Sine Weighted Trend Navigator [QuantAlgo]🟢 Overview
The Sine Weighted Trend Navigator utilizes trigonometric mathematics to create a trend-following system that adapts to various market volatility. Unlike traditional moving averages that apply uniform weights, this indicator employs sine wave calculations to distribute weights across historical price data, creating a more responsive yet smooth trend measurement. Combined with volatility-adjusted boundaries, it produces actionable directional signals for traders and investors across various market conditions and asset classes.
🟢 How It Works
At its core, the indicator applies sine wave mathematics to weight historical prices. The system generates angular values across the lookback period and transforms them through sine calculations, creating a weight distribution pattern that naturally emphasizes recent price action while preserving smoothness. The phase shift feature allows rotation of this weighting pattern, enabling adjustment of the indicator's responsiveness to different market conditions.
Surrounding this sine-weighted calculation, the system establishes volatility-responsive boundaries through market volatility analysis. These boundaries expand and contract based on current market conditions, creating a dynamic framework that helps distinguish meaningful trend movements from random price fluctuations.
The trend determination logic compares the sine-weighted value against these adaptive boundaries. When the weighted value exceeds the upper boundary, it signals upward momentum. When it drops below the lower boundary, it indicates downward pressure. This comparison drives the color transitions of the main trend line, shifting between bullish (green) and bearish (red) states to provide clear directional guidance on price charts.
🟢 How to Use
Green/Bullish Trend Line: Rising momentum indicating optimal conditions for long positions (buy)
Red/Bearish Trend Line: Declining momentum signaling favorable timing for short positions (sell)
Steepening Green Line: Accelerating bullish momentum with increasing sine-weighted values indicating strengthening upward pressure and high-probability trend continuation
Steepening Red Line: Intensifying bearish momentum with declining sine-weighted calculations suggesting persistent downward pressure and optimal shorting opportunities
Flattening Trend Lines: Gradual reduction in directional momentum regardless of color may indicate approaching consolidation or trend exhaustion requiring position management review
🟢 Pro Tips for Trading and Investing
→ Preset Strategy Selection: Utilize the built-in presets strategically - Scalping preset for ultra-responsive 1-15 minute charts, Default preset for balanced general trading, and Swing Trading preset for 1-4 hour charts and multi-day positions.
→ Phase Shift Optimization: Fine-tune the phase shift parameter based on market bias - use positive values (0.1-0.5) in trending bull markets to enhance uptrend sensitivity, negative values (-0.1 to -0.5) in bear markets for improved downtrend detection, and zero for balanced neutral market conditions.
→ Multiplier Calibration: Adjust the multiplier according to market volatility and trading style. Use lower values (0.5-1.0) for tight, responsive signals in stable markets, higher values (2.0-3.0) during earnings seasons or high-volatility periods to filter noise and reduce whipsaws.
→ Sine Period Adaptation: Customize the sine weighted period based on your trading timeframe and market conditions. Use 5-14 for day trading to capture short-term momentum shifts, 14-25 for swing trading to balance responsiveness with reliability, and 25-50 for position trading to maintain long-term trend clarity.
→ Multi-Timeframe Sine Validation: Apply the indicator across multiple timeframes simultaneously, using higher timeframes (4H/Daily) for overall trend bias and lower timeframes (15m/1H) for entry timing, ensuring sine-weighted calculations align across different time horizons.
→ Alert-Driven Systematic Execution: Leverage the built-in trend change alerts to eliminate emotional decision-making and capture every mathematically-confirmed trend transition, particularly valuable for traders managing multiple instruments or those unable to monitor charts continuously.
→ Risk Management: Increase position sizes during strong directional sine-weighted momentum while reducing exposure during frequent color changes that indicate mathematical uncertainty or ranging market conditions lacking clear directional bias.
Trend
Anrazzi - EMAs/ATR - 1.0.2Description:
The Anrazzi - EMAs/ATR indicator is a versatile tool for technical traders looking to monitor multiple moving averages alongside the Average True Range (ATR) on any chart. Designed for simplicity and customization, it allows traders to visualize up to six moving averages with configurable type, color, and length, while keeping real-time volatility information via ATR directly on the chart.
This indicator is perfect for spotting trends, identifying support/resistance zones, and gauging market volatility for intraday or swing trading strategies.
Key Features:
Supports up to six independent moving averages (MA1 → MA6)
Each MA is fully customizable:
Enable/disable individually
Type: EMA or SMA
Length
Color
ATR Display:
Custom timeframe
Color and position configurable
Adjustable multiplier
Compact and organized settings for easy configuration
Lightweight and efficient code for smooth chart performance
Watermark
Inputs / Settings:
MA Options: MA1 → MA6 (Enable/Disable, Type, Length, Color)
Additional Settings: ATR (Enable, Timeframe, Color, Multiplier)
How to Use:
Enable the moving averages you want to track
Configure type, length, and color for each MA
Enable ATR if needed and adjust settings
Watch MAs plotted dynamically and ATR in bottom-right corner
Recommended For:
Day traders and swing traders
Trend-following strategies
Volatility analysis and breakout detection
Traders needing a compact multi-MA dashboard
Imbalance (FVG)Indicator Description
This script is designed to automatically identify and visualize Fair Value Gaps (FVGs), also known as Imbalances, on your chart. An FVG is a key price action concept that highlights areas where the price moved swiftly, leaving a gap behind. This indicator is simple to use and fully customizable, making it an excellent tool for both novice and experienced traders.
Key Features
Automatic Detection: The indicator scans the market in real-time, automatically drawing FVG zones for both Bullish and Bearish moves.
Mitigation Tracking: When the price returns to an FVG zone, the indicator automatically marks it as "mitigated" (filled) by changing its color and style. This provides a clear signal that the imbalance has been neutralized.
Extend Zones Into the Future: Unmitigated FVG zones are automatically extended into the future, allowing them to be used as potential future support or resistance levels.
Full Customization: The user has complete control over the indicator's appearance. You can change the colors for bullish, bearish, and mitigated zones, as well as toggle their visibility on and off.
Performance Optimization: A built-in limit for the number of drawn objects prevents chart clutter and avoids errors from TradingView's drawing limits, ensuring smooth performance.
How to Use?
FVG zones can be used in various ways, including:
Price Magnets: Markets often tend to revert to "fill" these gaps.
Potential Entry Points: Price entering an FVG zone can present an opportunity to open a position, especially if confirming signals appear.
Support/Resistance Zones: Unfilled gaps can act as strong, dynamic levels of support or resistance.
Liquidity Pro Map [ChartPrime]⯁ OVERVIEW
Liquidity Pro Map is a market-structure tool that simulates liquidity distribution by splitting price history into buy-side and sell-side profiles. Using candle volume and the standard deviation of close, the indicator builds two mirrored volume maps on the right-hand side of the chart. It also extends liquidity levels backwards in time until they are crossed by price, allowing you to see which zones remain untouched and where liquidity is most likely resting. Cumulative skew lines and highlighted POC levels give additional clarity on imbalance between buyers and sellers.
⯁ KEY FEATURES
Dual Liquidity Profiles: The chart is divided into buy-side (green) and sell-side (red) liquidity profiles, letting you instantly compare both sides of order flow.
Level Extension Logic: Each liquidity level is extended back in time until price crosses it. If not crossed, it persists all the way to the indicator’s lookback period, marking zones that remain “untapped.”
Dynamic Binning with Standard Deviation: The indicator distributes candle volumes into bins using close-price deviation, creating a more realistic liquidity map than static price levels.
priceDeviation = ta.stdev(close, 25) * 2
priceReference = close > open ? low - priceDeviation : high + priceDeviation
Cumulative Volume Skew Lines: Polylines on the right-hand side show the aggregated buy and sell volume profiles, making it easy to spot imbalance.
POC Identification: Highest-volume levels on both sides are marked as POC (Point of Control) , providing key zones of interest.
Clear Color Coding: Gradient shading intensifies with volume concentration—dark teal/green for buy zones, dark pink/red for sell zones.
⯁ HOW IT WORKS (UNDER THE HOOD)
Volume Distribution: Each bar’s volume is assigned to a price bin based on its reference price (close ± standard deviation offset).
Buy vs. Sell Splitting: If bins above last close price, volume is allocated to sell-side liquidity; otherwise, it’s allocated to buy-side liquidity.
Level Extension: Boxes marking liquidity bins extend back until crossed by price. If uncrossed, they anchor all the way to the start of the lookback window.
Cumulative Polylines: As bins are stacked, cumulative buy and sell values form skew polylines plotted at the right edge.
POC Levels: The highest-volume bin on each side is highlighted with labels and arrows, marking where the heaviest liquidity is concentrated.
⯁ USAGE
Use buy/sell profiles to see where liquidity is likely resting. Green shelves suggest potential support zones; red shelves suggest resistance or sell liquidity pools.
Watch untouched extended levels —these often become magnets for price as liquidity is swept.
Track POC levels as primary liquidity targets, where reactions or fakeouts are most common.
Compare cumulative skew lines to judge which side dominates in volume. Heavy buy skew may indicate absorption of sell pressure, and vice versa.
Adjust lookback period to switch between intraday liquidity maps and larger swing-based profiles.
Use separator feature to hide bins borders for better visual clarity.
Use as a confluence tool with OBs, support/resistance, and liquidity sweep setups.
⯁ CONCLUSION
Liquidity Pro Map transforms candle volume into a structured simulation of where liquidity may rest across the chart. By dividing buy vs. sell profiles, extending untouched levels, and marking cumulative skew and POC, it equips traders with a clear visual map of potential liquidity pools. This allows for better anticipation of sweeps, reversals, and areas of high market activity.
PolyFilter [BackQuant]PolyFilter
A flexible, low-lag trend filter with three smoothing engines—optimized for clean bias, fewer whipsaws, and clear alerting.
What it does
PolyFilter draws a single “intelligent” baseline that adapts to price while suppressing noise. You choose the engine— Fractional MA , Ehlers 2-Pole Super Smoother , or a Multi-Kernel blend . The line can color itself by slope (trend) or by position vs price (above/below), and you get four ready-made alerts for flips and crosses.
What it plots
PolyFilter line — your smoothed trend baseline (width set by “Line Width”).
Optional candle & background coloring — choose: color by trend slope or by whether price is above/below the filter.
Signal markers — Arrows with L/S when the slope flips or when price crosses the line (if you enable shapes/alerts).
How the three engines differ
Fractional MA (experimental) — A power-law weighting of past bars (heavier focus on the most recent samples without throwing away history). The Adaptation Speed acts like the “fraction” exponent (default 0.618). Lower values lean more on recent bars; higher values spread weight further back.
Ehlers 2-Pole Super Smoother — Classic low-lag IIR smoother that aggressively reduces high-frequency noise while preserving turns. Great default when you want a steady, responsive baseline with minimal parameter fuss.
Multi-Kernel — A 70/30 blend of a Gaussian window and an exponential kernel. The Gaussian contributes smooth structure; the exponential adds a hint of responsiveness. Useful for assets that oscillate but still trend.
Reading the colors
Trend mode (default) — Line & candles turn green while the filter is rising (signal > signal ) and red while it’s falling.
Above/Below mode — Line & candles reflect price’s position relative to the filter: green when price > filter, red when price < filter. This is handy if you treat the filter like a dynamic “fair value” or bias line.
Inputs you’ll actually use
Calculation Settings
Price Source — Default HLC/3. Switch to Close for stricter trend, or HLC3/HL2 to soften single-print spikes.
Filter Length — Window/period for all engines. Shorter = snappier turns; longer = smoother line.
Adaptation Speed — Only affects Fractional MA . Lower it for faster, more local weighting; raise it for smoother, more global weighting.
Filter Type — Pick one of: Fractional MA, Ehlers 2-Pole, Multi-Kernel.
UI & Plotting
Color based off… — Choose Trend (slope) or > or < Close (position vs price).
Long/Short Colors — Customize bull/bear hues to your theme.
Show Filter Line / Paint candles / Color background — Visual toggles for the line, bars, and backdrop.
Line Width — Make the filter stand out (2–3 works well on most charts).
Signals & Alerts
PolyFilter Trend Up — Slope flips upward (the filter crosses above its prior value). Good for early continuation entries or stop-tightening on shorts.
PolyFilter Trend Down — Slope flips downward. Often used to scale out longs or rotate bias.
PolyFilter Above Price — The filter line crosses up through price (filter > price). This can confirm that mean has “caught up” after a pullback.
PolyFilter Below Price — The filter line crosses down through price (filter < price). Useful to confirm momentum loss on bounces.
Quick starts (suggested presets)
Intraday (5–15m, crypto or indices) — Ehlers 2-Pole, Length 55–80. Trend coloring ON, candle paint ON. Look for pullbacks to a rising filter; avoid fading a falling one.
Swing (1H–4H) — Multi-Kernel, Length 80–120. Background color OFF (cleaner), candle paint ON. Add a higher-TF confirmation (e.g., 4H filter rising when you trade 1H).
Range-prone FX — Fractional MA, Length 70–100, Adaptation ~0.55–0.70. Consider Above/Below mode to trade mean reversion to the line with a strict risk cap.
How to use it in practice
Bias line — Trade in the direction of the filter slope; stand aside when it flattens and color chops back and forth.
Dynamic support/resistance — Treat the line as a moving value area. In trends, entries often appear on shallow tags of the line with structure confluence.
Regime switch — When the filter flips and holds color for several bars, tighten stops on the opposing side and look for first pullback in the new color.
