Volume Distribution Before/After Top
Description
This script visualizes the distribution of volume before and after a price peak within a specified time interval. The green area represents the volume accumulated before the peak, and the red area represents the volume accumulated after the peak. The script also calculates and displays the volume-weighted average price (VWAP) on each side of the peak with a dotted line and a label.
The key features include:
Volume Visualization: Transparent green and red bars indicate volume fractions before and after the peak.
VWAP Markers: Centered labels with VWAP values are plotted above the corresponding levels.
Interactive Inputs: Define the start and end points of the analysis interval using customizable anchor times.
This tool is ideal for traders who want to analyze how volume dynamics are distributed around key price levels. It can help identify potential zones of support and resistance and improve the understanding of market behavior in response to volume accumulation.
Instructions
Select the start and end anchor times using the input fields.
Observe the volume distribution and VWAP levels on the chart.
Use the visual data to make more informed trading decisions.
在腳本中搜尋"accumulation"
Crypto SeasonDefinition
This indicator is an informative indicator aiming to predict when the Altcoin season will start and when Bitcoin will enter the month season.
The average of the graph shows the dominance of altcoins other than BTC, ETH and USDT. If this value is over 30, the BTC says that the bull season is over. This value indicates that 20 to 30 BTC is in the bull season or accumulation. If this value is less than 20, it means that the subcoin season has begun.
Disclaimer
This indicator is for informational purposes only and should be used for educational purposes only. You may lose money if you rely on this to trade without additional information. Use at your own risk.
Version
v1.0
Liquidity Swings [UAlgo]The "Liquidity Swings " indicator is designed to help traders identify liquidity swings within the market. This tool is particularly useful for visualizing areas where liquidity is accumulating and where it is being swept, providing valuable insights for making informed trading decisions. By tracking the pivots in price and associating them with volume, the indicator highlights zones of potential support and resistance, helping traders understand market dynamics more clearly.
🔶 Key Features
Liquidity Swing Sensitivity: Adjustable sensitivity settings to fine-tune the detection of liquidity swings according to market conditions and trader preferences.
Two modes of liquidity calculation:
Cumulative Liquidity: Aggregates unswept liquidity over multiple swings until it is swept, providing a broader view of liquidity accumulation.
Individual Liquidity: Displays the accumulated liquidity for each swing independently, offering a more granular perspective.
Visual Customization: Options to customize the colors and sizes of liquidity lines, areas, and informational text for better visual clarity.
Dynamic Updates: The indicator dynamically updates liquidity zones and labels, adjusting to new market data to keep traders informed in real-time.
🔶 Disclaimer
The "Liquidity Swings " indicator is provided for educational and informational purposes only.
It should not be considered as financial advice or a recommendation to buy or sell any financial instrument.
The use of this indicator involves inherent risks, and users should employ their own judgment and conduct their own research before making any trading decisions. Past performance is not indicative of future results.
🔷 Related Scripts
Liquidity Sweeps
Williams %R Liquidity Sweeps
Bitcoin Wave RainbowThis Bitcoin Wave Rainbow model is a powerful tool designed to help traders of all levels understand and navigate the Bitcoin market. It works only with BTC in any timeframe, but better looks in dayly or weekly timeframes. It provides valuable insights into historical price behavior and offers forecasts for the next decade, making it an essential asset for both short-term and long-term strategies.
How the Model Works
The model is built on a logarithmic trend, also known as a power law, represented by the green line on the chart. This line illustrates the expected price trajectory of Bitcoin over time. The model also incorporates a range of price fluctuations around this trend, represented by colored bands.
The width of these bands narrows over time, indicating that the model becomes increasingly accurate as it progresses. This is due to the exponential decrease in the range of price fluctuations, making the model a reliable tool for predicting future price movements.
Understanding the Zones
Blue Zone: This zone signifies that the price is below its trend, making it a recommended area for buying Bitcoin. It represents a level where the price is unlikely to fall further, providing a potential opportunity for accumulation.
Green Zone: This zone represents a fair price range, where the price is relatively close to its trend. In this zone, the price may continue to go up or down, depending on the halving season. ransiting up around any halving and transiting down around 2 years after each halving.
Yellow Zone: This zone indicates that the price is somewhat overheated, often due to the hype following a halving event. While there may still be room for the price to rise, traders should exercise caution in this zone, as a price correction could occur.
Red Zone: This zone represents a strong overbought condition, where the price is significantly above its trend. Traders should be extremely cautious in this zone and consider reducing their positions, as the price is likely to revert back towards the trend or even lower.
Using the Model in Your Trading Strategy
This indicator can be used in conjunction with the Bitcoin Wave Model, which complements it by showing harmonic price fluctuations associated with halving events. Together, these indicators provide a comprehensive view of the Bitcoin market, allowing traders to make informed decisions based on both historical data and future projections.
Benefits for Traders
This Bitcoin price model offers numerous benefits for traders, including:
Clear Visualization: The model provides a clear and concise visual representation of Bitcoin's price behavior, making it easy to understand and interpret.
Accurate Forecasting: The model's accuracy increases over time, providing reliable forecasts for future price movements.
Risk Management: The model helps traders identify overbought and oversold conditions, allowing them to manage their risk more effectively.
Strategic Decision-Making: By understanding the different zones and their implications, traders can make more informed decisions about when to buy, sell, or hold Bitcoin.
By incorporating this Bitcoin price model into your trading strategy, you can gain a deeper understanding of the market dynamics and improve your chances of success.
VWAP DivergenceThe "VWAP Divergence" indicator leverages the VWAP Rolling indicator available in TradingView's library to analyze price and volume dynamics. This custom indicator calculates a rolling VWAP (Volume Weighted Average Price) and compares it with a Simple Moving Average (SMA) over a specified historical period.
Advantages:
1. Accurate VWAP Calculation: The VWAP Rolling indicator computes a VWAP that dynamically adjusts based on recent price and volume data. VWAP is a vital metric used by traders to understand the average price at which a security has traded, factoring in volume.
2. SMA Comparison: By contrasting the rolling VWAP from the VWAP Rolling indicator with an SMA of the same length, the indicator highlights potential divergences. This comparison can reveal shifts in market sentiment.
3. Divergence Identification: The primary purpose of this indicator is to detect divergences between the rolling VWAP from VWAP Rolling and the SMA. Divergence occurs when the rolling VWAP significantly differs from the SMA, indicating potential changes in market dynamics.
