Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Cerca negli script per "track"
QT Previous Micro Cycle Range + SSMT [bilal]Previous Micro Cycle Range + SMTs - Indicator Description
📊 Overview
This indicator tracks 22.5-minute micro cycles within ICT's Quarterly Theory framework and automatically detects Smart Money Technique (SMT) divergences across correlated indices (NQ, ES, YM). It visualizes previous cycle ranges and identifies high-probability manipulation completions for precise intraday entries.
🎯 What It Does
Micro Cycle Tracking:
Divides each 90-minute session into four 22.5-minute micro quarters
Plots the previous micro cycle's High, Low, Equilibrium (EQ), and Quarter levels
Updates automatically as new micro cycles form
Works on any timeframe (recommended: 1-5 minute charts)
SMT Detection:
Compares current micro cycle vs previous micro cycle across NQ, ES, and YM
Detects Bearish SMT: Divergence at highs (signals distribution down)
Detects Bullish SMT: Divergence at lows (signals distribution up)
Draws visual SMT lines with directional arrows showing correlation/divergence
Optional SMT table showing all three indices' movements
💡 How To Use It
For Scalpers & Day Traders:
Wait for a new micro cycle to begin (lines will refresh every 22.5 minutes)
Watch for SMT formation in the current cycle
Bullish SMT = Buy signal (previous low is confirmed, expect move to previous high)
Bearish SMT = Sell signal (previous high is confirmed, expect move to previous low)
Key Concepts:
Minimum Target: Opposite extreme of previous cycle
SMT Confirmation: One or two indices sweep a level while the other(s) fail to sweep
Best Results: Trade with higher timeframe bias aligned
⚙️ Features
Customizable Display:
Toggle High/Low lines with multiple label styles (Timeframe, Label, %, Fib)
Optional Equilibrium (50%) level
Optional Quarter levels (25% / 75%)
Optional extended range projections (±50% to ±400%)
Adjustable line colors, widths, and label sizes
SMT Options:
Enable/disable SMT detection
Show/hide SMT text labels
Custom colors for bullish/bearish SMTs
Option to delete previous cycle SMTs (keeps chart clean)
Real-time SMT table showing all three indices
Comparison Assets:
Default: ES1! and YM1! (customize to your preference)
Set correlation type for each asset (correlated vs inverse)
Disable individual assets if needed
🔍 Understanding The Visuals
Lines:
Solid lines = Previous cycle High/Low (where price came from)
Dotted lines = EQ and Quarter levels (internal cycle structure)
Green lines = SMT divergence detected (buy/sell signal)
Labels:
▲ = Asset made higher high/low vs previous cycle
▼ = Asset made lower high/low vs previous cycle
🔺 = Inverse correlation (up when others down)
🔻 = Inverse correlation (down when others up)
SMT Logic:
If indices diverge (move opposite directions), SMT is confirmed
Bearish SMT = Highs diverge → Sell
Bullish SMT = Lows diverge → Buy
📈 Best Practices
Use on 1-5 minute charts for optimal micro cycle visualization
Combine with higher timeframe bias (Daily Cycle SSMT, session bias, etc.)
Wait for SMT confirmation before entering trades
Target previous cycle's opposite extreme as minimum profit target
Exit when opposing SMT forms or price reaches target
🛠️ Settings Guide
Essential Settings:
Comparison Symbols: Set to the indices you trade (default: ES1!, YM1!)
Show Cycle SMT: Toggle SMT detection on/off
Delete Previous Cycles SMTs: Keep chart clean by removing old SMTs
Visual Preferences:
Line Color/Width: Customize previous cycle lines
Label Style: Choose between Timeframe (22.5m), Label (descriptive), % (percentage), or Fib (0-1)
Show High/Low: Toggle previous cycle extremes
Show EQ/Quarters/Extended Ranges: Add more reference levels as needed
⚠️ Important Notes
This indicator shows previous cycle ranges, not predictive future levels
SMTs are confirmation signals for manipulation completion
Always use proper risk management and combine with your trading plan
Best results when aligned with higher timeframe directional bias
🎓 Based On ICT Concepts
This indicator implements concepts from Inner Circle Trader (ICT):
Quarterly Theory (fractal time structure)
Micro cycles (22.5-minute quarters)
Sequential SMT (mechanical divergence confirmation)
Smart Money accumulation, manipulation, distribution (AMD)
Perfect for: Scalpers, day traders, and anyone using ICT's Quarterly Theory and SMT concepts for precise intraday entries.
Note: This is a study indicator (overlay=true). It does not generate buy/sell signals automatically - you must interpret SMT formations based on your trading strategy.RéessayerGu should know it only works on the 30s chart btwPrevious Micro Cycle Range + SMTs - Indicator Description
📊 Overview
This indicator tracks 22.5-minute micro cycles within ICT's Quarterly Theory framework and automatically detects Smart Money Technique (SMT) divergences across correlated indices (NQ, ES, YM). It visualizes previous cycle ranges and identifies high-probability manipulation completions for precise intraday entries.
⚠️ IMPORTANT: This indicator is designed to work on the 30-second chart only. The micro cycle calculations are optimized for 30s timeframe data.
🎯 What It Does
Micro Cycle Tracking:
Divides each 90-minute session into four 22.5-minute micro quarters
Plots the previous micro cycle's High, Low, Equilibrium (EQ), and Quarter levels
Updates automatically as new micro cycles form every 22.5 minutes
Precise timing based on New York timezone session structure
SMT Detection:
Compares current micro cycle vs previous micro cycle across NQ, ES, and YM
Detects Bearish SMT: Divergence at highs (signals distribution down)
Detects Bullish SMT: Divergence at lows (signals distribution up)
Draws visual SMT lines with directional arrows showing correlation/divergence
Optional SMT table showing all three indices' movements in real-time
💡 How To Use It
Setup:
Switch to 30-second chart (required for accurate cycle timing)
Add indicator to your chart
Ensure you're viewing NQ, ES, or YM (or correlated futures)
For Scalpers & Day Traders:
Wait for a new micro cycle to begin (lines will refresh every 22.5 minutes)
Watch for SMT formation in the current cycle
Bullish SMT = Buy signal (previous low is confirmed, expect move to previous high)
Bearish SMT = Sell signal (previous high is confirmed, expect move to previous low)
Key Concepts:
Minimum Target: Opposite extreme of previous cycle
SMT Confirmation: One or two indices sweep a level while the other(s) fail to sweep
Best Results: Trade with higher timeframe bias aligned (Daily Cycle SSMT, session bias)
⚙️ Features
Customizable Display:
Toggle High/Low lines with multiple label styles (Timeframe, Label, %, Fib)
Optional Equilibrium (50%) level
Optional Quarter levels (25% / 75%)
Optional extended range projections (±50% to ±400%)
Adjustable line colors, widths, and label sizes
Line extension length (default: 15 bars ahead)
SMT Options:
Enable/disable SMT detection
Show/hide SMT text labels with ticker symbols and directional arrows
Custom colors for bullish/bearish SMT lines
Option to delete previous cycle SMTs (keeps chart clean)
Real-time SMT table showing all three indices' current status
Comparison Assets:
Default: ES1! and YM1! (customize to your preference)
Set correlation type for each asset (correlated vs inverse)
Disable individual assets if needed
Works with any correlated futures contracts
Debug Mode:
Toggle debug info to see current NY time, session, and micro cycle timing
Helpful for understanding cycle structure and troubleshooting
🔍 Understanding The Visuals
Lines:
Solid lines = Previous cycle High/Low (where price came from)
Dotted lines = EQ and Quarter levels (internal cycle structure)
Green lines (default) = SMT divergence detected (buy/sell signal)
Gray dotted lines = Extended range projections (if enabled)
Labels:
▲ = Asset made higher high/low vs previous cycle (correlated)
▼ = Asset made lower high/low vs previous cycle (correlated)
🔺 = Inverse correlation (up when others down)
🔻 = Inverse correlation (down when others up)
SMT Logic:
If indices diverge (move opposite directions), SMT is confirmed
Bearish SMT = Highs diverge → High is set, expect distribution down
Bullish SMT = Lows diverge → Low is set, expect distribution up
📈 Best Practices
Must use 30-second chart - indicator timing is calibrated for this timeframe
Combine with higher timeframe bias (Daily Cycle SSMT, 90-min SSMT, session bias)
Wait for SMT confirmation before entering trades (don't front-run)
Target previous cycle's opposite extreme as minimum profit target
Exit when opposing SMT forms or price reaches target
Best windows: Q2→Q3 or Q3→Q4 transitions within 90-minute sessions
Volatility injection times: Watch 09:30, 10:00, and 14:00 ET for strongest moves
🛠️ Settings Guide
Essential Settings:
Comparison Symbols: Set to the indices you monitor (default: ES1!, YM1!)
Correlation Type: Toggle "Correlated" on/off for each asset based on expected relationship
Show Cycle SMT: Enable/disable SMT detection
Show SMT Text: Toggle labels showing ticker divergence details
Delete Previous Cycles SMTs: Keep chart clean by removing old SMTs
Visual Preferences:
Line Color/Width: Customize previous cycle lines (default: black, width 1)
Label Style: Choose between:
Timeframe (shows "22.5m")
Label (descriptive: "previous micro cycle high/low")
% (shows "100%/0%")
Fib (shows "1/0")
Show High/Low: Toggle previous cycle extremes (recommended: ON)
Show EQ/Quarters/Extended Ranges: Add more reference levels as needed
SMT Customization:
SMT Colors: Customize bearish/bullish SMT line colors (default: green for both)
SMT Label Colors: Background and text color for SMT labels
SMT Table: Toggle real-time comparison table (bottom right)
⚠️ Important Notes
30-second chart required - will not work accurately on other timeframes
This indicator shows previous cycle ranges, not predictive future levels
SMTs are confirmation signals for manipulation completion, not entry triggers alone
Always use proper risk management and position sizing
Best results when aligned with higher timeframe directional bias
Monitor all three indices (NQ, ES, YM) for complete SMT picture
Micro cycles are part of a fractal structure - align with 90-min and Daily Cycle SMTs
🎓 Based On ICT Concepts
This indicator implements concepts from Inner Circle Trader (ICT):
Quarterly Theory (fractal time structure - 22.5 min micro quarters)
Micro cycles (four quarters within each 90-minute session)
Sequential SMT (mechanical divergence confirmation across correlated indices)
Smart Money AMD (Accumulation, Manipulation, Distribution pattern)
New York session timing (based on ICT's 6-hour daily cycles)
🕐 Micro Cycle Structure
Each 90-minute session divides into four 22.5-minute micro quarters:
Micro Q1: 00:00 - 22:30
Micro Q2: 22:30 - 45:00
Micro Q3: 45:00 - 67:30
Micro Q4: 67:30 - 90:00
This pattern repeats across all 16 daily 90-minute sessions (Q1.1 through Q4.4).
Perfect for: Scalpers and day traders using ICT's Quarterly Theory and SMT concepts for precise micro-level entries on 30-second charts.
Chart Requirement: 30-second timeframe only.
Note: This is a study indicator. It does not generate automatic buy/sell signals - you must interpret SMT formations based on your trading strategy and higher timeframe bias.
ATH대비 지정하락률에 도착 시 매수 - 장기홀딩 선물 전략(ATH Drawdown Re-Buy Long Only)본 스크립트는 과거 하락 데이터를 이용하여, 정해진 하락 %가 발생하는 경우 자기 자본의 정해진 %만큼을 진입하게 설계되어진 스트레티지입니다.
레버리지를 사용할 수 있으며 기본적으로 셋팅해둔 값이 내장되어있습니다.(자유롭게 바꿔서 쓰시면 됩니다.) 추가적으로 2번의 진입 외에도 다른 진입 기준, 진입 %를 설정하실 수 있으며 - ChatGPT에게 요청하면 수정해줄 것입니다.
실제 사용용도로는 KillSwitch 기능을 꺼주세요. 바 돋보기 기능을 켜주세요.
ATH Drawdown Re-Buy Long Only 전략 설명
1. 전략 개요
ATH Drawdown Re-Buy Long Only 전략은 자산의 역대 최고가(ATH, All-Time High)를 기준으로 한 하락폭(드로우다운)을 활용하여,
특정 구간마다 단계적으로 롱 포지션을 구축하는 자동 재매수(Long Only) 전략입니다.
본 전략은 다음과 같은 목적을 가지고 설계되었습니다.
급격한 조정 구간에서 체계적인 분할 매수 및 레버리지 활용
ATH를 기준으로 한 명확한 진입 규칙 제공
실시간으로
평단가
레버리지
청산가 추정
계좌 MDD
수익률
등을 시각적으로 제공하여 리스크와 포지션 상태를 직관적으로 확인할 수 있도록 지원
※ 본 전략은 교육·연구·백테스트 용도로 제공되며,
어떠한 형태의 투자 권유 또는 수익을 보장하지 않습니다.
2. 전략의 핵심 개념
2-1. ATH(역대 최고가) 기준 드로우다운
전략은 차트 상에서 항상 가장 높은 고가(High)를 ATH로 기록합니다.
새로운 고점이 형성될 때마다 ATH를 갱신하고, 해당 ATH를 기준으로 다음을 계산합니다.
현재 바의 저가(Low)가 ATH에서 몇 % 하락했는지
현재 바의 종가(Close)가 ATH에서 몇 % 하락했는지
그리고 사전에 설정한 두 개의 드로우다운 구간에서 매수를 수행합니다.
1차 진입 구간: ATH 대비 X% 하락 시
2차 진입 구간: ATH 대비 Y% 하락 시
각 구간은 ATH가 새로 갱신될 때마다 한 번씩만 작동하며,
새로운 ATH가 생성되면 다시 “1차 / 2차 진입 가능 상태”로 초기화됩니다.
2-2. 첫 포지션 100% / 300% 특수 규칙
이 전략의 중요한 특징은 **“첫 포지션 진입 시의 예외 규칙”**입니다.
전략이 현재 어떠한 포지션도 들고 있지 않은 상태에서
최초로 롱 포지션을 진입하는 시점(첫 포지션)에 대해:
기본적으로는 **자산의 100%**를 기준으로 포지션을 구축하지만,
만약 그 순간의 가격이 ATH 대비 설정값 이상(예: 약 –72.5% 이상 하락한 상황) 이라면
→ 자산의 300% 규모로 첫 포지션을 진입하도록 설계되어 있습니다.
이 규칙은 다음과 같이 동작합니다.
첫 진입이 1차 드로우다운 구간에서 발생하든,
첫 진입이 2차 드로우다운 구간에서 발생하든,
현재 하락폭이 설정된 기준 이상(예: –72.5% 이상) 이라면
→ “이 정도 하락이면 첫 진입부터 더 공격적으로 들어간다”는 의미로 300% 규모로 진입
그 이하의 하락폭이라면
→ 첫 진입은 100% 규모로 제한
즉, 전략은 다음 두 가지 모드로 동작합니다.
일반적인 상황의 첫 진입: 자산의 100%
심각한 드로우다운 구간에서의 첫 진입: 자산의 300%
이 특수 규칙은 깊은 하락에서는 공격적으로, 평소에는 상대적으로 보수적으로 진입하도록 설계된 것입니다.
3. 전략 동작 구조
3-1. 매수 조건
차트 상 High 기준으로 ATH를 추적합니다.
각 바마다 해당 ATH에서의 하락률을 계산합니다.
사용자가 설정한 두 개의 드로우다운 구간(예시):
1차 구간: 예를 들어 ATH – 50%
2차 구간: 예를 들어 ATH – 72.5%
각 구간에 대해 다음과 같은 조건을 확인합니다.
“이번 ATH 구간에서 아직 해당 구간 매수를 한 적이 없는 상태”이고,
현재 바의 저가(Low)가 해당 구간 가격 이하를 찍는 순간
→ 해당 바에서 매수 조건 충족으로 간주
실제 주문은:
해당 구간 가격에 맞춰 롱 포지션 진입(리밋/시장가 기반 시뮬레이션) 으로 처리됩니다.
3-2. ATH 갱신과 진입 기회 리셋
차트 상에서 새로운 고점(High)이 기존 ATH를 넘어서는 순간,
ATH가 갱신되고,
1차 / 2차 진입 여부를 나타내는 내부 플래그가 초기화됩니다.
이를 통해, 시장이 새로운 고점을 돌파해 나갈 때마다,
해당 구간에서 다시 한 번씩 1차·2차 드로우다운 진입 기회를 갖게 됩니다.
4. 포지션 사이징 및 레버리지
4-1. 계좌 자산(Equity) 기준 포지션 크기 결정
전략은 현재 계좌 자산을 다음과 같이 정의하여 사용합니다.
현재 자산 = 초기 자본 + 실현 손익 + 미실현 손익
각 진입 구간에서의 포지션 가치는 다음과 같이 결정됩니다.
1차 진입 구간:
“자산의 몇 %를 사용할지”를 설정값으로 입력
설정된 퍼센트를 계좌 자산에 곱한 뒤,
다시 전략 내 레버리지 배수(Leverage) 를 곱하여 실제 포지션 가치를 계산
2차 진입 구간:
동일한 방식으로, 독립된 퍼센트 설정값을 사용
즉, 포지션 가치는 다음과 같이 계산됩니다.
포지션 가치 = 현재 자산 × (해당 구간 설정 % / 100) × 레버리지 배수
그리고 이를 해당 구간의 진입 가격으로 나누어 실제 수량(토큰 단위) 를 산출합니다.
4-2. 첫 포지션의 예외 처리 (100% / 300%)
첫 포지션에 대해서는 위의 일반적인 퍼센트 설정 대신,
다음과 같은 고정 비율이 사용됩니다.
기본: 자산의 100% 규모로 첫 포지션 진입
단, 진입 시점의 ATH 대비 하락률이 설정값 이상(예: –72.5% 이상) 일 경우
→ 자산의 300% 규모로 첫 포지션 진입
이때 역시 다음 공식을 사용합니다.
포지션 가치 = 현재 자산 × (100% 또는 300%) × 레버리지
그리고 이를 가격으로 나누어 실제 진입 수량을 계산합니다.
이 규칙은:
첫 진입이 1차 구간이든 2차 구간이든 동일하게 적용되며,
“충분히 깊은 하락 구간에서는 첫 진입부터 더 크게,
평소에는 비교적 보수적으로” 라는 운용 철학을 반영합니다.
4-3. 실레버리지(Real Leverage)의 추적
전략은 각 바 단위로 다음을 추적합니다.
바가 시작할 때의 기존 포지션 크기
해당 바에서 새로 진입한 수량
이를 바탕으로, 진입이 발생한 시점에 다음을 계산합니다.
실제 레버리지 = (포지션 가치 / 현재 자산)
그리고 차트 상에 예를 들어:
Lev 2.53x 와 같은 형식의 레이블로 표시합니다.
이를 통해, 매수 시점마다 실제 계좌 레버리지가 어느 정도였는지를 직관적으로 확인할 수 있습니다.
5. 시각화 및 모니터링 요소
5-1. 차트 상 시각 요소
전략은 차트 위에 다음과 같은 정보를 직접 표시합니다.
ATH 라인
High 기준으로 계산된 역대 최고가를 주황색 선으로 표시
평단가(평균 진입가) 라인
현재 보유 포지션이 있을 때,
해당 포지션의 평균 진입가를 노란색 선으로 표시
추정 청산가(고정형 청산가) 라인
포지션 수량이 변화하는 시점을 감지하여,
당시의 평단가와 실제 레버리지를 이용해 근사적인 청산가를 계산
이를 빨간색 선으로 차트에 고정 표시
포지션이 없거나 레버리지가 1배 이하인 경우에는 청산가 라인을 제거
매수 마커 및 레이블
1차/2차 매수 조건이 충족될 때마다 해당 지점에 매수 마커를 표시
"Buy XX% @ 가격", "Lev XXx" 형태의 라벨로
진입 비율과 당시 레버리지를 함께 시각화
레이블의 위치는 설정에서 선택 가능:
바 아래 (Below Bar)
바 위 (Above Bar)
실제 가격 위치 (At Price)
5-2. 우측 상단 정보 테이블
차트 우측 상단에는 현재 계좌·포지션 상태를 요약한 정보 테이블이 표시됩니다.
대표적으로 다음 항목들이 포함됩니다.
Pos Qty (Token)
현재 보유 중인 포지션 수량(토큰 기준, 절대값 기준)
Pos Value (USDT)
현재 포지션의 시장 가치 (수량 × 현재 가격)
Leverage (Now)
현재 실레버리지 (포지션 가치 / 현재 자산)
DD from ATH (%)
현재 가격 기준, 최근 ATH에서의 하락률(%)
Avg Entry
현재 포지션의 평균 진입 가격
PnL (%)
현재 포지션 기준 미실현 손익률(%)
Max DD (Equity %)
전략 전체 기간 동안 기록된 계좌 기준 최대 손실(MDD, Max Drawdown)
Last Entry Price
가장 최근에 포지션을 추가로 진입한 직후의 평균 진입 가격
Last Entry Lev
위 “Last Entry Price” 시점에서의 실레버리지
Liq Price (Fixed)
위에서 설명한 고정형 추정 청산가
Return from Start (%)
전략 시작 시점(초기 자본) 대비 현재 계좌 자산의 총 수익률(%)
이 테이블을 통해 사용자는:
현재 계좌와 포지션의 상태
리스크 수준
누적 성과
를 직관적으로 파악할 수 있습니다.
6. 시간 필터 및 라벨 옵션
6-1. 전략 동작 기간 설정
전략은 옵션으로 특정 기간에만 전략을 동작시키는 시간 필터를 제공합니다.
“Use Date Range” 옵션을 활성화하면:
시작 시각과 종료 시각을 지정하여
해당 구간에 한해서만 매매가 발생하도록 제한
옵션을 비활성화하면:
전략은 전체 차트 구간에서 자유롭게 동작
6-2. 진입 라벨 위치 설정
사용자는 매수/레버리지 라벨의 위치를 선택할 수 있습니다.
바 아래 (Below Bar)
바 위 (Above Bar)
실제 가격 위치 (At Price)
이를 통해 개인 취향 및 차트 가독성에 맞추어
시각화 방식을 유연하게 조정할 수 있습니다.
7. 활용 대상 및 사용 예시
본 전략은 다음과 같은 목적에 적합합니다.
현물 또는 선물 롱 포지션 기준 장기·스윙 관점 추매 전략 백테스트
“고점 대비 하락률”을 기준으로 한 규칙 기반 운용 아이디어 검증
레버리지 사용 시
계좌 레버리지·청산가·MDD를 동시에 모니터링하고자 하는 경우
특정 자산에 대해
“새로운 고점이 형성될 때마다
일정한 규칙으로 깊은 조정 구간에서만 분할 진입하고자 할 때”
실거래에 그대로 적용하기보다는,
전략 아이디어 검증 및 리스크 프로파일 분석,
자신의 성향에 맞는 파라미터 탐색 용도로 사용하는 것을 권장합니다.
8. 한계 및 유의사항
백테스트 결과는 미래 성과를 보장하지 않습니다.
과거 데이터에 기반한 시뮬레이션일 뿐이며,
실제 시장에서는
유동성
슬리피지
수수료 체계
강제청산 규칙
등 다양한 변수가 존재합니다.
