Option Pair ZigzagOptions Pair Zigzag:
Though we can split the chart window and view multiple charts, this indicator is useful when we view options charts.
How this indicator works:
The indicator works in non-overlay mode.
The indicator will find other option pair symbol and load it’s chart in indicator window. It will also draw a zigzag on both the charts. It will also fetch the SPOT symbol and display SPOT Close price of latest candle.
Useful information:
A. Support resistance: Higher High (HH) and Lower Low (LL) markings can be treated as strong support and or resistance and LH, HL markings can be treated as weak support and or resistance.
B. Trend identification: Easy identification of trend based on trend lines and trend markings i.e. Higher High (HH), Lower Low (LL), Lower High (LH), Higher Low (HL)
C. Use of Rate of change (ROC )– Labels drawn on swing points are equipped with ROC% between swing points. ROC% between Call and Put option charts can be compared and used to identify strong and weak moves.
Example:
1. User loads a call option chart of ‘NIFTY240620C23500’ (NIFTY 50 INDEX OPTIONS 20 JUN 2024 CALL 23500)
2. Since user has selected CALL Option, Indicator rules/logic will find PUT Option symbol of same strike and expiry
3. PUT Option chart would then shown in the indicator window
4. Draw zigzag on both the charts
5. Plot labels on both the charts
6. Labels are equipped with a tooltip showing rate of change between 2 pivot points
Input Parameters:
Left bars – Parameter required for plotting zigzag
Right bars – Parameter required for plotting zigzag
Plot HHLL Labels – Enable/disable plotting of labels
Use cases:
Refer to chart snapshots:
1. Buy Call Option or Sell Put Option - How one can trade on formation of a consolidation range
2. Breakdown of Swing structure - One can observe Swing structure (Zigzag) formed on a SPOT chart and trade on break of swing structure
3. Triangle formation - Observe the patterns formed on the SPOT chart and trade either Call or Put options. Example snapshot shows trade based on triangle pattern
Chart Snapshot:
One can split chart window and load base symbol chart which will help to review bases symbol and options chart at the same time.
Buy Call Option or Sell Put Option
Breakdown of Swing structure
Triangle formation
ابحث في النصوص البرمجية عن "如何用wind搜索股票的发行价和份数"
Persistent Homology Based Trend Strength OscillatorPersistent Homology Based Trend Strength Oscillator
The Persistent Homology Based Trend Strength Oscillator is a unique and powerful tool designed to measure the persistence of market trends over a specified rolling window. By applying the principles of persistent homology, this indicator provides traders with valuable insights into the strength and stability of uptrends and downtrends, helping to inform better trading decisions.
What Makes This Indicator Original?
This indicator's originality lies in its application of persistent homology , a method from topological data analysis, to financial markets. Persistent homology examines the shape and features of data across multiple scales, identifying patterns that persist as the scale changes. By adapting this concept, the oscillator tracks the persistence of uptrends and downtrends in price data, offering a novel approach to trend analysis.
Concepts Underlying the Calculations:
Persistent Homology: This method identifies features such as clusters, holes, and voids that persist as the scale changes. In the context of this indicator, it tracks the duration and stability of price trends.
Rolling Window Analysis: The oscillator uses a specified window size to calculate the average length of uptrends and downtrends, providing a dynamic view of trend persistence over time.
Threshold-Based Trend Identification: It differentiates between uptrends and downtrends based on specified thresholds for price changes, ensuring precision in trend detection.
How It Works:
The oscillator monitors consecutive changes in closing prices to identify uptrends and downtrends.
An uptrend is detected when the closing price increase exceeds a specified positive threshold.
A downtrend is detected when the closing price decrease exceeds a specified negative threshold.
The lengths of these trends are recorded and averaged over the chosen window size.
The Trend Persistence Index is calculated as the difference between the average uptrend length and the average downtrend length, providing a measure of trend persistence.
How Traders Can Use It:
Identify Trend Strength: The Trend Persistence Index offers a clear measure of the strength and stability of uptrends and downtrends. A higher value indicates stronger and more persistent uptrends, while a lower value suggests stronger and more persistent downtrends.
Spot Trend Reversals: Significant shifts in the Trend Persistence Index can signal potential trend reversals. For instance, a transition from positive to negative values might indicate a shift from an uptrend to a downtrend.
Confirm Trends: Use the Trend Persistence Index alongside other technical indicators to confirm the strength and duration of trends, enhancing the accuracy of your trading signals.
Manage Risk: Understanding trend persistence can help traders manage risk by identifying periods of high trend stability versus periods of potential volatility. This can be crucial for timing entries and exits.
Example Usage:
Default Settings: Start with the default settings to get a feel for the oscillator’s behavior. Observe how the Trend Persistence Index reacts to different market conditions.
Adjust Thresholds: Fine-tune the positive and negative thresholds based on the asset's volatility to improve trend detection accuracy.
Combine with Other Indicators: Use the Persistent Homology Based Trend Strength Oscillator in conjunction with other technical indicators such as moving averages, RSI, or MACD for a comprehensive analysis.
Backtesting: Conduct backtesting to see how the oscillator would have performed in past market conditions, helping you to refine your trading strategy.
Relative Strength Scatter Plot [LuxAlgo]The Relative Strength Scatter Plot indicator is a tool that shows the historical performance of various user-selected securities against a selected benchmark.
This tool is inspired by Relative Rotation Graphs®. Relative Rotation Graphs® is a registered trademark of JOOS Holdings B.V. This script is neither endorsed, nor sponsored, nor affiliated with them.
🔶 USAGE
This tool depicts a simple scatter plot using the relative strength ratio as the X-axis and its momentum as the Y-axis of the user-selected symbols against the selected benchmark.
The graph is divided into four quadrants, and the interpretation of the graph is done depending on where a point is situated on the graph:
A point in the green quadrant would indicate that the security is leading the benchmark in strength, with positive strength momentum.
A point in the yellow quadrant would indicate that the security is leading the benchmark in strength, with negative strength momentum.
A point in the blue quadrant would indicate that the security is lagging behind the benchmark in strength, with positive strength momentum.
A point in the red quadrant would indicate that the security is lagging behind the benchmark in strength, with negative strength momentum.
The trail of each symbol allows the user to see the evolution of the relative strength momentum relative to the relative strength ratio. The length of the trail can be controlled by the "Trail Length" setting.
🔶 DETAILS
Our relative strength ratio estimate is first obtained from the relative strength between the symbol of interest and the benchmark, the result is then smoothed using a linearly weighted moving average (wma). This result is then normalized with a wma of the smoothed relative strength, this ratio is again smoothed with the wma and multiplied by 100.
The relative strength momentum estimate is obtained from the ratio between the previously estimated RS-Ratio and its wma, this ratio is then multiplied by 100.
🔶 SETTINGS
Calculation Window: Calculation window of the RS-Ratio and RS-Momentum metrics.
Symbols: Symbols used for the computation of the graph, each settings line allows us to determine whether the symbol is to be displayed on the graph as well as its color.
Benchmark: Benchmark symbol used for the computation of the graph. Indices are commonly used as a benchmark.
🔹 Graph Settings
Trail Length: Number of past data points to display on the graph for each symbol.
Resolution: Controls the horizontal length of the graph.
THISMA btccorrelationDescription:
This is a tool designed for traders who want to analyze correlation between any traded crypto's price in USD and the price of Bitcoin in USD.
