trend_vol_stopThe description below is copied from the script's comments. Because TradingView does not allow me to edit this description, please refer to the script's comments section, as well as the release notes, for the most up-to-date information.
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Usage:
The inputs define the trend and the volatility stop.
Trend:
The trend is defined by a moving average crossover. When the short
(or fast) moving average is above the long (slow) moving average, the
trend is up. Otherwise, the trend is down. The inputs are:
long: the number of periods in the long/slow moving average.
short: the number of periods in the short/fast moving average.
The slow moving average is shown in various colors (see explanation
below. The fast moving average is a faint blue.
Volatility stop:
The volatility stop has two modes, percentage and rank. The percentage
stop is given in terms of annualized volatility. The rank stop is given
in terms of percentile.
stop_pct and stop_rank are initialized with "-1". You need to set one of
these to the values you want after adding the indicator to your chart.
This is the only setting that requires your input.
mode: choose "rank" for a rank stop, "percentage" for a percentage stop.
vol_window: the number of periods in the historical volatility
calculation. e.g. "30" means the volatility will be a weighted
average of the previous 30 periods. applies to both types of stop.
stop_pct: the volatility limit, annualized. for example, "50" means
that the trend will not be followed when historical volatility rises
above 50%.
stop_rank: the trend will not be followed when the volatility is in the
N-th percentile. for example, "75" means the trend will not be
followed when the current historical volatility is greater than 75%
of previous volatilities.
rank_window: the number of periods in the rank percentile calculation.
for example, if rank_window is "252" and "stop_rank" is "80", the
trend will not be followed when current historical volatility is
greater than 80% of the previous 252 historical volatilities.
Outputs:
The outputs include moving averages, to visually identify the trend,
a volatility table, and a performance table.
Moving averages:
The slow moving average is colored green in an uptrend, red in a
downtrend, and black when the volatility stop is in place.
Volatility table:
The volatility table gives the current historical volatility, annualized
and expressed as a whole number percentage. E.g. "65" means the
instrument's one standard deviation annual move is 65% of its price.
The current rank is expressed, also as a whole number percentage. E.g.
"15" means the current volatility is greater than 15% of previous
volatilities. For convenience, the volatilities corresponding to the
0, 25, 50, 75, and 100th percentiles are also shown.
Performance table:
The performance table shows the current strategy's performance versus
buy-and-hold. If the trend is up, the instrument's return for that
period is added to the strategy's return, because the strategy is long.
If the trend is down, the negative return is added, because the strategy
is short. If the volatility stop is in (the slow moving average is
black), that period's return is excluded from the strategy returns.
Every period's return is added to the buy-and-hold returns.
The table shows the average return, the standard deviation of returns,
and the sharpe ratio (average return / standard deviation of returns).
All figures are expressed as per-period, whole number percentages.
For exmaple, "0.1" in the mean column on a daily chart means a
0.1% daily return.
The number of periods (samples) for each strategy is also shown.
在腳本中搜尋"Volatility"
Donchian Channel Width The Donchian Channel was developed by Richard Donchian and it could be compared
to the Bollinger Bands. When it comes to volatility analysis, the Donchian Channel
Width was created in the same way as the Bollinger Bandwidth technical indicator was.
As was mentioned above the Donchian Channel Width is used in technical analysis to measure
volatility. Volatility is one of the most important parameters in technical analysis.
A price trend is not just about a price change. It is also about volume traded during this
price change and volatility of a this price change. When a technical analyst focuses his/her
attention solely on price analysis by ignoring volume and volatility, he/she only sees a part
of a complete picture only. This could lead to a situation when a trader may miss something and
lose money. Lets take a look at a simple example how volatility may help a trader:
Most of the price based technical indicators are lagging indicators.
When price moves on low volatility, it takes time for a price trend to change its direction and
it could be ok to have some lag in an indicator.
When price moves on high volatility, a price trend changes its direction faster and stronger.
An indicator's lag acceptable under low volatility could be financially suicidal now - Buy/Sell signals could be generated when it is already too late.
Another use of volatility - very popular one - it is to adapt a stop loss strategy to it:
Smaller stop-loss recommended in low volatility periods. If it is not done, a stop-loss could
be generated when it is too late.
Bigger stop-loss recommended in high volatility periods. If it is not done, a stop-loss could
be triggered too often and you may miss good trades.
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.
Recency-Weighted Market Memory w/ Quantile-Based DriftRecency-Weighted Market Memory w/ Quantile-Based Drift
This indicator combines market memory, recency-weighted drift, quantile-based volatility analysis, momentum (RoC) filtering, and historical correlation checks to generate dynamic forecasts of possible future price levels. It calculates bullish and bearish forecast lines at each horizon, reflecting how the price might behave based on historical similarities.
Trading Concepts & Mathematical Foundations Explained
1) Market Memory
Concept:
Markets tend to repeat past behaviors under similar conditions. By identifying historical market states that closely match current conditions, we predict future price movements based on what happened historically.
Calculation Steps:
We select a historical lookback window (for example, 210 bars).
Each historical bar within this window is evaluated to see if its conditions match the current market. Conditions include:
Correlation between price change and bullish/bearish volume changes (over a user-defined correlation lookback period).
Momentum (Rate of Change, RoC) measured over a separate lookback period.
Only bars closely matching current conditions (within user-defined tolerance percentages) are included.
2) Recency-Weighted Drift
Concept:
Recent market movements often influence future direction. We assign more importance to recent bars to capture the current market bias effectively.
Calculation Steps:
Consider recent price changes between opens and closes for a user-defined drift lookback (for example, last 20 bars).
Give higher weight to recent bars (the most recent bar gets the highest weight, and weights decrease progressively for older bars).
Average these weighted changes separately for upward and downward movements, then combine these averages to calculate a final drift percentage relative to the current price.
3) Correlation Filtering
Concept:
Price changes often correlate strongly with bullish or bearish volume activity. By using historical correlation comparisons, we focus only on past market states with similar volume-price dynamics.