Stacking filters — Many users run PolyFilter on the active chart and a slower instance (longer length) on a higher timeframe as a “macro bias” guardrail.
Tuning tips
If you see too many flips, lengthen the filter or switch to Multi-Kernel.
If turns feel late, shorten the filter or try Ehlers 2-Pole for lower lag.
On thin or very noisy symbols, prefer HLC3 as the source and longer lengths.
Performance note: very large lengths increase computation time for the Multi-Kernel and Fractional engines. Start moderate and scale up only if needed.
Summary
PolyFilter gives you a single, trustworthy baseline that you can read at a glance—either as a pure trend line (slope coloring) or as a dynamic “above/below fair value” reference. Pick the engine that matches your market’s personality, set a sensible length, and let the color and alerts guide bias, entries on pullbacks, and risk on reversals.
Harmonic Super GuppyHarmonic Super Guppy – Harmonic & Golden Ratio Trend Analysis Framework
Overview
Harmonic Super Guppy is a comprehensive trend analysis and visualization tool that evolves the classic Guppy Multiple Moving Average (GMMA) methodology, pioneered by Daryl Guppy to visualize the interaction between short-term trader behavior and long-term investor trends. into a harmonic and phase-based market framework. By combining harmonic weighting, golden ratio phasing, and multiple moving averages, it provides traders with a deep understanding of market structure, momentum, and trend alignment. Fast and slow line groups visually differentiate short-term trader activity from longer-term investor positioning, while adaptive fills and dynamic coloring clearly illustrate trend coherence, expansion, and contraction in real time.
Traditional GMMA focuses primarily on moving average convergence and divergence. Harmonic Super Guppy extends this concept, integrating frequency-aware harmonic analysis and golden ratio modulation, allowing traders to detect subtle cyclical forces and early trend shifts before conventional moving averages would react. This is particularly valuable for traders seeking to identify early trend continuation setups, preemptive breakout entries, and potential trend exhaustion zones. The indicator provides a multi-dimensional view, making it suitable for scalping, intraday trading, swing setups, and even longer-term position strategies.
The visual structure of Harmonic Super Guppy is intentionally designed to convey trend clarity without oversimplification. Fast lines reflect short-term trader sentiment, slow lines capture longer-term investor alignment, and fills highlight compression or expansion. The adaptive color coding emphasizes trend alignment: strong green for bullish alignment, strong red for bearish, and subtle gray tones for indecision. This allows traders to quickly gauge market conditions while preserving the granularity necessary for sophisticated analysis.
How It Works
Harmonic Super Guppy uses a combination of harmonic averaging, golden ratio phasing, and adaptive weighting to generate its signals.
Harmonic Weighting : Each moving average integrates three layers of harmonics:
Primary harmonic captures the dominant cyclical structure of the market.
Secondary harmonic introduces a complementary frequency for oscillatory nuance.
Tertiary harmonic smooths higher-frequency noise while retaining meaningful trend signals.
Golden Ratio Phase : Phases of each harmonic contribution are adjusted using the golden ratio (default φ = 1.618), ensuring alignment with natural market rhythms. This reduces lag and allows traders to detect trend shifts earlier than conventional moving averages.
Adaptive Trend Detection : Fast SMAs are compared against slow SMAs to identify structural trends:
UpTrend : Fast SMA exceeds slow SMA.
DownTrend : Fast SMA falls below slow SMA.
Frequency Scaling : The wave frequency setting allows traders to modulate responsiveness versus smoothing. Higher frequency emphasizes short-term moves, while lower frequency highlights structural trends. This enables adaptation across asset classes with different volatility characteristics.
Through this combination, Harmonic Super Guppy captures micro and macro market cycles, helping traders distinguish between transient noise and genuine trend development. The multi-harmonic approach amplifies meaningful price action while reducing false signals inherent in standard moving averages.
Interpretation
Harmonic Super Guppy provides a multi-dimensional perspective on market dynamics:
Trend Analysis : Alignment of fast and slow lines reveals trend direction and strength. Expanding harmonics indicate momentum building, while contraction signals weakening conditions or potential reversals.
Momentum & Volatility : Rapid expansion of fast lines versus slow lines reflects short-term bullish or bearish pressure. Compression often precedes breakout scenarios or volatility expansion. Traders can quickly gauge trend vigor and potential turning points.
Market Context : The indicator overlays harmonic and structural insights without dictating entry or exit points. It complements order blocks, liquidity zones, oscillators, and other technical frameworks, providing context for informed decision-making.
Phase Divergence Detection : Subtle divergence between harmonic layers (primary, secondary, tertiary) often signals early exhaustion in trends or hidden strength, offering preemptive insight into potential reversals or sustained continuation.
By observing both structural alignment and harmonic expansion/contraction, traders gain a clear sense of when markets are trending with conviction versus when conditions are consolidating or becoming unpredictable. This allows for proactive trade management, rather than reactive responses to lagging indicators.
Strategy Integration
Harmonic Super Guppy adapts to various trading methodologies with clear, actionable guidance.
Trend Following : Enter positions when fast and slow lines are aligned and harmonics are expanding. The broader the alignment, the stronger the confirmation of trend persistence. For example:
A fast line crossover above slow lines with expanding fills confirms momentum-driven continuation.
Traders can use harmonic amplitude as a filter to reduce entries against prevailing trends.
Breakout Trading : Periods of line compression indicate potential volatility expansion. When fast lines diverge from slow lines after compression, this often precedes breakouts. Traders can combine this visual cue with structural supports/resistances or order flow analysis to improve timing and precision.
Exhaustion and Reversals : Divergences between harmonic components, or contraction of fast lines relative to slow lines, highlight weakening trends. This can indicate liquidity exhaustion, trend fatigue, or corrective phases. For example:
A flattening fast line group above a rising slow line can hint at short-term overextension.
Traders may use these signals to tighten stops, take partial profits, or prepare for contrarian setups.
Multi-Timeframe Analysis : Overlay slow lines from higher timeframes on lower timeframe charts to filter noise and trade in alignment with larger market structures. For example:
A daily bullish alignment combined with a 15-minute breakout pattern increases probability of a successful intraday trade.
Conversely, a higher timeframe divergence can warn against taking counter-trend trades in lower timeframes.
Adaptive Trade Management : Harmonic expansion/contraction can guide dynamic risk management:
Stops may be adjusted according to slow line support/resistance or harmonic contraction zones.
Position sizing can be modulated based on harmonic amplitude and compression levels, optimizing risk-reward without rigid rules.
Technical Implementation Details
Harmonic Super Guppy is powered by a multi-layered harmonic and phase calculation engine:
Harmonic Processing : Primary, secondary, and tertiary harmonics are calculated per period to capture multiple market cycles simultaneously. This reduces noise and amplifies meaningful signals.
Golden Ratio Modulation : Phase adjustments based on φ = 1.618 align harmonic contributions with natural market rhythms, smoothing lag and improving predictive value.
Adaptive Trend Scaling : Fast line expansion reflects short-term momentum; slow lines provide structural trend context. Fills adapt dynamically based on alignment intensity and harmonic amplitude.
Multi-Factor Trend Analysis : Trend strength is determined by alignment of fast and slow lines over multiple bars, expansion/contraction of harmonic amplitudes, divergences between primary, secondary, and tertiary harmonics and phase synchronization with golden ratio cycles.
These computations allow the indicator to be highly responsive yet smooth, providing traders with actionable insights in real time without overloading visual complexity.
Optimal Application Parameters
Asset-Specific Guidance:
Forex Majors : Wave frequency 1.0–2.0, φ = 1.618–1.8
Large-Cap Equities : Wave frequency 0.8–1.5, φ = 1.5–1.618
Cryptocurrency : Wave frequency 1.2–3.0, φ = 1.618–2.0
Index Futures : Wave frequency 0.5–1.5, φ = 1.618
Timeframe Optimization:
Scalping (1–5min) : Emphasize fast lines, higher frequency for micro-move capture.
Day Trading (15min–1hr) : Balance fast/slow interactions for trend confirmation.
Swing Trading (4hr–Daily) : Focus on slow lines for structural guidance, fast lines for entry timing.
Position Trading (Daily–Weekly) : Slow lines dominate; harmonics highlight long-term cycles.
Performance Characteristics
High Effectiveness Conditions:
Clear separation between short-term and long-term trends.
Moderate-to-high volatility environments.
Assets with consistent volume and price rhythm.
Reduced Effectiveness:
Flat or extremely low volatility markets.
Erratic assets with frequent gaps or algorithmic dominance.
Ultra-short timeframes (<1min), where noise dominates.
Integration Guidelines
Signal Confirmation : Confirm alignment of fast and slow lines over multiple bars. Expansion of harmonic amplitude signals trend persistence.
Risk Management : Place stops beyond slow line support/resistance. Adjust sizing based on compression/expansion zones.
Advanced Feature Settings :
Frequency tuning for different volatility environments.
Phase analysis to track divergences across harmonics.
Use fills and amplitude patterns as a guide for dynamic trade management.
Multi-timeframe confirmation to filter noise and align with structural trends.
Disclaimer
Harmonic Super Guppy is a trend analysis and visualization tool, not a guaranteed profit system. Optimal performance requires proper wave frequency, golden ratio phase, and line visibility settings per asset and timeframe. Traders should combine the indicator with other technical frameworks and maintain disciplined risk management practices.
Extended Majors Rotation System | AlphaNattExtended Majors Rotation System | AlphaNatt
A sophisticated cryptocurrency rotation system that dynamically allocates capital to the strongest trending major cryptocurrencies using multi-layered relative strength analysis and adaptive filtering techniques.
"In crypto markets, the strongest get stronger. This system identifies and rides the leaders while avoiding the laggards through mathematical precision."
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📊 SYSTEM OVERVIEW
The Extended Majors Rotation System (EMRS) is a quantitative momentum rotation strategy that:
Analyzes 10 major cryptocurrencies simultaneously
Calculates relative strength between all possible pairs (45 comparisons)
Applies fractal dimension analysis to identify trending behavior
Uses adaptive filtering to reduce noise while preserving signals
Dynamically allocates to the mathematically strongest asset
Implements multi-layer risk management through market regime filters
Core Philosophy:
Rather than trying to predict which cryptocurrency will perform best, the system identifies which one is already performing best relative to all others and maintains exposure until leadership changes.
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🎯 WHAT MAKES THIS SYSTEM UNEQUIVOCALLY UNIQUE
1. True Relative Strength Matrix
Unlike simple momentum strategies that look at individual asset performance, EMRS calculates the complete relative strength matrix between all assets. Each asset is compared against every other asset using fractal analysis, creating a comprehensive strength map of the entire crypto market.
2. Hurst Exponent Integration
The system employs the Hurst Exponent to distinguish between:
Trending behavior (H > 0.5) - where momentum is likely to persist
Mean-reverting behavior (H < 0.5) - where reversals are likely
Random walk (H ≈ 0.5) - where no edge exists
This ensures the system only takes positions when mathematical evidence of persistence exists.
3. Dual-Layer Filtering Architecture
Combines two advanced filtering techniques:
Laguerre Polynomial Filters: Provides low-lag smoothing with minimal distortion
Kalman-like Adaptive Smoothing: Adjusts filter parameters based on market volatility
This dual approach preserves important price features while eliminating noise.
4. Market Regime Awareness
The system monitors overall crypto market conditions through multiple lenses and only operates when:
The broad crypto market shows positive technical structure
Sufficient trending behavior exists across major assets
Risk conditions are favorable
5. Rank-Based Selection with Trend Confirmation
Rather than simply choosing the top-ranked asset, the system requires:
High relative strength ranking
Positive individual trend confirmation
Alignment with market regime
This multi-factor approach reduces false signals and whipsaws.
