Universal ATR Grid from Entry Price with AlertsUniversal ATR Grid from Entry Price with Alerts
This Pine Script v6 indicator creates a dynamic price grid based on a user-defined entry price and ATR for selected instruments (SOLUSDT, XRPUSDT, DOGEUSDT, PEPEUSDT, WIFUSDT).
Users can customize the entry price, ATR, number of levels (up to 5), and step multiplier per instrument.
The grid shows long (green) and short (red) levels around the entry price (gray), with labels offset right.
Lines extend from labels to the current bar, updating dynamically.
Alerts trigger on breakouts of long, short, and entry levels. Instrument names can be modified in the script.
Ketidakstabilan
Trend Strength Oscillator📌 Trend Strength Oscillator
📄 Description
Trend Strength Oscillator measures the directional strength of price relative to an adaptive dynamic trend band. It evaluates how far the current price is from the midpoint of a trend channel and normalizes this value by recent volatility range, allowing traders to detect trend strength, direction, and potential exhaustion in any market condition.
📌 Features
🔹 Adaptive Trend Band Logic: Uses a modified ATR and time-dependent spread formula to dynamically adjust upper and lower trend bands.
🔹 Trendline Midpoint Calculation: The central trendline is defined as the average between upper and lower bands.
🔹 Relative Positioning: Measures how far the close is from the center of the band as a percentage.
🔹 Range Normalization: Uses a normalized range to account for recent volatility, reducing noise in the oscillator reading.
🔹 Oscillator Output (±100 scale):
+100 indicates strong bullish momentum
-100 indicates strong bearish momentum
0 is the neutral centerline
🛠️ How to Use
✅ Trend Strength > +50: Indicates a strong bullish phase.
✅ Trend Strength < -50: Indicates a strong bearish phase.
⚠️ Crossing above 0: Potential bullish trend initiation.
⚠️ Crossing below 0: Potential bearish trend initiation.
📉 Values near 0: Suggest trend weakness or ranging conditions.
Best suited timeframes: 1H, 4H, Daily
Ideal combination with: RSI, MACD, volume-based oscillators, moving average crosses
✅ TradingView House Rules Compliance
This indicator is written in Pine Script v5 and fully open-source.
The script does not repaint, does not generate false alerts, and does not access external or private data.
It is intended strictly as a technical analysis tool, and not a buy/sell signal generator.
Users are encouraged to combine this tool with other confirmations and independent judgment in trading decisions.
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📌 Trend Strength Oscillator
📄 설명 (Description)
Trend Strength Oscillator는 가격이 동적 추세 밴드 내 어디에 위치해 있는지를 정량적으로 분석하여, 추세의 방향성과 강도를 시각적으로 보여주는 오실레이터 지표입니다. 최근 변동성을 반영한 밴드를 기반으로 가격 위치를 정규화하여, 과매수·과매도 상태나 추세의 소멸 가능성까지 탐지할 수 있도록 설계되었습니다.
📌 주요 특징 (Features)
🔹 적응형 추세 밴드 계산: ATR과 시간 경과를 기반으로 상단/하단 밴드를 동적으로 조정
🔹 중심 추세선 산출: 상단과 하단 밴드의 평균값을 중심선으로 활용하여 기준 축 제공
🔹 상대 위치 계산: 현재 종가가 중심선에서 얼마나 떨어져 있는지를 정규화하여 추세 강도 계산
🔹 변동성 기반 정규화: 최근 밴드 범위를 기준으로 상대 거리를 0~100 사이 값으로 변환
🔹 오실레이터 출력 (범위: ±100):
+100에 가까울수록 강한 상승 추세
-100에 가까울수록 강한 하락 추세
0에 가까울수록 횡보 구간 가능성
🛠️ 사용법 (How to Use)
✅ +50 이상: 강한 상승 추세 지속 중
✅ -50 이하: 강한 하락 추세 지속 중
⚠️ 0선 돌파 상향: 상승 추세 시작 가능성
⚠️ 0선 돌파 하향: 하락 추세 시작 가능성
🟡 0 근처 유지: 추세 약화 또는 횡보장 가능성
추천 시간대: 1시간봉, 4시간봉, 일봉
보조 지표로 추천: RSI, MACD, OBV, 이동평균 크로스 등과 함께 활용 시 효과적
✅ 트레이딩뷰 하우스룰 준수사항 (TradingView House Rules Compliance)
본 지표는 Pine Script v5로 작성된 오픈소스 공개용 스크립트입니다.
리페인트(Repaint) 현상이 없으며, **허위 경고(Spam Alerts)**나 성능 저하 요소도 없습니다.
외부 데이터 접근 없이 완전히 자체 계산으로 동작합니다.
이 지표는 투자 판단을 돕기 위한 분석용 도구이며, 직접적인 매수·매도 신호로 사용해서는 안 됩니다.
모든 트레이딩은 사용자의 독립적인 판단과 책임 하에 이루어져야 합니다.
Trend Band Oscillator📌 Trend Band Oscillator
📄 Description
Trend Band Oscillator is a momentum-based trend indicator that calculates the spread between two EMAs and overlays it with a volatility filter using a standard deviation band. It helps traders visualize not only the trend direction but also the strength and stability of the trend.
📌 Features
🔹 EMA Spread Calculation: Measures the difference between a fast and slow EMA to quantify short-term vs mid-term trend dynamics.
🔹 Volatility Band Overlay: Applies an EMA of standard deviation to the spread to filter noise and highlight valid momentum shifts.
🔹 Color-Based Visualization: Positive spread values are shown in lime (bullish), negative values in fuchsia (bearish) for quick directional insight.
🔹 Upper/Lower Bands: Help detect potential overbought/oversold conditions or strong trend continuation.
🔹 Zero Line Reference: A horizontal baseline at zero helps identify trend reversals and neutral zones.
🛠️ How to Use
✅ Spread > 0: Indicates a bullish trend. Consider maintaining or entering long positions.
✅ Spread < 0: Indicates a bearish trend. Consider maintaining or entering short positions.
⚠️ Spread exceeds bands: May signal overextension or strong momentum; consider using with additional confirmation indicators.
🔄 Band convergence: Suggests weakening trend and potential transition to a ranging market.
Recommended timeframes: 1H, 4H, Daily
Suggested complementary indicators: RSI, MACD, OBV, SuperTrend
✅ TradingView House Rules Compliance
This script is open-source and published under Pine Script v5.
It does not repaint, spam alerts, or cause performance issues.
It is designed as an analytical aid only and should not be considered financial advice.
All calculations are transparent, and no external data sources or insecure functions are used.
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📌 Trend Band Oscillator
📄 설명 (Description)
Trend Band Oscillator는 두 개의 EMA 간 스프레드(차이)를 기반으로 한 모멘텀 중심의 추세 오실레이터입니다. 여기에 표준편차 기반의 변동성 밴드를 적용하여, 추세의 방향뿐 아니라 강도와 안정성까지 시각적으로 분석할 수 있도록 설계되었습니다.
📌 주요 특징 (Features)
🔹 EMA 기반 스프레드 계산: Fast EMA와 Slow EMA의 차이를 활용해 시장 추세를 정량적으로 표현합니다.
🔹 표준편차 필터링: Spread에 대해 EMA 및 표준편차 기반의 밴드를 적용해 노이즈를 줄이고 유효한 추세를 강조합니다.
🔹 컬러 기반 시각화: 오실레이터 값이 양수일 경우 초록색, 음수일 경우 마젠타 색으로 추세 방향을 직관적으로 파악할 수 있습니다.
🔹 밴드 범위 시각화: 상·하위 밴드를 통해 스프레드의 평균 편차 범위를 보여주며, 추세의 강약과 포화 여부를 진단할 수 있습니다.
🔹 제로 라인 표시: 추세 전환 가능 지점을 시각적으로 확인할 수 있도록 중심선(0선)을 제공합니다.
