Kernel Market Dynamics [WFO - MAB]Kernel Market Dynamics
⚛️ CORE INNOVATION: KERNEL-BASED DISTRIBUTION ANALYSIS
The Kernel Market Dynamics system represents a fundamental departure from traditional technical indicators. Rather than measuring price levels, momentum, or oscillator extremes, KMD analyzes the statistical distribution of market returns using advanced kernel methods from machine learning theory. This allows the system to detect when market behavior has fundamentally changed—not just when price has moved, but when the underlying probability structure has shifted.
The Distribution Hypothesis:
Traditional indicators assume markets move in predictable patterns. KMD assumes something more profound: markets exist in distinct distributional regimes , and profitable trading opportunities emerge during regime transitions . When the distribution of recent returns diverges significantly from the historical baseline, the market is restructuring—and that's when edge exists.
Maximum Mean Discrepancy (MMD):
At the heart of KMD lies a sophisticated statistical metric called Maximum Mean Discrepancy. MMD measures the distance between two probability distributions by comparing their representations in a high-dimensional feature space created by a kernel function.
The Mathematics:
Given two sets of normalized returns:
• Reference period (X) : Historical baseline (default 100 bars)
• Test period (Y) : Recent behavior (default 20 bars)
MMD is calculated as:
MMD² = E + E - 2·E
Where:
• E = Expected kernel similarity within reference period
• E = Expected kernel similarity within test period
• E = Expected cross-similarity between periods
When MMD is low : Test period behaves like reference (stable regime)
When MMD is high : Test period diverges from reference (regime shift)
The final MMD value is smoothed with EMA(5) to reduce single-bar noise while maintaining responsiveness to genuine distribution changes.
The Kernel Functions:
The kernel function defines how similarity is measured. KMD offers four mathematically distinct kernels, each with different properties:
1. RBF (Radial Basis Function / Gaussian):
• Formula: k(x,y) = exp(-d² / (2·σ²·scale))
• Properties: Most sensitive to distribution changes, smooth decision boundaries
• Best for: Clean data, clear regime shifts, low-noise markets
• Sensitivity: Highest - detects subtle changes
• Use case: Stock indices, major forex pairs, trending environments
2. Laplacian:
• Formula: k(x,y) = exp(-|d| / σ)
• Properties: Medium sensitivity, robust to moderate outliers
• Best for: Standard market conditions, balanced noise/signal
• Sensitivity: Medium - filters minor fluctuations
• Use case: Commodities, standard timeframes, general trading
3. Cauchy (Default - Most Robust):
• Formula: k(x,y) = 1 / (1 + d²/σ²)
• Properties: Heavy-tailed, highly robust to outliers and spikes
• Best for: Noisy markets, choppy conditions, crypto volatility
• Sensitivity: Lower - only major distribution shifts trigger
• Use case: Cryptocurrencies, illiquid markets, volatile instruments
4. Rational Quadratic:
• Formula: k(x,y) = (1 + d²/(2·α·σ²))^(-α)
• Properties: Tunable via alpha parameter, mixture of RBF kernels
• Alpha < 1.0: Heavy tails (like Cauchy)
• Alpha > 3.0: Light tails (like RBF)
• Best for: Adaptive use, mixed market conditions
• Use case: Experimental optimization, regime-specific tuning
Bandwidth (σ) Parameter:
The bandwidth controls the "width" of the kernel, determining sensitivity to return differences:
• Low bandwidth (0.5-1.5) : Narrow kernel, very sensitive
- Treats small differences as significant
- More MMD spikes, more signals
- Use for: Scalping, fast markets
• Medium bandwidth (1.5-3.0) : Balanced sensitivity (recommended)
- Filters noise while catching real shifts
- Professional-grade signal quality
- Use for: Day/swing trading
• High bandwidth (3.0-10.0) : Wide kernel, less sensitive
- Only major distribution changes register
- Fewer, stronger signals
- Use for: Position trading, trend following
Adaptive Bandwidth:
When enabled (default ON), bandwidth automatically scales with market volatility:
Effective_BW = Base_BW × max(0.5, min(2.0, 1 / volatility_ratio))
• Low volatility → Tighter bandwidth (0.5× base) → More sensitive
• High volatility → Wider bandwidth (2.0× base) → Less sensitive
This prevents signal flooding during wild markets and avoids signal drought during calm periods.
Why Kernels Work:
Kernel methods implicitly map data to infinite-dimensional space where complex, nonlinear patterns become linearly separable. This allows MMD to detect distribution changes that simpler statistics (mean, variance) would miss. For example:
• Same mean, different shape : Traditional metrics see nothing, MMD detects shift
• Same volatility, different skew : Oscillators miss it, MMD catches it
• Regime rotation : Price unchanged, but return distribution restructured
The kernel captures the entire distributional signature —not just first and second moments.
🎰 MULTI-ARMED BANDIT FRAMEWORK: ADAPTIVE STRATEGY SELECTION
Rather than forcing one strategy on all market conditions, KMD implements a Multi-Armed Bandit (MAB) system that learns which of seven distinct strategies performs best and dynamically selects the optimal approach in real-time.
The Seven Arms (Strategies):
Each arm represents a fundamentally different trading logic:
ARM 0 - MMD Regime Shift:
• Logic: Distribution divergence with directional bias
• Triggers: MMD > threshold AND direction_bias confirmed AND velocity > 5%
• Philosophy: Trade the regime transition itself
• Best in: Volatile shifts, breakout moments, crisis periods
• Weakness: False alarms in choppy consolidation
ARM 1 - Trend Following:
• Logic: Aligned EMAs with strong ADX
• Triggers: EMA(9) > EMA(21) > EMA(50) AND ADX > 25
• Philosophy: Ride established momentum
• Best in: Strong trending regimes, directional markets
• Weakness: Late entries, whipsaws at reversals
ARM 2 - Breakout:
• Logic: Bollinger Band breakouts with volume
• Triggers: Price crosses BB outer band AND volume > 1.2× average
• Philosophy: Capture volatility expansion events
• Best in: Range breakouts, earnings, news events
• Weakness: False breakouts in ranging markets
ARM 3 - RSI Mean Reversion:
• Logic: RSI extremes with reversal confirmation
• Triggers: RSI < 30 with uptick OR RSI > 70 with downtick
• Philosophy: Fade overbought/oversold extremes
• Best in: Ranging markets, mean-reverting instruments
• Weakness: Fails in strong trends, catches falling knives
ARM 4 - Z-Score Statistical Reversion:
• Logic: Price deviation from 50-period mean
• Triggers: Z-score < -2 (oversold) OR > +2 (overbought) with reversal
• Philosophy: Statistical bounds reversion
• Best in: Stable volatility regimes, pairs trading
• Weakness: Trend continuation through extremes
ARM 5 - ADX Momentum:
• Logic: Strong directional movement with acceleration
• Triggers: ADX > 30 with DI+ or DI- strengthening
• Philosophy: Momentum begets momentum
• Best in: Trending with increasing velocity
• Weakness: Late exits, momentum exhaustion
ARM 6 - Volume Confirmation:
• Logic: OBV trend + volume spike + candle direction
• Triggers: OBV > EMA(20) AND volume > average AND bullish candle
• Philosophy: Follow institutional money flow
• Best in: Liquid markets with reliable volume
• Weakness: Manipulated volume, thin markets
Q-Learning with Rewards:
Each arm maintains a Q-value representing its expected reward. After every bar, the system calculates a reward based on the arm's signal and actual price movement:
Reward Calculation:
If arm signaled LONG:
reward = (close - close ) / close
If arm signaled SHORT:
reward = -(close - close ) / close
If arm signaled NEUTRAL:
reward = 0
Penalty multiplier: If loss > 0.5%, reward × 1.3 (punish big losses harder)
Q-Value Update (Exponential Moving Average):
Q_new = Q_old + α × (reward - Q_old)
Where α (learning rate, default 0.08) controls adaptation speed:
• Low α (0.01-0.05): Slow, stable learning
• Medium α (0.06-0.12): Balanced (recommended)
• High α (0.15-0.30): Fast, reactive learning
This gradually shifts Q-values toward arms that generate positive returns and away from losing arms.
Arm Selection Algorithms:
KMD offers four mathematically distinct selection strategies:
1. UCB1 (Upper Confidence Bound) - Recommended:
Formula: Select arm with max(Q_i + c·√(ln(t)/n_i))
Where:
• Q_i = Q-value of arm i
• c = exploration constant (default 1.5)
• t = total pulls across all arms
• n_i = pulls of arm i
Philosophy: Balance exploitation (use best arm) with exploration (try uncertain arms). The √(ln(t)/n_i) term creates an "exploration bonus" that decreases as an arm gets more pulls, ensuring all arms get sufficient testing.
Theoretical guarantee: Logarithmic regret bound - UCB1 provably converges to optimal arm selection over time.
2. UCB1-Tuned (Variance-Aware UCB):
Formula: Select arm with max(Q_i + √(ln(t)/n_i × min(0.25, V_i + √(2·ln(t)/n_i))))
Where V_i = variance of rewards for arm i
Philosophy: Incorporates reward variance into exploration. Arms with high variance (unpredictable) get less exploration bonus, focusing effort on stable performers.
Better bounds than UCB1 in practice, slightly more conservative exploration.
3. Epsilon-Greedy (Simple Random):
Algorithm:
With probability ε: Select random arm (explore)
With probability 1-ε: Select highest Q-value arm (exploit)
Default ε = 0.10 (10% exploration, 90% exploitation)
Philosophy: Simplest algorithm, easy to understand. Random exploration ensures all arms stay updated but may waste time on clearly bad arms.
4. Thompson Sampling (Bayesian):
The most sophisticated selection algorithm, using true Bayesian probability.
Each arm maintains Beta distribution parameters:
• α (alpha) = successes + 1
• β (beta) = failures + 1
Selection Process:
1. Sample θ_i ~ Beta(α_i, β_i) for each arm using Marsaglia-Tsang Gamma sampler
2. Select arm with highest sample: argmax_i(θ_i)
3. After reward, update:
- If reward > 0: α += |reward| × 100 (increment successes)
- If reward < 0: β += |reward| × 100 (increment failures)
Why Thompson Sampling Works:
The Beta distribution naturally represents uncertainty about an arm's true win rate. Early on with few trials, the distribution is wide (high uncertainty), leading to more exploration. As evidence accumulates, it narrows around the true performance, naturally shifting toward exploitation.
Unlike UCB which uses deterministic confidence bounds, Thompson Sampling is probabilistic—it samples from the posterior distribution of each arm's success rate, providing automatic exploration/exploitation balance without tuning.
Comparison:
• UCB1: Deterministic, guaranteed regret bounds, requires tuning exploration constant
• Thompson: Probabilistic, natural exploration, no tuning required, best empirical performance
• Epsilon-Greedy: Simplest, consistent exploration %, less efficient
• UCB1-Tuned: UCB1 + variance awareness, best for risk-averse
Exploration Constant (c):
For UCB algorithms, this multiplies the exploration bonus:
• Low c (0.5-1.0): Strongly prefer proven arms, rare exploration
• Medium c (1.2-1.8): Balanced (default 1.5)
• High c (2.0-3.0): Frequent exploration, diverse arm usage
Higher exploration constant in volatile/unstable markets, lower in stable trending environments.
🔬 WALK-FORWARD OPTIMIZATION: PREVENTING OVERFITTING
The single biggest problem in algorithmic trading is overfitting—strategies that look amazing in backtest but fail in live trading because they learned noise instead of signal. KMD's Walk-Forward Optimization system addresses this head-on.
How WFO Works:
The system divides time into repeating cycles:
1. Training Window (default 500 bars): Learn arm Q-values on historical data
2. Testing Window (default 100 bars): Validate on unseen "future" data
Training Phase:
• All arms accumulate rewards and update Q-values normally
• Q_train tracks in-sample performance
• System learns which arms work on historical data
Testing Phase:
• System continues using arms but tracks separate Q_test metrics
• Counts trades per arm (N_test)
• Testing performance is "out-of-sample" relative to training
Validation Requirements:
An arm is only "validated" (approved for live use) if:
1. N_test ≥ Minimum Trades (default 10): Sufficient statistical sample
2. Q_test > 0 : Positive out-of-sample performance
Arms that fail validation are blocked from generating signals, preventing the system from trading strategies that only worked on historical data.
Performance Decay:
At the end of each WFO cycle, all Q-values decay exponentially:
Q_new = Q_old × decay_rate (default 0.95)
This ensures old performance doesn't dominate forever. An arm that worked 10 cycles ago but fails recently will eventually lose influence.
Decay Math:
• 0.95 decay after 10 periods → 0.95^10 = 0.60 (40% forgotten)
• 0.90 decay after 10 periods → 0.90^10 = 0.35 (65% forgotten)
Fast decay (0.80-0.90): Quick adaptation, forgets old patterns rapidly
Slow decay (0.96-0.99): Stable, retains historical knowledge longer
WFO Efficiency Metric:
The key metric revealing overfitting:
Efficiency = (Q_test / Q_train) for each validated arm, averaged
• Efficiency > 0.8 : Excellent - strategies generalize well (LOW overfit risk)
• Efficiency 0.5-0.8 : Acceptable - moderate generalization (MODERATE risk)
• Efficiency < 0.5 : Poor - strategies curve-fitted to history (HIGH risk)
If efficiency is low, the system has learned noise. Training performance was good but testing (forward) performance is weak—classic overfitting.
The dashboard displays real-time WFO efficiency, allowing users to gauge system robustness. Low efficiency should trigger parameter review or reduced position sizing.
Why WFO Matters:
Consider two scenarios:
Scenario A - No WFO:
• Arm 3 (RSI Reversion) shows Q-value of 0.15 on all historical data
• System trades it aggressively
• Reality: It only worked during one specific ranging period
• Live trading: Fails because market has trended since backtest
Scenario B - With WFO:
• Arm 3 shows Q_train = 0.15 (good in training)
• But Q_test = -0.05 (loses in testing) with 12 test trades
• N_test ≥ 10 but Q_test < 0 → Arm BLOCKED
• System refuses to trade it despite good backtest
• Live trading: Protected from false strategy
WFO ensures only strategies that work going forward get used, not just strategies that fit the past.
Optimal Window Sizing:
Training Window:
• Too short (100-300): May learn recent noise, insufficient data
• Too long (1000-2000): May include obsolete market regimes
• Recommended: 4-6× testing window (default 500)
Testing Window:
• Too short (50-80): Insufficient validation, high variance
• Too long (300-500): Delayed adaptation to regime changes
• Recommended: 1/5 to 1/4 of training (default 100)
Minimum Trades:
• Too low (5-8): Statistical noise, lucky runs validate
• Too high (30-50): Many arms never validate, system rarely trades
• Recommended: 10-15 (default 10)
⚖️ WEIGHTED CONFLUENCE SYSTEM: MULTI-FACTOR SIGNAL QUALITY
Not all signals are created equal. KMD implements a sophisticated 100-point quality scoring system that combines eight independent factors with different importance weights.
The Scoring Framework:
Each potential signal receives a quality score from 0-100 by accumulating points from aligned factors:
CRITICAL FACTORS (20 points each):
1. Bandit Arm Alignment (20 points):
• Full points if selected arm's signal matches trade direction
• Zero points if arm disagrees
• Weight: Highest - the bandit selected this arm for a reason
2. MMD Regime Quality (20 points):
• Requires: MMD > dynamic threshold AND directional bias confirmed
• Scaled by MMD percentile (how extreme vs history)
• If MMD in top 10% of history: 100% of 20 points
• If MMD at 50th percentile: 50% of 20 points
• Weight: Highest - distribution shift is the core signal
HIGH IMPACT FACTORS (15 points each):
3. Trend Alignment (15 points):
• Full points if EMA(9) > EMA(21) > EMA(50) for longs (inverse for shorts)
• Scaled by ADX strength:
- ADX > 25: 100% (1.0× multiplier) - strong trend
- ADX 20-25: 70% (0.7× multiplier) - moderate trend
- ADX < 20: 40% (0.4× multiplier) - weak trend
• Weight: High - trend is friend, alignment increases probability
4. Volume Confirmation (15 points):
• Requires: OBV > EMA(OBV, 20) aligned with direction
• Scaled by volume ratio: vol_current / vol_average
- Volume 1.5×+ average: 100% of points (institutional participation)
- Volume 1.0-1.5× average: 67% of points (above average)
- Volume below average: 0 points (weak conviction)
• Weight: High - volume validates price moves
MODERATE FACTORS (10 points each):
5. Market Structure (10 points):
• Full points (10) if bullish structure (higher highs, higher lows) for longs
• Partial points (6) if near support level (within 1% of swing low)
• Similar logic inverted for bearish trades
• Weight: Moderate - structure context improves entries
6. RSI Positioning (10 points):
• For long signals:
- RSI < 50: 100% of points (1.0× multiplier) - room to run
- RSI 50-60: 60% of points (0.6× multiplier) - neutral
- RSI 60-70: 30% of points (0.3× multiplier) - elevated
- RSI > 70: 0 points (0× multiplier) - overbought
• Inverse for short signals
• Weight: Moderate - momentum context, not primary signal
BONUS FACTORS (10 points each):
7. Divergence (10 points):
• Full 10 points if bullish divergence detected for long (or bearish for short)
• Zero points otherwise
• Weight: Bonus - leading indicator, adds confidence when present
8. Multi-Timeframe Confirmation (10 points):
• Full 10 points if higher timeframe aligned (HTF EMA trending same direction, RSI supportive)
• Zero points if MTF disabled or HTF opposes
• Weight: Bonus - macro context filter, prevents counter-trend disasters
Total Maximum: 110 points (20+20+15+15+10+10+10+10)
Signal Quality Calculation:
Quality Score = (Accumulated_Points / Maximum_Possible) × 100
Where Maximum_Possible = 110 points if all factors active, adjusts if MTF disabled.
Example Calculation:
Long signal candidate:
• Bandit Arm: +20 (arm signals long)
• MMD Quality: +16 (MMD high, 80th percentile)
• Trend: +11 (EMAs aligned, ADX = 22 → 70% × 15)
• Volume: +10 (OBV rising, vol 1.3× avg → 67% × 15 = 10)
• Structure: +10 (higher lows forming)
• RSI: +6 (RSI = 55 → 60% × 10)
• Divergence: +0 (none present)
• MTF: +10 (HTF bullish)
Total: 83 / 110 × 100 = 75.5% quality score
This is an excellent quality signal - well above threshold (default 60%).
Quality Thresholds:
• Score 80-100 : Exceptional setup - all factors aligned
• Score 60-80 : High quality - most factors supportive (default minimum)
• Score 40-60 : Moderate - mixed confluence, proceed with caution
• Score 20-40 : Weak - minimal support, likely filtered out
• Score 0-20 : Very weak - almost certainly blocked
The minimum quality threshold (default 60) is the gatekeeper. Only signals scoring above this value can trigger trades.
Dynamic Threshold Adjustment:
The system optionally adjusts the threshold based on historical signal distribution:
If Dynamic Threshold enabled:
Recent_MMD_Mean = SMA(MMD, 50)
Recent_MMD_StdDev = StdDev(MMD, 50)
Dynamic_Threshold = max(Base_Threshold × 0.5,
min(Base_Threshold × 2.0,
MMD_Mean + MMD_StdDev × 0.5))
This auto-calibrates to market conditions:
• Quiet markets (low MMD): Threshold loosens (0.5× base)
• Active markets (high MMD): Threshold tightens (2× base)
Signal Ranking Filter:
When enabled, the system tracks the last 100 signal quality scores and only fires signals in the top percentile.
If Ranking Percentile = 75%:
• Collect last 100 signal scores in memory
• Sort ascending
• Threshold = Score at 75th percentile position
• Only signals ≥ this threshold fire
This ensures you're only taking the cream of the crop —top 25% of signals by quality, not every signal that technically qualifies.
🚦 SIGNAL GENERATION: TRANSITION LOGIC & COOLDOWNS
The confluence system determines if a signal qualifies , but the signal generation logic controls when triangles appear on the chart.
Core Qualification:
For a LONG signal to qualify:
1. Bull quality score ≥ signal threshold (default 60)
2. Selected arm signals +1 (long)
3. Cooldown satisfied (bars since last signal ≥ cooldown period)
4. Drawdown protection OK (current drawdown < pause threshold)
5. MMD ≥ 80% of dynamic threshold (slight buffer below full threshold)
For a SHORT signal to qualify:
1. Bear quality score ≥ signal threshold
2. Selected arm signals -1 (short)
3-5. Same as long
But qualification alone doesn't trigger a chart signal.
Three Signal Modes:
1. RESPONSIVE (Default - Recommended):
Signals appear on:
• Fresh qualification (wasn't qualified last bar, now is)
• Direction reversal (was qualified short, now qualified long)
• Quality improvement (already qualified, quality jumps 25%+ during EXTREME regime)
This mode shows new opportunities and significant upgrades without cluttering the chart with repeat signals.
2. TRANSITION ONLY:
Signals appear on:
• Fresh qualification only
• Direction reversal only
This is the cleanest mode - signals only when first qualifying or when flipping direction. Misses re-entries if quality improves mid-regime.
3. CONTINUOUS:
Signals appear on:
• Every bar that qualifies
Testing/debugging mode - shows all qualified bars. Very noisy but useful for understanding when system wants to trade.
Cooldown System:
Prevents signal clustering and overtrading by enforcing minimum bars between signals.
Base Cooldown: User-defined (default 5 bars)
Adaptive Cooldown (Optional):
If enabled, cooldown scales with volatility:
Effective_Cooldown = Base_Cooldown × volatility_multiplier
Where:
ATR_Pct = ATR(14) / Close × 100
Volatility_Multiplier = max(0.5, min(3.0, ATR_Pct / 2.0))
• Low volatility (ATR 1%): Multiplier ~0.5× → Cooldown = 2-3 bars (tight)
• Medium volatility (ATR 2%): Multiplier 1.0× → Cooldown = 5 bars (normal)
• High volatility (ATR 4%+): Multiplier 2.0-3.0× → Cooldown = 10-15 bars (wide)
This prevents excessive trading during wild swings while allowing more signals during calm periods.
Regime Filter:
Three modes controlling which regimes allow trading:
OFF: Trade in any regime (STABLE, TRENDING, SHIFTING, ELEVATED, EXTREME)
SMART (Recommended):
• Regime score = 1.0 for SHIFTING, ELEVATED (optimal)
• Regime score = 0.8 for TRENDING (acceptable)
• Regime score = 0.5 for EXTREME (too chaotic)
• Regime score = 0.2 for STABLE (too quiet)
Quality scores are multiplied by regime score. A 70% quality signal in STABLE regime becomes 70% × 0.2 = 14% → blocked.
STRICT:
• Regime score = 1.0 for SHIFTING, ELEVATED only
• Regime score = 0.0 for all others → hard block
Only trades during optimal distribution shift regimes.
Drawdown Protection:
If current equity drawdown exceeds pause threshold (default 8%), all signals are blocked until equity recovers.
This circuit breaker prevents compounding losses during adverse conditions or broken market structure.
🎯 RISK MANAGEMENT: ATR-BASED STOPS & TARGETS
Every signal generates volatility-normalized stop loss and target levels displayed as boxes on the chart.
Stop Loss Calculation:
Stop_Distance = ATR(14) × ATR_Multiplier (default 1.5)
For LONG: Stop = Entry - Stop_Distance
For SHORT: Stop = Entry + Stop_Distance
The stop is placed 1.5 ATRs away from entry by default, adapting automatically to instrument volatility.
Target Calculation:
Target_Distance = Stop_Distance × Risk_Reward_Ratio (default 2.0)
For LONG: Target = Entry + Target_Distance
For SHORT: Target = Entry - Target_Distance
Default 2:1 risk/reward means target is twice as far as stop.
Example:
• Price: $100
• ATR: $2
• ATR Multiplier: 1.5
• Risk/Reward: 2.0
LONG Signal:
• Entry: $100
• Stop: $100 - ($2 × 1.5) = $97.00 (-$3 risk)
• Target: $100 + ($3 × 2.0) = $106.00 (+$6 reward)
• Risk/Reward: $3 risk for $6 reward = 1:2 ratio
Target/Stop Box Lifecycle:
Boxes persist for a lifetime (default 20 bars) OR until an opposite signal fires, whichever comes first. This provides visual reference for active trade levels without permanent chart clutter.
When a new opposite-direction signal appears, all existing boxes from the previous direction are immediately deleted, ensuring only relevant levels remain visible.
Adaptive Stop/Target Sizing:
While not explicitly coded in the current version, the shadow portfolio tracking system calculates PnL based on these levels. Users can observe which ATR multipliers and risk/reward ratios produce optimal results for their instrument/timeframe via the dashboard performance metrics.
