15min intervalsindicator displays 4 15 minute intervals within the hour. this simple indicator can be used for effective scalping.
分形
4H CANDLES🕓 H4 Opening Candles – Pine Script v6
This indicator highlights only the very first candle that opens simultaneously with a new 4-hour (H4) candle, no matter which lower timeframe you're viewing (e.g., M1, M5, M15, M30, etc.).
🔍 Key Features:
Colors only the candles that mark the start of a new H4 session.
Leaves all other candles untouched – your chart style remains unchanged.
Great for identifying key H4 time anchors across any lower timeframe.
⚙️ Customization:
Adjustable candle color (body, wick, border).
Compatible with all chart intervals below 4H.
Fully timezone-independent – adapts to your TradingView settings automatically.
✅ Ideal for: time-based confluence zones, session-based strategies, or precise timing around key H4 opens.
💡 Pro tip: Combine this with support/resistance or momentum indicators for breakout or reversal trades.
NQ Lunch Model
This strategy runs on NQ (or MNQ) futures during the New York Lunch session (12:00–13:00 ET) and automatically:
Calculates contract size from your risk-per-trade (default $300) using ATR- or swing-based stops
Enforces a configurable minimum reward-to-risk ratio before entry
Draws on-chart R/R boxes showing entry, SL, TP, size, and RR
Displays a live status table with trade details (direction, entry, SL, TP, contracts)
(Optional) Sends Telegram alerts for entries, take-profits, and stop-losses
CRT Candle by aDiLThe CRT Candle indicator overlays candles from custom resolution timeframes (CRT) onto the current chart, aiding multi-timeframe analysis. Key features include:
➡️Custom Timeframes: Display up to three higher timeframe candles (e.g., H1, H4, 1D).
➡️Candle Styling: Customizable bullish/bearish candle appearance with adjustable width and spacing.
➡️Imbalance Visualization: Highlights Fair Value Gaps (FVG) and Volume Imbalances as boxes.
➡️Trace Lines: Optional lines connecting open, close, high, and low prices to candles.
➡️Labels & Timers: Shows timeframe labels and countdown timers, fully configurable.
This tool enhances trading strategies by integrating higher timeframe insights directly on the chart.
Pivot Swings w Table Pivot Swings w Table — Intraday Structure & Range Analyzer
This indicator identifies key pivot highs and lows on the chart and highlights market structure shifts using a real-time table display. It helps traders visually confirm potential trade setups by tracking unbroken swing points and measuring the range between the most recent pivots.
🔍 Features:
🔹 Automatic Pivot Detection using configurable left/right bar logic.
🔹 Unbroken Pivot Filtering — only pivots that haven't been invalidated by price are displayed.
🔹 Dynamic Range Table with:
Latest valid Pivot High and Pivot Low
Total Range Width
Upper & Lower 25% range thresholds (useful for value/imbalance analysis)
🔹 Trend-Based Color Coding — the table background changes based on which pivot (high or low) occurred more recently:
🟥 Red: Downward bias (last pivot was a lower high)
🟩 Green: Upward bias (last pivot was a higher low)
🔹 Optional extension of pivot levels to the right of the chart for support/resistance confluence.
⚙️ How to Use:
Adjust the Left Bars and Right Bars inputs to fine-tune how swings are defined.
Look for price reacting near the Upper or Lower 25% zones to anticipate mean reversion or breakout setups.
Use the trend color of the table to confirm directional bias, especially useful during consolidation or retracement periods.
💡 Best For:
Intraday or short-term swing traders
Traders who use market structure, support/resistance, or trend-based strategies
Those looking to avoid low-quality trades in tight ranges
✅ Built for overlay use on price charts
📈 Works on all symbols and timeframes
🧠 No repainting — pivots are confirmed with completed bars
Alpha Trader University - Market Structure AnalysisAlpha Trader University - Market Structure Analysis Indicator
EDUCATIONAL OVERVIEW:
This comprehensive indicator teaches traders professional market structure analysis through two proven methodologies: Fractal Analysis and Pivot Point Structure Tracking. Designed for educational purposes to help traders understand price action and market psychology.
