1A Monthly P&L Table - Using Library1A Monthly P&L Table: Track Your Performance Month-by-Month
Overview:
The 1A Monthly P&L Table is a straightforward yet powerful indicator designed to give you an immediate overview of your asset's (or strategy's) percentage performance on a monthly basis. Displayed conveniently in the bottom-right corner of your chart, this tool helps you quickly assess historical gains and losses, making it easier to analyze trends in performance over time.
Key Features:
Monthly Performance at a Glance: Clearly see the percentage change for each past month.
Cumulative P&L: A running total of the displayed monthly P&L is provided, giving you a quick sum of performance over the selected period.
Customizable Display:
Months to Display: Choose how many past months you want to see in the table (from 1 to 60 months).
Text Size: Adjust the text size (Tiny, Small, Normal, Large, Huge) to fit your viewing preferences.
Text Color: Customize the color of the text for better visibility against your chart background.
Intraday & Daily Compatibility: The table is optimized to display on daily and intraday timeframes, ensuring it's relevant for various trading styles. (Note: For very long-term analysis on weekly/monthly charts, you might consider other tools, as this focuses on granular monthly P&L.)
How It Works:
The indicator calculates the percentage change from the close of the previous month to the close of the current month. For the very first month displayed, it calculates the P&L from the opening price of the chart's first bar to the close of that month. This data is then neatly organized into a table, updated on the last bar of the day or session.
Ideal For:
Traders and investors who want a quick, visual summary of monthly performance.
Analyzing seasonal trends or consistent periods of profitability/drawdown.
Supplementing backtesting results with a clear month-by-month breakdown.
Settings:
Text Color: Changes the color of all text within the table.
Text Size: Controls the font size of the table content.
Months to Display: Determines the number of recent months included in the table.
Statistics
MonthlyPnLTableLibrary "MonthlyPnLTable"
monthlyPnL(currentClose, initialOpenPrice, monthsToDisplay)
Parameters:
currentClose (float)
initialOpenPrice (float)
monthsToDisplay (int)
displayPnLTable(pnls, pnlMonths, pnlYears, textSizeOption, labelColor)
Parameters:
pnls (array)
pnlMonths (array)
pnlYears (array)
textSizeOption (string)
labelColor (color)
Fibonacci & Volume Bell CurveBell Curve + Fibonacci Retracement
This custom indicator combines Fibonacci retracement levels with volume-weighted statistics (VWAP Bell Curve) to provide high-probability trading signals.
Indicator Components:
Fibonacci Retracement
Key Level Used:
Cyan (61.8%) – Golden Ratio: Most significant for identifying potential reversals.
Volume-Weighted Bell Curve (VWAP Bands)
White Line – VWAP (Volume Weighted Average Price).
Orange Bands (±2σ) – Represent two standard deviations above and below VWAP. Indicates the range where approximately 95% of volume-weighted price action occurs.
Trading Strategies:
Support & Resistance Trading
Fibonacci levels act as dynamic support/resistance.
The 61.8% level is especially effective for spotting reversal opportunities.
VWAP Mean Reversion
When price moves outside the ±2σ orange bands, expect a reversion back to the white VWAP line.
High-probability trades occur when price is extended to extremes.
Confluence Trading (High-Probability Setups). Strongest signals occur when Fibonacci levels align with VWAP bands. Look for overlap between Fib levels and VWAP support/resistance zones.
Pro Tips for Best Results:
Volume Confirmation: Look for increased volume at key levels for stronger signals.
Timeframes: Effective on all timeframes; higher timeframes offer more reliable signals.
Market Context: Always consider overall market direction and news events.
Multiple Touches: Levels become more valid when tested multiple times.
My settings:
Fibonacci Settings
Lookback Period: 50
Swing Detection Sensitivity: 5
Show Fibonacci Labels: ✅ Enabled
Bell Curve (VWAP Bands) Settings
Bell Curve Period: 100
VWAP Source: (H + L + C) / 3 (typical price)
Show Bell Curve Bands: ✅ Enabled
Confidence Levels: 2 Standard Deviations (±2σ)
Visual Settings
Fibonacci Line Width: 2
Bell Curve Line Width: 2
Extend Lines Right: ✅ Enabled
Fibonacci Levels
61.8% – ✅ Enabled, Color: Bright Blue
Other levels are disabled
VWAP & Bell Curve Bands
VWAP (White Line) – ✅ Enabled
Upper 1 SD – ✅ Enabled, Color: Gray
Lower 1 SD – ✅ Enabled, Color: Gray
Upper 2 SD – ✅ Enabled, Color: Orange-Red (with transparency)
Lower 2 SD – ✅ Enabled, Color: Orange-Red (with transparency)
LANZ Strategy 3.0 [Backtest]🔷 LANZ Strategy 3.0 — Asian Range Fibonacci Scalping Strategy
LANZ Strategy 3.0 is a precision-engineered backtesting tool tailored for intraday traders who rely on the Asian session range to determine directional bias. This strategy implements dynamic Fibonacci projections and strict time-window validation to simulate a clean and disciplined trading environment.
🧠 Core Components:
Asian Range Bias Definition: Direction is established between 01:15–02:15 a.m. NY time based on the candle’s close in relation to the midpoint of the Asian session range (18:00–01:15 NY).
Limit Order Execution: Only one trade is placed daily, using a limit order at the Asian range high (for sells) or low (for buys), between 01:15–08:00 a.m. NY.
Fibonacci-Based TP/SL:
Original Mode: TP = 2.25x range, SL = 0.75x range.
Optimized Mode: TP = 1.95x range, SL = 0.65x range.
No Trade After 08:00 NY: If the limit order is not executed before 08:00 a.m. NY, it is canceled.
Fallback Logic at 02:15 NY: If the market direction misaligns with the setup at 02:15 a.m., the system re-evaluates and can re-issue the order.
End-of-Day Closure: All positions are closed at 15:45 NY if still open.
📊 Backtest-Ready Design:
Entries and exits are executed using strategy.entry() and strategy.exit() functions.
Position size is fixed via capital risk allocation ($100 per trade by default).
Only one position can be active at a time, ensuring controlled risk.
📝 Notes:
This strategy is ideal for assets sensitive to the Asian/London session overlap, such as Forex pairs and indices.
Easily switch between Fibonacci versions using a single dropdown input.
Fully deterministic: all entries are based on pre-defined conditions and time constraints.
👤 Credits:
Strategy developed by rau_u_lanz using Pine Script v6. Built for traders who favor clean sessions, directional clarity, and consistent execution using time-based logic and Fibonacci projections.
Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis
A Journey from Theory to Trading Reality
What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals.
The Theoretical Foundation
Lorentzian Distance in Market Space
Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure:
d(x,y) = Σ ln(1 + |xi - yi|)
This logarithmic formulation naturally handles:
Scale invariance: Large price moves don't overwhelm small but significant patterns
Outlier robustness: Extreme values are dampened rather than dominating
Non-linear relationships: Captures market behavior better than linear metrics
K-Nearest Neighbors with Relativistic Weighting
The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance:
w = 1 / (1 + distance)
This creates a "gravitational" effect where closer patterns have stronger influence on predictions.
The Implementation Challenge
Creating meaningful market features required extensive experimentation:
Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average
Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio
Each feature undergoes min-max normalization to ensure equal weighting in distance calculations.
