MA Dispersion+MA Dispersion+ — read the “breathing space” between your moving-averages
Get instant feedback on trend strength, volatility expansion and mean-reversion — across any timeframe.
MA Dispersion+ turns the humble moving-average stack into a single, easy-to-read oscillator that tells you at a glance whether price is coiling or fanning out.
🧩 What it does
Plugs into your favourite MA setup
• Pick the classic 5 / 20 / 50 / 200 lengths or disable any combination with one click.
• Choose the MA engine you trust — SMA, EMA, RMA, VWMA or WMA.
• Works on any timeframe thanks to TradingView’s security() engine.
Measures “spread”
For every bar it calculates the absolute distance of each selected MA from their average.
The tighter the stack, the lower the value; the wider the fan, the higher the value.
Adds professional-grade controls
• Weighting — let short-term MAs dominate (Inverse Length), keep everything equal, or dial in your own custom weights.
• Normalisation — convert the raw distance into a percentage of price, ATR multiples, or scale by the MAs’ own mean so you can compare symbols of any price or volatility.
🔍 How traders use it
Trend confirmation – rising dispersion while price breaks out = momentum is genuine.
Volatility squeeze – dispersion parking near zero warns that a big move is loading.
Multi-TF outlook – drop one pane per timeframe (e.g. 5 m, 1 h, 1 D) and see which layer of the market is driving.
Mean-reversion plays – spikes that fade quickly often coincide with exhaustion and snap-backs.
⚙️ Quick-start
Add MA Dispersion+ to your chart.
Set the pane’s timeframe in the first input.
Tick the MA lengths you actually use.
(Optional) Pick a weighting scheme and a normaliser.
Repeat the indicator for as many timeframes as you like — each instance keeps its own settings.
✨ Why you’ll love it
Zero clutter – one orange line tells you what four separate MAs whisper.
Configurable yet bullet-proof – all lengths are hard-coded constants, so Pine never complains.
Context aware – normalisation lets you compare BTC’s $60 000 chaos with EURUSD’s four--decimals calm.
Lightweight – no labels, no drawings, no background processing — perfect for mobile and multi-pane layouts.
Give MA Dispersion+ a try and let your charts breathe — you’ll never look at moving-average ribbons the same way again.
Happy trading!
Oscillaltor
SynchroTrend Oscillator (STO) [PhenLabs]📊 SynchroTrend Oscillator
Version: PineScript™ v5
📌 Description
The SynchroTrend Oscillator (STO) is a multi-timeframe synchronization tool that combines trend information from three distinct timeframes into a single, easy-to-interpret oscillator ranging from -100 to +100.
This indicator solves the common problem of having to analyze multiple timeframe charts separately by consolidating trend direction and strength across different time horizons. The STO helps traders identify when markets are truly synchronized across timeframes, potentially indicating stronger trend conditions and higher probability trading opportunities.
Using either Moving Average crossovers or RSI analysis as the trend definition metric, the STO provides a comprehensive view of market structure that adapts to various trading strategies and market conditions.
🚀 Points of Innovation
Triple-timeframe synchronization in a single view eliminates chart switching
Dual trend detection methods (MA vs Price or RSI) for flexibility across different markets
Dynamic color intensity that automatically increases with signal strength
Scaled oscillator format (-100 to +100) for intuitive trend strength interpretation
Customizable signal thresholds to match your risk tolerance and trading style
Visual alerts when markets reach full synchronization states
🔧 Core Components
Trend Scoring System: Calculates a binary score (+1, -1, or 0) for each timeframe based on selected metrics, providing clear trend direction
Multi-Timeframe Synchronization: Combines and scales trend scores from all three timeframes into a single oscillator
Dynamic Visualization: Adjusts color transparency based on signal strength, creating an intuitive visual guide
Threshold System: Provides customizable levels for identifying potentially significant trading opportunities
🔥 Key Features
Triple Timeframe Analysis: Synchronizes three user-defined timeframes (default: 60min, 15min, 5min) into one view
Dual Trend Detection Methods: Choose between Moving Average vs Price or RSI-based trend determination
Adjustable Signal Smoothing: Apply EMA, SMA, or no smoothing to the oscillator output for your preferred signal responsiveness
Dynamic Color Intensity: Colors become more vibrant as signal strength increases, helping identify strongest setups
Customizable Thresholds: Set your own buy/sell threshold levels to match your trading strategy
Comprehensive Alerts: Six different alert conditions for crossing thresholds, zero line, and full synchronization states
🎨 Visualization
Oscillator Line: The main line showing the synchronized trend value from -100 to +100
Dynamic Fill: Area between oscillator and zero line changes transparency based on signal strength
Threshold Lines: Optional dotted lines indicating buy/sell thresholds for visual reference
Color Coding: Green for bullish synchronization, red for bearish synchronization
📖 Usage Guidelines
Timeframe Settings
Timeframe 1: Default: 60 (1 hour) - Primary higher timeframe for trend definition
Timeframe 2: Default: 15 (15 minutes) - Intermediate timeframe for trend definition
Timeframe 3: Default: 5 (5 minutes) - Lower timeframe for trend definition
Trend Calculation Settings
Trend Definition Metric: Default: “MA vs Price” - Method used to determine trend on each timeframe
MA Type: Default: EMA - Moving Average type when using MA vs Price method
MA Length: Default: 21 - Moving Average period when using MA vs Price method
RSI Length: Default: 14 - RSI period when using RSI method
RSI Source: Default: close - Price data source for RSI calculation
Oscillator Settings
Smoothing Type: Default: SMA - Applies smoothing to the final oscillator
Smoothing Length: Default: 5 - Period for the smoothing function
Visual & Threshold Settings
Up/Down Colors: Customize colors for bullish and bearish signals
Transparency Range: Control how transparency changes with signal strength
Line Width: Adjust oscillator line thickness
Buy/Sell Thresholds: Set levels for potential entry/exit signals
✅ Best Use Cases
Trend confirmation across multiple timeframes
Finding high-probability entry points when all timeframes align
Early detection of potential trend reversals
Filtering trade signals from other indicators
Market structure analysis
Identifying potential divergences between timeframes
⚠️ Limitations
Like all indicators, can produce false signals during choppy or ranging markets
Works best in trending market conditions
Should not be used in isolation for trading decisions
Past performance is not indicative of future results
May require different settings for different markets or instruments
💡 What Makes This Unique
Combines three timeframes in a single visualization without requiring multiple chart windows
Dynamic transparency feature that automatically emphasizes stronger signals
Flexible trend definition methods suitable for different market conditions
Visual system that makes multi-timeframe analysis intuitive and accessible
🔬 How It Works
1. Trend Evaluation:
For each timeframe, the indicator calculates a trend score (+1, -1, or 0) using either:
MA vs Price: Comparing close price to a moving average
RSI: Determining if RSI is above or below 50
2. Score Aggregation:
The three trend scores are combined and then scaled to a range of -100 to +100
A value of +100 indicates all timeframes show bullish conditions
A value of -100 indicates all timeframes show bearish conditions
Values in between indicate varying degrees of alignment
3. Signal Processing:
The raw oscillator value can be smoothed using EMA, SMA, or left unsmoothed
The final value determines line color, fill color, and transparency settings
Threshold levels are applied to identify potential trading opportunities
💡 Note:
The SynchroTrend Oscillator is most effective when used as part of a comprehensive trading strategy that includes proper risk management techniques. For best results, consider using the oscillator in conjunction with support/resistance levels, price action analysis, and other complementary indicators that align with your trading style.
Machine Learning RSI ║ BullVisionOverview:
Introducing the Machine Learning RSI with KNN Adaptation – a cutting-edge momentum indicator that blends the classic Relative Strength Index (RSI) with machine learning principles. By leveraging K-Nearest Neighbors (KNN), this indicator aims at identifying historical patterns that resemble current market behavior and uses this context to refine RSI readings with enhanced sensitivity and responsiveness.
Unlike traditional RSI models, which treat every market environment the same, this version adapts in real-time based on how similar past conditions evolved, offering an analytical edge without relying on predictive assumptions.
Key Features:
🔁 KNN-Based RSI Refinement
This indicator uses a machine learning algorithm (K-Nearest Neighbors) to compare current RSI and price action characteristics to similar historical conditions. The resulting RSI is weighted accordingly, producing a dynamically adjusted value that reflects historical context.
📈 Multi-Feature Similarity Analysis
Pattern similarity is calculated using up to five customizable features:
RSI level
RSI momentum
Volatility
Linear regression slope
Price momentum
Users can adjust how many features are used to tailor the behavior of the KNN logic.
🧠 Machine Learning Weight Control
The influence of the machine learning model on the final RSI output can be fine-tuned using a simple slider. This lets you blend traditional RSI and machine learning-enhanced RSI to suit your preferred level of adaptation.
🎛️ Adaptive Filtering
Additional smoothing options (Kalman Filter, ALMA, Double EMA) can be applied to the RSI, offering better visual clarity and helping to reduce noise in high-frequency environments.
🎨 Visual & Accessibility Settings
Custom color palettes, including support for color vision deficiencies, ensure that trend coloring remains readable for all users. A built-in neon mode adds high-contrast visuals to improve RSI visibility across dark or light themes.
How It Works:
Similarity Matching with KNN:
At each candle, the current RSI and optional market characteristics are compared to historical bars using a KNN search. The algorithm selects the closest matches and averages their RSI values, weighted by similarity. The more similar the pattern, the greater its influence.
Feature-Based Weighting:
Similarity is determined using normalized values of the selected features, which gives a more refined result than RSI alone. You can choose to use only 1 (RSI) or up to all 5 features for deeper analysis.
Filtering & Blending:
After the machine learning-enhanced RSI is calculated, it can be optionally smoothed using advanced filters to suppress short-term noise or sharp spikes. This makes it easier to evaluate RSI signals in different volatility regimes.
