PatternTransitionTablesPatternTransitionTables Library
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- 1. INTRODUCTION --------- 🌸
💮 Overview
This library provides precomputed state transition tables to enable ultra-efficient, O(1) computation of Ordinal Patterns. It is designed specifically to support high-performance indicators calculating Permutation Entropy and related complexity measures.
💮 The Problem & Solution
Calculating Permutation Entropy, as introduced by Bandt and Pompe (2002), typically requires computing ordinal patterns within a sliding window at every time step. The standard successive-pattern method (Equations 2+3 in the paper) requires ≤ 4d-1 operations per update.
Unakafova and Keller (2013) demonstrated that successive ordinal patterns "overlap" significantly. By knowing the current pattern index and the relative rank (position l) of just the single new data point, the next pattern index can be determined via a precomputed look-up table. Computing l still requires d comparisons, but the table lookup itself is O(1), eliminating the need for d multiplications and d additions. This reduces total operations from ≤ 4d-1 to ≤ 2d per update (Table 4). This library contains these precomputed tables for orders d = 2 through d = 5.
🌸 --------- 2. THEORETICAL BACKGROUND --------- 🌸
💮 Permutation Entropy
Bandt, C., & Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series.
doi.org
This concept quantifies the complexity of a system by comparing the order of neighbouring values rather than their magnitudes. It is robust against noise and non-linear distortions, making it ideal for financial time series analysis.
💮 Efficient Computation
Unakafova, V. A., & Keller, K. (2013). Efficiently Measuring Complexity on the Basis of Real-World Data.
doi.org
This library implements the transition function φ_d(n, l) described in Equation 5 of the paper. It maps a current pattern index (n) and the position of the new value (l) to the successor pattern, reducing the complexity of updates to constant time O(1).
🌸 --------- 3. LIBRARY FUNCTIONALITY --------- 🌸
💮 Data Structure
The library stores transition matrices as flattened 1D integer arrays. These tables are mathematically rigorous representations of the factorial number system used to enumerate permutations.
💮 Core Function: get_successor()
This is the primary interface for the library for direct pattern updates.
• Input: The current pattern index and the rank position of the incoming price data.
• Process: Routes the request to the specific transition table for the chosen order (d=2 to d=5).
• Output: The integer index of the next ordinal pattern.
💮 Table Access: get_table()
This function returns the entire flattened transition table for a specified dimension. This enables local caching of the table (e.g. in an indicator's init() method), avoiding the overhead of repeated library calls during the calculation loop.
💮 Supported Orders & Terminology
The parameter d is the order of ordinal patterns (following Bandt & Pompe 2002). Each pattern of order d contains (d+1) data points, yielding (d+1)! unique patterns:
• d=2: 3 points → 6 unique patterns, 3 successor positions
• d=3: 4 points → 24 unique patterns, 4 successor positions
• d=4: 5 points → 120 unique patterns, 5 successor positions
• d=5: 6 points → 720 unique patterns, 6 successor positions
Note: d=6 is not implemented. The resulting code size (approx. 191k tokens) exceeds the Pine Script limit of 100k tokens (as of 2025-12).
Patternrecognition
SMC N-Gram Probability Matrix [PhenLabs]📊 SMC N-Gram Probability Matrix
Version: PineScript™ v6
📌 Description
The SMC N-Gram Probability Matrix applies computational linguistics methodology to Smart Money Concepts trading. By treating SMC patterns as a discrete “alphabet” and analyzing their sequential relationships through N-gram modeling, this indicator calculates the statistical probability of which pattern will appear next based on historical transitions.
Traditional SMC analysis is reactive—traders identify patterns after they form and then anticipate the next move. This indicator inverts that approach by building a transition probability matrix from up to 5,000 bars of pattern history, enabling traders to see which SMC formations most frequently follow their current market sequence.
The indicator detects and classifies 11 distinct SMC patterns including Fair Value Gaps, Order Blocks, Liquidity Sweeps, Break of Structure, and Change of Character in both bullish and bearish variants, then tracks how these patterns transition from one to another over time.
🚀 Points of Innovation
First indicator to apply N-gram sequence modeling from computational linguistics to SMC pattern analysis
Dynamic transition matrix rebuilds every 50 bars for adaptive probability calculations
Supports bigram (2), trigram (3), and quadgram (4) sequence lengths for varying analysis depth
Priority-based pattern classification ensures higher-significance patterns (CHoCH, BOS) take precedence
Configurable minimum occurrence threshold filters out statistically insignificant predictions
Real-time probability visualization with graphical confidence bars
🔧 Core Components
Pattern Alphabet System: 11 discrete SMC patterns encoded as integers for efficient matrix indexing and transition tracking
Swing Point Detection: Uses ta.pivothigh/pivotlow with configurable sensitivity for non-repainting structure identification
Transition Count Matrix: Flattened array storing occurrence counts for all possible pattern sequence transitions
Context Encoder: Converts N-gram pattern sequences into unique integer IDs for matrix lookup
Probability Calculator: Transforms raw transition counts into percentage probabilities for each possible next pattern
🔥 Key Features
Multi-Pattern SMC Detection: Simultaneously identifies FVGs, Order Blocks, Liquidity Sweeps, BOS, and CHoCH formations
Adjustable N-Gram Length: Choose between 2-4 pattern sequences to balance specificity against sample size
Flexible Lookback Range: Analyze anywhere from 100 to 5,000 historical bars for matrix construction
Pattern Toggle Controls: Enable or disable individual SMC pattern types to customize analysis focus
Probability Threshold Filtering: Set minimum occurrence requirements to ensure prediction reliability
Alert Integration: Built-in alert conditions trigger when high-probability predictions emerge
🎨 Visualization
Probability Table: Displays current pattern, recent sequence, sample count, and top N predicted patterns with percentage probabilities
Graphical Probability Bars: Visual bar representation (█░) showing relative probability strength at a glance
Chart Pattern Markers: Color-coded labels placed directly on price bars identifying detected SMC formations
Pattern Short Codes: Compact notation (F+, F-, O+, O-, L↑, L↓, B+, B-, C+, C-) for quick pattern identification
Customizable Table Position: Place probability display in any corner of your chart
📖 Usage Guidelines
N-Gram Configuration
N-Gram Length: Default 2, Range 2-4. Lower values provide more samples but less specificity. Higher values capture complex sequences but require more historical data.
Matrix Lookback Bars: Default 500, Range 100-5000. More bars increase statistical significance but may include outdated market behavior.
Min Occurrences for Prediction: Default 2, Range 1-10. Higher values filter noise but may reduce prediction availability.
SMC Detection Settings
Swing Detection Length: Default 5, Range 2-20. Controls pivot sensitivity for structure analysis.
FVG Minimum Size: Default 0.1%, Range 0.01-2.0%. Filters insignificant gaps.
Order Block Lookback: Default 10, Range 3-30. Bars to search for OB formations.
Liquidity Sweep Threshold: Default 0.3%, Range 0.05-1.0%. Minimum wick extension beyond swing points.
Display Settings
Show Probability Table: Toggle the probability matrix display on/off.
Show Top N Probabilities: Default 5, Range 3-10. Number of predicted patterns to display.
Show SMC Markers: Toggle on-chart pattern labels.
