Lord Mathew ATSThe Smart Money Structure & Pattern Analyzer is a complete, all-in-one visual trading system that brings together every essential element of Smart Money Concepts (SMC), ICT methodology, and candlestick psychology into one powerful indicator.
It is designed to help traders instantly understand the market’s structure, liquidity flow, and potential turning points without switching tools or manually marking charts. Whether you trade forex, indices, crypto, or commodities, this indicator automatically identifies where institutional activity, imbalances, and price inefficiencies occur in real time.
With its advanced algorithm, it plots market structure shifts, equal highs and lows, liquidity zones, order blocks, fair value gaps (FVGs), and previous week and day levels (PWO, PWH, PWL, PWC, PDO, PDH, PDL, PDO). It also integrates a deep candlestick recognition engine that detects over ten classic and advanced candle formations including engulfing patterns, dojis, hammers, shooting stars, morning/evening stars, and spinning tops to provide precise confirmation at critical points of interest.
This indicator isn’t just a tool it’s a complete market map that helps traders visualize how institutional order flow and candlestick sentiment interact.
Core Features
📊 Market Structure Detection:
Automatically marks swing highs/lows, Break of Structure (BOS), and Change of Character (CHOCH) in real time.
💧 Liquidity Mapping:
Highlights equal highs/lows and liquidity grabs, showing where price is likely to target before a reversal or continuation.
🧱 Order Block Visualization:
Displays the last bullish or bearish candle before an impulsive displacement, acting as a potential institutional entry zone.
⚡ Fair Value Gap (FVG) Scanner:
Detects and highlights imbalances where price moved too fast, helping you identify high-probability retracement areas.
🕯️ Candlestick Pattern Recognition:
Recognizes key reversal and continuation patterns (engulfing, hammer, shooting star, doji, morning/evening star, etc.) in real time.
📅 Institutional Reference Points:
Plots previous week & day open (PWO, PDO), previous week & day high (PWH, PWH), previous week & day low (PWL, PDL), previous week & day close (PWC, PDC) and optionally previous day levels to help frame bias.
🎨 Customizable Design:
Toggle any feature, change colors, and set alerts when multiple Smart Money signals align for cleaner, faster decision-making.
How It Works
Add the indicator to your chart on any timeframe or market.
The algorithm automatically detects structure, liquidity, and imbalance zones.
Candlestick patterns are highlighted when they form near high-probability areas (like OBs or FVGs).
When confluence occurs such as a liquidity grab, FVG fill, and bullish engulfing candle—the indicator provides a visual signal zone for your confirmation-based entries.
You can refine your trades using higher-timeframe bias (HTF order flow) and lower-timeframe execution (LTF confirmation).
Best For
Traders using ICT, Smart Money Concepts, or price-action systems.
Intraday and swing traders looking for clear, data-driven chart structure.
Traders who want to simplify confluence analysis and focus on precision execution.
Why It Stands Out
Unlike standard candlestick or pattern scanners, this indicator merges institutional market logic with technical candle behavior, allowing traders to see where smart money might be entering or exiting positions.
It’s not about random signals it’s about context, structure, and confirmation.
Every feature in this indicator is built around the principle of liquidity engineering:
price creates liquidity, grabs it, and moves toward imbalance or order flow efficiency.
By merging that institutional logic with candlestick patterns, this tool gives traders an edge in recognizing not only where to trade but why price is reacting in that exact area.
Disclaimer
This indicator is intended for educational and analytical use. It does not provide financial advice or guaranteed trading results. Always backtest and manage your risk responsibly.
在脚本中搜索"pattern"
Market SessionsMarket Sessions (Asian, London, NY, Pacific)
Summary
This indicator plots the main global market sessions (Asian, European, American, Pacific) as boxes on your chart, complete with dynamic high/low tracking.
It's an essential tool for intraday traders to track session-based volatility patterns and visualize key support/resistance levels (like the Asian Range) that often define price action for the rest of the day.
Who it’s for
Intraday traders, scalpers, and day traders who need to visualize market hours and session-based ranges. If your strategy depends on the London open, the New York close, or the Asian range, this script will map it out for you.
What it shows
Customizable Session Boxes: Four fully configurable boxes for the Asian, European (London), American (New York), and Pacific (Sydney) sessions.
Session High & Low: The script tracks and boxes the highest high and lowest low of each session, dynamically updating as the session progresses.
Session Labels: Clear labels (e.g., "AS", "EU") mark each session, anchored to the start time.
Key Features
Powerful Timezone Control: This is the core feature.
Use Exchange Timezone (Default): Simply enter session times (e.g., 8:00 for London) relative to the exchange's timezone (e.g., "NASDAQ" or "BINANCE").
Use UTC Offset: Uncheck the box and enter a UTC offset (e.g., +3 or -5). Now, all session times you enter are relative to that specific UTC offset. This gives you full control regardless of the chart you're on.
Fully Customizable: Toggle any session on/off.
Style Control: Change the fill color, border color, transparency, border width, and line style (Solid, Dashed, Dotted) for each session individually.
Smart Labels: Labels stay anchored to the start of the session (no "sliding") and float just above the session high.
Why this helps
Track Volatility & Market Behavior: Visually identify the "personality" of each session. Some sessions might consistently produce powerful pumps or dumps, while others are prone to sideways "chop" or accumulation. This indicator helps you see these repeating patterns.
Find Key Support/Resistance Levels: The High and Low of a session (e.g., the Asian Range) often become critical support and resistance levels for the next session (e.g., London). This script makes it easy to spot these "session-to-session" S/R flips and reactions.
Aid Statistical Analysis: The script provides the core visual data for your statistical research. You can easily track how often the London session breaks the Asian high, or which session is most likely to reverse the trend, helping you build a robust trading plan.
Context is King: Instantly see which market is active, which are overlapping (like the high-volume London-NY overlap), and which have closed.
Quick setup
Go to Timezone Settings.
Decide how you want to enter times:
Easy (Default): Leave Use Exchange Timezone checked. Enter session times based on the chart's native exchange (e.g., for BTC/USDT on Binance, use UTC+0 times).
Manual (Pro): Uncheck Use Exchange Timezone. Enter your UTC Offset (e.g., +2 for Berlin). Now, enter all session times as they appear on the clock in Berlin.
Go to each session tab (Asian, European...) to enable/disable it and set the correct start/end hours and minutes.
Style the colors to match your chart theme.
Disclaimer
For educational/informational purposes only; not financial advice. Trading involves risk—manage it responsibly.
[AS] MACD-v & Hist [Alex Spiroglou | S.M.A.R.T. TRADER SYSTEMS] MACD-v & MACD-v Histogram
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Volatility Normalised Momentum 📈
Twice Awarded Indicator 🏆
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✅ 1. INTRODUCTION TO THE MACD-v ✅
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I created the MACD-v in 2015,
as a way to deal with the limitations
of well known indicators like the Stochastic, RSI, MACD.
I decided to publicly share a very small part of my research
in the form of a research paper I wrote in 2022,
titled "MACD-v: Volatility Normalised Momentum".
That paper was awarded twice:
1. The "Charles H. Dow" Award (2022),
for outstanding research in Technical Analysis,
by the Chartered Market Technicians Association (CMTA)
2. The "Founders" Award (2022),
for advances in Active Investment Management,
by the National Association of Active Investment Managers (NAAIM)
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❌ 2. WHY CREATE THE MACD-v ?
THE LIMITATIONS OF CONVENTIONAL MOMENTUM INDICATORS
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Technical Analysis indicators focused on momentum,
come in two general categories,
each with its own set of limitations:
(i) Range Bound Oscillators (RSI, Stochastics, etc)
These usually have a scaling of 0-100,
and thus have the advantage of having normalised readings,
that are comparable across time and securities.
However they have the following limitations (among others):
1. Skewing effect of steep trends
2. Indicator values do not adjust with and reflect true momentum
(indicator values are capped to 100)
(ii) Unbound Oscillators (MACD, RoC, etc)
These are boundless indicators,
and can expand with the market,
without being limited by a 0-100 scaling,
and thus have the advantage of really measuring momentum.
They have the main following limitations (among others):
1. Subjectivity of overbought / oversold levels
2. Not comparable across time
3. Not comparable across securities
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💡 3. THE SOLUTION TO SOLVE THESE LIMITATIONS
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In order to deal with these limitations,
I decided to create an indicator,
that would be the "Best of two worlds".
A unique & hybrid indicator,
that would have objective normalised readings
(similar to Range Bound Oscillators - RSI)
but would also be able to have no upper/lower boundaries
(similar to Unbound Oscillators - MACD).
This would be achieved by "normalising" a boundless oscillator (MACD)
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⛔ 4. DEEP DIVE INTO THE 5 LIMITATIONS OF THE MACD
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A Bloomberg study found that the MACD
is the most popular indicator after the RSI,
but the MACD has 5 BIG limitations.
Limitation 1: MACD values are not comparable across Time
The raw MACD values shift
as the underlying security's absolute value changes across time,
making historical comparisons obsolete
e.g S&P 500 maximum MACD was 1.56 in 1957-1971,
but reached 86.31 in 2019-2021 - not indicating 55x stronger momentum,
but simply different price levels.
Limitation 2: MACD values are not comparable across Assets
Traditional MACD cannot compare momentum between different assets.
S&P 500 MACD of 65 versus EUR/USD MACD of -0.5
reflects absolute price differences, not momentum differences
Limitation 3: MACD values cannot be Systematically Classified
Due to limitations #1 & #2, it is not possible to create
a momentum level classification scale
where one can define "fast", "slow", "overbought", "oversold" momentum
making systematic analysis impossible
Limitation 4: MACD Signal Line gives false crossovers in low-momentum ranges
In range-bound, low momentum environments,
most of the MACD signal line crossovers are false (noise)
Since there is no objective momentum classification system (limitation #3),
it is not possible to filter these signals out,
by avoiding them when momentum is low
Limitation 5: MACD Signal Line gives late crossovers in high momentum regimes.
Signal lag in strong trends not good at timing the turning point
— In high-momentum moves, MACD crossovers may come late.
Since there is no objective momentum classification system (limitation #3),
it is not possible to filter these signals out,
by avoiding them when momentum is high
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🏆 5. MACD-v : THE SOLUTION TO THE LIMITATIONS OF THE MACD , RSI, etc
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MACD-v is a volatility normalised momentum indicator.
It remedies these 5 limitations of the classic MACD,
while creating a tool with unique properties.
Formula: × 100
MACD-V enhances the classic MACD by normalizing for volatility,
transforming price-dependent readings into standardized momentum values.
This resolves key limitations of traditional MACD and adds significant analytical power.
Core Advantages of MACD-V
Advantage 1: Time-Based Stability
MACD-V values are consistent and comparable over time.
A reading of 100 has the same meaning today as it did in the past
(unlike traditional MACD which is influenced by changes in price and volatility over time)
Advantage 2: Cross-Market Comparability
MACD-V provides universal scaling.