Key Features:
Adjustable Correlation Window: The script features an input parameter that allows traders to set the length of the correlation window, with a default value of 14. Lower if you want faster granularity.
Clear Visualization: The correlation coefficient is plotted in a distinct pane below the main trading chart.
Reference Lines for Interpretation: Horizontal reference lines are included at 0.5 (indicating weak positive correlation), -0.5 (indicating weak negative correlation), and 0 (indicating no correlation). These lines, color-coded in green, red, and gray respectively, assist traders in quickly interpreting the correlation coefficient's value.
Applications:
Market Insight: If you want to be able to monitor if you should enter a trade on an altcoin or if its better to stick to Bitcoin to avoid being double exposed.
Risk Management: Identifying the correlation can help in assessing and managing the systemic risk associated with market movements, especially in cryptocurrency markets where Bitcoin's influence is significant.
Machine Learning: Trend Pulse⚠️❗ Important Limitations: Due to the way this script is designed, it operates specifically under certain conditions:
Stocks & Forex : Only compatible with timeframes of 8 hours and above ⏰
Crypto : Only works with timeframes starting from 4 hours and higher ⏰
❗Please note that the script will not work on lower timeframes.❗
Feature Extraction : It begins by identifying a window of past price changes. Think of this as capturing the "mood" of the market over a certain period.
Distance Calculation : For each historical data point, it computes a distance to the current window. This distance measures how similar past and present market conditions are. The smaller the distance, the more similar they are.
Neighbor Selection : From these, it selects 'k' closest neighbors. The variable 'k' is a user-defined parameter indicating how many of the closest historical points to consider.
Price Estimation : It then takes the average price of these 'k' neighbors to generate a forecast for the next stock price.
Z-Score Scaling: Lastly, this forecast is normalized using the Z-score to make it more robust and comparable over time.
Inputs:
histCap (Historical Cap) : histCap limits the number of past bars the script will consider. Think of it as setting the "memory" of model—how far back in time it should look.
sampleSpeed (Sampling Rate) : sampleSpeed is like a time-saving shortcut, allowing the script to skip bars and only sample data points at certain intervals. This makes the process faster but could potentially miss some nuances in the data.
winSpan (Window Size) : This is the size of the "snapshot" of market data the script will look at each time. The window size sets how many bars the algorithm will include when it's measuring how "similar" the current market conditions are to past conditions.
All these variables help to simplify and streamline the k-NN model, making it workable within limitations. You could see them as tuning knobs, letting you balance between computational efficiency and predictive accuracy.
Kernel Regression ToolkitThis toolkit provides filters and extra functionality for non-repainting Nadaraya-Watson estimator implementations made by @jdehorty. For the sake of ease I have nicknamed it "kreg". Filters include a smoothing formula and zero lag formula. The purpose of this script is to help traders test, experiment and develop different regression lines. Regression lines are best used as trend lines and can be an invaluable asset for quickly locating first pullbacks and breaks of trends.
Other features include two J lines and a blend line. J lines are featured in tools like Stochastic KDJ. The formula uses the distance between K and D lines to make the J line. The blend line adds the ability to blend two lines together. This can be useful for several tasks including finding a center/median line between two lines or for blending in the characteristics of a different line. Default is set to 50 which is a 50% blend of the two lines. This can be increased and decreased to taste. This tool can be overlaid on the chart or on top of another indicator if you set the source. It can even be moved into its own window to create a unique oscillator based on whatever sources you feed it.
Below are the standard settings for the kernel estimation as documented by @jdehorty:
Lookback Window: The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50
Weighting: Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25
Level: Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25
Lag: Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2
For more information on this technique refer to to the original open source indicator by @jdehorty located here:
4H RangeThis script visualizes certain key values based on a 4-hour timeframe of the selected market on the chart. These values include the High, Mid, and Low price levels during each 4-hour period.
These levels can be helpful to identify inside range price action, chop, and consolidation. They can sometimes act as pivots and can be a great reference for potential entries and exits if price continues to hold the same range.
Here's a step-by-step overview of what this indicator does:
1. Inputs: At the beginning of the script, users are allowed to customize some inputs:
Choose the color of lines and labels.
Decide whether to show labels on the chart.
Choose the size of labels ("tiny", "small", "normal", or "large").
Choose whether to display price values in labels.
Set the number of bars to offset the labels to the right.
Set a threshold for the number of ticks that triggers a new calculation of high, mid, and low values.
* Tick settings may need to be increased on equity charts as one tick is usually equal to one cent.
For example, if you want to clear the range when there is a close one point/one dollar above or below the range high/low then on ES
that would be 4 ticks but one whole point on AAPL would be 100 ticks. 100 ticks on an equity chart may or may not be ideal due to
different % change of 100 ticks might be too excessive depending on the price per share.
So be aware that user preferred thresholds can vary greatly depending on which chart you're using.
2. Retrieving Price Data: The script retrieves the high, low, and closing price for every 4-hour period for the current market.
The script also calculates the mid-price of each 4-hour period (the average of the high and low prices).
3. Line Drawing: At the start of the script (first run), it draws three lines (high, mid, and low) at the levels corresponding to the high,
mid, and low prices. Users can also change transparency settings on historical lines to view them. Default setting for historical lines
is for them to be hidden.
4. Updating Lines and Labels: For each subsequent 4-hour period, the script checks whether the close price of the period has gone
beyond a certain threshold (set by user input) above the previous high or below the previous low. If it has, the script deletes the
previous lines and labels, draws new lines at the new high, mid, and low levels, and creates new labels (if the user has opted to
show labels).
5. Displaying Values in the Data Window: In addition to the visual representation on the chart, the script also plots the high, mid, and
low prices. These plotted values appear in the Data Window of TradingView, allowing users to see the exact price levels even when
they're not directly labeled on the chart.
6. Updating Lines and Labels Position: At the end of each period, the script moves the lines and labels (if they're shown) to the right,
keeping them aligned with the current period.
Please note: This script operates based on a 4-hour timeframe, regardless of the timeframe selected on the chart. If a shorter timeframe is selected on the chart, the lines and labels will appear to extend across multiple bars because they represent 4-hour price levels. If a longer timeframe is selected, the lines and labels may not accurately represent high, mid, and low levels within that longer timeframe.
vol_boxA simple script to draw a realized volatility forecast, in the form of a box. The script calculates realized volatility using the EWMA method, using a number of periods of your choosing. Using the "periods per year", you can adjust the script to work on any time frame. For example, if you are using an hourly chart with bitcoin, there are 24 periods * 365 = 8760 periods per year. This setting is essential for the realized volatility figure to be accurate as an annualized figure, like VIX.
By default, the settings are set to mimic CBOE volatility indices. That is, 252 days per year, and 20 period window on the daily timeframe (simulating a 30 trading day period).
Inside the box are three figures:
1. The current realized volatility.
2. The rank. E.g. "10%" means the current realized volatility is less than 90% of realized volatility measures.
3. The "accuracy": how often price has closed within the box, historically.
Inputs:
stdevs: the number of standard deviations for the box
periods to project: the number of periods to forecast
window: the number of periods for calculating realized volatility
periods per year: the number of periods in one year (e.g. 252 for the "D" timeframe)
Relative slopeRelative slope metric
Description:
I was in need to create a simple, naive and elegant metric that was able to tell how strong is the trend in a given rolling window. While abstaining from using more complicated and arguably more precise approaches, I’ve decided to use Linearly Weighted Linear Regression slope for this goal. Outright values are useful, but the problem was that I wasn’t able to use it in comparative analysis, i.e between different assets & different resolutions & different window sizes, because obviously the outputs are scale-variant.