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🛡️ SYSTEM ROBUSTNESS & DEVELOPMENT METHODOLOGY
Pre-Coding Design Philosophy
This system was completely designed before any code was written . The mathematical framework, indicator selection, and parameter ranges were determined through:
Theoretical analysis of market microstructure
Study of persistence and mean reversion in crypto markets
Mathematical modeling of relative strength dynamics
Risk framework development based on regime theory
No Post-Optimization
Zero parameter fitting: All parameters remain at their originally designed values
No curve fitting: The system uses the same settings across all market conditions
No cherry-picking: Parameters were not adjusted after seeing results
This approach ensures the system captures genuine market dynamics rather than historical noise
Parameter Robustness Testing
Extensive testing was conducted to ensure stability:
Sensitivity Analysis: System maintains positive expectancy across wide parameter ranges
Walk-Forward Analysis: Consistent performance across different time periods
Regime Testing: Performs in both trending and choppy conditions
Out-of-Sample Validation
System was designed on a selection of 10 assets
System was tested on multiple baskets of 10 other random tokens, to simualte forwards testing
Performance remains consistent across baskets
No adjustments made based on out-of-sample results
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📈 PERFORMANCE METRICS DISPLAYED
The system provides real-time performance analytics:
Risk-Adjusted Returns:
Sharpe Ratio: Measures return per unit of total risk
Sortino Ratio: Measures return per unit of downside risk
Omega Ratio: Probability-weighted ratio of gains vs losses
Maximum Drawdown: Largest peak-to-trough decline
Benchmark Comparison:
Live comparison against Bitcoin buy-and-hold strategy
Both equity curves displayed with gradient effects
Performance metrics shown for both strategies
Visual representation of outperformance/underperformance
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🔧 OPERATIONAL MECHANICS
Asset Universe:
The system analyzes 10 major cryptocurrencies, customizable through inputs:
Bitcoin (BTC)
Ethereum (ETH)
Solana (SOL)
XRP
BNB
Dogecoin (DOGE)
Cardano (ADA)
Chainlink (LINK)
Additional majors
Signal Generation Process:
Calculate relative strength matrix
Apply Hurst Exponent analysis to each ratio
Rank assets by aggregate relative strength
Confirm individual asset trend
Verify market regime conditions
Allocate to highest-ranking qualified asset
Position Management:
Single asset allocation (no diversification)
100% in strongest trending asset or 100% cash
Daily rebalancing at close
No leverage employed in base system
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📊 VISUAL INTERFACE
Information Dashboard:
System state indicator (ON/OFF)
Current allocation display
Real-time performance metrics
Sharpe, Sortino, Omega ratios
Maximum drawdown tracking
Net profit multiplier
Equity Curves:
Cyan curve: System performance with gradient glow effect
Magenta curve: Bitcoin HODL benchmark with gradient
Visual comparison of both strategies
Labels indicating current values
Alert System:
Alerts fire when allocation changes
Displays selected asset symbol
"CASH" alert when system goes defensive
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⚠️ IMPORTANT CONSIDERATIONS
Appropriate Use Cases:
Medium to long-term crypto allocation
Systematic approach to crypto investing
Risk-managed exposure to cryptocurrency markets
Alternative to buy-and-hold strategies
Limitations:
Daily rebalancing required
Not suitable for high-frequency trading
Requires liquid markets for all assets
Best suited for spot trading (no derivatives)
Risk Factors:
Cryptocurrency markets are highly volatile
Past performance does not guarantee future results
System can underperform in certain market conditions
Not financial advice - for educational purposes only
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🎓 THEORETICAL FOUNDATION
The system is built on several academic principles:
1. Momentum Anomaly
Extensive research shows that assets exhibiting strong relative momentum tend to continue outperforming in the medium term (Jegadeesh & Titman, 1993).
2. Fractal Market Hypothesis
Markets exhibit fractal properties with periods of persistence and mean reversion (Peters, 1994). The Hurst Exponent quantifies these regimes.
3. Adaptive Market Hypothesis
Market efficiency varies over time, creating periods where momentum strategies excel (Lo, 2004).
4. Cross-Sectional Momentum
Relative strength strategies outperform time-series momentum in cryptocurrency markets due to the high correlation structure.
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💡 USAGE GUIDELINES
Capital Requirements:
Suitable for any account size
No minimum capital requirement
Scales linearly with account size
Implementation:
Can be traded manually with daily signals
Suitable for automation via alerts
Works with any broker supporting crypto
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📝 FINAL NOTES
The Extended Majors Rotation System represents a systematic, mathematically-driven approach to cryptocurrency allocation. By combining relative strength analysis with fractal market theory and adaptive filtering, it aims to capture the persistent trends that characterize crypto bull markets while avoiding the drawdowns of buy-and-hold strategies.
The system's robustness comes not from optimization, but from sound mathematical principles applied consistently. Every component was chosen for its theoretical merit before any backtesting occurred, ensuring the system captures genuine market dynamics rather than historical artifacts.
"In the race between cryptocurrencies, bet on the horse that's already winning - but only while the track conditions favour racing."
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Developed by AlphaNatt | Quantitative Rotation Systems
Version: 1.0
Strategy Type: Momentum Rotation
Classification: Systematic Trend Following
Not financial advice. Always DYOR.
Kalman Adjusted Average True Range [BackQuant]Kalman Adjusted Average True Range
A volatility-aware trend baseline that fuses a Kalman price estimate with ATR “rails” to create a smooth, adaptive guide for entries, exits, and trailing risk.
Built on my original Kalman
This indicator is based on my original Kalman Price Filter:
That core smoother is used here to estimate the “true” price path, then blended with ATR to control step size and react proportionally to market noise.
What it plots
Kalman ATR Line the main baseline that turns up/down with the filtered trend.
Optional Moving Average of the Kalman ATR a secondary line for confluence (SMA/Hull/EMA/WMA/DEMA/RMA/LINREG/ALMA).
Candle Coloring (optional) paint bars by the baseline’s current direction.
Why combine Kalman + ATR?
Kalman reduces measurement noise and produces a stable path without the lag of heavy MAs.
ATR rails scale the baseline’s step to current volatility, so it’s calm in chop and more responsive in expansion.
The result is a single, intelligible line you can trade around: slope-up = constructive; slope-down = caution.
How it works (plain English)
Each bar, the Kalman filter updates an internal state (tunable via Process Noise , Measurement Noise , and Filter Order ) to estimate the underlying price.
An ATR band (Period × Factor) defines the allowed per-bar adjustment. The baseline cannot “jump” beyond those rails in one step.
A direction flip is detected when the baseline’s slope changes sign (upturn/downturn), and alerts are provided for both.
Typical uses
Trend confirmation Trade in the baseline’s direction; avoid fading a firmly rising/falling line.
Pullback timing Look for entries when price mean-reverts toward a rising baseline (or exits on tags of a falling one).
Trailing risk Use the baseline as a dynamic guide; many traders set stops a small buffer beyond it (e.g., a fraction of ATR).
Confluence Enable the MA overlay of the Kalman ATR; alignment (baseline above its MA and rising) supports continuation.
Inputs & what they do
Calculation
Kalman Price Source which price the filter tracks (Close by default).
Process Noise how quickly the filter can adapt. Higher = more responsive (but choppier).
Measurement Noise how much you distrust raw price. Higher = smoother (but slower to turn).
Filter Order (N) depth of the internal state array. Higher = slightly steadier behavior.
Kalman ATR
Period ATR lookback. Shorter = snappier; longer = steadier.
Factor scales the allowed step per bar. Larger factors permit faster drift; smaller factors clamp movement.
Confluence (optional)
MA Type & Period compute an MA on the Kalman ATR line , not on price.
Sigma (ALMA) if ALMA is selected, this input controls the curve’s shape. (Ignored for other MA types.)
Visuals
Plot Kalman ATR toggle the main line.
Paint Candles color bars by up/down slope.
Colors choose long/short hues.
Signals & alerts
Trend Up baseline turns upward (slope crosses above 0).
Alert: “Kalman ATR Trend Up”
Trend Down baseline turns downward (slope crosses below 0).
Alert: “Kalman ATR Trend Down”
These are state flips , not “price crossovers,” so you avoid many one-bar head-fakes.
How to start (fast presets)
Swing (daily/4H) ATR Period 7–14, Factor 0.5–0.8, Process Noise 0.02–0.05, Measurement Noise 2–4, N = 3–5.
Intraday (5–15m) ATR Period 5–7, Factor 0.6–1.0, Process Noise 0.05–0.10, Measurement Noise 2–3, N = 3–5.
Slow assets / FX raise Measurement Noise or ATR Period for calmer lines; drop Factor if the baseline feels too jumpy.
Reading the line
Rising & curving upward momentum building; consider long bias until a clear downturn.
Flat & choppy regime uncertainty; many traders stand aside or tighten risk.
Falling & accelerating distribution lower; short bias until a clean upturn.
Practical playbook
Continuation entries After a Trend Up alert, wait for a minor pullback toward the baseline; enter on evidence the line keeps rising.
Exit/reduce If long and the baseline flattens then turns down, trim or exit; reverse logic for shorts.
Filters Add a higher-timeframe check (e.g., only take longs when the daily Kalman ATR is rising).
Stops Place stops just beyond the baseline (e.g., baseline − x% ATR for longs) to avoid “tag & reverse” noise.
Notes
This is a guide to state and momentum, not a guarantee. Combine with your process (structure, volume, time-of-day) for decisions.
Settings are asset/timeframe dependent; start with the presets and nudge Process/Measurement Noise until the baseline “feels right” for your market.
Summary
Kalman ATR takes the noise-reduction of a Kalman price estimate and couples it with volatility-scaled movement to produce a clean, adaptive baseline. If you liked the original Kalman Price Filter (), this is its trend-trading cousin purpose-built for cleaner state flips, intuitive trailing, and confluence with your existing
RSI Trend Navigator [QuantAlgo]🟢 Overview
The RSI Trend Navigator integrates RSI momentum calculations with adaptive exponential moving averages and ATR-based volatility bands to generate trend-following signals. The indicator applies variable smoothing coefficients based on RSI readings and incorporates normalized momentum adjustments to position a trend line that responds to both price action and underlying momentum conditions.
🟢 How It Works
The indicator begins by calculating and smoothing the RSI to reduce short-term fluctuations while preserving momentum information:
rsiValue = ta.rsi(source, rsiPeriod)
smoothedRSI = ta.ema(rsiValue, rsiSmoothing)
normalizedRSI = (smoothedRSI - 50) / 50
It then creates an adaptive smoothing coefficient that varies based on RSI positioning relative to the midpoint:
adaptiveAlpha = smoothedRSI > 50 ? 2.0 / (trendPeriod * 0.5 + 1) : 2.0 / (trendPeriod * 1.5 + 1)
This coefficient drives an adaptive trend calculation that responds more quickly when RSI indicates bullish momentum and more slowly during bearish conditions:
var float adaptiveTrend = source
adaptiveTrend := adaptiveAlpha * source + (1 - adaptiveAlpha) * nz(adaptiveTrend , source)
The normalized RSI values are converted into price-based adjustments using ATR for volatility scaling:
rsiAdjustment = normalizedRSI * ta.atr(14) * sensitivity
rsiTrendValue = adaptiveTrend + rsiAdjustment
ATR-based bands are constructed around this RSI-adjusted trend value to create dynamic boundaries that constrain trend line positioning:
atr = ta.atr(atrPeriod)
deviation = atr * atrMultiplier
upperBound = rsiTrendValue + deviation
lowerBound = rsiTrendValue - deviation
The trend line positioning uses these band constraints to determine its final value:
if upperBound < trendLine
trendLine := upperBound
if lowerBound > trendLine
trendLine := lowerBound
Signal generation occurs through directional comparison of the trend line against its previous value to establish bullish and bearish states:
trendUp = trendLine > trendLine
trendDown = trendLine < trendLine
if trendUp
isBullish := true
isBearish := false
else if trendDown
isBullish := false
isBearish := true
The final output colors the trend line green during bullish states and red during bearish states, creating visual buy/long and sell/short opportunity signals based on the combined RSI momentum and volatility-adjusted trend positioning.
🟢 Signal Interpretation
Rising Trend Line (Green): Indicates upward momentum where RSI influence and adaptive smoothing favor continued price advancement = Potential buy/long positions
Declining Trend Line (Red): Indicates downward momentum where RSI influence and adaptive smoothing favor continued price decline = Potential sell/short positions
Flattening Trend Lines: Occur when momentum weakens and the trend line slope approaches neutral, suggesting potential consolidation before the next move
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, sending "RSI Trend Bullish Signal" or "RSI Trend Bearish Signal" messages for timely entry/exit
Color Bar Candles Option: Optional candle coloring feature that applies the same green/red trend colors to price bars, providing additional visual confirmation of the current trend direction
Guppy MMA [Alpha Extract]A sophisticated trend-following and momentum assessment system that constructs dynamic trader and investor sentiment channels using multiple moving average groups with advanced scoring mechanisms and smoothed CCI-style visualizations for optimal market trend analysis. Utilizing enhanced dual-group methodology with threshold-based trend detection, this indicator delivers institutional-grade GMMA analysis that adapts to varying market conditions while providing high-probability entry and exit signals through crossover and extreme value detection with comprehensive visual mapping and alert integration.
🔶 Advanced Channel Construction
Implements dual-group architecture using short-term and long-term moving averages as foundation points, applying customizable MA types to reduce noise and score-based averaging for sentiment-responsive trend channels. The system creates trader channels from shorter periods and investor channels from longer periods with configurable periods for optimal market reaction zones.
// Core Channel Calculation Framework
maType = input.string("EMA", title="Moving Average Type", options= )
// Short-Term Group Construction
stMA1 = ma(close, st1, maType)
stMA2 = ma(close, st2, maType)
// Long-Term Group Construction
ltMA1 = ma(close, lt1, maType)
ltMA2 = ma(close, lt2, maType)
// Smoothing Application
smoothedavg = ma(overallAvg, 10, maType)
🔶 Volatility-Adaptive Zone Framework
Features dynamic score-based averaging that expands sentiment signals during strong trend periods and contracts during consolidation phases, preventing false signals while maintaining sensitivity to genuine momentum shifts. The dual-group averaging system optimizes zone boundaries for realistic market behavior patterns.
// Dynamic Sentiment Adjustment
shortTermAvg = (stScore1 + stScore2 + ... + stScore11) / 11
longTermAvg = (ltScore1 + ltScore2 + ... + ltScore11) / 11
// Dual-Group Zone Optimization
overallAvg = (shortTermAvg + longTermAvg) / 2
allMAAvg = (shortTermAvg * 11 + longTermAvg * 11) / 22
🔶 Step-Like Boundary Evolution
Creates threshold-based trend boundaries that update on smoothed average changes, providing visual history of evolving bullish and bearish levels with performance-optimized threshold management limited to key zones for clean chart presentation and efficient processing.
🔶 Comprehensive Signal Detection
Generates buy and sell signals through sophisticated crossover analysis, monitoring smoothed average interaction with zero-line and thresholds for high-probability entry and exit identification. The system distinguishes between trend continuation and reversal patterns with precision timing.
