TPO IQ [TradingIQ]Hello Traders!
Introducing "TPO IQ"!
TPO IQ offers a Time Price Opportunity profile with several customization options that packs several related features to help traders navigate the generated profiles!
Features
TPO Profiles
Single Print identification
Initial Balance Identification
Can be anchored to timeframe change
Can be anchored to fixed time interval
Last profile detailed visuals
Customizable value area percentage
POC identification
Mid-point identification
TPO Profiles
A TPO profile is a market profile visualization that details how much time was spent at each price level throughout the time interval.
The image above further explains what a TPO Profile is!
Each letter corresponds to a candlestick. With this information, traders are able to visualize how much time was spent at each price area.
With customizable gradient colors, specifically in this example, blocks colored red are the earliest times in the profile, blocks colored green are in the beginning half of the time midpoint of the profile. Blue blocks represent the first half of the end of the time period, and purple blocks correspond to the end of the time period.
Please note that this form of TPO profile generation will only occur when the most recent profile uses less than 500 alphabet characters! If more than 500 characters are preset, TPO IQ will revert to using labels!
Initial Balance
TPO IQ also identifies the initial balance range and all alphabet characters that form within it!
The image above exemplifies this feature. The initial balance range is denoted by a a neon-blue line, with a blue circle showing the opening price. All characters within the initial balance range are highlighted blue, which is a feature that can be disabled with customizable colors.
POC
TPO IQ also identifies the point of control (POC) of the TPO Profile.
The point of control for the profile is labeled yellow by default, and shows where price spent the most time throughout the time period.
The image above shows the POC for the time period being identified by TPO IQ.
Value Area
TPO IQ also identifies the value area of the profile. A customizable percentage that is 70% by default, the value area of a TPO profile shows where price traded the majority of the time.
The image above further explains this feature. For this example, with the value area percentage being set to 70%, the value area high and value area low show the price zone that prices traded at 70% of the time throughout the profile.
TPO Midpoint
In addition to the POC, the TPO profile midpoint is also identified by TPO IQ.
The TPO midpoint simply corresponds to the middle price between the session's high and low!
Fixed Interval Mode
By default, TPO IQ recalculates every day, but this can also be changed to a customizable session time, such as 4 hours. If 4 hours is selected, then a new TPO profile will be generated every 4 hours.
However, in Fixed Interval mode, a TPO profile will be generated through a user-defined time range, such as 1300-1700.
In the image above, Fixed Interval mode is applied with a time range of 1300-1700 and, consequently, TPO IQ generates a new profile throughout every 1300-1700 time range!
This feature allows traders to specify time ranges of interest to generate TPO profiles for!
TPO Overview Label
The TPO overview label shows key statistics for the TPO profile generated throughout the trading session!
The "TPO Count" statistic shows how many alphabetical letters were generated for the profile, which is an adequate method to determine the session's volatility and price range.
The "Tick Levels" statistic shows how many tick levels were used to create the profile - another method to determine the volatility and price range of the session.
The "Top Letter" statistic shows which letter appears most throughout the profile. In this example, the top letter was "f", which means throughout creation of the profile, the letter "f" appeared the most!
And that's all for now!
If you have any feedback or new feature ideas for TPO IQ please feel free to share them with us!
Thank you traders!
תנודתיות
DEMO QV | QuantEdgeBIntroducing DEMO QV by QuantEdgeB
Overview
The DEMO QV indicator is a dynamic momentum and volatility-based model, designed to identify high-probability trend shifts and breakout opportunities. By leveraging a double exponential moving average smoothing with percentile-based trend analysis, and ATR volatility filters, this tool adapts to market conditions efficiently and ensuring robust signal generation.
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Key Features
🔹 DEMA (Double Exponential Moving Average)
A faster and more responsive alternative to traditional EMAs, DEMA reduces lag, enhancing the ability to detect rapid market shifts.
🔹 Percentile-Based Trend Identification:
The system calculates 25th, 50th, and 75th percentile levels effectively segmenting price action into different regimes for trend confirmation and signal clarity.
🔹 ATR-Adjusted Volatility Filters:
By incorporating ATR multipliers the system adapts to different market conditions, ensuring that breakout signals are based on meaningful price movements rather than noise.
🔹 Momentum Confirmation (ROC-Based):
A rate-of-change (ROC) momentum filter is applied to validate trend strength, reducing false signals and aligning trades with prevailing market momentum.
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How It Works
✅ Long Signals:
- Price closes above the 75th DEMA Percentile level, adjusted with ATR for volatility filtering.
- Momentum is positive, confirming the trend shift.
- Shown by "Long" label
✅ Short Signals:
- Price closes above the 25th DEMA Percentile level, adjusted with ATR for volatility filtering.
- Momentum is negative, ensuring alignment with bearish trends.
- Shown by "Cash" label
This dual-layered signal mechanism makes the strategy smooth yet aggressive on shorts, quickly reacting to potential downturns.
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Use Cases
📌 Breakout & Trend-Following Strategy: Ideal for spotting breakout conditions based on percentile rank and ATR expansion.
📌 Momentum-Driven Trading: The ROC filter ensures signals align with price momentum, reducing premature entries.
📌 Adaptable Across Markets: Works across assets with different volatility, thanks to its ATR filtering and dual layer for signal confirmation.
📌 Smooth but Aggressive on Shorts:The dual-layered short logic enables reactive entries while maintaining a clean trend-following approach for longs.
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Behaviour across Crypto Majors:
BTC
ETH
SOL
Note : Past behaviour is not indicative of future results. Always conduct thorough testing and risk management before making trading decisions.
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Customization Options
⚙️ Color Mode Selection: Multiple preset themes for enhanced visualization.
⚙️ Long/Cash Signal Label: Default is turned off.