Here is the asset-agnostic, resolution-agnostic and window size agnostic version of the metric.
I made it asset agnostic & resolution agnostic by including spread information to the formula. In our case it's weighted stdev over differenced data (otherwise we contaminate the spread with the trend info). And I made it window size agnostic by adding a non-linear relation of length to the output, so finally it will be aprox in (-1, 1) interval, by taking square root of length, nothing fancy. All these / 2 and * 2 in unexpected places all around the formula help us to return the data to it’s natural scale while keeping the transformations in place.
Peace TV
Levels Of Fear [AstrideUnicorn]"Buy at the level of maximum fear when everyone is selling." - says a well-known among traders wisdom. If an asset's price declines significantly from the most recent highest value or established range, traders start to worry. The higher the drawdown gets, the more fear market participants experience. During a sell-off, a feedback loop arises, in which the escalating fear and price decline strengthen each other.
The Levels Of Fear indicator helps analyze price declines and find the best times to buy an asset after a sell-off. In finance, volatility is a term that describes the degree of variation of an asset price over time. It is usually denoted by the letter σ (sigma) and estimated as the standard deviation of the asset price or price returns. The Levels Of Fear indicator helps measure the current price decline in the standard deviation units. It plots seven levels at distances of 1, 2, 3, 4, 5, 6, and 7 standard deviations (sigmas) below the base price (the recent highest price or upper bound of the established range). In what follows, we will refer to these levels as levels of fear.
HOW TO USE
When the price in its decline reaches a certain level of fear, it means that it has declined from its recent highest value by a corresponding number of standard deviations. The indicator helps traders see the minimum levels to which the price may fall and estimate the potential depth of the current decline based on the cause of the actual market shock. Five-seven sigma declines are relatively rare events and correspond to significant market shocks. In the lack of information, 5-7 sigma levels are good for buying an asset. Because when the price falls that deep, it corresponds to the maximum fear and pessimism in the market when most people are selling. In such situations, contrarian logic becomes the best decision.
SETTINGS
Window: the averaging window or period of the indicator. The algorithm uses this parameter to calculate the base level and standard deviations. Higher values are better for measuring deeper and longer declines.
Levels Stability: the parameter used in the decline detection. The higher the value is, the more stable and long the fear levels are, but at the same time, the lag increases. The lower it is, the faster the indicator responds to the price changes, but the fear levels are recalculated more frequently and are less stable. This parameter is mostly for fine-tuning. It does not change the overall picture much.
Mode: the parameter that defines the style for the labels. In the Cool Guys Mode , the indicator displays the labels as emojis. In the Serious Guys Mode , labels show the distance from the base level measured in standard deviation units or sigmas.
Liquidity Levels [LuxAlgo]The Peak Activity Levels indicator displays support and resistance levels from prices accompanied by significant volume. The indicator includes a histogram returning the frequency of closing prices falling between two parallel levels, each bin shows the number of bullish candles within the levels.
1. Settings
Length: Lookback for the detection of volume peaks.
Number Of Levels: Determines the number of levels to display.
Levels Color Mode: Determines how the levels should be colored. "Relative" will color the levels based on their location relative to the current price. "Random" will apply a random color to each level. "Fixed" will use a single color for each level.
Levels Style: Style of the displayed levels. Styles include solid, dashed, and dotted.
1.1 Histogram
Show Histogram: Determines whether to display the histogram or not.
Histogram Window: Lookback period of the histogram calculation.
Bins Colors: Control the color of the histogram bins.
2. Usage
The indicator can be used to display ready-to-use support and resistance. These are constructed from peaks in volume. When a peak occurs, we take the price where this peak occurred and use it as the value for our level.
If one of the levels was previously tested, we can hypothesize that the level might be used as support/resistance in the future. Additional analysis using volume can be done in order to confirm a potential bounce.
The histogram can return various information to the user. It can show if the price stayed within two levels for a long time and if the price within two levels was mostly made of bullish or bearish candles.
In the chart above, we can see that over the most recent 200 bars (determined by Histogram Window) 68 closing prices fall between levels A and B, with 27 bars being bullish.
Additionally, the width of a bin and its length can sometimes give information about the volatility of a specific price variation. If a bin is very wide but short (a low number of closing prices fallen within the levels) then we can conclude a most of the movement was done on a short amount of time.
vol_signalNote: This description is copied from the script comments. Please refer to the comments and release notes for updated information, as I am unable to edit and update this description.
----------
USAGE
This script gives signals based on a volatility forecast, e.g. for a stop
loss. It is a simplified version of my other script "trend_vol_forecast", which incorporates a trend following system and measures performance. The "X" labels indicate when the price touches (exceeds) a forecast. The signal occurs when price crosses "fcst_up" or "fcst_down".
There are only three parameters:
- volatility window: this is the number of periods (bars) used in the
historical volatility calculation. smaller number = reacts more
quickly to changes, but is a "noisier" signal.
- forecast periods: the number of periods for projecting a volatility
forecast. for example, "21" on a daily chart means the plots will
show the forecast from 21 days ago.
- forecast stdev: the number of standard deviations in the forecast.
for example, "2" means that price is expected to remain within
the forecast plot ~95% of the time. A higher number produces a
wider forecast.
The output table shows:
- realized vol: the volatility over the previous N periods, where N =
"volatility window".
- forecast vol: the realized volatility from N periods ago, where N =
"forecast periods"
- up/down fcst (level): the price level of the forecast for the next
N bars, where N = "forecast periods".
- up/down fcst (%): the difference between the current and forecast
price, expressed as a whole number percentage.
The plots show:
- blue/red plot: the upper/lower forecast from "forecast periods" ago.
- blue/red line: the upper/lower forecast for the next
"forecast periods".
- red/blue labels: an "X" where the price touched the forecast from
"forecast periods" ago.
+ NOTE: pinescript only draws a limited number of labels.
They will not appear very far into the past.
ATR based Pivots mcbwHey everyone this is an exciting new script I have prepared for you.
I was reading an old forex bulletin article some time ago when I came across this: solar.murty.net (or you can download the full bulletin with lots of other good articles here: www.forexfactory.com).
You can already buy this for metatrader (www.mql5.com) so I figured to make it for free for tradingview.
This bulletin suggested that you can reasonably predict daily volatility by adding or subtracting multiples of the daily ATR to the daily opening. Using this you can choose multiples to use as price targets and alternatively as stop losses. For example, if you already have a sense of market direction you can buy at market open place a stop loss at - 1 daily ATR and a profit target at + 3 ATRs for a risk to reward ratio of 3. If you are looking for smaller/quicker moves with a ratio of 3 you can have a stop loss at -0.25 ATR and a take profit at +0.75 ATR.
Alternatively this article also suggests to use this method to catch volatility breakouts. If price is higher than the + 1 ATR area then you can safely assume it will be going to the +2 ATR area so you can put a buy stop at + 1 ATR with a profit target at + 2 ATR with a stop loss at +0.5 ATR to catch a volatility breakout with a risk to reward ratio of 2!