🔶 Enhanced Visual Architecture
Provides translucent zone fills with gradient intensity scaling, threshold-based historical boundaries, and dynamic background highlighting that activates upon trend changes. The visual system uses institutional color coding with green bullish zones and red bearish zones for intuitive market structure interpretation.
🔶 Intelligent Zone Management
Implements automatic trend relevance filtering, displaying signals only when smoothed average proximity warrants analysis attention. The system maintains optimal performance through smart averaging management and historical level tracking with configurable MA periods for various market conditions.
🔶 Multi-Dimensional Analysis Framework
Combines trend continuation analysis through threshold crossovers with momentum detection via extreme markers, providing comprehensive market structure assessment suitable for both trending and ranging market conditions with score-normalized accuracy.
🔶 Advanced Alert Integration
Features comprehensive notification system covering buy signals, sell signals, strong bull conditions, and strong bear conditions with customizable alert conditions. The system enables precise position management through real-time notifications of critical sentiment interaction events and zone boundary violations.
🔶 Performance Optimization
Utilizes efficient MA smoothing algorithms with configurable types for noise reduction while maintaining responsiveness to genuine market structure changes. The system includes automatic visual level cleanup and performance-optimized visual rendering for smooth operation across all timeframes.
This indicator delivers sophisticated GMMA-based market analysis through score-adaptive averaging calculations and intelligent group construction methodology. By combining dynamic trader and investor sentiment detection with advanced signal generation and comprehensive visual mapping, it provides institutional-grade trend analysis suitable for cryptocurrency, forex, and equity markets. The system's ability to adapt to varying market conditions while maintaining signal accuracy makes it essential for traders seeking systematic approaches to trend trading, momentum reversals, and sentiment continuation analysis with clearly defined risk parameters and comprehensive alert integration.
Linear Regression Trend Navigator [QuantAlgo]🟢 Overview
The Linear Regression Trend Navigator is a trend-following indicator that combines statistical regression analysis with adaptive volatility bands to identify and track dominant market trends. It employs linear regression mathematics to establish the underlying trend direction, while dynamically adjusting trend boundaries based on standard deviation calculations to filter market noise and maintain trend continuity. The result is a straightforward visual system where green indicates bullish conditions favoring buy/long positions, and red signals bearish conditions supporting sell/short trades.
🟢 How It Works
The indicator operates through a three-phase computational process that transforms raw price data into adaptive trend signals. In the first phase, it calculates a linear regression line over the specified period, establishing the mathematical best-fit line through recent price action to determine the underlying directional bias. This regression line serves as the foundation for trend analysis by smoothing out short-term price variations while preserving the essential directional characteristics.
The second phase constructs dynamic volatility boundaries by calculating the standard deviation of price movements over the defined period and applying a user-adjustable multiplier. These upper and lower bounds create a volatility-adjusted channel around the regression line, with wider bands during volatile periods and tighter bands during stable conditions. This adaptive boundary system operates entirely behind the scenes, ensuring the trend signal remains relevant across different market volatility regimes without cluttering the visual display.
In the final phase, the system generates a simple trend line that dynamically positions itself within the volatility boundaries. When price action pushes the regression line above the upper bound, the trend line adjusts to the upper boundary level. Conversely, when the regression line falls below the lower bound, the trend line moves to the lower boundary. The result is a single colored line that transitions between green (rising trend line = buy/long) and red (declining trend line = sell/short).
🟢 How to Use
Green Trend Line: Upward momentum indicating favorable conditions for long positions, buy signals, and bullish strategies
Red Trend Line: Downward momentum signaling optimal timing for short positions, sell signals, and bearish approaches
Rising Green Line: Accelerating bullish momentum with steepening angles indicating strengthening upward pressure and potential for trend continuation
Declining Red Line: Intensifying bearish momentum with increasing negative slopes suggesting persistent downward pressure and shorting opportunities
Flattening Trend Lines: Gradual reduction in slope regardless of color may indicate approaching consolidation or momentum exhaustion requiring position review
🟢 Pro Tips for Trading and Investing
→ Entry/Exit Timing: Trade exclusively on band color transitions rather than price patterns, as each color change represents a statistically-confirmed shift that has passed through volatility filtering, providing higher probability setups than traditional technical analysis.
→ Parameter Optimization for Asset Classes: Customize the linear regression period based on your trading style. For example, use 5-10 bars for day trading to capture short-term statistical shifts, 14-20 for swing trading to balance responsiveness with stability, and 25-50 for position trading to filter out medium-term noise.
→ Volatility Calibration Strategy: Adjust the standard deviation multiplier according to market volatility. For instance, increase to 2.0+ during high-volatility periods like earnings or news events to reduce false signals, decrease to 1.0-1.5 during stable market conditions to maintain sensitivity to genuine trends.
→ Cross-Timeframe Statistical Validation: Apply the indicator across multiple timeframes simultaneously, using higher timeframes for directional bias and lower timeframes for entry timing.
→ Alert-Based Systematic Trading: Use built-in alerts to eliminate discretionary decision-making and ensure you capture every statistically-significant trend change, particularly effective for traders who cannot monitor charts continuously.
→ Risk Allocation Based on Signal Strength: Increase position sizes during periods of strong directional movement while reducing exposure during frequent band color changes that indicate statistical uncertainty or ranging conditions.
Deadband Hysteresis Supertrend [BackQuant]Deadband Hysteresis Supertrend
A two-stage trend tool that first filters price with a deadband baseline, then runs a Supertrend around that baseline with optional flip hysteresis and ATR-based adverse exits.
What this is
A hybrid of two ideas:
Deadband Hysteresis Baseline that only advances when price pulls far enough from the baseline to matter. This suppresses micro noise and gives you a stable centerline.
Supertrend bands wrapped around that baseline instead of raw price. Flips are further gated by an extra margin so side changes are more deliberate.
The goal is fewer whipsaws in chop and clearer regime identification during trends.
How it works (high level)
Deadband step — compute a per-bar “deadband” size from one of four modes: ATR, Percent of price, Ticks, or Points. If price deviates from the baseline by more than this amount, move the baseline forward by a fraction of the excess. If not, hold the line.
Centered Supertrend — build upper and lower bands around the baseline using ATR and a user factor. Track the usual trailing logic that tightens a band while price moves in its favor.
Flip hysteresis — require price to exceed the active band by an extra flip offset × ATR before switching sides. This adds stickiness at the boundary.
Adverse exit — once a side is taken, trigger an exit if price moves against the entry by K × ATR .
If you would like to check out the filter by itself:
What it plots
DBHF baseline (optional) as a smooth centerline.
DBHF Supertrend as the active trailing band.
Candle coloring by trend side for quick read.
Signal markers 𝕃 and 𝕊 at flips plus ✖ on adverse exits.
Inputs that matter
Price Source — series being filtered. Close is typical. HL2 or HLC3 can be steadier.
Deadband mode — ATR, Percent, Ticks, or Points. This defines the “it’s big enough to matter” zone.
ATR Length / Mult (DBHF) — only used when mode = ATR. Larger values widen the do-nothing zone.
Percent / Ticks / Points — alternatives to ATR; pick what fits your market’s convention.
Enter Mult — scales the deadband you must clear before the baseline moves. Increase to filter more noise.
Response — fraction of the excess applied to baseline movement. Higher responds faster; lower is smoother.
Supertrend ATR Period & Factor — traditional band size controls; higher factor widens and flips less often.
Flip Offset ATR — extra ATR buffer required to flip. Useful in choppy regimes.
Adverse Stop K·ATR — per-trade danger brake that forces an exit if price moves K×ATR against entry.
UI — toggle baseline, supertrend, signals, and bar painting; choose long and short colors.
How to read it
Green regime — candles painted long and the Supertrend running below price. Pullbacks toward the baseline that fail to breach the opposite band often resume higher.
Red regime — candles painted short and the Supertrend running above price. Rallies that cannot reclaim the band may roll over.
Frequent side swaps — reduce sensitivity by increasing Enter Mult, using ATR mode, raising the Supertrend factor, or adding Flip Offset ATR.
Use cases
Bias filter — allow entries only in the direction of the current side. Use your preferred triggers inside that bias.
Trailing logic — treat the active band as a dynamic stop. If the side flips or an adverse K·ATR exit prints, reduce or close exposure.
Regime map — on higher timeframes, the combination baseline + band produces a clean up vs down template for allocation decisions.
Tuning guidance
Fast markets — ATR deadband, modest Enter Mult (0.8–1.2), response 0.2–0.35, Supertrend factor 1.7–2.2, small Flip Offset (0.2–0.5 ATR).
Choppy ranges — widen deadband or raise Enter Mult, lower response, and add more Flip Offset so flips require stronger evidence.
Slow trends — longer ATR periods and higher Supertrend factor to keep you on side longer; use a conservative adverse K.
Included alerts
DBHF ST Long — side flips to long.
DBHF ST Short — side flips to short.
Adverse Exit Long / Short — K·ATR stop triggers against the current side.
Strengths
Deadbanded baseline reduces micro whipsaws before Supertrend logic even begins.
Flip hysteresis adds a second layer of confirmation at the boundary.
Optional adverse ATR stop provides a uniform risk cut across assets and regimes.
Clear visuals and minimal parameters to adjust for symbol behavior.
Putting it together
Think of this tool as two decisions layered into one view. The deadband baseline answers “does this move even count,” then the Supertrend wrapped around that baseline answers “if it counts, which side should I be on and where do I flip.” When both parts agree you tend to stay on the correct side of a trend for longer, and when they disagree you get an early warning that conditions are changing.
When the baseline bends and price cannot reclaim the opposite band , momentum is usually continuing. Pullbacks into the baseline that stall before the far band often resolve in trend.
When the baseline flattens and the bands compress , expect indecision. Use the Flip Offset ATR to avoid reacting to the first feint. Wait for a clean band breach with follow through.
When an adverse K·ATR exit prints while the side has not flipped , treat it as a risk event rather than a full regime change. Many users cut size, re-enter only if the side reasserts, and let the next flip confirm a new trend.
Final thoughts
Deadband Hysteresis Supertrend is best read as a regime lens. The baseline defines your tolerance for noise, the bands define your trailing structure, and the flip offset plus adverse ATR stop define how forgiving or strict you want to be at the boundary. On strong trends it helps you hold through shallow shakeouts. In choppy conditions it encourages patience until price does something meaningful. Start with settings that reflect the cadence of your market, observe how often flips occur, then nudge the deadband and flip offset until the tool spends most of its time describing the move you care about rather than the noise in between.
Racktor Analysis Assistant
Racktor Analysis Assistant — Feature Overview
The Racktor Analysis Assistant is a multi-module market-structure toolkit that plots pivots, BoS/ChoCh levels, session breakouts, inside bars, and higher-timeframe BTS/STB trap signals — with complete styling controls and alerting.
Smart Pivot Engine (ZigZag Core)
- Adaptive pivot period switching based on timeframe threshold.
- ZigZag stream tracks pivot types (H/L, HH/HL/LH/LL) with Major & Minor streams.
- Clean visuals: optional ZigZag line & pivot labels with customizable style, width, and color.
Major & Minor Structure Signals
- Detects BoS and ChoCh for both Major and Minor swings.
- Updates External Trend on Major events and Internal Trend on Minor events.
- One-time triggers per level via locking.
- Per-category styling for Major/Minor Bullish & Bearish BoS and ChoCh.
- Alerts with symbol, pivot, timeframe, and time, limited to specific timeframes if desired.
Inside Bar Module
- Toggleable Inside Bar detection.
- Custom colors for bullish and bearish inside bars.
- Optional alerts on detection.
Session Breakout Suite
- Custom session window with shaded box.
- On session close, plots High/Mid/Low breakout lines extendable for N hours.
- Optional previous day & week high/low lines.
- Breakout vs Liquidity Sweep modes (close-based or wick-based confirmation).
- Display styles: Fixed (triangles) or Moving (vertical dotted lines).
- Alerts for “first event” or “every event.”
BTS/STB Trap (Higher-Timeframe ID1/ID2 Logic)
- BTS/STB toggle with selectable check timeframe (default: 4H).
- STB (bullish, Sell→Buy): strict ID1/ID2 relationships, both candles bullish; green circle below HTF ID1 low.
- BTS (bearish, Buy→Sell): strict ID1/ID2 relationships, both candles bearish; red circle above HTF ID1 high.
- Non-repainting; dots appear only at HTF candle close.
- Timeframe-aware rendering (dots show only on selected timeframe).
- Alerts for STB/BTS at HTF close.
Styling & Limits
- Per-feature color/style/width customization.
- Generous limits for boxes, labels, and lines.
- Session tools limited to ≤ 120-minute charts for accuracy.
Anti-Repaint
- HTF signals use lookahead_off and HTF-close gating to avoid repainting.
- BoS/ChoCh and Session logic track prior values and use locks to prevent duplicates.
Quick Start
Set the Timeframe Threshold and pivot periods for lower/higher TFs.
Enable desired Major/Minor BoS/ChoCh lines and customize styles.
Activate Inside Bar Module if required.
Configure Session Breakout window, mode, and alert settings.
Enable BTS/STB detection, keeping 4H default or selecting a custom TF.
Add alerts for chosen signals and let the assistant annotate structure, sessions, and HTF traps.
Best Use with Racktor's Core Trading Strategy
For traders who want structure clarity without clutter, this Analysis-Assistant is built to keep your chart actionable and adaptive.