The Prediction Mechanism
For each current market state:
Feature Vector Construction: 12-dimensional representation of market conditions
Historical Search: Scan lookback period for similar patterns using Lorentzian distance
Neighbor Selection: Identify K nearest historical matches
Outcome Analysis: Examine what happened N bars after each match
Weighted Prediction: Combine outcomes using distance-based weights
Confidence Calculation: Measure agreement between neighbors
Technical Hurdles Overcome
Array Management: Complex indexing to prevent look-ahead bias
Distance Calculations: Optimizing nested loops for performance
Memory Constraints: Balancing lookback depth with computational limits
Signal Filtering: Preventing clustering of identical signals
Advanced Dashboard System
Main Control Panel
The primary dashboard provides real-time market intelligence:
Signal Status: Current prediction with confidence percentage
Neighbor Analysis: How many historical patterns match current conditions
Market Regime: Trend strength, volatility, and volume analysis
Temporal Context: Real-time updates with timestamp
Performance Analytics
Comprehensive tracking system monitors:
Win Rate: Percentage of successful predictions
Signal Count: Total predictions generated
Streak Analysis: Current winning/losing sequence
Drawdown Monitoring: Maximum equity decline
Sharpe Approximation: Risk-adjusted performance estimate
Risk Assessment Panel
Multi-dimensional risk analysis:
RSI Positioning: Overbought/oversold conditions
ATR Percentage: Current volatility relative to price
Bollinger Position: Price location within volatility bands
MACD Alignment: Momentum confirmation
Confidence Heatmap
Visual representation of prediction reliability:
Historical Confidence: Last 10 periods of prediction certainty
Strength Analysis: Magnitude of prediction values over time
Pattern Recognition: Color-coded confidence levels for quick assessment
Input Parameters Deep Dive
Core Algorithm Settings
K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets.
Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes.
Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe.
Feature Selection
Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation
Signal Generation
Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading.
Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities.
Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag.
Visual Design Philosophy
Color Themes
Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics
Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors
Information Hierarchy
The dashboard system prioritizes information by importance:
Primary Signals: Largest, most prominent display
Confidence Metrics: Secondary but clearly visible
Supporting Data: Detailed but unobtrusive
Historical Context: Available but not distracting
Trading Applications
Signal Interpretation
Long Signals: Prediction > threshold with high confidence
Look for volume confirmation
- Check trend alignment
- Verify support levels
Short Signals: Prediction < -threshold with high confidence
Confirm with resistance levels
- Check for distribution patterns
- Verify momentum divergence
- Market Regime Adaptation
Trending Markets: Higher confidence in directional signals
Ranging Markets: Focus on reversal signals at extremes
Volatile Markets: Require higher confidence thresholds
Low Volume: Reduce position sizes, increase caution
Risk Management Integration
Confidence-Based Sizing: Larger positions for higher confidence signals
Regime-Aware Stops: Wider stops in volatile regimes
Multi-Timeframe Confirmation: Align signals across timeframes
Volume Confirmation: Require volume support for major signals
Originality and Innovation
This indicator represents genuine innovation in several areas:
Mathematical Approach
First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities.
Feature Engineering
Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form.
Visualization System
Professional-grade dashboard system providing comprehensive market intelligence in intuitive format.
Performance Tracking
Real-time performance analytics typically found only in institutional trading systems.
Development Journey
Creating this indicator involved overcoming numerous technical challenges:
Mathematical Complexity: Translating theoretical concepts into practical code
Performance Optimization: Balancing accuracy with computational efficiency
User Interface Design: Making complex data accessible and actionable
Signal Quality: Filtering noise while maintaining responsiveness
The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation.
Best Practices
- Parameter Optimization
- Start with default settings and adjust based on:
Market Characteristics: Volatile vs. stable
Trading Timeframe: Scalping vs. swing trading
Risk Tolerance: Conservative vs. aggressive
Signal Confirmation
Never trade on Lorentzian signals alone:
Price Action: Confirm with support/resistance
Volume: Verify with volume analysis
Multiple Timeframes: Check higher timeframe alignment
Market Context: Consider overall market conditions
Risk Management
Position Sizing: Scale with confidence levels
Stop Losses: Adapt to market volatility
Profit Targets: Based on historical performance
Maximum Risk: Never exceed 2-3% per trade
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution.
Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization.
Thank you for sharing the idea. You're more than a follower, you're a leader!
@vasanthgautham1221
Trade with precision. Trade with insight.
— Dskyz , for DAFE Trading Systems
SectorRotationRadarThe Sector Rotation Radar is a powerful visual analysis tool designed to track the relative strength and momentum of a stock compared to a benchmark index and its associated sector ETF. It helps traders and investors identify where an asset stands within the broader market cycle and spot rotation patterns across sectors and timeframes.
🔧 Key Features:
Benchmark Comparison: Measures the relative performance (strength and momentum) of the current symbol against a chosen benchmark (default: SPX), highlighting over- or underperformance.
Automatic Sector Detection: Automatically links stocks to their relevant sector ETFs (e.g., XLK, XLF, XLU), based on an extensive internal symbol map.
Multi-Timeframe Analysis: Supports simultaneous comparison across the current, next, and even third-higher timeframes (e.g., Daily → Weekly → Monthly), providing a bigger-picture perspective of trend shifts.
Tail Visualization: Displays a "trail" of price behavior over time, visualizing how the asset has moved in terms of relative strength and momentum across a user-defined period.
Quadrant-Based Layout: The chart is divided into four dynamic main zones, each representing a phase in the strength/momentum cycle:
🔄 Improving: Gaining strength and momentum
🚀 Leading: High strength and high momentum — top performers
💤 Weakening: Losing momentum while still strong
🐢 Lagging: Low strength and low momentum — underperformers
Clean Chart Visualization:
Background grid with axis labels
Dynamic tails and data points for each symbol
Option to include the associated sector ETF for context
Descriptive labels showing exact strength/momentum values per point
⚙️ Customization Options:
Benchmark Selector: Choose any symbol to compare against (e.g., SPX, Nasdaq, custom index)
Start Date Control: Option to fix a historical start point or use the current data range
Trail Length: Set the number of previous data points to display
Additional Timeframes: Enable analysis of one or two higher timeframes beyond the current
Sector ETF Display: Toggle to show or hide the related sector ETF alongside the asset
📚 Technical Architecture:
The indicator relies on external modules for:
Statistical modeling
Relative strength and momentum calculations
Chart rendering and label drawing
These components work together to compute and display a dynamic, real-time map of asset performance over time.
🧠 Use Case:
Sector Rotation Radar is ideal for traders looking to:
Spot stocks or sectors rotating into strength or weakness
Confirm alignment across multiple timeframes
Identify sector leaders and laggards
Understand how a symbol is positioned relative to the broader market and its peers
This tool is especially valuable for swing traders, sector rotation strategies, and macro-aware investors who want a visual edge in decision-making.
