50-Line Oscillator // (\_/)
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25-Line Oscillator
Description:
The 25-Line Oscillator is a sophisticated technical analysis tool designed to visualize market trends through the use of multiple Simple Moving Averages (SMAs). This indicator computes a series of 26 SMAs, incrementally increasing the base length, providing traders with a comprehensive view of price dynamics.
Features:
Customizable Base Length: Adjust the base length of the SMAs according to trading preferences, enhancing versatility for different market conditions.
Rainbow Effect: The indicator employs a visually appealing rainbow color scheme to differentiate between the various trend lines, making it easy to identify crossovers and momentum shifts.
Crossovers Detection: The script includes logic to detect crossover events between consecutive trend lines, which can serve as signals for potential entry or exit points in trading.
Clear Visualization: Suitable for both novice and seasoned traders, the plots enable quick interpretation of trends and market behavior.
How to Use:
Add the indicator to your chart and customize the base length as desired.
Observe the rainbow-colored lines for trend direction.
Look for crossover events between the SMAs as potential trading signals.
Application: This indicator is particularly useful for swing traders and trend followers who aim to capitalize on market momentum and identify reversals. By monitoring the behavior of multiple SMAs, traders can gain insights into the strength and direction of price movements over various time frames.
M-oscillator
Double RSI OscillatorThe Double RSI Oscillator
Hello Gs,
I came back from the dead and tried to see what a little tweak to RSI could do, and I think it is quite interesting and might be worth checking out.
Warning:
This indicator has lots of false signals unfortunatly
How does the DRSI Oscillator work?
Very simple, the DRSI oscillator at the very base is just 2 RSIs that should smooth each other out, making a smoother trend signal generation for trend analysis. One RSI is set to have lower values, by considering the lowest point of the price, and one RSI is set to have higher values using pretty much the same thing. The trend changes from positive to negative if RSI with higher values crosses negative treshhold, and from negative to positive if RSI with lower value crosses positive treshhold. On top of this I added some additional settings to smooth or speed it further, if these were a good idea, I guess only time will tell :D.
Settings
Here is a guide of what setting changes what and how it might be suitable for you:
RSI Optimism length: length of the RSI with higher values (higher values will be better for longer term, lower for medium term)
RSI Pesimism length: length of the RSI with lower values (higher values will be better for longer term, lower for medium term)
Positive treshhold: The value RSI pesimism needs to pass in order to change trends (in case of using RSI avg. the value the average needs to pass), making this higher can give you faster signals, but expect more false ones
Negative treshholds: The value RSI optimism needs to pass in order to change trends (in case of using RSI avg. the value the average needs to pass), lowering this can give you faster signals, but expect more false ones
Smoothing type: Select the type of smoothing (or none) to smooth your signals as you want, this one you need to play around with.
Smoothing length: The length of your smoothing method (if none is selected it wont change anything)
Use RSI average instead: self-explanatory, go figure
Above/Below Mean Trend: Changes the way trend logic works
Why consider using this indicator?
The DRSI Oscillator is a tool that has huge flexibility (due to tons of settings that base RSI doesnt, like trend treshholds), and is smoother allowing traders and investors to get high quality or high speed signals, allowing great entries and exits
Z SMMA | QuantEdgeB📈 Introducing Z-Score SMMA (Z SMMA) by QuantEdgeB
🛠️ Overview
Z SMMA is a momentum-driven oscillator designed to track the standardized deviation of a Smoothed Moving Average (SMMA). By applying Z-score normalization, this tool dynamically adapts to price volatility, enabling traders to detect meaningful directional shifts and trend changes with enhanced clarity.
It serves both as a trend-following and mean-reversion system, identifying opportunities through standardized thresholds while remaining robust across volatile and calm market conditions.
✨ Key Features
🔹 Z-Score Normalization Engine
Applies Z-score to a custom SMMA baseline, allowing traders to compare price action relative to its recent volatility-adjusted mean.
🔹 Dynamic Trend Detection
Generates actionable long/short signals based on customizable Z-thresholds, making it adaptable across different asset classes and timeframes.
🔹 Overbought/Oversold Zones
Highlight reversion and profit-taking zones (default OB: +2 to +4, OS: -2 to -4), great for counter-trend or mean-reversion strategies.
🔹 Visual Reinforcement Tools
Includes candle coloring, gradient fills, and optional ALMA/EMA band overlays to visualize trend regime transitions.
🔍 How It Works
1️⃣ Z-Score SMMA Calculation
The core is a custom Smoothed Moving Average (SMMA) that is normalized by its standard deviation over a lookback period.
Final Formula:
Z = (SMMA - Mean) / StdDev
2️⃣ Signal Generation
• ✅ Long Bias: Z-Score > Long Threshold (default: 0)
• ❌ Short Bias: Z-Score < Short Threshold (default: 0)
3️⃣ Visual Aids
• Candle Color → Shows trend bias
• Band Fills → Highlight trend strength
• Overlays → Optional ALMA/EMA bands for structure analysis
⚙️ Custom Settings
• SMMA Length → Default: 12
• Z-Score Lookback → Default: 30
• Long Threshold → Default: 0
• Short Threshold → Default: 0
• Color Themes → Choose from 6 visual modes
• Extra Plots → Toggle advanced overlays (ALMA, EMA, bands)
• Label Display → Show/hide “𝓛𝓸𝓷𝓰” & “𝓢𝓱𝓸𝓻𝓽” markers
👥 Who Should Use It?
✅ Trend Traders → For early entries with confirmation from Z-score expansion
✅ Quantitative Analysts → Standardized deviation enables comparison across assets
✅ Mean-Reversion Traders → Use OB/OS zones to fade parabolic spikes
✅ Swing & Systematic Traders → Identify momentum shifts with optional ALMA/EMA overlays
📌 Conclusion
Z SMMA offers a smart, adaptive framework for tracking deviation from equilibrium in a quant-friendly format. Whether you're looking to follow trends or catch exhaustion points, Z SMMA provides a clear, standardized view of momentum and price extremes.
🔹 Key Takeaways:
1️⃣ Z-Score standardization ensures dynamic range awareness
2️⃣ SMMA base filters out noise, offering smoother signals
3️⃣ Color-coded visuals support faster reaction and cleaner charts
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before
Kernel Weighted DMI | QuantEdgeB📊 Introducing Kernel Weighted DMI (K-DMI) by QuantEdgeB
🛠️ Overview
K-DMI is a next-gen momentum indicator that combines the traditional Directional Movement Index (DMI) with advanced kernel smoothing techniques to produce a highly adaptive, noise-resistant trend signal.
Unlike standard DMI that can be overly reactive or choppy in consolidation phases, K-DMI applies kernel-weighted filtering (Linear, Exponential, or Gaussian) to stabilize directional movement readings and extract a more reliable momentum signal.
