[astropark] Super RSI [strategy]Dear Followers,
today a new Scalper Tool , which works great on 3 minutes and 5 minutes timeframes , but also down to 1m and up to 30m!
I called it " Super RSI ", as it is based on RSI and inherits some basic functionality from it.
If you check its settings, you will see that you can have 3 different buy-sell sources (first two are active by default):
STRONG BUY/SELL : buy when white area deeply falls into oversold-red zone and sell when white area gets into overbought-green zone (you can set and edit at which level oversold and overbought zones start);
CROSSES : buy when the black RSI line in the middle between red and green area cross under the buy cross limit value and sell when it crosso over the sell cross limit value (you can set and edit specific cross ranges for both buy and sell);
WEAK BUY/SELL : buy when green area at least go below 50 level and sell when red area at least goes above 50 level (you can edit both levels); these conditions are highlighted as background color and are very useful for taking (at least partial) profits in trades.
By default the script will be placed as oscillator on a specific pane below the chart, but
> you can disable the oscillator plot by enabling the "hide all plot" option
> and place it as overlay on chart by clicking on the black arrow at then end on the indicator name in chart and selecting "move to -> existing pane above"
This strategy can trigger till 10 buy or 10 sell signals in a row before reverting, so use a proper money management .
Strategy results are calculated on 20 trading days using 1000$ as initial capital and working at 10x leverage.
The user who wants to use this strategy, especially via an automated bot, must always set a stoploss at 3-5% from entry point or use a proper risk management strategy .
This is not the "Holy Grail", so use it with caution.
This script will let you backtest the strategy and find best settings for your preferred market.
The alarms script version of this indicator, which will let you set all notifications you may need in order to be alerted on each triggered signals, can be found by searching for " Super RSI".
This is a premium indicator , so send me a private message in order to get access to this script .
"momentum"に関するスクリプトを検索
[astropark] Super Stochastic RSI [strategy]Dear Followers,
today a new Scalper Tool , which works great on 3 minutes and 5 minutes timeframes !
I called it " Super Stochastic RSI ", as it is based on StochRSI and inherits some basic functionality from it.
If you check its settings, you will see that you can have 3 different buy-sell sources (first two are active by default):
STRONG BUY/SELL : buy when green area deeply falls into oversold zone and sell when red area gets into overbought zone (you can set and edit at which level oversold and overbought zones start);
K/D CROSSES : buy when k-line cross above d-line and sell viceversa (you can set and edit specific cross ranges for both buy and sell);
WEAK BUY/SELL : buy when green area at least go below 50 level and sell when red area at least goes above 50 level (you can edit both levels); these conditions are highlighted as background color and are very useful for taking (at least partial) profits in trades.
By default the script will be placed as oscillator on a specific pane below the chart, but
> you can disable the oscillator plot by enabling the "hide all plot" option
> and place it as overlay on chart by clicking on the black arrow at then end on the indicator name in chart and selecting "move to -> existing pane above"
This strategy can trigger till 10 buy or 10 sell signals in a row before reverting, so use a proper money management .
Strategy results are calculated on 20 trading days using 1000$ as initial capital and working at 10x leverage.
The user who wants to use this strategy, especially via an automated bot, must always set a stoploss at 3-5% from entry point or use a proper risk management strategy .
This is not the "Holy Grail", so use it with caution.
This script will let you backtest the strategy and find best settings for your preferred market.
The alarms script version of this indicator, which will let you set all notifications you may need in order to be alerted on each triggered signals, can be found here below:
This is a premium indicator , so send me a private message in order to get access to this script .
S&P 500 Benchmark Strategy
This strategy is a Benchmark Trend trading strategy. I used it primarily to measure my private algorithms against. It works on a variety of instruments at intervals between 1m and 1d (you'll have to play with some of the ranged variables in these cases). It was primarily designed to trade the 15 minute interval on SPX derived products. S&P E-Mini contract featured above.
It hits what I consider to be key targets when developing an algo:
1. Avg Trade is above $50
2. Profit Factor is above 1.2 (preferably above 1.5)
3. Has a relatively small draw-down
4. Is able to be traded both long and short
Notes/Options:
Can trade within market hours (default), outside market hours (with open inside), or anytime
Can adjust lengths for trend calculations
Algo tries its best to avoid fake-outs by using a volume component, this means that it misses 'slow rises' sometimes
By default it tries to only enter trades between 0930 and 1600. If the trade has left the station, it will wait for the next setup.
Stop loss level has a big impact on performance per instrument - default is 20 ticks but this has to be changed per instrument (I plan on updating this with code to auto-magically generate appropriate stop levels
As a Trend Following algorithm, it is vulnerable to chop zones but has been particularly resilient over the past few months when traded at 15m or 1h intervals. It is designed to trade against the 'current' market that has more frequent whipsaws. When used over generic bull market periods, it fails due to the high number of failed short trades and trimmed long trades. It works in a medium/high volatility environment.
S&D Light+ Enhanced# S&D Light+ Enhanced - Supply & Demand Zone Trading Strategy
## 📊 Overview
**S&D Light+ Enhanced** is an advanced Supply and Demand zone identification and trading strategy that combines institutional order flow concepts with smart money techniques. This strategy automatically identifies high-probability reversal zones based on Break of Structure (BOS), momentum analysis, and first retest principles.
## 🎯 Key Features
### Smart Zone Detection
- **Automatic Supply & Demand Zone Identification** - Detects institutional zones where price is likely to react
- **Multi-Candle Momentum Analysis** - Validates zones with configurable momentum requirements
- **Break of Structure (BOS) Confirmation** - Ensures zones are created only after significant structure breaks
- **Quality Filters** - Minimum zone size and ATR-based filtering to eliminate weak zones
### Advanced Zone Management
- **Customizable Zone Display** - Choose between Geometric or Volume-Weighted midlines
- **First Retest Logic** - Option to trade only the first touch of each zone for higher probability setups
- **Zone Capacity Control** - Maintains a clean chart by limiting stored zones per type
- **Visual Zone Status** - Automatically marks consumed zones with faded midlines
### Risk Management
- **Dynamic Stop Loss** - Positioned beyond zone boundaries with adjustable buffer
- **Risk-Reward Ratio Control** - Customizable R:R for consistent risk management
- **Entry Spacing** - Minimum bars between signals prevents overtrading
- **Position Sizing** - Built-in percentage of equity allocation
## 🔧 How It Works
### Zone Creation Logic
**Supply Zones (Selling Pressure):**
1. Strong momentum downward movement (configurable body-to-range ratio)
2. Identified bullish base candle (where institutions accumulated shorts)
3. Break of Structure downward (price breaks below recent swing low)
4. Zone created at the base candle's high/low range
**Demand Zones (Buying Pressure):**
1. Strong momentum upward movement
2. Identified bearish base candle (where institutions accumulated longs)
3. Break of Structure upward (price breaks above recent swing high)
4. Zone created at the base candle's high/low range
### Entry Conditions
**Long Entry:**
- Price retests a demand zone (touches top of zone)
- Rejection confirmed (close above zone)
- Zone hasn't been used (if "first retest only" enabled)
- Minimum bars since last entry respected
**Short Entry:**
- Price retests a supply zone (touches bottom of zone)
- Rejection confirmed (close below zone)
- Zone hasn't been used (if "first retest only" enabled)
- Minimum bars since last entry respected
## ⚙️ Customizable Parameters
### Display Settings
- **Show Zones** - Toggle zone visualization on/off
- **Max Stored Zones** - Control number of active zones (1-50 per type)
- **Color Customization** - Adjust supply/demand colors and transparency
### Zone Quality Filters
- **Momentum Body Fraction** - Minimum body size for momentum candles (0.1-0.9)
- **Min Momentum Candles** - Number of consecutive momentum candles required (1-5)
- **Big Candle Body Fraction** - Alternative single-candle momentum threshold (0.5-0.95)
- **Min Zone Size %** - Minimum zone height as percentage of price (0.01-5.0%)
### BOS Configuration
- **Swing Length** - Lookback period for structure identification (3-20)
- **ATR Length** - Period for volatility measurement (1-50)
- **BOS Required Break** - ATR multiplier for valid structure break (0.1-3.0)
### Midline Options
- **None** - No midline displayed
- **Geometric** - Simple average of zone top/bottom
- **CenterVolume** - Volume-weighted center based on highest volume bar in window
### Risk Management
- **SL Buffer %** - Additional space beyond zone boundary (0-5%)
- **Take Profit RR** - Risk-reward ratio for target placement (0.5-10x)
### Entry Rules
- **Only 1st Retest per Zone** - Trade zones only once for higher quality
- **Min Bars Between Entries** - Prevent overtrading (1-20 bars)
## 📈 Recommended Settings
### Conservative (Lower Frequency, Higher Quality)
```
Momentum Body Fraction: 0.30
Min Momentum Candles: 2-3
BOS Required Break: 0.8-1.0
Min Zone Size: 0.15-0.20%
Only 1st Retest: Enabled
```
### Balanced (Default)
```
Momentum Body Fraction: 0.28
Min Momentum Candles: 2
BOS Required Break: 0.7
Min Zone Size: 0.12%
Only 1st Retest: Enabled
```
### Aggressive (Higher Frequency, More Signals)
```
Momentum Body Fraction: 0.20-0.25
Min Momentum Candles: 1-2
BOS Required Break: 0.4-0.5
Min Zone Size: 0.08-0.10%
Only 1st Retest: Disabled
```
## 🎨 Visual Elements
- **Red Boxes** - Supply zones (potential selling areas)
- **Green Boxes** - Demand zones (potential buying areas)
- **Dotted Midlines** - Center of each zone (fades when zone is used)
- **Debug Triangles** - Shows when zone creation conditions are met
- Red triangle down = Supply zone created
- Green triangle up = Demand zone created
## 📊 Best Practices
1. **Use on Higher Timeframes** - 1H, 4H, and Daily charts work best for institutional zones
2. **Combine with Trend** - Trade zones in direction of overall market structure
3. **Wait for Confirmation** - Don't enter immediately at zone touch; wait for rejection
4. **Adjust for Market Volatility** - Increase BOS multiplier in choppy markets
5. **Monitor Zone Quality** - Fresh zones typically have higher success rates
6. **Backtest Your Settings** - Optimize parameters for your specific market and timeframe
## ⚠️ Risk Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Always:
- Use proper position sizing
- Set appropriate stop losses
- Test thoroughly before live trading
- Consider market conditions and overall trend
- Never risk more than you can afford to lose
## 🔍 Data Window Information
The strategy provides real-time metrics visible in the data window:
- Supply Zones Count
- Demand Zones Count
- ATR Value
- Momentum Signals (Up/Down)
- BOS Signals (Up/Down)
## 📝 Version History
**v1.0 - Enhanced Edition**
- Improved BOS detection logic
- Extended base candle search range
- Added comprehensive input validation
- Enhanced visual feedback system
- Robust array bounds checking
- Debug signals for troubleshooting
## 💡 Tips for Optimization
- **Trending Markets**: Lower momentum requirements, tighter BOS filters
- **Ranging Markets**: Increase zone size minimum, enable first retest only
- **Volatile Assets**: Increase ATR multiplier and SL buffer
- **Lower Timeframes**: Reduce swing length, increase min bars between entries
- **Higher Timeframes**: Increase swing length, relax momentum requirements
---
**Created with focus on institutional order flow, smart money concepts, and practical risk management.**
*Happy Trading! 📈*
Fusion Trend Pulse V2SCRIPT TITLE
Adaptive Fusion Trend Pulse V2 - Multi-Regime Strategy
DETAILED DESCRIPTION FOR PUBLICATION
🚀 INNOVATION SUMMARY
The Adaptive Fusion Trend Pulse V2 represents a breakthrough in algorithmic trading by introducing real-time market regime detection that automatically adapts strategy parameters based on current market conditions. Unlike static indicator combinations, this system dynamically adjusts its behavior across trending, choppy, and volatile market environments, providing a sophisticated multi-layered approach to market analysis.
🎯 CORE INNOVATIONS JUSTIFYING PROTECTED STATUS
1. Adaptive Market Regime Engine
Trending Market Detection: Uses ADX >25 with directional movement analysis
Volatile Market Classification: ATR-based volatility regime scoring (>1.2 threshold)
Choppy Market Identification: ADX <20 combined with volatility patterns
Dynamic Parameter Adjustment: All thresholds adapt based on detected regime
2. Multi-Component Fusion Algorithm
McGinley Dynamic Trend Baseline: Self-adjusting moving average that adapts to price velocity
Adaptive RMI (Relative Momentum Index): Enhanced RSI with momentum period adaptation
Zero-Lag EMA Smoothed CCI: Custom implementation reducing lag while maintaining signal quality
Hull MA Gradient Analysis: Slope strength normalized by ATR for trend confirmation
Volume Spike Detection: Regime-adjusted volume confirmation (0.8x-1.3x multipliers)
3. Intelligence Layer Features
Cooldown System: Prevents overtrading with regime-specific waiting periods (1-3 bars)
Performance Tracking: Real-time adaptation based on recent trade outcomes
Multi-Exchange Alert Integration: JSON-formatted alerts for automated trading
Comprehensive Dashboard: 16-metric real-time performance monitoring
📊 TECHNICAL SPECIFICATIONS
Market Regime Detection Philosophy:
The system continuously monitors market structure through volatility analysis and directional strength measurements. Rather than applying fixed thresholds, it creates dynamic response profiles that adjust the strategy's sensitivity, timing, and filtering based on the current market environment.
