Şimdi başlat

Dönemler

Key Trading Session Times (UK) with DST Adjust//@version=5 indicator("Key Trading Session Times (UK) with DST Adjust", overlay=true) // === Inputs === showAsia = input.bool(true, "Show Asia Session (12 AM – 6 AM UK)") showLondon = input.bool(true, "Show London Open (6:30 AM – 9 AM UK)") showNY = input.bool(true, "Show NY Open (1 PM – 3 PM UK)") // === DST Adjustment Logic === // Define the start and end of daylight saving time (DST) isDST = (month >= 3 and month <= 10) // DST from March to October // === Time Ranges (Europe/London) with DST Adjustment === inSession(sessionStartHour, sessionStartMinute, sessionEndHour, sessionEndMinute) => t = time("1", "Europe/London") sessionStart = timestamp("Europe/London", year(t), month(t), dayofmonth(t), sessionStartHour, sessionStartMinute) sessionEnd = timestamp("Europe/London", year(t), month(t), dayofmonth(t), sessionEndHour, sessionEndMinute) if isDST sessionStart := sessionStart + 3600 // Add 1 hour during DST sessionEnd := sessionEnd + 3600 // Add 1 hour during DST time >= sessionStart and time <= sessionEnd // === Define Sessions === asiaSession = inSession(0, 0, 6, 0) londonSession = inSession(6, 30, 9, 0) nySession = inSession(13, 0, 15, 0) // === Background Highlights === bgcolor(showAsia and asiaSession ? color.new(color.blue, 85) : na, title="Asia Session") bgcolor(showLondon and londonSession ? color.new(color.green, 85) : na, title="London Session") bgcolor(showNY and nySession ? color.new(color.orange, 85) : na, title="New York Session")
Pine Script® göstergesi
frazerlittle tarafından
Solar Cycle (SOLAR)SOLAR: SOLAR CYCLE 🔍 OVERVIEW AND PURPOSE The Solar Cycle indicator is an astronomical calculator that provides precise values representing the seasonal position of the Sun throughout the year. This indicator maps the Sun's position in the ecliptic to a normalized value ranging from -1.0 (winter solstice) through 0.0 (equinoxes) to +1.0 (summer solstice), creating a continuous cycle that represents the seasonal progression throughout the year. The implementation uses high-precision astronomical formulas that include orbital elements and perturbation terms to accurately calculate the Sun's position. By converting chart timestamps to Julian dates and applying standard astronomical algorithms, this indicator achieves significantly greater accuracy than simplified seasonal approximations. This makes it valuable for traders exploring seasonal patterns, agricultural commodities trading, and natural cycle-based trading strategies. 🧩 CORE CONCEPTS Seasonal cycle integration: Maps the annual solar cycle (365.242 days) to a continuous wave Continuous phase representation: Provides a normalized -1.0 to +1.0 value Astronomical precision: Uses perturbation terms and high-precision constants for accurate solar position Key points detection: Identifies solstices (±1.0) and equinoxes (0.0) automatically The Solar Cycle indicator differs from traditional seasonal analysis tools by incorporating precise astronomical calculations rather than using simple calendar-based approximations. This approach allows traders to identify exact seasonal turning points and transitions with high accuracy. ⚙️ COMMON SETTINGS AND PARAMETERS Pro Tip: While the indicator itself doesn't have adjustable parameters, it's most effective when used on higher timeframes (daily or weekly charts) to visualize seasonal patterns. Consider combining it with commodity price data to analyze seasonal correlations. 🧮 CALCULATION AND MATHEMATICAL FOUNDATION Simplified explanation: The Solar Cycle indicator calculates the Sun's ecliptic longitude and transforms it into a sine wave that peaks at the summer solstice and troughs at the winter solstice, with equinoxes at the zero crossings. Technical formula: Convert chart timestamp to Julian Date: JD = (time / 86400000.0) + 2440587.5 Calculate Time T in Julian centuries since J2000.0: T = (JD - 2451545.0) / 36525.0 Calculate the Sun's mean longitude (L0) and mean anomaly (M), including perturbation terms: L0 = (280.46646 + 36000.76983T + 0.0003032T²) % 360 M = (357.52911 + 35999.05029T - 0.0001537T² - 0.00000025T³) % 360 Calculate the equation of center (C): C = (1.914602 - 0.004817T - 0.000014*T²)sin(M) + (0.019993 - 0.000101T)sin(2M) + 0.000289sin(3M) Calculate the Sun's true longitude and convert to seasonal value: λ = L0 + C seasonal = sin(λ) 🔍 Technical Note: The implementation includes terms for the equation of center to account for the Earth's elliptical orbit. This provides more accurate timing of solstices and equinoxes compared to simple harmonic approximations. 