🔥 DarkPool's Fear & Greed v4 🔥DarkPool Fear & Greed v4 is a composite sentiment indicator designed to gauge market psychology in real-time. Unlike standard oscillators that rely on a single metric, this tool aggregates data from four distinct technical sources—RSI, MACD, Bollinger Bands, and Moving Averages—to create a unified "Index Score" ranging from 0 to 100.
Beyond general sentiment, the script employs custom algorithms to detect specific market anomalies, including sustainable buying pressure (FOMO), capitulation events (Panic), and trend reversals (Divergences).
Key Features
Composite Index: A weighted average of Momentum, Trend, Volatility, and Price Location.
Anomaly Detection: Specialized logic to flag high-momentum "FOMO" events and high-volatility "Panic" drops.
Divergences: Automatically spots bearish and bullish discrepancies between the sentiment index and price action.
Live Dashboard: A real-time data table displaying current sentiment zones, intensity scores, and volume ratios.
How to Use
1. The Fear & Greed Index The main oscillator line moves between 0 and 100 to visualize market sentiment:
0-20 (Extreme Fear): Deeply oversold; potential capitulation or buying opportunity.
20-40 (Fear): General bearish sentiment.
40-60 (Neutral): Indecisive market.
60-80 (Greed): General bullish sentiment.
80-100 (Extreme Greed): Overbought conditions; potential for a pullback.
2. Visual Signals
FOMO (Triangle Up): Marks candles with excessive buying volume and RSI momentum.
Panic (Triangle Down): Marks candles with sharp percentage drops and volatility spikes.
Divergences (Circles): distinct markers appear when price action contradicts the sentiment index, often signaling a reversal.
3. The Dashboard Located on the chart, the dashboard provides a snapshot of the current market state, including the specific "Intensity" of FOMO or Panic events and a Volume-to-MA ratio to gauge participation.
4. Alerts The script is fully integrated with the alert system. You can set alerts for "Any alert() function call" to receive dynamic notifications for FOMO detections, Panic drops, Extreme Zone entries, and confirmed Divergences.
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.
波动率
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.
SCOTTGO - STOCK QUOTE V3This script is a customizable Pine Script indicator that creates a detailed, real-time table of essential trading metrics directly on your chart.
Its purpose is to provide day traders with a single-glance overview of the stock's most critical data points, including:
Valuation & Float (Market Cap, Shares Outstanding)
Daily Price Movement (High/Low, Daily Change %)
Volume & Momentum (RVOL, Volume Buzz, U/D Ratio, RSI)
Short Selling Pressure (Short Volume %)
It helps you quickly assess a stock's market structure, momentum, and technical context for efficient intraday decision-making.
Breakout with Alma & Slope - for high volatility playSometimes best not to overthink,
buy at line crosses ;)
NFA, DYOR
best for 15m-1Hr, high volatility FX,Gold etc
Long only when 3 conditions met:-
- Fast Alma crosses Slow Alma
-Angle Pointing UP
-ADX above 20
Short when
- aqua line below navy line
- navy line pointing down
- adx >20
EXIT
- Trailing Stop: The trade closes automatically if price hits the **Red Stepped Line** (this is your safety net that follows the price).
- Emergency Exit:** The trade closes immediately if the ALMA lines cross back in the opposite direction (Reversal).
Displacement Intelligence Channel (DIC) @darshaksscThe Displacement Intelligence Channel (DIC) is a clean, minimal, non-repainting analytical tool designed to help traders observe how price behaves around its dynamic equilibrium.
It does not generate buy/sell signals, does not predict future price movement, and should not be interpreted as financial advice.
All calculations are based strictly on confirmed historical bars.
⭐ What This Indicator Does
Price constantly fluctuates between expansion (large moves) and compression (small moves).
The DIC analyzes these changes through:
Displacement (how far price moves per bar)
ATR response (how volatility reacts over time)
Dynamic width calculation (channel widens or tightens as volatility changes)
EMA-based core midline (a smooth equilibrium reference)
The result is a smart two-line channel that adapts to market conditions without cluttering the chart.