Adaptive Parameter Concept:
All strategy components modify their behavior based on regime classification. Volume requirements become more or less stringent, momentum thresholds shift to match market character, and exit timing adjusts to prevent whipsaws in different market conditions.
Entry Conditions (Both Long/Short):
McGinley trend alignment (close vs trend line)
Hull MA slope confirmation with ATR-normalized strength
Adaptive CCI above/below regime-specific thresholds
RMI momentum confirmation (>50 for long, <50 for short)
Volume spike exceeding regime-adjusted threshold
Regime-specific additional filters
Exit Strategy:
Dual take-profit system (2% and 4% default, customizable)
Momentum weakness detection (CCI reversal)
Trend breakdown (close below/above McGinley line)
Regime-specific urgency multipliers for faster exits in choppy markets
🎛️ USER CUSTOMIZATION OPTIONS
Core Parameters:
RMI Length & Momentum periods
CCI smoothing length
McGinley Dynamic length
Hull MA period for gradient analysis
Volume spike detection (length & multiplier)
Take profit levels (separate for long/short)
Adaptive Settings:
Market regime detection period (21 bars default)
Adaptation period for performance tracking (60 bars)
Volatility adaptation toggle
Trend strength filtering toggle
Momentum sensitivity multiplier (0.5-2.0 range)
Dashboard & Alerts:
Dashboard position (4 corners)
Dashboard size (Small/Normal/Large)
Transparency settings (0-100%)
Custom alert messages for bot integration
Date range filtering
🏆 UNIQUE VALUE PROPOSITIONS
1. Market Intelligence: First Pine Script strategy to implement comprehensive regime detection with parameter adaptation - most strategies use static settings regardless of market conditions.
2. Fusion Methodology: Combines 5+ distinct technical approaches (trend-following, momentum, volatility, volume, regime analysis) in a cohesive adaptive framework rather than simple indicator stacking.
3. Performance Optimization: Built-in learning system tracks recent performance and adjusts sensitivity - providing evolution rather than static rule-following.
4. Professional Integration: Enterprise-ready with JSON alert formatting, multi-exchange compatibility, and comprehensive performance tracking suitable for institutional use.
5. Visual Intelligence: Advanced dashboard provides 16 real-time metrics including regime classification, signal strength, and performance analytics - far beyond basic P&L displays.
🔧 TECHNICAL IMPLEMENTATION HIGHLIGHTS
Primary Applications:
Swing Trading: 4H-1D timeframes with regime-adapted entries
Algorithmic Trading: Automated execution via webhook alerts
Portfolio Management: Multi-timeframe analysis across different market conditions
Risk Management: Regime-aware position sizing and exit timing
Target Markets:
Cryptocurrency pairs (high volatility adaptation)
Forex majors (trending market optimization)
Stock indices (choppy market handling)
Commodities (volatile regime management)
🎯 WHY THIS ISN'T JUST AN INDICATOR MASHUP
Integrated Adaptation Framework: Unlike scripts that simply combine multiple indicators with static settings, this system creates a unified intelligence layer where each component influences and adapts to the others. The McGinley trend baseline doesn't just provide signals - it dynamically adjusts its sensitivity based on market regime detection. The momentum components modify their thresholds based on trend strength analysis.
Feedback Loop Architecture: The strategy incorporates a closed-loop learning system where recent performance influences future parameter selection. This creates evolution rather than static rule application. Most indicator combinations lack this adaptive learning capability.
Contextual Decision Making: Rather than treating each signal independently, the system uses contextual analysis where the same technical setup may generate different responses based on the current market regime. A momentum signal in a trending market triggers different behavior than the identical signal in choppy conditions.
Unified Risk Management: The regime detection doesn't just affect entries - it creates a comprehensive risk framework that adjusts exit timing, cooldown periods, and position management based on market character. This holistic approach distinguishes it from simple indicator stacking.
Custom Implementation Depth: Each component uses proprietary implementations (custom McGinley calculation, zero-lag CCI smoothing, enhanced RMI) rather than standard built-in functions, creating a cohesive algorithmic ecosystem rather than disconnected indicator outputs.
Custom Functions:
mcginley(): Proprietary implementation of McGinley Dynamic MA
rmi(): Enhanced Relative Momentum Index with custom parameters
zlema(): Zero-lag EMA for CCI smoothing
Regime classification algorithms with multi-factor analysis
Performance Optimizations:
Efficient variable management with proper scoping
Minimal repainting through careful historical referencing
Optimized calculations to prevent timeout issues
Memory-efficient tracking systems
Alert System:
JSON-formatted messages for API integration
Dynamic symbol/exchange substitution
Separate entry/exit/TP alert conditions
Customizable message formatting
⚡ WHY THIS REQUIRES PROTECTION
This strategy represents months of research into adaptive trading systems and market regime analysis. The specific combination of:
Proprietary regime detection algorithms
Custom adaptive parameter calculations
Multi-indicator fusion methodology
Performance-based learning system
Professional-grade implementation
Creates intellectual property that provides genuine competitive advantage. The methodology is not available in existing open-source scripts and represents original research into algorithmic trading adaptation.
🎯 EDUCATIONAL VALUE
Users gain exposure to:
Advanced market regime analysis techniques
Adaptive parameter optimization concepts
Multi-timeframe indicator fusion
Professional strategy development practices
Automated trading integration methods
The comprehensive dashboard and parameter explanations serve as a learning tool for understanding how professional algorithms adapt to changing market conditions.
CATEGORY SELECTION
Primary: Strategy
Secondary: Trend Analysis
SUGGESTED TAGS
adaptive, trend, momentum, regime, strategy, alerts, dashboard, mcginley, rmi, cci, professional
MANDATORY DISCLAIMER
Disclaimer: This strategy is for educational and informational purposes only. It does not constitute financial advice. Trading cryptocurrencies involves substantial risk, and past performance is not indicative of future results. Always backtest and forward-test before using on a live account. Use at your own risk.
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.
EXODUS EXODUS by (DAFE) Trading Systems
EXODUS is a sophisticated trading algorithm built by Dskyz (DAFE) Trading Systems for competitive and competition purposes, designed to identify high-probability trades with robust risk management. this strategy leverages a multi-signal voting system, combining three core components—SPR, VWMO, and VEI—alongside ADX, choppiness filters, and ATR-based volatility gates to ensure trades are taken only in favorable market conditions. the algo uses a take-profit to stop-loss ratio, dynamic position sizing, and a strict voting mechanism requiring all signals to align before entering a trade.
EXODUS was not overfitted for any specific symbol. instead, it uses a generic tuned setting, making it versatile across various markets. while it can trade futures, it’s not currently set up for it but has the potential to do more with further development. visuals are intentionally minimal due to its competition focus, prioritizing performance over aesthetics. a more visually stunning version may be released in the future with enhanced graphics.
The Unique Core Components Developed for EXODUS
SPR (Session Price Recalibration)
SPR measures momentum during regular trading hours (RTH, 0930-1600, America/New_York) to catch session-specific trends.
spr_lookback = input.int(15, "SPR Lookback") this sets how many bars back SPR looks to calculate momentum (default 15 bars). it compares the current session’s price-volume score to the score 15 bars ago to gauge momentum strength.
how it works: a longer lookback smooths out the signal, focusing on bigger trends. a shorter one makes SPR more sensitive to recent moves.
how to adjust: on a 1-hour chart, 15 bars is 15 hours (about 2 trading days). if you’re on a shorter timeframe like 5 minutes, 15 bars is just 75 minutes, so you might want to increase it to 50 or 100 to capture more meaningful trends. if you’re trading a choppy stock, a shorter lookback (like 5) can help catch quick moves, but it might give more false signals.
spr_threshold = input.float (0.7, "SPR Threshold")
this is the cutoff for SPR to vote for a trade (default 0.7). if SPR’s normalized value is above 0.7, it votes for a long; below -0.7, it votes for a short.
how it works: SPR normalizes its momentum score by ATR, so this threshold ensures only strong moves count. a higher threshold means fewer trades but higher conviction.
how to adjust: if you’re getting too few trades, lower it to 0.5 to let more signals through. if you’re seeing too many false entries, raise it to 1.0 for stricter filtering. test on your chart to find a balance.
spr_atr_length = input.int(21, "SPR ATR Length") this sets the ATR period (default 21 bars) used to normalize SPR’s momentum score. ATR measures volatility, so this makes SPR’s signal relative to market conditions.
how it works: a longer ATR period (like 21) smooths out volatility, making SPR less jumpy. a shorter one makes it more reactive.
how to adjust: if you’re trading a volatile stock like TSLA, a longer period (30 or 50) can help avoid noise. for a calmer stock, try 10 to make SPR more responsive. match this to your timeframe—shorter timeframes might need a shorter ATR.
rth_session = input.session("0930-1600","SPR: RTH Sess.") rth_timezone = "America/New_York" this defines the session SPR uses (0930-1600, New York time). SPR only calculates momentum during these hours to focus on RTH activity.
how it works: it ignores pre-market or after-hours noise, ensuring SPR captures the main market action.
how to adjust: if you trade a different session (like London hours, 0300-1200 EST), change the session to match. you can also adjust the timezone if you’re in a different region, like "Europe/London". just make sure your chart’s timezone aligns with this setting.
VWMO (Volume-Weighted Momentum Oscillator)
VWMO measures momentum weighted by volume to spot sustained, high-conviction moves.
vwmo_momlen = input.int(21, "VWMO Momentum Length") this sets how many bars back VWMO looks to calculate price momentum (default 21 bars). it takes the price change (close minus close 21 bars ago).
how it works: a longer period captures bigger trends, while a shorter one reacts to recent swings.
how to adjust: on a daily chart, 21 bars is about a month—good for trend trading. on a 5-minute chart, it’s just 105 minutes, so you might bump it to 50 or 100 for more meaningful moves. if you want faster signals, drop it to 10, but expect more noise.
vwmo_volback = input.int(30, "VWMO Volume Lookback") this sets the period for calculating average volume (default 30 bars). VWMO weights momentum by volume divided by this average.
how it works: it compares current volume to the average to see if a move has strong participation. a longer lookback smooths the average, while a shorter one makes it more sensitive.
how to adjust: for stocks with spiky volume (like NVDA on earnings), a longer lookback (50 or 100) avoids overreacting to one-off spikes. for steady volume stocks, try 20. match this to your timeframe—shorter timeframes might need a shorter lookback.
vwmo_smooth = input.int(9, "VWMO Smoothing")
this sets the SMA period to smooth VWMO’s raw momentum (default 9 bars).
how it works: smoothing reduces noise in the signal, making VWMO more reliable for voting. a longer smoothing period cuts more noise but adds lag.
how to adjust: if VWMO is too jumpy (lots of false votes), increase to 15. if it’s too slow and missing trades, drop to 5. test on your chart to see what keeps the signal clean but responsive.
vwmo_threshold = input.float(10, "VWMO Threshold") this is the cutoff for VWMO to vote for a trade (default 10). above 10, it votes for a long; below -10, a short.
how it works: it ensures only strong momentum signals count. a higher threshold means fewer but stronger trades.
how to adjust: if you want more trades, lower it to 5. if you’re getting too many weak signals, raise it to 15. this depends on your market—volatile stocks might need a higher threshold to filter noise.
VEI (Velocity Efficiency Index)
VEI measures market efficiency and velocity to filter out choppy moves and focus on strong trends.
vei_eflen = input.int(14, "VEI Efficiency Smoothing") this sets the EMA period for smoothing VEI’s efficiency calc (bar range / volume, default 14 bars).
how it works: efficiency is how much price moves per unit of volume. smoothing it with an EMA reduces noise, focusing on consistent efficiency. a longer period smooths more but adds lag.
how to adjust: for choppy markets, increase to 20 to filter out noise. for faster markets, drop to 10 for quicker signals. this should match your timeframe—shorter timeframes might need a shorter period.
vei_momlen = input.int(8, "VEI Momentum Length") this sets how many bars back VEI looks to calculate momentum in efficiency (default 8 bars).
how it works: it measures the change in smoothed efficiency over 8 bars, then adjusts for inertia (volume-to-range). a longer period captures bigger shifts, while a shorter one reacts faster.
how to adjust: if VEI is missing quick reversals, drop to 5. if it’s too noisy, raise to 12. test on your chart to see what catches the right moves without too many false signals.
vei_threshold = input.float(4.5, "VEI Threshold") this is the cutoff for VEI to vote for a trade (default 4.5). above 4.5, it votes for a long; below -4.5, a short.
how it works: it ensures only strong, efficient moves count. a higher threshold means fewer trades but higher quality.
how to adjust: if you’re not getting enough trades, lower to 3. if you’re seeing too many false entries, raise to 6. this depends on your market—fast stocks like NQ1 might need a lower threshold.
