Hull modelius take profit and stop lossThis model has Hull moving average, fibs in form of Bollinger ,SMA and Modelius model with ATR for buy and sell power based on weis volume. Inside alerts for buy and sell. take profit and stop loss for both longs and shorts
so have fun
Buscar en scripts para "stop loss"
VSA Trade System HeatZone+ and ATR Stop Loss 1001Date Published : 1 Aug 2018 by Martin
VSA Trade System HeatZone + ATR Stop loss 1001, pinescripts encoded Expired 1 Aug 2019. -martin-
The Falcon v2 Long only strategy Using Stop loss and take profitHello,
Here is a backtest result from the beginning of the year on BTC. The white line is the Buy & Hold return.
Comission is set to 0.05% and there is no repainting : the price variable I'm using is heikenashi(tickerid).
The indicator is built upon RSI, EMAs and some other personnal tricks so predict trends.
I coded a stop loss and take profit system : the script will simply buy and sell upon conditions.
As usual I am selling access to the script, If some are interested I will publish an alert setup version. I am also open to development or reverse engineering commissions.
Profit/Stop Loss Pro - CryptoProToolsA quick visualization of where the market needs to move to reach your desired level of profit, plus your stop loss point.
Plan your trade entries and exits better with this easy to use visual indicator.
Adaptive Channels for for initial stop lossThe Adaptive Channels provide an initial stop loss value when a new trade is executed either long or short. They can also be used to enter and exit the trade.
SL + TP Dynamics - By M.LolasStop Loss e Take Profit dinâmicos para operações semiautomatizadas.
By M.Lolas
Stop loss and Take Profit in $$ exampleThis is a simple exit example in $$ (symbol's currency) for educational purpose .
STOP-LOSS-RSI with Edge-Preserving Filter Strategy V2Modified RSI strategy with entry and exit points.
I have allowed for High and Low risk for anyone interested in using it (NO GUARANTEES. DO YOUR OWN RESEARCH).
I will later consider incorporating the standard RSI for exit points. I am sure returns will increase based on current test runs :)
Returns look good based on the strategy result.
Kinetic Elasticity Reversion System - Adaptive Genesis Engine🧬 KERS-AGE - EVOLVED KINETIC ELASTICITY REVERSION SYSTEM
EDUCATIONAL GUIDE & THEORETICAL FOUNDATION
⚠️ IMPORTANT DISCLAIMER
This indicator and guide are provided for educational and informational purposes only. This is NOT financial advice, investment advice, or a recommendation to buy or sell any security.
Trading involves substantial risk of loss. Past performance does not guarantee future results. The performance metrics, win rates, and examples shown are from historical backtesting and do not represent actual trading results. Always conduct your own research, paper trade extensively, and never risk capital you cannot afford to lose.
The developers assume no responsibility for any trading losses incurred through use of this indicator.
INTRODUCTION
KERS-AGE (Kinetic Elasticity Reversion System - Adaptive Genetic Evolution) represents an educational exploration of adaptive trading systems. Unlike traditional indicators with fixed parameters, KERS-AGE demonstrates a dynamic, evolving approach that adjusts to market conditions through genetic algorithms and machine learning techniques.
This guide explains the theoretical concepts, technical implementation, and educational examples of how the system operates.
CONCEPTUAL FRAMEWORK
Traditional Indicators vs. Adaptive Systems:
Traditional Indicators:
Fixed parameters
Single strategy approach
Static behavior
Designed for specific conditions
Require manual optimization
Adaptive System Approach (KERS-AGE):
Dynamic parameters (adjust based on conditions)
Multiple strategies tested simultaneously
Pattern recognition (cluster analysis)
Regime-aware (speciation)
Automated optimization (genetic algorithms)
Transparent operation (detailed dashboard)
CORE CONCEPTS EXPLAINED
1. THE ELASTICITY ANALOGY 🎯
The indicator models price behavior as if connected to a moving average by an elastic band:
Price extends away → Elastic tension builds → Potential reversion point identified
Key Measurements:
STRETCH: Distance from price to equilibrium (MA)
TENSION: Normalized force calculation
THRESHOLD: Point where multiple factors align
Theoretical Foundation:
Markets have historically shown mean-reverting tendencies around fair value. This concept quantifies the deviation and identifies potential reversal zones based on multiple confluence factors.
Mathematical Approach:
text
Tension Score = (Price Distance from MA) / (Band Width) × Volatility Scaling
Signal Threshold = Multiple of ATR × Dynamic Volatility Ratio
Confluence = Tension Score + Additional Factors
2. THE 6 SIGNAL TYPES 📊
The system recognizes 6 distinct pattern categories:
A. ELASTIC SIGNALS
Pattern: Price reaches statistical band extremes
Theory: Maximum deviation from mean suggests potential reversion
Detection: Price touches outer zones (typically 2-3× ATR from MA)
Component: Mathematical band extension measurement
Historical Context: Often observed in markets with clear swing patterns
B. WICK SIGNALS
Pattern: Extended rejection wicks on candles
Theory: Failed breakout attempts may indicate directional exhaustion
Detection: Upper/lower wick exceeding 2× body size
Component: Real-time price rejection measurement
Historical Context: Common in volatile conditions with rapid reversals
C. EXHAUSTION SIGNALS
Pattern: Decelerating momentum despite price extension
Theory: Velocity and acceleration divergence may precede reversals
Detection: Decreasing velocity with negative acceleration
Component: Momentum derivative analysis
Historical Context: Often seen at trend maturity points
D. CLIMAX SIGNALS
Pattern: Volume spike at price extreme
Theory: Unusual volume at extremes historically correlates with turning points
Detection: Volume 1.5-2.5× average at band extreme
Component: Volume-price relationship analysis
Historical Context: Associated with institutional activity or capitulation
E. STRUCTURE SIGNALS
Pattern: Fractal pivot formations (swing highs/lows)
Theory: Market structure points have historically acted as support/resistance
Detection: 2-4 bar pivot patterns
Component: Classical technical analysis
Historical Context: Universal across timeframes and markets
F. DIVERGENCE SIGNALS
Pattern: RSI divergence versus price
Theory: Momentum divergence has historically preceded price reversals
Detection: Price makes new extreme but RSI does not
Component: Oscillator divergence detection
Historical Context: Considered a leading indicator in technical analysis
Pattern Confluence:
Historical testing suggests stronger signals when multiple types align:
Elastic + Wick + Volume = Higher confluence score
Elastic + Exhaustion + Divergence = Multiple confirmation factors
Any 3+ types = Increased pattern strength
Note: Past pattern performance does not guarantee future occurrence.
3. REGIME DETECTION 🌍
The system attempts to classify market conditions into three behavioral regimes:
📈 TREND REGIME
Detection Methodology:
text
Efficiency Ratio = Net Movement / Total Movement
Classification: Efficiency > 0.5 AND Volatility < 1.3 → TREND
Characteristics Observed:
Directional price movement
Relatively lower volatility
Defined higher highs/lower lows
Persistent directional momentum
System Response:
Reduces signal frequency
Prioritizes trend-specialist strategies
Applies additional filtering to counter-trend signals
Increases confluence requirements
Educational Note:
In trending conditions, counter-trend mean reversion signals historically have shown reduced reliability. Users may consider additional confirmation when trend regime is detected.
↔️ RANGE REGIME
Detection Methodology:
text
Classification: Efficiency < 0.5 AND Volatility 0.9-1.4 → RANGE
Characteristics Observed:
Oscillating price action
Defined support/resistance zones
Mean-reverting behavior patterns
Relatively balanced directional flow
System Response:
Increases signal frequency
Activates range-specialist strategies
Adjusts bands relative to volatility
Reduces confluence threshold
Educational Note:
Historical backtesting suggests mean reversion systems have performed better in ranging conditions. This does not guarantee future performance.
🌊 VOLATILE REGIME
Detection Methodology:
text
Classification: DVS (Dynamic Volatility Scaling) > 1.5 → VOLATILE
Characteristics Observed:
Erratic price swings
Expanded ranges
Elevated ATR readings
Often news or event-driven
System Response:
Activates volatility-specialist strategies
Widens bands automatically
Prioritizes wick rejection signals
Emphasizes volume confirmation
Educational Note:
Volatile conditions historically present both opportunity and increased risk. Wider stops may be appropriate for risk management.
4. GENETIC EVOLUTION EXPLAINED 🧬
The system employs genetic algorithms to optimize parameters - an approach used in computational finance research.
The Evolution Process:
STEP 1: INITIALIZATION
text
Initial State: System creates 4 starter strategies
- Strategy 0: Range-optimized parameters
- Strategy 1: Trend-optimized parameters
- Strategy 2: Volatility-optimized parameters
- Strategy 3: Balanced parameters
Each contains 14 adjustable parameters (genes):
- Band sensitivity
- Extension multiplier
- Wick threshold
- Momentum threshold
- Volume multiplier
- Component weights (elastic, wick, momentum, volume, fractal)
- Target percentage
STEP 2: COMPETITION (Shadow Trading)
text
Early Bars: All strategies generate signals in parallel
- Each tracks hypothetical performance independently
- Simulated P&L, win rate, Sharpe ratio calculated
- No actual trades executed (educational simulation)
- Performance metrics recorded for analysis
STEP 3: FITNESS EVALUATION
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Fitness Calculation =
0.25 × Win Rate +
0.25 × PnL Score +
0.15 × Drawdown Score +
0.30 × Sharpe Ratio Score +
0.05 × Trade Count Score
With Walk-Forward enabled:
Fitness = 0.60 × Test Score + 0.40 × Train Score
With Speciation enabled:
Fitness adjusted by Diversity Penalty
STEP 4: SELECTION (Tournament)
text
Periodically (default every 50 bars):
- Randomly select 4 active strategies
- Compare fitness scores
- Top 2 selected as "parents"
STEP 5: CROSSOVER (Breeding)
text
Parent 1 Fitness: 0.65
Parent 2 Fitness: 0.55
Weight calculation: 0.65/(0.65+0.55) = 54%
For each parameter:
Child Parameter = (0.54 × Parent1) + (0.46 × Parent2)
Example:
Band Sensitivity: (0.54 × 1.5) + (0.46 × 2.0) = 1.73
STEP 6: MUTATION
text
For each parameter:
if random(0-1) < Mutation Rate (default 0.15):
Add random variation: -12% to +12%
Purpose: Prevents premature convergence
Enables: Discovery of novel parameter combinations
ADAPTIVE MUTATION:
If population fitness converges → Mutation rate × 1.5
(Encourages exploration when diversity decreases)
STEP 7: INSERTION
text
New strategy added to population:
- Assigned unique ID number
- Generation counter incremented
- Begins shadow trading
- Competes with existing strategies
STEP 8: CULLING (Selection Pressure)
text
Periodically (default every 100 bars):
- Identify lowest fitness strategy
- Verify not elite (protected top performers)
- Verify not last of species
- Remove from population
Result: Maintains selection pressure
Effect: Prevents weak strategies from diluting signals
STEP 9: SIGNAL GENERATION LOGIC
text
When determining signals to display:
If Ensemble enabled:
- All strategies cast weighted votes
- Weights based on fitness scores
- Specialists receive boost in matching regime
- Signal generated if consensus threshold reached
If Ensemble disabled:
- Single highest-fitness strategy used
STEP 10: ADAPTATION OBSERVATION
text
Over time: Population characteristics may shift
- Lower-performing strategies removed
- Higher-performing strategies replicated
- Parameters adjust toward observed optima
- Fitness scores generally trend upward
Long-term: Population reaches maturity
- Strategies become specialized
- Parameters optimized for recent conditions
- Performance stabilizes
Educational Context:
Genetic algorithms are a recognized computational method for optimization problems. This implementation applies those concepts to trading parameter optimization. Past optimization results do not guarantee future performance.
5. SPECIATION (Niche Specialization) 🐟🦎🦅
Inspired by biological speciation theory applied to algorithmic trading.
The Three Species:
RANGE SPECIALISTS 📊
text
Optimized for: Sideways market conditions
Parameter tendencies:
- Tighter bands (1.0-1.5× ATR)
- Higher sensitivity to elastic stretch
- Emphasis on fractal structure
- More frequent signal generation
Typically emerge when:
- Range regime detected
- Clear support/resistance present
- Mean reversion showing historical success
Historical backtesting observations:
- Win rates often in 55-65% range
- Smaller reward/risk ratios (0.5-1.5R)
- Higher trade frequency
TREND SPECIALISTS 📈
text
Optimized for: Directional market conditions
Parameter tendencies:
- Wider bands (2.0-2.5× ATR)
- Focus on momentum exhaustion
- Emphasis on divergence patterns
- More selective signal generation
Typically emerge when:
- Trend regime detected
- Strong directional movement observed
- Counter-trend exhaustion signals sought
Historical backtesting observations:
- Win rates often in 40-55% range
- Larger reward/risk ratios (1.5-3.0R)
- Lower trade frequency
VOLATILITY SPECIALISTS 🌊
text
Optimized for: High-volatility conditions
Parameter tendencies:
- Expanded bands (1.5-2.0× ATR)
- Priority on wick rejection patterns
- Strong volume confirmation requirement
- Very selective signals
Typically emerge when:
- Volatile regime detected
- High DVS ratio (>1.5)
- News-driven or event-driven conditions
Historical backtesting observations:
- Win rates often in 50-60% range
- Variable reward/risk ratios (1.0-2.5R)
- Opportunistic trade timing
Species Protection Mechanism:
text
Minimum Per Species: Configurable (default 2)
If Range specialists = 1:
→ Preferential spawning of Range type
→ Protection from culling process
Purpose: Ensures coverage across regime types
Theory: Markets cycle between behavioral states
Goal: Prevent extinction of specialized approaches
Fitness Sharing:
text
If Species has 4 members:
Individual Fitness × 1 / (4 ^ 0.3)
Individual Fitness × 0.72
Purpose: Creates pressure toward species diversity
Effect: Prevents single approach from dominating population
Educational Note: Speciation is a theoretical framework for maintaining strategy diversity. Past specialization performance does not guarantee future regime classification accuracy or signal quality.
6. WALK-FORWARD VALIDATION 📈
An out-of-sample testing methodology used in quantitative research to reduce overfitting risk.
The Overfitting Problem:
text
Hypothetical Example:
In-Sample Backtest: 85% win rate
Out-of-Sample Results: 35% win rate
Explanation: Strategy may have optimized to historical noise
rather than repeatable patterns
Walk-Forward Methodology:
Timeline Structure:
text
┌──────────────────────────────────────────────────────┐
│ Train Window │ Test Window │ Train │ Test │
│ (200 bars) │ (50 bars) │ (200) │ (50) │
└──────────────────────────────────────────────────────┘
In-Sample Out-of-Sample IS OOS
(Optimize) (Validate) Cycle 2...
TRAIN PHASE (In-Sample):
text
Example Bars 1-200: Strategies optimize parameters
- Performance tracked
- Not yet used for primary fitness
- Learning period
TEST PHASE (Out-of-Sample):
text
Example Bars 201-250: Strategies use optimized parameters
- Performance tracked separately
- Validation period
- Out-of-sample evaluation
FITNESS CALCULATION EXAMPLE:
text
Train Win Rate: 65%
Test Win Rate: 58%
Composite Fitness:
= (0.40 × 0.65) + (0.60 × 0.58)
= 0.26 + 0.35
= 0.61
Note: Test results weighted 60%, Train 40%
Theory: Out-of-sample may better indicate forward performance
OVERFIT DETECTION MECHANISM:
text
Gap = Train WR - Test WR = 65% - 58% = 7%
If Gap > Overfit Threshold (default 25%):
Fitness Penalty = Gap × 2
Example with 30% gap:
Strategy shows: Train 70%, Test 40%
Gap: 30% → Potential overfit flagged
Penalty: 30% × 2 = 60% fitness reduction
Result: Strategy likely to be culled
WINDOW ROLLING:
text
Example Bar 250: Test window complete
→ Reset both windows
→ Start new cycle
→ Previous results retained for analysis
Cycle Count increments
Historical performance tracked across multiple cycles
Educational Context:
Walk-forward analysis is a recognized approach in quantitative finance research for evaluating strategy robustness. However, past out-of-sample performance does not guarantee future results. Market conditions can change in ways not represented in historical data.
7. CLUSTER ANALYSIS 🔬
An unsupervised machine learning approach for pattern recognition.
The Concept:
text
Scenario: System identifies a price pivot that wasn't signaled
→ Extract pattern characteristics
→ Store features for analysis
→ Adjust detection for similar future patterns
Implementation:
STEP 1: FEATURE EXTRACTION
text
When significant move occurs without signal:
Extract 5-dimensional feature vector:
Feature Vector =
Example:
Observed Pattern:
STEP 2: CLUSTER ASSIGNMENT
text
Compare to existing cluster centroids using distance metric:
Cluster 0:
Cluster 1: ← Minimum distance
Cluster 2:
...
Assign to nearest cluster
STEP 3: CENTROID UPDATE
text
Old Centroid 1:
New Pattern:
Decay Rate: 0.95
Updated Centroid:
= 0.95 × Old + 0.05 × New
= Exponential moving average update
=
STEP 4: PROFIT TRACKING
text
Cluster Average Profit (hypothetical):
Old Average: 2.5R
New Observation: 3.2R
Updated: 0.95 × 2.5 + 0.05 × 3.2 = 2.535R
STEP 5: LEARNING ADJUSTMENT
text
If Cluster Average Profit > Threshold (e.g., 2.0R):
Cluster Learning Boost += increment (e.g., 0.1)
(Maximum cap: 2.0)
Effect: Future signals resembling this cluster receive adjustment
STEP 6: SCORE MODIFICATION
text
For signals matching cluster characteristics:
Base Score × Cluster Learning Boost
Example:
Base Score: 5.2
Cluster Boost: 1.3
Adjusted Score: 5.2 × 1.3 = 6.76
Result: Pattern more likely to generate signal
Cluster Interpretation Example:
text
CLUSTER 0: "High elastic, low volume"
Centroid:
Avg Profit: 3.5R (historical backtest)
Interpretation: Pure elastic signals in ranges historically favorable
CLUSTER 1: "Wick rejection, volatile"
Centroid:
Avg Profit: 2.8R (historical backtest)
Interpretation: Wick signals in volatility showed positive results
CLUSTER 2: "Exhaustion divergence"
Centroid:
Avg Profit: 4.2R (historical backtest)
Interpretation: Momentum exhaustion in trends performed well
Learning Progress Metrics:
text
Missed Total: 47
Clusters Updated: 142
Patterns Learned: 28
Interpretation:
- System identified 47 significant moves without signals
- Clusters updated 142 times (incremental refinement)
- Made 28 parameter adjustments
- Theoretically improving pattern recognition
Educational Note: Cluster analysis is a recognized machine learning technique. This implementation applies it to trading pattern recognition. Past cluster performance does not guarantee future pattern profitability or accurate classification.
8. ENSEMBLE VOTING 🗳️
A collective decision-making approach common in machine learning.
The Wisdom of Crowds Concept:
text
Single Model:
- May have blind spots
- Subject to individual bias
- Limited perspective
Ensemble of Models:
- Blind spots may offset
- Biases may average out
- Multiple perspectives considered
Implementation:
STEP 1: INDIVIDUAL VOTES
text
Example Bar 247:
Strategy 0 (Range): LONG (fitness: 0.65)
Strategy 1 (Trend): FLAT (fitness: 0.58)
Strategy 2 (Volatile): LONG (fitness: 0.52)
Strategy 3 (Balanced): SHORT (fitness: 0.48)
Strategy 4 (Range): LONG (fitness: 0.71)
Strategy 5 (Trend): FLAT (fitness: 0.55)
STEP 2: WEIGHT CALCULATION
text
Base Weight = Fitness Score
If strategy's species matches current regime:
Weight × Specialist Boost (configurable, default 1.5)
If strategy has recent positive performance:
Weight × Recent Performance Factor
Example for Strategy 0:
Base: 0.65
Range specialist in Range regime: 0.65 × 1.5 = 0.975
Recent performance adjustment: 0.975 × 1.13 = 1.10
STEP 3: WEIGHTED TALLYING
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LONG votes:
S0: 1.10 + S2: 0.52 + S4: 0.71 = 2.33
SHORT votes:
S3: 0.48 = 0.48
FLAT votes:
S1: 0.58 + S5: 0.55 = 1.13
Total Weight: 2.33 + 0.48 + 1.13 = 3.94
STEP 4: CONSENSUS CALCULATION
text
LONG %: 2.33 / 3.94 = 59.1%
SHORT %: 0.48 / 3.94 = 12.2%
FLAT %: 1.13 / 3.94 = 28.7%
Minimum Consensus Setting: 60%
Result: NO SIGNAL (59.1% < 60%)
STEP 5: SIGNAL DETERMINATION
text
If LONG % >= Min Consensus:
→ Display LONG signal
→ Show consensus percentage in dashboard
If SHORT % >= Min Consensus:
→ Display SHORT signal
If neither threshold reached:
→ No signal displayed
Practical Examples:
text
Strong Consensus (85%):
5 strategies LONG, 0 SHORT, 1 FLAT
→ High agreement among models
Moderate Consensus (62%):
3 LONG, 2 SHORT, 1 FLAT
→ Borderline agreement
No Consensus (48%):
3 LONG, 2 SHORT, 1 FLAT
→ Insufficient agreement, no signal shown
Educational Note: Ensemble methods are widely used in machine learning to improve model robustness. This implementation applies ensemble concepts to trading signals. Past ensemble performance does not guarantee future signal quality or profitability.
9. THOMPSON SAMPLING 🎲
A Bayesian reinforcement learning technique for balancing exploration and exploitation.
The Exploration-Exploitation Dilemma:
text
EXPLOITATION: Use what appears to work
Benefit: Leverages observed success patterns
Risk: May miss better alternatives
EXPLORATION: Try less-tested approaches
Benefit: May discover superior methods
Risk: May waste resources on inferior options
Thompson Sampling Solution:
STEP 1: BETA DISTRIBUTIONS
text
For each signal type, maintain:
Alpha = Successes + 1
Beta = Failures + 1
Example for Elastic signals:
15 wins, 10 losses
Alpha = 16, Beta = 11
STEP 2: PROBABILITY SAMPLING
text
Rather than using simple Win Rate = 15/25 = 60%
Sample from Beta(16, 11) distribution:
Possible samples: 0.55, 0.62, 0.58, 0.64, 0.59...
Rationale: Incorporates uncertainty
- Type with 5 trades: High uncertainty, wide sample variation
- Type with 50 trades: Lower uncertainty, narrow sample range
STEP 3: TYPE PRIORITIZATION
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Example Bar 248:
Elastic sampled: 0.62
Wick sampled: 0.58
Exhaustion sampled: 0.71 ← Highest this sample
Climax sampled: 0.52
Structure sampled: 0.63
Divergence sampled: 0.45
Exhaustion type receives temporary boost
STEP 4: SIGNAL ADJUSTMENT
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If current signal is Exhaustion type:
Score × (0.7 + 0.71 × 0.6)
Score × 1.126
If current signal is other type with lower sample:
Score × (0.7 + sample × 0.6)
(smaller adjustment)
STEP 5: OUTCOME FEEDBACK
text
When trade completes:
If WIN:
Alpha += 1
(Beta unchanged)
If LOSS:
Beta += 1
(Alpha unchanged)
Effect: Shifts probability distribution for future samples
Educational Context:
Thompson Sampling is a recognized Bayesian approach to the multi-armed bandit problem. This implementation applies it to signal type selection. The mathematical optimality assumes stationary distributions, which may not hold in financial markets. Past sampling performance does not guarantee future type selection accuracy.
10. DYNAMIC VOLATILITY SCALING (DVS) 📉
An adaptive approach where parameters adjust based on current vs. baseline volatility.
The Adaptation Problem:
text
Fixed bands (e.g., always 1.5 ATR):
In low volatility environment (vol = 0.5):
Bands may be too wide → fewer signals
In high volatility environment (vol = 2.0):
Bands may be too tight → excessive signals
The DVS Approach:
STEP 1: BASELINE ESTABLISHMENT
text
Calculate volatility over baseline period (default 100 bars):
Method options: ATR / Close, Parkinson, or Garman-Klass
Example average volatility = 1.2%
This represents "normal" for recent conditions
STEP 2: CURRENT VOLATILITY
text
Current bar volatility = 1.8%
STEP 3: DVS RATIO
text
DVS Ratio = Current / Baseline
= 1.8 / 1.2
= 1.5
Interpretation: Volatility currently 50% above baseline
STEP 4: BAND ADJUSTMENT
text
Base Band Width: 1.5 ATR
Adjusted Band Width:
Upper: 1.5 × DVS = 1.5 × 1.5 = 2.25 ATR
Lower: Same
Result: Bands expand 50% to accommodate higher volatility
STEP 5: THRESHOLD ADJUSTMENT
text
Base Thresholds:
Wick: 0.15
Momentum: 0.6
Adjusted:
Wick: 0.15 / DVS = 0.10 (easier to trigger in high vol)
Momentum: 0.6 × DVS = 0.90 (harder to trigger in high vol)
DVS Calculation Methods:
text
ATR RATIO (Simplest):
DVS = (ATR / Close) / SMA(ATR / Close, 100)
PARKINSON (Range-based):
σ = √(∑(ln(H/L))² / (4×n×ln(2)))
DVS = Current σ / Baseline σ
GARMAN-KLASS (Comprehensive):
σ = √(0.5×(ln(H/L))² - (2×ln(2)-1)×(ln(C/O))²)
DVS = Current σ / Baseline σ
ENSEMBLE (Robust):
DVS = Median(ATR_Ratio, Parkinson, Garman_Klass)
Educational Note: Dynamic volatility scaling is an approach to normalize indicators across varying market conditions. The effectiveness depends on the assumption that recent volatility patterns continue, which is not guaranteed. Past volatility adjustment performance does not guarantee future normalization accuracy.
11. PRESSURE KERNEL 💪
A composite measurement attempting to quantify directional force beyond simple price movement.
Components:
1. CLOSE LOCATION VALUE (CLV)
text
CLV = ((Close - Low) - (High - Close)) / Range
Examples:
Close at top of range: CLV = +1.0 (bullish position)
Close at midpoint: CLV = 0.0 (neutral)
Close at bottom: CLV = -1.0 (bearish position)
2. WICK ASYMMETRY
text
Wick Pressure = (Lower Wick - Upper Wick) / Range
Additional factors:
If Lower Wick > Body × 2: +0.3 (rejection boost)
If Upper Wick > Body × 2: -0.3 (rejection penalty)
3. BODY MOMENTUM
text
Body Ratio = Body Size / Range
Body Momentum = Close > Open ? +Body Ratio : -Body Ratio
Strong bullish candle: +0.9
Weak bullish candle: +0.2
Doji: 0.0
4. PATH ESTIMATE
text
Close Position = (Close - Low) / Range
Open Position = (Open - Low) / Range
Path = Close Position - Open Position
Additional adjustments:
If closed high with lower wick: +0.2
If closed low with upper wick: -0.2
5. MOMENTUM CONFIRMATION
text
Price Change / ATR
Examples:
+1.5 ATR move: +1.0 (capped)
+0.5 ATR move: +0.5
-0.8 ATR move: -0.8
COMPOSITE CALCULATION:
text
Pressure =
CLV × 0.25 +
Wick Pressure × 0.25 +
Body Momentum × 0.20 +
Path Estimate × 0.15 +
Momentum Confirm × 0.15
Volume context applied:
If Volume > 1.5× avg: × 1.3
If Volume < 0.5× avg: × 0.7
Final smoothing: 3-period EMA
Pressure Interpretation:
text
Pressure > 0.3: Suggests buying pressure
→ May support LONG signals
→ May reduce SHORT signal strength
Pressure < -0.3: Suggests selling pressure
→ May support SHORT signals
→ May reduce LONG signal strength
-0.3 to +0.3: Neutral range
→ Minimal directional bias
Educational Note: The Pressure Kernel is a custom composite indicator combining multiple price action metrics. These weightings are theoretical constructs. Past pressure readings do not guarantee future directional movement or signal quality.
USAGE GUIDE - EDUCATIONAL EXAMPLES
Getting Started:
STEP 1: Add Indicator
Open TradingView
Add KERS-AGE to chart
Allow minimum 100 bars for initialization
Verify dashboard displays Gen: 1+
STEP 2: Initial Observation Period
text
First 200 bars:
- System is in learning phase
- Signal frequency typically low
- Population evolution occurring
- Fitness scores generally increasing
Recommendation: Observe without trading during initialization
STEP 3: Signal Evaluation Criteria
text
Consider evaluating signals based on:
- Confidence percentage
- Grade assignment (A+, A, B+, B, C)
- Position within bands
- Historical win rate shown in dashboard
- Train vs. Test performance gap
Example Signal Evaluation Checklist:
Educational Criteria to Consider:
Signal appeared (⚡ arrow displayed)
Confidence level meets personal threshold
Grade meets personal quality standard
Ensemble consensus (if enabled) meets threshold
Historical win rate acceptable
Test performance reasonable vs. Train
Price location at band extreme
Regime classification appropriate for strategy
If trending: Signal direction aligns with personal analysis
Stop loss distance acceptable for risk tolerance
Position size appropriate (example: 1-2% account risk)
Note: This is an educational checklist, not trading advice. Users should develop their own criteria based on personal risk tolerance and strategy.
Risk Management Educational Examples:
POSITION SIZING EXAMPLE:
text
Hypothetical scenario:
Account: $10,000
Risk tolerance: 1.5% per trade = $150
Indicated stop distance: 1.5 ATR = $300 per contract
Calculation: $150 / $300 = 0.5 contracts
This is an educational example only, not a recommendation.
STOP LOSS EXAMPLES:
text
System provides stop level (red line)
Typically calculated as 1.5 ATR from entry
Alternative approaches users might consider:
LONG: Below recent swing low
SHORT: Above recent swing high
Users should determine stops based on personal risk management.
TAKE PROFIT EXAMPLES:
text
System provides target level (green line)
Typically calculated as price stretch × 60%
Alternative approaches users might consider:
Scale out: Partial exit at 1R, remainder at 2R
Trailing stop: Adjust stop after profit threshold
Users should determine targets based on personal strategy.
Educational Note: These are theoretical examples for educational purposes. Actual position sizing and risk management should be determined by each user based on their individual risk tolerance, account size, and trading plan.
OPTIMIZATION BY MARKET TYPE - EDUCATIONAL SUGGESTIONS
RANGE-BOUND MARKETS
Suggested Settings for Testing:
Population Size: 6-8
Min Confluence: 5.0-6.0
Min Consensus: 70%
Enable Speciation: Consider enabling
Min Per Species: 2
Theoretical Rationale:
More strategies may provide better coverage
Moderate confluence may generate more signals
Higher consensus may filter quality
Speciation may encourage range specialist emergence
Historical Backtest Observations:
Win rates in testing: Varied, often 50-65% range
Reward/risk ratios observed: 0.5-1.5R
Signal frequency: Relatively frequent
Disclaimer: Past backtesting results do not guarantee future performance.
TRENDING MARKETS
Suggested Settings for Testing:
Population Size: 4-5
Min Confluence: 6.0-7.0
Consider enabling MTF filter
MTF Timeframe: 3-5× current timeframe
Specialist Boost: 1.8-2.0
Theoretical Rationale:
Fewer strategies may adapt faster
Higher confluence may filter counter-trend noise
MTF may reduce counter-trend signals
Specialist boost may prioritize trend specialists
Historical Backtest Observations:
Win rates in testing: Varied, often 40-55% range
Reward/risk ratios observed: 1.5-3.0R
Signal frequency: Less frequent
Disclaimer: Past backtesting results do not guarantee future performance.
VOLATILE MARKETS (e.g., Cryptocurrency)
Suggested Settings for Testing:
Base Length: 25-30
Band Multiplier: 1.8-2.0
DVS: Consider enabling (Ensemble method)
Consider enabling Volume Filter
Volume Multiplier: 1.5-2.0
Theoretical Rationale:
Longer base may smooth noise
Wider bands may accommodate larger swings
DVS may be critical for adaptation
Volume filter may confirm genuine moves
Historical Backtest Observations:
Win rates in testing: Varied, often 45-60% range
Reward/risk ratios observed: 1.0-2.5R
Signal frequency: Moderate
Disclaimer: Cryptocurrency markets are highly volatile and risky. Past backtesting results do not guarantee future performance.
SCALPING (1-5min timeframes)
Suggested Settings for Testing:
Base Length: 15-20
Train Window: 150
Test Window: 30
Spawn Interval: 30
Min Confluence: 5.5-6.5
Consider enabling Ensemble
Min Consensus: 75%
Theoretical Rationale:
Shorter base may increase responsiveness
Shorter windows may speed evolution cycles
Quick spawning may enable rapid adaptation
Higher confluence may filter noise
Ensemble may reduce false signals
Historical Backtest Observations:
Win rates in testing: Varied, often 50-65% range
Reward/risk ratios observed: 0.5-1.0R
Signal frequency: Frequent but filtered
Disclaimer: Scalping involves high frequency trading with increased transaction costs and slippage risk. Past backtesting results do not guarantee future performance.
SWING TRADING (4H-Daily timeframes)
Suggested Settings for Testing:
Base Length: 25-35
Train Window: 300
Test Window: 100
Population Size: 7-8
Consider enabling Walk-Forward
Cooldown: 8-10 bars
Theoretical Rationale:
Longer timeframe may benefit from longer lookbacks
Larger windows may improve robustness testing
More population may increase stability
Walk-forward may be valuable for multi-day holds
Longer cooldown may reduce overtrading
Historical Backtest Observations:
Win rates in testing: Varied, often 45-60% range
Reward/risk ratios observed: 2.0-4.0R
Signal frequency: Infrequent but potentially higher quality
Disclaimer: Swing trading involves overnight and weekend risk. Past backtesting results do not guarantee future performance.
