Enhanced Cycle IndicatorEnhanced Cycle Indicator Guide
DISCLAIMER
"This PineScript indicator evolved from a foundational algorithm designed to visualize cycle-based center average differentials. The original concept has been significantly enhanced and optimized through collaborative refinement with AI, resulting in improved functionality, performance, and visualization capabilities while maintaining the core mathematical principles of the original design"
Overview
The Enhanced Cycle Indicator is designed to identify market cycles with minimal lag while ensuring the cycle lows and highs correspond closely with actual price bottoms and tops. This indicator transforms price data into observable cycles that help you identify when a market is likely to change direction.
Core Principles
Cycle Detection: Identifies natural market rhythms using multiple timeframes
Dynamic Adaptation: Adjusts to changing market conditions for consistent performance
Precise Signals: Provides clear entry and exit points aligned with actual market turns
Reduced Lag: Uses advanced calculations to minimize delay in cycle identification
How To Use
1. Main Cycle Interpretation
Green Histogram Bars: Bullish cycle phase (upward momentum)
Red Histogram Bars: Bearish cycle phase (downward momentum)
Cycle Extremes: When the histogram reaches extreme values (+80/-80), the market is likely approaching a turning point
Zero Line: Crossovers often indicate a shift in the underlying market direction
2. Trading Signals
Green Triangle Up (bottom of chart): Strong bullish signal - ideal for entries or covering shorts
Red Triangle Down (top of chart): Strong bearish signal - ideal for exits or short entries
Diamond Shapes: Indicate divergence between price and cycle - early warning of potential reversals
Small Circles: Minor cycle turning points - useful for fine-tuning entries/exits
3. Optimal Signal Conditions
Bullish Signals Work Best When:
The cycle is deeply oversold (below -60)
RSI is below 40 or turning up
Price is near a significant low
Multiple confirmation bars have occurred
Bearish Signals Work Best When:
The cycle is heavily overbought (above +60)
RSI is above 60 or turning down
Price is near a significant high
Multiple confirmation bars have occurred
4. Parameter Adjustments
For Shorter Timeframes: Reduce cycle periods and smoothing factor for faster response
For Daily/Weekly Charts: Increase cycle periods and smoothing for smoother signals
For Volatile Markets: Reduce cycle responsiveness to filter noise
For Trending Markets: Increase signal confirmation requirement to avoid false signals
Recommended Settings
Default (All-Purpose)
Main Cycle: 50
Half Cycle: 25
Quarter Cycle: 12
Smoothing Factor: 0.5
RSI Filter: Enabled
Signal Confirmation: 2 bars
Faster Response (Day Trading)
Main Cycle: 30
Half Cycle: 15
Quarter Cycle: 8
Smoothing Factor: 0.3
Cycle Responsiveness: 1.2
Signal Confirmation: 1 bar
Smoother Signals (Swing Trading)
Main Cycle: 80
Half Cycle: 40
Quarter Cycle: 20
Smoothing Factor: 0.7
Cycle Responsiveness: 0.8
Signal Confirmation: 3 bars
Advanced Features
Adaptive Period
When enabled, the indicator automatically adjusts cycle periods based on recent price volatility. This is particularly useful in markets that alternate between trending and ranging behaviors.
Momentum Filter
Enhances cycle signals by incorporating price momentum, making signals more responsive during strong trends and less prone to whipsaws during consolidations.
RSI Filter
Adds an additional confirmation layer using RSI, helping to filter out lower-quality signals and improve overall accuracy.
Divergence Detection
Identifies situations where price makes a new high/low but the cycle doesn't confirm, often preceding significant market reversals.
Best Practices
Use the indicator in conjunction with support/resistance levels
Look for signal clusters across multiple timeframes
Reduce position size when signals appear far from cycle extremes
Pay special attention to signals that coincide with divergences
Customize cycle periods to match the natural rhythm of your traded instrument
Troubleshooting
Too Many Signals: Increase signal confirmation bars or reduce cycle responsiveness
Missing Major Turns: Decrease smoothing factor or increase cycle responsiveness
Signals Too Late: Decrease cycle periods and smoothing factor
False Signals: Enable RSI filter and increase signal confirmation requirement
在腳本中搜尋"AI功能更新"
Dskyz (DAFE) GENESIS Dskyz (DAFE) GENESIS: Adaptive Quant, Real Regime Power
Let’s be honest: Most published strategies on TradingView look nearly identical—copy-paste “open-source quant,” generic “adaptive” buzzwords, the same shallow explanations. I’ve even fallen into this trap with my own previously posted strategies. Not this time.
What Makes This Unique
GENESIS is not a black-box mashup or a pre-built template. It’s the culmination of DAFE’s own adaptive, multi-factor, regime-aware quant engine—built to outperform, survive, and visualize live edge in anything from NQ/MNQ to stocks and crypto.
True multi-factor core: Volume/price imbalances, trend shifts, volatility compression/expansion, and RSI all interlock for signal creation.
Adaptive regime logic: Trades only in healthy, actionable conditions—no “one-size-fits-all” signals.
Momentum normalization: Uses rolling, percentile-based fast/slow EMA differentials, ALWAYS normalized, ALWAYS relevant—no “is it working?” ambiguity.
Position sizing that adapts: Not fixed-lot, not naive—not a loophole for revenge trading.
No hidden DCA or pyramiding—what you see is what you trade.
Dashboard and visual system: Directly connected to internal logic. If it’s shown, it’s used—and nothing cosmetic is presented on your chart that isn’t quantifiable.
📊 Inputs and What They Mean (Read Carefully)
Maximum Raw Score: How many distinct factors can contribute to regime/trade confidence (default 4). If you extend the quant logic, increase this.
RSI Length / Min RSI for Shorts / Max RSI for Longs: Fine-tunes how “overbought/oversold” matters; increase the length for smoother swings, tighten floors/ceilings for more extreme signals.
⚡ Regime & Momentum Gates
Min Normed Momentum/Score (Conf): Raise to demand only the strongest trends—your filter to avoid algorithmic chop.
🕒 Volatility & Session
ATR Lookback, ATR Low/High Percentile: These control your system’s awareness of when the market is dead or ultra-volatile. All sizing and filter logic adapts in real time.
Trading Session (hours): Easy filter for when entries are allowed; default is regular trading hours—no surprise overnight fills.
📊 Sizing & Risk
Max Dollar Risk / Base-Max Contracts: All sizing is adaptive, based on live regime and volatility state—never static or “just 1 contract.” Control your max exposures and real $ risk. ATR will effect losses in high volatility times.
🔄 Exits & Scaling
Stop/Trail/Scale multipliers: You choose how dynamic/flexible risk controls and profit-taking need to be. ATR-based, so everything auto-adjusts to the current market mode.
Visuals That Actually Matter
Dashboard (Top Right): Shows only live, relevant stats: scoring, status, position size, win %, win streak, total wins—all from actual trade engine state (not “simulated”).
Watermark (Bottom Right): Momentum bar visual is always-on, regime-aware, reflecting live regime confidence and momentum normalization. If the bar is empty, you’re truly in no-momentum. If it glows lime, you’re riding the strongest possible edge.
*No cosmetics, no hidden code distractions.
Backtest Settings
Initial capital: $10,000
Commission: Conservative, realistic roundtrip cost:
15–20 per contract (including slippage per side) I set this to $25
Slippage: 3 ticks per trade
Symbol: CME_MINI:NQ1!
Timeframe: 1 min (but works on all timeframes)
Order size: Adaptive, 1–3 contracts
No pyramiding, no hidden DCA
Why these settings?
These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions.
Why It Wins
While others put out “AI-powered” strategies with little logic or soul, GENESIS is ruthlessly practical. It is built around what keeps traders alive:
- Context-aware signals, not just patterns
- Tight, transparent risk
- Inputs that adapt, not confuse
- Visuals that clarify, not distract
- Code that runs clean, efficient, and with minimal overfitting risk (try it on QQQ, AMD, SOL, etc. out of the box)
Disclaimer (for TradingView compliance):
Trading is risky. Futures, stocks, and crypto can result in significant losses. Do not trade with funds you cannot afford to lose. This is for educational and informational purposes only. Use in simulation/backtest mode before live trading. No past performance is indicative of future results. Always understand your risk and ownership of your trades.
