1D & 1W Institutional Trend The 1D & 1W Institutional Trend is a multi-timeframe (MTF) trend-following system designed to align traders with major "macro" market moves. Instead of relying on noisy intraday data, this indicator pulls data from the Daily (1D) and Weekly (1W) timeframes to construct a robust trend baseline, regardless of the chart timeframe you are currently viewing.
The core logic is based on the interaction between a Fast Institutional EMA (Daily) and a Slow Institutional EMA (Weekly). When the Daily trend crosses above the Weekly trend, it signals a significant shift in market structure. To ensure signal quality, the script incorporates a "Smart Filter" engine that checks for Momentum (RSI) and Volatility (ATR) before generating entry signals, preventing trades during exhausted or dead markets.
Key Features
Multi-Timeframe Engine: Projects Daily and Weekly moving averages onto lower timeframe charts (e.g., 1H or 4H) to show the "Big Picture."
Non-Repainting Logic: Utilizes closed-bar data to ensure that historical signals match live trading conditions strictly.
Algorithmic Filtering:
Momentum Filter: Rejects Buy signals if RSI is overbought and Sell signals if RSI is oversold.
Volatility Filter: Rejects signals during low-volatility "compression" zones using ATR.
Institutional Dashboard: A data panel tracking the macro trend status, trend strength (Spread %), and filter conditions.
How to Use
1. The Trend Cloud The visual core of the indicator is the "Cloud" formed between the two Moving Averages.
Green Cloud: The Daily Average is above the Weekly Average. The macro trend is Bullish. Look for long positions.
Red Cloud: The Daily Average is below the Weekly Average. The macro trend is Bearish. Look for short positions.
The Midline: The gray line represents the "Fair Value" price between the two timeframes. It often acts as dynamic support or resistance during a trend.
2. Signal Triangles Discrete shapes appear only when a crossover is confirmed AND all filters are met.
Up Triangle: Confirmed Bullish Crossover (Daily crosses over Weekly) + RSI is not overbought + Volatility is active.
Down Triangle: Confirmed Bearish Crossover (Daily crosses under Weekly) + RSI is not oversold + Volatility is active.
3. The Dashboard Located in the bottom right, this table provides a health check of the current trend:
Macro Trend: Displays BULLISH or BEARISH based on the cloud direction.
Trend Spread %: Measures the distance between the two EMAs. A widening percentage indicates a strengthening trend, while a narrowing percentage suggests momentum loss.
RSI Condition: Displays "SAFE" (good to trade) or "EXTENDED" (too risky).
Volatility: Displays "EXPANSION" (good movement) or "COMPRESSION" (flat market).
4. Timeframe Rules Because this indicator uses Daily and Weekly data, your chart timeframe must be lower than the Fast Trend Timeframe.
Correct: Viewing a 1-Hour chart with 1D/1W settings.
Incorrect: Viewing a Weekly chart with 1D/1W settings (this will trigger an error message on the screen).
Disclaimer: This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results.
ความผันผวน
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.
Position Sizing Calculator (Real-Time) - Futures Edition█ SUMMARY
The following indicator is a Position Sizing Calculator based on Average True Range (ATR), originally developed by market technician J. Welles Wilder Jr., intended for real-time trading.
This script utilizes the user's account size, acceptable risk percentage, and a stop-loss distance based on ATR to dynamically calculate the appropriate position size for each trade in real time.
█ BACKGROUND
Developed for use on the Micro E-mini Nasdaq-100 futures (MNQ), this script provides traders with continuously updated dynamic position sizes. It enables traders to instantly determine the exact number of contracts to use when entering a trade while staying within their acceptable risk tolerance.
This real-time position sizing tool helps traders make well-informed decisions when planning trade entries and calculating maximum stop-loss levels, ultimately enhancing risk management.
█ USER INPUTS
Trading Account Size: Total dollar value of the user's trading account.
Acceptable Risk (%): Maximum percentage of the trading account that the user is willing to risk per trade.
ATR Multiplier for Stop-Loss: Multiplier used to determine the distance of the stop-loss from the current price, based on the ATR value.
ATR Length: The length of the lookback period used to calculate the ATR value.
Show Target Risk Row: Toggle to hide/show the Target Risk Row
SL Levels Display: Option to see Both, Long Only, Short Only, or None of the Stop Loss Level Values.
Contract Point Value ($): Point value per contract. Tooltip highlights common values.
Tick Size: Minimum Price Movement (Default set to 0.25)
Minimum Contracts: Override the Minimum Contracts per trade to a user selected value.
(May Exceed User's Target Risk)
Ichimoku+RVOL Strategy Hariss 369Trading with Ichimoku Cloud is very simple.
It is the one of the self sufficient indicator takes every parameter into account.
Buy when price closes above the cloud. Stop loss just below the cloud and target 1.5 or 2 times of stop loss.
Sell when price closes below the cloud. Stop loss just above the cloud and target 1.5 or 2 times of stop loss.