⚙️ DEMA Length: Adjustable to fine-tune sensitivity. (Default: 14)
⚙️ Percentile Calculation Length: Defines trend zones. (Default: 35)
⚙️ ATR Length & Multipliers: Controls the threshold for breakout confirmation. (Default: 14, 1.3x for longs, 2.5x for shorts)
⚙️ Momentum Length: Fine-tunes responsiveness to trend shifts. (Default: 8)
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Conclusion
The DEMO QV indicator is a powerful trend and volatility-based tool, balancing smooth trend-following logic with aggressive short entries for optimized breakout detection. Whether used for momentum trading, breakouts, or adaptive trend filtering, its combination of percentile-based analysis, ATR filtering, and momentum validation ensures a robust and reliable trading experience.
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🔹 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.
Volatility-Volume Index (VVI)Volatility-Volume Index (VVI) – Indicator Description
The Volatility-Volume Index (VVI) is a custom trading indicator designed to identify market consolidation and anticipate breakouts by combining volatility (ATR) and trading volume into a single metric.
How It Works
Measures Volatility : Uses a 14-period Average True Range (ATR) to gauge price movement intensity.
Tracks Volume : Monitors trading activity to identify accumulation or distribution phases.
Normalization : ATR and volume are normalized using their respective 20-period Simple Moving Averages (SMA) for a balanced comparison.
Interpretation
VVI < 1: Low volatility and volume → Consolidation phase (range-bound market).
VVI > 1: Increased volatility and/or volume → Potential breakout or trend continuation.
How to Use VVI
Detect Consolidation:
Look for extended periods where VVI remains below 1.
Confirm with sideways price movement in a narrow range.
Anticipate Breakouts:
A spike above 1 signals a possible trend shift or breakout.
Why Use VVI?
Unlike traditional volatility indicators (ATR, Bollinger Bands) or volume-based tools (VWAP), VVI combines both elements to provide a clearer picture of consolidation zones and breakout potential.
GME Swapinator 5This indicator tracks likely GME swap expiration, which generally results in volatility to the upside in the stock. This is NOT valid on any stock except GME.
To view this indicator, add it to favorites by clicking the little rocket icon on the chart, then add it as an indicator by clicking the favorites tab (next to indicators if on desktop)
The magnitude of the spikes indicate the strength of the swap expiration. The more volatile the spikes, the more volatile the resultant price action might be.
This indicator does not catch all positive price increases, but only looks for swap expirations.
It also does not know what the options chain looks like, so a volatile options chain will make this indicator not show any spikes during that timeframe (see May-June 2024)
It also does not account for company actions like share offerings.
The indicator is only valid after Jan 2021.
The indicator gives a guideline with red/orange warnings on the trendline but you can use your own eyes to try and see when it is showing approaching volatility. The red and orange marks were added after the fact to try and make it a little more user friendly.
It was tuned to work on the DAILY timeframe. Using anything less than the daily timeframe is untested and likely not valid.
This is also just an indicator and does not predict the future. It is not guaranteed to work in the future, although it has done pretty well in the past.
Custom SL/TP ZonesThe "Please Don't Stop Me Now" Indicator 📊
Ever found yourself staring at a chart, thinking "This is DEFINITELY the bottom!" only to watch your stop loss get hit faster than your ex replacing you? Well, this indicator won't stop that from happening, but at least you'll know exactly where you're going to be wrong! 🎯
How it works:
See a setup you like? Pick your candle of choice (make sure it's closed - we're not fortune tellers here)
Hit either Bull or Bear (choose wisely, or don't - we all know it's 50/50 anyway)
3. Marvel at the beautiful boxes showing your:
Take Profit Zone (where you'll exit too early)
Stop Loss Zone (where you'll probably exit, let's be honest)
Features:
Uses ATR for dynamic zones because "one size fits all" only works in disappointing Halloween costumes
Extends 10 bars into the future, giving you plenty of time to watch your prediction go wrong
Price labels included so you know exactly where to set your alerts (and subsequently ignore them)
Customizable multipliers for when you're feeling extra brave (or foolish)
Clean interface that won't distract you from your bad decisions
Remember: The market can stay irrational longer than you can stay solvent, but at least with this indicator, you'll know exactly where your rationality ends and your "This time it's different" begins!
Happy Trading! (Results may vary, tears not included)
Settings:
TP Multiplier: How far to your dreams (Default: 4.0)
SL Multiplier: How far to your nightmares (Default: 2.0)
Bar Offset: Pick your poison (1 = last closed bar)
Colors: Because trading isn't painful enough in grayscale
Volty Expan Close Strategy (Simplified)This is a trading strategy based on the Average True Range (ATR), designed to help traders enter and exit trades using volatility-based indicators.
ATR Calculation: The strategy calculates the ATR over a defined period and multiplies it by a user-specified multiplier to set stop-loss and take-profit levels.
Position Sizing: It dynamically adjusts the trade size based on a percentage of available capital (for example, 1% of your equity per trade).
Entries and Exits: The strategy enters long and short positions based on the price moving in relation to the ATR-based stop and take-profit levels. It also includes a trailing stop that moves with the price to lock in profits as the trade goes in favor.
Capital Management: It manages risk by allocating a set percentage of equity to each trade and includes a take-profit multiplier to define potential exit points.
In summary, this strategy aims to trade with volatility-based stops and take-profits, adjusting dynamically to market conditions with capital management in mind.
ATR Volatility Expansion FilterThe ATR Volatility Expansion indicator helps traders identify when market volatility is increasing.
It compares two ATR values: the Baseline ATR, which tracks long-term volatility, and the Current ATR, which measures recent price movements.
The core concept is that when short-term volatility significantly surpasses the long-term average, it signals a period of heightened price movement. Traders can use this information to adjust their strategies accordingly.
Baseline ATR (blue): Represents long-term volatility, serving as a benchmark.
