TFPS - TradFi Pressure ScoreThe Data-Driven Answer to a New Market Reality.
This indicator quantifies the pressure exerted by Wall Street on the crypto market across four critical dimensions: Risk Appetite, Fear, Liquidity Flows, and the Opportunity Cost of Capital. Our research has found that the correlation between this 4-dimensional pressure vector and crypto price action reaches peak values of 0.87. This is your decisive macro edge, delivered in real-time.
The Irreversible Transformation
A fundamental analysis of the last five years of market data proves an irreversible transformation: The crypto market has matured into a high-beta risk asset, its fate now inextricably linked to Traditional Finance (TradFi).
The empirical data is clear:
Bitcoin increasingly behaves like a leveraged version of the S&P 500.
The correlation to major stock indices is statistically significant and persistent.
The "digital gold" narrative is refuted by the data; the correlation to gold is virtually non-existent.
This means standard technical indicators are no longer sufficient. Tools like RSI or MACD are blind to the powerful, external macro context that now dominates price action. They see the effect, but not the cause.
The Solution: A 4-Dimensional Macro-Lens
The TradFi Pressure Score (TFPS) is the answer. It is an institutional-grade dashboard that aggregates the four most dominant external forces into a single, actionable score:
S&P 500 (SPY): The Pulse of Risk Appetite. A rising S&P signals a "risk-on" environment, fueling capital flows into crypto.
VIX: The Market's Fear Gauge. A rising VIX signals a "risk-off" flight to safety, draining liquidity from crypto.
DXY (US-Dollar Index): The Anchor of Global Liquidity. A strong Dollar (rising DXY) tightens financial conditions, creating powerful headwinds for risk assets like Bitcoin.
US 10Y Yield: The Opportunity Cost of Capital. Rising yields make risk-free assets more attractive, pulling capital away from non-yielding assets like crypto.
What makes the TFPS truly unique?
1. Dynamic Weighting (The Secret Weapon):
Which macro factor matters most right now? Is it a surging Dollar or a collapsing stock market? The TFPS answers this automatically. It continuously analyzes the correlation of all four components to your chosen asset (e.g., Bitcoin) and adjusts their influence in real-time. The dashboard shows you the exact live weights, ensuring you are always focused on the factor that is currently driving the market.
2. Adaptive Engine:
The forces driving a 15-minute chart are different from those driving a daily chart. The TFPS engine automatically recalibrates its internal lookback periods to your chosen timeframe. This ensures the score is always optimally relevant, whether you are a day trader or a swing trader.
3. Designed for Actionable Insights
The Pressure Line: The indicator's core output. Is its value > 0 (tailwind) or < 0 (headwind)? This provides an instant, unambiguous read on the macro environment for your trade.
The Z-Score (The Contrarian Signal): The background "Stress Cloud" and the discrete dots provide early warnings of extreme macro greed or fear. Readings above +2 or below -2 have historically pinpointed moments of market exhaustion that often precede major trend reversals.
Lead/Lag Status: Gain a critical edge by knowing who is in the driver's seat. The dashboard tells you if TradFi is leading the price action or if crypto is moving independently, allowing you to validate your trade thesis against the dominant market force.
This is a public indicator with protected source code
Access is now available for traders who understand the new market reality at the intersection of crypto and traditional finance.
You are among the first to leverage what is a new standard for macro analysis in crypto trading. Your feedback is highly valued as I continue to refine this tool.
Follow for updates and trade with the full context!
ビットコイン (暗号資産)
TFPS - TradFi-Pressure-Score (Adaptive)The data-driven answer to an irreversible market reality.
This indicator quantifies the combined pressure from the S&P 500, VIX, DXY, and US10Y, whose correlation to crypto has reached peak values of 0.87. Your decisive macro edge, in real-time.
This indicator is built on a fundamental analysis of market data from the last five years. The analysis proves an irreversible transformation: The crypto market has evolved into a high-beta risk asset, its fate inextricably linked to Traditional Finance (TradFi).
The empirical data is clear:
Bitcoin increasingly behaves like a leveraged version of the S&P 500.
The correlation to stock indices, with peak values of up to 0.87, is statistically highly significant.
The "digital gold" safe-haven narrative is refuted by the data; the correlation to gold (0.04) is virtually non-existent and statistically insignificant.
This means: Standard indicators like RSI or MACD are insufficient for today's market conditions. They only see price, ignoring the powerful external context that now dominates price action.
The TradFi Pressure Score (TFPS) is the answer to this data-driven reality. It's your institutional-grade macro dashboard, aggregating the four most dominant external forces into a single, actionable score:
S&P 500 (SPY): The pulse of global risk appetite. A rising S&P signals a "risk-on" environment, fueling capital flows into crypto.
VIX: The market's "Fear Gauge". A rising VIX signals a "risk-off" flight to safety, draining liquidity from crypto.
DXY (US-Dollar Index): The counter-pole to risk assets. A strong Dollar (rising DXY) tightens global liquidity, creating significant headwinds for Bitcoin.
US 10Y Yield: The opportunity cost of capital. Rising yields make risk-free assets more attractive, pulling capital away from non-yielding assets like crypto.
What makes TFPS truly unique?
Dynamic Weighting (its secret weapon): Which factor matters most today? The DXY or the VIX? TFPS continuously analyzes the correlation of all four factors to your chosen asset (e.g., Bitcoin) and automatically adjusts their weight in real-time. This ensures you're always focused on what's currently driving the market.
Adaptive Engine : What drives a 15-minute chart is different from a daily chart. The TFPS engine automatically adapts its lookback periods and calculations to your chosen timeframe for optimal relevance.
Clear, Actionable Signals Designed for Traders:
Pressure Line (>0 or <0): Instantly see if the world's largest financial forces are providing a tailwind or a headwind for your trade.
Z-Score (Extreme Readings) : Get early warnings of extreme macro "Greed" or "Fear". Readings above +2 or below -2 have historically pinpointed moments of market exhaustion that often precede major trend reversals.
Regime Change : A fundamental shift in the nature of TradFi pressure is visualized with a clear signal, providing unambiguous macro insights.
Lead/Lag Status : Gain a critical edge by knowing who's in the driver's seat. The dashboard tells you if TradFi is LEADING the price action or if crypto is moving independently, allowing you to focus on the right information source.
This is a private beta. I am granting exclusive access to a limited number of traders who understand this new market reality. In exchange for your valuable feedback, you will be among the first to leverage what I believe is the new standard for macro analysis in crypto trading.
Request access to trade with the full context.
BTC PL Trend + Floor - Log PilotBTC Power Law Trend + Floor with forward projection.
Sky blue for the trend. Neon orange for the floor. Both project forward in dotted green.
Tracks Bitcoin’s long-term arc and structural support through time since Genesis.
DAX Inducere Simplă v1.3 – Confirmare InducereDAX Inducere Simplă v1.3 – Confirmare Inducere ,signals before fvg mss and displacement
PHANTOM STRIKE Z-4 [ApexLegion]Phantom Strike Z-4
STRATEGY OVERVIEW
This strategy represents an analytical framework using 6 detection systems that analyze distinct market dimensions through adaptive timeframe optimization. Each system targets specific market inefficiencies - automated parameter adjustment, market condition filtering, phantom strike pattern detection, SR exit management, order block identification, and volatility-aware risk management - with results processed through a multi-component scoring calculation that determines signal generation and position management decisions.
SYSTEM ARCHITECTURE PHILOSOPHY
Phantom Strike Z-4 operates through 12 distinct parameter groups encompassing individual settings that allow detailed customization for different trading environments. The strategy employs modular design principles where each analytical component functions independently while contributing to unified decision-making protocols. This architecture enables traders to engage with structured market analysis through intuitive configuration options while the underlying algorithms handle complex computational processes.
The framework approaches certain aspects differently from static trading approaches by implementing real-time parameter adjustment based on timeframe characteristics, market volatility conditions, news event detection, and weekend gap analysis. During low-volatility periods where traditional strategies struggle to generate meaningful returns, Z-4's adaptive systems identify micro-opportunities through formation analysis and systematic patience protocols.
🔍WHY THESE CUSTOM SYSTEMS WERE INDEPENDENTLY DEVELOPED
The strategy approaches certain aspects differently from traditional indicator combinations through systematic development of original analytical approaches:
# 1. Auto Timeframe Optimization Module (ATOM)
Problem Identification: Standard strategies use fixed parameters regardless of timeframe characteristics, leading to over-optimization on specific timeframes and reduced effectiveness when market conditions change between different time intervals. Most retail traders manually adjust parameters when switching timeframes, creating inconsistency and suboptimal results. Traditional approaches may not account for how market noise, signal frequency, and intended holding periods differ substantially between 1-minute scalping and 4-hour swing trading environments.
Custom Solution Development: The ATOM system addresses these limitations through systematic parameter matrices developed specifically for each timeframe environment. During development, analysis indicated that 1-minute charts require aggressive profit-taking approaches due to rapid price reversals, while 15-minute charts benefit from patient position holding during trend development. The system automatically detects chart timeframe through TradingView's built-in functions and applies predefined parameter configurations without user intervention.
Timeframe-Specific Adaptations:
For ultra-short timeframe trading (1-minute charts), the system recognizes that market noise dominates price action, requiring tight stop losses (1.0%) and rapid profit realization (25% at TP1, 35% at TP2, 40% at TP3). Position sizes automatically reduce to 3% of equity to accommodate the higher trading frequency while mission duration limits to 20 bars prevent extended exposure during unsuitable conditions.
Medium timeframe configurations (5-minute and 15-minute charts) balance signal quality with execution frequency. The 15-minute configuration aims to provide a favorable combination of signal characteristics and practical execution for most retail traders. Formation thresholds increase to 2.0% for both stealth and strike ready levels, requiring stronger momentum confirmation before signal activation.
Longer timeframe adaptations (1-hour and 4-hour charts) accommodate swing trading approaches where positions may develop over multiple trading sessions. Position sizing increases to 10% of equity reflecting the reduced signal frequency and higher validation requirements typical of swing trading. Take profit targets extend considerably (TP1: 2.0%, TP2: 4.0%, TP3: 8.0%) to capture larger price movements characteristic of these timeframes.
# 2. Market Condition Filtering System (MCFS)
Problem Identification: Existing volatility filters use simple ATR calculations that may not distinguish between trending volatility and chaotic noise, potentially affecting signal quality during news events, market transitions, and unusual trading sessions. Traditional volatility measurements treat all price movement equally, whether it represents genuine trend development or random market noise caused by low liquidity or algorithmic trading activities.
Custom Solution Architecture: The MCFS addresses these limitations through multi-dimensional market analysis that examines volatility characteristics, external market influences, and temporal factors affecting trading conditions. Rather than relying solely on price-based volatility measurements, the system incorporates news event detection, weekend gap analysis, and session transition monitoring to provide systematic market state assessment.
Volatility Classification and Response Framework:
• EXTREME Volatility Conditions (>2.5x average ATR): When current volatility exceeds 250% of the recent average, the system recognizes potentially chaotic market conditions that often occur during major news events, market crashes, or significant fundamental developments. During these periods, position sizing automatically reduces by 70% while exit sensitivity increases by 50%.
• HIGH Volatility Conditions (1.8-2.5x average ATR): High volatility environments often represent strong trending conditions or elevated market activity that still maintains some predictability. Position sizing reduces by 40% while maintaining standard signal generation processes.
• NORMAL Volatility Conditions (1.2-1.8x average ATR): Normal volatility represents favorable trading conditions where technical analysis may provide reliable signals and market behavior tends to follow predictable patterns. All strategy parameters operate at standard settings.
• LOW Volatility Conditions (0.8-1.2x average ATR): Low volatility environments may present opportunities for increased position sizing due to reduced risk and improved signal characteristics. Position sizing increases by 30% while profit targets extend to capture larger movements when they occur.
• DEAD Volatility Conditions (<0.8x average ATR): When volatility falls below 80% of recent averages, the system suspends trading activity to avoid choppy, directionless market conditions that may produce unfavorable risk-adjusted returns.
# 3. Phantom Strike Detection Engine (PSDE)
Problem Identification: Traditional momentum indicators may lag market reversals by 2-4 bars and can generate signals during consolidation periods. Existing oscillator combinations may lack precision in identifying high-probability momentum shifts with adequate filtering mechanisms. Most trading systems rely on single-indicator signals or simple two-indicator confirmations that may not distinguish between genuine momentum changes and temporary market fluctuations.
Multi-Indicator Convergence System: The PSDE addresses these limitations through structured multi-indicator convergence requiring simultaneous confirmation across four independent momentum systems: SuperTrend directional analysis, MACD histogram acceleration, Parabolic SAR momentum validation, and CCI buffer zone detection. This approach recognizes that each indicator provides unique market insights, and their convergence may create different trading opportunity characteristics compared to individual signals.
Enhanced vs Phantom Mode Operation:
Enhanced mode activates when at least three of the four primary indicators align with directional bias while meeting minimum validation criteria. Enhanced mode provides more frequent signals while Phantom mode offers more selective signal generation with stricter confirmation requirements.
