1MN Profitcosmos Gold Scalping📈 Profitcosmos Gold Scalping Indicator (1MN)
The Profitcosmos Gold Scalping Indicator is a high-precision scalping system designed specifically for XAUUSD (Gold) on the 1-minute timeframe. It blends ATR-based trend logic with smart session filtering to detect only the most actionable trading opportunities during high-liquidity market hours.
This indicator is built for traders who demand clean entries, structured risk management, and disciplined execution.
✅ Core Features
🔹 ATR Dynamic Stop System
Uses adaptive volatility-based trailing logic to detect strong directional moves.
🔹 Session-Based Trading Only
Trades are filtered to execute exclusively during high-probability sessions:
London Session
New York Session
Asian Session
🔹 Visual Trade Guidance
Every signal automatically draws:
✅ Entry level
🔴 Stop Loss (Swing-based)
🟢 Take Profit (3R risk-reward)
🔹 Clear BUY / SELL Markers
BUY below candle (arrow pointing up)
SELL above candle (arrow pointing down)
No confusion. No overtrading. Only precision.
🔹 Optional Heikin Ashi Mode
Smooth price data for cleaner trend detection.
🎯 How To Trade (Rules)
✅ Trade BUY signals only when price is trending up
✅ Trade SELL signals only when price is trending down
✅ Respect the Stop Loss and Take Profit levels
✅ Never revenge trade
✅ Focus on quality over quantity
🛡 Risk Management
Each signal follows a 3:1 reward-to-risk ratio, ensuring long-term profitability when combined with discipline and consistency.
⚠️ Disclaimer
This indicator is not financial advice. Trading involves risk. Use proper money management and test strategies on demo accounts before trading live capital.
Volatilità
TR-ATR-DATR+MAs shows the Range of selected Candle + 3 Moving Averages
True Range
Avg True Range
Daily Range
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.
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.
TriPrimeTriPrime is a multi-layer momentum-distance engine designed to capture structural trend behavior and directional transitions.
The system decomposes market displacement into three response-speed layers, representing different structural components of trend development:
Alpha – fast-response distance
Beta – medium-response distance
Gamma – slow-response distance
Together, the three layers reveal:
• Trend rising vs. trend falling cycles
• Multi-speed directional alignment
• Early-stage rotation signals
• Trend continuation and weakening phases
Bright colors indicate a rising trend.
Soft colors indicate a falling trend.
A synchronized-movement alert is included, highlighting moments when all three layers rise or fall together — conditions commonly associated with highly clear market direction.
TriPrime is designed for professional trading workflows, multi-layer momentum analysis, and structural trend validation.
TriPrime 是一套多层动能-距离分析引擎，用于捕捉结构性趋势、方向变化与趋势阶段特征。
系统将市场位移拆分为三个不同反应速度的层级，代表趋势结构中的多速度特性：
Alpha — 快速反应距离
Beta — 中速反应距离
Gamma — 慢速反应距离
三层结构可揭示：
• 趋势上升 / 趋势下降周期
• 多速度趋势一致性
• 趋势早期方向旋转信号
• 趋势延续与趋势衰减阶段
亮色代表趋势上升。
柔色代表趋势下降。
系统包含同步提醒
用于标记三层同时趋势上升或趋势下降的时刻 —— 通常对应趋势方向非常明确的行情阶段。
TriPrime 适用于专业交易流程、多层动能研究与趋势结构验证。
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.
Hyper Squeeze Sniper (Dual Side: Long + Short)Hyper Squeeze Sniper (Dual Side Strategy)
This script is a comprehensive Volatility Breakout System designed to identify and trade explosive price moves following periods of consolidation. It combines the classical "Squeeze" theory with Linear Regression Momentum, Volume Analysis, and an ATR-based Trailing Stop to filter false signals and manage risk effectively.
The script operates on a logic of "Compression -> Explosion -> Trend Following" suitable for both Long and Short positions.
🛠 Detailed Methodology (How it works)
1. The Squeeze Detection (Consolidation) The core concept relies on the relationship between Bollinger Bands (BB) and Keltner Channels (KC).