Current ATR (orange): Measures short-term volatility, highlighting recent market shifts.
Threshold ATR (red): A customizable multiplier of the Baseline ATR, setting the threshold for volatility expansion.
When the Current ATR exceeds the Threshold ATR, the background turns green, indicating volatility expansion. This provides traders with ability to get involved in moving markets or avoid choppy conditions.
The indicator is fully customizable, allowing you to adjust the ATR lengths, timeframe, and threshold multiplier to align with your trading strategy.
CSR Ultimate (Final)This indicator calculates and displays a "Candle Strength Ratio" (CSR) to help you gauge bullish versus bearish momentum on a given timeframe. Here’s what it does:
*Multiple Calculation Methods:*
*You can choose among three different methods:*
-Classic CSR: Compares the difference between the upper and lower parts of the candle relative to its total range.
-Weighted Body CSR: Gives more weight to the candle’s body relative to its wicks.
-Close-Focused CSR: Focuses on the net movement from open to close relative to the full range.
*Optional Enhancements:*
The indicator allows you to enable additional features to refine it:
-Volume Weighting: Adjusts the CSR based on the ratio of current volume to a moving average of volume, so a candle on higher-than-average volume might carry more weight.
-ATR Normalization: Normalizes the CSR using the Average True Range (ATR) to account for market volatility.
-Multi-Bar Averaging: Averages the CSR over a specified number of bars to smooth out noise.
-RSI Filter: Optionally checks an RSI condition (bullish if RSI > 50 or bearish if RSI < 50) to help filter out signals that might not be supported by overall momentum.
*Visual and Alert Features:*
The indicator plots the CSR line with color coding (green for bullish, red for bearish) and draws horizontal threshold lines. It also adjusts the chart background color when the CSR exceeds defined bullish or bearish levels and provides alerts when these thresholds are crossed.
MTF- Standard Deviation ChannelWhat Is Standard Deviation?
Standard deviation is a statistical measurement that looks at how far individual points in a dataset are dispersed from the mean of that set. If data points are further from the mean, there is a higher deviation within the data set. It is calculated as the square root of the variance.
Key Takeaways:
Standard deviation measures the dispersion of a dataset relative to its mean.
It is calculated as the square root of the variance.
Standard deviation, in finance, is often used as a measure of the relative riskiness of an asset.
A volatile stock has a high standard deviation, while the deviation of a stable blue-chip stock is usually rather low.
Standard deviation is also used by businesses to assess risk, manage business operations, and plan cash flows based on seasonal changes and volatility.
Source: Investopedia
--------------- UPDATE ---------------
The deviation is calculated automatically. (via stdev function).
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The targeted timeframe is available in the options (recalculation cycle).
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If the selected security is a contract the number of days before expiration is automatically managed, otherwise it will use the 'default' options.
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MT-Turnover.IndicatorMT-Turnover Indicator – Market Liquidity & Activity Gauge
Overview
The MT-Turnover Indicator is a TradingView tool designed to measure market liquidity and trading activity by tracking the turnover rate of a stock. It calculates the turnover percentage by comparing the trading volume to the number of outstanding shares, providing traders with insights into how actively a stock is being traded.
By incorporating a moving average (MA) of turnover and a customizable high turnover threshold, this indicator helps identify periods of increased market participation, potential breakouts, or distribution phases.
Key Features
✔ Turnover Rate Calculation – Expresses turnover as a percentage of outstanding shares
✔ Customizable Moving Average (MA) for Trend Analysis – Smoothens turnover fluctuations for better trend identification
✔ High Turnover Level Alert – Marks periods when turnover exceeds a predefined threshold
✔ Histogram Visualization – Shows turnover dynamics with clear green (above MA) and red (below MA) bars
✔ High Turnover Signal Markers – Flags exceptionally high turnover events for quick identification
How It Works
1. Turnover Rate Calculation
• Formula:

• Configurable Outstanding Shares (in millions) to match the stock being analyzed
2. Turnover Moving Average (MA) for Trend Analysis
• A simple moving average (SMA) of turnover is calculated over a user-defined period (default: 20 days)
• Green bars indicate turnover above MA, suggesting increased activity
• Red bars indicate turnover below MA, signaling lower participation
3. High Turnover Threshold
• Users can set a high turnover level (%) to mark exceptionally active trading periods
• When turnover exceeds this level, a red triangle marker appears above the bar
4. Reference Line & Informative Table
• A dashed red reference line marks the high turnover threshold
• A floating table in the top-right corner provides a quick summary
How to Use This Indicator
📈 For Breakout Traders – High turnover can indicate strong buying interest, often preceding breakouts
📉 For Risk Management – Spikes in turnover may signal distribution phases or panic selling
🔎 For Liquidity Analysis – Helps gauge how liquid a stock is, which can impact price stability
Conclusion
The MT-Turnover Indicator is a powerful tool for identifying periods of high market activity, helping traders detect potential breakouts, reversals, or strong accumulation/distribution phases. By visualizing turnover with a moving average and customizable threshold, it provides valuable insights into market participation trends.
➡ Add this indicator to your TradingView chart and improve your liquidity-based trading decisions today! 🚀
Long and Short Term Highs and LowsLong and Short Term Highs and Lows
Overview:
This indicator is designed to help traders identify significant price points by marking new highs and lows over two distinct timeframes—a long-term and a short-term period. It achieves this by drawing optional channel lines that outline the highest highs and lowest lows over the chosen time periods and by plotting visual markers (triangles) on the chart when a new high or low is detected.
Key Features:
Dual Timeframe Analysis:
Long Term: Uses a user-defined “Time Period” (default 52) and “Time Unit” (default: Weekly) to determine long-term high and low levels.
Short Term: Uses a separate “Time Period” (default 50) and “Time Unit” (default: Daily) to compute short-term high and low levels.