Interpretation:
1. Positive Oscillator Values: A positive oscillator (difference between rolling VWAP and SMA) suggests that the rolling VWAP, derived from the VWAP Rolling indicator, is above the SMA. This could indicate strong buying interest or accumulation.
2. Negative Oscillator Values: Conversely, a negative oscillator value indicates that the rolling VWAP is below the SMA. This might signal selling pressure or distribution.
3. Divergence Signals: Significant divergences between the rolling VWAP (from VWAP Rolling) and SMA can indicate shifts in market sentiment. For instance, a rising rolling VWAP diverging upwards from the SMA might suggest increasing bullish sentiment.
4. Confirmation with Price Movements: Traders often use these divergences alongside price action to confirm potential trend reversals or continuations.
Implementation:
1. Length Parameter: Adjust the Length input to modify the lookback period for computing both the rolling VWAP from VWAP Rolling and the SMA. A longer period provides a broader view of market sentiment, while a shorter period is more sensitive to recent price movements.
2. Visualization: The indicator plots the VWAP SMA Oscillator, which visually represents the difference (oscillator) between the rolling VWAP (from VWAP Rolling) and SMA over time.
3. Zero Line: The zero line (gray line) serves as a reference point. Oscillator values crossing above or below this line can be interpreted as bullish or bearish signals, respectively.
4. Contextual Analysis: Interpret signals from this indicator in conjunction with broader market conditions and other technical indicators to make informed trading decisions.
This indicator, utilizing the VWAP Rolling component, is valuable for traders seeking insights into the relationship between volume-weighted price levels and traditional moving averages, aiding in the identification of potential trading opportunities based on market dynamics.
Volume-Blended Candlesticks [QuantVue]Introducing the Volume-Blended Candlestick Indicator, a powerful tool that seamlessly integrates volume information with candlesticks, providing you with a comprehensive view of market dynamics in a single glance.
The Volume-Blended Candlestick Indicator employs a unique approach of projecting volume totals by calculating the total volume traded per second and comparing it to the time left in the session as well as the historical average length selected by the user.
The indicator then dynamically adjusts the opacity of the candlestick colors based on the intensity of the projected volume. As volume intensifies, the candlestick colors become more pronounced, while low volume will cause colors to fade allowing you to visually perceive the level of buying or selling.
One of the standout features of the Volume-Blended Candlestick Indicator is its ability to identify pocket pivots. A pocket pivot is an up day with volume greater than any of the down days volume in the past 10 days. By highlighting these pocket pivots on your chart, the indicator helps you identify potential stealth accumulation.
In addition to blending volume with candlesticks and spotting pocket pivots, this versatile indicator provides you with an insightful table displaying key volume metrics. The table includes the average volume, average dollar volume, and the up-down volume ratio, allowing you to get a clear picture of buying and selling pressure.
Settings Include:
🔹Sensitivty Level: Normal, More, Less
🔹Volume MA Length
🔹Toggle Color based on previous close
🔹Show or hide volume info
🔹Chose candlestick colors
🔹Show or hide pocket pivots
🔹Show or hide volume info table
Don't hesitate to reach out with any questions or concerns.
We hope you enjoy!
Cheers.
RelativeValue█ OVERVIEW
This library is a Pine Script™ programmer's tool offering the ability to compute relative values, which represent comparisons of current data points, such as volume, price, or custom indicators, with their analogous historical data points from corresponding time offsets. This approach can provide insightful perspectives into the intricate dynamics of relative market behavior over time.
█ CONCEPTS
Relative values
In this library, a relative value is a metric that compares a current data point in a time interval to an average of data points with corresponding time offsets across historical periods. Its purpose is to assess the significance of a value by considering the historical context within past time intervals.
For instance, suppose we wanted to calculate relative volume on an hourly chart over five daily periods, and the last chart bar is two hours into the current trading day. In this case, we would compare the current volume to the average of volume in the second hour of trading across five days. We obtain the relative volume value by dividing the current volume by this average.
This form of analysis rests on the hypothesis that substantial discrepancies or aberrations in present market activity relative to historical time intervals might help indicate upcoming changes in market trends.
Cumulative and non-cumulative values
In the context of this library, a cumulative value refers to the cumulative sum of a series since the last occurrence of a specific condition (referred to as `anchor` in the function definitions). Given that relative values depend on time, we use time-based conditions such as the onset of a new hour, day, etc. On the other hand, a non-cumulative value is simply the series value at a specific time without accumulation.
Calculating relative values
Four main functions coordinate together to compute the relative values: `maintainArray()`, `calcAverageByTime()`, `calcCumulativeSeries()`, and `averageAtTime()`. These functions are underpinned by a `collectedData` user-defined type (UDT), which stores data collected since the last reset of the timeframe along with their corresponding timestamps. The relative values are calculated using the following procedure:
1. The `averageAtTime()` function invokes the process leveraging all four of the methods and acts as the main driver of the calculations. For each bar, this function adds the current bar's source and corresponding time value to a `collectedData` object.
2. Within the `averageAtTime()` function, the `maintainArray()` function is called at the start of each anchor period. It adds a new `collectedData` object to the array and ensures the array size does not exceed the predefined `maxSize` by removing the oldest element when necessary. This method plays an essential role in limiting memory usage and ensuring only relevant data over the desired number of periods is in the calculation window.
3. Next, the `calcAverageByTime()` function calculates the average value of elements within the `data` field for each `collectedData` object that corresponds to the same time offset from each anchor condition. This method accounts for cases where the current index of a `collectedData` object exceeds the last index of any past objects by using the last available values instead.
4. For cumulative calculations, the `averageAtTime()` function utilizes the `isCumulative` boolean parameter. If true, the `calcCumulativeSeries()` function will track the running total of the source data from the last bar where the anchor condition was met, providing a cumulative sum of the source values from one anchor point to the next.
To summarize, the `averageAtTime()` function continually stores values with their corresponding times in a `collectedData` object for each bar in the anchor period. When the anchor resets, this object is added to a larger array. The array's size is limited by the specified number of periods to be averaged. To correlate data across these periods, time indexing is employed, enabling the function to compare corresponding points across multiple periods.
█ USING THIS LIBRARY
The library simplifies the complex process of calculating relative values through its intuitive functions. Follow the steps below to use this library in your scripts.