This is NOT a fair value gap, moving average ribbon, or premium/discount model.
It is a purely mathematical displacement-ATR engine.
⭐ How It Works
The indicator builds three elements:
1. Intelligence Midline
A smooth EMA that acts as the channel’s core “equilibrium.”
It gives a stable reference of where price is gravitating during the current session or trend.
2. Adaptive Upper Boundary
Calculated using displacement + ATR.
When volatility increases, the channel expands outward.
When volatility compresses, the channel tightens.
3. Adaptive Lower Boundary
Mirrors the upper boundary.
Also expands and contracts based on market conditions.
All lines update only on confirmed bar closes, keeping the script non-repainting.
⭐ What to Look For (Purely Analytical)
This indicator does not imply trend continuation, reversal, or breakout.
Instead, here’s what traders typically observe:
1. Price Reactions Around the Midline
Price often oscillates around the midline during equilibrium phases.
Strong deviation from the midline highlights expansion or momentum phases.
2. Channel Expansion / Contraction
Wider channel → increased volatility, displacement, and uncertainty
Tighter channel → compression and calm conditions
Traders may use this for context only — not for decision-making.
3. Respect of Channel Boundary
When market structure respects the upper/lower channel lines, it simply indicates volatility boundaries, not overbought/oversold conditions.
⭐ How to Add This Indicator
Open TradingView
Select any chart
Click Indicators → Invite-Only Scripts / My Scripts
Choose “Displacement Intelligence Channel (DIC)”
The channel will appear automatically on the chart
⭐ Recommended Settings (Optional)
These settings do not change signals (because the indicator has none).
They only adjust sensitivity:
Center EMA Length (default 34)
Smoother or faster midline
Displacement Lookback (default 21)
Controls how much recent displacement affects width
ATR Lookback (default 21)
Governs how volatility is interpreted
Min/Max Multipliers
Limits how tight or wide the channel can expand
Adjust them cautiously for different timeframes or asset classes.
⭐ Important Notes
This tool is non-repainting
It does not use future data
It does not repaint previous channel widths
It follows TradingView House Rules
It contains no signals, no alerts, and no predictions
The DIC is designed for visual context only and should be used as an analytical overlay, not as a stand-alone decision tool.
⭐ Disclaimer
This script is strictly for informational and educational purposes only.
It does not provide or imply any trading signals, financial advice, or expected outcomes.
Always do your own research and consult a licensed financial professional before making trading decisions.
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.
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.
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.
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.
EMA 7/21 + SuperTrend DEFINITIVOhe Ultimate 7/21 Signal: Trend-Filtered by Supertrend 🚀Tired of signals that trade against the main trend? This powerful indicator features the 7/21 EMA Crossover as its core signal, but with a massive upgrade in confirmation:Trend Alignment: Only signals that move in the direction of the Supertrend are confirmed, drastically reducing false entries.Momentum Filter: The ADX DI ensures the move has directional strength.Conviction Check: A Volume Filter validates the signal with market participation.This multi-stage filter provides clean, high-conviction signals for the $7/21$ strategy. The intuitive Informative Panel clearly shows when all conditions are met for a BUY or SELL.Trade with the trend. Trade with conviction.
