QLC - Gibaum 1.0 QLC - Gibaum 1.0
Good for Leverage AND Short - 5 to 20 minutes >70%.
Gibaum The Beast
Little Black Guy suffers in america
תנודתיות
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.
Monitor Posición Bollinger Multi-TFThis indicator provides a comprehensive dashboard that allows you to monitor the price position relative to Bollinger Bands across 7 different timeframes simultaneously, without the need to switch charts.
It uses the %B (Percent B) logic to normalize the price position, giving you an instant "Heatmap" view of the market state (Overbought/Oversold) from the 1-minute chart up to the Weekly chart.
Key Features:
Multi-Timeframe Monitoring: Watch 1m, 5m, 15m, 1h, 4h, Daily, and Weekly timeframes in a single panel.
Dynamic Color Coding:
Dark Red: Price breaking above the Upper Band (>100%).
Light Red: Price near the Upper Band (Resistance zone).
Gray: Price in the neutral middle zone.
Light Green: Price near the Lower Band (Support zone).
Dark Green: Price breaking below the Lower Band (<0%).
Trend Arrows: Indicates momentum (▲ or ▼) based on the previous candle's position.
Current Timeframe Highlight: Automatically highlights the row corresponding to your current chart view in orange.
Fully Customizable: Adjust Bollinger settings (Length, Mult), choose your preferred timeframes, and change the table position/size.
Movable Panel: Includes X/Y offset settings to prevent the table from blocking price action or menu buttons.
How to Use:
Add the indicator to your chart.
Use the dashboard to spot confluence across timeframes.
Example: If 15m, 1H, and 4H are all showing Red, the asset is likely overextended to the upside.
Example: If the lower timeframes are turning Green while the higher timeframes remain Gray/Bullish, it might indicate a pullback opportunity.
Settings:
Bollinger Config: Length (20) and Multiplier (2.0) by default.
Timeframes: Select the 7 specific TFs you want to track.
Visuals: Change table position, text size, and offset coordinates.
This tool is essential for scalpers and day traders who need situational awareness across multiple fractals instantly.
Institutional Valuation SuiteStandard volatility indicators often fail on long-term growth charts because they measure volatility in dollars rather than percentages. This causes bands to break or become irrelevant during exponential price moves (e.g., Bitcoin going from $1,000 to $100,000).
The Institutional Valuation Suite solves this by utilising Geometric (Log-Normal) Standard Deviation. This allows the model to adapt to the asset's price scale, providing accurate valuation zones regardless of price magnitude.
The model functions as a mean-reversion tool, visualizing price as an elastic band anchored to a "Fair Value" baseline. It identifies when the asset is statistically overextended (Bubble/FOMO) or undervalued (Deep Discount).
Key Features
1. Log-Normal Math Engine
Geometric Mode (Default): Calculates volatility in percentage terms. Essential for Crypto and Growth Stocks.
Arithmetic Mode: Available for Forex or range-bound assets where linear standard deviation is preferred.
2. Sentiment Heat map
Visualises valuation directly on the candles to remove interpretation bias.
GREEN: Deep Value / Accumulation Zone (< -0.5σ).
ORANGE: Overvalued / FOMO Zone (> 2.0σ).
RED: Speculative Bubble Zone (> 3.0σ).
3. Reversion Signals
"VALUE RECLAIM": Triggers when price re-enters the bottom band from below, filtering out "falling knife" scenarios.
"TOP EXIT": Triggers when price breaks down from the speculative top zone.
4. Statistical Dashboard
Displays the real-time Z-Score to quantify how "stretched" the price is relative to its baseline.
> 3.0: Statistical Anomaly (Top).
< -0.5: Statistical Discount (Bottom).
Optimisation Cheat Sheet
The "Cycle Length" input determines the lookback period for the baseline. Recommended settings:
Crypto Macro: 200 (Approx. 4 Years).
Altcoins: 100 (Approx. 2 Years).
Stocks (S&P 500): 50 (1 Year Trend).
Day Trading: Set "Timeframe Lock" to "Chart".
Technical Note
This indicator uses strict offset logic (`barmerge.lookahead_on`) to ensure historical consistency. The signals displayed on historical bars match exactly what would have appeared in real-time.
*Disclaimer: This script provides statistical analysis based on historical volatility and does not constitute financial advice.*
Compression Breakout [30min 65+33 EMA]Compression Breakout
by GhostMMXM (inspired by Chris Cady & Steidlmayer Market Profile principles)
This indicator automates the exact compression-to-displacement setup that veteran CBOT floor trader and Market Profile pioneer Chris Cady describes in interviews and his work with Peter Steidlmayer.
Core idea
Chris Cady uses two simple moving averages on the 30-minute chart — a 33-period and a 65-period — to visually detect when the market falls into “balance” (compression). When both lines go almost perfectly flat for several bars, the market is in a low-volatility, high-consensus state — the calm before a violent vertical breakout.
What this script does
• Detects when both the 33 EMA and 65 EMA are virtually flat (user-adjustable sensitivity)
• Requires a minimum of 6 consecutive flat bars (adjustable) before declaring compression
• Draws a light-grey background + live-updating box showing the detecting compression
• Triggers only on the first strong displacing bar that:
– closes entirely above the compression high OR entirely below the compression low
– has a range ≥ 1.5× the average bar range inside the compression zone (adjustable)
• Plots a clear “LONG Cady Break” or “SHORT Cady Break” label on the breakout bar
• Fires a clean alert instantly usable on entire watchlists:
BTC → Compression LONG breakout!
ES1! → Compression SHORT breakout!
Designed for 30-minute charts (BTC, ETH, SOL, NQ, CL, GC, etc.) but works on any timeframe.
Perfect for traders who want to catch the highest-conviction vertical moves that Chris Cady has traded for decades with only a few contracts scaled in aggressively on the break.
Settings
• Minimum flat bars for compression (default 6)
• Max % slope to be considered flat (default 0.08 %)
• Minimum range multiplier vs compression average (default 1.5×)
Enjoy the cleanest, most mechanical version of Chris Cady’s famous compression breakout strategy available on TradingView.
Happy trading!
able MACD Overview
Purpose: The indicator combines the traditional MACD (Moving Average Convergence Divergence) with a short-term “forecast” (projection) of MACD/histogram values to give early warning of momentum changes.
Typical outputs:
MACD line (fastEMA − slowEMA)
Signal line (EMA of MACD)
Histogram (MACD − signal)
Forecasted MACD or histogram projected N bars ahead
Optional buy/sell markers and alert conditions
Add the indicator to TradingView (Installation)
Open TradingView and the chart you want to apply the indicator to.
Click “Pine Editor” at the bottom of the chart.
Copy the contents of able_macd_forecast.pine into the Pine Editor window.
Click “Add to chart” (or Save then Add to chart). If it’s a study, it will appear on the chart below price.
If you plan to re-use the script, click Save and give it a meaningful name.
Inputs / Parameters (typical) Note: exact input names may differ in your script. Replace the names below with the script’s input labels when you inspect it.
Source: price source for calculations (close, hl2, etc.).
Fast Length: length for the fast EMA (commonly 12).
Slow Length: length for the slow EMA (commonly 26).
Signal Length: length for the MACD signal EMA (commonly 9).
Forecast Length / Horizon: how many bars ahead the script projects the MACD/histogram (e.g., 1–5).
Forecast Method / Smoothing: choice of projection method (linear regression, EMA extrapolation, simple slope * N, etc.) if available.
Histogram Thresholds: numeric thresholds to emphasize significant momentum (optional).
Show Forecast: toggle on/off the forecast plot.
Alerts On/Off toggles: enable or disable alert conditions baked into the indicator.
Visual / Style settings: colors, plot thickness, histogram style (columns/areas), show labels, show buy/sell arrows.
How the indicator is typically calculated (summary)
MACD line = EMA(source, fast) − EMA(source, slow)
Signal line = EMA(MACD line, signal length)
Histogram = MACD − Signal
Forecast = method-specific short-term projection of MACD or histogram (for example: extend the last slope forward, apply linear regression to MACD values and extrapolate N bars, or apply an additional smoothing and extend that value) Note: For exact math, I need to inspect the script; this is the typical approach.
How to read the indicator (signals & interpretation)
Bullish signal:
MACD line crossing above the signal line (MACD cross up).
Histogram turns positive (cross above zero).
Forecast shows MACD/histogram moving higher in the next N bars (if forecast is positive or trending up).
Bearish signal:
MACD line crossing below the signal line (MACD cross down).
Histogram turns negative (cross below zero).
Forecast shows MACD/histogram moving lower ahead.
Confirmations:
Use price action (higher highs/lows for bullish, lower highs/lows for bearish).
Volume or other momentum/confluence indicators (RSI, ADX).
Divergences:
Bullish divergence: price makes lower low while MACD histogram makes higher low.
Bearish divergence: price makes higher high while MACD histogram makes lower high.
Forecast behavior:
If the forecast leads the MACD cross (forecast crosses before the current MACD does), it’s an early warning.
Use caution: forecasts are prone to false signals; always confirm.
Common trading setups using this indicator
Conservative:
Wait for MACD to cross signal + histogram above zero + forecast already trending same direction.
Use stop below recent swing low (for long) or above recent swing high (for short).
Aggressive (early entry):
Enter when forecast turns positive while MACD still below signal (anticipating cross).
Use tighter stops and smaller position sizes.
Exit rules:
Opposite MACD cross, histogram flipping sign, or a target based on risk-reward.
Use trailing stop based on ATR or structure.
Example settings for different timeframes (starting points)
Scalping / 5–15 min:
Fast 8, Slow 21, Signal 5, Forecast 1–2
Intraday / 1H:
Fast 12, Slow 26, Signal 9, Forecast 2–3
Swing / 4H–Daily:
Fast 12, Slow 26, Signal 9, Forecast 3–5 Adjust based on the asset volatility and backtests.
Adding alerts (TradingView)
Click the “Alerts” button (clock icon) or press Alt + A.
In the Condition dropdown, select the indicator name (able_macd_forecast) and choose a plotted series or built-in alert condition (if the script uses alertcondition).
Common alert types:
MACD crosses Signal (Crossing)
Histogram crosses 0 (Crossing)
Forecast crosses 0 or Forecast trend change (if provided)
Message templates:
“{{ticker}}: MACD crossed above signal on {{interval}}”
“{{ticker}} Forecast positive: MACD forecast shows upward momentum”
Customize the message for your trade automation or notifications.
Configure frequency (Only once, Once per bar, or Once per bar close) — for signals like crossovers, “Once per bar close” is usually safer to avoid repainting issues. Note: If the script includes alertcondition() calls with explicit IDs/messages, use those directly — they are the most reliable for automation.
Backtesting / Strategy conversion
If this script is a study (indicator), you can:
Convert it to a strategy by adding strategy.* order calls (strategy.entry, strategy.close) using the entry/exit logic you prefer, or
Use TradingView’s “Bar Replay” to manually test signals across different markets/timeframes.
If you want, I can help convert or write a strategy wrapper that uses the indicator’s signals to place backtest trades (I’ll need the code).
Practical tips & best practices
Use higher timeframe confirmation for lower-timeframe entries (e.g., check daily MACD momentum before trading 15m signals).
Beware of choppy markets; MACD / forecast may produce whipsaws. Combine with trend filters (moving average direction, ADX).