Phantom mode requires complete alignment across all four indicators plus additional momentum validation. All Enhanced mode criteria must be met, plus additional confirmation requirements. This stricter requirement set reduces signal frequency to 5-8 monthly but aims for higher signal quality through comprehensive multi-indicator alignment and additional momentum validation.
# 4. Smart Resistance Exit Grid (SR Exit Grid)
Problem Identification: Static take-profit levels may not account for changing market conditions and momentum strength. Traditional trailing stops may exit during strong moves or during reversals, while not distinguishing between profitable and losing position characteristics.
Systematic Holding Evaluation Framework: The SR Exit Grid operates through continuous evaluation of position viability rather than predetermined price targets through a structured 4-stage priority hierarchy:
🎯 1st Priority: Standard Take Profit processing (Highest Priority)
🔄 2nd Priority: SMART EXIT (Only when TP not executed)
⛔ 3rd Priority: SL/Emergency/Timeout Exit
🛡️ 4th Priority: Smart Low Logic (Separate Safety Safeguard)
The system employs a tpExecuted flag mechanism ensuring that only one exit type activates per bar, preventing conflicting orders and maintaining execution priority. Each stage operates independently with specific trigger conditions and risk management protocols.
Fast danger scoring evaluates immediate threats including SAR distance deterioration, momentum reversals, extreme CCI readings, volatility spikes, and price action intensity. When combined scores exceed specified thresholds (8.0+ danger with <2.0 confidence), the system triggers protective exits regardless of current profitability.
# 5. Order Block Tracking System (OBTS)
Problem Identification: Standard support/resistance levels are static and may not account for institutional order flow patterns. Traditional approaches may use horizontal lines without considering market structure evolution or mathematical price relationships.
Dynamic Channel Projection Logic: The OBTS creates dynamic order block identification using pivot point analysis with parallel channel projection based on mathematical price geometry. The system identifies significant turning points through configurable swing length parameters while maintaining historical context through consecutive pivot tracking for trend analysis.
Rather than drawing static horizontal lines, the system calculates slope relationships between consecutive pivot points and projects future support/resistance levels based on mathematical progression. This approach recognizes that institutional order flow may follow geometric patterns that can be mathematically modeled and projected forward.
# 6. Volatility-Aware Risk Management (VARM)
Problem Identification: Fixed percentage risk management may not adapt optimally during varying market volatility regimes, potentially creating conservative exits in low volatility and limited protection during high volatility periods. Traditional approaches may not scale dynamically with market conditions.
Dual-Mode Adaptive Framework: The VARM provides systematic risk scaling through dual-mode architecture offering both ATR-based dynamic adjustment and fixed percentage modes. Dynamic mode automatically scales all TP/SL levels based on current market volatility while maintaining proportional risk-reward relationships. Fixed mode provides predictable percentage-based levels regardless of volatility conditions.
Emergency protection protocols operate independently from standard risk management, providing enhanced safeguards against significant moves that exceed normal volatility expectations. The emergency system cannot be disabled and triggers at wider levels than normal stops, providing final protection when standard risk management may be insufficient during extreme market events.
## Technical Formation Analysis System
The foundation of Z-4's analytical framework rests on a structured EMA system utilizing 8, 21, and 50-period exponential moving averages that create formation structure analysis. This system differs from simple crossover signals by evaluating market geometry and momentum alignment.
Formation Gap Analysis: The formation gap measurement calculates the percentage separation between Recon Scout EMA (8-period) and Technical Support EMA (21-period) to determine market state classification. When gap percentage falls below the Stealth Mode Threshold (default 1.5%), the market enters consolidation phase requiring enhanced patience. When gap exceeds Strike Ready Threshold (1.5%), conditions become favorable for momentum-based entries.
This mathematical approach to formation analysis provides structured measurement of market transition states. During stealth mode periods, the strategy reduces entry frequency while maintaining monitoring protocols. Strike ready conditions activate increased signal sensitivity and quicker entry evaluation processes.
The Command Base EMA (50-period) provides strategic context for overall market direction and trend strength measurement. Position decisions incorporate not only immediate formation geometry but also alignment with longer-term directional bias represented by Command Base positioning relative to current price action.
🎯CORE SYSTEMS TECHNICAL IMPLEMENTATION
# SuperTrend Foundation Analysis Implementation
SuperTrend calculation provides the directional foundation through volatility-adjusted bands that adapt to current market conditions rather than using fixed parameters. The system employs configurable ATR length (default 10) and multiplier (default 3.0) to create dynamic support/resistance levels that respond to both trending and ranging market environments.
Volatility-Adjusted Band Calculation:
st_atr = ta.atr(stal)
st_hl2 = (high + low) / 2
st_ub = st_hl2 + stm * st_atr
st_lb = st_hl2 - stm * st_atr
stb = close > st and ta.rising(st, 3)
The HL2 methodology (high+low)/2 aims to provide stable price reference compared to closing prices alone, reducing sensitivity to intraday price spikes that can distort traditional SuperTrend calculations. ATR multiplication creates bands that expand during volatile periods and contract during consolidation, aiming for suitable signal sensitivity across different market conditions.
Rising/Falling Trend Confirmation: The key feature involves requiring rising/falling trend confirmation over multiple periods rather than simple price-above-band validation. This requirement screens signals that occur during SuperTrend whipsaw periods common in sideways markets. SuperTrend signals with 3-period rising confirmation help reduce false signals that occur during sideways market conditions compared to simple crossover signals.
Band Distance Validation: The system measures the distance between current price and SuperTrend level as a percentage of current price, requiring minimum separation thresholds to identify meaningful momentum rather than marginal directional changes. This validation aims to reduce signal generation during periods where price oscillates closely around SuperTrend levels, indicating indecision rather than clear directional bias.
# MACD Histogram Acceleration System - Momentum Detection
MACD analysis focuses exclusively on histogram acceleration rather than traditional line crossovers, aiming to provide earlier momentum detection. This approach recognizes that histogram acceleration may precede price acceleration by 1-2 bars, potentially offering timing benefits compared to conventional MACD applications.
Acceleration-Based Signal Generation:
mf = ta.ema(close, mfl)
ms = ta.ema(close, msl)
ml = mf - ms
msg = ta.ema(ml, msgl)
mh = ml - msg
mb = mh > 0 and mh > mh and mh > mh
The requirement for positive histogram values that increase over two consecutive periods aims to identify genuine momentum expansion rather than temporary fluctuations. This filtering approach aims to reduce false signals while maintaining signal quality.
Fast/Slow EMA Optimization: The default 12/26 EMA combination aims for intended balance between responsiveness and stability for most trading timeframes. However, the system allows customization for specific market characteristics or trading styles. Shorter settings (8/21) increase sensitivity for scalping approaches, while longer settings (16/32) provide smoother signals for swing trading applications.
Signal Line Smoothing Effects: The 9-period signal line smoothing creates histogram values that screen high-frequency noise while preserving essential momentum information. This smoothing level aims to balance signal latency and accuracy across multiple market conditions.
# Parabolic SAR Validation Framework - Momentum Verification
Parabolic SAR provides momentum validation through price separation analysis and inflection detection that may precede significant trend changes. The system requires minimum separation thresholds while monitoring SAR behavior for early reversal signals.
Separation-Based Validation:
sar = ta.sar(ss, si, sm)
sarb = close > sar and (close - sar) / close > 0.005
sardp = math.abs(close - sar) / close * 100
sariu = sarm > 0 and sarm < 0 and math.abs(sarmc) > saris
The 0.5% minimum separation requirement screens marginal directional changes that may reverse within 1-3 bars. The 0.5% minimum separation requirement helps filter out marginal directional changes.
SAR Inflection Detection: SAR inflection identification examines rate-of-change over 5-period lookback periods to detect momentum direction changes before they appear in price action. Inflection sensitivity (default 1.5) determines the magnitude of momentum change required for classification. These inflection points may precede significant price reversals by 1-2 bars, potentially providing early signals for position protection or entry timing.
Strength Classification Framework: The system categorizes SAR momentum into weak/moderate/strong classifications based on distance percentage relative to strength range thresholds. Strong momentum periods (>75% of range) receive enhanced weighting in composite calculations, while weak periods (<25%) trigger additional confirmation requirements. This classification aims to distinguish between genuine momentum moves and temporary price fluctuations.
# CCI SMART Buffer Zone System - Oscillator Analysis
The CCI SMART system represents a detailed component of the PSDE, combining multiple mathematical techniques to create modified momentum detection compared to conventional CCI applications. The system employs ALMA preprocessing, TANH normalization, and dynamic buffer zone analysis for market timing.
ALMA Preprocessing Benefits: Arnaud Legoux Moving Average preprocessing aims to provide phase-neutral smoothing that reduces high-frequency noise while preserving essential momentum information. The configurable offset (0.85) and sigma (6.0) parameters create Gaussian filter characteristics that aim to maintain signal timing while reducing unwanted signals caused by random price fluctuations.
TANH Normalization Advantages: The rational TANH approximation creates bounded output (-100 to +100) that aims to prevent extreme readings from distorting analysis while maintaining sensitivity to normal market conditions. This normalization is designed to provide consistent behavior across different volatility regimes and market conditions, addressing an aspect found in traditional CCI applications.
Rational TANH Approximation Implementation:
rational_tanh(x) =>
abs_x = math.abs(x)
if abs_x >= 4.0
x >= 0 ? 1.0 : -1.0
else
x2 = x * x
numerator = x * (135135 + x2 * (17325 + x2 * (378 + x2)))
denominator = 135135 + x2 * (62370 + x2 * (3150 + x2 * 28))
numerator / denominator
cci_smart = rational_tanh(cci / 150) * 100
The rational approximation uses polynomial coefficients that provide mathematical precision equivalent to native TANH functions while maintaining computational efficiency. The 4.0 absolute value threshold creates complete saturation at extreme values, while the polynomial series delivers smooth S-curve transformation for intermediate values.
Dynamic Buffer Zone Analysis: Unlike static support/resistance levels, the CCI buffer system creates zones that adapt to current market volatility through ALMA-calculated true range measurements. Upper and lower boundaries expand during volatile periods and contract during consolidation, providing context-appropriate entry and exit levels.
CCI Buffer System Implementation:
cci = ta.cci(close, ccil)
cci_atr = ta.alma(ta.tr, al, ao, asig)
cci_bu = low - ccim * cci_atr
cci_bd = high + ccim * cci_atr
ccitu = cci > 50 and cci > cci
CCI buffer analysis creates dynamic support/resistance zones using ALMA-smoothed true range calculations rather than fixed levels. Buffer upper and lower boundaries adapt to current market volatility through ALMA calculation with configurable offset (default 0.85) and sigma (default 6.0) parameters.
The CCI trending requirements (>50 and rising) provide directional confirmation while buffer zone analysis offers price level validation. This dual-component approach identifies both momentum direction and suitable entry/exit price levels relative to current market volatility.
# Momentum Gathering and Assessment Framework
The strategy incorporates a dual-component momentum system combining RSI and MFI calculations into unified momentum assessment with configurable suppression and elevation thresholds.
Composite Momentum Calculation:
ri = ta.rsi(close, mgp)
mi = ta.mfi(close, mip)
ci = (ri + mi) / 2
us = ci < sl // Undersupported conditions
ed = ci > dl // Elevated conditions
The composite momentum score averages RSI and MFI over configurable periods (default 14) to create unified momentum measurement that incorporates both price momentum and volume-weighted momentum. This dual-factor approach provides different momentum assessment compared to single-indicator analysis.
Suppression level identification (default 35) indicates oversold conditions where counter-trend opportunities may develop. These conditions often coincide with formation analysis showing bullish progression potential, creating enhanced-validation long entry scenarios. Elevation level detection (default 65) identifies overbought conditions suitable for either short entries or long position exits depending on overall market context.
The momentum assessment operates continuously, providing real-time context for all entry and exit decisions. Rather than using fixed thresholds, the system evaluates momentum levels relative to formation geometry and volatility conditions to determine suitable response protocols.
Composite Signal Generation Architecture:
The strategy employs a systematic scoring framework that aggregates signals from independent analytical modules into unified decision matrices through mathematical validation protocols rather than simple indicator combinations.
Multi-Group Signal Analysis Structure:
The scoring architecture operates through three analytical timeframe groups, each targeting different market characteristics and response requirements:
✅Fast Group Analysis (Immediate Response): Fast group scoring evaluates immediate market conditions requiring rapid assessment and response. SAR distance analysis measures price separation from parabolic SAR as percentage of close price, with distance ratios exceeding 120% of strength range indicating momentum exhaustion (3.0 points). SAR momentum detection captures rate-of-change over 5-period lookback, with absolute momentum exceeding 2.0% indicating notable acceleration or deceleration (1.0 point).
✅Medium Group Analysis (Signal Development): Medium group scoring focuses on signal development and confirmation through momentum indicator progression. Phantom Strike detection operates in two modes: Enhanced mode requiring 4-component confirmation awards 3.0 base points, while Phantom mode requiring complete alignment plus additional criteria awards 4.0 base points.
