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Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Pine Script® indicator
by EdgeTools
5
TASC 2025.07 Laguerre Filters█ OVERVIEW This script implements the Laguerre filter and oscillator described by John F. Ehlers in the article "A Tool For Trend Trading, Laguerre Filters" from the July 2025 edition of TASC's Traders' Tips . The new Laguerre filter utilizes the UltimateSmoother filter in place of an exponential moving average (EMA) in its calculation, offering improved responsiveness and reduced lag. █ CONCEPTS As Ehlers explains in his article, the Laguerre filter is a form of transversal filter . A transversal filter calculates an output signal using a tapped delay line . It creates multiple delayed versions of an input signal, applies weight to each delay, and then calculates their sum to generate the filtered result. The Laguerre filter's structure relies on Laguerre polynomials — solutions to a differential equation solved by Edmond Laguerre in the 1800s. When Ehlers analyzed the formula for these polynomials on discrete systems (e.g., financial time series), he found that the first term's expression corresponds to an EMA response, and all subsequent terms correspond to an all-pass response. In contrast to other filter types, an all-pass filter produces phase shift (i.e., delay) in an input signal's components without affecting its amplitude. Ehlers observed that these characteristics of Laguerre polynomials make them suitable for use in a transversal filter structure, and thus the Laguerre filter was born. However, he notes that EMAs are not great filters in general. As such, to improve on the Laguerre filter's design, Ehlers modified it by replacing the EMA term with his UltimateSmoother filter. The resulting Laguerre filter has significantly reduced lag, achieving a tighter response to market fluctuations while maintaining smoothness. Ehlers suggests that traders can analyze crossings between the UltimateSmoother and this Laguerre filter, or those between two Laguerre filters of different order, for helpful buy and sell signals. In addition to the Laguerre filter, Ehlers derived a smooth, low-lag oscillator based on the difference between the first and second terms in the modified filter structure, scaled by the root mean square (RMS). The resulting oscillator provides an alternative filtered representation of market data, which can help traders identify swing and mean-reversion signals. █ USAGE This indicator calculates both the Laguerre filter and the Laguerre oscillator described in Ehlers' article. It displays the Laguerre filter on the main chart pane and the oscillator in a separate pane. Users can control the behavior of the filter and oscillator with the inputs in the "Settings/Inputs" tab: The "Period" input defines the critical period of the UltimateSmoother used in the Laguerre filter and oscillator calculations. Its default value is 30. The "Gamma" input determines the weighting behavior of the Laguerre filter and oscillator. It accepts a positive value between 0 and 1. Use a lower value for quicker responsiveness to market changes, and a higher value for trends. The default value is 0.5. The "RMS length" input determines the length of the RMS calculation for oscillator normalization. The default value is 100 bars.
Pine Script® indicator
by PineCodersTASC
13
Essa - Multi-Timeframe LevelsEnhanced Multi‐Timeframe Levels This indicator plots yearly, quarterly and monthly highs, lows and midpoints on your chart. Each level is drawn as a horizontal line with an optional label showing “ – ” (for example “Apr 2025 High – 1.2345”). If two or more timeframes share the same price (within two ticks), they are merged into a single line and the label lists each timeframe. A distance table can be shown in any corner of the chart. It lists up to five active levels closest to the current closing price and shows for each level: level name (e.g. “May 2025 Low”) exact price distance in pips or points (calculated according to the instrument’s tick size) percentage difference relative to the close Alerts can be enabled so that whenever price comes within a user-specified percentage of any level (for example 0.1 %), an alert fires. Once price decisively crosses a level, that level is marked as “broken” so it does not trigger again. Built-in alertcondition hooks are also provided for definite breaks of the current monthly, quarterly and yearly highs and lows. Monthly lookback is configurable (default 6 months), and once the number of levels exceeds a cap (calculated as 20 + monthlyLookback × 3), the oldest levels are automatically removed to avoid clutter. Line widths and colours (with adjustable opacity for quarterly and monthly) can be set separately for each timeframe. Touches of each level are counted internally to allow future extension (for example visually emphasising levels with multiple touches).
Pine Script® indicator
by Essa02
Enhanced Stock Ticker with 50MA vs 200MADescription The Enhanced Stock Ticker with 50MA vs 200MA is a versatile Pine Script indicator designed to visualize the relative position of a stock's price within its short-term and long-term price ranges, providing actionable bullish and bearish signals. By calculating normalized indices based on user-defined lookback periods (defaulting to 50 and 200 bars), this indicator helps traders identify potential reversals or trend continuations. It offers the flexibility to plot signals either on the main price chart or in a separate lower pane, leveraging Pine Script v6's force_overlay functionality for seamless integration. The indicator also includes a customizable ticker table, visual fills, and alert conditions for automated trading setups. Key Features Dual Lookback Indices: Computes short-term (default: 50 bars) and long-term (default: 200 bars) indices, normalizing the closing price relative to the high/low range over the specified periods. Flexible Signal Plotting: Users can toggle between plotting crossover signals (triangles) on the main price chart (location.abovebar/belowbar) or in the lower pane (location.top/bottom) using the Plot Signals on Main Chart option. Crossover Signals: Generates bullish (Golden Cross) and bearish (Death Cross) signals when the short or long index crosses above 5 or below 95, respectively. Visual Enhancements: Plots short-term (blue) and long-term (white) indices in a separate pane with customizable lookback periods. Includes horizontal reference lines at 0, 20, 50, 80, and 100, with green and red fills to highlight overbought/oversold zones. Dynamic fill between indices (green when short > long, red when long > short) for quick trend visualization. Displays a ticker and legend table in the top-right corner, showing the symbol and lookback periods. Alert Conditions: Supports alerts for bullish and bearish crossovers on both short and long indices, enabling integration with TradingView's alert system. Technical Innovation: Utilizes Pine Script v6's force_overlay parameter to plot signals on the main chart from a non-overlay indicator, combining the benefits of a separate pane and chart-based signals in a single script. Technical Details Calculation Logic: Uses confirmed bars (barstate.isconfirmed) to calculate indices, ensuring reliability by avoiding real-time bar fluctuations. Short-term index: (close - lowest(low, lookback_short)) / (highest(high, lookback_short) - lowest(low, lookback_short)) * 100 Long-term index: (close - lowest(low, lookback_long)) / (highest(high, lookback_long) - lowest(low, lookback_long)) * 100 Signals are triggered using ta.crossover() and ta.crossunder() for indices crossing 5 (bullish) and 95 (bearish). Signal Plotting: Main chart signals use force_overlay=true with location.abovebar/belowbar for precise alignment with price bars. Lower pane signals use location.top/bottom for visibility within the indicator pane. Plotting is controlled by boolean conditions (e.g., bullishLong and plot_on_chart) to ensure compliance with Pine Script's global scope requirements. Performance Considerations: Optimized for efficiency by calculating indices only on confirmed bars and using lightweight plotting functions. How to Use Add to Chart: Copy the script into TradingView's Pine Editor and add it to your chart. Configure Settings: Short Lookback Period: Adjust the short-term lookback (default: 50 bars) to match your trading style (e.g., 20 for shorter-term analysis). Long Lookback Period: Adjust the long-term lookback (default: 200 bars) for broader market context. Plot Signals on Main Chart: Check this box to display signals on the price chart; uncheck to show signals in the lower pane. Interpret Signals: Golden Cross (Bullish): Green (long) or blue (short) triangles indicate the index crossing above 5, suggesting a potential buying opportunity. Death Cross (Bearish): Red (long) or white (short) triangles indicate the index crossing below 95, signaling a potential selling opportunity. Set Alerts: Use TradingView's alert system to create notifications for the four alert conditions: Long Index Valley, Long Index Peak, Short Index Valley, and Short Index Peak. Customize Visuals: The ticker table displays the symbol and lookback periods in the top-right corner. Adjust colors and styles via TradingView's settings if desired. Example Use Cases Swing Trading: Use the short-term index (e.g., 50 bars) to identify short-term reversals within a broader trend defined by the long-term index. Trend Confirmation: Monitor the fill between indices to confirm whether the short-term trend aligns with the long-term trend. Automated Trading: Leverage alert conditions to integrate with bots or manual trading strategies. Notes Testing: Always backtest the indicator on your chosen market and timeframe to validate its effectiveness. Optional Histogram: The script includes a commented-out histogram for the index difference (index_short - index_long). Uncomment the plot(index_diff, ...) line to enable it. Compatibility: Built for Pine Script v6 and tested on TradingView as of May 27, 2025. Acknowledgments This indicator was inspired by the need for a flexible tool that combines lower-pane analysis with main chart signals, made possible by Pine Script's force_overlay feature. Share your feedback or suggestions in the comments below, and happy trading!
Pine Script® indicator
by chuckaschultz
EMA5/21 + VWAP + MACD HistogramScript Summary: EMA + VWAP + MACD + RSI Strategy Objective: Combine multiple technical indicators to identify market entry and exit opportunities, aiming to increase signal accuracy. Indicators Used: EMAs (Exponential Moving Averages): Periods of 5 (short-term) and 21 (long-term) to identify trend crossovers. VWAP (Volume Weighted Average Price): Serves as a reference to determine if the price is in a fair value zone. MACD (Moving Average Convergence Divergence): Standard settings of 12, 26, and 9 to detect momentum changes. RSI (Relative Strength Index): Period of 14 to identify overbought or oversold conditions. Entry Rules: Buy (Long): 5-period EMA crosses above the 21-period EMA, price is above VWAP, MACD crosses above the signal line, and RSI is above 40. Sell (Short): 5-period EMA crosses below the 21-period EMA, price is below VWAP, MACD crosses below the signal line, and RSI is below 60. Exit Rules: For long positions: When the 5-period EMA crosses below the 21-period EMA or MACD crosses below the signal line. For short positions: When the 5-period EMA crosses above the 21-period EMA or MACD crosses above the signal line. Visual Alerts: Buy and sell signals are highlighted on the chart with green (buy) and red (sell) arrows below or above the corresponding candles. Indicator Plotting: The 5 and 21-period EMAs, as well as the VWAP, are plotted on the chart to facilitate the visualization of market conditions. This script is a versatile tool for traders seeking to combine multiple technical indicators into a single strategy. It can be used across various timeframes and assets, allowing adjustments according to the trader's profile and market characteristics. Juliano Einhardt Ulguim, Brazil, 05/27/2025.
Pine Script® indicator
by JulianoEinhardt
Aftershock by Session [SAKANE]■ Background & Motivation In 24/7 markets like crypto, not all participants react simultaneously to major events. Instead, reactions unfold across different regional trading sessions — Asia (APAC), Europe (EU), and the United States (US) — each with its own tempo and sentiment. This indicator is designed to visualize which session drives the market after a key event — capturing the "aftershock" effect that ripples through time zones. ■ Key Features Tracks price return (open → close) for each session: APAC / EU / US Cumulative session returns are calculated and visualized Smoothing options: SMA, EMA, or Ehlers SuperSmoother Optimized for daily charts to highlight structural momentum shifts Toggle visibility of each session independently ■ Why “Aftershock”? Take April 2, 2025 — the day of the “Trump Tariff Opening.” That policy announcement triggered a market-wide response. But: Which session reacted first? Which session truly moved the market? This indicator is named “Aftershock” because it helps you see the ripple effect of such events — when and where momentum followed. ■ How to Use Search for “Aftershock by Session ” on TradingView Add it to your chart (use Daily timeframe) Customize sessions and smoothing options via settings You can also bookmark it for quick access. ■ Insights & Use Cases Detect which session initiated or led market moves after news events Understand geo-temporal dynamics — did the move start in Asia, Europe, or the US? For example, on April 2, 2025, the day Trump’s tariff pivot was announced: You can instantly see which session took the lead — the APAC session hesitated, while the US session drove the trend. This insight becomes visually obvious with the cumulative lines. ■ Unique Value Unlike typical indicators based on raw price action, Aftershock analyzes market movement through a session-based structural lens. It captures where capital actually moved — and when. A tool not just for technical analysis, but for event-driven, macro-aware market reading. ■ Final Thoughts To truly understand market mechanics, we must look beyond candles and trends. Aftershock by Session breaks down the 24-hour cycle into meaningful regional flows, allowing you to track the true drivers behind price momentum. Whether you're trading, researching, or tracking macro catalysts, this tool helps answer the key question: “Who moved the market — and when?”