DASHBOARD GUIDE - INTERPRETATION EXAMPLES
Reading Each Section:
HEADER:
text
🧬 KERS-AGE EVOLVED 📈 TREND
Regime indication:
Color coding suggests current classification
(Green = Range, Orange = Trend, Purple = Volatile)
POPULATION:
text
Pop: 6/6
Gen: 42
Interpretation:
- Population at target size
- System at generation 42
- May indicate mature evolution
SPECIES (if enabled):
text
R:2 T:3 V:1
Interpretation:
- 2 Range specialists
- 3 Trend specialists
- 1 Volatility specialist
In TREND regime this distribution may be expected
WALK-FORWARD (if enabled):
text
Phase: 🧪 TEST
Cycles: 5
Train: 65%
Test: 58%
Considerations:
- Currently in test phase
- Completed 5 full cycles
- 7% performance gap between train and test
- Gap under default 25% overfit threshold
ENSEMBLE (if enabled):
text
Vote: 🟢 LONG
Consensus: 72%
Interpretation:
- Weighted majority voting LONG
- 72% agreement level
- Exceeds default 60% consensus threshold
SELECTED STRATEGY:
text
ID:23
Trades: 47
Win%: 58%
P&L: +8.3R
Fitness: 0.62
Information displayed:
- Strategy ID 23, Trend specialist
- 47 historical simulated trades
- 58% historical win rate
- +8.3R historical cumulative reward/risk
- 0.62 fitness score
Note: These are historical simulation metrics
SIGNAL QUALITY:
text
Conf: 78%
Grade: B+
Elastic: ████████░░
Wick: ██████░░░░
Momentum: ███████░░░
Pressure: ███████░░░
Information displayed:
- 78% confluence score
- B+ grade assignment
- Elastic component strongest
- Visual representation of component strengths
LEARNING (if enabled):
text
Missed: 47
Learned: 28
Interpretation:
- System identified 47 moves without signals
- 28 pattern adjustments made
- Suggests ongoing learning process
POSITION:
text
POS: 🟢 LONG
Score: 7.2
Current state:
- Simulated long position active
- 7.2 confluence score
- Monitor for potential exit signal
Educational Note: Dashboard displays are for informational and educational purposes. All performance metrics are historical simulations and do not represent actual trading results or future expectations.
FREQUENTLY ASKED QUESTIONS - EDUCATIONAL RESPONSES
Q: Why aren't signals showing?
A: Several factors may affect signal generation:
System may still be initializing (check Gen: counter)
Confluence score may be below threshold
Ensemble consensus (if enabled) may be below requirement
Current regime may naturally produce fewer signals
Filters may be active (volume, noise reduction)
Consider adjusting settings or allowing more time for evolution.
Q: The win rate seems low compared to backtesting?
A: Consider these factors:
First 200 bars typically represent learning period
Focus on TEST % rather than TRAIN % for realistic expectations
Trend regime historically shows 40-55% win rates in backtesting
Different market conditions may affect performance
System emphasizes reward/risk ratio alongside win rate
Past performance does not guarantee future results
Q: Should I take all signals?
A: This is a personal decision. Some users may consider:
Taking higher grades (A+, A) in any regime
Being more selective in trend regimes
Requiring higher ensemble consensus
Only trading during specific regimes
Paper trading extensively before live trading
Each user should develop their own signal selection criteria.
Q: Signals appear then disappear?
A: This may be expected behavior:
Default requires 2-bar persistence
Designed to filter brief spikes
Confirmation delay intended to reduce false signals
Wait for persistence requirement to be met
This is an intentional feature, not a malfunction.
Q: Test % much lower than Train %?
A: This may indicate:
Overfit detection system functioning
Gap exceeding threshold triggers penalty
Strategy may be optimizing to in-sample noise
System designed to cull such strategies
Walk-forward protection working as intended
This is a safety feature to reduce overfitting risk.
Q: The population keeps culling strategies?
A: This is part of normal evolution:
Lower-performing strategies removed periodically
Higher-performing strategies replicate
Population quality theoretically improves over time
Total culled count shows selection pressure
This is expected evolutionary behavior.
Q: Which timeframe works best?
A: Backtesting suggests 15min to 4H may be suitable ranges:
Lower timeframes may be noisier, may need more filtering
Higher timeframes may produce fewer signals
Extensive historical testing recommended for chosen asset
Each asset may behave differently
Consider paper trading across multiple timeframes
Personal testing is recommended for your specific use case.
Q: Does it work on all asset types?
A: Historical testing suggests:
Cryptocurrency: Consider longer Base Length (25-30) due to volatility
Forex: Standard settings may be appropriate starting point
Stocks: Standard settings, possibly smaller population (4-5)
Indices: Trend-focused settings may be worth testing
Each asset class has unique characteristics. Extensive testing recommended.
Q: Can settings be changed after initialization?
A: Yes, but considerations:
Population will reset
Strategies restart evolution
Learning progress resets
Consider testing new settings on separate chart first
May want to compare performance before committing
Settings changes restart the evolutionary process.
Q: Walk-Forward enabled or disabled?
A: Educational perspective:
Walk-Forward adds out-of-sample validation
May reduce overfitting risk
Results may be more conservative
Considered best practice in quantitative research
Requires more bars for meaningful data
Recommended for those concerned about robustness
Individual users should assess based on their needs.
Q: Ensemble mode or single strategy?
A: Trade-offs to consider:
Ensemble approach:
Requires consensus threshold
May have higher consistency
Typically fewer signals
Multiple perspectives considered
Single strategy approach:
More signals (varying quality)
Faster response to conditions
Higher variability
More active signal generation
Personal preference and risk tolerance should guide this choice.
ADVANCED CONSIDERATIONS
Evolution Time: Consider allowing 200+ bars for population maturity
Regime Awareness: Historical performance varies by regime classification
Confluence Range: Testing suggests 70-85% may be informative range
Ensemble Levels: 80%+ consensus historically associated with stronger agreement
Out-of-Sample Focus: Test performance may be more indicative than train performance
Learning Metrics: "Learned" count shows pattern adjustment over time
Pressure Levels: >0.4 pressure historically added confirmation
DVS Monitoring: >1.5 DVS typically widens bands and affects frequency
Species Balance: Healthy distribution might be 2-2-2 or 3-2-1, avoid 6-0-0
Timeframe Testing: Match to personal trading style, test thoroughly
Volume Importance: May be more critical for stocks/crypto than forex
MTF Utility: Historically more impactful in trending conditions
Grade Significance: A+ in trend regime historically rare and potentially significant
Risk Parameters: Standard risk management suggests 1-2% per trade maximum
Stop Levels: System stops are pre-calculated, widening may affect reward/risk
THEORETICAL FOUNDATIONS
Genetic Algorithms in Finance:
Traditional Optimization Approaches:
Grid search: Exhaustive but computationally expensive
Gradient descent: Efficient but prone to local optima
Random search: Simple but inefficient
Genetic Algorithm Characteristics:
Explores parameter space through evolutionary process
Balances exploration (mutation) and exploitation (selection)
Mitigates local optima through population diversity
Parallel evaluation via population approach
Inspired by biological evolution principles
Academic Context: Genetic algorithms are studied in computational finance literature for parameter optimization. Effectiveness varies based on problem characteristics and implementation.
Ensemble Methods in Machine Learning:
Single Model Limitations:
May overfit to specific patterns
Can have blind spots in certain conditions
May be brittle to distribution shifts
Ensemble Theoretical Benefits:
Variance reduction through averaging
Robustness through diversity
Improved generalization potential
Widely used (Random Forests, Gradient Boosting, etc.)
Academic Context: Ensemble methods are well-studied in machine learning literature. Performance benefits depend on base model diversity and correlation structure.
Walk-Forward Analysis:
Alternative Approaches:
Simple backtest: Risk of overfitting to full dataset
Single train/test split: Limited validation
Cross-validation: May violate time-series properties
Walk-Forward Characteristics:
Continuous out-of-sample validation
Respects temporal ordering
Attempts to detect strategy degradation
Used in quantitative trading research
Academic Context: Walk-forward analysis is discussed in quantitative finance literature as a robustness check. However, it assumes future regimes will resemble recent test periods, which is not guaranteed.
FINAL EDUCATIONAL SUMMARY
KERS-AGE demonstrates an adaptive systems approach to technical analysis. Rather than fixed rules, it implements:
✓ Evolutionary Optimization: Parameter adaptation through genetic algorithms
✓ Regime Classification: Attempted market condition categorization
✓ Out-of-Sample Testing: Walk-forward validation methodology
✓ Pattern Recognition: Cluster analysis and learning systems
✓ Ensemble Methodology: Collective decision-making framework
✓ Full Transparency: Comprehensive dashboard and metrics
This indicator is an educational tool demonstrating advanced algorithmic concepts.
Critical Reminders:
The system:
✓ Attempts to identify potential reversal patterns
✓ Adapts parameters to changing conditions
✓ Provides multiple filtering mechanisms
✓ Offers detailed performance metrics
Users must understand:
✓ No system guarantees profitable results
✓ Past performance does not predict future results
✓ Extensive testing and validation recommended
✓ Risk management is user's responsibility
✓ Market conditions can change unpredictably
✓ This is educational software, not financial advice
Success in trading requires: Proper education, risk management, discipline, realistic expectations, and personal responsibility for all trading decisions.
For Educational Use
🧬 KERS-AGE Development Team
⚠️ FINAL DISCLAIMER
This indicator and documentation are provided strictly for educational and informational purposes.
NOT FINANCIAL ADVICE: Nothing in this guide constitutes financial advice, investment advice, trading advice, or any recommendation to buy, sell, or hold any security or to engage in any trading strategy.
NO GUARANTEES: No representation is made that any account will or is likely to achieve profits or losses similar to those shown in backtests, examples, or historical data. Past performance is not indicative of future results.
SUBSTANTIAL RISK: Trading stocks, forex, futures, options, and cryptocurrencies involves substantial risk of loss and is not suitable for every investor. The high degree of leverage can work against you as well as for you.
YOUR RESPONSIBILITY: You are solely responsible for your own investment and trading decisions. You should conduct your own research, perform your own analysis, and consult with qualified financial advisors before making any trading decisions.
NO LIABILITY: The developers, contributors, and distributors of this indicator disclaim all liability for any losses or damages, direct or indirect, that may result from use of this indicator or reliance on any information provided.
PAPER TRADE FIRST: Users are strongly encouraged to thoroughly test this indicator in a paper trading environment before risking any real capital.
By using this indicator, you acknowledge that you have read this disclaimer, understand the risks involved in trading, and agree that you are solely responsible for your own trading decisions and their outcomes.
Educational Software Only | Trade at Your Own Risk | Not Financial Advice
Taking you to school. — Dskyz , Trade with insight. Trade with anticipation.
RunRox - Pairs Strategy🧬 Pairs Strategy is a new indicator by RunRox included in our premium subscription.
It is a specialized tool for trading pairs, built around working with two correlated instruments at the same time.
The indicator is designed specifically for pair trading logic: it helps track the relationship between two assets, identify statistical deviations, and generate signals for opening and managing long/short combinations on both legs of the pair.
Below in this description I will go through the core functions of the indicator and the main concepts behind the strategy so you can clearly understand how to apply it in your trading.
📌 CONCEPT
The core idea of pair trading is to find and trade correlated instruments that usually move in a similar way.
When these two assets temporarily diverge from each other, a trading opportunity appears.
In such moments, the relatively overvalued asset is sold (short leg), and the relatively undervalued asset is bought (long leg).
When the spread between them narrows and both instruments revert back toward their typical relationship (mean), the position is closed and the trader captures the profit from this convergence.
In practice, one leg of the pair can end up in a loss while the other generates a larger profit.
Due to the difference in performance between the two assets, the combined result of the pair trade can still be positive.
✅ KEY FEATURES:
2 deviation types (Z-Score and S-Score)
Invert signals mode
Hedge Coefficient (position size balancing between both legs)
6 hedge modes
Entries based on Score or RSI
Extra entries based on Score or Spread
Stop Loss
Take Profit
RSI Filter
RSI Pivot Mode
Built-in Backtester Strategy
Lower Timeframe Backtester Strategy
Live trade panel for current position
Equity curve chart
21 performance metrics in the backtester
2 alert types
*And many more fine-tuning options for pair trading
🔗 SCORE
Score is the core deviation metric between the two assets in the pair.
For example, if you are trading ETHUSDT/BTCUSDT, the indicator analyzes the relationship ETH/BTC, and when one leg temporarily diverges from the other, this difference is reflected in the Score value.
In other words, Score shows how much the current spread between the two instruments deviates from its typical state and is used as the main signal source for pair entries and exits.
In the screenshot above you can see how Score looks in our indicator.
Depending on how large the difference is between the two assets, the Score value can move in a range from −N to +N
When Score is in the −N zone, this is a 🟢 long zone for the first asset and a short zone for the second.
Using the ETH/BTC example: when Score is deeply negative, you open a long on ETH and a short on BTC at the same time, then close both legs when Score returns back to the 0 zone (balance between the two assets).
When Score is in the +N zone, this is a 🔴 short zone for the first asset and a long zone for the second.
In the same ETH/BTC example: when Score is strongly positive, you short ETH and long BTC, and again close both positions when Score comes back to the neutral 0 zone.
☯️ Z/S SCORE
Inside the indicator we added two different formulas for calculating the spread between the two legs of the pair: Z-Score and S-Score.
These approaches measure deviation in different ways and can produce slightly different signals depending on the chosen pair and its behavior.
This allows you to switch between Z-Score and S-Score and choose the method that gives more stable and cleaner signals for your specific instruments.
As you can see in the screenshot above, we used the same pair but applied different Score types to measure the spread and deviation from the norm.
🟣 Z-Score – generated 9 entry signals .
It reacts to price fluctuations more smoothly and usually stays within a range of approximately −8 to +8 .
🟠 S-Score – generated 5 entry signals .
It reacts to price changes more aggressively and produces wider deviations, often reaching −15 to +15 .
This gives traders the choice between a more sensitive but smoother model (Z-Score) and a more selective, stronger-deviation model (S-Score)
⁉️ HOW DOES THE STRATEGY WORK
Here is a basic example of how you can trade this pair trading strategy using our indicator and its signals.
In the classic approach the trade consists of one initial entry and several scale-ins (averaging) if the spread continues to move against the position.
The first entry is opened when Score reaches a standard deviation of −2 or +2.
If price does not revert to the mean and moves further against the position so that Score expands to −3 or +3, the strategy performs the first scale-in.
If Score extends to −4 or +4, a second scale-in is added.
If the spread grows even more and Score reaches −5 or +5, a third scale-in is executed.
In our indicator the number of averaging steps can be up to 4 scale-ins .
After that the position waits until Score returns back to the 0 level , where the whole pair position is closed.
This is the standard model of classical pair trading.
However there are many variations:
using Stop Loss and Take Profit,
exiting earlier or later than the 0 zone,
scaling in not by Score but by Spread, since Score is not linear while Spread is linear,
entering when RSI on both tickers shows opposite extremes, for example RSI 20 on one asset and RSI 80 on the other, and so on.
The number of possible trading styles for this strategy is very large.
We designed the indicator to cover as many of these variations as possible and added flexible tools so you can build your own pair trading logic on top of it.
Below is an example of a classic pair trade with two entries: one main entry and one extra entry (scale-in) .
The pair SUIUSDT / PENGUUSDT shows a high correlation, and on one of the trades the sequence looked like this:
A −2 Score deviation occurred into the long zone and triggered the Main Entry .
🔹 Main Entry
Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5708
Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.011793
Price then moved further against the position, Score went deeper into deviation, and the strategy added one extra entry.
🔸 Extra Entry
Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5938
Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.012173
The trade was closed when Score reverted back toward the 0 zone (mean reversion of the spread):
❎ Exit
SUIUSDT P&L: −403.34 USD, Exit price: 1.5184
PENGUUSDT P&L: +743.73 USD, Exit price: 0.011089
✅ Total P&L: +340.39 USD
With a total margin of 10,000 USD used per side (20,000 USD combined), this trade yielded around +1.7% on the deployed margin.
On different assets the size and speed of the spread movement will vary, but the principle remains the same.
This is just one example to illustrate how the strategy works in practice using simplified theoretical balances.
⚙️ MAIN SETTINGS
After explaining how the strategy works, we can move to the indicator settings and their logic.
The first block is Main Settings, which controls how the pair is built, how the spread is calculated, and how the backtest is performed.
The core idea of the indicator is to backtest historical data, generate entry signals, show open-position parameters, and provide all necessary metrics for both discretionary and algorithmic trading.
This is a complete framework for analyzing a pair of assets and building a trading system around them. Below I will go through the main parameters one by one.
🔹 Exclude Dates
Allows you to exclude abnormal periods in the pair’s history to remove outlier trades from the backtest.
This is useful when the market experienced extreme news events, listing spikes, or other non-typical situations that distort statistics.
🔹 Pair
Here you select the second asset for your pair.
For example, if your main chart is BTCUSDT, in this field you choose a correlated asset such as ETHUSDT, and the working pair becomes BTCUSDT / ETHUSDT.
The indicator then calculates spread, Score, and all related metrics based on this asset combination.
🔹 Lower Timeframe
This is a special mode for backtesting on a lower timeframe while using a higher timeframe chart to extend the history limit.
For example, if your TradingView plan provides only 5,000 bars of history on the current timeframe, you can switch your chart to a higher timeframe and select a lower timeframe in this setting.
The indicator will then reconstruct the pair logic using up to 99,000 bars of lower timeframe data for backtesting.
This allows you to test the pair on a much longer historical period and find more stable combinations of assets.
🔹 Method
Here you choose which deviation model you want to use: Z-Score or S-Score.
Both methods calculate spread deviation but use different formulas, which can give different signal behavior depending on the pair.
Examples of these two methods are shown earlier in this description.
🔹 Period
This parameter defines how many bars are used to calculate the average deviation for the pair.
If you set Period = 300, the indicator looks back 300 bars and calculates the typical spread deviation over that window.
For example, if the average deviation over 300 bars is around 1%, then a move to 2% or more will push Z/S Score closer to its boundary levels, since such a deviation is considered abnormal for that lookback period.
A larger Period means that only bigger deviations will be treated as anomalies.
A smaller Period makes the model more sensitive and treats smaller deviations as anomalies.
This allows you to tune how aggressive or conservative your pair trading signals should be.
🔹 Invert
This setting is used for negatively correlated pairs.
Some instruments have a positive correlation in the range from +0.8 to +1.0 (strong positive correlation), while others show a negative correlation from −0.8 to −1.0, meaning they usually move in opposite directions.
A classic example is the pair EURUSD and DXY.
As shown in the screenshot above, these instruments often have strong negative correlation due to macro factors and typically move in opposite directions: when EURUSD is rising, DXY is falling, and vice versa.
Such pairs can also be traded with our indicator.
To do this, we use the Invert option, which effectively flips one of the assets (as shown in the screenshot below). After inversion, both instruments are brought to a “same-direction” behavior from the model’s point of view.
From there, you trade the pair in the same way as a positively correlated one:
you open both legs in the same direction (both long or both short) depending on the spread and Score, and then wait for the spread between the inverted pair to converge back toward its mean.
🔀 HEDGE COEFFICIENT
The next block of settings is related to the hedge coefficient.
This defines how much margin is allocated to each leg of the pair.
The classic approach in pair trading is to split the position equally between both assets.
For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other.
This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT
However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different.
They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period.
Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account.
This is the main idea behind the Hedge Coefficient section and its primary use.
The indicator includes 6 methods of calculating the coefficient:
Cumulative RMA
Beta OLS
Beta TLS
Beta EMA
RMA Range
RMA Delta
Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets.
We leave it to the trader to decide which algorithm works best for their specific pair and style.
Below are the settings inside this section:
🔹 Method
When Auto Hedge is enabled, you can select which method to use from the list above.
The chosen method will automatically calculate the hedge coefficient between the two legs.
🔹 Hedge Coefficient
This is the manual hedge ratio per trade when Auto Hedge is disabled.
By default it is set to 1, which means the position is opened 50/50 between the two assets.
🔹 Min Allowed Hedge Coef.
This is the minimum allowed hedge coefficient.
By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs.
🔹 MA Length
For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient.
🛠️ STRATEGY SETTINGS
The next important block is Strategy Settings .
Here you define the core parameters used for backtesting: trading commission, position size, entry / exit logic, Stop Loss, Take Profit, and other rules that describe how you want the strategy to operate.
Below are all parameters with a detailed explanation.
🔸 Commission %
In this field you set your broker’s fee percentage per trade .
The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required.
🔸 Main Entry Mode
There are two options for the main entry:
Score - This is the primary entry method based on Z/S Score.
When Score reaches the deviation level defined in the settings below, the strategy opens the first position.
For example, if you set “Entry at 2 deviations”, the trade will be opened when Score hits ±2.
RSI Only - Alternative entry method based on RSI divergence between the two assets.
The exact RSI levels are defined in the RSI settings section below.
For example, if you set the entry threshold at 30, then when one asset has RSI below 30 and the second one has RSI above 70, the first entry will be triggered.
🔸 Extra Entries Mode
This defines how scale-ins (averaging) are executed. There are two modes:
Score - Works the same way as the main entry, but for additional entries.
For example, the main entry can be at 2 deviations, the first scale-in at 3, the second at 4, etc.
Spread - This mode uses the Spread (difference between the two assets) starting from the main entry moment.
As the spread continues to widen, the strategy can add extra entries based on spread growth rather than Score.
Since Score is a non-linear metric and Spread is linear, in some configurations averaging by Spread can produce better results than averaging by Score. This is pair- and strategy-dependent. 🔸 Entry parameters
Deviation / Spread threshold
Entry size
Main Entry – first field (deviation / spread), second field (position size)
Entry 2 – first field (deviation / spread), second field (position size)
Entry 3 – first field (deviation / spread), second field (position size)
Entry 4 – first field (deviation / spread), second field (position size)
This allows you to define up to four scaling steps with different triggers and different sizing.
🔸 Exit Level
This parameter defines at what Score level you want to exit the trade.
By default it is 0, which means the backtester closes the position when Score returns to the neutral (0) zone.
You can also use positive or negative values. Example:
Assume your main entry is configured at a 3 deviation.
You can exit at the 0 level, or you can set Exit Level = 2.
If your initial entry was at −3, the position will be closed when Score reaches +2.
If your initial entry was at +3, the position will be closed when Score reaches −2.
This approach can increase the profit per trade due to a larger captured spread, but it may also increase the holding time of the position.
🔸 Stop Loss
Here you define the maximum loss per trade in PnL units.
If a trade reaches the negative PnL value specified in this field and the Stop Loss option is enabled, the indicator will close the trade at a loss.
The Cooldown parameter sets a pause after a losing trade:
the strategy will wait a specified number of bars before opening the next trade.
🔸 Take Profit
Works similar to Stop Loss but for profit targets.
You set the desired PnL value you want to reach.
The trade will be closed when either the Take Profit target is hit or when Score reaches the exit level defined in the settings, whichever occurs first (depending on your configuration).
🔸 Show Qty in currency
When enabled, trade size is displayed in currency (USD) instead of token quantity.
This is useful for quickly understanding position size in monetary terms.
You will see this in the Current Trade panel, which is described later.
🔸 Size Rounding
Controls how many decimal places are used when rounding position size (from 0 to 10 digits after the decimal).
This is also used for the Current Trade panel so you can adjust how detailed or compact the size display should be.
📊 RSI FILTERS
This section is used for additional trade filtering.
RSI can be used in two ways:
as a primary entry signal,
or as an extra filter for entries based on Z/S Score.
If in the Strategy Settings the Main Entry Mode is set to RSI, then RSI becomes the main trigger for opening a position.
In this case a trade is opened when the RSI of the two assets reaches opposite zones.
Example:
If the threshold is set to 30, then:
when one asset has RSI below 30, and
the second asset has RSI above 70 (100 − 30),
the strategy opens the first entry.
All extra entries after that will be executed either by Spread or by Z/S Score, depending on your Extra Entries Mode.
Below are the parameters in this block:
RSI Length – standard RSI period setting.
RSI Pivot Mode – when enabled, RSI is used as an additional filter together with Z/S Score. The indicator looks for a reversal pattern on RSI (pivot behavior). If RSI forms a reversal structure, the trade is allowed to open. If not, the signal is skipped until a proper RSI pivot is formed.
Entry RSI Filter – here you define the RSI thresholds used for RSI-based entries. These are the same boundary levels described in the example above.
Overall, this section helps filter out lower-quality trades using additional RSI conditions or lets you build RSI-only entry logic based on extreme levels.
🎨 MAIN CHART STYLING
This section controls the visual appearance of trades on the main chart.
You can customize how the second asset line is drawn, as well as the icons for entries, scale-ins, and exits, including their size and style.
▫️ Price Line
This is the line that shows the price of the second asset and the relative difference between the two instruments.
You can adjust the line thickness and color to make it more readable on your chart.
▫️ Adjust Price Line by Hedge Coefficient
When this option is enabled, the second asset’s line is normalized by the hedge coefficient.
If you turn it off, the hedge coefficient will not be applied to the second asset’s line, and it will be displayed in raw form.
▫️ Entry Label
Here you can customize how the entry markers look:
choose the color, icon style, and size of the label that marks each trade entry and scale-in on the chart.
▫️ Exit Label
Similarly, you can define the color, icon style, and size of the label used for exits.
This helps visually separate entries and exits and makes it easier to read the trade history directly from the chart.
🎯 INDICATOR PANEL
This section controls the settings of the indicator panel, which works like an oscillator and allows you to visualize multiple metrics in one place.
You can flexibly enable, style, and scale each parameter.
🔹 Score
Displays the main deviation metric between the two assets.
You can customize the color and line thickness of the Score plot.
🔹 Spread
Shows the spread between the two assets.
It starts calculating from the moment the trade is opened.
You can adjust its color and thickness for better visibility.
🔹 Total Profit
Displays the cumulative profit for this pair and strategy as a line that grows (or falls) over time.
Color, opacity, and line thickness can be customized.
🔹 Unrealized PNL
Once a trade is opened, this line shows the current PnL of the active position.
It also lets you see historical drawdowns on the pair.
Color and thickness can be adjusted.
🔹 Released PNL
Shows the realized PnL of each closed trade as bars.
Useful for quickly evaluating the result of every individual trade in the backtest.
🔹 Correlation
Plots the correlation coefficient between the two assets as a graph, so you can visually track how stable or unstable the relationship between them is over time.
🔹 Hedge Coefficient
Shows the hedge coefficient as a line, which helps understand how the model is rebalancing exposure between the two legs depending on their behavior.
For each metric there is also a 📎 Stretch option.
Stretch allows you to compress or expand the scale of a specific line to visually align metrics with different ranges on the same panel and make the chart easier to read.
📈 PROFIT CHART
Since TradingView does not natively support proper backtesting for pair trading, this indicator includes its own profit curve for the pair.
You can visually see how the strategy performed over historical data: whether there were deep drawdowns, abnormal profit spikes, or stable equity growth over time. This makes it much easier to evaluate the quality of the pair and the strategy on history.
In the settings of this section you can flexibly customize how the profit chart is displayed:
labels, position of the panel, padding, and other visual details.
Everything depends on your personal preferences, so we give full control over styling:
you can adjust the look of the profit chart to match your layout or completely hide it from the chart if you do not need it.
📌 CURRENT TRADE
This section controls the current trade table.
When there is an active trade on the chart, the panel displays all key information for the open position:
direction for each ticker (long or short),
required position size for each leg,
entry price for both assets,
and real-time PnL for each leg separately,
so you always have a clear view of the current situation.
The main thing you can do with this table is customize its appearance:
you can change the size, position on the chart, background and text colors, as well as separate coloring for positive / negative PnL and different colors for long and short positions.
📅 BACKTEST RESULTS
The next key block is Backtest Results.
This results table with detailed metrics gives you an extended view of how the pair and strategy perform: win rate, profit factor, long/short breakdown, and more than 20 additional stats that help you evaluate the potential of your setup.
⚠️ First of all, it is important to note ⚠️
past performance does not guarantee future results.
Every trader must keep this in mind and factor these risks into their strategy.
The table shows metrics in three cuts:
All Entries
Main Entries
Extra Entries (scale-ins)
Core metrics:
Profit – total profit for each entry type.
Winrate – win rate for this pair.
Profit Factor – ratio of gross profit to gross loss for the strategy.
Trades – number of trades in the backtest.
Wins – number of winning trades.
Losses – number of losing trades.
Long Profit – profit generated by long positions.
Short Profit – profit generated by short positions.
Longs – total number of long trades.
Shorts – total number of short trades.
Avg. Time – average time spent in a trade.
Additional metrics for a deeper evaluation of the pair:
Correlation – current correlation between the two assets in the pair.
Bars Processed – number of bars used in the analysis.
Max Drawdown – maximum historical drawdown of the strategy.
Biggest Loss – the largest single losing trade in the backtest.
Recommended Hedge – recommended hedge coefficient based on historical behavior.
Max Spread – maximum positive spread observed in history.
Min Spread – maximum negative spread observed in history.
Avg. Max Spread – average of positive extreme spread values (above 0).
Avg. Min Spread – average of negative extreme spread values (below 0).
Avg Positive Spread – average positive spread across all trades (only values above 0).
Avg Negative Spread – average negative spread across all trades (only values below 0).
Current Spread – current spread between the assets when a trade is open.
These metrics together allow you to quickly assess how stable the pair is, how the risk/return profile looks, and whether the strategy parameters are suitable for live trading. You can fully customize this results table to fit your workflow:
hide metrics you don’t need, change colors, opacity, and other visual styles, and reorder the focus of the stats according to your trading style.
This way the backtest block can show only the metrics that matter to you most and remain clean and readable during analysis.
📣 ALERTS
The next section is dedicated to alerts.
Here you can configure all signals you need, both for manual trading and for full automation of this pair trading strategy. This block is designed to cover most practical use cases. The indicator supports two alert modes:
Single Alert – one universal custom alert for all events.
Two Alerts – separate alerts for each ticker so you can receive different messages per asset.
Available alert events:
Main Entry – when the main entry is triggered.
Entry 2 – when the first scale-in is executed.
Entry 3 – when the second scale-in is executed.
Entry 4 – when the third scale-in is executed.
Exit Alert – when the position is closed.
StopLoss Alert – when Stop Loss is hit.
TakeProfit Alert – when Take Profit is hit.
All alerts are fully customizable and support a set of placeholders for building structured messages or JSON payloads.
🔹1 Alert Type
List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit').
{{dir_1}} – 'Long' or 'Short' for the main ticker.
{{dir_2}} – 'Long' or 'Short' for the other ticker.
{{action_1}} – 'Buy', 'Sell' or 'Close' for the main ticker.
{{action_2}} – 'Buy', 'Sell' or 'Close' for the other ticker.
{{price_1}} – price for the main ticker.
{{price_2}} – price for the other ticker.
{{qty_1}} – order size for the main ticker.
{{qty_2}} – order size for the other ticker.
{{ticker_1}} – main ticker (e.g. 'BTCUSD').
{{ticker_2}} – other ticker (e.g. 'ETHUSD').
{{time}} – candle open time in UTC.
{{timenow}} – signal time in UTC.
🔹2 Alert Type
List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit', 'SL', 'TP').
{{action}} – 'Buy', 'Sell' or 'Close'.
{{price}} – order price.
{{qty}} – order size.
{{ticker}} – ticker (e.g. 'BTCUSD').
{{time}} – candle open time in UTC.
{{timenow}} – signal time in UTC. You can use these placeholders to build any JSON structure or custom alert text required by your trading bot, exchange API, or automation service.
In this post I’ve explained how the indicator works, the core concept behind this pair trading strategy, and shown practical examples of trades together with a detailed breakdown of each unique feature inside the tool.
We have invested a lot of work into building this indicator and we truly hope it will help you trade pair strategies more efficiently and more profitably by giving you structured, strategy-specific information that is difficult to obtain in any other way.
⚠️ Please also remember that past performance does not guarantee future results.
Always evaluate the risks, the robustness of your setup, and your own risk tolerance before entering any position, and make independent, well-considered decisions when using this or any other strategy.
RunRox - Pairs Screener📊 Pairs Screener is part of our premium suite for pair trading.
This indicator is designed to scan and rank the most profitable and optimal pairs for the Pairs Strategy. The screener can backtest multiple metrics on deep historical data and display results for many pairs against one base asset at the same time.
This allows you to quickly detect market inefficiencies and select the most promising pairs for live trading.
HOW DOES THIS STRATEGY WORK⁉️
The core idea of the strategy is described in detail in our main indicator Pairs Strategy from the same product line.
There you can find a full explanation of the concept, the math behind pair trading, and the internal logic of the engine.
The Pairs Screener is built on top of the same core technology as the main indicator and uses the same internal logic and calculations.
It is designed as a key companion tool to the main strategy: it helps you find tradeable pairs, evaluate current deviations, sort and filter lists of candidates, and much more. All of these features will be described in this post.
✅ KEY FEATURES
More than 400+ assets available for scanning
Forex assets
Crypto assets
Lower Timeframe Backtester Strategy support
Invert signals mode
Hedge Coefficient (position size balancing between both legs)
6 hedge modes
Stop Loss support
Take Profit support
Whitelist with your own custom asset list
Blacklist to exclude unwanted assets
Custom filters
12 tracking metrics for pair evaluation
Customizable alerts
And many other tools for fine-tuning your search
The screener runs backtests simultaneously across a large number of assets and calculates metrics automatically.
This helps you very quickly find pairs with strong structural relationships or current inefficiencies that can be used as the basis for your pair trading strategies.
⚙️ MAIN SETTINGS
The first section controls the core parameters of the screener: Score, correlation, asset groups for scanning, and other base settings. All major crypto and forex symbols are embedded directly into the screener.
Since there are more than 400 assets, it is technically impossible to analyze everything at once, so we grouped them into batches of 40 assets per group.
The workflow is simple:
Open the chart of the asset you want to use as the base ticker.
In the screener settings choose the market (Crypto or Forex).
Select a Group (for example, Group 1) and the indicator will scan all assets inside that group against your base ticker.
Then you switch to Group 2, Group 3, etc., and repeat the scan.
Embedded universe:
400+ assets total
350+ Crypto – split into 10 groups
70+ Forex – split into 3 groups
Below is a description of each setting.
🔸 Exclude Dates
Allows you to specify a period that should be excluded from analysis.
Useful for removing abnormal spikes, news events, or any non-typical segments that distort the statistics for your pairs.
🔸 Market
Defines which universe will be used to build pairs with the current main asset:
Crypto – 350+ crypto symbols
Forex – 70+ FX symbols
Whitelist – your own custom list of assets
🔸 Group
Selects the asset group to scan.
