Liquidity Spectrum Visualizer (with option volume)This the Liquidity Spectrum Visualizer from BigBeluga, BUT, I took the script and changed it a little bit.
I added the ability to add option volume for a contract of your choosing. You can turn this off with a toggle switch.
If you are looking at option volume, its better to look at it on a smaller time frame (i.e., 15-min).
볼륨
RSI + SMA Strategy (Improved)The lower the timeframe, the more signals it will give; if the trend is too strong, it may give false signals, but it works well on lower timeframes in normal or sideways trends
If u have an idea contact me , TY
Pure Wyckoff V50R [Region Based]Pure Wyckoff V50R — Regional Wyckoff Volume-Price Structure Scanner
This script implements a semi-automatic Wyckoff volume–price analysis based purely on regional behaviour, not on single candles. Instead of trying to label every bar, it analyses the last N candles (default ≥ 50) and their volume distribution to estimate whether the market is in an accumulation, distribution or trend phase.
Main features:
🔍 Region-based structure detection
Scans the last regLen bars to find the trading range, then attempts to locate key Wyckoff points such as
SC (Selling Climax), AR, ST, Spring, UT, LPSY, and draws the SC–AR band when a structure is active.
⚖️ Supply–demand balance
Uses regional bullish vs bearish volume to show whether Demand > Supply, Supply > Demand, or Balanced for the current range.
🧠 Phase & decision panel
For the current bar the panel summarises:
overall structure (bullish / bearish / ranging),
approximate Wyckoff phase (e.g. “A phase: SC→AR rally”, “B phase: top distribution zone”, “Bottom testing zone”),
VSA-style bar reading (no supply, effort vs result, SOW, etc.),
current key signal (Spring / UT / LPSY / ST / Trend),
one-line short-term and long-term trading bias.
📊 Scoreboard
Simple scores for structure, volume and trend to give a quick “bullish / bearish / neutral” overview.
Recommended use:
Designed mainly for higher timeframes (Daily / 4H) where Wyckoff structures are clearer.
Parameters (window length, volume averages, multipliers) should be tuned to the instrument and timeframe.
This is a structure helper, not an automatic signal provider – always combine it with your own discretion and risk management.
Disclaimer: This script is for educational and analytical purposes only and does not constitute financial advice. Use at your own risk and feel free to share feedback or improvements.
Maximus imbalance
Maximus imbalance – Indicator Description
Maximus Precision Arrows is an advanced directional signal tool designed for high-accuracy intraday trading.
It detects early BUY and SELL shifts by combining:
• Delta Imbalance Analysis
• Volume-Normalized Pressure (Buy vs Sell Power)
• Trend Confirmation (MA20 / MA50)
• Signal Strength Ratio Filtering
• Smart Gap Control to avoid over-signaling
How it works
The indicator measures real-time buying and selling pressure (Delta), normalizes it by volume, and filters it through trend direction and strength-ratio logic.
Signals only appear when there is:
• A strong directional imbalance
• Confirmed trend alignment
• Valid momentum breakout
• Enough distance from the previous signal (noise reduction)
What the arrows mean
• Green Triangle (BUY):
Strong positive delta shift + bullish imbalance + price aligned with trend.
• Red Triangle (SELL):
Strong negative delta shift + bearish imbalance + price aligned with downtrend.
Best use
• Intraday scalping (1m–15m)
• Options trading (SPX, QQQ, NVDA, AAPL, futures)
• Identifying early reversals & continuation spots
• Filtering noise during consolidation
Important notes
• Signals are filtered to avoid choppy conditions.
• Works on any market, including equities, indices, futures, and CFDs.
• Not a repainting indicator.
able zone# able zone
## 📋 Overview
**able zone** is an advanced Support & Resistance zone detection indicator optimized for **15-minute timeframe trading**. It combines Price Action, Volume Profile, and intelligent zone analysis to identify high-probability trading areas with precise entry and exit points.
## 🎯 Core Features
### 1. **Zone Detection Methods**
- **Auto Detect**: Automatically finds the best zones using combined analysis
- **Price Action**: Based on pivot points and price structure
- **Volume Profile**: Identifies High Volume Nodes (HVN) where most trading occurred
- **Combined**: Uses all methods together for comprehensive analysis
### 2. **Zone Types & Colors**
- 🟢 **Support Zones** (Green): Price tends to bounce up from these areas
- 🔴 **Resistance Zones** (Red): Price tends to reverse down from these areas
- 🟣 **HVN Zones** (Purple): High volume areas from Volume Profile
- **Strong Zones**: Darker colors indicate zones with more touches (higher reliability)
### 3. **Zone Strength Indicators**
- **Labels**: "S3" = Support with 3 touches, "R5" = Resistance with 5 touches
- **Touch Count**: More touches = stronger zone
- **Min Touch Count Setting**: Adjust to filter weak zones (default: 3)
## ⚙️ Settings Guide
### **Zone Detection Settings**
- **Detection Method**: Choose your preferred analysis method
- **Lookback Period** (50-500): How many bars to analyze (default: 200)
- For 15min: 200 bars = ~50 hours of data
- Shorter = Recent zones only
- Longer = Historical zones included
- **Min Touch Count** (2-10): Minimum touches to qualify as a zone (default: 3)
- **Zone Thickness %** (0.1-2.0): How thick the zones appear (default: 0.5)
- Based on ATR for dynamic sizing on 15min chart
### **Zone Colors**
Fully customizable colors for:
- Support Zone (default: Green)
- Resistance Zone (default: Red)
- Strong Support/Resistance (darker shades)
- Volume Profile Zone (default: Purple)
### **Zone Touch Detection**
- **Enable Touch Alerts**: Get notifications when price enters zones
- **Touch Distance %** (0.1-1.0): How close to zone counts as "touch" (default: 0.3%)
- On 15min chart, this gives early warning signals
- **Show Touch Markers**: Visual indicators when price touches zones
- 🔺 = Support touch (potential buy)
- 🔻 = Resistance touch (potential sell)
- 💎 = HVN touch (watch for breakout/rejection)
### **Volume Profile Integration**
- **Show VP Zones**: Display high volume node zones
- **VP Resolution** (20-50): Number of price levels analyzed (default: 30)
- **POC Line** (orange): Point of Control - highest volume price level
- **POC Width**: Line thickness (1-3)
- **Show HVN**: Display High Volume Node zones
- **HVN Threshold** (0.5-0.9): Volume % to qualify as HVN (default: 0.7)
### **Display Options**
- **Zone Labels**: Show S/R labels with touch count
- **Zone Border Lines**: Dotted lines at zone boundaries
- **Extend Zones Right**: Project zones into future
- **Max Visible Zones** (5-50): Maximum number of zones displayed (default: 20)
- Adjust based on chart clarity needs
- **Info Table**: Real-time information dashboard
## 📊 Info Table Explained
The info table (top-right corner) provides real-time zone analysis:
### **Row 1: ZONE Header**
- Shows current timeframe (15m)
- Total active zones
- "able" branding
### **Row 2: 🎯 TOUCH Status**
- **RES**: Currently touching resistance (⚠️ potential reversal down)
- **SUP**: Currently touching support (🚀 potential bounce up)
- **HVN**: Currently in high volume area (⚡ watch for direction)
- **FREE**: Not near any zone (⏳ wait for setup)
- Progress bar shows proximity strength
- Arrows indicate zone type
### **Row 3: 🟢 SUP - Support Zones**
- Number of active support zones below current price
- Progress bar shows relative quantity
- More support = stronger floor
### **Row 4: 🔴 RES - Resistance Zones**
- Number of active resistance zones above current price
- Progress bar shows relative quantity
- More resistance = stronger ceiling
### **Row 5: 🟣 HVN - High Volume Nodes**
- Number of HVN zones (from Volume Profile)
- These are areas where most trading activity occurred
- Often act as magnets for price
### **Row 6: 📍 NEAR - Nearest Zone**
- Shows closest zone type (SUP/RES/HVN)
- Distance in % to nearest zone
- Arrow shows if zone is above or below
### **Row 7: POSITION - Price Position**
- **HIGH**: Price near range top (70%+) - watch for resistance
- **MID**: Price in middle range (30-70%) - neutral zone
- **LOW**: Price near range bottom (<30%) - watch for support
- Shows exact position % in lookback range
### **Row 8: ═ SIGNAL ═**
- **🚀 BUY**: Touching support zone (entry opportunity)
- **⚠️ SELL**: Touching resistance zone (exit/short opportunity)
- **⚡ WATCH**: At HVN (prepare for breakout or rejection)
- **⏳ WAIT**: No clear setup (be patient)
## 🎓 Trading Strategy for 15-Minute Timeframe
### **Basic Setup**
1. Set timeframe to **15 minutes**
2. Use **Auto Detect** or **Combined** method
3. Set **Lookback Period**: 200 bars (~50 hours)
4. Set **Min Touch Count**: 3 (proven zones)
### **Entry Signals**
#### **Long Entry (Buy)**
- Price touches green support zone
- Table shows "🚀 BUY" signal
- Look for bullish candle pattern (hammer, engulfing)
- Volume increases on bounce
- **Best Entry**: Bottom of support zone
- **Stop Loss**: Below support zone (1-2 ATR)
- **Target**: Next resistance zone or 2:1 RR
#### **Short Entry (Sell)**
- Price touches red resistance zone
- Table shows "⚠️ SELL" signal
- Look for bearish candle pattern (shooting star, engulfing)
- Volume increases on rejection
- **Best Entry**: Top of resistance zone
- **Stop Loss**: Above resistance zone (1-2 ATR)