Step 1: Import the library and declare inputs
Import the library and declare variables based on the user's input. These can include the timeframe for each period, the number of time intervals to include in the average, and whether the calculation uses cumulative values. For example:
//@version=5
import TradingView/RelativeValue/1 as TVrv
indicator("Relative Range Demo")
string resetTimeInput = input.timeframe("D")
int lengthInput = input.int(5, "No. of periods")
Step 2: Define the anchor condition
With these inputs declared, create a condition to define the start of a new period (anchor). For this, we use the change in the time value from the input timeframe:
bool anchor = timeframe.change(resetTimeInput)
Step 3: Calculate the average
At this point, one can calculate the average of a value's history at the time offset from the anchor over a number of periods using the `averageAtTime()` function. In this example, we use True Range (TR) as the `source` and set `isCumulative` to false:
float pastRange = TVrv.averageAtTime(ta.tr, lengthInput, anchor, false)
Step 4: Display the data
You can visualize the results by plotting the returned series. These lines display the non-cumulative TR alongside the average value over `lengthInput` periods for relative comparison:
plot(pastRange, "Past True Range Avg", color.new(chart.bg_color, 70), 1, plot.style_columns)
plot(ta.tr, "True Range", close >= open ? color.new(color.teal, 50) : color.new(color.red, 50), 1, plot.style_columns)
This example will display two overlapping series of columns. The green and red columns depict the current TR on each bar, and the light gray columns show the average over a defined number of periods, e.g., the default inputs on an hourly chart will show the average value at the hour over the past five days. This comparative analysis aids in determining whether the range of a bar aligns with its typical historical values or if it's an outlier.
█ NOTES
• The foundational concept of this library was derived from our initial Relative Volume at Time script. This library's logic significantly boosts its performance. Keep an eye out for a forthcoming updated version of the indicator. The demonstration code included in the library emulates a streamlined version of the indicator utilizing the library functions.
• Key efficiencies in the data management are realized through array.binary_search_leftmost() , which offers a performance improvement in comparison to its loop-dependent counterpart.
• This library's architecture utilizes user-defined types (UDTs) to create custom objects which are the equivalent of variables containing multiple parts, each able to hold independent values of different types . The recently added feature was announced in this blog post.
• To enhance readability, the code substitutes array functions with equivalent methods .
Look first. Then leap.
█ FUNCTIONS
This library contains the following functions:
calcCumulativeSeries(source, anchor)
Calculates the cumulative sum of `source` since the last bar where `anchor` was `true`.
Parameters:
source (series float) : Source used for the calculation.
anchor (series bool) : The condition that triggers the reset of the calculation. The calculation is reset when `anchor` evaluates to `true`, and continues using the values accumulated since the previous reset when `anchor` is `false`.
Returns: (float) The cumulative sum of `source`.
averageAtTime(source, length, anchor, isCumulative)
Calculates the average of all `source` values that share the same time difference from the `anchor` as the current bar for the most recent `length` bars.
Parameters:
source (series float) : Source used for the calculation.
length (simple int) : The number of reset periods to consider for the average calculation of historical data.
anchor (series bool) : The condition that triggers the reset of the average calculation. The calculation is reset when `anchor` evaluates to `true`, and continues using the values accumulated since the previous reset when `anchor` is `false`.
isCumulative (simple bool) : If `true`, `source` values are accumulated until the next time `anchor` is `true`. Optional. The default is `true`.
Returns: (float) The average of the source series at the specified time difference.
The Rush
█ OVERVIEW
This script shows when buyers are in a rush to buy and when sellers are in a rush to sell
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█ CONCEPTS
Prophet Mohamed Peace be upon Him once said something similar to this "It is not advisable to trade if you do not know the
Volume".
In his book "The Day Trader's Bible - Or My Secret In Day trading Of Stocks", Richard D. Kickoff wrote in page 55
"This shows that there was only 100 shares for sale at 180 1/8, none at all at 180f^, and only 500 at 3/8. The jump from 1 to 8 to 3/8
Emphasizes both the absence of pressure and persistency on the part of the buyers. They are not content to wait patiently until they can
Secure the stock at 180^/4; they "reach" for it."
This script was inspired by these two great men.
Prophet Mohamed Peace be upon Him showed the importance of the volume and Richard D. Kickoff explained what Prophet
Mohamed Peace be upon Him meant.
So I created this script that gauge the movement of the stock and the sentiments of the traders.
═════════════════════════════════════════════════════════════════════════
• FEATURES: The script calculates The Percentage Difference of the price and The Percentage Difference of the volume between
two success bullish candles (or two success bearish candles) and then it creates a ratio between these two Percentage
Differences and in the end the ratio is compared to the previous one to see if there is an increase or a decrease.
═════════════════════════════════════════════════════════════════════════
• HOW TO USE: if you see 2 or more successive red bars that mean bears are in hurry to sell and you can expect a bearish trend soon
if the Market Maker allows it or later if the Market Maker wants to do some distribution.
if you see 2 or more successive green bars that mean bulls are in hurry to buy and you can expect a bullish trend soon if the Market
Maker allows it or later if the Market Maker wants to do some accumulation.
═════════════════════════════════════════════════════════════════════════
• LIMITATIONS:
1- Use only Heikin Ashi chart
2- Good only if volume data is correct , meaning good for a centralized Market. (You can use it for forex or
crypto but at your own risk because those markets are not centralized)
═════════════════════════════════════════════════════════════════════════
• THANKS: I pay homage to Prophet Mohamed Peace be upon Him and Richard D. Kickoff who inspired the creation of this
Script.
═════════════════════════════════════════════════════════════════════════
taLibrary "ta"
█ OVERVIEW
This library holds technical analysis functions calculating values for which no Pine built-in exists.
Look first. Then leap.
█ FUNCTIONS
cagr(entryTime, entryPrice, exitTime, exitPrice)
It calculates the "Compound Annual Growth Rate" between two points in time. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two instruments. Because it annualizes values, the function requires a minimum of one day between the two end points (annualizing returns over smaller periods of times doesn't produce very meaningful figures).
Parameters:
entryTime : The starting timestamp.
entryPrice : The starting point's price.
exitTime : The ending timestamp.
exitPrice : The ending point's price.