Get_rich_aggressively_v5# 🚀 GET RICH AGGRESSIVELY v5 - TIER SYSTEM
### Precision Futures Scalping | NQ • ES • YM • GC • BTC
### *Leave Every Trade With Money*
---
## 📋 QUICK CHEATSHEET
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ GRA v5 SIGNAL REQUIREMENTS │
├─────────────────────────────────────────────────────────────────────────────┤
│ ✓ TIER MET Points ≥ 10 (B), ≥ 50 (A), ≥ 100 (S) │
│ ✓ VOLUME ≥ 1.3x average │
│ ✓ DELTA ≥ 55% dominance (buyers OR sellers) │
│ ✓ DIRECTION Candle color = Delta direction │
│ ✓ SESSION In London (3-5AM) or NY (9:30-11:30AM) if filter ON │
├─────────────────────────────────────────────────────────────────────────────┤
│ TIER ACTIONS │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🥇 S-TIER (100+ pts) │ HOLD LONGER │ Big institutional move │
│ 🥈 A-TIER (50-99 pts) │ HOLD A BIT │ Medium move, trail to BE │
│ 🥉 B-TIER (10-49 pts) │ CLOSE QUICK │ Scalp 5-10 pts, exit fast │
│ ❌ NO TIER (< 10 pts) │ NO TRADE │ Not enough conviction │
├─────────────────────────────────────────────────────────────────────────────┤
│ SESSION PRIORITY │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🔵 LONDON OPEN 03:00-05:00 ET │ IB forms 03:00-04:00 │
│ 🟢 NY OPEN 09:30-11:30 ET │ IB forms 09:30-10:30 │
│ 📊 IB BREAKOUT Close beyond IB + Impulse + 1.3x Vol = HIGH CONVICTION│
├─────────────────────────────────────────────────────────────────────────────┤
│ VOLUME PROFILE ZONES │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🔵 HVN (Blue BG) High volume = Support/Resistance, expect consolidation │
│ 🟡 LVN (Yellow BG) Low volume = Breakout acceleration, fast moves │
│ 🟣 POC Point of Control = Institutional fair value │
│ 🟣 VAH/VAL Value Area edges = S/R zones │
├─────────────────────────────────────────────────────────────────────────────┤
│ MARKET STATE DECODER │
├─────────────────────────────────────────────────────────────────────────────┤
│ TREND UP │ Price > EMA20 + CVD rising │ Trade WITH the trend │
│ TREND DN │ Price < EMA20 + CVD falling │ Trade WITH the trend │
│ RETRACE │ Price/CVD diverging │ Pullback, prepare for entry │
│ RANGE │ No clear direction │ Reduce size or skip │
├─────────────────────────────────────────────────────────────────────────────┤
│ 💎 HIGH CONVICTION UPGRADE │
├─────────────────────────────────────────────────────────────────────────────┤
│ Purple diamond (◆) appears when: │
│ • Strong delta (≥65%) + Strong volume (≥2x) + Market in imbalance │
│ → Consider upgrading tier (B→A, A→S) for position sizing │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 🎯 THE TIER SYSTEM
The tier system classifies candles by **point movement** to determine trade management:
| Tier | Points | Action | Expected R:R |
|:----:|:------:|:------:|:------------:|
| 🥇 **S-TIER** | 100+ | HOLD LONGER | 2:1+ |
| 🥈 **A-TIER** | 50-99 | HOLD A BIT | 1.5:1 |
| 🥉 **B-TIER** | 10-49 | CLOSE QUICK | 1:1 |
| ❌ **NO TIER** | < 10 | NO TRADE | — |
---
## ✅ SIGNAL REQUIREMENTS
**ALL conditions must be TRUE for a signal:**
```
SIGNAL = TIER + VOLUME + DELTA + DIRECTION + SESSION
☐ Points ≥ 10 (minimum B-tier)
☐ Volume ≥ 1.3x average
☐ Delta dominance ≥ 55%
☐ Candle direction = Delta direction
☐ In session (if filter ON)
ANY FALSE = NO SIGNAL = NO TRADE
```
---
## 📊 VOLUME DOMINANCE ANALYSIS
This is the **core edge** of GRA v5. We use intrabar analysis to determine who is in control:
```
VOLUME ANALYSIS BREAKDOWN
Total Volume = Buy Volume + Sell Volume
Buy Volume: Who pushed price UP within the bar
Sell Volume: Who pushed price DOWN within the bar
Delta = Buy Volume - Sell Volume
Buy Dominance = Buy Volume / Total Volume
Sell Dominance = Sell Volume / Total Volume
≥ 55% = ONE SIDE IN CONTROL
≥ 65% = STRONG DOMINANCE (high conviction)
```
**Direction Confirmation Matrix:**
| Candle | Delta | Signal |
|:-------|:------|:-------|
| 🟢 Bullish | 55%+ Buyers | ✅ LONG |
| 🟢 Bullish | 55%+ Sellers | ❌ Trap |
| 🔴 Bearish | 55%+ Sellers | ✅ SHORT |
| 🔴 Bearish | 55%+ Buyers | ❌ Trap |
---
## 🕐 SESSION CONTEXT
### Initial Balance (IB) Framework
The **first hour** of each session establishes the IB range. Institutions use this for the day's framework.