🛠️ 사용법 (How to Use)
✅ 오실레이터가 0 이상 유지: 상승 추세 구간이며, 롱 포지션 유지 또는 진입 검토
✅ 오실레이터가 0 이하 유지: 하락 추세 구간이며, 숏 포지션 유지 또는 진입 검토
⚠️ 상·하위 밴드를 이탈: 일시적인 과매수/과매도 혹은 강한 추세 발현 가능성 있음 → 다른 보조지표와 함께 필터링 권장
🔄 밴드 수렴: 추세가 약해지고 있음을 나타냄 → 변동성 하락 또는 방향성 상실 가능성 있음
권장 적용 시간대: 1시간봉, 4시간봉, 일봉
보조 적용 지표: RSI, MACD, OBV, SuperTrend 등과 함께 사용 시 신호 필터링에 유리
✅ 트레이딩뷰 하우스룰 준수사항 (TV House Rules Compliance)
이 지표는 **무료 공개용(Open-Source)**이며, Pine Script Version 5로 작성되어 있습니다.
과도한 리페인트, 비정상적 반복 경고(alert spam), 실시간 성능 저하 등의 요소는 포함되어 있지 않습니다.
사용자는 본 지표를 투자 결정의 참고용 보조 도구로 활용해야 하며, 독립적인 매매 판단이 필요합니다.
데이터 소스 및 계산 방식은 완전히 공개되어 있으며, 외부 API나 보안 취약점을 유발하는 구성 요소는 없습니다.
Volume-Confirmed Price Momentum# **Volume-Confirmed Price Momentum (VCPM) Indicator**
## **🔍 Overview**
Introducing the **Volume-Confirmed Price Momentum (VCPM)**, a sophisticated dual-metric indicator designed to identify high-probability momentum moves by analyzing the relationship between price action and volume dynamics. This indicator combines correlation analysis with volume strength validation to filter out weak signals and highlight institutional-backed movements.
---
## **⚙️ Core Mechanics**
**Price-Volume Correlation Engine:**
- Calculates real-time correlation between price movements and volume
- Configurable lookback period (default: 8 bars)
- Option to use price changes or absolute values
- Correlation range: -1.0 (perfect negative) to +1.0 (perfect positive)
**Volume Strength Analyzer:**
- Compares current volume against its moving average (default: 128 periods)
- Normalizes volume ratio to 0-1 scale for consistent interpretation
- Identifies when volume significantly exceeds historical norms
---
## **📊 Signal Generation**
### **🟢 Bullish Confirmation Signal**
**Trigger:** Positive correlation > 0.6 + Volume ratio > 0.5
- Price and volume moving in harmony upward
- Above-average volume confirms the move
- Indicates strong institutional buying interest
### **🔴 Bearish Confirmation Signal**
**Trigger:** Negative correlation < -0.6 + Volume ratio > 0.5
- Price declining with increasing volume
- Suggests distribution or institutional selling
- High-confidence bearish momentum
---
## **🎯 Trading Applications**
**Breakout Validation:**
Filter false breakouts by requiring volume confirmation before entering positions.
**Trend Continuation:**
Identify when existing trends have strong volume backing for continuation plays.
**Distribution Detection:**
Spot potential tops when price struggles despite high volume (negative correlation).
**Entry Timing:**
Built-in alert system notifies when both conditions align for optimal entry points.
---
## **🔧 Customization Features**
- **Correlation Period:** Adjust sensitivity (2-500 bars)
- **Volume Averaging:** Modify volume comparison timeframe
- **Alert Thresholds:** Fine-tune correlation and volume ratio triggers
- **Visual Options:** Toggle volume histogram display
- **Price Source:** Choose from OHLC or custom sources
---
## **💡 Why VCPM Works**
Traditional momentum indicators often generate false signals during low-volume periods. VCPM solves this by requiring **dual confirmation**: price momentum must be supported by corresponding volume activity. This approach:
- Reduces whipsaws and false breakouts
- Identifies institutional participation
- Provides higher conviction trade setups
- Works across all timeframes and markets
---
## **📈 Best Use Cases**
✅ **Crypto markets** (high volatility, volume-driven)
✅ **Stock breakouts** (earnings, news events)
✅ **Forex majors** (during high-impact news)
✅ **Futures trading** (momentum confirmation)
---
## **⚠️ Important Notes**
- Works best in liquid markets with consistent volume data
- Combine with support/resistance levels for enhanced accuracy
- Consider market context (trending vs. ranging conditions)
- Not recommended for extremely low-volume periods
---
## **🚀 Getting Started**
1. Add VCPM to your chart as a sub-panel indicator
2. Configure correlation threshold (start with 0.6)
3. Set volume ratio threshold (start with 0.5)
4. Enable alerts for automated signal detection
5. Backtest on your preferred timeframe and instrument
---
**Ready to enhance your momentum trading with volume confirmation? Try VCPM and experience the difference institutional-backed signals can make in your trading results.**
*Available in Pine Script v6 - Compatible with all TradingView accounts*
ATR > VXN Alert (5m)ATR > VXN Volatility Divergence Indicator
This custom TradingView indicator monitors real-time volatility divergence between realized volatility (via Average True Range, ATR) and implied volatility (via the CBOE NASDAQ Volatility Index, VXN). It is inspired by the GJR-GARCH (Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroskedasticity) model, which captures asymmetric volatility dynamics—particularly how markets respond more sharply to negative shocks than to positive ones.
Core Logic:
Chart on NQ (5 minute timeframe)
ATR (5-min) reflects realized intraday volatility of the Nasdaq 100 futures (NQ).
VXN (5-min, delayed) represents forward-looking implied volatility.
The indicator highlights regime shifts in volatility:
ATR < VXN: Volatility compression → potential energy building up (market coiling).
ATR > VXN: Volatility expansion → real movement exceeds expectations → potential breakout zone.
Visuals & Alerts:
Background turns green when ATR crosses above VXN, signaling a bullish expansion regime.
Background turns red when ATR drops below VXN, signaling compression or risk-off environment.
Custom alerts trigger on volatility regime shifts for breakout traders.
Application (Manual GJR-GARCH Strategy):
Similar to how the GJR-GARCH model captures volatility clustering and asymmetry, this indicator identifies when actual price volatility (ATR) begins to spike beyond implied forecasts (VXN), often after periods of contraction—mirroring a conditional variance shock in the GARCH framework.
Traders can align with directional bias using technical confluence (order flow, structure breaks, liquidity zones) once expansion is confirmed.
VoVix DEVMA🌌 VoVix DEVMA: A Deep Dive into Second-Order Volatility Dynamics
Welcome to VoVix+, a sophisticated trading framework that transcends traditional price analysis. This is not merely another indicator; it is a complete system designed to dissect and interpret the very fabric of market volatility. VoVix+ operates on the principle that the most powerful signals are not found in price alone, but in the behavior of volatility itself. It analyzes the rate of change, the momentum, and the structure of market volatility to identify periods of expansion and contraction, providing a unique edge in anticipating major market moves.
This document will serve as your comprehensive guide, breaking down every mathematical component, every user input, and every visual element to empower you with a profound understanding of how to harness its capabilities.
🔬 THEORETICAL FOUNDATION: THE MATHEMATICS OF MARKET DYNAMICS
VoVix+ is built upon a multi-layered mathematical engine designed to measure what we call "second-order volatility." While standard indicators analyze price, and first-order volatility indicators (like ATR) analyze the range of price, VoVix+ analyzes the dynamics of the volatility itself. This provides insight into the market's underlying state of stability or chaos.
1. The VoVix Score: Measuring Volatility Thrust
The core of the system begins with the VoVix Score. This is a normalized measure of volatility acceleration or deceleration.
Mathematical Formula:
VoVix Score = (ATR(fast) - ATR(slow)) / (StDev(ATR(fast)) + ε)
Where:
ATR(fast) is the Average True Range over a short period, representing current, immediate volatility.
ATR(slow) is the Average True Range over a longer period, representing the baseline or established volatility.
StDev(ATR(fast)) is the Standard Deviation of the fast ATR, which measures the "noisiness" or consistency of recent volatility.
ε (epsilon) is a very small number to prevent division by zero.
Market Implementation:
Positive Score (Expansion): When the fast ATR is significantly higher than the slow ATR, it indicates a rapid increase in volatility. The market is "stretching" or expanding.
Negative Score (Contraction): When the fast ATR falls below the slow ATR, it indicates a decrease in volatility. The market is "coiling" or contracting.
Normalization: By dividing by the standard deviation, we normalize the score. This turns it into a standardized measure, allowing us to compare volatility thrust across different market conditions and timeframes. A score of 2.0 in a quiet market means the same, relatively, as a score of 2.0 in a volatile market.