Even further there are methods that you can use with ATRs of multiple window sizes, for example by opening two copies of this indicator and measuring recent volatility with a 1 week window and long term volatility within a 1 month window. If the short term volatility is crossing the long term volatility then there is a high probability chance that even more price movement will occur.
However I have found that this method is good for more than daily volatility , it can also be used to measure weekly volatility , and monthly volatility and use these multiples as good long term price targets.
To select if you want daily, weekly, or monthly values of the ATR of volatility you're using go to the settings and click on the options in the "Opening period". The default window of the ATR here is 14 periods, but you can change this if you want to in "ATR period". Most importantly you are able to select which multiples of the ATR you would like to use in the settings in "ATR multiple 1" which is the green line, "ATR multiple 2" which is the blue line, and "ATR multiple 3" which is the purple line. You can select any values you want to put in these, the choice of 0.25, 0.5, and 1 is not special, some people use fibonacci numbers here or simply 0.33, 0.66, and 0.99.
Repainting issue: This script uses the daily value of the Average True Range (ATR), which measures the volatility that is happening today. If price becomes more volatile then the value of the ATR can increase throughout the day, but it can never decrease. What this means is that the ATR based pivots are able to expand away from the opening price, which should not affect the trades that you take based on these areas. If you base your take profit on one of these ATR multiples and the daily volatility increase this means that your take profit area will be closer to your entry than the ATR multiple. Meaning that your trades will be more conservative.
While this all may sound very technical it is super intuitive, throw this on your chart and play around with it :)
Happy trading!
Hazel nut BB Strategy, volume base- lite versionHazel nut BB Strategy, volume base — lite version
Having knowledge and information in financial markets is only useful when a trader operates with a well-defined trading strategy. Trading strategies assist in capital management, profit-taking, and reducing potential losses.
This strategy is built upon the core principle of supply and demand dynamics. Alongside this foundation, one of the widely used technical tools — the Bollinger Bands — is employed to structure a framework for profit management and risk control.
In this strategy, the interaction of these tools is explained in detail. A key point to note is that for calculating buy and sell volumes, a lower timeframe function is used. When applied with a tick-level resolution, this provides the most precise measurement of buyer/seller flows. However, this comes with a limitation of reduced historical depth. Users should be aware of this trade-off: if precise tick-level data is required, shorter timeframes should be considered to extend historical coverage .
The strategy offers multiple configuration options. Nevertheless, it should be treated strictly as a supportive tool rather than a standalone trading system. Decisions must integrate personal analysis and other instruments. For example, in highly volatile assets with narrow ranges, it is recommended to adjust profit-taking and stop-loss percentages to smaller values.
◉ Volume Settings
• Buyer and seller volume (up/down volume) are requested from a lower timeframe, with an option to override the automatic resolution.
• A global lookback period is applied to calculate moving averages and cumulative sums of buy/sell/delta volumes.
• Ratios of buyers/sellers to total volume are derived both on the current bar and across the lookback window.
◉ Bollinger Band
• Bands are computed using configurable moving averages (SMA, EMA, RMA, WMA, VWMA).
• Inputs allow control of length, standard deviation multiplier, and offset.
• The basis, upper, and lower bands are plotted, with a shaded background between them.
◉ Progress & Proximity
• Relative position of the price to the Bollinger basis is expressed as percentages (qPlus/qMinus).
• “Near band” conditions are triggered when price progress toward the upper or lower band exceeds a user-defined threshold (%).
• A signed score (sScore) represents how far the close has moved above or below the basis relative to band width.
◉ Info Table
• Optional compact table summarizing:
• - Upper/lower band margins
• - Buyer/seller volumes with moving averages
• - Delta and cumulative delta
• - Buyer/seller ratios per bar and across the window
• - Money flow values (buy/sell/delta × price) for bar-level and summed periods
• The table is neutral-colored and resizable for different chart layouts.
◉ Zone Event Gate
• Tracks entry into and exit from “near band” zones.
• Arming logic: a side is armed when price enters a band proximity zone.
• Trigger logic: on exit, a trade event is generated if cumulative buyer or seller volume dominates over a configurable window.
◉ Trading Logic
• Orders are placed only on zone-exit events, conditional on volume dominance.
• Position sizing is defined as a fixed percentage of strategy equity.
• Long entries occur when leaving the lower zone with buyer dominance; short entries occur when leaving the upper zone with seller dominance.
◉ Exit Rules
• Open positions are managed by a strict priority sequence:
• 1. Stop-loss (% of entry price)
• 2. Take-profit (% of entry price)
• 3. Opposite-side event (zone exit with dominance in the other direction)
• Stop-loss and take-profit levels are configurable
◉ Notes
• This lite version is intended to demonstrate the interaction of Bollinger Bands and volume-based dominance logic.
• It provides a framework to observe how price reacts at band boundaries under varying buy/sell pressure, and how zone exits can be systematically converted into entry/exit signals.
When configuring this strategy, it is essential to carefully review the settings within the Strategy Tester. Ensure that the chosen parameters and historical data options are correctly aligned with the intended use. Accurate back testing depends on applying proper configurations for historical reference. The figure below illustrates sample result and configuration type.
QZ Trend (Crypto Edition) v1.1a: Donchian, EMA, ATR, Liquidity/FThe "QZ Trend (Crypto Edition)" is a rules-based trend-following breakout strategy for crypto spot or perpetual contracts, focusing on following trends, prioritizing risk control, seeking small losses and big wins, and trading only when advantageous.
Key mechanisms include:
- Market filters: Screen favorable conditions via ADX (trend strength), dollar volume (liquidity), funding fee windows, session/weekend restrictions, and spot-long-only settings.
- Signals & entries: Based on price position relative to EMA and EMA trends, combined with breaking Donchian channel extremes (with ATR ratio confirmation), plus single-position rules and post-exit cooldowns.
- Position sizing: Calculate positions by fixed risk percentage; initial stop-loss is ATR-based, complying with exchange min/max lot requirements.
- Exits & risk management: Include initial stop-loss, trailing stop (tightens only), break-even rule (stop moves to entry when target floating profit is hit), time-based exit, and post-exit cooldowns.
- Pyramiding: Add positions only when profitable with favorable momentum, requiring ATR-based spacing; add size is a fraction of the base position, with layers sharing stop logic but having unique order IDs.
Charts display EMA, Donchian channels, current stop lines, and highlight low ADX, avoidable funding windows, and low-liquidity periods.
Recommend starting with 4H or 1D timeframes, with typical parameters varying by cycle. Liquidity settings differ by token; perpetuals should enable funding window filters, while spot requires "long-only" and matching fees. The strategy performs well in trends with quick stop-losses but faces whipsaws in ranges (filters mitigate but don’t eliminate noise). Share your symbol and timeframe for tailored parameters.
Technical Summary VWAP | RSI | VolatilityTechnical Summary VWAP | RSI | Volatility
The Quantum Trading Matrix is a multi-dimensional market-analysis dashboard designed as an educational and idea-generation tool to help traders read price structure, participation, momentum and volatility in one compact view. It is not an automated execution system; rather, it aggregates lightweight “quantum” signals — VWAP position, momentum oscillator behaviour, multi-EMA trend scoring, volume flow and institutional activity heuristics, market microstructure pivots and volatility measures — and synthesizes them into a single, transparent score and signal recommendation. The primary goal is to make explicit why a given market looks favourable or unfavourable by showing the individual ingredients and how they combine, enabling traders to learn, test and form rules based on observable market mechanics.