```
SESSION WINDOWS (Eastern Time):
LONDON:
├── IB Period: 03:00 - 04:00 ← Range established
├── Trade Window: 03:00 - 05:00 ← Best signals
└── Extension Targets: 1.5x, 2.0x
NY:
├── IB Period: 09:30 - 10:30 ← Range established
├── Trade Window: 09:30 - 11:30 ← Best signals
└── Extension Targets: 1.5x, 2.0x
```
### IB Breakout Signals
```
L▲ / L▼ = London IB Breakout (Blue)
N▲ / N▼ = NY IB Breakout (Orange)
Confirmation Required:
☐ Close beyond IB level (not just wick)
☐ Impulse candle (body > 60% of range)
☐ Volume > 1.3x average
```
**IB Statistics:**
- 97% of days break either IB high or low
- 1.5x extension = first profit target
- 2.0x extension = full range target
- ~66% of London sessions sweep Asian high/low first
---
## 📈 VIRTUAL VOLUME PROFILE ZONES
GRA v5 calculates volume profile zones **without drawing the profile**, giving you the key levels:
### Zone Types
| Zone | Background | Meaning | Action |
|:-----|:-----------|:--------|:-------|
| **HVN** | 🔵 Blue | High Volume Node | S/R zone, expect consolidation |
| **LVN** | 🟡 Yellow | Low Volume Node | Breakout zone, fast acceleration |
| **POC** | 🟣 Purple dots | Point of Control | Institutional fair value |
| **VAH/VAL** | 🟣 Purple lines | Value Area edges | S/R boundaries |
### How to Use
```
ENTERING A TRADE:
At HVN:
├── Expect price to consolidate
├── Look for rejection/absorption
└── Better for reversals
At LVN:
├── Expect fast price movement
├── Don't fight the direction
└── Better for breakouts
Near POC:
├── Institutional fair value
├── Strong magnet effect
└── Watch for volume at POC
```
---
## 🔄 MARKET STATE DETECTION
GRA v5 classifies the market into four states using **CVD + Price Action**:
```
CVD Direction
↑ Rising ↓ Falling
┌─────────────┬─────────────┐
Price > EMA20 │ TREND UP │ RETRACE │
│ (Go Long) │ (Pullback) │
├─────────────┼─────────────┤
Price < EMA20 │ RETRACE │ TREND DN │
│ (Pullback) │ (Go Short) │
└─────────────┴─────────────┘
```
| State | Meaning | Action |
|:------|:--------|:-------|
| **TREND UP** | Buyers in control | Trade long, follow signals |
| **TREND DN** | Sellers in control | Trade short, follow signals |
| **RETRACE** | Pullback against trend | Prepare for continuation entry |
| **RANGE** | No clear direction | Reduce size or wait |
---
## 💎 HIGH CONVICTION UPGRADES
When extra conditions align, GRA v5 marks the signal with a **purple diamond**:
```
HIGH CONVICTION = Base Signal + Strong Delta (65%+) + Strong Volume (2x+) + Imbalance State
```
**Action:** Consider upgrading tier for position sizing:
- B-Tier → A-Tier management
- A-Tier → S-Tier management
---
## 📋 TRADING BY TIER
### 🥇 S-TIER (100+ points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | IB extension / Next S/R |
| **Management** | HOLD LONGER |
**Rules:**
- Watch next candle - continues? HOLD
- Same tier same direction? ADD
- Opposite tier signal? EXIT on close
- Never close early unless reversal signal
### 🥈 A-TIER (50-99 points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | 1.5x initial risk minimum |
| **Management** | HOLD A BIT |
**Rules:**
- Target 1.5:1 R:R minimum
- Trail to breakeven after 1:1
- If stalls, take profit
- Upgrade to S-tier management if high conviction
### 🥉 B-TIER (10-49 points)
| | |
|:--|:--|
| **Entry** | Candle close |
| **Target** | 5-10 points MAX |
| **Management** | CLOSE QUICK |
**Rules:**
- Exit in 1-3 candles
- DO NOT hold for more
