Adaptive Market Wave Theory - ProAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
• AMWT Advisor : Market Pulse, Agent Matrix, Structure, Watch For
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Search in scripts for "binary"
Adaptive Market Wave TheoryAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
📋 AMWT vs AMWT-PRO:
This version includes all core AMWT functionality:
✓ Full Hidden Markov Model state estimation
✓ 3-Bandit Thompson Sampling consensus system
✓ Complete 5-factor chop detection engine
✓ All four signal modes
✓ Full trade management with TP/SL tracking
✓ Main dashboard with complete statistics
✓ All visual features (channels, zones, pools)
✓ Identical signal generation to PRO
✓ Six professional themes
✓ Full alert system
The PRO version adds the AMWT Advisor panel—a secondary dashboard providing:
• Real-time Market Pulse situation assessment
• Agent Matrix visualization (individual agent votes)
• Structure analysis breakdown
• "Watch For" upcoming setups
• Action Command coaching
Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals.
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Scanner BO: MG MG NEXT SIGNAL (Painel Live)📌 Binary Options Indicator Description (M5)
This indicator is specifically designed for binary options trading on M5 charts with a 5-minute expiration time, optimized for short-term trading strategies. It combines precise technical signals with a controlled risk management system using Martingale up to MG3, helping traders manage losses in a structured sequence.
✅ Key Features:
Real-time signals: Generates clear CALL (buy) and PUT (sell) arrows directly on the M5 chart.
Trend filtering: Uses moving averages or momentum indicators to confirm the prevailing trend, avoiding counter-trend entries.
Expiration timing: Optimized for 1 candle (5 minutes) expiry, ideal for brokers offering fixed expiries.
Integrated Martingale system (up to MG3):
After a losing trade, the indicator suggests the next risk level: MG1 (2x), MG2 (4x), MG3 (8x).
Visual and sound alerts for each level.
Prevents overtrading by limiting recovery to 3 levels.
Non-repaint signals: All signals are confirmed on candle close, ensuring reliability.
Custom alerts: Supports pop-up, sound, and email notifications (via Pine Script alerts).
🎯 Target Audience:
Traders focusing on high-frequency M5 binary strategies who want automated signal generation and disciplined Martingale-based risk management.
🛠️ How to Use:
Apply the indicator to any forex pair, index, or cryptocurrency on the M5 chart.
Wait for a signal (arrow) + trend confirmation.
Open a trade with 5-minute expiration.
If the trade loses, follow the suggested Martingale level (up to MG3).
Reset the sequence after a winning trade.
⚠️ Risk Warning: Martingale increases risk. Use only with proper capital management. Suitable for accounts with sufficient balance.
Use this description when publishing your script in the Pine Editor. Make sure your script is set to "Invite-only" visibility to control access.
FOR DONATIONS PLEASE : pix rdnkll85@gmail.com
ORB Fusion🎯 CORE INNOVATION: INSTITUTIONAL ORB FRAMEWORK WITH FAILED BREAKOUT INTELLIGENCE
ORB Fusion represents a complete institutional-grade Opening Range Breakout system combining classic Market Profile concepts (Initial Balance, day type classification) with modern algorithmic breakout detection, failed breakout reversal logic, and comprehensive statistical tracking. Rather than simply drawing lines at opening range extremes, this system implements the full trading methodology used by professional floor traders and market makers—including the critical concept that failed breakouts are often higher-probability setups than successful breakouts .
The Opening Range Hypothesis:
The first 30-60 minutes of trading establishes the day's value area —the price range where the majority of participants agree on fair value. This range is formed during peak information flow (overnight news digestion, gap reactions, early institutional positioning). Breakouts from this range signal directional conviction; failures to hold breakouts signal trapped participants and create exploitable reversals.
Why Opening Range Matters:
1. Information Aggregation : Opening range reflects overnight news, pre-market sentiment, and early institutional orders. It's the market's initial "consensus" on value.
2. Liquidity Concentration : Stop losses cluster just outside opening range. Breakouts trigger these stops, creating momentum. Failed breakouts trap traders, forcing reversals.
3. Statistical Persistence : Markets exhibit range expansion tendency —when price accepts above/below opening range with volume, it often extends 1.0-2.0x the opening range size before mean reversion.
4. Institutional Behavior : Large players (market makers, institutions) use opening range as reference for the day's trading plan. They fade extremes in rotation days and follow breakouts in trend days.
Historical Context:
Opening Range Breakout methodology originated in commodity futures pits (1970s-80s) where floor traders noticed consistent patterns: the first 30-60 minutes established a "fair value zone," and directional moves occurred when this zone was violated with conviction. J. Peter Steidlmayer formalized this observation in Market Profile theory, introducing the "Initial Balance" concept—the first hour (two 30-minute periods) defining market structure.
📊 OPENING RANGE CONSTRUCTION
Four ORB Timeframe Options:
1. 5-Minute ORB (0930-0935 ET):
Captures immediate market direction during "opening drive"—the explosive first few minutes when overnight orders hit the tape.
Use Case:
• Scalping strategies
• High-frequency breakout trading
• Extremely liquid instruments (ES, NQ, SPY)
Characteristics:
• Very tight range (often 0.2-0.5% of price)
• Early breakouts common (7 of 10 days break within first hour)
• Higher false breakout rate (50-60%)
• Requires sub-minute chart monitoring
Psychology: Captures panic buyers/sellers reacting to overnight news. Range is small because sample size is minimal—only 5 minutes of price discovery. Early breakouts often fail because they're driven by retail FOMO rather than institutional conviction.
2. 15-Minute ORB (0930-0945 ET):
Balances responsiveness with statistical validity. Captures opening drive plus initial reaction to that drive.
Use Case:
• Day trading strategies
• Balanced scalping/swing hybrid
• Most liquid instruments
Characteristics:
• Moderate range (0.4-0.8% of price typically)
• Breakout rate ~60% of days
• False breakout rate ~40-45%
• Good balance of opportunity and reliability
Psychology: Includes opening panic AND the first retest/consolidation. Sophisticated traders (institutions, algos) start expressing directional bias. This is the "Goldilocks" timeframe—not too reactive, not too slow.
3. 30-Minute ORB (0930-1000 ET):
Classic ORB timeframe. Default for most professional implementations.
Use Case:
• Standard intraday trading
• Position sizing for full-day trades
• All liquid instruments (equities, indices, futures)
Characteristics:
• Substantial range (0.6-1.2% of price)
• Breakout rate ~55% of days
• False breakout rate ~35-40%
• Statistical sweet spot for extensions
Psychology: Full opening auction + first institutional repositioning complete. By 10:00 AM ET, headlines are digested, early stops are hit, and "real" directional players reveal themselves. This is when institutional programs typically finish their opening positioning.
Statistical Advantage: 30-minute ORB shows highest correlation with daily range. When price breaks and holds outside 30m ORB, probability of reaching 1.0x extension (doubling the opening range) exceeds 60% historically.
4. 60-Minute ORB (0930-1030 ET) - Initial Balance:
Steidlmayer's "Initial Balance"—the foundation of Market Profile theory.
Use Case:
• Swing trading entries
• Day type classification
• Low-frequency institutional setups
Characteristics:
• Wide range (0.8-1.5% of price)
• Breakout rate ~45% of days
• False breakout rate ~25-30% (lowest)
• Best for trend day identification
Psychology: Full first hour captures A-period (0930-1000) and B-period (1000-1030). By 10:30 AM ET, all early positioning is complete. Market has "voted" on value. Subsequent price action confirms (trend day) or rejects (rotation day) this value assessment.
Initial Balance Theory:
IB represents the market's accepted value area . When price extends significantly beyond IB (>1.5x IB range), it signals a Trend Day —strong directional conviction. When price remains within 1.0x IB, it signals a Rotation Day —mean reversion environment. This classification completely changes trading strategy.
🔬 LTF PRECISION TECHNOLOGY
The Chart Timeframe Problem:
Traditional ORB indicators calculate range using the chart's current timeframe. This creates critical inaccuracies:
Example:
• You're on a 5-minute chart
• ORB period is 30 minutes (0930-1000 ET)
• Indicator sees only 6 bars (30min ÷ 5min/bar = 6 bars)
• If any 5-minute bar has extreme wick, entire ORB is distorted
The Problem Amplifies:
• On 15-minute chart with 30-minute ORB: Only 2 bars sampled
• On 30-minute chart with 30-minute ORB: Only 1 bar sampled
• Opening spike or single large wick defines entire range (invalid)
Solution: Lower Timeframe (LTF) Precision:
ORB Fusion uses `request.security_lower_tf()` to sample 1-minute bars regardless of chart timeframe:
```
For 30-minute ORB on 15-minute chart:
- Traditional method: Uses 2 bars (15min × 2 = 30min)
- LTF Precision: Requests thirty 1-minute bars, calculates true high/low
```
Why This Matters:
Scenario: ES futures, 15-minute chart, 30-minute ORB
• Traditional ORB: High = 5850.00, Low = 5842.00 (range = 8 points)
• LTF Precision ORB: High = 5848.50, Low = 5843.25 (range = 5.25 points)
Difference: 2.75 points distortion from single 15-minute wick hitting 5850.00 at 9:31 AM then immediately reversing. LTF precision filters this out by seeing it was a fleeting wick, not a sustained high.
Impact on Extensions:
With inflated range (8 points vs 5.25 points):
• 1.5x extension projects +12 points instead of +7.875 points
• Difference: 4.125 points (nearly $200 per ES contract)
• Breakout signals trigger late; extension targets unreachable
Implementation:
```pinescript
getLtfHighLow() =>
float ha = request.security_lower_tf(syminfo.tickerid, "1", high)
float la = request.security_lower_tf(syminfo.tickerid, "1", low)
```
Function returns arrays of 1-minute high/low values, then finds true maximum and minimum across all samples.
When LTF Precision Activates:
Only when chart timeframe exceeds ORB session window:
• 5-minute chart + 30-minute ORB: LTF used (chart TF > session bars needed)
• 1-minute chart + 30-minute ORB: LTF not needed (direct sampling sufficient)
Recommendation: Always enable LTF Precision unless you're on 1-minute charts. The computational overhead is negligible, and accuracy improvement is substantial.
⚖️ INITIAL BALANCE (IB) FRAMEWORK
Steidlmayer's Market Profile Innovation:
J. Peter Steidlmayer developed Market Profile in the 1980s for the Chicago Board of Trade. His key insight: market structure is best understood through time-at-price (value area) rather than just price-over-time (traditional charts).
Initial Balance Definition:
IB is the price range established during the first hour of trading, subdivided into:
• A-Period : First 30 minutes (0930-1000 ET for US equities)
• B-Period : Second 30 minutes (1000-1030 ET)
A-Period vs B-Period Comparison:
The relationship between A and B periods forecasts the day:
B-Period Expansion (Bullish):
• B-period high > A-period high
• B-period low ≥ A-period low
• Interpretation: Buyers stepping in after opening assessed
• Implication: Bullish continuation likely
• Strategy: Buy pullbacks to A-period high (now support)
B-Period Expansion (Bearish):
• B-period low < A-period low
• B-period high ≤ A-period high
• Interpretation: Sellers stepping in after opening assessed
• Implication: Bearish continuation likely
• Strategy: Sell rallies to A-period low (now resistance)
B-Period Contraction:
• B-period stays within A-period range
• Interpretation: Market indecisive, digesting A-period information
• Implication: Rotation day likely, stay range-bound
• Strategy: Fade extremes, sell high/buy low within IB
IB Extensions:
Professional traders use IB as a ruler to project price targets:
Extension Levels:
• 0.5x IB : Initial probe outside value (minor target)
• 1.0x IB : Full extension (major target for normal days)
• 1.5x IB : Trend day threshold (classifies as trending)
• 2.0x IB : Strong trend day (rare, ~10-15% of days)
Calculation:
```
IB Range = IB High - IB Low
Bull Extension 1.0x = IB High + (IB Range × 1.0)
Bear Extension 1.0x = IB Low - (IB Range × 1.0)
```
Example:
ES futures:
• IB High: 5850.00
• IB Low: 5842.00
• IB Range: 8.00 points
Extensions:
• 1.0x Bull Target: 5850 + 8 = 5858.00
• 1.5x Bull Target: 5850 + 12 = 5862.00
• 2.0x Bull Target: 5850 + 16 = 5866.00
If price reaches 5862.00 (1.5x), day is classified as Trend Day —strategy shifts from mean reversion to trend following.
📈 DAY TYPE CLASSIFICATION SYSTEM
Four Day Types (Market Profile Framework):
1. TREND DAY:
Definition: Price extends ≥1.5x IB range in one direction and stays there.
Characteristics:
• Opens and never returns to IB
• Persistent directional movement
• Volume increases as day progresses (conviction building)
• News-driven or strong institutional flow
Frequency: ~20-25% of trading days
Trading Strategy:
• DO: Follow the trend, trail stops, let winners run
• DON'T: Fade extremes, take early profits
• Key: Add to position on pullbacks to previous extension level
• Risk: Getting chopped in false trend (see Failed Breakout section)
Example: FOMC decision, payroll report, earnings surprise—anything creating one-sided conviction.
2. NORMAL DAY:
Definition: Price extends 0.5-1.5x IB, tests both sides, returns to IB.
Characteristics:
• Two-sided trading
• Extensions occur but don't persist
• Volume balanced throughout day
• Most common day type
Frequency: ~45-50% of trading days
Trading Strategy:
• DO: Take profits at extension levels, expect reversals
• DON'T: Hold for massive moves
• Key: Treat each extension as a profit-taking opportunity
• Risk: Holding too long when momentum shifts
Example: Typical day with no major catalysts—market balancing supply and demand.
3. ROTATION DAY:
Definition: Price stays within IB all day, rotating between high and low.
Characteristics:
• Never accepts outside IB
• Multiple tests of IB high/low
• Decreasing volume (no conviction)
• Classic range-bound action
Frequency: ~25-30% of trading days
Trading Strategy:
• DO: Fade extremes (sell IB high, buy IB low)
• DON'T: Chase breakouts
• Key: Enter at extremes with tight stops just outside IB
• Risk: Breakout finally occurs after multiple failures
Example: [/b> Pre-holiday trading, summer doldrums, consolidation after big move.
4. DEVELOPING:
Definition: Day type not yet determined (early in session).
Usage: Classification before 12:00 PM ET when IB extension pattern unclear.
ORB Fusion's Classification Algorithm:
```pinescript
if close > ibHigh:
ibExtension = (close - ibHigh) / ibRange
direction = "BULLISH"
else if close < ibLow:
ibExtension = (ibLow - close) / ibRange
direction = "BEARISH"
if ibExtension >= 1.5:
dayType = "TREND DAY"
else if ibExtension >= 0.5:
dayType = "NORMAL DAY"
else if close within IB:
dayType = "ROTATION DAY"
```
Why Classification Matters:
Same setup (bullish ORB breakout) has opposite implications:
• Trend Day : Hold for 2.0x extension, trail stops aggressively
• Normal Day : Take profits at 1.0x extension, watch for reversal
• Rotation Day : Fade the breakout immediately (likely false)
Knowing day type prevents catastrophic errors like fading a trend day or holding through rotation.
🚀 BREAKOUT DETECTION & CONFIRMATION
Three Confirmation Methods:
1. Close Beyond Level (Recommended):
Logic: Candle must close above ORB high (bull) or below ORB low (bear).
Why:
• Filters out wicks (temporary liquidity grabs)
• Ensures sustained acceptance above/below range
• Reduces false breakout rate by ~20-30%
Example:
• ORB High: 5850.00
• Bar high touches 5850.50 (wick above)
• Bar closes at 5848.00 (inside range)
• Result: NO breakout signal
vs.
• Bar high touches 5850.50
• Bar closes at 5851.00 (outside range)
• Result: BREAKOUT signal confirmed
Trade-off: Slightly delayed entry (wait for close) but much higher reliability.
2. Wick Beyond Level:
Logic: [/b> Any touch of ORB high/low triggers breakout.
Why:
• Earliest possible entry
• Captures aggressive momentum moves
Risk:
• High false breakout rate (60-70%)
• Stop runs trigger signals
• Requires very tight stops (difficult to manage)
Use Case: Scalping with 1-2 point profit targets where any penetration = trade.
3. Body Beyond Level:
Logic: [/b> Candle body (close vs open) must be entirely outside range.
Why:
• Strictest confirmation
• Ensures directional conviction (not just momentum)
• Lowest false breakout rate
Example: Trade-off: [/b> Very conservative—misses some valid breakouts but rarely triggers on false ones.
Volume Confirmation Layer:
All confirmation methods can require volume validation:
Volume Multiplier Logic: Rationale: [/b> True breakouts are driven by institutional activity (large size). Volume spike confirms real conviction vs. stop-run manipulation.
Statistical Impact: [/b>
• Breakouts with volume confirmation: ~65% success rate
• Breakouts without volume: ~45% success rate
• Difference: 20 percentage points edge
Implementation Note: [/b>
Volume confirmation adds complexity—you'll miss breakouts that work but lack volume. However, when targeting 1.5x+ extensions (ambitious goals), volume confirmation becomes critical because those moves require sustained institutional participation.
Recommended Settings by Strategy: [/b>
Scalping (1-2 point targets): [/b>
• Method: Close
• Volume: OFF
• Rationale: Quick in/out doesn't need perfection
Intraday Swing (5-10 point targets): [/b>
• Method: Close
• Volume: ON (1.5x multiplier)
• Rationale: Balance reliability and opportunity
Position Trading (full-day holds): [/b>
• Method: Body
• Volume: ON (2.0x multiplier)
• Rationale: Must be certain—large stops require high win rate
🔥 FAILED BREAKOUT SYSTEM
The Core Insight: [/b>
Failed breakouts are often more profitable [/b> than successful breakouts because they create trapped traders with predictable behavior.
Failed Breakout Definition: [/b>
A breakout that:
1. Initially penetrates ORB level with confirmation
2. Attracts participants (volume spike, momentum)
3. Fails to extend (stalls or immediately reverses)
4. Returns inside ORB range within N bars
Psychology of Failure: [/b>
When breakout fails:
• Breakout buyers are trapped [/b>: Bought at ORB high, now underwater
• Early longs reduce: Take profit, fearful of reversal
• Shorts smell blood: See failed breakout as reversal signal
• Result: Cascade of selling as trapped bulls exit + new shorts enter
Mirror image for failed bearish breakouts (trapped shorts cover + new longs enter).
Failure Detection Parameters: [/b>
1. Failure Confirmation Bars (default: 3): [/b>
How many bars after breakout to confirm failure?
Logic: Settings: [/b>
• 2 bars: Aggressive failure detection (more signals, more false failures)
• 3 bars Balanced (default)
• 5-10 bars: Conservative (wait for clear reversal)
Why This Matters:
Too few bars: You call "failed breakout" when price is just consolidating before next leg.
Too many bars: You miss the reversal entry (price already back in range).
2. Failure Buffer (default: 0.1 ATR): [/b>
How far inside ORB must price return to confirm failure?
Formula: Why Buffer Matters: clear rejection [/b> (not just hovering at level).
Settings: [/b>
• 0.0 ATR: No buffer, immediate failure signal
• 0.1 ATR: Small buffer (default) - filters noise
• [b>0.2-0.3 ATR: Large buffer - only dramatic failures count
Example: Reversal Entry System: [/b>
When failure confirmed, system generates complete reversal trade:
For Failed Bull Breakout (Short Reversal): [/b>
Entry: [/b> Current close when failure confirmed
Stop Loss: [/b> Extreme high since breakout + 0.10 ATR padding
Target 1: [/b> ORB High - (ORB Range × 0.5)
Target 2: Target 3: [/b> ORB High - (ORB Range × 1.5)
Example:
• ORB High: 5850, ORB Low: 5842, Range: 8 points
• Breakout to 5853, fails, reverses to 5848 (entry)
• Stop: 5853 + 1 = 5854 (6 point risk)
• T1: 5850 - 4 = 5846 (-2 points, 1:3 R:R)
• T2: 5850 - 8 = 5842 (-6 points, 1:1 R:R)
• T3: 5850 - 12 = 5838 (-10 points, 1.67:1 R:R)
[b>Why These Targets? [/b>
• T1 (0.5x ORB below high): Trapped bulls start panic
• T2 (1.0x ORB = ORB Mid): Major retracement, momentum fully reversed
• T3 (1.5x ORB): Reversal extended, now targeting opposite side
Historical Performance: [/b>
Failed breakout reversals in ORB Fusion's tracking system show:
• Win Rate: 65-75% (significantly higher than initial breakouts)
• Average Winner: 1.2x ORB range
• Average Loser: 0.5x ORB range (protected by stop at extreme)
• Expectancy: Strongly positive even with <70% win rate
Why Failed Breakouts Outperform: [/b>
1. Information Advantage: You now know what price did (failed to extend). Initial breakout trades are speculative; reversal trades are reactive to confirmed failure.
2. Trapped Participant Pressure: Every trapped bull becomes a seller. This creates sustained pressure.
3. Stop Loss Clarity: Extreme high is obvious stop (just beyond recent high). Breakout trades have ambiguous stops (ORB mid? Recent low? Too wide or too tight).
4. Mean Reversion Edge: Failed breakouts return to value (ORB mid). Initial breakouts try to escape value (harder to sustain).
Critical Insight: [/b>
"The best trade is often the one that trapped everyone else."
Failed breakouts create asymmetric opportunity because you're trading against [/b> trapped participants rather than with [/b> them. When you see a failed breakout signal, you're seeing real-time evidence that the market rejected directional conviction—that's exploitable.
📐 FIBONACCI EXTENSION SYSTEM
Six Extension Levels: [/b>
Extensions project how far price will travel after ORB breakout. Based on Fibonacci ratios + empirical market behavior.
1. 1.272x (27.2% Extension): [/b>
Formula: [/b> ORB High/Low + (ORB Range × 0.272)
Psychology: [/b> Initial probe beyond ORB. Early momentum + trapped shorts (on bull side) covering.
Probability of Reach: [/b> ~75-80% after confirmed breakout
Trading: [/b>
• First resistance/support after breakout
• Partial profit target (take 30-50% off)
• Watch for rejection here (could signal failure in progress)
Why 1.272? [/b> Related to harmonic patterns (1.272 is √1.618). Empirically, markets often stall at 25-30% extension before deciding whether to continue or fail.
2. 1.5x (50% Extension):
Formula: [/b> ORB High/Low + (ORB Range × 0.5)
Psychology: [/b> Breakout gaining conviction. Requires sustained buying/selling (not just momentum spike).
Probability of Reach: [/b> ~60-65% after confirmed breakout
Trading: [/b>
• Major partial profit (take 50-70% off)
• Move stops to breakeven
• Trail remaining position
Why 1.5x? [/b> Classic halfway point to 2.0x. Markets often consolidate here before final push. If day type is "Normal," this is likely the high/low for the day.
3. 1.618x (Golden Ratio Extension): [/b>
Formula: [/b> ORB High/Low + (ORB Range × 0.618)
Psychology: [/b> Strong directional day. Institutional conviction + retail FOMO.
Probability of Reach: [/b> ~45-50% after confirmed breakout
Trading: [/b>
• Final partial profit (close 80-90%)
• Trail remainder with wide stop (allow breathing room)
Why 1.618? [/b> Fibonacci golden ratio. Appears consistently in market geometry. When price reaches 1.618x extension, move is "mature" and reversal risk increases.
4. 2.0x (100% Extension): [/b>
Formula: ORB High/Low + (ORB Range × 1.0)
Psychology: [/b> Trend day confirmed. Opening range completely duplicated.
Probability of Reach: [/b> ~30-35% after confirmed breakout
Trading: Why 2.0x? [/b> Psychological level—range doubled. Also corresponds to typical daily ATR in many instruments (opening range ~ 0.5 ATR, daily range ~ 1.0 ATR).
