Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Analyse de la tendance
Kyoshiro - FVG + Order Blocks📌 Kyoshiro – FVG + Order Blocks
This indicator combines Order Block (OB) detection with an intelligent auto-management system and a clean visual display on the chart.
It is designed to help traders better identify institutional zones where price frequently reacts.
⚙️ Key Features:
✅ Real-time detection of bullish and bearish Order Blocks.
✅ Automatic cleanup: invalidated OBs are removed to keep the chart clean.
✅ Customizable display:
Maximum number of visible OBs (bullish / bearish).
Zone colors, outlines, and midlines.
Line styles (solid, dashed, dotted) and adjustable width.
✅ Choice of mitigation method:
Wick
Close
✅ Built-in alerts:
Formation of bullish or bearish OB.
Mitigation of an existing OB.
🔔 Available Alerts:
Bullish OB Formed → A bullish order block is detected.
Bearish OB Formed → A bearish order block is detected.
Bullish OB Mitigated → A bullish OB has been invalidated.
Bearish OB Mitigated → A bearish OB has been invalidated.
🎯 Use Cases:
Quickly identify key liquidity zones.
Track institutional activity in the market.
Improve entry and exit precision.
Multiple Divergence Scanner (move to candles and merge scales)This indicator detects and visualizes multiple types of RSI-based divergences, including Regular, Hidden, and Dual-source (Multi) Bullish/Bearish signals. Not limited with RSI only. You can add move functions and it will automaticly combine your options.
It offers customizable score filtering, label positioning, and visual styling.
Ideal for traders who seek both technical precision and symbolic clarity in their charts.
You have to drag it to your candles after adding to your chart. Then right click on price->Merge all scales to right/left.
ETFs Sector PerformanceDisplays a table of the Top 8 performing ETFs over a selected period (1M / 2M / 3M / 6M) to quickly identify industry strength.
Pre-Set Universe (39 ETFs)
ITA — iShares U.S. Aerospace & Defense ETF
DBA — Invesco DB Agriculture Fund
BOTZ — Global X Robotics & Artificial Intelligence ETF
JETS — U.S. Global Jets ETF
XLB — Materials Select Sector SPDR Fund
XBI — SPDR S&P Biotech ETF
PKB — Invesco Dynamic Building & Construction ETF
ICLN — iShares Global Clean Energy ETF
SKYY — First Trust Cloud Computing ETF
DBC — Invesco DB Commodity Index Tracking Fund
XLY — Consumer Discretionary Select Sector SPDR Fund
XLP — Consumer Staples Select Sector SPDR Fund
BLOK — Amplify Transformational Data Sharing ETF
KARS — KraneShares Electric Vehicles & Future Mobility ETF
XLE — Energy Select Sector SPDR Fund
ESPO — VanEck Video Gaming and eSports ETF
XLF — Financial Select Sector SPDR Fund
PBJ — Invesco Dynamic Food & Beverage ETF
ITB — iShares U.S. Home Construction ETF
XLI — Industrial Select Sector SPDR Fund
PAVE — Global X U.S. Infrastructure Development ETF
PEJ — Invesco Dynamic Leisure & Entertainment ETF
LIT — Global X Lithium & Battery Tech ETF
IHI — iShares U.S. Medical Devices ETF
XME — SPDR S&P Metals & Mining ETF
FCG — First Trust Natural Gas ETF
URA — Global X Uranium ETF
PPH — VanEck Pharmaceutical ETF
QTUM — Defiance Quantum Computing & Machine Learning ETF
IYR — iShares U.S. Real Estate ETF
XRT — SPDR S&P Retail ETF
SOXX — iShares Semiconductor ETF
BOAT — SonicShares Global Shipping ETF
IGV — iShares Expanded Tech-Software Sector ETF
TAN — Invesco Solar ETF
SLX — VanEck Steel ETF
IYZ — iShares U.S. Telecommunications ETF
IYT — iShares U.S. Transportation ETF
XLU — Utilities Select Sector SPDR Fund
Bollinger Breakout A3 updateBollinger Breakout A3 update from LuxAlgo signal
You can try it with some another signal.
All-In-One MA Stack ScalperWhat is this Indicator?
This tool is an advanced, multi-layered breakout and trend-following indicator designed for lower timeframes. It identifies high-conviction buy and sell signals by combining moving average stacking with a suite of professional-grade filters.
How Does It Work?
A signal is generated only when ALL of the following conditions are met:
Moving Average Stack (5M Chart):
Buy: The close price is above all five moving averages (MAs: 100, 48, 36, 24, 12).
Sell: The close price is below all five MAs.
Volatility Filter (ATR):
Signals only print when the current ATR (14) is at least 80% of its 100-period average, ensuring you only trade in actively moving markets.