CORE METHODOLOGY:
1. FRACTAL ANALYSIS (Bill Williams Approach):
- Identifies swing highs and lows using 3-bar or 5-bar patterns
- 3-bar fractals: More sensitive, catches smaller swings
- 5-bar fractals: More conservative, higher probability signals
- Visual markers help identify key support/resistance levels
2. MARKET STRUCTURE TRACKING:
- Automatically identifies Higher Highs (HH), Lower Highs (LH), Lower Lows (LL), Higher Lows (HL)
- Color-coded labels for easy trend identification
- Customizable pivot detection sensitivity
- Real-time structure classification
EDUCATIONAL VALUE:
- Learn to identify trend direction through structure analysis
- Understand the psychology behind market movements
- Develop skills in reading price action patterns
- Build foundation for advanced trading strategies
TRADING APPLICATIONS:
- Trend Identification: HH+HL = Uptrend, LH+LL = Downtrend
- Entry Timing: Enter after structure confirmation
- Stop Loss Placement: Use fractal levels for risk management
- Confluence Trading: Combine with other technical analysis tools
UNIQUE FEATURES:
- Dual analysis approach (Fractals + Structure)
- Educational tooltips and guidance
- Customizable sensitivity settings
- Professional color-coding system
- Clean, non-cluttered visual design
SETTINGS EXPLANATION:
- Fractal Sensitivity: Choose between 3-bar (sensitive) or 5-bar (conservative)
- Structure Labels: Toggle HH/LH/LL/HL display
- Pivot Settings: Adjust left/right bars for confirmation
- Color Customization: Personalize visual appearance
This indicator combines classical technical analysis principles with modern Pine Script efficiency, providing traders with essential market structure identification tools for educational and analytical purposes.
DISCLAIMER: This indicator is for educational purposes. Always combine with proper risk management and additional analysis before making trading decisions.
My Strategy: Uptrend Pullback ScreenerUptrend Pullback Screener. this will filter the stock who is in uptrend and ready to pullback from support.
Period Separator with DatesSimple Period Separator script for multiple timeframes with dates and months for higher timeframes, and time if you are using lower time frames.
[eLm] 0-1-2# 📈 Market Structure Indicator – Counter System
This indicator dynamically analyzes price action to detect key market structure shifts and trend strength using four essential formations:
## 🔍 Detected Structures
- **Higher High (HH):** A candle forming a new high above recent peaks
- **Lower Low (LL):** A candle forming a new low below recent bottoms
- **Higher Low (HL):** A higher dip, indicating potential trend continuation
- **Lower High (LH):** A lower peak, signaling potential weakness
## 🧠 Structure Protection Logic
After a HH or LL is formed, if price does not breach that level within a user-defined number of candles, the level is considered **"protected."**
This provides insight into trend strength and market reaction.
## 🔢 HL / LH Counter System
- Each HL or LH increases a counter.
- A new HH or LL **resets** the corresponding counter to zero.
> This helps visualize how many consecutive HL or LH structures have occurred — useful for measuring trend momentum.
## 🎯 Use Cases
- Trend following and confirmation
- Early trend reversal detection
- Building structure-based trading strategies
- Understanding price behavior and market intent
---
> **Note:** This indicator does not provide financial advice. It is designed to support technical analysis with clear, structure-based visual signals.
Auto Trendlines [AlgoXcalibur]Effortlessly visualize trendlines.
This algorithm does more than just draw lines connecting structural swing points — it reveals dynamic support & resistance breakouts with clarity and precision while significantly reducing your workload compared to the hassle of manually drawing trendlines.
🧠 Algorithm Logic
This advanced Auto Trendlines indicator delivers clear market structure through an intelligent multi-fractal design, revealing useful swing structures in real time. For those seeking maximum awareness, the optional Micro Trendlines (Dotted) constantly monitors even the most recent and minor structural shifts — keeping you fully in tune with evolving market dynamics. A Break Detection Engine constantly monitors each trendline and provides instant visual feedback when structural integrity is lost: broken lines turn gray, stop extending, and remain visible to enhance clarity and situational awareness. The algorithm is carefully refined to prevent chart distortion commonly caused by forcing entire trendline structures into view — preserving a natural and accurate charting experience. To further ensure optimal readability, an integrated Clutter Control mechanism limits the number of visible trendlines — focusing attention only on the most relevant structures.
⚙️ User-Selectable Features
• Micro Trendlines (Dotted): Ultra-responsive short-term trendlines that react to even the smallest structural shifts — ideal for staying ahead of early trend changes.
• Broken Trendline Declutter: Enable to display only the most recent broken trendlines to simplify chart visuals and maintain clarity, or disable to analyze previous price action.
💡 Modern Innovation
Auto Trendline indicators are often inaccurate, clumsy, and rely on slow methods that fail to adapt. AlgoXcalibur’s Auto Trendline indicator takes a modern, refined approach — combining smart pivot logic for both speed and stability, dynamic break detection with clear visual cues, and displaying only the most relevant trendlines while prioritizing accuracy, preventing distortion, and reducing clutter — automatically.
🔐 To get access or learn more, visit the Author’s Instructions section.
Advanced Day Separator with Future ProjectionsThe general indicator works on historical data, meaning they develop after the fact. The same is for indicators that show day separation. I was always forced to manually draw in vertical lines for the upcoming week. This indicator I built solves that issue by projecting vertical day separations for the upcoming week. Enjoy! :-)
headmapOverview
Advanced Price-Action Zones is a comprehensive, professional-grade indicator designed to automatically map and visualize the most critical historical price levels on your chart. Moving beyond simple lines, it renders these levels as dynamic, semi-transparent zones, providing an intuitive 'heatmap' of significant support, resistance, and potential liquidity areas.