Parameters Explained:
📊 RSI Settings:
Set the base RSI length and select your preferred smoothing method from 10+ moving average types (e.g., EMA, ALMA, TEMA).
🧠 Machine Learning Controls:
Enable or disable the KNN engine
Select how many nearest neighbors to compare (K)
Choose the number of features used in similarity detection
Control how much the machine learning engine affects the RSI calculation
🔍 Filtering Options:
Enable one of several advanced smoothing techniques (Kalman Filter, ALMA, Double EMA) to adjust the indicator’s reactivity and stability.
📏 Threshold Levels:
Define static overbought/oversold boundaries or reference dynamically adjusted thresholds based on historical context identified by the KNN algorithm.
🎨 Visual Enhancements:
Select between trend-following or impulse coloring styles. Customize color palettes to accommodate different types of color blindness. Enable neon-style effects for visual clarity.
Use Cases:
Swing & Trend Traders
Can use the indicator to explore how current RSI readings compare to similar market phases, helping to assess trend strength or potential turning points.
Intraday Traders
Benefit from adjustable filters and fast-reacting smoothing to reduce noise in shorter timeframes while retaining contextual relevance.
Discretionary Analysts
Use the adaptive OB/OS thresholds and visual cues to supplement broader confluence zones or market structure analysis.
Customization Tips:
Higher Volatility Periods: Use more neighbors and enable filtering to reduce noise.
Lower Volatility Markets: Use fewer features and disable filtering for quicker RSI adaptation.
Deeper Contextual Analysis: Increase KNN lookback and raise the feature count to refine pattern recognition.
Accessibility Needs: Switch to Deuteranopia or Monochrome mode for clearer visuals in specific color vision conditions.
Final Thoughts:
The Machine Learning RSI combines familiar momentum logic with statistical context derived from historical similarity analysis. It does not attempt to predict price action but rather contextualizes RSI behavior with added nuance. This makes it a valuable tool for those looking to elevate traditional RSI workflows with adaptive, research-driven enhancements.
RSI Full [Titans_Invest]RSI Full
One of the most complete RSI indicators on the market.
While maintaining the classic RSI foundation, our indicator integrates multiple entry conditions to generate more accurate buy and sell signals.
All conditions are fully configurable, allowing complete customization to fit your trading strategy.
⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
Overbought: When the RSI is above 70, indicating that the asset may be overbought.
Oversold: When the RSI is below 30, indicating that the asset may be oversold.
Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
______________________________________________________
🔹 CONDITIONS TO BUY 📈
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
______________________________________________________
______________________________________________________
🔸 CONDITIONS TO SELL 📉
______________________________________________________
• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
______________________________________________________
______________________________________________________
🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
______________________________________________________
______________________________________________________
⯁ UNIQUE FEATURES
______________________________________________________
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
______________________________________________________
📜 SCRIPT : RSI Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy the Spell!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
CCI with Signals & Divergence [AIBitcoinTrend]👽 CCI with Signals & Divergence (AIBitcoinTrend)
The Hilbert Adaptive CCI with Signals & Divergence takes the traditional Commodity Channel Index (CCI) to the next level by dynamically adjusting its calculation period based on real-time market cycles using Hilbert Transform Cycle Detection. This makes it far superior to standard CCI, as it adapts to fast-moving trends and slow consolidations, filtering noise and improving signal accuracy.
Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, helping traders identify potential reversals and manage risk effectively.
👽 What Makes the Hilbert Adaptive CCI Unique?
Unlike the traditional CCI, which uses a fixed-length lookback period, this version automatically adjusts its lookback period using Hilbert Transform to detect the dominant cycle in the market.
✅ Hilbert Transform Adaptive Lookback – Dynamically detects cycle length to adjust CCI sensitivity.
✅ Real-Time Divergence Detection – Instantly identifies bullish and bearish divergences for early reversal signals.
✅ Implement Crossover/Crossunder signals tied to ATR-based trailing stops for risk management
👽 The Math Behind the Indicator
👾 Hilbert Transform Cycle Detection
The Hilbert Transform estimates the dominant market cycle length based on the frequency of price oscillations. It is computed using the in-phase and quadrature components of the price series:
tp = (high + low + close) / 3
smooth = (tp + 2 * tp + 2 * tp + tp ) / 6
detrender = smooth - smooth
quadrature = detrender - detrender
inPhase = detrender + quadrature
outPhase = quadrature - inPhase
instPeriod = 0.0
deltaPhase = math.abs(inPhase - inPhase ) + math.abs(outPhase - outPhase )
instPeriod := nz(3.25 / deltaPhase, instPeriod )
dominantCycle = int(math.min(math.max(instPeriod, cciMinPeriod), 500))
Where:
In-Phase & Out-Phase Components are derived from a detrended version of the price series.
Instantaneous Frequency measures the rate of cycle change, allowing the CCI period to adjust dynamically.
The result is bounded within a user-defined min/max range, ensuring stability.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence Setup:
Price makes a lower low, while CCI forms a higher low.
Buy signal is confirmed when CCI shows upward momentum.
Bearish Divergence Setup:
Price makes a higher high, while CCI forms a lower high.
Sell signal is confirmed when CCI shows downward momentum.
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ CCI crosses above -100 → Buy signal.
✅ A bullish trailing stop is placed at Low - (ATR × Multiplier).
✅ Exit if the price crosses below the stop.
Bearish Setup:
✅ CCI crosses below 100 → Sell signal.
✅ A bearish trailing stop is placed at High + (ATR × Multiplier).
✅ Exit if the price crosses above the stop.
👽 Why It’s Useful for Traders
Hilbert Adaptive Period Calculation – No more fixed-length periods; the indicator dynamically adapts to market conditions.
Real-Time Divergence Alerts – Helps traders anticipate market reversals before they occur.
ATR-Based Risk Management – Stops automatically adjust based on volatility.
Works Across Multiple Markets & Timeframes – Ideal for stocks, forex, crypto, and futures.
👽 Indicator Settings
Min & Max CCI Period – Defines the adaptive range for Hilbert-based lookback.
Smoothing Factor – Controls the degree of smoothing applied to CCI.
Enable Divergence Analysis – Toggles real-time divergence detection.
Lookback Period – Defines the number of bars for detecting pivot points.
Enable Crosses Signals – Turns on CCI crossover-based trade signals.
ATR Multiplier – Adjusts trailing stop sensitivity.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
WaveTrend Divergences, Candle Colouring and TP Signal [LuciTech]WaveTrend is a momentum-based oscillator designed to track trend strength, detect divergences, and highlight potential take-profit zones using Bollinger Bands. It provides a clear visualization of market conditions to help traders identify trend shifts and exhaustion points.
The WaveTrend Oscillator consists of a smoothed momentum line (WT Line) and a signal line, which work together to indicate trend direction and possible reversals. When the WT Line crosses above the signal line, it suggests bullish momentum, while crossing below signals bearish momentum.
Candle colouring changes dynamically based on WaveTrend crossovers. If the WT Line crosses above the signal line, candles turn bullish. If the WT Line crosses below the signal line, candles turn bearish. This provides an immediate visual cue for trend direction.
Divergence Detection identifies when price action contradicts the WaveTrend movement.
Bullish Divergence appears when price makes a lower low, but the WT Line forms a higher low, suggesting weakening bearish pressure.
Bearish Divergence appears when price makes a higher high, but the WT Line forms a lower high, indicating weakening bullish pressure.
Plus (+) Divergences are stronger signals that occur when the first pivot of the divergence happens at an extreme level—above +60 for bearish divergence or below -60 for bullish divergence. These levels suggest the market is overbought or oversold, making the divergence more significant.
Bollinger Band Signals highlight potential take-profit zones by detecting when the WT Line moves beyond its upper or lower Bollinger Band.
If the WT Line crosses above the upper band, it signals stretched bullish momentum, suggesting a possible pullback or reversal.
If the WT Line crosses below the lower band, it indicates stretched bearish momentum, warning of a potential bounce.
How It Works
The WaveTrend momentum calculation is based on an EMA-smoothed moving average to filter out noise and provide a more reliable trend indication.
The WT Line (momentum line) fluctuates based on market momentum.
The signal line smooths out the WT Line to help identify trend shifts.
When the WT Line crosses above the signal line, it suggests buying pressure, and when it crosses below, it indicates selling pressure.
Divergences are detected by comparing pivot highs and lows in price with pivot highs and lows in the WT Line.
A pivot forms when a local high or low is confirmed after a certain number of bars.
The indicator tracks whether price action and the WT Line are making opposite movements.
If a divergence occurs and the first pivot was beyond ±60, it is marked as a Plus Divergence, making it a stronger reversal signal.
Bollinger Bands are applied directly to the WT Line instead of price, identifying when the WT Line moves outside its volatility range. This helps traders recognize when momentum is overstretched and a potential reversal or retracement is likely.
Settings
Channel Length (default: 8) controls the period used to calculate the WT Line.
Average Length (default: 16) smooths the WT Line for better trend detection.
Divergences (on/off) enables or disables divergence plotting.
Candle colouring (on/off) applies or removes trend-based candle colour changes.
Bollinger Band Signals (on/off) toggles take-profit signals when the WT Line crosses the bands.
Bullish/Bearish colours allow customization of divergence and signal colours.
Interpretation
The WaveTrend Oscillator helps traders assess market momentum and trend strength.
Crossovers between the WT Line and signal line indicate potential trend reversals.
Divergences warn of weakening momentum and possible reversals, with Plus Divergences acting as stronger signals.
Bollinger Band Crosses highlight areas where momentum is overstretched, signaling potential profit-taking opportunities.
[COG]StochRSI Zenith📊 StochRSI Zenith
This indicator combines the traditional Stochastic RSI with enhanced visualization features and multi-timeframe analysis capabilities. It's designed to provide traders with a comprehensive view of market conditions through various technical components.