This will not be my last—my goal is to keep raising the bar until DAFE is a brand or I’m forced to take this private.
Use with discipline, use with clarity, and always trade smarter.
— Dskyz , powered by DAFE Trading Systems.
NeuroTrendNeuroTrend is an advanced, self-adjusting trend analysis system that continuously adapts to changing market conditions using volatility-aware smoothing, momentum weighting, and intelligent trend classification. It provides real-time trend detection, confidence scoring, early reversal warnings, and slope projection, all delivered through a coaching dashboard and structured rule-based commentary system.
At its core, NeuroTrend uses two EMAs whose smoothing lengths change automatically based on current volatility, measured by the ATR relative to price, and momentum bias, measured by RSI displacement from the neutral level. These adaptive EMAs create a flexible baseline that adjusts to the pace of the market. From these EMAs, the system calculates angular slope and derives a slope power score, which reflects directional momentum weighted by volatility.
NeuroTrend classifies each bar into one of five market phases: Impulse, Cooling, Reversal Risk, Stall, or Neutral. This classification is based on slope strength, slope variability, and RSI behavior. Each phase offers specific context for whether to enter, continue, or avoid a position.
The indicator uses what is referred to as a neural memory engine, which is inspired by the idea of memory but is not a neural network or machine learning model. Instead, it is a statistical recalibration system that adjusts thresholds using recent ATR conditions and slope standard deviation. This allows the indicator to remain aligned with the current market environment without the need for manual tuning.
Although NeuroTrend is fully adaptive, it includes inputs for the base fast and slow EMAs. These inputs define the central anchor points around which the adaptive logic operates. This gives the trader the ability to control the default behavior of the indicator while still benefiting from real-time responsiveness to volatility and momentum.
To assess the strength of a trend, NeuroTrend computes a confidence score based on four elements: DMI trend strength, directional bias from DI+ and DI–, slope normalization, and volatility efficiency measured by ATR in relation to EMA distance. This score is used to inform alerts, commentary, and dashboard visualization.
The indicator also includes a slope projection engine that estimates near-term direction based on slope change and acceleration. This projection is scaled and clamped using a dynamic volatility factor to prevent unrealistic or unstable values.
Reversal and stall detection are built in. Reversal detection is based on slope collapsing, sign flipping, and RSI weakness. Stall detection is triggered when slope magnitude is low, RSI is flat, and ATR is compressed. These filters help prevent entries in low-quality or high-risk environments.
The system also includes AI-style commentary. This feature is not powered by machine learning or natural language processing. It is rule-based, using prioritized conditions to generate clear statements that reflect the current market state. Messages such as "Strong trend forming" or "Reversal risk rising" are created by predefined logic that adapts to the market.
A visual dashboard is provided on the chart. It displays the current phase, trend direction, slope score, confidence level, reversal status, stall condition, and projected slope angle. This helps traders interpret market behavior at a glance without scanning multiple indicators.
Alerts are triggered only when specific conditions are met: trend strength must be in the impulse phase, confidence must be high, and there must be no active reversal or stall conditions. This ensures alerts are reserved for high-quality setups with strong directional alignment.
Disclaimer:
This script is intended for educational and informational use only. It does not constitute financial advice. The author accepts no responsibility for any trading or investment decisions made using this tool. Always do your own research and consult a licensed financial advisor before making financial decisions.
ADX Forecast [Titans_Invest]ADX Forecast
This isn’t just another ADX indicator — it’s the most powerful and complete ADX tool ever created, and without question the best ADX indicator on TradingView, possibly even the best in the world.
ADX Forecast represents a revolutionary leap in trend strength analysis, blending the timeless principles of the classic ADX with cutting-edge predictive modeling. For the first time on TradingView, you can anticipate future ADX movements using scientifically validated linear regression — a true game-changer for traders looking to stay ahead of trend shifts.
1. Real-Time ADX Forecasting
By applying least squares linear regression, ADX Forecast projects the future trajectory of the ADX with exceptional accuracy. This forecasting power enables traders to anticipate changes in trend strength before they fully unfold — a vital edge in fast-moving markets.
2. Unmatched Customization & Precision
With 26 long entry conditions and 26 short entry conditions, this indicator accounts for every possible ADX scenario. Every parameter is fully customizable, making it adaptable to any trading strategy — from scalping to swing trading to long-term investing.
3. Transparency & Advanced Visualization
Visualize internal ADX dynamics in real time with interactive tags, smart flags, and fully adjustable threshold levels. Every signal is transparent, logic-based, and engineered to fit seamlessly into professional-grade trading systems.
4. Scientific Foundation, Elite Execution
Grounded in statistical precision and machine learning principles, ADX Forecast upgrades the classic ADX from a reactive lagging tool into a forward-looking trend prediction engine. This isn’t just an indicator — it’s a scientific evolution in trend analysis.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the ADX, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an ADX time series like this:
Time →
ADX →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted ADX, which can be crossed with the actual ADX to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public ADX with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining ADX with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
ADX Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🥇 This is the world’s first ADX indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE ADX❓
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ HOW TO USE THE ADX❓
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
• Strong Trend: When the ADX is above 25, indicating a strong trend.
• Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
• Neutral Zone: Between 20 and 25, where the trend strength is unclear.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔹 +DI > -DI
🔹 +DI < -DI
🔹 +DI > ADX
🔹 +DI < ADX
🔹 -DI > ADX
🔹 -DI < ADX
🔹 ADX > Threshold
🔹 ADX < Threshold
🔹 +DI > Threshold
🔹 +DI < Threshold
🔹 -DI > Threshold
🔹 -DI < Threshold
🔹 +DI (Crossover) -DI
🔹 +DI (Crossunder) -DI
🔹 +DI (Crossover) ADX
🔹 +DI (Crossunder) ADX
🔹 +DI (Crossover) Threshold
🔹 +DI (Crossunder) Threshold
🔹 -DI (Crossover) ADX
🔹 -DI (Crossunder) ADX
🔹 -DI (Crossover) Threshold
🔹 -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
🔸 +DI > -DI
🔸 +DI < -DI
🔸 +DI > ADX
🔸 +DI < ADX
🔸 -DI > ADX
🔸 -DI < ADX
🔸 ADX > Threshold
🔸 ADX < Threshold
🔸 +DI > Threshold
🔸 +DI < Threshold
🔸 -DI > Threshold
🔸 -DI < Threshold
🔸 +DI (Crossover) -DI
🔸 +DI (Crossunder) -DI
🔸 +DI (Crossover) ADX
🔸 +DI (Crossunder) ADX
🔸 +DI (Crossover) Threshold
🔸 +DI (Crossunder) Threshold
🔸 -DI (Crossover) ADX
🔸 -DI (Crossunder) ADX
🔸 -DI (Crossover) Threshold
🔸 -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
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
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : ADX Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Machine Learning | Adaptive Trend Signals [Bitwardex]⚙️🧠Machine Learning | Adaptive Trend Signals
🔷Overview
Machine Learning | Adaptive Trend Signals is a Pine Script™ v6 indicator designed to visualize market trends and generate signals through a combination of volatility clustering, Gaussian smoothing, and adaptive trend calculations. Built as an overlay indicator, it integrates advanced techniques inspired by machine learning concepts, such as K-Means clustering, to adapt to changing market conditions. The script is highly customizable, includes a backtesting module, and supports alert conditions, making it suitable for traders exploring trend-based strategies and developers studying volatility-driven indicator design.
🔷Functionality
The indicator performs the following core functions:
• Volatility Clustering: Uses K-Means clustering to categorize market volatility into high, medium, and low states, adjusting trend sensitivity accordingly.
• Trend Calculation: Computes adaptive trend lines (SmartTrend) based on volatility-adjusted standard deviation, smoothed RSI, and ADX filters.
• Signal Generation: Identifies potential buy and sell points through trend line crossovers and directional confirmation.