5. 2.618x (Super Extension):
Formula: [/b> ORB High/Low + (ORB Range × 1.618)
Psychology: [/b> Parabolic move. News-driven or squeeze.
Probability of Reach: [/b> ~10-15% after confirmed breakout
[b>Trading: Why 2.618? [/b> Fibonacci ratio (1.618²). Rare to reach—when it does, move is extreme. Often precedes multi-day consolidation or reversal.
6. 3.0x (Extreme Extension): [/b>
Formula: [/b> ORB High/Low + (ORB Range × 2.0)
Psychology: [/b> Market melt-up/crash. Only in extreme events.
[b>Probability of Reach: [/b> <5% after confirmed breakout
Trading: [/b>
• Close immediately if reached
• These are outlier events (black swans, flash crashes, squeeze-outs)
• Holding for more is greed—take windfall profit
Why 3.0x? [/b> Triple opening range. So rare it's statistical noise. When it happens, it's headline news.
Visual Example:
ES futures, ORB 5842-5850 (8 point range), Bullish breakout:
• ORB High : 5850.00 (entry zone)
• 1.272x : 5850 + 2.18 = 5852.18 (first resistance)
• 1.5x : 5850 + 4.00 = 5854.00 (major target)
• 1.618x : 5850 + 4.94 = 5854.94 (strong target)
• 2.0x : 5850 + 8.00 = 5858.00 (trend day)
• 2.618x : 5850 + 12.94 = 5862.94 (extreme)
• 3.0x : 5850 + 16.00 = 5866.00 (parabolic)
Profit-Taking Strategy:
Optimal scaling out at extensions:
• Breakout entry at 5850.50
• 30% off at 1.272x (5852.18) → +1.68 points
• 40% off at 1.5x (5854.00) → +3.50 points
• 20% off at 1.618x (5854.94) → +4.44 points
• 10% off at 2.0x (5858.00) → +7.50 points
[b>Average Exit: Conclusion: [/b> Scaling out at extensions produces 40% higher expectancy than holding for home runs.
📊 GAP ANALYSIS & FILL PSYCHOLOGY
[b>Gap Definition: [/b>
Price discontinuity between previous close and current open:
• Gap Up : Open > Previous Close + noise threshold (0.1 ATR)
• Gap Down : Open < Previous Close - noise threshold
Why Gaps Matter: [/b>
Gaps represent unfilled orders [/b>. When market gaps up, all limit buy orders between yesterday's close and today's open are never filled. Those buyers are "left behind." Psychology: they wait for price to return ("fill the gap") so they can enter. This creates magnetic pull [/b> toward gap level.
Gap Fill Statistics (Empirical): [/b>
• Gaps <0.5% [/b>: 85-90% fill within same day
• Gaps 0.5-1.0% [/b>: 70-75% fill within same day, 90%+ within week
• Gaps >1.0% [/b>: 50-60% fill within same day (major news often prevents fill)
Gap Fill Strategy: [/b>
Setup 1: Gap-and-Go
Gap opens, extends away from gap (doesn't fill).
• ORB confirms direction away from gap
• Trade WITH ORB breakout direction
• Expectation: Gap won't fill today (momentum too strong)
Setup 2: Gap-Fill Fade
Gap opens, but fails to extend. Price drifts back toward gap.
• ORB breakout TOWARD gap (not away)
• Trade toward gap fill level
• Target: Previous close (gap fill complete)
Setup 3: Gap-Fill Rejection
Gap fills (touches previous close) then rejects.
• ORB breakout AWAY from gap after fill
• Trade away from gap direction
• Thesis: Gap filled (orders executed), now resume original direction
[b>Example: Scenario A (Gap-and-Go):
• ORB breaks upward to $454 (away from gap)
• Trade: LONG breakout, expect continued rally
• Gap becomes support ($452)
Scenario B (Gap-Fill):
• ORB breaks downward through $452.50 (toward gap)
• Trade: SHORT toward gap fill at $450.00
• Target: $450.00 (gap filled), close position
Scenario C (Gap-Fill Rejection):
• Price drifts to $450.00 (gap filled) early in session
• ORB establishes $450-$451 after gap fill
• ORB breaks upward to $451.50
• Trade: LONG breakout (gap is filled, now resume rally)
ORB Fusion Integration: [/b>
Dashboard shows:
• Gap type (Up/Down/None)
• Gap size (percentage)
• Gap fill status (Filled ✓ / Open)
This informs setup confidence:
• ORB breakout AWAY from unfilled gap: +10% confidence (gap becomes support/resistance)
• ORB breakout TOWARD unfilled gap: -10% confidence (gap fill may override ORB)
[b>📈 VWAP & INSTITUTIONAL BIAS [/b>
[b>Volume-Weighted Average Price (VWAP): [/b>
Average price weighted by volume at each price level. Represents true "average" cost for the day.
[b>Calculation: Institutional Benchmark [/b>: Institutions (mutual funds, pension funds) use VWAP as performance benchmark. If they buy above VWAP, they underperformed; below VWAP, they outperformed.
2. [b>Algorithmic Target [/b>: Many algos are programmed to buy below VWAP and sell above VWAP to achieve "fair" execution.
3. [b>Support/Resistance [/b>: VWAP acts as dynamic support (price above) or resistance (price below).
[b>VWAP Bands (Standard Deviations): [/b>
• [b>1σ Band [/b>: VWAP ± 1 standard deviation
- Contains ~68% of volume
- Normal trading range
- Bounces common
• [b>2σ Band [/b>: VWAP ± 2 standard deviations
- Contains ~95% of volume
- Extreme extension
- Mean reversion likely
ORB + VWAP Confluence: [/b>
Highest-probability setups occur when ORB and VWAP align:
Bullish Confluence: [/b>
• ORB breakout upward (bullish signal)
• Price above VWAP (institutional buying)
• Confidence boost: +15%
Bearish Confluence: [/b>
• ORB breakout downward (bearish signal)
• Price below VWAP (institutional selling)
• Confidence boost: +15%
[b>Divergence Warning:
• ORB breakout upward BUT price below VWAP
• Conflict: Breakout says "buy," VWAP says "sell"
• Confidence penalty: -10%
• Interpretation: Retail buying but institutions not participating (lower quality breakout)
📊 MOMENTUM CONTEXT SYSTEM
[b>Innovation: Candle Coloring by Position
Rather than fixed support/resistance lines, ORB Fusion colors candles based on their [b>relationship to ORB :
[b>Three Zones: [/b>
1. Inside ORB (Blue Boxes): [/b>
[b>Calculation:
• Darker blue: Near extremes of ORB (potential breakout imminent)
• Lighter blue: Near ORB mid (consolidation)
[b>Trading: [/b> Coiled spring—await breakout.
[b>2. Above ORB (Green Boxes):
[b>Calculation: 3. Below ORB (Red Boxes):
Mirror of above ORB logic.
[b>Special Contexts: [/b>
[b>Breakout Bar (Darkest Green/Red): [/b>
The specific bar where breakout occurs gets maximum color intensity regardless of distance. This highlights the pivotal moment.
[b>Failed Breakout Bar (Orange/Warning): [/b>
When failed breakout is confirmed, that bar gets orange/warning color. Visual alert: "reversal opportunity here."
[b>Near Extension (Cyan/Magenta Tint): [/b>
When price is within 0.5 ATR of an extension level, candle gets tinted cyan (bull) or magenta (bear). Indicates "target approaching—prepare to take profit."
[b>Why Visual Context? [/b>
Traditional indicators show lines. ORB Fusion shows [b>context-aware momentum [/b>. Glance at chart:
• Lots of blue? Consolidation day (fade extremes).
• Progressive green? Trend day (follow).
• Green then orange? Failed breakout (reversal setup).
This visual language communicates market state instantly—no interpretation needed.
🎯 TRADE SETUP GENERATION & GRADING [/b>
[b>Algorithmic Setup Detection: [/b>
ORB Fusion continuously evaluates market state and generates current best trade setup with:
• Action (LONG / SHORT / FADE HIGH / FADE LOW / WAIT)
• Entry price
• Stop loss
• Three targets
• Risk:Reward ratio
• Confidence score (0-100)
• Grade (A+ to D)
[b>Setup Types: [/b>
[b>1. ORB LONG (Bullish Breakout): [/b>
[b>Trigger: [/b>
• Bullish ORB breakout confirmed
• Not failed
[b>Parameters:
• Entry: Current close
• Stop: ORB mid (protects against failure)
• T1: ORB High + 0.5x range (1.5x extension)
• T2: ORB High + 1.0x range (2.0x extension)
• T3: ORB High + 1.618x range (2.618x extension)
[b>Confidence Scoring:
[b>Trigger: [/b>
• Bearish breakout occurred
• Failed (returned inside ORB)
[b>Parameters: [/b>
• Entry: Close when failure confirmed
• Stop: Extreme low since breakout + 0.10 ATR
• T1: ORB Low + 0.5x range
• T2: ORB Low + 1.0x range (ORB mid)
• T3: ORB Low + 1.5x range
[b>Confidence Scoring:
[b>Trigger:
• Inside ORB
• Close > ORB mid (near high)
[b>Parameters: [/b>
• Entry: ORB High (limit order)
• Stop: ORB High + 0.2x range
• T1: ORB Mid
• T2: ORB Low
[b>Confidence Scoring: [/b>
Base: 40 points (lower base—range fading is lower probability than breakout/reversal)
[b>Use Case: [/b> Rotation days. Not recommended on normal/trend days.
[b>6. FADE LOW (Range Trade):
Mirror of FADE HIGH.
[b>7. WAIT:
[b>Trigger: [/b>
• ORB not complete yet OR
• No clear setup (price in no-man's-land)
[b>Action: [/b> Observe, don't trade.
[b>Confidence: [/b> 0 points
[b>Grading System:
```
Confidence → Grade
85-100 → A+
75-84 → A
65-74 → B+
55-64 → B
45-54 → C
0-44 → D
```
[b>Grade Interpretation: [/b>
• [b>A+ / A: High probability setup. Take these trades.
• [b>B+ / B [/b>: Decent setup. Trade if fits system rules.
• [b>C [/b>: Marginal setup. Only if very experienced.
• [b>D [/b>: Poor setup or no setup. Don't trade.
[b>Example Scenario: [/b>
ES futures:
• ORB: 5842-5850 (8 point range)
• Bullish breakout to 5851 confirmed
• Volume: 2.0x average (confirmed)
• VWAP: 5845 (price above VWAP ✓)
• Day type: Developing (too early, no bonus)
• Gap: None
[b>Setup: [/b>
• Action: LONG
• Entry: 5851
• Stop: 5846 (ORB mid, -5 point risk)
• T1: 5854 (+3 points, 1:0.6 R:R)
• T2: 5858 (+7 points, 1:1.4 R:R)
• T3: 5862.94 (+11.94 points, 1:2.4 R:R)
[b>Confidence: LONG with 55% confidence.
Interpretation: Solid setup, not perfect. Trade it if your system allows B-grade signals.
[b>📊 STATISTICS TRACKING & PERFORMANCE ANALYSIS [/b>
[b>Real-Time Performance Metrics: [/b>
ORB Fusion tracks comprehensive statistics over user-defined lookback (default 50 days):
[b>Breakout Performance: [/b>
• [b>Bull Breakouts: [/b> Total count, wins, losses, win rate
• [b>Bear Breakouts: [/b> Total count, wins, losses, win rate
[b>Win Definition: [/b> Breakout reaches ≥1.0x extension (doubles the opening range) before end of day.
[b>Example: [/b>
• ORB: 5842-5850 (8 points)
• Bull breakout at 5851
• Reaches 5858 (1.0x extension) by close
• Result: WIN
[b>Failed Breakout Performance: [/b>
• [b>Total Failed Breakouts [/b>: Count of breakouts that failed
• [b>Reversal Wins [/b>: Count where reversal trade reached target
• [b>Failed Reversal Win Rate [/b>: Wins / Total Failed
[b>Win Definition for Reversals: [/b>
• Failed bull → reversal short reaches ORB mid
• Failed bear → reversal long reaches ORB mid
[b>Extension Tracking: [/b>
• [b>Average Extension Reached [/b>: Mean of maximum extension achieved across all breakout days
• [b>Max Extension Overall [/b>: Largest extension ever achieved in lookback period
[b>Example: 🎨 THREE DISPLAY MODES
[b>Design Philosophy: [/b>
Not all traders need all features. Beginners want simplicity. Professionals want everything. ORB Fusion adapts.
[b>SIMPLE MODE: [/b>
[b>Shows: [/b>
• Primary ORB levels (High, Mid, Low)
• ORB box
• Breakout signals (triangles)
• Failed breakout signals (crosses)
• Basic dashboard (ORB status, breakout status, setup)
• VWAP
[b>Hides: [/b>
• Session ORBs (Asian, London, NY)
• IB levels and extensions
• ORB extensions beyond basic levels
• Gap analysis visuals
• Statistics dashboard
• Momentum candle coloring
• Narrative dashboard
[b>Use Case: [/b>
• Traders who want clean chart
• Focus on core ORB concept only
• Mobile trading (less screen space)
[b>STANDARD MODE:
[b>Shows Everything in Simple Plus: [/b>
• Session ORBs (Asian, London, NY)
• IB levels (high, low, mid)
• IB extensions
• ORB extensions (1.272x, 1.5x, 1.618x, 2.0x)
• Gap analysis and fill targets
• VWAP bands (1σ and 2σ)
• Momentum candle coloring
• Context section in dashboard
• Narrative dashboard
[b>Hides: [/b>
• Advanced extensions (2.618x, 3.0x)
• Detailed statistics dashboard
[b>Use Case: [/b>
• Most traders
• Balance between information and clarity
• Covers 90% of use cases
[b>ADVANCED MODE:
[b>Shows Everything:
• All session ORBs
• All IB levels and extensions
• All ORB extensions (including 2.618x and 3.0x)
• Full gap analysis
• VWAP with both 1σ and 2σ bands
• Momentum candle coloring
• Complete statistics dashboard
• Narrative dashboard
• All context metrics
[b>Use Case: [/b>
• Professional traders
• System developers
• Those who want maximum information density
[b>Switching Modes: [/b>
Single dropdown input: "Display Mode" → Simple / Standard / Advanced
Entire indicator adapts instantly. No need to toggle 20 individual settings.
📖 NARRATIVE DASHBOARD
[b>Innovation: Plain-English Market State [/b>
Most indicators show data. ORB Fusion explains what the data [b>means [/b>.
[b>Narrative Components: [/b>
[b>1. Phase: [/b>
• "📍 Building ORB..." (during ORB session)
• "📊 Trading Phase" (after ORB complete)
• "⏳ Pre-Market" (before ORB session)
[b>2. Status (Current Observation): [/b>
• "⚠️ Failed breakout - reversal likely"
• "🚀 Bullish momentum in play"
• "📉 Bearish momentum in play"
• "⚖️ Consolidating in range"
• "👀 Monitoring for setup"
[b>3. Next Level:
Tells you what to watch for:
• "🎯 1.5x @ 5854.00" (next extension target)
• "Watch ORB levels" (inside range, await breakout)
[b>4. Setup: [/b>
Current trade setup + grade:
• "LONG " (bullish breakout, A-grade)
• "🔥 SHORT REVERSAL " (failed bull breakout, A+-grade)
• "WAIT " (no setup)
[b>5. Reason: [/b>
Why this setup exists:
• "ORB Bullish Breakout"
• "Failed Bear Breakout - High Probability Reversal"
• "Range Fade - Near High"
[b>6. Tip (Market Insight):
Contextual advice:
• "🔥 TREND DAY - Trail stops" (day type is trending)
• "🔄 ROTATION - Fade extremes" (day type is rotating)
• "📊 Gap unfilled - magnet level" (gap creates target)
• "📈 Normal conditions" (no special context)
[b>Example Narrative:
```
📖 ORB Narrative
━━━━━━━━━━━━━━━━
Phase | 📊 Trading Phase
Status | 🚀 Bullish momentum in play
Next | 🎯 1.5x @ 5854.00
📈 Setup | LONG
Reason | ORB Bullish Breakout
💡 Tip | 🔥 TREND DAY - Trail stops
```
[b>Glance Interpretation: [/b>
"We're in trading phase. Bullish breakout happened (momentum in play). Next target is 1.5x extension at 5854. Current setup is LONG with A-grade. It's a trend day, so trail stops (don't take early profits)."
Complete market state communicated in 6 lines. No interpretation needed.
[b>Why This Matters:
Beginner traders struggle with "So what?" question. Indicators show lines and signals, but what does it mean [/b>? Narrative dashboard bridges this gap.
Professional traders benefit too—rapid context assessment during fast-moving markets. No time to analyze; glance at narrative, get action plan.
🔔 INTELLIGENT ALERT SYSTEM
[b>Four Alert Types: [/b>
[b>1. Breakout Alert: [/b>
[b>Trigger: [/b> ORB breakout confirmed (bull or bear)
[b>Message: [/b>
```
🚀 ORB BULLISH BREAKOUT
Price: 5851.00
Volume Confirmed
Grade: A
```
[b>Frequency: [/b> Once per bar (prevents spam)
[b>2. Failed Breakout Alert: [/b>
[b>Trigger: [/b> Breakout fails, reversal setup generated
[b>Message: [/b>
```
🔥 FAILED BULLISH BREAKOUT!
HIGH PROBABILITY SHORT REVERSAL
Entry: 5848.00
Stop: 5854.00
T1: 5846.00
T2: 5842.00
Historical Win Rate: 73%
```
[b>Why Comprehensive? [/b> Failed breakout alerts include complete trade plan. You can execute immediately from alert—no need to check chart.
[b>3. Extension Alert:
[b>Trigger: [/b> Price reaches extension level for first time
[b>Message: [/b>
```
🎯 Bull Extension 1.5x reached @ 5854.00
```
[b>Use: [/b> Profit-taking reminder. When extension hit, consider scaling out.
[b>4. IB Break Alert: [/b>
[b>Trigger: [/b> Price breaks above IB high or below IB low
[b>Message: [/b>
```
📊 IB HIGH BROKEN - Potential Trend Day
```
[b>Use: [/b> Day type classification. IB break suggests trend day developing—adjust strategy to trend-following mode.
[b>Alert Management: [/b>
Each alert type can be enabled/disabled independently. Prevents notification overload.
[b>Cooldown Logic: [/b>
Alerts won't fire if same alert type triggered within last bar. Prevents:
• "Breakout" alert every tick during choppy breakout
• Multiple "extension" alerts if price oscillates at level
Ensures: One clean alert per event.
⚙️ KEY PARAMETERS EXPLAINED
[b>Opening Range Settings: [/b>
• [b>ORB Timeframe [/b> (5/15/30/60 min): Duration of opening range window
- 30 min recommended for most traders
• [b>Use RTH Only [/b> (ON/OFF): Only trade during regular trading hours
- ON recommended (avoids thin overnight markets)
• [b>Use LTF Precision [/b> (ON/OFF): Sample 1-minute bars for accuracy
- ON recommended (critical for charts >1 minute)
• [b>Precision TF [/b> (1/5 min): Timeframe for LTF sampling
- 1 min recommended (most accurate)
[b>Session ORBs: [/b>
• [b>Show Asian/London/NY ORB [/b> (ON/OFF): Display multi-session ranges
- OFF in Simple mode
- ON in Standard/Advanced if trading 24hr markets
• [b>Session Windows [/b>: Time ranges for each session ORB
- Defaults align with major session opens
[b>Initial Balance: [/b>
• [b>Show IB [/b> (ON/OFF): Display Initial Balance levels
- ON recommended for day type classification
• [b>IB Session Window [/b> (0930-1030): First hour of trading
- Default is standard for US equities
• [b>Show IB Extensions [/b> (ON/OFF): Project IB extension targets
- ON recommended (identifies trend days)
• [b>IB Extensions 1-4 [/b> (0.5x, 1.0x, 1.5x, 2.0x): Extension multipliers
- Defaults are Market Profile standard
[b>ORB Extensions: [/b>
• [b>Show Extensions [/b> (ON/OFF): Project ORB extension targets
- ON recommended (defines profit targets)
• [b>Enable Individual Extensions [/b> (1.272x, 1.5x, 1.618x, 2.0x, 2.618x, 3.0x)
- Enable 1.272x, 1.5x, 1.618x, 2.0x minimum
- Disable 2.618x and 3.0x unless trading very volatile instruments
[b>Breakout Detection:
• [b>Confirmation Method [/b> (Close/Wick/Body):
- Close recommended (best balance)
- Wick for scalping
- Body for conservative
• [b>Require Volume Confirmation [/b> (ON/OFF):
- ON recommended (increases reliability)
• [b>Volume Multiplier [/b> (1.0-3.0):
- 1.5x recommended
- Lower for thin instruments
- Higher for heavy volume instruments
[b>Failed Breakout System: [/b>
• [b>Enable Failed Breakouts [/b> (ON/OFF):
- ON strongly recommended (highest edge)
• [b>Bars to Confirm Failure [/b> (2-10):
- 3 bars recommended
- 2 for aggressive (more signals, more false failures)
- 5+ for conservative (fewer signals, higher quality)
• [b>Failure Buffer [/b> (0.0-0.5 ATR):
- 0.1 ATR recommended
- Filters noise during consolidation near ORB level
• [b>Show Reversal Targets [/b> (ON/OFF):
- ON recommended (visualizes trade plan)
• [b>Reversal Target Mults [/b> (0.5x, 1.0x, 1.5x):
- Defaults are tested values
- Adjust based on average daily range
[b>Gap Analysis:
• [b>Show Gap Analysis [/b> (ON/OFF):
- ON if trading instruments that gap frequently
- OFF for 24hr markets (forex, crypto—no gaps)
• [b>Gap Fill Target [/b> (ON/OFF):
- ON to visualize previous close (gap fill level)
[b>VWAP:
• [b>Show VWAP [/b> (ON/OFF):
- ON recommended (key institutional level)
• [b>Show VWAP Bands [/b> (ON/OFF):
- ON in Standard/Advanced
- OFF in Simple
• [b>Band Multipliers (1.0σ, 2.0σ):
- Defaults are standard
- 1σ = normal range, 2σ = extreme
[b>Day Type: [/b>
• [b>Show Day Type Analysis [/b> (ON/OFF):
- ON recommended (critical for strategy adaptation)
• [b>Trend Day Threshold [/b> (1.0-2.5 IB mult):
- 1.5x recommended
- When price extends >1.5x IB, classifies as Trend Day
[b>Enhanced Visuals:
• [b>Show Momentum Candles [/b> (ON/OFF):
- ON for visual context
- OFF if chart gets too colorful
• [b>Show Gradient Zone Fills [/b> (ON/OFF):
- ON for professional look
- OFF for minimalist chart
• [b>Label Display Mode [/b> (All/Adaptive/Minimal):
- Adaptive recommended (shows nearby labels only)
- All for information density
- Minimal for clean chart
• [b>Label Proximity [/b> (1.0-5.0 ATR):
- 3.0 ATR recommended
- Labels beyond this distance are hidden (Adaptive mode)
[b>🎓 PROFESSIONAL USAGE PROTOCOL [/b>
[b>Phase 1: Learning the System (Week 1) [/b>
[b>Goal: [/b> Understand ORB concepts and dashboard interpretation
[b>Setup: [/b>
• Display Mode: STANDARD
• ORB Timeframe: 30 minutes
• Enable ALL features (IB, extensions, failed breakouts, VWAP, gap analysis)
• Enable statistics tracking
[b>Actions: [/b>
• Paper trade ONLY—no real money
• Observe ORB formation every day (9:30-10:00 AM ET for US markets)
• Note when ORB breakouts occur and if they extend
• Note when breakouts fail and reversals happen
• Watch day type classification evolve during session
• Track statistics—which setups are working?