🔑 Key Features:
• Advanced StochRSI Implementation
- Customizable RSI and Stochastic calculation periods
- Multiple moving average type options (SMA, EMA, SMMA, LWMA)
- Adjustable signal line parameters
• Visual Enhancement System
- Dynamic wave effect visualization
- Energy field display for momentum visualization
- Customizable color schemes for bullish and bearish signals
- Adaptive transparency settings
• Multi-Timeframe Analysis
- Higher timeframe confirmation
- Synchronized market structure analysis
- Cross-timeframe signal validation
• Divergence Detection
- Automated bullish and bearish divergence identification
- Customizable lookback period
- Clear visual signals for confirmed divergences
• Signal Generation Framework
- Price action confirmation
- SMA-based trend filtering
- Multiple confirmation levels for reduced noise
- Clear entry signals with customizable display options
📈 Technical Components:
1. Core Oscillator
- Base calculation: 13-period RSI (adjustable)
- Stochastic calculation: 8-period (adjustable)
- Signal lines: 5,3 smoothing (adjustable)
2. Visual Systems
- Wave effect with three layers of visualization
- Energy field display with dynamic intensity
- Reference bands at 20/30/50/70/80 levels
3. Confirmation Mechanisms
- SMA trend filter
- Higher timeframe alignment
- Price action validation
- Divergence confirmation
⚙️ Customization Options:
• Visual Parameters
- Wave effect intensity and speed
- Energy field sensitivity
- Color schemes for bullish/bearish signals
- Signal display preferences
• Technical Parameters
- All core calculation periods
- Moving average types
- Divergence detection settings
- Signal confirmation criteria
• Display Settings
- Chart and indicator signal placement
- SMA line visualization
- Background highlighting options
- Label positioning and size
🔍 Technical Implementation:
The indicator combines several advanced techniques to generate signals. Here are key components with code examples:
1. Core StochRSI Calculation:
// Base RSI calculation
rsi = ta.rsi(close, rsi_length)
// StochRSI transformation
stochRSI = ((ta.highest(rsi, stoch_length) - ta.lowest(rsi, stoch_length)) != 0) ?
(100 * (rsi - ta.lowest(rsi, stoch_length))) /
(ta.highest(rsi, stoch_length) - ta.lowest(rsi, stoch_length)) : 0
2. Signal Generation System:
// Core signal conditions
crossover_buy = crossOver(sk, sd, cross_threshold)
valid_buy_zone = sk < 30 and sd < 30
price_within_sma_bands = close <= sma_high and close >= sma_low
// Enhanced signal generation
if crossover_buy and valid_buy_zone and price_within_sma_bands and htf_allows_long
if is_bullish_candle
long_signal := true
else
awaiting_bull_confirmation := true
3. Multi-Timeframe Analysis:
= request.security(syminfo.tickerid, mtf_period,
)
The HTF filter looks at a higher timeframe (default: 4H) to confirm the trend
It only allows:
Long trades when the higher timeframe is bullish
Short trades when the higher timeframe is bearish
📈 Trading Application Guide:
1. Signal Identification
• Oversold Opportunities (< 30 level)
- Look for bullish crosses of K-line above D-line
- Confirm with higher timeframe alignment
- Wait for price action confirmation (bullish candle)
• Overbought Conditions (> 70 level)
- Watch for bearish crosses of K-line below D-line
- Verify higher timeframe condition
- Confirm with bearish price action
2. Divergence Trading
• Bullish Divergence
- Price makes lower lows while indicator makes higher lows
- Most effective when occurring in oversold territory
- Use with support levels for entry timing
• Bearish Divergence
- Price makes higher highs while indicator shows lower highs
- Most reliable in overbought conditions
- Combine with resistance levels
3. Wave Effect Analysis
• Strong Waves
- Multiple wave lines moving in same direction indicate momentum
- Wider wave spread suggests increased volatility
- Use for trend strength confirmation
• Energy Field
- Higher intensity in trading zones suggests stronger moves
- Use for momentum confirmation
- Watch for energy field convergence with price action
The energy field is like a heat map that shows momentum strength
It gets stronger (more visible) when:
Price is in oversold (<30) or overbought (>70) zones
The indicator lines are moving apart quickly
A strong signal is forming
Think of it as a "strength meter" - the more visible the energy field, the stronger the potential move
4. Risk Management Integration
• Entry Confirmation
- Wait for all signal components to align
- Use higher timeframe for trend direction
- Confirm with price action and SMA positions
• Stop Loss Placement
- Consider placing stops beyond recent swing points
- Use ATR for dynamic stop calculation
- Account for market volatility
5. Position Management
• Partial Profit Taking
- Consider scaling out at overbought/oversold levels
- Use wave effect intensity for exit timing
- Monitor energy field for momentum shifts
• Trade Duration
- Short-term: Use primary signals in trading zones
- Swing trades: Focus on divergence signals
- Position trades: Utilize higher timeframe signals
⚠️ Important Usage Notes:
• Avoid:
- Trading against strong trends
- Relying solely on single signals
- Ignoring higher timeframe context
- Over-leveraging based on signals
Remember: This tool is designed to assist in analysis but should never be used as the sole decision-maker for trades. Always maintain proper risk management and combine with other forms of analysis.
Smart Money Index + True Strength IndexThe Smart Money Index + True Strength Index indicator is a combination of two popular technical analysis indicators: the Smart Money Index (SMI) and the True Strength Index (TSI). This combined indicator helps traders identify potential entry points for long and short positions based on signals from both indexes.
Main Components:
Smart Money Index (SMI):
The SMI measures the difference between the closing and opening price of a candle multiplied by the trading volume over a certain period of time. This allows you to assess the activity of large players ("smart money") in the market. If the SMI value is above a certain threshold (smiThreshold), it may indicate a bullish trend, and if lower, it may indicate a bearish trend.
True Strength Index (TSI):
The TSI is an oscillator that measures the strength of a trend by comparing the price change of the current bar with the previous bar. It uses two exponential moving averages (EMAS) to smooth the data. TSI values can fluctuate around zero, with values above the overbought level indicating a possible downward correction, and values below the oversold level signaling a possible upward correction.
Parameters:
SMI Length: Defines the number of candles used to calculate the average SMI value. The default value is 14.
SMI Threshold: A threshold value that is used to determine a buy or sell signal. The default value is 0.
Length of the first TSI smoothing (tsiLength1): The length of the first EMA for calculating TSI. The default value is 25.
Second TSI smoothing length (tsiLength2): The length of the second EMA for additional smoothing of TSI values. The default value is 13.
TSI Overbought level: The level at which the market is considered to be overbought. The default value is 25.
Oversold level TSI: The level at which it is considered that the market is in an oversold state. The default value is -25.
Logic of operation:
SMI calculation:
First, the difference between the closing and opening price of each candle (close - open) is calculated.
This difference is then multiplied by the trading volume.
The resulting product is averaged using a simple moving average (SMA) over a specified period (smiLength).
Calculation of TSI:
The price change relative to the previous bar is calculated (close - close ).
The first EMA with the length tsiLength1 is applied.
Next, a second EMA with a length of tsiLength2 is applied to obtain the final TSI value.
The absolute value of price changes is calculated in the same way, and two emas are also applied.
The final TSI index is calculated as the ratio of these two values multiplied by 100.
Graphical representation:
The SMI and TSI lines are plotted on the graph along with their respective thresholds.
For SMI, the line is drawn in orange, and the threshold level is dotted in gray.
For the TSI, the line is plotted in blue, the overbought and oversold levels are indicated by red and green dotted lines, respectively.
Conditions for buy/sell signals:
A buy (long) signal is generated when:
SMI is greater than the threshold (smi > smiThreshold)
TSI crosses the oversold level from bottom to top (ta.crossover(tsi, oversold)).
A sell (short) signal is generated when:
SMI is less than the threshold (smi < smiThreshold)
TSI crosses the overbought level from top to bottom (ta.crossunder(tsi, overbought)).
Signal display:
When the conditions for a long or short are met, labels labeled "LONG" or "SHORT" appear on the chart.
The label for the long is located under the candle and is colored green, and for the short it is above the candle and is colored red.
Notification generation:
The indicator also supports notifications via the TradingView platform. Notifications are sent when conditions arise for a long or short position.
This combined indicator provides the trader with the opportunity to use both SMI and TSI signals simultaneously, which can improve the accuracy of trading decisions.
[COG] Adaptive Squeeze Intensity 📊 Adaptive Squeeze Intensity (ASI) Indicator
🎯 Overview
The Adaptive Squeeze Intensity (ASI) indicator is an advanced technical analysis tool that combines the power of volatility compression analysis with momentum, volume, and trend confirmation to identify high-probability trading opportunities. It quantifies the degree of price compression using a sophisticated scoring system and provides clear entry signals for both long and short positions.
