VIX Term StructureThis script allows users to visualize the state of the VIX Futures Term Structure. The user is able to select from five CBOE VIX Indices; VIX, VIX9D, VIX3M, VIX6M, and VIX1Y and the script will color the candles based on the price relationship between selected indices. Visit the CBOE website for more info on how the various VIX indices are calculated.
Cerca negli script per "implied"
OBHOW-TO: Advanced Order Block & Liquidity Mapping Tool
This script combines order block detection using 3- and 5-bar fractal structures with dynamic and static liquidity zones, plus optional time-based label calculations.
Key features:
– Fractal-based detection of bullish and bearish order blocks
– Automatic removal of broken OBs to keep charts clean
– Optional FVG validation for higher-quality OBs
– Custom time label logic for bar/session analysis
– Configurable visuals: line styles, widths, label placement, and time offsets
The logic for liquidity detection is adapted from the open-source LiquidityFinderLibrary by TFlab. This version adds custom cleanup behavior and visual enhancements to improve usability.
This script is published as open-source and intended for educational and analytical use only. No performance guarantees are implied.
If you have questions or suggestions, feel free to message me privately on TradingView.
Trailing Monster StrategyTrailing Monster Strategy
This is an experimental trend-following strategy that incorporates a custom adaptive moving average (PKAMA), RSI-based momentum filtering, and dynamic trailing stop-loss logic. It is designed for educational and research purposes only, and may require further optimization or risk management considerations prior to live deployment.
Strategy Logic
The strategy attempts to participate in sustained price trends by combining:
- A Power Kaufman Adaptive Moving Average (PKAMA) for dynamic trend detection,
- RSI and Simple Moving Average (SMA) filters for market condition confirmation,
- A delayed trailing stop-loss to manage exits once a trade is in profit.
Entry Conditions
Long Entry:
- RSI exceeds the overbought threshold (default: 70),
- Price is trading above the 200-period SMA,
- PKAMA slope is positive (indicating upward momentum),
- A minimum number of bars have passed since the last entry.
Short Entry:
- RSI falls below the oversold threshold (default: 30),
- Price is trading below the 200-period SMA,
- PKAMA slope is negative (indicating downward momentum),
-A minimum number of bars have passed since the last entry.
Exit Conditions
- A trailing stop-loss is applied once the position has been open for a user-defined number of bars.
- The trailing distance is calculated as a fixed percentage of the average entry price.
Technical Notes
This script implements a custom version of the Power Kaufman Adaptive Moving Average (PKAMA), conceptually inspired by alexgrover’s public implementation on TradingView .
Unlike traditional moving averages, PKAMA dynamically adjusts its responsiveness based on recent market volatility, allowing it to better capture trend changes in fast-moving assets like altcoins.
Disclaimer
This strategy is provided for educational purposes only.
It is not financial advice, and no guarantee of profitability is implied.
Always conduct thorough backtesting and forward testing before using any strategy in a live environment.
Adjust inputs based on your individual risk tolerance, asset class, and trading style.
Feedback is encouraged. You are welcome to fork and modify this script to suit your own preferences and market approach.
Enhanced Fuzzy SMA Analyzer (Multi-Output Proxy) [FibonacciFlux]EFzSMA: Decode Trend Quality, Conviction & Risk Beyond Simple Averages
Stop Relying on Lagging Averages Alone. Gain a Multi-Dimensional Edge.
The Challenge: Simple Moving Averages (SMAs) tell you where the price was , but they fail to capture the true quality, conviction, and sustainability of a trend. Relying solely on price crossing an average often leads to chasing weak moves, getting caught in choppy markets, or missing critical signs of trend exhaustion. Advanced traders need a more sophisticated lens to navigate complex market dynamics.
The Solution: Enhanced Fuzzy SMA Analyzer (EFzSMA)
EFzSMA is engineered to address these limitations head-on. It moves beyond simple price-average comparisons by employing a sophisticated Fuzzy Inference System (FIS) that intelligently integrates multiple critical market factors:
Price deviation from the SMA ( adaptively normalized for market volatility)
Momentum (Rate of Change - ROC)
Market Sentiment/Overheat (Relative Strength Index - RSI)
Market Volatility Context (Average True Range - ATR, optional)
Volume Dynamics (Volume relative to its MA, optional)
Instead of just a line on a chart, EFzSMA delivers a multi-dimensional assessment designed to give you deeper insights and a quantifiable edge.
Why EFzSMA? Gain Deeper Market Insights
EFzSMA empowers you to make more informed decisions by providing insights that simple averages cannot:
Assess True Trend Quality, Not Just Location: Is the price above the SMA simply because of a temporary spike, or is it supported by strong momentum, confirming volume, and stable volatility? EFzSMA's core fuzzyTrendScore (-1 to +1) evaluates the health of the trend, helping you distinguish robust moves from noise.
Quantify Signal Conviction: How reliable is the current trend signal? The Conviction Proxy (0 to 1) measures the internal consistency among the different market factors analyzed by the FIS. High conviction suggests factors are aligned, boosting confidence in the trend signal. Low conviction warns of conflicting signals, uncertainty, or potential consolidation – acting as a powerful filter against chasing weak moves.
