Adaptive Genesis Engine [AGE]ADAPTIVE GENESIS ENGINE (AGE)
Pure Signal Evolution Through Genetic Algorithms
Where Darwin Meets Technical Analysis
🧬 WHAT YOU'RE GETTING - THE PURE INDICATOR
This is a technical analysis indicator - it generates signals, visualizes probability, and shows you the evolutionary process in real-time. This is NOT a strategy with automatic execution - it's a sophisticated signal generation system that you control .
What This Indicator Does:
Generates Long/Short entry signals with probability scores (35-88% range)
Evolves a population of up to 12 competing strategies using genetic algorithms
Validates strategies through walk-forward optimization (train/test cycles)
Visualizes signal quality through premium gradient clouds and confidence halos
Displays comprehensive metrics via enhanced dashboard
Provides alerts for entries and exits
Works on any timeframe, any instrument, any broker
What This Indicator Does NOT Do:
Execute trades automatically
Manage positions or calculate position sizes
Place orders on your behalf
Make trading decisions for you
This is pure signal intelligence. AGE tells you when and how confident it is. You decide whether and how much to trade.
🔬 THE SCIENCE: GENETIC ALGORITHMS MEET TECHNICAL ANALYSIS
What Makes This Different - The Evolutionary Foundation
Most indicators are static - they use the same parameters forever, regardless of market conditions. AGE is alive . It maintains a population of competing strategies that evolve, adapt, and improve through natural selection principles:
Birth: New strategies spawn through crossover breeding (combining DNA from fit parents) plus random mutation for exploration
Life: Each strategy trades virtually via shadow portfolios, accumulating wins/losses, tracking drawdown, and building performance history
Selection: Strategies are ranked by comprehensive fitness scoring (win rate, expectancy, drawdown control, signal efficiency)
Death: Weak strategies are culled periodically, with elite performers (top 2 by default) protected from removal
Evolution: The gene pool continuously improves as successful traits propagate and unsuccessful ones die out
This is not curve-fitting. Each new strategy must prove itself on out-of-sample data through walk-forward validation before being trusted for live signals.
🧪 THE DNA: WHAT EVOLVES
Every strategy carries a 10-gene chromosome controlling how it interprets market data:
Signal Sensitivity Genes
Entropy Sensitivity (0.5-2.0): Weight given to market order/disorder calculations. Low values = conservative, require strong directional clarity. High values = aggressive, act on weaker order signals.
Momentum Sensitivity (0.5-2.0): Weight given to RSI/ROC/MACD composite. Controls responsiveness to momentum shifts vs. mean-reversion setups.
Structure Sensitivity (0.5-2.0): Weight given to support/resistance positioning. Determines how much price location within swing range matters.
Probability Adjustment Genes
Probability Boost (-0.10 to +0.10): Inherent bias toward aggressive (+) or conservative (-) entries. Acts as personality trait - some strategies naturally optimistic, others pessimistic.
Trend Strength Requirement (0.3-0.8): Minimum trend conviction needed before signaling. Higher values = only trades strong trends, lower values = acts in weak/sideways markets.
Volume Filter (0.5-1.5): Strictness of volume confirmation. Higher values = requires strong volume, lower values = volume less important.
Risk Management Genes
ATR Multiplier (1.5-4.0): Base volatility scaling for all price levels. Controls whether strategy uses tight or wide stops/targets relative to ATR.
Stop Multiplier (1.0-2.5): Stop loss tightness. Lower values = aggressive profit protection, higher values = more breathing room.
Target Multiplier (1.5-4.0): Profit target ambition. Lower values = quick scalping exits, higher values = swing trading holds.
Adaptation Gene
Regime Adaptation (0.0-1.0): How much strategy adjusts behavior based on detected market regime (trending/volatile/choppy). Higher values = more reactive to regime changes.
The Magic: AGE doesn't just try random combinations. Through tournament selection and fitness-weighted crossover, successful gene combinations spread through the population while unsuccessful ones fade away. Over 50-100 bars, you'll see the population converge toward genes that work for YOUR instrument and timeframe.
📊 THE SIGNAL ENGINE: THREE-LAYER SYNTHESIS
Before any strategy generates a signal, AGE calculates probability through multi-indicator confluence:
Layer 1 - Market Entropy (Information Theory)
Measures whether price movements exhibit directional order or random walk characteristics:
The Math:
Shannon Entropy = -Σ(p × log(p))
Market Order = 1 - (Entropy / 0.693)
What It Means:
High entropy = choppy, random market → low confidence signals
Low entropy = directional market → high confidence signals
Direction determined by up-move vs down-move dominance over lookback period (default: 20 bars)
Signal Output: -1.0 to +1.0 (bearish order to bullish order)
Layer 2 - Momentum Synthesis
Combines three momentum indicators into single composite score:
Components:
RSI (40% weight): Normalized to -1/+1 scale using (RSI-50)/50
Rate of Change (30% weight): Percentage change over lookback (default: 14 bars), clamped to ±1
MACD Histogram (30% weight): Fast(12) - Slow(26), normalized by ATR
Why This Matters: RSI catches mean-reversion opportunities, ROC catches raw momentum, MACD catches momentum divergence. Weighting favors RSI for reliability while keeping other perspectives.
Signal Output: -1.0 to +1.0 (strong bearish to strong bullish)
Layer 3 - Structure Analysis
Evaluates price position within swing range (default: 50-bar lookback):
Position Classification:
Bottom 20% of range = Support Zone → bullish bounce potential
Top 20% of range = Resistance Zone → bearish rejection potential
Middle 60% = Neutral Zone → breakout/breakdown monitoring
Signal Logic:
At support + bullish candle = +0.7 (strong buy setup)
At resistance + bearish candle = -0.7 (strong sell setup)
Breaking above range highs = +0.5 (breakout confirmation)
Breaking below range lows = -0.5 (breakdown confirmation)
Consolidation within range = ±0.3 (weak directional bias)
Signal Output: -1.0 to +1.0 (bearish structure to bullish structure)
Confluence Voting System
Each layer casts a vote (Long/Short/Neutral). The system requires minimum 2-of-3 agreement (configurable 1-3) before generating a signal:
Examples:
Entropy: Bullish, Momentum: Bullish, Structure: Neutral → Signal generated (2 long votes)
Entropy: Bearish, Momentum: Neutral, Structure: Neutral → No signal (only 1 short vote)
All three bullish → Signal generated with +5% probability bonus
This is the key to quality. Single indicators give too many false signals. Triple confirmation dramatically improves accuracy.
📈 PROBABILITY CALCULATION: HOW CONFIDENCE IS MEASURED
Base Probability:
Raw_Prob = 50% + (Average_Signal_Strength × 25%)
Then AGE applies strategic adjustments:
Trend Alignment:
Signal with trend: +4%
Signal against strong trend: -8%
Weak/no trend: no adjustment
Regime Adaptation:
Trending market (efficiency >50%, moderate vol): +3%
Volatile market (vol ratio >1.5x): -5%
Choppy market (low efficiency): -2%
Volume Confirmation:
Volume > 70% of 20-bar SMA: no change
Volume below threshold: -3%
Volatility State (DVS Ratio):
High vol (>1.8x baseline): -4% (reduce confidence in chaos)
Low vol (<0.7x baseline): -2% (markets can whipsaw in compression)
Moderate elevated vol (1.0-1.3x): +2% (trending conditions emerging)
Confluence Bonus:
All 3 indicators agree: +5%
2 of 3 agree: +2%
Strategy Gene Adjustment:
Probability Boost gene: -10% to +10%
Regime Adaptation gene: scales regime adjustments by 0-100%
Final Probability: Clamped between 35% (minimum) and 88% (maximum)
Why These Ranges?
Below 35% = too uncertain, better not to signal
Above 88% = unrealistic, creates overconfidence
Sweet spot: 65-80% for quality entries
🔄 THE SHADOW PORTFOLIO SYSTEM: HOW STRATEGIES COMPETE
Each active strategy maintains a virtual trading account that executes in parallel with real-time data:
Shadow Trading Mechanics
Entry Logic:
Calculate signal direction, probability, and confluence using strategy's unique DNA
Check if signal meets quality gate:
Probability ≥ configured minimum threshold (default: 65%)
Confluence ≥ configured minimum (default: 2 of 3)
Direction is not zero (must be long or short, not neutral)
Verify signal persistence:
Base requirement: 2 bars (configurable 1-5)
Adapts based on probability: high-prob signals (75%+) enter 1 bar faster, low-prob signals need 1 bar more
Adjusts for regime: trending markets reduce persistence by 1, volatile markets add 1
Apply additional filters:
Trend strength must exceed strategy's requirement gene
Regime filter: if volatile market detected, probability must be 72%+ to override
Volume confirmation required (volume > 70% of average)
If all conditions met for required persistence bars, enter shadow position at current close price
Position Management:
Entry Price: Recorded at close of entry bar
Stop Loss: ATR-based distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit: ATR-based distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Position: +1 (long) or -1 (short), only one at a time per strategy
Exit Logic:
Check if price hit stop (on low) or target (on high) on current bar
Record trade outcome in R-multiples (profit/loss normalized by ATR)
Update performance metrics:
Total trades counter incremented
Wins counter (if profit > 0)
Cumulative P&L updated
Peak equity tracked (for drawdown calculation)
Maximum drawdown from peak recorded
Enter cooldown period (default: 8 bars, configurable 3-20) before next entry allowed
Reset signal age counter to zero
Walk-Forward Tracking:
During position lifecycle, trades are categorized:
Training Phase (first 250 bars): Trade counted toward training metrics
Testing Phase (next 75 bars): Trade counted toward testing metrics (out-of-sample)
Live Phase (after WFO period): Trade counted toward overall metrics
Why Shadow Portfolios?
No lookahead bias (uses only data available at the bar)
Realistic execution simulation (entry on close, stop/target checks on high/low)
Independent performance tracking for true fitness comparison
Allows safe experimentation without risking capital
Each strategy learns from its own experience
🏆 FITNESS SCORING: HOW STRATEGIES ARE RANKED
Fitness is not just win rate. AGE uses a comprehensive multi-factor scoring system:
Core Metrics (Minimum 3 trades required)
Win Rate (30% of fitness):
WinRate = Wins / TotalTrades
Normalized directly (0.0-1.0 scale)
Total P&L (30% of fitness):
Normalized_PnL = (PnL + 300) / 600
Clamped 0.0-1.0. Assumes P&L range of -300R to +300R for normalization scale.
Expectancy (25% of fitness):
Expectancy = Total_PnL / Total_Trades
Normalized_Expectancy = (Expectancy + 30) / 60
Clamped 0.0-1.0. Rewards consistency of profit per trade.
Drawdown Control (15% of fitness):
Normalized_DD = 1 - (Max_Drawdown / 15)
Clamped 0.0-1.0. Penalizes strategies that suffer large equity retracements from peak.
Sample Size Adjustment
Quality Factor:
<50 trades: 1.0 (full weight, small sample)
50-100 trades: 0.95 (slight penalty for medium sample)
100 trades: 0.85 (larger penalty for large sample)
Why penalize more trades? Prevents strategies from gaming the system by taking hundreds of tiny trades to inflate statistics. Favors quality over quantity.
Bonus Adjustments
Walk-Forward Validation Bonus:
if (WFO_Validated):
Fitness += (WFO_Efficiency - 0.5) × 0.1
Strategies proven on out-of-sample data receive up to +10% fitness boost based on test/train efficiency ratio.
Signal Efficiency Bonus (if diagnostics enabled):
if (Signals_Evaluated > 10):
Pass_Rate = Signals_Passed / Signals_Evaluated
Fitness += (Pass_Rate - 0.1) × 0.05
Rewards strategies that generate high-quality signals passing the quality gate, not just profitable trades.
Final Fitness: Clamped at 0.0 minimum (prevents negative fitness values)
Result: Elite strategies typically achieve 0.50-0.75 fitness. Anything above 0.60 is excellent. Below 0.30 is prime candidate for culling.
🔬 WALK-FORWARD OPTIMIZATION: ANTI-OVERFITTING PROTECTION
This is what separates AGE from curve-fitted garbage indicators.
The Three-Phase Process
Every new strategy undergoes a rigorous validation lifecycle:
Phase 1 - Training Window (First 250 bars, configurable 100-500):
Strategy trades normally via shadow portfolio
All trades count toward training performance metrics
System learns which gene combinations produce profitable patterns
Tracks independently: Training_Trades, Training_Wins, Training_PnL
Phase 2 - Testing Window (Next 75 bars, configurable 30-200):
Strategy continues trading without any parameter changes
Trades now count toward testing performance metrics (separate tracking)
This is out-of-sample data - strategy has never seen these bars during "optimization"
Tracks independently: Testing_Trades, Testing_Wins, Testing_PnL
Phase 3 - Validation Check:
Minimum_Trades = 5 (configurable 3-15)
IF (Train_Trades >= Minimum AND Test_Trades >= Minimum):
WR_Efficiency = Test_WinRate / Train_WinRate
Expectancy_Efficiency = Test_Expectancy / Train_Expectancy
WFO_Efficiency = (WR_Efficiency + Expectancy_Efficiency) / 2
IF (WFO_Efficiency >= 0.55): // configurable 0.3-0.9
Strategy.Validated = TRUE
Strategy receives fitness bonus
ELSE:
Strategy receives 30% fitness penalty
ELSE:
Validation deferred (insufficient trades in one or both periods)
What Validation Means
Validated Strategy (Green "✓ VAL" in dashboard):
Performed at least 55% as well on unseen data compared to training data
Gets fitness bonus: +(efficiency - 0.5) × 0.1
Receives priority during tournament selection for breeding
More likely to be chosen as active trading strategy
Unvalidated Strategy (Orange "○ TRAIN" in dashboard):
Failed to maintain performance on test data (likely curve-fitted to training period)
Receives 30% fitness penalty (0.7x multiplier)
Makes strategy prime candidate for culling
Can still trade but with lower selection probability
Insufficient Data (continues collecting):
Hasn't completed both training and testing periods yet
OR hasn't achieved minimum trade count in both periods
Validation check deferred until requirements met
Why 55% Efficiency Threshold?