Readings (e.g., ±50) apply consistently across all asset classes—stocks,
bonds, commodities, or currencies,
allowing traders to compare momentum across markets reliably.
Advantage 3: Objective Momentum Classification
MACD-V includes a defined 5-range momentum lifecycle
with standardized thresholds (e.g., -150 to +150).
This offers an objective framework for analyzing market conditions
and supports integration with broader models.
Advantage 4: False Signal Reduction in Low-Momentum Regimes
MACD-V introduces a "neutral zone" (typically -50 to +50)
to filter out these low-probability signals.
Advantage 5: Improved Signal Timing in High-Momentum Regimes
MACD-V identifies extremely strong trends,
allowing for more precise entry and exit points.
Advantage 6: Trend-Adaptive Scaling
Unlike bounded oscillators like RSI or Stochastic,
MACD-V dynamically expands with trend strength,
providing clearer momentum insights without artificial limits.
Advantage 7: Enhanced Divergence Detection
MACD-V offers more reliable divergence signals
by avoiding distortion at extreme levels,
a common flaw in bounded indicators (RSI, etc)
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⚒️ 5. HOW TO USE THE MACD-v: 7 CORE PATTERNS
HOW TO USE THE MACD-v Histogram: 2 CORE PATTERNS
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>>>>>> BASIC USE (RANGE RULES) <<<<<<
The MACD-v has 7 Core Patterns (Ranges) :
1. Risk Range (Overbought)
Condition: MACD-V > Signal Line and MACD-V > +150
Interpretation: Extremely strong bullish momentum—potential exhaustion or reversal zone.
2. Retracing
Condition: MACD-V < Signal Line and MACD-V > -50
Interpretation: Mild pullback within a bullish trend.
3. Rundown
Condition: MACD-V < Signal Line and -50 > MACD-V > -150
Interpretation: Momentum is weakening—bearish pressure building.
4. Risk Range (Oversold)
Condition: MACD-V < Signal Line and MACD-V < -150
Interpretation: Extreme bearish momentum—potential for reversal or capitulation.
5. Rebounding
Condition: MACD-V > Signal Line and MACD-V > -150
Interpretation: Bullish recovery from oversold or weak conditions.
6. Rallying
Condition: MACD-V > Signal Line and MACD-V > +50
Interpretation: Strengthening bullish trend—momentum accelerating.
7. Ranging (Neutral Zone)
Condition: MACD-V remains between -50 and +50 for 20+ bars
Interpretation: Sideways market—low conviction and momentum.
The MACD-v Histogram has 2 Core Patterns (Ranges) :
1. Risk (Overbought)
Condition: Histogram > +40
Interpretation: Short-term bullish momentum is stretched—possible overextension or reversal risk.
2. Risk (Oversold)
Condition: Histogram < -40
Interpretation: Short-term bearish momentum is stretched—potential for rebound or reversal.
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📈 6. ADVANCED PATTERNS WITH MACD-v
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Thanks to its volatility normalization,
the MACD-V framework enables the development
of a wide range of advanced pattern recognition setups,
trading signals, and strategic models.
These patterns go beyond basic crossovers,
offering deeper insight into momentum structure,
regime shifts, and high-probability trade setups.
These are not part of this script
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⚙️ 7. FUNCTIONALITY - HOW TO ADD THE INDICATORS TO YOUR CHART
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The script allows you to see :
1. MACD-v
The indicator with the ranges (150,50,0,-50,-150)
and colour coded according to its 7 basic patterns
2. MACD-v Histogram
The indicator The indicator with the ranges (40,0,-40)
and colour coded according to its 2 basic ranges / patterns
3. MACD-v Heatmap
You can see the MACD-v in a Multiple Timeframe basis,
using a colour-coded Heatmap
Note that lowest timeframe in the heatmap must be the one on the chart
i.e. if you see the daily chart, then the Heatmap will be Daily, Weekly, Monthly
4. MACD-v Dashboard
You can see the MACD-v for 7 markets,
in a multiple timeframe basis
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🤝 CONTRIBUTIONS 🤝
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I would like to thank the following people:
1. Mike Christensen for coding the indicator
@TradersPostInc, @Mik3Christ3ns3n,
2. @Indicator-Jones For allowing me to use his Scanner
3. @Daveatt For allowing me to use his heatmap
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⚠️ LEGAL - Usage and Attribution Notice ⚠️
=======================================
Use of this Script is permitted
for personal or non-commercial purposes,
including implementation by coders and TradingView users.
However, any form of paid redistribution,
resale, or commercial exploitation is strictly prohibited.
Proper attribution to the original author is expected and appreciated,
in order to acknowledge the source
and maintain the integrity of the original work.
Failure to comply with these terms,
or to take corrective action within 48 hours of notification,
will result in a formal report to TradingView’s moderation team,
and will actively pursue account suspension and removal of the infringing script(s).
Continued violations may result in further legal action, as deemed necessary.
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⚠️ DISCLAIMER ⚠️
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This indicator is For Educational Purposes Only (F.E.P.O.).
I am just Teaching by Example (T.B.E.)
It does not constitute investment advice.
There are no guarantees in trading - except one.
You will have losses in trading.
I can guarantee you that with 100% certainty.
The author is not responsible for any financial losses
or trading decisions made based on this indicator. 🙏
Always perform your own analysis and use proper risk management. 🛡️
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Inside Bar + Harami ComboThis indicator visually highlights Inside Bars, Outside Bars, and Harami candlestick patterns directly on your chart using clean color-coded candles — no labels, no shapes, just visual clarity.
It helps traders quickly identify potential reversal and continuation setups by coloring candles according to the detected pattern type.
🔍 Patterns Detected
🟨 Inside Bar — Current candle’s range is completely inside the previous candle’s range.
Often signals price contraction before a breakout.
💗 Outside Bar — Current candle’s high and low exceed the previous candle’s range.
Indicates volatility expansion and possible trend continuation.
🟩 Bullish Harami — A small bullish candle within the body of a prior bearish candle.
Suggests potential reversal to the upside.
🟥 Bearish Harami — A small bearish candle within the body of a prior bullish candle.
Suggests potential reversal to the downside.
⚙️ Features
Customizable colors for each pattern type.
Simple overlay visualization — no shapes, no labels, just colored candles.
Harami colors automatically override Inside/Outside colors when both occur on the same bar.
Lightweight logic for smooth performance on any timeframe or symbol.
💡 How to Use
Apply the indicator to your chart.
Configure colors in the settings panel if desired.
Watch for highlighted candles:
Inside Bars often precede breakouts.
Harami patterns can mark reversal zones.
Combine with trend tools (like moving averages) to confirm setups.
⚠️ Note
This indicator is for visual pattern detection and educational use only.
Always combine candlestick signals with broader technical or market context before trading decisions.
MACD with Smart Entry Signals & Trend Filter
This advanced MACD indicator combines traditional MACD analysis with intelligent entry signal detection and an optional EMA trend filter. It identifies high-probability entry points by analyzing histogram patterns, consolidation phases, and trend continuation setups.
### Key Features
**🎯 Smart Entry Detection**
- **Consolidation Breakouts**: Identifies exits from consolidation zones (weak bars) with strong momentum
- **Trend Reversals**: Detects potential trend changes after extended weak phases
- **Correction/Continuation Patterns**: Recognizes brief corrections within strong trends that offer continuation opportunities
**📊 Enhanced MACD Visualization**
- Color-coded histogram showing four distinct states:
- Strong Bullish (dark green): Rising histogram above zero
- Weak Bullish (light green): Falling histogram above zero
- Weak Bearish (light red): Rising histogram below zero
- Strong Bearish (dark red): Falling histogram below zero
**🔍 Multi-Layer Filtering System**
- **Candle Size Filter**: Eliminates signals during high volatility/large candle ranges
- **EMA Trend Filter**: Optional filter ensuring entries align with the dominant trend direction
- Visual markers for rejected signals (orange X for candle size, blue E for EMA trend)
**⚙️ Customizable Parameters**
- Adjustable MACD periods (default: 34/144/9)
- Configurable consolidation bar requirements
- Flexible correction pattern detection
- EMA trend filter with adjustable sensitivity
- Multiple alert types for all signal conditions
### How to Use
1. **Enable/disable filters** based on your trading style and market conditions
2. **Green triangles (L)**: Long entry signals when all conditions are met
3. **Red triangles (S)**: Short entry signals when all conditions are met
4. **Rejected signal markers**: Help you understand why certain setups were filtered out
5. **Background coloring**: Provides visual confirmation of signal zones and correction patterns
### Alert System
Comprehensive alerts for:
- Long and short entry signals
- Specific pattern types (consolidation, reversal, continuation)
- Rejected signals (helps refine strategy)
- Traditional MACD histogram crossovers
### Best Practices
- Use the EMA trend filter in trending markets to avoid counter-trend trades
- Adjust candle size filter based on your instrument's typical volatility
- Consider combining with support/resistance levels for confirmation
- Test different consolidation bar settings for your timeframe
### Parameters Summary
- Fast/Slow Length: MACD calculation periods
- Signal Smoothing: Signal line period
- Consolidation Bars: Minimum weak bars before breakout
- Max Candle Range: Filter for oversized candles
- EMA Period & Sensitivity: Trend filter configuration
---
*This indicator is designed for traders who want a systematic approach to identifying MACD-based entry opportunities with built-in risk management through filtering.*
Indian Gold Festival Dates HistoricalIndian Gold Festival Dates (1975-2025)
Marks 8 major Indian festivals associated with gold buying over 50 years of historical data. Essential for analyzing seasonal patterns and cultural demand cycles in gold markets.
Festivals Included:
Dhanteras (Gold) - Most auspicious gold buying day
Diwali (Orange) - Festival of Lights
Akshaya Tritiya (Green) - "Never-ending" prosperity
Dussehra (Red) - Victory and success
Makar Sankranti (Cyan) - Solar new year
Gudi Padwa (Magenta) - Hindu New Year (Maharashtra)
Ugadi (Purple) - Hindu New Year (South India)
Navratri (Yellow) - 9-day festival
Features:
✓ 408 exact historical dates (1975-2025)
✓ Color-coded vertical lines for easy identification
✓ Toggle individual festivals on/off
✓ Adjustable line width and labels
✓ Works on all timeframes (best on daily/weekly)
Perfect for traders analyzing gold seasonality, Indian market sentiment, and cultural demand patterns. Use on XAUUSD, GC1!, or Indian gold futures.
HA Reversal + Doji 🔥 Heikin Ashi Reversal + Stochastic Filter (Precision Entry System)
This indicator is designed to detect high–quality reversal entries using a Heikin Ashi candle pattern (Doji + 2 no–wick confirmation) combined with a strict Stochastic filter that uses memory of extreme touches to control trade direction.