Candle Structure Filter:
The current candle must have a real body that is at least 35% of the candle’s total range, filtering out dojis and indecision bars.
Big Candle Filter:
The candle’s total range must be at least 40% of the current ATR, avoiding signals on minor, insignificant moves.
Volume Filter:
The current volume must be at least 80% of its 50-period average, filtering out signals during illiquid or quiet market conditions.
Minimum Distance from All MAs:
Price must be a minimum distance (20% ATR) away from each MA, confirming a clean breakout and avoiding signals in tight MA clusters or ranging markets.
RSI Momentum Filter:
Buy: RSI(14) must be greater than 55.
Sell: RSI(14) must be less than 45.
This ensures trades are only taken in the direction of momentum.
ADX Trend Filter:
ADX(14,14) must be above 20, ensuring signals only print in trending conditions (not in chop/range).
Minimum Bars Between Signals:
Only one signal per direction is allowed every 10 bars to avoid overtrading and signal clustering.
What Does This Achieve?
Reduces noise and false signals common in basic MA cross or stack systems.
Captures only strong, high-momentum, and high-conviction moves.
Helps you avoid chop, range, and news whipsaws by combining multiple market filters.
Perfect for advanced scalpers, intraday trend followers, or as a trade filter for algos/EAs.
How to Use It:
Apply to your 5-minute chart.
Green BUY signals: Only when all bullish conditions align.
Red SELL signals: Only when all bearish conditions align.
Use as a stand-alone system or as a filter for your own entries.
Recommended For:
Scalpers & intraday traders who want only the best opportunities.
EA and bot builders seeking reliable signal logic.
Manual traders seeking confirmation of high-probability breakouts.
Tip:
Adjust any of the filters (e.g., RSI/ADX thresholds, minBars, minDist) to make it more/less selective for your style or market.
Ripping and Dipping (Reversal + Trend Signals)Waits for a series of EMAs to be stacking from fastest to slowest for a user input X bars, then signals trend or reversal trades based on a simple close above/below the high/low of the last bar. Designed to catch quick trend trades once strength is confirmed, and quick reversal trades once trend has overextended.
STOCK SCHOOL | FVGThe Stock School FVG Indicator is designed to help traders identify and trade Fair Value Gaps (FVGs) and Inverse FVGs (IFVGs) with precision.
Built for both intraday and swing traders, this tool highlights high-probability trading zones where institutions leave imbalances in the market.
✨ Key Features:
Auto-detects FVGs & IFVGs in real-time
Works on all timeframes and instruments (Nifty, BankNifty, Stocks, Forex, Crypto)
Non-repainting logic for reliable signals
Clean and easy-to-use interface with Stock School styling
Perfect for Smart Money Concept (SMC) traders
🚀 With this indicator, you can:
Spot institutional footprints quickly
Combine with BOS, CHoCH, Order Blocks for high accuracy
Trade liquidity sweeps + FVG collisions with confidence
💡 Disclaimer:
This indicator is for educational purposes only. Trading involves risk. Always use proper risk management.
TRAPPER TRENDLINES — RSIBuilds dynamic RSI trendlines by connecting the two most recent confirmed RSI swing points (highs→highs for resistance, lows→lows for support). Includes optional channel shading for the 30–70 zone, an RSI moving average, clean break alerts, and simple bullish/bearish divergence alerts versus price.
How it works
RSI pivots: A point on RSI is a swing high/low only if it is the most extreme value compared with a set number of bars on the left and the right (the Pivot Lookback).
RSI trendlines:
Resistance connects the last two confirmed RSI swing highs.
Support connects the last two confirmed RSI swing lows.
Lines can be Full Extend (update into the future) or Pivot Only.
Channel block: Optional fill of the 30–70 range for fast visual context.
Alerts:
Breaks of RSI support/resistance trendlines.
Basic bullish/bearish RSI divergences versus price pivots.
Inputs
RSI
RSI Length: Default 14 (standard).
Pivot Lookback: Bars to the left/right required to confirm an RSI swing.
Overbought / Oversold: 70 / 30 by default.
Line Extension: Full Extend or Pivot Only.
Visuals
Show RSI Moving Average / Signal Length: Optional smoothing line on RSI.
RSI/Signal colors: Customize plot colors.
Show 30–70 Channel Block: Toggle the middle-zone fill.
Tint pane background when RSI in channel: Optional subtle background when RSI is between OB/OS.
Divergences & Alerts
Enable RSI TL Break Alerts: Alert conditions for RSI line breaks.
Enable Divergence Alerts: Bullish/Bearish divergence alerts versus price.
Pairing with price for confluence/divergence
For accurate confluence and clearer divergences, align this RSI tool with your price trendline tool (for example, TRAPPER TRENDLINES — PRICE):
Set RSI Pivot Lookback equal to the Pivot Left/Right size used on price.
