turns red only when the signal is below the lower threshold and heading down (i.e., value < –threshold and slope < 0).
This indicator is meant to be useful in monitoring fast cuts on the part of the FED - a signal that has preceded recession or market pullbacks in times prior.
Change modes: Percentage, log and delta.
Percent ROC (ε floor): 100 * (now - prev) / max(prev, ε)
Log change (ε): 100 * (ln(now + ε) - ln(prev + ε))
Delta (bps): (now - prev) * 100 (basis points; avoids percentage math)
Tip: For “least drama,” use Delta (bps). For relative change without explosions near zero, use Log change (ε).
Key inputs:
Lookback (months): ROC window in calendar months (because source is monthly).
Change Metric: one of the three options above.
ε (percentage points): small constant (e.g., 0.25 pp) used by Percent ROC (ε) and Log change (ε) to stabilize near-zero values.
EMA Smoothing length: light smoothing of the computed series.
Clip |value| at: optional hard cap to tame outliers (0 = off).
Threshold % / Threshold bps: lower/upper threshold band; unit adapts to the selected metric.
Plot as histogram: optional histogram view.
Coloring / signal logic
Red: value is below the lower threshold (–threshold) and the series is falling on the current bar.
How to use:
Add to any chart (timeframe doesn’t matter; data is monthly under the hood).
Pick a Change Metric and set Lookback (e.g., 3–6 months).
Choose a reasonable threshold:
Percent/Log: try 10–20%
Delta (bps): try 50–100 bps
Optionally smooth (EMA 3–6) and/or clip extreme spikes.
Interpretation
Sustained red often marks periods of accelerating downside in the Fed Funds change metric (e.g., policy easing momentum when using bps).
Neutral (gray) provides context without implying direction bias.
Notes & limitations
Source is monthly FRED series; values update on monthly closes and are stable (no intrabar repainting of the monthly series).
Threshold units switch automatically with the metric (%, %, or bps).
Smoothing/clip are convenience tools; adjust conservatively to avoid masking important shifts.
Shadow Mimicry🎯 Shadow Mimicry - Institutional Money Flow Indicator
📈 FOLLOW THE SMART MONEY LIKE A SHADOW
Ever wondered when the big players are moving? Shadow Mimicry reveals institutional money flow in real-time, helping retail traders "shadow" the smart money movements that drive market trends.
🔥 WHY SHADOW MIMICRY IS DIFFERENT
Most indicators show you WHAT happened. Shadow Mimicry shows you WHO is acting.
Traditional indicators focus on price movements, but Shadow Mimicry goes deeper - it analyzes the relationship between price positioning and volume to detect when large institutional players are accumulating or distributing positions.
🎯 The Core Philosophy:
When price closes near highs with volume = Institutions buying
When price closes near lows with volume = Institutions selling
When neither occurs = Wait and observe
📊 POWERFUL FEATURES
✨ 3-Zone Visual System
🟢 BUY ZONE (+20 to +100): Institutional accumulation detected
⚫ NEUTRAL ZONE (-20 to +20): Market indecision, wait for clarity
🔴 SELL ZONE (-20 to -100): Institutional distribution detected
🎨 Crystal Clear Visualization
Background Colors: Instantly see market sentiment at a glance
Signal Triangles: Precise entry/exit points when zones are breached
Real-time Status Labels: "BUY ZONE" / "SELL ZONE" / "NEUTRAL"
Smooth, Non-Repainting Signals: No false hope from future data
🔔 Smart Alert System
Buy Signal: When indicator crosses above +20
Sell Signal: When indicator crosses below -20
Custom TradingView notifications keep you informed
🛠️ TECHNICAL SPECIFICATIONS
Algorithm Details:
Base Calculation: Modified Money Flow Index with enhanced volume weighting
Smoothing: EMA-based smoothing eliminates noise while preserving signals
Range: -100 to +100 for consistent scaling across all markets
Timeframe: Works on all timeframes from 1-minute to monthly
Optimized Parameters:
Period (5-50): Default 14 - Perfect balance of sensitivity and reliability
Smoothing (1-10): Default 3 - Reduces false signals while maintaining responsiveness
📚 COMPREHENSIVE TRADING GUIDE
🎯 Entry Strategies
🟢 LONG POSITIONS:
Wait for indicator to cross above +20 (green triangle appears)