Calculation Steps:
Compute current correlations between price changes and bullish/bearish volume over the user-defined correlation lookback.
Evaluate each historical bar to see if its correlation closely matches the current correlation (within a user-specified percentage tolerance).
Only historical bars meeting this correlation criterion are selected.
4) Momentum (RoC) Filtering
Concept:
Two market periods may exhibit similar correlation structures but differ in how fast prices move (momentum). To ensure true similarity, momentum is checked as an additional filter.
Calculation Steps:
Compute the current Rate of Change (RoC) over the specified RoC lookback.
For each candidate historical bar, calculate its historical RoC.
Only include historical bars whose RoC closely matches the current RoC (within the RoC percentage tolerance).
5) Quantile-Based Volatility and Drift Amplification
Concept:
Quantiles (such as the 95th, 50th, and 5th percentiles) help gauge if current prices are near historical extremes or the median. Quantile bands measure volatility expansions and contractions.
Calculation Steps:
Calculate the 95%, 50%, and 5% quantiles of price over the quantile lookback period.
Add and subtract multiples of the standard deviation to these quantiles, creating upper and lower bands.
Measure the bands' widths relative to the current price as volatility indicators.
Determine the active quantile (95%, 50%, or 5%) based on proximity to the current price (within a percentage tolerance).
Compute the rate of change (RoC) of the active quantile to detect directional bias.
Combine volatility and quantile RoC into a scaling factor that amplifies or dampens expected price moves.
6) Expected Value (EV) Computation & Forecast Lines
Concept:
We forecast future prices based on how similarly-conditioned historical periods performed. We average historical moves to estimate the expected future price.
Calculation Steps:
For each forecast horizon (e.g., 1 to 27 bars ahead), collect all historical price moves that passed correlation and RoC filters.
Calculate average historical moves for bullish and bearish cases separately.
Adjust these averages by applying recency-weighted drift and quantile-based scaling.
Translate adjusted percentages into absolute future price forecasts.
Draw bullish and bearish forecast lines accordingly.
Indicator Inputs & Their Roles
Correlation Tolerance (%)
Adjusts how strictly the indicator matches historical correlation. Higher tolerance includes more matches, lower tolerance selects fewer but closer matches.
Price RoC Lookback and Price RoC Tolerance (%)
Controls how momentum (speed of price moves) is matched historically. Increasing tolerance broadens historical matches.
Drift Lookback (bars)
Determines the number of recent bars influencing current drift estimation.
Quantile Lookback Period and Std Dev Multipliers
Defines quantile calculation and the size of the volatility bands.
Quantile Contact Tolerance (%)
Sets how close the current price must be to a quantile for it to be considered "active."
Forecast Horizons
Specifies how many future bars to forecast.
Continuous Forecast Lines
Toggles between drawing continuous lines or separate horizontal segments for each forecast horizon.
Practical Trading Applications
Bullish & Bearish EV Lines
These forecast lines indicate expected price levels based on historical similarity. Green indicates positive expectations; red indicates negative.
Momentum vs. Mean Reversion
Wide quantile bands and high drift suggest momentum, while extremes may signal possible reversals.
Volatility Sensitivity
Forecasts adapt dynamically to market volatility. Broader bands increase forecasted price movements.
Filtering Non-Relevant Historical Data
By using both correlation and RoC filtering, irrelevant past periods are excluded, enhancing forecast reliability.
Multi-Timeframe Suitability
Adaptable parameters make this indicator suitable for different trading styles and timeframes.
Complementary Tool
This indicator provides probabilistic projections rather than direct buy or sell signals. Combine it with other trading signals and analyses for optimal results.
Important Considerations
While historically-informed forecasts are valuable, market behavior can evolve unpredictably. Always manage risks and use supplementary analysis.
Experiment extensively with input settings for your specific market and timeframe to optimize forecasting performance.
Summary
The Recency-Weighted Market Memory w/ Quantile-Based Drift indicator uniquely merges multiple sophisticated concepts, delivering dynamic, historically-informed price forecasts. By combining historical similarity, adaptive drift, momentum filtering, and quantile-driven volatility scaling, traders gain an insightful perspective on future price possibilities.
Feel free to experiment, explore, and enjoy this powerful addition to your trading toolkit!
G-VIDYA | QuantEdgeBIntroducing G-VIDYA by QuantEdgeB
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🔹 Overview
The G-VIDYA | QuantEdgeB is a dynamic trend-following indicator that enhances market trend detection using Gaussian smoothing and an adaptive Variable Index Dynamic Average (VIDYA). It is designed to reduce noise, improve responsiveness, and adapt to volatility, making it a powerful tool for traders looking to capture long-term trends efficiently.
By integrating ATR-based filtering, the indicator creates a dynamic support and resistance band around VIDYA, allowing for more accurate trend confirmations. Additionally, traders have the option to enable trade labels for clearer visual signals.
This indicator is well-suited for medium to long-term trend traders, combining mathematical precision with market adaptability for robust trading strategies.
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🚀 Key Features
1. Gaussian Smoothing → Reduces market noise and smoothens price action.
2. VIDYA Adaptive Calculation → Adjusts dynamically based on market volatility.
3. ATR-Based Filtering → Creates a volatility-driven range around VIDYA.
4. Dynamic Trend Confirmation → Identifies bullish and bearish momentum shifts.
5. Trade Labels (Optional) → Can display Long/Cash labels on chart for better clarity.
6. Customizable Color Modes → Offers multiple visual themes for personalized experience.
7. Automated Alerts → Sends buy/sell alerts for crossover trend changes.
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📊 How It Works
1. Gaussian Smoothing is applied to the closing price to remove noise and improve signal clarity.
2. VIDYA Calculation dynamically adjusts to price movements, making it more reactive during high-volatility periods and stable in low-volatility environments.
3. ATR-Based Filtering establishes a dynamic range (Upper & Lower ATR Bands) around VIDYA:
- If price breaks above the upper ATR band, it signals a potential long trend.