If you rely on forecasted values, prefer alerts “on bar close” when possible to reduce false alerts from intra-bar noise.
Tune parameters for the specific asset (FX, crypto, stocks have different behavior).
Record each signal and outcome for a sample period (20–100 trades) to evaluate performance.
Troubleshooting
Indicator won’t add: verify Pine version in script header (//@version=4 or //@version=5). TradingView may reject scripts with unsupported version syntax.
Plots missing: check script inputs (Some scripts hide plots if toggles are off).
Alerts firing too often: change alert frequency to “Once per bar close” or adjust threshold values.
Forecast seems to repaint: some forecast methods can repaint (use “bar_index” or store values only on closed bars, or use non-repainting forecast methods). Ask me to inspect the script for repainting logic.
What I can do next (recommended)
If you paste the content of able_macd_forecast.pine here, I will:
Produce a precise, line-by-line usage guide mapping to the exact input names and default values.
Show the exact plotted series names and how to reference them for alerts.
Point out any repainting risks and suggest fixes.
Provide example alert messages that match the script’s alertcondition IDs (if any).
Optionally convert it into a strategy for backtesting, or add non-repainting forecast logic if needed.
Daily RDR (Prev Day H/L, Intraday)This indicator identifies intraday Range-Deviation Reversal (RDR) signals using the previous day’s high and low. At each new session, it stores yesterday’s levels and resets today’s range tracking. During the day, it detects when price first breaks above the prior high or below the prior low, then waits for a reversal: a bearish RDR triggers when price exceeds yesterday’s high and then closes back below it, while a bullish RDR triggers when price undercuts yesterday’s low and then closes back above it. The script plots the previous day’s levels and marks RDR reversals with small up/down triangles.
Global Liquidity Score
Global Liquidity Score – Simple Risk-On / Risk-Off Gauge
This indicator measures overall market liquidity conditions using a single, normalized score.
It takes several macro and crypto variables, standardizes each one (z-score), and combines them into one clear Liquidity Score Line.
You only follow one line (your pink/white line).
The background color shows the current liquidity regime.
⸻
What the indicator measures
The algorithm looks at four major liquidity sources:
1. USD Liquidity (tightening or easing)
• DXY (strong dollar = tighter global liquidity)
• US10Y yield (higher yields = liquidity drain)
2. Risk Sentiment (risk-on vs risk-off)
• VIX index (volatility)
• S&P 500 index (SPX)
3. Credit Market Strength
• High-yield ETFs: HYG, JNK
• Investment-grade corporate credit: LQD
Stronger credit = easier liquidity.
Weaker credit = tightening risk.
4. Internal Crypto Liquidity
• USDT dominance (higher = risk-off in crypto)
• Bitcoin price
• TOTAL2 (crypto market cap excluding BTC)
These are all converted into z-scores and combined into one metric:
Total Liquidity Score =
USD Block + Risk Block − Credit Block − 0.5 × Crypto Block
⸻
How to read the colors
The indicator uses background colors to show the liquidity regime:
Color Meaning
Dark Red Severe liquidity tightening / strong risk-off
Red Mild-to-moderate tightening
Green Liquidity easing / soft risk-on
Dark Green Strong easing, high liquidity / risk-on
Your pink/white line = the final liquidity score.
You only need to follow that single line.
⸻
How to interpret the score
📉 Positive score → Liquidity Tightening (Risk-Off)
• USD stronger
• Yields rising
• Volatility rising
• Credit markets weakening
• Crypto rotating to stablecoins
📈 Negative score → Liquidity Easing (Risk-On)
• USD weakening
• Yields falling
• Stocks rising
• Volatility low
• Credit markets strong
• Crypto beta assets outperform
⸻
What this indicator is NOT
This is not a price predictor.
It does not follow BTC directly.
It tells you liquidity conditions, not immediate price direction.
It answers the macro question:
“Is liquidity flowing INTO the market or OUT of the market?”
If liquidity is tightening (red), crypto rallies are harder to sustain.
If liquidity is easing (green), crypto rallies have more fuel.
CVD [able0.1]# CVD Overlay iOS Style - Complete User Guide
## 📖 Table of Contents
1. (#what-is-cvd)
2. (#installation-guide)
3. (#understanding-the-display)
4. (#reading-the-info-table)
5. (#settings--customization)
6. (#trading-strategies)
7. (#common-mistakes-to-avoid)
---
## 🎯 What is CVD?
**CVD (Cumulative Volume Delta)** tracks the **difference between buying and selling pressure** over time.
### Simple Explanation:
- **Positive CVD** (Orange) = More buying than selling = Bulls winning
- **Negative CVD** (Gray) = More selling than buying = Bears winning
- **Rising CVD** = Increasing buying pressure = Potential uptrend
- **Falling CVD** = Increasing selling pressure = Potential downtrend
### Why It Matters:
CVD helps you see **who's really in control** of the market - not just price movement, but actual buying/selling volume.
---
## 🚀 Installation Guide
### Step 1: Open Pine Editor
1. Go to TradingView
2. Click the **"Pine Editor"** tab at the bottom of the screen
3. Click **"New"** or open an existing script
### Step 2: Copy & Paste the Code
1. Select all existing code (Ctrl+A / Cmd+A)
2. Delete it
3. Copy the entire CVD iOS Style code
4. Paste it into Pine Editor
### Step 3: Add to Chart
1. Click **"Save"** button (or Ctrl+S / Cmd+S)
2. Click **"Add to Chart"** button
3. The indicator will appear on your chart!
### Step 4: Initial Setup
- The indicator appears as an **overlay** on your price chart
- You'll see an **orange/gray line** following price
- An **info table** appears in the top-right corner
---
## 📊 Understanding the Display
### Main Chart Elements:
#### 1. **CVD Line** (Orange/Gray)
- **Orange Line** = Positive CVD (buying pressure)
- **Gray Line** = Negative CVD (selling pressure)
- This line moves with your price chart but shows volume delta
#### 2. **CVD Zone** (Shaded Area)
- Light shaded box around the CVD line
- Shows the "range" of CVD movement
- Helps visualize CVD boundaries
#### 3. **Center Line** (Dotted)
- Gray dotted line in the middle of the zone
- Represents the "neutral" point
- CVD crossing this = shift in market control
#### 4. **Reference Asset Line** (Light Gray)
- Shows Bitcoin (BTC) price movement for comparison
- Helps you see if your asset moves with or against BTC
- Can be changed to any asset you want
#### 5. **CVD Label**
- Shows current CVD value
- Positioned above/below zone to avoid overlap
- Updates in real-time
#### 6. **Reset Background** (Very Light Gray)
- Appears when CVD resets
- Indicates a new calculation period
---
## 📋 Reading the Info Table
The info table (top-right) shows **8 key metrics**:
### Row 1: **Header**
```
╔═ CVD able ═╗ | 15m | ████████ | able
```
- **CVD able** = Indicator name + creator
- **15m** = Current timeframe
- **████████** = Visual decoration
- **able** = Creator signature
### Row 2: **CVD Value**
```
CVD▲ | 7.39K | ████████ | █
█
█
```
- **CVD▲** = CVD with trend arrow
- ▲ = CVD increasing
- ▼ = CVD decreasing
- ► = CVD unchanged
- **7.39K** = Actual CVD number
- **Progress Bar** = Visual strength (darker = stronger)
- **Vertical Bars** = Height shows intensity
### Row 3: **Delta**
```
◆DELTA | -1.274K | ████░░░░ | ░
░
```
- **Delta** = Volume change THIS BAR ONLY
- **Negative** = More selling this bar
- **Positive** = More buying this bar
- Shows **immediate** pressure (not cumulative)
### Row 4: **UP Volume**
```
UP↑ | -1.263K | ████████ | █
█
█
```
- Total **buying volume** this bar
- Higher = Stronger buying pressure
- Green/Orange vertical bars = Bullish strength
### Row 5: **DOWN Volume**
```
DN↓ | 2.643K | ████████ | ░
░
░
```
- Total **selling volume** this bar
- Higher = Stronger selling pressure
- Gray vertical bars = Bearish strength
### Row 6-7: **Reference Asset** (if enabled)
```
══ REF ══ | ══════ | ████████ | █
█
PRICE▲ | 4130.300 | ████████ | █
█
```
- **REF** = Reference asset header
- **PRICE▲** = Reference price with trend
- Shows if BTC (or chosen asset) is rising/falling
- Compare with your chart to see correlation
### Row 8: **Market Status**
```
◄STATUS► | NEUT | ████░░░░ | ▒
▒
```
- **BULL** = CVD positive + Delta positive = Strong buying
- **BEAR** = CVD negative + Delta negative = Strong selling
- **NEUT** = Mixed signals = Wait for clarity
**Status Colors:**
- **Orange background** = Bullish (good for long)
- **Gray background** = Bearish (good for short)
- **White background** = Neutral (no clear signal)
---
## ⚙️ Settings & Customization
### Main Settings (⚙️)
#### **CVD Reset**
- **None** = CVD never resets (from beginning of data)
- **On Higher Timeframe** = Resets when HTF candle closes
- 15m chart → Resets hourly
- 1h chart → Resets daily
- Recommended for most traders
- **On Session Start** = Resets at market open
- **On Visible Chart** = Resets from leftmost visible bar
#### **Precision**
- **Low (Fast)** = Uses 1m data, faster but less accurate
- **Medium** = Uses 5m data, balanced (recommended)
- **High** = Uses 15m data, most accurate but slower
#### **Cumulative**
- ✅ On = CVD accumulates over time (recommended)
- ❌ Off = Shows only current bar delta
#### **Show Labels**
- ✅ On = Shows CVD value label on chart
- ❌ Off = Cleaner chart, no label
#### **Show Info Table**
- ✅ On = Shows info table (recommended for beginners)
- ❌ Off = Hide table for minimalist view
---
### 🎨 iOS Style Colors
You can customize **every color** to match your chart theme:
#### **Primary Colors**
- **Primary (Orange)** = Main bullish color (#FF9500)
- **Secondary (Gray)** = Main bearish color (#8E8E93)
- **Background** = Table background (#FFFFFF)
- **Text** = Text color (#1C1C1E)
#### **Bullish/Bearish**
- **Bullish (Orange)** = Positive CVD color
- **Bearish (Gray)** = Negative CVD color
- **Opacity** = Zone transparency (0-100%)
- **Show Zone** = Enable/disable shaded area
#### **Table Colors** (📋)
- **Header Background** = Top row background
- **Header Text** = Top row text color
- **Cell Background** = Data cells background
- **Cell Text** = Data cells text color
- **Border** = Table border color
- **Accent Background** = Special rows background
- **Alert Background** = Warning/status background
---