⭐ Key Features
- 📈 Comprehensive squeeze intensity scoring system (0-100)
- 📏 Multiple Keltner Channel compression zones
- 📊 Volume analysis integration
- 🎯 EMA-based trend confirmation
- 🎨 Proximity-based entry validation
- 📱 Visual status monitoring
- 🎨 Customizable color schemes
- ⚡ Clear entry signals with directional indicators
🔧 Components
1. 📐 Squeeze Intensity Score (0-100)
The indicator calculates a total squeeze intensity score based on four components:
- 📊 Band Convergence (0-40 points): Measures the relationship between Bollinger Bands and Keltner Channels
- 📍 Price Position (0-20 points): Evaluates price location relative to the base channels
- 📈 Volume Intensity (0-20 points): Analyzes volume patterns and thresholds
- ⚡ Momentum (0-20 points): Assesses price momentum and direction
2. 🎨 Compression Zones
Visual representation of squeeze intensity levels:
- 🔴 Extreme Squeeze (80-100): Red zone
- 🟠 Strong Squeeze (60-80): Orange zone
- 🟡 Moderate Squeeze (40-60): Yellow zone
- 🟢 Light Squeeze (20-40): Green zone
- ⚪ No Squeeze (0-20): Base zone
3. 🎯 Entry Signals
The indicator generates entry signals based on:
- ✨ Squeeze release confirmation
- ➡️ Momentum direction
- 📊 Candlestick pattern confirmation
- 📈 Optional EMA trend alignment
- 🎯 Customizable EMA proximity validation
⚙️ Settings
🔧 Main Settings
- Base Length: Determines the calculation period for main indicators
- BB Multiplier: Sets the Bollinger Bands deviation multiplier
- Keltner Channel Multipliers: Three separate multipliers for different compression zones
📈 Trend Confirmation
- Four customizable EMA periods (default: 21, 34, 55, 89)
- Optional trend requirement for entry signals
- Adjustable EMA proximity threshold
📊 Volume Analysis
- Customizable volume MA length
- Adjustable volume threshold for signal confirmation
- Option to enable/disable volume analysis
🎨 Visualization
- Customizable bullish/bearish colors
- Optional intensity zones display
- Status monitor with real-time score and state information
- Clear entry arrows and background highlights
💻 Technical Code Breakdown
1. Core Calculations
// Base calculations for EMAs
ema_1 = ta.ema(close, ema_length_1)
ema_2 = ta.ema(close, ema_length_2)
ema_3 = ta.ema(close, ema_length_3)
ema_4 = ta.ema(close, ema_length_4)
// Proximity calculation for entry validation
ema_prox_raw = math.abs(close - ema_1) / ema_1 * 100
is_close_to_ema_long = close > ema_1 and ema_prox_raw <= prox_percent
```
### 2. Squeeze Detection System
```pine
// Bollinger Bands setup
BB_basis = ta.sma(close, length)
BB_dev = ta.stdev(close, length)
BB_upper = BB_basis + BB_mult * BB_dev
BB_lower = BB_basis - BB_mult * BB_dev
// Keltner Channels setup
KC_basis = ta.sma(close, length)
KC_range = ta.sma(ta.tr, length)
KC_upper_high = KC_basis + KC_range * KC_mult_high
KC_lower_high = KC_basis - KC_range * KC_mult_high
```
### 3. Scoring System Implementation
```pine
// Band Convergence Score
band_ratio = BB_width / KC_width
convergence_score = math.max(0, 40 * (1 - band_ratio))
// Price Position Score
price_range = math.abs(close - KC_basis) / (KC_upper_low - KC_lower_low)
position_score = 20 * (1 - price_range)
// Final Score Calculation
squeeze_score = convergence_score + position_score + vol_score + mom_score
```
### 4. Signal Generation
```pine
// Entry Signal Logic
long_signal = squeeze_release and
is_momentum_positive and
(not use_ema_trend or (bullish_trend and is_close_to_ema_long)) and
is_bullish_candle
short_signal = squeeze_release and
is_momentum_negative and
(not use_ema_trend or (bearish_trend and is_close_to_ema_short)) and
is_bearish_candle
```
📈 Trading Signals
🚀 Long Entry Conditions
- Squeeze release detected
- Positive momentum
- Bullish candlestick
- Price above relevant EMAs (if enabled)
- Within EMA proximity threshold (if enabled)
- Sufficient volume confirmation (if enabled)
🔻 Short Entry Conditions
- Squeeze release detected
- Negative momentum
- Bearish candlestick
- Price below relevant EMAs (if enabled)
- Within EMA proximity threshold (if enabled)
- Sufficient volume confirmation (if enabled)
⚠️ Alert Conditions
- 🔔 Extreme squeeze level reached (score crosses above 80)
- 🚀 Long squeeze release signal
- 🔻 Short squeeze release signal
💡 Tips for Usage
1. 📱 Use the status monitor to track real-time squeeze intensity and state
2. 🎨 Pay attention to the color gradient for trend direction and strength
3. ⏰ Consider using multiple timeframes for confirmation
4. ⚙️ Adjust EMA and proximity settings based on your trading style
5. 📊 Use volume analysis for additional confirmation in liquid markets
📝 Notes
- 🔧 The indicator combines multiple technical analysis concepts for robust signal generation
- 📈 Suitable for all tradable markets and timeframes
- ⭐ Best results typically achieved in trending markets with clear volatility cycles
- 🎯 Consider using in conjunction with other technical analysis tools for confirmation
⚠️ Disclaimer
This technical indicator is designed to assist in analysis but should not be considered as financial advice. Always perform your own analysis and risk management when trading.
VWAP Divergence | dobofulopOverview :
This script identifies potential bullish and bearish divergence signals using the Volume Weighted Average Price (VWAP). It calculates VWAP resets based on a selected “Anchor Period” (Session, Week, Month, Quarter, Year, Decade, Century, or corporate events like Earnings, Dividends, Splits). When price action and VWAP move in opposite directions with a sufficiently large ATR-based move over a chosen lookback period, the script plots divergence dots on the chart.
Key Features:
VWAP Anchoring : Choose an anchor period for resetting VWAP. This could be daily, weekly, monthly, or based on specific corporate events (Earnings, Dividends, Splits).
Divergence Detection : Looks for instances where the price is moving up while VWAP moves down (potential bullish divergence), and vice versa for bearish divergence.
ATR Filter : Uses the ATR (Average True Range) to filter out minor or insignificant price moves, helping to reduce noise.
Gap Check : Automatically invalidates signals if large price gaps occur within the lookback range.
Visual Signals : Bullish divergences are plotted below the bar, while bearish divergences are plotted above, making it easy to spot potential reversal zones.
How to Us
Inputs:
- Anchor Period (Session, Week, Month, etc.) – determines when the VWAP calculation restarts.
- Source (Default: HLC3) – Price source for the VWAP.
- ATR Multiplier and Lookback Period – Fine-tune the threshold for detecting significant moves vs. VWAP.
Interpretation:
- Bullish Divergence Dot: Suggests potential price strength when price moves higher but VWAP moves lower.
- Bearish Divergence Dot: Suggests potential price weakness when price moves lower but VWAP moves higher.
Disclaimer:
This script is provided for educational purposes only and should not be interpreted as financial advice. Past performance does not guarantee future results. Always conduct your own analysis and consider consulting a financial professional before making trading decisions.
Flow-Weighted Volume Oscillator (FWVO)Volume Dynamics Oscillator (VDO)
Description
The Volume Dynamics Oscillator (VDO) is a powerful and innovative tool designed to analyze volume trends and provide traders with actionable insights into market dynamics. This indicator goes beyond simple volume analysis by incorporating a smoothed oscillator that visualizes the flow and momentum of trading activity, giving traders a clearer understanding of volume behavior over time.
What It Does
The VDO calculates the flow of volume by scaling raw volume data relative to its highest and lowest values over a user-defined period. This scaled volume is then smoothed using an exponential moving average (EMA) to eliminate noise and highlight significant trends. The oscillator dynamically shifts above or below a zero line, providing clear visual cues for bullish or bearish volume pressure.
Key features include:
Smoothed Oscillator: Displays the direction and momentum of volume using gradient colors.
Threshold Markers: Highlights overbought or oversold zones based on upper and lower bounds of the oscillator.
Visual Fill Zones: Uses color-filled areas to emphasize positive and negative volume flow, making it easy to interpret market sentiment.
How It Works
The calculation consists of several steps:
Smoothing with EMA: An EMA of the scaled volume is applied to reduce noise and enhance trends. A separate EMA period can be adjusted by the user (Volume EMA Period).
Dynamic Thresholds: The script determines upper and lower bounds around the smoothed oscillator, derived from its recent highest and lowest values. These thresholds indicate critical zones of volume momentum.
How to Use It
Bullish Signals: When the oscillator is above zero and green, it suggests strong buying pressure. A crossover from negative to positive can signal the start of an uptrend.
Bearish Signals: When the oscillator is below zero and blue, it indicates selling pressure. A crossover from positive to negative signals potential bearish momentum.
Overbought/Oversold Zones: Use the upper and lower threshold levels as indicators of extreme volume momentum. These can act as early warnings for trend reversals.
Traders can adjust the following inputs to customize the indicator:
High/Low Period: Defines the period for volume scaling.
Volume EMA Period: Adjusts the smoothing factor for the oscillator.
Smooth Factor: Controls the responsiveness of the smoothed oscillator.
Originality and Usefulness
The VDO stands out by combining dynamic volume scaling, EMA smoothing, and gradient-based visualization into a single, cohesive tool. Unlike traditional volume indicators, which often display raw or cumulative data, the VDO emphasizes relative volume strength and flow, making it particularly useful for spotting reversals, confirming trends, and identifying breakout opportunities.
The integration of color-coded fills and thresholds enhances usability, allowing traders to quickly interpret market conditions without requiring deep technical expertise.
Chart Recommendations
To maximize the effectiveness of the VDO, use it on a clean chart without additional indicators. The gradient coloring and filled zones make it self-explanatory, but traders can overlay basic trendlines or support/resistance levels for additional context.
For advanced users, the VDO can be paired with price action strategies, candlestick patterns, or other trend-following indicators to improve accuracy and timing.
PGO For Loop | mad_tiger_slayerPGO For Loop Indicator
The PGO For Loop indicator, inspired by Alex Orekhov's "Pretty Good Oscillator," and indicator originally made by Mark Johnson, the PGO designed as a fast and responsive tool to capture quick price movements in financial markets. This oscillator leverages a combination of moving averages and Average True Range (ATR) to measure price deviations, providing a concise yet powerful framework for identifying potential trade entry and exit points. What makes this
"enhanced" PGO indicator special is its ability to identify trending periods more accurately. By using thresholds, this allows the script to enter accurate long and short conditions extremely quickly.
Intended Uses:
Used to capture long-term trends:
Used to identify quick reversals:
Used on higher timeframes above 8hrs for more accurate signals
Used in strategies to enter and exit trades quickly
Can be used for Scalping
NOT Intended Uses:
Not to be used as Mean Reversion
Not to be used as valuation (Overbought or Oversold)
Key Features:
Quick Detection of Market Movements:
The indicator's primary focus is on speed, making it suitable for medium-term traders looking to capitalize on rapid price changes. It is particularly effective in trending or volatile markets.