✨ Key Features
🔹 Kernel Smoothing Engine
Smooths DMI using your choice of kernel (Linear, Exponential, Gaussian) for flexible noise reduction and clarity.
🔹 Dynamic Trend Signal
Generates real-time long/short trend bias based on signal crossing upper or lower thresholds (defaults: ±1).
🔹 Visual Encoding
Includes directional gradient fills, candle coloring, and momentum-based overlays for instant signal comprehension.
🔹 Multi-Mode Plotting
Optional moving average overlays visualize structure and compression/expansion within price action.
📐 How It Works
1️⃣ Directional Movement Index (DMI)
Calculates the traditional +DI and -DI differential to derive directional bias.
2️⃣ Kernel-Based Smoothing
Applies a custom-weighted average across historical DMI values using one of three smoothing methods:
• Linear → Simple tapering weights
• Exponential → Decay curve for recent emphasis
• Gaussian → Bell-shaped weight for centered precision
3️⃣ Signal Generation
• ✅ Long → Signal > Long Threshold (default: +1)
• ❌ Short → Signal < Short Threshold (default: -1)
Additional overlays signal potential compression zones or trend resumption using gradient and line fills.
⚙️ Custom Settings
• DMI Length: Default = 7
• Kernel Type: Options → Linear, Exponential, Gaussian (Def:Linear)
• Kernel Length: Default = 25
• Long Threshold: Default = 1
• Short Threshold: Default = -1
• Color Mode: Strategy, Solar, Warm, Cool, Classic, Magic
• Show Labels: Optional entry signal labels (Long/Short)
• Enable Extra Plots: Toggle MA overlays and dynamic bands
👥 Who Is It For?
✅ Trend Traders → Identify sustained directional bias with smoother signal lines
✅ Quant Analysts → Leverage advanced smoothing models to enhance data clarity
✅ Discretionary Swing Traders → Visualize clean breakouts or fades within choppy zones
✅ MA Compression Traders → Use overlay MAs to detect expansion opportunities
📌 Conclusion
Kernel Weighted DMI is the evolution of classic momentum tracking—merging traditional DMI logic with adaptable kernel filters. It provides a refined lens for trend detection, while optional visual overlays support price structure analysis.
🔹 Key Takeaways:
1️⃣ Smoothed and stabilized DMI for reliable trend signal generation
2️⃣ Optional Gaussian/exponential weighting for adaptive responsiveness
3️⃣ Custom gradient fills, dynamic MAs, and candle coloring to support visual clarity
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Normalized DEMA Oscillator SD| QuantEdgeB📊 Introducing Normalized DEMA Oscillator SD (NDOSD) by QuantEdgeB
🛠️ Overview
Normalized DEMA Oscillator SD (NDOSD) is a powerful trend and momentum indicator that blends DEMA-based smoothing with a standard deviation-based normalization engine. The result is an oscillator that adapts to volatility, filters noise, and highlights both trend continuations and reversal zones with exceptional clarity.
It normalizes price momentum within an adaptive SD envelope, allowing comparisons across assets and market conditions. Whether you're a trend trader or mean-reverter, NDOSD provides the insight needed for smarter decision-making.
✨ Key Features
🔹 DEMA-Powered Momentum Core
Utilizes a Double EMA (DEMA) for smoother trend detection with reduced lag.
🔹 Normalized SD Bands
Price momentum is standardized using a dynamic 2× standard deviation range—enabling consistent interpretation across assets and timeframes.
🔹 Overbought/Oversold Detection
Includes clear OB/OS zones with shaded thresholds to identify potential reversals or trend exhaustion areas.
🔹 Visual Trend Feedback
Color-coded oscillator zones, candle coloring, and optional signal labels help traders immediately see trend direction and strength.
📐 How It Works
1️⃣ DEMA Calculation
The core of NDOSD is a smoothed price line using a Double EMA, designed to reduce false signals in choppy markets.
2️⃣ Normalization with SD
The DEMA is normalized within a volatility range using a 2x SD calculation, producing a bounded oscillator from 0–100. This transforms the raw signal into a structured format, allowing for OB/OS detection and trend entry clarity.
3️⃣ Signal Generation
• ✅ Long Signal → Oscillator crosses above the long threshold (default: 55) and price holds above the lower SD boundary.
• ❌ Short Signal → Oscillator drops below short threshold (default: 45), often within upper SD boundary context.
4️⃣ OB/OS Thresholds
• Overbought Zone: Above 100 → Caution / Consider profit-taking.
• Oversold Zone: Below 0 → Watch for accumulation setups.
⚙️ Custom Settings
• Calculation Source: Default = close
• DEMA Period: Default = 30
• Base SMA Period: Default = 20
• Long Threshold: Default = 55
• Short Threshold: Default = 45
• Color Mode: Choose from Strategy, Solar, Warm, Cool, Classic, or Magic
• Signal Labels Toggle: Show/hide Long/Short markers on chart
👥 Ideal For
✅ Trend Followers – Identify breakout continuation zones using oscillator thrust and SD structure
✅ Swing Traders – Catch mid-trend entries or mean reversion setups at OB/OS extremes
✅ Quant/Systemic Traders – Normalize signals for algorithmic integration across assets
✅ Multi-Timeframe Analysts – Easily compare trend health using standardized oscillator ranges
📌 Conclusion
Normalized DEMA Oscillator SD is a sleek and adaptive momentum toolkit that helps traders distinguish true momentum from false noise. With its fusion of DEMA smoothing and SD normalization, it works equally well in trending and range-bound conditions.
🔹 Key Takeaways:
1️⃣ Smoother momentum tracking using DEMA
2️⃣ Cross-asset consistency via SD-based normalization
3️⃣ Versatile for both trend confirmation and reversal identification
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Let me know if you want a strategy script or publish-ready layout for TradingView next!
Median RSI SD| QuantEdgeB📈 Introducing Median RSI SD by QuantEdgeB
🛠️ Overview
Median RSI SD is a hybrid momentum tool that fuses two powerful techniques: Median Price Filtering and RSI-based Momentum. The result? A cleaner, more responsive oscillator designed to reduce noise and increase clarity in trend detection and potential reversals.
By applying the RSI not to raw price but to the percentile-based median, the indicator adapts better to real structural shifts in the market while filtering out temporary price spikes.
✨ Key Features
🔹 Smoothed RSI Momentum
Utilizes a percentile-based median as input to RSI, reducing volatility and enhancing signal reliability.
🔹 Volatility-Weighted SD Zones
Automatically detects overbought/oversold extremes using ±1 standard deviation bands on the median, adapting to current market volatility.
🔹 Trend Signal Overlay
A directional trend signal (Long / Short / Neutral) is derived from the RSI crossing custom thresholds, combined with position relative to SD bands.
🔹 Visual Labeling System
Optional in-chart labels for Long / Short signals and fully color-customizable theme modes.
