50 & 200 SMA + RSI Average Strategy (Long Only, Single Trade)It works better in trending markets. It delivers its best performance in the 4-hour to 1-day timeframes.
M-oscillator
Fat Tony Composite Histogram Dual SettingsThis is an adaptation of Rob Booker's Fat Tony Composite Histogram which allows you to put two levels for signals.
Dual MACD📘 Dual MACD — Synopsis
The Dual MACD indicator displays two separate MACD systems inside the same pane, allowing traders to compare fast and slow momentum behavior simultaneously.
What It Includes
Two fully adjustable MACDs
MACD 1 default: 12 / 12 / 9
MACD 2 default: 8 / 20 / 6
Show/Hide Toggles so each MACD can be viewed independently or together.
MACD Lines, Signal Lines, and Histograms for both systems.
Clean layout with a compact panel title: “MACD x2”
What It Helps You See
Short-term vs. longer-term momentum shifts
Faster MACD reacting to quick trend changes
Slower MACD confirming or filtering signals
Trend strength, momentum acceleration, and crossover behavior in a single pane
Why It’s Useful
The Dual MACD gives you momentum confirmation, fakeout filtering, and multi-speed trend insight—making it valuable for scalpers, intraday traders, and swing traders who want to reduce noise and improve signal quality.
UM Nadaraya-Watson OscillatorDescription
This is a different take on the Nadaraya-Watson Estimator from both Jdhorty and LuxAlgo. Both great scripts, I encourage everyone to check them out. Think of this script as a measure of trend direction, direction change, and trend acceleration or deceleration. It is not a Moving Average, but you could think of it as loosely as an intelligent adaptive regression curve with the focus on trend direction. The Gaussian calculations prefer and add more weight to the most recent bars. The end result is the oscillator is more responsive with less lag and less prone to pure price noise.
How it Works
The indicator was added to the chart twice; once with an MA, once without. The oscillator indicates trend change by crossing up through the zero line or down through the zero line. Once the indicator turns positive, we are in a positive trend until it crosses below zero and then the trend turns negative. I implemented a Moving Average overlay for additional signal determination; if the configured MA (EMA, SMA, WMA, or Nadaraya-Watson Estimator) trends higher, it is green. When trending down, it is red. The indicator also changes the color of the price bars; when the indicator below zero and red, the price bars are red. When the indicator is above zero and green, the price bars are green.
I marked up the chart and indicator to identify LONG, SHORT, and divergences between price and oscillator.
Default Settings
The default settings are 16 for Bandwidth and a WMA with 110. This is shown in the chart example. There directional arrows, but they are off by default. The Price bars are colored green or red to match the oscillator and the bar coloring is on by default.
All settings are user-configurable including bandwidth, MA type, MA length, bar coloring, and arrows.
Suggested Settings and uses
I personally like the 30 min chart with a bandwidth of 16 and a WMA of 110. The bandwidth 8 and 8 period EMA or WMA also work well on 6 hour and daily charts. Add this to your chart arsenal and use your favorite indicators for confirmation. This indicator works well on the 30 minute chart for inverse ETFs as well (SQQQ, SOXS, TZA). Also, the oscillator is good for identifying divergences between price and and indicator. (see chart for illustration)
Experiment with settings and adapt them to your trading style.
Alerts
If you right click the indicator, and select add alert, I have configured 4 standard alerts: A bullish cross above zero, A bearish cross below zero, An MA bullish turned up to trend higher, (green), and an MA bearish turned down to trend lower (red).
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.
LiquidityPulse RSI Candle Strength MomentumLiquidity-Pulse RSI Candle Strength Momentum is a multifunctional and original candle-analysis tool designed to highlight the potential internal strength of each candle using a combination of body size and volume.
To view the candle-strength scores clearly: right-click on the chart, go to Settings, and in the Symbol tab untick Body, Borders and Wicks.
Candle Strength Scores
The indicator calculates the average body size and average volume over a user-defined lookback period. Each candle is then compared to these averages, and the indicator combines relative body expansion and relative volume expansion with a square-root calculation to create a (normalised) candle-strength score from 1 to 10.
10 – exceptionally strong compared to the lookback average (large body size and volume)
1 – very weak compared to the lookback average (small body size and volume)
Bullish and bearish candles are evaluated independently, producing separate bull-strength and bear-strength scores.
Optional ATR and volume floors can be enabled to restrict strength scoring to candles that exceed a minimum volatility or participation threshold. This helps users who prefer to filter out low-impact candles during quiet market periods. This option can be enabled or adjusted in the settings but is turned off by default.
Candle Colours
This tool also shows candles coloured based on the candle-strength scores (10 colours in each theme), which makes it easier to visualise the scores and see whether the candle score was high or not. There are several options in the 'colour theme' dropdown menu in the settings. Users can also customise all colours manually.
RSI Candle Strength Arrows
The Relative Strength Index is a long-established momentum tool that calculates the ratio of average upward moves to average downward moves over a defined period, allowing traders to identify potential overbought and oversold market conditions where momentum may be stretched. As well as this, strong early momentum and participation are often associated with more sustained moves.
This indicator combines this methodology and provides optional arrows that appear only when candle strength and RSI conditions align:
– A candle meets or exceeds a chosen strength threshold
– RSI has recently reached an overbought or oversold level
– The candle direction matches the expected momentum shift
For example, if price has reached an oversold RSI level and a strong bullish candle forms (high candle-strength number), an upside arrow may plot.
Users can customise the RSI oversold and overbought thresholds, the minimum candle-strength threshold, and how many bars back the RSI condition must have occurred in the settings.
These arrows are not buy or sell signals but instead highlight rare moments where strong candle behaviour aligns with meaningful RSI extremes. This is useful to users because it allows the candle-strength logic to be applied only when momentum is genuinely stretched, filtering out noise and focusing attention on the most statistically significant market moves.
This indicator brings together a quantitative candle-strength model and a momentum-based RSI filter to give users a clearer view of how individual candles behave relative to their recent environment, while also highlighting when those movements occur during meaningful shifts in market momentum. By combining both forms of analysis, the tool helps traders distinguish ordinary price changes from potentially significant structural behaviour.
How traders can use this indicator
– Stronger candle scores in the trend direction can confirm continuation pressure.
– Powerful opposing candles appearing at RSI extremes may signal potential reversals or exhaustion points.
– If breakouts occur with high candle scores, price may be more likely to follow through.
– Weak candles with low scores help traders avoid false signals or low-quality setups.