Returns: CAGR in % (50 is 50%). Returns `na` if there is not >=1D between `entryTime` and `exitTime`, or until the two time points have not been reached by the script.
█ v2, Mar. 8, 2022
Added functions `allTimeHigh()` and `allTimeLow()` to find the highest or lowest value of a source from the first historical bar to the current bar. These functions will not look ahead; they will only return new highs/lows on the bar where they occur.
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `high`.
Returns: (float) The highest value tracked.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `low`.
Returns: (float) The lowest value tracked.
█ v3, Sept. 27, 2022
This version includes the following new functions:
aroon(length)
Calculates the values of the Aroon indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the Aroon-Up and Aroon-Down values.
coppock(source, longLength, shortLength, smoothLength)
Calculates the value of the Coppock Curve indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
longLength (simple int) : (simple int) Number of bars for the fast ROC value (length).
shortLength (simple int) : (simple int) Number of bars for the slow ROC value (length).
smoothLength (simple int) : (simple int) Number of bars for the weigted moving average value (length).
Returns: (float) The oscillator value.
dema(source, length)
Calculates the value of the Double Exponential Moving Average (DEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `source`.
dema2(src, length)
An alternate Double Exponential Moving Average (Dema) function to `dema()`, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `src`.
dm(length)
Calculates the value of the "Demarker" indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
ema2(src, length)
An alternate ema function to the `ta.ema()` built-in, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Number of bars (length).
Returns: (float) The exponentially weighted moving average of the `src`.
eom(length, div)
Calculates the value of the Ease of Movement indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
div (simple int) : (simple int) Divisor used for normalzing values. Optional. The default is 10000.
Returns: (float) The oscillator value.
frama(source, length)
The Fractal Adaptive Moving Average (FRAMA), developed by John Ehlers, is an adaptive moving average that dynamically adjusts its lookback period based on fractal geometry.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The fractal adaptive moving average of the `source`.
ft(source, length)
Calculates the value of the Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
ht(source)
Calculates the value of the Hilbert Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
ichimoku(conLength, baseLength, senkouLength)
Calculates values of the Ichimoku Cloud indicator, including tenkan, kijun, senkouSpan1, senkouSpan2, and chikou. NOTE: offsets forward or backward can be done using the `offset` argument in `plot()`.
Parameters:
conLength (int) : (series int) Length for the Conversion Line (Tenkan). The default is 9 periods, which returns the mid-point of the 9 period Donchian Channel.
baseLength (int) : (series int) Length for the Base Line (Kijun-sen). The default is 26 periods, which returns the mid-point of the 26 period Donchian Channel.
senkouLength (int) : (series int) Length for the Senkou Span 2 (Leading Span B). The default is 52 periods, which returns the mid-point of the 52 period Donchian Channel.
Returns: ( [float, float, float, float, float ]) A tuple of the Tenkan, Kijun, Senkou Span 1, Senkou Span 2, and Chikou Span values. NOTE: by default, the senkouSpan1 and senkouSpan2 should be plotted 26 periods in the future, and the Chikou Span plotted 26 days in the past.
ift(source)
Calculates the value of the Inverse Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
kvo(fastLen, slowLen, trigLen)
Calculates the values of the Klinger Volume Oscillator.
Parameters:
fastLen (simple int) : (simple int) Length for the fast moving average smoothing parameter calculation.
slowLen (simple int) : (simple int) Length for the slow moving average smoothing parameter calculation.
trigLen (simple int) : (simple int) Length for the trigger moving average smoothing parameter calculation.
Returns: ( [float, float ]) A tuple of the KVO value, and the trigger value.
pzo(length)
Calculates the value of the Price Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
rms(source, length)
Calculates the Root Mean Square of the `source` over the `length`.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The RMS value.
rwi(length)
Calculates the values of the Random Walk Index.
Parameters:
length (simple int) : (simple int) Lookback and ATR smoothing parameter length.
Returns: ( [float, float ]) A tuple of the `rwiHigh` and `rwiLow` values.
stc(source, fast, slow, cycle, d1, d2)
Calculates the value of the Schaff Trend Cycle indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
fast (simple int) : (simple int) Length for the MACD fast smoothing parameter calculation.
slow (simple int) : (simple int) Length for the MACD slow smoothing parameter calculation.
cycle (simple int) : (simple int) Number of bars for the Stochastic values (length).
d1 (simple int) : (simple int) Length for the initial %D smoothing parameter calculation.
d2 (simple int) : (simple int) Length for the final %D smoothing parameter calculation.
Returns: (float) The oscillator value.
stochFull(periodK, smoothK, periodD)
Calculates the %K and %D values of the Full Stochastic indicator.
Parameters:
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
stochRsi(lengthRsi, periodK, smoothK, periodD, source)
Calculates the %K and %D values of the Stochastic RSI indicator.
Parameters:
lengthRsi (simple int) : (simple int) Length for the RSI smoothing parameter calculation.
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
source (float) : (series int/float) Series of values to process. Optional. The default is `close`.
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
supertrend(factor, atrLength, wicks)
Calculates the values of the SuperTrend indicator with the ability to take candle wicks into account, rather than only the closing price.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is false.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
szo(source, length)
Calculates the value of the Sentiment Zone Oscillator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
t3(source, length, vf)
Calculates the value of the Tilson Moving Average (T3).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
t3Alt(source, length, vf)
An alternate Tilson Moving Average (T3) function to `t3()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
tema(source, length)
Calculates the value of the Triple Exponential Moving Average (TEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
tema2(source, length)
An alternate Triple Exponential Moving Average (TEMA) function to `tema()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
trima(source, length)
Calculates the value of the Triangular Moving Average (TRIMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `source`.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `src`.
trix(source, length, signalLength, exponential)
Calculates the values of the TRIX indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
signalLength (simple int) : (simple int) Length for smoothing the signal line.
exponential (simple bool) : (simple bool) Condition to determine whether exponential or simple smoothing is used. Optional. The default is `true` (exponential smoothing).
Returns: ( [float, float, float ]) A tuple of the TRIX value, the signal value, and the histogram.
uo(fastLen, midLen, slowLen)
Calculates the value of the Ultimate Oscillator.
Parameters:
fastLen (simple int) : (series int) Number of bars for the fast smoothing average (length).
midLen (simple int) : (series int) Number of bars for the middle smoothing average (length).
slowLen (simple int) : (series int) Number of bars for the slow smoothing average (length).