As mentioned above, assets are split into groups of about 40 instruments:
350+ Crypto → 10 groups
70+ Forex → 3 groups
The screener will calculate all metrics only for the group you select.
🔸 Lower Timeframe
This option enables deep history analysis.
Each TradingView plan has a limit on the number of visible bars (for example, 5,000 bars on the basic plan). In standard mode you would only get statistics for the last 5,000 bars of your current timeframe.
If you want a deeper backtest on a lower timeframe, you can do the following:
Suppose your target timeframe for analysis is 5 minutes.
Switch your chart to a 30-minute timeframe.
Enable Lower Timeframe in the indicator.
Select 5 minutes as the lower timeframe inside the screener.
In this mode the screener can reconstruct and analyze up to 99,000 bars of data for your assets. This allows you to evaluate pairs on a much deeper history and see whether the results are stable over a larger sample.
🔸 Method
Here you choose the deviation model:
preferred Z-Score or S-Score for your analysis,
plus you can enable Invert to search for negatively correlated pairs and calculate their profit correctly.
🔸 Period
This is the lookback period for Z/S Score.
It defines how many bars are used to calculate the deviation metric for each pair.
🔸 Correlation Period
This is the number of bars used to calculate correlation between the base asset and each candidate in the group.
The resulting correlation value is also displayed in the results table.
🔀 HEDGE COEFFICIENT
The next block of settings is related to the hedge coefficient.
This defines how much margin is allocated to each leg of the pair.
The classic approach in pair trading is to split the position equally between both assets.
For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other.
This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT
However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different.
They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period.
Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account.
This is the main idea behind the Hedge Coefficient section and its primary use.
The indicator includes 6 methods of calculating the coefficient:
Cumulative RMA
Beta OLS
Beta TLS
Beta EMA
RMA Range
RMA Delta
Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets.
We leave it to the trader to decide which algorithm works best for their specific pair and style.
Below are the settings inside this section:
🔹 Method
When Auto Hedge is enabled, you can select which method to use from the list above.
The chosen method will automatically calculate the hedge coefficient between the two legs.
🔹 Hedge Coefficient
This is the manual hedge ratio per trade when Auto Hedge is disabled.
By default it is set to 1, which means the position is opened 50/50 between the two assets.
🔹 Min Allowed Hedge Coef.
This is the minimum allowed hedge coefficient.
By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs.
🔹 MA Length
For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient.
💰 STRATEGY SETTINGS
This section defines the base backtesting settings for all assets in the screener.
Here you configure entries, exits, Stop Loss, and other parameters used to find the most optimal pairs for your strategy. 🔸 Commission %
In this field you set your broker’s fee percentage per trade.
The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required.
🔸 Qty $
The margin amount used for backtesting across all assets in the screener.
This margin is split between both legs of the pair either equally or according to the selected hedge coefficient.
🔸 Entry
The Z/S Score deviation level at which the backtest opens a trade for each pair.
🔸 Exit
The Z/S Score level at which the backtest closes trades for the tested assets.
🔸 Stop Loss
PnL threshold at which a trade is force-closed during the historical test.
🔸 Cooldown
Number of bars the strategy will wait after a Stop Loss before opening the next trade.
This block gives you flexible control over how your strategy is tested on 400+ assets, helping you standardize the rules and compare pairs under the exact same conditions.
🗒️ WHITELIST
In this section you can define your own custom list of assets for monitoring and backtesting.
This is useful if you want to work with symbols that are not included in the built-in lists, such as exotic crypto from smaller exchanges, specific stocks, or any custom universe 🔹 Exchange Prefix
Enter the exchange prefix used for your tickers.
Example: BINANCE, OANDA, etc.
🔹 Ticker Postfix
Enable this option if the tickers require a postfix.
Example 1: .P for Binance Futures perpetual contracts.
Example 2: USDT if you only provide the base asset in the ticker list.
🔹 Ticker List
Enter a comma-separated list of tickers to analyze.
Example 1: BTCUSDT, ETHUSDT, BNBUSDT (when the exchange prefix is set).
Example 2: BTC, ETH, BNB (when using postfix USDT).
Example 3: BINANCE:BTCUSDT.P, OANDA:EURUSD (when different exchanges are used and the prefix option is disabled).
This gives you full flexibility to build a screener universe that matches exactly the assets you trade.
⛔ BLACKLIST
In this section you can enable a blacklist of unwanted assets that should be skipped during analysis. Enter a comma-separated list of tickers to exclude from the screener:
Example 1: BTCUSDT, ETHUSDT
Example 2: BTC, ETH (all tickers that contain these symbols will be excluded)
This helps you quickly remove illiquid, noisy, or unwanted instruments from the results without changing your main groups or whitelist.
📈 DASHBOARD
This section controls the results dashboard: table position, style, and sorting logic.
Here is what you can configure:
Result Table – position of the results table on the chart.
Background / Text – colors and opacity for the table background and text.
Table Size – overall size of the results table (from 0 to 30).
Show Results – how many rows (pairs) to display in the table.
Sort by (stat) – which metric to use for sorting the results.
Available options: Profit Factor, Profit, Winrate, Correlation, Score.
This lets you quickly focus on the most interesting pairs according to the exact metric that matters most for your strategy.
📎 FILTER SETTINGS
This section lets you filter the results table by metric values.
For example, you can show only pairs with a minimum correlation of 0.8 to focus on more stable relationships. 🔸 Min Correlation
Minimum allowed correlation between the two assets over the selected lookback period.
🔸 Min Score
Minimum absolute Score (Z-Score or S-Score) required to include a pair in the results.
For example, 2.0 means only pairs with Score >= 2.0 or <= -2.0 will be displayed.
🔸 Min Winrate
Minimum win rate percentage for a pair to be included in the table.
🔸 Min Profit Factor
Minimum profit factor required for a pair to stay in the results. These filters help you quickly narrow the list down to pairs that meet your quality criteria and match your risk profile.
📌 COLUMN SELECTION
This section lets you fully customize which metrics are displayed in the results table.
You can enable or hide any column to focus only on the data you need to identify the best pairs for trading. The screener allows you to show up to 12 metrics at the same time, which gives a detailed view of pair quality. Available columns:
🔹 Exchange Prefix
Show the exchange prefix in the ticker.
🔹 Correlation
Correlation between the two assets’ prices over the lookback period.
🔹 Score
Current Score value (Z-Score or S-Score).
On lower timeframe research, Score is not displayed.
🔹 Spread
Shows spread as % change since entry.
Positive value = profit on the main position.
🔹 Unrealized PnL
Shows unrealized PnL as a $ value based on current prices.
🔹 Profit
Total profit from all trades: Gross Profit − Gross Loss.
🔹 Winrate
Percentage of profitable trades out of all executed trades.
🔹 Profit Factor
Gross Profit / Gross Loss.
🔹 Trades
Total number of trades.
🔹 Max Drawdown
Maximum observed loss from peak to trough before a new peak is made.
🔹 Max Loss
Largest loss recorded on a single trade.
🔹 Long/Short Profit
Separate profit/loss for long trades and short trades.
🔹 Avg. Trade Time
Average duration of trades.
All these metrics are designed to help you quickly identify the strongest pairs for your strategy.
You can change colors, opacity, and hide any columns that are not relevant to your workflow.
🔔 ALERT
The alert system in this screener works in a specific way.
Alerts are tied directly to the filters you set in the Filter Settings section:
Minimum Correlation
Minimum Score
Minimum Winrate
Minimum Profit Factor
You can configure alerts to trigger when a new pair appears that matches all your filter conditions. 💡 Example
You set:
Minimum Score = 3
Then you create an alert based on the screener.
When any pair reaches a Score greater than +3 or less than −3, you will receive a notification.
This is how alerts work in this screener.
The idea is to deliver the most relevant information about the current market situation without forcing you to watch the screener all the time.
Supported placeholders for alert messages: {{ticker_1}} – main ticker (the one on the chart).
{{ticker_2}} – the paired ticker listed in the table.
{{corr}} – correlation value.
{{score}} – Score value (Z-Score or S-Score).
{{time}} – bar open time (UTC).
{{timenow}} – alert trigger time (UTC). You can use these placeholders to build alert text or JSON payloads in any format required by your tools.
The screener is designed to significantly enhance your pair trading workflow: it helps you quickly identify working pairs and current market inefficiencies, and with the alert system you can react to opportunities without constantly sitting in front of the screen.
Always remember that past performance does not guarantee future results.
Use the screener data within a risk-controlled trading system and adjust position sizing according to your own risk management rules.
DAISOB Q3Description: SMC , ICT, FVG, Fair Value Gap
Strategy, Non Repainting
Overview This strategy is designed for precision trading based on Smart Money Concepts (SMC) and Fair Value Gaps (FVG). It acts as a "Sniper" tool: it identifies high-probability institutional levels on Higher Timeframes (HTF) while allowing you to execute entries on Lower Timeframes (LTF) with strict confirmation logic.
The core philosophy of this script is Stability & Precision. Unlike many FVG indicators that repaint or flicker during candle formation, this strategy uses strict bar_index confirmation. An FVG is only drawn and valid after the candle has fully closed, ensuring that what you see on the chart is permanent and backtestable.
Key Features
🚀 Auto-Timeframe Pairing (Dynamic HTF) Forget about manually changing settings. The strategy automatically detects your current chart timeframe and scans for FVGs on a specific higher timeframe to ensure you are trading with the trend.
1m Chart → Scans 15m FVGs.
5m Chart → Scans 30m FVGs.
15m Chart → Scans 1H FVGs.
30m Chart → Scans 4H FVGs. (You can also disable this and set a manual fixed timeframe).
🛡️ Zero Repainting (Hard Confirmation) The script utilizes lookahead_off and references historical closed candles ( , ). An FVG box will only appear once the formation is mathematically confirmed and the candle is closed.
⚡ Smart Alerts (Pre-Entry Focus) Instead of spamming you with signals, the script includes a "Pre-Entry" mechanism.
"P" Label: Appears when price approaches the FVG (95% retracement).
Alerts: You can set alerts specifically for this Pre-Entry phase, giving you time to prepare for the trade before the actual execution.
🎯 Q3 Time Logic The strategy filters for FVGs formed only during high-volatility "Q3" windows (New York Session specific times), filtering out low-probability setups during off-hours.
🔥 One Trade Per FVG To prevent over-trading, once an FVG has generated a signal (Win or Loss), it is marked as "Used" (dashed line) and will not generate further signals.
How to Use
Set Up: Open your chart on a low timeframe (e.g., 1m or 5m).
Configuration: Ensure "Use Fixed TF Pairs" is enabled in the settings.
Wait for "P": When price approaches a valid FVG, a gray "P" (Pre-Entry) label will appear. This is your "Get Ready" signal.
Entry Execution:
LONG: Price must touch the bottom of the Bullish FVG and close inside/above it.
SHORT: Price must touch the top of the Bearish FVG and close inside/below it.
Visual Results: The script draws a Green box (Profit) or Red box (Loss) representing the trade outcome based on your SL/TP settings.
Settings
Risk Reward Ratio: Define your target (default 1:2.5).
Stop Loss Points: Fixed point stop loss.
Pre-Entry %: Distance from the FVG to trigger the "P" warning.
Time Filters: Adjust the Q3 hours if needed.
Disclaimer This tool is for educational and analytical purposes only. Past performance does not guarantee future results. Always manage your risk properly.
Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4hInvestment Strategy (Quantitative Trading)
| 🛑 | Watch "LIVE" and 'COPY' this strategy in real time:
🔗 Link: www.tradingview.com
Hello, welcome, feel free 🌹💐
Since the stone age to the most technological age, one thing has not changed, that which continues impress human beings the most, is the other human being!
Deep down, it's all very simple or very complicated, depends on how you look at it.
I believe that everyone was born to do something very well in life.
But few are those who have, let's use the word 'luck' .
Few are those who have the 'luck' to discover this thing.
That is why few are happy and successful in their jobs and professions.
Thank God I had this 'luck' , and discovered what I was born to do well.
And I was born to program. 👨💻
📋 Summary : Project Titan
0️⃣ : 🦄 Project Titan
1️⃣ : ⚖️ Quantitative THEMIS
2️⃣ : 🏛️ Titan Community
3️⃣ : 👨💻 Who am I ❔
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
8️⃣ : ❓ What is Backtest ❓
9️⃣ : ❓ How to build a Consistent system ❓
🔟 : ❓ What is a Quantitative Trading system ❓
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
1️⃣4️⃣ : 🔧 Fixed Technical
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
1️⃣6️⃣ : ⚠️ Risk Profile
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
1️⃣8️⃣ : 💸 Initial Capital
1️⃣9️⃣ : ⚙️ Entry Options
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
3️⃣0️⃣ : 🛠️ Roadmap
3️⃣1️⃣ : 🧻 Notes ❕
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
3️⃣3️⃣ : ♻️ ® No Repaint
3️⃣4️⃣ : 🔒 Copyright ©️
3️⃣5️⃣ : 👏 Acknowledgments
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
0️⃣ : 🦄 Project Titan
This is the first real, 100% automated Quantitative Strategy made available to the public and the pinescript community for TradingView.
You will be able to automate all signals of this strategy for your broker , centralized or decentralized and also for messaging services : Discord, Telegram or Twitter .
This is the first strategy of a larger project, in 2023, I will provide a total of 6 100% automated 'Quantitative' strategies to the pinescript community for TradingView.
The future strategies to be shared here will also be unique , never before seen, real 'Quantitative' bots with real, validated results in real operation.
Just like the 'Quantitative THEMIS' strategy, it will be something out of the loop throughout the pinescript/tradingview community, truly unique tools for building mutual wealth consistently and continuously for our community.
1️⃣ : ⚖️ Quantitative THEMIS : Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
This is a truly unique and out of the curve strategy for BTC /USD .
A truly real strategy, with real, validated results and in real operation.
A unique tool for building mutual wealth, consistently and continuously for the members of the Titan community.
Initially we will operate on a monthly, quarterly, annual or biennial subscription service.
Our goal here is to build a great community, in exchange for an extremely fair value for the use of our truly unique tools, which bring and will bring real results to our community members.
With this business model it will be possible to provide all Titan users and community members with the purest and highest degree of sophistication in the market with pinescript for tradingview, providing unique and truly profitable strategies.
My goal here is to offer the best to our members!
The best 'pinescript' tradingview service in the world!
We are the only Start-Up in the world that will decentralize real and full access to truly real 'quantitative' tools that bring and will bring real results for mutual and ongoing wealth building for our community.
2️⃣ : 🏛️ Titan Community : 👽 Pro 🔁 Aff 🛸
Become a Titan Pro 👽
To get access to the strategy: "Quantitative THEMIS" , and future Titan strategies in a 100% automated way, along with all tutorials for automation.
Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months.
👽 Pro 🅼 Monthly
👽 Pro 🆀 Quarterly
👽 Pro🅰 Annual
👽 Pro👾Two Years
You will have access to a truly unique system that is out of the curve .
A 100% real, 100% automated, tested, validated, profitable, and in real operation strategy.
Become a Titan Affiliate 🛸
By becoming a Titan Affiliate 🛸, you will automatically receive 50% of the value of each new subscription you refer .
You will receive 50% for any of the above plans that you refer .
This way we will encourage our community to grow in a fair and healthy way, because we know what we have in our hands and what we deliver real value to our users.
We are at the highest level of sophistication in the market, the consistency here and the results here speak for themselves.
So growing our community means growing mutual wealth and raising collective conscience.
Wealth must be created not divided.
And here we are creating mutual wealth on all ends and in all ways.
A non-zero sum system, where everybody wins.
3️⃣ : 👨💻 Who am I ❔
My name is FilipeSoh I am 26 years old, Technical Analyst, Trader, Computer Engineer, pinescript Specialist, with extensive experience in several languages and technologies.
For the last 4 years I have been focusing on developing, editing and creating pinescript indicators and strategies for Tradingview for people and myself.
Full-time passionate workaholic pinescript developer with over 10,000 hours of pinescript development.
• Pinescript expert ▬Tradingview.
• Specialist in Automated Trading
• Specialist in Quantitative Trading.
• Statistical/Probabilistic Trading Specialist - Mark Douglas Scholl.
• Inventor of the 'Classic Forecast' Indicators.
• Inventor of the 'Backtest Table'.
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
Statistical/probabilistic trading is the only way to get a positive mathematical expectation regarding the market and consequently that is the only way to make money consistently from it.
I will present below some more details about the Quantitative THEMIS strategy, it is a real strategy, tested, validated and in real operation, 'Skin in the Game' , a consistent way to make money with statistical/probabilistic trading in a 100% automated.
I am a Technical Analyst , I used to be a Discretionary Trader , today I am 100% a Statistical Trader .
I've gotten rich and made a lot of money, and I've also lost a lot with 'leverage'.
That was a few years ago.
The book that changed everything for me was "Trading in The Zone" by Mark Douglas.
That's when I understood that the market is just a game of statistics and probability, like a casino!
It was then that I understood that the human brain is not prepared for trading, because it involves triggers and mental emotions.
And emotions in trading and in making trading decisions do not go well together, not in the long run, because you always have the burden of being wrong with the outcome of that particular position.
But remembering that the market is just a statistical game!
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
Let's use a 'coin' as an example:
If we toss a 'coin' up 10 times.
Do you agree that it is impossible for us to know exactly the result of the 'plays' before they actually happen?
As in the example above, would you agree, that we cannot "guess" the outcome of a position before it actually happens?
As much as we cannot "guess" whether the coin will drop heads or tails on each flip.
We can analyze the "backtest" of the 10 moves made with that coin:
If we analyze the 10 moves and count the number of times the coin fell heads or tails in a specific sequence, we then have a percentage of times the coin fell heads or tails, so we have a 'backtest' of those moves.
Then on the next flip we can now assume a point or a favorable position for one side, the side with the highest probability .
In a nutshell, this is more or less how probabilistic statistical trading works.
As Statistical Traders we can never say whether such a Trader/Position we take will be a winner or a loser.
But still we can have a positive and consistent result in a "sequence" of trades, because before we even open a position, backtests have already been performed so we identify an anomaly and build a system that will have a positive statistical advantage in our favor over the market.
The advantage will not be in one trade itself, but in the "sequence" of trades as a whole!
Because our system will work like a casino, having a positive mathematical expectation relative to the players/market.
Design, develop, test models and systems that can take advantage of market anomalies, until they change.
Be the casino! - Mark Douglas
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
In recent years I have focused and specialized in developing 100% automated trading systems, essentially for the cryptocurrency market.
I have developed many extremely robust and efficient systems, with positive mathematical expectation towards the market.
These are not complex systems per se , because here we want to avoid 'over-optimization' as much as possible.
As Da Vinci said: "Simplicity is the highest degree of sophistication".
I say this because I have tested, tried and developed hundreds of systems/strategies.
I believe I have programmed more than 10,000 unique indicators/strategies, because this is my passion and purpose in life.
I am passionate about what I do, completely!
I love statistical trading because it is the only way to get consistency in the long run!
This is why I have studied, applied, developed, and specialized in 100% automated cryptocurrency trading systems.
The reason why our systems are extremely "simple" is because, as I mentioned before, in statistical trading we want to exploit the market anomaly to the maximum, that is, this anomaly will change from time to time, usually we can exploit a trading system efficiently for about 6 to 12 months, or for a few years, that is; for fixed 'scalpers' systems.
Because at some point these anomalies will be identified , and from the moment they are identified they will be exploited and will stop being anomalies .
With the system presented here; you can even copy the indicators and input values shared here;
However; what I have to offer you is: it is me , our team , and our community !
That is, we will constantly monitor this system, for life , because our goal here is to create a unique , perpetual , profitable , and consistent system for our community.
Myself , our team and our community will keep this script periodically updated , to ensure the positive mathematical expectation of it.
So we don't mind sharing the current parameters and values , because the real value is also in the future updates that this system will receive from me and our team , guided by our culture and our community of real users !
As we are hosted on 'tradingview', all future updates for this strategy, will be implemented and updated automatically on your tradingview account.
What we want here is: to make sure you get gains from our system, because if you get gains , our ecosystem will grow as a whole in a healthy and scalable way, so we will be generating continuous mutual wealth and raising the collective consciousness .
People Need People: 3️⃣🅿
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
Today my greatest skill is to develop statistically profitable and 100% automated strategies for 'pinescript' tradingview.
Note that I said: 'profitable' because in fact statistical trading is the only way to make money in a 'consistent' way from the market.
And consequently have a positive wealth curve every cycle, because we will be based on mathematics, not on feelings and news.
Because the human brain is not prepared to do trading.
Because trading is connected to the decision making of the cerebral cortex.
And the decision making is automatically linked to emotions, and emotions don't match with trading decision making, because in those moments, we can feel the best and also the worst sensations and emotions, and this certainly affects us and makes us commit grotesque mistakes!
That's why the human brain is not prepared to do trading.
If you want to participate in a fully automated, profitable and consistent trading system; be a Titan Pro 👽
I believe we are walking an extremely enriching path here, not only in terms of financial returns for our community, but also in terms of knowledge about probabilistic and automated statistical trading.
You will have access to an extremely robust system, which was built upon very strong concepts and foundations, and upon the world's main asset in a few years: Bitcoin .
We are the tip of the best that exists in the cryptocurrency market when it comes to probabilistic and automated statistical trading.
Result is result! Me being dressed or naked.
This is just the beginning!
But there is a way to consistently make money from the market.
Being the Casino! - Mark Douglas
8️⃣ : ❓ What is Backtest ❓
Imagine the market as a purely random system, but even in 'randomness' there are patterns.
So now imagine the market and statistical trading as follows:
Repeating the above 'coin' example, let's think of it as follows:
If we toss a coin up 10 times again.
It is impossible to know which flips will have heads or tails, correct?
But if we analyze these 10 tosses, then we will have a mathematical statistic of the past result, for example, 70 % of the tosses fell 'heads'.
That is:
7 moves fell on "heads" .
3 moves fell on "tails" .
So based on these conditions and on the generic backtest presented here, we could adopt " heads " as our system of moves, to have a statistical and probabilistic advantage in relation to the next move to be performed.
That is, if you define a system, based on backtests , that has a robust positive mathematical expectation in relation to the market you will have a profitable system.
For every move you make you will have a positive statistical advantage in your favor over the market before you even make the move.
Like a casino in relation to all its players!
The casino does not have an advantage over one specific player, but over all players, because it has a positive mathematical expectation about all the moves that night.
The casino will always have a positive statistical advantage over its players.
Note that there will always be real players who will make real, million-dollar bankrolls that night, but this condition is already built into the casino's 'strategy', which has a pre-determined positive statistical advantage of that night as a whole.
Statistical trading is the same thing, as long as you don't understand this you will keep losing money and consistently.
9️⃣ : ❓ How to build a Consistent system ❓
See most traders around the world perform trades believing that that specific position taken will make them filthy rich, because they simply believe faithfully that the position taken will be an undoubted winner, based on a trader's methodology: 'trading a trade' without analyzing the whole context, just using 'empirical' aspects in their system.
But if you think of trading, as a sequence of moves.
You see, 'a sequence' !
When we think statistically, it doesn't matter your result for this , or for the next specific trade , but the final sequence of trades as a whole.
As the market has a random system of results distribution , if your system has a positive statistical advantage in relation to the market, at the end of that sequence you'll have the biggest probability of having a winning bank.
That's how you do real trading!
And with consistency!
Trading is a long term game, but when you change the key you realize that it is a simple game to make money in a consistent way from the market, all you need is patience.
Even more when we are based on Bitcoin, which has its 'Halving' effect where, in theory, we will never lose money in 3 to 4 years intervals, due to its scarcity and the fact that Bitcoin is the 'discovery of digital scarcity' which makes it the digital gold, we believe in this thesis and we follow Satoshi's legacy.
So align Bitcoin with a probabilistic statistical trading system with a positive mathematical expectation of the market and 100% automated with the long term, and all you need is patience, and you will become rich.
In fact Bitcoin by itself is already a path, buy, wait for each halving and your wealth will be maintained.
No inflation, unlike fiat currencies.
This is a complete and extremely robust strategy, with the most current possible and 'not possible' techniques involved and applied here.
Today I am at another level in developing 100% automated 'quantitative' strategies.
I was born for this!
🔟 : ❓ What is a Quantitative Trading system ❓
In addition to having access to a revolutionary strategy you will have access to disruptive 100% multifunctional tables with the ability to perform 'backtests' for better tracking and monitoring of your system on a customized basis.
I would like to emphasize one thing, and that is that you keep this in mind.
Today my greatest skill in 'pinescript' is to build indicators, but mainly strategies, based on statistical and probabilistic trading, with a postive mathematical expectation in relation to the market, in a 100% automated way.
This with the goal of building a consistent and continuous positive equity curve through mathematics using data, converting it into statistical / probabilistic parameters and applying them to a Quantitative model.
Before becoming a Quantitative Trader , I was a Technical Analyst and a Discretionary Trader .
First as a position trader and then as a day trader.
Before becoming a Trader, I trained myself as a Technical Analyst , to masterly understand the shape and workings of the market in theory.
But everything changed when I met 'Mark Douglas' , when I got to know his works, that's when my head exploded 🤯, and I started to understand the market for good!
The market is nothing more than a 'random' system of distributing results.
See that I said: 'random' .
Do yourself a mental exercise.
Is there really such a thing as random ?
I believe not, as far as we know maybe the 'singularity'.
So thinking this way, to translate, the market is nothing more than a game of probability, statistics and pure mathematics.
Like a casino!
What happens is that most traders, whenever they take a position, take it with all the empirical certainty that such position will win or lose, and do not take into consideration the total sequence of results to understand their place in the market.
Understanding your place in the market gives you the ability to create and design systems that can exploit the present market anomaly, and thus make money statistically, consistently, and 100% automated.
Thinking of it this way, it is easy to make money from the market.
There are many ways to make money from the market, but the only consistent way I know of is through 'probabilistic and automated statistical trading'.
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
There are some fundamental points that must be addressed here in order to understand what makes up a system based on statistics and probability applied to a quantitative model.
When we talk about 'discretionary' trading, it is a trading system based on human decisions after the defined 'empirical' conditions are met.
It is quite another thing to build a fully automated system without any human interference/interaction .
That said:
Building a statistically profitable system is perfectly possible, but this is a high level task , but with possible high rewards and consistent gains.
Here you will find a real "Skin In The Game" strategy.
With all due respect, but the vast majority of traders who post strategies on TradingView do not understand what they are doing.
Most of them do not understand the minimum complexity involved in the main variable for the construction of a real strategy, the mother variable: "strategy".
I say this by my own experience, because I have analyzed practically all the existing publications of TradingView + 200,000 indicators and strategies.
I breathe pinescript, I eat pinescript, I sleep pinescript, I bathe pinescript, I live TradingView.
But the main advantage for the TradingView users, is that all entry and exit orders made by this strategy can be checked and analyzed thoroughly, to validate and prove the veracity of this strategy, because this is a 100% real strategy.
Here there is a huge world of possibilities, but only one way to build a 'pinescript strategy' that will work correctly aligned to the real world with real results .
There are some fundamental points to take into consideration when building a profitable trading system:
The most important of these for me is: 'DrawDown' .
Followed by: 'Hit Rate' .
And only after that we use the parameter: 'Profit'.
See, this is because here, we are dealing with the 'imponderable' , and anything can happen in this scenario.
But there is one thing that makes us sleep peacefully at night, and that is: controlling losses .
That is, in other words: controlling the DrawDown .
The amateur is concerned with 'winning', the professional is concerned with conserving capital.
If we have the losses under control, then we can move on to the other two parameters: hit rate and profit.
See, the second most important factor in building a system is the hit rate.
I say this from my own experience.
I have worked with many systems with a 'low hit rate', but extremely profitable.
For example: systems with hit rates of 40 to 50%.
But as much as statistically and mathematically the profit is rewarding, operating systems with a low hit rate is always very stressful psychologically.
That's why there are two big reasons why when I build an automated trading system, I focus on the high hit rate of the system, they are
1 - To reduce psychological damage as much as possible .
2 - And more important , when we create a system with a 'high hit rate' , there is a huge intrinsic advantage here, that most statistic traders don't take in consideration.
That is: knowing more quickly when the system stops being functional.
The main advantage of a system with a high hit rate is: to identify when the system stops being functional and stop exploiting the market's anomaly.
Look: When we are talking about trading and random distribution of results on the market, do you agree that when we create a trading system, we are focused on exploring some anomaly of that market?
When that anomaly is verified by the market, it will stop being functional with time.
That's why trading systems, 'scalpers', especially for cryptocurrencies, need constant monitoring, quarterly, semi-annually or annually.
Because market movements change from time to time.
Because we go through different cycles from time to time, such as congestion cycles, accumulation , distribution , volatility , uptrends and downtrends .
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
You see there is a very important point that must be stressed here.
As we are always trying to exploit an 'anomaly' in the market.
So the 'number' of indicators/tools that will integrate the system is of paramount importance.
But most traders do not take this into consideration.
To build a professional, robust, consistent, and profitable system, you don't need to use hundreds of indicators to build your setup.
This will actually make it harder to read when the setup stops working and needs some adjustment.
So focusing on a high hit rate is very important here, this is a fundamental principle that is widely ignored , and with a high hit rate, we can know much more accurately when the system is no longer functional much faster.
As Darwin said: "It is not the strongest or the most intelligent that wins the game of life, it is the most adapted.
So simple systems, as contradictory as it may seem, are more efficient, because they help to identify inflection points in the market much more quickly.
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
See I have built, hundreds of thousands of indicators and 'pinescript' strategies, hundreds of thousands.
This is an extremely professional, robust and profitable system.
Based on the currency pairs: BTC /USDT
There are many ways and avenues to build a profitable trading setup/system.
And actually this is not a difficult task, taking in consideration, as the main factor here, that our trading and investment plan is for the long term, so consequently we will face scenarios with less noise.
He who is in a hurry eats raw.
As mentioned before.
Defining trends in pinescript is technically a simple task, the hardest task is to determine congestion zones with low volume and volatility, it's in these moments that many false signals are generated, and consequently is where most setups face their maximum DrawDown.
That's why this strategy was strictly and thoroughly planned, built on a very solid foundation, to avoid as much noise as possible, for a positive and consistent equity curve in each market cycle, 'Consistency' is our 'Mantra' around here.
1️⃣4️⃣ : 🔧 Fixed Technical
• Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
• Pair: BTC/USDTP
• Time Frame: 4 hours
• Broker: Binance (Recommended)
For a more conservative scenario, we have built the Quantitative THEMIS for the 4h time frame, with the main focus on consistency.
So we can avoid noise as much as possible!
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
In order to build a 'perpetual' system specific to BTC/USDT, it took a lot of testing, and more testing, and a lot of investment and research.
There is one initial and fundamental point that we can address to justify the incredible consistency presented here.
That fundamental point is our exit via Take Profit or Stop Loss percentage (%).
🎯 Take Profit (%)
🛑 Stop Loss (%)
See, today I have been testing some more advanced backtesting models for some cryptocurrency systems.
In which I perform 'backtest of backtest', i.e. we use a set of strategies each focused on a principle, operating individually, but they are part of something unique, i.e. we do 'backtests' of 'backtests' together.
What I mean is that we do a lot of backtesting around here.
I can assure you, that always the best output for a trading system is to set fixed output values!
In other words:
🎯 Take Profit (%)
🛑 Stop Loss (%)
This happens because statistically setting fixed exit structures in the vast majority of times, presents a superior result on the capital/equity curve, throughout history and for the vast majority of setups compared to other exit methods.
This is due to a mathematical principle of simplicity, 'avoiding more noise'.
Thus whenever the Quantitative THEMIS strategy takes a position it has a target and a defined maximum stop percentage.
1️⃣6️⃣ : ⚠️ Risk Profile
The strategy, currently has 3 risk profiles ⚠️ patterns for 'fixed percentage exits': Take Profit (%) and Stop Loss (%) .
They are: ⚠️ Rich's Profiles
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
You will be able to select and switch between the above options and profiles through the 'input' menu of the strategy by navigating to the "⚠️ Risk Profile" menu.
You can then select, test and apply the Risk Profile above that best suits your risk management, expectations and reality , as well as customize all the 'fixed exit' values through the TP and SL menus below.
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
The strategy currently also has 'Moving Exits' based on indicator signals.
These are Moving Exits (Indicators)
📈 LONG : (EXIT)
🧃 (MAO) Short : true
📉 SHORT : (EXIT)
🧃 (MAO) Long: false
You can select and toggle between the above options through the 'input' menu of the strategy by navigating to the "LONG : Exit" and "SHORT : Exit" menu.
1️⃣8️⃣ : 💸 Initial Capital
By default the "Initial Capital" set for entries and backtests of this strategy is: 10000 $
You can set another value for the 'Starting Capital' through the tradingview menu under "properties" , and edit the value of the "Initial Capital" field.
This way you can set and test other 'Entry Values' for your trades, tests and backtests.
1️⃣9️⃣ : ⚙️ Entry Options
By default the 'order size' set for this strategy is 100 % of the 'initial capital' on each new trade.
You can set and test other entry options like : contracts , cash , % of equity
You should make these changes directly in the input menu of the strategy by navigating to the menu "⚙️ Properties : TradingView" below.
⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100
Leverage: 1
So you can define and test other 'Entry Options' for your trades, tests and backtests.
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
It is possible to automate the signals of this strategy for any centralized or decentralized broker, as well as for messaging services: Discord, Telegram and Twitter.
All in an extremely simple and uncomplicated way through the tutorials available in PDF /VIDEO for our Titan Pro 👽 subscriber community.
With our tutorials in PDF and Video it will be possible to automate the signals of this strategy for the chosen service in an extremely simple way with less than 10 steps only.
Tradingview naturally doesn't count with native integration between brokers and tradingview.
But it is possible to use 'third party services' to do the integration and automation between Tradingview and your centralized or decentralized broker.
Here are the standard, available and recommended 'third party services' to automate the signals from the 'Quantitative THEMIS' strategy on the tradingview for your broker:
1) Wundertrading (Recommended):
2) 3commas:
3) Zignaly:
4) Aleeert.com (Recommended):
5) Alertatron:
Note! 'Third party services' cannot perform 'withdrawals' via their key 'API', they can only open positions, so your funds will always be 'safe' in your brokerage firm, being traded via the 'API', when they receive an entry and exit signal from this strategy.