– Candle-strength scoring helps users quickly interpret both volume and candle-body behaviour without manual analysis.
Open source, if anyone has any ideas on how to make the script better or have any questions please let me know :)
Disclaimer
This indicator is provided for educational and analytical purposes only and should not be interpreted as financial advice or a recommendation to buy or sell any asset. The candle-strength values displayed by this tool are not literal or definitive measures of market strength; they are derived from a custom mathematical model designed to highlight relative differences in candle behaviour. These values should be viewed as a simplified representation of candle dynamics, not as an objective or universal measure of strength.
Users should be aware that this calculation does not replace the importance of analysing real traded volume, order flow, liquidity conditions, or broader market context. As with any technical tool, results should be considered alongside other forms of analysis, and past performance does not guarantee future outcomes. Use at your own discretion and risk.
Multi Condition Stock Screener & Alert SystemMulti Condition Stock Screener & Strategy Builder
This script is a comprehensive Stock Screener and Strategy Builder designed to scan predefined groups of stocks (specifically focused on BIST/Istanbul Stock Exchange symbols) or a custom list of symbols based on user-defined technical conditions.
It allows users to combine multiple technical indicators to create complex entry or exit conditions without writing code. The script iterates through a list of symbols and triggers alerts when the conditions are met.
Key Features
• Custom Strategy Building: Users can define up to 6 separate conditions. • Logical Operators: Conditions can be linked using logical operators (AND / OR) to create flexible strategies. • Predefined Groups: Includes 14 groups of stocks (covering BIST symbols) for quick scanning. • Custom Scanner: Users can select the "SPECIAL" group to manually input up to 40 custom symbols to scan. • Directional Scanning: Capable of scanning for both Buy/Long and Sell/Short signals. • Alert Integration: Generates JSON-formatted alert messages suitable for webhook integrations (e.g., sending notifications to Telegram bots).
Supported Indicators for Conditions
The script utilizes built-in ta.* functions to calculate the following indicators:
• MA (Moving Average): Supports EMA, SMA, RMA, and WMA. • RSI (Relative Strength Index) • CCI (Commodity Channel Index) • ATR (Average True Range) • BBW (Bollinger Bands Width) • ADX (Average Directional Index) • MFI (Money Flow Index) • MOM (Momentum)
How it Works
The script uses request.security() to fetch data for the selected group of symbols based on the current timeframe. It evaluates the user-defined logic (Condition 1 to 6) for each symbol.
• Comparison Logic: You can compare an indicator against a value (e.g., RSI > 50 ) or against another indicator (e.g., MA1 CrossOver MA2 ). • Signal Generation: If the logical result is TRUE based on the "AND/OR" settings, a visual label is plotted on the chart, and an alert condition is triggered.
Alert Configuration
The script produces a JSON output containing the Ticker, Signal Type, Period, and Price. This is optimized for users who want to parse alerts programmatically or send them to external messaging apps via webhooks.
Disclaimer This tool is for informational purposes only and does not constitute financial advice. Since it uses request.security across multiple symbols, please allow time for the script to load data on the chart.
RSI + Psy + ADXRSI + Psychological Line + ADX (with RCI-replacement logic)
This custom TradingView indicator combines three major technical analysis tools—RSI, Psychological Line (Psy), and ADX—to help traders identify trend strength, market momentum, and overbought/oversold conditions with improved clarity.
1. Multi-Period RSI
The indicator calculates three RSI values:
Short-term RSI (9)
Mid-term RSI (26)
Long-term RSI (52)
These help users observe short-, mid-, and long-term momentum simultaneously.
Threshold lines are drawn at 70, 50, and 30 for standard RSI overbought/oversold analysis.
2. Psychological Line (Psy) with Dynamic Column Display
The Psy indicator counts how many closes within the selected period (default: 12) were higher than the previous close.
Values above 75 indicate overbought markets.
Values below 25 indicate oversold markets.
When Psy crosses these thresholds, it is displayed as a column chart centered at 50, visually expanding upward (overbought) or downward (oversold).
3. ADX Trend Strength with Color Coding
ADX is calculated from DI+ and DI− values (using true range and directional movement).
The ADX line changes color based on trend strength:
Blue: Weak trend (below 20)
Yellow: Moderate trend (20–30)
Red: Strong trend (above 30)
This helps traders easily recognize when the market transitions from low-volatility to strong-trend conditions.
Adaptive MACD PROAdaptive MACD PRO
Highlights structural momentum changes using dynamic normalization of MACD and Signal.
Phase Momentum Core
Adds directional confirmation based on short-term phase behavior.
Visual Output
• MACD & Signal lines with trend-based coloring
• Adaptive histogram reflecting momentum strength
• Fixed-position Buy/Sell dots at predefined levels
• AutoCalib dots on MACD_z threshold crossings
• Optional HUD panel displaying calibration levels and MACD_z
Features
• Selectable MA types (EMA, SMA, KAMA)
• Z-score normalization
• ATR-based volatility weighting
• Higher timeframe alignment
• Auto-calibration with SAFE / AGGRESSIVE modes
• Unified long/short triggers
• Full bar-coloring control
• Works on all assets and timeframes
The full source code is visible and may be modified or extended.
This script is intended for technical analysis and research only.
This indicator is published as a free, open-source script with full visible code.
1M XAU Cumulative Delta Volume with OB Breakouts
### Overview
This is a **session-based CVD strategy** built around the **00:00–07:00 CEST range**. It finds the high/low of that session, turns them into **adaptive ATR-based support (yellow)** and **resistance (purple)** zones, and trades only **CVD-confirmed reversals** off those levels.
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### How it Works
* For each day, the script:
* Builds a 00:00–07:00 CEST **profile high/low**.
* Creates a **support zone** around the session low and a **resistance zone** around the session high.
* Using lower timeframe data, it reconstructs **Cumulative Volume Delta (CVD)** and a **recent delta** filter.
* It arms “pending” states when price **enters a zone from the correct side**, then confirms:
* **BUY (long):** price reclaims above support and recent CVD is strongly positive.
* **SELL (short):** price rejects below resistance and recent CVD is strongly negative.
Only these two CVD signals (`buySignal` / `sellSignal`) open trades.
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### Strategy Logic
* **Entries**
* `buySignal` → open **long** (if flat).
* `sellSignal` → open **short** (if flat).
* No pyramiding; one position at a time.
* **Exits (only TP & SL)**
* Long: TP at `avg_price * (0.5 + TP%)`, SL at `avg_price * (1 – SL%)`.