청산가는 단순화된 공식에 따른 추정치입니다.
거래소별 실제 청산 규칙, 유지 증거금, 수수료, 펀딩비 등은
본 전략의 계산과 다를 수 있으며,
청산가 추정 라인은 참고용 지표일 뿐입니다.
레버리지 및 진입 비율 설정에 따라 손실 폭이 매우 커질 수 있습니다.
특히 **“첫 포지션 300% 진입”**과 같이 매우 공격적인 설정은
시장 급락 시 계좌 손실과 청산 리스크를 크게 증가시킬 수 있으므로
신중한 검토가 필요합니다.
실거래 연동 시에는 별도의 리스크 관리가 필수입니다.
개별 손절 기준
포지션 상한선
전체 포트폴리오 내 비중 관리 등
본 전략 외부에서 추가적인 안전장치가 필요합니다.
9. 결론
ATH Drawdown Re-Buy Long Only 전략은 단순한 “저가 매수”를 넘어서,
ATH 기준으로 드로우다운을 구조적으로 활용하고,
첫 포지션에 대한 **특수 규칙(100% / 300%)**을 적용하며,
레버리지·청산가·MDD·수익률을 통합적으로 시각화함으로써,
하락 구간에서의 규칙 기반 롱 포지션 구축과
리스크 모니터링을 동시에 지원하는 전략입니다.
사용자는 본 전략을 통해:
자신의 시장 관점과 리스크 허용 범위에 맞는
드로우다운 구간
진입 비율
레버리지 설정
다양한 시나리오에 대한 백테스트와 분석
을 수행할 수 있습니다.
다시 한 번 강조하지만,
본 전략은 연구·학습·백테스트를 위한 도구이며,
실제 투자 판단과 책임은 전적으로 사용자 본인에게 있습니다.
/ENG Version.
This script is designed to use historical drawdown data and automatically enter positions when a predefined percentage drop from the all-time high occurs, using a predefined percentage of your account equity.
You can use leverage, and default parameter values are provided out of the box (you can freely change them to suit your style).
In addition to the two main entry levels, you can add more entry conditions and custom entry percentages – just ask ChatGPT to modify the script.
For actual/live usage, please turn OFF the KillSwitch function and turn ON the Bar Magnifier feature.
ATH Drawdown Re-Buy Long Only Strategy
1. Strategy Overview
The ATH Drawdown Re-Buy Long Only strategy is an automatic re-buy (Long Only) system that builds long positions step-by-step at specific drawdown levels, based on the asset’s all-time high (ATH) and its subsequent drawdown.
This strategy is designed with the following goals:
Systematic scaled buying and leverage usage during sharp correction periods
Clear, rule-based entry logic using drawdowns from ATH
Real-time visualization of:
Average entry price
Leverage
Estimated liquidation price
Account MDD (Max Drawdown)
Return / performance
This allows traders to intuitively monitor both risk and position status.
※ This strategy is provided for educational, research, and backtesting purposes only.
It does not constitute investment advice and does not guarantee any profits.
2. Core Concepts
2-1. Drawdown from ATH (All-Time High)
On the chart, the strategy always tracks the highest high as the ATH.
Whenever a new high is made, ATH is updated, and based on that ATH the following are calculated:
How many percent the current bar’s Low is below the ATH
How many percent the current bar’s Close is below the ATH
Using these, the strategy executes buys at two predefined drawdown zones:
1st entry zone: When price drops X% from ATH
2nd entry zone: When price drops Y% from ATH
Each zone is allowed to trigger only once per ATH cycle.
When a new ATH is created, the “1st / 2nd entry possible” flags are reset, and new opportunities open up for that ATH leg.
2-2. Special Rule for the First Position (100% / 300%)
A key feature of this strategy is the special rule for the very first position.
When the strategy currently holds no position and is about to open the first long position:
Under normal conditions, it builds the position using 100% of account equity.
However, if at that moment the price has dropped by at least a predefined threshold from ATH (e.g. around –72.5% or more),
→ the strategy will open the first position using 300% of account equity.
This rule works as follows:
Whether the first entry happens at the 1st drawdown zone or at the 2nd drawdown zone,
If the current drawdown from ATH is at or below the threshold (e.g. –72.5% or worse),
→ the strategy interprets this as “a sufficiently deep crash” and opens the initial position with 300% of equity.
If the drawdown is less severe than the threshold,
→ the first entry is capped at 100% of equity.
So the strategy has two modes for the first entry:
Normal market conditions: 100% of equity
Deep drawdown conditions: 300% of equity
This special rule is intended to be aggressive in extremely deep crashes while staying more conservative in normal corrections.
3. Strategy Logic & Execution
3-1. Entry Conditions
The strategy tracks the ATH using the High price.
For each bar, it calculates the drawdown from ATH.
The user defines two drawdown zones, for example:
1st zone: ATH – 50%
2nd zone: ATH – 72.5%
For each zone, the strategy checks:
If no buy has been executed yet for that zone in the current ATH leg, and
If the current bar’s Low touches or falls below that zone’s price level,
→ That bar is considered to have triggered a buy condition.
Order simulation:
The strategy simulates entering a long position at that zone’s price level
(using a limit/market-like approximation for backtesting).
3-2. ATH Reset & Entry Opportunity Reset
When a new High goes above the previous ATH:
The ATH is updated to this new high.
Internal flags that track whether the 1st and 2nd entries have been used are reset.
This means:
Each time the market makes a new ATH,
The strategy once again has a fresh opportunity to execute 1st and 2nd drawdown entries for that new ATH leg.
4. Position Sizing & Leverage
4-1. Position Size Based on Account Equity
The strategy defines current equity as:
Current Equity = Initial Capital + Realized PnL + Unrealized PnL
For each entry zone, the position value is calculated as follows:
The user inputs:
“What % of equity to use at this zone”
The strategy:
Multiplies current equity by that percentage
Then multiplies by the strategy’s leverage factor
Thus:
Position Value = Current Equity × (Zone % / 100) × Leverage
Finally, this position value is divided by the entry price to determine the actual position size in tokens.
4-2. Exception for the First Position (100% / 300%)
For the very first position (when there is no open position),
the strategy does not use the zone % parameters. Instead, it uses fixed ratios:
Default: Enter the first position with 100% of equity.
If the drawdown from ATH at that moment is greater than or equal to a predefined threshold (e.g. –72.5% or more)
→ Enter the first position with 300% of equity.
The position value is computed as:
Position Value = Current Equity × (100% or 300%) × Leverage
Then it is divided by the entry price to obtain the token quantity.
This rule:
Applies regardless of whether the first entry occurs at the 1st zone or 2nd zone.
Embeds the philosophy:
“In very deep crashes, go much larger on the first entry; otherwise, stay more conservative.”
4-3. Tracking Real Leverage
On each bar, the strategy tracks:
The existing position size at the start of the bar
The newly added size (if any) on that bar
When a new entry occurs, it calculates the real leverage at that moment:
Real Leverage = (Position Value / Current Equity)
This is then displayed on the chart as a label, for example:
Lev 2.53x
This makes it easy to see the actual leverage level at each entry point.
5. Visualization & Monitoring
5-1. On-Chart Visual Elements
The strategy plots the following directly on the chart:
ATH Line
The all-time high (based on High) is plotted as an orange line.
Average Entry Price Line
When a position is open, the average entry price of that position is plotted as a yellow line.
Estimated Liquidation Price (Fixed) Line
The strategy detects when the position size changes.
At each size change, it uses the current average entry price and real leverage to compute an approximate liquidation price.
This “fixed liquidation price” is then plotted as a red line on the chart.
If there is no position, or if leverage is 1x or lower, the liquidation line is removed.
Entry Markers & Labels
When 1st/2nd entry conditions are met, the strategy:
Marks the entry point on the chart.
Displays labels such as "Buy XX% @ Price" and "Lev XXx",
showing both entry percentage and real leverage at that time.
The label placement is configurable:
Below Bar
Above Bar
At Price
5-2. Information Table (Top-Right Panel)
In the top-right corner of the chart, the strategy displays a summary table of the current account and position status. It typically includes:
Pos Qty (Token)
Absolute size of the current position (in tokens)
Pos Value (USDT)
Market value of the current position (qty × current price)
Leverage (Now)
Current real leverage (position value / current equity)
DD from ATH (%)
Current drawdown (%) from the latest ATH, based on current price
Avg Entry
Average entry price of the current position
PnL (%)
Unrealized profit/loss (%) of the current position
Max DD (Equity %)
The maximum equity drawdown (MDD) recorded over the entire backtest period
Last Entry Price
Average entry price immediately after the most recent add-on entry
Last Entry Lev
Real leverage at the time of the most recent entry
Liq Price (Fixed)
The fixed estimated liquidation price described above
Return from Start (%)
Total return (%) of equity compared to the initial capital
Through this table, users can quickly grasp:
Current account and position status
Current risk level
Cumulative performance
6. Time Filters & Label Options
6-1. Strategy Date Range Filter
The strategy provides an option to restrict trading to a specific time range.
When “Use Date Range” is enabled:
You can specify start and end timestamps.
The strategy will only execute trades within that range.
When this option is disabled:
The strategy operates over the entire chart history.
6-2. Entry Label Placement
Users can customize where entry/leverage labels are drawn:
Below Bar (Below Bar)
Above Bar (Above Bar)
At the actual price level (At Price)
This allows you to adjust visualization according to personal preference and chart readability.
7. Use Cases & Applications
This strategy is suitable for the following purposes:
Long-term / swing-style re-buy strategies for spot or futures long positions
Testing rule-based strategies that rely on “drawdown from ATH” as a main signal
Monitoring account leverage, liquidation price, and MDD when using leverage
Handling situations where, for a given asset:
“Every time a new ATH is formed,
you want to wait for deep corrections and enter only at specific drawdown zones”
It is generally recommended to use this strategy not as a direct plug-and-play live system, but as a tool for:
Strategy idea validation
Risk profile analysis
Parameter exploration to match your personal risk tolerance and style
8. Limitations & Warnings
Backtest results do not guarantee future performance.
They are based on historical data only.
In live markets, additional factors exist:
Liquidity
Slippage
Fee structures
Exchange-specific liquidation rules
Funding fees, etc.
The liquidation price is only an approximate estimate, derived from a simplified formula.
Actual liquidation rules, maintenance margin requirements, fees, and other details differ by exchange.
The liquidation line should be treated as a reference indicator, not an exact guarantee.
Depending on the configured leverage and entry percentages, losses can be very large.
In particular, extremely aggressive settings such as “first position 300% of equity” can greatly increase the risk of large account drawdowns and liquidation during sharp market crashes.
Use such settings with extreme caution.
For live trading, additional risk management is essential:
Your own stop-loss rules
Maximum position size limits
Portfolio-level exposure controls
And other external safety mechanisms beyond this strategy
9. Conclusion
The ATH Drawdown Re-Buy Long Only strategy goes beyond simple “buy the dip” logic. It:
Systematically utilizes drawdowns from ATH as a structural signal
Applies a special first-position rule (100% / 300%)
Integrates visualization of leverage, liquidation price, MDD, and returns
All of this supports rule-based long position building in drawdown phases and comprehensive risk monitoring.
With this strategy, users can:
Explore different:
Drawdown zones
Entry percentages
Leverage levels
Run various backtests and scenario analyses
Better understand the risk/return profile that fits their own market view and risk tolerance
Once again, this strategy is intended for research, learning, and backtesting only.
All real trading decisions and their consequences are solely the responsibility of the user.
Curvature Tensor Pivots - HIVECurvature Tensor Pivots - HIVE
I. CORE CONCEPT & ORIGINALITY
Curvature Tensor Pivots - HIVE is an advanced, multi-dimensional pivot detection system that combines differential geometry, reinforcement learning, and statistical physics to identify high-probability reversal zones before they fully form. Unlike traditional pivot indicators that rely on simple price comparisons or lagging moving averages, this system models price action as a smooth curve in geometric space and calculates its mathematical curvature (how sharply the price trajectory is "bending") to detect pivots with scientific precision.
What Makes This Original:
Differential Geometry Engine: The script calculates first and second derivatives of price using Kalman-filtered trajectory analysis, then computes true mathematical curvature (κ) using the classical formula: κ = |y''| / (1 + y'²)^(3/2). This approach treats price as a physical phenomenon rather than discrete data points.
Ghost Vertex Prediction: A proprietary algorithm that detects pivots 1-3 bars BEFORE they complete by identifying when velocity approaches zero while acceleration is high—this is the mathematical definition of a turning point.
Multi-Armed Bandit AI: Four distinct pivot detection strategies (Fast, Balanced, Strict, Tensor) run simultaneously in shadow portfolios. A Thompson Sampling reinforcement learning algorithm continuously evaluates which strategy performs best in current market conditions and automatically selects it.
Hive Consensus System: When 3 or 4 of the parallel strategies agree on the same price zone, the system generates "confluence zones"—areas of institutional-grade probability.
Dynamic Volatility Scaling (DVS): All parameters auto-adjust based on current ATR relative to historical average, making the indicator adaptive across all timeframes and instruments without manual re-optimization.
II. HOW THE COMPONENTS WORK TOGETHER
This is NOT a simple mashup —each subsystem feeds data into the others in a closed-loop learning architecture:
The Processing Pipeline:
Step 1: Geometric Foundation
Raw price is normalized against a 50-period SMA to create a trajectory baseline
A Zero-Lag EMA smooths the trajectory while preserving edge response
Kalman filter removes noise while maintaining signal integrity
Step 2: Calculus Layer
First derivative (y') measures velocity of price movement
Second derivative (y'') measures acceleration (rate of velocity change)
Curvature (κ) is calculated from these derivatives, representing how sharply price is turning
Step 3: Statistical Validation
Z-Score measures how many standard deviations current price deviates from the Kalman-filtered "true price"
Only pivots with Z-Score > threshold (default 1.2) are considered statistically significant
This filters out noise and micro-fluctuations
Step 4: Tensor Construction
Curvature is combined with volatility (ATR-based) and momentum (ROC-based) to create a multidimensional "tensor score"
This tensor represents the geometric stress in the price field
High tensor magnitude = high probability of structural failure (reversal)
Step 5: AI Decision Layer
All 4 bandit strategies evaluate current conditions using different sensitivity thresholds
Each strategy maintains a virtual portfolio that trades its signals in real-time
Thompson Sampling algorithm updates Bayesian priors (alpha/beta distributions) based on each strategy's Sharpe ratio, win rate, and drawdown
The highest-performing strategy's signals are displayed to the user
Step 6: Confluence Aggregation
When multiple strategies agree on the same price zone, that zone is highlighted as a confluence area. These represent "hive mind" consensus—the strongest setups
Why This Integration Matters:
Traditional indicators either detect pivots too late (lagging) or generate too many false signals (noisy). By requiring geometric confirmation (curvature), statistical significance (Z-Score), multi-strategy agreement (hive voting), and performance validation (RL feedback) , this system achieves institutional-grade precision. The reinforcement learning layer ensures the system adapts as market regimes change, rather than degrading over time like static algorithms.
III. DETAILED METHODOLOGY
A. Curvature Calculation (Differential Geometry)
The system models price as a parametric curve where:
x-axis = time (bar index)
y-axis = normalized price
The curvature at any point represents how quickly the direction of the tangent line is changing. High curvature = sharp turn = potential pivot.
Implementation:
Lookback window (default 8 bars) defines the local curve segment
Smoothing (default 5 bars) applies adaptive EMA to reduce tick noise
Curvature is normalized to 0-1 scale using local statistical bounds (mean ± 2 standard deviations)
B. Ghost Vertex (Predictive Pivot Detection)
Classical pivot detection waits for price to form a swing high/low and confirm. Ghost Vertex uses calculus to predict the turning point:
Conditions for Ghost Pivot:
Velocity (y') ≈ 0 (price rate of change approaching zero)
Acceleration (y'') ≠ 0 (change is decelerating/accelerating)
Z-Score > threshold (statistically abnormal position)
This allows detection 1-3 bars before the actual high/low prints, providing an early entry edge.
C. Multi-Armed Bandit Reinforcement Learning
The system runs 4 parallel "bandits" (agents), each with different detection sensitivity:
Bandit Strategies:
Fast: Low curvature threshold (0.1), low Z-Score requirement (1.0) → High frequency, more signals
Balanced: Standard thresholds (0.2 curvature, 1.5 Z-Score) → Moderate frequency
Strict: High thresholds (0.4 curvature, 2.0 Z-Score) → Low frequency, high conviction
Tensor: Requires tensor magnitude > 0.5 → Geometric-weighted detection
Learning Algorithm (Thompson Sampling):
Each bandit maintains a Beta distribution with parameters (α, β)
After each trade outcome, α is incremented for wins, β for losses
Selection probability is proportional to sampled success rate from the distribution
This naturally balances exploration (trying underperformed strategies) vs exploitation (using best strategy)
Performance Metrics Tracked:
Equity curve for each shadow portfolio
Win rate percentage
Sharpe ratio (risk-adjusted returns)
Maximum drawdown
Total trades executed
The system displays all metrics in real-time on the dashboard so users can see which strategy is currently "winning."
D. Dynamic Volatility Scaling (DVS)
Markets cycle between high volatility (trending, news-driven) and low volatility (ranging, quiet). Static parameters fail when regime changes.
DVS Solution:
Measures current ATR(30) / close as normalized volatility
Compares to 100-bar SMA of normalized volatility
Ratio > 1 = high volatility → lengthen lookbacks, raise thresholds (prevent noise)
Ratio < 1 = low volatility → shorten lookbacks, lower thresholds (maintain sensitivity)
This single feature is why the indicator works on 1-minute crypto charts AND daily stock charts without parameter changes.
E. Confluence Zone Detection
The script divides the recent price range (200 bars) into 200 discrete zones. On each bar:
Each of the 4 bandits votes on potential pivot zones
Votes accumulate in a histogram array
Zones with ≥ 3 votes (75% agreement) are drawn as colored boxes
Red boxes = resistance confluence, Green boxes = support confluence
These zones act as magnet levels where price often returns multiple times.
IV. HOW TO USE THIS INDICATOR
For Scalpers (1m - 5m timeframes):
Settings: Use "Aggressive" or "Adaptive" pivot mode, Curvature Window 5-8, Min Pivot Strength 50-60
Entry Signal: Triangle marker appears (🔺 for longs, 🔻 for shorts)
Confirmation: Check that Hive Sentiment on dashboard agrees (3+ votes)
Stop Loss: Use the dotted volatility-adjusted target line in reverse (if pivot is at 100 with target at 110, stop is ~95)
Take Profit: Use the projected target line (default 3× ATR)
Advanced: Wait for confluence zone formation, then enter on retest of the zone
For Day Traders (15m - 1H timeframes):
Settings: Use "Adaptive" mode (default settings work well)
Entry Signal: Pivot marker + Hive Consensus alert
Confirmation: Check dashboard—ensure selected bandit has Sharpe > 1.5 and Win% > 55%
Filter: Only take pivots with Pivot Strength > 70 (shown in dashboard)
Risk Management: Monitor the Live Position Tracker—if your selected bandit is holding a position, consider that as market structure context
Exit: Either use target lines OR exit when opposite pivot appears
For Swing Traders (4H - Daily timeframes):
Settings: Use "Conservative" mode, Curvature Window 12-20, Min Bars Between Pivots 15-30
Focus on Confluence: Only trade when 4/4 bandits agree (unanimous hive consensus)
Entry: Set limit orders at confluence zones rather than market orders at pivot signals
Confirmation: Look for breakout diamonds (◆) after pivot—these signal momentum continuation
Risk Management: Use wider stops (base stop loss % = 3-5%)
Dashboard Interpretation:
Top Section (Real-Time Metrics):
κ (Curv): Current curvature. >0.6 = active pivot forming
Tensor: Geometric stress. Positive = bullish bias, Negative = bearish bias
Z-Score: Statistical deviation. >2.0 or <-2.0 = extreme outlier (strong signal)
Bandit Performance Table:
α/β: Bayesian parameters. Higher α = more wins in history
Win%: Self-explanatory. >60% is excellent
Sharpe: Risk-adjusted returns. >2.0 is institutional-grade
Status: Shows which strategy is currently selected
Live Position Tracker:
Shows if the selected bandit's shadow portfolio is currently holding a position
Displays entry price and real-time P&L
Use this as "what the AI would do" confirmation
Hive Sentiment:
Shows vote distribution across all 4 bandits
"BULLISH" with 3+ green votes = high-conviction long setup
"BEARISH" with 3+ red votes = high-conviction short setup
Alert Setup:
The script includes 6 alert conditions:
"AI High Pivot" = Selected bandit signals short
"AI Low Pivot" = Selected bandit signals long
"Hive Consensus BUY" = 3+ bandits agree on long
"Hive Consensus SELL" = 3+ bandits agree on short
"Breakout Up" = Resistance breakout (continuation long)
"Breakdown Down" = Support breakdown (continuation short)
Recommended Alert Strategy:
Set "Hive Consensus" alerts for high-conviction setups
Use "AI Pivot" alerts for active monitoring during your trading session
Use breakout alerts for momentum/trend-following entries
V. PARAMETER OPTIMIZATION GUIDE
Core Geometry Parameters:
Curvature Window (default 8):
Lower (3-5): Detects micro-structure, best for scalping volatile pairs (crypto, forex majors)
Higher (12-20): Detects macro-structure, best for swing trading stocks/indices
Rule of thumb: Set to ~0.5% of your typical trade duration in bars
Curvature Smoothing (default 5):
Increase if you see too many false pivots (noisy instrument)
Decrease if pivots lag (missing entries by 2-3 bars)
Inflection Threshold (default 0.20):
This is advanced. Lower = more inflection zones highlighted
Useful for identifying order blocks and liquidity voids
Most users can leave default
Pivot Detection Parameters:
Pivot Sensitivity Mode:
Aggressive: Use in low-volatility range-bound markets
Normal: General purpose
Adaptive: Recommended—auto-adjusts via DVS
Conservative: Use in choppy, whipsaw conditions or for swing trading
Min Bars Between Pivots (default 8):
THIS IS CRITICAL for visual clarity
If chart looks cluttered, increase to 12-15
If missing pivots, decrease to 5-6
Match to your timeframe: 1m charts use 3-5, Daily charts use 20+
Min Z-Score (default 1.2):
Statistical filter. Higher = fewer but stronger signals
During news events (NFP, FOMC), increase to 2.0+
In calm markets, 1.0 works well
Min Pivot Strength (default 60):
Composite quality score (0-100)
80+ = institutional-grade pivots only
50-70 = balanced
Below 50 = will show weak setups (not recommended)
RL & DVS Parameters:
Enable DVS (default ON):
Leave enabled unless you want to manually tune for a specific market condition
This is the "secret sauce" for cross-timeframe performance
DVS Sensitivity (default 1.0):
Increase to 1.5-2.0 for extremely volatile instruments (meme stocks, altcoins)
Decrease to 0.5-0.7 for stable instruments (utilities, bonds)
RL Algorithm (default Thompson Sampling):
Thompson Sampling: Best for non-stationary markets (recommended)
UCB1: Best for stable, mean-reverting markets
Epsilon-Greedy: For testing only
Contextual: Advanced—uses market regime as context
Risk Parameters:
Base Stop Loss % (default 2.0):
Set to 1.5-2× your instrument's average ATR as a percentage
Example: If SPY ATR = $3 and price = $450, ATR% = 0.67%, so use 1.5-2.0%
Base Take Profit % (default 4.0):
Aim for 2:1 reward/risk ratio minimum
For mean-reversion strategies, use 1.5-2.0%
For trend-following, use 3-5%
VI. UNDERSTANDING THE UNDERLYING CONCEPTS
Why Differential Geometry?