If a strategy earned 10R during training but only 5.5R during testing, it still proved an edge exists beyond random luck. Requiring 100% efficiency would be unrealistic - market conditions change between periods. But requiring >50% ensures the strategy didn't completely degrade on fresh data.
The Protection: Strategies that work great on historical data but fail on new data are automatically identified and penalized. This prevents the population from being polluted by overfitted strategies that would fail in live trading.
🌊 DYNAMIC VOLATILITY SCALING (DVS): ADAPTIVE STOP/TARGET PLACEMENT
AGE doesn't use fixed stop distances. It adapts to current volatility conditions in real-time.
Four Volatility Measurement Methods
1. ATR Ratio (Simple Method):
Current_Vol = ATR(14) / Close
Baseline_Vol = SMA(Current_Vol, 100)
Ratio = Current_Vol / Baseline_Vol
Basic comparison of current ATR to 100-bar moving average baseline.
2. Parkinson (High-Low Range Based):
For each bar: HL = log(High / Low)
Parkinson_Vol = sqrt(Σ(HL²) / (4 × Period × log(2)))
More stable than close-to-close volatility. Captures intraday range expansion without overnight gap noise.
3. Garman-Klass (OHLC Based):
HL_Term = 0.5 × ²
CO_Term = (2×log(2) - 1) × ²
GK_Vol = sqrt(Σ(HL_Term - CO_Term) / Period)
Most sophisticated estimator. Incorporates all four price points (open, high, low, close) plus gap information.
4. Ensemble Method (Default - Median of All Three):
Ratio_1 = ATR_Current / ATR_Baseline
Ratio_2 = Parkinson_Current / Parkinson_Baseline
Ratio_3 = GK_Current / GK_Baseline
DVS_Ratio = Median(Ratio_1, Ratio_2, Ratio_3)
Why Ensemble?
Takes median to avoid outliers and false spikes
If ATR jumps but range-based methods stay calm, median prevents overreaction
If one method fails, other two compensate
Most robust approach across different market conditions
Sensitivity Scaling
Scaled_Ratio = (Raw_Ratio) ^ Sensitivity
Sensitivity 0.3: Cube root - heavily dampens volatility impact
Sensitivity 0.5: Square root - moderate dampening
Sensitivity 0.7 (Default): Balanced response to volatility changes
Sensitivity 1.0: Linear - full 1:1 volatility impact
Sensitivity 1.5: Exponential - amplified response to volatility spikes
Safety Clamps: Final DVS Ratio always clamped between 0.5x and 2.5x baseline to prevent extreme position sizing or stop placement errors.
How DVS Affects Shadow Trading
Every strategy's stop and target distances are multiplied by the current DVS ratio:
Stop Loss Distance:
Stop_Distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit Distance:
Target_Distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Example Scenario:
ATR = 10 points
Strategy's ATR_Mult gene = 2.5
Strategy's Stop_Mult gene = 1.5
Strategy's Target_Mult gene = 2.5
DVS_Ratio = 1.4 (40% above baseline volatility - market heating up)
Stop = 10 × 2.5 × 1.5 × 1.4 = 52.5 points (vs. 37.5 in normal vol)
Target = 10 × 2.5 × 2.5 × 1.4 = 87.5 points (vs. 62.5 in normal vol)
Result:
During volatility spikes: Stops automatically widen to avoid noise-based exits, targets extend for bigger moves
During calm periods: Stops tighten for better risk/reward, targets compress for realistic profit-taking
Strategies adapt risk management to match current market behavior
🧬 THE EVOLUTIONARY CYCLE: SPAWN, COMPETE, CULL
Initialization (Bar 1)
AGE begins with 4 seed strategies (if evolution enabled):
Seed Strategy #0 (Balanced):
All sensitivities at 1.0 (neutral)
Zero probability boost
Moderate trend requirement (0.4)
Standard ATR/stop/target multiples (2.5/1.5/2.5)
Mid-level regime adaptation (0.5)
Seed Strategy #1 (Momentum-Focused):
Lower entropy sensitivity (0.7), higher momentum (1.5)
Slight probability boost (+0.03)
Higher trend requirement (0.5)
Tighter stops (1.3), wider targets (3.0)
Seed Strategy #2 (Entropy-Driven):
Higher entropy sensitivity (1.5), lower momentum (0.8)
Slight probability penalty (-0.02)
More trend tolerant (0.6)
Wider stops (1.8), standard targets (2.5)
Seed Strategy #3 (Structure-Based):
Balanced entropy/momentum (0.8/0.9), high structure (1.4)
Slight probability boost (+0.02)
Lower trend requirement (0.35)
Moderate risk parameters (1.6/2.8)
All seeds start with WFO validation bypassed if WFO is disabled, or must validate if enabled.
Spawning New Strategies
Timing (Adaptive):
Historical phase: Every 30 bars (configurable 10-100)
Live phase: Every 200 bars (configurable 100-500)
Automatically switches to live timing when barstate.isrealtime triggers
Conditions:
Current population < max population limit (default: 8, configurable 4-12)
At least 2 active strategies exist (need parents)
Available slot in population array
Selection Process:
Run tournament selection 3 times with different seeds
Each tournament: randomly sample active strategies, pick highest fitness
Best from 3 tournaments becomes Parent 1
Repeat independently for Parent 2
Ensures fit parents but maintains diversity
Crossover Breeding:
For each of 10 genes:
Parent1_Fitness = fitness
Parent2_Fitness = fitness
Weight1 = Parent1_Fitness / (Parent1_Fitness + Parent2_Fitness)
Gene1 = parent1's value
Gene2 = parent2's value
Child_Gene = Weight1 × Gene1 + (1 - Weight1) × Gene2
Fitness-weighted crossover ensures fitter parent contributes more genetic material.
Mutation:
For each gene in child:
IF (random < mutation_rate):
Gene_Range = GENE_MAX - GENE_MIN
Noise = (random - 0.5) × 2 × mutation_strength × Gene_Range
Mutated_Gene = Clamp(Child_Gene + Noise, GENE_MIN, GENE_MAX)
Historical mutation rate: 20% (aggressive exploration)
Live mutation rate: 8% (conservative stability)
Mutation strength: 12% of gene range (configurable 5-25%)
Initialization of New Strategy:
Unique ID assigned (total_spawned counter)
Parent ID recorded
Generation = max(parent generations) + 1
Birth bar recorded (for age tracking)
All performance metrics zeroed
Shadow portfolio reset
WFO validation flag set to false (must prove itself)
Result: New strategy with hybrid DNA enters population, begins trading in next bar.
Competition (Every Bar)
All active strategies:
Calculate their signal based on unique DNA
Check quality gate with their thresholds
Manage shadow positions (entries/exits)
Update performance metrics
Recalculate fitness score
Track WFO validation progress
Strategies compete indirectly through fitness ranking - no direct interaction.
Culling Weak Strategies
Timing (Adaptive):
Historical phase: Every 60 bars (configurable 20-200, should be 2x spawn interval)
Live phase: Every 400 bars (configurable 200-1000, should be 2x spawn interval)
Minimum Adaptation Score (MAS):
Initial MAS = 0.10
MAS decays: MAS × 0.995 every cull cycle
Minimum MAS = 0.03 (floor)
MAS represents the "survival threshold" - strategies below this fitness level are vulnerable.
Culling Conditions (ALL must be true):
Population > minimum population (default: 3, configurable 2-4)
At least one strategy has fitness < MAS
Strategy's age > culling interval (prevents premature culling of new strategies)
Strategy is not in top N elite (default: 2, configurable 1-3)
Culling Process:
Find worst strategy:
For each active strategy:
IF (age > cull_interval):
Fitness = base_fitness
IF (not WFO_validated AND WFO_enabled):
Fitness × 0.7 // 30% penalty for unvalidated
IF (Fitness < MAS AND Fitness < worst_fitness_found):
worst_strategy = this_strategy
worst_fitness = Fitness
IF (worst_strategy found):
Count elite strategies with fitness > worst_fitness
IF (elite_count >= elite_preservation_count):
Deactivate worst_strategy (set active flag = false)
Increment total_culled counter
Elite Protection:
Even if a strategy's fitness falls below MAS, it survives if fewer than N strategies are better. This prevents culling when population is generally weak.
Result: Weak strategies removed from population, freeing slots for new spawns. Gene pool improves over time.
Selection for Display (Every Bar)
AGE chooses one strategy to display signals:
Best fitness = -1
Selected = none
For each active strategy:
Fitness = base_fitness
IF (WFO_validated):
Fitness × 1.3 // 30% bonus for validated strategies
IF (Fitness > best_fitness):
best_fitness = Fitness
selected_strategy = this_strategy
Display selected strategy's signals on chart
Result: Only the highest-fitness (optionally validated-boosted) strategy's signals appear as chart markers. Other strategies trade invisibly in shadow portfolios.
🎨 PREMIUM VISUALIZATION SYSTEM
AGE includes sophisticated visual feedback that standard indicators lack:
1. Gradient Probability Cloud (Optional, Default: ON)
Multi-layer gradient showing signal buildup 2-3 bars before entry:
Activation Conditions:
Signal persistence > 0 (same directional signal held for multiple bars)
Signal probability ≥ minimum threshold (65% by default)
Signal hasn't yet executed (still in "forming" state)
Visual Construction:
7 gradient layers by default (configurable 3-15)
Each layer is a line-fill pair (top line, bottom line, filled between)
Layer spacing: 0.3 to 1.0 × ATR above/below price
Outer layers = faint, inner layers = bright
Color transitions from base to intense based on layer position
Transparency scales with probability (high prob = more opaque)
Color Selection:
Long signals: Gradient from theme.gradient_bull_mid to theme.gradient_bull_strong
Short signals: Gradient from theme.gradient_bear_mid to theme.gradient_bear_strong
Base transparency: 92%, reduces by up to 8% for high-probability setups
Dynamic Behavior:
Cloud grows/shrinks as signal persistence increases/decreases
Redraws every bar while signal is forming
Disappears when signal executes or invalidates
Performance Note: Computationally expensive due to linefill objects. Disable or reduce layers if chart performance degrades.
2. Population Fitness Ribbon (Optional, Default: ON)
Histogram showing fitness distribution across active strategies:
Activation: Only draws on last bar (barstate.islast) to avoid historical clutter
Visual Construction:
10 histogram layers by default (configurable 5-20)
Plots 50 bars back from current bar
Positioned below price at: lowest_low(100) - 1.5×ATR (doesn't interfere with price action)
Each layer represents a fitness threshold (evenly spaced min to max fitness)
Layer Logic:
For layer_num from 0 to ribbon_layers:
Fitness_threshold = min_fitness + (max_fitness - min_fitness) × (layer / layers)
Count strategies with fitness ≥ threshold
Height = ATR × 0.15 × (count / total_active)
Y_position = base_level + ATR × 0.2 × layer
Color = Gradient from weak to strong based on layer position
Line_width = Scaled by height (taller = thicker)
Visual Feedback:
Tall, bright ribbon = healthy population, many fit strategies at high fitness levels
Short, dim ribbon = weak population, few strategies achieving good fitness
Ribbon compression (layers close together) = population converging to similar fitness
Ribbon spread = diverse fitness range, active selection pressure
Use Case: Quick visual health check without opening dashboard. Ribbon growing upward over time = population improving.
3. Confidence Halo (Optional, Default: ON)
Circular polyline around entry signals showing probability strength:
Activation: Draws when new position opens (shadow_position changes from 0 to ±1)
Visual Construction:
20-segment polyline forming approximate circle
Center: Low - 0.5×ATR (long) or High + 0.5×ATR (short)
Radius: 0.3×ATR (low confidence) to 1.0×ATR (elite confidence)
Scales with: (probability - min_probability) / (1.0 - min_probability)
Color Coding:
Elite (85%+): Cyan (theme.conf_elite), large radius, minimal transparency (40%)
Strong (75-85%): Strong green (theme.conf_strong), medium radius, moderate transparency (50%)
Good (65-75%): Good green (theme.conf_good), smaller radius, more transparent (60%)
Moderate (<65%): Moderate green (theme.conf_moderate), tiny radius, very transparent (70%)
Technical Detail:
Uses chart.point array with index-based positioning
5-bar horizontal spread for circular appearance (±5 bars from entry)
Curved=false (Pine Script polyline limitation)
Fill color matches line color but more transparent (88% vs line's transparency)
Purpose: Instant visual probability assessment. No need to check dashboard - halo size/brightness tells the story.