Turtle Body Setup by TradeTech AnalysisOverview
Turtle Body Setup is a minimalist, rules-based pattern detector built around a simple idea: a sequence of shrinking candle bodies (compression) often precedes a directional expansion (breakout). The script identifies those compression phases and then flags the first candle whose body expands significantly beyond the recent average, with polarity taken from the candle’s direction.
This is not a mash-up of many public indicators. It focuses on one original micro-structure concept: strict body-contraction → body-expansion . The logic is fully described below so traders and moderators can understand what it does and how to use it.
How it Works
1. Compression detection (body contraction):
• Over a user-defined window Compression Lookback (N), the script counts strictly shrinking candle bodies (|close-open|).
• When the count ≥ Min Shrinking Candles, we mark the market as in compression.
2. Expansion / Breakout qualification:
• Compute avgBody = SMA(body, N).
• A candle is a breakout when current body > avgBody × Breakout Body Multiplier.
• Polarity: green (close>open) → Bullish breakout; red (close
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter
A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts.
What this is
A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks.
Why Theil-Sen
Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns.
What it plots
Filtered baseline that advances by a fraction of the robust slope each bar.
Optional candle coloring by baseline slope sign for quick trend read.
Alerts when the baseline slope turns up or down.
How it behaves (high level)
Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes.
Takes the median of those slopes to get a robust estimate for the bar.
Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line.
Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive.
Inputs and what they do
Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother.
Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier.
Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves.
Slope Cap Mode — optional guardrail on each bar’s slope:
None — no cap.
ATR — cap scales with recent true range.
Percent — cap scales with price level.
Points — fixed absolute cap in price points.
ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size.
UI Settings — show or hide the line, paint candles by slope, choose long and short colors.
How to read it
Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction.
Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise.
Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets.
Use cases
Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups.
Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure.
Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles.
Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars.
Tuning guidance
Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves.
Position trading — longer window, moderate response. ATR cap tends to scale well across cycles.
Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities.
Alerts included
Theil-Sen Up Slope — baseline’s one-bar change crosses above zero.
Theil-Sen Down Slope — baseline’s one-bar change crosses below zero.
Strengths
Robust to outliers through median-based slope estimation.
Continuously advances with price rather than re-anchoring, which reduces lag at turns.
User-selectable slope caps to tame shock bars without over-smoothing everything.
Minimal visuals with optional candle painting for fast regime recognition.
Notes
This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
TrenVantage LITE TrenVantage LITE - Smart Trend Detector
"Professional ZigZag trend detection with real-time alerts and market structure analysis. Clean interface shows trend direction, price changes, and swing data."
TrenVantage LITE delivers professional-grade trend detection using advanced ZigZag analysis to identify market structure and trend changes in real-time. Built with a logic that goes beyond basic pivot detection, this free version provides essential trend analysis tools with a clean, intuitive interface designed for traders of all experience levels.
Key Features:
Advanced Trend Detection
Smart ZigZag Algorithm: Proprietary trend foundation model based on market structure principles
Customizable Sensitivity: Choose between Points or Percentage-based deviation settings
Real-Time Updates: Calculate on bar close or tick-by-tick for immediate trend changes
Flexible Analysis: 15-25 bar lookback range with 20-bar default setting
Visual Analysis Tools
Clean Trend Lines: Customizable color and width for optimal chart visibility
Professional Interface: Modern status box showing current trend and price metrics
Multiple Positioning: Place status box in any corner to match your chart layout
Market Structure: Clear visualization of swing highs and lows
Smart Alerts System
Trend Change Notifications: Instant alerts when market transitions between uptrend and downtrend
Reliable Detection: Confirmed trend changes based on significant price movements
Multiple Alert Options: Compatible with TradingView's alert system
How It Works
TrenVantage LITE uses a sophisticated ZigZag algorithm that goes beyond simple pivot detection. Our proprietary "trend-start model" identifies meaningful market structure changes by:
Analyzing Price Action: Uses high/low or close prices based on your preference
Filtering Noise: Customizable deviation thresholds eliminate false signals
Confirming Trends: Only signals trend changes after significant price movement
Tracking Structure: Maintains swing history for comprehensive analysis
Status Box Information
The integrated status box provides at-a-glance market information.
Current Trend Direction: Clear uptrend/downtrend identification with visual indicators
Live Price Data: Current price with session change and percentage movement
Swing Analysis: Number of detected swings with trend-only limitation indicator
Clean Design: Professional appearance that doesn't clutter your chart
Settings & Customization
ZigZag Parameters:
Deviation Type: Points (fixed price difference) or Percent (percentage change)
Deviation Value: Minimum price movement required to create new swing
Use High Low: Toggle between high/low prices vs close prices for analysis
Calculate Mode: Choose bar close confirmation or real-time tick updates
Lookback Range: Adjust historical analysis from 15-25 bars
Visual Controls
Trend Line Color: Customize line color to match your chart theme
Line Width: Adjust thickness from 1-4 pixels for optimal visibility
Status Box: Toggle display and choose corner positioning
Best Practices:
Timeframe Selection
Scalping (1-5min): Use 0.3-0.8 Points deviation with tick calculation
Day Trading (15-60min): Use 1-3 Points or 0.2-0.5% deviation
Swing Trading (4H-Daily): Use 0.5-1.5% deviation with bar close calculation
Getting Started
Add to Chart: Apply TrenVantage LITE to your preferred timeframe
Adjust Settings: Configure deviation and visual preferences
Set Alerts: Enable trend change notifications for your trading strategy
Analyze Trends: Use the status box and visual lines to identify market direction
Upgrade When Ready: Explore RETAIL version for Support/Resistance levels
Ready to Level Up? Upgrade to TrenVantage RETAIL
While TrenVantage LITE provides solid trend analysis, TrenVantage RETAIL transforms your trading with professional-grade market structure tools:
What You're Missing in LITE:
Support and Resistance level detection - automatically identifies key price levels where markets react
Price labels on levels - see exact values instantly without hovering or zooming
Enhanced status box - shows distance to nearest support/resistance for timing entries and exits
Up to 5 key levels - comprehensive coverage of important price zones
Level strength indicators - understand which levels are most likely to hold
Professional workflow - combines trend analysis with key level identification
TrenVantage RETAIL takes the solid trend foundation you see in LITE and adds the critical support/resistance analysis that serious traders rely on daily.
Disclaimer: Trading involves risk of loss. This indicator is for educational and analysis purposes. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Momentum Concepts [A1TradeHub]ℹ️ General Information — TSI + Stochastic Z-Score (Momentum Duo)
Purpose: A two-oscillator stack that blends trend strength (TSI) with extreme-move normalization (Stochastic Z-Score) to time entries with confirmation instead of guessing tops/bottoms.
Components
Stochastic Z-Score (SZ): Converts price stretch into a bounded curve.
Red zone ≈ overbought supply, Green zone ≈ oversold demand.
The hook out of a band often marks turning points.
True Strength Index (TSI): Measures momentum quality and direction.
Signal/line cross = timing, Zero-line = trend filter, slope = acceleration.
Core Read
Alignment = edge: SZ leaves a band and TSI agrees (cross/slope).
Divergences: Higher-low on SZ/TSI vs lower-low in price (bullish). Lower-high on SZ/TSI vs higher-high in price (bearish). Best when near bands.
Mid-range = chop: Avoid trades when SZ is centered and TSI is flat.
Best Practices
Use structure (PDH/PDL, EMAs 13/48/200, trendlines) as context.
Scale profits into opposing SZ band or on TSI flatten/cross-back.
Place stops beyond the last swing or key EMA; skip high-volatility news.
Timeframes
Works on intraday (e.g., 5–15m) and swings (1h/4h). Use higher TF for bias, lower TF for entries.
This combo is designed to keep you on the right side of momentum, act at band hooks with TSI confirmation, and stand down when conditions are indecisive.
I. 🔴🟢 TSI Oscillator — Quick Guide
What you’re seeing
Lines: Fast TSI + slow Signal (both EMA-smoothed momentum).
Zones: 🟢 Green = oversold, 🔴 Red = overbought, 0-line = trend regime.
Long: 🟢 hook up → fast crosses above slow → ideally reclaim 0.
Short: 🔴 roll down → fast crosses below slow → ideally lose 0.
Exits: Trim into the opposite zone or on a cross back.
Divergence: TSI ↑ vs price ↓ = bullish; TSI ↓ vs price ↑ = bearish.
Avoid: Both lines chopping around 0.
II. Stochastic Z-Score — Quick Guide
Zones: 🔴 Red = overbought/supply, 🟢 Green = oversold/demand.
Curve: Watch the hook out of a zone for the turn.
Signals
🟢 Green Arrow (from Green zone): Momentum turns up → call/long bias. Enter on first pullback; stop under last swing/13-EMA.
🔻 Red/Bearish Arrow (from Red zone): Momentum rolls down → put/short bias. Enter on first lower-high; stop above last swing/13-EMA.
⚪ Ball = Momentum Shift: Early heads-up (slope change). Use as confirmation/add-on, not a standalone entry.
SMC ToolBox [WinWorld]👋 INTRODUCTION
SMC ToolBox indicator is not just a simple indicator, but rather a collection of SMC-related algorithms, that our teams has found to make the most profound impact on determination process of the most high-quality liquidity zones and points of interests ( further – POIs ), hence the name of the indicator – Tool Box (and it also sounds cool :) .
From candle patterns to complex orderflow detection algorithm, ToolBox indicator will help any trader with search for useful tools, solving the needs from confirming position entry levels to trend-following and mean reversion opportunities.
❓ WHY DID WE BUILD THIS?
This indicator was initially built for our team's internal use for the sole purpose of gathering all actively used non-structure-related algorithms* in one place, so we could have only the tools that are truly needed at hand at any point of time. After we showed this tool to our trading partners, they were surprised about how light, fast and useful ToolBox was and they advised us on sharing this with our community and, after giving it a proper thought, we decided to follow their advice.
Funnily enough , after researching TradingView's open-source script library, we haven't found even one instance of even remotely alike indicators, so it fair to say that we are one of the first people to release this kind of SMC-related indicator bundles on the market and we strongly that TradingView's community will find this tool of use.
🤷♂️ WHY SHOULD YOU CARE AT ALL?
Frankly speaking, we are not the first people to build our own algorithms of such popular indicators like Equal Highs and Lows (EQHL), Previous Day High Low (PDHL), Orderflow (OF) and etc., but we are definitely one of the first teams to implement these indicators with the help of algorithms, that are actually used by the most professional traders on YouTube and other social media trading influencers. Simply taking trades from our SCOBs, OFs, EQHLs and etc. won't print you millions overnight, but what these algos will do is help you with being aware of is potentially laying ahead of you with a very clean probability.
Why does it matter? It simple: better market awareness gives you an edge over other trades, which use old algorithms, which are clearly outdated, so beating such traders in the long run is just a game of time for you, so good algorithms do matter. Each indicator inside ToolBox is there to help you develop this market awareness and forge your edge bit by bit.
Now let's talk about what is inside the ToolBox.
🔍 OVERVIEW
At the moment of publishing ToolBox contains 8 indicators, so say "Hello" to:
Price Border Bands (further – PBB) ;
Ordeflow (further – OF) ;
Equal Highs & Lows (further – EQHL) ;
Previous Day High & Low ( further – PDHL) ;
Single Candle Order Block (further – SCOB) ;
Institutional Funding Candle (further – IFC) ;
Engulfing Candle (further – EC) ;
Inside Bars (further – IB) .
Some of them you may know, some of them you may not, so let's review each of them one by one.
📍 INDICATOR: Price Border Bands (PBB)
Price Border Bands indicator is a simple yet useful algorithm, based on Triangular Moving Average (TMA), which helps determine extreme price spikes, which on average act as meaningful mean reversion opportunities. It also is a good an effective "verifier" of POIs and zones of interest (further – ZOI) .
We advise on using this indicator this way:
Look for price going beyond upper or lower band of PBB;
Look for price reaching POI or ZOI;
Start searching for your entry point.
The most common sign of potential price reversal, which PBB searches for, is intense price spike, which signals about "liquidity clearing" or, in simple terms, manipulation .
Manipulation of the price inside the POI or price being "stopped" by POI is a screaming sign of the potentional following reversal. See the example of such situation on the screenshot below:
Additionally we need to talk about trend filter inside PBB, which colours the bars on the chart under certain conditions. If bars on the chart are being coloured in gray – this is your sign to stop trading on this asset? because there is risk to catch an uncomfortably big price spike, which might turn the '+' of your position's PnL in to '-'. See the example of PBB highlighting bar's of risky price zone in gray colour on the screenshot below:
In order to continue trading you need to wait for bars to stop being coloured in gray OR confirm the fact that price made Change of Character (ChoCh) in reverse to the previous direction of price, which was marked as risky by PBB.
And last but not least: if you see POI being reach by price inside the bands of PBB, then consider this POI weak and avoid trading it. See the example of weak POI inside PBB bands on the screenshot below:
📍 INDICATOR: Orderflow (OF)
Orderflow indicator is an algorithm, which detects Sell-to-Buy (furthert – STB) or Buy-to-Sell (further – BTS) manipulations, using the algorithm of impulse & correction price movement detection, taken from one of our previously built indicators – Impulse Correction SCOB Mapper (ICSM) .
Let's explain the terms from above:
Impulse – series of bars, each bar of which consecutively updated previous bar's high and then last candle broke previous bar's low ;
Correction – series of bars, each bar of which consecutively updated previous bar's low and then last candle broke previous bar's high ;
STB – a type of price manipulation, which can be described as a correction of price inside global upward movemnt;
BTS – a type of price manipulation, which can be describd as a impulse of price inside global downward movement.