Condition: When the Bollinger Bands (Standard Deviation) contract and fall inside the Keltner Channels (ATR based), it indicates a period of extremely low volatility (The Squeeze).
Visual: The background turns Gray to indicate "Do Not Trade / Wait Mode".
2. Momentum Confirmation (Linear Regression) Instead of using standard lagging indicators, this script utilizes Linear Regression of the price deviation to determine the direction of the breakout.
If the Linear Regression Slope > 0, the bias is Bullish.
If the Linear Regression Slope < 0, the bias is Bearish.
3. Volume Validation To avoid fake breakouts, a Volume Spike filter is applied. A signal is only valid if the current volume exceeds its moving average by a defined multiplier (Default x1.2).
4. Risk Management: ATR Trailing Stop Once a trade is entered, the script calculates a dynamic Trailing Stop based on the Average True Range (ATR).
- Long: The stop line trails below the price and never moves down.
- Short: The stop line trails above the price and never moves up.
- Exit: The position is closed immediately when the price breaches this volatility-based safety line.
How to Use
1. Wait: Look for the Gray Background. This is the accumulation phase.
2. Entry:
LONG: Wait for a Green Triangle ▲ (Price breaks Upper BB + Vol Spike + Bullish Momentum).
SHORT: Wait for a Red Triangle ▼ (Price breaks Lower BB + Vol Spike + Bearish Momentum).
3. Exit: Close the position when the "X" mark appears or when candles cross the trailing safety line.
Settings
- BB Length/Mult: Adjust the sensitivity of the squeeze detection.
- Vol Spike Factor: Increase this to filter out low-volume breakouts.
- ATR Period/Mult: Adjust the trailing stop distance (Higher = Wider stop for swing trading).
HC HighCrew Volume Intelligence Surge TrackerThis indicator measures coordinated market activity by comparing live volume flow across multiple timeframes against its normalized baseline.
It detects when institutional participation increases beyond historical averages, signaling either a breakout ignition, sustained trend pressure, or liquidity cooling.
Each timeframe is classified by surge intensity, and the system aggregates those readings into a unified “market energy” output that reveals whether participation is concentrated, fading, or fragmented.
The goal is to help traders differentiate between real accumulation and low-resistance drift, improving timing on breakouts or exits.
Use cases: breakout validation, liquidity-flow analysis, volume confirmation with trend bias.
Low Volatility Breakout + TP/SL Levels█ OVERVIEW
"Low Volatility Breakout + TP/SL Levels" is a breakout indicator designed to detect and trade breakouts from periods of low volatility (consolidation). Unlike classic strategies based on fixed support/resistance levels, this indicator dynamically identifies consolidations characterized by small candle bodies and only generates a signal when the breakout occurs with a large, decisive candle. It also automatically plots 3 Take Profit levels and a Stop Loss (with two calculation modes), making it a complete breakout trading tool.
█ CONCEPTS
The strongest market moves most often start after a prolonged period of very low volatility — when candles become small and the market "falls asleep". The indicator first detects such consolidations (small bodies for at least X bars), draws a box around them, and then waits for a breakout with a candle significantly larger than the average. Additional filters (e.g., the box height cannot exceed the average candle body by too much) eliminate false consolidations and volatility traps. Immediately after the breakout, TP1, TP2, TP3, and SL levels are plotted.