This tool is built for traders who demand a clean, data-rich chart that adapts in real-time to market movements, with a fully customizable interface for personal tuning.
Core Features
Automated Multi-Timeframe Analysis: Automatically plots the high and low of the previous Daily, Weekly, Monthly, and 12-Hour sessions, forming the backbone of your structural analysis.
Heatmap-Style Visualization: Levels are displayed as colored zones rather than simple lines. This allows you to instantly spot areas of confluence where multiple historical levels overlap, indicating stronger S/R.
Dynamic Daily Highlighting: The most recent previous day's high/low is shown in a primary color (default: yellow). As a new day begins, these zones automatically fade to a distinct historical color (default: grey), keeping your focus on the most relevant and recent price action.
Intraday Liquidity Targets: Temporary 12-hour zones are plotted with "L. Shorts" and "L. Longs" labels, highlighting potential short-term reversal areas or stop-run targets. These zones automatically expire to keep the chart clean.
Daily Range Context: A subtle background fill visualizes the entire range of the previous trading day, extending into the current session to provide immediate context for breakouts or range-bound behavior.
Toggleable Volume Data: Get deeper insights with floating labels showing the volume on the bar that created a key Daily, Weekly, or Monthly level. This feature can be turned on or off in the settings.
Full Customization: Every color for every zone type, background, and text element is fully adjustable via the indicator's input menu.
On-Chart Informative Legend: A clean legend in the top-right corner explains the color-coding and the implied importance of each timeframe.
How to Interpret the Visuals
High-Timeframe Levels (Monthly/Red, Weekly/Orange): Use these major zones to identify significant market turning points, high-probability reversal areas, and logical take-profit targets for swing trades.
Mid-Timeframe Levels (Daily): The yellow zones (most recent) represent the immediate battlefield. A decisive move beyond this area can set the tone for the session. The grey zones provide a historical map of prior daily structures.
Short-Timeframe Levels (12-Hour/Blue): Treat these as intraday targets. They often represent areas where stop-losses and liquidations might be clustered, making them magnets for price in the short term.
Disclaimer & Technical Notes
Disclaimer: This tool is for educational and analytical purposes only and does not constitute financial advice or trade signals. All trading involves significant risk.
Technical Note on lookahead: This indicator correctly uses lookahead = barmerge.lookahead_on when requesting higher-timeframe data. This is the standard, industry-accepted method to ensure that a completed historical value (e.g., the previous day's high) is plotted consistently and accurately across all chart timeframes. It does not "repaint" in the conventional sense of changing past signals, but rather ensures data stability.
This is a protected, closed-source script.
Liquidity Point LinesLiquidity Point Lines
The "Liquidity Point Lines" indicator helps traders identify potential areas of liquidity in the market by drawing lines at specific price levels where significant "liquidation events" may have occurred. These events are determined by analyzing the MACD Histogram and identifying pivot points that suggest strong movements, which are often associated with the flushing out of short or long positions.
How It Works
This indicator leverages the MACD Histogram to gauge the strength of price momentum. It then identifies pivot highs and lows within the MACD Histogram's values. When a significant pivot is detected, the indicator interprets this as a potential "liquidity point" — a price level where a substantial amount of buy or sell orders (often due to liquidations) may have been executed.
The indicator distinguishes between:
Shorts Liquidation Points (Resistance): These are identified when the MACD Histogram registers a pivot high, suggesting a strong upward movement that could have liquidated short positions. Lines are drawn at the high price of the bar where this pivot occurred.
Longs Liquidation Points (Support): Conversely, these are identified when the MACD Histogram registers a pivot low, indicating a strong downward movement that might have liquidated long positions. Lines are drawn at the low price of the bar where this pivot occurred.
Key Features and Settings
The "Liquidity Point Lines" indicator offers extensive customization to tailor its sensitivity and visual representation:
MACD Settings for Liquidity: Configure the underlying MACD calculation with adjustable Fast Length, Slow Length, Source, Signal Smoothing, and MA Types (SMA/EMA) for both the Oscillator and Signal Line.
Liquidity Points Settings:
Pivot Lookback Left/Right: Define the number of bars to look back on either side to identify a pivot in the MACD Histogram.
Dynamic Strength Thresholds: This powerful feature allows the indicator to dynamically calculate the significance of a liquidation event. When enabled, it uses the average absolute histogram value over a specified Dynamic Threshold Lookback Period and applies Small and Medium Threshold Factors to determine the strength (Small, Medium, or Large) of the liquidity point.
Fixed Strength Thresholds: If dynamic thresholds are disabled, you can set fixed numerical values for Small and Medium Histogram Thresholds to define the strength categories.
Color & Style Customization: Assign distinct colors for Small, Medium, and Large liquidation points, choose the Line Style (Solid, Dashed, Dotted), and set the Label Text Color.