Traditional technical analysis treats price as discrete data points. Differential geometry models price as a continuous manifold —a smooth surface that can be analyzed using calculus. This allows us to ask: "At what rate is the trend changing?" rather than just "Is price going up or down?"
The curvature metric captures something fundamental: inflection points in market psychology . When buyers exhaust and sellers take over (or vice versa), the price trajectory must curve. By measuring this curvature mathematically, we detect these psychological shifts with precision.
Why Reinforcement Learning?
Markets are non-stationary —statistical properties change over time. A strategy that works in Q1 may fail in Q3. Traditional indicators have fixed parameters and degrade over time.
The multi-armed bandit framework solves this by:
Running multiple strategies in parallel (diversification)
Continuously measuring performance (feedback loop)
Automatically shifting capital to what's working (adaptation)
This is how professional hedge funds operate—they don't use one strategy, they use ensembles with dynamic allocation.
Why Kalman Filtering?
Raw price contains two components: signal (true movement) and noise (random fluctuations). Kalman filters are the gold standard in aerospace and robotics for extracting signal from noisy sensors.
By applying this to price data, we get a "clean" trajectory to measure curvature against. This prevents false pivots from bid-ask bounce or single-print anomalies.
Why Z-Score Validation?
Not all high-curvature points are tradeable. A sharp turn in a ranging market might just be noise. Z-Score ensures that pivots occur at statistically abnormal price levels —places where price has deviated significantly from its Kalman-filtered "fair value."
This filters out 70-80% of false signals while preserving true reversal points.
VII. COMMON USE CASES & STRATEGIES
Strategy 1: Confluence Zone Reversal Trading
Wait for confluence zone to form (red or green box)
Wait for price to approach zone
Enter when pivot marker appears WITHIN the confluence zone
Stop: Beyond the zone
Target: Opposite confluence zone or 3× ATR
Strategy 2: Hive Consensus Scalping
Set alert for "Hive Consensus BUY/SELL"
When alert fires, check dashboard—ensure 3-4 votes
Enter immediately (market order or 1-tick limit)
Stop: Tight, 1-1.5× ATR
Target: 2× ATR or opposite pivot signal
Strategy 3: Bandit-Following Swing Trading
On Daily timeframe, monitor which bandit has best Sharpe ratio over 30+ days
Take ONLY that bandit's signals (ignore others)
Enter on pivot, hold until opposite pivot or target line
Position size based on bandit's current win rate (higher win% = larger position)
Strategy 4: Breakout Confirmation
Identify key support/resistance level manually
Wait for pivot to form AT that level
If price breaks level and diamond breakout marker appears, enter in breakout direction
This combines support/resistance with geometric confirmation
Strategy 5: Inflection Zone Limit Orders
Enable "Show Inflection Zones"
Place limit buy orders at bottom of purple zones
Place limit sell orders at top of purple zones
These zones represent structural change points where price often pauses
VIII. WHAT THIS INDICATOR DOES NOT DO
To set proper expectations:
This is NOT:
A "holy grail" with 100% win rate
A strategy that works without risk management
A replacement for understanding market fundamentals
A signal copier (you must interpret context)
This DOES NOT:
Predict black swan events
Account for fundamental news (you must avoid trading during major news if not experienced)
Work well in extremely low liquidity conditions (penny stocks, microcap crypto)
Generate signals during consolidation (by design—prevents whipsaw)
Best Performance:
Liquid instruments (SPY, ES, NQ, EUR/USD, BTC/USD, etc.)
Clear trend or range conditions (struggles in choppy transition periods)
Timeframes 5m and above (1m can work but requires experience)
IX. PERFORMANCE EXPECTATIONS
Based on shadow portfolio backtesting across multiple instruments:
Conservative Mode:
Signal frequency: 2-5 per week (Daily charts)
Expected win rate: 60-70%
Average RRR: 2.5:1
Adaptive Mode:
Signal frequency: 5-15 per day (15m charts)
Expected win rate: 55-65%
Average RRR: 2:1
Aggressive Mode:
Signal frequency: 20-40 per day (5m charts)
Expected win rate: 50-60%
Average RRR: 1.5:1
Note: These are statistical expectations. Individual results depend on execution, risk management, and market conditions.
X. PRIVACY & INVITE-ONLY NATURE
This script is invite-only to:
Maintain signal quality (prevent market impact from mass adoption)
Provide dedicated support to users
Continuously improve the algorithm based on user feedback
Ensure users understand the complexity before deploying real capital
The script is closed-source to protect proprietary research in:
Ghost Vertex prediction mathematics
Tensor construction methodology
Bandit reward function design
DVS scaling algorithms
XI. FINAL RECOMMENDATIONS
Before Trading Live:
Paper trade for minimum 2 weeks to understand signal timing
Start with ONE timeframe and master it before adding others
Monitor the dashboard —if selected bandit Sharpe drops below 1.0, reduce size
Use confluence and hive consensus for highest-quality setups
Respect the Min Bars Between Pivots setting —this prevents overtrading
Risk Management Rules:
Never risk more than 1-2% of account per trade
If 3 consecutive losses occur, stop trading and review (possible regime change)
Use the shadow portfolio as a guide—if ALL bandits are losing, market is in transition
Combine with other analysis (order flow, volume profile) for best results
Continuous Learning:
The RL system improves over time, but only if you:
Keep the indicator running (it learns from bar data)
Don't constantly change parameters (confuses the learning)
Let it accumulate at least 50 samples before judging performance
Review the dashboard weekly to see which bandits are adapting
CONCLUSION
Curvature Tensor Pivots - HIVE represents a fusion of advanced mathematics, machine learning, and practical trading experience. It is designed for serious traders who want institutional-grade tools and understand that edge comes from superior methodology, not magic formulas.
The system's strength lies in its adaptive intelligence —it doesn't just detect pivots, it learns which detection method works best right now, in this market, under these conditions. The hive consensus mechanism provides confidence, the geometric foundation provides precision, and the reinforcement learning provides evolution.
Use it wisely, manage risk properly, and let the mathematics work for you.
Disclaimer: This indicator is a tool for analysis and does not constitute financial advice. Past performance of shadow portfolios does not guarantee future results. Trading involves substantial risk of loss. Always perform your own due diligence and never trade with capital you cannot afford to lose.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Overnight Gap Detector - 4H Body to BodyThis TradingView indicator automatically detects and tracks overnight price gaps based on 4-hour candle bodies, displaying them as colored rectangles on your chart.
Key Features:
Gap Detection:
Identifies true wick-to-wick gaps that occur at the start of each new trading day
Gap Up: Detected when previous candle's high is below current candle's low
Gap Down: Detected when previous candle's low is above current candle's high
Rectangles are drawn from candle body to body (not wicks), providing clean gap zones
Gap Tracking:
Gaps are marked as "GAP HOLE" when first detected
Automatically tracks when gaps get filled
Changes to "FILLED" label and color when price closes through the gap zone
Gaps extend horizontally until filled or chart end
Customizable Display:
Label Position: Choose between "Inside" (centered in box) or "Outside" the gap rectangle
Label Offset: Adjust how far from the right edge labels appear (0-50 bars)
Minimum Gap Size: Filter out small gaps by setting minimum percentage threshold (default 0.05%)
Max Stored Gaps: Control how many gaps are kept on chart (default 200)
Visual Options:
Optional midline showing the 50% fill level of each gap
Fully customizable colors for Gap Up, Gap Down, and Filled gaps
Separate transparency controls for box backgrounds and label backgrounds
Adjustable border and midline widths
Toggle labels and midlines on/off
Color Coding:
Green: Gap Up (default)
Red: Gap Down (default)
Yellow: Filled gaps (default)
Perfect for traders who use gap-fill strategies or want to track key price levels where gaps occurred
JOPA Channel (Dual-Volumed) v1 [JopAlgo]JOPA Channel (Dual-Volumed) v1
Short title: JOPAV1 • License: MPL-2.0 • Provider: JopAlgo
We have developed our own, first channel-based trading indicator and we’re making it available to all traders. The goal was a channel that breathes with the tape—built on a volume-weighted backbone—so the outcome stays lively instead of static. That led to the JOPA Channel.
All important features (at a glance)
In one line: A Rolling-VWAP channel whose width adapts with two volumes (RVOL + dollar-flow), adds order-flow asymmetry (OBV tilt) and regime awareness (Efficiency Ratio), and frames risk with outer containment bands from residual extremes—so you see fair value, momentum, and exhaustion in one view.
Feature list
Rolling VWAP centerline: Tracks where volume traded (fair value).
Dual-volume width: Bands expand/contract with relative volume and value traded (price×volume).
OBV tilt: Upper/lower widths skew toward the side actually pushing.
Regime adapter (ER): Tighter in trend, wider in chop—automatically.
Outer containment rails: Residual-extreme ceilings/floors, smoothed + margin.
20% / 80% guides: 20% light blue (discount), 80% light red (premium).
Squeeze dots (optional): Orange circles below candles during compression.
Non-repainting: Uses rolling sums and past-only math; no lookahead.
Default visual in this release
Containment rails + fill: ON (stepline, medium).
Inner Value rails + fill: Rails OFF (stepline, thin), fill ON (drawn only if rails are shown).
20% & 80% guides: ON (dashed, thin; 20% light blue, 80% light red).
Squeeze dots: OFF by default (orange circles when enabled).
What you see on the chart
RVWAP (centerline): Your compass for fair value.
Inner Value Bands (optional): Tight rails for breakouts and pullback timing.
Outer Containment Bands (default ON): High-confidence ceilings/floors for targets and fades.
20% / 80% guides: Quick read of “where in the channel” price is sitting.
Squeeze dots (optional): Volatility compression heads-up (no text labels).
Non-repainting note: The indicator does not revise closed bars. Forecast-Lock uses linear regression to extrapolate 1–3 bars ahead without using future data.
How to use it
Core reads (works on any timeframe)
Bias: Above a rising RVWAP → long bias; below a falling RVWAP → short bias.
Breakouts (momentum): Close beyond an Inner Value rail with RVOL ≥ threshold (alert provided).
Reversions (fades): Tag Outer Containment, stall, then close back inside → expect mean reversion toward RVWAP.
20/80 timing:
At/above 80% (light red) → premium/exhaustion risk; trim longs or consider fades if RVOL cools.
At/below 20% (light blue) → discount/exhaustion risk; trim shorts or consider longs if RVOL cools.
Squeeze clusters: When dots bunch up, expect a range break; use the Breakout alert as confirmation.
Playbooks by trading style
Day Trading (1–5m)
Setup: Keep the chart clean (Containment ON, Value rails OFF). Toggle Inner Value ON when hunting a breakout or timing a pullback.
Pullback Long: Dip to RVWAP / Lower Value with sub-threshold RVOL, then a close back above RVWAP → long.
Stop: Just beyond Lower Containment or the pullback swing.
Targets (1:1:1): ⅓ at RVWAP, ⅓ at Upper Value, ⅓ trail toward Upper Containment.
Breakout Long: After a squeeze cluster, take the Breakout Long alert (close > Upper Value, RVOL ≥ min). If no retest, demand the next bar holds outside.
Range Fade: Only when RVWAP is flat and dots cluster; short Upper Containment → RVWAP (mirror for longs at the lower rail).
Intraday (15m–1H)
HTF compass: Take bias from 4H.
Pullback Long: “Touch & reclaim” of RVWAP while RVOL cools; enter on the reclaim close or break of that candle’s high.
Breakout: Run Inner Value ON; act on Breakout alerts (RVOL gate ≈ 1.10–1.15 typical).
Avoid low-probability fades against the 4H slope unless RVWAP is flat.
Swing (4H–1D)
Continuation: In uptrends, buy pullbacks to RVWAP / Lower Value with sub-threshold RVOL; scale at Upper Containment.
Adds: Post-squeeze Breakout Long adds; trail on RVWAP or Lower Value.
Fades: Prefer when RVWAP flattens and price oscillates between containments.
Position (1D+)
Framework: Daily RVWAP slope + position within containment.
Add rule: Each reclaim of RVWAP after a dip is an add; trim into Upper Containment or near 80% light red.
Sizing: Containment distance is larger—size down and trail on RVWAP.
Inputs & Settings (complete)
Core
Source: Price input for RVWAP.
Rolling VWAP Length: Window of the centerline (higher = smoother).
Volume Baseline (RVOL): SMA window for relative volume.
Inner Value Bands (volatility-based width)
k·StdDev(residuals), k·ATR, k·MAD(residuals): Blend three measures into base width.
StdDev / ATR / MAD Lengths: Lookbacks for each.
Two-Volume Fusion
RVOL Exponent: How aggressively width responds to relative volume.
Dollar-Flow Gain: Adds push from price×volume (value traded).
Dollar-Flow Z-Window: Standardization window for dollar-flow.
Asymmetry (Order-Flow Tilt)
Enable Tilt (OBV): Lets flow skew upper/lower widths.
Tilt Strength (0..1): Gain applied to OBV slope z-score.
OBV Slope Z-Window: Window to standardize OBV slope.
Regime Adapter
Efficiency Ratio Lookback: Measures trend vs chop.
ER Width Min/Max: Maps ER into a width factor (tighter in trend, wider in chop).
Band Tracking (inner value rails)
Tracking Mode:
Base: Pure base rails.
Parallel-Lock: Smooth RVWAP & width; track in parallel.
Slope-Lock: Adds a fraction of recent slope (momentum-friendly).
Forecast-Lock: 1–3 bar extrapolation via linreg (non-repainting on closed bars).
Attach Strength (0..1): Blend tracked rails vs base rails.
Tracking Smooth Length: EMA smoothing of RVWAP and width.
Slope Influence / Forecast Lead Bars: Gains for the chosen mode.
Outer Containment Bands
Show Containment Bands: Master toggle (default ON).
Residual Extremes Lookback: Highest/lowest residual window.
Extreme Smoothing (EMA): Stability on extreme lines.
Margin vs inner width: Extra padding relative to smoothed inner width.
Squeeze & Alerts
Squeeze Window / Threshold: Width vs average; at/under threshold = dot (when enabled).
Min RVOL for Breakout: Required RVOL for breakout alerts.
Style (defaults in this release)
Inner Value rails: OFF (stepline, thin).
Inner & Containment fills: ON.
Containment rails: ON (stepline, medium).
20% / 80% guides: ON — 20% light blue, 80% light red, dashed, thin.
Squeeze dots: OFF by default (orange circles below candles when enabled).
Practical templates (copy/paste into a plan)
Momentum Breakout
Context: Squeeze cluster near RVWAP; Inner Value ON.
Trigger: Breakout Long (close > Upper Value & RVOL ≥ min).
Stop: Below Lower Value (tight) or below RVWAP (safer).
Targets (1:1:1): ⅓ Value → ⅓ Containment → ⅓ trail on RVWAP.
Pullback Continuation
Context: Uptrend; dip to RVWAP / Lower Value with cooling RVOL.
Trigger: Close back above RVWAP or break of reclaim candle’s high.
Stop: Just outside Lower Containment or pullback swing.
Targets: RVWAP → Upper Value → Upper Containment.
Containment Reversion (range)
Context: RVWAP flat; repeated containment tags.
Trigger: Stall at containment, then close back inside.
Stop: A step beyond that containment.
Target: RVWAP; runner only if RVOL stays muted.
Alerts included
DVWAP Breakout Long / Short (Value Bands)
Top Zone / Bottom Zone (20% / 80% guides)
Tip: On lower TFs, act on Breakout alerts with higher-TF bias (e.g., trade 5–15m in the direction of 1H/4H RVWAP slope/position).
Best practices
Let RVWAP be the compass; if unsure, wait until price picks a side.
Respect RVOL; low-RVOL breaks are prone to fail.
Use guides for timing, not certainty. Pair 20/80 zones with flow context.
Start with defaults; change one knob at a time.
Common pitfalls
Fading every containment touch → only fade when RVWAP is flat or RVOL cools.
Over-tuning inputs → the defaults are robust; small tweaks go a long way.
Fighting the higher timeframe on low TFs → expensive habit.
Footer — License & Publishing
License: Mozilla Public License 2.0 (MPL-2.0). You may modify and redistribute; keep this file under MPL and provide source for this file.
Originality: © 2025 JopAlgo. No third-party code reused; Pine built-ins and common formulas only.
Publishing: Keep this header/description intact when releasing on TradingView. Avoid promotional links in the public script text.
Liquidity PocketLiquidity Pocket Indicator
This indicator identifies and tracks institutional liquidity zones through pivot-based support and resistance analysis, providing visual confirmation when price returns to test these critical levels.
Core Functionality:
Dynamic Pivot Detection: Automatically identifies swing highs and lows using customizable lookback parameters
Multi-Timeframe Analysis: Option to analyze pivots from higher timeframes while displaying on current chart
Liquidity Line Tracking: Draws horizontal lines from pivot points that extend until price interference occurs
Sweep Confirmation: Generates signals when price returns to test previously established pivot levels
Key Features:
Adaptive Timeframe Selection: Choose specific timeframes or use automatic multiplier system (Lvl1-Lvl4) for systematic higher timeframe analysis
Dynamic Line Management: Automatically manages active lines with performance optimization through maximum line limits
Visual Confirmation System: Customizable display options including line styles, candle coloring, and liquidity sweep signals
ATR-Based Signal Positioning: Intelligent signal placement using Average True Range calculations for optimal visibility
Signal Logic:
The indicator monitors when price returns to previously established pivot levels, interpreting these interactions as liquidity sweeps or institutional order execution zones. Signals trigger upon contact with tracked levels, providing confirmation of institutional interest areas.
Customization Options:
Adjustable pivot sensitivity through left/right bar lookback settings
Comprehensive visual customization including colors, line styles, and signal symbols
Performance controls with maximum active line limits
Alert system for real-time liquidity event notifications
This tool excels at identifying where institutional players may have placed orders, making it valuable for understanding market structure and potential reversal zones.
52SIGNAL RECIPE Whale Smart Money Detector52SIGNAL RECIPE Whale Smart Money Detector
◆ Overview
52SIGNAL RECIPE Whale Smart Money Detector is an innovative indicator that detects the movements of whales (large investors) in the cryptocurrency market in real-time. This powerful tool tracks large-scale trading activities that significantly impact the market, providing valuable signals before important market direction changes occur. It can be applied to any cryptocurrency chart, allowing traders to follow the movements of big money anytime, anywhere.
The unique strength of this indicator lies in its comprehensive analysis of volume surges, price volatility, and trend strength to accurately capture whale market entries and exits. By providing clear visual representation of large fund flow data that is difficult for ordinary traders to detect, you gain the opportunity to move alongside the big players in the market.
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◆ Key Features
• Whale Activity Detection System: Analyzes volume surges and price impacts to capture large investor movements in real-time
• Precise Volume Analysis: Distinguishes between regular volume and whale volume to track only meaningful market movements
• Market Impact Measurement: Quantifies and analyzes the real impact of whale buying/selling on the market
• Continuity Tracking: Follows market direction continuity after whale activity to confirm signal validity
• Intuitive Visualization: Easily identifies whale activity points through color bar charts and clear labels
• Trend Strength Display: Calculates and displays current market buy/sell strength in real-time in a table
• Whale Signal Filtering: Applies multiple filtering systems to detect only genuine whale activity
• Customizable Sensitivity Settings: Offers flexible parameters to adjust whale detection sensitivity according to market conditions
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◆ Understanding Signal Types
■ Whale Buy Signal
• Definition: Occurs when volume increases significantly above average, immediate volume impact is large, and price rises beyond normal volatility
• Visual Representation: Translucent blue bar coloring with "🐋Whale Buying Detected!" label on the candle where the buy signal occurs
• Market Interpretation: Indicates that large funds are actively buying the coin, which is likely to lead to price increases
■ Whale Sell Signal
• Definition: Occurs when volume increases significantly above average, immediate volume impact is large, and price falls beyond normal volatility
• Visual Representation: Translucent pink bar coloring with "🐋Whale Selling Detected!" label on the candle where the sell signal occurs
• Market Interpretation: Indicates that large funds are actively selling the coin, which is likely to lead to price decreases
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◆ Understanding Trend Analysis
■ Trend Analysis Method
• Definition: Measures current trend and strength by analyzing the ratio of up/down candles over a set period
• Visual Representation: Displayed in the table as "BUY" and "SELL" percentages, with the current trend clearly marked as "BULLISH", "BEARISH", or "NEUTRAL"
• Calculation Method:
▶ Buy ratio = (Number of up candles) / (Total analysis period)
▶ Sell ratio = (Number of down candles) / (Total analysis period)
▶ Current trend determined by the dominant ratio as "BULLISH" or "BEARISH"
■ Trend Utilization Methods
• Whale Signal Confirmation: Signal reliability increases when whale signals align with the current trend
• Reversal Point Identification: Opposing whale signals during strong trends may indicate important reversal points
• Market Strength Assessment: Understand the balance of power in the current market through buy/sell ratios
• Signal Context Understanding: Consider trend information alongside whale signals for interpretation in a broader market context
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◆ Indicator Settings Guide
■ Key Setting Parameters
• Volume Impact Factor:
▶ Purpose: Sets the minimum multiplier for immediate volume impact to be considered whale activity
▶ Lower values: Generate more signals, detect smaller whales
▶ Higher values: Fewer signals, detect only very large whales
▶ Recommended range: 2.0-4.0 (adjust according to market conditions)
• Sensitivity Factor:
▶ Purpose: Adjusts sensitivity of price movement relative to normal volatility
▶ Lower values: Increased sensitivity, more signals generated
▶ Higher values: Decreased sensitivity, only stronger price impacts detected
▶ Recommended range: 0.2-0.5 (set higher in highly volatile markets)
• Trend Analysis Period:
▶ Purpose: Sets the number of candles to calculate buy/sell ratios
▶ Lower values: More responsive to recent trends
▶ Higher values: More stable analysis considering longer-term trends
▶ Recommended range: 30-70 (adjust according to trading style)
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◆ Synergy with Other Indicators
• Key Support/Resistance Levels:
▶ Whale signals occurring near important technical levels have higher reliability
▶ Coincidence of weekly/monthly pivot points and whale signals confirms important price points
• Moving Averages:
▶ Pay attention to whale signals near key moving averages (50MA, 200MA)
▶ Simultaneous occurrence of moving average breakouts and whale signals indicates important technical events
• Volume Profile:
▶ Whale activity near high volume nodes confirms important price levels
▶ Whale signals at low volume nodes may indicate possibility of rapid price movements
• Volatility Indicators:
▶ Whale signals after periods of low volatility may mark the beginning of new market movements
▶ Whale signals after Bollinger Band contraction may be precursors to large movements
• Market Structure:
▶ Whale signals near key market structures (higher highs/lows, lower highs/lows) suggest structural changes
▶ Coincidence of market structure changes and whale activity may signal important trend changes
─────────────────────────────────────
◆ Conclusion
52SIGNAL RECIPE Whale Smart Money Detector tracks the trading activities of large investors in the cryptocurrency market in real-time, providing traders with valuable insights. Because it can be applied to any cryptocurrency chart, you can utilize it immediately on your preferred trading platform.