// Simplified Concept: Conviction reflects agreement vs. conflict among fuzzy inputs
bullStrength = strength_SB + strength_WB
bearStrength = strength_SBe + strength_WBe
dominantStrength = max(bullStrength, bearStrength)
conflictingStrength = min(bullStrength, bearStrength) + strength_N
convictionProxy := (dominantStrength - conflictingStrength) / (dominantStrength + conflictingStrength + 1e-10)
// Modifiers (Volatility/Volume) applied...
Anticipate Potential Reversals: Trends don't last forever. The Reversal Risk Proxy (0 to 1) synthesizes multiple warning signs – like extreme RSI readings, surging volatility, or diverging volume – into a single, actionable metric. High reversal risk flags conditions often associated with trend exhaustion, providing early warnings to protect profits or consider counter-trend opportunities.
Adapt to Changing Market Regimes: Markets shift between high and low volatility. EFzSMA's unique Adaptive Deviation Normalization adjusts how it perceives price deviations based on recent market behavior (percentile rank). This ensures more consistent analysis whether the market is quiet or chaotic.
// Core Idea: Normalize deviation by recent volatility (percentile)
diff_abs_percentile = ta.percentile_linear_interpolation(abs(raw_diff), normLookback, percRank) + 1e-10
normalized_diff := raw_diff / diff_abs_percentile
// Fuzzy sets for 'normalized_diff' are thus adaptive to volatility
Integrate Complexity, Output Clarity: EFzSMA distills complex, multi-factor analysis into clear, interpretable outputs, helping you cut through market noise and focus on what truly matters for your decision-making process.
Interpreting the Multi-Dimensional Output
The true power of EFzSMA lies in analyzing its outputs together:
A high Trend Score (+0.8) is significant, but its reliability is amplified by high Conviction (0.9) and low Reversal Risk (0.2) . This indicates a strong, well-supported trend.
Conversely, the same high Trend Score (+0.8) coupled with low Conviction (0.3) and high Reversal Risk (0.7) signals caution – the trend might look strong superficially, but internal factors suggest weakness or impending exhaustion.
Use these combined insights to:
Filter Entry Signals: Require minimum Trend Score and Conviction levels.
Manage Risk: Consider reducing exposure or tightening stops when Reversal Risk climbs significantly, especially if Conviction drops.
Time Exits: Use rising Reversal Risk and falling Conviction as potential signals to take profits.
Identify Regime Shifts: Monitor how the relationship between the outputs changes over time.
Core Technology (Briefly)
EFzSMA leverages a Mamdani-style Fuzzy Inference System. Crisp inputs (normalized deviation, ROC, RSI, ATR%, Vol Ratio) are mapped to linguistic fuzzy sets ("Low", "High", "Positive", etc.). A rules engine evaluates combinations (e.g., "IF Deviation is LargePositive AND Momentum is StrongPositive THEN Trend is StrongBullish"). Modifiers based on Volatility and Volume context adjust rule strengths. Finally, the system aggregates these and defuzzifies them into the Trend Score, Conviction Proxy, and Reversal Risk Proxy. The key is the system's ability to handle ambiguity and combine multiple, potentially conflicting factors in a nuanced way, much like human expert reasoning.
Customization
While designed with robust defaults, EFzSMA offers granular control:
Adjust SMA, ROC, RSI, ATR, Volume MA lengths.
Fine-tune Normalization parameters (lookback, percentile). Note: Fuzzy set definitions for deviation are tuned for the normalized range.
Configure Volatility and Volume thresholds for fuzzy sets. Tuning these is crucial for specific assets/timeframes.
Toggle visual elements (Proxies, BG Color, Risk Shapes, Volatility-based Transparency).
Recommended Use & Caveats
EFzSMA is a sophisticated analytical tool, not a standalone "buy/sell" signal generator.
Use it to complement your existing strategy and analysis.
Always validate signals with price action, market structure, and other confirming factors.
Thorough backtesting and forward testing are essential to understand its behavior and tune parameters for your specific instruments and timeframes.
Fuzzy logic parameters (membership functions, rules) are based on general heuristics and may require optimization for specific market niches.
Disclaimer
Trading involves substantial risk. EFzSMA is provided for informational and analytical purposes only and does not constitute financial advice. No guarantee of profit is made or implied. Past performance is not indicative of future results. Use rigorous risk management practices.
Volume-Based Reversal and Breakout [The_lurker]Indicator Overview:
The "Volume-Based Reversal and Breakout Indicator" is designed for use on the TradingView platform. Its primary function is to identify potential reversal candles using volume and price criteria and to mark significant breakout points. This tool is particularly valuable for traders who incorporate reversal patterns and volume analysis in their trading strategies.
Detailed Functionality:
Customizable Label Color:
Traders can choose the color of the labels that mark breakout points, allowing for personalization and better visibility on different chart backgrounds.
Volume Multiplier Input:
Users can set a 'Volume Multiplier' to define what constitutes significant trading volume. This multiplier is used to compare the current candle's volume with that of the previous candle. A higher volume on the current candle, as defined by this multiplier, is indicative of a significant trading activity.
Reversal Candle Criteria:
The script identifies a candle as a reversal candle if it meets the following conditions:
The closing price of the candle is lower than its opening price, indicating a bearish sentiment.