2. Deviation Analysis (DEV): Gauging Volatility's Own Volatility
The script then takes the analysis a step further. It calculates the standard deviation of the VoVix Score itself.
Mathematical Formula:
DEV = StDev(VoVix Score, lookback_period)
Market Implementation:
This DEV value represents the magnitude of chaos or stability in the market's volatility dynamics. A high DEV value means the volatility thrust is erratic and unpredictable. A low DEV value suggests the change in volatility is smooth and directional.
3. The DEVMA Crossover: Identifying Regime Shifts
This is the primary signal generator. We take two moving averages of the DEV value.
Mathematical Formula:
fastDEVMA = SMA(DEV, fast_period)
slowDEVMA = SMA(DEV, slow_period)
The Core Signal:
The strategy triggers on the crossover and crossunder of these two DEVMA lines. This is a profound concept: we are not looking at a moving average of price or even of volatility, but a moving average of the standard deviation of the normalized rate of change of volatility.
Bullish Crossover (fastDEVMA > slowDEVMA): This signals that the short-term measure of volatility's chaos is increasing relative to the long-term measure. This often precedes a significant market expansion and is interpreted as a bullish volatility regime.
Bearish Crossunder (fastDEVMA < slowDEVMA): This signals that the short-term measure of volatility's chaos is decreasing. The market is settling down or contracting, often leading to trending moves or range consolidation.
⚙️ INPUTS MENU: CONFIGURING YOUR ANALYSIS ENGINE
Every input has been meticulously designed to give you full control over the strategy's behavior. Understanding these settings is key to adapting VoVix+ to your specific instrument, timeframe, and trading style.
🌀 VoVix DEVMA Configuration
🧬 Deviation Lookback: This sets the lookback period for calculating the DEV value. It defines the window for measuring the stability of the VoVix Score. A shorter value makes the system highly reactive to recent changes in volatility's character, ideal for scalping. A longer value provides a smoother, more stable reading, better for identifying major, long-term regime shifts.
⚡ Fast VoVix Length: This is the lookback period for the fastDEVMA. It represents the short-term trend of volatility's chaos. A smaller number will result in a faster, more sensitive signal line that reacts quickly to market shifts.
🐌 Slow VoVix Length: This is the lookback period for the slowDEVMA. It represents the long-term, baseline trend of volatility's chaos. A larger number creates a more stable, slower-moving anchor against which the fast line is compared.
How to Optimize: The relationship between the Fast and Slow lengths is crucial. A wider gap (e.g., 20 and 60) will result in fewer, but potentially more significant, signals. A narrower gap (e.g., 25 and 40) will generate more frequent signals, suitable for more active trading styles.
🧠 Adaptive Intelligence
🧠 Enable Adaptive Features: When enabled, this activates the strategy's performance tracking module. The script will analyze the outcome of its last 50 trades to calculate a dynamic win rate.
⏰ Adaptive Time-Based Exit: If Enable Adaptive Features is on, this allows the strategy to adjust its Maximum Bars in Trade setting based on performance. It learns from the average duration of winning trades. If winning trades tend to be short, it may shorten the time exit to lock in profits. If winners tend to run, it will extend the time exit, allowing trades more room to develop. This helps prevent the strategy from cutting winning trades short or holding losing trades for too long.
⚡ Intelligent Execution
📊 Trade Quantity: A straightforward input that defines the number of contracts or shares for each trade. This is a fixed value for consistent position sizing.
🛡️ Smart Stop Loss: Enables the dynamic stop-loss mechanism.
🎯 Stop Loss ATR Multiplier: Determines the distance of the stop loss from the entry price, calculated as a multiple of the current 14-period ATR. A higher multiplier gives the trade more room to breathe but increases risk per trade. A lower multiplier creates a tighter stop, reducing risk but increasing the chance of being stopped out by normal market noise.
💰 Take Profit ATR Multiplier: Sets the take profit target, also as a multiple of the ATR. A common practice is to set this higher than the Stop Loss multiplier (e.g., a 2:1 or 3:1 reward-to-risk ratio).
🏃 Use Trailing Stop: This is a powerful feature for trend-following. When enabled, instead of a fixed stop loss, the stop will trail behind the price as the trade moves into profit, helping to lock in gains while letting winners run.
🎯 Trail Points & 📏 Trail Offset ATR Multipliers: These control the trailing stop's behavior. Trail Points defines how much profit is needed before the trail activates. Trail Offset defines how far the stop will trail behind the current price. Both are based on ATR, making them fully adaptive to market volatility.
⏰ Maximum Bars in Trade: This is a time-based stop. It forces an exit if a trade has been open for a specified number of bars, preventing positions from being held indefinitely in stagnant markets.
⏰ Session Management
These inputs allow you to confine the strategy's trading activity to specific market hours, which is crucial for day trading instruments that have defined high-volume sessions (e.g., stock market open).
🎨 Visual Effects & Dashboard
These toggles give you complete control over the on-chart visuals and the dashboard. You can disable any element to declutter your chart or focus only on the information that matters most to you.
📊 THE DASHBOARD: YOUR AT-A-GLANCE COMMAND CENTER
The dashboard centralizes all critical information into one compact, easy-to-read panel. It provides a real-time summary of the market state and strategy performance.
🎯 VOVIX ANALYSIS
Fast & Slow: Displays the current numerical values of the fastDEVMA and slowDEVMA. The color indicates their direction: green for rising, red for falling. This lets you see the underlying momentum of each line.
Regime: This is your most important environmental cue. It tells you the market's current state based on the DEVMA relationship. 🚀 EXPANSION (Green) signifies a bullish volatility regime where explosive moves are more likely. ⚛️ CONTRACTION (Purple) signifies a bearish volatility regime, where the market may be consolidating or entering a smoother trend.
Quality: Measures the strength of the last signal based on the magnitude of the DEVMA difference. An ELITE or STRONG signal indicates a high-conviction setup where the crossover had significant force.
PERFORMANCE
Win Rate & Trades: Displays the historical win rate of the strategy from the backtest, along with the total number of closed trades. This provides immediate feedback on the strategy's historical effectiveness on the current chart.
EXECUTION
Trade Qty: Shows your configured position size per trade.
Session: Indicates whether trading is currently OPEN (allowed) or CLOSED based on your session management settings.
POSITION
Position & PnL: Displays your current position (LONG, SHORT, or FLAT) and the real-time Profit or Loss of the open trade.
🧠 ADAPTIVE STATUS
Stop/Profit Mult: In this simplified version, these are placeholders. The primary adaptive feature currently modifies the time-based exit, which is reflected in how long trades are held on the chart.
🎨 THE VISUAL UNIVERSE: DECIPHERING MARKET GEOMETRY
The visuals are not mere decorations; they are geometric representations of the underlying mathematical concepts, designed to give you an intuitive feel for the market's state.
The Core Lines:
FastDEVMA (Green/Maroon Line): The primary signal line. Green when rising, indicating an increase in short-term volatility chaos. Maroon when falling.
SlowDEVMA (Aqua/Orange Line): The baseline. Aqua when rising, indicating a long-term increase in volatility chaos. Orange when falling.
🌊 Morphism Flow (Flowing Lines with Circles):
What it represents: This visualizes the momentum and strength of the fastDEVMA. The width and intensity of the "beam" are proportional to the signal strength.
Interpretation: A thick, steep, and vibrant flow indicates powerful, committed momentum in the current volatility regime. The floating '●' particles represent kinetic energy; more particles suggest stronger underlying force.
📐 Homotopy Paths (Layered Transparent Boxes):
What it represents: These layered boxes are centered between the two DEVMA lines. Their height is determined by the DEV value.
Interpretation: This visualizes the overall "volatility of volatility." Wider boxes indicate a chaotic, unpredictable market. Narrower boxes suggest a more stable, predictable environment.
🧠 Consciousness Field (The Grid):
What it represents: This grid provides a historical lookback at the DEV range.
Interpretation: It maps the recent "consciousness" or character of the market's volatility. A consistently wide grid suggests a prolonged period of chaos, while a narrowing grid can signal a transition to a more stable state.
📏 Functorial Levels (Projected Horizontal Lines):
What it represents: These lines extend from the current fastDEVMA and slowDEVMA values into the future.