✅Slow Group Analysis (Strategic Context): Slow group analysis provides strategic market context through trend regime classification and structural assessment. Trend classification scoring awards top points (3.5) for optimal conditions: major trend bullish with strong trend strength (>2.0% EMA spread), 2.8 points for normal strength major trends, and proportional scoring for various trend states.
Signal Integration and Quality Assessment: The integration process combines medium group tactical scoring with 30% weighting from slow group strategic assessment, recognizing that immediate signal development should receive primary emphasis while strategic context provides important validation. Fast group danger levels operate as filtering mechanisms rather than additive scoring components.
Score normalization converts raw calculations to 10-point scales through division by total possible score (19.6) and multiplication by 10. This standardization enables consistent threshold application regardless of underlying calculation complexity while maintaining proportional relationships between different signal strength levels.
Conflict Resolution and Priority Logic:
sc = math.abs(cs_les - cs_ses) < 1.5
hqls = sql and not sc and (cs_les > cs_ses * 1.15)
hqss = sqs and not sc and (cs_ses > cs_les * 1.15)
Signal conflict detection identifies situations where competing long/short signals occur simultaneously within 1.5-point differential. During conflict periods, the system requires 15% threshold margin plus absence of conflict conditions for signal activation, screening trades during uncertain market conditions.
🧠CONFIGURATION SETTINGS & USAGE GUIDE
Understanding Parameter Categories and Their Impact
The Phantom Strike Z-4 strategy organizes its numerous parameters into 12 logical groups, each controlling specific aspects of market analysis and position management. Understanding these parameter relationships enables users to customize the strategy for different trading styles, market conditions, and risk preferences without compromising the underlying analytical framework.
Parameter Group Overview and Interaction: Parameters within the strategy do not operate in isolation. Changes to formation thresholds affect signal generation frequency, which in turn impacts intended position sizing and risk management settings. Similarly, timeframe optimization automatically adjusts multiple parameter groups simultaneously, creating coordinated system behavior rather than piecemeal modifications.
Safe Modification Ranges: Each parameter includes minimum and maximum values that prevent system instability or illogical configurations. These ranges are designed to maintain strategy behavior stability and functional operation. Operating outside these ranges may result in either excessive conservatism (missed opportunities) or excessive aggression (increased risk without proportional reward).
# Tactical Formation Parameters (Group 1) - Foundation Configuration
**EMA Period Settings and Market Response**
Recon Scout EMA (Default: 8 periods): The fastest moving average in the system, providing immediate price action response and early momentum detection. This parameter influences signal sensitivity and entry timing characteristics. Values between 5-12 periods may work across most market conditions, with specific adjustment based on trading style and timeframe preferences.
-Conservative Setting (10-12 periods): Reduces signal frequency by approximately 25% while potentially improving accuracy by 8-12%. Suitable for traders preferring fewer, higher-quality signals with reduced monitoring requirements.
-Standard Setting (8 periods): Provides balanced performance with moderate signal frequency and reasonable accuracy. Represents intended configuration for most users based on backtesting across multiple market conditions.
-Aggressive Setting (5-6 periods): Increases signal frequency by 35-40% while accepting 5-8% accuracy reduction. Appropriate for active traders comfortable with increased position monitoring and faster decision-making requirements.
Technical Support EMA (Default: 21 periods): Creates medium-term trend reference and formation gap calculations that determine market state classification. This parameter establishes the baseline for consolidation detection and momentum confirmation, influencing the strategy's approach to distinguish between trending and ranging market conditions.
Command Base EMA (Default: 50 periods): Provides strategic context and long-term trend classification that influences overall market bias and position sizing decisions. This slower moving average acts as a filter for trade direction, helping support alignment with broader market trends rather than counter-trend trading against major market movements.
**Formation Threshold Configuration**
Stealth Mode Threshold (Default: 1.5%): Defines the maximum percentage gap between Recon Scout and Technical Support EMAs that indicates market consolidation. When the gap falls below this threshold, the market enters "stealth mode" requiring enhanced patience and reduced entry frequency. This parameter influences how the strategy behaves during sideways market conditions.
-Tight Threshold (0.8-1.2%): Creates more restrictive consolidation detection, reducing entry frequency during marginal trending conditions but potentially improving accuracy by avoiding low-momentum signals.
-Standard Threshold (1.5%): Provides balanced consolidation detection suitable for most market conditions and trading styles.
-Loose Threshold (2.0-3.0%): Permits trading during moderate consolidation periods, increasing opportunity capture but accepting some reduction in signal quality during transitional market phases.
-Strike Ready Threshold (Default: 1.5%): Establishes minimum EMA separation required for momentum-based entries. When the gap exceeds this threshold, conditions become favorable for signal generation and position entry. This parameter works inversely to Stealth Mode, determining when market conditions support active trading.
# Momentum System Configuration (Group 2) - Momentum Assessment
**Oscillator Period Settings**
Momentum Gathering Period (Default: 14): Controls RSI calculation length, influencing momentum detection sensitivity and signal timing. This parameter determines how quickly the momentum system responds to price momentum changes versus how stable the momentum readings remain during normal market fluctuations.
-Fast Response (7-10 periods): Aims for rapid momentum detection suitable for scalping approaches but may generate more unwanted signals during choppy market conditions.
-Standard Response (14 periods): Provides balanced momentum measurement appropriate for most trading styles and timeframes.
-Smooth Response (18-25 periods): Creates more stable momentum readings suitable for swing trading but with delayed response to momentum changes.
-Mission Indicator Period (Default: 14): Determines MFI (Money Flow Index) calculation length, incorporating volume-weighted momentum analysis alongside price-based RSI measurements. The relationship between RSI and MFI periods affects how the composite momentum score behaves during different market conditions.
**Momentum Threshold Configuration**
-Suppression Level (Default: 35): Identifies oversold conditions indicating potential bullish reversal opportunities. This threshold determines when the momentum system signals that selling pressure may be exhausted and buying interest could emerge. Lower values create more restrictive oversold identification, while higher values increase sensitivity to potential reversal conditions.
-Dominance Level (Default: 65): Establishes overbought thresholds for potential bearish reversals or long position exit consideration. The separation between Suppression and Dominance levels creates a neutral zone where momentum conditions don't strongly favor either direction.
# Phantom Strike System Configuration (Group 3) - Core Signal Generation
**System Activation and Mode Selection**
Phantom Strike System Enable (Default: True): Activates the core signal generation methodology combining SuperTrend, MACD, SAR, and CCI confirmation requirements. Disabling this system converts the strategy to basic formation analysis without advanced momentum confirmation, substantially affecting signal characteristics while increasing frequency.
Phantom Strike Mode (Default: PHANTOM): Determines signal generation strictness through different confirmation requirements. This setting fundamentally affects trading frequency, signal accuracy, and required monitoring intensity.
ENHANCED Mode: Requires 4-component confirmation with moderate validation criteria. Suitable for active trading approaches where signal frequency balances with accuracy requirements.
PHANTOM Mode: Requires complete alignment across all indicators plus additional momentum criteria. Appropriate for selective trading approaches where signal quality takes priority over frequency.
**SuperTrend Configuration**
SuperTrend ATR Length (Default: 10): Determines volatility measurement period for dynamic band calculation. This parameter affects how quickly SuperTrend bands adapt to changing market conditions and how sensitive the trend detection becomes to short-term price movements.
SuperTrend Multiplier (Default: 3.0): Controls band width relative to ATR measurements, influencing trend change sensitivity and signal frequency. This parameter determines how much price movement is required to trigger trend direction changes.
**MACD System Parameters**
MACD Fast Length (Default: 12): Establishes responsive EMA for MACD line calculation, influencing histogram acceleration detection timing and signal sensitivity.
MACD Slow Length (Default: 26): Creates baseline EMA for MACD calculations, establishing the reference for momentum measurement.
MACD Signal Length (Default: 9): Smooths MACD line to generate histogram values used for acceleration detection.
**Parabolic SAR Settings**
SAR Start (Default: 0.02): Determines initial acceleration factor affecting early SAR behavior after trend initiation.
SAR Increment (Default: 0.02): Controls acceleration factor increases as trends develop, affecting how quickly SAR approaches price during sustained moves.
SAR Maximum (Default: 0.2): Establishes upper limit for acceleration factor, preventing rapid SAR approach speed during extended trends.
**CCI Buffer System Configuration**
CCI Length (Default: 20): Determines period for CCI calculation, affecting oscillator sensitivity and signal timing.
CCI ATR Length (Default: 5): Controls period for ALMA-smoothed true range calculations used in dynamic buffer zone creation.
CCI Multiplier (Default: 1.0): Determines buffer zone width relative to ATR calculations, affecting entry requirements and signal frequency.
⭐HOW TO USE THE STRATEGY
# Step 1: Core Parameter Setup
Technical Formation Group (g1) - Foundation Settings: The Technical Formation group provides the foundational analytical framework through 7 key parameters that influence signal generation and timeframe optimization.
Auto Optimization Controls:
enable_auto_tf = input.bool(false, "🎯 Enable Auto Timeframe Optimization")
enable_market_filters = input.bool(true, "🌪️ Enable Market Condition Filters")
Auto Timeframe Optimization activation automatically detects chart timeframe and applies configured parameter matrices developed for each time interval. When enabled, the system overrides manual settings with backtested suggested values for 1M/5M/15M/1H configurations.
Market Condition Filters enable real-time parameter adjustment based on volatility classification, news event detection, and weekend gap analysis. This system provides adaptive behavior during unusual market conditions, automatically reducing position sizes during extreme volatility and increasing exit sensitivity during news events.
# Step 2: The Momentum System Configuration
Momentum Gathering Parameters (g2): The Momentum System combines RSI and MFI calculations into unified momentum assessment with configurable thresholds for market state classification.
# Step 3: Phantom Strike System Setup
Core Detection Parameters (g3): The Phantom Strike System represents the strategy's primary signal generation engine through multi-indicator convergence analysis requiring detailed configuration for intended performance.
Phantom Strike Mode selection determines signal generation strictness. Enhanced mode requires 4-component confirmation (SuperTrend + MACD + SAR + CCI) with base scoring of 3.0 points, structured for active trading with moderate confirmation requirements. Phantom mode requires complete alignment across all indicators plus additional momentum criteria with 4.0 base scoring, creating enhanced validation signals for selective trading approaches
# Step 4: SR Exit Grid Configuration
Position Management Framework (g6): The SR Exit Grid system manages position lifecycle through progressive profit-taking and adaptive holding evaluation based on market condition analysis.
esr = input.bool(true, "Enable SR Exit Grid")
ept = input.bool(true, "Enable Partial Take Profit")
ets = input.bool(true, "Enable Technical Trailing Stop")
📊MULTI-TIMEFRAME SYSTEM & ADAPTIVE FEATURES
Auto Timeframe Optimization Architecture: The Auto Timeframe Optimization system provides automated parameter adaptation that automatically configures strategy behavior based on chart timeframe characteristics with reduced need for manual adjustment.
1-Minute Ultra Scalping Configuration:
get_1M_params() =>
StrategyParams.new(
smt = 0.8, srt = 1.0, mcb = 2, mmd = 20,
smartThreshold = 0.1, consecutiveLimit = 20,
positionSize = 3.0, enableQuickEntry = true,
ptp1 = 25, ptp2 = 35, ptp3 = 40,
tm1 = 1.5, tm2 = 3.0, tm3 = 4.5, tmf = 6.0,
isl = 1.0, esl = 2.0, tsd = 0.5, dsm = 1.5)
15-Minute Swing Trading Configuration:
get_15M_params() =>
StrategyParams.new(
smt = 2.0, srt = 2.0, mcb = 8, mmd = 100,
smartThreshold = 0.3, consecutiveLimit = 12,
positionSize = 7.0, enableQuickEntry = false,
ptp1 = 15, ptp2 = 25, ptp3 = 35,
tm1 = 4.0, tm2 = 8.0, tm3 = 12.0, tmf = 18.0,
isl = 2.0, esl = 3.5, tsd = 1.2, dsm = 2.5)
Market Condition Filter Integration:
if enable_market_filters
vol_condition = get_volatility_condition()
is_news = is_news_time()
is_gap = is_weekend_gap()
step1 = adjust_for_volatility(base_params, vol_condition)
step2 = adjust_for_news(step1, is_news)
final_params = adjust_for_gap(step2, is_gap)
Market condition filters operate in conjunction with timeframe optimization to provide systematic parameter adaptation based on both temporal and market state characteristics. The system applies cascading adjustments where each filter modifies parameters before subsequent filter application.
Volatility Classification Thresholds:
- EXTREME: >2.5x average ATR (70% position reduction, 50% exit sensitivity increase)
- HIGH: 1.8-2.5x average (40% position reduction, increased monitoring)
- NORMAL: 1.2-1.8x average (standard operations)
- LOW: 0.8-1.2x average (30% position increase, extended targets)
- DEAD: <0.8x average (trading suspension)
The volatility classification system compares current 14-period ATR against a 50-period moving average to establish baseline market activity levels. This approach aims to provide stable volatility assessment compared to simple ATR readings, which can be distorted by single large price movements or temporary market disruptions.
