CCI with Subjective NormalizationCCI (Commodity Channel Index) with Subjective Normalization
This indicator computes the classic CCI over a user-defined length, then applies a subjective mean and scale to transform the raw CCI into a pseudo Z‑score range. By adjusting the “Subjective Mean” and “Subjective Scale” inputs, you can shift and rescale the oscillator to highlight significant tops and bottoms more clearly in historical data.
1. CCI Calculation:
- Uses the standard formula \(\text{CCI} = \frac{\text{price} - \text{SMA(price, length)}}{0.015 \times \text{mean deviation}}\) over a user-specified length (default 500 bars).
2. Subjective Normalization:
- After CCI is calculated, it is divided by “Subjective Scale” and offset by “Subjective Mean.”
- This step effectively re-centers and re-scales the oscillator, helping you align major lows or highs at values like –2 or +2 (or any desired range).
3. Usage Tips:
- CCI Length controls how far back the script measures average price and deviation. Larger values emphasize multi-year cycles.
- Subjective Mean and Scale let you align the oscillator’s historical lows and highs with numeric levels you prefer (e.g., near ±2).
- Adjust these parameters to fit your particular market analysis or to match known cycle tops/bottoms.
4. Plot & Zero Line:
- The indicator plots the normalized CCI in yellow, along with a zero line for quick reference.
- Positive values suggest price is above its long-term mean, while negative values suggest it’s below.
This approach offers a straightforward momentum oscillator (CCI) combined with a customizable normalization, making it easier to spot historically significant overbought/oversold conditions without writing complex code yourself.
Indicators and strategies
[blackcat] L3 Breakout IndicatorOVERVIEW
This script provides a breakout detection system ( L3 Breakout Indicator) analyzing price momentum across timeframes. It identifies market entry/exit zones through dynamically scaled thresholds and visual feedback layers.
FEATURES
Dual momentum visualization: • Price Momentum Ratio Plot ( yellow ) • Filtered Signal Value Plot ( fuchsia )
Adjustable trade boundaries: ▪ Lower Threshold (default: 0.5) ▪ Upper Threshold (default: 2.9) ▪ Central boundary ( fixed at 2.0 )
Real-time visual feedback: ☀ Buy zone highlights ( lime ) on momentum crossover ⚠ Sell zone highlights ( red ) on momentum cross-under ♦ Dynamic convergence area between plots ( colored gradient )
HOW TO USE
Interpretation Flow
Monitor momentum plots relative to threshold lines
Actionable signals occur when momentum crosses thresholds
Persistent movement above/below central boundary indicates trend continuation
Key Zones
• Below 0.5: Potential buying opportunity zone
• Above 2.0: Cautionary selling region
• Between 0.5-2.0: Neutral consolidation phase
Optimization Tips
Adjust thresholds based on asset volatility
Combine with volume metrics for confirmation
Backtest parameters using historical data
LIMITATIONS
• Lag induced by 4-period EMA smoothing
• Historical dependency in calculating extremes (lowest(100)/highest(250))
• No built-in risk management protocols (stop loss take profit)
• Performance variability during sideways markets
ChartPatternSetupsLibrary "ChartPatternSetups"
detectSymmetricalTriangle(lookback)
Detects a Symmetrical Triangle (Bullish or Bearish) and provides trade levels.
Parameters:
lookback (int) : Number of bars to look back for pivots.
Returns: Tuple of (isBullish, isBearish, entry, sl, tp).
detectAscendingTriangle(lookback)
Detects an Ascending Triangle (Bullish) and provides trade levels.
Parameters:
lookback (int) : Number of bars to look back for pivots.
Returns: Tuple of (isDetected, entry, sl, tp).
detectDescendingTriangle(lookback)
Detects a Descending Triangle (Bearish) and provides trade levels.
Parameters:
lookback (int) : Number of bars to look back for pivots.
Returns: Tuple of (isDetected, entry, sl, tp).
detectFallingWedge(lookback)
Detects a Falling Wedge (Bullish) and provides trade levels.
Parameters:
lookback (int) : Number of bars to look back for pivots.
Returns: Tuple of (isDetected, entry, sl, tp).
detectRisingWedge(lookback)
Detects a Rising Wedge (Bearish) and provides trade levels.
Parameters:
lookback (int) : Number of bars to look back for pivots.
Returns: Tuple of (isDetected, entry, sl, tp).