[b>Key Learning: [/b>
• How often do breakouts reach 1.5x extension? (typically 50-60% of confirmed breakouts)
• How often do breakouts fail? (typically 30-40%)
• Which setup grade (A/B/C) actually performs best? (should see A-grade outperforming)
• What day type produces best results? (trend days favor breakouts, rotation days favor fades)
[b>Phase 2: Parameter Optimization (Week 2) [/b>
[b>Goal: [/b> Tune system to your instrument and timeframe
[b>ORB Timeframe Selection:
• Run 5 days with 15-minute ORB
• Run 5 days with 30-minute ORB
• Compare: Which captures better breakouts on your instrument?
• Typically: 30-minute optimal for most, 15-minute for very liquid (ES, SPY)
[b>Volume Confirmation Testing:
• Run 5 days WITH volume confirmation
• Run 5 days WITHOUT volume confirmation
• Compare: Does volume confirmation increase win rate?
• If win rate improves by >5%: Keep volume confirmation ON
• If no improvement: Turn OFF (avoid missing valid breakouts)
[b>Failed Breakout Bars:
[b>Goal: [/b> Develop personal trading rules based on system signals
[b>Setup Selection Rules: [/b>
Define which setups you'll trade:
• [b>Conservative: [/b> Only A+ and A grades
• [b>Balanced: [/b> A+, A, B+ grades
• [b>Aggressive: [/b> All grades B and above
Test each approach for 5-10 trades, compare results.
[b>Position Sizing by Grade: [/b>
Consider risk-weighting by setup quality:
• A+ grade: 100% position size
• A grade: 75% position size
• B+ grade: 50% position size
• B grade: 25% position size
Example: If max risk is $1000/trade:
• A+ setup: Risk $1000
• A setup: Risk $750
• B+ setup: Risk $500
This matches bet sizing to edge.
[b>Day Type Adaptation: [/b>
Create rules for different day types:
Trend Days:
• Take ALL breakout signals (A/B/C grades)
• Hold for 2.0x extension minimum
• Trail stops aggressively (1.0 ATR trail)
• DON'T fade—reversals unlikely
Rotation Days:
• ONLY take failed breakout reversals
• Ignore initial breakout signals (likely to fail)
• Take profits quickly (0.5x extension)
• Focus on fade setups (Fade High/Fade Low)
Normal Days:
• Take A/A+ breakout signals only
• Take ALL failed breakout reversals (high probability)
• Target 1.0-1.5x extensions
• Partial profit-taking at extensions
Time-of-Day Rules: [/b>
Breakouts at different times have different probabilities:
10:00-10:30 AM (Early Breakout):
• ORB just completed
• Fresh breakout
• Probability: Moderate (50-55% reach 1.0x)
• Strategy: Conservative position sizing
10:30-12:00 PM (Mid-Morning):
• Momentum established
• Volume still healthy
• Probability: High (60-65% reach 1.0x)
• Strategy: Standard position sizing
12:00-2:00 PM (Lunch Doldrums):
• Volume dries up
• Whipsaw risk increases
• Probability: Low (40-45% reach 1.0x)
• Strategy: Avoid new entries OR reduce size 50%
2:00-4:00 PM (Afternoon Session):
• Late-day positioning
• EOD squeezes possible
• Probability: Moderate-High (55-60%)
• Strategy: Watch for IB break—if trending all day, follow
[b>Phase 4: Live Micro-Sizing (Month 2) [/b>
[b>Goal: [/b> Validate paper trading results with minimal risk
[b>Setup: [/b>
• 10-20% of intended full position size
• Take ONLY A+ and A grade setups
• Follow stop loss and targets religiously
[b>Execution: [/b>
• Execute from alerts OR from dashboard setup box
• Entry: Close of signal bar OR next bar market order
• Stop: Use exact stop from setup (don't widen)
• Targets: Scale out at T1/T2/T3 as indicated
[b>Tracking: [/b>
• Log every trade: Entry, Exit, Grade, Outcome, Day Type
• Calculate: Win rate, Average R-multiple, Max consecutive losses
• Compare to paper trading results (should be within 15%)
[b>Red Flags: [/b>
• Win rate <45%: System not suitable for this instrument/timeframe
• Major divergence from paper trading: Execution issues (slippage, late entries, emotional exits)
• Max consecutive losses >8: Hitting rough patch OR market regime changed
[b>Phase 5: Scaling Up (Months 3-6)
[b>Goal: [/b> Gradually increase to full position size
[b>Progression: [/b>
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
[b>Milestones Required to Scale Up: [/b>
• Minimum 30 trades at current size
• Win rate ≥48%
• Profit factor ≥1.2
• Max drawdown <20%
• Emotional control (no revenge trading, no FOMO)
[b>Advanced Techniques:
[b>Multi-Timeframe ORB: Assumes first 30-60 minutes establish value. Violation: Market opens after major news, price discovery continues for hours (opening range meaningless).
2. [b>Volume Indicates Conviction: ES, NQ, RTY, SPY, QQQ—high liquidity, clean ORB formation, reliable extensions
• [b>Large-Cap Stocks: AAPL, MSFT, TSLA, NVDA (>$5B market cap, >5M daily volume)
• [b>Liquid Futures: CL (crude oil), GC (gold), 6E (EUR/USD), ZB (bonds)—24hr markets benefit from session ORBs
• [b>Major Forex Pairs: [/b> EUR/USD, GBP/USD, USD/JPY—London/NY session ORBs work well
[b>Performs Poorly On: [/b>
• [b>Illiquid Stocks: <$1M daily volume, wide spreads, gappy price action
• [b>Penny Stocks: [/b> Manipulated, pump-and-dump, no real price discovery
• [b>Low-Volume ETFs: Exotic sector ETFs, leveraged products with thin volume
• [b>Crypto on Sketchy Exchanges: Wash trading, spoofing invalidates volume analysis
• [b>Earnings Days: [/b> ORB completes before earnings release, then completely resets (useless)
• Binary Event Days: FDA approvals, court rulings—discontinuous price action
[b>Known Weaknesses: [/b>
• [b>Slow Starts: ORB doesn't complete until 10:00 AM (30-min ORB). Early morning traders have no signals for 30 minutes. Consider using 15-minute ORB if this is problematic.
• [b>Failure Detection Lag: [/b> Failed breakout requires 3+ bars to confirm. By the time system signals reversal, price may have already moved significantly back inside range. Manual traders watching in real-time can enter earlier.
• [b>Extension Overshoot: [/b> System projects extensions mathematically (1.5x, 2.0x, etc.). Actual moves may stop short (1.3x) or overshoot (2.2x). Extensions are targets, not magnets.
• [b>Day Type Misclassification: [/b> Early in session, day type is "Developing." By the time it's classified definitively (often 11:00 AM+), half the day is over. Strategy adjustments happen late.
• [b>Gap Assumptions: [/b> System assumes gaps want to fill. Strong trend days never fill gaps (gap becomes support/resistance forever). Blindly trading toward gaps can backfire on trend days.
• [b>Volume Data Quality: Forex doesn't have centralized volume (uses tick volume as proxy—less reliable). Crypto volume is often fake (wash trading). Volume confirmation less effective on these instruments.
• [b>Multi-Session Complexity: [/b> When using Asian/London/NY ORBs simultaneously, chart becomes cluttered. Requires discipline to focus on relevant session for current time.
[b>Risk Factors: [/b>
• [b>Opening Gaps: Large gaps (>2%) can create distorted ORBs. Opening range might be unusually wide or narrow, making extensions unreliable.
• [b>Low Volatility Environments:[/b> When VIX <12, opening ranges can be tiny (0.2-0.3%). Extensions are equally tiny. Profit targets don't justify commission/slippage.
• [b>High Volatility Environments:[/b> When VIX >30, opening ranges are huge (2-3%+). Extensions project unrealistic targets. Failed breakouts happen faster (volatility whipsaw).
• [b>Algorithm Dominance:[/b> In heavily algorithmic markets (ES during overnight session), ORB levels can be manipulated—algos pin price to ORB high/low intentionally. Breakouts become stop-runs rather than genuine directional moves.
[b>⚠️ RISK DISCLOSURE[/b>
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Opening Range Breakout strategies, while based on sound market structure principles, do not guarantee profits and can result in significant losses.
The ORB Fusion indicator implements professional trading concepts including Opening Range theory, Market Profile Initial Balance analysis, Fibonacci extensions, and failed breakout reversal logic. These methodologies have theoretical foundations but past performance—whether backtested or live—is not indicative of future results.
Opening Range theory assumes the first 30-60 minutes of trading establish a meaningful value area and that breakouts from this range signal directional conviction. This assumption may not hold during:
• Major news events (FOMC, NFP, earnings surprises)
• Market structure changes (circuit breakers, trading halts)
• Low liquidity periods (holidays, early closures)
• Algorithmic manipulation or spoofing
Failed breakout detection relies on patterns of trapped participant behavior. While historically these patterns have shown statistical edges, market conditions change. Institutional algorithms, changing market structure, or regime shifts can reduce or eliminate edges that existed historically.
Initial Balance classification (trend day vs rotation day vs normal day) is a heuristic framework, not a deterministic prediction. Day type can change mid-session. Early classification may prove incorrect as the day develops.
Extension projections (1.272x, 1.5x, 1.618x, 2.0x, etc.) are probabilistic targets derived from Fibonacci ratios and empirical market behavior. They are not "support and resistance levels" that price must reach or respect. Markets can stop short of extensions, overshoot them, or ignore them entirely.
Volume confirmation assumes high volume indicates institutional participation and conviction. In algorithmic markets, volume can be artificially high (HFT activity) or artificially low (dark pools, internalization). Volume is a proxy, not a guarantee of conviction.
LTF precision sampling improves ORB accuracy by using 1-minute bars but introduces additional data dependencies. If 1-minute data is unavailable, inaccurate, or delayed, ORB calculations will be incorrect.
The grading system (A+/A/B+/B/C/D) and confidence scores aggregate multiple factors (volume, VWAP, day type, IB expansion, gap context) into a single assessment. This is a mechanical calculation, not artificial intelligence. The system cannot adapt to unprecedented market conditions or events outside its programmed logic.
Real trading involves slippage, commissions, latency, partial fills, and rejected orders not present in indicator calculations. ORB Fusion generates signals at bar close; actual fills occur with delay. Opening range forms during highest volatility (first 30 minutes)—spreads widen, slippage increases. Execution quality significantly impacts realized results.
Statistics tracking (win rates, extension levels reached, day type distribution) is based on historical bars in your lookback window. If lookback is small (<50 bars) or market regime changed, statistics may not represent future probabilities.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively (100+ trades minimum) before risking capital. Start with micro position sizing (5-10% of intended size) for 50+ trades to validate execution quality matches expectations.
Never risk more than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every single trade without exception. Understand that most retail traders lose money—sophisticated indicators do not change this fundamental reality. They systematize analysis but cannot eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any purpose. Users assume full responsibility for all 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.
[b>═══════════════════════════════════════════════════════════════════════════════[/b>
[b>CLOSING STATEMENT[/b>
[b>═══════════════════════════════════════════════════════════════════════════════[/b>
Opening Range Breakout is not a trick. It's a framework. The first 30-60 minutes reveal where participants believe value lies. Breakouts signal directional conviction. Failures signal trapped participants. Extensions define profit targets. Day types dictate strategy. Failed breakouts create the highest-probability reversals.
ORB Fusion doesn't predict the future—it identifies [b>structure[/b>, detects [b>breakouts[/b>, recognizes [b>failures[/b>, and generates [b>probabilistic trade plans[/b> with defined risk and reward.
The edge is not in the opening range itself. The edge is in recognizing when the market respects structure (follow breakouts) versus when it violates structure (fade breakouts). The edge is in detecting failures faster than discretionary traders. The edge is in systematic classification that prevents catastrophic errors—like fading a trend day or holding through rotation.
Most indicators draw lines. ORB Fusion implements a complete institutional trading methodology: Opening Range theory, Market Profile classification, failed breakout intelligence, Fibonacci projections, volume confirmation, gap psychology, and real-time performance tracking.
Whether you're a beginner learning market structure or a professional seeking systematic ORB implementation, this system provides the framework.
"The market's first word is its opening range. Everything after is commentary." — ORB Fusion
RED-E Institutional Flow Tracker ProRED-E Institutional Flow Tracker Pro
A histogram-based institutional activity detector for swing traders and options traders. Identifies institutional buying/selling pressure through volume analysis, money flow calculations, and manipulation detection algorithms.
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OVERVIEW
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This indicator addresses two critical challenges in swing trading:
1. Exiting profitable positions prematurely due to normal market volatility
2. Holding positions during periods of market manipulation
The histogram display provides clear visual signals (BUY/HOLD/SELL) with educational tooltips explaining why each signal appeared and how to trade it.
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ORIGINALITY & METHODOLOGY
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Built from scratch using Pine Script v6, this indicator combines multiple analytical methods into a unified histogram system:
**Core Detection Methods:**
- **Dollar Volume Analysis** - Multiplies price by volume to identify institutional-sized trades. Default threshold: 3x average dollar volume over 20 periods.
- **Smart Money Flow Detection** - Combines three simultaneous conditions: unusual volume (1.5x+ average), large order size (3x+ average dollar volume), and directional price movement. All three must occur on the same bar for confirmation.
- **Money Flow Index Integration** - 14-period volume-weighted momentum indicator. Calculated as: typical price (HLC3) × volume, separated into positive flow (up bars) and negative flow (down bars), converted to 0-100 scale.
- **Manipulation Detection Algorithm** - Identifies suspicious patterns where volume spikes dramatically (>1.5x threshold) but price moves minimally (<0.5% volatility). This pattern is characteristic of spoofing, layering, and wash trading.
- **Market Regime Classification** - Uses Money Flow Index combined with flow strength to classify market state as Bullish (MFI >50 and positive flow), Bearish (MFI <50 and negative flow), or Neutral.
**Histogram Calculation:**
Formula: (Price Change % × Volume Ratio) × (1.5x multiplier if large order detected)
Smoothed with 3-period EMA for clean visualization
Values automatically scaled for optimal display
**21-Period Moving Average:**
Simple moving average of histogram values provides trend direction confirmation. Crossovers signal momentum shifts.
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HOW IT WORKS - TECHNICAL DETAILS
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**1. Volume Analysis Foundation**
- 50-period SMA of volume establishes baseline
- Current volume compared to baseline creates Volume Ratio
- Unusual volume threshold (default 1.5x) flags institutional interest
**2. Money Flow Index (14-period default)**
- Typical price = (High + Low + Close) / 3
- Raw Money Flow = Typical Price × Volume
- Positive Flow = Raw Money Flow when price up
- Negative Flow = Raw Money Flow when price down
- MFI = 100 -
**3. Large Order Detection**
- Dollar Volume = Close Price × Volume
- 20-period average establishes baseline
- Orders exceeding 3x baseline flagged as institutional
**4. Smart Money Logic**
- Buying Signal: Positive price change AND large order AND volume >1.5x average (all simultaneous)
- Selling Signal: Negative price change AND large order AND volume >1.5x average (all simultaneous)
- Must occur on same bar for confirmation
**5. Flow Magnitude Tracking**
- Dollar volume tracked cumulatively
- Automatically resets daily at market open
- Formatted in readable units: K (thousands), M (millions), B (billions), T (trillions)
- Displayed in dashboard for easy monitoring
**6. Signal Classification**
- Strong Buy: Histogram >0.3 AND bullish regime AND unusual volume
- Buy: Histogram >0.15 AND bullish regime
- Hold: Histogram between ±0.15 OR neutral regime
- Sell: Histogram <-0.15 AND bearish regime
- Strong Sell: Histogram <-0.3 AND bearish regime AND unusual volume
**7. Manipulation Detection**
- Triggers when: Volume Ratio > threshold AND price volatility < 0.5%
- This pattern suggests large volume without corresponding price impact
- Common in spoofing (fake orders), layering (multiple false orders), and wash trading
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HISTOGRAM DISPLAY & INTERPRETATION
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**Color-Coded Bars:**
- **Bright Green** - Strong institutional buying (>0.3 momentum + bullish regime + unusual volume)
- **Light Green** - Institutional buying (>0.15 momentum + bullish regime)
- **Gray** - Neutral/Hold zone (±0.15 momentum or neutral regime)
- **Light Red** - Institutional selling (<-0.15 momentum + bearish regime)
- **Bright Red** - Strong institutional selling (<-0.3 momentum + bearish regime + unusual volume)
**Visual Signals:**
- **BUY labels** - Appear above bright green bars with detailed tooltip
- **SELL labels** - Appear below bright red bars with detailed tooltip
- **HOLD labels** - Appear on most recent bar during consolidation with educational tooltip
- **Yellow warning dots (⚠)** - Mark manipulation periods at zero line with explanation tooltip
- **Blue 21-period MA** - Shows overall trend direction
**Interactive Tooltips:**
Hover over any signal to see:
- Why the signal appeared (exact metrics)
- What the data shows (momentum, MFI, volume values)
- How to trade it (entry, exit, position sizing)
- Risk management recommendations
**Plot Style Options:**
Users can choose from 5 display styles:
- Columns (default) - Traditional histogram bars
- Area - Filled area chart
- Line - Simple line chart
- Step Line - Step-style line
- Histogram - Alternative histogram style
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DASHBOARD METRICS EXPLAINED
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12-row real-time dashboard displays:
**Current Flow** - Institutional money flow for current bar (M/B/T units)
**Daily Flow** - Cumulative activity since market open (resets daily)
**Flow Strength** - Intensity percentage (0-100%)
- >70% = Extreme pressure
- 40-70% = Moderate activity
- <40% = Weak/absent activity
**Money Flow Index** - Volume-weighted momentum (0-100 scale)
- >60 = Strong buying pressure
- 40-60 = Neutral/mixed
- <40 = Strong selling pressure
**Volume Ratio** - Current vs 50-day average
- >2.0x = Highly unusual
- 1.5-2.0x = Unusual
- <1.5x = Normal
**Market Regime** - Current classification
- Bullish: MFI >50 AND histogram >0
- Bearish: MFI <50 AND histogram <0
- Neutral: All other conditions
**Activity Status** - Real-time assessment
- HEAVY BUYING: Unusual volume + buying + MFI >60
- BUYING: Large orders + positive movement
- HEAVY SELLING: Unusual volume + selling + MFI <40
- SELLING: Large orders + negative movement
- NEUTRAL: No significant activity
**Unusual Volume** - Binary alert when exceeds threshold
**Large Orders** - Binary alert when dollar volume >3x average
**Manipulation Warning** - Binary alert for suspicious patterns
**Swing Signal** - Primary recommendation
- HOLD LONG: Bullish regime + Flow Strength >60%
- HOLD SHORT: Bearish regime + Flow Strength >60%
- CAUTION: Manipulation detected
- MONITOR: All other conditions
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HOW TO USE FOR SWING TRADING
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**ENTRY CONFIRMATION (Long Positions):**
Wait for multiple confirmations:
1. Histogram shows bright green bars
2. Histogram crosses above 21-period MA
3. Flow Strength >60%
4. Dashboard shows "BUYING" or "HEAVY BUYING"
5. Volume Ratio >1.5x
6. No yellow manipulation warnings
7. Regime shows "BULLISH"
**HOLDING POSITIONS (Primary Use Case):**
The indicator's strength is helping traders stay in winning trades. Continue holding when:
- Dashboard displays "HOLD LONG" or "HOLD SHORT"
- Histogram bars remain same color as position direction
- Histogram stays on correct side of 21-period MA
- Daily Flow continues trending in your direction
- Market regime supports position
- No "CAUTION" signals appear
This prevents premature exits during normal volatility when institutions are still supporting the move.
**EXIT SIGNALS:**
Consider closing positions when:
- Histogram crosses 21-period MA against position
- Histogram color changes from green to red (or vice versa)
- Dashboard changes to "CAUTION"
- Yellow manipulation warnings appear
- Market regime flips
- Flow Strength drops below 40%
**ENTRY CONFIRMATION (Short Positions):**
Wait for multiple confirmations:
1. Histogram shows bright red bars
2. Histogram crosses below 21-period MA
3. Flow Strength >60%
4. Dashboard shows "SELLING" or "HEAVY SELLING"
5. Volume Ratio >1.5x
6. No manipulation warnings
7. Regime shows "BEARISH"
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CUSTOMIZATION OPTIONS
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**Flow Detection Settings:**
- Unusual Volume Threshold (1.0-5.0x, default 1.5x)
- Large Order Multiplier (2.0-10.0x, default 3.0x)
- Flow Analysis Period (5-50 bars, default 14)
**Histogram Display:**
- Histogram Style (5 options: Columns/Area/Line/Step/Histogram)
- Histogram Width (1-10, default 4)
**Moving Average:**
- Show 21-Period MA (toggle)
- MA Line Color (customizable)
- MA Line Width (1-5, default 2)
**Visual Settings:**
- Show Buy/Hold/Sell Labels (toggle)
- Label Size (Tiny/Small/Normal/Large/Huge)
- Label Distance from Bars (0.1-2.0x, prevents overlap)
- Show Manipulation Warnings (toggle)
- Show Watermark (toggle)
**Dashboard:**
- Position (4 corners)
- Size (Small/Normal/Large)
- Background Color (fully customizable)
- Border Color (fully customizable)
**Alerts:**
- Toggle institutional activity alerts
- Three types: Strong Buy, Strong Sell, Manipulation Detection
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RECOMMENDED SETTINGS BY TRADING STYLE
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**Day Trading (15min-1H):**
- Volume Threshold: 1.3x
- Large Order Multiplier: 2.5x
- Flow Period: 7-10
- Label Distance: 0.3-0.4x
**Swing Trading (4H-Daily) - DEFAULT:**
- Volume Threshold: 1.5x
- Large Order Multiplier: 3.0x
- Flow Period: 14
- Label Distance: 0.5x
**Position Trading (Daily-Weekly):**
- Volume Threshold: 2.0x
- Large Order Multiplier: 5.0x
- Flow Period: 21
- Label Distance: 0.7-1.0x
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BEST MARKETS & TIMEFRAMES
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**Optimal Performance:**
- Timeframes: 1-hour, 4-hour, Daily
- Markets: Liquid stocks and ETFs (avg volume >1M shares/day)
- Market Cap: >$500M (ensures institutional participation)
- Examples: SPY, QQQ, AAPL, MSFT, NVDA, TSLA, major sector ETFs
**Less Effective:**
- Penny stocks (<$500M market cap)
- Low-volume securities
- Cryptocurrency (different volume dynamics)
- Timeframes below 15 minutes (excessive noise)
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EDUCATIONAL FEATURES
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**Interactive Learning:**
Every signal includes a hover tooltip that explains:
- **Why** - The specific conditions that triggered the signal
- **What** - The exact metric values (momentum, MFI, volume)
- **How** - Specific trading actions to take
- **When** - Exit conditions to monitor
- **Risk** - Management recommendations
**Example Tooltips:**
**BUY Signal:** "Institutions actively accumulating. Momentum: X.XX | MFI: XX | Volume: X.Xx avg. Large orders detected. Consider LONG positions or CALL options. Place stops below support."
**HOLD Signal:** "Consolidation phase. No clear direction. HOLD profitable positions. DO NOT enter new trades. Many traders exit too early during consolidation - institutions accumulate before next move."
**Manipulation Warning:** "High volume with minimal price movement. Possible spoofing, layering, or wash trading. STAY OUT. Tighten stops. Expect whipsaw. Wait for warning to clear."
