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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®指標
由EdgeTools提供
4
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®指標
由PineCodersTASC提供
12
BackToBasic XEMAบทความอธิบายสคริปต์ “BackToBasic XEMA” ภาษาไทย แนวคิดโดยย่อ BackToBasic XEMA เกิดจากแนวคิด “กลับสู่พื้นฐานแต่เพิ่มประโยชน์” โดยใช้สัญญาณ EMA Crossover เป็นแกนหลัก แล้วต่อยอดด้วยการแสดงกำไร/ขาดทุนจริง (PnL) และเส้น Trailing Stop แนวนอน เพื่อช่วยวัดประสิทธิภาพและป้องกันการคืนกำไร กลไกการทำงาน Dual EMA – คำนวณ EMA สองเส้น (Fast และ Slow) Crossover Signal – ออกสัญญาณ Buy เมื่อ Fast ตัดขึ้น Slow และ Sell เมื่อ Fast ตัดลง Slow PnL Lines & Labels – เมื่อทิศทางกลับตัว ระบบจะคำนวณส่วนต่างราคา × จำนวน Contracts แล้ววาดเส้นเชื่อมจุดเข้า–ออก พร้อมป้ายกำไร/ขาดทุนสีเขียว / แดง Horizontal Trailing Stop – เมื่อราคาวิ่งไปทางกำไรเกิน trailStartPips ระบบจะสร้างเส้น Trail ห่างจาก EMA อ้างอิงด้วย trailBufferPips และเลื่อนเฉพาะในทางที่ล็อกกำไร การตั้งค่าใช้งาน (สรุปเป็นคำอธิบาย) ปรับค่า Fast/Slow EMA ให้สัมพันธ์กับกรอบเวลาและความผันผวนของสินทรัพย์ กรอกจำนวน Contracts ตามขนาดโพซิชันจริงเพื่อให้ค่า PnL สมจริง ค่า Trail เริ่มต้นเหมาะกับกราฟ 1 ชั่วโมงขึ้นไป หากเทรดสั้นอาจลด trailStartPips และ trailBufferPips แนะนำใช้กับสินทรัพย์สภาพคล่องสูง (คู่เงินหลัก, XAUUSD, ดัชนี) และทดสอบบนบัญชีเดโมก่อนเสมอ จุดเด่นเมื่อเทียบกับ EMA Crossover พื้นฐาน เห็นผลกำไร/ขาดทุนของแต่ละการเทรดทันที ไม่ต้องคำนวณย้อนหลัง มีเส้น Trailing Stop แนวนอนช่วยล็อกกำไรและจำกัดขาดทุน เปิด–ปิดฟังก์ชัน PnL และ Trailing ได้จากหน้าตั้งค่า ไม่ยุ่งยาก ข้อจำกัดและคำเตือน ไม่เหมาะกับกราฟแบบ Heikin Ashi หรือ Renko เพราะอาจเกิด repaint PnL คำนวณจากส่วนต่างราคาเท่านั้น ไม่รวมค่าคอมมิชชันหรือสลิปเพจ ผลลัพธ์ในอดีตไม่รับประกันอนาคต ควรจัดการความเสี่ยงและทดลองก่อนใช้งานจริง ลิขสิทธิ์ สคริปต์นี้พัฒนาใหม่ทั้งหมดโดย , © 2025 English Concept BackToBasic XEMA extends a classic EMA-crossover setup with real-time profit-and-loss tracking and a horizontal trailing-stop line, giving traders both clear entry/exit signals and built-in risk management. How It Works Dual EMAs – Calculates Fast and Slow EMAs. Crossover Signals – Generates a Buy when the Fast EMA crosses above the Slow EMA, and a Sell when it crosses below. PnL Lines & Labels – On every direction flip the script computes price difference × contracts, draws a line from entry to exit, and labels the result in green (profit) or red (loss). Horizontal Trailing Stop – After price moves in profit by at least trailStartPips, a trail line is placed trailBufferPips away from the chosen EMA and moves only in the trade’s favour. Practical Settings (plain-language guide) Adjust Fast/Slow EMA lengths to suit your timeframe and the instrument’s volatility. Enter your position size in Contracts so PnL lines reflect real cash values. For shorter timeframes, lower trailStartPips and trailBufferPips; for swing trading, larger values work better. Best used on 1-hour-and-above charts of liquid symbols (major FX pairs, gold, indices). Forward-test on demo first. Advantages over a Basic EMA Cross Instant visual feedback on each trade’s profit or loss. Built-in horizontal trailing stop to lock in gains and limit downside. Modular design – PnL and trailing features can be toggled on or off in the input panel. Limitations & Disclaimer Not repaint-safe on non-standard chart types such as Heikin Ashi or Renko. PnL lines show raw price change only; commissions and slippage are not included. Past performance does not guarantee future results – trade responsibly and test thoroughly. License Original Pine Script by , © 2025
Pine Script®指標
由CJSuperTrader提供
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®指標
由Essa02提供
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®指標
由amusedMagpie26867提供
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®指標
由PineCodersTASC提供
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®策略
由DskyzInvestments提供
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®指標