The trading volume of the candle is greater than the product of the previous candle's volume and the user-set volume multiplier. This implies increased trading activity during the formation of this candle.
The length of the candle's lower tail is greater than its body, suggesting a rejection of lower prices and potential bullish sentiment building up.
Breakout Identification and Marking:
Upon detecting a reversal candle, the indicator draws lines at the high and low of this candle.
These lines represent potential breakout levels. A breakout is confirmed if the price crosses above the high (indicating a bullish breakout) or below the low (indicating a bearish breakout) of the reversal candle.
When a breakout occurs, the indicator places an arrow marker at the breakout point. The direction of the arrow (upwards or downwards) and its color (customizable by the user) indicate the nature of the breakout.
Breakout Alerts:
The indicator includes an alert condition that notifies traders when a breakout occurs. This feature helps traders to quickly react to potential trading opportunities.
Practical Application:
The indicator is best used in markets with distinct volume patterns, as volume is a key component of its analysis.
It can be combined with other technical analysis tools, such as trend lines or moving averages, for additional confirmation of trading signals.
Traders should consider adjusting the volume multiplier based on the typical volume characteristics of the specific asset they are analyzing.
Conclusion:
This "Volume-Based Reversal and Breakout Indicator" is a robust tool that aids traders in identifying potential reversals and breakouts with an emphasis on volume analysis. It's customizable and alert-enabled features make it a versatile addition to a trader's toolkit, suitable for various trading styles and market conditions.
Disclaimer:
This indicator is provided "as is" without any warranties, either express or implied. The information and data contained within this indicator do not constitute investment advice or a recommendation to buy or sell any security. Users assume full responsibility for any trading decisions made based on the use of this indicator.
Past performance of indicators does not guarantee future results. Investing in financial markets involves risks, including the potential loss of capital. It is strongly advised to consult with a qualified financial advisor before making any investment decisions.
The development of this indicator does not constitute an endorsement or recommendation by TradingView or any other entity. All trademarks and trade names mentioned herein are the property of their respective owners.
ADR, ATR & VOL OverlayThis is a combined version of 2 of my other indicators:
ADR / ATR Overlay
VOL / AVG Overlay
This indicator will display the following as an overlay on your chart:
ADR
% of ADR
ADR % of Price
ATR
% of ATR
ATR % of Price
Custom Session Volume
Average For Selected Session
Volume Percentage Comparison
Description:
ADR : Average Day Range
% of ADR : Percentage that the current price move has covered its average.
ADR % of Price : The percentage move implied by the average range.
ATR : Average True Range
% of ATR : Percentage that the current price move has covered its average.
ATR % of Price : The percentage move implied by the average true range.
Custom Session Volume : User chosen time frame to monitor volume
Average For Selected Session : Average for the custom session volume
Volume Percentage Comparison : Current session compared to the average (calculated at session close)
Options:
ADR/ATR:
Time Frame
Length
Smoothing
Volume:
Set Custom Time Frame For Calculations
Set Custom Time Frame For Average Comparison
Set Custom Time Zone
Table:
Enable / Disable Each Value
Change Text Color
Change Background Color
Change Table location
Add/Remove extra row for placement
ADR / ATR Example:
The ADR and ATR can be used to provide information about average price moves to help set targets, stop losses, entries and exits based on the potential average moves.
Example: If the "% of ADR" is reading 100%, then 100% of the asset's average price range has been covered, suggesting that an additional move beyond the range has a lower probability.
Example: "ADR % of Price" provides potential price movement in percentage which can be used to asses R/R for asset.
Example: ADR (D) reading is 100% at market close but ATR (D) is at 70% at close. This suggests that there is a potential (coverage) move of 30% in Pre/Post market as suggested by averages.
Custom Volume Session Example:
Set indicator to 30 period average. Set custom time frame to 9:30am to 10:30am Eastern/New York.
When the time frame for the calculation is closed, the indicator will provide a comparison of the current days volume compared to the average of 30 previous days for that same time frame and display it as a percentage in the table.
In this example you could compare how the first hour of the trading day compares to the previous 30 day's average, aiding in evaluating the potential volume for the remainder of the day.
Notes:
Times must be entered in 24 hour format. (1pm = 13:00 etc.)
Volume indicator is for Intra-day time frames, not > Day.
How I use these values:
I use these calculations to determine if a ticker symbol has the necessary range to achieve target gains, to determine if the price oscillation is within "normal" ranges to determine if the trading day will be choppy, and to determine placement of stops and targets within average ranges in combination with support, resistance and retracement levels.
ADR & ATR OverlayADR & ATR Overlay
This indicator will display the following as an overlay on your chart:
ADR
% of ADR
ADR % of Price
ATR
% of ATR
ATR % of Price
Description:
ADR : Average Day Range
% of ADR : Percentage that the current price move has covered its average.
ADR % of Price : The percentage move implied by the average range.
ATR : Average True Range
% of ATR : Percentage that the current price move has covered its average.
ATR % of Price : The percentage move implied by the average true range.
Options:
Time Frame
Length
Smoothing
Enable or Disable each value
Text Color
Background Color
How to use this indicator:
The ADR and ATR can be used to provide information about average price moves to help set targets, stop losses, entries and exits based on the potential average moves.