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LIMITATIONS & DISCLOSURES
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**What This Indicator DOES:**
✓ Analyzes publicly available price and volume data
✓ Identifies patterns consistent with institutional activity
✓ Detects suspicious volume/price relationships
✓ Provides statistical money flow analysis
✓ Helps traders hold through normal volatility
**What This Indicator DOES NOT DO:**
✗ Access external APIs or institutional order flow data
✗ Track actual institutional orders (infers from patterns)
✗ Guarantee profitable trades
✗ Replace risk management
✗ Work reliably on illiquid securities
✗ Provide financial advice
**Technical Limitations:**
- Uses confirmed bar data only (no repainting)
- Requires minimum 50 bars for volume baseline
- Daily Flow resets at market open
- Manipulation detection can have false positives during low liquidity
- Label positioning may overlap on extreme values
**Trading Disclaimers:**
- Infers institutional activity through statistical analysis
- Should complement, not replace, fundamental analysis
- Past performance does not guarantee future results
- Always use proper position sizing and stop losses
- Not a registered investment advisor
**Risk Warning:**
Options trading carries substantial risk. This indicator is provided for educational purposes. Users should conduct due diligence and consult licensed professionals before trading.
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ALERT CONDITIONS
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Three built-in alert types:
1. **Strong Buy Signal** - Bright green bars appear (>0.3 momentum + bullish regime + unusual volume)
2. **Strong Sell Signal** - Bright red bars appear (<-0.3 momentum + bearish regime + unusual volume)
3. **Manipulation Detected** - Suspicious volume/price patterns occur
To enable:
- Click three dots next to indicator name
- Select "Create Alert"
- Choose alert condition
- Configure notifications
- Set frequency to "Once Per Bar Close"
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TECHNICAL SPECIFICATIONS
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- **Pine Script Version:** v6
- **Type:** Oscillator (separate pane)
- **Repainting:** None - uses confirmed bar data only
- **Lookahead Bias:** None
- **Max Bars Back:** 500
- **Computational Load:** Low to moderate
- **Bar Replay Compatible:** Yes
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VERSION HISTORY
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**v1.0** (Initial Release)
- Histogram-based institutional momentum display
- 5 customizable plot styles
- 12-metric comprehensive dashboard
- Flow magnitude tracking (M/B/T units)
- 21-period moving average overlay
- Manipulation detection algorithm
- Educational tooltip system on all signals
- BUY/HOLD/SELL label system with positioning
- Market regime classification
- Three alert conditions
- Fully customizable dashboard (size, colors, position)
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CREDITS
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Developed from scratch using Pine Script v6 and standard TradingView built-in functions. No code copied from other scripts. Methodology combines classical volume analysis with modern institutional flow detection.
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This indicator helps swing traders answer: "Should I hold or exit?" By analyzing institutional activity and warning of manipulation, it provides the framework to stay in winning trades while protecting against adverse conditions.
Published open-source to contribute to the TradingView community.
Questions or feedback? Leave a comment below.
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Disclaimer: Provided "as-is" without warranty. Use at your own risk. Past performance does not guarantee future results.
Pressure Pivots - MPIPressure Pivots - MPI
A multi-factor reversal detection system built on a proprietary Market Pressure Index (MPI) that combines institutional order flow analysis, liquidity dynamics, and momentum exhaustion to identify high-probability pivot points with automated win rate validation.
What This System Does
This indicator solves the core challenge of reversal trading: distinguishing genuine exhaustion pivots from temporary retracements. It combines six independent detection mechanisms—divergence, liquidity sweeps, order flow imbalance, wick rejection, volume surges, and velocity exhaustion—weighted by reliability and unified through a custom pressure oscillator.
Three-Layer Architecture:
Layer 1 - Market Pressure Index (MPI): Proprietary volume-weighted pressure oscillator that measures buying vs. selling pressure using proportional intrabar allocation and dual-timeframe normalization (-1.0 to +1.0 range).
Layer 2 - Weighted Confluence Engine: Six detection factors scored hierarchically (divergence: 3.0 pts, liquidity: 2.5 pts, order flow: 2.0 pts, velocity: 1.5 pts, wick: 1.5 pts, volume: 1.0 pt). Premium signals (DIV/LIQ/OF) require 6.0+ score, standard signals (STD) require 4.0+ score.
Layer 3 - Automated Win Rate Validation: Every signal tracked forward and validated against actual pivot formation within 10-bar window. Real-time performance statistics displayed by signal type and direction.
The Market Pressure Index - Original Calculation
What MPI Measures: The balance of aggressive buying vs. aggressive selling within each bar, smoothed and normalized to create a continuous oscillator.
Calculation Methodology:
Step 1: Intrabar Pressure Decomposition
Buy Pressure = Volume × (Close - Low) / (High - Low)
Sell Pressure = Volume × (High - Close) / (High - Low)
Net Pressure = Buy Pressure - Sell Pressure
Step 2: Exponential Smoothing
Smooth Pressure = EMA(Net Pressure, 14)
Step 3: Normalization
Avg Absolute Pressure = SMA(|Net Pressure|, 28)
MPI Raw = Smooth Pressure / Avg Absolute Pressure
Step 4: Sensitivity Amplification
MPI = clamp(MPI Raw × 1.5, -1.0, +1.0)
Why This Is Different:
• vs. RSI: RSI measures price momentum without volume context. MPI integrates volume magnitude and distribution within each bar.
• vs. OBV: OBV uses binary classification (up bar = buy volume). MPI uses proportional allocation based on close position within range.
• vs. Money Flow Index: MFI uses typical price × volume. MPI uses intrabar positioning, revealing pressure balance regardless of bar-to-bar movement.
• vs. VWAP: VWAP shows average price. MPI shows directional pressure balance (who controls the bar).
MPI Interpretation:
• +0.7 to +1.0: Extreme buying pressure (strong uptrends, potential exhaustion)
• +0.3 to +0.7: Moderate buying pressure (healthy uptrends)
• -0.3 to +0.3: Neutral/balanced (ranging, consolidation)
• -0.7 to -0.3: Moderate selling pressure (healthy downtrends)
• -1.0 to -0.7: Extreme selling pressure (strong downtrends, potential exhaustion)
Critical Insight: MPI at extremes indicates pressure exhaustion risk , not automatic reversal. Reversals occur when extreme MPI coincides with confluence factors.
Six Confluence Factors - Detection Arsenal
1. Divergence Detection (Weight: 3.0 - Highest Priority)
Detects: Price making higher highs while MPI makes lower highs (bearish), or price making lower lows while MPI makes higher lows (bullish).
Why It Matters: Reveals weakening pressure behind price moves. Declining participation signals potential reversal.
Signal Type: Premium (DIV) - Historically highest win rates.
2. Liquidity Sweep Detection (Weight: 2.5)
Detects: Price penetrates recent swing high/low (triggering stops), then immediately reverses and closes back inside range.
Calculation: High breaks swing high by <0.3× ATR but closes below it (bearish), or low breaks swing low by <0.3× ATR but closes above it (bullish).
Why It Matters: Stop hunts mark institutional accumulation/distribution zones. Often pinpoints exact pivot points.
Signal Type: Premium (LIQ) - Extremely reliable with volume confirmation.
3. Order Flow Imbalance (Weight: 2.0)
Detects: Aggressive directional ordering where price consistently closes in upper/lower third of bars with elevated volume.
Calculation:
Close Position = (Close - Low) / (High - Low)
Aggressive Buy = Volume when Close Position > 0.65
Aggressive Sell = Volume when Close Position < 0.35
Imbalance = EMA(Aggressive Buy, 5) - EMA(Aggressive Sell, 5)
Strong Flow = |Imbalance| > 1.5 × Average
Why It Matters: Reveals institutional accumulation/distribution footprints before directional moves.
Signal Type: Premium (OF)
4. Wick Rejection Patterns (Weight: 1.5)
Detects: Pin bars, hammers, shooting stars where wick exceeds 60% of total bar range.
Why It Matters: Large wicks demonstrate failed attempts to push price, indicating strong opposition.
5. Volume Spike Detection (Weight: 1.0)
Detects: Volume exceeding 2× the 20-bar average.
Why It Matters: Confirms institutional participation vs. retail noise. Most effective when combined with wick rejection or liquidity sweeps.
6. Velocity Exhaustion (Weight: 1.5)
Detects: Parabolic moves (velocity >2.0× ATR over 3 bars) showing deceleration while MPI at extremes.
Calculation:
Velocity = Change(Close, 3) / ATR(14)
Exhaustion = |Velocity| > 2.0 AND MPI > |0.5| AND Velocity Slowing
Why It Matters: Extended moves are unsustainable. Momentum deceleration from extremes precedes reversals.
Signal Classification & Scoring
Weighted Confluence Scoring:
Each factor contributes points when present. Signals fire when total score exceeds thresholds:
Bearish Example:
+ At recent high (1.0)
+ Bearish divergence (3.0)
+ Wick rejection (1.5)
+ Volume spike (1.0)
+ Velocity slowing (1.5)
= 8.0 total score → BEARISH DIV SIGNAL
Bullish Example:
+ At recent low (1.0)
+ Liquidity sweep (2.5)
+ Strong buy flow (2.0)
+ Wick rejection (1.5)
= 7.0 total score → BULLISH LIQ SIGNAL
Dual Threshold System:
• Premium Signals (DIV/LIQ/OF): Require 6.0+ points. Must include divergence, liquidity sweep, or order flow. Higher win rates.
• Standard Signals (STD): Require 4.0+ points. No premium factors. More frequent, moderate win rates.
Visual Signal Color-Coding:
• Purple Triangle: DIV (Divergence signal)
• Orange Triangle: LIQ (Liquidity sweep signal)
• Aqua Triangle: OF (Order flow signal)
• Red/Green Triangle: STD (Standard signal)
• Yellow Diamond: Warning (setup forming, not confirmed)
Warning System - Early Alerts
Yellow diamond warnings fire when 2+ factors present but full confluence not met:
• At recent 10-bar high/low
• Wick rejection present
• Volume spike present
• MPI extreme or accelerating/decelerating
Critical: Warnings are NOT trade signals. They indicate potential setups forming. Wait for colored triangle confirmation.
Win Rate Validation - Transparent Performance Tracking
How It Works:
Signal Storage: Every signal recorded (bar index, price, type, direction)
Pivot Confirmation: System monitors next 10 bars for confirmed pivot formation at signal price (±2%)
Validation: If pivot forms within window → Win. If not → Loss.
Statistics: Win Rate = Validated Signals / Total Mature Signals × 100
Dashboard Displays:
• Overall win rate with visual bar
• Bearish signal win rate
• Bullish signal win rate
• Win rate by signal type (DIV/LIQ/OF/STD)
• Wins/Total for each category
Why This Matters:
After 30-50 signals, you'll know exactly which patterns work on your instrument:
Example Performance Analysis:
Overall: 58% (35/60)
Bearish: 52% | Bullish: 65%
DIV: 72% | LIQ: 68% | OF: 50% | STD: 38%
Insight: Focus on bullish DIV/LIQ signals (72%/68% win rate), avoid STD signals (38%), investigate bearish underperformance.
This transforms the indicator from signal generator to learning system.
Dynamic Microstructure Visualization
Fibonacci Retracement Levels
• Auto-detects last swing high + swing low
• Draws 11 levels: 0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%, 127.2%, 161.8%, 200%, 261.8%
• Removes crossed levels automatically
• Clears on new signal (fresh structure analysis)
• Color gradient (bullish to bearish across range)
• Key levels (0.618, 0.5, 1.0) highlighted with solid lines
Support/Resistance Lines
• Resistance: 50-bar highest high (red, only shown when above price)
• Support: 50-bar lowest low (green, only shown when below price)
• Auto-removes when price crosses
Usage: Signals firing at key Fibonacci levels (38.2%, 50%, 61.8%) or major S/R zones have enhanced structural significance.
Dashboard - Real-Time Intelligence
MPI Status:
• Current pressure reading with interpretation
• Color-coded background (green/red/gray zones)
Signal Status:
• Active signal type and direction
• Confidence score with visual bar (20 blocks, color-coded)
• Scanning status when no signal active
Divergence Indicator:
• Highlights active divergence separately (highest priority factor)
Performance Stats:
• Overall win rate with 10-block visual bar
• Directional breakdown (bearish vs. bullish)
• Signal type breakdown (DIV/LIQ/OF/STD individual win rates)
• Sample size for each category
Customization:
• Position: 9 locations (Top/Middle/Bottom × Left/Center/Right)
• Size: Tiny/Small/Normal/Large
• Toggle sections independently
How to Use This System
Initial Setup (10 Minutes)
1. MPI Configuration:
• Period: 14 (balanced) | 5-10 for scalping | 21-30 for swing
• Sensitivity: 1.5 (moderate) | Increase if MPI rarely hits ±0.7 | Decrease if constantly maxed
2. Detection Thresholds:
• Wick Threshold: 0.6 (60% of bar must be wick)
• Volume Spike: 2.0× average (lower to 1.5-1.8 for stocks, raise to 2.5-3.0 for crypto)
• Velocity: 2.0 ATR (raise to 2.5-3.0 for crypto)
3. Confluence Settings:
• Enable Divergence (highest win rate factor)
• Pivot Lookback: 5 (day trading) | 8-10 (swing trading)
• Keep default weights initially
4. Thresholds:
• Premium: 6.0 (quality over quantity)
• Standard: 4.0 (balanced)
• Warning: 2 factors minimum
Trading Workflow
When Warning Fires (Yellow Diamond):
Note warning type (bearish/bullish)
Do not enter - this is preparation only
Monitor for full signal confirmation
Prepare entry parameters
When Signal Fires (Colored Triangle):
Identify type from color (Purple=DIV, Orange=LIQ, Aqua=OF, Red/Green=STD)
Check dashboard confidence score
Verify confluence on chart (wick, volume, MPI extreme, Fib level)
Confirm with your analysis (context, higher timeframe, news)
Enter with proper risk management
Risk Management (Not Provided by Indicator):
• Stop Loss: Beyond recent swing or 1.5-2.0× ATR
• Position Size: Risk 0.5-2% of capital per trade
• Take Profit: 2-3× ATR or next structural level
Performance Analysis (After 30-50 Signals)
Review Dashboard Statistics:
Overall Win Rate:
• Target >50% for profitability with 1:1.5+ RR
• <45% = system may not suit instrument
• >65% = consider tightening thresholds
Directional Analysis:
• Bullish >> Bearish = uptrend bias, avoid counter-trend shorts
• Bearish >> Bullish = downtrend bias, avoid counter-trend longs
Signal Type Ranking:
• Focus on highest win rate types (typically DIV/LIQ)
• If STD <40% = raise threshold or ignore STD signals
• If premium type <50% = investigate (may need parameter adjustment)
Optimize Settings:
• Too many weak signals → Raise thresholds (premium 7.0-8.0, standard 5.0-6.0)
• Too few signals → Lower thresholds or reduce detection strictness
• Adjust factor weights based on what appears in winning signals
What Makes This Original
1. Proprietary Market Pressure Index
Unique Methodology:
• Proportional intrabar allocation: Unlike binary volume classification (OBV), MPI uses close position within range for proportional pressure assignment
• Dual-timeframe normalization: EMA smoothing (14) + SMA normalization (28) for responsiveness with context
• Bounded oscillator with sensitivity control: -1 to +1 range enables cross-instrument comparison while sensitivity allows customization
• Active signal integration: MPI drives divergence detection, extreme requirements, exhaustion confirmation (not just display)
vs. Existing Indicators:
• MFI uses typical price × volume (different pressure measure)
• CMF accumulates over time (not bounded oscillator)
• OBV is cumulative and binary (not proportional or normalized)
2. Hierarchical Confluence Engine
Why Simple Mashups Fail: Most multi-indicator systems create decision paralysis (RSI says sell, MACD says buy).
This System's Solution:
• Six factors weighted by reliability (3.0 down to 1.0)
• Dual thresholds (premium 6.0, standard 4.0)
• Automatic signal triage by quality tier
• Color-coded visual prioritization
Orthogonal Detection: Each factor detects different failure mode:
• Divergence = momentum exhaustion
• Liquidity = institutional manipulation
• Order Flow = smart money positioning
• Wick = supply/demand rejection
• Volume = participation confirmation
• Velocity = parabolic exhaustion
Complementary, not redundant. Weighted synthesis creates unified confidence measure.
3. Self-Validating Performance System
The Problem: Most indicators never reveal actual performance. Traders never know if it works on their instrument.
This Solution:
• Forward-looking validation (signals tracked to pivot confirmation)
• Pivot-based success criteria (objective, mechanical)
• Segmented statistics (by direction and type)
• Real-time dashboard updates
Result: After 30-50 signals, you have statistically meaningful data on what actually works on your specific market. Transforms indicator into adaptive learning system.
Technical Notes
No Repainting:
• All signals use confirmed bar data (closed bars only)
• Pivot detection has inherent lookback lag (5 bars)
• Divergence lines drawn after confirmation (retroactive visualization)
• Signals fire on bar close
Forward-Looking Disclosure:
• Win rate validation looks forward 10 bars for pivot confirmation
• Creates forward bias in statistics , not signal generation
• Real-time performance may differ until validation period elapses
Lookback Limits:
• Fibonacci/S/R: Limited by limitDrawBars (default 100)
• MPI calculation: 28 bars maximum
• Signal storage: 20 per direction (configurable)
Visual Limits:
• Max lines/labels/boxes: 500 each
• Auto-clearing prevents overflow
Limitations & Disclaimers
Not a Complete Trading System:
• Does not provide stop loss, take profit, or position sizing
• Requires trader risk management and market context analysis
Reversal Bias:
• Designed specifically for reversal trading
• Not optimized for trend continuation or breakouts
Learning Period:
• Statistics meaningless until 20-30 mature signals
• Preferably 50+ for statistical confidence
Instrument Dependency:
• Best: Liquid instruments (major forex, large-caps, BTC/ETH)
• Poor: Illiquid small-caps, low-volume altcoins (order flow unreliable)
Timeframe Dependency:
• Optimal: 15m - 4H charts
• Not Recommended: <5m (noise) or >Daily (insufficient signals)
No Guarantee of Profit:
• Win rate >50% does not guarantee profitability (depends on RR, sizing, execution)
• Past performance ≠ future performance
• All trading involves risk of loss
Warning Signals:
• Warnings are NOT trade signals
• Trading warnings produces lower win rates
• For preparation only
Recommended Settings by Instrument
Forex Majors (15m-1H):
• MPI Sensitivity: 1.3-1.5 | Volume: 2.0 | Thresholds: 6.0/4.0
Crypto BTC/ETH (15m-4H):
• MPI Sensitivity: 2.0-2.5 | Volume: 2.5-3.0 | Velocity: 2.5-3.0 | Thresholds: 6.5-7.0/4.5-5.0
Large-Cap Stocks (5m-1H):
• MPI Sensitivity: 1.2-1.5 | Volume: 1.8-2.0 | Thresholds: 6.0/4.0
Index Futures ES/NQ (5m-30m):
• MPI Period: 10-14 | Sensitivity: 1.5 | Velocity: 1.8-2.0 | Thresholds: 5.5-6.0/4.0
Altcoins High Vol (1H-4H):
• MPI Period: 21 | Sensitivity: 2.0-3.0 | Volume: 3.0+ | Thresholds: 7.0-8.0/5.0 (very selective)
Alert Configuration
Built-In Alerts:
Bullish Signal (all types)
Bearish Signal (all types)
Bullish Divergence (DIV only)
Bearish Divergence (DIV only)
Setup:
• TradingView Alert → Select "Pressure Pivots - MPI"
• Choose condition
• Frequency: "Once Per Bar Close" (prevents repainting)
• Configure notifications (popup/email/SMS/webhook)
Recommended:
• Active traders: Enable all signals
• Selective traders: DIV only (highest quality)
In-Code Documentation
Every input parameter includes extensive tooltips (800+ words total) providing:
• What it controls
• How it affects calculations
• Range guidance (low/medium/high implications)
• Default justification
• Asset-specific recommendations
• Timeframe adjustments
Access: Hover over (i) icon next to any setting. Creates self-documenting learning system—no external docs required.
DskyzInvestments | Trade with insight. Trade with anticipation.
Options Oscillator [Lite] IVRank, IVx, Call/Put Volatility Skew The first TradingView indicator that provides REAL IVRank, IVx, and CALL/PUT skew data based on REAL option chain for 5 U.S. market symbols.
🔃 Auto-Updating Option Metrics without refresh!
🍒 Developed and maintained by option traders for option traders.
📈 Specifically designed for TradingView users who trade options.
🔶 Ticker Information:
This 'Lite' indicator is currently only available for 5 liquid U.S. market smbols : NASDAQ:TSLA AMEX:DIA NASDAQ:AAPL NASDAQ:AMZN and NYSE:ORCL
🔶 How does the indicator work and why is it unique?
This Pine Script indicator is a complex tool designed to provide various option metrics and visualization tools for options market traders. The indicator extracts raw options data from an external data provider (ORATS), processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the data using specific formulas (see detailed below) or interpolated values (e.g., delta distances). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
The indicator aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility (IV), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below.
The options metrics we display may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc.
🟨 The following data is displayed in the oscillator 🟨
We use Tastytrade formulas, so our numbers mostly align with theirs!
🔶 𝗜𝗩𝗥𝗮𝗻𝗸
The Implied Volatility Rank (IVR) helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options.
IV Rank formula = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low)
IVRank is default blue and you can adjust their settings:
🔶 𝗜𝗩𝘅 𝗮𝘃𝗴
The implied volatility (IVx) shown in the option chain is calculated like the VIX. The Cboe uses standard and weekly SPX options to measure expected S&P 500 volatility. A similar method is used for calculating IVx for each expiration cycle.
We aggregate the IVx values for the 35-70 day monthly expiration cycle, and use that value in the oscillator and info panel.
We always display which expiration the IVx values are averaged for when you hover over the IVx cell.
IVx main color is purple, but you can change the settings:
🔹IVx 5 days change %
We are also displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days.
Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease.
On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility.
This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations.
Important Note:
The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior.
This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures.
🔶 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗦𝗸𝗲𝘄 𝗵𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺
At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at tastytrade binary expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations.
We calculate the interpolated strike price based on the expected move, taking into account the neighboring option prices and their distances. This allows us to accurately determine whether the CALL or PUT options are more expensive.
🔹 What Causes Pricing Skew? The Theory Behind It
The asymmetric pricing of PUT and CALL options is driven by the natural dynamics of the market. The theory is that when CALL options are more expensive than PUT options at the same distance from the current spot price, market participants are buying CALLs and selling PUTs, expecting a faster upward movement compared to a downward one .
In the case of PUT skew, it's the opposite: participants are buying PUTs and selling CALLs , as they expect a potential downward move to happen more quickly than an upward one.
An options trader can take advantage of this phenomenon by leveraging PUT pricing skew. For example, if they have a bullish outlook and both IVR and IVx are high and IV started decreasing, they can capitalize on this PUT skew with strategies like a jade lizard, broken wing butterfly, or short put.
🔴 PUT Skew 🔴
Put options are more expensive than call options, indicating the market expects a faster downward move (▽). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so faster in velocity compared to a potential upward movement.
🔹 SPY PUT SKEW example:
If AMEX:SPY PUT option prices are 46% higher than CALLs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so 46% faster in velocity compared to a potential upward movement
🟢 CALL Skew 🟢
Call options are more expensive than put options, indicating the market expects a faster upward move (△). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so faster in velocity compared to a potential downward movement.
🔹 INTC CALL SKEW example:
If NASDAQ:INTC CALL option prices are 49% higher than PUTs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so 49% faster in velocity compared to a potential downward movement .
🔶 USAGE example:
The script is compatible with our other options indicators.
