Fourier Adjusted Average True Range [BackQuant]Fourier Adjusted Average True Range
1. Conceptual Foundation and Innovation
The FA-ATR leverages the principles of Fourier analysis to dissect market prices into their constituent cyclical components. By applying Fourier Transform to the price data, the FA-ATR captures the dominant cycles and trends which are often obscured in noisy market data. This integration allows the FA-ATR to adapt its readings based on underlying market dynamics, offering a refined view of volatility that is sensitive to both market direction and momentum.
2. Technical Composition and Calculation
The core of the FA-ATR involves calculating the traditional ATR, which measures market volatility by decomposing the entire range of price movements. The FA-ATR extends this by incorporating a Fourier Transform of price data to assess cyclical patterns over a user-defined period 'N'. This process synthesizes both the magnitude of price changes and their rhythmic occurrences, resulting in a more comprehensive volatility indicator.
Fourier Transform Application: The Fourier series is calculated using price data to identify the fundamental frequency of market movements. This frequency helps in adjusting the ATR to reflect more accurately the current market conditions.
Dynamic Adjustment: The ATR is then adjusted by the magnitude of the dominant cycle from the Fourier analysis, enhancing or reducing the ATR value based on the intensity and phase of market cycles.
3. Features and User Inputs
Customizability: Traders can modify the Fourier period, ATR period, and the multiplication factor to suit different trading styles and market environments.
Visualization : The FA-ATR can be plotted directly on the chart, providing a visual representation of volatility. Additionally, the option to paint candles according to the trend direction enhances the usability and interpretative ease of the indicator.
Confluence with Moving Averages: Optionally, a moving average of the FA-ATR can be displayed, serving as a confluence factor for confirming trends or potential reversals.
4. Practical Applications
The FA-ATR is particularly useful in markets characterized by periodic fluctuations or those that exhibit strong cyclical trends. Traders can utilize this indicator to:
Adjust Stop-Loss Orders: More accurately set stop-loss orders based on a volatility measure that accounts for cyclical market changes.
Trend Confirmation: Use the FA-ATR to confirm trend strength and sustainability, helping to avoid false signals often encountered in volatile markets.
Strategic Entry and Exit: The indicator's responsiveness to changing market dynamics makes it an excellent tool for planning entries and exits in a trend-following or a breakout trading strategy.
5. Advantages and Strategic Value
By integrating Fourier analysis, the FA-ATR provides a volatility measure that is both adaptive and anticipatory, giving traders a forward-looking tool that adjusts to changes before they become apparent through traditional indicators. This anticipatory feature makes it an invaluable asset for traders looking to gain an edge in fast-paced and rapidly changing market conditions.
6. Summary and Usage Tips
The Fourier Adjusted Average True Range is a cutting-edge development in technical analysis, offering traders an enhanced tool for assessing market volatility with increased accuracy and responsiveness. Its ability to adapt to the market's cyclical nature makes it particularly useful for those trading in highly volatile or cyclically influenced markets.
Traders are encouraged to integrate the FA-ATR into their trading systems as a supplementary tool to improve risk management and decision-making accuracy, thereby potentially increasing the effectiveness of their trading strategies.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Chart
Normalised T3 Oscillator [BackQuant]Normalised T3 Oscillator
The Normalised T3 Oscillator is an technical indicator designed to provide traders with a refined measure of market momentum by normalizing the T3 Moving Average. This tool was developed to enhance trading decisions by smoothing price data and reducing market noise, allowing for clearer trend recognition and potential signal generation. Below is a detailed breakdown of the Normalised T3 Oscillator, its methodology, and its application in trading scenarios.
1. Conceptual Foundation and Definition of T3
The T3 Moving Average, originally proposed by Tim Tillson, is renowned for its smoothness and responsiveness, achieved through a combination of multiple Exponential Moving Averages and a volume factor. The Normalised T3 Oscillator extends this concept by normalizing these values to oscillate around a central zero line, which aids in highlighting overbought and oversold conditions.
2. Normalization Process
Normalization in this context refers to the adjustment of the T3 values to ensure that the oscillator provides a standard range of output. This is accomplished by calculating the lowest and highest values of the T3 over a user-defined period and scaling the output between -0.5 to +0.5. This process not only aids in standardizing the indicator across different securities and time frames but also enhances comparative analysis.
3. Integration of the Oscillator and Moving Average
A unique feature of the Normalised T3 Oscillator is the inclusion of a secondary smoothing mechanism via a moving average of the oscillator itself, selectable from various types such as SMA, EMA, and more. This moving average acts as a signal line, providing potential buy or sell triggers when the oscillator crosses this line, thus offering dual layers of analysis—momentum and trend confirmation.
4. Visualization and User Interaction
The indicator is designed with user interaction in mind, featuring customizable parameters such as the length of the T3, normalization period, and type of moving average used for signals. Additionally, the oscillator is plotted with a color-coded scheme that visually represents different strength levels of the market conditions, enhancing readability and quick decision-making.
5. Practical Applications and Strategy Integration
Traders can leverage the Normalised T3 Oscillator in various trading strategies, including trend following, counter-trend plays, and as a component of a broader trading system. It is particularly useful in identifying turning points in the market or confirming ongoing trends. The clear visualization and customizable nature of the oscillator facilitate its adaptation to different trading styles and market environments.
6. Advanced Features and Customization
Further enhancing its utility, the indicator includes options such as painting candles according to the trend, showing static levels for quick reference, and alerts for crossover and crossunder events, which can be integrated into automated trading systems. These features allow for a high degree of personalization, enabling traders to mold the tool according to their specific trading preferences and risk management requirements.
7. Theoretical Justification and Empirical Usage
The use of the T3 smoothing mechanism combined with normalization is theoretically sound, aiming to reduce lag and false signals often associated with traditional moving averages. The practical effectiveness of the Normalised T3 Oscillator should be validated through rigorous backtesting and adjustment of parameters to match historical market conditions and volatility.
8. Conclusion and Utility in Market Analysis
Overall, the Normalised T3 Oscillator by BackQuant stands as a sophisticated tool for market analysis, providing traders with a dynamic and adaptable approach to gauging market momentum. Its development is rooted in the understanding of technical nuances and the demand for a more stable, responsive, and customizable trading indicator.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Pine Script Chart ViewerDisplay your custom charts exported from anywhere in TradingView.
Put your candles on candles :
var Candle candles = array.from(...)