📊 COMPREHENSIVE VISUAL SYSTEM
KMD provides rich visual feedback through four distinct layers:
1. PROBABILITY CLOUD (Adaptive Volatility Bands):
Two sets of bands around price that expand/contract with MMD:
Calculation:
Std_Multiplier = 1 + MMD × 3
Upper_1σ = Close + ATR × Std_Multiplier × 0.5
Lower_1σ = Close - ATR × Std_Multiplier × 0.5
Upper_2σ = Close + ATR × Std_Multiplier
Lower_2σ = Close - ATR × Std_Multiplier
• Inner band (±0.5× adjusted ATR) : 68% probability zone (1 standard deviation equivalent)
• Outer band (±1.0× adjusted ATR) : 95% probability zone (2 standard deviation equivalent)
When MMD spikes, bands widen dramatically, showing increased uncertainty. When MMD calms, bands tighten, showing normal price action.
2. MOMENTUM FLOW VECTORS (Directional Arrows):
Dynamic arrows that visualize momentum strength and direction:
Arrow Properties:
• Length: Proportional to momentum magnitude (2-10 bars forward)
• Width: 1px (weak), 2px (medium), 3px (strong)
• Transparency: 30-100 (more opaque = stronger momentum)
• Direction: Up for bullish, down for bearish
• Placement: Below bars (bulls) or above bars (bears)
Trigger Logic:
• Always appears every 5 bars (regular sampling)
• Forced appearance if momentum strength > 50 OR regime shift OR MMD velocity > 10%
Strong momentum (>75%) gets:
• Secondary support arrow (70% length, lighter color)
• Label showing "75%" strength
Very strong momentum (>60%) gets:
• Gradient flow lines (thick vertical lines showing momentum vector)
This creates a dynamic "flow field" showing where market pressure is pushing price.
3. REGIME ZONES (Distribution Shift Highlighting):
Boxes drawn around price action during periods when MMD > threshold:
Zone Detection:
• System enters "in_regime" mode when MMD crosses above threshold
• Tracks highest high and lowest low during regime
• Exits "in_regime" when MMD crosses back below threshold
• Draws box from regime_start to current bar, spanning high to low
Zone Colors:
• EXTREME regime: Red with 90% transparency (dangerous)
• SHIFTING regime: Amber with 92% transparency (active)
• Other regimes: Teal with 95% transparency (normal)
Emphasis Boxes:
When regime_shift occurs (MMD crosses above threshold that bar), a special 4-bar wide emphasis box highlights the exact transition moment with thicker borders and lower transparency.
This visual immediately shows "the market just changed" moments.
4. SIGNAL CONNECTION LINES:
Lines connecting consecutive signals to show trade sequences:
Line Types:
• Solid line : Same direction signals (long → long, short → short)
• Dotted line : Reversal signals (long → short or short → long)
Visual Purpose:
• Identify signal clusters (multiple entries same direction)
• Spot reversal patterns (system changing bias)
• See average bars between signals
• Understand system behavior patterns
Connections are limited to signals within 100 bars of each other to avoid across-chart lines.
📈 COMPREHENSIVE DASHBOARD: REAL-TIME SYSTEM STATE
The dashboard provides complete transparency into system internals with three size modes:
MINIMAL MODE:
• Header (Regime + WFO phase)
• Signal Status (LONG READY / SHORT READY / WAITING)
• Core metrics only
COMPACT MODE (Default):
• Everything in Minimal
• Kernel info
• Active bandit arm + validation
• WFO efficiency
• Confluence scores (bull/bear)
• MMD current value
• Position status (if active)
• Performance summary
FULL MODE:
• Everything in Compact
• Signal Quality Diagnostics:
- Bull quality score vs threshold with progress bar
- Bear quality score vs threshold with progress bar
- MMD threshold check (✓/✗)
- MMD percentile (top X% of history)
- Regime fit score (how well current regime suits trading)
- WFO confidence level (validation strength)
- Adaptive cooldown status (bars remaining vs required)
• All Arms Signals:
- Shows all 7 arm signals (▲/▼/○)
- Q-value for each arm
- Indicates selected arm with ◄
• Thompson Sampling Parameters (if TS mode):
- Alpha/Beta values for selected arm
- Probability estimate (α/(α+β))
• Extended Performance:
- Expectancy per trade
- Sharpe ratio with star rating
- Individual arm performance (if enough data)
Key Dashboard Sections:
REGIME: Current market regime (STABLE/TRENDING/SHIFTING/ELEVATED/EXTREME) with color-coded background
SIGNAL STATUS:
• "▲ LONG READY" (cyan) - Long signal qualified
• "▼ SHORT READY" (red) - Short signal qualified
• "○ WAITING" (gray) - No qualified signals
• Signal Mode displayed (Responsive/Transition/Continuous)
KERNEL:
• Active kernel type (RBF/Laplacian/Cauchy/Rational Quadratic)
• Current bandwidth (effective after adaptation)
• Adaptive vs Fixed indicator
• RBF scale (if RBF) or RQ alpha (if RQ)
BANDIT:
• Selection algorithm (UCB1/UCB1-Tuned/Epsilon/Thompson)
• Active arm name (MMD Shift, Trend, Breakout, etc.)
• Validation status (✓ if validated, ? if unproven)
• Pull count (n=XXX) - how many times selected
• Q-Value (×10000 for readability)
• UCB score (exploration + exploitation)
• Train Q vs Test Q comparison
• Test trade count
WFO:
• Current period number
• Progress through period (XX%)
• Efficiency percentage (color-coded: green >80%, yellow 50-80%, red <50%)
• Overfit risk assessment (LOW/MODERATE/HIGH)
• Validated arms count (X/7)
CONFLUENCE:
• Bull score (X/7) with progress bar (███ full, ██ medium, █ low, ○ none)
• Bear score (X/7) with progress bar
• Color-coded: Green/red if ≥ minimum, gray if below
MMD:
• Current value (3 decimals)
• Threshold (2 decimals)
• Ratio (MMD/Threshold × multiplier, e.g. "1.5x" = 50% above threshold)
• Velocity (+/- percentage change) with up/down arrows
POSITION:
• Status: LONG/SHORT/FLAT
• Active indicator (● if active, ○ if flat)
• Bars since entry
• Current P&L percentage (if active)
• P&L direction (▲ profit / ▼ loss)
• R-Multiple (how many Rs: PnL / initial_risk)
PERFORMANCE:
• Total Trades
• Wins (green) / Losses (red) breakdown
• Win Rate % with visual bar and color coding
• Profit Factor (PF) with checkmark if >1.0
• Expectancy % (average profit per trade)
• Sharpe Ratio with star rating (★★★ >2, ★★ >1, ★ >0, ○ negative)
• Max DD % (maximum drawdown) with "Now: X%" showing current drawdown
🔧 KEY PARAMETERS EXPLAINED
Kernel Configuration:
• Kernel Function : RBF / Laplacian / Cauchy / Rational Quadratic
- Start with Cauchy for stability, experiment with others
• Bandwidth (σ) (0.5-10.0, default 2.0): Kernel sensitivity
- Lower: More signals, more false positives (scalping: 0.8-1.5)
- Medium: Balanced (swing: 1.5-3.0)
- Higher: Fewer signals, stronger quality (position: 3.0-8.0)
• Adaptive Bandwidth (default ON): Auto-adjust to volatility
- Keep ON for most markets
• RBF Scale (0.1-2.0, default 0.5): RBF-specific scaling
- Only matters if RBF kernel selected
- Lower = more sensitive (0.3 for scalping)
- Higher = less sensitive (1.0+ for position)
• RQ Alpha (0.5-5.0, default 2.0): Rational Quadratic tail behavior
- Only matters if RQ kernel selected
- Low (0.5-1.0): Heavy tails, robust to outliers (like Cauchy)
- High (3.0-5.0): Light tails, sensitive (like RBF)
Analysis Windows:
• Reference Period (30-500, default 100): Historical baseline
- Scalping: 50-80
- Intraday: 80-150
- Swing: 100-200
- Position: 200-500
• Test Period (5-100, default 20): Recent behavior window
- Should be 15-25% of Reference Period
- Scalping: 10-15
- Intraday: 15-25
- Swing: 20-40
- Position: 30-60
• Sample Size (10-40, default 20): Data points for MMD
- Lower: Faster, less reliable (scalping: 12-15)
- Medium: Balanced (standard: 18-25)
- Higher: Slower, more reliable (position: 25-35)
Walk-Forward Optimization:
• Enable WFO (default ON): Master overfitting protection
- Always ON for live trading
• Training Window (100-2000, default 500): Learning data
- Should be 4-6× Testing Window
- 1m-5m: 300-500
- 15m-1h: 500-800
- 4h-1D: 500-1000
- 1D-1W: 800-2000
• Testing Window (50-500, default 100): Validation data
- Should be 1/5 to 1/4 of Training
- 1m-5m: 50-100
- 15m-1h: 80-150
- 4h-1D: 100-200
- 1D-1W: 150-500
• Min Trades for Validation (5-50, default 10): Statistical threshold
- Active traders: 8-12
- Position traders: 15-30
• Performance Decay (0.8-0.99, default 0.95): Old data forgetting
- Aggressive: 0.85-0.90 (volatile markets)
- Moderate: 0.92-0.96 (most use cases)
- Conservative: 0.97-0.99 (stable markets)
Multi-Armed Bandit:
• Learning Rate (α) (0.01-0.3, default 0.08): Adaptation speed
- Low: 0.01-0.05 (position trading, stable)
- Medium: 0.06-0.12 (day/swing trading)
- High: 0.15-0.30 (scalping, fast adaptation)
• Selection Strategy : UCB1 / UCB1-Tuned / Epsilon-Greedy / Thompson
- UCB1 recommended for most (proven, reliable)
- Thompson for advanced users (best empirical performance)
• Exploration Constant (c) (0.5-3.0, default 1.5): Explore vs exploit
- Low: 0.5-1.0 (conservative, proven strategies)
- Medium: 1.2-1.8 (balanced)
- High: 2.0-3.0 (experimental, volatile markets)
• Epsilon (0.0-0.3, default 0.10): Random exploration (ε-greedy only)
- Only applies if Epsilon-Greedy selected
- Standard: 0.10 (10% random)
Signal Configuration:
• MMD Threshold (0.05-1.0, default 0.15): Distribution divergence trigger
- Low: 0.08-0.12 (scalping, sensitive)
- Medium: 0.12-0.20 (day/swing)
- High: 0.25-0.50 (position, strong signals)
- Stocks/indices: 0.12-0.18
- Forex: 0.15-0.25
- Crypto: 0.20-0.35
• Confluence Filter (default ON): Multi-factor requirement
- Keep ON for quality signals
• Minimum Confluence (1-7, default 2): Factors needed
- Very low: 1 (high frequency)
- Low: 2-3 (active trading)
- Medium: 4-5 (swing)
- High: 6-7 (rare perfect setups)
• Cooldown (1-20, default 5): Bars between signals
- Short: 1-3 (scalping, allows rapid re-entry)
- Medium: 4-7 (day/swing)
- Long: 8-20 (position, ensures development)
• Signal Mode : Responsive / Transition Only / Continuous
- Responsive: Recommended (new + upgrades)
- Transition: Cleanest (first + reversals)
- Continuous: Testing (every qualified bar)
Advanced Signal Control:
• Minimum Signal Strength (30-90, default 60): Quality floor
- Lower: More signals (scalping: 40-50)
- Medium: Balanced (standard: 55-65)
- Higher: Fewer signals (position: 70-80)
• Dynamic MMD Threshold (default ON): Auto-calibration
- Keep ON for adaptive behavior
• Signal Ranking Filter (default ON): Top percentile only
- Keep ON to trade only best signals
• Ranking Percentile (50-95, default 75): Selectivity
- 75 = top 25% of signals
- 85 = top 15% of signals
- 90 = top 10% of signals
• Adaptive Cooldown (default ON): Volatility-scaled spacing
- Keep ON for intelligent spacing
• Regime Filter : Off / Smart / Strict
- Off: Any regime (maximize frequency)
- Smart: Avoid extremes (recommended)
- Strict: Only optimal regimes (maximum quality)
Risk Parameters:
• Risk:Reward Ratio (1.0-5.0, default 2.0): Target distance multiplier
- Conservative: 1.0-1.5 (higher WR needed)
- Balanced: 2.0-2.5 (standard professional)
- Aggressive: 3.0-5.0 (lower WR acceptable)
• Stop Loss (ATR mult) (0.5-4.0, default 1.5): Stop distance
- Tight: 0.5-1.0 (scalping, low vol)
- Medium: 1.2-2.0 (day/swing)
- Wide: 2.5-4.0 (position, high vol)
• Pause After Drawdown (2-20%, default 8%): Circuit breaker
- Aggressive: 3-6% (small accounts)
- Moderate: 6-10% (most traders)
- Relaxed: 10-15% (large accounts)
Multi-Timeframe:
• MTF Confirmation (default OFF): Higher TF filter
- Turn ON for swing/position trading
- Keep OFF for scalping/day trading
• Higher Timeframe (default "60"): HTF for trend check
- Should be 3-5× chart timeframe
- 1m chart → 5m or 15m
- 5m chart → 15m or 60m
- 15m chart → 60m or 240m
- 1h chart → 240m or D
Display:
• Probability Cloud (default ON): Volatility bands
• Momentum Flow Vectors (default ON): Directional arrows
• Regime Zones (default ON): Distribution shift boxes
• Signal Connections (default ON): Lines between signals
• Dashboard (default ON): Stats table
• Dashboard Position : Top Left / Top Right / Bottom Left / Bottom Right
• Dashboard Size : Minimal / Compact / Full
• Color Scheme : Default / Monochrome / Warm / Cool
• Show MMD Debug Plot (default OFF): Overlay MMD value
- Turn ON temporarily for threshold calibration
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Parameter Calibration (Week 1)
Goal: Find optimal kernel and bandwidth for your instrument/timeframe
Setup:
• Enable "Show MMD Debug Plot"
• Start with Cauchy kernel, 2.0 bandwidth
• Run on chart with 500+ bars of history
Actions:
• Watch yellow MMD line vs red threshold line
• Count threshold crossings per 100 bars
• Adjust bandwidth to achieve desired signal frequency:
- Too many crossings (>20): Increase bandwidth (2.5-3.5)
- Too few crossings (<5): Decrease bandwidth (1.2-1.8)
• Try other kernels to see sensitivity differences
• Note: RBF most sensitive, Cauchy most robust
Target: 8-12 threshold crossings per 100 bars for day trading
Phase 2: WFO Validation (Weeks 2-3)
Goal: Verify strategies generalize out-of-sample
Requirements:
• Enable WFO with default settings (500/100)
• Let system run through 2-3 complete WFO cycles
• Accumulate 50+ total trades
Actions:
• Monitor WFO Efficiency in dashboard
• Check which arms validate (green ✓) vs unproven (yellow ?)
• Review Train Q vs Test Q for selected arm
• If efficiency < 0.5: System overfitting, adjust parameters
Red Flags:
• Efficiency consistently <0.4: Serious overfitting
• Zero arms validate after 2 cycles: Windows too short or thresholds too strict
• Selected arm never validates: Investigate arm logic relevance
Phase 3: Signal Quality Tuning (Week 4)
Goal: Optimize confluence and quality thresholds
Requirements:
• Switch dashboard to FULL mode
• Enable all diagnostic displays
• Track signals for 100+ bars
Actions:
• Watch Bull/Bear quality scores in real-time
• Note quality distribution of fired signals (are they all 60-70% or higher?)
• If signal ranking on, check percentile cutoff appropriateness
• Adjust "Minimum Signal Strength" to filter weak setups
• Adjust "Minimum Confluence" if too many/few signals
Optimization:
• If win rate >60%: Lower thresholds (capture more opportunities)
• If win rate <45%: Raise thresholds (improve quality)
• If Profit Factor <1.2: Increase minimum quality by 5-10 points
Phase 4: Regime Awareness (Week 5)
Goal: Understand which regimes work best
Setup:
• Track performance by regime using notes/journal
• Dashboard shows current regime constantly
Actions:
• Note signal quality and outcomes in each regime:
- STABLE: Often weak signals, low confidence
- TRENDING: Trend-following arms dominate
- SHIFTING: Highest signal quality, core opportunity
- ELEVATED: Good signals, moderate success
- EXTREME: Mixed results, high variance
• Adjust Regime Filter based on findings
• If losing in EXTREME consistently: Use "Smart" or "Strict" filter
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate forward performance with minimal capital
Requirements:
• Paper trading shows: WR >45%, PF >1.2, Efficiency >0.6
• Understand why signals fire and why they're blocked
• Comfortable with dashboard interpretation
Setup:
• 10-25% intended position size
• Focus on ML-boosted signals (if any pattern emerges)
• Keep detailed journal with screenshots
Actions:
• Execute every signal the system generates (within reason)
• Compare your P&L to shadow portfolio metrics
• Track divergence between your results and system expectations
• Review weekly: What worked? What failed? Any execution issues?
Red Flags:
• Your WR >20% below paper: Execution problems (slippage, timing)
• Your WR >20% above paper: Lucky streak or parameter mismatch
• Dashboard metrics drift significantly: Market regime changed
Phase 6: Full Scale Deployment (Month 3+)
Goal: Progressively increase to full position sizing
Requirements:
• 30+ micro live trades completed
• Live WR within 15% of paper WR
• Profit Factor >1.0 live
• Max DD <15% live
• Confidence in parameter stability
Progression:
• Months 3-4: 25-50% intended size
• Months 5-6: 50-75% intended size
• Month 7+: 75-100% intended size
Maintenance:
• Weekly dashboard review for metric drift
• Monthly WFO efficiency check (should stay >0.5)
• Quarterly parameter re-optimization if market character shifts
• Annual deep review of arm performance and kernel relevance
Stop/Reduce Rules:
• WR drops >20% from baseline: Reduce to 50%, investigate
• Consecutive losses >12: Reduce to 25%, review parameters
• Drawdown >20%: Stop trading, reassess system fit
• WFO efficiency <0.3 for 2+ periods: System broken, retune completely
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Kernel Discovery:
Early versions used simple moving average crossovers and momentum indicators—they captured obvious moves but missed subtle regime changes. The breakthrough came from reading academic papers on two-sample testing and kernel methods. Applying Maximum Mean Discrepancy to financial returns revealed distribution shifts 10-20 bars before traditional indicators signaled. This edge—knowing the market had fundamentally changed before it was obvious—became the core of KMD.
Testing showed Cauchy kernel outperformed others by 15% win rate in crypto specifically because its heavy tails ignored the massive outlier spikes (liquidation cascades, bot manipulation) that fooled RBF into false signals.
The Seven Arms Revelation:
Originally, the system had one strategy: "Trade when MMD crosses threshold." Performance was inconsistent—great in ranging markets, terrible in trends. The insight: different market structures require different strategies. Creating seven distinct arms based on different market theories (trend-following, mean-reversion, breakout, volume, momentum) and letting them compete solved the problem.
The multi-armed bandit wasn't added as a gimmick—it was the solution to "which strategy should I use right now?" The system discovers the answer automatically through reinforcement learning.
The Thompson Sampling Superiority:
UCB1 worked fine, but Thompson Sampling empirically outperformed it by 8% over 1000+ trades in backtesting. The reason: Thompson's probabilistic selection naturally hedges uncertainty. When two arms have similar Q-values, UCB1 picks one deterministically (whichever has slightly higher exploration bonus). Thompson samples from both distributions, sometimes picking the "worse" one—and often discovering it's actually better in current conditions.
Implementing true Beta distribution sampling (Box-Muller + Marsaglia-Tsang) instead of fake approximations was critical. Fake Thompson (using random with bias) underperformed UCB1. Real Thompson with proper Bayesian updating dominated.
The Walk-Forward Necessity:
Initial backtests showed 65% win rate across 5000 trades. Live trading: 38% win rate over first 100 trades. Crushing disappointment. The problem: overfitting. The training data included the test data (look-ahead bias). Implementing proper walk-forward optimization with out-of-sample validation dropped backtest win rate to 51%—but live performance matched at 49%. That's a system you can trust.
WFO efficiency metric became the North Star. If efficiency >0.7, live results track paper. If efficiency <0.5, prepare for disappointment.
The Confluence Complexity:
First signals were simple: "MMD high + arm agrees." This generated 200+ signals on 1000 bars with 42% win rate—not tradeable. Adding confluence (must have trend + volume + structure + RSI) reduced signals to 40 with 58% win rate. The math clicked: fewer, better signals outperform many mediocre signals .
The weighted system (20pt critical factors, 15pt high-impact, 10pt moderate/bonus) emerged from analyzing which factors best predicted wins. Bandit arm alignment and MMD quality were 2-3× more predictive than RSI or divergence, so they got 2× the weight. This isn't arbitrary—it's data-driven.
The Dynamic Threshold Insight:
Fixed MMD threshold failed across different market conditions. 0.15 worked perfectly on ES but fired constantly on Bitcoin. The adaptive threshold (scaling with recent MMD mean + stdev) auto-calibrated to instrument volatility. This single change made the system deployable across forex, crypto, stocks without manual tuning per instrument.
The Signal Mode Evolution:
Originally, every qualified bar showed a triangle. Charts became unusable—dozens of stacked triangles during trending regimes. "Transition Only" mode cleaned this up but missed re-entries when quality spiked mid-regime. "Responsive" mode emerged as the optimal balance: show fresh qualifications, reversals, AND significant quality improvements (25%+) during extreme regimes. This captures the signal intent ("something important just happened") without chart pollution.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : KMD doesn't forecast prices. It identifies when the current distribution differs from historical baseline, suggesting regime transition—but not direction or magnitude.
• NOT Holy Grail : Typical performance is 48-56% win rate with 1.3-1.8 avg R-multiple. This is a probabilistic edge, not certainty. Expect losing streaks of 8-12 trades.
• NOT Universal : Performs best on liquid, auction-driven markets (futures, major forex, large-cap stocks, BTC/ETH). Struggles with illiquid instruments, thin order books, heavily manipulated markets.
• NOT Hands-Off : Requires monitoring for news events, earnings, central bank announcements. MMD cannot detect "Fed meeting in 2 hours" or "CEO stepping down"—it only sees statistical patterns.
• NOT Immune to Regime Persistence : WFO helps but cannot predict black swans or fundamental market structure changes (pandemic, war, regulatory overhaul). During these events, all historical patterns may break.
Core Assumptions:
1. Return Distributions Exhibit Clustering : Markets alternate between relatively stable distributional regimes. Violation: Permanent random walk, no regime structure.
2. Distribution Changes Precede Price Moves : Statistical divergence appears before obvious technical signals. Violation: Instantaneous regime flips (gaps, news), no statistical warning.
3. Volume Reflects Real Activity : Volume-based confluence assumes genuine participation. Violation: Wash trading, spoofing, exchange manipulation (common in crypto).
4. Past Arm Performance Predicts Future Arm Performance : The bandit learns from history. Violation: Fundamental strategy regime change (e.g., market transitions from mean-reverting to trending permanently).
5. ATR-Based Stops Are Rational : Volatility-normalized risk management avoids premature exits. Violation: Flash crashes, liquidity gaps, stop hunts precisely targeting ATR multiples.
6. Kernel Similarity Maps to Economic Similarity : Mathematical similarity (via kernel) correlates with economic similarity (regime). Violation: Distributions match by chance while fundamentals differ completely.
Performs Best On:
• ES, NQ, RTY (S&P 500, Nasdaq, Russell 2000 futures)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY, AUD/USD
• Liquid commodities: CL (crude oil), GC (gold), SI (silver)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M avg daily volume)
• Major crypto on reputable exchanges: BTC, ETH (Coinbase, Kraken)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume)
• Exotic forex pairs with erratic spreads
• Illiquid crypto altcoins (manipulation, unreliable volume)
• Pre-market/after-hours (thin liquidity, gaps)
• Instruments with frequent corporate actions (splits, dividends)
• Markets with persistent one-sided intervention (central bank pegs)
Known Weaknesses:
• Lag During Instantaneous Shifts : MMD requires (test_window) bars to detect regime change. Fast-moving events (5-10 bar crashes) may bypass detection entirely.
• False Positives in Choppy Consolidation : Low-volatility range-bound markets can trigger false MMD spikes from random noise crossing threshold. Regime filter helps but doesn't eliminate.
• Parameter Sensitivity : Small bandwidth changes (2.0→2.5) can alter signal frequency by 30-50%. Requires careful calibration per instrument.
• Bandit Convergence Time : MAB needs 50-100 trades per arm to reliably learn Q-values. Early trades (first 200 bars) are essentially random exploration.
• WFO Warmup Drag : First WFO cycle has no validation data, so all arms start unvalidated. System may trade rarely or conservatively for first 500-600 bars until sufficient test data accumulates.
• Visual Overload : With all display options enabled (cloud, vectors, zones, connections), chart can become cluttered. Disable selectively for cleaner view.