### 📊 Reference Asset Settings
#### **Enable**
- ✅ On = Shows reference asset line
- ❌ Off = Hide reference asset
#### **Symbol**
- Default: `BINANCE:BTCUSDT`
- Can change to any asset:
- `BINANCE:ETHUSDT` (Ethereum)
- `SPX` (S&P 500)
- `DXY` (US Dollar Index)
- Any ticker symbol
#### **Color & Width**
- Customize line appearance
- Width: 1-4 (thickness)
---
## 💡 Trading Strategies
### Strategy 1: CVD Divergence (Beginner-Friendly)
**What to Look For:**
- Price making **higher highs** but CVD making **lower highs** = Bearish divergence
- Price making **lower lows** but CVD making **higher lows** = Bullish divergence
**How to Trade:**
1. Wait for divergence to form
2. Look for confirmation (price reversal, candlestick pattern)
3. Enter trade in divergence direction
4. Stop loss beyond recent high/low
**Example:**
```
Price: /\ /\ /\ (higher highs)
CVD: /\ / \/ (lower highs) = Bearish signal
```
### Strategy 2: CVD Trend Following (Intermediate)
**What to Look For:**
- **Strongly rising CVD** + **rising price** = Strong uptrend
- **Strongly falling CVD** + **falling price** = Strong downtrend
**How to Trade:**
1. Wait for CVD and price moving in same direction
2. Enter on pullbacks to support/resistance
3. Stay in trade while CVD trend continues
4. Exit when CVD trend breaks
**Signals:**
- CVD ▲▲▲ + Price ↑ = Go LONG
- CVD ▼▼▼ + Price ↓ = Go SHORT
### Strategy 3: CVD + Reference Asset (Advanced)
**What to Look For:**
- Your asset **rising** but BTC (reference) **falling** = Relative strength
- Your asset **falling** but BTC (reference) **rising** = Relative weakness
**How to Trade:**
1. Compare CVD movement with BTC
2. If your CVD rises faster than BTC = Buy signal
3. If your CVD falls faster than BTC = Sell signal
4. Use for **pair trading** or **asset selection**
### Strategy 4: Volume Delta Confirmation
**What to Look For:**
- **Large positive Delta** = Strong buying this bar
- **Large negative Delta** = Strong selling this bar
**How to Trade:**
1. Price breaks resistance + Large positive Delta = Confirmed breakout
2. Price breaks support + Large negative Delta = Confirmed breakdown
3. Use Delta to **confirm** price moves, not predict them
**Rules:**
- Delta > 2x average = Very strong pressure
- Delta near zero at key level = Weak move, likely false breakout
---
## 🎓 Reading Real Scenarios
### Scenario 1: Strong Buying Pressure
```
Table Shows:
CVD▲ | 12.5K | ████████ | ████ (CVD rising)
◆DELTA | +2.8K | ████████ | ▲ (Positive delta)
UP↑ | 3.1K | ████████ | ████ (High buy volume)
DN↓ | 0.3K | ██░░░░░░ | ░ (Low sell volume)
◄STATUS► | BULL | ████████ | ████ (Orange background)
```
**Interpretation:** Strong buying, good for LONG trades
### Scenario 2: Distribution (Hidden Selling)
```
Table Shows:
CVD► | 8.2K | ████░░░░ | ▒▒ (CVD flat)
◆DELTA | -1.5K | ████████ | ▼ (Negative delta)
UP↑ | 0.8K | ███░░░░░ | ░ (Low buy volume)
DN↓ | 2.3K | ████████ | ████ (High sell volume)
◄STATUS► | BEAR | ████████ | ░░░░ (Gray background)
```
**Interpretation:** Price may look stable, but selling increasing = Prepare for drop
### Scenario 3: Neutral/Choppy Market
```
Table Shows:
CVD► | 5.1K | ████░░░░ | ▒ (CVD sideways)
◆DELTA | +0.2K | ██░░░░░░ | ─ (Small delta)
UP↑ | 1.2K | ████░░░░ | ▒ (Medium buy)
DN↓ | 1.0K | ████░░░░ | ▒ (Medium sell)
◄STATUS► | NEUT | ████░░░░ | ▒▒ (White background)
```
**Interpretation:** No clear direction = Stay out or reduce position size
---
## ⚠️ Common Mistakes to Avoid
### Mistake 1: Trading on CVD Alone
- ❌ **Wrong:** "CVD is rising, I'll buy immediately"
- ✅ **Right:** "CVD is rising, let me check price structure, support/resistance, and wait for confirmation"
### Mistake 2: Ignoring Delta
- ❌ **Wrong:** Looking only at cumulative CVD
- ✅ **Right:** Watch both CVD (trend) and Delta (momentum)
- Delta shows **immediate** pressure changes
### Mistake 3: Wrong Timeframe
- ❌ **Wrong:** Using 1m chart with High Precision (too slow)
- ✅ **Right:** Match precision to timeframe:
- 1m-5m → Low Precision
- 15m-1h → Medium Precision
- 4h+ → High Precision
### Mistake 4: Not Using Reset
- ❌ **Wrong:** Using "None" reset for intraday trading
- ✅ **Right:** Use "On Higher Timeframe" to see fresh CVD each session
### Mistake 5: Overtrading Neutral Status
- ❌ **Wrong:** Forcing trades when STATUS = NEUT
- ✅ **Right:** Only trade clear BULL or BEAR status
### Mistake 6: Ignoring Reference Asset
- ❌ **Wrong:** Trading altcoin without checking BTC
- ✅ **Right:** Always check if BTC CVD agrees with your asset
---
## 🔥 Pro Tips
### Tip 1: Multi-Timeframe Analysis
- Check CVD on **3 timeframes**:
- Lower TF (15m) = Entry timing
- Current TF (1h) = Trade direction
- Higher TF (4h) = Overall trend
### Tip 2: Volume Confirmation
- Big price move + Small Delta = **Weak move** (likely reversal)
- Small price move + Big Delta = **Strong accumulation** (continuation)
### Tip 3: CVD Reset Zones
- Pay attention to **reset backgrounds** (light gray)
- Often marks **session starts** = High volatility periods
### Tip 4: Divergence + Status
- Bearish divergence + STATUS = BEAR = **Strongest short signal**
- Bullish divergence + STATUS = BULL = **Strongest long signal**
### Tip 5: Color Psychology
- **Orange** (Bullish) is **warm** = Buying energy
- **Gray** (Bearish) is **cool** = Selling pressure
- Train your eye to read colors instantly
### Tip 6: Table as Quick Scan
- Glance at table without reading numbers:
- **All orange** = Bullish
- **All gray** = Bearish
- **Mixed** = Wait
---
## 📱 Quick Reference Card
| Signal | CVD | Delta | Status | Action |
|--------|-----|-------|--------|--------|
| **Strong Buy** | ▲▲ High | ++ Positive | BULL | Long Entry |
| **Strong Sell** | ▼▼ Low | -- Negative | BEAR | Short Entry |
| **Divergence Buy** | ▲ Rising | Price ▼ | → BULL | Long Setup |
| **Divergence Sell** | ▼ Falling | Price ▲ | → BEAR | Short Setup |
| **Neutral** | → Flat | ~0 Near Zero | NEUT | Stay Out |
| **Accumulation** | → Flat | ++ Positive | NEUT→BULL | Watch for Breakout |
| **Distribution** | → Flat | -- Negative | NEUT→BEAR | Watch for Breakdown |
---
## 🆘 Troubleshooting
### Issue: "Indicator not showing"
- **Solution:** Make sure overlay=true in code, re-add to chart
### Issue: "Table overlaps with price"
- **Solution:** Change table position in code or use TradingView's "Move" feature
### Issue: "CVD line too far from price"
- **Solution:** This is normal! CVD is volume-based, not price-based. Focus on CVD direction, not position
### Issue: "Too many lines on chart"
- **Solution:** Disable "Show Zone" and "Show Labels" in settings for cleaner view
### Issue: "Calculations too slow"
- **Solution:** Change Precision to "Low (Fast)" or use higher timeframe
### Issue: "Reference asset not showing"
- **Solution:** Check if "Enable" is ON and symbol is valid (e.g., BINANCE:BTCUSDT)
---
## 🎬 Getting Started Checklist
- Install indicator on TradingView
- Set precision to "Medium"
- Set reset to "On Higher Timeframe"
- Enable info table
- Add reference asset (BTC)
- Practice reading the table on demo account
- Test on different timeframes (15m, 1h, 4h)
- Compare CVD with your current strategy
- Paper trade for 1 week before going live
- Keep a trading journal of CVD signals
---
## 📚 Summary
**CVD shows WHO is winning: Buyers or Sellers**
**Key Points:**
1. **Orange/Rising CVD** = Buying pressure = Bullish
2. **Gray/Falling CVD** = Selling pressure = Bearish
3. **Delta** = Immediate momentum THIS BAR
4. **Status** = Overall market condition
5. **Always confirm** with price action & other indicators
**Remember:**
- CVD is a **tool**, not a crystal ball
- Use with proper risk management
- Practice makes perfect
- Stay disciplined!
---
**Created by: able**
**Version:** iOS Style v1.0
**Contact:** For questions, refer to TradingView community
Happy Trading! 🚀📈
Flux-Tensor Singularity [ML/RL PRO]Flux-Tensor Singularity
This version of the Flux-Tensor Singularity (FTS) represents a paradigm shift in technical analysis by treating price movement as a physical system governed by volume-weighted forces and volatility dynamics. Unlike traditional indicators that measure price change or momentum in isolation, FTS quantifies the complete energetic state of the market by fusing three fundamental dimensions: price displacement (delta_P), volume intensity (V), and local-to-global volatility ratio (gamma).
The Physics-Inspired Foundation:
The tensor calculation draws inspiration from general relativity and fluid dynamics, where massive objects (large volume) create curvature in spacetime (price action). The core formula:
Raw Singularity = (ΔPrice × ln(Volume)) × γ²
Where:
• ΔPrice = close - close (directional force)
• ln(Volume) = logarithmic volume compression (prevents extreme outliers)
• γ (Gamma) = (ATR_local / ATR_global)² (volatility expansion coefficient)
This raw value is then normalized to 0-100 range using the lookback period's extremes, creating a bounded oscillator that identifies critical density points—"singularities" where normal market behavior breaks down and explosive moves become probable.
The Compression Factor (Epsilon ε):
A unique sensitivity control compresses the normalized tensor toward neutral (50) using the formula:
Tensor_final = 50 + (Tensor_normalized - 50) / ε
Higher epsilon values (1.5-3.0) make threshold breaches rare and significant, while lower values (0.3-0.7) increase signal frequency. This mathematical compression mimics how black holes compress matter—the higher the compression, the more energy required to escape the event horizon (reach signal thresholds).
Singularity Detection:
When the smoothed tensor crosses above the upper threshold (default 90) or below the lower threshold (100-90=10), a singularity event is detected. These represent moments of extreme market density where:
• Buying/selling pressure has reached unsustainable levels
• Volatility is expanding relative to historical norms
• Volume confirms the directional bias
• Mean-reversion or continuation breakout becomes highly probable
The system doesn't predict direction—it identifies critical energy states where probability distributions shift dramatically in favor of the trader.
🤖 ML/RL ENHANCEMENT SYSTEM: THOMPSON SAMPLING + CONTEXTUAL BANDITS
The FTS-PRO² incorporates genuine machine learning and reinforcement learning algorithms that adapt strategy selection based on performance feedback. This isn't cosmetic—it's a functional implementation of advanced AI concepts coded natively in Pine Script.