For instance:
var Candle candles = array.from(Candle.new(2.0, 4.0, 1.0, 3.0), Candle.new(3.0, 5.0, 2.0, 4.0))
Candle details:
Candle.new(open_1, high_1, low_1, close_1)
Gaussian Price Filter [BackQuant]Gaussian Price Filter
Overview and History of the Gaussian Transformation
The Gaussian transformation, often associated with the Gaussian (normal) distribution, is a mathematical function characteristically prominent in statistics and probability theory. The bell-shaped curve of the Gaussian function, expressing the normal distribution, is ubiquitously employed in various scientific and engineering disciplines, including financial market analysis. This transformation's core utility in trading and economic forecasting is derived from its efficacy in smoothing data series and highlighting underlying trends, which are pivotal for making strategic trading decisions.
The Gaussian filter, specifically, is a type of data-smoothing algorithm that mitigates the random "noise" of market price data, thus enhancing the visibility of crucial trend changes and patterns. Historically, this concept was adapted from fields such as signal processing and image editing, where precise extraction of useful information from noisy environments is critical.
1. What is a Gaussian Transformation?
A Gaussian transformation involves the application of a Gaussian function to a set of data points. The function is applied as a filter in the context of trading algorithms to smooth time series data, which helps in identifying the intrinsic trends obscured by market volatility. The transformation is characterized by its parameter, sigma (σ), representing the standard deviation, which determines the width of the Gaussian bell curve. The breadth of this curve impacts the degree of smoothing: a wider curve (higher sigma value) results in more smoothing, beneficial for longer-term trend analysis.
2. Filtering Price with Gaussian Transformation and its Benefits
In the provided Script, the Gaussian transformation is utilized to filter price data. The filtering process involves convolving the price data with Gaussian weights, which are calculated based on the chosen length (the number of data points considered) and sigma. This convolution process smooths out short-term fluctuations and highlights longer-term movements, facilitating a clearer analysis of market trends.
Benefits:
Reduces noise: It filters out minor price movements and random fluctuations, which are often misleading.
Enhances trend recognition: By smoothing the data, it becomes easier to identify significant trends and reversals.
Improves decision-making: Traders can make more informed decisions by focusing on substantive, smoothed data rather than reacting to random noise.
3. Potential Limitations and Issues
While Gaussian filters are highly effective in smoothing data, they are not without limitations:
Lag introduction: Like all moving averages, the Gaussian filter introduces a lag between the actual price movements and the output signal, which can delay decision-making.
Feature blurring: Over-smoothing might obscure significant price movements, especially if a large sigma is used.
Parameter sensitivity: The choice of length and sigma significantly affects the output, requiring optimization and backtesting to determine the best settings for specific market conditions.
4. Extending Gaussian Filters to Other Indicators
The methodology used to filter price data with a Gaussian filter can similarly be applied to other technical indicators, such as RSI (Relative Strength Index) or MACD (Moving Average Convergence Divergence). By smoothing these indicators, traders can reduce false signals and enhance the reliability of the indicators' outputs, leading to potentially more accurate signals and better timing for entering or exiting trades.
5. Application in Trading
In trading, the Gaussian Price Filter can be strategically used to:
Spot trend reversals: Smoothed price data can more clearly indicate when a trend is starting to change, which is crucial for catching reversals early.
Define entry and exit points: The filtered data points can help in setting more precise entry and exit thresholds, minimizing the risk and maximizing the potential return.
Filter other data streams: Apply the Gaussian filter on volume or open interest data to identify significant changes in market dynamics.
6. Functionality of the Script
The script is designed to:
Calculate Gaussian weights (f_gaussianWeights function): Generates the weights used for the Gaussian kernel based on the provided length and sigma.
Apply the Gaussian filter (f_applyGaussianFilter function): Uses the weights to compute the smoothed price data.
Conditional Trend Detection and Coloring: Determines the trend direction based on the filtered price and colors the price bars on the chart to visually represent the trend.
7. Specific Actions of This Code
The Pine Script provided by BackQuant executes several specific actions:
Input Handling: It allows users to specify the source data (src), kernel length, and sigma directly in the chart settings.
Weight Calculation and Normalization: Computes the Gaussian weights and normalizes them to ensure their sum equals one, which maintains the original data scale.
Filter Application: Applies the normalized Gaussian kernel to the price data to produce a smoothed output.
Trend Identification and Visualization: Identifies whether the market is trending upwards or downwards based on the smoothed data and colors the bars green (up) or red (down) to indicate the trend direction.
Volatility Adjusted Weighted DEMA [BackQuant]Volatility Adjusted Weighted DEMA
The Volatility Adjusted Weighted Double Exponential Moving Average (VAWDEMA) by BackQuant is a sophisticated technical analysis tool designed for traders seeking to integrate volatility into their moving average calculations. This innovative indicator adjusts the weighting of the Double Exponential Moving Average (DEMA) according to recent volatility levels, offering a more dynamic and responsive measure of market trends.
Primarily, the single Moving average is very noisy, but can be used in the context of strategy development, where as the crossover, is best used in the context of defining a trading zone/ macro uptrend on higher timeframes.
Why Volatility Adjustment is Beneficial
Volatility is a fundamental aspect of financial markets, reflecting the intensity of price changes. A volatility adjustment in moving averages is beneficial because it allows the indicator to adapt more quickly during periods of high volatility, providing signals that are more aligned with the current market conditions. This makes the VAWDEMA a versatile tool for identifying trend strength and potential reversal points in more volatile markets.
Understanding DEMA and Its Advantages
DEMA is an indicator that aims to reduce the lag associated with traditional moving averages by applying a double smoothing process. The primary benefit of DEMA is its sensitivity and quicker response to price changes, making it an excellent tool for trend following and momentum trading. Incorporating DEMA into your analysis can help capture trends earlier than with simple moving averages.
The Power of Combining Volatility Adjustment with DEMA
By adjusting the weight of the DEMA based on volatility, the VAWDEMA becomes a powerful hybrid indicator. This combination leverages the quick responsiveness of DEMA while dynamically adjusting its sensitivity based on current market volatility. This results in a moving average that is both swift and adaptive, capable of providing more relevant signals for entering and exiting trades.
Core Logic Behind VAWDEMA
The core logic of the VAWDEMA involves calculating the DEMA for a specified period and then adjusting its weighting based on a volatility measure, such as the average true range (ATR) or standard deviation of price changes. This results in a weighted DEMA that reflects both the direction and the volatility of the market, offering insights into potential trend continuations or reversals.