- Any doubt = EXIT
- Quick scalp mentality
---
## ⚙️ SETTINGS BY INSTRUMENT
| Setting | NQ/ES | YM | GC | BTC |
|:--------|:-----:|:--:|:--:|:---:|
| **Timeframe** | 1-5 min | 1-5 min | 5-15 min | 1-15 min |
| **S-Tier** | 100 pts | 100 pts | 15 pts | 500 pts |
| **A-Tier** | 50 pts | 50 pts | 8 pts | 250 pts |
| **B-Tier** | 10 pts | 15 pts | 3 pts | 50 pts |
| **Min Volume** | 1.3x | 1.3x | 1.5x | 1.3x |
| **Delta %** | 55% | 55% | 58% | 55% |
| **Best Time** | 9:30-11:30 ET | 9:30-11:30 ET | 3-5AM & 8:30-10:30 ET | 24/7 |
---
## 📊 TABLE LEGEND
The info panel displays real-time market data:
| Row | Shows | Colors |
|:----|:------|:-------|
| **Pts** | Candle points | Gold/Green/Yellow by tier |
| **Tier** | S/A/B/X | Gold/Green/Yellow/White |
| **Vol** | Volume ratio | Yellow (2x+) / Green (1.3x+) / Red |
| **Delta** | Buy/Sell % | Green (buy) / Red (sell) / White |
| **CVD** | Direction | Green ▲ / Red ▼ |
| **State** | Market state | Green/Red/Orange/Gray |
| **Sess** | Session | Yellow if active |
| **Zone** | VP zone | Blue/Yellow/Purple |
| **Sig** | Signal | Green/Red if active |
---
## 🔔 ALERTS
| Alert | When | Action |
|:------|:-----|:-------|
| **S-TIER LONG/SHORT** | S-tier signal | Hold longer |
| **A-TIER LONG/SHORT** | A-tier signal | Hold a bit |
| **B-TIER LONG/SHORT** | B-tier signal | Close quick |
| **LON IB BREAK UP/DN** | London IB breakout | Major session move |
| **NY IB BREAK UP/DN** | NY IB breakout | Major session move |
| **HIGH CONVICTION** | Upgraded signal | Consider larger size |
| **LONDON/NY OPEN** | Session start | Get ready |
---
## 💰 THE GOLDEN RULE
> ### **LEAVE EVERY TRADE WITH MONEY**
>
> | Situation | Rule |
> |:----------|:-----|
> | B-Tier | Small win > Small loss |
> | A-Tier | Trail to BE, lock profit |
> | S-Tier | Let it run to target |
> | No Signal | NO TRADE |
> | Wrong Side | EXIT immediately |
>
> **Capital preserved = Trade tomorrow**
---
## ⚠️ DISCLAIMER
> Risk management is **YOUR** responsibility.
> Never risk more than 1-2% per trade.
> Paper trade until you understand the signals.
> Past performance ≠ future results.
---
### Get Rich. Stay Rich. Trade Aggressively. 🚀
**Get Rich Aggressively v5**
*Precision Futures Scalping*
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.
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.
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?
@Complete Squeeze Cycle Detector v2.0 FINALDescription:
The Complete Squeeze Cycle Detector identifies and tracks the full lifecycle of squeeze formations, from pre-squeeze consolidation through active squeeze periods to squeeze completion. The indicator systematically detects the characteristic conditions that precede and accompany squeeze events.
The indicator monitors multiple factors associated with squeeze development including:
• Volatility compression relative to recent volume activity
• Elevated market stress conditions as measured by VIX levels
• Momentum compression through rate of change measurements across multiple time periods
• Alignment of multiple exponential moving averages indicating consolidation
The squeeze cycle is classified into three distinct phases: Pre-Squeeze Setup, Active Squeeze, and Squeeze Complete. Each phase is identified based on threshold levels of multiple compression metrics, with adjustable sensitivity settings to control the strictness of detection.