• Backtesting Module: Tracks trade outcomes based on the SmartTrend3 value, displaying win rate and total trades.
• Visualization: Plots trend lines with gradient colors and optional signal markers (bullish 🐮 and bearish 🐻).
• Alerts: Provides configurable alerts for trend shifts and volatility state changes.
🔷Technical Methodology
Volatility Clustering with K-Means
The indicator employs a K-Means clustering algorithm to classify market volatility, measured via the Average True Range (ATR), into three distinct clusters:
• Data Collection: Gathers ATR values over a user-defined training period (default: 100 bars).
• Centroid Initialization: Sets initial centroids at the highest, lowest, and midpoint ATR values within the training period.
• Iterative Clustering: Assigns ATR data points to the nearest centroid, recalculates centroid means, and repeats until convergence.
• Dynamic Adjustment: Assigns a volatility state (high, medium, or low) based on the closest centroid, adjusting the trend factor (e.g., tighter for high volatility, wider for low volatility).
This approach allows the indicator to adapt its sensitivity to varying market conditions, providing a data-driven foundation for trend calculations.
🔷Gaussian Smoothing
To enhance signal clarity and reduce noise, the indicator applies Gaussian kernel smoothing to:
• RSI: Smooths the Relative Strength Index (calculated from OHLC4) to filter short-term fluctuations.
• SmartTrend: Smooths the primary trend line for a more stable output.
The Gaussian kernel uses a sigma value derived from the user-defined smoothing length, ensuring mathematically consistent noise reduction.
🔷SmartTrend Calculation
The pineSmartTrend function is the core of the indicator, producing three trend lines:
• SmartTrend: The primary trend line, calculated using a volatility-adjusted standard deviation, smoothed RSI, and ADX conditions.
• SmartTrend2: A secondary trend line with a wider factor (base factor * 1.382) for signal confirmation.
SmartTrend3: The average of SmartTrend and SmartTrend2, used for plotting and backtesting.
Key components of the calculation include:
• Dynamic Standard Deviation: Scales based on ATR relative to its 50-period smoothed average, with multipliers (1.0 to 1.4) applied according to volatility thresholds.
• RSI and ADX Filters: Requires RSI > 50 for bullish trends or < 50 for bearish trends, alongside ADX > 15 and rising to confirm trend strength.
Volatility-Adjusted Bands: Constructs upper and lower bands around price action, adjusted by the volatility cluster’s dynamic factor.
🔷Signal Generation
The generate_signals function generates signals as follows:
• Buy Signal: Triggered when SmartTrend crosses above SmartTrend2 and the price is above SmartTrend, with directional confirmation.
• Sell Signal: Triggered when SmartTrend crosses below SmartTrend2 and the price is below SmartTrend, with directional confirmation.
Directional Logic: Tracks trend direction to filter out conflicting signals, ensuring alignment with the broader market context.
Signals are visualized as small circles with bullish (🐮) or bearish (🐻) emojis, with an option to toggle visibility.
🔷Backtesting
The get_backtest function evaluates signal outcomes using the SmartTrend3 value (rather than closing prices) to align with the trend-based methodology.
It tracks:
• Total Trades: Counts completed long and short trades.
• Win Rate: Calculates the percentage of trades where SmartTrend3 moves favorably (higher for longs, lower for shorts).
Position Management: Closes opposite positions before opening new ones, simulating a single-position trading system.
Results are displayed in a table at the top-right of the chart, showing win rate and total trades. Note that backtest results reflect the indicator’s internal logic and should not be interpreted as predictive of real-world performance.
🔷Visualization and Alerts
• Trend Lines: SmartTrend3 is plotted with gradient colors reflecting trend direction and volatility cluster, accompanied by a secondary line for visual clarity.
• Signal Markers: Optional buy/sell signals are plotted as small circles with customizable colors.
• Alerts: Supports alerts for:
• Bullish and bearish trend shifts (confirmed on bar close).
Transitions to high, medium, or low volatility states.
🔷Input Parameters
• ATR Length (default: 14): Period for ATR calculation, used in volatility clustering.
• Period (default: 21): Common period for RSI, ADX, and standard deviation calculations.
• Base SmartTrend Factor (default: 2.0): Base multiplier for volatility-adjusted bands.
• SmartTrend Smoothing Length (default: 10): Length for Gaussian smoothing of the trend line.
• Show Buy/Sell Signals? (default: true): Enables/disables signal markers.
• Bullish/Bearish Color: Customizable colors for trend lines and signals.
🔷Usage Instructions
• Apply to Chart: Add the indicator to any TradingView chart.
• Configure Inputs: Adjust parameters to align with your trading style or market conditions (e.g., shorter ATR length for faster markets).
• Interpret Output:
• Trend Lines: Use SmartTrend3’s direction and color to gauge market bias.
• Signals: Monitor bullish (🐮) and bearish (🐻) markers for potential entry/exit points.
• Backtest Table: Review win rate and total trades to understand the indicator’s behavior in historical data.
• Set Alerts: Configure alerts for trend shifts or volatility changes to support manual or automated trading workflows.
• Combine with Analysis: Use the indicator alongside other tools or market context, as it is designed to complement, not replace, comprehensive analysis.
🔷Technical Notes
• Data Requirements: Requires at least 100 bars for accurate volatility clustering. Ensure sufficient historical data is loaded.
• Market Suitability: The indicator is designed for trend detection and may perform differently in ranging or volatile markets due to its reliance on RSI and ADX filters.
• Backtesting Scope: The backtest module uses SmartTrend3 values, which may differ from price-based outcomes. Results are for informational purposes only.
• Computational Intensity: The K-Means clustering and Gaussian smoothing may increase processing time on lower timeframes or with large datasets.
🔷For Developers
The script is modular, well-commented, encouraging reuse and modification with proper attribution.
Key functions include:
• gaussianSmooth: Applies Gaussian kernel smoothing to any data series.
• pineSmartTrend: Computes adaptive trend lines with volatility and momentum filters.
• getDynamicFactor: Adjusts trend sensitivity based on volatility clusters.
• get_backtest: Evaluates signal performance using SmartTrend3.
Developers can extend these functions for custom indicators or strategies, leveraging the volatility clustering and smoothing methodologies. The K-Means implementation is particularly useful for adaptive volatility analysis.
🔷Limitations
• The indicator is not predictive and should be used as part of a broader trading strategy.
• Performance varies by market, timeframe, and parameter settings, requiring user experimentation.
• Backtest results are based on historical data and internal logic, not real-world trading conditions.
• Volatility clustering assumes sufficient historical data; incomplete data may affect accuracy.
🔷Acknowledgments
Developed by Bitwardex, inspired by machine learning concepts and adaptive trading methodologies. Community feedback is welcome via TradingView’s platform.
🔷 Risk Disclaimer
Trading involves significant risks, and most traders may incur losses. Bitwardex AI Algo is provided for informational and educational purposes only and does not constitute financial advice or a recommendation to buy or sell any financial instrument . The signals, metrics, and features are tools for analysis and do not guarantee profits or specific outcomes. Past performance is not indicative of future results. Always conduct your own due diligence and consult a financial advisor before making trading decisions.
EMA Oscillating Trend📈 EMA Oscillating Trend by AI-123
The EMA Oscillating Trend indicator is a dynamic trend visualizer that enhances traditional EMA behavior by offsetting the line based on trend direction, providing a more intuitive and visually distinct representation of market momentum.
🔍 Key Features:
🔵 Bullish Color Customization – Define your preferred color for bullish trends
🔴 Bearish Color Customization – Set a different tone for bearish phases
🪄 Adjustable Line Thickness – Tailor the EMA's appearance to your chart style
📐 Offset Multiplier Input – Automatically pushes the EMA above price in a downtrend and below price in an uptrend for enhanced clarity
⚙️ User-Friendly Inputs – No coding knowledge required; full customization in the settings panel
🧠 How It Works:
Calculates a primary EMA line (OV) and a sub-component to compare against (OV2)
Determines the trend based on whether OV is above or below OV2
Shifts the EMA line above price during bearish trends and below price during bullish trends
The offset is percentage-based and scales dynamically with the price for optimal readability
✅ Ideal For:
Trend-followers seeking visual clarity
Discretionary traders who want less clutter and more signal
Anyone who likes their EMAs with a little more flair and insight
🛠️ Author: @alphainvestor123
This tool was crafted with simplicity and clarity in mind. If you enjoy the indicator, consider dropping feedback or sharing your use case!