⚠️ 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 Kernel Market Dynamics system, including its multi-armed bandit and walk-forward optimization components, is provided for educational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The adaptive learning algorithms optimize based on historical data—there is no guarantee that learned strategies will remain profitable or that kernel-detected regime changes will lead to profitable trades. Market conditions change, correlations break, and distributional regimes shift in ways that historical data cannot predict. Black swan events occur.
Walk-forward optimization reduces but does not eliminate overfitting risk. WFO efficiency metrics indicate likelihood of forward performance but cannot guarantee it. A system showing high efficiency on one dataset may show low efficiency on another timeframe or instrument.
The dashboard shadow portfolio simulates trades under idealized conditions: instant fills, no slippage, no commissions, perfect execution. Real trading involves slippage (often 1-3 ticks per trade), commissions, latency, partial fills, rejected orders, requotes, and liquidity constraints that significantly reduce performance below simulated results.
Maximum Mean Discrepancy is a statistical distance metric—high MMD indicates distribution divergence but does not indicate direction, magnitude, duration, or profitability of subsequent moves. MMD can spike during sideways chop, producing signals with no directional follow-through.
Users must independently validate system performance on their specific instruments, timeframes, broker execution, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 trades) and start with micro position sizing (10-25% 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 (1-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they systematize decision-making but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any particular purpose. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read and understood these risk disclosures and accept full responsibility for all trading activity and potential losses.
📁 SUGGESTED TRADINGVIEW CATEGORIES
PRIMARY CATEGORY: Statistics
The Kernel Market Dynamics system is fundamentally a statistical learning framework . At its core lies Maximum Mean Discrepancy—an advanced two-sample statistical test from the academic machine learning literature. The indicator compares probability distributions using kernel methods (RBF, Laplacian, Cauchy, Rational Quadratic) that map data to high-dimensional feature spaces for nonlinear similarity measurement.
The multi-armed bandit framework implements reinforcement learning via Q-learning with exponential moving average updates. Thompson Sampling uses true Bayesian inference with Beta posterior distributions. Walk-forward optimization performs rigorous out-of-sample statistical validation with train/test splits and efficiency metrics that detect overfitting.
The confluence system aggregates multiple statistical indicators (RSI, ADX, OBV, Z-scores, EMAs) with weighted scoring that produces a 0-100 quality metric. Signal ranking uses percentile-based filtering on historical quality distributions. The dashboard displays comprehensive statistics: win rates, profit factors, Sharpe ratios, expectancy, drawdowns—all computed from trade return distributions.
This is advanced statistical analysis applied to trading: distribution comparison, kernel methods, reinforcement learning, Bayesian inference, hypothesis testing, and performance analytics. The statistical sophistication distinguishes KMD from simple technical indicators.
SECONDARY CATEGORY: Volume
Volume analysis plays a crucial role in KMD's signal generation and validation. The confluence system includes volume confirmation as a high-impact factor (15 points): signals require above-average volume (>1.2× mean) for full points, with scaling based on volume ratio. The OBV (On-Balance Volume) trend indicator determines directional bias for Arm 6 (Volume Confirmation strategy).
Volume ratio (current / 20-period average) directly affects confluence scores—higher volume strengthens signal quality. The momentum flow vectors scale width and opacity based on volume momentum relative to average. Energy particle visualization specifically marks volume burst events (>2× average volume) as potential market-moving catalysts.
Several bandit arms explicitly incorporate volume:
• Arm 2 (Breakout): Requires volume confirmation for Bollinger Band breaks
• Arm 6 (Volume Confirmation): Primary logic based on OBV trend + volume spike
The system recognizes volume as the "conviction" behind price moves—distribution changes matter more when accompanied by significant volume, indicating genuine participant behavior rather than noise. This volume-aware filtering improves signal reliability in liquid markets.
TERTIARY CATEGORY: Volatility
Volatility measurement and adaptation permeate the KMD system. ATR (Average True Range) forms the basis for all risk management: stops are placed at ATR × multiplier, targets are scaled accordingly. The adaptive bandwidth feature scales kernel bandwidth (0.5-2.0×) inversely with volatility—tightening during calm markets, widening during volatile periods.
The probability cloud (primary visual element) directly visualizes volatility: bands expand/contract based on (1 + MMD × 3) multiplier applied to ATR. Higher MMD (distribution divergence) + higher ATR = dramatically wider uncertainty bands.
Adaptive cooldown scales minimum bars between signals based on ATR percentage: higher volatility = longer cooldown (up to 3× base), preventing overtrading during whipsaw conditions. The gamma parameter in the tensor calculation (from related indicators) and volatility ratio measurements influence MMD sensitivity.
Regime classification incorporates volatility metrics: high volatility with ranging price action produces "RANGE⚡" regime, while volatility expansion with directional movement produces trending regimes. The system adapts its behavior to volatility regimes—tighter requirements during extreme volatility, looser requirements during stable periods.
ATR-based risk management ensures position sizing and exit levels automatically adapt to instrument volatility, making the system deployable across instruments with different average volatilities (stocks vs crypto) without manual recalibration.
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CLOSING STATEMENT
══════════════════════════════════════════
Kernel Market Dynamics doesn't just measure price—it measures the probability structure underlying price. It doesn't just pick one strategy—it learns which strategies work in which conditions. It doesn't just optimize on history—it validates on the future.
This is machine learning applied correctly to trading: not curve-fitting oscillators to maximize backtest profit, but implementing genuine statistical learning algorithms (kernel methods, multi-armed bandits, Bayesian inference) that adapt to market evolution while protecting against overfitting through rigorous walk-forward testing.
The seven arms compete. The Thompson sampler selects. The kernel measures. The confluence scores. The walk-forward validates. The signals fire.
Most indicators tell you what happened. KMD tells you when the game changed.
"In the space between distributions, where the kernel measures divergence and the bandit learns from consequence—there, edge exists." — KMD-WFO-MAB v2
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
ค้นหาในสคริปต์สำหรับ "bear"
Smart RSI Composite [DotGain]Summary
Do you want to know the "True Direction" of the market without getting distracted by noise on a single timeframe?
The Smart RSI Composite simplifies market analysis by aggregating momentum data from 10 different timeframes (5m to 12M) into a single, easy-to-read Histogram.
Instead of looking at 10 separate charts or dots, this indicator calculates the Average RSI of the entire market structure. It answers one simple question: "Is the market predominantly Bullish or Bearish right now?"
⚙️ Core Components and Logic
This indicator works like a consensus mechanism for momentum:
Data Aggregation: It pulls RSI values from 10 customizable slots (Default: 5m, 15m, 1h, 4h, 1D, 1W, 1M, 3M, 6M, 12M). All slots are enabled by default.
Smart Averaging: It calculates the arithmetic mean of all active timeframes. If the 5m chart is bearish but the Monthly chart is bullish, this indicator balances them out to show you the net result.
Histogram Visualization: The result is plotted as a histogram centered around the 50-line (Neutral).
🚦 How to Read the Histogram
The histogram bars indicate the aggregate strength of the trend based on the Average RSI:
🟩 DARK GREEN (Strong Bullish)
Condition: Average RSI > 60.
Meaning: The market is in a strong uptrend across most timeframes. Momentum is firmly on the buyers' side.
🟢 LIGHT GREEN (Weak Bullish)
Condition: Average RSI between 50 and 60.
Meaning: Slight bullish bias. The bulls are in control, but momentum is not yet extreme.
🔴 LIGHT RED (Weak Bearish)
Condition: Average RSI between 40 and 50.
Meaning: Slight bearish bias. The bears are taking control.
🟥 DARK RED (Strong Bearish)
Condition: Average RSI < 40.
Meaning: The market is in a strong downtrend across most timeframes. Momentum is firmly on the sellers' side.
Visual Elements
Center Line (50): This acts as the Zero-Line. Above 50 is bullish, below 50 is bearish.
Zone Lines (30/70): Dashed lines indicate the traditional Overbought/Oversold levels applied to the aggregate average.
Key Benefit
The Smart RSI Composite acts as a powerful Macro Trend Filter .
Pro Tip: Never go long if the Histogram is Dark Red, and avoid shorting when it is Dark Green. Use this tool to align your trades with the overall market momentum.
Have fun :)
Disclaimer
This "Smart RSI Composite" indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice.
The signals generated by this tool (both "Buy" and "Sell" indications) are the result of a specific set of algorithmic conditions. They are not a direct recommendation to buy or sell any asset. All trading and investing in financial markets involves substantial risk of loss. You can lose all of your invested capital.
Past performance is not indicative of future results. The signals generated may produce false or losing trades. The creator (© DotGain) assumes no liability for any financial losses or damages you may incur as a result of using this indicator.
You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR) and consider your personal risk tolerance before making any trades.
Patrice - GC M1 Bot (MACD EMA RSI)//@version=6
indicator("Patrice - GC M1 Bot (MACD EMA RSI)", overlay = true)
//----------------------
// Inputs (optimisés GC)
//----------------------
emaLenFast = input.int(9, "EMA rapide")
emaLenSlow = input.int(14, "EMA lente")
rsiLen = input.int(14, "RSI length")
atrLen = input.int(14, "ATR length")
volLen = input.int(20, "Volume moyenne")
slMult = input.float(0.4, "SL = ATR x", step = 0.1)
tpMult = input.float(0.7, "TP = ATR x", step = 0.1)
minAtr = input.float(0.7, "ATR minimum pour trader", step = 0.1)
maxDistEmaPct = input.float(0.3, "Distance max EMA9 (%)", step = 0.1)
//----------------------
// Indicateurs
//----------------------
ema9 = ta.ema(close, emaLenFast)
ema14 = ta.ema(close, emaLenSlow)
= ta.macd(close, 12, 26, 9)
hist = macdLine - signalLine
rsi = ta.rsi(close, rsiLen)
atr = ta.atr(atrLen)
volMa = ta.sma(volume, volLen)
//----------------------
// Session 9:30 - 11:00 (NY)
//----------------------
hourSession = hour(time, "America/New_York")
minuteSession = minute(time, "America/New_York")
inSession = (hourSession == 9 and minuteSession >= 30) or
(hourSession > 9 and hourSession < 11) or
(hourSession == 11 and minuteSession == 0)
//----------------------
// Filtres vol / ATR / distance EMA
//----------------------
volFilter = volume > volMa
atrFilter = atr > minAtr
distEmaPct = math.abs(close - ema9) / close * 100.0
distFilter = distEmaPct < maxDistEmaPct
//----------------------
// Tendance
//----------------------
bullTrend = close > ema9 and close > ema14 and ema9 > ema14
bearTrend = close < ema9 and close < ema14 and ema9 < ema14
//----------------------
// MACD : 2e barre
//----------------------
bullSecondBar = hist > 0 and hist > 0 and hist <= 0
bearSecondBar = hist < 0 and hist < 0 and hist >= 0
//----------------------
// Filtres RSI
//----------------------
rsiLongOk = rsi < 70 and rsi >= 45 and rsi <= 65
rsiShortOk = rsi > 30 and rsi >= 35 and rsi <= 55
//----------------------
// Gestion du risque (simple pour l'instant)
//----------------------
canTradeRisk = true
//----------------------
// Conditions d'entrée
//----------------------
longCond = bullTrend and bullSecondBar and rsiLongOk and inSession and volFilter and atrFilter and distFilter and canTradeRisk
shortCond = bearTrend and bearSecondBar and rsiShortOk and inSession and volFilter and atrFilter and distFilter and canTradeRisk
//----------------------
// SL / TP (info seulement, pas d'ordres)
//----------------------
slPoints = atr * slMult
tpPoints = atr * tpMult
longSL = close - slPoints
longTP = close + tpPoints
shortSL = close + slPoints
shortTP = close - tpPoints
//----------------------
// Visuels
//----------------------
plot(ema9, title = "EMA 9")
plot(ema14, title = "EMA 14")
plotshape(longCond, title = "Signal Long", style = shape.triangleup, location = location.belowbar, size = size.tiny, text = "L")
plotshape(shortCond, title = "Signal Short", style = shape.triangledown, location = location.abovebar, size = size.tiny, text = "S")
//----------------------
// Conditions d'ALERTE
//----------------------
alertcondition(longCond, title = "ALERTE LONG", message = "Signal LONG Patrice GC bot")
alertcondition(shortCond, title = "ALERTE SHORT", message = "Signal SHORT Patrice GC bot")
Super momentum DBSISuper momentum DBSI: The Ultimate Guide
1. What is this Indicator?
The Super momentum DBSI is a "Consensus Engine." Instead of relying on a single line (like an RSI) to tell you where the market is going, this tool calculates 33 distinct technical indicators simultaneously for every single candle.
It treats the market like a democracy. It asks 33 mathematical "voters" (Momentum, Trend, Volume, Volatility) if they are Bullish or Bearish.
If 30 out of 33 say "Buy," the score is high (Yellow), and the trend is extremely strong.
If only 15 say "Buy," the score is low (Teal), and the trend is weak or choppy.
2. Visual Guide: How to Read the Numbers
The Scores
Top Number (Bears): Represents Selling Pressure.
Bottom Number (Bulls): Represents Buying Pressure.
The Colors (The Traffic Lights)
The colors are your primary signal. They tell you who is currently winning the war.
🟡 YELLOW (Dominance):
This indicates the Winning Side.
If the Bottom Number is Yellow, Bulls are in control.
If the Top Number is Yellow, Bears are in control.
🔴 RED (Weakness):
This appears on the Top. It means Bears are present but losing.
🔵 TEAL (Weakness):
This appears on the Bottom. It means Bulls are present but losing.
3. Trading Strategy
Scenario A: The "Strong Buy" (Long Entry)
The Setup: You are looking for a shift in momentum where Buyers overwhelm Sellers.
Watch the Bottom Number: Wait for it to turn Yellow.
Confirm Strength: Ensure the score is above 15 and rising (e.g., 12 → 18 → 22).
Check the Top: The Top Number should be Red and low (below 10).
Trigger: Enter on the candle close.
Scenario B: The "Strong Sell" (Short Entry)
The Setup: You are looking for Sellers to crush the Buyers.
Watch the Top Number: Wait for it to turn Yellow.
Confirm Strength: Ensure the score is above 15 and rising.
Check the Bottom: The Bottom Number should be Teal and low.
Trigger: Enter on the candle close.
Scenario C: The "No Trade Zone" (Choppy Market)
The Setup: The market is confused.
Visual: Top is Red, Bottom is Teal.
Meaning: NOBODY IS WINNING. There is no Yellow number.
Action: Do not trade. This usually happens during lunch hours, weekends, or right before big news. This filter alone will save you from many false breakouts.
4. What is Inside? (The 33 Indicators)
To give you confidence in the signals, here is exactly what the script is checking:
Group 1: Momentum (Oscillators)
Detects if price is moving fast.
RSI (Relative Strength Index)
CCI (Commodity Channel Index)
Stochastic
Williams %R
Momentum
Rate of Change (ROC)
Ultimate Oscillator
Awesome Oscillator
True Strength Index (TSI)
Stoch RSI
TRIX
Chande Momentum Oscillator
Group 2: Trend Direction
Detects the general path of the market.
13. MACD
14. Parabolic SAR
15. SuperTrend
16. ALMA (Moving Average)
17. Aroon
18. ADX (Directional Movement)
19. Coppock Curve
20. Ichimoku Conversion Line
21. Hull Moving Average
Group 3: Price Action
Detects where price is relative to averages.
22. Price vs EMA 20
23. Price vs EMA 50
24. Price vs EMA 200
Group 4: Volume & Force
Detects if there is money behind the move.
25. Money Flow Index (MFI)
26. On Balance Volume (OBV)
27. Chaikin Money Flow (CMF)
28. VWAP (Intraday)
29. Elder Force Index
30. Ease of Movement
Group 5: Volatility
Detects if price is pushing the outer limits.
31. Bollinger Bands
32. Keltner Channels
33. Donchian Channels
5. Pro Tips for Success
Don't Catch Knives: If the Bear score (Top) is Yellow and 25+, do not try to buy the dip. Wait for the Yellow score to break.
Exit Early: If you are Long and the Yellow Bull score drops from 28 to 15 in one candle, TAKE PROFIT. The momentum has died.
Use Higher Timeframes: This indicator works best on 15m, 1H, and 4H charts. On the 1m chart, it may be too volatile.
Directional Imbalance Index [BigBeluga]🔵 OVERVIEW
The Directional Imbalance Index is designed to track market strength by counting how often price sets new highs or lows over a defined lookback period. Every time a bar forms a new extreme, the indicator records a +1 count for either bullish (highs) or bearish (lows). These counts are aggregated into a rolling calculation, allowing traders to see which side dominates and how directional imbalance evolves.
🔵 CONCEPTS
Each new highest high → adds a bullish count (+1).
Each new lowest low → adds a bearish count (+1).
Counts are stored inside arrays over a user-defined Calculation Period .
for i = 0 to period-1
h = high
l = low
if h == upper
countUp.push(1)
if l == lower
countDn.push(1)
The balance between bullish and bearish counts highlights dominance and imbalance.
Normalized percentages help compare both sides (e.g., 65% bullish vs 35% bearish).
🔵 FEATURES
Counts new highs/lows over a chosen Highest/Lowest Length .
Aggregates values over a rolling Calculation Period .
Plots cumulative bullish vs bearish totals in the subchart.
Displays % share of bulls vs bears from total counts.
On-chart labels mark bars where a count was added.
Plots reference lines of the current upper (high) and lower (low) ranges.
Dynamic fill between bullish/bearish plots to visualize which side dominates.
🔵 HOW TO USE
Look for persistent bullish imbalance (bull % > bear %) as confirmation of upward momentum.
Look for persistent bearish imbalance (bear % > bull %) as confirmation of downward momentum.
Watch for shifts in % dominance — often early signs of trend reversal or weakening strength.
Use labels on the chart to visually confirm which bars contributed to directional bias.
Combine with trend or volume tools to confirm whether imbalance aligns with market direction.
🔵 CONCLUSION
The Directional Imbalance Index offers a systematic way to measure directional pressure. By counting how often price pushes into new territory, the indicator reveals whether bulls or bears are taking control. This makes it a valuable tool for detecting early signs of trend continuation or exhaustion, helping traders align with the side most likely to dominate.
DTC - 1.3.6 DTC Trading ClubDTC – 1.3.6 | Advanced Multi-Timeframe Trend & Algo Trading System
Version: 1.3.6
Platform: TradingView
Category: Trend Following | Algo Automation | EMA-Based System
⚙️ 1. Algo Trading Integration (NEW FEATURE)
The DTC 1.3.6 system bridges TradingView charts and MT4/MT5 execution through PineConnector for full trade automation.
This allows signals generated by the indicator to instantly execute trades on your broker account using your unique Trading UID.
Purpose & Justification:
Many traders rely on chart-based strategies but struggle with delayed manual execution. The Algo module removes this limitation by automating execution directly from the chart while retaining full user control. This makes it particularly useful for systematic traders who want precision, consistency, and hands-off operation.
Key Features:
🔑 Trading UID Input: Enter your PineConnector UID (e.g., 88888999) for authenticated trade routing.
📊 Auto-Formatted Trade Message: All signals are converted into PineConnector-ready format:
88888999, buy, EURUSD, risk=0.01, comment=DTCAlgoMT4/MT5
⚙️ Full Customization: Users decide every aspect of execution — direction, position risk, TP/SL handling, signal triggers, comments, and automation level.
Practical Example:
If a Bullish EMA alignment occurs on EURUSD while the ATR filter confirms valid volatility, the Algo instantly sends a buy order to MT4/MT5 with all trade parameters defined in TradingView — enabling disciplined, automated execution without delay.
Requirements:
TradingView paid plan (for webhook automation)
PineConnector account (free tier supported)
Once connected, the DTC system becomes a fully autonomous trading solution, handling entries, exits, and TP/SL management in real-time.
📈 2. Multi-Timeframe Trend Dashboard
This feature provides an at-a-glance overview of market bias across key timeframes (1H–Monthly), powered by the EMA 30 vs EMA 60 trend structure.
Purpose & Justification:
Instead of switching between multiple charts, traders can instantly see the dominant market direction across all major timeframes. This prevents counter-trend trades and encourages entries that align with broader market momentum.
Dashboard Highlights:
Customizable position (top/bottom left/right)
Adjustable text size for clarity
Displays active trend for 1H, 4H, Daily, Weekly, and Monthly
Shows Unrealized PnL of the current open position
Displays ATR Filter Status (Active/Inactive with color coding)
Practical Example:
A trader sees a Bullish signal on the 1H chart, but the dashboard shows Bearish trends on higher timeframes — indicating possible short-term pullback rather than a trend reversal. This cross-timeframe awareness improves decision quality.
💹 3. EMA Trend System
At the core of DTC lies a 6-layer EMA engine (30–60) designed to identify strong, sustained market trends with minimal lag.
Logic:
Bullish: EMA 30 > 35 > 40 > 45 > 50 > 60
Bearish: EMA 30 < 35 < 40 < 45 < 50 < 60
Purpose & Justification:
Unlike simple two-EMA systems, this multi-layer method filters out temporary volatility and confirms trend stability before triggering signals. It forms the foundation upon which all other modules (signals, dashboard, algo, and volatility filter) operate.
🚦 4. Buy/Sell Signal Engine
Signals are automatically generated when EMA layers achieve full alignment — a clear confirmation of market direction change.
Conditions:
🟢 Buy Signal: Bearish/Neutral → Bullish alignment
🔴 Sell Signal: Bullish/Neutral → Bearish alignment
Signals appear as chart markers and can trigger PineConnector automation.
Purpose & Justification:
This systematic approach removes emotional decision-making and allows traders to execute only when objective technical conditions are met. It ensures that trades align with verified EMA-based momentum, not short-term price noise.
🎯 5. Dynamic TP/SL and Entry Visualization
Every trade signal comes with a structured plan:
Entry, Stop-Loss, and seven Take-Profit levels
Accurate price labels and color-coded zones
Customizable SL modes: Tiny, Small, Mid, or Large
Purpose & Justification:
Visual clarity is vital for risk control. The indicator presents all levels directly on the chart, so traders instantly see potential reward-to-risk scenarios and manage trades with confidence. These levels also integrate seamlessly with PineConnector automation for direct execution.
📊 6. ATR Volatility Filter
The ATR (Average True Range) module filters low-quality setups during flat or choppy markets.
Mechanics:
Adjustable ATR period and multiplier
Filters weak signals when volatility drops below threshold
Purpose & Justification:
ATR adds a volatility-based confirmation layer to the EMA logic. For instance, if EMAs align but ATR volatility is below the active threshold, no trade is executed — reducing false signals during quiet sessions. When ATR exceeds the threshold, trades are allowed, improving accuracy and overall system efficiency.
Example:
During a ranging session on GBPUSD, EMAs may align briefly, but low ATR prevents a false breakout entry. Once volatility returns, the same logic allows a valid trade — demonstrating how ATR integrates into the system to maintain trade quality.
🎨 7. EMA Cloud Visualization
The EMA Cloud fills the area between EMA 30 and EMA 60 to visualize momentum strength and transitions.
🟩 Green cloud → Bullish bias
🟥 Red cloud → Bearish bias
Purpose & Justification:
This feature provides immediate visual cues for traders to identify trend continuation or potential reversal zones. It reinforces the EMA Trend System, ensuring that market structure and momentum remain visually synchronized.
🧭 8. Full Customization Control
DTC 1.3.6 allows traders to tailor every aspect of their experience:
Enable/disable components such as Algo, Dashboard, TP/SL, and Cloud
Modify color themes, layouts, and text sizes
Adjust to suit manual or automated trading preferences
Purpose & Justification:
Different traders have different workflows. By allowing total flexibility, DTC can adapt to short-term scalping, swing trading, or long-term automation setups without performance compromise.
✅ Summary
DTC 1.3.6 is a professional-grade, multi-layer trading system that integrates EMA-based trend detection, volatility filtering, dashboard visualization, and algo automation into one cohesive tool.
Each module contributes to a unified goal — identifying high-probability market conditions, confirming them through volatility, and executing with precision.
This system is Invite-Only because it represents a complete, ready-to-deploy professional framework — not a single indicator — and requires responsible use of automation features that directly impact live trading accounts.
DTC = Smart Visuals + Total Control + True Automation.
⚠️ Disclaimer
This indicator is for educational and research purposes only.
The DTC Team, developers, and affiliates do not provide financial advice and assume no responsibility for profits or losses from its use.
Trading involves substantial risk.
Past performance is not indicative of future results.
Users should test thoroughly on demo accounts, perform independent analysis, and consult a licensed financial advisor before live trading.
By using this tool, you acknowledge that all actions are taken at your own risk and that the DTC Team bears no liability for any outcomes.
ICS🏛️ Institutional Confluence Suite (ICS) Indicator
The Institutional Confluence Suite is a powerful and highly customizable TradingView indicator built to help traders identify key institutional trading concepts across multiple timeframes. It visualizes essential market components like Market Structures (MS), Order Blocks (OB)/Breaker Blocks (BB), Liquidity Zones, and Volume Profile, providing a confluence of institutional price action data.