Pine Script® indicator
by amusedMagpie26867
TASC 2025.06 Cybernetic Oscillator█ OVERVIEW This script implements the Cybernetic Oscillator introduced by John F. Ehlers in his article "The Cybernetic Oscillator For More Flexibility, Making A Better Oscillator" from the June 2025 edition of the TASC Traders' Tips . It cascades two-pole highpass and lowpass filters, then scales the result by its root mean square (RMS) to create a flexible normalized oscillator that responds to a customizable frequency range for different trading styles. █ CONCEPTS Oscillators are indicators widely used by technical traders. These indicators swing above and below a center value, emphasizing cyclic movements within a frequency range. In his article, Ehlers explains that all oscillators share a common characteristic: their calculations involve computing differences . The reliance on differences is what causes these indicators to oscillate about a central point. The difference between two data points in a series acts as a highpass filter — it allows high frequencies (short wavelengths) to pass through while significantly attenuating low frequencies (long wavelengths). Ehlers demonstrates that a simple difference calculation attenuates lower-frequency cycles at a rate of 6 dB per octave. However, the difference also significantly amplifies cycles near the shortest observable wavelength, making the result appear noisier than the original series. To mitigate the effects of noise in a differenced series, oscillators typically smooth the series with a lowpass filter, such as a moving average. Ehlers highlights an underlying issue with smoothing differenced data to create oscillators. He postulates that market data statistically follows a pink spectrum , where the amplitudes of cyclic components in the data are approximately directly proportional to the underlying periods. Specifically, he suggests that cyclic amplitude increases by 6 dB per octave of wavelength. Because some conventional oscillators, such as RSI, use differencing calculations that attenuate cycles by only 6 dB per octave, and market cycles increase in amplitude by 6 dB per octave, such calculations do not have a tangible net effect on larger wavelengths in the analyzed data. The influence of larger wavelengths can be especially problematic when using these oscillators for mean reversion or swing signals. For instance, an expected reversion to the mean might be erroneous because oscillator's mean might significantly deviate from its center over time. To address the issues with conventional oscillator responses, Ehlers created a new indicator dubbed the Cybernetic Oscillator. It uses a simple combination of highpass and lowpass filters to emphasize a specific range of frequencies in the market data, then normalizes the result based on RMS. The process is as follows: Apply a two-pole highpass filter to the data. This filter's critical period defines the longest wavelength in the oscillator's passband. Apply a two-pole SuperSmoother (lowpass filter) to the highpass-filtered data. This filter's critical period defines the shortest wavelength in the passband. Scale the resulting waveform by its RMS. If the filtered waveform follows a normal distribution, the scaled result represents amplitude in standard deviations. The oscillator's two-pole filters attenuate cycles outside the desired frequency range by 12 dB per octave. This rate outweighs the apparent rate of amplitude increase for successively longer market cycles (6 dB per octave). Therefore, the Cybernetic Oscillator provides a more robust isolation of cyclic content than conventional oscillators. Best of all, traders can set the periods of the highpass and lowpass filters separately, enabling fine-tuning of the frequency range for different trading styles. █ USAGE The "Highpass period" input in the "Settings/Inputs" tab specifies the longest wavelength in the oscillator's passband, and the "Lowpass period" input defines the shortest wavelength. The oscillator becomes more responsive to rapid movements with a smaller lowpass period. Conversely, it becomes more sensitive to trends with a larger highpass period. Ehlers recommends setting the smallest period to a value above 8 to avoid aliasing. The highpass period must not be smaller than the lowpass period. Otherwise, it causes a runtime error. The "RMS length" input determines the number of bars in the RMS calculation that the indicator uses to normalize the filtered result. This indicator also features two distinct display styles, which users can toggle with the "Display style" input. With the "Trend" style enabled, the indicator plots the oscillator with one of two colors based on whether its value is above or below zero. With the "Threshold" style enabled, it plots the oscillator as a gray line and highlights overbought and oversold areas based on the user-specified threshold. Below, we show two instances of the script with different settings on an equities chart. The first uses the "Threshold" style with default settings to pass cycles between 20 and 30 bars for mean reversion signals. The second uses a larger highpass period of 250 bars and the "Trend" style to visualize trends based on cycles spanning less than one year:
Pine Script® indicator
by PineCodersTASC
13
EXODUS EXODUS by (DAFE) Trading Systems EXODUS is a sophisticated trading algorithm built by Dskyz (DAFE) Trading Systems for competitive and competition purposes, designed to identify high-probability trades with robust risk management. this strategy leverages a multi-signal voting system, combining three core components—SPR, VWMO, and VEI—alongside ADX, choppiness filters, and ATR-based volatility gates to ensure trades are taken only in favorable market conditions. the algo uses a take-profit to stop-loss ratio, dynamic position sizing, and a strict voting mechanism requiring all signals to align before entering a trade. EXODUS was not overfitted for any specific symbol. instead, it uses a generic tuned setting, making it versatile across various markets. while it can trade futures, it’s not currently set up for it but has the potential to do more with further development. visuals are intentionally minimal due to its competition focus, prioritizing performance over aesthetics. a more visually stunning version may be released in the future with enhanced graphics. The Unique Core Components Developed for EXODUS SPR (Session Price Recalibration) SPR measures momentum during regular trading hours (RTH, 0930-1600, America/New_York) to catch session-specific trends. spr_lookback = input.int(15, "SPR Lookback") this sets how many bars back SPR looks to calculate momentum (default 15 bars). it compares the current session’s price-volume score to the score 15 bars ago to gauge momentum strength. how it works: a longer lookback smooths out the signal, focusing on bigger trends. a shorter one makes SPR more sensitive to recent moves. how to adjust: on a 1-hour chart, 15 bars is 15 hours (about 2 trading days). if you’re on a shorter timeframe like 5 minutes, 15 bars is just 75 minutes, so you might want to increase it to 50 or 100 to capture more meaningful trends. if you’re trading a choppy stock, a shorter lookback (like 5) can help catch quick moves, but it might give more false signals. spr_threshold = input.float (0.7, "SPR Threshold") this is the cutoff for SPR to vote for a trade (default 0.7). if SPR’s normalized value is above 0.7, it votes for a long; below -0.7, it votes for a short. how it works: SPR normalizes its momentum score by ATR, so this threshold ensures only strong moves count. a higher threshold means fewer trades but higher conviction. how to adjust: if you’re getting too few trades, lower it to 0.5 to let more signals through. if you’re seeing too many false entries, raise it to 1.0 for stricter filtering. test on your chart to find a balance. spr_atr_length = input.int(21, "SPR ATR Length") this sets the ATR period (default 21 bars) used to normalize SPR’s momentum score. ATR measures volatility, so this makes SPR’s signal relative to market conditions. how it works: a longer ATR period (like 21) smooths out volatility, making SPR less jumpy. a shorter one makes it more reactive. how to adjust: if you’re trading a volatile stock like TSLA, a longer period (30 or 50) can help avoid noise. for a calmer stock, try 10 to make SPR more responsive. match this to your timeframe—shorter timeframes might need a shorter ATR. rth_session = input.session("0930-1600","SPR: RTH Sess.") rth_timezone = "America/New_York" this defines the session SPR uses (0930-1600, New York time). SPR only calculates momentum during these hours to focus on RTH activity. how it works: it ignores pre-market or after-hours noise, ensuring SPR captures the main market action. how to adjust: if you trade a different session (like London hours, 0300-1200 EST), change the session to match. you can also adjust the timezone if you’re in a different region, like "Europe/London". just make sure your chart’s timezone aligns with this setting. VWMO (Volume-Weighted Momentum Oscillator) VWMO measures momentum weighted by volume to spot sustained, high-conviction moves. vwmo_momlen = input.int(21, "VWMO Momentum Length") this sets how many bars back VWMO looks to calculate price momentum (default 21 bars). it takes the price change (close minus close 21 bars ago). how it works: a longer period captures bigger trends, while a shorter one reacts to recent swings. how to adjust: on a daily chart, 21 bars is about a month—good for trend trading. on a 5-minute chart, it’s just 105 minutes, so you might bump it to 50 or 100 for more meaningful moves. if you want faster signals, drop it to 10, but expect more noise. vwmo_volback = input.int(30, "VWMO Volume Lookback") this sets the period for calculating average volume (default 30 bars). VWMO weights momentum by volume divided by this average. how it works: it compares current volume to the average to see if a move has strong participation. a longer lookback smooths the average, while a shorter one makes it more sensitive. how to adjust: for stocks with spiky volume (like NVDA on earnings), a longer lookback (50 or 100) avoids overreacting to one-off spikes. for steady volume stocks, try 20. match this to your timeframe—shorter timeframes might need a shorter lookback. vwmo_smooth = input.int(9, "VWMO Smoothing") this sets the SMA period to smooth VWMO’s raw momentum (default 9 bars). how it works: smoothing reduces noise in the signal, making VWMO more reliable for voting. a longer smoothing period cuts more noise but adds lag. how to adjust: if VWMO is too jumpy (lots of false votes), increase to 15. if it’s too slow and missing trades, drop to 5. test on your chart to see what keeps the signal clean but responsive. vwmo_threshold = input.float(10, "VWMO Threshold") this is the cutoff for VWMO to vote for a trade (default 10). above 10, it votes for a long; below -10, a short. how it works: it ensures only strong momentum signals count. a higher threshold means fewer but stronger trades. how to adjust: if you want more trades, lower it to 5. if you’re getting too many weak signals, raise it to 15. this depends on your market—volatile stocks might need a higher threshold to filter noise. VEI (Velocity Efficiency Index) VEI measures market efficiency and velocity to filter out choppy moves and focus on strong trends. vei_eflen = input.int(14, "VEI Efficiency Smoothing") this sets the EMA period for smoothing VEI’s efficiency calc (bar range / volume, default 14 bars). how it works: efficiency is how much price moves per unit of volume. smoothing it with an EMA reduces noise, focusing on consistent efficiency. a longer period smooths more but adds lag. how to adjust: for choppy markets, increase to 20 to filter out noise. for faster markets, drop to 10 for quicker signals. this should match your timeframe—shorter timeframes might need a shorter period. vei_momlen = input.int(8, "VEI Momentum Length") this sets how many bars back VEI looks to calculate momentum in efficiency (default 8 bars). how it works: it measures the change in smoothed efficiency over 8 bars, then adjusts for inertia (volume-to-range). a longer period captures bigger shifts, while a shorter one reacts faster. how to adjust: if VEI is missing quick reversals, drop to 5. if it’s too noisy, raise to 12. test on your chart to see what catches the right moves without too many false signals. vei_threshold = input.float(4.5, "VEI Threshold") this is the cutoff for VEI to vote for a trade (default 4.5). above 4.5, it votes for a long; below -4.5, a short. how it works: it ensures only strong, efficient moves count. a higher threshold means fewer trades but higher quality. how to adjust: if you’re not getting enough trades, lower to 3. if you’re seeing too many false entries, raise to 6. this depends on your market—fast stocks like NQ1 might need a lower threshold. Features Multi-Signal Voting: requires all three signals (SPR, VWMO, VEI) to align for a trade, ensuring high-probability setups. Risk Management: uses ATR-based stops (2.1x) and take-profits (4.1x), with dynamic position sizing based on a risk percentage (default 0.4%). Market Filters: ADX (default 27) ensures trending conditions, choppiness index (default 54.5) avoids sideways markets, and ATR expansion (default 1.12) confirms volatility. Dashboard: provides real-time stats like SPR, VWMO, VEI values, net P/L, win rate, and streak, with a clean, functional design. Visuals EXODUS prioritizes performance over visuals, as it was built for competitive and competition purposes. entry/exit signals are marked with simple labels and shapes, and a basic heatmap highlights market regimes. a more visually stunning update may be released later, with enhanced graphics and overlays. Usage EXODUS is designed for stocks and ETFs but can be adapted for futures with adjustments. it performs best in trending markets with sufficient volatility, as confirmed by its generic tuning across symbols like TSLA, AMD, NVDA, and NQ1. adjust inputs like SPR threshold, VWMO smoothing, or VEI momentum length to suit specific assets or timeframes. Setting I used: (Again, these are a generic setting, each security needs to be fine tuned) SPR LB = 19 SPR TH = 0.5 SPR ATR L= 21 SPR RTH Sess: 9:30 – 16:00 VWMO L = 21 VWMO LB = 18 VWMO S = 6 VWMO T = 8 VEI ES = 14 VEI ML = 21 VEI T = 4 R % = 0.4 ATR L = 21 ATR M (S) =1.1 TP Multi = 2.1 ATR min mult = 0.8 ATR Expansion = 1.02 ADX L = 21 Min ADX = 25 Choppiness Index = 14 Chop. Max T = 55.5 Backtesting: TSLA Frame: Jan 02, 2018, 08:00 — May 01, 2025, 09:00 Slippage: 3 Commission .01 Disclaimer this strategy is for educational purposes. past performance is not indicative of future results. trading involves significant risk, and you should only trade with capital you can afford to lose. always backtest and validate any strategy before using it in live markets. (This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.) About the Author Dskyz (DAFE) Trading Systems is dedicated to building high-performance trading algorithms. EXODUS is a product of rigorous research and development, aimed at delivering consistent, and data-driven trading solutions. Use it with discipline. Use it with clarity. Trade smarter. **I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract. 2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Pine Script® strategy
by DskyzInvestments
2
BTC Daily DCA CalculatorThe BTC Daily DCA Calculator is an indicator that calculates how much Bitcoin (BTC) you would own today by investing a fixed dollar amount daily (Dollar-Cost Averaging) over a user-defined period. Simply input your start date, end date, and daily investment amount, and the indicator will display a table on the last candle showing your total BTC, total invested, portfolio value, and unrealized yield (in USD and percentage). Features Customizable Inputs: Set the start date, end date, and daily dollar amount to simulate your DCA strategy. Results Table: Displays on the last candle (top-right of the chart) with: Total BTC: The accumulated Bitcoin from daily purchases. Total Invested ($): The total dollars invested. Portfolio Value ($): The current value of your BTC holdings. Unrealized Yield ($): Your profit/loss in USD. Unrealized Yield (%): Your profit/loss as a percentage. Visual Markers: Green triangles below the chart mark each daily investment. Overlay on Chart: The table and markers appear directly on the BTCUSD price chart for easy reference. Daily Timeframe: Designed for Daily (1D) charts to ensure accurate calculations. How to Use Add the Indicator: Apply the indicator to a BTCUSD chart (e.g., Coinbase:BTCUSD, Binance:BTCUSDT). Set Daily Timeframe: Ensure your chart is on the Daily (1D) timeframe, or the script will display an error. Configure Inputs: Open the indicator’s Settings > Inputs tab and set: Start Date: When to begin the DCA strategy (e.g., 2024-01-01). End Date: When to end the strategy (e.g., 2025-04-27 or earlier). Daily Investment ($): The fixed dollar amount to invest daily (e.g., $100). View Results: Scroll to the last candle in your date range to see the results table in the top-right corner of the chart. Green triangles below the bars indicate investment days. Settings Start Date: Choose the start date for your DCA strategy (default: 2024-01-01). End Date: Choose the end date (default: 2025-04-27). Must be after the start date and within available chart data. Daily Investment ($): Set the daily investment amount (default: $100). Minimum is $0.01. Notes Timeframe: The indicator requires a Daily (1D) chart. Other timeframes will trigger an error. Data: Ensure your BTCUSD chart has historical data for the selected date range. Use reliable pairs like Coinbase:BTCUSD or Binance:BTCUSDT. Limitations: Does not account for trading fees or slippage. Future dates (beyond the current date) will not display results. Performance: Works best with historical data. Free TradingView accounts may have limited historical data; consider premium for longer ranges.