Interpretation: Think of these as dynamic support and resistance levels for the volatility structure itself. A crossover becomes more significant if it breaks cleanly through a prior established level.
🌊 Flow Boxes (Spaced Out Boxes):
What it represents: These are compact visual footprints of the current regime, colored green for Expansion and red for Contraction.
Interpretation: They provide a quick, at-a-glance confirmation of the dominant volatility flow, reinforcing the background color.
Background Color:
This provides an immediate, unmistakable indication of the current volatility regime. Light Green for Expansion and Light Aqua/Blue for Contraction, allowing you to assess the market environment in a split second.
📊 BACKTESTING PERFORMANCE REVIEW & ANALYSIS
The following is a factual, transparent review of a backtest conducted using the strategy's default settings on a specific instrument and timeframe. This information is presented for educational purposes to demonstrate how the strategy's mechanics performed over a historical period. It is crucial to understand that these results are historical, apply only to the specific conditions of this test, and are not a guarantee or promise of future performance. Market conditions are dynamic and constantly change.
Test Parameters & Conditions
To ensure the backtest reflects a degree of real-world conditions, the following parameters were used. The goal is to provide a transparent baseline, not an over-optimized or unrealistic scenario.
Instrument: CME E-mini Nasdaq 100 Futures (NQ1!)
Timeframe: 5-Minute Chart
Backtesting Range: March 24, 2024, to July 09, 2024
Initial Capital: $100,000
Commission: $0.62 per contract (A realistic cost for futures trading).
Slippage: 3 ticks per trade (A conservative setting to account for potential price discrepancies between order placement and execution).
Trade Size: 1 contract per trade.
Performance Overview (Historical Data)
The test period generated 465 total trades , providing a statistically significant sample size for analysis, which is well above the recommended minimum of 100 trades for a strategy evaluation.
Profit Factor: The historical Profit Factor was 2.663 . This metric represents the gross profit divided by the gross loss. In this test, it indicates that for every dollar lost, $2.663 was gained.
Percent Profitable: Across all 465 trades, the strategy had a historical win rate of 84.09% . While a high figure, this is a historical artifact of this specific data set and settings, and should not be the sole basis for future expectations.
Risk & Trade Characteristics
Beyond the headline numbers, the following metrics provide deeper insight into the strategy's historical behavior.
Sortino Ratio (Downside Risk): The Sortino Ratio was 6.828 . Unlike the Sharpe Ratio, this metric only measures the volatility of negative returns. A higher value, such as this one, suggests that during this test period, the strategy was highly efficient at managing downside volatility and large losing trades relative to the profits it generated.
Average Trade Duration: A critical characteristic to understand is the strategy's holding period. With an average of only 2 bars per trade , this configuration operates as a very short-term, or scalping-style, system. Winning trades averaged 2 bars, while losing trades averaged 4 bars. This indicates the strategy's logic is designed to capture quick, high-probability moves and exit rapidly, either at a profit target or a stop loss.
Conclusion and Final Disclaimer
This backtest demonstrates one specific application of the VoVix+ framework. It highlights the strategy's behavior as a short-term system that, in this historical test on NQ1!, exhibited a high win rate and effective management of downside risk. Users are strongly encouraged to conduct their own backtests on different instruments, timeframes, and date ranges to understand how the strategy adapts to varying market structures. Past performance is not indicative of future results, and all trading involves significant risk.
🔧 THE DEVELOPMENT PHILOSOPHY: FROM VOLATILITY TO CLARITY
The journey to create VoVix+ began with a simple question: "What drives major market moves?" The answer is often not a change in price direction, but a fundamental shift in market volatility. Standard indicators are reactive to price. We wanted to create a system that was predictive of market state. VoVix+ was designed to go one level deeper—to analyze the behavior, character, and momentum of volatility itself.
The challenge was twofold. First, to create a robust mathematical model to quantify these abstract concepts. This led to the multi-layered analysis of ATR differentials and standard deviations. Second, to make this complex data intuitive and actionable. This drove the creation of the "Visual Universe," where abstract mathematical values are translated into geometric shapes, flows, and fields. The adaptive system was intentionally kept simple and transparent, focusing on a single, impactful parameter (time-based exits) to provide performance feedback without becoming an inscrutable "black box." The result is a tool that is both profoundly deep in its analysis and remarkably clear in its presentation.
⚠️ RISK DISCLAIMER AND BEST PRACTICES
VoVix+ is an advanced analytical tool, not a guarantee of future profits. All financial markets carry inherent risk. The backtesting results shown by the strategy are historical and do not guarantee future performance. This strategy incorporates realistic commission and slippage settings by default, but market conditions can vary. Always practice sound risk management, use position sizes appropriate for your account equity, and never risk more than you can afford to lose. It is recommended to use this strategy as part of a comprehensive trading plan. This was developed specifically for Futures
"The prevailing wisdom is that markets are always right. I take the opposite view. I assume that markets are always wrong. Even if my assumption is occasionally wrong, I use it as a working hypothesis."
— George Soros
— Dskyz, Trade with insight. Trade with anticipation.
Fear and Greed Indicator [DunesIsland]The Fear and Greed Indicator is a TradingView indicator that measures market sentiment using five metrics. It displays:
Tiny green circles below candles when the market is in "Extreme Fear" (index ≤ 25), signalling potential buys.
Tiny red circles above candles when the market is in "Greed" (index > 75), indicating potential sells.
Purpose: Helps traders spot market extremes for contrarian trading opportunities.Components (each weighted 20%):
Market Momentum: S&P 500 (SPX) vs. its 125-day SMA, normalized over 252 days.
Stock Price Strength: Net NYSE 52-week highs (INDEX:HIGN) minus lows (INDEX:LOWN), normalized.
Put/Call Ratio: 5-day SMA of Put/Call Ratio (USI:PC).
Market Volatility: VIX (VIX), inverted and normalized.
Stochastic RSI: 14-period RSI on SPX with 3-period Stochastic SMA.
Alerts:
Buy: Index ≤ 25 ("Extreme Fear - Potential Buy").
Sell: Index > 75 ("Greed - Potential Sell").
HL/OL Histogram + (Close-Open)🧠 Core Concept
This indicator is designed to detect meaningful directional intent in price action using a combination of:
Intrabar candle structure (high - open, open - low)
Net price momentum (close - open)
Timed trigger levels (frozen buy/sell prices based on selected timeframe closes)
The core idea is to visually separate bullish and bearish energy in the current bar, and to mark the price at which momentum flips from down to up or vice versa, based on a change in the close - open differential.
🔍 Components Breakdown
1. Histogram Bars
Green Bars (high - open): Represent bullish upper wicks, showing intrabar strength above the open.
Red Bars (open - low): Represent bearish lower wicks, showing pressure below the open.
Plotted as histograms above and below the zero line.
2. Close–Open Line (White)
Plots the difference between close and open for each bar.
Helps you visually track when momentum flips from negative to positive, or vice versa.
A bold black zero line provides clear reference for these flips.
3. Buy/Sell Signal Logic
A Buy Trigger is generated when close - open crosses above zero
A Sell Trigger occurs when close - open crosses below zero
These trigger events are one-shot, meaning they’re only registered once per signal direction. No retriggers occur until the opposite condition is met.
📈 Trigger Price Table (Static)
On a signal trigger, the close price from a lower timeframe (15S, 30S, 1, 2, 3, or 5 min) is captured.
This price is frozen and displayed in a table at the top-right of the pane.
The price remains fixed until the opposite trigger condition fires, at which point it is replaced.
Why close price?
Using the close from the lower timeframe gives a precise, decisive reference point — ideal for planning limit entries or confirming breakout commitment.
🛠️ Use Cases
Momentum traders can use the histogram and line to time entries after strong open rejection or close breakouts.
Scalpers can quickly gauge intrabar sentiment reversals and react to new momentum without waiting for candle closes.
Algo builders can use the frozen price logic as precise entry or confirmation points in automated strategies.
VIX-Price Covariance MonitorThe VIX-Price Covariance Monitor is a statistical tool that measures the evolving relationship between a security's price and volatility indices such as the VIX (or VVIX).
