Quantum Flow ScannerOverview
The Quantum Flow Scanner is a comprehensive technical analysis indicator that combines trend detection, momentum analysis, and dynamic band systems to identify potential market opportunities. This indicator uses advanced filtering techniques and multi-factor detection strength calculations to help traders make informed decisions.
Key Features
Trend Detection System
Dual-period momentum analysis (Fast/Slow periods configurable)
Pattern recognition engine that analyzes recent price movements
Normalized momentum calculations adjusted for volatility
Bull and Bear detection generation based on trend changes
Dynamic Band System
Adaptive bands that adjust to market volatility using ATR (Average True Range)
Customizable band width and distance multipliers
Optional midline, upper band, and lower band displays
Visual channel fill options for enhanced clarity
Background color coding for trend direction
Detection Strength Rating
Multi-factor detection strength calculation (25-92% range)
Considers volatility, momentum, trend duration, and volume
Higher timeframe alignment analysis
Swing position evaluation
Real-time percentage display on detections
Performance Tracking
Live performance statistics table
Total detections counter
Successful detections vs unsuccessful detections tracking based on configurable stop loss and take profit levels
Success rate percentage calculation
Average detection strength monitoring
How It Works
The indicator employs a sophisticated filtering mechanism based on pole-zero placement algorithms to smooth price data and calculate dynamic bands. When price crosses these bands in conjunction with momentum shifts, the indicator generates Bull or Bear detections.
Detection strength is calculated using eight weighted factors:
Market volatility assessment
Momentum cluster analysis
Distance from dynamic midline
Trend consistency duration
Higher timeframe trend alignment
Volume profile analysis
Candle strength evaluation
Swing position context
Configuration Options
Period Settings:
Fast Period (1-200): Controls short-term momentum sensitivity
Slow Period (1-500): Defines longer-term trend context
Pattern Recognition Length (5-50): Sets momentum analysis window
Sensitivity Controls:
Distance Multiplier (1.0-10.0): Adjusts band width relative to volatility
Cluster Size (1-15): Number of bars analyzed for momentum clustering
Display Options:
Customizable detection colors
Optional detection markers and percentage labels
Dynamic band visibility toggles
Channel fill options
Background color coding
Performance Tracking:
Configurable stop loss and take profit levels (in points)
Optional performance statistics table
Success rate monitoring
Use Cases
This indicator is designed for:
Trend identification across multiple timeframes
Entry and exit timing optimization
Market volatility assessment
Detection quality evaluation through strength ratings
Strategy performance tracking
Important Notes
This indicator is for educational and informational purposes only
Past performance does not guarantee future results
Always use proper risk management and position sizing
Detections should be used as part of a comprehensive trading strategy
Test thoroughly on historical data before live trading
No indicator is 100% accurate; losses are part of trading
Cari dalam skrip untuk "track"
GOGO SCALPER# GOGO SCALPER - Advanced Multi-Timeframe Trading Indicator
## Overview
GOGO SCALPER is a comprehensive trading indicator that combines multiple proven trading concepts into one powerful tool. It provides automated bias detection, session analysis, market structure tracking, and high-probability entry signals for scalpers and day traders.
## Key Features
### 🎯 Auto Bias System
- **Dual Timeframe Analysis**: Automatically tracks both Lower Timeframe (LTF) and Higher Timeframe (HTF) bias using EMA-based momentum
- **Dynamic Confidence Scoring**: Real-time confidence percentage (0-100%) for BUY/SELL signals based on multiple market factors
- **Smart Signal Generation**: Only triggers entries when both timeframes align during active trading sessions
### 📊 Market Phase Detection
- **Expansion vs Consolidation**: Automatically identifies whether the market is in an expansion or consolidation phase
- **Multi-Metric Analysis**: Uses Bollinger Band Width, Average Daily Range (ADR), and ATR ratios to determine market conditions
- **Trend Strength Indicator**: Shows whether the current trend is STRONG or WEAK
### 🕒 Killzone Session Management
- **Four Major Sessions**: Asia, London, NY AM, and NY PM killzones with customizable times
- **Visual Session Boxes**: Color-coded boxes highlighting active trading sessions
- **Session Range Tracking**: Displays the price range for each killzone session
- **Auto Time Remaining**: Shows countdown timer for active sessions
### 📈 Multi-Timeframe Structure Analysis
- **HTF Candle Visualization**: Displays H1, H4, and Daily candles as mini-charts on your current timeframe
- **Sweep Detection**: Automatically identifies bullish and bearish liquidity sweeps
- **Numbered Candle System**: Labels candles 1-5 leading up to sweeps for pattern recognition
- **Counter-Sweep Protection**: Filters out invalidated sweeps automatically
### 🔍 Market Structure Tools
- **CISD (Close in Structure Detection)**: Identifies when price closes through pivot highs/lows
- **FVG Detection**: Automatically plots Fair Value Gaps (Bullish & Bearish) with mitigation tracking
- **H4 & Daily Open Lines**: Tracks key opening prices with dynamic extension
- **High/Low Levels**: Plots session highs and lows with breakout alerts
### 📋 Information Dashboard
- **Comprehensive Table Display**: Shows all critical information at a glance
- Current HTF and LTF bias
- Active session
- Trend strength
- Signal direction
- Confidence percentage
- Entry confirmation status
- Market phase (Expansion/Consolidation)
- Killzone ranges
### ⚡ Entry Signal System
- **BUY Signal**: Triggers when price crosses above Bollinger Band upper level during bullish bias
- **SELL Signal**: Triggers when price crosses below Bollinger Band lower level during bearish bias
- **Session Filter**: Signals only activate during configured killzone sessions
- **Confirmation Labels**: Clear "Long Confirm!" or "Short Confirm!" messages with "Wait!" during invalid conditions
## How It Works
### Bias Calculation
The indicator compares current price against EMA on both lower and higher timeframes:
- **BULLISH**: Price above EMA
- **BEARISH**: Price below EMA
- **NEUTRAL**: Price at EMA
### Confidence Scoring
The confidence score (0-100%) is calculated using:
- HTF/LTF bias alignment (25%)
- Active session quality (20%)
- Volume analysis (15%)
- ATR momentum (15%)
- RSI position (15%)
- Trend strength (10%)
- BB position (10%)
### Market Phase Detection
Uses a voting system from three metrics:
- Bollinger Band Width relative to average
- Average Daily Range achievement percentage
- ATR ratio to moving average
When 2+ metrics vote for expansion, market is in "EKSPANSI" phase, otherwise "KONSOLIDASI".
## Best Use Cases
- **Scalping**: 1-5 minute charts with 15m/1H/4H higher timeframes
- **Day Trading**: 5-15 minute charts with 1H/4H/Daily higher timeframes
- **Session Trading**: Focus on London and NY AM sessions for highest probability setups
- **Confluence Trading**: Wait for HTF/LTF alignment + high confidence + active session
## Customization Options
- Adjustable EMA length and Bollinger Band settings
- Customizable killzone session times and colors
- Configurable HTF timeframes and candle count
- Toggle visibility for all components (FVGs, sweeps, lines, boxes)
- Flexible table position and display options
## Recommended Settings
- **1-3 minute charts**: Use 5m/15m/1H for HTF analysis
- **5-15 minute charts**: Use 1H/4H/Daily for HTF analysis
- **Focus on major sessions**: Enable London and NY AM for best results
- **Wait for 60%+ confidence**: Higher confidence = higher probability trades
## Notes
- Works best on liquid markets (Forex majors, indices, major crypto pairs)
- Designed for active trading sessions (avoid low-volume periods)
- Combines with price action for best results
- Not a standalone system - use proper risk management
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**Disclaimer**: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose.
Smart Money Concepts [Riz]Smart Money Concepts is a comprehensive technical analysis tool for identifying institutional trading patterns and market structure. This indicator combines Smart Money Concepts (SMC), ICT methodology, and Wyckoff principles into one professional tool.
✨ KEY FEATURES
📊 VOLUMETRIC ORDER BLOCKS
• Visual representation of supply/demand zones with volume distribution
• Horizontal volume bars showing buy/sell composition inside each Order Block
• Automatic mitigation tracking
• Breaker Block detection (invalidated OBs acting as reversal zones)
• Strength rating system: ★ Weak, ★★ Medium, ★★★ Strong
• ATR-based size filtering to show only significant zones
📈 MARKET STRUCTURE DETECTION
• Break of Structure (BOS) and Change of Character (CHoCH) identification
• Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), Lower Lows (LL) labels
• Internal structure pivots (iH/iL) for intraday analysis
• Auto-adjusting swing length based on timeframe
• Configurable confirmation methods (Close vs Wick-based)
💎 FAIR VALUE GAPS (FVG)
• Automatic detection of bullish and bearish imbalances
• Configurable mitigation percentage (default 50%)
• Visual tracking until gaps are filled
• Separate color schemes for clarity
💧 LIQUIDITY ANALYSIS
• Buy Side Liquidity (BSL) identification at swing highs
• Sell Side Liquidity (SSL) identification at swing lows
• Automatic sweep detection with visual confirmation
• Real-time alerts when liquidity is taken
⚖️ PREMIUM & DISCOUNT ZONES
• Dynamic range calculation based on configurable lookback period
• Equilibrium (EQ) level identification
• Previous Day High (PDH) and Previous Day Low (PDL) levels
• Helps identify favorable entry zones
📊 REAL-TIME DASHBOARD
• Live statistics on all detected patterns
• Active Order Blocks and FVGs count
• BOS/CHoCH occurrence tracking
• Liquidity sweep counters
• Recent market activity indicators
• Current trend bias display
• Fully customizable position and size
⚙️ CUSTOMIZATION OPTIONS
All aspects are fully customizable:
• Swing Length (1-50 bars) with auto-adjust for timeframe
• Max Active Order Blocks (10-100)
• Volume bar position (Left/Right) with mirror option
• Volume bar width percentage (10-50%)
• ATR size filter for Order Blocks
• Strength rating method (Touches/Age/Distance/Volume/Combined)
• All colors and transparency levels
• Dashboard position (9 locations available)
• Comprehensive alert system for all events
🎓 HOW IT WORKS
ORDER BLOCKS: Identified at the last candle before a Break of Structure. These represent institutional supply and demand zones. Volume is estimated based on candle characteristics and displayed as horizontal bars.
MARKET STRUCTURE: Tracks pivot highs and lows to determine if price is making Higher Highs/Higher Lows (bullish structure) or Lower Highs/Lower Lows (bearish structure). BOS indicates trend continuation, while CHoCH signals potential trend reversal.
LIQUIDITY: Swing highs represent Buy Side Liquidity where short positions have their stop losses. Swing lows represent Sell Side Liquidity where long positions have stop losses. The indicator tracks when these levels are "swept" by price.
FAIR VALUE GAPS: Three-candle patterns where the current candle's range doesn't overlap with the candle two bars ago, creating price imbalances that often get filled later.
📚 BEST PRACTICES
• Use on all timeframes - Auto-adjust feature optimizes settings automatically
• Look for confluence - Best setups occur when multiple concepts align (e.g., Order Block + liquidity sweep + discount zone)
• Consider risk/reward - Use Premium/Discount zones to identify favorable entry areas
• Respect market context - Order Blocks in the direction of overall trend tend to be more reliable
• Volume matters - Higher volume percentages in the expected direction may indicate stronger zones
⚠️ IMPORTANT NOTES
EDUCATIONAL TOOL: This indicator is designed for analysis and education, not as trading signals or investment advice.
VOLUME ESTIMATION: Buy/sell volume distribution is estimated based on candle characteristics since true buy/sell volume data is not available in Pine Script.
NO GUARANTEES: Past performance is not indicative of future results. All trading involves substantial risk.
RISK MANAGEMENT: Always use proper risk management and seek additional confirmation before making trading decisions.
OBJECT LIMITS: On very fast timeframes (1m, 5m) in highly volatile markets, the indicator may approach Pine Script's 500-object limit. Reduce max OBs/FVGs in settings if needed.
🔧 TECHNICAL SPECIFICATIONS
• Pine Script Version: v6
• Indicator Type: Overlay (displays on price chart)
• Maximum Objects: Optimized to stay within Pine Script limits
• Performance: Efficient rendering with configurable history management
• Updates: Real-time on every bar close
📖 METHODOLOGY
This indicator combines concepts from:
• Inner Circle Trader (ICT) methodology
• Smart Money Concepts (SMC) framework
• Wyckoff market analysis principles
• Order flow and volume spread analysis
⚖️ DISCLAIMER
This indicator is for educational and informational purposes only. It is not financial advice. Trading financial instruments carries substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. Always conduct your own research and consult with a licensed financial advisor before making any investment decisions. The author assumes no responsibility for any losses incurred from using this indicator.
FVG Snper PRO🎯 FVG Sniper — Fair Value Gap Signal Engine
FVG Sniper is a professional imbalance-based entry tool built around the Nasdaq futures (NQ/MNQ) — but the signal logic is general enough to apply to many liquid instruments (indices, FX, crypto, metals).
It automatically detects Fair Value Gaps (FVGs), tracks their lifecycle, and fires rule-based long/short signals only when price shows decisive intent away from those imbalances.
🔍 What FVG Sniper Does
Detects FVGs automatically (no pivots)
Uses a strict 3-candle pattern to locate bullish and bearish imbalances directly from price action.
Tracks each FVG over time
For every FVG, FVG Sniper tracks:
When it was created
Whether it has ever been tapped
Whether it has been tapped since the last trade
Whether it has been invalidated (“inversion close”)
Session-gated execution
FVGs can be formed and tapped any time.
Only bars inside a defined signal session (e.g. 09:30–12:00 New York time) are allowed to trigger entries.
FVGs are only eligible if they were created on the same trading day as the signal and after a specific time cutoff (e.g. 08:30 ET).
Tap-aware, breakout-based entries
The indicator looks for:
An FVG that has been tapped at least once since the last signal (if tap is required).
A decisive breakout of the previous bar’s high or low coming off that FVG.
Multi-strategy overlay (for advanced use)
On top of the core engine, FVG Sniper offers several optional “Sniper profiles” (strategies) tuned around:
Session timing (e.g. morning / midday windows)
Volatility regimes
Lane cleanliness / opposite-side structure behavior
Range context (distance from session extremes)
You can toggle these profiles on/off to restrict signals to specific conditions — but the exact internal filters and thresholds are not disclosed.
If at least one profile is enabled, a signal prints when any enabled profile likes the setup.
If no profiles are enabled, FVG Sniper shows the raw base FVG breakout signals from the core engine.
🧠 How to Use It
Primary use case: intraday futures (NQ/MNQ) on 1M timeframe.
FVG Sniper works best as:
A signal engine feeding your execution plans, or
A confirmation layer on top of your own context (HTF bias, news, higher-timeframe levels, etc.).
🎨 Visuals & Controls
Bullish and bearish FVG zones are drawn directly on the chart.
Optional mid-lines through each FVG.
Automatic delete or “fade” behavior when FVGs are invalidated.
Clear long/short markers at the signal bar.
Optional debug label to inspect which FVG produced the signal and key reference times.
⚠️ Disclaimer
This script is for educational and research purposes only and is not financial advice.
Past performance does not guarantee future results. Always validate any signal logic in a simulator and adapt it to your own risk management, instrument, and timeframe.
VWAP Kalman FilterOverview
This indicator applies Kalman filtering techniques to Volume Weighted Average Price (VWAP) calculations, providing a statistically optimized approach to VWAP analysis. The Kalman filter reduces noise while maintaining responsiveness to genuine price movements, addressing common VWAP limitations in volatile or low-volume conditions.
