Elastic Volume-Weighted Momentum 🔥Elastic Volume-Weighted Momentum (EVWM) is a hybrid oscillator that measures the "force" of a price move by combining distance from the mean (elasticity) with relative trading volume. Unlike standard momentum indicators that only look at price speed, EVWM assumes that volume is the fuel for sustainable trends.
The indicator calculates a baseline (default: Hull Moving Average) and measures how far price stretches away from it, normalized by market volatility (ATR). This "elasticity" is then multiplied by a Volume Factor. The result is a histogram and signal line that distinguishes between high-conviction moves (high volume) and weak speculation (low volume).
How to Use
The EVWM is designed to filter false breakouts and identify mean-reversion opportunities through three distinct signal types:
1. Ignition Signals (Triangles) These occur when the momentum breaks outside the standard deviation bands with high volume.
Signal: Yellow Triangles.
Interpretation: This represents a valid breakout. The market has stretched away from the average with significant participation. This is often a signal to enter in the direction of the breakout.
2. Zombie Signals (X Marks) These occur when the momentum breaks outside the bands with low volume.
Signal: Grey "X".
Interpretation: This is a "fakeout" or a trap. The price moved, but there is no volume supporting it. Traders should exercise caution or consider fading the move, as it lacks the energy to sustain the trend.
3. Snap-Back Signals (Circles) These occur when the momentum line returns inside the bands after being overextended.
Signal: Red/Green Circles.
Interpretation: The "rubber band" is snapping back. This is a classic mean-reversion signal, often used to take profits on an existing position or to enter a counter-trend trade targeting the baseline.
4. Divergences The indicator includes an optional feature to detect discrepancies between price action and momentum.
Bearish Divergence: Price makes a higher high, but EVWM makes a lower high.
Bullish Divergence: Price makes a lower low, but EVWM makes a higher low. These patterns often precede a trend reversal.
Configuration Guide
Lookback Length: Controls the speed of the indicator. Use lower values (21) for scalping and higher values (50+) for swing trading.
Baseline Type: Selects the moving average used as the center of gravity. "Hull MA" is the default for its responsiveness, while "SMA" offers a smoother, slower baseline.
Trend Filter: A safety mechanism that checks a higher timeframe (e.g., 4-hour or Daily). If enabled, the indicator will block "Buy" signals if the higher timeframe trend is bearish, helping traders stay on the right side of the market.
Volume Threshold: Adjusts what defines "High Volume." Increasing this value makes "Ignition" signals rarer but potentially more reliable.
Disclaimer: This indicator is provided for educational purposes only. Past performance does not guarantee future results.
Oscillators
Divergence Scanner
Scanner and Indication (Divergence Scanner & Signal)An advanced experimental indicator designed to detect instances of Divergence between price action and key oscillator metrics (e.g., RSI or MACD).The primary function of this script is for Screener use. It plots a numerical value (a value greater than zero) on the chart when a confirmed bullish or bearish divergence signal appears."
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.
RSI HTF Hardcoded (A/B Presets) + Regimes [CHE]RSI HTF Hardcoded (A/B Presets) + Regimes — Higher-timeframe RSI emulation with acceptance-based regime filter and on-chart diagnostics
Summary
This indicator emulates a higher-timeframe RSI on the current chart by resolving hardcoded “HTF-like” lengths from a time-bucket mapping, avoiding cross-timeframe requests. It computes RSI on a resolved length, smooths it with a resolved moving average, and derives a histogram-style difference (RSI minus its smoother). A four-state regime classifier is gated by a dead-band and an acceptance filter requiring consecutive bars before a regime is considered valid. An on-chart table reports the active preset, resolved mapping tag, resolved lengths, and the current filtered regime.