📈 INTERPRETATION DETAILS The Solar Cycle indicator provides several analytical perspectives: Summer Solstice (+1.0): Maximum solar elevation, longest day Winter Solstice (-1.0): Minimum solar elevation, shortest day Vernal Equinox (0.0 crossing up): Day and night equal length, spring begins Autumnal Equinox (0.0 crossing down): Day and night equal length, autumn begins Transition rates: Steepest near equinoxes, flattest near solstices Cycle alignment: Market cycles that align with seasonal patterns may show stronger trends Confirmation points: Solstices and equinoxes often mark important seasonal turning points ⚠️ LIMITATIONS AND CONSIDERATIONS Geographic relevance: Solar cycle timing is most relevant for temperate latitudes Market specificity: Seasonal effects vary significantly across different markets Timeframe compatibility: Most effective for longer-term analysis (weekly/monthly) Complementary tool: Should be used alongside price action and other indicators Lead/lag effects: Market reactions to seasonal changes may precede or follow astronomical events Statistical significance: Seasonal patterns should be verified across multiple years Global markets: Consider opposite seasonality in Southern Hemisphere markets 📚 REFERENCES Meeus, J. (1998). Astronomical Algorithms (2nd ed.). Willmann-Bell. Hirshleifer, D., & Shumway, T. (2003). Good day sunshine: Stock returns and the weather. Journal of Finance, 58(3), 1009-1032. Hong, H., & Yu, J. (2009). Gone fishin': Seasonality in trading activity and asset prices. Journal of Financial Markets, 12(4), 672-702. Bouman, S., & Jacobsen, B. (2002). The Halloween indicator, 'Sell in May and go away': Another puzzle. American Economic Review, 92(5), 1618-1635.
Pine Script® göstergesi
mihakralj tarafından
MVRV | Lyro RS📊 MVRV | Lyro RS is a powerful on-chain valuation tool designed to assess the relative market positioning of Bitcoin (BTC) or Ethereum (ETH) based on the Market Value to Realized Value (MVRV) ratio. It highlights potential undervaluation or overvaluation zones, helping traders and investors anticipate cyclical tops and bottoms. ✨ Key Features : 🔁 Dual Asset Support: Analyze either BTC or ETH with a single toggle. 📐 Dynamic MVRV Thresholds: Automatically calculates median-based bands at 50%, 64%, 125%, and 170%. 📊 Median Calculation: Period-based median MVRV for long-term trend context. 💡 Optional Smoothing: Use SMA to smooth MVRV for cleaner analysis. 🎯 Visual Threshold Alerts: Background and bar colors change based on MVRV position relative to thresholds. ⚠️ Built-in Alerts: Get notified when MVRV enters under- or overvalued territory. 📈 How It Works : 💰 MVRV Calculation: Uses data from IntoTheBlock and CoinMetrics to obtain real-time MVRV values. 🧠 Threshold Bands: Median MVRV is used as a baseline. Ratios like 50%, 64%, 125%, and 170% signal various levels of market extremes. 🎨 Visual Zones: Green zones for undervaluation and red zones for overvaluation, providing intuitive visual cues. 🛠️ Custom Highlights: Toggle individual threshold zones on/off for a cleaner view. ⚙️ Customization Options : 🔄 Switch between BTC or ETH for analysis. 📏 Adjust period length for median MVRV calculation. 🔧 Enable/disable threshold visibility (50%, 64%, 125%, 170%). 📉 Toggle smoothing to reduce noise in volatile markets. 📌 Use Cases : 🟢 Identify undervalued zones for long-term entry opportunities. 🔴 Spot potential overvaluation zones that may precede corrections. 🧭 Use in confluence with price action or macro indicators for better timing. ⚠️ Disclaimer : This indicator is for educational purposes only. It should not be used in isolation for making trading or investment decisions. Always combine with price action, fundamentals, and proper risk management.