Technical Implementation
Kalman Filter Mathematics
The indicator implements a state-space model for VWAP estimation:
- Prediction Step: x̂(k|k-1) = x̂(k-1|k-1) + v(k-1)
- Update Step: x̂(k|k) = x̂(k|k-1) + K(k)
- Kalman Gain: K(k) = P(k|k-1) / (P(k|k-1) + R)
Where:
- x̂ = estimated VWAP state
- K = Kalman gain (adaptive weighting factor)
- P = error covariance
- R = measurement noise
- Q = process noise
- v = optional velocity component
Core Components
Dual VWAP System
- Standard VWAP: Traditional volume-weighted calculation
- Kalman-filtered VWAP: Noise-reduced estimation with optional velocity tracking
- Real-time divergence measurement between filtered and unfiltered values
Adaptive Filtering
- Process Noise (Q): Controls adaptation to price changes (0.001-1.0)
- Measurement Noise (R): Determines smoothing intensity (0.01-5.0)
- Optional velocity tracking for momentum-based filtering
Multi-Timeframe Anchoring
- Session, Weekly, Monthly, Quarterly, and Yearly anchor periods
- Automatic Kalman state reset on anchor changes
- Maintains VWAP integrity across timeframes
Features
Visual Components
- Dual VWAP Lines: Compare filtered vs. unfiltered in real-time
- Dynamic Bands: Three-level deviation bands (1σ, 2σ, 3σ)
- Trend Coloring: Automatic color adaptation based on price position
- Cloud Visualization: Highlights divergence between standard and Kalman VWAP
- Signal Markers: Crossover and band-touch indicators
Trading Signals
- VWAP crossover detection with Kalman filtering
- Band touch alerts at multiple standard deviation levels
- Velocity-based momentum confirmation (optional)
- Divergence warnings when filtered/unfiltered values separate
Information Display
- Real-time VWAP values (both standard and filtered)
- Trend direction indicator
- Velocity/momentum reading (when enabled)
- Divergence percentage calculation
- Anchor period display
Input Parameters
VWAP Settings
- Anchor Period: Choose calculation reset period
- Band Multipliers: Customize deviation band distances
- Display Options: Toggle standard VWAP and bands
Kalman Parameters
- Length: Base period for calculations (5-200)
- Process Noise (Q: Higher values increase responsiveness
- Measurement Noise (R): Higher values increase smoothing
- Velocity Tracking: Enable momentum-based filtering
Visual Controls
- Toggle filtered/unfiltered VWAP display
- Band visibility options
- Signal markers on/off
- Cloud fill between VWAPs
- Bar coloring by trend
Use Cases
Noise Reduction
Particularly effective during:
- Low volume periods (pre-market, lunch hours)
- Volatile market conditions
- Fast-moving markets where standard VWAP whipsaws
Trend Identification
- Cleaner trend signals with reduced false crosses
- Earlier trend detection through velocity component
- Confirmation through divergence analysis
Support/Resistance
- Filtered VWAP provides more stable S/R levels
- Bands adapt to filtered values for better zone identification
- Reduced false breakout signals
Technical Advantages
1. Optimal Estimation: Mathematically optimal under Gaussian noise assumptions
2. Adaptive Response: Self-adjusting to market conditions
3. Predictive Element: Velocity component provides forward-looking insight
4. Noise Immunity: Superior noise rejection vs. simple moving average smoothing
Limitations
- Assumes linear price dynamics
- Requires parameter optimization for different instruments
- May lag during sudden volatility regime changes
- Not suitable as standalone trading system
Mathematical Background
Based on control systems theory, the Kalman filter provides recursive Bayesian estimation originally developed for aerospace applications. This implementation adapts the algorithm specifically for financial time series, maintaining VWAP's volume-weighted properties while adding statistical filtering.
Comparison with Standard VWAP
Standard VWAP Issues Addressed:
- Choppy behavior in low volume
- Whipsaws around VWAP line
- Lag in trend identification
- Noise in deviation bands
Kalman VWAP Benefits:
- Smooth yet responsive line
- Fewer false signals
- Optional momentum tracking
- Statistically optimized filtering
Alert Conditions
The indicator includes several pre-configured alert conditions:
- Bullish/Bearish VWAP crosses
- Upper/Lower band touches
- High divergence warnings
- Velocity shifts (if enabled)
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This open-source indicator is provided as-is for educational and trading purposes. No guarantees are made regarding trading performance. Users should conduct their own testing and validation before using in live trading.
Prophet Model [TakingProphets]The Prophet Model — context pipeline (HTF PDA → Sweep → CISD → EPE) with dynamic risk
Purpose
Informational overlay for organizing institutional context in real time. It does not issue buy/sell signals and is not financial advice. Use it to structure analysis and checklist-driven execution—not to automate decisions.
What it does (modules at a glance)
Projects HTF PD Arrays (FVGs) onto your current chart and maintains only the nearest active array.
Validates directional bias using Candle Range Theory (CRT) on the same HTF.
Tracks Liquidity Sweeps (BSL/SSL) on HTF-aware pivots.
Confirms Change in State of Delivery (CISD) via displacement after a sweep.
Optionally refines entries with EPE when a local (internal) imbalance forms right after CISD.
Derives dynamic TP/BE/SL from measured displacement and recent extremes (not fixed distances).
Keeps a rules checklist (PDA tap → CRT → Sweep → CISD) and a relationships table (common HTF↔LTF pairings) to enforce process.
How it works (integration, not a mashup)
The modules are sequenced on one HTF time base so each step gates the next:
HTF PD Arrays (context zone). The model identifies valid HTF FVGs, filters tiny/weekend gaps, removes arrays that are invalidated by clean trades-through, and persists only the nearest PDA. This focuses attention on the institutional zone most likely to matter now.
CRT (directional gating). CRT on the same HTF establishes a provisional bias. No entries are implied; CRT simply permits or forbids the following steps. If CRT disagrees with the PDA context, the checklist remains incomplete.
Liquidity Sweep (event). The model tracks HTF-aware BSL/SSL pivots. A sweep only “counts” if it occurs in relation to the active PDA (tap/engagement). This prevents generic swing-high/low tags from triggering downstream logic.
CISD (confirmation). After a qualified sweep, the tool looks for displacement through the sequence open (the open of the impulsive leg beginning at or immediately after the sweep). Crossing that threshold confirms CISD, which marks a structural delivery shift consistent with the CRT bias.
EPE (refinement, optional). Immediately following CISD, the model scans for a fresh internal imbalance. If found quickly, it promotes that price area as the Easiest Point of Entry (EPE) and relabels the reference. If not, the CISD level remains primary.
Dynamic risk levels. TP/BE/SL are derived from the measured displacement around the CISD leg (e.g., BE ≈ 1× leg, TP ≈ 2.25× stretch; SL aligned to nearby structural extremes rather than a fixed pip offset). Levels update with structure and can display prices.
By chaining PDA → CRT → Sweep → CISD → (EPE) → Risk on a single HTF backbone, the tool creates a coherent workflow where later signals simply do not appear without earlier context. That’s why this is not a bundle of independent features: each module’s output is another module’s input.
Concepts & operational rules (high level)
HTF PD Arrays (FVGs)
Uses a standard three-candle gap definition on the chosen HTF, with filters for weekend/tiny gaps.
Inverse mitigation: if price trades cleanly through an array, the box is removed and internal state resets.
Nearest-PDA persistence: when multiple arrays exist, only the closest remains visible to reduce clutter.
Optional right-extension draws lingering influence X bars forward.
Candle Range Theory (CRT)
Bullish CRT: candle 2 wicks below candle 1’s low but closes back inside candle 1’s range, without taking its high.
Bearish CRT: candle 2 wicks above candle 1’s high but closes back inside candle 1’s range, without taking its low.
Role: bias validation paired to CISD when alignments match the active PDA.
Liquidity Sweeps (BSL/SSL)
Tracks candidate HTF pivots as buy-/sell-side liquidity.
A sweep registers when price takes a tracked pivot in the vicinity of the active PDA.
CISD (Change in State of Delivery)
Finds the sequence open for the impulsive leg that begins at/after the sweep.
Bearish path (after BSL sweep): CISD when close < sequence-open.
Bullish path (after SSL sweep): CISD when close > sequence-open.
On confirmation, the model plots a CISD line, checks the box in the Strategy Checklist, and triggers risk calc.
EPE (Easiest Point of Entry)
Within a short window after CISD, scans for a local imbalance; if present, promotes that level as EPE.
If no imbalance forms, CISD remains the operative reference.
Dynamic TP / BE / SL
Built from the measured leg around CISD (not fixed pip steps).
Approximate geometry: BE ≈ 1× leg, TP ≈ 2.25× leg; SL respects nearby structural extremes.
Labels and price markers are optional.
Architecture notes
Maps the current chart to a higher timeframe (e.g., 15s→M5, M1→M15, M5→H1, M15→H4, H1→D, H4→W, D→M).
Retrieves HTF OHLC/time with no lookahead so structures update intrabar until the HTF bar closes.
Periodic cleanup clears obsolete lines/labels/boxes to keep charts responsive.
Inputs (summary)
FVGs/PD Arrays: show/hide, colors, borders, label size, right-extension, nearest-only toggle.
CRT: enable/disable, label style.
Sweeps/CISD/EPE: enable/disable, line/label styles, EPE window.
Risk Levels (TP/BE/SL): enable each, price labels on/off, colors.
Tables/Checklist: strategy checklist on/off; relationships table (common HTF↔LTF pairings); text sizes and header colors.
Alerts (optional)
You may add alertconditions aligned with these events in your own workspace:
HTF PDA tap (bullish/bearish box)
CRT detected (bullish/bearish)
CISD confirmed (bullish/bearish)
EPE set/updated
Example messages:
“Prophet: CISD confirmed on {{ticker}} / {{interval}}”
“Prophet: EPE refined at {{close}} ({{time}})”
Notes & limitations
HTF values are provisional until the HTF bar closes; labels/levels can update while forming.
CISD/EPE are live conditions; they can form and later invalidate within the same HTF bar.
Liquidity relationships vary by market/regime; thin sessions and large gaps can affect clarity.
Educational tool only. No performance claims; no trade signals.
Originality & scope (for protected/invite-only publications)
A single HTF-synchronized engine sequences PDA → CRT → Sweep → CISD → (EPE) and withholds later steps unless prerequisites are met.
Nearest-PDA persistence and inverse-mitigation enforce focus on the most relevant institutional zone.
Displacement-based risk math ties TP/BE/SL to structure instead of static offsets.
Checklist + relationships table promote consistent, rules-first behavior and reduce discretionary drift.
Attribution: Concepts inspired by ICT (PD arrays/FVGs, CRT, sweeps, displacement, refined entries). Design, integration logic, and risk framework by TakingProphets.
Copeland Dynamic Dominance Matrix System | GForgeCopeland Dynamic Dominance Matrix System | GForge - v1
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📊 COMPREHENSIVE SYSTEM OVERVIEW
The GForge Dynamic BB% TrendSync System represents a revolutionary approach to algorithmic portfolio management, combining cutting-edge statistical analysis, momentum detection, and regime identification into a unified framework. This system processes up to 39 different cryptocurrency assets simultaneously, using advanced mathematical models to determine optimal capital allocation across dynamic market conditions.
Core Innovation: Multi-Dimensional Analysis
Unlike traditional single-asset indicators, this system operates on multiple analytical dimensions:
Momentum Analysis: Dual Bollinger Band Modified Deviation (DBBMD) calculations
Relative Strength: Comprehensive dominance matrix with head-to-head comparisons
Fundamental Screening: Alpha and Beta statistical filtering
Market Regime Detection: Five-component statistical testing framework
Portfolio Optimization: Dynamic weighting and allocation algorithms
Risk Management: Multi-layered protection and regime-based positioning
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🔧 DETAILED COMPONENT BREAKDOWN
1. Dynamic Bollinger Band % Modified Deviation Engine (DBBMD)
The foundation of this system is an advanced oscillator that combines two independent Bollinger Band systems with asymmetric parameters to create unique momentum readings.
Technical Implementation:
[
// BB System 1: Fast-reacting with extended standard deviation
primary_bb1_ma_len = 40 // Shorter MA for responsiveness
primary_bb1_sd_len = 65 // Longer SD for stability
primary_bb1_mult = 1.0 // Standard deviation multiplier
// BB System 2: Complementary asymmetric design
primary_bb2_ma_len = 8 // Longer MA for trend following
primary_bb2_sd_len = 66 // Shorter SD for volatility sensitivity
primary_bb2_mult = 1.7 // Wider bands for reduced noise
Key Features:
Asymmetric Design: The intentional mismatch between MA and Standard Deviation periods creates unique oscillation characteristics that traditional Bollinger Bands cannot achieve
Percentage Scale: All readings are normalized to 0-100% scale for consistent interpretation across assets
Multiple Combination Modes:
BB1 Only: Fast/reactive system
BB2 Only: Smooth/stable system
Average: Balanced blend (recommended)
Both Required: Conservative (both must agree)
Either One: Aggressive (either can trigger)
Mean Deviation Filter: Additional volatility-based layer that measures the standard deviation of the DBBMD% itself, creating dynamic trigger bands
Signal Generation Logic:
// Primary thresholds
primary_long_threshold = 71 // DBBMD% level for bullish signals
primary_short_threshold = 33 // DBBMD% level for bearish signals
// Mean Deviation creates dynamic bands around these thresholds
upper_md_band = combined_bb + (md_mult * bb_std)
lower_md_band = combined_bb - (md_mult * bb_std)
// Signal triggers when DBBMD crosses these dynamic bands
long_signal = lower_md_band > long_threshold
short_signal = upper_md_band < short_threshold
For more information on this BB% indicator, find it here:
2. Revolutionary Dominance Matrix System
This is the system's most sophisticated innovation - a comprehensive framework that compares every asset against every other asset to determine relative strength hierarchies.
Mathematical Foundation:
The system constructs a mathematical matrix where each cell represents whether asset i dominates asset j:
// Core dominance matrix (39x39 for maximum assets)
var matrix dominance_matrix = matrix.new(39, 39, 0)
// For each qualifying asset pair (i,j):
for i = 0 to active_count - 1
for j = 0 to active_count - 1
if i != j
// Calculate price ratio BB% TrendSync for asset_i/asset_j
ratio_array = calculate_price_ratios(asset_i, asset_j)
ratio_dbbmd = calculate_dbbmd(ratio_array)
// Asset i dominates j if ratio is in uptrend
if ratio_dbbmd_state == 1
matrix.set(dominance_matrix, i, j, 1)
Copeland Scoring Algorithm:
Each asset receives a dominance score calculated as:
Dominance Score = Total Wins - Total Losses
// Calculate net dominance for each asset
for i = 0 to active_count - 1
wins = 0
losses = 0
for j = 0 to active_count - 1
if i != j
if matrix.get(dominance_matrix, i, j) == 1
wins += 1
else
losses += 1
copeland_score = wins - losses
array.set(dominance_scores, i, copeland_score)
Head-to-Head Analysis Process:
Ratio Construction: For each asset pair, calculate price_asset_A / price_asset_B
DBBMD Application: Apply the same DBBMD analysis to these ratios
Trend Determination: If ratio DBBMD shows uptrend, Asset A dominates Asset B
Matrix Population: Store dominance relationships in mathematical matrix
Score Calculation: Sum wins minus losses for final ranking
This creates a tournament-style ranking where each asset's strength is measured against all others, not just against a benchmark.
3. Advanced Alpha & Beta Filtering System
The system incorporates fundamental analysis through Capital Asset Pricing Model (CAPM) calculations to filter assets based on risk-adjusted performance.