✅ Entry Logic
🔹 Bullish BUY Signal
A BUY is triggered only when:
A valid reversal pattern is detected:
Doji candle (pivot) 3 bars back
Followed by 2 bullish candles with no lower wicks
Stochastic touched Oversold (≤ 20) at least once before the signal
Pattern + Stoch alignment = BUY
🔹 Bearish SELL Signal
A SELL is triggered only when:
Valid bearish reversal pattern:
Doji candle (pivot) 3 bars back
Followed by 2 bearish candles with no upper wicks
Stochastic touched Overbought (≥ 80) before the signal
Pattern + Stoch alignment = SELL
🧠 Stochastic “Memory” Filter
This is not a basic OB/OS filter — it uses event memory:
If Stochastic touches Oversold, the system becomes ready for BUY
If it touches Overbought, it becomes ready for SELL
Both directions can be armed at once
Once a BUY or SELL actually triggers, memory resets to neutral
Prevents “signal spam” during chop and keeps direction meaningful
🎯 Why This Works
✔ Filters out random countertrend noise
✔ Only trades after momentum exhaustion
✔ Uses strict Heikin Ashi reversal structure
✔ Works great across crypto, forex, indices, metals
✔ Designed for precision entries and swing continuation traps
⚙️ Customizable Options
Doji detection mode (body % / ticks / hybrid)
Wick tolerance
Heikin Ashi source (chart or calculated)
Stochastic source (raw or smoothed)
Option to avoid duplicate same-direction signals
Visual aids: pattern markers, blocked signals, doji debugging
📌 Best Use Cases
Reversal scalping on 5m/15m
Swing entries on 1H/4H
Trend exhaustion confirmation
Smart Money Concepts entry refinement
Entry timing after liquidity sweeps
🚨 Important
This is not a repainting system. Signals are generated at bar close only. Always combine with proper risk management and market context.
Let me know if you want:
✅ A shorter description
✅ An SEO optimized TradingView title
✅ A strategy version with backtesting
✅ Alerts version for automation
DAMMU Swing Trading PRODammu Scalping Pro – Short Notes
1️⃣ Purpose:
Scalping and swing trading tool for 15-min and 1-min charts.
Designed for trend continuation, pullbacks, and reversals.
Works well with Heikin Ashi candles (optional).
2️⃣ Core Components:
EMAs:
Fast: EMA5-12
Medium: EMA12-36 Ribbon
Long: EMA75/89 (1-min), EMA180/200 (15-min), EMA540/633
Price Action Channel (PAC): EMA-based High, Low, Close channel.
Fractals: Regular & filtered (BW) fractals for swing recognition.
Higher Highs / Lower Highs / Higher Lows / Lower Lows (HH, LH, HL, LL).
Pivot Points: Optional display with labels.
3️⃣ Bar Coloring:
Blue: Close above PAC
Red: Close below PAC
Gray: Close inside PAC
4️⃣ Alerts:
Swing Buy/Sell arrows based on PAC breakout and EMA200 filter.
Optional “Big Arrows” mode for visibility.
Alert messages: "SWING_UP" and "SWING_DN"
5️⃣ Workflow / Usage Tips:
Set chart to 15-min (for trend) + 1-min (for entry).
Optionally enable Heikin Ashi candles.
Trade long only above EMA200, short only below EMA200.
Watch for pullbacks into EMA channels or ribbons.
Confirm trend resumption via PAC breakout & bar color change.
Use fractals and pivot points to draw trendlines and locate support/resistance.
6️⃣ Optional Filters:
Filter PAC signals with 200 EMA.
Filter fractals for “Pristine/Ideal” patterns (BW filter).
7️⃣ Visuals:
EMA ribbons, PAC fill, HH/LL squares, fractal triangles.
Pivot labels & candle numbering for patterns.
8️⃣ Notes:
No extra indicators needed except optionally SweetSpot Gold2 for major S/R levels.
Suitable for scalping pullbacks with trend confirmation.
If you want, I can make an even shorter “one-screen cheat sheet” with colors, alerts, and EMAs, perfect for real-time chart reference.
Do you want me to do that?
DAMMU Swing Trading PRODammu Scalping Pro – Short Notes
1️⃣ Purpose:
Scalping and swing trading tool for 15-min and 1-min charts.
Designed for trend continuation, pullbacks, and reversals.
Works well with Heikin Ashi candles (optional).
2️⃣ Core Components:
EMAs:
Fast: EMA5-12
Medium: EMA12-36 Ribbon
Long: EMA75/89 (1-min), EMA180/200 (15-min), EMA540/633
Price Action Channel (PAC): EMA-based High, Low, Close channel.
Fractals: Regular & filtered (BW) fractals for swing recognition.
Higher Highs / Lower Highs / Higher Lows / Lower Lows (HH, LH, HL, LL).
Pivot Points: Optional display with labels.
3️⃣ Bar Coloring:
Blue: Close above PAC
Red: Close below PAC
Gray: Close inside PAC
4️⃣ Alerts:
Swing Buy/Sell arrows based on PAC breakout and EMA200 filter.
Optional “Big Arrows” mode for visibility.
Alert messages: "SWING_UP" and "SWING_DN"
5️⃣ Workflow / Usage Tips:
Set chart to 15-min (for trend) + 1-min (for entry).
Optionally enable Heikin Ashi candles.
Trade long only above EMA200, short only below EMA200.
Watch for pullbacks into EMA channels or ribbons.
Confirm trend resumption via PAC breakout & bar color change.
Use fractals and pivot points to draw trendlines and locate support/resistance.
6️⃣ Optional Filters:
Filter PAC signals with 200 EMA.
Filter fractals for “Pristine/Ideal” patterns (BW filter).
7️⃣ Visuals:
EMA ribbons, PAC fill, HH/LL squares, fractal triangles.
Pivot labels & candle numbering for patterns.
8️⃣ Notes:
No extra indicators needed except optionally SweetSpot Gold2 for major S/R levels.
Suitable for scalping pullbacks with trend confirmation.
If you want, I can make an even shorter “one-screen cheat sheet” with colors, alerts, and EMAs, perfect for real-time charT
Forecast PriceTime Oracle [CHE] Forecast PriceTime Oracle — Prioritizes quality over quantity by using Power Pivots via RSI %B metric to forecast future pivot highs/lows in price and time
Summary
This indicator identifies potential pivot highs and lows based on out-of-bounds conditions in a modified RSI %B metric, then projects future occurrences by estimating time intervals and price changes from historical medians. It provides visual forecasts via diagonal and horizontal lines, tracks achievement with color changes and symbols, and displays a dashboard for statistical overview including hit rates. Signals are robust due to median-based aggregation, which reduces outlier influence, and optional tolerance settings for near-misses, making it suitable for anticipating reversals in ranging or trending markets.
Motivation: Why this design?
Standard pivot detection often lags or generates false signals in volatile conditions, missing the timing of true extrema. This design leverages out-of-bounds excursions in RSI %B to capture "Power Pivots" early—focusing on quality over quantity by prioritizing significant extrema rather than every minor swing—then uses historical deltas in time and price to forecast the next ones, addressing the need for proactive rather than reactive analysis. It assumes that pivot spacing follows statistical patterns, allowing users to prepare entries or exits ahead of confirmation.
What’s different vs. standard approaches?
- Reference baseline: Diverges from traditional ta.pivothigh/low, which require fixed left/right lengths and confirm only after bars close, often too late for dynamic markets.
- Architecture differences:
- Detects extrema during OOB runs rather than post-bar symmetry.
- Aggregates deltas via medians (or alternatives) over a user-defined history, capping arrays to manage resources.
- Applies tolerance thresholds for hit detection, with options for percentage, absolute, or volatility-adjusted (ATR) flexibility.
- Freezes achieved forecasts with visual states to avoid clutter.
- Practical effect: Charts show proactive dashed projections instead of retrospective dots; the dashboard reveals evolving hit rates, helping users gauge reliability over time without manual calculation.
How it works (technical)
The indicator first computes a smoothed RSI over a specified length, then applies Bollinger Bands to derive %B, flagging out-of-bounds below zero or above one hundred as potential run starts. During these runs, it tracks the extreme high or low price and bar index. Upon exit from the OOB state, it confirms the Power Pivot at that extreme and records the time delta (bars since prior) and price change percentage to rolling arrays.
For forecasts, it calculates the median (or selected statistic) of recent deltas, subtracts the confirmation delay (bars from apex to exit), and projects ahead by that adjusted amount. Price targets use the median change applied to the origin pivot value. Lines are drawn from the apex to the target bar and price, with a short horizontal at the endpoint. Arrays store up to five active forecasts, pruning oldest on overflow.
Tolerance adjusts hit checks: for highs, if the high reaches or exceeds the target (adjusted by tolerance); for lows, if the low drops to or below. Once hit, the forecast freezes, changing colors and symbols, and extends the horizontal to the hit bar. Persistent variables maintain last pivot states across bars; arrays initialize empty and grow until capped at history length.
Parameter Guide
Source: Specifies the data input for the RSI computation, influencing how price action is captured. Default is close. For conservative signals in noisy environments, switch to high; using low boosts responsiveness but may increase false positives.
RSI Length: Sets the smoothing period for the RSI calculation, with longer values helping to filter out whipsaws. Default is 32. Opt for shorter lengths like 14 to 21 on faster timeframes for quicker reactions, or extend to 50 or more in strong trends to enhance stability at the cost of some lag.
BB Length: Defines the period for the Bollinger Bands applied to %B, directly affecting how often out-of-bounds conditions are triggered. Default is 20. Align it with the RSI length: shorter periods detect more potential runs but risk added noise, while longer ones provide better filtering yet might overlook emerging extrema.
BB StdDev: Controls the multiplier for the standard deviation in the bands, where wider settings reduce false out-of-bounds alerts. Default is 2.0. Narrow it to 1.5 for highly volatile assets to catch more signals, or broaden to 2.5 or higher to emphasize only major movements.
Show Price Forecast: Enables or disables the display of diagonal and target lines along with their updates. Default is true. Turn it off for simpler chart views, or keep it on to aid in trade planning.
History Length: Determines the number of recent pivot samples used for median-based statistics, where more history leads to smoother but potentially less current estimates. Default is 50. Start with a minimum of 5 to build data; limit to 100 to 200 to prevent outdated regimes from skewing results.
Max Lookahead: Limits the number of bars projected forward to avoid overly extended lines. Default is 500. Reduce to 100 to 200 for intraday focus, or increase for longer swing horizons.
Stat Method: Selects the aggregation technique for time and price deltas: Median for robustness against outliers, Trimmed Mean (20%) for a balanced trim of extremes, or 75th Percentile for a conservative upward tilt. Default is Median. Use Median for even distributions; switch to Percentile when emphasizing potential upside in trending conditions.
Tolerance Type: Chooses the approach for flexible hit detection: None for exact matches, Percentage for relative adjustments, Absolute for fixed point offsets, or ATR for scaling with volatility. Default is None. Begin with Percentage at 0.5 percent for currency pairs, or ATR for adapting to cryptocurrency swings.
Tolerance %: Provides the relative buffer when using Percentage mode, forgiving small deviations. Default is 0.5. Set between 0.2 and 1.0 percent; higher values accommodate gaps but can overstate hit counts.
