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").
Ketidakstabilan
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.
Price Extension from 8 EMAOverview
This indicator can be used to see how far away the price is from the 8 EMA. It compares this to the Average Daily Range % to see if the stock may be overextended. The "Extension Multiplier" represents how far the stock is extended away from the 8 EMA.
Core Concept
This indicator is best used for breakout trades that are trying to make sure they are not chasing the stock.
How to Use This Indicator
This tool is primarily intended for analyzing daily charts of individual stocks and is often used by breakout traders to evaluate potential entry areas.
If the stock is far away from the 8 EMA, it is likely not ready to break out. If it is close to the 8ema, it could be ready to move higher.
This indicator can also be used in the opposite way. For example, shorting or puts.
Understanding the colors
Green (Not Extended): Indicates the price is close to the 8 EMA. This often corresponds to periods of consolidation.
Yellow (Slightly Extended): The price is beginning to move away from the 8 EMA.
Orange (Extended): The price has moved a considerable distance from the 8 EMA.
Red (Very Extended): The price is at an extreme distance from the 8 EMA, historically increasing the likelihood of a pullback or consolidation.
Settings
Info Row Position: Adjusts the vertical position of the display table on the chart. Useful when using other indicators.
ADR Length: Sets the lookback period for calculating the Average Daily Range. Or the average range % for different timeframes.
Timeframe: Determines the timeframe for the EMA and ADR calculation (the default is Daily).
+ ATR Table and BracketsHi, all. I'm back with a new indicator—one I firmly believe could be one of the most valuable indicators you keep in your indicator toolshed—based around true range.
This is a simple, streamlined indicator utilizing true range and average true range that will help any trader with stoploss, trailing stoploss, and take-profit placement—things that I know many traders use average true range for. It could also be useful for trade entries as well, depending on the trader's style.
Typically, most traders (or at least what I've seen recommended across websites, video tutorials on YouTube, etc.) are taught to simply take the ATR number and use that, and possibly some sort of multiplier, as your stoploss and take-profit. This is fine, but I thought that it might be possible to dive a bit deeper into these values. Because an average is a combination of values, some higher, some lower, and we often see ATR spikes during periods of high volatility, I thought wouldn't it be useful to know what value those ATR spikes are, and how do they relate to the ATR? Then I thought to myself, well, what about the most volatile candle within that ATR (the candle with the greatest true range)? Couldn't knowing that value be useful to a trader? So then the idea of a table displaying these values, along with the ATR and the ATR times some multiplier number, would be a useful, simple way to display this information. That's what we have here.
The table is made up of two columns, one with the name of the metric being measured, and the other with its value. That's it. Simple.
As nice as this was, I thought an additional, great, and perhaps better, way to visualize this information would be in the form of brackets extending from the current bar. These are simply lines/labels plotted at the price values of the ATR, ATR times X, highest ATR, highest ATR times X, and highest TR value. These labels supply the actual values of the ATR, etc., but may also display the price if you should choose (both of these values are toggleable in the 'Inputs' section of the indicator.). Additionally, you can choose to display none of these labels, or all five if you wish (leaves the chart a bit cluttered, as shown in the image below), though I suspect you'll determine your preferences for which information you'd like to see and which not.
Chart with all five lines/labels displayed. I adjusted the ATRX value to 3 just to make the screenshot as legible as possible. Default is set to 1.5. As you can see, the label doesn't show the multiplier number, but the table does.
Here's a screenshot of the labels showing the price in addition to the value of the ATR, set to "Previous Closing Price," (see next paragraph for what that means) and highest TR. Personally, I don't see the value in the displaying the price, but I thought some people might want that. It's not available in the table as of now, but perhaps if I get enough requests for it I will add it.
That's basically it, but one last detail I need to go over is the dropdown box labeled "Bar Value ATR Levels are Oriented To." Firstly, this has no effect on Highest ATR, Highest ATRX, and Highest TR levels. Those are based on the ATR up to the last closed candle, meaning they aren't including the value of the currently open candle (this would be useless). However, knowing that different traders trade different ways it seemed to me prudent to allow for traders to select which opening or closing value the trader wishes to have the ATR brackets based on. For example, as someone who has consumed much No Nonsense Forex content I know that traders are urged to enter their trades in the last fifteen minutes of the trading day because the ATR is unlikely to change significantly in that period (ATR being the centerpiece of NNFX money management), so one of three selections here is to plot the brackets based on the ATR's inclusion of this value (this of course means the brackets will move while the candle is still open). The other options are to set the brackets to the current opening price, or the previous closing price. Depending on what you're trading many times these prices are virtually identical, but sometimes price gaps (stocks in particular), so, wanting your brackets placed relative to the previous close as opposed to the current open might be preferable for some traders.
And that's it. I really hope you guys like this indicator. I haven't seen anything closely similar to it on TradingView, and I think it will be something you all will find incredibly handy.