Alpha Calculation (Excess Return Analysis):
// CAPM Alpha calculation
f_calc_alpha(asset_prices, benchmark_prices, alpha_length, beta_length, risk_free_rate) =>
// Calculate asset and benchmark returns
asset_returns = calculate_returns(asset_prices, alpha_length)
benchmark_returns = calculate_returns(benchmark_prices, alpha_length)
// Get beta for expected return calculation
beta = f_calc_beta(asset_prices, benchmark_prices, beta_length)
// Average returns over period
avg_asset_return = array_average(asset_returns) * 100
avg_benchmark_return = array_average(benchmark_returns) * 100
// Expected return using CAPM: E(R) = Beta * Market_Return + Risk_Free_Rate
expected_return = beta * avg_benchmark_return + risk_free_rate
// Alpha = Actual Return - Expected Return
alpha = avg_asset_return - expected_return
Beta Calculation (Volatility Relationship):
// Beta measures how much an asset moves relative to benchmark
f_calc_beta(asset_prices, benchmark_prices, length) =>
// Calculate return series for both assets
asset_returns =
benchmark_returns =
// Populate return arrays
for i = 0 to length - 1
asset_return = (current_price - previous_price) / previous_price
benchmark_return = (current_bench - previous_bench) / previous_bench
// Calculate covariance and variance
covariance = calculate_covariance(asset_returns, benchmark_returns)
benchmark_variance = calculate_variance(benchmark_returns)
// Beta = Covariance(Asset, Market) / Variance(Market)
beta = covariance / benchmark_variance
Filtering Applications:
Alpha Filter: Only includes assets with alpha above specified threshold (e.g., >0.5% monthly excess return)
Beta Filter: Screens for desired volatility characteristics (e.g., beta >1.0 for aggressive assets)
Combined Screening: Both filters must pass for asset qualification
Dynamic Thresholds: User-configurable parameters for different market conditions
4. Intelligent Tie-Breaking Resolution System
When multiple assets have identical dominance scores, the system employs sophisticated methods to determine final rankings.
Standard Tie-Breaking Hierarchy:
// Primary tie-breaking logic
if score_i == score_j // Tied dominance scores
// Level 1: Compare Beta values (higher beta wins)
beta_i = array.get(beta_values, i)
beta_j = array.get(beta_values, j)
if beta_j > beta_i
swap_positions(i, j)
else if beta_j == beta_i
// Level 2: Compare Alpha values (higher alpha wins)
alpha_i = array.get(alpha_values, i)
alpha_j = array.get(alpha_values, j)
if alpha_j > alpha_i
swap_positions(i, j)
Advanced Tie-Breaking (Head-to-Head Analysis):
For the top 3 performers, an enhanced tie-breaking mechanism analyzes direct head-to-head price ratio performance:
// Advanced tie-breaker for top performers
f_advanced_tiebreaker(asset1_idx, asset2_idx, lookback_period) =>
// Calculate price ratio over lookback period
ratio_history =
for k = 0 to lookback_period - 1
price_ratio = price_asset1 / price_asset2
array.push(ratio_history, price_ratio)
// Apply simplified trend analysis to ratio
current_ratio = array.get(ratio_history, 0)
average_ratio = calculate_average(ratio_history)
// Asset 1 wins if current ratio > average (trending up)
if current_ratio > average_ratio
return 1 // Asset 1 dominates
else
return -1 // Asset 2 dominates
5. Five-Component Aggregate Market Regime Filter
This sophisticated framework combines multiple statistical tests to determine whether market conditions favor trending strategies or require defensive positioning.
Component 1: Augmented Dickey-Fuller (ADF) Test
Tests for unit root presence to distinguish between trending and mean-reverting price series.
// Simplified ADF implementation
calculate_adf_statistic(price_series, lookback) =>
// Calculate first differences
differences =
for i = 0 to lookback - 2
diff = price_series - price_series
array.push(differences, diff)
// Statistical analysis of differences
mean_diff = calculate_mean(differences)
std_diff = calculate_standard_deviation(differences)
// ADF statistic approximation
adf_stat = mean_diff / std_diff
// Compare against threshold for trend determination
is_trending = adf_stat <= adf_threshold
Component 2: Directional Movement Index (DMI)
Classic Wilder indicator measuring trend strength through directional movement analysis.
// DMI calculation for trend strength
calculate_dmi_signal(high_data, low_data, close_data, period) =>
// Calculate directional movements
plus_dm_sum = 0.0
minus_dm_sum = 0.0
true_range_sum = 0.0
for i = 1 to period
// Directional movements
up_move = high_data - high_data
down_move = low_data - low_data
// Accumulate positive/negative movements
if up_move > down_move and up_move > 0
plus_dm_sum += up_move
if down_move > up_move and down_move > 0
minus_dm_sum += down_move
// True range calculation
true_range_sum += calculate_true_range(i)
// Calculate directional indicators
di_plus = 100 * plus_dm_sum / true_range_sum
di_minus = 100 * minus_dm_sum / true_range_sum
// ADX calculation
dx = 100 * math.abs(di_plus - di_minus) / (di_plus + di_minus)
adx = dx // Simplified for demonstration
// Trending if ADX above threshold
is_trending = adx > dmi_threshold
Component 3: KPSS Stationarity Test
Complementary test to ADF that examines stationarity around trend components.
// KPSS test implementation
calculate_kpss_statistic(price_series, lookback, significance_level) =>
// Calculate mean and variance
series_mean = calculate_mean(price_series, lookback)
series_variance = calculate_variance(price_series, lookback)
// Cumulative sum of deviations
cumulative_sum = 0.0
cumsum_squared_sum = 0.0
for i = 0 to lookback - 1
deviation = price_series - series_mean
cumulative_sum += deviation
cumsum_squared_sum += math.pow(cumulative_sum, 2)
// KPSS statistic
kpss_stat = cumsum_squared_sum / (lookback * lookback * series_variance)
// Compare against critical values
critical_value = significance_level == 0.01 ? 0.739 :
significance_level == 0.05 ? 0.463 : 0.347
is_trending = kpss_stat >= critical_value
Component 4: Choppiness Index
Measures market directionality using fractal dimension analysis of price movement.
// Choppiness Index calculation
calculate_choppiness(price_data, period) =>
// Find highest and lowest over period
highest = price_data
lowest = price_data
true_range_sum = 0.0
for i = 0 to period - 1
if price_data > highest
highest := price_data
if price_data < lowest
lowest := price_data
// Accumulate true range
if i > 0
true_range = calculate_true_range(price_data, i)
true_range_sum += true_range
// Choppiness calculation
range_high_low = highest - lowest
choppiness = 100 * math.log10(true_range_sum / range_high_low) / math.log10(period)
// Trending if choppiness below threshold (typically 61.8)
is_trending = choppiness < 61.8
Component 5: Hilbert Transform Analysis
Phase-based cycle detection and trend identification using mathematical signal processing.
// Hilbert Transform trend detection
calculate_hilbert_signal(price_data, smoothing_period, filter_period) =>
// Smooth the price data
smoothed_price = calculate_moving_average(price_data, smoothing_period)
// Calculate instantaneous phase components
// Simplified implementation for demonstration
instant_phase = smoothed_price
delayed_phase = calculate_moving_average(price_data, filter_period)
// Compare instantaneous vs delayed signals
phase_difference = instant_phase - delayed_phase
// Trending if instantaneous leads delayed
is_trending = phase_difference > 0
Aggregate Regime Determination:
// Combine all five components
regime_calculation() =>
trending_count = 0
total_components = 0
// Test each enabled component
if enable_adf and adf_signal == 1
trending_count += 1
if enable_adf
total_components += 1
// Repeat for all five components...
// Calculate trending proportion
trending_proportion = trending_count / total_components
// Market is trending if proportion above threshold
regime_allows_trading = trending_proportion >= regime_threshold
The system only allows asset positions when the specified percentage of components indicate trending conditions. During choppy or mean-reverting periods, the system automatically positions in USD to preserve capital.
6. Dynamic Portfolio Weighting Framework
Six sophisticated allocation methodologies provide flexibility for different market conditions and risk preferences.
Weighting Method Implementations:
1. Equal Weight Distribution:
// Simple equal allocation
if weighting_mode == "Equal Weight"
weight_per_asset = 1.0 / selection_count
for i = 0 to selection_count - 1
array.push(weights, weight_per_asset)
2. Linear Dominance Scaling:
// Linear scaling based on dominance scores
if weighting_mode == "Linear Dominance"
// Normalize scores to 0-1 range
min_score = array.min(dominance_scores)
max_score = array.max(dominance_scores)
score_range = max_score - min_score
total_weight = 0.0
for i = 0 to selection_count - 1
score = array.get(dominance_scores, i)
normalized = (score - min_score) / score_range
weight = 1.0 + normalized * concentration_factor
array.push(weights, weight)
total_weight += weight
// Normalize to sum to 1.0
for i = 0 to selection_count - 1
current_weight = array.get(weights, i)
array.set(weights, i, current_weight / total_weight)
3. Conviction Score (Exponential):
// Exponential scaling for high conviction
if weighting_mode == "Conviction Score"
// Combine dominance score with DBBMD strength
conviction_scores =
for i = 0 to selection_count - 1
dominance = array.get(dominance_scores, i)
dbbmd_strength = array.get(dbbmd_values, i)
conviction = dominance + (dbbmd_strength - 50) / 25
array.push(conviction_scores, conviction)
// Exponential weighting
total_weight = 0.0
for i = 0 to selection_count - 1
conviction = array.get(conviction_scores, i)
normalized = normalize_score(conviction)
weight = math.pow(1 + normalized, concentration_factor)
array.push(weights, weight)
total_weight += weight
// Final normalization
normalize_weights(weights, total_weight)
Advanced Features:
Minimum Position Constraint: Prevents dust allocations below specified threshold
Concentration Factor: Adjustable parameter controlling weight distribution aggressiveness
Dominance Boost: Extra weight for assets exceeding specified dominance thresholds
Dynamic Rebalancing: Automatic weight recalculation on portfolio changes
7. Intelligent USD Management System
The system treats USD as a competing asset with its own dominance score, enabling sophisticated cash management.
USD Scoring Methodologies:
Smart Competition Mode (Recommended):
f_calculate_smart_usd_dominance() =>
usd_wins = 0
// USD beats assets in downtrends or weak uptrends
for i = 0 to active_count - 1
asset_state = get_asset_state(i)
asset_dbbmd = get_asset_dbbmd(i)
// USD dominates shorts and weak longs
if asset_state == -1 or (asset_state == 1 and asset_dbbmd < long_threshold)
usd_wins += 1
// Calculate Copeland-style score
base_score = usd_wins - (active_count - usd_wins)
// Boost during weak market conditions
qualified_assets = count_qualified_long_assets()
if qualified_assets <= active_count * 0.2
base_score := math.round(base_score * usd_boost_factor)
base_score
Auto Short Count Mode:
// USD dominance based on number of bearish assets
usd_dominance = count_assets_in_short_state()
// Apply boost during low activity
if qualified_long_count <= active_count * 0.2
usd_dominance := usd_dominance * usd_boost_factor
Regime-Based USD Positioning:
When the five-component regime filter indicates unfavorable conditions, the system automatically overrides all asset signals and positions 100% in USD, protecting capital during choppy markets.
8. Multi-Asset Infrastructure & Data Management
The system maintains comprehensive data structures for up to 39 assets simultaneously.
Data Collection Framework:
// Full OHLC data matrices (200 bars depth for performance)
var matrix open_data = matrix.new(39, 200, na)
var matrix high_data = matrix.new(39, 200, na)
var matrix low_data = matrix.new(39, 200, na)
var matrix close_data = matrix.new(39, 200, na)
// Real-time data collection
if barstate.isconfirmed
for i = 0 to active_count - 1
ticker = array.get(assets, i)
= request.security(ticker, timeframe.period,
[open , high , low , close ],
lookahead=barmerge.lookahead_off)
// Store in matrices with proper shifting
matrix.set(open_data, i, 0, nz(o, 0))
matrix.set(high_data, i, 0, nz(h, 0))
matrix.set(low_data, i, 0, nz(l, 0))
matrix.set(close_data, i, 0, nz(c, 0))
Asset Configuration:
The system comes pre-configured with 39 major cryptocurrency pairs across multiple exchanges:
Major Pairs: BTC, ETH, XRP, SOL, DOGE, ADA, etc.
Exchange Coverage: Binance, KuCoin, MEXC for optimal liquidity
Configurable Count: Users can activate 2-39 assets based on preferences
Custom Tickers: All asset selections are user-modifiable
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⚙️ COMPREHENSIVE CONFIGURATION GUIDE
Portfolio Management Settings
Maximum Portfolio Size (1-10):
Conservative (1-2): High concentration, captures strong trends
Balanced (3-5): Moderate diversification with trend focus
Diversified (6-10): Lower concentration, broader market exposure
Dominance Clarity Threshold (0.1-1.0):
Low (0.1-0.4): Prefers diversification, holds multiple assets frequently
Medium (0.5-0.7): Balanced approach, context-dependent allocation
High (0.8-1.0): Concentration-focused, single asset preference
Signal Generation Parameters
DBBMD Thresholds:
// Standard configuration
primary_long_threshold = 71 // Conservative: 75+, Aggressive: 65-70
primary_short_threshold = 33 // Conservative: 25-30, Aggressive: 35-40
// BB System parameters
bb1_ma_len = 40 // Fast system: 20-50
bb1_sd_len = 65 // Stability: 50-80
bb2_ma_len = 8 // Trend: 60-100
bb2_sd_len = 66 // Sensitivity: 10-20
Risk Management Configuration
Alpha/Beta Filters:
Alpha Threshold: 0.0-2.0% (higher = more selective)
Beta Threshold: 0.5-2.0 (1.0+ for aggressive assets)
Calculation Periods: 20-50 bars (longer = more stable)
Regime Filter Settings:
Trending Threshold: 0.3-0.8 (higher = stricter trend requirements)
Component Lookbacks: 30-100 bars (balance responsiveness vs stability)
Enable/Disable: Individual component control for customization
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📊 PERFORMANCE TRACKING & VISUALIZATION
Real-Time Dashboard Features
The compact dashboard provides essential information:
Current Holdings: Asset names and allocation percentages
Dominance Score: Current position's relative strength ranking
Active Assets: Qualified long signals vs total asset count
Returns: Total portfolio performance percentage
Maximum Drawdown: Peak-to-trough decline measurement
Trade Count: Total portfolio transitions executed
Regime Status: Current market condition assessment
Comprehensive Ranking Table
The left-side table displays detailed asset analysis:
Ranking Position: Numerical order by dominance score
Asset Symbol: Clean ticker identification with color coding
Dominance Score: Net wins minus losses in head-to-head comparisons
Win-Loss Record: Detailed breakdown of dominance relationships
DBBMD Reading: Current momentum percentage with threshold highlighting
Alpha/Beta Values: Fundamental analysis metrics when filters enabled
Portfolio Weight: Current allocation percentage in signal portfolio
Execution Status: Visual indicator of actual holdings vs signals
Visual Enhancement Features
Color-Coded Assets: 39 distinct colors for easy identification
Regime Background: Red tinting during unfavorable market conditions
Dynamic Equity Curve: Portfolio value plotted with position-based coloring
Status Indicators: Symbols showing execution vs signal states
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🔍 ADVANCED TECHNICAL FEATURES
State Persistence System
The system maintains asset states across bars to prevent excessive switching:
// State tracking for each asset and ratio combination
var array asset_states = array.new(1560, 0) // 39 * 40 ratios
// State changes only occur on confirmed threshold breaks
if long_crossover and current_state != 1
current_state := 1
array.set(asset_states, asset_index, 1)
else if short_crossover and current_state != -1
current_state := -1
array.set(asset_states, asset_index, -1)
Transaction Cost Integration
Realistic modeling of trading expenses:
// Transaction cost calculation
transaction_fee = 0.4 // Default 0.4% (fees + slippage)
// Applied on portfolio transitions
if should_execute_transition
was_holding_assets = check_current_holdings()
will_hold_assets = check_new_signals()
// Charge fees for meaningful transitions
if transaction_fee > 0 and (was_holding_assets or will_hold_assets)
fee_amount = equity * (transaction_fee / 100)
equity -= fee_amount
total_fees += fee_amount
Dynamic Memory Management
Optimized data structures for performance:
200-Bar History: Sufficient for calculations while maintaining speed
Matrix Operations: Efficient storage and retrieval of multi-asset data
Array Recycling: Memory-conscious data handling for long-running backtests
Conditional Calculations: Skip unnecessary computations during initialization
12H 30 assets portfolio
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🚨 SYSTEM LIMITATIONS & TESTING STATUS
CURRENT DEVELOPMENT PHASE: ACTIVE TESTING & OPTIMIZATION
This system represents cutting-edge algorithmic trading technology but remains in continuous development. Key considerations:
Known Limitations:
Requires significant computational resources for 39-asset analysis
Performance varies significantly across different market conditions
Complex parameter interactions may require extensive optimization
Slippage and liquidity constraints not fully modeled for all assets
No consideration for market impact in large position sizes
Areas Under Active Development:
Enhanced regime detection algorithms
Improved transaction cost modeling
Additional portfolio weighting methodologies
Machine learning integration for parameter optimization
Cross-timeframe analysis capabilities
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🔒 ANTI-REPAINTING ARCHITECTURE & LIVE TRADING READINESS
One of the most critical aspects of any trading system is ensuring that signals and calculations are based on confirmed, historical data rather than current bar information that can change throughout the trading session. This system implements comprehensive anti-repainting measures to ensure 100% reliability for live trading .