4. Evolution Event Markers (Optional, Default: ON)
Visual indicators of genetic algorithm activity:
Spawn Markers (Diamond, Cyan):
Plots when total_spawned increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.spawn_marker (cyan/bright blue)
Size: tiny
Indicates new strategy just entered population
Cull Markers (X-Cross, Red):
Plots when total_culled increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.cull_marker (red/pink)
Size: tiny
Indicates weak strategy just removed from population
What It Tells You:
Frequent spawning early = population building, active exploration
Frequent culling early = high selection pressure, weak strategies dying fast
Balanced spawn/cull = healthy evolutionary churn
No markers for long periods = stable population (evolution plateaued or optimal genes found)
5. Entry/Exit Markers
Clear visual signals for selected strategy's trades:
Long Entry (Triangle Up, Green):
Plots when selected strategy opens long position (position changes 0 → +1)
Location: below bar (location.belowbar)
Color: theme.long_primary (green/cyan depending on theme)
Transparency: Scales with probability:
Elite (85%+): 0% (fully opaque)
Strong (75-85%): 10%
Good (65-75%): 20%
Acceptable (55-65%): 35%
Size: small
Short Entry (Triangle Down, Red):
Plots when selected strategy opens short position (position changes 0 → -1)
Location: above bar (location.abovebar)
Color: theme.short_primary (red/pink depending on theme)
Transparency: Same scaling as long entries
Size: small
Exit (X-Cross, Orange):
Plots when selected strategy closes position (position changes ±1 → 0)
Location: absolute (at actual exit price if stop/target lines enabled)
Color: theme.exit_color (orange/yellow depending on theme)
Transparency: 0% (fully opaque)
Size: tiny
Result: Clean, probability-scaled markers that don't clutter chart but convey essential information.
6. Stop Loss & Take Profit Lines (Optional, Default: ON)
Visual representation of shadow portfolio risk levels:
Stop Loss Line:
Plots when selected strategy has active position
Level: shadow_stop value from selected strategy
Color: theme.short_primary with 60% transparency (red/pink, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Take Profit Line:
Plots when selected strategy has active position
Level: shadow_target value from selected strategy
Color: theme.long_primary with 60% transparency (green, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Purpose:
Shows where shadow portfolio would exit for stop/target
Helps visualize strategy's risk/reward ratio
Useful for manual traders to set similar levels
Disable for cleaner chart (recommended for presentations)
7. Dynamic Trend EMA
Gradient-colored trend line that visualizes trend strength:
Calculation:
EMA(close, trend_length) - default 50 period (configurable 20-100)
Slope calculated over 10 bars: (current_ema - ema ) / ema × 100
Color Logic:
Trend_direction:
Slope > 0.1% = Bullish (1)
Slope < -0.1% = Bearish (-1)
Otherwise = Neutral (0)
Trend_strength = abs(slope)
Color = Gradient between:
- Neutral color (gray/purple)
- Strong bullish (bright green) if direction = 1
- Strong bearish (bright red) if direction = -1
Gradient factor = trend_strength (0 to 1+ scale)
Visual Behavior:
Faint gray/purple = weak/no trend (choppy conditions)
Light green/red = emerging trend (low strength)
Bright green/red = strong trend (high conviction)
Color intensity = trend strength magnitude
Transparency: 50% (subtle, doesn't overpower price action)
Purpose: Subconscious awareness of trend state without checking dashboard or indicators.
8. Regime Background Tinting (Subtle)
Ultra-low opacity background color indicating detected market regime:
Regime Detection:
Efficiency = directional_movement / total_range (over trend_length bars)
Vol_ratio = current_volatility / average_volatility
IF (efficiency > 0.5 AND vol_ratio < 1.3):
Regime = Trending (1)
ELSE IF (vol_ratio > 1.5):
Regime = Volatile (2)
ELSE:
Regime = Choppy (0)
Background Colors:
Trending: theme.regime_trending (dark green, 92-93% transparency)
Volatile: theme.regime_volatile (dark red, 93% transparency)
Choppy: No tint (normal background)
Purpose:
Subliminal regime awareness
Helps explain why signals are/aren't generating
Trending = ideal conditions for AGE
Volatile = fewer signals, higher thresholds applied
Choppy = mixed signals, lower confidence
Important: Extremely subtle by design. Not meant to be obvious, just subconscious context.
📊 ENHANCED DASHBOARD
Comprehensive real-time metrics in single organized panel (top-right position):
Dashboard Structure (5 columns × 14 rows)
Header Row:
Column 0: "🧬 AGE PRO" + phase indicator (🔴 LIVE or ⏪ HIST)
Column 1: "POPULATION"
Column 2: "PERFORMANCE"
Column 3: "CURRENT SIGNAL"
Column 4: "ACTIVE STRATEGY"
Column 0: Market State
Regime (📈 TREND / 🌊 CHAOS / ➖ CHOP)
DVS Ratio (current volatility scaling factor, format: #.##)
Trend Direction (▲ BULL / ▼ BEAR / ➖ FLAT with color coding)
Trend Strength (0-100 scale, format: #.##)
Column 1: Population Metrics
Active strategies (count / max_population)
Validated strategies (WFO passed / active total)
Current generation number
Total spawned (all-time strategy births)
Total culled (all-time strategy deaths)
Column 2: Aggregate Performance
Total trades across all active strategies
Aggregate win rate (%) - color-coded:
Green (>55%)
Orange (45-55%)
Red (<45%)
Total P&L in R-multiples - color-coded by positive/negative
Best fitness score in population (format: #.###)
MAS - Minimum Adaptation Score (cull threshold, format: #.###)
Column 3: Current Signal Status
Status indicator:
"▲ LONG" (green) if selected strategy in long position
"▼ SHORT" (red) if selected strategy in short position
"⏳ FORMING" (orange) if signal persisting but not yet executed
"○ WAITING" (gray) if no active signal
Confidence percentage (0-100%, format: #.#%)
Quality assessment:
"🔥 ELITE" (cyan) for 85%+ probability
"✓ STRONG" (bright green) for 75-85%
"○ GOOD" (green) for 65-75%
"- LOW" (dim) for <65%
Confluence score (X/3 format)
Signal age:
"X bars" if signal forming
"IN TRADE" if position active
"---" if no signal
Column 4: Selected Strategy Details
Strategy ID number (#X format)
Validation status:
"✓ VAL" (green) if WFO validated
"○ TRAIN" (orange) if still in training/testing phase
Generation number (GX format)
Personal fitness score (format: #.### with color coding)
Trade count
P&L and win rate (format: #.#R (##%) with color coding)
Color Scheme:
Panel background: theme.panel_bg (dark, low opacity)
Panel headers: theme.panel_header (slightly lighter)
Primary text: theme.text_primary (bright, high contrast)
Secondary text: theme.text_secondary (dim, lower contrast)
Positive metrics: theme.metric_positive (green)
Warning metrics: theme.metric_warning (orange)
Negative metrics: theme.metric_negative (red)
Special markers: theme.validated_marker, theme.spawn_marker
Update Frequency: Only on barstate.islast (current bar) to minimize CPU usage
Purpose:
Quick overview of entire system state
No need to check multiple indicators
Trading decisions informed by population health, regime state, and signal quality
Transparency into what AGE is thinking
🔍 DIAGNOSTICS PANEL (Optional, Default: OFF)
Detailed signal quality tracking for optimization and debugging:
Panel Structure (3 columns × 8 rows)
Position: Bottom-right corner (doesn't interfere with main dashboard)
Header Row:
Column 0: "🔍 DIAGNOSTICS"
Column 1: "COUNT"
Column 2: "%"
Metrics Tracked (for selected strategy only):
Total Evaluated:
Every signal that passed initial calculation (direction ≠ 0)
Represents total opportunities considered
✓ Passed:
Signals that passed quality gate and executed
Green color coding
Percentage of evaluated signals
Rejection Breakdown:
⨯ Probability:
Rejected because probability < minimum threshold
Most common rejection reason typically
⨯ Confluence:
Rejected because confluence < minimum required (e.g., only 1 of 3 indicators agreed)
⨯ Trend:
Rejected because signal opposed strong trend
Indicates counter-trend protection working
⨯ Regime:
Rejected because volatile regime detected and probability wasn't high enough to override
Shows regime filter in action
⨯ Volume:
Rejected because volume < 70% of 20-bar average
Indicates volume confirmation requirement
Color Coding:
Passed count: Green (success metric)
Rejection counts: Red (failure metrics)
Percentages: Gray (neutral, informational)
Performance Cost: Slight CPU overhead for tracking counters. Disable when not actively optimizing settings.
How to Use Diagnostics
Scenario 1: Too Few Signals
Evaluated: 200
Passed: 10 (5%)
⨯ Probability: 120 (60%)
⨯ Confluence: 40 (20%)
⨯ Others: 30 (15%)
Diagnosis: Probability threshold too high for this strategy's DNA.
Solution: Lower min probability from 65% to 60%, or allow strategy more time to evolve better DNA.
Scenario 2: Too Many False Signals
Evaluated: 200
Passed: 80 (40%)
Strategy win rate: 45%
Diagnosis: Quality gate too loose, letting low-quality signals through.
Solution: Raise min probability to 70%, or increase min confluence to 3 (all indicators must agree).
Scenario 3: Regime-Specific Issues
⨯ Regime: 90 (45% of rejections)
Diagnosis: Frequent volatile regime detection blocking otherwise good signals.
Solution: Either accept fewer trades during chaos (recommended), or disable regime filter if you want signals regardless of market state.
Optimization Workflow:
Enable diagnostics
Run 200+ bars
Analyze rejection patterns
Adjust settings based on data
Re-run and compare pass rate
Disable diagnostics when satisfied
⚙️ CONFIGURATION GUIDE
🧬 Evolution Engine Settings
Enable AGE Evolution (Default: ON):
ON: Full genetic algorithm (recommended for best results)
OFF: Uses only 4 seed strategies, no spawning/culling (static population for comparison testing)
Max Population (4-12, Default: 8):
Higher = more diversity, more exploration, slower performance
Lower = faster computation, less exploration, risk of premature convergence
Sweet spot: 6-8 for most use cases
4 = minimum for meaningful evolution
12 = maximum before diminishing returns
Min Population (2-4, Default: 3):
Safety floor - system never culls below this count
Prevents population extinction during harsh selection
Should be at least half of max population
Elite Preservation (1-3, Default: 2):
Top N performers completely immune to culling
Ensures best genes always survive
1 = minimal protection, aggressive selection
2 = balanced (recommended)
3 = conservative, slower gene pool turnover
Historical: Spawn Interval (10-100, Default: 30):
Bars between spawning new strategies during historical data
Lower = faster evolution, more exploration
Higher = slower evolution, more evaluation time per strategy
30 bars = ~1-2 hours on 15min chart
Historical: Cull Interval (20-200, Default: 60):
Bars between culling weak strategies during historical data
Should be 2x spawn interval for balanced churn
Lower = aggressive selection pressure
Higher = patient evaluation
Live: Spawn Interval (100-500, Default: 200):
Bars between spawning during live trading
Much slower than historical for stability
Prevents population chaos during live trading
200 bars = ~1.5 trading days on 15min chart
Live: Cull Interval (200-1000, Default: 400):
Bars between culling during live trading
Should be 2x live spawn interval
Conservative removal during live trading
Historical: Mutation Rate (0.05-0.40, Default: 0.20):
Probability each gene mutates during breeding (20% = 2 out of 10 genes on average)
Higher = more exploration, slower convergence
Lower = more exploitation, faster convergence but risk of local optima
20% balances exploration vs exploitation
Live: Mutation Rate (0.02-0.20, Default: 0.08):
Mutation rate during live trading
Much lower for stability (don't want population to suddenly degrade)
8% = mostly inherits parent genes with small tweaks
Mutation Strength (0.05-0.25, Default: 0.12):
How much genes change when mutated (% of gene's total range)
0.05 = tiny nudges (fine-tuning)
0.12 = moderate jumps (recommended)
0.25 = large leaps (aggressive exploration)
Example: If gene range is 0.5-2.0, 12% strength = ±0.18 possible change
📈 Signal Quality Settings
Min Signal Probability (0.55-0.80, Default: 0.65):
Quality gate threshold - signals below this never generate
0.55-0.60 = More signals, accept lower confidence (higher risk)
0.65 = Institutional-grade balance (recommended)
0.70-0.75 = Fewer but higher-quality signals (conservative)
0.80+ = Very selective, very few signals (ultra-conservative)
Min Confluence Score (1-3, Default: 2):
Required indicator agreement before signal generates
1 = Any single indicator can trigger (not recommended - too many false signals)
2 = Requires 2 of 3 indicators agree (RECOMMENDED for balance)
3 = All 3 must agree (very selective, few signals, high quality)
Base Persistence Bars (1-5, Default: 2):
Base bars signal must persist before entry
System adapts automatically:
High probability signals (75%+) enter 1 bar faster
Low probability signals (<68%) need 1 bar more
Trending regime: -1 bar (faster entries)
Volatile regime: +1 bar (more confirmation)
1 = Immediate entry after quality gate (responsive but prone to whipsaw)
2 = Balanced confirmation (recommended)
3-5 = Patient confirmation (slower but more reliable)
Cooldown After Trade (3-20, Default: 8):
Bars to wait after exit before next entry allowed
Prevents overtrading and revenge trading
3 = Minimal cooldown (active trading)
8 = Balanced (recommended)
15-20 = Conservative (position trading)
Entropy Length (10-50, Default: 20):
Lookback period for market order/disorder calculation
Lower = more responsive to regime changes (noisy)
Higher = more stable regime detection (laggy)
20 = works across most timeframes
Momentum Length (5-30, Default: 14):
Period for RSI/ROC calculations
14 = standard (RSI default)
Lower = more signals, less reliable
Higher = fewer signals, more reliable
Structure Length (20-100, Default: 50):
Lookback for support/resistance swing range
20 = short-term swings (day trading)
50 = medium-term structure (recommended)
100 = major structure (position trading)
Trend EMA Length (20-100, Default: 50):
EMA period for trend detection and direction bias
20 = short-term trend (responsive)
50 = medium-term trend (recommended)