* Short: TP at `avg_price * (0.5 – TP%)`, SL at `avg_price * (1 + SL%)`.
* No opposite-signal exits.
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### Extras
* **Reversal markers** on yellow/purple zones and **breakout/retest markers** are plotted for context and alerts but **do not trigger entries**.
* Zone width and “thickening” are ATR-based so important touches and near-touches are easy to see.
* Only suited for **1m intraday scalping** (e.g. XAU/USD), but can be tested on other markets/timeframes.
Global Macro IndexGlobal Macro Index
The Global Macro Index is a comprehensive economic sentiment indicator that aggregates 23 real-time macroeconomic data points from the world's largest economies (US, EU, China, Japan, Taiwan). It provides a single normalized score that reflects the overall health and momentum of the global economy, helping traders identify macro trends that drive asset prices.
⚠️ Important: Timeframe Settings
This indicator is designed exclusively for the 1W (weekly) timeframe. The indicator is hardcoded to pull weekly data and will not function correctly on other timeframes.
What It Measures
The indicator tracks normalized Trend Power Index (TPI) values across multiple economic categories:
United States (7 components)
Business Confidence Index (BCOI) - Business sentiment and outlook
Composite Leading Indicator (CLI) - Forward-looking economic indicators
Consumer Confidence Index (CCI) - Consumer sentiment and spending intentions
Terms of Trade (TOT) - Import/export price relationships
Manufacturing Composite - Combines business confidence, production, and new orders
Comprehensive Economic Composite - Broad aggregation including employment, business activity, and regional indicators
Business Inventory (BI) - Stock levels and supply chain health
European Union (10 components)
Sentiment Survey (SS) - Overall economic sentiment
Business Confidence Index - EU business outlook
Economic Sentiment Indicator (ESI) - Combined confidence metrics
Manufacturing Production (MPRYY) - Industrial output year-over-year
New Orders - Germany, France, Netherlands, Spain manufacturing orders
Composite Leading Indicators - Germany, France forward-looking metrics
Business Climate Index (BCLI) - France business conditions
Asia (6 components)
New Orders - China, Japan, Taiwan manufacturing demand
Composite Leading Indicators - China, Japan economic momentum
The Formula
The indicator calculates a weighted average of normalized TPI scores:
Global Macro Index = (1/23) × Σ
Each of the 23 economic indicators is:
Converted to a Trend Power Index (TPI) using 4-day Bitcoin normalization
Weighted equally (1/23 ≈ 4.35% each)
Summed and smoothed with a 1-period SMA
The result is a single oscillator that ranges typically between -1 and +1, with extreme readings beyond ±0.6.
Z-Score Signal System
The indicator includes an optional Z-Score overlay that identifies extreme macro conditions:
Calculation:
Z-Score = (Current Value - 50-period Mean) / Standard Deviation
Smoothed with 35-period Hull Moving Average
Inverted for intuitive interpretation
Signals:
Green background (Z-Score ≥ 2) = Extremely positive macro conditions, potential overbought
Red background (Z-Score ≤ -2) = Extremely negative macro conditions, potential oversold
These extreme readings occur approximately 5% of the time statistically
How to Use It
Interpreting the Main Plot (Red Line):
Above 0 = Positive macro momentum, risk-on environment
Below 0 = Negative macro momentum, risk-off environment
Above +0.6 = Strong expansion, bullish for equities and crypto
Below -0.6 = Severe contraction, bearish conditions
Trend direction = More important than absolute level
Z-Score Signals:
Z ≥ 2 (Green) = Macro sentiment extremely positive, consider taking profits or preparing for pullback
Z ≤ -2 (Red) = Macro sentiment extremely negative, potential buying opportunity for contrarians
Works best as a regime filter, not precise timing tool
Best Practices:
Use as a macro regime filter for other strategies
Combines well with liquidity indicators and price action
Leading indicator for risk assets (equities, Bitcoin, emerging markets)
Lagging indicator - confirms macro trends rather than predicting reversals
Watch for divergences: price making new highs while macro weakens (bearish) or vice versa (bullish)
Settings
Show Zscore Signals: Toggle green/red background shading for extreme readings
Overlay Zscore Signals: Display Z-Score signals on the price chart as well as the indicator panel
Reference Lines
0 (gray) = Neutral macro conditions
+0.6 (green) = Strong positive threshold
-0.6 (red) = Strong negative threshold
Data Sources
Real-time economic data from TradingView's ECONOMICS database, including:
OECD leading indicators
Manufacturing PMIs and new orders
Consumer and business confidence surveys
Trade and inventory metrics
Regional economic sentiment indices
Notes
This is a macro trend indicator, not a day-trading tool. Economic data updates weekly and reflects the aggregate health of global growth. Best used on weekly timeframes to identify favorable or unfavorable macro regimes for risk asset allocation.
The indicator distills complex global economic data into a single actionable score, answering: "Is the global economy expanding or contracting right now?"
Debt-Cycle vs Bitcoin-CycleDebt-Cycle vs Bitcoin-Cycle Indicator
The Debt-Cycle vs Bitcoin-Cycle indicator is a macro-economic analysis tool that compares traditional financial market cycles (debt/credit cycles) against Bitcoin market cycles. It uses Z-score normalization to track the relative positioning of global financial conditions versus cryptocurrency market sentiment, helping identify potential turning points and divergences between traditional finance and digital assets.