Label X Offset (To Right): Adjust the horizontal position of the liquidity point labels on your chart.
Liquidity Points Management:
Max Active Liquidity Lines: Control the maximum number of liquidity lines displayed simultaneously on your chart. Older lines are automatically removed to maintain clarity, except for lines that have been "touched" (i.e., price has interacted with that liquidity level).
Visual Interpretation
Each liquidity line is colored according to the strength of the detected liquidation event, making it easy to visually assess the potential significance of the price level. Lines extend to the right, serving as ongoing reference points. When the price interacts with a liquidity line (i.e., "touches" it), the line and its corresponding label are removed, indicating that the liquidity at that level may have been absorbed.
This indicator can be a valuable tool for identifying potential support and resistance levels, understanding market reactions to "liquidation cascades," and informing your trading decisions.
Obsession.FX Sessions IndicatorSession & Fractal Structure Tool — for Market Phases and Microstructure Analysis
This script combines session timeframes with local fractal identification to help traders analyze market context and structural shifts. It is designed for those applying Smart Money Concepts, where precise session boundaries and reactions to key levels are critical.
Session Zones
The script visualizes the three main trading sessions: Asia, London, and New York. It helps to:
– identify active phases of market participation;
– track each session’s Highs and Lows;
– interpret price behavior in relation to intraday liquidity shifts.
Each session is fully customizable — including start/end time, background and border colors, and display style (box, high/low lines, filled zones).
Fractal Structure
Fractals are shown as local highs and lows, supporting:
– detection of BOS/CHOCH on lower timeframes;
– building A→B ranges for structural flow;
– confirming short-term directional changes.
The fractal algorithm is optimized for responsiveness and clarity, with no repainting and minimal delay.
Purpose and Usage
This tool is intended for contextual reading of price action: determining the active session and monitoring price behavior within its boundaries, with a focus on microstructural reactions. It supports building precise entry logic based on the interaction between session ranges and fractal structure.
Closed Source Justification
The script is closed-source due to its use of proprietary logic for fractal detection and session integration, developed as part of a private trading methodology not available in public libraries.
Fractal Adaptive Moving Average (FRAMA)Core Concept
Unlike traditional moving averages that use fixed smoothing factors, FRAMA adapts its responsiveness based on how "fractal" or chaotic the price movement is:
In trending markets (low fractal dimension), it becomes more responsive
In choppy/sideways markets (high fractal dimension), it becomes smoother
How It Works
1. Fractal Dimension Calculation:
Splits the lookback period into two halves
Calculates price ranges for each half and the total period
Uses logarithmic ratios to determine the fractal dimension (bounded between 1.0 and 2.0)
2. Dynamic Alpha Calculation:
Converts fractal dimension to a smoothing factor (alpha)
Higher fractal dimension = lower alpha = smoother average
Lower fractal dimension = higher alpha = more responsive average
3. Adaptive Smoothing:
Applies the calculated alpha to create the moving average
FRAMA = alpha × current_price + (1 - alpha) × previous_FRAMA
Key Parameters
Length (16): Lookback period for calculations
Fast Constant (4.0): Maximum responsiveness limit
Slow Constant (300.0): Minimum responsiveness limit
Visual Features
Line Color: Green when rising, red when falling
Background: Light green above FRAMA (bullish), light red below (bearish)
Information Table: Shows current FRAMA value, price, trend direction, and efficiency ratio
Close Price: Plotted as a semi-transparent white line for comparison
Trading Applications
FRAMA is particularly useful for:
Trend Following: More responsive in strong trends, less noisy in consolidations
Support/Resistance: Acts as dynamic support in uptrends, resistance in downtrends
Market Regime Detection: The efficiency ratio helps identify trending vs. ranging markets
Entry/Exit Signals: Crossovers and price position relative to FRAMA
The indicator automatically balances between being fast enough to catch trends early while being smooth enough to avoid false signals in choppy markets.
In this FRAMA script, fractal refers to measuring the complexity or "roughness" of price movements over time, not the self-similar geometric patterns we typically think of.
What the Script Measures
The script calculates a fractal dimension that quantifies how chaotic or smooth the price action is:
Low fractal dimension (closer to 1.0): Price moves in a relatively straight, trending manner
High fractal dimension (closer to 2.0): Price moves in a jagged, choppy, sideways manner.
The Logic
If the market is trending smoothly:
The sum of the two half-period ranges will be close to the total range
This gives a fractal dimension closer to 1.0
The indicator becomes more responsive (faster)
If the market is choppy/sideways:
The sum of the two half-period ranges will be much larger than the total range
This gives a fractal dimension closer to 2.0
The indicator becomes less responsive (smoother)
Practical Example
Imagine a 16-period lookback:
Trending market: Price goes from 100 → 116 steadily
Choppy market: Price bounces 100→108→102→114→106→116
The choppy market has a higher fractal dimension because there's more "path length" relative to the actual distance traveled.