Example: Price uses Pivot Left = 50 and Pivot Right = 50 → set RSI Pivot Lookback = 50.
Keep RSI Length = 14 and OB/OS = 70/30 unless you have a specific edge.
Interpretation:
Confluence: Price reacts at its trendline while RSI reacts at its own line in the same direction.
Divergence: Price makes a higher high while RSI makes a lower high (bearish), or price makes a lower low while RSI makes a higher low (bullish), using matched pivot windows.
Suggested settings
Higher timeframes (4H / 1D / 1W): Pivot Lookback = 50; optional RSI MA length 14; channel block ON.
Intraday (15m / 30m / 1H): Pivot Lookback = 30; optional RSI MA length 14.
Always mirror your price pivot size to this RSI Pivot Lookback for consistent swings.
Reading the signals
RSI trendline touch/hold: Momentum reacting at structure; look for confluence with price levels.
RSI Trendline Break Up / Down: Momentum shift; consider price structure and retests.
Bullish/Bearish Divergence: Confirm only when pivots are matched and the new swing is confirmed.
Notes & limitations
Pivots require future bars to confirm by design; trendlines update as new swings confirm.
Divergence logic compares RSI pivots to price pivots with the same lookback; mismatched windows can produce false positives.
No strategy entries/exits or performance claims are provided. This is an analytical tool.
Alerts (titles/messages)
RSI: Trendline Break Up — “RSI broke falling resistance line.”
RSI: Trendline Break Down — “RSI broke rising support line.”
RSI: Bullish Divergence — “Bullish RSI divergence confirmed.”
RSI: Bearish Divergence — “Bearish RSI divergence confirmed.”
Quick start
Add the indicator to a separate pane.
Set Pivot Lookback to match your price tool’s pivot size (e.g., 50).
Optionally toggle the RSI MA and Channel Block for clarity.
Enable alerts if you want notifications on RSI line breaks and divergences.
Use with TRAPPER TRENDLINES — PRICE or any price-based trendline tool for confluence/divergence analysis.
Compliance
This script is for educational purposes only and does not constitute financial advice. Trading involves risk. Past performance does not guarantee future results. No performance claims are made.
TRAPPER TRENDLINES — PRICEDraws dynamic trendlines on price by connecting the two most recent confirmed swing points (highs to highs for resistance, lows to lows for support). Swings are defined with a symmetric left/right pivot window. Old anchors are ignored so lines stay attached to current structure. Optional break alerts are included.
How it works (plain language)
Pivots: A bar is a swing high (or low) only if it’s the most extreme point compared with a set number of bars on the left and the right.
Lines:
Support connects the last two confirmed swing lows.
Resistance connects the last two confirmed swing highs.
Lines can be extended right only or both left & right (toggle).
Recency filter: Only swings within the last N bars are kept. This avoids anchoring to very old pivots far from current price.
Alerts: Optional alerts fire when price closes above resistance or below support.
Inputs
Auto Settings
Auto pivot size by chart timeframe: When ON, the script picks a pivot size suitable for the current timeframe (you can scale it with Auto pivot multiplier). When OFF, the manual left/right inputs are used.
Auto pivot multiplier: Scales the auto pivot size (e.g., 1.5 makes pivots stricter).
Manual Pivots
Pivot Left / Pivot Right: Bars to the left/right required to confirm a swing. Example: Left=50 & Right=50 keeps only major swings.
Recency Filter
Use last N bars for pivots: Swings older than this window are discarded so trendlines stay relevant to current price.
Style
Support/Resistance color: Line colors.
Extend Left & Right: When ON, both endpoints extend; when OFF, lines extend to the right only.
Alerts
Enable Break Alerts: When ON, alert conditions are exposed:
Price: Break Up — close above resistance.
Price: Break Down — close below support.
Suggested settings
Higher timeframes (4H / 1D / 1W):
Manual: Pivot Left = 50, Pivot Right = 50, Use last N bars = 400–800.
Or enable Auto with Auto pivot multiplier = 1.0–1.5.
Intraday (15m / 30m / 1H):
Manual: Pivot Left = 30, Pivot Right = 30, Use last N bars = 300–500.
Or enable Auto with multiplier ≈ 1.0–1.2.
Pairing with RSI for confluence/divergence
This tool is designed to pair with a companion TRAPPER TRENDLINES — RSI (or any RSI trendline script):
To mirror swings, set RSI Pivot Lookback equal to the price Pivot Left/Right you use here.
Example: Price = 50/50 → RSI Pivot Lookback = 50.
Keep RSI at Length 14 with 70/30 channel for clarity.