Example: If the "% of ADR" is reading 100%, then 100% of the asset's average price range has been covered, suggesting that an additional move beyond the range has a lower probability.
Example: "ADR % of Price" provides potential price movement in percentage which can be used to asses R/R for asset.
Example: ADR (D) reading is 100% at market close but ATR (D) is at 70% at close. This suggests that there is a potential move of 30% in Pre/Post market as suggested by averages.
Notes:
These indicators are available as oscillators to place under your chart through trading view but this indicator will place them on the chart in numerical only format.
Please feel free to modify this script if you like but please acknowledge me, I am only a hobby coder so this takes some time & effort.
Tomas' Financial Conditions Z Score"The indicator is a composite z-score comprised of the following four components (equally-weighted):
Credit spreads - ICE BofA High Yield Option Adjusted Spread (BAMLH0A0HYM2) and ICE BofA Corporate Index Option Adjusted Spread (BAMLC0A0CM)
Volatility indexes - VIX (S&P 500 implied volatility) and MOVE (US Treasury bond implied volatility)
I've got it set to a 160-day lookback period, which I think is roughly the best setting after some tinkering.
When the z-score is above zero, it throws a red signal - and when the z-score is below zero, it throws a green signal.
This indicator is a follow-on from the "traffic light financial conditions indicator" that I wrote a thread about a couple of months ago.
I moved on from that previous indicator because it is based on the Federal Reserve's NFCI, which is regularly revised, but I didn't take that into account at the time.
So not a great real-time indicator, if the signal can be subsequently revised in the opposite direction weeks later.
This new indicator is based on real-time market data, so there's no revisions, and it also updates daily, as opposed to weekly for the NFCI"
IV Rank/Percentile with Williams VIX FixDisplay IV Rank / IV Percentile
This indicator is based on William's VixFix, which replicates the VIX—a measure of the implied volatility of the S&P 500 Index (SPX). The key advantage of the VixFix is that it can be applied to any security, not just the SPX.
IV Rank is calculated by identifying the highest and lowest implied volatility (IV) values over a selected number of past periods. It then determines where the current IV lies as a percentage between these two extremes. For example, if over the past five periods the highest IV was 30%, the lowest was 10%, and the current IV is 20%, the IV Rank would be 50%, since 20% is halfway between 10% and 30%.
IV Percentile, on the other hand, considers all past IV values—not just the highest and lowest—and calculates the percentage of these values that are below the current IV. For instance, if the past five IV values were 30%, 10%, 11%, 15%, and 17%, and the current IV is 20%, the IV Rank remains at 50%. However, the IV Percentile is 80% because 4 out of the 5 past values (80%) are below the current IV of 20%.
VIX Statistical Sentiment Index [Nasan]** THIS IS ONLY FOR US STOCK MARKET**
The indicator analyzes market sentiment by computing the Rate of Change (ROC) for the VIX and S&P 500, visualizing the data as histograms with conditional coloring. It measures the correlation between the VIX, the specific stock, and the S&P 500, displaying the results on the chart. The reliability measure combines these correlations, offering an overall assessment of data robustness. One can use this information to gauge the inverse relationship between VIX and S&P 500, the alignment of the specific stock with the market, and the overall reliability of the correlations for informed decision-making based on the inverse relationship of VIX and price movement.
**WHEN THE VIX ROC IS ABOVE ZERO (RED COLOR) AND RASING ONE CAN EXPECT THE PRICE TO MOVE DOWNWARDS, WHEN THE VIX ROC IS BELOW ZERO (GREEN)AND DECREASING ONE CAN EXPECT THE PRICE TO MOVE UPWARDS"
Understanding the VIX Concept:
The VIX, or Volatility Index, is a widely used indicator in finance that measures the market's expectation of volatility over the next 30 days. Here are key points about the VIX:
Fear Gauge:
Often referred to as the "fear gauge," the VIX tends to rise during periods of market uncertainty or fear and fall during calmer market conditions.
Inverse Relationship with Market:
The VIX typically has an inverse relationship with the stock market. When the stock market experiences a sell-off, the VIX tends to rise, indicating increased expected volatility.
Implied Volatility:
The VIX is derived from the prices of options on the S&P 500. It represents the market's expectations for future volatility and is often referred to as "implied volatility."
Contrarian Indicator:
Extremely high VIX levels may indicate oversold conditions, suggesting a potential market rebound. Conversely, very low VIX levels may signal complacency and a potential reversal.
VIX vs. SPX Correlation:
This correlation measures the strength and direction of the relationship between the VIX (Volatility Index) and the S&P 500 (SPX).
A negative correlation indicates an inverse relationship. When the VIX goes up, the SPX tends to go down, and vice versa.
The correlation value closer to -1 suggests a stronger inverse relationship between VIX and SPX.
Stock vs. SPX Correlation:
This correlation measures the strength and direction of the relationship between the closing price of the stock (retrieved using src1) and the S&P 500 (SPX).
This correlation helps assess how closely the stock's price movements align with the broader market represented by the S&P 500.
A positive correlation suggests that the stock tends to move in the same direction as the S&P 500, while a negative correlation indicates an opposite movement.
Reliability Measure:
Combines the squared values of the VIX vs. SPX and Stock vs. SPX correlations and takes the square root to create a reliability measure.