- If price breaks below the lower ATR band, it signals a potential short trend.
4. The indicator assigns color-coded candles based on trend direction:
- Bullish Trend → Blue/Green (Uptrend)
- Bearish Trend → Red/Maroon (Downtrend)
5. Labels & Alerts (Optional)
- Users can activate Long/Cash labels to mark buy/sell opportunities.
- Built-in alerts trigger automatic notifications when trend direction changes.
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🎨 Visual Representation
- VIDYA Line → A smooth, trend-following line that dynamically adjusts to market conditions.
- Upper & Lower ATR Bands → Establishes a volatility-based corridor around VIDYA.
- Bar Coloring → Candles change color according to the detected trend.
- Long/Short Labels (Optional) → Displays trade entry/exit signals (can be enabled/disabled).
- Alerts → Generates trade notifications based on trend reversals.
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⚙️ Default Settings
- Gaussian Smoothing
- Length: 4
- Sigma: 2.0
- VIDYA Settings
- VIDYA Length: 46
- Standard Deviation Length: 28
- ATR Settings
- ATR Length: 14
- ATR Multiplier: 1.3
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💡 Who Should Use It?
✅ Trend Traders → Those who rely on medium-to-long-term trends for trading decisions.
✅ Swing Traders → Ideal for traders who want to capture trend reversals and ride momentum.
✅ Quantitative Analysts → Provides statistically driven smoothing and adaptive trend detection.
✅ Risk-Averse Traders → ATR filtering helps manage market volatility effectively.
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Conclusion
The G-VIDYA | QuantEdgeB is an advanced trend-following indicator that combines Gaussian smoothing, adaptive VIDYA filtering, and ATR-based dynamic trend analysis to deliver robust and reliable trade signals.
✅ Key Takeaways
📌 Adaptive & Dynamic: Adjusts to market conditions, making it effective for trend-following strategies.
📌 Noise Reduction: Gaussian smoothing helps filter out short-term fluctuations, improving signal clarity.
📌 Volatility Awareness: ATR-based filtering ensures better handling of market swings and trend reversals.
By blending mathematical precision and quantitative market analysis, G-VIDYA | QuantEdgeB offers a powerful edge in trend trading strategies.
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Dynamic Ticks Oscillator Model (DTOM)The Dynamic Ticks Oscillator Model (DTOM) is a systematic trading approach grounded in momentum and volatility analysis, designed to exploit behavioral inefficiencies in the equity markets. It focuses on the NYSE Down Ticks, a metric reflecting the cumulative number of stocks trading at a lower price than their previous trade. As a proxy for market sentiment and selling pressure, this indicator is particularly useful in identifying shifts in investor behavior during periods of heightened uncertainty or volatility (Jegadeesh & Titman, 1993).
Theoretical Basis
The DTOM builds on established principles of momentum and mean reversion in financial markets. Momentum strategies, which seek to capitalize on the persistence of price trends, have been shown to deliver significant returns in various asset classes (Carhart, 1997). However, these strategies are also susceptible to periods of drawdown due to sudden reversals. By incorporating volatility as a dynamic component, DTOM adapts to changing market conditions, addressing one of the primary challenges of traditional momentum models (Barroso & Santa-Clara, 2015).
Sentiment and Volatility as Core Drivers
The NYSE Down Ticks serve as a proxy for short-term negative sentiment. Sudden increases in Down Ticks often signal panic-driven selling, creating potential opportunities for mean reversion. Behavioral finance studies suggest that investor overreaction to negative news can lead to temporary mispricings, which systematic strategies can exploit (De Bondt & Thaler, 1985). By incorporating a rate-of-change (ROC) oscillator into the model, DTOM tracks the momentum of Down Ticks over a specified lookback period, identifying periods of extreme sentiment.
In addition, the strategy dynamically adjusts entry and exit thresholds based on recent volatility. Research indicates that incorporating volatility into momentum strategies can enhance risk-adjusted returns by improving adaptability to market conditions (Moskowitz, Ooi, & Pedersen, 2012). DTOM uses standard deviations of the ROC as a measure of volatility, allowing thresholds to contract during calm markets and expand during turbulent ones. This approach helps mitigate false signals and aligns with findings that volatility scaling can improve strategy robustness (Barroso & Santa-Clara, 2015).
Practical Implications
The DTOM framework is particularly well-suited for systematic traders seeking to exploit behavioral inefficiencies while maintaining adaptability to varying market environments. By leveraging sentiment metrics such as the NYSE Down Ticks and combining them with a volatility-adjusted momentum oscillator, the strategy addresses key limitations of traditional trend-following models, such as their lagging nature and susceptibility to reversals in volatile conditions.
References
• Barroso, P., & Santa-Clara, P. (2015). Momentum Has Its Moments. Journal of Financial Economics, 116(1), 111–120.
• Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, 52(1), 57–82.
• De Bondt, W. F., & Thaler, R. (1985). Does the Stock Market Overreact? The Journal of Finance, 40(3), 793–805.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.
• Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228–250.
Volatility Adaptive Signal Tracker (VAST)The Adaptive Trend Following Buy/Sell Signals Pine Script is designed to help traders identify and capitalize on market trends using an adaptive trend-following strategy. This script focuses on generating reliable buy and sell signals by analyzing market trends and volatility. It simplifies the trading process by providing clear signals without plotting additional lines, making it easy to use and interpret.
Key Features:
Adaptive Trend Following:
The script employs an adaptive trend-following approach that leverages market volatility to generate buy and sell signals. This method is effective in both trending and volatile markets.
Inputs and Customization:
The script includes customizable parameters for the Simple Moving Average (SMA) length, the Average True Range (ATR) length, and the ATR multiplier. These inputs allow traders to adjust the sensitivity of the signals to match their trading style and market conditions.
Signal Generation:
Buy Signal: Generated when the closing price crosses above the upper adaptive band, indicating a potential upward trend.
Sell Signal: Generated when the closing price crosses below the lower adaptive band, indicating a potential downward trend.