Features
Multi-Signal Voting: requires all three signals (SPR, VWMO, VEI) to align for a trade, ensuring high-probability setups.
Risk Management: uses ATR-based stops (2.1x) and take-profits (4.1x), with dynamic position sizing based on a risk percentage (default 0.4%).
Market Filters: ADX (default 27) ensures trending conditions, choppiness index (default 54.5) avoids sideways markets, and ATR expansion (default 1.12) confirms volatility.
Dashboard: provides real-time stats like SPR, VWMO, VEI values, net P/L, win rate, and streak, with a clean, functional design.
Visuals
EXODUS prioritizes performance over visuals, as it was built for competitive and competition purposes. entry/exit signals are marked with simple labels and shapes, and a basic heatmap highlights market regimes. a more visually stunning update may be released later, with enhanced graphics and overlays.
Usage
EXODUS is designed for stocks and ETFs but can be adapted for futures with adjustments. it performs best in trending markets with sufficient volatility, as confirmed by its generic tuning across symbols like TSLA, AMD, NVDA, and NQ1. adjust inputs like SPR threshold, VWMO smoothing, or VEI momentum length to suit specific assets or timeframes.
Setting I used: (Again, these are a generic setting, each security needs to be fine tuned)
SPR LB = 19 SPR TH = 0.5 SPR ATR L= 21 SPR RTH Sess: 9:30 – 16:00
VWMO L = 21 VWMO LB = 18 VWMO S = 6 VWMO T = 8
VEI ES = 14 VEI ML = 21 VEI T = 4
R % = 0.4
ATR L = 21 ATR M (S) =1.1 TP Multi = 2.1 ATR min mult = 0.8 ATR Expansion = 1.02
ADX L = 21 Min ADX = 25
Choppiness Index = 14 Chop. Max T = 55.5
Backtesting: TSLA
Frame: Jan 02, 2018, 08:00 — May 01, 2025, 09:00
Slippage: 3
Commission .01
Disclaimer
this strategy is for educational purposes. past performance is not indicative of future results. trading involves significant risk, and you should only trade with capital you can afford to lose. always backtest and validate any strategy before using it in live markets.
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
About the Author
Dskyz (DAFE) Trading Systems is dedicated to building high-performance trading algorithms. EXODUS is a product of rigorous research and development, aimed at delivering consistent, and data-driven trading solutions.
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Ichimoku by FarmerBTCLegal Disclaimer
This strategy, "Ichimoku by FarmerBTC," is provided for educational and informational purposes only. It does not constitute financial advice and should not be relied upon as such. Trading and investing involve substantial risk, including the potential for losing more than your initial investment. Past performance is not indicative of future results. Always consult with a qualified financial advisor before making trading or investment decisions. The author of this strategy is not responsible for any financial losses incurred through its use.
Overview
The "Ichimoku by FarmerBTC" strategy is a trend-following system built on the Ichimoku Cloud indicator, enhanced with volume analysis and a high-timeframe Simple Moving Average (HTF SMA) condition. It is designed to identify long-only trade opportunities and performs optimally on higher timeframes, such as the daily chart or above.
Core Components
1. Ichimoku Cloud
The Ichimoku Cloud is a comprehensive trend-following indicator that helps identify the overall market direction and momentum. It consists of:
Conversion Line (Tenkan-Sen): Measures short-term momentum.
Base Line (Kijun-Sen): Filters medium-term trends.
Leading Span A: The average of the Conversion and Base Lines, forming one cloud boundary.
Leading Span B: The midpoint of the highest high and lowest low over a longer period, forming the other cloud boundary.
Key Ichimoku Rules Applied:
The strategy identifies bullish trends when:
The price is above the cloud.
The cloud is bullish (Leading Span A > Leading Span B).
2. High-Timeframe Simple Moving Average (HTF SMA)
This condition ensures alignment with the broader trend:
Default SMA Length: 13 periods.
Default Timeframe: 1 day.
HTF SMA Rule:
Trades are allowed only when the price is above the HTF SMA, ensuring alignment with the larger trend.
3. Volume Analysis
The strategy uses volume to validate trade setups:
Volume MA: A 20-period moving average of volume is calculated.
Trades are allowed only when the current volume is at least 1.5x the Volume MA, indicating strong market participation.
Entry and Exit Rules
Entry Condition (Long Only):
Price above the Ichimoku Cloud: Confirms a bullish trend.
Bullish Cloud: Leading Span A > Leading Span B indicates upward momentum.
Price above the HTF SMA: Ensures alignment with the broader trend.
Volume exceeds threshold: Confirms strong market participation.
Exit Condition:
The strategy exits the position when the price moves below the Ichimoku Cloud, signaling a potential trend reversal.
Best Timeframes
This strategy is optimized for daily (1D) or higher timeframes (e.g., weekly 1W). Using it on lower timeframes may produce false signals due to increased noise in price and volume data.
Default Settings
Ichimoku Settings:
Conversion Line Period: 10
Base Line Period: 30
Lagging Span Period: 53
Displacement: 26
HTF SMA Settings:
SMA Length: 13
Timeframe: 1 Day
Volume Settings:
Volume MA Length: 20
Volume Multiplier: 1.5x
Visualization
Ichimoku Cloud:
Dynamic cloud coloring (green for bullish, red for bearish) helps identify the current trend.
HTF SMA:
A purple line overlays the chart, providing a clear representation of the high-timeframe trend.
Volume Panel:
An optional panel displays volume (blue histogram) and the Volume Moving Average (orange line) to analyze market participation.
Advantages of This Strategy
High Accuracy on Higher Timeframes:
Filtering trades using the Ichimoku Cloud, HTF SMA, and volume ensures robust trend alignment, reducing false signals.
Volume Confirmation:
Incorporates volume as a validation metric to enter trades only during strong market participation.
Easy Customization:
Parameters like Ichimoku periods, SMA length, timeframe, and volume thresholds can be adjusted to suit different assets or trading styles.
Limitations
Not Suitable for Low Timeframes:
Lower timeframes can produce excessive noise, leading to false signals.
Long-Only:
The strategy is designed only for bullish markets and does not support short trades.
Lagging Nature of Indicators:
Both the Ichimoku Cloud and SMA are lagging indicators, meaning they react to past price movements.
Conclusion
The "Ichimoku by FarmerBTC" strategy is an excellent tool for trend-following on daily or higher timeframes. Its combination of Ichimoku Cloud, high-timeframe SMA, and volume ensures a robust framework for identifying high-probability long trades in trending markets. However, users are advised to test the strategy thoroughly and manage their risk appropriately. Always consult with a financial professional before making trading decisions.
Ultimate Oscillator Trading StrategyThe Ultimate Oscillator Trading Strategy implemented in Pine Script™ is based on the Ultimate Oscillator (UO), a momentum indicator developed by Larry Williams in 1976. The UO is designed to measure price momentum over multiple timeframes, providing a more comprehensive view of market conditions by considering short-term, medium-term, and long-term trends simultaneously. This strategy applies the UO as a mean-reversion tool, seeking to capitalize on temporary deviations from the mean price level in the asset’s movement (Williams, 1976).
Strategy Overview:
Calculation of the Ultimate Oscillator (UO):
The UO combines price action over three different periods (short-term, medium-term, and long-term) to generate a weighted momentum measure. The default settings used in this strategy are:
Short-term: 6 periods (adjustable between 2 and 10).
Medium-term: 14 periods (adjustable between 6 and 14).
Long-term: 20 periods (adjustable between 10 and 20).
The UO is calculated as a weighted average of buying pressure and true range across these periods. The weights are designed to give more emphasis to short-term momentum, reflecting the short-term mean-reversion behavior observed in financial markets (Murphy, 1999).
Entry Conditions:
A long position is opened when the UO value falls below 30, indicating that the asset is potentially oversold. The value of 30 is a common threshold that suggests the price may have deviated significantly from its mean and could be due for a reversal, consistent with mean-reversion theory (Jegadeesh & Titman, 1993).
Exit Conditions:
The long position is closed when the current close price exceeds the previous day’s high. This rule captures the reversal and price recovery, providing a defined point to take profits.
The use of previous highs as exit points aligns with breakout and momentum strategies, as it indicates sufficient strength for a price recovery (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages two well-established phenomena in financial markets: momentum and mean-reversion. Momentum, identified in earlier studies like those by Jegadeesh and Titman (1993), describes the tendency of assets to continue in their direction of movement over short periods. Mean-reversion, as discussed by Poterba and Summers (1988), indicates that asset prices tend to revert to their mean over time after short-term deviations. This dual approach aims to buy assets when they are temporarily oversold and capitalize on their return to the mean.
Multi-timeframe Analysis:
The UO’s incorporation of multiple timeframes (short, medium, and long) provides a holistic view of momentum, unlike single-period oscillators such as the RSI. By combining data across different timeframes, the UO offers a more robust signal and reduces the risk of false entries often associated with single-period momentum indicators (Murphy, 1999).
Trading and Market Efficiency:
Studies in behavioral finance, such as those by Shiller (2003), show that short-term inefficiencies and behavioral biases can lead to overreactions in the market, resulting in price deviations. This strategy seeks to exploit these temporary inefficiencies, using the UO as a signal to identify potential entry points when the market sentiment may have overly pushed the price away from its average.
Strategy Performance:
Backtests of this strategy show promising results, with profit factors exceeding 2.5 when the default settings are optimized. These results are consistent with other studies on short-term trading strategies that capitalize on mean-reversion patterns (Jegadeesh & Titman, 1993). The use of a dynamic, multi-period indicator like the UO enhances the strategy’s adaptability, making it effective across different market conditions and timeframes.
Conclusion:
The Ultimate Oscillator Trading Strategy effectively combines momentum and mean-reversion principles to trade on temporary market inefficiencies. By utilizing multiple periods in its calculation, the UO provides a more reliable and comprehensive measure of momentum, reducing the likelihood of false signals and increasing the profitability of trades. This aligns with modern financial research, showing that strategies based on mean-reversion and multi-timeframe analysis can be effective in capturing short-term price movements.
References:
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Williams, L. (1976). Ultimate Oscillator. Market research and technical trading analysis.
Triple ThreatThis indicator provides buy and sell signals for Bitcoin based on confluence from well-known momentum, volatility, and trend indicators. It has successfully captured the major directional trends on Bitcoin's daily chart since 2018, and the settings are currently optimized for this chart in particular. This indicator implements RSI to gauge momentum, BBWP to gauge volatility, and an EMA to gauge trend. Maximum confluence signals are represented by horizontal bars in the indicator's pane, where the tallest green bar is a confirmed buy signal, and the tallest red bar is a confirmed sell signal. The shortest bar represents a momentum-only signal, and the second-shortest bar represents a volatility signal in confluence with the previously given momentum signal.
To track momentum, the RSI is plotted to the indicator plane against a moving average of the RSI. A momentum signal is generated when the RSI crosses over its moving average, retests/approaches the moving average, and then continues in the crossover direction (i.e., it fails to cross the moving average to the opposite side, creating a successful retest). The settings that affect this trigger are the "Crossover Threshold," which specifies how much the RSI should exceed the moving average to be considered a crossover, and the "Retest threshold," which specifies how closely the RSI should approach the moving average to be considered a retest. A momentum signal is ALSO generated if the RSI or its moving average exceed their counterpart by a certain threshold. For example, if the threshold was set at 10, a BUY signal would be generated when the RSI exceeds the moving average by 10, or a SELL signal would be generated when the moving average exceeds the RSI by 10. This threshold can be set using the "Instant Signal Threshold" setting. Either type of momentum signal will be plotted on the pane as the shortest horizontal bar, with its color indicating the signal's direction.