📈 Key Features & Components
1. Market Structures (MS)
Purpose: Automatically identifies and labels shifts in market trends (Market Structure Shift, MSS) and continuations (Break of Structure, BOS).
Timeframe Detection: You can select detection across Short Term, Intermediate Term, or Long Term swings to match your trading horizon.
Visualization: Plots colored lines (Bullish: Teal, Bearish: Red) to mark the structures and optional text labels (BOS/MSS) for clear identification.
2. Order & Breaker Blocks (OB/BB)
Purpose: Detects and projects potential Supply and Demand zones based on recent price action that led to a swing high or low.
Block Types: Distinguishes between standard Order Blocks and Breaker Blocks (OBs that fail to hold and are traded through, often serving as support/resistance in the opposite direction).
Customization:
Detection Term: Adjusts sensitivity (Short, Intermediate, Long Term).
Display Limit: Sets the maximum number of recent Bullish and Bearish blocks to display.
Price Reference: Option to use the Candle Body (Open/Close) or Candle Wicks (High/Low) to define the block boundaries.
Visualization: Displays blocks as colored boxes (Bullish: Green, Bearish: Red) extending into the future, with a dotted line marking the 50% equilibrium level. Breaker Blocks are indicated by a change in color/line style upon being broken.
3. Buyside & Sellside Liquidity (BSL/SSL)
Purpose: Highlights areas where retail stops/limit orders are likely clustered, often represented by a series of relatively equal highs (Buyside Liquidity) or lows (Sellside Liquidity).
Detection Term: Adjustable sensitivity (Short, Intermediate, Long Term).
Margin: Uses a margin (derived from ATR) to group similar swing points into a single liquidity zone.
Visualization: Plots a line and text label marking the swing point, and a box indicating the clustered liquidity zone.
4. Liquidity Voids (LV) / Fair Value Gaps (FVG)
Purpose: Identifies areas where price moved sharply and inefficiency was created, often referred to as Fair Value Gaps or Imbalances. These are price ranges where minimal trading volume occurred.
Threshold: Uses a multiplier applied to the 200-period ATR to filter for significant gaps.
Mode: Can be set to Present (only show voids near the current price) or Historical (show all detected voids).
Visualization: Fills the price gap with colored boxes (Bullish/Bearish zones), often segmented to represent the price delivery across the gap.
5. Enhanced Liquidity Detection
Purpose: A complementary feature that uses volume and price action to highlight areas of high liquidity turnover, potentially indicating stronger Support and Resistance zones.
Calculation: Utilizes a volume-weighted approach to color-grade liquidity zones based on their significance.
Visualization: Plots shaded boxes (gradient-colored) around swing highs/lows, with text displaying the normalized volume strength.
6. Swing Highs/Lows
Purpose: Directly marks the price points identified as Swing Highs and Swing Lows based on the lookback periods.
Timeframe Detection: Can be enabled for Short Term, Intermediate Term, or Long Term swings.
Visualization: Plots a small colored dot/label (e.g., "⦁") at the swing point.
This indicator is an invaluable tool for traders employing ICT (Inner Circle Trader), Smart Money Concepts (SMC), or general price action strategies, as it automatically aggregates and displays these critical structural and liquidity elements.
LE ScannerGENERAL OVERVIEW:
The LE Scanner is a multi-ticker dashboard that scans up to 20 tickers in real time and displays their current trend, price, volume, and key level conditions directly on your chart. It tracks how each ticker interacts with both the Previous Day’s High/Low (PDH/PDL) and Pre-Market High/Low (PMH/PML) to determine whether price is breaking above, below, or remaining inside those levels. The indicator automatically classifies each ticker as Bullish, Bearish, or Neutral based on these break conditions.
This indicator was developed by Flux Charts in collaboration with Ellis Dillinger (Ellydtrades).
What is the purpose of the indicator?:
The LE Scanner helps traders keep track of up to 20 tickers at once without switching between charts. It puts all the key information in one place, including price, daily percentage change, volume, and how each ticker is reacting around the previous day’s and pre-market highs and lows. The layout is simple and easy to read, with progress bars that show where price is relative to those levels. The goal is to save time and make it easier to understand market strength and weakness across your watchlist.
What’s the theory behind the indicator?:
The LE Scanner is built around the idea that key levels define bias. The previous day’s high and low show where the market traded most actively during the prior session, and the pre-market range reveals how price behaved before the open. When a ticker breaks both the previous day’s high and the pre-market high, it shows that buyers are in control. When it breaks both the previous day’s low and the pre-market low, sellers are in control. If neither side has full control, the bias is seen as neutral.
LE SCANNER FEATURES:
Multi-Ticker Dashboard
Key Level Tracking
Trend Classification
Sorting
Customization
Multi-Ticker Dashboard:
The LE Scanner can monitor up to 20 tickers at the same time. Each ticker has its own row in the dashboard showing:
Ticker Name
Current Price
Volume
Daily % Change
PDH Break
PDL Break
PMH Break
PML Break
Trend (bullish, bearish, or neutral)
You can enable or disable each ticker individually, so if you only want to track 5 or 10 tickers, you can simply toggle the rest off. Each ticker input lets you type in any valid ticker that’s available on TradingView.
Ticker Name:
Shows the ticker you selected in your input settings
Current Price:
Displays the latest price of that ticker based on your chart’s selected timeframe.
Volume:
Tracks the total trading volume for the current session.
Daily % Change:
Measures how much price has moved since the previous session’s close.
The remaining elements of the dashboard are explained in full detail throughout the remaining sections of this write-up.
Key Level Tracking:
The core of the LE Scanner is its ability to track and visualize how price interacts with four key levels for every ticker:
Previous Day High (PDH)
Previous Day Low (PDL)
Pre-Market High (PMH)
Pre-Market Low (PML)
These levels are updated automatically and compared to the current market price for each ticker inputted into the indicator. They show you whether the market is staying inside yesterday’s range or expanding beyond it.
🔹Previous Day High (PDH) & Previous Day Low (PDL)
The Previous Day High (PDH) marks where price reached its highest point during the last full trading session, while the Previous Day Low (PDL) marks the lowest point. Together, they define the previous day’s range and help traders understand where price is trading relative to that prior structure.
When the current price of a user-selected ticker moves above the PDH, it signals that buyers are taking control and that the ticker is now trading above yesterday’s range. In the dashboard, this change triggers a 🟢 icon under the “PDH Break” column. Once the PDH Break is confirmed, the opposite PDL Break column for that same ticker becomes blank.
When the current price of the user-selected ticker moves below the PDL, it shows that sellers are taking control and that the ticker is trading below yesterday’s range. In the dashboard, this change triggers a 🔴 icon under the “PDL Break” column. Once the PDL Break is confirmed, the opposite PDH Break column for that same ticker becomes blank.
🔹 Pre-Market High (PMH) & Pre-Market Low (PML)
The Pre-Market High (PMH) and Pre-Market Low (PML) show where price reached its highest and lowest points before the main trading session begins. On most U.S. exchanges, the pre-market session is from 4:00 AM to 9:29 AM Eastern Standard Time (EST), just before the New York session opens at 9:30 AM EST. These levels are important because they reflect how traders positioned themselves during the early morning hours. Many traders use the pre-market session to react to overnight news. The PMH and PML outline that entire pre-market range, showing where buyers and sellers fought for control and where the early balance between the two sides was established before the market opens.
When the current price of a ticker moves above the Pre-Market High, it means buyers are in control and that price has pushed through the top of the pre-market range. In the dashboard, this triggers a 🟢 icon under the “PMH Break” column. Once this break is confirmed, the opposite PML Break column for that ticker becomes blank.
When the current price moves below the Pre-Market Low, it means sellers are in control and that price has fallen beneath the pre-market range. In the dashboard, this triggers a 🔴 icon under the “PML Break” column. Once a PML Break is confirmed, the opposite PMH Break column for that ticker becomes blank.
🔹Progress Bars
The LE Scanner indicator includes progress bars that show how far the current price is from key levels.
When price is between the Previous Day High (PDH) and Previous Day Low (PDL), the progress bar measures price’s distance relative to those two points.
When price is between the Pre-Market High (PMH) and Pre-Market Low (PML), the bar tracks how far price is from those pre-market boundaries.
The closer price gets to either side, the more the bar fills, giving you a quick visual sense of how close a breakout or breakdown might be. A bar that’s nearly full means price is approaching one of the levels, while a shorter bar means it’s still far away from it. By seeing this relationship directly in the dashboard, you can see which tickers are getting ready to test key levels without flipping through multiple charts.
🔹PDH Progress Bar
The PDH progress bar measures how close price is to breaking above the previous day’s high.
When the bar is nearly full, it means the current price is trading just below yesterday’s high.
When the bar is low or mostly empty, it means price is far from the PDH and trading near the middle or lower end of the previous day’s range.
Once price breaks above the PDH, the progress bar is replaced with a green confirmation icon in the PDH Break column.
🔹Previous Day Low (PDL) Progress Bar
The PDL progress bar measures how close price is to breaking below the previous day’s low.
When the bar is nearly full, it means the current price is trading just above yesterday’s low.
When the bar is low or mostly empty, it means price is far from the PDL and trading near the middle or upper end of the previous day’s range.
Once price breaks below the PDL, the progress bar is replaced with a red confirmation icon in the PDL Break column.
🔹Pre-Market High (PMH) Progress Bar
The PMH progress bar shows how close price is to breaking above the pre-market high.
When the bar is nearly full, it means the current price is trading just below the pre-market high.
When the bar is low or mostly empty, it means price is far from the PMH and trading near the middle or lower end of the pre-market range.
Once price breaks above the PMH, the progress bar is replaced with a green confirmation icon in the PMH Break column.
🔹Pre-Market Low (PML) Progress Bar
The PML progress bar shows how close price is to breaking below the pre-market low.
When the bar is nearly full, it means the current price is trading just above the pre-market low.
When the bar is low or mostly empty, it means price is far from the PML and trading near the middle or upper end of the pre-market range.
Once price breaks below the PML, the progress bar is replaced with a red confirmation icon in the PML Break column.
Trend Classification:
The LE Scanner automatically classifies each user-inputted ticker as bullish, bearish, or neutral based on how price is interacting with its key levels.
Each trend type follows a specific set of conditions and is displayed in its own column under Trend on the dashboard.
🔹 Bullish Trend
A bullish trend occurs when price has broken above both the Previous Day High (PDH) and the Pre-Market High (PMH). This shows that buyers are in full control and that the ticker is trading firmly above the prior session’s and pre-market range.
When this condition is met, the Trend column displays a green background with an upward-facing triangle icon (▲).
🔹 Bearish Trend
A bearish trend occurs when price has broken below both the Previous Day Low (PDL) and the Pre-Market Low (PML). This indicates that sellers are in control and that the ticker is trading firmly below the prior session’s and pre-market range.
When this happens, the Trend column switches to a red background with a downward-facing triangle icon (▼).
🔹 Neutral Trend
A neutral trend occurs when price is trading inside the range, meaning it hasn’t broken above the PDH/PMH or below the PDL/PML. This indicates that neither bulls nor bears has clear control, and the ticker is consolidating between the prior session’s and pre-market range.
When this condition is active, the Trend column appears with a warning sign icon (⚠️). This helps distinguish tickers that are still forming setups from those that have already shown decisive strength or weakness.
Sorting:
The LE Scanner includes a built-in sorting feature that lets you reorder the dashboard in either descending or ascending order based on one of four metrics:
% Change
Volume
Price
Trend
Sorting is handled directly in the indicator settings, where you can toggle “Sort By” and then select your preferred Sort By criteria and Order (Ascending or Descending). When enabled, the dashboard automatically repositions every ticker to match the selected sorting method.
🔹 % Change Sorting
When you sort by % Change, the dashboard ranks tickers based on their daily percentage movement relative to the previous session’s close.
If you choose descending order, the biggest gainers appear at the top.
If you choose ascending order, the biggest decliners appear at the top.
🔹 Volume Sorting
When you sort by Volume, the dashboard arranges tickers based on their total traded volume for the current session.
If you choose descending order, the highest-volume tickers appear at the top.
If you choose ascending order, the lowest-volume tickers appear at the top.
🔹 Price Sorting
When you sort by Price, the dashboard arranges tickers by their current market price.
If you choose descending order, the highest-priced tickers appear at the top.
If you choose ascending order, the lowest-priced tickers appear at the top.
🔹 Trend Sorting
When you sort by Trend, the dashboard organizes tickers based on their directional classification.
If you choose descending order, bullish tickers appear first, followed by neutral and bearish.
If you choose ascending order, bearish tickers appear first, followed by neutral and bullish.
Customization:
The LE Scanner includes several settings that let you customize how the dashboard appears on your chart. All visual and positional elements can be adjusted to fit your personal layout preferences.
🔹 Dashboard Position
You can move the dashboard anywhere on your chart using the “Table Position” setting. Options include:
Bottom-Center
Bottom-Left
Bottom-Right
Middle-Center
Middle-Left
Middle-Right
Top-Center
Top-Left
Top-Right
🔹 Dashboard Size
The dashboard size can be adjusted to be larger or smaller. Users can choose between the following options:
Tiny
Small
Normal
Large
Huge
🔹 Color Customization
All color elements in the dashboard are customizable. You can change the following:
Background Color
Border Color
Frame Color
Text Color
Bullish Trend Color
Bearish Trend Color
Important Notes:
Because the LE Scanner tracks multiple tickers and updates all data in real time, it performs several background calculations at once. On rare occasions, this can cause the following issue:
Computation Error:
Scanning up to 20 tickers at the same time requires multiple request.security() calls. This process is resource-intensive and can sometimes trigger a calculation timeout message in TradingView. If this occurs, simply force the indicator to refresh by changing one of its settings (for example, toggling a ticker off and back on) or by removing and re-adding the indicator to your chart.
Uniqueness:
The LE Scanner is unique because it combines real-time multi-ticker tracking, sortable data, and visual feedback into one tool. It can track up to 20 tickers simultaneously, automatically sort them by % change, volume, price, or trend. The built-in progress bars provide a clear visual of how close price is to breaking key levels, while the trend classification instantly shows whether each ticker is bullish, bearish, or neutral.
Algorithm Predator - ProAlgorithm Predator - Pro: Advanced Multi-Agent Reinforcement Learning Trading System
Algorithm Predator - Pro combines four specialized market microstructure agents with a state-of-the-art reinforcement learning framework . Unlike traditional indicator mashups, this system implements genuine machine learning to automatically discover which detection strategies work best in current market conditions and adapts continuously without manual intervention.
Core Innovation: Rather than forcing traders to interpret conflicting signals, this system uses 15 different multi-armed bandit algorithms and a full reinforcement learning stack (Q-Learning, TD(λ) with eligibility traces, and Policy Gradient with REINFORCE) to learn optimal agent selection policies. The result is a self-improving system that gets smarter with every trade.
Target Users: Swing traders, day traders, and algorithmic traders seeking systematic signal generation with mathematical rigor. Suitable for stocks, forex, crypto, and futures on liquid instruments (>100k daily volume).
Why These Components Are Combined
The Fundamental Problem
No single indicator works consistently across all market regimes. What works in trending markets fails in ranging conditions. Traditional solutions force traders to manually switch indicators (slow, error-prone) or interpret all signals simultaneously (cognitive overload).
This system solves the problem through automated meta-learning: Deploy multiple specialized agents designed for specific market microstructure conditions, then use reinforcement learning to discover which agent (or combination) performs best in real-time.
Why These Specific Four Agents?
The four agents provide orthogonal failure mode coverage —each agent's weakness is another's strength:
Spoofing Detector - Optimal in consolidation/manipulation; fails in trending markets (hedged by Exhaustion Detector)
Exhaustion Detector - Optimal at trend climax; fails in range-bound markets (hedged by Liquidity Void)
Liquidity Void - Optimal pre-breakout compression; fails in established trends (hedged by Mean Reversion)
Mean Reversion - Optimal in low volatility; fails in strong trends (hedged by Spoofing Detector)
This creates complete market state coverage where at least one agent should perform well in any condition. The bandit system identifies which one without human intervention.
Why Reinforcement Learning vs. Simple Voting?
Traditional consensus systems have fatal flaws: equal weighting assumes all agents are equally reliable (false), static thresholds don't adapt, and no learning means past mistakes repeat indefinitely.
Reinforcement learning solves this through the exploration-exploitation tradeoff: Continuously test underused agents (exploration) while primarily relying on proven winners (exploitation). Over time, the system builds a probability distribution over agent quality reflecting actual market performance.
Mathematical Foundation: Multi-armed bandit problem from probability theory, where each agent is an "arm" with unknown reward distribution. The goal is to maximize cumulative reward while efficiently learning each arm's true quality.
The Four Trading Agents: Technical Explanation
Agent 1: 🎭 Spoofing Detector (Institutional Manipulation Detection)
Theoretical Basis: Market microstructure theory on order flow toxicity and information asymmetry. Based on research by Easley, López de Prado, and O'Hara on high-frequency trading manipulation.
What It Detects:
1. Iceberg Orders (Hidden Liquidity Absorption)
Method: Monitors volume spikes (>2.5× 20-period average) with minimal price movement (<0.3× ATR)
Formula: score += (close > open ? -2.5 : 2.5) when volume > vol_avg × 2.5 AND abs(close - open) / ATR < 0.3
Interpretation: Large volume without price movement indicates institutional absorption (buying) or distribution (selling) using hidden orders
Signal Logic: Contrarian—fade false breakouts caused by institutional manipulation
2. Spoofing Patterns (Fake Liquidity via Layering)
Method: Analyzes candlestick wick-to-body ratios during volume spikes
Formula: if upper_wick > body × 2 AND volume_spike: score += 2.0
Mechanism: Spoofing creates large wicks (orders pulled before execution) with volume evidence
Signal Logic: Wick direction indicates trapped participants; trade against the failed move
3. Post-Manipulation Reversals
Method: Tracks volume decay after manipulation events
Formula: if volume > vol_avg × 3 AND volume / volume < 0.3: score += (close > open ? -1.5 : 1.5)
Interpretation: Sharp volume drop after manipulation indicates exhaustion of manipulative orders
Why It Works: Institutional manipulation creates detectable microstructure anomalies. While retail traders see "mysterious reversals," this agent quantifies the order flow patterns causing them.
Parameter: i_spoof (sensitivity 0.5-2.0) - Controls detection threshold
Best Markets: Consolidations before breakouts, London/NY overlap windows, stocks with institutional ownership >70%
Agent 2: ⚡ Exhaustion Detector (Momentum Failure Analysis)
Theoretical Basis: Technical analysis divergence theory combined with VPIN reversals from market microstructure literature.
What It Detects:
1. Price-RSI Divergence (Momentum Deceleration)
Method: Compares 5-bar price ROC against RSI change
Formula: if price_roc > 5% AND rsi_current < rsi : score += 1.8
Mathematics: Second derivative detecting inflection points
Signal Logic: When price makes higher highs but momentum makes lower highs, expect mean reversion
2. Volume Exhaustion (Buying/Selling Climax)
Method: Identifies strong price moves (>5% ROC) with declining volume (<-20% volume ROC)
Formula: if price_roc > 5 AND vol_roc < -20: score += 2.5
Interpretation: Price extension without volume support indicates retail chasing while institutions exit
3. Momentum Deceleration (Acceleration Analysis)
Method: Compares recent 3-bar momentum to prior 3-bar momentum
Formula: deceleration = abs(mom1) < abs(mom2) × 0.5 where momentum significant (> ATR)
Signal Logic: When rate of price change decelerates significantly, anticipate directional shift
Why It Works: Momentum is lagging, but momentum divergence is leading. By comparing momentum's rate of change to price, this agent detects "weakening conviction" before reversals become obvious.
Parameter: i_momentum (sensitivity 0.5-2.0)
Best Markets: Strong trends reaching climax, parabolic moves, instruments with high retail participation
Agent 3: 💧 Liquidity Void Detector (Breakout Anticipation)
Theoretical Basis: Market liquidity theory and order book dynamics. Based on research into "liquidity holes" and volatility compression preceding expansion.
What It Detects:
1. Bollinger Band Squeeze (Volatility Compression)
Method: Monitors Bollinger Band width relative to 50-period average
Formula: bb_width = (upper_band - lower_band) / middle_band; triggers when < 0.6× average
Mathematical Foundation: Regression to the mean—low volatility precedes high volatility
Signal Logic: When volatility compresses AND cumulative delta shows directional bias, anticipate breakout
2. Volume Profile Gaps (Thin Liquidity Zones)
Method: Identifies sharp volume transitions indicating few limit orders
Formula: if volume < vol_avg × 0.5 AND volume < vol_avg × 0.5 AND volume > vol_avg × 1.5
Interpretation: Sudden volume drop after spike indicates price moved through order book to low-opposition area
Signal Logic: Price accelerates through low-liquidity zones
3. Stop Hunts (Liquidity Grabs Before Reversals)
Method: Detects new 20-bar highs/lows with immediate reversal and rejection wick
Formula: if new_high AND close < high - (high - low) × 0.6: score += 3.0
Mechanism: Market makers push price to trigger stop-loss clusters, then reverse
Signal Logic: Enter reversal after stop-hunt completes
Why It Works: Order book theory shows price moves fastest through zones with minimal liquidity. By identifying these zones before major moves, this agent provides early entry for high-reward breakouts.
Parameter: i_liquidity (sensitivity 0.5-2.0)
Best Markets: Range-bound pre-breakout setups, volatility compression zones, instruments prone to gap moves
Agent 4: 📊 Mean Reversion (Statistical Arbitrage Engine)
Theoretical Basis: Statistical arbitrage theory, Ornstein-Uhlenbeck mean-reverting processes, and pairs trading methodology applied to single instruments.
What It Detects:
1. Z-Score Extremes (Standard Deviation Analysis)
Method: Calculates price distance from 20-period and 50-period SMAs in standard deviation units
Formula: zscore_20 = (close - SMA20) / StdDev(50)
Statistical Interpretation: Z-score >2.0 means price is 2 standard deviations above mean (97.5th percentile)
Trigger Logic: if abs(zscore_20) > 2.0: score += zscore_20 > 0 ? -1.5 : 1.5 (fade extremes)
2. Ornstein-Uhlenbeck Process (Mean-Reverting Stochastic Model)
Method: Models price as mean-reverting stochastic process: dx = θ(μ - x)dt + σdW
Implementation: Calculates spread = close - SMA20, then z-score of spread vs. spread distribution
Formula: ou_signal = (spread - spread_mean) / spread_std
Interpretation: Measures "tension" pulling price back to equilibrium
3. Correlation Breakdown (Regime Change Detection)
Method: Compares 50-period price-volume correlation to 10-period correlation
Formula: corr_breakdown = abs(typical_corr - recent_corr) > 0.5
Enhancement: if corr_breakdown AND abs(zscore_20) > 1.0: score += zscore_20 > 0 ? -1.2 : 1.2
Why It Works: Mean reversion is the oldest quantitative strategy (1970s pairs trading at Morgan Stanley). While simple, it remains effective because markets exhibit periodic equilibrium-seeking behavior. This agent applies rigorous statistical testing to identify when mean reversion probability is highest.
Parameter: i_statarb (sensitivity 0.5-2.0)
Best Markets: Range-bound instruments, low-volatility periods (VIX <15), algo-dominated markets (forex majors, index futures)
Multi-Armed Bandit System: 15 Algorithms Explained
What Is a Multi-Armed Bandit Problem?
Origin: Named after slot machines ("one-armed bandits"). Imagine facing multiple slot machines, each with unknown payout rates. How do you maximize winnings?
Formal Definition: K arms (agents), each with unknown reward distribution with mean μᵢ. Goal: Maximize cumulative reward over T trials. Challenge: Balance exploration (trying uncertain arms to learn quality) vs. exploitation (using known-best arm for immediate reward).
Trading Application: Each agent is an "arm." After each trade, receive reward (P&L). Must decide which agent to trust for next signal.
Algorithm Categories
Bayesian Approaches (probabilistic, optimal for stationary environments):
Thompson Sampling
Bootstrapped Thompson Sampling
Discounted Thompson Sampling
Frequentist Approaches (confidence intervals, deterministic):
UCB1
UCB1-Tuned
KL-UCB
SW-UCB (Sliding Window)
D-UCB (Discounted)
Adversarial Approaches (robust to non-stationary environments):
EXP3-IX
Hedge
FPL-Gumbel
Reinforcement Learning Approaches (leverage learned state-action values):
Q-Values (from Q-Learning)
Policy Network (from Policy Gradient)
Simple Baseline:
Epsilon-Greedy
Softmax
Key Algorithm Details
Thompson Sampling (DEFAULT - RECOMMENDED)
Theoretical Foundation: Bayesian decision theory with conjugate priors. Published by Thompson (1933), rediscovered for bandits by Chapelle & Li (2011).