✅ Best Use Cases
Anticipating continuation or reversal patterns after liquidity sweeps
Identifying high-probability BOS/CHoCH sequences for trend trading
Filtering FVG and Order Block signals based on historical follow-through rates
Building confluence by comparing predicted patterns with other technical analysis
Studying how SMC patterns typically sequence on specific instruments or timeframes
⚠️ Limitations
Predictions are based solely on historical pattern frequency and do not account for fundamental factors
Low sample counts produce unreliable probabilities—always check the Samples display
Market regime changes can invalidate historical transition patterns
The indicator requires sufficient historical data to build meaningful probability matrices
Pattern detection uses standardized parameters that may not capture all institutional activity
💡 What Makes This Unique
Linguistic Modeling Applied to Markets: Treats SMC patterns like words in a language, analyzing how they “flow” together
Quantified Pattern Relationships: Transforms subjective SMC analysis into objective probability percentages
Adaptive Learning: Matrix rebuilds periodically to incorporate recent pattern behavior
Comprehensive SMC Coverage: Tracks all major Smart Money Concepts in a unified probability framework
🔬 How It Works
1. Pattern Detection Phase
Each bar is analyzed for SMC formations using configurable detection parameters
A priority hierarchy assigns the most significant pattern when multiple detections occur
2. Sequence Encoding Phase
Detected patterns are stored in a rolling history buffer of recent classifications
The current N-gram context is encoded into a unique integer identifier
3. Matrix Construction Phase
Historical pattern sequences are iterated to count transition occurrences
Each context-to-next-pattern transition increments the appropriate matrix cell
4. Probability Calculation Phase
Current context ID retrieves corresponding transition counts from the matrix
Raw counts are converted to percentages based on total context occurrences
5. Visualization Phase
Probabilities are sorted and the top N predictions are displayed in the table
Chart markers identify the current detected pattern for visual reference
💡 Note:
This indicator performs best when used as a confluence tool alongside traditional SMC analysis. The probability predictions highlight statistically common pattern sequences but should not be used as standalone trading signals. Always verify predictions against price action context, higher timeframe structure, and your overall trading plan. Monitor the sample count to ensure predictions are based on adequate historical data.
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
Trade PullBack - EMA Pullback System with Auto Risk-Reward# Trade Pull Back - Professional Pullback Trading System
## 📊 Overview
**Trade Pull Back** is a comprehensive pullback trading system that combines trend-following principles with precise entry timing using candlestick pattern confirmation. This indicator is designed for traders who want to enter trending markets at optimal retracement levels with pre-calculated risk-reward ratios.
---
## 🎯 Core Methodology
### Why This System Works
Most traders struggle with two key challenges:
1. **Entering too early** - jumping into trades before the pullback completes
2. **Entering too late** - missing the momentum after the pullback reverses
This system solves both problems by using a **3-Phase Confirmation Process**:
**Phase 1: Trend Identification** → **Phase 2: Pullback Detection** → **Phase 3: Reversal Confirmation**
---
## 🔧 How It Works
### 1. Triple EMA Framework (The Foundation)
Unlike traditional single EMA systems, this indicator uses **3 separate EMAs** with different purposes:
- **EMA Trend (default: 50)** - Determines the overall market direction
- Source: HL/2 for balanced trend reading
- Acts as the primary filter - we only trade in its direction
- **EMA High (default: 20)** - Dynamic resistance in uptrends
- Source: High prices for accurate resistance mapping
- Entry trigger for bullish setups when price closes above it
- **EMA Low (default: 20)** - Dynamic support in downtrends
- Source: Low prices for accurate support mapping
- Entry trigger for bearish setups when price closes below it
**Why 3 EMAs?**
- Single EMA can't distinguish between trend and pullback zones
- Two EMAs (like MACD) don't provide clear entry/exit levels
- Three EMAs create a **channel system** that identifies both trend direction AND optimal entry zones
### 2. Pattern Recognition Engine
The system detects two high-probability reversal patterns:
#### Engulfing Patterns
- **Bullish Engulfing**: Previous bearish candle completely engulfed by bullish candle
- **Bearish Engulfing**: Previous bullish candle completely engulfed by bearish candle
- Validates: Strong momentum reversal with volume confirmation
#### Pin Bar Patterns
- **Bullish Pin Bar (Hammer)**: Long lower wick (60%+ of total range) rejecting lower prices
- **Bearish Pin Bar (Inverted Hammer)**: Long upper wick (60%+ of total range) rejecting higher prices
- Validates: Institutional rejection at support/resistance levels
**Pattern Quality Filter:**
- Body-to-wick ratio must meet minimum standards
- Checks previous candle momentum
- Requires trend alignment before signaling
### 3. Pullback Confirmation System
The system includes **5 mandatory conditions** before generating a signal:
#### For Bullish Signals (BUY):
1. ✅ Close > EMA Trend (uptrend confirmed)
2. ✅ EMA High > EMA Trend AND EMA Low > EMA Trend (healthy trend structure)
3. ✅ Bullish Engulfing OR Bullish Pin Bar (pattern detected)
4. ✅ Close > EMA High (breakout confirmation)
5. ✅ Optional: Low < EMA High (pullback occurred)
#### For Bearish Signals (SELL):
1. ✅ Close < EMA Trend (downtrend confirmed)
2. ✅ EMA High < EMA Trend AND EMA Low < EMA Trend (healthy trend structure)
3. ✅ Bearish Engulfing OR Bearish Pin Bar (pattern detected)
4. ✅ Close < EMA Low (breakdown confirmation)
5. ✅ Optional: High > EMA Low (pullback occurred)
**Additional Filters:**
- **Consecutive Bars Check**: Ensures pullback had momentum (1-5 bearish/bullish bars)
- **Signal Spacing**: Minimum 4 bars between signals to avoid noise
- **Confirmation Delay**: Signal appears only AFTER bar closes (no repainting)
---
## 💰 Automatic Risk-Reward Calculator
### Smart Position Sizing
When a signal triggers, the system automatically calculates:
**For Long Positions:**
- **Entry**: High of signal candle
- **Stop Loss**: Lower of last 2 candle lows (protects against false breakouts)
- **Target 1 (1R)**: Entry + 1x Risk
- **Target 2 (2R)**: Entry + 2x Risk
- **Target 3 (3R)**: Entry + 3x Risk
**For Short Positions:**
- **Entry**: Low of signal candle
- **Stop Loss**: Higher of last 2 candle highs
- **Targets**: Calculated based on risk multiple
### Auto-Remove Feature
Lines and labels automatically disappear when:
- Price hits Stop Loss (trade invalidated)
- Price reaches 3R target (trade complete)
This keeps your chart clean and focuses only on active trades.
---
## 📈 Multi-Timeframe Trend Analysis
### Confluence Trading
The built-in MTF trend box shows trend status across 7 timeframes simultaneously:
- M1, M5, M15, M30, H1, H4, D1
**Color Coding:**
- 🟢 **Green**: Uptrend (Price > EMA Trend AND EMAs aligned bullish)
- 🔴 **Red**: Downtrend (Price < EMA Trend AND EMAs aligned bearish)
- ⚪ **Gray**: No clear trend
**Why This Matters:**
- Trade with higher timeframe trends for better win rate
- Avoid counter-trend trades when all timeframes show same direction
- Identify divergences between timeframes for reversal opportunities
---
## 🎨 Customization Options
### EMA Settings
- Adjust periods for different trading styles (scalping vs swing trading)
- Choose price sources (HL/2, Close, HLC/3) for sensitivity tuning
### Pattern Selection
- Enable/disable Engulfing patterns
- Enable/disable Pin Bar patterns
- Trade only your preferred pattern type
### Signal Filters
- **Require Pullback**: Force pullback condition (stricter entries)
- **Consecutive Bars**: Set momentum requirement (1-5 bars)
### Display Options
- Show/hide EMA lines
- Show/hide signals
- Enable/disable alerts
- Customize Risk-Reward line styles and extensions
---
## 📋 How to Use This Indicator
### Step 1: Identify the Trend
- Wait for price to establish clear direction relative to EMA Trend (50)
- Check MTF box to confirm higher timeframe alignment
### Step 2: Wait for Pullback
- In uptrend: Watch for price to pull back toward EMA High
- In downtrend: Watch for price to pull back toward EMA Low
### Step 3: Pattern Confirmation
- Look for Engulfing or Pin Bar pattern (triangle/diamond markers)
- Ensure pattern forms at or near the EMA High/Low zone
### Step 4: Entry & Risk Management
- Enter when signal appears (after bar closes)
- Use displayed Stop Loss and Take Profit levels
- Consider partial profits at 1R and 2R, let remainder run to 3R
### Step 5: Trade Management
- If price hits SL, lines disappear automatically (trade invalidated)
- If price reaches 3R, lines disappear (trade complete)
- Consider trailing stop after 1R is reached
---
## ⚙️ Recommended Settings
### For Scalping (M1-M5)
- EMA Trend: 20-30
- EMA High/Low: 10-15
- Require Pullback: OFF
- Consecutive Bars: 1
### For Day Trading (M15-H1)
- EMA Trend: 50 (default)
- EMA High/Low: 20 (default)
- Require Pullback: ON
- Consecutive Bars: 2-3
### For Swing Trading (H4-D1)
- EMA Trend: 100-200
- EMA High/Low: 50
- Require Pullback: ON
- Consecutive Bars: 3-5
---
## ✅ What Makes This Script Original
### 1. Systematic Approach
This isn't just a collection of indicators. It's a **complete trading system** with:
- Defined entry rules (5-point confirmation checklist)
- Automatic risk management (SL/TP calculation)
- Trade validation (consecutive bars, signal spacing)
### 2. Smart EMA Framework
The 3-EMA system creates a **dynamic channel** that adapts to market conditions:
- Trend EMA = Direction filter
- High/Low EMAs = Entry/Exit zones
- Together they form a "trade zone" that standard EMAs can't provide
### 3. Pattern Quality Control
Not all Engulfing or Pin Bar patterns are equal. This system:
- Validates body-to-wick ratios
- Checks previous candle momentum
- Requires trend alignment before signaling
### 4. Auto Risk-Reward Management
Most indicators just show signals. This one:
- Calculates exact entry prices
- Places stop loss at optimal location (lower of 2 lows)
- Projects 3 profit targets based on risk
- Auto-removes when trade is complete/invalidated
### 5. No Repainting
- All signals appear AFTER bar closes
- No future data leaking
- What you see in backtest = what you get in real-time
---
## 🚨 Alerts
Built-in alerts notify you when:
- Bullish signal confirmed
- Bearish signal confirmed
Alerts fire once per bar (no spam) and only after bar closes (no false alerts).