📊 How It Works
1️⃣ Median RSI Calculation
Instead of using the close price directly, the script first computes a smoothed median via percentile ranking. RSI is then applied to this filtered stream, improving reactivity without overfitting to short-term noise.
2️⃣ Standard Deviation Filtering
Upper and lower SD bands are calculated around the median to identify extreme conditions. A position near the upper SD while RSI is below the short threshold triggers bearish bias. The reverse applies for longs.
3️⃣ Signal Generation
• ✅ Long Signal → RSI crosses above the Long Threshold (default: 65) and price holds above lower SD.
• ❌ Short Signal → RSI crosses below the Short Threshold (default: 45), typically within upper SD range.
4️⃣ Contextual Highlighting
Zone fills on the chart and RSI subgraph indicate Overbought (>75) and Oversold (<25) conditions for added clarity.
⚙️ Custom Settings
• RSI Length → Default: 21
• Median Length → Default: 10
• Long Threshold → Default: 65
• Short Threshold → Default: 45
• Color Mode → Choose from Strategy, Solar, Warm, Cool, Classic, Magic
• Signal Labels Toggle → Optional in-chart long/short labels
👥 Who Should Use It?
✅ Swing & Momentum Traders → Filter entries based on confirmed directional RSI setups.
✅ Range-Bound Traders → Use SD thresholds to spot fakeouts or exhaustion zones.
✅ Intraday Strategists → Enhanced signal clarity makes it usable even on lower timeframes.
✅ System Builders → Combine this signal with price action or confluence layers for smarter rules.
📌 Conclusion
Median RSI SD by QuantEdgeB is more than just a modified oscillator—it's a robust momentum confirmation framework designed for modern volatility. By replacing noisy price feeds with a statistically stable input and layering RSI + SD logic, this tool provides high-clarity signals without sacrificing responsiveness.
🔹 Key Takeaways:
1️⃣ Median-filtered RSI eliminates noise without lag
2️⃣ Standard deviation bands identify exhaustion zones
3️⃣ Reliable for both trend continuation and mean-reversion strategies
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Coppock Curve
The Coppock Curve is a long-term momentum indicator, also known as the "Coppock Guide," used to identify potential long-term market turning points, particularly major downturns and upturns, by smoothing the sum of 14-month and 11-month rates of change with a 10-month weighted moving average.
Here's a more detailed breakdown:
What it is:
The Coppock Curve is a technical indicator designed to identify long-term buy and sell signals in major stock market indices and related ETFs.
How it's calculated:
Rate of Change (ROC): The indicator starts by calculating the rate of change (ROC) for 14 and 11 periods (usually months).
Sum of ROCs: The ROC for the 14-period and 11-period are summed.
Weighted Moving Average (WMA): A 10-period weighted moving average (WMA) is then applied to the sum of the ROCs.
Interpreting the Curve:
Buy Signals: A buy signal is often generated when the Coppock Curve crosses above the zero line, suggesting a potential transition from a bearish to a bullish phase.
Sell Signals: While primarily designed to identify market bottoms, some traders may interpret a cross below the zero line as a sell signal or a bearish warning.
Origin and Purpose:
The Coppock Curve was introduced by economist Edwin Coppock in 1962.
It was originally designed to help investors identify opportune moments to enter the market.
Coppock's inspiration came from the Episcopal Church's concept of the average mourning period, which he believed mirrored the stock market's recovery period.
Limitations:
The Coppock Curve is primarily used for long-term analysis and may not be as effective for short-term or intraday trading.
It may lag in rapidly changing markets, and its signals may not always be reliable.
EMA Price Range by tuanduongEMA Price Range Indicator – Dynamic Range Analysis with Custom EMA (tuanduong2511)
Overview
The EMA Price Range Indicator is designed to help traders visualize the distance between price action and a key Exponential Moving Average (EMA). This indicator dynamically calculates the range from each candle to a user-defined EMA and displays it in a real-time table. By understanding the relationship between price and the EMA, traders can better gauge potential support, resistance, and overextension in the market.
Key Features
✅ Customizable EMA – Allows users to choose the EMA period that best suits their strategy (default: 144).
✅ Real-Time Range Calculation – Computes the absolute difference between the EMA and the price (using the high or low, depending on whether the candle is above or below the EMA).
✅ Minimalist UI – The EMA is plotted directly on the chart, while a small table in the bottom-right corner provides numerical insights, reducing chart clutter.
✅ Versatile Use Cases – Suitable for trend-following traders (identifying pullbacks to EMA) and mean-reversion traders (spotting extended price movements).
How It Works
User-Defined EMA:
The script calculates an Exponential Moving Average (EMA) based on the selected period.
EMA adapts dynamically, giving more weight to recent price movements.
Range Calculation:
If the price is above the EMA, the range is measured from the high point of the candle to the EMA.
If the price is below the EMA, the range is measured from the low point of the candle to the EMA.
This approach ensures that we’re measuring the most relevant distance for price interaction.
Live Table Display:
The current EMA value and the distance (range) from the price are displayed in a small table in the bottom-right corner of the chart.
How to Use It
📌 Trend Traders: Use the indicator to track pullbacks to key EMAs (e.g., EMA 50, 144, or 200). When the price is far from the EMA, it may indicate an overextended trend or potential retracement zone.
📌 Mean Reversion Traders: Look for extreme deviations between price and the EMA. Large distances can signal potential price snapbacks to the mean.
📌 Scalping & Day Trading: Short-term traders can use it with fast EMAs (e.g., EMA 21 or 34) to measure quick price movements relative to short-term momentum.
Why This Indicator?
Unlike traditional EMA indicators, which only plot a moving average, this script provides quantifiable price distance to the EMA, helping traders make data-driven decisions. It allows traders to answer:
✅ Is the price stretched too far from the EMA?
✅ Should I wait for a pullback before entering?
✅ Is the trend strong, or is the price losing momentum?
By integrating EMA-based range analysis, traders gain a clearer understanding of market conditions and can improve their entry, exit, and risk management strategies.
Gioteen-NormThe "Gioteen-Norm" indicator is a versatile and powerful technical analysis tool designed to help traders identify key market conditions such as divergences, overbought/oversold levels, and trend strength. By normalizing price data relative to a moving average and standard deviation, this indicator provides a unique perspective on price behavior, making it easier to spot potential reversals or continuations in the market.
The indicator calculates a normalized value based on the difference between the selected price and its moving average, scaled by the standard deviation over a user-defined period. Additionally, an optional moving average of this normalized value (Green line) can be plotted to smooth the output and enhance signal clarity. This dual-line approach makes it an excellent tool for both short-term and long-term traders.
***Key Features
Divergence Detection: The Gioteen-Norm excels at identifying divergences between price action and the normalized indicator value. For example, if the price makes a higher high while Red line forms a lower high, it may signal a bearish divergence, hinting at a potential reversal.