由bitcoin_briefs提供
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®策略
由PineCodersTASC提供
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®指標
由Mereep01提供
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®指標
由PineCodersTASC提供
5
Cash And Carry Arbitrage BTC Compare Month 6 by SeoNo1Detailed Explanation of the BTC Cash and Carry Arbitrage Script Script Title: BTC Cash And Carry Arbitrage Month 6 by SeoNo1 Short Title: BTC C&C ABT Month 6 Version: Pine Script v5 Overlay: True (The indicators are plotted directly on the price chart) Purpose of the Script This script is designed to help traders analyze and track arbitrage opportunities between the spot market and futures market for Bitcoin (BTC). Specifically, it calculates the spread and Annual Percentage Yield (APY) from a cash-and-carry arbitrage strategy until a specific expiry date (in this case, June 27, 2025). The strategy helps identify profitable opportunities when the futures price of BTC is higher than the spot price. Traders can then buy BTC in the spot market and short BTC futures contracts to lock in a risk-free profit. 1. Input Settings Spot Symbol: The real-time BTC spot price from Binance (BTCUSDT). Futures Symbol: The BTC futures contract that expires in June 2025 (BTCUSDM2025). Expiry Date: The expiration date of the futures contract, set to June 27, 2025. These inputs allow users to adjust the symbols or expiry date according to their trading needs. 2. Price Data Retrieval Spot Price: Fetches the latest closing price of BTC from the spot market. Futures Price: Fetches the latest closing price of BTC futures. Spread: The difference between the futures price and the spot price (futures_price - spot_price). The spread indicates how much higher (or lower) the futures price is compared to the spot market. 3. Time to Maturity (TTM) and Annual Percentage Yield (APY) Calculation Current Date: Gets the current timestamp. Time to Maturity (TTM): The number of days left until the futures contract expires. APY Calculation: Formula: APY = ( Spread / Spot Price ) x ( 365 / TTM Days ) x 100 This represents the annualized return from holding a cash-and-carry arbitrage position if the trader buys BTC at the spot price and sells BTC futures. 4. Display Information Table on the Chart A table is created on the chart's top-right corner showing the following data: Metric: Labels such as Spread and APY Value: Displays the calculated spread and APY The table automatically updates at the latest bar to display the most recent data. 5. Alert Condition This sets an alert condition that triggers every time the script runs. In practice, users can modify this alert to trigger based on specific conditions (e.g., APY exceeds a threshold). 6. Plotting the APY and Spread APY Plot: Displays the annualized yield as a blue line on the chart. Spread Plot: Visualizes the futures-spot spread as a red line. This helps traders quickly identify arbitrage opportunities when the spread or APY reaches desirable levels. How to Use the Script Monitor Arbitrage Opportunities: A positive spread indicates a potential cash-and-carry arbitrage opportunity. The larger the APY, the more profitable the arbitrage opportunity could be. Timing Trades: Execute a buy on the BTC spot market and simultaneously sell BTC futures when the APY is attractive. Close both positions upon futures contract expiry to realize profits. Risk Management: Ensure you have sufficient margin to hold both positions until expiry. Monitor funding rates and volatility, which could affect returns. Conclusion This script is an essential tool for traders looking to exploit price discrepancies between the BTC spot market and futures market through a cash-and-carry arbitrage strategy. It provides real-time data on spreads, annualized returns (APY), and visual alerts, helping traders make informed decisions and maximize their profit potential.