ES vs Bond ROCThis Pine Script plots the Relative Rate of Change (ROC) between the S&P 500 E-mini Futures (ES) and 30-Year Treasury Bond Futures (ZB) over a specified period. It helps identify when equities are overperforming or underperforming relative to long-term bonds—an insight often used to detect risk-on/risk-off sentiment shifts in the market.
Adaptive KDJ (MTF)Hey guys,
this is an adaptive MTF KDJ oscillator.
Pick up to 3 different timeframes, choose a weighting if you want and enjoy the beautiful signals it will show you.
The length of every timeframe is adaptive and based of the timeframe's ATR.
The plot shows the smoothed average of the 3 KDJ values.
Large triangles show KDJ crossings.
Small triangles show anticipations of possible crossings.
I found out it works best with 1m, 5m, 15m and weighting=1 for forex scalping in 1m.
Use other indicators for confluence.
HBND ReferenceChart the HBND as an index based on weighting found on the HBND Etf website. For best results display the adjusted close since HBND is a high yielding fund. The weightings have to be updated manually.
There are three display options:
1. Normalize the index relative to the symbol on the chart (presumably HBND) and this is the default.
2. Percentage change relative to the first bar of the index
3. The raw value which will be the tlt price * tlt percentage weighting + vglt price * vglt percentage weighting + edv percentage weighting * edv price.
Relative Crypto Dominance Polar Chart [LuxAlgo]The Relative Crypto Dominance Polar Chart tool allows traders to compare the relative dominance of up to ten different tickers in the form of a polar area chart, we define relative dominance as a combination between traded dollar volume and volatility, making it very easy to compare them at a glance.
🔶 USAGE
The use is quite simple, traders just have to load the indicator on the chart, and the graph showing the relative dominance will appear.
The 10 tickers loaded by default are the major cryptocurrencies by market cap, but traders can select any ticker in the settings panel.
Each area represents dominance as volatility (radius) by dollar volume (arc length); a larger area means greater dominance on that ticker.
🔹 Choosing Period
The tool supports up to five different periods
Hourly
Daily
Weekly
Monthly
Yearly
By default, the tool period is set on auto mode, which means that the tool will choose the period depending on the chart timeframe
timeframes up to 2m: Hourly
timeframes up to 15m: Daily
timeframes up to 1H: Weekly
timeframes up to 4H: Monthly
larger timeframes: Yearly
🔹 Sorting & Sizing
Traders can sort the graph areas by volatility (radius of each area) in ascending or descending order; by default, the tickers are sorted as they are in the settings panel.
The tool also allows you to adjust the width of the chart on a percentage basis, i.e., at 100% size, all the available width is used; if the graph is too wide, just decrease the graph size parameter in the settings panel.
🔹 Set your own style
The tool allows great customization from the settings panel, traders can enable/disable most of the components, and add a very nice touch with curved lines enabled for displaying the areas with a petal-like effect.
🔶 SETTINGS
Period: Select up to 5 different time periods from Hourly, Daily, Weekly, Monthly and Yearly. Enable/disable Auto mode.
Tickers: Enable/disable and select tickers and colors
🔹 Style
Graph Order: Select sort order
Graph Size: Select percentage of width used
Labels Size: Select size for ticker labels
Show Percent: Show dominance in % under each ticker
Curved Lines: Enable/disable petal-like effect for each area
Show Title: Enable/disable graph title
Show Mean: Enable/disable volatility average and select color
DOPT---
## 🔍 **DOPT - Daily Open & Price Time Markers**
This script is designed to support directional bias development and price behavior analysis around key time-based reference points on the **1H and 4H timeframes**.
### ✨ **What It Does**
- **1800 Open Marker** (6 PM NY time): Plots the **daily open** from 1800 in **black dotted lines**.
- **0000 Open Marker** (Midnight NY time): Plots the **midnight open** in **blue dotted lines**.
- **Day Letters**: Each 1800 open is labeled with the corresponding **day of the week** (e.g., M, T, W...), helping visually segment your chart.
- **Hour Labels**: Select specific candles (e.g., 0000 = '0', 0800 = '8') to be labeled above the bar. These are fully customizable.
- **Candle Midpoints**: Option to mark the **50% level** of a specific candle (good for CE or CRT references).
- **CRT High/Low Tracking**: Ability to plot **extended high and low lines** from a selected candle back (e.g., for CRT modeling).