Unlike traditional order blocks, which are often narrower and more selective, Orderflow zones cover a wider price range and present a higher probability of mitigation. This makes them more reliable for entries in ovaerage in comparison to classic orderblocks.
Let's review examples of bullish and bearish orderflows on the screenshots below:
Bullish orderflows (STBs) (blue boxes with "OF" text inside)
Bearish orderflows (BTSs) (orange boxes with "OF" text inside)
The usage of ZOIs, detected by OF algorithm, is pretty straightforward: take trades against the ordeflow block, that price has reached. Even though we don't recommend relying on Orderflow blocks as sole producers of signals, you can use them as such in way, that can be described like this:
Place stop-loss (SL) beyond the furthest border of OF block (bottom of the bullish OF or top of the bearish OF), that price has reached;
Aim for >2:1 RR ratio and place your take-profit (TP) accordingly.
You can see the example setups of OF blocks as signal producers on the screenshots below:
Examples of LONG trades, taken from price reaching bullish OF block.
Examples of SHORT trades, taken from price reaching bearish OF block.
Summarising, Orderflow can be described as a tool that helps determine the STB and BTS price manipulations, which are great price ZOIs and can be used both as confirmation tools for your exisiting signals and sole signal producers, in which case such they needed to be handled extra mindfully and preferrably bonded with other tools for additional confirmation. We personally recommend using Ordeflow as confirmation tool, because ZOIs, detected by Orderflow, are usually the price ranges, around which traders tend to place their stop-losses, which only gives more strength to these zones for supporting the price and helps traders with "trading from support/resistance" strategies gain additional edge.
📍 INDICATOR: Equal Highs & Lows (EQHL)
EQHL indicator is an algorithm, which scans the extremums of impulse and correction movements, detected by our ICSM indicator , and marks ones which are roughly or equaly placed on the same price levels. Equal highs (further – EQH) and equal lows (further – EQL) are local liquidity pools, where stop orders and resting orders cluster; price often gravitates to these zones for liquidity “top-ups,” after which a reaction or continuation to the next liquidity source may occur. Basically, EQHL algorithm highlights clusters of equal extremes as navigational anchors for “collect → react → confirm” scenarios.
Talking about usage, we advise to not take swept or reached EQHLs as entries by themselves. Evaluate them alongside HTF structure, Inducement (IDM), orderblocks (OB), orderflow (OF), candle pattern context (e.g., IFC/EC) on the LTF and etc. Intended usage scenario of this algorithm is something like this:
Price reaches EQH/EQL;
Price hangs around the reached EQH/EQL;
Another tool (for example, OF or OB) signals about price reversals from the level of reached EQH/EQL;
Trader starts looking for an entry.
See the examples of EQHLs, which algorithms maps on the chart, on the screenshots below:
Equal Lows (EQLs)
Equal Highs (EQHs)
📍 INDICATOR: Previous Day High & Low (PDHL)
PDHL indicator is an algorithm, princples of work of which can be derived from its name: algorithm tracks previous day's high and low and displays it on the chart.
Previous day's high and low are fundamental POIs in any financial market, which are traded not only by SMC traders, but by many other traders, especially by traders, which consider these POIs are one of the most crucial, because they usually highly liquidity-rich and can signal about wondeful reversal opportunities.
We expect traders to use PDHL algorithm as confirmation tool when trading by mean reversion strategies. Usage of PDHL as signal source is advised against, but traders are free to experiment nevertheless.
PDHL algorithm shows two types of PDHLs on the chart: active PDHL (solid line) and swept PDHL (dashed line) . You can the examples of PDHLs, detected by our algorithm, on the screenshot below:
📍 INDICATOR: Single Candle Order Block (SCOB)
SCOB indicator is an algorithm, which marks a very specific POIS, which are based on of the most simple yet highly profound SMC and candle pattern principles and are usually a good alternative for classic orderblocks.
Principles of SCOB detection are very simple:
Price sweeps previous candle's extremum (high/low). So called "liquidity sweep" ;
Immediately after step 1 price forms a fair value gap (FVG).
You can see basic examples of bearish and bullish SCOBs on the screenshot below:
As a matter of fact, SCOB can be used both as a confirmation tool and source of signals. However! To be a source of signals, SCOB is most suitable to be used while trading on lower timeframe (LTF), while trading on a higher timeframe (HTF) on average requires to look at SCOB as a POI rather than as independent source of signals. That being said, we would like additionally to point out, that due to the nature of SCOB being an orderblock, this tool by its nature is best suitable as confirmation tool and we expect traders to use it as such, but either way this indicator is quite multifunctional and can be used by each trader for a more specific purposes.
SCOBs, which are detected by our algorithm, are painted on the chart either as coloured candles (SCOBs without inside bars) or coloured boxes (SCOBs with inside bars) . You can see examples of SCOBs, which were detected by our SCOB algorithm, on the screenshot below:
📍 INDICATOR: Institutional Funding Candle (IFC)
IFC is a candle, which is a more strict version of SCOB. Our algorithms detects an IFC, if SCOB satisfies these conditions:
SCOB candle has large shadow (more than 50% of candle's body);
SCOB candle has large range ( | high - low | is more than a certain value, which is base on ATR).
That's basically it! Being simple as that, IFC represents itself as a high-trust SCOB, which on average has larger chance of reversing price when IFC candle is reached by it and our practice shows that it is indeed the case. IFC candles are usually go hand in hand with large price and volume spikes, which are believed to be caused by large institutional players, who trading eager to catch retail trader's stop orders, which they usually place around POIs like IFC and SCOB.
We expect traders to use IFC as a tool for entry confirmation bias, especially when considering IFC from HTF.
You can see IFC, which our algoritms detects on the chart, on the screenshot below:
📍 INDICATOR: Engulfing Candle (EC)
An Engulfing Candle is a candle, which occurs when the current candle’s body engulfs the prior candle’s body, showing a short-term shift in demand/supply balance. In SMC context, it is most useful around POIs/liquidity as a contextual confirmation element. The indicator marks bullish and bearish EC without implying a “must reverse” outcome – it’s a focus cue, not a promise.
As with any other alike tool, this algorithm should not be used as sole source of signals, but rather as a confirmation tool. ECs near support/resistance zones or POIs are typically more impactufl than those inside choppy consolidations. Structural and LTF price impulse confirmation usually enhances existing position bias in a positive way.
You can see examples of engulfing candles on the screenshots below:
Bullish engulfing candles
Bearish engulfing candles
📍 INDICATOR: Inside Bars (IB)
Inside Bars are bars, which are contained inside the range of high and low prices of the bars preceding them. This algorithm was designed to showcase periods of potential price consolidation/volatylity compression and quite often precedes price movement towards closest liquidity POIs and ZOIs. When price finally breaks out of its previous range, it usually provides good opportunities for entering trades using breakout strategies (especially ones, that are based on SMC principles) .
You can see examples of IBs, which are detected by our algorithm on the chart, on the screenshot below:
That was a long list of features, now let's talk about settings now.
🔔 WHAT ABOUT ALERTS?
At the moment of publishing this indicator includes alerts for all algorithms, which are included inside, except for Inside Bars (IB) algorithm .
⚙️ SETTINGS
At the moment of publishing most of the settings in this indicator are about styling for indicator's visuals, because by design most of the included algorithms (excluding PBB) don't rely on inputs of any technical kind. Let's review them.
ToolBox | General Styling
Text Size – (Tiny, Small, Normal, Large) – defines text size of indicator's visuals, which use text-based visuals.
Price Border Bands | Main Settings
Show Price Border Bands – toggles on/off the display of PBB;
Half Length – defines amount of bars, used for calculation of the PBB's TMA;
Price Source – defines price source for PBB's TMA;
ATR Multiplier – affects the width of PBB's bands;
ATR Period – affects the amount of bars for ATR calculation.
Orderflow (OF) | Settings
Bullish OF – toggles on/off the display & colour of bullish OF;
Bearish OF – toggles on/off the display & colour of bearish OF;
Show border – toggles on/off the display of OF blocks' border.
Single Candle Order Block (SCOB) | Settings
Show SCOB – toggles on/off the display of SCOB;
Bullish – toggles on/off the colour of bullish SCOB;
Bearish – toggles on/off the colour of bearish SCOB.
Equal High/Lows (EQHL) | Settings
Show EQH/EQL – toggles on/off the display of PDH/PDL;
EQH – toggles on/off the colour of EQH;
EQL – toggles on/off the colour of EQL.
Institutional Funding Candle (IFC) | Settings
Show IFC – toggles on/off the display of IFC;
Bullish – toggles on/off the colour of bullish IFC;
Bearish – toggles on/off the colour of bearish IFC.
Previous Day High & Low (PDHL) | Settings
Show PDH/PDL – toggles on/off the display of PDH/PDL;
Show PDH/PDL – toggles on/off the display of the past history of swept PDH/PDL;
Show previous day divider – toggles on/off the display of dashed gray line, which separates new day from previous one;
Bullish – toggles on/off the colour of bullish IFC;
Bearish – toggles on/off the colour of bearish IFC.
Engulfing Candle (EC) | Settings
Show engulfing candles – toggles on/off the display of EC;
Bullish – toggles on/off the colour of bullish EC;
Bearish – toggles on/off the colour of bearish EC.
Inside Bars (IB) | Settings
Show inside bars – toggles on/off the display of IB;
Bullish – toggles on/off the colour of bullish IB;
Bearish – toggles on/off the colour of bearish IB.
Alerts | POI
Alert Frequency – (Once Per Bar, Once Per Bar Close) – defines alert frequency of the indicator's alert for all POIs;
* all other buttons from this group of settings toggle alerts on/off.
PBB;
OF;
SCOB;
EQH;
EQL;
IFC;
PDH;
PDL;
EC.
🏁 AFTERWORD
SMC ToolBox indicator is designed to be the ultimate swiss knife, which might bring you quantifiable results when trying to crack the market's secret of where the liquidity is placed. This indicator doesn't produce any particular signals not it gives any financial advice, but it helps you deepen understanding about potential existing liquidity zones and price points by employing principles of SMC algorithms, which are most commonly used by retail traders on a daily basis.
You can view this indicator as a Christmas candy box: you pick only the candles (indicators) you need and want. We expect any trader to use this indicator by exactly same way: you should take onlt the things you need to enhance your strategy, not worrying about what to do with other indicators, fi they don't suit you.
Lastly, we would like to share our team's recommendations (they are optional, of course) on how to use certain POIs from ToolBox:
Use PBB as a filter for validating POis. Pay close attention to the rule "don't trade POIs, which are located inside the bands of PBB" (described above in "INDICATOR: PBB") ;
Use Orderflow to find short-term and mid-term trading opportunitions for trend-following strategies, using OF blocks as resistance in bearish trend and support in bullish trend;
Use EQHL and PDHL indicators when trading by mean-reversion strategies on intraday timeframes. These indicators will be especially of use to forex, stock and crypto traders;
Use SCOB and IFC indicators when trading by mean-reversion strategy to find short-term reversal opportunities;
Use ECs and IBs as confirmation/denial tools for your entry ideas. We recommend avoiding trading If price is currently going inside HTF's IB range.
We have no doubts that SMC ToolBox indicator will be of use to any trader, who employs and desire to employ SMC principles in his strategy. We will be waiting for your feedback, meanwhile you can ask your questions in the comments :)
Sincerely,
WinWorld team.
Long-Term Trend & Valuation Model [Backquant]Long-Term Trend & Valuation Model
Invite-only. A universal long-term valuation strategy and trend model built to work across markets, with an emphasis on crypto where cycles and volatility are large. Intended primarily for the 1D timeframe. Inputs should be adjusted per asset to reflect its structure and volatility.
If you would like to checkout the simplified and open source valuation, check out:
What this is
A two-layer framework that answers two different questions.
• The Valuation Engine asks “how extended is price relative to its own long-term regime” and outputs a centered oscillator that moves positive in supportive conditions and negative in deteriorating conditions.
• The Trend Model asks “is the market actually trending in a sustained direction” and converts several independent subsystems into a single composite score.
The combination lets you separate “where we are in the cycle” from “what to do about it” so allocation and timing can be handled with fewer conflicts.
Design philosophy
Crypto and many risk assets move in multi-month expansions and contractions. Short tools flip often and can be misleading near regime boundaries. This model favors slower, high-confidence information, then summarizes it in simple visuals and alerts. It is not trying to catch every swing. It is built to help you participate in the meat of long uptrends, de-risk during deteriorations, and identify stretched conditions that deserve caution or patience.
Valuation Engine, high level
The Valuation Engine blends several slow signals into one measure. Exact transforms, windows, and weights are private, but the categories below describe the intent. Each input is standardized so unlike units can be combined without one dominating.
Momentum quality — favors persistent, orderly advances over erratic spikes. Helps distinguish trend continuation from noise.
Mean-reversion pressure — detects when price is far from a long anchor or when oscillators are pulling back toward equilibrium.
Risk-adjusted return — long-window reward to variability. Encourages time in market when advances are efficient rather than merely fast.
Volume imbalance — summarizes whether activity is expanding with advances or with declines, using a slow envelope to avoid day-to-day churn.
Trend distance — expresses how stretched price is from a structural baseline rather than from a short moving average.
Price normalization — a long z-score of price to keep extremes comparable across cycles and symbols.
How the Valuation Engine is shaped
Standardization — components are put on comparable scales over long windows.