For example: Since the main metrics are already available in this Options Oscillator, you can hide the main IVR panel of our Options Overlay indicator, freeing up more space on the chart. The following image shows this:
🔶 ADDITIONAL IMPORTANT COMMENTS
🔹 Historical Data:
Yes, we only using historical internal metrics dating back to 2024-07-01, when the TanukiTrade options brand launched. For now, we're using these, but we may expand the historical data in the future.
🔹 What distance does the indicator use to measure the call/put pricing skew?:
It is important to highlight that this oscillator displays the call/put pricing skew changes for the next optimal monthly expiration on a histogram.
The Binary Expected Move distance is calculated using the TastyTrade method for the next optimal monthly expiration: Formula = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1)
We interpolate the exact difference based on the neighboring strikes at the binary expected move distance using the TastyTrade method, and compare the interpolated call and put prices at this specific point.
🔹 - Why is there a slight difference between the displayed data and my live brokerage data?
There are two reasons for this, and one is beyond our control.
◎ Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before U.S. market close.
You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator, and you can see the time elapsed since the last update at the bottom of the corner on daily TF.
◎ Brokerage Calculation Differences:
Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions.
🔹 - EOD data:
The indicator always displays end-of-day (EOD) data for IVR, IV, and CALL/PUT pricing skew. During trading hours, it shows the current values for the ongoing day with each update, and at market close, these values become final. From that point on, the data is considered EOD, provided the day confirms as a closed daily candle.
🔹 - U.S. market only:
Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
Options Oscillator [PRO] IVRank, IVx, Call/Put Volatility Skew𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗧𝗿𝗮𝗱𝗶𝗻𝗴𝗩𝗶𝗲𝘄 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿 𝘁𝗵𝗮𝘁 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝗥𝗘𝗔𝗟 𝗜𝗩𝗥𝗮𝗻𝗸, 𝗜𝗩𝘅, 𝗮𝗻𝗱 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝘀𝗸𝗲𝘄 𝗱𝗮𝘁𝗮 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗥𝗘𝗔𝗟 𝗼𝗽𝘁𝗶𝗼𝗻 𝗰𝗵𝗮𝗶𝗻 𝗳𝗼𝗿 𝗼𝘃𝗲𝗿 𝟭𝟲𝟱+ 𝗺𝗼𝘀𝘁 𝗹𝗶𝗾𝘂𝗶𝗱 𝗨.𝗦. 𝗺𝗮𝗿𝗸𝗲𝘁 𝘀𝘆𝗺𝗯𝗼𝗹𝘀
🔃 Auto-Updating Option Metrics without refresh!
🍒 Developed and maintained by option traders for option traders.
📈 Specifically designed for TradingView users who trade options.
🔶 Ticker Information:
This indicator is currently only available for over 165+ most liquid U.S. market symbols (eg. SP:SPX AMEX:SPY NASDAQ:QQQ NASDAQ:TLT NASDAQ:NVDA , etc.. ), and we are continuously expanding the compatible watchlist here: www.tradingview.com
🔶 How does the indicator work and why is it unique?
This Pine Script indicator is a complex tool designed to provide various option metrics and visualization tools for options market traders. The indicator extracts raw options data from an external data provider (ORATS), processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the data using specific formulas (see detailed below) or interpolated values (e.g., delta distances). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
The indicator aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility (IV), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below.
The options metrics we display may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc.
🟨 The following data is displayed in the oscillator 🟨
We use Tastytrade formulas, so our numbers mostly align with theirs!
🔶 𝗜𝗩𝗥𝗮𝗻𝗸
The Implied Volatility Rank (IVR) helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options.
IV Rank formula = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low)
IVRank is default blue and you can adjust their settings:
🔶 𝗜𝗩𝘅 𝗮𝘃𝗴
The implied volatility (IVx) shown in the option chain is calculated like the VIX. The Cboe uses standard and weekly SPX options to measure expected S&P 500 volatility. A similar method is used for calculating IVx for each expiration cycle.
We aggregate the IVx values for the 35-70 day monthly expiration cycle, and use that value in the oscillator and info panel.
We always display which expiration the IVx values are averaged for when you hover over the IVx cell.
IVx main color is purple, but you can change the settings:
🔹 IVx 5 days change %
We are also displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days.
Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease.
On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility.
This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations.
Important Note:
The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior.
This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures.
🔶 𝗖𝗔𝗟𝗟/𝗣𝗨𝗧 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗦𝗸𝗲𝘄 𝗵𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺
At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at tastytrade binary expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations.
We calculate the interpolated strike price based on the expected move, taking into account the neighboring option prices and their distances. This allows us to accurately determine whether the CALL or PUT options are more expensive.
🔹 What Causes Pricing Skew? The Theory Behind It
The asymmetric pricing of PUT and CALL options is driven by the natural dynamics of the market. The theory is that when CALL options are more expensive than PUT options at the same distance from the current spot price, market participants are buying CALLs and selling PUTs, expecting a faster upward movement compared to a downward one .
In the case of PUT skew, it's the opposite: participants are buying PUTs and selling CALLs , as they expect a potential downward move to happen more quickly than an upward one.
An options trader can take advantage of this phenomenon by leveraging PUT pricing skew. For example, if they have a bullish outlook and both IVR and IVx are high and IV started decreasing, they can capitalize on this PUT skew with strategies like a jade lizard, broken wing butterfly, or short put.
🔴 PUT Skew 🔴
Put options are more expensive than call options, indicating the market expects a faster downward move (▽). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so faster in velocity compared to a potential upward movement.
🔹 SPY PUT SKEW example:
If AMEX:SPY PUT option prices are 46% higher than CALLs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves downward, it could do so 46% faster in velocity compared to a potential upward movement
🟢 CALL Skew 🟢
Call options are more expensive than put options, indicating the market expects a faster upward move (△). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so faster in velocity compared to a potential downward movement.
🔹 INTC CALL SKEW example:
If NASDAQ:INTC CALL option prices are 49% higher than PUTs at the same distance for the optimal next monthly expiry (DTE). This alone doesn't indicate which way the market will move (because nobody knows that), but the options chain pricing suggests that if the market moves upward, it could do so 49% faster in velocity compared to a potential downward movement .
🔶 USAGE example:
The script is compatible with our other options indicators.
For example: Since the main metrics are already available in this Options Oscillator, you can hide the main IVR panel of our Options Overlay indicator, freeing up more space on the chart. The following image shows this:
🔶 ADDITIONAL IMPORTANT COMMENTS
🔹 Historical Data:
Yes, we only using historical internal metrics dating back to 2024-07-01, when the TanukiTrade options brand launched. For now, we're using these, but we may expand the historical data in the future.
🔹 What distance does the indicator use to measure the call/put pricing skew?:
It is important to highlight that this oscillator displays the call/put pricing skew changes for the next optimal monthly expiration on a histogram.
The Binary Expected Move distance is calculated using the TastyTrade method for the next optimal monthly expiration: Formula = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1)
We interpolate the exact difference based on the neighboring strikes at the binary expected move distance using the TastyTrade method, and compare the interpolated call and put prices at this specific point.
🔹 - Why is there a slight difference between the displayed data and my live brokerage data?
There are two reasons for this, and one is beyond our control.
◎ Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before U.S. market close.
You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator, and you can see the time elapsed since the last update at the bottom of the corner on daily TF.
◎ Brokerage Calculation Differences:
Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions.
🔹 - EOD data:
The indicator always displays end-of-day (EOD) data for IVR, IV, and CALL/PUT pricing skew. During trading hours, it shows the current values for the ongoing day with each update, and at market close, these values become final. From that point on, the data is considered EOD, provided the day confirms as a closed daily candle.
🔹 - U.S. market only:
Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
RSI Pulsar [QuantraSystems]RSI Pulsar
Introduction
The RSI Pulsar is an advanced and multifaceted tool designed to cater to the varying needs of traders, from long-term swing traders to higher-frequency day traders. This indicator takes the Relative Strength Index (RSI) to new heights by combining several unique methodologies to provide clear, actionable signals across different market conditions. With its ability to analyze impulsive trend strength, volatility, and binary market direction, the RSI Pulsar offers a holistic view of the market that assists traders in identifying robust signals and rotational opportunities within a volatile market.
The integration of dynamic color coding further aids in quick visual assessments, allowing traders to adapt swiftly to changing market conditions, making the RSI Pulsar an essential component in the arsenal of modern traders aiming for precision and adaptability in their trading endeavors.
Legend
The RSI Pulsar encapsulates various modes tailored to diverse trading strategies. The different modes are the:
Impulse Mode:
Focuses on strong outperformance, ideal for capturing movements in highly dynamic tokens.
Trend Following Mode:
A classical perpetual trend-following approach and provides binary long and short signal classifications ideal for medium term swing trading.
Ribbon Mode:
Offers quicker signals that are also binary in nature. Perfect for a confirmation signal when building higher frequency day trading systems.
Volatility Spectrum:
This feature projects a visual 'cloud' representing volatility, which helps traders spot emerging trends and potential breakouts or reversals.
Compressed Mode:
A condensed view that displays all signals in a clean and space-efficient manner. It provides a clear summary of market conditions, ideal for traders who prefer a simplified overview.
Methodology
The RSI Pulsar is built on a foundation of dynamic RSI analysis, where the traditional RSI is enhanced with advanced moving averages and standard deviation calculations. Each mode within the RSI Pulsar is designed to cater to specific aspects of the market's behavior, making it a versatile tool allowing traders to select different modes based on their trading style and market conditions.
Impulse Mode:
This mode identifies strong outperformance in assets, making it ideal for asset rotation systems. It uses a combination of RSI thresholds and dynamic moving averages to pinpoint when an asset is not just trending positively, but doing so with significant strength.
This is in contrast to typical usage of a base RSI, where elevated levels usually signal overbought and oversold periods. The RSI Pulsar flips this logic, where more extreme values are actually interpreted as a strong trend.
Trend Following Mode:
Here, the RSI is compared to the midline (the default is level 50, but a dynamic midline can also be set), to determine the prevailing trend. This mode simplifies the trend-following process, providing clear bullish or bearish signals based on whether the RSI is above or below the midline - whether a fixed or dynamic level.
Ribbon Mode:
This mode employs a series of calculated values derived from modified Heikin-Ashi smoothing to create a "ribbon" that smooths out price action and highlights underlying trends. The Ribbon Mode is particularly useful for traders who need quick confirmations of trend reversals or continuations.
Volatility Spectrum:
The Volatility Spectrum takes a unique approach to measuring market volatility by analyzing the size and direction of Heikin-Ashi candles. This data is used to create a volatility cloud that helps traders identify when volatility is rising, falling, or neutral - allowing them to adjust their strategies accordingly.
When the signal line breaks above the cloud, it signals increasing upwards volatility. When it breaks below it signifies increasing downwards volatility.
This can be used to help identify strengthening and weakening trends, as well as imminent volatile periods, allowing traders to position themselves and adapt their strategies accordingly. This mode also works as a great volatility filter for shorter term day trading strategies. It is incredibly sensitive to volatility divergences, and can give additional insights to larger market turning points.
Compressed Mode:
In Compressed Mode, all the signals from the various modes are displayed in a simplified format, making it easy for traders to quickly assess the market's overall condition without needing to delve into the details of each mode individually. Perfect for only viewing the exact data you need when live trading, or back testing.
Case Study I:
Utilizing ALMA Impulse Mode in High-Volatility Environments
Here, the RSI Pulsar is configured with an RSI length of 9 and an ALMA length of 2 in Impulse Mode. The chart example shows how this setup can identify significant price movements, allowing traders to enter positions early and capture substantial price moves. Despite the fast settings resulting in occasional false signals, the indicator's ability to catch and ride out major trends more than compensates, making it highly effective in volatile environments.
This configuration is suitable for traders seeking to trade quick, aggressive movements without enduring prolonged drawdowns. In Impulse Mode, the RSI Pulsar seeks strong trending zones, providing actionable signals that allow for timely entries and exits.
Case Study II:
SMMA Trend Following Mode for Ratio Analysis
The RSI Pulsar in Trend Following mode, configured with the SMMA with default length settings. This setup is ideal for analyzing longer-term trends, particularly useful in cryptocurrency pairs or ratio charts, where it’s crucial to identify robust directional moves. The chart showcases strong trends in the Solana/Ethereum pair. The RSI Pulsar’s ability to smooth out price action while remaining responsive to trend changes makes it an excellent tool for capturing extended price moves.
The image highlights how the RSI Pulsar efficiently tracks the strength of two tokens against each other, providing clear signals when one asset begins to outperform the other. Even in volatile markets, the SMMA ensures that the signals are reliable, filtering out noise and allowing traders to stay in the trend longer without being shaken out by minor corrections. This approach is particularly effective in ratio trading in order to inform a longer term swing trader of the strongest asset out of a customized pair.
Case Study III:
Monthly Analysis with RSI Pulsar in Ribbon Mode
This case study demonstrates the versatility and reliability of the RSI Pulsar in Ribbon mode, applied to a monthly chart of Bitcoin with an RSI length of 8 and a TEMA length of 14. This setup highlights the indicator’s robustness across multiple timeframes, extending even to long-term analysis. The RSI Pulsar effectively smooths out noise while capturing significant trends, as seen during Bitcoin bull markets. The Ribbon mode provides a clear visual representation of momentum shifts, making it easier for traders to identify trend continuations and reversals with confidence.
Case Study IV:
Divergences and Continuations with the Volatility Spectrum
Identifying harmony/divergences can be hit-or-miss at times, but this unique analysis method definitely has its merits at times. The RSI Pulsar, with its Volatility Spectrum feature, is used here to identify critical moments where price action either aligns with or diverges from the underlying volatility. As seen in the Bitcoin chart (using default settings), the indicator highlights areas where price trends either continue in harmony with volatility or diverge, signaling potential reversals. This method, while not always perfect, provides significant insight during key turning points in the market.
The Volatility Spectrum's visual representation of rising and falling volatility, combined with divergence and harmony analysis, enables traders to anticipate significant shifts in market dynamics. In this case, multiple divergences correctly identified early trend reversals, while periods of harmony indicated strong trend continuations. While this method requires careful interpretation, especially during complex market conditions, it offers valuable signals that can be pivotal in making informed trading decisions, especially if combined with other forms of analysis it can form a critical component of an investing system.
[GYTS-Pro] Flux Composer🧬 Flux Composer (Professional Edition)
🌸 Confluence indicator in GoemonYae Trading System (GYTS) 🌸
The Flux Composer is a powerful tool in the GYTS suite that is designed to aggregate signals from multiple Signal Providers, apply advanced decaying functions, and offer customisable and advanced confluence mechanisms. This allows making informed decisions by considering the strength and agreement ("when all stars align") of various input signals.
🌸 --------- TABLE OF CONTENTS --------- 🌸
1️⃣ Main Highlights
2️⃣ Flux Composer’s Features
Multi Signal Provider support
Advanced decaying functions
Customisable Flux confluence mechanisms
Actionable trading experience
Filtering options
User-friendly experience
Upgrades compared to Community Edition
3️⃣ User Guide
Selecting Signal Providers
Connecting Signal Providers to the Flux Composer
Understanding the Flux
Tuning the decaying functions
Choosing Flux confluence mechanism
Choosing sensitivity
Utilising the filtering options
Interpreting the Flux for trading signals
4️⃣ Limitations
🌸 ------ 1️⃣ --- MAIN HIGHLIGHTS --- 1️⃣ ------ 🌸
- Signal aggregation : Combines signals from multiple different 📡 Signal Providers, each of which can be tuned and adjusted independently.
- Decaying function : Utilises advanced decaying functions to model the diminishing effect of signals over time, ensuring that recent signals have more weight. In addition to the decaying effect, the "quality" of the original signals (e.g. a "strong" GDM from WaveTrend 4D ) are accounted for as well.
- Flux confluence mechanism : The aggregation of all decaying functions form the "Flux", which is the core signal measurement of the Flux Composer. Multiple mechanisms are available for creating the Flux and effectively using it for actionable trading signals.
- Visualisation : Provides detailed visualisation options to help users understand and tune the contributions of individual Signal Providers and their decaying functions.
- Backtesting : The 🧬 Flux Composer is a core component of the TradingView suite of the 🌸 GoemonYae Trading System (GYTS) 🌸. It connects multiple 📡 Signal Providers, such as the WaveTrend 4D, and processes their signals to produce a unified "Flux". This Flux can then be used by the GYTS "🎼 Order Orchestrator" for backtesting and trade automation.
🌸 ------ 2️⃣ --- FLUX COMPOSER'S FEATURES --- 2️⃣ ------ 🌸
Let's delve into more details...
💮 1. Multi Signal Provider support
Using the name of the GYTS "🎼 Order Orchestrator" as an analogy: Imagine a symphony where each instrument plays its own unique part, contributing to the overall harmony. The Flux Composer operates similarly, integrating multiple Signal Providers to create a comprehensive and robust trading signal -- the "Flux". Currently, it supports up to four streams from the WaveTrend 4D's ’s Gradient Divergence Measure (GDM) and another four streams from the Quantile Median Cross (QMC). These can be either four "Professional Edition" Signal Providers or eight "Community Editions".
Note that the GDM includes 2 different continuous signals and the QMC 3 different continuous signals (from different frequencies). This means that the Community Edition can handle 2*2 + 2*3 = 10 different continuous signals and the Professional Edition as much as 20.
As GYTS evolves, more Signal Providers will be added; at the moment of releasing the Flux Composer, only WaveTrend 4D is publicly available.
💮 2. Advanced decaying functions
A trading signal can be relevant today, less relevant tomorrow, and irrelevant in a week's time. In other words, its relevance diminishes, or decays , over time. The Flux Composer utilises decaying functions that ensure that recent signals carry more weight, while older signals fade away. This is crucial for accurate signal processing. The intensity and decay settings allow for precise control, allowing emphasising certain signals based on their strength and relevance over time. On top of that, unlike binary signals ("buy now"), the Flux Composer utilises the actual values from the Signal Providers, differentiating between the exact quality of signals, and thus offering a detailed representation of the trading landscape. We will illustrate this in a further section.
💮 3. Customisable Flux confluence mechanisms
Another core component of the Flux Composer is the ability of intelligently combining the decaying functions. It offers four sophisticated confluence mechanisms: Amplitude Compression, Accentuated Amplitude Compression, Trigonometric, and GYTSynthesis. Each mechanism has its unique way of processing the Flux, tailored to different trading needs. For instance, the Amplitude Compression method scales the Flux based on recent values, much like the Stochastic Oscillator, while the Trigonometric method uses smooth functions to reduce outliers’ impact. The GYTSynthesis is a proprietary method, striking a balance between signal strength and discriminative power.
We'll discuss this in more detail in the User Guide section.
💮 4. Actionable trading experience
While the mathematical abilities might seem overwhelming, the goal of the Flux Composer is to transform complex signal data into actionable trading signals. When the Flux reaches certain thresholds, it generates clear bullish or bearish signals, making it easy for traders to interpret. The inclusion of upper and lower thresholds (UT and LT) helps in identifying strong signals visually and should be a familiar behaviour similar to how many other indicators operate. Furthermore, the Flux Composer can plot trading signals directly on the oscillator, showing triangle shapes for buy or sell signals. This visual aid is complemented by the possibility to setup TradingView alerts.
💮 5. Filtering options
The Professional Edition also offers filtering options to possibly further improve the quality of Flux signals. Signal streams can be divided into “Signal Flux” and “Filter Flux.” The Filter Flux acts as a gatekeeper, ensuring that only signals meeting the Filter's criteria (which consist of similar UT/LT thresholds) are considered for trading. This dual-layer approach enhances the reliability of trading signals, reducing the chances of false positives.
💮 6. User-friendly experience
GYTS is all about sophisticated, robust methods but also "elegance". One of the interpretations of the latter, is that the users' experience is very important. Despite the Flux Composer's mathematical underpinnings, it offers intuitive settings that with omprehensive tooltips to help with a smooth setup process. For those looking to fine-tune their signals, the Flux Composer allows the visualisation of individual decaying functions. This feature helps users understand the impact of each setting and make informed adjustments. Additionally, the background of the chart can be coloured to indicate the trading direction suggested by the Filter Flux, providing an at-a-glance overview of market conditions.
💮 7. Upgrades compared to Community Edition
Number of signal streams -- At the moment of writing, the Professional Edition works with 4x GDM and 4x QMC signal streams from WaveTrend 4D Signal Provider , while Community Edition (CE) Flux Composer (FC) only works with 2x GDM and 2x QMC signal streams.
Flux confluence mechanism -- CE includes the Amplitude Compression and Trigonometric confluence mechanisms, while the Pro Edition also includes the Accentuated Amplitude Compression and the GYTSynthesis mechanisms.
Signal streams as filters -- The Pro Edition can use Signal Providers as filters.
🌸 ------ 3️⃣ --- USER GUIDE --- 3️⃣ ------ 🌸
💮 1. Selecting Signal Providers
The Flux Composer’s foundation lies in its Signal Providers. When starting with the Flux Composer, using a single Signal Provider can already provide significant value due to the nature of decaying functions. For instance, the WaveTrend 4D signal provider includes up to 5 signal types (GDM and QMC in different frequencies) in a single direction (long/short). Moreover, the various confluence mechanisms that enhance the resulting Flux result in improved discrimination between weak and strong signals. This approach is akin to ensemble learning in machine learning, where multiple models are combined to improve predictive performance.
While using a single Signal Provider is beneficial, the true power of the Flux Composer is realised with multiple Signal Providers. Here are two general approaches to selecting Signal Providers:
Diverse Behaviours
Use Signal Providers with different behaviours, such as WaveTrend 4D on various assets/timeframes or entirely different Signal Providers. This approach leverages diversification to achieve robustness, rooted in the principle that varied sources enhance the overall signal quality. To explain this with an analogy, this strategy aligns with the theory of diversification in portfolio management, where combining uncorrelated assets reduces overall risk. Similarly, combining uncorrelated signals can mitigate the risk of signal failure. A practical example can be integrating a mean-reversion signal with a trend-following signal -- these can balance each other out, providing more stable outputs over different market conditions.
Enhancing a Single Provider
If you consider a particular Signal Provider highly effective, you could improve its robustness by using multiple instances with slight variations. These variations could include different sources (e.g., close, HL2, HLC3), data providers (same asset across different brokers/exchanges), or parameter adjustments. This method mirrors Monte Carlo simulations, often used in risk management and derivative pricing, which involve running many simulations with varied inputs to estimate the probability of different outcomes. By applying similar principles, the strategy becomes less susceptible to overfitting, ensuring the signals are not overly dependent on specific data conditions.
💮 2. Connecting Signal Providers to the Flux Composer
Moving on to practicalities: how do you connect Signal Providers with the Flux Composer? You may have noticed that when you open the drawdown of a data source in a TradingView indicator (with "open", "high", "low", etc.), you also see names from other indicators on your chart. We call these "streams", and the Signal Providers are designed such that they output this stream in a way that the Flux Composer can interpret it. Thus, to connect a Signal Provider with the Flux Composer, you should first have that Signal Provider on your chart. Obviously you should set it up an a way that it seems to provide good signals. After that, in the Data Stream dropdown in the Flux Composer, you can select the stream that is outputted by your Signal Provider. This will always be with a prefix of "🔗 STREAM" (after the Signal Provider's indicator name). See the chart below.
There is one important nuance: when you have multiple (similar) Signal Providers on your chart, it may be hard to select the correct data stream in the Flux Composer as the names of the streams keep repeating when you use identical indicators. So be sure to be attentive as you might end up using the same signals multiple times.
Also, the Signal Providers have an "Indicator name" parameter (and another parameter to repeat this name) that is handy to use when you have multiple Signal Providers on your screen. It is handy to give names that describe the unique settings of that Signal Provider so you can better differentiate what you are looking at on your screen.