The Repainting Problem in Trading Systems
Repainting occurs when an indicator uses current, unconfirmed bar data in its calculations, causing:
False Historical Signals: Backtests appear better than reality because calculations change as bars develop
Live Trading Failures: Signals that looked profitable in testing fail when deployed in real markets
Inconsistent Results: Different results when running the same indicator at different times during a trading session
Misleading Performance: Inflated win rates and returns that cannot be replicated in practice
GForge Anti-Repainting Implementation
This system eliminates repainting through multiple technical safeguards:
1. Historical Data Usage for All Calculations
// CRITICAL: All calculations use PREVIOUS bar data (note the offset)
= request.security(ticker, timeframe.period,
[open , high , low , close , close],
lookahead=barmerge.lookahead_off)
// Store confirmed previous bar OHLC for calculations
matrix.set(open_data, i, 0, nz(o1, 0)) // Previous bar open
matrix.set(high_data, i, 0, nz(h1, 0)) // Previous bar high
matrix.set(low_data, i, 0, nz(l1, 0)) // Previous bar low
matrix.set(close_data, i, 0, nz(c1, 0)) // Previous bar close
// Current bar close only for visualization
matrix.set(current_prices, i, 0, nz(c0, 0)) // Live price display
2. Confirmed Bar State Processing
// Only process data when bars are confirmed and closed
if barstate.isconfirmed
// All signal generation and portfolio decisions occur here
// using only historical, unchanging data
// Shift historical data arrays
for i = 0 to active_count - 1
for bar = math.min(data_bars, 199) to 1
// Move confirmed data through historical matrices
old_data = matrix.get(close_data, i, bar - 1)
matrix.set(close_data, i, bar, old_data)
// Process new confirmed bar data
calculate_all_signals_and_dominance()
3. Lookahead Prevention
// Explicit lookahead prevention in all security calls
request.security(ticker, timeframe.period, expression,
lookahead=barmerge.lookahead_off)
// This ensures no future data can influence current calculations
// Essential for maintaining signal integrity across all timeframes
4. State Persistence with Historical Validation
// Asset states only change based on confirmed threshold breaks
// using historical data that cannot change
var array asset_states = array.new(1560, 0)
// State changes use only confirmed, previous bar calculations
if barstate.isconfirmed
=
f_calculate_enhanced_dbbmd(confirmed_price_array, ...)
// Only update states after bar confirmation
if long_crossover_confirmed and current_state != 1
current_state := 1
array.set(asset_states, asset_index, 1)
Live Trading vs. Backtesting Consistency
The system's architecture ensures identical behavior in both environments:
Backtesting Mode:
Uses historical offset data for all calculations
Processes confirmed bars with `barstate.isconfirmed`
Maintains identical signal generation logic
No access to future information
Live Trading Mode:
Uses same historical offset data structure
Waits for bar confirmation before signal updates
Identical mathematical calculations and thresholds
Real-time price display without affecting signals
Technical Implementation Details
Data Collection Timing
// Example of proper data collection timing
if barstate.isconfirmed // Wait for bar to close
// Collect PREVIOUS bar's confirmed OHLC data
for i = 0 to active_count - 1
ticker = array.get(assets, i)
// Get confirmed previous bar data (note offset)
=
request.security(ticker, timeframe.period,
[open , high , low , close , close],
lookahead=barmerge.lookahead_off)
// ALL calculations use prev_* values
// current_close only for real-time display
portfolio_calculations_use_previous_bar_data()
Signal Generation Process
// Signal generation workflow (simplified)
if barstate.isconfirmed and data_bars >= minimum_required_bars
// Step 1: Calculate DBBMD using historical price arrays
for i = 0 to active_count - 1
historical_prices = get_confirmed_price_history(i) // Uses offset data
= calculate_dbbmd(historical_prices)
update_asset_state(i, state)
// Step 2: Build dominance matrix using confirmed data
calculate_dominance_relationships() // All historical data
// Step 3: Generate portfolio signals
new_portfolio = generate_target_portfolio() // Based on confirmed calculations
// Step 4: Compare with previous signals for changes
if portfolio_signals_changed()
execute_portfolio_transition()
Verification Methods for Users
Users can verify the anti-repainting behavior through several methods:
1. Historical Replay Test
Run the indicator on historical data
Note signal timing and portfolio changes
Replay the same period - signals should be identical
No retroactive changes in historical signals
2. Intraday Consistency Check
Load indicator during active trading session
Observe that previous day's signals remain unchanged
Only current day's final bar should show potential signal changes
Refresh indicator - historical signals should be identical
Live Trading Deployment Considerations
Data Quality Assurance
Exchange Connectivity: Ensure reliable data feeds for all 39 assets
Missing Data Handling: System includes safeguards for data gaps
Price Validation: Automatic filtering of obvious price errors
Timeframe Synchronization: All assets synchronized to same bar timing
Performance Impact of Anti-Repainting Measures
The robust anti-repainting implementation requires additional computational resources:
Memory Usage: 200-bar historical data storage for 39 assets
Processing Delay: Signals update only after bar confirmation
Calculation Overhead: Multiple historical data validations
Alert Timing: Slight delay compared to current-bar indicators
However, these trade-offs are essential for reliable live trading performance and accurate backtesting results.
Critical: Equity Curve Anti-Repainting Architecture
The most sophisticated aspect of this system's anti-repainting design is the temporal separation between signal generation and performance calculation . This creates a realistic trading simulation that perfectly matches live trading execution.
The Timing Sequence
// STEP 1: Store what we HELD during the current bar (for performance calc)
if barstate.isconfirmed
// Record positions that were active during this bar
array.clear(held_portfolio)
array.clear(held_weights)
for i = 0 to array.size(execution_portfolio) - 1
array.push(held_portfolio, array.get(execution_portfolio, i))
array.push(held_weights, array.get(execution_weights, i))
// STEP 2: Calculate performance based on what we HELD
portfolio_return = 0.0
for i = 0 to array.size(held_portfolio) - 1
held_asset = array.get(held_portfolio, i)
held_weight = array.get(held_weights, i)
// Performance from current_price vs reference_price
// This is what we ACTUALLY earned during this bar
if held_asset != "USD"
current_price = get_current_price(held_asset) // End of bar
reference_price = get_reference_price(held_asset) // Start of bar
asset_return = (current_price - reference_price) / reference_price
portfolio_return += asset_return * held_weight
// STEP 3: Apply return to equity (realistic timing)
equity := equity * (1 + portfolio_return)
// STEP 4: Generate NEW signals for NEXT period (using confirmed data)
= f_generate_target_portfolio()
// STEP 5: Execute transitions if signals changed
if signal_changed
// Update execution_portfolio for NEXT bar
array.clear(execution_portfolio)
array.clear(execution_weights)
for i = 0 to array.size(new_signal_portfolio) - 1
array.push(execution_portfolio, array.get(new_signal_portfolio, i))
array.push(execution_weights, array.get(new_signal_weights, i))
Why This Prevents Equity Curve Repainting
Performance Attribution: Returns are calculated based on positions that were **actually held** during each bar, not future signals
Signal Timing: New signals are generated **after** performance calculation, affecting only **future** bars
Realistic Execution: Mimics real trading where you earn returns on current positions while planning future moves
No Retroactive Changes: Once a bar closes, its performance contribution to equity is permanent and unchangeable
The One-Bar Offset Mechanism
This system implements a critical one-bar timing offset:
// Bar N: Performance Calculation
// ================================
// 1. Calculate returns on positions held during Bar N
// 2. Update equity based on actual holdings during Bar N
// 3. Plot equity point for Bar N (based on what we HELD)
// Bar N: Signal Generation
// ========================
// 4. Generate signals for Bar N+1 (using confirmed Bar N data)
// 5. Send alerts for what will be held during Bar N+1
// 6. Update execution_portfolio for Bar N+1
// Bar N+1: The Cycle Continues
// =============================
// 1. Performance calculated on positions from Bar N signals
// 2. New signals generated for Bar N+2
Alert System Timing
The alert system reflects this sophisticated timing:
Transaction Cost Realism
Even transaction costs follow realistic timing:
// Fees applied when transitioning between different portfolios
if should_execute_transition
// Charge fees BEFORE taking new positions (realistic timing)
if transaction_fee > 0
fee_amount = equity * (transaction_fee / 100)
equity -= fee_amount // Immediate cost impact
total_fees += fee_amount
// THEN update to new portfolio
update_execution_portfolio(new_signals)
transitions += 1
// Fees reduce equity immediately, affecting all future calculations
// This matches real trading where fees are deducted upon execution
LIVE TRADING CERTIFICATION:
This system has been specifically designed and tested for live trading deployment. The comprehensive anti-repainting measures ensure that:
Backtesting results accurately represent real trading potential
Signals are generated using only confirmed, historical data
No retroactive changes can occur to previously generated signals
Portfolio transitions are based on reliable, unchanging calculations
Performance metrics reflect realistic trading outcomes including proper timing
Users can deploy this system with confidence that live trading results will closely match backtesting performance, subject to normal market execution factors such as slippage and liquidity.
---
⚡ ALERT SYSTEM & AUTOMATION
The system provides comprehensive alerting for automation and monitoring:
Available Alert Conditions
Portfolio Signal Change: Triggered when new portfolio composition is generated
Regime Override Active: Alerts when market regime forces USD positioning
Individual Asset Signals: Can be configured for specific asset transitions
Performance Thresholds: Drawdown or return-based notifications
---
📈 BACKTESTING & PERFORMANCE ANALYSIS
8 Comprehensive Metrics Tracking
The system maintains detailed performance statistics:
Equity Curve: Real-time portfolio value progression
Returns Calculation: Total and annualized performance metrics
Drawdown Analysis: Peak-to-trough decline measurements
Transaction Counting: Portfolio transition frequency
Fee Tracking: Cumulative transaction cost impact
Win Rate Analysis: Success rate of position changes
Backtesting Configuration
// Backtesting parameters
initial_capital = 10000.0 // Starting capital
use_custom_start = true // Enable specific start date
custom_start = timestamp("2023-09-01") // Backtest beginning
transaction_fee = 0.4 // Combined fees and slippage %
// Performance calculation
total_return = (equity - initial_capital) / initial_capital * 100
current_drawdown = (peak_equity - equity) / peak_equity * 100
---
🔧 TROUBLESHOOTING & OPTIMIZATION
Common Configuration Issues
Insufficient Data: Ensure 100+ bars available before start date
[*} Not Compiling: Go on an asset's price chart with 2 or 3 years of data to
make the system compile or just simply reapply the indicator again
Too Many Assets: Reduce active count if experiencing timeouts
Regime Filter Too Strict: Lower trending threshold if always in USD
Excessive Switching: Increase MD multiplier or adjust thresholds
---
💡 USER FEEDBACK & ENHANCEMENT REQUESTS
The continuous evolution of this system depends heavily on user experience and community feedback. Your insights will help motivate me for new improvements and new feature developments.
---
⚖️ FINAL COMPREHENSIVE RISK DISCLAIMER
TRADING INVOLVES SUBSTANTIAL RISK OF LOSS
This indicator is a sophisticated analytical tool designed for educational and research purposes. Important warnings and considerations:
System Limitations:
No algorithmic system can guarantee profitable outcomes
Complex systems may fail in unexpected ways during extreme market events
Historical backtesting does not account for all real-world trading challenges
Slippage, liquidity constraints, and market impact can significantly affect results
System parameters require careful optimization and ongoing monitoring
The creator and distributor of this indicator assume no liability for any financial losses, system failures, or adverse outcomes resulting from its use. This tool is provided "as is" without any warranties, express or implied.
By using this indicator, you acknowledge that you have read, understood, and agreed to assume all risks associated with algorithmic trading and cryptocurrency investments.
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (🐑):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (💧):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (🏦):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (⚖️):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (🛡️):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
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.
Kijun Shifting Band Oscillator | QuantMAC🎯 Kijun Shifting Band Oscillator | QuantMAC
📊 **Revolutionary Technical Analysis Tool Combining Ancient Ichimoku Wisdom with Cutting-Edge Statistical Methods**
🌟 Overview
The Kijun Shifting Band Oscillator represents a sophisticated fusion of traditional Japanese technical analysis and modern statistical theory. Built upon the foundational concepts of the Ichimoku Kinko Hyo system, this indicator transforms the classic Kijun-sen (base line) into a dynamic, multi-dimensional analysis tool that provides traders with unprecedented market insights.
This advanced oscillator doesn't just show you where price has been – it reveals the underlying momentum dynamics and volatility patterns that drive market movements, giving you a statistical edge in your trading decisions.