Optional Channel Display:
For both long and short term periods, you have the option to display a channel by plotting the highest and lowest values as lines. This visual channel helps to delineate the range within which the price has traded over the selected period.
New High/Low Markers:
The indicator identifies moments when the highest high or lowest low is updated relative to the previous bar.
When a new high is established, an up triangle is plotted above the bar.
Conversely, when a new low occurs, a down triangle is plotted below the bar.
Separate input toggles allow you to enable or disable these markers independently for the long-term and short-term setups.
Inputs and Settings:
Long Term High/Low Period Settings:
Show New High/Low? (STW): Toggle to enable or disable the plotting of new high/low markers for the long-term period.
Time Period: The number of bars used to calculate the highest high and lowest low (default is 52).
Time Unit: The timeframe on which the long-term calculation is based (default is Weekly).
Show Channel? (SCW): Toggle to display the channel lines that connect the long-term high and low levels.
Short Term High/Low Period Settings:
Show New High/Low?: Toggle to enable or disable the plotting of new high/low markers for the short-term period.
Time Period: The number of bars used for calculating the short-term extremes (default is 50).
Time Unit: The timeframe on which the short-term calculations are based (default is Daily).
Show Channel?: Toggle to display the channel lines for the short-term highs and lows.
Indicator Logic:
Channel Calculation:
The script uses the request.security function to pull data from the specified timeframes. For each timeframe:
It calculates the lowest low over the defined period using ta.lowest.
It calculates the highest high over the defined period using ta.highest.
These values can be optionally plotted as channel lines when the “Show Channel?” option is enabled.
New High/Low Detection:
For each timeframe, the indicator compares the current high (or low) with its immediate previous value:
New High: When the current high exceeds the previous bar’s high, an up triangle is drawn above the bar.
New Low: When the current low falls below the previous bar’s low, a down triangle is drawn below the bar.
Usage and Interpretation:
Trend Identification:
When new highs (or lows) occur, they can signal the start of a strong upward (or downward) movement. The indicator helps you visually track these critical turning points over both longer and shorter periods.
Channel Breakouts:
The optional channel display offers additional context. Price movement beyond these channels may indicate a breakout or a significant shift in trend.
Customizable Timeframes:
You can adjust both the time period and time unit to fit your trading style—whether you’re focusing on longer-term trends or short-term price action.
Conclusion:
This indicator provides a dual-layer analysis by combining long-term and short-term perspectives, making it a versatile tool for identifying key highs and lows. Whether you are looking to confirm trend strength or spot potential breakouts, the “Long and Short Term Highs and Lows” indicator adds a valuable visual element to your TradingView charts.
SMA with Std Dev Bands (Futures/US Stocks RTH)Rolling Daily SMA With Std Dev Bands
Upgrade your technical analysis with Rolling Daily SMA With Std Dev Bands, a powerful indicator that dynamically adjusts to your trading instrument. Whether you’re analyzing futures or US stocks during regular trading hours (RTH), this indicator seamlessly applies the correct logic to calculate a rolling daily Simple Moving Average (SMA) with customizable standard deviation bands for precise trend and volatility tracking.
Key Features:
✅ Automatic Instrument Detection– The indicator automatically recognizes whether you're trading futures or US equities and applies the correct daily lookback period based on your chart’s timeframe.
- Futures: Uses full trading day lengths (e.g., 1380 bars for 1‑minute charts).
- US Stocks (RTH): Uses regular session lengths (e.g., 390 bars for 1‑minute charts).
✅ Rolling Daily SMA (3‑pt Purple Line) – A continuously updated daily moving average, giving you an adaptive trend indicator based on market structure.
✅ Three Standard Deviation Bands (1‑pt White Lines) –
- Customizable multipliers allow you to adjust each band’s width.
- Toggle each band on or off to tailor the indicator to your strategy.
- The inner band area is color-filled: light green when the SMA is rising, light red when falling, helping you quickly identify trend direction.
✅ Works on Any Chart Timeframe – Whether you trade on 1-minute, 3-minute, 5-minute, or 15-minute charts, the indicator adjusts dynamically to provide accurate rolling daily calculations.
# How to Use:
📌 Identify Trends & Volatility Zones – The rolling daily SMA acts as a dynamic trend guide, while the standard deviation bands help spot potential overbought/oversold conditions.
📌 Customize for Precision – Adjust band multipliers and toggle each band on/off to match your trading style.
📌 Trade Smarter – The filled inner band offers instant visual feedback on market momentum, while the outer bands highlight potential breakout zones.
🔹 This is the perfect tool for traders looking to combine trend-following with volatility analysis in an easy-to-use, adaptive indicator.
🚀 Add Rolling Daily SMA With Std Dev Bands to your chart today and enhance your market insights!
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*Disclaimer: This indicator is for informational and educational purposes only and should not be considered financial advice. Always use proper risk management and conduct your own research before trading.*
Donchian and Keltner Channels Trend Following with Trailing StopLong Only Trend-following model based on Keltner Channels and Donchian Channels.
These indicators include a noise region, which allows prices to oscillate without requiring position adjustments.
When price trades above the upper band, it signals strength; when it trades below the lower band, it signals weakness.
Keltner Channels
Keltner Channels are volatility-based envelopes set above and below an exponential moving average. Keltner Channels use the Average True Range (ATR), which measures daily volatility, to set channel distance.
Donchian Channel
Donchian Channels are are used to identify market trends and volatility. The upper and lower bands are based on the highest high and lowest low of a specified period. When the price moves above the upper band, it indicates a bullish breakout, while a
move below the lower band indicates a bearish breakout. The distance between the upper and lower channel of the Donchian Channel indicates the asset’s volatility.
Trend Following Model
The default settings are:
Upper Keltner and Upper Donchian Channel Length : 20
Lower Keltner and Lower Donchian Channel Length : 40
Keltner ATR Multiplier: 2
Entries, Exits and Trailing Stop
Entry : When price exceeds the upper band of at least one of these indicators.