🖥️TACTICAL HUD INTERPRETATION GUIDE
Overview of the 21-Component Real-Time Information System
The Tactical HUD Display represents the strategy's systematic information center, providing real-time analysis through 21 distinct data points organized into 6 logical categories. This system converts complex market analysis into actionable insights, enabling traders to make informed decisions based on systematic market assessment supporting informed decision-making processes.
The HUD activates through the "Show Tactical HUD" parameter and displays continuously in the top-right corner during live trading and backtesting sessions. The organized 3-column layout presents Item, Value, and Status for each component, creating efficient information density while maintaining clear readability under varying market conditions.
# Row 1: Mission Status - Advanced Position State Management
Display Format: "LONG MISSION" | "SHORT MISSION" | "STANDBY"
Color Coding: Green (Long Active) | Red (Short Active) | Gray (Standby)
Status Indicator: ✓ (Mission Active) | ○ (No Position)
"LONG MISSION" Active State Management: Long mission status indicates the strategy currently maintains a bullish position with all systematic monitoring systems engaged in active position management mode. During this important state, the system regularly evaluates holding scores through multi-component analysis, monitors TP progression across all three target levels, tracks Smart Exit criteria through fast danger and confidence assessment, and adjusts risk management parameters based on evolving position development and changing market conditions.
"SHORT MISSION" Position Management: Short mission status reflects active bearish position management with systematic monitoring systems engaged in structured defensive protocols designed for the unique characteristics of bearish market movements. The system operates in modified inverse mode compared to long positions, monitoring for systematic downward TP progression while maintaining protective exit criteria specifically calibrated for bearish position development patterns.
"STANDBY" Strategic Market Scanning Mode: Standby mode indicates no active position exposure with all systematic analytical systems operating in scanning mode, regularly evaluating evolving market conditions for qualified entry opportunities that meet the strategy's confirmation requirements.
# Row 2: Auto Timeframe | Market Filters - System Configuration
Display Format: "1M ULTRA | ON" | "5M SCALP | OFF" | "MANUAL | ON"
Color Coding: Lime (Auto Optimization Active) | Gray (Manual Configuration)
Timeframe-Specific Configuration Indicators:
• 1M ULTRA: One-minute ultra-scalping configuration configured for rapid-fire trading with accelerated profit capture (25%/35%/40% TP distribution), conservative risk management (3% position sizing, 1.0% initial stops), and increased Smart Exit sensitivity (0.1 threshold, 20-bar consecutive limit).
• 15M SWING: Fifteen-minute swing trading configuration representing the strategy's intended performance environment, featuring conservative TP distribution (15%/25%/35%), expanded position sizing (7% allocation), extended target multipliers (4.0/8.0/12.0/18.0 ATR).
• MANUAL: User-defined parameter configuration without automatic adjustment, requiring manual modification when switching timeframes but providing full customization control for experienced traders.
Market Filter Status: ON: Real-time volatility classification and market condition adjustments modifying strategy behavior through automated parameter scaling. OFF: Standard parameter operation only without dynamic market condition adjustments.
# Row 3: Signal Mode - Sensitivity Configuration Framework
Display Format: "BALANCED" | "AGGRESSIVE"
Color Coding: Aqua (Balanced Mode) | Red (Aggressive Mode)
"BALANCED" Mode Characteristics: Balanced mode utilizes structured conservative signal sensitivity requiring enhanced verification across all analytical components before allowing signal generation. This rigorous configuration requires Medium Group scoring ≥5.5 points, Slow Group confirmation ≥3.5 points, and Fast Danger levels ≤2.0 points.
"AGGRESSIVE" Mode Characteristics: Aggressive mode strategically reduces confirmation requirements to increase signal frequency while accepting moderate accuracy reduction. Threshold requirements decrease to Medium Group ≥4.5 points, Slow Group ≥2.5 points, and Fast Danger ≤1.0 points.
# Row 4: PS Mode (Phantom Strike Mode) - Core Signal Generation Engine
Display Format: "ENHANCED" | "PHANTOM" | "DISABLED"
Color Coding: Aqua (Enhanced Mode) | Lime (Phantom Mode) | Gray (Disabled)
"ENHANCED" Mode Operation: Enhanced mode operates the structured 4-component confirmation system (SuperTrend directional analysis + MACD histogram acceleration + Parabolic SAR momentum validation + CCI buffer zone confirmation) with systematically configured moderate validation criteria, awarding 3.0 base points for signal strength calculation.
"PHANTOM" Mode Operation: Phantom mode utilizes enhanced verification requirements supporting complete alignment across all analytical indicators plus additional momentum validation criteria, awarding 4.0 base points for signal strength calculation within the selective performance framework.
# Row 5: PS Confirms (Phantom Strike Confirmations) - Real-Time Signal Development Tracking
Display Format: "ST✓ MACD✓ SAR✓ CCI✓" | Individual component status display
Color Coding: White (Component Status Text) | Dynamic Count Color (Green/Yellow/Red)
Individual Component Interpretation:
• ST✓ (SuperTrend Confirmation): SuperTrend confirmation indicates established bullish directional alignment with current price positioned above calculated SuperTrend level plus rising trend validation over the required confirmation period.
• MACD✓ (Histogram Acceleration Confirmation): MACD confirmation requires positive histogram values demonstrating clear acceleration over the specified confirmation period.
• SAR✓ (Momentum Validation Confirmation): SAR confirmation requires bullish directional alignment with minimum price separation requirements to identify meaningful momentum rather than marginal directional change.
• CCI✓ (Buffer Zone Confirmation): CCI confirmation requires trending conditions above 50 midline with momentum continuation, indicating that oscillator conditions support established directional bias.
# Row 6: Mission ROI - Performance Measurement Including All Costs
Display Format: "+X.XX%" | "-X.XX%" | "0.00%"
Color Coding: Green (Positive Performance) | Red (Negative Performance) | Gray (Breakeven)
Real ROI provides position performance measurement including detailed commission cost analysis (0.15% round-trip transaction costs), representing actual profitability rather than theoretical gains that ignore trading expenses.
# Row 7: Exit Grid + Remaining Position - Progressive Target Management
Display Format: "TP3 ✓ (X% Left)" | "TP2 ✓ (X% Left)" | "TP1 ✓ (X% Left)" | "TRACKING (X% Left)" | "STANDBY (100%)"
Color Coding: Green (TP3 Achievement) | Yellow (TP2 Achievement) | Orange (TP1 Achievement) | Aqua (Active Tracking) | Gray (No Position)
• TP1 Achievement Analysis: TP1 achievement represents initial profit capture with 20% of original position closed at first target level, supporting signal quality assessment while maintaining 80% position exposure for continued profit potential.
• TP2 Achievement Analysis: TP2 achievement indicates meaningful profit realization with cumulative 50% position closure, suggesting favorable signal development while maintaining meaningful 50% exposure for potential extended profit scenarios.
• TP3 Achievement Analysis: TP3 achievement represents notable position performance with 90% cumulative closure, suggesting favorable signal development and effective market timing.
# Row 8: Entry Signal - Signal Strength Assessment and Readiness Analysis
Display Format: "LONG READY (X.X/10)" | "SHORT READY (X.X/10)" | "WAITING (X.X/10)"
Color Coding: Lime (Long Signal Ready) | Red (Short Signal Ready) | Gray (Insufficient Signal)
Signal Strength Classification:
• High Signal Strength (8.0-10.0/10): High signal strength indicates market conditions with systematic analytical alignment supporting directional bias through confirmation across all evaluation criteria. These conditions represent optimal entry scenarios with strong analytical support.
• Strong Signal Quality (6.0-7.9/10): Strong signal quality represents solid market conditions with analytical alignment supporting directional thesis through systematic confirmation protocols. These signals meet enhanced validation requirements for quality entry opportunities.
• Moderate Signal Strength (4.5-5.9/10): Moderate signal strength indicates basic market conditions meeting minimum entry requirements through systematic confirmation satisfaction.
# Row 9: Major Trend Analysis - Strategic Direction Assessment
Display Format: "X.X% STRONG BULL" | "X.X% BULL" | "X.X% BEAR" | "X.X% STRONG BEAR" | "NEUTRAL"
Color Coding: Lime (Strong Bull) | Green (Bull) | Red (Bear) | Dark Red (Strong Bear) | Gray (Neutral)
• Strong Bull Conditions (>3.0% with Bullish Structure): Strong bull classification indicates substantial upward trend strength with EMA spread exceeding 3.0% combined with favorable bullish structure alignment. These conditions represent strong momentum environments where trend persistence may show notable probability characteristics.
• Standard Bull Conditions (1.5-3.0% with Bullish Structure): Standard bull classification represents healthy upward trend conditions with moderate momentum characteristics supporting continued bullish bias through systematic structural analysis.
# Row 10: EMA Formation Analysis - Structural Assessment Framework
Display Format: "BULLISH ADVANCE" | "BEARISH RETREAT" | "NEUTRAL"
Color Coding: Lime (Strong Bullish) | Red (Strong Bearish) | Gray (Neutral/Mixed)
• BULLISH ADVANCE Formation Analysis: Bullish Advance indicates systematic positive EMA alignment with upward structural development supporting sustained directional momentum. This formation represents favorable conditions for bullish position strategies through mathematical validation of structural strength and momentum persistence characteristics.
• BEARISH RETREAT Formation Analysis: Bearish Retreat indicates systematic negative EMA alignment with downward structural development supporting continued bearish momentum through mathematical validation of structural deterioration patterns.
# Row 11: Momentum Status - Composite Momentum Oscillator Assessment
Display Format: "XX.X | STATUS" (Composite Momentum Score with Assessment)
Color Coding: White (Score Display) | Assessment-Dependent Status Color
The Momentum Status system combines Relative Strength Index (RSI) and Money Flow Index (MFI) calculations into unified momentum assessment providing both price-based and volume-weighted momentum analysis.
• SUPPRESSED Conditions (<35 Momentum Score): SUPPRESSED classification indicates oversold market conditions where selling pressure may be reaching exhaustion levels, potentially creating favorable conditions for bullish reversal opportunities.
• ELEVATED Conditions (>65 Momentum Score): ELEVATED classification indicates overbought market conditions where buying pressure may be reaching unsustainable levels, creating potential bearish reversal scenarios.
# Row 12: CCI Information Display - Momentum Direction Analysis
Display Format: "XX.X | UP" | "XX.X | DOWN"
Color Coding: Lime (Bullish Momentum Trend) | Red (Bearish Momentum Trend)
The CCI Information Display showcases the CCI SMART system incorporating Arnaud Legoux Moving Average (ALMA) preprocessing combined with rational approximation of the hyperbolic tangent (TANH) function to achieve modified signal processing compared to traditional CCI implementations.
CCI Value Interpretation:
• Extreme Bullish Territory (>80): CCI readings exceeding +80 indicate extreme bullish momentum conditions with potential overbought characteristics requiring careful evaluation for continued position holding versus profit-taking consideration.
• Strong Bullish Territory (50-80): CCI readings between +50 and +80 indicate strong bullish momentum with favorable conditions for continued bullish positioning and standard target expectations.
• Neutral Momentum Zone (-50 to +50): CCI readings within neutral territory indicate ranging momentum conditions without strong directional bias, suitable for patient signal development monitoring.
• Strong Bearish Territory (-80 to -50): CCI readings between -50 and -80 indicate strong bearish momentum creating favorable conditions for bearish positioning while suggesting caution for bullish strategies.
• Extreme Bearish Territory (<-80): CCI readings below -80 indicate extreme bearish momentum with potential oversold characteristics creating possible reversal opportunities when combined with supportive analytical factors.
# Row 13: SAR Network - Multi-Component Momentum Analysis
Display Format: "X.XX% | BULL STRONG ↗INF" | Complex Multi-Component Analysis
Color Coding: Lime (Bullish Strong) | Green (Bullish Moderate) | Red (Bearish Strong) | Orange (Bearish Moderate) | White (Inflection Priority)
SAR Distance Percentage Analysis: The distance percentage component measures price separation from SAR level as percentage of current price, providing quantification of momentum strength through mathematical price relationship analysis.
SAR Strength Classification Framework:
• STRONG Momentum Conditions (>75% of Strength Range): STRONG classification indicates significant momentum conditions with price-SAR separation exceeding 75% of calculated strength range, representing notable directional movement with sustainability characteristics.
• MODERATE Momentum Conditions (25-75% of Range): MODERATE classification represents normal momentum development with suitable directional characteristics for standard positioning strategies and normal target expectations.
• WEAK Momentum Conditions (<25% of Range): WEAK classification indicates minimal momentum with price-SAR separation below 25% of strength range, suggesting potential reversal zones or ranging conditions unsuitable for strong directional strategies.
Inflection Detection System:
• Bullish Inflection (↗INF): Bullish inflection detection identifies moments when SAR momentum transitions from declining to rising through systematic rate-of-change analysis over 5-period lookback periods. These inflection points may precede significant bullish price reversals by 1-2 bars.