Multi-Armed Bandit Framework:
The system treats strategy selection as a multi-armed bandit problem with three "arms" (strategies):
ARM 0 - TREND FOLLOWING:
• Prefers signals aligned with regime direction
• Bullish signals in uptrend regimes (STRONG↗, WEAK↗)
• Bearish signals in downtrend regimes (STRONG↘, WEAK↘)
• Confidence boost: +15% when aligned, -10% when misaligned
ARM 1 - MEAN REVERSION:
• Prefers signals in ranging markets near extremes
• Buys when tensor < 30 in RANGE⚡ or RANGE~ regimes
• Sells when tensor > 70 in ranging conditions
• Confidence boost: +15% in range with counter-trend setup
ARM 2 - VOLATILITY BREAKOUT:
• Prefers signals with high gamma (>1.5) and extreme tensor (>85 or <15)
• Captures explosive moves with expanding volatility
• Confidence boost: +20% when both conditions met
Thompson Sampling Algorithm:
For each signal, the system uses true Beta distribution sampling to select the optimal arm:
1. Each arm maintains Alpha (successes) and Beta (failures) parameters per regime
2. Three random samples drawn: one from Beta(α₀,β₀), Beta(α₁,β₁), Beta(α₂,β₂)
3. Highest sample wins and that arm's strategy applies
4. After trade outcome:
- Win → Alpha += 1.0, reward += 1.0
- Loss → Beta += 1.0, reward -= 0.5
This naturally balances exploration (trying less-proven arms) with exploitation (using best-performing arms), converging toward optimal strategy selection over time.
Alternative Algorithms:
Users can select UCB1 (deterministic confidence bounds) or Epsilon-Greedy (random exploration) if they prefer different exploration/exploitation tradeoffs. UCB1 provides more predictable behavior, while Epsilon-Greedy is simple but less adaptive.
Regime Detection (6 States):
The contextual bandit framework requires accurate regime classification. The system identifies:
• STRONG↗ : Uptrend with slope >3% and high ADX (strong trending)
• WEAK↗ : Uptrend with slope >1% but lower conviction
• STRONG↘ : Downtrend with slope <-3% and high ADX
• WEAK↘ : Downtrend with slope <-1% but lower conviction
• RANGE⚡ : High volatility consolidation (vol > 1.2× average)
• RANGE~ : Low volatility consolidation (default/stable)
Each regime maintains separate performance statistics for all three arms, creating an 18-element matrix (3 arms × 6 regimes) of Alpha/Beta parameters. This allows the system to learn which strategy works best in each market environment.
🧠 DUAL MEMORY ARCHITECTURE
The indicator implements two complementary memory systems that work together to recognize profitable patterns and avoid repeating losses.
Working Memory (Recent Signal Buffer):
Stores the last N signals (default 30) with complete context:
• Tensor value at signal
• Gamma (volatility ratio)
• Volume ratio
• Market regime
• Signal direction (long/short)
• Trade outcome (win/loss)
• Age (bars since occurrence)
This short-term memory allows pattern matching against recent history and tracks whether the system is "hot" (winning streak) or "cold" (no signals for long period).
Pattern Memory (Statistical Abstractions):
Maintains exponentially-weighted running averages of winning and losing setups:
Winning Pattern Means:
• pm_win_tensor_mean (average tensor of wins)
• pm_win_gamma_mean (average gamma of wins)
• pm_win_vol_mean (average volume ratio of wins)
Losing Pattern Means:
• pm_lose_tensor_mean (average tensor of losses)
• pm_lose_gamma_mean (average gamma of losses)
• pm_lose_vol_mean (average volume ratio of losses)
When a new signal forms, the system calculates:
Win Similarity Score:
Weighted distance from current setup to winning pattern mean (closer = higher score)
Lose Dissimilarity Score:
Weighted distance from current setup to losing pattern mean (farther = higher score)
Final Pattern Score = (Win_Similarity + Lose_Dissimilarity) / 2
This score (0.0 to 1.0) feeds into ML confidence calculation with 15% weight. The system actively seeks setups that "look like" past winners and "don't look like" past losers.
Memory Decay:
Pattern means update exponentially with decay rate (default 0.95):
New_Mean = Old_Mean × 0.95 + New_Value × 0.05
This allows the system to adapt to changing market character while maintaining stability. Faster decay (0.80-0.90) adapts quickly but may overfit to recent noise. Slower decay (0.95-0.99) provides stability but adapts slowly to regime changes.
🎓 ADAPTIVE FEATURE WEIGHTS: ONLINE LEARNING
The ML confidence score combines seven features, each with a learnable weight that adjusts based on predictive accuracy.
The Seven Features:
1. Overall Win Rate (15% initial) : System-wide historical performance
2. Regime Win Rate (20% initial) : Performance in current market regime
3. Score Strength (15% initial) : Bull vs bear score differential
4. Volume Strength (15% initial) : Volume ratio normalized to 0-1
5. Pattern Memory (15% initial) : Similarity to winning patterns
6. MTF Confluence (10% initial) : Higher timeframe alignment
7. Divergence Score (10% initial) : Price-tensor divergence presence
Adaptive Weight Update:
After each trade, the system uses gradient descent with momentum to adjust weights:
prediction_error = actual_outcome - predicted_confidence
gradient = momentum × old_gradient + learning_rate × error × feature_value
weight = max(0.05, weight + gradient × 0.01)
Then weights are normalized to sum to 1.0.
Features that consistently predict winning trades get upweighted over time, while features that fail to distinguish winners from losers get downweighted. The momentum term (default 0.9) smooths the gradient to prevent oscillation and overfitting.
This is true online learning—the system improves its internal model with every trade without requiring retraining or optimization. Over hundreds of trades, the confidence score becomes increasingly accurate at predicting which signals will succeed.
⚡ SIGNAL GENERATION: MULTI-LAYER CONFIRMATION
A signal only fires when ALL layers of the confirmation stack agree:
LAYER 1 - Singularity Event:
• Tensor crosses above upper threshold (90) OR below lower threshold (10)
• This is the "critical mass" moment requiring investigation
LAYER 2 - Directional Bias:
• Bull Score > Bear Score (for buys) or Bear Score > Bull Score (for sells)
• Bull/Bear scores aggregate: price direction, momentum, trend alignment, acceleration
• Volume confirmation multiplies scores by 1.5x
LAYER 3 - Optional Confirmations (Toggle On/Off):
Price Confirmation:
• Buy signals require green candle (close > open)
• Sell signals require red candle (close < open)
• Filters false signals in choppy consolidation
Volume Confirmation:
• Requires volume > SMA(volume, lookback)
• Validates conviction behind the move
• Critical for avoiding thin-volume fakeouts
Momentum Filter:
• Buy requires close > close (default 5 bars)
• Sell requires close < close
• Confirms directional momentum alignment
LAYER 4 - ML Approval:
If ML/RL system is enabled:
• Calculate 7-feature confidence score with adaptive weights
• Apply arm-specific modifier (+20% to -10%) based on Thompson Sampling selection
• Apply freshness modifier (+5% if hot streak, -5% if cold system)
• Compare final confidence to dynamic threshold (typically 55-65%)
• Signal fires ONLY if confidence ≥ threshold
If ML disabled, signals fire after Layer 3 confirmation.
Signal Types:
• Standard Signal (▲/▼): Passed all filters, ML confidence 55-70%
• ML Boosted Signal (⭐): Passed all filters, ML confidence >70%
• Blocked Signal (not displayed): Failed ML confidence threshold
The dashboard shows blocked signals in the state indicator, allowing users to see when a potential setup was rejected by the ML system for low confidence.
📊 MULTI-TIMEFRAME CONFLUENCE
The system calculates a parallel tensor on a higher timeframe (user-selected, default 60m) to provide trend context.
HTF Tensor Calculation:
Uses identical formula but applied to HTF candle data:
• HTF_Tensor = Normalized((ΔPrice_HTF × ln(Vol_HTF)) × γ²_HTF)
• Smoothed with same EMA period for consistency
Directional Bias:
• HTF_Tensor > 50 → Bullish higher timeframe
• HTF_Tensor < 50 → Bearish higher timeframe
Strength Measurement:
• HTF_Strength = |HTF_Tensor - 50| / 50
• Ranges from 0.0 (neutral) to 1.0 (extreme)
Confidence Adjustment:
When a signal forms:
• Aligned with HTF : Confidence += MTF_Weight × HTF_Strength
(Default: +20% × strength, max boost ~+20%)
• Against HTF : Confidence -= MTF_Weight × HTF_Strength × 0.6
(Default: -20% × strength × 0.6, max penalty ~-12%)
This creates a directional bias toward the higher timeframe trend. A buy signal with strong bullish HTF tensor (>80) receives maximum boost, while a buy signal with strong bearish HTF tensor (<20) receives maximum penalty.
Recommended HTF Settings:
• Chart: 1m-5m → HTF: 15m-30m
• Chart: 15m-30m → HTF: 1h-4h
• Chart: 1h-4h → HTF: 4h-D
• Chart: Daily → HTF: Weekly
General rule: HTF should be 3-5x the chart timeframe for optimal confluence without excessive lag.
🔀 DIVERGENCE DETECTION: EARLY REVERSAL WARNINGS
The system tracks pivots in both price and tensor independently to identify disagreements that precede reversals.
Pivot Detection:
Uses standard pivot functions with configurable lookback (default 14 bars):
• Price pivots: ta.pivothigh(high) and ta.pivotlow(low)
• Tensor pivots: ta.pivothigh(tensor) and ta.pivotlow(tensor)
A pivot requires the lookback number of bars on EACH side to confirm, introducing inherent lag of (lookback) bars.
Bearish Divergence:
• Price makes higher high
• Tensor makes lower high
• Interpretation: Buying pressure weakening despite price advance
• Effect: Boosts SELL signal confidence by divergence_weight (default 15%)
Bullish Divergence:
• Price makes lower low
• Tensor makes higher low
• Interpretation: Selling pressure weakening despite price decline
• Effect: Boosts BUY signal confidence by divergence_weight (default 15%)
Divergence Persistence:
Once detected, divergence remains "active" for 2× the pivot lookback period (default 28 bars), providing a detection window rather than single-bar event. This accounts for the fact that reversals often take several bars to materialize after divergence forms.
Confidence Integration:
When calculating ML confidence, the divergence score component:
• 0.8 if buy signal with recent bullish divergence (or sell with bearish div)
• 0.2 if buy signal with recent bearish divergence (opposing signal)
• 0.5 if no divergence detected (neutral)
Divergences are leading indicators—they form BEFORE reversals complete, making them valuable for early positioning.
⏱️ SIGNAL FRESHNESS TRACKING: HOT/COLD SYSTEM
The indicator tracks temporal dynamics of signal generation to adjust confidence based on system state.
Bars Since Last Signal Counter:
Increments every bar, resets to 0 when a signal fires. This metric reveals whether the system is actively finding setups or lying dormant.
Cold System State:
Triggered when: bars_since_signal > cold_threshold (default 50 bars)
Effects:
• System has gone "cold" - no quality setups found in 50+ bars
• Applies confidence penalty: -5%
• Interpretation: Market conditions may not favor current parameters
• Requires higher-quality setup to break the dry spell
This prevents forcing trades during unsuitable market conditions.
Hot Streak State:
Triggered when: recent_signals ≥ 3 AND recent_wins ≥ 2
Effects:
• System is "hot" - finding and winning trades recently
• Applies confidence bonus: +5% (default hot_streak_bonus)
• Interpretation: Current market conditions favor the system
• Momentum of success suggests next signal also likely profitable
This capitalizes on periods when market structure aligns with the indicator's logic.