Exit : When price undercuts the lower band of at least one of these indicators.
Trailing Stop : See below.
Trailing Stop
This is a stop-loss order that moves with the price of the underlying. It is designed to “trail” the price up (in the case of a long position) or down (for a short position), locking in profits as the price moves in a favorable direction.
At the end of day t, there was a Trailing Stop level in place. For the next day (day t + 1), the Trailing Stop will be adjusted. The new Trailing Stop will be the higher of two values:
The Trailing Stop from the previous day (day t).
The Lower Band computed at the end of day t + 1.
G-FRAMA | QuantEdgeBIntroducing G-FRAMA by QuantEdgeB
Overview
The Gaussian FRAMA (G-FRAMA) is an adaptive trend-following indicator that leverages the power of Fractal Adaptive Moving Averages (FRAMA), enhanced with a Gaussian filter for noise reduction and an ATR-based dynamic band for trade signal confirmation. This combination results in a highly responsive moving average that adapts to market volatility while filtering out insignificant price movements.
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1. Key Features
- 📈 Gaussian Smoothing – Utilizes a Gaussian filter to refine price input, reducing short-term noise while maintaining responsiveness.
- 📊 Fractal Adaptive Moving Average (FRAMA) – A self-adjusting moving average that adapts its sensitivity to market trends.
- 📉 ATR-Based Volatility Bands – Dynamic upper and lower bands based on the Average True Range (ATR), improving signal reliability.
- ⚡ Adaptive Trend Signals – Automatically detects shifts in market structure by evaluating price in relation to FRAMA and its ATR bands.
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2. How It Works
- Gaussian Filtering
The Gaussian function preprocesses the price data, giving more weight to recent values and smoothing fluctuations. This reduces whipsaws and allows the FRAMA calculation to focus on meaningful trend developments.
- Fractal Adaptive Moving Average (FRAMA)
Unlike traditional moving averages, FRAMA uses fractal dimension calculations to adjust its smoothing factor dynamically. In trending markets, it reacts faster, while in sideways conditions, it reduces sensitivity, filtering out noise.
- ATR-Based Volatility Bands
ATR is applied to determine upper and lower thresholds around FRAMA:
- 🔹 Long Condition: Price closes above FRAMA + ATR*Multiplier
- 🔻 Short Condition: Price closes below FRAMA - ATR
This setup ensures entries are volatility-adjusted, preventing premature exits or false signals in choppy conditions.
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3. Use Cases
✔ Adaptive Trend Trading – Automatically adjusts to different market conditions, making it ideal for both short-term and long-term traders.
✔ Noise-Filtered Entries – Gaussian smoothing prevents false breakouts, allowing for cleaner entries.
✔ Breakout & Volatility Strategies – The ATR bands confirm valid price movements, reducing false signals.
✔ Smooth but Aggressive Shorts – While the indicator is smooth in overall trend detection, it reacts aggressively to downside moves, making it well-suited for traders focusing on short opportunities.
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4. Customization Options
- Gaussian Filter Settings – Adjust length & sigma to fine-tune the smoothness of the input price. (Default: Gaussian length = 4, Gaussian sigma = 2.0, Gaussian source = close)
- FRAMA Length & Limits – Modify how quickly FRAMA reacts to price changes.(Default: Base FRAMA = 20, Upper FRAMA Limit = 8, Lower FRAMA Limit = 40)
- ATR Multiplier – Control how wide the volatility bands are for long/short entries.(Default: ATR Length = 14, ATR Multiplier = 1.9)
- Color Themes – Multiple visual styles to match different trading environments.
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Conclusion
The G-FRAMA is an intelligent trend-following tool that combines the adaptability of FRAMA with the precision of Gaussian filtering and volatility-based confirmation. It is versatile across different timeframes and asset classes, offering traders an edge in trend detection and trade execution.
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🔹 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.
Smart MA Crossover BacktesterSmart MA Crossover Backtester - Strategy Overview
Strategy Name: Smart MA Crossover Backtester
Published on: TradingView
Applicable Markets: Works well on crypto (tested profitably on ETH)
Strategy Concept
The Smart MA Crossover Backtester is an improved Moving Average (MA) crossover strategy that incorporates a trend filter and an ATR-based stop loss & take profit mechanism for better risk management. It aims to capture trends efficiently while reducing false signals by only trading in the direction of the long-term trend.
Core Components & Logic
Moving Averages (MA) for Entry Signals
Fast Moving Average (9-period SMA)
Slow Moving Average (21-period SMA)
A trade signal is generated when the fast MA crosses the slow MA.
Trend Filter (200-period SMA)
Only enters long positions if price is above the 200-period SMA (bullish trend).
Only enters short positions if price is below the 200-period SMA (bearish trend).
This helps in avoiding counter-trend trades, reducing whipsaws.
ATR-Based Stop Loss & Take Profit
Uses the Average True Range (ATR) with a multiplier of 2 to calculate stop loss.
Risk-Reward Ratio = 1:2 (Take profit is set at 2x ATR).
This ensures dynamic stop loss and take profit levels based on market volatility.
Trading Rules
✅ Long Entry (Buy Signal):
Fast MA (9) crosses above Slow MA (21)
Price is above the 200 MA (bullish trend filter active)
Stop Loss: Below entry price by 2× ATR
Take Profit: Above entry price by 4× ATR
✅ Short Entry (Sell Signal):
Fast MA (9) crosses below Slow MA (21)
Price is below the 200 MA (bearish trend filter active)
Stop Loss: Above entry price by 2× ATR
Take Profit: Below entry price by 4× ATR
Why This Strategy Works Well for Crypto (ETH)?
🔹 Crypto markets are highly volatile – ATR-based stop loss adapts dynamically to market conditions.