Pine Script® indicator
by bitcoin_briefs
TASC 2025.05 Trading The Channel█ OVERVIEW This script implements channel-based trading strategies based on the concepts explained by Perry J. Kaufman in the article "A Test Of Three Approaches: Trading The Channel" from the May 2025 edition of TASC's Traders' Tips . The script explores three distinct trading methods for equities and futures using information from a linear regression channel. Each rule set corresponds to different market behaviors, offering flexibility for trend-following, breakout, and mean-reversion trading styles. █ CONCEPTS Linear regression Linear regression is a model that estimates the relationship between a dependent variable and one or more independent variables by fitting a straight line to the observed data. In the context of financial time series, traders often use linear regression to estimate trends in price movements over time. The slope of the linear regression line indicates the strength and direction of the price trend. For example, a larger positive slope indicates a stronger upward trend, and a larger negative slope indicates the opposite. Traders can look for shifts in the direction of a linear regression slope to identify potential trend trading signals, and they can analyze the magnitude of the slope to support trading decisions. One caveat to linear regression is that most financial time series data does not follow a straight line, meaning a regression line cannot perfectly describe the relationships between values. Prices typically fluctuate around a regression line to some degree. As such, analysts often project ranges above and below regression lines, creating channels to model the expected extent of the data's variability. This strategy constructs a channel based on the method used in Kaufman's article. It measures the maximum distances from points on the linear regression line to historical price values, then adds those distances and the current slope to the regression points. Depending on the trading style, traders might look for prices to move outside an established channel for breakout signals, or they might look for price action to reach extremes within the channel for potential mean reversion opportunities. █ STRATEGY CALCULATIONS Primary trade rules This strategy implements three distinct sets of rules for trend, breakout, and mean-reversion trades based on the methods Kaufman describes in his article: Trade the trend (Rule 1) : Open new positions when the sign of the slope changes, indicating a potential trend reversal. Close short trades and enter a long trade when the slope changes from negative to positive, and do the opposite when the slope changes from positive to negative. Trade channel breakouts (Rule 2) : Open new positions when prices cross outside the linear regression channel for the current sample. Close short trades and enter a long trade when the price moves above the channel, and do the opposite when the price moves below the channel. Trade within the channel (Rule 3) : Open new positions based on price values within the channel's range. Close short trades and enter a long trade when the price is near the channel's low, within a specified percentage of the channel's range, and do the opposite when the price is near the channel's high. With this rule, users can also filter the trades based on the channel's slope. When the filter is active, long positions are allowed only when the slope is positive, and short positions are allowed only when it is negative. Position sizing Kaufman's strategy uses specific trade sizes for equities and futures markets: For an equities symbol, the number of shares traded is $10,000 divided by the current price. For a futures symbol, the number of contracts traded is based on a volatility-adjusted formula that divides $25,000 by the product of the 20-bar average true range and the instrument's point value. By default, this script automatically uses these sizes for its trade simulation on equities and futures symbols and does not simulate trading on other symbols. However, users can control position sizes from the "Settings/Properties" tab and enable trade simulation on other symbol types by selecting the "Manual" option in the script's "Position sizing" input. Stop-loss This strategy includes the option to place an accompanying stop-loss order for each trade, which users can enable from the "SL %" input in the "Settings/Inputs" tab. When enabled, the strategy places a stop-loss order at a specified percentage distance from the closing price where the entry order occurs, allowing users to compare how the strategy performs with added loss protection. █ USAGE This strategy adapts its display logic for the three trading approaches based on the rule selected in the "Trade rule" input: For all rules, the script plots the linear regression slope in a separate pane. The plot is color-coded to indicate whether the current slope is positive or negative. When the selected rule is "Trade the trend", the script plots triangles in the separate pane to indicate when the slope's direction changes from positive to negative or vice versa. Additionally, it plots a color-coded SMA on the main chart pane, allowing visual comparison of the slope to directional changes in a moving average. When the rule is "Trade channel breakouts" or "Trade within the channel", the script draws the current period's linear regression channel on the main chart pane, and it plots bands representing the history of the channel values from the specified start time onward. When the rule is "Trade within the channel", the script plots overbought and oversold zones between the bands based on a user-specified percentage of the channel range to indicate the value ranges where new trades are allowed. Users can customize the strategy's calculations with the following additional inputs in the "Settings/Inputs" tab: Start date : Sets the date and time when the strategy begins simulating trades. The script marks the specified point on the chart with a gray vertical line. The plots for rules 2 and 3 display the bands and trading zones from this point onward. Period : Specifies the number of bars in the linear regression channel calculation. The default is 40. Linreg source : Specifies the source series from which to calculate the linear regression values. The default is "close". Range source : Specifies whether the script uses the distances from the linear regression line to closing prices or high and low prices to determine the channel's upper and lower ranges for rules 2 and 3. The default is "close". Zone % : The percentage of the channel's overall range to use for trading zones with rule 3. The default is 20, meaning the width of the upper and lower zones is 20% of the range. SL% : If the checkbox is selected, the strategy adds a stop-loss to each trade at the specified percentage distance away from the closing price where the entry order occurs. The checkbox is deselected by default, and the default percentage value is 5. Position sizing : Determines whether the strategy uses Kaufman's predefined trade sizes ("Auto") or allows user-defined sizes from the "Settings/Properties" tab ("Manual"). The default is "Auto". Long trades only : If selected, the strategy does not allow short positions. It is deselected by default. Trend filter : If selected, the strategy filters positions for rule 3 based on the linear regression slope, allowing long positions only when the slope is positive and short positions only when the slope is negative. It is deselected by default. NOTE: Because of this strategy's trading rules, the simulated results for a specific symbol or channel configuration might have significantly fewer than 100 trades. For meaningful results, we recommend adjusting the start date and other parameters to achieve a reasonable number of closed trades for analysis. Additionally, this strategy does not specify commission and slippage amounts by default, because these values can vary across market types. Therefore, we recommend setting realistic values for these properties in the "Cost simulation" section of the "Settings/Properties" tab.
Pine Script® strategy
by PineCodersTASC
1
Psych Level ScreenerThis Script is intended for Pine Screener and is not designed as a indicator!!! Pine Screener is something TradingView has recently added and is still only a Beta version. Pine Screener itself is currently only available to members that are Premium and above. What it does: This screener will actively look for tickers that are close to Pysch level in your watchlist. Psych level here refers to price levels that are round numbers such as 50,100,1000. Users can specify the offset from a psych level (in %) and scanner will scan for tickers that are within the offset. For example if offset is set at 5% then it will scan for tickers that are within +/-5% of a ticker. (for $100 psych level it will scan for ticker in $95-105 range) Once scan is completed you will be able to see: - Current price of ticker - Closest psych level for that ticker - % and $ move required for it to hit that psych level - Ticker's day range and Average range (with % of average range completed for the day) - Ticker volume and average volume Setting up: www.tradingview.com Above link will help you guide how to setup Pine screener. Use steps below to guide you the setup for this specific screener: 1. Open Pine Screener (open new tab, select screener the "Pine") 2. At the top, click on "Choose Indicator" and select "Psych Level Screener" 3. At the top again, click "Indicator Psych Level Screener" and select settings. 4. Change setting to your needs. Hit Apply when done. a)"% offset from Psych Level" will scan for any stocks in your watchlist which are +/- from the offset you chose for any given psych level. Default is 5. (e.g. If offset is 5%, it will scan for stocks that are between $95-$105 vs $100 psych level, $190-$210 for $200 psych level and so on) b) ATR length is number of previous trading days you want to include in your calculation. Moving Average Type is calculation method. c) Rvol length is number of previous trading days you want to include in your calculation. 5. On top left, click "Price within specified offset of Psych. Level" and select true. Then select "Scan" which is located at the top next to "Indicator Psych Level Screener". This will filter out all the stock that meets the condition. 6. At the end of the column on the right there is a "+" symbol. From there you can add/remove columns. 30min/1hr/4hr/1D Trend are disabled by default so if this is needed please enable them. 7. You can change the order of ticker by ascending and descending order of each column label if needed. Just click on the arrow that comes up when you move the cursor to any of the column items. 8. You can specify advanced filter settings based on the variables in the column. (e.g., set price range of stock to filter out further) To do so, click on the column variable name in interest, located above the screener table (or right below "scan") and select "manual setup". How to read the column: Current Price: Shows current price of the ticker when scan was done. Currently Pine Screener does NOT support pre/post-hours data so no PM and AH price. Psych Level: Psych level the current price is near to. % to Psych Level: Price movement in % necessary to get to the Psych level. $ to Psych Level: Price movement in $ necessary to get to the Psych level. DTR: Daily True Range of the stock. i.e. High - Low of the ticker on the day. ATR: Average True Range of stock in the last x days, where x is a value selected in the setting. (See step 3 in Previous section) DTR vs ATR: Amount of DTR a ticker has done in % with respect to ATR. (e.g., 90% means DTR is 90% of ATR) Vol.: Volume of a ticker for the day. Currently Pine Screener does NOT support pre/post-hours data so no PM and AH volume. Avg. Vol: Average volume of a ticker in the last x days, where x is a value selected in the setting. (See step 3 in Previous section) Rvol: Relative volume in percentage, measured by the ratio of day's volume and average volume. 30min/1hr/4hr/1D Trend: Trend status to see if the chart is Bullish or Bearish on each of the time frame. Bullishness or Bearishness is defined by the price being over or under the 34/50 cloud on each of the time frame. Output of 1 is Bullish, -1 is Bearish. 0 means price is sitting inside the 34/50 cloud. Currently Pine Screener does NOT support pre/post-hours data so 34/50 cloud is based on regular trading hours data ONLY. Some things user should be aware of: - Pine Screener itself is currently only available to TradingView members with Premium Subscription and above. (I can't to anything about this as this is NOT set by me, I have no control) For more info: www.tradingview.com - The Pine Screener itself is a Beta version and this screener can stop working anytime depending on changes made by TradingView themselves. (Again I cannot control this) - Pine Screener can only run on Watchlists for now. (as of 03/31/2025) You will have to prepare your own watchlists. In a Watchlist no more than 1000 tickers may be added. (This is TradingView rules) - Psych level included are currently 50 to 1500 in steps of 50. If you need a specific number please let me know. Will add accordingly. - Unfortunately this screener does not update automatically, so please hit "scan" to get latest screener result. - I cannot add 10min trend to the column as Pine Screener does NOT support 10min timeframe as of now. (03/31/2025) - This code is only meant for Pine Screener. I do NOT recommend using this as an indicator. - Currently Pine Screener does NOT support pre/post-hours data. So data such as Price, Volume and EMA values are based on market hours data ONLY! (If I'm wrong about this please correct me / let me know and will make look into and make changes to the code) Other useful links about Pine Screener: Quick overview of the Screener’s functionality: www.tradingview.com what do you need to know before you start working? : www.tradingview.com These links will go over the setting up with GIFs so is easier to understand. ----------------------------------------------------------------------------------------------------------------- If there are other column variables that you think is worth adding please let me know! Will try add it to the screener! If you have any questions let me know as well, will reply soon as I can! Have a good trading day and hope it helps!
Pine Script® indicator
by Mereep01
US Presidents (Alternating Fills by Order)📜 Indicator Description: US Presidents Background Fill This indicator highlights the terms of U.S. Presidents on your chart with alternating red and blue background fills based on their political party: • 🟥 Republicans = Red • 🟦 Democrats = Blue • 🎨 Dark/Light shading alternates with each new president to clearly distinguish consecutive terms, even within the same party. The fill starts from President Ulysses S. Grant (18th President, 1873) through to the 47th president in 2025. It is designed to work with any asset and automatically adapts to the visible date range on your chart. Ideal for visualizing macro trends, historical context, and how markets may have reacted under different political administrations.