Pine version: v6
Overlay: false
Primary outputs: RSI line, SMA(RSI) line, RSI–SMA histogram columns, reference levels (30/50/70), regime-change alert, info table
Motivation
Cross-timeframe RSI implementations often rely on `request.security`, which can introduce repaint pathways and additional update latency. This design uses deterministic, on-series computation: it infers a coarse target bucket (or uses a forced bucket) and resolves lengths accordingly. The dead-band reduces noise at the decision boundaries (around RSI 50 and around the RSI–SMA difference), while the acceptance filter suppresses rapid flip-flops by requiring sustained agreement across bars.
Differences
Baseline: Standard RSI with a user-selected length on the same timeframe, or HTF RSI via cross-timeframe requests.
Key differences:
Hardcoded preset families and a bucket-based mapping to resolve “HTF-like” lengths on the current chart.
No `request.security`; all calculations run on the chart’s own series.
Regime classification uses two independent signals (RSI relative to 50 and RSI–SMA difference), gated by a configurable dead-band and an acceptance counter.
Always-on diagnostics via a persistent table (optional), showing preset, mapping tag, resolved lengths, and filtered regime.
Practical effect: The oscillator behaves like a slower, higher-timeframe variant with more stable regime transitions, at the cost of delayed recognition around sharp turns (by design).
How it works
1. Bucket selection: The script derives a coarse “target bucket” from the chart timeframe (Auto) or uses a user-forced bucket.
2. Length resolution: A chosen preset defines base lengths (RSI length and smoothing length). A bucket/timeframe mapping resolves a multiplier, producing final lengths used for RSI and smoothing.
3. Oscillator construction: RSI is computed on the resolved RSI length. A moving average of RSI is computed on the resolved smoothing length. The difference (RSI minus its smoother) is used as the histogram series.
4. Regime classification: Four regimes are defined from:
RSI relative to 50 (bullish above, bearish below), with a dead-band around 50
Difference relative to 0 (positive/negative), with a dead-band around 0
These two axes produce strong/weak bull and bear states, plus a neutral state when inside the dead-band(s).
5. Acceptance filter: The raw regime must persist for `n` consecutive bars before it becomes the filtered regime. The alert triggers when the filtered regime changes.
6. Diagnostics and visualization: Histogram columns change shade based on sign and whether the difference is rising/falling. The table displays preset, mapping tag, resolved lengths, and the filtered regime description.
Parameter Guide
Source — Input series for RSI — Default: Close — Smoother sources reduce noise but add lag.
Preset — Base lengths family — Default: A(14/14) — Switch presets to change RSI and smoothing responsiveness.
Target Bucket — Auto or forced bucket — Default: Auto — Force a bucket to lock behavior across chart timeframe changes.
Table X / Table Y — Table anchor — Default: right / top — Move to avoid covering content.
Table Size — Table text size — Default: normal — Increase for presentations, decrease for dense layouts.
Dark Mode — Table theme — Default: enabled — Match chart background for readability.
Show Table — Toggle diagnostics table — Default: enabled — Disable for a cleaner pane.
Epsilon (dead-band) — Noise gate for decisions — Default: 1.0 — Raise to reduce flips near boundaries; lower to react faster.
Acceptance bars (n) — Bars required to confirm a regime — Default: 3 — Higher reduces whipsaw; lower increases reactivity.
Reading
Histogram (RSI–SMA):
Above zero indicates RSI is above its smoother (positive momentum bias).
Below zero indicates RSI is below its smoother (negative momentum bias).
Darker/lighter shading indicates whether the difference is increasing or decreasing versus the previous bar.
RSI vs SMA(RSI):
RSI’s position relative to 50 provides broad directional bias.
RSI’s position relative to its smoother provides momentum confirmation/contra-signal.
Regimes:
Strong bull: RSI meaningfully above 50 and difference meaningfully above 0.
Weak bull: RSI above 50 but difference below 0 (pullback/transition).
Strong bear: RSI meaningfully below 50 and difference meaningfully below 0.
Weak bear: RSI below 50 but difference above 0 (pullback/transition).
Neutral: inside the dead-band(s).
Table:
Use it to validate the active preset, the mapping tag, the resolved lengths, and the filtered regime output.