█ FEATURES
Dynamic detection of low-volatility consolidations
- candles with small bodies (< average body × consolidationMultiplier)
- minimum number of bars in consolidation: confirmBars (default 5)
Automatic drawing of consolidation boxes
- green (bullish) or red (bearish) with transparent background (85)
- adjustable border thickness (border_width 1–5)
- box height filter (boxHeightMultiplier, default 6.0 × average body) – removes overly stretched/false consolidations
Breakout conditions
- current candle must be larger than average body × threshold (default 1.5)
- must be the largest candle in the entire consolidation
- must close above the highest high (long) or below the lowest low (short)
Breakout signals
- small green triangles below the bar (long)
- small red triangles above the bar (short)
Automatic Take Profit and Stop Loss levels (drawn 5 bars forward)
- two calculation modes:
• Candle Multiplier – based on average true range (high-low) over tp_sl_length period
• Percentage – fixed percentage from breakout close price (percentages must be manually adjusted to the asset and timeframe)
- 3 TP levels (default 2×, 3×, 4× or 2%, 3%, 4%)
- 1 SL level (default 2× or 1.5%)
Live TP/SL price table (top-right corner)
- displays exact current values of SL, TP1, TP2, TP3 immediately after each new signal
- colors identical to drawn lines (red background for SL, green for TP levels)
- updates automatically with every new breakout
Built-in alerts
- “Bullish Breakout Alert” and “Bearish Breakout Alert”
█ HOW TO USE
Add the indicator to your TradingView chart → Indicators → search “Low Volatility Breakout + TP/SL Levels”.
After each valid breakout you will immediately see:
- the colored box
- signal triangle
- horizontal TP/SL lines
- updated table in the top-right corner showing precise price levels for the current trade
Key settings to adjust:
Consolidation Settings
- Volatility Window (length) – period for average body calculation (default 20)
- Consolidation Multiplier – how small bodies must be to count as consolidation (default 2.0)
- Breakout Multiplier – minimum size of breakout candle (default 1.5)
- Box Height Multiplier – maximum allowed box height (default 6.0)
- Min Consolidation Bars – minimum bars required (default 5)
Risk Management Settings
- Choose TP/SL mode: Candle Multiplier or Percentage
- Adjust TP1–3 and SL multipliers/percentages to match your risk management style
Signal interpretation:
- Green triangle below bar + green box + green TP levels in table = long signal
- Red triangle above bar + red box + red SL level in table = short signal
- Boxes remain on chart until broken — they highlight accumulation/distribution zones
█ APPLICATIONS
- Trading breakouts from consolidation on all markets and timeframes
- Recommended to trade in the direction of the higher-timeframe trend or with additional confirmations (e.g., key level breaks). Aggressive mode (trading both directions) is also possible — provided box and TP/SL settings are properly optimized
- Experiment with different TP/SL ratios — higher reward-to-risk setups (e.g., SL 1×, TP3 6–8×) with lower win rate are often more profitable in the long run
- Strongly encourage testing various box parameters (consolidationMultiplier, boxHeightMultiplier, confirmBars) — small changes can dramatically affect signal frequency and quality
█ NOTES
Always test and optimize parameters for the specific instrument and timeframe.
HighCrew Sniper Entry/Exit This system uses a multi-timeframe momentum-forecast model that detects pressure shifts before standard confirmation signals trigger.
It calculates real-time Force, Speed, Power, and Acceleration values derived from live RSI and price-velocity behavior, then adapts dynamically between lower (scalp) and higher (swing) intervals.
When acceleration and power converge, the system identifies early directional intent and prints a bias signal for traders to confirm entry or manage exits.
The framework continuously self-adjusts its thresholds based on volatility and relative strength to maintain precision during fast market changes.
Use cases: intraday scalping, micro-trend reversal timing, swing-bias validation.
Disclaimer: Algorithmic forecasts only; practice proper risk management.
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*
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.
FVG + Breaker Block v8.6This indicator helps you identify FVGs, inverse FVGs (which are marked with a dashed line in the middle of the FVG) and potential Breaker Blocks on every time frame. You can spot 4 different types of BBs: highly potential bullish and bearish BBs are colored blue and red, and the less potential bullish and bearish BBs are colored magenta and orange, but you can change the colors to your desire.
Due to internal constraints of TradingView there is a limit on how many boxes you can plot on your screen so it is advisable not to increase the "Max Bars Back to keep FVG (candles)" above 100 or TV will not be able to plot many BB´s due to its internal limits. Increasing the number above 100 will limit the quantity of BBs boxes to be plot on TradingView.
VWAP SESSION BUY SELL STRATEGY (INDICATOR) (PDK1977)VWAP SESSION BUY SELL STRATEGY (INDICATOR) (PDK1977)
This indicator combines the proven UT Bot breakout engine but with VWAP directional filtering and ATR-based take profit levels.