No trade when price is inside the cloud as price consolidates here.
RVOL has been taken into account to initiate the trade with volume.
Bollinger Bands HTF Hardcoded (Len 20 / Dev 2) [CHE]Bollinger Bands HTF Hardcoded (Len 20 / Dev 2) — Higher-timeframe BB emulation with bucket-based length scaling and on-chart diagnostics
Summary
This indicator emulates higher-timeframe Bollinger Bands directly on the current chart by scaling a fixed base length (20) via a timeframe-to-bucket multiplier map. It avoids cross-timeframe requests and instead applies the “HTF feel” by using a longer effective lookback on lower timeframes. Bands use the classic deviation of 2 and the original color scheme (Basis blue, Upper red, Lower green, blue fill). An on-chart table reports the resolved bucket, multiplier, and effective length.
Pine version: v6
Overlay: true
Primary outputs: Basis (SMA), Upper/Lower bands, background fill, optional info table
Motivation: Why this design?
Cross-timeframe Bollinger Bands typically rely on `request.security`, which can introduce complexity, mixed-bar alignment issues, and potential repaint paths depending on how users consume signals intrabar. This design offers a deterministic alternative: a single-series calculation on the chart timeframe, with a hardcoded “HTF emulation” achieved by scaling the BB length according to coarse higher-timeframe buckets. The result is a smoother, slower band structure on low timeframes without external timeframe calls.
What’s different vs. standard approaches?
Baseline: Standard Bollinger Bands with a fixed user length on the current timeframe, or true HTF bands via `request.security`.
Architecture differences:
Fixed base parameters: Length = 20, Deviation = 2.
Bucket mapping derived from the chart timeframe (or manually overridden).
No `request.security`; all computations occur on the current series.
Effective length is “20 × multiplier”, where multiplier approximates aggregation into the chosen bucket.
Diagnostics table for transparency (bucket, multiplier, resolved length, bandwidth).
Practical effect: On lower timeframes, the effective length becomes much larger, behaving like a higher-timeframe Bollinger structure (smoother basis and wider stability), while remaining purely local to the chart series.
How it works (technical)
The script first resolves a target bucket (“Auto” or a manual selection such as 60/240/1D/…/12M). It then computes a multiplier that approximates how many current bars fit into that bucket (e.g., 1m→60m uses mult≈60, 5m→60m uses mult≈12). The effective Bollinger length becomes:
`bb_len = 20 mult` (clamped to at least 1)
Using the effective length, it calculates:
`basis = ta.sma(src, bb_len)`
`dev = 2 ta.stdev(src, bb_len)`
`upper = basis + dev`
`lower = basis - dev`
A “bandwidth” diagnostic is also computed as `(upper-lower) / basis` (guarded against division by zero) and shown in the table as a percentage. A persistent table object is created/deleted based on the visibility toggle and updated only on the last bar for performance.
Parameter Guide
Source — Input series for the bands — Default: Close
Use close for classic behavior; smoother sources reduce responsiveness.
Bucket — HTF bucket selection — Default: Auto
Auto derives a bucket from the chart timeframe; manual selection forces the intended target bucket.
Offset — Plot offset — Default: 0
Shifts plots forward/back for visual alignment, displayed in the data window.
Table X / Table Y — Table anchor — Default: Right / Top
Places the diagnostics table in one of nine anchor points.
Table Size — Table text size — Default: Normal
Use small on dense charts, large for presentations.
Dark Mode — Table theme — Default: Enabled
Switches table palette for readability against chart background.
Show Table — Toggle diagnostics table — Default: Enabled
Disable for a cleaner chart.
Reading & Interpretation
Basis (blue): The moving average centerline of the bands (SMA of effective length).
Upper (red) / Lower (green): ±2 standard deviations around the basis using the same effective length.
Fill (blue tint): Visual band zone to quickly see compression/expansion.
Interpretation staples:
Price riding the upper band suggests strong bullish pressure; riding the lower band suggests strong bearish pressure.
Band expansion indicates rising volatility; contraction indicates volatility compression.
Mean reversion setups often key off the basis and re-entries from outside bands, while breakout/trend setups often key off sustained band rides.
Diagnostics table:
HTF Tag: Human-readable label showing the current timeframe → bucket mapping.
Bucket: The resolved target bucket (Auto result or manual selection).
Multiplier: The integer factor applied to the base length.
Len/Dev: Shows base length (20) and the effective length result plus deviation (2).
Bandwidth: Normalized width of the band (percent), useful for spotting squeezes.
Practical Workflows & Combinations
HTF context on LTF charts: Use this as “slow structure” bands on 1m–15m charts without requesting HTF data.
Squeeze detection: Watch bandwidth shrink to historically low levels, then look for break/hold outside bands.
Trend filtering: Favor long bias when price stays above the basis and repeatedly respects it; favor short bias when below.
Confluence: Combine with market structure (swing highs/lows), volume tools, or a trend filter (e.g., a longer MA) for confirmation.