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
You can automate this strategy for any of the brokers below, through your broker's 'API' by connecting it to the 'third party automation services' for tradingview available and mentioned in the menu above:
1) Binance (Recommended)
2) Bitmex
3) Bybit
4) KuCoin
5) Deribit
6) OKX
7) Coinbase
8) Huobi
9) Bitfinex
10) Bitget
11) Bittrex
12) Bitstamp
13) Gate. io
14) Kraken
15) Gemini
16) Ascendex
17) VCCE
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
You can also automate and monitor the signals of this strategy much more efficiently by sending them to the following popular messaging services:
1) Discord
2) Telegram
3) Twitter
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
It will also be possible to copy/replicate the entries and exits of this strategy to your broker in an extremely simple and agile way, through the available copy-trader services.
This way it will be possible to replicate the signals of this strategy at each entry and exit to your broker through the API connecting it to the integrated copy-trader services available through the tradingview automation services below:
1) Wundetrading:
2) Zignaly:
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
I believe that today I am at another level in 'pinescript' development.
I consider myself today a true unicorn as a pinescript developer, someone unique and very rare.
If you choose another tool or another pinescript service, this tool will be just another one, with no real results.
But if you join our Titan community, you will have access to a unique tool! And you will get real results!
I already earn money consistently with statistical and automated trading and as an expert pinescript developer.
I am here to evolve my skills as much as possible, and one day become a pinescript 'Wizard'.
So excellence, quality and professionalism will always be my north here.
You will never find a developer like me, and who will take so seriously such a revolutionary project as this one. A Maverick! ▬ The man never stops!
Here you will find the highest degree of sophistication and development in the market for 'pinescript'.
You will get the best of me and the best of pinescript possible.
Let me show you how a professional in my field does it.
Become a Titan Pro Member 👽 and get Full Access to this strategy and all the Automation Tutorials.
Be the Titan in your life!
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
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2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Pro|BINANCE:BTCUSDTP:4h
® Titan Investimentos | Quantitative THEMIS ⚖️ | Pro 👽 2.6 | Dev: © FilipeSoh 🧙 | 🤖 100% Automated : Discord, Telegram, Twitter, Wundertrading, 3commas, Zignaly, Aleeert, Alertatron, Uniswap-v3 | BINANCE:BTCUSDTPERP 4h
🛒 Subscribe this strategy ❗️ Be a Titan Member 🏛️
🛒 Titan Pro 👽 🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🛒 Titan Affiliate 🛸 🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
📋 Summary : QT THEMIS ⚖️
🕵️♂️ Check This Strategy..................................................................0
🦄 ® Titan Investimentos...............................................................1
👨💻 © Developer..........................................................................2
📚 Signal Automation Tutorials : (PDF/VIDEO).......................................3
👨🔧 Revision...............................................................................4
📊 Table : (BACKTEST)..................................................................5
📊 Table : (INFORMATIONS).............................................................6
⚙️ Properties : (TRADINGVIEW)........................................................7
📆 Backtest : (TRADINGVIEW)..........................................................8
⚠️ Risk Profile...........................................................................9
🟢 On 🔴 Off : (LONG/SHORT).......................................................10
📈 LONG : (ENTRY)....................................................................11
📉 SHORT : (ENTRY)...................................................................12
📈 LONG : (EXIT).......................................................................13
📉 SHORT : (EXIT)......................................................................14
🧩 (EI) External Indicator.............................................................15
📡 (QT) Quantitative...................................................................16
🎠 (FF) Forecast......................................................................17
🅱 (BB) Bollinger Bands................................................................18
🧃 (MAP) Moving Average Primary......................................................19
🧃 (MAP) Labels.........................................................................20
🍔 (MAQ) Moving Average Quaternary.................................................21
🍟 (MACD) Moving Average Convergence Divergence...............................22
📣 (VWAP) Volume Weighted Average Price........................................23
🪀 (HL) HILO..........................................................................24
🅾 (OBV) On Balance Volume.........................................................25
🥊 (SAR) Stop and Reverse...........................................................26
🛡️ (DSR) Dynamic Support and Resistance..........................................27
🔊 (VD) Volume Directional..........................................................28
🧰 (RSI) Relative Momentum Index.................................................29
🎯 (TP) Take Profit %..................................................................30
🛑 (SL) Stop Loss %....................................................................31
🤖 Automation Selected...............................................................32
📱💻 Discord............................................................................33
📱💻 Telegram..........................................................................34
📱💻 Twitter...........................................................................35
🤖 Wundertrading......................................................................36
🤖 3commas............................................................................37
🤖 Zignaly...............................................................................38
🤖 Aleeert...............................................................................39
🤖 Alertatron...........................................................................40
🤖 Uniswap-v3..........................................................................41
🧲🤖 Copy-Trading....................................................................42
♻️ ® No Repaint........................................................................43
🔒 Copyright ©️..........................................................................44
🏛️ Be a Titan Member..................................................................45
Nº Active Users..........................................................................46
⏱ Time Left............................................................................47
| 0 | 🕵️♂️ Check This Strategy
🕵️♂️ Version Demo: 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
🕵️♂️ Version Pro: 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
| 1 | 🦄 ® Titan Investimentos
Decentralizing the World 🗺
Raising the Collective Conscience 🗺
🦄Site:
🦄TradingView: www.tradingview.com
🦄Discord:
🦄Telegram:
🦄Youtube:
🦄Twitter:
🦄Instagram:
🦄TikTok:
🦄Linkedin:
🦄E-mail:
| 2 | 👨💻 © Developer
🧠 Developer: @FilipeSoh🧙
📺 TradingView: www.tradingview.com
☑️ Linkedin:
✅ Fiverr:
✅ Upwork:
🎥 YouTube:
🐤 Twitter:
🤳 Instagram:
| 3 | 📚 Signal Automation Tutorials : (PDF/VIDEO)
📚 Discord: 🔗 Link: 🔒Titan Pro👽
📚 Telegram: 🔗 Link: 🔒Titan Pro👽
📚 Twitter: 🔗 Link: 🔒Titan Pro👽
📚 Wundertrading: 🔗 Link: 🔒Titan Pro👽
📚 3comnas: 🔗 Link: 🔒Titan Pro👽
📚 Zignaly: 🔗 Link: 🔒Titan Pro👽
📚 Aleeert: 🔗 Link: 🔒Titan Pro👽
📚 Alertatron: 🔗 Link: 🔒Titan Pro👽
📚 Uniswap-v3: 🔗 Link: 🔒Titan Pro👽
📚 Copy-Trading: 🔗 Link: 🔒Titan Pro👽
| 4 | 👨🔧 Revision
👨🔧 Start Of Operations: 01 Jan 2019 21:00 -0300 💡 Start Of Operations (Skin in the game) : Revision 1.0
👨🔧 Previous Review: 01 Jan 2022 21:00 -0300 💡 Previous Review : Revision 2.0
👨🔧 Current Revision: 01 Jan 2023 21:00 -0300 💡 Current Revision : Revision 2.6
👨🔧 Next Revision: 28 May 2023 21:00 -0300 💡 Next Revision : Revision 2.7
| 5 | 📊 Table : (BACKTEST)
📊 Table: true
🖌️ Style: label.style_label_left
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 6 | 📊 Table : (INFORMATIONS)
📊 Table: false
🖌️ Style: label.style_label_right
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 7 | ⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100 %
🚀 Leverage: 1
| 8 | 📆 Backtest : (TradingView)
🗓️ Mon: true
🗓️ Tue: true
🗓️ Wed: true
🗓️ Thu: true
🗓️ Fri: true
🗓️ Sat: true
🗓️ Sun: true
📆 Range: custom
📆 Start: UTC 31 Oct 2008 00:00
📆 End: UTC 31 Oct 2030 23:45
📆 Session: 0000-0000
📆 UTC: UTC
| 9 | ⚠️ Risk Profile
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
| 10 | 🟢 On 🔴 Off : (LONG/SHORT)
🟢📈 LONG: true
🟢📉 SHORT: true
| 11 | 📈 LONG : (ENTRY)
📡 (QT) Long: true
🧃 (MAP) Long: false
🅱 (BB) Long: false
🍟 (MACD) Long: false
🅾 (OBV) Long: false
| 12 | 📉 SHORT : (ENTRY)
📡 (QT) Short: true
🧃 (MAP) Short: false
🅱 (BB) Short: false
🍟 (MACD) Short: false
🅾 (OBV) Short: false
| 13 | 📈 LONG : (EXIT)
🧃 (MAP) Short: true
| 14 | 📉 SHORT : (EXIT)
🧃 (MAP) Long: false
| 15 | 🧩 (EI) External Indicator
🧩 (EI) Connect your external indicator/filter: false
🧩 (EI) Connect your indicator here (Study mode only): close
🧩 (EI) Connect your indicator here (Study mode only): close
| 16 | 📡 (QT) Quantitative
📡 (QT) Quantitative: true
📡 (QT) Market: BINANCE:BTCUSDTPERP
📡 (QT) Dice: openai
| 17 | 🎠 (FF) Forecast
🎠 (FF) Include current unclosed current candle: true
🎠 (FF) Forecast Type: flat
🎠 (FF) Nº of candles to use in linear regression: 3
| 18 | 🅱 (BB) Bollinger Bands
🅱 (BB) Bollinger Bands: true
🅱 (BB) Type: EMA
🅱 (BB) Period: 20
🅱 (BB) Source: close
🅱 (BB) Multiplier: 2
🅱 (BB) Linewidth: 0
🅱 (BB) Color: #131722
| 19 | 🧃 (MAP) Moving Average Primary
🧃 (MAP) Moving Average Primary: true
🧃 (MAP) BarColor: false
🧃 (MAP) Background: false
🧃 (MAP) Type: SMA
🧃 (MAP) Source: open
🧃 (MAP) Period: 100
🧃 (MAP) Multiplier: 2.0
🧃 (MAP) Linewidth: 2
🧃 (MAP) Color P: #42bda8
🧃 (MAP) Color N: #801922
| 20 | 🧃 (MAP) Labels
🧃 (MAP) Labels: true
🧃 (MAP) Style BUY ZONE: shape.labelup
🧃 (MAP) Color BUY ZONE: #42bda8
🧃 (MAP) Style SELL ZONE: shape.labeldown
🧃 (MAP) Color SELL ZONE: #801922
| 21 | 🍔 (MAQ) Moving Average Quaternary
🍔 (MAQ) Moving Average Quaternary: true
🍔 (MAQ) BarColor: false
🍔 (MAQ) Background: false
🍔 (MAQ) Type: SMA
🍔 (MAQ) Source: close
🍔 (MAQ) Primary: 14
🍔 (MAQ) Secondary: 22
🍔 (MAQ) Tertiary: 44
🍔 (MAQ) Quaternary: 16
🍔 (MAQ) Linewidth: 0
🍔 (MAQ) Color P: #42bda8
🍔 (MAQ) Color N: #801922
| 22 | 🍟 (MACD) Moving Average Convergence Divergence
🍟 (MACD) Macd Type: EMA
🍟 (MACD) Signal Type: EMA
🍟 (MACD) Source: close
🍟 (MACD) Fast: 12
🍟 (MACD) Slow: 26
🍟 (MACD) Smoothing: 9
| 23 | 📣 (VWAP) Volume Weighted Average Price
📣 (VWAP) Source: close
📣 (VWAP) Period: 340
📣 (VWAP) Momentum A: 84
📣 (VWAP) Momentum B: 150
📣 (VWAP) Average Volume: 1
📣 (VWAP) Multiplier: 1
📣 (VWAP) Diviser: 2
| 24 | 🪀 (HL) HILO
🪀 (HL) Type: SMA
🪀 (HL) Function: Maverick🧙
🪀 (HL) Source H: high
🪀 (HL) Source L: low
🪀 (HL) Period: 20
🪀 (HL) Momentum: 26
🪀 (HL) Diviser: 2
🪀 (HL) Multiplier: 1
| 25 | 🅾 (OBV) On Balance Volume
🅾 (OBV) Type: EMA
🅾 (OBV) Source: close
🅾 (OBV) Period: 16
🅾 (OBV) Diviser: 2
🅾 (OBV) Multiplier: 1
| 26 | 🥊 (SAR) Stop and Reverse
🥊 (SAR) Source: close
🥊 (SAR) High: 1.8
🥊 (SAR) Mid: 1.6
🥊 (SAR) Low: 1.6
🥊 (SAR) Diviser: 2
🥊 (SAR) Multiplier: 1
| 27 | 🛡️ (DSR) Dynamic Support and Resistance
🛡️ (DSR) Source D: close
🛡️ (DSR) Source R: high
🛡️ (DSR) Source S: low
🛡️ (DSR) Momentum R: 0
🛡️ (DSR) Momentum S: 2
🛡️ (DSR) Diviser: 2
🛡️ (DSR) Multiplier: 1
| 28 | 🔊 (VD) Volume Directional
🔊 (VD) Type: SMA
🔊 (VD) Period: 68
🔊 (VD) Momentum: 3.8
🔊 (VD) Diviser: 2
🔊 (VD) Multiplier: 1
| 29 | 🧰 (RSI) Relative Momentum Index
🧰 (RSI) Type UP: EMA
🧰 (RSI) Type DOWN: EMA
🧰 (RSI) Source: close
🧰 (RSI) Period: 29
🧰 (RSI) Smoothing: 22
🧰 (RSI) Momentum R: 64
🧰 (RSI) Momentum S: 142
🧰 (RSI) Diviser: 2
🧰 (RSI) Multiplier: 1
| 30 | 🎯 (TP) Take Profit %
🎯 (TP) Take Profit: false
🎯 (TP) %: 2.2
🎯 (TP) Color: #42bda8
🎯 (TP) Linewidth: 1
| 31 | 🛑 (SL) Stop Loss %
🛑 (SL) Stop Loss: false
🛑 (SL) %: 2.7
🛑 (SL) Color: #801922
🛑 (SL) Linewidth: 1
| 32 | 🤖 Automation : Discord | Telegram | Twitter | Wundertrading | 3commas | Zignaly | Aleeert | Alertatron | Uniswap-v3
🤖 Automation Selected : Discord
| 33 | 🤖 Discord
🔗 Link Discord: discord.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Long: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Long: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Short: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Short: 🔒Titan Pro👽
| 34 | 🤖 Telegram
🔗 Link Telegram: telegram.org
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Short: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Short: 🔒Titan Pro👽
| 35 | 🤖 Twitter
🔗 Link Twitter: twitter.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Short: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Short: 🔒Titan Pro👽
| 36 | 🤖 Wundertrading : Binance | Bitmex | Bybit | KuCoin | Deribit | OKX | Coinbase | Huobi | Bitfinex | Bitget
🔗 Link Wundertrading: wundertrading.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Short: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Short: 🔒Titan Pro👽
| 37 | 🤖 3commas : Binance | Bybit | OKX | Bitfinex | Coinbase | Deribit | Bitmex | Bittrex | Bitstamp | Gate.io | Kraken | Gemini | Huobi | KuCoin
🔗 Link 3commas: 3commas.io
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Long: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Long: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Short: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Short: 🔒Titan Pro👽
| 38 | 🤖 Zignaly : Binance | Ascendex | Bitmex | Kucoin | VCCE
🔗 Link Zignaly: zignaly.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
🤖 Type Automation: Profit Sharing
🤖 Type Provider: Webook
🔑 Key: 🔒Titan Pro👽
🤖 pair: BTCUSDTP
🤖 exchange: binance
🤖 exchangeAccountType: futures
🤖 orderType: market
🚀 leverage: 1x
% positionSizePercentage: 100 %
💸 positionSizeQuote: 10000 $
🆔 signalId: @Signal1234
| 39 | 🤖 Aleeert : Binance
🔗 Link Aleeert: aleeert.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Short: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Short: 🔒Titan Pro👽
| 40 | 🤖 Alertatron : Binance | Bybit | Deribit | Bitmex
🔗 Link Alertatron: alertatron.com
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Short: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Short: 🔒Titan Pro👽
| 41 | 🤖 Uniswap-v3
🔗 Link Alertatron: uniswap.org
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Short: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Short: 🔒Titan Pro👽
| 42 | 🧲🤖 Copy-Trading : Zignaly | Wundertrading
🔗 Link 📚 Copy-Trading: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Zignaly: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Wundertrading: 🔒Titan Pro👽
| 43 | ♻️ ® Don't Repaint!
♻️ This Strategy does not Repaint!: ® Signs Do not repaint❕
♻️ This is a Real Strategy!: Quality : ® Titan Investimentos
📋️️ Get more information about Repainting here:
| 44 | 🔒 Copyright ©️
🔒 Copyright ©️: Copyright © 2023-2024 All rights reserved, ® Titan Investimentos
🔒 Copyright ©️: ® Titan Investimentos
🔒 Copyright ©️: Unique and Exclusive Strategy. All rights reserved
| 45 | 🏛️ Be a Titan Members
🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
| 46 | ⏱ Time Left
Time Left Titan Demo 🐄: ⏱♾ | ⏱ : ♾ Titan Demo 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
Time Left Titan Pro 👽: 🔒Titan Pro👽 | ⏱ : Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months. (👽 Pro 🅼 Monthly, 👽 Pro 🆀 Quarterly, 👽 Pro🅰 Annual, 👽 Pro👾Two Years)
| 47 | Nº Active Users
Nº Active Subscribers Titan Pro 👽: 5️⃣6️⃣ | 1✔️ 5✔️ 10✔️ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
Nº Active Affiliates Titan Aff 🛸: 6️⃣ | 1✔️ 5✔️ 10❌ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🆑 Conservative: 🎯 TP=2.7 % | 🛑 SL=2.7 %
• 📆All years: 🆑 Conservative: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1669.89 %
💲 + 166989.43 USD
Total Close Trades:
⚪️ 369
Percent Profitable:
🟡 64.77 %
Profit Factor:
🟢 2.314
DrawDrown Maximum:
🔴 -24.82 %
💲 -10221.43 USD
Avg Trade:
💲 + 452.55 USD
✔️ Trades Winning: 239
❌ Trades Losing: 130
✔️ Average Gross Win: + 12.31 %
❌ Average Gross Loss: - 9.78 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 499.33 %
% Average Gain Monthly: 41.61 %
% Average Gain Weekly: 9.6 %
% Average Gain Day: 1.37 %
💲 Average Gain Annual: 49933 $
💲 Average Gain Monthly: 4161 $
💲 Average Gain Weekly: 960 $
💲 Average Gain Day: 137 $
• 📆 Year: 2020: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🆑 Conservative: 🚀 Leverage 1️⃣x
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: Ⓜ️ Moderate: 🎯 TP=2.8 % | 🛑 SL=2.7 %
• 📆 All years: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1472.04 %
💲 + 147199.89 USD
Total Close Trades:
⚪️ 362
Percent Profitable:
🟡 63.26 %
Profit Factor:
🟢 2.192
DrawDrown Maximum:
🔴 -22.69 %
💲 -9269.33 USD
Avg Trade:
💲 + 406.63 USD
✔️ Trades Winning: 229
❌ Trades Losing : 133
✔️ Average Gross Win: + 11.82 %
❌ Average Gross Loss: - 9.29 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 8
% Average Gain Annual: 440.15 %
% Average Gain Monthly: 36.68 %
% Average Gain Weekly: 8.46 %
% Average Gain Day: 1.21 %
💲 Average Gain Annual: 44015 $
💲 Average Gain Monthly: 3668 $
💲 Average Gain Weekly: 846 $
💲 Average Gain Day: 121 $
• 📆 Year: 2020: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🅰 Aggressive: 🎯 TP=1.6 % | 🛑 SL=6.9 %
• 📆 All years: 🅰 Aggressive: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 989.38 %
💲 + 98938.38 USD
Total Close Trades:
⚪️ 380
Percent Profitable:
🟢 84.47 %
Profit Factor:
🟢 2.156
DrawDrown Maximum:
🔴 -17.88 %
💲 -9182.84 USD
Avg Trade:
💲 + 260.36 USD
✔️ Trades Winning: 321
❌ Trades Losing: 59
✔️ Average Gross Win: + 5.75 %
❌ Average Gross Loss: - 14.51 %
✔️ Maximum Consecutive Wins: 21
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 295.84 %
% Average Gain Monthly: 24.65 %
% Average Gain Weekly: 5.69 %
% Average Gain Day: 0.81 %
💲 Average Gain Annual: 29584 $
💲 Average Gain Monthly: 2465 $
💲 Average Gain Weekly: 569 $
💲 Average Gain Day: 81 $
• 📆 Year: 2020: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🅰 Aggressive: 🚀 Leverage 1️⃣x
3️⃣0️⃣ : 🛠️ Roadmap
🛠️• 14/ 01 /2023 : Titan THEMIS Launch
🛠️• Updates January/2023 :
• 📚 Tutorials for Automation 🤖 already Available : ✔️
• ✔️ Discord
• ✔️ Wundertrading
• ✔️ Zignaly
• 📚 Tutorials for Automation 🤖 In Preparation : ⭕
• ⭕ Telegram
• ⭕ Twitter
• ⭕ 3comnas
• ⭕ Aleeert
• ⭕ Alertatron
• ⭕ Uniswap-v3
• ⭕ Copy-Trading
🛠️• Updates February/2023 :
• 📰 Launch of advertising material for Titan Affiliates 🛸
• 🛍️🎥🖼️📊 (Sales Page/VSL/Videos/Creative/Infographics)
🛠️• 28/05/2023 : Titan THEMIS update ▬ Version 2.7
🛠️• 28/05/2023 : BOT BOB release ▬ Version 1.0
• (Native Titan THEMIS Automation - Through BOT BOB, a bot for automation of signals, indicators and strategies of TradingView, of own code ▬ in validation.
• BOT BOB
Automation/Connection :
• API - For Centralized Brokers.
• Smart Contracts - Wallet Web - For Decentralized Brokers.
• This way users can automate any indicator or strategy of TradingView and Titan in a decentralized, secure and simplified way.
• Without having the need to use 'third party services' for automating TradingView indicators and strategies like the ones available above.
🛠️• 28/05/2023 : Release ▬ Titan Culture Guide 📝
3️⃣1️⃣ : 🧻 Notes ❕
🧻 • Note ❕ The "Demo 🐄" version, ❌does not have 'integrated automation', to automate the signals of this strategy and enjoy a fully automated system, you need to have access to the Pro version with '100% integrated automation' and all the tutorials for automation available. Become a Titan Pro 👽
🧻 • Note ❕ You will also need to be a "Pro User or higher on Tradingview", to be able to use the webhook feature available only for 'paid' profiles on the platform.
With the webhook feature it is possible to send the signals of this strategy to almost anywhere, in our case to centralized or decentralized brokerages, also to popular messaging services such as: Discord, Telegram or Twiter.
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
🚨 • Disclaimer ❕❕ Past positive result and performance of a system does not guarantee its positive result and performance for the future!
🚨 • Disclaimer ❗❗❗ When using this strategy: Titan Investments is totally Exempt from any claim of liability for losses. The responsibility on the management of your funds is solely yours. This is a very high risk/volatility market! Understand your place in the market.
3️⃣3️⃣ : ♻️ ® No Repaint
This Strategy does not Repaint! This is a real strategy!
3️⃣4️⃣ : 🔒 Copyright ©️
Copyright © 2022-2023 All rights reserved, ® Titan Investimentos
3️⃣5️⃣ : 👏 Acknowledgments
I want to start this message in thanks to TradingView and all the Pinescript community for all the 'magic' created here, a unique ecosystem! rich and healthy, a fertile soil, a 'new world' of possibilities, for a complete deepening and improvement of our best personal skills.
I leave here my immense thanks to the whole community: Tradingview, Pinecoders, Wizards and Moderators.
I was not born Rich .
Thanks to TradingView and pinescript and all its transformation.
I could develop myself and the best of me and the best of my skills.
And consequently build wealth and patrimony.
Gratitude.
One more story for the infinite book !
If you were born poor you were born to be rich !
Raising🔼 the level and raising🔼 the ruler! 📏
My work is my 'debauchery'! Do better! 💐🌹
Soul of a first-timer! Creativity Exudes! 🦄
This is the manifestation of God's magic in me. This is the best of me. 🧙
You will copy me, I know. So you owe me. 💋
My mission here is to raise the consciousness and self-esteem of all Titans and Titanids! Welcome! 🧘 🏛️
The only way to accomplish great work is to do what you love ! Before I learned to program I was wasting my life!
Death is the best creation of life .
Now you are the new , but in the not so distant future you will gradually become the old . Here I stay forever!
Playing the game like an Athlete! 🖼️ Enjoy and Enjoy 🍷 🗿
In honor of: BOB ☆
1 name, 3 letters, 3 possibilities, and if read backwards it's the same thing, a palindrome. ☘
Gratitude to the oracles that have enabled me the 'luck' to get this far: Dal&Ni&Fer
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
House Rules : This publication and strategy follows all TradingView house guidelines and rules:
📺 TradingView House Rules: www.tradingview.com
📺 Script publication rules: www.tradingview.com
📺 Vendor requirements: www.tradingview.com
📺 Links/References rules: www.tradingview.com
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
🟩 Titan Pro 👽 🟩
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
🟥 Titan Affiliate 🛸 🟥
Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4hInvestment Strategy (Quantitative Trading)
| 🛑 | Watch "LIVE" and 'COPY' this strategy in real time:
🔗 Link: www.tradingview.com
Hello, welcome, feel free 🌹💐
Since the stone age to the most technological age, one thing has not changed, that which continues impress human beings the most, is the other human being!
Deep down, it's all very simple or very complicated, depends on how you look at it.
I believe that everyone was born to do something very well in life.
But few are those who have, let's use the word 'luck' .
Few are those who have the 'luck' to discover this thing.
That is why few are happy and successful in their jobs and professions.
Thank God I had this 'luck' , and discovered what I was born to do well.
And I was born to program. 👨💻
📋 Summary : Project Titan
0️⃣ : 🦄 Project Titan
1️⃣ : ⚖️ Quantitative THEMIS
2️⃣ : 🏛️ Titan Community
3️⃣ : 👨💻 Who am I ❔
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
8️⃣ : ❓ What is Backtest ❓
9️⃣ : ❓ How to build a Consistent system ❓
🔟 : ❓ What is a Quantitative Trading system ❓
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
1️⃣4️⃣ : 🔧 Fixed Technical
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
1️⃣6️⃣ : ⚠️ Risk Profile
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
1️⃣8️⃣ : 💸 Initial Capital
1️⃣9️⃣ : ⚙️ Entry Options
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
3️⃣0️⃣ : 🛠️ Roadmap
3️⃣1️⃣ : 🧻 Notes ❕
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
3️⃣3️⃣ : ♻️ ® No Repaint
3️⃣4️⃣ : 🔒 Copyright ©️
3️⃣5️⃣ : 👏 Acknowledgments
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
0️⃣ : 🦄 Project Titan
This is the first real, 100% automated Quantitative Strategy made available to the public and the pinescript community for TradingView.
You will be able to automate all signals of this strategy for your broker , centralized or decentralized and also for messaging services : Discord, Telegram or Twitter .
This is the first strategy of a larger project, in 2023, I will provide a total of 6 100% automated 'Quantitative' strategies to the pinescript community for TradingView.
The future strategies to be shared here will also be unique , never before seen, real 'Quantitative' bots with real, validated results in real operation.
Just like the 'Quantitative THEMIS' strategy, it will be something out of the loop throughout the pinescript/tradingview community, truly unique tools for building mutual wealth consistently and continuously for our community.
1️⃣ : ⚖️ Quantitative THEMIS : Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
This is a truly unique and out of the curve strategy for BTC /USD .
A truly real strategy, with real, validated results and in real operation.
A unique tool for building mutual wealth, consistently and continuously for the members of the Titan community.
Initially we will operate on a monthly, quarterly, annual or biennial subscription service.
Our goal here is to build a great community, in exchange for an extremely fair value for the use of our truly unique tools, which bring and will bring real results to our community members.
With this business model it will be possible to provide all Titan users and community members with the purest and highest degree of sophistication in the market with pinescript for tradingview, providing unique and truly profitable strategies.
My goal here is to offer the best to our members!
The best 'pinescript' tradingview service in the world!
We are the only Start-Up in the world that will decentralize real and full access to truly real 'quantitative' tools that bring and will bring real results for mutual and ongoing wealth building for our community.
2️⃣ : 🏛️ Titan Community : 👽 Pro 🔁 Aff 🛸
Become a Titan Pro 👽
To get access to the strategy: "Quantitative THEMIS" , and future Titan strategies in a 100% automated way, along with all tutorials for automation.
Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months.
👽 Pro 🅼 Monthly
👽 Pro 🆀 Quarterly
👽 Pro🅰 Annual
👽 Pro👾Two Years
You will have access to a truly unique system that is out of the curve .
A 100% real, 100% automated, tested, validated, profitable, and in real operation strategy.
Become a Titan Affiliate 🛸
By becoming a Titan Affiliate 🛸, you will automatically receive 50% of the value of each new subscription you refer .
You will receive 50% for any of the above plans that you refer .
This way we will encourage our community to grow in a fair and healthy way, because we know what we have in our hands and what we deliver real value to our users.
We are at the highest level of sophistication in the market, the consistency here and the results here speak for themselves.
So growing our community means growing mutual wealth and raising collective conscience.
Wealth must be created not divided.
And here we are creating mutual wealth on all ends and in all ways.
A non-zero sum system, where everybody wins.
3️⃣ : 👨💻 Who am I ❔
My name is FilipeSoh I am 26 years old, Technical Analyst, Trader, Computer Engineer, pinescript Specialist, with extensive experience in several languages and technologies.
For the last 4 years I have been focusing on developing, editing and creating pinescript indicators and strategies for Tradingview for people and myself.
Full-time passionate workaholic pinescript developer with over 10,000 hours of pinescript development.
• Pinescript expert ▬Tradingview.
• Specialist in Automated Trading
• Specialist in Quantitative Trading.
• Statistical/Probabilistic Trading Specialist - Mark Douglas Scholl.
• Inventor of the 'Classic Forecast' Indicators.
• Inventor of the 'Backtest Table'.
4️⃣ : ❓ What is Statistical/Probabilistic Trading ❓
Statistical/probabilistic trading is the only way to get a positive mathematical expectation regarding the market and consequently that is the only way to make money consistently from it.
I will present below some more details about the Quantitative THEMIS strategy, it is a real strategy, tested, validated and in real operation, 'Skin in the Game' , a consistent way to make money with statistical/probabilistic trading in a 100% automated.
I am a Technical Analyst , I used to be a Discretionary Trader , today I am 100% a Statistical Trader .
I've gotten rich and made a lot of money, and I've also lost a lot with 'leverage'.
That was a few years ago.
The book that changed everything for me was "Trading in The Zone" by Mark Douglas.
That's when I understood that the market is just a game of statistics and probability, like a casino!
It was then that I understood that the human brain is not prepared for trading, because it involves triggers and mental emotions.
And emotions in trading and in making trading decisions do not go well together, not in the long run, because you always have the burden of being wrong with the outcome of that particular position.
But remembering that the market is just a statistical game!
5️⃣ : ❓ How Statistical/Probabilistic Trading works ❓
Let's use a 'coin' as an example:
If we toss a 'coin' up 10 times.
Do you agree that it is impossible for us to know exactly the result of the 'plays' before they actually happen?
As in the example above, would you agree, that we cannot "guess" the outcome of a position before it actually happens?
As much as we cannot "guess" whether the coin will drop heads or tails on each flip.
We can analyze the "backtest" of the 10 moves made with that coin:
If we analyze the 10 moves and count the number of times the coin fell heads or tails in a specific sequence, we then have a percentage of times the coin fell heads or tails, so we have a 'backtest' of those moves.
Then on the next flip we can now assume a point or a favorable position for one side, the side with the highest probability .
In a nutshell, this is more or less how probabilistic statistical trading works.
As Statistical Traders we can never say whether such a Trader/Position we take will be a winner or a loser.
But still we can have a positive and consistent result in a "sequence" of trades, because before we even open a position, backtests have already been performed so we identify an anomaly and build a system that will have a positive statistical advantage in our favor over the market.
The advantage will not be in one trade itself, but in the "sequence" of trades as a whole!
Because our system will work like a casino, having a positive mathematical expectation relative to the players/market.
Design, develop, test models and systems that can take advantage of market anomalies, until they change.
Be the casino! - Mark Douglas
6️⃣ : ❓ Why use a Statistical/Probabilistic system ❓
In recent years I have focused and specialized in developing 100% automated trading systems, essentially for the cryptocurrency market.
I have developed many extremely robust and efficient systems, with positive mathematical expectation towards the market.
These are not complex systems per se , because here we want to avoid 'over-optimization' as much as possible.
As Da Vinci said: "Simplicity is the highest degree of sophistication".
I say this because I have tested, tried and developed hundreds of systems/strategies.
I believe I have programmed more than 10,000 unique indicators/strategies, because this is my passion and purpose in life.
I am passionate about what I do, completely!
I love statistical trading because it is the only way to get consistency in the long run!
This is why I have studied, applied, developed, and specialized in 100% automated cryptocurrency trading systems.
The reason why our systems are extremely "simple" is because, as I mentioned before, in statistical trading we want to exploit the market anomaly to the maximum, that is, this anomaly will change from time to time, usually we can exploit a trading system efficiently for about 6 to 12 months, or for a few years, that is; for fixed 'scalpers' systems.
Because at some point these anomalies will be identified , and from the moment they are identified they will be exploited and will stop being anomalies .
With the system presented here; you can even copy the indicators and input values shared here;
However; what I have to offer you is: it is me , our team , and our community !
That is, we will constantly monitor this system, for life , because our goal here is to create a unique , perpetual , profitable , and consistent system for our community.
Myself , our team and our community will keep this script periodically updated , to ensure the positive mathematical expectation of it.
So we don't mind sharing the current parameters and values , because the real value is also in the future updates that this system will receive from me and our team , guided by our culture and our community of real users !
As we are hosted on 'tradingview', all future updates for this strategy, will be implemented and updated automatically on your tradingview account.
What we want here is: to make sure you get gains from our system, because if you get gains , our ecosystem will grow as a whole in a healthy and scalable way, so we will be generating continuous mutual wealth and raising the collective consciousness .