Tolerance Points: Establishes a fixed offset in price units for Absolute mode. Default is 0.0010. Tailor to the asset, such as 0.0001 for forex pairs, and validate against past wick behavior.
ATR Length: Specifies the period for the Average True Range in dynamic tolerance calculations. Default is 14. This is the standard setting; shorten to 10 to reflect more recent volatility.
ATR Multiplier: Adjusts the ATR scale for tolerance width in ATR mode. Default is 0.5. Range from 0.3 for tighter precision to 0.8 for greater leniency.
Dashboard Location: Positions the summary table on the chart. Default is Bottom Right. Consider Top Left for better visibility on mobile devices.
Dashboard Size: Controls the text scaling for dashboard readability. Default is Normal. Choose Tiny for dense overlays or Large for detailed review sessions.
Text/Frame Color: Sets the color scheme for dashboard text and borders. Default is gray. Align with your chart theme, opting for lighter shades on dark backgrounds.
Reading & Interpretation
Forecast lines appear as dashed diagonals from confirmed pivots to projected targets, with solid horizontals at endpoints marking price levels. Open targets show a target symbol (🎯); achieved ones switch to a trophy symbol (🏆) in gray, with lines fading to gray. The dashboard summarizes median time/price deltas, sample counts, and hit rates—rising rates indicate improving forecast alignment. Colors differentiate highs (red) from lows (lime); frozen states signal validated projections.
Practical Workflows & Combinations
- Trend following: Enter long on low forecast hits during uptrends (higher highs/lower lows structure); filter with EMA crossovers to ignore counter-trend signals.
- Reversal setups: Short above high projections in overextended rallies; use volume spikes as confirmation to reduce false breaks.
- Exits/Stops: Trail stops to prior pivot lows; conservative on low hit rates (below 50%), aggressive above 70% with tight tolerance.
- Multi-TF: Apply on 1H for entries, 4H for time projections; combine with Ichimoku clouds for confluence on targets.
- Risk management: Position size inversely to delta uncertainty (wider history = smaller bets); avoid low-liquidity sessions.
Behavior, Constraints & Performance
Confirmation occurs on OOB exit, so live-bar pivots may adjust until close, but projections update only on events to minimize repaint. No security or HTF calls, so no external lookahead issues. Arrays cap at history length with shifts; forecasts limited to five active, pruning FIFO. Loops iterate over small fixed sizes (e.g., up to 50 for stats), efficient on most hardware. Max lines/labels at 500 prevent overflow.
Known limits: Sensitive to OOB parameter tuning—too tight misses runs; assumes stationary pivot stats, which may shift in regime changes like low vol. Gaps or holidays distort time deltas.
Sensible Defaults & Quick Tuning
Defaults suit forex/crypto on 1H–4H: RSI 32/BB 20 for balanced detection, Median stats over 50 samples, None tolerance for exactness.
- Too many false runs: Increase BB StdDev to 2.5 or RSI Length to 50 for filtering.
- Lagging forecasts: Shorten History Length to 20; switch to 75th Percentile for forward bias.
- Missed near-hits: Enable Percentage tolerance at 0.3% to capture wicks without overcounting.
- Cluttered charts: Reduce Max Lookahead to 200; disable dashboard on lower TFs.
What this indicator is—and isn’t
This is a forecasting visualization layer for pivot-based analysis, highlighting statistical projections from historical patterns. It is not a standalone system—pair with price action, volume, and risk rules. Not predictive of all turns; focuses on OOB-derived extrema, ignoring volume or news impacts.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Cumulative Volume Delta Z Score [BackQuant]Cumulative Volume Delta Z Score
The Cumulative Volume Delta Z Score indicator is a sophisticated tool that combines the cumulative volume delta (CVD) with Z-Score normalization to provide traders with a clearer view of market dynamics. By analyzing volume imbalances and standardizing them through a Z-Score, this tool helps identify significant price movements and market trends while filtering out noise.
Core Concept of Cumulative Volume Delta (CVD)
Cumulative Volume Delta (CVD) is a popular indicator that tracks the net difference between buying and selling volume over time. CVD helps traders understand whether buying or selling pressure is dominating the market. Positive CVD signals buying pressure, while negative CVD indicates selling pressure.
The addition of Z-Score normalization to CVD makes it easier to evaluate whether current volume imbalances are unusual compared to past behavior. Z-Score helps in detecting extreme conditions by showing how far the current CVD is from its historical mean in terms of standard deviations.
Key Features
Cumulative Volume Delta (CVD): Tracks the net buying vs. selling volume, allowing traders to gauge the overall market sentiment.
Z-Score Normalization: Converts CVD into a standardized value to highlight extreme movements in volume that are statistically significant.
Divergence Detection: The indicator can spot bullish and bearish divergences between price and CVD, which can signal potential trend reversals.
Pivot-Based Divergence: Identifies price and CVD pivots, highlighting divergence patterns that are crucial for predicting price changes.
Trend Analysis: Colors bars according to trend direction, providing a visual indication of bullish or bearish conditions based on Z-Score.
How It Works
Cumulative Volume Delta (CVD): The CVD is calculated by summing the difference between buying and selling volume for each bar. It represents the net buying or selling pressure, giving insights into market sentiment.
Z-Score Normalization: The Z-Score is applied to the CVD to normalize its values, making it easier to compare current conditions with historical averages. A Z-Score greater than 0 indicates a bullish market, while a Z-Score less than 0 signals a bearish market.
Divergence Detection: The indicator detects regular and hidden bullish and bearish divergences between price and CVD. These divergences often precede trend reversals, offering traders a potential entry point.
Pivot-Based Analysis: The indicator uses pivot highs and lows in both price and CVD to identify divergence patterns. A bullish divergence occurs when price makes a lower low, but CVD fails to follow, suggesting weakening selling pressure. Conversely, a bearish divergence happens when price makes a higher high, but CVD doesn't confirm the move, indicating potential selling pressure.
Trend Coloring: The bars are colored based on the trend direction. Green bars indicate an uptrend (CVD is positive), and red bars indicate a downtrend (CVD is negative). This provides an easy-to-read visualization of market conditions.
Standard Deviation Levels: The indicator plots ±1σ, ±2σ, and ±3σ levels to indicate the degree of deviation from the average CVD. These levels act as thresholds for identifying extreme buying or selling pressure.
Customization Options
Anchor Timeframe: The user can define an anchor timeframe to aggregate the CVD, which can be customized based on the trader’s needs (e.g., daily, weekly, custom lower timeframes).
Z-Score Period: The period for calculating the Z-Score can be adjusted, allowing traders to fine-tune the indicator's sensitivity.
Divergence Detection: The tool offers controls to enable or disable divergence detection, with the ability to adjust the lookback periods for pivot detection.
Trend Coloring and Visuals: Traders can choose whether to color bars based on trend direction, display standard deviation levels, or visualize the data as a histogram or line plot.
Display Options: The indicator also allows for various display options, including showing the Z-Score values and divergence signals, with customizable colors and line widths.
Alerts and Signals
The Cumulative Volume Delta Z Score comes with pre-configured alert conditions for:
Z-Score Crossovers: Alerts are triggered when the Z-Score crosses the 0 line, indicating a potential trend reversal.
Shifting Trend: Alerts for when the Z-Score shifts direction, signaling a change in market sentiment.
Divergence Detection: Alerts for both regular and hidden bullish and bearish divergences, offering potential reversal signals.
Extreme Imbalances: Alerts when the Z-Score reaches extreme positive or negative levels, indicating overbought or oversold market conditions.
Applications in Trading
Trend Identification: Use the Z-Score to confirm bullish or bearish trends based on cumulative volume data, filtering out noise and false signals.
Reversal Signals: Divergences between price and CVD can help identify potential trend reversals, making it a powerful tool for swing traders.
Volume-Based Confirmation: The Z-Score allows traders to confirm price movements with volume data, providing more reliable signals compared to price action alone.
Divergence Strategy: Use the divergence signals to identify potential points of entry, particularly when regular or hidden divergences appear.
Volatility and Market Sentiment: The Z-Score provides insights into market volatility by measuring the deviation of CVD from its historical mean, helping to predict price movement strength.
The Cumulative Volume Delta Z Score is a powerful tool that combines volume analysis with statistical normalization. By focusing on volume imbalances and applying Z-Score normalization, this indicator provides clear, reliable signals for trend identification and potential reversals. It is especially useful for filtering out market noise and ensuring that trades are based on significant price movements driven by substantial volume changes.
This indicator is perfect for traders looking to add volume-based analysis to their strategy, offering a more robust and accurate way to gauge market sentiment and trend strength.
Enhanced Holt-Winters RSI [BOSWaves]Enhanced Holt-Winters RSI – Next-Level Momentum Smoothing & Signal Precision
Overview
The Enhanced Holt-Winters RSI transforms the classic Relative Strength Index into a robust, lag-minimized momentum oscillator through Holt-Winters triple exponential smoothing. By modeling the level, trend, and cyclical behavior of the RSI series, this indicator delivers smoother, more responsive signals that highlight overbought/oversold conditions, momentum shifts, and high-conviction trading setups without cluttering the chart with noise.
Unlike traditional RSI, which reacts to historical data and produces frequent whipsaws, the Enhanced Holt-Winters RSI filters transient price fluctuations, enabling traders to detect emerging momentum and potential reversal zones earlier.
Theoretical Foundation
The traditional RSI measures relative strength by comparing average gains and losses, but suffers from:
Lag in trend recognition : Signals often arrive after momentum has shifted.
Noise sensitivity : High-frequency price movements generate unreliable crossovers.
Limited insight into structural market shifts : Standard RSI cannot contextualize cyclical or momentum patterns.
The Enhanced Holt-Winters RSI addresses these limitations by applying triple exponential smoothing directly to the RSI series. This decomposes the series into:
Level (Lₜ) : Represents the smoothed central tendency of RSI.
Trend (Tₜ) : Captures rate-of-change in smoothed momentum.
Seasonal Component (Sₜ) : Models short-term cyclical deviations in momentum.
By incorporating these elements, the oscillator produces smoothed RSI values that react faster to emerging trends while suppressing erratic noise. Its internal forecast is mathematical, influencing the smoothed RSI output and signals, rather than being directly plotted.
How It Works
The Enhanced Holt-Winters RSI builds its signal framework through several layers:
1. Base RSI Calculation
Computes standard RSI over the selected period as the primary momentum input.
2. Triple Exponential Smoothing (Holt-Winters)
The RSI is smoothed recursively to extract underlying momentum structure:
Level, trend, and seasonal components are combined to produce a smoothed RSI.
This internal smoothing reduces lag and enhances signal reliability.
3. Momentum Analysis
Short-term momentum shifts are tracked via a moving average of the smoothed RSI, highlighting acceleration or deceleration in directional strength.
4. Volume Confirmation (Optional)
Buy/sell signals can be filtered through a configurable volume threshold, ensuring only high-conviction moves trigger alerts.