Pine Script® indicator
by WordsBySimon
TASC 2025.04 The Ultimate Oscillator█ OVERVIEW This script implements an alternative, refined version of the Ultimate Oscillator (UO) designed to reduce lag and enhance responsiveness in momentum indicators, as introduced by John F. Ehlers in his article "Less Lag In Momentum Indicators, The Ultimate Oscillator" from the April 2025 edition of TASC's Traders' Tips . █ CONCEPTS In his article, Ehlers states that indicators are essentially filters that remove unwanted noise (i.e., unnecessary information) from market data. Simply put, they process a series of data to place focus on specific information, providing a different perspective on price dynamics. Various filter types attenuate different periodic signals within the data. For instance, a lowpass filter allows only low-frequency signals, a highpass filter allows only high-frequency signals, and a bandpass filter allows signals within a specific frequency range . Ehlers explains that the key to removing indicator lag is to combine filters of different types in such a way that the result preserves necessary, useful signals while minimizing delay (lag). His proposed UltimateOscillator aims to maintain responsiveness to a specific frequency range by measuring the difference between two highpass filters' outputs. The oscillator uses the following formula: UO = (HP1 - HP2) / RMS Where: HP1 is the first highpass filter. HP2 is another highpass filter that allows only shorter wavelengths than the critical period of HP1. RMS is the root mean square of the highpass filter difference, used as a scaling factor to standardize the output. The resulting oscillator is similar to a bandpass filter , because it emphasizes wavelengths between the critical periods of the two highpass filters. Ehlers' UO responds quickly to value changes in a series, providing a responsive view of momentum with little to no lag. █ USAGE Ehlers' UltimateOscillator sets the critical periods of its highpass filters using two parameters: BandEdge and Bandwidth : The BandEdge sets the critical period of the second highpass filter, which determines the shortest wavelengths in the response. The Bandwidth is a multiple of the BandEdge used for the critical period of the first highpass filter, which determines the longest wavelengths in the response. Ehlers suggests that a Bandwidth value of 2 works well for most applications. However, traders can use any value above or equal to 1.4. Users can customize these parameters with the "Bandwidth" and "BandEdge" inputs in the "Settings/Inputs" tab. The script plots the UO calculated for the specified "Source" series in a separate pane, with a color based on the chart's foreground color. Positive UO values indicate upward momentum or trends, and negative UO values indicate the opposite. Additionally, this indicator provides the option to display a "cloud" from 10 additional UO series with different settings for an aggregate view of momentum. The "Cloud" input offers four display choices: "Bandwidth", "BandEdge", "Bandwidth + BandEdge", or "None". The "Bandwidth" option calculates oscillators with different Bandwidth values based on the main oscillator's setting. Likewise, the "BandEdge" option calculates oscillators with varying BandEdge values. The "Bandwidth + BandEdge" option calculates the extra oscillators with different values for both parameters. When a user selects any of these options, the script plots the maximum and minimum oscillator values and fills their space with a color gradient. The fill color corresponds to the net sum of each UO's sign , indicating whether most of the UOs reflect positive or negative momentum. Green hues mean most oscillators are above zero, signifying stronger upward momentum. Red hues mean most are below zero, indicating stronger downward momentum.
Pine Script® indicator
by PineCodersTASC
5
Mogwai Method with RSI and EMA - BTCUSD 15mThis is a custom TradingView indicator designed for trading Bitcoin (BTCUSD) on a 15-minute timeframe. It’s based on the Mogwai Method—a mean-reversion strategy—enhanced with the Relative Strength Index (RSI) for momentum confirmation. The indicator generates buy and sell signals, visualized as green and red triangle arrows on the chart, to help identify potential entry and exit points in the volatile cryptocurrency market. Components Bollinger Bands (BB): Purpose: Identifies overextended price movements, signaling potential reversions to the mean. Parameters: Length: 20 periods (standard for mean-reversion). Multiplier: 2.2 (slightly wider than the default 2.0 to suit BTCUSD’s volatility). Role: Buy signal when price drops below the lower band (oversold). Sell signal when price rises above the upper band (overbought). Relative Strength Index (RSI): Purpose: Confirms momentum to filter out false signals from Bollinger Bands. Parameters: Length: 14 periods (classic setting, effective for crypto). Overbought Level: 70 (price may be overextended upward). Oversold Level: 30 (price may be overextended downward). Role: Buy signal requires RSI < 30 (oversold). Sell signal requires RSI > 70 (overbought). Exponential Moving Averages (EMAs) (Plotted but not currently in signal logic): Purpose: Provides trend context (included in the script for visualization, optional for signal filtering). Parameters: Fast EMA: 9 periods (short-term trend). Slow EMA: 50 periods (longer-term trend). Role: Can be re-added to filter signals (e.g., buy only when Fast EMA > Slow EMA). Signals (Triangles): Buy Signal: Green upward triangle below the bar when price is below the lower Bollinger Band and RSI is below 30. Sell Signal: Red downward triangle above the bar when price is above the upper Bollinger Band and RSI is above 70. How It Works The indicator combines Bollinger Bands and RSI to spot mean-reversion opportunities: Buy Condition: Price breaks below the lower Bollinger Band (indicating oversold conditions), and RSI confirms this with a reading below 30. Sell Condition: Price breaks above the upper Bollinger Band (indicating overbought conditions), and RSI confirms this with a reading above 70. The strategy assumes that extreme price movements in BTCUSD will often revert to the mean, especially in choppy or ranging markets. Visual Elements Green Upward Triangles: Appear below the candlestick to indicate a buy signal. Red Downward Triangles: Appear above the candlestick to indicate a sell signal. Bollinger Bands: Gray lines (upper, middle, lower) plotted for reference. EMAs: Blue (Fast) and Orange (Slow) lines for trend visualization. How to Use the Indicator Setup Open TradingView: Log into TradingView and select a BTCUSD chart from a supported exchange (e.g., Binance, Coinbase, Bitfinex). Set Timeframe: Switch the chart to a 15-minute timeframe (15m). Add the Indicator: Open the Pine Editor (bottom panel in TradingView). Copy and paste the script provided. Click “Add to Chart” to apply it. Verify Display: You should see Bollinger Bands (gray), Fast EMA (blue), Slow EMA (orange), and buy/sell triangles when conditions are met. Trading Guidelines Buy Signal (Green Triangle Below Bar): What It Means: Price is oversold, potentially ready to bounce back toward the Bollinger Band middle line. Action: Enter a long position (buy BTCUSD). Set a take-profit near the middle Bollinger Band (bb_middle) or a resistance level. Place a stop-loss 1-2% below the entry (or based on ATR, e.g., ta.atr(14) * 2). Best Context: Works well in ranging markets; avoid during strong downtrends. Sell Signal (Red Triangle Above Bar): What It Means: Price is overbought, potentially ready to drop back toward the middle line. Action: Enter a short position (sell BTCUSD) or exit a long position. Set a take-profit near the middle Bollinger Band or a support level. Place a stop-loss 1-2% above the entry. Best Context: Effective in ranging markets; avoid during strong uptrends. Trend Filter (Optional): To reduce false signals in trending markets, you can modify the script: Add and ema_fast > ema_slow to the buy condition (only buy in uptrends). Add and ema_fast < ema_slow to the sell condition (only sell in downtrends). Check the Fast EMA (blue) vs. Slow EMA (orange) alignment visually. Tips for BTCUSD on 15-Minute Charts Volatility: BTCUSD can be erratic. If signals are too frequent, increase bb_mult (e.g., to 2.5) or adjust RSI levels (e.g., 75/25). Confirmation: Use volume spikes or candlestick patterns (e.g., doji, engulfing) to confirm signals. Time of Day: Mean-reversion works best during low-volume periods (e.g., Asian session in crypto). Backtesting: Use TradingView’s Strategy Tester (convert to a strategy by adding entry/exit logic) to evaluate performance with historical BTCUSD data up to March 13, 2025. Risk Management Position Size: Risk no more than 1-2% of your account per trade. Stop Losses: Always use stops to protect against BTCUSD’s sudden moves. Avoid Overtrading: Wait for clear signals; don’t force trades in choppy or unclear conditions. Example Scenario Chart: BTCUSD, 15-minute timeframe. Buy Signal: Price drops to $58,000, below the lower Bollinger Band, RSI at 28. A green triangle appears. Action: Buy at $58,000, target $59,000 (middle BB), stop at $57,500. Sell Signal: Price rises to $60,500, above the upper Bollinger Band, RSI at 72. A red triangle appears. Action: Sell at $60,500, target $59,500 (middle BB), stop at $61,000. This indicator is tailored for mean-reversion trading on BTCUSD. Let me know if you’d like to tweak it further (e.g., add filters, alerts, or alternative indicators)!
Pine Script® indicator
by Crypto_wheels
1
Full Moon and New Moon IndicatorThe Full Moon & New Moon Indicator is a custom Pine Script indicator which marks Full Moon (Pournami) and New Moon (Amavasya) events on the price chart. This indicator helps traders who incorporate lunar cycles into their market analysis, as certain traders believe these cycles influence market sentiment and price action. The current script is added for the year 2024 and 2025 and the dates are considered as per the Telugu calendar. Features ✅ Identifies and labels Full Moon & New Moon days on the chart for the year 2024 and 2025 How it Works! On a Full Moon day, it places a yellow label ("Pournami") above the corresponding candle. On a New Moon day, it places a blue label ("Amavasya") above the corresponding candle. Example Usage When a Full Moon label appears, check for potential trend reversals or high volatility. When a New Moon label appears, watch for market consolidation or a shift in sentiment. Combine with candlestick patterns, support/resistance, or momentum indicators for a stronger trading setup. 🚀 Add this indicator to your TradingView chart and explore the market’s reaction to lunar cycles! 🌕
Pine Script® indicator
by Karthikeyan_G
TASC 2025.03 A New Solution, Removing Moving Average Lag█ OVERVIEW This script implements a novel technique for removing lag from a moving average, as introduced by John Ehlers in the "A New Solution, Removing Moving Average Lag" article featured in the March 2025 edition of TASC's Traders' Tips . █ CONCEPTS In his article, Ehlers explains that the average price in a time series represents a statistical estimate for a block of price values, where the estimate is positioned at the block's center on the time axis. In the case of a simple moving average (SMA), the calculation moves the analyzed block along the time axis and computes an average after each new sample. Because the average's position is at the center of each block, the SMA inherently lags behind price changes by half the data length. As a solution to removing moving average lag, Ehlers proposes a new projected moving average (PMA) . The PMA smooths price data while maintaining responsiveness by calculating a projection of the average using the data's linear regression slope. The slope of linear regression on a block of financial time series data can be expressed as the covariance between prices and sample points divided by the variance of the sample points. Ehlers derives the PMA by adding this slope across half the data length to the SMA, creating a first-order prediction that substantially reduces lag: PMA = SMA + Slope * Length / 2 In addition, the article includes methods for calculating predictions of the PMA and the slope based on second-order and fourth-order differences. The formulas for these predictions are as follows: PredictPMA = PMA + 0.5 * (Slope - Slope ) * Length PredictSlope = 1.5 * Slope - 0.5 * Slope Ehlers suggests that crossings between the predictions and the original values can help traders identify timely buy and sell signals. █ USAGE This indicator displays the SMA, PMA, and PMA prediction for a specified series in the main chart pane, and it shows the linear regression slope and prediction in a separate pane. Analyzing the difference between the PMA and SMA can help to identify trends. The differences between PMA or slope and its corresponding prediction can indicate turning points and potential trade opportunities. The SMA plot uses the chart's foreground color, and the PMA and slope plots are blue by default. The plots of the predictions have a green or red hue to signify direction. Additionally, the indicator fills the space between the SMA and PMA with a green or red color gradient based on their differences: Users can customize the source series, data length, and plot colors via the inputs in the "Settings/Inputs" tab. █ NOTES FOR Pine Script® CODERS The article's code implementation uses a loop to calculate all necessary sums for the slope and SMA calculations. Ported into Pine, the implementation is as follows: pma(float src, int length) => float PMA = 0., float SMA = 0., float Slope = 0. float Sx = 0.0 , float Sy = 0.0 float Sxx = 0.0 , float Syy = 0.0 , float Sxy = 0.0 for count = 1 to length float src1 = src Sx += count Sy += src Sxx += count * count Syy += src1 * src1 Sxy += count * src1 Slope := -(length * Sxy - Sx * Sy) / (length * Sxx - Sx * Sx) SMA := Sy / length PMA := SMA + Slope * length / 2 However, loops in Pine can be computationally expensive, and the above loop's runtime scales directly with the specified length. Fortunately, Pine's built-in functions often eliminate the need for loops. This indicator implements the following function, which simplifies the process by using the ta.linreg() and ta.sma() functions to calculate equivalent slope and SMA values efficiently: pma(float src, int length) => float Slope = ta.linreg(src, length, 0) - ta.linreg(src, length, 1) float SMA = ta.sma(src, length) float PMA = SMA + Slope * length * 0.5 To learn more about loop elimination in Pine, refer to this section of the User Manual's Profiling and optimization page.