This measure provides an overall assessment of how reliable the correlation information is in guiding decision-making.
Interpretation:
A higher reliability measure implies that the correlations between VIX and SPX, as well as between the stock and SPX, are more robust and consistent.
One can use this reliability measure to gauge the confidence they can place in the correlations when making decisions about the specific stock based on VIX data and its correlation with the broader market.
Rule of 16 - LowerThe "Rule of 16" is a simple guideline used by traders and investors to estimate the expected annualized volatility of the S&P 500 Index (SPX) based on the level of the CBOE Volatility Index (VIX). The VIX, often referred to as the "fear gauge" or "fear index," measures the market's expectations for future volatility. It is calculated using the implied volatility of a specific set of S&P 500 options.
The Rule of 16 provides a rough approximation of the expected annualized percentage change in the S&P 500 based on the VIX level. Here's how it works:
Find the VIX level: Look up the current value of the VIX. Let's say it's currently at 20.
Apply the Rule of 16: Divide the VIX level by 16. In this example, 20 divided by 16 equals 1.25.
Result: The result of this calculation represents the expected annualized percentage change in the S&P 500. In this case, 1.25% is the estimated annualized volatility.
So, according to the Rule of 16, a VIX level of 20 suggests an expected annualized volatility of approximately 1.25% in the S&P 500.
Here's how you can use the Rule of 16:
Market Sentiment: The VIX is often used as an indicator of market sentiment. When the VIX is high (above its historical average), it suggests that investors expect higher market volatility, indicating potential uncertainty or fear in the markets. Conversely, when the VIX is low, it suggests lower expected volatility and potentially more confidence in the markets.
Risk Management: Traders and investors can use the Rule of 16 to estimate the potential risk associated with their portfolios. For example, if you have a portfolio of S&P 500 stocks and the VIX is at 20, you can use the Rule of 16 to estimate that the annualized volatility of your portfolio may be around 1.25%. This information can help you make decisions about position sizing and risk management.
Option Pricing: Options traders may use the Rule of 16 to get a quick estimate of the implied annualized volatility priced into S&P 500 options. It can help them assess whether options are relatively expensive or cheap based on the VIX level.
It's important to note that the Rule of 16 is a simplification and provides only a rough estimate of expected volatility. Market conditions and the relationship between the VIX and the S&P 500 can change over time. Therefore, it should be used as a guideline rather than a precise forecasting tool. Traders and investors should consider other factors and use additional analysis to make informed decisions.
[blackcat] L1 Dynamic Volatility IndicatorThe volatility indicator (Volatility) is used to measure the magnitude and instability of price changes in financial markets or a specific asset. This thing is usually used to assess how risky the market is. The higher the volatility, the greater the fluctuation in asset prices, but brother, the risk is also relatively high! Here are some related terms and explanations:
- Historical Volatility: The actual volatility of asset prices over a certain period of time in the past. This thing is measured by calculating historical data.
- Implied Volatility: The volatility inferred from option market prices, used to measure market expectations for future price fluctuations.
- VIX Index (Volatility Index): Often referred to as the "fear index," it predicts the volatility of the US stock market within 30 days in advance. This is one of the most famous volatility indicators in global financial markets.
Volatility indicators are very important for investors and traders because they can help them understand how unstable and risky the market is, thereby making wiser investment decisions.
Today I want to introduce a volatility indicator that I have privately held for many years. It can use colors to judge sharp rises and falls! Of course, if you are smart enough, you can also predict some potential sharp rises and falls by looking at the trend!
In the financial field, volatility indicators measure the magnitude and instability of price changes in different assets. They are usually used to assess the level of market risk. The higher the volatility, the greater the fluctuation in asset prices and therefore higher risk. Historical Volatility refers to the actual volatility of asset prices over a certain period of time in the past, which can be measured by calculating historical data; while Implied Volatility is derived from option market prices and used to measure market expectations for future price fluctuations. In addition, VIX Index is commonly known as "fear index" and is used to predict volatility in the US stock market within 30 days. It is one of the most famous volatility indicators in global financial markets.
Volatility indicators are very important for investors and traders because they help them understand market uncertainty and risk, enabling them to make wiser investment decisions. The L1 Dynamic Volatility Indicator that I am introducing today is an indicator that measures volatility and can also judge sharp rises and falls through colors!
This indicator combines two technical indicators: Dynamic Volatility (DV) and ATR (Average True Range), displaying warnings about sharp rises or falls through color coding. DV has a slow but relatively smooth response, while ATR has a fast but more oscillating response. By utilizing their complementary characteristics, it is possible to construct a structure similar to MACD's fast-slow line structure. Of course, in order to achieve fast-slow lines for DV and ATR, first we need to unify their coordinate axes by normalizing them. Then whenever ATR's yellow line exceeds DV's purple line with both curves rapidly breaking through the threshold of 0.2, sharp rises or falls are imminent.
However, it is important to note that relying solely on the height and direction of these two lines is not enough to determine the direction of sharp rises or falls! Because they only judge the trend of volatility and cannot determine bull or bear markets! But it's okay, I have already considered this issue early on and added a magical gradient color band. When the color band gradually turns warm, it indicates a sharp rise; conversely, when the color band tends towards cool colors, it indicates a sharp fall! Of course, you won't see the color band in sideways consolidation areas, which avoids your involvement in unnecessary trades that would only waste your funds! This indicator is really practical and with it you can better assess market risks and opportunities!