Pine Script®指標
由seono1提供
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®指標
由Karthikeyan_G提供
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®指標
由PineCodersTASC提供
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®指標
由PineCodersTASC提供
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®指標
由kennethkhan提供
2
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®指標
由PineCodersTASC提供
12
Ripple (XRP) Model PriceAn article titled Bitcoin Stock-to-Flow Model was published in March 2019 by "PlanB" with mathematical model used to calculate Bitcoin model price during the time. We know that Ripple has a strong correlation with Bitcoin. But does this correlation have a definite rule? In this study, we examine the relationship between bitcoin's stock-to-flow ratio and the ripple(XRP) price. The Halving and the stock-to-flow ratio Stock-to-flow is defined as a relationship between production and current stock that is out there. SF = stock / flow The term "halving" as it relates to Bitcoin has to do with how many Bitcoin tokens are found in a newly created block. Back in 2009, when Bitcoin launched, each block contained 50 BTC, but this amount was set to be reduced by 50% every 210,000 blocks (about 4 years). Today, there have been three halving events, and a block now only contains 6.25 BTC. When the next halving occurs, a block will only contain 3.125 BTC. Halving events will continue until the reward for minors reaches 0 BTC. With each halving, the stock-to-flow ratio increased and Bitcoin experienced a huge bull market that absolutely crushed its previous all-time high. But what exactly does this affect the price of Ripple? Price Model I have used Bitcoin's stock-to-flow ratio and Ripple's price data from April 1, 2014 to November 3, 2021 (Daily Close-Price) as the statistical population. Then I used linear regression to determine the relationship between the natural logarithm of the Ripple price and the natural logarithm of the Bitcoin's stock-to-flow (BSF). You can see the results in the image below: Basic Equation : ln(Model Price) = 3.2977 * ln(BSF) - 12.13 The high R-Squared value (R2 = 0.83) indicates a large positive linear association. Then I "winsorized" the statistical data to limit extreme values to reduce the effect of possibly spurious outliers (This process affected less than 4.5% of the total price data). ln(Model Price) = 3.3297 * ln(BSF) - 12.214 If we raise the both sides of the equation to the power of e, we will have: ============================================ Final Equation: ■ Model Price = Exp(- 12.214) * BSF ^ 3.3297 Where BSF is Bitcoin's stock-to-flow ============================================ If we put current Bitcoin's stock-to-flow value (54.2) into this equation we get value of 2.95USD. This is the price which is indicated by the model. There is a power law relationship between the market price and Bitcoin's stock-to-flow (BSF). Power laws are interesting because they reveal an underlying regularity in the properties of seemingly random complex systems. I plotted XRP model price (black) over time on the chart. Estimating the range of price movements I also used several bands to estimate the range of price movements and used the residual standard deviation to determine the equation for those bands. Residual STDEV = 0.82188 ln(First-Upper-Band) = 3.3297 * ln(BSF) - 12.214 + Residual STDEV => ln(First-Upper-Band) = 3.3297 * ln(BSF) – 11.392 => ■ First-Upper-Band = Exp(-11.392) * BSF ^ 3.3297 In the same way: ■ First-Lower-Band = Exp(-13.036) * BSF ^ 3.3297 I also used twice the residual standard deviation to define two extra bands: ■ Second-Upper-Band = Exp(-10.570) * BSF ^ 3.3297 ■ Second-Lower-Band = Exp(-13.858) * BSF ^ 3.3297 These bands can be used to determine overbought and oversold levels. Estimating of the future price movements Because we know that every four years the stock-to-flow ratio, or current circulation relative to new supply, doubles, this metric can be plotted into the future. At the time of the next halving event, Bitcoins will be produced at a rate of 450 BTC / day. There will be around 19,900,000 coins in circulation by August 2025 It is estimated that during first year of Bitcoin (2009) Satoshi Nakamoto (Bitcoin creator) mined around 1 million Bitcoins and did not move them until today. It can be debated if those coins might be lost or Satoshi is just waiting still to sell them but the fact is that they are not moving at all ever since. We simply decrease stock amount for 1 million BTC so stock to flow value would be: BSF = (19,900,000 – 1.000.000) / (450 * 365) =115.07 Thus, Bitcoin's stock-to-flow will increase to around 115 until AUG 2025. If we put this number in the equation: Model Price = Exp(- 12.214) * 114 ^ 3.3297 = 36.06$ Ripple has a fixed supply rate. In AUG 2025, the total number of coins in circulation will be about 56,000,000,000. According to the equation, Ripple's market cap will reach $2 trillion. Note that these studies have been conducted only to better understand price movements and are not a financial advice.