Returns: (float) The oscillator value.
vhf(source, length)
Calculates the value of the Vertical Horizontal Filter.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
vi(length)
Calculates the values of the Vortex Indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the viPlus and viMinus values.
vzo(length)
Calculates the value of the Volume Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
williamsFractal(period)
Detects Williams Fractals.
Parameters:
period (int) : (series int) Number of bars (length).
Returns: ( [bool, bool ]) A tuple of an up fractal and down fractal. Variables are true when detected.
wpo(length)
Calculates the value of the Wave Period Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
█ v7, Nov. 2, 2023
This version includes the following new and updated functions:
atr2(length)
An alternate ATR function to the `ta.atr()` built-in, which allows a "series float" `length` argument.
Parameters:
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The ATR value.
changePercent(newValue, oldValue)
Calculates the percentage difference between two distinct values.
Parameters:
newValue (float) : (series int/float) The current value.
oldValue (float) : (series int/float) The previous value.
Returns: (float) The percentage change from the `oldValue` to the `newValue`.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
highestSince(cond, source)
Tracks the highest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the highest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `high`.
Returns: (float) The highest `source` value since the last time the `cond` was `true`.
lowestSince(cond, source)
Tracks the lowest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the lowest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `low`.
Returns: (float) The lowest `source` value since the last time the `cond` was `true`.
relativeVolume(length, anchorTimeframe, isCumulative, adjustRealtime)
Calculates the volume since the last change in the time value from the `anchorTimeframe`, the historical average volume using bars from past periods that have the same relative time offset as the current bar from the start of its period, and the ratio of these volumes. The volume values are cumulative by default, but can be adjusted to non-accumulated with the `isCumulative` parameter.
Parameters:
length (simple int) : (simple int) The number of periods to use for the historical average calculation.
anchorTimeframe (simple string) : (simple string) The anchor timeframe used in the calculation. Optional. Default is "D".
isCumulative (simple bool) : (simple bool) If `true`, the volume values will be accumulated since the start of the last `anchorTimeframe`. If `false`, values will be used without accumulation. Optional. The default is `true`.
adjustRealtime (simple bool) : (simple bool) If `true`, estimates the cumulative value on unclosed bars based on the data since the last `anchor` condition. Optional. The default is `false`.
Returns: ( [float, float, float ]) A tuple of three float values. The first element is the current volume. The second is the average of volumes at equivalent time offsets from past anchors over the specified number of periods. The third is the ratio of the current volume to the historical average volume.
rma2(source, length)
An alternate RMA function to the `ta.rma()` built-in, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The rolling moving average of the `source`.
supertrend2(factor, atrLength, wicks)
An alternate SuperTrend function to `supertrend()`, which allows a "series float" `atrLength` argument.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is `false`.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
vStop(source, atrLength, atrFactor)
Calculates an ATR-based stop value that trails behind the `source`. Can serve as a possible stop-loss guide and trend identifier.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
vStop2(source, atrLength, atrFactor)
An alternate Volatility Stop function to `vStop()`, which allows a "series float" `atrLength` argument.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
Removed Functions:
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a
"series int" length argument.
Binance Z VolumeBTC perpetual volume on Binance is about 4x spot volume.
Comparing spot and perpetual volumes could provide useful insights into market sentiment.
Abnormal increases in the spot market could be associated with accumulation. Abnormal increases in the perpetual market, on the other hand, could predict volatility as well lows and highs.
This script represents a Z-score of the volume of perpetual and 4xspot on Binance.
High values above 0 mean that the volume is skewed towards perpetual contracts. Values below 0 mean that the volume is skewed towards spot contracts.
Feel free to suggest changes and improvements of this script.
Translated with www.DeepL.com (free version)
BIO
Cumulative Volume v3The script, for Pine Script version 3, shows how to accumulate volume values during a defined session/period.
The input is the period to use for accumulation. "D" is the default value, useful to view data for each session.
This is slower than version 4 because there is no "var" and you need to use a loop. Also, you can't use "sum( volume , cnt_new_day)" with a variable length argument instead of "for".
Relative Volume Strength IndexRVSI is an alternative volume-based indicator that measures the rate of change of average OBV.
How to read a chart using it?
First signal to buy is when you see RVSI is close to green oversold levels.
Once RVSI passes above it's orange EMA, that would be the second alert of accumulation.
Be always cautious when it reaches 50 level as a random statistical correction can be expected because of "market noises".
You know it's a serious uptrend when it reaches above 75 and fluctuates there, grading behind EMA.
The best signal to sell would be a situation where you see RVSI passing below it's EMA when the whole thing is close to Red overbought level
It looks simple, but it's powerful!
I'd use RVSI in combination with price-based indicators.
Cumulative VolumeThe script shows how to accumulate volume values during a defined session/period.
The input is the period to use for accumulation. "D" is the default value, useful to view data for each session.
X-volume assessment numberSee source code for more details. Src1 = distribution and Src2 = accumulation.
SN Smoothed Balance of Power v2Hi all,
here is an updated version of the indicator script I published yesterday.
The goal of this indicator is to try and find darkpool activity. The indicator itself is not enough to fully identify darkpool but it should be able to detect quiet accumulation. What makes this Balance of Power different from others on TV is that it is smoothed by using a moving average.
Notes:
- The values that are default are completely arbitrary except for the VWMA length (a 14-day period for the 1D chart is the norm). For instance the limit where it shows red/green I picked because it works best for the 1D chart I am using. Other TF's and charts will need tweaking of all the values you find in the options menu to get the best results.
- I modified the indicator such that it is usable on charts that do not show volume. HOWEVER, this chart is default to NYMEX: CL1!. To get different volume data this needs to be changed in the option menu.
- I am in no way an expert on darkpool/HFT trading and am merely going from the information I found on the internet. Consider this an experiment.
Credits:
- Lazybear for some of the plotting-code
- Igor Livshin for the formula
- TahaBintahir for the Symbol-code (although I'm not sure who the original author is...)
Indicators: Volume Zone Indicator & Price Zone IndicatorVolume Zone Indicator (VZO) and Price Zone Indicator (PZO) are by Waleed Aly Khalil.
Volume Zone Indicator (VZO)
------------------------------------------------------------
VZO is a leading volume oscillator that evaluates volume in relation to the direction of the net price change on each bar.