Utilizing the Crossover in a Trading System
The VAWDEMA crossover occurs when two VAWDEMAs of different lengths cross, signaling potential bullish or bearish market conditions. In a trading system, a crossover can be used as a trigger for entry or exit points:
Bullish Signal: When a shorter-period VAWDEMA crosses above a longer-period VAWDEMA, it may indicate an uptrend, suggesting a potential entry point for a long position.
Bearish Signal: Conversely, when a shorter-period VAWDEMA crosses below a longer-period VAWDEMA, it might signal a downtrend, indicating a possible exit point or a short entry.
Incorporating VAWDEMA crossovers into a trading strategy can enhance decision-making by providing timely and adaptive signals that account for both trend direction and market volatility. Traders should combine these signals with other forms of analysis and risk management techniques to develop a well-rounded trading strategy.
Alert Conditions For Trading
alertcondition(vwdema>vwdema , title="VWDEMA Long", message="VWDEMA Long - {{ticker}} - {{interval}}")
alertcondition(vwdema<vwdema , title="VWDEMA Short", message="VWDEMA Short - {{ticker}} - {{interval}}")
alertcondition(ta.crossover(crossover, 0), title="VWDEMA Crossover Long", message="VWDEMA Crossover Long - {{ticker}} - {{interval}}")
alertcondition(ta.crossunder(crossover, 0), title="VWDEMA Crossover Short", message="VWDEMA Crossover Short - {{ticker}} - {{interval}}")
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Kalman Hull Supertrend [BackQuant]Kalman Hull Supertrend
At its core, this indicator uses a Kalman filter of price, put inside of a hull moving average function (replacing the weighted moving averages) and then using that as a price source for the supertrend instead of the normal hl2 (high+low/2).
Therefore, making it more adaptive to price and also sensitive to recent price action.
PLEASE Read the following, knowing what an indicator does at its core before adding it into a system is pivotal. The core concepts can allow you to include it in a logical and sound manner.
1. What is a Kalman Filter
The Kalman Filter is an algorithm renowned for its efficiency in estimating the states of a linear dynamic system amidst noisy data. It excels in real-time data processing, making it indispensable in fields requiring precise and adaptive filtering, such as aerospace, robotics, and financial market analysis. By leveraging its predictive capabilities, traders can significantly enhance their market analysis, particularly in estimating price movements more accurately.
If you would like this on its own, with a more in-depth description please see our Kalman Price Filter.
2. Hull Moving Average (HMA) and Its Core Calculation
The Hull Moving Average (HMA) improves on traditional moving averages by combining the Weighted Moving Average's (WMA) smoothness and reduced lag. Its core calculation involves taking the WMA of the data set and doubling it, then subtracting the WMA of the full period, followed by applying another WMA on the result over the square root of the period's length. This methodology yields a smoother and more responsive moving average, particularly useful for identifying market trends more rapidly.
3. Combining Kalman Filter with HMA
The innovative combination of the Kalman Filter with the Hull Moving Average (KHMA) offers a unique approach to smoothing price data. By applying the Kalman Filter to the price source before its incorporation into the HMA formula, we enhance the adaptiveness and responsiveness of the moving average. This adaptive smoothing method reduces noise more effectively and adjusts more swiftly to price changes, providing traders with clearer signals for market entries or exits.
The calculation is like so:
KHMA(_src, _length) =>
f_kalman(2 * f_kalman(_src, _length / 2) - f_kalman(_src, _length), math.round(math.sqrt(_length)))
4. Integration with Supertrend
Incorporating this adaptive price smoothing technique into the Supertrend indicator further enhances its efficiency. The Supertrend, known for its proficiency in identifying the prevailing market trend and providing clear buy or sell signals, becomes even more powerful with an adaptive price source. This integration allows the Supertrend to adjust more dynamically to market changes, offering traders more accurate and timely trading signals.
5. Application in a Trading System
In a trading system, the Kalman Hull Supertrend indicator can serve as a critical component for identifying market trends and generating signals for potential entry and exit points. Its adaptiveness and sensitivity to price changes make it particularly useful for traders looking to minimize lag in signal generation and improve the accuracy of their market trend analysis. Whether used as a standalone tool or in conjunction with other indicators, its dynamic nature can significantly enhance trading strategies.
6. Core Calculations and Benefits
The core of this indicator lies in its sophisticated filtering and averaging techniques, starting with the Kalman Filter's predictive adjustments, followed by the adaptive smoothing of the Hull Moving Average, and culminating in the trend-detecting capabilities of the Supertrend. This multi-layered approach not only reduces market noise but also adapts to market volatility more effectively. Benefits include improved signal accuracy, reduced lag, and the ability to discern trend changes more promptly, offering traders a competitive edge.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Candles ThemesGood morning,
Here is my first script as a pinecoder.
So I present to you my indicator: the “Candles Theme”.
Instead of searching for a long time in the chart settings to change the style of the chart, you can use this indicator which offers:
- 8 default themes.
- The ability to create a custom theme.
Themes :
- Pink - Blue : Dark and Light
- Classic : Dark and Light
- Blue - Orange Classic : Dark and Light
- Dark Monochrome : Only Dark
- Light Monochrome : Only Light
- Blue - Orange 2 : Light and Dark
- Pastel 1 : Light and Dark
- Pastel 2 : Only Light
Being a trader and PineScript developer, I often create scripts according to my needs like this, but this is the first time I have published it.
If you have any questions or suggestions for improvement, please let me know in the comments.
End
Standardized Median Proximity [AlgoAlpha]Introducing the Standardized Median Proximity by AlgoAlpha 🚀📊 – a dynamic tool designed to enhance your trading strategy by analyzing price fluctuations relative to the median value. This indicator is built to provide clear visual cues on the price deviation from its median, allowing for a nuanced understanding of market trends and potential reversals.