---
## 📊 Best Practices
### ✅ DO:
- Trade in direction of higher timeframe trends
- Wait for full confirmation (all 5 conditions met)
- Use proper position sizing (1-2% risk per trade)
- Let winners run to at least 2R
### ❌ DON'T:
- Trade against major trend on MTF box
- Enter before signal bar closes
- Ignore the Stop Loss level
- Overtrade - respect the 4-bar minimum spacing
---
## 🔍 Limitations
This indicator is a **tool**, not a crystal ball:
- No indicator wins 100% of the time
- False signals occur in choppy/ranging markets
- Best results in trending conditions
- Requires proper risk management
- Should be combined with fundamental analysis and market context
---
## 📚 Educational Value
This script teaches:
- How to combine trend following with mean reversion
- Pattern recognition and validation
- Risk-reward ratio calculation
- Multi-timeframe analysis
- Proper trade entry timing
---
## 🎓 Credits & Disclaimer
**Original Work**: All code written from scratch
**Methodology**: Based on classical technical analysis principles (EMA crossovers, candlestick patterns, support/resistance)
**Disclaimer**: This indicator is for educational purposes. Past performance does not guarantee future results. Always practice proper risk management.
---
## 📞 Support
If you find this indicator helpful:
- Leave a review
- Share with fellow traders
- Provide feedback for improvements
**Note**: This is a closed-source script to protect the proprietary signal logic and filtering algorithms. The description above provides comprehensive understanding of the methodology without revealing exact implementation details.
---
**Version**: 1.0
**Pine Script Version**: 5
**Type**: Indicator (Overlay)
**Category**: Trend Following + Pattern Recognition
---
*Happy Trading! 🚀*
# 🇹🇭 คู่มือภาษาไทย / Thai Guide
# Trade Pull Back - คู่มือภาษาไทย
## 📊 ภาพรวม
**Trade Pull Back** เป็นระบบเทรด Pullback ที่ผสมผสานการเทรดตามเทรนด์กับการจับจังหวะเข้าออเดอร์ด้วย Candlestick Pattern พร้อมคำนวณ Risk-Reward อัตโนมัติ
---
## 🎯 หลักการทำงาน
### ทำไมระบบนี้ได้ผล?
แก้ปัญหา 2 ข้อหลักของเทรดเดอร์:
1. **เข้าเร็วเกินไป** - เข้าก่อน Pullback เสร็จ
2. **เข้าช้าเกินไป** - พลาดโมเมนตัมหลังกลับตัว
**วิธีแก้**: ใช้กระบวนการยืนยัน 3 ขั้นตอน
- **ขั้น 1**: ระบุเทรนด์ → **ขั้น 2**: ตรวจจับ Pullback → **ขั้น 3**: ยืนยันการกลับตัว
---
## 🔧 ส่วนประกอบหลัก
### 1. ระบบ EMA 3 เส้น
ต่างจาก EMA ทั่วไป ระบบนี้ใช้ 3 เส้นที่มีหน้าที่แยกกัน:
- **EMA Trend (50)** - กำหนดทิศทางเทรนด์หลัก
- **EMA High (20)** - แนวต้านไดนามิก (สำหรับ Buy)
- **EMA Low (20)** - แนวรับไดนามิก (สำหรับ Sell)
**ทำไมต้อง 3 เส้น?**
- 1 เส้น = แยกเทรนด์กับ Pullback ไม่ได้
- 2 เส้น = ไม่มีจุด Entry/Exit ชัดเจน
- 3 เส้น = สร้าง Channel ที่บอกทั้งเทรนด์และโซนเข้าออเดอร์
### 2. ตรวจจับ Pattern
ระบบตรวจจับ 2 Pattern หลัก:
**Engulfing (แท่งกลืน)**
- Bullish: แท่งเขียวกลืนแท่งแดงทั้งหมด
- Bearish: แท่งแดงกลืนแท่งเขียวทั้งหมด
**Pin Bar (แท่งหาง)**
- Bullish: หางล่างยาว 60%+ ของช่วงทั้งหมด
- Bearish: หางบนยาว 60%+ ของช่วงทั้งหมด
### 3. เงื่อนไขยืนยันสัญญาณ (5 ข้อ)
**สัญญาณ Buy:**
1. ✅ ราคาปิด > EMA Trend (เทรนด์ขาขึ้น)
2. ✅ EMA High และ Low เหนือ EMA Trend (โครงสร้างดี)
3. ✅ เกิด Bullish Engulfing หรือ Pin Bar
4. ✅ ราคาปิด > EMA High (ยืนยัน Breakout)
5. ✅ ตัวเลือก: มี Pullback มาแตะ EMA High
**สัญญาณ Sell:**
1. ✅ ราคาปิด < EMA Trend (เทรนด์ขาลง)
2. ✅ EMA High และ Low ใต้ EMA Trend (โครงสร้างดี)
3. ✅ เกิด Bearish Engulfing หรือ Pin Bar
4. ✅ ราคาปิด < EMA Low (ยืนยัน Breakdown)
5. ✅ ตัวเลือก: มี Pullback มาแตะ EMA Low
**ตัวกรองเพิ่มเติม:**
- ต้องมีแท่งติดกัน 1-5 แท่ง (กำหนดได้)
- ห่างสัญญาณก่อนหน้าอย่างน้อย 4 แท่ง
- สัญญาณปรากฏหลังแท่งปิดเท่านั้น (ไม่ Repaint)
---
## 💰 คำนวณ Risk-Reward อัตโนมัติ
เมื่อสัญญาณเกิด ระบบคำนวณให้อัตโนมัติ:
**Long Position:**
- Entry = High ของแท่งสัญญาณ
- Stop Loss = Low ที่ต่ำกว่าของ 2 แท่งล่าสุด
- Target = 1R, 2R, 3R
**Short Position:**
- Entry = Low ของแท่งสัญญาณ
- Stop Loss = High ที่สูงกว่าของ 2 แท่งล่าสุด
- Target = 1R, 2R, 3R
**ลบอัตโนมัติ:** เส้นหายเมื่อราคาชน SL หรือถึง 3R
---
## 📈 กล่องเทรนด์หลาย Timeframe
แสดงเทรนด์พร้อมกัน 7 Timeframe:
- M1, M5, M15, M30, H1, H4, D1
**สีแสดงผล:**
- 🟢 เขียว = Uptrend
- 🔴 แดง = Downtrend
- ⚪ เทา = ไม่มีเทรนด์
**ประโยชน์:** เทรดตาม Timeframe ใหญ่เพื่อเพิ่ม Win Rate
---
## 📋 วิธีใช้งาน (5 ขั้นตอน)
1. **ระบุเทรนด์** - เช็คราคาเทียบกับ EMA Trend และกล่อง MTF
2. **รอ Pullback** - เฝ้าราคา Pullback มาที่ EMA High/Low
3. **เช็ค Pattern** - มองหาลูกศรสามเหลี่ยม (Engulfing) หรือเพชร (Pin Bar)
4. **เข้าออเดอร์** - เข้าเมื่อสัญญาณปรากฏ ใช้ SL/TP ที่แสดง
5. **จัดการเทรด** - เส้นจะหายเองเมื่อชน SL หรือถึง 3R
---
## ⚙️ การตั้งค่าแนะนำ
**Scalping (M1-M5)**
- EMA Trend: 20-30
- EMA High/Low: 10-15
- Require Pullback: ปิด
**Day Trading (M15-H1)**
- EMA Trend: 50 (ค่าเริ่มต้น)
- EMA High/Low: 20 (ค่าเริ่มต้น)
- Require Pullback: เปิด
**Swing Trading (H4-D1)**
- EMA Trend: 100-200
- EMA High/Low: 50
- Require Pullback: เปิด
---
## ✅ จุดเด่นที่แตกต่าง
1. **เป็นระบบสมบูรณ์** - ไม่ใช่แค่รวม Indicator
2. **EMA 3 เส้นสร้าง Channel** - บอกทั้งเทรนด์และโซนเข้า
3. **ตรวจสอบคุณภาพ Pattern** - ไม่ใช่ทุก Pattern ที่ให้สัญญาณ
4. **คำนวณ RR อัตโนมัติ** - วาง SL/TP ให้เลย
5. **ไม่ Repaint** - สัญญาณปรากฏหลังแท่งปิดเท่านั้น
---
## 📊 ควรทำ / ไม่ควรทำ
### ✅ ควรทำ:
- เทรดตามเทรนด์ Timeframe ใหญ่
- รอยืนยันครบ 5 เงื่อนไข
- เสี่ยง 1-2% ต่อเทรด
- ปล่อยกำไรไปอย่างน้อย 2R
### ❌ ไม่ควรทำ:
- เทรดทวนเทรนด์ในกล่อง MTF
- เข้าก่อนแท่งปิด
- ละเลย Stop Loss
- เทรดบ่อยเกินไป
---
## 🔍 ข้อจำกัด
- ไม่มี Indicator ไหนชนะ 100%
- สัญญาณผิดพลาดเกิดในตลาด Sideways
- ผลดีสุดในตลาดที่มีเทรนด์ชัด
- ต้องใช้ Money Management
- ควรดูปัจจัยพื้นฐานประกอบ
---
## 🎓 คำเตือน
**Disclaimer**: อินดิเคเตอร์นี้สำหรับการศึกษา ผลในอดีตไม่รับประกันอนาคต ใช้ Risk Management ที่เหมาะสมเสมอ
---
**เวอร์ชั่น**: 1.0
**Pine Script**: v5
**ประเภท**: Indicator (Overlay)
*Happy Trading! 🚀*