100 = long-term trend (position trading)
ATR Period (5-30, Default: 14):
Period for volatility measurement
14 = standard ATR
Lower = more responsive to vol changes
Higher = smoother vol calculation
📊 Volatility Scaling (DVS) Settings
Enable DVS (Default: ON):
Dynamic volatility scaling for adaptive stop/target placement
Highly recommended to leave ON
OFF only for testing fixed-distance stops
DVS Method (Default: Ensemble):
ATR Ratio: Simple, fast, single-method (good for beginners)
Parkinson: High-low range based (good for intraday)
Garman-Klass: OHLC based (sophisticated, considers gaps)
Ensemble: Median of all three (RECOMMENDED - most robust)
DVS Memory (20-200, Default: 100):
Lookback for baseline volatility comparison
20 = very responsive to vol changes (can overreact)
100 = balanced adaptation (recommended)
200 = slow, stable baseline (minimizes false vol signals)
DVS Sensitivity (0.3-1.5, Default: 0.7):
How much volatility affects scaling (power-law exponent)
0.3 = Conservative, heavily dampens vol impact (cube root)
0.5 = Moderate dampening (square root)
0.7 = Balanced response (recommended)
1.0 = Linear, full 1:1 vol response
1.5 = Aggressive, amplified response (exponential)
🔬 Walk-Forward Optimization Settings
Enable WFO (Default: ON):
Out-of-sample validation to prevent overfitting
Highly recommended to leave ON
OFF only for testing or if you want unvalidated strategies
Training Window (100-500, Default: 250):
Bars for in-sample optimization
100 = fast validation, less data (risky)
250 = balanced (recommended) - about 1-2 months on daily, 1-2 weeks on 15min
500 = patient validation, more data (conservative)
Testing Window (30-200, Default: 75):
Bars for out-of-sample validation
Should be ~30% of training window
30 = minimal test (fast validation)
75 = balanced (recommended)
200 = extensive test (very conservative)
Min Trades for Validation (3-15, Default: 5):
Required trades in BOTH training AND testing periods
3 = minimal sample (risky, fast validation)
5 = balanced (recommended)
10+ = conservative (slow validation, high confidence)
WFO Efficiency Threshold (0.3-0.9, Default: 0.55):
Minimum test/train performance ratio required
0.30 = Very loose (test must be 30% as good as training)
0.55 = Balanced (recommended) - test must be 55% as good
0.70+ = Strict (test must closely match training)
Higher = fewer validated strategies, lower risk of overfitting
🎨 Premium Visuals Settings
Visual Theme:
Neon Genesis: Cyberpunk aesthetic (cyan/magenta/purple)
Carbon Fiber: Industrial look (blue/red/gray)
Quantum Blue: Quantum computing (blue/purple/pink)
Aurora: Northern lights (teal/orange/purple)
⚡ Gradient Probability Cloud (Default: ON):
Multi-layer gradient showing signal buildup
Turn OFF if chart lags or for cleaner look
Cloud Gradient Layers (3-15, Default: 7):
More layers = smoother gradient, more CPU intensive
Fewer layers = faster, blockier appearance
🎗️ Population Fitness Ribbon (Default: ON):
Histogram showing fitness distribution
Turn OFF for cleaner chart
Ribbon Layers (5-20, Default: 10):
More layers = finer fitness detail
Fewer layers = simpler histogram
⭕ Signal Confidence Halo (Default: ON):
Circular indicator around entry signals
Size/brightness scales with probability
Minimal performance cost
🔬 Evolution Event Markers (Default: ON):
Diamond (spawn) and X (cull) markers
Shows genetic algorithm activity
Minimal performance cost
🎯 Stop/Target Lines (Default: ON):
Shows shadow portfolio stop/target levels
Turn OFF for cleaner chart (recommended for screenshots/presentations)
📊 Enhanced Dashboard (Default: ON):
Comprehensive metrics panel
Should stay ON unless you want zero overlays
🔍 Diagnostics Panel (Default: OFF):
Detailed signal rejection tracking
Turn ON when optimizing settings
Turn OFF during normal use (slight performance cost)
📈 USAGE WORKFLOW - HOW TO USE THIS INDICATOR
Phase 1: Initial Setup & Learning
Add AGE to your chart
Recommended timeframes: 15min, 30min, 1H (best signal-to-noise ratio)
Works on: 5min (day trading), 4H (swing trading), Daily (position trading)
Load 1000+ bars for sufficient evolution history
Let the population evolve (100+ bars minimum)
First 50 bars: Random exploration, poor results expected
Bars 50-150: Population converging, fitness improving
Bars 150+: Stable performance, validated strategies emerging
Watch the dashboard metrics
Population should grow toward max capacity
Generation number should advance regularly
Validated strategies counter should increase
Best fitness should trend upward toward 0.50-0.70 range
Observe evolution markers
Diamond markers (cyan) = new strategies spawning
X markers (red) = weak strategies being culled
Frequent early activity = healthy evolution
Activity slowing = population stabilizing
Be patient. Evolution takes time. Don't judge performance before 150+ bars.
Phase 2: Signal Observation
Watch signals form
Gradient cloud builds up 2-3 bars before entry
Cloud brightness = probability strength
Cloud thickness = signal persistence
Check signal quality
Look at confidence halo size when entry marker appears
Large bright halo = elite setup (85%+)
Medium halo = strong setup (75-85%)
Small halo = good setup (65-75%)
Verify market conditions
Check trend EMA color (green = uptrend, red = downtrend, gray = choppy)
Check background tint (green = trending, red = volatile, clear = choppy)
Trending background + aligned signal = ideal conditions
Review dashboard signal status
Current Signal column shows:
Status (Long/Short/Forming/Waiting)
Confidence % (actual probability value)
Quality assessment (Elite/Strong/Good)
Confluence score (2/3 or 3/3 preferred)
Only signals meeting ALL quality gates appear on chart. If you're not seeing signals, population is either still learning or market conditions aren't suitable.
Phase 3: Manual Trading Execution
When Long Signal Fires:
Verify confidence level (dashboard or halo size)
Confirm trend alignment (EMA sloping up, green color)
Check regime (preferably trending or choppy, avoid volatile)
Enter long manually on your broker platform
Set stop loss at displayed stop line level (if lines enabled), or use your own risk management
Set take profit at displayed target line level, or trail manually
Monitor position - exit if X marker appears (signal reversal)
When Short Signal Fires:
Same verification process
Confirm downtrend (EMA sloping down, red color)
Enter short manually
Use displayed stop/target levels or your own
AGE tells you WHEN and HOW CONFIDENT. You decide WHETHER and HOW MUCH.
Phase 4: Set Up Alerts (Never Miss a Signal)
Right-click on indicator name in legend
Select "Add Alert"
Choose condition:
"AGE Long" = Long entry signal fired
"AGE Short" = Short entry signal fired
"AGE Exit" = Position reversal/exit signal
Set notification method:
Sound alert (popup on chart)
Email notification
Webhook to phone/trading platform
Mobile app push notification
Name the alert (e.g., "AGE BTCUSD 15min Long")
Save alert
Recommended: Set alerts for both long and short, enable mobile push notifications. You'll get alerted in real-time even if not watching charts.
Phase 5: Monitor Population Health
Weekly Review:
Check dashboard Population column:
Active count should be near max (6-8 of 8)
Validated count should be >50% of active
Generation should be advancing (1-2 per week typical)
Check dashboard Performance column:
Aggregate win rate should be >50% (target: 55-65%)
Total P&L should be positive (may fluctuate)
Best fitness should be >0.50 (target: 0.55-0.70)
MAS should be declining slowly (normal adaptation)
Check Active Strategy column:
Selected strategy should be validated (✓ VAL)
Personal fitness should match best fitness
Trade count should be accumulating
Win rate should be >50%
Warning Signs:
Zero validated strategies after 300+ bars = settings too strict or market unsuitable
Best fitness stuck <0.30 = population struggling, consider parameter adjustment
No spawning/culling for 200+ bars = evolution stalled (may be optimal or need reset)
Aggregate win rate <45% sustained = system not working on this instrument/timeframe
Health Check Pass:
50%+ strategies validated
Best fitness >0.50
Aggregate win rate >52%
Regular spawn/cull activity
Selected strategy validated
Phase 6: Optimization (If Needed)
Enable Diagnostics Panel (bottom-right) for data-driven tuning:
Problem: Too Few Signals
Evaluated: 200
Passed: 8 (4%)
⨯ Probability: 140 (70%)
Solutions:
Lower min probability: 65% → 60% or 55%
Reduce min confluence: 2 → 1
Lower base persistence: 2 → 1
Increase mutation rate temporarily to explore new genes
Check if regime filter is blocking signals (⨯ Regime high?)
Problem: Too Many False Signals
Evaluated: 200
Passed: 90 (45%)
Win rate: 42%
Solutions:
Raise min probability: 65% → 70% or 75%
Increase min confluence: 2 → 3
Raise base persistence: 2 → 3
Enable WFO if disabled (validates strategies before use)
Check if volume filter is being ignored (⨯ Volume low?)
Problem: Counter-Trend Losses
⨯ Trend: 5 (only 5% rejected)
Losses often occur against trend
Solutions:
System should already filter trend opposition
May need stronger trend requirement
Consider only taking signals aligned with higher timeframe trend
Use longer trend EMA (50 → 100)
Problem: Volatile Market Whipsaws
⨯ Regime: 100 (50% rejected by volatile regime)
Still getting stopped out frequently
Solutions:
System is correctly blocking volatile signals
Losses happening because vol filter isn't strict enough
Consider not trading during volatile periods (respect the regime)
Or disable regime filter and accept higher risk
Optimization Workflow:
Enable diagnostics
Run 200+ bars with current settings
Analyze rejection patterns and win rate
Make ONE change at a time (scientific method)
Re-run 200+ bars and compare results
Keep change if improvement, revert if worse
Disable diagnostics when satisfied
Never change multiple parameters at once - you won't know what worked.
Phase 7: Multi-Instrument Deployment
AGE learns independently on each chart:
Recommended Strategy:
Deploy AGE on 3-5 different instruments
Different asset classes ideal (e.g., ES futures, EURUSD, BTCUSD, SPY, Gold)
Each learns optimal strategies for that instrument's personality
Take signals from all 5 charts
Natural diversification reduces overall risk
Why This Works:
When one market is choppy, others may be trending
Different instruments respond to different news/catalysts
Portfolio-level win rate more stable than single-instrument
Evolution explores different parameter spaces on each chart
Setup:
Same settings across all charts (or customize if preferred)
Set alerts for all
Take every validated signal across all instruments
Position size based on total account (don't overleverage any single signal)
⚠️ REALISTIC EXPECTATIONS - CRITICAL READING
What AGE Can Do
✅ Generate probability-weighted signals using genetic algorithms
✅ Evolve strategies in real-time through natural selection
✅ Validate strategies on out-of-sample data (walk-forward optimization)
✅ Adapt to changing market conditions automatically over time
✅ Provide comprehensive metrics on population health and signal quality
✅ Work on any instrument, any timeframe, any broker
✅ Improve over time as weak strategies are culled and fit strategies breed
What AGE Cannot Do
❌ Win every trade (typical win rate: 55-65% at best)
❌ Predict the future with certainty (markets are probabilistic, not deterministic)
❌ Work perfectly from bar 1 (needs 100-150 bars to learn and stabilize)
❌ Guarantee profits under all market conditions
❌ Replace your trading discipline and risk management
❌ Execute trades automatically (this is an indicator, not a strategy)
❌ Prevent all losses (drawdowns are normal and expected)
❌ Adapt instantly to regime changes (re-learning takes 50-100 bars)
Performance Realities
Typical Performance After Evolution Stabilizes (150+ bars):
Win Rate: 55-65% (excellent for trend-following systems)
Profit Factor: 1.5-2.5 (realistic for validated strategies)
Signal Frequency: 5-15 signals per 100 bars (quality over quantity)
Drawdown Periods: 20-40% of time in equity retracement (normal trading reality)
Max Consecutive Losses: 5-8 losses possible even with 60% win rate (probability says this is normal)
Evolution Timeline:
Bars 0-50: Random exploration, learning phase - poor results expected, don't judge yet
Bars 50-150: Population converging, fitness climbing - results improving
Bars 150-300: Stable performance, most strategies validated - consistent results
Bars 300+: Mature population, optimal genes dominant - best results
Market Condition Dependency:
Trending Markets: AGE excels - clear directional moves, high-probability setups
Choppy Markets: AGE struggles - fewer signals generated, lower win rate
Volatile Markets: AGE cautious - higher rejection rate, wider stops, fewer trades
Market Regime Changes:
When market shifts from trending to choppy overnight
Validated strategies can become temporarily invalidated
AGE will adapt through evolution, but not instantly
Expect 50-100 bar re-learning period after major regime shifts
Fitness may temporarily drop then recover
This is NOT a holy grail. It's a sophisticated signal generator that learns and adapts using genetic algorithms. Your success depends on:
Patience during learning periods (don't abandon after 3 losses)
Proper position sizing (risk 0.5-2% per trade, not 10%)
Following signals consistently (cherry-picking defeats statistical edge)
Not abandoning system prematurely (give it 200+ bars minimum)
Understanding probability (60% win rate means 40% of trades WILL lose)
Respecting market conditions (trending = trade more, choppy = trade less)
Managing emotions (AGE is emotionless, you need to be too)
Expected Drawdowns:
Single-strategy max DD: 10-20% of equity (normal)
Portfolio across multiple instruments: 5-15% (diversification helps)
Losing streaks: 3-5 consecutive losses expected periodically
No indicator eliminates risk. AGE manages risk through:
Quality gates (rejecting low-probability signals)
Confluence requirements (multi-indicator confirmation)
Persistence requirements (no knee-jerk reactions)
Regime awareness (reduced trading in chaos)
Walk-forward validation (preventing overfitting)
But it cannot prevent all losses. That's inherent to trading.