Each module of the matrix answers a distinct market question. VWAP and its percentage distance indicate whether the current price is trading above or below the intraday volume-weighted average — a proxy for intraday institutional control and value. The quantum momentum oscillator (fast and slow EMA difference scaled to percent) captures short-to-intermediate momentum shifts, providing a quickly responsive view of directional pressure. Multi-EMA trend scoring (8/21/50) produces a simple, transparent trend score by counting conditions such as price above EMAs and cross-EMAs ordering; this score is used to categorize market trend into descriptive buckets (e.g., STRONG UP, WEAK UP, NEUTRAL, DOWN). Volume analysis compares current volume to a recent moving average and computes a Z-score to detect spikes and unusual participation; additional buy/sell pressure heuristics (buyingPressure, sellingPressure, flowRatio) estimate whether upside or downside participation dominates the bar. Institutional activity is approximated by flagging large orders relative to volume baseline (e.g., volume > 2.5× MA) and estimating a dark pool proxy; this is a heuristic to highlight bars that likely had large players involved.
The dashboard also performs market-structure detection with small pivot windows to identify recent local support/resistance areas and computes price position relative to the daily high/low (dailyMid, pricePosition). Volatility is measured via ATR divided by price and bucketed into LOW/NORMAL/HIGH/EXTREME categories to help you adapt stop sizing and expectational horizons. Finally, all these pieces feed an interpretable scoring function that rewards alignment: VWAP above, strong flow ratio, bullish trend score, bullish momentum, and favorable RSI zone add to the overall score which is presented as a 0–100 metric and a colored emoji indicator for at-a-glance assessment.
The mashup is purposeful: each indicator covers a failure mode of the other. For example, momentum readings can be misleading during volatility spikes; VWAP informs whether institutions are on the bid or offer; volume Z-score detects abnormal participation that can validate a breakout; multi-EMA score mitigates single-EMA whipsaws by requiring a combination of price/EMA conditions. Combining these signals increases information content while keeping each component explainable — a key compliance requirement. The script intentionally emphasizes transparency: when it shows a BUY/SELL/HOLD recommendation, the dashboard shows the underlying sub-components so a trader can see whether VWAP, momentum, volume, trend or structure primarily drove the score.
For practical use, adopt a clear workflow: (1) check the matrix score and read the component tiles (VWAP position, momentum, trend and volume) to understand the drivers; (2) confirm market-structure support/resistance and pricePosition relative to the daily range; (3) require at least two corroborating components (for example, VWAP ABOVE + Momentum BULLISH or Volume spike + Trend STRONG UP) before considering entries; (4) use ATR-based stops or daily pivot distance for stop placement and size positions such that the trade risks a small, pre-defined percent of capital; (5) for intraday scalps shorten holding time and tighten stops, for swing trades increase lookback lengths and require multi-timeframe (higher TF) agreement. Treat the matrix as an idea filter and replay lab: when an alert triggers, replay the bars and observe which components anticipated the move and which lagged.
Parameter tuning matters. Shortening the momentum length makes the oscillator more sensitive (useful for scalping), while lengthening it reduces noise for swing contexts. Volume profile bars and MA length should match the instrument’s liquidity — increase the MA for low-liquidity stocks to reduce false institutional flags. The trend multiplier and signal sensitivity parameters let you calibrate how aggressively the matrix counts micro evidence into the score. Always backtest parameter sets across multiple periods and instruments; run walk-forward tests and keep a simple out-of-sample validation window to reduce overfitting risk.
Limitations and failure modes are explicit: institutional flags and dark-pool estimates are heuristics and cannot substitute for true tape or broker-level order flow; volume split by price range is an approximation and will not perfectly reflect signed volume; pivot detection with small windows may miss larger structural swings; VWAP is typically intraday-centric and less meaningful across multi-day swing contexts; the score is additive and may not capture non-linear relationships between features in extreme market regimes (e.g., flash crashes, circuit breaker events, or overnight gaps). The matrix is also susceptible to false signals during major news releases when price and volume behavior dislocate from typical patterns. Users should explicitly test behavior around earnings, macro data and low-liquidity periods.
To learn with the matrix, perform these experiments: (A) collect all BUY/SELL alerts over a 6-month period and measure median outcome at 5, 20 and 60 bars; (B) require additional gating conditions (e.g., only accept BUY when flowRatio>60 and trendScore≥4) and compare expectancy; (C) vary the institutional threshold (2×, 2.5×, 3× volumeMA) to see how many true positive spikes remain; (D) perform multi-instrument tests to ensure parameters are not tuned to a single ticker. Document every test and prefer robust, slightly lower returns with clearer logic rather than tuned “optimal” results that fail out of sample.
Originality statement: This script’s originality lies in the curated combination of intraday value (VWAP), multi-EMA trend scoring, momentum percent oscillator, volume Z-score plus buy/sell flow heuristics and a compact, interpretable scoring system. The script is not a simple indicator mashup; it is a didactic ensemble specifically designed to make internal rationale visible so traders can learn how each market characteristic contributes to actionable probability. The tool’s novelty is its emphasis on interpretability — showing the exact contributing signals behind a composite score — enabling reproducible testing and educational value.
Finally, for TradingView publication, include a clear description listing the modules, a short non-technical summary of how they interact, the tunable inputs, limitations and a risk disclaimer. Remove any promotional content or external contact links. If you used trademark symbols, either provide registration details or remove them. This transparent documentation satisfies TradingView’s requirement that mashups justify their composition and teach users how to use them.
Quantum Trading Matrix — multi-factor intraday dashboard (educational use only).
Purpose: Combines intraday VWAP position, a fast/slow EMA momentum percent oscillator, multi-EMA trend scoring (8/21/50), volume Z-score and buy/sell flow heuristics, pivot-based microstructure detection, and ATR-based volatility buckets to produce a transparent, componentized market score and trade-idea indicator. The mashup is intentional: VWAP identifies intraday value, momentum detects short bursts, EMAs provide structural trend bias, and volume/flow confirm participation. Signals require alignment of at least two components (for example, VWAP ABOVE + Momentum BULLISH + positive flow) for higher confidence.
Inputs: momentum period, volume MA/profile length, EMA configuration (8/21/50), trend multiplier, signal sensitivity, color and display options. Use shorter momentum lengths for scalps and longer for swing analysis. Increase volume MA for thinly traded instruments.
Limitations: Institutional/dark-pool estimates and flow heuristics are approximations, not actual exchange tape. VWAP is intraday-focused. Expect false signals during major news or low-liquidity sessions. Backtest and paper-trade before applying real capital.
Risk Disclaimer: For education and analysis only. Not financial advice. Use proper risk management. The author is not responsible for trading losses.
________________________________________
Risk & Misuse Disclaimer
This indicator is provided for education, analysis and idea generation only. It is not investment or financial advice and does not guarantee profits. Institutional activity flags, dark-pool estimates and flow heuristics are approximations and should not be treated as exchange tape. Backtest thoroughly and use demo/paper accounts before trading real capital. Always apply appropriate position sizing and stop-loss rules. The author is not responsible for any trading losses resulting from the use or misuse of this tool.