It can give indication of potential market reversal, as typically, volatility and the VIX increase before markets turn red,
This indicator calculates the Pearson correlation coefficient using the formula:
ρ(X,Y) = cov(X,Y) / (σₓ × σᵧ)
Where:
ρ is the correlation coefficient
cov(X,Y) is the covariance between price and the volatility index
σₓ and σᵧ are the standard deviations of price and the volatility index
Enjoy!
Features
Dual Correlation Periods: Analyze both short-term and long-term correlation trends simultaneously
Adaptive Color Coding: Correlation strength is visually represented through color intensity
Market Condition Assessment: Automatic interpretation of correlation values into actionable market insights
Leading/Lagging Analysis: Optional time-shift analysis to detect predictive relationships
Detailed Information Panel: Real-time statistics including current correlation values, historical averages, and trading implications
Interpretation
Positive Correlation (Red): Typically bearish for price, as rising VIX correlates with falling markets. This is what traders should be looking for.
Negative Correlation (Green): Typically bullish for price, as falling VIX correlates with rising markets
How to use it
Apply the indicator to any chart to see its correlation with the default VIX index
Adjust the correlation length to match your trading timeframe (shorter for day trading, longer for swing trading)
Enable the secondary correlation period to compare different timeframes simultaneously
For advanced analysis, enable the Leading/Lagging feature to detect if VIX changes precede or follow price movements
Use the information panel to quickly assess the current market condition and potential trading implications
Time-Specific Volume AverageA volume indicator based on historic volume.
Checks for the average volume in the past few days at the same time of day. This helps you determine when there is truly volume in the markets.
We will see often see sustained volume above the average during a clear trend. If you see spikes in volume without it being sustained above the average, it is very likely that the trend will die off quickly.
This is very helpful in determining whether to trade based on a trend following system, or a range based system.
Settings are below:
Days to average: Number of days to look back(tradingview has limits depending on your plan)
SMA Length: Number of "volume averages" to look at. Keep this at 1 if you want the average volume at the exact moment in the day. If you increase it, will also average in the past few candles of "volume averages".
SMA Multiplier: Multiplies the SMA by this amount(helps to get higher quality trends)
Dynamic Ray BandsAbout Dynamic Ray Bands
Dynamic Ray Bands is a volatility-adaptive envelope indicator that adjusts in real time to evolving market conditions. It uses a Double Exponential Moving Average (DEMA) as its central trend reference, with upper and lower bands scaled according to current volatility measured by the Average True Range (ATR).
This creates a dynamic structure that visually frames price action, helping traders identify areas of potential trend continuation, overextension, or mean reversion.
How It Works
🟡 Centerline (DEMA)
The central yellow line is a Double Exponential Moving Average, which offers a smoother, less laggy trend signal than traditional moving averages. It represents the market’s short- to medium-term “equilibrium.”
🔵 Outer Bands
Plotted at:
Upper Band = DEMA + (ATR × outerMultiplier)
Lower Band = DEMA - (ATR × outerMultiplier)
These bands define the extreme bounds of current volatility. When price breaks above or below them, it can signal strong directional momentum or overbought/oversold conditions, depending on context. They're often used as trend breakout zones or to time exits after extended runs.
🟣 Inner Bands
Plotted closer to the DEMA:
Inner Upper = DEMA + (ATR × innerMultiplier)
Inner Lower = DEMA - (ATR × innerMultiplier)
These are preliminary volatility thresholds, offering early cues for potential expansion or reversal. They may be used for scalping, tight stop zones, or pre-breakout positioning.
🔁 Dynamic Width (Bands are Dynamically Adjusted Per Tick)
The width of both inner and outer bands is based on ATR (Average True Range), which is recalculated in real time. This means:
During high volatility, the bands expand, allowing for wider price fluctuations.
During low volatility, the bands contract, tightening range expectations.
Unlike fixed-width channels or standard Bollinger Bands (which use standard deviation), this per-tick adjustment via ATR enables Dynamic Ray Bands to reduce false signals in choppy markets and remain more reactive during trending conditions.
⚙️ Inputs
DMA Length — Period for the central DEMA.
ATR Length — Lookback used for ATR volatility calculations.
Outer Band Multiplier — Controls sensitivity of extreme bands.
Inner Band Multiplier — Controls proximity of inner bands.
Show Inner Bands — Toggle for plotting the inner zone.
🔔 Alerts
Alert conditions are included for:
Price closing above/below the outer bands (trend momentum or overextension)
Price closing above/below the inner bands (early signs of strength/weakness)
🧭 Use Cases
Breakout detection — Catch price continuation beyond the outer bands.
Volatility filtering — Adjust trade logic based on band width.
Mean reversion — Monitor for snapbacks toward the DEMA after price stretches too far.
Trend guidance — Use band slope and price position to confirm direction.
⚠️ Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice or a recommendation to trade any specific market or security. Always test indicators thoroughly before using them in live trading.
Adaptive Causal Wavelet Trend FilterThe Adaptive Causal Wavelet Trend Filter is a technical indicator implementing causal approximations of wavelet transform properties for better trend detection with adaptive volatility response.
The Adaptive Causal Wavelet Trend Filter (ACWTF) applies mathematical principles derived from wavelet analysis to financial time series, providing robust trend identification with minimal lag. Unlike conventional moving averages, it preserves significant price movements while filtering market noise through signal processing that i describe below.
I was inspired to build this indicator after reading " Wavelet-Based Trend Identification in Financial Time Series " by In, F., & Kim, S. 2013 and reading about Mexican Hat wavelet filters.
The ACWTF maintains optimal performance across varying market regimes without requiring parameter adjustments by adapting filter characteristics to current volatility conditions.
Mathematical Foundation
Inspired by the Mexican Hat wavelet (Ricker wavelet), this indicator implements causal approximations of wavelet filters optimized for real-time financial analysis. The multi-resolution approach identifies features at different scales and the adaptive component dynamically adjusts filtering characteristics based on local volatility measurements.
Key mathematical properties include:
Non-linear frequency response adaptation
Edge-preserving signal extraction
Scale-space analysis through dual filter implementation
Volatility-dependent coefficient adjustment, which I love
Filter Methods
Adaptive: Implements a volatility-weighted combination of multiple filter types to optimize the time-frequency resolution trade-off
Hull: Provides a causal approximation of wavelet edge detection properties with forward-projection characteristics
VWMA: Incorporates volume information into the filtering process for enhanced signal detection
EMA Cascade: Creates a multi-pole filter structure that approximates certain wavelet scaling properties
Suggestion: try all as they will provide slightly different signals. Try also different time-frames.
Practical Applications
Trend Direction Identification: Clear visual trend direction with reduced noise and lag
Regime Change Detection: Early identification of significant trend reversals
Market Condition Analysis: Integrated volatility metrics provide context for current market behavior
Multi-timeframe Confirmation: Alignment between primary and secondary filters offers additional confirmation
Entry/Exit Timing: Filter crossovers and trend changes provide potential trading signals
The comprehensive information panel provides:
Current filter method and trend state
Trend alignment between timeframes
Real-time volatility assessment
Price position relative to filter
Overall trading bias based on multiple factors
Implementation Notes
Log returns option provides improved statistical properties for financial time series
Primary and secondary filter lengths can be adjusted to optimize for specific instruments and timeframes
The indicator performs particularly well during trend transitions and regime changes
The indicator reduces the need for using additional indicators to check trend reversion
Capital Risk OptimizerCapital Risk Optimizer 🛡️
The Capital Risk Optimizer is an educational tool designed to help traders study capital efficiency, risk management, and scaling strategies when using leverage.
This script calculates and visualizes essential metrics for managing leveraged positions, including:
Entry Price – The current market price.
Stop Loss Level – Automatically derived using the 30-bar lowest low minus 1 ATR (default: 14-period ATR), an approach designed to create a dynamic, volatility-adjusted stop loss.
Stop Loss Distance (%) – The percentage distance between entry and stop.
Maximum Safe Leverage – The highest leverage allowable without risking liquidation before your stop is reached.
Margin Required – The amount of collateral necessary to support the desired position size at the calculated leverage.
Position Size – The configurable notional value of your trade.
These outputs are presented in a clean, customizable table overlay so you can quickly understand how position sizing, volatility, and leverage interact.
By default, the script uses a 14-period ATR combined with the lowest low of the past 30 bars, providing an optimal balance between sensitivity and noise for defining stop placement. This methodology helps traders account for market volatility in a systematic way.