RSI Forecast [Titans_Invest]RSI Forecast
Introducing one of the most impressive RSI indicators ever created – arguably the best on TradingView, and potentially the best in the world.
RSI Forecast is a visionary evolution of the classic RSI, merging powerful customization with groundbreaking predictive capabilities. While preserving the core principles of traditional RSI, it takes analysis to the next level by allowing users to anticipate potential future RSI movements.
Real-Time RSI Forecasting:
For the first time ever, an RSI indicator integrates linear regression using the least squares method to accurately forecast the future behavior of the RSI. This innovation empowers traders to stay one step ahead of the market with forward-looking insight.
Highly Customizable:
Easily adapt the indicator to your personal trading style. Fine-tune a variety of parameters to generate signals perfectly aligned with your strategy.
Innovative, Unique, and Powerful:
This is the world’s first RSI Forecast to apply this predictive approach using least squares linear regression. A truly elite-level tool designed for traders who want a real edge in the market.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ 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.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or 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
📈 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or 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
📉 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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Linear Regression: (Forecast)
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
Linear Regression (Forecast)
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
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📜 SCRIPT : RSI Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
GranDoc - Week, Day, Month, and Session Separator5Indicator Name: GranDoc's - Week, Day, Month, and Session Separator
Version: Pine Script v5
Author: Jonpaul Nnamdi Opara (GranDoc )
Description
The "GranDoc - Week, Day, Month, and Session Separator" is a highly customizable TradingView indicator designed to enhance chart analysis by visually marking critical time-based transitions. Developed by Jonpaul Nnamdi Opara, this tool plots vertical lines with labels or background highlights to denote the start and end of weeks, days, months, and major trading sessions (Frankfurt, London, NY Morning, NY Afternoon, Sydney, and Tokyo). Traders can tailor colors, line styles, widths, transparency, and session times to align with their strategies and timezones.
Ideal for forex, stocks, futures, and crypto traders, this indicator simplifies the identification of key market periods—such as session openings/closings or new weeks—that often signal increased volatility or trend shifts. It’s optimized for intraday timeframes for session separators but supports all timeframes for week, day, and month markers, making it a versatile addition to any trader’s toolkit.
Features
Week Separators: Marks Monday starts with customizable lines and "Week Start" labels.
Day Separators: Highlights daily openings with lines and "Day Start" labels.
Month Separators: Indicates new months with lines and "Month Start" labels.
Session Separators: Plots lines and labels for major trading sessions’ start and end:
Frankfurt (default: 07:00–15:00 UTC)
London (default: 08:00–16:00 UTC)
NY Morning (default: 13:00–16:00 UTC)
NY Afternoon (default: 16:00–21:00 UTC)
Sydney (default: 22:00–06:00 UTC)
Tokyo (default: 00:00–08:00 UTC)
Timezone Support: Adjusts session times with a UTC offset (±12 hours).
Display Flexibility : Toggle between labeled vertical lines or background highlights.
Customization: Fine-tune colors, line styles (solid, dashed, dotted), widths, and transparency.
Background Mode: Highlights periods with translucent backgrounds for cleaner charts.
[ i]Labeled Lines: Each line includes descriptive labels (e.g., "London Open", "Tokyo Closed") when not in background mode.
How to Use
Add to Chart:
Copy the script into TradingView’s Pine Editor.
Click "Add to Chart" to apply the indicator.
Customize Settings:
Open settings via double-click or the "Settings" gear icon.
Timezone Offset: Set your UTC offset (e.g., -5 for EST) to align sessions.
Toggles: Enable/disable week, day, month, or session separators.
Appearance: Adjust colors, line styles, widths, and transparency for each separator.
Session Times: Modify start/end hours and minutes if defaults don’t suit your market.
Background Mode: Enable "Show as Background" for colored backgrounds instead of lines, and tweak "Session Background Transparency."
Labels: Labeled lines (e.g., "Sydney Open") appear automatically unless background mode is active.
Chart Compatibility:
Session separators require intraday timeframes (e.g., 1-minute to 4-hour).
Week, day, and month separators work across all timeframes.
Confirm your chart’s timezone aligns with your analysis.
Analyze:
Use separators to pinpoint session transitions, daily openings, or weekly shifts for trade planning.
Labels make it easy to spot key periods on busy charts.
Pair with indicators like RSI, volume, or support/resistance for deeper insights.
Example Use Cases
Forex Trading: Highlight London and NY session opens/closes for high-liquidity entries.
Day Trading: Reset strategies at daily separators and monitor intraday volatility.
Swing Trading: Use week/month separators to track longer-term trends.
Session Focus: Isolate sessions like Tokyo for regional market analysis.
Chart Clarity: Background mode declutters charts while marking key times.
Notes
Session separators are disabled on daily+ timeframes to prevent clutter.
Verify timezone offset for accurate session alignment.
Background mode suits lower timeframes for readability.
Labels are visible only when background mode is disabled.
Feedback
Share your thoughts or suggestions to make this indicator even better! Reach out via TradingView or connect with the author for insights. Happy trading!
About the Author
Dr. Jonpaul Nnamdi Opara, a PhD graduate from Ehime University, Japan, is a researcher and developer specializing in AI and machine learning. His work on automated landslide mapping and defect detection, published in journals like GEOMATE, showcases his precision-driven approach. With the "GranDoc" indicator, Jonpaul brings intuitive, data-driven clarity to financial markets, reflecting his expertise in creating impactful tools.
Tetris with Auto-PlayThis indicator is implemented in Pine Script™ v6 and serves as a demonstration of TradingView's capabilities. The core concept is to simulate a classic Tetris game by creating a grid-based environment and managing game state entirely within Pine Script.
Key Technical Aspects:
Grid Representation:
The script defines a custom grid structure using a user-defined type that holds the grid’s dimensions and a one-dimensional array to simulate a two-dimensional board. This structure is used to track occupied cells, clear full rows, and determine stack height.
Piece Management:
A second custom type is used to represent the state of a tetromino piece, including its type, rotation, and position. The code includes functions to calculate the block offsets for each tetromino based on its rotation state.
Collision Detection and Piece Locking:
Dedicated functions check for collisions against the grid borders and existing blocks. When a collision is detected during a downward move, the piece is locked into the grid, and any complete lines are cleared.
AIgo-Driven Placement:
The script incorporates a simple heuristic to determine the best placement for the next tetromino. It simulates different rotations and horizontal positions, evaluating each based on aggregated column height, cleared lines, holes, and bumpiness. This decision-making process is encapsulated in an AI-like function that returns the optimal rotation and placement.
Rendering Using Tables:
The visual representation is managed via TradingView’s table objects. The game board is rendered with a bordered layout, while a separate preview table displays the next piece and the current score. Each cell is updated with text and background colors that correspond to the state of the game.
Execution Flow and Timing:
The main execution loop handles real-time updates by dropping pieces at set intervals and checking for game-over conditions. The code leverages persistent variables and time comparisons to control game speed and manage transitions between piece drops.
Executing:
Add the indicator to the chart
It starts playing itself till game over
There are no parameters to change in this version but the grid in the code directly
p.s. Sadly we have no interactive buttons in the current pinescript versions to play ourself, but its about the possibilitys what we could do ;-)
Maybe in a future version there is more possible, if i find time to enhance and expand the idea
Have fun :-)
Machine Learning Trendlines Cluster [LuxAlgo]The ML Trendlines Cluster indicator allows traders to automatically identify trendlines using a machine learning algorithm based on k-means clustering and linear regression, highlighting trendlines from clustered prices.
For trader's convenience, trendlines can be filtered based on their slope, allowing them to filter out trendlines that are too horizontal, or instead keep them depending on the user-selected settings.
🔶 USAGE
Traders only need to set the number of trendlines (clusters) they want the tool to detect and the algorithm will do the rest.