5. Visual Output
Colored Candles : Represent overbought (red), oversold (green), or neutral (yellow) conditions.
Oscillator Panel : Plots the smoothed RSI with dynamic color coding for immediate trend context.
Signals : Triangular markers indicate bullish or bearish setups, with stronger signals flagged in extreme zones.
Interpretation
The Enhanced Holt-Winters RSI provides a multi-dimensional perspective on price action:
Trend Strength : Smoothed RSI slope and color coding reflect the direction and momentum intensity.
Momentum Shifts : Rapid changes in the smoothed RSI indicate emerging strength or weakness.
Overbought/Oversold Zones : Highlight areas where price is stretched relative to recent momentum.
High-Conviction Signals : Combined with volume filtering, markers indicate optimal entries/exits.
Cycle Awareness : Smoothing reveals structural patterns, helping traders avoid reacting to noise.
By combining these elements, traders gain early insight into market structure and momentum without relying on raw, lag-prone RSI data.
Strategy Integration
The Enhanced Holt-Winters RSI can be applied across trading styles:
Trend Following
Enter when RSI is aligned with price momentum and color-coded signals confirm trend direction.
Strong slope in the smoothed RSI signals trend continuation.
Reversal Trading
Look for RSI extremes with momentum shifts and strong signal markers.
Compression in oscillator values often precedes reversal setups.
Breakout Detection
Oscillator flattening in neutral zones followed by directional expansion indicates potential breakout conditions.
Multi-Timeframe Confluence
Higher timeframes provide directional bias; lower timeframes refine entry timing using smoothed RSI dynamics.
Technical Implementation Details
Input Source : Close, open, high, low, or price.
Smoothing : Holt-Winters triple exponential smoothing applied to RSI.
Parameters :
Level (α) : Controls smoothing of RSI.
Trend (β) : Adjusts responsiveness to momentum changes.
Seasonal Length : Defines cycles for short-term adjustments.
Delta Smoothing : Reduces choppiness in smoothed RSI difference.
Outputs :
Smoothed RSI
Colored candles and oscillator panel
Buy/Sell signal markers (with optional strength filtering)
Volume Filtering : Optional threshold to confirm signals.
Optimal Application Parameters
Asset-Specific Guidance:
Forex : Use moderate smoothing (α, β) to capture medium-term momentum swings while filtering minor price noise. Works best when combined with volume or volatility filters.
Equities : Balance responsiveness and smoothness to identify sustained sector momentum or rotational shifts; ideal for capturing clean directional transitions.
Cryptocurrency : Increase smoothing parameters slightly to stabilize RSI during extreme volatility; optional volume confirmation can help filter false signals.
Futures/Indices : Lower smoothing sensitivity emphasizes macro momentum and structural trend durability over short-term fluctuations.
Timeframe Optimization:
Scalping (1-5m) : Use higher sensitivity (lower smoothing factors) to react quickly to micro-momentum reversals.
Intraday (15m-1h) : Balance smoothing and responsiveness for detecting short-term acceleration and exhaustion zones.
Swing (4h-Daily) : Apply moderate smoothing to reveal underlying directional persistence and cyclical reversals.
Position (Daily-Weekly) : Use stronger smoothing to isolate dominant momentum trends and filter temporary pullbacks.
Integration Guidelines
Combine with trend filters (EMAs, SuperSmoother MA, ATR-based tools) for confirmation.
Use volume and signal strength markers to filter low-conviction trades.
Slope, color, and signal alignment can guide entry, stop placement, and scaling.
Disclaimer
The Enhanced Holt-Winters RSI is a technical analysis tool, not a guaranteed profit system. Effectiveness depends on proper settings, market structure, and disciplined risk management. Always backtest before live trading.
Oscillator Matrix [Alpha Extract]A comprehensive multi-oscillator system that combines volume-weighted money flow analysis with enhanced momentum detection, providing traders with a unified framework for identifying high-probability market opportunities across all timeframes. By integrating two powerful oscillators with advanced confluence analysis, this indicator delivers precise entry and exit signals while filtering out market noise through sophisticated threshold-based regime detection.
🔶 Volume-Weighted Money Flow Analysis
Utilizes an advanced money flow calculation that tracks volume-weighted price movements to identify institutional activity and smart money flow. This approach provides superior signal quality by emphasizing high-volume price movements while filtering out low-volume market noise.
// Volume-weighted flows
up_volume = price_up ? volume : 0
down_volume = price_down ? volume : 0
// Money Flow calculation
up_vol_sum = ta.sma(up_volume, mf_length)
down_vol_sum = ta.sma(down_volume, mf_length)
total_volume = up_vol_sum + down_vol_sum
money_flow_ratio = total_volume > 0 ? (up_vol_sum - down_vol_sum) / total_volume : 0
🔶 Enhanced Hyper Wave Oscillator
Features a sophisticated MACD-based momentum oscillator with advanced normalization techniques that adapt to different price ranges and market volatility. The system uses percentage-based calculations to ensure consistent performance across various instruments and timeframes.
// Enhanced MACD-based oscillator
fast_ma = ta.ema(src, hw_fast)
slow_ma = ta.ema(src, hw_slow)
macd_line = fast_ma - slow_ma
signal_line = ta.ema(macd_line, hw_signal)
// Proper normalization using percentage of price
price_base = ta.sma(close, 50)
macd_normalized = macd_line / price_base
hyper_wave = macd_range > 0 ? macd_normalized / macd_range : 0
🔶 Multi-Factor Confluence System
Implements an intelligent confluence scoring mechanism that combines signals from both oscillators to identify high-probability trading opportunities. The system assigns strength scores based on multiple confirmation factors, significantly reducing false signals.
🔶 Fixed Threshold Levels
Uses predefined threshold levels optimized for standard oscillator ranges to distinguish between normal market fluctuations and significant momentum shifts. The dual-threshold system provides clear visual cues for overbought/oversold conditions while maintaining consistent signal criteria across different market conditions.
🔶 Overflow Detection Technology
Advanced overflow indicators identify extreme market conditions that often precede major reversals or continuation patterns. These signals highlight moments when market momentum reaches critical levels, providing early warning for potential turning points.
🔶 Dual Oscillator Integration
The indicator simultaneously tracks volume-weighted money flow and momentum-based price action through two independent oscillators. This dual approach ensures comprehensive market analysis by capturing both institutional activity and technical momentum patterns.
// Multi-factor confluence scoring
confluence_bull = (mf_bullish ? 1 : 0) + (hw_bullish ? 1 : 0) +
(mf_overflow_bull ? 1 : 0) + (hw_overflow_bull ? 1 : 0)
confluence_bear = (mf_bearish ? 1 : 0) + (hw_bearish ? 1 : 0) +
(mf_overflow_bear ? 1 : 0) + (hw_overflow_bear ? 1 : 0)
confluence_strength = confluence_bull > confluence_bear ? confluence_bull / 4 : -confluence_bear / 4
🔶 Intelligent Signal Generation
The system generates two tiers of reversal signals: strong signals that require multiple confirmations across both oscillators, and weak signals that identify early momentum shifts. This hierarchical approach allows traders to adjust position sizing based on signal strength.
🔶 Visual Confluence Zones
Background coloring dynamically adjusts based on confluence strength, creating visual zones that immediately communicate market sentiment. The intensity of background shading corresponds to the strength of the confluent signals, making pattern recognition effortless.
🔶 Threshold Visualization
Color-coded threshold zones provide instant visual feedback about oscillator positions relative to key levels. The fill areas between thresholds create clear overbought and oversold regions with graduated color intensity.
🔶 Candle Color Integration
Optional candle coloring applies confluence-based color logic directly to price bars, creating a unified visual framework that helps traders correlate indicator signals with actual price movements for enhanced decision-making.
🔶 Overflow Alert System
Specialized circular markers highlight extreme overflow conditions on both oscillators, drawing attention to potential climax moves that often precede significant reversals or accelerated trend continuation.
🔶 Customizable Display Options
Comprehensive display controls allow traders to toggle individual components on or off, enabling focused analysis on specific aspects of the indicator. This modularity ensures the indicator adapts to different trading styles and analytical preferences.
1 Week
1 Day
15 Min
This indicator provides a complete analytical framework by combining volume analysis with momentum detection in a single, coherent system. By offering multiple confirmation layers and clear visual hierarchies, it empowers traders to identify high-probability opportunities while maintaining precise risk management across all market conditions and timeframes. The sophisticated confluence system ensures that signals are both timely and reliable, making it an essential tool for serious technical analysts.
Institutional Levels (CNN) - [PhenLabs]📊Institutional Levels (Convolutional Neural Network-inspired)
Version : PineScript™v6
📌Description
The CNN-IL Institutional Levels indicator represents a breakthrough in automated zone detection technology, combining convolutional neural network principles with advanced statistical modeling. This sophisticated tool identifies high-probability institutional trading zones by analyzing pivot patterns, volume dynamics, and price behavior using machine learning algorithms.
The indicator employs a proprietary 9-factor logistic regression model that calculates real-time reaction probabilities for each detected zone. By incorporating CNN-inspired filtering techniques and dynamic zone management, it provides traders with unprecedented accuracy in identifying where institutional money is likely to react to price action.