Why This Matters
The fractal dimension becomes the adaptive mechanism that automatically adjusts the moving average's sensitivity based on current market conditions - making it faster in trends and smoother in consolidations.
what is efficiency that is shown in tables
The efficiency shown in the table measures how "efficient" or smooth the FRAMA line is compared to the actual price movement.
What It Means
Efficiency = FRAMA Movement ÷ Price Movement
Values close to 0: FRAMA is very smooth/stable while price is moving significantly
Values close to 1: FRAMA is moving almost as much as the price
Values > 1: FRAMA is moving more than the raw price (rare, usually in very short periods)
Practical Interpretation
Low Efficiency (0.1 - 0.3):
FRAMA is doing a good job of smoothing out noise
Market is likely choppy/sideways
The adaptive mechanism is working - keeping the average stable during consolidation
High Efficiency (0.7 - 1.0):
FRAMA is closely following price movements
Market is likely trending strongly
The adaptive mechanism is making the average more responsive
Medium Efficiency (0.3 - 0.7):
Balanced market conditions
FRAMA is providing moderate smoothing
Trading Context
This efficiency ratio helps you understand:
Market regime: Is this a trending or ranging market?
Signal quality: Low efficiency periods might produce fewer but higher-quality signals
Adaptive performance: How well the FRAMA is adapting to current conditions
For example, if you see efficiency at 0.15, it means the FRAMA moved only 15% as much as the price did in the last bar, indicating it's successfully filtering out noise in a choppy market. If efficiency is 0.85, the FRAMA is closely tracking price, suggesting a trending environment where you want the average to be responsive.
Previous Daily High/LowThe previous day’s high and low are critical price levels that traders use to identify potential support, resistance, and intraday trading opportunities. These levels represent the highest and lowest prices reached during the prior trading session and often act as reference points for future price action.
Why Are Previous Daily High/Low Important?
Support & Resistance Zones
The previous day’s low often acts as support (buyers defend this level).
The previous day’s high often acts as resistance (sellers defend this level).
Breakout Trading
A move above the previous high suggests bullish momentum.
A move below the previous low suggests bearish momentum.
Mean Reversion Trading
Traders fade moves toward these levels, expecting reversals.
Example: Buying near the previous low in an uptrend.
Institutional Order Flow
Market makers and algos often reference these levels for liquidity.
How to Use Previous Daily High/Low in Trading
1. Breakout Strategy
Long Entry: Price breaks & closes above previous high → bullish continuation.
Short Entry: Price breaks & closes below previous low → bearish continuation.
2. Reversal Strategy
Long at Previous Low: If price pulls back to the prior day’s low in an uptrend.
Short at Previous High: If price rallies to the prior day’s high in a downtrend.
3. Range-Bound Markets
Buy near previous low, sell near previous high if price oscillates between them.
Example Trade Setup
Scenario: Price opens near the previous day’s high.
Bullish Case: A breakout above it targets next resistance.
Bearish Case: Rejection at the high signals a pullback.
TIME-SPLT ACADEMY INDICATOR# TIME-SPLT ACADEMY CISD + FVG + TSM FRACTALS - Comprehensive Market Structure Analysis Tool
## Overview
This indicator combines three essential market structure analysis components into a unified trading tool: Change in State Direction (CISD), Fair Value Gaps (FVG), and TSM Fractals. This integration provides traders with a complete framework for identifying market structure breaks, price imbalances, and key pivot levels on any timeframe.
## Component 1: CISD (Change in State Direction)
**What it is:** CISD identifies significant breaks in market structure by tracking when price decisively breaks above previous swing highs (bullish CISD) or below previous swing lows (bearish CISD). This concept is fundamental to understanding trend changes and continuation patterns.
**How it works:**
- Monitors swing highs and lows using customizable pivot periods
- Tracks when price closes above a previous swing high (bullish structure break)
- Tracks when price closes below a previous swing low (bearish structure break)
- Draws horizontal lines from the pivot point to the break point with "CISD" labels
- Works on multiple timeframes simultaneously
**Trading Applications:**
- Identifies trend changes and continuation signals
- Provides entry signals on structure breaks
- Helps determine market bias and direction
## Component 2: FVG (Fair Value Gaps)
**What it is:** Fair Value Gaps are price imbalances that occur when there's a gap between the high of one candle and the low of another candle two periods later, with the middle candle not filling this gap. These represent areas where price moved inefficiently and often return to "fill" the gap.