The Capital Risk Optimizer is particularly useful as a portfolio management tool, supporting traders who want to study how to scale into positions using risk-adjusted sizing and capital efficiency principles. It pairs best with backtested strategies, and does not directly produce signals of any kind.
How to Use:
Set your desired position size.
Adjust the ATR and lookback settings to fine-tune stop loss placement.
Study the resulting leverage and margin requirements in real time.
Use this information to simulate and visualize potential trade scenarios and capital allocation models.
Disclaimer:
This script is provided for educational and informational purposes only. It does not constitute financial advice and should not be relied upon for live trading decisions. Always do your own research and consult with a qualified professional before making any trading or investment decisions.
K Bands v2.2K Bands v2 - Settings Breakdown (Timeframe Agnostic)
K Bands v2 is an adaptive volatility envelope tool designed for flexibility across different trading
styles and timeframes.
The settings below allow complete control over how the bands are constructed, smoothed, and how
they respond to market volatility.
1. Upstream MA Type
Controls the core smoothing applied to price before calculating the bands.
Options:
- EMA: Fast, responsive, reacts quickly to price changes.
- SMA: Classic moving average, slower but provides stability.
- Hull: Ultra smooth, reduces noise significantly but may react differently to choppy conditions.
- GeoMean: Geometric mean smoothing, creates a unique, slightly smoother line.
- SMMA: Wilder-style smoothing, balances noise reduction and responsiveness.
- WMA: Weighted Moving Average, emphasizes recent price action for sharper responsiveness.
2. Smoothing Length
Lookback period for the upstream moving average.
- Lower values: Faster reaction, captures short-term shifts.
- Higher values: Smoother trend depiction, filters out noise.
3. Multiplier
Determines the width of the bands relative to calculated volatility.
- Lower multiplier: Tighter bands, more signals, but increased false breakouts.
- Higher multiplier: Wider bands, fewer false signals, more conservative.
4. Downstream MA Type
Applies final smoothing to the band plots after initial calculation.
Same options as Upstream MA.
5. Downstream Smoothing Length
Lookback period for downstream smoothing.
- Lower: More responsive bands.
- Higher: Smoother, visually cleaner bands.
6. Band Width Source
Selects the method used to calculate band width based on market volatility.
Options:
- ATR (Average True Range): Smooth, stable bands based on price range expansion.
- Stdev (Standard Deviation): More reactive bands highlighting short-term volatility spikes.
7. ATR Smoothing Type
Controls how the ATR or Stdev value is smoothed before applying to band width.
Options:
- Wilder: Classic, stable smoothing.
- SMA: Simple moving average smoothing.
- EMA: Faster, more reactive smoothing.
- Hull: Ultra-smooth, noise-reducing smoothing.
- GeoMean: Geometric mean smoothing.
8. ATR Length
Lookback period for smoothing the volatility measurement (ATR or Stdev).
- Lower: More reactive bands, captures quick shifts.
- Higher: Smoother, more stable bands.
9. Dynamic Multiplier Based on Volatility
Allows the band multiplier to adapt automatically to changes in market volatility.
- ON: Bands expand during high volatility and contract during low volatility.
- OFF: Bands remain fixed based on the set multiplier.
10. Dynamic Multiplier Sensitivity
Controls how aggressively the dynamic multiplier responds to volatility changes.
- Lower values: Subtle adjustments.
- Higher values: More aggressive band expansion/contraction.
K Bands v2 is designed to be adaptable across any market or timeframe, helping visualize price
structure, trend, and volatility behavior.
Kelly Optimal Leverage IndicatorThe Kelly Optimal Leverage Indicator mathematically applies Kelly Criterion to determine optimal position sizing based on market conditions.
This indicator helps traders answer the critical question: "How much capital should I allocate to this trade?"
Note that "optimal position sizing" does not equal the position sizing that you should have. The Optima position sizing given by the indicator is based on historical data and cannot predict a crash, in which case, high leverage could be devastating.
Originally developed for gambling scenarios with known probabilities, the Kelly formula has been adapted here for financial markets to dynamically calculate the optimal leverage ratio that maximizes long-term capital growth while managing risk.
Key Features
Kelly Position Sizing: Uses historical returns and volatility to calculate mathematically optimal position sizes
Multiple Risk Profiles: Displays Full Kelly (aggressive), 3/4 Kelly (moderate), 1/2 Kelly (conservative), and 1/4 Kelly (very conservative) leverage levels
Volatility Adjustment: Automatically recommends appropriate Kelly fraction based on current market volatility
Return Smoothing: Option to use log returns and smoothed calculations for more stable signals
Comprehensive Table: Displays key metrics including annualized return, volatility, and recommended exposure levels
How to Use
Interpret the Lines: Each colored line represents a different Kelly fraction (risk tolerance level). When above zero, positive exposure is suggested; when below zero, reduce exposure. Note that this is based on historical returns. I personally like to increase my exposure during market downturns, but this is hard to illustrate in the indicator.
Monitor the Table: The information panel provides precise leverage recommendations and exposure guidance based on current market conditions.
Follow Recommended Position: Use the "Recommended Position" guidance in the table to determine appropriate exposure level.
Select Your Risk Profile: Conservative traders should follow the Half Kelly or Quarter Kelly lines, while more aggressive traders might consider the Three-Quarter or Full Kelly lines.
Adjust with Volatility: During high volatility periods, consider using more conservative Kelly fractions as recommended by the indicator.
Mathematical Foundation
The indicator calculates the optimal leverage (f*) using the formula:
f* = μ/σ²
Where:
μ is the annualized expected return
σ² is the annualized variance of returns
This approach balances potential gains against risk of ruin, offering a scientific framework for position sizing that maximizes long-term growth rate.
Notes
The Full Kelly is theoretically optimal for maximizing long-term growth but can experience significant drawdowns. You should almost never use full kelly.
Most practitioners use fractional Kelly strategies (1/2 or 1/4 Kelly) to reduce volatility while capturing most of the growth benefits
This indicator works best on daily timeframes but can be applied to any timeframe
Negative Kelly values suggest reducing or eliminating market exposure
The indicator should be used as part of a complete trading system, not in isolation
Enjoy the indicator! :)
P.S. If you are really geeky about the Kelly Criterion, I recommend the book The Kelly Capital Growth Investment Criterion by Edward O. Thorp and others.
EVaR Indicator and Position SizingThe Problem:
Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress.
Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing.
The Solution: Entropic Value at Risk (EVAR)
EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets.
Entropy is more adaptive than standard deviations and volatility measures.
I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui.
Key advantages of EVAR over traditional risk measures:
Superior tail risk capture: More accurately quantifies the probability of extreme market moves
Adaptability to market regimes: Self-calibrates to changing volatility environments
Non-parametric flexibility: Makes less assumptions about the underlying return distribution
Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :)
Mathematically, EVAR is defined as:
EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))}
Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior.
Technical Implementation
This indicator implements EVAR through a q-exponential approach from Tsallis statistics:
Returns Calculation: Price returns are calculated over the lookback period
Moment Generating Function: Approximated using q-exponentials to account for fat tails
EVAR Computation: Derived from the MGF and confidence parameter
Normalization: Scaled to for intuitive visualization
Position Sizing: Inversely modulated based on normalized EVAR
The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions.
Indicator Components
1. EVAR Risk Visualization
Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1)
Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones
2. Position Sizing Matrix
Risk Assessment: Current risk level and raw EVAR value
Position Recommendations: Percentage allocation, dollar value, and quantity
Stop Parameters: Mathematically derived stop price with percentage distance
Drawdown Projection: Maximum theoretical loss if stop is triggered
Interpretation and Application
The normalized EVAR reading provides a probabilistic risk assessment:
< 0.3: Low risk environment with minimal tail concerns
0.3-0.5: Moderate risk with standard tail behavior
0.5-0.7: Elevated risk with increased probability of significant moves
> 0.7: High risk environment with substantial tail risk present
Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated.
Parameter Optimization
For optimal risk assessment across market conditions:
Lookback Period: Determines the historical window for risk calculation
Q Parameter: Controls tail sensitivity (higher values increase conservatism)
Confidence Level: Sets the statistical threshold for risk assessment
For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches.