🚀Points of Innovation
● CNN-Inspired Pivot Analysis - Advanced binning system using convolutional neural network principles for superior pattern recognition
● Real-Time Probability Engine - Live reaction probability calculations using 9-factor logistic regression model
● Dynamic Zone Intelligence - Automatic zone merging using Intersection over Union (IoU) algorithms
● Volume-Weighted Scoring - Time-of-day volume Z-score analysis for enhanced zone strength assessment
● Adaptive Decay System - Intelligent zone lifecycle management based on touch frequency and recency
● Multi-Filter Architecture - Optional gradient, smoothing, and Difference of Gaussians (DoG) convolution filters
🔧Core Components
● Pivot Detection Engine - Advanced pivot identification with configurable left/right bars and ATR-normalized strength calculations
● Neural Network Binning - Price level clustering using CNN-inspired algorithms with ATR-based bin sizing
● Logistic Regression Model - 9-factor probability calculation including distance, width, volume, VWAP deviation, and trend analysis
● Zone Management System - Intelligent creation, merging, and decay algorithms for optimal zone lifecycle control
● Visualization Layer - Dynamic line drawing with opacity-based scoring and optional zone fills
🔥Key Features
● High-Probability Zone Detection - Automatically identifies institutional levels with reaction probabilities above configurable thresholds
● Real-Time Probability Scoring - Live calculation of zone reaction likelihood using advanced statistical modeling
● Session-Aware Analysis - Optional filtering to specific trading sessions for enhanced accuracy during active market hours
● Customizable Parameters - Full control over lookback periods, zone sensitivity, merge thresholds, and probability models
● Performance Optimized - Efficient processing with controlled update frequencies and pivot processing limits
● Non-Repainting Mode - Strict mode available for backtesting accuracy and live trading reliability
🎨Visualization
● Dynamic Zone Lines - Color-coded support and resistance levels with opacity reflecting zone strength and confidence scores
● Probability Labels - Real-time display of reaction probabilities, touch counts, and historical hit rates for active zones
● Zone Fills - Optional semi-transparent zone highlighting for enhanced visual clarity and immediate pattern recognition
● Adaptive Styling - Automatic color and opacity adjustments based on zone scoring and statistical significance
📖Usage Guidelines
● Lookback Bars - Default 500, Range 100-1000, Controls the historical data window for pivot analysis and zone calculation
● Pivot Left/Right - Default 3, Range 1-10, Defines the pivot detection sensitivity and confirmation requirements
● Bin Size ATR units - Default 0.25, Range 0.1-2.0, Controls price level clustering granularity for zone creation
● Base Zone Half-Width ATR units - Default 0.25, Range 0.1-1.0, Sets the minimum zone width in ATR units for institutional level boundaries
● Zone Merge IoU Threshold - Default 0.5, Range 0.1-0.9, Intersection over Union threshold for automatic zone merging algorithms
● Max Active Zones - Default 5, Range 3-20, Maximum number of zones displayed simultaneously to prevent chart clutter
● Probability Threshold for Labels - Default 0.6, Range 0.3-0.9, Minimum reaction probability required for zone label display and alerts
● Distance Weight w1 - Controls influence of price distance from zone center on reaction probability
● Width Weight w2 - Adjusts impact of zone width on probability calculations
● Volume Weight w3 - Modifies volume Z-score influence on zone strength assessment
● VWAP Weight w4 - Controls VWAP deviation impact on institutional level significance
● Touch Count Weight w5 - Adjusts influence of historical zone interactions on probability scoring
● Hit Rate Weight w6 - Controls prior success rate impact on future reaction likelihood predictions
● Wick Penetration Weight w7 - Modifies wick penetration analysis influence on probability calculations
● Trend Weight w8 - Adjusts trend context impact using ADX analysis for directional bias assessment
✅Best Use Cases
● Swing Trading Entries - Enter positions at high-probability institutional zones with 60%+ reaction scores
● Scalping Opportunities - Quick entries and exits around frequently tested institutional levels
● Risk Management - Use zones as dynamic stop-loss and take-profit levels based on institutional behavior
● Market Structure Analysis - Identify key institutional levels that define current market structure and sentiment
● Confluence Trading - Combine with other technical indicators for high-probability trade setups
● Session-Based Strategies - Focus analysis during high-volume sessions for maximum effectiveness
⚠️Limitations
● Historical Pattern Dependency - Algorithm effectiveness relies on historical patterns that may not repeat in changing market conditions
● Computational Intensity - Complex calculations may impact chart performance on lower-end devices or with multiple indicators
● Probability Estimates - Reaction probabilities are statistical estimates and do not guarantee actual market outcomes
● Session Sensitivity - Performance may vary significantly between different market sessions and volatility regimes
● Parameter Sensitivity - Results can be highly dependent on input parameters requiring optimization for different instruments
💡What Makes This Unique
● CNN Architecture - First indicator to apply convolutional neural network principles to institutional-level detection
● Real-Time ML Scoring - Live machine learning probability calculations for each zone interaction
● Advanced Zone Management - Sophisticated algorithms for zone lifecycle management and automatic optimization
● Statistical Rigor - Comprehensive 9-factor logistic regression model with extensive backtesting validation
● Performance Optimization - Efficient processing algorithms designed for real-time trading applications
🔬How It Works
● Multi-timeframe pivot identification - Uses configurable sensitivity parameters for advanced pivot detection
● ATR-normalized strength calculations - Standardizes pivot significance across different volatility regimes
● Volume Z-score integration - Enhanced pivot weighting based on time-of-day volume patterns
● Price level clustering - Neural network binning algorithms with ATR-based sizing for zone creation
● Recency decay applications - Weights recent pivots more heavily than historical data for relevance
● Statistical filtering - Eliminates low-significance price levels and reduces market noise
● Dynamic zone generation - Creates zones from statistically significant pivot clusters with minimum support thresholds
● IoU-based merging algorithms - Combines overlapping zones while maintaining accuracy using Intersection over Union
● Adaptive decay systems - Automatic removal of outdated or low-performing zones for optimal performance
● 9-factor logistic regression - Incorporates distance, width, volume, VWAP, touch history, and trend analysis
● Real-time scoring updates - Zone interaction calculations with configurable threshold filtering
● Optional CNN filters - Gradient detection, smoothing, and Difference of Gaussians processing for enhanced accuracy
💡Note
This indicator represents advanced quantitative analysis and should be used by traders familiar with statistical modeling concepts. The probability scores are mathematical estimates based on historical patterns and should be combined with proper risk management and additional technical analysis for optimal trading decisions.
FSVZO [Alpha Extract]A sophisticated volume-weighted momentum oscillator that combines Fourier smoothing with Volume Zone Oscillator methodology to deliver institutional-grade flow analysis and divergence detection. Utilizing advanced statistical filtering including ADF trend analysis and multi-dimensional volume dynamics, this indicator provides comprehensive market sentiment assessment through volume-price relationships with extreme zone detection and intelligent divergence recognition for high-probability reversal and continuation signals.
🔶 Advanced VZO Calculation Engine
Implements enhanced Volume Zone Oscillator methodology using relative volume analysis combined with smoothed price changes to create momentum-weighted oscillator values. The system applies exponential smoothing to both volume and price components before calculating positive and negative momentum ratios with trend factor integration for market regime awareness.
🔶 Fourier-Based Smoothing Architecture
Features advanced Fourier approximation smoothing using cosine-weighted calculations to reduce noise while preserving signal integrity. The system applies configurable Fourier length parameters with weighted sum normalization for optimal signal clarity across varying market conditions with enhanced responsiveness to genuine trend changes.
// Fourier Smoothing Algorithm
fourier_smooth(src, length) =>
sum = 0
weightSum = 0
for i = 0 to length - 1
weight = cos(2 * π * i / length)
sum += src * weight
weightSum += weight
sum / weightSum
🔶 Intelligent Divergence Detection System
Implements comprehensive divergence analysis using pivot point methodology with configurable lookback periods for both standard and hidden divergence patterns. The system validates divergence conditions through range analysis and provides visual confirmation through plot lines, labels, and color-coded identification for precise timing analysis.
15MIN
4H
12H
🔶 Flow Momentum Analysis Framework
Calculates flow momentum by measuring oscillator deviation from its exponential moving average, providing secondary confirmation of volume flow dynamics. The system creates momentum-based fills and visual indicators that complement the primary oscillator analysis for comprehensive market flow assessment.
🔶 Extreme Zone Detection Engine
Features sophisticated extreme zone identification at ±98 levels with specialized marker system including white X markers for signals occurring in extreme territory and directional triangles for potential reversal points. The system provides clear visual feedback for overbought/oversold conditions with institutional-level threshold accuracy.
🔶 Dynamic Visual Architecture
Provides advanced visualization engine with bullish/bearish color transitions, dynamic fill regions between oscillator and signal lines, and flow momentum overlay with configurable transparency levels. The system includes flip markers aligned to color junction points for precise signal timing with optional bar close confirmation to prevent repainting.
🔶 ADF Trend Filtering Integration
Incorporates Augmented Dickey-Fuller inspired trend filtering using normalized price statistics to enhance signal quality during trending versus ranging market conditions. The system calculates trend factors based on mean deviation and standard deviation analysis for improved oscillator accuracy across market regimes.
🔶 Comprehensive Alert System
Features intelligent multi-tier alert framework covering bullish/bearish flow detection, extreme zone reversals, and divergence confirmations with customizable message templates. The system provides real-time notifications for critical volume flow changes and structural market shifts with exchange and ticker integration.
🔶 Performance Optimization Framework
Utilizes efficient calculation methods with optimized variable management and configurable smoothing parameters to balance signal quality with computational efficiency. The system includes automatic pivot validation and range checking for consistent performance across extended analysis periods with minimal resource usage.
This indicator delivers sophisticated volume-weighted momentum analysis through advanced Fourier smoothing and comprehensive divergence detection capabilities. Unlike traditional volume oscillators that focus solely on volume patterns, the FSVZO integrates volume dynamics with price momentum and statistical trend filtering to provide institutional-grade flow analysis. The system's combination of extreme zone detection, intelligent divergence recognition, and multi-dimensional visual feedback makes it essential for traders seeking systematic approaches to volume-based market analysis across cryptocurrency, forex, and equity markets with clearly defined reversal and continuation signals.
Measured Move Volume XIndicator Description
The "Measured Move Volume X" indicator, developed for TradingView using Pine Script version 6, projects potential price targets based on the measured move concept, where the magnitude of a prior price leg (Leg A) is used to forecast a subsequent move. It overlays translucent boxes on the chart to visualize bullish (green) or bearish (red) price projections, extending them to the right for a user-specified number of bars. The indicator integrates volume analysis (relative to a simple moving average), RSI for momentum, and VWAP for price-volume weighting, combining these into a confidence score to filter entry signals, displayed as triangles on breakouts. Horizontal key level lines (large, medium, small) are drawn at significant price points derived from the measured moves, with customizable thresholds, colors, and styles. Exhaustion hints, shown as orange labels near box extremes, indicate potential reversal points. Anomalous candles, marked with diamond shapes, are identified based on volume spikes and body-to-range ratios. Optional higher timeframe candle coloring enhances context. The indicator is fully customizable through input groups for lookback periods, transparency, and signal weights, making it adaptable to various assets and timeframes.
Adjustment Tips for Optimization
To optimize the "Measured Move Volume X" indicator for specific assets or timeframes, adjust the following input parameters:
Leg A Lookback (default: 14 bars): Increase to 20-30 for volatile markets (e.g., cryptocurrencies) to capture larger price swings; decrease to 5-10 for intraday charts (e.g., stocks) for faster signals.
Extend Box to the Right (default: 30 bars): Extend to 50+ for daily or weekly charts to project further targets; shorten to 10-20 for lower timeframes to reduce clutter.
Volume SMA Length (default: 20) and Relative Volume Threshold (default: 1.5): Lower the threshold to 1.2-1.3 for low-volume assets (e.g., commodities) to detect subtler spikes; raise to 2.0+ for high-volume equities to filter noise. Match SMA length to RSI length for consistency.
RSI Parameters (default: length 14, overbought 70, oversold 30): Set overbought to 80 and oversold to 20 in trending markets to reduce premature exit signals; shorten length to 7-10 for scalping.
Key Level Thresholds (default: large 10%, medium 5%, small 5%): Increase thresholds (e.g., large to 15%) for volatile assets to focus on significant moves; disable medium or small lines to declutter charts.
Confidence Score Weights (default: volume 0.5, VWAP 0.3, RSI 0.2): Increase volume weight (e.g., 0.7) for volume-driven markets like futures; emphasize RSI (e.g., 0.4) for momentum-focused strategies.