VWAP Fibonacci S&R with Bell CurveThis indicator is a sophisticated trading tool that combines three powerful technical analysis concepts to identify high-probability trading opportunities. Let me break down how it works:
Core Components:
1. VWAP (Volume Weighted Average Price)
Calculates the average price weighted by volume over a specified period
Acts as a dynamic support/resistance level that institutions often use
Can reset daily, weekly, or monthly depending on your trading timeframe
The yellow line on your chart represents the current VWAP
2. Bell Curve Probability Analysis
Measures how far the current price deviates from the VWAP in statistical terms
Calculates a Z-score (standard deviations away from the mean)
Creates probability bands around the VWAP based on price volatility
The theory: extreme deviations from VWAP tend to revert back to the mean
3. Fibonacci Retracement Levels
Uses recent highs and lows to calculate key Fibonacci levels (38.2%, 50%, 61.8%)
These levels often act as support and resistance zones
Combined with VWAP analysis for confluence trading
How the Signals Work:
BUY Signals (Green arrows below candles)
Generated when either condition is met:
Mean Reversion Buy: Price is below VWAP + high probability of reversion + extreme statistical deviation
Fibonacci Support Buy: Price is above VWAP + near key Fibonacci support levels (38.2% or 50%)
SELL Signals (Red arrows above candles)
Generated when either condition is met:
Mean Reversion Sell: Price is above VWAP + high probability of reversion + extreme statistical deviation
Fibonacci Resistance Sell: Price is below VWAP + near key Fibonacci resistance levels (61.8% or 50%)
Visual Elements
Yellow Line: Main VWAP
Blue Bands: Probability zones based on standard deviation
Orange/White/Purple Lines: Key Fibonacci levels (38.2%, 50%, 61.8%)
Yellow Background: High probability mean reversion zones
⚠ Symbol: Extreme deviation warning (Z-score > 2.5)
The Information Table
Shows real-time statistics:
VWAP: Current VWAP value
Distance: How far price is from VWAP (percentage)
Z-Score: Statistical measure of deviation (>2 is significant)
Reversion %: Probability of mean reversion
Fib 50%: Key Fibonacci midpoint level
Status: Current signal state
Trading Logic
The indicator works on the principle that:
Extreme deviations from VWAP are unsustainable and tend to revert
Fibonacci levels provide natural support/resistance zones
Volume confirmation ensures the move has institutional backing
Statistical probability helps time entries when odds are favorable
Best Use Cases
Scalping: Quick mean reversion trades when price gets too far from VWAP
Swing Trading: Using Fibonacci levels with VWAP for longer-term positions
Risk Management: Avoiding trades when probability is low
Confluence Trading: Waiting for multiple signals to align
Eigenvector Centrality Drift (ECD) - Market State Network What is Eigenvector Centrality Drift (ECD)?
Eigenvector Centrality Drift (ECD) is a groundbreaking indicator that applies concepts from network science to financial markets. Instead of viewing price as a simple series, ECD models the market as a dynamic network of “micro-states”—distinct combinations of price, volatility, and volume. By tracking how the influence of these states changes over time, ECD helps you spot regime shifts and transitions in market character before they become obvious in price.
This is not another moving average or momentum oscillator. ECD is inspired by eigenvector centrality—a measure of influence in network theory—and adapts it to the world of price action, volatility, and volume. It’s about understanding which market states are “in control” and when that control is about to change.
Theoretical Foundation
Network Science: In complex systems, nodes (states) and edges (transitions) form a network. Eigenvector centrality measures how influential a node is, not just by its direct connections, but by the influence of the nodes it connects to.
Market Micro-States: Each bar is classified into a “state” based on price change, volatility, and volume. The market transitions between these states, forming a network of possible regimes.
Centrality Drift: By tracking the centrality (influence) of the current state, and how it changes (drifts) over time, ECD highlights when the market’s “center of gravity” is shifting—often a precursor to major moves or regime changes.
How ECD Works
State Classification: Each bar is assigned to one of N market micro-states, based on a weighted combination of normalized price change, volatility, and volume.
Transition Matrix: Over a rolling window, ECD tracks how often the market transitions from each state to every other state, forming a transition probability matrix.
Centrality Calculation: Using a simplified eigenvector approach, ECD calculates the “influence” score for each state, reflecting how central it is to the network of recent market behavior.
Centrality Drift: The indicator tracks the Z-score of the change in centrality for the current state. Rapid increases or decreases, or a shift in the dominant state, signal a potential regime shift.
Dominant State: ECD also highlights which state currently has the highest influence, providing insight into the prevailing market character.
Inputs:
🌐 Market State Configuration
Number of Market States (n_states, default 6): Number of distinct micro-states to track.
3–4: Simple (Up/Down/Sideways)
5–6: Balanced (recommended)
7–9: Complex, more nuanced
Price Change Weight (price_weight, default 0.4):
How much price movement defines a state. Higher = more directional.
Volatility Weight (vol_weight, default 0.3):
How much volatility defines a state. Higher = more regime focus.
Volume Weight (volume_weight, default 0.3):
How much volume defines a state. Higher = more participation focus.
🔗 Network Analysis
Transition Matrix Window (transition_window, default 50): Lookback for building the state transition matrix.
Shorter: Adapts quickly
Longer: More stable
Influence Decay Factor (influence_decay, default 0.85): How much influence propagates through the network.
Higher: Distant transitions matter more
Lower: Only immediate transitions matter
Drift Detection Sensitivity (drift_sensitivity, default 1.5): Z-score threshold for significant centrality drift.
Lower: More signals
Higher: Only major shifts
🎨 Visualization
Show Network Visualization (show_network, default true): Background color and effects based on network structure.
Show Centrality Score (show_centrality, default true): Plots the current state’s centrality measure.
Show Drift Indicator (show_drift, default true): Plots the centrality drift Z-score.
Show State Map (show_state_map, default true): Dashboard showing all state centralities and which is dominant.
Color Scheme (color_scheme, default "Quantum"):
“Quantum”: Cyan/Magenta
“Neural”: Green/Blue
“Plasma”: Yellow/Pink
“Matrix”: Green/Black
Color Schemes
Dynamic gradients reflect the current state’s centrality and drift, using your chosen color palette.
Background network effect: The more central the current state, the more intense the background.
Centrality and drift lines: Color-coded for clarity and regime shift detection.
Visual Logic
Centrality Score Line: Plots the influence of the current state, with glow for emphasis.
Drift Indicator: Histogram of centrality drift Z-score, green for positive, red for negative.
Threshold Lines: Dotted lines mark the drift sensitivity threshold for regime shift alerts.
State Map Dashboard: Top-right panel shows all state centralities, highlights the current and dominant state, and visualizes influence with bars.
Information Panel: Bottom-left panel summarizes current state, centrality, dominant state, drift Z-score, and regime shift status.
How to Use ECD
Centrality Score: High = current state is highly influential; low = state is peripheral.
Drift Z-Score:
Large positive/negative = rapid change in influence, regime shift likely.
Near zero = stable network, no major shift.
Dominant State: The state with the highest centrality is “in control” of the market’s transitions.
State Map: Use to see which states are rising or falling in influence.
Tips:
Use fewer states for simple markets, more for nuanced analysis.
Watch for drift Z-score crossing the threshold—these are your regime shift signals.
Combine with your own system for confirmation.
Alerts:
ECD Regime Shift: Significant centrality drift detected—potential regime change.
ECD State Change: Market state transition occurred.
ECD Dominance Shift: Dominant market state has changed.
Originality & Usefulness
ECD is not a mashup or rehash of standard indicators. It is a novel application of network science and eigenvector centrality to market microstructure, providing a new lens for understanding regime shifts and market transitions. The state network, centrality drift, and dashboard are unique to this script. ECD is designed for anticipation, not confirmation—helping you see the market’s “center of gravity” shift before price action makes it obvious.
Chart Info
Script Name: Eigenvector Centrality Drift (ECD) – Market State Network
Recommended Use: Any asset, any timeframe. Tune parameters to your style.
Disclaimer
This script is for research and educational purposes only. It does not provide financial advice or direct buy/sell signals. Always use proper risk management and combine with your own strategy. Past performance is not indicative of future results.
See the market as a network. Anticipate the shift in influence.
— Dskyz , for DAFE Trading Systems
H4 Swing Grade Checklist English V.1✅ H4 Swing Grade Checklist – Auto Grading for Smart Money Setups
This script helps manual traders assess the quality of a Smart Money swing trade setup by checking 7 key criteria. The system assigns a grade (A+, A, A−, or B) based on how many and which checklist items are met.