🔍 Key Features:
1. 📈 Median Tracking: At the core of this indicator is the calculation of the median price over a specified lookback period. By evaluating the current price against this median, the indicator provides a sense of whether the price is trending above or below its recent median value.
medianValue = ta.median(priceSource, lookbackLength)
2. 🌡️ Normalization of Price Deviation: The deviation of the price from the median is normalized using standard deviation, ensuring that the indicator's readings are consistent and comparable across different time frames and instruments.
standardDeviation = ta.stdev(priceDeviation, 45)
normalizedValue = priceDeviation / (standardDeviation + standardDeviation)
3. 📌 Boundary Calculations: The indicator sets upper and lower boundaries based on the normalized values, helping to identify overbought and oversold conditions.
upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier
lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
4. 🎨 Visual Appeal and Clarity: With carefully chosen colors, the plots provide an intuitive and clear representation of market states. Rising trends are indicated in a shade of green, while falling trends are shown in red.
5. 🚨 Alert Conditions: Stay ahead of market movements with customizable alerts for trend shifts and impulse signals, enabling timely decisions.
alertcondition(ta.crossover(normalizedValue, 0), "Bullish Trend Shift", "Median Proximity Crossover Zero Line")
🔧 How to Use:
- 🎯 Set your preferred lookback lengths and standard deviation multipliers to tailor the indicator to your trading style.
- 💹 Utilize the boundary plots to understand potential overbought or oversold conditions.
- 📈 Analyze the color-coded column plots for quick insights into the market's direction relative to the median.
- ⏰ Set alerts to notify you of significant trend changes or conditions that match your trading criteria.
Basic Logic Explained:
- The indicator first calculates the median of the selected price source over your chosen lookback period. This median serves as a baseline for measuring price deviation.
- It then standardizes this deviation by dividing it by the standard deviation of the price deviation over a 45-period lookback, creating a normalized value.
- Upper and lower boundaries are computed using the exponential moving average (EMA) and standard deviation of these normalized values, adjusted by your selected multiplier.
- Finally, color-coded plots provide a visual representation of these calculations, offering at-a-glance insights into market conditions.
Remember, while this tool offers valuable insights, it's crucial to use it as part of a comprehensive trading strategy, complemented by other analysis and indicators. Happy trading!
🚀
Median Proximity Percentile [AlgoAlpha]📊🚀 Introducing the "Median Proximity Percentile" by AlgoAlpha, a dynamic and sophisticated trading indicator designed to enhance your market analysis! This tool efficiently tracks median price proximity over a specified lookback period and finds it's percentile between 2 dynamic standard deviation bands, offering valuable insights for traders looking to make informed decisions.
🌟 Key Features:
Color-Coded Visuals: Easily interpret market trends with color-coded plots indicating bullish or bearish signals.
Flexibility: Customize the indicator with your preferred price source and lookback lengths to suit your trading strategy.
Advanced Alert System: Stay ahead with customizable alerts for key trend shifts and market conditions.
🔍 Deep Dive into the Code:
Choose your preferred price data source and define lookback lengths for median and EMA calculations. priceSource = input.source(close, "Source") and lookbackLength = input.int(21, minval = 1, title = "Lookback Length")
Calculate median value, price deviation, and normalized value to analyze market position relative to the median. medianValue = ta.median(priceSource, lookbackLength)
Determine upper and lower boundaries based on standard deviation and EMA. upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier
lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
Compute the percentile value to track market position within these boundaries. percentileValue = 100 * (normalizedValue - lowerBoundary)/(upperBoundary - lowerBoundary) - 50
Enhance your analysis with Hull Moving Average (HMA) for smoother trend identification. emaValue = ta.hma(percentileValue, emaLookbackLength)
Visualize trends with color-coded plots and characters for easy interpretation. plotColor = percentileValue > 0 ? colorUp : percentileValue < 0 ? colorDown : na
Set up advanced alerts to stay informed about significant market movements. // Alerts
alertcondition(ta.crossover(emaValue, 0), "Bullish Trend Shift", "Median Proximity Percentile Crossover Zero Line")
alertcondition(ta.crossunder(emaValue, 0), "Bearish Trend Shift", "Median Proximity Percentile Crossunder Zero Line")
alertcondition(ta.crossunder(emaValue,emaValue ) and emaValue > 90, "Bearish Reversal", "Median Proximity Percentile Bearish Reversal")
alertcondition(ta.crossunder(emaValue ,emaValue) and emaValue < -90, "Bullish Reversal", "Median Proximity Percentile Bullish Reversal")
🚨 Remember, the "Median Proximity Percentile " is a tool to aid your analysis. It’s essential to combine it with other analysis techniques and market understanding for best results. Happy trading! 📈📉
Momentum Bias Index [AlgoAlpha]Description:
The Momentum Bias Index by AlgoAlpha is designed to provide traders with a powerful tool for assessing market momentum bias. The indicator calculates the positive and negative bias of momentum to gauge which one is greater to determine the trend.
Key Features:
Comprehensive Momentum Analysis: The script aims to detect momentum-trend bias, typically when in an uptrend, the momentum oscillator will oscillate around the zero line but will have stronger positive values than negative values, similarly for a downtrend the momentum will have stronger negative values. This script aims to quantify this phenomenon.
Overlay Mode: Traders can choose to overlay the indicator on the price chart for a clear visual representation of market momentum.
Take-profit Signals: The indicator includes signals to lock in profits, they appear as labels in overlay mode and as crosses when overlay mode is off.
Impulse Boundary: The script includes an impulse boundary, the impulse boundary is a threshold to visualize significant spikes in momentum.
Standard Deviation Multiplier: Users can adjust the standard deviation multiplier to increase the noise tolerance of the impulse boundary.
Bias Length Control: Traders can customize the length for evaluating bias, enabling them to fine-tune the indicator according to their trading preferences. A higher length will give a longer-term bias in trend.
Volume Exhaustion [AlgoAlpha]Introducing the Volume Exhaustion by AlgoAlpha, is an innovative tool that aims to identify potential exhaustion or peaks in trading volume , which can be a key indicator for reversals or continuations in market trends 🔶.
Key Features:
Signal Plotting : A special feature is the plotting of 'Release' signals, marked by orange diamonds, indicating points where the exhaustion index crosses under its previous value and is above a certain boundary. This could signify critical market points 🚨.