Practical Applications
Adaptive Risk Management: Quantify and respond to changing tail risk conditions
Volatility-Normalized Positioning: Maintain consistent exposure across market regimes
Black Swan Detection: Early identification of potential extreme market conditions
Portfolio Construction: Apply consistent risk-based sizing across diverse instruments
This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets.
Enjoy!
H BollingerBollinger Bands are a widely used technical analysis indicator that helps spot relative price highs and lows. The tool comprises three lines: a central band representing the 20-period simple moving average (SMA), and upper and lower bands usually placed two standard deviations above and below the SMA. These bands adjust with market volatility, offering insights into price fluctuations and trading conditions.
How this indicator works
Bollinger Bands helps traders assess price volatility and potential price reversals. They consist of three bands: the middle band, the upper band, and the lower band. Here's how Bollinger Bands work:
Middle band: This is typically a simple moving average (SMA) of the asset's price over a specified period. The most common period used is 20 days.
Upper band: This is calculated by adding a specified number of standard deviations to the middle band. The standard deviation measures the asset's price volatility. Commonly, two standard deviations are added to the middle band.
Lower band: Similar to the upper band, it is calculated by subtracting a specified number of standard deviations from the middle band.
What do Bollinger Bands tell you?
Bollinger bands primarily indicate the level of market volatility and trading opportunities. Narrow bands indicate low market volatility, while wide bands suggest high market volatility. Bollinger bands indicators can be used by traders to assess potential buy or sell signals. For instance, a sell signal may be interpreted or generated if the asset’s price moves closer or crosses the upper band, as it may indicate that the asset is overbought. Alternatively, a buy signal may be interpreted or generated if the price moves closer to the lower band, as it may signify that the asset is oversold.
However, traders should be cautious when using Bollinger Bands as standalone indicators when making trading decisions. Experienced traders refrain from confirming signals based on one indicator. Instead, they generally combine various technical indicators and fundamental analysis methods to make informed trading decisions. Basing trading decisions on only one indicator can result in misinterpretation of signals and heavy losses.
Bollinger Bands assist in identifying whether prices are relatively high or low. They are applied as a pair—upper and lower bands—alongside a moving average. However, these bands are not designed to be used in isolation. Instead, they should be used to validate signals generated by other technical indicators.
Calculation of Bollinger Band
Fear and Greed Index [DunesIsland]The Fear and Greed Index is a sentiment indicator designed to measure the emotions driving the stock market, specifically investor fear and greed. Fear represents pessimism and caution, while greed reflects optimism and risk-taking. This indicator aggregates multiple market metrics to provide a comprehensive view of market sentiment, helping traders and investors gauge whether the market is overly fearful or excessively greedy.How It WorksThe Fear and Greed Index is calculated using four key market indicators, each capturing a different aspect of market sentiment:
Market Momentum (30% weight)
Measures how the S&P 500 (SPX) is performing relative to its 125-day simple moving average (SMA).
A higher value indicates that the market is trading well above its moving average, signaling greed.
Stock Price Strength (20% weight)
Calculates the net number of stocks hitting 52-week highs minus those hitting 52-week lows on the NYSE.
A greater number of net highs suggests strong market breadth and greed.
Put/Call Options (30% weight)
Uses the 5-day average of the put/call ratio.
A lower ratio (more call options being bought) indicates greed, as investors are betting on rising prices.
Market Volatility (20% weight)
Utilizes the VIX index, which measures market volatility.
Lower volatility is associated with greed, as investors are less fearful of large market swings.
Each component is normalized using a z-score over a 252-day lookback period (approximately one trading year) and scaled to a range of 0 to 100. The final Fear and Greed Index is a weighted average of these four components, with the weights specified above.Key FeaturesIndex Range: The index value ranges from 0 to 100:
0–25: Extreme Fear (red)
25–50: Fear (orange)
50–75: Neutral (yellow)
75–100: Greed (green)
Dynamic Plot Color: The plot line changes color based on the index value, visually indicating the current sentiment zone.
Reference Lines: Horizontal lines are plotted at 0, 25, 50, 75, and 100 to represent the different sentiment levels: Extreme Fear, Fear, Neutral, Greed, and Extreme Greed.
How to Interpret
Low Values (0–25): Indicate extreme fear, which may suggest that the market is oversold and could be due for a rebound.
High Values (75–100): Indicate greed, which may signal that the market is overbought and could be at risk of a correction.
Neutral Range (25–75): Suggests a balanced market sentiment, neither overly fearful nor greedy.
This indicator is a valuable tool for contrarian investors, as extreme readings often precede market reversals. However, it should be used in conjunction with other technical and fundamental analysis tools for a well-rounded view of the market.
Institutional Momentum Scanner [IMS]Institutional Momentum Scanner - Professional Momentum Detection System
Hunt explosive price movements like the professionals. IMS identifies maximum momentum displacement within 10-bar windows, revealing where institutional money commits to directional moves.
KEY FEATURES:
▪ Scans for strongest momentum in rolling 10-bar windows (institutional accumulation period)
▪ Adaptive filtering reduces false signals using efficiency ratio technology
▪ Three clear states: LONG (green), SHORT (red), WAIT (gray)
▪ Dynamic volatility-adjusted thresholds (8% ATR-scaled)
▪ Visual momentum flow with glow effects for signal strength
BASED ON:
- Pocket Pivot concept (O'Neil/Morales) applied to price momentum
- Adaptive Moving Average principles (Kaufman KAMA)
- Market Wizards momentum philosophy
- Institutional order flow patterns (5-day verification window)
HOW IT WORKS:
The scanner finds the maximum price displacement in each 10-bar window - where the market showed its hand. An adaptive filter (5-bar regression) separates real moves from noise. When momentum exceeds the volatility-adjusted threshold, states change.
IDEAL FOR:
- Momentum traders seeking explosive moves
- Swing traders (especially 4H timeframe)
- Position traders wanting institutional footprints
- Anyone tired of false breakout signals
Default parameters (10,5) optimized for 4H charts but adaptable to any timeframe. Remember: The market rewards patience and punishes heroes. Wait for clear signals.
"The market is honest. Are you?"
Adiyogi Trend🟢🔴 “Adiyogi” Trend — Market Alignment Visualizer
“Adiyogi” Trend is a powerful, non-intrusive trend detection system built for traders who seek clarity, discipline, and alignment with true market flow. Inspired by the meditative stillness of Adiyogi and the need for mindful, high-probability decisions, this tool offers a clean and intuitive visual guide to trending environments — without cluttering the chart or pushing forced trades.
This is not a buy/sell signal generator. Instead, it is designed as a background confirmation engine that helps you stay on the right side of the market by identifying moments of true directional strength.
🧠 Core Logic
The “Adiyogi” Trend indicator highlights the background of your chart in green or red when multiple layers of strength and structure align — including momentum, market positioning, and relative force. Only when these internal components agree does the system activate a directional state.
It’s built on three foundational energies of trend confirmation:
Strength of movement
Structure in price action
Conviction in momentum
By combining these into one visual background, the indicator filters out indecision and helps you stay focused during real trend phases — whether you're day trading, swing trading, or holding longer-term positions.
📌 Core Concepts Behind the Tool
The indicator integrates three essential market filters—each confirming a different dimension of trend strength:
ADX (Average Directional Index) – Measures trend momentum.
You’ve chosen a very responsive setting (ADX Length = 2), which helps catch the earliest possible signs of momentum emergence.
The threshold is ADX ≥ 22, ensuring that weak or sideways markets are filtered out.
SuperTrend (10,1) – Captures short-term trend direction.
This setup follows price closely and reacts quickly to reversals, making it ideal for fast-moving assets or intraday strategies.
SuperTrend acts as the structural confirmation of directional bias.
RSI (Relative Strength Index) – Measures strength based on recent price closes.
You’ve configured RSI > 50 for bullish zones and < 50 for bearish—a neutral midpoint standard often used by professional traders.
This ensures that only trades in sync with momentum and recent strength are highlighted.
🌈 How It Visually Works
Background turns GREEN when:
ADX ≥ 22, indicating strong momentum
Price is above the 20 EMA and above SuperTrend (10,1)
RSI > 50, confirming recent strength
Background turns RED when:
ADX ≥ 22, indicating strong momentum
Price is below the 20 EMA and below SuperTrend (10,1)
RSI < 50, confirming recent weakness
The background remains neutral (transparent) when trend conditions are not clearly aligned—this is the tool's way of keeping you out of indecisive markets.