It delivers clean, high-probability trend entries and automatic volatility-calibrated exits.
How it works:
Buy only when price is above VWAP
Sell only when price is below VWAP
UT Bot confirms momentum with ATR-based trailing logic
ATR Take Profit gives consistent exits based on volatility
Bars turn green/red only while in a trade, back to normal after TP
Best For:
Intraday and swing trading
Indices, FX, crypto, and high-volume stocks but also for Forex with right TF and settings
Traders who want clean signals and minimal noise
2. Trade Checklist:
Use this before every entry. Quick, simple, reliable.
BUY Checklist:
Price confirmed above VWAP
Label prints BUY
Enter on Buy label (on bar close)
→ Hold until ATR TP hits
SELL Checklist:
Price below VWAP
Label prints SELL
Enter on Sell label
→ Hold until ATR TP hits
Avoid Entries When:
Price is chopping tightly around VWAP
Major news events are about to release
Volume is extremely low
ATR is shrinking rapidly (market compression)
3. Risk-Management Guide
This is tailored to how your system actually behaves.
1. Use ATR TP as Primary Exit:
The system automatically calculates a TP based on volatility:
High volatility → larger TP
Low volatility → smaller TP
This keeps trades consistent and avoids lingering too long.
2. Stop-Loss Recommendation
This strategy is designed for TP-only exits, but if you want a SL:
Recommended Stop-Loss:
Use ATR’s opposite trail, OR
Use 1 × ATR behind your entry candle
This matches the system's internal logic.
3. Position Sizing
Since this system enters during momentum expansions, use:
1–2% max risk per trade
Reduce risk during high-impact news sessions
4. Understand VWAP Environment
Different VWAP conditions require different expectations:
Strong Trend (price far from VWAP)
Higher TP probability
Fewer whipsaws
Excellent continuation setups
Chop Zone (price flips above/below VWAP)
Do NOT trade
Signals lose edge
Wait for a clean break + UT confirmation
5. Choose Correct VWAP Reset for your trading and session
This dramatically improves results.
Market Recommended VWAP Reset
US Stocks New York Session
Indices Daily
Forex London or New York
Crypto Daily or None
4. Final Tips for Best Performance
✔ Enter only in the direction of VWAP
✔ Don’t chase signals far away from entry candle
✔ Avoid trading right into major support/resistance
✔ ATR TP will handle exits mostly
Disclaimer
This script is provided for educational and informational purposes only.
It does not constitute financial advice, investment advice, trading advice, or any form of recommendation to buy or sell any financial instrument. The auto take no responsibility for loss or error in the script.
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?
Swift Algo X🧠 Swift Algo X - Adaptive Volume-Drift & Optimization System
Swift Algo X is a sophisticated quantitative trading system designed to solve a big failure point in technical analysis: Parameter Inefficiency.
While most indicators rely on static input settings that fail when market volatility shifts, Swift Algo X solves this by combining a Volume-Drift Model with an integrated Brute-Force Optimization Engine.
The system does not just guess the trend or entry signals, it runs 24 parallel historical simulations in the background to mathematically identify the optimal settings for the asset you are currently trading.
🔍 How It Works
The algorithm operates on a "Dual-Core" architecture: The Signal Engine generates possible trade setups, while the Optimization Engine validates and ranks them in real-time.
1. The Signal Engine: Volume-Drift Calculation Unlike standard indicators that rely on lagging price averages, Swift Algo X calculates the underlying "Volume Force".
It applies a Z-Score Normalization to measure how far the current volume flow has drifted from its statistical mean.
This creates a "Fair Value Estimate" derived purely from volume pressure rather than just price action.
Signals are generated when price breaks out of the volatility bands surrounding this estimate.
2. The Macro Anchor To filter out lower-timeframe noise: The system anchors all logic to a dynamic Macro Baseline.
Bullish Setups: Valid only when the Volume Estimate is sustaining above the Macro Baseline.
Bearish Setups: Valid only when the Volume Estimate is sustaining below the Macro Baseline.