🔹 Long-term trend filter (200 MA) ensures trading in the dominant direction, reducing false signals.
🔹 Risk-reward ratio of 1:2 allows for profitable trades even with a lower win rate.
This strategy has been tested on Ethereum (ETH) and has shown profitable performance, making it a strong choice for crypto traders looking for trend-following setups with solid risk management. 🚀
VolatilityThis is a filtering indicator Volatility in the CTA contract of BG Exchange. According to their introduction, it should be calculated using this simple method.
However, you may have seen the problem. According to the exchange's introduction, the threshold should still be divided by 100, which is in percentage form. The result I calculated, even if not divided by 100, still shows a significant difference, which may be due to the exchange's mistake. Smart netizens, do you know how the volatility of BG Exchange is calculated.
The official introduction of BG Exchange is as follows: Volatility (K, Fluctuation) is an additional indicator used to filter out positions triggered by CTA strategy signals in low volatility markets. Usage: Select the fluctuation range composed of the nearest K candlesticks, and choose the highest and lowest closing prices. Calculation: 100 * (highest closing price - lowest closing price) divided by the lowest closing price to obtain the recent amplitude. When the recent amplitude is greater than Fluctuation, it is considered that the current market volatility meets the requirements. When the CTA strategy's position building signal is triggered, position building can be executed. Otherwise, warehouse building cannot be executed.
Anchored VWAP with Buy/Sell SignalsAnchored VWAP Calculation:
The script calculates the AVWAP starting from a user-defined anchor point (anchor_date).
The AVWAP is calculated using the formula:
AVWAP
=
∑
(
Volume
×
Average Price
)
∑
Volume
AVWAP=
∑Volume
∑(Volume×Average Price)
where the average price is
(
h
i
g
h
+
l
o
w
+
c
l
o
s
e
)
/
3
(high+low+close)/3.
Buy Signal:
A buy signal is generated when the price closes above the AVWAP (ta.crossover(close, avwap)).
Sell Signal:
A sell signal is generated when the price closes below the AVWAP (ta.crossunder(close, avwap)).
Plotting:
The AVWAP is plotted on the chart.
Buy and sell signals are displayed as labels on the chart.
Background Highlighting:
The background is highlighted in green for buy signals and red for sell signals (optional).
True Range & ATRDescription : This indicator plots both the True Range (TR) and the Average True Range (ATR) in a separate pane below the main chart.
- TR represents the absolute price movement range within each candle.
- ATR is a smoothed version of TR over a user-defined period (default: 14), providing insight into market volatility.
- TR is displayed as a histogram for a clearer view of individual candle ranges.
- ATR is plotted as a line to show the smoothed trend of volatility.
This indicator helps traders assess market volatility and potential price movements.
Dual SD Median | QuantEdgeBIntroducing Dual SD Median by QuantEdgeB
The Dual SD Median indicator is a powerful statistical tool designed to enhance market analysis through median-based trend detection and standard deviation filtering. By leveraging median price smoothing, adaptive standard deviation bands, and normalized statistical filtering, it provides traders with a structured approach to identifying breakouts, reversals, and stable market trends.
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1. Key Features
🔹 Median-Based Trend Calculation: Uses the median price instead of simple moving averages to create a more robust and stable trend baseline, reducing noise in volatile markets.
🔹 Standard Deviation Bands: Dynamically adjusts upper and lower bands based on market volatility, helping traders spot key breakout zones and trend reversals.
🔹 Normalized Filtering: Incorporates a normalized median structure, ensuring that trends are identified with greater accuracy, and filtering out insignificant price fluctuations.
🔹 Multi-Market Adaptability: Optimized for crypto, but its calibration settings allow adaptation to other markets through adjustable SD multipliers and other inputs.
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2. How It Works
The Dual SD Median calculates a smoothed median price over a defined period, providing a stable central value for trend tracking. It then applies standard deviation bands to dynamically adjust to market conditions.
To further enhance precision, the indicator normalizes the median price against the underlying asset’s price fluctuations, ensuring that only significant trend shifts trigger signals.
Long & Short Signals:
✔ Long Signal: Triggered when the price breaks above both the upper SD median band and the normalized median threshold.
✔ Short Signal: Activated when the price drops below the lower SD median band and the normalized threshold.
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3. How to Use it
📌 Trend Confirmation: Use this indicator to confirm trends by observing breakouts beyond the SD bands. A strong price move past the median SD zone signals potential continuation.
📌 Reversal Identification: If price moves aggressively into SD bands but fails to sustain momentum, it may indicate overextension and reversal potential.
📌 Volatility-Based Trading: Traders can adjust the SD multipliers to match different asset classes and market conditions.
📌 Cross-Market Applicability: While optimized for crypto, the system can be fine-tuned for stocks, forex, and commodities through custom parameter adjustments.
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4. Customization Options
⚙️ SD Median Length (Default: 14) – Defines the median price calculation window.
⚙️ Normalized Median Length (Default: 50) – Smooths long-term trends for stability.
⚙️ Standard Deviation Length (Default: 30) – Adjusts volatility sensitivity.
⚙️ SD Multipliers (Default: 0.98 for Longs, 1.06 for Shorts) – Determines breakout thresholds.
⚙️ Smoothing Factors (Default: 1) – Fine-tunes signal sensitivity.
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Conclusion
The Dual SD Median is a versatile, statistically-driven tool that helps traders navigate volatile market conditions with greater accuracy. By combining median smoothing, standard deviation filtering, and normalized trend detection, it reduces noise while maintaining responsiveness to price movements.
🔹 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.