- **4H Timeframe Candle Numbering**: Helpful when analyzing sequences on the 4-hour timeframe. Candles are numbered `1`, `5`, and `9` for reference.
---
### 🧠 **How I Use It**
- I mostly use this on the **1-hour timeframe** to decide **directional bias** for the day:
- If price **closes above 1800 open**, I consider that a **green daily close** — potential bullish sentiment.
- If price **closes below**, I treat it as a **red daily close** — potential bearish behavior.
- Price often uses these opens as **support/resistance**, so I watch for reactions there.
- On the **4H**, the candle numbers help track structure and flow.
- Combine with CRT tools to mark **key candle highs/lows** and their **equilibrium (50%)** — great for refining entries or understanding how price is respecting a particular candle.
---
### ⚠️ **Note on Daylight Savings**
This is a **daylight saving time-dependent script**. When DST kicks in or out, you’ll need to **adjust the time inputs** accordingly to keep the opens accurate (e.g., 1800 might shift to 1700 depending on the season).
---
### 🔁 **Backtesting & Reference**
- The **1800 and 0000 opens** are plotted for **as far back** as your chart loads, making it great for backtesting historical reactions.
- The CRT marking tools only go back **50 candles max**, so use that for recent structure only.
---
Advanced Swing High/Low Trend Lines with MA Filter# Advanced Swing High/Low Trend Lines Indicator
## Overview
This advanced indicator identifies and draws trend lines based on swing highs and lows across three different timeframes (large, middle, and small trends). It's designed to help traders visualize market structure and potential support/resistance levels at multiple scales simultaneously.
## Key Features
- *Multi-Timeframe Analysis*: Simultaneously tracks trends at large (200-bar), middle (100-bar), and small (50-bar) scales
- *Customizable Visualization*: Different colors, widths, and styles for each trend level
- *Trend Confirmation System*: Requires minimum consecutive pivot points to validate trends
- *Trend Filter Option*: Can align trends with 200 EMA direction for consistency
## Recommended Settings
### For Long-Term Investors:
- Large Swing Length: 200-300
- Middle Swing Length: 100-150
- Small Swing Length: 50-75
- Enable Trend Filter: Yes
- Confirmation Points: 4-5
### For Swing Traders:
- Large Swing Length: 100
- Middle Swing Length: 50
- Small Swing Length: 20-30
- Enable Trend Filter: Optional
- Confirmation Points: 3
### For Day Traders:
- Large Swing Length: 50
- Middle Swing Length: 20
- Small Swing Length: 5-10
- Enable Trend Filter: No
- Confirmation Points: 2-3
## How to Use
### Identification:
1. *Large Trend Lines* (Red/Green): Show major market structure
2. *Middle Trend Lines* (Purple/Aqua): Intermediate levels
3. *Small Trend Lines* (Orange/Blue): Short-term price action
### Trading Applications:
- *Breakout Trading*: Watch for price breaking through multiple trend lines
- *Bounce Trading*: Look for reactions at confluence of trend lines
- *Trend Confirmation*: Aligned trends across timeframes suggest stronger moves
### Best Markets:
- Works well in trending markets (forex, indices)
- Effective in higher timeframes (1H+)
- Can be used in ranging markets to identify boundaries
## Customization Tips
1. For cleaner charts, reduce line widths in congested markets
2. Use dotted styles for smaller trends to reduce visual clutter
3. Adjust confirmation points based on market volatility (higher for noisy markets)
## Limitations
- May repaint on current swing points
- Works best in trending conditions
- Requires sufficient historical data for longer swing lengths
This indicator provides a comprehensive view of market structure across multiple timeframes, helping traders make more informed decisions by visualizing the hierarchy of support and resistance levels.
Trading Capital Management for Option SellingTrading Capital Management for Option Selling
This Pine Script indicator helps manage trading capital allocation for option selling strategies based on price percentile ranking. It provides dynamic allocation recommendations for index options (NIFTY and BANKNIFTY) and individual stock positions.