The indicator provides visual identification of each phase through labels, background coloring, and an optional dashboard, allowing users to distinguish between the preparation phase where volatility contracts, the active squeeze phase where compression reaches critical levels, and the completion phase where the squeeze releases and directional movement resumes.
This systematic approach enables users to identify squeeze formations throughout their complete development cycle rather than focusing only on the breakout phase.
Santhosh Zero lag Trend change AlertThis indicator alert whenever these is a change in trend direction. Change input to match with your Asset/Index. This works well in all time frame, I recommend this for Scalping and Position trading
ES-VIX Expected Daily MoveThis indicator calculates the expected daily price movement for ES futures based on current volatility levels as measured by the VIX (CBOE Volatility Index).
Formula:
Expected Daily Move = (ES Price × VIX Price) / √252 / 100
The calculation converts the annualized VIX volatility into an expected daily move by dividing by the square root of 252 (the approximate number of trading days per year).
Features:
Real-time calculation using current ES futures price and VIX level
Histogram visualization in a separate pane for easy trend analysis
Information table displaying:
Current ES futures price
Current VIX level
Expected daily move in points
Expected daily move as a percentage
Elite Energy Alpha MatrixThe Elite Energy Alpha Matrix indicator provides comprehensive analysis of the energy sector, focusing on the complex relationships between crude oil benchmarks, natural gas, energy-related ETFs, and the performance dynamics across various energy sub-sectors.
The indicator tracks multiple energy price data sources including WTI crude oil, Brent crude, natural gas, and oil ETFs, enabling detailed monitoring of price relationships and divergences within the energy complex.
Key analytical components include:
• Correlation analysis between major energy benchmarks
• Multi-timeframe examination of energy price relationships
• Sector rotation detection within energy sub-sectors including integrated oil majors, exploration and production companies, oilfield services, refiners, pipelines, and renewable energy
• Performance monitoring across different energy market segments
The indicator provides a structured framework for analyzing the internal dynamics of the energy sector, identifying periods of alignment or divergence between different energy price instruments, and monitoring relative performance across energy sub-sectors.
This approach enables users to assess the consistency of price movements across the energy complex and identify situations where different components of the energy market are exhibiting divergent behavior, which can provide insight into the underlying drivers affecting the sector.2.6s
Dynamic Ratchet Trend Strategy [VIX Filter]Overview This strategy is a long-only trend-following system designed to capture major market moves while strictly managing downside risk through a state-machine based "Ratchet" exit logic. It incorporates a volatility filter using the CBOE VIX index to stay out of (or exit) the market during high-stress environments.
Key Features
1. Multi-Condition Entries The strategy looks for momentum shifts and trend breakouts using four Simple Moving Averages (25, 50, 100, 200).
Momentum Cross: SMA 25 crossover above SMA 50.
Trend Breakouts: A specific "3-Bar Breakout" logic above the SMA 50, 100, or 200. This requires the price to hold above the SMA for 3 consecutive bars after being below it, reducing false signals compared to simple closes.
2. VIX Volatility Filter Before entering any trade, the script checks the CBOE:VIX.
Filter: If VIX is above the threshold (default 32), new entries are blocked.
Panic Exit: If you are in a position and the VIX spikes above the threshold, the strategy executes an immediate "Panic Exit" to preserve capital during market crashes.
3. The "Ratchet" Exit System (3 Stages) Unlike a standard trailing stop, this strategy uses a 3-stage dynamic exit mechanism that tightens as profits grow:
Stage 0 (Initial Risk): Standard percentage-based Stop Loss from the entry price.
Stage 1 (The Lock-In): Triggered when profit hits 10% (configurable).
Unique Logic: Instead of trailing from the highest high, the stop is calculated based on the price at the exact moment this stage was triggered. It "steps up" once and holds, securing the initial move without being prematurely stopped out by normal volatility.
Stage 2 (Trailing Mode): Triggered when profit hits 15% (configurable).