Visual Signals:
The script uses plotshape to mark buy signals with green labels below the bars and sell signals with red labels above the bars. This clear visual representation helps traders quickly identify trading opportunities.
Alert Conditions:
The script sets up alert conditions for both buy and sell signals. Traders can use these alerts to receive notifications when a signal is generated, ensuring they do not miss any trading opportunities.
How It Works:
SMA Calculation: The script calculates the Simple Moving Average (SMA) over a specified period, which helps in identifying the general trend direction.
ATR Calculation: The Average True Range (ATR) is calculated to measure market volatility.
Adaptive Bands: Upper and lower adaptive bands are created by adding and subtracting a multiple of the ATR to the SMA, respectively.
Signal Logic: Buy signals are generated when the closing price crosses above the upper band, while sell signals are generated when the closing price crosses below the lower band.
Example Use Case:
A trader looking to capitalize on medium-term trends in the Nifty futures market can use this script to receive timely buy and sell signals. By customizing the SMA length and ATR parameters, the trader can fine-tune the script to match their trading strategy, ensuring they enter and exit trades at optimal points.
Benefits:
Simplicity: The script provides clear buy and sell signals without cluttering the chart with additional lines or indicators.
Adaptability: Customizable parameters allow traders to adapt the script to various market conditions and trading styles.
Alerts: Built-in alert conditions ensure traders receive timely notifications, helping them to act quickly on trading signals.
How to Use:
Open TradingView: Go to the TradingView website and log in.
Create a New Chart: Click on the “Chart” button to open a new chart.
Open the Pine Script Editor: Click on the “Pine Editor” tab at the bottom of the chart.
Create a New Script: Delete any default code in the Pine Script editor and paste the provided script.
Add to Chart: Click on the “Add to Chart” button to compile and add the script to your chart.
Save the Script: Click “Save” and name the script.
Set Alerts: Right-click on the chart, select “Add Alert,” and choose the appropriate condition to set alerts for buy and sell signals.
VIX Dashboard [NariCapitalTrading]Overview
This VIX Dashboard is designed to provide traders with a quick visual reference into the current volatility and trend direction of the market as measured by CBOE VIX. It uses statistical measures and indicators including Rate of Change (ROC), Average True Range (ATR), and simple moving averages (SMA) to analyze the VIX.
Components
ATR Period : The ATR Period is used to calculate the Average True Range. The default period set is 24.
Trend Period : This period is used for the Simple Moving Average (SMA) to determine the trend direction. The default is set to 48.
Speed Up/Down Thresholds : These thresholds are used to determine significant increases or decreases in the VIX’s rate of change, signaling potential market volatility spikes or drops. These are customizable in the input section.
VIX Data : The script fetches the closing price of the VIX from a specified source (CBOE:VIX) with a 60-minute interval.
Rate of Change (ROC) : The ROC measures the percentage change in price from one period to the next. The script uses a default period of 20. The period can be customized in the input section.
VIX ATR : This is the Average True Range of the VIX, indicating the daily volatility level.
Trend Direction : Determined by comparing the VIX data with its SMA, indicating if the trend is up, down, or neutral. The trend direction can be customized in the input section.
Dashboard Display : The script creates a table on the chart that dynamically updates with the VIX ROC, ATR, trend direction, and speed.
Calculations
VIX ROC : Calculated as * 100
VIX ATR : ATR is calculated using the 'atrPeriod' and is a measure of volatility.
Trend Direction : Compared against the SMA over 'trendPeriod'.
Trader Interpretation
High ROC Value : Indicates increasing volatility, which could signal a market turn or increased uncertainty.
High ATR Value : Suggests high volatility, often seen in turbulent market conditions.
Trend Direction : Helps in understanding the overall market sentiment and trend.
Speed Indicators : “Mooning” suggests rapid increase in volatility, whereas “Cratering” indicates a rapid decrease.
The interpretation of these indicators should be combined with other market analysis tools for best results.
cankardesler stoploss v2This stoploss allows to filter high volatility fake trends;
But how we are made it; we are calculating the last spikes value average and calculating the standart deviation, after we added to the standart stoploss formula price+2atr and voila!!
Your stop loss is ready.
The idea behind this formula: what is explosing our stops? fake-out spikes.
We think if we get the last spikes average and calculate the standart deviation on it and after add it to the original stop formula, its gonna help for bypassing the spikes.
Thanks a lot @ocankardes for helping me to developing this formula
Damiani Volatmeter [loxx]I wasn't going to publish this since it's one my go to private indicators, but I decided to push this out anyway. This is a variation on Damiani Volatmeter to make it easier to understand what's going on. Damiani Volatmeter uses ATR and Standard deviation to tease out ticker volatility so you can better understand when it's the ideal time to trade. The idea here is that you only take trades when volatility is high so this indicator is to be coupled with various other indicators to validate the other indicator's signals. This is also useful for detecting crabbing and chopping markets.
Shoutout to user @xinolia for the DV function used here.
Anything red means that volatility is low. Remember volatility doesn't have a direction. Anything green means volatility high despite the direction of price. The core signal line here is the green and red line that dips below two while threshold lines to "recharge". Maximum recharge happen when the core signal line shows a yellow ping. Soon after one or many yellow pings you should expect a massive upthrust of volatility. The idea here is you don't trade unless volatility is rising or green. This means that the Volatmeter has to dip into the recharge zone, recharge and then spike upward. You can also attempt to buy or sell reversals with confluence indicators when volatility is in the recharge zone, but I wouldn't recommend this. However, if you so choose to do this, then use the following indicator for confluence.
And last reminder, volatility doesn't have a direction ! Red doesn't mean short, and green doesn't mean long, Red means don't trade period regardless of direction long/short, and green means trade no matter the direction long/short. This means you'll have to add an indicator that does show direction such as a mean reversion indicator like Fisher Transform or a Gaussian Filter. You can search my public scripts for various Fisher Transform and Gaussian Filter indicators.