Pine Script® indicator
by PineCodersTASC
3
TASC 2025.02 Autocorrelation Indicator█ OVERVIEW This script implements the Autocorrelation Indicator introduced by John Ehlers in the "Drunkard's Walk: Theory And Measurement By Autocorrelation" article from the February 2025 edition of TASC's Traders' Tips . The indicator calculates the autocorrelation of a price series across several lags to construct a periodogram , which traders can use to identify market cycles, trends, and potential reversal patterns. █ CONCEPTS Drunkard's walk A drunkard's walk , formally known as a random walk , is a type of stochastic process that models the evolution of a system or variable through successive random steps. In his article, John Ehlers relates this model to market data. He discusses two first- and second-order partial differential equations, modified for discrete (non-continuous) data, that can represent solutions to the discrete random walk problem: the diffusion equation and the wave equation. According to Ehlers, market data takes on a mixture of two "modes" described by these equations. He theorizes that when "diffusion mode" is dominant, trading success is almost a matter of luck, and when "wave mode" is dominant, indicators may have improved performance. Pink spectrum John Ehlers explains that many recent academic studies affirm that market data has a pink spectrum , meaning the power spectral density of the data is proportional to the wavelengths it contains, like pink noise . A random walk with a pink spectrum suggests that the states of the random variable are correlated and not independent. In other words, the random variable exhibits long-range dependence with respect to previous states. Autocorrelation function (ACF) Autocorrelation measures the correlation of a time series with a delayed copy, or lag , of itself. The autocorrelation function (ACF) is a method that evaluates autocorrelation across a range of lags , which can help to identify patterns, trends, and cycles in stochastic market data. Analysts often use ACF to detect and characterize long-range dependence in a time series. The Autocorrelation Indicator evaluates the ACF of market prices over a fixed range of lags, expressing the results as a color-coded heatmap representing a dynamic periodogram. Ehlers suggests the information from the periodogram can help traders identify different market behaviors, including: Cycles : Distinguishable as repeated patterns in the periodogram. Reversals : Indicated by sharp vertical changes in the periodogram when the indicator uses a short data length . Trends : Indicated by increasing correlation across lags, starting with the shortest, over time. █ USAGE This script calculates the Autocorrelation Indicator on an input "Source" series, smoothed by Ehlers' UltimateSmoother filter, and plots several color-coded lines to represent the periodogram's information. Each line corresponds to an analyzed lag, with the shortest lag's line at the bottom of the pane. Green hues in the line indicate a positive correlation for the lag, red hues indicate a negative correlation (anticorrelation), and orange or yellow hues mean the correlation is near zero. Because Pine has a limit on the number of plots for a single indicator, this script divides the periodogram display into three distinct ranges that cover different lags. To see the full periodogram, add three instances of this script to the chart and set the "Lag range" input for each to a different value, as demonstrated in the chart above. With a modest autocorrelation length, such as 20 on a "1D" chart, traders can identify seasonal patterns in the price series, which can help to pinpoint cycles and moderate trends. For instance, on the daily ES1! chart above, the indicator shows repetitive, similar patterns through fall 2023 and winter 2023-2024. The green "triangular" shape rising from the zero lag baseline over different time ranges corresponds to seasonal trends in the data. To identify turning points in the price series, Ehlers recommends using a short autocorrelation length, such as 2. With this length, users can observe sharp, sudden shifts along the vertical axis, which suggest potential turning points from upward to downward or vice versa.
Pine Script® indicator
by PineCodersTASC
8
Highs & Lows RTH/OVN/IBs/D/W/M/YOverview Plots the highs and lows of RTH, OVN/ETH, IBs of those sessions, previous Day, Week, Month, and Year. Features Allows the user to enable/disable plotting the high/low of each period. Lines' length, offset, and colors can be customized Labels' position, size, color, and style can be customized Support Questions, feedbacks, and requests are welcomed. Please feel free to use Comments or direct private message via TradingView. Disclaimer This stock chart indicator provided is for informational purposes only and should not be considered as financial or investment advice. The data and information presented in this indicator are obtained from sources believed to be reliable, but we do not warrant its completeness or accuracy. Users should be aware that: Any investment decisions made based on this indicator are at your own risk. The creators and providers of this indicator disclaim all liability for any losses, damages, or other consequences resulting from its use. By using this stock chart indicator, you acknowledge and accept the inherent risks associated with trading and investing in financial markets. Release Date: 2025-01-17 Release Version: v1 r1 Release Notes Date: 2025-01-17
Pine Script® indicator
by kennethkhan
2
SW monthly Gann Days**Script Description:** The script you are looking at is based on the work of W.D. Gann, a famous trader and market analyst in the early 20th century, known for his use of geometry, astrology, and numerology in market analysis. Gann believed that certain days in the market had significant importance, and he observed that markets often exhibited significant price moves around specific dates. These dates were typically associated with cyclical patterns in price movements, and Gann referred to these as "Gann Days." In this script, we have focused on highlighting certain days of the month that Gann believed to have an influence on market behavior. The specific days in question are the **6th to 7th**, **9th to 10th**, **14th to 15th**, **19th to 20th**, **23rd to 24th**, and **29th to 31st** of each month. These ranges are based on Gann’s theory that there are recurring time cycles in the market that cause turning points or critical price movements to occur around certain days of the month. ### **Why Gann Used These Days:** 1. **Mathematical and Astrological Cycles:** Gann believed that markets were influenced by natural cycles, and that certain dates (or combinations of dates) played a critical role in the price movements. These specific days are part of his broader theory of "time cycles" where the market would often change direction, reverse, or exhibit significant volatility on particular days. Gann's research was based on both mathematical principles and astrological observations, leading him to assign importance to these days. 2. **Gann's Universal Timing Theory:** According to Gann, financial markets operate in a universe governed by geometric and astrological principles. These cycles repeat themselves over time, and specific days in a given month correspond to key turning points within these repeating cycles. Gann found that the 6th to 7th, 9th to 10th, 14th to 15th, 19th to 20th, 23rd to 24th, and 29th to 31st often marked significant changes in the market, making them particularly important for traders to watch. 3. **Market Psychology and Sentiment:** These specific days likely correspond to key moments where market participants tend to react in predictable ways, influenced by past market behavior on similar dates. For example, news events or scheduled economic reports might fall within these time windows, causing the market to respond in a particular way. Gann's method involves using these cyclical patterns to predict turning points in market prices, enabling traders to anticipate when the market might make a reversal or face a significant shift in direction. 4. **Turning Points:** Gann believed that markets often reversed or encountered critical points around specific dates. This is why he considered certain days more important than others. By identifying and focusing on these days, traders can better anticipate the market’s movement and make more informed trading decisions. 5. **Numerology:** Gann also utilized numerology in his trading system, believing that numbers, and particularly certain key numbers, had significance in predicting market movements. The days selected in this script may correspond to numerological patterns that Gann identified in his analysis of the markets, such as recurring numbers in his astrological and geometric systems. ### **Purpose of the Script:** This script highlights these "Gann Days" within a trading chart for 2024 and 2025. The color-coding or background highlighting is intended to draw attention to these dates, so traders can observe the potential for significant market movements during these times. By identifying these specific dates, traders following Gann's theories may gain insights into possible turning points, corrections, or key price movements based on the market's historical behavior around these days. Overall, Gann’s use of specific days was based on his deep belief in the cyclical nature of the market and his attempt to tie those cycles to the natural laws of time, geometry, and astrology. By focusing on these dates, Gann aimed to give traders an edge in predicting significant market events and price shifts.
Pine Script® indicator
by scott_wyatt14
TASC 2025.01 Linear Predictive Filters█ OVERVIEW This script implements a suite of tools for identifying and utilizing dominant cycles in time series data, as introduced by John Ehlers in the "Linear Predictive Filters And Instantaneous Frequency" article featured in the January 2025 edition of TASC's Traders' Tips . Dominant cycle information can help traders adapt their indicators and strategies to changing market conditions. █ CONCEPTS Conventional technical indicators and strategies often rely on static, unchanging parameters, which may fail to account for the dynamic nature of market data. In his article, John Ehlers applies digital signal processing principles to address this issue, introducing linear predictive filters to identify cyclic information for adapting indicators and strategies to evolving market conditions. This approach treats market data as a complex series in the time domain. Analyzing the series in the frequency domain reveals information about its cyclic components. To reduce the impact of frequencies outside a range of interest and focus on a specific range of cycles, Ehlers applies second-order highpass and lowpass filters to the price data, which attenuate or remove wavelengths outside the desired range. This band-limited analysis isolates specific parts of the frequency spectrum for various trading styles, e.g., longer wavelengths for position trading or shorter wavelengths for swing trading. After filtering the series to produce band-limited data, Ehlers applies a linear predictive filter to predict future values a few bars ahead. The filter, calculated based on the techniques proposed by Lloyd Griffiths, adaptively minimizes the error between the latest data point and prediction, successively adjusting its coefficients to align with the band-limited series. The filter's coefficients can then be applied to generate an adaptive estimate of the band-limited data's structure in the frequency domain and identify the dominant cycle. █ USAGE This script implements the following tools presented in the article: Griffiths Predictor This tool calculates a linear predictive filter to forecast future data points in band-limited price data. The crosses between the prediction and signal lines can provide potential trade signals. Griffiths Spectrum This tool calculates a partial frequency spectrum of the band-limited price data derived from the linear predictive filter's coefficients, displaying a color-coded representation of the frequency information in the pane. This mode's display represents the data as a periodogram . The bottom of each plotted bar corresponds to a specific analyzed period (inverse of frequency), and the bar's color represents the presence of that periodic cycle in the time series relative to the one with the highest presence (i.e., the dominant cycle). Warmer, brighter colors indicate a higher presence of the cycle in the series, whereas darker colors indicate a lower presence. Griffiths Dominant Cycle This tool compares the cyclic components within the partial spectrum and identifies the frequency with the highest power, i.e., the dominant cycle . Traders can use this dominant cycle information to tune other indicators and strategies, which may help promote better alignment with dynamic market conditions. Notes on parameters Bandpass boundaries: In the article, Ehlers recommends an upper bound of 125 bars or higher to capture longer-term cycles for position trading. He recommends an upper bound of 40 bars and a lower bound of 18 bars for swing trading. If traders use smaller lower bounds, Ehlers advises a minimum of eight bars to minimize the potential effects of aliasing. Data length: The Griffiths predictor can use a relatively small data length, as autocorrelation diminishes rapidly with lag. However, for optimal spectrum and dominant cycle calculations, the length must match or exceed the upper bound of the bandpass filter. Ehlers recommends avoiding excessively long lengths to maintain responsiveness to shorter-term cycles.