The strategy switches to a classic Trailing Stop, following the percentage distance from the Highest High.
4. Emergency Backup A "Dead Cross" (SMA 25 crossing under SMA 50) acts as a final fail-safe to close positions if the trend reverses completely before hitting a stop.
Settings & Inputs
SMAs: Customize the lengths for all four moving averages.
VIX Filter: Toggle the filter on/off and set the panic threshold.
Exit Logic: Fully customizable percentages for Initial SL, Stage 1 Trigger/Distance, and Stage 2 Trigger/Trailing Distance.
Disclaimer This script is for educational purposes only. Past performance is not indicative of future results. Always manage your risk appropriately.
Ratchet Exit Trend Strategy with VIX FilterThis strategy is a trend-following system designed specifically for volatile markets. Instead of focusing solely on the "perfect entry," this script emphasizes intelligent trade management using a custom **"Ratchet Exit System."**
Additionally, it integrates a volatility filter based on the CBOE Volatility Index (VIX) to minimize risk during extreme market phases.
### 🎯 The Concept: Ratchet Exit
The "Ratchet" system operates like a mechanical ratchet tool: the Stop Loss can only move in one direction (up, for long trades) and "locks" into specific stages. The goal is to give the trade "room to breathe" initially to avoid being stopped out by noise, then aggressively reduce risk as the trade moves into profit.
The exit logic moves through 3 distinct phases:
1. **Phase 0 (Initial Risk):** At the start of the trade, a wide Stop Loss is set (Default: 10%) to tolerate normal market volatility.
2. **Phase 1 (Risk Reduction):** Once the trade reaches a specific floating profit (Default: +10%), the Stop Loss is raised and "pinned" to a fixed value (Default: -8% from entry). This drastically reduces risk while keeping the trade alive.
3. **Phase 2 (Trailing Mode):** If the trend extends to a higher profit zone (Default: +15%), the Stop switches to a dynamic Trailing Mode. It follows the **Highest High** at a fixed percentage distance (Default: 8%).
### 🛡️ VIX Filter & Panic Exit
High volatility is often the enemy of trend-following strategies.
* **Entry Filter:** The system will not enter new positions if the VIX is above a user-defined threshold (Default: 32). This helps avoid entering "falling knife" markets.
* **Panic Exit:** If the VIX spikes above the threshold (32) while a trade is open, the position is closed immediately to protect capital (Emergency Exit).
### 📈 Entry Signals
The strategy trades **LONG only** and uses Simple Moving Averages (SMAs) to identify trends:
* **Golden Cross:** SMA 25 crosses over SMA 50.
* **3-Bar Breakouts:** A confirmation logic where the price must close above the SMA 50, 100, or 200 for 3 consecutive bars.
### ⚙️ Settings (Inputs)
All parameters are fully customizable via the settings menu:
* **SMAs:** Lengths for the trend indicators (Default: 25, 50, 100, 200).
* **VIX Filter:** Toggle the filter on/off and adjust the panic threshold.
* **Ratchet Settings:** Percentages for Initial Stop, Trigger Levels for Stages 1 & 2, and the Trailing Distance.
### ⚠️ Technical Note & Risk Warning
This script uses `request.security` to fetch VIX data. Please ensure you understand the risks associated with trading leveraged or volatile assets. Past performance is not indicative of future results.
Z-EMA Fusion BandsDesigned with crypto markets in mind, particularly Bitcoin , it builds on the concept that the 1-Week 50 EMA often serves as a long-term bull/bear market threshold — an area where institutional bias, momentum shifts, and cyclical rotations tend to occur.
🔹 Core Components & Synergies:
1. 1W 50 EMA (Higher Timeframe)
- This EMA is calculated on a weekly timeframe, regardless of your current chart.
- In crypto, price above the 1W 50 EMA typically aligns with long-term bull market phases, while extended periods below can signify bearish macro structure.
- The slope of the EMA is also analyzed to add directional confidence to trend strength.
2. ±1 Standard Deviation Bands
- Surrounding the 50 EMA, these bands visualize normal price dispersion relative to trend.