Customizable Thresholds:
Users can set upper and lower thresholds to define long and short conditions, offering flexibility to adapt the indicator to different trading styles and asset classes.
Noisy but Purposeful:
While the PGO For Loop may generate frequent signals, it is specifically tuned for traders aiming to enter and exit trades quickly, embracing the noise as part of its effectiveness in capturing rapid market dynamics.
Integrated Visuals:
The script plots key levels and provides dynamic visual feedback through colored candles and shapes, enabling intuitive and quick decision-making.
How It Works:
Oscillator Calculation:
The PGO value is derived by comparing the source price's deviation from its moving average to the ATR. This highlights price movements relative to recent volatility.
Signal Identification:
When the oscillator exceeds the upper threshold, it signals potential long opportunities UNTIL the PGO reaches the lower threshold.
When the oscillator drops below the lower threshold, it signals potential short opportunities UNTIL the oscillator reaches above the upper threshold.
No signals occur when the oscillator lies between these thresholds.
Visual Cues:
Color-coded candles indicate market bias (green for long, red for short, gray for neutral).
Upward and downward triangles highlight changes in signal direction.
Note:
This indicator is intentionally "noisy," as it prioritizes capturing fast movements over filtering out minor fluctuations. Users should pair it with other tools or techniques to confirm signals and manage risk effectively.
Enhanced Price Z-Score OscillatorThe Enhanced Price Z-Score Oscillator by tkarolak is a powerful tool that transforms raw price data into an easy-to-understand statistical visualization using Z-Score-derived candlesticks. Simply put, it shows how far prices stray from their average in terms of standard deviations (Z-Scores), helping traders identify when prices are unusually high (overbought) or unusually low (oversold).
The indicator’s default feature displays Z-Score Candlesticks, where each candle reflects the statistical “distance” of the open, high, low, and close prices from their average. This creates a visual map of market extremes and potential reversal points. For added flexibility, you can also switch to Z-Score line plots based on either Close prices or OHLC4 averages.
With clear threshold lines (±2σ and ±3σ) marking moderate and extreme price deviations, and color-coded zones to highlight overbought and oversold areas, the oscillator simplifies complex statistical concepts into actionable trading insights.
True Amplitude Envelopes (TAE)The True Envelopes indicator is an adaptation of the True Amplitude Envelope (TAE) method, based on the research paper " Improved Estimation of the Amplitude Envelope of Time Domain Signals Using True Envelope Cepstral Smoothing " by Caetano and Rodet. This indicator aims to create an asymmetric price envelope with strong predictive power, closely following the methodology outlined in the paper.
Due to the inherent limitations of Pine Script, the indicator utilizes a Kernel Density Estimator (KDE) in place of the original Cepstral Smoothing technique described in the paper. While this approach was chosen out of necessity rather than superiority, the resulting method is designed to be as effective as possible within the constraints of the Pine environment.
This indicator is ideal for traders seeking an advanced tool to analyze price dynamics, offering insights into potential price movements while working within the practical constraints of Pine Script. Whether used in dynamic mode or with a static setting, the True Envelopes indicator helps in identifying key support and resistance levels, making it a valuable asset in any trading strategy.
Key Features:
Dynamic Mode: The indicator dynamically estimates the fundamental frequency of the price, optimizing the envelope generation process in real-time to capture critical price movements.
High-Pass Filtering: Uses a high-pass filtered signal to identify and smoothly interpolate price peaks, ensuring that the envelope accurately reflects significant price changes.
Kernel Density Estimation: Although implemented as a workaround, the KDE technique allows for flexible and adaptive smoothing of the envelope, aimed at achieving results comparable to the more sophisticated methods described in the original research.
Symmetric and Asymmetric Envelopes: Provides options to select between symmetric and asymmetric envelopes, accommodating various trading strategies and market conditions.
Smoothness Control: Features adjustable smoothness settings, enabling users to balance between responsiveness and the overall smoothness of the envelopes.
The True Envelopes indicator comes with a variety of input settings that allow traders to customize the behavior of the envelopes to match their specific trading needs and market conditions. Understanding each of these settings is crucial for optimizing the indicator's performance.
Main Settings
Source: This is the data series on which the indicator is applied, typically the closing price (close). You can select other price data like open, high, low, or a custom series to base the envelope calculations.
History: This setting determines how much historical data the indicator should consider when calculating the envelopes. A value of 0 will make the indicator process all available data, while a higher value restricts it to the most recent n bars. This can be useful for reducing the computational load or focusing the analysis on recent market behavior.
Iterations: This parameter controls the number of iterations used in the envelope generation algorithm. More iterations will typically result in a smoother envelope, but can also increase computation time. The optimal number of iterations depends on the desired balance between smoothness and responsiveness.
Kernel Style: The smoothing kernel used in the Kernel Density Estimator (KDE). Available options include Sinc, Gaussian, Epanechnikov, Logistic, and Triangular. Each kernel has different properties, affecting how the smoothing is applied. For example, Gaussian provides a smooth, bell-shaped curve, while Epanechnikov is more efficient computationally with a parabolic shape.
Envelope Style: This setting determines whether the envelope should be Static or Dynamic. The Static mode applies a fixed period for the envelope, while the Dynamic mode automatically adjusts the period based on the fundamental frequency of the price data. Dynamic mode is typically more responsive to changing market conditions.
High Q: This option controls the quality factor (Q) of the high-pass filter. Enabling this will increase the Q factor, leading to a sharper cutoff and more precise isolation of high-frequency components, which can help in better identifying significant price peaks.
Symmetric: This setting allows you to choose between symmetric and asymmetric envelopes. Symmetric envelopes maintain an equal distance from the central price line on both sides, while asymmetric envelopes can adjust differently above and below the price line, which might better capture market conditions where upside and downside volatility are not equal.
Smooth Envelopes: When enabled, this setting applies additional smoothing to the envelopes. While this can reduce noise and make the envelopes more visually appealing, it may also decrease their responsiveness to sudden market changes.
Dynamic Settings
Extra Detrend: This setting toggles an additional high-pass filter that can be applied when using a long filter period. The purpose is to further detrend the data, ensuring that the envelope focuses solely on the most recent price oscillations.
Filter Period Multiplier: This multiplier adjusts the period of the high-pass filter dynamically based on the detected fundamental frequency. Increasing this multiplier will lengthen the period, making the filter less sensitive to short-term price fluctuations.
Filter Period (Min) and Filter Period (Max): These settings define the minimum and maximum bounds for the high-pass filter period. They ensure that the filter period stays within a reasonable range, preventing it from becoming too short (and overly sensitive) or too long (and too sluggish).
Envelope Period Multiplier: Similar to the filter period multiplier, this adjusts the period for the envelope generation. It scales the period dynamically to match the detected price cycles, allowing for more precise envelope adjustments.
Envelope Period (Min) and Envelope Period (Max): These settings establish the minimum and maximum bounds for the envelope period, ensuring the envelopes remain adaptive without becoming too reactive or too slow.
Static Settings
Filter Period: In static mode, this setting determines the fixed period for the high-pass filter. A shorter period will make the filter more responsive to price changes, while a longer period will smooth out more of the price data.
Envelope Period: This setting specifies the fixed period used for generating the envelopes in static mode. It directly influences how tightly or loosely the envelopes follow the price action.
TAE Smoothing: This controls the degree of smoothing applied during the TAE process in static mode. Higher smoothing values result in more gradual envelope curves, which can be useful in reducing noise but may also delay the envelope’s response to rapid price movements.
Visual Settings
Top Band Color: This setting allows you to choose the color for the upper band of the envelope. This band represents the resistance level in the price action.
Bottom Band Color: Similar to the top band color, this setting controls the color of the lower band, which represents the support level.
Center Line Color: This is the color of the central price line, often referred to as the carrier. It represents the detrended price around which the envelopes are constructed.
Line Width: This determines the thickness of the plotted lines for the top band, bottom band, and center line. Thicker lines can make the envelopes more visible, especially when overlaid on price data.
Fill Alpha: This controls the transparency level of the shaded area between the top and bottom bands. A lower alpha value will make the fill more transparent, while a higher value will make it more opaque, helping to highlight the envelope more clearly.
The envelopes generated by the True Envelopes indicator are designed to provide a more precise and responsive representation of price action compared to traditional methods like Bollinger Bands or Keltner Channels. The core idea behind this indicator is to create a price envelope that smoothly interpolates the significant peaks in price action, offering a more accurate depiction of support and resistance levels.
One of the critical aspects of this approach is the use of a high-pass filtered signal to identify these peaks. The high-pass filter serves as an effective method of detrending the price data, isolating the rapid fluctuations in price that are often lost in standard trend-following indicators. By filtering out the lower frequency components (i.e., the trend), the high-pass filter reveals the underlying oscillations in the price, which correspond to significant peaks and troughs. These oscillations are crucial for accurately constructing the envelope, as they represent the most responsive elements of the price movement.
The algorithm works by first applying the high-pass filter to the source price data, effectively detrending the series and isolating the high-frequency price changes. This filtered signal is then used to estimate the fundamental frequency of the price movement, which is essential for dynamically adjusting the envelope to current market conditions. By focusing on the peaks identified in the high-pass filtered signal, the algorithm generates an envelope that is both smooth and adaptive, closely following the most significant price changes without overfitting to transient noise.
Compared to traditional envelopes and bands, such as Bollinger Bands and Keltner Channels, the True Envelopes indicator offers several advantages. Bollinger Bands, which are based on standard deviations, and Keltner Channels, which use the average true range (ATR), both tend to react to price volatility but do not necessarily follow the peaks and troughs of the price with precision. As a result, these traditional methods can sometimes lag behind or fail to capture sudden shifts in price momentum, leading to either false signals or missed opportunities.