- **Target**: Next support zone or 2:1 RR
#### **HVN Breakout Strategy**
- Price approaches purple HVN zone
- Table shows "⚡ WATCH"
- Wait for breakout with strong volume
- **If breaks up**: Go long, target next resistance
- **If breaks down**: Go short, target next support
### **Zone Strength Rules**
- **S5+ or R5+**: Very strong zones (high probability)
- **S3-S4 or R3-R4**: Reliable zones (good setups)
- **S2 or R2**: Weak zones (use caution)
### **Best Trading Times (15min)**
- **London Open**: 08:00-12:00 GMT (high volume)
- **NY Open**: 13:00-17:00 GMT (high volatility)
- **Overlap**: 13:00-16:00 GMT (best setups)
- **Avoid**: Asian session low volatility periods
### **Risk Management**
- Never risk more than 1-2% per trade
- Use stop loss ALWAYS (place outside zones)
- Take partial profits at 1:1, let rest run to 2:1 or 3:1
- If price consolidates in zone > 3 candles, exit
## ⚠️ Important Notes
### **When Zones Work Best**
✅ Clear trending markets
✅ After significant price movements
✅ At session opens (London/NY)
✅ When multiple zones align
✅ Strong zone with 5+ touches
### **When to Be Cautious**
❌ During major news releases (use economic calendar)
❌ Very low volume periods
❌ Price consolidating inside zone
❌ Weak zones with only 2 touches
❌ Conflicting signals from multiple indicators
### **15-Minute Specific Tips**
- **Lookback 200**: Captures 2-3 trading days of zones
- **Touch Distance 0.3%**: Early signals on 15min moves
- **Max Zones 20**: Keeps chart clean but comprehensive
- **Watch POC**: Often acts as pivot on 15min
- **Volume spike + zone touch** = high probability setup
## 🔧 Recommended Settings for 15min
### **Conservative Trader**
- Detection Method: Combined
- Min Touch Count: 4
- Max Zones: 15
- Touch Distance: 0.2%
### **Aggressive Trader**
- Detection Method: Auto Detect
- Min Touch Count: 2
- Max Zones: 25
- Touch Distance: 0.5%
### **Volume Profile Focused**
- Detection Method: Volume Profile
- Show HVN: Yes
- HVN Threshold: 0.6
- Show POC: Yes
## 📈 Example Trade Scenario (15min)
**Setup**: BTC/USD on 15-minute chart
1. Price approaching green support zone at $42,000
2. Zone label shows "S4" (touched 4 times)
3. Table shows "🚀 BUY" signal
4. Volume increasing on approach
5. Bullish hammer candle forms
**Entry**: $42,050 (bottom of zone)
**Stop Loss**: $41,900 (below zone)
**Target 1**: $42,350 (2:1 RR)
**Target 2**: Next resistance at $42,650
**Result**: Price bounces, hits Target 1 in 3 candles (~45min)
## 💡 Pro Tips
1. **Combine with trend**: Trade in direction of higher timeframe trend
2. **Multiple touches**: Zones with 5+ touches are highest probability
3. **Volume confirmation**: Always check volume on zone touch
4. **POC magnet**: Price often returns to POC line
5. **False breakouts**: If price barely breaks zone and returns = strong signal
6. **Zone-to-zone**: Trade from support to resistance, resistance to support
7. **Time of day**: Best setups occur during peak volume hours
8. **Chart timeframe**: Use 1H to confirm trend, 15min for entry
9. **News avoidance**: Close trades before high-impact news
10. **Zone clusters**: Multiple zones together = strong area
---
**Created by able** | Optimized for 15-minute trading
**Version**: 1.0 | Compatible with TradingView Pine Script v5
For support and updates, enable alerts and monitor the info table in real-time!
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
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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.
Cumulative Volume Delta CandlesCVD Trend Candles
Visualize buying and selling pressure directly on your price candles. This indicator colors your candlesticks based on Cumulative Volume Delta (CVD), helping you see the underlying order flow driving price action.
WHAT IS CVD?
Cumulative Volume Delta estimates the difference between aggressive buying and selling volume on each bar. Positive delta indicates more aggressive buying; negative delta indicates more aggressive selling.
COLOR METHODS
▸ CVD Raw
The simplest view—candles are colored based purely on the raw delta of each bar.
• Cyan = Positive delta (net buying)
• Red = Negative delta (net selling)
▸ Rule-Based (Default)
Uses Heikin Ashi-smoothed CVD candles with intensity based on trend strength:
• Bright colors = Strong conviction (larger body + continuation)
• Medium colors = Moderate conviction (continuation)
• Dark colors = Weak/indecision (inside candles, hesitation)
▸ Size-Based
Colors intensity based on z-score of delta changes:
• Bright colors = Statistically significant delta (above strong threshold)
• Medium colors = Moderate delta (above moderate threshold)
• Dark colors = Normal/quiet delta
KEY FEATURES
◆ Kalman Filter Smoothing
Adaptive filtering reduces noise while staying responsive to genuine shifts in order flow. Adjust sensitivity with the Responsiveness and Kalman Gain settings.
◆ Inside Candle Rule
When enabled, prevents false signals from inside candles that show a direction change but lack conviction. The candle retains the previous trend's color (dimmed) instead of flipping.
◆ Session Anchoring
Optionally reset cumulative delta at a specific time (e.g., market open) for intraday analysis.
◆ Z-Score Thresholds
Fine-tune what constitutes "strong" vs "moderate" delta activity for Size-Based coloring.
HOW TO USE
1. Add the indicator to your chart
2. Set your chart type to "Line" or bring the indicator to front via Visual Order → Bring to Front
3. Select your preferred Color Method
4. Look for:
• Sequences of bright cyan candles → Strong buying pressure / bullish momentum
• Sequences of bright red candles → Strong selling pressure / bearish momentum
• Fading colors → Weakening conviction, potential reversal or consolidation
• Color flips → Shift in order flow dominance
Notes
• This indicator estimates delta from OHLCV data. For true order flow analysis, consider using tick or trade data from your broker/exchange.
• Works on all timeframes and instruments with volume data.
• Best used in conjunction with support/resistance levels, market structure, or other confluence factors.
Physics Visualizer [RSI + Vol] bars ( educational Purpose only )This code is a TradingView Pine Script (Version 6) for a custom indicator named "Physics Visualizer ".
Here is a breakdown of what it does:
1. What It Is: It is a visual tool designed to show you the relationship between Price Momentum (RSI) and Volume (Fuel) in a single, easy-to-read panel. It tries to answer the question: "Is this price move supported by real volume, or is it fake?"
2. How It Works (The "Physics"): It calculates the "Slope" (direction) of both the RSI and Volume over a short period (3 bars).
Explosion (Lime Green): RSI is going UP + Volume is going UP. This is a strong, healthy move.
Fakeout (Orange): RSI is going UP (Price rising) + Volume is going DOWN. This warns of a weak move that might reverse.
Churn (Maroon): RSI is going DOWN (Price falling) + Volume is going UP. This suggests heavy selling or absorption (fighting).
3. Visuals: It draws a "Bar in Bar" chart:
Background (Gray Bar): Represents the Volume (scaled 0-100). Wide and transparent.
Foreground (Colored Stick): Represents the RSI (Momentum). Thin and colored based on the "Physics State" (Green/Orange/Maroon).
Can we use it as a confirmation? Yes. This is an excellent confirmation tool.
Rule: Only take a Buy signal from your main strategy if this indicator shows a Lime Green (Explosion) bar,
The Map - RMAConcept This indicator is designed to be the ultimate "Map" for intraday traders. Instead of guessing where support and resistance are, it automatically projects Higher Timeframe (HTF) Market Structure onto your chart and combines it with Institutional Volume Analysis. It answers two critical questions instantly: "Where are we?" (Premium vs. Discount) and "Who is trading?" (Whales vs. Retail).
Key Features
Dynamic Market Structure (The Map):
Automatically fetches the Highest High and Lowest Low from a higher timeframe (Default: 4-Hour) over a user-defined lookback period.
Premium Zone (Red): The upper 50% of the range. Ideally used for looking for Short/Sell setups.
Discount Zone (Green): The lower 50% of the range. Ideally used for looking for Long/Buy setups.
Equilibrium (Gray): The 50% midpoint. A key target for mean reversion strategies.
Whale Volume Detection (The Fuel):
Identifies "Whale Candles" where the current volume significantly exceeds the average (e.g., 2x the 20-period average).
Plots visual Bubbles (Green for Up-close, Red for Down-close) to highlight where big money is entering the market.
Filters out noise by only showing bubbles on candles with significant price movement.
Live Dashboard:
A clean table in the top-right corner displays the current Zone status (Premium vs. Discount) and Volume status in real-time.
How to Use
Trend Following: If price breaks out of the H4 High with a Green Whale Bubble, it indicates strong bullish momentum.
Reversal Trading: If price enters the Red (Premium) Zone and prints a Red Whale Bubble (rejection), it suggests institutional selling pressure at resistance.
Confluence: This tool is best used as a "Context Filter" alongside your favorite entry trigger (like a London Breakout or MACD crossover).
Settings
Structure Timeframe: Choose the HTF for your map (Default: 240/4-Hour).
Lookback: How many bars to scan for Highs/Lows (Default: 20).
Whale Multiplier: How much larger than average volume must be to trigger a bubble (Default: 2.0x).