Key Features:
- Dynamic buying power (BP) allocation based on close price percentile
- Flexible index allocation between NIFTY and BANKNIFTY
- Automated calculation of recommended number of stock positions
- Risk management through position size limits
- Real-time INDIA VIX monitoring
Main Parameters:
1. Window Length: Period for percentile calculation (default: 252 days)
2. Thresholds: Low (30%) and High (70%) percentile thresholds
3. Capital Settings:
- Trading Capital: Total capital available
- Max BP% per Stock: Maximum allocation per stock position
4. Buying Power Range:
- Low Percentile BP%: Base BP usage at low percentile
- High Percentile BP%: Maximum BP usage at high percentile
5. Index Allocation:
- NIFTY/BANKNIFTY split ratio
- Minimum and maximum allocation thresholds
Display:
The indicator shows two tables:
1. Common Metrics:
- Total BP Usage with percentage
- Current INDIA VIX value
- Current Close Price Percentile
2. Capital Allocation:
- Index-wise BP allocation (NIFTY and BANKNIFTY)
- Stock allocation pool
- Recommended number of stock positions with BP per stock
Usage:
This indicator helps traders:
1. Scale positions based on market conditions using price percentile
2. Maintain balanced exposure between indices and stocks
3. Optimize capital utilization while managing risk
4. Adjust position sizing dynamically with market volatility
Volume Pro Indicator## Volume Pro Indicator
A powerful volume indicator that visualizes volume distribution across different price levels. This tool helps you easily identify where trading activity concentrates within the price range.
### Key Features:
- **Volume visualization by price levels**: Green (lower zone), Magenta (middle zone), Cyan (upper zone)
- **VPOC (Volume Point of Control)**: Shows the price level with the highest volume concentration
- **High and Low lines**: Highlights the extreme levels of the analyzed price range
- **Customizable historical analysis**: Configurable number of days for calculation
### How to use it:
- Colored volumes show where trading activity concentrates within the price range
- The VPOC helps identify the most significant price levels
- Different colors allow you to quickly visualize volume distribution in different price areas
Customizable with numerous options, including analysis period, calculation resolution, colors, and visibility of different components.
### Note:
This indicator works best on higher timeframes (1H, 4H, 1D) and liquid markets. It's a visual analysis tool that enhances your understanding of market structure.
#volume #vpoc #distribution #volumeprofile #trading #analysis #indicator #professional #pricelevels #volumedistribution
volume profile ranking indicator📌 Introduction
This script implements a volume profile ranking indicato for TradingView. It is designed to visualize the distribution of traded volume over price levels within a defined historical window. Unlike TradingView’s built-in Volume Profile, this script gives full customization of the profile drawing logic, binning, color gradient, and the ability to anchor the profile to a specific date.
⚙️ How It Works (Logic)
1. Inputs
➤POC Lookback Days (lookback): Defines how many bars (days) to look back from a selected point to calculate the volume distribution.
➤Bin Count (bin_count): Determines how many price bins (horizontal levels) the price range will be divided into.
➤Use Custom Lookback Date (useCustomDate): Enables/disables manually selecting a backtest start date.
➤Custom Lookback Date (customDate): When enabled, the profile will calculate volume based on this date instead of the most recent bar.
2. Target Bar Determination
➤If a custom date is selected, the script searches for the bar closest to that date within 1000 bars.
➤If not, it defaults to the latest bar (bar_index).
➤The profile is drawn only when the current bar is close to the target bar (within ±2 bars), to avoid unnecessary recalculations and performance issues.
3. Volume Binning
➤The price range over the lookback window is divided into bin_count segments.
➤For each bar within the lookback window, its volume is added to the appropriate bin based on price.
➤If the price falls outside the expected range, it is clamped to the first or last bin.
4. Ranking and Sorting
➤A bubble sort ranks each bin by total volume.
➤The most active bin (POC, or Point of Control) is highlighted with a thicker bar.
5. Rendering
➤Horizontal bars (line.new) represent volume intensity in each price bin.
➤Each bar is color-coded by volume heat: more volume = more intense color.
➤Labels (label.new) show:
➤Total volume
➤Rank
➤Percentage of total volume
➤Price range of the bin
🧑💻 How to Use
1. Add the Script to Your Chart
➤Copy the code into TradingView’s Pine Script editor and add it to your chart.
2. Set Lookback Period
➤Default is 252 bars (about one year for daily charts), but can be changed via the input.
3. (Optional) Use Custom Date
●Toggle "Use Custom Lookback Date" to true.
➤Pick a date in the "Custom Lookback Date" input to anchor the profile.
4. Analyze the Volume Distribution
➤The longest (thickest) red/orange bar represents the Point of Control (POC) — the price with the most volume traded.
➤Other bars show volume distribution across price.
➤Labels display useful metrics to evaluate areas of high/low interest.
✅ Features
🔶 Customizable anchor point (custom date).