People Need People: 3️⃣🅿
7️⃣ : ❓ Why the human brain is not prepared to do Trading ❓
Today my greatest skill is to develop statistically profitable and 100% automated strategies for 'pinescript' tradingview.
Note that I said: 'profitable' because in fact statistical trading is the only way to make money in a 'consistent' way from the market.
And consequently have a positive wealth curve every cycle, because we will be based on mathematics, not on feelings and news.
Because the human brain is not prepared to do trading.
Because trading is connected to the decision making of the cerebral cortex.
And the decision making is automatically linked to emotions, and emotions don't match with trading decision making, because in those moments, we can feel the best and also the worst sensations and emotions, and this certainly affects us and makes us commit grotesque mistakes!
That's why the human brain is not prepared to do trading.
If you want to participate in a fully automated, profitable and consistent trading system; be a Titan Pro 👽
I believe we are walking an extremely enriching path here, not only in terms of financial returns for our community, but also in terms of knowledge about probabilistic and automated statistical trading.
You will have access to an extremely robust system, which was built upon very strong concepts and foundations, and upon the world's main asset in a few years: Bitcoin .
We are the tip of the best that exists in the cryptocurrency market when it comes to probabilistic and automated statistical trading.
Result is result! Me being dressed or naked.
This is just the beginning!
But there is a way to consistently make money from the market.
Being the Casino! - Mark Douglas
8️⃣ : ❓ What is Backtest ❓
Imagine the market as a purely random system, but even in 'randomness' there are patterns.
So now imagine the market and statistical trading as follows:
Repeating the above 'coin' example, let's think of it as follows:
If we toss a coin up 10 times again.
It is impossible to know which flips will have heads or tails, correct?
But if we analyze these 10 tosses, then we will have a mathematical statistic of the past result, for example, 70 % of the tosses fell 'heads'.
That is:
7 moves fell on "heads" .
3 moves fell on "tails" .
So based on these conditions and on the generic backtest presented here, we could adopt " heads " as our system of moves, to have a statistical and probabilistic advantage in relation to the next move to be performed.
That is, if you define a system, based on backtests , that has a robust positive mathematical expectation in relation to the market you will have a profitable system.
For every move you make you will have a positive statistical advantage in your favor over the market before you even make the move.
Like a casino in relation to all its players!
The casino does not have an advantage over one specific player, but over all players, because it has a positive mathematical expectation about all the moves that night.
The casino will always have a positive statistical advantage over its players.
Note that there will always be real players who will make real, million-dollar bankrolls that night, but this condition is already built into the casino's 'strategy', which has a pre-determined positive statistical advantage of that night as a whole.
Statistical trading is the same thing, as long as you don't understand this you will keep losing money and consistently.
9️⃣ : ❓ How to build a Consistent system ❓
See most traders around the world perform trades believing that that specific position taken will make them filthy rich, because they simply believe faithfully that the position taken will be an undoubted winner, based on a trader's methodology: 'trading a trade' without analyzing the whole context, just using 'empirical' aspects in their system.
But if you think of trading, as a sequence of moves.
You see, 'a sequence' !
When we think statistically, it doesn't matter your result for this , or for the next specific trade , but the final sequence of trades as a whole.
As the market has a random system of results distribution , if your system has a positive statistical advantage in relation to the market, at the end of that sequence you'll have the biggest probability of having a winning bank.
That's how you do real trading!
And with consistency!
Trading is a long term game, but when you change the key you realize that it is a simple game to make money in a consistent way from the market, all you need is patience.
Even more when we are based on Bitcoin, which has its 'Halving' effect where, in theory, we will never lose money in 3 to 4 years intervals, due to its scarcity and the fact that Bitcoin is the 'discovery of digital scarcity' which makes it the digital gold, we believe in this thesis and we follow Satoshi's legacy.
So align Bitcoin with a probabilistic statistical trading system with a positive mathematical expectation of the market and 100% automated with the long term, and all you need is patience, and you will become rich.
In fact Bitcoin by itself is already a path, buy, wait for each halving and your wealth will be maintained.
No inflation, unlike fiat currencies.
This is a complete and extremely robust strategy, with the most current possible and 'not possible' techniques involved and applied here.
Today I am at another level in developing 100% automated 'quantitative' strategies.
I was born for this!
🔟 : ❓ What is a Quantitative Trading system ❓
In addition to having access to a revolutionary strategy you will have access to disruptive 100% multifunctional tables with the ability to perform 'backtests' for better tracking and monitoring of your system on a customized basis.
I would like to emphasize one thing, and that is that you keep this in mind.
Today my greatest skill in 'pinescript' is to build indicators, but mainly strategies, based on statistical and probabilistic trading, with a postive mathematical expectation in relation to the market, in a 100% automated way.
This with the goal of building a consistent and continuous positive equity curve through mathematics using data, converting it into statistical / probabilistic parameters and applying them to a Quantitative model.
Before becoming a Quantitative Trader , I was a Technical Analyst and a Discretionary Trader .
First as a position trader and then as a day trader.
Before becoming a Trader, I trained myself as a Technical Analyst , to masterly understand the shape and workings of the market in theory.
But everything changed when I met 'Mark Douglas' , when I got to know his works, that's when my head exploded 🤯, and I started to understand the market for good!
The market is nothing more than a 'random' system of distributing results.
See that I said: 'random' .
Do yourself a mental exercise.
Is there really such a thing as random ?
I believe not, as far as we know maybe the 'singularity'.
So thinking this way, to translate, the market is nothing more than a game of probability, statistics and pure mathematics.
Like a casino!
What happens is that most traders, whenever they take a position, take it with all the empirical certainty that such position will win or lose, and do not take into consideration the total sequence of results to understand their place in the market.
Understanding your place in the market gives you the ability to create and design systems that can exploit the present market anomaly, and thus make money statistically, consistently, and 100% automated.
Thinking of it this way, it is easy to make money from the market.
There are many ways to make money from the market, but the only consistent way I know of is through 'probabilistic and automated statistical trading'.
1️⃣1️⃣ : ❓ How to build a Quantitative Trading system ❓
There are some fundamental points that must be addressed here in order to understand what makes up a system based on statistics and probability applied to a quantitative model.
When we talk about 'discretionary' trading, it is a trading system based on human decisions after the defined 'empirical' conditions are met.
It is quite another thing to build a fully automated system without any human interference/interaction .
That said:
Building a statistically profitable system is perfectly possible, but this is a high level task , but with possible high rewards and consistent gains.
Here you will find a real "Skin In The Game" strategy.
With all due respect, but the vast majority of traders who post strategies on TradingView do not understand what they are doing.
Most of them do not understand the minimum complexity involved in the main variable for the construction of a real strategy, the mother variable: "strategy".
I say this by my own experience, because I have analyzed practically all the existing publications of TradingView + 200,000 indicators and strategies.
I breathe pinescript, I eat pinescript, I sleep pinescript, I bathe pinescript, I live TradingView.
But the main advantage for the TradingView users, is that all entry and exit orders made by this strategy can be checked and analyzed thoroughly, to validate and prove the veracity of this strategy, because this is a 100% real strategy.
Here there is a huge world of possibilities, but only one way to build a 'pinescript strategy' that will work correctly aligned to the real world with real results .
There are some fundamental points to take into consideration when building a profitable trading system:
The most important of these for me is: 'DrawDown' .
Followed by: 'Hit Rate' .
And only after that we use the parameter: 'Profit'.
See, this is because here, we are dealing with the 'imponderable' , and anything can happen in this scenario.
But there is one thing that makes us sleep peacefully at night, and that is: controlling losses .
That is, in other words: controlling the DrawDown .
The amateur is concerned with 'winning', the professional is concerned with conserving capital.
If we have the losses under control, then we can move on to the other two parameters: hit rate and profit.
See, the second most important factor in building a system is the hit rate.
I say this from my own experience.
I have worked with many systems with a 'low hit rate', but extremely profitable.
For example: systems with hit rates of 40 to 50%.
But as much as statistically and mathematically the profit is rewarding, operating systems with a low hit rate is always very stressful psychologically.
That's why there are two big reasons why when I build an automated trading system, I focus on the high hit rate of the system, they are
1 - To reduce psychological damage as much as possible .
2 - And more important , when we create a system with a 'high hit rate' , there is a huge intrinsic advantage here, that most statistic traders don't take in consideration.
That is: knowing more quickly when the system stops being functional.
The main advantage of a system with a high hit rate is: to identify when the system stops being functional and stop exploiting the market's anomaly.
Look: When we are talking about trading and random distribution of results on the market, do you agree that when we create a trading system, we are focused on exploring some anomaly of that market?
When that anomaly is verified by the market, it will stop being functional with time.
That's why trading systems, 'scalpers', especially for cryptocurrencies, need constant monitoring, quarterly, semi-annually or annually.
Because market movements change from time to time.
Because we go through different cycles from time to time, such as congestion cycles, accumulation , distribution , volatility , uptrends and downtrends .
1️⃣2️⃣ : ❓ How to Exploit Market Anomalies ❓
You see there is a very important point that must be stressed here.
As we are always trying to exploit an 'anomaly' in the market.
So the 'number' of indicators/tools that will integrate the system is of paramount importance.
But most traders do not take this into consideration.
To build a professional, robust, consistent, and profitable system, you don't need to use hundreds of indicators to build your setup.
This will actually make it harder to read when the setup stops working and needs some adjustment.
So focusing on a high hit rate is very important here, this is a fundamental principle that is widely ignored , and with a high hit rate, we can know much more accurately when the system is no longer functional much faster.
As Darwin said: "It is not the strongest or the most intelligent that wins the game of life, it is the most adapted.
So simple systems, as contradictory as it may seem, are more efficient, because they help to identify inflection points in the market much more quickly.
1️⃣3️⃣ : ❓ What Defines a Robust, Profitable and Consistent System ❓
See I have built, hundreds of thousands of indicators and 'pinescript' strategies, hundreds of thousands.
This is an extremely professional, robust and profitable system.
Based on the currency pairs: BTC /USDT
There are many ways and avenues to build a profitable trading setup/system.
And actually this is not a difficult task, taking in consideration, as the main factor here, that our trading and investment plan is for the long term, so consequently we will face scenarios with less noise.
He who is in a hurry eats raw.
As mentioned before.
Defining trends in pinescript is technically a simple task, the hardest task is to determine congestion zones with low volume and volatility, it's in these moments that many false signals are generated, and consequently is where most setups face their maximum DrawDown.
That's why this strategy was strictly and thoroughly planned, built on a very solid foundation, to avoid as much noise as possible, for a positive and consistent equity curve in each market cycle, 'Consistency' is our 'Mantra' around here.
1️⃣4️⃣ : 🔧 Fixed Technical
• Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
• Pair: BTC/USDTP
• Time Frame: 4 hours
• Broker: Binance (Recommended)
For a more conservative scenario, we have built the Quantitative THEMIS for the 4h time frame, with the main focus on consistency.
So we can avoid noise as much as possible!
1️⃣5️⃣ : ❌ Fixed Outputs : 🎯 TP(%) & 🛑SL(%)
In order to build a 'perpetual' system specific to BTC/USDT, it took a lot of testing, and more testing, and a lot of investment and research.
There is one initial and fundamental point that we can address to justify the incredible consistency presented here.
That fundamental point is our exit via Take Profit or Stop Loss percentage (%).
🎯 Take Profit (%)
🛑 Stop Loss (%)
See, today I have been testing some more advanced backtesting models for some cryptocurrency systems.
In which I perform 'backtest of backtest', i.e. we use a set of strategies each focused on a principle, operating individually, but they are part of something unique, i.e. we do 'backtests' of 'backtests' together.
What I mean is that we do a lot of backtesting around here.
I can assure you, that always the best output for a trading system is to set fixed output values!
In other words:
🎯 Take Profit (%)
🛑 Stop Loss (%)
This happens because statistically setting fixed exit structures in the vast majority of times, presents a superior result on the capital/equity curve, throughout history and for the vast majority of setups compared to other exit methods.
This is due to a mathematical principle of simplicity, 'avoiding more noise'.
Thus whenever the Quantitative THEMIS strategy takes a position it has a target and a defined maximum stop percentage.
1️⃣6️⃣ : ⚠️ Risk Profile
The strategy, currently has 3 risk profiles ⚠️ patterns for 'fixed percentage exits': Take Profit (%) and Stop Loss (%) .
They are: ⚠️ Rich's Profiles
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
You will be able to select and switch between the above options and profiles through the 'input' menu of the strategy by navigating to the "⚠️ Risk Profile" menu.
You can then select, test and apply the Risk Profile above that best suits your risk management, expectations and reality , as well as customize all the 'fixed exit' values through the TP and SL menus below.
1️⃣7️⃣ : ⭕ Moving Exits : (Indicators)
The strategy currently also has 'Moving Exits' based on indicator signals.
These are Moving Exits (Indicators)
📈 LONG : (EXIT)
🧃 (MAO) Short : true
📉 SHORT : (EXIT)
🧃 (MAO) Long: false
You can select and toggle between the above options through the 'input' menu of the strategy by navigating to the "LONG : Exit" and "SHORT : Exit" menu.
1️⃣8️⃣ : 💸 Initial Capital
By default the "Initial Capital" set for entries and backtests of this strategy is: 10000 $
You can set another value for the 'Starting Capital' through the tradingview menu under "properties" , and edit the value of the "Initial Capital" field.
This way you can set and test other 'Entry Values' for your trades, tests and backtests.
1️⃣9️⃣ : ⚙️ Entry Options
By default the 'order size' set for this strategy is 100 % of the 'initial capital' on each new trade.
You can set and test other entry options like : contracts , cash , % of equity
You should make these changes directly in the input menu of the strategy by navigating to the menu "⚙️ Properties : TradingView" below.
⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100
Leverage: 1
So you can define and test other 'Entry Options' for your trades, tests and backtests.
2️⃣0️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Third-Party Services'
It is possible to automate the signals of this strategy for any centralized or decentralized broker, as well as for messaging services: Discord, Telegram and Twitter.
All in an extremely simple and uncomplicated way through the tutorials available in PDF /VIDEO for our Titan Pro 👽 subscriber community.
With our tutorials in PDF and Video it will be possible to automate the signals of this strategy for the chosen service in an extremely simple way with less than 10 steps only.
Tradingview naturally doesn't count with native integration between brokers and tradingview.
But it is possible to use 'third party services' to do the integration and automation between Tradingview and your centralized or decentralized broker.
Here are the standard, available and recommended 'third party services' to automate the signals from the 'Quantitative THEMIS' strategy on the tradingview for your broker:
1) Wundertrading (Recommended):
2) 3commas:
3) Zignaly:
4) Aleeert.com (Recommended):
5) Alertatron:
Note! 'Third party services' cannot perform 'withdrawals' via their key 'API', they can only open positions, so your funds will always be 'safe' in your brokerage firm, being traded via the 'API', when they receive an entry and exit signal from this strategy.
2️⃣1️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Exchanges
You can automate this strategy for any of the brokers below, through your broker's 'API' by connecting it to the 'third party automation services' for tradingview available and mentioned in the menu above:
1) Binance (Recommended)
2) Bitmex
3) Bybit
4) KuCoin
5) Deribit
6) OKX
7) Coinbase
8) Huobi
9) Bitfinex
10) Bitget
11) Bittrex
12) Bitstamp
13) Gate. io
14) Kraken
15) Gemini
16) Ascendex
17) VCCE
2️⃣2️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : 'Messaging Services'
You can also automate and monitor the signals of this strategy much more efficiently by sending them to the following popular messaging services:
1) Discord
2) Telegram
3) Twitter
2️⃣3️⃣ : ❓ How to Automate this Strategy ❓ : 🤖 Automation : '🧲🤖Copy-Trading'
It will also be possible to copy/replicate the entries and exits of this strategy to your broker in an extremely simple and agile way, through the available copy-trader services.
This way it will be possible to replicate the signals of this strategy at each entry and exit to your broker through the API connecting it to the integrated copy-trader services available through the tradingview automation services below:
1) Wundetrading:
2) Zignaly:
2️⃣4️⃣ : ❔ Why be a Titan Pro 👽❔
I believe that today I am at another level in 'pinescript' development.
I consider myself today a true unicorn as a pinescript developer, someone unique and very rare.
If you choose another tool or another pinescript service, this tool will be just another one, with no real results.
But if you join our Titan community, you will have access to a unique tool! And you will get real results!
I already earn money consistently with statistical and automated trading and as an expert pinescript developer.
I am here to evolve my skills as much as possible, and one day become a pinescript 'Wizard'.
So excellence, quality and professionalism will always be my north here.
You will never find a developer like me, and who will take so seriously such a revolutionary project as this one. A Maverick! ▬ The man never stops!
Here you will find the highest degree of sophistication and development in the market for 'pinescript'.
You will get the best of me and the best of pinescript possible.
Let me show you how a professional in my field does it.
Become a Titan Pro Member 👽 and get Full Access to this strategy and all the Automation Tutorials.
Be the Titan in your life!
2️⃣5️⃣ : ❔ Why be a Titan Aff 🛸❔
Get financial return for your referrals, Decentralize the World, and raise the collective consciousness.
2️⃣6️⃣ : 📋 Summary : ⚖️ Strategy: Titan Investments|Quantitative THEMIS|Demo|BINANCE:BTCUSDTP:4h
® Titan Investimentos | Quantitative THEMIS ⚖️ | Demo 🐄 2.6 | Dev: © FilipeSoh 🧙 | 🤖 100% Automated : Discord, Telegram, Twitter, Wundertrading, 3commas, Zignaly, Aleeert, Alertatron, Uniswap-v3 | BINANCE:BTCUSDTPERP 4h
🛒 Subscribe this strategy ❗️ Be a Titan Member 🏛️
🛒 Titan Pro 👽 🔗 🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🛒 Titan Affiliate 🛸 🔗 🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
📋 Summary : QT THEMIS ⚖️
🕵️♂️ Check This Strategy..................................................................0
🦄 ® Titan Investimentos...............................................................1
👨💻 © Developer..........................................................................2
📚 Signal Automation Tutorials : (PDF/VIDEO).......................................3
👨🔧 Revision...............................................................................4
📊 Table : (BACKTEST)..................................................................5
📊 Table : (INFORMATIONS).............................................................6
⚙️ Properties : (TRADINGVIEW)........................................................7
📆 Backtest : (TRADINGVIEW)..........................................................8
⚠️ Risk Profile...........................................................................9
🟢 On 🔴 Off : (LONG/SHORT).......................................................10
📈 LONG : (ENTRY)....................................................................11
📉 SHORT : (ENTRY)...................................................................12
📈 LONG : (EXIT).......................................................................13
📉 SHORT : (EXIT)......................................................................14
🧩 (EI) External Indicator.............................................................15
📡 (QT) Quantitative...................................................................16
🎠 (FF) Forecast......................................................................17
🅱 (BB) Bollinger Bands................................................................18
🧃 (MAP) Moving Average Primary......................................................19
🧃 (MAP) Labels.........................................................................20
🍔 (MAQ) Moving Average Quaternary.................................................21
🍟 (MACD) Moving Average Convergence Divergence...............................22
📣 (VWAP) Volume Weighted Average Price........................................23
🪀 (HL) HILO..........................................................................24
🅾 (OBV) On Balance Volume.........................................................25
🥊 (SAR) Stop and Reverse...........................................................26
🛡️ (DSR) Dynamic Support and Resistance..........................................27
🔊 (VD) Volume Directional..........................................................28
🧰 (RSI) Relative Momentum Index.................................................29
🎯 (TP) Take Profit %..................................................................30
🛑 (SL) Stop Loss %....................................................................31
🤖 Automation Selected...............................................................32
📱💻 Discord............................................................................33
📱💻 Telegram..........................................................................34
📱💻 Twitter...........................................................................35
🤖 Wundertrading......................................................................36
🤖 3commas............................................................................37
🤖 Zignaly...............................................................................38
🤖 Aleeert...............................................................................39
🤖 Alertatron...........................................................................40
🤖 Uniswap-v3..........................................................................41
🧲🤖 Copy-Trading....................................................................42
♻️ ® No Repaint........................................................................43
🔒 Copyright ©️..........................................................................44
🏛️ Be a Titan Member..................................................................45
Nº Active Users..........................................................................46
⏱ Time Left............................................................................47
| 0 | 🕵️♂️ Check This Strategy
🕵️♂️ Version Demo: 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
🕵️♂️ Version Pro: 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
| 1 | 🦄 ® Titan Investimentos
Decentralizing the World 🗺
Raising the Collective Conscience 🗺
🦄Site:
🦄TradingView: www.tradingview.com
🦄Discord:
🦄Telegram:
🦄Youtube:
🦄Twitter:
🦄Instagram:
🦄TikTok:
🦄Linkedin:
🦄E-mail:
| 2 | 👨💻 © Developer
🧠 Developer: @FilipeSoh🧙
📺 TradingView: www.tradingview.com
☑️ Linkedin:
✅ Fiverr:
✅ Upwork:
🎥 YouTube:
🐤 Twitter:
🤳 Instagram:
| 3 | 📚 Signal Automation Tutorials : (PDF/VIDEO)
📚 Discord: 🔗 Link: 🔒Titan Pro👽
📚 Telegram: 🔗 Link: 🔒Titan Pro👽
📚 Twitter: 🔗 Link: 🔒Titan Pro👽
📚 Wundertrading: 🔗 Link: 🔒Titan Pro👽
📚 3comnas: 🔗 Link: 🔒Titan Pro👽
📚 Zignaly: 🔗 Link: 🔒Titan Pro👽
📚 Aleeert: 🔗 Link: 🔒Titan Pro👽
📚 Alertatron: 🔗 Link: 🔒Titan Pro👽
📚 Uniswap-v3: 🔗 Link: 🔒Titan Pro👽
📚 Copy-Trading: 🔗 Link: 🔒Titan Pro👽
| 4 | 👨🔧 Revision
👨🔧 Start Of Operations: 01 Jan 2019 21:00 -0300 💡 Start Of Operations (Skin in the game) : Revision 1.0
👨🔧 Previous Review: 01 Jan 2022 21:00 -0300 💡 Previous Review : Revision 2.0
👨🔧 Current Revision: 01 Jan 2023 21:00 -0300 💡 Current Revision : Revision 2.6
👨🔧 Next Revision: 28 May 2023 21:00 -0300 💡 Next Revision : Revision 2.7
| 5 | 📊 Table : (BACKTEST)
📊 Table: true
🖌️ Style: label.style_label_left
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 6 | 📊 Table : (INFORMATIONS)
📊 Table: false
🖌️ Style: label.style_label_right
📐 Size: size_small
📏 Line: defval
🎨 Color: #131722
| 7 | ⚙️ Properties : (TradingView)
📊 Strategy Type: strategy.position_size != 1
📝💲 % Order Type: % of equity
📝💲 % Order Size: 100 %
🚀 Leverage: 1
| 8 | 📆 Backtest : (TradingView)
🗓️ Mon: true
🗓️ Tue: true
🗓️ Wed: true
🗓️ Thu: true
🗓️ Fri: true
🗓️ Sat: true
🗓️ Sun: true
📆 Range: custom
📆 Start: UTC 31 Oct 2008 00:00
📆 End: UTC 31 Oct 2030 23:45
📆 Session: 0000-0000
📆 UTC: UTC
| 9 | ⚠️ Risk Profile
✔️🆑 Conservative: 🎯 TP=2.7 % 🛑 SL=2.7 %
❌Ⓜ️ Moderate: 🎯 TP=2.8 % 🛑 SL=2.7 %
❌🅰 Aggressive: 🎯 TP=1.6 % 🛑 SL=6.9 %
| 10 | 🟢 On 🔴 Off : (LONG/SHORT)
🟢📈 LONG: true
🟢📉 SHORT: true
| 11 | 📈 LONG : (ENTRY)
📡 (QT) Long: true
🧃 (MAP) Long: false
🅱 (BB) Long: false
🍟 (MACD) Long: false
🅾 (OBV) Long: false
| 12 | 📉 SHORT : (ENTRY)
📡 (QT) Short: true
🧃 (MAP) Short: false
🅱 (BB) Short: false
🍟 (MACD) Short: false
🅾 (OBV) Short: false
| 13 | 📈 LONG : (EXIT)
🧃 (MAP) Short: true
| 14 | 📉 SHORT : (EXIT)
🧃 (MAP) Long: false
| 15 | 🧩 (EI) External Indicator
🧩 (EI) Connect your external indicator/filter: false
🧩 (EI) Connect your indicator here (Study mode only): close
🧩 (EI) Connect your indicator here (Study mode only): close
| 16 | 📡 (QT) Quantitative
📡 (QT) Quantitative: true
📡 (QT) Market: BINANCE:BTCUSDTPERP
📡 (QT) Dice: openai
| 17 | 🎠 (FF) Forecast
🎠 (FF) Include current unclosed current candle: true
🎠 (FF) Forecast Type: flat
🎠 (FF) Nº of candles to use in linear regression: 3
| 18 | 🅱 (BB) Bollinger Bands
🅱 (BB) Bollinger Bands: true
🅱 (BB) Type: EMA
🅱 (BB) Period: 20
🅱 (BB) Source: close
🅱 (BB) Multiplier: 2
🅱 (BB) Linewidth: 0
🅱 (BB) Color: #131722
| 19 | 🧃 (MAP) Moving Average Primary
🧃 (MAP) Moving Average Primary: true
🧃 (MAP) BarColor: false
🧃 (MAP) Background: false
🧃 (MAP) Type: SMA
🧃 (MAP) Source: open
🧃 (MAP) Period: 100
🧃 (MAP) Multiplier: 2.0
🧃 (MAP) Linewidth: 2
🧃 (MAP) Color P: #42bda8
🧃 (MAP) Color N: #801922
| 20 | 🧃 (MAP) Labels
🧃 (MAP) Labels: true
🧃 (MAP) Style BUY ZONE: shape.labelup
🧃 (MAP) Color BUY ZONE: #42bda8
🧃 (MAP) Style SELL ZONE: shape.labeldown
🧃 (MAP) Color SELL ZONE: #801922
| 21 | 🍔 (MAQ) Moving Average Quaternary
🍔 (MAQ) Moving Average Quaternary: true
🍔 (MAQ) BarColor: false
🍔 (MAQ) Background: false
🍔 (MAQ) Type: SMA
🍔 (MAQ) Source: close
🍔 (MAQ) Primary: 14
🍔 (MAQ) Secondary: 22
🍔 (MAQ) Tertiary: 44
🍔 (MAQ) Quaternary: 16
🍔 (MAQ) Linewidth: 0
🍔 (MAQ) Color P: #42bda8
🍔 (MAQ) Color N: #801922
| 22 | 🍟 (MACD) Moving Average Convergence Divergence
🍟 (MACD) Macd Type: EMA
🍟 (MACD) Signal Type: EMA
🍟 (MACD) Source: close
🍟 (MACD) Fast: 12
🍟 (MACD) Slow: 26
🍟 (MACD) Smoothing: 9
| 23 | 📣 (VWAP) Volume Weighted Average Price
📣 (VWAP) Source: close
📣 (VWAP) Period: 340
📣 (VWAP) Momentum A: 84
📣 (VWAP) Momentum B: 150
📣 (VWAP) Average Volume: 1
📣 (VWAP) Multiplier: 1
📣 (VWAP) Diviser: 2
| 24 | 🪀 (HL) HILO
🪀 (HL) Type: SMA
🪀 (HL) Function: Maverick🧙
🪀 (HL) Source H: high
🪀 (HL) Source L: low
🪀 (HL) Period: 20
🪀 (HL) Momentum: 26
🪀 (HL) Diviser: 2
🪀 (HL) Multiplier: 1
| 25 | 🅾 (OBV) On Balance Volume
🅾 (OBV) Type: EMA
🅾 (OBV) Source: close
🅾 (OBV) Period: 16
🅾 (OBV) Diviser: 2
🅾 (OBV) Multiplier: 1
| 26 | 🥊 (SAR) Stop and Reverse
🥊 (SAR) Source: close
🥊 (SAR) High: 1.8
🥊 (SAR) Mid: 1.6
🥊 (SAR) Low: 1.6
🥊 (SAR) Diviser: 2
🥊 (SAR) Multiplier: 1
| 27 | 🛡️ (DSR) Dynamic Support and Resistance
🛡️ (DSR) Source D: close
🛡️ (DSR) Source R: high
🛡️ (DSR) Source S: low
🛡️ (DSR) Momentum R: 0
🛡️ (DSR) Momentum S: 2
🛡️ (DSR) Diviser: 2
🛡️ (DSR) Multiplier: 1
| 28 | 🔊 (VD) Volume Directional
🔊 (VD) Type: SMA
🔊 (VD) Period: 68
🔊 (VD) Momentum: 3.8
🔊 (VD) Diviser: 2
🔊 (VD) Multiplier: 1
| 29 | 🧰 (RSI) Relative Momentum Index
🧰 (RSI) Type UP: EMA
🧰 (RSI) Type DOWN: EMA
🧰 (RSI) Source: close
🧰 (RSI) Period: 29
🧰 (RSI) Smoothing: 22
🧰 (RSI) Momentum R: 64
🧰 (RSI) Momentum S: 142
🧰 (RSI) Diviser: 2
🧰 (RSI) Multiplier: 1
| 30 | 🎯 (TP) Take Profit %
🎯 (TP) Take Profit: false
🎯 (TP) %: 2.2
🎯 (TP) Color: #42bda8
🎯 (TP) Linewidth: 1
| 31 | 🛑 (SL) Stop Loss %
🛑 (SL) Stop Loss: false
🛑 (SL) %: 2.7
🛑 (SL) Color: #801922
🛑 (SL) Linewidth: 1
| 32 | 🤖 Automation : Discord | Telegram | Twitter | Wundertrading | 3commas | Zignaly | Aleeert | Alertatron | Uniswap-v3
🤖 Automation Selected : Discord
| 33 | 🤖 Discord
🔗 Link Discord:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Long: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Long: 🔒Titan Pro👽
📱💻 Discord ▬ Enter Short: 🔒Titan Pro👽
📱💻 Discord ▬ Exit Short: 🔒Titan Pro👽
| 34 | 🤖 Telegram
🔗 Link Telegram:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Long: 🔒Titan Pro👽
📱💻 Telegram ▬ Enter Short: 🔒Titan Pro👽
📱💻 Telegram ▬ Exit Short: 🔒Titan Pro👽
| 35 | 🤖 Twitter
🔗 Link Twitter:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Long: 🔒Titan Pro👽
📱💻 Twitter ▬ Enter Short: 🔒Titan Pro👽
📱💻 Twitter ▬ Exit Short: 🔒Titan Pro👽
| 36 | 🤖 Wundertrading : Binance | Bitmex | Bybit | KuCoin | Deribit | OKX | Coinbase | Huobi | Bitfinex | Bitget
🔗 Link Wundertrading:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Long: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Enter Short: 🔒Titan Pro👽
📱💻 Wundertrading ▬ Exit Short: 🔒Titan Pro👽
| 37 | 🤖 3commas : Binance | Bybit | OKX | Bitfinex | Coinbase | Deribit | Bitmex | Bittrex | Bitstamp | Gate.io | Kraken | Gemini | Huobi | KuCoin
🔗 Link 3commas:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Long: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Long: 🔒Titan Pro👽
📱💻 3commas ▬ Enter Short: 🔒Titan Pro👽
📱💻 3commas ▬ Exit Short: 🔒Titan Pro👽
| 38 | 🤖 Zignaly : Binance | Ascendex | Bitmex | Kucoin | VCCE
🔗 Link Zignaly:
🔗 Link 📚 Automation: 🔒Titan Pro👽
🤖 Type Automation: Profit Sharing
🤖 Type Provider: Webook
🔑 Key: 🔒Titan Pro👽
🤖 pair: BTCUSDTP
🤖 exchange: binance
🤖 exchangeAccountType: futures
🤖 orderType: market
🚀 leverage: 1x
% positionSizePercentage: 100 %
💸 positionSizeQuote: 10000 $
🆔 signalId: @Signal1234
| 39 | 🤖 Aleeert : Binance
🔗 Link Aleeert:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Long: 🔒Titan Pro👽
📱💻 Aleeert ▬ Enter Short: 🔒Titan Pro👽
📱💻 Aleeert ▬ Exit Short: 🔒Titan Pro👽
| 40 | 🤖 Alertatron : Binance | Bybit | Deribit | Bitmex
🔗 Link Alertatron:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Long: 🔒Titan Pro👽
📱💻 Alertatron ▬ Enter Short: 🔒Titan Pro👽
📱💻 Alertatron ▬ Exit Short: 🔒Titan Pro👽
| 41 | 🤖 Uniswap-v3
🔗 Link Alertatron:
🔗 Link 📚 Automation: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Long: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Enter Short: 🔒Titan Pro👽
📱💻 Uniswap-v3 ▬ Exit Short: 🔒Titan Pro👽
| 42 | 🧲🤖 Copy-Trading : Zignaly | Wundertrading
🔗 Link 📚 Copy-Trading: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Zignaly: 🔒Titan Pro👽
🧲🤖 Copy-Trading ▬ Wundertrading: 🔒Titan Pro👽
| 43 | ♻️ ® Don't Repaint!