Behavior, Constraints & Performance
Repaint/confirmation: No cross-timeframe requests. Values can still evolve intrabar and settle on close, as with any indicator computed on live bars.
History requirements: Very large effective lengths need sufficient historical bars; expect a warm-up period after loading or switching symbols/timeframes.
Known limits: Because the method approximates HTF behavior by scaling lookback, it is not identical to true HTF Bollinger Bands computed on aggregated candles. In particular, volatility and mean can differ slightly versus a real HTF series.
Sensible Defaults & Quick Tuning
Default workflow:
Bucket: Auto
Source: Close
Table: On (until you trust the mapping), then optionally off
If bands feel too slow on your timeframe: choose a smaller bucket (e.g., 60 instead of 240).
If bands feel too reactive/noisy: choose a larger bucket (e.g., 1D or 3D).
If chart looks cluttered: hide the table; keep only the bands and fill.
What this indicator is—and isn’t
This is a Bollinger Band visualization layer that emulates higher-timeframe “slowness” via deterministic length scaling. It is not a complete trading system and does not include entries, exits, sizing, or risk management. Use it as context alongside your execution rules and protective stops.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino.
RSI Regimes + Cardwell Sweet SpotsRSI based upon Cardwell principles, with a strength evaluation based upon the ADX, VWAP, velocity of both, and Cardwell RSI principles of a sweet spot of a RSI.
DarkPool's Squeeze Momentum @author LazyBearDarkPool's Squeeze Momentum Pro is a comprehensive overhaul of the classic volatility indicator, designed for the modern trader who requires deeper market insight. While staying true to the core logic of the original TTM Squeeze, this version introduces advanced features like automatic divergence detection, dynamic moving average selection, and main-chart integration to help you time entries and exits with precision.
Credit: This script is built upon the foundational "Squeeze Momentum Indicator" originally developed by LazyBear. This version expands on that legacy with enhanced visualization, alert systems, and divergence logic.
Key Features
1. Advanced Divergence Detection
The indicator automatically scans for Regular Bullish and Regular Bearish divergences between price action and momentum.
Bullish Divergence (Green "BULL" Label): Occurs when Price makes a Lower Low, but Momentum makes a Higher Low. This often precedes a bullish reversal.
Bearish Divergence (Red "BEAR" Label): Occurs when Price makes a Higher High, but Momentum makes a Lower High. This often precedes a bearish reversal.
2. Multi-Mode Squeeze Detection
The central dots on the zero line tell you the state of market volatility:
Red Dot (Squeeze ON): Volatility is compressed. The Bollinger Bands are inside the Keltner Channels. The market is "coiling" and preparing for an explosive move. Do not trade yet—wait for the fire.
Grey Dot (Squeeze OFF): The squeeze has "fired." Volatility is expanding, and price is moving.
Blue Dot (Wide Bands): Volatility is extremely high. The bands are exceptionally wide, often indicating the end of a trend or a period of high risk.
3. "Ghost" Histogram & Visual Depth
The momentum histogram features a "Ghost" fill (transparent background) to help visualize the volume of momentum without cluttering the screen.
Bright Green: Strong Bullish Momentum (Rising).
Dark Green: Weakening Bullish Momentum (Fading).
Bright Red: Strong Bearish Momentum (Falling).
Dark Red: Weakening Bearish Momentum (Recovering).
4. Dynamic Candle Coloring
Enabled by default, this feature colors the candles on your main chart to match the momentum histogram. This allows you to instantly gauge the trend strength without looking down at the oscillator pane.
5. Adaptive Calculation Engines
Unlike standard versions fixed to SMA, you can now select the moving average algorithm that drives the Bollinger Bands and Keltner Channels:
SMA: Standard, stable signals.
EMA: More reactive to recent price action.
WMA/RMA: Weighted options for specific strategies.
🛠 How to Operate
The "Squeeze & Fire" Strategy
Identify the Squeeze: Look for a series of Red Dots on the zero line. This indicates the market is resting and building energy.
The Trigger: Wait for the dot to turn Gray AND for the histogram to expand clearly in one direction.
Long Signal: Squeeze fires (Red -> Gray) + Histogram turns Green.
Short Signal: Squeeze fires (Red -> Gray) + Histogram turns Red.
The "Divergence Reversal" Strategy
Watch for "BULL" or "BEAR" labels appearing near the peaks or valleys of the histogram.
Confirmation: A divergence is a warning. Wait for the histogram color to change (e.g., from Bright Red to Dark Red) before entering a reversal trade.
⚙️ Settings Guide
Basis MA Type: Choose between SMA, EMA, WMA, or RMA to tune the sensitivity of the squeeze.
BB/KC Settings: Fully customizable Length and Multipliers to adapt to different assets (Crypto, Forex, or Stocks).
Pivot Lookback: Controls how strict the divergence detection is. Higher numbers = fewer, more significant signals.
Colour Main Chart Candles: Toggle this OFF if you prefer your standard candle colours.