Volatility is primarily measured using the Bollinger Band Width Percentile (BBWP) indicator, which was created by The_Caretaker. BBWP plots the volatility of the asset's price, given by Bollinger Band width, relative to past volatility by assigning the volatility readings into percentiles. The indicator also includes a moving average of the BBWP itself, where a crossover to the upside represents expanding volatility and a crossover to the downside represents contracting volatility. This indicator is used to confirm a signal given by the momentum indicators - a momentum signal that is given during a period of expanding volatility has a greater likelihood of success. Therefore, when the BBWP crosses above its moving average by a given threshold, a previously triggered momentum signal is considered to be "confirmed." The threshold for this crossover can be set using the "BBWP Confirmation Threshold" setting. However, it is also relevant that periods of extreme volatility often accompany an extremity in price action (a "top" or "bottom"), in which case the BBWP is likely to contract after price reaches such an extremity. This phenomenon is captured by also using "extreme reads" on the momentum indicator to signal that there has already been enough volatility to confirm a momentum signal. If the RSI gives an "extreme read" before triggering a signal, the momentum signal is also considered to be confirmed. For example, if the RSI is above 80, breaks below 80, and then gives a SELL signal, this sell signal is considered to be confirmed without requiring the BBWP to crossover its moving average to the upside. The threshold that would confirm a SELL signal can be set with the "Overbought" setting, and the threshold that would confirm a BUY signal can be set with the "Oversold" setting. Whenever a volatility signal confirms a momentum signal, a medium-sized horizontal bar will be plotted on the pane in the same directional color as the momentum signal. Note that a momentum signal may trigger at the exact same time as the volatility signal which confirms it; in this case, only the medium-sized bar will be visible on the pane, but its direction can still be identified by its color.
Lastly, to reduce the likelihood of "false signals," a trend indicator is used to confirm the direction of the signal. This is typically an exponential moving average. If a confirmed volatility SELL signal is given, and the closing price is below the moving average, then the SELL signal is also confirmed by the trend. Likewise, if a confirmed volatility BUY signal is given, and the closing price is above the moving average, then the BUY signal is confirmed by the trend. The type and length of the moving average used to verify the trend can be set using the "Moving Average Type" and "Moving Average Length" settings found below the momentum/volatility settings. A trend signal is plotted on the pane as a tall horizontal bar, and is more deeply colored than the momentum and volatility signals.
For maximum confluence, it is recommended that the trend signal, given by the tallest bar, is the one that forms the basis of trades executed while using the Triple Threat indicator. It is possible to enter more aggressive trades with better entries by using only the volatility signal, given by the medium-sized bar, however this entails greater risk and should only be done in confluence with an additional trading strategy of your own discretion. Backtesting has shown that using the volatility signal alone underperforms using the volatility signal in confluence with the trend signal.
Please also be advised that the default setting are optimized for Bitcoin's daily chart only. The indicator is still applicable to other timeframes and asset classes, but the settings may need to be modified. I have a list of settings for other Bitcoin timeframes, and I would be happy to share them upon request.
I hope you can find this indicator to be of some use to your trading strategies. I'd be happy to hear any feedback from the community, so please don't hesitate to reach out. Stay safe, and happy trading.
OPTIMISED FOR 15Min on certain FOREX Ichimoku & Friends Strategy
Timeframe
15-Minute Chart
Entry Rules
Required Conditions ALL Must Be True
For LONG Entries:
Trend: Price is above EMA 200 (purple line)
Ichimoku: Tenkan (blue) is above Kijun (red)
Price Position: Close is above BOTH Tenkan AND Kijun
ADX: Must be above 22 (shows strong trend)
RSI: Between 50 and 70 (has momentum, not overbought)
Cooldown: At least 12 bars since last trade closed
For SHORT Entries:
Trend: Price is below EMA 200 (purple line)
Ichimoku: Tenkan (blue) is below Kijun (red)
Price Position: Close is below BOTH Tenkan AND Kijun
ADX: Must be above 22 (shows strong trend)
RSI: Between 30 and 50 (has momentum, not oversold)
Cooldown: At least 12 bars since last trade closed
Entry Signals Any ONE of These
Signal Type 1: Cross (C)
Long: Tenkan crosses above Kijun AND price closes above Kijun
Short: Tenkan crosses below Kijun AND price closes below Kijun
Wait 1 bar to confirm the cross holds
Signal Type 2: Bounce (B) - Most Reliable
Long: Price touches/dips to Kijun, then bounces up with strong bullish candle
Short: Price touches/spikes to Kijun, then rejects down with strong bearish candle
Must occur within last 3 bars
Signal Type 3: Breakout (K)
Long: Price breaks above Kijun with strong bullish momentum candle
Short: Price breaks below Kijun with strong bearish momentum candle
Candle body must be at least 40% of ATR
Risk Management
Stop Loss Placement
Placed at the lower of:
Recent swing low (last 5 bars) for longs
Kijun minus 0.5 ATR for longs
Minimum distance: 2.5 x ATR
FOR SHORTS: Mirror logic using swing highs
Take Profit
2x the stop loss distance
Example: If stop is 20 pips away, target is 40 pips
Position Size
100% of equity per trade (as per current settings)
Adjust based on your risk tolerance
Trade Management
When to Enter
Only when ALL entry conditions are met
Check that background is shaded (green for long, red for short)
Small letter markers (C, B, K) show which signal type triggered
When to Exit
Take Profit hit (2x R:R ratio)
Stop Loss hit (smart placement protects capital)
Strategy closes position (conditions reverse)
Cooldown Period
Wait 12 bars (3 hours on 15m chart) after any trade closes
Prevents revenge trading and overtrading
Visual Indicators on Chart
Lines
Blue (Tenkan): 9-period conversion line
Red (Kijun): 26-period base line
Purple (EMA 200): Long-term trend line
Orange (EMA 50): Not used in current rules
Signals
Large Green Triangle Up: LONG entry
Large Red Triangle Down: SHORT entry
Small Letters (C/B/K): Which signal type triggered
Background Colors
Light Green: Conditions favorable for LONG (ADX good, uptrend)
Light Red: Conditions favorable for SHORT (ADX good, downtrend)
No Color: Not safe to trade
Top Right Display
ADX Value: Green = above threshold, Red = below
Win Rate: Shows current performance
Quick Checklist Before Entry
LONG Trade Checklist:
Price above purple EMA 200
Blue line above red line
Price above both blue AND red lines
ADX number is green (above 22)
RSI between 50-70
Background is light green
At least 12 bars since last trade
Signal marker appeared (triangle or letter)
SHORT Trade Checklist:
Price below purple EMA 200
Blue line below red line
Price below both blue AND red lines
ADX number is green (above 22)
RSI between 30-50
Background is light red
At least 12 bars since last trade
Signal marker appeared (triangle or letter)
Tips for Success
Best Signal Type: Bounce (B) signals typically have highest win rate
ADX is Critical: Do not trade when ADX is red - wait for trends
Be Patient: 2-3 trades per day on 15m is normal and healthy
Trust the System: Do not second-guess the signals
Respect Cooldown: Waiting prevents emotional trading
Monitor Win Rate: Keep above 50% for profitability with 2:1 R:R
Adjustable Settings
If you want to modify strategy performance:
For Higher Win Rate Fewer Trades:
Increase "Minimum ADX" to 25
Increase "Cooldown Bars" to 15
Turn OFF breakout signals
For More Trades Slightly Lower Win Rate:
Decrease "Minimum ADX" to 20
Decrease "Cooldown Bars" to 8
Keep all signal types enabled
For Better Risk:Reward:
Increase "Risk:Reward Ratio" to 2.5 or 3.0
This means bigger targets, letting winners run more
What NOT to Do
Do not trade without ADX confirmation (when number is red)
Do not enter during cooldown period
Do not trade when price is chopping around EMA 200
Do not override the stop loss - let it work
Do not take signals when Tenkan and Kijun are flat/parallel
Do not force trades - wait for all conditions
Do not trade if you see no background shading
Notes
Current Performance: 67% win rate (2/3 trades)
Timeframe: 15-minute (3 hours = 12 bars cooldown)
Profit Factor Target: Above 1.5 is excellent
Strategy works best during: European and US trading sessions when volatility is higher
DYOR NFA
2026 CHRISTMAS PRESENT CHRISTMAS PRESENT
Overview
The Cash Detector is a comprehensive trading strategy that combines momentum analysis with price action confirmation to identify high-probability entry points. This strategy is designed to capture trend reversals and continuation moves by requiring multiple confirming signals before entry, significantly reducing false signals common in single-indicator systems.
Strategy Background
The strategy is built on the principle of confluence trading requiring multiple technical factors to align before taking a position. It focuses on two critical phases of market rotation:
Q2 Momentum Phase: Uses MACD crossovers to identify shifts in market momentum, signaling when bulls or bears are gaining control.
Q4 Trigger Phase: Employs engulfing candlestick patterns to confirm strong directional pressure and validate the momentum signal with actual price action.
By combining these elements, the strategy filters out weak signals and focuses only on setups where both momentum AND price action agree on direction.
Key Features
Dual Confirmation System: Requires both MACD momentum shift and engulfing candle pattern
RSI Filter: Optional overbought/oversold filter to avoid extreme conditions
Built-in Risk Management: Configurable stop loss and take profit levels
Performance Dashboard: Real-time ROI metrics displayed on chart
Full Backtesting: Strategy mode allows historical performance analysis
Trading Rules
LONG ENTRY BUY
All conditions must occur on the same candle:
1. Momentum Confirmation:
MACD line crosses above signal line bullish crossover
2. Price Action Confirmation:
Bullish engulfing pattern forms:
Current close greater than previous open
Current open less than previous close
Current close greater than current open
3. RSI Filter Optional:
RSI less than 70 not overbought
Visual Signal: Green LONG label appears below the candle
SHORT ENTRY SELL
All conditions must occur on the same candle:
1. Momentum Confirmation:
MACD line crosses below signal line bearish crossover
2. Price Action Confirmation:
Bearish engulfing pattern forms:
Current close less than previous open
Current open greater than previous close
Current close less than current open
3. RSI Filter Optional:
RSI greater than 30 not oversold
Visual Signal: Red SHORT label appears above the candle
Exit Rules
Stop Loss Default 2 percent
Long: Exit if price drops 2 percent below entry
Short: Exit if price rises 2 percent above entry
Take Profit Default 4 percent
Long: Exit if price rises 4 percent above entry
Short: Exit if price drops 4 percent below entry
Input Parameters
Indicator Settings
MACD Fast Length: 12 default
MACD Slow Length: 26 default
RSI Length: 14 default
Risk Management
Use Stop Loss: Enable or disable stop loss
Stop Loss percent: Percentage risk per trade default 2 percent
Use Take Profit: Enable or disable take profit
Take Profit percent: Target profit per trade default 4 percent
Filters
Use RSI Filter: Enable or disable RSI overbought oversold filter
RSI Overbought: Upper threshold default 70
RSI Oversold: Lower threshold default 30
Performance Metrics
The built-in dashboard displays:
Net Profit: Total profit loss in currency and percentage
Total Trades: Number of completed trades
Win Rate: Percentage of profitable trades
Profit Factor: Ratio of gross profit to gross loss
Average Win Loss: Mean profit per winning losing trade
Max Drawdown: Largest peak to trough decline
Best Practices
1. Timeframe Selection: Works on multiple timeframes test on 15min 1H 4H and daily
2. Market Conditions: Most effective in trending markets with clear momentum
3. Risk Reward Ratio: Default 1:2 ratio 2 percent risk 4 percent reward is conservative adjust based on backtesting
4. Combine with Context: Consider overall market trend and support resistance levels
5. Backtest First: Always backtest on your specific instrument and timeframe before live trading
Risk Disclaimer
This strategy is for educational purposes. Past performance does not guarantee future results. Always:
Backtest thoroughly on historical data
Paper trade before using real capital
Use proper position sizing and risk management
Never risk more than you can afford to lose
Customization Tips
Aggressive traders: Reduce stop loss to 1.5 percent increase take profit to 5 percent
Conservative traders: Increase stop loss to 3 percent reduce take profit to 3 percent
Ranging markets: Enable RSI filter to avoid false breakouts
Strong trends: Disable RSI filter to catch all momentum shifts
Technical Details
Indicators Used:
Moving Average Convergence Divergence MACD
Relative Strength Index RSI
Candlestick Pattern Recognition
Strategy Type: Trend following with momentum confirmation
Best Suited For: Stocks Forex Crypto Indices
Version 1.0
Compatible with Pine Script v5
VWolf – Slope GuardOVERVIEW
Slope Guard combines a momentum core (WaveTrend + RSI/MFI + QQE family) with a directional bias (EMA/DEMA and a DEMA-slope filter). Trade direction can be constrained by the Supertrend regime (Normal or Pivot). Risk is managed with ATR-based stops and targets, optional Supertrend-anchored dynamic levels, and a two-stage take-profit that can shift the stop to break-even after the first partial. The strategy supports explicit Backtest and Forward-test windows and adapts certain thresholds by market type (Forex vs. Stocks).
RECOMMENDED USE
Markets: Forex and equities; use Market Type to properly scale the DEMA-slope gate.