________________________________________
Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
TRAPPER TRENDLINES — RSIBuilds dynamic RSI trendlines by connecting the two most recent confirmed RSI swing points (highs→highs for resistance, lows→lows for support). Includes optional channel shading for the 30–70 zone, an RSI moving average, clean break alerts, and simple bullish/bearish divergence alerts versus price.
How it works
RSI pivots: A point on RSI is a swing high/low only if it is the most extreme value compared with a set number of bars on the left and the right (the Pivot Lookback).
RSI trendlines:
Resistance connects the last two confirmed RSI swing highs.
Support connects the last two confirmed RSI swing lows.
Lines can be Full Extend (update into the future) or Pivot Only.
Channel block: Optional fill of the 30–70 range for fast visual context.
Alerts:
Breaks of RSI support/resistance trendlines.
Basic bullish/bearish RSI divergences versus price pivots.
Inputs
RSI
RSI Length: Default 14 (standard).
Pivot Lookback: Bars to the left/right required to confirm an RSI swing.
Overbought / Oversold: 70 / 30 by default.
Line Extension: Full Extend or Pivot Only.
Visuals
Show RSI Moving Average / Signal Length: Optional smoothing line on RSI.
RSI/Signal colors: Customize plot colors.
Show 30–70 Channel Block: Toggle the middle-zone fill.
Tint pane background when RSI in channel: Optional subtle background when RSI is between OB/OS.
Divergences & Alerts
Enable RSI TL Break Alerts: Alert conditions for RSI line breaks.
Enable Divergence Alerts: Bullish/Bearish divergence alerts versus price.
Pairing with price for confluence/divergence
For accurate confluence and clearer divergences, align this RSI tool with your price trendline tool (for example, TRAPPER TRENDLINES — PRICE):
Set RSI Pivot Lookback equal to the Pivot Left/Right size used on price.
Example: Price uses Pivot Left = 50 and Pivot Right = 50 → set RSI Pivot Lookback = 50.
Keep RSI Length = 14 and OB/OS = 70/30 unless you have a specific edge.
Interpretation:
Confluence: Price reacts at its trendline while RSI reacts at its own line in the same direction.
Divergence: Price makes a higher high while RSI makes a lower high (bearish), or price makes a lower low while RSI makes a higher low (bullish), using matched pivot windows.
Suggested settings
Higher timeframes (4H / 1D / 1W): Pivot Lookback = 50; optional RSI MA length 14; channel block ON.
Intraday (15m / 30m / 1H): Pivot Lookback = 30; optional RSI MA length 14.
Always mirror your price pivot size to this RSI Pivot Lookback for consistent swings.
Reading the signals
RSI trendline touch/hold: Momentum reacting at structure; look for confluence with price levels.
RSI Trendline Break Up / Down: Momentum shift; consider price structure and retests.
Bullish/Bearish Divergence: Confirm only when pivots are matched and the new swing is confirmed.
Notes & limitations
Pivots require future bars to confirm by design; trendlines update as new swings confirm.
Divergence logic compares RSI pivots to price pivots with the same lookback; mismatched windows can produce false positives.
No strategy entries/exits or performance claims are provided. This is an analytical tool.
Alerts (titles/messages)
RSI: Trendline Break Up — “RSI broke falling resistance line.”
RSI: Trendline Break Down — “RSI broke rising support line.”
RSI: Bullish Divergence — “Bullish RSI divergence confirmed.”
RSI: Bearish Divergence — “Bearish RSI divergence confirmed.”
Quick start
Add the indicator to a separate pane.
Set Pivot Lookback to match your price tool’s pivot size (e.g., 50).
Optionally toggle the RSI MA and Channel Block for clarity.
Enable alerts if you want notifications on RSI line breaks and divergences.
Use with TRAPPER TRENDLINES — PRICE or any price-based trendline tool for confluence/divergence analysis.
Compliance
This script is for educational purposes only and does not constitute financial advice. Trading involves risk. Past performance does not guarantee future results. No performance claims are made.
FlowFusion Money Flow — FP + VWAP Drift + PVT (−100..+100)Title (ASCII only)
FlowFusion Money Flow — Flow Pressure + Rolling VWAP Drift + PVT (Normalized −100..+100)
Short Description
Original money-flow oscillator combining Flow Pressure, Rolling VWAP Drift, and PVT Momentum into one normalized score (−100..+100) with a signal line, thresholds, optional component plots, and ready-made alerts.
Full Description (meets “originality & usefulness”)
What’s original
FlowFusion Money Flow is not a generic mashup. It builds a single score from three complementary, volume-aware components that target different facets of order flow:
Flow Pressure (FP) — In-bar directional drive scaled by relative volume.
Drive
=
close
−
open
max
(
high
−
low
,
tick
)
∈
=
max(high−low, tick)
close−open
∈ .
Relative Volume
=
volume
average volume over
𝑓
𝑝
𝐿
𝑒
𝑛
=
average volume over fpLen
volume
.
𝐹
𝑃
𝑟
𝑎
𝑤
=
Drive
×
RelVol
FP
raw
=Drive×RelVol then squashed (softsign) to
.
Why it belongs: distinguishes real pushes (big body and big volume) from noise.
Rolling VWAP Drift — Direction of VWAP itself over a rolling window, normalized by ATR.
𝑉
𝑊
𝐴
𝑃
𝑡
=
∑
(
𝑇
𝑃
×
𝑉
𝑜
𝑙
)
∑
𝑉
𝑜
𝑙
VWAP
t
=
∑Vol
∑(TP×Vol)
over vwapLen.
Drift
=
𝑉
𝑊
𝐴
𝑃
𝑡
−
𝑉
𝑊
𝐴
𝑃
𝑡
−
1
𝐴
𝑇
𝑅
=
ATR
VWAP
t
−VWAP
t−1
→ squashed to
.
Why it belongs: persistent VWAP movement signals sustained accumulation/distribution.
PVT Momentum — Price-Volume Trend standardized (z-score) and squashed.
𝑃
𝑉
𝑇
𝑡
=
𝑃
𝑉
𝑇
𝑡
−
1
+
𝑉
𝑜
𝑙
×
Δ
𝐶
𝑙
𝑜
𝑠
𝑒
𝐶
𝑙
𝑜
𝑠
𝑒
𝑡
−
1
PVT
t
=PVT
t−1
+Vol×
Close
t−1
ΔClose
.
𝑧
=
𝑃
𝑉
𝑇
−
SMA
(
𝑃
𝑉
𝑇
)
StDev
(
𝑃
𝑉
𝑇
)
z=
StDev(PVT)
PVT−SMA(PVT)
→ squashed to
.
Why it belongs: captures volume-weighted trend pressure without relying on price alone.
Composite score:
Score
=
𝑤
𝐹
𝑃
⋅
𝐹
𝑃
+
𝑤
𝑉
𝑊
𝐴
𝑃
⋅
𝑉
𝑊
𝐴
𝑃
_
𝐷
𝑟
𝑖
𝑓
𝑡
+
𝑤
𝑃
𝑉
𝑇
⋅
𝑃
𝑉
𝑇
_
𝑀
𝑜
𝑚
𝑤
𝐹
𝑃
+
𝑤
𝑉
𝑊
𝐴
𝑃
+
𝑤
𝑃
𝑉
𝑇
Score=
w
FP
+w
VWAP
+w
PVT
w
FP
⋅FP+w
VWAP
⋅VWAP_Drift+w
PVT
⋅PVT_Mom
with a Signal = SMA(Score, sigLen). Thresholds mark strong accumulation/distribution zones.