Disclaimer
Trading involves a high level of risk and is not suitable for all investors. This indicator is a tool for technical analysis and does not constitute financial advice. Past performance is not indicative of future results. Always use proper risk management and do not trade based solely on a single indicator.
ATR Risk Manager v5.2 [Auto-Extrapolate]If you ever had problems knowing how much contracts to use for a particular timeframe to keep your risk within acceptable levels, then this indicator should help. You just have to define your accepted risk based on ATR and also percetage of your drawdown, then the indicator will tell you how many contracts you should use. If the risk is too high, it will also tell you not to trade. This is only for futures NQ MNQ ES MES GC MGC CL MCL MYM and M2K.
EMA 7/21 + SuperTrend INDICADOR CREADO PARA MI ESTRATEGIA That sounds like a complex and multi-layered trading indicator you've developed! Combining an Exponential Moving Average (EMA) crossover with an Average Directional Index (ADX) filter, a Volume filter, and a Supertrend for overall trend confirmation can certainly help to filter out false signals and potentially increase the reliability of your entry and exit points.Here's a breakdown of the components you're using and why this combination can be powerful:📊 Indicator Components and Their Roles1. 7/21 EMA Crossover (Signal Generator)The $7$-period EMA and $21$-period EMA crossover is a common strategy used to generate initial buy or sell signals.Buy Signal: The short-term EMA (7) crosses above the long-term EMA (21).Sell Signal: The short-term EMA (7) crosses below the long-term EMA (21).2. ADX DI Filter (Momentum and Direction)The Average Directional Index (ADX) and its directional indicators ($+DI$ and $-DI$) are key to confirming the strength and direction of the move.Directional Confirmation: The EMA crossover must be confirmed by the appropriate directional index. For a buy, the $+DI$ should be above the $-DI$. For a sell, the $-DI$ should be above the $+DI$.Trend Strength ( NYSE:ADX $): A rising NYSE:ADX $ (typically above 20 or 25) suggests the current trend has sufficient momentum, making the signal more reliable.3. Volume Filter (Conviction)Adding a Volume filter ensures that the price movement accompanying the EMA crossover is supported by significant trading activity.Confirmation: A strong signal (buy or sell) is often accompanied by above-average volume. This suggests that market participants are actively supporting the move, adding conviction to the trade.4. Supertrend (Overall Trend Confirmation)The Supertrend indicator is based on the Average True Range (ATR) and is excellent for identifying the dominant market trend.Trend Alignment: The EMA crossover signal should align with the Supertrend's current signal. For a buy signal, the price should be above the Supertrend line (green). For a sell signal, the price should be below the Supertrend line (red). This helps ensure you are trading with the prevailing trend.📈 Why This is a Powerful CombinationYour indicator is essentially a multi-stage confirmation system:Speed (7/21 EMA): Generates a fast, responsive signal.Momentum (ADX DI): Confirms the direction and strength of the signal.Conviction (Volume): Validates the signal with market participation.Safety/Trend (Supertrend): Ensures the trade is in the direction of the long-term trend.The Informative Panel is a great feature, as it simplifies the decision-making process by summarizing the findings of all these components—e.g., "BUY: EMA Crossover $\checkmark$, +DI > -DI $\checkmark$, High Volume $\checkmark$, Supertrend Green $\checkmark$."💡 Next Steps for RefinementTo finalize and test this indicator, you may want to consider:Parameter Optimization: The best settings for the ADX level (e.g., 20 vs. 25) and the Supertrend ATR parameters may need to be optimized for the specific asset (e.g., stocks, forex) and timeframe you are using.Exit Strategy: Since this primarily focuses on entries, define clear Stop-Loss (perhaps based on the Supertrend line or a recent swing low/high) and Take-Profit (e.g., a fixed Risk/Reward ratio or previous resistance/support levels) rules.Would you like to explore specific parameters for any of these components or look into ways to backtest your strategy?
Adaptive ATR% Grid + SuperTrend + OrderFlipDescription:
This indicator combines multiple technical analysis tools to identify key price levels and trading signals:
ATR% Grid – automatic plotting of support and resistance levels based on current price and volatility (ATR). Useful for identifying potential targets and entry/exit zones.
SuperTrend – a classic trend indicator with an adaptive ATR multiplier that adjusts based on average volatility.
OrderFlip – identifies price reversal points relative to a moving average with ATR-based sensitivity, optionally filtered by OBV and DMI.
MTF Confirmation – multi-timeframe trend verification using EMA to reduce false signals.
Signal Labels – "LONG" and "SHORT" labels appear on the chart with an offset from the price for better visibility.
JSON Alerts – ready-to-use format for automated alerts, including price, SuperTrend direction, Fair Zone, and ATR%.
Features:
Fully compatible with Pine Script v6
Lines and signals are fixed on the chart, do not shift with new bars
Configurable grid, ATR, SuperTrend, and filter parameters
Works with MTF analysis and classic indicators (OBV/DMI)
Usage:
Best used with additional indicators and risk management strategies. ATR% Grid is ideal for both positional trading and intraday setups.