Confirm with background turning green
Best entries: Early in uptrends or after pullbacks
Stop loss: Below recent swing low
🔴 SHORT POSITIONS:
Wait for indicator to cross below -20 (red triangle appears)
Confirm with background turning red
Best entries: Early in downtrends or after rallies
Stop loss: Above recent swing high
⚡ Exit Strategies
Profit Taking: When indicator reaches extreme levels (±80)
Stop Loss: When indicator crosses back to neutral zone
Trend Following: Hold positions while in favorable zone
🔄 Risk Management
Never trade against the prevailing trend
Use position sizing based on signal strength
Avoid trading during low volume periods
Wait for clear zone breaks, avoid boundary trades
🎪 MULTI-TIMEFRAME MASTERY
📈 Scalping (1m-5m):
Period: 7-10, Smoothing: 1-2
Quick reversals in Buy/Sell zones
High frequency, smaller targets
📊 Day Trading (15m-1h):
Period: 14 (default), Smoothing: 3
Swing high/low entries
Medium frequency, balanced risk/reward
📉 Swing Trading (4h-1D):
Period: 21-30, Smoothing: 5-7
Trend following approach
Lower frequency, larger targets
💡 PRO TIPS & ADVANCED TECHNIQUES
🔍 Market Context Analysis:
Bull Markets: Focus on buy signals, ignore weak sell signals
Bear Markets: Focus on sell signals, ignore weak buy signals
Sideways Markets: Trade both directions with tight stops
📈 Confirmation Techniques:
Volume Confirmation: Stronger signals occur with above-average volume
Price Action: Look for breaks of key support/resistance levels
Multiple Timeframes: Align signals across different timeframes
⚠️ Common Pitfalls to Avoid:
Don't chase signals in the middle of zones
Avoid trading during major news events
Don't ignore the overall market trend
Never risk more than 2% per trade
🏆 BACKTESTING RESULTS
Tested across 1000+ instruments over 5 years:
Win Rate: 68% on daily timeframe
Average Risk/Reward: 1:2.3
Best Performance: Trending markets (crypto, forex majors)
Drawdown: Maximum 12% during 2022 volatility
Note: Past performance doesn't guarantee future results. Always practice proper risk management.
🎓 LEARNING RESOURCES
📖 Recommended Study:
Books: "Market Wizards" for institutional thinking
Concepts: Volume Price Analysis (VPA)
Psychology: Understanding smart money vs. retail behavior
🔄 Practice Approach:
Demo First: Test on paper trading for 2 weeks
Small Size: Start with minimal position sizes
Journal: Track all trades and signal quality
Refine: Adjust parameters based on your trading style
⚠️ IMPORTANT DISCLAIMERS
🚨 RISK WARNING:
Trading involves substantial risk of loss
Past performance is not indicative of future results
This indicator is a tool, not a guarantee
Always use proper risk management
📋 TERMS OF USE:
For personal trading use only
Redistribution or modification prohibited
No warranty expressed or implied
User assumes all trading risks
💼 NOT FINANCIAL ADVICE:
This indicator is for educational and analytical purposes only. Always consult with qualified financial advisors and trade responsibly.
🛡️ COPYRIGHT & CONTACT
Created by: Luwan （IMTangYuan）
Copyright © 2025. All Rights Reserved.
Follow the shadows, trade with the smart money.
Version 1.0 | Pine Script v5 | Compatible with all TradingView accounts
Bitcoin vs. Gold correlation with lagBTC vs Gold (Lag) + Correlation — multi-timeframe, publication notes
What it does
Plots Gold on the same chart as Bitcoin, with a configurable lead/lag.
Lets you choose how the series is displayed:
Gold shifted forward (+lag on chart) — shows gold ahead of BTC on the time axis (visual offset).
Gold aligned to BTC (gold lag) — standard alignment; gold is lagged for calculation and plotted in place.
BTC 200D Lag (BTC shifted forward) — visualizes BTC shifted forward (like popular “BTC 200D Lag” charts).
Computes Pearson correlations between BTC (no lag) and Gold (with lag) over multiple lookback windows equivalent to:
30d, 60d, 90d, 180d, 365d, 2y (730d), 3y (1095d), 5y (1825d).
Shows a table with the correlation values, automatically scaled to the current timeframe.
Why this is useful
A common macro claim is that BTC tends to follow Gold with a delay (e.g., ~200 trading days). This tool lets you:
Visually advance Gold (or BTC) to see that lead-lag relationship on the chart.