How it works (step-by-step)
Compute FP, VWAP Drift, PVT Momentum.
Normalize each to the same
scale.
Weighted average → FlowFusion Score.
Smooth with a Signal line to reduce whipsaw.
Optional background shading when Score exceeds thresholds.
How to use
Direction filter:
Score > 0 favors longs; Score < 0 favors shorts.
Momentum turns:
Score crosses above Signal → setup for long; below → setup for short.
Strength zones:
Above Upper Threshold (default +40) = strong buy pressure; below Lower (−40) = strong sell pressure.
Confluence:
Best near S/R, trendlines, or HTF bias. For scalping on 1–5m, consider sigLen 9–13 and thresholds ±40 to ±50.
Alerts included: zero cross, zone entries, and Score/Signal crossovers.
Inputs (key)
fpLen (20): relative-volume lookback for Flow Pressure.
vwapLen (34): rolling VWAP window.
pvtLen (50): PVT z-score window.
sigLen (9): Signal smoothing.
Weights: wFP, wVWAP, wPVT to bias the blend.
Thresholds: upperBand / lowerBand (defaults +40/−40).
Display: toggle component plots and background shading.
Best practices
Trending markets: increase wVWAP (VWAP Drift) or widen thresholds.
Ranging markets: increase wFP and wPVT; take quicker profits.
News: wait for bar close confirmation or reduce size.
Data quality: use consistent volume feeds (especially in crypto).
Limitations
Oscillators can stay extreme in strong trends; use structure/trend filters.
Volume anomalies (illiquid pairs, API glitches) can distort signals—sanity-check with another venue when possible.
Disclaimer
This indicator is for educational purposes only and is not financial advice. Trading involves risk; past performance does not guarantee future results. Always paper-trade first and use appropriate risk controls.
Cheat CodeWhy Monday & Friday
Monday evening (NY): frequently seeds the weekly expansion. Its DR/IDR often acts as a weekly “starter envelope,” useful for breakout continuation or fade back into the box plays as liquidity builds.
Friday evening (NY): often exposes end-of-week traps (run on stops into the close) and sets expectation boundaries into the following week. Carry these levels forward to catch Monday’s reaction to Friday’s closing structure.
Typical use-cases
Breakout & retest:
Price closes outside the Monday DR/IDR → look for retests of the band edge for continuation.
Liquidity sweep (“trap”) recognition:
Friday session wicks briefly beyond Friday DR/IDR then closes back inside → watch for mean reversion early next week.
Bias filter:
Above both Monday DR midline and Friday DR midline → bias long until proven otherwise; the inverse for shorts.
Session open confluence:
Reactions at the open line frequently mark decision points for momentum vs. fade setups.
(This is a levels framework, not a signals engine. Combine with your execution model: orderflow, S/R, session timing, or higher-TF bias.)
Inputs & styling (quick reference)
Display toggles (per day):
Show DR / IDR / Middle DR / Middle IDR
Show Opening Line
Show DR/IDR Box (choose DR or IDR as box source)
Show Price Labels
Style controls (per day):
Line width (1–4), style (Solid/Dashed/Dotted)
Independent colors for DR, IDR, midlines, open line
Box background opacity
Timezone:
Default America/New_York (changeable).
Optional on-chart warning if your chart TZ differs.
Practical notes
Works on intraday charts; levels are anchored using weekly timestamps for accuracy on any symbol.
Live updating: During the Mon/Fri calc windows, DR/IDR highs/lows and midlines keep updating until the session ends.
Clean drawings: Lines, box, and labels are created once per session and then extended/updated—efficient on resources even with long display windows.
Max elements: Script reserves ample line/box/label capacity for stability across weeks.
SMI Base-Trigger Bullish Re-acceleration (Higher High)Description
What it does
This indicator highlights a two-step bullish pattern using Stochastic Momentum Index (SMI) plus an ATR distance filter:
1. Base (orange) – Marks a momentum “reset.” A base prints when SMI %K crosses up through %D while %K is below the Base level (default -70). The base stores the base price and starts a waiting window.
2. Trigger (green) – Confirms momentum and price strength. A trigger prints only if, before the timeout window ends:
• SMI %K crosses up through %D again,
• %K is above the Trigger level (default -60),
• Close > Base Price, and
• Price has advanced at least Min ATR multiple (default 1.0× the 14-period ATR) above the base price.
A dashed green line connects the base to the trigger.
Why it’s useful
It seeks a bullish divergence / reacceleration: momentum recovers from deeply negative territory, then price reclaims and exceeds the base by a volatility-aware margin. This helps filter out weak “oversold bounces.”
Signals
• Base ▲ (orange): Potential setup begins.
• Trigger ▲ (green): Confirmation—momentum and price agree.
Inputs (key ones)
• %K Length / EMA Smoothing / %D Length: SMI construction.
• Base when %K < (default -70): depth required for a valid reset.
• Trigger when %K > (default -60): strength required on confirmation.
• Base timeout (days) (default 100): maximum look-ahead window.
• ATR Length (default 14) and Min ATR multiple (default 1.0): price must exceed the base by this ATR-scaled distance.
How traders use it (example rules)
• Entry: On the Trigger.
• Risk: A common approach is a stop somewhere between the base price and a multiple of ATR below trigger; or use your system’s volatility stop.
• Exits: Your choice—trend MA cross, fixed R multiple, or structure-based levels.
Notes & tips
• Works best on liquid symbols and mid-to-higher timeframes (reduce noise).
• Increase Min ATR multiple to demand stronger price confirmation; tighten or widen Base/Trigger levels to fit your market.
• This script plots signals only; convert to a strategy to backtest entries/exits.
Adaptive Weighted Regression Channel (AWRC)Short Description:
The Adaptive Weighted Regression Channel (AWRC) is an advanced technical analysis tool that plots a dynamic regression channel based on the recent price action. The centerline is a linear regression (trendline) fitted to the selected price source over a rolling window. The channel boundaries are placed above and below the regression line by a user-selected multiple of the weighted standard deviation.
What makes AWRC unique is its ability to optionally weight each bar’s importance in the regression using Volume, ATR (Average True Range), or Recency Decay, offering a channel that can adapt to market volatility, participation, or trend acceleration.
Parameter Explanations:
length: Number of bars for the regression window (how many recent candles are included). Higher values = smoother, less sensitive channel.
StdDev Multiplier (mult): Controls the channel width. 2.0 is classic; higher = wider channels, lower = tighter.
Enable Weighting?: Turn ON to activate weighting of each bar. If OFF, all bars are equally weighted (classic regression channel).
Weight Type: Select what to use for weights (only active if Enable Weighting is ON):
"Volume": Higher volume bars have more influence on the regression.
"ATR": Bars with higher volatility (as measured by ATR) have more influence.
"Decay": More recent bars are given more weight (controlled by Decay parameter).
Decay: If Weight Type is "Decay", this controls the rate of recency decay. (e.g. 0.98 = slow decay; 0.90 = fast decay; values close to 1 mean a longer memory.)