🔧 TECHNICAL SPECIFICATIONS
Platform: TradingView Pine Script v5
Indicator Type: Overlay indicator (plots on price chart)
Execution Type: Signals only - no automatic order placement
Computational Load:
Moderate to High (genetic algorithms + shadow portfolios)
8 strategies × shadow portfolio simulation = significant computation
Premium visuals add additional load (gradient cloud, fitness ribbon)
TradingView Resource Limits (Built-in Caps):
Max Bars Back: 500 (sufficient for WFO and evolution)
Max Labels: 100 (plenty for entry/exit markers)
Max Lines: 150 (adequate for stop/target lines)
Max Boxes: 50 (not heavily used)
Max Polylines: 100 (confidence halos)
Recommended Chart Settings:
Timeframe: 15min to 1H (optimal signal/noise balance)
5min: Works but noisier, more signals
4H/Daily: Works but fewer signals
Bars Loaded: 1000+ (ensures sufficient evolution history)
Replay Mode: Excellent for testing without risk
Performance Optimization Tips:
Disable gradient cloud if chart lags (most CPU intensive visual)
Disable fitness ribbon if still laggy
Reduce cloud layers from 7 to 3
Reduce ribbon layers from 10 to 5
Turn off diagnostics panel unless actively tuning
Close other heavy indicators to free resources
Browser/Platform Compatibility:
Works on all modern browsers (Chrome, Firefox, Safari, Edge)
Mobile app supported (full functionality on phone/tablet)
Desktop app supported (best performance)
Web version supported (may be slower on older computers)
Data Requirements:
Real-time or delayed data both work
No special data feeds required
Works with TradingView's standard data
Historical + live data seamlessly integrated
🎓 THEORETICAL FOUNDATIONS
AGE synthesizes advanced concepts from multiple disciplines:
Evolutionary Computation
Genetic Algorithms (Holland, 1975): Population-based optimization through natural selection metaphor
Tournament Selection: Fitness-based parent selection with diversity preservation
Crossover Operators: Fitness-weighted gene recombination from two parents
Mutation Operators: Random gene perturbation for exploration of new parameter space
Elitism: Preservation of top N performers to prevent loss of best solutions
Adaptive Parameters: Different mutation rates for historical vs. live phases
Technical Analysis
Support/Resistance: Price structure within swing ranges
Trend Following: EMA-based directional bias
Momentum Analysis: RSI, ROC, MACD composite indicators
Volatility Analysis: ATR-based risk scaling
Volume Confirmation: Trade activity validation
Information Theory
Shannon Entropy (1948): Quantification of market order vs. disorder
Signal-to-Noise Ratio: Directional information vs. random walk
Information Content: How much "information" a price move contains
Statistics & Probability
Walk-Forward Analysis: Rolling in-sample/out-of-sample optimization
Out-of-Sample Validation: Testing on unseen data to prevent overfitting
Monte Carlo Principles: Shadow portfolio simulation with realistic execution
Expectancy Theory: Win rate × avg win - loss rate × avg loss
Probability Distributions: Signal confidence quantification
Risk Management
ATR-Based Stops: Volatility-normalized risk per trade
Volatility Regime Detection: Market state classification (trending/choppy/volatile)
Drawdown Control: Peak-to-trough equity measurement
R-Multiple Normalization: Performance measurement in risk units
Machine Learning Concepts
Online Learning: Continuous adaptation as new data arrives
Fitness Functions: Multi-objective optimization (win rate + expectancy + drawdown)
Exploration vs. Exploitation: Balance between trying new strategies and using proven ones
Overfitting Prevention: Walk-forward validation as regularization
Novel Contribution:
AGE is the first TradingView indicator to apply genetic algorithms to real-time indicator parameter optimization while maintaining strict anti-overfitting controls through walk-forward validation.
Most "adaptive" indicators simply recalibrate lookback periods or thresholds. AGE evolves entirely new strategies through competitive selection - it's not parameter tuning, it's Darwinian evolution of trading logic itself.
The combination of:
Genetic algorithm population management
Shadow portfolio simulation for realistic fitness evaluation
Walk-forward validation to prevent overfitting
Multi-indicator confluence for signal quality
Dynamic volatility scaling for adaptive risk
...creates a system that genuinely learns and improves over time while avoiding the curse of curve-fitting that plagues most optimization approaches.
🏗️ DEVELOPMENT NOTES
This project represents months of intensive development, facing significant technical challenges:
Challenge 1: Making Genetics Actually Work
Early versions spawned garbage strategies that polluted the gene pool:
Random gene combinations produced nonsensical parameter sets
Weak strategies survived too long, dragging down population
No clear convergence toward optimal solutions
Solution:
Comprehensive fitness scoring (4 factors: win rate, P&L, expectancy, drawdown)
Elite preservation (top 2 always protected)
Walk-forward validation (unproven strategies penalized 30%)
Tournament selection (fitness-weighted breeding)
Adaptive culling (MAS decay creates increasing selection pressure)
Challenge 2: Balancing Evolution Speed vs. Stability
Too fast = population chaos, no convergence. Too slow = can't adapt to regime changes.
Solution:
Dual-phase timing: Fast evolution during historical (30/60 bar intervals), slow during live (200/400 bar intervals)
Adaptive mutation rates: 20% historical, 8% live
Spawn/cull ratio: Always 2:1 to prevent population collapse
Challenge 3: Shadow Portfolio Accuracy
Needed realistic trade simulation without lookahead bias:
Can't peek at future bars for exits
Must track multiple portfolios simultaneously
Stop/target checks must use bar's high/low correctly
Solution:
Entry on close (realistic)
Exit checks on current bar's high/low (realistic)
Independent position tracking per strategy
Cooldown periods to prevent unrealistic rapid re-entry
ATR-normalized P&L (R-multiples) for fair comparison across volatility regimes
Challenge 4: Pine Script Compilation Limits
Hit TradingView's execution limits multiple times:
Too many array operations
Too many variables
Too complex conditional logic
Solution:
Optimized data structures (single DNA array instead of 8 separate arrays)
Minimal visual overlays (only essential plots)
Efficient fitness calculations (vectorized where possible)
Strategic use of barstate.islast to minimize dashboard updates
Challenge 5: Walk-Forward Implementation
Standard WFO is difficult in Pine Script:
Can't easily "roll forward" through historical data
Can't re-optimize strategies mid-stream
Must work in real-time streaming environment
Solution:
Age-based phase detection (first 250 bars = training, next 75 = testing)
Separate metric tracking for train vs. test
Efficiency calculation at fixed interval (after test period completes)
Validation flag persists for strategy lifetime
Challenge 6: Signal Quality Control
Early versions generated too many signals with poor win rates:
Single indicators produced excessive noise
No trend alignment
No regime awareness
Instant entries on single-bar spikes
Solution:
Three-layer confluence system (entropy + momentum + structure)
Minimum 2-of-3 agreement requirement
Trend alignment checks (penalty for counter-trend)
Regime-based probability adjustments
Persistence requirements (signals must hold multiple bars)
Volume confirmation
Quality gate (probability + confluence thresholds)
The Result
A system that:
Truly evolves (not just parameter sweeps)
Truly validates (out-of-sample testing)
Truly adapts (ongoing competition and breeding)
Stays within TradingView's platform constraints
Provides institutional-quality signals
Maintains transparency (full metrics dashboard)
Development time: 3+ months of iterative refinement
Lines of code: ~1500 (highly optimized)
Test instruments: ES, NQ, EURUSD, BTCUSD, SPY, AAPL
Test timeframes: 5min, 15min, 1H, Daily
🎯 FINAL WORDS
The Adaptive Genesis Engine is not just another indicator - it's a living system that learns, adapts, and improves through the same principles that drive biological evolution. Every bar it observes adds to its experience. Every strategy it spawns explores new parameter combinations. Every strategy it culls removes weakness from the gene pool.
This is evolution in action on your charts.
You're not getting a static formula locked in time. You're getting a system that thinks , that competes , that survives through natural selection. The strongest strategies rise to the top. The weakest die. The gene pool improves generation after generation.
AGE doesn't claim to predict the future - it adapts to whatever the future brings. When markets shift from trending to choppy, from calm to volatile, from bullish to bearish - AGE evolves new strategies suited to the new regime.
Use it on any instrument. Any timeframe. Any market condition. AGE will adapt.
This indicator gives you the pure signal intelligence. How you choose to act on it - position sizing, risk management, execution discipline - that's your responsibility. AGE tells you when and how confident . You decide whether and how much .
Trust the process. Respect the evolution. Let Darwin work.
"In markets, as in nature, it is not the strongest strategies that survive, nor the most intelligent - but those most responsive to change."
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
— Happy Holiday's
Volatilité
Volatility-Dynamic Risk Manager MNQ [HERMAN]Title: Volatility-Dynamic Risk Manager MNQ
Description:
The Volatility-Dynamic Risk Manager is a dedicated risk management utility designed specifically for traders of Micro Nasdaq 100 Futures (MNQ).
Many traders struggle with position sizing because they use a fixed Stop Loss size regardless of market conditions. A 10-point stop might be safe in a slow market but easily stopped out in a high-volatility environment. This indicator solves that problem by monitoring real-time volatility (using ATR) and automatically suggesting the appropriate Stop Loss size and Position Size (Contracts) to keep your dollar risk constant.
Note: This tool is hardcoded for MNQ (Micro Nasdaq) with a tick value calculation of $2 per point.
📈 How It Works
-This script operates on a logical flow that adapts to market behavior:
-Volatility Measurement: It calculates the Average True Range (ATR) over a user-defined length (Default: 14) to gauge the current "speed" of the market.
-State Detection: Based on the current ATR, the script classifies the market into one of three states:
Low Volatility: The market is chopping or moving slowly.
Normal Volatility: Standard trading conditions.
High Volatility: The market is moving aggressively.
Dynamic Stop Loss Selection: Depending on the detected state, the script selects a pre-defined Stop Loss (in points) that you have configured for that specific environment.
Position Sizing Calculation: Finally, it calculates how many MNQ contracts you can trade so that if your Stop Loss is hit, you do not lose more than your defined "Max Risk per Trade."
🧮 Methodology & Calculations
Since this script handles risk management, transparency in calculation is vital.
Here is the exact math used:
ATR Calculation: Contracts = Max Risk / Risk Per Contract
⚙️ Settings
You can fully customize the behavior of the risk manager via the settings panel:
Risk Management
-Max Risk per Trade ($): The maximum amount of USD you are willing to lose on a single trade.
Volatility Thresholds (ATR)
-ATR Length: The lookback period for volatility calculation.
-Upper Limit for LOW Volatility: If ATR is below this number, the market is "Low Volatility."
-Lower Limit for HIGH Volatility: If ATR is above this number, the market is "High Volatility." (Anything between Low and High is considered "Normal").
Stop Loss Settings (Points)
-SL for Low/Normal/High: Define how wide your stop loss should be in points for each of the three market states.
Visual Settings
-Color Theme: Switch between Light and Dark modes.
-Panel Position: Move the dashboard to any corner or center of your chart.
-Panel Size: Adjust the scale (Tiny to Large) to fit your screen resolution.
📊 Dashboard Overview
-The on-screen panel provides a quick-glance summary for live execution:
-Market State: Color-coded status (Green = Low Vol, Orange = Normal, Red = High Vol).
-Current ATR: The live volatility reading.
-Suggested SL: The Stop Loss size you should enter in your execution platform.
-CONTRACTS: The calculated position size.
-Est. Loss: The actual dollar amount you will lose if the stop is hit (usually slightly less than your Max Risk due to rounding down).
Who is this for?
-Discretionary and systematic futures traders on MNQ (/MNQ or MES also works with small adjustments)
-Anyone who wants perfect risk consistency regardless of whether the market is asleep or exploding
-Traders who hate manual position-size calculations on every trade
No repainting
Works on any timeframe
Real-time updates on every bar
Overlay indicator (no signals, pure risk-management tool)
⚠️ Disclaimer
This tool is for informational and educational purposes only. It calculates mathematical position sizes based on user inputs. It does not execute trades, nor does it guarantee profits. Past performance (volatility) is not indicative of future results. Always manually verify your order size before executing trades on your broker platform.