🔶Adjustable bin count and lookback length.
🔶 Clear visualization with heatmap coloring.
🔶 Lightweight and performance-optimized (especially with the shouldDrawProfile filter)
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Buy on 5% dip strategy with time adjustment
This script is a strategy called "Buy on 5% Dip Strategy with Time Adjustment 📉💡," which detects a 5% drop in price and triggers a buy signal 🔔. It also automatically closes the position once the set profit target is reached 💰, and it has additional logic to close the position if the loss exceeds 14% after holding for 230 days ⏳.
Strategy Explanation
Buy Condition: A buy signal is triggered when the price drops 5% from the highest price reached 🔻.
Take Profit: The position is closed when the price hits a 1.22x target from the average entry price 📈.
Forced Sell Condition: If the position is held for more than 230 days and the loss exceeds 14%, the position is automatically closed 🚫.
Leverage & Capital Allocation: Leverage is adjustable ⚖️, and you can set the percentage of capital allocated to each trade 💸.
Time Limits: The strategy allows you to set a start and end time ⏰ for trading, making the strategy active only within that specific period.
Code Credits and References
Credits: This script utilizes ideas and code from @QuantNomad and jangdokang for the profit table and algorithm concepts 🔧.
Sources:
Monthly Performance Table Script by QuantNomad:
ZenAndTheArtOfTrading's Script:
Strategy Performance
This strategy provides risk management through take profit and forced sell conditions and includes a performance table 📊 to track monthly and yearly results. You can compare backtest results with real-time performance to evaluate the strategy's effectiveness.
The performance numbers shown in the backtest reflect what would have happened if you had used this strategy since the launch date of the SOXL (the Direxion Daily Semiconductor Bull 3x Shares ETF) 📅. These results are not hypothetical but based on actual performance from the day of the ETF’s launch 📈.
Caution ⚠️
No Guarantee of Future Results: The results are based on historical performance from the launch of the SOXL ETF, but past performance does not guarantee future results. It’s important to approach with caution when applying it to live trading 🔍.
Risk Management: Leverage and capital allocation settings are crucial for managing risk ⚠️. Make sure to adjust these according to your risk tolerance ⚖️.
Master Litecoin Miner Sell PressureBrief Description:
Purpose: The indicator overlays on a chart to highlight periods of high miner sell pressure for Litecoin.
Data Sources:
miner_out: Fetches daily Litecoin miner outflows (amount of LTC moved out by miners) using the INTOTHEBLOCK:LTC_MINEROUTFLOWS dataset.
miner_res: Fetches daily Litecoin miner reserves (amount of LTC held by miners) using the INTOTHEBLOCK:LTC_MINERRESERVES dataset.
Calculation:
Computes a ratio m by taking the 14-day sum of miner outflows and dividing it by the 14-day simple moving average (SMA) of miner reserves.
Calculates Bollinger Bands around m:
bbl: Lower band (200-day SMA of m minus 1 standard deviation).
bbu: Upper band (200-day SMA of m plus 1 standard deviation).
Visualization:
If the ratio m exceeds the upper Bollinger Band (bbu), the background is colored blue with 30% opacity, indicating potential high sell pressure from miners.
Master Global Liquidity Shifted 75 DaysThe Global Liquidity Index is a Pine Script (version 5) technical indicator designed to measure and visualize global financial liquidity by aggregating data from various central bank balance sheets and money supply metrics. The indicator is plotted as an overlay on the price chart using the left scale, with the entire line shifted left by 75 days.
Key features:
Data Sources: Incorporates balance sheet data from major central banks including the Federal Reserve (FED), European Central Bank (ECB), People's Bank of China (PBC), Bank of Japan (BOJ), and other central banks, along with optional M2 money supply data from various countries.
Components: Includes options to toggle specific liquidity factors such as FED balance sheet, Treasury General Account (TGA), Reverse Repurchase Agreements (RRP), and regional M2 money supplies, all converted to USD.
75-Day Shift: The indicator's output is shifted left by 75 days on the chart, aligning historical liquidity data with earlier price action, with this shift period adjustable via the "Shift Days Left" input.
Calculations:
Computes a total liquidity value by summing enabled central bank and M2 data (adjusted for RRP and TGA as drains)
Scales the total by dividing by 1 trillion (10^12)
Applies a Simple Moving Average (SMA) and Rate of Change (ROC) with user-defined periods
Final output is either the SMA of ROC or SMA alone, depending on ROC length
Visualization: Plots the shifted result as a yellow line with a linewidth of 2.