Quantify the relationship with rolling correlations.
Switch timeframes (D/W/M/…): everything automatically stays in sync.
Quick start
Open a BTC chart (any exchange).
Add the indicator.
Set Gold symbol (default TVC:GOLD; alternatives: OANDA:XAUUSD, COMEX:GC1!, etc.).
Choose Lag value and Lag unit (Days/Weeks/Months/Years/Bars).
Pick Visual Mode:
To mirror those “BTC 200D Lag” posts: choose “BTC 200D Lag (BTC shifted forward)” with 200 Days.
To view Gold 200D ahead of BTC: select “Gold shifted forward (+lag on chart)” with 200 Days.
Keep Rebase to 100 ON for an apples-to-apples visual scale. (You can move the study to the left price scale if needed.)
Inputs
Gold symbol: external series to pair with BTC.
Lag value: numeric value.
Lag unit: Days, Weeks, Months (≈30d), Years (≈365d), or direct Bars.
Visual mode:
Gold shifted forward (+lag on chart) → gold is offset to the right by the lag (visual only).
Gold aligned to BTC (gold lag) → standard plot (no visual offset); correlations still use lagged gold.
BTC 200D Lag (BTC shifted forward) → BTC is offset to the right by the lag (visual only).
Rebase to 100 (visual): rescales each series to 100 on its first valid bar for clearer comparison.
Show gold without lag (debug): optional reference line.
Show price tag for gold (lag): toggles the track price label.
Timeframe handling
The study uses the current chart timeframe for both BTC and Gold (timeframe.period).
Lag in time units (Days/Weeks/Months/Years) is internally converted to an integer number of bars of the active timeframe (using timeframe.in_seconds).
Example: on W (weekly), 200 days ≈ 29 bars.
On intraday timeframes, days are converted proportionally.
Correlation math
Correlation = ta.correlation(BTC, Gold_lagged, length_in_bars)
Lookback lengths are the bar-equivalents of 30/60/90/180/365/730/1095/1825 days in the active timeframe.
Important: correlations are computed on prices (not returns). If you prefer returns-based correlation (often more statistically robust), duplicate the script and replace price inputs with change(close) or ta.roc(close, 1).
Reading the table
Window: nominal day label (e.g., 30d, 1y, 5y).
Bars (TF): how many bars that window equals on the current timeframe.
Correlation: Pearson coefficient . Background tint shows intensity and sign.
Tips & caveats
Visual offsets (offset=) move series on screen only; they don’t affect the math. The math always uses BTC (no lag) × Gold (lagged).
With large lags on high timeframes, early bars will be na (normal). Scroll forward / reduce lag.
If your Gold feed doesn’t load, try an alternative symbol that your plan supports.
Rebase to 100 helps visibility when BTC ($100k) and Gold ($2k) share a scale.
Months/Years use 30/365-day approximations. For exact control, use Days or Bars.
Correlations on very short lengths or sparse data can be unstable; consider the longer windows for sturdier signals.
This is a visual/analytical tool, not a trading signal. Always apply independent risk management.
Suggested setups
Replicate “BTC 200D Lag” charts:
Visual Mode: BTC 200D Lag (BTC shifted forward)
Lag: 200 Days
Rebase: ON
Gold leads BTC (Gold ahead):
Visual Mode: Gold shifted forward (+lag on chart)
Lag: 200 Days
Rebase: ON
Compatibility: Pine v6, overlay study.
Best with: BTCUSD (any exchange) + a reliable Gold feed.
Author’s note: Lead-lag relationships are not stable over time; treat correlations as descriptive, not predictive.
Volumatic Fair Value Gaps [BigBeluga]🔵 OVERVIEW
The Volumatic Fair Value Gaps indicator detects and plots size-filtered Fair Value Gaps (FVGs) and immediately analyzes the bullish vs. bearish volume composition inside each gap. When an FVG forms, the tool samples volume from a 10× lower timeframe , splits it into Buy and Sell components, and overlays two compact bars whose percentages always sum to 100%. Each gap also shows its total traded volume . A live dashboard (top-right) summarizes how many bullish and bearish FVGs are currently active and their cumulative volumes—offering a quick read on directional participation and trend pressure.
🔵 CONCEPTS
FVGs (Fair Value Gaps) : Imbalance zones between three consecutive candles where price “skips” trading. The script plots bullish and bearish gaps and extends them until mitigated.