Timeframes: M15–H4 for intraday-swing and H1–D1 for slower swing; avoid ultra-low TFs without tightening ADX/QQE.
Assets: Instruments with persistent trends and orderly pullbacks; avoid flat ranges without sufficient ADX.
Strengths
Multi-layer confluence: trend bias + momentum + regime + strength.
Flexible risk engine: ATR vs. Supertrend anchoring, staged exits, and automatic break-even.
Clean research workflow: separated Backtest and Forward-test windows.
Precautions
Structural latency: Pivot-based constructs confirm with delay; validate with Forward-test.
Filter interaction: QQE Strict + ADX + WT zero-line can become overly selective; calibrate by asset/TF.
Overfitting risk: Prefer simple, portable parameter sets and validate across symbols/TFs.
CONCLUSION
Slope Guard is a “trend + momentum” framework with risk control at its core. By enforcing a baseline bias, validating momentum with the Vuman composite, and offering ATR or Supertrend-anchored exits—plus staged profits and break-even shifts—it seeks to capture the core of directional swings while compressing drawdowns. Keep testing windows isolated, start with moderate filters (QQE Normal, ADX ~20–25), and only add stricter gates (WT zero-line, DEMA slope) once they demonstrably improve stability without starving signals.
FOR MORE INFORMATION VISIT vwolftrading.com
VWolf – Pivot VumanSkewOVERVIEW
This strategy blends a lightweight trend scaffold (EMA/DEMA) with a skew-of-volatility filter and VuManchu/WaveTrend momentum signals. It’s designed to participate only when trending structure, momentum alignment, and volatility asymmetry converge, while delegating execution management to either a standard SuperTrend or a Pivot-based SuperTrend. Position sizing is risk‑based, with optional two‑step profit taking and automatic stop movement once price confirms in favor.
RECOMMENDED USE
Markets: Designed for Forex and equities, and readily adaptable to indices or liquid futures.
Timeframes: Performs best from 15m to 4h where momentum and trend layers both matter; daily can be used for confirmation/context.
Conditions: Trending or range‑expansion phases with clear volatility asymmetry. Avoid extremely compressed sessions unless thresholds are relaxed.
Strengths
Multi‑layer confluence (trend + skew + momentum) reduces random signals.
Dual SuperTrend modes provide flexible trailing and regime control.
Built‑in hygiene (ADX/DMI, lockout after loss, ATR gap) curbs over‑trading.
Risk‑% sizing and two‑step exits support consistent, plan‑driven execution.
Precautions
Over‑tight thresholds can lead to missed opportunities; start from defaults and tune gradually.
High sensitivity in momentum settings may overfit to a single instrument/timeframe.
In very low volatility, ATR‑gap or skew filters may block entries—consider adaptive thresholds.
CONCLUSION
VWolf – Pivot VumanSkew is a disciplined trend‑participation strategy that waits for directional structure, volatility asymmetry, and synchronized momentum before acting. Its execution layer—selectable between Normal and Pivot SuperTrend—keeps management pragmatic: scale out early when appropriate, trail intelligently, and defend capital with volatility‑aware stops. For users building a diversified playbook, Pivot VumanSkew serves as a trend‑continuation workhorse that can be tightened for precision or relaxed for higher participation depending on the market’s rhythm.
12M Return Strategy This strategy is based on the original Dual Momentum concept presented by Gary Antonacci in his book “Dual Momentum Investing.”
It implements the absolute momentum portion of the framework using a 12-month rate of change, combined with a moving-average filter for trend confirmation.
The script automatically adapts the lookback period depending on chart timeframe, ensuring the return calculation always represents approximately one year, whether you are on daily, weekly, or monthly charts.
How the Strategy Works
1. 12-Month Return Calculation
The core signal is the 12-month price return, computed as:
(Current Price ÷ Price from ~1 year ago) − 1
This return:
Plots as a histogram
Turns green when positive
Turns red when negative
The lookback adjusts automatically:
1D chart → 252 bars
1W chart → 52 bars
1M chart → 12 bars
Other timeframes → estimated to approximate 1 calendar year
2. Trend Filter (Moving Average of Return)
To smooth volatility and avoid noise, the strategy applies a moving average to the 12M return:
Default length: 12 periods
Plotted as a white line on the indicator panel
This becomes the benchmark used for crossovers.
3. Trade Signals (Long / Short / Cash)
Trades are generated using a simple crossover mechanism:
Bullish Signal (Go Long)
When:
12M Return crosses ABOVE its MA
Action:
Close short (if any)
Enter long
Bearish Signal (Go Short or Go Flat)
When:
12M Return crosses BELOW its MA
Action:
If shorting is enabled → Enter short
If shorting is disabled → Exit position and go to cash
Shorting can be enabled or disabled with a single input switch.
4. Position Sizing
The strategy uses:
Percent of Equity position sizing
You can specify the percentage of your portfolio to allocate (default 100%).
No leverage is required, but the strategy supports it if your account settings allow.
5. Visual Signals
To improve clarity, the strategy marks signals directly on the indicator panel:
Green Up Arrows: return > MA
Red Down Arrows: return < MA
A status label shows the current mode:
LONG
SHORT
CASH
6. Backtest-Ready
This script is built as a full TradingView strategy, not just an indicator.
This means you can:
Run complete backtests
View performance metrics
Compare long-only vs long/short behavior
Adjust inputs to tune the system
It provides a clean, rule-driven interpretation of the classic absolute momentum approach.
Inspired By: Gary Antonacci – Dual Momentum Investing
This script reflects the absolute momentum side of Antonacci’s original research:
Uses 12-month momentum (the most statistically validated lookback)
Applies a trend-following overlay to control downside risk
Recreates the classic signal structure used in academic studies
It is a simplified, transparent version intended for practical use and educational clarity.
Disclaimer
This script is for educational and research purposes only.
Historical performance does not guarantee future results.
Always use proper risk management.
TrendSight📌 TrendSight — The All-in-One Multi-Timeframe Trend Engine
Key Features & Logic
Multi-Timeframe Trend Confirmation:
Entries are filtered by confirming bullish/bearish alignment across three distinct Supertrend timeframes (e.g., 5-min, 15-min, 45-min, etc.), combined with an EMA and volatility filter, to ensure high-conviction trades that's a powerful combination! Designing the entire strategy around the 15-minute timeframe (M15) and focusing on high-volatility coins maximizes the strategy's effectiveness .
Guaranteed Single-Entry per Signal:
The strategy uses a powerful manual flag and counter system to ensure trades fire only once when a new signal begins. It absolutely prevents immediate re-entry if the signal remains true, waiting instead for the entire trend condition to reset to false.
Dynamic Trailing Stop Loss:
The Stop Loss is set to a moving Supertrend line (current_supertrend), ensuring tight risk management that trails the price as the trade moves into profit.Guaranteed Take Profit (4% Run-up): Uses a precise Limit Order via strategy.exit() to capture profits instantly at a 4% run-up. This ensures accurate profit capture, even on sudden spikes (wicks).
Automated Risk Management:
Position size is dynamically calculated based on a fixed risk percentage (default 2% of equity) relative to the distance to the trailing stop.
🔥 Core Components
1. Adaptive Multi-Timeframe SuperTrend Dashboard
The backbone of mTrendSight is a fully customizable SuperTrend system, enhanced with a multi-timeframe confirmation table displaying ST direction & value.
This compact “Trend Dashboard” provides instant clarity on higher-timeframe direction, trend strength, and market bias.
2. Dynamic Support & Resistance Channels
Automatically detects the strongest support/resistance zones using pivot clustering.
Key Features:
Clustered S/R Channels instead of thin lines
Adaptive width based on recent swings
Breakout markers (optional) for continuation signals
Helps identify structural zones, retest areas, and liquidity pockets
3. Multi-Timeframe Color-Coded EMAs
Plot up to three EMAs, each optionally pulled from a higher timeframe.
Benefits:
Instant visual trend alignment
Bullish/Bearish dynamic color shifts
Precision EMA value table for trade planning
Works perfectly with ST & RSI for multi-layer confirmation
4. Linear Regression Trend Channel
A statistically driven trend channel that measures the most probable path of price action.
Highlights:
Uses Pearson’s R to determine trend reliability
Provides a Confidence Level to judge whether trend slope is credible
Ideal for determining over-extension and mean-reversion zones
5. ATR Volatility Analyzer
A lightweight but powerful volatility classifier using ATR.
Features:
Detects High, Low, or Normal volatility
Clean table display
Helps filter entries during low-energy markets
Strengthens trend-following filters when volatility expands
6. RSI Momentum & Trend Classifier
A significantly improved RSI with multi-layer smoothing and structure-based classification.
Provides:
Bullish / Bearish / Neutral momentum states
Short-term momentum vs long-term RSI trend
Perfect for early trend shifts, pullback entries, and momentum confirmation
⚙️ How the Strategy Works (Execution Logic)
📌 Multi-Timeframe Supertrend + EMA + Volatility Confirmation
Entries are only triggered when:
Multiple Supertrend timeframes align (e.g., 5m + 15m + 45m)
EMA direction aligns with the trend
Volatility conditions (ATR filter) is not Low allow high-probability moves
This ensures strong directional confluence before every trade.
📌 Guaranteed Single-Entry Logic
The strategy uses a flag + counter system to ensure:
Only one entry is allowed per trend signal
Re-entries do not happen until the entire trend condition resets
The Strategy Tester remains clean, without duplicate overlapping trades
This eliminates revenge trades, repeated fills, and choppy overtrading.
📌 Dynamic Supertrend Trailing Stop
Stop Loss is anchored to current Supertrend value, creating:
Automatic trailing
Tight downside control
Protection against deep pullbacks
High responsiveness during volatility expansions
📌 Precision Take-Profit (4% Run-Up Capture)
A dedicated global exit block ensures:
Take Profit triggers exactly at 4% price run-up
Uses strategy.exit() with limit orders to catch spikes (wicks)
Works consistently on all timeframes & assets
📌 Automated Position Sizing (2% Risk Default)
Position size is dynamically calculated based on:
Account Equity
Distance to trailing stop
Configured risk %
This enforces proper risk management without manual adjustments.
📈 How to Interpret Results
Reliable Exits: All exits are globally managed, so stops and take profits trigger accurately on every bar.
Clean Trade History: Because of single-entry logic, backtests show one trade per valid signal.
Consistency: Multi-timeframe logic ensures only high-quality, structured trades.
Dual MTF Confirmed Trend Strategy (5m Entry / 15m MACD & RSI) v1That is a detailed Dual Multi-Timeframe (MTF) Confirmed Trend Strategy written in Pine Script for TradingView. The core idea of this strategy is to only take entry signals on a faster timeframe (5-minute) when the trend is strongly confirmed on a slower, higher timeframe (15-minute). This aims to reduce false signals and trade in the direction of the dominant trend. Here is an explanation of how the strategy works, broken down by section:
1. 5-Minute Entry Filters 🚀This section calculates several indicators on the current 5-minute chart to identify potential trade setups. A position is only considered if all 5-minute conditions align.
Supertrend: A trend-following indicator based on Average True Range (ATR).
Long Condition: The closing price must be above the Supertrend line.
Short Condition: The closing price must be below the Supertrend line.
Gann Hi-Lo (GHL): A trend indicator using Simple Moving Averages (SMA) of the high and low prices. GHL Line: Switches between the SMA of the Highs and the SMA of the Lows based on price action.
Long Condition: The closing price must be above the GHL line.
Short Condition: The closing price must be below the GHL line.
Exponential Moving Averages (EMAs): It uses a 50-period EMA and a 100-period EMA to confirm the short-term trend direction.
Long Condition: The closing price must be above both the 50 EMA and the 100 EMA.
Short Condition: The closing price must be below both the 50 EMA and the 100 EMA.
2. 15-Minute MTF Confirmation Filters ⏳This is the crucial step where the strategy verifies the trend on the slower, 15-minute timeframe using the request security function. This step acts as a gatekeeper to ensure the 5-minute trade aligns with the larger trend.
MACD Histogram (12, 26, 9): The difference between the MACD Line and the Signal Line.
Long Confirmation: The 15m MACD Histogram must be greater than 0 (MACD line is above the Signal line, indicating bullish momentum).
Short Confirmation: The 15m MACD Histogram must be less than 0 (MACD line is below the Signal line, indicating bearish momentum).
RSI (Relative Strength Index) (14): A momentum oscillator. The 50 level is often used to determine the general market trend.
Long Confirmation: The 15m RSI must be greater than 50 (indicating stronger bullish momentum).
Short Confirmation: The 15m RSI must be less than 50 (indicating stronger bearish momentum).
The Total 15m Confirmation is only true if both the MACD and the RSI confirmation signals align.