Fourier Trend Energy (Prototype)Fourier Trend Energy (Prototype)
This indicator brings the logic of Fourier-based trend analysis into Pine Script.
It estimates two key components:
Low-Frequency Energy — representing the strength of the underlying trend
High-Frequency Energy — representing noise, volatility, or deviation from the trend
🔹 Green line → trend strength
🔸 Orange line → short-term noise
🟩🟥 Background color → shows whether trend energy is increasing or decreasing
You can use it to:
Detect early trend formation
Filter fakeouts during consolidation
Spot momentum shifts based on energy crossovers
This is not a traditional oscillator — it’s a frequency-inspired tool to help you understand when the market is charging for a move.
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies.
Key Features
Volume-Weighted Momentum Calculation:
The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume).
Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close.
These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula:
\text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}}
where
\text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}}
.
Oscillator Range and Interpretation:
The VW RSI oscillates between 0 and 100, with a centerline at 50.
Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend.
Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend.
Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points:
VW RSI > 70: Overbought, indicating a possible pullback or reversal.
VW RSI < 30: Oversold, indicating a possible bounce or reversal.
Visual Elements:
VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value:
Green when above 50 (bullish momentum).
Red when below 50 (bearish momentum).
Gray when at 50 (neutral).
Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum.
Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions.
Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones.
Alerts:
Built-in alerts for key events:
Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum.
Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum.
Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback.
Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce.
Input Parameters
VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity.
Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback.
Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce.
Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones.
Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines.
How It Works
Volume Classification:
For each bar, the indicator determines whether the price movement is upward or downward:
If the current close is higher than the previous close, the bar’s volume is classified as up-volume.
If the current close is lower than the previous close, the bar’s volume is classified as down-volume.
If the close is unchanged, both up-volume and down-volume are set to 0 for that bar.
Smoothing:
The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum.
VW RSI Calculation:
The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume.
The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100.
Visualization and Alerts:
The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels.
Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings.
Usage
Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals.
Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts.
Trading Strategies:
Trend Confirmation:
Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50.
In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50.
Overbought/Oversold Conditions:
When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators.
When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals.
Divergences:
Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend.
Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend.
Momentum Shifts:
A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades.
A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions.
Example
On a 4-hour SOLUSDT chart:
During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback.
During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward.
A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal.
Limitations
Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends.
False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this.
Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced.
Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early.
Potential Improvements
Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness.
Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals.
Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading.
Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets.
The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
Dynamic Trend Indicator (DTI) - VWAP FilterThe Dynamic Trend Indicator (DTI) with VWAP Filter is a trend-following indicator.
It aims to identify and follow market trends while minimizing false signals in choppy or ranging markets.
The DTI combines a dynamically adjusted Exponential Moving Average (EMA) with a daily Volume Weighted Average Price (VWAP) confirmation filter and a cooldown mechanism to enhance signal reliability. This indicator is particularly useful for traders on intraday timeframes (e.g., 4-hour charts) who want to align their trades with the broader daily trend while avoiding whipsaws.
Key Features:
Dynamic Trend Line:
The core of the DTI is a trend line calculated using a custom EMA that adjusts its period dynamically based on market conditions.
The period of the EMA is determined by a combination of volatility (measured via ATR) and trend strength (measured via price momentum). In strong trends, the period shortens for faster responsiveness; in weak or ranging markets, it lengthens to reduce noise.
An optional smoothing EMA can be applied to the dynamic trend line to further reduce noise, with a user-defined smoothing length.
Daily VWAP Confirmation Filter:
A daily VWAP is calculated to provide a higher-timeframe trend bias. VWAP represents the average price paid for an asset during the day, weighted by volume, and is often used as a benchmark by institutional traders.
Buy signals are only generated when the price is above the daily VWAP (indicating a bullish daily bias), and sell signals are only generated when the price is below the VWAP (indicating a bearish daily bias).
The VWAP resets at the start of each day, ensuring it reflects the current day’s trading activity.
Cooldown Mechanism:
To prevent rapid signal reversals (whipsaws), the indicator includes a cooldown period between signals. After a buy or sell signal is generated, no new signals can be generated for a user-defined number of bars (default: 5 bars).
This helps filter out noise in choppy markets, ensuring signals are spaced out and more likely to align with significant trend changes.