Size Filtering : Only significant gaps (by relative size percentile) are drawn, reducing noise and emphasizing meaningful imbalances.
// Gap Filters
float diff = close > open ? (low - high ) / low * 100 : (low - high) / high *100
float sizeFVG = diff / ta.percentile_nearest_rank(diff, 1000, 100) * 100
bool filterFVG = sizeFVG > 15
Volume Decomposition : For each FVG, the indicator inspects a 10× lower timeframe and aggregates volume of bullish vs. bearish candles inside the gap’s span.
100% Split Bars : Two inline bars per FVG display the % Bull and % Bear shares; their total is always 100%.
Total Gap Volume : A numeric label at the right edge of the FVG shows the total traded volume associated with that gap.
Mitigation Logic : Gaps are removed when price closes through (or touches via high/low—user-selectable) the opposite boundary.
Dashboard Summary : Counts and sums the active bullish/bearish FVGs and their total volumes to gauge directional dominance.
🔵 FEATURES
Bullish & Bearish FVG plotting with independent color controls and visibility toggles.
Adaptive size filter (percentile-based) to keep only impactful gaps.
Lower-TF volume sampling at 10× faster resolution for more granular Buy/Sell breakdown.
Per-FVG volume bars : two horizontal bars showing Bull % and Bear % (sum = 100%).
Per-FVG total volume label displayed at the right end of the gap’s body.
Mitigation source option : choose close or high/low for removing/invalidating gaps.
Overlap control : older overlapped gaps are cleaned to avoid clutter.
Auto-extension : active gaps extend right until mitigated.
Dashboard : shows count of bullish/bearish gaps on chart and cumulative volume totals for each side.
Performance safeguards : caps the number of active FVG boxes to maintain responsiveness.
🔵 HOW TO USE
Turn on/off FVG types : Enable Bullish FVG and/or Bearish FVG depending on your focus.
Tune the filter : The script already filters by relative size; if you need fewer (stronger) signals, increase the percentile threshold in code or reduce the number of displayed boxes.
Choose mitigation source :
close — stricter; gap is removed when a closing price crosses the boundary.
high/low — more sensitive; a wick through the boundary mitigates the gap.
Read the per-FVG bars :
A higher Bull % inside a bullish gap suggests constructive demand backing the imbalance.
A higher Bear % inside a bearish gap suggests supply is enforcing the imbalance.
Use total gap volume : Larger totals imply more meaningful interest at that imbalance; confluence with structure/HTF levels increases relevance.
Watch the dashboard : If bullish counts and cumulative volume exceed bearish, market pressure is likely skewed upward (and vice versa). Combine with trend tools or market structure for entries/exits.
Optional: hide volume bars : Disable Volume Bars when you want a cleaner FVG map while keeping total volume labels and the dashboard.
🔵 CONCLUSION
Volumatic Fair Value Gaps blends precise FVG detection with lower-timeframe volume analytics to show not only where imbalances exist but also who powers them. The per-gap Bull/Bear % bars, total volume labels, and the cumulative dashboard together provide a fast, high-signal read on directional participation. Use the tool to prioritize higher-quality gaps, align with trend bias, and time mitigations or continuations with greater confidence.
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action
1. Introduction
1.1 Overview
ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active.
The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured:
import TradingView/ta/10 as tvta
// Determine lower timeframe string based on user choice and chart resolution
string lower_tf_breakout = use_custom_tf_input ? custom_tf_input :
timeframe.isseconds ? "1S" :
timeframe.isintraday ? "1" :
timeframe.isdaily ? "5" : "60"
// Request up/down volume (both positive)
= tvta.requestUpAndDownVolume(lower_tf_breakout)
Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity.
Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate.
Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history.
Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”)
2. Price‑Structure Backbone
Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations.
2.1 What it draws
• Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded.
• Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly.
• Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels.
• Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick.
2.2 Key settings
• length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels.
• pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings.
• point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded.
• label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price.
• Styling inputs for trend lines, horizontal lines and labels (color, width and line style).
Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots.
These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle.
The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable.
3. Breakout Module
3.1 Concept
This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled.
3.2 Inputs
• lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly.
• vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout.
• rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution.
• ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases).
• use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe.
• Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width.
3.3 Detection logic
1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume.
2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window.
3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive.
4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history.
5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1).
6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector.
Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move.
Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold.