Overbought/Oversold Conditions: Extreme values of Red line (e.g., significantly above or below zero) can indicate overbought or oversold conditions, helping traders anticipate pullbacks or bounces.
Trend Strength Insight: The normalized output reflects how far the price deviates from its average, providing a measure of momentum and trend strength.
**Customizable Parameters
Traders can adjust the period, moving average type, applied price, and shift to suit their trading style and timeframe.
**How It Works
Label1 (Red Line): Represents the normalized price deviation from a user-selected moving average (SMA, EMA, SMMA, or LWMA) divided by the standard deviation over the specified period. This line highlights the relative position of the price compared to its historical range.
Label2 (Green Line, Optional): A moving average of Label1, which smooths the normalized data to reduce noise and provide clearer signals. This can be toggled on or off via the "Draw MA" option.
**Inputs
Period: Length of the lookback period for normalization (default: 100).
MA Method: Type of moving average for normalization (SMA, EMA, SMMA, LWMA; default: EMA).
Applied Price: Price type used for calculation (Close, Open, High, Low, HL2, HLC3, HLCC4; default: Close).
Shift: Shifts the indicator forward or backward (default: 0).
Draw MA: Toggle the display of the Label2 moving average (default: true).
MA Period: Length of the moving average for Label2 (default: 50).
MA Method (Label2): Type of moving average for Label2 (SMA, EMA, SMMA, LWMA; default: SMA).
**How to Use
Divergence Trading: Look for discrepancies between price action and Label1. A bullish divergence (higher low in Label1 vs. lower low in price) may suggest a buying opportunity, while a bearish divergence could indicate a selling opportunity.
Overbought/Oversold Levels: Monitor extreme Label1 values. For instance, values significantly above +2 or below -2 could indicate overextension, though traders should define thresholds based on the asset and timeframe.
Trend Confirmation: Use Label2 to confirm trend direction. A rising Label2 suggests increasing bullish momentum, while a declining Label2 may indicate bearish pressure.
Combine with Other Tools: Pair Gioteen-Norm with support/resistance levels, RSI, or volume indicators for a more robust trading strategy.
**Notes
The indicator is non-overlay, meaning it plots below the price chart in a separate panel.
Avoid using a Period value of 1, as it may lead to unstable results due to insufficient data for standard deviation calculation.
This tool is best used as part of a broader trading system rather than in isolation.
**Why Use Gioteen-Norm?
The Gioteen-Norm indicator offers a fresh take on price normalization, blending statistical analysis with moving average techniques. Its flexibility and clarity make it suitable for traders of all levels—whether you're scalping on short timeframes or analyzing long-term trends. By publishing this for free, I hope to contribute to the TradingView community and help traders uncover hidden opportunities in the markets.
**Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice. Always backtest and validate any strategy before trading with real capital, and use proper risk management.
Market Push Meter - CoffeeStyleMarket Push Meter - CoffeeKiller Indicator Guide
Welcome traders! This guide will walk you through the Market Push Meter indicator, a sophisticated volume analysis tool developed by CoffeeKiller with the help and assistance of FindBetterTrades that measures and visualizes the ongoing battle between buyers and sellers through volume pressure analysis.
🔔 **Warning: This Is Not a Standard Volume Indicator** 🔔 This indicator analyzes volume pressure in a unique way, combining directional volume with price action to identify market imbalances between buyers and sellers. All credit for the core logic for this indicator goes to FindBetterTrades and his/hers Volume Pressure Histogram (Normalized) (this is my adaptation and style added to that core logic, thus the CoffeeStyle name was added).
Core Concept: Volume Pressure Analysis
The foundation of this indicator lies in measuring the imbalance between buying and selling volume, providing insights into which market participants are exerting more pressure on price movements.
Volume Pressure Columns: Buying vs Selling Force
- Positive Green Columns: Net buying pressure
- Negative Red Columns: Net selling pressure
- Color intensity varies based on pressure strength
- Special coloring for new high/low boundaries
Marker Lines: Dynamic Support/Resistance
- High Marker Line (Magenta): Tracks the highest point reached during buying phases
- Low Marker Line (Cyan): Tracks the lowest point reached during selling phases
- Creates visual boundaries showing pressure extremes
Peak Detection System:
- Triangular markers identify significant local maxima and minima
- Background highlighting shows important pressure peaks
- Helps identify potential reversal points and pressure exhaustion
Reference Lines:
- Overbought Level: Threshold for extreme selling pressure
- Oversold Level: Threshold for extreme buying pressure
- Used to identify potential reversal zones
Core Components
1. Volume Pressure Calculation
- Separation of up-volume and down-volume
- Calculation of net volume pressure
- Smoothing for consistent visualization
- Normalization against total volume for percentage scaling
2. Boundary Tracking System
- Automatic detection of highest values in buying phases
- Automatic detection of lowest values in selling phases
- Step-line visualization of boundaries
- Color-coded for easy identification
3. Peak Detection System
- Identification of local maxima and minima
- Background highlighting of significant peaks
- Triangle markers for peak visualization
- Zero-line cross detection for trend changes
4. Threshold Settings
- Extreme threshold multiplier for identifying significant pressure
- Overbought/oversold levels for potential reversals
- Dynamic color coding based on pressure intensity
- Alert conditions for key pressure levels
Main Features
Volume Analysis Settings
- Customizable volume MA length
- Signal smoothing for clearer readings
- Optional log scale for handling wide range variations
- Adjustable threshold multiplier for sensitivity
Visual Elements
- Color-coded columns showing pressure direction and strength
- Dynamic marker lines for pressure boundaries
- Peak triangles for significant turning points
- Background highlighting for peak identification
- Overbought/oversold reference lines
Signal Generation
- Zero-line crosses for trend change signals
- Boundary breaks for pressure strength
- Peak formation for potential reversals
- Color changes for pressure direction and intensity
- Alert conditions for extreme pressure levels
Customization Options
- Volume analysis parameters
- Marker line visibility and colors
- Peak marker display options
- Log scale toggle for handling various markets
- Overbought/oversold threshold adjustments
Trading Applications
1. Trend Identification
- Volume pressure crossing above zero: buying pressure emerging
- Volume pressure crossing below zero: selling pressure emerging
- Column color: indicates pressure direction
- Column height: indicates pressure strength
- Signal line: confirms overall trend direction
2. Reversal Detection
- Peak triangles after extended trend: potential exhaustion
- Background highlighting: significant reversal points
- Volume pressure approaching marker lines: potential trend change
- Color shifts from bright to muted: decreasing pressure
- Readings beyond overbought/oversold levels: potential reversal zones
3. Pressure Analysis
- Breaking above previous high boundary: accelerating buying pressure
- Breaking below previous low boundary: accelerating selling pressure
- Special coloring (magenta/cyan): boundary breaks indicating strength
- Extreme readings: potential climactic buying/selling
4. Market Structure Assessment
- Consecutive higher peaks: strengthening buying structure
- Consecutive lower troughs: strengthening selling structure
- Peak comparisons: relative strength of pressure phases
- Boundary line steps: market structure levels
Optimization Guide