Visual Elements:
Trend Line: Plotted on the chart, colored green when the price is above (uptrend) and red when below (downtrend). A gray color indicates a neutral trend.
Buy/Sell Signals: Displayed as green triangles below the bar for buy signals and red triangles above the bar for sell signals.
Background Coloring: The chart background is shaded green during uptrends and red during downtrends, providing a quick visual cue of the trend direction.
Daily VWAP Line: Optionally plotted as a purple step line, allowing traders to see the VWAP level and its relationship to the price.
Alerts:
The indicator includes built-in alerts for buy and sell signals, triggered when the price crosses the trend line and satisfies the VWAP filter and cooldown conditions.
Alert messages specify whether the signal is a buy or sell and confirm that the VWAP condition was met (e.g., "DTI Buy Signal: Price crossed above trend line and VWAP").
Input Parameters
Base Length (default: 14): The base period for calculating volatility and trend strength, used to adjust the dynamic EMA period.
Volatility Multiplier (default: 1.5): Adjusts the sensitivity of the dynamic period to market volatility (via ATR).
Trend Threshold (default: 0.5): Controls the sensitivity of the dynamic period to trend strength (via price momentum).
Use Smoothing (default: true): Enables/disables smoothing of the trend line with an additional EMA.
Smoothing Length (default: 3): The period for the smoothing EMA, if enabled.
Cooldown Bars (default: 5): The minimum number of bars between consecutive signals, reducing signal frequency in choppy markets.
Show Daily VWAP (default: true): Toggles the display of the daily VWAP line on the chart.
How It Works
Dynamic Trend Line Calculation:
Volatility is measured using the Average True Range (ATR) over the base length, scaled by the volatility multiplier.
Trend strength is calculated as the absolute price momentum (change in price over the base length) divided by the volatility factor.
The dynamic EMA period is adjusted based on the trend strength: stronger trends result in a shorter period (faster response), while weaker trends result in a longer period (more stability). The period is constrained between 5 and 50 to avoid extreme values.
A custom EMA function is used to handle the dynamic period, as Pine Script’s built-in ta.ema() requires a fixed length. The trend line is optionally smoothed with a secondary EMA.
Signal Generation:
A buy signal is generated when the price crosses above the trend line, the price is above the daily VWAP, and the cooldown period has elapsed.
A sell signal is generated when the price crosses below the trend line, the price is below the daily VWAP, and the cooldown period has elapsed.
The cooldown mechanism ensures that signals are not generated too frequently, reducing false signals in ranging markets.
Daily VWAP Calculation:
The VWAP is calculated by accumulating the price-volume product (close * volume) and total volume for the day, resetting at the start of each new day.
The VWAP is then computed as the cumulative price-volume divided by the cumulative volume, providing a volume-weighted average price for the day.
Usage
Timeframe: Best suited for intraday timeframes (e.g., 1-hour, 4-hour) where the daily VWAP provides a higher-timeframe trend bias. It can also be used on daily charts with adjustments to the cooldown period.
Markets: Works well in trending markets (e.g., forex, crypto, stocks) where the dynamic trend line can capture sustained price movements. The VWAP filter helps align signals with the daily trend, making it effective for assets with clear daily biases.
Trading Strategy:
Buy: Enter a long position when a green triangle (buy signal) appears, indicating the price has crossed above the trend line and is above the daily VWAP.
Sell: Enter a short position (or exit a long) when a red triangle (sell signal) appears, indicating the price has crossed below the trend line and is below the daily VWAP.
Use the trend line and VWAP as dynamic support/resistance levels to set stop-losses or take-profit targets.
Backtesting: Use TradingView’s strategy tester to evaluate the indicator’s performance on your chosen market and timeframe, adjusting parameters like cooldown_bars and volatility_mult to optimize for profitability.
Example
On a 4-hour SOLUSDT chart, the DTI with VWAP Filter might show:
An uptrend with the price above the green trend line and above the daily VWAP, generating buy signals as the price continues to rise.
A downtrend where the price falls below the red trend line and the daily VWAP, generating sell signals that align with the bearish daily bias.
During choppy periods, the cooldown mechanism and VWAP filter reduce false signals, ensuring trades are taken only when the price aligns with the daily trend.
Limitations
Lagging Nature: Like all trend-following indicators, the DTI may lag during sharp price reversals, as the dynamic EMA needs time to adjust.