How It Works:
Model each agent's reward distribution as Beta(α, β) where α = wins, β = losses
Each step, sample from each agent's beta distribution: θᵢ ~ Beta(αᵢ, βᵢ)
Select agent with highest sample: argmaxᵢ θᵢ
Update winner's distribution after observing outcome
Mathematical Properties:
Optimality: Achieves logarithmic regret O(K log T) (proven optimal)
Bayesian: Maintains probability distribution over true arm means
Automatic Balance: High uncertainty → more exploration; high certainty → exploitation
⚠️ CRITICAL APPROXIMATION: This is a pseudo-random approximation of true Thompson Sampling. True implementation requires random number generation from beta distributions, which Pine Script doesn't provide. This version uses Box-Muller transform with market data (price/volume decimal digits) as entropy source. While not mathematically pure, it maintains core exploration-exploitation balance and learns agent preferences effectively.
When To Use: Best all-around choice. Handles non-stationary markets reasonably well, balances exploration naturally, highly sample-efficient.
UCB1 (Upper Confidence Bound)
Formula: UCB_i = reward_mean_i + sqrt(2 × ln(total_pulls) / pulls_i)
Interpretation: First term (exploitation) + second term (exploration bonus for less-tested arms)
Mathematical Properties:
Deterministic : Always selects same arm given same state
Regret Bound: O(K log T) — same optimality as Thompson Sampling
Interpretable: Can visualize confidence intervals
When To Use: Prefer deterministic behavior, want to visualize uncertainty, stable markets
EXP3-IX (Exponential Weights - Adversarial)
Theoretical Foundation: Adversarial bandit algorithm. Assumes environment may be actively hostile (worst-case analysis).
How It Works:
Maintain exponential weights: w_i = exp(η × cumulative_reward_i)
Select agent with probability proportional to weights: p_i = (1-γ)w_i/Σw_j + γ/K
After outcome, update with importance weighting: estimated_reward = observed_reward / p_i
Mathematical Properties:
Adversarial Regret: O(sqrt(TK log K)) even if environment is adversarial
No Assumptions: Doesn't assume stationary or stochastic reward distributions
Robust: Works even when optimal arm changes continuously
When To Use: Extreme non-stationarity, don't trust reward distribution assumptions, want robustness over efficiency
KL-UCB (Kullback-Leibler Upper Confidence Bound)
Theoretical Foundation: Uses KL-divergence instead of Hoeffding bounds. Tighter confidence intervals.
Formula (conceptual): Find largest q such that: n × KL(p||q) ≤ ln(t) + 3×ln(ln(t))
Mathematical Properties:
Tighter Bounds: KL-divergence adapts to reward distribution shape
Asymptotically Optimal: Better constant factors than UCB1
Computationally Intensive: Requires iterative binary search (15 iterations)
When To Use: Maximum sample efficiency needed, willing to pay computational cost, long-term trading (>500 bars)
Q-Values & Policy Network (RL-Based Selection)
Unique Feature: Instead of treating agents as black boxes with scalar rewards, these algorithms leverage the full RL state representation .
Q-Values Selection:
Uses learned Q-values: Q(state, agent_i) from Q-Learning
Selects agent via softmax over Q-values for current market state
Advantage: Selects based on state-conditional quality (which agent works best in THIS market state)
Policy Network Selection:
Uses neural network policy: π(agent | state, θ) from Policy Gradient
Direct policy over agents given market features
Advantage: Can learn non-linear relationships between market features and agent quality
When To Use: After 200+ RL updates (Q-Values) or 500+ updates (Policy Network) when models converged
Machine Learning & Reinforcement Learning Stack
Why Both Bandits AND Reinforcement Learning?
Critical Distinction:
Bandits treat agents as contextless black boxes: "Agent 2 has 60% win rate"
Reinforcement Learning adds state context: "Agent 2 has 60% win rate WHEN trend_score > 2 and RSI < 40"
Power of Combination: Bandits provide fast initial learning with minimal assumptions. RL provides state-dependent policies for superior long-term performance.
Component 1: Q-Learning (Value-Based RL)
Algorithm: Temporal Difference Learning with Bellman equation.
State Space: 54 discrete states formed from:
trend_state = {0: bearish, 1: neutral, 2: bullish} (3 values)
volatility_state = {0: low, 1: normal, 2: high} (3 values)
RSI_state = {0: oversold, 1: neutral, 2: overbought} (3 values)
volume_state = {0: low, 1: high} (2 values)
Total states: 3 × 3 × 3 × 2 = 54 states
Action Space: 5 actions (No trade, Agent 1, Agent 2, Agent 3, Agent 4)
Total state-action pairs: 54 × 5 = 270 Q-values
Bellman Equation:
Q(s,a) ← Q(s,a) + α ×
Parameters:
α (learning rate): 0.01-0.50, default 0.10 - Controls step size for updates
γ (discount factor): 0.80-0.99, default 0.95 - Values future rewards
ε (exploration): 0.01-0.30, default 0.10 - Probability of random action
Update Mechanism:
Position opens with state s, action a (selected agent)
Every bar position is open: Calculate floating P&L → scale to reward
Perform online TD update
When position closes: Perform terminal update with final reward
Gradient Clipping: TD errors clipped to ; Q-values clipped to for stability.
Why It Works: Q-Learning learns "quality" of each agent in each market state through trial and error. Over time, builds complete state-action value function enabling optimal state-dependent agent selection.
Component 2: TD(λ) Learning (Temporal Difference with Eligibility Traces)
Enhancement Over Basic Q-Learning: Credit assignment across multiple time steps.
The Problem TD(λ) Solves:
Position opens at t=0
Market moves favorably at t=3
Position closes at t=8
Question: Which earlier decisions contributed to success?
Basic Q-Learning: Only updates Q(s₈, a₈) ← reward
TD(λ): Updates ALL visited state-action pairs with decayed credit
Eligibility Trace Formula:
e(s,a) ← γ × λ × e(s,a) for all s,a (decay all traces)
e(s_current, a_current) ← 1 (reset current trace)
Q(s,a) ← Q(s,a) + α × TD_error × e(s,a) (update all with trace weight)
Lambda Parameter (λ): 0.5-0.99, default 0.90
λ=0: Pure 1-step TD (only immediate next state)
λ=1: Full Monte Carlo (entire episode)
λ=0.9: Balance (recommended)
Why Superior: Dramatically faster learning for multi-step tasks. Q-Learning requires many episodes to propagate rewards backwards; TD(λ) does it in one.
Component 3: Policy Gradient (REINFORCE with Baseline)
Paradigm Shift: Instead of learning value function Q(s,a), directly learn policy π(a|s).
Policy Network Architecture:
Input: 12 market features
Hidden: None (linear policy)
Output: 5 actions (softmax distribution)
Total parameters: 12 features × 5 actions + 5 biases = 65 parameters
Feature Set (12 Features):
Price Z-score (close - SMA20) / ATR
Volume ratio (volume / vol_avg - 1)
RSI deviation (RSI - 50) / 50
Bollinger width ratio
Trend score / 4 (normalized)
VWAP deviation
5-bar price ROC
5-bar volume ROC
Range/ATR ratio - 1
Price-volume correlation (20-period)
Volatility ratio (ATR / ATR_avg - 1)
EMA50 deviation
REINFORCE Update Rule:
θ ← θ + α × ∇log π(a|s) × advantage
where advantage = reward - baseline (variance reduction)
Why Baseline? Raw rewards have high variance. Subtracting baseline (running average) centers rewards around zero, reducing gradient variance by 50-70%.
Learning Rate: 0.001-0.100, default 0.010 (much lower than Q-Learning because policy gradients have high variance)
Why Policy Gradient?
Handles 12 continuous features directly (Q-Learning requires discretization)
Naturally maintains exploration through probability distribution
Can converge to stochastic optimal policy
Component 4: Ensemble Meta-Learner (Stacking)
Architecture: Level-1 meta-learner combines Level-0 base learners (Q-Learning, TD(λ), Policy Gradient).
Three Meta-Learning Algorithms:
1. Simple Average (Baseline)
Final_prediction = (Q_prediction + TD_prediction + Policy_prediction) / 3
2. Weighted Vote (Reward-Based)
weight_i ← 0.95 × weight_i + 0.05 × (reward_i + 1)
3. Adaptive Weighting (Gradient-Based) — RECOMMENDED
Loss Function: L = (y_true - ŷ_ensemble)²
Gradient: ∂L/∂weight_i = -2 × (y_true - ŷ_ensemble) × agent_contribution_i
Updates weights via gradient descent with clipping and normalization
Why It Works: Unlike simple averaging, meta-learner discovers which base learner is most reliable in current regime. If Policy Gradient excels in trending markets while Q-Learning excels in ranging, meta-learner learns these patterns and weights accordingly.
Feature Importance Tracking
Purpose: Identify which of 12 features contribute most to successful predictions.
Update Rule: importance_i ← 0.95 × importance_i + 0.05 × |feature_i × reward|
Use Cases:
Feature selection: Drop low-importance features
Market regime detection: Importance shifts reveal regime changes
Agent tuning: If VWAP deviation has high importance, consider boosting agents using VWAP
RL Position Tracking System
Critical Innovation: Proper reinforcement learning requires tracking which decisions led to outcomes.
State Tracking (When Signal Validates):
active_rl_state ← current_market_state (0-53)
active_rl_action ← selected_agent (1-4)
active_rl_entry ← entry_price
active_rl_direction ← 1 (long) or -1 (short)
active_rl_bar ← current_bar_index
Online Updates (Every Bar Position Open):
floating_pnl = (close - entry) / entry × direction
reward = floating_pnl × 10 (scale to meaningful range)
reward = clip(reward, -5.0, 5.0)
Update Q-Learning, TD(λ), and Policy Gradient
Terminal Update (Position Close):
Final Q-Learning update (no next Q-value, terminal state)
Update meta-learner with final result
Update agent memory
Clear position tracking
Exit Conditions:
Time-based: ≥3 bars held (minimum hold period)
Stop-loss: 1.5% adverse move
Take-profit: 2.0% favorable move
Market Microstructure Filters
Why Microstructure Matters
Traditional technical analysis assumes fair, efficient markets. Reality: Markets have friction, manipulation, and information asymmetry. Microstructure filters detect when market structure indicates adverse conditions.
Filter 1: VPIN (Volume-Synchronized Probability of Informed Trading)
Theoretical Foundation: Easley, López de Prado, & O'Hara (2012). "Flow Toxicity and Liquidity in a High-Frequency World."
What It Measures: Probability that current order flow is "toxic" (informed traders with private information).
Calculation:
Classify volume as buy or sell (close > close = buy volume)
Calculate imbalance over 20 bars: VPIN = |Σ buy_volume - Σ sell_volume| / Σ total_volume
Compare to moving average: toxic = VPIN > VPIN_MA(20) × sensitivity
Interpretation:
VPIN < 0.3: Normal flow (uninformed retail)
VPIN 0.3-0.4: Elevated (smart money active)
VPIN > 0.4: Toxic flow (informed institutions dominant)
Filter Logic:
Block LONG when: VPIN toxic AND price rising (don't buy into institutional distribution)
Block SHORT when: VPIN toxic AND price falling (don't sell into institutional accumulation)
Adaptive Threshold: If VPIN toxic frequently, relax threshold; if rarely toxic, tighten threshold. Bounded .
Filter 2: Toxicity (Kyle's Lambda Approximation)
Theoretical Foundation: Kyle (1985). "Continuous Auctions and Insider Trading."
What It Measures: Price impact per unit volume — market depth and informed trading.
Calculation:
price_impact = (close - close ) / sqrt(Σ volume over 10 bars)
impact_zscore = (price_impact - impact_mean) / impact_std
toxicity = abs(impact_zscore)
Interpretation:
Low toxicity (<1.0): Deep liquid market, large orders absorbed easily
High toxicity (>2.0): Thin market or informed trading
Filter Logic: Block ALL SIGNALS when toxicity > threshold. Most dangerous when price breaks from VWAP with high toxicity.
Filter 3: Regime Filter (Counter-Trend Protection)
Purpose: Prevent counter-trend trades during strong trends.
Trend Scoring:
trend_score = 0
trend_score += close > EMA8 ? +1 : -1
trend_score += EMA8 > EMA21 ? +1 : -1
trend_score += EMA21 > EMA50 ? +1 : -1
trend_score += close > EMA200 ? +1 : -1
Range:
Regime Classification:
Strong Bull: trend_score ≥ +3 → Block all SHORT signals
Strong Bear: trend_score ≤ -3 → Block all LONG signals
Neutral: -2 ≤ trend_score ≤ +2 → Allow both directions
Filter 4: Liquidity Boost (Signal Enhancer)
Unique: Unlike other filters (which block), this amplifies signals during low liquidity.
Logic: if volume < vol_avg × 0.7: agent_scores × 1.2
Why It Works: Low liquidity often precedes explosive moves (breakouts). By increasing agent sensitivity during compression, system catches pre-breakout signals earlier.
Technical Implementation & Approximations
⚠️ Critical Approximations Required by Pine Script
1. Thompson Sampling: Pseudo-Random Beta Distribution
Academic Standard: True random sampling from beta distributions using cryptographic RNG
This Implementation: Box-Muller transform for normal distribution using market data (price/volume decimal digits) as entropy source, then scale to beta distribution mean/variance
Impact: Not cryptographically random, may have subtle biases in specific price ranges, but maintains correct mean and approximate variance. Sufficient for bandit agent selection.
2. VPIN: Simplified Volume Classification
Academic Standard: Lee-Ready algorithm or exchange-provided aggressor flags with tick-by-tick data
This Implementation: Bar-based classification: if close > close : buy_volume += volume
Impact: 10-15% precision loss. Works well in directional markets, misclassifies in choppy conditions. Still captures order flow imbalance signal.
3. Policy Gradient: Simplified Per-Action Updates
Academic Standard: Full softmax gradient updating all actions (selected action UP, others DOWN proportionally)
This Implementation: Only updates selected action's weights
Impact: Valid approximation for small action spaces (5 actions). Slower convergence than full softmax but still learns optimal policy.
4. Kyle's Lambda: Simplified Price Impact
Academic Standard: Regression over multiple time scales with signed order flow
This Implementation: price_impact = Δprice_10 / sqrt(Σvolume_10); z_score calculation
Impact: 15-20% precision loss. No proper signed order flow. Still detects informed trading signals at extremes (>2σ).
5. Other Simplifications:
Hawkes Process: Fixed exponential decay (0.9) not MLE-optimized
Entropy: Ratio approximation not true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Feature Engineering: 12 features vs. potential 100+ with polynomial interactions
RL Hybrid Updates: Both online and terminal (non-standard but empirically effective)
Overall Precision Loss Estimate: 10-15% compared to academic implementations with institutional data feeds.
Practical Trade-off: For retail trading with OHLCV data, these approximations provide 90%+ of the edge while maintaining full transparency, zero latency, no external dependencies, and runs on any TradingView plan.
How to Use: Practical Guide
Initial Setup (5 Minutes)
Select Trading Mode: Start with "Balanced" for most users
Enable ML/RL System: Toggle to TRUE, select "Full Stack" ML Mode
Bandit Configuration: Algorithm: "Thompson Sampling", Mode: "Switch" or "Blend"
Microstructure Filters: Enable all four filters, enable "Adaptive Microstructure Thresholds"
Visual Settings: Enable dashboard (Top Right), enable all chart visuals
Learning Phase (First 50-100 Signals)
What To Monitor:
Agent Performance Table: Watch win rates develop (target >55%)
Bandit Weights: Should diverge from uniform (0.25 each) after 20-30 signals
RL Core Metrics: "RL Updates" should increase when position open
Filter Status: "Blocked" count indicates filter activity
Optimization Tips:
Too few signals: Lower min_confidence to 0.25, increase agent sensitivities to 1.1-1.2
Too many signals: Raise min_confidence to 0.35-0.40, decrease agent sensitivities to 0.8-0.9
One agent dominates (>70%): Consider "Lock Agent" feature
Signal Interpretation
Dashboard Signal Status:
⚪ WAITING FOR SIGNAL: No agent signaling
⏳ ANALYZING...: Agent signaling but not confirmed
🟡 CONFIRMING 2/3: Building confirmation (2 of 3 bars)
🟢 LONG ACTIVE : Validated long entry
🔴 SHORT ACTIVE : Validated short entry
Kill Zone Boxes: Entry price (triangle marker), Take Profit (Entry + 2.5× ATR), Stop Loss (Entry - 1.5× ATR). Risk:Reward = 1:1.67
Risk Management
Position Sizing:
Risk per trade = 1-2% of capital
Position size = (Capital × Risk%) / (Entry - StopLoss)
Stop-Loss Placement:
Initial: Entry ± 1.5× ATR (shown in kill zone)
Trailing: After 1:1 R:R achieved, move stop to breakeven
Take-Profit Strategy:
TP1 (2.5× ATR): Take 50% off
TP2 (Runner): Trail stop at 1× ATR or use opposite signal as exit
Memory Persistence
Why Save Memory: Every chart reload resets the system. Saving learned parameters preserves weeks of learning.
When To Save: After 200+ signals when agent weights stabilize
What To Save: From Memory Export panel, copy all alpha/beta/weight values and adaptive thresholds
How To Restore: Enable "Restore From Saved State", input all values into corresponding fields
What Makes This Original
Innovation 1: Genuine Multi-Armed Bandit Framework
This implements 15 mathematically rigorous bandit algorithms from academic literature (Thompson Sampling from Chapelle & Li 2011, UCB family from Auer et al. 2002, EXP3 from Auer et al. 2002, KL-UCB from Garivier & Cappé 2011). Each algorithm maintains proper state, updates according to proven theory, and converges to optimal behavior. This is real learning, not superficial parameter changes.
Innovation 2: Full Reinforcement Learning Stack
Beyond bandits learning which agent works best globally, RL learns which agent works best in each market state. After 500+ positions, system builds 54-state × 5-action value function (270 learned parameters) capturing context-dependent agent quality.
Innovation 3: Market Microstructure Integration
Combines retail technical analysis with institutional-grade microstructure metrics: VPIN from Easley, López de Prado, O'Hara (2012), Kyle's Lambda from Kyle (1985), Hawkes Processes from Hawkes (1971). These detect informed trading, manipulation, and liquidity dynamics invisible to technical analysis.
Innovation 4: Adaptive Threshold System
Dynamic quantile-based thresholds: Maintains histogram of each agent's score distribution (24 bins, exponentially decayed), calculates 80th percentile threshold from histogram. Agent triggers only when score exceeds its own learned quantile. Proper non-parametric density estimation automatically adapts to instrument volatility, agent behavior shifts, and market regime changes.
Innovation 5: Episodic Memory with Transfer Learning
Dual-layer architecture: Short-term memory (last 20 trades, fast adaptation) + Long-term memory (condensed episodes, historical patterns). Transfer mechanism consolidates knowledge when STM reaches threshold. Mimics hippocampus → neocortex consolidation in human memory.
Limitations & Disclaimers
General Limitations
No Predictive Guarantee: Pattern recognition ≠ prediction. Past performance ≠ future results.
Learning Period Required: Minimum 50-100 bars for reliable statistics. Initial performance may be suboptimal.
Overfitting Risk: System learns patterns in historical data. May not generalize to unprecedented conditions.
Approximation Limitations: See technical implementation section (10-15% precision loss vs. academic standards)
Single-Instrument Limitation: No multi-asset correlation, sector context, or VIX integration.
Forward-Looking Bias Disclaimer
CRITICAL TRANSPARENCY: The RL system uses an 8-bar forward-looking window for reward calculation.
What This Means: System learns from rewards incorporating future price information (bars 101-108 relative to entry at bar 100).
Why Acceptable:
✅ Signals do NOT look ahead: Entry decisions use only data ≤ entry bar
✅ Learning only: Forward data used for optimization, not signal generation
✅ Real-time mirrors backtest: In live trading, system learns identically
⚠️ Implication: Dashboard "Agent Win%" reflects this 8-bar evaluation. Real-time performance may differ slightly if positions held longer, slippage/fees not captured, or market microstructure changes.
Risk Warnings
No Guarantee of Profit: All trading involves risk of loss
System Failures: Bugs possible despite extensive testing
Market Conditions: Optimized for liquid markets (>100k daily volume). Performance degrades in illiquid instruments, major news events, flash crashes
Broker-Specific Issues: Execution slippage, commission/fees, overnight financing costs
Appropriate Use
This Indicator Is:
✅ Entry trigger system
✅ Risk management framework (stop/target)
✅ Adaptive agent selection engine
✅ Learning system that improves over time
This Indicator Is NOT:
❌ Complete trading strategy (requires position sizing, portfolio management)
❌ Replacement for fundamental analysis
❌ Guaranteed profit generator
❌ Suitable for complete beginners without training
Recommended Complementary Analysis: Market context (support/resistance), volume profile, fundamental catalysts, correlation with related instruments, broader market regime
Recommended Settings by Instrument
Stocks (Large Cap, >$1B):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Switch
Agent Sensitivity: Spoofing 1.0-1.2, Exhaustion 0.9-1.1, Liquidity 0.8-1.0, StatArb 1.1-1.3
Microstructure: All enabled, VPIN 1.2, Toxicity 1.5 | Timeframe: 15min-1H
Forex Majors (EURUSD, GBPUSD):
Mode: Balanced to Conservative | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Blend
Agent Sensitivity: Spoofing 0.8-1.0, Exhaustion 0.9-1.1, Liquidity 0.7-0.9, StatArb 1.2-1.5
Microstructure: All enabled, VPIN 1.0-1.1, Toxicity 1.3-1.5 | Timeframe: 5min-30min
Crypto (BTC, ETH):
Mode: Aggressive to Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling OR EXP3-IX
Agent Sensitivity: Spoofing 1.2-1.5, Exhaustion 1.1-1.3, Liquidity 1.2-1.5, StatArb 0.7-0.9
Microstructure: All enabled, VPIN 1.4-1.6, Toxicity 1.8-2.2 | Timeframe: 15min-4H
Futures (ES, NQ, CL):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: UCB1 or Thompson Sampling
Agent Sensitivity: All 1.0-1.2 (balanced)
Microstructure: All enabled, VPIN 1.1-1.3, Toxicity 1.4-1.6 | Timeframe: 5min-30min
Conclusion
Algorithm Predator - Pro synthesizes academic research from market microstructure theory, reinforcement learning, and multi-armed bandit algorithms. Unlike typical indicator mashups, this system implements 15 mathematically rigorous bandit algorithms, deploys a complete RL stack (Q-Learning, TD(λ), Policy Gradient), integrates institutional microstructure metrics (VPIN, Kyle's Lambda), adapts continuously through dual-layer memory and meta-learning, and provides full transparency on approximations and limitations.
The system is designed for serious algorithmic traders who understand that no indicator is perfect, but through proper machine learning, we can build systems that improve over time and adapt to changing markets without manual intervention.
Use responsibly. Risk disclosure applies. Past performance ≠ future results.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Trend Strength IndicatorThis is a Trend Strength Indicator that shows you the immediate trend and historical trend of price for up to 7 higher timeframes.
It shows the strength of each timeframe by showing a red or green dot based on where price is at compared to the previous higher timeframe candle. The brighter red or green the dot is, the stronger the trend is compared to that higher timeframe candle.
The colors and timeframes can be customized to suit your preference and you can also turn off as many timeframes as you’d like if you want less time frames to show up on the indicator.
It also includes alerts for when all timeframes are bullish or all timeframes are bearish.
Keep these timeframes set to higher time frames than your chart so you can trade in the direction of the overall higher timeframe trend.
Bullish Scoring & Colors
If the current candle close is above the midline of the higher time frame candle, it is given a score of 1 and a dark green dot. If the current candle close is above the higher timeframe candle body, then it is given a score of 2 and a medium green dot. If the current candle close is above the high of the higher time frame candle, it is given a score of 3 and a bright green dot.
The higher the score the stronger the bullish trend and the brighter green the dot will be.
Bearish Scoring & Colors
If the current candle close is below the midline of the higher timeframe candle, it is given a score of -1 and a dark red dot. If the current candle close is below the higher timeframe candle body, then it is given a score of -2 and a medium red dot. If the current candle close is below the low of the higher timeframe candle, it is given a score of -3 and a bright red dot.
The lower the score, the stronger the bearish trend and the brighter red the dot will be.
Trend Scoring Modes
We gave you the option to set the trend scoring mode to either score based on price above or below the midline for quick and easy trend identification, or using the midline, candle body and highs and lows to give you a more detailed view of the trend strength. You can switch between these modes by selecting your preferred mode in the settings panel. The default is Open, High, Low, Close + Midline.
Sending Trend Direction To External Indicators
We coded in the ability to use the trend strength score as a signal that you can use to filter other indicators. This feature is great for notifying signal generating indicators what direction the market is trending in so that the signal generating indicator only gives signals in the direction of the trend.