A label (BULL / BEAR) appears only when the bias flips from the previous one. This helps avoid repeated or redundant alerts, focusing your attention only when something changes.
📊 Practical Uses & Benefits
✅ Stay with the trend: Perfectly filters out choppy or sideways markets by only activating when conditions align across momentum, structure, and strength.
✅ Pre-trade confirmation: Use this tool to confirm trade setups from other indicators or price action patterns.
✅ Avoid noise: Prevent overtrading by focusing only on high-quality trend conditions.
✅ Visual clarity: Unlike arrows or plots that clutter the chart, this tool subtly highlights trend conditions in the background, preserving your price action view.
📍 Important Notes
This is not a buy/sell signal generator. It is a trend-confirmation system.
Use it in conjunction with your existing entry setups—such as breakouts, order blocks, retests, or candlestick patterns.
The tool helps you stay in sync with the dominant direction, especially when combining multiple timeframes.
Can be used on any market (stocks, forex, crypto, indices) and on any timeframe.
Faytterro Bands Breakout📌 Faytterro Bands Breakout 📌
This indicator was created as a strategy showcase for another script: Faytterro Bands
It’s meant to demonstrate a simple breakout strategy based on Faytterro Bands logic and includes performance tracking.
❓ What Is It?
This script is a visual breakout strategy based on a custom moving average and dynamic deviation bands, similar in concept to Bollinger Bands but with unique smoothing (centered regression) and performance features.
🔍 What Does It Do?
Detects breakouts above or below the Faytterro Band.
Plots visual trade entries and exits.
Labels each trade with percentage return.
Draws profit/loss lines for every trade.
Shows cumulative performance (compounded return).
Displays key metrics in the top-right corner:
Total Return
Win Rate
Total Trades
Number of Wins / Losses
🛠 How Does It Work?
Bullish Breakout: When price crosses above the upper band and stays above the midline.
Bearish Breakout: When price crosses below the lower band and stays below the midline.
Each trade is held until breakout invalidation, not a fixed TP/SL.
Trades are compounded, i.e., profits stack up realistically over time.
📈 Best Use Cases:
For traders who want to experiment with breakout strategies.
For visual learners who want to study past breakouts with performance metrics.
As a template to develop your own logic on top of Faytterro Bands.
⚠ Notes:
This is a strategy-like visual indicator, not an automated backtest.
It doesn't use strategy.* commands, so you can still use alerts and visuals.
You can tweak the logic to create your own backtest-ready strategy.
Unlike the original Faytterro Bands, this script does not repaint and is fully stable on closed candles.
Frahm Factor Position Size CalculatorThe Frahm Factor Position Size Calculator is a powerful evolution of the original Frahm Factor script, leveraging its volatility analysis to dynamically adjust trading risk. This Pine Script for TradingView uses the Frahm Factor’s volatility score (1-10) to set risk percentages (1.75% to 5%) for both Margin-Based and Equity-Based position sizing. A compact table on the main chart displays Risk per Trade, Frahm Factor, and Average Candle Size, making it an essential tool for traders aligning risk with market conditions.
Calculates a volatility score (1-10) using true range percentile rank over a customizable look-back window (default 24 hours).
Dynamically sets risk percentage based on volatility:
Low volatility (score ≤ 3): 5% risk for bolder trades.
High volatility (score ≥ 8): 1.75% risk for caution.
Medium volatility (score 4-7): Smoothly interpolated (e.g., 4 → 4.3%, 5 → 3.6%).
Adjustable sensitivity via Frahm Scale Multiplier (default 9) for tailored volatility response.
Position Sizing:
Margin-Based: Risk as a percentage of total margin (e.g., $175 for 1.75% of $10,000 at high volatility).
Equity-Based: Risk as a percentage of (equity - minimum balance) (e.g., $175 for 1.75% of ($15,000 - $5,000)).
Compact 1-3 row table shows:
Risk per Trade with Frahm score (e.g., “$175.00 (Frahm: 8)”).
Frahm Factor (e.g., “Frahm Factor: 8”).
Average Candle Size (e.g., “Avg Candle: 50 t”).
Toggles to show/hide Frahm Factor and Average Candle Size rows, with no empty backgrounds.
Four sizes: XL (18x7, large text), L (13x6, normal), M (9x5, small, default), S (8x4, tiny).
Repositionable (9 positions, default: top-right).
Customizable cell color, text color, and transparency.
Set Frahm Factor:
Frahm Window (hrs): Pick how far back to measure volatility (e.g., 24 hours). Shorter for fast markets, longer for chill ones.
Frahm Scale Multiplier: Set sensitivity (1-10, default 9). Higher makes the score jumpier; lower smooths it out.
Set Margin-Based:
Total Margin: Enter your account balance (e.g., $10,000). Risk auto-adjusts via Frahm Factor.
Set Equity-Based:
Total Equity: Enter your total account balance (e.g., $15,000).
Minimum Balance: Set to the lowest your account can go before liquidation (e.g., $5,000). Risk is based on the difference, auto-adjusted by Frahm Factor.
Customize Display:
Calculation Method: Pick Margin-Based or Equity-Based.
Table Position: Choose where the table sits (e.g., top_right).
Table Size: Select XL, L, M, or S (default M, small text).
Table Cell Color: Set background color (default blue).
Table Text Color: Set text color (default white).
Table Cell Transparency: Adjust transparency (0 = solid, 100 = invisible, default 80).
Show Frahm Factor & Show Avg Candle Size: Check to show these rows, uncheck to hide (default on).
Machine Learning Key Levels [AlgoAlpha]🟠 OVERVIEW
This script plots Machine Learning Key Levels on your chart by detecting historical pivot points and grouping them using agglomerative clustering to highlight price levels with the most past reactions. It combines a pivot detection, hierarchical clustering logic, and an optional silhouette method to automatically select the optimal number of key levels, giving you an adaptive way to visualize price zones where activity concentrated over time.
🟠 CONCEPTS
Agglomerative clustering is a bottom-up method that starts by treating each pivot as its own cluster, then repeatedly merges the two closest clusters based on the average distance between their members until only the desired number of clusters remain. This process creates a hierarchy of groupings that can flexibly describe patterns in how price reacts around certain levels. This offers an advantage over K-means clustering, since the number of clusters does not need to be predefined. In this script, it uses an average linkage approach, where distance between clusters is computed as the average pairwise distance of all contained points.
The script finds pivot highs and lows over a set lookback period and saves them in a buffer controlled by the Pivot Memory setting. When there are at least two pivots, it groups them using agglomerative clustering: it starts with each pivot as its own group and keeps merging the closest pairs based on their average distance until the desired number of clusters is left. This number can be fixed or chosen automatically with the silhouette method, which checks how well each point fits in its cluster compared to others (higher scores mean cleaner separation). Once clustering finishes, the script takes the average price of each cluster to create key levels, sorts them, and draws horizontal lines with labels and colors showing their strength. A metrics table can also display details about the clusters to help you understand how the levels were calculated.
🟠 FEATURES
Agglomerative clustering engine with average linkage to merge pivots into level groups.
Dynamic lines showing each cluster’s price level for clarity.
Labels indicating level strength either as percent of all pivots or raw counts.
A metrics table displaying pivot count, cluster count, silhouette score, and cluster size data.
Optional silhouette-based auto-selection of cluster count to adaptively find the best fit.
🟠 USAGE
Add the indicator to any chart. Choose how far back to detect pivots using Pivot Length and set Pivot Memory to control how many are kept for clustering (more pivots give smoother levels but can slow performance). If you want the script to pick the number of levels automatically, enable Auto No. Levels ; otherwise, set Number of Levels . The colored horizontal lines represent the calculated key levels, and circles show where pivots occurred colored by which cluster they belong to. The labels beside each level indicate its strength, so you can see which levels are supported by more pivots. If Show Metrics Table is enabled, you will see statistics about the clustering in the corner you selected. Use this tool to spot areas where price often reacts and to plan entries or exits around levels that have been significant over time. Adjust settings to better match volatility and history depth of your instrument.