Confluence: Price holds/rejects at a trendline while RSI trendline agrees.
Divergence: Price prints a higher high (resistance line rising) while RSI prints a lower high (RSI resistance line falling), or vice-versa for lows. Matching pivot windows makes these relationships clear and reduces false signals.
Reading the signals
Trendline touch/hold: Potential reaction area; wait for follow-through.
Break Up / Break Down (alerts): Close beyond the line. Consider retest behavior, higher-timeframe context, and volume/RSI confirmation.
Notes & limitations
Pivots require future bars to confirm (by design). Lines update as pivots confirm.
“Use last N bars” purposely ignores very old swings. Increase this value if you need legacy structure.
Lines are based on two most recent confirmed pivots per side; rapidly changing markets can replace anchors as new swings confirm.
This is a visual/analytical tool. No strategy entries/exits or performance claims are provided.
Compliance
This script is for educational purposes only and does not constitute financial advice. Trading involves risk. Past results do not guarantee future outcomes. No promises of profit, accuracy, or performance are made.
Alerts (titles/messages)
Price: Break Up — “Price broke above resistance trendline.”
Price: Break Down — “Price broke below support trendline.”
Quick start
Add the indicator to your chart.
Choose Auto or set Pivot Left/Right manually.
Set Use last N bars for how far back to consider swings.
Toggle Extend Left & Right to your preference.
(Optional) Add your RSI trendline indicator and match Pivot Lookback with your price pivot size for clean confluence/divergence.
Enable alerts if you want notifications on breaks.
Simplified Market ForecastSimplified Market Forecast Indicator
This indicator pairs nicely with the Contrarian 100 MA and can be located here:
Overview
The "Simplified Market Forecast" (SMF) indicator is a streamlined technical analysis tool designed for traders to identify potential buy and sell opportunities based on a momentum-based oscillator. By analyzing price movements relative to a defined lookback period, SMF generates clear buy and sell signals when the oscillator crosses customizable threshold levels. This indicator is versatile, suitable for various markets (e.g., forex, stocks, cryptocurrencies), and optimized for daily timeframes, though it can be adapted to other timeframes with proper testing. Its intuitive design and visual cues make it accessible for both novice and experienced traders.
How It Works
The SMF indicator calculates a momentum oscillator based on the price’s position within a specified range over a user-defined lookback period. It then smooths this value to reduce noise and plots the result as a line in a separate lower pane. Buy and sell signals are generated when the smoothed oscillator crosses above a user-defined buy level or below a user-defined sell level, respectively. These signals are visualized as triangles either on the main chart or in the lower pane, with a table displaying the current ticker and oscillator value for quick reference.
Key Components
Momentum Oscillator: The indicator measures the price’s position relative to the highest high and lowest low over a specified period, normalized to a 0–100 scale.
Signal Generation: Buy signals occur when the oscillator crosses above the buy level (default: 15), indicating potential oversold conditions. Sell signals occur when the oscillator crosses below the sell level (default: 85), suggesting potential overbought conditions.
Visual Aids: The indicator includes customizable horizontal lines for buy and sell levels, shaded zones for clarity, and a table showing the ticker and current oscillator value.
Mathematical Concepts
Oscillator Calculation: The indicator uses the following formula to compute the raw oscillator value:
c1I = close - lowest(low, medLen)
c2I = highest(high, medLen) - lowest(low, medLen)
fastK_I = (c1I / c2I) * 100
The result is smoothed using a 5-period Simple Moving Average (SMA) to produce the final oscillator value (inter).
Signal Logic:
A buy signal is triggered when the smoothed oscillator crosses above the buy level (ta.crossover(inter, buyLevel)).
A sell signal is triggered when the smoothed oscillator crosses below the sell level (ta.crossunder(inter, sellLevel)).
Entry and Exit Rules
Buy Signal (Blue Triangle): Triggered when the oscillator crosses above the buy level (default: 15), indicating a potential oversold condition and a buying opportunity. The signal appears as a blue triangle either below the price bar (if plotted on the main chart) or at the bottom of the lower pane.
Sell Signal (White Triangle): Triggered when the oscillator crosses below the sell level (default: 85), indicating a potential overbought condition and a selling opportunity. The signal appears as a white triangle either above the price bar (if plotted on the main chart) or at the top of the lower pane.
Exit Rules: Traders can exit positions when an opposite signal occurs (e.g., exit a buy on a sell signal) or based on additional technical analysis tools (e.g., support/resistance, trendlines). Always apply proper risk management.
Recommended Usage
The SMF indicator is optimized for the daily timeframe but can be adapted to other timeframes (e.g., 1H, 4H) with careful testing. It performs best in markets with clear momentum shifts, such as trending or range-bound conditions. Traders should:
Backtest the indicator on their chosen asset and timeframe to validate signal reliability.