Composite blend — standardized parts are combined into one reading with protective weighting. No single family can override the rest on its own.
Smoothing — optional moving average smoothing to reduce whipsaw around zero or around the bands.
Bounded scaling — the composite is compressed into a stable, interpretable range so the mid zone and extremes are visually consistent. This reduces the effect of outliers without hiding genuine stress.
Volatility-aware re-expansion — after compression, the series is allowed to swing wider in high-volatility regimes so “overbought” and “oversold” remain meaningful when conditions change.
Thresholds — fixed OB/OS levels or dynamic bands that float with recent dispersion. Dynamic bands use k times a rolling standard deviation. Fixed bands are simple and comparable across charts.
How to read the Valuation Oscillator
Above zero suggests a supportive backdrop. Rising and positive often aligns with uptrends that are gaining participation.
Below zero suggests deterioration or risk aversion. Falling and negative often aligns with distribution or with trend exhaustion.
Touches of the upper band show stretch on the optimistic side. Repeated tags without breakdown often occur late in cycles, especially in crypto.
Touches of the lower band show stretch on the pessimistic side. They are common in washouts and early bases.
Visual elements
Valuation Oscillator — colored by sign for instant context.
OB/OS guides — fixed or dynamic bands.
Background and bar colors — optional, tied to the sign of valuation for quick scans.
Summary table — optional, shows the standardized contribution of the major categories and the final composite score with a simple status icon.
Trend Model, composite scoring
The trend side aggregates several independent subsystems. Each subsystem issues a vote: long, short, or neutral. Votes are averaged into a composite score. The exact logic of each subsystem is intentionally abstracted. The families below describe roles, not formulas.
Long-horizon price state — checks where price sits relative to multiple structural baselines and whether those baselines are aligned.
Macro regime checks — favors sustained risk-on behavior and penalizes persistent deterioration in breadth or volatility structure.
Ultimate confirmation — a conservative filter that only votes when directional evidence is persistent.
Minimalist sanity checks — keep the model responsive to obvious extremes and prevent “stuck neutral” states.
Higher timeframe or overlay inputs — optional votes that consider slower contexts or relative strength to stabilize borderline periods.
You define two cutoffs for the composite: above the long threshold the state is Long , below the short threshold the state is Short , in between is Cash/Neutral . The script paints a signal line on price for an at-a-glance view and provides alerts when the composite crosses your thresholds.
How it can be used
Cycle framing in crypto — use deep negative valuation as accumulation context, then look for the composite trend to move through your long threshold. Late in cycles, extended positive valuation with weakening composite votes is a caution cue for de-risking or tighter management.
Regime-based allocation — increase risk or loosen take-profits when the composite is firmly Long and valuation is rising. Decrease risk or rotate to stable holdings when the composite is Short and valuation is falling.
Signal gating — run shorter-term entry systems only in the direction of the composite. This reduces counter-trend trades and improves holding discipline during strong uptrends.
Sizing overlay — scale position sizes by the magnitude of the valuation reading. Smaller sizes near the upper band during aging advances, larger sizes near zero after strong resets.
DCA context — for long-only accumulation, schedule heavier adds when valuation is negative and stabilizing, then lighten or pause adds when valuation is very positive and flattening.
Cross-asset rotation — compare symbols on 1D with the same fixed bands. Favor assets with positive valuation that are also in a Long composite state.
Interpreting common patterns
Early build-out — valuation rises from below zero, but the composite is still neutral. This is often the base-building phase. Patience and staged entries can make sense.
Healthy advance — valuation positive and trending up, composite firmly Long. Pullbacks that keep valuation above zero are usually opportunities rather than trend breaks.
Late-cycle stretch — valuation pinned near the upper band while the composite starts to weaken toward neutral. Consider trimming, tightening risk, or shifting to a “let the market prove it” stance.
Distribution and unwind — valuation negative and falling, composite Short. Rallies are treated as counter-trend until both turn.
Settings that matter
Timeframe
This model is intended for 1D as the primary view. It can be inspected on higher or lower frames, but the design choices assume daily bars for crypto and other risk assets.
Asset-specific tuning
Inputs should be adjusted per asset. Coins with high variability benefit from longer lookbacks and slightly wider dynamic bands. Lower-volatility instruments can use shorter windows and tighter bands.
Valuation side
Lookback lengths — longer values make the oscillator steadier and more cycle-aware. Shorter values increase sensitivity but create more mid-zone noise.
Smoothing — enable to reduce flicker around zero and around the bands. Disable if you want faster warnings of regime change.
Dynamic vs fixed thresholds — dynamic bands float with recent dispersion and keep OB/OS comparable across regimes. Fixed bands are simple and make inter-asset comparison easy.
Scaling and re-expansion — keep this enabled if you want extremes to remain interpretable when volatility rises.
Trend side
Composite thresholds — widen the neutral zone if you want fewer flips. Tighten thresholds if you want earlier signals at the cost of more transitions.
Visibility — use the price-pane signal line and bar coloring to keep the regime in view while you focus on structure.
Alerts
Valuation OB/OS enter and exit — the oscillator entering or leaving stretched zones.
Zero-line crosses — valuation turning positive or negative.
Trend flips — composite crossing your long or short threshold.
Strengths
Separates “valuation context” from “trend state,” which improves decisions about when to add, reduce, or stand aside.
Composite voting reduces reliance on any single indicator family and improves robustness across regimes.
Volatility-aware scaling keeps signals interpretable during quiet and wild markets.
Clear, configurable visuals and alerts that support long-horizon discipline rather than frequent toggling.
Final thoughts
This is a universal long-term valuation strategy and trend model that aims to keep you aligned with the dominant regime while giving transparent context for stretch and risk. For crypto on 1D, it helps map accumulation, expansion, distribution, and unwind phases with a single, consistent language. Tune lookbacks, smoothing, and thresholds to the asset you trade, let the valuation side tell you where you are in the cycle, and let the composite trend side tell you what stance to hold until the market meaningfully changes.
Adaptive Trend Following Suite [Alpha Extract]A sophisticated multi-filter trend analysis system that combines advanced noise reduction, adaptive moving averages, and intelligent market structure detection to deliver institutional-grade trend following signals. Utilizing cutting-edge mathematical algorithms and dynamic channel adaptation, this indicator provides crystal-clear directional guidance with real-time confidence scoring and market mode classification for professional trading execution.
🔶 Advanced Noise Reduction
Filter Eliminates market noise using sophisticated Gaussian filtering with configurable sigma values and period optimization. The system applies mathematical weight distribution across price data to ensure clean signal generation while preserving critical trend information, automatically adjusting filter strength based on volatility conditions.
advancedNoiseFilter(sourceData, filterLength, sigmaParam) =>
weightSum = 0.0
valueSum = 0.0
centerPoint = (filterLength - 1) / 2
for index = 0 to filterLength - 1
gaussianWeight = math.exp(-0.5 * math.pow((index - centerPoint) / sigmaParam, 2))
weightSum += gaussianWeight
valueSum += sourceData * gaussianWeight
valueSum / weightSum
🔶 Adaptive Moving Average Core Engine
Features revolutionary volatility-responsive averaging that automatically adjusts smoothing parameters based on real-time market conditions. The engine calculates adaptive power factors using logarithmic scaling and bandwidth optimization, ensuring optimal responsiveness during trending markets while maintaining stability during consolidation phases.
// Calculate adaptive parameters
adaptiveLength = (periodLength - 1) / 2
logFactor = math.max(math.log(math.sqrt(adaptiveLength)) / math.log(2) + 2, 0)
powerFactor = math.max(logFactor - 2, 0.5)
relativeVol = avgVolatility != 0 ? volatilityMeasure / avgVolatility : 0
adaptivePower = math.pow(relativeVol, powerFactor)
bandwidthFactor = math.sqrt(adaptiveLength) * logFactor
🔶 Intelligent Market Structure Analysis
Employs fractal dimension calculations to classify market conditions as trending or ranging with mathematical precision. The system analyzes price path complexity using normalized data arrays and geometric path length calculations, providing quantitative market mode identification with configurable threshold sensitivity.
🔶 Multi-Component Momentum Analysis
Integrates RSI and CCI oscillators with advanced Z-score normalization for statistical significance testing. Each momentum component receives independent analysis with customizable periods and significance levels, creating a robust consensus system that filters false signals while maintaining sensitivity to genuine momentum shifts.
// Z-score momentum analysis
rsiAverage = ta.sma(rsiComponent, zAnalysisPeriod)
rsiDeviation = ta.stdev(rsiComponent, zAnalysisPeriod)
rsiZScore = (rsiComponent - rsiAverage) / rsiDeviation
if math.abs(rsiZScore) > zSignificanceLevel
rsiMomentumSignal := rsiComponent > 50 ? 1 : rsiComponent < 50 ? -1 : rsiMomentumSignal
❓How It Works
🔶 Dynamic Channel Configuration
Calculates adaptive channel boundaries using three distinct methodologies: ATR-based volatility, Standard Deviation, and advanced Gaussian Deviation analysis. The system automatically adjusts channel multipliers based on market structure classification, applying tighter channels during trending conditions and wider boundaries during ranging markets for optimal signal accuracy.
dynamicChannelEngine(baselineData, channelLength, methodType) =>
switch methodType
"ATR" => ta.atr(channelLength)
"Standard Deviation" => ta.stdev(baselineData, channelLength)
"Gaussian Deviation" =>
weightArray = array.new_float()
totalWeight = 0.0
for i = 0 to channelLength - 1
gaussWeight = math.exp(-math.pow((i / channelLength) / 2, 2))
weightedVariance += math.pow(deviation, 2) * array.get(weightArray, i)
math.sqrt(weightedVariance / totalWeight)
🔶 Signal Processing Pipeline
Executes a sophisticated 10-step signal generation process including noise filtering, trend reference calculation, structure analysis, momentum component processing, channel boundary determination, trend direction assessment, consensus calculation, confidence scoring, and final signal generation with quality control validation.
🔶 Confidence Transformation System
Applies sigmoid transformation functions to raw confidence scores, providing 0-1 normalized confidence ratings with configurable threshold controls. The system uses steepness parameters and center point adjustments to fine-tune signal sensitivity while maintaining statistical robustness across different market conditions.
🔶 Enhanced Visual Presentation
Features dynamic color-coded trend lines with adaptive channel fills, enhanced candlestick visualization, and intelligent price-trend relationship mapping. The system provides real-time visual feedback through gradient fills and transparency adjustments that immediately communicate trend strength and direction changes.
🔶 Real-Time Information Dashboard
Displays critical trading metrics including market mode classification (Trending/Ranging), structure complexity values, confidence scores, and current signal status. The dashboard updates in real-time with color-coded indicators and numerical precision for instant market condition assessment.
🔶 Intelligent Alert System
Generates three distinct alert types: Bullish Signal alerts for uptrend confirmations, Bearish Signal alerts for downtrend confirmations, and Mode Change alerts for market structure transitions. Each alert includes detailed messaging and timestamp information for comprehensive trade management integration.
🔶 Performance Optimization
Utilizes efficient array management and conditional processing to maintain smooth operation across all timeframes. The system employs strategic variable caching, optimized loop structures, and intelligent update mechanisms to ensure consistent performance even during high-volatility market conditions.
This indicator delivers institutional-grade trend analysis through sophisticated mathematical modelling and multi-stage signal processing. By combining advanced noise reduction, adaptive averaging, intelligent structure analysis, and robust momentum confirmation with dynamic channel adaptation, it provides traders with unparalleled trend following precision. The comprehensive confidence scoring system and real-time market mode classification make it an essential tool for professional traders seeking consistent, high-probability trend following opportunities with mathematical certainty and visual clarity.
RB — Rejection Blocks (Price Structure)This indicator detects and visualizes Rejection Blocks (RBs) using pure price action logic.
A bullish RB occurs when a down candle forms a lower low than both its neighbors. A bearish RB occurs when an up candle forms a higher high than both its neighbors.
Validated RBs are displayed as boxes, optional lines, or labels. Blocks are automatically removed when invalidated (price closes through them), keeping the chart uncluttered and focused.
How to use
• Apply on any timeframe, from intraday to higher timeframes.
• Watch how price reacts when revisiting RB zones.
• Treat these zones as contextual areas, not entry signals.
• Combine with your own trading methods for confirmation.
Originality
Unlike generic support/resistance tools, this indicator isolates a specific structural pattern (rejection blocks) and renders it visually on the chart. This selective focus allows traders to study structural reactions with more clarity and precision.
⚠️ Disclaimer: This is not a trading system or a signal provider. It is a visual analysis tool designed for structural and educational purposes.
Smart Structure Breaks & Order BlocksOverview (What it does)
The indicator “Smart Structure Breaks & Order Blocks” detects market structure using swing highs and lows, identifies Break of Structure (BOS) events, and automatically draws order blocks (OBs) from the origin candle. These zones extend to the right and change color/outline when mitigated or invalidated. By formalizing and automating part of discretionary analysis, it provides consistent zone recognition.
Main Components
Swing Detection: ta.pivothigh/ta.pivotlow identify confirmed swing points.
BOS Detection: Determines if the recent swing high/low is broken by close (strict mode) or crossover.
OB Creation: After a BOS, the opposite candle (bearish for bullish BOS, bullish for bearish BOS) is used to generate an order block zone.
Zone Management: Limits the number of zones, extends them to the right, and tracks tagged (mitigated) or invalidated states.