3. The Optimization Engine (The Core Innovation) This is the distinguishing feature of Swift Algo X. On every bar update, the script utilizes Pine Script to:
- Simulate 24 different permutation sets of Volatility Factors and Periods.
- Backtest every permutation against historical price action in real-time.
- Rank them by Win Rate and display the most profitable mathematical fit on the dashboard.
⚙ Key Features
🚀 Live Strategy Tester: A built-in dashboard displays the Win Rate for your current settings vs. the calculated "Best Settings."
🧠 Self-Optimizing Logic: The system recommends the exact "Multiplier" and "Period" that have historically yielded the highest probability for the specific ticker.
✅ Volume-Weighted Signals: Entries are based on volume accumulation, offering a distinct advantage over price-only indicators.
🎯 Adaptive Bands: The volatility bands expand and contract based on the Z-Score drift, naturally filtering out chop during low-volume consolidation.
📘 How to Use
1) Apply to Chart: Load Swift Algo X on your preferred timeframe (e.g., 15m, 1H, 4H).
2) Consult the Dashboard: Look at the "Backtesting" table in the top right corner.
Row 1 (Current): Shows how your current inputs are performing.
Row 2 (Backtest): Shows the theoretical performance of the optimal settings found by the engine.
3) Align Parameters: If the "Backtest Setting" shows a significantly higher Win %, adjust your Multiplier and Period inputs to match the dashboard's recommendation.
4) Wait for BUY / SELL Labels to appear. Use these as confirmation or as tools within your own strategy.
5) Always complement signals with independent risk management and your own analysis.
💡 Originality & Concept
Swift Algo X innovates by transforming the chart from a passive display into an active Simulation Environment.
While the underlying concept of Trailing Stops is a familiar tool, Swift Algo X’s originality lies in its Permutation Engine. By leveraging complex array sorting and loop structures, the script performs a Historical Analysis inside the indicator itself.
This effectively turns a standard script into a dynamic "Strategy Analyzer," allowing traders to adapt the Volume-Drift model to the specific volatility profile of any asset class (Crypto, Forex, or Indices) instantly without leaving the chart.
⚠ Disclaimer
Swift Algo X is a quantitative analysis tool designed for educational purposes. The "Best Settings" are derived from historical data and do not guarantee future performance. Traders should always apply independent risk management.
Multi Condition Stock Screener & Alert SystemMulti Condition Stock Screener & Strategy Builder
This script is a comprehensive Stock Screener and Strategy Builder designed to scan predefined groups of stocks (specifically focused on BIST/Istanbul Stock Exchange symbols) or a custom list of symbols based on user-defined technical conditions.
It allows users to combine multiple technical indicators to create complex entry or exit conditions without writing code. The script iterates through a list of symbols and triggers alerts when the conditions are met.
Key Features
• Custom Strategy Building: Users can define up to 6 separate conditions. • Logical Operators: Conditions can be linked using logical operators (AND / OR) to create flexible strategies. • Predefined Groups: Includes 14 groups of stocks (covering BIST symbols) for quick scanning. • Custom Scanner: Users can select the "SPECIAL" group to manually input up to 40 custom symbols to scan. • Directional Scanning: Capable of scanning for both Buy/Long and Sell/Short signals. • Alert Integration: Generates JSON-formatted alert messages suitable for webhook integrations (e.g., sending notifications to Telegram bots).
Supported Indicators for Conditions
The script utilizes built-in ta.* functions to calculate the following indicators:
• MA (Moving Average): Supports EMA, SMA, RMA, and WMA. • RSI (Relative Strength Index) • CCI (Commodity Channel Index) • ATR (Average True Range) • BBW (Bollinger Bands Width) • ADX (Average Directional Index) • MFI (Money Flow Index) • MOM (Momentum)
How it Works
The script uses request.security() to fetch data for the selected group of symbols based on the current timeframe. It evaluates the user-defined logic (Condition 1 to 6) for each symbol.