Combine with other indicators (e.g., moving averages, support/resistance) or price action for confirmation.
Adjust the lookback period and buy/sell levels to suit market volatility and trading style.
Customization Options
Intermediate Length: Adjust the lookback period for the oscillator calculation (default: 31 bars).
Buy/Sell Levels: Customize the threshold levels for buy (default: 15) and sell (default: 85) signals.
Colors: Modify the colors of the oscillator line, buy/sell signals, and threshold lines.
Signal Display: Toggle whether signals appear on the main chart or in the lower pane.
Visual Aids: The indicator includes dotted horizontal lines at the buy (green) and sell (red) levels, with shaded zones between 0–buy level (green) and sell level–100 (red) for clarity.
Ticker Table: A table in the top-right corner displays the current ticker and oscillator value (in percentage), with customizable colors.
Why Use This Indicator?
The "Simplified Market Forecast" indicator provides a straightforward, momentum-based approach to identifying potential reversals in overbought or oversold markets. Its clear signals, customizable settings, and visual aids make it easy to integrate into various trading strategies. Whether you’re a swing trader or a day trader, SMF offers a reliable tool to enhance decision-making and improve market timing.
Tips for Users
Test the indicator thoroughly on your chosen asset and timeframe to optimize settings.
Use in conjunction with other technical tools for stronger trade confirmation.
Adjust the buy and sell levels based on market conditions (e.g., lower levels for less volatile markets).
Monitor the ticker table for real-time oscillator values to gauge market momentum.
Happy trading with the Simplified Market Forecast indicator!
Multi Timeframe Indian Stocks TrendsThis script, "Multi Timeframe Indian Stocks Trends," is designed for swing trading in the Indian stock market, with a specific focus on Nifty50. It provides a comprehensive view of trends across multiple timeframes: 1-hour, 4-hour, Daily, Weekly, and Monthly.
Key Features:
Multi-Timeframe Analysis: Gain insights into trends across 1H, 4H, D, W, and M timeframes, helping you make informed swing trading decisions.
Trend Calculation Methods: Choose between two popular trend calculation methods:
Supertrend: A widely used indicator that identifies trend direction and provides potential entry and exit points.
EMA (Exponential Moving Average): Utilizes the relationship between a fast and slow EMA to determine trend direction.
Customizable Trend Table: A clear and concise table displays the trend direction for each selected timeframe, making it easy to grasp the overall market sentiment.
Nifty50 Reference: The script is tailored for Indian stocks and includes a reference to Nifty50, allowing you to gauge the broader market trend.
Visual Customization: Adjust the colors of the trend table, background, and text to suit your preferences.
Adjustable Settings: Fine-tune the parameters for Supertrend (ATR Length, Factor) and EMA (Fast EMA, Slow EMA) to optimize the indicator for your trading style.
This script is ideal for traders who want to:
Identify swing trading opportunities in Indian stocks.
Confirm trends across various timeframes.
Utilize either Supertrend or EMA for trend analysis.
Have a quick and clear overview of market trends.
By providing a multi-timeframe perspective and customizable trend analysis, this script empowers traders to make more confident and well-informed swing trading decisions in the Indian stock market.
[blackcat] L2 Trend LinearityOVERVIEW
The L2 Trend Linearity indicator is a sophisticated market analysis tool designed to help traders identify and visualize market trend linearity by analyzing price action relative to dynamic support and resistance zones. This powerful Pine Script indicator utilizes the Arnaud Legoux Moving Average (ALMA) algorithm to calculate weighted price calculations and generate dynamic support/resistance zones that adapt to changing market conditions. By visualizing market zones through colored candles and histograms, the indicator provides clear visual cues about market momentum and potential trading opportunities. The script generates buy/sell signals based on zone crossovers, making it an invaluable tool for both technical analysis and automated trading strategies. Whether you're a day trader, swing trader, or algorithmic trader, this indicator can help you identify market regimes, support/resistance levels, and potential entry/exit points with greater precision.