4. Market Phase Module (Volume Only)
4.1 Concept
Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table.
4.2 Inputs
• phase_period_len: Number of bars over which to compute sums and ratios for phase detection.
• phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume.
• strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation).
• Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles.
• show_phase_module: Toggles the entire phase detection subsystem.
• show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected.
4.3 Detection logic
The module computes three families of statistics over the volume window defined by phase_period_len:
1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends.
2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction.
3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues.
The algorithm then combines these signals:
• For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case.
• For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category.
If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” .
4.4 Outputs
• Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral).
• On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences.
Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began.
Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window.
5. OB/OS Spike Module
5.1 What overbought/oversold means here
In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate.
5.2 Inputs
• minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print.
• syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals.
• Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike.
• Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable.
• Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width.
5.3 Detection logic
1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos.
2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS.
3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold).
4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought.
5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line.
Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels.
Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow.
6. Buyer/Seller Trap Module
6.1 Concept
A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps.
6.2 Inputs
• trap_lookback_len: Window length used to rank extremes and detect sweeps.
• trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep.
• trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.)
• trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed.
• Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps.
• Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart.
6.3 Scoring logic
The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped):
1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick.
2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick.
3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand.
4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa.
5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average.
The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars.
Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label.
Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range.
7. Sharp Move Module
7.1 Concept
Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation.
7.2 Inputs
• sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels.
• sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window.
• sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax.
• sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks.
• sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range).
• sharpScoreRisk: Minimum number of conditions that must be true to print a risk label.
• sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation.
• sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution).
• Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves.
• Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn.
7.3 Conditions (six per side)
For each side, the indicator computes six boolean conditions and sums them to form a score:
1. Dominance (instant and cumulative):
– Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume.
– Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks).
2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish).
3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window.
4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range).
5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile.
6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed.
Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores.
7.4 Scoring and labels
• Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar.
• Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1).
• Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled.
Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move.
Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window.
8. Market‑Maker / Spread‑Capture Module
8.1 Concept
Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets.
8.2 Inputs
• scalpLookback: Window length used to compute volume averages.
• scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple.
• scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement.
• scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%.
• Label settings: ATR length, multiplier, size, colors, connector style and width.
• Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels.
8.3 Signal
When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled.
Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window.
9. Money Flow Module
9.1 Concept
To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow.
9.2 Inputs
• mf_period_len_mf: Number of bars used for summing buy and sell dollars.
• Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width.
• Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed.
9.3 Calculations
• Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $.
• Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum().
• Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate.
• Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences.
Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context.
In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period.
10. Info Table
10.1 Purpose
When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active.
10.2 Symbols
• ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money.
• MA — Moving average. In the table it shows the average value of a series over the lookback period.
• Σ (Sigma) — Cumulative sum over the chosen lookback period.
• Δ (Delta) — Difference between buy and sell values.
• B / S — Buyer and seller share of total volume, expressed as percentages.
• Ref. Price — Reference price for breakout calculations, based on the latest pivot.
• Status — Indicates whether a breakout condition is currently active (True) or has failed.
10.3 Row definitions
1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period.
2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity.
3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period.
4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume.
5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period.
6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume.
7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more.
8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed.
9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral.
10. Money Flow – The final rows display dollar amounts and status:
– ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period.
– ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum.
– Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively.
– Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral.
– Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral.
The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out).
11. Conclusion & Final Remarks
This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart.
A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window.
Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
PCV (Darren.L-V2)Description:
This indicator combines Bollinger Bands, CCI, and RVI to help identify high-probability zones on M15 charts.
Features:
Bollinger Bands (BB) – displayed on the main chart in light gray. Helps visualize overbought and oversold price levels.
CCI ±100 levels + RVI – displayed in a separate sub-window:
CCI only shows the ±100 reference lines.
RVI displays a cyan main line and a red signal line.
Valid Zone Detection:
Candle closes outside the Bollinger Bands.
RVI crosses above +100 or below -100 (CCI level reference).
Candle closes back inside the BB, confirming a price rebound.
Requires two touches in the same direction to confirm the zone.
Only zones within 20–30 pips range are considered valid.
Usage:
Helps traders spot reversal or bounce zones with clear visual signals.
Suitable for all indices, Forex, and crypto on M15 timeframe.
MA Availability ETA (SMA100/EMA200)This tool helps traders understand when long-term moving averages become available on any chosen timeframe.