Universal Strategy | QuantEdgeBIntroducing the Universal Strategy by QuantEdgeB
The Universal Strategy | QuantEdgeB is a dynamic, multi-indicator strategy designed to operate across various asset classes with precision and adaptability. This cutting-edge system utilizes four sophisticated methodologies, each integrating advanced trend-following, volatility filtering, and normalization techniques to provide robust signals. Its modular architecture and customizable features ensure suitability for diverse market conditions, empowering traders with data-driven decision-making tools. Its adaptability to different price behaviors and volatility levels makes it a robust and versatile tool, equipping traders with data-driven confidence in their market decisions.
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1. Core Methodologies and Features
1️⃣ DEMA ATR
Strength : Fast responsiveness to trend shifts.
The double exponential moving average is inherently aggressive, designed to reduce lag and quickly identify early signs of trend reversals or breakout opportunities. ATR bands add a volatility-sensitive layer, dynamically adjusting the breakout thresholds to match current market conditions, ensuring it remains responsive while filtering out noise
How It Fits :
This indicator is the first responder, providing early signals of potential trend shifts. While its aggressiveness can result in quick entries, it may occasionally overreact in noisy markets. This is where the smoother indicators step in to confirm signals.
2️⃣ Gaussian - VIDYA ATR (Variable Index Dynamic Average)
Strength : Smooth, adaptive trend identification.
Unlike DEMA, VIDYA adapts to market volatility through its standard deviation-based formula, making it smoother and less reactive to short-term fluctuations. ATR filtering ensures the indicator remains effective in volatile markets by dynamically adjusting its sensitivity.
How It Fits :
The smoother complement to DEMA ATR, VIDYA ATR filters out false signals from minor price movements. It provides confirmation for the trends identified by DEMA ATR, ensuring entries are based on robust, sustained price movements.
3️⃣ VIDYA Loop Trend Scoring
Strength : Historical trend scoring for consistent momentum detection.
This module evaluates the relative strength of trends by comparing the current VIDYA value to its historical values over a defined range. The loop mechanism provides a trend confidence score, quantifying the momentum behind price movements.
How It Fits :
VIDYA For-Loop adds a quantitative measure of trend strength, ensuring that trades are backed by sustained momentum. It balances the early signals from DEMA ATR and the smoothness of VIDYA ATR by providing a statistical check on the underlying trend.
4️⃣ Median SD with Normalization
Strength : Precision in breakout detection and market normalization.
The Median price serves as a robust baseline for detecting breakouts and reversals.
SD bands expand dynamically during periods of high volatility, making the indicator particularly effective for spotting strong trends or breakout opportunities. Normalization ensures the indicator adapts seamlessly across different assets and timeframes, providing consistent performance.
How It Fits :
The Median SD module provides final confirmation by focusing on price breakouts and market normalization. While the other indicators focus on momentum and trend strength, Median SD emphasizes precision, ensuring entries align with significant price movements rather than random fluctuations.
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2. How The Single Components Work Together
1️⃣ Balance of Speed and Smoothness :
The strategy blends quick responsiveness (DEMA ATR) with smooth and adaptive confirmation (VIDYA ATR & For-Loop), ensuring timely reactions without overreacting to market fluctuations. Median SD with Normalization refines breakout detection and stabilizes performance across assets using statistical anchors like price median and standard deviation.
Adaptability to Market Dynamics:
2️⃣ Adaptability to Market Dynamics :
The indicators complement each other seamlessly in trending markets, with the DEMA ATR and Median SD with Normalization quickly identifying shifts and confirming sustained momentum. In volatile or choppy markets, normalization and SD bands work together to filter out noise and reduce false signals, ensuring precise entries and exits. Meanwhile, the For-Loop scoring and Gaussian-Filtered VIDYA ATR focus on providing smoother, more reliable trend detection, offering consistent performance regardless of market conditions.
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3. Scoring and Signal Confirmation
The Universal Strategy consolidates signals from all four methodologies, calculating a Trend Probability Index (TPI). The four core indicators operate independently but contribute to a unified TPI, enabling highly adaptive behavior across asset classes.
- Each methodology generates a trend score: 1 for bullish trends, -1 for bearish trends.
- The TPI averages the scores, creating a unified signal.
- Long Position: Triggered when the TPI exceeds the long threshold (default: 0).
- Short Position: Triggered when the TPI falls below the short threshold (default: 0).
The strategy’s customizable settings allow traders to tailor its behavior to different market conditions—whether smoother trends in low-volatility assets or quick reaction to high-volatility breakouts. The long and short thresholds can be fine-tuned to match a trader’s risk tolerance and preferences.
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4. Use Cases:
The Universal Strategy | QuantEdgeB is designed to excel across a wide range of trading scenarios, thanks to its modular architecture and adaptability. Whether you're navigating trending, volatile, or range-bound markets, this strategy offers robust tools to enhance your decision-making. Below are the key use cases for its application:
1️⃣ Trend Trading
The strategy’s Gaussian-Filtered DEMA ATR and VIDYA ATR modules are perfect for identifying and riding sustained trends.
Ideal For: Traders looking to capture long-term momentum or position trades.
2️⃣ Breakout and Volatility-Based Strategies
With its Median SD with Normalization, the strategy excels in detecting volatility breakouts and significant price movements.
Ideal For: Traders aiming to capitalize on sudden market movements, especially in assets like cryptocurrencies and commodities.
3️⃣ Momentum and Strength Assessment
By generating a trend confidence score, the VIDYA For-Loop quantifies momentum strength—helping traders distinguish temporary spikes from sustainable trends.
Ideal For: Swing traders and those focusing on momentum-driven setups.
4️⃣ Adaptability Across Multiple Assets
The strategy’s robust framework ensures it performs consistently across different assets and timeframes.
Ideal For: Traders managing diverse portfolios or shifting between asset classes.
5️⃣ Backtesting and Optimization
Built-in backtesting and equity visualization tools make this strategy ideal for testing and refining parameters in real-world conditions.
• How It Helps: The strategy equity curve and metrics table offer a clear picture of performance, helping traders identify optimal settings for their chosen market and timeframe.