FEATURES
Dynamic Support/Resistance Zones: Calculates dynamic support (bear market zone) and resistance (bull market zone) using weighted price calculations and ALMA smoothing
Visual Market Representation: Color-coded candles and histograms provide immediate visual feedback about market conditions
Smart Signal Generation: Automatic buy/sell signals generated from zone crossovers with clear visual indicators
Customizable Parameters: Four different ALMA smoothing parameters for various timeframes and trading styles
Multi-Timeframe Compatibility: Works across different timeframes from 1-minute to weekly charts
Real-time Analysis: Provides instant feedback on market momentum and trend direction
Clear Visual Cues: Green candles indicate bullish momentum, red candles indicate bearish momentum, and white candles indicate neutral conditions
Histogram Visualization: Blue histogram shows bear market zone (below support), aqua histogram shows bull market zone (above resistance)
Signal Labels: "B" labels mark buy signals (price crosses above resistance), "S" labels mark sell signals (price crosses below support)
Overlay Functionality: Works as an overlay indicator without cluttering the chart with unnecessary elements
Highly Customizable: All parameters can be adjusted to suit different trading strategies and market conditions
HOW TO USE
Add the Indicator to Your Chart
Open TradingView and navigate to your desired trading instrument
Click on "Indicators" in the top menu and select "New"
Search for "L2 Trend Linearity" or paste the Pine Script code
Click "Add to Chart" to apply the indicator
Configure the Parameters
ALMA Length Short: Set the short-term smoothing parameter (default: 3). Lower values provide more responsive signals but may generate more false signals
ALMA Length Medium: Set the medium-term smoothing parameter (default: 5). This provides a balance between responsiveness and stability
ALMA Length Long: Set the long-term smoothing parameter (default: 13). Higher values provide more stable signals but with less responsiveness
ALMA Length Very Long: Set the very long-term smoothing parameter (default: 21). This provides the most stable support/resistance levels
Understand the Visual Elements
Green Candles: Indicate bullish momentum when price is above the bear market zone (support)
Red Candles: Indicate bearish momentum when price is below the bull market zone (resistance)
White Candles: Indicate neutral market conditions when price is between support and resistance zones
Blue Histogram: Shows bear market zone when price is below support level
Aqua Histogram: Shows bull market zone when price is above resistance level
"B" Labels: Mark buy signals when price crosses above resistance
"S" Labels: Mark sell signals when price crosses below support
Identify Market Regimes
Bullish Regime: Price consistently above resistance zone with green candles and aqua histogram
Bearish Regime: Price consistently below support zone with red candles and blue histogram
Neutral Regime: Price oscillating between support and resistance zones with white candles
Generate Trading Signals
Buy Signals: Look for price crossing above the bull market zone (resistance) with confirmation from green candles
Sell Signals: Look for price crossing below the bear market zone (support) with confirmation from red candles
Confirmation: Always wait for confirmation from candle color changes before entering trades
Optimize for Different Timeframes
Scalping: Use shorter ALMA lengths (3-5) for 1-5 minute charts
Day Trading: Use medium ALMA lengths (5-13) for 15-60 minute charts
Swing Trading: Use longer ALMA lengths (13-21) for 1-4 hour charts
Position Trading: Use very long ALMA lengths (21+) for daily and weekly charts
LIMITATIONS
Whipsaw Markets: The indicator may generate false signals in choppy, sideways markets where price oscillates rapidly between support and resistance
Lagging Nature: Like all moving average-based indicators, there is inherent lag in the calculations, which may result in delayed signals
Not a Standalone Tool: This indicator should be used in conjunction with other technical analysis tools and risk management strategies
Market Structure Dependency: Performance may vary depending on market structure and volatility conditions
Parameter Sensitivity: Different markets may require different parameter settings for optimal performance
No Volume Integration: The indicator does not incorporate volume data, which could provide additional confirmation signals
Limited Backtesting: Pine Script limitations may restrict comprehensive backtesting capabilities
Not Suitable for All Instruments: May perform differently on stocks, forex, crypto, and futures markets
Requires Confirmation: Signals should always be confirmed with other indicators or price action analysis
Not Predictive: The indicator identifies current market conditions but does not predict future price movements
NOTES
ALMA Algorithm: The indicator uses the Arnaud Legoux Moving Average (ALMA) algorithm, which is known for its excellent smoothing capabilities and reduced lag compared to traditional moving averages
Weighted Price Calculations: The bear market zone uses (2low + close) / 3, while the bull market zone uses (high + 2close) / 3, providing more weight to recent price action
Dynamic Zones: The support and resistance zones are dynamic and adapt to changing market conditions, making them more responsive than static levels
Color Psychology: The color scheme follows traditional trading psychology - green for bullish, red for bearish, and white for neutral
Signal Timing: The signals are generated on the close of each bar, ensuring they are based on complete price action
Label Positioning: Buy signals appear below the bar (red "B" label), while sell signals appear above the bar (green "S" label)
Multiple Timeframes: The indicator can be applied to multiple timeframes simultaneously for comprehensive analysis
Risk Management: Always use proper risk management techniques when trading based on indicator signals
Market Context: Consider the overall market context and trend direction when interpreting signals
Confirmation: Look for confirmation from other indicators or price action patterns before entering trades
Practice: Test the indicator on historical data before using it in live trading
Customization: Feel free to experiment with different parameter combinations to find what works best for your trading style
THANKS
Special thanks to the TradingView community and the Pine Script developers for creating such a powerful and flexible platform for technical analysis. This indicator builds upon the foundation of the ALMA algorithm and various moving average techniques developed by technical analysis pioneers. The concept of dynamic support and resistance zones has been refined over decades of market analysis, and this script represents a modern implementation of these timeless principles. We acknowledge the contributions of all traders and developers who have contributed to the evolution of technical analysis and continue to push the boundaries of what's possible with algorithmic trading tools.