Calculation Length Customization : Users can adjust the calculation and Signal lengths to suit their trading style, allowing for flexibility in analysis over different time periods. ☝️
len = input(50, "Calculation Length")
len2 = input(8, "Signal Length")
Visual Appeal : The script offers customizable colors (col for the indicator and col1 for the background) enhancing the visual clarity and user experience 💡.
col = input.color(color.white, "Indicator Color")
col1 = input.color(color.gray, "Background Color")
Advanced Volume Processing : At its core, the script utilizes a combination of Hull Moving Average (HMA) and Exponential Moving Average (EMA) applied to the volume data. This sophisticated approach helps in smoothing out the volume data and reducing lag.
sv = ta.hma(volume, len)
ssv = ta.hma(sv, len)
Volume Exhaustion Detection : The script calculates the difference between the volume and its smoothed version, normalizing this value to create an exhaustion index (fff). Positive values of this index suggest potential volume exhaustion.
f = sv-ssv
ff = (f) / (ta.ema(ta.highest(f, len) - ta.lowest(f, len), len)) * 100
fff = ff > 0 ? ff : 0
Boundary and Zero Line : The script includes a boundary line (boundary) and a zero line (zero), with the area between them filled for enhanced visual interpretation. This helps in assessing the relative position of the exhaustion index.
Customizable Background : The script colors the background of the chart for better readability and to distinguish the indicator’s area clearly.
Overall, Volume Exhaustion is designed for traders who focus on volume analysis. It provides a unique perspective on volume trends and potential exhaustion points, which can be crucial for making informed trading decisions. This script is a valuable addition for traders looking to enhance their trading experience with advanced volume analysis tools.
Squeeze & Release [AlgoAlpha]Introduction:
💡The Squeeze & Release by AlgoAlpha is an innovative tool designed to capture price volatility dynamics using a combination of EMA-based calculations and ATR principles. This script aims to provide traders with clear visual cues to spot potential market squeezes and release scenarios. Hence it is important to note that this indicator shows information on volatility, not direction.
Core Logic and Components:
🔶EMA Calculations: The script utilizes the Exponential Moving Average (EMA) in multiple ways to smooth out the data and provide indicator direction. There are specific lengths for the EMAs that users can modify as per their preference.
🔶ATR Dynamics: Average True Range (ATR) is a core component of the script. The differential between the smoothed ATR and its EMA is used to plot the main line. This differential, when represented as a percentage of the high-low range, provides insights into volatility.
🔶Squeeze and Release Detection: The script identifies and highlights squeeze and release scenarios based on the crossover and cross-under events between our main line and its smoothed version. Squeezes are potential setups where the market may be consolidating, and releases indicate a potential breakout or breakdown.
🔶Hyper Squeeze Detection: A unique feature that detects instances when the main line is rising consistently over a user-defined period. Hyper squeeze marks areas of extremely low volatility.
Visual Components:
The main line (ATR-based) changes color depending on its position relative to its EMA.
A middle line plotted at zero level which provides a quick visual cue about the main line's position. If the main line is above the zero level, it indicates that the price is squeezing on a longer time horizon, even if the indicator indicates a shorter-term release.
"𝓢" and "𝓡" characters are plotted to represent 'Squeeze' and 'Release' scenarios respectively.
Standard Deviation Bands are plotted to help users gauge the extremity and significance of the signal from the indicator, if the indicator is closer to either the upper or lower deviation bands, this means that statistically, the current value is considered to be more extreme and as it is further away from the mean where the indicator is oscillating at for the majority of the time. Thus indicating that the price has experienced an unusual amount or squeeze or release depending on the value of the indicator.
Usage Guidelines:
☝️Traders can use the script to:
Identify potential consolidation (squeeze) zones.
Gauge potential breakout or breakdown scenarios (release).
Fine-tune their entries and exits based on volatility.
Adjust the various lengths provided in the input for better customization based on individual trading styles and the asset being traded.
Liquidity Weighted Moving Averages [AlgoAlpha]Description:
The Liquidity Weighted Moving Averages by AlgoAlpha is a unique approach to identifying underlying trends in the market by looking at candle bars with the highest level of liquidity. This script offers a modified version of the classical MA crossover indicator that aims to be less noisy by using liquidity to determine the true fair value of price and where it should place more emphasis on when calculating the average.
Rationale:
It is common knowledge that liquidity makes it harder for market participants to move the price of assets, using this logic, we can determine the coincident liquidity of each bar by looking at the volume divided by the distance between the opening and closing price of that bar. If there is a higher volume but the opening and closing prices are near each other, this means that there was a high level of liquidity in that bar. We then use standard deviations to filter out high spikes of liquidity and record the closing prices on those bars. An average is then applied to these recorded prices only instead of taking the average of every single bar to avoid including outliers in the data processing.
Key features:
Customizable:
Fast Length - the period of the fast-moving average
Slow Length - the period of the slow-moving average
Outlier Threshold Length - the period of the outlier processing algorithm to detect spikes in liquidity
Significant Noise reduction from outliers:
Amazing Oscillator (AO) [Algoalpha]Description:
Introducing the Amazing Oscillator indicator by Algoalpha, a versatile tool designed to help traders identify potential trend shifts and market turning points. This indicator combines the power of the Awesome Oscillator (AO) and the Relative Strength Index (RSI) to create a new indicator that provides valuable insights into market momentum and potential trade opportunities.
Key Features:
Customizable Parameters: The indicator allows you to customize the period of the RSI calculations to fine-tune the indicator's responsiveness.
Visual Clarity: The indicator uses user-defined colors to visually represent upward and downward movements. You can select your preferred colors for both bullish and bearish signals, making it easy to spot potential trade setups.
AO and RSI Integration: The script combines the AO and RSI indicators to provide a comprehensive view of market conditions. The RSI is applied to the AO, which results in a standardized as well as a less noisy version of the Awesome Oscillator. This makes the indicator capable of pointing out overbought or oversold conditions as well as giving fewer false signals
Signal Plots: The indicator plots key levels on the chart, including the RSI threshold(Shifted down by 50) at 30 and -30. These levels are often used by traders to identify potential trend reversal points.
Signal Alerts: For added convenience, the indicator includes "x" markers to signal potential buy (green "x") and sell (red "x") opportunities based on RSI crossovers with the -30 and 30 levels. These alerts can help traders quickly identify potential entry and exit points.
Trend Flow Profile [AlgoAlpha]Description:
The "Trend Flow Profile" indicator is a powerful tool designed to analyze and interpret the underlying trends and reversals in a financial market. It combines the concepts of Order Flow and Rate of Change (ROC) to provide valuable insights into market dynamics, momentum, and potential trade opportunities. By integrating these two components, the indicator offers a comprehensive view of market sentiment and price movements, facilitating informed trading decisions.