3. Trade Orders (Entry Logic) ⚖️
The strategy only executes a trade when the 5-minute entry conditions are met AND the 15-minute confirmation conditions are met.
Final Long Condition:
5m Conditions (Supertrend, GHL, EMA alignment) AND
15m Confirmation (MACD Hist > 0 AND RSI > 50)
Final Short Condition:
5m Conditions (Supertrend, GHL, EMA alignment) AND
15m Confirmation (MACD Hist < 0 AND RSI < 50)
When a trade signal is generated, the strategy:
Closes any opposite position (e.g., closes a "Short" trade if a "Long" signal appears).
Enters the new position (e.g., enters a "Long" trade).
This is designed as a reversal strategy where a new entry automatically closes the previous opposing trade.
In Summary
The strategy operates on a principle of Trend Alignment:
5-Minute Chart: Is used for Signal Timing (when exactly to enter the market).
15-Minute Chart: Is used for Trend Validation (is the overall market momentum supporting the signal?).
It's an attempt to capture short-term moves (5m signals) that are backed by strong medium-term momentum (15m confirmation), thereby aiming for higher probability trades.
This is not investment advice; it is recommended to perform optimization and backtesting for the assets intended for implementation.
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.
Stochastic Hash Strat [Hash Capital Research]# Stochastic Hash Strategy by Hash Capital Research
## 🎯 What Is This Strategy?
The **Stochastic Slow Strategy** is a momentum-based trading system that identifies oversold and overbought market conditions to capture mean-reversion opportunities. Think of it as a "buy low, sell high" approach with smart mathematical filters that remove emotion from your trading decisions.
Unlike fast-moving indicators that generate excessive noise, this strategy uses **smoothed stochastic oscillators** to identify only the highest-probability setups when momentum truly shifts.
---
## 💡 Why This Strategy Works
Most traders fail because they:
- **Chase prices** after big moves (buying high, selling low)
- **Overtrade** in choppy, directionless markets
- **Exit too early** or hold losses too long
This strategy solves all three problems:
1. **Entry Discipline**: Only trades when the stochastic oscillator crosses in extreme zones (oversold for longs, overbought for shorts)
2. **Cooldown Filter**: Prevents revenge trading by forcing a waiting period after each trade
3. **Fixed Risk/Reward**: Pre-defined stop-loss and take-profit levels ensure consistent risk management
**The Math Behind It**: The stochastic oscillator measures where the current price sits relative to its recent high-low range. When it's below 25, the market is oversold (time to buy). When above 70, it's overbought (time to sell). The crossover with its moving average confirms momentum is shifting.
---
## 📊 Best Markets & Timeframes
### ⭐ OPTIMAL PERFORMANCE:
**Crude Oil (WTI) - 12H Timeframe**
- **Why it works**: Oil markets have predictable volatility patterns and respect technical levels
**AAVE/USD - 4H to 12H Timeframe**
- **Why it works**: DeFi tokens exhibit strong momentum cycles with clear extremes
### ✅ Also Works Well On:
- **BTC/USD** (12H, Daily) - Lower frequency but high win rate
- **ETH/USD** (8H, 12H) - Balanced volatility and liquidity
- **Gold (XAU/USD)** (Daily) - Classic mean-reversion asset
- **EUR/USD** (4H, 8H) - Lower volatility, requires patience
### ❌ Avoid Using On:
- Timeframes below 4H (too much noise)
- Low-liquidity altcoins (wide spreads kill performance)
- Strongly trending markets without pullbacks (Bitcoin in 2021)
- News-driven instruments during major events
---
## 🎛️ Understanding The Settings
### Core Stochastic Parameters
**Stochastic Length (Default: 16)**
- Controls the lookback period for price comparison
- Lower = faster reactions, more signals (10-14 for volatile markets)
- Higher = smoother signals, fewer trades (16-21 for stable markets)
- **Pro tip**: Use 10 for crypto 4H, 16 for commodities 12H
**Overbought Level (Default: 70)**
- Threshold for short entries
- Lower values (65-70) = more trades, earlier entries
- Higher values (75-80) = fewer but higher-conviction trades
- **Sweet spot**: 70 works for most assets
**Oversold Level (Default: 25)**
- Threshold for long entries
- Higher values (25-30) = more trades, earlier entries
- Lower values (15-20) = fewer but stronger bounce setups
- **Sweet spot**: 20-25 depending on market conditions
**Smooth K & Smooth D (Default: 7 & 3)**
- Additional smoothing to filter out whipsaws
- K=7 makes the indicator slower and more reliable
- D=3 is the signal line that confirms the trend
- **Don't change these unless you know what you're doing**
---
### Risk Management
**Stop Loss % (Default: 2.2%)**
- Automatically exits losing trades
- Should be 1.5x to 2x your average market volatility
- Too tight = death by a thousand cuts
- Too wide = uncontrolled losses
- **Calibration**: Check ATR indicator and set SL slightly above it
**Take Profit % (Default: 7%)**
- Automatically exits winning trades
- Should be 2.5x to 3x your stop loss (reward-to-risk ratio)
- This default gives 7% / 2.2% = 3.18:1 R:R
- **The golden rule**: Never have R:R below 2:1
---
### Trade Filters
**Bar Cooldown Filter (Default: ON, 3 bars)**
- **What it does**: Forces you to wait X bars after closing a trade before entering a new one
- **Why it matters**: Prevents emotional revenge trading and overtrading in choppy markets
- **Settings guide**:
- 3 bars = Standard (good for most cases)
- 5-7 bars = Conservative (oil, slow-moving assets)
- 1-2 bars = Aggressive (only for experienced traders)
**Exit on Opposite Extreme (Default: ON)**
- Closes your long when stochastic hits overbought (and vice versa)
- Acts as an early profit-taking mechanism
- **Leave this ON** unless you're testing other exit strategies
**Divergence Filter (Default: OFF)**
- Looks for price/momentum divergences for additional confirmation
- **When to enable**: Trending markets where you want fewer but higher-quality trades
- **Keep OFF for**: Mean-reverting markets (oil, forex, most of the time)
---
## 🚀 Quick Start Guide
### Step 1: Set Up in TradingView
1. Open TradingView and navigate to your chart
2. Click "Pine Editor" at the bottom
3. Copy and paste the strategy code
4. Click "Add to Chart"
5. The strategy will appear in a separate pane below your price chart
### Step 2: Choose Your Market
**If you're trading Crude Oil:**
- Timeframe: 12H
- Keep all default settings
- Watch for signals during London/NY overlap (8am-11am EST)
**If you're trading AAVE or crypto:**
- Timeframe: 4H or 12H
- Consider these adjustments:
- Stochastic Length: 10-14 (faster)
- Oversold: 20 (more aggressive)
- Take Profit: 8-10% (higher targets)
### Step 3: Wait for Your First Signal
**LONG Entry** (Green circle appears):
- Stochastic crosses up below oversold level (25)
- Price likely near recent lows
- System places limit order at take profit and stop loss
**SHORT Entry** (Red circle appears):
- Stochastic crosses down above overbought level (70)
- Price likely near recent highs
- System places limit order at take profit and stop loss
**EXIT** (Orange circle):
- Position closes either at stop, target, or opposite extreme
- Cooldown period begins
### Step 4: Let It Run
The biggest mistake? **Interfering with the system.**
- Don't close trades early because you're scared
- Don't skip signals because you "have a feeling"
- Don't increase position size after a big win
- Don't revenge trade after a loss
**Follow the system or don't use it at all.**
---
### Important Risks:
1. **Drawdown Pain**: You WILL experience losing streaks of 5-7 trades. This is mathematically normal.
2. **Whipsaw Markets**: Choppy, range-bound conditions can trigger multiple small losses.
3. **Gap Risk**: Overnight gaps can cause your actual fill to be worse than the stop loss.
4. **Slippage**: Real execution prices differ from backtested prices (factor in 0.1-0.2% slippage).
---
## 🔧 Optimization Guide
### When to Adjust Settings:
**Market Volatility Increased?**
- Widen stop loss by 0.5-1%
- Increase take profit proportionally
- Consider increasing cooldown to 5-7 bars
**Getting Too Few Signals?**
- Decrease stochastic length to 10-12
- Increase oversold to 30, decrease overbought to 65
- Reduce cooldown to 2 bars
**Getting Too Many Losses?**
- Increase stochastic length to 18-21 (slower, smoother)
- Enable divergence filter
- Increase cooldown to 5+ bars
- Verify you're on the right timeframe
### A/B Testing Method:
1. **Run default settings for 50 trades** on your chosen market
2. Document: Win rate, profit factor, max drawdown, emotional tolerance
3. **Change ONE variable** (e.g., oversold from 25 to 20)
4. Run another 50 trades
5. Compare results
6. Keep the better version
**Never change multiple settings at once** or you won't know what worked.
---
## 📚 Educational Resources
### Key Concepts to Learn:
**Stochastic Oscillator**
- Developed by George Lane in the 1950s
- Measures momentum by comparing closing price to price range
- Formula: %K = (Close - Low) / (High - Low) × 100
- Similar to RSI but more sensitive to price movements
**Mean Reversion vs. Trend Following**
- This is a **mean reversion** strategy (price returns to average)
- Works best in ranging markets with defined support/resistance
- Fails in strong trending markets (2017 Bitcoin, 2020 Tech stocks)
- Complement with trend filters for better results
**Risk:Reward Ratio**
- The cornerstone of profitable trading
- Winning 40% of trades with 3:1 R:R = profitable
- Winning 60% of trades with 1:1 R:R = breakeven (after fees)
- **This strategy aims for 45% win rate with 2.5-3:1 R:R**
### Recommended Reading:
- *"Trading Systems and Methods"* by Perry Kaufman (Chapter on Oscillators)
- *"Mean Reversion Trading Systems"* by Howard Bandy
- *"The New Trading for a Living"* by Dr. Alexander Elder
---
## 🛠️ Troubleshooting
### "I'm not seeing any signals!"
**Check:**
- Is your timeframe 4H or higher?
- Is the stochastic actually reaching extreme levels (check if your asset is stuck in middle range)?
- Is cooldown still active from a previous trade?
- Are you on a low-liquidity pair?
**Solution**: Switch to a more volatile asset or lower the overbought/oversold thresholds.
---
### "The strategy keeps losing money!"
**Check:**
- What's your win rate? (Below 35% is concerning)
- What's your profit factor? (Below 0.8 means serious issues)
- Are you trading during major news events?
- Is the market in a strong trend?
**Solution**:
1. Verify you're using recommended markets/timeframes
2. Increase cooldown period to avoid choppy markets
3. Reduce position size to 5% while you diagnose
4. Consider switching to daily timeframe for less noise
---
### "My stop losses keep getting hit!"
**Check:**
- Is your stop loss tighter than the average ATR?
- Are you trading during high-volatility sessions?
- Is slippage eating into your buffer?
**Solution**:
1. Calculate the 14-period ATR
2. Set stop loss to 1.5x the ATR value
3. Avoid trading right after market open or major news
4. Factor in 0.2% slippage for crypto, 0.1% for oil
---
## 💪 Pro Tips from the Trenches
### Psychological Discipline
**The Three Deadly Sins:**
1. **Skipping signals** - "This one doesn't feel right"
2. **Early exits** - "I'll just take profit here to be safe"
3. **Revenge trading** - "I need to make back that loss NOW"
**The Solution:** Treat your strategy like a business system. Would McDonald's skip making fries because the cashier "doesn't feel like it today"? No. Systems work because of consistency.
---
### Position Management
**Scaling In/Out** (Advanced)
- Enter 50% position at signal
- Add 50% if stochastic reaches 10 (oversold) or 90 (overbought)
- Exit 50% at 1.5x take profit, let the rest run
**This is NOT for beginners.** Master the basic system first.
---
### Market Awareness
**Oil Traders:**
- OPEC meetings = volatility spikes (avoid or widen stops)
- US inventory reports (Wed 10:30am EST) = avoid trading 2 hours before/after
- Summer driving season = different patterns than winter
**Crypto Traders:**
- Monday-Tuesday = typically lower volatility (fewer signals)
- Thursday-Sunday = higher volatility (more signals)
- Avoid trading during exchange maintenance windows
---
## ⚖️ Legal Disclaimer
This trading strategy is provided for **educational purposes only**.
- Past performance does not guarantee future results
- Trading involves substantial risk of loss
- Only trade with capital you can afford to lose
- No one associated with this strategy is a licensed financial advisor
- You are solely responsible for your trading decisions
**By using this strategy, you acknowledge that you understand and accept these risks.**
---
## 🙏 Acknowledgments
Strategy development inspired by:
- George Lane's original Stochastic Oscillator work
- Modern quantitative trading research
- Community feedback from hundreds of backtests
Built with ❤️ for retail traders who want systematic, disciplined approaches to the markets.