Key Features
Dual-Cycle Analysis: Simultaneously tracks traditional financial cycles and Bitcoin-specific cycles
Z-Score Normalization: Standardizes diverse data sources for meaningful comparison
Multi-Asset Coverage: Analyzes currencies, commodities, bonds, monetary aggregates, and on-chain metrics
Divergence Detection: Identifies when Bitcoin cycles move independently from traditional finance
21-Day Timeframe: Optimized for Long-term cycle analysis
What It Measures
Finance-Cycle (White Line)
Tracks traditional financial market health through:
Currencies: USD strength (DXY), global currency weights (USDWCU, EURWCU)
Commodities: Oil, gold, natural gas, agricultural products, and Bitcoin price
Corporate Bonds: Investment-grade spreads, high-yield spreads, credit conditions
Monetary Aggregates: M2 money supply, foreign exchange reserves (weighted by currency)
Treasury Bonds: Yield curve (2Y/10Y, 3M/10Y), term premiums, long-term rates
Bitcoin-Cycle (Orange Line)
Tracks Bitcoin market positioning through:
On-Chain Metrics:
MVRV Ratio (Market Value to Realized Value)
NUPL (Net Unrealized Profit/Loss)
Profit/Loss Address Distribution
Technical Indicators:
Bitcoin price Z-score
Moving average deviation
Relative Strength:
ETH/BTC ratio (altcoin strength indicator)
Visual Elements
White Line: Finance-Cycle indicator (positive = expansionary conditions, negative = contractionary)
Orange Line: Bitcoin-Cycle indicator (positive = bullish positioning, negative = bearish)
Zero Line: Neutral reference point
Interpretation
Cycle Alignment
Both positive: Risk-on environment, favorable for crypto
Both negative: Risk-off environment, caution warranted
Divergence: Potential opportunities or warning signals
Divergence Signals
Finance positive, Bitcoin negative: Bitcoin may be undervalued relative to macro conditions
Finance negative, Bitcoin positive: Bitcoin may be overextended or decoupling from traditional finance
Important Limitations
This indicator uses some technical and macro data but still has significant gaps:
⚠️ Limited monetary data - missing:
Funding rates (repo, overnight markets)
Comprehensive bond spread analysis
Collateral velocity and quality metrics
Central bank balance sheet details
⚠️ Basic economic coverage - missing:
GDP growth rates
Inflation expectations
Employment data
Manufacturing indices
Consumer confidence
⚠️ Simplified on-chain analysis - missing:
Exchange flow data
Whale wallet movements
Mining difficulty adjustments
Hash rate trends
Network fee dynamics
⚠️ No sentiment data - missing:
Fear & Greed Index
Options positioning
Futures open interest
Social media sentiment
The indicator provides a high-level cycle comparison but should be combined with comprehensive fundamental analysis, detailed on-chain research, and proper risk management.
Settings
Offset: Adjust the horizontal positioning of the indicators (default: 0)
Timeframe: Fixed at 21 days for optimal cycle detection
Use Cases
Macro-crypto correlation analysis: Understand when Bitcoin moves with or against traditional markets
Cycle timing: Identify potential tops and bottoms in both cycles
Risk assessment: Gauge overall market conditions across asset classes
Divergence trading: Spot opportunities when cycles diverge significantly
Portfolio allocation: Balance traditional and crypto assets based on cycle positioning
Technical Notes
Uses Z-score normalization with varying lookback periods (40-60 bars)
Applies HMA (Hull Moving Average) smoothing to reduce noise
Asymmetric multipliers for upside/downside movements in certain metrics
Requires access to FRED economic data, Glassnode, CoinMetrics, and IntoTheBlock feeds
21-day timeframe optimized for cycle analysis
Strategy Applications
This indicator is particularly useful for:
Cross-asset allocation - Decide between traditional finance and crypto exposure
Cycle positioning - Identify where we are in credit/debt cycles vs. Bitcoin cycles
Regime changes - Detect shifts in market leadership and correlation patterns
Risk management - Reduce exposure when both cycles turn negative
Disclaimer: This indicator is a cycle analysis tool and should not be used as the sole basis for investment decisions. It has limited coverage of monetary conditions, economic fundamentals, and on-chain metrics. The indicator provides directional insight but cannot predict exact timing or magnitude of market moves. Always conduct thorough research, consider multiple data sources, and maintain proper risk management in all investment decisions.
Smart RSI Composite [DotGain]Summary
Do you want to know the "True Direction" of the market without getting distracted by noise on a single timeframe?
The Smart RSI Composite simplifies market analysis by aggregating momentum data from 10 different timeframes (5m to 12M) into a single, easy-to-read Histogram.
Instead of looking at 10 separate charts or dots, this indicator calculates the Average RSI of the entire market structure. It answers one simple question: "Is the market predominantly Bullish or Bearish right now?"
⚙️ Core Components and Logic
This indicator works like a consensus mechanism for momentum:
Data Aggregation: It pulls RSI values from 10 customizable slots (Default: 5m, 15m, 1h, 4h, 1D, 1W, 1M, 3M, 6M, 12M). All slots are enabled by default.
Smart Averaging: It calculates the arithmetic mean of all active timeframes. If the 5m chart is bearish but the Monthly chart is bullish, this indicator balances them out to show you the net result.
Histogram Visualization: The result is plotted as a histogram centered around the 50-line (Neutral).
🚦 How to Read the Histogram
The histogram bars indicate the aggregate strength of the trend based on the Average RSI:
🟩 DARK GREEN (Strong Bullish)
Condition: Average RSI > 60.
Meaning: The market is in a strong uptrend across most timeframes. Momentum is firmly on the buyers' side.
🟢 LIGHT GREEN (Weak Bullish)
Condition: Average RSI between 50 and 60.
Meaning: Slight bullish bias. The bulls are in control, but momentum is not yet extreme.
🔴 LIGHT RED (Weak Bearish)
Condition: Average RSI between 40 and 50.
Meaning: Slight bearish bias. The bears are taking control.
🟥 DARK RED (Strong Bearish)
Condition: Average RSI < 40.
Meaning: The market is in a strong downtrend across most timeframes. Momentum is firmly on the sellers' side.
Visual Elements
Center Line (50): This acts as the Zero-Line. Above 50 is bullish, below 50 is bearish.
Zone Lines (30/70): Dashed lines indicate the traditional Overbought/Oversold levels applied to the aggregate average.
Key Benefit
The Smart RSI Composite acts as a powerful Macro Trend Filter .
Pro Tip: Never go long if the Histogram is Dark Red, and avoid shorting when it is Dark Green. Use this tool to align your trades with the overall market momentum.
Have fun :)
Disclaimer
This "Smart RSI Composite" indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice.
The signals generated by this tool (both "Buy" and "Sell" indications) are the result of a specific set of algorithmic conditions. They are not a direct recommendation to buy or sell any asset. All trading and investing in financial markets involves substantial risk of loss. You can lose all of your invested capital.
Past performance is not indicative of future results. The signals generated may produce false or losing trades. The creator (© DotGain) assumes no liability for any financial losses or damages you may incur as a result of using this indicator.
You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR) and consider your personal risk tolerance before making any trades.
Smart RSI MTF Matrix [DotGain]Summary
Are you tired of trading trend signals, only to miss the bigger picture because you are focused on a single timeframe?
The Smart RSI MTF Matrix is the ultimate "Cockpit View" for momentum traders. Unlike chart overlays that can sometimes clutter your price action, this indicator organizes RSI conditions across 10 different timeframes simultaneously into a clean, separate Heatmap pane.
It monitors everything from the 5-minute chart all the way up to the 12-Month view , giving you a complete X-ray vision of the market's momentum structure instantly.