Workflows
Trend confirmation:
Favor long bias when strong bull is active; favor short bias when strong bear is active.
Treat weak regimes as pullback/transition context rather than immediate reversals, especially with higher acceptance.
Structure + oscillator:
Combine regimes with swing structure, breakouts, or a baseline trend filter to avoid trading against dominant structure.
Use regime change alerts as a “state change” notification, not as a standalone entry.
Multi-asset consistency:
The bucket mapping helps keep a consistent “feel” across different chart timeframes without relying on external timeframe series.
Behavior/Constraints
Intrabar behavior:
No cross-timeframe requests are used; values can still evolve on the live bar and settle at close depending on your chart/update timing.
Warm-up requirements:
Large resolved lengths require sufficient history to seed RSI and smoothing. Expect a warm-up period after loading or switching symbols/timeframes.
Latency by design:
Dead-band and acceptance filtering reduce noise but can delay regime changes during sharp reversals.
Chart types:
Intended for standard time-based charts. Non-time-based or synthetic chart types (e.g., Heikin-Ashi, Renko, Kagi, Point-and-Figure, Range) can distort oscillator behavior and regime stability.
Tuning
Too many flips near decision boundaries:
Increase Epsilon and/or increase Acceptance bars.
Too sluggish in clean trends:
Reduce Acceptance bars by one, or choose a faster preset (shorter base lengths).
Too sensitive on lower timeframes:
Choose a slower preset (longer base lengths) or force a higher Target Bucket.
Want less clutter:
Disable the table and keep only the alert + plots you need.
What it is/isn’t
This indicator is a regime and visualization layer for RSI using higher-timeframe emulation and stability gates. It is not a complete trading system and does not provide position sizing, risk management, or execution rules. Use it alongside structure, liquidity/volatility context, and protective risk controls.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Best regards and happy trading
Chervolino.
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.
Delta Force Index - DFI [TCMaster]This indicator provides a proxy measurement of hidden buying and selling pressure inside each candle by combining tick volume with candle direction. It calculates a simulated delta volume (buy vs. sell imbalance), applies customizable scaling factors, and displays three components:
Delta Columns (green/red): Show estimated hidden buy or sell pressure per candle.
Delta Moving Average (orange line): Smooths delta values to highlight underlying momentum.
Cumulative Delta (blue line): Tracks the long-term accumulation of hidden order flow.
How to use:
Rising green columns with a positive Delta MA and upward Cumulative Delta suggest strong hidden buying pressure.
Falling red columns with a negative Delta MA and downward Cumulative Delta suggest strong hidden selling pressure.
Scaling parameters allow you to adjust the visual balance between columns and lines for different timeframes.
Note: This tool uses tick volume and candle direction as a proxy for order flow. It does not display actual bid/ask data or Level II market depth. For professional order flow analysis, footprint charts or DOM data are required.
Smart ATR ProSmart ATR Pro - Adaptive Volatility & Smart Money Indicator
Advanced oscillator combining Adaptive ATR filtering with Smart Money detection. Features:
🎯 Smart Signals
BUY/SELL alerts with star rating system (1-5 stars)
STRONG signals for high-probability entries
ATR color status (Green/Yellow/Red) for volatility conditions
📊 Multi-Timeframe Analysis
MFI with overbought/oversold zones
Cumulative Delta volume analysis
Smart Money Power histogram
Price-action divergences detection
⚡ Adaptive Technology
Auto-adjusts ATR ranges based on market conditions
Smart Money strength calculation (0-6 points)
Volume spike detection
🎨 Professional UI
Centered table with adjustable opacity
Color-coded indicators for quick reading
Clean oscillator display with multiple plots
Perfect for swing traders and day traders seeking confirmed entries with volatility filtering and smart money confirmation.
*Settings: ATR Period 14, MFI Period 12, 100-bar analysis*
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.