---
**Good luck, stay disciplined, and trade the system, not your emotions.**
RSI-Adaptive T3 & SAR Strategy [PrimeAutomation]⯁ OVERVIEW
The RSI-Adaptive T3 and SAR Confluence Strategy combines adaptive smoothing with dynamic trend confirmation to identify precise trend reversals and continuation opportunities. It fuses the power of an RSI-based adaptive T3 moving average with the Parabolic SAR filter , aiming to trade in harmony with dominant momentum shifts while maintaining tight control through automatic stop-loss placement.
The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson, designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations.
To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market.
⯁ LOGIC
The T3 moving average length dynamically adjusts based on RSI values — when RSI is high, the smoothing period shortens to react faster; when RSI is low, the period increases for stability in slow markets.
A Parabolic SAR filter confirms directional bias, ensuring trades only occur in alignment with the broader market trend.
Long Entries: Trigger when the T3 curve crosses upward while the current price remains above the SAR — signaling bullish momentum alignment.
Short Entries: Trigger when the T3 crosses downward while the price remains below the SAR — confirming bearish trend alignment.
Stops: Dynamic stops are placed using the highest or lowest price over a set lookback period, adapting automatically to market volatility.
⯁ FEATURES
RSI-Adaptive T3 Filter: Adjusts smoothing in real time to market conditions, blending responsiveness with noise reduction.
SAR Confluence Check: Prevents counter-trend entries by confirming momentum direction via the Parabolic SAR.
Automatic Stop Placement: Uses recent highs or lows as stop-loss anchors, minimizing risk exposure.
Color-coded Visualization: The T3 line dynamically changes color based on slope direction, making momentum shifts visually intuitive.
Smoothed Trend Structure: Reduces market noise, allowing cleaner, more reliable trend recognition across different assets.
⯁ CONCLUSION
The RSI-Adaptive T3 and SAR Confluence Strategy delivers an advanced fusion of adaptive smoothing and structural confirmation. By combining RSI-driven reactivity with Parabolic SAR trend validation, this strategy offers a balanced approach to identifying sustainable momentum reversals while maintaining strong risk management through automatic stop levels. Ideal for traders who seek precision entries aligned with adaptive trend dynamics.
Pressure Pivots - MPI (Strategy)⇋ PRESSURE PIVOTS — MARKET PRESSURE INDEX STRATEGY
A comprehensive reversal trading system that combines order flow pressure analysis, multi-factor confluence detection, and adaptive machine learning to identify high-probability turning points in liquid markets.
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CORE INNOVATION: MARKET PRESSURE INDEX (MPI)
Traditional indicators measure price movement. The Market Pressure Index measures the force behind the movement.
How MPI Works:
Every bar tells two stories through volume distribution:
• Buy Pressure: Volume × (Close - Low) / (High - Low)
• Sell Pressure: Volume × (High - Close) / (High - Low)
• Net Pressure: Buy Pressure - Sell Pressure
This raw pressure is then normalized against baseline activity to create the bounded MPI (-1.0 to +1.0):
• Smooth Pressure: EMA(Net Pressure, period)
• Baseline Activity: SMA(|Net Pressure|, period × 2)
• MPI: (Smooth Pressure / Baseline) × Sensitivity
What MPI Reveals:
MPI > +0.7: Extreme buy pressure → Exhaustion potential
MPI = +0.2 to +0.7: Healthy bullish momentum
MPI = -0.2 to +0.2: Neutral/balanced pressure
MPI = -0.7 to -0.2: Healthy bearish momentum
MPI < -0.7: Extreme sell pressure → Exhaustion potential
Why It Works:
Two bars can both move 10 points, but if one closes at the high on high volume (aggressive buying) and the other closes mid-range on average volume (weak buying), only MPI distinguishes between sustainable momentum and exhaustion. This volume-weighted pressure analysis reveals conviction behind price moves—the key to timing reversals.
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SEVEN-FACTOR CONFLUENCE SYSTEM
MPI extremes alone aren't enough. The system requires multiple independent confirmations through weighted scoring:
1. DIVERGENCE (Weight: 3.0) — Premium Signal Type: DIV
Price makes new high but MPI makes lower high (or inverse for bullish)
• Detection: Tracks pivots with 5-bar lookback, compares price vs MPI at pivot points
• Signal: Purple triangles, highest weight (pressure weakening while price extends)
2. LIQUIDITY SWEEP (Weight: 2.5) — Premium Signal Type: LIQ
Price breaks swing high/low within 0.3 ATR then reverses
• Detection: Break within tolerance + close back through level
• Signal: Orange triangles, second-highest weight (stop hunt reversal)
3. ORDER FLOW IMBALANCE (Weight: 2.0) — Premium Signal Type: OF
Aggressive buying/selling 50% above normal
• Detection: EMA(aggressive volume) vs SMA(imbalance) threshold
• Signal: Aqua triangles, institutional positioning
4. VELOCITY EXHAUSTION (Weight: 1.5)
Parabolic move (2+ ATRs in 3 bars) + extreme MPI
• Detection: |3-bar price change / ATR| > threshold + MPI > ±0.5
• Indicates: Momentum deceleration, blow-off top/bottom
5. WICK REJECTION (Weight: 1.5)
Single bar: wick > 60% of range, or sequence: 2 bars with 40% + 30% wicks
• Detection: Shooting stars (bearish) or hammers (bullish)
• Indicates: Intrabar rejection, battle won by opposing side
6. VOLUME SPIKE (Weight: 1.0)
Volume > 20-bar average × multiplier (default: 2.0x)
• Detection: Participation surge confirmation
• Lowest weight: Can be manipulated, better as confirmation
7. POSITION FACTOR (Weight: 1.0)
At 10-bar highest (bearish) or lowest (bullish)
• Detection: Structural positioning for reversal
• Base requirement: Must be at extreme to score
Scoring Logic:
Premium Signals (DIV/LIQ/OF): Must score ≥6.0 (default premiumThreshold)
Standard Signals (STD): Must score ≥4.0 (default standardThreshold)
Example Scoring:
Divergence (3.0) + Liquidity Sweep (2.5) + Volume (1.0) = 6.5 → FIRES (DIV signal)
Recent High (1.0) + Wick (1.5) + Volume (1.0) + Velocity (1.5) = 5.0 → FIRES (STD signal)
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ADAPTIVE LEARNING ENGINE
Unlike static strategies, this system learns from every trade and optimizes itself.
Performance Tracking:
Every trade records:
• Entry Score: Confluence level at entry
• Signal Type: DIV / LIQ / OF / STD
• Win/Loss: Boolean outcome
• R-Multiple: (Exit - Entry) / (Entry - Stop)
• MAE: Maximum Adverse Excursion (worst drawdown)
• MFE: Maximum Favorable Excursion (best profit reached)
Three Adaptive Parameters:
1. Signal Threshold Adaptation
If Win Rate < Target (45%): RAISE threshold → fewer signals, better quality
If Win Rate > Target + 10% AND good R: LOWER threshold → more signals, profitable
2. Stop Distance Adaptation
If Avg MAE > 0.85 AND WR < 50%: WIDEN stops → reduce premature exits
If Avg MAE < 0.4 AND WR > 55%: TIGHTEN stops → reduce risk
3. Target Distance Adaptation
If Avg MFE > Target × 1.5: EXTEND targets → capture more of runners
If Avg MFE < Target × 0.7: SHORTEN targets → take profits faster
Signal Type Filtering:
The system tracks performance by type (DIV/LIQ/OF/STD):
• If Type WR < 40% AND Avg R < 0.8: Type DISABLED
• If Type WR ≥ 40% OR Avg R ≥ 0.8: Type RE-ENABLED
Example: If OF signals consistently lose while DIV signals win, system automatically stops taking OF signals and focuses on DIV.
Warmup Period:
First 30 trades (default) gather baseline data with relaxed thresholds. After warmup, full adaptation activates.
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COMPLETE POSITION MANAGEMENT
Dynamic Position Sizing:
Base Contracts = (Equity × Risk%) / (Stop Distance × Point Value)
Then multiplied by:
• Score Bonus: Up to +50% for highest-scoring signals
• Signal Type Bonus: DIV signals +50%, LIQ signals +30%
• Streak Multiplier: After 3 losses: 50% reduction, After 3 wins: 25% increase
Example: High-scoring DIV signal on winning streak = 3-4× larger position than weak STD signal on losing streak
Entry Modes:
Single Entry: Full size at once, exit at TP2 (or partial at TP1)
Tiered Entry: 40% at TP1 (2R), 60% at TP2 (4R adaptive)
Stop Management (3 Modes):
Structural: Beyond recent 20-bar swing high/low + buffer
ATR: Fixed ATR multiplier (default: 2.0 ATR, then adapts)
Hybrid: Attempt structural, fallback to ATR if invalid
Plus:
• Breakeven: Move stop to entry ± 1 tick when 1R reached
• Trailing: Activate when 1.5R reached, trail 0.8R behind price
• Max Loss Override: Cap dollar risk regardless of calculation
Target Management:
Fixed Mode: TP1 = 2R, TP2 = 4R
Adaptive Mode: TP1 = 2R fixed, TP2 adapts based on MFE analysis
Partial Exits: Default 50% at TP1, remainder at TP2 or trailing stop
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COMPREHENSIVE RISK CONTROLS
Daily Limits:
• Max Daily Loss: $2,000 default → HALT trading
• Max Daily Trades: 15 default → prevent overtrading
• Max Concurrent: 2 positions → limit correlation risk
Session Controls:
• Trading Hours: Specify start/end times + timezone
• Weekend Block: Optional (avoid crypto weekend volatility)
Prop Firm Protection (Live Trading Only):
• Daily Loss Limit: Stricter of general or prop limit ($1,000 default)
• Trailing Drawdown: Tracks high water mark, HALTS if breach ($2,500 default)
• Reset on Reload: Optional high water mark reset
Liquidity Filter (Optional):
• Time-Based: Avoid first/last X minutes of session
• Volume-Based: Require minimum volume ratio (0.5× average default)
Market Regime Filter (Optional):
• ADX-Based: Only trade when ADX > threshold (trending)
• Block: Consolidation (ADX < 20) or Transitional regimes
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REAL-TIME DASHBOARD
MPI Gauge Section:
Shows current pressure: 🟢 STRONG BUY (+0.5 to +1.0), 🟩 BUY PRESSURE (+0.2 to +0.5), ⚪ NEUTRAL (-0.2 to +0.2), 🟥 SELL PRESSURE (-0.5 to -0.2), 🔴 STRONG SELL (-1.0 to -0.5)
Signal Status Section:
• Active Signals: "🔴 DIV SELL" (purple background), "🟢 LIQ BUY" (orange), "🔵 OF SELL" (aqua), "🟢 STD BUY" (green)
• Warnings: "⚠️ BEAR WARNING" / "⚠️ BULL WARNING" (yellow) — setup forming, not full signal
• Scanning: "⏳ SCANNING..." (gray) — no signal active
• Confidence Bar: Visual score display "██████░░░░" showing confluence strength
Divergence Indicator:
"🟣 BEARISH DIVERGENCE" or "🟡 BULLISH DIVERGENCE" when detected
Performance Statistics:
• Overall Win Rate: Wins/Total with visual bar (lime ≥70%, yellow 50-70%, red <50%)
• Directional: Bearish vs Bullish win rates separately
• By Signal Type: DIV / LIQ / OF / STD individual performance tracking
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KEY PARAMETERS EXPLAINED
🎯 Pressure Engine:
• MPI Period (5-50, default: 14): Smoothing period — lower for scalping, higher for position trading
• MPI Sensitivity (0.5-5.0, default: 1.5): Amplification — lower compresses range, higher more extremes
🔍 Detection:
• Wick Threshold (0.3-0.9, default: 0.6): Minimum wick-to-range ratio for rejection
• Volume Spike (1.2-3.0x, default: 2.0): Multiplier above average for spike
• Aggressive Ratio (0.5-0.9, default: 0.65): Close position in range for aggressive orders
• Velocity Threshold (1.0-5.0 ATR, default: 2.0): ATR-normalized move for exhaustion
• MPI Extreme (0.5-0.95, default: 0.7): Level considered overbought/oversold
⚖️ Weights:
• Divergence: 3.0 (highest — pressure weakening)
• Liquidity: 2.5 (second — stop hunts)
• Order Flow: 2.0 (institutional positioning)
• Velocity: 1.5 (momentum exhaustion)
• Wick: 1.5 (rejection patterns)
• Volume: 1.0 (lowest — can be manipulated)
🎚️ Thresholds:
• Premium (4.0-15.0, default: 6.0): Score for DIV/LIQ/OF signals
• Standard (2.0-8.0, default: 4.0): Score for STD signals
• Warning Confluence (1-4, default: 2): Factors for yellow diamond warnings
🧬 Adaptive:
• Enable (true/false, default: true): Master learning switch
• Warmup Trades (5-100, default: 30): Data collection before adaptation
• Lookback (20-200, default: 50): Recent trades for performance calculation
• Adapt Speed (0.05-0.50, default: 0.15): Parameter adjustment rate
• Target Win Rate (30-70%, default: 45%): Optimization goal
• Target R-Multiple (0.5-5.0, default: 1.5): Risk/reward goal
💼 Position:
• Base Risk (0.1-10.0%, default: 1.5%): Equity risked per trade
• Max Contracts (1-100, default: 10): Hard position limit
• DIV Bonus (1.0-3.0x, default: 1.5): Size multiplier for divergence signals
• LIQ Bonus (1.0-3.0x, default: 1.3): Size multiplier for liquidity signals
🛡️ Stops:
• Mode (Structural/ATR/Hybrid, default: ATR): Stop placement method
• ATR Multiplier (0.5-5.0, default: 2.0): Stop distance in ATRs (adapts)
• Breakeven at (0.3-3.0R, default: 1.0R): When to move stop to entry
• Trail Trigger (0.5-5.0R, default: 1.5R): When to activate trailing
• Trail Offset (0.3-3.0R, default: 0.8R): Distance behind price
🎯 Targets:
• Mode (Fixed/Adaptive, default: Fixed): Target placement method
• TP1 (0.5-10.0R, default: 2.0R): First target for partial exit
• TP2 (1.0-15.0R, default: 4.0R): Final target (adapts in adaptive mode)
• Partial % (0-100%, default: 50%): Position percentage to exit at TP1
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PROFESSIONAL USAGE PROTOCOL
Phase 1: Paper Trading (Weeks 1-4)
• Setup: Default settings, all adaptive features ON, 0.5% base risk
• Goal: 30+ trades for warmup, observe MPI behavior and signal frequency
• Adjust: MPI sensitivity if stuck near neutral or always at extremes
• Threshold: Raise/lower if too many/few signals
Phase 2: Micro Live (Weeks 5-8)
• Requirements: WR >43%, at least one type >55%, Avg R >0.8
• Setup: 10-25% intended size, 0.5-1.0% risk, 1 position max
• Focus: Execution quality, match dashboard performance
• Journal: Screenshot every signal, track outcomes
Phase 3: Full Scale (Month 3+)
• Requirements: WR >45% over 50+ trades, Avg R >1.2, drawdown <15%
• Progression: Months 3-4 (1.0-1.5% risk), 5-6 (1.5-2.0%), 7+ (1.5-2.5%)
• Maintenance: Weekly dashboard review, monthly deep analysis
• Warnings: Reduce size if WR drops >10%, consecutive losses >7, or drawdown >20%
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DEVELOPMENT INSIGHTS
The Pressure Insight: Emerged from analyzing intrabar volume distribution. Within every candlestick, volume accumulates at different price levels. MPI deconstructs this to reveal conviction behind moves.