Price-Filtered Spearman Rank Correl. w/ Floating Levels is considered the Mercedes Benz of reversal indcators
How signals work
RV = Rising Volatility
VD = Volatility Dump
Plots
White line is signal
Thick red/green line is the Volatmeter line
The dotted lower lines are the zero line and minimum recharging line
Included
Bar coloring
Alerts
Signals
Related indicators
Variety Moving Average Waddah Attar Explosion (WAE)
vol_boxA simple script to draw a realized volatility forecast, in the form of a box. The script calculates realized volatility using the EWMA method, using a number of periods of your choosing. Using the "periods per year", you can adjust the script to work on any time frame. For example, if you are using an hourly chart with bitcoin, there are 24 periods * 365 = 8760 periods per year. This setting is essential for the realized volatility figure to be accurate as an annualized figure, like VIX.
By default, the settings are set to mimic CBOE volatility indices. That is, 252 days per year, and 20 period window on the daily timeframe (simulating a 30 trading day period).
Inside the box are three figures:
1. The current realized volatility.
2. The rank. E.g. "10%" means the current realized volatility is less than 90% of realized volatility measures.
3. The "accuracy": how often price has closed within the box, historically.
Inputs:
stdevs: the number of standard deviations for the box
periods to project: the number of periods to forecast
window: the number of periods for calculating realized volatility
periods per year: the number of periods in one year (e.g. 252 for the "D" timeframe)
vol_premiaThis script shows the volatility risk premium for several instruments. The premium is simply "IV30 - RV20". Although Tradingview doesn't provide options prices, CBOE publishes 30-day implied volatilities for many instruments (most of which are VIX variations). CBOE calculates these in a standard way, weighting at- and out-of-the-money IVs for options that expire in 30 days, on average. For realized volatility, I used the standard deviation of log returns. Since there are twenty trading periods in 30 calendar days, IV30 can be compared to RV20. The "premium" is the difference, which reflects market participants' expectation for how much upcoming volatility will over- or under-shoot recent volatility.
The script loads pretty slow since there are lots of symbols, so feel free to delete the ones you don't care about. Hopefully the code is straightforward enough. I won't list the meaning of every symbols here, since I might change them later, but you can type them into tradingview for data, and read about their volatility index on CBOE's website. Some of the more well-known ones are:
ES: S&P futures, which I prefer to the SPX index). Its implied volatility is VIX.
USO: the oil ETF representing WTI future prices. Its IV is OVX.
GDX: the gold miner's ETF, which is usually more volatile than gold. Its IV is VXGDX.
FXI: a china ETF, whose volatility is VXFXI.
And so on. In addition to the premium, the "percentile" column shows where this premium ranks among the previous 252 trading days. 100 = the highest premium, 0 = the lowest premium.
pricing_tableThis script helps you evaluate the fair value of an option. It poses the question "if I bought or sold an option under these circumstances in the past, would it have expired in the money, or worthless? What would be its expected value, at expiration, if I opened a position at N standard deviations, given the volatility forecast, with M days to expiration at the close of every previous trading day?"
The default (and only) "hv" volatility forecast is based on the assumption that today's volatility will hold for the next M days.
To use this script, only one step is mandatory. You must first select days to expiration. The script will not do anything until this value is changed from the default (-1). These should be CALENDAR days. The script will convert to these to business days for forecasting and valuation, as trading in most contracts occurs over ~250 business days per year.
Adjust any other variables as desired:
model: the volatility forecasting model
window: the number of periods for a lagged model (e.g. hv)
filter: a filter to remove forecasts from the sample
filter type: "none" (do not use the filter), "less than" (keep forecasts when filter < volatility), "greater than" (keep forecasts when filter > volatility)
filter value: a whole number percentage. see example below
discount rate: to discount the expected value to present value
precision: number of decimals in output
trim outliers: omit upper N % of (generally itm) contracts
The theoretical values are based on history. For example, suppose days to expiration is 30. On every bar, the 30 days ago N deviation forecast value is compared to the present price. If the price is above the forecast value, the contract has expired in the money; otherwise, it has expired worthless. The theoretical value is the average of every such sample. The itm probabilities are calculated the same way.
The default (and only) volatility model is a 20 period EWMA derived historical (realized) volatility. Feel free to extend the script by adding your own.
The filter parameters can be used to remove some forecasts from the sample.
Example A:
filter:
filter type: none
filter value:
Default: the filter is not used; all forecasts are included in the the sample.
Example B:
filter: model
filter type: less than
filter value: 50
If the model is "hv", this will remove all forecasts when the historical volatility is greater than fifty.
Example C:
filter: rank
filter type: greater than
filter value: 75
If the model volatility is in the top 25% of the previous year's range, the forecast will be included in the sample apart from "model" there are some common volatility indexes to choose from, such as Nasdaq (VXN), crude oil (OVX), emerging markets (VXFXI), S&P; (VIX) etc.
Refer to the middle-right table to see the current forecast value, its rank among the last 252 days, and the number of business days until
expiration.
NOTE: This script is meant for the daily chart only.
vstop5 (RA)Upgrade standart Volatility Stop with 5 fixed values for selected tickers.
When switching between tickers - VStop multiplier will be changed to desired fixed value for fixed tickers.
If nothing mached - will be used standart value
See the example of setting here
As You can see on screenshot 5 different VStops can be set up for different tickers.
and as a result:
Доработка стандартного индикатора VStop, но с возможностью зафиксировать для 5-ти разных инструментов свое значение мультипликатора.
Далее при переключении с одного инструмента на другой - значение Мультипликатора VStop будет меняться в соответствии с сохраненными привязанными настройками. для всех НЕ привязанных инструментов - будет использовано значение Мультипликатора по умолчанию, которое также задается в Настройках.
Пример настроек тут
VoluTility🌊 VoluTility forecasts trend exhaustion, breakout pressure, and structural inflection by measuring volatility within the effort stream. Built on the concept of ATR applied to volume, it doesn’t read raw volume — it reveals whether that volume is stable, chaotic, or compressing ahead of a move. The goal is to detect structural setups before they resolve. The lower the timeframe, the greater the alpha.