CE - Market Performance TableThe 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is a sophisticated market tool designed to provide valuable insights into the current market trends and the approximate current position in the Macroeconomic Regime.
Furthermore the 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 provides the Correlation Implied Trend for the Asset on the Chart. Lastly it provides information about current "RISK ON" or "RISK OFF" periods.
Methodology:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 tracks the 15 underlying Stock ETF's to identify their performance and puts the combined performances together to visualize 42MACRO's GRID Equity Model.
For this it uses the below ETF's:
Dividends (SPHD)
Low Beta (SPLV)
Quality (QUAL)
Defensives (DEF)
Growth (IWF)
High Beta (SPHB)
Cyclicals (IYT, IWN)
Value (IWD)
Small Caps (IWM)
Mid Caps (IWR)
Mega Cap Growth (MGK)
Size (OEF)
Momentum (MTUM)
Large Caps (IWB)
Overall Settings:
The main time values you want to change are:
Correlation Length
- Defines the time horizon for the Correlation Table
ROC Period
- Defines the time horizon for the Performance Table
Normalization lookback
- Defines the time horizon for the Trend calculation of the ETF's
- For longer term Trends over weeks or months a length of 50 is usually pretty accurate
Visuals:
There is a variety of options to change the visual settings of what is being plotted and the two table positions and additional considerations.
Everything that is relevant in the underlying logic that can help comprehension can be visualized with these options.
Market Correlation:
The Market Correlation Table takes the Correlation of the above ETF's to the Asset on the Chart, it furthermore uses the Normalized KAMA Oscillator by IkkeOmar to analyse the current trend of every single ETF.
It then Implies a Correlation based on the Trend and the Correlation to give a probabilistically adjusted expectation for the future Chart Asset Movement. This is strengthened by taking the average of all Implied Trends.
With this the Correlation Table provides valuable insights about probabilistically likely Movement of the Asset, for Traders and Investors alike, over the defined time duration.
Market Performance:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is the actual valuable part of this Indicator.
It provides valuable information about the current market environment (whether it's risk on or risk off), the rough GRID models from 42MACRO and the actual market performance.
This allows you to obtain a deeper understanding of how the market works and makes it simple to identify the actual market direction.
Utility:
The 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is divided in 4 Sections which are the GRID regimes:
Economic Growth:
Goldilocks
Reflation
Economic Contraction:
Inflation
Deflation
Top 5 Equity Style Factors:
Are the values green for a specific Column? If so then the market reflects the corresponding GRID behavior.
Bottom 5 Equity Style Factors:
Are the values red for a specific Column? If so then the market reflects the corresponding GRID behavior.
So if we have Goldilocks as current regime we would see green values in the Top 5 Goldilocks Cells and red values in the Bottom 5 Goldilocks Cells.
You will find that Reflation will look similar, as it is also a sign of Economic Growth.
Same is the case for the two Contraction regimes.
VIX HeatmapVIX HeatMap
Instructions:
- To be used with the S&P500 index (ES, SPX, SPY, any S&P ETF) as that's the input from where the CBOE calculates and measures the VIX. Can also be used with the Dow Jones, Nasdaq, & Nasdaq100.
Description:
- Expected Implied Volatility regime simplified & visualized. Know if we are in a high, medium, or low volatility regime, instantly.
- Ranges from Hot to Cold: The hotter the heat-map, the higher the implied volatility and fear & vice versa.
- The VIX HeatMap, color-maps important VIX levels (7 in this case) in measuring volatility for day trading & swing trading.
Using the VIX HeatMap:
- A LOW level volatility environment: Represented by "cooler" colors (Blue & White) depicts that the level of volatility and fear is low. Percentage moves on the index level are going to be tame and less volatile more often than not. Low fear = low perceived risk.
- A MEDIUM level volatility environment: Represented by "warmer" colors (Green & Yellow) depicts that the markets are transitioning from a calmer period or from a more fearful period. Market volatility here will be higher and provide more volatile swings in price.
- A HIGH level volatility environment: Represented by "hotter" colors (Orange, Red, & Purple) depicts that the markets are very fearful at the moment and will have big swings in both directions. Historically, extreme VIX levels tend to coincide with bottoms but are in no way predictive of the exact timing as the volatile moves can continue for an extended period of time.
- Transitioning between the 7 VIX Zones: Each and every one of these specific VIX zone levels is important.
1. Extreme low: <16
2. Low: 16 to 20
3. Normal: 20 to 24
4. Medium: 24 to 28
5. Med-High: 28 to 32
6. High: 32 to 36
7. Extreme high: >36
- These VIX levels in particular measure volatility changes that have a major impact on switching between smaller time frames and measuring depths of a sell move and vice versa. Each level also behaves as its own support & resistance level in terms of taking a bit of effort to switch regimes, and aids in identifying and measuring the potential depth of pullbacks in bull markets and bounces in bear markets to reveal reversal points.
- Examples of VIX level supports depicted on the chart marked with arrows. From left to right:
1. March 10th: Markets jumped 2 volatility levels in 2 days. The fluctuations from blue to yellow to green where a sign that price action would reverse from the selloff.