**How it works:**
- Analyzes 3-candle patterns to identify gaps
- Bearish FVG: Gap between low and high where price dropped leaving unfilled space above
- Bullish FVG: Gap between high and low where price rose leaving unfilled space below
- Tracks 8 different candle body combinations for each direction (up, down, doji patterns)
- Monitors gap mitigation when price returns to fill the imbalance
- Changes color when gaps are partially or fully mitigated
**Gap Detection Logic:**
- Bearish FVG patterns: DDD, DDJ, JDD, UDJ, JDU, UDD, DDU, UDU
- Bullish FVG patterns: DUD, DUJ, JUD, UUJ, JUU, UUD, DUU, UUU
- (D=Down candle, U=Up candle, J=Doji candle)
**Trading Applications:**
- High-probability reversal zones when price returns to FVGs
- Support and resistance levels
- Target areas for limit orders
- Risk management reference points
## Component 3: TSM Fractals
**What it is:** TSM Fractals identify significant pivot highs and lows using Williams Fractal methodology. These mark potential reversal points and key support/resistance levels.
**How it works:**
- Identifies fractal highs: peaks where the center candle's high is higher than surrounding candles
- Identifies fractal lows: valleys where the center candle's low is lower than surrounding candles
- Uses customizable lookback periods (default 15) for fractal identification
- Displays horizontal lines with "$" symbols at fractal levels
- Maintains a configurable number of recent fractals on the chart
**Trading Applications:**
- Key support and resistance levels
- Potential reversal zones
- Confluence with other analysis tools
- Stop loss placement reference points
## Why This Combination Works
**Synergistic Analysis:** Each component provides different but complementary information:
1. **CISD** shows when market structure changes, indicating trend shifts or continuation
2. **FVGs** reveal where price has moved inefficiently and may return for rebalancing
3. **Fractals** highlight key pivot points that often act as support/resistance
**Trading Edge:** The combination allows for:
- **Entry Confirmation:** Wait for CISD breaks near unfilled FVGs at fractal levels
- **Risk Management:** Use FVG boundaries and fractal levels for stop placement
- **Target Selection:** Project moves to opposite FVGs or fractal levels
- **Market Context:** Understand whether you're trading with or against structure
## Key Features
**Multi-Timeframe CISD:**
- Customizable timeframe settings (Minute, Hour, Day, Week, Month)
- Adjustable swing length for pivot identification
- Customizable line styles, widths, and colors
- Optional alerts on structure breaks
**Advanced FVG Management:**
- Automatic gap size filtering
- Real-time mitigation tracking
- Color-coded active vs. mitigated gaps
- Optional pip value labels
- Large gap alerts for significant imbalances
**Intelligent Fractal Display:**
- Configurable fractal periods
- Maximum fractal count management
- Clean visual presentation
- Historical fractal preservation
## Settings & Customization
**CISD Settings:**
- Timeframe selection and multipliers
- Swing length adjustment (default 7)
- Line styling options
- Color customization for bullish/bearish breaks
- Alert toggle options
**FVG Settings:**
- Show/hide toggles for each direction
- Minimum gap size filtering
- Alert threshold for large gaps
- Color schemes for active and mitigated gaps
- Optional size labels in pips
**Fractal Settings:**
- Fractal period adjustment (default 15)
- Maximum display count (default 10)
- Show/hide toggle
## Educational Value
This indicator teaches traders to:
- Understand market structure concepts
- Recognize price inefficiencies
- Identify key pivot points
- Combine multiple analysis methods
- Develop systematic trading approaches
## Use Cases
**Swing Trading:** Identify major structure breaks with FVG confluence
**Day Trading:** Use lower timeframe CISDs with intraday FVGs
**Scalping:** Quick entries at FVG mitigation near fractal levels
**Position Trading:** Higher timeframe structure analysis with major FVGs
## Technical Implementation
- Utilizes Pine Script v6 for optimal performance
- Efficient array management for historical data
- Real-time calculations without repainting
- Memory-optimized box and line management
- Multi-timeframe data handling with proper security functions
This comprehensive tool eliminates the need for multiple separate indicators, providing everything needed for complete market structure analysis in one cohesive package. The educational component helps traders understand not just what the signals are, but why they work and how to use them effectively in different market conditions.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Hidden Markov Model [Extension] | FractalystWhat's the indicator's purpose and functionality?
The Hidden Markov Model is specifically designed to integrate with the Quantify Trading Model framework, serving as a probabilistic market regime identification system for institutional trading analysis.
Hidden Markov Models are particularly well-suited for market regime detection because they can model the unobservable (hidden) state of the market, capture probabilistic transitions between different states, and account for observable market data that each state generates.
The indicator uses Hidden Markov Model mathematics to automatically detect distinct market regimes such as low-volatility bull markets, high-volatility bear markets, or range-bound consolidation periods.
This approach provides real-time regime probabilities without requiring optimization periods that can lead to overfitting, enabling systematic trading based on genuine probabilistic market structure.
How does this extension work with the Quantify Trading Model?
The Hidden Markov Model | Fractalyst serves as a probabilistic state estimation engine for systematic market analysis.
Instead of relying on traditional technical indicators, this system automatically identifies market regimes using forward algorithm implementation with three-state probability calculation (bullish/neutral/bearish), Viterbi decoding process for determining most likely regime sequence without repainting, online parameter learning with adaptive emission probabilities based on market observations, and multi-feature analysis combining normalized returns, volatility comprehensive regime assessment.