A value of 40 or above shows bullish accumulation. Low values (< 40) are bearish. Near zero or between +/- 20, the market is either in consolidation or near a break out. When VZO is near +/- 60, an end to the bull/bear run should be expected soon. If that run has been opposite to the long term price trend direction, then a reversal often will occur.
Traditional way of looking at this also works:
* +/- 40 levels are overbought / oversold
* +/- 60 levels are extreme overbought / oversold
More info:
drive.google.com
Price Zone Indicator (PZO)
------------------------------------------------------------
PZO is interpreted the same way as VZO (same formula with "close" substituted for "volume").
Chart Markings
------------------------------------------------------------
In the chart above,
* The red circles indicate a run-end (or reversal) zones (VZO +/- 60).
* Blue rectangle shows the consolidation zone (VZO betwen +/- 20)
I have been trying out VZO only for a week now, but I think this has lot of potential. Give it a try, let me know what you think.
BK AK-SILENCER (P8N)🚨Introducing BK AK-SILENCER (P8N) — Institutional Order Flow Tracking for Silent Precision🚨
After months of meticulous tuning and refinement, I'm proud to unleash the next weapon in my trading arsenal—BK AK-SILENCER (P8N).
🔥 Why "AK-SILENCER"? The True Meaning
Institutions don’t announce their moves—they move silently, hidden beneath the noise. The SILENCER is built specifically to detect and track these stealth institutional maneuvers, giving you the power to hunt quietly, execute decisively, and strike precisely before the market catches on.
🔹 "AK" continues the legacy, honoring my mentor, A.K., whose teachings on discipline, precision, and clarity form the cornerstone of my trading.
🔹 "SILENCER" symbolizes the stealth aspect of institutional trading—quiet but deadly moves. This indicator equips you to silently track, expose, and capitalize on their hidden footprints.
🧠 What Exactly is BK AK-SILENCER (P8N)?
It's a next-generation Cumulative Volume Delta (CVD) tool crafted specifically for traders who hunt institutional order flow, combining adaptive volatility bands, enhanced momentum gradients, and precise divergence detection into a single deadly-accurate weapon.
Built for silent execution—tracking moves quietly and trading with lethal precision.
⚙️ Core Weapon Systems
✅ Institutional CVD Engine
→ Dynamically measures hidden volume shifts (buying/selling pressure) to reveal institutional footprints that price alone won't show.
✅ Adaptive AK-9 Bollinger Bands
→ Bollinger Bands placed around a custom CVD signal line, pinpointing exactly when institutional accumulation or distribution reaches critical extremes.
✅ Gradient Momentum Intelligence
→ Color-coded momentum gradients reveal the strength, speed, and silent intent behind institutional order flow:
🟢 Strong Bullish (aggressive buying)
🟡 Moderate Bullish (steady accumulation)
🔵 Neutral (balance)
🟠 Moderate Bearish (quiet distribution)
🔴 Strong Bearish (aggressive selling)
✅ Silent Divergence Detection
→ Instantly spots divergence between price and hidden volume—your earliest indication that institutions are stealthily reversing direction.
✅ Background Flash Alerts
→ Visually highlights institutional extremes through subtle background flashes, alerting you quietly yet powerfully when market-moving players make their silent moves.
✅ Structural & Institutional Clarity
→ Optional structural pivots, standard deviation bands, volume profile anchors, and session lines clearly identify the exact levels institutions defend or attack silently.
🛡️ Why BK AK-SILENCER (P8N) is Your Edge
🔹 Tracks Institutional Footprints—Silently identifies hidden volume signals of institutional intentions before they’re obvious.
🔹 Precision Execution—Cuts through noise, allowing you to execute silently, confidently, and precisely.
🔹 Perfect for Traders Using:
Elliott Wave
Gann Methods (Angles, Squares)
Fibonacci Time & Price
Harmonic Patterns
Market Profile & Order Flow Analysis
🎯 How to Use BK AK-SILENCER (P8N)
🔸 Institutional Reversal Hunting (Stealth Mode)
Bearish divergence + CVD breaking below lower BB → stealth short signal.
Bullish divergence + CVD breaking above upper BB → quiet, early long entry.
🔸 Momentum Confirmation (Silent Strength)
Strong bullish gradient + CVD above upper BB → follow institutional buying quietly.
Strong bearish gradient + CVD below lower BB → confidently short institutional selling.
🔸 Noise Filtering (Patience & Precision)
Neutral gradient (blue) → remain quiet, wait patiently to strike precisely when institutional activity resumes.
🔸 Structural Precision (Institutional Levels)
Optional StdDev, POC, Value Areas, Session Anchors clearly identify exact institutional defense/offense zones.
🙏 Final Thoughts
Institutions move in silence, leaving subtle footprints. BK AK-SILENCER (P8N) is your specialized weapon for tracking and hunting their quiet, decisive actions before the market reacts.
🔹 Dedicated in deep gratitude to my mentor, A.K.—whose silent wisdom shapes every line of code.
🔹 Engineered for the disciplined, quiet hunter who knows when to wait patiently and when to strike decisively.
Above all, honor and gratitude to Gd—the ultimate source of wisdom, clarity, and disciplined execution. Without Him, markets are chaos. With Him, we move silently, purposefully, and precisely.
⚡ Stay Quiet. Stay Precise. Hunt Silently.
🔥 BK AK-SILENCER (P8N) — Track the Silent Moves. Strike with Precision. 🔥
May Gd bless every silent step you take. 🙏
Buy Opportunity Score Table (21 Points)## 📊 Script Title:
**Buy Opportunity Score Table (21 Points)**
---
## 📝 Script Description:
This TradingView script is a **custom multi-indicator scoring system** designed to identify **buying opportunities** in stocks, indices, or ETFs by evaluating momentum, volume strength, and overall market sentiment. It gives a score out of **21 points** using 6 key indicators, and highlights **potential buy signals** when the score crosses **8 points or more**.