This feature works by providing a data output of 1, 0 or -1. 1 means the trend is bullish, 0 means the trend is neutral and -1 means the trend is bearish.
This score is calculated by using the score of each timeframe that is turned on and checking if all timeframes are in the same direction or not. So if 3 timeframes are turned on and they are all bullish, the indicator will provide a data output of 1. This tells your external indicators that the trend is bullish.
This data output can be found in the data window and is labeled Trend Direction To Send To External Indicators.
At the bottom of the settings panel, there is a setting called Trend Score Threshold For External Indicators. This setting is the score threshold that all timeframes will need to meet to allow a trend strength signal to go through. So if set to 1, then all timeframes must be scored 1 or higher for bullish or -1 or lower for bearish. If set to 2, then all timeframes must be 2 or higher for bullish or -2 or lower for bearish. If set to 3, then all timeframes must be 3 for bullish or -3 for bearish. If all timeframes have met this threshold, then a bullish or bearish signal can be sent to your external indicator as a trend filter.
Labels
There are labels to the right of each row of dots, telling you which timeframe is which so you can easily identify what timeframe each row is showing the trend for.
Alerts
You can set alerts for when all timeframes are bullish or when all timeframes are bearish. If you have some time frames turned off at the time of creating your alerts, then it will only require all timeframes that are on to be all bullish or bearish to generate an alert. Make sure to set your alerts to once per bar close to ensure you don’t get premature alerts that aren’t yet valid.
Backtesting
This indicator helps you quickly identify and backtest the trend direction, how strong that trend is on multiple timeframes and helps you spot reversals and trend continuations. Make sure you look back at a lot of historical data to see how price moves when trend changes take place and how well price continues in each direction compared to the overall trend. This will help you gain confidence in reading the indicator and using it to your advantage when trading.
Best Way To Use The Indicator
This indicator is designed to help you quickly identify the trend on various different timeframes. The brighter the green dots are, the stronger the bullish trend is. The brighter the red dots are, the stronger the bearish trend is.
Trade in the direction of the trend. If the colors are mixed green and red, then price is likely to chop back and forth, so only trade the extremes of the ranges when that happens.
When most of the lower timeframe dots are the same color, that means it is a strong trend and you should place trades in the direction of the trend to be safe. The lower timeframes will start trending before the higher timeframes, so take notice of the lower timeframe colors starting to agree with each other and then take advantage of the trend that is forming.
You can also spot reversals with this indicator by watching for the lower timeframes to start changing color after a strong trend in one direction. The lower timeframes will start to change color one by one, indicating that the trend is actually changing direction.
For best results, make sure you wait for the trend to show all bullish or all bearish at the same time before you place any trades. If you can be patient enough to do that, you will increase the probability of winning your trade because you are trading with the direction of the overall higher timeframe trend which is typically an easy way to win more trades. Of course wait for pullbacks during the trend so you can keep a tight stop loss after entering your trade.
If you are scalping, you can turn off the higher timeframes and just use the 1 hour through 1 day. This won’t be as reliable as using all timeframes and waiting for them to align, but it is suitable for scalping quick intraday movements.
Other Indicators To Pair This With
Use this in combination with our Higher Timeframe Candle Levels indicator so you can see all of these levels being used to calculate the trend strength scores and watch how price reacts to those levels. You should also use our Breakout Scanner to find other markets with strong trends so you always know which market is trending the strongest and can trade those. Trend Strength Indicator, Higher Timeframe Candle Levels and the Breakout Scanner all use the same levels and calculate the trend scores the same way so they are designed to work all together to help you quickly be able to read a chart and find what direction to trade in.
BK AK-13⚔️ BK AK-13 — The Mentor’s 13. Revealed on 11. Command the Band. Punish the Extremes. ⚔️
This is my 11th release—and that matters. 11 is a sacred number to me, so for release eleven I’m doing something I never planned to do: I’m putting my mentor’s secret 13 MA into the open.
For years, this 13-based MA framework was part of our private playbook—quietly doing work behind the scenes. Now I’m handing it to you fully armed, because I believe in karma in, karma out: I took years of wisdom from the market. I took years of wisdom from the men who taught me. This is one of the ways I give back—with structure, respect, and intent.
🎖 Full Credit — Respect the Origin
The core architecture of BK AK-13 is not mine. It stands firmly on the work of DZIV.
What comes from DZIV:
The Heikin Ashi MA engine (MA calculated on HA Open/High/Low/Close)
The multi-MA engine on the HA feed (ALMA / HMA / SMA / RMA / VWMA / WMA / ZLEMA / EMA)
The Body / Wick / Band zone classification for price
The dynamic body & wick clouds that give this structure its clean visual form
If this framework changes the way you see trend and price location, remember the name: DZIV.
On top of his backbone, I forged the BK AK-13 enhancement layer: trend-strength regimes, background modes, structured band-reversal arrows, momentum acceleration dots, extreme pivot markers, historical band-touch rails, the info panel, and a complete alert suite.
And as always, the “AK” in the name is not branding—it’s honor. It belongs to my mentor A.K. His secret 13 MA is the spine of this system, and his obsession with clarity, patience, and zero shortcuts sits behind every decision in this tool. Above that, all glory and gratitude to Gd—the real source of any wisdom, edge, or endurance we have in this game.
🧠 Why “BK AK-13”?
BK — my mark, the house I’m building.
AK — my mentor, the standard I’m still chasing.
13 — his secret moving average, the length that quietly shaped how I see trend, location, and pressure.
For years, 13 stayed off the public record—used, not discussed. Now, on indicator number 11, I’m putting that weapon in the open: 11th release. Sacred number. Secret 13 revealed, not for hype—but as karmic give-back. Karma in. Karma out.
🧱 What BK AK-13 Actually Is
BK AK-13 is a Heikin Ashi MA battle band with a brain and a conscience.
It does three big things:
Builds a smoothed HA-MA band using Heikin Ashi OHLC to create a cleaner, truer band around price.
Maps price into zones: Body, Upper Wick, Lower Wick, Above Band, Below Band—so every bar has a role.
Assigns a trend regime by computing a normalized trend-strength %, classifying the environment as Weak / Normal / Strong / Extreme.
You’re never guessing: Is this real trend or just drift? Am I in the spine, the wick, or off the rails? Is this where I press, fade, or stand down? The band, zones, and regimes answer that for you.
🎨 Visual Architecture — Band, Clouds, Regimes
Body & Wick Clouds (DZIV’s craft)
Body cloud between HA-MA Open & Close.
Wick clouds between body and HA-MA High/Low.
Color follows trend: bull, bear, or neutral.
You’re not decoding noisy candles—you’re reading the spine and skin of the move.
Background Regime Modes (BK layer)
Standard – background always on, soft trend-follow color.
Hybrid (Extreme + Breaks) – lights only on extreme trend states or reversal break events.
Hybrid (Strong/Extreme + Breaks) – shows strong & extreme regimes, darker tone on true extremes.
Breaks Only – background flashes only on reversal arrows.
When the background goes quiet, you’re in ordinary flow. When it lights up, something is strategic, not cosmetic.
🎯 Weapons Inside BK AK-13
⭐ Trend Change Stars
Stars appear when the internal band trend crosses zero: bull star when it flips negative → positive, bear star from positive → negative. They’re your pivot flags for swing shifts when aligned with your higher timeframe bias.
🔁 Band Reversal Arrows — Edge Flip Logic
Not every band tap—only structured reversals:
Reversal Down (short idea): first a break of the upper band, then later, for the first time, a break of the lower band.
Reversal Up (long idea): first a break of the lower band, then later, for the first time, a break of the upper band.
You can require a close outside the band and set a minimum break distance (% of band range) so only real punches count. These arrows mark campaign flips, not noise.
💡 Momentum Acceleration Dots
In strong trend regimes only:
Green dot = trend accelerating in its own direction (uptrend steepening, downtrend deepening).
Red dot = trend decelerating, even if direction hasn’t flipped yet.
They protect you from chasing late when the engine is dying and from staying stubborn when momentum is bleeding out.
⚠ Extreme Pivot Markers
Pivot highs/lows are found with a configurable lookback and only marked when trend strength at that pivot bar is above your threshold. You’ll see ⚠ above likely exhaustion tops in strong bulls and ⚠ below likely exhaustion lows in strong bears—perfect for final scale-outs, countertrend scouts, and knowing where campaigns commonly run out of blood.
📏 Historical Band-Touch Rails
Over your lookback window, BK AK-13 tracks the highest upper band touch and lowest lower band touch, drawing them as dashed rails. They’re dynamic SR built from real band extremes—ideal for trend targets, fade zones, and stop/scale-out context.
🧭 Info Panel — On-Chart War Room
The Info Panel compresses everything into a single strip: direction + strength codes (BULL STR, BEAR EXT, NEUT WEAK), four segments that brighten as |trend| climbs from weak → normal → strong → extreme, and a zone + deviation label (BDY/UW/LW/AB/BL × OK/AL/EX).
Hover and you get a full tactical brief: trend, momentum change, acceleration, band levels, distances to upper/lower/nearest band in ticks, outer-band streaks, strategic state, plus “Action” guidance and a “What-if” forward scenario. It doesn’t just tell you where you are—it pushes you toward a structured thought process on each bar.
🕹 How to Use BK AK-13 with Intent
1️⃣ Trend-Rider Mode
In Strong/Extreme bull with price in Body or Lower Wick: buy dips into the band (mid/lower) instead of chasing tops; target the upper band / upper rail while structure holds.
In Strong/Extreme bear with price in Body or Upper Wick: sell rallies into the band; target lower band / lower rail while acceleration stays healthy.
The band defines where you’re allowed to do business.
2️⃣ Extreme Snapback Hunter
Prime conditions: trend tagged Extreme, price pressed into the outer band in trend direction, strategic state lit + Hybrid background active. That’s where pressing fresh risk often flips from reward to punishment. Use it to stop adding, start harvesting, or launch controlled mean-reversion probes back to the midline—if your system and risk rules allow it.
3️⃣ Exhaustion & Turn Zones
Watch for confluence: red momentum dots, extreme pivot ⚠ markers, a reversal arrow, and a nearby historical rail or your own key level (Fibs, VWAP, volume structure, etc.). That’s where campaigns often end, traps are set, and new campaigns begin.
🔔 Alerts — The Chart Calls You
Included alerts: Bullish/Bearish Trend Change, Strategic Extreme at Outer Band, Reversal Up/Down, Extreme Pivot High/Low, and Body Zone Entry during Strong Trend. Use them so you respond to events, not impulses.
🔧 Tuning the Extremes — Help Me Perfect the Advanced Side
The extreme thresholds and advanced features are powerful but sensitive, and there is no single perfect universal setting. I’m still tuning them myself across instruments and timeframes: strong/extreme trend thresholds, extreme background thresholds, momentum acceleration threshold, pivot lookback + pivot trend filter, band-touch lookback, and minimum break distance for reversals.
Different markets and timeframes breathe differently.
If you find killer settings for a specific symbol + timeframe, please share:
Instrument & timeframe
Your tuned values for extremes and advanced modules
A few charts showing why they work
Experiment. Dial it in. Then share your best settings for the extremes and advanced features. Let this become a crowd-forged battle manual: I gave you the engine, you tune it to your battleground, and we all benefit from what’s discovered in live fire. Karma in. Karma out.
🤝 Pay It Forward
If BK AK-13 sharpens your read, don’t just flex screenshots—teach structure. Show newer traders body vs wick vs edge. Talk about when you didn’t take a trade because the band said “danger,” not just the wins. Share your settings, charts, and lessons—especially around the extremes and advanced modules. I’m sharing a mentor’s secret on release 11 for a reason. If it blesses you, don’t let it stop with you.
📜 King Solomon’s Lens
King Solomon said: “The prudent sees danger and hides himself, but the simple go on and suffer for it.”
BK AK-13 is built exactly around that dividing line: the simple chase candles at the outer band in extreme regimes and get punished; the prudent see danger in the structure, hide their size, hedge, or reverse with intent.
This indicator won’t make you prudent. It just removes your excuse for being simple.
⚔️ BK AK-13 — The mentor’s secret 13, revealed on 11. Let the band define the field. Let wisdom define your strike.
May Gd bless your eyes, your patience, your settings, and every decision you make at the edge. 🙏
Market Trend statusBullTrading Free Indicator Series
What is the Trend State Machine?
A “trend state machine” that fuses DMI (+DI/−DI) with ADX strength. It avoids bells and whistles and answers three things with minimal rules:
1. Whether the market is range-bound (chop) or trending;
2. If trending, whether it is bullish (long) or bearish (short);
3. The trend intensity tier (Strong / Extreme / Decaying) plus a 0–100 strength score.
1-Minute Quick Start (beginners can stop here)
1. Timeframe – pick your trading anchor first
• Crypto: 5–15m
• Gold: 5m or 15m
• FX: 15–30m
2. Mode – top of the panel: set Mode = Simple.
3. Sensitivity – set Sensitivity (1 conservative – 5 aggressive). Recommended:
• Crypto: 3 (use 4 in high volatility)
• Gold: 2–3
• FX: 2–3
• Indices: 2
4. Read the card (top-right)
• Environment: Range/Invalid, Bull Trend (Watch), Bull Trend (Confirmed) (bearish equivalents apply)
• Add-ons: | Strong, | Extreme, | Decay
• Also shows ADX, Enter/Exit thresholds, ΔDI, and Score.
5. Background & lines
• Green/Red background = in trend; deeper shade = stronger.
• Orange thick line = ADX, Green = +DI, Red = −DI; shaded band between lines is the enter/exit zone.
6. Minimal execution rules
• Trade with the trend only: consider entries only when Environment = Confirmed and direction is bull/bear.
• Prioritize strength: when Strong Trend triggers or Score > 70, prefer trend-following adds / enable trailing take-profit.
• Exit: when Exit/Flip alert fires, or after Decay if ADX falls back below the enter threshold, reduce/close.
Note: In Simple mode, built-in hysteresis (Enter > Exit) cuts whipsaws significantly—no need to hand-tune thresholds.
How to Use Alerts
• Three built-in fixed alerts:
1. Trend Confirmed (Bull/Bear) — entry/add trigger
2. Strong Trend — momentum reinforcement (chase/add or tighten trailing TP)
3. Exit or Flip — scale-out/close/observe the other side
• Want dynamic messages with numbers? Check “Enable dynamic alerts (alert())” and, when creating the alert, choose Any alert() function call.
Parameter Guidance (rules of thumb)
• Sensitivity: Higher = earlier entries but more false signals; lower = later confirmation but steadier.
• Timeframe: The smaller the timeframe, the lower the sensitivity you usually need; on higher timeframes you may nudge it up.
• Combos:
• Crypto: 5m/15m + Sens 3 (4 in heavy vol)
• Gold: 5m/15m + Sens 2–3
• FX: 15m/30m + Sens 2–3
• Indices: 15m/30m + Sens 2
Pro Mode Highlights (optional)
• Threshold Mode: switch from Fixed (default) to Percentile Adaptive for better robustness across regimes/markets.
• ΔDI / Slope / Hold / Cool-down:
• ΔDI min separation filters weak price/volume divergences.
• ADX slope > threshold on entry rejects “breakouts without growing strength”.
• Min hold bars confirms before output to reduce whipsaws.
• Cool-down bars prevent immediate re-entry after exit/flip.
• MTF Aggregation: enable MTF, default 3× current timeframe, HTF weight 0.3–0.5.
• Turn on Require HTF not opposite & HTF_ADX ≥ exit threshold to effectively filter higher-TF noise.
Reading Cheat Sheet (what you see = what it means)
• Environment: Range/Invalid → Stand down; avoid counter-trend.
• Trend (Watch) → Just entered the zone; wait for Confirmed or buy the pullback with small size.
• Trend (Confirmed) → Trend-following allowed; use Score and Strong/Decay to size/manage.
• Strong Trend → Consider chasing/relaxing TP; momentum is increasing.
• Extreme → Overheated; be cautious chasing—favor trailing to lock gains.
• Decay → Momentum bending down; prepare to trim or tighten stops.
Common Pitfalls & Fixes
• Whipsaws in ranges → Lower sensitivity or move up a timeframe; in Pro mode, enable Slope filter.
• Confirmation too late → During Trend (Watch), try a probe with smaller size; add on confirmation.
• Cross-asset differences → Use Percentile thresholds and MTF weight, or adjust via market presets (Gold/FX/Index).
• Single-signal bias → Always combine Environment + Score + Strong/Decay to avoid tunnel vision.
⸻
Disclaimer: This tool is for educational and research purposes only and does not constitute investment advice or a promise of profit. Trading involves risk; you are solely responsible for your gains and losses.
BullTrading免费指标系列
趋势状态机 是什么：
一个把 DMI(+DI/-DI) 与 ADX 强度合成的“趋势状态机”。它不追求花哨，而是用最小规则输出三件事：
1. 市场当前是 震荡还是趋势；
2. 如是趋势，是 多还是 空；
3. 趋势的 强弱等级（强趋势/极端/衰减）与一个 0–100 的强度分数。
一分钟上手（新手用这个就够）
1. 时间周期：先选你交易的主周期（例：加密 5–15m；黄金 5m 或 15m；外汇 15–30m）。
2. 模式：面板最上方“模式”= 简单。
3. 敏感度：设“敏感度（1保守–5激进）”。推荐：
• Crypto：3（波动大可 4）
• Gold：2–3
• FX：2–3
• 指数/股指：2
4. 读卡片（右上角）
• 环境：震荡/无效、多头趋势(观察)、多头趋势(已确认)（空头同理）
• 附加：｜强趋势、｜极端、｜衰减
• 同时显示 ADX、进入/退出阈值、ΔDI、评分。
5. 底色 & 线
• 绿色/红色底色=处于趋势；颜色越实=越强。
• 橙色粗线=ADX，绿色=+DI，红色=-DI；中间阴影为进入/退出带。
6. 最小执行规则
• 只顺势：环境=已确认 且方向为多/空时才考虑进场。
• 强势优先：出现 强趋势 或评分>70 时，优先做顺势加仓/启动追踪止盈。
• 退出：出现 退出/翻转 告警，或 衰减 后 ADX 再跌回进入阈值下方时，减仓/平仓。
提醒：简单模式下，脚本已内置迟滞（进入>退出），可显著减少抖动；无需再手动校准阈值。
告警怎么用
• 已内置三条固定告警：
1. 趋势已确认（多/空） — 入场/加仓触发器
2. 强趋势 — 趋势强化（可做追击或加速移动止盈）
3. 退出或翻转 — 减仓/止盈/反向观察
• 想带数值的动态文案：勾选“启用动态告警 alert()”，创建告警时选择 Any alert() function call。
参数建议（简易法则）
• 敏感度：更激进（数字大）=更早进场但更易假信号；更保守（数字小）=更迟确认但更稳。
• 时间周期：越小周期越需要降低敏感度；越大周期可略升敏感度。
• 组合：
• Crypto：5m/15m + 敏感度 3（波动大时 4）
• Gold：5m/15m + 敏感度 2–3
• FX：15m/30m + 敏感度 2–3
• 指数：15m/30m + 敏感度 2
专业模式要点（进阶可选）
• 阈值模式：从“固定阈值（默认）”切到“百分位自适应”，在大波动/换市场时更鲁棒。
• ΔDI/斜率/驻留/冷却：
• ΔDI 最小分离度 过滤弱量价背离；
• 进入需 ADX 斜率>阈值 可拒绝“强度不增”的假突破；
• 最小驻留K数 确认后再输出，减少回撤抖动；
• 冷却K数 防止来回打脸。
• MTF 聚合：勾选“启用 MTF”，默认自动 3× 当前周期，HTF 权重 0.3–0.5。
• 要求HTF不反向且HTF_ADX≥退出阈值 打开，能有效剔除逆大级别噪音。
读图速查（你看到=代表什么）
• 环境：震荡/无效 → 暂停；不要逆势开单。
• 趋势(观察) → 刚进入阈值，等待 已确认 或回踩二次确认。
• 趋势(已确认) → 允许顺势；用评分和“强趋势/衰减”微调仓位。
• 强趋势 → 追击或放宽止盈，趋势动能在增强。
• 极端 → 过热区；谨慎追高，更多用移动止盈锁定。
• 衰减 → 动能下弯，准备减仓或收紧止盈。
常见坑 & 对策
• 在震荡箱体频繁进出：降低敏感度或升周期；专业模式勾选“斜率过滤”。
• 确认太慢错过起点：在确认前的“趋势(观察)”阶段，可用更小仓位的试探单，确认后加仓。
• 不同品种差异大：用“百分位”阈值与 MTF 权重；或按市场预设（Gold/FX/Index）微调。
• 只看一个信号：至少同时看 环境状态 + 评分 + 强/衰 三个维度，避免单指标偏差。
本指标仅供教育与研究，不构成投资建议或收益承诺；交易有风险，盈亏自负。
Gildenburg ValueGildenburg Value
Purpose: Trend-strength indicator using three moving averages (fast/mid/slow) to identify Bull/Bear/Neutral zones and generate precise crossover signals.
Default parameters: fast = 8, mid = 13, slow = 21, MA type = EMA.
Zones:
Bull: fast > mid > slow
Bear: fast < mid < slow
Neutral: any other ordering
Signals:
BUY: fast crosses above mid, the post-crossing order is fast > mid > slow, and close > slow.
SELL: fast crosses below mid, the post-crossing order is fast < mid < slow, and close < slow.
Optional filter: minimum percentage gap between fast and mid to ignore weak crosses.
Visualization: three MAs on the chart, optional cloud between fast and mid colored by zone, signal arrows/labels, and alertconditions.
Adjustable settings: MA type (SMA/EMA/WMA/VWMA), MA periods, cloud on/off, show signals, minimum distance threshold.
Use to confirm trends and filter false entries.
Opening Range Breakout with Multi-Timeframe Liquidity]═══════════════════════════════════════
OPENING RANGE BREAKOUT WITH MULTI-TIMEFRAME LIQUIDITY
═══════════════════════════════════════
A professional Opening Range Breakout (ORB) indicator enhanced with multi-timeframe liquidity detection, trading session visualization, volume analysis, and trend confirmation tools. Designed for intraday trading with comprehensive alert system.
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WHAT THIS INDICATOR DOES
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This indicator combines multiple trading concepts:
- Opening Range Breakout (ORB) - Customizable time period detection with automatic high/low identification
- Multi-Timeframe Liquidity - HTF (Higher Timeframe) and LTF (Lower Timeframe) key level detection
- Trading Sessions - Tokyo, London, New York, and Sydney session visualization
- Volume Analysis - Volume spike detection and strength measurement
- Multi-Timeframe Confirmation - Trend bias from higher timeframes
- EMA Integration - Trend filter and dynamic support/resistance
- Smart Alerts - Quality-filtered breakout notifications
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HOW IT WORKS
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OPENING RANGE BREAKOUT (ORB):
Concept:
The Opening Range is a period at the start of a trading session where price establishes an initial high and low. Breakouts beyond this range often indicate the direction of the day's trend.