⚙️ Core Components and Logic
The Smart RSI MTF Matrix relies on a sophisticated hierarchy to deliver clear, actionable context:
Multi-Timeframe Engine: The script runs 10 independent RSI calculations in the background, organized in rows from bottom (Short Term) to top (Long Term).
Classic RSI Thresholds:
Overbought (> 70): Indicates price may be extended to the upside.
Oversold (< 30): Indicates price may be extended to the downside.
Smart Visibility System (The "Secret Sauce"): Not all signals are equal. A 5-minute signal is "noise" compared to a Yearly signal. This indicator automatically applies Transparency to differentiate importance. The visibility increases by 10% for each higher timeframe slot (Row).
🚦 How to Read the Matrix
The indicator plots dots in 10 stacked rows. The position and opacity tell you the direction and significance:
🟥 RED DOTS (Overbought Condition)
Trigger: RSI is above 70 on that specific timeframe.
Meaning: Potential bearish reversal or pullback.
🟩 GREEN DOTS (Oversold Condition)
Trigger: RSI is below 30 on that specific timeframe.
Meaning: Potential bullish reversal or bounce.
⚪ GRAY DOTS (Neutral)
Trigger: RSI is between 30 and 70.
Meaning: No extreme momentum present.
👻 TRANSPARENCY (Signal Strength)
The visibility of the dot tells you exactly which Timeframe (Row) is triggered. The higher the row, the more solid the color:
Faint (10-30% Visibility): Rows 1-3 (5m, 15m, 1h). Used for scalping entries.
Medium (40-60% Visibility): Rows 4-6 (4h, 1D, 1W). Used for swing trading context.
Solid (70-100% Visibility): Rows 7-10 (1M, 3M, 6M, 12M). Used for identifying major macro cycles.
Visual Elements
Structure: Row 1 (Bottom) represents the 5-minute timeframe. Row 10 (Top) represents the 12-Month timeframe.
Vertical Alignment: If you see a vertical column of Red or Green dots, it indicates Multi-Timeframe Confluence —a highly probable reversal point.
Key Benefit
The goal of the Smart RSI MTF Matrix is to keep your main chart clean while providing maximum information. You can instantly see if a short-term pullback (Faint Green Dot) is happening within a long-term uptrend (Solid Gray/Red Dot), allowing for precision entries.
Have fun :)
Disclaimer
This "Smart RSI MTF Matrix" indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice.
The signals generated by this tool (both "Buy" and "Sell" indications) are the result of a specific set of algorithmic conditions. They are not a direct recommendation to buy or sell any asset. All trading and investing in financial markets involves substantial risk of loss. You can lose all of your invested capital.
Past performance is not indicative of future results. The signals generated may produce false or losing trades. The creator (© DotGain) assumes no liability for any financial losses or damages you may incur as a result of using this indicator.
You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR) and consider your personal risk tolerance before making any trades.
[CT] Kurutoga MTF HistogramWhat is Kurutoga MTF Histogram?
The Kurutoga MTF Histogram is a multi-time-frame momentum and mean-deviation tool.
It measures how far the current close is trading away from a rolling midpoint of price and then displays that deviation as a color-coded histogram.
Instead of looking only at one lookback, this version plots three Kurutoga “leads” at the same time:
Kurutoga Lead (x1) – base length
Kurutoga Lead 2x – slower, 2 × base length
Kurutoga Lead 4x – slowest, 4 × base length
Each lead is calculated both on the chart’s timeframe (LTF) and on a Higher Time Frame (HTF) of your choice, so you can see short-term deviation inside a higher-time-frame structure.
4-color Kurutoga scheme
Each Kurutoga lead uses a 4-color MACD-style scheme:
For a given lead:
Up Light – divergence ≥ 0 and rising compared to the previous bar
Up Dark – divergence ≥ 0 and falling (positive but losing momentum)
Down Light – divergence < 0 and falling (bearish momentum increasing)
Down Dark – divergence < 0 and rising (negative but contracting)
By default the same four teal / red hues are shared across x1, x2, and x4. The only difference between the leads is transparency:
x1 = strongest (least transparent)
x2 = medium opacity
x4 = faintest
This lets you see all three layers at once without the chart becoming a solid block of color.
The HTF areas use the same palette but with an extra transparency offset applied, so they appear as soft background bands rather than competing with the histograms.
Inputs and how to use them
1. Base Length
Defines the lookback for the main Kurutoga Lead.
The script automatically creates:
len1 = baseLength
len2 = baseLength × 2
len3 = baseLength × 4
Smaller base lengths → faster, more reactive histograms.
Larger base lengths → smoother, trend-focused behavior.
2. Higher Time Frame
This is the HTF used for the area plots and HTF midpoints.
Examples:
5-minute chart with HTF = 30 or 60 minutes
15-minute chart with HTF = 4H or 1D
The idea is to trade on the lower timeframe while seeing how far price is stretched relative to a higher-time-frame range midpoint.
3. Show / Hide toggles
Under “Show / Hide” you can independently turn on/off:
Kurutoga Lead (x1)
Kurutoga Lead 2x
Kurutoga Lead 4x
HTF Lead, HTF Lead 2x, HTF Lead 4x
This lets you:
Run only a single Kurutoga if you want a clean panel, or
Stack multiple leads for a “multi-speed” view of extension and mean reversion.
4. Color Scheme (4-color Kurutoga)
Up Light / Up Dark / Down Light / Down Dark – base hues used for every lead.
Lead opacity (x1, 2x, 4x) – sets how strong or faint each lead appears.
x1 is usually your primary “trading speed.”
x2 and x4 can be faded so they act as context.
Extra transparency for HTF areas – additional opacity applied on top of each lead’s opacity when drawing HTF areas. This keeps the HTF layer subtle.
You can fine-tune the exact teal/red values here to match your personal palette.
Practical reading & trade ideas
Trend alignment
When all three Kurutoga leads (x1, 2x, 4x) are above zero, price is trading above its rolling mid-range on multiple speeds → bullish environment.
When all three are below zero, you have a multi-speed bearish environment.
Mixed readings (e.g., x1 above zero, x4 below zero) can signal transition or mean-reversion areas.
Momentum vs exhaustion
Up Light / Down Light (light colors) show momentum expanding in that direction.
Up Dark / Down Dark (dark colors) show momentum contracting – price still on that side of zero, but the push is weakening.
After a run of Up Light bars, a shift to Up Dark may hint at a stall or pullback.