By default the tool is set to detect 4 clusters over the last 500 bars, in the image above it is set to detect 10 clusters over the same period.
This approach only focuses on drawing trendlines from prices that share a common trading range, offering a unique perspective to traditional trendlines. Trendlines with a significant slope can highlight higher dispersion within its cluster.
🔹 Trendline Slope Filtering
Traders can filter trendlines by their slope to display only steep or flat trendlines relative to a user-defined threshold.
The image above shows the three different configurations of this feature:
Filtering disabled
Filter slopes above threshold
Filter slopes below threshold
🔶 DETAILS
K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group.
The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized.
The trendlines are displayed according to the linear regression function calculated for each cluster.
🔶 SETTINGS
Window Size: Maximum number of bars to get data from
Clusters: Maximum number of clusters (trendlines) to detect
🔹 Optimization
Maximum Iteration Steps: Maximum loop iterations for cluster computation
🔹 Slope Filter
Threshold Multiplier: Multiplier applied to a volatility measure, higher multiplier equals higher threshold
Filter Slopes: Enable/Disable Trendline Slope Filtering, select to filter trendlines with slopes ABOVE or BELOW the threshold
🔹 Style
Upper Zone: Color to display in the top zone
Lower Zone: Color to display in the bottom zone
Lines: Style for the lines
Size: Line size
aivance_Multi-Index Performance Comparison# Multi-Index Performance Comparison
This indicator allows traders and investors to easily compare the performance of multiple global market and sector indexes from a user-defined start date. All indexes are normalized to 100% at the specified start date, making relative performance comparisons straightforward.
## Features:
- Customizable start date for performance comparison
- Toggleable global market indexes (S&P 500, MSCI World, DAX, Nasdaq 100, EURO STOXX 50, Japan, Hong Kong)
- Toggleable sector indexes (Materials, Health Care, Financial, Technology, AI & Robotics)
- Clear visualization with distinct colors for each index
- Reference line at 100% for easy benchmark comparison
## How to Use:
1. Set your desired start date for normalizing performance
2. Toggle indexes on/off under the "Inputs" tab
3. Compare relative performance across different markets and sectors
Perfect for identifying relative strength, sector rotation, or global market correlations over your specific timeframe of interest.
Zen ABR### **Average Bar Range (ABR) Levels** – A Simple Yet Powerful Scalping Tool
The **ABR Levels** indicator dynamically calculates the **average bar range** and plots three key levels on your chart:
✅ **0.5x ABR** – Half the average bar range
✅ **1x ABR** – The full average bar range
✅ **2x ABR** – Double the average bar range
This provides an **instant volatility gauge**, helping traders adjust position size, stop losses, and targets **based on current market conditions** rather than arbitrary numbers.
### **How It Works**
- The indicator calculates **the average range of the last 8 bars** (default setting, adjustable).
- The **ABR levels update in real time**, appearing in the **top-right corner of your chart** for easy reference.
- Works across **any instrument and timeframe** – great for scalpers and intraday traders.
### **Why Use ABR Levels?**
🚀 **Avoid oversized losses** – Trade dynamically instead of using fixed stops and targets.
📉 **Recognize shifts in market conditions** – Identify when volatility is expanding or contracting.
🎯 **Refine your entries & exits** – Use ABR levels to **scale** your positions intelligently.
### **Pro Tip:**
Never enter a trade **aiming for half-R** profits. If a trade goes badly, **you might manage it to break even or half-R**, but aiming for tiny wins will **wreck your long-term edge**.
🔹 **This script is open-source!** If you're not on TradingView, you can copy the code into any AI tool to generate it for your platform. 🚀
Smart Adaptive Signal SystemSmart Adaptive Signal System
Description: The Smart Adaptive Signal System is a sophisticated indicator that generates intelligent buy/sell signals by dynamically adapting to market conditions. It predicts target prices based on momentum and volatility, providing more accurate and reliable trading opportunities.
How It Works:
Dynamic Signal Generation: The system predicts target prices by considering factors such as volatility and momentum. This allows it to react instantly to trend changes and market fluctuations.
Adaptive Thresholds: Buy and sell signals are triggered with adaptive thresholds, adjusting according to market volatility. This ensures flexibility in the face of sudden market changes.
Trend-Based Reset: Users can choose to reset threshold values based on a time interval or trend change. This feature helps the system re-adapt to current market conditions for greater accuracy.
Target Price Prediction: Target prices are calculated using momentum and volatility, helping the system predict future price movements.
How to Use:
Buy/Sell Signals: The indicator generates buy and sell signals based on market conditions. Look for a "down arrow" for a buy signal and an "up arrow" for a sell signal on the chart.
Target Price Lines: Along with buy and sell signals, the system draws target price lines. This helps you visualize potential future price levels.
Flexible Settings: Users can customize analysis periods, minimum change percentages, and other parameters to fit their needs.
Features:
Dynamic buy and sell signals
Target price predictions
Volatility and momentum-based analysis
User-friendly and flexible settings
Trend-based adaptive resetting
Alerts: The Smart Adaptive Signal System responds quickly to sudden market changes, but always use it in conjunction with other indicators like support and resistance levels. Signal accuracy may vary depending on market conditions.
Machine Learning SupertrendThe Machine Learning Supertrend is an advanced trend-following indicator that enhances the traditional Supertrend with Gaussian Process Regression (GPR) and kernel-based learning. Unlike conventional methods that rely purely on historical ATR values, this indicator integrates machine learning techniques to dynamically estimate volatility and forecast future price movements, resulting in a more adaptive and robust trend detection system.
At the core of this indicator lies Gaussian Process Regression (GPR), which utilizes a Radial Basis Function (RBF) kernel to model price distributions and anticipate future trends. Instead of simply looking at past price action, it constructs a kernel matrix, enabling a probabilistic approach to price forecasting. This allows the indicator to not only detect current trends but also project potential trend reversals with greater accuracy.
By applying machine learning to ATR estimation, the ML Supertrend dynamically adjusts its thresholds based on predicted values rather than a fixed multiplier. This makes the trend signals more responsive to market conditions, reducing false signals and minimizing whipsaws often seen with traditional Supertrend indicators. The upper and lower bands are no longer static but evolve based on the underlying price structure, improving the reliability of trend shifts.
When the price crosses these adaptive levels, the indicator detects a trend change and plots it accordingly. Green signifies a bullish trend, while red indicates a bearish one. Alerts can also be triggered when the trend shifts, allowing traders to react quickly to potential reversals.
What makes this approach powerful is its ability to adapt to different market conditions. Traditional ATR-based methods use fixed parameters that might not always be optimal, whereas this ML-driven Supertrend continuously refines its estimations based on real-time data. The result is a more intelligent, less lagging, and highly adaptive trend-following tool.
This indicator is particularly useful for traders looking to enhance trend-following strategies with AI-driven insights. It reduces noise, improves signal reliability, and even offers a degree of trend forecasting, making it ideal for those who want a more advanced and dynamic alternative to standard Supertrend indicators.
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, and past performance is not indicative of future results. Trading involves risk, and users should conduct their own research and use proper risk management before making investment decisions.
Johnny's Volatility-Driven Trend Identifier w/ Reversal SignalsJohnny's Volatility-Driven Trend Identifier w/ Reversal Signals is designed to identify high-probability trend shifts and reversals by incorporating volatility, momentum, and impulse-based filtering. It is specifically built for traders who want to capture strong trend movements while minimizing false signals caused by low volatility noise.
By leveraging Rate of Change (ROC), Relative Strength Index (RSI), and Average True Range (ATR)-based volatility detection, the indicator dynamically adapts to market conditions. It highlights breakout trends, reversals, and early signs of momentum shifts using strategically placed labels and color-coded trend visualization.
Inspiration taken from Top G indicator .
What This Indicator Does
The Volatility-Driven Trend Identifier works by:
Measuring Market Extremes & Momentum:
Uses ROC normalization with standard deviation to identify impulse moves in price action.
Implements RSI filtering to determine overbought/oversold conditions that validate trend strength.