♻️ This Strategy does not Repaint!: ® Signs Do not repaint❕
♻️ This is a Real Strategy!: Quality : ® Titan Investimentos
📋️️ Get more information about Repainting here:
| 44 | 🔒 Copyright ©️
🔒 Copyright ©️: Copyright © 2023-2024 All rights reserved, ® Titan Investimentos
🔒 Copyright ©️: ® Titan Investimentos
🔒 Copyright ©️: Unique and Exclusive Strategy. All rights reserved
| 45 | 🏛️ Be a Titan Members
🏛️ Titan Pro 👽 Version with ✔️100% Integrated Automation 🤖 and 📚 Automation Tutorials ✔️100% available at: (PDF/VIDEO)
🏛️ Titan Affiliate 🛸 (Subscription Sale) 🔥 Receive 50% commission
| 46 | ⏱ Time Left
Time Left Titan Demo 🐄: ⏱♾ | ⏱ : ♾ Titan Demo 🐄 Version with ❌non-integrated automation 🤖 and 📚 Tutorials for automation ❌not available
Time Left Titan Pro 👽: 🔒Titan Pro👽 | ⏱ : Pro Plans: 30 Days, 90 Days, 12 Months, 24 Months. (👽 Pro 🅼 Monthly, 👽 Pro 🆀 Quarterly, 👽 Pro🅰 Annual, 👽 Pro👾Two Years)
| 47 | Nº Active Users
Nº Active Subscribers Titan Pro 👽: 5️⃣6️⃣ | 1✔️ 5✔️ 10✔️ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
Nº Active Affiliates Titan Aff 🛸: 6️⃣ | 1✔️ 5✔️ 10❌ 100❌ 1K❌ 10K❌ 50K❌ 100K❌ 1M❌ 10M❌ 100M❌ : ⏱ Active Users is updated every 24 hours (Check on indicator)
2️⃣7️⃣ : 📊 PERFORMANCE : 🆑 Conservative
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🆑 Conservative: 🎯 TP=2.7 % | 🛑 SL=2.7 %
• 📆All years: 🆑 Conservative: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1669.89 %
💲 + 166989.43 USD
Total Close Trades:
⚪️ 369
Percent Profitable:
🟡 64.77 %
Profit Factor:
🟢 2.314
DrawDrown Maximum:
🔴 -24.82 %
💲 -10221.43 USD
Avg Trade:
💲 + 452.55 USD
✔️ Trades Winning: 239
❌ Trades Losing: 130
✔️ Average Gross Win: + 12.31 %
❌ Average Gross Loss: - 9.78 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 499.33 %
% Average Gain Monthly: 41.61 %
% Average Gain Weekly: 9.6 %
% Average Gain Day: 1.37 %
💲 Average Gain Annual: 49933 $
💲 Average Gain Monthly: 4161 $
💲 Average Gain Weekly: 960 $
💲 Average Gain Day: 137 $
• 📆 Year: 2020: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🆑 Conservative: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🆑 Conservative: 🚀 Leverage 1️⃣x
2️⃣8️⃣ : 📊 PERFORMANCE : Ⓜ️ Moderate
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: Ⓜ️ Moderate: 🎯 TP=2.8 % | 🛑 SL=2.7 %
• 📆 All years: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 1472.04 %
💲 + 147199.89 USD
Total Close Trades:
⚪️ 362
Percent Profitable:
🟡 63.26 %
Profit Factor:
🟢 2.192
DrawDrown Maximum:
🔴 -22.69 %
💲 -9269.33 USD
Avg Trade:
💲 + 406.63 USD
✔️ Trades Winning: 229
❌ Trades Losing : 133
✔️ Average Gross Win: + 11.82 %
❌ Average Gross Loss: - 9.29 %
✔️ Maximum Consecutive Wins: 9
❌ Maximum Consecutive Losses: 8
% Average Gain Annual: 440.15 %
% Average Gain Monthly: 36.68 %
% Average Gain Weekly: 8.46 %
% Average Gain Day: 1.21 %
💲 Average Gain Annual: 44015 $
💲 Average Gain Monthly: 3668 $
💲 Average Gain Weekly: 846 $
💲 Average Gain Day: 121 $
• 📆 Year: 2020: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: Ⓜ️ Moderate: 🚀 Leverage 1️⃣x
2️⃣9️⃣ : 📊 PERFORMANCE : 🅰 Aggressive
📊 Exchange: Binance
📊 Pair: BINANCE: BTCUSDTPERP
📊 TimeFrame: 4h
📊 Initial Capital: 10000 $
📊 Order Type: % equity
📊 Size Per Order: 100 %
📊 Commission: 0.03 %
📊 Pyramid: 1
• ⚠️ Risk Profile: 🅰 Aggressive: 🎯 TP=1.6 % | 🛑 SL=6.9 %
• 📆 All years: 🅰 Aggressive: 🚀 Leverage 1️⃣x
📆 Start: September 23, 2019
📆 End: January 11, 2023
📅 Days: 1221
📅 Bars: 7325
Net Profit:
🟢 + 989.38 %
💲 + 98938.38 USD
Total Close Trades:
⚪️ 380
Percent Profitable:
🟢 84.47 %
Profit Factor:
🟢 2.156
DrawDrown Maximum:
🔴 -17.88 %
💲 -9182.84 USD
Avg Trade:
💲 + 260.36 USD
✔️ Trades Winning: 321
❌ Trades Losing: 59
✔️ Average Gross Win: + 5.75 %
❌ Average Gross Loss: - 14.51 %
✔️ Maximum Consecutive Wins: 21
❌ Maximum Consecutive Losses: 6
% Average Gain Annual: 295.84 %
% Average Gain Monthly: 24.65 %
% Average Gain Weekly: 5.69 %
% Average Gain Day: 0.81 %
💲 Average Gain Annual: 29584 $
💲 Average Gain Monthly: 2465 $
💲 Average Gain Weekly: 569 $
💲 Average Gain Day: 81 $
• 📆 Year: 2020: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2021: 🅰 Aggressive: 🚀 Leverage 1️⃣x
• 📆 Year: 2022: 🅰 Aggressive: 🚀 Leverage 1️⃣x
3️⃣0️⃣ : 🛠️ Roadmap
🛠️• 14/ 01 /2023 : Titan THEMIS Launch
🛠️• Updates January/2023 :
• 📚 Tutorials for Automation 🤖 already Available : ✔️
• ✔️ Discord
• ✔️ Wundertrading
• ✔️ Zignaly
• 📚 Tutorials for Automation 🤖 In Preparation : ⭕
• ⭕ Telegram
• ⭕ Twitter
• ⭕ 3comnas
• ⭕ Aleeert
• ⭕ Alertatron
• ⭕ Uniswap-v3
• ⭕ Copy-Trading
🛠️• Updates February/2023 :
• 📰 Launch of advertising material for Titan Affiliates 🛸
• 🛍️🎥🖼️📊 (Sales Page/VSL/Videos/Creative/Infographics)
🛠️• 28/05/2023 : Titan THEMIS update ▬ Version 2.7
🛠️• 28/05/2023 : BOT BOB release ▬ Version 1.0
• (Native Titan THEMIS Automation - Through BOT BOB, a bot for automation of signals, indicators and strategies of TradingView, of own code ▬ in validation.
• BOT BOB
Automation/Connection :
• API - For Centralized Brokers.
• Smart Contracts - Wallet Web - For Decentralized Brokers.
• This way users can automate any indicator or strategy of TradingView and Titan in a decentralized, secure and simplified way.
• Without having the need to use 'third party services' for automating TradingView indicators and strategies like the ones available above.
🛠️• 28/05/2023 : Release ▬ Titan Culture Guide 📝
3️⃣1️⃣ : 🧻 Notes ❕
🧻 • Note ❕ The "Demo 🐄" version, ❌does not have 'integrated automation', to automate the signals of this strategy and enjoy a fully automated system, you need to have access to the Pro version with '100% integrated automation' and all the tutorials for automation available. Become a Titan Pro 👽
🧻 • Note ❕ You will also need to be a "Pro User or higher on Tradingview", to be able to use the webhook feature available only for 'paid' profiles on the platform.
With the webhook feature it is possible to send the signals of this strategy to almost anywhere, in our case to centralized or decentralized brokerages, also to popular messaging services such as: Discord, Telegram or Twiter.
3️⃣2️⃣ : 🚨 Disclaimer ❕❗
🚨 • Disclaimer ❕❕ Past positive result and performance of a system does not guarantee its positive result and performance for the future!
🚨 • Disclaimer ❗❗❗ When using this strategy: Titan Investments is totally Exempt from any claim of liability for losses. The responsibility on the management of your funds is solely yours. This is a very high risk/volatility market! Understand your place in the market.
3️⃣3️⃣ : ♻️ ® No Repaint
This Strategy does not Repaint! This is a real strategy!
3️⃣4️⃣ : 🔒 Copyright ©️
Copyright © 2022-2023 All rights reserved, ® Titan Investimentos
3️⃣5️⃣ : 👏 Acknowledgments
I want to start this message in thanks to TradingView and all the Pinescript community for all the 'magic' created here, a unique ecosystem! rich and healthy, a fertile soil, a 'new world' of possibilities, for a complete deepening and improvement of our best personal skills.
I leave here my immense thanks to the whole community: Tradingview, Pinecoders, Wizards and Moderators.
I was not born Rich .
Thanks to TradingView and pinescript and all its transformation.
I could develop myself and the best of me and the best of my skills.
And consequently build wealth and patrimony.
Gratitude.
One more story for the infinite book !
If you were born poor you were born to be rich !
Raising🔼 the level and raising🔼 the ruler! 📏
My work is my 'debauchery'! Do better! 💐🌹
Soul of a first-timer! Creativity Exudes! 🦄
This is the manifestation of God's magic in me. This is the best of me. 🧙
You will copy me, I know. So you owe me. 💋
My mission here is to raise the consciousness and self-esteem of all Titans and Titanids! Welcome! 🧘 🏛️
The only way to accomplish great work is to do what you love ! Before I learned to program I was wasting my life!
Death is the best creation of life .
Now you are the new , but in the not so distant future you will gradually become the old . Here I stay forever!
Playing the game like an Athlete! 🖼️ Enjoy and Enjoy 🍷 🗿
In honor of: BOB ☆
1 name, 3 letters, 3 possibilities, and if read backwards it's the same thing, a palindrome. ☘
Gratitude to the oracles that have enabled me the 'luck' to get this far: Dal&Ni&Fer
3️⃣6️⃣ : 👮 House Rules : 📺 TradingView
House Rules : This publication and strategy follows all TradingView house guidelines and rules:
📺 TradingView House Rules: www.tradingview.com
📺 Script publication rules: www.tradingview.com
📺 Vendor requirements: www.tradingview.com
📺 Links/References rules: www.tradingview.com
3️⃣7️⃣ : 🏛️ Become a Titan Pro member 👽
🟩 Titan Pro 👽 🟩
3️⃣8️⃣ : 🏛️ Be a member Titan Aff 🛸
🟥 Titan Affiliate 🛸 🟥
Order Block Finder [MHA Finverse]A comprehensive order block detection indicator that identifies institutional buying and selling zones with advanced volume filtering and King Order Block (KOB) detection.
Key Features
Dual Structure Detection
- Internal Order Blocks: Short-term zones based on 5-bar pivots for precise entries
- Swing Order Blocks: Higher timeframe zones based on 50-bar pivots for major support/resistance levels
- Toggle between structures or display both simultaneously
King Order Block (KOB)
The indicator automatically identifies the order block with the highest volume and marks it as the "King Order Block" - typically the most significant institutional zone that deserves priority attention. You can optionally color-code KOB blocks differently for instant visual recognition.
Intelligent Volume Filtering
Uses ATR or Cumulative Mean Range to filter out low-quality blocks and focus only on institutional-sized moves. This ensures you're trading zones where smart money has left clear footprints.
Volume Metrics Display
Each order block shows its actual volume and percentage contribution, helping you prioritize which zones to trade. Higher volume blocks indicate stronger institutional interest.
Flexible Lookback Periods
Choose from preset trading styles (Scalping: 50 bars, Day Trading: 150 bars, Swing Trading: 400 bars, Position Trading: 750 bars) or set custom lookback periods to match your strategy.
Advanced Mitigation Options
Select between Close or High/Low mitigation methods to control when order blocks are considered invalidated.
King Order Block (KOB)
The King Order Block is the order block with the highest volume among all active blocks on your chart. It represents the zone where the largest institutional activity occurred, making it the most significant level for potential reversals. When multiple order blocks are present, the KOB should be your primary focus as it typically offers higher probability setups. The indicator automatically identifies and marks the KOB with a special label, and you can optionally color-code it differently for instant visual recognition. KOB zones often produce stronger reactions when price returns to test them, allowing for more confident entries with tighter stop losses.
How to Use
1. For Short-Term Trading: Enable Internal Order Blocks to identify quick reversals and entries on lower timeframes.
2. For Position Trading: Enable Swing Order Blocks to find major institutional zones on higher timeframes.
3. Focus on KOB: When multiple blocks appear, prioritize the King Order Block as it represents the highest volume zone.
4. Volume Confirmation: Check the volume metrics - blocks with 30%+ volume contribution are typically stronger zones.
5. Entry Strategy: Wait for price to return to an order block, look for rejection signals (pin bars, engulfing patterns), then enter with stops below/above the block.
6. Adjust Lookback: Match the lookback period to your trading timeframe for optimal block visibility.
Color Themes
- Colored Mode: Distinct colors for bullish/bearish and internal/swing blocks
- Monochrome Mode: Professional grayscale palette for clean charts
Alert System
Comprehensive alerts for:
- Order block creation (internal/swing, bullish/bearish)
- Order block mitigation
- King Order Block events
Perfect for traders who follow Smart Money Concepts (SMC), institutional order flow, and supply/demand zones.
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice or trading recommendations. Past performance does not guarantee future results. Always conduct your own analysis, use proper risk management, and never risk more than you can afford to lose. Trading involves substantial risk of loss.
TradingView.To Strategy Template (with Dyanmic Alerts)Hello traders,
If you're tired of manual trading and looking for a solid strategy template to pair with your indicators, look no further.
This Pine Script v5 strategy template is engineered for maximum customization and risk management.
Best part?
This Pine Script v5 template facilitates the dynamic construction of TradingView.TO alerts, sparing users the time and effort of mastering the TradingView.TO syntax and manually create alert commands.
This powerful tool gives much power to those who don't know how to code in Pinescript and want to automate their indicators' signals via TradingView.TO bot.
IMPORTANT NOTES
TradingView.TO is a trading bot software that forwards TradingView alerts to your brokers (examples: Binance, Oanda, Coinbase, Bybit, Metatrader 4/5, ...) for automating trading.
Many traders don't know how to create TradingView.TO dynamically-compatible alerts using the data from their TradingView scripts.
Traders using trading bots want their alerts to reflect the stop-loss/take-profit/trailing-stop/stop-loss to break options from your script and then create the orders accordingly.
This script showcases how to create TradingView.TO alerts dynamically.
TRADINGVIEW ALERTS
1) You'll have to create one alert per asset X timeframe = 1 chart.
Example: 1 alert for BTC/USDT on the 5 minutes chart, 1 alert for BTC/USDT on the 15-minute chart (assuming you want your bot to trade the BTC/USDT on the 5 and 15-minute timeframes)
2) Select the Order fills and alert() function calls condition
3) For each alert, the alert message is pre-configured with the text below
{{strategy.order.alert_message}}
Please leave it as it is.
It's a TradingView native variable that will fetch the alert text messages built by the script.
4) TradingView.TO uses webhook technology - setting a webhook URL from the alerts notifications tab is required.
KEY FEATURES
I) Modular Indicator Connection
* plug your existing indicator into the template.
* Only two lines of code are needed for full compatibility.
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
To do so:
1) Find in your indicator where the conditions print the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator, whether a MACD , ZigZag, Pivots , higher-highs, lower-lows or whatever indicator with clear buy and sell conditions.
//@version=5
indicator("Supertrend", overlay = true, timeframe = "", timeframe_gaps = true)
atrPeriod = input.int(10, "ATR Length", minval = 1)
factor = input.float(3.0, "Factor", minval = 0.01, step = 0.01)
= ta.supertrend(factor, atrPeriod)
supertrend := barstate.isfirst ? na : supertrend
bodyMiddle = plot(barstate.isfirst ? na : (open + close) / 2, display = display.none)
upTrend = plot(direction < 0 ? supertrend : na, "Up Trend", color = color.green, style = plot.style_linebr)
downTrend = plot(direction < 0 ? na : supertrend, "Down Trend", color = color.red, style = plot.style_linebr)
fill(bodyMiddle, upTrend, color.new(color.green, 90), fillgaps = false)
fill(bodyMiddle, downTrend, color.new(color.red, 90), fillgaps = false)
buy = ta.crossunder(direction, 0)
sell = ta.crossunder(direction, 0)
//////// CONNECTOR SECTION ////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title = "Signal", display = display.data_window)
//////// CONNECTOR SECTION ////////
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal and -1 for the bearish signal
Now, you can connect your indicator to the Strategy Template using the method below or that one.
Step 2: Connect the connector
1) Add your updated indicator to a TradingView chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings, and in the Data Source field, select your 🔌Connector🔌 (which comes from your indicator)
Note it doesn’t have to be named 🔌Connector🔌 - you can name it as you want - however, I recommend an explicit name you can easily remember.
From then, you should start seeing the signals and plenty of other stuff on your chart.
🔥 Note that whenever you update your indicator values, the strategy statistics and visuals on your chart will update in real-time
II) BOT Risk Management:
- Max Drawdown:
Mode: Select whether the max drawdown is calculated in percentage (%) or USD.
Value: If the max drawdown reaches this specified value, set a value to halt the bot.
- Max Consecutive Days:
Use Max Consecutive Days BOT Halt: Enable/Disable halting the bot if the max consecutive losing days value is reached.
- Max Consecutive Days: Set the maximum number of consecutive losing days allowed before halting the bot.
- Max Losing Streak:
Use Max Losing Streak: Enable/Disable a feature to prevent the bot from taking too many losses in a row.
- Max Losing Streak Length: Set the maximum length of a losing streak allowed.
Margin Call:
- Use Margin Call: Enable/Disable a feature to exit when a specified percentage away from a margin call to prevent it.
Margin Call (%): Set the percentage value to trigger this feature.
- Close BOT Total Loss:
Use Close BOT Total Loss: Enable/Disable a feature to close all trades and halt the bot if the total loss is reached.
- Total Loss ($): Set the total loss value in USD to trigger this feature.
Intraday BOT Risk Management:
- Intraday Losses:
Use Intraday Losses BOT Halt: Enable/Disable halting the bot on reaching specified intraday losses.
Mode: Select whether the intraday loss is calculated in percentage (%) or USD.
- Max Intraday Losses (%): Set the value for maximum intraday losses.
Limit Intraday Trades:
- Use Limit Intraday Trades: Enable/Disable a feature to limit the number of intraday trades.
- Max Intraday Trades: Set the maximum number of intraday trades allowed.
Restart Intraday EA:
III) Order Types and Position Sizing
- Choose between market or limit orders.
- Set your position size directly in the template.
Please use the position size from the “Inputs” and not the “Properties” tab.
I know it's redundant. - the template needs this value from the "Inputs" tab to build the alerts, and the Backtester needs it from the "Properties" tab.
IV) Advanced Take-Profit and Stop-Loss Options
- Choose to set your SL/TP in either USD or percentages.
- Option for multiple take-profit levels and trailing stop losses.
- Move your stop loss to break even +/- offset in USD for “risk-free” trades.
V) Miscellaneous:
Retry order openings if they fail.
Order Types:
Select and specify order type and price settings.
Position Size:
Define the type and size of positions.
Leverage:
Leverage settings, including margin type and hedge mode.
Session:
Limit trades to specific sessions.
Dates:
Limit trades to a specific date range.
Trades Direction:
Direction: Specify the market direction for opening positions.
VI) Logger
The TradingView.TO commands are logged in the TradingView logger.
You'll find more information about it in this TradingView blog post .
WHY YOU MIGHT NEED THIS TEMPLATE
1) Transform your indicator into a TradingView.TO trading bot more easily than before
Connect your indicator to the template
Create your alerts
Set your EA settings
2) Save Time
Auto-generated alert messages for TradingView.TO.
I tested them all and checked with the support team what could/couldn’t be done.
3) Be in Control
Manage your trading risks with advanced features.
4) Customizable
Fits various trading styles and asset classes.
REQUIREMENTS
* Make sure you have your TradingView.TO account
* If there is any issue with the template, ask me in the comments section - I’ll answer quickly.
BACKTEST RESULTS FROM THIS POST
1) I connected this strategy template to a dummy Supertrend script.
I could have selected any other indicator or concept for this script post.
I wanted to share an example of how you can quickly upgrade your strategy, making it compatible with TradingView.TO.
2) The backtest results aren't relevant for this educational script publication.
I used realistic backtesting data but didn't look too much into optimizing the results, as this isn't the point of why I'm publishing this script.
This strategy is a template to be connected to any indicator - the sky is the limit. :)
3) This template is made to take 1 trade per direction at any given time.
Pyramiding is set to 1 on TradingView.
The strategy default settings are:
* Initial Capital: 100000 USD
* Position Size: 1%
* Commission Percent: 0.075%
* Slippage: 1 tick
* No margin/leverage used
ProfitView Strategy TemplateHello traders,
This script took me a full week of coding/testing, sweat, and tears - and I’m too nice as I’m giving it for free to the community.
If you're tired of manual trading and looking for a solid strategy template to pair with your indicators, look no further.
This Pine Script v5 strategy template is engineered for maximum customization and risk management.
Best part?
This Pine Script v5 template facilitates the dynamic construction of ProfitView alerts, sparing users the time and effort of mastering the ProfitView syntax and manually creating alert commands.
This powerful tool gives much power to those who don't know how to code in Pinescript and want to automate their indicators' signals via the ProfitView Chrome extension.
IMPORTANT NOTES
ProfitView is a trading bot software that forwards TradingView alerts to your brokers (examples: Binance, Oanda, Coinbase, Bybit, etc.) for automating trading.
Many traders don't know how to dynamically create ProfitView-compatible alerts using the data from their TradingView scripts.
Traders using trading bots want their alerts to reflect the stop-loss/take-profit/trailing-stop/stop-loss to break options from your script and then create the orders accordingly.
This script showcases how to create ProfitView alerts dynamically.
TRADINGVIEW ALERTS
1) You'll have to create one alert per asset X timeframe = 1 chart.
Example: 1 alert for EUR/USD on the 5 minutes chart, 1 alert for EUR/USD on the 15-minute chart (assuming you want your bot to trade the EUR/USD on the 5 and 15-minute timeframes)
2) Select the Order fills and alert() function calls condition
3) For each alert, the alert message is pre-configured with the text below
{{strategy.order.alert_message}}
Please leave it as it is.
It's a TradingView native variable that will fetch the alert text messages built by the script.
4) ProfitView doesn't use webhook technology, so setting a webhook URL from the alerts notifications tab is unnecessary.
KEY FEATURES
I) Modular Indicator Connection
* plug your existing indicator into the template.
* Only two lines of code are needed for full compatibility.
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
To do so:
1) Find in your indicator where the conditions print the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator, whether a MACD , ZigZag, Pivots , higher-highs, lower-lows or whatever indicator with clear buy and sell conditions.
//@version=5
indicator("Supertrend", overlay = true, timeframe = "", timeframe_gaps = true)
atrPeriod = input.int(10, "ATR Length", minval = 1)
factor = input.float(3.0, "Factor", minval = 0.01, step = 0.01)
= ta.supertrend(factor, atrPeriod)
supertrend := barstate.isfirst ? na : supertrend
bodyMiddle = plot(barstate.isfirst ? na : (open + close) / 2, display = display.none)
upTrend = plot(direction < 0 ? supertrend : na, "Up Trend", color = color.green, style = plot.style_linebr)
downTrend = plot(direction < 0 ? na : supertrend, "Down Trend", color = color.red, style = plot.style_linebr)
fill(bodyMiddle, upTrend, color.new(color.green, 90), fillgaps = false)
fill(bodyMiddle, downTrend, color.new(color.red, 90), fillgaps = false)
buy = ta.crossunder(direction, 0)
sell = ta.crossunder(direction, 0)
//////// CONNECTOR SECTION ////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title = "Signal", display = display.data_window)
//////// CONNECTOR SECTION ////////
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal and -1 for the bearish signal
Now, you can connect your indicator to the Strategy Template using the method below or that one.
Step 2: Connect the connector
1) Add your updated indicator to a TradingView chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings, and in the Data Source field, select your 🔌Connector🔌 (which comes from your indicator)
Note it doesn’t have to be named 🔌Connector🔌 - you can name it as you want - however, I recommend an explicit name you can easily remember.
From then, you should start seeing the signals and plenty of other stuff on your chart.
🔥 Note that whenever you update your indicator values, the strategy statistics and visuals on your chart will update in real-time
II) BOT Risk Management:
- Max Drawdown:
Mode: Select whether the max drawdown is calculated in percentage (%) or USD.
Value: If the max drawdown reaches this specified value, set a value to halt the bot.
- Max Consecutive Days:
Use Max Consecutive Days BOT Halt: Enable/Disable halting the bot if the max consecutive losing days value is reached.
- Max Consecutive Days: Set the maximum number of consecutive losing days allowed before halting the bot.
- Max Losing Streak:
Use Max Losing Streak: Enable/Disable a feature to prevent the bot from taking too many losses in a row.
- Max Losing Streak Length: Set the maximum length of a losing streak allowed.
Margin Call:
- Use Margin Call: Enable/Disable a feature to exit when a specified percentage away from a margin call to prevent it.
Margin Call (%): Set the percentage value to trigger this feature.
- Close BOT Total Loss:
Use Close BOT Total Loss: Enable/Disable a feature to close all trades and halt the bot if the total loss is reached.
- Total Loss ($): Set the total loss value in USD to trigger this feature.
Intraday BOT Risk Management:
- Intraday Losses:
Use Intraday Losses BOT Halt: Enable/Disable halting the bot on reaching specified intraday losses.
Mode: Select whether the intraday loss is calculated in percentage (%) or USD.
- Max Intraday Losses (%): Set the value for maximum intraday losses.
Limit Intraday Trades:
- Use Limit Intraday Trades: Enable/Disable a feature to limit the number of intraday trades.
- Max Intraday Trades: Set the maximum number of intraday trades allowed.
Restart Intraday EA:
- Use Restart Intraday EA: Enable/Disable a feature to restart the bot at the first bar of the next day if it has been stopped with an intraday risk management safeguard.
III) Order Types and Position Sizing
- Choose between market, limit, or stop orders.
- Set your position size directly in the template.
Please use the position size from the “Inputs” and not the “Properties” tab.
I know it's redundant. - the template needs this value from the "Inputs" tab to build the alerts, and the Backtester needs it from the "Properties" tab.
IV) Advanced Take-Profit and Stop-Loss Options
- Choose to set your SL/TP in either pips or percentages.
- Option for multiple take-profit levels and trailing stop losses.
- Move your stop loss to break even +/- offset in pips for “risk-free” trades.
V) Miscellaneous
Retry order openings if they fail.
Order Types:
Select and specify order type and price settings.
Position Size:
Define the type and size of positions.
Leverage:
Leverage settings, including margin type and hedge mode.
Session:
Limit trades to specific sessions.
Dates:
Limit trades to a specific date range.
Trades Direction:
Direction: Specify the market direction for opening positions.
VI) Notifications (Telegram/Discord/Email/IFTTT/Twilio/SMS)
Customize notifications sent to Telegram, Discord, Email, IFTTT, Twilio, and ProfitView Logger.
VII) Logger
The ProfitView commands are logged in the TradingView logger.
You'll find more information about it in this TradingView blog post .
WHY YOU MIGHT NEED THIS TEMPLATE
1) Transform your indicator into a ProfitView trading bot more easily than before
Connect your indicator to the template
Create your alerts
Set your EA settings
2) Save Time
Auto-generated alert messages for ProfitView.
I tested them all and checked with the support team what could/couldn’t be done.
3) Be in Control
Manage your trading risks with advanced features.
4) Customizable
Fits various trading styles and asset classes.
REQUIREMENTS
* Make sure you have your ProfitView account and do the settings correctly in your Chrome extension. If you don't know how to do it, read the documentation + ask for help in the ProfitView Discord support channel.
* If there is any issue with the template, ask me in the comments section - I’ll answer quickly.
BACKTEST RESULTS FROM THIS POST
1) I connected this strategy template to a dummy Supertrend script.
I could have selected any other indicator or concept for this script post.
I wanted to share an example of how you can quickly upgrade your strategy, making it compatible with ProfitView.
2) The backtest results aren't relevant for this educational script publication.
I used realistic backtesting data but didn't look too much into optimizing the results, as this isn't the point of why I'm publishing this script.
This strategy is a template to be connected to any indicator - the sky is the limit. :)
3) This template is made to take 1 trade per direction at any given time.
Pyramiding is set to 1 on TradingView.
The strategy default settings are:
* Initial Capital: 100000 USD
* Position Size: 1%
* Commission Percent: 0.075%
* Slippage: 1 tick
* No margin/leverage used
Best regards,
Dave
Quantum Reversal Engine [ApexLegion]Quantum Reversal Engine
STRATEGY OVERVIEW
This strategy is constructed using 5 custom analytical filters that analyze different market dimensions - trend structure, momentum expansion, volume confirmation, price action patterns, and reversal detection - with results processed through a multi-component scoring calculation that determines signal generation and position management decisions.
Why These Custom Filters Were Independently Developed:
This strategy employs five custom-developed analytical filters:
1. Apex Momentum Core (AMC) - Custom oscillator with volatility-scaled deviation calculation
Standard oscillators lag momentum shifts by 2-3 bars. Custom calculation designed for momentum analysis
2. Apex Wick Trap (AWT) - Wick dominance analysis for trap detection
Existing wick analysis tools don't quantify trap conditions. Uses specific ratios for wick dominance detection
3. Apex Volume Pulse (AVP) - Volume surge validation with participation confirmation
Volume indicators typically use simple averages. Uses surge multipliers with participation validation
4. Apex TrendGuard (ATG) - Angle-based trend detection with volatility band integration
EMA slope calculations often produce false signals. Uses angle analysis with volatility bands for confirmation
5. Quantum Composite Filter (QCF) - Multi-component scoring and signal generation system
Composite scoring designed to filter noise by requiring multiple confirmations before signal activation.
Each filter represents mathematical calculations designed to address specific analytical requirements.
Framework Operation: The strategy functions as a scoring framework where each filter contributes weighted points based on market conditions. Entry signals are generated when minimum threshold scores are met. Exit management operates through a three-tier system with continued signal strength evaluation determining position holds versus closures at each TP level.
Integration Challenge: The core difficulty was creating a scoring system where five independent filters could work together without generating conflicting signals. This required backtesting to determine effective weight distributions.
Custom Filter Development:
Each of the five filters represents analytical approaches developed through testing and validation:
Integration Validation: Each filter underwent individual testing before integration. The composite scoring system required validation to verify that filters complement rather than conflict with each other, resulting in a cohesive analytical framework that was tested during the development period.
These filters represent custom-developed components created specifically for this strategy, with each component addressing different analytical requirements through testing and parameter adjustment.
Programming Features:
Multi-timeframe data handling with backup systems
Performance optimization techniques
Error handling for live trading scenarios
Parameter adaptation based on market conditions
Strategy Features:
Uses multi-filter confirmation approach
Adapts position holding based on continued signal strength
Includes analysis tools for trade review and optimization
Ongoing Development: The strategy was developed through testing and validation processes during the creation period.
COMPONENT EXPLANATION
EMA System
Uses 8 exponential moving averages (7, 14, 21, 30, 50, 90, 120, 200 periods) for trend identification. Primary signals come from 8/21 EMA crossovers, while longer EMAs provide structural context. EMA 1-4 determine short-term structure, EMA 5-8 provide long-term trend confirmation.
Apex Momentum Core (AMC)
Built custom oscillator mathematics after testing dozens of momentum calculation methods. Final algorithm uses price deviation from EMA baseline with volatility scaling to reduce lag while maintaining accuracy across different market conditions.
Custom momentum oscillator using price deviation from EMA baseline:
apxCI = 100 * (source - emaBase) / (sensitivity * sqrt(deviation + 1))
fastLine = EMA(apxCI, smoothing)
signalLine = SMA(fastLine, 4)
Signals generate when fastLine crosses signalLine at +50/-50 thresholds.
This identifies momentum expansion before traditional oscillators.
Apex Volume Pulse (AVP)
Created volume surge analysis that goes beyond simple averages. Extensive testing determined 1.3x multiplier with participation validation provides reliable confirmation while filtering false volume spikes.
Compares current volume to 21-period moving average.
Requires 1.3x average volume for signal confirmation. This filters out low-volume moves during quiet periods and confirms breakouts with actual participation.
Apex Wick Trap (AWT)
Developed proprietary wick trap detection through analysis of failed breakout patterns. Tested various ratio combinations before settling on 60% wick dominance + 20% body limit as effective trap identification parameters.
Analyzes candle structure to identify failed breakouts:
candleRange = math.max(high - low, 0.00001)
candleBody = math.abs(close - open)
bodyRatio = candleBody / candleRange
upperWick = high - math.max(open, close)
lowerWick = math.min(open, close) - low
upperWickRatio = upperWick / candleRange
lowerWickRatio = lowerWick / candleRange
trapWickLong = showAWT and lowerWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close > open
trapWickShort = showAWT and upperWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close < open This catches reversals after fake breakouts.
Apex TrendGuard (ATG)
Built angle-based trend detection after standard EMA crossovers proved insufficient. Combined slope analysis with volatility bands through iterative testing to eliminate false trend signals.
