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RRC Sniper SetupRRC Sniper Setup, this looks at candles this way: Go to Market Scanner Create New Scan → "RRC Sniper Setup" Add filters listed below with timeframe logic (e.g. 1m/5m) Run scan on: Your Watchlist SPY 500 QQQ 100 AI/Momentum names 1. Reclaim Filter Find price breaking back above a key level (VWAP or EMA113) Last 1m Close > EMA 113 (1m) OR Last 5m Close > VWAP 2. Retrace Filter Price pulls back into the zone and holds within a tight range Current Price < VWAP * 1.0025 AND Current Price > VWAP * 0.9975 AND Volume (Current Candle) < Volume (Previous Candle) ✅ 3. Confirm Filter Price begins moving back up with confirmation candle and volume Last Candle Close > Last Candle Open AND Volume (Current Candle) > Volume (Previous Candle)
Pine Script® göstergesi
Gimmedatgamma tarafından
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
Pine Script® göstergesi
Actragea tarafından
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.
Pine Script® stratejisi
DskyzInvestments tarafından
6
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.
Pine Script® göstergesi
AresIQ tarafından
28
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: _______________________________________________________________________ 🥇 This is the world’s first ADX indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________ _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ 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. ______________________________________________________ ______________________________________________________ ⯁ 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. ______________________________________________________ ______________________________________________________ ⯁ 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 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 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  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 ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ 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 ______________________________________________________ 📜 SCRIPT : ADX Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Pine Script® göstergesi
Titans_Invest tarafından
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.
Pine Script® göstergesi
bitwardex tarafından
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!
Pine Script® göstergesi
alphainvestor123 tarafından
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: _______________________________________________________________________ 🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________ _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ 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. ______________________________________________________ ______________________________________________________ ⯁ 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 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 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  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 ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ 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 ______________________________________________________ 📜 SCRIPT : RSI Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Pine Script® göstergesi
Titans_Invest tarafından
3
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.
Pine Script® göstergesi
Grandoc tarafından
1
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 :-)
Pine Script® göstergesi
djmad tarafından
15
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
Pine Script® göstergesi
LuxAlgo tarafından
13
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.
Pine Script® göstergesi
bar_rog tarafından
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. 🚀
Pine Script® göstergesi
Zen_Trading_Tech tarafından
1
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.
Pine Script® göstergesi
alwaysfree2021 tarafından
9
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.
Pine Script® göstergesi
Julien_Eche tarafından
2
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.
Pine Script® göstergesi
JonathanLeyva tarafından
6