• Ideal For: Traders focused on rigorous testing and long-term optimization.
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5. Signal Composition Table:
This table presents a real-time breakdown of each indicator’s trend score (+1 bullish, -1 bearish) alongside the final aggregated signal. By visualizing the contribution of each methodology, traders gain greater transparency, confidence, and clarity in identifying long or short opportunities.
6. Customized Settings:
1️⃣ General Inputs
• Strategy Long Threshold (Lu): 0
• Strategy Short Threshold (Su): 0
2️⃣ Gaussian Filter
• Gaussian Length (len_FG): 4
• Gaussian Source (src_FG): close
• Gaussian Sigma (sigma_FG): 2.0
3️⃣ DEMA ATR
• DEMA Length (len_D): 30
• DEMA Source (src_D): close
• ATR Length (atr_D): 14
• ATR Multiplier (mult_D): 1.0
4️⃣ VIDYA ATR
• VIDYA Length (len_V1): 9
• SD Length (len_VHist1): 30
• ATR Length (atr_V): 14
• ATR Multiplier (mult_V): 1.7
5️⃣ VIDYA For-Loop
• VIDYA Length (len_V2): 2
• SD Length (len_VHist2): 5
• VIDYA Source (src_V2): close
• Start Loop (strat_loop): 1
• End Loop (end_loop): 60
• Long Threshold (long_t): 40
• Short Threshold (short_t): 8
6️⃣ Median SD
• Median Length (len_m): 24
• Normalized Median Length (len_msd): 50
• SD Length (SD_len): 32
• Long SD Weight (w1): 0.98
• Short SD Weight (w2): 1.02
• Long Normalized Smooth (smooth_long): 1
• Short Normalized Smooth (smooth_short): 1
Conclusion
The Universal Strategy | QuantEdgeB is a meticulously crafted, multi-dimensional trading system designed to thrive across diverse market conditions and asset classes. By combining Gaussian-Filtered DEMA ATR, VIDYA ATR, VIDYA For-Loop, and Median SD with Normalization, this strategy provides a seamless balance between speed, smoothness, and adaptability. Each component complements the others, ensuring traders benefit from early responsiveness, trend confirmation, momentum scoring, and breakout precision.
Its modular structure ensures versatility across trending, volatile, and consolidating markets. Whether applied to equities, forex, commodities, or crypto, it delivers data-driven precision while minimizing reliance on randomness, reinforcing confidence in decision-making.
With built-in backtesting tools, traders can rigorously evaluate performance under real-world conditions, while customization options allow fine-tuning for specific market dynamics and individual trading styles.
Why It Stands Out
The Universal Strategy | QuantEdgeB isn’t just a trading algorithm—it’s a comprehensive framework that empowers traders to make confident, informed decisions in the face of ever-changing market conditions. Its emphasis on precision, reliability, and transparency makes it a powerful tool for both professional and retail traders seeking consistent performance and enhanced risk management.
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🔹 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.
Smoothed Low-Pass Butterworth Filtered Median [AlphaAlgos]Smoothed Low-Pass Butterworth Filtered Median
This indicator is designed to smooth price action and filter out noise while maintaining the dominant trend. By combining a Butterworth low-pass filter with a median-based smoothing approach , it effectively reduces short-term fluctuations, allowing traders to focus on the true market direction.
How It Works
Median Smoothing: The indicator calculates the 50th percentile (median) of closing prices over a customizable period , making it more robust against outliers compared to traditional moving averages.
Butterworth Filtering: A low-pass filter is applied using an approximation of the Butterworth formula , controlled by the Cutoff Frequency , helping to eliminate high-frequency noise while preserving trends.
EMA Refinement: A 7-period EMA is applied to further smooth the signal, providing a more reliable trend representation.
Features
Trend Smoothing: Reduces market noise and highlights the dominant trend.
Dynamic Color Signals: The EMA line changes color to indicate trend strength and direction.
Configurable Parameters: Customize the median length, cutoff frequency, and EMA length to fit your strategy.
Versatile Use Case: Suitable for both trend-following and mean-reversion strategies.
How to Use
Bullish Signal: When the EMA is below the price and rising , indicating upward momentum.
Bearish Signal: When the EMA is above the price and falling , signaling a potential downtrend.
Reversal Zones: Monitor for trend shifts when the color of the EMA changes.
This indicator provides a clear, noise-free view of market trends , making it ideal for traders seeking improved trend identification and entry signals .
Dynamic Stop Loss & Take ProfitDynamic Stop Loss & Take Profit is a versatile risk management indicator that calculates dynamic stop loss and take profit levels based on the Average True Range (ATR). This indicator helps traders set adaptive exit points by using a configurable ATR multiplier and defining whether they are in a Long (Buy) or Short (Sell) trade.
How It Works
ATR Calculation – The indicator calculates the ATR value over a user-defined period (default: 14).
Stop Loss and Take Profit Multipliers – The ATR value is multiplied by a configurable factor (ranging from 1.5 to 4) to determine volatility-adjusted stop loss and take profit levels.
Trade Type Selection – The user can specify whether they are in a Long (Buy) or Short (Sell) trade.
Long (Buy) Trade:
Stop Loss = Entry Price - (ATR × Stop Loss Multiplier)
Take Profit = Entry Price + (ATR × Take Profit Multiplier)
Short (Sell) Trade:
Stop Loss = Entry Price + (ATR × Stop Loss Multiplier)
Take Profit = Entry Price - (ATR × Take Profit Multiplier)
Features
Configurable ATR length and multipliers
Supports both long and short trades
Clearly plotted Stop Loss (red) and Take Profit (green) levels on the chart
Helps traders manage risk dynamically based on market volatility
This indicator is ideal for traders looking to set adaptive stop loss and take profit levels without relying on fixed price targets.
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