🔥 Key Features & Innovations
Dual Trading Modes for Maximum Flexibility: 🚀
Long/Short Mode: Full bidirectional trading capability for aggressive traders seeking to capitalize on both bullish and bearish market conditions
Long/Cash Mode: Conservative approach perfect for risk-averse traders, taking long positions during uptrends and moving to cash during downtrends (avoiding short exposure)
Advanced Visual Intelligence: 🎨
9 Professional Color Schemes: From classic blue/navy to vibrant orange/purple combinations, each optimized for different chart backgrounds and personal preferences
Dynamic Gradient Histogram: Color intensity reflects oscillator strength, providing instant visual feedback on momentum magnitude
Intelligent Overlay Bands: Semi-transparent fills create clear visual boundaries without cluttering your chart
Smart Candle Coloring: Real-time color changes reflect current market state and trend direction
Customizable Threshold Lines: Clearly marked entry and exit levels with contrasting colors
Professional-Grade Analytics: 📊
Real-Time Performance Metrics: Live calculation of 9 key performance indicators
Risk-Adjusted Returns: Sharpe, Sortino, and Omega ratios for comprehensive performance evaluation
Position Sizing Guidance: Half-Kelly percentage for optimal risk management
Drawdown Analysis: Maximum drawdown tracking for risk assessment
📈 Deep Technical Foundation
Kijun-Based Mathematical Framework: 🧮
The indicator begins with the traditional Kijun-sen calculation but extends it significantly:
Statistical Enhancements: 📉
Adaptive Volatility: Bands expand and contract based on market volatility
Momentum Filtering: EMA smoothing of oscillator for trend confirmation
State Management: Intelligent signal filtering prevents whipsaws and false signals
Multi-Timeframe Compatibility: Optimized algorithms work across all timeframes
⚙️ Comprehensive Parameter Control
Kijun Core Settings: 🎛️
Kijun Length (Default: 30): Controls the lookback period for the base calculation. Shorter periods = more responsive, longer periods = smoother signals
Source Selection: Choose from Close, Open, High, Low, or HL2. Close price recommended for most applications
Calculation Method: Uses traditional Ichimoku methodology ensuring compatibility with classic analysis
Advanced Oscillator Configuration: 📊
Standard Deviation Length (Default: 36): Determines volatility measurement period. Affects band width and sensitivity
SD Multiplier (Default: 2.1): Fine-tune band distance from basis line. Higher values = wider bands, lower values = tighter bands
Oscillator Multiplier (Default: 100): Scales the final oscillator output. Useful for matching other indicators or personal preference
Smoothing Algorithm: Built-in EMA smoothing prevents noise while maintaining responsiveness
Signal Threshold Optimization: 🎯
Long Threshold (Default: 83): Oscillator level that triggers long entries. Higher values = fewer but stronger signals
Short Threshold (Default: 42): Oscillator level that triggers short entries. Lower values = fewer but stronger signals
Threshold Logic: Crossover-based system with state management prevents signal overlap
Customization Range: Fully adjustable to match your trading style and risk tolerance
Precision Date Control: 📅
Start Date/Month/Year: Precise backtesting control down to the day
Historical Analysis: Test strategies on specific market periods or events
Strategy Validation: Isolate performance during different market conditions
📊 Professional Metrics Dashboard
Risk Assessment Metrics: 💼
Maximum Drawdown %: Largest peak-to-trough decline in portfolio value. Critical for understanding worst-case scenarios and position sizing
Sortino Ratio: Risk-adjusted return measure focusing only on downside volatility. Superior to Sharpe ratio for asymmetric return distributions
Sharpe Ratio: Classic risk-adjusted performance metric. Values above 1.0 considered good, above 2.0 excellent
Omega Ratio: Probability-weighted ratio capturing all moments of return distribution. More comprehensive than Sharpe or Sortino
Performance Analytics: 📈
Profit Factor: Gross Profit ÷ Gross Loss. Values above 1.0 indicate profitability, above 2.0 considered excellent
Win Rate %: Percentage of profitable trades. Consider alongside average win/loss size for complete picture
Net Profit %: Total return on initial capital. Accounts for compounding effects
Total Trades: Sample size for statistical significance assessment
Advanced Position Sizing: 🎯
Half Kelly %: Optimal position size based on Kelly Criterion, reduced by 50% for safety margin
Risk Management: Helps determine appropriate position size relative to account equity
Mathematical Foundation: Based on win probability and profit factor calculations
Practical Application: Directly usable percentage for position sizing decisions
🎨 Advanced Display Options
Flexible Interface Design: 🖥️
6 Positioning Options: Top/Bottom/Middle × Left/Right combinations for optimal chart organization
Toggle Functionality: Show/hide metrics table for clean chart presentation during analysis
Color Coordination: Metrics table colors match selected oscillator color scheme
Professional Styling: Clean, readable format with proper spacing and alignment
Visual Hierarchy: 🎭
Oscillator Histogram: Primary focus with gradient intensity showing momentum strength
Threshold Lines: Clear horizontal references for entry/exit levels
Zero Line: Neutral reference point for trend bias determination
Background Bands: Subtle overlay context without chart clutter
🚀 Advanced Signal Generation System
Multi-Layer Signal Logic: ⚡
Primary Signal Generation: Oscillator crossover above Long Threshold (default 83) triggers long entries
Exit Signal Processing: Oscillator crossunder below Short Threshold (default 42) triggers position exits
State Management System: Prevents duplicate signals and ensures clean position transitions
Mode-Specific Logic: Different behavior for Long/Short vs Long/Cash modes
Date Range Filtering: Signals only generated within specified backtesting period
Confirmation Requirements: Bar confirmation prevents false signals from intrabar price spikes
Intelligent Position Management: 🧠
Entry Tracking: Precise entry price recording for accurate P&L calculations
Position State Monitoring: Continuous tracking of long/short/cash positions
Automatic Exit Logic: Seamless position closure and new position initiation
Performance Calculation: Real-time P&L tracking with compounding effects
📉📈 Comprehensive Band Interpretation Guide
Dynamic Band Analysis: 🔍
Upper Band Function: Represents dynamic resistance based on recent volatility. Price approaching upper band suggests potential reversal or breakout
Lower Band Function: Represents dynamic support with volatility adjustment. Price near lower band indicates oversold conditions or support testing
Middle Line (Basis): Trend direction indicator. Price above = bullish bias, price below = bearish bias
Band Width Interpretation: Wide bands = high volatility, narrow bands = low volatility/potential breakout setup
Band Slope Analysis: Rising bands = strengthening trend, falling bands = weakening trend
Oscillator Interpretation: 📊
Values Above 50: Price in upper half of recent range, bullish momentum
Values Below 50: Price in lower half of recent range, bearish momentum
Extreme Values (>80 or <20): Overbought/oversold conditions, potential reversal zones
Momentum Divergence: Oscillator direction vs price direction for early reversal signals
Trend Confirmation: Oscillator direction confirming or contradicting price trends
💡 Strategic Trading Applications
Primary Trading Strategies: 🎯
Trend Following: Use threshold crossovers to capture major directional moves. Best in trending markets with clear directional bias
Mean Reversion: Identify extreme oscillator readings for counter-trend opportunities. Effective in range-bound markets
Breakout Trading: Monitor band compressions followed by expansions for breakout signals
Swing Trading: Combine oscillator signals with band interactions for swing position entries/exits
Risk Management: Use metrics dashboard for position sizing and risk assessment
Market Condition Optimization: 🌊
Trending Markets: Increase threshold separation for fewer, stronger signals
Choppy Markets: Decrease threshold separation for more responsive signals
High Volatility: Increase SD multiplier for wider bands
Low Volatility: Decrease SD multiplier for tighter bands and earlier signals
⚙️ Advanced Configuration Tips
Parameter Optimization Guidelines: 🔧
Kijun Length Adjustment: Shorter periods (10-20) for faster signals, longer periods (50-100) for smoother trends
SD Length Tuning: Match to your trading timeframe - shorter for responsive, longer for stability
Threshold Calibration: Backtest different levels to find optimal entry/exit points for your market
Color Scheme Selection: Choose schemes that provide best contrast with your chart background and other indicators
Integration with Other Indicators: 🔗
Volume Indicators: Confirm oscillator signals with volume spikes
Support/Resistance: Use key levels to filter oscillator signals
Momentum Indicators: RSI, MACD confirmation for signal strength
Trend Indicators: Moving averages for overall trend bias confirmation
⚠️ Important Usage Notes & Limitations
Indicator Characteristics: ⚡
Lagging Nature: Based on historical price data - signals occur after moves have begun
Best Practice: Combine with leading indicators and price action analysis
Market Dependency: Performance varies across different market conditions and instruments
Backtesting Essential: Always validate parameters on historical data before live implementation
Optimization Recommendations: 🎯
Parameter Testing: Systematically test different combinations on your preferred instruments
Walk-Forward Analysis: Regularly re-optimize parameters to maintain effectiveness
Market Regime Awareness: Adjust parameters for different market conditions (trending vs ranging)
Risk Controls: Implement maximum drawdown limits and position size controls
🔧 Technical Specifications
Performance Optimization: ⚡
Efficient Algorithms: Optimized calculations for smooth real-time operation
Memory Management: Smart array handling for metrics calculations
Visual Optimization: Balanced detail vs performance for responsive charts
Multi-Symbol Ready: Consistent performance across different assets
---
The Kijun Shifting Band Oscillator represents the evolution of technical analysis, bridging the gap between traditional methods and modern quantitative approaches. This indicator provides traders with a comprehensive toolkit for market analysis, combining the intuitive wisdom of Japanese candlestick analysis with the precision of statistical mathematics.
🎯 Designed for serious traders who demand professional-grade analysis tools with institutional-quality metrics and risk management capabilities. Whether you're a discretionary trader seeking visual confirmation or a systematic trader building quantitative strategies, this indicator provides the foundation for informed trading decisions.
⚠️ IMPORTANT DISCLAIMER
Past Performance Warning: 📉⚠️
PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. Historical backtesting results, while useful for strategy development and parameter optimization, do not guarantee similar performance in live trading conditions. Market conditions change continuously, and what worked in the past may not work in the future.
Remember: Successful trading requires discipline, continuous learning, and adaptation to changing market conditions. No indicator or strategy guarantees profits, and all trading involves substantial risk of loss.
Machine Learning | Adaptive Trend Signals [Bitwardex]⚙️🧠Machine Learning | Adaptive Trend Signals
🔷Overview
Machine Learning | Adaptive Trend Signals is a Pine Script™ v6 indicator designed to visualize market trends and generate signals through a combination of volatility clustering, Gaussian smoothing, and adaptive trend calculations. Built as an overlay indicator, it integrates advanced techniques inspired by machine learning concepts, such as K-Means clustering, to adapt to changing market conditions. The script is highly customizable, includes a backtesting module, and supports alert conditions, making it suitable for traders exploring trend-based strategies and developers studying volatility-driven indicator design.
🔷Functionality
The indicator performs the following core functions:
• Volatility Clustering: Uses K-Means clustering to categorize market volatility into high, medium, and low states, adjusting trend sensitivity accordingly.
• Trend Calculation: Computes adaptive trend lines (SmartTrend) based on volatility-adjusted standard deviation, smoothed RSI, and ADX filters.
• Signal Generation: Identifies potential buy and sell points through trend line crossovers and directional confirmation.
• Backtesting Module: Tracks trade outcomes based on the SmartTrend3 value, displaying win rate and total trades.
• Visualization: Plots trend lines with gradient colors and optional signal markers (bullish 🐮 and bearish 🐻).
• Alerts: Provides configurable alerts for trend shifts and volatility state changes.
🔷Technical Methodology
Volatility Clustering with K-Means
The indicator employs a K-Means clustering algorithm to classify market volatility, measured via the Average True Range (ATR), into three distinct clusters:
• Data Collection: Gathers ATR values over a user-defined training period (default: 100 bars).
• Centroid Initialization: Sets initial centroids at the highest, lowest, and midpoint ATR values within the training period.
• Iterative Clustering: Assigns ATR data points to the nearest centroid, recalculates centroid means, and repeats until convergence.
• Dynamic Adjustment: Assigns a volatility state (high, medium, or low) based on the closest centroid, adjusting the trend factor (e.g., tighter for high volatility, wider for low volatility).
This approach allows the indicator to adapt its sensitivity to varying market conditions, providing a data-driven foundation for trend calculations.
🔷Gaussian Smoothing
To enhance signal clarity and reduce noise, the indicator applies Gaussian kernel smoothing to:
• RSI: Smooths the Relative Strength Index (calculated from OHLC4) to filter short-term fluctuations.
• SmartTrend: Smooths the primary trend line for a more stable output.
The Gaussian kernel uses a sigma value derived from the user-defined smoothing length, ensuring mathematically consistent noise reduction.
🔷SmartTrend Calculation
The pineSmartTrend function is the core of the indicator, producing three trend lines:
• SmartTrend: The primary trend line, calculated using a volatility-adjusted standard deviation, smoothed RSI, and ADX conditions.
• SmartTrend2: A secondary trend line with a wider factor (base factor * 1.382) for signal confirmation.
SmartTrend3: The average of SmartTrend and SmartTrend2, used for plotting and backtesting.
Key components of the calculation include:
• Dynamic Standard Deviation: Scales based on ATR relative to its 50-period smoothed average, with multipliers (1.0 to 1.4) applied according to volatility thresholds.
• RSI and ADX Filters: Requires RSI > 50 for bullish trends or < 50 for bearish trends, alongside ADX > 15 and rising to confirm trend strength.
Volatility-Adjusted Bands: Constructs upper and lower bands around price action, adjusted by the volatility cluster’s dynamic factor.
🔷Signal Generation
The generate_signals function generates signals as follows:
• Buy Signal: Triggered when SmartTrend crosses above SmartTrend2 and the price is above SmartTrend, with directional confirmation.
• Sell Signal: Triggered when SmartTrend crosses below SmartTrend2 and the price is below SmartTrend, with directional confirmation.
Directional Logic: Tracks trend direction to filter out conflicting signals, ensuring alignment with the broader market context.
Signals are visualized as small circles with bullish (🐮) or bearish (🐻) emojis, with an option to toggle visibility.
🔷Backtesting
The get_backtest function evaluates signal outcomes using the SmartTrend3 value (rather than closing prices) to align with the trend-based methodology.
It tracks:
• Total Trades: Counts completed long and short trades.
• Win Rate: Calculates the percentage of trades where SmartTrend3 moves favorably (higher for longs, lower for shorts).
Position Management: Closes opposite positions before opening new ones, simulating a single-position trading system.
Results are displayed in a table at the top-right of the chart, showing win rate and total trades. Note that backtest results reflect the indicator’s internal logic and should not be interpreted as predictive of real-world performance.
🔷Visualization and Alerts
• Trend Lines: SmartTrend3 is plotted with gradient colors reflecting trend direction and volatility cluster, accompanied by a secondary line for visual clarity.
• Signal Markers: Optional buy/sell signals are plotted as small circles with customizable colors.
• Alerts: Supports alerts for:
• Bullish and bearish trend shifts (confirmed on bar close).
Transitions to high, medium, or low volatility states.
🔷Input Parameters
• ATR Length (default: 14): Period for ATR calculation, used in volatility clustering.
• Period (default: 21): Common period for RSI, ADX, and standard deviation calculations.
• Base SmartTrend Factor (default: 2.0): Base multiplier for volatility-adjusted bands.
• SmartTrend Smoothing Length (default: 10): Length for Gaussian smoothing of the trend line.
• Show Buy/Sell Signals? (default: true): Enables/disables signal markers.
• Bullish/Bearish Color: Customizable colors for trend lines and signals.
🔷Usage Instructions
• Apply to Chart: Add the indicator to any TradingView chart.
• Configure Inputs: Adjust parameters to align with your trading style or market conditions (e.g., shorter ATR length for faster markets).
• Interpret Output:
• Trend Lines: Use SmartTrend3’s direction and color to gauge market bias.
• Signals: Monitor bullish (🐮) and bearish (🐻) markers for potential entry/exit points.
• Backtest Table: Review win rate and total trades to understand the indicator’s behavior in historical data.
• Set Alerts: Configure alerts for trend shifts or volatility changes to support manual or automated trading workflows.
• Combine with Analysis: Use the indicator alongside other tools or market context, as it is designed to complement, not replace, comprehensive analysis.
🔷Technical Notes
• Data Requirements: Requires at least 100 bars for accurate volatility clustering. Ensure sufficient historical data is loaded.
• Market Suitability: The indicator is designed for trend detection and may perform differently in ranging or volatile markets due to its reliance on RSI and ADX filters.
• Backtesting Scope: The backtest module uses SmartTrend3 values, which may differ from price-based outcomes. Results are for informational purposes only.
• Computational Intensity: The K-Means clustering and Gaussian smoothing may increase processing time on lower timeframes or with large datasets.
🔷For Developers
The script is modular, well-commented, encouraging reuse and modification with proper attribution.
Key functions include:
• gaussianSmooth: Applies Gaussian kernel smoothing to any data series.
• pineSmartTrend: Computes adaptive trend lines with volatility and momentum filters.
• getDynamicFactor: Adjusts trend sensitivity based on volatility clusters.
• get_backtest: Evaluates signal performance using SmartTrend3.
Developers can extend these functions for custom indicators or strategies, leveraging the volatility clustering and smoothing methodologies. The K-Means implementation is particularly useful for adaptive volatility analysis.
🔷Limitations
• The indicator is not predictive and should be used as part of a broader trading strategy.
• Performance varies by market, timeframe, and parameter settings, requiring user experimentation.
• Backtest results are based on historical data and internal logic, not real-world trading conditions.