## Screenshots
**Bearish Signals with Risk-Reward:**
! (drive.google.com)
**Bullish Signal with Risk-Reward:**
! (drive.google.com)
**Multi-Timeframe Trend Box:**
! (drive.google.com)
**Settings Panel:**
! (drive.google.com)
CNN Statistical Trading System [PhenLabs]📌 DESCRIPTION
An advanced pattern recognition system utilizing Convolutional Neural Network (CNN) principles to identify statistically significant market patterns and generate high-probability trading signals.
CNN Statistical Trading System transforms traditional technical analysis by applying machine learning concepts directly to price action. Through six specialized convolution kernels, it detects momentum shifts, reversal patterns, consolidation phases, and breakout setups simultaneously. The system combines these pattern detections using adaptive weighting based on market volatility and trend strength, creating a sophisticated composite score that provides both directional bias and signal confidence on a normalized -1 to +1 scale.
🚀 CONCEPTS
• Built on Convolutional Neural Network pattern recognition methodology adapted for financial markets
• Six specialized kernels detect distinct price patterns: upward/downward momentum, peak/trough formations, consolidation, and breakout setups
• Activation functions create non-linear responses with tanh-like behavior, mimicking neural network layers
• Adaptive weighting system adjusts pattern importance based on current market regime (volatility < 2% and trend strength)
• Multi-confirmation signals require CNN threshold breach (±0.65), RSI boundaries, and volume confirmation above 120% of 20-period average
🔧 FEATURES
Six-Kernel Pattern Detection:
Simultaneous analysis of upward momentum, downward momentum, peak/resistance, trough/support, consolidation, and breakout patterns using mathematically optimized convolution kernels.
Adaptive Neural Architecture:
Dynamic weight adjustment based on market volatility (ATR/Price) and trend strength (EMA differential), ensuring optimal performance across different market conditions.
Professional Visual Themes:
Four sophisticated color palettes (Professional, Ocean, Sunset, Monochrome) with cohesive design language. Default Monochrome theme provides clean, distraction-free analysis.
Confidence Band System:
Upper and lower confidence zones at 150% of threshold values (±0.975) help identify high-probability signal areas and potential exhaustion zones.
Real-Time Information Panel:
Live display of CNN score, market state with emoji indicators, net momentum, confidence percentage, and RSI confirmation with dynamic color coding based on signal strength.
Individual Feature Analysis:
Optional display of all six kernel outputs with distinct visual styles (step lines, circles, crosses, area fills) for advanced pattern component analysis.
User Guide
• Monitor CNN Score crossing above +0.65 for long signals or below -0.65 for short signals with volume confirmation
• Use confidence bands to identify optimal entry zones - signals within confidence bands carry higher probability
• Background intensity reflects signal strength - darker backgrounds indicate stronger conviction
• Enter long positions when blue circles appear above oscillator with RSI < 75 and volume > 120% average
• Enter short positions when dark circles appear below oscillator with RSI > 25 and volume confirmation
• Information panel provides real-time confidence percentage and momentum direction for position sizing decisions
• Individual feature plots allow granular analysis of specific pattern components for strategy refinement
💡Conclusion
CNN Statistical Trading System represents the evolution of technical analysis, combining institutional-grade pattern recognition with retail accessibility. The six-kernel architecture provides comprehensive market pattern coverage while adaptive weighting ensures relevance across all market conditions. Whether you’re seeking systematic entry signals or advanced pattern confirmation, this indicator delivers mathematically rigorous analysis with intuitive visual presentation.
Market Timing(Mastersinnifty)Overview
Market Timing (Mastersinnifty) is a proprietary visualization tool designed to help traders study historical market behavior through structural pattern similarity.
The script analyzes the most recent session’s price action and identifies the closest-matching historical sequence among thousands of past patterns. Once a match is found, the script projects the subsequent historical price path onto the current chart for easy visual reference.
Unlike traditional indicators, Market Timing (Mastersinnifty) does not generate trade signals. Instead, it offers a unique historical scenario analysis based on quantified structural similarity.
---
How It Works
- The script captures the last 20 closing prices and compares them to historical price sequences from the past 8000 bars.
- Similarity is computed using the Euclidean distance formula (sum of squared differences) between the current pattern and historical candidates.
- Upon finding the most similar past pattern, the subsequent historical movement is normalized relative to session opening and plotted onto the current chart using projection lines.
- The projection automatically adapts to intraday, daily, weekly, or monthly timeframes, with the option for manual or automatic projection length settings.
- Session start detection is handled automatically based on volume thresholds and price-time analysis to adjust for market openings across different instruments.
---
Key Features
- Historical Pattern Matching: Quantitative matching of the most similar past price structure.
- Dynamic Projections: Visualizes likely historical scenarios based on past market behavior.