💮 3. Understanding the Flux
Let's understand how the Signal Provider's signals are processed. In the chart below, you see we have one Signal Provider (WaveTrend 4D) connected to the Flux Composer and that it gives a bearish QMC signal. The Flux Composer converts this into a decaying function. You can show these functions per Signal Provider when the option "Show decaying function of Signal Provider" is enabled (as it is in the chart).
In our opinion, of crucial importance is the ability to process the quality of signals, rather than just any signal. In mathematical terms, we are interested in continuous signals as these provide a spectrum of values. These signals can reflect varying degrees of market sentiment or trend strength, offering richer information than binary signals, which offer only two states (e.g., buy/sell). Especially in the context of the Flux Composer, where you aggregate multiple signals, it makes a big difference whether you combine 10 weak signals or 10 strong signals. To illustrate this principle, look at the chart below where there are 4 signals of different strengths. As you can see, each of the signals affects the Flux with different intensities.
💮 4. Tuning the decaying functions
As previously mentioned, the decaying functions are a way to give more importance to recent signals while allowing older ones to fade away gradually. This mimics the natural way we assess information, giving more weight to recent events. The decaying functions in the Flux Composer are highly customisable while remaining easy to use. You can adjust the initial intensity , which sets the starting strength of a signal, and the decay rate, which determines how quickly this signal diminishes over time. Let's look at specific examples.
If we add 3 Flux Composers on the chart, connect the same Signal Provider, keep all settings the same with one exception, we get the chart below. Here we have changed the "intensity" parameter of the specific signal. As you can see, the decaying functions are different. The intensity determines the initial strength of the decayed function. Adjusting the intensity allows you to emphasise certain signal types based on their perceived reliability or importance.
Let's now keep the intensity the same ("normal"), but change the "decay" parameter. As you can see in the image below, the decay controls how quickly the signal’s strength diminishes over time. By adjusting the decay, you can model the longevity of the signal’s impact. A faster decay means the signal loses its influence quickly, while a slower decay means it remains relevant for a longer period.
So how do multiple signals interact? You can see this as a simple "stacking of decaying functions" (although there is more to it, see next section). In the chart below we different strenghts of signals and different decay rates to illustrate how the Flux is constructed.
Hopefully this helps with developing some intuition how signals are converted to decaying functions, how you can control them, and how the Flux is constructed. When tuning these parameters, use the visualisation options to see how individual decaying functions contribute to the overall Flux. This helps in understanding and refining the parameters to achieve the desired trading signal behaviour.
💮 5. Choosing Flux confluence mechanism
While we mentioned that the Flux is a "stacking of individual decaying functions", in the back-end, that is not exactly that simple. Like previously mentioned, for GYTS, "elegance" is very important. One of the interpretations is "user friendliness" and the Flux confluence mechanism is one of the essential developments for this characteristic. The Flux confluence mechanism is critical in synthesising the aggregated signals into the Flux. The choice of mechanism affects how the signals are combined and the resulting trading signals. The Professional Edition offers four distinct mechanisms, each with its strengths.
The Amplitude Compression mechanism is intuitive, scaling the Flux based on recent values, intuitively not unlike the method of the well-known Stochastic Oscillator. The Accentuated Amplitude Compression method takes this a step further, giving more weight to strong Flux values. The Trigonometric mechanism smooths the Flux and reduces the impact of outliers, providing a balanced approach. Finally, the GYTSynthesis mechanism, a proprietary approach, balances signal strength and discriminative power, making it easier to tune and generalise.
It's difficult to convey the workings of the Flux confluence mechanism in a chart, but let's take the opportunity to show how the Flux would look like when connecting both one WaveTrend 4D Signal Provider signals to four Flux Composers with default settings, except the Flux confluence mechanism:
You may notice subtle differences between the four methods. They react differently to different values and their overall shape is slightly be different. The Amplitude Compression is more "pointy" and GYTSynthesis doesn't react to low values. There are many nuances, especially in combination with tuning the sensitivity and upper/lower threshold (UT/LT) parameters.
💮 6. Choosing sensitivity
Speaking of the sensitivity , this parameters fine-tunes how responsive the Flux is to the input signals. Higher sensitivity results in more pronounced responses, leading to more frequent trading signals. Lower sensitivity makes the Flux less responsive, resulting in fewer but potentially more reliable signals.
You might think that changing the upper/lower threshold (UT/LT) parameters would be equivalent, but that's not the case. The sensitivity In case of the Amplitude Compression mechanisms, changing the sensitivity would change the relative Flux shape over time, and with the Trigonometric and GYTSynthesis mechanisms, the Flux shape itself (independent of time) would change. In other words, these are all good parameters for tuning.
💮 7. Utilising the filtering options
When choosing the signal stream of a Signal Provider, you can also change the default "Signal" category of that Signal Provider to a "Filter". In the example below, two Signal Providers are connected; the second is set as a filter. You can see that a second row of a Flux is shown in the Flux Composer (this visualisation can be disabled), corresponding with the signals of the second Signal Provider.
Logically, only when the Filter Flux gives a signal in a certain direction, signals from the regular Signal Flux are registered. Generally speaking, for this use case it is handy to set the thresholds for the Filter Flux low and possibly to decrease the decay rate so that the filtering is active for a long enough time.
💮 8. Interpreting the Flux for trading signals
Lastly, the Signal Flux gives buy and sell signals when it crosses the upper/lower thresholds (UT/LT), when the filter allows it (if enabled). This can be visualised with the triangles as you may have seen in the charts in the previous sections. For people using TradingView's alerts -- these would work too out of the box. And finally, for backtesting and possibly trade automation, we will have the GYTS "🎼 Order Orchestrator" that connects with the Flux Composer.
🌸 ------ 4️⃣ --- LIMITATIONS --- 4️⃣ ------ 🌸
Only 🌸 GYTS 📡 Signal Providers are supported, as there is a specific method to pass continuous (non-binary) data in the data stream
At the moment of release, only the WaveTrend 4D Signal Provider is available. Other Signal Providers will be gradually released.
[GYTS-CE] Flux Composer🧬 Flux Composer (Community Edition)
🌸 Confluence indicator in GoemonYae Trading System (GYTS) 🌸
The Flux Composer is a powerful tool in the GYTS suite that is designed to aggregate signals from multiple Signal Providers, apply customisable decaying functions, and offer customisable and advanced confluence mechanisms. This allows making informed decisions by considering the strength and agreement ("when all stars align") of various input signals.
🌸 --------- TABLE OF CONTENTS --------- 🌸
1️⃣ Main Highlights
2️⃣ Flux Composer’s Features
Multi Signal Provider support
Advanced decaying functions
Customisable Flux confluence mechanisms
Actionable trading experience
User-friendly experience
3️⃣ User Guide
Selecting Signal Providers
Connecting Signal Providers to the Flux Composer
Understanding the Flux
Tuning the decaying functions
Choosing Flux confluence mechanism
Choosing sensitivity
Interpreting the Flux for trading signals
4️⃣ Limitations
🌸 ------ 1️⃣ --- MAIN HIGHLIGHTS --- 1️⃣ ------ 🌸
- Signal aggregation : Combines signals from multiple different 📡 Signal Providers, each of which can be tuned and adjusted independently.
- Decaying function : Utilises advanced decaying functions to model the diminishing effect of signals over time, ensuring that recent signals have more weight. In addition to the decaying effect, the "quality" of the original signals (e.g. a "strong" GDM from WaveTrend 4D with GDM ) are accounted for as well.
- Flux confluence mechanism : The aggregation of all decaying functions form the "Flux", which is the core signal measurement of the Flux Composer. Multiple mechanisms are available for creating the Flux and effectively using it for actionable trading signals.
- Visualisation : Provides detailed visualisation options to help users understand and tune the contributions of individual Signal Providers and their decaying functions.
- Backtesting : The 🧬 Flux Composer is a core component of the TradingView suite of the 🌸 GoemonYae Trading System (GYTS) 🌸. It connects multiple 📡 Signal Providers, such as the WaveTrend 4D, and processes their signals to produce a unified "Flux". This Flux can then be used by the GYTS "🎼 Order Orchestrator" for backtesting and trade automation.
🌸 ------ 2️⃣ --- FLUX COMPOSER'S FEATURES --- 2️⃣ ------ 🌸
Let's delve into more details...
💮 1. Multi Signal Provider support
Using the name of the GYTS "🎼 Order Orchestrator" as an analogy: Imagine a symphony where each instrument plays its own unique part, contributing to the overall harmony. The Flux Composer operates similarly, integrating multiple Signal Providers to create a comprehensive and robust trading signal -- the "Flux". Currently, it supports up to two streams from the WaveTrend 4D’s Gradient Divergence Measure (GDM) and another two streams from the WaveTrend 4D's Quantile Median Cross (QMC) .
Note that the GDM includes 2 different continuous signals and the QMC 3 different continuous signals (from different frequencies). This means that the Community Edition can handle 2*2 + 2*3 = 10 different continuous signals.
As GYTS evolves, more Signal Providers will be added; at the moment of releasing the Flux Composer, only WaveTrend 4D with GDM and with QMC are publicly available.
💮 2. Advanced decaying functions
A trading signal can be relevant today, less relevant tomorrow, and irrelevant in a week's time. In other words, its relevance diminishes, or decays , over time. The Flux Composer utilises decaying functions that ensure that recent signals carry more weight, while older signals fade away. This is crucial for accurate signal processing. The intensity and decay settings allow for precise control, allowing emphasising certain signals based on their strength and relevance over time. On top of that, unlike binary signals ("buy now"), the Flux Composer utilises the actual values from the Signal Providers, differentiating between the exact quality of signals, and thus offering a detailed representation of the trading landscape. We will illustrate this in a further section.
💮 3. Customisable Flux confluence mechanisms
Another core component of the Flux Composer is the ability of intelligently combining the decaying functions. It offers two sophisticated confluence mechanisms: Amplitude Compression and Trigonometric. Each mechanism has its unique way of processing the Flux, tailored to different trading needs. The Amplitude Compression method scales the Flux based on recent values, much like the Stochastic Oscillator, while the Trigonometric method uses smooth functions to reduce outliers’ impact We'll discuss this in more detail in the User Guide section.
💮 4. Actionable trading experience
While the mathematical abilities might seem overwhelming, the goal of the Flux Composer is to transform complex signal data into actionable trading signals. When the Flux reaches certain thresholds, it generates clear bullish or bearish signals, making it easy for traders to interpret. The inclusion of upper and lower thresholds (UT and LT) helps in identifying strong signals visually and should be a familiar behaviour similar to how many other indicators operate. Furthermore, the Flux Composer can plot trading signals directly on the oscillator, showing triangle shapes for buy or sell signals. This visual aid is complemented by the possibility to setup TradingView alerts.
💮 5. User-friendly experience
GYTS is all about sophisticated, robust methods but also "elegance". One of the interpretations of the latter, is that the users' experience is very important. Despite the Flux Composer's mathematical underpinnings, it offers intuitive settings that with omprehensive tooltips to help with a smooth setup process. For those looking to fine-tune their signals, the Flux Composer allows the visualisation of individual decaying functions. This feature helps users understand the impact of each setting and make informed adjustments.
🌸 ------ 3️⃣ --- USER GUIDE --- 3️⃣ ------ 🌸
💮 1. Selecting Signal Providers
The Flux Composer’s foundation lies in its Signal Providers. When starting with the Flux Composer, using a single Signal Provider can already provide significant value due to the nature of decaying functions. For instance, the WaveTrend 4D signal provider includes up to two GDM and three QMC signals in a single direction (long/short). Moreover, the various confluence mechanisms that enhance the resulting Flux result in improved discrimination between weak and strong signals. This approach is akin to ensemble learning in machine learning, where multiple models are combined to improve predictive performance.
While using a single Signal Provider is beneficial, the true power of the Flux Composer is realised with multiple Signal Providers. Here are two general approaches to selecting Signal Providers:
Diverse Behaviours
Use Signal Providers with different behaviours, such as WaveTrend 4D on various assets/timeframes or entirely different Signal Providers. This approach leverages diversification to achieve robustness, rooted in the principle that varied sources enhance the overall signal quality. To explain this with an analogy, this strategy aligns with the theory of diversification in portfolio management, where combining uncorrelated assets reduces overall risk. Similarly, combining uncorrelated signals can mitigate the risk of signal failure. A practical example can be integrating a mean-reversion signal with a trend-following signal -- these can balance each other out, providing more stable outputs over different market conditions.
Enhancing a Single Provider
If you consider a particular Signal Provider highly effective, you could improve its robustness by using multiple instances with slight variations. These variations could include different sources (e.g., close, HL2, HLC3), data providers (same asset across different brokers/exchanges), or parameter adjustments. This method mirrors Monte Carlo simulations, often used in risk management and derivative pricing, which involve running many simulations with varied inputs to estimate the probability of different outcomes. By applying similar principles, the strategy becomes less susceptible to overfitting, ensuring the signals are not overly dependent on specific data conditions.
💮 2. Connecting Signal Providers to the Flux Composer
Moving on to practicalities: how do you connect Signal Providers with the Flux Composer? You may have noticed that when you open the drawdown of a data source in a TradingView indicator (with "open", "high", "low", etc.), you also see names from other indicators on your chart. We call these "streams", and the Signal Providers are designed such that they output this stream in a way that the Flux Composer can interpret it. Thus, to connect a Signal Provider with the Flux Composer, you should first have that Signal Provider on your chart. Obviously you should set it up an a way that it seems to provide good signals. After that, in the Data Stream dropdown in the Flux Composer, you can select the stream that is outputted by your Signal Provider. This will always be with a prefix of "🔗 STREAM" (after the Signal Provider's indicator name). See the chart below.
There is one important nuance: when you have multiple (similar) Signal Providers on your chart, it may be hard to select the correct data stream in the Flux Composer as the names of the streams keep repeating when you use identical indicators. So be sure to be attentive as you might end up using the same signals multiple times.
Also, the Signal Providers have an "Indicator name" parameter (and another parameter to repeat this name) that is handy to use when you have multiple Signal Providers on your screen. It is handy to give names that describe the unique settings of that Signal Provider so you can better differentiate what you are looking at on your screen.
💮 3. Understanding the Flux
Let's understand how the Signal Provider's signals are processed. In the chart below, you see we have one Signal Provider (WaveTrend 4D) connected to the Flux Composer and that it gives a bearish QMC signal. The Flux Composer converts this into a decaying function. You can show these functions per Signal Provider when the option "Show decaying function of Signal Provider" is enabled (as it is in the chart).
In our opinion, of crucial importance is the ability to process the quality of signals, rather than just any signal. In mathematical terms, we are interested in continuous signals as these provide a spectrum of values. These signals can reflect varying degrees of market sentiment or trend strength, offering richer information than binary signals, which offer only two states (e.g., buy/sell). Especially in the context of the Flux Composer, where you aggregate multiple signals, it makes a big difference whether you combine 10 weak signals or 10 strong signals. To illustrate this principle, look at the chart below where there are 4 signals of different strengths. As you can see, each of the signals affects the Flux with different intensities.
💮 4. Tuning the decaying functions
As previously mentioned, the decaying functions are a way to give more importance to recent signals while allowing older ones to fade away gradually. This mimics the natural way we assess information, giving more weight to recent events. The decaying functions in the Flux Composer are highly customisable while remaining easy to use. You can adjust the initial intensity , which sets the starting strength of a signal, and the decay rate, which determines how quickly this signal diminishes over time. Let's look at specific examples.
If we add 3 Flux Composers on the chart, connect the same Signal Provider, keep all settings the same with one exception, we get the chart below. Here we have changed the "intensity" parameter of the specific signal. As you can see, the decaying functions are different. The intensity determines the initial strength of the decayed function. Adjusting the intensity allows you to emphasise certain signal types based on their perceived reliability or importance.
Let's now keep the intensity the same ("normal"), but change the "decay" parameter. As you can see in the image below, the decay controls how quickly the signal’s strength diminishes over time. By adjusting the decay, you can model the longevity of the signal’s impact. A faster decay means the signal loses its influence quickly, while a slower decay means it remains relevant for a longer period.
So how do multiple signals interact? You can see this as a simple "stacking of decaying functions" (although there is more to it, see next section). In the chart below we use different "intensity" and "decay" parameters to discuss how the Flux is created.
Hopefully this helps with developing some intuition how signals are converted to decaying functions, how you can control them, and how the Flux is constructed. When tuning these parameters, use the visualisation options to see how individual decaying functions contribute to the overall Flux. This helps in understanding and refining the parameters to achieve the desired trading signal behaviour.
💮 5. Choosing Flux confluence mechanism
While we mentioned that the Flux is a "stacking of individual decaying functions", in the back-end, that is not exactly that simple. Like previously mentioned, for GYTS, "elegance" is very important. One of the interpretations is "user friendliness" and the Flux confluence mechanism is one of the essential developments for this characteristic. The Flux confluence mechanism is critical in synthesising the aggregated signals into the Flux. The choice of mechanism affects how the signals are combined and the resulting trading signals. The Community Edition offers two distinct mechanisms, each with its strengths.
The Amplitude Compression mechanism is intuitive, scaling the Flux based on recent values, intuitively not unlike the method of the well-known Stochastic Oscillator. On the other hand, the Trigonometric mechanism smooths the Flux and reduces the impact of outliers, providing a balanced approach. It's difficult to convey the workings of the Flux confluence mechanism in a chart, but let's take the opportunity to show how the Flux would look like when connecting both GDM and QMC signals to two Flux Composers with default settings, except the Flux confluence mechanism:
You can notice that the upper Flux Converter (FC) triggered two signals while the other FC triggered only one. There are more nuances, especially in combination with tuning the sensitivity and upper/lower threshold (UT/LT) parameters.
💮 6. Choosing sensitivity
Speaking of the sensitivity , this parameters fine-tunes how responsive the Flux is to the input signals. Higher sensitivity results in more pronounced responses, leading to more frequent trading signals. Lower sensitivity makes the Flux less responsive, resulting in fewer but potentially more reliable signals.
You might think that changing the upper/lower threshold (UT/LT) parameters would be equivalent, but that's not the case. The sensitivity In case of the Amplitude Compression mechanism, changing the sensitivity would change the relative Flux shape over time, and with the Trigonometric mechanism, the Flux shape itself (independent of time) would change. In other words, these are all good parameters for tuning.
💮 8. Interpreting the Flux for trading signals
Lastly, the Signal Flux gives buy and sell signals when it crosses the upper/lower thresholds (UT/LT) This can be visualised with the triangles as you may have seen in the charts in the previous sections. For people using TradingView's alerts -- these would work out of the box. And finally, for backtesting and possibly trade automation, we will have the GYTS "🎼 Order Orchestrator" that connects with the Flux Composer.
🌸 ------ 4️⃣ --- LIMITATIONS --- 4️⃣ ------ 🌸
Only 🌸 GYTS 📡 Signal Providers are supported, as there is a specific method to pass continuous (non-binary) data in the data stream
At the moment of release, only WaveTrend 4D with GDM and with QMC are available. Other Signal Providers will be gradually released.
Beta Coefficient & RSI Table (Midcaps vs Majors)Beta Coefficient & RSI Table (Midcaps vs Majors)
This script builds a comprehensive beta comparison framework between midcap assets and majors for benchmarks, enhanced with a simple RSI midline strategy for clean entry and exit signaling.
In addition to beta-based relative analysis, the script:
Computes raw RSI values on midcap assets for standalone trend qualification
Evaluates every midcap/major ratio combination using the same RSI-based regime logic
Produces binary (0 / 1) signals suitable for systematic filtering and automation
Designed with automation in mind, this script is perfect for daily alerts that can send webhooks externally, and is fully compatible to reliably daily close updates for:
Ratio beta comparisons (midcaps vs majors)
Binary RSI crossover signals on each ratio
Base midcap trend state (RSI > 45 indicating an active uptrend) - 45 made for a slightly faster entry signal if used as a preliminary filter
This makes the table ideal for automated system building, signal aggregates, and hands-off portfolio logic.
Full credits to @MarktQuant and @NianiaFrania🐸 for the original script source.
Neeson Mayer MultipleIntegrating the Mayer Multiple Indicator: A Practical Guide for Market Analysis
Introduction
The Mayer Multiple indicator is a specialized tool designed to assess asset valuations relative to their long-term historical trends. By comparing current price action against a long-term simple moving average, this indicator provides a quantitative framework for identifying potential overbought and oversold conditions. This article explains the rationale behind its design, operational mechanics, practical applications, and unique value proposition.
Purpose and Functionality
The primary function of the Mayer Multiple indicator is to measure how far current prices deviate from a long-term moving average, expressed as a ratio. This measurement helps traders and investors identify:
Extreme valuation levels that may signal potential reversal points
Long-term trend strength and sustainability
Market psychology shifts between fear and greed cycles
Originally popularized in Bitcoin analysis, the indicator's principles apply to any volatile asset class where mean reversion tendencies exist alongside strong trend characteristics.
Operational Principles
The indicator operates through several interconnected components:
Core Calculation Mechanism
At its heart, the indicator calculates the Mayer Multiple by dividing the current closing price by a configurable simple moving average (default: 200 periods). This ratio represents how many times the current price exceeds its long-term average, providing an immediate visual reference for valuation extremes.
Multi-Level Threshold System
Four configurable thresholds create distinct market condition zones:
Optimal Buy Zone (default: 0.7) - Historically extreme undervaluation
Undervalued Zone (default: 1.0) - Moderate undervaluation
Overvalued Zone (default: 2.4) - Moderate overvaluation
Optimal Sell Zone (default: 3.5) - Historically extreme overvaluation
These thresholds create a graduated scale of market conditions rather than binary signals.
Visual Signal Hierarchy
A sophisticated color-coding system prioritizes different signal types based on their significance:
White/Gray: Neutral territory (between undervalued and overvalued thresholds)
Aqua: Entering undervalued territory (potential accumulation zone)
White: Reaching optimal buying conditions (historically rare opportunities)
Yellow: Entering overvalued territory (potential distribution zone)
Orange: Reaching optimal selling conditions (historically rare extremes)
Green: Emerging from optimal buying conditions (momentum shift confirmation)
Red: Retreating from optimal selling conditions (momentum reversal confirmation)
This hierarchy helps users distinguish between entry signals, exit signals, and confirmation signals.
Integration Rationale
The integration of these components follows a logical progression:
Mathematical Foundation
The moving average provides a stable reference point that filters out short-term noise while maintaining sensitivity to long-term trend changes. The ratio format normalizes values across different price levels and timeframes, enabling cross-asset comparisons.
Behavioral Finance Alignment
The threshold system corresponds to documented market psychology patterns. The extreme thresholds (optimal buy/sell) represent points where fear or greed typically reach maximum intensity, while the moderate thresholds represent early warning levels.
Progressive Signal Detection
The indicator tracks both threshold breaches and retreats from extreme zones. This dual-tracking approach captures not only when conditions become extreme but also when they begin to normalize—often the most actionable moments for position adjustments.
Component Synergy
The indicator's components work together through a continuous feedback loop:
Calculation Engine: Continuously computes the core ratio, serving as the foundation for all subsequent analysis.
Threshold Comparator: Compares the current ratio against user-defined thresholds, categorizing market conditions in real-time.
Signal Generator: Identifies specific events (threshold crossings, zone entries/exits) and assigns appropriate visual representations.
Visual Renderer: Displays the information through colored histograms, reference lines, and data tables, creating an intuitive interface.
Alert System: Monitors for predefined conditions and notifies users of significant developments without requiring constant screen monitoring.
This integrated approach transforms raw price data into structured, actionable information while maintaining mathematical rigor and visual clarity.