📋 Checklist Items (7 total):
✅ Sweep occurs within 4 candles
✅ MSS (strong break candle)
✅ Entry is placed outside the wick of the sweep
✅ FVG is fresh (not previously used)
✅ FVG overlaps Fibonacci 0.705 level
✅ FVG lies within Premium or Discount zone
✅ Entry is placed at 0.705 Fibonacci retracement
🏅 Grading Criteria:
A+ → All 7 checklist items are satisfied
A → Only missing #5 (FVG Overlap with 0.705)
A− → Only missing #4 (FVG Fresh)
B → Only missing #2 (MSS – clear break of structure)
– → Any other combinations / fewer than 6 conditions met
⚙️ Features:
Toggle visibility with one click
Fixed display in top-right or bottom-right of the chart
Color-coded grading logic (Green, Yellow, Orange, Blue)
Clear checklist feedback for trade journaling or evaluation
🚀 Ideal For:
ICT / Smart Money traders
Prop firm evaluations
Swing trade quality control
Information Asymmetry Gradient (IAG) What is the Information Asymmetry Gradient (IAG)?
The Information Asymmetry Gradient (IAG) is a unique market regime and imbalance detector that quantifies the subtle, directional “information flow” in price and volume. Inspired by information theory and market microstructure, IAG is designed to help traders spot the early buildup of conviction or surprise—the kind of hidden imbalance that often precedes major price moves.
Unlike traditional volume or momentum indicators, IAG focuses on the efficiency and directionality of information transfer: how much “informational energy” is being revealed by up-moves versus down-moves, normalized by price movement. It’s not just about net flow, but about the quality and asymmetry of that flow.
Theoretical Foundation
Information Asymmetry: Markets move when new information is revealed. If one side (buyers or sellers) is consistently more “informationally efficient” per unit of price change, an imbalance is building—even if price hasn’t moved much yet.
Gradient: By tracking the rate of change (gradient) between fast and slow information flows, IAG highlights when a subtle imbalance is accelerating.
Volatility of Asymmetry: Sudden spikes in the volatility of information asymmetry often signal regime uncertainty or the approach of a “surprise” move.
How IAG Works
Directional Information Content: For each bar, IAG estimates the “information per unit of price change” for both up-moves and down-moves, using volume and price action.
Asymmetry Calculation: Computes the difference (or ratio) between up and down information content, revealing directional bias.
Gradient Detection: Calculates both a fast and slow EMA of the asymmetry, then measures their difference (the “gradient”), normalized as a Z-score.
Volatility of Asymmetry: Tracks the standard deviation of asymmetry over a rolling window, with Z-score normalization to spot “information shocks.”
Flow Strength: Quantifies the conviction of the current information flow on a 0–100 scale.
Regime Detection: Flags “extreme” asymmetry, “building” flow, and “high volatility” states.
Inputs:
🌌 Core Asymmetry Parameters
Fast Information Period (short_len, default 8): EMA period for detecting immediate information flow changes.
5–8: Scalping (1–5min)
8–12: Day trading (15min–1hr)
12–20: Swing trading (4hr+)
Slow Information Period (long_len, default 34): EMA period for baseline information context. Should be 3–5x fast period.
Default (34): Fibonacci number, stable for most assets.
Gradient Smoothing (gradient_smooth, default 3): Smooths the gradient calculation.
1–2: Raw, responsive
3–5: Balanced
6–10: Very smooth
📊 Asymmetry Method
Calculation Mode (calc_mode, default "Weighted"):
“Simple”: Basic volume split by direction
“Weighted”: Volume × price movement (default, most robust)
“Logarithmic”: Log-scaled for large moves
Use Ratio (show_ratio, default false):
“Difference”: UpInfo – DownInfo (additive)
“Ratio”: UpInfo / DownInfo (multiplicative, better for comparing volatility regimes)
🌊 Volatility Analysis
Volatility Window (stdev_len, default 21): Lookback for measuring asymmetry volatility.
Volatility Alert Level (vol_threshold, default 1.5): Z-score threshold for volatility alerts.
🎨 Visual Settings
Color Theme (color_theme, default "Starry Night"):
Van Gogh-inspired palettes:
“Starry Night”: Deep blues and yellows
“Sunflowers”: Warm yellows and browns
“Café Terrace”: Night blues and warm lights
“Wheat Field”: Golden and sky blue
Show Swirl Effects (show_swirls, default true): Adds swirling background to visualize information turbulence.
Show Signal Stars (show_stars, default true): Star markers at significant asymmetry points.
Show Info Dashboard (show_dashboard, default true): Top-right panel with current metrics and market state.
Show Flow Visualization (show_flow, default true): Main gradient line with artistic effects.
Color Schemes
Dynamic color gradients adapt to both the direction and intensity of the information gradient, using Van Gogh-inspired palettes for visual clarity and artistic flair.
Glow and aura effects: The main line is layered with glows for depth and to highlight strong signals.
Swirl background: Visualizes the “turbulence” of information flow, darker and more intense as flow strength and volatility rise.
Visual Logic
Main Gradient Line: Plots the normalized information gradient (Z-score), color-coded by direction and intensity.
Glow/Aura: Multiple layers for visual depth and to highlight strong signals.
Threshold Zones: Dotted lines and filled areas mark “Building” and “Extreme” asymmetry zones.
Volatility Ribbon: Area plot of volatility Z-score, highlighting information shocks.
Signal Stars: Circular markers at each “Extreme” event, color-coded for bullish/bearish; cross markers for volatility spikes.
Dashboard: Top-right panel shows current status (Extreme, Building, High Volatility, Balanced), gradient value, flow strength, information balance, and volatility status.
Trading Guide: Bottom-left panel explains all states and how to interpret them.
How to Use IAG
🌟 EXTREME: Major information imbalance—potential for explosive move or reversal.
🌙 BUILDING: Asymmetry is forming—watch for a breakout or trend acceleration.
🌪️ HIGH VOLATILITY: Information flow is unstable—expect regime uncertainty or “surprise” moves.
☁️ BALANCED: No clear bias—market is in equilibrium.
Positive Gradient: Bullish information flow (buyers have the edge).
Negative Gradient: Bearish information flow (sellers have the edge).
Flow >66%: Strong conviction—crowd is acting in unison.
Volatility Spike: Regime uncertainty—be alert for sudden moves.
Tips:
- Use lower periods for scalping, higher for swing trading.
- “Weighted” mode is most robust for most assets.
- Combine with price action or your own system for confirmation.
- Works on all assets and timeframes—tune to your style.
Alerts
IAG Extreme Asymmetry: Extreme information asymmetry detected.
IAG Building Flow: Information flow building.
IAG High Volatility: Information volatility spike.
IAG Bullish/Bearish Extreme: Directional extreme detected.
Originality & Usefulness
IAG is not a mashup of existing indicators. It is a novel approach to quantifying the “surprise” or “conviction” element in market moves, focusing on the efficiency and directionality of information transfer per unit of price change. The multi-layered color logic, artistic visual effects, and regime dashboard are unique to this script. IAG is designed for anticipation, not confirmation—helping you see subtle imbalances before they become obvious in price.
Chart Info
Script Name: Information Asymmetry Gradient (IAG) – Starry Night
Recommended Use: Any asset, any timeframe. Tune parameters to your style.
Disclaimer
This script is for research and educational purposes only. It does not provide financial advice or direct buy/sell signals. Always use proper risk management and combine with your own strategy. Past performance is not indicative of future results.
Trade with insight. Trade with anticipation.
— Dskyz , for DAFE Trading Systems
multi-tf standard devs [keypoems]Multi-Timeframe Standard Deviations Levels
A visual map of “how far is too far” across any three higher time-frames.
1. What it does
This script plots dynamic price “rails” built from standard deviation (StDev)—the same math that underpins the bell curve—on up to three higher-time-frames (HTFs) at once.