🧠 Core Logic
A zero-centered histogram shows the deviation of smoothed volume from its own volatility baseline. Positive bars indicate expansion; negative bars signal compression. Color reflects rate-of-change in volume volatility. Opacity tracks effort/result strength — showing when moves are real or hollow.
The overlaid ribbon (EMA vs HMA) highlights rhythm shifts. Orange fill signals real expansion; yellow shows decay or absorption. Together, they expose pre-breakout compression and exhaustion tails before price reacts.
🏗️ Structural Read
On the 1H BTC chart shown, price coils into a shallow pullback, compressing within a narrow range marked by shrinking candle bodies and muted wick aggression. A sudden expansion candle breaks the coil cleanly, with no immediate rejection or wick reversion. Price holds above the breakout pivot, establishing a baseline for structural acceptance and shifting bias toward continuation.
🔰 Zone Descriptions
🔴 Volatile blowout
🟠 Clean expansion
🟡 Passive or absorbed effort
🟢 Steady-state rhythm
🔵 Compression coil
🧐 Suggested Use
VoluTility is expressly designed as an overlay for sub-pane indicators, where it acts as a second-order rhythm map — exposing hidden structural pressure within volume or volatility streams. When paired with volume (like ZVOL or OBVX), it highlights when flow is expanding with intent versus fading into noise. When layered over volatility signals (like ATR Turbulence or WIRE), it reveals whether expansion has real effort behind it — or is just structural slack.
It pairs especially well with the Relative Directional Index (RDI), where its histogram and ribbon offer early exhaustion signals before traditional trend or momentum fades appear. On raw momentum tools, it acts as a filter: softening false breaks and confirming pressure-backed continuation.
Run on 15m or lower charts for early entry cues or breakout anticipation. On 1H charts, use it to validate compression resolution or detect fatigue before structure turns. It doesn’t react to price — it forecasts readiness.
Volatility-Adjusted Trend Deviation Statistics (C-Ratios)The Pine Script logic provided generates and displays a table with key information derived from VWMA, EMA, and ATR-based "C Ratios," alongside stochastic oscillators, correlation coefficients, Z-scores, and bias indicators. Here’s an explanation of the logic and what the output in the table informs:
Key Calculations and Their Purpose
VWMA and EMA (Smoothing Lengths):
Multiple EMAs are calculated using VWMA as the source, with lengths spanning short-term (13) to long-term (233).
These EMAs provide a hierarchy of smoothed price levels to assess trends over various time horizons.
ATR-Based "C Ratios":
The C Ratios measure deviations of smoothed prices (a_1 to a_7) from the source price relative to ATR at corresponding lengths.
These values normalize deviations, giving insight into the price's relative movement strength and direction over various periods.
Stochastic Oscillator for C Ratios:
Calculates normalized stochastic values for each C Ratio to assess overbought/oversold conditions dynamically over a rolling window.
Helps identify short-term momentum trends within the broader context of C Ratios.
Displays the average stochastic value derived from all C Ratios.
Text: Shows overbought/oversold conditions (Overbought, Oversold, or ---).
Color: Green for strong upward momentum, red for downward, and white for neutral.
Weighted and Mean C Ratio:
The script computes both an arithmetic mean (c_mean) and a weighted mean (c_mean_w) for all C Ratios.
Weighted mean emphasizes short-term values using predefined weights.
Trend Bias and Reversal Detection:
The script calculates Z-scores for c_mean to identify statistically significant deviations.
It combines Z-scores and weighted C Ratio values to determine:
Bias (Bullish/Bearish based on Z-score thresholds and mean values).
Reversals (Based on relative positioning and how the weighted c_mean and un-weighted C_mean move. ).
Correlation Coefficient:
Correlation of mean C Ratios (c_mean) with bar indices over the short-term length (sl) assesses the strength and direction of trend consistency.
Table Output and Its Meaning
Stochastic Strength:
Long-term Correlation:
List of Lengths: Define the list of lengths for EMA and ATR explicitly (e.g., ).
Calculate Mean C Ratios: For each length in the list, calculate the mean C Ratio
Average these values over the entire dataset.
Store Lengths and Mean C Ratios: Maintain arrays for lengths and their corresponding mean C Ratios.
Correlation: compute the Pearson correlation between the list of lengths and the mean C Ratios.
Text: Indicates Uptrend, Downtrend, or neutral (---).
Color: Green for positive (uptrend), red for negative (downtrend), and white for neutral.
Z-Score Bias:
Assesses the statistical deviation of C Ratios from their historical mean.
Text: Bullish Bias, Bearish Bias, or --- (neutral).
Color: Green or red based on the direction and significance of the Z-score.
C-Ratio Mean:
Displays the weighted average C Ratio (c_mean_w) or a reversal condition.
Text: If no reversal is detected, shows c_mean_w; otherwise, a reversal condition (Bullish Reversal, Bearish Reversal).
Color: Indicates the strength and direction of the bias or reversal.
Practical Insights
Trend Identification: Correlation coefficients, Z-scores, and stochastic values collectively highlight whether the market is trending and the trend's direction.
Momentum and Volatility: Stochastic and ATR-normalized C Ratios provide insights into the momentum and price movement consistency across different timeframes.
Bias and Reversal Detection: The script highlights potential shifts in market sentiment or direction (bias or reversal) using statistical measures.
Customization: Users can toggle plots and analyze specific EMA lengths or focus on combined metrics like the weighted C Ratio.
Volume-Adjusted Bollinger BandsThe Volume-Adjusted Bollinger Bands (VABB) indicator is an advanced technical analysis tool that enhances the traditional Bollinger Bands by incorporating volume data. This integration allows the bands to dynamically adjust based on market volume, providing a more nuanced view of price movements and volatility. The key qualities of the VABB indicator include:
1. Dynamic Adjustment with Volume: Traditional Bollinger Bands are based solely on price data and standard deviations. The VABB indicator adjusts the width of the bands based on the volume ratio, making them more responsive to changes in market activity. This means that during periods of high volume, the bands will expand, and during periods of low volume, they will contract. This adjustment helps to reinforce the significance of price movements relative to the central line (VWMA).