Pine Script®指標
由BobRivera990提供
10
[Pandora] Laguerre Ultimate Explorations MulticatorIt's time to begin demonstrations differentiating the difference between known and actual feasibility beyond imagination... Welcome to my algorithmic twilight zone . INTRODUCTION: Hot off my press, I present this Laguerre multicator employing PSv6.0, originally formulated by John Ehlers for TASC - July 2025 Traders Tips. Basically I transcended Ehlers' notions of transversal filtration with an overhaul of his Laguerre design with my "what if" Pandora notions included. Striving beyond John Ehlers' original intended design. This action packed indicator is a radically revamped version of his original filter using novel techniques. My aim was to explore whether providing even more enhanced responsiveness and lesser lag is possible and how. Presented here is my mind warping results to witness. EHLERS' LAGUERRE EXPLAINED: First and foremost, the concept of Ehlers' Laguerre-izing method deserves a comprehensive deep dive. Ehlers' Laguerre filter design, as it functions originally, begins with his Ultimate Smoother (US) followed by a gang of four LERP (jargon for Linear intERPolation) filters. Following a myriad of cascading LERPs is a window-like FIR filter tapped into the LERP delay values to provide extra smoothness via the output. On a side note, damping factor controlled LERP filters resemble EMAs indeed, but aren't exactly "periodic" filters that would have a period/length parameter and their subsequent calculations. I won't go into fine-grained relationship details, but EMA and LERP are indeed related in approach, being cousins of similar pedigree. EXAMINING LAGUERRE: I focused firstly on US initialization obstacles at Pine's bar_index==0 with nz() in abundance. The next primary notion of intrigue I mostly wondered about was, why are there four LERP elements instead of fewer or more. Why not three or why not two LERPs, etc... 1-4-6-4-1, I remember seeing those coefficients before in high pass filters. Gathering my thoughts from that highpass knowledge base, I devised other tapped configuration modes to inspect their behavior out of curiosity. Eureka! There is actually more to Laguerre than Ehlers' mind provided, now that I had formulated additional modes. Each mode exhibits it's own lag/smoothness characteristics better than the quad LERPed version. I narrowed it down to a total of 5 modes for exploration. Mode 0 is just the raw US by itself. ANALYZING FILTER BEHAVIORS: Which option might be possibly superior, and how may I determine that? Fortunately, I have a custom-built analyzer allowing me to thoroughly examine transient responses across multiple periodicities simultaneously, providing remarkable visual insights. While Ehlers has meagerly touched upon presenting general frequency responses in his books, I have excelled far beyond that. This robust filter analysis capability enables me to observe finer aspects hidden to others, ultimately leading to the deprecation of numerous existing filters. Not only this, but inventing entirely new species of filtration whether lowpass, highpass, or bandpass is already possible with a thorough comprehensive evaluation. Revealing what's quirky with each filter and having the ability to discover what filters may be lacking in performance, is one of it's implications. I'm just going to explain this: For example US has a little too much overshoot to my liking, along with nonconformant cutoff frequency compliance with the period parameter. Perhaps Ehlers should inspect US coefficients a bit closer... I hope stating this is not received in an ill manner, as it's not my intention here. What this technically eludes to is that UltimateSmoother can be further improved, analogous to my Laguerre alterations described above. I will also state Laguerre can indeed be reformulated to an even greater extent concerning group delay, from what I have already discussed. Another exciting time though... More investigative research is warranted. LAGUERRE CONCLUSIONS: After analyzing Laguerre's frequency compliance, transient responses, amplitudes, lag, symmetry across periodicities, noise rejection, and smoothness... I favor