Visuals: Toggle the Zones map on/off to fix chart scaling if needed.
Disclaimer This indicator is for educational and analytical purposes only. Past performance (structure levels) does not guarantee future price action. Always manage your risk.
CVD Smart ReversalCVD Smart Reversal - Indicator Description
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🎯 OVERVIEW
Advanced reversal detection system based on Cumulative Volume Delta (CVD) analysis with intelligent quality filtering. Each signal is rated 1-5 stars based on multiple confirmation factors.
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🌟 KEY FEATURES
1. Quality Score System (⭐1-5)
• 5 independent criteria evaluate each signal
• Filter weak setups - show only 3+ star signals
• Higher scores = higher probability setups
2. Adaptive Thresholds
• Automatically adjusts to market volatility
• High volatility = stricter criteria
• Works across all market conditions
3. Volume Context Analysis
• Compares current vs historical volume
• Calculates buy/sell pressure (requires >60%)
• Filters reversals with weak volume
4. Multi-Timeframe Confirmation (Optional)
• Validates signals on higher timeframe
• Ensures trading with the trend
• Reduces counter-trend entries
5. Smart Signal Management
• Minimum 5-bar spacing between signals
• Automatic label cleanup (max 20)
• Clean chart, no clutter
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📊 HOW IT WORKS
CVD Calculation:
Custom volume delta calculation using intrabar polarity estimation.
Signal Detection:
Combines CVD reversal, candlestick patterns (Hammer, Shooting Star, Engulfing, Pin Bar), and divergence analysis.
Quality Scoring:
Each signal scores 0-5 points based on:
• CVD strength (statistical deviation)
• Pattern quality (professional recognition)
• Divergence presence
• Volume context (ratio + pressure)
• Trend confirmation (MTF or acceleration)
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🎮 USAGE MODES
Sniper Mode (High Quality):
• Min Score: 4-5 stars
• MTF: ON
• Result: 2-5 signals/day, highest win-rate
Active Mode (Balanced):
• Min Score: 3 stars
• MTF: OFF
• Result: 5-15 signals/day, good balance
Scalping Mode (High Frequency):
• Min Score: 2 stars
• Divergence: Weak
• Result: Many signals, fast execution needed
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💡 BEST PRACTICES
• Use on liquid markets with reliable volume data
• Combine with key support/resistance levels
• Pay attention to quality scores - 4-5★ have significantly higher success
• Enable MTF confirmation for intraday trading
• Use stricter settings during high-impact news events
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⚙️ DEFAULT SETTINGS
• Quality Filter: ON
• Minimum Score: 3 stars
• MTF Confirmation: OFF
• Volume Analysis: ON
• Divergence Strength: Medium
These settings provide 5-15 quality signals per day on active instruments.
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🔔 ALERTS
Four alert types available:
• Strong Bullish Reversal (4-5★ only)
• Strong Bearish Reversal (4-5★ only)
• Regular Bullish Reversal (all qualified)
• Regular Bearish Reversal (all qualified)
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⚠️ LIMITATIONS
• Requires volume data (not suitable for markets without volume)
• MTF confirmation adds lag by design
• Lower timeframes may need adjusted settings
• Quality filter reduces signal frequency by design
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🎯 ORIGINALITY
This indicator combines multiple unique elements:
• Multi-factor quality scoring (not found in other CVD tools)
• Adaptive volatility-based thresholds
• Volume pressure calculation with directional filter
• Integrated MTF confirmation within scoring system
• Smart label management with automatic cleanup
The quality scoring system transforms CVD analysis from binary signals into a ranked opportunity system, allowing traders to prioritize setups based on confluence strength.
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📈 DISPLAY ELEMENTS
• Background highlighting on signal bars
• Triangle markers at entry points
• Labels showing CVD, Delta, Divergence, Quality Score, Volume flag
• Real-time info panel with CVD metrics
• Clean visual presentation
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✅ SUITABLE FOR
• Crypto (BTC, ETH, etc.)
• Stocks (AAPL, TSLA, SPY, etc.)
• Futures (ES, NQ, CL, etc.)
• Forex (brokers with volume data)
• All timeframes (1m to 1D)
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Ants Pro - MVP Leaders [LevelUp]Ants Pro identifies exceptionally strong momentum, volume, and price action (MVP) — often one of the earliest signs of institutional accumulation. It offers extensive customization, powerful historical analysis tools, and advanced screening features to give traders a meaningful edge.
Ants Pro was developed in collaboration with David Ryan, three-time winner of the U.S. Investing Championship. David worked with William O’Neil and managed the New USA Growth Fund at William O’Neil + Company, where the Ants idea originated.
Ants Pro helps answer an important question posed by David:
“What separates a stock that makes a 15–20% move from one that rises 15–20%, builds a base, and then continues significantly higher?”
Through his research, David found that many of the market’s biggest winners showed consistent buying over 12 to 15 days, on high volume — a sign of steady institutional accumulation that often unfolds over days or weeks as institutions establish large positions in a stock.
In addition to spotting early accumulation, Ants Pro can flag signs of topping patterns, alerting traders to possible shifts in market sentiment and helping them navigate momentum changes effectively.
🔹—— Key Features ——🔹
▪ Automated detection and highlighting of Ants.
▪ Extensive customization options to match your trading style.
▪ Hover over Ants for detailed stats.
▪ Optional table showing progress towards a new Ant.
▪ Pine Screener support to find new and historical Ants.
▪ Create symbol or watchlist alerts to get real-time notifications of new Ants.
🔹—— Ants Pro Overview ——🔹
The original Ants indicator was published on TradingView in 2021, before Ant integration became available in MarketSurge — a premium charting platform developed by Investor’s Business Daily, the company founded by William O’Neil. Ants Pro is a complete rewrite designed to deliver a similar visual experience while adding extensive customization options, real-time and historical Ant statistics, unique alert features, and support for the Pine Screener to enable comprehensive stock screening.
🔹—— Ants ▪ Momentum, Volume & Price (MVP) ——🔹
The default criteria for a new Ant are based on the daily timeframe and are as follows:
▪ Momentum: Stock closed higher at least 12 of the past 15 days.
▪ Volume: Volume 20%+ above its 50-day average over the past 15 days.
▪ Price: Price up 20%+ over the past 15 days.
You can adjust these parameters based on your trading style and preferences. See the Settings section below for more details.
If you’re wondering about the name “Ants,” it comes from the original implementation, where small black marks were plotted above price bars whenever the MVP criteria were met, resembling ants on the chart.
🔹—— Ants As MVP Leaders ——🔹
Ants highlight significant strength in price and volume, yet they aren’t a buy signal on their own. With the default criteria, a stock that’s up 12 of the past 15 days with price and volume running 20%+ above average is showing exceptional momentum — yet it's important to avoid chasing price.
Instead, add stocks showing Ants to a watchlist and wait for a pullback to an area of support, such as a moving average or a prior price zone where support was evident. Another strong setup is sideways consolidation followed by a decisive breakout above the consolidation high.
CELH
FTAI
IREN
🔹—— Ants As Topping Signal ——🔹
The Ants indicator can be helpful for spotting topping formations. When you compare the definition of a climax top with Ants, they have similar price and volume characteristics.
Climax Top
▪ Stock in a strong, extended uptrend, followed by a 20%+ surge in price over 2 to 3 weeks.
▪ Multiple high-volume up days and/or a large gap up near the absolute peak.
▪ Highest price of move occurs, followed immediately by a reversal.
Because the default Ant settings are essentially looking for the same combination of extreme price acceleration and volume surge, the indicator will often show Ants at or just before a topping pattern. That visual cue begs the question, is this the final blow-off, or just another leg higher?
Context is everything. Paying close attention to where the stock has already been — how extended it is from your preferred moving averages, a prior base, or institutional support levels — is what separates a high-probability profit-taking opportunity from an early exit on a still trending leader.
The distance from the 50-day SMA helps show how far price has stretched above its intermediate trend; when a stock extends too far above this level, it often reflects unsustainable strength and a higher risk of a pullback.
The Average True Range (ATR) multiple helps quantify how far price has moved relative to its average volatility, giving a normalized read on how stretched a stock is. The ATR multiple is simply the distance between price and the 50-SMA expressed in ATR units. For example, an ATR multiple of 5 means price is five times its ATR above the 50-SMA. Ants Pro uses a 20-day ATR.
OKLO
APLD
🔹—— Stats Table ▪ Progress Towards New Ant ——🔹
There is an optional table that highlights every requirement and how current price and volume are tracking toward qualifying as a new Ant. When conditions are close, a shallow pullback or consolidation may offer a possible early entry.
TSLA
🔹—— Hover Over Ants For Stats ——🔹
As shown above in the charts of OKLO and APLD, you can hover your cursor over any Ant to get detailed price and volume stats.
▪ Close Up: number of bars up versus the requirement.
▪ Volume % Change: % change versus the requirement.
▪ Price % Change: % change versus the requirement.
▪ From 50-SMA: how far is the price from the 50-SMA.
▪ ATR Multiple: how many ATR multiples is the price from the 50-SMA.
Note: To hover over an Ant, the Ants Pro indicator needs to be shown on top of all other indicators. Follow the steps in the chart below to bring Ants Pro to the front.
🔹—— Context-Sensitive Help ——🔹
All help tooltips are context-aware and update based on your Settings. If you adjust the Ant requirements, for example, changing the default 12 of 15 days to 7 of 10 days, the Ants popup and table values will automatically reflect those changes.