• Volatility clustering assumes sufficient historical data; incomplete data may affect accuracy.
🔷Acknowledgments
Developed by Bitwardex, inspired by machine learning concepts and adaptive trading methodologies. Community feedback is welcome via TradingView’s platform.
🔷 Risk Disclaimer
Trading involves significant risks, and most traders may incur losses. Bitwardex AI Algo is provided for informational and educational purposes only and does not constitute financial advice or a recommendation to buy or sell any financial instrument . The signals, metrics, and features are tools for analysis and do not guarantee profits or specific outcomes. Past performance is not indicative of future results. Always conduct your own due diligence and consult a financial advisor before making trading decisions.
Volume Profile & Smart Money Explorer🔍 Volume Profile & Smart Money Explorer: Decode Institutional Footprints
Master the art of institutional trading with this sophisticated volume analysis tool. Track smart money movements, identify peak liquidity windows, and align your trades with major market participants.
🌟 Key Features:
📊 Triple-Layer Volume Analysis
• Total Volume Patterns
• Directional Volume Split (Up/Down)
• Institutional Flow Detection
• Real-time Smart Money Tracking
• Historical Pattern Recognition
⚡ Smart Money Detection
• Institutional Trade Identification
• Large Block Order Tracking
• Smart Money Concentration Periods
• Whale Activity Alerts
• Volume Threshold Analysis
📈 Advanced Profiling
• Hourly Volume Distribution
• Directional Bias Analysis
• Liquidity Heat Maps
• Volume Pattern Recognition
• Custom Threshold Settings
🎯 Strategic Applications:
Institutional Trading:
• Track Big Player Movements
• Identify Accumulation/Distribution
• Follow Smart Money Flow
• Detect Institutional Trading Windows
• Monitor Block Orders
Risk Management:
• Identify High Liquidity Windows
• Avoid Thin Market Periods
• Optimize Position Sizing
• Track Market Participation
• Monitor Volume Quality
Market Analysis:
• Volume Pattern Recognition
• Smart Money Flow Analysis
• Liquidity Window Identification
• Institutional Activity Cycles
• Market Depth Analysis
💡 Perfect For:
• Professional Traders
• Volume Profile Traders
• Institutional Traders
• Risk Managers
• Algorithmic Traders
• Smart Money Followers
• Day Traders
• Swing Traders
📊 Key Metrics:
• Normalized Volume Profiles
• Institutional Thresholds
• Directional Volume Split
• Smart Money Concentration
• Historical Patterns
• Real-time Analysis
⚡ Trading Edge:
• Trade with Institution Flow
• Identify Optimal Entry Points
• Recognize Distribution Patterns
• Follow Smart Money Positioning
• Avoid Thin Markets
• Capitalize on Peak Liquidity
🎓 Educational Value:
• Understand Market Structure
• Learn Volume Analysis
• Master Institutional Patterns
• Develop Market Intuition
• Track Smart Money Flow
🛠️ Customization:
• Adjustable Time Windows
• Flexible Volume Thresholds
• Multiple Timeframe Analysis
• Custom Alert Settings
• Visual Preference Options
Whether you're tracking institutional flows in crypto markets or following smart money in traditional markets, the Volume Profile & Smart Money Explorer provides the deep insights needed to trade alongside the biggest players.
Transform your trading from retail guesswork to institutional precision. Know exactly when and where smart money moves, and position yourself ahead of major market shifts.
#VolumeProfile #SmartMoney #InstitutionalTrading #MarketAnalysis #TradingView #VolumeAnalysis #CryptoTrading #ForexTrading #TechnicalAnalysis #Trading #PriceAction #MarketStructure #OrderFlow #Liquidity #RiskManagement #TradingStrategy #DayTrading #SwingTrading #AlgoTrading #QuantitativeTrading
X Levels [Pro+] (TradeX)Introduction:
The X-Levels Indicator is a cutting-edge trading tool to help identify key price levels around Premium / Discount Arrays (PD arrays) at Higher Timeframe Points of Interest. It aids the trader by automatically measuring dealing ranges across multiple Timeframes and highlighting the percentages within which define a Premium & Discount Range. These percentages, known as X-Levels, are where the trader seeks an entry around a relevant PD array. This approach allows a trader to optimize entry and exit points around X-Levels. Suitable for traders of all levels, X-Levels enhances analysts' trade location and framework, providing crucial insights into market movements.
What is an X-Level? A specific percentage within any given dealing range that defines a premium and discount. X Levels are defined as the following percents: 0,12,21,29.5,38,50,61.8,70.5,79,88,100. Percentages below 50% indicate a discount and above 50% indicate a premium.
Foundation: This methodology, developed by TradeX, defines a consistent way for defining dealing ranges and his key percentage levels. Built upon Inner Circle Trader (ICT)’s principles of price delivery, it recognizes that price moves between premium and discount levels, seeking liquidity and inefficiencies.
After extensive refinements, this tool now automates the identification of these dealing ranges across any Timeframe, whilst presenting the X-Levels in a clear and precise manner allowing traders to track price movements with precision and efficiency across multiple time frames.
As price moves between X-Levels, it is the trader’s role to analyze which PD Array offers the best entry opportunity around a given X-Level. The true value of this tool lies in its ability to automatically update Dealing Ranges in real Time, eliminating the need for manual measurement or adjustment. This not only saves Time but also allows analysts to focus on trading rather than manually drawing and updating dealing ranges, removing guess work from defining the correct X Levels dealing range.
When X-Levels are applied across multiple Timeframes, traders gain a comprehensive view of the current market conditions. A key principle of this approach is aligning with price at Higher Timeframe (HTF) Point of Interest. By tracking dealing ranges from HTF while operating in Lower Timeframes, analysts can maintain a granular view while keeping track of the HTF framework.
Explanation of Killzones
Killzones refer to the times when major financial markets are open and active, particularly the London and New York sessions. For example, the London Open Killzone typically runs from 2:00 AM to 4:00 AM Eastern Time, while the New York Killzone is often from 8:00 AM to 10:00 AM Eastern Time. During these times, traders can expect more significant price movements due to higher trading volumes and the overlap of market participants.
The X-Levels indicator includes customizable killzone delineations, allowing traders to tailor this setting to their preferred trading sessions.
Key Opening Prices
Finally, the X-Levels indicator also includes Opening Price Delineations in both Horizontal and Vertical delineation. The "opening price" in trading refers to the first price at which a security is traded when the market opens. This price is significant because it can set the tone for the day's trading and is often used as a reference point for analyzing market movements. We are tracking midnight open, 8:30am and 9:30am. This is due to Midnight Theory.
Midnight Theory:
This is following the principles of Power of 3 (PO3) where if a trader is seeking a bullish expansion on a daily candle he is looking to frame entries below the midnight opening price. The principle of midnight theory comes in the form of buying at a hypothetical discount. A trade entering below midnight would be considered a discount, below midnight and 9:30am on a bullish day would be considered a deep discount.
Settings Summary:
Dealing Ranges: Traders can choose which Timeframe to track and can choose up to a maximum of 3 per chart. The styles of which are fully customizable. Solid lines, dotted lines or dashed lines are all available options for presenting each X-Level on each Dealing Range.
Dealing Range Labels: Above and below each Dealing Range extreme, analysts can find a label marking what Timeframe it originates from to differentiate between multiple Dealing Ranges. The size of this label can be hidden, and if shown its size can be customized.
Customizable Colors: Each Dealing Range Discount, Fair Value, and Premium, can be customized at the choice of the trader to suit their preferences.
Manual Dealing Range: If a trader would like to manually set their own Dealing Range, they can do so by marking the beginning of the Dealing Range view window visually through a tailored Manual Dealing Range setting.
Table Presentation: A table that can be presented in different locations on the chart, showing the percentages in relation to where price is trading in any given active Dealing Range. This is an incredibly useful tool for those wanting to see where they sit across Timeframes quickly.
Killzone Delineations: Traders can customize the Times of their preferred Killzones, whether conventional sessions, or their own preference. Their individual colors can also be customized to the trader's liking and preference.
Opening Prices: Traders can customize the colours to suit preferences and change the line thickness, plus adjust and label size.
Conclusion
The X-Levels Indicator is a powerful tool designed to streamline and enhance a trader’s ability to identify key price levels, track Dealing Ranges automatically, and highlight opportunities around Premium and Discount. By automating the measurement of dealing ranges and dynamically updating X-Levels across multiple Timeframes, this indicator eliminates the need for manual calculations, saving Time and allowing traders to focus on narrative.
When combined with Killzone delineations and Opening Prices, the indicator provides a comprehensive framework for aligning trades within the broader market context.
Whether used by beginners or experienced traders, the X-Levels Indicator empowers market participants with a structured approach to price action, liquidity dynamics, and trade location.
Usage Guidance:
Add X Levels° (TradeX) to your TradingView chart.
Select your preferred Timeframes for Dealing Ranges, Killzones, and Opening Prices.
Automate your analysis process with X Levels° (TradeX) and leverage it into your existing strategies to fine-tune your view through automatic Dealing Range tracking and charting.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
AMD Session Structure Levels# Market Structure & Manipulation Probability Indicator
## Overview
This advanced indicator is designed for traders who want a systematic approach to analyzing market structure, identifying manipulation, and assessing probability-based trade setups. It incorporates four core components:
### 1. Session Price Action Analysis
- Tracks **OHLC (Open, High, Low, Close)** within defined sessions.
- Implements a **dual tracking system**:
- **Official session levels** (fixed from the session open to close).
- **Real-time max/min tracking** to differentiate between temporary spikes and real price acceptance.
### 2. Market Manipulation Detection
- Identifies **manipulative price action** using the relationship between the open and close:
- If **price closes below open** → assumes **upward manipulation**, followed by **downward distribution**.
- If **price closes above open** → assumes **downward manipulation**, followed by **upward distribution**.
- Normalized using **ATR**, ensuring adaptability across different volatility conditions.
### 3. Probability Engine
- Tracks **historical wick ratios** to assess trend vs. reversal conditions.
- Calculates **conditional probabilities** for price moves.
- Uses a **special threshold system (0.45 and 0.03)** for reversal signals.
- Provides **real-time probability updates** to enhance trade decision-making.
### 4. Market Condition Classification
- Classifies market conditions using a **wick-to-body ratio**:
```pine
wick_to_body_ratio = open > close ? upper_wick / (high - low) : lower_wick / (high - low)
```
- **Low ratio (<0.25)** → Likely a **trend day**.
- **High ratio (>0.25)** → Likely a **range day**.
---
## Why This Indicator Stands Out
### ✅ Smarter Level Detection
- Uses **ATR-based dynamic levels** instead of static support/resistance.
- Differentiates **manipulation from distribution** for better decision-making.
- Updates probabilities **in real-time**.
### ✅ Memory-Efficient Design
- Implements **circular buffers** to maintain efficiency:
```pine
var float manipUp = array.new_float(lookbackPeriod, 0.0)
var float manipDown = array.new_float(lookbackPeriod, 0.0)
```
- Ensures **constant memory usage**, even over extended trading sessions.
### ✅ Advanced Probability Calculation
- Utilizes **conditional probabilities** instead of simple averages.
- Incorporates **market context** through wick analysis.
- Provides **actionable signals** via a probability table.
---
## Trading Strategy Guide
### **Best Entry Setups**
✅ Wait for **price to approach manipulation levels**.
✅ Confirm using the **probability table**.
✅ Check the **wick ratio for context**.
✅ Enter when **conditional probability aligns**.
### **Smart Exit Management**
✅ Use **distribution levels** as **profit targets**.
✅ Scale out **when probabilities shift**.
✅ Monitor **wick percentiles** for confirmation.
### **Risk Management**
✅ Size positions based on **probability readings**.
✅ Place stops at **manipulation levels**.
✅ Adjust position size based on **trend vs. range classification**.
---
## Configuration Tips
### **Session Settings**
```pine
sessionTime = input.session("0830-1500", "Session Hours")
weekDays = input.string("23456", "Active Days")
```
- Match these to your **primary trading session**.
- Adjust for different **market opens** if needed.
### **Analysis Parameters**
```pine
lookbackPeriod = input.int(50, "Lookback Period")
low_threshold = input.float(0.25, "Trend/Range Threshold")
```
- **50 periods** is a good starting point but can be optimized per instrument.
- The **0.25 threshold** is ideal for most markets but may need adjustments.
---
## Market Structure Breakdown
### **Trend/Continuation Days**
- **Characteristics:**
✅ Small **opposing wicks** (minimal counter-pressure).
✅ Clean, **directional price movement**.
- **Bullish Trend Day Example:**
✅ Small **lower wicks** (minimal downward pressure).
✅ Strong **closes near the highs** → **Buyers in control**.
- **Bearish Trend Day Example:**
✅ Small **upper wicks** (minimal upward pressure).
✅ Strong **closes near the lows** → **Sellers in control**.
### **Reversal Days**
- **Characteristics:**
✅ **Large opposing wicks** → Failed momentum in the initial direction.
- **Bullish Reversal Example:**
✅ **Large upper wick early**.
✅ **Strong close from the lows** → **Sellers failed to maintain control**.
- **Bearish Reversal Example:**
✅ **Large lower wick early**.
✅ **Weak close from the highs** → **Buyers failed to maintain control**.
---
## Summary
This indicator systematically quantifies market structure by measuring **manipulation, distribution, and probability-driven trade setups**. Unlike traditional indicators, it adapts dynamically using **ATR, historical probabilities, and real-time tracking** to offer a structured, data-driven approach to trading.
🚀 **Use this tool to enhance your decision-making and gain an objective edge in the market!**
Liquidations Zones [ChartPrime]The Liquidation Zones indicator is designed to detect potential liquidation zones based on common leverage levels such as 10x, 25x, 50x, and 100x. By calculating percentage distances from recent pivot points, the indicator shows where leveraged positions are most likely to get liquidated. It also tracks buy and sell volumes in these zones, helping traders assess market pressure and predict liquidation scenarios. Additionally, the indicator features a heat map mode to highlight areas where orders and stop-losses might be clustered.
⯁ KEY FEATURES AND HOW TO USE
⯌ Leverage Zones Detection :
The indicator identifies zones where positions with leverage ratios of 100x, 50x, 25x, and 10x are at risk of liquidation. These zones are based on percentage moves from recent pivots: a 1% move can liquidate 100x positions, a 4% move affects 25x positions, and so on.
⯌ Liquidated Zones and Volume Tracking :
The indicator displays liquidated zones by plotting gray areas where the price potentually liquidate positons. It calculates the volume needed to liquidate positions in these zones, showing volume from bullish candles if short positions were liquidated and volume from bearish candles for long positions. This feature helps traders assess the risk of liquidation as the price approaches these zones.
⯌ Buy/Sell Volume Calculation :
Buy and sell volumes are calculated from the most recent pivot high or low. For buy volume, only bullish candles are considered, while for sell volume, only bearish candles are summed. This data helps traders gauge the strength of potential liquidation in different zones.
Example of buy and sell volume tracking in active zones:
⯌ Liquidity Heat Map :
In heat map mode, the indicator visualizes potential liquidity areas where orders and stop-losses may be clustered. This map highlights zones that are likely to experience liquidations based on leverage ratios. Additionally, it tracks the highest and lowest price levels for the past 100 bars, while also displaying buy and sell volumes. This feature is useful for predicting market moves driven by liquidation events.
⯁ USER INPUTS
Length : Determines the number of bars used to calculate pivots for liquidation zones.
Extend : Controls how far the liquidation zones are extended on the chart.
Leverage Options : Toggle options to display zones for different leverage levels: 10x, 25x, 50x, and 100x.
Display Heat Map : Enables or disables the liquidity heat map feature.