The core value of this indicator is providing intuitive visualization of large fund flows that are easily missed by ordinary traders. By comprehensively analyzing volume surges, immediate price impacts, and trend continuity to accurately capture whale activity, you gain the opportunity to move alongside the big players in the market.
Clear buy/sell signals and real-time trend strength measurements help traders quickly grasp market conditions and understand market direction. By integrating this powerful tool into your trading system, gain insights into where the market's smart money is flowing for better market understanding.
─────────────────────────────────────
※ Disclaimer: Like all trading tools, the 52SIGNAL RECIPE Whale Smart Money Detector should be used as a supplementary indicator and not relied upon exclusively for trading decisions. Past patterns of whale behavior may not guarantee future market movements. Always employ appropriate risk management strategies in your trading.
52SIGNAL RECIPE Whale Smart Money Detector
◆ 개요
52SIGNAL RECIPE Whale Smart Money Detector는 암호화폐 시장에서 고래(대형 투자자)의 움직임을 실시간으로 감지하는 혁신적인 지표입니다. 이 강력한 도구는 시장에 큰 영향을 미치는 대규모 트레이딩 활동을 추적하여 중요한 시장 방향 전환이 일어나기 전에 귀중한 신호를 제공합니다. 모든 암호화폐 차트에 적용 가능하여 트레이더들이 언제 어디서든 대형 자금의 움직임을 따라갈 수 있게 해줍니다.
이 지표의 독보적인 강점은 거래량 급증, 가격 변동성, 그리고 추세 강도를 종합적으로 분석하여 고래의 시장 진입과 퇴출을 정확히 포착한다는 점입니다. 일반 트레이더들이 놓치기 쉬운 대형 자금의 흐름 데이터를 시각적으로 명확하게 제공함으로써, 여러분은 시장의 큰 손들과 함께 움직일 수 있는 기회를 얻게 됩니다.
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◆ 주요 특징
• 고래 활동 감지 시스템: 거래량 급증과 가격 임팩트를 분석하여 대형 투자자의 움직임을 실시간으로 포착
• 정밀한 거래량 분석: 일반 거래량과 고래 거래량을 구분하여 의미 있는 시장 움직임만 추적
• 시장 영향력 측정: 고래의 매수/매도가 시장에 미치는 실질적 영향력을 수치화하여 분석
• 연속성 추적: 고래 활동 이후 시장 방향의 지속성을 추적하여 신호의 유효성 확인
• 직관적 시각화: 컬러 바 차트와 명확한 라벨을 통해 고래 활동 지점을 쉽게 식별
• 추세 강도 표시: 현재 시장의 매수/매도 강도를 실시간으로 계산하여 테이블에 표시
• 고래 신호 필터링: 진정한 고래 활동만 감지하도록 다중 필터링 시스템 적용
• 맞춤형 감도 설정: 시장 상황에 따라 고래 감지 감도를 조절할 수 있는 유연한 파라미터 제공
─────────────────────────────────────
◆ 신호 유형 이해하기
■ 고래 매수 신호
• 정의: 거래량이 평균보다 크게 증가하고, 즉각적인 거래량 충격이 크며, 가격이 정상 변동성을 초과하여 상승할 때 발생
• 시각적 표현: 매수 신호가 발생한 캔들에 반투명 파란색 바 컬러링과 함께 "🐋Whale Buying Detected!" 라벨 표시
• 시장 해석: 대형 자금이 적극적으로 코인을 매수하고 있으며, 이는 곧 가격 상승으로 이어질 가능성이 높음을 의미
■ 고래 매도 신호
• 정의: 거래량이 평균보다 크게 증가하고, 즉각적인 거래량 충격이 크며, 가격이 정상 변동성을 초과하여 하락할 때 발생
• 시각적 표현: 매도 신호가 발생한 캔들에 반투명 분홍색 바 컬러링과 함께 "🐋Whale Selling Detected!" 라벨 표시
• 시장 해석: 대형 자금이 적극적으로 코인을 매도하고 있으며, 이는 곧 가격 하락으로 이어질 가능성이 높음을 의미
─────────────────────────────────────
◆ 추세 분석 이해하기
■ 추세 분석 방식
• 정의: 설정된 기간 동안의 상승/하락 캔들 비율을 분석하여 시장의 현재 추세와 강도를 측정
• 시각적 표현: 테이블에 "BUY"와 "SELL" 비율이 백분율로 표시되며, 현재 추세가 "BULLISH", "BEARISH" 또는 "NEUTRAL"로 명확하게 표시됨
• 계산 방식:
▶ 매수 비율 = (상승 캔들 수) / (전체 분석 기간)
▶ 매도 비율 = (하락 캔들 수) / (전체 분석 기간)
▶ 우세한 비율에 따라 "BULLISH" 또는 "BEARISH" 추세 결정
■ 추세 활용 방법
• 고래 신호 확인: 고래 신호가 현재 추세와 일치할 때 신호의 신뢰도가 높아짐
• 반전 포인트 식별: 강한 추세 속에서 발생하는 반대 방향의 고래 신호는 중요한 반전 포인트일 수 있음
• 시장 강도 평가: 매수/매도 비율을 통해 현재 시장의 세력 균형 파악
• 신호 발생 맥락 이해: 추세 정보와 고래 신호를 함께 고려하여 더 넓은 시장 컨텍스트에서 해석
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◆ 지표 설정 가이드
■ 주요 설정 매개변수
• Volume Impact Factor (거래량 임팩트 요소):
▶ 목적: 고래 활동으로 간주할 즉각적인 거래량 충격의 최소 배수 설정
▶ 낮은 값: 더 많은 신호 생성, 작은 고래도 감지
▶ 높은 값: 더 적은 신호, 매우 큰 고래만 감지
▶ 권장 범위: 2.0-4.0 (시장 상황에 따라 조정)
• Sensitivity Factor (민감도 요소):
▶ 목적: 정상 변동성 대비 가격 변동의 민감도 조절
▶ 낮은 값: 민감도 증가, 더 많은 신호 생성
▶ 높은 값: 민감도 감소, 더 강한 가격 충격만 감지
▶ 권장 범위: 0.2-0.5 (변동성이 높은 시장에서는 높게 설정)
• Trend Analysis Period (추세 분석 기간):
▶ 목적: 매수/매도 비율을 계산할 캔들 수 설정
▶ 낮은 값: 최근 추세에 더 민감하게 반응
▶ 높은 값: 더 긴 기간의 추세를 고려하여 안정적인 분석
▶ 권장 범위: 30-70 (트레이딩 스타일에 따라 조정)
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◆ 다른 지표와의 시너지
• 주요 지지/저항 레벨:
▶ 중요한 기술적 레벨 근처에서 발생하는 고래 신호는 더 높은 신뢰도를 가짐
▶ 주간/월간 피봇 포인트와 고래 신호의 일치는 중요한 가격 지점을 확인해줌
• 이동평균선:
▶ 주요 이동평균선(50MA, 200MA) 근처에서 발생하는 고래 신호에 주목
▶ 이동평균선 돌파와 고래 신호가 동시 발생 시 중요한 기술적 이벤트 확인
• 볼륨 프로필:
▶ 높은 볼륨 노드 근처에서의 고래 활동은 중요한 가격 레벨 확인
▶ 낮은 볼륨 노드에서 발생하는 고래 신호는 급격한 가격 이동 가능성 암시
• 변동성 지표:
▶ 낮은 변동성 구간 이후 발생하는 고래 신호는 새로운 시장 움직임의 시작일 수 있음
▶ 볼린저 밴드 수축 후 발생하는 고래 신호는 큰 움직임의 전조일 수 있음
• 시장 구조:
▶ 주요 시장 구조(높은 고점/저점, 낮은 고점/저점) 근처에서 발생하는 고래 신호는 구조 변화 암시
▶ 시장 구조 변화와 고래 활동의 일치는 중요한 트렌드 변화 신호일 수 있음
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◆ 결론
52SIGNAL RECIPE Whale Smart Money Detector는 암호화폐 시장에서 대형 투자자들의 거래 활동을 실시간으로 추적하여 트레이더들에게 귀중한 통찰력을 제공합니다. 모든 암호화폐 차트에 적용 가능하기 때문에, 여러분이 선호하는 트레이딩 플랫폼에서 바로 활용할 수 있습니다.
이 지표의 핵심 가치는 일반 트레이더들이 놓치기 쉬운 대형 자금의 흐름을 직관적으로 시각화하여 제공한다는 점입니다. 거래량 급증, 즉각적인 가격 충격, 그리고 추세 지속성을 종합적으로 분석하여 고래의 활동을 정확히 포착함으로써, 여러분은 시장을 움직이는 큰 손들과 함께할 수 있는 기회를 얻게 됩니다.
명확한 매수/매도 신호와 실시간 추세 강도 측정은 트레이더들이 시장 상황을 한눈에 파악하고 시장의 방향성을 이해하는 데 도움을 줍니다. 이 강력한 도구를 여러분의 트레이딩 시스템에 통합함으로써, 시장의 스마트 머니가 어디로 흘러가는지 파악하고 더 나은 통찰력을 얻으세요.
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※ 면책 조항: 모든 트레이딩 도구와 마찬가지로, 52SIGNAL RECIPE Whale Smart Money Detector는 보조 지표로 사용해야 하며 트레이딩 결정을 전적으로 의존해서는 안 됩니다. 과거의 고래 행동 패턴이 미래 시장 움직임을 보장하지는 않습니다. 항상 적절한 리스크 관리 전략을 트레이딩에 활용하세요.
Daily Performance Analysis [Mr_Rakun]The Daily Performance Analysis indicator is a comprehensive trading performance tracker that analyzes your strategy's success rate and profitability across different days of the week and month. This powerful tool provides detailed statistics to help traders identify patterns in their trading performance and optimize their strategies accordingly.
Weekly Performance Analysis:
Tracks wins/losses for each day of the week (Monday through Sunday)
Calculates net profit/loss for each trading day
Shows profit factor (gross profit ÷ gross loss) for each day
Displays win rate percentage for each day
Monthly Performance Analysis:
Monitors performance for each day of the month (1-31)
Provides the same detailed metrics as weekly analysis
Helps identify monthly patterns and trends
Add to Your Strategy:
Copy the performance analysis code and integrate it into your existing Pine Script strategy
Optimize Strategy: Use insights to refine entry/exit timing or avoid trading on poor-performing days
Pattern Recognition: Identify which days of the week/month work best for your strategy
Risk Management: Avoid trading on historically poor-performing days
Strategy Optimization: Fine-tune your approach based on empirical data
Performance Tracking: Monitor long-term trends in your trading success
Data-Driven Decisions: Make informed adjustments to your trading schedule
Uptrick: Asset Rotation SystemOverview
The Uptrick: Asset Rotation System is a high-level performance-based crypto rotation tool. It evaluates the normalized strength of selected assets and dynamically simulates capital rotation into the strongest asset while optionally sidestepping into cash when performance drops. Built to deliver an intelligent, low-noise view of where capital should move, this system is ideal for traders focused on strength-driven allocation without relying on standard technical indicators.
Purpose
The purpose of this tool is to identify outperforming assets based strictly on relative price behavior and automatically simulate how a portfolio would evolve if it consistently moved into the strongest performer. By doing so, it gives users a realistic and dynamic model for capital optimization, making it especially suitable during trending markets and major crypto cycles. Additionally, it includes an optional safety fallback mechanism into cash to preserve capital during risk-off conditions.
Originality
This system stands out due to its strict use of normalized performance as the only basis for decision-making. No RSI, no MACD, no trend oscillators. It does not rely on any traditional indicator logic. The rotation logic depends purely on how each asset is performing over a user-defined lookback period. There is a single optional moving average filter, but this is used internally for refinement, not for entry or exit logic. The system’s intelligence lies in its minimalism and precision — using normalized asset scores to continuously rotate capital with clarity and consistency.
Inputs
General
Normalization Length: Defines how many bars are used to calculate each asset’s normalized score. This score is used to compare asset performance.
Visuals: Selects between Equity Curve (show strategy growth over time) or Asset Performance (compare asset strength visually).
Detect after bar close: Ensures changes only happen after a candle closes (for safety), or allows bar-by-bar updates for quicker reactions.
Moving Average
Used internally for optional signal filtering.
MA Type: Lets you choose which moving average type to use (EMA, SMA, WMA, RMA, SMMA, TEMA, DEMA, LSMA, EWMA, SWMA).
MA Length: Sets how many bars the moving average should calculate over.
Use MA Filter: Turns the filter on or off. It doesn’t affect the signal directly — just adds a layer of control.
Backtest
Used to simulate equity tracking from a chosen starting point. All calculations begin from the selected start date. Prior data is ignored for equity tracking, allowing users to isolate specific market cycles or testing periods.
Starting Day / Month / Year: The exact day the strategy starts tracking equity.
Initial Capital $: The amount of simulated starting capital used for performance calculation.
Rotation Assets
Each asset has 3 controls:
Enable: Include or exclude this asset from the rotation engine.
Symbol: The ticker for the asset (e.g., BINANCE:BTCUSDT).
Color: The color for visualization (labels, plots, tables).
Assets supported by default:
BTC, ETH, SOL, XRP, BNB, NEAR, PEPE, ADA, BRETT, SUI
Cash Rotation
Normalization Threshold USDC: If all assets fall below this threshold, the system rotates into cash.
Symbol & Color: Sets the cash color for plots and tables.
Customization
Dynamic Label Colors: Makes labels change color to match the current asset.
Enable Asset Label: Plots asset name labels on the chart.
Asset Table Position: Choose where the key asset usage table appears.
Performance Table Position: Choose where the backtest performance table appears.
Enable Realism: Enables slippage and fee simulation for realistic equity tracking. Adjusted profit is shown in the performance table.
Equity Styling
Show Equity Curve (STYLING): Toggles an extra-thick visual equity curve.
Background Color: Adds a soft background color that matches the current asset.
Features
Dual Visualization Modes
The script offers two powerful modes for real-time visual insights:
Equity Curve Mode: Simulates the growth of a portfolio over time using dynamic asset rotation. It visually tracks capital as it moves between outperforming assets, showing compounded returns and the current allocation through both line plots and background color.
Asset Performance Mode: Displays the normalized performance of all selected assets over the chosen lookback period. This mode is ideal for comparing relative strength and seeing how different coins perform in real-time against one another, regardless of price level.
Multi-Asset Rotation Logic
You can choose up to 10 unique assets, each fully customizable by symbol and color. This allows full flexibility for different strategies — whether you're rotating across majors like BTC, ETH, and SOL, or including meme tokens and stablecoins. You decide the rotation universe. If none of the selected assets meet the strength threshold, the system automatically moves to cash as a protective fallback.
Key Asset Selection Table
This on-screen table displays how frequently each enabled asset was selected as the top performer. It updates in real time and can help traders understand which assets the system has historically favored.
Asset Name: Shortened for readability
Color Box: Visual color representing the asset
% Used: How often the asset was selected (as a percentage of strategy runtime)
This table gives clear insight into historical rotation behavior and asset dominance over time.
Performance Comparison Table
This second table shows a full backtest vs. chart comparison, broken down into key performance metrics:
Backtest Start Date
Chart Asset Return (%) – The performance of the asset you’re currently viewing
System Return (%) – The equity growth of the rotation strategy
Outperformed By – Shows how many times the system beat the chart (e.g., 2.1x)
Slippage – Estimated total slippage costs over the strategy
Fees – Estimated trading fees based on rotation activity
Total Switches – Number of times the system changed assets
Adjusted Profit (%) – Final net return after subtracting fees and slippage
Equity Curve Styling
To enhance visual clarity and aesthetics, the equity curve includes styling options:
Custom Thickness Curve: A second stylized line plots a shadow or highlight of the main equity curve for stronger visual feedback
Dynamic Background Coloring: The chart background changes color to match the currently held asset, giving instant visual context
Realism Mode
By enabling Realism, the system calculates estimated:
Trading Fees (default 0.1%)
Slippage (default 0.05%)
These costs are subtracted from the equity curve in real time, and shown in the table to produce an Adjusted Return metric — giving users a more honest and execution-aware picture of system performance.
Adaptive Labeling System
Each time the asset changes, an on-chart label updates to show:
Current Asset
Live Equity Value
These labels dynamically adjust in color and visibility depending on the asset being held and your styling preferences.
Full Customization
From visual position settings to table placements and custom asset color coding, the entire system is fully modular. You can move tables around the screen, toggle background visuals, and control whether labels are colored dynamically or uniformly.
Key Concepts
Normalized values represent how much an asset has changed relative to its past price over a fixed period, allowing performance comparisons across different assets. Outperforming refers to the asset with the highest normalized value at a given time. Cash fallback means the system moves into a stable asset like USDC when no strong performers are available. The equity curve is a running total of simulated capital over time. Slippage is the small price difference between expected and actual trade execution due to market movement.
Use Case Flexibility
You don’t need to use all 10 assets. The system works just as effectively with only 1 asset — such as rotating between CASH and SOL — for a simple, minimal strategy. This is ideal for more focused portfolios or thematic rotation systems.
How to Use the Indicator
To use the Uptrick: Asset Rotation System, start by selecting which assets to include and entering their symbols (e.g., BINANCE:BTCUSDT). Choose between Equity Curve mode to see simulated portfolio growth, or Asset Performance mode to compare asset strength. Set your lookback period, backtest start date, and optionally enable the moving average filter or realism settings for slippage and fees. The system will then automatically rotate into the strongest asset, or into cash if no asset meets the strength threshold. Use alerts to be notified when a rotation occurs.
Asset Switch Alerts
The script includes built-in alert conditions for when the system rotates into a new asset. You can enable these to be notified when the system reallocates to a different coin or to cash. Each alert message is labeled by target asset and can be used for automation or monitoring purposes.
Conclusion
The Uptrick: Asset Rotation System is a next-generation rotation engine designed to cut through noise and overcomplication. It gives users direct insight into capital strength, without relying on generic indicators. Whether used to track a broad basket or focus on just two assets, it is built for accuracy, adaptability, and transparency — all in real-time.
Disclaimer
This script is for research and educational purposes only. It is not intended as financial advice. Past performance is not a guarantee of future results. Always consult with a financial professional and evaluate risks before trading or investing.
EMA POD Indicator #gangesThis script is a technical analysis indicator that uses multiple Exponential Moving Averages (EMAs) to identify trends and track price changes in the market. Here's a breakdown:
EMA Calculation: It calculates six different EMAs (for periods 5, 10, 20, 50, 100, and 150) to track short- and long-term trends.
Trend Identification:
Uptrend: The script identifies an uptrend when the EMAs are in ascending order (EMA5 > EMA10 > EMA20 > EMA50 > EMA100 > EMA150).
Downtrend: A downtrend is identified when the EMAs are not in ascending order.
Trend Change Tracking: It tracks when an uptrend starts and ends, displaying the duration of the trend and the percentage price change during the trend.
Visuals:
It plots the EMAs on the chart with different colors.
It adds green and red lines to represent the ongoing uptrend and downtrend.
Labels are displayed showing when the uptrend starts and ends, along with the trend's duration and price change percentage.
In short, this indicator helps visualize trends, track their changes, and measure the impact of those trends on price.
Visual Range Position Size CalculatorVisual Range Position Size Calculator
The "VR Position Size Calculator" helps traders determine the appropriate position size based on their risk tolerance and the current market conditions. Below is a detailed description of the script, its functionality, and how to use it effectively.
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Key Features
1. Risk Calculation: The script allows users to input their desired risk in monetary terms (in the currency of the ticker). It then calculates the position sizes for both long and short trades based on this risk.
2. Dynamic High and Low Tracking: The script dynamically tracks the highest and lowest prices within the visible range of the chart, allowing for more accurate position sizing.
3. Formatted Output: The calculated values are displayed in a user-friendly table format with thousands separators for better readability.
4. Visual Indicators: Dashed lines are drawn on the chart at the high and low points of the visible range, providing a clear visual reference for traders.
5. If the risk in security price is 1% or less, the background of the cells displaying position sizes will be green for long positions and red for short positions. If the risk is between 1% and 5%, the background changes to gray, indicating that the risk may be too high for an effective trade. If the risk exceeds 5% of the price, the text also turns gray, rendering it invisible, which signifies that there is no justification for such a trade.
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Code Explanation
The script identifies the start and end times of the visible range on the chart, ensuring calculations are based only on the data currently in view. It updates and stores the highest (hh) and lowest (ll) prices within this visible range. At the end of the range, dashed lines are drawn at the high and low prices, providing a visual cue for traders.
Users can input their risk amount, which is then used to calculate potential position sizes for both long and short trades based on the current price relative to the tracked high and low. The calculated risk values and position sizes are displayed in a table on the right side of the chart, with color coding to indicate whether the calculated position size meets specific criteria.
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Usage Instructions
1. Add the Indicator: To use this script, copy and paste it into Pine Script editor, then add it to your chart.
2. Input Your Risk: Adjust the 'Risk in money' input to reflect your desired risk amount for trading.
3. Analyze Position Sizes: Observe the calculated position sizes for both long and short trades displayed in the table. Use this information to guide your trading decisions.
4. Visual Cues: Utilize the dashed lines on the chart to understand recent price extremes within your visible range.
Absorption AnalysisThe Absorption Analysis indicator identifies potential market turning points by analyzing volume, price patterns, and market structure across multiple dimensions. It combines traditional technical signals with volume analysis and success rate tracking to provide high-probability reversal opportunities.