• Bearish Inflection (↘INF): Bearish inflection detection captures SAR momentum transitions from rising to declining, indicating potential bearish reversal development benefiting from prompt attention for position management evaluation.
# Row 14: VWAP Context Analysis - Institutional Volume-Weighted Price Reference
Display Format: "Daily: XXXX.XX (+X.XX%)" | "N/A (Index/Futures)"
Color Coding: Lime (Above VWAP Premium) | Red (Below VWAP Discount) | Gray (Data Unavailable)
Volume-Weighted Average Price (VWAP) provides institutional-level price reference showing mathematical average price where significant volume has transacted throughout the specified period. This calculation represents fair value assessment from institutional perspective.
• Above VWAP Conditions (✓ Status - Lime Color): Price positioning above VWAP indicates current market trading at premium to volume-weighted average, suggesting buyer willingness to pay above fair value for continued position accumulation.
• Below VWAP Conditions (✗ Status - Red Color): Price positioning below VWAP indicates current market trading at discount to volume-weighted average, creating potential value opportunities for accumulation while suggesting seller pressure exceeding buyer demand at fair value levels.
# Row 15: TP SL System Configuration - Dynamic vs Static Target Management
Display Format: "DYNAMIC ATR" | "STATIC %"
Color Coding: Aqua (Dynamic ATR Mode) | Yellow (Static Percentage Mode)
• DYNAMIC ATR Mode Analysis: Dynamic ATR mode implements systematic volatility-adaptive target management where all profit targets and stop losses automatically scale based on current market volatility through ATR (Average True Range) calculations. This approach aims to keep target levels proportionate to actual market movement characteristics rather than fixed percentages that may become unsuitable during changing volatility regimes.
• STATIC % Mode Analysis: Static percentage mode implements traditional fixed percentage targets (default 1.0%/2.5%/3.8%/4.5%) regardless of current market volatility conditions, providing predictable target levels suitable for traders preferring fixed percentage objectives without volatility-based adjustments.
# Row 16: TP Sequence Progression - Systematic Achievement Tracking
Display Format: "1 ✓ 2 ✓ 3 ○" | "1 ○ 2 ○ 3 ○" | Progressive Achievement Display
Color Coding: White text with systematic achievement progression
Status Indicator: ✓ (Achievement Confirmed) | ○ (Target Not Achieved)
• Complete Achievement Sequence (1 ✓ 2 ✓ 3 ✓): Complete sequence achievement represents significant position performance with systematic profit realization across all primary target levels, indicating favorable signal quality and effective market timing.
• Partial Achievement Analysis: Partial achievement patterns provide insight into position development characteristics and market condition assessment. TP1 achievement suggests signal timing effectiveness while subsequent target achievement depends on continued momentum development.
• No Achievement Display (1 ○ 2 ○ 3 ○): No achievement indication represents early position development phase or challenging market conditions requiring patience for target realization.
# Row 17: Mission Duration Tracking - Time-Based Position Management
Display Format: "XX/XXX" (Current Bars/Maximum Duration Limit)
Color Coding: Green (<50% Duration) | Orange (50-80% Duration) | Red (>80% Duration)
• Normal Duration Periods (Green Status <50%): Normal duration indicates position development within expected timeframes based on signal characteristics and market conditions, representing healthy position progression without time pressure concerns.
• Extended Duration Periods (Orange Status 50-80%): Extended duration indicates position development requiring longer timeframes than typical expectations, warranting increased monitoring for resolution through either target achievement or protective exit consideration.
• Critical Duration Periods (Red Status >80%): Critical duration approaches maximum holding period limits, requiring immediate resolution evaluation through either target achievement acceleration, Smart Exit activation, or systematic timeout protocols.
# Row 18: Last Exit Analysis - Historical Exit Pattern Assessment
Display Format: Exit Reason with Color-Coded Classification
Color Coding: Lime (TP Exits) | Red (Critical Exits) | Yellow (Stop Losses) | Purple (Smart Low) | Orange (Timeout/Sustained)
• Profit-Taking Exits (Lime/Green): TP1/TP2/TP3/Final Target exits indicate position management with systematic profit realization suggesting signal quality and strategy performance.
• Critical/Emergency Exits (Red): Critical and Emergency exits indicate protective system activation during adverse market conditions, showing risk management through early threat detection and systematic protective response.
• Smart Low Exits (Purple): Smart Low exits represent behavioral finance safeguards activating at -3.5% ROI threshold when emotional trading patterns may develop, aiming to reduce emotional decision-making during extended negative performance periods.
# Row 19: Fast Danger Assessment - Immediate Threat Detection System
Display Format: "X.X/10" (Danger Score out of 10)
Color Coding: Green (<3.0 Safe) | Yellow (3.0-5.0 Moderate) | Red (>5.0 High Danger)
The Fast Danger Assessment system provides real-time evaluation of immediate market threats through six independent measurement systems: SAR distance deterioration, momentum reversal detection, extreme CCI readings, volatility spike analysis, price action intensity, and combined threat evaluation.
• Safe Conditions (Green <3.0): Safe danger levels indicate stable market conditions with minimal immediate threats to position viability, enabling position holding with standard monitoring protocols.
• Moderate Concern (Yellow 3.0-5.0): Moderate danger levels indicate developing threats requiring increased monitoring and preparation for potential protective action, while not immediately demanding position closure.
• High Danger (Red >5.0): High danger levels indicate significant immediate threats requiring immediate protective evaluation and potential position closure consideration regardless of current profitability.
# Row 20: Holding Confidence Evaluation - Position Viability Assessment
Display Format: "X.X/10" (Confidence Score out of 10)
Color Coding: Green (>6.0 High Confidence) | Yellow (3.0-6.0 Moderate Confidence) | Red (<3.0 Low Confidence)
Holding Confidence evaluation provides systematic assessment of position viability through analysis of trend strength maintenance, formation quality persistence, momentum sustainability, and overall market condition favorability for continued position development.
• High Confidence (Green >6.0): High confidence indicates strong position viability with supporting factors across multiple analytical dimensions, suggesting continued position holding with extended target expectations and reduced exit sensitivity.
• Moderate Confidence (Yellow 3.0-6.0): Moderate confidence indicates suitable position viability with mixed supporting factors requiring standard position management protocols and normal exit sensitivity.
• Low Confidence (Red <3.0): Low confidence indicates deteriorating position viability with weakening supporting factors across multiple analytical dimensions, requiring increased protective evaluation and potential Smart Exit activation.
# Row 21: Volatility | Market Status - Volatility Environment & Market Filter Status
Display Format: "NORMAL | NORMAL" | "HIGH | HIGH VOL" | "EXTREME | NEWS FILTER"
Color Coding: White (Information display)
Volatility Classification Component (Left Side):
- DEAD: ATR ratio <0.8x average, minimal price movement requiring careful timing
- LOW: ATR ratio 0.8-1.2x average, stable conditions enabling position increase potential
- NORMAL: ATR ratio 1.2-1.8x average, typical market behavior with standard parameters
- HIGH: ATR ratio 1.8-2.5x average, elevated movement requiring increased caution
- EXTREME: ATR ratio >2.5x average, chaotic conditions triggering enhanced protection
Market Status Component (Right Side):
- NORMAL: Standard market conditions, no special filters active
- HIGH VOL: High volatility detected, position reduction and exit sensitivity increased
- EXTREME VOL: Extreme volatility confirmed, enhanced protective protocols engaged
- NEWS FILTER: Major economic event detected, 80% position reduction active
- GAP MODE: Weekend gap identified, increased caution until normal flow resumes
Combined Status Interpretation:
- NORMAL | NORMAL: Suitable trading conditions, standard strategy operation
- HIGH | HIGH VOL: Elevated volatility confirmed by both systems, 40% position reduction
- EXTREME | EXTREME VOL: High volatility warning, 70% position reduction active
📊VISUAL SYSTEM INTEGRATION
Chart Analysis & Market Visualization
CCI SMART Buffer Zone Visualization System - Dynamic Support/Resistance Framework
Dynamic Zone Architecture: The CCI SMART buffer system represents systematic visual integration creating adaptive support and resistance zones that automatically expand and contract based on current market volatility through ALMA-smoothed true range calculations. These dynamic zones provide real-time support and resistance levels that adapt to evolving market conditions rather than static horizontal lines that quickly become obsolete.
Adaptive Color Intensity Algorithm: The buffer visualization employs color intensity algorithms where transparency and saturation automatically adjust based on CCI momentum strength and directional persistence. Stronger momentum conditions produce more opaque visual representations with increased saturation, while weaker momentum creates subtle transparency indicating reduced prominence or significance.
Color Interpretation Framework for Strategic Decision Making:
-Intense Blue/Purple (High Opacity): Strong CCI readings exceeding ±80 with notable momentum strength indicating support/resistance zones suitable for increased position management decisions
• Moderate Blue/Purple (Medium Opacity): Standard CCI readings ranging ±40-80 with normal momentum indicating support/resistance areas for standard position management protocols
• Faded Blue/Purple (High Transparency): Weak CCI readings below ±40 with minimal momentum suggesting cautious interpretation and conservative position management approaches
• Dynamic Color Transitions: Automatic real-time shifts between bullish (blue spectrum) and bearish (purple spectrum) based on CCI trend direction and momentum persistence characteristics
CCI Inflection Circle System - Momentum Reversal Identification: The inflection detection system creates distinctive visual alerts through dual-circle design combining solid cores with transparent glow effects for enhanced visibility across different chart backgrounds and timeframe configurations.
Inflection Circle Classification:
• Neon Green Circles: CCI extreme bullish inflection detected (>80 threshold) with systematic core + glow effect indicating bearish reversal warning for position management evaluation
• Hot Pink Circles: CCI extreme bearish inflection detected (<-80 threshold) with dual-layer visualization indicating bullish reversal opportunity for strategic entry consideration
• Dual-Circle Design Architecture: Solid tiny core providing location identification with large transparent glow ensuring visibility without chart obstruction across multiple timeframe analyses
SAR Visual Network - Multi-Layer Momentum Display Architecture
SAR Visualization Framework: The SAR visual system implements structured multi-layer display architecture incorporating trend lines, strength classification markers, and momentum analysis through various visual elements that automatically adapt to current momentum conditions and strength characteristics.
SAR Strength Visual Classification System:
• Bright Triangles (High Intensity): Strong SAR momentum exceeding 75% of calculated strength range, indicating significant momentum quality suitable for increased positioning considerations and extended target scenarios
• Standard Circles (Medium Intensity): Moderate SAR momentum within 25-75% strength range, representing normal momentum development appropriate for standard positioning approaches and regular target expectations
• Faded Markers (Low Intensity): Weak SAR momentum below 25% strength range, suggesting caution and conservative positioning during minimal momentum conditions with increased exit sensitivity
⚠️IMPORTANT DISCLAIMERS AND RISK WARNINGS
Past Performance Limitations: The backtesting results presented represent hypothetical performance based on historical market data and do not guarantee future results. All trading involves substantial risk of loss. This strategy is provided for informational purposes and does not constitute financial advice. No trading strategy can guarantee 100% success or eliminate the risk of loss.
Users must approach trading with appropriate caution, never risking more than they can afford to lose.
Users are responsible for their own trading decisions, risk management, and compliance with applicable regulations in their jurisdiction.
Market to NAV Premium Arbitrage Alpha IndicatorBitcoin treasury companies such as Microstrategy are known for trading at significant premiums. but how big exactly is the premium? And how can we measure it in real time?
I developed this quantitative tool to identify statistical mispricings between market capitalization and net asset value (NAV), specifically designed for arbitrage strategies and alpha generation in Bitcoin-holding companies, such as MicroStrategy or Sharplink Gaming, or SPACs used primarily to hold cryptocurrencies, Bitcoin ETFs, and other NAV-based instruments. It can probably also be used in certain spin-offs.
KEY FEATURES:
✅ Real-time Premium/Discount Calculation
• Automatically retrieves market cap data from TradingView
• Calculates precise NAV based on underlying asset holdings (for example Bitcoin)
• Formula: (Market Cap - NAV) / NAV × 100
✅ Statistical Analysis
• Historical percentile rankings (customizable lookback period)
• Standard deviation bands (2σ) for extreme value detection (close to these values might be seen as interesting points to short or go long)
• Smoothing period to reduce noise
✅ Multi-Source Market Cap Detection
• You can add the ticker of the NAV asset, but if necessary, you can also put it manually. Priority system: TradingView data → Calculated → Manual override
✅ Advanced NAV Modeling
• Basic NAV: Asset holdings + cash.
• Adjusted NAV: Includes software business value, debt, preferred shares. If the company has a lot of this kind of intrinsic value, put it in the "cash" field
• Support for any underlying asset (BTC, ETH, etc.)