- Auto/Manual Projection Length: Flexible control over the number of projected bars.
- Multi-Timeframe Support: Works seamlessly across intraday, daily, weekly, and monthly charts.
- Purely Visual Context: Designed to support human decision-making without replacing it with automatic trade signals.
---
Who Can Benefit
- Traders studying market structure repetition and price symmetry.
- Visual thinkers who prefer scenario-based planning over fixed indicator systems.
- Intraday, swing, and position traders looking for historical context to complement price action, volume, and momentum studies.
---
How to Use
- Apply the script to any asset — including indices, stocks, commodities, forex, or crypto.
- Select your preferred timeframe.
- Choose "Auto" or "Custom" for the projection length.
- Observe the projected lines:
- Upward slope = Historical bullish continuation.
- Downward slope = Historical bearish continuation.
- Flat movement = Historical sideways movement.
- Combine insights with volume, support/resistance, and price action for better decision-making.
---
Important Notes
- This script does not predict the future. It offers a visual reference based on historical similarity.
- Always validate projected scenarios with live market conditions.
- Market structure evolves; past behavior may not repeat under new market dynamics.
- Use this tool for educational and research purposes only.
---
Disclaimer
This is not financial advice. The Market Timing (Mastersinnifty) tool is intended for research and educational purposes only. Trading involves risk, and past performance does not guarantee future results. Always apply sound risk management practices.
Volume Predictor [PhenLabs]📊 Volume Predictor
Version: PineScript™ v6
📌 Description
The Volume Predictor is an advanced technical indicator that leverages machine learning and statistical modeling techniques to forecast future trading volume. This innovative tool analyzes historical volume patterns to predict volume levels for upcoming bars, providing traders with valuable insights into potential market activity. By combining multiple prediction algorithms with pattern recognition techniques, the indicator delivers forward-looking volume projections that can enhance trading strategies and market analysis.
🚀 Points of Innovation:
Machine learning pattern recognition using Lorentzian distance metrics
Multi-algorithm prediction framework with algorithm selection
Ensemble learning approach combining multiple prediction methods
Real-time accuracy metrics with visual performance dashboard
Dynamic volume normalization for consistent scale representation
Forward-looking visualization with configurable prediction horizon
🔧 Core Components
Pattern Recognition Engine : Identifies similar historical volume patterns using Lorentzian distance metrics
Multi-Algorithm Framework : Offers five distinct prediction methods with configurable parameters
Volume Normalization : Converts raw volume to percentage scale for consistent analysis
Accuracy Tracking : Continuously evaluates prediction performance against actual outcomes
Advanced Visualization : Displays actual vs. predicted volume with configurable future bar projections
Interactive Dashboard : Shows real-time performance metrics and prediction accuracy
🔥 Key Features
The indicator provides comprehensive volume analysis through:
Multiple Prediction Methods : Choose from Lorentzian, KNN Pattern, Ensemble, EMA, or Linear Regression algorithms
Pattern Matching : Identifies similar historical volume patterns to project future volume
Adaptive Predictions : Generates volume forecasts for multiple bars into the future
Performance Tracking : Calculates and displays real-time prediction accuracy metrics
Normalized Scale : Presents volume as a percentage of historical maximums for consistent analysis
Customizable Visualization : Configure how predictions and actual volumes are displayed
Interactive Dashboard : View algorithm performance metrics in a customizable information panel
🎨 Visualization
Actual Volume Columns : Color-coded green/red bars showing current normalized volume
Prediction Columns : Semi-transparent blue columns representing predicted volume levels
Future Bar Projections : Forward-looking volume predictions with configurable transparency
Prediction Dots : Optional white dots highlighting future prediction points
Reference Lines : Visual guides showing the normalized volume scale
Performance Dashboard : Customizable panel displaying prediction method and accuracy metrics
📖 Usage Guidelines
History Lookback Period
Default: 20
Range: 5-100
This setting determines how many historical bars are analyzed for pattern matching. A longer period provides more historical data for pattern recognition but may reduce responsiveness to recent changes. A shorter period emphasizes recent market behavior but might miss longer-term patterns.
🧠 Prediction Method
Algorithm
Default: Lorentzian
Options: Lorentzian, KNN Pattern, Ensemble, EMA, Linear Regression
Selects the algorithm used for volume prediction:
Lorentzian: Uses Lorentzian distance metrics for pattern recognition, offering excellent noise resistance
KNN Pattern: Traditional K-Nearest Neighbors approach for historical pattern matching
Ensemble: Combines multiple methods with weighted averaging for robust predictions
EMA: Simple exponential moving average projection for trend-following predictions
Linear Regression: Projects future values based on linear trend analysis
Pattern Length
Default: 5
Range: 3-10
Defines the number of bars in each pattern for machine learning methods. Shorter patterns increase sensitivity to recent changes, while longer patterns may identify more complex structures but require more historical data.
Neighbors Count
Default: 3
Range: 1-5
Sets the K value (number of nearest neighbors) used in KNN and Lorentzian methods. Higher values produce smoother predictions by averaging more historical patterns, while lower values may capture more specific patterns but could be more susceptible to noise.
Prediction Horizon
Default: 5
Range: 1-10
Determines how many future bars to predict. Longer horizons provide more forward-looking information but typically decrease accuracy as the prediction window extends.
📊 Display Settings
Display Mode
Default: Overlay
Options: Overlay, Prediction Only
Controls how volume information is displayed:
Overlay: Shows both actual volume and predictions on the same chart
Prediction Only: Displays only the predictions without actual volume
Show Prediction Dots
Default: false
When enabled, adds white dots to future predictions for improved visibility and clarity.
Future Bar Transparency (%)
Default: 70
Range: 0-90
Controls the transparency of future prediction bars. Higher values make future bars more transparent, while lower values make them more visible.
📱 Dashboard Settings
Show Dashboard
Default: true
Toggles display of the prediction accuracy dashboard. When enabled, shows real-time accuracy metrics.
Dashboard Location
Default: Bottom Right
Options: Top Left, Top Right, Bottom Left, Bottom Right
Determines where the dashboard appears on the chart.
Dashboard Text Size
Default: Normal
Options: Small, Normal, Large
Controls the size of text in the dashboard for various display sizes.
Dashboard Style
Default: Solid
Options: Solid, Transparent
Sets the visual style of the dashboard background.
Understanding Accuracy Metrics
The dashboard provides key performance metrics to evaluate prediction quality:
Average Error
Shows the average difference between predicted and actual values
Positive values indicate the prediction tends to be higher than actual volume
Negative values indicate the prediction tends to be lower than actual volume
Values closer to zero indicate better prediction accuracy
Accuracy Percentage
A measure of how close predictions are to actual outcomes
Higher percentages (>70%) indicate excellent prediction quality
Moderate percentages (50-70%) indicate acceptable predictions
Lower percentages (<50%) suggest weaker prediction reliability
The accuracy metrics are color-coded for quick assessment:
Green: Strong prediction performance
Orange: Moderate prediction performance
Red: Weaker prediction performance
✅ Best Use Cases
Anticipate upcoming volume spikes or drops
Identify potential volume divergences from price action
Plan entries and exits around expected volume changes
Filter trading signals based on predicted volume support
Optimize position sizing by forecasting market participation
Prepare for potential volatility changes signaled by volume predictions
Enhance technical pattern analysis with volume projection context
⚠️ Limitations
Volume predictions become less accurate over longer time horizons
Performance varies based on market conditions and asset characteristics
Works best on liquid assets with consistent volume patterns
Requires sufficient historical data for pattern recognition
Sudden market events can disrupt prediction accuracy
Volume spikes may be muted in predictions due to normalization
💡 What Makes This Unique
Machine Learning Approach : Applies Lorentzian distance metrics for robust pattern matching
Algorithm Selection : Offers multiple prediction methods to suit different market conditions
Real-time Accuracy Tracking : Provides continuous feedback on prediction performance
Forward Projection : Visualizes multiple future bars with configurable display options
Normalized Scale : Presents volume as a percentage of maximum volume for consistent analysis
Interactive Dashboard : Displays key metrics with customizable appearance and placement
🔬 How It Works
The Volume Predictor processes market data through five main steps:
1. Volume Normalization:
Converts raw volume to percentage of maximum volume in lookback period
Creates consistent scale representation across different timeframes and assets
Stores historical normalized volumes for pattern analysis
2. Pattern Detection:
Identifies similar volume patterns in historical data
Uses Lorentzian distance metrics for robust similarity measurement
Determines strength of pattern match for prediction weighting
3. Algorithm Processing:
Applies selected prediction algorithm to historical patterns
For KNN/Lorentzian: Finds K nearest neighbors and calculates weighted prediction
For Ensemble: Combines multiple methods with optimized weighting
For EMA/Linear Regression: Projects trends based on statistical models
4. Accuracy Calculation:
Compares previous predictions to actual outcomes
Calculates average error and prediction accuracy
Updates performance metrics in real-time
5. Visualization:
Displays normalized actual volume with color-coding
Shows current and future volume predictions
Presents performance metrics through interactive dashboard
💡 Note:
The Volume Predictor performs optimally on liquid assets with established volume patterns. It’s most effective when used in conjunction with price action analysis and other technical indicators. The multi-algorithm approach allows adaptation to different market conditions by switching prediction methods. Pay special attention to the accuracy metrics when evaluating prediction reliability, as sudden market changes can temporarily reduce prediction quality. The normalized percentage scale makes the indicator consistent across different assets and timeframes, providing a standardized approach to volume analysis.