Signal Types & Classification
1. Pattern-Based Signals (W-Bottom & M-Top)
**W-Bottom Pattern**
- Pattern Structure:
* Price makes a low below the lower Bollinger Band
* First bounce occurs with price moving higher
* Secondary test forms a higher low
* Final confirmation with bullish close above lower band
- Volume Requirements:
* Must exceed 1.5x the 20-period volume moving average
- Visual Indicators:
* Blue dotted line appears at pattern low
* Line remains until broken by price
* Label shows volume and percentage from baseline
- Success Tracking:
* Pattern stored in historical database
* Success measured by upward price movement
* Historical success rate displayed with signal
**M-Top Pattern**
- Pattern Structure:
* Price makes a high above the upper Bollinger Band
* First pullback occurs with price moving lower
* Secondary push forms a lower high
* Final confirmation with bearish close below upper band
- Volume Requirements:
* Must exceed 1.5x the 20-period volume moving average
- Visual Indicators:
* Orange dotted line appears at pattern high
* Line remains until broken by price
* Label shows volume and percentage from baseline
- Success Tracking:
* Pattern stored in historical database
* Success measured by downward price movement
* Historical success rate displayed with signal
2. Technical Reversals
**Bullish Reversal**
- Entry Conditions:
* Previous candle closes below lower Bollinger Band
* Previous candle must be bearish
* Current candle closes above lower band
* Current candle must be bullish
- Volume Validation:
* Volume must exceed 1.5x 20-period MA
- Visual Markers:
* Green label at reversal point
* Includes volume context
- Trading Implementation:
* Suggests strong buying pressure overcoming selling
* Often marks end of downward price exhaustion
**Bearish Reversal**
- Entry Conditions:
* Previous candle closes above upper Bollinger Band
* Previous candle must be bullish
* Current candle closes below upper band
* Current candle must be bearish
- Volume Validation:
* Volume must exceed 1.5x 20-period MA
- Visual Markers:
* Red label at reversal point
* Includes volume context
- Trading Implementation:
* Suggests strong selling pressure overcoming buying
* Often marks end of upward price exhaustion
3. Volume-Based Reversals
**High Volume Bear to Bull**
- Signal Formation:
* High volume bearish candle (2.5σ above mean)
* Immediately followed by high volume bullish candle
- Market Psychology:
* Shows strong selling being absorbed by buying
* Often indicates institutional accumulation
- Visual Identification:
* Purple "HV Bull" label
* Includes volume statistics
- Trading Context:
* Strong signal for trend reversal
* Most effective at support levels
**High Volume Bull to Bear**
- Signal Formation:
* High volume bullish candle (2.5σ above mean)
* Immediately followed by high volume bearish candle
- Market Psychology:
* Shows strong buying being absorbed by selling
* Often indicates institutional distribution
- Visual Identification:
* Purple "HV Bear" label
* Includes volume statistics
- Trading Context:
* Strong signal for trend reversal
* Most effective at resistance levels
4. Absorption Signals
**Buy Absorption**
- Technical Requirements:
* High volume conditions (2.5σ above mean)
* Spread momentum must be negative
* Fast spread MA below slow spread MA
* Bullish closing candle
- Market Interpretation:
* Indicates buying pressure absorbing selling
* Often precedes upward movement
- Visual Markers:
* Red label with volume context
* Placed at significant price levels
**Sell Absorption**
- Technical Requirements:
* High volume conditions (2.5σ above mean)
* Spread momentum must be negative
* Fast spread MA below slow spread MA
* Bearish closing candle
- Market Interpretation:
* Indicates selling pressure absorbing buying
* Often precedes downward movement
- Visual Markers:
* Green label with volume context
* Placed at significant price levels
Volume Analysis Components
Volume Calculation
- Rolling baseline volume calculated based on timeframe:
* Monthly: 6-period sum
* Weekly: 12-period sum
* Daily: 20-period sum
* Intraday: Proportional to timeframe
- Net volume = Bullish volume - Bearish volume
- Volume percentage calculated against baseline
- High volume threshold = 2.5 standard deviations
- Pattern volume threshold = 1.5x 20MA
Exchange Aggregation
- Primary symbol (chart) always included
- Optional secondary symbol data
- Combines volume data for stronger signals
- Useful for crypto markets with split liquidity
Success Rate Implementation
Rate Calculation
- Based on user-defined lookback period
- Separately tracked for each pattern type
- Bullish patterns: Percentage of times price moved higher
- Bearish patterns: Percentage of times price moved lower
- Used to filter alerts with minimum threshold
Pattern Storage
- Arrays maintain historical pattern data
- Limited to lookback period size
- Oldest patterns removed as new ones form
- Constantly updated success rates
## Trading Implementation
### Signal Priority
1. Pattern Signals (W/M)
- Highest reliability due to complex criteria
- Must meet all volume and price conditions
- Line break provides clear invalidation
2. High Volume Reversals
- Strong indication of institutional activity
- Clear volume confirmation
- Immediate reversal potential
3. Technical Reversals
- Traditional technical analysis backbone
- Enhanced with volume confirmation
- Good for trend trading
4. Absorption Signals
- Early warning system
- Best used with other confirmations
- Good for position building
Best Practices
- Look for multiple signal types aligning
- Consider higher timeframe context
- Use success rates to filter setups
- Monitor volume context closely
- Wait for candle closes
- Use line breaks for clear invalidation
- Consider market structure
- Pay attention to success rates
- Use appropriate position sizing
Risk Management
- Use pattern breaks for stop losses
- Consider historical success rates
- Larger positions for multiple signal confluence
- Respect timeframe hierarchy
- Monitor volume for confirmation
- Use proper position sizing
- Consider market volatility
This indicator provides a comprehensive framework for identifying potential market turning points while maintaining rigorous risk management through multiple confirmation factors and clear invalidation levels.
Trend Continuation RatioThis TradingView indicator calculates the likelihood of consecutive bullish or bearish days over a specified period, giving insights into day-to-day continuation patterns within the market.
How It Works
Period Length Input:
The user sets the period length (e.g., 20 days) to analyze.
After each period, the counts reset, allowing fresh data for each new interval.
Bullish and Bearish Day Definitions:
A day is considered bullish if the closing price is higher than the opening price.
A day is considered bearish if the closing price is lower than the opening price.
Count Tracking:
Within each specified period, the indicator tracks:
Total Bullish Days: The number of days where the close is greater than the open.
Total Bearish Days: The number of days where the close is less than the open.
Bullish to Bullish Continuations: Counts each instance where a bullish day is followed by another bullish day.
Bearish to Bearish Continuations: Counts each instance where a bearish day is followed by another bearish day.
Calculating Continuation Ratios:
The Bullish Continuation Ratio is calculated as the percentage of bullish days that were followed by another bullish day:
Bullish Continuation Ratio = (Bullish to Bullish Continuations /Total Bullish Days)×100
Bullish Continuation Ratio=( Total Bullish Days/Bullish to Bullish Continuations )×100
The Bearish Continuation Ratio is the percentage of bearish days followed by another bearish day:
Bearish Continuation Ratio = (Bearish to Bearish Continuations/Total Bearish Days)×100
Bearish Continuation Ratio=( Total Bearish Days/Bearish to Bearish Continuations )×100
Display on Chart:
The indicator displays a table in the top-right corner of the chart with:
Bullish Continuation Ratio (%): Percentage of bullish days that led to another bullish day within the period.
Bearish Continuation Ratio (%): Percentage of bearish days that led to another bearish day within the period.
Usage Insights
High Ratios: If the bullish or bearish continuation ratio is high, it suggests a trend where bullish/bearish days often lead to similar days, indicating possible momentum.
Low Ratios: Low continuation ratios indicate frequent reversals, which could suggest a range-bound or volatile market.
This indicator is helpful for assessing short-term trend continuation tendencies, allowing traders to gauge whether they are more likely to see follow-through on bullish or bearish days within a chosen timeframe.
LumleyTrading GapsName: LumleyTrading Gaps
Description:
The Gap Tracker Indicator is a powerful tool designed for traders to identify, monitor, and capitalize on price gaps in financial markets. It serves two primary functions:
Identifying Gaps: The indicator scans price action to detect instances where the current trading session's opening price significantly differs from the previous session's closing price. These disparities indicate the presence of price gaps.
Tracking Gap Fills: Once a gap is identified, the indicator continues to monitor the price movement. It dynamically adjusts its parameters to track whether and when the price retraces back to fill the gap. As soon as the gap is filled, the indicator generates a signal to notify traders of this occurrence.
Key Features:
Customizable Parameters: Traders can adjust the sensitivity and criteria for what constitutes a significant gap based on their trading preferences and the market conditions.
Visual Alerts: The indicator provides clear visual signals on price charts, highlighting the presence of gaps and indicating when they are filled. This helps traders to easily spot trading opportunities and make informed decisions.
Alert Notifications: In addition to visual cues, traders can opt to receive real-time alerts via email, SMS, or within their trading platform, ensuring they never miss an opportunity or a filled gap.
Historical Analysis: The indicator may also offer historical gap data, allowing traders to conduct backtesting and analyze the performance of trading strategies based on gap patterns.
Benefits:
Gap Trading Opportunities: Traders can use the indicator to identify potential areas of price continuation or reversal, leveraging the phenomenon of gap trading for profit.
Risk Management: By tracking gap fills, traders can manage their risk more effectively, knowing when a gap is likely to act as support or resistance and adjusting their positions accordingly.
Enhanced Decision Making: With real-time gap detection and fill tracking, traders gain valuable insights into market sentiment and price dynamics, empowering them to make timely and informed trading decisions.
Compatibility:
The Gap Tracker Indicator is compatible with popular trading platforms and can be seamlessly integrated into various technical analysis tools and strategies.
Conclusion:
In the fast-paced world of financial markets, identifying and understanding price gaps is crucial for successful trading. The Gap Tracker Indicator provides traders with a reliable tool to spot, track, and capitalize on gap opportunities, enhancing their trading efficiency and profitability.
Ultimate Multi-Asset Correlation System by able eiei Ultimate Multi-Asset Correlation System - User Guide
Overview
This advanced TradingView indicator combines WaveTrend oscillator analysis with comprehensive multi-asset correlation tracking. It helps traders understand market relationships, identify regime changes, and spot high-probability trading opportunities across different asset classes.
Key Features
1. WaveTrend Oscillator
Main Signal Lines: WT1 (blue) and WT2 (red) plot momentum and its moving average
Overbought/Oversold Zones: Default levels at +60/-60
Cross Signals:
🟢 Bullish: WT1 crosses above WT2 in oversold territory
🔴 Bearish: WT1 crosses below WT2 in overbought territory
Higher Timeframe (HTF) Analysis: Shows WT1 from 4H, Daily, and Weekly timeframes for trend confirmation
2. Multi-Asset Correlation Tracking
Monitors relationships between:
Major Assets: Gold (XAUUSD), Dollar Index (DXY), US 10-Year Yield, S&P 500
Crypto Assets: Bitcoin, Ethereum, Solana, BNB
Cross-Asset Analysis: Correlation between traditional markets and crypto
3. Market Regime Detection
Automatically identifies market conditions:
Risk-On: High correlation + positive sentiment (🟢 Green background)
Risk-Off: High correlation + negative sentiment (🔴 Red background)
Crypto-Risk-On: Strong crypto correlations (🟠 Orange background)
Low-Correlation: Divergent market behavior (⚪ Gray background)
Neutral: Mixed signals (🟡 Yellow background)
How to Use
Basic Setup
Add to Chart: Apply the indicator to any chart (works on all timeframes)
Choose Display Mode (Display Options):
All: Shows everything (recommended for comprehensive analysis)
WaveTrend Only: Focus on momentum signals
Correlation Only: View market relationships
Heatmap Only: Simplified correlation view
Enable Asset Groups:
✅ Major Assets: Traditional markets (stocks, bonds, commodities)
✅ Crypto Assets: Digital currencies
Mix and match based on your trading focus
Reading the Charts
WaveTrend Section (Bottom Panel)
Above 0 = Bullish momentum
Below 0 = Bearish momentum
Above +60 = Overbought (potential reversal)
Below -60 = Oversold (potential bounce)
Lighter lines = Higher timeframe trends
Correlation Histogram (Colored Bars)
Blue bars: Major asset correlations
Orange bars: Crypto correlations
Purple bars: Cross-asset correlations
Bar height: Correlation strength (-50 to +50 scale)
Background Color
Intensity reflects correlation strength
Color shows market regime
Dashboard Elements
🎯 Market Regime Analysis (Top Left)
Current Regime: Overall market condition
Average Correlation: Strength of relationships (0-1 scale)
Risk Sentiment: -100% (risk-off) to +100% (risk-on)
HTF Alignment: Multi-timeframe trend agreement
Signal Quality: Confidence level for current signals
📊 Correlation Matrix (Top Right)
Shows correlation values between asset pairs:
1.00: Perfect positive correlation
0.75+: Strong correlation (🟢 Green)
0.50+: Medium correlation (🟡 Yellow)
0.25+: Weak correlation (🟠 Orange)
Below 0.25: Negative/no correlation (🔴 Red)
🔥 Correlation Heatmap (Bottom Right)
Visual matrix showing:
Gold vs. DXY, BTC, ETH
DXY vs. BTC, ETH
BTC vs. ETH
Color-coded strength
📈 Performance Tracker (Bottom Left)
Tracks individual asset momentum:
WT1 Values: Current momentum reading
Status: OB (overbought) / OS (oversold) / Normal
Trading Strategies
1. High-Probability Trend Following
✅ Entry Conditions:
WaveTrend bullish/bearish cross
HTF Alignment matches signal direction
Signal Quality > 70%
Correlation supports direction
2. Regime Change Trading
🎯 Watch for regime shifts:
Risk-Off → Risk-On = Consider long positions
High correlation → Low correlation = Reduce position size
Crypto-Risk-On = Focus on crypto longs
3. Divergence Trading
🔍 Look for:
Strong correlation breakdown = Potential volatility
Cross-asset correlation surge = Follow the leader
Volume-price correlation extremes = Trend confirmation
4. Overbought/Oversold Reversals
⚡ Trade reversals when:
WT crosses in extreme zones (-60/+60)
HTF alignment shows opposite trend weakening
Correlation confirms mean reversion setup
Customization Tips
Fine-Tuning Parameters
WaveTrend Core:
Channel Length (10): Lower = more sensitive, Higher = smoother
Average Length (21): Adjust for your timeframe
Correlation Settings:
Length (50): Longer = more stable, Shorter = more responsive
Smoothing (5): Reduce noise in correlation readings
Market Regime:
Risk-On Threshold (0.6): Lower = earlier regime signals
High Correlation Threshold (0.75): Adjust sensitivity
Custom Asset Selection
Replace default symbols with your preferred markets:
Major Assets: Any forex, indices, bonds
Crypto: Any digital currencies
Must use correct exchange prefix (e.g., BINANCE:BTCUSDT)
Alert System
Enable "Advanced Alerts" to receive notifications for:
✅ Market regime changes
✅ Correlation breakdowns/surges
✅ Strong signals with high correlation
✅ Extreme volume-price correlation
✅ Complete HTF alignment
Correlation Interpretation Guide
ValueMeaningTrading Implication+0.75 to +1.0Strong positiveAssets move together+0.5 to +0.75Moderate positiveGenerally aligned+0.25 to +0.5Weak positiveLoose relationship-0.25 to +0.25No correlationIndependent movements-0.5 to -0.25Weak negativeSlight inverse relationship-0.75 to -0.5Moderate negativeTend to move opposite-1.0 to -0.75Strong negativeStrongly inversely correlated
Best Practices
Use Multiple Timeframes: Check HTF alignment before trading
Confirm with Correlation: Strong signals work best with supportive correlations
Watch Regime Changes: Adjust strategy based on market conditions
Volume Matters: Enable volume-price correlation for confirmation
Quality Over Quantity: Trade only high-quality setups (>70% signal quality)
Common Patterns to Watch
🔵 Risk-On Environment:
Gold-BTC positive correlation
DXY negative correlation with risk assets
High crypto correlations
🔴 Risk-Off Environment:
Flight to safety (Gold up, stocks down)
DXY strength
Correlation breakdowns
🟡 Transition Periods:
Low correlation across assets
Mixed HTF signals
Use caution, reduce position sizes
Technical Notes
Calculation Period: Uses HLC3 (average of high, low, close)
Correlation Window: Rolling correlation over specified length
HTF Data: Accurately calculated using security() function
Performance: Optimized for real-time calculation on all timeframes
Support
For optimal performance:
Use on 15-minute to daily timeframes
Enable only needed asset groups
Adjust correlation length based on trading style
Combine with your existing strategy for confirmation
Enjoy comprehensive multi-asset analysis! 🚀
Inversion Fair Value Gap Model [PJ Trades]GENERAL OVERVIEW:
The Inversion Fair Value Gap Model indicator is a complete rule-based system designed to identify trade setups using the Inversion Fair Value Gap strategy taught by PJ Trades. It automates the strategy’s workflow by detecting liquidity sweeps, confirming V-shape recoveries, identifying valid Inversion Fair Value Gaps, validating higher-timeframe Fair Value Gap taps, and checking for a clear opposite Draw On Liquidity. These factors are evaluated together to produce a signal rating of A, A+, or A++, based on how many of these criteria the setup satisfies. When a long or short setup is confirmed, the indicator automatically plots an entry, stop-loss, break-even, and two take-profit levels.
A dashboard that updates in real-time displays the current directional bias, liquidity sweep activity, Inversion Fair Value Gap confirmation state, V Shape Recovery state, higher-timeframe Fair Value Gap context, opposite Draw on Liquidity, SMT divergence, and other key information relevant to the trading model. The indicator also includes optional trade statistics on the dashboard that tracks the recent win rates for A, A+, and A++ setups, as well as separate long and short win rates.
This indicator was developed by Flux Charts, in collaboration with PJ Trades.
What is the theory behind the indicator?:
The Inversion Fair Value Gap model is built on the idea that when the market pushes above a high or below a low, it often does so to sweep liquidity. If that move quickly fails and price reverses, it shows the sweep was a grab for orders and not a continuation. That quick rejection is the V Shape Recovery behavior. An Inversion Fair Value Gap forms when a Fair Value Gap that once supported the original move gets invalidated afterward. That invalidation confirms the shift in direction and becomes the new reference point for trades. The Inversion Fair Value Gap model uses this sequence because it highlights when the market has taken liquidity, rejected continuation, and started delivering in the opposite direction.
INVERSION FAIR VALUE GAP MODEL FEATURES:
The Inversion Fair Value Gap Model indicator includes 15 main features:
Sessions
Key Levels & Swing Levels
Liquidity Levels
Liquidity Sweeps
V Shape Recoveries
Higher-Timeframe Fair Value Gaps
Inversion Fair Value Gaps
Macros
Bias
Signals
New Day Opening Gap
New Week Opening Gap
SMT Divergences
Dashboard
Alerts
SESSIONS:
The Inversion Fair Value Gap Model indicator includes five trading sessions (times in EST):
Asia: 20:00 - 00:00
London: 02:00 - 05:00
NY AM: 09:30 - 12:15
NY Lunch: 12:15 - 13:30
NY PM: 13:30 - 16:00
Session highs and lows are automatically tracked and used within the indicator’s signal logic.
🔹Session Zones:
Each session has a zone that outlines its active time window. These zones can be toggled on or off independently. When active, they visually separate each part of the trading day. Users can adjust the color and opacity of each session box. Users can also enable session labels, which place a label above each session zone showing its corresponding session name.
🔹Session Time:
Users can toggle on ‘Time’ which will display each session’s time window next to its session title.
🔹Session Highs/Lows:
Every session can display its own high and low as horizontal lines. Users can customize the line style for session highs/lows, choosing between solid, dashed, or dotted. The color of the lines will match the same color used for the session box. Users can adjust the color of the labels as well, which is applied to all session high/low labels.
When price has moved above a session high, or below a session low, the label will not be displayed anymore.
🔹Extend Levels:
When enabled, each session’s high and low levels can be extended forward by a set number of bars.
Please Note: Disabling a session under the main Sessions section only hides its visuals (boxes, lines, or labels). It does not impact signal detection or logic.
KEY LEVELS:
The Inversion Fair Value Gap Model indicator includes 11 key market levels that outline important structural price areas across daily, weekly, and monthly timeframes. These levels include the Daily Open, Previous Day High/Low, Weekly Open, Previous Week High/Low, Monthly Open, Previous Month High/Low, Midnight Open, and 08:30 Open. The levels can be enabled or disabled and customized in color and line style. All of the levels except the Midnight Open and 08:30 Open are used for the indicator’s signal logic.
🔹Daily Open
The Daily Open marks where the current trading day began.
🔹Previous Day High/Low
The Previous Day High (PDH) marks the highest price reached during the previous regular trading session. It shows where buyers pushed price to its highest point before the market closed.
The Previous Day Low (PDL) marks the lowest price reached during the previous regular trading session. It shows where selling pressure reached its lowest point before buyers stepped in.
When price pushes above the PDH or below the PDL, the level is removed from the chart.
🔹Weekly Open
The Weekly Open marks the first price of the current trading week.
🔹Previous Week High/Low
The Previous Week High (PWH) marks the highest price reached during the previous trading week. It shows where buying pressure reached its peak before the weekly close.
The Previous Week Low (PWL) marks the lowest price reached during the previous trading week. It shows where sellers pushed price to its lowest point before buyers regained control.
When price pushes above the PWH or below the PWL, the level is removed from the chart.
🔹Monthly Open
The Monthly Open marks the opening price of the current month.
🔹Previous Month High/Low
The Previous Month High (PMH) marks the highest price reached during the previous calendar month. It represents the point at which buyers achieved the strongest push before the monthly close.
The Previous Month Low (PML) marks the lowest price reached during the previous calendar month. It shows where selling pressure was strongest before buyers stepped back in.
When price pushes above the PMH or below the PML, the level is removed from the chart.
🔹Midnight Open
The Midnight Open marks the first price of the trading day at 00:00 EST.
🔹08:30 Open
The 08:30 Open marks the opening price at 08:30 EST.
🔹Customization Options:
Users can fully customize the appearance of all key levels, including the following:
Labels
Label Size
Line Style
Line Colors
Labels:
Users can toggle on ‘Show Labels’ to display labels for each toggled-on level that price hasn’t pushed above/below. Users can also adjust the size of labels, choosing between auto, tiny, small, normal, large, or huge.
Line Style:
Users can select a line style, choosing between solid, dashed, or dotted, which is applied to all toggled-on key levels.
Line Color:
Users can choose different colors for each of the following key levels:
Daily Open, Previous Day High, Previous Day Low
Weekly Open, Previous Week High, Previous Week Low,
Monthly Open, Previous Month High, Previous Month Low
Midnight Open
08:30 Open
🔹Extend Levels:
When enabled, each key level is extended forward by a set number of bars.
Please Note: Disabling a level in the “Key Levels” section only hides its visuals and does not affect the indicator’s signals.
🔹Swing Levels
The indicator automatically plots Swing Highs and Swing Lows which are used in the indicator’s signal generation logic.
A swing high forms when a candle’s high is greater than the highs of the bars immediately before and after it.
A swing low forms when a candle’s low is lower than the lows of the bars immediately before and after it.
🔹Swing Level Colors
Users can customize the color of Active Levels and Swept Levels.
Active Levels are levels that price has not pushed above or below
Swept Levels are levels that price pushed above or below.
🔹Swing Levels – Show Nearest
This setting determines how many swing highs/lows are displayed on the chart. The indicator will display the nearest X highs to price and the nearest X lows to price.
For example, if ‘Show Nearest’ is set to 2, the nearest 2 swing highs and nearest 2 swing lows to price will be plotted on the chart.
LIQUIDITY LEVELS:
The Inversion Fair Value Gap Model indicator automatically identifies and plots liquidity at key structural points in the market. These include swing highs and swing lows, session highs and lows, and major higher timeframe reference points as explained in the SESSIONS and KEY LEVELS sections above. All of these areas are treated as potential pools of resting orders and are used throughout the indicator’s signal logic.
🔹What is Buyside Liquidity?:
Buyside Liquidity (BSL) represents price levels where many buy stop orders are sitting, usually from traders holding short positions. When price moves into these areas, those stop-loss orders get triggered and short sellers are forced to buy back their positions. These zones often form above key highs such as the previous day, week, or month. Understanding BSL is important because when price reaches these levels, the sudden wave of buy orders can create sharp reactions or reversals as liquidity is taken from the market.