EMA slope analysis with volatility bands:
Fast EMA (21) vs Slow EMA (55) for trend direction
Angle calculation: atan(fast - slow) * 180 / π
ATR bands (1.75x multiplier) for breakout confirmation
Minimum 25° angle for strong trend classification
Core Algorithm Framework
1. Composite Signal Generation
calculateCompositeSignals() =>
// Component Conditions
structSignalLong = trapWickLong
structSignalShort = trapWickShort
momentumLong = amcBuySignal
momentumShort = amcSellSignal
volumeSpike = volume > volAvg_AVP * volMult_AVP
priceStrength_Long = close > open and close > close
priceStrength_Short = close < open and close < close
rsiMfiComboValue = (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
reversalTrigger_Long = ta.crossover(rsiMfiComboValue, 50)
reversalTrigger_Short = ta.crossunder(rsiMfiComboValue, 50)
isEMACrossUp = ta.crossover(emaFast_ATG, emaSlow_ATG)
isEMACrossDown = ta.crossunder(emaFast_ATG, emaSlow_ATG)
// Enhanced Composite Score Calculation
scoreBuy = 0.0
scoreBuy += structSignalLong ? scoreStruct : 0.0
scoreBuy += momentumLong ? scoreMomentum : 0.0
scoreBuy += flashSignal ? weightFlash : 0.0
scoreBuy += blinkSignal ? weightBlink : 0.0
scoreBuy += volumeSpike_AVP ? scoreVolume : 0.0
scoreBuy += priceStrength_Long ? scorePriceAction : 0.0
scoreBuy += reversalTrigger_Long ? scoreReversal : 0.0
scoreBuy += emaAlignment_Bull ? weightTrendAlign : 0.0
scoreBuy += strongUpTrend ? weightTrendAlign : 0.0
scoreBuy += highRisk_Long ? -1.2 : 0.0
scoreBuy += signalGreenDot ? 1.0 : 0.0
scoreBuy += isAMCUp ? 0.8 : 0.0
scoreBuy += isVssBuy ? 1.5 : 0.0
scoreBuy += isEMACrossUp ? 1.0 : 0.0
scoreBuy += signalRedX ? -1.0 : 0.0
scoreSell = 0.0
scoreSell += structSignalShort ? scoreStruct : 0.0
scoreSell += momentumShort ? scoreMomentum : 0.0
scoreSell += flashSignal ? weightFlash : 0.0
scoreSell += blinkSignal ? weightBlink : 0.0
scoreSell += volumeSpike_AVP ? scoreVolume : 0.0
scoreSell += priceStrength_Short ? scorePriceAction : 0.0
scoreSell += reversalTrigger_Short ? scoreReversal : 0.0
scoreSell += emaAlignment_Bear ? weightTrendAlign : 0.0
scoreSell += strongDownTrend ? weightTrendAlign : 0.0
scoreSell += highRisk_Short ? -1.2 : 0.0
scoreSell += signalRedX ? 1.0 : 0.0
scoreSell += isAMCDown ? 0.8 : 0.0
scoreSell += isVssSell ? 1.5 : 0.0
scoreSell += isEMACrossDown ? 1.0 : 0.0
scoreSell += signalGreenDot ? -1.0 : 0.0
compositeBuySignal = enableComposite and scoreBuy >= thresholdCompositeBuy
compositeSellSignal = enableComposite and scoreSell >= thresholdCompositeSell
if compositeBuySignal and compositeSellSignal
compositeBuySignal := false
compositeSellSignal := false
= calculateCompositeSignals()
// Final Entry Signals
entryCompositeBuySignal = compositeBuySignal and ta.rising(emaFast_ATG, 2)
entryCompositeSellSignal = compositeSellSignal and ta.falling(emaFast_ATG, 2)
Calculates weighted scores from independent modules and activates signals only when threshold requirements are met.
2. Smart Exit Hold Evaluation System
evaluateSmartHold() =>
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
avgVolume = ta.sma(volume, 20)
volumeSpike = volume > avgVolume * volMultiplier
// MTF Bull/Bear conditions
mtf_bull = mtf_emaFast_final > mtf_emaSlow_final
mtf_bear = mtf_emaFast_final < mtf_emaSlow_final
emaBackupDivergence = math.abs(mtf_emaFast_backup - mtf_emaSlow_backup) / mtf_emaSlow_backup
emaBackupStrong = emaBackupDivergence > 0.008
mtfConflict_Long = inLong and mtf_bear and emaBackupStrong
mtfConflict_Short = inShort and mtf_bull and emaBackupStrong
// Layer 1: ATR-Based Dynamic Threshold (Market Volatility Intelligence)
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : (atrRatio > 0.01 ? 1.5 : 2.8)
// Layer 2: ROI-Conditional Time Intelligence (Selective Pressure)
timeMultiplier_Long = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Long <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Long <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
timeMultiplier_Short = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Short <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Short <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
// Dual-Layer Threshold Calculation
baseThreshold_Long = mtfConflict_Long ? dynamicThreshold + 1.0 : dynamicThreshold
baseThreshold_Short = mtfConflict_Short ? dynamicThreshold + 1.0 : dynamicThreshold
timeAdjustedThreshold_Long = baseThreshold_Long * timeMultiplier_Long
timeAdjustedThreshold_Short = baseThreshold_Short * timeMultiplier_Short
// Final Smart Hold Decision with Dual-Layer Intelligence
smartHold_Long = not mtfConflict_Long and smartScoreLong >= timeAdjustedThreshold_Long and compositeBuyRecentCount >= signalMinCount
smartHold_Short = not mtfConflict_Short and smartScoreShort >= timeAdjustedThreshold_Short and compositeSellRecentCount >= signalMinCount
= evaluateSmartHold()
Evaluates whether to hold positions past TP1/TP2/TP3 levels based on continued signal strength, volume confirmation, and multi-timeframe trend alignment
HOW TO USE THE STRATEGY
Step 1: Initial Setup
Apply strategy to your preferred timeframe (backtested on 15M)
Enable "Use Heikin-Ashi Base" for smoother signals in volatile markets
"Show EMA Lines" and "Show Ichimoku Cloud" are enabled for visual context
Set default quantities to match your risk management (5% equity default)
Step 2: Signal Recognition
Visual Signal Guide:
Visual Signal Guide - Complete Reference:
🔶 Red Diamond: Bearish momentum breakdown - short reversal signal
🔷 Blue Diamond: Strong bullish momentum - long reversal signal
🔵 Blue Dot: Volume-confirmed directional move - trend continuation
🟢 Green Dot: Bullish EMA crossover - trend reversal confirmation
🟠 Orange X: Oversold reversal setup - counter-trend opportunity
❌ Red X: Bearish EMA breakdown - trend reversal warning
✡ Star Uprising: Strong bullish convergence
💥 Ultra Entry: Ultra-rapid downward momentum acceleration
▲ VSS Long: Velocity-based bullish momentum confirmation
▼ VSS Short: Velocity-based bearish momentum confirmation
Step 3: Entry Execution
For Long Positions:
1. ✅ EMA1 crossed above EMA2 exactly 3 bars ago [ta.crossover(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 > EMA2 (maintained)
3. ✅ Composite score ≥ 5.0 points (6.5+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Volume spike confirmation (green dot/blue dot signals)
6. ✅ Bullish candle closes above EMA structure
For Short Positions:
1. ✅ EMA1 crossed below EMA2 exactly 3 bars ago [ta.crossunder(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 < EMA2 (maintained)
3. ✅ Composite score ≥ 5.4 points (7.0+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Momentum breakdown (red diamond/red X signals)
6. ✅ Bearish candle closes below EMA structure
🎯 Critical Timing Note: The strategy requires EMA crossover to have occurred 3 bars prior to entry, not at the current bar. This attempts to avoid premature entries and may improve signal reliability.
Step 4: Reading Market Context
EMA Ribbon Interpretation:
All EMAs ascending = Strong uptrend context
EMAs 1-3 above EMAs 4-8 = Bullish structure
Tight EMA spacing = Low volatility/consolidation
Wide EMA spacing = High volatility/trending
Ichimoku Cloud Context:
Price above cloud = Bullish environment
Price below cloud = Bearish environment
Cloud color intensity = Momentum strength
Thick cloud = Strong support/resistance
THE SMART EXIT GRID SYSTEM
Smart Exit Grid Approach:
The Smart Exit Grid uses dynamic hold evaluation that continuously analyzes market conditions after position entry. This differs from traditional fixed profit targets by adapting exit timing based on real-time signal strength.
How Smart Exit Grid System Works
The system operates through three evaluation phases:
Smart Score Calculation:
The smart score calculation aggregates 22 signal components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. MTF analysis provides additional confirmation as a separate validation layer.
Signal Stack Management:
The per-tick signal accumulation system monitors 22 active signal types with MTF providing trend validation and conflict detection as a separate confirmation layer.
Take Profit Progression:
Smart Exit Activation:
The QRE system activates Smart Exit Grid immediately upon position entry. When strategy.entry() executes, the system initializes monitoring systems designed to track position progress.
Upon position opening, holdTimer begins counting, establishing the foundation for subsequent decisions. The Smart Exit Grid starts accumulating signals from entry, with all 22 signal components beginning real-time tracking when the trade opens.
The system operates on continuous evaluation where smartScoreLong and smartScoreShort calculate from the first tick after entry. QRE's approach is designed to capture market structure changes, trend deteriorations, or signal pattern shifts that can trigger protective exits even before the first take profit level is reached.
This activation creates a proactive position management framework. The 8-candle sliding window starts from entry, meaning that if market conditions change rapidly after entry - due to news events, liquidity shifts, or technical changes - the system can respond within the configured lookback period.
TP Markers as Reference Points:
The TP1, TP2, and TP3 levels function as reference points rather than mandatory exit triggers. When longTP1Hit or shortTP1Hit conditions activate, they serve as profit confirmation markers that inform the Smart Exit algorithm about achieved reward levels, but don't automatically initiate position closure.
These TP markers enhance the Smart Exit decision matrix by providing profit context to ongoing signal evaluation. The system recognizes when positions have achieved target returns, but the actual exit decision remains governed by continuous smart score evaluation and signal stack analysis.
TP2 Reached: Enhanced Monitoring
TP2 represents significant profit capture with additional monitoring features:
This approach is designed to help avoid premature profit-taking during trending conditions. If TP2 is reached but smartScoreLong remains above the dynamic threshold and the 8-candle sliding window shows persistent signals, the position continues holding. If market structure deteriorates before reaching TP2, the Smart Exit can trigger closure based on signal analysis.
The visual TP circles that appear when levels are reached serve as performance tracking tools, allowing users to see how frequently entries achieve various profit levels while understanding that actual exit timing depends on market structure analysis.
Risk Management Systems:
Operating independently from the Smart Exit Grid are two risk management systems: the Trap Wick Detection Protocol and the Stop Loss Mechanism. These systems maintain override authority over other exit logic.
The Trap Wick System monitors for conditionBearTrapExit during long positions and conditionBullTrapExit during short positions. When detected, these conditions trigger position closure with state reset, bypassing Smart Exit evaluations. This system recognizes that certain candlestick patterns may indicate reversal risk.
Volatility Exit Monitoring: The strategy monitors for isStrongBearCandle combined with conditionBearTrapExit, recognizing when market structure may be shifting.
Volume Validation: Before exiting on volatility, the strategy requires volume confirmation: volume > ta.sma(volume, 20) * 1.8. This is designed to filter exits on weak, low-volume movements.
The Stop Loss Mechanism operates through multiple triggers including traditional price-based stops (longSLHit, shortSLHit) and early exit conditions based on smart score deterioration combined with negative ROI. The early exit logic activates when smartScoreLong < 1.0 or smartScoreShort < 1.0 while realROI < -0.9%.
These risk management systems are designed so that risk scenarios can trigger protective closure with state reset across all 22 signal counters, TP tracking variables, and smart exit states.
This architecture - Smart Exit activation, TP markers as navigation tools, and independent risk management - creates a position management system that adapts to market conditions while maintaining risk discipline through dedicated protection protocols.
TP3 Reached: Enhanced Protection
Once TP3 is hit, the strategy shifts into enhanced monitoring:
EMA Structure Monitoring: isEMAStructureDown becomes a primary exit trigger
MTF Alignment: The higher timeframe receives increased consideration
Wick Trap Priority: conditionBearTrapExit becomes an immediate exit signal
Approach Differences:
Traditional Fixed Exits:
Exit at predetermined levels regardless of market conditions
May exit during trend continuation
May exit before trend completion
Limited adaptation to changing volatility
Smart Exit Grid Approach:
Adaptive timing based on signal conditions
Exits when supporting signals weaken
Multi-timeframe validation for trend confirmation
Volume confirmation requirements for holds
Structural monitoring for trend analysis
Dynamic ATR-Based Smart Score Threshold System
Market Volatility Adaptive Scoring
// Real-time ATR Analysis
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
// Three-Tier Dynamic Threshold Matrix
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
The market volatility adaptive scoring calculates real-time ATR with a 2% fallback for new markets. The atrRatio represents the relationship between current volatility and price, creating a foundation for threshold adjustment.
The three-tier dynamic threshold matrix responds to market conditions by adjusting requirements based on volatility levels: lowering thresholds during high volatility periods above 2% ATR ratio to 1.0 points, maintaining standard requirements at 1.5 points for medium volatility between 1-2%, and raising standards to 2.8 points during low volatility periods below 1%.
Profit-Loss Adaptive Management:
The system applies different evaluation criteria based on position performance:
Winning Positions (realROI ≥ 0%):
→ timeMultiplier = 1.0 (No additional pressure)
→ Maintains base threshold requirements
→ Allows natural progression to TP2/TP3 levels
Losing Positions (realROI < 0%):
→ Progressive time pressure activated
→ Increasingly strict requirements over time
→ Faster decision-making on underperforming trades
ROI-Adaptive Smart Hold Decision Process:
The strategy uses a profit-loss adaptive system:
Winning Position Management (ROI ≥ 0%):
✅ Standard threshold requirements maintained
✅ No additional time-based pressure applied
✅ Allows positions to progress toward TP2/TP3 levels
✅ timeMultiplier remains at 1.0 regardless of hold duration
Losing Position Management (ROI < 0%):
⚠️ Time-based threshold adjustments activated
⚠️ Progressive increase in required signal strength over time
⚠️ Earlier exit evaluation on underperforming positions
⚠️ timeMultiplier increases from 1.0 → 1.1 → 1.3 based on hold duration
Real-Time Monitoring:
Monitor Analysis Table → "Smart" filter → "Score" vs "Dynamic Threshold"
Winning positions: Evaluation based on signal strength deterioration only
Losing positions: Evaluation considers both signal strength and progressive time adjustments
Breakeven positions (0% ROI): Treated as winning positions - no time adjustments
This approach differentiates between winning and losing positions in the hold evaluation process, requiring higher signal thresholds for extended holding of losing positions while maintaining standard requirements for winning ones.
ROI-Conditional Decision Matrix Examples:
Scenario 1 - Winning Position in Any Market:
Position ROI: +0.8% → timeMultiplier = 1.0 (regardless of hold time)
ATR Medium (1.2%) → dynamicThreshold = 1.5
Final Threshold = 1.5 × 1.0 = 1.5 points ✅ Position continues
Scenario 2 - Losing Position, Extended Hold:
Position ROI: -0.5% → Time pressure activated
Hold Time: 20 bars → timeMultiplier = 1.3
ATR Low (0.8%) → dynamicThreshold = 2.8
Final Threshold = 2.8 × 1.3 = 3.64 points ⚡ Enhanced requirements
Scenario 3 - Fresh Losing Position:
Position ROI: -0.3% → Time pressure activated
Hold Time: 5 bars → timeMultiplier = 1.0 (still early)
ATR High (2.1%) → dynamicThreshold = 1.0
Final Threshold = 1.0 × 1.0 = 1.0 points 📊 Recovery opportunity
Scenario 4 - Breakeven Position:
Position ROI: 0.0% → timeMultiplier = 1.0 (no pressure)
Hold Time: 15 bars → No time penalty applied
Final Threshold = dynamicThreshold only ⚖️ Neutral treatment
🔄8-Candle Sliding Window Signal Rotation System
Composite Signal Counting Mechanism
// Dynamic Lookback Window (configurable: default 8)
signalLookbackBars = input.int(8, "Composite Lookback Bars", minval=1, maxval=50)
// Rolling Signal Analysis
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
Candle Flow Example (8-bar window):
→
✓ ✓ ✗ ✓ ✗ ✓ ✗ ✓ 🗑️
New Signal Count = 5/8 signals in window
Threshold Check: 5 ≥ signalMinCount (2) = HOLD CONFIRMED
Signal Decay & Refresh Mechanism
// Signal Persistence Tracking
if compositeBuyRecentCount >= signalMinCount
smartHold_Long = true
else
smartHold_Long = false
The composite signal counting operates through a configurable sliding window. The system maintains rolling counters that scan backward through the specified number of candles.
During each evaluation cycle, the algorithm iterates through historical bars, incrementing counters when composite signals are detected. This creates a dynamic signal persistence measurement where recent signal density determines holding decisions.
The sliding window rotation functions like a moving conveyor belt where new signals enter while the oldest signals drop off. For example, in an 8-bar window, if 5 out of 8 recent candles showed composite buy signals, and the minimum required count is 2, the system confirms the hold condition. As new bars form, the window slides forward, potentially changing the signal count and triggering exit conditions when signal density falls below the threshold.
Signal decay and refresh occur continuously where smartHold_Long remains true only when compositeBuyRecentCount exceeds signalMinCount. When recent signal density drops below the minimum requirement, the system switches to exit mode.
Advanced Signal Stack Management - 22-Signal Real-Time Evaluation
// Long Position Signal Stacking (calc_on_every_tick=true)
if inLong
// Primary Reversal Signals
if signalRedDiamond: signalCountRedDiamond += 1 // -0.5 points
if signalStarUprising: signalCountStarUprising += 1 // +1.5 points
if entryUltraShort: signalCountUltra += 1 // -1.0 points
// Trend Confirmation Signals
if strongUpTrend: trendUpCount_Long += 1 // +1.5 points
if emaAlignment_Bull: bullAlignCount_Long += 1 // +1.0 points
// Risk Assessment Signals
if highRisk_Long: riskCount_Long += 1 // -1.5 points
if topZone: tzoneCount_Long += 1 // -0.5 points
The per-tick signal accumulation system operates with calc_on_every_tick=true for real-time responsiveness. During long positions, the system monitors primary reversal signals where Red Diamond signals subtract 0.5 points as reversal warnings, Star Uprising adds 1.5 points for continuation signals, and Ultra Short signals deduct 1.0 points as counter-trend warnings.
Trend confirmation signals provide weighted scoring where strongUpTrend adds 1.5 points for aligned momentum, emaAlignment_Bull contributes 1.0 point for structural support, and various EMA-based confirmations contribute to the overall score. Risk assessment signals apply negative weighting where highRisk_Long situations subtract 1.5 points, topZone conditions deduct 0.5 points, and other risk factors create defensive scoring adjustments.
The smart score calculation aggregates all 22 components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. This score updates continuously, providing the foundation for hold-or-exit decisions.
MULTI-TIMEFRAME (MTF) SYSTEM
MTF Data Collection
The strategy requests higher timeframe data (default 30-minute) for trend confirmation:
= request.security(syminfo.tickerid, mtfTimeframe, , lookahead=barmerge.lookahead_off, gaps=barmerge.gaps_off)
MTF Watchtower System - Implementation Logic
The system employs a timeframe discrimination protocol where currentTFInMinutes is compared against a 30-minute threshold. This creates different operational behavior between timeframes:
📊 Timeframe Testing Results:
30M+ charts: Full MTF confirmation → Tested with full features
15M charts: Local EMA + adjusted parameters → Standard testing baseline
5M charts: Local EMA only → Requires parameter adjustment
1M charts: High noise → Limited testing conducted
When the chart timeframe is 30 minutes or above, the strategy activates useMTF = true and requests external MTF data through request.security(). For timeframes below 30 minutes, including your 5-minute setup, the system deliberately uses local EMA calculations to avoid MTF lag and data inconsistencies.
The triple-layer data sourcing architecture works as follows: timeframes from 1 minute to 29 minutes rely on chart-based EMA calculations for immediate responsiveness. Timeframes of 30 minutes and above utilize MTF data through the security function, with a backup system that doubles the EMA length (emaLen * 2) if MTF data fails. When MTF data is unavailable or invalid, the system falls back to local EMA as the final safety net.
Data validation occurs through a pipeline where mtf_dataValid checks not only for non-null values but also verifies that EMA values are positive above zero. The system tracks data sources through mtf_dataSource which displays "MTF Data" for successful external requests, "Backup EMA" for failed MTF with backup system active, or "Chart EMA" for local calculations.
🔄 MTF Smart Score Caching & Recheck System
// Cache Update Decision Logic
mtfSmartIntervalSec = input.int(300, "Smart Grid Recheck Interval (sec)") // 5-minute cache
canRecheckSmartScore = na(timenow) ? false :
(na(lastCheckTime) or (timenow - lastCheckTime) > mtfSmartIntervalSec * 1000)
// Cache Management
if canRecheckSmartScore
lastCheckTime := timenow
cachedSmartScoreLong := smartScoreLong // Store current calculation
cachedSmartScoreShort := smartScoreShort
The performance-optimized caching system addresses the computational intensity of continuous MTF analysis through intelligent interval management. The mtfSmartIntervalSec parameter, defaulting to 300 seconds (5 minutes), determines cache refresh frequency. The system evaluates canRecheckSmartScore by comparing current time against lastCheckTime plus the configured interval.
When cache updates trigger, the system stores current calculations in cachedSmartScoreLong and cachedSmartScoreShort, creating stable reference points that reduce excessive MTF requests. This cache management balances computational efficiency with analytical accuracy.
The cache versus real-time hybrid system creates a multi-layered decision matrix where immediate signals update every tick for responsive market reaction, cached MTF scores refresh every 5 minutes for stability filtering, dynamic thresholds recalculate every bar for volatility adaptation, and sliding window analysis updates every bar for trend persistence validation.
This architecture balances real-time signal detection with multi-timeframe strategic validation, creating adaptive trading intelligence that responds immediately to market changes while maintaining strategic stability through cached analysis and volatility-adjusted decision thresholds.
⚡The Execution Section Deep Dive
The execution section represents the culmination of all previous systems – where analysis transforms into action.
🚪 Entry Execution: The Gateway Protocol
Primary Entry Validation:
Entry isn't just about seeing a signal – it's about passing through multiple security checkpoints, each designed to filter out low-quality opportunities.
Stage 1: Signal Confirmation
entryCompositeBuySignal must be TRUE for longs
entryCompositeSellSignal must be TRUE for shorts
Stage 2: Enhanced Entry Validation
The strategy employs an "OR" logic system that recognizes different types of market opportunities:
Path A - Trend Reversal Entry:
When emaTrendReversal_Long triggers, it indicates the market structure is shifting in favor of the trade direction. This isn't just about a single EMA crossing – it represents a change in market momentum that experienced traders recognize as potential high-probability setups.
Path B - Momentum Breakout Entry:
The strongBullMomentum condition is where QRE identifies accelerating market conditions:
Criteria:
EMA1 rising for 3+ candles AND
EMA2 rising for 2+ candles AND
Close > 10-period high
This combination captures those explosive moves where the market doesn't just trend – it accelerates, creating momentum-driven opportunities.
Path C - Recovery Entry:
When previous exit states are clean (no recent stop losses), the strategy permits entry based purely on signal strength. This pathway is designed to help avoid the strategy becoming overly cautious after successful trades.
🛡️ The Priority Exit Matrix: When Rules Collide
Not all exit signals are created equal. QRE uses a strict hierarchy that is designed to avoid conflicting signals from causing hesitation:
Priority Level 1 - Exception Exits (Immediate Action):
Condition: TP3 reached AND Wick Trap detected
Action: Immediate exit regardless of other signals
Rationale: Historical analysis suggests wick traps at TP3 may indicate potential reversals
Priority Level 2 - Structural Breakdown:
Condition: TP3 active AND EMA structure deteriorating AND Smart Score insufficient
Logic: isEMAStructureDown AND NOT smartHold_Long
This represents the strategy recognizing that the underlying market structure that justified the trade is failing. It's like a building inspector identifying structural issues – you don't wait for additional confirmation.
Priority Level 3 - Enhanced Volatility Exits:
Conditions: TP2 active AND Strong counter-candle AND Wick trap AND Volume spike
Logic: Multiple confirmation required to reduce false exits
Priority Level 4 - Standard Smart Score Exits:
Condition: Any TP level active AND smartHold evaluates to FALSE
This is the bread-and-butter exit logic where signal deterioration triggers exit
⚖️ Stop Loss Management: Risk Control Protocol
Dual Stop Loss System:
QRE provides two stop loss modes that users can select based on their preference:
Fixed Mode (Default - useAdaptiveSL = false):
Uses predetermined percentage levels regardless of market volatility:
- Long SL = entryPrice × (1 - fixedRiskP - slipBuffer)
- Short SL = entryPrice × (1 + fixedRiskP + slipBuffer)
- Default: 0.6% risk + 0.3% slippage buffer = 0.9% total stop
- Consistent and predictable stop loss levels
- Recommended for users who prefer stable risk parameters
Adaptive Mode (Optional - useAdaptiveSL = true):
Dynamic system that adjusts stop loss based on market volatility:
- Base Calculation uses ATR (Average True Range)
- Long SL = entryPrice × (1 - (ATR × atrMultSL) / entryPrice - slipBuffer)
- Short SL = entryPrice × (1 + (ATR × atrMultSL) / entryPrice + slipBuffer)
- Automatically widens stops during high volatility periods
- Tightens stops during low volatility periods
- Advanced users can enable for volatility-adaptive risk management
Trend Multiplier Enhancement (Both Modes):
When strongUpTrend is detected for long positions, the stop loss receives 1.5x breathing room. Strong trends often have deeper retracements before continuing. This is designed to help avoid the strategy being shaken out of active trades by normal market noise.
Mode Selection Guidance:
- New Users: Start with Fixed Mode for predictable risk levels
- Experienced Users: Consider Adaptive Mode for volatility-responsive stops
- Volatile Markets: Adaptive Mode may provide better stop placement
- Stable Markets: Fixed Mode often sufficient for consistent risk management
Early Exit Conditions:
Beyond traditional stop losses, QRE implements "smart stops" that trigger before price-based stops:
Early Long Exit: (smartScoreLong < 1.0 OR prev5BearCandles) AND realROI < -0.9%
🔄 State Management: The Memory System
Complete State Reset Protocol:
When a position closes, QRE doesn't just wipe the slate clean – it performs a methodical reset:
TP State Cleanup:
All Boolean flags: tp1/tp2/tp3HitBefore → FALSE
All Reached flags: tp1/tp2/tp3Reached → FALSE
All Active flags: tp1/tp2/tp3HoldActive → FALSE
Signal Counter Reset:
Every one of the 22 signal counters returns to zero.
This is designed to avoid signal "ghosting" where old signals influence new trades.
Memory Preservation:
While operational states reset, certain information is preserved for learning:
killReasonLong/Short: Why did this trade end?
lastExitWasTP1/TP2/TP3: What was the exit quality?
reEntryCount: How many consecutive re-entries have occurred?
🔄 Re-Entry Logic: The Comeback System
Re-Entry Conditions Matrix:
QRE implements a re-entry system that recognizes not all exits are created equal:
TP-Based Re-Entry (Enabled):
Criteria: Previous exit was TP1, TP2, or TP3
Cooldown: Minimal or bypassed entirely
Logic: Target-based exits indicate potentially viable market conditions
EMA-Based Re-Entry (Conditional):
Criteria: Previous exit was EMA-based (structural change)
Requirements: Must wait for EMA confirmation in new direction
Minimum Wait: 5 candles
Advanced Re-Entry Features:
When adjustReEntryTargets is enabled, the strategy becomes more aggressive with re-entries:
Target Adjustment: TP1 multiplied by reEntryTP1Mult (default 2.0)
Stop Adjustment: SL multiplied by reEntrySLMult (default 1.5)
Logic: If we're confident enough to re-enter, we should be confident enough to hold for bigger moves
Performance Tracking: Strategy tracks re-entry win rate, average ROI, and total performance separately from initial entries for optimization analysis.
📊 Exit Reason Analytics: Learning from Every Trade
Kill Reason Tracking:
Every exit is categorized and stored:
"TP3 Exit–Wick Trap": Exit at target level with wick pattern detection
"Smart Exit–EMA Down": Structural breakdown exit
"Smart Exit–Volatility": Volatility-based protection exit
"Exit Post-TP1/TP2/TP3": Standard smart exit progression
"Long SL Exit" / "Short SL Exit": Stop loss exits
Performance Differentiation:
The strategy tracks performance by exit type, allowing for continuous analysis:
TP-based exits: Achieved target levels, analyze for pattern improvement
EMA-based exits: Mixed results, analyze for pattern improvement
SL-based exits: Learning opportunities, adjust entry criteria
Volatility exits: Protective measures, monitor performance
🎛️ Trailing Stop Implementation:
Conditional Trailing Activation:
Activation Criteria: Position profitable beyond trailingStartPct AND
(TP hold active OR re-entry trade)
Dynamic Trailing Logic:
Unlike simple trailing stops, QRE's implementation considers market context:
Trending Markets: Wider trail offsets to avoid whipsaws
Volatile Markets: Tighter offsets to protect gains
Re-Entry Trades: Enhanced trailing to maximize second-chance opportunities
Return-to-Entry Protection:
When deactivateOnReturn is enabled, the strategy will close positions that return to entry level after being profitable. This is designed to help avoid the frustration of watching profitable trades turn into losers.
🧠 How It All Works Together
The beauty of QRE lies not in any single component, but in how everything integrates:
The Entry Decision: Multiple pathways are designed to help identify opportunities while maintaining filtering standards.
The Progression System: Each TP level unlocks new protection features, like achieving ranks in a video game.
The Exit Matrix: Prioritized decision-making aims to reduce analysis paralysis while providing appropriate responses to different market conditions.
The Memory System: Learning from each trade while preventing contamination between separate opportunities.
The Re-Entry Logic: Re-entry system that balances opportunity with risk management.
This creates a trading system where entry conditions filter for quality, progression systems adapt to changing market conditions, exit priorities handle conflicting signals intelligently, memory systems learn from each trade cycle, and re-entry logic maximizes opportunities while managing risk exposure.
📊 ANALYSIS TABLE INTERPRETATION -
⚙️ Enabling Analysis Mode
Navigate to strategy settings → "Testing & Analysis" → Enable "Show Analysis Table". The Analysis Table displays different information based on the selected test filter and provides real-time insight into all strategy components, helping users understand current market conditions, position status, and system decision-making processes.
📋 Filter Mode Interpretations
"All" Mode (Default View):
Composite Section:
Buy Score: Aggregated strength from all 22 bullish signals (threshold 5.0+ triggers entry consideration)
Sell Score: Aggregated strength from all 22 bearish signals (threshold 5.4+ triggers entry consideration)
APEX Filters:
ATG Trend: Shows current trend direction analysis
Indicates whether momentum filters are aligned for directional bias
ReEntry Section:
Most Recent Exit: Displays exit type and timeframe since last position closure
Status: Shows if ReEntry system is Ready/Waiting/Disabled
Count: Current re-entry attempts versus maximum allowed attempts
Position Section (When Active):
Status: Current position state (LONG/SHORT/FLAT)
ROI: Dual calculation showing Custom vs Real ROI percentages
Entry Price: Original position entry level
Current Price: Live market price for comparison
TP Tracking: Progress toward profit targets
"Smart" Filter (Critical for Active Positions):
Smart Exit Section:
Hold Timer: Time elapsed since position opened (bar-based counting)
Status: Whether Smart Exit Grid is Enabled/Disabled
Score: Current smart score calculation from 22-component matrix
Dynamic Threshold: ATR-based minimum score required for holding
Final Threshold: Time and ROI-adjusted threshold actually used for decisions
Score Check: Pass/Fail based on Score vs Final Threshold comparison
Smart Hold: Current hold decision status
Final Hold: Final recommendation based on all factors
🎯 Advanced Smart Exit Debugging - ROI & Time-Based Threshold System
Understanding the Multi-Layer Threshold System:
Layer 1: Dynamic Threshold (ATR-Based)
atrRatio = ATR / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
Layer 2: Time Multiplier (ROI & Duration-Based)
Winning Positions (ROI ≥ 0%):
→ timeMultiplier = 1.0 (No time pressure, regardless of hold duration)
Losing Positions (ROI < 0%):
→ holdTimer ≤ 8 bars: timeMultiplier = 1.0 (Early stage, standard requirements)
→ holdTimer 9-16 bars: timeMultiplier = 1.1 (10% stricter requirements)
→ holdTimer 17+ bars: timeMultiplier = 1.3 (30% stricter requirements)
Layer 3: Final Threshold Calculation
finalThreshold = dynamicThreshold × timeMultiplier
Examples:
- Winning Position: 2.8 × 1.0 = 2.8 (Always standard)
- Losing Position (Early): 2.8 × 1.0 = 2.8 (Same as winning initially)
- Losing Position (Extended): 2.8 × 1.3 = 3.64 (Much stricter)
Real-Time Debugging Display:
Smart Exit Section shows:
Score: 3.5 → Current smartScoreLong/Short value
Dynamic Threshold: 2.8 → Base ATR-calculated threshold
Final Threshold: 3.64 (ATR×1.3) → Actual threshold used for decisions
Score Check: FAIL (3.5 vs 3.64) → Pass/Fail based on final comparison
Final Hold: NO HOLD → Actual system decision
Position Status Indicators:
Winner + Early: ATR×1.0 (No pressure)
Winner + Extended: ATR×1.0 (No pressure - winners can run indefinitely)
Loser + Early: ATR×1.0 (Recovery opportunity)
Loser + Extended: ATR×1.1 or ATR×1.3 (Increasing pressure to exit)
MTF Section:
Data Source: Shows whether using MTF Data/EMA Backup/Local EMA
Timeframe: Configured watchtower timeframe setting
Data Valid: Confirms successful MTF data retrieval status
Trend Signal: Higher timeframe directional bias analysis
Close Price: MTF price data availability confirmation
"Composite" Filter:
Composite Section:
Buy Score: Real-time weighted scoring from multiple indicators
Sell Score: Opposing directional signal strength
Threshold: Minimum scores required for signal activation
Components:
Flash/Blink: Momentum acceleration indicators (F = Flash active, B = Blink active)
Individual filter contributions showing which specific signals are firing
"ReEntry" Filter:
ReEntry System:
System: Shows if re-entry feature is Enabled/Disabled
Eligibility: Conditions for new entries in each direction
Performance: Success metrics of re-entry attempts when enabled
🎯 Key Status Indicators
Status Column Symbols:
✓ = Condition met / System active / Signal valid
✗ = Condition not met / System inactive / No signal
⏳ = Cooldown active (waiting period)
✅ = Ready state / Good condition
🔄 = Processing / Transitioning state
🔍 Critical Reading Guidelines
For Active Positions - Smart Exit Priority Reading:
1. First Check Position Type:
ROI ≥ 0% = Winning Position (Standard requirements)
ROI < 0% = Losing Position (Progressive requirements)
2. Check Hold Duration:
Early Stage (≤8 bars): Standard multiplier regardless of ROI
Extended Stage (9-16 bars): Slight pressure on losing positions
Long Stage (17+ bars): Strong pressure on losing positions
3. Score vs Final Threshold Analysis:
Score ≥ Final Threshold = HOLD (Continue position)
Score < Final Threshold = EXIT (Close position)
Watch for timeMultiplier changes as position duration increases
4. Understanding "Why No Hold?"
Common scenarios when Score Check shows FAIL:
Losing position held too long (timeMultiplier increased to 1.1 or 1.3)
Low volatility period (dynamic threshold raised to 2.8)
Signal deterioration (smart score dropped below required level)
MTF conflict (higher timeframe opposing position direction)
For Entry Signal Analysis:
Composite Score Reading: Signal strength relative to threshold requirements
Component Analysis: Individual filter contributions to overall score
EMA Structure: Confirm 3-bar crossover requirement met
Cooldown Status: Ensure sufficient time passed since last exit
For ReEntry Opportunities (when enabled):
System Status: Availability and eligibility for re-engagement
Exit Type Analysis: TP-based exits enable immediate re-entry, SL-based exits require cooldown
Condition Monitoring: Requirements for potential re-entry signals
Debugging Common Issues:
Issue: "Score is high but no hold?"