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms.
👽 Core Features
Segment-Based Pattern Recognition
At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions.
AI-Powered k-Nearest Neighbors (kNN) Prediction
The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages.
Dynamic Dictionary of Historical Patterns
The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for:
Candle body ranges, High-low ranges, Wick highs and lows.
This dynamic approach ensures the model adapts continuously to evolving market conditions.
Volatility-Normalized Forecasting
Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments.
AI-Driven Trend Detection
The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes.
👽 Deep Dive into the Core Mathematics
👾 Segment-Based Multivariate Pattern Analysis
The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as:
Body Ranges: Differences between the open and close prices.
High-Low Ranges: Capturing the full volatility of a bar.
Wick Extremes: Quantifying deviations beyond the body, both above and below.
Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically.
👾 Volatility Normalization Using ATR
To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations.
👾 k-Nearest Neighbors (kNN) Algorithm
The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns.
Euclidean Distance Calculation:
The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns.
Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings.
👾 Prediction Horizon and Refinement
The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity.
👽 AI-Driven Pattern Recognition
Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions.
Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias.
Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models.
Visual Outputs
Forecast Line (Y_hat_line):
A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns.
Trend Regime Bands:
Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range.
Historical Pattern Matching:
The nearest historical pattern is displayed, allowing traders to visualize similarities
👽 Applications
Trend Identification:
Detect and follow emerging trends early using dynamic trend regime analysis.
Reversal Signals:
Anticipate market reversals with high-confidence predictions based on historically similar scenarios.
Range and Momentum Trading:
Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
TechniTrend: Advance Custom Candle Finder (CCF)🟦 Description:
The TechniTrend: Advanced Custom Candle Finder (CCF) is a versatile tool designed to help traders identify custom candlestick patterns using various configurable criteria. This indicator provides a flexible framework to filter and highlight specific candles based on volume, volatility, candle characteristics, and other important metrics. Below is a detailed explanation of each filter and its customization options:
🟦 Volume-Based Filters
🔸Volume Spike Filter:
Enable filtering based on volume spikes. Use the Volume Spike Multiplier to define what constitutes a significant increase in volume compared to the average. A spike indicates unusually high trading interest.
🔸Volume Range Filter:
Filter candles based on specific volume ranges. Set Minimum Volume and Maximum Volume thresholds to isolate candles with trading volumes within your desired boundaries.
🟦 Candle Body & Wick Filters
🔸Body Size Filter:
Filter candles based on the size of their body. A Body Size Multiplier determines what is considered a large body relative to historical averages.
🔸Body Percentage Filter:
Filter based on the proportion of the body to the entire candle size. Use the Body Percentage Threshold to highlight candles where the body makes up a certain percentage of the total candle range.
🔸Wick-to-Body Ratio Filter:
Identify candles with specific wick-to-body ratios. A higher Wick-to-Body Ratio can indicate indecision or reversals.
🟦 Volatility & Range Filters
🔸Volatility Filter:
Highlight candles based on price changes relative to volume. The Volatility Multiplier sets the threshold for what is considered a volatile candle.
🔸Candle Range Filter:
Filter based on the range (High - Low) of each candle. Use Minimum Candle Range and Maximum Candle Range to specify your desired candle size in points or pips.
🔸Short-Term and Long-Term Volatility Filters:
Analyze volatility over different periods. Enable Short-Term Volatility or Long-Term Volatility filters to compare recent volatility against historical averages, helping you detect sudden market shifts.
🟦 Candle Color & Open/Close Filters
🔸Candle Color Filter:
Filter based on the candle's color. Choose between Bullish (close > open) or Bearish (close < open) to focus on specific market sentiments.
🔸Open/Close Price Range Filter:
Filter based on the difference between the open and close prices. Use Minimum Open/Close Range and Maximum Open/Close Range to specify your acceptable range in price movements.
🟦 Core Functionality
The CCF indicator combines these filters to provide a final signal whenever a candle meets all the enabled criteria. By default, it highlights any qualifying candle directly on the chart and changes the background color for added visibility.
🟦 Key Features:
🔸Highly Customizable Filters: Adjust the parameters for each filter to tailor the indicator to your specific needs.
🔸Multiple Conditions: Combine several conditions to identify complex candlestick patterns.
🔸Real-Time Alerts: Receive instant notifications when a matching candle pattern is found based on your custom criteria.
🟦 How to Use:
🔸Enable the filters you wish to apply (e.g., Volume Spike, Candle Body Size, Volatility).
🔸Adjust the thresholds for each filter to fine-tune the pattern recognition criteria.
🔸Observe the chart to see visual cues for candles that match your specified conditions.
🟦 Notes:
🔸Ensure that you clearly understand each filter’s role. Over-filtering with very strict criteria may reduce the number of signals.
🔸This indicator is designed to be a customizable tool, not providing buy or sell recommendations.
🔸Use in combination with other analysis tools and indicators for the best results.
Lune Technical Analysis Premium⬛️ Overview
Lune Technical Analysis is a state-of-the-art TradingView indicator, meticulously designed to provide real-time market insights. Distinguished by its non-repainting features that operate in real-time, this tool brings enhanced accuracy and timeliness to your market analysis.
🟦 Features
Lune Technical Analysis equips traders with an array of innovative features:
🔹 Candle Coloring: The Candle Coloring feature introduces an innovative approach to visualizing market sentiment by coloring chart candles. It is devised to streamline your market analysis, offering a readily digestible snapshot of market trends. For example, if you aim to gauge the predominant market sentiment promptly, enable this feature for instant candle color-coding in accordance with prevailing bullish or bearish market structures. Though it currently supports only Market Structure-based Candle Coloring, its settings can be manipulated for enabling or disabling this feature. This feature operates by applying predefined algorithms that interpret market sentiment, coloring the candles accordingly.
🔹 Chart Pattern Detection: This sophisticated tool automatically detects and illustrates common chart patterns on your chart, simplifying the process of pattern identification. It identifies a range of patterns such as Head & Shoulders, Inverted Head & Shoulders, Ascending/Descending Wedges, Broadening Wedges, various Triangles, and Double Tops/Bottoms, enhancing your confluence detection in the market. For example, upon detecting a Double Top pattern, you could anticipate a potential price reversal due to this bearish signal. The sensitivity of this tool can be customized according to your trading style, with lower settings for short-term changes and higher for long-term. This feature leverages predefined formulas and price action analysis to identify these patterns.