TRADING APPLICATIONS:
🎯 Pairs Trading Signals
• Long/Short opportunities when premium reaches statistical extremes
• Mean reversion strategies based on historical ranges
• Risk-adjusted position sizing using percentile ranks
🎯 Arbitrage Detection
• Identifies when market pricing significantly deviates from fair value
• Quantifies the magnitude of mispricing for profit potential
• Historical context for timing entry/exit points
CONFIGURATION OPTIONS:
• Underlying Asset: Any symbol (default: COINBASE:BTCUSD) NEEDS MANUAL INPUT
• Asset Quantity: Precise holdings amount (for example, how much BTC does the company currently hold). NEEDS MANUAL INPUT
• Cash Holdings: Additional liquid assets. NEEDS MANUAL INPUT
• Market Cap Mode: Auto-detect, calculated, or manual
• Advanced Adjustments: Business value, debt, preferred shares
• Display Settings: Lookback period, smoothing, custom colors
IT CAN BE USED BY:
• Quantitative traders focused on statistical arbitrage
• Institutional investors monitoring NAV-based instruments
• Bitcoin ETF and MSTR traders seeking alpha generation
• Risk managers tracking premium/discount exposures
• Academic researchers studying market efficiency (as you can see, markets are not efficient 😉)
Durdens Global M2 Liquidity Tracker🧠 Durdens Global M2 Liquidity Tracker | Bitcoin vs Liquidity, Visualized
If you’re not watching global liquidity, you’re not really trading macro.
This indicator tracks FX-adjusted M2 money supply across 20+ countries, aggregated into a single global liquidity signal. It can then be used to overlay against Bitcoin for timing macro shifts with precision.
🔍 Core Features:
🌐 USD-adjusted M2 from the US, China, Eurozone, UK, Japan, and more
📊 Normalization modes: None (raw), Index (Based to 100), Z-Score
⏳ Offset input to shift liquidity data forward — aligns with Bitcoin's delayed reaction (84–107 days common)
🧠 BTC correlation matrix: 30D, 90D, 365D correlation values
🧪 Top 3 M2 delta signals: Tracks 90-day % change for US, China, EU
🧮 Fibonacci SMAs: 13 / 34 / 89 for structural macro context
🟢🔴 Liquidity regime engine: EMA 89 defines "Risk-On" vs "Risk-Off" states
🧩 How It Works:
Each country’s M2 is multiplied by its FX rate (to USD) and summed into a single global M2 line. This ensures comparability across nations. The user can choose to:
Normalize the output (raw, indexed, or z-scored)
Shift the global M2 forward in time (offset), simulating the lag effect liquidity has on Bitcoin
Visualize macro risk conditions using EMA 89 as a liquidity regime filter
Analyze BTC correlation across 3 windows and track key regions’ M2 delta
❓ FAQ:
Why does this matter?
M2 is the monetary fuel behind asset bubbles. When liquidity rises, Bitcoin follows; with a delay. This tracker helps you front-run macro flows before they hit the chart.
Why use Index or Z-Score modes?
Raw values skew long-term visual analysis. Index mode rebases data for comparative trend tracking. Z-Score shows when liquidity is overheated or suppressed (mean reversion).
What does the offset input do?
Liquidity doesn’t hit Bitcoin instantly. Many traders use an 84–107 day forward shift to align M2 changes with BTC price action. The offset helps you visualize this.
Why track top 3 M2 regions?
US, China, and Eurozone are the heavyweights in global liquidity. Tracking their offset-day % change gives immediate insight into capital expansion or contraction.
Can I use this to trade?
Absolutely; but it’s best used as a macro filter. Combine with price structure, funding, or on-chain data to optimize timing and conviction.
⚡ Use Cases:
Spot early pivots in liquidity regimes (Risk-Off to Risk-On)
Quantify macro backdrop for Bitcoin or altcoin cycles
Understand when the Fed or PBOC are tightening or easing
Ditch the hopium. Trade with context.
—
Built by: @DurdensBitcoinLedger
Follow for updates — future upgrades include:
• Regional toggles
• Custom M2 baskets
• Alert conditions
• Continued revisions & updates
Stay liquid, not wrecked.
Ultimate Market Structure [Alpha Extract]Ultimate Market Structure
A comprehensive market structure analysis tool that combines advanced swing point detection, imbalance zone identification, and intelligent break analysis to identify high-probability trading opportunities.Utilizing a sophisticated trend scoring system, this indicator classifies market conditions and provides clear signals for structure breaks, directional changes, and fair value gap detection with institutional-grade precision.
🔶 Advanced Swing Point Detection
Identifies pivot highs and lows using configurable lookback periods with optional close-based analysis for cleaner signals. The system automatically labels swing points as Higher Highs (HH), Lower Highs (LH), Higher Lows (HL), and Lower Lows (LL) while providing advanced classifications including "rising_high", "falling_high", "rising_low", "falling_low", "peak_high", and "valley_low" for nuanced market analysis.
swingHighPrice = useClosesForStructure ? ta.pivothigh(close, swingLength, swingLength) : ta.pivothigh(high, swingLength, swingLength)
swingLowPrice = useClosesForStructure ? ta.pivotlow(close, swingLength, swingLength) : ta.pivotlow(low, swingLength, swingLength)
classification = classifyStructurePoint(structureHighPrice, upperStructure, true)
significance = calculateSignificance(structureHighPrice, upperStructure, true)
🔶 Significance Scoring System
Each structure point receives a significance level on a 1-5 scale based on its distance from previous points, helping prioritize the most important levels. This intelligent scoring system ensures traders focus on the most meaningful structure breaks while filtering out minor noise.
🔶 Comprehensive Trend Analysis
Calculates momentum, strength, direction, and confidence levels using volatility-normalized price changes and multi-timeframe correlation. The system provides real-time trend state tracking with bullish (+1), bearish (-1), or neutral (0) direction assessment and 0-100 confidence scoring.
// Calculate trend momentum using rate of change and volatility
calculateTrendMomentum(lookback) =>
priceChange = (close - close ) / close * 100
avgVolatility = ta.atr(lookback) / close * 100
momentum = priceChange / (avgVolatility + 0.0001)
momentum
// Calculate trend strength using multiple timeframe correlation
calculateTrendStrength(shortPeriod, longPeriod) =>
shortMA = ta.sma(close, shortPeriod)
longMA = ta.sma(close, longPeriod)
separation = math.abs(shortMA - longMA) / longMA * 100
strength = separation * slopeAlignment
❓How It Works
🔶 Imbalance Zone Detection
Identifies Fair Value Gaps (FVGs) between consecutive candles where price gaps create unfilled areas. These zones are displayed as semi-transparent boxes with optional center line mitigation tracking, highlighting potential support and resistance levels where institutional players often react.
// Detect Fair Value Gaps
detectPriceImbalance() =>
currentHigh = high
currentLow = low
refHigh = high
refLow = low
if currentOpen > currentClose
if currentHigh - refLow < 0
upperBound = currentClose - (currentClose - refLow)
lowerBound = currentClose - (currentClose - currentHigh)
centerPoint = (upperBound + lowerBound) / 2
newZone = ImbalanceZone.new(
zoneBox = box.new(bar_index, upperBound, rightEdge, lowerBound,
bgcolor=bullishImbalanceColor, border_color=hiddenColor)
)
🔶 Structure Break Analysis
Determines Break of Structure (BOS) for trend continuation and Directional Change (DC) for trend reversals with advanced classification as "continuation", "reversal", or "neutral". The system compares pre-trend and post-trend states for each break, providing comprehensive trend change momentum analysis.
🔶 Intelligent Zone Management
Features partial mitigation tracking when price enters but doesn't fully fill zones, with automatic zone boundary adjustment during partial fills. Smart array management keeps only recent structure points for optimal performance while preventing duplicate signals from the same level.
🔶 Liquidity Zone Detection
Automatically identifies potential liquidity zones at key structure points for institutional trading analysis. The system tracks broken structure points and provides adaptive zone extension with configurable time-based limits for imbalance areas.
🔶 Visual Structure Mapping
Provides clear visual indicators including swing labels with color-coded significance levels, dashed lines connecting break points with BOS/DC labels, and break signals for continuation and reversal patterns. The adaptive zones feature smart management with automatic mitigation tracking.
🔶 Market Structure Interpretation
HH/HL patterns indicate bullish market structure with trend continuation likelihood, while LH/LL patterns signal bearish structure with downtrend continuation expected. BOS signals represent structure breaks in trend direction for continuation opportunities, while DC signals warn of potential reversals.
🔶 Performance Optimization
Automatic cleanup of old structure points (keeps last 8 points), recent break tracking (keeps last 5 break events), and efficient array management ensure smooth performance across all timeframes and market conditions.
Why Choose Ultimate Market Structure ?
This indicator provides traders with institutional-grade market structure analysis, combining multiple analytical approaches into one comprehensive tool. By identifying key structure levels, imbalance zones, and break patterns with advanced significance scoring, it helps traders understand market dynamics and position themselves for high-probability trade setups in alignment with smart money concepts. The sophisticated trend scoring system and intelligent zone management make it an essential tool for any serious trader looking to decode market structure with precision and confidence.
HIFI BTC Daily Hashrate Momentum OscillatorThe "HIFI BTC Daily Hashrate Momentum Oscillator" indicator is an oscillator that analyzes the "health" and confidence of miners in the Bitcoin network. It measures the momentum of hashrate changes using its deviation from the 30-day and 60-day moving averages. A rising hashrate is often a leading or confirming bullish trend indicator for the price of BTC.
Main Idea
Hashrate is the total computing power involved in mining. Its growth indicates increased investment in network security and miners' confidence in future profitability.
Blue Oscillator (fast): Shows the short-term dynamics of hashrate growth.
Green Oscillator (slow): Indicates the long-term trend of hash rate changes.
Chart background: The green background signals the acceleration of the hash rate growth (short-term momentum is higher than long-term), which is a positive sign.
How to Read Signals
A Buy signal appears when two fundamental conditions coincide:
Growth acceleration: The short-term hashrate momentum becomes stronger than the long-term one (the blue line crosses the green one from bottom to top). This indicates that miners are actively building capacity.
Exit from stagnation: This acceleration occurs after a period of weak hashrate growth or decline (the green line is below the red dashed line).
This combination indicates the potential start of a new cycle of growth and confidence in the network, which historically has often preceded the rise in the price of Bitcoin itself.
Disclamer: This indicator is an analysis tool and should not be considered as a direct financial recommendation. Always do your own analysis before making trades.
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator
The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi.
It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle.
🔶 USAGE
Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models.
As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale.
This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality.
According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle.
🔹 Enable/Disable Elements
All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain.
The different charts above show various combinations. Traders can customize the tool to their needs.
🔹 Auto scale
The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel.
The image above shows different configurations of this feature.
🔶 SETTINGS
🔹 Price
Price: Enable/disable price spiral, select color, and enable/disable curved mode
Average: Enable/disable average spiral, select color, and enable/disable curved mode
🔹 Style
Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals
Lines width: Width of each spiral line
Text Size: Select text size for date tags and price scales
Prices: Enable/disable price scales on the x-axis
Handle: Enable/disable clock handle
Halvings: Enable/disable Halvings
Hours: Enable/disable hours and key cycle points
🔹 Time & Price Dashboard
Show Time & Price: Enable/disable time & price dashboard
Location: Dashboard location
Size: Dashboard size
Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
CHN BUY SELL with EMA 200Overview
This indicator combines RSI 7 momentum signals with EMA 200 trend filtering to generate high-probability BUY and SELL entry points. It uses colored candles to highlight key market conditions and displays clear trading signals with built-in cooldown periods to prevent signal spam.
Key Features
Colored Candles: Visual momentum indicators based on RSI 7 levels
Trend Filtering: EMA 200 confirms overall market direction
Signal Cooldown: Prevents over-trading with adjustable waiting periods
Clean Interface: Simple BUY/SELL labels without clutter
How It Works
Candle Coloring System
Yellow Candles: Appear when RSI 7 ≥ 70 (overbought momentum)
Purple Candles: Appear when RSI 7 ≤ 30 (oversold momentum)
Normal Candles: All other market conditions
Trading Signals
BUY Signal: Triggered when closing price > EMA 200 AND yellow candle appears
SELL Signal: Triggered when closing price < EMA 200 AND purple candle appears
Signal Cooldown
After a BUY or SELL signal appears, the same signal type is suppressed for a specified number of candles (default: 5) to prevent excessive signals in ranging markets.
Settings
RSI 7 Length: Period for RSI calculation (default: 7)
RSI 7 Overbought: Threshold for yellow candles (default: 70)
RSI 7 Oversold: Threshold for purple candles (default: 30)
EMA Length: Period for trend filter (default: 200)
Signal Cooldown: Candles to wait between same signal type (default: 5)
How to Use
Apply the indicator to your chart
Look for yellow or purple colored candles
For LONG entries: Wait for yellow candle above EMA 200, then enter BUY when signal appears
For SHORT entries: Wait for purple candle below EMA 200, then enter SELL when signal appears
Use appropriate risk management and position sizing
Best Practices
Works best on timeframes M15 and higher
Suitable for Forex, Gold, Crypto, and Stock markets
Consider market volatility when setting stop-loss and take-profit levels
Use in conjunction with proper risk management strategies
Technical Details
Overlay: True (plots directly on price chart)
Calculation: Based on RSI momentum and EMA trend analysis
Signal Logic: Combines momentum exhaustion with trend direction
Visual Feedback: Colored candles provide immediate market condition awareness
SectorRotationRadarThe Sector Rotation Radar is a powerful visual analysis tool designed to track the relative strength and momentum of a stock compared to a benchmark index and its associated sector ETF. It helps traders and investors identify where an asset stands within the broader market cycle and spot rotation patterns across sectors and timeframes.