After a run of Down Light bars, a shift to Down Dark may hint at short covering / bounce potential.
Multi-time-frame confluence
Use the HTF areas as a backdrop:
If LTF Kurutoga leads are above zero while the HTF area is also positive (and ideally expanding), that’s strong bullish alignment.
If LTF leads are trying to flip up while HTF divergence is still deeply negative, you may be looking at a counter-trend bounce rather than a true trend change.
Example setups
Trend-following entries:
Look for x2 & x4 leads on the same side of zero as the HTF area, then use x1 color shifts (from Down Dark → Up Light or vice versa) to fine-tune entries in the direction of that higher-time-frame bias.
Mean-reversion fades:
Watch for extreme Kurutoga values where x1/x2 are strongly extended beyond zero while color flips from Light to Dark (momentum stalling) against an opposing HTF backdrop .
Notes
The indicator is non-directional by itself – it measures distance from a rolling midpoint rather than trend structure or order flow. It works best when combined with your existing price action/trend tools (moving averages, HLBO, structure zones, etc.).
Because HTF values are brought down via request.security, choose HTF settings that make sense for your product and session (for example, don’t use very high HTFs on thin intraday markets).
Use the Kurutoga MTF Histogram as a visual scanner for extension, momentum regime, and multi-speed alignment, then layer your own entry/exit rules on top.
Predictive Analysis Engine — Adaptive MACD Forecasting with R² SProfessional and Rule-Compliant Description (Ready for Publishing)
This description explains every component of the script in detail, highlights its originality, and provides traders with clear usage instructions — exactly what TradingView expects.
Predictive Analysis Engine (PAE)
This script is a predictive analysis model that combines trend filtering, linear forecasting, stability analysis (R²), and outlier filtering using ATR to produce an advanced, leading-style version of MACD rather than a traditional lagging one.
The indicator does not rely on random elements; it is built on four core components that work together:
1. Stability Measurement Using R²
The coefficient of determination (R²) is calculated based on the correlation between price and time, then normalized to a 0–1 scale.
A higher R² indicates more stable price movement, allowing the script to increase forecast accuracy.
Here, R² acts as a primary component of the Confidence Filter.
2. Forecasted Price Using Linear Regression
Instead of relying solely on the current price, the script uses:
Linear Regression
Weighted blending between the forecasted price and actual price
This enables the script to build a Leading MACD based on an “advanced” price that anticipates probable movement.
3. Advanced MACD With Adaptive Smoothing
MACD is applied to the blended (real + forecasted) price using:
Fast EMA
Slow EMA
MACD base
Optional TEMA for reducing signal lag
Adjustable histogram smoothing
This process makes MACD more responsive with significantly less lag, reacting faster to predicted movements.
4. Predictive MACD (Projected MACD)
Linear Regression is applied again — but this time to:
MACD
Signal
Histogram
to generate projected versions of each line (proj_macd, proj_signal), while proj_hist is used to produce early signals before the actual crossover occurs.
5. Volatility Filtering Using ATR & Volatility Ratio
ATR is used to evaluate:
Strength of movement
Overextension levels
Signal quality
ATR is combined with R² to compute:
Confidence = R² × Volatility Ratio
This suppresses weak signals and boosts high-quality, reliable ones.
6. Predictive Signals + Safety Filters
A signal is triggered when:
proj_hist crosses the 0 level
Confidence exceeds the required threshold
The real histogram is not excessively stretched (extra safety)
The script includes:
BUY / SELL
BUY_STRONG / SELL_STRONG
based on the smoothed histogram trend.
7. Coloring, Background & Visual Enhancements
The script colors:
The histogram
Chart background
Signal lines
to clearly highlight momentum direction and confidence conditions.
8. Built-In Alerts
The script provides ready-to-use alerts:
BUY Alert
SELL Alert
Both based on the predictive MACD model.
How to Use the Script
Add it to any timeframe and any market.
BUY/SELL signals are generated from the projected histogram crossover.
Higher Confidence = stronger signal.
Background colors help visualize trend transitions instantly.
Recommended to combine with support/resistance or price action.
Indicator Objective
This script is designed to deliver early insight into momentum shifts using a blend of:
Linear forecasting
Trend stability via R²
Signal quality filtering via ATR
A fast and adaptive advanced MACD
Smart RSI MTF [DotGain]Summary
Are you tired of constantly switching between timeframes to check the RSI, only to miss the bigger picture?
The Smart RSI MTF (Multi-Timeframe) is designed to solve this exact problem. It is a streamlined chart overlay that monitors RSI conditions across up to 10 different timeframes simultaneously —from the 1-minute chart all the way up to the Monthly view.
This indicator removes the need for multiple open tabs and declutters your analysis by plotting signals directly on your main chart using a smart "visual hierarchy" system based on transparency.
⚙️ Core Components and Logic
The Smart RSI MTF relies on a sophisticated 3-layer logic to deliver clear, actionable context:
Multi-Timeframe Engine: The script runs 10 independent RSI calculations in the background. It checks standard intervals (5m, 15m, 1h, 4h, Daily, Weekly, Monthly) to ensure you never miss a momentum extreme on any scale.
Classic RSI Thresholds:
Overbought (> 70): Indicates price may be extended to the upside.
Oversold (< 30): Indicates price may be extended to the downside.
Smart Visibility System (The "Secret Sauce"): Not all signals are equal. A 5-minute Overbought signal is "noise" compared to a Weekly Overbought signal. This indicator automatically applies Transparency to differentiate importance:
Minutes = High Transparency (Faint).
Hours = Medium Transparency.
Days/Weeks/Months = No Transparency (Solid/Bold).
🚦 How to Read the Indicator
The indicator plots shapes (Labels by default) directly above or below the candles. The appearance tells you the direction and the timeframe significance:
🟥 RED SIGNALS (Overbought Condition)
Trigger: RSI is above 70 on a specific timeframe.
Location: Placed above the candle bar.
Meaning: Potential bearish reversal or pullback.
🟩 GREEN SIGNALS (Oversold Condition)
Trigger: RSI is below 30 on a specific timeframe.
Location: Placed below the candle bar.
Meaning: Potential bullish reversal or bounce.
👻 TRANSPARENCY (Signal Strength)
Faint/Ghostly: The signal comes from a lower timeframe (e.g., 5m, 15m). Use for scalping or entry timing.
Solid/Bright: The signal comes from a major timeframe (e.g., Daily, Weekly). Use for swing trading and identifying major market turns.