🔹What is Sellside Liquidity?:
Sellside Liquidity (SSL) represents price levels where many sell stop orders are waiting, usually from traders holding long positions. When price drops into these areas, those stop-loss orders are triggered and long traders are forced to sell their positions. These zones often form below key lows such as the previous day, week, or month. Understanding SSL is important because when price reaches these levels, the surge of sell orders can cause sharp reactions or reversals as liquidity is taken from the market.
🔹 Which Liquidity Levels Are Used
The indicator tracks liquidity at the following areas:
Asia Session High/Low
London High/Low
NY AM High/Low
NY Lunch High/Low
NY PM High/Low
Previous Day High and Low
Previous Week High and Low
Previous Month High and Low
Daily Open
Weekly Open
Monthly Open
Swing Highs/Lows
🔹 How Liquidity Levels Are Used
All tracked levels across sessions, swing points, and higher timeframes serve as potential liquidity targets. When price trades above one of these highs, the indicator looks for short setups if other confluences align. When price trades below lows, the indicator looks for long setups if other confluences align.
LIQUIDITY SWEEPS:
The indicator automatically detects Buyside Liquidity and Sellside Liquidity sweeps using the liquidity levels mentioned in the previous section.
🔹What is a Liquidity Sweep?
Liquidity sweeps occur when price trades beyond a key high or low and activates resting buy-stop or sell-stop orders in that area. It’s how the market gathers the liquidity needed for larger participants to enter positions.
Traders often place stop-loss orders around obvious highs and lows, such as the previous day’s, week’s, or month’s levels. When price pushes through one of these areas, it triggers the stops placed there and generates a burst of volume. This can lead to quick movements in price as those orders are executed.
🔹Sellside Liquidity Sweep
These occur when price dips below a Sellside Liquidity (SSL) level, taking out the stop-loss orders placed by long traders below that low. When this happens, the indicator records the sweep and begins monitoring for potential long setups as the next step in the IFVG trading strategy. Long trades are only eligible after a SSL sweep.
🔹Buyside Liquidity Sweep
These occur when price dips above a Buyside Liquidity (BSL) level, taking out the stop-loss orders placed by short seller traders above that high. When this happens, the indicator records the sweep and begins monitoring for potential short setups as the next step in the trading strategy. Short trades are only eligible after a BSL sweep.
🔹How to Use Liquidity Sweeps
Liquidity sweeps are not direct trade signals. They are best used as context when forming a directional bias. A sweep shows that the market has removed liquidity from one side, which can hint at where the next move may develop.
For example:
When BSL is swept, it often signals that buy stops have been triggered and the market may be preparing to move lower. Traders may then begin looking for short opportunities.
When SSL is swept, it often signals that sell stops have been triggered and the market may be preparing to move higher. Traders may then begin looking for long opportunities.
V SHAPE RECOVERIES:
🔹 What Is a V Shape Recovery?
A V shape recovery is a sharp, immediate reversal that happens right after price sweeps BSL or SSL. It indicates that price quickly moved back in the opposite direction after trading through the level. This behavior signals a shift in momentum and is a required confirmation in the indicator for signal generation. The indicator will not look for long trades after a SSL sweep unless a V shape recovery occurs. It will not look for short trades after a BSL sweep unless a V shape recovery occurs. Without this behavior, the indicator assumes that price may still be delivering in the direction of the sweep, so no valid setups can form.
🔹 Why V Shape Recoveries Matter
V shape recoveries help confirm that the liquidity the sweep did not immediately continue in the same direction. They separate false breaks from true continuation. A sweep without recovery often means price may keep trending, so the indicator does not generate signals in those cases. A sweep with a V shape recovery confirms rejection and sets the foundation for valid Inversion Fair Value Gap formation. This makes the V shape recovery one of the most important sequence steps in the Inversion Fair Value Gap Model.
🔹 How the Indicator Detects V Shape Recoveries
V shape recoveries can be visually intuitive when looking at a chart, but they are difficult to define consistently programmatically. To ensure reliable and repeatable detection, the indicator uses a rules-based method that evaluates candle size, candle direction, and the strength of the move immediately following the liquidity sweep. This approach removes subjectivity and allows the indicator to confirm V shape behavior the same way every time.
The indicator does not plot any visual elements specifically for V shape recoveries. Instead, the presence of a V shape recovery is implied through the signals themselves. Every valid long or short signal that appears after a liquidity sweep requires a confirmed V shape recovery. This means that if a signal is generated following a sweep, a V shape recovery has occurred.
🔹 V Shape Recovery After a Sellside Sweep (SSL Sweep)
After price trades below a sellside liquidity level, long positions are liquidated. If buyers quickly step in and force price upward with strong momentum, this forms a V shape recovery. This signals that the sweep below the low was rejected and that buyers have reclaimed control. When this occurs, the indicator begins monitoring for long setups.
🔹 V Shape Recovery After a Buyside Sweep (BSL Sweep)
After price pushes above a buyside liquidity level, many short positions are stopped out. If sellers immediately step in and drive price back down with strong movement, this forms a V shape recovery. This behavior reflects a quick change in candle direction immediately following the sweep. When this occurs, the indicator begins monitoring for short setups.
🔹Failed V Shape Recoveries
These examples show failed V shape recoveries, where price did not reverse decisively after the BSL or SSL sweep. The lack of strong response from buyers or sellers indicates that momentum did not shift. Thus, the indicator will not detect valid long/short setups using these liquidity sweeps.
HIGHER-TIMEFRAME FAIR VALUE GAPS:
Higher-timeframe Fair Value Gaps (HTF FVGs) provide important context in the Inversion Fair Value Gap Model because they show where significant imbalance occurred on larger market structures. The indicator automatically detects HTF FVGs and uses them as part of the signal rating system.
🔹 What Is a Fair Value Gap?
A Fair Value Gap (FVG) is an area where the market’s perception of fair value suddenly changes. On your chart, it appears as a three-candle pattern: a large candle in the middle, with smaller candles on each side that don’t fully overlap it.
A bullish FVG forms when a bullish candle is between two smaller bullish/bearish candles, where the first and third candles’ wicks don’t overlap each other at all.
A bearish FVG forms when a bearish candle is between two smaller bullish/bearish candles, where the first and third candles’ wicks don’t overlap each other at all.
This creates an imbalance because price moved so quickly that one side of the auction did not trade.
Examples:
🔹 What Makes an FVG “Higher-Timeframe”?
In this indicator, HTF FVGs are Fair Value Gaps detected on timeframes higher than the chart’s current timeframe. For example, on a 5-minute chart, a 1-hour FVG would be considered a HTF FVG. The indicator automatically plots and checks whether price interacts with these HTF FVGs during a liquidity sweep and incorporates this into the signal rating (A, A+, A++).
🔹 How the Indicator Uses Higher-Timeframe FVGs
The indicator automatically scans up to three user-selected higher timeframes for valid bullish and bearish FVGs and tracks price’s behavior around them in the background. When any of these higher timeframes are enabled, their FVGs are used directly within the signal logic.
During a liquidity sweep, the indicator checks whether price taps into any enabled HTF FVG. A tap occurs when price trades inside the boundaries of a higher-timeframe FVG during or immediately after the sweep.
A bullish HTF FVG tap during a sellside sweep supports a long setup.
A bearish HTF FVG tap during a buyside sweep supports a short setup.
When an HTF FVG tap aligns with the direction of the setup, the signal’s rating is increased. This can increase a setup’s rating from A to A+ or from A+ to A++.
🔹 Higher-Timeframe FVG Customization
Users can select up to three higher timeframes for HTF FVG detection. When a higher timeframe is enabled, its FVGs are used in the model’s signal logic. Users can also choose whether to display these HTF FVGs visually on the chart, by enabling the ‘Plot HTF FVGs’ setting.
Each enabled HTF FVG can be customized with the following options:
Bullish and Bearish Colors: Users can set different fill colors for bullish and bearish HTF FVGs for each selected timeframe.
Midline: When enabled, a midline is drawn through the center of each HTF FVG. Users can customize the midline’s line style, choosing between solid, dashed, or dotted and also customize the midline’s color.
Labels: When enabled, each plotted HTF FVG displays a label that shows its originating timeframe (for example, 1H, 4H).
Plot HTF FVGs: When disabled, the HTF FVG zones are hidden from the chart while the logic remains active in the background for signals.
Show Nearest:
This setting controls how many HTF FVGs are displayed based on proximity to current price. Users can choose to show the nearest X bullish HTF FVGs and the nearest X bearish HTF FVGs. This filter is applied across all enabled higher timeframes and does not limit by timeframe individually.
🔹When are Higher Timeframe Fair Value Gaps mitigated?
A Higher Timeframe Fair Value Gap is considered mitigated when a candle from the chart’s timeframe closes above the gap for a bearish FVG or below the gap for a bullish FVG.
INVERSION FAIR VALUE GAPS:
Inversion Fair Value Gaps (IFVGs) are a core requirement of the Inversion Fair Value Gap Model. Every long and short signal generated by the indicator requires a valid IFVG, just like liquidity sweeps and V shape recoveries. Without a confirmed IFVG, the model will not produce a setup.
🔹 What Is an Inversion Fair Value Gap?
An Inversion Fair Value Gap is a Fair Value Gap that becomes invalidated by a candle close in the opposite direction. This “flip” confirms that the original imbalance failed and that the market has shifted.
A bullish IFVG forms when a bearish FVG is invalidated by a candle closing above it.
A bearish IFVG forms when a bullish FVG is invalidated by a candle closing below it.
In the indicator, IFVGs are not used as retracement areas. Signals are generated immediately when a valid IFVG forms, not after price returns to the gap. The IFVG itself is the confirmation event that finalizes a setup sequence after a liquidity sweep and V shape recovery.
🔹 How the Indicator Plots IFVGs
The indicator only plots IFVGs that are used in long or short setups. Not every possible IFVG is shown on the chart. Only the IFVG involved in a confirmed signal is displayed. Users can disable IFVG plots entirely if they prefer a minimal view. This hides the visual gaps but does not affect the signal logic.
🔹 Customization Options
Users can customize how IFVGs appear on the chart:
Color Settings: Choose separate fill colors for bullish IFVGs and bearish IFVGs.
Midline: Toggle an optional midline inside the IFVG and choose between a solid, dashed, or dotted line.
Midline Color: Adjust the color of the IFVG Midline.
MACROS:
Macros are short, predefined time windows, where price is more likely to seek liquidity or rebalance imbalances. These periods often create sharp movements or shifts in delivery, giving additional context to setups. In the Inversion Fair Value Gap Model, macros are used as a confluence factor. When a long or short signal forms during a macro time window, the setup’s rating can increase from A to A+ or from A+ to A++.
Macros are not required for a signal to form, but they increase the signal’s rating when the setup aligns with macro timing.
🔹 How the Indicator Uses Macros
The indicator allows users to enable up to five macros. Each macro has its own start and end time, which the user can customize. These time windows are used directly in the signal logic. If a valid IFVG setup forms while price is inside any of the enabled macro windows, the indicator increases the signal’s rating.
Users may visually disable macros on the chart without affecting signal logic. Disabling visuals hides the macro zones, labels, and lines, but the underlying macro logic continues to function in the background for signals.
The indicator’s default macros use the following time periods (in EST):
09:50 - 10:10
10:50 - 11:10
11:50 - 12:10
12:50 - 13:10
13:50 - 14:10
🔹 Macro Settings
Each macro displays a shaded zone representing the active time window. This zone can be toggled on or off. Users can customize:
The color of each macro zone
The opacity of each zone
Whether the zones display at all (‘Show Zones’)
These visuals help identify whether price is currently inside a macro window.
🔹 Macro Labels:
Users can enable macro labels, which place a text label showing the macro’s title and its time window. The label color is global (applies to all macros), and the label size can be adjusted. Individual macros cannot have unique label colors.
🔹 Macro Start/End Lines
For additional clarity, the indicator draws two vertical markers for each macro:
One at the start of the macro
One at the end of the macro
A horizontal macro line is then drawn between the highs of these two candles to highlight the full duration of the macro window. Users can customize:
The line styles (solid, dashed, dotted) of the Macro Line and Start/End Lines
BIAS:
Bias determines which direction the indicator is allowed to generate signals. A bullish bias means only long setups can be confirmed. A bearish bias means only short setups can be confirmed. The bias acts as the final directional filter after a liquidity sweep, V shape recovery, and IFVG have all been validated. Even if all model conditions are met, the indicator will only confirm the setup if the direction aligns with the active bias.
Users are able to manually set a bias or use an automatic bias filter, which is explained below.
🔹 Manual Bias
Users can manually choose the directional bias at any time and choose between Bullish, Bearish, or Both.
When set to Bullish, the indicator will only confirm long setups, regardless of market structure.
When set to Bearish, only short setups are allowed.
When set to Both, the indicator can confirm both long and short setups if all requirements are met.
🔹 Automatic Bias
Automatic bias is fully rules-based and determined by how the previous session interacted with major draw-on-liquidity (DOL) levels. These levels include 1-hour highs and lows, 4-hour highs and lows, previous session highs and lows (such as Asia or London), and the previous day’s high and low. The indicator evaluates whether the previous session consolidated, manipulated liquidity, or manipulated and reversed before closing. Based on this behavior, the indicator establishes a directional bias for the current session.
◇ Previous Session Consolidation:
If the previous session did not sweep any major liquidity levels and price remained inside its range, the session is classified as consolidation.
After the current session sweeps a key low, the bias becomes bullish.
After the current session sweeps a key high, the bias becomes bearish.
The bias is determined live based on which side the current session manipulates first.
◇ Previous Session Manipulation (No Reversal):
If the previous session swept a major high-timeframe level but did not reverse before the session closed, the model assigns a reversal-based bias at the start of the current session.
If the previous session swept a low, the current session bias is bullish.
If the previous session swept a high, the current session bias is bearish.
Here, bias is determined immediately because the previous session’s manipulation defines the directional framework for the current session.
◇ Previous Session Manipulation + Reversal:
If the previous session swept a DOL level and also reversed away from it within the same session, the model assigns a continuation-based bias at the start of the current session.
If the previous session swept a low and reversed upward, the bias for the current session is bullish.
If the previous session swept a high and reversed downward, the bias is bearish.
🔹 How the Indicator Uses Bias in Practice
After the indicator validates the liquidity sweep, V shape recovery, and IFVG, it checks the active bias before confirming a signal.
If bias is bullish, only long setups are allowed.
If bias is bearish, only short setups are allowed.
If bias is Both, setups of either direction may form.
The bias does not influence the detection of liquidity sweeps, V shape recoveries, or IFVGs. It only determines whether those validated components are allowed to produce a final signal. Automatic bias updates based on session behavior, while manual bias remains fixed until the user changes it.
SIGNALS:
Signals are the final output of the Inversion Fair Value Gap Model indicator. A signal is only generated when all model conditions are satisfied in a clear, rules-based sequence.
A signal consists of:
An Entry
A Stop-Loss (SL)
A Breakeven (BE) level
Two Take-Profit levels (TP1 and TP2)
These components are plotted immediately once the final requirement (the IFVG confirmation) is met and the directional filter (bias) allows the setup.
Signals can be rated A, A+, or A++, based on whether certain confluences were present during the setup’s formation.
🔹 What All Signals Have in Common
Each signal type (A, A+, A++) requires the same four mandatory conditions. If any of these four are missing, the indicator will not print a signal.
◇ Required Component #1 – Valid Directional Bias
The bias determines whether the indicator can confirm a long or short setup.
Bullish bias → only long setups allowed
Bearish bias → only short setups allowed
Both → long or short setups allowed
Automatic bias → bias determined by session-based liquidity logic explained above
◇ Required Component #2 – Liquidity Sweep
The indicator must detect one of the following:
Sellside Liquidity Sweep (SSL Sweep) for potential long setups
Buyside Liquidity Sweep (BSL Sweep) for potential short setups
◇ Required Component #3 – V Shape Recovery
After a liquidity sweep, the indicator evaluates whether price produced a valid V shape recovery.
◇ Required Component #4 – Inversion Fair Value Gap (IFVG)
An IFVG must form in the direction of the potential setup.
A bullish IFVG forms when a bearish FVG is invalidated by a candle closing above that gap
A bearish IFVG forms when a bullish FVG is invalidated by a candle closing below that gap
The IFVG must occur after the V Shape Recovery and Liquidity Sweep. The IFVG confirmation is the final structural requirement. Once it forms, the setup is considered structurally complete.
🔹 A Signals
An A-rated signal contains exactly the four required components:
Valid Bias
Liquidity Sweep
V Shape Recovery
IFVG
An A signals represent the foundational implementation of the IFVG Model.
🔹 A+ Signals
An A+ signal includes the full A-signal structure plus ONE of the following:
Higher-Timeframe FVG Tap
Multi-Liquidity Sweep
Inside a Macro Window
◇ Higher-Timeframe FVG Tap
During a liquidity sweep, the indicator checks whether price taps into any enabled HTF FVG. A tap occurs when price trades inside the boundaries of a higher-timeframe FVG during or immediately after the sweep.
A bullish HTF FVG tap during a sellside sweep supports a long setup.
A bearish HTF FVG tap during a buyside sweep supports a short setup.
◇ Multi-Liquidity Sweep
A Multi-Liquidity Sweep occurs when price sweeps two liquidity levels of the same type in the same directional push.
Sweeping two lows in one move: Multi-Sellside Liquidity Sweep (long setups).
Sweeping two highs in one move → Multi-Buyside Liquidity Sweep (short setups).
◇ Inside a Macro Window
The final IFVG confirmation must occur inside a macro time window defined by the user.
If exactly one of these additional confluences is present, the signal rating is A+.
🔹 A++ Signals (Two Additional Confluences)
An A++ signal contains the full A signal structure plus TWO of the three confluences listed above.
HTF FVG tap + Multi-Liquidity Sweep
HTF FVG tap + Inside a Macro Window
Multi-Liquidity Sweep + Inside a Macro Window
If two confluences are present, the rating becomes A++. If all three are present, the setup is still rated a A++ (there is no A+++).
🔹 Signal Plots
When a valid long/short setup is detected, a signal with its rating appears with the following:
Entry: At the close of the candle that inverted a FVG
Stop-Loss: At the nearest swing high for short setups or nearest swing low for long setups
Breakeven Level: At the nearest swing high for long setups or the nearest swing low for short setups
Take-Profit 1: At the second nearest swing high for long setups or the second nearest swing low for short setups.
Take-Profit 2: At the third nearest swing high for long setups or the third nearest swing low for short setups.
After a signal reaches either TP2 or SL, the levels for Entry, SL, BE, TP1, and TP2 are removed from the chart. If another signal appears before the prior signal reaches either TP2 or SL, the levels are also removed.
Users can hover over any signal label to view a short summary of the exact criteria that were met for that setup. This includes whether a HTF FVG tap occurred, whether a multi-liquidity sweep was detected, whether the setup formed inside a macro window, and which liquidity level was swept prior to the V shape recovery.
🔹 Long Setup – A Rating
A long A-rated setup forms when all four core requirements of the IFVG Model occur without any additional confluences. First, price must sweep a Sellside Liquidity level. Immediately after the sweep, price must form a valid V shape recovery. Once the recovery completes, a bullish IFVG must form by invalidating a bearish Fair Value Gap with a candle close above it.
For a confirmed long signal, the indicator marks:
Entry: At the candle close that invalidates the bearish FVG and creates the IFVG
Stop Loss: At the nearest swing low
Breakeven: Midpoint between entry and stop-loss
Take Profit 1: At the second nearest swing high
Take Profit 2: At the third nearest swing high
In this example, price sweeps a swing low, has a V Shape recovery, and forms a bullish IFVG:
🔹 Short Setup – A Rating
A short A-rated setup forms when all four core requirements of the IFVG Model occur without any additional confluences. Price must first sweep a Buyside Liquidity level. Immediately after the sweep, price must form a valid V shape recovery. Once the recovery completes, a bearish IFVG must form by invalidating a bullish Fair Value Gap with a candle close below it.
For a confirmed short signal, the indicator marks:
Entry: At the candle close that invalidates the bullish FVG and creates the IFVG
Stop Loss: At the nearest swing high
Breakeven: Midpoint between entry and stop-loss
Take Profit 1: At the second nearest swing low
Take Profit 2: At the third nearest swing low
In this example, price sweeps a swing high, has a V shape recovery, and forms a bearish IFVG:
🔹 Long Setup – A+ Rating
A long A+ setup forms when the four core requirements of the IFVG Model occur and exactly one additional confluence is present. Price must sweep a Sellside Liquidity level, form a valid V shape recovery, and create a bullish IFVG by invalidating a bearish FVG. One of the following must also occur: a bullish HTF FVG tap during the liquidity sweep, a multi-sellside liquidity sweep, or the IFVG confirmation forms inside a macro window.
For a confirmed long A+ signal, the indicator marks:
Entry: At the candle close that creates the bullish IFVG
Stop Loss: At the nearest swing low
Breakeven: Midpoint between entry and stop-loss
Take Profit 1: At the second nearest swing high
Take Profit 2: At the third nearest swing high
In this example, price sweeps the NY AM Session Low, taps a 30-minute HTF FVG during the sweep, has a V shape recovery, and forms a bullish IFVG:
🔹 Short Setup – A+ Rating
A short A+ setup forms when the four core requirements of the IFVG Model occur and exactly one additional confluence is present. Price must sweep a Buyside Liquidity level, form a valid V shape recovery, and create a bearish IFVG by invalidating a bullish FVG. One of the following must also occur: a bearish HTF FVG tap, a multi-buyside liquidity sweep, or the IFVG confirmation forms inside a macro window.
For a confirmed short A+ signal, the indicator marks:
Entry: At the candle close that creates the bearish IFVG
Stop Loss: At the nearest swing high
Breakeven: Midpoint between entry and stop-loss
Take Profit 1: At the second nearest swing low
Take Profit 2: At the third nearest swing low
In this example, price sweeps a swing high, has a V shape recovery, and forms a bearish IFVG inside of the 13:50-14:10 macro:
🔹 Long Setup – A++ Rating
A long A++ setup forms when the four core requirements of the IFVG Model occur and at least two additional confluences are present. Price must sweep a Sellside Liquidity level, form a valid V shape recovery, and create a bullish IFVG. The setup must also include any two or three of the following: a bullish HTF FVG tap, a multi-sellside liquidity sweep, or the IFVG confirmation forming inside a macro window.
For a confirmed long A++ signal, the indicator marks:
Entry: At the candle close that creates the bullish IFVG
Stop Loss: At the nearest swing low
Breakeven: Midpoint between entry and stop-loss
Take Profit 1: At the second nearest swing high
Take Profit 2: At the third nearest swing high
In this example, price sweeps two swing lows, has a V shape recovery, taps a bullish 30-minute HTF FVG during the liquidity sweep, and forms a bullish IFVG inside of the 10:50-11:10 macro:
🔹 Short Setup – A++ Rating
A short A++ setup forms when the four core requirements of the IFVG Model occur and at least two additional confluences are present. Price must sweep a Buyside Liquidity level, form a valid V shape recovery, and create a bearish IFVG. The setup must also include any two or three of the following: a bearish HTF FVG tap, a multi-buyside liquidity sweep, or the IFVG confirmation forming inside a macro window.