mode 3 for a multitude of reasons over the mode 4 configuration, but mostly superb smoothing with less lag, AND I also appreciated mode 1 & 2 for it's lower lag performance options. Each mode and lag (phase shift) damping value has it's own unique characteristics at extremes, yet they demonstrate additional finesse in it's new hybrid form without adding too much more complexity. This multicator has a bunch of Laguerre filters in the overlay chart over many periodicities so you can easily witness it's differing periodic symmetries on an input signal while adjusting lag and mode. LAGUERRE OSCILLATOR: The oscillator is integrated into the laguerreMulti() function for the intention of posterity only. I performed no evaluation on it, only providing the code in Pine. That wasn't part of my intended exploration adventure, as I'm more TREND oriented for the time being, focusing my efforts there. Market analysis has two primary aspects in my observations, one cyclic while the other is trending dynamics... There's endless oscillators, but my expectations for trend analysis seems a little lesser explored in my opinion, hence my laborious trend endeavors. Ehlers provided both indicator facets this time around, and I hope you find the filtration aspect more intriguing after absorption of this reading. FUNCTION MODULES EXPLAINED: The Ultimate Smoother is an advanced IIR lowpass smoothing filter intended to minimize noise in time series data with minimal group delay, similar to a traditional biquad filter. This calculation helps to create a smoother version of the original signal without the distortions of short-term fluctuations and with minimal lag, adjustable by period. The Modified Laguerre Lowpass Filter (MLLF) enhances the functionality of US by introducing a Laguerre mode parameter along side the lag parameter to refine control over the amount of additional smoothing/lag applied to the signal. By tethering US with this LERPed lag mechanism, MLLF achieves an effective balance between responsiveness and smoothness, allowing for customizable lag adjustments via multiple inputs. This filter ends with selecting from a choice of weighted averages derived from a gang of up to four cascading LERP calculations, resulting with smoother representations of the data. The Laguerre Oscillator is a momentum-like indicator derived from the output of US and a singular LERPed lowpass filter. It calculates the difference between the US data and Laguerre filter data, normalizing it by the root mean square (RMS). This quasi-normalization technique helps to assess the intensity of the momentum on any timeframe within an expected bound range centered around 0.0. When the Laguerre Oscillator is positive, it suggests that the smoothed data is trending upward, while a negative value indicates a downward trend. Adjustability is controlled with period, lag, Laguerre mode, and RMS period.
Pine Script®指標
由ImmortalFreedom提供
5
Bitcoin Power Law ModelBitcoin Power Law Model with Cycle Predictions Scientific Price Modeling for Bitcoin This indicator implements **Dr. Giovanni Santostasi's Bitcoin Power Law Theory** - a discovery that Bitcoin's price follows mathematical laws similar to natural phenomena. Unlike traditional financial models, this treats Bitcoin as a scale-invariant system that grows predictably over time. What Makes This Special Dr. Santostasi, an astrophysicist who studied gravitational waves, discovered that Bitcoin's price forms a perfect straight line when plotted on a log-log scale over its entire 15-year history. This isn't just another technical indicator - it's a fundamental law that has held true through multiple 80%+ crashes and recoveries. Core Features Power Law Model - Orange Line: The power law trajectory showing Bitcoin's long-term growth path - Yellow Line: Fair value (geometric mean between support and resistance) - Green/Red Bands: Support and resistance levels that have historically contained price movements - Band Position %: Shows exactly where price sits within the power law channel (0-100%) How to Use It For Long-term Investors 1. Accumulate when price is near the green support line (band position < 20%) 2. Hold when price is