🔹—— Configuring Alerts ——🔹
New Ant Alert
Using the TradingView alert dialog, choose the option for "New Ant" to be notified when price and volume meet the requirements for a new Ant.
Watchlist Alerts
To be notified when there is a new Ant across a range symbols, you can use a watchlist alert as outlined below.
Historical Ants Alert
In the Condition drop-down menu of the alert dialog, there is an option for Historical Ants . This setting is intended for use with the Pine Screener. If you select this for an alert on a stock, an alert will be generated if there are one or more Ants going back in time based on the Historical Bars To Search value in Settings. For example, if Historical Bars To Search is set to 50, and there is an Ant on the chart within the past 50 bars, an alert will be triggered.
🔹—— Stock Screening ——🔹
Ants Pro works with the Pine Screener, eliminating the need for a separate screening indicator.
Screening For New Ants
To search for new Ants on the most recent bar:
The new Ant might appear only on the last bar, or it could be part of a longer series of Ants.
Screening For Historical Ants
When searching historical bars, you can configure how far back to search:
Screening And Custom Ant Requirements
You can change any of the default price and volume requirements. For example, instead of 12 of 15 days up and 20%+ gains, your preference may be 8 of 10 days up and 10%+ gains.
🔹—— Settings ——🔹
Ant Requirements
You can customize the default price and volume requirements to align with your preferences.
Table Of Ant Stats
The table showing status towards the progress of a new Ant has several configurable options:
▪ Current Progress: shows the stats of price and volume.
▪ Always On: table will always be visible, even if there is an Ant on the last bar.
Historical Bars To Search
This option is only applicable when using the Pine Screener. By default, searching historical bars will look back approximately one year (250 daily bars). However, you might prefer to screen over a shorter period of time. For example, change the value to 50 to look for Ants that occurred over the past 50 bars.
🔹—— Studying Past Winners & Reviewing Trades ——🔹
TradingView’s Bar Replay is an incredibly useful feature that lets you step through any historical chart bar by bar, simulating real-time price movement as it unfolded. You can revisit past big winners, review your own trades, test whether a pattern would have influenced your decisions at the time, and use those insights to refine your price and volume analysis.
AXON
🔹—— Best Practices ——🔹
In technical analysis, it’s essential to understand where price is coming from. Never evaluate a pattern in isolation — always zoom out and study the broader context of price and volume.
The same applies to Ants. Remember, Ants are not a buy signal. When they appear, zoom out on the chart and assess where price is in relation to moving averages and prior areas of support or resistance. Review higher timeframes to see the bigger picture.
▪ Build a watchlist as new Ants appear. Review the watchlist regularly for potential trades.
▪ Relative strength is essential. Look for the RS Line to be trending up.
▪ Look for earnings and sales acceleration as confirmation of strength.
▪ Always define risk before entering a trade — know where you’ll exit.
▪ Size positions based on volatility and conviction, not emotion.
▪ Be patient — trends take time to develop.
🔹—— Acknowledgements ——🔹
A sincere thank you to David Ryan for sharing his expertise on Ant requirements and for offering insightful suggestions to improve the Ants Pro indicator.
유료 스크립트
Liquidity Structure & Sweeps [Visualized]Liquidity Structure & Sweeps | 流动性结构与猎杀
1. Design Philosophy & Logic
This indicator is designed based on Smart Money Concepts (SMC) and Market Microstructure principles. Unlike traditional indicators that rely on lagging averages or repainting fractals, this script focuses on "Objective Structure" and "Liquidity Grabs".
The core design philosophy rests on three pillars:
Zero Repainting (Real-time Integrity): We utilize a strict "Left-Side Confirmation" algorithm. A structure level is only stored in memory when the candle is fully closed (barstate.isconfirmed). This ensures that the historical signals you see are exactly what happened in real-time.
Institutional Memory (Visualized): Markets "remember" key levels. This script draws dashed lines extending from valid pivot points. These lines represent "resting liquidity" (Stop Orders). They remain on the chart until the price interacts with them.
Sweep vs. Breakout: Not all breaches are equal. We specifically look for "Sweeps" (Liquidity Grabs) — where price pierces a level but closes back inside. This is a classic sign of absorption and potential reversal, distinct from a structural breakout.
2. Key Features
Visualized Order Blocks: Automatically draws potential support (Green Dotted) and resistance (Red Dotted) lines based on fractal points.
Wick Detection: Filters out strong momentum breakouts. Signals are only generated when a specific "Wick Ratio" is met, indicating a rejection.
Clean Charts: Features a "Garbage Collection" mechanism. Once a level is swept, the line is removed, and a signal dot is placed. Old, untouched levels are automatically cycled out to prevent chart clutter.
3. How to Use
The Lines (Context):
Red Dotted Line: Buy-side Liquidity (Resistance). Expect potential shorts or breakouts here.
Green Dotted Line: Sell-side Liquidity (Support). Expect potential longs or breakdowns here.
The Signals (Action):
Red Dot (Bearish Sweep): Price spiked above a Resistance Line but closed below it. This suggests long stops were hunted, and bears are stepping in.
Green Dot (Bullish Sweep): Price spiked below a Support Line but closed above it. This suggests short stops were hunted, and bulls are stepping in.
Configuration:
Structure Length: Adjusts sensitivity. Higher values (e.g., 20-50) find major swing points; lower values (e.g., 5-10) find scalping setups.
Wick Filter %: The minimum size of the wick relative to the breakout. Increase this to filter for only the most dramatic rejections.
4. Developer Notes & Considerations
Why do lines disappear? In this logic, liquidity is treated as "Fuel". Once a level is swept (the stop orders are triggered), the fuel is consumed. Keeping the line would clutter the chart with invalid data.
Why is the dot small? The indicator is designed to be part of a toolchain, not a standalone signal. The minimalist design prevents visual interference with price action or other indicators.
1. 设计思路与核心逻辑
本指标基于 聪明钱概念 (SMC) 与 市场微观结构 原理设计。不同于依赖滞后均线或存在重绘问题的传统分形指标，本脚本专注于捕捉 “客观结构” 与 “流动性猎杀 (Liquidity Grabs)”。
核心设计哲学包含三大支柱：
零重绘 (Zero Repainting): 我们采用了严格的“左侧确认”算法。所有的结构位仅在K线完全收盘 (barstate.isconfirmed) 后才会被记录。这保证了您回测看到的信号与实盘完全一致，杜绝“未来函数”陷阱。
可视化的机构记忆: 市场是有记忆的。本脚本会从有效的波段高低点引出虚线。这些虚线代表了“沉睡的流动性”（止损盘聚集区）。它们会一直延伸，直到价格触碰它们。
区分“猎杀”与“突破”: 并不是所有的破位都是一样的。我们专注于识别“扫损（Sweep）”——即价格刺破了关键位，但收盘价收回了关键位内部。这是典型的吸筹或派发信号，与趋势延续的真突破有本质区别。
2. 主要功能
结构可视化: 自动基于分形点绘制潜在的支撑线（绿色虚线）和阻力线（红色虚线）。
插针检测: 过滤掉强势的实体突破。只有当价格出现明显的“长影线”拒绝行为时，才会触发信号。
图表自清洁: 内置“垃圾回收”机制。一旦某个关键位的流动性被猎杀（触发信号），该线条会被自动删除。过旧且未被触碰的线条也会被自动替换，保持图表整洁。
3. 使用指南
线条 (市场语境):
红色虚线: 买方流动性池（阻力位）。
绿色虚线: 卖方流动性池（支撑位）。
信号点 (交易动作):
红色圆点 (看跌猎杀): 价格刺破了红色阻力线，但收盘价回落到线下方。这暗示多头止损被触发，主力可能正在建立空单。
绿色圆点 (看涨猎杀): 价格刺破了绿色支撑线，但收盘价反弹到线上方。这暗示空头止损被触发，主力可能正在建立多单。
参数设置建议:
Structure Length (结构周期): 调整灵敏度。数值越大（如 20-50）锁定大级别波段；数值越小（如 5-10）适合短线剥头皮。
Wick Filter % (影线过滤): 设置影线占价格波动的最小比例。调大该数值可以只看最剧烈的反转信号。
4. 开发者注记与潜在考量
为什么线条会消失？ 在本逻辑中，流动性被视为“燃料”。一旦发生猎杀（止损单成交），该位置的燃料即被消耗。移除线条是为了防止无效数据干扰判断。
为什么圆点设计得很小？ 该指标旨在成为您交易工具链的一部分，而非唯一的决策依据。极简设计是为了避免干扰裸K形态或其他指标的观察。
===============================================================
这个脚本（我们称之为 Liq Structure Script）本质上是一个基于价格行为（Price Action）的结构猎杀探测器。
以下是详细的深度对比分析：
1. 如何使用？ (实战操作手册)
不要把它当作“红灯停绿灯行”的傻瓜指标。把它当作一个**“战场地图”**。
第一阶段：观察结构 (The Setup)
图表上会自动画出 红色虚线（上方压力）和 绿色虚线（下方支撑）。
解读：告诉自己，“这里埋着很多人的止损单”。不要在这里盲目追涨杀跌。
第二阶段：等待猎杀 (The Trigger)
耐心等待价格冲向这些虚线。
关键动作：价格刺破虚线，然后迅速收回。
信号确认：虚线消失，留下一个 红点（顶部猎杀）或 绿点（底部猎杀）。
第三阶段：进场逻辑 (The Execution)
做空逻辑：出现红点 + K线留长上影线 → 说明多头试图突破失败，被主力“倒了一盆冷水”。此时可尝试做空，止损设在刚刚那个最高点上方一点点。
做多逻辑：出现绿点 + K线留长下影线 → 说明空头试图砸盘失败，被主力接住了。
传统爆量是“燃料”，Liq 脚本是“引爆点”。没有引爆点的爆量可能是空转；没有爆量的引爆点可能是假摔。Liq 脚本是一个免费、轻量级、基于K线逻辑的替代品。它不需要你买昂贵的数据服务，它利用的是“图表形态学”中的流动性共识。
结论：如何定位这个工具？
这个脚本不是“预测未来的水晶球”，而是一个**“高胜率区域提示器”**。
用它来找位置（哪里有陷阱？）。
用成交量来做确认（是不是真的有主力介入？）。
用宏观逻辑来定方向（现在该做多还是做空？）。
它是你交易工具链中负责**“微观入场时机（Timing）”**的那一环。
SMC Strategy Companion [Pro Dashboard & Smart TP]A comprehensive Smart Money Concepts (SMC) toolkit designed for precision trading. Features an institutional-grade dashboard, auto-detection of Order Blocks/FVG, Premium/Discount valuation, and a smart "Obstacle-Aware" Take Profit system. Perfect for traders seeking confluence.