2. March 28th: As soon as we move from green to the blue VIX level (<20), markets began to rally and only ended when the volatility level moved sub VIX 16 (white).
3. May 4th & 24th: Next we see the 2 dips where volatility levels went from blue to green (VIX > 20), marked bottoms and reversed higher.
4. June 1st: We see a change in VIX regime yet again into lower VIX level and markets rocket higher.
Knowing the current VIX regime is a very important tool and aid in trading, now easily visualized.
SPX Expected MoveThis indicator plots the "expected move" of SPX for today's trading session. Expected move is the amount that SPX is predicted to increase or decrease from its current price, based on the current level of implied volatility. The implied volatility in this indicator is computed from the current value of the VIX (or one of several volatility symbols available on Trading view). The computation is done using standard formula. The resulting plots are labeled as 1 and 2 standard deviations. The default values are to use VIX as well as 252 trading days in the years.
Use the square root of (days to expiration, or in this case a fraction of the day remaining) divided but the square root of (252, or number of trading days in a year).
timeRemaining = math.sqrt(DTE) / math.sqrt(252)
Standard deviation move = SPX bar closing price * (VIX/100) * timeRemaining
4C Expected Move (Weekly Options)This indicator plots the Expected Move (EM) calculated from weekly options pricing, for a quick visual reference.
The EM is the amount that a stock is predicted to increase or decrease from its current price, based on the current level of implied volatility.
This range can be viewed as support and resistance, or once price gets outside of the range, institutional hedging actions can accelerate the move in that direction.
The EM range is based on the Weekly close of the prior week.
It can be useful to know what the weekly EM range is for a stock to understand the probabilities of the overall distance, direction and volatility for the week.
To use this indicator you must have access to a broker with options data (not available on Tradingview).
Look at the stock's option chain and find the weekly expected move. You will have to do your own research to find where this information is displayed depending on your broker.
See screenshot example on the chart. This is the Thinkorswim platform's option chain, and the Implied Volatility % and the calculated EM is circled in red. Use the +- number in parentheses, NOT the % value.
Input that number into the indicator on a weekly basis, ideally on the weekend sometime after the cash market close on Friday, and before the Market open at the beginning of the trading week.
The indicator must be manually updated each week.
It will automatically start over at the beginning of the week.
Volatility barometerIt is the indicator that analyzes the behaviour of VIX against CBOE volaility indices (VIX3M, VIX6M and VIX1Y) and VIX futures (next contract to the front one - VX!2). Because VIX is a derivate of SPX, the indicator shall be used on the SPX chart (or equivalent like SPY).
When the readings get above 90 / below 10, it means the market is overbought / oversold in terms of implied volatility. However, it does not mean it will reverse - if the price go higher along with the indicator readings then everything is fine. There is an alarming situation when the SPX is diverging - e.g. the price go higher, the readings lower. It means the SPX does not play in the same team as IVOL anymore and might reverse.
You can use it in conjunction with other implied volatility indicators for stronger signals: the Correlation overlay ( - the indicator that measures the correlation between VVIX and VIX) and VVIX/VIX ratio (it generates a signal the ratio makes 50wk high).
Black Scholes Model [racer8]This is the Black Scholes Model. This indicator tells you the prices of both a call option & a put option.
Input variables are spot price, strike price, risk free rate %, days to maturity, and implied volatility %.
This indicator was made generally for educational purposes.
By using this indicator, you will develop a better understanding of how options are priced.
This indicator was made to be as simple as possible so that the user can easily understand it.
I recreated the Black Scholes Model because there is very little scripts on TV that are based on the Black Scholes Model.
I am aware that are Black Scholes Model (BSM) scripts already on TV, but mine is not the same. Correct me if I'm wrong, but I don't think there is a BSM script out there yet that relies on the exact same inputs that mine does.
Why use this indicator?
If you don't already have your own IV indicator...
You can use this indicator to approximate the value of implied volatility %.
You already know every input variable except IV%, and you know the call & put option prices.
So put in the numbers for each input and put a random number between 0 to 100 into the IV% input to get the options prices.
Adjust that random number for IV% until the output (options prices) matches correctly with what you already know they are to be.
This is called the trial and error method.
On the other hand, if you already know all input variables including IV%. Then you can use this indicator to find the call & put options prices directly.
Hope this helps. Enjoy 🙂
Volatility Tracker: VIX, MOVE, RealizedRealized Volatility (Blue): Based on daily price changes.
VIX (Red): Implied equity volatility.
MOVE Index (Orange): Bond market volatility expectations.
Volatility Spread (Gray): VIX minus Realized Vol — useful for detecting complacency or fear premiums.
Volatility Tracker (VIX vs Realized)Plots Realized Volatility (historical, blue).
Plots Implied Volatility (VIX) (red).
Shows the spread between VIX and realized vol (gray), helping spot fear premium or complacency.
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
Z-Score Normalized VIX StrategyThis strategy leverages the concept of the Z-score applied to multiple VIX-based volatility indices, specifically designed to capture market reversals based on the normalization of volatility. The strategy takes advantage of VIX-related indicators to measure extreme levels of market fear or greed and adjusts its position accordingly.