For a confirmed short A++ signal, the indicator marks:
Entry: At the candle close that creates the bearish IFVG
Stop Loss: At the nearest swing high
Breakeven: Midpoint between entry and stop-loss
Take Profit 1: At the second nearest swing low
Take Profit 2: At the third nearest swing low
In this example, price sweeps a swing high, has a V shape recovery, taps a bearish 30-minute HTF FVG during the liquidity sweep, and forms a bearish IFVG inside of the 09:50-10:10 macro:
🔹Signal Settings
◇ Liquidity Levels Used:
Users can select which type of liquidity levels the indicator uses for identifying liquidity sweeps:
Swing Points: Only uses Swing Highs/Lows
Session Highs/Lows: Only uses Session Highs/Lows
Both: Uses both Swing Highs/Lows and Session Highs/Lows
◇ Bias:
This setting determines which signal directions are allowed.
Manual Bias: Users can manually choose the directional bias, picking between Bullish, Bearish, or Both.
Automatic Bias: The indicator automatically determines a directional bias based on the criteria mentioned in the previous Bias section.
◇ IFVG Sensitivity:
This setting determines the minimum gap size required for an FVG to qualify as an Inversion FVG.
Higher values: only larger FVGs become IFVGs
Lower values: smaller gaps are allowed
◇ Use First Presented IFVG:
This setting determines whether the indicator limits signals to only the first IFVG created within the manipulation leg.
What Is the First Presented IFVG?
It is the earliest FVG formed inside the displacement that causes the liquidity sweep.
For a bearish manipulation leg (price moving downward into the sweep), the first presented IFVG is the first FVG created at the start of that downward move:
For a bullish manipulation leg (price moving upward into the sweep), the first presented IFVG is the first FVG created at the start of that upward move:
When this setting is enabled, the indicator will only confirm signals when the IFVG used is derived from this first presented FVG. IFVGs that form later in the manipulation leg are not used for signal generation.
◇ Only Take Trades:
This setting allows users to restrict signals to a defined time window.
If a complete setup occurs inside the time window, it is allowed and plotted
If it occurs outside the window, the signal will not appear
For example, if you only wanted to see long/short signals between 9:30 AM and 12:00 PM, you would enable this setting and set the time window from 09:30 - 12:00.
◇ Minimum R:R
This setting allows users to require a minimum risk-to-reward ratio before a signal is confirmed and plotted on the chart. The risk-to-reward ratio is calculated using the distance from the Entry to the Stop-Loss (risk) and the distance from the Entry to TP2 (reward). The indicator compares these distances and determines whether the setup meets or exceeds the minimum R:R value selected by the user.
If the calculated R:R is equal to or greater than the chosen threshold, the signal will be displayed.
If the calculated R:R is lower than the threshold, the signal will not appear on the chart.
🔹 Signal Rating Minimum
Users can restrict which signal ratings appear:
A: shows all signals
A+: shows only A+ and A++
A++: shows only A++ setups
🔹 Signal Styling and Customization
The indicator provides full control over how signal labels and levels appear on your chart. Users can customize long signals, short signals, all plotted lines, and the visibility of every individual element.
◇ Long Signal Styling
Users can customize:
Long Signal Label Color
Long Signal Text Color
Long Signal Label Size
◇ Short Signal Styling
Users can customize:
Short Signal Label Color
Short Signal Text Color
Short Signal Label Size
◇ Entry, Stop Loss, Breakeven, and Take Profit Lines
Each line type can be enabled or disabled individually:
Entry Line
Stop Loss Line
Breakeven Line
Take Profit 1 & 2 Lines
Users can also set custom colors for each line so every level is easy to track during live price movement.
◇ Show Price Labels
Price labels can be toggled on or off individually for each level. Users can choose whether to show or hide the price for:
Entry
Stop loss
Breakeven
Take Profit 1 & 2
NEW DAY OPENING GAP:
The New Day Opening Gap (NDOG) highlights the price difference between the previous day’s closing candle and the first candle of the new trading day. The indicator tracks this gap automatically each day and makes it available as optional context for users.
🔹 What Is the New Day Opening Gap?
A New Day Opening Gap forms when the trading day opens at a price different from the previous day’s final closing price.
If the new day opens above the prior day’s close → Bullish NDOG
If the new day opens below the prior day’s close → Bearish NDOG
This gap acts as a short-term draw on liquidity because the market may revisit the gap to rebalance price delivery. While the NDOG is not a required component for IFVG signals.
🔹 How the Indicator Uses the New Day Opening Gap
When enabled, the indicator plots the gap as a rectangular zone spanning from the previous day’s close to the new day’s open. The zone remains active until it is fully filled by price or until the next day’s opening gap forms. Once price trades through the entire gap, or once a new NDOG replaces it the following day, the zone becomes inactive and is removed from the chart. The indicator does not use the NDOG for signal generation. It is strictly a visual tool that helps traders identify areas where price may retrace or seek liquidity during the session.
🔹 Customization Options
Users have full control over how the New Day Opening Gap displays on the chart:
Show New Day Opening Gap: Toggle the NDOG zone on or off
Bullish NDOG Color: Customize the fill color for gaps formed above the prior close
Bearish NDOG Color: Customize the fill color for gaps formed below the prior close
NEW WEEK OPENING GAP:
The New Week Opening Gap (NWOG) highlights the price difference between the previous week’s final closing candle and the first candle of the new trading week. The indicator tracks this gap automatically each week and provides it as optional context for users.
🔹 What Is the New Week Opening Gap?
A New Week Opening Gap forms when the new trading week opens at a price different from the previous week’s closing price.
If the new week opens above the prior week’s close → Bullish NWOG
If the new week opens below the prior week’s close → Bearish NWOG
This gap often serves as a medium-term draw on liquidity because price may return to rebalance the weekly displacement. The NWOG is not a required component for IFVG signals.
🔹 How the Indicator Uses the New Week Opening Gap
When enabled, the indicator plots the gap as a rectangular zone spanning from the previous week’s close to the new week’s open. The zone remains active until it is fully filled by price or until the next week’s opening gap forms. Once price trades through the entire gap, or once a new NWOG replaces it the following week, the zone becomes inactive and is removed from the chart. The indicator does not use the NWOG for signal generation. It is purely a visual reference to help traders identify areas where price may rebalance or seek liquidity during the week.
🔹 Customization Options
Users have full control over how the New Week Opening Gap displays on the chart:
Show New Week Opening Gap: Toggle the NWOG zone on or off
Bullish NWOG Color: Set the fill color for gaps formed above the prior weekly close
Bearish NWOG Color: Set the fill color for gaps formed below the prior weekly close
SMT DIVERGENCES:
The indicator automatically marks SMT Divergences that occur between the current selected chart ticker and a second user-selected ticker.
A SMT Divergence forms when the prices of the currently selected chart ticker and the user-selected ticker don’t follow each other. For example, if the current chart’s ticker symbol is SEED_ALEXDRAYM_SHORTINTEREST2:NQ and the user-selected ticker is $ES. If SEED_ALEXDRAYM_SHORTINTEREST2:NQ does not sweep the low of the NY AM Session, but NYSE:ES sweeps that same exact session’s low during the same candle, then a SMT Divergence is detected.
In the images below, SEED_ALEXDRAYM_SHORTINTEREST2:NQ and NYSE:ES form a low at 12:20 AM on November 12th. At 12:35 AM, the 12:20 AM low is taken out on $NQ. However, on NYSE:ES , price failed to take out this exact low at 12:35 AM. Thus, an SMT Divergence is detected, and a line is drawn between the two lows on $NQ.
NYSE:ES Chart:
SEED_ALEXDRAYM_SHORTINTEREST2:NQ Chart:
🔹 SMT Divergence Settings
The indicator includes settings that allow users to control how SMT Divergences are detected and displayed.
◇ Length
Length controls how sensitive the pivot detection is when finding highs and lows for SMT.
Lower Length: confirms swings with fewer bars, so more swings qualify.
Higher Length: requires more bars to confirm a swing, so fewer swings qualify.
◇ Divergence Length
The Divergence Length setting defines how many bars apart the two swing points may be for them to count as part of the same SMT Divergence.
Higher Values: The two instruments can form their swing highs or lows farther apart in time. As long as both swings occur within this wider bar window, the indicator compares them for divergence.
Lower Values: The two swing points must occur very close to each other.
◇ Show Last
This setting limits how many recent SMT Divergences are displayed on the chart. For example, setting Show Last to 1 will only show the most recent SMT Divergence, while higher values allow more historical SMT Divergences to remain visible on the chart.
◇ Divergence Ticker
Users can change the ticker used for detections. Since SMT Divergences occur by comparing two tickers, the inputted ticker within the settings will always be compared to the current selected ticker on your chart.
DASHBOARD:
The dashboard provides a live summary of all major components of the Inversion Fair Value Gap Model. It updates every candle and displays the current state of each requirement used in the setup logic.
🔹 Real-Time Model Components
The state of each component is displayed with the following:
✔️ = condition is satisfied
❌ = condition is not satisfied
🐂 / 🐻 = current directional bias (bullish or bearish)
The dashboard actively tracks the following:
◇ Bias (🐂 Bullish, 🐻 Bearish, or Both)
Shows the current bias with a bull or bear emoji. If using automatic bias, the dashboard updates as soon as the session logic determines a direction.
◇ Liquidity Sweep
Displays ✔️ once a valid BSL Sweep (for shorts) or SSL Sweep (for longs) is detected.
Shows ❌ when no sweep is present.
◇ V Shape Recovery
Displays ✔️ when a confirmed V shape recovery forms after the sweep.
Shows ❌ until a valid V shape appears.
◇ Inversion Fair Value Gap (IFVG)
Shows ✔️ once a bullish or bearish IFVG forms in the correct direction.
Shows ❌ when no IFVG has yet confirmed.
◇ Higher-Timeframe FVG Interaction
Displays ✔️ when price is currently inside any enabled HTF FVG or taps a HTF FVG during a liquidity sweep.
Displays ❌ when price is not inside a HTF imbalance.
◇ Clear Opposite Draw on Liquidity (DOL)
Shows ✔️ when a clear opposite-side draw is present in the model logic.
Shows ❌ if no clear opposite draw is detected.
◇ SMT Divergence
Shows ✔️ for 20 candles immediately after an SMT Divergence forms.
After 20 candles, it returns to ❌ unless a new SMT Divergence is detected.
🔹 Signal Information Display
When a valid long or short signal appears, the dashboard expands to show the full details of the setup, including:
Signal Rating
Entry Price
Stop-Loss Price
Breakeven Price
Take Profit 1 Price
Take Profit 2 Price
🔹 Trade Statistics Module
Users can enable a built-in statistics panel to view historical performance of signals across all ratings. The trade stats include:
A Signal Win Rate
A+ Signal Win Rate
A++ Signal Win Rate
Long Signal Win Rate
Short Signal Win Rate
Total Number of Trades Used in the Calculations
A trade is counted as a win if price reaches breakeven before stop-loss. A trade is counted as a loss if price hits stop-loss before breakeven.
🔹 Dashboard Customization
The dashboard includes several options to control its appearance and position:
Show Dashboard: Toggle the entire dashboard on or off
Dashboard Size: Choose the size of the dashboard
Dashboard Position: Choose the location of the dashboard on the chart
Trade Stats Text Color: Customize the color of the 2nd column outputs under the Trade Stats section in the dashboard
◇ Component Toggles
Users can enable or disable the display of any model component based on preference. Each of these items can be shown or hidden independently:
Setup Rating
Entry
Stop-Loss
Breakeven
Take Profit 1
Take Profit 2
Bias
Liquidity Sweep
Higher-Timeframe FVG Interaction
V Shape Recovery
Inversion FVG
Clear Opposite Draw on Liquidity
Trade Stats
These toggles only affect visual display. Disabling any of them does not affect the underlying indicator’s logic.
ALERTS:
The Inversion Fair Value Gap Model includes full alert functionality using AnyAlert(), allowing users to receive notifications in real time for all major model components and signal events.
Users can enable or disable each alert type in the “Alerts” section of the settings. After selecting which alerts they want active, they can create a single TradingView alert using the AnyAlert() condition. This will automatically trigger alerts for all enabled events as soon as they occur on the chart.
Available Alerts:
Long Signal
Short Signal
Breakeven Hit (BE)
Take Profit 1 Hit (TP1)
Take Profit 2 Hit (TP2)
Stop-Loss Hit (SL)
Liquidity Sweep Detected
SMT Divergence Detected
How to Receive Alerts:
Open the TradingView alert creation window.
Select the IFVG Model indicator as the alert condition.
Choose AnyAlert() from the condition dropdown.
Create the alert.
IMPORTANT NOTES:
TradingView has limitations when running features on multiple timeframes such as the HTF FVGs, which can result in the following restriction:
Computation Error:
The computation of using MTF features is very intensive on TradingView. This can sometimes cause calculation timeouts. When this occurs, simply force the recalculation by modifying one indicator’s settings or by removing the indicator and adding it to your chart again.
UNIQUENESS:
This indicator is unique because it organizes every part of the Inversion Fair Value Gap Model into one structured, rules based system. It detects liquidity sweeps, confirms V shape recoveries, identifies valid IFVGs, checks higher timeframe FVG taps, reads macro timing, and applies a session based directional bias. All of these components are evaluated in a fixed sequence so users always know exactly why a signal appears. Every part of the logic is customizable, including which liquidity types are used, which IFVGs qualify for signals, which time windows allow trades, the minimum risk to reward for a setup, and all visual elements on the chart. The tool also includes optional SMT Divergence detection, daily and weekly opening gaps, a live dashboard that shows the state of each model requirement, and optional signal performance statistics.
Stock Management (Zeiierman)█ Overview
Stock Management (Zeiierman) gives investors a complete, real-time view of their portfolio directly inside TradingView. It tracks performance, allocation, volatility, and dividends in one unified interface, making it easy to understand both how your portfolio is performing and how it behaves in terms of risk and exposure.
Rather than analyzing each chart in isolation, Stock Management (Zeiierman) turns TradingView into a lightweight portfolio cockpit. You can define up to 20 stock positions (ticker, shares, average cost), and the tool will:
Normalize all positions into a single user-selected currency
Calculate live position value, PnL, PnL%, and daily movement
Compute total portfolio value, performance, and volatility
Optionally generate a risk-parity style Recommended Allocation
Display upcoming dividend amounts, ex-dates, and pay-dates for your holdings
All of this appears as clean on-chart tables, including a main portfolio table, an optional dividend table, and an optional summary panel, allowing you to manage your portfolio while still watching price action. It is a visual portfolio layer built entirely around your own inputs, integrated seamlessly into the TradingView environment.
⚪ Why This One Is Unique
Most investors rely on basic broker dashboards that show position values but provide little insight into risk, exposure, or how each holding interacts with the rest of the portfolio. Stock Management (Zeiierman) goes far beyond that by building an intelligent, unified portfolio layer directly inside TradingView.
It automatically normalizes global holdings into a single reporting currency using live FX data, stabilizes allocation with a volatility-aware weighting engine, and structures your information through an adaptive column framework that highlights performance and risk in real time. A weighted summary blends portfolio movement, volatility, and long-horizon behavior into a clean snapshot, while dividend schedules and projected payouts are fully integrated into the same interface.
█ Main Features
⚪ 1. Portfolio Tracker
The core of Stock Management (Zeiierman) is a dynamic, real-time portfolio table that brings all key position data into one intelligent view. Each holding is displayed with:
Ticker
Sector
Price
Average Paid Price
Shares
Position Value
Position Weight
Profit & Loss
Profit & Loss %
Today % Change
Recommended Allocation
The table updates continuously with market prices, giving investors an immediate understanding of performance, exposure, and risk across all positions.
⚪ 2. Dividend Information
Dividend data for your holdings is automatically fetched, organized, and presented alongside your positions. This includes dividend amount, ex-date, and pay-date, along with projected payouts based on your share count. All dividend-related information is integrated directly into the portfolio view, so you can plan cash flow without switching tools.
⚪ 3. Portfolio Summary
A dedicated summary panel consolidates the entire portfolio into a single snapshot: total value, total PnL, YTD %, today’s change, and overall volatility. The volatility reading is particularly valuable, providing a quick gauge of your portfolio’s risk level and how sensitive it may be to market movement.
⚪ 4. Portfolio Weight Recommendation
An intelligent weighting engine reviews your current allocations and highlights where your portfolio is overexposed or underweighted. It offers recommended allocation levels designed to reduce concentration risk and improve balance, giving you a clearer path toward a more stable long-term positioning.
█ How to Use
⚪ Performance Tracking
Quickly assess your entire portfolio’s profit, loss, daily movement, and volatility from one centralized dashboard. The summary panel gives you an instant read on how your holdings are performing and how sensitive they are to market swings.
⚪ Dividend Management
Monitor upcoming dividend amounts, ex-dates, and pay-dates directly inside your portfolio table. This ensures you never miss a payout opportunity and can plan your expected cash flow with complete clarity.
⚪ Risk Management & Optimization
Use portfolio-wide volatility and the intelligent Recommended Allocation engine to identify imbalances in your holdings. These insights help you adjust position sizes, reduce concentration risk, and maintain a more stable long-term portfolio profile.
⚪ Currency Comparison
Switch between different base currencies to evaluate performance in local or international terms. All positions are automatically normalized using live FX data, making global portfolio management effortless.
█ How It Works
Stock Management (Zeiierman) continuously gathers price, currency, dividend, and volatility data for every ticker you track. All values are automatically converted into your selected reporting currency, so global holdings remain comparable in one unified view.
It builds a live portfolio snapshot of each bar, updating position values, PnL, daily returns, YTD performance, and overall volatility. This gives you an always-current understanding of how your portfolio is performing and how each holding contributes to risk and exposure.
An intelligent, volatility-aware allocation model generates recommended portfolio weights and position sizes, helping you identify where you may be overexposed or underweighted. Dividend information is integrated directly into the table, projecting future payouts and highlighting upcoming ex-dates and pay-dates.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. 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.
BORSA 321 - Care PackageOverview
Care Package is a complete higher-timeframe and intraday context tool designed to map out every important environmental factor on your chart: sessions, opening levels, gaps, market structure, order blocks, fair value gaps, volume imbalance and more.
It automatically plots:
Sessions / killzones (Asia, London, New York AM/Lunch/PM)
Key opening levels (00:00, 08:30, 09:30, 13:30)
Previous day AM/PM high–low ranges
New Day and New Week Opening Gaps (NDOG / NWOG)
RTH gap and RTH zone levels
Multi-timeframe Fair Value Gaps (up to 4)
Fractals and Order Blocks (with optional FVG confirmation)
Market structure (HH/HL/LL/LH, CHoCH, BOS)
Volume Imbalance zones with mitigation logic
All session logic runs on IANA time zones (like America/New_York), giving accurate sessions and market opens regardless of DST or broker feed.
Care Package serves as the full “context layer” for intraday execution charts.
What It Shows
1. Sessions / Killzones
The indicator automatically highlights:
Asia Session
London Session
New York AM
New York Lunch
New York PM
Each session displays:
A high–low range box
Labels for session high and session low
A midline showing the mean price
Optional forward extensions of session levels to the current bar
This cleanly outlines intraday phases for ICT/SMC execution.
2. Opening Price Levels
Key market open levels tracked:
00:00
08:30
09:30
13:30
For each open, the script draws:
A horizontal line at the opening price
A label showing time and price
An optional vertical line marking the opening bar
These opens often act as liquidity or reversal areas.
3. Previous Day AM/PM Levels
The script splits the prior day into:
Previous Day AM (first half)
Previous Day PM (second half)
Both provide:
PD AM High, PD AM Low
PD PM High, PD PM Low
Forward-projected levels
Labels for easy identification
Useful for navigating intraday targets and reaction zones.
4. Last N Days High/Low
Tracks a rolling daily range:
Each day’s High and Low
Labels containing the date
Forward extension into today’s price action
This shows where price sits relative to recent daily extremes.
5. New Day & New Week Opening Gaps (NDOG / NWOG)
The script automatically identifies:
NDOG (New Day Open Gap)
NWOG (New Week Open Gap)
Each gap includes:
A shaded zone between the two opens
Labels showing the gap type and date/week
Forward extension (optional)
Limiting the number of historical gaps (optional)
Critical for identifying unfilled imbalance zones across sessions and weeks.
6. RTH Gap & RTH Zone
You define RTH open/close times, and the indicator:
Detects RTH gaps
Draws a full zone based on direction
Plots subdivision lines (top, 75%, mid, 25%, bottom)
Extends the RTH Close reference line forward
Can extend old RTH zones automatically
Ideal for futures traders and equities.
7. Higher-Timeframe Fair Value Gaps (up to 4 TFs)
Supports up to four selectable FVG timeframes such as:
Chart timeframe
5m, 15m, 1H, 4H, 1D, 1W, 1M
Each FVG includes:
Top and bottom boundary
A midline (mean threshold)
Colored bullish or bearish fill
A label showing FVG + timeframe
Automatic cleanup when mitigated (close/wick based)
You get a clean and accurate HTF FVG map without clutter.
8. Fractals & Order Blocks
Fractals:
Standard or 5-bar fractals
Plotted as swing highs and lows
Order Blocks:
Bullish OB → down candle before up displacement
Bearish OB → up candle before down displacement
Optionally require OB to be near an FVG
Wick-based or body-based OB size
Forward-projected OB boxes
Auto-delete after mitigation
This keeps your OBs clean and execution-focused.
9. Market Structure (HH/HL/LL/LH, CHoCH, BOS)
The indicator automatically detects:
HH (Higher High)
HL (Higher Low)
LH (Lower High)
LL (Lower Low)
And also identifies:
CHoCH (Change of Character)
BOS (Break of Structure)
Each break includes:
A horizontal level at the break point
A color-coded label
Bullish (green) or bearish (red) styling
A complete market structure map is built automatically.
10. Volume Imbalances (VI)
Detects and displays:
Bullish VI (VI+)
Bearish VI (VI-)
Features:
Configurable colors
Custom label size
Max visible boxes
Extension until mitigation
Automatic mitigation detection (close or wick)
Highlight when price enters an active VI
Perfect for tracking aggressive buying/selling footprints.
11. Timezone & Date/Time Widget
Uses IANA timezones for:
Accurate session boundaries
Proper DST handling
Multi-market consistency
Also includes a small on-chart table showing:
Your timezone date/time
Exchange timezone date/time
Great for globally active traders.
12. Max Display Timeframe
To prevent clutter, the script disables visuals above a chosen timeframe.
If you exceed it:
A clean on-chart message appears
Tells you to lower your chart TF or adjust the Max Display TF
Keeps charts fast and clean
Key Inputs & Customization
Timezone (IANA format)
Max Display Timeframe
Session/Killzone toggles, colors, naming
Opening levels (00:00 / 08:30 / 09:30 / 13:30)
Previous Day AM/PM highs/lows
NDOG / NWOG gap settings
RTH gap settings
FVG batching (4 independent timeframes)
Fractal type
Order Block settings (range type, deletion, FVG filter)
Market structure settings
Volume Imbalance settings
Date/time widget settings
Everything is modular — turn features on/off individually.