• It measures the volatility of intraday open-to-close increments, reaching back as far as 5000 bars (≈ 20 years on daily data).
• Each HTF can be extended to the next session or truncated at session close for tidy dashboards.
• Lines can be mirrored so you see symmetric positive/negative bands, and optional background fills shade the “probability cone.”
Because ≈ 68 % of moves live inside ±1 StDev, ≈ 95 % inside ±2, and ≈ 99.7 % inside ±3, the plot instantly shows when price is statistically stretched or compressed.
3. Key settings
Higher Time-Frame #1-3 Turn each HTF on/off, pick the interval (anything from 1 min to 1 year), and decide whether lines should extend into the next period.
Show levels for last X days Keep your chart clean by limiting how many historical sessions are displayed (1-50).
Based on last X periods Length of the StDev sample. Long look-backs (e.g. 5 000) iron-out day-to-day noise; short look-backs make the bands flex with recent volatility.
Fib Settings Toggle each multiple, line thickness/style/colour, label size, whether to print the numeric level, the live price, the HTF label, and whether to tint the background (choose your own opacity).
4. Under-the-hood notes
StDev is calculated on (close – open) / open rather than absolute prices, making the band width scale-agnostic.
Watch for tests of ±1:
Momentum traders ride the breakout with a target at the next band.
Mean-reversion traders wait for the first stall candle and trade back to zero line or VWAP.
Bottom line: Multi-Timeframe Standard-Deviations turns raw volatility math into an intuitive “price terrain map,” helping you instantly judge whether a move is ordinary, stretched, or extreme—across the time-frames that matter to you.
Original code by fadizeidan and stats by NQStats's ProbableChris.
Bullish Volume AnomalyAnomaly is designed to spot hidden bullish accumulation before price actually breaks out, by blending a trend-aware volume measure with a volatility-adjusted price channel. Here’s how it works:
First, it runs a simple ATR-based zigzag to identify the current swing direction. Volume is then signed (+ for up-trends, – for down-trends) and cumulatively summed. By converting that cumulative signed volume into a z-score over the past 480 bars, we get a sense of when buying or selling pressure is unusually strong relative to its own history.
At the same time, price itself is normalized into a z-score over the same 480-bar window, and its change over that period is also tracked. These two measures—volume z-score (s) and price z-score (p)—are compared, and the indicator looks for moments when s outpaces p by at least two standard deviations (s – p > 2), while price momentum change remains low (c < 1) and the net volume is positive (s > 0). That combination flags instances where heavy buying is taking place but price hasn’t yet reacted.
To define a dynamic trading zone, it plots a 288-bar EMA of price as the middle band (t2), and builds upper and lower bands around it using the average close-to-open range multiplied by a user-set factor. The lower band (t1) sits beneath the EMA by that volatility-based margin. A signal fires only when the bar’s high stays below t1—meaning price is still “sleeping” under the lower volatility boundary even as bullish volume builds up.
Together, these filters home in on anomalies: strong, trend-aligned volume surges that outstrip price movement, occurring while price sits below its lower volatility band. In practice, that often marks early accumulation before a breakout. You can tweak the ATR length and multiplier for the zigzag, as well as the channel period and range factor, to suit different markets or timeframes.
Normalized DXY+Custom USD Index (DXY+) – Normalized Dollar Strength with Bitcoin, Gold, and Yuan.
This custom USD strength index replicates the structure of the official U.S. Dollar Index (DXY), while expanding it to include modern financial assets such as Bitcoin (BTC), Ethereum (ETH), gold (XAU), and the Chinese yuan (CNY).
Weights for the core fiat currencies (EUR, JPY, GBP, CAD, SEK, CHF) follow the official ICE DXY methodology. Additional components are weighted proportionally based on their estimated global economic influence.
The index is normalized from its initial valid data point, meaning it starts at 100 on the first day all asset inputs are available. From that point forward, it tracks the relative strength of the U.S. dollar against this expanded basket.
This provides a more comprehensive and modernized view of the dollar's strength—not only against traditional fiat currencies, but also in the context of rising decentralized assets and non-Western trade power.
HGDA Hany Ghazy Digital Analytics area zone'sIndicator Name: HGDA Hany Ghazy Digital Analytics area zones
Description:
This indicator plots several key price zones based on the highest high and lowest low over a user-defined lookback period.
The plotted zones represent dynamic support and resistance levels calculated using specific ratios of the price range (High - Low), as follows:
- Zone 1 (Light Red): Represents an upper resistance zone.
- Zone 2 (Medium Green): Represents a medium support zone.
- Zone 3 (Dark Red): Represents a lower resistance zone.
- Zone 4 (Dark Green): Represents a strong support zone.
Additionally, the indicator plots a yellow "Zero" line representing the midpoint price of the selected period, serving as a balance point for price action.
This indicator is ideal for identifying the overall market trend, as prices typically move from the upper resistance zones (light red) downwards to the end of the wave in the lower zones (dark green). This helps traders better understand wave nature and direction.
Usage:
- The colored zones assist in identifying potential reversal or continuation areas.
- These zones can be used to plan entries, exits, and risk management.
- Default lookback period is 20 bars, adjustable in the settings to suit the timeframe.
Notes:
- This indicator relies on historical price data and does not guarantee market predictions.
- It is recommended to combine it with other indicators and analytical tools for improved trading decisions.
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Developed by Hany Ghazy Digital Analytics (HGDA).
Custom USD IndexThis is a modernized, expanded version of the U.S. Dollar Index (DXY), designed to provide a more accurate representation of the dollar’s global strength in today’s diversified economy.
Unlike the traditional DXY, which excludes major players like China and entirely omits real-world stores of value, this custom index (DXY+) includes:
Fiat Currencies (78.3% total weight):
EUR, JPY, GBP, CAD, AUD, CHF, and CNY — equally weighted to reflect the global currency landscape.
Gold (17.5%):
Gold (XAUUSD) is included as a traditional reserve asset and inflation hedge, acknowledging its continued monetary relevance.
Cryptocurrencies (2.8% total weight):
Bitcoin (BTC) and Ethereum (ETH) represent the emerging digital monetary layer.
The index rises when the U.S. dollar strengthens relative to this blended basket, and falls when the dollar weakens against it. This is ideal for traders, economists, and macro analysts seeking a more inclusive and up-to-date measure of dollar performance.
Float, Daily % Change & Short %This TradingView Pine Script displays a compact table on your chart showing four key metrics for any stock:
📊 What It Shows:
Float – Number of publicly available shares, formatted in K/M/B.
Daily % Change – Price change from yesterday’s close to the current price.
Intraday % Change – Price change from today’s open to the current price.
Short Volume % – Estimated short volume as a percentage of total daily volume.
⚙️ How to Use:
Add the script to your TradingView chart.
Choose table size and screen position from the settings panel.
The values update in real-time on the latest candle only, so they stay out of the way but always visible.
Ideal for momentum traders, short float hunters, and day traders who need quick access to real-time float, price action, and short volume stats.
SOFR Spread (proxy: FEDFUNDS - US03MY)📊 SOFR Spread (Proxy: FEDFUNDS - US03MY) – Monitoring USD Money Market Liquidity
In 2008, the spread exhibits a sharp vertical spike, signaling a severe liquidity dislocation: investors rushed into short-term U.S. Treasuries, pushing their yields down dramatically, while the FEDFUNDS rate remained relatively high.
This behavior indicates extreme systemic stress in the interbank lending market, preceding massive Federal Reserve interventions such as rate cuts, emergency liquidity operations, and the launch of quantitative easing (QE).