Rationale:
The combination of Order Flow and ROC in the "Trend Flow Profile" indicator stems from the recognition that both factors play critical roles in understanding market behavior. Order Flow represents the net buying or selling pressure in the market, while ROC measures the rate at which prices change. By merging these elements, the indicator captures the interplay between market participants' actions and the momentum of price movements, enabling traders to identify trends, spot reversals, and gauge the strength of price acceleration or deceleration.
Calculation:
The Order Flow component is computed by summing the volume when prices move up and subtracting the volume when prices move down. This cumulative measure reflects the overall order imbalance in the market, providing insights into the dominant buying or selling pressure.
The ROC component calculates the percentage change in price over a given period. It compares the current price to a previous price and expresses the change as a percentage. This measurement indicates the velocity and direction of price movement, allowing traders to assess the market's momentum.
How to Use It?
The "Trend Flow Profile" indicator offers valuable information to traders for making informed trading decisions. It enables the identification of underlying trends and potential reversals, providing a comprehensive view of market sentiment and momentum. Here are some key ways to utilize the indicator:
Spotting Trends: The indicator helps identify the prevailing market trend, whether bullish or bearish. A consistent positive (green) histogram indicates a strong uptrend, while a consistent negative (red) histogram suggests a robust downtrend.
Reversal Signals: Reversal patterns can be identified when the histogram changes color, transitioning from positive to negative (or vice versa). These reversals can signify potential turning points in the market, highlighting opportunities for counter-trend trades.
Momentum Assessment: By observing the width and intensity of the histogram, traders can assess the acceleration or deceleration of price momentum. A wider histogram suggests strong momentum, while a narrower histogram indicates a potential slowdown.
Utility:
The "Trend Flow Profile" indicator serves as a valuable tool for traders, providing several benefits. Traders can easily identify the prevailing market trend, enabling them to align their trading strategies with the dominant direction of the market. The indicator also helps spot potential reversals, allowing traders to anticipate market turning points and capture counter-trend opportunities. Additionally, the green and red histogram colors provide visual cues to determine the optimal duration of a long or short position. Following the green histogram signals when in a long position and the red histogram signals when in a short position can assist traders in managing their trades effectively. Moreover, the width and intensity of the histogram offer insights into the acceleration or deceleration of momentum. Traders can gauge the strength of price movements and adjust their trading strategies accordingly. By leveraging the "Trend Flow Profile" indicator, traders gain a comprehensive understanding of market dynamics, which enhances their decision-making and improves their overall trading outcomes.
Bollinger Bands Percentile + Stdev Channels (BBPct) [AlgoAlpha]Description:
The "Bollinger Bands Percentile (BBPct) + STD Channels" mean reversion indicator, developed by AlgoApha, is a technical analysis tool designed to analyze price positions using Bollinger Bands and Standard Deviation Channels (STDC). The combination of these two indicators reinforces a stronger reversal signal. BBPct calculates the percentile rank of the price's standard deviation relative to a specified lookback period. Standard deviation channels operate by utilizing a moving average as the central line, with upper and lower lines equidistant from the average based on the market's volatility, helping to identify potential price boundaries and deviations.
How it Works:
The BBPct indicator utilizes Bollinger Bands, which consist of a moving average (basis) and upper and lower bands based on a specified standard deviation multiplier. By default, it uses a 20-period moving average and a standard deviation multiplier of 2. The upper band is calculated by adding the basis to the standard deviation multiplied by the multiplier, while the lower band is calculated by subtracting the same value. The BBPct indicator calculates the position of the current price between the lower and upper Bollinger Bands as a percentile value. It determines this position by comparing the price's distance from the lower band to the overall range between the upper and lower bands. A value of 0 indicates that the price is at the lower band, while a value of 100 indicates that the price is at the upper band. The indicator also includes an optional Bollinger Band standard deviation percentage (%Stdev) histogram, representing the deviation of the current price from the moving average as a percentage of the price itself.
Standard deviation channels, also known as volatility channels, aid in identifying potential buying and selling opportunities while minimizing unfavorable trades. These channels are constructed by two lines that run parallel to a moving average. The separation between these lines is determined by the market's volatility, represented by standard deviation. By designating upper and lower channel lines, the channels demarcate the borders between typical and atypical price movements. Consequently, when the market's price falls below the lower channel line, it suggests undervaluation, whereas prices surpassing the upper channel line indicate overvaluation.
Signals
The chart displays potential reversal points through the use of red and green arrows. A red arrow indicates a potential bearish retracement, signaling a possible downward movement, while a green arrow represents a potential pullback to the positive, suggesting a potential upward movement. These signals are generated only when both the BBPct (Bollinger Bands Percentage) and the STDC (Standard Deviation Channel) indicators align with bullish or bearish conditions. Consequently, traders might consider opening long positions when the green arrow appears and short positions when the red arrow is plotted.
Usage:
This indicator can be utilized by traders and investors to effectively identify pullbacks, reversals, and mean regression, thereby enhancing their trading opportunities. Notably, extreme values of the BBPct, such as below -5 or above 105, indicate oversold or overbought conditions, respectively. Moreover, the presence of extreme STDC zones occurs when prices fall below the lower channel line or cross above the upper channel line. Traders can leverage this information as a mean reversion tool by identifying instances of peak overbought and oversold values. These distinctive characteristics facilitate the identification of potential entry and exit points, thus augmenting trading decisions and enhancing market analysis.
The indicator's parameters, such as the length of the moving average, the data source, and the standard deviation multiplier, can be customized to align with individual trading strategies and preferences.
Originality:
The BBPct + STDC indicator, developed by AlgoAlpha, is an original implementation that combines the calculation of Bollinger Bands, percentile ranking, the %Stdev histogram and the STDC. While it shares some similarities with the Bollinger Bands %B indicator, the BBPct indicator introduces additional elements and customization options tailored to AlgoAlpha's methodology. The script is released under the Mozilla Public License 2.0, granting users the freedom to utilize and modify it while adhering to the license terms.
Peak & Valley Levels [AlgoAlpha]The Peak & Valley Levels indicator is a sophisticated script designed to pinpoint key support and resistance levels in the market. By utilizing candle length and direction, it accurately identifies potential reversal points, offering traders valuable insights for their strategies.
Core Components:
Peak and Valley Detection: The script recognizes peaks and valleys in price action. Peaks (potential resistance levels) are identified when a candle is longer than the previous one, changes direction, and closes lower, especially on lower volume. Valleys (potential support levels) are detected under similar conditions but with the candle closing higher.