🔹 Trendlines: With the Automatic Trendline Drawing tool, your technical analysis becomes significantly more efficient and precise. This feature is engineered to identify upward and downward Trendlines, aiding in locating potential pivots, and market support/resistance. For instance, if the price consistently rebounds off a Trendline, it may continue to do so, serving as a support/resistance level. However, a break through the Trendline could signal potential volatility and trend change. This feature's sensitivity to price changes can be adjusted to either short or long-term. It works by tracing Trendlines based on price action and wick formations to detect potential pivots.
🔹 Liquidity Bubbles: Liquidity Bubbles is an advanced tool that pinpoints key liquidity areas and large positions in real-time. This feature significantly contributes to effective trading strategy formulation by highlighting potential entry and exit points. It operates in real-time, ensuring zero repaint or lag, and supports two modes: Enhanced Bubbles and Basic Bubbles. For instance, the detection of multiple bullish Liquidity Bubbles during a ranging market could suggest an upward price movement due to dominant bullish volume. This feature's settings include thresholds for insignificant bubble filtering and a mode selection feature. Liquidity Bubbles operates by applying a proprietary formula to volume data, determining general volume direction and potential positions.
🔹 Market Structure: The Market Structure tool identifies key market structures such as Break of Structures (BoS) and Change of Character (ChoCh), thereby enhancing your ability to read market trends and sentiment. This smart money concept gives you a unique insight into short-term and long-term market trends. For instance, the appearance of a bullish Break of Structure and Change of Character after a predominantly bearish market sentiment could suggest a new bullish trend. This feature allows users to select which Market Structures to display and calculates these structures based on the market's high and low points.
🔹 Order Blocks: Order Blocks provide a visual representation of areas where large market participants are likely to place orders. These zones, where significant buying or selling activity has occurred in the past, offer insightful data for future price movements. The Order Blocks feature operates in real-time, providing real-time Order Blocks without any lag. For instance, if the price enters a large Order Block with predominantly bullish volume, an upward price movement can be anticipated. However, if the price breaks through the block, it could suggest the block's invalidation and a likely continued price fall. You can configure the settings to enable an additional Order Block, customize timeframes, overlap functions, and apply a quality filter. This feature calculates Order Blocks using the volume and candle size data.
🔹 Supply/Demand Zones: This real-time tool identifies crucial supply and demand zones, revealing potential price reaction points. These zones, where supply (selling pressure) and demand (buying pressure) have historically impacted price significantly, provide traders with insights into potential areas of strong support (demand) and resistance (supply). For example, if the price enters a large supply zone, a price rejection could be anticipated due to historical selling pressure at this zone. The settings enable users to add an additional Supply/Demand Zone, customize the timeframe, and apply a quality filter. This feature identifies common Supply/Demand Zones patterns based on volume and the size of the zone and displays them on the chart.
🔹 Fair Value Gaps: The Fair Value Gaps tool is designed to identify potential price correction zones or "gaps". These areas, where the market price sharply deviated from the fair value, suggest potential price adjustments in the future. For instance, the formation of a bullish Fair Value Gap could indicate a future price drop to fill this gap, potentially followed by an upward movement if the gap was of fair value. The settings allow users to enable additional Fair Value Gaps, customize the timeframe, and apply a quality filter. This feature measures large market gaps based on the size of the gap and its volume.
These features and tools collectively offer a comprehensive solution for traders to understand and navigate the financial markets. It's important to remember that they are designed to assist in making informed trading decisions and should be used as part of a balanced trading strategy.
🟧 Usage
Lune Technical Analysis's unique feature set can be leveraged both individually and synergistically. It is important to understand each feature and experiment with different configurations to best suit your unique trading needs.
🔸 Example #1: The following example demonstrates how the Order Block and Liquidity Bubbles feature can be used together to enhance your market analysis.
Order Blocks work in real-time to identify key order zones based on price action. These zones are often crucial for predicting price fluctuations. Meanwhile, Liquidity Bubbles act as real-time visual cues that detect significant market positions, facilitating an understanding of market accumulation, distribution, and trapped positions.
In this instance, at point 1, a bearish Basic and Enhanced Liquidity Bubble is visible within a crucial Order Block. The combination of these indicators augments the bearish sentiment, leading to a potential price decrease. Similarly, at point 2, the conjunction of two bullish Basic Liquidity Bubbles within an Order Block strengthens the bullish sentiment, culminating in a subsequent price increase.
🔸 Example #2: The following example demonstrates how Supply and Demand Zones can be used to detect strong and quality supports and resistance.
Supply and Demand Zones operate in real-time, detecting crucial zones based on price action and volume. This feature is invaluable for predicting potential price reaction points.
At point 1, the price enters a Supply Zone, a historical hotspot for selling activity, which usually leads to a price rejection and consequent decrease. At point 2, a Demand Zone indicating a bullish sentiment suggests a potential reversal when the price touches this level.
🔸 Example #3: The following example demonstrates how the Chart Pattern Detection feature is able to detect chart patterns to help enhance your trades.
Chart Pattern Detection employs formulas and price action analysis to identify common chart patterns as they form. Here, it successfully detects a 'Head and Shoulders' pattern, a conventionally bearish pattern, indicating a potential price drop.
🟥 Conclusion
Lune Technical Analysis stands as an exceptional blend of real-time insights into market activity. Its real-time, non-repainting features offer traders a more precise and timely approach to market analysis, promoting improved decision making in ever-changing market conditions.
🔻 Access
You can see the Author's instructions below to get instant access to this indicator & our Premium Suite.
🔻 Disclaimer
Lune Technical Analysis is a tool for aiding in market analysis and is not a guarantee of future market performance or individual trading success. We strongly recommend that users combine our tool with their trading strategies and do their due diligence before making any trading decisions.
Remember, past performance is not indicative of future results. Please trade responsibly.
Sniffer
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Overview
A vast majority of modern data analysis & modelling techniques rely upon the idea of hidden patterns, wether it is some type of visualisation tool or some form of a complex machine learning algorithm, the one thing that they have in common is the belief, that patterns tell us what’s hidden behind plain numbers. The same philosophy has been adopted by many traders & investors worldwide, there’s an entire school of thought that operates purely based on chart patterns. This is where Sniffer comes in, it is a tool designed to simplify & quantify the job of pattern recognition on any given price chart, by combining various factors & techniques that generate high-quality results.
This tool analyses bars selected by the user, and highlights bar clusters on the chart that exhibit similar behaviour across multiple dimensions. It can detect a single candle pattern like hammers or dojis, or it can handle multiple candles like morning/evening stars or double tops/bottoms, and many more. In fact, the tool is completely independent of such specific candle formations, instead, it works on the idea of vector similarity and generates a degree of similarity for every single combination of candles. Only the top-n matches are highlighted, users get to choose which patterns they want to analyse and to what degree, by customising the feature-space.
Background
In the world of trading, a common use-case is to scan a price chart for some specific candlestick formations & price structures, and then the chart is further analysed in reference to these events. Traders are often trying to answer questions like, when was the last time price showed similar behaviour, what are the instances similar to what price is doing right now, what happens when price forms a pattern like this, what were some of other indicators doing when this happened last(RSI, CCI, ADX etc), and many other abstract ideas to have a stronger confluence or to confirm a bias.Having such a context can be vital in making better informed decisions, but doing this manually on a chart that has thousands of candles can have many disadvantages. It’s tedious, human errors are rather likely, and even if it’s done with pin-point accuracy, chances are that we’ll miss out on many pieces of information. This is the thought that gave birth to Sniffer .
Sniffer tries to provide a general solution for pattern-based analysis by deploying vector-similarity computation techniques, that cover the full-breadth of a price chart and generate a list of top-n matches based on the criteria selected by the user. Most of these techniques come from the data science space, where vector similarity is often implemented to solve classification & clustering problems. Sniffer uses same principles of vector comparison, and computes a degree of similarity for every single candle formation within the selected range, and as a result generates a similarity matrix that captures how similar or dissimilar a set of candles is to the input set selected by the user.
How It Works
A brief overview of how the tool is implemented:
- Every bar is processed, and a set of features are mapped to it.
- Bars selected by the user are captured, and saved for later use.
- Once the all the bars have been processed, candles are back-tracked and degree of similarity is computed for every single bar(max-limit is 5000 bars).