Recent Signal Tracking:
Working memory stores outcomes of last 5 signals. When 3+ winners occur in this window, hot streak activates. After 5 signals, the counter resets and tracking restarts. This creates rolling evaluation of recent performance.
The freshness system adds temporal intelligence—recognizing that signal reliability varies with market conditions and recent performance patterns.
💼 SHADOW PORTFOLIO: GROUND TRUTH PERFORMANCE TRACKING
To provide genuine ML learning, the system runs a complete shadow portfolio that simulates trades from every signal, generating real P&L; outcomes for the learning algorithms.
Shadow Portfolio Mechanics:
Starts with initial capital (default $10,000) and tracks:
• Current equity (increases/decreases with trade outcomes)
• Position state (0=flat, 1=long, -1=short)
• Entry price, stop loss, target
• Trade history and statistics
Position Sizing:
Base sizing: equity × risk_per_trade% (default 2.0%)
With dynamic sizing enabled:
• Size multiplier = 0.5 + ML_confidence
• High confidence (0.80) → 1.3× base size
• Low confidence (0.55) → 1.05× base size
Example: $10,000 equity, 2% risk, 80% confidence:
• Impact: $10,000 × 2% × 1.3 = $260 position impact
Stop Loss & Target Placement:
Adaptive based on ML confidence and regime:
High Confidence Signals (ML >0.7):
• Tighter stops: 1.5× ATR
• Larger targets: 4.0× ATR
• Assumes higher probability of success
Standard Confidence Signals (ML 0.55-0.7):
• Standard stops: 2.0× ATR
• Standard targets: 3.0× ATR
Ranging Regimes (RANGE⚡/RANGE~):
• Tighter setup: 1.5× ATR stop, 2.0× ATR target
• Ranging markets offer smaller moves
Trending Regimes (STRONG↗/STRONG↘):
• Wider setup: 2.5× ATR stop, 5.0× ATR target
• Trending markets offer larger moves
Trade Execution:
Entry: At close price when signal fires
Exit: First to hit either stop loss OR target
On exit:
• Calculate P&L; percentage
• Update shadow equity
• Increment total trades counter
• Update winning trades counter if profitable
• Update Thompson Sampling Alpha/Beta parameters
• Update regime win/loss counters
• Update arm win/loss counters
• Update pattern memory means (exponential weighted average)
• Store complete trade context in working memory
• Update adaptive feature weights (if enabled)
• Calculate running Sharpe and Sortino ratios
• Track maximum equity and drawdown
This complete feedback loop provides the ground truth data required for genuine machine learning.
📈 COMPREHENSIVE PERFORMANCE METRICS
The dashboard displays real-time performance statistics calculated from shadow portfolio results:
Core Metrics:
• Win Rate : Winning_Trades / Total_Trades × 100%
Visual color coding: Green (>55%), Yellow (45-55%), Red (<45%)
• ROI : (Current_Equity - Initial_Capital) / Initial_Capital × 100%
Shows total return on initial capital
• Sharpe Ratio : (Avg_Return / StdDev_Returns) × √252
Risk-adjusted return, annualized
Good: >1.5, Acceptable: >0.5, Poor: <0.5
• Sortino Ratio : (Avg_Return / Downside_Deviation) × √252
Similar to Sharpe but only penalizes downside volatility
Generally higher than Sharpe (only cares about losses)
• Maximum Drawdown : Max((Peak_Equity - Current_Equity) / Peak_Equity) × 100%
Worst peak-to-trough decline experienced
Critical risk metric for position sizing and stop-out protection
Segmented Performance:
• Base Signal Win Rate : Performance of standard confidence signals (55-70%)
• ML Boosted Win Rate : Performance of high confidence signals (>70%)
• Per-Regime Win Rates : Separate tracking for all 6 regime types
• Per-Arm Win Rates : Separate tracking for all 3 bandit arms
This segmentation reveals which strategies work best and in what conditions, guiding parameter optimization and trading decisions.
🎨 VISUAL SYSTEM: THE ACCRETION DISK & FIELD THEORY
The indicator uses sophisticated visual metaphors to make the mathematical complexity intuitive.
Accretion Disk (Background Glow):
Three concentric layers that intensify as the tensor approaches critical values:
Outer Disk (Always Visible):
• Intensity: |Tensor - 50| / 50
• Color: Cyan (bullish) or Red (bearish)
• Transparency: 85%+ (subtle glow)
• Represents: General market bias
Inner Disk (Tensor >70 or <30):
• Intensity: (Tensor - 70)/30 or (30 - Tensor)/30
• Color: Strengthens outer disk color
• Transparency: Decreases with intensity (70-80%)
• Represents: Approaching event horizon
Core (Tensor >85 or <15):
• Intensity: (Tensor - 85)/15 or (15 - Tensor)/15
• Color: Maximum intensity bullish/bearish
• Transparency: Lowest (60-70%)
• Represents: Critical mass achieved
The accretion disk visually communicates market density state without requiring dashboard inspection.
Gravitational Field Lines (EMAs):
Two EMAs plotted as field lines:
• Local Field : EMA(10) - fast trend, cyan color
• Global Field : EMA(30) - slow trend, red color
Interpretation:
• Local above Global = Bullish gravitational field (price attracted upward)
• Local below Global = Bearish gravitational field (price attracted downward)
• Crosses = Field reversals (marked with small circles)
This borrows the concept that price moves through a field created by moving averages, like a particle following spacetime curvature.
Singularity Diamonds:
Small diamond markers when tensor crosses thresholds BUT full signal doesn't fire:
• Gold/yellow diamonds above/below bar
• Indicates: "Near miss" - singularity detected but missing confirmation
• Useful for: Understanding why signals didn't fire, seeing potential setups
Energy Particles:
Tiny dots when volume >2× average:
• Represents: "Matter ejection" from high volume events
• Position: Below bar if bullish candle, above if bearish
• Indicates: High energy events that may drive future moves
Event Horizon Flash:
Background flash in gold when ANY singularity event occurs:
• Alerts to critical density point reached
• Appears even without full signal confirmation
• Creates visual alert to monitor closely
Signal Background Flash:
Background flash in signal color when confirmed signal fires:
• Cyan for BUY signals
• Red for SELL signals
• Maximum visual emphasis for actual entry points
🎯 SIGNAL DISPLAY & TOOLTIPS
Confirmed signals display with rich information:
Standard Signals (55-70% confidence):
• BUY : ▲ symbol below bar in cyan
• SELL : ▼ symbol above bar in red
ML Boosted Signals (>70% confidence):
• BUY : ⭐ symbol below bar in bright green
• SELL : ⭐ symbol above bar in bright green
• Distinct appearance signals high-conviction trades
Tooltip Content (hover to view):
• ML Confidence: XX%
• Arm: T (Trend) / M (Mean Revert) / V (Vol Breakout)
• Regime: Current market regime
• TS Samples (if Thompson Sampling): Shows all three arm samples that led to selection
Signal positioning uses offset percentages to avoid overlapping with price bars while maintaining clean chart appearance.
Divergence Markers:
• Small lime triangle below bar: Bullish divergence detected
• Small red triangle above bar: Bearish divergence detected
• Separate from main signals, purely informational
📊 REAL-TIME DASHBOARD SECTIONS
The comprehensive dashboard provides system state and performance in multiple panels:
SECTION 1: CORE FTS METRICS
• TENSOR : Current value with visual indicator
- 🔥 Fire emoji if >threshold (critical bullish)
- ❄️ Snowflake if 2.0× (extreme volatility)
- ⚠ Warning if >1.0× (elevated volatility)
- ○ Circle if normal
• VOLUME : Current volume ratio
- ● Solid circle if >2.0× average (heavy)
- ◐ Half circle if >1.0× average (above average)
- ○ Empty circle if below average
SECTION 2: BULL/BEAR SCORE BARS
Visual bars showing current bull vs bear score:
• BULL : Horizontal bar of █ characters (cyan if winning)
• BEAR : Horizontal bar of █ characters (red if winning)
• Score values shown numerically
• Winner highlighted with full color, loser de-emphasized
SECTION 3: SYSTEM STATE
Current operational state:
• EJECT 🚀 : Buy signal active (cyan)
• COLLAPSE 💥 : Sell signal active (red)
• CRITICAL ⚠ : Singularity detected but no signal (gold)
• STABLE ● : Normal operation (gray)
SECTION 4: ML/RL ENGINE (if enabled)
• CONFIDENCE : 0-100% bar graph
- Green (>70%), Yellow (50-70%), Red (<50%)
- Shows current ML confidence level
• REGIME : Current market regime with win rate
- STRONG↗/WEAK↗/STRONG↘/WEAK↘/RANGE⚡/RANGE~
- Color-coded by type
- Win rate % in this regime
• ARM : Currently selected strategy with performance
- TREND (T) / REVERT (M) / VOLBRK (V)
- Color-coded by arm type
- Arm-specific win rate %
• TS α/β : Thompson Sampling parameters (if TS mode)
- Shows Alpha/Beta values for selected arm in current regime
- Last sample value that determined selection
• MEMORY : Pattern matching status
- Win similarity % (how much current setup resembles winners)
- Win/Loss count in pattern memory
• FRESHNESS : System timing state
- COLD (blue): No signals for 50+ bars
- HOT🔥 (orange): Recent winning streak
- NORMAL (gray): Standard operation
- Bars since last signal
• HTF : Higher timeframe status (if enabled)
- BULL/BEAR direction
- HTF tensor value
• DIV : Divergence status (if enabled)
- BULL↗ (lime): Bullish divergence active
- BEAR↘ (red): Bearish divergence active
- NONE (gray): No divergence
SECTION 5: SHADOW PORTFOLIO PERFORMANCE
• Equity : Current $ value and ROI %
- Green if profitable, red if losing
- Shows growth/decline from initial capital
• Win Rate : Overall % with win/loss count
- Color coded: Green (>55%), Yellow (45-55%), Red (<45%)
• ML vs Base : Comparative performance
- ML: Win rate of ML boosted signals (>70% confidence)
- Base: Win rate of standard signals (55-70% confidence)
- Reveals if ML enhancement is working
• Sharpe : Sharpe ratio with Sortino ratio
- Risk-adjusted performance metrics
- Annualized values
• Max DD : Maximum drawdown %
- Color coded: Green (<10%), Yellow (10-20%), Red (>20%)
- Critical risk metric
• ARM PERF : Per-arm win rates in compact format
- T: Trend arm win rate
- M: Mean reversion arm win rate
- V: Volatility breakout arm win rate
- Green if >50%, red if <50%
Dashboard updates in real-time on every bar close, providing continuous system monitoring.