🚀 Overview
SMC Strategy Companion is an all-in-one decision support system based on Smart Money Concepts (SMC). Unlike standard indicators that simply draw boxes, this script acts as a professional trading assistant. It filters market noise using multi-timeframe analysis, valuations, and trend strength to help you find high-probability setups like the "Unicorn" and "Turtle Soup".
It is specifically designed for traders who want to avoid "over-trading" and focus only on A+ quality setups.
🛠️ Key Features
1. 🧠 Intelligent Dashboard
The heads-up display (HUD) provides a real-time snapshot of the market condition:
HTF Trend: Monitors the higher timeframe trend (default: 4H) to ensure you trade with the flow.
Mkt State (ADX Filter): Detects if the market is Trending or Choppy. It automatically downgrades signal quality during low-momentum range bound markets.
Valuation (Premium/Discount): Using institutional logic, it warns you against buying in Premium zones or selling in Discount zones.
Confluence Score: A live scoring system (0-6) that rates every potential setup based on trend, structure, and zone validity.
2. 🎯 Smart Execution Levels (Auto EP/TP/SL)
The script doesn't just show you where to trade, but how:
EP (Entry Price): Identifies the optimal entry within the Order Block.
Smart Obstacle TP: This is a unique feature. Instead of a fixed R:R, the script scans for "Roadblocks" (e.g., opposing unmitigated OBs or EMA walls). If an obstacle is detected before the structural target, the TP is automatically adjusted to ensure you secure profits safely.
Risk Management: Automatically calculates Risk-to-Reward (R:R). If a setup offers less than 1.5R, the label turns gray to warn you of poor expectancy.
3. 🛡️ Strict Confirmation Mode
No Repainting/Flickering: Includes a "Strict Mode" that only generates historical signals after candle closes to ensure validity.
Smart Alerts: Built-in logic prevents alert spamming. You receive one pre-alert when price enters a zone, and one confirmation alert when the setup is valid.
📊 How to Use
Setup A: Turtle Soup (Reversal/Sweep)
Logic: Price sweeps a major Liquidity level (Swing High/Low) and closes back within the range.
Best For: Choppy markets or catching the absolute bottom/top of a pullback.
Action: Look for the ★ Setup A label.
Setup B: Unicorn (Trend Continuation)
Logic: A confluence of a Breaker/Order Block + Fair Value Gap (FVG) in the direction of the HTF Trend.
Best For: Strong trending markets.
Action: Look for the ★ Setup B label. Ideally, execute when the dashboard shows "Trending" and "Discount".
⚙️ Settings & Customization
Trend Filter: You can toggle the HTF trend filter on/off.
Time Filter (Killzones): Option to filter signals based on London/New York sessions (Recommended for Forex/Crypto/US Stocks). Note: Turn this OFF for Asian markets like TWSE.
Target Mode: Choose between "Smart (Structure + OBs)" or "Fixed R:R".
⚠️ Disclaimer
This tool is designed to assist with technical analysis and does not constitute financial advice. SMC involves understanding liquidity and market structure; please backtest thoroughly before using it on live accounts.
Volume Gaps & Imbalances (Zeiierman)█ Overview
Volume Gaps & Imbalances (Zeiierman) is an advanced market-structure and order-flow visualizer that maps where the market traded, where it did not, and how buyer-vs-seller pressure accumulated across the entire price range.
The core of the indicator is a price-by-price volume profile built from Bullish and Bearish volume assignments. The script highlights:
True zero-volume voids (regions of no traded volume)
Bull/Bear imbalance rows (horizontal volume slices)
A multi-section Delta Panel, showing aggregated Buy–Sell pressure per vertical sector
A clean separation between profile structure, volume efficiency, and delta flows
Together, these components reveal market inefficiencies, displacement zones, and fair-value regions that price tends to revisit — making it an exceptional tool for structural trading, order-flow analysis, and contextual confluence.
Highlights
Identifies true volume voids (untraded price regions), more precisely than standard FVG tools
Plots Bull vs Bear volume at each price row for fine-grained imbalance reading
Includes a sector-based Delta Grid that aggregates Buy–Sell dominance
█ How It Works
⚪ Profile Construction
The indicator scans a user-defined Lookback window and divides the full high–low range into Rows. Each bar's volume is allocated into the correct price bucket:
Bullish volume when close > open
Bearish volume when close <= open
This produces three values per price level:
Bull Volume
Bear Volume
Total Volume & Imbalance Profile
Rows where no volume at all occurred are marked as volume gaps — signaling true untraded zones, often produced by impulsive imbalanced moves.
⚪ Zero-Volume Gaps (True Voids)
Unlike candle-based Fair Value Gaps (FVGs), volume gaps identify the deeper, structural inefficiency: Price moved so fast through a region that no trades occurred at those prices. These areas often attract revisits because liquidity never exchanged hands there.
⚪ Bull/Bear Volume Imbalance
Every price row is drawn using two colored horizontal segments:
Bull segment proportional to bullish volume
Bear segment proportional to bearish volume
This reveals where buyers or sellers dominated individual price levels.
⚪ Delta Panel
The full volume profile is cut into Summary Sections. For each block, the script computes: Δ = (Bull Volume − Bear Volume) ÷ Total Volume × 100%
█ How to Use
⚪ Spot True Voids & Inefficiencies
Zero-volume zones highlight where the price moved without trading. These areas often behave like:
Refill zones during retracements
Targets during displacement
Thin regions price slices through quickly
Ideal for both SMC-style trading and structural mapping.
⚪ Identify Bull/Bear Control at Each Price Level
Broad bullish segments show zones of buyer absorption, while wide bearish slices reveal seller control.
This helps you interpret:
Where buyers supported the price
Where sellers defended a level
Which price levels matter for continuation or reversal
⚪ Use Delta Sectors for Contextual Direction
The delta panel shows where market pressure is accumulating, revealing whether the profile is dominated by:
Bullish flow (positive delta)
Bearish flow (negative delta)
Neutral flow (balanced or minimal delta)
█ Settings
Lookback – Number of bars scanned to build the profile.
Rows – Vertical resolution of price bins.
Source – Price source used to assign volume into rows.
Summary Sections – Number of vertical delta sectors.
Summary Width – Horizontal size of the delta bar panel.
Gap From Profile – Distance between profile and delta grid.
Show Delta Text – Toggle Δ% labels.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Float Rotation TrackerFloat Rotation Tracker - Quick Reference Guide
What is Float Rotation?
Float Rotation = Cumulative Daily Volume ÷ Float
Example:
Float = 5,000,000 shares
Day Volume = 7,500,000 shares
Rotation = 7.5M ÷ 5M = 1.5x (150%)
When rotation hits 1x (100%), every available share has theoretically changed hands at least once during the trading day.
Why It Matters
RotationMeaningImplication0.5x50% of float tradedInterest building1.0x 🔥Full rotationExtreme interest confirmed2.0x 🔥🔥Double rotationVery high volatility3.0x 🔥🔥🔥Triple rotationRare - maximum volatility
Key insight: High rotation on a low-float stock = explosive potential
Float Classification
Float SizeClassificationRotation Impact≤ 2M🔥 MICROExtremely volatile, fast rotation≤ 5M🔥 VERY LOWExcellent momentum potential≤ 10MLOWGood for rotation plays> 10MNORMALNeeds massive volume to rotate
Rule of thumb: Focus on stocks with float under 10M for meaningful rotation signals.