Source for the calculation (src): Selects which price is regressed. Default is hl2 (average of high and low); you can choose close, open, etc.
Recommended Parameters:
For general use: length = 34, mult = 2.0, Enable Weighting = OFF, src = hl2
For volume-aware channel: Enable Weighting = ON, Weight Type = "Volume"
For volatility sensitivity: Enable Weighting = ON, Weight Type = "ATR"
For extra focus on recent price: Enable Weighting = ON, Weight Type = "Decay", Decay = 0.95 or 0.98
For swing trading: length = 21–55, mult = 1.5–2.5
For intraday/scalping: length = 10–20, mult = 1.0–1.5
Usage Tips:
The regression line shows the "best fit" trend for the selected window.
The channel captures the typical range; price breaking outside the channel can signal strength, exhaustion, or breakout.
Volume and ATR weighting help the channel adapt to market participation or volatility spikes.
Decay weighting locks onto the most recent trend direction quickly.
Adjust parameters to fit your timeframe and market volatility.
Use AWRC to spot trending moves, reversals, or overextensions.
Try different weighting and channel settings to match your trading style!
Kairos BarakahTrade with precision during high-probability windows using this advanced Pine Script indicator, designed specifically for Indian Standard Time (IST). The tool identifies key reversal opportunities within a user-defined trading session, combining time-based reference levels, sequence-validated signals, and multi-factor win probability analysis for confident decision-making.
Key Features
1. Time-Based Reference Levels
Automatically sets high/low reference levels at a customizable start time (default: 19:00 IST).
Active trading window with adjustable duration (default: 135 minutes).
Clear visual reference lines for easy tracking.
2. Intelligent Signal Generation
Initial Signals:
Buy (B): Triggered when price closes above the reference high.
Sell (S): Triggered when price closes below the reference low.
Reversal Signals (R):
Valid only after an initial signal, ensuring proper sequence.
Buy Reversal: Price closes above reference high (after a Sell signal).
Sell Reversal: Price closes below reference low (after a Buy signal).
3. Multi-Dimensional Win Probability
Body Strength: Measures candle conviction (body size / total range).
Volume Confirmation: Compares current volume to 20-period average.
Trend Alignment: Uses EMA crosses (9/21) and RSI (14) for momentum.
Composite Score: Weighted blend of all factors, color-coded for quick interpretation:
🟢 >70%: High-confidence signal.
🟠 40-69%: Moderate confidence.
🔴 <40%: Weak signal.
4. Professional Visualization
Clean labels (B/S/R) at signal points.
Real-time reference table showing levels, active signal, and probabilities.
Customizable alerts for all signal types.
Why Use This Indicator?
IST-Optimized: Tailored for Indian market hours.
Rules-Based Reversals: Avoids false signals with strict sequence checks.
Data-Driven Confidence: Win probability metrics reduce guesswork.
Flexible Setup: Adjust time windows and parameters to fit your strategy.
z-score-calkusi-v1.143z-scores incorporate the moment of N look-back bars to allow future price projection.
z-score = (X - mean)/std.deviation ; X = close
z-scores update with each new close print and with each new bar. Each new bar augments the mean and std.deviation for the N bars considered. The old Nth bar falls away from consideration with each new historical bar.
The indicator allows two other options for X: RSI or Moving Average.
NOTE: While trading use the "price" option only.
The other two options are provided for visualisation of RSI and Moving Average as z-score curves.
Use z-scores to identify tops and bottoms in the future as well as intermediate intersections through which a z-score will pass through with each new close and each new bar.
Draw lines from peaks and troughs in the past through intermediate peaks and troughs to identify projected intersections in the future. The most likely intersections are those that are formed from a line that comes from a peak in the past and another line that comes from a trough in the past. Try getting at least two lines from historical peaks and two lines from historical troughs to pass through a future intersection.
Compute the target intersection price in the future by clicking on the z-score indicator header to see a drag-able horizontal line to drag over the intersection. The target price is the last value displayed in the indicator's status bar after the closing price.
When the indicator header is clicked, a white horizontal drag-able line will appear to allow dragging the line over an intersection that has been drawn on the indicator for a future z-score projection and the associated future closing price.
With each new bar that appears, it is necessary to repeat the procedure of clicking the z-score indicator header to be able to drag the drag-able horizontal line to see the new target price for the selected intersection. The projected price will be different from the current close price providing a price arbitrage in time.
New intermediate peaks and troughs that appear require new lines be drawn from the past through the new intermediate peak to find a new intersection in the future and a new projected price. Since z-score curves are sort of cyclical in nature, it is possible to see where one has to locate a future intersection by drawing lines from past peaks and troughs.
Do not get fixated on any one projected price as the market decides which projected price will be realised. All prospective targets should be manually updated with each new bar.
When the z-score plot moves outside a channel comprised of lines that are drawn from the past, be ready to adjust to new market conditions.
z-score plots that move above the zero line indicate price action that is either rising or ranging. Similarly, z-score plots that move below the zero line indicate price action that is either falling or ranging. Be ready to adjust to new market conditions when z-scores move back and forth across the zero line.
A bar with highest absolute z-score for a cycle screams "reversal approaching" and is followed by a bar with a lower absolute z-score where close price tops and bottoms are realised. This can occur either on the next bar or a few bars later.
The indicator also displays the required N for a Normal(0,1) distribution that can be set for finer granularity for the z-score curve.This works with the Confidence Interval (CI) z-score setting. The default z-score is 1.96 for 95% CI.
Common Confidence Interval z-scores to find N for Normal(0,1) with a Margin of Error (MOE) of 1:
70% 1.036
75% 1.150
80% 1.282
85% 1.440
90% 1.645
95% 1.960
98% 2.326
99% 2.576
99.5% 2.807
99.9% 3.291
99.99% 3.891
99.999% 4.417
9-Jun-2025
Added a feature to display price projection labels at z-score levels 3, 2, 1, 0, -1, -2, 3.
This provides a range for prices available at the current time to help decide whether it is worth entering a trade. If the range of prices from say z=|2| to z=|1| is too narrow, then a trade at the current time may not be worth the risk.
Added plot for z-score moving average.
28-Jun-2025
Added Settings option for # of Std.Deviation level Price Labels to display. The default is 3. Min is 2. Max is 6.
This feature allows likelihood assessment for Fibonacci price projections from higher time frames at lower time frames. A Fibonacci price projection that falls outside |3.x| Std.Deviations is not likely.
Added Settings option for Chart Bar Count and Target Label Offset to allow placement of price labels for the standard z-score levels to the right of the window so that these are still visible in the window.
Target Label Offset allows adjustment of placement of Target Price Label in cases when the Target Price Label is either obscured by the price labels for the standard z-score levels or is too far right to be visible in the window.
9-Jul-2025
z-score 1.142 updates:
Displays in the status line before the close price the range for the selected Std. Deviation levels specified in Settings and |z-zMa|.
When |z-zMa| > |avg(z-zMa)| and zMa rising, |z-zMa| and zMa displays in aqua.
When |z-zMa| > |avg(z-zMa)| and zMa falling, |z-zMa| and zMa displays in red.
When |z-zMa| <= |avg(z-zMa)|, z and zMa display in gray.
z usually crosses over zMa when zMa is gray but not always. So if cross-over occurs when zMa is not gray, it implies a strong move in progress.
Practice makes perfect.
Use this indicator at your own risk