---
### 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.
---
### 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.
---
### 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.
RSI Strategy [PrimeAutomation]⯁ OVERVIEW
The RSI Strategy is a momentum-driven trading system built around the behavior of the Relative Strength Index (RSI).
Instead of using traditional overbought/oversold zones, this strategy focuses on RSI breakouts with volatility-based trailing stops, adaptive profit-targets, and optional early-exit logic.
It is designed to capture strong continuation moves after momentum shifts while protecting trades using ATR-based dynamic risk management.
⯁ CONCEPTS
RSI Breakout Momentum: Entries happen when RSI breaks above/below custom thresholds, signaling a shift in momentum rather than mean reversion.
Volatility-Adjusted Risk: ATR defines both stop-loss and profit-target distances, scaling positions based on market volatility.
Dynamic Trailing Stop: The strategy maintains an adaptive trailing level that tightens as price moves in the trade’s favor.
Single-Position System: Only one trade at a time (no pyramiding), maximizing clarity and simplifying execution.
⯁ KEY FEATURES
RSI Signal Engine
• Long when RSI crosses above Upper threshold
• Short when RSI crosses below Lower threshold
These levels are configurable and optimized for trend-momentum detection.
ATR-Based Stop-Loss
A custom ATR multiplier defines the initial stop.
• Long stop = price – ATR × multiplier
• Short stop = price + ATR × multiplier
Stops adjust continuously using a trailing model.
ATR-Based Take Profit (Optional)
Profit targets scale with volatility.
• Long TP = entry + ATR × TP-multiplier
• Short TP = entry – ATR × TP-multiplier
Users can disable TP and rely solely on trailing stops.
Real-Time Trailing Logic
The stop updates bar-by-bar:
• In a long trade → stop moves upward only
• In a short trade → stop moves downward only
This keeps the stop tight as trends develop.
Early Exit Module (Optional)
After X bars in a trade, opposite RSI signals trigger exit.
This reduces holding time during weak follow-through phases.
Full Visual Layer
• RSI plotted with threshold fills
• Entry/TP/Stop visual lines
• Color-coded zones for clarity
⯁ HOW TO USE
Look for RSI Breakouts:
Focus on RSI crossing above the upper boundary (long) or below the lower boundary (short). These moments identify fresh momentum surges.
Use ATR Levels to Manage Risk:
Because stops and targets scale with volatility, the strategy adapts well to both quiet and explosive market phases.
Monitor Trailing Stops for Trend Continuation:
The trailing stop is the primary driver of exits—often outperforming fixed targets by catching larger runs.
Use on Liquid Markets & Mid-Higher Timeframes:
The system performs best where RSI and ATR signals are clean—crypto majors, FX, and indices.
⯁ CONCLUSION
The RSI Strategy is a modern RSI breakout system enhanced with volatility-adaptive risk management and flexible exit logic. It is designed for traders who prefer momentum confirmation over mean reversion, offering a disciplined framework with robust protections and dynamic trend-following capability.
Its blend of ATR-based stops, optional profit targets, and RSI-driven entries makes it a reliable strategy across a wide range of market conditions.
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?"
RSI UpDown [DivineTrade]This indicator displays the RSI values across multiple timeframes in real time. It provides a compact panel showing RSI readings for 1W, 1D, 4H, 1H, 15M, 5M and 1M, updating continuously as new price data arrives. Each value is color-coded based on market conditions: strong overbought levels, moderate overbought zones, neutral ranges and oversold areas. This allows traders to quickly assess multi-timeframe momentum and identify alignment or divergence across different market horizons.
Moving Average Difference//@version=5
indicator("Moving Average Difference", overlay=false)
fastLength = input.int(9, "Fast MA Length")
slowLength = input.int(21, "Slow MA Length")
fastMA = ta.sma(close, fastLength)
slowMA = ta.sma(close, slowLength)
difference = fastMA - slowMA
plot(difference, color = difference >= 0 ? color.green : color.red, linewidth=2)
hline(0, "Zero Line", color=color.white)
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.