перевод на русский
Описание:
Этот индикатор объединяет несколько методов технического анализа для выявления ключевых уровней цены и сигналов на покупку/продажу:
Сетка ATR% (ATR% Grid) – автоматическое построение уровней поддержки и сопротивления на основе текущей цены и волатильности (ATR). Позволяет видеть потенциальные цели и зоны входа/выхода.
SuperTrend – классический трендовый индикатор с адаптивным множителем ATR, который корректируется на основе средней волатильности.
OrderFlip – определение моментов разворота цены относительно скользящей средней с учетом ATR, с возможностью фильтрации по OBV и DMI.
MTF-подтверждение – проверка направления тренда на нескольких таймфреймах с помощью EMA, чтобы снизить ложные сигналы.
Сигнальные метки – на графике появляются "LONG" и "SHORT" с отступом от цены для наглядности.
JSON Alerts – готовый формат для автоматических уведомлений, включающий цену, направление SuperTrend, Fair Zone и ATR%.
Особенности:
Поддержка Pine Script v6
Линии и сигналы закреплены на графике, не двигаются при обновлении свечей
Настраиваемые параметры сетки, ATR, SuperTrend и фильтров
Совместимость с MTF-анализом и классическими индикаторами OBV/DMI
Рекомендации:
Используйте в сочетании с другими индикаторами и стратегиями управления риском. Сетка ATR% отлично подходит для позиционной торговли и интрадей.
ATR% Grid – automatic plotting of support and resistance levels based on current price and volatility (ATR). Useful for identifying potential targets and entry/exit zones.
SuperTrend – a classic trend indicator with an adaptive ATR multiplier that adjusts based on average volatility.
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.
QLC - Gibaum 1.0 QLC - Gibaum 1.0
Good for Leverage AND Short - 5 to 20 minutes >70%.
Gibaum The Beast
Little Black Guy suffers in america
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*
ProCrypto OI Candles — by ruben_procryptoThis indicator visualizes aggregated Open Interest (OI) from multiple futures exchanges (Binance, Bybit, OKX).
It plots OI as colored candles (blue for increasing OI, orange for decreasing OI), combined with a smoothed OI line for clearer trend reading.
Key Features:
Multiple exchange support (Binance / Bybit / OKX)
Aggregated OI calculation
OI candlesticks with custom opacity
Smoothed OI trend line
Optional OI Delta bars
Adjustable smoothing length, range offset, and lookback settings
Works on all timeframes
What it helps with:
Spotting liquidity traps
Identifying fake pumps / fake dumps
Detecting aggressive long/short positioning
Reading funding cycles and OI expansions
Tracking market strength/weakness behind price movements
OI is one of the most powerful tools for understanding leverage behavior and true market intent.
This script gives a clear, clean, real-time view of OI so traders can see where momentum is actually coming from.
Built for traders who use liquidity, leverage, OI shifts, and momentum to understand price movement more accurately.
Created by @ruben_procrypto.
Monitor Posición Bollinger Multi-TFThis indicator provides a comprehensive dashboard that allows you to monitor the price position relative to Bollinger Bands across 7 different timeframes simultaneously, without the need to switch charts.
It uses the %B (Percent B) logic to normalize the price position, giving you an instant "Heatmap" view of the market state (Overbought/Oversold) from the 1-minute chart up to the Weekly chart.
Key Features:
Multi-Timeframe Monitoring: Watch 1m, 5m, 15m, 1h, 4h, Daily, and Weekly timeframes in a single panel.
Dynamic Color Coding:
Dark Red: Price breaking above the Upper Band (>100%).
Light Red: Price near the Upper Band (Resistance zone).
Gray: Price in the neutral middle zone.
Light Green: Price near the Lower Band (Support zone).
Dark Green: Price breaking below the Lower Band (<0%).
Trend Arrows: Indicates momentum (▲ or ▼) based on the previous candle's position.
Current Timeframe Highlight: Automatically highlights the row corresponding to your current chart view in orange.
Fully Customizable: Adjust Bollinger settings (Length, Mult), choose your preferred timeframes, and change the table position/size.
Movable Panel: Includes X/Y offset settings to prevent the table from blocking price action or menu buttons.
How to Use:
Add the indicator to your chart.
Use the dashboard to spot confluence across timeframes.
Example: If 15m, 1H, and 4H are all showing Red, the asset is likely overextended to the upside.
Example: If the lower timeframes are turning Green while the higher timeframes remain Gray/Bullish, it might indicate a pullback opportunity.
Settings:
Bollinger Config: Length (20) and Multiplier (2.0) by default.
Timeframes: Select the 7 specific TFs you want to track.
Visuals: Change table position, text size, and offset coordinates.
This tool is essential for scalpers and day traders who need situational awareness across multiple fractals instantly.