Description:
This indicator plots the spread between the Effective Federal Funds Rate (FEDFUNDS) and the 3-Month US Treasury Bill yield (US03MY), used here as a proxy for the SOFR spread.
It serves as a simple yet powerful tool to detect liquidity dislocations and stress signals in the US short-term funding markets.
Interpretation:
🔴 Spread > 0.20% → Possible liquidity stress: elevated repo rates, cash shortage, interbank distrust.
🟡 Spread ≈ 0% → Normal market conditions, balanced liquidity.
🟢 Spread < 0% → Excess liquidity: strong demand for T-Bills, “flight to safety”, or distortion due to expansionary monetary policy.
Ideal for:
Monitoring Fed policy impact
Anticipating market-wide liquidity squeezes
Correlation with DXY, SPX, VIX, MOVE Index, and risk sentiment
🧠 Note: As SOFR is not directly available on TradingView, FEDFUNDS is used as a reliable proxy, closely tracking the same trends in most macro conditions.
LANZ Strategy 2.0 [Backtest]🔷 LANZ Strategy 2.0 — Structural Breakout Logic with Dynamic Swing Protection
LANZ Strategy 2.0 is a precision-focused backtesting system built for intraday traders who rely on structural confirmations before the London session to guide directional bias. This tool uses smart swing detection, risk-defined position sizing, and strict time-based execution to simulate real trading conditions with clarity and control.
🧠 Core Components:
Structural Confirmation (Trend & BoS): Detects trend direction and break of structure (BoS) using a three-swing logic, aligning trade entries with valid structural movement.
Time-Based Execution: Trades are triggered exclusively at 02:00 a.m. New York time, ensuring disciplined and repeatable intraday testing.
Swing-Based SL Models: Traders can select between three stop-loss protection types:
First Swing: Most recent structural level
Second Swing: Prior level
Full Coverage: All recent swing levels + configurable pip buffer
Dynamic TP Calculation: Take-Profit is projected as a risk-based multiple (RR), fully adjustable via input.
Capital-Based Risk Management: Risk is defined as a percentage of a fixed account size (e.g., $100 per trade from $10,000), and lot size is automatically calculated based on SL distance.
Fallback Entry Logic: If structural breakout is present but trend is not confirmed, a secondary entry is triggered.
End-of-Session Management: Any open trades are automatically closed at 11:45 a.m. NY time, with optional manual labeling or review.
📊 Visual Features (Optional in Indicator Version):
(Note: Visuals apply to the indicator version of LANZ 2.0, not this backtest script)
Swing level labels (1st, 2nd) and dynamic SL/TP lines.
Real-time session coloring for clarity: Pre-London, Entry Window, and NY Close.
Outcome labels: +RR, -RR, or net % at close.
Auto-cleanup of previous drawings for a clean chart per session.
⚙️ How It Works:
Detects last trend and BoS using swing logic before 02:00 a.m. NY.
At 02:00 a.m., evaluates directional bias and executes BUY or SELL if confirmed.
Applies selected SL logic (1st, 2nd, or full swing protection).
Sets TP based on the RR multiplier.
Closes the trade either on SL, TP, or at 11:45 a.m. NY manually.
🔔 Alerts:
Time-of-day alert at 02:00 a.m. NY to monitor execution.
Can be extended to cover SL/TP triggers or new BoS events.
📝 Notes:
Designed for backtesting precision and discretionary decision-making.
Ideal for Forex pairs, indices, or assets active during the London session.
Fully customizable: session timing, swing logic, SL buffer, and RR.
👤 Credits:
Strategy built by @rau_u_lanz using Pine Script v6, combining structural logic, capital-based risk control, and London-session timing in a backtest-ready framework for traders who demand accuracy and structure.
Statistical Pairs Trading IndicatorZ-Score Stat Trading — Statistical Pairs Trading Indicator
📊🔗
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What is it?
Z-Score Stat Trading is a powerful indicator for statistical pairs trading and quantitative analysis of two correlated assets.
It calculates the Z-Score of the log-price spread between any two symbols you choose, providing both long-term and short-term Z-Score signals.
You’ll also see real-time correlation, volatility, spread, and the number of long/short signals in a handy on-chart table!
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How to Use 🛠️
1. Add the indicator to your chart.
2. Select two assets (symbols) to analyze in the settings.
3. Watch the Z-Score plots (blue and orange lines) and threshold levels (+2, -2 by default).
4. Check the info table for:
- Correlation
- Volatility
- Spread
- Number of long (NL) and short (NS) signals in the last 1000 bars
5. Set up alerts for signal generation or threshold crossings if you want to be notified automatically.
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Trading Strategy 💡
- This indicator is designed for statistical arbitrage (mean reversion) strategies.
- Long Signal (🟢):
When both Z-Scores drop below the negative threshold (e.g., -2), a long signal is generated.
→ Buy Symbol A, Sell Symbol B, expecting the spread to revert to the mean.
- Short Signal (🔴):
When both Z-Scores rise above the positive threshold (e.g., +2), a short signal is generated.
→ Sell Symbol A, Buy Symbol B, again expecting mean reversion.
- The info table helps you quickly assess the frequency of signals and the current statistical relationship between your chosen assets.
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Best Practices & Warnings 🚦
- Avoid high leverage! Pairs trading can be risky, especially during periods of divergence. Use conservative position sizing.
- Check for cointegration: Before using this indicator, make sure both assets are cointegrated or have a strong historical relationship. This increases the reliability of mean reversion signals.
- Check correlation: Only use asset pairs with a high correlation (preferably 0.8–0.9 or higher) for best results. The correlation value is shown in the info table.
- Scale in and out gradually: When entering or exiting positions, consider doing so in parts rather than all at once. This helps manage slippage and risk, especially in volatile markets.
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⚠️ Note on Performance:
This indicator may work a bit slowly, especially on large timeframes or long chart histories, because the calculation of NL and NS (number of long/short signals) is computationally intensive.
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Disclaimer ⚠️
This script is provided for educational and informational purposes only .
It is not financial advice or a recommendation to buy or sell any asset.
Use at your own risk. The author assumes no responsibility for any trading decisions or losses.
Fibo Normalized RSI & RSI RibbonPlots both standard and Z-score normalized RSI ribbons using Fibonacci-based periods. Supports adjustable normalization, optional 0–100 scaling, and multi-line visualizations for momentum and deviation analysis.
This tool is designed for traders who want to go beyond standard RSI by adding:
Statistical normalization (Z-score)
Multi-period analysis (Fibonacci structure)
Advanced divergence and exhaustion detection
It gives you both classical momentum context and mathematically rigorous deviation insight, making it ideal for:
Swing traders
Quant-inclined discretionary traders
Multi-timeframe analysts
Trend Confirmation
When both RSI and normalized RSI across short and long periods are stacked in the same direction (e.g., above 50 or with high Z-scores), the trend is likely strong.
Disagreement between the two ribbons (e.g., RSI high but normalized RSI flat) may indicate late-stage trend or false strength.
Mean Reversion Trades
Look for normalized RSI values > +2 or < -2 (i.e., ~2 standard deviations).
Cross-check with standard RSI to see if the move aligns with a traditional overbought/oversold level.
Great for fade/reversal setups when Z-score RSI is extreme but classic RSI is just beginning to turn.
Divergence Detection
Compare the slope of RSI vs. normalized RSI over same period:
If RSI is rising but normalized RSI is falling → momentum is fading despite apparent strength.
Excellent for early warnings before reversals.
Multi-Timeframe Confluence
Use short-period ribbons (e.g., 3–13) for tactical entries/exits.
Use long-period ribbons (e.g., 55–233) for macro trend bias.