⯁ CONCLUSION
The Liquidation Zones indicator provides a powerful tool for identifying potential liquidation zones, tracking volume pressure, and visualizing liquidity areas on the chart. With its real-time updates and multiple features, this indicator offers valuable insights for managing risk and anticipating market moves driven by leveraged positions.
Multi-Step FlexiMA - Strategy [presentTrading]It's time to come back! hope I can not to be busy for a while.
█ Introduction and How It Is Different
The FlexiMA Variance Tracker is a unique trading strategy that calculates a series of deviations between the price (or another indicator source) and a variable-length moving average (MA). Unlike traditional strategies that use fixed-length moving averages, the length of the MA in this system varies within a defined range. The length changes dynamically based on a starting factor and an increment factor, creating a more adaptive approach to market conditions.
This strategy integrates Multi-Step Take Profit (TP) levels, allowing for partial exits at predefined price increments. It enables traders to secure profits at different stages of a trend, making it ideal for volatile markets where taking full profits at once might lead to missed opportunities if the trend continues.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
🔶 FlexiMA Concept
The FlexiMA (Flexible Moving Average) is at the heart of this strategy. Unlike traditional MA-based strategies where the MA length is fixed (e.g., a 50-period SMA), the FlexiMA varies its length with each iteration. This is done using a **starting factor** and an **increment factor**.
The formula for the moving average length at each iteration \(i\) is:
`MA_length_i = indicator_length * (starting_factor + i * increment_factor)`
Where:
- `indicator_length` is the user-defined base length.
- `starting_factor` is the initial multiplier of the base length.
- `increment_factor` increases the multiplier in each iteration.
Each iteration applies a **simple moving average** (SMA) to the chosen **indicator source** (e.g., HLC3) with a different length based on the above formula. The deviation between the current price and the moving average is then calculated as follows:
`deviation_i = price_current - MA_i`
These deviations are normalized using one of the following methods:
- **Max-Min normalization**:
`normalized_i = (deviation_i - min(deviations)) / range(deviations)`
- **Absolute Sum normalization**:
`normalized_i = deviation_i / sum(|deviation_i|)`
The **median** and **standard deviation (stdev)** of the normalized deviations are then calculated as follows:
`median = median(normalized deviations)`
For the standard deviation:
`stdev = sqrt((1/(N-1)) * sum((normalized_i - mean)^2))`
These values are plotted to provide a clear indication of how the price is deviating from its variable-length moving averages.
For more detail:
🔶 Multi-Step Take Profit
This strategy uses a multi-step take profit system, allowing for exits at different stages of a trade based on the percentage of price movement. Three take-profit levels are defined:
- Take Profit Level 1 (TP1): A small, quick profit level (e.g., 2%).
- Take Profit Level 2 (TP2): A medium-level profit target (e.g., 8%).
- Take Profit Level 3 (TP3): A larger, more ambitious target (e.g., 18%).
At each level, a corresponding percentage of the trade is exited:
- TP Percent 1: E.g., 30% of the position.
- TP Percent 2: E.g., 20% of the position.
- TP Percent 3: E.g., 15% of the position.
This approach ensures that profits are locked in progressively, reducing the risk of market reversals wiping out potential gains.
Local
🔶 Trade Entry and Exit Conditions
The entry and exit signals are determined by the interaction between the **SuperTrend Polyfactor Oscillator** and the **median** value of the normalized deviations:
- Long entry: The SuperTrend turns bearish, and the median value of the deviations is positive.
- Short entry: The SuperTrend turns bullish, and the median value is negative.
Similarly, trades are exited when the SuperTrend flips direction.
* The SuperTrend Toolkit is made by @EliCobra
█ Trade Direction
The strategy allows users to specify the desired trade direction:
- Long: Only long positions will be taken.
- Short: Only short positions will be taken.
- Both: Both long and short positions are allowed based on the conditions.
This flexibility allows the strategy to adapt to different market conditions and trading styles, whether you're looking to buy low and sell high, or sell high and buy low.
█ Usage
This strategy can be applied across various asset classes, including stocks, cryptocurrencies, and forex. The primary use case is to take advantage of market volatility by using a flexible moving average and multiple take-profit levels to capture profits incrementally as the market moves in your favor.
How to Use:
1. Configure the Inputs: Start by adjusting the **Indicator Length**, **Starting Factor**, and **Increment Factor** to suit your chosen asset. The defaults work well for most markets, but fine-tuning them can improve performance.
2. Set the Take Profit Levels: Adjust the three **TP levels** and their corresponding **percentages** based on your risk tolerance and the expected volatility of the market.
3. Monitor the Strategy: The SuperTrend and the FlexiMA variance tracker will provide entry and exit signals, automatically managing the positions and taking profits at the pre-set levels.
█ Default Settings
The default settings for the strategy are configured to provide a balanced approach that works across different market conditions:
Indicator Length (10):
This controls the base length for the moving average. A lower length makes the moving average more responsive to price changes, while a higher length smooths out fluctuations, making the strategy less sensitive to short-term price movements.
Starting Factor (1.0):
This determines the initial multiplier applied to the moving average length. A higher starting factor will increase the average length, making it slower to react to price changes.
Increment Factor (1.0):
This increases the moving average length in each iteration. A larger increment factor creates a wider range of moving average lengths, allowing the strategy to track both short-term and long-term trends simultaneously.
Normalization Method ('None'):
Three methods of normalization can be applied to the deviations:
- None: No normalization applied, using raw deviations.
- Max-Min: Normalizes based on the range between the maximum and minimum deviations.
- Absolute Sum: Normalizes based on the total sum of absolute deviations.
Take Profit Levels:
- TP1 (2%): A quick exit to capture small price movements.
- TP2 (8%): A medium-term profit target for stronger trends.
- TP3 (18%): A long-term target for strong price moves.
Take Profit Percentages:
- TP Percent 1 (30%): Exits 30% of the position at TP1.
- TP Percent 2 (20%): Exits 20% of the position at TP2.
- TP Percent 3 (15%): Exits 15% of the position at TP3.
Effect of Variables on Performance:
- Short Indicator Lengths: More responsive to price changes but prone to false signals.
- Higher Starting Factor: Slows down the response, useful for longer-term trend following.
- Higher Increment Factor: Widens the variability in moving average lengths, making the strategy adapt to both short-term and long-term price trends.
- Aggressive Take Profit Levels: Allows for quick profit-taking in volatile markets but may exit positions prematurely in strong trends.
The default configuration offers a moderate balance between short-term responsiveness and long-term trend capturing, suitable for most traders. However, users can adjust these variables to optimize performance based on market conditions and personal preferences.
ICT Killzones and Sessions W/ Silver Bullet + MacrosForex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
Usage:
To maximize your experience, minimize the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience.
Forex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
ICT Sessions and Kill Zones
What They Are:
ICT Sessions: These are specific times during the trading day when market activity is expected to be higher, such as the London Open, New York Open, and the Asian session.
Kill Zones: These are specific time windows within these sessions where the probability of significant price movements is higher. For example, the New York AM Kill Zone is typically from 8:30 AM to 11:00 AM EST.
How to Use Them:
Identify the Session: Determine which trading session you are in (London, New York, or Asian).
Focus on Kill Zones: Within that session, focus on the kill zones for potential trade setups. For instance, during the New York session, look for setups between 8:30 AM and 11:00 AM EST.
Silver Bullets
What They Are:
Silver Bullets: These are specific, high-probability trade setups that occur within the kill zones. They are designed to be "one shot, one kill" trades, meaning they aim for precise and effective entries and exits.
How to Use Them:
Time-Based Setup: Look for these setups within the designated kill zones. For example, between 10:00 AM and 11:00 AM for the New York AM session .
Chart Analysis: Start with higher time frames like the 15-minute chart and then refine down to 5-minute and 1-minute charts to identify imbalances or specific patterns .
Macros
What They Are:
Macros: These are broader market conditions and trends that influence your trading decisions. They include understanding the overall market direction, seasonal tendencies, and the Commitment of Traders (COT) reports.
How to Use Them:
Understand Market Conditions: Be aware of the macroeconomic factors and market conditions that could affect price movements.
Seasonal Tendencies: Know the seasonal patterns that might influence the market direction.
COT Reports: Use the Commitment of Traders reports to understand the positioning of large traders and commercial hedgers .
Putting It All Together
Preparation: Understand the macro conditions and review the COT reports.
Session and Kill Zone: Identify the trading session and focus on the kill zones.
Silver Bullet Setup: Look for high-probability setups within the kill zones using refined chart analysis.
Execution: Execute the trade with precision, aiming for a "one shot, one kill" outcome.
By following these steps, you can effectively use ICT sessions, kill zones, silver bullets, and macros to enhance your trading strategy.
Usage:
To maximize your experience, shrink the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience. All credit goes to itradesize for the SB + Macro boxes
Heikin Ashi RSI + OTT [Erebor]Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a popular momentum oscillator used in technical analysis to measure the speed and change of price movements. Developed by J. Welles Wilder, the RSI is calculated using the average gains and losses over a specified period, typically 14 days. Here's how it works:
Description and Calculation:
1. Average Gain and Average Loss Calculation:
- Calculate the average gain and average loss over the chosen period (e.g., 14 days).
- The average gain is the sum of gains divided by the period, and the average loss is the sum of losses divided by the period.
2. Relative Strength (RS) Calculation:
- The relative strength is the ratio of average gain to average loss.
The RSI oscillates between 0 and 100. Traditionally, an RSI above 70 indicates overbought conditions, suggesting a potential sell signal, while an RSI below 30 suggests oversold conditions, indicating a potential buy signal.
Pros of RSI:
- Identifying Overbought and Oversold Conditions: RSI helps traders identify potential reversal points in the market due to overbought or oversold conditions.
- Confirmation Tool: RSI can be used in conjunction with other technical indicators or chart patterns to confirm signals, enhancing the reliability of trading decisions.
- Versatility: RSI can be applied to various timeframes, from intraday to long-term charts, making it adaptable to different trading styles.
Cons of RSI:
- Whipsaws: In ranging markets, RSI can generate false signals, leading to whipsaws (rapid price movements followed by a reversal).
- Not Always Accurate: RSI may give false signals, especially in strongly trending markets where overbought or oversold conditions persist for extended periods.
- Subjectivity: Interpretation of RSI levels (e.g., 70 for overbought, 30 for oversold) is somewhat subjective and can vary depending on market conditions and individual preferences.
Checking RSIs in Different Periods:
Traders often use multiple timeframes to analyze RSI for a more comprehensive view:
- Fast RSI (e.g., 8-period): Provides more sensitive signals, suitable for short-term trading and quick decision-making.
- Slow RSI (e.g., 32-period): Offers a smoother representation of price movements, useful for identifying longer-term trends and reducing noise.
By comparing RSI readings across different periods, traders can gain insights into the momentum and strength of price movements over various timeframes, helping them make more informed trading decisions. Additionally, divergence between fast and slow RSI readings may signal potential trend reversals or continuation patterns.
Heikin Ashi Candles
Let's consider a modification to the traditional “Heikin Ashi Candles” where we introduce a new parameter: the period of calculation. The traditional HA candles are derived from the open 01, high 00 low 00, and close 00 prices of the underlying asset.
Now, let's introduce a new parameter, period, which will determine how many periods are considered in the calculation of the HA candles. This period parameter will affect the smoothing and responsiveness of the resulting candles.
In this modification, instead of considering just the current period, we're averaging or aggregating the prices over a specified number of periods . This will result in candles that reflect a longer-term trend or sentiment, depending on the chosen period value.
For example, if period is set to 1, it would essentially be the same as traditional Heikin Ashi candles. However, if period is set to a higher value, say 5, each candle will represent the average price movement over the last 5 periods, providing a smoother representation of the trend but potentially with delayed signals compared to lower period values.
Traders can adjust the period parameter based on their trading style, the timeframe they're analyzing, and the level of smoothing or responsiveness they prefer in their candlestick patterns.
Optimized Trend Tracker
The "Optimized Trend Tracker" is a proprietary trading indicator developed by TradingView user ANIL ÖZEKŞİ. It is designed to identify and track trends in financial markets efficiently. The indicator attempts to smooth out price fluctuations and provide clear signals for trend direction.
The Optimized Trend Tracker uses a combination of moving averages and adaptive filters to detect trends. It aims to reduce lag and noise typically associated with traditional moving averages, thereby providing more timely and accurate signals.
Some of the key features and applications of the OTT include:
• Trend Identification: The indicator helps traders identify the direction of the prevailing trend in a market. It distinguishes between uptrends, downtrends, and sideways consolidations.
• Entry and Exit Signals: The OTT generates buy and sell signals based on crossovers and direction changes of the trend. Traders can use these signals to time their entries and exits in the market.
• Trend Strength: It also provides insights into the strength of the trend by analyzing the slope and momentum of price movements. This information can help traders assess the conviction behind the trend and adjust their trading strategies accordingly.
• Filter Noise: By employing adaptive filters, the indicator aims to filter out market noise and false signals, thereby enhancing the reliability of trend identification.
• Customization: Traders can customize the parameters of the OTT to suit their specific trading preferences and market conditions. This flexibility allows for adaptation to different timeframes and asset classes.
Overall, the OTT can be a valuable tool for traders seeking to capitalize on trending market conditions while minimizing false signals and noise. However, like any trading indicator, it is essential to combine its signals with other forms of analysis and risk management strategies for optimal results. Additionally, traders should thoroughly back-test the indicator and practice using it in a demo environment before applying it to live trading.
The following types of moving average have been included: "SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA", "HMA", "KAMA", "LSMA", "TRAMA", "VAR", "DEMA", "ZLEMA", "TSF", "WWMA". Thanks to the authors.
Thank you for your indicator “Optimized Trend Tracker”. © kivancozbilgic
Thank you for your programming language, indicators and strategies. © TradingView
Kind regards.
© Erebor_GIT
Heikin Ashi TSI and OTT [Erebor]TSI (True Strength Index)
The TSI (True Strength Index) is a momentum-based trading indicator used to identify trend direction, overbought/oversold conditions, and potential trend reversals in financial markets. It was developed by William Blau and first introduced in 1991.
Here's how the TSI indicator is calculated:
• Double Smoothed Momentum (DM): This is calculated by applying double smoothing to the price momentum. First, the single smoothed momentum is calculated by subtracting the smoothed closing price from the current closing price. Then, this single smoothed momentum is smoothed again using an additional smoothing period.
• Absolute Smoothed Momentum (ASM): This is calculated by applying smoothing to the absolute value of the price momentum. Similar to DM, ASM applies a smoothing period to the absolute value of the difference between the current closing price and the smoothed closing price.
• TSI Calculation: The TSI is calculated as the ratio of DM to ASM, multiplied by 100 to express it as a percentage. Mathematically, TSI = (DM / ASM) * 100.
The TSI indicator oscillates around a centerline (typically at zero), with positive values indicating bullish momentum and negative values indicating bearish momentum. Traders often look for crossovers of the TSI above or below the centerline to identify shifts in momentum and potential trend reversals. Additionally, divergences between price and the TSI can signal weakening trends and potential reversal points.
Pros of the TSI indicator:
• Smoothed Momentum: The TSI uses double smoothing techniques, which helps to reduce noise and generate smoother signals compared to other momentum indicators.
• Versatility: The TSI can be applied to various financial instruments and timeframes, making it suitable for both short-term and long-term trading strategies.
• Trend Identification: The TSI is effective in identifying the direction and strength of market trends, helping traders to align their positions with the prevailing market sentiment.
Cons of the TSI indicator:
• Lagging Indicator: Like many momentum indicators, the TSI is a lagging indicator, meaning it may not provide timely signals for entering or exiting trades during rapidly changing market conditions.
• False Signals: Despite its smoothing techniques, the TSI can still produce false signals, especially during periods of low volatility or ranging markets.
• Subjectivity: Interpretation of the TSI signals may vary among traders, leading to subjective analysis and potential inconsistencies in trading decisions.