Visual Elements
Symbol Shapes: Fully customizable (Label, Diamond, Circle, Triangle, etc.) via settings.
Stacking: If multiple timeframes trigger at once, symbols will overlay, creating a visually denser and darker color, indicating Confluence .
Key Benefit
The goal of the Smart RSI MTF is to help traders instantly spot Confluence . When you see a faint short-term signal align with a solid long-term signal, you have identified a high-probability reversal zone without leaving your chart.
Have fun :)
Disclaimer
This "Smart RSI MTF" indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice.
The signals generated by this tool (both "Buy" and "Sell" indications) are the result of a specific set of algorithmic conditions. They are not a direct recommendation to buy or sell any asset. All trading and investing in financial markets involves substantial risk of loss. You can lose all of your invested capital.
Past performance is not indicative of future results. The signals generated may produce false or losing trades. The creator (© DotGain) assumes no liability for any financial losses or damages you may incur as a result of using this indicator.
You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR) and consider your personal risk tolerance before making any trades.
Nuh's Stochastic + Structure 1.0Nuh's Stochastic + Structure 1.0 is an advanced momentum–structure fusion indicator designed to identify high-probability reversal and continuation zones using a multi-layer confirmation engine. The script combines enhanced Stochastic analysis, market structure detection (HH/HL/LH/LL), divergence tracking, volume spikes, higher-timeframe trend alignment, and extreme-duration filters to deliver highly reliable buy/sell signals. Each signal is dynamically scored for strength, and a compact one-line trend panel provides real-time market state at a glance. Colors and visual elements follow a clear and intuitive hierarchy optimized for fast decision-making. Ideal for crypto, indices, and forex traders who want precision entries with minimal noise.
Order Flow AnalysisOrder Flow Pressure Suite — Wick, Volume & Absorption-Based Pressure Map
This indicator builds a composite buying/selling pressure score from candle structure, volume behavior, and absorption signals.
It is designed to infer the “intent” behind price moves by looking at how candles form, where they close, and how volume behaves — even without access to true bid/ask or footprint data.
Core Concepts
Wick-to-Body Analysis
The script evaluates the ratio of upper and lower wicks to the total candle range.
Strong wicks with relatively small bodies are treated as rejections :
Long upper wick → potential selling pressure / rejection of higher prices
Long lower wick → potential buying pressure / rejection of lower prices
Close Position Analysis
The close is normalized within the candle range:
Close near the high → bullish pressure
Close near the low → bearish pressure
Close near the middle → more neutral , context taken from wicks and volume
Volume Delta Estimation
Since true bid/ask data is not available on standard charts, the script estimates “volume delta” by distributing total volume between buyers and sellers based on candle characteristics:
Bull candles receive more “buying volume,” weighted toward closes near the high
Bear candles receive more “selling volume,” weighted toward closes near the low
This is an approximation of order flow, not a direct time & sales feed.
Absorption Detection
The script looks for candles where volume is high but price movement is relatively small .
This combination often suggests:
Bullish absorption → buyers absorbing aggressive selling (potential accumulation)
Bearish absorption → sellers absorbing aggressive buying (potential distribution)
Absorption zones are tracked over a configurable lookback and can be shaded in the background.
Composite Pressure Oscillator
All the above components (wicks, close position, heuristic volume delta, absorption bias) are blended into a single pressure score :
Values > 0 → net buying pressure
Values < 0 → net selling pressure
The raw score is smoothed with an EMA to reduce noise and create a cleaner oscillator line.
Divergence Detection
The indicator compares price pivots to pressure pivots:
Bullish divergence : price makes a lower low while pressure makes a higher low
Bearish divergence : price makes a higher high while pressure makes a lower high
These conditions can help highlight potential exhaustion or hidden participation from larger players.
Visual Elements
Histogram showing the intensity of buying/selling pressure
Color-coding for increasing vs. decreasing pressure
Background shading for detected absorption zones
Status table summarizing current pressure, trend bias, volume delta, wick signal, and absorption state in real time
How To Use
Use the pressure oscillator to gauge whether the current bar sequence is dominated by buyers or sellers. Strong positive readings may indicate sustained buying pressure; strong negatives may indicate sustained selling pressure.
Watch for divergences between price and the pressure oscillator around key levels, swings, or zones you already care about.
Use absorption zones and wick rejection signals as additional context around support/resistance, breakouts, or failed moves.
Treat all signals as context and confluence , not as stand-alone trade entries or exits. This tool is best used alongside your existing price action, volume, and risk management framework.
Important Notes & Limitations
This script does not access real bid/ask, footprint, or order book data . All volume delta and absorption interpretations are heuristic estimates derived from OHLCV candles.
Signals are probabilistic , not guarantees. They can be early, late, or outright wrong in fast or low-liquidity markets.
Always validate signals with your own analysis, timeframe alignment, and risk management. This indicator is intended as an analytical tool , not financial advice.
Abacus Community Williams %R + Bollinger %B📌 Indicator Description (Professional & Clear)
Williams %R + Bollinger %B Momentum Indicator (ThinkOrSwim Style)
This custom indicator combines Williams %R and Bollinger %B into a single, unified panel to provide a powerful momentum-and-positioning view of price action. Modeled after the ThinkOrSwim version used by professional traders, it displays:
✅ Williams %R (10-period) – Yellow Line
This oscillator measures the market's position relative to recent highs and lows.
It plots on a 0% to 100% scale, where:
80–100% → Overbought region
20–0% → Oversold region
50% → Momentum equilibrium
Williams %R helps identify exhaustion, trend strength, and potential reversal zones.
✅ Bollinger %B (20, 2.0) – Turquoise Histogram Bars
%B shows where price is trading relative to the Bollinger Bands:
Above 50% → Price is in the upper half of the band (bullish pressure)
Below 50% → Price is in the lower half (bearish pressure)
Near 100% → Price pushing upper band (possible breakout)
Near 0% → Price testing lower band (possible breakdown)
The histogram visually represents momentum shifts in real time, creating a clean profile of volatility and strength.
🎯 Why This Combination Works
Together, Williams %R and Bollinger %B reveal:
Momentum direction
Overbought/oversold conditions
Volatility compression & expansion
Trend continuation vs reversal zones
High-probability inflection points
Williams %R shows oscillation and exhaustion, while %B shows pressure inside volatility bands.
The combination helps identify whether momentum supports the current trend or is weakening.