Anomaly Detection (default: volume multiplier 1.5, small body ratio 0.2, large body ratio 0.75): Adjust the volume multiplier higher for stricter anomaly detection in noisy markets; fine-tune body-to-range ratios based on asset-specific candle patterns.
Use TradingView’s replay feature to test adjustments on historical data, ensuring settings suit the chosen market and timeframe.
Tips for Using the Indicator
Interpreting Signals: Green upward triangles indicate bullish breakout entries when price exceeds the prior high with a confidence score ≥40; red downward triangles signal bearish breakouts. Use these to identify potential entry points aligned with the projected box targets.
Box Projections: Bullish boxes project upward targets (top of box) equal to the prior leg’s height added to the breakout price; bearish boxes project downward. Monitor price action near box edges for target completion or reversal.
Exhaustion Hints: Orange labels near box tops (bullish) or bottoms (bearish) suggest potential exhaustion when price deviates within the set percentage (default: 5%) and RSI or volume conditions are met. Use these as cues to watch for reversals.
Key Level Lines: Large, medium, and small lines mark significant price levels from box tops/bottoms. Use these as potential support/resistance zones, especially when drawn with high volume (colored differently).
Anomaly Candles: Orange diamonds highlight candles with unusual volume/body characteristics, indicating potential reversals or pauses. Combine with box levels for context.
Higher Timeframe Coloring: Enable to color bars based on higher timeframe candle closures (e.g., 1, 2, 5, or 15 minutes) for added trend context.
Customization: Toggle "Only Show Bullish Moves" to focus on bullish setups. Adjust transparency and line styles for visual clarity. Test settings to balance signal frequency and chart readability.
Inputs: Organized into groups (e.g., "Measured Move Settings") using input.int, input.float, input.color, and input.bool for user customization, with tooltips for clarity.
Calculations: Computes relative volume (ta.sma(volume, volLookback)), VWAP (ta.vwap(hlc3)), RSI (ta.rsi(close, rsiLength)), and prior leg extremes (ta.highest/lowest) using prior bar data ( ) to prevent repainting.
Boxes and Lines: Creates boxes (box.new) for bullish/bearish projections and lines (line.new) for key levels. The f_addLine function manages line arrays (array.new_line), capping at maxLinesCount to avoid clutter.
Confidence Score: Combines volume, VWAP distance, and RSI into a weighted score (confScore), filtering entries (≥40). Rounded for display.
Exhaustion Hints: Functions like f_plotBullExitHint assess price deviation, RSI, and volume decrease, using label.new for dynamic orange labels.
Entry Signals and Plots: plotshape displays triangles for breakouts; plot and hline show VWAP and RSI levels; request.security handles higher timeframe coloring.
Anomaly Detection: Identifies candles with small-body high-volume or large-body average-volume patterns via ratios, plotted as diamonds.
Guppy MMA [Alpha Extract]A sophisticated trend-following and momentum assessment system that constructs dynamic trader and investor sentiment channels using multiple moving average groups with advanced scoring mechanisms and smoothed CCI-style visualizations for optimal market trend analysis. Utilizing enhanced dual-group methodology with threshold-based trend detection, this indicator delivers institutional-grade GMMA analysis that adapts to varying market conditions while providing high-probability entry and exit signals through crossover and extreme value detection with comprehensive visual mapping and alert integration.
🔶 Advanced Channel Construction
Implements dual-group architecture using short-term and long-term moving averages as foundation points, applying customizable MA types to reduce noise and score-based averaging for sentiment-responsive trend channels. The system creates trader channels from shorter periods and investor channels from longer periods with configurable periods for optimal market reaction zones.
// Core Channel Calculation Framework
maType = input.string("EMA", title="Moving Average Type", options= )
// Short-Term Group Construction
stMA1 = ma(close, st1, maType)
stMA2 = ma(close, st2, maType)
// Long-Term Group Construction
ltMA1 = ma(close, lt1, maType)
ltMA2 = ma(close, lt2, maType)
// Smoothing Application
smoothedavg = ma(overallAvg, 10, maType)
🔶 Volatility-Adaptive Zone Framework
Features dynamic score-based averaging that expands sentiment signals during strong trend periods and contracts during consolidation phases, preventing false signals while maintaining sensitivity to genuine momentum shifts. The dual-group averaging system optimizes zone boundaries for realistic market behavior patterns.
// Dynamic Sentiment Adjustment
shortTermAvg = (stScore1 + stScore2 + ... + stScore11) / 11
longTermAvg = (ltScore1 + ltScore2 + ... + ltScore11) / 11
// Dual-Group Zone Optimization
overallAvg = (shortTermAvg + longTermAvg) / 2
allMAAvg = (shortTermAvg * 11 + longTermAvg * 11) / 22
🔶 Step-Like Boundary Evolution
Creates threshold-based trend boundaries that update on smoothed average changes, providing visual history of evolving bullish and bearish levels with performance-optimized threshold management limited to key zones for clean chart presentation and efficient processing.
🔶 Comprehensive Signal Detection
Generates buy and sell signals through sophisticated crossover analysis, monitoring smoothed average interaction with zero-line and thresholds for high-probability entry and exit identification. The system distinguishes between trend continuation and reversal patterns with precision timing.
🔶 Enhanced Visual Architecture
Provides translucent zone fills with gradient intensity scaling, threshold-based historical boundaries, and dynamic background highlighting that activates upon trend changes. The visual system uses institutional color coding with green bullish zones and red bearish zones for intuitive market structure interpretation.
🔶 Intelligent Zone Management
Implements automatic trend relevance filtering, displaying signals only when smoothed average proximity warrants analysis attention. The system maintains optimal performance through smart averaging management and historical level tracking with configurable MA periods for various market conditions.
🔶 Multi-Dimensional Analysis Framework
Combines trend continuation analysis through threshold crossovers with momentum detection via extreme markers, providing comprehensive market structure assessment suitable for both trending and ranging market conditions with score-normalized accuracy.
🔶 Advanced Alert Integration
Features comprehensive notification system covering buy signals, sell signals, strong bull conditions, and strong bear conditions with customizable alert conditions. The system enables precise position management through real-time notifications of critical sentiment interaction events and zone boundary violations.
🔶 Performance Optimization
Utilizes efficient MA smoothing algorithms with configurable types for noise reduction while maintaining responsiveness to genuine market structure changes. The system includes automatic visual level cleanup and performance-optimized visual rendering for smooth operation across all timeframes.
This indicator delivers sophisticated GMMA-based market analysis through score-adaptive averaging calculations and intelligent group construction methodology. By combining dynamic trader and investor sentiment detection with advanced signal generation and comprehensive visual mapping, it provides institutional-grade trend analysis suitable for cryptocurrency, forex, and equity markets. The system's ability to adapt to varying market conditions while maintaining signal accuracy makes it essential for traders seeking systematic approaches to trend trading, momentum reversals, and sentiment continuation analysis with clearly defined risk parameters and comprehensive alert integration.
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
POC Migration Velocity (POC-MV) [PhenLabs]📊POC Migration Velocity (POC-MV)
Version: PineScript™v6
📌Description
The POC Migration Velocity indicator revolutionizes market structure analysis by tracking the movement, speed, and acceleration of Point of Control (POC) levels in real-time. This tool combines sophisticated volume distribution estimation with velocity calculations to reveal hidden market dynamics that conventional indicators miss.
POC-MV provides traders with unprecedented insight into volume-based price movement patterns, enabling the early identification of continuation and exhaustion signals before they become apparent to the broader market. By measuring how quickly and consistently the POC migrates across price levels, traders gain early warning signals for significant market shifts and can position themselves advantageously.
The indicator employs advanced algorithms to estimate intra-bar volume distribution without requiring lower timeframe data, making it accessible across all chart timeframes while maintaining sophisticated analytical capabilities.
🚀Points of Innovation
Micro-POC calculation using advanced OHLC-based volume distribution estimation
Real-time velocity and acceleration tracking normalized by ATR for cross-market consistency
Persistence scoring system that quantifies directional consistency over multiple periods
Multi-signal detection combining continuation patterns, exhaustion signals, and gap alerts
Dynamic color-coded visualization system with intensity-based feedback
Comprehensive customization options for resolution, periods, and thresholds
🔧Core Components
POC Calculation Engine: Estimates volume distribution within each bar using configurable price bands and sophisticated weighting algorithms
Velocity Measurement System: Tracks the rate of POC movement over customizable lookback periods with ATR normalization
Acceleration Calculator: Measures the rate of change of velocity to identify momentum shifts in POC migration
Persistence Analyzer: Quantifies how consistently POC moves in the same direction using exponential weighting
Signal Detection Framework: Combines trend analysis, velocity thresholds, and persistence requirements for signal generation
Visual Rendering System: Provides dynamic color-coded lines and heat ribbons based on velocity and price-POC relationships
🔥Key Features
Real-time POC calculation with 10-100 configurable price bands for optimal precision
Velocity tracking with customizable lookback periods from 5 to 50 bars
Acceleration measurement for detecting momentum changes in POC movement
Persistence scoring to validate signal strength and filter false signals
Dynamic visual feedback with blue/orange color scheme indicating bullish/bearish conditions
Comprehensive alert system for continuation patterns, exhaustion signals, and POC gaps
Adjustable information table displaying real-time metrics and current signals
Heat ribbon visualization showing price-POC relationship intensity
Multiple threshold settings for customizing signal sensitivity
Export capability for use with separate panel indicators
🎨Visualization
POC Connecting Lines: Color-coded lines showing POC levels with intensity based on velocity magnitude
Heat Ribbon: Dynamic colored ribbon around price showing POC-price basis intensity
Signal Markers: Clear exhaustion top/bottom signals with labeled shapes
Information Table: Real-time display of POC value, velocity, acceleration, basis, persistence, and current signal status
Color Gradients: Blue gradients for bullish conditions, orange gradients for bearish conditions
📖Usage Guidelines
POC Calculation Settings
POC Resolution (Price Bands): Default 20, Range 10-100. Controls the number of price bands used to estimate volume distribution within each bar
Volume Weight Factor: Default 0.7, Range 0.1-1.0. Adjusts the influence of volume in POC calculation
POC Smoothing: Default 3, Range 1-10. EMA smoothing period applied to the calculated POC to reduce noise
Velocity Settings
Velocity Lookback Period: Default 14, Range 5-50. Number of bars used to calculate POC velocity
Acceleration Period: Default 7, Range 3-20. Period for calculating POC acceleration
Velocity Significance Threshold: Default 0.5, Range 0.1-2.0. Minimum normalized velocity for continuation signals
Persistence Settings
Persistence Lookback: Default 5, Range 3-20. Number of bars examined for persistence score calculation
Persistence Threshold: Default 0.7, Range 0.5-1.0. Minimum persistence score required for continuation signals
Visual Settings
Show POC Connecting Lines: Toggle display of colored lines connecting POC levels
Show Heat Ribbon: Toggle display of colored ribbon showing POC-price relationship
Ribbon Transparency: Default 70, Range 0-100. Controls transparency level of heat ribbon
Alert Settings
Enable Continuation Alerts: Toggle alerts for continuation pattern detection
Enable Exhaustion Alerts: Toggle alerts for exhaustion pattern detection
Enable POC Gap Alerts: Toggle alerts for significant POC gaps
Gap Threshold: Default 2.0 ATR, Range 0.5-5.0. Minimum gap size to trigger alerts
✅Best Use Cases
Identifying trend continuation opportunities when POC velocity aligns with price direction
Spotting potential reversal points through exhaustion pattern detection
Confirming breakout validity by monitoring POC gap behavior
Adding volume-based context to traditional technical analysis
Managing position sizing based on POC-price basis strength
⚠️Limitations
POC calculations are estimations based on OHLC data, not true tick-by-tick volume distribution
Effectiveness may vary in low-volume or highly volatile market conditions
Requires complementary analysis tools for complete trading decisions
Signal frequency may be lower in ranging markets compared to trending conditions
Performance optimization needed for very short timeframes below 1-minute
💡What Makes This Unique
Advanced Estimation Algorithm: Sophisticated method for calculating POC without requiring lower timeframe data
Velocity-Based Analysis: Focus on POC movement dynamics rather than static levels
Comprehensive Signal Framework: Integration of continuation, exhaustion, and gap detection in one indicator
Dynamic Visual Feedback: Intensity-based color coding that adapts to market conditions
Persistence Validation: Unique scoring system to filter signals based on directional consistency
🔬How It Works
Volume Distribution Estimation:
Divides each bar into configurable price bands for volume analysis
Applies sophisticated weighting based on OHLC relationships and proximity to close
Identifies the price level with maximum estimated volume as the POC
Velocity and Acceleration Calculation:
Measures POC rate of change over specified lookback periods
Normalizes values using ATR for consistent cross-market performance
Calculates acceleration as the rate of change of velocity
Signal Generation Process:
Combines trend direction analysis using EMA crossovers
Applies velocity and persistence thresholds to filter signals
Generates continuation, exhaustion, and gap alerts based on specific criteria
💡Note:
This indicator provides estimated POC calculations based on available OHLC data and should be used in conjunction with other analysis methods. The velocity-based approach offers unique insights into market structure dynamics but requires proper risk management and complementary analysis for optimal trading decisions.