Color-Coded Visualization:
Red lines mark resistance levels, signifying peaks in the price action.
Green lines indicate support levels, representing valleys.
Dynamic Level Adjustment: The script adapts these levels based on ongoing market movements, enhancing their relevance and accuracy.
Rejection Functions:
Bullish Rejection: Determines if a candlestick pattern rejects a level as potential support.
Bearish Rejection: Identifies if a pattern rejects a level as possible resistance.
Usage and Strategy Integration:
Visual Aid for Support and Resistance: The indicator is invaluable for visualizing key market levels where price reversals may occur.
Entry and Exit Points: Traders can use the identified support and resistance levels to fine-tune entry and exit points in their trading strategies.
Trend Reversal Signals: The detection of peaks and valleys serves as an early indicator of potential trend reversals.
Application in Trading:
Versatile for Various Trading Styles: This indicator can be applied across different trading styles, including swing trading, scalping, or trend-following approaches.
Complementary Tool: For best results, it should be used alongside other technical analysis tools to confirm trading signals and strategies.
Customization and Adaptability: Traders are encouraged to experiment with different settings and timeframes to tailor the indicator to their specific trading needs and market conditions.
In summary, the Peak & Valley Levels by AlgoAlpha is a dynamic and adaptable tool that enhances a trader’s ability to identify crucial market levels. Its integration of candlestick analysis with dynamic level adjustment offers a robust method for spotting potential reversal points, making it a valuable addition to any trader's toolkit.
Limited Growth Stock-to-Flow (LGS2F) [AlgoAlpha]Description:
The "∂ Limited Growth Stock-to-Flow (LG-S2F)" indicator, developed by AlgoAlpha, is a technical analysis tool designed to analyze the price of Bitcoin (BTC) based on the Stock-to-Flow model. The indicator calculates the expected price range of BTC by incorporating variables such as BTC supply, block height, and model parameters. It also includes error bands to indicate potential overbought and oversold conditions.
How it Works:
The LG-S2F indicator utilizes the Stock-to-Flow model, which measures the scarcity of an asset by comparing its circulating supply (stock) to its newly produced supply (flow). In this script, the BTC supply and block height data are obtained to calculate the price using the model formula. The formula includes coefficients (a, b, c) and exponentiation functions to derive the expected price.
The script incorporates error bands based on uncertainty values derived from the standard errors of the model parameters. These error bands indicate the potential range of variation in the expected price, accounting for uncertainties in the model's parameters. The upper and lower error bands visualize potential overbought and oversold conditions, respectively.
Usage:
Traders can utilize the LG-S2F indicator to gain insights into the potential price movements of Bitcoin. The indicator's main line represents the expected price, while the error bands highlight the potential range of variation. Traders may consider taking long positions when the price is near or below the lower error band and short positions when the price is close to or above the upper error band.
It's important to note that the LG-S2F indicator is specifically designed for Bitcoin and relies on the Stock-to-Flow model. Users should exercise caution and consider additional analysis and factors before making trading decisions solely based on this indicator.
Originality:
The LG-S2F indicator, developed by QuantMario and AlgoAlpha, is an original implementation that combines the Stock-to-Flow model with error bands to provide a comprehensive view of BTC's potential price range. While the concept of Stock-to-Flow analysis exists, the specific calculations, incorporation of error bands, and customization options in this script are unique to QuantMario's methodology. The script is released under Mozilla Public License 2.0, allowing users to utilize and modify it while adhering to the license terms.
Lower timeframe chartHi all!
I've made this script to help with my laziness (and to help me (and now you) with efficiency). It's purpose is to, without having to change the chart timeframe, being able to view the lower timeframe bars (and trend) within the last chart bar. The defaults are just my settings (It's based on daily bars), so feel free to change them and maybe share yours! It's also based on stocks, which have limited trading hours, but if you want to view this for forex trading I suggest changing the 'lower time frame' to a higher value since it has more trading hours.
The script prints a label chart (ASCII) based on your chosen timeframe and the trend, based on @KivancOzbilgic script SuperTrend The printed ASCII chart has rows (slots) that are based on ATR (14 bars) and empty gaps are removed. The current trend is decided by a percentage of bars (user defined but defaults to 80%, which is really big but let's you be very conservative in defining a trend to be bullish. Set to 50% to have the trend being decided equally or lower to be more conservative in defining a trend to be bearish) that must have a bullish SuperTrend, it's considered to be bearish otherwise. Big price range (based on the ATR for 14 bars) and big volume (true if the volume is bigger than a user defined simple moving average (defaults to 20 bars)) can be disabled for faster execution.
The chart displayed will consist of bars and thicker bars that has a higher volume than the defined simple moving average. The bars that has a 'big range' (user defined value of ATR (14 days) factor that defaults to 0.5) will also have a wick. The characters used are the following:
Green bar = ┼
Green bar with large volume = ╪
Green bar wick = │
Red bar = ╋
Red bar with large volume = ╬
Red bar wick = ┃
Bar with no range = ─
Bar with no range and high volume = ═
Best of trading!
Fair Value Gap ChartThe Fair Value Gap chart is a new charting method that displays fair value gap imbalances as Japanese candlesticks, allowing traders to quickly see the evolution of historical market imbalances.
The script is additionally able to compute an exponential moving average using the imbalances as input.
🔶 USAGE
The Fair Value Gap chart allows us to quickly display historical fair value gap imbalances. This also allows for filtering out potential noisy variations, showing more compact trends.
Most like other charting methods, we can draw trendlines/patterns from the displayed results, this can be helpful to potentially predict future imbalances locations.
Users can display an exponential moving average computed from the detected fvg's imbalances. Imbalances above the ema can be indicative of an uptrend, while imbalances under the ema are indicative of a downtrend.
Note that due to pinescript limitations a maximum of 500 lines can be displayed, as such displaying the EMA prevent candle wicks from being displayed.
🔶 DETAILS
🔹 Candle Structure
The Fair Value Gap Chart is constructed by keeping a record of all detected fair value gaps on the chart. Each fvg is displayed as a candlestick, with the imbalance range representing the body of the candle, and the range of the imbalance interval being used for the wicks.
🔹 EMA Source Input
The exponential moving average uses the imbalance range to get its input source, the extremity of the range used depends on whether the fvg is bullish or bearish.
When the fvg is bullish, the maximum of the imbalance range is used as ema input, else the minimum of the fvg imbalance is used.