Alignment across both = high-confidence zone.
ATS DELTABAR V5.0ATS DeltaBar Indicator: Volume Trend Momentum Analysis System
——Precisely Capturing "Price-Volume Resonance" Signals for Trend Reversals
Core Positioning
The ATS DeltaBar is a sub-chart indicator focused on the synergy between volume trends and price action. It dynamically monitors changes in volume momentum and price deviations to identify trend strengthening, exhaustion, and reversal signals. Its core value lies in:
Red/Green Bars: Visually reflect volume increases/decreases, revealing capital flow direction.
Divergence Signals: Warn of potential trend reversals (top/bottom divergence).
Resonance Breakouts/Breakdowns: Confirm high-probability trend continuation signals.
Red/Green Zones: Clearly define bullish/bearish phases (red = bearish, green = bullish).
I. Core Logic & Algorithm
1. Volume Trend Visualization
Bar Color Volume State Market Implication
Green Bar Volume ↑ vs. prior period Capital inflow, trend momentum strengthens
Red Bar Volume ↓ vs. prior period Capital outflow, trend momentum weakens
Bar Height Magnitude of volume change Quantifies intensity (higher = stronger shift)
📌 Key Insight:
Green bars + rising price = Healthy uptrend.
Red bars + price新高 = Potential top divergence risk.
2. Divergence Detection
Top Divergence: Price makes higher highs, but DeltaBar peaks lower (red bars accumulate) → Bullish exhaustion.
Bottom Divergence: Price makes lower lows, but DeltaBar troughs rise (green bars accumulate) → Bearish exhaustion.
3. Resonance Signal System
Resonance Breakout: Price breaks resistance + DeltaBar green volume spike → Confirmed uptrend acceleration.
Resonance Breakdown: Price breaks support + DeltaBar red volume spike → Confirmed downtrend weakness.
4. Bullish/Bearish Zone划分
Green Zone: DeltaBar consistently above neutral line → Bullish dominance (favor longs).
Red Zone: DeltaBar consistently below neutral line → Bearish dominance (caution for downside).
II. Signal Types & Practical Applications
1. Basic Trading Signals
Signal Type DeltaBar Behavior Trading Suggestion
Green Zone + Green Bar Price & volume rise together Hold/add to longs
Red Zone + Red Bar Price & volume decline together Short/exit longs
Top Divergence Price ↑ + DeltaBar peaks ↓ (red bars) Reduce longs/test shorts
Bottom Divergence Price ↓ + DeltaBar troughs ↑ (green bars) Prepare for reversal/cover shorts
2. Advanced Resonance Strategies
Breakout Trade: Enter when price breaks a key level + DeltaBar shows green volume spike (resonance breakout) → High-probability long.
Breakdown Trade: Enter when price breaks support + DeltaBar shows red volume spike (resonance breakdown) → High-probability short.
III. Comparison with Traditional Indicators
Aspect Traditional Volume (e.g., OBV) ATS DeltaBar
Signal Dimension Single cumulative volume direction 3D analysis: divergence + resonance + zone划分
Visualization Monotonic curve Dynamic dual-color bars + zones + threshold lines
Practicality Lags price action Real-time捕捉 divergence/resonance points
IV. Usage Scenarios & Tips
1. Trend Following
In Green Zone: Price above MA + DeltaBar green bars expanding → Hold longs.
In Red Zone: Price below MA + DeltaBar red bars expanding → Stay short/avoid longs.
2. Reversal Trading
Top Divergence + Bearish candlestick (e.g., Evening Star) + red bars → Short.
Bottom Divergence + Bullish engulfing + green bars → Long.
3. Breakout Filtering
Only trade breakouts where price and DeltaBar confirm共振 (avoids false breakouts).
V. Case Study (BTC/USDT 1H Chart)
Successful Long: Price broke resistance + DeltaBar green volume spike → 15% rally.
Successful Short: Price consolidated with red bar accumulation (top divergence) → 8% drop.
VI.注意事项
Combine with price structure (support/resistance) for higher accuracy.
Prioritize divergence in ranging markets; focus on共振 signals in trending markets.
"Volume is the fuel of price" — ATS DeltaBar quantifies this relationship to pinpoint trend ignition and reversal points.
ATS Net Volume V5.0ATS Net Volume V5.0
Smart Net Volume Analysis System
Overview
ATS Net Volume V5.0 is an advanced volume-based indicator designed for institutional-level capital flow analysis. By monitoring net buying/selling pressure, it identifies the movements of major market players. The system integrates large-order filtering and dynamic price-volume equilibrium algorithms to distinguish genuine demand from market noise, providing traders with clear signals for capital inflow/outflow.
Key Features
🔹 Net Volume Dynamics
Real-time calculation of the difference between buy-side vs. sell-side volume (units: millions/billions)
Positive values indicate capital inflow (green), negative values indicate outflow (red)
🔹 Large Order Detection
Automatically filters out retail-sized trades, focusing on institutional block orders (e.g., "60M" = 60 million, "05B" = 5 billion)
Evaluates accumulation/distribution behavior relative to price levels
🔹 Multi-Timeframe Compatibility
Supports analysis from tick data to daily charts
🔹 Visual Signals
Histogram + numerical labels for intuitive net volume strength display
Threshold-based alerts (e.g., extreme values trigger overbought/oversold signals)
Data Interpretation
Use Cases
✅ Trend Confirmation
Price rise + expanding net buys → Healthy trend
Price rise + net sells → Potential bull trap
✅ Reversal Warning
Price新高 + net volume divergence → Possible topping signal
✅ Institutional Activity
Sustained large net inflows → Smart money accumulation
Sudden massive outflows → Emergency liquidation event
Signal Classification
Net Volume Range Market Implication
Above +50M Strong inflow (bullish)
+10M to +50M Moderate buying
-10M to +10M Balanced market
-10M to -50M Moderate selling
Below -50M Extreme outflow (bearish)
Advantages
🚨 Filters False Breakouts: Responds only to large-order-driven price movements
📊 Price-Volume Synergy: Avoids "low-volume rally" traps
💡 Universal Applicability: Stocks/Futures/Cryptocurrencies
Note: Always combine with price structure (support/resistance). Not a standalone trading signal.
Sentival | QuantEdgeBIntroducing Sentival by QuantEdgeB.
An Adaptive Multi-Factor Indicator for Market Valuation & Trend Strength
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Overview
The Sentival Valuation System is a medium-term, multi-factor valuation tool designed to assess market conditions using a combination of momentum, mean reversion, and risk-adjusted metrics. It provides traders and investors with a dynamic score reflecting market valuation, ranging from strongly oversold to strongly overbought conditions.
This system leverages a diverse range of technical indicators, including momentum oscillators, volatility measures, risk ratios, and mean-reversion metrics, providing a holistic view of market conditions.
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1. Key Features
🛠 Multi-Factor Valuation Model
Sentival aggregates nine different indicators, normalizing and rescaling them into a standardized z-score-based valuation system. The final output represents an average of the selected indicators, allowing for flexible customization based on the user’s preference.
📊 Customizable Indicator Selection
Users can enable or disable any of the nine valuation factors, ensuring the system adapts to different market environments, trading styles, and assets.
🔄 Multi-Timeframe Adaptability
Sentival can be used across different time horizons, making it suitable for short-term mean reversion, medium-term traders, or long-term valuation analysis by simply adjusting the timeframe and indicator settings. This flexibility allows traders to adapt Sentival to various market conditions and trading objectives.
🎨 Intuitive Dashboard & Color Coding
- Dynamic Heatmap & Dashboard: Displays valuation strength across multiple factors.
- Gradient-Based Overbought/Oversold Signals: Clear color-coded signals for easy interpretation.