Institutional Valuation SuiteStandard volatility indicators often fail on long-term growth charts because they measure volatility in dollars rather than percentages. This causes bands to break or become irrelevant during exponential price moves (e.g., Bitcoin going from $1,000 to $100,000).
The Institutional Valuation Suite solves this by utilising Geometric (Log-Normal) Standard Deviation. This allows the model to adapt to the asset's price scale, providing accurate valuation zones regardless of price magnitude.
The model functions as a mean-reversion tool, visualizing price as an elastic band anchored to a "Fair Value" baseline. It identifies when the asset is statistically overextended (Bubble/FOMO) or undervalued (Deep Discount).
Key Features
1. Log-Normal Math Engine
Geometric Mode (Default): Calculates volatility in percentage terms. Essential for Crypto and Growth Stocks.
Arithmetic Mode: Available for Forex or range-bound assets where linear standard deviation is preferred.
2. Sentiment Heat map
Visualises valuation directly on the candles to remove interpretation bias.
GREEN: Deep Value / Accumulation Zone (< -0.5σ).
ORANGE: Overvalued / FOMO Zone (> 2.0σ).
RED: Speculative Bubble Zone (> 3.0σ).
3. Reversion Signals
"VALUE RECLAIM": Triggers when price re-enters the bottom band from below, filtering out "falling knife" scenarios.
"TOP EXIT": Triggers when price breaks down from the speculative top zone.
4. Statistical Dashboard
Displays the real-time Z-Score to quantify how "stretched" the price is relative to its baseline.
> 3.0: Statistical Anomaly (Top).
< -0.5: Statistical Discount (Bottom).
Optimisation Cheat Sheet
The "Cycle Length" input determines the lookback period for the baseline. Recommended settings:
Crypto Macro: 200 (Approx. 4 Years).
Altcoins: 100 (Approx. 2 Years).
Stocks (S&P 500): 50 (1 Year Trend).
Day Trading: Set "Timeframe Lock" to "Chart".
Technical Note
This indicator uses strict offset logic (`barmerge.lookahead_on`) to ensure historical consistency. The signals displayed on historical bars match exactly what would have appeared in real-time.
*Disclaimer: This script provides statistical analysis based on historical volatility and does not constitute financial advice.*
ADR / $Volume DashboardSee 5 / 20 days ADR / Volume and price %age from low of day on top of the chart
LazyTradeLazyTrade is a clean, high-confidence trend-following indicator built on TradingView’s non-repainting SuperTrend V6 engine. It adds intelligent RSI confirmation, profit-tracking labels, trend-flip markers, and optional background shading to highlight momentum shifts. Designed for intraday and swing traders who want fast, reliable signals without chart clutter.
Features:
• Non-repainting Buy/Sell signals
• Smart RSI confirmation (Aggressive / Standard / Conservative)
• Auto P&L between opposite signals
• Trend-flip circles and transparent background zones
• Clean visual structure optimized for daily and leveraged ETF trading
A simple, intuitive tool that keeps you aligned with the dominant trend—no noise, no over-complication.
Compression Breakout [30min 65+33 EMA]Compression Breakout
by GhostMMXM (inspired by Chris Cady & Steidlmayer Market Profile principles)
This indicator automates the exact compression-to-displacement setup that veteran CBOT floor trader and Market Profile pioneer Chris Cady describes in interviews and his work with Peter Steidlmayer.
Core idea
Chris Cady uses two simple moving averages on the 30-minute chart — a 33-period and a 65-period — to visually detect when the market falls into “balance” (compression). When both lines go almost perfectly flat for several bars, the market is in a low-volatility, high-consensus state — the calm before a violent vertical breakout.
What this script does
• Detects when both the 33 EMA and 65 EMA are virtually flat (user-adjustable sensitivity)
• Requires a minimum of 6 consecutive flat bars (adjustable) before declaring compression
• Draws a light-grey background + live-updating box showing the detecting compression
• Triggers only on the first strong displacing bar that:
– closes entirely above the compression high OR entirely below the compression low
– has a range ≥ 1.5× the average bar range inside the compression zone (adjustable)
• Plots a clear “LONG Cady Break” or “SHORT Cady Break” label on the breakout bar
• Fires a clean alert instantly usable on entire watchlists:
BTC → Compression LONG breakout!
ES1! → Compression SHORT breakout!
Designed for 30-minute charts (BTC, ETH, SOL, NQ, CL, GC, etc.) but works on any timeframe.
Perfect for traders who want to catch the highest-conviction vertical moves that Chris Cady has traded for decades with only a few contracts scaled in aggressively on the break.
Settings
• Minimum flat bars for compression (default 6)
• Max % slope to be considered flat (default 0.08 %)
• Minimum range multiplier vs compression average (default 1.5×)
Enjoy the cleanest, most mechanical version of Chris Cady’s famous compression breakout strategy available on TradingView.