---
## ✅ Key Features:
### 📌 1. **Indicators Used (21 Points Max)**
Each of the following indicators contributes up to 4 points (except Nifty trend, which adds 1 point):
| Indicator | Max Points | Logic |
| ----------- | ---------- | ---------------------------------- |
| Volume | 4 | Volume > 20-day average and rising |
| OBV | 4 | OBV rising for 3+ days |
| MFI | 4 | MFI > 60 and increasing |
| RSI | 4 | RSI > 50 and increasing |
| ROC | 4 | Rate of change positive and rising |
| Nifty Trend | 1 | Nifty > yesterday and above 20 EMA |
---
### 📌 2. **Scoring Output**
* The **total score** is plotted on the chart and capped at 21.
* Score of **8 or higher** is treated as a **buy trigger**.
* Permanent horizontal lines are plotted at **8**, **13**, and **17** to show key levels:
* **8** → Entry zone
* **13** → Accumulation/Build zone
* **17** → Strong bullish momentum
---
### 📌 3. **Table Panel (Top Right Corner)**
A table shows:
* Current and previous day’s score for each indicator
* Total score and change from previous day
---
### 📌 4. **Visual Aids**
* Background turns **green** when score is **≥ 8**
* Permanent **horizontal lines** at score levels **8, 13, and 17** for easy reference
---
### 📌 5. **Alerts**
* An `alertcondition` is triggered **when score crosses 8 from below**
* You can use this for mobile/email alerts or automated strategies
---
## ⚙️ Inputs:
You can customize:
* Length of **ROC**, **MFI**, **RSI**, and **Volume SMA**
Default values are:
* ROC: 10
* MFI: 14
* RSI: 14
* Volume MA: 20
---
## 🎯 Use Case:
This tool is ideal for:
* **Swing traders** looking for early signals of accumulation
* **Investors** filtering stocks with rising internal strength
* **Algo traders** building signal-based strategies based on multi-factor models
Momentum TrendThis indicator suite aims to confirm strong uptrends or downtrends by combining ADX, MACD, ATR, and Accumulation/Distribution signals—green dots suggest confirmed upward momentum supported by accumulation at that moment, red dots indicate strong downward momentum, while separate A/D and ADX panels help visually assess trend strength and volume pressure for confirmation.
Volatility & Momentum Nexus (VMN)Volatility & Momentum Nexus (VMN)
This indicator was designed to solve a common trader's problem: chart clutter from dozens of indicators that often contradict each other. The Volatility & Momentum Nexus ( VMN ) is not just another indicator; it's a complete analysis system that synthesizes four essential market pillars into a single, clean, and intuitive visual signal.
The goal of VMN is to identify high-probability moments where a period of accumulation (low volatility) is about to erupt into an explosive move, confirmed by trend, momentum, and volume.
VMN analyzes the real-time confluence of four critical elements:
The Trend (The Main Filter): A 100-period Exponential Moving Average (EMA) sets the overall context. The indicator will only look for buy signals above this line (in an uptrend) and sell signals below it (in a downtrend). The line's color changes for quick visualization.
Volatility (Energy Accumulation): Using Bollinger Bands Width (BBW), the indicator identifies "Squeeze" periods—when the price contracts and builds up energy. These zones are marked with a yellow background on the chart, signaling that a major move is imminent.
Momentum (The Trigger): An RSI (Relative Strength Index) acts as the trigger. A signal is only validated if momentum confirms the direction of the breakout (e.g., RSI > 55 for a buy), ensuring we enter the market with force.
Volume (The Final Confirmation): No breakout move is credible without volume. VMN checks if the volume at the time of the signal is significantly higher than its recent average, adding a vital layer of confirmation.
Green Arrow (Buy Signal): Appears ONLY when ALL the following conditions are met simultaneously:
Price is above the 100 EMA (Bullish Trend).
The chart is exiting a Squeeze zone (yellow background on the previous bar).
Price breaks above the upper Bollinger Band.
RSI is above the buy threshold (default 55).
Volume is above average.
Red Arrow (Sell Signal): Appears ONLY when all the opposite conditions are met.
Do not treat signals as blind commands to trade. They are high-probability confirmations.
Look for signals near key Support/Resistance levels for an even higher success rate.
Always set a Stop Loss (e.g., below the low of the signal candle or below the lower Bollinger Band for a buy).
All parameters (EMA, RSI, Bollinger Bands lengths, thresholds, etc.) can be customized from the settings menu to adapt the indicator to any financial asset or timeframe.
Disclaimer: This indicator is a tool for educational and analytical purposes. It does not constitute and should not be interpreted as financial advice. Trading involves significant risk. Always perform your own analysis and backtesting before risking real capital.
Rainbow Price Chart This indicator is a technical and on-chain analysis tool for Bitcoin, designed to help investors better understand the different phases of the market cycle and underlying sentiment. It directly overlays on the price chart (overlay=true).
Indicator Name: "Rainbow Price Chart & V/T Ratio Signals"
General Purpose:
It combines two popular methodologies for visualizing Bitcoin's value and sentiment: the classic "Rainbow Price Chart" and signals derived from the "Value per Transaction Ratio" (V/T Ratio) based on blockchain data. It is ideal for long-term investors looking for strategic entry/exit points.
Main Components:
Rainbow Price Chart:
Concept: Divides Bitcoin's price range into different market "sentiment zones" (e.g., "Bubble Zone," "FOMO Zone," "HODL Zone," "Accumulation Zone," "Buy Zone," "Fire Sale Zone") using colored bands. These bands are calculated as ascending and descending multiples of a base Exponential Moving Average (EMA), configurable by default to 200 periods.
Visualization: The zones are represented with transparent color fills on the price chart. A detailed legend in the top right corner of the chart explains the meaning of each color and sentiment zone.
Important Note: This type of chart is designed to be viewed and analyzed correctly on a logarithmic price scale. The indicator includes a visual reminder to activate this scale.
Value per Transaction (V/T) Ratio Signals:
Concept: Measures the average value per transaction on the Bitcoin blockchain by dividing the total transacted volume in USD by the number of transactions. This ratio is smoothed with an Exponential Moving Average (by default, 7 periods) and is framed within a dynamic Linear Regression Channel (LRC) based on standard deviation.
Signal Generation: Based on the position of the smoothed V/T Ratio within this LRC channel, the indicator generates signals directly on the price chart, such as:
"BOTTOM": Low price, V/T Ratio in the lower band of the LRC.
"SEMI-LOW" / "SEMI-HIGH": Intermediate phases within the channel.
"ATH" (All-Time High): Potentially overvalued price, V/T Ratio in the upper band of the LRC.