between the bands 3. Consider profits when approaching red resistance (band position > 80%) 4. Never panic - the model shows $30K+ is now the permanent floor Key Metrics to Watch - **Band Position: <20% = Oversold, >80% = Overbought - Fair Value: Price above = Overvalued, below = Undervalued - Support Line: Breaking below suggests model invalidation Current Cycle Projections Based on the November 2022 bottom at ~$15,500: - Cycle Peak: ~$155,000-$230,000 (October 2025) - Next Bottom: ~$70,000-$100,000 (October 2026) - Long-term: $1 million by 2033 (power law projection) Customizable Settings Model Parameters - Intercept & Slope: Fine-tune the power law formula - Band Offsets: Adjust support/resistance distances Display Options - Toggle each visual element on/off - Show/hide future projections - Enable/disable cycle analysis - Customize halving markers Understanding the Math The model uses the formula: **Price = 10^(A + B × log10(days since genesis))** Where: - A = -17.01 (intercept) - B = 5.82 (slope) - Days counted from Bitcoin's genesis block (Jan 3, 2009) This creates parallel support/resistance lines in log-log space that have contained Bitcoin's price for 15+ years. Important 1.Not Financial Advice: This is a mathematical model, not a guarantee 2. Long-term Focus: Best suited for macro analysis, not day trading 3. Model Limitations: Past performance doesn't ensure future results 4. Volatility Expected: 50-80% drawdowns are normal within the model Background Dr. Giovanni Santostasi discovered this model while analyzing Bitcoin through the lens of physics. He found that Bitcoin behaves more like a city or organism than a financial asset, growing according to universal power laws found throughout
Pine Script®指標
由CryptoForMoney提供
Bitcoin Stock-to-Flow Model Price Bands# Bitcoin Stock-to-Flow Model Price Bands Overview This indicator implements the famous Stock-to-Flow (S2F) model created by PlanB (@100trillionUSD), which uses Bitcoin's scarcity to predict its long-term value. The S2F model has gained significant attention for its historical accuracy in capturing Bitcoin's price movements across multiple market cycles. What is Stock-to-Flow? Stock-to-Flow is a ratio that measures scarcity by dividing the current supply (stock) by the annual production (flow). The model suggests that as Bitcoin becomes scarcer through halving events, its value should increase proportionally. This indicator features: Dynamic S2F Calculation - Automatically calculates Bitcoin's current supply based on block height - Adjusts for halving events (every 210,000 blocks) - Updates the S2F ratio in real-time Visual Elements - Orange Line: S2F model price based on the formula: Price = 0.4 × S2F³ - Confidence Bands: Upper (red) and lower (green) bands showing expected price ranges - Colored Candles: Green when above model price, red when below - Info Table: Displays current S2F ratio, model price, actual price, and price multiple Customizable Parameters - Model Coefficient: Adjust the multiplier (default: 0.4) - Model Exponent: Modify the power factor (default: 3.0) - Band Width: Control confidence band spread (1-5 standard deviations) - Display Options: Toggle individual elements on/off Built-in Alerts - Price crossing above/below S2F model price - Price exceeding upper/lower confidence bands How to Use 1. Trend Identification: When price is above the orange S2F line, Bitcoin may be overvalued; below suggests undervaluation 2. Cycle Analysis: The model steps up at each halving, creating distinct price "floors" 3. Risk Management: Use confidence bands to identify extreme deviations from the model 4. Long-term Perspective: Best suited for macro analysis rather than short-term trading Important to understand: This is a model, not a guarantee. The S2F model: - Assumes scarcity is the primary driver of value - Doesn't account for demand-side factors - Has shown deviations during certain market conditions - Should be used alongside other analysis methods Model Performance Historically, the S2F model has captured major Bitcoin price movements: - 2013 Bull Run: Price followed model predictions - 2017 Peak: Reached model targets - 2021 Cycle: Initially tracked, then deviated - 2024-2025: Model suggests $500k-$1M potential Technical Details - Uses logarithmic regression similar to the original S2F model - Accounts for "lost" coins (est. 1M BTC from early mining) - Implements dynamic supply calculation through halving cycles - Confidence bands use log-normal distribution Best Timeframes - Weekly/Monthly: Ideal for long-term trend analysis Credits Based on the Stock-to-Flow model by PlanB (@100trillionUSD) Original article: "Modeling Bitcoin's Value with Scarcity" (2019)