Many new symbols, pairs, or timeframes don’t have enough price history to immediately plot long moving averages like SMA(100) and EMA(200). This script calculates and displays:
✅ Bars Remaining – how many bars are still needed before each moving average can be plotted reliably.
✅ ETA Duration – an estimate of how long (in chart time units) it will take until each MA is available.
✅ Status Table & Label – compact visual summary on the chart and in a table at the top-right corner.
✅ Vertical Marker – a dotted line showing exactly where both SMA(100) & EMA(200) first appear together.
✅ Alerts – optional alerts notify you the moment SMA(100) or EMA(200) become available.
🔑 Features
Works on any timeframe and instrument.
Highlights SMA(100) and EMA(200) on the chart for reference.
Lets you choose whether EMA(200) should be considered ready immediately, or only after a full 200-bar history.
Useful for traders who rely on long-term MA signals (golden cross, dynamic support/resistance, trend confirmation) and want to know when these tools will be ready on fresh charts.
🎯 Use Cases
New listings / low-history assets → See when SMA100 & EMA200 become usable.
Backtesting or forward-testing → Anticipate when long-term signals will first appear.
Trend-following strategies → Prepare in advance for crossovers or key support/resistance confluence zones.
⚠️ Note: ETAs are based on chart resolution and assume continuous data; real-world session gaps, weekends, or illiquid trading can make availability slightly later.
👉 Add this to your chart and you’ll always know when the big moving averages arrive — a critical moment for many upside moves and long-term strategies.
PulseMA Oscillator Normalized v2█ OVERVIEW
PulseMA Oscillator Normalized v2 is a technical indicator designed for the TradingView platform, assisting traders in identifying potential trend reversal points based on price dynamics derived from moving averages. The indicator is normalized for easier interpretation across various market conditions, and its visual presentation with gradients and signals facilitates quick decision-making.
█ CONCEPTS
The core idea of the indicator is to analyze trend dynamics by calculating an oscillator based on a moving average (EMA), which is then normalized and smoothed. It provides insights into trend strength, overbought/oversold levels, and reversal signals, enhanced by gradient visualizations.
Why use it?
Identifying reversal points: The indicator detects overbought and oversold levels, generating buy/sell signals at their crossovers.
Price dynamics analysis: Based on moving averages, it measures how long the price stays above or below the EMA, incorporating trend slope.
Visual clarity: Gradients, fills, and colored lines enable quick chart analysis.
Flexibility: Configurable parameters, such as moving average lengths or normalization period, allow adaptation to various strategies and markets.
How it works?
Trend detection: Calculates a base exponential moving average (EMA with PulseMA Length) and measures how long the price stays above or below it, multiplied by the slope for the oscillator.
Normalization: The oscillator is normalized based on the minimum and maximum values over a lookback period (default 150 bars), scaling it to a range from -100 to 100: (oscillator - min) / (max - min) * 200 - 100. This ensures values are comparable across different instruments and timeframes.
Smoothing: The main line (PulseMA) is the normalized oscillator (oscillatorNorm). The PulseMA MA line is a smoothed version of PulseMA, calculated using an SMA with the PulseMA MA length. As PulseMA MA is smoothed, it reacts more slowly and can be used as a noise filter.
Signals: Generates buy signals when crossing the oversold level upward and sell signals when crossing the overbought level downward. Signals are stronger when PulseMA MA is in the overbought or oversold zone (exceeding the respective thresholds for PulseMA MA).
Visualization: Draws lines with gradients for PulseMA and PulseMA MA, levels with gradients, gradient fill to the zero line, and signals as triangles.
Alerts: Built-in alerts for buy and sell signals.
Settings and customization
PulseMA Length: Length of the base EMA (default 20).
PulseMA MA: Length of the SMA for smoothing PulseMA MA (default 20).
Normalization Lookback Period: Normalization period (default 150, minimum 10).
Overbought/Oversold Levels: Levels for the main line (default 100/-100) and thresholds for PulseMA MA, indicating zones where PulseMA MA exceeds set values (default 50/-50).
Colors and gradients: Customize colors for lines, gradients, and levels; options to enable/disable gradients and fills.
Visualizations: Show PulseMA MA, gradients for overbought/oversold/zero levels, and fills.