1. Volume Analysis Settings
- Volume MA Length: Default 25 provides balanced signals
- Lower values (10-15): More responsive, potentially noisier
- Higher values (30-50): Smoother, fewer false signals
- Signal Smoothing Length: Default 8 provides good balance
- Lower values: More responsive to pressure changes
- Higher values: Smoother trend identification
2. Threshold Settings
- Extreme Threshold Multiplier: Default 20.0
- Lower values: More signals, potentially more noise
- Higher values: Fewer signals, but more significant
- Overbought/Oversold Levels: Defaults at 20/-20
- Adjust based on instrument volatility
- Wider settings for more volatile instruments
3. Visual Customization
- Marker Line Colors: Adjust for visibility on your chart
- Peak Marker Color: Default yellow provides good contrast
- Enable/disable background highlights based on preference
- Consider log scale for instruments with wide volume ranges
4. Alert Settings
- Configure alerts for high buying pressure
- Configure alerts for high selling pressure
- Set additional alerts for zero-line crosses
- Consider timeframe when setting alert sensitivity
Best Practices
1. Signal Confirmation
- Wait for zero-line crosses to confirm pressure changes
- Look for peak formations to identify potential reversals
- Check for boundary breaks to confirm strong pressure
- Use with price action for entry/exit precision
- Consider extreme threshold crossings as significant signals
2. Timeframe Selection
- Lower timeframes: more signals, potential noise
- Higher timeframes: cleaner signals, less frequent
- Multiple timeframes: confirm signals across time horizons
- Match to your trading style and holding period
3. Market Context
- Strong buying phase: positive columns breaking above marker line
- Strong selling phase: negative columns breaking below marker line
- Columns approaching zero: potential pressure shift
- Columns beyond overbought/oversold: extreme conditions, potential reversal
4. Combining with Other Indicators
- Use with trend indicators for confirmation
- Pair with price action oscillators for divergence detection
- Combine with traditional volume indicators for validation
- Consider support/resistance levels with boundary lines
Advanced Trading Strategies
1. Boundary Break Strategy
- Enter long when volume pressure breaks above previous high marker line
- Enter short when volume pressure breaks below previous low marker line
- Use zero-line as initial stop-loss reference
- Take profits at formation of opposing peaks
2. Peak Trading Strategy
- Identify significant peaks with triangular markers
- Look for consecutive lower peaks in buying phases for shorting opportunities
- Look for consecutive higher troughs in selling phases for buying opportunities
- Use zero-line crosses as confirmation
3. Extreme Reading Strategy
- Look for volume pressure beyond overbought/oversold levels
- Watch for color changes and peak formations
- Enter counter-trend positions after confirmed peaks
- Use tight stops due to extreme market conditions
4. Volume Color Strategy
- Enter long when columns turn bright green (increasing buying pressure)
- Enter short when columns turn bright red (increasing selling pressure)
- Exit when color intensity fades (decreasing pressure)
- Use marker lines as dynamic support/resistance
Practical Analysis Examples
Bullish Market Scenario
- Volume pressure crosses above zero line
- Green columns grow in height and intensity
- High marker line forms steps upward
- Peak triangles appear at local maxima
- Background highlights appear at significant buying pressure peaks
Bearish Market Scenario
- Volume pressure crosses below zero line
- Red columns grow in depth and intensity
- Low marker line forms steps downward
- Peak triangles appear at local minima
- Background highlights appear at significant selling pressure troughs
Consolidation Scenario
- Volume pressure oscillates around zero line
- Column colors alternate frequently
- Marker lines remain relatively flat
- Few or no new peak highlights appear
- Pressure values remain small
Understanding Market Dynamics Through Market Push Meter
At its core, this indicator provides a unique lens to visualize market pressure through volume analysis:
1. Volume Imbalance: By separating and comparing buying volume (up candles) from selling volume (down candles), the indicator provides insights into which side is exerting more pressure in the market.
2. Normalized Pressure: The indicator normalizes volume pressure as a percentage of total volume, making it more comparable across different market conditions and instruments.
3. Dynamic Boundaries: The marker lines create a visual representation of the "high water marks" of pressure in both directions, helping to identify when markets are making new pressure extremes.
4. Exhaustion Signals: The peak detection system highlights moments where pressure has reached a local maximum or minimum, often precursors to reversals or consolidations.
Remember:
- Combine signals from volume pressure, marker lines, and peak formations
- Use appropriate timeframe settings for your trading style
- Customize the indicator to match your visual preferences and market
- Consider overall market conditions and correlate with price action
This indicator works best when:
- Used as part of a comprehensive trading system
- Combined with proper risk management
- Applied with an understanding of current market conditions
- Signals are confirmed by price action and other indicators
DISCLAIMER: This indicator and its signals are intended solely for educational and informational purposes. They do not constitute financial advice. Trading involves significant risk of loss. Always conduct your own analysis and consult with financial professionals before making trading decisions.
DAMA OSC - Directional Adaptive MA OscillatorOverview:
The DAMA OSC (Directional Adaptive MA Oscillator) is a highly customizable and versatile oscillator that analyzes the delta between two moving averages of your choice. It detects trend progression, regressions, rebound signals, MA cross and critical zone crossovers to provide highly contextual trading information.
Designed for trend-following, reversal timing, and volatility filtering, DAMA OSC adapts to market conditions and highlights actionable signals in real-time.
Features:
Support for 11 custom moving average types (EMA, DEMA, TEMA, ALMA, KAMA, etc.)
Customizable fast & slow MA periods and types
Histogram based on percentage delta between fast and slow MA
Trend direction coloring with “Green”, “Blue”, and “Red” zones
Rebound detection using close or shadow logic
Configurable thresholds: Overbought, Oversold, Underbought, Undersold
Optional filters: rebound validation by candle color or flat-zone filter
Full visual overlay: MA lines, crossover markers, rebound icons
Complete alert system with 16 preconfigured conditions
How It Works:
Histogram Logic:
The histogram measures the percentage difference between the fast and slow MA:
hist_value = ((FastMA - SlowMA) / SlowMA) * 100
Trend State Logic (Green / Blue / Red):
Green_Up = Bullish acceleration
Blue_Up (or Red_Up, depending the display settings) = Bullish deceleration
Blue_Down (or Green_Down, depending the display settings) = Bearish deceleration
Red_Down = Bearish acceleration
Rebound Logic:
A rebound is detected when price:
Crosses back over a selected MA (fast or slow)
After being away for X candles (rebound_backstep)
Optional: filtered by histogram zones or candle color
Inputs:
Display Options:
Show/hide MA lines
Show/hide MA crosses
Show/hide price rebounds
Enable/disable blue deceleration zones
DAMA Settings:
Fast/Slow MA type and length
Source input (close by default)
Overbought/Oversold levels
Underbought/Undersold levels
Rebound Settings:
Use Close and/or Shadow
Rebound MA (Fast/Slow)
Candle color validation
Flat zone filter rebounds (between UnderSold and UnderBought)
Available MA type:
SMA (Simple MA)
EMA (Exponential MA)
DEMA (Double EMA)
TEMA (Triple EMA)
WMA (Weighted MA)
HMA (Hull MA)
VWMA (Volume Weighted MA)
Kijun (Ichimoku Baseline)
ALMA (Arnaud Legoux MA)
KAMA (Kaufman Adaptive MA)
HULLMOD (Modified Hull MA, Same as HMA, tweaked for Pine v6 constraints)
Notes:
**DEMA/TEMA** reduce lag compared to EMA, useful for faster reaction in trending markets.
**KAMA/ALMA** are better suited to noisy or volatile environments (e.g., BTC).
**VWMA** reacts strongly to volume spikes.
**HMA/HULLMOD** are great for visual clarity in fast moves.
Alerts Included (Fully Configurable):
Golden Cross:
Fast MA crosses above Slow MA
Death Cross:
Fast MA crosses below Slow MA
Bullish Rebound:
Rebound from below MA in uptrend
Bearish Rebound:
Rebound from above MA in downtrend
Bull Progression:
Transition into Green_Up with positive delta
Bear Progression:
Transition into Red_Down with negative delta
Bull Regression:
Exit from Red_Down into Blue/Green with negative delta
Bear Regression:
Exit from Green_Up into Blue/Red with positive delta
Crossover Overbought:
Histogram crosses above Overbought
Crossunder Overbought:
Histogram crosses below Overbought
Crossover Oversold:
Histogram crosses above Oversold
Crossunder Oversold:
Histogram crosses below Oversold
Crossover Underbought:
Histogram crosses above Underbought
Crossunder Underbought:
Histogram crosses below Underbought
Crossover Undersold:
Histogram crosses above Undersold
Crossunder Undersold:
Histogram crosses below Undersold
Credits:
Created by Eff_Hash. This code is shared with the TradingView community and full free. do not hesitate to share your best settings and usage.
Pivot Length Percentiles Oscillator# Pivot Length Percentiles Oscillator: Technical Mechanics Explained
## Introduction
The Pivot Length Percentiles Oscillator is a statistical approach to identifying potential market reversals by analyzing the distribution of price movements relative to pivot points. This publication explains the technical mechanics behind the indicator.
## Core Mechanics
### 1. Pivot Point Detection
The indicator begins by identifying significant pivot highs and lows using a user-defined lookback period:
- `lft`: Number of bars to the left of potential pivot point
- `rht`: Number of bars to the right of potential pivot point
These parameters determine how "significant" a pivot needs to be to qualify for analysis.
### 2. Distance Measurement & Historical Database
For each new pivot point identified, the indicator:
- Calculates the absolute price distance from the previous pivot of the same type
- Records the number of candles between consecutive pivots
- Stores these measurements in dynamic arrays that build a historical database
### 3. Statistical Distribution Analysis
Rather than using fixed values, the oscillator analyzes the complete distribution of historical pivot distances and calculates key percentile values:
- `lw` (Low Percentile): Lower boundary for statistical significance
- `md` (Mid Percentile): Median statistical boundary
- `hi` (High Percentile): Upper boundary for statistical extremes
### 4. Oscillator Construction
Two primary oscillator lines are calculated:
- Green line (`osc1`): Measures current price's fall below recent highs with `low - ta.highest(high, lft)`
- Red line (`osc2`): Measures current price's rise above recent lows with `high - ta.lowest(low, lft)`
### 5. Threshold Generation
The percentile values from the historical distribution create dynamic threshold lines:
- For downside movements: Scaled versions of the low percentile (`lw_distance_low`) and high percentile (`hi_distance_low`)
- For upside movements: Scaled versions of the low percentile (`lw_distance_high`) and high percentile (`hi_distance_high`)
### 6. Signal Logic
Entry signals are generated when:
- **Bullish Signal**: The downside oscillator crosses below a statistical threshold while price continues showing downward momentum (close < previous close AND close < previous open)
- **Bearish Signal**: The upside oscillator crosses above a statistical threshold while price continues showing upward momentum (close > previous close AND close > previous open)
### 7. Visualization Options
Users can toggle between:
- Standard view: Shows the oscillator and threshold lines
- Percentile view: Displays the current movement's percentile rank within the historical distribution
## Implementation Notes
- The indicator scales threshold values by 0.9 to create a slight buffer that reduces false signals
- The movement's continuation is confirmed by checking both close-to-close and close-to-open relationships
- Arrays dynamically update throughout the chart's history, making the indicator increasingly accurate as more data is processed
## Mathematical Framework
The core statistical function calculates percentiles using linear interpolation between values when needed:
```
calculate_percentile(array, percentile) =
sortedValue +
fraction * (sortedValue - sortedValue )
```
where `index = (array.size - 1) * percentile / 100`
This mathematical approach ensures the thresholds adapt dynamically to changing market conditions rather than relying on fixed values.
Oracle Prediction Futur
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Indicator Description: Oracle Prediction Futur
The Oracle Prediction Futur is a sophisticated technical indicator designed for traders and analysts looking to gain insights into market trends through the analysis of price movements. This Pine Script™ code integrates innovative elements to enhance the trading experience and is governed by the Mozilla Public License 2.0.
Key Features:
Normalization of Closing Prices:
The indicator normalizes closing prices over a defined lookback period (100 periods) to provide a percentage-based representation of the current price relative to its historical range. This helps in identifying potential price extremes.
Peak and Trough Detection:
It identifies and plots peak tops and bottom troughs based on normalized closing values. Peak tops are marked with vibrant magenta circles, while peak bottoms are indicated by soothing cyan circles, helping traders visually spot significant turning points in the price action.
Dynamic Background Gradient:
The indicator features a visually appealing gradient background that represents market sentiment. The background color transitions between bear and bull colors based on the position of the normalized close within the 0-100 range. This provides an immediate visual cue about the strength or weakness of the market.
Horizontal Reference Lines:
The indicator includes horizontal lines at key levels (9.51 and 92.5) for quick reference, which can help to gauge areas of potential support or resistance.
User-Friendly Visuals:
The combination of background colors, dynamic plots, and clear labeling offers a user-friendly visual representation, making it easier to interpret market conditions at a glance.
Overlay Options:
As an overlay-free indicator, it maintains clarity on the price chart while providing insightful trends and forecasts.
Practical Application:
Traders can utilize the Oracle Prediction Futur indicator to identify potential entry and exit points in their trading strategies. By observing the peaks, troughs, and background color shifts, users can better understand market momentum and price action.