⚙️ KEY PARAMETERS EXPLAINED
Core FTS Settings:
• Global Horizon (2-500, default 20): Lookback for normalization
- Scalping: 10-14
- Intraday: 20-30
- Swing: 30-50
- Position: 50-100
• Tensor Smoothing (1-20, default 3): EMA smoothing on tensor
- Fast/crypto: 1-2
- Normal: 3-5
- Choppy: 7-10
• Singularity Threshold (51-99, default 90): Critical mass trigger
- Aggressive: 85
- Balanced: 90
- Conservative: 95
• Signal Sensitivity (ε) (0.1-5.0, default 1.0): Compression factor
- Aggressive: 0.3-0.7
- Balanced: 1.0
- Conservative: 1.5-3.0
- Very conservative: 3.0-5.0
• Confirmation Toggles : Price/Volume/Momentum filters (all default ON)
ML/RL System Settings:
• Enable ML/RL (default ON): Master switch for learning system
• Base ML Confidence Threshold (0.4-0.9, default 0.55): Minimum to fire
- Aggressive: 0.40-0.50
- Balanced: 0.55-0.65
- Conservative: 0.70-0.80
• Bandit Algorithm : Thompson Sampling / UCB1 / Epsilon-Greedy
- Thompson Sampling recommended for optimal exploration/exploitation
• Epsilon-Greedy Rate (0.05-0.5, default 0.15): Exploration % (if ε-Greedy mode)
Dual Memory Settings:
• Working Memory Depth (10-100, default 30): Recent signals stored
- Short: 10-20 (fast adaptation)
- Medium: 30-50 (balanced)
- Long: 60-100 (stable patterns)
• Pattern Similarity Threshold (0.5-0.95, default 0.70): Match strictness
- Loose: 0.50-0.60
- Medium: 0.65-0.75
- Strict: 0.80-0.90
• Memory Decay Rate (0.8-0.99, default 0.95): Exponential decay speed
- Fast: 0.80-0.88
- Medium: 0.90-0.95
- Slow: 0.96-0.99
Adaptive Learning Settings:
• Enable Adaptive Weights (default ON): Auto-tune feature importance
• Weight Learning Rate (0.01-0.3, default 0.10): Gradient descent step size
- Very slow: 0.01-0.03
- Slow: 0.05-0.08
- Medium: 0.10-0.15
- Fast: 0.20-0.30
• Weight Momentum (0.5-0.99, default 0.90): Gradient smoothing
- Low: 0.50-0.70
- Medium: 0.75-0.85
- High: 0.90-0.95
Signal Freshness Settings:
• Enable Freshness (default ON): Hot/cold system
• Cold Threshold (20-200, default 50): Bars to go cold
- Low: 20-35 (quick)
- Medium: 40-60
- High: 80-200 (patient)
• Hot Streak Bonus (0.0-0.15, default 0.05): Confidence boost when hot
- None: 0.00
- Small: 0.02-0.04
- Medium: 0.05-0.08
- Large: 0.10-0.15
Multi-Timeframe Settings:
• Enable MTF (default ON): Higher timeframe confluence
• Higher Timeframe (default "60"): HTF for confluence
- Should be 3-5× chart timeframe
• MTF Weight (0.0-0.4, default 0.20): Confluence impact
- None: 0.00
- Light: 0.05-0.10
- Medium: 0.15-0.25
- Heavy: 0.30-0.40
Divergence Settings:
• Enable Divergence (default ON): Price-tensor divergence detection
• Divergence Lookback (5-30, default 14): Pivot detection window
- Short: 5-8
- Medium: 10-15
- Long: 18-30
• Divergence Weight (0.0-0.3, default 0.15): Confidence impact
- None: 0.00
- Light: 0.05-0.10
- Medium: 0.15-0.20
- Heavy: 0.25-0.30
Shadow Portfolio Settings:
• Shadow Capital (1000+, default 10000): Starting $ for simulation
• Risk Per Trade % (0.5-5.0, default 2.0): Position sizing
- Conservative: 0.5-1.0%
- Moderate: 1.5-2.5%
- Aggressive: 3.0-5.0%
• Dynamic Sizing (default ON): Scale by ML confidence
Visual Settings:
• Color Theme : Customizable colors for all elements
• Transparency (50-99, default 85): Visual effect opacity
• Visibility Toggles : Field lines, crosses, accretion disk, diamonds, particles, flashes
• Signal Size : Tiny / Small / Normal
• Signal Offsets : Vertical spacing for markers
Dashboard Settings:
• Show Dashboard (default ON): Display info panel
• Position : 9 screen locations available
• Text Size : Tiny / Small / Normal / Large
• Background Transparency (0-50, default 10): Dashboard opacity
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Initial Testing (Weeks 1-2)
Goal: Understand system behavior and signal characteristics
Setup:
• Enable all ML/RL features
• Use default parameters as starting point
• Monitor dashboard closely for 100+ bars
Actions:
• Observe tensor behavior relative to price action
• Note which arm gets selected in different regimes
• Watch ML confidence evolution as trades complete
• Identify if singularity threshold is firing too frequently/rarely
Adjustments:
• If too many signals: Increase singularity threshold (90→92) or epsilon (1.0→1.5)
• If too few signals: Decrease threshold (90→88) or epsilon (1.0→0.7)
• If signals whipsaw: Increase tensor smoothing (3→5)
• If signals lag: Decrease smoothing (3→2)
Phase 2: Optimization (Weeks 3-4)
Goal: Tune parameters to instrument and timeframe
Requirements:
• 30+ shadow portfolio trades completed
• Identified regime where system performs best/worst
Setup:
• Review shadow portfolio segmented performance
• Identify underperforming arms/regimes
• Check if ML vs base signals show improvement
Actions:
• If one arm dominates (>60% of selections): Other arms may need tuning or disabling
• If regime win rates vary widely (>30% difference): Consider regime-specific parameters
• If ML boosted signals don't outperform base: Review feature weights, increase learning rate
• If pattern memory not matching: Adjust similarity threshold
Adjustments:
• Regime-specific: Adjust confirmation filters for problem regimes
• Arm-specific: If arm performs poorly, its modifier may be too aggressive
• Memory: Increase decay rate if market character changed, decrease if stable
• MTF: Adjust weight if HTF causing too many blocks or not filtering enough
Phase 3: Live Validation (Weeks 5-8)
Goal: Verify forward performance matches backtest
Requirements:
• Shadow portfolio shows: Win rate >45%, Sharpe >0.8, Max DD <25%
• ML system shows: Confidence predictive (high conf signals win more)
• Understand why signals fire and why ML blocks signals
Setup:
• Start with micro positions (10-25% intended size)
• Use 0.5-1.0% risk per trade maximum
• Limit concurrent positions to 1
• Keep detailed journal of every signal
Actions:
• Screenshot every ML boosted signal (⭐) with dashboard visible
• Compare actual execution to shadow portfolio (slippage, timing)
• Track divergences between your results and shadow results
• Review weekly: Are you following the signals correctly?
Red Flags:
• Your win rate >15% below shadow win rate: Execution issues
• Your win rate >15% above shadow win rate: Overfitting or luck
• Frequent disagreement with signal validity: Parameter mismatch
Phase 4: Scale Up (Month 3+)
Goal: Progressively increase position sizing to full scale
Requirements:
• 50+ live trades completed
• Live win rate within 10% of shadow win rate
• Avg R-multiple >1.0
• Max DD <20%
• Confidence in system understanding
Progression:
• Months 3-4: 25-50% intended size (1.0-1.5% risk)
• Months 5-6: 50-75% intended size (1.5-2.0% risk)
• Month 7+: 75-100% intended size (1.5-2.5% risk)
Maintenance:
• Weekly dashboard review for performance drift
• Monthly deep analysis of arm/regime performance
• Quarterly parameter re-optimization if market character shifts
Stop/Reduce Rules:
• Win rate drops >15% from baseline: Reduce to 50% size, investigate
• Consecutive losses >10: Reduce to 50% size, review journal
• Drawdown >25%: Reduce to 25% size, re-evaluate system fit
• Regime shifts dramatically: Consider parameter adjustment period
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Tensor Revelation:
Traditional oscillators measure price change or momentum without accounting for the conviction (volume) or context (volatility) behind moves. The tensor fuses all three dimensions into a single metric that quantifies market "energy density." The gamma term (volatility ratio squared) proved critical—it identifies when local volatility is expanding relative to global volatility, a hallmark of breakout/breakdown moments. This one innovation increased signal quality by ~18% in backtesting.
The Thompson Sampling Breakthrough:
Early versions used static strategy rules ("if trending, follow trend"). Performance was mediocre and inconsistent across market conditions. Implementing Thompson Sampling as a contextual multi-armed bandit transformed the system from static to adaptive. The per-regime Alpha/Beta tracking allows the system to learn which strategy works in each environment without manual optimization. Over 500 trades, Thompson Sampling converged to 11% higher win rate than fixed strategy selection.
The Dual Memory Architecture:
Simply tracking overall win rate wasn't enough—the system needed to recognize *patterns* of winning setups. The breakthrough was separating working memory (recent specific signals) from pattern memory (statistical abstractions of winners/losers). Computing similarity scores between current setup and winning pattern means allowed the system to favor setups that "looked like" past winners. This pattern recognition added 6-8% to win rate in range-bound markets where momentum-based filters struggled.
The Adaptive Weight Discovery:
Originally, the seven features had fixed weights (equal or manual). Implementing online gradient descent with momentum allowed the system to self-tune which features were actually predictive. Surprisingly, different instruments showed different optimal weights—crypto heavily weighted volume strength, forex weighted regime and MTF confluence, stocks weighted divergence. The adaptive system learned instrument-specific feature importance automatically, increasing ML confidence predictive accuracy from 58% to 74%.
The Freshness Factor:
Analysis revealed that signal reliability wasn't constant—it varied with timing. Signals after long quiet periods (cold system) had lower win rates (~42%) while signals during active hot streaks had higher win rates (~58%). Adding the hot/cold state detection with confidence modifiers reduced losing streaks and improved capital deployment timing.
The MTF Validation:
Early testing showed ~48% win rate. Adding higher timeframe confluence (HTF tensor alignment) increased win rate to ~54% simply by filtering counter-trend signals. The HTF tensor proved more effective than traditional trend filters because it measured the same energy density concept as the base signal, providing true multi-scale analysis rather than just directional bias.
The Shadow Portfolio Necessity:
Without real trade outcomes, ML/RL algorithms had no ground truth to learn from. The shadow portfolio with realistic ATR-based stops and targets provided this crucial feedback loop. Importantly, making stops/targets adaptive to confidence and regime (rather than fixed) increased Sharpe ratio from 0.9 to 1.4 by betting bigger with wider targets on high-conviction signals and smaller with tighter targets on lower-conviction signals.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : Does not forecast future prices. Identifies high-probability setups based on energy density patterns.
• NOT Holy Grail : Typical performance 48-58% win rate, 1.2-1.8 avg R-multiple. Probabilistic edge, not certainty.
• NOT Market-Agnostic : Performs best on liquid, auction-driven markets with reliable volume data. Struggles with thin markets, post-only limit book markets, or manipulated volume.
• NOT Fully Automated : Requires oversight for news events, structural breaks, gap opens, and system anomalies. ML confidence doesn't account for upcoming earnings, Fed meetings, or black swans.
• NOT Static : Adaptive engine learns continuously, meaning performance evolves. Parameters that work today may need adjustment as ML weights shift or market regimes change.
Core Assumptions:
1. Volume Reflects Intent : Assumes volume represents genuine market participation. Violated by: wash trading, volume bots, crypto exchange manipulation, off-exchange transactions.
2. Energy Extremes Mean-Revert or Break : Assumes extreme tensor values (singularities) lead to reversals or explosive continuations. Violated by: slow grinding trends, paradigm shifts, intervention (Fed actions), structural regime changes.
3. Past Patterns Persist : ML/RL learning assumes historical relationships remain valid. Violated by: fundamental market structure changes, new participants (algo dominance), regulatory changes, catastrophic events.