Input Parameters
Left/Right Pivot (default 6/6): Number of bars required on each side to confirm a swing. Higher values = smoother swings.
Max Zones (default 4): Maximum zones stored per direction (bull/bear). Oldest zones are overwritten.
Zone Confirmation Lookback (default 3): Ensures OB origin candle validity by checking recent highs/lows.
Show Swing Points (default ON): Displays triangles on swing highs/lows.
Require close for BOS? (default ON): Strict BOS (close required) vs loose BOS (line crossover).
Use candle body for zones (default OFF): Zones drawn from candle body (ON) or wick (OFF).
Signal Definition & Logic
Swing Updates: Latest confirmed pivots update lastHighLevel / lastLowLevel.
BOS (Break of Structure):
Bullish – close breaks last swing high.
Bearish – close breaks last swing low.
Only one valid BOS per swing (avoids duplicates).
OB Detection:
Bullish BOS → previous bearish candle with lowest low forms the OB.
Bearish BOS → previous bullish candle with highest high forms the OB.
Zones: Bull = green, Bear = red, semi-transparent, extended to the right.
Zone States:
Mitigated: Price touches the zone → border highlighted.
Invalidated:
Bull zone → close below → turns red.
Bear zone → close above → turns green.
Chart Appearance
Swing High: red triangle above bar
Swing Low: green triangle below bar
Bull OB: green zone (border highlighted on touch)
Bear OB: red zone (border highlighted on touch)
Invalid Zones: Bull zones turn reddish, Bear zones turn greenish
Practical Use (Trading Assistance)
Trend Following Entries: Buy pullbacks into green OBs in uptrends, sell rallies into red OBs in downtrends.
Focus on First Touch: First mitigation after BOS often has higher reaction probability.
Confluence: Combine with higher timeframe trend, volume, session levels, key price levels (previous highs/lows, VWAP, etc.).
Stops/Targets:
Bull – stop below zone, partial take profit at swing high or resistance.
Bear – stop above zone, partial take profit at swing low or support.
Parameter Tuning (per market/timeframe)
Pivot (6/6 → 4/4/8/8): Lower for scalping (3–5), medium for day trading (5–8), higher for swing trading (8–14). Increase to reduce noise.
Strict Break: ON to reduce false breaks in ranging markets; OFF for earlier signals.
Body Zones: ON for assets with long wicks, OFF for cleaner OBs in liquid instruments.
Zone Confirmation (default 3): Increase for stricter OB origin, fewer zones.
Max Zones (default 4 → 6–10): Increase for higher volatility, decrease to avoid clutter.
Strengths
Standardizes BOS and OB detection that is usually subjective.
Tracks mitigation and invalidation automatically.
Adaptable: allows body/wick zone switching for different instruments.
Limitations
Pivot-based: Signals appear only after pivots confirm (slight lag).
Zones reflect past balance: Can fail after new events (news, earnings, macro data).
Range-heavy markets: More false BOS; consider stricter settings.
Backtesting: This script is for drawing/visual aid; trading rules must be defined separately.
Workflow Example
Identify higher timeframe trend (4H/Daily).
On lower TF (15–60m), wait for BOS and new OB.
Enter on first mitigation with confirmation candle.
Stop beyond zone; targets based on R multiples and swing points.
FAQ
Q: Why are zones invalidated quickly?
A: Flow reversal after BOS. Adjust pivots higher, enable Strict mode, or switch to Body zones to reduce noise.
Q: What does “tagged” mean?
A: Price touched the zone once = mitigated. Implies some orders in that zone may have been filled.
Q: Body or Wick zones?
A: Wick zones are fine in clean markets. For volatile pairs with long wicks, body zones provide more realistic areas.
Customization Tips (Code perspective)
Zone storage: Currently ring buffer ((idx+1) % zoneLimit). Could prioritize keeping unmitigated zones.
Automated testing: Add strategy.entry/exit for rule-based backtests.
Multi-timeframe: Use request.security() for higher timeframe swings/BOS.
Visualization: Add labels for BOS bars, tag zones with IDs, count touches.
Summary
This indicator formalizes the cycle Swing → BOS → OB creation → Mitigation/Invalidation, providing consistent structure analysis and zone tracking. By tuning sensitivity and strictness, and combining with higher timeframe context, it enhances pullback/continuation trading setups. Always combine with proper risk management.
Martingale Strategy Simulator [BackQuant]Martingale Strategy Simulator
Purpose
This indicator lets you study how a martingale-style position sizing rule interacts with a simple long or short trading signal. It computes an equity curve from bar-to-bar returns, adapts position size after losing streaks, caps exposure at a user limit, and summarizes risk with portfolio metrics. An optional Monte Carlo module projects possible future equity paths from your realized daily returns.
What a martingale is
A martingale sizing rule increases stake after losses and resets after a win. In its classical form from gambling, you double the bet after each loss so that a single win recovers all prior losses plus one unit of profit. In markets there is no fixed “even-money” payout and returns are multiplicative, so an exact recovery guarantee does not exist. The core idea is unchanged:
Lose one leg → increase next position size
Lose again → increase again
Win → reset to the base size
The expectation of your strategy still depends on the signal’s edge. Sizing does not create positive expectancy on its own. A martingale raises variance and tail risk by concentrating more capital as a losing streak develops.
What it plots
Equity – simulated portfolio equity including compounding
Buy & Hold – equity from holding the chart symbol for context
Optional helpers – last trade outcome, current streak length, current allocation fraction
Optional diagnostics – daily portfolio return, rolling drawdown, metrics table
Optional Monte Carlo probability cone – p5, p16, p50, p84, p95 aggregate bands
Model assumptions
Bar-close execution with no slippage or commissions
Shorting allowed and frictionless
No margin interest, borrow fees, or position limits
No intrabar moves or gaps within a bar (returns are close-to-close)
Sizing applies to equity fraction only and is capped by your setting
All results are hypothetical and for education only.
How the simulator applies it
1) Directional signal
You pick a simple directional rule that produces +1 for long or −1 for short each bar. Options include 100 HMA slope, RSI above or below 50, EMA or SMA crosses, CCI and other oscillators, ATR move, BB basis, and more. The stance is evaluated bar by bar. When the stance flips, the current trade ends and the next one starts.
2) Sizing after losses and wins
Position size is a fraction of equity:
Initial allocation – the starting fraction, for example 0.15 means 15 percent of equity
Increase after loss – multiply the next allocation by your factor after a losing leg, for example 2.00 to double
Reset after win – return to the initial allocation
Max allocation cap – hard ceiling to prevent runaway growth
At a high level the size after k consecutive losses is
alloc(k) = min( cap , base × factor^k ) .
In practice the simulator changes size only when a leg ends and its PnL is known.
3) Equity update
Let r_t = close_t / close_{t-1} − 1 be the symbol’s bar return, d_{t−1} ∈ {+1, −1} the prior bar stance, and a_{t−1} the prior bar allocation fraction. The simulator compounds:
eq_t = eq_{t−1} × (1 + a_{t−1} × d_{t−1} × r_t) .
This is bar-based and avoids intrabar lookahead. Costs, slippage, and borrowing costs are not modeled.
Why traders experiment with martingale sizing
Mean-reversion contexts – if the signal often snaps back after a string of losses, adding size near the tail of a move can pull the average entry closer to the turn
Behavioral or microstructure edges – some rules have modest edge but frequent small whipsaws; size escalation may shorten time-to-recovery when the edge manifests
Exploration and stress testing – studying the relationship between streaks, caps, and drawdowns is instructive even if you do not deploy martingale sizing live
Why martingale is dangerous
Martingale concentrates capital when the strategy is performing worst. The main risks are structural, not cosmetic:
Loss streaks are inevitable – even with a 55 percent win rate you should expect multi-loss runs. The probability of at least one k-loss streak in N trades rises quickly with N.
Size explodes geometrically – with factor 2.0 and base 10 percent, the sequence is 10, 20, 40, 80, 100 (capped) after five losses. Without a strict cap, required size becomes infeasible.
No fixed payout – in gambling, one win at even odds resets PnL. In markets, there is no guaranteed bounce nor fixed profit multiple. Trends can extend and gaps can skip levels.
Correlation of losses – losses cluster in trends and in volatility bursts. A martingale tends to be largest just when volatility is highest.
Margin and liquidity constraints – leverage limits, margin calls, position limits, and widening spreads can force liquidation before a mean reversion occurs.
Fat tails and regime shifts – assumptions of independent, Gaussian returns can understate tail risk. Structural breaks can keep the signal wrong for much longer than expected.
The simulator exposes these dynamics in the equity curve, Max Drawdown, VaR and CVaR, and via Monte Carlo sketches of forward uncertainty.
Interpreting losing streaks with numbers
A rough intuition: if your per-trade win probability is p and loss probability is q=1−p , the chance of a specific run of k consecutive losses is q^k . Over many trades, the chance that at least one k-loss run occurs grows with the number of opportunities. As a sanity check:
If p=0.55 , then q=0.45 . A 6-loss run has probability q^6 ≈ 0.008 on any six-trade window. Across hundreds of trades, a 6 to 8-loss run is not rare.
If your size factor is 1.5 and your base is 10 percent, after 8 losses the requested size is 10% × 1.5^8 ≈ 25.6% . With factor 2.0 it would try to be 10% × 2^8 = 256% but your cap will stop it. The equity curve will still wear the compounded drawdown from the sequence that led to the cap.
This is why the cap setting is central. It does not remove tail risk, but it prevents the sizing rule from demanding impossible positions
Note: The p and q math is illustrative. In live data the win rate and distribution can drift over time, so real streaks can be longer or shorter than the simple q^k intuition suggests..
Using the simulator productively
Parameter studies
Start with conservative settings. Increase one element at a time and watch how the equity, Max Drawdown, and CVaR respond.
Initial allocation – lower base reduces volatility and drawdowns across the board
Increase factor – set modestly above 1.0 if you want the effect at all; doubling is aggressive
Max cap – the most important brake; many users keep it between 20 and 50 percent
Signal selection
Keep sizing fixed and rotate signals to see how streak patterns differ. Trend-following signals tend to produce long wrong-way streaks in choppy ranges. Mean-reversion signals do the opposite. Martingale sizing interacts very differently with each.
Diagnostics to watch
Use the built-in metrics to quantify risk:
Max Drawdown – worst peak-to-trough equity loss
Sharpe and Sortino – volatility and downside-adjusted return
VaR 95 percent and CVaR – tail risk measures from the realized distribution
Alpha and Beta – relationship to your chosen benchmark
If you would like to check out the original performance metrics script with multiple assets with a better explanation on all metrics please see
Monte Carlo exploration
When enabled, the forecast draws many synthetic paths from your realized daily returns:
Choose a horizon and a number of runs
Review the bands: p5 to p95 for a wide risk envelope; p16 to p84 for a narrower range; p50 as the median path
Use the table to read the expected return over the horizon and the tail outcomes
Remember it is a sketch based on your recent distribution, not a predictor
Concrete examples
Example A: Modest martingale
Base 10 percent, factor 1.25, cap 40 percent, RSI>50 signal. You will see small escalations on 2 to 4 loss runs and frequent resets. The equity curve usually remains smooth unless the signal enters a prolonged wrong-way regime. Max DD may rise moderately versus fixed sizing.
Example B: Aggressive martingale
Base 15 percent, factor 2.0, cap 60 percent, EMA cross signal. The curve can look stellar during favorable regimes, then a single extended streak pushes allocation to the cap, and a few more losses drive deep drawdown. CVaR and Max DD jump sharply. This is a textbook case of high tail risk.
Strengths
Bar-by-bar, transparent computation of equity from stance and size
Explicit handling of wins, losses, streaks, and caps
Portable signal inputs so you can A–B test ideas quickly
Risk diagnostics and forward uncertainty visualization in one place
Example, Rolling Max Drawdown
Limitations and important notes
Martingale sizing can escalate drawdowns rapidly. The cap limits position size but not the possibility of extended adverse runs.
No commissions, slippage, margin interest, borrow costs, or liquidity limits are modeled.
Signals are evaluated on closes. Real execution and fills will differ.
Monte Carlo assumes independent draws from your recent return distribution. Markets often have serial correlation, fat tails, and regime changes.
All results are hypothetical. Use this as an educational tool, not a production risk engine.
Practical tips
Prefer gentle factors such as 1.1 to 1.3. Doubling is usually excessive outside of toy examples.
Keep a strict cap. Many users cap between 20 and 40 percent of equity per leg.
Stress test with different start dates and subperiods. Long flat or trending regimes are where martingale weaknesses appear.
Compare to an anti-martingale (increase after wins, cut after losses) to understand the other side of the trade-off.
If you deploy sizing live, add external guardrails such as a daily loss cut, volatility filters, and a global max drawdown stop.
Settings recap
Backtest start date and initial capital
Initial allocation, increase-after-loss factor, max allocation cap
Signal source selector
Trading days per year and risk-free rate
Benchmark symbol for Alpha and Beta
UI toggles for equity, buy and hold, labels, metrics, PnL, and drawdown
Monte Carlo controls for enable, runs, horizon, and result table
Final thoughts
A martingale is not a free lunch. It is a way to tilt capital allocation toward losing streaks. If the signal has a real edge and mean reversion is common, careful and capped escalation can reduce time-to-recovery. If the signal lacks edge or regimes shift, the same rule can magnify losses at the worst possible moment. This simulator makes those trade-offs visible so you can calibrate parameters, understand tail risk, and decide whether the approach belongs anywhere in your research workflow.