How to Use:
Deploy this indicator on your trading platform, and analyze the peaks and troughs along with the normalized close line and background gradient to inform your trading decisions. Look for alignment between price action and the signaling provided by the indicator for optimized trading results.
RSI Pro+ (Bear market, financial crisis and so on EditionIn markets defined by volatility, fear, and uncertainty – the battlegrounds of bear markets and financial crises – you need tools forged in resilience. Introducing RSI Pro+, a strategy built upon a legendary indicator born in 1978, yet engineered with modern visual clarity to remain devastatingly effective even in the chaotic financial landscapes of 3078.
This isn't about complex algorithms predicting the unpredictable. It's about harnessing the raw, time-tested power of the Relative Strength Index (RSI) to identify potential exhaustion points and capitalize on oversold conditions. RSI Pro+ cuts through the noise, providing clear, actionable signals when markets might be poised for a relief bounce or reversal.
Core Technology (The 1978 Engine):
RSI Crossover Entry: The strategy initiates a LONG position when the RSI (default period 11) crosses above a user-defined low threshold (default 30). This classic technique aims to enter when selling pressure may be waning, offering potential entry points during sharp downturns or periods of consolidation after a fall.
Modern Enhancements (The 3078 Cockpit):
RSI Pro+ isn't just about the signal; it's about providing a professional-grade visual experience directly on your chart:
Entry Bar Highlight: A subtle background flash on the chart signals the exact bar where the RSI crossover condition is met, alerting you to potential entry opportunities.
Trade Bar Coloring: Once a trade is active, the price bars are subtly colored, giving you immediate visual confirmation that the strategy is live in the market.
Entry Price Line: A clear, persistent line marks your exact average entry price for the duration of the trade, serving as a crucial visual anchor.
Take Profit Line: Your calculated Take Profit target is plotted as a distinct line, keeping your objective clearly in sight.
Custom Entry Marker: A precise shape (▲) appears below the bar where the trade entry was actually executed, pinpointing the start of the position.
On-Chart Info Table (HUD): A clean, customizable Heads-Up Display appears when a trade is active, showing vital information at a glance:
Entry Price: Your position's average cost basis.
TP Target: The calculated price level for your Take Profit exit.
Current PnL%: Real-time Profit/Loss percentage for the open trade.
Full Customization: Nearly every aspect is configurable via the settings menu:
RSI Period & Crossover Level
Take Profit Percentage
Toggle ALL visual enhancements on/off individually
Position the Info Table wherever you prefer on the chart.
How to Use RSI Pro+:
Add to Chart: Apply the "RSI Pro+ (Bear market...)" strategy to your TradingView chart. Ensure any previous versions are removed.
Access Settings: Click the cogwheel icon (⚙️) next to the strategy name on your chart.
Configure Inputs (Crucial Step):
RSI Crossover Level: This is key. The default (30) targets standard oversold conditions. In severe downturns, you might experiment with lower levels (e.g., 25, 20) or higher ones (e.g., 40) depending on the asset and timeframe. Observe where RSI(11) typically bottoms out on your chart.
Take Profit Percentage (%): Define your desired profit target per trade (e.g., enter 0.5 for 0.5%, 1.0 for 1%). The default is a very small 0.11%.
RSI Period: While default is 11, you can adjust this (e.g., the standard 14).
Visual Enhancements: Enable or disable the visual features (background highlights, bar coloring, lines, markers, table) according to your preference using the checkboxes. Adjust table position.
Observe & Backtest: Watch how the strategy behaves on your chosen asset and timeframe. Use TradingView's Strategy Tester to analyze historical performance based on your settings. No strategy works perfectly everywhere; testing is essential.
Important Considerations:
Risk Management: This specific script version focuses on a Take Profit exit. It does not include an explicit Stop Loss. You MUST manage risk through appropriate position sizing, potentially adding a Stop Loss manually, or by modifying the script.
Oversold ≠ Reversal: An RSI crossover is an indicator of potential exhaustion, not a guarantee of a price reversal.
Fixed TP: A fixed percentage TP ensures small wins but may exit before larger potential moves.
Backtesting Limitations: Past performance does not guarantee future results.
RSI Pro+ strips away complexity to focus on a robust, time-honored principle, enhanced with modern visuals for the discerning trader navigating today's (and tomorrow's) challenging markets
FiveFactorEdgeUses ATR14, TSI, RSI, Fast Stochastic and Slow Stochastic information to determine potential high and low price, trend strength and direction. The information ia easy to read, self-descriptive and color coded for quick reference. Since it incorporates 5 different elements it could be used by itself but as with any indicator it's highly recommended to use it with other tried and true indicators.
OG Trend MeterDescription:
The OG Trend Meter gives you a visual snapshot of multiple timeframe trends in one glance. Built for speed and clarity, it helps confirm direction across key intraday timeframes: 1m, 5m, 15m, and 30m.
How it works:
Each timeframe analyzes EMA alignment, price action, and momentum.
Displays clear green/red indicators for bullish/bearish trends on each timeframe.
Great for aligning trades with higher timeframe bias.
Best for:
Traders who want multi-timeframe confirmation before pulling the trigger.
Reducing fakeouts by staying with the dominant trend.
Scalping with the 1m chart while respecting 5m–30m direction.
Pair With: OG Supertrend or EMA Stack for high-probability confluence.
OG ATR RangeDescription:
The OG ATR Tool is a clean, visualized version of the Average True Range indicator for identifying volatility, stop-loss levels, and realistic price movement expectations.
How it works:
Calculates the average range (in points/pips) of recent candles.
Overlays ATR bands to help define breakout potential or squeeze zones.
Can be used to size trades or set dynamic stop-loss and target levels.
Best for:
Intraday traders who want to avoid unrealistic targets.
Volatility-based setups and breakout strategies.
Creating position sizing rules based on instrument volatility.
Pro Tip: Combine with your trend indicators to set sniper entries and exits that respect volatility.
[blackcat] L2 Gradient RSIVWAPOVERVIEW
The L2 Gradient RSIVWAP indicator offers traders a powerful tool for assessing market conditions by combining Relative Strength Index (RSI) with Volume Weighted Average Price (VWAP). It features dynamic coloring and clear buy/sell signals to enhance decision-making.
Customizable Inputs: Adjust key parameters such as RSI-VWAP length, oversold/overbought levels, and smoothing period.
Gradient Color Visualization: Provides intuitive gradient coloring to represent RSI-VWAP values.
Buy/Sell Indicators: On-chart labels highlight potential buying and selling opportunities.
Transparent Fills: Visually distinguishes overbought and oversold zones without obscuring other data.
Access the TradingView platform and select the chart where you wish to implement the indicator.
Go to “Indicators” in the toolbar and search for “ L2 Gradient RSIVWAP.”
Click “Add to Chart” to integrate the indicator into your chart.
Customize settings via the input options:
Toggle between standard RSI and RSI-based VWAP.