The indicator outputs regime probabilities and confidence levels that can be used for systematic trading decisions, portfolio allocation, or risk management protocols.
Why doesn't this use optimization periods like other indicators?
The Hidden Markov Model | Fractalyst deliberately avoids optimization periods to prevent overfitting bias that destroys out-of-sample performance.
The system uses a fixed mathematical framework based on Hidden Markov Model theory rather than optimized parameters, probabilistic state estimation using forward algorithm calculations that work across all market conditions, online learning methodology with adaptive parameter updates based on real-time market observations, and regime persistence modeling using fixed transition probabilities with 70% diagonal bias for realistic regime behavior.
This approach ensures the regime detection signals remain robust across different market cycles without the performance degradation typical of over-optimized traditional indicators.
Can this extension be used independently for discretionary trading?
No, the Hidden Markov Model | Fractalyst is specifically engineered for systematic implementation within institutional trading frameworks.
The indicator is designed to provide regime filtering for systematic trading algorithms and risk management systems, enable automated backtesting through mathematical regime identification without subjective interpretation, and support institutional-level analysis when combined with systematic entry/exit models.
Using this indicator independently would miss the primary value proposition of systematic regime-based strategy optimization that institutional frameworks provide.
How do I integrate this with the Quantify Trading Model?
Integration enables institutional-grade systematic trading through advanced machine learning and statistical validation:
- Add both HMM Extension and Quantify Trading Model to your chart
- Select HMM Extension as the bias source using input.source()
- Quantify automatically uses the extension's bias signals for entry/exit analysis
- The built-in machine learning algorithms score optimal entry and exit levels based on trend intensity, and market structure patterns identified by the extension
The extension handles all bias detection complexity while Quantify focuses on optimal trade timing, position sizing, and risk management along with PineConnector automation
What markets and assets does the indicator Extension work best on?
The Hidden Markov Model | Fractalyst performs optimally on markets with sufficient price movement since the system relies on statistical analysis of returns, volatility, and momentum patterns for regime identification.
Recommended asset classes include major forex pairs (EURUSD, GBPUSD, USDJPY) with high liquidity and clear regime transitions, stock index futures (ES, NQ, YM) providing consistent regime behavior patterns, individual equities (large-cap stocks with sufficient volatility for regime detection), cryptocurrency markets (BTC, ETH with pronounced regime characteristics), and commodity futures (GC, CL showing distinct market cycles and regime transitions).
These markets provide sufficient statistical variation in returns and volatility patterns, ensuring the HMM system's mathematical framework can effectively distinguish between bullish, neutral, and bearish regime states.
Any timeframe from 15-minute to daily charts provides sufficient data points for regime calculation, with higher timeframes (4H, Daily) typically showing more stable regime identification with fewer false transitions, while lower timeframes (30m, 1H) provide more responsive regime detection but may show increased noise.
Acceptable Timeframes and Portfolio Integration:
- Any timeframe that can be evaluated within Quantify Trading Model's backtesting engine is acceptable for live trading implementation.
Legal Disclaimers and Risk Acknowledgments
Trading Risk Disclosure
The HMM Extension is provided for informational, educational, and systematic bias detection purposes only and should not be construed as financial, investment, or trading advice. The extension provides institutional analysis but does not guarantee profitable outcomes, accurate bias predictions, or positive investment returns.
Trading systems utilizing bias detection algorithms carry substantial risks including but not limited to total capital loss, incorrect bias identification, market regime changes, and adverse conditions that may invalidate analysis. The extension's performance depends on accurate data, TradingView infrastructure stability, and proper integration with Quantify Trading Model, any of which may experience data errors, technical failures, or service interruptions that could affect bias detection accuracy.
System Dependency Acknowledgment
The extension requires continuous operation of multiple interconnected systems: TradingView charts and real-time data feeds, accurate reporting from exchanges, Quantify Trading Model integration, and stable platform connectivity. Any interruption or malfunction in these systems may result in incorrect bias signals, missed transitions, or unexpected analytical behavior.
Users acknowledge that neither Fractalyst nor the creator has control over third-party data providers, exchange reporting accuracy, or TradingView platform stability, and cannot guarantee data accuracy, service availability, or analytical performance. Market microstructure changes, reporting delays, exchange outages, and technical factors may significantly affect bias detection accuracy compared to theoretical or backtested performance.
Intellectual Property Protection
The HMM Extension, including all proprietary algorithms, classification methodologies, three-state bias detection systems, and integration protocols, constitutes the exclusive intellectual property of Fractalyst. Unauthorized reproduction, reverse engineering, modification, or commercial exploitation of these proprietary technologies is strictly prohibited and may result in legal action.