ASFX - Automatic VWAPs & Key LevelsAutomate your AVWAPs and key levels for day trading! NY Market open VWAP, Previous day NY VWAP, and more are included. Inital Balance and Opening Range are also automated.
Smart Money Concepts [MHA Finverse]A comprehensive Smart Money Concepts (SMC) indicator designed to identify institutional trading behavior and market structure shifts. This tool helps traders align with "smart money" by detecting key supply and demand zones, structural breaks, and liquidity patterns.
Core Features
Market Structure Analysis
- Real-time Internal Structure: Detects short-term BOS (Break of Structure) and CHoCH (Change of Character) with customizable filters
- Swing Structure: Identifies major trend shifts and structural breaks on higher timeframes
- Adjustable pivot detection with customizable swing point visualization
- Strong/Weak High/Low identification for bias confirmation
Order Blocks (OB)
- Internal and Swing Order Blocks with independent control
- Volume-based metrics showing OB strength and percentage contribution
- Two filtering methods: ATR-based and Cumulative Mean Range
- Flexible mitigation options (Close or High/Low)
- Display up to 20 order blocks per type with auto-cleanup on mitigation
- Color-coded zones with transparency control
Liquidity Detection
- Equal Highs (EQH) and Equal Lows (EQL) identification
- Threshold-based detection using ATR calculation
- Visual confirmation lines connecting equal levels
- Adjustable sensitivity and bar confirmation settings
Fair Value Gaps (FVG)
- Multi-timeframe FVG detection
- Auto-threshold calculation based on price momentum
- Bullish and Bearish gap visualization
- Extendable gap boxes for tracking unfilled imbalances
Premium & Discount Zones
- Automated premium, equilibrium, and discount zone plotting
- Based on current swing range extremes
- Visual representation of optimal entry zones
- Helps identify potential reversal and continuation areas
Multi-Timeframe Levels
- Previous Daily, Weekly, and Monthly High/Low levels
- Customizable line styles (solid, dashed, dotted)
- Independent color controls for each timeframe
- Auto-adjusted labels (PDH, PDL, PWH, PWL, PMH, PML)
Display Modes
- Historical Mode: Shows all past structures and maintains drawing history
- Present Mode: Displays only current active structures for cleaner charts
Visual Themes
- Colored: Full color customization for all elements
- Monochrome: Clean grey-scale design for minimal distraction
Smart Features
- Confluence filter for internal structure to reduce noise
- Automatic candle coloring based on market bias
- 16 pre-configured alert conditions for all major signals
- Efficient rendering with automatic cleanup of broken structures
- Independent control over each feature for modular usage
Use Cases
- Identify institutional entry and exit points through order blocks
- Spot potential reversals at premium/discount zones
- Confirm trend direction with BOS and CHoCH signals
- Find liquidity grabs at equal highs and lows
- Trade imbalances at fair value gaps
- Align entries with multi-timeframe key levels
Settings Organization
All features are neatly organized into logical groups:
- Smart Money Concepts (general settings)
- Real Time Internal Structure
- Real Time Swing Structure
- Order Blocks
- EQH/EQL
- Fair Value Gaps
- Highs & Lows MTF
- Premium & Discount Zones
Note: This indicator works on all timeframes and instruments. For optimal results, combine multiple SMC concepts together to find high-probability setups with confluence.
Credits
Special thanks to Dau_tu_hieu_goc and BigBeluga for their code examples and inspiration that contributed to the development of this indicator.
Disclaimer
This indicator is for educational purposes only and does not constitute financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and conduct your own analysis before making trading decisions. The developer is not responsible for any trading losses incurred.
Happy Trading
Trinity Extreme Rope Trend [SamRecio]Original work and credit to Sam and you can find him here (www.tradingview.com) and his script available from
Why change... just some small tweaks to enhance and here is the summary of Changes vs the Original Script...
- Rope smoothing algorithm kept 100% identical (same brilliant “pull-only-when-exceeded-ATR” logic)
- Direction logic unchanged (still instantly resets on price crossing the rope)
- Old linebr + fill method completely replaced with clean box.new() consolidation zones
- Added “BR” breakout arrows (cyan triangle up for bullish break, magenta triangle down for bearish break)
- Arrows fire only on the exact breakout bar — zero repaint, zero lag
- Added subtle yellow background tint while in consolidation
- Full alertconditions + optional popup/sound on every BR break
- Auto-finalizes and cleans boxes properly, no chart clutter
Primary rule: only take trades on the BR arrow in the direction of the higher-timeframe trend.
Typical high-probability setups
- Wait for yellow rope + box → price consolidates
- BR arrow appears and candle closes outside the box → enter immediately
- Stop-loss just inside the box (opposite side)
- Target: next major liquidity pool, previous swing high/low, or 3–5R
Suggested Settings for Different Styles/Timeframes
Scalping (1 m – 5 m)
ATR Length: 10–12
ATR Multiplier: 1.0–1.3
→ tighter rope = faster signals, perfect for killing 1-minute London/NY open raids
Intraday aggression (5 m – 15 m)
ATR Length: 14 (default)
ATR Multiplier: 1.5–1.8
→ this is the sweet spot most funded traders use right now
Swing / position trading (1 H – 4 H)
ATR Length: 20–30
ATR Multiplier: 2.0–2.5
→ wider rope filters out noise, only catches the real macro moves
Daily / weekly bias filter
ATR Length: 50
ATR Multiplier: 3.0–4.0
→ use only the rope color (ignore boxes) to determine weekly bias — cyan = only longs all week, magenta = only shorts
That’s it. Drop the script, choose one of the above settings based on your style, turn on alerts, and hope you enjoy what is a wonderful script.
Adaptive Risk Management [sgbpulse]1. Introduction:
Adaptive Risk Management is an advanced indicator designed to provide traders with a comprehensive risk management tool directly on the chart. Instead of relying on complex manual calculations, the indicator automates all critical steps of trade planning. It dynamically calculates the estimated Entry Price , the Stop Loss location, the required Position Size (Quantity) based on your capital and risk limits, and the three Take Profit targets based on your defined Reward/Risk ratios. The indicator displays all these essential data points clearly and visually on the chart, ensuring you always know the potential risk-reward profile of every trade.
ARM : The A daptive R isk M anagement every trader needs to ARM themselves with.
2. The Critical Importance of Risk Management
Proper risk management is the cornerstone of successful trading. Consistent profitability in the market is impossible without rigorously defining risk limits.
Risk Control: This starts by setting the maximum risk amount you are willing to lose in a single trade (Risk per Trade), and limiting the total capital allocated to the position (Max Capital per Trade).
Defining Boundaries (Stop Loss & Take Profit): It is mandatory to define a technical Stop Loss and a Take Profit target. A fundamental rule of risk management is that the Reward/Risk Ratio (R/R) must be a minimum of 1:1.
3. Core Features, Adaptivity, and Customization
The Adaptive Risk Management indicator is engineered for use across all major trading styles, including Swing Trading, Intraday Trading, and Scalping, providing consistent risk control regardless of the chosen timeframe.
Real-Time Dynamic Adaptivity: The indicator calculates all risk management parameters (Entry, Stop Loss, Quantity) dynamically with every new bar, thus adapting instantly to changing market conditions.
Trend Direction Adjustment: Define the analysis direction (Long/Uptrend or Short/Downtrend).
Intraday Session Data Control: Full control over whether lookback calculations will include data from Extended Trading Hours (ETH), or if the daily calculations will start actively only from the first bar of Regular Trading Hours (RTH).
Status Validation: The indicator performs critical status checks and displays clear Warning Messages if risk conditions are not met.
4. Intuitive Visualization and Real-Time Data
Dynamic Tracking Lines: The Entry Price and Stop Loss lines are updated with every new bar. Crucially, the length of these lines dynamically reflects the calculation's lookback range (e.g., the extent of Lookback Bars or the location of the confirmed Pivot Point), providing a visual anchor for the calculated price.
Risk and Reward Zones: The indicator creates a graphical background fill between Entry and Stop Loss (marked with the risk color) and between Entry and the Reward Targets (marked with the reward color).
Essential Information Labels: Labels are placed at the end of each line, providing critical data: Estimated Entry Price, Stock/Contract Quantity (Quantity), Total Entry Amount, Estimated Stop Loss, Risk per Share, Total Financial Risk (Risk Amount), Exit Amount, Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3.
4.1. Data Window Metrics (16 Full Series)
The indicator displays 16 full data series in the TradingView Data Window, allowing precise tracking of every calculation parameter:
Entry Data: Estimated Entry, Quantity, Entry Amount.
Risk Data (Stop Loss): Estimated Stop Loss, Risk per Share, Risk Amount, Exit Amount.
Reward Data (Take Profit): Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3.
4.2. Instant Tracking in the Status Line
The indicator displays 6 critical parameters continuously in the indicator's Status Line: Estimated Entry, Quantity, Estimated Stop Loss, Estimated Take Profit 1/2/3.
5. Detailed Indicator Inputs
5.1 General
Focused Trend: Defines the analysis direction (Uptrend / Downtrend).
Max Capital per Trade: The maximum amount allocated to purchasing stocks/contracts (in account currency).
Risk per Trade: The maximum amount the user is willing to risk in this single trade (in account currency).
ATR Length: The lookback period for the Average True Range (ATR) calculation.
5.2 Intraday Session Data Control
Regular Hours Limitation : If enabled, all daily lookback calculations (for Entry/Stop Loss anchor points) will begin strictly from the first Regular Trading Hours (RTH) bar. This limits the lookback range to the current RTH session, excluding preceding Extended Trading Hours (ETH) data. Only relevant for Intraday charts. Default: False (Off)
5.3 Entry Inputs
Entry Method: Selects the entry price calculation method:
Current Price: Uses the closing price of the current bar as the estimated entry point (Market Entry).
ATR Real Bodies Margin :
- Uptrend: Calculates the Maximum Real Body over the lookback period + the calculated safety margin.
- Downtrend: Calculates the Minimum Real Body over the lookback period - the calculated safety margin.
ATR Bars Margin :
- Uptrend: Calculates the Maximum High price over the lookback period + the calculated safety margin.
- Downtrend: Calculates the Minimum Low price over the lookback period - the calculated safety margin.
Lookback Bars: The number of bars used to calculate the extremes in the ATR-based entry methods (Relevant only for ATR Real Bodies Margin and ATR Bars Margin methods).
ATR Multiplier (Entry): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to determine the estimated Entry Price.
5.4 Risk Inputs (Stop Loss)
Risk Method: Selects the Stop Loss price calculation method.
ATR Current Price Margin :
- Uptrend: Entry Price - the calculated safety margin.
- Downtrend: Entry Price + the calculated safety margin.
ATR Current Bar Margin :
- Uptrend: Current Bar's Low price - the calculated safety margin.
- Downtrend: Current Bar's High price + the calculated safety margin.
ATR Bars Margin :
- Uptrend: Lowest Low over lookback period - the calculated safety margin.
- Downtrend: Highest High over lookback period + the calculated safety margin.
ATR Pivot Margin :
- Uptrend: The first confirmed Pivot Low point - the calculated safety margin.
- Downtrend: The first confirmed Pivot High point + the calculated safety margin.
Lookback Bars: The lookback period for finding the extreme price used in the 'ATR Bars Margin' calculation.
ATR Multiplier (Risk): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to place the estimated Stop Loss. Note: If set to 0, the Stop Loss will be placed exactly at the technical anchor point, provided the Minimum Margin Value is also 0.
Minimum Margin Value: The minimum price value (e.g., $0.01) the Stop Loss margin buffer must be.
Pivot (Left / Right): The number of bars required on either side of the pivot bar for confirmation (relevant only for the ATR Pivot Margin method).
5.5 Reward Inputs (Take Profit)
Show Take Profit 1/2/3: ON/OFF switch to control the visibility of each Take Profit target.
Reward/Risk Ratio 1/ 2/ 3: Defines the R/R ratio for the profit target. Must be ≥1.0.
6. Indicator Status/Warning Messages
In situations where the Stop Loss location cannot be calculated logically and validly, often caused by a mismatch between the configured Focused Trend (Uptrend/Downtrend) and the actual price action, the indicator will display a warning message, explaining the reason and suggesting corrective action.
Status Message 1: Pivot reference unavailable
Condition: The Stop Loss is set to the "ATR Pivot Margin" method, but the anchor point (Pivot) is missing or inaccessible.
Message Displayed: "Pivot reference unavailable. Wait for valid price action, or adjust the Regular Hours Limitation setting or Pivot Left/Right inputs."
Status Message 2: Calculated Stop Loss is unsafe
Condition: The calculated Stop Loss is placed illogically or unsafely relative to the trend direction and the Entry price.
Message Displayed: "Calculated Stop Loss is unsafe for current trend. Wait for valid price action or adjust SL Lookback/Multiplier."
7. Summary
The Adaptive Risk Management (ARM) indicator provides a seamless and systematic approach to trade execution and risk control. By dynamically automating all critical trade parameters—from Entry Price and Stop Loss placement to Position Sizing and Take Profit targets—ARM removes emotional bias and ensures every trade adheres strictly to your predefined risk profile.
Key Benefits:
Systematic Risk Control: Strict enforcement of maximum capital allocation and risk per trade limits.
Adaptivity: Dynamic calculation of prices and quantities based on real-time market data (ATR and Lookback).
Clarity and Trust: Clear on-chart visualization, precise data metrics (16 series), and unambiguous Status/Warning Messages ensure transparency and reliability.