Happy trading!
able MACD Overview
Purpose: The indicator combines the traditional MACD (Moving Average Convergence Divergence) with a short-term “forecast” (projection) of MACD/histogram values to give early warning of momentum changes.
Typical outputs:
MACD line (fastEMA − slowEMA)
Signal line (EMA of MACD)
Histogram (MACD − signal)
Forecasted MACD or histogram projected N bars ahead
Optional buy/sell markers and alert conditions
Add the indicator to TradingView (Installation)
Open TradingView and the chart you want to apply the indicator to.
Click “Pine Editor” at the bottom of the chart.
Copy the contents of able_macd_forecast.pine into the Pine Editor window.
Click “Add to chart” (or Save then Add to chart). If it’s a study, it will appear on the chart below price.
If you plan to re-use the script, click Save and give it a meaningful name.
Inputs / Parameters (typical) Note: exact input names may differ in your script. Replace the names below with the script’s input labels when you inspect it.
Source: price source for calculations (close, hl2, etc.).
Fast Length: length for the fast EMA (commonly 12).
Slow Length: length for the slow EMA (commonly 26).
Signal Length: length for the MACD signal EMA (commonly 9).
Forecast Length / Horizon: how many bars ahead the script projects the MACD/histogram (e.g., 1–5).
Forecast Method / Smoothing: choice of projection method (linear regression, EMA extrapolation, simple slope * N, etc.) if available.
Histogram Thresholds: numeric thresholds to emphasize significant momentum (optional).
Show Forecast: toggle on/off the forecast plot.
Alerts On/Off toggles: enable or disable alert conditions baked into the indicator.
Visual / Style settings: colors, plot thickness, histogram style (columns/areas), show labels, show buy/sell arrows.
How the indicator is typically calculated (summary)
MACD line = EMA(source, fast) − EMA(source, slow)
Signal line = EMA(MACD line, signal length)
Histogram = MACD − Signal
Forecast = method-specific short-term projection of MACD or histogram (for example: extend the last slope forward, apply linear regression to MACD values and extrapolate N bars, or apply an additional smoothing and extend that value) Note: For exact math, I need to inspect the script; this is the typical approach.
How to read the indicator (signals & interpretation)
Bullish signal:
MACD line crossing above the signal line (MACD cross up).
Histogram turns positive (cross above zero).
Forecast shows MACD/histogram moving higher in the next N bars (if forecast is positive or trending up).
Bearish signal:
MACD line crossing below the signal line (MACD cross down).
Histogram turns negative (cross below zero).
Forecast shows MACD/histogram moving lower ahead.
Confirmations:
Use price action (higher highs/lows for bullish, lower highs/lows for bearish).
Volume or other momentum/confluence indicators (RSI, ADX).
Divergences:
Bullish divergence: price makes lower low while MACD histogram makes higher low.
Bearish divergence: price makes higher high while MACD histogram makes lower high.
Forecast behavior:
If the forecast leads the MACD cross (forecast crosses before the current MACD does), it’s an early warning.
Use caution: forecasts are prone to false signals; always confirm.
Common trading setups using this indicator
Conservative:
Wait for MACD to cross signal + histogram above zero + forecast already trending same direction.
Use stop below recent swing low (for long) or above recent swing high (for short).
Aggressive (early entry):
Enter when forecast turns positive while MACD still below signal (anticipating cross).
Use tighter stops and smaller position sizes.
Exit rules:
Opposite MACD cross, histogram flipping sign, or a target based on risk-reward.
Use trailing stop based on ATR or structure.
Example settings for different timeframes (starting points)
Scalping / 5–15 min:
Fast 8, Slow 21, Signal 5, Forecast 1–2
Intraday / 1H:
Fast 12, Slow 26, Signal 9, Forecast 2–3
Swing / 4H–Daily:
Fast 12, Slow 26, Signal 9, Forecast 3–5 Adjust based on the asset volatility and backtests.
Adding alerts (TradingView)
Click the “Alerts” button (clock icon) or press Alt + A.
In the Condition dropdown, select the indicator name (able_macd_forecast) and choose a plotted series or built-in alert condition (if the script uses alertcondition).
Common alert types:
MACD crosses Signal (Crossing)
Histogram crosses 0 (Crossing)
Forecast crosses 0 or Forecast trend change (if provided)
Message templates:
“{{ticker}}: MACD crossed above signal on {{interval}}”
“{{ticker}} Forecast positive: MACD forecast shows upward momentum”
Customize the message for your trade automation or notifications.
Configure frequency (Only once, Once per bar, or Once per bar close) — for signals like crossovers, “Once per bar close” is usually safer to avoid repainting issues. Note: If the script includes alertcondition() calls with explicit IDs/messages, use those directly — they are the most reliable for automation.
Backtesting / Strategy conversion
If this script is a study (indicator), you can:
Convert it to a strategy by adding strategy.* order calls (strategy.entry, strategy.close) using the entry/exit logic you prefer, or
Use TradingView’s “Bar Replay” to manually test signals across different markets/timeframes.