The Confluence Challenge: Early versions using MPI extremes alone achieved only 42% win rate. The seven-factor confluence system emerged from testing which combinations produced reliable reversals. Divergence + liquidity sweep became the strongest setup (68% win rate in isolation).
The Adaptive Breakthrough: Per-signal-type performance tracking revealed DIV signals winning at 71% while OF signals languished at 38%. Adaptive filtering disabled weak types automatically, recovering win rate from 39% to 54% during the 2022 volatility spike.
The Position Sizing Revelation: Dynamic sizing based on signal quality and recent performance increased Sharpe ratio from 1.2 to 1.9 while decreasing max drawdown from 18% to 12% over 500 trades. Bigger positions on better signals = geometric edge amplification.
The Risk Control Lesson: Testing with $50K accounts revealed catastrophic failure modes: daily loss cascades, overtrading commission bleed, weekend gap blowouts. Multi-layer controls (daily limits, concurrent caps, prop firm protection) became essential.
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LIMITATIONS & ASSUMPTIONS
What This Is NOT:
• NOT a Holy Grail: Typical performance 52-58% WR, 1.3-1.8 avg R, probabilistic edge
• NOT Predictive: Identifies high-probability conditions, doesn't forecast prices
• NOT Market-Agnostic: Best on liquid auction-driven markets (futures, forex, major crypto)
• NOT Hands-Off: Requires oversight for news events, gaps, system anomalies
• NOT Immune to Regime Changes: Adaptive engine helps but cannot predict black swans
Critical Assumptions:
1. Volume reflects intent (valid for regulated markets, violated by wash trading)
2. Pressure extremes mean-revert (true in ranging/exhaustion, fails in paradigm shifts)
3. Stop hunts exist (valid in liquid markets, less in thin/random walk periods)
4. Past patterns persist (valid in stable regimes, fails when structure fundamentally changes)
Works Best On: Major futures (ES, NQ, CL), liquid forex pairs (EUR/USD, GBP/USD), large-cap stocks, BTC
Performs Poorly On: Low-volume stocks, illiquid crypto pairs, news-driven headline events
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RISK DISCLOSURE
Trading futures, forex, and leveraged instruments involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. This strategy is provided for educational purposes only and should not be construed as financial advice.
The adaptive engine learns from historical data—there is no guarantee that past relationships will persist. Market conditions change, volatility regimes shift, and black swan events occur. No strategy can eliminate the risk of loss.
Users must validate performance on their specific instruments and timeframes before risking capital. The developer makes no warranties regarding profitability or suitability. Users assume all responsibility for trading decisions and outcomes.
"The market doesn't care about your indicators. It only cares about pressure—who's willing to pay more, who's desperate to sell. Find the exhaustion. Trade the reversal. Let the system learn the rest."
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Multi-Endeks KAMA & RSI Stratejisi v6 (Long & Short)Multi-Index KAMA & RSI Strategy v6 (Long & Short)
This is a hybrid trading strategy that combines two powerful technical analysis tools—the Kaufman's Adaptive Moving Average (KAMA) for trend following and the Relative Strength Index (RSI) for measuring momentum and identifying overbought/oversold conditions.
The term "Multi-Index" suggests that the decision-making process might incorporate data or conditions from several different market indices or timeframes, rather than just the single asset being traded.
🧭 Core Components
1. KAMA (Kaufman's Adaptive Moving Average)
KAMA is an adaptive moving average developed by quantitative financial theorist Perry J. Kaufman.
Adaptivity: Unlike standard moving averages, KAMA automatically adjusts its smoothing factor (speed) based on market volatility.
Mechanism:
Trending Markets (Low Noise): When prices move clearly in one direction (low volatility), KAMA speeds up, hugging the price closely and providing fast signals.
Sideways Markets (High Noise): When prices are choppy (high volatility/noise), KAMA slows down, smoothing out price fluctuations to reduce the risk of whipsaws (false signals).
Role in Strategy: To define the main trend direction. The position of the price relative to the KAMA line determines the base directional bias (Long or Short).
2. RSI (Relative Strength Index)
RSI is a momentum oscillator developed by J. Welles Wilder Jr. that measures the speed and change of price movements.
Overbought/Oversold: It oscillates between 0 and 100. Conventionally, a reading above 70 suggests overbought conditions (potential sell signal), and a reading below 30 suggests oversold conditions (potential buy signal).
Role in Strategy: Timing and Confirmation. Once the trend is confirmed by KAMA, the RSI acts as a timing filter, often confirming an entry as it moves away from extreme overbought (for Short) or oversold (for Long) levels.
📉 Potential Trading Logic (V6)
This "v6" strategy likely aims to capture more reliable entries by requiring both trend (KAMA) and momentum (RSI) alignment:
1. LONG (Buy) Entry Conditions
Trend Confirmation (KAMA): The asset's price (Closing Price) must be above the KAMA line (confirming an uptrend).
Momentum Confirmation (RSI):
Option A (Reversal): The RSI must cross above the 30 level (exiting oversold) or decisively move above the 50 level.
Option B (Trend-Continuation): In a strong uptrend, the RSI might bounce off the 40-50 zone and turn upwards, confirming trend continuation.
2. SHORT (Sell) Entry Conditions
Trend Confirmation (KAMA): The asset's price (Closing Price) must be below the KAMA line (confirming a downtrend).
Momentum Confirmation (RSI):
Option A (Reversal): The RSI must cross below the 70 level (exiting overbought) or decisively move below the 50 level.
Option B (Trend-Continuation): In a strong downtrend, the RSI might be rejected from the 50-60 zone and turn downwards, confirming continuation.
3. Exit Management
The strategy likely utilizes dynamic risk controls:
Stop-Loss: A dynamic stop placed on the opposite side of the KAMA, or an ATR-based distance to adjust to volatility.
Take-Profit: Conditions such as the RSI reaching extreme levels or the KAMA line being crossed in the reverse direction.
🌟 Implication of the "V6" Version
The "v6" designation implies that the strategy has been refined and iterated upon over time to address weaknesses in prior versions (v1, v2, etc.). These improvements might include:
Filters: Adding stricter RSI or KAMA cross filters to reduce false signals.
Multi-Index Logic: Using the RSI or KAMA of a secondary instrument (e.g., a major index or volatility measure) as a macro filter for the main trade execution.
Optimization: Optimizing the default lookback periods for KAMA and RSI for different asset classes.
AlosAlgo V2 (BETA)— V2 BETA —
V2 – 2025-11-21 (Update)
• Rebuilt the core signal engine to remove repainting – higher-timeframe Heikin Ashi / Renko now use confirmed bars only for more stable signals & alerts.
• Added Trend Filter MA so longs are only taken above the MA and shorts only below (optional).
• Added MACD momentum filter and Price Action filter (Higher Low for longs, Lower High for shorts) to cut a lot of chop.
• Introduced a loss-streak “circuit breaker” – after X consecutive losing trades the strategy pauses for a set number of bars.
• New TP/SL engine with 2 modes: ATR-based or Fixed % moves, with 4 staged TPs plus an optional runner and break-even SL after TP2.
• Cleaned up TP/SL lines & labels so levels are fixed per trade and easier to read.
• General refactor for more realistic backtests, better live behaviour and easier parameter tuning compared to V1.
ABOUT
AlosAlgo V2 is a multi-timeframe trend + momentum strategy designed for BTC and other high-liquidity markets. It takes directional bias from a higher timeframe, then filters that bias with volatility, momentum and simple price-action structure before it ever opens a trade.
Purely rule-based, no AI / Bayesian / ML.
Core idea
– Use higher-timeframe structure for direction.
– Only trade when trend, momentum and basic price action agree.
– Manage exits with multiple TPs, an optional runner and a hard SL so risk is defined from the start.
Setups
Two main engines:
• Open/Close – Higher-timeframe Heikin Ashi body direction (close vs open) as the core trend signal.
• Renko – ATR-based Renko feed with EMA cross (fast vs slow) as the core trend signal.
Classic sideways filters (ATR + RSI) can be layered on top if you want to only trade in trending or ranging conditions.
Filters added in V2
• Trend Filter MA – Longs only above the MA, shorts only below (length configurable).
• Momentum Filter – Optional MACD filter; only takes longs when MACD is bullish and shorts when MACD is bearish.
• Price Action Filter – Optional HL/LH logic using pivots: longs after a Higher Low, shorts after a Lower High.
• Loss-Streak Circuit Breaker – After N losing trades in a row, the strategy pauses entries for a set number of bars to avoid bad regimes / tilt.
Risk & exits
Two TP/SL modes:
• ATR mode – SL and TP1–TP4 based on ATR at entry (stopFactor / profitFactor).
• Fixed % mode – SL and TP1–TP4 defined as % moves from entry.
On entry the strategy:
• Opens a single position.
• Places 4 staged TPs (TP1–TP4) with user-defined % sizing.
• Optionally leaves a “runner” managed only by SL and trend changes.
• Can move SL to break-even automatically after TP2 (toggle).
All TP/SL levels are locked at entry and drawn on the chart with labels so you can see exactly what the trade is trying to do.
Non-repainting behaviour
V2 is refactored to avoid the repainting behaviour that V1 used. Higher-timeframe and Renko data are taken from confirmed bars only, and entries are based on state (e.g. > / <) instead of repaint-prone crosses. Backtests are much closer to what you’ll see live, and alerts line up with executed trades more reliably.
How to use (suggested defaults)
• Setup: Open/Close
• TPSType: Fixed %
• Trend Filter: ON
• Momentum Filter: ON
• Price Action Filter: ON
• Sideways Filter: No Filtering
Then tweak TP/SL distances and filters per asset + timeframe, and forward-test before sizing up.
Disclaimer
This is not financial advice, not a guarantee of profit and not a “set and forget” money printer. Always forward-test, paper trade and tune risk before using real capital or automation. Markets change – this is a tool, not a promise.