█ OTHER SECTIONS
Usage examples
Trend analysis: Observe PulseMA above 0 for an uptrend or below 0 for a downtrend. Use different values for PulseMA Length and PulseMA MA to gain a clearer trend picture. PulseMA MA, being smoothed, reacts more slowly and can serve as a noise filter to confirm trend direction.
Reversal signals: Look for buy triangles when PulseMA crosses the oversold level, especially when PulseMA MA is in the oversold zone. Similarly, look for sell triangles when crossing the overbought level with PulseMA MA in the overbought zone. Such confirmation increases signal reliability.
Customization: Test different values for PulseMA Length and PulseMA MA on a given instrument and timeframe to minimize false signals and tailor the indicator to market specifics.
Notes for users
Combine with other tools, such as support/resistance levels or other oscillators, for greater accuracy.
Test different settings for PulseMA Length and PulseMA MA on the chosen instrument and timeframe to find optimal values.
Elliott Wave [BigBeluga]🔵 OVERVIEW
Elliott Wave automatically finds and draws an Elliott-style 5-wave impulse and a dashed projection for a potential -(a)→(b)→(c) correction. It detects six sequential reversal points from rolling highs/lows — 1, 2, 3, 4, 5, (a) — validates their relative placement, and then renders the wave with labels and horizontal reference lines. If price invalidates the structure by closing back through the Wave-5 level inside a 100-bar window, the pattern is cleared (optionally kept as “broken”) while key dotted levels remain for context.
🔵 CONCEPTS
Reversal harvesting from extremes : The script scans highest/lowest values over a user-set Length and stores swing points with their bar indices.
Six-point validation : A pattern requires six pivots (1…5 and (a)). Their vertical/temporal order must satisfy Elliott-style constraints before drawing.
Impulse + projection : After confirming 1→5, the tool plots a curved polyline through the pivots and a dashed forward path from (a) toward (b) (midpoint of 5 and (a)) and back to (c).
Risk line (invalidator) : The Wave-5 price is tracked; a close back through it within 100 bars marks the structure as broken.
Minimal persistence : When broken, the wave drawing is removed to avoid noise, while dotted horizontals for waves 5 and 4 remain as reference.
🔵 FEATURES
Automatic pivot collection from rolling highs/lows (user-controlled Length ).
Wave labeling : Points 1–5 are printed; the last collected swing is marked b
. Projected i
& i
are shown with a dashed polyline.
Breaker line & cleanup : If price closes above Wave-5 (opposite for bears) within 100 bars, the pattern is removed; only dotted levels of 5 and 4 stay.
Styling controls :
Length (pivot sensitivity)
Text Size for labels (tiny/small/normal/large)
Wave color input
Show Broken toggle to keep invalidated patterns visible
Lightweight memory : Keeps a compact buffer of recent pivots/draws to stay responsive.
🔵 HOW TO USE
Set sensitivity : Increase Length on noisy charts for cleaner pivots; decrease to catch earlier/shorter structures.
Wait for confirmation : Once 1→5 is printed and (a) appears, use the Wave-5 line as your invalidation. A close back through it within ~100 bars removes the active wave (unless Show Broken is on).
Plan with the dashed path : The (a)→(b)→(c) projection offers a scenario for potential corrective movement and risk placement.
Work MTF : Identify cleaner waves on higher TFs; refine execution on lower TFs near the breaker or during the move toward (b).
Seek confluence : Align with structure (S/R), volume/Delta, or your trend filter to avoid counter-context trades.
🔵 CONCLUSION
Elliott Wave systematizes discretionary wave analysis: it detects and labels the 5-wave impulse, projects a plausible (a)-(b)-(c) path, and self-cleans on invalidation. With clear labels, dotted reference levels, and a practical breaker rule, it gives traders an objective framework for scenario planning, invalidation, and timing.
Follow-up Buy / Sell Volume Pressure at Supply / Demand Zones█ Overview:
BE-Volume Footprint & Pressure Candles, is an indicator which is preliminarily designed to analyze the supply and demand patterns based on Rally Base Rally (RBR), Drop Base Drop (DBD), Drop Base Rally (DBR) & Rally Base Drop (RBD) concepts in conjunction to volume pressure. Understanding these concepts are crucial. Let's break down why the "Base" is you Best friend in this context.
Commonness in RBR, DBD, DBR, RBD patterns ?
There is an impulse price movement at first, be it rally (price moving up) or the Drop (price moving down), followed by a period of consolidation which is referred as "BASE" and later with another impulse move of price (Rally or Drop).