🔧 Key Features:
Benchmark Comparison: Measures the relative performance (strength and momentum) of the current symbol against a chosen benchmark (default: SPX), highlighting over- or underperformance.
Automatic Sector Detection: Automatically links stocks to their relevant sector ETFs (e.g., XLK, XLF, XLU), based on an extensive internal symbol map.
Multi-Timeframe Analysis: Supports simultaneous comparison across the current, next, and even third-higher timeframes (e.g., Daily → Weekly → Monthly), providing a bigger-picture perspective of trend shifts.
Tail Visualization: Displays a "trail" of price behavior over time, visualizing how the asset has moved in terms of relative strength and momentum across a user-defined period.
Quadrant-Based Layout: The chart is divided into four dynamic main zones, each representing a phase in the strength/momentum cycle:
🔄 Improving: Gaining strength and momentum
🚀 Leading: High strength and high momentum — top performers
💤 Weakening: Losing momentum while still strong
🐢 Lagging: Low strength and low momentum — underperformers
Clean Chart Visualization:
Background grid with axis labels
Dynamic tails and data points for each symbol
Option to include the associated sector ETF for context
Descriptive labels showing exact strength/momentum values per point
⚙️ Customization Options:
Benchmark Selector: Choose any symbol to compare against (e.g., SPX, Nasdaq, custom index)
Start Date Control: Option to fix a historical start point or use the current data range
Trail Length: Set the number of previous data points to display
Additional Timeframes: Enable analysis of one or two higher timeframes beyond the current
Sector ETF Display: Toggle to show or hide the related sector ETF alongside the asset
📚 Technical Architecture:
The indicator relies on external modules for:
Statistical modeling
Relative strength and momentum calculations
Chart rendering and label drawing
These components work together to compute and display a dynamic, real-time map of asset performance over time.
🧠 Use Case:
Sector Rotation Radar is ideal for traders looking to:
Spot stocks or sectors rotating into strength or weakness
Confirm alignment across multiple timeframes
Identify sector leaders and laggards
Understand how a symbol is positioned relative to the broader market and its peers
This tool is especially valuable for swing traders, sector rotation strategies, and macro-aware investors who want a visual edge in decision-making.
SuperSmoothed Volume Zone Oscillator------------------------------------------------------------------------------------
SUPERSMOOTHED VOLUME ZONE OSCILLATOR (SSVZO)
TECHNICAL INDICATOR DOCUMENTATION
------------------------------------------------------------------------------------
Table of Contents:
1. Original VZO Background
2. SuperSmoother Technology
3. SSVZO Components
3.1. Main SSVZO Oscillator
3.2. Momentum Velocity Component
3.3. Adaptive Levels
3.4. Static Levels
3.5. Trend Shift Detection
3.6. Glow Effect Visualization
4. References & Further Reading
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1. ORIGINAL VOLUME ZONE OSCILLATOR (VZO) BACKGROUND
------------------------------------------------------------------------------------
Creator: Walid Khalil (November 2009, Technical Analysis of Stocks & Commodities)
History: Khalil designed the VZO to address limitations in other volume indicators
by focusing on the relative balance between buying and selling volume while filtering
out market noise. The indicator identifies accumulation and distribution patterns.
Traditional Usage: The classic VZO uses a 14-period calculation setting and is
interpreted on a scale from -60% to +60%:
- Readings above +40% indicate strong buying pressure (potential overbought)
- Readings below -40% indicate strong selling pressure (potential oversold)
- The zero line acts as a key reference for trend changes
- Divergences between VZO and price offer valuable trading signals
Difference from Other Volume Indicators: Unlike simple volume indicators that only
track total volume, the VZO tracks the relative difference between up-volume and
down-volume, more effectively identifying buying/selling pressure imbalances and
potential reversal points.
------------------------------------------------------------------------------------
2. SUPERSMOOTHER FILTER TECHNOLOGY
------------------------------------------------------------------------------------
Creator: John F. Ehlers, an engineer specializing in digital signal processing for
trading systems.
Origins: Introduced in "Rocket Science for Traders" (2001) and refined in "Cybernetic
Analysis for Stocks and Futures" (2004). Represents the application of digital signal
processing techniques to financial markets.
Technical Foundation: The SuperSmoother is a two-pole low-pass filter specifically
designed to eliminate noise while preserving the underlying signal. It combines
principles of Butterworth and Gaussian filters to minimize both phase shift and
passband ripple.
Mathematical Implementation:
a1 = exp(-π * sqrt(2) / period)
b1 = 2 * a1 * cos(sqrt(2) * π / period)
c2 = b1
c3 = -a1²
c1 = 1 - c2 - c3
Advantages Over Traditional Filters:
- Reduces lag compared to simple moving averages
- Eliminates high-frequency market noise more effectively
- Minimizes unwanted ripples in the output signal
- Preserves important turning points in the data
- Superior handling of sudden market movements
According to Ehlers: "Conventional moving averages are plagued by excessive lag and/or
rippling in their passband. The SuperSmoother eliminates virtually all of this ripple
and has excellent transient response characteristics." (TASC Magazine, 2014)
------------------------------------------------------------------------------------
3. SSVZO COMPONENTS
------------------------------------------------------------------------------------
3.1. MAIN SSVZO OSCILLATOR
------------------------------------------------------------------------------------
Description: The core component measuring buying vs. selling volume pressure using
the SuperSmoother filter for enhanced noise reduction.
Calculation: SSVZO analyzes the relationship between up-volume (volume on rising
prices) and down-volume (volume on falling prices), applying exponential moving
averages to both components, then calculating their relative strength. The
SuperSmoother filter reduces market noise while preserving the underlying trend signal.
Implementation Advantage: By applying the SuperSmoother filter to the VZO calculation,
the SSVZO provides significantly cleaner signals with fewer false crossovers and more
accurate identification of true trend changes.
Interpretation:
- Values above zero indicate bullish volume dominance
- Values below zero indicate bearish volume dominance
- Readings above +60 suggest overbought conditions
- Readings below -60 suggest oversold conditions
- Crossovers of the zero line signal potential trend changes
Trading Application: Use SSVZO as a primary volume-based momentum indicator to
confirm price trends, identify divergences, and spot potential reversal zones.
------------------------------------------------------------------------------------
3.2. MOMENTUM VELOCITY COMPONENT
------------------------------------------------------------------------------------
Description: A histogram displaying the rate of change of momentum, showing how
quickly buying or selling pressure is accelerating or decelerating.
Calculation: Derived from price momentum over a user-defined period, with optional
adaptive filtering that adjusts sensitivity based on market volatility. The velocity
component shows the first derivative of momentum – essentially the "acceleration" of
market movement.
Technical Origin: Inspired by Ehlers' work on Hilbert Transforms and research on
cyclic components in financial markets, as detailed in "Cycle Analytics for Traders"
(2013).
Interpretation:
- Positive readings (teal bars) indicate accelerating upward momentum
- Negative readings (orange bars) suggest accelerating downward momentum
- Larger bars indicate stronger momentum acceleration
- Shrinking bars signal momentum deceleration
Trading Application: Use as an early warning system for potential trend exhaustion
or confirmation of a new trending move. When momentum velocity diverges from price,
it often precedes a reversal.
------------------------------------------------------------------------------------
3.3. ADAPTIVE LEVELS
------------------------------------------------------------------------------------
Description: Dynamic overbought and oversold boundaries that adjust to market
conditions, providing context-aware trading signals.
Calculation: Uses statistical methods based on the standard deviation of the SSVZO
values over a longer period. These levels automatically widen during higher volatility
periods and narrow during consolidation.
Research Base: Draws from Perry Kaufman's work on Adaptive Moving Averages (AMA) and
Bollinger's research on dynamic volatility bands, as published in "Trading Systems
and Methods" (2013).
Interpretation:
- Adaptive Overbought (dotted circles above): Dynamic ceiling that expands/contracts
based on market volatility
- Adaptive Oversold (dotted circles below): Dynamic floor that expands/contracts based
on market volatility
Trading Application: More reliable for identifying extremes than static levels,
particularly in changing market conditions or different instruments. Touching these
levels often provides higher-probability reversal signals.
------------------------------------------------------------------------------------
3.4. STATIC LEVELS
------------------------------------------------------------------------------------
Description: Fixed overbought and oversold horizontal lines that provide consistent
reference points for excess market conditions.
Calculation: Preset at +60 (overbought) and -60 (oversold) based on historical
analysis of volume behavior across multiple markets, extending the classic VZO range.
Interpretation:
- Readings above +60 suggest potential buying exhaustion
- Readings below -60 indicate potential selling exhaustion
- Duration spent beyond these levels correlates with reversal probability
Trading Application: Use as baseline reference points for extreme conditions. Most
effective when combined with other confirmation signals like divergences or
candlestick patterns.
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3.5. TREND SHIFT DETECTION
------------------------------------------------------------------------------------
Description: Visual markers and optional background shading highlighting potential
trend changes when the SSVZO crosses the zero line.
Calculation: Based on mathematical crossovers of the SSVZO value above or below the
zero line, with pattern recognition to reduce false signals.
Research Foundation: Incorporates concepts from Dr. Alexander Elder's "triple screen
trading system" and Mark Chaikin's volume-based trend identification research.
Interpretation:
- Upward triangles indicate bullish trend shifts (SSVZO crossing above zero)
- Downward triangles indicate bearish trend shifts (SSVZO crossing below zero)
- Background shading emphasizes the new trend direction
Trading Application: These signals often precede price trend changes and can serve
as entry triggers when aligned with the higher timeframe trend.
------------------------------------------------------------------------------------
3.6. GLOW EFFECT VISUALIZATION
------------------------------------------------------------------------------------
Description: An aesthetic enhancement creating a gradient "glow" around the main SSVZO
line, improving visual clarity and emphasizing signal strength.
Calculation: Generated using percentage-based bands around the main SSVZO value, with
multiple translucent layers to create a subtle illumination effect.
Design Inspiration: Inspired by modern UI/UX design principles for financial
dashboards and the MATS (Moving Average Trend Sniper) indicator's visual presentation,
enhancing perception of signal strength through visual intensity.
Interpretation:
- Teal glow indicates positive SSVZO values (bullish)
- Orange glow indicates negative SSVZO values (bearish)
- Glow intensity correlates with the strength of the signal
Trading Application: Beyond aesthetics, the glow creates visual emphasis that makes
trend direction, strength, and changes more immediately apparent, particularly useful
during fast-moving market conditions.
------------------------------------------------------------------------------------
4. REFERENCES & FURTHER READING
------------------------------------------------------------------------------------
1. Ehlers, J. F. (2001). "Rocket Science for Traders: Digital Signal Processing
Applications." John Wiley & Sons.
2. Ehlers, J. F. (2004). "Cybernetic Analysis for Stocks and Futures: Cutting-Edge
DSP Technology to Improve Your Trading." John Wiley & Sons.
3. Ehlers, J. F. (2013). "Cycle Analytics for Traders: Advanced Technical Trading
Concepts." John Wiley & Sons.
4. Khalil, W. (2009). "The Volume Zone Oscillator." Technical Analysis of Stocks &
Commodities, November 2009.
5. Kaufman, P. J. (2013). "Trading Systems and Methods." 5th Edition, Wiley Trading.
6. Elder, A. (2002). "Come Into My Trading Room: A Complete Guide to Trading."
John Wiley & Sons.
7. Bollinger, J. (2002). "Bollinger on Bollinger Bands." McGraw-Hill Education.
------------------------------------------------------------------------------------
END OF DOCUMENTATION
------------------------------------------------------------------------------------
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B(Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
PORTFOLIO TABLE Full [Titans_Invest]PORTFOLIO TABLE Full
This is a complete table for monitoring your assets or cryptocurrencies in your SPOT wallet without needing to access your broker’s website or app.
⯁ HOW TO USE THIS TABLE❓
Simply select the asset and enter the amount you hold.
The table will display the value of each asset and the total value of your portfolio.
You can monitor up to 19 assets in real time.
⯁ CONVERT VALUES
You can also enable and select a currency for conversion.
For example, cryptocurrencies are calculated in US dollars by default, but you can choose euros as the conversion currency.
The values originally in dollars will then be displayed in euros.
⯁ TRACK THE DAILY VARIATION OF YOUR PORTFOLIO
You’ll be able to monitor your portfolio’s raw daily variation in real time.