How It Helps Traders
For Intraday Traders / Scalpers:
Session mapping for timing setups
Exact key opening prices
RTH gaps and internals
Precise daily AM/PM high–low context
HTF FVGs, OBs, VI zones for higher-timeframe bias
Real-time CHoCH/BOS for entry timing
For Swing Traders:
Daily/weekly context plotted automatically
NDOG, NWOG, RTH gap awareness
Macro structure levels
HTF FVGs and OBs for HTF targets
ICT 1st Presented FVG After RTH OpenICT 1st Presented FVG After RTH Open
Overview
This indicator identifies and tracks the first Fair Value Gap (FVG) that forms after the Regular Trading Hours (RTH) open, based on Inner Circle Trader (ICT) concepts. It monitors price behavior and reaction to this initial FVG throughout the trading session.
Key Features
📊 Smart FVG Detection
• Automatically identifies the first valid FVG after RTH open (default: 9:30-10:00 AM ET)
• Filters noise using ATR-based minimum gap size validation
• Option to display all FVGs or just the first one
• Visual distinction between the first FVG and subsequent ones
⏰ Customizable Time Settings
• Adjustable RTH window (default: 9:30-10:00 AM)
• Multiple timezone support (New York, Chicago, London, Tokyo)
• Flexible tracking duration and sampling intervals
📈 Price Reaction Tracking
• Monitors price behavior relative to the first FVG over time
• Tracks whether price remains above, below, or inside the FVG zone
• Records price distance from FVG boundaries
• Displays real-time data in an easy-to-read table
• Volume tracking at each sample interval
🎨 Visual Elements
• Color-coded FVG boxes (green for bullish, red for bearish)
• Timestamp labels showing when each FVG formed
• Extendable boxes to track ongoing validity
• Optional background highlighting during RTH window
• Customizable table positions and display options
🔔 Alert System
• Visual markers on chart for easy backtesting
• Real-time programmatic alerts with detailed FVG information
• TradingView alert conditions for custom notifications
• Alerts include price range, gap size, and timestamp
Settings
Time Configuration:
• Timezone selection
• RTH start/end times
• Tracking duration (default: 120 minutes)
• Sample interval (default: 5 minutes)
FVG Validation:
• ATR length for gap size calculation
• Minimum gap size as ATR percentage
• Option to show all valid FVGs
Display Options:
• Custom colors for bullish/bearish FVGs
• Label visibility toggle
• Box extension options
• Maximum historical FVGs to display
• Info and reaction table positions
Use Cases
1. Entry Timing: Use the first FVG as a potential entry zone when price returns to fill the gap
2. Trend Confirmation: Monitor whether price respects or violates the first FVG
3. Session Analysis: Track how the first inefficiency of the session plays out over time
4. Backtesting: Visual markers allow easy historical analysis of FVG behavior
How It Works
The indicator waits for RTH to begin, then identifies the first three-candle pattern that creates a valid Fair Value Gap. Once detected, it:
1. Marks the FVG zone with a colored box
2. Begins tracking price position at regular intervals
3. Records data in a reaction table showing price behavior over time
4. Continues monitoring until the tracking duration expires or a new trading day begins
Notes
• Resets daily to track each session independently
• Works on any timeframe, though lower timeframes (1-5 min) are recommended for intraday FVG detection
• The "first presented" FVG concept emphasizes the importance of the initial inefficiency created after market open
• Historical FVGs are preserved up to the display limit for reference
This indicator is designed for traders familiar with ICT concepts and Fair Value Gap trading strategies. It combines automated detection with comprehensive tracking to help identify high-probability trading opportunities.
iFVG Ultimate+ | DodgysDDOVERVIEW
iFVG Ultimate+ | DodgysDD is a professional-grade visualization framework that automates the identification and management of Inversion Fair Value Gaps (IFVGs)
It is designed for analysts and educators studying institutional price behavior, liquidity dynamics, and displacement-based imbalances.
This indicator does not provide trading signals or forecasts.
All logic serves educational and analytical purposes only.
A Fair Value Gap (FVG) appears when strong directional displacement prevents candle bodies from overlapping.When a liquidity sweep occurs and price later closes through that gap, the imbalance is considered inverted. This often marks a shift in order-flow.
iFVG Ultimate+ tracks these transitions using a rule-based sequence:
Liquidity Sweep – Price sweeps a previous swing high or low.
Displacement – Body-to-body gap forms as price accelerates away.
Inversion – Full candle body closes through the gap after raid.
Validation and Tracking – Confirmed inversions are stored and managed until completion or invalidation.
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PURPOSE AND SCOPE
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The framework serves as a research tool to document and analyze IFVG behavior within liquidity and session contexts.
It is commonly used to:
-Record and journal IFVG formations for back-testing and model study.
-Assess how often gaps complete or invalidate after sweeps.
-Evaluate session-based patterns (London, Asia, New York).
-Overlay HTF PD Arrays to observe inter-timeframe delivery.
-Receive custom alerts to your phone
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LOGIC STRUCTURE
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iFVG Ultimate+ runs a five-stage validation process to ensure sequential, non-repainting behavior.
Liquidity Framework:
• Detects swing highs and lows on aligned timeframes (automatic or manual selection).
• Logs session highs/lows for Asia (20:00–00:00 NY) and London (02:00–05:00 NY).
• Includes data wicks around 08:30 NY for event reference.
FVG Detection and Displacement Filter:
• Identifies body-based imbalances using ATR-scaled sensitivity modes (Sensitive / Normal / Strict).
• Supports “Single” or “Series” modes to merge adjacent gaps.
• Excludes weak displacements using minimum ATR thresholds.
Inversion Validation:
• Confirms only when a complete candle body closes through a qualifying FVG within a user-defined window (6 or 15 bars).
• Duplicate detections are ignored; mitigation states are recorded.
HTF Context Integration:
• Maps higher-time-frame PD Arrays and tracks their delivery status.
• Labels active zones (e.g. “H4 PDA”) and updates on HTF close.
Model Lifecycle and Limits:
• Plots the inversion line and derives educational limit levels: Break-Even and Stop-Loss.
• Tracks until opposing liquidity is swept (model complete) or an invalidation event occurs.
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COMPONENTS AND VISUALS
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-IFVG Line — Marks confirmed inversion at close.
-Break-Even / Stop-Loss Lines — Calculated retrospectively for journal grading.
-Session High/Low Markers — London and Asia reference levels.
-Data Wicks — 8:30 NY “DATA.H/L” labels for event volatility.
-SMTs — Compares current symbol to correlated instrument for divergence confirmation.
-Checklist Panel — Tracks liquidity, momentum, HTF delivery, and SMT conditions.
-Setup Grade Display — Computes qualitative score (A+ to C) based on met conditions.
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INPUT CATEGORIES
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General — Detection mode, ATR strictness, bias filter, long/short window.
Liquidity — Automatic or manual timeframe alignment, session visuals.
FVG — Color themes, label sizes, inversion color change, HTF inclusion.
Entry / Limits — Enable or hide Entry, Break-Even, and Stop-Loss levels.
Alerts — Individual toggles for IFVG formation, session sweeps, multi-TF inversions, and invalidations.
Display — Info Box, relationship table, and grade styling.
All alerts output plain text messages only and do not execute orders.
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ALERT FRAMEWORK
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When enabled, alerts may notify for:
-Potential inversion detected.
-Confirmed IFVG formation.
-Liquidity sweeps (high/low or session).
-Multi-time-frame inversion.
-Invalidation or close warning.
-Alerts serve as educational markers only, not trade triggers.
The user will have the ability to create custom messages for each of these alert events.
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USAGE GUIDELINES
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iFVG Ultimate+ is suited for review and documentation of displacement-based price behavior.
Recommended educational workflows:
-Annotate IFVG events and review delivery into PD Arrays.
-Analyze frequency by session or timeframe.
-Assess how often IFVGs complete versus invalidate.
-Teach ICT-style liquidity mechanics in mentorship or training contexts.
-The indicator works across forex, futures, and crypto markets.
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OPERATIONAL NOTES AND LIMITATIONS
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-HTF calculations finalize on bar close (no look-ahead).
-ATR filter strength affects small-gap visibility.
-Session windows use New York time.
-Break-Even and Stop-Loss lines are visual aids only.
-Performance depends on chart density and bar count.
-No strategy module or backtest engine is included.
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ORIGINALITY AND PROTECTION
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iFVG Ultimate+ | DodgysDD integrates multiple independent systems into a single engine:
-PD Array context alignment with liquidity tracking.
-Dynamic session detection and macro data integration.
-Sequential IFVG validation pipeline with grade assignment.
-Multi-time-frame SMT confirmation module.
-Structured alerts and mitigation tracking.
The logic is entirely original, written in Pine v6, and protected as invite-only to preserve methodology integrity.
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ATTRIBUTION
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Core concepts such as Fair Value Gaps, Liquidity Sweeps, PD Arrays, and SMT Divergence are publicly taught within ICT-style market education. This implementation was designed and engineered by TakingProphets as iFVG Ultimate+ | DodgysDD, authored for TradingView publication by TakingProphets.
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TERMS AND DISCLAIMER
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This indicator is for educational and informational use only. It does not provide financial advice or predictive output. Historical patterns do not guarantee future results. All users remain responsible for their own decisions.Use of this script implies agreement with TradingView’s Vendor Requirements and Terms of Use.
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ACCESS INSTRUCTIONS
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Access is managed through TradingView’s invite-only framework. Users request access via private message to TakingProphets or access link
Squeeze Weekday Frequency [CHE] Squeeze Weekday Frequency — Tracks historical frequency of low-volatility squeezes by weekday to inform timing of low-risk setups.
Summary
This indicator monitors periods of unusually low volatility, defined as when the average true range falls below a percentile threshold, and tallies their occurrences across each weekday. By aggregating these counts over the chart's history, it reveals patterns in squeeze frequency, helping traders avoid or target specific days for reduced noise. The approach uses persistent counters to ensure accurate daily tallies without duplicates, providing a robust view of weekday biases in volatility regimes.
Motivation: Why this design?
Traders often face inconsistent signal quality due to varying volatility patterns tied to the trading calendar, such as quieter mid-week sessions or busier Mondays. This indicator addresses that by binning low-volatility events into weekday buckets, allowing users to spot recurring low-activity days where trends may develop with less whipsaw. It focuses on historical aggregation rather than real-time alerts, emphasizing pattern recognition over prediction.
What’s different vs. standard approaches?
- Reference baseline: Traditional volatility trackers like simple moving averages of range or standalone Bollinger Band squeezes, which ignore temporal distribution.
- Architecture differences:
- Employs array-based persistent counters for each weekday to accumulate events without recounting.
- Includes duplicate prevention via day-key tracking to handle sparse data.
- Features on-demand sorting and conditional display modes for focused insights.
- Practical effect: Charts show a persistent table of ranked weekdays instead of transient plots, making it easier to glance at biases like higher squeezes on Fridays, which reduces the need for manual logging and highlights calendar-driven edges.
How it works (technical)
The indicator first computes the average true range over a specified lookback period to gauge recent volatility. It then ranks this value against its own history within a sliding window to identify squeezes when the rank drops below the threshold. Each bar's timestamp is resolved to a weekday using the selected timezone, and a unique day identifier is generated from the date components.
On detecting a squeeze and valid price data, it checks against a stored last-marked day for that weekday to avoid multiple counts per day. If it's a new occurrence, the corresponding weekday counter in an array increments. Total days and data-valid days are tracked separately for context.
At the chart's last bar, it sums all counters to compute shares, sorts weekdays by their squeeze proportions, and populates a table with the selected subset. The table alternates row colors and highlights the peak weekday. An info label above the final bar summarizes totals and the top day. Background shading applies a faint red to squeeze bars for visual confirmation. State persists via variable arrays initialized once, ensuring counts build incrementally without resets.
Parameter Guide
ATR Length — Sets the lookback for measuring average true range, influencing squeeze sensitivity to short-term swings. Default: 14. Trade-offs/Tips: Shorter values increase responsiveness but raise false positives in chop; longer smooths for stability, potentially missing early squeezes.
Percentile Window (bars) — Defines the history length for ranking the current ATR, balancing recent relevance with sample size. Default: 252. Trade-offs/Tips: Narrower windows adapt faster to regime shifts but amplify noise; wider ones stabilize ranks yet lag in fast markets—aim for 100-500 bars on daily charts.
Squeeze threshold (PR < x) — Determines the cutoff for low-volatility classification; lower values flag rarer, tighter squeezes. Default: 10.0. Trade-offs/Tips: Tighter thresholds (under 5) yield fewer but higher-quality signals, reducing clutter; looser (over 20) captures more events at the cost of relevance.
Timezone — Selects the reference for weekday assignment; exchange default aligns with asset's session. Default: Exchange. Trade-offs/Tips: Use custom for cross-market analysis, but verify alignment to avoid offset errors in global pairs.
Show — Toggles the results table visibility for quick on/off of the display. Default: true. Trade-offs/Tips: Disable in multi-indicator setups to save screen space; re-enable for periodic reviews.
Pos — Positions the table on the chart pane for optimal viewing. Default: Top Right. Trade-offs/Tips: Bottom options suit long-term charts; test placements to avoid overlapping price action.
Font — Adjusts text size in the table for readability at different zooms. Default: normal. Trade-offs/Tips: Smaller fonts fit more data but strain eyes on small screens; larger for presentations.
Dark — Applies a dark color scheme to the table for contrast against chart backgrounds. Default: true. Trade-offs/Tips: Toggle false for light themes; ensures legibility without manual recoloring.
Display — Filters table rows to show all, top three, or bottom three weekdays by squeeze share. Default: All. Trade-offs/Tips: Use "Top 3" for focus on high-frequency days in active trading; "All" for full audits.
Reading & Interpretation
Red-tinted backgrounds mark individual squeeze bars, indicating current low-volatility conditions. The table's summary row shows the highest squeeze count, its percentage of total events, and the associated weekday in teal. Detail rows list selected weekdays with their absolute counts, proportional shares, and a left arrow for the peak day—higher percentages signal days where squeezes cluster, suggesting potential for calmer trend development. The info label reports overall days observed, valid data days, and reiterates the top weekday with its count. Drifting counts toward zero on a weekday imply rarity, while elevated ones point to habitual low-activity sessions.
Practical Workflows & Combinations
- Trend following: Scan for squeezes on high-frequency weekdays as entry filters, confirming with higher highs or lower lows in the structure; pair with momentum oscillators to time breaks.
- Exits/Stops: On low-squeeze days, widen stops for breathing room, tightening them during peak squeeze periods to guard against false breaks—use the table's percentages as a regime proxy.
- Multi-asset/Multi-TF: Defaults work across forex and indices on hourly or daily frames; for stocks, adjust percentile window to 100 for shorter histories. Scale thresholds up by 5-10 points for high-vol assets like crypto to maintain signal sparsity.
Behavior, Constraints & Performance
- Repaint/confirmation: Counts update only on confirmed bars via day-key changes, with no future references—live bars may shade red tentatively but tallies finalize at session close.
- security()/HTF: Not used, so no higher-timeframe repaint risks; all computations stay in the chart's resolution.
- Resources: Relies on a fixed-size array of seven elements and small loops for sorting and table fills, capped at 5000 bars back—efficient for most charts but may slow on very long intraday histories.
- Known limits: Ignores weekends and holidays implicitly via data presence; early chart bars lack full percentile context, leading to initial undercounting; assumes continuous sessions, so gaps in data (e.g., news halts) skew totals.
Sensible Defaults & Quick Tuning
Start with the built-in values for broad-market daily charts: ATR at 14, window at 252, threshold at 10. For noisier environments, lower the threshold to 5 and shorten the window to 100 to prioritize rare squeezes. If too few events appear, raise the threshold to 15 and extend ATR to 20 for broader capture. To combat overcounting in sparse data, widen the window to 500 while keeping others stock—monitor the info label's data-days count before trusting patterns.
What this indicator is—and isn’t
This serves as a statistical overlay for spotting calendar-based volatility biases, aiding in session selection and filter design. It is not a standalone signal generator, predictive model, or risk manager—integrate it with price action, volume, and broader strategy rules for decisions.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Elite Zone Master Pro - Advanced Multi-Session Trading System🚀 Elite Zone Master Pro - Advanced Multi-Session Trading System
🎯 ORIGINALITY & UNIQUE VALUE PROPOSITION
Elite Zone Master Pro is NOT a simple mashup of existing indicators. It's a proprietary trading system that combines three distinct methodologies into a unified, synergistic approach:
Multi-Session Zone Analysis - Original algorithm for tracking global market sessions
Dynamic Opening Range Breakout (ORB) - Enhanced ORB with bias-aware signal filtering
Advanced Fair Value Gap Detection - Proprietary FVG identification with smart mitigation tracking
🔧 Why This Combination Works
The power lies in how these components work together, not separately:
Session zones provide market context and volatility windows
ORB system identifies key breakout levels during optimal timeframes
FVG detection pinpoints precise entry locations within the ORB framework
Integrated bias system filters signals based on range direction momentum
🧠 DETAILED METHODOLOGY & CALCULATIONS
🌍 1. Multi-Session Zone Framework
What it does: Tracks and visualizes three major global trading sessions simultaneously.
How it works:
Dynamic zone tracking algorithm that calculates session highs/lows in real-time
Adaptive box rendering that expands/contracts based on actual price movement
Session overlap detection for identifying high-volatility periods
Time-weighted zone positioning using custom timezone calculations
Original concepts:
Simultaneous multi-session visualization (not found in standard session indicators)
Dynamic zone expansion based on volatility, not fixed time periods
Cross-session momentum analysis for bias determination
🎯 2. Enhanced Opening Range Breakout System
What it does: Identifies breakout opportunities from predefined session ranges with intelligent bias filtering.
How it works:
Multi-session ORB calculation: Supports US (16:30-16:45), EU (10:00-10:15), Asian (03:00-03:15), and custom sessions
Dynamic range establishment: Range is built in real-time during active session periods
Bias-aware signal filtering: Two-tier breakout system based on range midpoint momentum
Range direction analysis: Compares current range midpoint to previous session's midpoint
Original methodology:
Range Bias Calculation:
- If Current_Midpoint > Previous_Midpoint = Bullish Bias (+1)
- If Current_Midpoint < Previous_Midpoint = Bearish Bias (-1)
- If Current_Midpoint = Previous_Midpoint = Neutral Bias (0)
Signal Logic:
- Bullish Bias: Standard breakout above range high
- Bearish Bias: Enhanced breakout (range_high + 0.5 * range_width) for bullish signals
- Neutral Bias: Standard breakouts both directions
⚡ 3. Advanced Fair Value Gap (FVG) Detection
What it does: Identifies and tracks fair value gaps with automatic mitigation detection.
How it works:
Three-bar gap analysis: Compares current bar relationships to identify true gaps
Dynamic threshold calculation: Auto-adjusting sensitivity based on market volatility
Smart mitigation tracking: Automatically removes filled gaps from display
Directional bias integration: Color-codes gaps based on their directional implication
Proprietary algorithms:
Bullish FVG Criteria:
- Current_Low > High (gap condition)
- Close > High (confirmation)
- (Current_Low - High ) / High > Threshold (significance filter)
Bearish FVG Criteria:
- Current_High < Low (gap condition)
- Close < Low (confirmation)
- (Low - Current_High) / Current_High > Threshold (significance filter)
Mitigation Logic:
- Bullish FVG: Mitigated when Close < FVG_Low
- Bearish FVG: Mitigated when Close > FVG_High
📈 4. Session-Based Moving Average System
What it does: Calculates moving averages that reset and adapt to session boundaries.
How it works:
Session-aware length calculation: Effective length = min(bars_since_session_start, user_length)
Multiple MA types: EMA, SMA, RMA, WMA, VWMA with session-specific calculations
Dynamic smoothing: Adapts to session length for consistent signals across different session durations
🔄 INTEGRATED SYSTEM SYNERGY
🎯 How Components Work Together
Context Layer: Session zones provide market timing context
Setup Layer: ORB system identifies breakout opportunities within optimal timeframes
Entry Layer: FVG detection pinpoints precise entry levels
Filter Layer: Bias system ensures alignment with momentum direction
Confirmation Layer: Session MA provides trend confirmation
🧭 Signal Generation Process
Step 1: Session Analysis
- Identify active trading session
- Calculate session volatility metrics
- Establish range boundaries
Step 2: Range Bias Calculation
- Compare current vs previous range midpoints
- Assign directional bias (-1, 0, +1)
- Adjust breakout thresholds accordingly
Step 3: Breakout Detection
- Monitor price interaction with range boundaries
- Apply bias-specific breakout criteria
- Generate preliminary signals
Step 4: FVG Confirmation
- Scan for fair value gaps within range
- Validate gap significance using dynamic thresholds
- Provide entry refinement opportunities
Step 5: Signal Validation
- Cross-reference with session MA direction
- Ensure alignment with overall bias
- Output final trading signals
📊 PRACTICAL IMPLEMENTATION
🎯 Trading Strategy Framework
Setup Phase:
Configure session times for your timezone
Enable preferred sessions (US/EU/Asian)
Adjust FVG sensitivity based on instrument volatility
Execution Phase:
Wait for range establishment during active session
Monitor for bias-aligned breakouts
Look for FVG retest opportunities
Enter trades with ORB-based stop losses
Risk Management:
Stop loss placement: Outside ORB range boundaries
Position sizing: Based on range width volatility
Trade direction: Must align with calculated range bias
🎨 UNIQUE VISUAL IMPLEMENTATION
📊 Advanced Visualization Features
Multi-layered zone rendering with transparency controls
Dynamic range boxes that adapt to price movement
Smart label positioning to avoid chart clutter
Color-coded bias indication through range fills
Progressive FVG display with automatic cleanup
🔧 TECHNICAL SPECIFICATIONS
⚙️ Performance Optimizations
Efficient array management for FVG tracking
Memory optimization through historical data cleanup
Smart rendering to prevent chart overload
Error handling for edge cases and invalid timeframes
📈 Compatibility
All timeframes under 1 day
All instruments (Forex, Stocks, Crypto, Futures)
All chart types with overlay capability
Mobile and desktop platform support
🏆 WHAT MAKES THIS DIFFERENT FROM OTHER INDICATORS
❌ Standard ORB indicators: Only show basic range breakouts without bias consideration
❌ Basic FVG indicators: Don't integrate with session analysis or range systems
❌ Session indicators: Simply highlight time periods without actionable trading signals
❌ Moving average indicators: Don't adapt to session dynamics
✅ Elite Zone Master Pro: Combines all elements with proprietary logic for a complete trading system
📋 USE CASES & MARKET APPLICATION
🎯 Primary Applications
Forex day trading during major session overlaps
Index futures scalping using session-specific ranges
Cryptocurrency swing trading with 24/7 session analysis
Stock market opening range breakout strategies
📊 Performance Characteristics
Best performance: During high-volatility session transitions
Optimal timeframes: 1m to 4H for intraday trading
Risk-reward ratios: Typically 1:2 to 1:4 based on range width
Win rate: Higher probability when all components align
This indicator represents months of development combining institutional trading concepts with retail accessibility. It's not just another indicator - it's a complete trading methodology in one comprehensive tool.