Utilizes ATR-based volatility tracking to ensure signals only appear when meaningful market movements are occurring.
Identifying Key Trend Events:
Power Peak (🔥): Marks a confirmed strong downtrend, ideal for shorting opportunities.
Surge (🚀): Indicates a confirmed strong uptrend, signaling a potential long entry.
Soft Surge (↗): Highlights a mild bullish reentry or early uptrend formation.
Soft Peak (↘): Shows a mild bearish reentry or early downtrend formation.
Providing Adaptive Filtering for Reliable Signals:
Filters out weak trends with a volatility check, ensuring signals appear only in strong market conditions.
Implements multi-level confirmation by combining trend strength metrics, preventing false breakouts.
Uses gradient-based visualization to color-code market sentiment for quick interpretation.
What This Indicator Signals
Breakouts & Impulse Moves: 🚀🔥
The Surge (🚀) and Power Peak (🔥) labels indicate confirmed momentum breakouts, where the trend has been validated by a combination of ROC impulse, RSI confirmation, and ATR volatility filtering.
These signals suggest that the market is entering a strong trend, and traders can align their entries accordingly.
Early Trend Formation & Reentries: ↗ ↘
The Soft Surge (↗) and Soft Peak (↘) labels indicate areas where a trend might be forming, but is not yet fully confirmed.
These signals help traders anticipate potential entries before the trend gains full strength.
Volatility-Adaptive Trend Filtering: 📊
Since the indicator only activates in volatile conditions, it avoids the pitfalls of low-range choppy markets where false signals frequently occur.
ATR-driven adaptive windowing allows the indicator to dynamically adjust its sensitivity based on real-time volatility conditions.
How to Use This Indicator
1. Identifying High-Probability Entries
Bullish Entries (Long Trades)
Look for 🚀 Surge signals in an uptrend.
Confirm with RSI (should be above 50 for momentum).
Ensure volatility is increasing to validate the breakout.
Use ↗ Soft Surge signals for early entries before the trend fully confirms.
Bearish Entries (Short Trades)
Look for 🔥 Power Peak signals in a downtrend.
RSI should be below 50, indicating downward momentum.
Volatility should be rising, ensuring market momentum is strong.
Use ↘ Soft Peak signals for early entries before a full bearish confirmation.
2. Avoiding False Signals
Ignore signals when the market is ranging (low ATR).
Check RSI and ROC alignment to ensure trend confirmation.
Use additional confluences (e.g., price action, support/resistance levels, moving averages) for enhanced accuracy.
3. Trend Confirmation & Filtering
The stronger the trend, the higher the likelihood that Surge (🚀) and Power Peak (🔥) signals will continue in their direction.
Soft Surge (↗) and Soft Peak (↘) act as early warning signals before major breakouts occur.
What Makes This a Machine Learning-Inspired Moving Average?
While this indicator is not a direct implementation of machine learning (as Pine Script lacks AI/ML capabilities), it mimics machine learning principles by adapting dynamically to market conditions using the following techniques:
Adaptive Trend Selection:
It does not rely on fixed moving averages but instead adapts dynamically based on volatility expansion and momentum detection.
ATR-based filtering adjusts the indicator’s sensitivity to real-time conditions.
Multi-Factor Confirmation (Feature Engineering Equivalent in ML):
Combines ROC, RSI, and ATR in a structured way, similar to how ML models use multiple inputs to filter and classify data.
Implements conditional trend recognition, ensuring that only valid signals pass through the filter.
Noise Reduction with Data Smoothing:
The algorithm avoids false signals by incorporating trend intensity thresholds, much like how ML models remove outliers to refine predictions.
Adaptive filtering ensures that low-volatility environments do not produce misleading signals.
Why Use This Indicator?
✔ Reduces False Signals: Multi-factor validation ensures only high-confidence signals are triggered.
✔ Works in All Market Conditions: Volatility-adaptive nature allows the indicator to perform well in both trending and ranging markets.
✔ Great for Swing & Intraday Trading: It helps spot momentum shifts early and allows traders to catch major market moves before they fully develop.
✔ Visually Intuitive: Color-coded trends and clear signal markers make it easy to interpret.
Price in BTC (x1000)I'm not a coder. I just knocked this together with AI
Shows how the current asset performed relative to BTC (COINBASE:BTCUSD) on the current timeframe
Works with assets priced in USD, USDT and USDC but you can easily add more
Had to multiply the price by 1000 to mitigate leading zeros and improve compatibility with low-denomination assets (e.g. PEPE)
MAs and crossovers included
Feel free to use it however you want
TOL LANGIT ATR v7 - AI EnhancedThe TOL LANGIT ATR v7 is an adaptive technical indicator designed to identify market trends, support and resistance levels, and breakout points. It uses the Average True Range (ATR) and volatility to dynamically adjust trend bands, with visual markers for buy and sell signals. The indicator also highlights key support (blue) and resistance (orange) levels, and alerts users when these levels are broken. It’s perfect for trend following, breakout trading, and reversal strategies, and includes easy-to-set alerts for key market changes.
Market Cycles
The Market Cycles indicator transforms market price data into a stochastic wave, offering a unique perspective on market cycles. The wave is bounded between positive and negative values, providing clear visual cues for potential bullish and bearish trends. When the wave turns green, it signals a bullish cycle, while red indicates a bearish cycle.
Designed to show clarity and precision, this tool helps identify market momentum and cyclical behavior in an intuitive way. Ideal for fine-tuning entries or analyzing broader trends, this indicator aims to enhance the decision-making process with simplicity and elegance.
Inside Bar Breakout/Fakeout with AI Scenarios [Yosiet]Inside Bar Breakout/Fakeout Indicator with Scenarios
The Indicator is a powerful tool for traders looking to identify potential breakout and fakeout opportunities based on inside bar patterns. This indicator combines multiple technical analysis concepts to provide a comprehensive view of market behavior, helping traders make more informed decisions.
Key Features
Inside bar detection with filtering
Breakout and fakeout identification
Three distinct scenario detections
Customizable moving average calculations
Flexible visualization options
Alert conditions for various events
How It Works
The indicator identifies inside bars and filters them based on a maximum number of consecutive inside bars. It then detects breakouts and fakeouts using user-defined parameters. The script also calculates moving averages to determine trend direction.
Three specific scenarios are detected:
Strong breakout followed by a strong reversal
Weak breakout with multiple doji/weak candles
Strong breakout without reversal
These scenarios are visually represented on the chart, allowing traders to quickly identify potential trading opportunities.
How to Use
Apply the indicator to your chart
Adjust the input parameters to suit your trading style
Look for inside bar patterns and subsequent breakouts/fakeouts
Pay attention to the three scenario markers for additional context
Use the alert conditions to stay informed of potential opportunities
EMD Oscillator (Zeiierman)█ Overview
The Empirical Mode Decomposition (EMD) Oscillator is an advanced indicator designed to analyze market trends and cycles with high precision. It breaks down complex price data into simpler parts called Intrinsic Mode Functions (IMFs), allowing traders to see underlying patterns and trends that aren’t visible with traditional indicators. The result is a dynamic oscillator that provides insights into overbought and oversold conditions, as well as trend direction and strength. This indicator is suitable for all types of traders, from beginners to advanced, looking to gain deeper insights into market behavior.
█ How It Works
The core of this indicator is the Empirical Mode Decomposition (EMD) process, a method typically used in signal processing and advanced scientific fields. It works by breaking down price data into various “layers,” each representing different frequencies in the market’s movement. Imagine peeling layers off an onion: each layer (or IMF) reveals a different aspect of the price action.
⚪ Data Decomposition (Sifting): The indicator “sifts” through historical price data to detect natural oscillations within it. Each oscillation (or IMF) highlights a unique rhythm in price behavior, from rapid fluctuations to broader, slower trends.
⚪ Adaptive Signal Reconstruction: The EMD Oscillator allows traders to select specific IMFs for a custom signal reconstruction. This reconstructed signal provides a composite view of market behavior, showing both short-term cycles and long-term trends based on which IMFs are included.