Reading the Indicator
Rotation Line (Yellow)
Shows current rotation level
Rises throughout the day as volume accumulates
Crosses horizontal level lines at milestones
Level Lines
LineColorMeaning0.5Gray dotted50% rotation1.0Orange solidFull rotation2.0Red solidDouble rotation3.0Fuchsia solidTriple rotation
Volume Bars (Bottom)
ColorMeaningGrayBelow average volumeBlueNormal volume (1-2x avg)GreenHigh volume (2-5x avg)LimeExtreme volume (5x+ avg)
Milestone Markers
Circles appear when rotation crosses key levels
Labels show "50%", "1x", "2x", "3x🔥"
Background Color
Changes as rotation increases
Darker = higher rotation level
Info Table Explained
FieldDescriptionFloatShare count + classification (MICRO/LOW/NORMAL)SourceAuto ✓ = TradingView data / Manual = user enteredRotationCurrent rotation with emoji indicatorRotation %Same as rotation × 100Day VolumeCumulative volume todayTo XxVolume needed to reach next milestoneBar RVolCurrent bar's relative volumeMilestonesWhich levels have been hit todayPer RotationShares equal to one full rotationEst. TimeBars until next milestone (at current pace)
Trading with Float Rotation
Entry Signals
Early Entry (Higher Risk, Higher Reward)
Rotation approaching 0.5x
Strong price action (bull flag, breakout)
Rising relative volume bars
Confirmation Entry (Lower Risk)
Rotation at or above 1x
Price holding above VWAP
Continuous green/lime volume bars
Late Entry (Highest Risk)
Rotation above 2x
Only enter on clear pullback pattern
Tight stop required
Exit Signals
Warning Signs:
Rotation very high (2x+) with declining volume bars
Reversal candle after milestone
Price breaking below key support
Volume bars turning gray/blue after being green/lime
Take Profits:
Partial profit at each rotation milestone
Trail stop as rotation increases
Full exit on reversal pattern after 2x+ rotation
Best Setups
Ideal Float Rotation Play
✓ Float under 10M (preferably under 5M)
✓ Stock up 5%+ on the day
✓ News catalyst driving interest
✓ Rotation approaching or exceeding 1x
✓ Price above VWAP
✓ Volume bars green or lime
✓ Clear chart pattern (bull flag, flat top)
Red Flags to Avoid
✗ Float over 50M (hard to rotate meaningfully)
✗ Rotation high but price declining
✗ Volume bars turning gray after spike
✗ No clear catalyst
✗ Price below VWAP with high rotation
✗ Late in day (3pm+) after 2x rotation
Float Data Sources
If auto-detect doesn't work, get float from:
SourceHow to FindFinvizfinviz.com → ticker → "Shs Float"Yahoo FinanceFinance.yahoo.com → Statistics → "Float"MarketWatchMarketwatch.com → ticker → ProfileYour BrokerUsually in stock details/fundamentals
Note: Float can change due to offerings, buybacks, lockup expirations. Check recent data.
Settings Guide
Conservative Settings
Alert Level 1: 0.75 (75%)
Alert Level 2: 1.0 (100%)
Alert Level 3: 2.0 (200%)
Alert Level 4: 3.0 (300%)
High Vol Multiplier: 2.0
Extreme Vol Multiplier: 5.0
Aggressive Settings
Alert Level 1: 0.3 (30%)
Alert Level 2: 0.5 (50%)
Alert Level 3: 1.0 (100%)
Alert Level 4: 2.0 (200%)
High Vol Multiplier: 1.5
Extreme Vol Multiplier: 3.0
Alert Setup
Recommended Alerts
100% Rotation (1x) - Primary signal
Most important milestone
Confirms extreme interest
High Rotation + Extreme Volume
Combined condition
Very high probability signal
How to Set
Right-click chart → Add Alert
Condition: Float Rotation Tracker
Select desired milestone
Set notification (popup/email/phone)
Set expiration
Common Questions
Q: Why is my float showing "Manual (no data)"?
A: TradingView doesn't have float data for this stock. Enter the float manually in settings after looking it up on Finviz or Yahoo Finance.
Q: The rotation seems too high/low - is the float wrong?
A: Possibly. Cross-check float on Finviz. Recent offerings or share structure changes may not be reflected in TradingView's data.
Q: What if float rotates early in the day?
A: Early 1x rotation (within first hour) is very bullish - indicates massive interest. Watch for continuation patterns.
Q: High rotation but price is dropping?
A: This is distribution - large holders are selling into demand. High rotation doesn't guarantee price direction, just volatility.
Q: Can I use this for swing trading?
A: The indicator resets daily, so it's designed for intraday use. You could note multi-day rotation patterns manually.
Quick Decision Matrix
RotationPrice ActionVolumeDecision<0.5xStrong upHighWatch, early stage0.5-1xConsolidatingSteadyPrepare entry1x+Breaking outIncreasingEntry on pattern1x+DroppingHighAvoid - distribution2x+Strong upExtremePartial profit, trail stop2x+Reversal candleDecliningExit or avoid
Workflow Integration
MORNING ROUTINE:
1. Scan for gappers (5%+, high volume)
2. Check float on each candidate
3. Apply Float Rotation Tracker
4. Prioritize lowest float with building rotation
DURING SESSION:
5. Watch rotation levels on active trades
6. Enter on patterns when rotation confirms (0.5-1x)
7. Scale out as rotation increases
8. Exit or trail after 2x rotation
END OF DAY:
9. Note which stocks hit 2x+ rotation
10. Review rotation vs price action
11. Learn patterns for future trades
Combining with Other Indicators
IndicatorHow to Use Together5 PillarsScreen for low-float stocks firstGap & GoCheck rotation on gappersBull FlagEnter bull flags with 1x+ rotationVWAPOnly trade rotation plays above VWAPRSIWatch for divergence at high rotation
Key Takeaways
Float size matters - Lower float = faster rotation = more volatility
1x is the key level - Full rotation confirms extreme interest
Volume quality matters - Green/lime bars better than gray
Combine with price action - Rotation confirms, patterns trigger
Know when you're late - 2x+ rotation is late stage
Check your float data - Wrong float = wrong rotation calculation
Happy Trading! 🔥
Orderbook Table1. Indicator Name
Orderbook Table
This is an order book style trading volume map
that upgraded the price from my first script to label
2. One-line Introduction
A visual heatmap-style orderbook simulator that displays volume and delta clustering across price levels.
3. Overall Description
Orderbook Table is a powerful visual tool designed to replicate an on-chart approximation of a traditional order book.
It scans historical candles within a specified lookback window and accumulates traded volume into price "bins" or levels.
Each level is color-coded based on total volume and directional bias (delta), offering a layered view of where market interest was concentrated.
The indicator approximates order flow by analyzing each candle's directional volume, separating bullish and bearish volume.
With adjustable parameters such as level depth, price bin density, delta sensitivity, and opacity, it provides a highly customizable visualization.
Displayed directly on the chart, each level shows the volume at that price zone, along with a price label, offset to the right of the current bar.
Traders can use this tool to detect high liquidity zones, support/resistance clusters, and volume imbalances that may precede future price movements.
4. Key Benefits (Title + Description)
✅ On-Chart Volume Heatmap
Shows volume distribution across price levels in real-time directly on the price chart, creating a live “orderbook” view.
✅ Delta-Based Bias Coloring
Color changes based on net buying/selling pressure (delta), making aggressive demand/supply zones easy to spot.
✅ High Customizability
Users can adjust lookback bars, price bins, opacity levels, and delta usage to fit any market condition or asset class.
✅ Lightweight Simulation
Approximates orderbook depth using candle data without needing L2 feed access—works on all assets and timeframes.
✅ Clear Visual Anchoring
Volume quantities and price levels are offset to the right for easy viewing without cluttering the active chart area.
✅ Fast Market Context Recognition
Quickly identify price levels where volume concentrated historically, improving decision-making for entries/exits.
5. Indicator User Guide
📌 Basic Concept
Orderbook Table analyzes a configurable number of past bars and distributes traded volume into price "bins."
Each bin shows how much volume occurred around that price level, optionally adjusted for bullish/bearish candle direction.
⚙️ Settings Overview
Lookback Bars: Number of candles to scan for volume history
Levels (Total): Number of price levels to display around the current price
Price Bins: Granularity of price segmentation for volume distribution
Shift Right: How far to offset labels to the right of the current bar
Max/Min Opacity: Controls visual strength of volume coloring
Use Candle Delta Approx.: If enabled, colors the volume based on candle direction (green for up, red for down)
📈 Example Timing
Look for green clusters (bullish bias) below current price → possible strong demand zones
Price enters a high-volume level with previously aggressive buyers (green), suggesting support
📉 Example Timing
Red clusters (bearish bias) above current price can act as resistance or supply zones
Price stalling at a red-heavy volume band may indicate exhaustion or reversal opportunity
🧪 Recommended Use
Use as a support/resistance mapping tool in ranging and trending markets
Pair with candlestick analysis or momentum indicators for refined entry/exit points
Combine with VWAP or volume profile for multi-dimensional volume insight
🔒 Cautions
This is an approximation, not a true L2 orderbook—volume is based on historical candles, not actual limit order data
In low-volume markets or higher timeframes, bin granularity may be too coarse—adjust "Price Bins" accordingly
Delta calculation is based on open-close direction and does not reflect true buy/sell volume splits
Avoid overinterpreting low-opacity (light color) zones—they may indicate low interest rather than true resistance/support
+++
Dynamic Support and Resistance with Trend LinesDynamic Support and Resistance with Trend Lines (DSRTL)
1. Introduction & Methodology
The DSRTL indicator is designed to provide a multidimensional analysis of market structure. Unlike traditional tools that rely solely on price pivots, this script combines Static Volume-based Zones with Dynamic Trend Lines to evaluate the price's position relative to critical market components.
The S/R Identification Technique
Instead of standard pivot points, DSRTL utilizes Volume Analysis to highlight areas of significant trader participation:
- Strategy A:
Matrix Climax: Identifies candles within the lookback period that are near price extremes (Highs/Lows) and coincide with significant buying or selling volume.