2. Volume-Weighted Moving Average (VWMA): Instead of using a simple moving average (SMA) as the central line, the VABB uses the VWMA, which weights prices by volume. This provides a more accurate representation of the average price level, considering the trading volume.
3. Enhanced Signal Reliability: By incorporating volume, the VABB can filter out false signals that might occur in low-volume conditions. This makes the indicator particularly useful for identifying significant price movements that are supported by strong trading activity.
How to Use and Interpret the VABB Indicator
To use the VABB indicator, you need to set it up on your trading platform with the following parameters:
1. BB Length: The number of periods for calculating the Bollinger Bands (default is 20).
2. BB Multiplier: The multiplier for the standard deviation to set the width of the Bollinger Bands (default is 2.0).
3. Volume MA Length: The number of periods for calculating the moving average of the volume (default is 14).
Volume Ratio Smoothing Length: The number of periods for smoothing the volume ratio (default is 5).
Interpretation
1.Trend Identification: The VWMA serves as the central line. When the price is above the VWMA, it indicates an uptrend, and when it is below, it indicates a downtrend. The direction of the VWMA itself can also signal the trend's strength.
2. Volatility and Volume Analysis: The width of the VABB bands reflects both volatility and volume. Wider bands indicate high volatility and/or high volume, suggesting significant price movements. Narrower bands indicate low volatility and/or low volume, suggesting consolidation.
3. Trading Signals:
Breakouts: A price move outside the adjusted upper or lower bands can signal a potential breakout. High volume during such moves reinforces the breakout's validity.
Reversals: When the price touches or crosses the adjusted upper band, it may indicate overbought conditions, while touching or crossing the adjusted lower band may indicate oversold conditions. These conditions can signal potential reversals, especially if confirmed by other indicators or volume patterns.
Volume Confirmation: The volume ratio component helps confirm the strength of price movements. For instance, a breakout accompanied by a high volume ratio is more likely to be sustained than one with a low volume ratio.
Practical Example
Bullish Scenario: If the price crosses above the adjusted upper band with a high volume ratio, it suggests a strong bullish breakout. Traders might consider entering a long position, setting a stop-loss just below the VWMA or the lower band.
Bearish Scenario: Conversely, if the price crosses below the adjusted lower band with a high volume ratio, it suggests a strong bearish breakout. Traders might consider entering a short position, setting a stop-loss just above the VWMA or the upper band.
Conclusion
The Volume-Adjusted Bollinger Bands (VABB) indicator is a powerful tool that enhances traditional Bollinger Bands by incorporating volume data. This dynamic adjustment helps traders better understand market conditions and make more informed trading decisions. By using the VABB indicator, traders can identify significant price movements supported by volume, improving the reliability of their trading signals.
The Volume-Adjusted Bollinger Bands (VABB) indicator is provided for educational and informational purposes only. It is not financial advice and should not be construed as a recommendation to buy, sell, or hold any financial instrument. Trading involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results.
Elastic Buy-Sell Volume Wighted SupertrendCredits: This uses Trading View's buy and sell volume script and the Super trend script.
Elastic Buy-Sell Volume Wighted Supertrend can be used like a traditional supertrend indicator however we do not have to arbitrarily choose a multiplier depending on the stock and time frame the code dynamically adjust the multiplier and this is described below.
The buy and sell ATR (Average True Range) play a crucial role in determining the levels for potential buy and sell signals in the market. These ATR values are calculated based on volume-weighted averages, providing insights into the strength of buying and selling pressures. By incorporating volume into the ATR calculation, the indicator can better adapt to market dynamics, as volume often reflects the intensity of price movements. Instead of using Volume as whole this uses up and down volume derived from lower time frames which is used to calculate buy and sell ATR.
The multiplier is a key factor in the Supertrend calculation, which adjusts the width of the trend bands. The multiplier in this indicator dynamically adjusts itself based on two key components: the ratio of the asset's Average True Range (ATR) to that of a broader market benchmark and the coefficient of variation (CV) of the True Range (TR). The ratio comparison provides a historical context of the asset's volatility relative to the wider market over a longer time frame, while the CV accounts for short-term fluctuations in volatility.
By comparing the asset's ATR to that of the benchmark, traders gain insights into the asset's historical volatility behavior. A higher multiplier suggests that the asset's volatility has historically exceeded that of the benchmark, indicating potentially larger price movements compared to the broader market. Conversely, a lower multiplier suggests the opposite.
The CV component measures short-term variability in the asset's volatility, ensuring that the multiplier adapts to both long-term trends and short-term fluctuations. This combined approach enables traders to make informed decisions, considering both historical volatility relative to the broader market and short-term variability. Ultimately, the dynamic multiplier enhances traders' ability to adjust their strategies effectively across various market conditions.
Overall, the use of buy and sell ATR, along with a dynamically adjusted multiplier, enhances the indicator's ability to identify trend directions and to use a dynamic stop loss level.
NASDAQ 100 Peak Hours StrategyNASDAQ 100 Peak Hours Trading Strategy
Description
Our NASDAQ 100 Peak Hours Trading Strategy leverages a carefully designed algorithm to trade within specific hours of high market activity, particularly focusing on the first two hours of the trading session from 09:30 AM to 11:30 AM GMT-5. This period is identified for its increased volatility and liquidity, offering numerous trading opportunities.
The strategy incorporates a blend of technical indicators to identify entry and exit points for both long and short positions. These indicators include:
Exponential Moving Averages (EMAs) : A short-term 9-period EMA and a longer-term 21-period EMA to determine the market trend and momentum.
Relative Strength Index (RSI) : A 14-period RSI to gauge the market's momentum.
Average True Range (ATR) : A 14-period ATR to assess market volatility and to set dynamic stop losses and trailing stops.