Ranging Markets: While the VWAP filter and cooldown mechanism reduce whipsaws, the indicator may still generate some false signals in strongly ranging markets. Combining it with a trend strength filter (e.g., ADX) can help.
VWAP Dependency: The effectiveness of the VWAP filter depends on the market’s respect for the daily VWAP as a support/resistance level. In markets with low volume or erratic price action, the VWAP may be less reliable.
Potential Improvements
VWAP Buffer: Add a percentage buffer around the VWAP (e.g., require the price to be 1% above/below) to further reduce noise.
Multi-Timeframe VWAP: Incorporate a weekly VWAP for additional trend confirmation on longer timeframes.
Trend Strength Filter: Add an ADX filter to ensure signals are generated only during strong trends (e.g., ADX > 25).
ACCURATE TREND LEVELS - TABLE PSv6.1Accurate Trend Level Indicator
Description:
The "Accurate Trend Level" indicator is a powerful tool designed to identify market trends and potential reversals with precision. Built on the concept (foundation) of Swing Highs and Swing Lows, this indicator easily detects uptrends and downtrends, providing traders with clear signals for trend continuation or reversal. Whether you are a swing trader or a trend follower, this indicator offers customization options to suit your trading style.
Key Features:
Trend Identification: Accurately identifies uptrends and downtrends based on Swing High and Swing Low points. This indicator provides signals for Up after Down and Down after Up.
Percentage Adjustment: Includes a customizable percentage factor that reduces false signals and helps identify accurate and strong trends.
Trend Table: Displays essential data in a table, such as:
Last and running Trend Position (Uptrend/Downtrend)
Date and Time of the last and running trend change
Reversal Level (price level for the next potential trend change)
Max. Run-up feature is also provided, which shows how much the market has moved according to the trend.
How It Works:
The indicator analyzes price action using Swing Highs and Lows to determine the current trend direction. A user-defined percentage threshold filters out minor fluctuations, ensuring only significant trends are highlighted. The table provides a quick snapshot of the latest trend data, while reversal levels help traders anticipate the next move.
Cumulative Histogram TickThis script is designed to create a cumulative histogram based on tick data from a specific financial instrument. The histogram resets at the start of each trading session, which is defined by a fixed time.
Key Components:
Tick Data Retrieval:
The script fetches the closing tick values from the specified instrument using request.security("TICK.NY", timeframe.period, close). This line ensures that the script works with the tick data for each bar on the chart.
Session Start and End Detection:
Start Hour: The script checks if the current bar's time is 9:30 AM (hour == 9 and minute == 30). This is used to reset the cumulative value at the beginning of each trading session.
End Hour: It also checks if the current bar's time is 4:00 PM (hour == 16). However, this condition is used to prevent further accumulation after the session ends.
Cumulative Value Management:
Reset: When the start hour condition is met (startHour), the cumulative value (cumulative) is reset to zero. This ensures that each trading session starts with a clean slate.
Accumulation: For all bars that are not at the end hour (not endHour), the tick value is added to the cumulative total. This process continues until the end of the trading session.
Histogram Visualization:
The cumulative value is plotted as a histogram using plot.style_histogram. The color of the histogram changes based on whether the cumulative value is positive (green) or negative (red).
Usage
This script is useful for analyzing intraday market activity by visualizing the accumulation of tick data over a trading session. It helps traders identify trends or patterns within each session, which can be valuable for making informed trading decisions.
VIX Implied MovesKey Features:
Three Timeframe Bands:
Daily: Blue bands showing ±1σ expected move
Weekly: Green bands showing ±1σ expected move
30-Day: Red bands showing ±1σ expected move
Calculation Methodology:
Uses VIX's annualized volatility converted to specific timeframes using square root of time rule
Trading day convention (252 days/year)
Band width = Price × (VIX/100) ÷ √(number of periods)
Visual Features:
Colored semi-transparent backgrounds between bands
Progressive line thickness (thinner for shorter timeframes)
Real-time updates as VIX and ES prices change
Example Calculation (VIX=20, ES=5000):
Daily move = 5000 × (20/100)/√252 ≈ ±63 points
Weekly move = 5000 × (20/100)/√50 ≈ ±141 points
Monthly move = 5000 × (20/100)/√21 ≈ ±218 points
This indicator helps visualize expected price ranges based on current volatility conditions, with wider bands indicating higher market uncertainty. The probabilistic ranges represent 68% confidence levels (1 standard deviation) derived from options pricing.