→ Check Final Threshold vs Score (not Dynamic Threshold)
→ Losing position may have increased timeMultiplier
→ Extended hold duration applying pressure
Issue: "Why different thresholds for same score?"
→ Position ROI status affects multiplier
→ Time elapsed since entry affects multiplier
→ Market volatility affects base threshold
Issue: "MTF conflicts with local signals?"
→ Higher timeframe trend opposing position
→ System designed to exit on MTF conflicts
→ Check MTF Data Valid status
⚡ Performance Optimization Notes
For Better Performance:
Analysis table updates may impact performance on some devices
Use specific filters rather than "All" mode for focused monitoring
Consider disabling during live trading for optimal chart performance
Enable only when needed for debugging or analysis
Strategic Usage:
Monitor "Smart" filter when positions are active for exit timing decisions
Use "Composite" filter during setup phases for signal strength analysis
Reference "ReEntry" filter after position closures for re-engagement opportunities
Track Final Threshold changes to understand exit pressure evolution
Advanced Debugging Workflow:
Position Entry Analysis:
Check Composite score vs threshold
Verify EMA crossover timing (3 bars prior)
Confirm cooldown completion
Hold Decision Monitoring:
Track Score vs Final Threshold progression
Monitor timeMultiplier changes over time
Watch for MTF conflicts
Exit Timing Analysis:
Identify which threshold layer caused exit
Track performance by exit type
Analyze re-entry eligibility
This analysis system provides transparency into strategy decision-making processes, allowing users to understand how signals are generated and positions are managed according to the programmed logic during various market conditions and position states.
SIGNAL TYPES AND CHARACTERISTICS
🔥 Core Momentum Signals
Flash Signal
Calculation: ta.rma(math.abs(close - close ), 5) > ta.sma(math.abs(close - close ), 7)
Purpose: Detects sudden price acceleration using smoothed momentum comparison
Characteristics: Triggers when recent price movement exceeds historical average movement
Usage: Primary momentum confirmation across multiple composite calculations
Weight: 1.3 points in composite scoring
Blink Signal
Calculation: math.abs(ta.change(close, 1)) > ta.sma(math.abs(ta.change(close, 1)), 5)
Purpose: Identifies immediate price velocity spikes
Characteristics: More sensitive than Flash, captures single-bar momentum bursts
Usage: Secondary momentum confirmation, often paired with Flash
Weight: 1.3 points in composite scoring
⚡ Advanced Composite Signals
Apex Pulse Signal
Calculation: apexAngleValue > 30 or apexAngleValue < -30
Purpose: Detects extreme EMA angle momentum
Characteristics: Identifies when trend angle exceeds ±30 degrees
Usage: Confirms directional momentum strength in trend-following scenarios
Pressure Surge Signal
Calculation: volSpike_AVP and strongTrendUp_ATG
Purpose: Combines volume expansion with trend confirmation
Characteristics: Requires both volume spike and strong uptrend simultaneously
Usage: bullish signal for trend continuation
Shift Wick Signal
Calculation: ta.crossunder(ema1, ema2) and isWickTrapDetected and directionFlip
Purpose: Detects bearish reversal with wick trap confirmation
Characteristics: Combines EMA crossunder with upper wick dominance and directional flip
Usage: Reversal signal for trend change identification
🛡️ Trap Exit Protection Signals
Bear Trap Exit
Calculation: isUpperWickTrap and isBearEngulfNow
Conditions: Previous bullish candle with 80%+ upper wick, followed by current bearish engulfing
Purpose: Emergency exit signal for long positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
Bull Trap Exit
Calculation: isLowerWickTrap and isBullEngulfNow
Conditions: Previous bearish candle with 80%+ lower wick, followed by current bullish engulfing
Purpose: Emergency exit signal for short positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
📊 Technical Analysis Foundation Signals
RSI-MFI Hybrid System
Base Calculation: (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
Oversold Threshold: < 35
Overbought Threshold: > 65
Weak Condition: < 35 and declining
Strong Condition: > 65 and rising
Usage: Momentum confirmation and reversal identification
ADX-DMI Trend Classification
Strong Up Trend: (adx > 25 and diplus > diminus and (diplus - diminus) > 5) or (ema1 > ema2 and ema2 > ema3 and ta.rising(ema2, 3))
Strong Down Trend: (adx > 20 and diminus > diplus - 5) or (ema1 < ema2 and ta.falling(ema1, 3))
Trend Weakening: adx < adx and adx < adx
Usage: Primary trend direction confirmation
Bollinger Band Squeeze Detection
Calculation: bbWidth < ta.lowest(bbWidth, 20) * 1.2
Purpose: Identifies low volatility periods before breakouts
Usage: Entry filter - avoids trades during consolidation
🎨 Visual Signal Indicators
Red X Signal
Calculation: isBearCandle and ta.crossunder(ema1, ema2)
Visual: Red X above price
Purpose: Bearish EMA crossunder with confirming candle
Composite Weight: +1.0 for short positions, -1.0 for long positions
Characteristics: Simple but effective trend change indicator
Green Dot Signal
Calculation: isBullCandle and ta.crossover(ema1, ema2)
Visual: Green dot below price
Purpose: Bullish EMA crossover with confirming candle
Composite Weight: +1.0 for long positions, -1.0 for short positions
Characteristics: Entry confirmation for trend-following strategies
Blue Diamond Signal
Trigger Conditions: amcBuySignal and score >= 4
Scoring Components: 11 different technical conditions
Key Requirements: AMC bullish + momentum rise + EMA expansion + volume confirmation
Visual: Blue diamond below price
Purpose: Bullish reversal or continuation signal
Characteristics: Multi-factor confirmation requiring 4+ technical alignments
Red Diamond Signal
Trigger Conditions: amcSellSignal and score >= 5
Scoring Components: 11 different technical conditions (stricter than Blue Diamond)
Key Requirements: AMC bearish + momentum crash + EMA compression + volume decline
Visual: Red diamond above price
Purpose: Potential bearish reversal or continuation signal
Characteristics: Requires higher threshold (5 vs 4) for more selective triggering
🔵 Specialized Detection Signals
Blue Dot Signal
Calculation: volumePulse and isCandleStrong and volIsHigh
Requirements: Volume > 2.0x MA, strong candle body > 35% of range, volume MA > 55
Purpose: Volume-confirmed momentum signal
Visual: Blue dot above price
Characteristics: Volume-centric signal for high-liquidity environments
Orange X Signal
Calculation: Complex multi-factor oversold reversal detection
Requirements: AMC oversold + wick trap + flash/blink + RSI-MFI oversold + bullish flip
Purpose: Oversold bounce signal with multiple confirmations
Visual: Orange X below price
Characteristics: Reversal signal requiring 5+ simultaneous conditions
VSS (Velocity Signal System)
Components: Volume spike + EMA angle + trend direction
Buy Signal: vssTrigger and vssTrendDir == 1
Sell Signal: vssTrigger and vssTrendDir == -1
Visual: Green/Red triangles
Purpose: Velocity-based momentum detection
Characteristics: Fast-response signal for momentum trading
⭐ Elite Composite Signals
Star Uprising Signal
Base Requirements: entryCompositeBuySignal and echoBodyLong and strongUpTrend and isAMCUp
Additional Confirmations: RSI hybrid strong + not high risk
Special Conditions: At bottom zone OR RSI bottom bounce OR strong volume bounce
Visual: Star symbol below price
Purpose: Bullish reversal signal from oversold conditions
Characteristics: Most selective bullish signal requiring multiple confirmations
Ultra Short Signal
Scoring System: 7-component scoring requiring 4+ points
Key Components: EMA trap + volume decline + RSI weakness + composite confirmation
Additional Requirements: Falling EMA structure + volume spike + flash confirmation
Visual: Explosion emoji above price
Purpose: Aggressive short entry for trend reversal or continuation
Characteristics: Complex multi-layered signal for experienced short selling
🎯 Composite Signal Architecture
Enhanced Composite Scoring
Long Composite: 15+ weighted components including structure, momentum, flash/blink, volume, price action, reversal triggers, trend alignment
Short Composite: Mirror structure with bearish bias
Threshold: 5.0 points required for signal activation
Conflict Resolution: If both long and short signals trigger simultaneously, both are disabled
Final Validation: Requires EMA momentum confirmation (ta.rising(emaFast_ATG, 2) for longs, ta.falling(emaFast_ATG, 2) for shorts)
Risk Assessment Integration
High Risk Long: RSI > 70 OR close > upper Bollinger Band 80%
High Risk Short: RSI < 30 OR close < lower Bollinger Band 80%
Zone Analysis: Top zone (95% of 50-bar high) vs Bottom zone (105% of 50-bar low)
Risk Penalty: High risk conditions subtract 1.5 points from composite scores
This signal architecture creates a multi-layered detection system where simple momentum signals provide foundation, technical analysis adds structure, visual indicators offer clarity, specialized detectors capture different market conditions, and composite signals identify potential opportunities while integrated risk assessment is designed to filter risky entries.
VISUAL FEATURES SHOWCASE
Ichimoku Cloud Visualization
Dynamic Color Intensity: Cloud transparency adapts to momentum strength - darker colors indicate stronger directional moves, while lighter transparency shows weakening momentum phases.
Gradient Color Mapping: Bullish momentum renders blue-purple spectrum with increasing opacity, while bearish momentum displays corresponding color gradients with intensity-based transparency.
Real-time Momentum Feedback: Color saturation provides immediate visual feedback on market structure strength, allowing traders to assess levels at a glance without additional indicators.
EMA Ribbon Bands
The 8-level exponential moving average system creates a comprehensive trend structure map with gradient color coding.
Signal Type Visualization
STRATEGY PROPERTIES & BACKTESTING DISCLOSURE
📊 Default Strategy Configuration:
✅ Initial Capital: 100,000 USD (realistic for average traders)
✅ Commission: 0.075% per trade (realistic exchange fees)
✅ Slippage: 3 ticks (market impact consideration)
✅ Position Size: 5% equity per trade (sustainable risk level)
✅ Pyramiding: Disabled (single position management)
✅ Sample Size: 185 trades over 12-month backtesting period
✅ Risk Management: Adaptive stop loss with maximum 1% risk per trade
COMPREHENSIVE BACKTESTING RESULTS
Testing Period & Market Conditions:
Backtesting Period: June 25, 2024 - June 25, 2025 (12 months)
Timeframe: 15-minute charts (MTF system active)
Market: BTCUSDT (Bitcoin/Tether)
Market Conditions: Full market cycle including volatility periods
Deep Backtesting: Enabled for maximum accuracy
📈 Performance Summary:
Total Return: +2.19% (+2,193.59 USDT)
Total Trades Executed: 185 trades
Win Rate: 34.05% (63 winning trades out of 185)
Profit Factor: 1.295 (gross profit ÷ gross loss)
Maximum Drawdown: 0.65% (653.17 USDT)
Risk-Adjusted Returns: Consistent with conservative risk management approach
📊 Detailed Trade Analysis:
Position Distribution:
Long Positions: 109 trades (58.9%) | Win Rate: 36.70%
Short Positions: 76 trades (41.1%) | Win Rate: 30.26%
Average Trade Duration: Optimized for 15-minute timeframe efficiency
Profitability Metrics:
Average Profit per Trade: 11.74 USDT (0.23%)
Average Winning Trade: 151.17 USDT (3.00%)
Average Losing Trade: 60.27 USDT (1.20%)
Win/Loss Ratio: 2.508 (winners are 2.5x larger than losses)
Largest Single Win: 436.02 USDT (8.69%)
Largest Single Loss: 107.41 USDT (controlled risk management)
💰 Financial Performance Breakdown:
Gross Profit: 9,523.93 USDT (9.52% of capital)
Gross Loss: 7,352.48 USDT (7.35% of capital)
Net Profit After Costs: 2,171.44 USDT (2.17%)
Commission Costs: 1,402.47 USDT (realistic trading expenses)
Maximum Equity Run-up: 2,431.66 USDT (2.38%)
⚖️ Risk Management Validation:
Maximum Drawdown: 0.65% showing controlled risk management
Drawdown Recovery: Consistent equity curve progression
Risk per Trade: Successfully maintained below 1.5% per position
Position Sizing: 5% equity allocation proved sustainable throughout testing period
📋 Strategy Performance Characteristics:
✅ Strengths Demonstrated:
Controlled Risk: Maximum drawdown well below industry standards (< 1%)
Positive Expectancy: Win/loss ratio of 2.5+ creates profitable edge
Consistent Performance: Steady equity curve without extreme volatility
Realistic Costs: Includes actual commission and slippage impacts
Sample Size: 185 trades during testing period
⚠️ Performance Considerations:
Win Rate: 34% win rate requires discipline to follow system signals
Market Dependency: Performance may vary significantly in different market conditions
Timeframe Sensitivity: Optimized for 15-minute charts; other timeframes may show different results
Slippage Impact: Real trading conditions may affect actual performance
📊 Benchmark Comparison:
Strategy Return: +2.19% over 12 months
Buy & Hold Bitcoin: +71.12% over same period
Strategy Advantage: Significantly lower drawdown and volatility
Risk-Adjusted Performance: Different risk profile compared to holding cryptocurrency
🎯 Real-World Application Insights:
Expected Trading Frequency:
Average: 15.4 trades per month (185 trades ÷ 12 months)
Weekly Frequency: Approximately 3-4 trades per week
Active Management: Requires regular monitoring during market hours
Capital Requirements:
Minimum Used in Testing: $10,000 for sustainable position sizing
Tested Range: $50,000-$100,000 for comfortable risk management
Commission Impact: 0.075% per trade totaled 1.4% of capital over 12 months
⚠️ IMPORTANT BACKTESTING DISCLAIMERS:
📈 Performance Reality:
Past performance does not guarantee future results. Backtesting results represent hypothetical performance and may not reflect actual trading outcomes due to market changes, execution differences, and emotional factors.
🔄 Market Condition Dependency:
This strategy's performance during the tested period may not be representative of performance in different market conditions, volatility regimes, or trending vs. sideways markets.
💸 Cost Considerations:
Actual trading costs may vary based on broker selection, market conditions, and trade size. Commission rates and slippage assumptions may differ from real-world execution.
🎯 Realistic Expectations:
The 34% win rate requires psychological discipline to continue following signals during losing streaks. Risk management and position sizing are critical for replicating these results.
⚡ Technology Dependencies:
Strategy performance assumes reliable internet connection, platform stability, and timely signal execution. Technical failures may impact actual results.
CONFIGURATION OPTIMIZATION
5-Minute Timeframe Optimization (Advanced Users Only)
⚠️ Important Warning: 5-minute timeframes operate without MTF confirmation, resulting in reduced signal quality and higher false signal rates.
Example 5-Minute Parameters:
Composite Thresholds: Long 6.5, Short 7.0 (vs 15M default 5.0/5.4)
Signal Lookback Bars: 12 (vs 15M default 8)
Volume Multiplier: 2.2 (vs 15M default 1.8)
MTF Timeframe: Disabled (automatic below 30M)
Risk Management Adjustments:
Position Size: Reduce to 3% (vs 5% default)
TP1: 0.8%, TP2: 1.2%, TP3: 2.0% (tighter targets)
SL: 0.8% (tighter stop loss)
Cooldown Minutes: 8 (vs 5 default)
Usage Notes for 5-Minute Trading:
- Wait for higher composite scores before entry
- Require stronger volume confirmation
- Monitor EMA structure more closely
15-Minute Scalping Setup:
TP1: 1.0%, TP2: 1.5%, TP3: 2.5%
Composite Threshold: 5.0 (higher filtering)
TP ATR Multiplier: 7.0
SL ATR Multiplier: 2.5
Volume Multiplier: 1.8 (requires stronger confirmation)
Hold Time: 2 bars minimum
3-Hour Swing Setup:
TP1: 2.0%, TP2: 4.0%, TP3: 8.0%
Composite Threshold: 4.5 (more signals)
TP ATR Multiplier: 8.0
SL ATR Multiplier: 3.2
Volume Multiplier: 1.2
Hold Time: 6 bars minimum
Market-Specific Adjustments
High Volatility Periods:
Increase ATR multipliers (TP: 2.0x, SL: 1.2x)
Raise composite thresholds (+0.5 points)
Reduce position size
Enable cooldown periods
Low Volatility Periods:
Decrease ATR multipliers (TP: 1.2x, SL: 0.8x)
Lower composite thresholds (-0.3 points)
Standard position sizing
Disable extended cooldowns
News Events:
Temporarily disable strategy 30 minutes before major releases
Increase volume requirements (2.0x multiplier)
Reduce position sizes by 50%
Monitor for unusual price action
RISK MANAGEMENT
Dual ROI System: Adaptive vs Fixed Mode
Adaptive RR Mode:
Uses ATR (Average True Range) for automatic adjustment
TP1: 1.0x ATR from entry price
TP2: 1.5x ATR from entry price
TP3: 2.0x ATR from entry price
Stop Loss: 1.0x ATR from entry price
Automatically adjusts to market volatility
Fixed Percentage Mode:
Uses predetermined percentage levels
TP1: 1.0% (default)
TP2: 1.5% (default)
TP3: 2.5% (default)
Stop Loss: 0.9% total (0.6% risk tolerance + 0.3% slippage buffer)(default)
Consistent levels regardless of volatility
Mode Selection: Enable "Use Adaptive RR" for ATR-based targets, disable for fixed percentages. Adaptive mode works better in varying volatility conditions, while fixed mode provides predictable risk/reward ratios.
Stop Loss Management
In Adaptive SL Mode:
Automatically scales with market volatility
Tight stops during low volatility (smaller ATR)
Wider stops during high volatility (larger ATR)
Include 0.3% slippage buffer in both modes
In Fixed Mode:
Consistent percentage-based stops
2% for crypto, 1.5% for forex, 1% for stocks
Manual adjustment needed for different market conditions
Trailing Stop System
Configuration:
Enable Trailing: Activates dynamic stop loss adjustment
Start Trailing %: Profit level to begin trailing (default 1.0%)
Trailing Offset %: Distance from current price (default 0.5%)
Close if Return to Entry: Optional immediate exit if price returns to entry level
Operation: Once position reaches trailing start level, stop loss automatically adjusts upward (longs) or downward (shorts) maintaining the offset distance from favorable price movement.
Timeframe-Specific Risk Considerations
15-Minute and Above (Tested):
✅ Full MTF system active
✅ Standard risk parameters apply
✅ Backtested performance metrics valid
✅ Standard position sizing (5%)
5-Minute Timeframes (Advanced Only):
⚠️ MTF system inactive - local signals only
⚠️ Higher false signal rate expected
⚠️ Reduced position sizing preferred (3%)
⚠️ Tighter stop losses required (0.8% vs 1.2%)
⚠️ Requires parameter optimization
⚠️ Monitor performance closely
1-Minute Timeframes (Limited Testing):
❌ Excessive noise levels
❌ Strategy not optimized for this frequency
Risk Management Practices
Allocate no more than 5% of your total investment portfolio to high-risk trading
Never trade with funds you cannot afford to lose
Thoroughly backtest and validate the strategy with small amounts before full implementation
Always maintain proper risk management and stop-loss settings
IMPORTANT DISCLAIMERS
Performance Disclaimer
Past performance does not guarantee future results. All trading involves substantial risk of loss. This strategy is provided for informational purposes and does not constitute financial advice.
Market Risk
Cryptocurrency and forex markets are highly volatile. Prices can move rapidly against positions, resulting in significant losses. Users should never risk more than they can afford to lose.
Strategy Limitations
This strategy relies on technical analysis and may not perform well during fundamental market shifts, news events, or unprecedented market conditions. No trading strategy can guarantee 100% success or eliminate the risk of loss.
Legal Compliance
You are responsible for compliance with all applicable regulations and laws in your jurisdiction. Consult with licensed financial professionals when necessary.
User Responsibility
Users are responsible for their own trading decisions, risk management, and compliance with applicable regulations in their jurisdiction.
RC - Crypto Scalper v3Cryptocurrency scalping strategy for perpetual futures with risk management and automation capabilities.
## Strategy Overview
This strategy identifies high-probability scalping opportunities in cryptocurrency perpetual futures markets using adaptive position sizing, dynamic stop losses, and intelligent exit management to maintain consistent risk-adjusted returns across varying market conditions.
## Technical Foundation
The strategy employs exponential moving averages for trend detection, Bollinger Bands for volatility measurement and mean reversion signals, RSI for momentum confirmation and overbought/oversold conditions, ATR for dynamic volatility-based stop placement, and VWAP for institutional price level identification. These technical indicators are combined with volume analysis and optional multi-timeframe confirmation to filter low-probability setups.
## Entry Methodology
The strategy identifies trading opportunities using three complementary approaches that can be enabled individually or in combination:
Momentum-Based Entries: Detects directional price movements aligned with short-term and intermediate-term trend indicators, with momentum oscillator confirmation to avoid entries at exhaustion points. Volume analysis provides additional confirmation of institutional participation.
Mean Reversion Entries: Identifies price extremes using statistical volatility bands combined with momentum divergence, targeting high-probability reversal zones in ranging market conditions. Entries require initial price structure confirmation to reduce false signals.
Institutional Flow Entries: Monitors volume-weighted price levels to identify areas where institutional orders are likely concentrated, entering on confirmed breaks of these key levels with supporting directional bias from trend indicators.
Each methodology uses distinct combinations of the technical indicators mentioned above, with specific parameter relationships and confirmation requirements that can be customized based on trader preference and market conditions.
## Exit Framework
Adaptive Stop Loss: Uses ATR-based stops (default 0.7x multiplier on 14-period ATR) that automatically adjust to current market volatility. Stop distance expands during volatile periods to avoid premature stops while tightening during consolidation to protect capital. Alternative percentage-based stops available for traders preferring fixed-distance risk management.
Trailing Profit System: Employs a dual-target exit approach combining fixed limit orders with dynamic trailing stops. The system activates trailing stops when positions reach profitable thresholds, allowing winning trades to capture extended moves while protecting accumulated gains. The high fixed limit (6R default) serves as a ceiling for exceptional moves while the trailing mechanism handles the majority of exits at optimal profit levels.
Time-Based Management: Implements maximum holding period constraints (50 bars default) to prevent capital from being trapped in directionless price action. This ensures consistent capital turnover and prevents the strategy from holding through extended consolidation periods.
Breakeven Protection: Automatically adjusts stop loss to entry price plus commission costs once trades reach predefined profit thresholds (0.7R default), eliminating downside risk on positions that have demonstrated directional follow-through.
## Risk Management
Position Sizing: Dynamic position sizing based on account equity percentage risk model (2% default). Calculates optimal position size based on entry price, stop distance, and account risk tolerance. Includes maximum position exposure caps and minimum position size thresholds to ensure practical trade execution.
Daily Loss Limits: Automatic trading suspension when intraday losses exceed configured threshold (5% of equity default). Prevents catastrophic drawdown days and removes emotional decision-making during adverse market conditions. Resets automatically at the start of each new trading day.
Leverage Controls: Comprehensive leverage monitoring with built-in liquidation protection for margined positions. Strategy calculates liquidation prices based on leverage settings and automatically closes positions approaching critical margin levels, preventing forced liquidations.
Exposure Management: Multiple layers of position size controls including maximum position value as percentage of equity (50% default), leverage-adjusted margin requirements, and minimum capital availability thresholds before opening new positions.
## Market Filters
Session-Based Filtering: Configurable trading windows for Asian (00:00-08:00 UTC), London (08:00-16:00 UTC), and New York (13:00-21:00 UTC) sessions. Allows traders to focus on specific market hours or avoid illiquid periods based on their asset and trading style.
Volatility Requirements: Minimum and maximum ATR percentage thresholds ensure strategy only operates within optimal volatility ranges. Prevents trading during both insufficient movement periods and extreme volatility events where execution quality deteriorates.
Trend Alignment: Optional higher timeframe trend filter ensures directional bias aligns with broader market structure, reducing counter-trend entries during strong directional moves.
Volume Confirmation: Configurable volume requirements for entry validation, ensuring sufficient market participation and reducing false signals during low-liquidity periods.
## Automation Support
Built-in webhook integration generates JSON payloads compatible with popular broker automation platforms. Alert system provides comprehensive notifications for all entry signals, exit executions, risk limit breaches, and daily trading status updates. Supports both automated and manual execution workflows.
## Settings Explanation
Initial Capital: $5,000
Selected as realistic starting point for retail traders entering crypto futures markets. Strategy scales proportionally - larger accounts show similar percentage returns with proportionally larger absolute gains and position sizes.
Risk Per Trade: 2%
Conservative default providing significant drawdown tolerance. With 51% historical win rate and positive expectancy, risking 2% per trade allows for extended losing streaks without account impairment. Adjustable from 0.5% (very conservative) to 5% (aggressive, experienced traders only).
Leverage: 10x
Standard cross-margin leverage for cryptocurrency perpetual futures. Combined with 2% risk setting and maximum 50% equity position size caps, actual exposure remains controlled despite leverage. Built-in liquidation protection provides additional safety layer.
Commission: 0.055%
Modeled on major exchange maker fee structures (Bybit, Binance Futures).
**Slippage: 50 ticks**
Ultra-conservative slippage assumption representing extreme worst-case execution scenarios. ETH perpetual tick size is $0.01, therefore 50 ticks equals $0.50 per side or $1.00 round trip slippage per trade.
Real-world slippage on 30-minute timeframe typically ranges from 2-5 ticks ($0.02-0.05 round trip) under normal conditions, with 10-20 ticks during highly volatile periods. The 50-tick setting assumes every single trade executes during extreme market stress conditions.
This ultra-conservative modeling approach means real-world trading performance under typical market conditions may exceed backtest results, as the strategy has been tested under punishing execution cost assumptions that represent worst-case scenarios rather than expected outcomes.
Stop Loss: ATR-based (0.7x multiplier)
Volatility-adaptive stops optimized for 30-minute cryptocurrency perpetuals. The 0.7x multiplier balances protection against premature stops due to normal market noise. Lower multipliers (0.5-0.6x) suitable for lower timeframes, higher multipliers (0.8-1.2x) for higher timeframes.
Take Profit: 6R (Risk:Reward)
High target designed to work in conjunction with trailing stop system rather than as primary exit mechanism. Historical analysis shows most profitable trades exit via trailing stops at lower multiples, with the 6R limit capturing occasional extended moves. This configuration allows the trailing stop system to operate optimally while providing upside capture on exceptional price runs.
Trailing Stop: Activates at 1R | Offset 0.5R
Trailing mechanism engages when position reaches 1:1 risk-reward, then maintains 0.5R distance from peak favourable price. This configuration allows profitable trades room to develop while protecting accumulated gains from reversals.
Maximum Holding Period: 50 bars
Automatic exit trigger after 50 bars (25 hours on 30-minute timeframe) prevents capital commitment to non-trending price action. Adjustable based on timeframe and trading style preferences.
## Backtest Performance
Test Period: November 2023 - November 2025 (2 years)
Asset: ETH/USDT Perpetual Futures
Timeframe: 30 minutes
Initial Capital: $5,000
Performance Metrics:
- Final Equity: $25,353.99
- Net Profit: $20,353.99
- Total Return: 407.08%
- Annualized Return: ~204%
- Total Trades: 2,549
- Winning Trades: 1,308 (51.28%)
- Losing Trades: 1,241 (48.72%)
- Profit Factor: 1.215
- Sharpe Ratio: 0.813
- Sortino Ratio: 6.428
- Maximum Drawdown: 11.53%
- Average Drawdown: <2%
Trade Statistics:
- Average Win: 1.15% per trade
- Average Loss: -0.98% per trade
- Win/Loss Ratio: 1.17:1
- Largest Win: 7.14%
- Largest Loss: -2.31%
- Average Trade Duration: ~8 hours
- Trades Per Month: ~106
Cost Analysis:
- Total Commission Paid: $21,277.06
- Commission as % of Gross Profit: 18.5%
- Modeled Slippage Impact: $2,549.00 (50 ticks per trade)
- Total Trading Costs: $23,826.06
- Net Profit After All Costs: $20,353.99
Risk-Adjusted Performance:
- Return/Max DD Ratio: 35.3
- Profit Per Trade: $7.98 average
- Risk of Ruin: <0.001% (with 2% risk, 51% win rate, 1.17 R:R)
## Bear Market Validation
To validate robustness across different market conditions, the strategy was additionally tested during the 2022 cryptocurrency bear market:
Test Period: May 2022 - November 2022 (7 months)
Market Conditions: ETH declined 57% (from ~$2,900 to ~$1,200)
Bear Market Results:
- Net Profit: $4,959.69
- Return: 99.19%
- Total Trades: 845
- Win Rate: 51.72%
- Maximum Drawdown: 18.54%
- Profit Factor: 1.235
- Outperformance vs Buy & Hold: +156.3%
The strategy demonstrated profitable performance during severe market decline, with short positions showing particular strength (54.1% win rate on shorts vs 49.4% on longs). This validates that the edge is not dependent on bullish market conditions and the multiple entry methodologies adapt naturally to different market environments.
## Recommended Usage
Optimal Timeframes:
- Primary: 30-minute (tested and optimized)
- Alternative: 1-hour (more selective, fewer trades)
- Not recommended: <15-minute (execution quality deteriorates)
Suitable Assets:
High-liquidity cryptocurrency perpetual futures recommended:
- BTC/USDT (>$2B daily volume)
- ETH/USDT (>$1B daily volume)
- SOL/USDT, AVAX/USDT (>$100M daily volume)
- Avoid low-liquidity pairs (<$50M daily volume)
Risk Configuration:
- Conservative: 1-1.5% per trade
- Moderate: 2-3% per trade (default: 2%)
- Aggressive: 3-5% per trade (requires discipline)
## Important Considerations
Backtesting vs Live Trading: Always paper trade first. Real-world results vary based on execution quality, broker-specific factors, network latency, and individual trade management decisions. Backtest performance represents historical simulation with ultra-conservative cost assumptions, not guaranteed future results.
Market Conditions: Strategy designed for liquid, actively-traded markets. Performance characteristics:
- Strong trends: Optimal (trailing stops capture extended moves)
- Ranging markets: Moderate (mean reversion component provides edge)
- Low volatility: Reduced (ATR filter prevents most entries)
- Extreme volatility: Protected (maximum volatility filter prevents entries)
Cost Impact: Commission represents approximately 18.5% of gross profit in backtests. The 50-tick slippage assumption is deliberately punitive - typical execution will likely be 5-10x better (2-10 ticks actual vs 50 ticks modeled), meaning real-world net results may significantly exceed backtest performance under normal market conditions.
Execution Quality: 30-minute timeframe provides sufficient time for order placement and management. Automated execution recommended for consistency. Manual execution requires discipline to follow signals without hesitation or second-guessing.
Starting Procedures:
1. Run backtest on your specific asset and timeframe
2. Paper trade for minimum 50 trades or 2 weeks
3. Start with minimum position sizes (0.5-1% risk)
4. Gradually scale to target risk levels as confidence builds
5. Monitor actual execution costs vs backtest assumptions
## Strategy Limitations
- Requires liquid markets; performance degrades significantly on low-volume pairs
- No built-in news event calendar; traders should manually avoid scheduled high-impact events
- Weekend/holiday trading may experience wider spreads and different price behaviour
- Does not model spread costs (assumes mid-price fills); add 1-2 ticks additional cost for market orders
- Performance during market structure changes (regime shifts) may differ from backtest period
- Requires consistent monitoring during active trading hours for optimal automated execution
- Slippage assumptions are deliberately extreme; actual slippage will typically be much lower
## Risk Disclosure
Cryptocurrency trading involves substantial risk of loss. Leverage amplifies both gains and losses. This strategy will experience losing streaks and drawdowns. The 11.53% maximum historical drawdown in bull market testing and 18.54% in bear market testing do not represent ceilings - larger drawdowns are possible and should be expected in live trading.
Past performance does not guarantee future results. Market conditions evolve, and historical edge may diminish or disappear. No strategy works in all market conditions. The strategy has been tested with extremely conservative slippage assumptions (50 ticks per trade) that significantly exceed typical execution costs; this provides a safety margin but does not eliminate risk.
Capital at Risk: Only trade with capital you can afford to lose completely. The strategy's positive historical performance across both bull and bear markets does not eliminate the possibility of significant losses or account impairment.
Not Financial Advice: This strategy is an educational tool, not investment advice. Users are solely responsible for their trading decisions, risk management, and outcomes. The developer assumes no liability for trading losses.
Leverage Warning: Trading with leverage can result in losses exceeding initial investment. Ensure you understand leverage mechanics and liquidation risks before using leveraged products.
## Technical Requirements
- TradingView Premium subscription (for strategy testing and alerts)
- Understanding of risk management principles
- Familiarity with perpetual futures mechanics
- Broker account supporting crypto perpetuals (if trading live)
- For automation: Webhook-compatible execution platform
## Version History
v3.0 - November 2025 (Initial Release)
- Multi-methodology entry system (Momentum, Mean Reversion, VWAP)
- Comprehensive risk management framework
- Adaptive exit system with trailing stops
- Session and volatility filtering
- Webhook automation support
- Validated across bull market (2024-25) and bear market (2022) periods
- Tested with ultra-conservative 50-tick slippage assumptions
Disclaimer: This strategy is provided "as-is" for educational purposes. Past performance does not indicate future results. All backtests conducted with 50-tick slippage (ultra-conservative assumptions). Actual trading costs typically significantly lower. Trade responsibly and at your own risk.