Liability Limitation
By utilizing this extension, users acknowledge and agree that they assume full responsibility and liability for all trading decisions, financial outcomes, and potential losses resulting from reliance on the extension's bias detection signals. Fractalyst shall not be liable for any unfavorable outcomes, financial losses, missed opportunities, or damages resulting from the development, use, malfunction, or performance of this extension.
Past performance of bias detection accuracy, classification effectiveness, or integration with Quantify Trading Model does not guarantee future results. Trading outcomes depend on numerous factors including market regime changes, pattern evolution, institutional behavior shifts, and proper system configuration, all of which are beyond the control of Fractalyst.
User Responsibility Statement
Users are solely responsible for understanding the risks associated with algorithmic bias detection, properly configuring system parameters, maintaining appropriate risk management protocols, and regularly monitoring extension performance. Users should thoroughly validate the extension's bias signals through comprehensive backtesting before live implementation and should never base trading decisions solely on automated bias detection.
This extension is designed to provide systematic institutional flow analysis but does not replace the need for proper market understanding, risk management discipline, and comprehensive trading methodology. Users should maintain active oversight of bias detection accuracy and be prepared to implement manual overrides when market conditions invalidate analysis assumptions.
Terms of Service Acceptance
Continued use of the HMM Extension constitutes acceptance of these terms, acknowledgment of associated risks, and agreement to respect all intellectual property protections. Users assume full responsibility for compliance with applicable laws and regulations governing automated trading system usage in their jurisdiction.
Previous Daily OHLCPrevious Daily OHLC Indicator
Overview:
This professional TradingView indicator displays the previous day's key price levels (Open, High, Low, Close, and 50% midpoint) as horizontal lines on your chart. These levels are essential for traders who use previous day data as support and resistance zones in their technical analysis.
What It Does
Displays Previous Day Levels: Automatically shows horizontal lines for yesterday's OHLC data
Real-Time Updates: Lines update dynamically each new trading day
Fully Customizable: Complete control over which levels to display and how they appear
Smart Line Management: Choose between showing lines for recent bars or across the entire chart
Professional Labels: Clear labels with optional price values for each level
Color Coded System: Distinct colors for each level type for instant recognition
Key Features
Five Important Price Levels
Previous Day Open: Yesterday's opening price - often acts as psychological level
Previous Day High: Yesterday's highest price - key resistance level for breakout trading
Previous Day Low: Yesterday's lowest price - important support level for breakdowns
Previous Day Close: Yesterday's closing price - significant reference point
50% Midpoint: Calculated midpoint between previous day's high and low - bias indicator
Modified Fractal Open/CloseModified Fractal (Open/Close Based) - Indicator
The Modified Fractal (Open/Close Based) indicator offers a new way to detect fractal patterns on your chart by analyzing the open and close prices instead of the traditional high and low values.
🧮 How it works:
The indicator evaluates a group of 5 consecutive candles.
The central candle (2 bars ago) is analyzed.
For a Bullish Fractal:
The open or close of the central candle must be lower than the open and close of the other 4 surrounding candles.
For a Bearish Fractal:
The open or close of the central candle must be higher than the open and close of the other 4 surrounding candles.
Once a valid pattern is detected, a visual symbol (triangle) is plotted directly on the chart and an alert can be triggered.
✅ Key Features:
Non-repainting signals (evaluated after candle close)
Fully mechanical detection logic
Easy-to-use visual signals
Alert conditions ready to be integrated into TradingView’s alert system
Suitable for multiple timeframes (can be used from M1 to Daily and beyond)
🎯 Use case:
This modified fractal approach can help traders:
Spot potential swing points
Identify possible reversals
Confirm price exhaustion zones
Support breakout or mean reversion strategies
⚠ Note:
This indicator does not provide trade signals by itself. It is recommended to be combined with additional tools, price action analysis, or risk management rules.
ICT SMT Divergence Lines - EnhancedThis indicator identifies and highlights Smart Money Technique (SMT) divergence between two user-selected symbols across any timeframe. It compares the last two completed candles of both symbols and marks potential SMT signals only when market context aligns with smart money principles.
⚡ Key Features
✅ SMT Bullish Signal
Symbol 1 forms a lower low compared to its prior candle.
Symbol 2 forms a higher low compared to its prior candle.
The previous candle on the chart was bearish (close < open).
✅ SMT Bearish Signal
Symbol 1 forms a higher high compared to its prior candle.
Symbol 2 forms a lower high compared to its prior candle.
The previous candle on the chart was bullish (close > open).
✅ Visual Representation
Draws a clean, subtle line between the two SMT candles — green for bullish SMT and red for bearish SMT — making divergence easy to spot.
Optional small triangle markers above or below the bar where SMT is detected.
Old lines are automatically removed to keep the chart uncluttered.
✅ Multi-timeframe compatible
Works on any chart timeframe.
Compares the last two completed candles in the current chart timeframe, or can be adapted for a fixed higher timeframe.