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter
A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts.
What this is
A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks.
Why Theil-Sen
Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns.
What it plots
Filtered baseline that advances by a fraction of the robust slope each bar.
Optional candle coloring by baseline slope sign for quick trend read.
Alerts when the baseline slope turns up or down.
How it behaves (high level)
Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes.
Takes the median of those slopes to get a robust estimate for the bar.
Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line.
Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive.
Inputs and what they do
Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother.
Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier.
Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves.
Slope Cap Mode — optional guardrail on each bar’s slope:
None — no cap.
ATR — cap scales with recent true range.
Percent — cap scales with price level.
Points — fixed absolute cap in price points.
ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size.
UI Settings — show or hide the line, paint candles by slope, choose long and short colors.
How to read it
Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction.
Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise.
Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets.
Use cases
Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups.
Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure.
Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles.
Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars.
Tuning guidance
Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves.
Position trading — longer window, moderate response. ATR cap tends to scale well across cycles.
Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities.
Alerts included
Theil-Sen Up Slope — baseline’s one-bar change crosses above zero.
Theil-Sen Down Slope — baseline’s one-bar change crosses below zero.
Strengths
Robust to outliers through median-based slope estimation.
Continuously advances with price rather than re-anchoring, which reduces lag at turns.
User-selectable slope caps to tame shock bars without over-smoothing everything.
Minimal visuals with optional candle painting for fast regime recognition.
Notes
This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
Fisher Volume Transform | AlphaNattFisher Volume Transform | AlphaNatt
A powerful oscillator that applies the Fisher Transform - converting price into a Gaussian normal distribution - while incorporating volume weighting to identify high-probability reversal points with institutional participation.
"The Fisher Transform reveals what statistics professors have known for decades: when you transform market data into a normal distribution, turning points become crystal clear."
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🎲 THE MATHEMATICS
Fisher Transform Formula:
The Fisher Transform converts any bounded dataset into a Gaussian distribution:
y = 0.5 × ln((1 + x) / (1 - x))
Where x is normalized price (-1 to 1 range)
Why This Matters:
Market extremes become statistically identifiable
Turning points are amplified and clarified
Removes the skew from price distributions
Creates nearly instantaneous signals at reversals
Volume Integration:
Unlike standard Fisher Transform, this version weights price by relative volume:
High volume moves get more weight
Low volume moves get filtered out
Identifies institutional participation
Reduces false signals from retail chop
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💎 KEY ADVANTAGES
Statistical Edge: Transforms price into normal distribution where extremes are mathematically defined
Volume Confirmation: Only signals with volume support
Early Reversal Detection: Fisher Transform amplifies turning points
Clean Signals: Gaussian distribution reduces noise
No Lag: Mathematical transformation, not averaging
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⚙️ SETTINGS OPTIMIZATION
Fisher Period (5-30):
5-9: Very sensitive, many signals
10: Default - balanced sensitivity
15-20: Moderate smoothing
25-30: Major reversals only
Volume Weight (0.1-1.0):
0.1-0.3: Minimal volume influence
0.5-0.7: Balanced price/volume
0.7: Default - strong volume weight
0.8-1.0: Volume dominant
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📊 TRADING SIGNALS
Primary Signals:
Zero Cross Up: Bullish momentum shift
Zero Cross Down: Bearish momentum shift
Signal Line Cross: Early reversal warning
Extreme Readings (±75): Potential reversal zones
Visual Interpretation:
Cyan zones: Bullish momentum
Magenta zones: Bearish momentum
Gradient intensity: Strength of move
Histogram: Raw momentum power
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🎯 OPTIMAL USAGE
Best Market Conditions:
Range-bound markets (reversals clear)
High volume periods
Major support/resistance levels
Divergence hunting
Trading Strategies:
1. Extreme Reversal:
Enter when oscillator exceeds ±75 and reverses
2. Zero Line Momentum:
Trade crosses of zero line with volume confirmation
3. Signal Line Strategy:
Early entry on signal line crosses
4. Divergence Trading:
Price makes new high/low but Fisher doesn't
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Developed by AlphaNatt | Quantitative Trading Systems
Version: 1.0
Classification: Statistical Transform Oscillator
Not financial advice. Always DYOR.