BTC/USD 3-Min Binary Prediction [v7.2 EN]BTC/USD 3-Minute Binary Prediction Indicator v7.2 - Complete Guide
Overview
This is an advanced technical analysis indicator designed for Bitcoin/USD binary options trading with 3-minute expiration times. The system aims for an 83% win rate by combining multiple analysis layers and pattern recognition.
How It Works
Core Prediction Logic
- Timeframe: Predicts whether BTC price will be ±$25 higher (HIGH) or lower (LOW) after 3 minutes
- Entry Signals: Generates HIGH/LOW signals when confidence exceeds threshold (default 75%)
- Verification: Automatically tracks and displays win/loss statistics in real-time
5-Layer Filter System
The indicator uses a sophisticated scoring system (0-100 points):
1. Trend Filter (25 points) - Analyzes EMA alignments and price momentum
2. Leading Indicators (25 points) - RSI and MACD divergence analysis
3. Volume Confirmation (20 points) - Detects unusual volume patterns
4. Support/Resistance (15 points) - Identifies key price levels
5. Momentum Alignment (15 points) - Measures acceleration and deceleration
Pattern Recognition
Automatically detects and visualizes:
- Double Tops/Bottoms - Reversal patterns
- Triangles - Ascending, descending, symmetrical
- Channels - Trending price channels
- Candlestick Patterns - Engulfing, hammer, hanging man
Multi-Timeframe Analysis
- Uses 1-minute and 5-minute data for confirmation
- Aligns multiple timeframes for higher probability trades
- Monitors trend consistency across timeframes
Key Features
Display Panels
1. Statistics Panel (Top Right)
- Overall win rate percentage
- Hourly performance (wins/losses)
- Daily performance
- Current system status
2. Analysis Panel (Left Side)
- Market trend analysis
- RSI status (overbought/oversold)
- Volume conditions
- Filter scores for each component
- Final HIGH/LOW/WAIT decision
Visual Signals
- Green Triangle (↑) = HIGH prediction
- Red Triangle (↓) = LOW prediction
- Yellow Background = Entry opportunity
- Blue Background = Waiting for result
Configuration Options
Basic Settings
- Range Width: Target price movement (default $50 = ±$25)
- Min Confidence: Minimum confidence to enter (default 75%)
- Max Daily Trades: Risk management limit (default 5)
Filters (Can be toggled on/off)
- Trend Filter
- Volume Confirmation
- Support/Resistance Filter
- Momentum Alignment
Display Options
- Show/hide signals, statistics, analysis
- Minimal Mode for cleaner charts
- EMA line visibility
Important Risk Warnings
Binary Options Trading Risks:
1. High Risk Product - Binary options are extremely risky and banned in many countries
2. Not Investment Advice - This tool is for educational/analytical purposes only
3. No Guaranteed Returns - Past performance doesn't predict future results
4. Capital at Risk - You can lose your entire investment in seconds
Technical Limitations:
- Requires stable internet connection
- Performance varies with market conditions
- High volatility can reduce accuracy
- Not suitable for news events or low liquidity periods
Best Practices
1. Paper Trade First - Test thoroughly on demo accounts
2. Risk Management - Never risk more than 1-2% per trade
3. Market Conditions - Works best in normal volatility conditions
4. Avoid Major Events - Don't trade during major news releases
5. Monitor Performance - Track your actual results vs displayed statistics
Setup Instructions
1. Add to TradingView chart (BTC/USD preferred)
2. Use 30-second or 1-minute chart timeframe
3. Adjust settings based on your risk tolerance
4. Monitor F-Score (should be >65 for entries)
5. Wait for clear HIGH/LOW signals with high confidence
Alert Configuration
The indicator provides three alert types:
- HIGH Signal alerts
- LOW Signal alerts
- General entry opportunity alerts
Legal Disclaimer
Binary options trading may not be legal in your jurisdiction. Many countries including the USA, Canada, and EU nations have restrictions or outright bans on binary options. Always check local regulations and consult with financial advisors before trading.
Remember: This is a technical analysis tool, not a money-printing machine. Successful trading requires discipline, risk management, and continuous learning. The displayed statistics are historical and don't guarantee future performance.
Simple Symmetrical Triangle Strategy (6 points)Overview
This strategy identifies triangle patterns formed by a series of key high and low price points. A trade is triggered when the price breaks out from the pattern's final confirmation points: a buy signal occurs on a close above the last high point, and a sell signal on a close below the last low point. To ensure relevance, any pattern that doesn't break out within 10 bars is automatically discarded.
This helps filter out patterns that lose momentum and focuses only on the most imminent breakouts.
How It Works
1. Pattern Detection: The script continuously scans for a sequence of three declining highs (points H1, H2, H3) and three rising lows (points L1, L2, L3) to form a triangle.
2. Entry Logic: The logic is straightforward and based on breaking the last confirmed pivot:
* Long Entry: A buy order is executed if the price closes above the level of the last high (H3).
* Short Entry: A sell order is executed if the price closes below the level of the last low (L3).
3. Pattern Expiration: A triangle only remains "active" for 10 bars after its formation. If a breakout doesn't occur within this window, the pattern is removed from analysis, avoiding trades on prolonged, unresolved consolidations.
Key Features
* Automatic Detection: Identifies and draws triangles for you.
* Simple Breakout Logic: Easy to understand, trades by following the price action.
* Time Filter: Its main advantage is discarding patterns that do not resolve quickly.
* Customizable: You can adjust the sensitivity of the pivot detection in the settings.
Important Disclaimer
This strategy is designed as an entry system and DOES NOT INCLUDE A STOP LOSS OR TAKE PROFIT.
Automation Ready
Want to automate this or ANY strategy on your broker or MetaTrader (MT4/MT5) without keeping your computer on or needing a VPS? You can use WebhookTrade.
Symmetrical Triangle Strategy (Real and Trap confirmation)Overview
This is an advanced strategy that not only detects symmetrical triangle patterns but also attempts to differentiate between a genuine breakout and a false breakout (a trap) to trade accordingly.
Instead of blindly following every breakout, it analyzes the "quality" of the move using Volume and RSI filters. If the breakout appears weak, it prepares to trade in the opposite direction, capitalizing on the pattern's failure.
How It Works
The strategy employs a dual logic that activates after the price breaks the last pivot (H3 or L3):
1. Scenario A: The Real Breakout
* If the price breaks the triangle AND the breakout is confirmed by a surge in volume and/or a favorable RSI, the strategy considers the move genuine and enters in the direction of the breakout.
2. Scenario B: The False Breakout (Trap)
* If the price breaks the triangle BUT the indicators fail to confirm it (e.g., low volume), the strategy interprets it as a potential trap.
* It waits for the price to return inside the pattern.
* Once the price has re-entered, it opens a trade AGAINST the initial breakout, betting that the first move was a fake-out.
Key Features
* Hybrid Logic: It's not just a simple breakout strategy; it adapts to market conditions.
* Confirmation Filters: Uses Volume and RSI to validate the strength of a breakout (fully configurable).
* Capitalizes on Traps: Its greatest strength is the ability to identify and trade false breakouts, a common market scenario.
* Optional Confirmation: For trap trades, an extra confirmation via an EMA crossover can be enabled for added safety.
* Opportunity Timeout: Potential traps have a time limit to be confirmed, preventing the strategy from getting stuck in an undecided scenario.
Important Disclaimer
This strategy is designed as an entry system and DOES NOT INCLUDE A STOP LOSS OR TAKE PROFIT.
Automation Ready
Want to automate this or ANY strategy on your broker or MetaTrader (MT4/MT5) without keeping your computer on or needing a VPS? You can use WebhookTrade.
Average hourly move by @zeusbottradingThis Pine Script called "Average hourly move by @zeusbottrading" calculates and displays the average percentage price movement for each hour of the day using the full available historical data.
How the script works:
It tracks the high and low price within each full hour (e.g., 10:00–10:59).
It calculates the percentage move as the range between high and low relative to the average price during that hour.
For each hour of the day, it stores the total of all recorded moves and the count of occurrences across the full history.
At the end, the script computes the average move for each hour (0 to 23) and determines the minimum and maximum averages.
Using these values, it creates a color gradient, where the hours with the lowest average volatility are red and the highest are green.
It then displays a table in the top-right corner of the chart showing each hour and its average percentage move, color‑coded according to volatility.
What it can be used for:
Identifying when the market is historically most volatile or calm during the day.
Helping plan trade entries and exits based on expected volatility.
Comparing hourly volatility patterns across different markets or instruments.
Adjusting position size and risk management according to the anticipated volatility in a particular hour.
Using long-term historical data to understand recurring daily volatility patterns.
In short, this script is a useful tool for traders who want to fine‑tune their trading strategies and risk management by analyzing time‑based volatility profiles.