ARM allows traders to focus on strategy and analysis, confident that their execution complies with the core principles of professional risk management.
Important Note: Trading Risk
This indicator is intended for educational and informational purposes only and does not constitute investment advice or a recommendation for trading in any form whatsoever.
Trading in financial markets involves significant risk of capital loss. It is important to remember that past performance is not indicative of future results. All trading decisions are your sole responsibility. Never trade with money you cannot afford to lose.
Hash Ratings EngineHash Ratings Engine - Technical Consensus Strategy
A systematic trading strategy that harnesses TradingView's Technical Ratings to generate high-conviction entries with institutional-grade risk management.
What It Does
This strategy aggregates the consensus of 26+ technical indicators (RSI, MACD, Stochastics, multiple Moving Averages, etc.) into a single actionable signal. When enough indicators align bullish or bearish, the engine triggers an entry. Built-in trend filtering and ATR-based exits keep you on the right side of the market.
Key Features
Trend Filter - Only takes longs in uptrends, shorts in downtrends. This single filter typically improves results by 20-40% by avoiding counter-trend trades.
ATR-Based Risk Management - Stop loss and trailing stops adapt to current market volatility. Tight stops in calm markets, wider stops in volatile conditions.
Cooldown System - After a losing trade, the strategy waits before re-entering. This prevents the consecutive loss streaks that destroy accounts.
Clean Visuals - Fluorescent entry/exit signals with price level references. See exactly where you got in and out.
Settings Guide
Indicator Timeframe: Leave blank for current chart. Use higher timeframe for fewer, higher-quality signals.
Rating Source: "All" for balanced approach. "MAs" for trend-following. "Oscillators" for mean-reversion.
Entry Thresholds
Strong Signal Threshold: Higher = fewer trades but better conviction. Start at 0.5, test 0.4-0.6.
Risk Management
ATR Period: 12 is responsive, 14 is standard, 20+ is smoother.
Stop Loss: 2-3x ATR for tight stops, 3.5-4x for moderate, 5x+ for wide.
Trail Activation: How far price must move in profit before trailing begins.
Trail Offset: How closely the trail follows price.
Trend Filter
EMA Length: 150 works well on 4H charts. Use 100 for lower timeframes, 200 for daily.
Trade Timing
Cooldown: Keep enabled. 5 bars is a good starting point.
Best Practices
Start with default settings and backtest on your preferred instrument. Adjust the Strong Signal Threshold first - this has the biggest impact on trade frequency. Then tune the EMA length to match your timeframe. Finally, optimize the ATR multipliers for your risk tolerance.
Works on any liquid market - crypto, forex, stocks, futures. Higher timeframes (4H, Daily) tend to produce cleaner signals than lower timeframes.
Disclaimer
Past performance does not guarantee future results. Always backtest thoroughly and use proper position sizing. This strategy is for educational purposes - trade at your own risk.
VOLX+ VWAP Range BandsVOLX+ plots multiple VWAP-weighted high/low channels across different lookback periods to show how price behaves relative to short-term and long-term value zones.
Instead of using a single VWAP line, this tool creates four rolling VWAP envelopes:
Short-term range (fast reaction)
Mid-term range
Mid-mid range (transitional layer)
Long-term range (macro context)
Each band is computed as:
VWAP-High = SMA(high × volume, length) ÷ SMA(volume, length)
VWAP-Low = SMA(low × volume, length) ÷ SMA(volume, length)
This produces dynamic price channels that account for both price and traded volume, offering a clearer sense of where the market is accepting or rejecting value.
What It Shows
Four VWAP-weighted high/low bands
A short-term VWAP midline
Price line
Three SMAs for trend context
Optional visibility switches for each VWAP band
The filled regions between VWAP highs and lows create a layered “value map,” helping you interpret:
Trend continuation (price hugging outer VWAP bands)
Mean reversion (price returning toward inner bands)
Volatility contraction/expansion
Shifts in short-term vs long-term balance
🧠 How to Use
Use the short-term band for day-trading context or detecting short-term excess.
Use mid-term and mid-mid bands to confirm developing structure.
Use the long-term VWAP band to understand broader value zones.
Combine VWAP bands with SMAs and structure analysis for confluence.
This indicator is intended for price interpretation and analytical support.
✔ Does Not Repaint
The script uses rolling VWAP formulas and standard MAs; everything is stable and non-repainting.
FVG with Fibonacci Levels [MHA Finverse]FVG with Fibonacci Levels - Professional Fair Value Gap Indicator
This advanced Fair Value Gap (FVG) indicator automatically identifies and tracks market imbalances with integrated Fibonacci retracement levels, providing traders with precise entry and exit opportunities.
Key Features:
Smart Gap Detection
• Automatically identifies bullish and bearish fair value gaps in real-time
• Customizable minimum gap percentage filter to avoid noise
• Visual color-coded boxes for easy identification
Fibonacci Integration
• Built-in 0.5 and 0.618 Fibonacci retracement levels
• Fully customizable fib levels, colors, and line styles
• Helps identify optimal entry zones within each gap
Intelligent Gap Management
• Tracks multiple gaps simultaneously (up to 20)
• Automatic gap mitigation detection (Close or Wicks)
• Option to remove or highlight filled gaps
• Auto-hide boxes after specified bar count
Advanced Alert System
• Alerts when gaps are filled
• Fibonacci level touch alerts for both 0.5 and 0.618 levels
• Separate alerts for bullish and bearish setups
• Customizable alert preferences
Clean Visual Display
• Transparent boxes that don't clutter your chart
• Extending lines that update in real-time
• Customizable colors for both bullish and bearish gaps
• Option to change border style when gaps are filled
Perfect For:
Smart Money Concepts (SMC) traders, Price Action traders, and anyone looking to trade market structure and liquidity gaps with precision.
How to Use:
The indicator draws boxes around identified fair value gaps and extends them forward until they are filled. Fibonacci levels within each gap provide optimal entry zones. Set up alerts to get notified when price interacts with these key levels.
Credits
Special thanks to Quant Vue for their code examples and inspiration that contributed to the development of this indicator.
Disclaimer:
This indicator is for educational and informational purposes only. It does not constitute financial advice. Trading involves substantial risk of loss. Always conduct your own research and consider your risk tolerance before making any trading decisions. Past performance does not guarantee future results.
Triple ATR Adaptive MAs + VWAP Option + Clouds + Candle Trend V2Another one of my experiences ... combining things...
📘 Indicator Description – Triple ATR Adaptive Moving Averages with VWAP Influence
This indicator plots three adaptive moving averages whose behavior changes dynamically based on market volatility (ATR) and optionally VWAP deviation.
Because they adapt in real time to both volatility and VWAP pressure, their movement, slope, and reaction speed differ significantly from traditional moving averages.
🔶 1. ATR-Adaptive Moving Averages
Each of the three MAs uses a custom adaptive formula:
ATR (Average True Range) is measured over a chosen period.
Higher ATR → more volatility → the MA becomes more reactive and moves closer to price.
Lower ATR → stable market → the MA becomes smoother and slower.
This creates a volatility-aware smoothing factor, making the MA expand, contract, and respond to market conditions in ways a classic SMA, EMA, or HMA cannot.
🔷 2. Optional VWAP Influence
Each MA has an independent toggle allowing it to be influenced by VWAP.
When enabled:
The MA is gently “pulled” toward VWAP.
The strength of this attraction is determined by the VWAP Influence parameter (0–1).
This causes the moving averages to behave differently from normal MAs:
In trending markets, the ATR and price push the MA away from VWAP.
In mean-reverting or balanced conditions, VWAP pulls the MA back toward fair value.
The result is an MA that reflects both trend pressure and fair-value pressure.
🔶 3. Visual Behavior: Non-Traditional Movement
Because each MA is simultaneously influenced by volatility, trend magnitude, and VWAP deviation, their shape is often very distinct from normal moving averages.
They may:
Respond faster during high volatility
Flatten out earlier during consolidation
Curve toward VWAP when price becomes extended
Separate or compress depending on ATR strength
This is intentional and essential, since the goal is to show:
✔ Volatility expansion
✔ Trend exhaustion
✔ Overextended price relative to VWAP
✔ Dynamic trend confirmation
Rather than simply smoothing past price.
🔷 4. Three Independent Adaptive Lines
Each of the three moving averages has:
Its own ATR length
Its own sensitivity multiplier
Its own optional VWAP influence
Its own color and trail
This allows the user to combine:
a fast volatility-adaptive trend line
a mid-range adaptive baseline
a slow adaptive long-trend MA
All adapting independently to volatility and VWAP conditions.
🔶 5. Optional Candle Coloring
The indicator can color candles according to trend strength derived from the fast/slow MAs.
Stronger trends produce more vivid colors. Neutral or conflicting trends produce softer colors.
This adds a visual layer to identify:
Trend direction
Trend strength
Volatility state
Market compression
at a glance.
📌 Summary
This indicator does not behave like standard SMAs or EMAs because each line dynamically adapts to:
🔸 ATR (volatility)
🔸 VWAP (fair value)
This makes the indicator extremely responsive to market conditions while still reducing noise during stable phases.
It provides a more realistic, context-aware, and intelligent representation of price behavior compared to traditional moving averages.
ATR + BJ Signal（GOLD）This script visualizes a price-based counting pattern that highlights potential market exhaustion and reversal areas.
When a series of candles continues in one direction, the indicator measures price momentum loss and marks possible turning points.
Features
Counts consecutive upward or downward price movement
Highlights possible exhaustion or reversal areas
Optional alerts, take-profit and stop-loss visual levels
Fully customizable colors and display settings
Useful as a confirmation tool with trend or volume indicators
This indicator is designed to assist decision-making, not to generate mechanical buy/sell signals.
Best used together with other trend or volatility tools.
📎 Short Description (for compact field)
Counts consecutive price movement to highlight potential market exhaustion and reversal zones.
Helps identify when strong trends may be weakening.
Macro-Sentiment (Macro_Serie 1:7)Part of a 7-indicator macro series. Combines yield curve dynamics, VIX structure, employment data (jobless claims, NFP), ISM manufacturing, US-Japan carry trade flows, and consumer sentiment into a single adaptive stress score. Color-coded regimes guide strategy from "Aggressive" to "Buy the Crash."
8EMA+BB-SubiProvides the facility to display 8 EMAs along with Bollinger Bands in the same indicator.
CRR Darvas Nemesis SCALP 1m–5m v1.5CRR Darvas Nemesis Scalp 1m–5m – What it is and how it works
The CRR Darvas Nemesis Scalp is an indicator designed to help you detect strong and reliable breakouts in scalping, using a smart Darvas box with professional confirmations.
What does it do?
It automatically draws a "Darvas Box" on a higher timeframe (usually 5 minutes).
This box represents an area where the price has been accumulating or consolidating.
It detects the actual breakout of that box only when:
There is strong volume (higher than average).
The trend is favorable (measured with the EMA of the higher timeframe).
It provides A+ entry and exit signals for scalping trades:
✔️ LONG A+ when it breaks the top of the box with volume + trend.
❌ EXIT when it breaks below the bottom of the box.
Includes a professional HUD with:
Current status (In box, Breakout A+, Exit, etc.)
Box High / Box Low
Suggested Stop (bottom of the box)
Volume strength
📌 How to get the most out of it? (SUMMARY)
1. Wait for the Darvas Box to form
The box marks the accumulation zone.
While the price is inside: DO NOT trade yet.
2. Only look for movement when the box is broken
The upward breakout is only valid if:
There is high volume
The trend is aligned
The indicator already filters this for you → it shows you LONG A+.
3. Enter only on A+ signals (the strongest ones)
The green triangle indicates:
Legitimate breakout
High volume
Favorable trend
This is the highest probability entry.
4. Use the bottom of the box as a stop
Each LONG signal automatically comes with a suggested stop:
Stop = bottom of the Darvas Box
Simple, clear, and professional.
5. Exit the trade when the system indicates EXIT
If a red triangle or "Long Exit" text appears, it means:
The breakout failed or ran out of momentum
It's time to close the trade
📌 In short
This indicator allows you to identify real breakouts and avoid traps.
It filters trend, volume, and structure to give you only A+ signals. Ideal for fast and precise scalping on 1m–5m timeframes.
Scary Flush Indicator R0Work in progress.
Calculates the gradient based on candle lows (previous low to current low). Works on all time frames.
Looks for a selling gradient of >0.75pts per minute then highlights. Anything less than this indicates a lazy grind down and indicates a potential invalidation for the FBD.
BTC - FRIC: Friction & Realized Intensity CompositeTitle: BTC - FRIC: Friction & Realized Intensity Composite
Data: IntoTheBlock
Overview & Philosophy
FRIC (Friction & Realized Intensity Composite) is a specialized on-chain oscillator designed to visualize the "psychological battlegrounds" of the Bitcoin network.
Most indicators focus on Price or Momentum. FRIC focuses on Cost Basis. It operates on the thesis that the market experiences maximum "Friction" when the price revisits the cost basis of a large number of holders. These are the zones where investors are emotionally triggered to react—either to exit "at breakeven" after a loss (creating resistance) or to defend their entry (creating support).
This indicator answers two questions simultaneously:
Intensity: Is the market hitting a Wall (High Friction) or a Vacuum (Low Friction)?
Valuation: Is this happening at a market bottom or a top?
The "Alpha" (Wall vs. Vacuum)
Why we visualize both extremes: This indicator filters out the "Noise" (the middle range) to show you only the statistically significant anomalies.