- Degree of similarity is computed by comparing attributes like price range, candle breadth & volume etc.
- Similarity matrix is sorted and top-n results are highlighted on the chart through boxes of different colors.
A brief overview of the features space for bars:
- Range: Difference between high & low
- Body: Difference between close & open
- Volume: Traded volume for that candle
- Head: Upper wick for green candles & lower wick for red candles
- Tail: Lower wick for green candles & upper wick for red candles
- BTR: Body to Range ratio
- HTR: Head to Range ratio
- TTR: Tail to Range ratio
- HTB: Head to Body ratio
- TTB: Tail to Body ratio
- ROC: Rate of change for HL2 for four different periods
- RSI: Relative Strength Index
- CCI: Commodity Channel Index
- Stochastic: Stochastic Index
- ADX: DMI+, DMI- & ADX
A brief overview of how degree of similarity is calculated:
- Each bar set is compared to the inout bar set within the selected feature space
- Features are represented as vectors, and distance between the vectors is calculated
- Shorter the distance, greater the similarity
- Different distance calculation methods are available to choose from, such as Cosine, Euclidean, Lorentzian, Manhattan, & Pearson
- Each method is likely to generate slightly different results, users are expected to select the method & the feature space that best fits their use-case
How To Use It
- Usage of this tool is relatively straightforward, users can add this indicator to their chart and similar clusters will be highlighted automatically
- Users need to select a time range that will be treated as input, and bars within that range become the input formation for similarity calculations
- Boxes will be draw around the clusters that fit the matching criteria
- Boxes are color-coded, green color boxes represent the top one-third of the top-n matches, yellow boxes represent the middle third, red boxes are for bottom third, and white box represents user-input
- Boxes colors will be adjusted as you adjust input parameters, such as number of matches or look-back period
User Settings
Users can configure the following options:
- Select the time-range to set input bars
- Select the look-back period, number of candles to backtrack for similarity search
- Select the number of top-n matches to show on the chart
- Select the method for similarity calculation
- Adjust the feature space, this enables addition of custom features, such as pattern recognition, technical indicators, rate of change etc
- Toggle verbosity, shows degree of similarity as a percentage value inside the box
Top Features
- Pattern Agnostic: Designed to work with variable number of candles & complex patterns
- Customisable Feature Space: Users get to add custom features to each bar
- Comprehensive Comparison: Generates a degree of similarity for all possible combinations
Final Note
- Similarity matches will be shown only within last 4500 bars.
- In theory, it is possible to compute similarity for any size candle formations, indicator has been tested with formations of 50+ candles, but it is recommended to select smaller range for faster & cleaner results.
- As you move to smaller time frames, selected time range will provide a larger number of candles as input, which can produce undesired results, it is advised to adjust your selection when you change time frames. Seeking suggestions on how to directly receive bars as user input, instead of time range.
- At times, users may see array index out of bound error when setting up this indicator, this generally happens when the input range is not properly configured. So, it should disappear after you select the input range, still trying to figure out where it is coming from, suggestions are welcome.
Credits
- @HeWhoMustNotBeNamed for publishing such a handy PineScript Logger, it certainly made the job a lot easier.
FunctionPatternDecompositionLibrary "FunctionPatternDecomposition"
Methods for decomposing price into common grid/matrix patterns.
series_to_array(source, length) Helper for converting series to array.
Parameters:
source : float, data series.
length : int, size.
Returns: float array.
smooth_data_2d(data, rate) Smooth data sample into 2d points.
Parameters:
data : float array, source data.
rate : float, default=0.25, the rate of smoothness to apply.
Returns: tuple with 2 float arrays.
thin_points(data_x, data_y, rate) Thin the number of points.
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
rate : float, default=2.0, minimum threshold rate of sample stdev to accept points.
Returns: tuple with 2 float arrays.
extract_point_direction(data_x, data_y) Extract the direction each point faces.
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
Returns: float array.
find_corners(data_x, data_y, rate) ...
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
rate : float, minimum threshold rate of data y stdev.
Returns: tuple with 2 float arrays.
grid_coordinates(data_x, data_y, m_size) transforms points data to a constrained sized matrix format.
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
m_size : int, default=10, size of the matrix.
Returns: flat 2d pseudo matrix.
Double Top/Bottom V2This is an enhanced version of Double Top/Bottom detector.
Initial basic version can be found here:
Concept of deriving pattern is similar but there are few major changes.
Double Top:
Get the highest pivot high from last X pivot highs ( DTHigh1 )
Look for next top most pivot high which happened after DTHigh1 ( DTHigh2 )
Look for lowest pivot low between DTHigh1 and DTHigh2 ( DTLow )
Double Bottom:
Get the lowest pivot low from last X pivot lows ( DBLow1 )
Look for next lowest pivot low which happened after DBLow1 ( DBLow2 )
Look for highest pivot high between DBLow1 and DBLow2 ( DbHigh )
Other Key parameters:
checkForAbsolutePeaks and absolutePeakLoopback work together. When selected, double top and double bottom is formed only if DTHigh1 / DBLow1 are highest/lowest points from last absolutePeakLoopback bars back.
considerPivotDistance will make sure distance between Highs(in double top) and lows(in double bottom) are below 2 ( MaxAtrDistanceBase ) times ATR. And distance between average Highs/Lows to Low/High forming triangle is less than 6 ( MaxAtrDistanceHighLow ) times ATR. This will avoid showing steep triangles as double top/bottoms.
showLastLevels option allows users to display dashed lines on double top/bottom confirmation and invalidation levels for last formed double bottom and tops. These can be treated as strong support and resistence. Dashed lines are permanently formed on double top/bottom setups when an invalidation or confirmation occurs by price crossing either lowest or highest points of double top/bottom triangles.
Alerts:
Probable double top/bottom - when double top or bottom triangles formed.
Double top/bottom confirmation - when double top or bottom is confirmed.
Double top/bottom invalidation - when double top or bottom is invalidated.
Few important points about adjusting parameters:
Lower pivot lengths will generate more signals. But, too frequent signals may not be desirable as well.
higher absolutePeakLoopback will reduce number of signals while strengthening them.
Unchecking consider pivot distance or reducing MaxAtrDistanceBase/MaxAtrDistanceHighLow may considerably increase the number of pivots specially when pivot lengths are lower side. This may result in reduced quality of signals as well.
Moving average condition which is part of basic script is not included in this. We can add Hull Suite or moving average crossover on the chart as confirmation of strong signal.
Pattern Recognition Probabilities [racer8]Brief 🌟
Pattern Recognition Probabilities (PRP) is a REALLY smart indicator. It uses the correlation coefficient formula to determine if the current set of bars resembles that of past patterns. It counts the number of times the current pattern has occurred in the past and looks at how it performed historically to determine the probability of an up move, down move, or neutral move.
I'd like to say, I'm proud of this indicator 😆🤙 This is the SMARTEST indicator I have ever made 🧠🧠🧠
Note: PRP doesn't give you actual probabilities, but gives you instead the historical occurrences of up, down, and neutral moves that resulted after the pattern. So you can calculate probabilities based on these valuable statistics. So for example, PRP can tell you this pattern has historically resulted in 55 up moves, 20 down moves, and 60 neutral moves.
Parameters 🌟
You can adjust the Pattern length, Minimum correlation, Statistics lookback, Exit after time, and Atr multiplier parameters.
Pattern length - determines how long the pattern is
Minimum correlation - determines the minimum correlation coefficient needed to pass as a similiar enough pattern.
Statistics lookback - lookback period for gathering all the patterns in the past.
Exit after time - determines when exit occurred (number of periods after pattern) ; is the point that represents the pattern's result.
Atr multiplier - determines minimum atr move needed to qualify whether result was an up/down move or a neutral move. If a particular historical pattern resulted in a move that was less than the min atr, then it is recorded as a neutral move in the statistics.
Thanks for reading! 🙏
Good luck 🍀 Stay safe 😷 Drink lots of water💧
Enjoy! 🥳 and Hit the like button! 👍
Test: Pattern RecognitionEXPERIMENTAL:
a test on how to compare price at different frequency's with static patterns.