In contrast, the True Envelopes indicator, by using a high-pass filtered signal and a dynamic period estimation, adapts more quickly to changes in price behavior. The envelopes generated by this method are less prone to the lag that often affects standard deviation or ATR-based bands, and they provide a more accurate representation of the price's immediate oscillations. This can result in better predictive power and more reliable identification of support and resistance levels, making the True Envelopes indicator a valuable tool for traders looking for a more responsive and precise approach to market analysis.
In conclusion, the True Envelopes indicator is a powerful tool that blends advanced theoretical concepts with practical implementation, offering traders a precise and responsive way to analyze price dynamics. By adapting the True Amplitude Envelope (TAE) method through the use of a Kernel Density Estimator (KDE) and high-pass filtering, this indicator effectively captures the most significant price movements, providing a more accurate depiction of support and resistance levels compared to traditional methods like Bollinger Bands and Keltner Channels. The flexible settings allow for extensive customization, ensuring the indicator can be tailored to suit various trading strategies and market conditions.
Monest Value Indicator (MVI)
Description
The Monest Value Indicator (MVI) is a modern oscillator designed to address common issues in traditional oscillators like RSI or MACD. Unlike classical oscillators, the MVI dynamically adjusts to relative price movements and market volatility, providing a transparent and reliable valuation for short-term trading decisions.
This indicator normalizes price data around a consensus line and accounts for market volatility using the Average True Range (ATR). It highlights overbought and oversold conditions, offering a unique perspective for traders.
Key Features
Dynamic Overbought/Oversold Levels : Highlights significant price extremes for better entry and exit signals. Volatility Normalization : Adapts to market conditions, ensuring consistent readings across various assets. Consensus-Based Valuation : Uses a moving average of the midrange price for baseline calculations. No Lag or Stickiness : Reacts promptly to price movements without getting stuck in extreme zones.
How It Works
Consensus Line :
Calculated as a 5-day moving average of the midrange:
Consensus = SMA((High + Low) / 2, 5) .
Offset OHLC Data :
All prices are adjusted relative to the consensus line:
Offset Price = Price - Consensus .
Volatility Normalization :
Adjusted prices are normalized using a 5-day ATR divided by 5:
Normalized Price = Offset Price / (ATR / 5) .
MVI Calculation :
The normalized closing price is plotted as the MVI.
Overbought/Oversold Levels :
Default levels are set at +8 (overbought) and -8 (oversold).
How to Use
Identifying Overbought/Oversold Conditions :
When the MVI crosses above +8 , the asset is overbought, signaling a potential reversal or pullback.
When the MVI drops below -8 , the asset is oversold, indicating a potential bounce or upward move.
Trend Confirmation :
Use the MVI to confirm trends by observing sustained movements above or below zero.
Combine with other trend indicators (e.g., Moving Averages) for robust analysis.
Alerts :
Set alerts for when the MVI crosses overbought or oversold levels to stay informed about potential trading opportunities.
Inputs
ATR Length : Default is 5. Adjust to modify the sensitivity of volatility normalization. Consensus Length : Default is 5. Change to tweak the baseline calculation.
Example
Overbought Signal : MVI exceeds +8 , indicating the asset may reverse from an overvalued position. Oversold Signal : MVI drops below -8 , suggesting the asset may recover from an undervalued state. Flat Market : MVI hovers near zero, indicating price consolidation.
Kalman Trend Strength Index (K-TSI)The Kalman Trend Strength Index (K-TSI) is an innovative technical indicator that combines the Kalman filter with correlation analysis to measure trend strength in financial markets. This sophisticated tool aims to provide traders with a more refined method for trend analysis and market dynamics interpretation.
The use of the Kalman filter is a key feature of the K-TSI. This advanced algorithm is renowned for its ability to extract meaningful signals from noisy data. In financial markets, this translates to smoothing out price action while maintaining responsiveness to genuine market movements. By applying the Kalman filter to price data before performing correlation analysis, the K-TSI potentially offers more stable and reliable trend signals.
The synergy between the Kalman-filtered price data and correlation analysis creates an oscillator that attempts to capture market dynamics more effectively. The correlation component contributes by measuring the strength and consistency of price movements relative to time, while the Kalman filter adds robustness by reducing the impact of market noise. Basing these calculations on Kalman-filtered data may help reduce false signals and provide a clearer picture of underlying market trends.
A notable aspect of the K-TSI is its normalization process. This approach adjusts the indicator's values to a standardized range (-1 to 1), allowing for consistent interpretation across different market conditions and timeframes. This flexibility, combined with the noise-reduction properties of the Kalman filter, positions the K-TSI as a potentially useful tool for various market environments.
In practice, traders might find that the K-TSI offers several potential benefits:
Smoother trend identification, which could aid in detecting the start and end of trends more accurately.
Possibly reduced false signals, particularly in choppy or volatile markets.
Potential for improved trend strength assessment, which might lead to more confident trading decisions.
Consistent performance across different timeframes, due to the adaptive nature of the Kalman filter and the normalization process.
The K-TSI's visual representation as a color-coded histogram further enhances its utility. The changing colors and intensities provide an intuitive way to gauge both the direction and strength of trends, making it easier for traders to quickly assess market conditions.
While the K-TSI builds upon existing concepts in technical analysis, its integration of the Kalman filter with correlation analysis offers traders an interesting tool for market analysis. It represents an attempt to address common challenges in technical analysis, such as noise reduction and trend strength quantification.
As with any technical indicator, the K-TSI should be used as part of a broader trading strategy rather than in isolation. Its effectiveness will depend on how well it aligns with a trader's individual approach and market conditions. For traders looking to explore a more refined trend strength oscillator, the Kalman Trend Strength Index could be a worthwhile addition to their analytical toolkit.
Kalman Synergy Oscillator (KSO)The Kalman Synergy Oscillator (KSO) is an innovative technical indicator that combines the Kalman filter with two well-established momentum oscillators: the Relative Strength Index (RSI) and Williams %R. This combination aims to provide traders with a more refined tool for market analysis.
The use of the Kalman filter is a key feature of the KSO. This sophisticated algorithm is known for its ability to extract meaningful signals from noisy data. In financial markets, this translates to smoothing out price action while maintaining responsiveness to genuine market movements. By applying the Kalman filter to price data before calculating the RSI and Williams %R, the KSO potentially offers more stable and reliable signals.
The synergy between the Kalman-filtered price data and the two momentum indicators creates an oscillator that attempts to capture market dynamics more effectively. The RSI contributes its strength in measuring the magnitude and speed of price movements, while Williams %R adds sensitivity to overbought and oversold conditions. Basing these calculations on Kalman-filtered data may help reduce false signals and provide a clearer picture of underlying market trends.
A notable aspect of the KSO is its dynamic weighting system. This approach adjusts the relative importance of the RSI and Williams %R based on their current strengths, allowing the indicator to emphasize the most relevant information as market conditions change. This flexibility, combined with the noise-reduction properties of the Kalman filter, positions the KSO as a potentially useful tool for different market conditions.
In practice, traders might find that the KSO offers several potential benefits:
Smoother oscillator movements, which could aid in trend identification and reversal detection.
Possibly reduced whipsaws, particularly in choppy or volatile markets.
Potential for improved divergence detection, which might lead to more timely reversal signals.
Consistent performance across different timeframes, due to the adaptive nature of the Kalman filter.
While the KSO builds upon existing concepts in technical analysis, its integration of the Kalman filter with traditional momentum indicators offers traders an interesting tool for market analysis. It represents an attempt to address common challenges in technical analysis, such as noise reduction and false signal minimization.
As with any technical indicator, the KSO should be used as part of a broader trading strategy rather than in isolation. Its effectiveness will depend on how well it aligns with a trader's individual approach and market conditions. For traders looking to explore a more refined momentum oscillator, the Kalman Synergy Oscillator could be a worthwhile addition to their analytical toolkit.
Dynamic Score PSAR [QuantAlgo]Dynamic Score PSAR 📈🧬
The Dynamic Score PSAR by QuantAlgo introduces an innovative approach to trend detection by utilizing a dynamic trend scoring technique in combination with the Parabolic SAR. This method goes beyond traditional trend-following indicators by evaluating market momentum through a scoring system that analyzes price behavior over a customizable window. By dynamically adjusting to evolving market conditions, this indicator provides clearer, more adaptive trend signals that help traders and investors anticipate market reversals and capitalize on momentum shifts with greater precision.
💫 Conceptual Foundation and Innovation
At the core of the Dynamic Score PSAR is the dynamic trend score system, which assesses price movements by comparing normalized PSAR values across a range of historical data points. This dynamic trend scoring technique offers a unique, probabilistic approach to trend analysis by evaluating how the current market compares to past price movements. Unlike traditional PSAR indicators that rely on static parameters, this scoring mechanism allows the indicator to adjust in real time to market fluctuations, offering traders and investors a more responsive and insightful view of trends. This innovation makes the Dynamic Score PSAR particularly effective in detecting shifts in momentum and potential reversals, even in volatile or complex market environments.
✨ Technical Composition and Calculation
The Dynamic Score PSAR is composed of several advanced components designed to provide a higher probability of detecting accurate trend shifts. The key innovation lies in the dynamic trend scoring technique, which iterates over historical PSAR values and evaluates price momentum through a dynamic scoring system. By comparing the current normalized PSAR value with previous data points over a user-defined window, the system generates a score that reflects the strength and direction of the trend. This allows for a more refined and responsive detection of trends compared to static, traditional indicators.
To enhance clarity, the PSAR values are normalized against an Exponential Moving Average (EMA), providing a standardized framework for comparison. This normalization ensures that the indicator adapts dynamically to market conditions, making it more effective in volatile markets. The smoothing process reduces noise, helping traders and investors focus on significant trend signals.
Additionally, users can adjust the length of the data window and the sensitivity thresholds for detecting uptrends and downtrends, providing flexibility for different trading and investing environments.