⚪ Normalization: To make the oscillator easy to interpret, the reconstructed signal is scaled between -1 and 1. This normalization lets traders quickly spot overbought and oversold conditions, as well as trend direction, without worrying about the raw magnitude of price changes.
The indicator adapts to changing market conditions, making it effective for identifying real-time market cycles and potential turning points.
█ Key Calculations: The Math Behind the EMD Oscillator
The EMD Oscillator’s advanced nature lies in its high-level mathematical operations:
⚪ Intrinsic Mode Functions (IMFs)
IMFs are extracted from the data and act as the building blocks of this indicator. Each IMF is a unique oscillation within the price data, similar to how a band might be divided into treble, mid, and bass frequencies. In the EMD Oscillator:
Higher-Frequency IMFs: Represent short-term market “noise” and quick fluctuations.
Lower-Frequency IMFs: Capture broader market trends, showing more stable and long-term patterns.
⚪ Sifting Process: The Heart of EMD
The sifting process isolates each IMF by repeatedly separating and refining the data. Think of this as filtering water through finer and finer mesh sieves until only the clearest parts remain. Mathematically, it involves:
Extrema Detection: Finding all peaks and troughs (local maxima and minima) in the data.
Envelope Calculation: Smoothing these peaks and troughs into upper and lower envelopes using cubic spline interpolation (a method for creating smooth curves between data points).
Mean Removal: Calculating the average between these envelopes and subtracting it from the data to isolate one IMF. This process repeats until the IMF criteria are met, resulting in a clean oscillation without trend influences.
⚪ Spline Interpolation
The cubic spline interpolation is an advanced mathematical technique that allows smooth curves between points, which is essential for creating the upper and lower envelopes around each IMF. This interpolation solves a tridiagonal matrix (a specialized mathematical problem) to ensure that the envelopes align smoothly with the data’s natural oscillations.
To give a relatable example: imagine drawing a smooth line that passes through each peak and trough of a mountain range on a map. Spline interpolation ensures that line is as smooth and close to reality as possible. Achieving this in Pine Script is technically demanding and demonstrates a high level of mathematical coding.
⚪ Amplitude Normalization
To make the oscillator more readable, the final signal is scaled by its maximum amplitude. This amplitude normalization brings the oscillator into a range of -1 to 1, creating consistent signals regardless of price level or volatility.
█ Comparison with Other Signal Processing Methods
Unlike standard technical indicators that often rely on fixed parameters or pre-defined mathematical functions, the EMD adapts to the data itself, capturing natural cycles and irregularities in real-time. For example, if the market becomes more volatile, EMD adjusts automatically to reflect this without requiring parameter changes from the trader. In this way, it behaves more like a “smart” indicator, intuitively adapting to the market, unlike most traditional methods. EMD’s adaptive approach is akin to AI’s ability to learn from data, making it both resilient and robust in non-linear markets. This makes it a great alternative to methods that struggle in volatile environments, such as fixed-parameter oscillators or moving averages.
█ How to Use
Identify Market Cycles and Trends: Use the EMD Oscillator to spot market cycles that represent phases of buying or selling pressure. The smoothed version of the oscillator can help highlight broader trends, while the main oscillator reveals immediate cycles.
Spot Overbought and Oversold Levels: When the oscillator approaches +1 or -1, it may indicate that the market is overbought or oversold, signaling potential entry or exit points.
Confirm Divergences: If the price movement diverges from the oscillator's direction, it may indicate a potential reversal. For example, if prices make higher highs while the oscillator makes lower highs, it could be a sign of weakening trend strength.
█ Settings
Window Length (N): Defines the number of historical bars used for EMD analysis. A larger window captures more data but may slow down performance.
Number of IMFs (M): Sets how many IMFs to extract. Higher values allow for a more detailed decomposition, isolating smaller cycles within the data.
Amplitude Window (L): Controls the length of the window used for amplitude calculation, affecting the smoothness of the normalized oscillator.
Extraction Range (IMF Start and End): Allows you to select which IMFs to include in the reconstructed signal. Starting with lower IMFs captures faster cycles, while ending with higher IMFs includes slower, trend-based components.
Sifting Stopping Criterion (S-number): Sets how precisely each IMF should be refined. Higher values yield more accurate IMFs but take longer to compute.
Max Sifting Iterations (num_siftings): Limits the number of sifting iterations for each IMF extraction, balancing between performance and accuracy.
Source: The price data used for the analysis, such as close or open prices. This determines which price movements are decomposed by the indicator.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Backside Bubble ScalpingFrom LIHKG
Pine from Perplexity AI
以下是Backside Bubble Scalping策略的使用說明，旨在幫助交易者理解如何在美股交易中應用這一策略。
使用說明：Backside Bubble Scalping 策略
1. 前提條件
交易時間：此策略適用於香港時間晚上9:30 PM至12:00 AM。
圖表類型：使用1分鐘圖表進行交易。
2. 策略概述
Backside Bubble Scalping策略包含兩種主要的設置：尖backside和鈍backside。這些設置通常在10:00 PM至12:00 AM之間出現。
3. 指標設定
VWAP（粉紅色）：成交量加權平均價格，用於識別市場趨勢。
9 EMA（綠色）：9期指數移動平均線，用於捕捉短期價格變化。
4. 識別 Backside 設置
尖backside
特徵：
當市場趨勢為純紅色下跌，並形成尖尖的V形底部。
入場條件：
當價格突破9 EMA並經過小幅盤整後，進場做多。
鈍backside
特徵：
在混合顏色的趨勢中，形成鈍鈍的V形底部。
入場條件：
在盤整期間進場做多。
5. 止損和止盈設置
止損位置：
尖backside：設置在9 EMA上方的盤整範圍底部加上0.2。
鈍backside：設置在V底部的最低點加上0.2。
止盈位置：
尖backside：當價格跌破VWAP或出現一根K線沒有跟隨時出場。
鈍backside：當一根K線的三分之二身體向下突破9 EMA時出場。
6. 操作步驟
監控市場動態：在指定的交易時間內，觀察VWAP和9 EMA的變化。
識別入場信號：根據尖backside或鈍backside的條件進行判斷，確定何時進場。
設置止損和止盈：根據上述條件設置止損和止盈位，以管理風險。
執行交易：根據信號執行交易，並持續監控市場情況以調整策略。
7. 注意事項
避免在VWAP附近進行交易，以減少失敗風險。
如果出現影線（wick bar），建議不要進行交易，因為這可能表示該設置失敗。
Machine Learning Signal FilterIntroducing the "Machine Learning Signal Filter," an innovative trading indicator designed to leverage the power of machine learning to enhance trading strategies. This tool combines advanced data processing capabilities with user-friendly customization options, offering traders a sophisticated yet accessible means to optimize their market analysis and decision-making processes. Importantly, this indicator does not repaint, ensuring that signals remain consistent and reliable after they are generated.
Machine Learning Integration
The "Machine Learning Signal Filter" employs machine learning algorithms to analyze historical price data and identify patterns that may not be immediately apparent through traditional technical analysis. By utilizing techniques such as regression analysis and neural networks, the indicator continuously learns from new data, refining its predictive capabilities over time. This dynamic adaptability allows the indicator to adjust to changing market conditions, potentially improving the accuracy of trading signals.
Key Features and Benefits
Dynamic Signal Generation: The indicator uses machine learning to generate buy and sell signals based on complex data patterns. This approach enables it to adapt to evolving market trends, offering traders timely and relevant insights. Crucially, the indicator does not repaint, providing reliable signals that traders can trust.
Customizable Parameters: Users can fine-tune the indicator to suit their specific trading styles by adjusting settings such as the temporal synchronization and neural pulse rate. This flexibility ensures that the indicator can be tailored to different market environments.
Visual Clarity and Usability: The indicator provides clear visual cues on the chart, including color-coded signals and optional display of signal curves. Users can also customize the table's position and text size, enhancing readability and ease of use.
Comprehensive Performance Metrics: The indicator includes a detailed metrics table that displays key performance indicators such as return rates, trade counts, and win/loss ratios. This feature helps traders assess the effectiveness of their strategies and make data-driven decisions.