- Background Highlighting: Optional oversold/overbought background zones.
🏆 Statistical & Risk-Based Insights
- Standardized Rescaling & Z-Score Analysis to prevent bias from individual indicators.
- Risk-Adjusted Metrics such as Sharpe, Sortino, and Omega Ratios help assess the overall market risk appetite.
- Trend Following Mode (TF Display): Users can enable the "Trend Following" option to display the trend direction, helping to align valuation signals with the broader market trend.
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2. How It Works
Sentival is a multi-factor trend and momentum analysis system, designed to track market cycle shifts using a combination of volatility, momentum, risk assessment, and valuation mechanisms. Instead of focusing on one dimension of the market, Sentival integrates multiple methodologies to cross-validate signals and reduce noise. Each indicator in Sentival plays a specific role, ensuring confirmation across different market conditions.
How Each Component Works Together
1️⃣ Chande Momentum Oscillator (CMO)
• A momentum-based measure that determines whether price action is dominated by upward or downward forces.
• Works well in combination with volatility measures to confirm whether a move is sustainable.
2️⃣ Disparity Index
• Measures the distance between price and its moving average, acting as an overextension filter.
• Ensures that trend-following signals are not driven by short-term spikes but sustained trends.
3️⃣ Bollinger Bands % (BB%)
• A volatility measure that indicates how far price is from the statistical mean.
• Helps identify trend exhaustion points where price moves become unstable.
4️⃣ Relative Strength Index (RSI)
• A trend confirmation layer, ensuring that momentum strength aligns with price movement.
• Adds an additional check to prevent false breakouts.
5️⃣ Rate of Change (RoC)
• Captures the speed of price movement, ensuring that the market has enough momentum for trend continuation.
• Works well with risk indicators to filter weaker moves.
6️⃣ Price Z-Score
• A statistical tool to measure how far price is from its long-term equilibrium.
• Helps prevent entering overstretched trends too late.
7️⃣ Risk Ratios (Sharpe, Sortino, Omega)
• This is the risk-adjusted performance component, ensuring that trends have a healthy risk-reward balance.
• Helps determine when a trend has structurally strong backing rather than speculative movement.
8️⃣ Hurst Cycle Analysis
• Measures the persistence of trends by analyzing price fractals.
• Ensures that the market regime is either trending or mean-reverting, improving trade confidence.
9️⃣ Commodity Channel Index (CCI)
• Helps identify strong trend conditions, adding another layer of momentum confirmation.
• Works well with other oscillators to prevent misreading counter-trends.
🔗 Why These Components Work Well Together
• Momentum + Volatility + Risk → Instead of relying on a single category, Sentival merges multiple dimensions of market behavior into a cohesive signal.
• Filters Out False Signals → Combining momentum oscillators, volatility measures, and risk-adjusted metrics ensures high-confidence entries.
• Adaptability Across Market Regimes → Whether the market is trending, consolidating, or volatile, the system adjusts dynamically.
• Cross-Validation for Trend Strength → If multiple components align, it increases certainty that a trend is real and sustainable.
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3. Sentival Scanner - table breakdown
The dashboard-style table generated is designed to give traders a holistic market view at a glance. It processes a variety of technical signals and distills them into readable labels, visual strength bars, and actionable trend states. Here's a breakdown of what each section means:
1. Direction
This section analyzes whether the average Z-score (a composite of several indicators) is increasing, decreasing, or neutral over time. It does this using a smoothed trend of the Z-score, comparing recent values to older ones.
2. Momentum
Momentum is derived from the rate of change (RoC) of the average Z-score. It evaluates how strong the current move is. If momentum is above a certain positive threshold, it’s considered positive, if below a negative threshold, it’s negative, otherwise it’s neutral.
3. Impulse
Impulse reflects the velocity of momentum — in other words, is the market speeding up or slowing down? High positive values suggest strong acceleration (strong impulse), while negative values show deceleration or stalling.
4. Drive
This metric combines momentum and velocity to create a descriptive phrase that captures the market’s behavior. For example:
• “Strong Upside” means strong momentum with acceleration.
• “Fading Downside” means bearish momentum losing steam.
• “Neutral” appears when momentum is indecisive.
5. Deviation Distance
This represents how far the market price is from fair value in terms of standard deviation units (σ). It’s calculated using Z-scores and classified as:
• +1σ, +2σ, etc., for overvalued regions.
• −1σ, −2σ, etc., for undervalued areas.
• “At Fair Value” if close to the mean.
6. Bull and Bear Strength Bars
The system computes both bullish and bearish strength, using distance from fair value, the rate of change, and the velocity. These strengths are displayed as progress bars, giving a quick visual cue of conviction. The table labels them as:
• “Bull Conviction” if there's a long bias.
• “Bull Potential” if bullish but undecided.
• “Bear Conviction” or “Bear Potential” for short-side equivalents.
7. Trend Signal
This is a simple label that tells you if the scanner recommends a Long, Short, or Cash (neutral) stance based on threshold logic. It is based on whether the average Z-score crosses above a long threshold or below a short one.
8. Stage
The “Stage” label summarizes the valuation environment based on the composite Z-score:
• Strong Undervalued
• Moderately Undervalued
• Fair Value
• Overvalued, etc.
This stage helps traders know whether they are operating in cheap or expensive territory statistically.
Summary
Overall, this table merges advanced technical signals like momentum, volatility, valuation, and risk into a digestible format that updates dynamically with each bar. The goal is to provide traders with a 360° perspective on market conditions, tailored for both trend-following and mean-reversion strategies.
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4. Sentival Valuation Score & Interpretation
🔹 Sentival Score Ranges
- 📉 Strongly Oversold (-2 and below) → Market is extremely undervalued; potential reversal.
- 📉 Moderately Oversold (-1.5 to -2) → Discounted market conditions, buying interest may emerge.
- 📉 Slightly Oversold (-0.5 to -1.5) → Possible accumulation phase.
- ⚖ Fair Value (-0.5 to +0.5) → Market trading at equilibrium.
- 📈 Slightly Overbought (+0.5 to +1.5) → Initial signs of market strength.
- 📈 Moderately Overbought (+1.5 to +2) → Market heating up, caution warranted, selling interest may emerge.
- 📈 Strongly Overbought (+2 and above) → Extreme valuation, increased risk of correction.
This classification helps traders gauge overall market sentiment and make better allocation decisions.
Note: Past valuations and buy/sell signals generated by Sentival do not guarantee future performance. Market conditions can change, and proper risk management should always be applied.
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5. Use Cases & Applications
🔹 📊 Market Rotation & Asset Allocation
- Used as a valuation model to determine if a market or asset is undervalued or overvalued.
- Rotational strategies can benefit from the valuation score by switching exposure between assets.
🔹 📈 Medium-Term Trend Identification
- Detects overbought and oversold conditions while filtering out short-term noise.
- Can be combined with other trend-following indicators for confluence-based strategies.
🔹 🔄 Mean Reversion & Momentum Trading
- Provides statistical validation for momentum breakouts or mean reversion signals.
- Useful for long-short trading strategies, determining optimal entry & exit points.
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Conclusion
Sentival is a powerful universal valuation system for traders and investors seeking a data-driven, multi-factor approach to market valuation. With its combination of momentum, trend, risk-adjusted, and mean-reversion indicators, it provides a robust, adaptable, and statistically sound framework for making informed market decisions.
🔹 Who Should Use Sentival?
✅ Swing Traders & Medium-Term Investors looking for structured valuation metrics.
✅ Quantitative & Systematic Traders incorporating multi-factor models.
✅ Portfolio Managers optimizing exposure to different market regimes.
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