📈 Features and Practical Applications
Customizable Window Length: Adjust the window length to control the indicator’s sensitivity to recent price movements. This provides flexibility for short-term or long-term trend analysis.
Uptrend/Downtrend Thresholds: Set customizable thresholds for identifying uptrends and downtrends. These thresholds define when trend signals are triggered, offering adaptability to different market conditions.
Bar Coloring and Gradient Visualization: Visual cues, including color-coded bars and gradient fills, make it easier to interpret market trends and identify key moments for potential trend reversals.
Momentum Confirmation: The dynamic trend scoring system evaluates price action over time, providing a probabilistic measure of market momentum to confirm the strength and direction of a trend.
⚡️ How to Use
✅ Add the Indicator: Add the Dynamic Score PSAR to your favourites, then to your chart and adjust the PSAR settings, window length, and trend thresholds to match your preferences. Customize the sensitivity to price movements by tweaking the window length and thresholds for different market conditions.
👀 Monitor Trend Shifts: Watch for trend changes as the normalized PSAR values cross key thresholds, and use the dynamic score to confirm the strength and direction of trends. Bar coloring and background fills visually highlight key moments for trend shifts, making it easier to spot reversals.
🔔 Set Alerts: Configure alerts for significant trend crossovers and reversals, ensuring you can act on market movements promptly, even when you’re not actively monitoring the charts.
🌟 Summary and Usage Tips
The Dynamic Score PSAR by QuantAlgo is a powerful tool that combines traditional trend-following techniques with the flexibility of a dynamic trend scoring system. This innovative approach provides clearer, more adaptive trend signals, reducing the risk of false entries and exits while helping traders and investors capture significant market moves. The ability to adjust the indicator’s sensitivity and thresholds makes it versatile across different trading and investing environments, whether you’re focused on short-term pivots or long-term trend reversals. To maximize its effectiveness, fine-tune the sensitivity settings based on current market conditions and use the visual cues to confirm trend shifts.
RSI (Kernel Optimized) | Flux Charts💎 GENERAL OVERVIEW
Introducing our new KDE Optimized RSI Indicator! This indicator adds a new aspect to the well-known RSI indicator, with the help of the KDE (Kernel Density Estimation) algorithm, estimates the probability of a candlestick will be a pivot or not. For more information about the process, please check the "HOW DOES IT WORK ?" section.
Features of the new KDE Optimized RSI Indicator :
A New Approach To Pivot Detection
Customizable KDE Algorithm
Realtime RSI & KDE Dashboard
Alerts For Possible Pivots
Customizable Visuals
❓ HOW TO INTERPRET THE KDE %
The KDE % is a critical metric that reflects how closely the current RSI aligns with the KDE (Kernel Density Estimation) array. In simple terms, it represents the likelihood that the current candlestick is forming a pivot point based on historical data patterns. a low percentage suggests a lower probability of the current candlestick being a pivot point. In these cases, price action is less likely to reverse, and existing trends may continue. At moderate levels, the possibility of a pivot increases, indicating potential trend shifts or consolidations.Traders should start monitoring closely for confirmation signals. An even higher KDE % suggests a strong likelihood that the current candlestick could form a pivot point, which could lead to a reversal or significant price movement. These points often align with overbought or oversold conditions in traditional RSI analysis, making them key moments for potential trade entry or exit.
📌 HOW DOES IT WORK ?
The RSI (Relative Strength Index) is a widely used oscillator among traders. It outputs a value between 0 - 100 and gives a glimpse about the current momentum of the price action. This indicator then calculates the RSI for each candlesticks, and saves them into an array if the candlestick is a pivot. The low & high pivot RSIs' are inserted into two different arrays. Then the a KDE array is calculated for both of the low & high pivot RSI arrays. Explaining the KDE might be too much for this write-up, but for a brief explanation, here are the steps :
1. Define the necessary options for the KDE function. These are : Bandwidth & Nº Steps, Array Range (Array Max - Array Min)
2. After that, create a density range array. The array has (steps * 2 - 1) elements and they are calculated by (arrMin + i * stepCount), i being the index.
3. Then, define a kernel function. This indicator has 3 different kernel distribution modes : Uniform, Gaussian and Sigmoid
4. Then, define a temporary value for the current element of KDE array.
5. For each element E in the pivot RSI array, add "kernel(densityRange.get(i) - E, 1.0 / bandwidth)" to the temporary value.
6. Add 1.0 / arrSize * to the KDE array.
Then the prefix sum array of the KDE array is calculated. For each candlestick, the index closest to it's RSI value in the KDE array is found using binary search. Then for the low pivot KDE calculation, the sum of KDE values from found index to max index is calculated. For the high pivot KDE, the sum of 0 to found index is used. Then if high or low KDE value is greater than the activation threshold determined in the settings, a bearish or bullish arrow is plotted after bar confirmation respectively. The arrows are drawn as long as the KDE value of current candlestick is greater than the threshold. When the KDE value is out of the threshold, a less transparent arrow is drawn, indicating a possible pivot point.
🚩 UNIQUENESS
This indicator combines RSI & KDE Algorithm to get a foresight of possible pivot points. Pivot points are important entry, confirmation and exit points for traders. But to their nature, they can be only detected after more candlesticks are rendered after them. The purpose of this indicator is to alert the traders of possible pivot points using KDE algorithm right away when they are confirmed. The indicator also has a dashboard for realtime view of the current RSI & Bullish or Bearish KDE value. You can fully customize the KDE algorithm and set up alerts for pivot detection.
⚙️ SETTINGS
1. RSI Settings
RSI Length -> The amount of bars taken into account for RSI calculation.
Source -> The source value for RSI calculation.
2. Pivots
Pivot Lengths -> Pivot lengths for both high & low pivots. For example, if this value is set to 21; 21 bars before AND 21 bars after a candlestick must be higher for a candlestick to be a low pivot.
3. KDE
Activation Threshold -> This setting determines the amount of arrows shown. Higher options will result in more arrows being rendered.
Kernel -> The kernel function as explained in the upper section.
Bandwidth -> The bandwidth variable as explained in the upper section. The smoothness of the KDE function is tied to this setting.
Nº Bins -> The Nº Steps variable as explained in the upper section. It determines the precision of the KDE algorithm.
Inverse Fisher Oscillator [BigBeluga]The Inverse Fisher Oscillator is a powerful tool for identifying market trends and potential reversal points by applying the Inverse Fisher Transform to normalized price data. This indicator plots multiple smoothed oscillators, each color-coded to signify their relation to dynamic volatility bands. Additionally, the Butterworth filter is incorporated to further refine trend signals. The Inverse Fisher Oscillator offers traders a visually appealing and insightful approach to trend analysis and market direction detection.
🔵 KEY FEATURES
● Inverse Fisher Oscillator Visualization
Multiple Oscillators : The indicator calculates and plots six different Inverse Fisher Oscillators, each smoothed at increasing levels to provide a layered view of price momentum.
Color-Coded Signals : The oscillator lines are color-coded based on their relation to the volatility bands—green for bullish momentum, red for bearish momentum, and yellow for neutral movements.
● Butterworth Filter Integration
Filtering : The Butterworth filter is applied to mid-line Bands to reduce noise, allowing for clearer trend detection.
// Calculate constants for the Butterworth filter
float piPrd = math.pi / mid_len
float g = math.sqrt(2)
float a1 = math.exp(-g * piPrd)
float b1 = 2 * a1 * math.cos(g * piPrd)
float coef2 = b1
float coef3 = -a1 * a1
float coef1 = (1 - b1 + a1 * a1) / 4
// Source data for the Butterworth filter
float source = ifish // The first inverse Fisher Oscillator is used as the source
// Previous source and butter filter values
var float butter = na // Initialize the 'butter' variable
// Handle null values using the nz function
float prevB1 = nz(butter , source) // Use 'source' as a fallback if butter is null
float prevB2 = nz(butter , source) // Use 'source' as a fallback if butter is null
// Calculate the Butterworth filter value
butter := coef1 * (source + (2 * source ) + source ) + (coef2 * prevB1) + (coef3 * prevB2)
● Numbered Signal Marks
Signal Markers : The indicator plots numbered signals on the chart when an oscillator crosses above the upper volatility band or below the lower volatility band.
Numbered Lines : Numbers correspond to the different oscillators (1-6), helping traders easily identify which smoothing level generated the signal.
Visual Cues : The signals are color-coded—green for bullish crossovers and red for bearish crossunders—providing clear visual cues for trend accumulation phases.
Mid-Line Option : Traders can choose between plotting the Butterworth filter as a dynamic mid-line or simply displaying it as part of the bands.
Volatility Bands : Dynamic volatility bands provide additional context for interpreting the strength and sustainability of trends.
● Dashboard Display
Real-Time Market Trend Overview : The dashboard in the bottom-right corner of the chart displays the market trend based on the Inverse Fisher Oscillator for six different smoothing levels, providing a clear visual summary of market direction.
Direction Symbols : Directional symbols (up, down, or neutral) are displayed in the dashboard, color-coded to represent bullish, bearish, or neutral momentum.
Current Price Display : The dashboard also shows the current price and highlights whether it is above or below the opening price.
🔵 HOW TO USE
● Identifying Trend Reversals
Bullish Reversals : When the oscillators short period lines start to cross above the upper volatility band (green), it indicates potential bullish momentum.
Bearish Reversals : When the oscillator crosses below the lower volatility band (red), it signals potential bearish momentum.
Neutral Signals : When the oscillator remains within the bands (yellow), it suggests that the market is in a neutral or consolidating state. Traders may choose to wait for a clearer trend signal.
● Using the Dashboard for Trend Overview
Market Trend Summary : The dashboard provides a quick overview of market direction across six different smoothing levels. Green arrows indicate bullish momentum, red arrows indicate bearish momentum, and wavy lines suggest neutrality.
Price Context : The dashboard also displays the current price, helping traders quickly assess whether the price is moving in the expected direction relative to their trend analysis.