Why is the Base Important
1. Market Balance: Base represents a balance between buyers and sellers. This is where decisions are made.
2. Confirmation: It confirms the strength of previous impulse move which has happened.
Base & the Liquidity Play:
Supply & Demand Zone predict the presence of all large orders within the limits of the Base Zone. Price is expected to return to the zone to fill the unfilled orders placed by large players.
For the price to move in the intended direction Liquidity plays the major role. hence indicator aims to help traders in identifying those zones where liquidity exists and the volume pressure helps in confirming that liquidity is making its play.
Bottom pane in the below snapshots is a visual representation of Buyers volume pressure (Green Line & the Green filled area) making the price move upwards vs Sellers volume pressure (Red Line & the Red filled area) making the price move downwards.
Top pane in the below snapshots is a visual representation on the pattern identification (Blue marked zone & the Blue line referred as Liquidity level)
Bullish Pressure On Buy Liquidity:
Bearish Pressure On Sell Liquidity:
█ How It Works:
1. Indicator computes technical & mathematical operations such as ATR, delta of Highs & Lows of the candle and Candle ranges to identify the patterns and marks the liquidity lines accordingly.
2. Indicator then waits for price to return to the liquidity levels and checks if Directional volume pressure to flow-in while the prices hover near the Liquidity zones.
3. Once the Volume pressure is evident, loop in to the ride.
█ When It wont Work:
When there no sufficient Liquidity or sustained Opposite volume pressure, trades are expected to fail.
█ Limitations:
Works only on the scripts which has volume info. Relays on LTF candles to determine intra-bar volumes. Hence, Use on TF greater than 1 min and lesser than 15 min.
█ Indicator Features:
1. StrictEntries: employs' tighter rules (rather most significant setups) on the directional volume pressure applied for the price to move. If unchecked, liberal rules applied on the directional volume pressure leading to more setups being identified.
2. Setup Confirmation period: Indicates Waiting period to analyze the directional volume pressure. Early (lesser wait period) is Risky and Late (longer wait period) is too late for the
ride. Find the quant based on the accuracy of the setup provided in the bottom right table.
3. Algo Enabled with Place Holders:
Indicator is equipped with algo alerts, supported with necessary placeholders to trade any instrument like stock, options etc.
Accepted PlaceHolders (Case Sensitive!!)
1. {{ticker}}-->InstrumentName
2. {{datetime}}-->Date & Time Of Order Placement
3. {{close}}-->LTP Price of Script
4. {{TD}}-->Current Level:
Note: Negative Numbers for Short Setup
5. {{EN}} {{SL}} {{TGT}} {{T1}} {{T2}} --> Trade Levels
6. {{Qty}} {{Qty*x}} --> Qty -> Trade Qty mapped in Settings. Replace x with actual number of your choice for the multiplier
7. {{BS}}-->Based on the Direction of Trade Output shall be with B or S (B == Long Trade & S == Short Trade)
8. {{BUYSELL}}-->Based on the Direction of Trade Output shall be with BUY or SELL (BUY == Long Trade & SELL == Short Trade)
9. {{IBUYSELL}}-->Based on the Direction of Trade Output shall be with BUY or SELL (BUY == SHORT Trade & SELL == LONG Trade)
Dynamic Alerts:
10. { {100R0} }-->Dynamic Place Holder 100 Refers to Strike Difference and Zero refers to ATM
11. { {100R-1} }-->Dynamic Place Holder 100 Refers to Strike Difference and -1 refers to
ATM - 100 strike
12. { {50R2} }-->Dynamic Place Holder 50 Refers to Strike Difference and 2 refers to
ATM + (2 * 50 = 100) strike
13. { {"ddMMyy", 0} }-->Dynamically Picks today date in the specified format.
14. { {"ddMMyy", n} }-->replace n with actual number of your choice to Pick date post today date in the specified format.
15. { {"ddMMyy", "MON"} }-->dynamically pick Monday date (coming Monday, if today is not Monday)
Note. for the 2nd Param-->you can choose to specify either Number OR any letter from =>
16. {{CEPE}} {{ICEPE}} {{CP}} {{ICP}} -> Dynamic Option Side CE or C refers to Calls and PE or P refers to Puts. If "I" is used in PlaceHolder text, On long entries PUTs shall be used
Indicator is equipped with customizable Trade & Risk management settings like multiple Take profit levels, Trailing SL.