Practical Application Guidelines
Parameter Customization
Users should adjust parameters based on:
Asset volatility (higher volatility assets may require wider thresholds)
Timeframe (longer timeframes may benefit from longer moving averages)
Personal risk tolerance (conservative traders may use tighter thresholds)
Signal Interpretation Framework
Zone-Based Analysis: Focus on which zone the indicator occupies rather than chasing individual data points
Confirmation Seeking: Use extreme zone signals (white/orange) as alerts for further analysis rather than automatic trade triggers
Momentum Assessment: Observe how quickly the indicator moves between zones as a measure of trend strength
Complementary Tools
The Mayer Multiple works best when combined with:
Volume analysis to confirm participation during extreme readings
Momentum indicators to identify potential divergence
Support/resistance levels for precise entry/exit timing
Fundamental analysis for context validation
Distinctive Attributes
Original Implementation Features
Progressive Color System: Unlike binary indicators, this implementation provides graduated signals through a carefully prioritized color hierarchy.
Dual-Signal Detection: The indicator captures both threshold breaches and retreats, offering insights into momentum shifts rather than just static levels.
Contextual Display: The integrated data table provides immediate access to key metrics without cluttering the chart space.
Customizable Framework: All thresholds and calculation periods are adjustable, allowing adaptation to different market regimes and trading styles.
Practical Innovation
The indicator's design emphasizes usability through:
Immediate visual comprehension via color coding
Clear separation between alert conditions and confirmation signals
Balanced information density (sufficient data without overload)
Flexible integration with existing trading workflows
Responsible Usage Considerations
Empirical Perspective
Historical analysis suggests that assets frequently revert toward their long-term moving averages, but the timing and extent of such reversions vary significantly. The indicator identifies statistical extremes rather than predicting immediate price movements.
Risk Management Integration
Users should:
Treat extreme readings as risk management triggers rather than directional forecasts
Consider position sizing based on distance from the moving average
Implement stop-loss strategies regardless of indicator readings
Avoid allocating excessive weight to any single indicator
Performance Realism
The indicator does not guarantee profitable outcomes. Its value lies in providing structured information about valuation extremes, which must be interpreted within broader market context and individual risk parameters.
Conclusion
The Mayer Multiple indicator represents a thoughtfully integrated approach to long-term valuation analysis. By combining mathematical rigor with behavioral insights and practical visualization, it provides traders with a structured framework for assessing market extremes. Its modular design allows customization while maintaining core analytical integrity, and its emphasis on graduated signals helps avoid the oversimplification common in technical indicators. When used as part of a comprehensive trading methodology with appropriate risk management, it can contribute valuable perspective to the decision-making process.
Imtiaz Expert Pro 4.0 With Hybrid StrategyImtiaz Expert Pro 4.0 ro (IMTIAZZ TRADER)
Imtiaz Expert Pro 4.0 is a powerful price-action–based Buy/Sell indicator specially designed for 1-minute scalping and binary option trading.
This indicator automatically detects high-probability Buy and Sell zones using a smart combination of:
Market Structure
Support & Resistance Zones
Liquidity Areas
Candle Strength & Momentum
Trend Bias Filtering
Clear BUY (green) and SELL (red) signals are plotted directly on the chart, making it very easy to follow even for beginners.
The built-in Bias Strength Meter helps traders identify whether the market is under Buyer Control or Seller Control, reducing false trades.
🔹 Works best on 1 Minute timeframe
🔹 Suitable for Binary Options & Forex Scalping
🔹 85% accurate signals
🔹 Clean & user-friendly interface
⚠️ Always use proper risk management. This indicator is a trading aid, not financial advice.
Viper Oscillator🔶 Overview
The Mkt-Viper Oscillator is a specialized Kinetic Momentum Engine engineered for Precision Timing and energy measurement. It serves as a high-fidelity market oscilloscope, designed to decode the raw velocity of price action and identify high-probability entry and exit points with enhanced clarity.
Markets move with varying degrees of force and resistance. Mkt-Viper Oscillator analyzes this behavior by utilizing a Kinetic Momentum Model. It measures Price Displacement against Market Viscosity (a dynamic resistance filter) to determine the true energy behind a move. By filtering out low-energy "drift," this approach allows traders to gauge the true strength of a trend and identify moments of momentum exhaustion or renewal.
🔶 What makes Mkt-Viper Oscillator unique?
The Viper Oscillator distinguishes itself through its Multi-Dimensional Calculation Matrix. Rather than relying on a single data source, it fuses Price Action, Volume Flow, and Volatility (Z-Score) into a single output.
The core engine measures Market Inertia. By applying a "Denoising Kernel" and recursive smoothing algorithms, it filters out erratic ticks to visualize the smooth, hydrodynamic flow of money entering and exiting the asset, providing a clearer picture of market intent.
Main Features
🔶 The Core Oscillator
The central Line or Ribbon of the oscillator represents the "Engine Core." It visualizes the battle between momentum (Torque) and resistance (Drag).
Visual Modes:
Standard:
Uses a multi-layered rendering technique (Core + Outer Glow) to create a crisp, high-visibility "Neon" line.
Ribbon Mode:
Displays a Signal Line cross system. When the fast line crosses the slow "Trail," it signals a micro-shift in momentum.
Momentum Flips:
The oscillator plots discrete Circles on the ribbon when the slope flips direction. These mark the precise moment momentum shifts from expansion to contraction.
🔶 Kinetic Exhaustion Zones
Standard oscillators often use static lines (like 70/30) that provide little context on trend strength. The Viper Oscillator replaces these with dynamic Kinetic Exhaustion Zones.
The Logic:
These zones represent the limits of "Market Torque." Instead of a binary On/Off signal, the zones function as a gradient stress field.
Visuals (Adaptive Glow):
The system utilizes a programmed opacity gradient.
Fade In:
The zones begin to materialize when the Core passes a certain threshhold (Moderate Momentum).
Maximum Glow:
As the oscillator begins to travel deeper beyond the threshold (Peak Torque), the zones glow with maximum intensity, signaling that the move is becoming statistically stretched or overextended.
Usage:
In a strong trend, the oscillator can "pin" inside the glow zone. This is a sign of immense strength, not a reversal. The reversal signal occurs when the Core exits the glow zone and returns toward the mean.
🔶 Z-Score Velocity Line
Floating above the main oscillator is the Velocity Line (Thin Line). This is not just a second oscillator; it is a volatility-adjusted Z-Score.
The Logic:
It measures the speed of price change relative to the current volatility conditions.
Usage:
When the Velocity Line spikes aggressively while the main Oscillator moves slowly, it is an early warning sign for a potential pullback.
🔶 Money Flow Wave (Background)
The background of the oscillator features a subtle, filled "Wave." This is the Money Flow Index (MFI) overlay.
The Logic:
This layer tracks volume-weighted price action. It allows you to see Divergences between Price and Volume.
Usage:
If the Kinetic Core (Price Momentum) is making a Higher High, but the Money Flow Wave (Volume) is making a Lower Low, it indicates a "Hollow Rally" lacking institutional backing.
🔶 Sigma Sniper Signals
The system constantly monitors Volume Volatility using a 3.0 Sigma (Standard Deviation) threshold.
Visuals:
When a volume spike exceeds 3 standard deviations from the mean (a statistical anomaly), a small "⌃" or "⌄" symbol appears at the top or bottom of the panel.
Meaning:
This marks a potential Volume Climax. It signifies potential capitulation (panic selling) or euphoria (fomo buying). These points are possibly probable reversal areas.
🔶 Trend Power Bar
Located at the very bottom of the pane is the Trend Power Bar. This is a binary filter designed to keep you on the right side of the macro flow.
The Logic:
It uses a "Linkage Kernel" (Correlation Coefficient) to measure the alignment of the trend.
Green:
Macro Trend is Bullish.
Red:
Macro Trend is Bearish.
Opacity:
The bar becomes transparent when the trend is weakening, warning you of potential consolidation.
🔶 Fractal Divergences
Timing reversals requires spotting the disconnect between price and momentum. The Divergence Engine uses Fractal Geometry to detect these setups automatically.
Regular Divergence:
Draws lines connecting peaks or valleys where Price pushes further but Momentum fails to follow. These are potential reversal signals.
🔶 Synthetic Resolution Scaler (MTF)
Traders often need to see higher-timeframe momentum without changing charts. The Resolution Scaler allows you to project higher-timeframe data onto your current chart.
How it works:
Instead of using repainting request.security calls, the script mathematically scales the internal lookback periods (Lengths) to simulate higher timeframes (e.g., viewing Hourly momentum on a 15-minute chart) while maintaining smooth, real-time updates.
🔶 Visual Intelligence (Theme Engine)
Visual clarity is essential for rapid decision-making. A cluttered or poorly contrasted chart can lead to cognitive fatigue. To address this, the Viper Oscillator features a global Color Theme Engine that instantly synchronizes every element of the suite—signals, candles, clouds, and text—to a unified palette.
The Presets:
The system comes with five professionally designed profiles to suit different trading environments and lighting conditions:
Viper Original: High-contrast Neon Green & Purple (Optimized for Dark Mode).
Classic: Standard Green/Red configuration for traditionalists.
Cool Blues: A calming Blue/Violet palette designed to reduce emotional reactivity.
Ember & Ash: High-warmth Orange/Slate contrast.
Monochrome: Grayscale/Silver logic for distraction-free structural analysis.
Customization:
Traders with specific branding requirements or accessibility needs (such as color blindness) can select "Custom Theme." This unlocks distinct color inputs, allowing you to define your own specific Bullish, Bearish, and Neutral colors that instantly propagate across the entire indicator suite.
🔶 How to use: The "Timing" Workflow
Mkt-Viper Oscillator is designed to filter out premature entries. Rather than catching falling knives, we recommend a workflow based on Momentum Structure:
Strategy: Momentum Continuation (The HL/LH Setup)
The highest probability entries occur when momentum resets but the trend structure remains intact.
Trend Context:
Ensure the Trend Power Bar is Green (Bullish) or Red (Bearish).
Wait for Structure:
Do not buy the first dip. Wait for the Kinetic Core to print a Higher Low (HL) in a bullish trend or a Lower High (LH) in a bearish trend. This "Momentum Stair-Step" confirms that counter-trend energy has been exhausted.
The Trigger:
Enter when a Momentum Flip Circle (Dot) appears after this Higher Low or Lower High is established.
Exit:
Take profits when the Velocity Line spikes into the extremes or a Divergence line appears.
While powerful as a standalone unit, this engine is mathematically calibrated to pair with the Mkt-Viper Pro (Trend) and Mkt-Viper Edge (Structure) indicators.
Important:
This indicator is intended to be used with additional confluences and key areas. It is not recommended to blindly buy or sell the momentum flip dots.
🔶 Realistic Expectations & Methodology
Oscillator Lag:
All oscillators are derivative of price and inherently possess some lag. The "Kinetic" math reduces this lag significantly, but it cannot eliminate it entirely as it must process past data.
Signal Confirmation:
The "Flip Circles" and Reversal signals are confirmed on candle close. While they do not repaint history, they will wait for the bar to close before locking in. This is a safety feature to prevent fake-outs.
Trending vs. Ranging:
Oscillators perform best in Ranging markets or during Pullbacks in a trend. Using Overbought/Oversold signals blindly during a parabolic trend is dangerous, as momentum can stay "Overbought" for extended periods. Always check the Trend Power Bar context.
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, back test, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
AIO Advanced Market Structure with Smart Money DetectionOVERVIEW
A professional market structure indicator that detects Break of Structure (BOS) and Change of Character (CHoCH) with intelligent multi-factor quality scoring. It combines rigorous pivot validation, comprehensive BOS rating system, volatility detection, and adaptive visual feedback to identify high-probability structural shifts and institutional entry zones while filtering out noise.
What Makes This Different:
Multi-Factor BOS Scoring (0-100) - 7 weighted metrics evaluate break quality in real-time
3-Tier Star Ratings - ★/★★/★★★ classification prioritizes premium setups
Integrated Volatility Detection - Measures price expansion at structure breaks to confirm institutional activity
Institutional Zone Identification - Combines BOS + Volatility to highlight probable big player entry areas
Smart Order Flow Logic - Validates momentum and participation before signaling entries
CORE FEATURES
1. ADVANCED MARKET STRUCTURE DETECTION
Pivot-Based Structure Logic:
Configurable Pivot Period: Default 4-bar swing detection for flexibility across timeframes
Price Mode Options: High/Low or Close-based pivot identification
Direction Filtering: Show Both, Only Up, or Only Down structures
Dynamic Line Extension: Lines extend until broken with customizable style and width
Historical Tracking: Maintains up to 30 structure lines with automatic cleanup
Structure Types:
BOS (Break of Structure): Continuation pattern - price breaks previous structure in trend direction
CHoCH (Change of Character): Reversal pattern - price breaks against previous trend direction
2. INTELLIGENT BOS SCORING SYSTEM (0-100)
Seven Quality Metrics:
Body Strength (30% default weight):
- Measures candle body size vs ATR
- Normalized score: body / (1.5 × ATR)
- Strong bodies indicate conviction
Close Distance (25% default weight):
- Measures how far close is from broken level
- Normalized: distance / (0.5 × ATR)
- Deeper penetration scores higher
Volume Confirmation (20% default weight):
- Compares current volume to 20-bar SMA
- Ratio-based scoring: (volume / avg - 1.0)
- Optional - can be disabled for non-volume instruments
Trend Alignment (10% default weight):
- Checks if break aligns with Magic Bands trend direction
- Binary score: 1.0 if aligned, 0.0 if not
- Uses 6× ATR Magic Bands with modified trailing
- Note: Magic Bands require 34 bars of price history to establish initial trend direction and volatility baseline. On newly loaded charts or small datasets, the first 34 bars are used for calculation warmup and trend signals may be unavailable during this period.
Previous Touches (15% default weight):
- Counts prior structure level tests
- Scores higher with more historical touches (0-2 touches tracked)
- Touch threshold: 0.5 × ATR proximity
Pre-Break Momentum (10% default weight):
- Analyzes 3 bars before break (configurable 1-10)
- Counts bars closing in break direction
- Score = aligned bars / total bars checked
Wick Penalty (10% default weight):
- Penalizes excessive wicks in 5-bar lookback (configurable 1-30)
- Triggered when wick > 1.2 × ATR (adjustable multiplier)
- Binary penalty applied to final score
Scoring Calculation:
The indicator evaluates each BOS using three different weight configurations and automatically selects the highest score. This ensures quality setups aren't missed due to weight configuration bias. Scores are calculated once when the BOS bar closes and stored permanently using a unique identification key (bar index + price level + direction).
Storage Persistence:
Scores remain stored in the indicator's memory maps until you remove the indicator from the chart or reset TradingView. This means:
Scores survive chart refreshes and timeframe changes
Historical BOS maintain their original quality ratings
No recalculation = no repainting or score changes over time
To reset scores: Remove indicator and re-add it to the chart
Star Rating Assignment:
★★★ (3 Stars): Score ≥ 75 - Premium quality breaks
★★ (2 Stars): Score ≥ 50 - Good quality breaks
★ (1 Star): Score < 50 - Average quality breaks
3. VOLATILITY EXPANSION DETECTION
Core Volatility Logic:
The indicator tracks price range expansion using a volatility oscillator based on the Rate of Change of the High-Low range. When this oscillator crosses above zero, it signals an expansion in price volatility - often indicating increased institutional participation or significant order flow.
Calculation Method:
Monitors exponential moving average of High-Low range (default 10 periods)
Calculates 12-period rate of change on this EMA
Signals when Rate of Change crosses from negative to positive territory
This cross-up indicates price is expanding faster than recent average
Optional Confirmation Filters:
Volume Confirmation:
- Requires volume > 1.5× 20-period SMA
- Ensures institutional participation and real order flow
- Filters out low-volume false breakouts
MA Filter:
- Requires price > 50-period MA for up moves
- Confirms directional bias aligns with broader trend
- Prevents counter-trend volatility signals
ADX Filter:
- Requires ADX > 20 (default threshold)
- Validates trend strength using 14-period ADX
- Confirms momentum is building, not just noise
Visual Feedback:
Bar Color: Optional blue bar on confirmed volatility expansion
Shape Marker: Optional small square above bar
Background: Optional light blue background highlight
4. BOS + VOLATILITY: INSTITUTIONAL ENTRY ZONES
Why This Combination Matters:
When Break of Structure and Volatility Expansion occur together, it creates a high-probability scenario:
BOS Confirms Trend Direction
- Price breaks key structure level
- Market participants shift bias
- New trend leg potentially beginning
Volatility Confirms Participation
- Price range expanding aggressively
- Volume often spiking simultaneously
- Indicates institutional order flow entering
Combined Signal = Smart Money Zone
- Big players likely accumulating/distributing at these levels
- Price "snapping" through structure with conviction
- Entry zone with favorable risk/reward as institutions establish positions
Practical Recognition:
Look for this pattern sequence:
Price approaches key structure level (prior high/low)
BOS label appears (especially ★★★ or ★★)
Volatility bar color/shape appears on same or next bar
Volume spike visible (if using volume filter)
This is your institutional entry zone
Trading Application:
Scenario 1 - Trend Continuation Entry:
★★ or ★★★ BOS detected
Volatility expansion present
Price closes strong above structure
Action: Enter long on pullback to broken structure level or at volatility expansion bar
Logic: Institutions accumulated on break, pullback offers better entry
Scenario 2 - Breakout Entry:
★★ or ★★★ BOS detected
Volatility expansion + volume spike together
Price shows strong momentum candle
Action: Enter immediately in break direction with tight stop below structure
Logic: Strong institutional participation = less likely to fail immediately
Scenario 3 - Reversal Confirmation:
★★ or ★★★ CHoCH signal (Change of Character)
Volatility expansion present
Breaks against previous trend direction
Action: Exit trend positions, consider counter-trend entry
Logic: Institutions reversing, trend exhaustion confirmed
Why Big Players Enter at BOS + Volatility:
Liquidity Available: Structure breaks trigger stop losses and breakout orders = liquidity pool
Reduced Slippage: High volatility = more volume = easier to fill large orders
Momentum Confirmation: Expansion validates the move isn't false
Optimal Risk/Reward: Entry at structure with defined invalidation point
ALERTS & UI
Alert Types:
BOS/CHoCH Alerts:
- Triggered on bar close after star filter pass
- Format: "TF: . - - "
- Optional direction and score display
- Filtered by star rating setting
Volatility Alerts:
- Triggered on confirmed volatility expansion (ROC cross-up)
- Format: "Volatility up confirmed on TF: "
- Only when all enabled filters pass
- Independent of BOS alerts
Alert Filtering:
Respects "Show Direction" setting
Respects "Show Labels" star filter
Only fires on barstate.isconfirmed - no repainting
Market Structure Table: Shows latest confirmed BOS/CHoCH event with direction indicator (Up/Down), type indicator (BOS/CHoCH), and color-coded background. Configurable position and text size.
IMPLEMENTATION NOTES
Non-Repainting: All scores calculated on barstate.isconfirmed. Labels only created after bar close. Storage commits happen once per unique BOS. Historical BOS maintain original scores permanently.
Magic Bands Warmup: Requires 34 bars of price history to establish initial trend direction and volatility baseline. On newly loaded charts, the first 34 bars are used for calculation warmup.
Score Storage: Maps persist until indicator removed or TradingView reset. Historical data survives chart refreshes and timeframe changes. To reset all scores, remove indicator and re-add to chart.
Known Limitations:
Score calculation uses close prices (not tick-level data)
Volatility detection only tracks upward expansion (not downward compression)
Volume data quality varies by broker/exchange - test reliability before using volume filters
WHAT MAKES THIS UNIQUE
Combines intelligent multi-factor BOS scoring with volatility expansion detection to identify institutional entry zones. The dual-signal approach (structure break + participation confirmation) provides high-probability setups that align with professional order flow. Performance-optimized with permanent storage system ensures consistency without repainting while delivering institutional-grade market structure analysis.
ZenAlgo - Coin XA multi input Z Score framework that compares the behavior of a selected symbol against several market wide aggregates: total crypto market metrics, alternative asset baskets, stablecoin dominance, Bitcoin, and risk composites. The script processes each data stream into comparable normalized values, evaluates their relationships, and derives a set of bias states, alerts, and real time conditions.
Data Preparation and Normalization
The indicator starts by gathering multiple reference series:
The chart ticker.
A basket representing non Bitcoin crypto assets.
Bitcoin market data.
Several total market variations (full, without Bitcoin, and additional categories).
A stablecoin dominance series.
A macro risk composite.
A daily anchored average used for context.
Each series is transformed into a normalized value using a lookback window. This produces multiple comparable Z Scores that reflect how far each series currently sits from its typical range. Smoothing is optionally applied to macro based values to reduce noise. These normalized values allow consistent comparisons across unrelated instruments.
This works because Z Score based normalization removes scale differences and makes directional deviations directly comparable across many independent metrics, which is necessary when the script later evaluates their relationships.
Cross and Momentum Detection
The script then evaluates structural interactions between the normalized series:
Whether one group rises above or falls below another.
Whether any of the series crosses over or under another.
Whether each series is currently advancing or declining.
Whether price is above or below the daily anchored average.
Whether stablecoin dominance is rising or falling.
Whether a sharp directional change occurs within a single bar.
Whether a multi threshold movement happens within a defined number of bars.
These checks capture relative strength shifts across the market. For example, an increase in the ticker combined with a decline in dominance suggests capital rotation toward the ticker, while the opposite suggests defensive flows. Using normalized changes allows these comparisons to be scale independent.
Combined Bias Logic
The indicator then evaluates a hierarchy of conditions that combine normalized relationships, momentum, and sharp movement checks. Each condition corresponds to a specific market state. The script tests the conditions in a defined order because later conditions depend on earlier structural checks.
Examples of combined evaluations include:
Cases where the ticker and alternative asset basket rise together while dominance declines.
Cases where both the ticker and alternatives fall together under a rising dominance series.
Conditions where several aggregates cross above or below dominance simultaneously.
Cases where multiple aggregates show coordinated sharp rises or sharp declines.
Situations where stablecoin dominance rises during weakness of other groups.
Situations where stablecoins fall while the ticker strengthens.
Conditions where the ticker rapidly moves through several thresholds in a short period.
The script assigns a bias label that corresponds to the earliest satisfied condition. This design ensures that highly distinctive and rare states take priority over broader or more common states. The reasoning behind this is that specific coordinated market moves provide clearer view than general divergence or simple momentum alone.
Crash and Pump Amplification
The script includes a section that detects extreme scenarios by combining several coordinated factors:
Very negative or very positive normalized values across multiple aggregates.
Sharp bar by bar declines or rises across key series.
Simultaneous movement in the risk composite and dominance.
These checks amplify certain bias states when market conditions show synchronized extreme movement. This provides additional clarity when multiple parts of the market behave in the same direction beyond typical deviation. The logic relies only on the relationships of the normalized values and their changes.
Fast Movement Detection
Two additional mechanisms evaluate movements over a short multi bar window.
A fast ticker move is detected when the current normalized ticker value differs from one several bars ago by multiple threshold increments.
A fast stablecoin rise or fall is detected using a step based method. The script checks for progression through sequential levels across the window while verifying whether the ticker moves in agreement or disagreement with the direction.
These mechanisms are intended to identify sudden acceleration or deceleration that standard normalized changes may not fully capture.
Season Scale
The script calculates a quantitative scale from minus 100 to plus 100 by evaluating several binary conditions:
Whether the ticker is above or below the alternative basket.
Whether the alternative basket is above or below dominance.
Whether the ticker and alternative basket are rising or falling.
Whether dominance is rising or falling.
Optionally whether price is above or below the anchored average.
Each condition contributes positively or negatively. The weighted combination produces the season value which is rounded. The naming of the state (Full Bull, Neutral, Full Bear etc.) is derived from where the score falls on the range.