If you want, I can help convert or write a strategy wrapper that uses the indicator’s signals to place backtest trades (I’ll need the code).
Practical tips & best practices
Use higher timeframe confirmation for lower-timeframe entries (e.g., check daily MACD momentum before trading 15m signals).
Beware of choppy markets; MACD / forecast may produce whipsaws. Combine with trend filters (moving average direction, ADX).
If you rely on forecasted values, prefer alerts “on bar close” when possible to reduce false alerts from intra-bar noise.
Tune parameters for the specific asset (FX, crypto, stocks have different behavior).
Record each signal and outcome for a sample period (20–100 trades) to evaluate performance.
Troubleshooting
Indicator won’t add: verify Pine version in script header (//@version=4 or //@version=5). TradingView may reject scripts with unsupported version syntax.
Plots missing: check script inputs (Some scripts hide plots if toggles are off).
Alerts firing too often: change alert frequency to “Once per bar close” or adjust threshold values.
Forecast seems to repaint: some forecast methods can repaint (use “bar_index” or store values only on closed bars, or use non-repainting forecast methods). Ask me to inspect the script for repainting logic.
What I can do next (recommended)
If you paste the content of able_macd_forecast.pine here, I will:
Produce a precise, line-by-line usage guide mapping to the exact input names and default values.
Show the exact plotted series names and how to reference them for alerts.
Point out any repainting risks and suggest fixes.
Provide example alert messages that match the script’s alertcondition IDs (if any).
Optionally convert it into a strategy for backtesting, or add non-repainting forecast logic if needed.
Daily RDR (Prev Day H/L, Intraday)This indicator identifies intraday Range-Deviation Reversal (RDR) signals using the previous day’s high and low. At each new session, it stores yesterday’s levels and resets today’s range tracking. During the day, it detects when price first breaks above the prior high or below the prior low, then waits for a reversal: a bearish RDR triggers when price exceeds yesterday’s high and then closes back below it, while a bullish RDR triggers when price undercuts yesterday’s low and then closes back above it. The script plots the previous day’s levels and marks RDR reversals with small up/down triangles.
Simple Grid Trading v1.0 [PUCHON]Simple Grid Trading v1.0
Overview
This is a Long-Only Grid Trading Strategy developed in Pine Script v6 for TradingView. It is designed to profit from market volatility by placing a series of Buy Limit orders at predefined price levels. As the price drops, the strategy accumulates positions. As the price rises, it sells these positions at a profit.
Features
Grid Types : Supports both Arithmetic (equal price spacing) and Geometric (equal percentage spacing) grids.
Flexible Order Management : Uses strategy.order for precise control and prevents duplicate orders at the same level.
Performance Dashboard : A real-time table displaying key metrics like Capital, Cashflow, and Drawdown.
Advanced Metrics : Includes Max Drawdown (MaxDD) , Avg Monthly Return , and CAGR calculations.
Customizable : Fully adjustable price range, grid lines, and lot size.
Dashboard Metrics
The dashboard (default: Bottom Right) provides a quick snapshot of the strategy's performance:
Initial Capital : The starting capital defined in the strategy settings.
Lot Size : The fixed quantity of assets purchased per grid level.
Avg. Profit per Grid : The average realized profit for each closed trade.
Cashflow : The total realized net profit (closed trades only).
MaxDD : Maximum Drawdown . The largest percentage drop in equity (realized + unrealized) from a peak.
Avg Monthly Return : The average percentage return generated per month.
CAGR : Compound Annual Growth Rate . The mean annual growth rate of the investment over the specified time period.
Strategy Settings (Inputs)
Grid Settings
Upper Price : The highest price level for the grid.
Lower Price : The lowest price level for the grid.
Number of Grid Lines : The total number of levels (lines) in the grid.
Grid Type :
Arithmetic: Distance between lines is fixed in price terms (e.g., $10, $20, $30).
Geometric: Distance between lines is fixed in percentage terms (e.g., 1%, 2%, 3%).
Lot Size : The fixed amount of the asset to buy at each level.
Dashboard Settings
Show Dashboard : Toggle to hide/show the performance table.
Position : Choose where the dashboard appears on the chart (e.g., Bottom Right, Top Left).
How It Works
Initialization : On the first bar, the script calculates the price levels based on your Upper/Lower price and Grid Type.
Entry Logic :
The strategy places Buy Limit orders at every grid level below the current price.
It checks if a position already exists at a specific level to avoid "stacking" multiple orders on the same line.
Exit Logic :
For every Buy order, a corresponding Sell Limit (Take Profit) order is placed at the next higher grid level.
MaxDD Calculation :
The script continuously tracks the highest equity peak.
It calculates the drawdown on every bar (including intra-bar movements) to ensure accuracy.
Displayed as a percentage (e.g., 5.25%).
Disclaimer
This script is for educational and backtesting purposes only. Grid trading involves significant risk, especially in strong trending markets where the price may move outside your grid range. Always use proper risk management.