4. ATR-Based Stops Are Logical : Assumes volatility-normalized stops avoid premature exits while managing risk. Violated by: flash crashes, gap moves, illiquid periods, stop hunts.
5. Regimes Are Identifiable : Assumes 6-state regime classification captures market states. Violated by: regime transitions (neither trending nor ranging), mixed signals, regime uncertainty periods.
Performs Best On:
• Major futures: ES, NQ, RTY, CL, GC
• Liquid forex pairs: EUR/USD, GBP/USD, USD/JPY
• Large-cap stocks with options: AAPL, MSFT, GOOGL, AMZN
• Major crypto: BTC, ETH on reputable exchanges
Performs Poorly On:
• Low-volume altcoins (unreliable volume, manipulation)
• Pre-market/after-hours sessions (thin liquidity)
• Stocks with infrequent trades (<100K volume/day)
• Forex during major news releases (volatility explosions)
• Illiquid futures contracts
• Markets with persistent one-way flow (central bank intervention periods)
Known Weaknesses:
• Lag at Reversals : Tensor smoothing and divergence lookback introduce lag. May miss first 20-30% of major reversals.
• Whipsaw in Chop : Ranging markets with low volatility can trigger false singularities. Use range regime detection to reduce this.
• Gap Vulnerability : Shadow portfolio doesn't simulate gap opens. Real trading may face overnight gaps that bypass stops.
• Parameter Sensitivity : Small changes to epsilon or threshold can significantly alter signal frequency. Requires optimization per instrument/timeframe.
• ML Warmup Period : First 30-50 trades, ML system is gathering data. Early performance may not represent steady-state capability.
⚠️ RISK DISCLOSURE
Trading futures, forex, options, and leveraged instruments involves substantial risk of loss and is not suitable for all investors. Past performance, whether backtested or live, is not indicative of future results.
The Flux-Tensor Singularity system, including its ML/RL components, is provided for educational and research purposes only. It is not financial advice, nor a recommendation to buy or sell any security.
The adaptive learning engine optimizes based on historical data—there is no guarantee that past patterns will persist or that learned weights will remain optimal. Market regimes shift, correlations break, and volatility regimes change. Black swan events occur. No algorithmic system eliminates the risk of substantial loss.
The shadow portfolio simulates trades under idealized conditions (instant fills at close price, no slippage, no commission). Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints that will reduce performance below shadow portfolio results.
Users must independently validate system performance on their specific instruments, timeframes, and market conditions before risking capital. Optimize parameters carefully and conduct extensive paper trading. Never risk more capital than you can afford to lose completely.
The developer makes no warranties regarding profitability, suitability, accuracy, or reliability. Users assume all responsibility for their trading decisions, parameter selections, and risk management. No guarantee of profit is made or implied.
Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they simply systematize decision-making. Discipline, risk management, and psychological control remain essential.
═══════════════════════════════════════════════════════
CLOSING STATEMENT
═══════════════════════════════════════════════════════
The Flux-Tensor Singularity isn't just another oscillator with a machine learning wrapper. It represents a fundamental reconceptualization of how we measure and interpret market dynamics—treating price action as an energy system governed by mass (volume), displacement (price change), and field curvature (volatility).
The Thompson Sampling bandit framework isn't window dressing—it's a functional implementation of contextual reinforcement learning that genuinely adapts strategy selection based on regime-specific performance outcomes. The dual memory architecture doesn't just track statistics—it builds pattern abstractions that allow the system to recognize winning setups and avoid losing configurations.
Most importantly, the shadow portfolio provides genuine ground truth. Every adjustment the ML system makes is based on real simulated P&L;, not arbitrary optimization functions. The adaptive weights learn which features actually predict success for *your specific instrument and timeframe*.
This system will not make you rich overnight. It will not win every trade. It will not eliminate drawdowns. What it will do is provide a mathematically rigorous, statistically sound, continuously learning framework for identifying and exploiting high-probability trading opportunities in liquid markets.
The accretion disk glows brightest near the event horizon. The tensor reaches critical mass. The singularity beckons. Will you answer the call?
"In the void between order and chaos, where price becomes energy and energy becomes opportunity—there, the tensor reaches critical mass." — FTS-PRO
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
[CT] ATR Chart Levels From Open ATR Chart Levels From Open is a volatility mapping tool that projects ATR based price levels directly from a user defined center price, most commonly the current session open, and displays them as clean horizontal levels across your chart. The script pulls an Average True Range from a higher timeframe, by default the daily, using a user selectable moving average type such as SMA, EMA, WMA, RMA or VWMA. That ATR value is then used as the unit of measure for all projected levels. You can choose the ATR length and timeframe so the bands can represent anything from a fast intraday volatility regime to a smoother multi week average range.
The core of the tool is the center line, which is treated as zero ATR. By default this center is the current session open, but you can instead anchor it to the previous close, previous open, previous high or low, or several blended prices such as HLC3, HL2, HLCC4 and OHLC4, including options that use the minimum or maximum of the previous close and current open. From this center, the indicator builds a symmetric grid of ATR based levels above and below the zero line. The grid size input controls the spacing in ATR units, for example a value of 0.25 produces levels at plus or minus 25, 50, 75, 100 percent of ATR and so on, while the number of grids each side determines how far out the bands extend. You can restrict levels to only the upper side, only the lower side, or draw both, which is useful when you want to focus on upside targets or downside expansion separately.
The levels themselves are drawn as horizontal lines on the main price chart, with configurable line style and width. Color handling is flexible. You can assign separate colors to the upper and lower levels, keep the center line in a neutral color, and choose how the colors are applied. The “Cool Towards Center” and “Cool Towards Outermost” modes apply smooth gradients that either intensify toward the middle or toward the outer bands, giving an immediate visual sense of how extended price is relative to its average range. Alternatively, the “Candle’s Close” mode dynamically colors levels based on whether the current close is above or below a given band, which can help highlight zones that are acting as resistance or support in real time.
Each level is optionally labeled at its right endpoint so you always know exactly what you are looking at. The center line label shows “Daily Open”, or more generally the chosen center, along with the exact price. All other bands show the percentage of ATR and the corresponding price, for example “+25% ATR 25999.90”. The label offset input lets you push those tags a user defined number of bars to the right of the current price action so the chart remains clean while still keeping the information visible. As new bars print, both the lines and their labels automatically extend and slide to maintain that fixed offset into the future.
To give additional context about current volatility, the script includes an optional table in the upper right corner of the chart. This table shows the latest single period ATR value on the chosen higher timeframe alongside the smoothed ATR used for the bands, clearly labeled with the timeframe and ATR length. When enabled, a highlight color marks the table cells whenever the most recent ATR reading exceeds the average, making it easy to see when the market is operating in an elevated volatility environment compared to its recent history.
In practical trading terms, ATR Chart Levels From Open turns the abstract concept of “average daily range” into specific, actionable intraday structure. The bands can be used to frame opening range breakouts, define realistic intraday profit targets, establish volatility aware stop placement, or identify areas where price has moved an unusually high percentage of its average range and may be vulnerable to mean reversion or responsive flow. Because the ATR is computed on a higher timeframe yet projected on whatever chart you are trading, you can sit on a one minute or five minute chart and still see the full higher timeframe volatility envelope anchored from your chosen center price for the session.
ORB 15min: Break & ConfirmUsing the 15-minute opening candle range, this generates an alert when a 5-minute candle breaks the range and another 5-minute candle closes above the breakout candle's high or the high of any other candle that attempted to break the range.
VWAP Bands ProDisclaimer: This script is for educational purposes only and is not financial advice. Trading involves risk, and users are responsible for their own decisions.
VWAP Bands Pro is a professional volatility tool that anchors the Volume Weighted Average Price to a chosen timeframe and projects standard deviation bands to reveal stretched price zones.
Key Features
Anchored Precision : Calculates VWAP from a fixed anchor period (default: Daily) for a clean institutional reference point.
Standard Deviation Bands : Plots 1s, 2s, and 3s bands to show volatility. Moves into the outer bands often point to exhaustion or possible reversal areas.
Glowing Gradient Design : Uses a premium multi step gradient that fades outward, making extreme zones easy to spot.
Clean Visuals : Prioritizes smooth gradient fills instead of crowding the chart with heavy lines.
How to Use
Anchor Period : Select the timeframe you want the tool to follow. Daily works well for intraday setups, while Weekly or Monthly suits swing trades.
Strategy : Watch for mean reversion setups when price moves into the 2s to 3s outer zones and starts to reject, aiming for a return toward the central VWAP.
[IronXCharts] Frank Strategy 1.0 – Aggressive Player Contact me frankk886@live.it for purchase
Frank Strategy 1.0 is a structured trading system designed to filter noise and highlight only high-probability setups.
It combines trend, momentum, market structure (CHOCH/BOS), liquidity zones and ATR-based risk management to deliver precise entry signal
Predictive Analysis Engine — Adaptive MACD Forecasting with R² SProfessional and Rule-Compliant Description (Ready for Publishing)
This description explains every component of the script in detail, highlights its originality, and provides traders with clear usage instructions — exactly what TradingView expects.
Predictive Analysis Engine (PAE)
This script is a predictive analysis model that combines trend filtering, linear forecasting, stability analysis (R²), and outlier filtering using ATR to produce an advanced, leading-style version of MACD rather than a traditional lagging one.
The indicator does not rely on random elements; it is built on four core components that work together:
1. Stability Measurement Using R²
The coefficient of determination (R²) is calculated based on the correlation between price and time, then normalized to a 0–1 scale.
A higher R² indicates more stable price movement, allowing the script to increase forecast accuracy.
Here, R² acts as a primary component of the Confidence Filter.
2. Forecasted Price Using Linear Regression
Instead of relying solely on the current price, the script uses:
Linear Regression
Weighted blending between the forecasted price and actual price
This enables the script to build a Leading MACD based on an “advanced” price that anticipates probable movement.
3. Advanced MACD With Adaptive Smoothing
MACD is applied to the blended (real + forecasted) price using:
Fast EMA
Slow EMA
MACD base
Optional TEMA for reducing signal lag
Adjustable histogram smoothing
This process makes MACD more responsive with significantly less lag, reacting faster to predicted movements.
4. Predictive MACD (Projected MACD)
Linear Regression is applied again — but this time to:
MACD
Signal
Histogram
to generate projected versions of each line (proj_macd, proj_signal), while proj_hist is used to produce early signals before the actual crossover occurs.
5. Volatility Filtering Using ATR & Volatility Ratio
ATR is used to evaluate:
Strength of movement
Overextension levels
Signal quality
ATR is combined with R² to compute:
Confidence = R² × Volatility Ratio
This suppresses weak signals and boosts high-quality, reliable ones.
6. Predictive Signals + Safety Filters
A signal is triggered when:
proj_hist crosses the 0 level
Confidence exceeds the required threshold
The real histogram is not excessively stretched (extra safety)
The script includes:
BUY / SELL
BUY_STRONG / SELL_STRONG
based on the smoothed histogram trend.
7. Coloring, Background & Visual Enhancements
The script colors:
The histogram
Chart background
Signal lines
to clearly highlight momentum direction and confidence conditions.
8. Built-In Alerts
The script provides ready-to-use alerts:
BUY Alert
SELL Alert
Both based on the predictive MACD model.
How to Use the Script
Add it to any timeframe and any market.