Please enjoy!
Momentum Regression [BackQuant]Momentum Regression
The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window.
Purpose & Design Philosophy
Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious.
The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes:
Volatility normalization to account for differences in scale and distribution.
Regression analysis to model the relationship between past and present standardized returns.
Deviation bands to highlight overbought/oversold zones around the predicted trendline.
Statistical summary tables to assess the reliability of the detected momentum.
Core Concepts and Calculations
The model uses the following:
Independent variable (x): The volatility-adjusted return over the chosen momentum period.
Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility.
A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct:
A predicted momentum trendline across time.
Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors).
These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average.
Key Metrics Provided
On each update, the indicator dynamically displays:
Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect.
Intercept (β₀): The predicted return when x = 0.
Pearson’s R: Correlation coefficient between x and y.
R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y.
Standard Error of Residuals: Measures dispersion around the trendline.
t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope.
These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators.
Features and Inputs
✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes.
✅ Custom Lookback Control : Set the number of bars to analyze regression.
✅ Extendable Trendlines : For continuous visualization into the future.
✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action.
✅ Contextual Tables : Help determine strength, direction, and significance of momentum.
✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart.
How It Helps Traders
📉 Quantitative Strategy Validation:
Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe.
🔍 Regime Detection:
Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts.
📊 Trade Filtering:
Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable.
📈 Backtest Preparation:
Before running costly simulations, use this tool to pre-screen assets for exploitable return structures.
When to Use It
Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable.
During market transitions : Detect early signs of fading strength or reversal.
As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters.
Conclusion
The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
Omori Law Recovery PhasesWhat is the Omori Law?
Originally a seismological model, the Omori Law describes how earthquake aftershocks decay over time. It follows a power law relationship: the frequency of aftershocks decreases roughly proportionally to 1/(t+c)^p, where:
t = time since the main shock
c = time offset constant
p = power law exponent (typically around 1.0)
Application to the markets
Financial markets experience "aftershocks" similar to earthquakes:
Market Crashes as Main Shocks: Major market declines (crashes) represent the initial shock event.
Volatility Decay: After a crash, market volatility typically declines following a power law pattern rather than a linear or exponential one.
Behavioral Components: The decay pattern reflects collective market psychology - initial panic gives way to uncertainty, then stabilization, and finally normalization.
The Four Recovery Phases
The Omori decay pattern in markets can be divided into distinct phases:
Acute Phase: Immediately after the crash, characterized by extreme volatility, panic selling, and sharp reversals. Trading is hazardous.
Reaction Phase: Volatility begins decreasing, but markets test previous levels. False rallies and retests of lows are common.
Repair Phase: Structure returns to the market. Volatility approaches normal levels, and traditional technical analysis becomes more reliable.
Recovery Phase: The final stage where market behavior normalizes completely. The impact of the original shock has fully decayed.
Why It Matters for Traders
Understanding where the market stands in this recovery cycle provides valuable context:
Risk Management: Adjust position sizing based on the current phase
Strategy Selection: Different strategies work in different phases
Psychological Preparation: Know what to expect based on the phase
Time Horizon Guidance: Each phase suggests appropriate time frames for trading
ATR Stop-Loss with Fibonacci Take-Profit [jpkxyz]ATR Stop-Loss with Fibonacci Take-Profit Indicator
This comprehensive indicator combines Average True Range (ATR) volatility analysis with Fibonacci extensions to create dynamic stop-loss and take-profit levels. It's designed to help traders set precise risk management levels and profit targets based on market volatility and mathematical ratios.
Two Operating Modes
Default Mode (Rolling Levels)
In default mode, the indicator continuously plots evolving stop-loss and take-profit levels based on real-time price action. These levels update dynamically as new bars form, creating rolling horizontal lines across the chart. I use this mode primarily to plot the rolling ATR-Level which I use to trail my Stop-Loss into profit.
Characteristics:
Levels recalculate with each new bar
All selected Fibonacci levels display simultaneously
Uses plot() functions with trackprice=true for price tracking
Custom Anchor Mode (Fixed Levels)
This is the primary mode for precision trading. You select a specific timestamp (typically your entry bar), and the indicator locks all calculations to that exact moment, creating fixed horizontal lines that represent your actual trade levels.
Characteristics:
Entry line (blue) marks your anchor point
Stop-loss calculated using ATR from the anchor bar
Fibonacci levels projected from entry-to-stop distance
Lines terminate when price breaks through them
Includes comprehensive alert system
Core Calculation Logic
ATR Stop-Loss Calculation:
Stop Loss = Entry Price ± (ATR × Multiplier)
Long positions: SL = Entry - (ATR × Multiplier)
Short positions: SL = Entry + (ATR × Multiplier)
ATR uses your chosen smoothing method (RMA, SMA, EMA, or WMA)
Default multiplier is 1.5, adjustable to your risk tolerance
Fibonacci Take-Profit Projection:
The distance from entry to stop-loss becomes the base unit (1.0) for Fibonacci extensions:
TP Level = Entry + (Entry-to-SL Distance × Fibonacci Ratio)
Available Fibonacci Levels:
Conservative: 0.618, 1.0, 1.618
Extended: 2.618, 3.618, 4.618
Complete range: 0.0 to 4.764 (23 levels total)
Multi-Timeframe Functionality
One of the indicator's most powerful features is timeframe flexibility. You can analyze on one timeframe while using stop-loss and take-profit calculations from another.