Open Interest Chart [LuxAlgo]The Open Interest Chart displays Commitments of Traders %change of futures open interest , with a unique circular plotting technique, inspired from this publication Periodic Ellipses .
🔶 USAGE
Open interest represents the total number of contracts that have been entered by market participants but have not yet been offset or delivered. This can be a direct indicator of market activity/liquidity, with higher open interest indicating a more active market.
Increasing open interest is highlighted in green on the circular plot, indicating money coming into the market, while decreasing open interests highlighted in red indicates money coming out of the market.
You can set up to 6 different Futures Open interest tickers for a quick follow up:
🔶 DETAILS
Circles are drawn, using plot() , with the functions createOuterCircle() (for the largest circle) and createInnerCircle() (for inner circles).
Following snippet will reload the chart, so the circles will remain at the right side of the chart:
if ta.change(chart.left_visible_bar_time ) or
ta.change(chart.right_visible_bar_time)
n := bar_index
Here is a snippet which will draw a 39-bars wide circle that will keep updating its position to the right.
//@version=5
indicator("")
n = bar_index
barsTillEnd = last_bar_index - n
if ta.change(chart.left_visible_bar_time ) or
ta.change(chart.right_visible_bar_time)
n := bar_index
createOuterCircle(radius) =>
var int end = na
var int start = na
var basis = 0.
barsFromNearestEdgeCircle = 0.
barsTillEndFromCircleStart = radius
startCylce = barsTillEnd % barsTillEndFromCircleStart == 0 // start circle
bars = ta.barssince(startCylce)
barsFromNearestEdgeCircle := barsTillEndFromCircleStart -1
basis := math.min(startCylce ? -1 : basis + 1 / barsFromNearestEdgeCircle * 2, 1) // 0 -> 1
shape = math.sqrt(1 - basis * basis)
rad = radius / 2
isOK = barsTillEnd <= barsTillEndFromCircleStart and barsTillEnd > 0
hi = isOK ? (rad + shape * radius) - rad : na
lo = isOK ? (rad - shape * radius) - rad : na
start := barsTillEnd == barsTillEndFromCircleStart ? n -1 : start
end := barsTillEnd == 0 ? start + radius : end
= createOuterCircle(40)
plot(h), plot(l)
🔶 LIMITATIONS
Due to the inability to draw between bars, from time to time, drawings can be slightly off.
Bar-replay can be demanding, since it has to reload on every bar progression. We don't recommend using this script on bar-replay. If you do, please choose the lowest speed and from time to time pause bar-replay for a second. You'll see the script gets reloaded.
🔶 SETTINGS
🔹 TICKERS
Toggle :
• Enabled -> uses the first column with a pre-filled list of Futures Open Interest tickers/symbols
• Disabled -> uses the empty field where you can enter your own ticker/symbol
Pre-filled list : the first column is filled with a list, so you can choose your open interest easily, otherwise you would see COT:088691_F_OI aka Gold Futures Open Interest for example.
If applicable, you will see 3 different COT data:
• COT: Legacy Commitments of Traders report data
• COT2: Disaggregated Commitments of Traders report data
• COT3: Traders in Financial Futures report data
Empty field : When needed, you can pick another ticker/symbol in the empty field at the right and disable the toggle.
Timeframe : Commitments of Traders (COT) data is tallied by the Commodity Futures Trading Commission (CFTC) and is published weekly. Therefore data won't change every day.
Default set TF is Daily
🔹 STYLE
From middle:
• Enabled (default): Drawings start from the middle circle -> towards outer circle is + %change , towards middle of the circle is - %change
• Disabled: Drawings start from the middle POINT of the circle, towards outer circle is + OR -
-> in both options, + %change will be coloured green , - %change will be coloured red .
-> 0 %change will be coloured blue , and when no data is available, this will be coloured gray .
Size circle : options tiny, small, normal, large, huge.
Angle : Only applicable if "From middle" is disabled!
-> sets the angle of the spike:
Show Ticker : Name of ticker, as seen in table, will be added to labels.
Text - fill
• Sets colour for +/- %change
Table
• Sets 2 text colours, size and position
Circles
• Sets the colour of circles, style can be changed in the Style section.
You can make it as crazy as you want:
EquiVolume [LuxAlgo]EquiVolume is a charting method that aims to incorporate volume information to a candlestick chart. Volume is highlighted through the candle body width, with wider candles suggesting more significant volume.
Our script shows an EquiVolume chart for the visible chart range. Additionally regular volume can be plotted as a column plot with the column's width controlled by volume.
🔶 SETTINGS
🔹 Options
Chart: Shows candles with volume adjusted width.
Volume: Shows volume with volume adjusted width.
🔹 Intrabar Analysis
Enable/disable: When LTF is enabled, the script will calculate the % volume/candles in the same direction than current timeframe.
You can choose a LTF between 1 and 240 minutes.
Type %:
- Volume: sum of volume of all LTF candles, which are in the same direction.
- #bars: sum of all LTF candles, which are in the same direction.
🔹 Width Boxes (bars)
Minimum width: sets the minimum width of a box (candle/volume)
Maximum width: sets the maximum width of a box (candle/volume)
🔶 USAGE
This charting method makes it easier to spot large volume candles, against comparing candles to volume.
Another example:
Additionally, users can make the script perform an intrabar analysis on the chart candles, allowing to highlight bullish/bearish activity within a candle. The script can estimate bullish/bearish trading activity within a candle or simply use intrabar candle signs.
Example
- 15-minute candle is green
- 10 1-minute candles (LTF) IN that 15-minute candle are green -> 10/15 = 66,667%
-> The current 15-minute candle will be 66,667% filled with green color.
Note that the script will draw everything from last visible bar at the right to left, as such you can scroll backwards, and the script will show you the data of the visible chart.
Scrolling back will return the following result:
🔶 REMARKS
When the LTF is too far apart from current timeframe, you should get an error. To prevent this, the LTF will automatically rise, giving no error.
When this happens, the adjusted LTF will be displayed. Do note, due to a maximum available LTF data, sometimes boxes won't always be visible (since there is no LTF data anymore)
To solve this, just elevate your LTF:
When the set LTF is higher than current TF, you would normally get an error as well.
This script will automatically adjust the LTF to current TF, together with a visible warning (no error though).
Due to the inability to draw a line in the space between bars, sometimes a wick won't be placed exactly in the middle.