Logarithmic Bollinger Bands with Secondary BandThe Logarithmic Bollinger Bands indicator is a variation of standard Bollinger Bands that applies the calculation on a logarithmic scale rather than directly on price values. This approach is particularly useful in markets where assets experience exponential growth or large percentage-based moves, as it allows the bands to adapt proportionally instead of being distorted by absolute price changes. The indicator calculates a moving average of the log-transformed price (the basis), then adds and subtracts multiples of the standard deviation of the log price to form the upper and lower bands. Finally, the results are converted back to normal price scale for plotting. In addition to the primary bands set at 2.0 standard deviations, this version includes a secondary band set at 0.5 standard deviations, offering a closer inner envelope around the mean.
To use this indicator
Traders can observe how price interacts with both the inner and outer bands. The outer 2.0 standard deviation bands represent traditional Bollinger-style boundaries, highlighting potential overbought or oversold conditions when price pushes beyond them.
The inner 0.5 bands provide an earlier signal of price compression and breakout potential, as moves outside these tighter bands often precede larger volatility expansions.
Together, these dual layers give traders a way to monitor both short-term fluctuations and broader trend extremes, making it easier to spot opportunities for entries, exits, or risk management in markets where percentage-based scaling is more meaningful than raw price levels.
Custom Price Labels (10 liquidity key levels)A simple indicator for liquidity key level trader:
Add your key level price and key note.
You can adjust the color and font.
How to find key level:
Daily high and Low for key event
eg: NVDA earning, Jackson Hole Day Pump, AI bubble report day dump, Aug Labor Data Revision day dump. If market is consolidating, these key event price level are trend target and reversal level.
TEWMA - [JTCAPITAL]Triple Exponential Weighted Moving Average is a modified way to use Weighted Triple Moving Averages for Trend-Following
The indicator works by calculating in the following steps:
1. The length gets multiplied by the multi to get the second length.
2. The Triple Exponential Moving Average gets calculated using the Weighted Moving Average as input.
3. This calculation is done over the first and the second length.
4. The average from both calculations is taken and used for buy and sell conditions.
--Buy and sell conditions--
-The buy and sell conditions are defined by the average of both indicators having a higher value than the previous bar.
-Average higher than the previous average = Long
-Average lower than the previous average = Short
--Features and Parameters--
-Allows the usage of different sources
-Allows the changing of the calculation length
-Allows the changing of the multiplier to determine the second length
-Allows the use of alerts for signal changes
--Details--
This script uses the result of the calculation of the Weighted Moving Averages as inputs for the Triple Moving averages. The usage of 2 separate calculations and using the average of them for trend determination is to allow for faster entries and exits while limiting potential false signals.
Enjoy!
Trend Following S/R Fibonacci StrategyTrend Following S/R Fibonacci Strategy
Trend Following S/R Fibonacci Strategy
Distance from EMAThis indicator measures the percentage distance from an EMA. When price gets too far away from it's EMA, it can show that the trend is overheated
Auto-Anchored MA with Deviation BandsAuto-Anchored MA with Deviation Bands
✨ Features
📈 Auto-Anchored MA: Calculates moving averages (EMA, SMA, EWMA, WMA, VWAP, TEMA) anchored to user-defined periods (Hour, Day, Week, etc.).📏 Deviation Bands: Plots upper/lower bands using Percentage or Standard Deviation modes for volatility analysis.⚙️ Customizable Timeframes: Choose anchor periods from Hour to Year for flexible trend analysis.🎨 Visuals: Displays MA and bands with gradient fills, customizable colors, and adjustable display bars.⏱️ Countdown Table: Shows bars since the last anchor for easy tracking.🛠️ Smoothing: Applies smoothing to bands for cleaner visuals.
🛠️ How to Use
Add to Chart: Apply the indicator on TradingView.
Configure Inputs:
Anchor Settings: Select anchor period (e.g., Day, Week).
MA Settings: Choose MA type (e.g., VWAP, TEMA).
Deviation Settings: Set deviation mode (Percentage/Std Dev) and multipliers.
Display Settings: Adjust bars to display, colors, and gradient fill.
Analyze: View MA, deviation bands, and countdown table on the chart.
Track Trends: Use bands as dynamic support/resistance and monitor anchor resets.
🎯 Why Use It?
Dynamic Analysis: Auto-anchors MA to key timeframes for adaptive trend tracking.
Volatility Insight: Deviation bands highlight potential breakouts or reversals.
Customizable: Tailor MA type, timeframe, and visuals to your trading style.
User-Friendly: Clear visuals and countdown table simplify analysis.
📝 Notes
Ensure sufficient bars for accurate MA and deviation calculations.
Gradient fill enhances readability but can be disabled for simplicity.
Best used with complementary indicators like RSI or Bollinger Bands for robust strategies.
Happy trading! 🚀📈