- When price consistently hugs or breaks outside these bands, it often reflects market expansion, volatility events, or mean-reversion opportunity.
3. Z-Score Gradient Fill
- The area between the bands is filled using a Z-score-based gradient, which dynamically adjusts color based on how far price is from the EMA (in terms of standard deviations).
- Color shifts from aqua (near EMA) to fuchsia (far from EMA) help you spot price compression, equilibrium, or overextension at a glance.
- The fill also uses transparency scaling, making it fade as price stretches further, emphasizing the core structure.
4. Directional EMA Coloring
- The EMA line itself is colored based on:
- The slope of the EMA (rising/falling)
- Whether the HTF candle is bullish or bearish
- This provides intuitive color-coded confirmation of momentum alignment or potential exhaustion.
5. Price/EMA Divergence Detection
- The script detects bullish and bearish divergence between price and the EMA (rather than using a traditional oscillator).
- Bullish Divergence: Price makes a lower low, EMA makes a higher low.
- Bearish Divergence: Price makes a higher high, EMA makes a lower high.
- These signals often mark transitional zones where momentum fades before a trend reversal or correction.
📊 Suggested Uses:
🔸 Swing and Position Trading:
- Use the 1W 50 EMA as a macro-trend anchor.
- Stay long-biased when price is above with positive slope, and short-biased when below.
- Consider entries near band edges for mean-reversion plays, especially if confluence forms with divergence signals.
🔸 Volatility-Based Filtering:
- Use the Z-score fill to identify volatility compression (near EMA) or expansion (edge of bands).
- Combine this with breakout strategies or dynamic position sizing.
🔸 Divergence Confirmation:
- Combine divergence markers with HTF EMA slope for high-probability setups.
- Bullish div + EMA flattening/rising can signal the start of accumulation after a macro dip.
🔸 Multi-Timeframe Analysis:
- Works well as a structural overlay on intraday charts (1H, 4H, 1D).
- Use this indicator to track long-term bias while executing lower timeframe trades.
⚠️ Disclaimer:
This indicator is designed for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset.
Always use proper risk management, and combine with your own analysis, tools, and strategy. Performance in past market conditions does not guarantee future results.
Simple Price ChannelSimple Price Channel
This indicator plots a basic volatility-based channel around a moving average.
Features:
Midline using Simple Moving Average (SMA)
Upper & lower bands using ATR or true range
Channel fill for easy trend visualisation
This script is designed for educational and analytical purposes only.
It does not provide signals, alerts, or financial advice.
Vibha Jha TQQQ Clean Buy/Sell📈 Vibha Jha TEQQ Hybrid Strategy — Buy/Sell Signals
This script replicates the high-performance buy/sell methodology of Vibha Jha, one of the top money-manager performers in the U.S. Investing Championship (USIC). Her hybrid system generated triple-digit returns in both 2020 and 2021, and strong follow-up performance in 2023–2024 through a strict, rules-based combination of:
✔ CANSLIM-style market leadership tracking
✔ Position-trading fundamentals
✔ Rules-based swing trading using TQQQ/QQQ
✔ Tight entries & disciplined sells
✔ Market-timed exposure based on follow-through days, 21-EMA, and distribution clusters
🚀 What This Indicator Does
This indicator plots clean BUY and SELL signals based on Vibha’s core rule set:
BUY Signals
Three consecutive higher highs AND higher lows (her famous “3-day up” rule)
Strong up-day with rising volume
Designed to catch early trend reversals and early-stage rally attempts
SELL Signals
Two closes below the 21-day EMA
Three consecutive down days
Distribution cluster (4+ distribution days in the last 6 bars)
Captures exhaustion, weakening trend, and institutional selling
🧠 Why This Works
Vibha’s system is built on the reality that:
🔹 Markets give early warning before reversing
🔹 Momentum shifts appear before fundamentals
🔹 Distribution clusters precede pullbacks
🔹 3-day up patterns often kick off powerful rallies
🔹 TQQQ/QQQ respond clearly to technical signals
This indicator applies those insights directly to your chart—stocks, crypto, indices, or leveraged ETFs.