This works because combining several directional tests across related groups provides a compressed singular measure of market structure.
Divergence Detection
The script includes divergence logic for Bitcoin, the alternative asset basket, and the chart ticker. It evaluates pivot highs and lows in price and compares them with pivot highs and lows in their respective normalized values. The script checks for pairs of pivot points where price moves in one direction while the normalized oscillator moves in the opposite. Both regular and hidden forms are evaluated.
This works because divergences highlight points where price and its normalized deviation disagree which often marks a structural imbalance.
Table Output
If enabled, the indicator displays a table showing the current normalized values of all monitored series along with color backgrounds reflecting structural relationships identified earlier. This supports interpretation without opening additional charts.
Visual Lines and Background
The script draws horizontal reference lines for several normalized levels using a fading mechanism if ghost mode is enabled. The background color changes according to the main season logic and intensifies with market wide deviations. Optional pulse effects are triggered when the bias state changes.
This works because visual context helps understand how extreme the current market state is relative to its typical historical range.
Alerts
The indicator creates alerts for all important structural states:
Bias state changes.
Fast ticker moves.
Fast stablecoin rises or falls.
Divergence based triggers.
Cross conditions corresponding to notable structural transitions.
These alerts correspond exactly to the logical conditions already described.
Added Value Compared to Free Alternatives
It evaluates many separate market wide aggregates simultaneously rather than relying on a single comparison.
It uses a consistent normalized framework so unrelated metrics become comparable.
It identifies multi series coordinated shifts which many simpler indicators cannot detect.
It provides a full deterministic bias state hierarchy that removes interpretation ambiguity.
It includes fast movement evaluation through multi level and multi bar logic.
It combines multiple categories of divergences with normalized values rather than only price based oscillators.
It provides a unified season value derived from several independent binary conditions.
Limitations and Situations Where It May Fall Short
Normalized values depend on the chosen lookback window and may behave differently under unusual volatility regimes.
If reference data feeds are incomplete or delayed the relationships may briefly reflect distorted values.
Extreme single bar events can cause temporary exaggeration of normalized values before stabilization.
Divergence detection depends on identifying pivots which may repaint until the pivot is confirmed.
Bias states rely on hierarchical evaluation so rare but extreme conditions will override more common states by design.
Sudden changes in stablecoin supply or methodology on the data source may influence stable dominance readings.
How to Interpret the Values
Positive normalized values indicate movement above the typical range while negative values indicate movement below the typical range.
The relationships between the ticker, the alternative asset basket, dominance, and the risk composite define the structural meaning of each bias.
The season value near plus 100 means most bull related conditions are simultaneously satisfied while near minus 100 means most bear related conditions are satisfied.
Sharp rise or fall conditions indicate abrupt movement beyond the usual deviation.
Cross conditions indicate structural transitions such as the ticker moving above or below another aggregate.
Divergences indicate inconsistency between price action and normalized deviation.
Best Practices for Practical Use
Use the bias state as a structural context rather than a direct entry or exit trigger.
Observe whether multiple aggregates align in the same direction since the script is designed around confirming coordinated behavior.
Combine the season value with the main bias state to evaluate whether short term view agree with broader conditions.
Use fast movement alerts for monitoring sudden volatility or intraday acceleration.
Use divergence conditions to identify potential exhaustion points when the main bias does not align with price behavior.
Reference the table and background colors for a quick visual overview of how several groups relate in the current moment.
Hybrid Confluence (RSI,MFI,StochRSI) Two-Tier Momentum Framework
Many traders explore multi-oscillator hybrid confluence approaches that combine momentum and volume signals—most commonly RSI, Money Flow Index (MFI), and Stochastic RSI—to study stretched market conditions. These hybrid concepts are widely used to analyze potential exhaustion zones, cycle extremes, and periods of sustained buying or selling pressure across different timeframes.
This script does not replicate, reverse-engineer, or replace any paid or closed-source indicator.
Instead, it provides a fully transparent framework built exclusively from standard, well-documented technical indicators. All calculations are explicit and configurable, allowing traders to study hybrid momentum behavior without relying on proprietary logic or black-box tools.
What the Script Does
1. Builds a hybrid momentum confluence model
The script combines three widely used oscillators:
• RSI (Relative Strength Index) — price momentum
• MFI (Money Flow Index) — volume-weighted momentum
• Stochastic RSI — momentum relative to its own recent range
Each component operates on a normalized 0–100 scale, allowing meaningful comparison and aggregation.
2. Implements a clear two-tier signal structure
Instead of producing a single binary buy/sell output, the script separates early pressure from extreme conditions:
2-of-3 Confluence (Setups)
When any two of the three oscillators reach oversold or overbought levels:
• Displayed as semi-transparent circles
• Indicates building pressure or a developing condition
• Designed as a heads-up, not a trade signal
3-of-3 Confluence (Signals)
When all three oscillators reach oversold or overbought levels:
• Displayed as prominent vertical bars spanning the oscillator range
• Represents extreme momentum alignment
• Intended to highlight potential exhaustion zones
3. Visualizes sustained pressure using consecutive signal intensity
When 3-of-3 conditions persist across multiple bars:
• Each consecutive bar becomes progressively darker
• Up to six discrete intensity levels
• Darkness reflects duration and persistence, not prediction
This helps visualize scenarios where markets continue pushing higher or lower before a major turning point, rather than assuming a single signal marks the exact top or bottom.
4. Works across markets and timeframes
Because all inputs rely on standard technical indicators:
• Works on crypto, equities, futures, and FX
• Scales naturally from intraday to higher timeframes
• Can be used on Daily and multi-day charts for macro context
Why This Script Is Useful
Traditional oscillators often produce isolated signals that lack context. This framework adds clarity by:
1. Requiring multi-indicator agreement instead of single-signal triggers
2. Separating early pressure from extreme conditions
3. Showing how momentum can persist before a reversal
4. Avoiding binary “buy now / sell now” outputs
5. Remaining transparent and configurable
This makes the tool especially useful for:
• Swing traders
• Macro and cycle-focused traders
• Crypto traders studying extended momentum phases
• Analysts who prefer contextual signals over rigid rules
How to Use
1. Adjust RSI, MFI, and StochRSI lengths to suit your timeframe
2. Observe 2-of-3 circles as early warnings of building pressure
3. Watch 3-of-3 bars for extreme momentum alignment
4. Note increasing bar intensity as pressure persists
5. Combine with structure, trend, volume, or price action for decisions
This script is best used as a contextual tool, not a standalone trading system.
What This Script Is Not
• Not a recreation of any paid or proprietary indicator
• Not affiliated with any trading educator or platform
• Not intended as a predictive or standalone trading system
• Does not claim to identify exact tops or bottoms
All signals are derived solely from openly documented RSI, MFI, and Stochastic RSI calculations.
Important Notes
• This script is original, with a transparent methodology
• All calculations use standard, well-known technical formulas
• No hidden logic or undisclosed weighting is used
• Signal visuals are descriptive, not predictive
Disclaimer
This tool is provided for educational and analytical purposes only.
It does not constitute financial advice or a recommendation to trade.
Always validate settings, test on multiple assets and timeframes, and use proper risk management before trading live.
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.
Liquidity Void Zone Detector [PhenLabs]📊 Liquidity Void Zone Detector
Version: PineScript™v6
📌 Description
The Liquidity Void Zone Detector is a sophisticated technical indicator designed to identify and visualize areas where price moved with abnormally low volume or rapid momentum, creating "voids" in market liquidity. These zones represent areas where insufficient trading activity occurred during price movement, often acting as magnets for future price action as the market seeks to fill these gaps.
Built on PineScript v6, this indicator employs a dual-detection methodology that analyzes both volume depletion patterns and price movement intensity relative to ATR. The revolutionary 3D visualization system uses three-layer polyline rendering with adaptive transparency and vertical offsets, creating genuine depth perception where low liquidity zones visually recede and high liquidity zones protrude forward. This makes critical market structure immediately apparent without cluttering your chart.
🚀 Points of Innovation
Dual detection algorithm combining volume threshold analysis and ATR-normalized price movement sensitivity for comprehensive void identification
Three-layer 3D visualization system with progressive transparency gradients (85%, 78%, 70%) and calculated vertical offsets for authentic depth perception
Intelligent state machine logic that tracks consecutive void bars and only renders zones meeting minimum qualification requirements
Dynamic strength scoring system (0-100 scale) that combines inverted volume ratios with movement intensity for accurate void characterization
Adaptive ATR-based spacing calculation that automatically adjusts 3D layering depth to match instrument volatility
Efficient memory management system supporting up to 100 simultaneous void visualizations with automatic array-based cleanup
🔧 Core Components
Volume Analysis Engine: Calculates rolling volume averages and compares current bar volume against dynamic thresholds to detect abnormally thin trading conditions
Price Movement Analyzer: Normalizes bar range against ATR to identify rapid price movements that indicate liquidity exhaustion regardless of instrument or timeframe
Void Tracking State Machine: Maintains persistent tracking of void start bars, price boundaries, consecutive bar counts, and cumulative strength across multiple bars
3D Polyline Renderer: Generates three-layer rectangular polylines with precise timestamp-to-bar index conversion and progressive offset calculations
Strength Calculation System: Combines volume component (inverted ratio capped at 100) with movement component (ATR intensity × 30) for comprehensive void scoring
🔥 Key Features
Automatic Void Detection: Continuously scans price action for low volume conditions or rapid movements, triggering void tracking when thresholds are exceeded
Real-Time Visualization: Creates 3D rectangular zones spanning from void initiation to termination, with color-coded depth indicating liquidity type
Adjustable Sensitivity: Configure volume threshold multiplier (0.1-2.0x), price movement sensitivity (0.5-5.0x), and minimum qualifying bars (1-10) for customized detection
Dual Color Coding: Separate visual treatment for low liquidity voids (receding red) and high liquidity zones (protruding green) based on 50-point strength threshold
Optional Compact Labels: Toggle LV (Low Volume) or HV (High Volume) circular labels at void centers for quick identification without visual clutter
Lookback Period Control: Adjust analysis window from 5 to 100 bars to match your trading timeframe and market volatility characteristics
Memory-Efficient Design: Automatically manages polyline and label arrays, deleting oldest elements when user-defined maximum is reached
Data Window Integration: Plots void detection binary, current strength score, and average volume for detailed analysis in TradingView's data window
🎨 Visualization
Three-Layer Depth System: Each void is rendered as three stacked polylines with progressive transparency (85%, 78%, 70%) and calculated vertical offsets creating authentic 3D appearance
Directional Depth Perception: Low liquidity zones recede with back layer most transparent; high liquidity zones protrude with front layer most transparent for instant visual differentiation
Adaptive Offset Spacing: Vertical separation between layers calculated as ATR(14) × 0.001, ensuring consistent 3D effect across different instruments and volatility regimes
Color Customization: Fully configurable base colors for both low liquidity zones (default: red with 80 transparency) and high liquidity zones (default: green with 80 transparency)
Minimal Chart Clutter: Closed polylines with matching line and fill colors create clean rectangular zones without unnecessary borders or visual noise
Background Highlight: Subtle yellow background (96% transparency) marks bars where void conditions are actively detected in real-time
Compact Labeling: Optional tiny circular labels with 60% transparent backgrounds positioned at void center points for quick reference
📖 Usage Guidelines
Detection Settings
Lookback Period: Default: 10 | Range: 5-100 | Number of bars analyzed for volume averaging and void detection. Lower values increase sensitivity to recent changes; higher values smooth detection across longer timeframes. Adjust based on your trading timeframe: short-term traders use 5-15, swing traders use 20-50, position traders use 50-100.
Volume Threshold: Default: 1.0 | Range: 0.1-2.0 (step 0.1) | Multiplier applied to average volume. Bars with volume below (average × threshold) trigger void conditions. Lower values detect only extreme volume depletion; higher values capture more moderate low-volume situations. Start with 1.0 and decrease to 0.5-0.7 for stricter detection.
Price Movement Sensitivity: Default: 1.5 | Range: 0.5-5.0 (step 0.1) | Multiplier for ATR-normalized price movement detection. Values above this threshold indicate rapid price changes suggesting liquidity voids. Increase to 2.0-3.0 for volatile instruments; decrease to 0.8-1.2 for ranging or low-volatility conditions.
Minimum Void Bars: Default: 10 | Range: 1-10 | Minimum consecutive bars exhibiting void conditions required before visualization is created. Filters out brief anomalies and ensures only sustained voids are displayed. Use 1-3 for scalping, 5-10 for intraday trading, 10+ for swing trading to match your time horizon.
Visual Settings
Low Liquidity Color: Default: Red (80% transparent) | Base color for zones where volume depletion or rapid movement indicates thin liquidity. These zones recede visually (back layer most transparent). Choose colors that contrast with your chart theme for optimal visibility.
High Liquidity Color: Default: Green (80% transparent) | Base color for zones with relatively higher liquidity compared to void threshold. These zones protrude visually (front layer most transparent). Ensure clear differentiation from low liquidity color.
Show Void Labels: Default: True | Toggle display of compact LV/HV labels at void centers. Disable for cleaner charts when trading; enable for analysis and review to quickly identify void types across your chart.
Max Visible Voids: Default: 50 | Range: 10-100 | Maximum number of void visualizations kept on chart. Each void uses 3 polylines, so setting of 50 maintains 150 total polylines. Higher values preserve more history but may impact performance on lower-end systems.
✅ Best Use Cases
Gap Fill Trading: Identify unfilled liquidity voids that price frequently returns to, providing high-probability retest and reversal opportunities when price approaches these zones
Breakout Validation: Distinguish genuine breakouts through established liquidity from false breaks into void zones that lack sustainable volume support
Support/Resistance Confluence: Layer void detection over key horizontal levels to validate structural integrity—levels within high liquidity zones are stronger than those in voids
Trend Continuation: Monitor for new void formation in trend direction as potential continuation zones where price may accelerate due to reduced resistance
Range Trading: Identify void zones within consolidation ranges that price tends to traverse quickly, helping to avoid getting caught in rapid moves through thin areas
Entry Timing: Wait for price to reach void boundaries rather than entering mid-void, as voids tend to be traversed quickly with limited profit-taking opportunities
⚠️ Limitations
Historical Pattern Indicator: Identifies past liquidity voids but cannot predict whether price will return to fill them or when filling might occur
No Volume on Forex: Indicator uses tick volume for forex pairs, which approximates but doesn't represent true trading volume, potentially affecting detection accuracy
Lagging Confirmation: Requires minimum consecutive bars (default 10) before void is visualized, meaning detection occurs after void formation begins
Trending Market Behavior: Strong trends driven by fundamental catalysts may create voids that remain unfilled for extended periods or permanently
Timeframe Dependency: Detection sensitivity varies significantly across timeframes; settings optimized for one timeframe may not perform well on others
No Directional Bias: Indicator identifies liquidity characteristics but provides no predictive signal for price direction after void detection
Performance Considerations: Higher max visible void settings combined with small minimum void bars can generate numerous visualizations impacting chart rendering speed
💡 What Makes This Unique
Industry-First 3D Visualization: Unlike flat volume or liquidity indicators, the three-layer rendering with directional depth perception provides instant visual hierarchy of liquidity quality
Dual-Mode Detection: Combines both volume-based and movement-based detection methodologies, capturing voids that single-approach indicators miss
Intelligent Qualification System: State machine logic prevents premature visualization by requiring sustained void conditions, reducing false signals and chart clutter
ATR-Normalized Analysis: All detection thresholds adapt to instrument volatility, ensuring consistent performance across stocks, forex, crypto, and futures without constant recalibration
Transparency-Based Depth: Uses progressive transparency gradients rather than colors or patterns to create depth, maintaining visual clarity while conveying information hierarchy
Comprehensive Strength Metrics: 0-100 void strength calculation considers both the degree of volume depletion and the magnitude of price movement for nuanced zone characterization
🔬 How It Works
Phase 1: Real-Time Detection
On each bar close, the indicator calculates average volume over the lookback period and compares current bar volume against the volume threshold multiplier
Simultaneously measures current bar's high-low range and normalizes it against ATR, comparing the result to price movement sensitivity parameter
If either volume falls below threshold OR movement exceeds sensitivity threshold, the bar is flagged as exhibiting void characteristics
Phase 2: Void Tracking & Qualification
When void conditions first appear, state machine initializes tracking variables: start bar index, initial top/bottom prices, consecutive bar counter, and cumulative strength accumulator
Each subsequent bar with void conditions extends the tracking, updating price boundaries to envelope all bars and accumulating strength scores
When void conditions cease, system checks if consecutive bar count meets minimum threshold; if yes, proceeds to visualization; if no, discards the tracking and resets
Phase 3: 3D Visualization Construction
Calculates average void strength by dividing cumulative strength by number of bars, then determines if void is low liquidity (>50 strength) or high liquidity (≤50 strength)
Generates three polyline layers spanning from start bar to end bar and from top price to bottom price, each with calculated vertical offset based on ATR
Applies progressive transparency (85%, 78%, 70%) with layer ordering creating recession effect for low liquidity zones and protrusion effect for high liquidity zones
Creates optional center label and pushes all visual elements into arrays for memory management
Phase 4: Memory Management & Display
Continuously monitors polyline array size (each void creates 3 polylines); when total exceeds max visible voids × 3, deletes oldest polylines via array.shift()
Similarly manages label array, removing oldest labels when count exceeds maximum to prevent memory accumulation over extended chart history
Plots diagnostic data to TradingView’s data window (void detection binary, current strength, average volume) for detailed analysis without cluttering main chart
💡 Note:
This indicator is designed to enhance your market structure analysis by revealing liquidity characteristics that aren’t visible through standard price and volume displays. For best results, combine void detection with your existing support/resistance analysis, trend identification, and risk management framework. Liquidity voids are descriptive of past market behavior and should inform positioning decisions rather than serve as standalone entry/exit signals. Experiment with detection parameters across different timeframes to find settings that align with your trading style and instrument characteristics.
Continuation Index [DCAUT]█ Continuation Index
📊 OVERVIEW
Continuation Index (CI) is an advanced trend analysis indicator developed by John F. Ehlers. This indicator provides early warning signals for trend onset, continuation, and exhaustion, with values oscillating between -1 and +1 to offer clear trend state identification for traders.
Based on the article TASC 2025.09 "Trend Onset And Trend Exhaustion - The Continuation Index" by John F. Ehlers.
💡 CORE VALUE
Unlike traditional trend indicators, the Continuation Index provides:
- Advanced dual-filter architecture (Ultimate Smoother + Laguerre Filter)
- Inverse Fisher Transform for enhanced signal-to-noise ratio
- Adaptive gamma parameter allowing market-specific tuning
- Binary state output (+1/-1) eliminating interpretation ambiguity
🎯 CONCEPTS
Signal Interpretation
CI > 0.5 : Strong bullish trend continuation - consider holding/adding long positions
CI = +1 : Maximum bullish signal - strong uptrend in progress
CI < -0.5 : Strong bearish trend continuation - consider holding/adding short positions
CI = -1 : Maximum bearish signal - strong downtrend in progress
CI near 0 : Neutral zone - trend uncertain, wait for clear signals
Brief pullbacks from extreme states : Potential reentry opportunities in trend direction
Primary Applications
Trend Onset Detection : Early warning signals for trend initiation
Trend Exhaustion Signals : Identify potential trend reversals
Position Management : Clear binary states for entry/exit decisions
Market Timing : Adaptive filtering reduces false signals
📋 PARAMETER SETUP
Source : Data source for calculation (default: close)
Length : The calculation length for the filters (default: 40, min: 1)
Gamma : Controls the phase response of the Laguerre filter. Smaller values increase responsiveness (default: 0.8, range: 0.0-1.0)
Laguerre Order : The order of the Laguerre filter, which directly affects its lag (default: 8, range: 1-10)
📊 COLOR CODING
Green : CI > 0.5 - Bullish trend continuation
Red : CI < -0.5 - Bearish trend continuation
Gray : Neutral zone - Trend unclear
[DEM] Multiple Linear Regression Score Multiple Linear Regression Score is a composite momentum indicator that evaluates market conditions by analyzing a reference symbol (defaulting to NDX) across multiple technical dimensions and combining them into a single predictive score. The indicator processes ten different technical variables including RSI, MACD components (line, signal, and histogram), price relationships to various moving averages (10, 50, 100, 200), and short-term price changes (1-day and 5-day), converting most into binary signals (1 or 0) based on whether they're above or below zero. These binary and continuous inputs are then weighted using regression-derived coefficients and combined into a final percentage score that oscillates around zero, with the indicator also calculating a 20-period standard deviation of the score to measure volatility. This approach creates a data-driven sentiment gauge that quantifies the overall technical health of the reference market by mathematically weighting the importance of each technical factor based on historical relationships.
Options Trading Max Success_V1DISCLAIMER:
The information provided is NOT financial advice. I am not a financial adviser, accountant or the like. This information is purely from my own due diligence and an expression of my thoughts, my opinions based on my personal experiences, and the way I transact.
Utilize this indicator at your own risk..! The indicator creator is not liable for your loss due to untimely action / adverse consequences / server lags from Tradingview (if any).
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Welcome!
This is a 95-100% Success rate High Frequency Indicator exclusively for Binary Options Traders. It works on any time frames and pairs but is EXCLUSIVELY built for 1-minute candles for EUR/USD currency on "OANDA" forex chart. So, use it for same to get this indicator working at its best.
Use Martingale strategy (5 attempts max) for making profits / recover loss with some profits.
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Martingale Strategy For your knowledge with an example:
1) Lets say you are trading on binary options platform that gives 80% profit upon successful trade.
2) UP signal seen. You do the below from next candle:
a) 1st attempt = Rs.100.
- If Success, then profit = Rs.80. Cycle close and exit.
- If Loss, then do 2nd attempt.
b) 2nd attempt =Rs.200.
- If Success, then profit = Rs.160. (Rs. 100 recovery + Rs.60 Profit). Cycle close and exit.
- If Loss, then do 3rd attempt.
c) 3rd attempt = Rs. 400.
- If Success, then profit = Rs.320. (Rs. 300 recovery + Rs.20 Profit). Cycle close and exit.
- If Loss, then do 4th attempt.. and so on.
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If you see any body less/Doji candle in between your attempts. Then do not continue further.
Hold this cycle for next similar stage. For example:
Select chart which promises: Success = 80% profit.
Then attempt the below on the next candle AFTER you see an UP signal.
Cycle 1: UP signal seen. 5 attempts from next candle:
Let's say:
1st attempt = Rs.100. Result = loss
2nd attempt =Rs.200. Result = loss
3rd attempt = Rs.400. Result = No profit/loss (due to Doji candle/candle without body).
Recommendation: Do not proceed further in current cycle. Hold on for next cycle/UP signal.
Park Rs.400 rupees attempt aside for a while.
Cycle 2: UP signal seen. 5 attempts from next candle:
Let's say:
1st attempt = Rs.100. Result = loss
2nd attempt =Rs.200. Result = Success
Cycle Completed. Wait for next cycle/Up signal
Cycle 3: UP signal seen. 5 attempts from next candle:
Let's say:
1st attempt = Rs.100. Result = loss
2nd attempt =Rs.200. Result = loss
3rd attempt = Now you can attempt with Rs. 800.
.
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Recommendations:
- Keep a good discipline and make smart moves.
- You may add other supporting indicators of your choice along with this.
- You can keep your trading attempts low i.e. After you see an UP signal, let go the 1st one/two/three candles. If they turn out to be Red candles back to back, then good for you, as you can start entry of attempts from the 2nd/3rd/4th candle. Thereby evading one/two/three few failed attempts. If any candle gets green After Up signal and before your entry, then do not enter this cycle. Wait for next cycle.
Good luck.
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