• Comparison Logic: You can compare an indicator against a value (e.g., RSI > 50 ) or against another indicator (e.g., MA1 CrossOver MA2 ). • Signal Generation: If the logical result is TRUE based on the "AND/OR" settings, a visual label is plotted on the chart, and an alert condition is triggered.
Alert Configuration
The script produces a JSON output containing the Ticker, Signal Type, Period, and Price. This is optimized for users who want to parse alerts programmatically or send them to external messaging apps via webhooks.
Disclaimer This tool is for informational purposes only and does not constitute financial advice. Since it uses request.security across multiple symbols, please allow time for the script to load data on the chart.
QuantMotions - TPR Sentinel LineTPR Sentinel Line is an advanced adaptive Support/Resistance system that combines multi-layered trend analysis with a directional Time-Price Ratio (TPR) engine. The indicator dynamically builds a stabilized support or resistance line that adjusts to market volatility, trend strength, ATR expansion and contraction, and real-time slope changes.
This creates a high-precision, self-adjusting trend barrier that acts as support in uptrends, resistance in downtrends, and a neutral anchor during sideways phases.
Key Features
✔ Adaptive Trend Base
- A composite trend model blending:
- Kijun-style midpoint
- Donchian midline
- SMA & EMA smoothing
This creates a stable baseline that reacts smoothly but reliably to structural trend shifts.
✔ Directional TPR Calculation
The indicator measures slope across short, medium, and long trend windows, normalizes it with ATR, and determines:
- Trend direction
- Trend strength
- Momentum quality
✔ Dynamic Support/Resistance Line
Depending on trend direction:
- In uptrends → the line becomes adaptive support
- In downtrends → the line becomes adaptive resistance
- In neutral phases → the line centers around the smoothed trend base
A built-in lag factor prevents unrealistic jumps and keeps the level stable.
✔ Automatic Support/Resistance Zones
The indicator expands the main line into upper and lower zones based on ATR and trend strength, creating a dynamic volatility envelope around the trend structure.
✔ Signals & Alerts
- Support bounce
- Resistance rejection
- Breakouts above/below the dynamic line
These events help identify high-probability continuation or reversal moments.
✔ Information Panel
A real-time status table displays:
- Trend direction
- Trend strength
- Current S/R level
🎯 Ideal For
- Precision entries on pullbacks
- Detecting trend shifts earlier
- Identifying strong or weak trend phases
- Adaptive take-profit and stop-loss zones
- Filtering false breakouts
💡 Summary
TPR Sentinel Line gives you a living, breathing support/resistance structure that evolves with the market.
Instead of relying on static levels, you get a continuously adapting trend barrier that reflects real strength, real volatility, and real momentum.
A powerful tool for traders who want structure, clarity, and trend confidence.
Adaptive ATR% Grid + SuperTrend + OrderFlipDescription:
This indicator combines multiple technical analysis tools to identify key price levels and trading signals:
ATR% Grid – automatic plotting of support and resistance levels based on current price and volatility (ATR). Useful for identifying potential targets and entry/exit zones.
SuperTrend – a classic trend indicator with an adaptive ATR multiplier that adjusts based on average volatility.
OrderFlip – identifies price reversal points relative to a moving average with ATR-based sensitivity, optionally filtered by OBV and DMI.
MTF Confirmation – multi-timeframe trend verification using EMA to reduce false signals.
Signal Labels – "LONG" and "SHORT" labels appear on the chart with an offset from the price for better visibility.
JSON Alerts – ready-to-use format for automated alerts, including price, SuperTrend direction, Fair Zone, and ATR%.
Features:
Fully compatible with Pine Script v6
Lines and signals are fixed on the chart, do not shift with new bars
Configurable grid, ATR, SuperTrend, and filter parameters
Works with MTF analysis and classic indicators (OBV/DMI)
Usage:
Best used with additional indicators and risk management strategies. ATR% Grid is ideal for both positional trading and intraday setups.
перевод на русский
Описание:
Этот индикатор объединяет несколько методов технического анализа для выявления ключевых уровней цены и сигналов на покупку/продажу:
Сетка ATR% (ATR% Grid) – автоматическое построение уровней поддержки и сопротивления на основе текущей цены и волатильности (ATR). Позволяет видеть потенциальные цели и зоны входа/выхода.