🔍 Use Cases
Detect early trend reversals
Validate breakouts and breakdowns
Spot momentum failure in price extremes
Confirm pullbacks and continuation setups
Time entries and exits with higher precision
💡 Best For
Swing traders
Momentum traders
Trend-followers
Options traders (for timing premium decay or volatility expansion)
Zfr RSI Pozitif - Negatif Uyumsuzluk TaramasıIt helps to track the direction of RSI with moving average while displaying divergence and related scans on the standard RSI.
Tactical Deviation🎯 TACTICAL DEVIATION - Volume-Backed VWAP Deviation Analysis
What Makes This Different?
Unlike basic VWAP indicators, Tactical Deviation combines:
• Multi-timeframe VWAP deviation bands (Daily/Weekly/Monthly)
• Volume spike intelligence - signals only appear with volume confirmation
• Pivot reversal detection at deviation extremes
• Optional multi-VWAP confluence system
• Smart defaults for quality over quantity
This unique combination filters weak setups and identifies high-probability entries at extreme price deviations from fair value.
📊 DEFAULT SETTINGS (Ready to Use)
✅ Daily VWAP with ±2σ deviation bands
✅ Volume spike detection (1.5x average required)
✅ 2σ minimum deviation for signals
❌ Weekly/Monthly VWAPs (enable for multi-timeframe)
❌ Pivot reversal requirement (enable for stronger signals)
❌ Fill zones (optional visual enhancement)
Why: Daily VWAP is most relevant for intraday trading. 2σ bands catch meaningful moves. Volume spikes ensure conviction. Clean chart focuses on what matters.
🚀 HOW TO USE
BASIC USAGE:
• Green triangles (below bars) = Long signals at oversold deviations
• Red triangles (above bars) = Short signals at overbought deviations
SIGNAL QUALITY:
• Normal size, bright colors = Volume spike (best quality)
• Small size, lighter colors = Volume momentum
• Tiny size = No volume confirmation
DEVIATION ZONES:
• ±2σ = Extreme deviation (signals appear here)
• ±1σ to ±2σ = Extended but not extreme
• Within ±1σ = Normal range
TRADING APPROACHES:
Mean Reversion:
→ Enter when price reaches ±2σ with volume spike
→ Target: Return to VWAP or opposite band
→ Stop: Beyond extreme deviation
Trend Continuation:
→ Use bands to identify pullbacks
→ Enter pullback to VWAP in trending market
→ Volume confirms continuation
Reversal Trading:
→ Enable "Require Pivot Reversal" for stronger signals
→ Signals only when deviation + pivot reversal occur
→ Higher probability, fewer signals
⚙️ EXPLORE SETTINGS FOR FULL USE
VWAP SETTINGS:
• Show Weekly/Monthly VWAP = Multi-timeframe context
• Show ±1σ Bands = Normal deviation range
• Show ±3σ Bands = Extreme extremes (rare but powerful)
SIGNAL SETTINGS:
• Min Deviation: 1σ (more signals) | 2σ (default) | 3σ (fewer, extreme only)
• Require Pivot Reversal: OFF (default) | ON (stronger but fewer)
• Volume Spike Threshold: 1.5x (default) | 2.0x+ (major spikes) | 1.2x (more signals)
CONFLUENCE SETTINGS:
• Require Multi-VWAP Confluence: OFF (default) | ON (2+ VWAPs must agree)
• Min VWAPs: 2 (Daily + Weekly/Monthly) | 3 (all must agree)
VISUAL SETTINGS:
• Show Fill Zones = Shaded areas between bands
• Fill Opacity = Transparency adjustment
• Line Widths = Customize thickness
💡 PRO TIPS
1. Start with defaults, then enable features as you learn
2. Volume spike requirement filters weak moves - keep it enabled
3. Enable Weekly/Monthly VWAPs for higher timeframe context
4. Enable confluence for swing trading setups
5. Pivot reversals: ON for reversals, OFF for continuations
6. Check top-right info table for current deviation levels
🎨 VISUAL GUIDE
• Cyan Line = Daily VWAP (fair value)
• Cyan Bands = Daily deviation zones
• Orange Line = Weekly VWAP (if enabled)
• Purple Line = Monthly VWAP (if enabled)
• Green Triangle = Long signal (oversold)
• Red Triangle = Short signal (overbought)
⚠️ IMPORTANT
Educational purposes only. Always use proper risk management. Signals are based on statistical deviation, not guarantees. Volume confirmation improves quality but doesn't guarantee outcomes. Combine with your own analysis.
The unique combination of VWAP deviation analysis, volume profile confirmation, pivot identification, and multi-timeframe confluence in a single clean interface makes Tactical Deviation different from basic VWAP indicators.
Happy Trading! 📈
Average Directional Index infoAverage Directional Index (ADX) is a technical indicator created by J. Welles Wilder that measures trend strength (not direction!). Values range from 0 to 100.
This indicator is a supplementary tool for assessing whether trend strategies are worthwhile, monitoring changes in trend strength and avoiding weak, choppy movements
Value Interpretation:
0-25: Weak trend or sideways market
25-50: Moderate to strong trend
50-75: Very strong trend
75-100: Extremely strong trend (rare)
Important: ADX does not indicate trend direction (up/down), only its strength!
This script indicator includes additional features:
1. ADX Plot (purple line)
Basic ADX value showing current trend strength.
2. ADX Trend Analysis (arrows)
The script compares current ADX with its 10-period moving average with ±5% tolerance:
↑ (green): ADX rising → trend strengthening
↓ (red): ADX falling → trend weakening
⮆ (gray): ADX stable → trend strength unchanged
3. Information Table
Displays current ADX value with trend arrow in the top-right corner.
Parameters to Configure
Smoothing (default: 14) - Indicator smoothing period
Lower values (e.g., 7): more sensitive, more signals
Higher values (e.g., 21): more stable, less noise
Indicator Length (default: 14) - Period for calculating directional movement (+DI/-DI)
Wilder's standard value is 14
Trend Length (default: 10) - Period for moving average to analyze ADX dynamics
Determines how quickly changes in trend strength are detected
Practical Application
✅ Strategy 1: Trend Strength Filter
1. ADX > 25 → look for positions aligned with the trend
2. ADX < 25 → avoid trend strategies, consider oscillators
✅ Strategy 2: Entries on Strengthening Trend
1. ADX crosses above 25 + arrow ↑ → trend gaining momentum
2. Combine with other indicators (e.g., EMA) for direction confirmation
✅ Strategy 3: Exhaustion Warning
1. ADX > 50 + arrow ↓ → strong trend may be exhausting
2. Consider profit protection or trailing stop