- Strategy B:
Volume Extremes: Detects candles with the absolute highest buy/sell volumes within the selected lookback window, creating extreme volume-based S/R zones.
- Result:
This creates Support/Resistance (S/R) zones that are validated by actual market activity, not just price geometry.
Dynamic Trend Lines
To complement the static zones, the indicator employs two adaptive channel methods:
- Pivot Span: Connects recent significant pivots for a fast, reactive trend corridor.
- 5-Point Channel: Segments the lookback period into 5 parts to perform a linear regression analysis, creating a stable and statistically significant channel.
2. Volume Calculation Methodology
Accurate S/R detection requires distinguishing Buy Volume from Sell Volume. DSRTL offers two calculation modes:
- Geometry (Source File): Estimates buy/sell volume based on the Close price's position relative to the High/Low of the candle.
Note: This is an approximation that works on all plan types as it does not require intrabar data.
- Intrabar (Precise): Analyzes historical lower-timeframe data (e.g., 15S) to calculate intrabar-based volume deltas with higher precision compared to the geometric method.
Note: This offers superior accuracy. It requires access to historical intrabar data (depending on your plan limits). For the best analytical results, use this mode if available.
3. The Smart Matrix Engine (3D Analysis)
The core of DSRTL is its dashboard, powered by the "Smart Matrix Engine." This engine evaluates the current price in a multi-layer market structure context (Static Volume Zones + Dynamic Channels + Volume Metrics).:
A. S-State (Static): Where is the price relative to the Volume S/R zones?
B. D-State (Dynamic): Where is the price relative to the Trend Channels?
How to read the Matrix Map:
The dashboard displays a 5x5 grid representing 25 possible market scenarios.
- Rows (S1-S5): Represent the Static State (S1=Breakout, S3=Mid-Range, S5=Breakdown).
- Columns (D1-D5): Represent the Dynamic State (D1=Overextended Up, D3=Neutral, D5=Overextended Down).
- Active Cell: Marked with a dot, indicating the specific intersection of price action and market structure.
4. Matrix Interpretations (The 25 Scenarios)
Below is the detailed logic for every possible state displayed on the dashboard, explaining the Title, Bias, and actionable Signal.
Section I: S1 - Static Breakout (Price > Static Resistance)
The price has cleared the static volume resistance zone.
- S1 / D1: HYPER EXTENSION
Bias: Extreme Bullish
Signal: Caution: Exhaustion Risk. Trail stops tight.
- S1 / D2: RESISTANCE CLASH
Bias: Bullish
Signal: Breakout confirmed but facing immediate dynamic resistance.
- S1 / D3: CHANNEL BREAKOUT
Bias: Strong Bullish
Signal: Ideal Trend Continuation. Look to buy dips.
- S1 / D4: SMART PULLBACK
Bias: Bullish (Pullback)
Signal: A pullback occurring after a breakout. Strong buy opportunity.
- S1 / D5: CONFLICT (DIV)
Bias: Conflict/Reversal
Signal: Major Divergence. Static breakout is failing against dynamic structure. High Risk.
Section II: S2 - Inside Static Resistance
The price is currently testing the overhead resistance zone.
- S2 / D1: WEAK SPIKE
Bias: Neutral/Bullish
Signal: Testing resistance, but short-term overextended.
- S2 / D2: IRON FORTRESS (R)
Bias: Rejection Risk
Signal: Double Resistance (Static + Dynamic). High probability of rejection.
- S2 / D3: TESTING RES
Bias: Neutral
Signal: Consolidating at resistance. Wait for a clear break or rejection.
- S2 / D4: COMPRESSION (UP)
Bias: Conflict (Squeeze)
Signal: Squeezed between Static Resistance and Dynamic Support. Volatility imminent.
- S2 / D5: RES vs DOWN-TREND
Bias: Bearish
Signal: Strong downtrend meeting static resistance. Potential Short entry.
Section III: S3 - Mid-Range
The price is floating between significant Static Support and Resistance.
- S3 / D1: OVERBOUGHT RANGE
Bias: Rejection Risk (OB)
Signal: Overextended within the range. Potential fade (short).
- S3 / D2: RANGE HIGH LIMIT
Bias: Neutral/Bearish
Signal: At the top of the dynamic channel. Look for rejection signs.
- S3 / D3: NEUTRAL / CHOPPY
Bias: Neutral
Signal: Dead Center. Low probability environment. Avoid trading.
- S3 / D4: RANGE DIP BUY
Bias: Neutral/Bullish
Signal: At the bottom of the dynamic channel. Look for bounce signs.
- S3 / D5: WEAK RANGE (OS)
Bias: Bounce Risk (OS)
Signal: Oversold within the range. Potential fade (long).
Section IV: S4 - Inside Static Support
The price is currently testing the floor support zone.
- S4 / D1: SUP vs UP-TREND
Bias: Bullish
Signal: Strong uptrend meeting static support. Potential Long entry.
- S4 / D2: COMPRESSION (DN)
Bias: Conflict (Squeeze)
Signal: Squeezed between Static Support and Dynamic Resistance. Volatility imminent.
- S4 / D3: TESTING SUPPORT
Bias: Neutral
Signal: Consolidating at support. Wait for a bounce or breakdown.
- S4 / D4: IRON FLOOR (S)
Bias: Bounce Risk
Signal: Double Support (Static + Dynamic). High probability of a bounce.
- S4 / D5: WEAK DIP
Bias: Neutral/Bearish
Signal: Testing support, but short-term oversold.
Section V: S5 - Static Breakdown (Price < Static Support)
The price has dropped below the static volume support zone.
- S5 / D1: CONFLICT (DIV)
Bias: Conflict/Reversal
Signal: Major Divergence. Static breakdown is failing. High Risk.
- S5 / D2: BEAR PULLBACK
Bias: Bearish (Pullback)
Signal: A pullback occurring after a breakdown. Strong selling opportunity.
- S5 / D3: CHANNEL BREAKDOWN
Bias: Strong Bearish
Signal: Ideal Trend Continuation (Down). Sell rallies.
- S5 / D4: SUPPORT CLASH
Bias: Bearish
Signal: Breakdown confirmed but facing immediate dynamic support.
- S5 / D5: HYPER DROP (VOID)
Bias: Extreme Bearish
Signal: Caution: Climax risk. Trail stops for shorts.
DISCLAIMER & EDUCATIONAL PURPOSE
This indicator is strictly an educational tool designed to visualize complex market structure concepts. Its primary purpose is to help traders "bridge the gap" between academic theory and real-time market behavior by providing a visual representation of support, resistance, and volume dynamics.
Please Note:
1. Not a Trading Strategy: This script is an analytical assistant, not a standalone "Black Box" trading system. It does not generate buy or sell signals that should be followed blindly.
2. No Financial Advice: The data provided by this tool is for informational purposes only. It is not a recommendation to buy or sell any asset.
3. Risk Warning: Trading involves significant risk. Always use your own judgment, perform your own technical analysis, and use proper risk management. Do not use this tool as the sole basis for your trading decisions.
4. Data Precision & Platform Limits: The "Intrabar (Precise)" calculation mode relies on high-resolution historical data to provide exact results. Access to this specific data depth depends entirely on your platform's subscription capabilities. If your plan does not support this level of historical intrabar data, the Precise mode may have limited coverage. In that case, you should switch to "Geometry" mode for a fully populated view.
Filter Volume1. Indicator Name
Filter Volume
2. One-line Introduction
A regression-based trend filter that quantifies and visualizes market direction and strength using price behavior.
3. Overall Description
Filter Volume+ is a trend-detection indicator that uses linear regression to evaluate the dominant direction of price movement over a given period.
It compares historical regression values to determine whether the market is in a bullish, bearish, or neutral state.
The indicator applies a percentage threshold to filter out weak or indecisive trends, highlighting only significant movements.
Each trend state is visualized through distinct colors: bullish (greenish), bearish (reddish), and neutral (gray), with intensity reflecting trend strength.
To reduce noise and create smooth visual signals, a three-step smoothing process is applied to the raw trend intensity.
Users can customize the regression source, lookback period, and sensitivity, allowing the indicator to adapt to various assets and timeframes.
This tool is especially useful in filtering entry signals based on clear directional bias, making it suitable for trend-following or confirmation strategies.
4. Key Benefits (Title + Description)
✅ Quantified Trend Strength
Only displays trend signals when a statistically significant direction is detected using linear regression comparisons.
✅ Visual Clarity with Color Coding
Each market state (bullish, bearish, neutral) is represented with distinct colors and transparency, enabling fast interpretation.
✅ Custom Regression Source
Users can define the data input (e.g., close, open, indicator output) for regression calculation, increasing strategic flexibility.
✅ Multi-Level Smoothing
Applies three layers of smoothing (via moving averages) to eliminate noise and produce a stable, flowing trend curve.
✅ Area Fill Visualization
Plots a colored band between the trend value and zero-line, helping users quickly gauge the market's dominant force.
✅ Adjustable Sensitivity Settings
Includes tolerance and lookback controls, allowing traders to fine-tune how reactive or conservative the trend detection should be.
5. Indicator User Guide
📌 Basic Concept
Filter Volume+ assesses the direction of price by comparing regression values over a selected period.
If the percentage of upward comparisons exceeds a threshold, a bullish state is shown; if downward comparisons dominate, it shows a bearish state.