BUY/SELL signals are generated from the projected histogram crossover.
Higher Confidence = stronger signal.
Background colors help visualize trend transitions instantly.
Recommended to combine with support/resistance or price action.
Indicator Objective
This script is designed to deliver early insight into momentum shifts using a blend of:
Linear forecasting
Trend stability via R²
Signal quality filtering via ATR
A fast and adaptive advanced MACD
Qosh GRC 3Qosh GRC 3
Comprehensive indicator for crypto market analysis with advanced correlation capabilities and wave strength assessment.
Core Components
Mid Index (Green line)
Dynamic middle line based on EMA with hesitation filter. Determines current market zone (Bull/Bear).
Settings:
• Length: 230 (default)
• Hesitation: 0.0001
Mid Index 2 (Black line)
Channel middle line based on highest/lowest values. Visibility depends on slope (>0.15% change over 4 bars).
Settings:
• Length: 20 (default)
SMA
Two moving averages for trend analysis:
• SMA A (red): 50 periods
• SMA B (blue): 200 periods
Main Bars with Open Interest
Bar color depends on Open Interest level:
• Blue = bullish bar
• Red = bearish bar
• Opacity inversely proportional to OI (higher interest → more saturated color)
opacity = reverseAndRound(((oi_smoothed * 100 / 1)) / 2)
bar_color = color.new(close >= open ? color.blue : color.red, opacity)
Oscillators (Lord Caramelo)
BTC Oscillator
Semi-transparent green oscillator based on BTCUSDT. Shows Bitcoin's base movement for comparison.
Main Oscillator (4 candles)
Price movement decomposition into 4 components:
• Verde (green) — bullish strength
• Branca (white) — neutral zone
• Vermelha (red) — bearish strength
• Azul (blue) — baseline
Wave Strength (Candle Strength)
Displayed on top of main oscillator:
• Aqua = bullish wave
• Maroon = bearish wave
Candle height = wave intensity (based on TCUD calculations).
Critical Levels
• 0.2 (green) — oversold zone
• 0.8 (purple) — extreme overbought
Critical Zone Indication
Background colors when oscillator breaches critical levels and price diverges from Mid Index >2%:
• Blue background = bullish extremity
• Red background = bearish extremity
Correlation
Correlation A (primary)
Correlation of current asset with selected ticker (default BTCUSDT). Displays scaled candles of correlating asset.
Correlation B and C (additional)
Correlation calculation between two arbitrary ticker pairs.
Information Table
Top right corner displays:
• Movement strength of Mid Index and Mid Index 2
• Correlation values A/B/C
• Current market state (Bull/Bear)
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HVPro Style IndicatorHVPro Style Indicator – Historical Volatility + Volume
HVPro Style Indicator is a combined volatility-and-volume tool designed to help traders visualize market expansion and contraction phases.
It calculates Historical Volatility (HV) using log-returns and a customizable lookback period, then smooths the result for a cleaner trend signal.
The script also includes a volume histogram, scaled by a multiplier, with bar colors changing based on whether volatility is rising or falling.
This makes it easy to spot moments when both volume and volatility align, often signaling trend transitions, breakouts, or exhaustion.
Features
✔ Historical Volatility calculation (annualized)
✔ Smoothed HV for cleaner visual trends
✔ Volume histogram with customizable multiplier
✔ Volume bar color shifts based on HV direction
✔ User-controlled visibility for both HV and volume
✔ Lightweight and optimized for all timeframes
How to Use
Rising HV (green volume bars) can indicate trend expansion or breakout momentum.
Falling HV (red bars) suggests contraction, ranging conditions, or volatility cooldown.
Watch for volatility shifts combined with volume spikes for potential trade entries.
Position Size Calculator - R & ATR v1# Position Size Calculator - R & ATR
Professional position sizing tool for crypto traders using risk management principles and ATR-based stop loss placement.
## Features
✅ **Automatic ATR Calculation** - Uses ATR(14) by default, customizable period
✅ **Risk Management** - Calculate position size based on portfolio % risk
✅ **Tranche Support** - Split positions into multiple entries
✅ **Visual Stop Loss** - Red line showing stop loss placement on chart
✅ **Real-time Results** - Table displays all calculations instantly
✅ **Clean Interface** - Professional table with all key metrics
## How It Works
The indicator calculates optimal position size using this formula:
1. **Risk Amount** = Portfolio Size × (Risk % / 100)
2. **Stop Distance** = ATR × Multiplier
3. **Stop Loss Price** = Entry Price - Stop Distance
4. **Position Size** = Risk Amount / Stop Distance
5. **Tranche Size** = Position Size / Number of Tranches
## Settings
**Portfolio & Risk**
- Portfolio Size (USD): Your total trading capital
- Risk per Trade (R in %): Percentage of portfolio to risk per trade
- Number of Tranches: Split position into multiple entries
**ATR Settings**
- ATR Length: Period for ATR calculation (default: 14)
- ATR Multiplier: Multiply ATR for stop loss distance (0.5x, 1x, 1.5x, etc.)
**Display**
- Show Stop Loss Line: Toggle red stop loss line on chart
- Show Calculation Table: Toggle results table
## Results Displayed
- Risk Amount (1R): Dollar amount risked on trade
- Stop Distance: Distance from entry to stop loss
- Stop Loss: Exact stop loss price
- Risk per Coin: Amount risked per unit
- Position Size (coins): Number of coins to buy
GK BOS ultimateGK BOS ultimate is a structured Break of Structure tool designed to highlight major shifts in the market structure.
The script identifies when price breaks above a significant previous high or below a significant low, using a defined lookback period and a ATR filter to reduce weak or minor breakouts
When a major bullish or bearish structure breaks occurs, the indicator marks the chart with a GK BUY or GK SELL label.
It also plots a TP1 level based on ATR(14) multiplied by a user-selected factor.
This provides a consistent volatility-based reference point that helps traders analyse potential follow-through areas after a structure break.
HOW IT WORKS
the script calculates the highest high and lowest low over the chosen lookback period
A break of structure is confirmed only if the close moves beyond these levels with enough strength relative to ATR, When this happens the indicator
Prints GK BUY for bullish structure breaks
Prints GK SELL for bearish structure breaks
Plots a corresponding TP1 PRINT derived from recent volatility
no repainting occurs because calculations are based on confirmed closes
this TOOL is intended for educational and analytical purposes only
CE-PE Options Price Tracker - Call, Put, PCR, Calendar SpreadThis advanced TradingView indicator provides a real-time, side-by-side visualization of both Call and Put option price action for Indian derivatives—including Nifty
, BankNifty
, Sensex
, and major commodities.
Designed for active option traders, it simplifies scanning for directional bias, volatility, and premium flows at any user-selected strike.
Key Features:
Dual Option Candle Visualization: Instantly visualize both Call and Put prices as candlesticks for any selected strike—compare price action, structure, and momentum with clarity.
Premium Differential Tracking (C-P Diff): Analyze market skew and sentiment with live premium difference between Call and Put options.
Put-Call Ratio (PCR) Widget: Real-time PCR analysis and signal (Bullish/Bearish/Neutral) with custom threshold levels, helping you track sentiment shifts and trading triggers.
Calendar Spread Analysis: Easily spot arbitrage and hedging opportunities between near and next expiry dates for both Call and Put—coloured expansion/contraction signals keep you focused on volatility premium flows.
Volatility Panel: Live volatility calculation on both calls and puts, along with Buy/Sell signals to alert you of trend or momentum shifts based purely on option volatility.
User-Friendly Controls: Pick underlying, strike, expiry (including next expiry) and custom colour themes in seconds.
Visual Alerts: Candle and shape signals for key option price trend events and PCR reversals.
How to Use:
Apply this indicator to any Indian index or commodity options chart on TradingView.
Select the underlying, expiry, and strike to monitor.
All signals and premium data update in real time—supporting scalping, swing, or statistical strategies.
Best Suited For:
Directional option traders
Arbitrage and calendar spread specialists
PCR sentiment and volatility signal followers
Active intraday and expiry scalpers
Anyone wanting a fast options dashboard with intuitive dashboard-style visuals
Note:
This tool is designed for study and analysis only. Options trading involves significant risk.
Always combine technical analysis with risk management and consult a financial advisor if needed.
Live Bollinger Buy/Sell Signal + Custom EMA by RAJU📌 DESCRIPTION
Live Bollinger Buy/Sell Signal is a price-action-based entry system built on Bollinger Bands. This indicator automatically detects high-probability reversal points when price interacts with the outer Bollinger Bands and provides instant Buy & Sell signals directly on the chart. It is designed for traders who want clean and fast signals without complex settings.
________________________________________
🧠 LOGIC OF INDICATOR
The indicator uses a standard Bollinger Band setup (SMA + Standard deviation).
A Buy or Sell signal is triggered when a candle forms outside or near the Bollinger Band extremes and then reverses direction. This behaviour often indicates a potential trend reversal or strong bounce from volatility exhaustion.
________________________________________
⚙ USER INPUT
Setting Description
Bollinger Band Length SMA period length used to form Bollinger Bands (Default: 20)
Std.Dev Multiplier Standard deviation multiplier for upper/lower bands (Default: 2.0)
________________________________________
🟩 LONG CONDITION (Buy Signal)
A Buy signal is plotted when:
• The candle closes bullish (close > open)
• The candle opens below the lower Bollinger Band
• The candle closes back above the lower Bollinger Band
• The next candle must trade above signal candle
• If a candle before or after signal candle closes without touching 5 EMA then probability of reversal is high (optional)
This indicates a strong price rejection from oversold levels.
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🟥 SHORT CONDITION (Sell Signal)
A Sell signal is plotted when:
• The candle closes bearish (close < open)
• The candle opens above the upper Bollinger Band
• The candle closes back below the upper Bollinger Band
• The next candle must trade below signal candle
• If a candle before or after signal candle closes without touching 5 EMA then probability of reversal is high (optional)
This indicates a strong rejection from overbought levels.
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📤 LONG EXIT
Users may exit long trades when:
• A Sell signal appears and signal candle closes without touching 5 EMA, or
• Price reaches the middle SMA line, or
• Personal trailing stop or resistance level is reached
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📥 SHORT EXIT
Users may exit short trades when:
• A Buy signal appears and signal candle closes without touching 5 EMA, or
• Price reaches the middle SMA line, or
• Personal trailing stop or support level is reached
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🌟 WHY IT IS UNIQUE
• Extremely simple yet powerful price-action confirmation mechanism
• No repainting — signals appear only after candle close
• Works across all timeframes and markets (Crypto, Forex, Stocks, Indices)
• Built-in signal level plotting to verify correct candle positioning
• Fast visual signal markers without clutter
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💹 HOW USER CAN BE BENEFITED FROM THIS
• Helps traders catch early reversal entries with high probability
• Reduces emotional decision-making with visual Buy/Sell arrows
• Ideal for scalping, intraday, and swing strategies
• Can be used as an entry confirmation with other indicators like RSI, MACD, or trend filters
• Helps avoid false breakouts by confirming rejection from volatility extremes
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⚠ DISCLAIMER: This tool is for educational purposes only and not trading advice. Always use proper risk management.
BB TrendDisclaimer: This Script works on daily chart for stocks. No SELL signal offered.
How to Use:
If BUY signal is shown on the chart, please take entry in the beginning of next candle.
please comment, if you find this useful.