Pine Script® indicator
by PineCodersTASC
12
4h 相对超跌筛选器 · Webhook v2.0## 指标用途 用于你的「框架第2步」：在**美股 RTH**里，按**4h 收盘**（06:30–10:30 PT 为首根）筛出相对大盘/行业**显著超跌**且结构健康的候选标的，并可**通过 Webhook 自动推送**`symbol + ts`给下游 AI 执行新闻甄别（第3步）与进出场评估（第4步）。 ## 工作原理（核心逻辑） * **结构健康**：最近 80 根 4h 中，收盘 > 4h_SMA50 的占比 ≥ 阈值（默认 55%）。 * **跌深条件**：4h 跌幅 ≤ −4%，且近两根累计（≈8h）≤ −6%。 * **相对劣化**：相对大盘（SPY/QQQ）与相对行业（XLK/XLF/… 或 KWEB/CQQQ）各 ≤ −3%。 * **流动性与价格**：ADV20_USD ≥ 2000 万；价格 ≥ 3 美元。 * **只在 4h 收盘刻评估与触发**，历史点位全部保留，便于回放核验。 * **冷却**：同一标的信号间隔 ≥ N 天（默认 10）。 ## 主要输入参数 * **bench / sector**：大盘与行业基准（例：SPY/QQQ，XLK/XLF/XLY；中概用 KWEB/CQQQ）。 * **advMinUSD / priceMin**：20 日美元成交额下限、最小价格。 * **pctAboveTh**：结构健康阈值（%）。 * **drop4hTh / drop8hTh**：4h/8h 跌幅阈值（%）。 * **relMktTh / relSecTh**：相对大盘/行业阈值（%）。 * **coolDays**：冷却天数。 * **fromDate**：仅显示此日期后的历史信号（图表拥挤时可用）。 * **showTable / tableRows**：是否显示右上角“最近信号表”及行数。 ## 图表信号 * **S2 绿点**：当根 4h 收盘满足全部筛选条件。 * **右上角表格**：滚动列出最近 N 条命中（`SYMBOL @ yyyy-MM-dd HH:mm`，按图表本地时区）。 ## Webhook 联动（生产用） 1. 添加指标 → 🔔 新建警报（Alert）： * **Condition**：`Any alert() function call` * **Options**：`Once per bar close` * **Webhook URL**：填你的接收地址（可带 `?token=...`） * **Message**：留空（脚本内部 `alert(payload)` 会发送 JSON）。 2. 典型 JSON 载荷（举例）： ```json { "event": "step2_signal", "symbol": "LULU", "symbol_id": "NASDAQ:LULU", "venue": "NASDAQ", "bench": "SPY", "sector": "XLY", "ts_bar_close_ms": 1754524200000, "ts_bar_close_local": "2025-06-06 10:30", "price_close": 318.42, "ret_4h_pct": -5.30, "ret_8h_pct": -7.45, "rel_mkt_pct": -4.90, "rel_sec_pct": -3.80 } ``` > 建议以 `symbol + ts_bar_close_ms` 做去重键；接收端先快速 `200 OK`，后续异步处理并交给第3步 AI。 ## 使用建议 * **时间框架**：任意周期可用，指标内部统一拉取 240 分钟数据并仅在 4h 收盘刻触发。 * **行业映射**：尽量选与个股业务最贴近的 ETF；中国 ADR 可用 `PGJ/KWEB/CQQQ` 叠加细分行业对照。 * **回放验证**：Bar Replay **不发送真实 Webhook**；仅用于查看历史命中与表格。测试接收端请用 Alert 面板的 **Test**。 ## 适配说明 * Pine Script **v5**。 * 不含成分筛查逻辑（请在你的 500–600 只候选池内使用）。 * 数字常量不使用下划线分隔；如需大数可用 `20000000` 或 `2e7`。 ## 常见问题 * ⛔️ 报错 `tostring(...)`：Pine 无时间格式化重载，脚本已内置 `timeToStr()`。 * ⛔️ `syminfo.exchange` 不存在：已改用 `syminfo.prefix`（交易所前缀）。 * ⛔️ 多行字符串拼接报 `line continuation`：本脚本已用括号包裹或 `str.format` 规避。 ## 免责声明 该指标仅供筛选与研究使用，不构成投资建议。请结合你的第3步新闻/基本面甄别与第4步执行规则共同决策。
Pine Script® indicator
by lvyaofeng19920413
Hellenic EMA Matrix - Α Ω PremiumHellenic EMA Matrix - Alpha Omega Premium Complete User Guide Table of Contents Introduction Indicator Philosophy Mathematical Constants EMA Types Settings Trading Signals Visualization Usage Strategies FAQ Introduction Hellenic EMA Matrix is a premium indicator based on mathematical constants of nature: Phi (Phi - Golden Ratio), Pi (Pi), e (Euler's number). The indicator uses these universal constants to create dynamic EMAs that adapt to the natural rhythms of the market. Key Features: 6 EMA types based on mathematical constants Premium visualization with Neon Glow and Gradient Clouds Automatic Fast/Mid/Slow EMA sorting STRONG signals for powerful trends Pulsing Ribbon Bar for instant trend assessment Works on all timeframes (M1 - MN) Indicator Philosophy Why Mathematical Constants? Traditional EMAs use arbitrary periods (9, 21, 50, 200). Hellenic Matrix goes further, using universal mathematical constants found in nature: Phi (1.618) - Golden Ratio: galaxy spirals, seashells, human body proportions Pi (3.14159) - Pi: circles, waves, cycles e (2.71828) - Natural logarithm base: exponential growth, radioactive decay Markets are also a natural system composed of millions of participants. Using mathematical constants allows tuning into the natural rhythms of market cycles. Mathematical Constants Phi (Phi) - Golden Ratio Phi = 1.618033988749895 Properties: Phi² = Phi + 1 = 2.618 Phi³ = 4.236 Phi⁴ = 6.854 Application: Ideal for trending movements and Fibonacci corrections Pi (Pi) - Pi Number Pi = 3.141592653589793 Properties: 2Pi = 6.283 (full circle) 3Pi = 9.425 4Pi = 12.566 Application: Excellent for cyclical markets and wave structures e (Euler) - Euler's Number e = 2.718281828459045 Properties: e² = 7.389 e³ = 20.085 e⁴ = 54.598 Application: Suitable for exponential movements and volatile markets EMA Types 1. Phi (Phi) - Golden Ratio EMA Description: EMA based on the golden ratio Period Formula: Period = Phi^n × Base Multiplier Parameters: Phi Power Level (1-8): Power of Phi Phi¹ = 1.618 → ~16 period (with Base=10) Phi² = 2.618 → ~26 period Phi³ = 4.236 → ~42 period (recommended) Phi⁴ = 6.854 → ~69 period Recommendations: Phi² or Phi³ for day trading Phi⁴ or Phi⁵ for swing trading Works excellently as Fast EMA 2. Pi (Pi) - Circular EMA Description: EMA based on Pi for cyclical movements Period Formula: Period = Pi × Multiple × Base Multiplier Parameters: Pi Multiple (1-10): Pi multiplier 1Pi = 3.14 → ~31 period (with Base=10) 2Pi = 6.28 → ~63 period (recommended) 3Pi = 9.42 → ~94 period Recommendations: 2Pi ideal as Mid or Slow EMA Excellently identifies cycles and waves Use on volatile markets (crypto, forex) 3. e (Euler) - Natural EMA Description: EMA based on natural logarithm Period Formula: Period = e^n × Base Multiplier Parameters: e Power Level (1-6): Power of e e¹ = 2.718 → ~27 period (with Base=10) e² = 7.389 → ~74 period (recommended) e³ = 20.085 → ~201 period Recommendations: e² works excellently as Slow EMA Ideal for stocks and indices Filters noise well on lower timeframes 4. Delta (Delta) - Adaptive EMA Description: Adaptive EMA that changes period based on volatility Period Formula: Period = Base Period × (1 + (Volatility - 1) × Factor) Parameters: Delta Base Period (5-200): Base period (default 20) Delta Volatility Sensitivity (0.5-5.0): Volatility sensitivity (default 2.0) How it works: During low volatility → period decreases → EMA reacts faster During high volatility → period increases → EMA smooths noise Recommendations: Works excellently on news and sharp movements Use as Fast EMA for quick adaptation Sensitivity 2.0-3.0 for crypto, 1.0-2.0 for stocks 5. Sigma (Sigma) - Composite EMA Description: Composite EMA combining multiple active EMAs Composition Methods: Weighted Average (default): Sigma = (Phi + Pi + e + Delta) / 4 Simple average of all active EMAs Geometric Mean: Sigma = fourth_root(Phi × Pi × e × Delta) Geometric mean (more conservative) Harmonic Mean: Sigma = 4 / (1/Phi + 1/Pi + 1/e + 1/Delta) Harmonic mean (more weight to smaller values) Recommendations: Enable for additional confirmation Use as Mid EMA Weighted Average - most universal method 6. Lambda (Lambda) - Wave EMA Description: Wave EMA with sinusoidal period modulation Period Formula: Period = Base Period × (1 + Amplitude × sin(2Pi × bar / Frequency)) Parameters: Lambda Base Period (10-200): Base period Lambda Wave Amplitude (0.1-2.0): Wave amplitude Lambda Wave Frequency (10-200): Wave frequency in bars How it works: Period pulsates sinusoidally Creates wave effect following market cycles Recommendations: Experimental EMA for advanced users Works well on cyclical markets Frequency = 50 for day trading, 100+ for swing Settings Matrix Core Settings Base Multiplier (1-100) Multiplies all EMA periods Base = 1: Very fast EMAs (Phi³ = 4, 2Pi = 6, e² = 7) Base = 10: Standard (Phi³ = 42, 2Pi = 63, e² = 74) Base = 20: Slow EMAs (Phi³ = 85, 2Pi = 126, e² = 148) Recommendations by timeframe: M1-M5: Base = 5-10 M15-H1: Base = 10-15 (recommended) H4-D1: Base = 15-25 W1-MN: Base = 25-50 Matrix Source Data source selection for EMA calculation: close - closing price (standard) open - opening price high - high low - low hl2 - (high + low) / 2 hlc3 - (high + low + close) / 3 ohlc4 - (open + high + low + close) / 4 When to change: hlc3 or ohlc4 for smoother signals high for aggressive longs low for aggressive shorts Manual EMA Selection Critically important setting! Determines which EMAs are used for signal generation. Use Manual Fast/Slow/Mid Selection Enabled (default): You select EMAs manually Disabled: Automatic selection by periods Fast EMA Fast EMA - reacts first to price changes Recommendations: Phi Golden (recommended) - universal choice Delta Adaptive - for volatile markets Must be fastest (smallest period) Slow EMA Slow EMA - determines main trend Recommendations: Pi Circular (recommended) - excellent trend filter e Natural - for smoother trend Must be slowest (largest period) Mid EMA Mid EMA - additional signal filter Recommendations: e Natural (recommended) - excellent middle level Pi Circular - alternative None - for more frequent signals (only 2 EMAs) IMPORTANT: The indicator automatically sorts selected EMAs by their actual periods: Fast = EMA with smallest period Mid = EMA with middle period Slow = EMA with largest period Therefore, you can select any combination - the indicator will arrange them correctly! Premium Visualization Neon Glow Enable Neon Glow for EMAs - adds glowing effect around EMA lines Glow Strength: Light - subtle glow Medium (recommended) - optimal balance Strong - bright glow (may be too bright) Effect: 2 glow layers around each EMA for 3D effect Gradient Clouds Enable Gradient Clouds - fills space between EMAs with gradient Parameters: Cloud Transparency (85-98): Cloud transparency 95-97 (recommended) Higher = more transparent Dynamic Cloud Intensity - automatically changes transparency based on EMA distance Cloud Colors: Phi-Pi Cloud: Blue - when Pi above Phi (bullish) Gold - when Phi above Pi (bearish) Pi-e Cloud: Green - when e above Pi (bullish) Blue - when Pi above e (bearish) 2 layers for volumetric effect Pulsing Ribbon Bar Enable Pulsing Indicator Bar - pulsing strip at bottom/top of chart Parameters: Ribbon Position: Top / Bottom (recommended) Pulse Speed: Slow / Medium (recommended) / Fast Symbols and colors: Green filled square - STRONG BULLISH Pink filled square - STRONG BEARISH Blue hollow square - Bullish (regular) Red hollow square - Bearish (regular) Purple rectangle - Neutral Effect: Pulsation with sinusoid for living market feel Signal Bar Highlights Enable Signal Bar Highlights - highlights bars with signals Parameters: Highlight Transparency (88-96): Highlight transparency Highlight Style: Light Fill (recommended) - bar background fill Thin Line - bar outline only Highlights: Golden Cross - green Death Cross - pink STRONG BUY - green STRONG SELL - pink Show Greek Labels Shows Greek alphabet letters on last bar: Phi - Phi EMA (gold) Pi - Pi EMA (blue) e - Euler EMA (green) Delta - Delta EMA (purple) Sigma - Sigma EMA (pink) When to use: For education or presentations Show Old Background Old background style (not recommended): Green background - STRONG BULLISH Pink background - STRONG BEARISH Blue background - Bullish Red background - Bearish Not recommended - use new Gradient Clouds and Pulsing Bar Info Table Show Info Table - table with indicator information Parameters: Position: Top Left / Top Right (recommended) / Bottom Left / Bottom Right Size: Tiny / Small (recommended) / Normal / Large Table contents: EMA list - periods and current values of all active EMAs Effects - active visual effects TREND - current trend state: STRONG UP - strong bullish STRONG DOWN - strong bearish Bullish - regular bullish Bearish - regular bearish Neutral - neutral Momentum % - percentage deviation of price from Fast EMA Setup - current Fast/Slow/Mid configuration Trading Signals Show Golden/Death Cross Golden Cross - Fast EMA crosses Slow EMA from below (bullish signal) Death Cross - Fast EMA crosses Slow EMA from above (bearish signal) Symbols: Yellow dot "GC" below - Golden Cross Dark red dot "DC" above - Death Cross Show STRONG Signals STRONG BUY and STRONG SELL - the most powerful indicator signals Conditions for STRONG BULLISH: EMA Alignment: Fast > Mid > Slow (all EMAs aligned) Trend: Fast > Slow (clear uptrend) Distance: EMAs separated by minimum 0.15% Price Position: Price above Fast EMA Fast Slope: Fast EMA rising Slow Slope: Slow EMA rising Mid Trending: Mid EMA also rising (if enabled) Conditions for STRONG BEARISH: Same but in reverse Visual display: Green label "STRONG BUY" below bar Pink label "STRONG SELL" above bar Difference from Golden/Death Cross: Golden/Death Cross = crossing moment (1 bar) STRONG signal = sustained trend (lasts several bars) IMPORTANT: After fixes, STRONG signals now: Work on all timeframes (M1 to MN) Don't break on small retracements Work with any Fast/Mid/Slow combination Automatically adapt thanks to EMA sorting Show Stop Loss/Take Profit Automatic SL/TP level calculation on STRONG signal Parameters: Stop Loss (ATR) (0.5-5.0): ATR multiplier for stop loss 1.5 (recommended) - standard 1.0 - tight stop 2.0-3.0 - wide stop Take Profit R:R (1.0-5.0): Risk/reward ratio 2.0 (recommended) - standard (risk 1.5 ATR, profit 3.0 ATR) 1.5 - conservative 3.0-5.0 - aggressive Formulas: LONG: Stop Loss = Entry - (ATR × Stop Loss ATR) Take Profit = Entry + (ATR × Stop Loss ATR × Take Profit R:R) SHORT: Stop Loss = Entry + (ATR × Stop Loss ATR) Take Profit = Entry - (ATR × Stop Loss ATR × Take Profit R:R) Visualization: Red X - Stop Loss Green X - Take Profit Levels remain active while STRONG signal persists Trading Signals Signal Types 1. Golden Cross Description: Fast EMA crosses Slow EMA from below Signal: Beginning of bullish trend How to trade: ENTRY: On bar close with Golden Cross STOP: Below local low or below Slow EMA TARGET: Next resistance level or 2:1 R:R Strengths: Simple and clear Works well on trending markets Clear entry point Weaknesses: Lags (signal after movement starts) Many false signals in ranging markets May be late on fast moves Optimal timeframes: H1, H4, D1 2. Death Cross Description: Fast EMA crosses Slow