Detection Method:
- Default: 15-minute opening range (configurable)
- Custom Range: Set specific session times with timezone support
- Automatically identifies ORH (Opening Range High) and ORL (Opening Range Low)
- Tracks ORB mid-point for reference
Range Establishment:
1. Session starts (or custom time begins)
2. Tracks highest high and lowest low during the period
3. Range confirmed at end of opening period
4. Levels extend throughout the session
Breakout Detection:
- Bullish Breakout: Close above ORH
- Bearish Breakout: Close below ORL
- Mid-point acts as bias indicator
Visual Display:
- Shaded box during range formation
- Horizontal lines for ORH, ORL, and mid-point
- Labels showing level values
- Color-coded fills based on selected method
Fill Color Methods:
1. Session Comparison:
- Green: Current OR mid > Previous OR mid
- Red: Current OR mid < Previous OR mid
- Gray: Equal or first session
- Shows day-over-day momentum
2. Breakout Direction (Recommended):
- Green: Price currently above ORH (bullish breakout)
- Red: Price currently below ORL (bearish breakout)
- Gray: Price inside range (no breakout)
- Real-time breakout status
MULTI-TIMEFRAME LIQUIDITY:
Two-Tier System for comprehensive level identification:
HTF (Higher Timeframe) Key Liquidity:
- Default: 4H timeframe (configurable to Daily, Weekly)
- Identifies major institutional levels
- Uses pivot detection with adjustable parameters
- Suitable for swing highs/lows where large orders rest
LTF (Lower Timeframe) Key Liquidity:
- Default: 1H timeframe (configurable)
- Provides precision entry/exit levels
- Finer granularity for intraday trading
- Captures minor swing points
Calculation Method:
- Pivot high/low detection algorithm
- Configurable left bars (lookback) and right bars (confirmation)
- Timeframe multiplier for accurate multi-timeframe detection
- Automatic level extension
Mitigation System:
- Tracks when levels are swept (broken)
- Configurable mitigation type: Wick or Close-based
- Option to remove or show mitigated levels
- Display limit prevents chart clutter
Asset-Specific Optimization:
The indicator includes quick reference settings for different assets:
- Major Forex (EUR/USD, GBP/USD): Default settings optimal
- Crypto (BTC/ETH): Left=12, Right=4, Display=7
- Gold: HTF=1D, Left=20
TRADING SESSIONS:
Four Major Sessions with Full Customization:
Tokyo Session:
- Default: 04:00-13:00 UTC+4
- Asian trading hours
- Often sets daily range
London Session:
- Default: 11:00-20:00 UTC+4
- Highest liquidity period
- Major institutional activity
New York Session:
- Default: 16:00-01:00 UTC+4
- US market hours
- High-impact news events
Sydney Session:
- Default: 01:00-10:00 UTC+4
- Earliest Asian activity
- Lower volatility
Session Features:
- Shaded background boxes
- Session name labels
- Optional open/close lines
- Session high/low tracking with colored lines
- Each session has independent color settings
- Fully customizable times and timezones
VOLUME ANALYSIS:
Volume-Based Trade Confirmation:
Volume MA:
- Configurable period (default: 20)
- Establishes average volume baseline
- Used for spike detection
Volume Spike Detection:
- Identifies when volume exceeds MA * multiplier
- Default: 1.5x average volume
- Confirms breakout strength
Volume Strength Measurement:
- Calculates current volume as percentage of average
- Shows relative volume intensity
- Used in alert quality filtering
High Volume Bars:
- Identifies bars above 50th percentile
- Additional confirmation layer
- Indicates institutional participation
MULTI-TIMEFRAME CONFIRMATION:
Trend Bias from Higher Timeframes:
HTF 1 (Trend):
- Default: 1H timeframe
- Uses EMA to determine intermediate trend
- Compares current timeframe EMA to HTF EMA
HTF 2 (Bias):
- Default: 4H timeframe
- Uses 50 EMA for longer-term bias
- Confirms overall market direction
Bias Classifications:
- Bullish Bias: HTF close > HTF 50 EMA AND Current EMA > HTF1 EMA
- Bearish Bias: HTF close < HTF 50 EMA AND Current EMA < HTF1 EMA
- Neutral Bias: Mixed signals between timeframes
EMA Stack Analysis:
- Compares EMA alignment across timeframes
- +1: Bullish stack (lower TF EMA > higher TF EMA)
- -1: Bearish stack (lower TF EMA < higher TF EMA)
- 0: Neutral/crossed
Usage:
- Filters false breakouts
- Confirms trend direction
- Improves trade quality
EMA INTEGRATION:
Dynamic EMA for Trend Reference:
Features:
- Configurable period (default: 20)
- Customizable color and width
- Acts as dynamic support/resistance
- Trend filter for ORB trades
Application:
- Above EMA: Favor long breakouts
- Below EMA: Favor short breakouts
- EMA cross: Potential trend change
- Distance from EMA: Momentum gauge
SMART ALERT SYSTEM:
Quality-Filtered Breakout Notifications:
Alert Types:
1. Standard ORB Breakout
2. High Quality ORB Breakout
Quality Criteria:
- Volume Confirmation: Volume > 1.2x average
- MTF Confirmation: Bias aligned with breakout direction
Standard Alert:
- Basic breakout detection
- Price crosses ORH or ORL
- Icon: 🚀 (bullish) or 🔻 (bearish)
High Quality Alert:
- Both volume AND MTF confirmed
- Stronger probability setup
- Icon: 🚀⭐ (bullish) or 🔻⭐ (bearish)
Alert Information Includes:
- Alert quality rating
- Breakout level and current price
- Volume strength percentage (if enabled)
- MTF bias status (if enabled)
- Recommended action
One Alert Per Bar:
- Prevents alert spam
- Uses flag system to track sent alerts
- Resets on new ORB session
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HOW TO USE
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OPENING RANGE SETUP:
Basic Configuration:
1. Select time period for opening range (default: 15 minutes)
2. Choose fill color method (Breakout Direction recommended)
3. Enable historical data display if needed
Custom Range (Advanced):
1. Enable Custom Range toggle
2. Set specific session time (e.g., 0930-0945)
3. Select appropriate timezone
4. Useful for specific market opens (NYSE, LSE, etc.)
LIQUIDITY LEVELS SETUP:
Quick Configuration by Asset:
- Forex: Use default settings (Left=15, Right=5)
- Crypto: Set Left=12, Right=4, Display=7
- Gold: Set HTF=1D, Left=20
HTF Liquidity:
- Purpose: Major support/resistance levels
- Recommended: 4H for day trading, 1D for swing trading
- Use as profit targets or reversal zones
LTF Liquidity:
- Purpose: Entry/exit refinement
- Recommended: 1H for day trading, 4H for swing trading
- Use for position management
Mitigation Settings:
- Wick-based: More sensitive (default)
- Close-based: More conservative
- Remove or Show mitigated levels based on preference
TRADING SESSIONS SETUP:
Enable/Disable Sessions:
- Master toggle for all sessions
- Individual session controls
- Show/hide session names
Session High/Low Lines:
- Enable to see session extremes
- Each session has custom colors
- Useful for range trading
Customization:
- Adjust session times for your broker
- Set timezone to match your location
- Customize colors for visibility
VOLUME ANALYSIS SETUP:
Enable Volume Analysis:
1. Toggle on Volume Analysis
2. Set MA length (20 recommended)
3. Adjust spike multiplier (1.5 typical)
Usage:
- Confirm breakouts with volume
- Identify climactic moves
- Filter false signals
MULTI-TIMEFRAME SETUP:
HTF Selection:
- HTF 1 (Trend): 1H for day trading, 4H for swing
- HTF 2 (Bias): 4H for day trading, 1D for swing
Interpretation:
- Trade only with bias alignment
- Neutral bias: Be cautious
- Bias changes: Potential reversals
EMA SETUP:
Configuration:
- Period: 20 for responsive, 50 for smoother
- Color: Choose contrasting color
- Width: 1-2 for visibility
Usage:
- Filter trades: Long above, Short below
- Dynamic support/resistance reference
- Trend confirmation
ALERT SETUP:
TradingView Alert Creation:
1. Enable alerts in indicator settings
2. Enable ORB Breakout Alerts
3. Right-click chart → Add Alert
4. Select this indicator
5. Choose "Any alert() function call"
6. Configure delivery method (mobile, email, webhook)
Alert Filtering:
- All alerts include quality rating
- High Quality alerts = Volume + MTF confirmed
- Standard alerts = Basic breakout only
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TRADING STRATEGIES
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CLASSIC ORB STRATEGY:
Setup:
1. Wait for opening range to complete
2. Price breaks and closes above ORH or below ORL
3. Volume > average (if enabled)
4. MTF bias aligned (if enabled)
Entry:
- Bullish: Buy on break above ORH
- Bearish: Sell on break below ORL
- Consider retest entries for better risk/reward
Stop Loss:
- Bullish: Below ORL or range mid-point
- Bearish: Above ORH or range mid-point
- Adjust based on volatility
Targets:
- Initial: Range width extension (ORH + range width)
- Secondary: HTF liquidity levels
- Final: Session high/low or major support/resistance
ORB + LIQUIDITY CONFLUENCE:
Enhanced Setup:
1. Opening range established
2. HTF liquidity level near or beyond ORH/ORL
3. Breakout occurs with volume
4. Price targets the liquidity level
Entry:
- Enter on ORB breakout
- Target the HTF liquidity level
- Use LTF liquidity for position management
Management:
- Partial profits at ORB + range width
- Move stop to breakeven at LTF liquidity
- Final exit at HTF liquidity sweep
ORB REJECTION STRATEGY (Counter-Trend):
Setup:
1. Price breaks above ORH or below ORL
2. Weak volume (below average)
3. MTF bias opposite to breakout
4. Price closes back inside range
Entry:
- Failed bullish break: Short below ORH
- Failed bearish break: Long above ORL
Stop Loss:
- Beyond the failed breakout level
- Or beyond session extreme
Target:
- Opposite end of opening range
- Range mid-point for partial profit
SESSION-BASED ORB TRADING:
Tokyo Session:
- Typically narrower ranges
- Good for range trading
- Wait for London open breakout
London Session:
- Highest volume and volatility
- Strong ORB setups
- Major liquidity sweeps common
New York Session:
- Strong trending moves
- News-driven volatility
- Good for momentum trades
Sydney Session:
- Quieter conditions
- Suitable for range strategies
- Sets up Tokyo session
EMA-FILTERED ORB:
Rules:
- Only take bullish breaks if price > EMA
- Only take bearish breaks if price < EMA
- Ignore counter-trend breaks
Benefits:
- Reduces false signals
- Aligns with larger trend
- Improves win rate
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CONFIGURATION GUIDE
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OPENING RANGE SETTINGS:
Time Period:
- 15 min: Standard for most markets
- 30 min: Wider range, fewer breakouts
- 60 min: For slower markets or swing trades
Custom Range:
- Use for specific market opens
- NYSE: 0930-1000 EST
- LSE: 0800-0830 GMT
- Set timezone to match exchange
Historical Display:
- Enable: See all previous session data
- Disable: Cleaner chart, current session only
LIQUIDITY SETTINGS:
Left Bars (5-30):
- Lower: More frequent, sensitive levels
- Higher: Fewer, more significant levels
- Recommended: 15 for most markets
Right Bars (1-25):
- Confirmation period
- Higher: More reliable, less frequent
- Recommended: 5 for balance
Display Limit (1-20):
- Number of active levels shown
- Higher: More context, busier chart
- Recommended: 7 for clarity
Extension Options:
- Short: Levels visible near formation
- Current: Extended to current bar (recommended)
- Max: Extended indefinitely
VOLUME SETTINGS:
MA Length (5-50):
- Shorter: More responsive to spikes
- Longer: Smoother baseline
- Recommended: 20 for balance
Spike Multiplier (1.0-3.0):
- Lower: More sensitive spike detection
- Higher: Only extreme spikes
- Recommended: 1.5 for day trading
MULTI-TIMEFRAME SETTINGS:
HTF 1 (Trend):
- 5m chart: Use 15m or 1H
- 15m chart: Use 1H or 4H
- 1H chart: Use 4H or 1D
HTF 2 (Bias):
- One level higher than HTF 1
- Provides longer-term context
- Don't use same as HTF 1
EMA SETTINGS:
Length:
- 20: Responsive, more signals
- 50: Smoother, stronger filter
- 200: Long-term trend only
Style:
- Choose contrasting color
- Width 1-2 for visibility
- Match your trading style
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BEST PRACTICES
───────────────────────────────────────
Chart Timeframe Selection:
- ORB Trading: Use 5m or 15m charts
- Session Review: Use 1H or 4H charts
- Swing Trading: Use 1H or 4H charts
Quality Over Quantity:
- Wait for high-quality alerts (volume + MTF)
- Avoid trading every breakout
- Focus on confluence setups
Risk Management:
- Position size based on range width
- Wider ranges = smaller positions
- Use stop losses always
- Take partial profits at targets
Market Conditions:
- Best results in trending markets
- Reduce position size in choppy conditions
- Consider session overlaps for volatility
- Avoid trading near major news if inexperienced
Continuous Improvement:
- Track win rate by session
- Note which confluence factors work best
- Adjust settings based on market volatility
- Review performance weekly
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PERFORMANCE OPTIMIZATION
───────────────────────────────────────
This indicator is optimized with:
- max_bars_back declarations for efficient processing
- Conditional calculations based on enabled features
- Proper memory management for drawing objects
- Minimal recalculation on each bar
Best Practices:
- Disable unused features (sessions, MTF, volume)
- Limit historical display to reduce rendering
- Use appropriate timeframe for your strategy
- Clear old drawing objects periodically
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EDUCATIONAL DISCLAIMER
───────────────────────────────────────
This indicator combines established trading concepts:
- Opening Range Breakout theory (price action)
- Liquidity level detection (pivot analysis)
- Session-based trading (time-of-day patterns)
- Volume analysis (confirmation technique)
- Multi-timeframe analysis (trend alignment)
All calculations use standard technical analysis methods:
- Pivot high/low detection algorithms
- Moving averages for trend and volume
- Session time filtering
- Timeframe security functions
The indicator identifies potential trading setups but does not predict future price movements. Success requires proper application within a complete trading strategy including risk management, position sizing, and market context.
───────────────────────────────────────
USAGE DISCLAIMER
───────────────────────────────────────
This tool is for educational and analytical purposes. Opening Range Breakout trading involves substantial risk. The alert system and quality filters are designed to identify potential setups but do not guarantee profitability. Always conduct independent analysis, use proper risk management, and never risk capital you cannot afford to lose. Past performance does not indicate future results. Trading intraday breakouts requires experience and discipline.
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CREDITS & ATTRIBUTION
───────────────────────────────────────
ORIGINAL SOURCE:
This indicator builds upon concepts from LuxAlgo's-ORB
FVG MagicFVG Magic — Fair Value Gaps with Smart Mitigation, Inversion & Auto-Clean-up
FVG Magic finds every tradable Fair Value Gap (FVG), shows who powered it, and then manages each gap intelligently as price interacts with it—so your chart stays actionable and clean.
Attribution
This tool is inspired by the idea popularized in “Volumatic Fair Value Gaps ” by BigBeluga (licensed CC BY-NC-SA 4.0). Credit to BigBeluga for advancing FVG visualization in the community.
Important: This is a from-scratch implementation—no code was copied from the original. I expanded the concept substantially with a different detection stack, a gap state machine (ACTIVE → 50% SQ → MITIGATED → INVERSED), auto-clean up rules, lookback/nearest-per-side pruning, zoom-proof volume meters, and timeframe auto-tuning for 15m/H1/H4.
What makes this version more accurate
Full-coverage detection (no “missed” gaps)
Default ICT-minimal rule (Bullish: low > high , Bearish: high < low ) catches all valid 3-candle FVGs.
Optional Strict filter (stricter structure checks) for traders who prefer only “clean” gaps.
Optional size percentile filter—off by default so nothing is hidden unless you choose to filter.
Correct handling of confirmations (wick vs close)
Mitigation Source is user-selectable: high/low (wick-based) or close (strict).
This avoids false “misses” when you expect wick confirmations (50% or full fill) but your logic required closes.
State-aware labelling to prevent misleading data
The Bull%/Bear% meter is shown only while a gap is ACTIVE.
As soon as a gap is 50% SQ, MITIGATED, or INVERSED, the meter is hidden and replaced with a clear tag—so you never read stale participation stats.
Robust zoom behaviour
The meter uses a fixed bar-width (not pixels), so it stays proportional and readable at any zoom level.
Deterministic lifecycle (no stale boxes)
Remove on 50% SQ (instant or delayed).
Inversion window after first entry: if price enters but doesn’t invert within N bars, the box auto-removes once fully filled.
Inversion clean up: after a confirmed flip, keep for N bars (context) then delete (or 0 = immediate).
Result: charts auto-maintain themselves and never “lie” about relevance.
Clarity near current price
Nearest-per-side (keep N closest bullish & bearish gaps by distance to the midpoint) focuses attention where it matters without altering detection accuracy.
Lookback (bars) ensures reproducible behaviour across accounts with different data history.
Timeframe-aware defaults
Sensible auto-tuning for 15m / H1 / H4 (right-extension length, meter width, inversion windows, clean up bars) to reduce setup friction and improve consistency.
What it does (under the hood)
Detects FVGs using ICT-minimal (default) or a stricter rule.
Samples volume from a 10× lower timeframe to split participation into Bull % / Bear % (sum = 100%).
Manages each gap through a state machine:
ACTIVE → 50% SQ (midline) → MITIGATED (full) → INVERSED (SR flip after fill).
Auto-clean up keeps only relevant levels, per your rules.
Dashboard (top-right) displays counts by side and the active state tags.
How to use it
First run (show everything)
Use Strict FVG Filter: OFF
Enable Size Filter (percentile): OFF
Mitigation Source: high/low (wick-based) or close (stricter), as you prefer.
Remove on 50% SQ: ON, Delay: 0
Read the context
While ACTIVE, use the Bull%/Bear% meter to gauge demand/supply behind the impulse that created the gap.
Confluence with your HTF structure, sessions, VWAP, OB/FVG, RSI/MACD, etc.
Trade interactions
50% SQ: often the highest-quality interaction; if removal is ON, the box clears = “job done.”
Full mitigation then rejection through the other side → tag changes to INVERSED (acts like SR). Keep for N bars, then auto-remove.
Keep the chart tidy (optional)
If too busy, enable Size Filter or set Nearest per side to 2–4.
Use Lookback (bars) to make behaviour consistent across symbols and histories.
Inputs (key ones)
Use Strict FVG Filter: OFF(default)/ON
Enable Size Filter (percentile): OFF(default)/ON + threshold
Mitigation Source: high/low or close
Remove on 50% SQ + Delay
Inversion window after entry (bars)
Remove inversed after (bars)
Lookback (bars), Nearest per side (N)
Right Extension Bars, Max FVGs, Meter width (bars)
Colours: Bullish, Bearish, Inversed fill
Suggested defaults (per TF)
15m: Extension 50, Max 12, Inversion window 8, Clean up 8, Meter width 20
H1: Extension 25, Max 10, Inversion window 6, Clean up 6, Meter width 15
H4: Extension 15, Max 8, Inversion window 5, Clean up 5, Meter width 10
Notes & edge cases
If a wick hits 50% or the far edge but state doesn’t change, you’re likely on close mode—switch to high/low for wick-based behaviour.
If a gap disappears, it likely met a clean up condition (50% removal, inversion window, inversion clean up, nearest-per-side, lookback, or max-cap).
Meters are hidden after ACTIVE to avoid stale percentages.
TheStrat: Failed 2 + 2 ConfirmationTheStrat: Failed-2 + 2 Confirmation (2-2 option)
This indicator spots a classic Strat “failed 2 → 2” sequence on consecutive bars, and fires alerts only when both conditions are met:
Failed-2 bar on the prior candle
Failed 2-Down (F2D): Took the prior low but closed green
Failed 2-Up (F2U): Took the prior high but closed red
By default this is strict (excludes outside bars/3’s). You can toggle to allow 3’s if you prefer a looser definition.
Current bar is a clean “2”
2U = broke prior high only; 2D = broke prior low only (excludes 3’s).
Optional: Require flip so the second bar must reverse direction (F2D→2U for longs, F2U→2D for shorts). Turn this off if you want any 2 after a Failed-2.
What gets plotted
Tiny markers on each Failed-2 as they occur (F2U/F2D).
A label when the combo confirms on the next bar:
Bullish: F2D→2U (or F2D→2 if flip is off) below the bar
Bearish: F2U→2D (or F2U→2) above the bar
Alerts (set from the “Add Alert” dialog)
Failed2 + 2 (Bull): Prior bar was F2D; current bar is a 2 (flip optional).
Failed2 + 2 (Bear): Prior bar was F2U; current bar is a 2 (flip optional).
Inputs
Colors for Failed-2 markers and combo labels
Require flip (on by default) → focuses on true 2-2 reversals
Count outside bars (3) as Failed-2? (off by default) → stricter = fewer, cleaner signals
How to trade it (typical Strat style)
Bullish combo (F2D→2U): Entry on break of the combo bar’s high; risk under its low (or under the prior bar).
Bearish combo (F2U→2D): Entry on break of the combo bar’s low; risk above its high.
First target = prior pivot (“magnitude”), then nearby pivots/BF levels. Improve odds by aligning with time-frame continuity (e.g., day/week in your direction).
Notes / Best practices
Uses barstate.isconfirmed → signals/labels are based on closed bars to avoid repaint.
If you see too many Failed-2 tags, keep outside-bar counting OFF (strict mode).
Gaussian RibbonSummary
Adaptive Gaussian ribbon with inner/outer sigma bands and soft regime colors—green trend, red pressure, gray neutral.
What it is
A clean Gaussian filter ribbon that maps trend + volatility without the jitter. It uses a Gaussian smoother, a tiny EMA basis, and two standard-deviation bands. Color fades with distance from the basis, and flips softly (no knife-edge).
How it works
Gaussian core: IIR-style smoothing on your chosen source (default hlc3).
Basis: EMA(3) of the Gaussian for a steadier slope.
Bands: Inner = Basis ± (σ × Inner Mult), Outer = Basis ± (σ × Outer Mult).
Regime: z-score → softsign → EMA(3) → bull / neutral / bear.
Faded look: opacity ramps with distance; neutral turns gray.
What “Regime” Means (Simple)
A regime is the market’s “weather.” It shifts between Bull, Neutral, and Bear. Different tactics work in each.
How this indicator detects regime
Builds a smoothed score of price vs. the basis (z-score → softsign → EMA).
Score > 0 = Bull, score < 0 = Bear. Inside the inner band = Neutral filter to cut noise.
Color changes are soft (faded) so flips don’t knife-edge.
Playbook (What to do)
Bull (Green): Buy pullbacks to the inner band; add on strength; cut fast if price falls back inside the ribbon.
Neutral (Gray): Reduce size, fade extremes, or stand down. Wait for a clean break in either direction.
Bear (Red): Sell/short rallies to the ribbon; protect capital; flip long only after a confirmed regime turn.
For Pros (Tuning & Confirmation)
Timeframe bias: Use higher TF (1W/2W) for context; trade on 1D/4H in the higher-TF direction.
Smoother vs faster: Increase Length to reduce flip-flop; decrease for earlier turns.
Vol filter: Widen Outer/Inner multipliers in choppy markets; narrow in strong trends.
Confirm: Use structure (HH/HL vs LH/LL), volume/OBV, or your MA; ribbon = context, not a standalone trigger.
How to read it
Green = trend support; pullbacks to the inner band are typical buy-the-dip zones.
Gray = inside ribbon; chop/mean reversion. Size down or wait.
Red = trend pressure; rallies into ribbon are fade zones until regime flips.
Opacity increases with distance = stronger momentum.
Good starting presets
Macro (1W–2W): Length 90–110, Outer 2.3, Inner 1.3, Source hlc3.
Swing (1D): Length 60–80, Outer 2.0, Inner 1.4.
Intraday (1–4H): Length 30–40, Outer 1.8, Inner 1.2.
Options
Opens in a separate pane (overlay=false). Set overlay=true to place on candles (consider +5 transparency on fills).
Watermark is “CAYEN” (table-based, no editor drama).
Why it’s “safe”
No repaint. No lookahead; uses only closed-bar data.
Deterministic state and divide-by-zero guards.
Limitations
It’s a context tool. It will lag at regime turns (by design). Use structure/volume to time entries.
Credits
Script by Jason Cayen. Gaussian smoothing is classic DSP math (public domain).
Release notes (v1.0)
Initial public release: faded bands, neutral zone, soft regime colors, Non-repainting; pane by default.
Dual ATR Trailing Stop with Buy/Sell SignalsThis indicator applies two ATR-based trailing stops (Fast and Slow) together with a four-state market classifier to visualize trend strength and place crossover Buy/Sell markers. It is a trend-following tool and does not predict future prices. Use it alongside independent analysis and risk management.
How it works
The Fast trail uses ATR with period 5 and multiplier 0.5 by default. The Slow trail uses ATR with period 10 and multiplier 3.0 by default. When price remains above the prior trail, the new trail equals the maximum of the previous trail and close minus the stop-loss distance derived from ATR. When price remains below the prior trail, the new trail equals the minimum of the previous trail and close plus that distance. On a side switch the trail is re-anchored from the current bar. The Fast trail reacts more quickly, while the Slow trail aims to confirm direction and filter noise.
Signals
Buy is when the Fast trail crosses above the Slow trail. Sell is when the Fast trail crosses below the Slow trail. These crossovers are continuation cues rather than standalone trade advice.
Four-state coloring
Strong Bull occurs when Fast is above Slow, the close is above Slow, and the low is above Slow. Bull with Pullback occurs when Fast is above Slow and the close is above Slow, but the low dips below Slow. Strong Bear occurs when Slow is above Fast, the close is below Slow, and the high is below Slow. Bear with Bounce occurs when Slow is above Fast and the close is below Slow, but the high pierces above Slow.
Directional bias
Bias is inferred by comparing which of Strong Bull or Strong Bear occurred more recently using a barsSince comparison. The most recent state defines the current bias and can help filter frequent flips in ranges.
Inputs
You can adjust Fast ATR period from 1 to 50 and its multiplier from 0.1 to 5.0. You can adjust Slow ATR period from 1 to 50 and its multiplier from 0.1 to 10.0. Optional toggles control candle coloring, trail visibility, fill between trails, alerts, and the information panel.
On-chart outputs
The indicator plots the Fast trail and the Slow trail, with optional fill between them. It can show Buy and Sell markers at crossovers. The optional information panel can display last signal side, entry price, current price, P&L since last signal, current market state, Fast and Slow ATR values, and inferred bias.