SuperTrend – классический трендовый индикатор с адаптивным множителем ATR, который корректируется на основе средней волатильности.
OrderFlip – определение моментов разворота цены относительно скользящей средней с учетом ATR, с возможностью фильтрации по OBV и DMI.
MTF-подтверждение – проверка направления тренда на нескольких таймфреймах с помощью EMA, чтобы снизить ложные сигналы.
Сигнальные метки – на графике появляются "LONG" и "SHORT" с отступом от цены для наглядности.
JSON Alerts – готовый формат для автоматических уведомлений, включающий цену, направление SuperTrend, Fair Zone и ATR%.
Особенности:
Поддержка Pine Script v6
Линии и сигналы закреплены на графике, не двигаются при обновлении свечей
Настраиваемые параметры сетки, ATR, SuperTrend и фильтров
Совместимость с MTF-анализом и классическими индикаторами OBV/DMI
Рекомендации:
Используйте в сочетании с другими индикаторами и стратегиями управления риском. Сетка ATR% отлично подходит для позиционной торговли и интрадей.
ATR% Grid – automatic plotting of support and resistance levels based on current price and volatility (ATR). Useful for identifying potential targets and entry/exit zones.
SuperTrend – a classic trend indicator with an adaptive ATR multiplier that adjusts based on average volatility.
BSSSv2BSSSv2 is a market-structure-based tool designed to highlight potential liquidity zones and liquidity voids on the chart. It detects recurring pivot-based price levels using a custom zigzag structure and marks buyside and sellside liquidity areas with dynamic boxes and lines. The script also tracks breaches of these zones and visually updates levels as new structure forms. Optional liquidity-void visualization is included for users who want to study displacement or imbalance behavior.
This tool is intended for chart analysis and helps traders observe how price interacts with liquidity-related areas. It does not provide trade signals or recommendations.
EGGY SIGNALEGGY SIGNAL is a custom trading system designed to eliminate market noise and provide clean, high-probability entry signals. Unlike standard indicators that often give conflicting information, this script uses a Triple Confirmation Algorithm that works in the background.
The script combines three essential market elements:
Trend Filter: Determines the overall market direction to prevent counter-trend trading.
Momentum Detector: Identifies the speed of price movement.
Strength Validator: Confirms the power of the current candle.
How it Works: This indicator utilizes a "Clean Chart" philosophy. You will not see messy lines or clouds.
BUY Signal: Appears only when the Trend is Bullish, Momentum is shifting upwards, and Market Strength is confirmed.
SELL Signal: Appears only when the Trend is Bearish, Momentum is shifting downwards, and Market Strength is confirmed.
Features:
No Repaint: Signals are permanent once the candle closes.
False Signal Filtering: The algorithm automatically filters out signals during choppy/sideways markets.
Proprietary Settings: The input parameters are hardcoded and optimized for specific market conditions to ensure strategy integrity.
How to Use: Simply wait for the "BUY" or "SELL" label to appear. If no label is present, the market is considered neutral or too risky for entry.
Adaptive MACD PROAdaptive MACD PRO
Highlights structural momentum changes using dynamic normalization of MACD and Signal.
Phase Momentum Core
Adds directional confirmation based on short-term phase behavior.
Visual Output
• MACD & Signal lines with trend-based coloring
• Adaptive histogram reflecting momentum strength
• Fixed-position Buy/Sell dots at predefined levels
• AutoCalib dots on MACD_z threshold crossings
• Optional HUD panel displaying calibration levels and MACD_z
Features
• Selectable MA types (EMA, SMA, KAMA)
• Z-score normalization
• ATR-based volatility weighting
• Higher timeframe alignment
• Auto-calibration with SAFE / AGGRESSIVE modes
• Unified long/short triggers
• Full bar-coloring control
• Works on all assets and timeframes
The full source code is visible and may be modified or extended.
This script is intended for technical analysis and research only.
This indicator is published as a free, open-source script with full visible code.