How It Works
The core of the "Machine Learning Signal Filter" is its ability to process and learn from large datasets. By applying machine learning models, the indicator identifies potential trading opportunities based on historical data patterns. It uses regression techniques to predict future price movements and neural networks to enhance pattern recognition. As new data is introduced, the indicator refines its algorithms, improving its accuracy and reliability over time.
Use Cases
Trend Following: Ideal for traders seeking to capitalize on market trends, the indicator helps identify the direction and strength of price movements.
Scalping: With its ability to provide quick signals, the indicator is suitable for scalpers aiming for rapid profits in volatile markets.
Risk Management: By offering insights into trade performance, the indicator aids in managing risk and optimizing trade setups.
In summary, the "Machine Learning Signal Filter" is a powerful tool that combines the analytical strength of machine learning with the practical needs of traders. Its ability to adapt and provide actionable insights makes it an invaluable asset for navigating the complexities of financial markets.
The "Machine Learning Signal Filter" is a tool designed to assist traders by providing insights based on historical data and machine learning techniques. It does not guarantee profitable trades and should be used as part of a comprehensive trading strategy. Users are encouraged to conduct their own research and consider their financial situation before making trading decisions. Trading involves significant risk, and it is possible to lose more than the initial investment. Always trade responsibly and be aware of the risks involved.
CVD with Moving Average (Trend Colors) [SYNC & TRADE]Yesterday I wrote a simple and easy code for the indicator "Cumulative Delta Volume with a moving average" using AI.
Introduction:
Delta is the difference between buys and sells. If there are more purchases, the delta is positive, if there are more sales, the delta is negative. We look at each candle separately on a particular time frame, which does not give us an overall picture over time.
Cumulative volume delta is in many ways an extension of volume delta, but it covers longer periods of time and provides different trading signals. Like the volume delta indicator, the Cumulative Volume Delta (CVD) indicator measures the relationship between buying and selling pressure, but does not focus on one specific candle (or other chart element), but rather gives a picture over time.
What did you want to get?
I have often seen that they tried to attach RSI and the Ichimoku cloud to the cumulative delta of volume, but I have never seen a cumulative delta of volume with a moving average. A moving average that takes data from the cumulative volume delta will be different from the moving average of the underlying asset. It has been noted that often at the intersection of the cumulative volume delta and the moving average, this is a more accurate signal to buy or sell than the same intersections for the underlying asset.
Initially, 5 moving averages were made with values of 21, 55, 89, 144 and 233, but I realized that this overloads the chart. It is easier to change the length of the moving average depending on the time frame you are using than to overload the chart. The final version with one moving SMA, EMA, RMA, WMA, HMA.
The logic for applying a moving average to a cumulative volume delta:
You choose a moving average, just like you would on your underlying asset. Use the moving average you like and the period you are used to working with. Each TF has its own settings.
What we see on the graph:
This is not an oscillator, but an adapted version for a candlestick chart (line only). Using it, you can clearly see where the market is moving based on the cumulative volume delta. The cool thing is that you can include your moving average applied to the cumulative volume delta. Thanks to this, you can see a trend movement, a return to the moving average to continue the trend.
Opportunities not lost:
The most interesting thing is that it remains possible to observe the divergence of the asset and the cumulative delta of the volume. This gives a great advantage. Those who have not worked with divergence do not rush into it right away. There may be 3 peaks in divergence (as with oversold/overbought), but it works many times more clearly than RSI and MACD.
Here's a good example on the daily chart. The moment we were all waiting for 75,000. The cumulative Delta Volume fell with each peak, while the price chart (tops) were approximately level.
Usually they throw (allow to buy) without volume for sales (delta down, price up) in order to merge at a more interesting price. And they also drain without the volume of purchases for a squeeze (price down / delta up) and again I buy back at a more interesting price. There are more complex estimation options; you can read about the divergence of the cumulative delta of the CVD volume. I just recommend doing a backtest.
Recommendations:
One more moment. Use the indicator on the stock exchange, where there is the most money, by turnover and by asset. Choose Binance, not Bybit. Those. choose the BTC asset, for example, but on the Binance exchange. Not futures, but spot.
The greater the turnover on the exchange for an asset, and the fewer opportunities to enter with leverage, the less volatile the price and the more beautiful and accurate the chart.
Works on all assets. There is a subscription limit (the number of calculated bars) that has little effect on anything. Can be applied to any asset where there is volume (not SPX, but ES1, not MOEX, but MX1!).
Перевод на русский.
Вчера написал с помощью AI простой и легкий код индикатора "Кумулятивная Дельта Объема со скользящей средней".
Введение:
Дельта (Delta) — это разница между покупками и продажами. Если покупок больше — дельта положительная, если больше продаж — дельта отрицательная. Мы смотрим на каждую свечу отдельно на том или ином таймфрейме, что не дает нам общей картины во времени.
Кумулятивная дельта объема — во многом продолжение дельты объёмов, но она включает более длительные периоды времени и дает другие торговые сигналы. Как и индикатор дельты объёма, индикатор кумулятивной дельты объема (Cumulative Volume Delta, CVD) измеряет связь между давлением покупателей и продавцов, но при этом не фокусируется на одной конкретной свече (или другом элементе графика), а дает картину во времени.
Что хотел получить?
Часто видел, что к кумулятивной детьте объема пытались прикрепить RSI и облако ишимоку, но никогда не видел кумулятивную дельту объема со скользящей средней. Скользящая средняя которая берет данные от кумулятивной дельты объема будет отличатся от скользящей средней основного актива. Было замечено, что часто в местах пересечения кумулятивной дельты объема и скользящей средней - это более точный сигнал к покупке или продаже, чем такие же пересечения по основному активу.
Изначально было сделанно 5 скользящих со значениями 21, 55, 89, 144 и 233, но я понял, что это перегружает график. Проще менять длину скользящей средней от используемого таймфрейма, чем перегружать график. Финальный вариант с одной скользящей SMA, EMA, RMA, WMA, HMA.
Логика применения скользящей средней к кумулятивной дельте объема:
Вы выбираете скользящую среднюю, так же как и на основном активе. Применяйте ту скользящую среднюю, которая вам нравится и период, с которым привыкли работать. На каждом TF свои настройки.
Что мы видим на графике:
Это не осциллятор, а адаптированная версия к свечному графику (только линия). С помощью него вы можете наглядно посмотреть куда движется рынок по кумулятивной дельте объема. Самое интересное, что вы можете включить свою скользящую среднюю, применимую к кумулятивной дельте объема. Благодаря этому вы можете видеть трендовое движение, возврат к средней скользящей для продолжения тренда.
Не потерянные возможности:
Самое интересное, что осталась возможность наблюдать за дивергенцией актива и кумулятивной дельтой объема. Это дает большое преимущество. Те кто не работал с дивергенцией не бросайтесь на нее сразу. Может быть и 3 пика в дивергенции (как с перепроданностью / перекупленностью), но работает в разы четче чем RSI и MACD.
Вот хороший пример на дневном графике. Момент когда мы все ждали 75000. Кумулятивная Дельта Объема падала с каждым пиком, в то время как ценовой график (вершины) были примерно на уровне.
Обычно закидывают (разрешают покупать) без объема на продажи (дельта вниз цена вверх), чтобы слить по более интересной цене. И также сливают без объема покупок для сквиза (цена вниз / дельта вверх) и опять откупаю по более интересной цене. Существуют более сложные варианты оценки, можете почитать про дивергенцию кумулятивной дельты объема CVD. Только рекомендую сделать бэктест.
Рекомендации:
Еще момент. Используйте индикатор, на бирже, там где больше всего денег, по обороту и по активу. Выбирайте не Bybit, а Binance. Т.е. выбираете актив BTC, к примеру, но на бирже Binance. Не фьючерс, а спот.
Чем более большие обороты на бирже, по активу, и меньше возможностей заходить с плечами, тем менее волатильная цена и более красивый и точный график.
Работает на всех активах. Есть ограничение по подписке (количество рассчитываемых баров) мало влияет на что. Можно применить к любому активу где есть объем (не SPX, а ES1, не MOEX, а MX1!).