On-Chain Data: The indicator requests external daily on-chain data for total transacted volume (TVTVR) and number of transactions (NTRAN) from the Bitcoin blockchain.
Diagnostic Panes: Includes plots of the raw on-chain data (volume and number of transactions) in a separate pane, which are useful for debugging or verifying the data source. The lines for the V/T Ratio itself and its LRC channel are not plotted by default but can be activated in the code for deeper analysis.
Ideal for:
Bitcoin investors and "hodlers" who desire a visual tool that combines price-based market cycle context with fundamental signals derived from on-chain activity, to help identify key moments for accumulation or potential distribution.
Considerations:
Relies on the availability of external on-chain data (QUANDL:BCHAIN) within TradingView.
Functions best on a daily timeframe.
Bullish Volume AnomalyAnomaly is designed to spot hidden bullish accumulation before price actually breaks out, by blending a trend-aware volume measure with a volatility-adjusted price channel. Here’s how it works:
First, it runs a simple ATR-based zigzag to identify the current swing direction. Volume is then signed (+ for up-trends, – for down-trends) and cumulatively summed. By converting that cumulative signed volume into a z-score over the past 480 bars, we get a sense of when buying or selling pressure is unusually strong relative to its own history.
At the same time, price itself is normalized into a z-score over the same 480-bar window, and its change over that period is also tracked. These two measures—volume z-score (s) and price z-score (p)—are compared, and the indicator looks for moments when s outpaces p by at least two standard deviations (s – p > 2), while price momentum change remains low (c < 1) and the net volume is positive (s > 0). That combination flags instances where heavy buying is taking place but price hasn’t yet reacted.
To define a dynamic trading zone, it plots a 288-bar EMA of price as the middle band (t2), and builds upper and lower bands around it using the average close-to-open range multiplied by a user-set factor. The lower band (t1) sits beneath the EMA by that volatility-based margin. A signal fires only when the bar’s high stays below t1—meaning price is still “sleeping” under the lower volatility boundary even as bullish volume builds up.
Together, these filters home in on anomalies: strong, trend-aligned volume surges that outstrip price movement, occurring while price sits below its lower volatility band. In practice, that often marks early accumulation before a breakout. You can tweak the ATR length and multiplier for the zigzag, as well as the channel period and range factor, to suit different markets or timeframes.
[blackcat] L1 Net Volume DifferenceOVERVIEW
The L1 Net Volume Difference indicator serves as an advanced analytical tool designed to provide traders with deep insights into market sentiment by examining the differential between buying and selling volumes over precise timeframes. By leveraging these volume dynamics, it helps identify trends and potential reversal points more accurately, thereby supporting well-informed decision-making processes. The key focus lies in dissecting intraday changes that reflect short-term market behavior, offering critical input for both swing and day traders alike. 📊
Key benefits encompass:
• Precise calculation of net volume differences grounded in real-time data.
• Interactive visualization elements enhancing interpretability effortlessly.
• Real-time generation of buy/sell signals driven by dynamic volume shifts.
TECHNICAL ANALYSIS COMPONENTS
📉 Volume Accumulation Mechanisms:
Monitors cumulative buy/sell volumes derived from comparative closing prices.
Periodically resets accumulation counters aligning with predefined intervals (e.g., 5-minute bars).
Facilitates identification of directional biases reflecting underlying market forces accurately.
🕵️♂️ Sentiment Detection Algorithms:
Employs proprietary logic distinguishing between bullish/bearish sentiments dynamically.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy.
Supports adaptive thresholds adjusting sensitivities based on changing market conditions flexibly.
🎯 Dynamic Signal Generation:
Detects transitions indicating dominance shifts between buyers/sellers promptly.
Triggers timely alerts enabling swift reactions to evolving market dynamics effectively.
Integrates conditional logic reinforcing signal validity minimizing erroneous activations.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Utilizes moving averages along with standardized deviation formulas generating precise net volume measurements.
Implements Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent alignment with established statistical principles preserving fidelity.
🖱️ User Interface Elements:
Dedicated plots displaying real-time net volume markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively.
Background shading highlighting proximity to key threshold activations enhancing visibility.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals.
Validate entry decisions considering concurrent market sentiment factors.
Assess alignment between net volume readings and broader trend directions ensuring coherence.
🚫 Exit Mechanisms:
Trigger exits upon hitting predetermined thresholds derived from historical analyses.
Monitor continuous breaches signifying potential trend reversals promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Reset Interval: Governs responsiveness versus stability balancing sensitivity/stability.
Price Source: Dictates primary data series driving volume calculations selecting relevant inputs accurately.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts.
Evaluate adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity.
Sustain balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines.
Mandatorily apply trailing stop-loss orders conforming to script outputs reinforcing discipline.
Allocate positions proportionately relative to available capital reserves managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically.
Prepare contingency plans mitigating margin call possibilities preparing proactive responses effectively.
Continuously assess automated system reliability amidst fluctuating conditions ensuring seamless functionality.
PERFORMANCE AUDITS & REFINEMENTS
🔍 Critical Evaluation Metrics:
Assess win percentages consistently across diverse trading instruments gauging reliability.
Calculate average profit ratios per successful execution measuring profitability efficiency accurately.
Measure peak drawdown durations alongside associated magnitudes evaluating downside risks comprehensively.
Analyze signal generation frequencies revealing hidden patterns potentially skewing outcomes uncovering systematic biases.
📈 Historical Data Analysis Tools:
Maintain comprehensive records capturing every triggered event meticulously documenting results.
Compare realized profits/losses against backtested simulations benchmarking actual vs expected performances accurately.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily.
Document evolving performance metrics tracking progress dynamically addressing identified shortcomings proactively.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities.
Overfitted models yielding suboptimal results post-extensive tuning demanding recalibrations.
Inaccuracies stemming from incomplete/inaccurate data feeds necessitating verification procedures.
💡 Effective Resolution Pathways:
Exclude low-liquidity assets prone to erratic movements enhancing signal integrity.
Introduce buffer intervals safeguarding major news/event impacts mitigating distortions effectively.
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations reliably.
USER ENGAGEMENT SEGMENT
🤝 Community Contributions Welcome
Highly encourage active participation sharing experiences & recommendations!
THANKS
Heartfelt acknowledgment extends to all developers contributing invaluable insights about volume-based trading methodologies! ✨