⚙️ Settings Overview
Lookback Period (n): The number of bars to compare for trend analysis
Range Tolerance (%): Minimum threshold for declaring a strong trend
Regression Source: The data used for regression (e.g., close, open)
Linear Regression Length: Number of bars used to compute each regression value
Bull/Bear Color: Custom colors for bullish and bearish trends
📈 Example Timing
When the trend line stays above zero and the green color intensity increases → trend gaining strength
After a neutral phase (gray), the color shifts quickly to greenish → early trend reversal
📉 Example Timing
When the trend line stays below zero with deepening red color → strong bearish continuation
Sudden change from bullish to bearish color with rising intensity
🧪 Recommended Use
Use as a trend confirmation filter alongside entry/exit strategies
Ideal for swing or position trades in trending markets
Combine with oscillators like RSI or MACD for improved signal validation
🔒 Cautions
In ranging (sideways) markets, the color may change frequently – avoid relying solely on this indicator in those zones.
Low-intensity colors (faded) suggest weak trends – better to stay on the sidelines.
A short lookback period may cause over-sensitivity and false signals.
When using non-price regression sources, expect the indicator to behave differently – test before deploying.
+++
MTF EMA Hariss 369The strategy has been prepared in a simplistic manner and easy to understand the concept by any novice trader.
Indicators used:
Current Time frame 20 EMA- Gives clear look about current time frame dynamic support and resistance and trend as well.
Higher Time Frame 20 EMA: Gives macro level trend, support and resistance
Kama: Capture volatility and trend direction.
RVOL: Main factor of price movement.
Buy when price closes above current time frame 20 ema and current time frame 20 ema is above higher time frame 20 ema. Stop loss just below the low of last candle. One can use current time frame 20 ema, higher time frame 20 ema or kama as stop loss depending upon type of asset class and risk appetite. The ideal way is to keep 20 ema as trailing sl if one wants to trail with trend.
Sell when price closes below current time frame 20 ema and current time frame 20 ema is lower than higher time frame 20 ema. Stop loss just above high of last candle.
Ideal target is 1.5 or 2 times of stop loss.
Entry and exit time depends on trading style. Eg. if you want to enter and exit in 5 min time frame, then choose 15 min or 1h as higher time frame as trend filter. Buy and sell signals are also plotted based on this strategy. One should always go with the higher time frame trend. Opting higher time frame trend filter always filters out market noises.
QuantMotions - Smart Money BlocksSmart Money Blocks – Clean Edition is a minimalistic, high-precision Smart Money Concepts (SMC) tool designed for traders who want clean and reliable market structure signals without chart clutter.
This script detects and visualizes Order Blocks, Fair Value Gaps (FVGs), and Liquidity Levels using a strictly filtered, volume-based institutional logic.
Unlike many SMC indicators that overload the chart with noise, this version is built to stay fast, clean, and accurate — ideal for both scalpers and higher-timeframe traders.
🔍 Features
✔ Institutional Order Block Detection
• Identifies bullish and bearish order blocks
• Uses high-volume + price-displacement confirmation
• OBs extend forward and deactivate when broken
• Includes volume + tick range info-box
✔ Fair Value Gaps (FVG)
• Auto-detects bullish and bearish FVGs
• Marks imbalance zones until they are fully filled
• Clean, non-intrusive visualization
✔ Liquidity Levels
• Smart swing-high/swing-low liquidity detection
• Tracks touches to distinguish strong vs weak levels
• Marks support/resistance liquidity with labels only (no chart clutter)
⚙️ Clean & Minimal Design
This script is optimized for a clean workflow:
• No volume profile
• No BOS/CHOCH spam
• No unused SMC elements
• Only high-value SMC signals
• Clean color theme for dark charts
The goal is to provide only what matters, nothing more.
📈 Use Cases
• Smart Money / ICT style trading
• Scalping (1s – 1m)
• Intraday / London & New York session trading
• Swing trading
• Market structure analysis
• Liquidity and imbalance mapping
Whether you're identifying points of interest (POIs), building a bias, or mapping high-probability reaction zones — this tool helps you see structure clearly.
🔔 Alerts Included
• Order Block creation
• FVG creation
• Price touching an active Order Block
• Volume surge
• Institutional candle detection
• Structure break detection
Great for automation or bias confirmation.
🧠 Why This Script?
Many SMC indicators try to do everything — which often results in clutter, lag, and unreliable signals.
This edition focuses on precision, clarity, and real usability.
The logic is light, efficient, and suited for real-time execution on very fast charts.
📌 Note
This tool does not generate trade signals by itself.
It is designed as a market structure map for traders who already understand Smart Money principles such as:
• Displacement
• Imbalance
• Institutional candles
• OB mitigation
• Liquidity sweeps
Use it as part of your confluence system.
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.
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CLOSING STATEMENT
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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.
DCA + Liquidation LevelsThe indicator combines a Dollar-Cost Averaging (DCA) strategy after downtrends with liquidation level detection, providing comprehensive market analysis.
📈 Working Principle
1. DCA Strategy Foundation
EMA 50 and EMA 200 - used as primary trend indicators
EMA-CD Histogram - difference between EMA50 and EMA200 with signal line
BHD Levels - dynamic support/resistance levels based on volatility
2. DCA Entry Logic
pinescript
// Entry Conditions
entry_condition1 = nPastCandles > entryNumber * 24 * 30 // Monthly interval
entry_condition2 = emacd < 0 and hist < 0 and hist > hist // Downtrend reversal
ENTRY_CONDITIONS = entry_condition1 and entry_condition2
Entry triggers when:
Specified time has passed since last entry (monthly intervals)
EMA-CD is negative but showing reversal signs (histogram increasing)
Market is emerging from downtrend
3. Price Zone Coloring System
pinescript
// BHD Unit Calculation
bhd_unit = ta.rma(high - low, 200) * 2
price_level = (close - ema200) / bhd_unit
Color Zones:
🔴 Red Zone: Level > 5 (Extreme Overbought)
🟠 Orange Zone: Level 4-5 (Strong Overbought)
🟡 Yellow Zone: Level 3-4 (Overbought)
🟢 Green Zone: Level 2-3 (Moderate Overbought)
🔵 Light Blue: Level 1-2 (Slightly Overbought)
🔵 Blue: Level 0-1 (Near EMA200)
🔵 Dark Blue: Level -1 to -4 (Oversold)
🔵 Extreme Blue: Level < -4 (Extreme Oversold)
4. Liquidation Levels Detection
pinescript
// Open Interest Delta Analysis
OI_delta = OI - nz(OI )
OI_delta_abs_MA = ta.sma(math.abs(OI_delta), maLength)
Liquidation Level Types:
Large Liquidation Level: OI Delta ≥ 3x MA
Middle Liquidation Level: OI Delta 2x-3x MA
Small Liquidation Level: OI Delta 1.2x-2x MA
Leverage Calculations:
5x, 10x, 25x, 50x, 100x leverage levels
Both long and short liquidation prices
⚙️ Technical Components
1. Moving Averages
EMA 50: Short-term trend direction
EMA 200: Long-term trend foundation
EMA-CD: Momentum and trend strength measurement
2. BHD Levels Calculation
pinescript
bhd_unit = ta.rma(high - low, 200) * 2
bhd_upper = ema200 + bhd_unit * N // Resistance levels
bhd_lower = ema200 - bhd_unit * N // Support levels
Where N = 1 to 5 for multiple levels
3. Open Interest Integration
Fetches Binance USDT perpetual contract OI data
Calculates OI changes to detect large position movements
Identifies potential liquidation clusters
🔔 Alert System
Zone Transition Alerts
Triggers: When price moves between different BHD zones
Customizable: Each zone alert can be enabled/disabled individually
Information: Includes exact level, price, and EMA200 value
Alert Types Available:
🔴 Red Zone Alert
🟠 Orange Zone Alert
🟡 Yellow Zone Alert
🟢 Green Zone Alert
🔵 Light Blue Zone Alert
🔵 Blue Zone Alert
🔵 Dark Blue Zone Alert
🔵 Extreme Blue Zone Alert
🎨 Visual Features
1. Candle Coloring
Real-time color coding based on price position relative to EMA200
Immediate visual identification of market conditions
2. Level Displays
EMA lines (50 & 200)
BHD support/resistance levels
Liquidation level lines with different styles based on significance
3. Entry Markers
Green upward labels below bars indicating DCA entry points
Numbered sequentially for tracking
📊 Input Parameters
DCA Settings
Start/End dates for backtesting
EMA periods customization
Liquidation Levels Settings
MA Length for OI Delta
Threshold multipliers for different liquidation levels
Display toggles for lines and histogram
Alert Settings
Individual zone alert enable/disable
Customizable sensitivity
🔧 Usage Recommendations
For DCA Strategy:
Enter positions at marked DCA points after downtrends
Use BHD levels for position sizing and take-profit targets
Monitor zone transitions for market condition changes
For Liquidation Analysis:
Watch for price approaches to liquidation levels
Use histogram for density of liquidation clusters
Combine with zone analysis for entry/exit timing
⚠️ Limitations
Data Dependency: Requires Binance OI data availability
Market Specific: Optimized for cryptocurrency markets
Timeframe: Works best on 1H+ timeframes for reliable signals
Volatility: BHD levels may need adjustment for different volatility regimes
🔄 Updates and Maintenance
Regular compatibility checks with TradingView updates
Performance optimization for different market conditions
User feedback incorporation for feature improvements
This indicator provides institutional-grade market analysis combined with systematic DCA strategy implementation, suitable for both manual trading and algorithmic strategy development.