Best Practices:
Identify your entry point on execution timeframe
Enable "Custom Anchor" mode
Set anchor timestamp to your entry bar
Select appropriate analysis timeframe
Choose relevant Fibonacci levels
Enable alerts for automated notifications
Example Scenario:
Analyse trend on 4-hour chart
Execute entry on 5-minute chart for precision
Set custom anchor to your 5-minute entry bar
Configure timeframe setting to "4h" for swing-level targets
Select appropriate Fibonacci Extension levels
Result: Precise entry with larger timeframe risk management
Visual Intelligence System
Line Behaviour in Custom Anchor Mode:
Active levels: Lines extend to the right edge
Hit levels: Lines terminate at the breaking bar
Entry line: Always visible in blue
Stop-loss: Red line, terminates when hit
Take-profits: Green lines (1.618 level in gold for emphasis)
Customisation Options:
Line width (1-4 pixels)
Show/hide individual Fibonacci levels
ATR length and smoothing method
ATR multiplier for stop-loss distance
Rolling Log Returns [BackQuant]Rolling Log Returns
The Rolling Log Returns indicator is a versatile tool designed to help traders, quants, and data-driven analysts evaluate the dynamics of price changes using logarithmic return analysis. Widely adopted in quantitative finance, log returns offer several mathematical and statistical advantages over simple returns, making them ideal for backtesting, portfolio optimization, volatility modeling, and risk management.
What Are Log Returns?
In quantitative finance, logarithmic returns are defined as:
ln(Pₜ / Pₜ₋₁)
or for rolling periods:
ln(Pₜ / Pₜ₋ₙ)
where P represents price and n is the rolling lookback window.
Log returns are preferred because:
They are time additive : returns over multiple periods can be summed.
They allow for easier statistical modeling , especially when assuming normally distributed returns.
They behave symmetrically for gains and losses, unlike arithmetic returns.
They normalize percentage changes, making cross-asset or cross-timeframe comparisons more consistent.
Indicator Overview
The Rolling Log Returns indicator computes log returns either on a standard (1-period) basis or using a rolling lookback period , allowing users to adapt it to short-term trading or long-term trend analysis.
It also supports a comparison series , enabling traders to compare the return structure of the main charted asset to another instrument (e.g., SPY, BTC, etc.).
Core Features
✅ Return Modes :
Normal Log Returns : Measures ln(price / price ), ideal for day-to-day return analysis.
Rolling Log Returns : Measures ln(price / price ), highlighting price drift over longer horizons.
✅ Comparison Support :
Compare log returns of the primary instrument to another symbol (like an index or ETF).
Useful for relative performance and market regime analysis .
✅ Moving Averages of Returns :
Smooth noisy return series with customizable MA types: SMA, EMA, WMA, RMA, and Linear Regression.
Applicable to both primary and comparison series.
✅ Conditional Coloring :
Returns > 0 are colored green ; returns < 0 are red .
Comparison series gets its own unique color scheme.
✅ Extreme Return Detection :
Highlight unusually large price moves using upper/lower thresholds.
Visually flags abnormal volatility events such as earnings surprises or macroeconomic shocks.
Quantitative Use Cases
🔍 Return Distribution Analysis :
Gain insight into the statistical properties of asset returns (e.g., skewness, kurtosis, tail behavior).
📉 Risk Management :
Use historical return outliers to define drawdown expectations, stress tests, or VaR simulations.
🔁 Strategy Backtesting :
Apply rolling log returns to momentum or mean-reversion models where compounding and consistent scaling matter.
📊 Market Regime Detection :
Identify periods of consistent overperformance/underperformance relative to a benchmark asset.
📈 Signal Engineering :
Incorporate return deltas, moving average crossover of returns, or threshold-based triggers into machine learning pipelines or rule-based systems.
Recommended Settings
Use Normal mode for high-frequency trading signals.
Use Rolling mode for swing or trend-following strategies.
Compare vs. a broad market index (e.g., SPY or QQQ ) to extract relative strength insights.
Set upper and lower thresholds around ±5% for spotting major volatility days.
Conclusion
The Rolling Log Returns indicator transforms raw price action into a statistically sound return series—equipping traders with a professional-grade lens into market behavior. Whether you're conducting exploratory data analysis, building factor models, or visually scanning for outliers, this indicator integrates seamlessly into a modern quant's toolbox.