1. The "Wall" (Positive Z-Score Bars)
What it is : A statistically high number of addresses are at breakeven.
The Implication : Expect a grind. Price action often slows down or reverses here because "Bag Holders" are selling into strength to get out flat, or new buyers are establishing a floor.
2. The "Vacuum" (Negative Z-Score Bars)
What it is : A statistically low number of addresses are at breakeven.
The Implication : Expect acceleration. The price is moving through a zone where very few people have a cost basis. With no natural "breakeven supply" to block the path, price often enters Price Discovery or Free Fall.
Methodology
The indicator constructs a composite view using two premium metrics from IntoTheBlock:
1. The "Activity" (Friction Z-Score): We utilize the Breakeven Addresses Percentage. This measures the % of all addresses where the current price equals the average cost basis.
- Normalization: We apply a rolling Z-Score (Standard Deviation) to this data.
- The Filter: We hide the "Noise" (e.g., Z-Scores between -2.0 and +2.0) to isolate only the events where market structure is truly stretched.
2. The "Context" (Valuation Heatmap): We utilize the MVRV Ratio to color-code the friction.
Deep Value (< 1.0): Price is below the average "Fair Value" of the network.
Overheated (> 3.0): Price is significantly extended above the "Fair Value."
Credit: The MVRV Ratio was originally conceptualized by Murad Mahmudov and David Puell. It remains one of the gold standards for detecting Bitcoin's fair value deviations.
How to Read the Indicator
The chart is visualized as a Noise-Filtered Heatmap.
1. The Bars (Intensity)
Bars Above Zero: High Friction (Congestion). The market is fighting through a supply wall.
Bars Below Zero: Low Friction (Vacuum). The market is accelerating through thin air.
Gray/Ghosted: Noise. Routine market activity; no significant signal.
2. The Colors (Valuation Context) The color tells you why the friction is happening:
🟦 Deep Blue (The "Capitulation Buy"):
Signal: High Friction + Low MVRV.
Meaning : Investors are panic-selling at breakeven/loss, but the asset is fundamentally undervalued. Historically, these are high-conviction cycle bottoms.
🟥 Dark Red (The "FOMO Sell"):
Signal: High Friction + High MVRV.
Meaning : Investors are churning at high valuations. Smart money is often distributing to late retail arrivers. Historically marks cycle tops.
🟨 Yellow/Orange (The "Trend Battle"):
Signal: High Friction + Neutral MVRV.
Meaning : The market is contesting a level within a trend (e.g., a mid-cycle correction).
Visual Guide & Features
10-Zone Heatmap: A granular color gradient that shifts from Dark Blue (Deep Value) → Sky Blue → Grey (Neutral) → Orange → Dark Red (Top).
Noise Filter
A unique feature that "ghosts out" insignificant data, leaving only the statistically relevant signals visible.
Data Check Monitor
A diagnostic table in the bottom-right corner that confirms the live connection to IntoTheBlock data streams and displays the current regime in real-time.
Settings
Lookback Period (Default: 90): The rolling window used for the Z-Score calculation. Shortening this (e.g., to 30) makes the indicator more sensitive to local volatility; lengthening it (e.g., to 365) aligns it with macro cycles.
Noise Threshold (Default: 2.0): The strictness of the filter. Only friction events exceeding this Z-Score will be highlighted in full color.
Show Status Table : Toggles the on-screen dashboard.
Disclaimer
This script is for research and educational purposes only. It relies on third-party on-chain data which may be subject to latency or revision. Past performance of on-chain metrics does not guarantee future price action.
Tags
bitcoin, btc, on-chain, mvrv, intotheblock, friction, z-score, fundamental, valuation, cycle
Realized Volatility — Wang Shi JieA realized volatility indicator based on return standard deviation. Displays volatility for either a selected date range or the latest N bars, helping identify periods of strong or weak price movement.”**
Trend & Pullback Cycle How to use.
Trend Identification:
Green Columns: The cycle is above 50. Look for Longs.
Red Columns: The cycle is below 50. Look for Shorts.
Pullback Detection:
I added a Colour Change feature. If the Green bars turn Dark Green, it means momentum is fading (a pullback is happening). This is your signal to get ready to enter or add to a position once it turns Bright Green again.
The Yellow Line:
This is your trigger. In the screenshot, you see the bars cross the yellow line.
Entry Signal: When the Histogram crosses above the Yellow line (while generally green) or crosses below it (while generally red).
AlphaStrike: Volatility & Pinbar Reversion SystemDescription:
The Concept: Solving the "Context" Problem One of the hardest challenges in trading is identifying whether the market is in a "Trend State" or a "Mean Reversion State." Using trend indicators in a range leads to false breakouts, while using reversal indicators in a strong trend leads to catching falling knives.
This script solves this issue by combining an ATR-based Trend Filter with a conditional Price Action Reversion engine. It does not simply overlay two indicators; it uses a filtering logic to ensure that Reversal signals are only generated when Momentum, Volatility, and Candle Geometry all align at the same time.
How It Works (The Logic) This script functions as a "Hybrid" system with two distinct engines running simultaneously:
1. The Trend Engine (Bias Filter) We use an ATR-based SuperTrend calculation to determine the dominant market direction.
Purpose: This acts as a "No Trade Zone" filter.
Logic: If the Trend Line is Green, the statistical bias is bullish. If Red, the bias is bearish. This helps traders avoid shorting strong uptrends or buying weak downtrends.
2. The Reversal Engine (Signal Generator) This is where the script differentiates itself from standard "Bollinger + RSI" mashups. A signal is NOT generated just because price hits a band. The script requires a specific "Pinbar" candle pattern to validate the move.
The "Blue Dot" (Bullish Reversal) Logic:
Condition A: Price must be below the Lower Bollinger Band (2 Standard Deviations).
Condition B: RSI (14) must be Oversold (< 35).
Condition C (The Filter): The candle must form a Bullish Pinbar. The script calculates the ratio of the lower wick to the body. If the wick is 2x longer than the body, it confirms that buyers actively rejected the lower prices.
The "Orange Dot" (Bearish Reversal) Logic:
Condition A: Price must be above the Upper Bollinger Band.
Condition B: RSI (14) must be Overbought (> 65).
Condition C (The Filter): The candle must form a Bearish Pinbar (long upper wick), indicating buyer exhaustion.
Visual Guide & Usage
Green/Red Line: Use this to trail your Stop Loss or determine trend direction.
Triangles (Breakouts): These marks indicate a shift in volatility where the trend officially flips.
Dots (Reversals): These are high-probability zones for scalps or entering on pullbacks.
Built-In Risk Management To assist with position sizing, a "Smart Risk" table is included in the bottom right corner.
It automatically detects the nearest market structure (Swing Highs/Lows).
It calculates the distance from the current price to that structure.
It displays the suggested position size to maintain a fixed risk percentage (configurable in Settings).
Note: You must input your Account Balance in the settings for this to work.
Settings
Crypto: Default settings (Factor 3.5) are optimized for high-volatility assets like BTC/ETH to reduce noise.
TradFi: For Forex or Stocks, consider lowering the Factor to 3.0.
Disclaimer This tool is designed for educational analysis and risk management assistance. It does not constitute financial advice. Past performance of signals (like those shown on the chart) does not guarantee future results. Always manage your risk.
RV − IV Spread Alert (SPY vs VIX)Realized vs Implied Volatility Spread (RV − IV) for the S&P 500 / SPY.
Plots the daily difference between 30-day realized volatility (SPY) and implied volatility (VIX) in basis points.
Key insight from the research: when the spread turns and stays above ≈ +50 bps, forward returns historically degrade and volatility of returns rises sharply — a useful early-warning regime flag.
Features:
- Clean daily plot of RV − IV in bps
- Horizontal lines at 0, −50 bps and +50 bps
- Red background when spread > +50 bps
- Built-in alert condition that fires once per bar close when spread closes above +50 bps
- Optional “all-clear” alert when it drops back below
Use on SPY or ES1! daily chart. Perfect for anyone wanting a simple notification when the market enters the “risk-on” volatility regime highlighted by Machina Quanta and the original Bali & Hovakimian (2007) paper.
Value Charts by Mark Helweg1. Introduction
This script is a simplified implementation of the Value Charts concept introduced by Mark Helweg and David Stendahl in their work on “Dynamic Trading Indicators”. It converts raw price into value units by normalizing distance from a dynamic fair‑value line, making it easier to see when price is relatively overvalued or undervalued across different markets and timeframes. The code focuses on plotting Value Chart candlesticks and clean visual bands, keeping the logic close to the original idea while remaining lightweight for intraday and swing trading.
2. Key Features
- Dynamic fair‑value axis
Uses a moving average of the chosen price source as the fair‑value line and a volatility‑based deviation (smoothed True Range) to scale all price moves into comparable value units.
- Normalized Value Chart candlesticks
OHLC prices are transformed into value units and displayed as a dedicated candlestick panel, visually similar to standard candles but detached from raw price, highlighting relative extremes instead of absolute levels.
- Custom upper and lower visual limits
User‑defined upper and lower bands frame the majority of action and emphasize extreme value zones, helping the trader spot potential exhaustion or mean‑reversion conditions at a glance.
- Clean, publishing‑friendly layout
Only the normalized candles and three simple reference lines (top, bottom, zero) are plotted, keeping the chart uncluttered and compliant with presentation standards for published scripts.
3. How to Use
1. Attach the indicator to a separate pane (overlay = false) on any market and timeframe you trade.
2. Set the “Period (Value Chart)” to control how fast the fair‑value line adapts: shorter values react more quickly, longer values smooth more.
3. Adjust the “Volatility Factor” so that most candles stay between the upper and lower limits, with only true extremes touching or exceeding them.
4. Use the Value Chart candlesticks as a relative overbought/oversold tool:
- Candles pressing into the Top band suggest overvalued conditions and potential for pullbacks or reversions.
- Candles pressing into the Bottom band suggest undervalued conditions and potential for bounces.
5. Combine the signals with your existing price‑action, volume, or trend‑filter rules on the main chart; the Value Chart panel is designed as a context and timing tool, not a standalone trading system.
Combined: Net Volume, RSI & ATR# Combined: Net Volume, RSI & ATR Indicator
## Overview
This custom TradingView indicator overlays **Net Volume** and **RSI (Relative Strength Index)** on the same chart panel, with RSI scaled to match the visual range of volume spikes. It also displays **ATR (Average True Range)** values in a table.
## Key Features
### Net Volume
- Calculates buying vs selling pressure by analyzing lower timeframe data
- Displays as a **yellow line** centered around zero
- Automatically selects optimal timeframe or allows manual override
- Shows net buying pressure (positive values) and selling pressure (negative values)
### RSI (Relative Strength Index)
- Traditional 14-period RSI displayed as a **blue line**
- **Overlays directly on the volume chart** - scaled to match volume spike heights
- Includes **70/30 overbought/oversold levels** (shown as dotted red/green lines)
- Adjustable scale factor to fine-tune visual sizing relative to volume
- Optional **smoothing** with multiple moving average types (SMA, EMA, RMA, WMA, VWMA)
- Optional **Bollinger Bands** around RSI smoothing line
- **Divergence detection** - identifies regular bullish/bearish divergences with labels
### ATR (Average True Range)
- Displays current ATR value in a **table at top-right corner**
- Configurable period length (default: 50)
- Multiple smoothing methods: RMA, SMA, EMA, or WMA
- Helps assess current market volatility
## Use Cases
- **Momentum & Volume Confirmation**: See if RSI trends align with net volume flows
- **Divergence Trading**: Automatically spots when price makes new highs/lows but RSI doesn't
- **Volatility Assessment**: Monitor ATR for position sizing and stop-loss placement
- **Overbought/Oversold + Volume**: Identify exhaustion when RSI hits extremes with volume spikes
## Customization
All components can be toggled on/off independently. RSI scale factor allows you to adjust how prominent the RSI line appears relative to volume bars.
Volume profilerMulti-Range Volume Analysis & Absorption Detection
This tool visualises market activity through multi-range volume profiling and absorption signal detection. It helps you quickly identify where volume expands, compresses, or diverges from expected behaviour.
What it does
Volume Profiler plots four volume EMAs (short / mid / long / longer) so you can gauge how current volume compares to different market regimes.
It also highlights structural volume extremes:
• Low-volume bars (liquidity withdrawal)
These are potential signs of exhaustion, pauses, or low liquidity environments.
• High-volume + Low-range absorption
A classic footprint-style signal where aggressive volume fails to move price.
Often seen during:
absorption of one side of the book
liquidity collection
failed breakouts
institutional accumulation/distribution
You can choose:
which EMA defines “high volume”
how to measure candle range (High-Low, True Range, or Body)
how to define baseline volatility (ATR or average range)
Alerts are included so you can monitor absorption automatically.
Features
Multi-range volume EMAs (10 / 50 / 100 / 300 by default)
Low-volume bar flags
Absorption detection based on custom thresholds
Customisable volatility baseline
Optional bar colouring
Labels displayed directly in the volume pane
Alert conditions for absorption events
How to use
This indicator is valuable for:
confirming trend strength or weakness
detecting absorption before reversal or breakout continuation
finding low-liquidity pauses
identifying volume expansion across different time horizons
footprint-style behavioural confirmation without needing order-flow data
Works across all markets and timeframes.
Notes
This script is intended for educational and analytical use.
It does not repaint.