EMA from above Signal: Beginning of bearish trend How to trade: ENTRY: On bar close with Death Cross STOP: Above local high or above Slow EMA TARGET: Next support level or 2:1 R:R Application: Mirror of Golden Cross 3. STRONG BUY Description: All EMAs aligned + trend + all EMAs rising Signal: Powerful bullish trend How to trade: ENTRY: On bar close with STRONG BUY or on pullback to Fast EMA STOP: Below Fast EMA or automatic SL (if enabled) TARGET: Automatic TP (if enabled) or by levels TRAILING: Follow Fast EMA Entry strategies: Aggressive: Enter immediately on signal Conservative: Wait for pullback to Fast EMA, then enter on bounce Pyramiding: Add positions on pullbacks to Mid EMA Position management: Hold while STRONG signal active Exit on STRONG SELL or Death Cross appearance Move stop behind Fast EMA Strengths: Most reliable indicator signal Doesn't break on pullbacks Catches large moves Works on all timeframes Weaknesses: Appears less frequently than other signals Requires confirmation (multiple conditions) Optimal timeframes: All (M5 - D1) 4. STRONG SELL Description: All EMAs aligned down + downtrend + all EMAs falling Signal: Powerful bearish trend How to trade: Mirror of STRONG BUY Visual Signals Pulsing Ribbon Bar Quick market assessment at a glance: Symbol Color State Filled square Green STRONG BULLISH Filled square Pink STRONG BEARISH Hollow square Blue Bullish Hollow square Red Bearish Rectangle Purple Neutral Pulsation: Sinusoidal, creates living effect Signal Bar Highlights Bars with signals are highlighted: Green highlight: STRONG BUY or Golden Cross Pink highlight: STRONG SELL or Death Cross Gradient Clouds Colored space between EMAs shows trend strength: Wide clouds - strong trend Narrow clouds - weak trend or consolidation Color change - trend change Info Table Quick reference in corner: TREND: Current state (STRONG UP, Bullish, Neutral, Bearish, STRONG DOWN) Momentum %: Movement strength Effects: Active visual effects Setup: Fast/Slow/Mid configuration Usage Strategies Strategy 1: "Golden Trailing" Idea: Follow STRONG signals using Fast EMA as trailing stop Settings: Fast: Phi Golden (Phi³) Mid: Pi Circular (2Pi) Slow: e Natural (e²) Base Multiplier: 10 Timeframe: H1, H4 Entry rules: Wait for STRONG BUY Enter on bar close or on pullback to Fast EMA Stop below Fast EMA Management: Hold position while STRONG signal active Move stop behind Fast EMA daily Exit on STRONG SELL or Death Cross Take Profit: Partially close at +2R Trail remainder until exit signal For whom: Swing traders, trend followers Pros: Catches large moves Simple rules Emotionally comfortable Cons: Requires patience Possible extended drawdowns on pullbacks Strategy 2: "Scalping Bounces" Idea: Scalp bounces from Fast EMA during STRONG trend Settings: Fast: Delta Adaptive (Base 15, Sensitivity 2.0) Mid: Phi Golden (Phi²) Slow: Pi Circular (2Pi) Base Multiplier: 5 Timeframe: M5, M15 Entry rules: STRONG signal must be active Wait for price pullback to Fast EMA Enter on bounce (candle closes above/below Fast EMA) Stop behind local extreme (15-20 pips) Take Profit: +1.5R or to Mid EMA Or to next level For whom: Active day traders Pros: Many signals Clear entry point Quick profits Cons: Requires constant monitoring Not all bounces work Requires discipline for frequent trading Strategy 3: "Triple Filter" Idea: Enter only when all 3 EMAs and price perfectly aligned Settings: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (3Pi) Base Multiplier: 15 Timeframe: H4, D1 Entry rules (LONG): STRONG BUY active Price above all three EMAs Fast > Mid > Slow (all aligned) All EMAs rising (slope up) Gradient Clouds wide and bright Entry: On bar close meeting all conditions Or on next pullback to Fast EMA Stop: Below Mid EMA or -1.5 ATR Take Profit: First target: +3R Second target: next major level Trailing: Mid EMA For whom: Conservative swing traders, investors Pros: Very reliable signals Minimum false entries Large profit potential Cons: Rare signals (2-5 per month) Requires patience Strategy 4: "Adaptive Scalper" Idea: Use only Delta Adaptive EMA for quick volatility reaction Settings: Fast: Delta Adaptive (Base 10, Sensitivity 3.0) Mid: None Slow: Delta Adaptive (Base 30, Sensitivity 2.0) Base Multiplier: 3 Timeframe: M1, M5 Feature: Two different Delta EMAs with different settings Entry rules: Golden Cross between two Delta EMAs Both Delta EMAs must be rising/falling Enter on next bar Stop: 10-15 pips or below Slow Delta EMA Take Profit: +1R to +2R Or Death Cross For whom: Scalpers on cryptocurrencies and forex Pros: Instant volatility adaptation Many signals on volatile markets Quick results Cons: Much noise on calm markets Requires fast execution High commissions may eat profits Strategy 5: "Cyclical Trader" Idea: Use Pi and Lambda for trading cyclical markets Settings: Fast: Pi Circular (1Pi) Mid: Lambda Wave (Base 30, Amplitude 0.5, Frequency 50) Slow: Pi Circular (3Pi) Base Multiplier: 10 Timeframe: H1, H4 Entry rules: STRONG signal active Lambda Wave EMA synchronized with trend Enter on bounce from Lambda Wave For whom: Traders of cyclical assets (some altcoins, commodities) Pros: Catches cyclical movements Lambda Wave provides additional entry points Cons: More complex to configure Not for all markets Lambda Wave may give false signals Strategy 6: "Multi-Timeframe Confirmation" Idea: Use multiple timeframes for confirmation Scheme: Higher TF (D1): Determine trend direction (STRONG signal) Middle TF (H4): Wait for STRONG signal in same direction Lower TF (M15): Look for entry point (Golden Cross or bounce from Fast EMA) Settings for all TFs: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (2Pi) Base Multiplier: 10 Rules: All 3 TFs must show one trend Entry on lower TF Stop by lower TF Target by higher TF For whom: Serious traders and investors Pros: Maximum reliability Large profit targets Minimum false signals Cons: Rare setups Requires analysis of multiple charts Experience needed Practical Tips DOs Use STRONG signals as primary - they're most reliable Let signals develop - don't exit on first pullback Use trailing stop - follow Fast EMA Combine with levels - S/R, Fibonacci, volumes Test on demo before real Adjust Base Multiplier for your timeframe Enable visual effects - they help see the picture Use Info Table - quick situation assessment Watch Pulsing Bar - instant state indicator Trust auto-sorting of Fast/Mid/Slow DON'Ts Don't trade against STRONG signal - trend is your friend Don't ignore Mid EMA - it adds reliability Don't use too small Base Multiplier on higher TFs Don't enter on Golden Cross in range - check for trend Don't change settings during open position Don't forget risk management - 1-2% per trade Don't trade all signals in row - choose best ones Don't use indicator in isolation - combine with Price Action Don't set too tight stops - let trade breathe Don't over-optimize - simplicity = reliability Optimal Settings by Asset US Stocks (SPY, AAPL, TSLA) Recommendation: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (2Pi) Base: 10-15 Timeframe: H4, D1 Features: Use on daily for swing STRONG signals very reliable Works well on trending stocks Forex (EUR/USD, GBP/USD) Recommendation: Fast: Delta Adaptive (Base 15, Sens 2.0) Mid: Phi Golden (Phi²) Slow: Pi Circular (2Pi) Base: 8-12 Timeframe: M15, H1, H4 Features: Delta Adaptive works excellently on news Many signals on M15-H1 Consider spreads Cryptocurrencies (BTC, ETH, altcoins) Recommendation: Fast: Delta Adaptive (Base 10, Sens 3.0) Mid: Pi Circular (2Pi) Slow: e Natural (e²) Base: 5-10 Timeframe: M5, M15, H1 Features: High volatility - adaptation needed STRONG signals can last days Be careful with scalping on M1-M5 Commodities (Gold, Oil) Recommendation: Fast: Pi Circular (1Pi) Mid: Phi Golden (Phi³) Slow: Pi Circular (3Pi) Base: 12-18 Timeframe: H4, D1 Features: Pi works excellently on cyclical commodities Gold responds especially well to Phi Oil volatile - use wide stops Indices (S&P500, Nasdaq, DAX) Recommendation: Fast: Phi Golden (Phi³) Mid: e Natural (e²) Slow: Pi Circular (2Pi) Base: 15-20 Timeframe: H4, D1, W1 Features: Very trending instruments STRONG signals last weeks Good for position trading Alerts The indicator supports 6 alert types: 1. Golden Cross Message: "Hellenic Matrix: GOLDEN CROSS - Fast EMA crossed above Slow EMA - Bullish trend starting!" When: Fast EMA crosses Slow EMA from below 2. Death Cross Message: "Hellenic Matrix: DEATH CROSS - Fast EMA crossed below Slow EMA - Bearish trend starting!" When: Fast EMA crosses Slow EMA from above 3. STRONG BULLISH Message: "Hellenic Matrix: STRONG BULLISH SIGNAL - All EMAs aligned for powerful uptrend!" When: All conditions for STRONG BUY met (first bar) 4. STRONG BEARISH Message: "Hellenic Matrix: STRONG BEARISH SIGNAL - All EMAs aligned for powerful downtrend!" When: All conditions for STRONG SELL met (first bar) 5. Bullish Ribbon Message: "Hellenic Matrix: BULLISH RIBBON - EMAs aligned for uptrend" When: EMAs aligned bullish + price above Fast EMA (less strict condition) 6. Bearish Ribbon Message: "Hellenic Matrix: BEARISH RIBBON - EMAs aligned for downtrend" When: EMAs aligned bearish + price below Fast EMA (less strict condition) How to Set Up Alerts: Open indicator on chart Click on three dots next to indicator name Select "Create Alert" In "Condition" field select needed alert: Golden Cross Death Cross STRONG BULLISH STRONG BEARISH Bullish Ribbon Bearish Ribbon Configure notification method: Pop-up in browser Email SMS (in Premium accounts) Push notifications in mobile app Webhook (for automation) Select frequency: Once Per Bar Close (recommended) - once on bar close Once Per Bar - during bar formation Only Once - only first time Click "Create" Tip: Create separate alerts for different timeframes and instruments FAQ 1. Why don't STRONG signals appear? Possible reasons: Incorrect Fast/Mid/Slow order Solution: Indicator automatically sorts EMAs by periods, but ensure selected EMAs have different periods Base Multiplier too large Solution: Reduce Base to 5-10 on lower timeframes Market in range Solution: STRONG signals appear only in trends - this is normal Too strict EMA settings Solution: Try classic combination: Phi³ / Pi×2 / e² with Base=10 Mid EMA too close to Fast or Slow Solution: Select Mid EMA with period between Fast and Slow 2. How often should STRONG signals appear? Normal frequency: M1-M5: 5-15 signals per day (very active markets) M15-H1: 2-8 signals per day H4: 3-10 signals per week D1: 2-5 signals per month W1: 2-6 signals per year If too many signals - market very volatile or Base too small If too few signals - market in range or Base too large 4. What are the best settings for beginners? Universal "out of the box" settings: Matrix Core: Base Multiplier: 10 Source: close Phi Golden: Enabled, Power = 3 Pi Circular: Enabled, Multiple = 2 e Natural: Enabled, Power = 2 Delta Adaptive: Enabled, Base = 20, Sensitivity = 2.0 Manual Selection: Fast: Phi Golden Mid: e Natural Slow: Pi Circular Visualization: Gradient Clouds: ON Neon Glow: ON (Medium) Pulsing Bar: ON (Medium) Signal Highlights: ON (Light Fill) Table: ON (Top Right, Small) Signals: Golden/Death Cross: ON STRONG Signals: ON Stop Loss: OFF (while learning) Timeframe for learning: H1 or H4 5. Can I use only one EMA? No, minimum 2 EMAs (Fast and Slow) for signal generation. Mid EMA is optional: With Mid EMA = more reliable but rarer signals Without Mid EMA = more signals but less strict filtering Recommendation: Start with 3 EMAs (Fast/Mid/Slow), then experiment 6. Does the indicator work on cryptocurrencies? Yes, works excellently! Especially good on: Bitcoin (BTC) Ethereum (ETH) Major altcoins (SOL, BNB, XRP) Recommended settings for crypto: Fast: Delta Adaptive (Base 10-15, Sensitivity 2.5-3.0) Mid: Pi Circular (2Pi) Slow: e Natural (e²) Base: 5-10 Timeframe: M15, H1, H4 Crypto market features: High volatility → use Delta Adaptive 24/7 trading → set alerts Sharp movements → wide stops 7. Can I trade only with this indicator? Technically yes, but NOT recommended. Best approach - combine with: Price Action - support/resistance levels, candle patterns Volume - movement strength confirmation Fibonacci - retracement and extension levels RSI/MACD - divergences and overbought/oversold Fundamental analysis - news, company reports Hellenic Matrix: Excellently determines trend and its strength Provides clear entry/exit points Doesn't consider fundamentals Doesn't see major levels 8. Why do Gradient Clouds change color? Color depends on EMA order: Phi-Pi Cloud: Blue - Pi EMA above Phi EMA (bullish alignment) Gold - Phi EMA above Pi EMA (bearish alignment) Pi-e Cloud: Green - e EMA above Pi EMA (bullish alignment) Blue - Pi EMA above e EMA (bearish alignment) Color change = EMA order change = possible trend change 9. What is Momentum % in the table? Momentum % = percentage deviation of price from Fast EMA Formula: Momentum = ((Close - Fast EMA) / Fast EMA) × 100 Interpretation: +0.5% to +2% - normal bullish momentum +2% to +5% - strong bullish momentum +5% and above - overheating (correction possible) -0.5% to -2% - normal bearish momentum -2% to -5% - strong bearish momentum -5% and below - oversold (bounce possible) Usage: Monitor momentum during STRONG signals Large momentum = don't enter (wait for pullback) Small momentum = good entry point 10. How to configure for scalping? Settings for scalping (M1-M5): Base Multiplier: 3-5 Source: close or hlc3 (smoother) Fast: Delta Adaptive (Base 8-12, Sensitivity 3.0) Mid: None (for more signals) Slow: Phi Golden (Phi²) or Pi Circular (1Pi) Visualization: - Gradient Clouds: ON (helps see strength) - Neon Glow: OFF (doesn't clutter chart) - Pulsing Bar: ON (quick assessment) - Signal Highlights: ON Signals: - Golden/Death Cross: ON - STRONG Signals: ON - Stop Loss: ON (1.0-1.5 ATR, R:R 1.5-2.0) Scalping rules: Trade only STRONG signals Enter on bounce from Fast EMA Tight stops (10-20 pips) Quick take profit (+1R to +2R) Don't hold through news 11. How to configure for long-term investing? Settings for investing (D1-W1): Base Multiplier: 20-30 Source: close Fast: Phi Golden (Phi³ or Phi⁴) Mid: e Natural (e²) Slow: Pi Circular (3Pi or 4Pi) Visualization: - Gradient Clouds: ON - Neon Glow: ON (Medium) - Everything else - to taste Signals: - Golden/Death Cross: ON - STRONG Signals: ON - Stop Loss: OFF (use percentage stop) Investing rules: Enter only on STRONG signals Hold while STRONG active (weeks/months) Stop below Slow EMA or -10% Take profit: by company targets or +50-100% Ignore short-term pullbacks 12. What if indicator slows down chart? Indicator is optimized, but if it slows: Disable unnecessary visual effects: Neon Glow: OFF (saves 8 plots) Gradient Clouds: ON but low quality Lambda Wave EMA: OFF (if not using) Reduce number of active EMAs: Sigma Composite: OFF Lambda Wave: OFF Leave only Phi, Pi, e, Delta Simplify settings: Pulsing Bar: OFF Greek Labels: OFF Info Table: smaller size 13. Can I use on different timeframes simultaneously? Yes! Multi-timeframe analysis is very powerful: Classic scheme: Higher TF (D1, W1) - determine global trend Wait for STRONG signal This is our trading direction Middle TF (H4, H1) - look for confirmation STRONG signal in same direction Precise entry zone Lower TF (M15, M5) - entry point Golden Cross or bounce from Fast EMA Precise stop loss Example: W1: STRONG BUY active (global uptrend) H4: STRONG BUY appeared (confirmation) M15: Wait for Golden Cross or bounce from Fast EMA → ENTRY Advantages: Maximum reliability Clear timeframe hierarchy Large targets 14. How does indicator work on news? Delta Adaptive EMA adapts excellently to news: Before news: Low volatility → Delta EMA becomes fast → pulls to price During news: Sharp volatility spike → Delta EMA slows → filters noise After news: Volatility normalizes → Delta EMA returns to normal Recommendations: Don't trade at news release moment (spreads widen) Wait for STRONG signal after news (2-5 bars) Use Delta Adaptive as Fast EMA for quick reaction Widen stops by 50-100% during important news Advanced Techniques Technique 1: "Divergences with EMA" Idea: Look for discrepancies between price and Fast EMA Bullish divergence: Price makes lower low Fast EMA makes higher low = Possible reversal up Bearish divergence: Price makes higher high Fast EMA makes lower high = Possible reversal down How to trade: Find divergence Wait for STRONG signal in divergence direction Enter on confirmation Technique 2: "EMA Tunnel" Idea: Use space between Fast and Slow EMA as "tunnel" Rules: Wide tunnel - strong trend, hold position Narrow tunnel - weak trend or consolidation, caution Tunnel narrowing - trend weakening, prepare to exit Tunnel widening - trend strengthening, can add Visually: Gradient Clouds show this automatically! Trading: Enter on STRONG signal (tunnel starts widening) Hold while tunnel wide Exit when tunnel starts narrowing Technique 3: "Wave Analysis with Lambda" Idea: Lambda Wave EMA creates sinusoid matching market cycles Setup: Lambda Base Period: 30 Lambda Wave Amplitude: 0.5 Lambda Wave Frequency: 50 (adjusted to asset cycle) How to find correct Frequency: Look at historical cycles (distance between local highs) Average distance = your Frequency Example: if highs every 40-60 bars, set Frequency = 50 Trading: Enter when Lambda Wave at bottom of sinusoid (growth potential) Exit when Lambda Wave at top (fall potential) Combine with STRONG signals Technique 4: "Cluster Analysis" Idea: When all EMAs gather in narrow cluster = powerful breakout soon Cluster signs: All EMAs (Phi, Pi, e, Delta) within 0.5-1% of each other Gradient Clouds almost invisible Price jumping around all EMAs Trading: Identify cluster (all EMAs close) Determine breakout direction (where more volume, higher TFs direction) Wait for breakout and STRONG signal Enter on confirmation Target = cluster size × 3-5 This is very powerful technique for big moves! Technique 5: "Sigma as Dynamic Level" Idea: Sigma Composite EMA = average of all EMAs = magnetic level Usage: Enable Sigma Composite (Weighted Average) Sigma works as dynamic support/resistance Price often returns to Sigma before trend continuation Trading: In trend: Enter on bounces from Sigma In range: Fade moves from Sigma (trade return to Sigma) On breakout: Sigma becomes support/resistance Risk Management Basic Rules 1. Position Size Conservative: 1% of capital per trade Moderate: 2% of capital per trade (recommended) Aggressive: 3-5% (only for experienced) Calculation formula: Lot Size = (Capital × Risk%) / (Stop in pips × Pip value) 2. Risk/Reward Ratio Minimum: 1:1.5 Standard: 1:2 (recommended) Optimal: 1:3 Aggressive: 1:5+ 3. Maximum Drawdown Daily: -3% to -5% Weekly: -7% to -10% Monthly: -15% to -20% Upon reaching limit → STOP trading until end of period Position Management Strategies 1. Fixed Stop Method: Stop below/above Fast EMA or local extreme DON'T move stop against position Can move to breakeven For whom: Beginners, conservative traders 2. Trailing by Fast EMA Method: Each day (or bar) move stop to Fast EMA level Position closes when price breaks Fast EMA Advantages: Stay in trend as long as possible Automatically exit on reversal For whom: Trend followers, swing traders 3. Partial Exit Method: 50% of position close at +2R 50% hold with trailing by Mid EMA or Slow EMA Advantages: Lock profit Leave position for big move Psychologically comfortable For whom: Universal method (recommended) 4. Pyramiding Method: First entry on STRONG signal (50% of planned position) Add 25% on pullback to Fast EMA Add another 25% on pullback to Mid EMA Overall stop below Slow EMA Advantages: Average entry price Reduce risk Increase profit in strong trends Caution: Works only in trends In range leads to losses For whom: Experienced traders Trading Psychology Correct Mindset 1. Indicator is a tool, not holy grail Indicator shows probability, not guarantee There will be losing trades - this is normal Important is series statistics, not one trade 2. Trust the system If STRONG signal appeared - enter Don't search for "perfect" moment Follow trading plan 3. Patience STRONG signals don't appear every day Better miss signal than enter against trend Quality over quantity 4. Discipline Always set stop loss Don't move stop against position Don't increase risk after losses Beginner Mistakes 1. "I know better than indicator" Indicator says STRONG BUY, but you think "too high, will wait for pullback" Result: miss profitable move Solution: Trust signals or don't use indicator 2. "Will reverse now for sure" Trading against STRONG trend Result: stops, stops, stops Solution: Trend is your friend, trade with trend 3. "Will hold a bit more" Don't exit when STRONG signal disappears Greed eats profit Solution: If signal gone - exit! 4. "I'll recover" After losses double risk Result: huge losses Solution: Fixed % risk ALWAYS 5. "I don't like this signal" Skip signals because of "feeling" Result: inconsistency, no statistics Solution: Trade ALL signals or clearly define filters Trading Journal What to Record For each trade: 1. Entry/exit date and time 2. Instrument and timeframe 3. Signal type Golden Cross STRONG BUY STRONG SELL Death Cross 4. Indicator settings Fast/Mid/Slow EMA Base Multiplier Other parameters 5. Chart screenshot Entry moment Exit moment 6. Trade parameters Position size Stop loss Take Profit R:R 7. Result Profit/Loss in $ Profit/Loss in % Profit/Loss in R 8. Notes What was right What was wrong Emotions during trade Lessons Journal Analysis Analyze weekly: 1. Win Rate Win Rate = (Profitable trades / All trades) × 100% Good: 50-60% Excellent: 60-70% Exceptional: 70%+ 2. Average R Average R = Sum of all R / Number of trades Good: +0.5R Excellent: +1.0R Exceptional: +1.5R+ 3. Profit Factor Profit Factor = Total profit / Total losses Good: 1.5+ Excellent: 2.0+ Exceptional: 3.0+ 4. Maximum Drawdown Track consecutive losses If more than 5 in row - stop, check system 5. Best/Worst Trades What was common in best trades? (do more) What was common in worst trades? (avoid) Pre-Trade Checklist Technical Analysis STRONG signal active (BUY or SELL) All EMAs properly aligned (Fast > Mid > Slow or reverse) Price on correct side of Fast EMA Gradient Clouds confirm trend Pulsing Bar shows STRONG state Momentum % in normal range (not overheated) No close strong levels against direction Higher timeframe doesn't contradict Risk Management Position size calculated (1-2% risk) Stop loss set Take profit calculated (minimum 1:2) R:R satisfactory Daily/weekly risk limit not exceeded No other open correlated positions Fundamental Analysis No important news in coming hours Market session appropriate (liquidity) No contradicting fundamentals Understand why asset is moving Psychology Calm and thinking clearly No emotions from previous trades Ready to accept loss at stop Following trading plan Not revenging market for past losses If at least one point is NO - think twice before entering! Learning Roadmap Week 1: Familiarization Goals: Install and configure indicator Study all EMA types Understand visualization Tasks: Add indicator to chart Test all Fast/Mid/Slow settings Play with Base Multiplier on different timeframes Observe Gradient Clouds and Pulsing Bar Study Info Table Result: Comfort with indicator interface Week 2: Signals Goals: Learn to recognize all signal types Understand difference between Golden Cross and STRONG Tasks: Find 10 Golden Cross examples in history Find 10 STRONG BUY examples in history Compare their results (which worked better) Set up alerts Get 5 real alerts Result: Understanding signals Week 3: Demo Trading Goals: Start trading signals on demo account Gather statistics Tasks: Open demo account Trade ONLY STRONG signals Keep journal (minimum 20 trades) Don't change indicator settings Strictly follow stop losses Result: 20+ documented trades Week 4: Analysis Goals: Analyze demo trading results Optimize approach Tasks: Calculate win rate and average R Find patterns in profitable trades Find patterns in losing trades Adjust approach (not indicator!) Write trading plan Result: Trading plan on 1 page Month 2: Improvement Goals: Deepen understanding Add additional techniques Tasks: Study multi-timeframe analysis Test combinations with Price Action Try advanced techniques (divergences, tunnels) Continue demo trading (minimum 50 trades) Achieve stable profitability on demo Result: Win rate 55%+ and Profit Factor 1.5+ Month 3: Real Trading Goals: Transition to real account Maintain discipline Tasks: Open small real account Trade minimum lots Strictly follow trading plan DON'T increase risk Focus on process, not profit Result: Psychological comfort on real Month 4+: Scaling Goals: Increase account Become consistently profitable Tasks: With 60%+ win rate can increase risk to 2% Upon doubling account can add capital Continue keeping journal Periodically review and improve strategy Share experience with community Result: Stable profitability month after month Additional Resources Recommended Reading Technical Analysis: "Technical Analysis of Financial Markets" - John Murphy "Trading in the Zone" - Mark Douglas (psychology) "Market Wizards" - Jack Schwager (trader interviews) EMA and Moving Averages: "Moving Averages 101" - Steve Burns Articles on Investopedia about EMA Risk Management: "The Mathematics of Money Management" - Ralph Vince "Trade Your Way to Financial Freedom" - Van K. Tharp Trading Journals: Edgewonk (paid, very powerful) Tradervue (free version + premium) Excel/Google Sheets (free) Screeners: TradingView Stock Screener Finviz (stocks) CoinMarketCap (crypto) Conclusion Hellenic EMA Matrix is a powerful tool based on universal mathematical constants of nature. The indicator combines: Mathematical elegance - Phi, Pi, e instead of arbitrary numbers Premium visualization - Neon Glow, Gradient Clouds, Pulsing Bar Reliable signals - STRONG BUY/SELL work on all timeframes Flexibility - 6 EMA types, adaptation to any trading style Automation - auto-sorting EMAs, SL/TP calculation, alerts Key Success Principles: Simplicity - start with basic settings (Phi/Pi/e, Base=10) Discipline - follow STRONG signals strictly Patience - wait for quality setups Risk Management - 1-2% per trade, ALWAYS Journal - document every trade Learning - constantly improve skills Remember: Indicator shows probability, not guarantee Important is series statistics, not one trade Psychology more important than technique Quality more important than quantity Process more important than result Acknowledgments Thank you for using Hellenic EMA Matrix - Alpha Omega Premium! The indicator was created with love for mathematics, markets, and beautiful visualization. Wishing you profitable trading! Guide Version: 1.0 Date: 2025 Compatibility: Pine Script v6, TradingView "In the simplicity of mathematical constants lies the complexity of market movements"
Pine Script® indicator
by Sesilya