Set preferred lengths and thresholds for RSI-VWAP calculations.
Configure the smoothing period for ALMA.
Performance can vary based on asset characteristics like liquidity and volatility.
Historical backtests do not predict future market behavior accurately.
The ALMA function, developed by Arnaud Legoux, enhances response times relative to simple moving averages.
Buy and sell signals are derived from RSI-VWAP crossovers; consider additional factors before making trades.
Special thanks to Arnaud Legoux for creating the ALMA function.
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.
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.
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.
Enhanced Fuzzy SMA Analyzer (Multi-Output Proxy) [FibonacciFlux]EFzSMA: Decode Trend Quality, Conviction & Risk Beyond Simple Averages
Stop Relying on Lagging Averages Alone. Gain a Multi-Dimensional Edge.
The Challenge: Simple Moving Averages (SMAs) tell you where the price was , but they fail to capture the true quality, conviction, and sustainability of a trend. Relying solely on price crossing an average often leads to chasing weak moves, getting caught in choppy markets, or missing critical signs of trend exhaustion. Advanced traders need a more sophisticated lens to navigate complex market dynamics.
The Solution: Enhanced Fuzzy SMA Analyzer (EFzSMA)
EFzSMA is engineered to address these limitations head-on. It moves beyond simple price-average comparisons by employing a sophisticated Fuzzy Inference System (FIS) that intelligently integrates multiple critical market factors:
Price deviation from the SMA ( adaptively normalized for market volatility)
Momentum (Rate of Change - ROC)
Market Sentiment/Overheat (Relative Strength Index - RSI)
Market Volatility Context (Average True Range - ATR, optional)
Volume Dynamics (Volume relative to its MA, optional)
Instead of just a line on a chart, EFzSMA delivers a multi-dimensional assessment designed to give you deeper insights and a quantifiable edge.
Why EFzSMA? Gain Deeper Market Insights
EFzSMA empowers you to make more informed decisions by providing insights that simple averages cannot:
Assess True Trend Quality, Not Just Location: Is the price above the SMA simply because of a temporary spike, or is it supported by strong momentum, confirming volume, and stable volatility? EFzSMA's core fuzzyTrendScore (-1 to +1) evaluates the health of the trend, helping you distinguish robust moves from noise.
Quantify Signal Conviction: How reliable is the current trend signal? The Conviction Proxy (0 to 1) measures the internal consistency among the different market factors analyzed by the FIS. High conviction suggests factors are aligned, boosting confidence in the trend signal. Low conviction warns of conflicting signals, uncertainty, or potential consolidation – acting as a powerful filter against chasing weak moves.
// Simplified Concept: Conviction reflects agreement vs. conflict among fuzzy inputs
bullStrength = strength_SB + strength_WB
bearStrength = strength_SBe + strength_WBe
dominantStrength = max(bullStrength, bearStrength)
conflictingStrength = min(bullStrength, bearStrength) + strength_N
convictionProxy := (dominantStrength - conflictingStrength) / (dominantStrength + conflictingStrength + 1e-10)
// Modifiers (Volatility/Volume) applied...
Anticipate Potential Reversals: Trends don't last forever. The Reversal Risk Proxy (0 to 1) synthesizes multiple warning signs – like extreme RSI readings, surging volatility, or diverging volume – into a single, actionable metric. High reversal risk flags conditions often associated with trend exhaustion, providing early warnings to protect profits or consider counter-trend opportunities.
Adapt to Changing Market Regimes: Markets shift between high and low volatility. EFzSMA's unique Adaptive Deviation Normalization adjusts how it perceives price deviations based on recent market behavior (percentile rank). This ensures more consistent analysis whether the market is quiet or chaotic.
// Core Idea: Normalize deviation by recent volatility (percentile)
diff_abs_percentile = ta.percentile_linear_interpolation(abs(raw_diff), normLookback, percRank) + 1e-10
normalized_diff := raw_diff / diff_abs_percentile
// Fuzzy sets for 'normalized_diff' are thus adaptive to volatility
Integrate Complexity, Output Clarity: EFzSMA distills complex, multi-factor analysis into clear, interpretable outputs, helping you cut through market noise and focus on what truly matters for your decision-making process.
Interpreting the Multi-Dimensional Output
The true power of EFzSMA lies in analyzing its outputs together:
A high Trend Score (+0.8) is significant, but its reliability is amplified by high Conviction (0.9) and low Reversal Risk (0.2) . This indicates a strong, well-supported trend.
Conversely, the same high Trend Score (+0.8) coupled with low Conviction (0.3) and high Reversal Risk (0.7) signals caution – the trend might look strong superficially, but internal factors suggest weakness or impending exhaustion.
Use these combined insights to:
Filter Entry Signals: Require minimum Trend Score and Conviction levels.
Manage Risk: Consider reducing exposure or tightening stops when Reversal Risk climbs significantly, especially if Conviction drops.
Time Exits: Use rising Reversal Risk and falling Conviction as potential signals to take profits.
Identify Regime Shifts: Monitor how the relationship between the outputs changes over time.
Core Technology (Briefly)
EFzSMA leverages a Mamdani-style Fuzzy Inference System. Crisp inputs (normalized deviation, ROC, RSI, ATR%, Vol Ratio) are mapped to linguistic fuzzy sets ("Low", "High", "Positive", etc.). A rules engine evaluates combinations (e.g., "IF Deviation is LargePositive AND Momentum is StrongPositive THEN Trend is StrongBullish"). Modifiers based on Volatility and Volume context adjust rule strengths. Finally, the system aggregates these and defuzzifies them into the Trend Score, Conviction Proxy, and Reversal Risk Proxy. The key is the system's ability to handle ambiguity and combine multiple, potentially conflicting factors in a nuanced way, much like human expert reasoning.
Customization
While designed with robust defaults, EFzSMA offers granular control:
Adjust SMA, ROC, RSI, ATR, Volume MA lengths.
Fine-tune Normalization parameters (lookback, percentile). Note: Fuzzy set definitions for deviation are tuned for the normalized range.
Configure Volatility and Volume thresholds for fuzzy sets. Tuning these is crucial for specific assets/timeframes.
Toggle visual elements (Proxies, BG Color, Risk Shapes, Volatility-based Transparency).
Recommended Use & Caveats
EFzSMA is a sophisticated analytical tool, not a standalone "buy/sell" signal generator.
Use it to complement your existing strategy and analysis.
Always validate signals with price action, market structure, and other confirming factors.
Thorough backtesting and forward testing are essential to understand its behavior and tune parameters for your specific instruments and timeframes.
Fuzzy logic parameters (membership functions, rules) are based on general heuristics and may require optimization for specific market niches.
Disclaimer
Trading involves substantial risk. EFzSMA is provided for informational and analytical purposes only and does not constitute financial advice. No guarantee of profit is made or implied. Past performance is not indicative of future results. Use rigorous risk management practices.