1. Overview of the Z-Score Methodology
The Z-score is a statistical measure that describes the position of a value relative to the mean of a distribution in terms of standard deviations. In this strategy, the Z-score is calculated for various volatility indices to assess how far their values are from their historical averages, thus normalizing volatility levels. The Z-score is calculated as follows:
Z = \frac{X - \mu}{\sigma}
Where:
• X is the current value of the volatility index.
• \mu is the mean of the index over a specified period.
• \sigma is the standard deviation of the index over the same period.
This measure tells us how many standard deviations the current value of the index is away from its average, indicating whether the market is experiencing unusually high or low volatility (fear or calm).
2. VIX Indices Used in the Strategy
The strategy utilizes four commonly referenced volatility indices:
• VIX (CBOE Volatility Index): Measures the market’s expectations of 30-day volatility based on S&P 500 options.
• VIX3M (3-Month VIX): Reflects expectations of volatility over the next three months.
• VIX9D (9-Day VIX): Reflects shorter-term volatility expectations.
• VVIX (VIX of VIX): Measures the volatility of the VIX itself, indicating the level of uncertainty in the volatility index.
These indices provide a comprehensive view of the current volatility landscape across different time horizons.
3. Strategy Logic
The strategy follows a long entry condition and an exit condition based on the combined Z-score of the selected volatility indices:
• Long Entry Condition: The strategy enters a long position when the combined Z-score of the selected VIX indices falls below a user-defined threshold, indicating an abnormally low level of volatility (suggesting a potential market bottom and a bullish reversal). The threshold is set as a negative value (e.g., -1), where a more negative Z-score implies greater deviation below the mean.
• Exit Condition: The strategy exits the long position when the combined Z-score exceeds the threshold (i.e., when the market volatility increases above the threshold, indicating a shift in market sentiment and reduced likelihood of continued upward momentum).
4. User Inputs
• Z-Score Lookback Period: The user can adjust the lookback period for calculating the Z-score (e.g., 6 periods).
• Z-Score Threshold: A customizable threshold value to define when the market has reached an extreme volatility level, triggering entries and exits.
The strategy also allows users to select which VIX indices to use, with checkboxes to enable or disable each index in the calculation of the combined Z-score.
5. Trade Execution Parameters
• Initial Capital: The strategy assumes an initial capital of $20,000.
• Pyramiding: The strategy does not allow pyramiding (multiple positions in the same direction).
• Commission and Slippage: The commission is set at $0.05 per contract, and slippage is set at 1 tick.
6. Statistical Basis of the Z-Score Approach
The Z-score methodology is a standard technique in statistics and finance, commonly used in risk management and for identifying outliers or unusual events. According to Dumas, Fleming, and Whaley (1998), volatility indices like the VIX serve as a useful proxy for market sentiment, particularly during periods of high uncertainty. By calculating the Z-score, we normalize volatility and quantify the degree to which the current volatility deviates from historical norms, allowing for systematic entry and exit based on these deviations.
7. Implications of the Strategy
This strategy aims to exploit market conditions where volatility has deviated significantly from its historical mean. When the Z-score falls below the threshold, it suggests that the market has become excessively calm, potentially indicating an overreaction to past market events. Entering long positions under such conditions could capture market reversals as fear subsides and volatility normalizes. Conversely, when the Z-score rises above the threshold, it signals increased volatility, which could be indicative of a bearish shift in the market, prompting an exit from the position.
By applying this Z-score normalized approach, the strategy seeks to achieve more consistent entry and exit points by reducing reliance on subjective interpretation of market conditions.
8. Scientific Sources
• Dumas, B., Fleming, J., & Whaley, R. (1998). “Implied Volatility Functions: Empirical Tests”. The Journal of Finance, 53(6), 2059-2106. This paper discusses the use of volatility indices and their empirical behavior, providing context for volatility-based strategies.
• Black, F., & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities”. Journal of Political Economy, 81(3), 637-654. The original Black-Scholes model, which forms the basis for many volatility-related strategies.
ATR 3x Multiplier StrategyBeta version
Volatility and Candle Spikes in Trading
Volatility
Volatility refers to the degree of variation in the price of a financial asset over time. It measures how much the price fluctuates and is often associated with risk and uncertainty in the market. High volatility means larger price swings, while low volatility indicates more stable price movements.
Key aspects of volatility:
Measured using indicators like Average True Range (ATR), Bollinger Bands, and Implied Volatility (IV).
Influenced by factors such as market news, economic events, and liquidity.
Higher volatility increases both risk and potential profit opportunities.
Candle Spikes
A candle spike (or wick) refers to a sudden price movement that forms a long shadow or wick on a candlestick chart. These spikes can indicate strong buying or selling pressure, liquidity hunts, or stop-loss triggers.
Types of candle spikes:
Bullish Spike (Long Lower Wick): Indicates buyers rejected lower prices, pushing the price higher.
Bearish Spike (Long Upper Wick): Suggests sellers rejected higher prices, pushing the price lower.
Stop-Loss Hunt: Market makers may trigger stop-losses by creating artificial spikes before reversing the price.
News-Induced Spikes: Economic data releases or unexpected events can cause sudden price jumps.
Understanding volatility and candle spikes can help traders manage risk, spot entry/exit points, and avoid false breakouts. 🚀📈