🔶 Track your Portfolio in real time:
🔶 Add your local Currency to Convert Values:
🔶 Follow your Portfolio Live:
___________________________________________________________
📜 SCRIPT : PORTFOLIO TABLE Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
___________________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Bitcoin Weekend FadeThis indicator is a tool for setting a bias based on weekend price movements, with the assumption that the crypto market often experiences stronger moves over the weekend due to thinner order books. It helps identify potential fade opportunities, suggesting that price movements from Saturday and Sunday may reverse during the weekdays.
How to use:
Sets a bias based on weekend price action.
Sets a bias based on weekend price action.
Use weekday price action for confirmation before acting on the bias.
Best suited for range-bound markets, where the price tends to revert to the mean.
Avoid fading high-timeframe breakouts, as they often indicate strong trends.
SwingTrade VWAP Strategy[TiamatCrypto]V1.1This Pine Script® code creates a trading strategy called "SwingTrade VWAP Strategy V1.1." This strategy incorporates various trading tools, such as VWAP (Volume Weighted Average Price), ADX (Average Directional Index), and volume signals. Below is an explanation of the components and logic within the script:
### Overview of Features
- **VWAP:** A volume-weighted moving average that assesses price trends relative to the VWAP level.
- **ADX:** A trend strength indicator that helps confirm the strength of bullish or bearish trends.
- **Volume Analysis:** Leverages volume data to gauge momentum and identify volume-weighted buy/sell conditions.
- **Dynamic Entry/Exit Signals:** Combines the above indicators to produce actionable buy/sell or exit signals.
- **Customizable Inputs:** Inputs for tuning parameters like VWAP period, ADX thresholds, and volume sensitivity.
---
### **Code Breakdown**
#### **Input Parameters**
The script begins by defining several user-configurable variables under groups. These include indicators' on/off switches (`showVWAP`, `enableADX`, `enableVolume`) and input parameters for VWAP, ADX thresholds, and volume sensitivity:
- **VWAP Period and Threshold:** Controls sensitivity for VWAP signal generation.
- **ADX Settings:** Allows users to configure the ADX period and strength threshold.
- **Volume Ratio:** Detects bullish/bearish conditions based on relative volume patterns.
---
#### **VWAP Calculation**
The script calculates VWAP using the formula:
\
Where `P` is the typical price (`(high + low + close)/3`) and `V` is the volume.
- It resets cumulative values (`sumPV` and `sumV`) at the start of each day.
- Delta percentage (`deltaPercent`) is calculated as the percentage difference between the close price and the VWAP.
---
#### **Indicators and Signals**
1. **VWAP Trend Signals:**
- Identifies bullish/bearish conditions based on price movement (`aboveVWAP`, `belowVWAP`) and whether the price is crossing the VWAP level (`crossingUp`, `crossingDown`).
- Also detects rising/falling delta changes based on the VWAP threshold.
2. **ADX Calculation:**
- Calculates the directional movement (`PlusDM`, `MinusDM`) and smoothed values for `PlusDI`, `MinusDI`, and `ADX`.
- Confirms strong bullish/bearish trends when ADX crosses the defined threshold.
3. **Volume-Based Signals:**
- Evaluates the ratio of bullish volume (when `close > VWAP`) to bearish volume (when `close < VWAP`) over a specified lookback period.
---
#### **Trade Signals**
The buy and sell signals are determined by combining conditions from the VWAP, ADX, and volume signals:
- **Buy Signal:** Triggered when price upward crossover VWAP, delta rises above the threshold, ADX indicates a strong bullish trend, and volume confirms bullish momentum.
- **Sell Signal:** Triggered under inverse conditions.
- Additionally, exit conditions (`exitLong` and `exitShort`) are based on VWAP crossovers combined with the reversal of delta values.
---
#### **Plotting and Display**
The strategy plots VWAP on the chart and adds signal markers for:
- **Buy/Long Entry:** Green triangle below bars.
- **Sell/Short Entry:** Red triangle above bars.
- **Exit Signals:** Lime or orange "X" shapes for exits from long/short positions.
- Additionally, optional text labels are displayed to indicate the type of signal.
---
#### **Trading Logic**
The script's trading logic executes as follows:
- **Entries:**
- Executes long trades when the `buySignal` condition is true.
- Executes short trades when the `sellSignal` condition is true.
- **Exits:**
- Closes long positions upon `exitLong` conditions.
- Closes short positions upon `exitShort` conditions.
- The strategy calculates profits and visualizes the trade entry, exit, and running profit within the chart.
---
#### **Alerts**
Alerts are set up to notify traders via custom signals for buy and sell trades.
---
### **Use Case**
This script is suitable for day traders, swing traders, or algorithmic traders who rely on confluence signals from VWAP, ADX, and volume momentum. Its modular structure (e.g., the ability to enable/disable specific indicators) makes it highly customizable for various trading styles and financial instruments.
#### **Customizability**
- Adjust VWAP, ADX, and volume sensitivity levels to fit unique market conditions or asset classes.
- Turn off specific criteria to focus only on VWAP or ADX signals if desired.
#### **Caution**
As with all trading strategies, this script should be used for backtesting and analysis before live implementation. It's essential to validate its performance on historical data while considering factors like slippage and transaction costs.
PORTFOLIO TABLE Simple [Titans_Invest]PORTFOLIO TABLE Simple
This is a simple table for you to monitor your assets or cryptocurrencies in your SPOT wallet without needing to access your broker’s website or wallet app.
⯁ HOW TO USE THIS TABLE❓
You only need to select the asset and enter the amount of each one.
The table will show how much you have of each asset and the total value of your portfolio.
You’ll be able to monitor up to 39 assets in real time.
⯁ CONVERT VALUES
You can also activate and select a currency for conversion.
For example, cryptocurrency assets are calculated in US dollars, but you can select euros as the conversion currency.
The values originally in dollars will then be displayed in euros.
⯁ Track your Portfolio in real time:
⯁ Add your local Currency to Convert Values:
⯁ Follow your Portfolio Live:
___________________________________________________________
📜 SCRIPT : PORTFOLIO TABLE Simple
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
___________________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
ADX Forecast [Titans_Invest]ADX Forecast
This isn’t just another ADX indicator — it’s the most powerful and complete ADX tool ever created, and without question the best ADX indicator on TradingView, possibly even the best in the world.
ADX Forecast represents a revolutionary leap in trend strength analysis, blending the timeless principles of the classic ADX with cutting-edge predictive modeling. For the first time on TradingView, you can anticipate future ADX movements using scientifically validated linear regression — a true game-changer for traders looking to stay ahead of trend shifts.
1. Real-Time ADX Forecasting
By applying least squares linear regression, ADX Forecast projects the future trajectory of the ADX with exceptional accuracy. This forecasting power enables traders to anticipate changes in trend strength before they fully unfold — a vital edge in fast-moving markets.
2. Unmatched Customization & Precision
With 26 long entry conditions and 26 short entry conditions, this indicator accounts for every possible ADX scenario. Every parameter is fully customizable, making it adaptable to any trading strategy — from scalping to swing trading to long-term investing.
3. Transparency & Advanced Visualization
Visualize internal ADX dynamics in real time with interactive tags, smart flags, and fully adjustable threshold levels. Every signal is transparent, logic-based, and engineered to fit seamlessly into professional-grade trading systems.
4. Scientific Foundation, Elite Execution
Grounded in statistical precision and machine learning principles, ADX Forecast upgrades the classic ADX from a reactive lagging tool into a forward-looking trend prediction engine. This isn’t just an indicator — it’s a scientific evolution in trend analysis.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the ADX, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an ADX time series like this:
Time →
ADX →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted ADX, which can be crossed with the actual ADX to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public ADX with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining ADX with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
ADX Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🥇 This is the world’s first ADX indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
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🔮 Linear Regression: PineScript Technical Parameters 🔮
_________________________________________________
Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE ADX❓
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ HOW TO USE THE ADX❓
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
• Strong Trend: When the ADX is above 25, indicating a strong trend.
• Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
• Neutral Zone: Between 20 and 25, where the trend strength is unclear.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 +DI > -DI
🔹 +DI < -DI
🔹 +DI > ADX
🔹 +DI < ADX
🔹 -DI > ADX
🔹 -DI < ADX
🔹 ADX > Threshold
🔹 ADX < Threshold
🔹 +DI > Threshold
🔹 +DI < Threshold
🔹 -DI > Threshold
🔹 -DI < Threshold
🔹 +DI (Crossover) -DI
🔹 +DI (Crossunder) -DI
🔹 +DI (Crossover) ADX
🔹 +DI (Crossunder) ADX
🔹 +DI (Crossover) Threshold
🔹 +DI (Crossunder) Threshold
🔹 -DI (Crossover) ADX
🔹 -DI (Crossunder) ADX
🔹 -DI (Crossover) Threshold
🔹 -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 +DI > -DI
🔸 +DI < -DI
🔸 +DI > ADX
🔸 +DI < ADX
🔸 -DI > ADX
🔸 -DI < ADX
🔸 ADX > Threshold
🔸 ADX < Threshold
🔸 +DI > Threshold
🔸 +DI < Threshold
🔸 -DI > Threshold
🔸 -DI < Threshold
🔸 +DI (Crossover) -DI
🔸 +DI (Crossunder) -DI
🔸 +DI (Crossover) ADX
🔸 +DI (Crossunder) ADX
🔸 +DI (Crossover) Threshold
🔸 +DI (Crossunder) Threshold
🔸 -DI (Crossover) ADX
🔸 -DI (Crossunder) ADX
🔸 -DI (Crossover) Threshold
🔸 -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : ADX Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Spent Output Profit Ratio Z-Score | Vistula LabsOverview
The Spent Output Profit Ratio (SOPR) Z-Score indicator is a sophisticated tool designed by Vistula Labs to help cryptocurrency traders analyze market sentiment and identify potential trend reversals. It leverages on-chain data from Glassnode to calculate the Spent Output Profit Ratio (SOPR) for Bitcoin and Ethereum, transforming this metric into a Z-Score for easy interpretation.
What is SOPR?
Spent Output Profit Ratio (SOPR) measures the profit ratio of spent outputs (transactions) on the blockchain:
SOPR > 1: Indicates that, on average, coins are being sold at a profit.
SOPR < 1: Suggests that coins are being sold at a loss.
SOPR = 1: Break-even point, often seen as a key psychological level.
SOPR provides insights into holder behavior—whether they are locking in profits or cutting losses—making it a valuable gauge of market sentiment.
How It Works
The indicator applies a Z-Score to the SOPR data to normalize it relative to its historical behavior:
Z-Score = (Smoothed SOPR - Moving Average of Smoothed SOPR) / Standard Deviation of Smoothed SOPR
Smoothed SOPR: A moving average (e.g., WMA) of SOPR over a short period (default: 30 bars) to reduce noise.
Moving Average of Smoothed SOPR: A longer moving average (default: 180 bars) of the smoothed SOPR.
Standard Deviation: Calculated over a lookback period (default: 200 bars).
This Z-Score highlights how extreme the current SOPR is compared to its historical norm, helping traders spot significant deviations.
Key Features
Data Source:
Selectable between BTC and ETH, using daily SOPR data from Glassnode.
Customization:
Moving Average Types: Choose from SMA, EMA, DEMA, RMA, WMA, or VWMA for both smoothing and main averages.
Lengths: Adjust the smoothing period (default: 30) and main moving average length (default: 180).
Z-Score Lookback: Default is 200 bars.
Thresholds: Set levels for long/short signals and overbought/oversold conditions.
Signals:
Long Signal: Triggered when Z-Score crosses above 1.02, suggesting potential upward momentum.
Short Signal: Triggered when Z-Score crosses below -0.66, indicating potential downward momentum.
Overbought/Oversold Conditions:
Overbought: Z-Score > 2.5, signaling potential overvaluation.
Oversold: Z-Score < -2.0, indicating potential undervaluation.
Visualizations:
Z-Score Plot: Teal for long signals, magenta for short signals.
Threshold Lines: Dashed for long/short, solid for overbought/oversold.
Candlestick Coloring: Matches signal colors.
Arrows: Green up-triangles for long entries, red down-triangles for short entries.
Background Colors: Magenta for overbought, teal for oversold.
Alerts:
Conditions for Long Opportunity, Short Opportunity, Overbought, and Oversold.
Usage Guide
Select Cryptocurrency: Choose BTC or ETH.
Adjust Moving Averages: Customize types and lengths for smoothing and main averages.
Set Thresholds: Define Z-Score levels for signals and extreme conditions.
Monitor Signals: Use color changes, arrows, and background highlights to identify opportunities.
Enable Alerts: Stay informed without constant chart watching.
Interpretation
High Z-Score (>1.02): SOPR is significantly above its historical mean, potentially indicating overvaluation or strong bullish momentum.
Low Z-Score (<-0.66): SOPR is below its mean, suggesting undervaluation or bearish momentum.
Extreme Conditions: Z-Scores above 2.5 or below -2.0 highlight overbought or oversold markets, often preceding reversals.
Conclusion
The SOPR Z-Score indicator combines on-chain data with statistical analysis to provide traders with a clear, actionable view of market sentiment. Its customizable settings, visual clarity, and alert system make it an essential tool for both novice and experienced traders seeking an edge in the cryptocurrency markets.