Volume Weighted Average Price (VWAP) : To identify the market's average price weighted by volume, serving as a benchmark for the trading day.
Our strategy uniquely applies a volatility filter using the ATR, ensuring trades are only executed in conditions that favor our setup. Additionally, we consider the direction of the EMAs to confirm the market's trend before entering trades.
Originality and Usefulness
This strategy stands out by combining these indicators within the NASDAQ 100's peak hours, exploiting the specific market conditions that prevail during these times. The inclusion of a volatility filter and dynamic stop-loss mechanisms based on the ATR provides a robust method for managing risk.
By focusing on the early trading hours, the strategy aims to capture the initial market movements driven by overnight news and the opening rush, often characterized by higher volatility. This approach is particularly useful for traders looking to maximize gains from short-term fluctuations while limiting exposure to longer-term market uncertainty.
Strategy Results
To ensure the strategy's effectiveness and reliability, it has undergone rigorous backtesting over a significant dataset to produce a sample size of more than 100 trades. This testing phase helps in identifying the strategy's potential in various market conditions, its consistency, and its risk-to-reward ratio.
Our backtesting adheres to realistic trading conditions, accounting for slippage and commission to reflect actual trading scenarios accurately. The strategy is designed with a conservative approach to risk management, advising not to risk more than 5-10% of equity on a single trade. The default settings in the script align with these principles, ensuring that users can replicate our tested conditions.
Using the Strategy
The strategy is designed for simplicity and ease of use:
Trade Hours : Focuses on 09:30 AM to 11:30 AM GMT-5, during the NASDAQ 100's peak activity hours.
Entry Conditions : Trades are initiated based on the alignment of EMAs, RSI, VWAP, and the ATR's volatility filter within the designated time frame.
Exit Conditions : Includes dynamic trailing stops based on ATR, a predefined time exit strategy, and a trend reversal exit condition for risk management.
This script is a powerful tool for traders looking to leverage the NASDAQ 100's peak hours, providing a structured approach to navigating the early market hours with a robust set of criteria for making informed trading decisions.
Average True Range Level█ Overview
The indicator uses color-coded columns to represent different levels of normalized ATR, helping traders identify periods of high or low volatility.
█ Calculations
The normalization process involves dividing the current True Range by the Average True Range. The formula for normalized ATR in the code is:
nAtr = nz(barRange/atr)
█ How To Use
Level < 1
During periods when the normalized ATR is less than 1, suggesting a lower level of volatility, traders may explore inside bar strategies. These strategies focus on trading within the range of the previous bar, aiming to capitalize on potential breakout opportunities.
Level between 1 and 3
In instances where the normalized ATR falls between 1 and 3, indicating moderate volatility, a pullback strategy may be considered. Traders look for temporary corrections against the prevailing trend, entering positions in anticipation of the trend's resumption
Level between 2 and 3
Within the range of normalized ATR between 2 and 3, signifying a balanced level of volatility, traders might explore breakout strategies. These strategies involve identifying potential breakout levels using support and resistance or other indicators and entering trades in the direction of the breakout.
Level > 3
When the normalized ATR exceeds 3, signaling high volatility, traders should approach with caution. While not ideal for typical mean reversion strategies, this condition may indicate that the price has become overextended. Traders might wait for subsequent candles, observing a normalized ATR between 2 and 3, to consider mean reversion opportunities after potential overpricing during the high volatility period.
* Note: These strategies are suggestions and may not be suitable for all trading scenarios. Traders should exercise discretion, conduct their own analysis, and adapt strategies based on individual preferences and risk tolerance.
Bull / Bear Market RegimeBull / Bear Market Regime
Instructions:
- A simple risk on or risk off indicator based on CBOE's Implied Correlation and VIX to highlight and indicate Bull / Bear Markets. To be used with the S&P500 index as that's the source from where the CBOE calculates and measures implied volatility & implied correlation. Can also be used with the other indices such as: Dow Jones, S&P 500, Nasdaq, & Nasdaq100, & Index ETF's such as DIA, SPY, QQQ, etc.
- Know the active regime, see the larger picture using the Daily or Weekly view, and visualize the current "Risk On (Bull) or Risk Off (Bear)" environment.
Description:
- Risk On and Risk Off simplified & visualized. Know if we are in a RISK ON or RISK OFF environment (Bull or Bear Market). (Absolute bottoms and tops will occur BEFORE a Risk On (Bull Market) or Risk Off (Bear Market) environment is confirmed!) This indicator is not meant to bottom tick or uptick market price action, but to show the active regime.
- Green: Bull Market, Risk On, low volatility, and low risk.
- Red: Bear Market, Risk Off, high volatility, and higher risk.
Buy & Sell Indicators (DAILY time frame)
- Nothing is 100% guaranteed! Can be used for short to medium term trades at the users discretion in BEAR MARKETS!!
- These signals are meant to be used during a RISK OFF / BEAR MARKET environment that tends to be accompanied with high volatility. A Risk on / Bull Market environment tends to have low volatility and endless rallies, so the signals will differ and in most instances not apply for Bull market / Risk on regime.
- The SELL signal will more often than not signal that a pullback is near in a BULL market and that a BMR-Bear Market Rally is almost over in a BEAR market.
- The BUY signal will have far more accuracy in a BEAR market-high volatility environment and can Identify short-term and major bottoms.
Always use proper sizing and risk management!
intraday_bondsStatistics for assisting with intraday bond trading, using five minute periods and one hour ranges. There are two tables, a volatility table and a correlation table. The correlation table shows the correlation of five minute returns (absolute) between the four different bond contracts that trade on the CME. The volatility table shows for each contract:
- The current realized volatility, based on the previous one hour of realized volatility. This figure is annualized for easy comparison with options contracts.
- The current realized volatility's z-score, based on all available data.
- The tick range of an "N" standard deviation move over one hour. Choose "N" using the stdevs input.
- The previous hour's true range (high - low).
The ranges are expressed in ticks.