● Volatility Band Interpretation
Volatility-Based Signals : Pay attention to how the oscillators interact with the volatility bands. Strong trends will often result in oscillators staying above or below the bands, while weaker trends or consolidations will see oscillators hovering within the bands.
🔵 CUSTOMIZATION
Length and Smoothing : Adjust the length and smoothing parameters to fit different market conditions and timeframes.
Bands Multiplier : Customize the multiplier for the volatility bands to make them more or less sensitive to price changes.
Mid-Line Type : Choose whether to display the Butterworth filter as a mid-line or incorporate it into the volatility bands.
Signal Markers : Toggle on or off the number markers for signal crossovers, making it easier to identify key entry and exit points.
🔵 CONCLUSION
The Inverse Fisher Oscillator combines the power of the Inverse Fisher Transform and the Butterworth filter to provide a sophisticated approach to trend and reversal detection. By leveraging volatility-based analysis and visually intuitive signals, this indicator helps traders spot potential entry and exit points with greater clarity. The customizable dashboard display adds further value, offering a real-time summary of market conditions to enhance decision-making. Use this tool in conjunction with other technical analysis methods to develop a well-rounded trading strategy.
GannLSVZO Indicator [Algo Alert]The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction.
The Gann Laplace Smoothed Volume Zone Oscillator GannLSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the upgraded Discrete Fourier Transform, the Laplace Stieltjes Transform. Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends.
By combining the Laplace with Gann Swing Entries and Exits (orange X) and with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions.
HOW TO USE THE INDICATOR:
The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well)
The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in a natural trend.
ORIGINALITY & USFULLNESS:
Personal combination of Gann swings and Laplace Stieltjes Transform of a price which results in less noise Volume Zone Oscillator.
The Laplace Stieltjes Transform is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element.
This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding.
The Gann swings and the Gan swing strategy is developed by meomeo105, this Gann high and low algorithm forms the basis of the EMA modification.
DETAILED DESCRIPTION:
My detailed description of the indicator and use cases which I find very valuable.
What is oscillator?
Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets.
What is volume zone oscillator?
Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price.
Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average.
What does this mean?
If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely.
If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely.
Rising means that VZO on close is higher than the previous day.
Falling means that VZO on close is lower than the previous day.
What if VZO is falling above 0 line?
It means we have a high probability of a bearish trend.
Thus the indicator returns 0 and Strategy closes all it's positions when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend.
What is approximation and smoothing?
They are mathematical concepts for making a discrete set of numbers a
continuous curved line.
Laplace Stieltjes Transform approximation of a close price are taken from aprox library.
Key Features:
You can tailor the Indicator/Strategy to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length.
Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification.
Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics.
Choose between the traditional Fast Laplace Stieltjes Transform (FLT) and the innovative Double Discrete Fourier Transform (DTF32) soothed price series to suit your analytical needs.
Use dynamic calculation of Laplace coefficient or the static one. You may modify those inputs and Strategy entries with Gann swings.
I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame. I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
DSL Oscillator [BigBeluga]DSL Oscillator BigBeluga
The DSL (Discontinued Signal Lines) Oscillator is an advanced technical analysis tool that combines elements of the Relative Strength Index (RSI), Discontinued Signal Lines, and Zero-Lag Exponential Moving Average (ZLEMA). This versatile indicator is designed to help traders identify trend direction, momentum, and potential reversal points in the market.
What are Discontinued Signal Lines (DSL)?
Discontinued Signal Lines are an extension of the traditional signal line concept used in many indicators. While a standard signal line compares an indicator's value to its smoothed (slightly lagging) state, DSL takes this idea further by using multiple adaptive lines that respond to the indicator's current value. This approach provides a more nuanced view of the indicator's state and momentum, making it easier to determine trends and desired states of the indicator.
🔵 KEY FEATURES
● Discontinued Signal Lines (DSL)
Uses multiple adaptive lines that respond to the indicator's value
Provides a more nuanced view of the indicator's state and momentum
Helps determine trends and desired states of the indicator more effectively
Available in "Fast" and "Slow" modes for different responsiveness
Acts as dynamic support and resistance levels for the oscillator
● DSL Oscillator
Based on a combination of RSI and Discontinued Signal Lines
// Discontinued Signal Lines
dsl_lines(src, length)=>
UP = 0.
DN = 0.
UP := (src > ta.sma(src, length)) ? nz(UP ) + dsl_mode / length * (src - nz(UP )) : nz(UP )
DN := (src < ta.sma(src, length)) ? nz(DN ) + dsl_mode / length * (src - nz(DN )) : nz(DN )
Smoothed using Zero-Lag Exponential Moving Average for reduced lag
// Zero-Lag Exponential Moving Average function
zlema(src, length) =>
lag = math.floor((length - 1) / 2)
ema_data = 2 * src - src
ema2 = ta.ema(ema_data, length)
ema2
Oscillates between 0 and 100
Color-coded for easy interpretation of market conditions
● Signal Generation
Generates buy signals when the oscillator crosses above the lower DSL line below 50
Generates sell signals when the oscillator crosses below the upper DSL line above 50
Signals are visualized on both the oscillator and the main chart
● Visual Cues
Background color changes on signal occurrences for easy identification
Candles on the main chart are colored based on the latest signal
Oscillator line color changes based on its position relative to the DSL lines
🔵 HOW TO USE
● Trend Identification
Use the color and position of the DSL Oscillator relative to its Discontinued Signal Lines to determine the overall market trend
● Entry Signals
Look for buy signals (green circles) when the oscillator crosses above the lower DSL line
Look for sell signals (blue circles) when the oscillator crosses below the upper DSL line
Confirm signals with the triangles on the main chart and background color changes
● Exit Signals
Consider exiting long positions on exit signals and short positions on Entery signals
Watch for the oscillator crossing back between the DSL lines as a potential early exit signal
● Momentum Analysis
Strong momentum is indicated when the oscillator moves rapidly towards extremes and away from the DSL lines
Weakening momentum can be spotted when the oscillator struggles to reach new highs or lows, or starts converging with the DSL lines
The space between the DSL lines can indicate potential momentum strength - wider gaps suggest stronger trends
● Confirmation
Use the DSL lines as dynamic support/resistance levels for the oscillator
Look for convergence between oscillator signals and price action on the main chart
Combine signals with other technical indicators or chart patterns for stronger confirmation
🔵 CUSTOMIZATION
The DSL Oscillator offers several customization options:
Adjust the main calculation length for the DSL lines
Choose between "Fast" and "Slow" modes for the DSL lines calculation
By fine-tuning these settings, traders can adapt the DSL Oscillator to various market conditions and personal trading strategies.
The DSL Oscillator provides a multi-faceted approach to market analysis, combining trend identification, momentum assessment, and signal generation in one comprehensive tool. Its dynamic nature and visual cues make it suitable for both novice and experienced traders across various timeframes and markets. The integration of RSI, Discontinued Signal Lines, and ZLEMA offers traders a sophisticated yet intuitive tool to inform their trading decisions.
The use of Discontinued Signal Lines sets this oscillator apart from traditional indicators by providing a more adaptive and nuanced view of market conditions. This can potentially lead to more accurate trend identification and signal generation, especially in markets with varying volatility.
Traders can use the DSL Oscillator to identify trends, spot potential reversals, and gauge market momentum. The combination of the oscillator, dynamic signal lines, and clear visual signals provides a holistic view of market conditions. As with all technical indicators, it's recommended to use the DSL Oscillator in conjunction with other forms of analysis and within the context of a well-defined trading strategy.
S&P Short-Range Oscillator**SHOULD BE USED ON THE S&P 500 ONLY**
The S&P Short-Range Oscillator (SRO), inspired by the principles of Jim Cramer's oscillator, is a technical analysis tool designed to help traders identify potential buy and sell signals in the stock market, specifically for the S&P 500 index. The SRO combines several market indicators to provide a normalized measure of market sentiment, assisting traders in making informed decisions.
The SRO utilizes two simple moving averages (SMAs) of different lengths: a 5-day SMA and a 10-day SMA. It also incorporates the daily price change and market breadth (the net change of closing prices). The 5-day and 10-day SMAs are calculated based on the closing prices. The daily price change is determined by subtracting the opening price from the closing price. Market breadth is calculated as the difference between the current closing price and the previous closing price.
The raw value of the oscillator, referred to as SRO Raw, is the sum of the daily price change, the 5-day SMA, the 10-day SMA, and the market breadth. This raw value is then normalized using its mean and standard deviation over a 20-day period, ensuring that the oscillator is centered and maintains a consistent scale. Finally, the normalized value is scaled to fit within the range of -15 to 15.
When interpreting the SRO, a value below -5 indicates that the market is potentially oversold, suggesting it might be a good time to start buying stocks as the market could be poised for a rebound. Conversely, a value above 5 suggests that the market is potentially overbought. In this situation, it may be prudent to hold on to existing positions or consider selling if you have substantial gains.
The SRO is visually represented as a blue line on a chart, making it easy to track its movements. Red and green horizontal lines mark the overbought (5) and oversold (-5) levels, respectively. Additionally, the background color changes to light red when the oscillator is overbought and light green when it is oversold, providing a clear visual cue.
By incorporating the S&P Short-Range Oscillator into your trading strategy, you can gain valuable insights into market conditions and make more informed decisions about when to buy, sell, or hold your stocks. However, always consider other market factors and perform your own analysis before making any trading decisions.
The S&P Short-Range Oscillator is a powerful tool for traders looking to gain insights into market sentiment. It provides clear buy and sell signals through its combination of multiple indicators and normalization process. However, traders should be aware of its lagging nature and potential complexity, and use it in conjunction with other analysis methods for the best results.
Disclaimer
The S&P Short-Range Oscillator is for informational purposes only and should not be considered financial advice. Trading involves risk, and you should conduct your own research or consult a financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Use at your own risk.