Overall, the TSI indicator can be a valuable tool for traders when used in conjunction with other technical analysis tools and risk management strategies. It can help traders identify potential trading opportunities and confirm trends, but it's essential to consider its limitations and incorporate additional analysis for more robust trading decisions.
Heikin Ashi Candles
Let's consider a modification to the traditional “Heikin Ashi Candles” where we introduce a new parameter: the period of calculation. The traditional HA candles are derived from the open , high low , and close prices of the underlying asset.
Now, let's introduce a new parameter, period, which will determine how many periods are considered in the calculation of the HA candles. This period parameter will affect the smoothing and responsiveness of the resulting candles.
In this modification, instead of considering just the current period, we're averaging or aggregating the prices over a specified number of periods . This will result in candles that reflect a longer-term trend or sentiment, depending on the chosen period value.
For example, if period is set to 1, it would essentially be the same as traditional Heikin Ashi candles. However, if period is set to a higher value, say 5, each candle will represent the average price movement over the last 5 periods, providing a smoother representation of the trend but potentially with delayed signals compared to lower period values.
Traders can adjust the period parameter based on their trading style, the timeframe they're analyzing, and the level of smoothing or responsiveness they prefer in their candlestick patterns.
Optimized Trend Tracker
The "Optimized Trend Tracker" is a proprietary trading indicator developed by TradingView user ANIL ÖZEKŞİ. It is designed to identify and track trends in financial markets efficiently. The indicator attempts to smooth out price fluctuations and provide clear signals for trend direction.
The Optimized Trend Tracker uses a combination of moving averages and adaptive filters to detect trends. It aims to reduce lag and noise typically associated with traditional moving averages, thereby providing more timely and accurate signals.
Some of the key features and applications of the OTT include:
• Trend Identification: The indicator helps traders identify the direction of the prevailing trend in a market. It distinguishes between uptrends, downtrends, and sideways consolidations.
• Entry and Exit Signals: The OTT generates buy and sell signals based on crossovers and direction changes of the trend. Traders can use these signals to time their entries and exits in the market.
• Trend Strength: It also provides insights into the strength of the trend by analyzing the slope and momentum of price movements. This information can help traders assess the conviction behind the trend and adjust their trading strategies accordingly.
• Filter Noise: By employing adaptive filters, the indicator aims to filter out market noise and false signals, thereby enhancing the reliability of trend identification.
• Customization: Traders can customize the parameters of the OTT to suit their specific trading preferences and market conditions. This flexibility allows for adaptation to different timeframes and asset classes.
Overall, the OTT can be a valuable tool for traders seeking to capitalize on trending market conditions while minimizing false signals and noise. However, like any trading indicator, it is essential to combine its signals with other forms of analysis and risk management strategies for optimal results. Additionally, traders should thoroughly back-test the indicator and practice using it in a demo environment before applying it to live trading.
The following types of moving average have been included: "SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA", "HMA", "KAMA", "LSMA", "TRAMA", "VAR", "DEMA", "ZLEMA", "TSF", "WWMA". Thanks to the authors.
Thank you for your indicator “Optimized Trend Tracker”. © kivancozbilgic
Thank you for your programming language, indicators and strategies. © TradingView
Kind regards.
© Erebor_GIT
Z Algo (Expo)█ Overview
Z Algo (Expo) is a sophisticated and user-friendly trading tool designed to meet the needs of both novice and seasoned traders. With its real-time signals, trend analysis, and risk management capabilities, this tool can be a valuable addition to any trader's toolkit.
█ Main Features & How to Use
Buy/Sell signals: Z Algo provides real-time buy and sell signals, which assist traders in identifying the most opportune moments to enter or exit a trade.
Strong Buy/Sell signals: In addition to regular buy and sell signals, the tool also offers strong buy and sell signals. These are generated when the market conditions align with a higher probability of a significant price movement.
Sniper Signals: This feature is specifically designed for contrarian traders who look to exploit temporary market inefficiencies or take advantage of price reversals. When enabled, Sniper Signals identify potential market turning points, offering traders the opportunity to profit from sharp price fluctuations.
Reversal Cloud: The Reversal Cloud is a unique visual representation of the market's potential trend reversals. It offers traders an easy-to-understand display of changing market dynamics, enabling them to quickly identify potential entry and exit points based on trend reversals.
Support and Resistance (S/R) Levels: Z Algo automatically calculates and displays support and resistance levels on the chart. These are crucial price points where buying or selling pressure may change, providing valuable insights for traders looking to enter or exit positions based on these levels.
Trend Tracker: This feature helps traders monitor and analyze the prevailing market trend. Trend Tracker identifies and highlights the direction of the trend, allowing traders to align their strategies accordingly and increase their chances of success.
Trend Background Color: To improve the user experience and simplify the interpretation of market data, Z Algo changes the chart's background color based on the identified trend direction. This visual cue makes it easier for traders to recognize bullish or bearish trends at a glance.
Bar Coloring: In addition to the trend background color, Z Algo also provides bar coloring for both contrarian and trend bars. This feature helps traders visualize price movements and trends more effectively, enabling them to identify potential opportunities for both trend-following and contrarian trading strategies.
Risk Management: The tool incorporates risk management features that help traders to protect their capital and maximize potential returns. Users can set stop-loss and take-profit levels, as well as customize their risk exposure according to their individual preferences and trading style.
█ Calculations
█ What are the Buy/Sell signals based on?
The Buy/Sell signals use volatility and price range with a weighting function that can help reduce lag and respond faster to recent price changes. The function gives more weight to the most recent volatility values and absolute price changes, making the algorithm more responsive to changes in volatility and price moves. Using a model that factors in both price changes and volatility gives a bias toward more recent data. This advanced approach to trading signal generation incorporates the concepts of trend following and mean reversion while accounting for changing market volatility.
Traditional systems often use fixed parameters, which may not adapt quickly to changes in market conditions. This can lead to late entries or exits, potentially reducing profitability or increasing risk. Our algorithm uses a weighting function to give more importance to recent volatility values, and absolute price changes can make these signals more responsive. This is especially useful in dynamic markets where price swings and volatility can change rapidly.
Adapting to Recent Price Changes: Markets can often exhibit trending behavior over certain periods. By weighing recent price changes more heavily, the model can quickly identify and react to the emergence of new trends. This can lead to earlier entries in a new trend, potentially increasing profitability.
Adapting to Recent Volatility Changes: Markets can shift from low to high volatility regimes (and vice versa) quite rapidly. A model that gives more weight to recent volatility can adapt its signals to these changing conditions. For example, in high volatility conditions, the model might generate fewer signals to reduce the risk of false breakouts. Conversely, in low volatility conditions, the model might generate more signals to capitalize on trending behavior.
Adaptive Trading: The approach inherently leads to an adaptive trading system. Rather than using fixed parameters, the system can adjust its behavior based on recent market activity. This can lead to a more robust system that performs well across different market conditions.
█ What are the Sniper signals (contrarian signals) based on?
Our contrarian signals are based on deviation from the expected value. The algorithm quantifies the amount of variation or dispersion in a set of values. Non-expected values are the fundamental core of the signal generation process.
█ Reversal Cloud Calculation
The cloud uses the information of how much the price fluctuates over a specific time period and updates its equilibrium value automatically at new price changes. The price changes are used to predict what will happen next, and the band adapts accordingly. The algorithm assumes that past price changes can predict future market behavior.
█ Support and Resistance (S/R) Levels Calculation
The support and resistance levels use historical overbought and oversold levels combined with a weighted atr function to predict future support and resistance areas. This calculation can potentially give traders a great heads-up on where the price may find support and resistance at.
█ Trend & Bar coloring Calculation
Trend calculations with dynamic events are key in ever-changing markets. The main idea of the calculation method is to find the mathematical function that best fits the data points, by minimizing the sum of the squares of the vertical distances of each data point from the equilibrium. The outcome is a function that finds the best mathematical description of that data. Hence the trend output may vary depending on the asset and timeframe. A unique approach where the same settings can give different results.
█ Risk Management Calculation
The risk management system is not unique in itself and contains everything that can help traders to manage their risk, such as different types of stop losses, Take Profits calculations.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Skrip berbayar
FlashTrade 20 Asset ScreenerThe FlashTrade 20 Asset Screener is a powerful screening tool written in Pinescript and designed for use in Tradingview. It simultaneously monitors a combination of seven (7) lagging and three (3) leading indicators for twenty (20) assets, such as; stocks, cryptocurrencies, or forex pairs.
The screener sends summarized numeric data as an alert to external programs that specialize in automated trading. This proprietary technology developed by the algorithmic trading firm known as FlashTrade.AI is now integrated with the rules-based trading platform TradeLab.AI.
The lagging indicators tracked by the screener are as follows:
1. The 8 Period Exponential Moving Average (8EMA): It determines whether it is over or under the 21 Period Exponential Moving Average (21EMA).
2. Two Closes of the 8 Period Exponential Average (8EMA): This confirms a trend as either bullish or bearish when it occurs over or under the 21 Period Exponential Moving Average (21EMA).
3. Ichimoku Cloud: This indicator identifies whether the price is above or below the cloud, indicating bullish or bearish trends.
4. Ichimoku Cloud: Conversion Line Above Base or Conversion Line Below Base: This measures the trend strength based on the distance between the conversion line and the base line.
5. Ichimoku Cloud: Lag Line Above Price or Lag Line Below Price: This tracks the lag line's position relative to the current price to confirm trend direction.
6. Ichimoku Cloud: Leading Cloud Green or Leading Cloud Red: This identifies the trend based on the color of the leading cloud.
7. MACD: This oscillator tracks the difference between two exponential moving averages and helps identify bullish or bearish momentum in the market.
The screener also tracks three leading indicators that primarily measure trading volume and momentum. These leading indicators are:
1. The Relative Strength Index (RSI): This oscillator measures the speed and change of price movements to identify overbought or oversold conditions in the market.
2. The Stochastic: This oscillator measures the momentum of price movements and helps identify potential trend reversals in the market.
3. The relative Vigor Index (RVGI): This indicator measures the strength of price movements by comparing the closing price to the trading range and helps identify trend reversals.
Overall, the FlashTrade 20 Asset Screener is a powerful tool for traders looking to automate their trading strategies. By monitoring multiple indicators for multiple assets simultaneously, it can identify trends and capitalize on opportunities when they present themselves.
Portfolio_Tracking_TRThis is a portfolio tracker that will track individual, overall and daily profit/loss for up. You can set the size of your buys and price of your buys for accurate, up to date profit and loss data right on your chart. It works on all markets and timeframes.
Next we get into setting up your , order size and price. Each ticker lets you set which stock you bought, then set how much you purchased and then what price you purchased them at.
FEATURES
Top Section
The portfolio tracker has 2 sections. The top section shows each ticker in your portfolio individually with the following data:
- Ticker Name
- Weight of that asset compared to your total portfolio in %
- Current value of that position in TL
- Profit or loss value from purchase price in %
- Todays change in value from yesterday’s close in %
Bottom Section
The bottom section of the tracker will give you info for your portfolio as a whole. It has the following data:
- Total cost of your entire portfolio in TL
- Current value of your entire portfolio in TL
- Current profit or loss of your entire portfolio in TL
- Current profit or loss of your entire portfolio in %
- Todays change of your entire portfolio value compared to yesterday’s close in %
This indicator was compiled from FriendOfTheTrend's indicator named Portfolio Tracker For Stocks & Crypto.
Prophit Ninja: Hidden ScrollStay ahead of the markets moves with "Prophit Ninja: Hidden Scroll".
Our legendary senseis have mastered the arts of wielding the Katana and Shuriken over many decades of focused practice and distilled their systems and techniques down to their most vital form- storing their knowledge in this ancient scroll for easy adoption by any ninja practiced enough to be able to decipher it.
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█ INTERPRETATION
Each sub-indicator in this package can be used as a confirmation tool to check your bias and give you a more informed decision as they all take into account every reading shown and not shown being calculated across all Prophit Ninja packages. A sentiment rating below the candle shows bullish bias while a green color emphasizes bullish strength- a sentiment rating above the candle shows bearish bias while a red color emphasizes bearish strength- gold color signifies a strong turn in the market while grey/dark grey is a weak reading. A green trend sensei reading is bullish- while a red trend sensei reading is bearish. A green bull trade sensei label signifies a possible bullish trade set up, while a red bear label trade sensei signifies a possible bearish trade set up. Stat sensei gives you tick by tick multi-time frame readings to always keep you aware of the exact environment you're in. Lastly risk sensei will give you the most optimal least risk trade set-up based on user defined variables and give you tick by tick readings of your trade status. This can be used as a standalone decision-maker, or used in confluence with other indicator packages in our Prophit Ninja bundle to get higher precision.
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█ OVERVIEW
1 — Sentiment Sensei: A toggle-able tick by tick rating system (0-100%) for each candle based on over 100 individual readings .
2 — Trend Sensei: A toggle-able background coloring that easily shows you the trend bias behind the moves.
3 — Trade Sensei: A toggle-able trade finder that finds confluent trade set-ups to give you the upper hand.
4 — Stat Sensei: A toggle-able multi-time frame candle progress tracker with a built in trend bias and price/volume/momentum change/ratio trackers.
5 — Risk Sensei: A toggle-able risk manager with two variations of auto profit target zones , three variations of trailing stop losses , a win/loss tracker , trade duration timer and all the information you need to stay updated with the status of your trade at a glance whether long or short.
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█ EASY CUSTOMIZATION
i.imgur.com
With a fully customizable and easy-to-use input menu , this indicator gives you the ability to tailor your trading experience to your needs and see as much (or as little) information as you want to; presented in the manner you deem most viable with the following options in just a few clicks:
Indicator Package- This option allows you to switch between the four display modes available so in any moment you can completely change the metrics you’re reading in just two clicks. This allows you the ability to make decisions based on not only what you’re comfortable with; but also to find confirmation or disagreement with other systems instantly.
Color Theme- There are four color themes available which include original, colorful, monochrome and solid. These not only allow you a quick and easy way to change the colors to suit your style; they also make it so you can challenge your bias in an instant by viewing the data in a completely different way.
Dual Attack Modes- Whether you’re a scalper, day trader, swing trader, or investor; this option allows you to see the chart based on four different risk tolerance/time expectancy mentalities for the Katana and Shuriken separately in just two clicks. Investors can see what the scalpers are thinking and vice/versa to broaden their decision making and/or hone in when optimal.
Dual Sharpness Levels- This algorithm allows the user to display the data on five different smoothness levels for the Katana and Shuriken separately without suffering the inherent lag that accompanies most other indicators. Whether you like to see every tick of a choppy movement, or filter out the false signals into smooth readings, you can do so at any moment.
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█ RISK SENSEI EXAMPLE
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█ PRE-BUILT ALERTS
With Prophit Ninja: Hidden Scroll's built-in alerts you can enable alerts for any piece of the Hidden Scrollin just a few clicks. These alerts are way more specific and optimized than you can possibly achieve with the custom alert settings. Each checking for multiple possible activation triggers instead of one and populating the message field automatically so you can just click create.
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As you can see; this ancient scroll has the ability to adapt to any reader or adversary and give those in control of its power the upper hand. Any mode of battle, any opponent, any circumstance- 'Prophit Ninja: Hidden Scroll' was polished by our finest artists to inform any reader and make sure they know when to attack, defend or simply allow the fight to play out by its easy-to-read coloring system. As long as you learn the techniques you'll have a much better chance of making the right decision than when you didn't.
This state-of-the-art tool is great for experienced traders, those who just started learning to trade, or anyone in between- truly made to suit the needs of any trader, in any moment, with any mindset (along with the other indicators in our Prophit Ninja bundle) you'll notice an immediate improvement in your market dexterity after learning it.
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*everything displayed is part of the Prophit Ninja indicator bundle; this is an otherwise blank chart*