Alerts
Available alerts are Fast crossing above Slow, Fast crossing below Slow, entering Strong Bull, and entering Strong Bear.
Usage guidelines
For trend following, many users wait for a Buy crossover together with Strong Bull coloring and then manage risk against the Slow trail. For scalping, consider lower timeframes and a smaller Fast ATR such as period 3 and multiplier 0.3, while avoiding over-optimization. When publishing screenshots, use a clean chart with the symbol, timeframe, and the indicator’s name visible.
Limitations and realism
The tool works best in directional markets; ranging conditions may cause frequent side switches. It is designed for standard OHLC charts only and is not intended for Heikin Ashi, Renko, Kagi, Point & Figure, or Range charts. No claims are made about accuracy, profits, or future performance. Always use position sizing, stop-losses, and additional confirmation.
Compatibility and version
Pine Script v6. Default inputs in the publication match the values described above. Version 1.0 initial public release.
Notes
For questions and feedback, please use the comments section on the script page.
RSI DD – RSI Divergence DetectorRSI DD – RSI Divergence Detector (closed-source):
What it does:
Detects and plots regular and hidden RSI–price divergences using confirmed pivots on both series. Lines are drawn between the two most recent qualifying pivots; optional marks highlight OB/OS peaks at confirmation.
Detection method:
1. Compute RSI on a user-selected source and length. Optional EMA/SMA smoothing controls lag.
2. Build price and RSI pivot points with left/right lookbacks; a pivot confirms on the bar where right completes.
3. Pair the latest two pivots of the same type within a user-defined bar-distance window:
• Regular Bullish: price makes a lower low while RSI makes a higher low.
• Hidden Bullish: price makes a higher low while RSI makes a lower low.
• Regular Bearish: price makes a higher high while RSI makes a lower high.
• Hidden Bearish: price makes a lower high while RSI makes a higher high.
4. When a valid pair is found, draw a line on the RSI pane from the first RSI pivot to the second; color encodes divergence type.
5. Optional ticks mark RSI extremes when the confirming pivot is beyond OB/OS thresholds.
Inputs (key settings):
• RSI Period / Source: oscillator base.
• Pivot Lookback Left/Right: structure sensitivity; larger = fewer but stronger pivots.
• Min/Max Pairing Range: bars allowed between the two pivots; filters stale or too-tight pairs.
• Plot Toggles: enable/disable each divergence class.
• Signal Pair (visual): optional fast/slow MA pair and smoothing plotted as context; not used in detection.
• Levels: OB/OS and midline for visual regime.
Plots:
• RSI line.
• Optional RSI signal line.
• Midline (50), OB, OS levels.
• Colored divergence lines on RSI:
o Regular Bullish (aqua), Hidden Bullish (lime), Regular Bearish (yellow), Hidden Bearish (red).
• Optional OB/OS ticks at confirming pivots.
How to use:
• Works on any symbol and timeframe; higher timeframes reduce noise.
• Treat divergences as context. Combine with structure, trend, volume, and risk rules.
• Tighten stops or scale when divergence aligns with S/R and higher-TF bias.
• Increase right lookback and raise Min Range to reduce whipsaws; lower them to catch earlier turns.
Practical guidance:
• Swing trading: RSI 14, left=3/right=5, min=8/max=80, OB/OS 70/30.
• Intraday: RSI 14, left=2/right=3, min=4/max=40; consider slightly higher smoothing.
• If you see too many short lines, raise Min Range or increase right.
• If valid turns are missed, lower right or Min Range.
Limitations:
• Divergences can persist in strong trends.
• Pivot detection waits for confirmation, so signals are not predictive on the unconfirmed bar.
• OB/OS thresholds are conventional and not optimized to any asset.
Alerts (if you add alertcondition)
• Regular/Hidden Bullish/ Bearish detected on confirmation bar.
• Optional alert when RSI crosses back through midline after a divergence.
Version notes:
v2: pivot-pair range filter, optional OB/OS peak markers, object count management to prevent clutter, cosmetic controls, and visual signal pair.
Copyright © 2025 imaclone (Zen Silva). All rights reserved.
License: Private. No copying, sharing, or derivative works. Use limited to this TradingView script. Contact owner for access.
VMS Momentum Trend Matrix Indicator [09.00 to 23.30]VMS Momentum Trend Matrix Indicator - Detailed Explanation
🎯 Overview & Core Philosophy
This is a multi-dimensional trading and a multi-confirmation system that combines 4 independent analytical approaches into one unified framework. The indicator operates on the principle of "consensus trading" - where signals are only considered reliable when multiple systems confirm each other. The system is designed for 9:00 AM to 23:30 PM trading sessions (Indian Market) with dynamic support/resistance levels.
Five Pillars of Analysis:
1. Trend Matrix – Multiple indicator voting system
2. Momentum Suite – Multiple Hybrid oscillator
3. Volume Analysis - Buy/sell pressure quantification
4. Key Level Identification - Dynamic support/resistance
5. EMA Trend: Indicates the overall long-term direction.
📊 DASHBOARD INTERPRETATION - ROW BY ROW
ROW 1: Indicator Name and Cell background colour changes with Trend Matrix
ROW 2: EMA ANALYSIS (It analyses independently and does not combine this analysis with the Combined Analysis and Trading View. Background Colour on price chart is based on this)
Purpose: Long-term trend identification using Exponential Moving Averages
What to Watch:
• Major Trend: Overall market direction (Bullish/Bearish/Neutral)
• Bullish Condition: All EMAs aligned upward
• Bearish Condition: All EMAs aligned downward
• Neutral: Mixed alignment
Trading Significance:
• Trading Condition: Current bias based on EMA alignment
• Bullish Market: Focus on LONG positions only
• Bearish Market: Focus on SHORT positions only
• Neutral Market: Wait for clearer direction
ROW 3-4: KEY LEVELS
Purpose: Dynamic support and resistance identification
Levels to Monitor:
• VMS Line-1 (Support): Dynamic Support for long positions
• VMS Line-2 (Resistance): Dynamic Resistance for short positions
• Up/Down: Daily base levels from opening price calculations
• Up: Daily support level based on opening price
• Down: Daily resistance level based on opening price
How Levels Work:
• Wait for Line-1 and 2 Crossing
• In the Upward movement, Line-1 will move with the price, and Line-2 will be moved as a straight line
• In the Downward movement, Line-2 will move with the price, and Line-2 will be moved as a straight line
• Provide clear entry/exit points
• If the price is between these levels, it is mostly a sideways market. After the Upward movement, if the price crosses Line-1 and other bearish conditions are supported, a short position can be taken. And in the Downward movement, it is the reverse condition.
• If the price is above the up level, it can be considered as bullish and below as bearish
ROW 5-6: VOLUME ANALYSIS
Purpose: Measure buying vs selling pressure
Key Metrics:
• Total Buy Volume: Cumulative buying pressure
• Total Sell Volume: Cumulative selling pressure
• Bullish Candles: Number of up-candles in session
• Bearish Candles: Number of down-candles in session
Interpretation:
• Buy Volume > Sell Volume: Bullish sentiment
• Sell Volume > Buy Volume: Bearish sentiment
• Bullish Candles Dominating: Upward momentum
• Bearish Candles Dominating: Downward momentum
ROW 7-8: MOMENTUM SUITE (Background colour of Oscillator is based on this)
Purpose: Short-term momentum strength and direction
Critical Components:
• Direction: Current momentum (BULLISH/BEARISH)
• Strength: 0-100% strength measurement
• Bullish Height: Positive momentum magnitude
• Bearish Height: Negative momentum magnitude
Strength Classification:
• 80-100%: Very Strong - High conviction trades
• 60-80%: Strong - Good trading opportunities
• 40-60%: Moderate - Caution advised
• 20-40%: Weak - Avoid trading
• 0-20%: Very Weak - No trade zone
ROW 9-11: TREND MATRIX
Purpose: Consensus from Multiple technical indicators
Matrix Scoring:
• Bullish Signals: Number voting UP
• Bearish Signals: Number voting DOWN
• Neutral Signals: Non-committed indicators
• Net Score: Bullish - Bearish signals
Trend Classification:
• Strong Uptrend: Net Score ≥ +5
• Uptrend: Net Score +1 to +4
• Neutral: Net Score = 0
• Downtrend: Net Score -1 to -4
• Strong Downtrend: Net Score ≤ -5
ROW 12: COMBINED ANALYSIS
Purpose: Final integrated signal from all systems
Bias Levels:
• STRONG BULLISH: All systems aligned upward
• BULLISH: Majority systems upward
• NEUTRAL: Mixed or weak signals
• BEARISH: Majority systems downward
• STRONG BEARISH: All systems aligned downward
Confidence Score: 0-100% reliability measurement
ROW 13: TRADING VIEW
Purpose: Clear action recommendations
Possible Actions:
• STRONG LONG: High conviction buy signal
• MODERATE LONG: Medium conviction buy signal
• WAIT FOR CONFIRMATION: No clear signal
• MODERATE SHORT: Medium conviction sell signal
• STRONG SHORT: High conviction sell signal
🎯 COMPLETE TRADING RULES
BUY ENTRY CONDITIONS (All Must Be True)
Primary Conditions:
1. Combined Bias: BULLISH or STRONG BULLISH
2. Trading Action: MODERATE LONG or STRONG LONG
3. Momentum Strength: ≥ 40% (≥60% for STRONG LONG)
4. Trend Matrix: Net Score ≥ +3
5. EMA Trend: Bullish or Neutral
Confirmation Conditions:
6. Price Position: Above VMS Line-1 AND Base Up
7. Volume Confirmation: Buy Volume > Sell Volume
8. Bullish Candles: More bullish than bearish candles
Risk Management:
9. Stop Loss: Below VMS Line-1 OR Base Down (whichever is lower)
10. Position Size: Based on confidence score (higher score = larger position)
11. Take Profit: When Combined Bias turns "NEUTRAL" or momentum strength drops below 20%
12. Exit Signal: Trading Action shows "WAIT FOR CONFIRMATION"
SELL/SHORT ENTRY CONDITIONS (All Must Be True)
Primary Conditions:
1. Combined Bias: BEARISH or STRONG BEARISH
2. Trading Action: MODERATE SHORT or STRONG SHORT
3. Momentum Strength: ≥ 40% (≥60% for STRONG SHORT)
4. Bearish Signals: ≥ 12 in Trend Matrix
5. Trend Matrix: Net Score ≤ -3
6. EMA Trend: Bearish or Neutral
Confirmation Conditions:
6. Price Position: Below VMS Line-2 AND Base Down
7. Volume Confirmation: Sell Volume > Buy Volume
8. Bearish Candles: More bearish than bullish candles
Risk Management:
9. Stop Loss: Above VMS Line-2 OR Base Up (whichever is higher)
10. Position Size: Based on confidence score
11. Take Profit: When Combined Bias turns "NEUTRAL" or momentum strength drops below 20%
12. Exit Signal: Trading Action shows "WAIT FOR CONFIRMATION"
⏰ ENTRY/EXIT TIMING
Best Entry Times:
• 9:30-11:00 AM: Early session momentum established
• 12:30-16:30 AM: Mid-session confirmation
• 21:30-23:00 PM: closing session momentum shifts
Avoid Trading:
• First 15 minutes: Excessive volatility
• 12:00-18:00 PM: Low liquidity period
• After 22:00 PM: Session closing volatility
Exit Triggers:
Profit Taking:
• Target 1: 1:1 Risk-Reward (exit 50% position)
• Target 2: 1.5:1 Risk-Reward (exit remaining 50%)
• Trailing Stop: Move stop to breakeven after Target 1
Stop Loss Triggers:
• Price crosses opposite VMS line
• Combined Bias changes to NEUTRAL
• Momentum Strength drops below 20%
• Volume confirmation reverses
•
Emergency Exit:
• Trend Matrix Net Score reverses direction
• 6-EMA trend changes direction
• Key support/resistance breaks against position
📈 TRADING SCENARIOS
Scenario 1: STRONG BULLISH SETUP
- Combined Bias: STRONG BULLISH
- Trading Action: STRONG LONG
- Momentum Strength: 75%
- Trend Matrix: Net Score +8
- Price: Above VMS Line-1 and Base Up
- Volume: Strong buy volume dominance
ACTION: Enter LONG with full position size
STOP LOSS: Below VMS Line-1
TARGET: 1.5:1 Risk-Reward ratio
Scenario 2: MODERATE BEARISH SETUP
- Combined Bias: BEARISH
- Trading Action: MODERATE SHORT
- Momentum Strength: 55%
- Trend Matrix: Net Score -4
- Price: Below VMS Line-2 but above Base Down
- Volume: Moderate sell volume dominance
ACTION: Enter SHORT with half position size
STOP LOSS: Above VMS Line-2
TARGET: 1:1 Risk-Reward ratio
Scenario 3: NEUTRAL/WAIT SETUP
- Combined Bias: NEUTRAL
- Trading Action: WAIT FOR CONFIRMATION
- Momentum Strength: 35%
- Trend Matrix: Net Score 0
- Mixed volume signals
ACTION: NO TRADE - Wait for clearer signals
________________________________________
⚠️ RISK MANAGEMENT RULES
Position Sizing:
• STRONG Signals (80-100% confidence): 100% normal position
• MODERATE Signals (60-79% confidence): 50-75% position
• WEAK Signals (40-59% confidence): 25% position or avoid
• VERY WEAK (<40% confidence): NO TRADE
Daily Loss Limits:
• Maximum 2% capital loss per day
• Maximum 3 consecutive losing trades
• Stop trading after the daily limit is reached
Trade Management:
• Never move the stop loss against a position
• Take partial profits at predetermined levels
• Never average down losing positions
• Respect all exit signals immediately
________________________________________
🔄 SIGNAL CONFIRMATION PROCESS
Step 1: Trend Direction
Check EMA alignment and Combined Bias
Step 2: Momentum Strength
Verify Momentum Strength ≥ 40% and direction matches trend
Step 3: Volume Confirmation
Confirm volume supports the direction
Step 4: Matrix Consensus
Ensure Trend Matrix agrees (Net Score ≥ |3|)
Step 5: Price Position
Verify price is on the correct side of key levels
Step 6: Entry Execution
Enter on a pullback to support/resistance with a stop loss
________________________________________
This system works best when you wait for all conditions to align. Patience is key - only trade when all systems confirm the same direction with adequate strength. The multiple confirmation layers significantly increase the probability of success but reduce trading frequency.
Bull Market Support Band (20W SMA + 21W EMA)🟩 Bull Market Support Band (20W SMA + 21W EMA)
OVERVIEW
The Bull Market Support Band is a long-term trend indicator that combines the 20-week SMA and 21-week EMA.
When price is above the band → bullish trend.
When below → bearish trend.
When within → neutral or transition phase.
This v6 version adds full customization, dynamic coloring, event labels, candle coloring, and a configurable on-chart legend table with trend arrows.
HOW IT WORKS
Calculates the 20W SMA and 21W EMA from a user-selectable higher timeframe (weekly by default).
Plots both averages and shades the area between them to form the support band.
The fill and line colors change automatically based on the market phase:
🟩 Green = Bullish (price above)
🟥 Red = Bearish (price below)
⬜ Gray = Neutral (price within).
Labels appear only when the trend state changes.
Alerts trigger when price crosses the band or either moving average.
Optional candle coloring applies the same bull/bear/neutral logic to price bars.
A convenience legend table displays current 20W SMA and 21W EMA values, along with slope arrows (^, v, =).
INCLUDED FEATURES
Configurable calculation timeframe.
Automatic band fill and line coloring.
Optional candle coloring overlay.
Compact or detailed regime-change labels.
ATR-based label offset and spacing control.
Customizable legend table with background color.
Horizontal or vertical legend layout.
Custom arrow characters (ASCII or Unicode).
Built-in alerts for band and MA crosses.
Optimized for higher-timeframe accuracy using request.security().
USAGE TIPS
Keep the default weekly setting for macro trend context.
On lower chart timeframes, use the band as higher-level support/resistance.
Combine with momentum or volume indicators (RSI, MACD, OBV) for confirmation.
Adjust ATR multiplier to move labels further from price candles.
Increase “Min bars between labels” to reduce visual clutter.
Disable auto-coloring if you prefer a static color theme.
Toggle compact legend or switch between horizontal/vertical layouts for best fit.
INTERPRETATION GUIDE
🟩 Bullish: Price above the band → trend continuation likely.
⬜ Neutral: Price within the band → consolidation or potential reversal zone.
🟥 Bearish: Price below the band → downtrend continuation or distribution.
Sustained closes above or below the band typically align with major market cycle shifts.
NOTES
Uses request.security() for higher-timeframe precision — accurate even on smaller charts.
The legend table is screen-anchored and remains fixed as you pan or zoom.
Plots and labels are price-anchored and move with candles.
Lightweight and efficient for all markets and intervals.
CREDITS
Original concept: The Bull Market Support Band (popularized in crypto analysis).
Enhanced Pine Script v6 version: Ricco .
Modernized for clarity, flexibility, and multi-timeframe accuracy.
SUMMARY
A modern, customizable Bull Market Support Band indicator highlighting long-term market regimes with clean visuals, color logic, and convenience features.
Designed for traders seeking macro clarity, minimal clutter, and professional presentation.
Session-Conditioned Regime ATRWhy this exists
Classic ATR is great—until the open. The first few bars often inherit overnight gaps and 24-hour noise that have nothing to do with the intraday regime you actually trade. That inflates early ATR, scrambles thresholds, and invites hyper-recency bias (“today is crazy!”) when it’s just the open being the open.
This tool was built to:
Separate session reality from 24h noise. Measure volatility only inside your defined session (e.g., NYSE 09:30–16:00 ET).
Judge candles against the current regime, not the last 2–3 bars. A rolling statistic from the last N completed sessions defines what “typical” means right now.
Label “large” and “small” objectively. Bars are colored only when True Range meaningfully departs from the session regime—no gut feel, no open-bar distortion (gap inclusion optional).
Overview
Purpose: objectively identify unusually big or small candles within the active trading session, compared to the recent session regime.
Use cases: volatility filters, entry/exit confirmation, session bias detection, adaptive sizing.
This indicator replaces generic ATR with a session-conditioned, regime-aware measure. It colors candles only when their True Range (TR) is abnormally large/small versus the last N completed sessions of the same session window.
How it works
Session gating: Only bars inside the selected session are evaluated (presets for NYSE, CME RTH, FX NY; custom supported).
Per-bar TR: TR = max(high, prevRef) − min(low, prevRef).
prevRef is the prior close for in-session bars.
First bar of the session can include the overnight gap (optional; default off).
Regime statistic: For any bar in session k, aggregate all in-session TRs from the previous N completed sessions (k−N … k−1), then compute Median (default) or Mean.
Today’s anchor: Running statistic from today’s session start → current bar (for context and the on-chart ratio).
Color logic:
Big if TR ≥ bigMult × RegimeStat
Small if TR ≤ smallMult × RegimeStat
Colored states: big bull, big bear, small bull, small bear.
Non-triggering bars retain the chart’s native colors.
Panel (top-right by default)
Regime ATR (Nd): session-conditioned statistic over the past N completed sessions.
Today ATR (anchored): running statistic for the current session.
Ratio (Today/Regime): intraday volatility vs regime.
Sample size n: number of bars used in the regime calculation.
Inputs
Session Preset: NYSE (09:30–16:00 ET), CME RTH (08:30–15:00 CT), FX NY (08:00–17:00 ET), Custom (session + IANA timezone).
Regime Window: number of completed sessions (default 5).
Statistic: Median (robust) or Mean.
Include Open Gap: include overnight gap in the first in-session bar’s TR (default off).
Big/Small thresholds: multipliers relative to RegimeStat (defaults: Big=1.5×, Small=0.67×).
Colors: four independent colors for big/small × bull/bear.
Panel position & text size.
Hidden outputs: expose RegimeStat, TodayStat, Ratio, and Z-score to other scripts.
Alerts
RegimeATR: BIG bar — triggers when a bar meets the “Big” condition.
RegimeATR: SMALL bar — triggers when a bar meets the “Small” condition.
Hidden outputs (for strategies/screeners)
RegimeATR_stat, TodayATR_stat, Today_vs_Regime_Ratio, BarTR_Zscore.
Notes & limitations
No look-ahead: calculations only use information available up to that bar. Historical colors reflect what would have been known then.
Warm-up: colors begin once there are at least N completed sessions; before that, regime is undefined by design.
Changing inputs (session window, multipliers, median/mean, gap toggle) recomputes the full series using the same rolling regime logic per bar.
Designed for standard candles. Styling respects existing chart colors when no condition triggers.
Practical tips
For a broader or tighter notion of “unusual,” adjust Big/Small multipliers.
Prefer Median in markets prone to outliers; use Mean if you want Z-score alignment with the panel’s regime mean/std.
Use the Ratio readout to spot compression/expansion days quickly (e.g., <0.7× = compressed session, >1.3× = expanded).
Roadmap
More session presets:
24h continuous (crypto, index CFDs).
23h/Globex futures (CME ETH with a 60-minute maintenance break).
Regional equities (LSE, Xetra, TSE), Asia/Europe/NY overlaps for FX.
Half-day/holiday templates and dynamic calendars.
Multi-regime comparison: track multiple overlapping regimes (e.g., RTH vs ETH for futures) and show separate stats/ratios.
Robust stats options: trimmed mean, MAD/Huber alternatives; optional percentile thresholds instead of fixed multipliers.
Subpanel visuals: rolling TodayATR and Ratio plots; optional Z-score ribbon.
Screener/strategy hooks: export boolean series for BIG/SMALL, plus a lightweight strategy template for backtesting entries/exits conditioned on regime volatility.
Performance/QOL: per-symbol presets, smarter warm-up, and finer control over sample caps for ultra-low TF charts.
Changelog
v0.9b (Beta)
Session presets (NYSE/CME RTH/FX NY/Custom) with timezone handling.
Panel enhancements: ratio + sample size n.
Four-state bar coloring (big/small × bull/bear).
Alerts for BIG/SMALL bars.
Hidden Z-score stream for downstream use.
Gap-in-TR toggle for the first in-session bar.
Disclaimer
For educational purposes only. Not investment advice. Validate thresholds and session settings across symbols/timeframes before live use.
RSI Cloud v1.0 [PriceBlance] RSI Cloud v1.0 — Ichimoku-style Cloud on RSI(14), not on price.
Recalibrated baselines: EMA9 (Tenkan) for speed, WMA45 (Kijun) for stability.
Plus ADX-on-RSI to grade strength so you know when momentum persists or fades.
1. Introduction
RSI Cloud v1.0 applies an Ichimoku Cloud directly on RSI(14) to reveal momentum regimes earlier and cleaner than price-based views. We replaced Tenkan with EMA9 (faster, more responsive) and Kijun with WMA45 (slower, more stable) to fit a bounded oscillator (0–100). Forward spans (+26) and a lagging line (−26) provide a clear framework for trend bias and transitions.
To qualify signals, the indicator adds ADX computed on RSI—highlighting whether strength is weak, strong, or very strong, so you can decide when to follow, fade, or stand aside.
2. Core Mapping (Hook + Bullets)
At a glance: Ichimoku on RSI(14) with recalibrated baselines for a bounded oscillator.
Source: RSI(14)
Tenkan → EMA9(RSI) (fast, responsive)
Kijun → WMA45(RSI) (slow, stable)
Span A: classic Ichimoku midline, displaced +26
Span B: classic Ichimoku baseline, displaced +26
Lagging line: RSI shifted −26
3. Key Benefits (Why traders care)
Momentum regimes on RSI: position vs. Cloud = bull / bear / transition at a glance.
Cleaner confirmations: EMA9/WMA45 pairing cuts noise vs. raw 30/70 flips.
Earlier warnings: Cloud breaks on RSI often lead price-based confirmations.
4. ADX on RSI (Enhanced Strength Normalization)
Grade strength inside the RSI domain using ADX from ΔRSI:
ADX ≤ 20 → Weak (transparency = 60)
ADX ≤ 40 → Strong (transparency = 15)
ADX > 40 → Very strong (transparency = 0)
Use these tiers to decide when to trust, fade, or ignore a signal.
5. How to Read (Quick rules)
Bias / Regime
Bullish: RSI above Cloud and RSI > WMA45
Bearish: RSI below Cloud and RSI < WMA45
Neutral / Transition: all other cases
6. Settings (Copy & use)
RSI Length: 14 (default)
Tenkan: EMA9 on RSI · Kijun: WMA45 on RSI
Displacement: +26 (Span A/B) · −26 (Lagging)
Theme: PriceBlance Dark/Light
Visibility toggles: Cloud, Baselines, Lagging, labels/panel, Overbought/Oversold, Divergence, ADX-on-RSI (via transparency coloring)
7. Credits & License
Author/Brand: PriceBlance
Version: v1.0 (Free)
Watermark: PriceBlance • RSI Cloud v1.0
Disclaimer: Educational content; not financial advice.
8. CTA
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