Pine Script®指標
由CryptoForMoney提供
MCC Smart Reversal Signals📊 MCC Smart Reversal Signals (Stoch RSI) v1.0.9 © 2025 Jawid Iqbal Anwar — MIT License This indicator is designed to help traders detect potential market reversal points by combining multiple confirmation tools such as: Stochastic RSI Classic RSI Trend direction (EMA-based) Candlestick reversal patterns Re-entry logic Dollar-Cost Averaging (DCA) strategy Built-in alert system 🔄 What’s New in v1.0.9 ✅ Time-Frame Presets: Choose from Custom, Weekly, Daily, 4H, 1H, or 15m profiles — each with optimized parameters. 🤖 Auto Mode: The script can automatically detect the chart timeframe and apply the best preset settings. 🎯 Dynamic Overbought/Oversold Levels: On 15m and 1H, RSI and Stoch RSI use 6 (Oversold) and 94 (Overbought) On 4H, Daily, and Weekly, it uses 30/70 for RSI and 20/80 for Stoch RSI 🧠 All original logic from v1.0.8 is retained: signal throttling, strong confirmation, and DCA logic. 🔧 How It Works 1. Watch Buy / Watch Sell Signals These are early signals that notify you of a possible reversal forming. 🟠 Watch Buy: Triggered when the Stoch RSI enters oversold territory. Will only appear if the market isn’t trending strongly downward. 🔵 Watch Sell: Triggered when the Stoch RSI moves into overbought territory. Once fired, it will pause for a few candles unless the zone is exited. ⚠️ OB/OS zones are adjusted based on the selected preset or Auto mode. 2. Strong Reversal Signals (Higher Confidence) These provide stronger confirmation when all of the following align: RSI is in an extreme zone (very low or very high) A reversal candlestick pattern appears (doji, bullish/bearish) RSI recently changed direction EMA trend supports the move (not against a strong trend) When this happens, you’ll see a green (Buy) or red (Sell) label. These are typically high-probability entries. 3. Re-Entry Dots If the price stays oversold or overbought for a while and forms a new valid candle setup, a new “Watch” dot will appear. 🔁 These help you catch continuation or delayed reversal setups. 4. DCA (Dollar-Cost Averaging) Signals ✅ DCA Buy: RSI is very low and Stoch RSI remains in the oversold zone — a good place to scale into a long position. ❌ DCA Sell: RSI and Stoch RSI are both in the overbought zone — ideal for scaling out or taking profit. 5. Signal Throttling (Smarter Alerts) To avoid over-signal noise, the script includes throttling logic: A signal won’t repeat unless the zone is exited and re-entered. There’s a minimum bar gap between signals to reduce clutter. 🔔 Built-in Alerts (No Setup Needed) The script includes alerts for all signal types: Watch Buy / Watch Sell Strong Buy / Strong Sell DCA Buy / DCA Sell Once you add the script to your chart, you can enable alerts immediately. 🕒 How to Use Timeframes With v1.0.9, the indicator adapts to your chart: Auto Mode: Just load the indicator — it adjusts based on your active timeframe. Manual Presets: Choose a preset if you want specific control. Timeframe Default OB/OS Settings 15m & 1H RSI & Stoch RSI: 6 / 94 4H+ RSI: 30 / 70, Stoch RSI: 20 / 80 🛠️ Suggested Trading Flow Add the indicator and select a timeframe preset (or use Auto). Wait for 🟠 Watch Buy or 🔵 Watch Sell — watch the price behavior. If a 🟢 Strong Buy or 🔴 Strong Sell appears, consider entering with confirmation. Use ✅ DCA Buy or ⏺️ DCA Sell to scale in/out slowly. Use EMA trend and candlestick context to refine your decision. 💡 Final Tips Let signals come to you — don’t force trades. Use the OB/OS zones and alerts as part of a structured strategy. Combine this tool with your price action knowledge for best results.
Pine Script®指標
由Malaysiancryptocommunity提供