Pine Script® göstergesi
LyroRS tarafından
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here. Understanding the Bitcoin Power Law Model Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship. By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges. In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term. The Bitcoin Power Law has the following function: Power Law = 10^(a + b * log10(d)) Consisting of the following parameters: a: Power Law Intercept (default: -17.668). b: Power Law Slope (default: 5.926). d: Number of days since a reference point(calculated by counting bars from the reference point with an offset). Explanation of the a and b parameters: Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post. Applying vertical shifts to generate the other lines: Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept. In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest): Base Power Law Line (no shift) 1-year shifted line 2-year shifted line 3-year shifted line 4-year shifted line 5-year shifted line The six power law lines: Bitcoin Power Law Oscillator This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) . The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula: normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line) Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines. Interpretation of the Bitcoin Power Law Model: The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.). By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame. On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines. In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples: Example 1: In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B(Note: Vertical yearly grid lines have been added for this purpose👍). Example 2: In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line. Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe : Example 3: In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Pine Script® göstergesi
Cryptonerds tarafından
Smoothed ROC Z-Score with TableSmoothed ROC Z-Score with Table This indicator calculates the Rate of Change (ROC) of a chosen price source and transforms it into a smoothed Z-Score oscillator, allowing you to identify market cycle tops and bottoms with reduced noise. How it works: The ROC is calculated over a user-defined length. A moving average and standard deviation over a separate window are used to standardize the ROC into a Z-Score. This Z-Score is further smoothed using an exponential moving average (EMA) to filter noise and highlight clearer cycle signals. The smoothed Z-Score oscillates around zero, with upper and lower bands defined by user inputs (default ±2 standard deviations). When the Z-Score reaches or exceeds ±3 (customizable), the value shown in the table is clamped at ±2 for clearer interpretation. The indicator plots the smoothed Z-Score line with zero and band lines, and displays a colored Z-Score table on the right for quick reference. How to read it: Values near zero indicate neutral momentum. Rising Z-Scores towards the upper band suggest increasing positive momentum, possible market tops or strength. Falling Z-Scores towards the lower band indicate negative momentum, potential bottoms or weakness. The color-coded table gives an easy visual cue: red/orange for strong positive signals, green/teal for strong negative signals, and gray for neutral zones. Use cases: Identify turning points in trending markets. Filter noisy ROC data for cleaner signals. Combine with other indicators to time entries and exits more effectively.
Pine Script® göstergesi
simonweisser_business tarafından
S&P 500 Estimated PE (Sampled Every 4)📊 **S&P 500 Estimated PE Ratio (from CSV)** This indicator visualizes the forward-looking estimated PE ratio of the S&P 500 index, imported from external CSV data. 🔹 **Features:** - Real historical daily data from 2008 onward - Automatically aligns PE values to closest available trading date - Useful for macro valuation trends and long-term entry signals 📌 **Best for:** - Investors interested in forward-looking valuation - Analysts tracking over/undervaluation trends - Long-term timing overlay on price action Category: `Breadth indicators`, `Cycles`
Pine Script® göstergesi
tctctc999 tarafından
Gold ValuationGold Value Index The Gold Value Index (GVI) is a macro-driven oscillator that estimates the relative value of gold based on real-time movements in the US Dollar Index (DXY) and the 10-Year US Treasury Yield (US10Y). It helps traders contextualize gold’s price within broader macroeconomic pressure — identifying when gold may be over- or undervalued relative to these key drivers. How It Works – Macro Inputs: DXY (US Dollar Index): Typically moves inversely to gold. A rising dollar suggests downward pressure on gold value. US10Y Yield: Higher yields increase the opportunity cost of holding gold, often leading to weaker gold prices. Both inputs are Z-score normalized and inverted to reflect their typical negative correlation with gold. When combined, they form a single, scaled index from 0 (undervalued) to 100 (overvalued). Why Use This Tool? Gold reacts to macro forces as much as technical ones. The GVI blends these inputs into a clear, visual gauge to: Anticipate mean-reversion setups. Avoid emotionally-driven trades in extreme macro conditions. Enhance timing by understanding gold's macro context. Important Notes: Data sources include ICEUS:DXY and TVC:US10Y via TradingView. Code is protected — this is a private, invite-only script.
Pine Script® göstergesi
Ramon_S tarafından
Simplified STH-MVRV + Z-ScoreSimplified Short Term Holder MVRV (STH-MVRV) + Z-Score Indicator Description: This indicator visualizes the Short Term Holder Market Value to Realized Value ratio (STH-MVRV) and its normalized Z-Score, providing insight into Bitcoin’s market cycle phases and potential overbought or oversold conditions. How it works: The STH-MVRV ratio compares the market value of coins held by short-term holders to their realized value, helping to identify periods of profit-taking or accumulation by these holders. The indicator calculates three versions: STH-MVRV (MVRV): Ratio of current MVRV to its 155-day SMA. STH-MVRV (Price): Ratio of BTC price to its 155-day SMA. STH-MVRV (AVG): Average of the above two ratios. You can select which ratio to display via the input dropdown. Threshold Lines: Adjustable upper and lower threshold lines mark significant levels where market sentiment might shift. The indicator also plots a baseline at 1.0 as a reference. Z-Score Explanation: The Z-Score is a normalized value scaled between -3 and +3, calculated relative to the chosen threshold levels. When the ratio hits the upper threshold, the Z-Score approaches +2, indicating potential overbought conditions. Conversely, reaching the lower threshold corresponds to a Z-Score near -2, signaling potential oversold conditions. This Z-Score is shown in a clear table in the top right corner of the chart for easy monitoring. Data Sources: MVRV data is fetched from the BTC_MVRV dataset. Price data is sourced from the BTC/USD index. Usage: Use this indicator to assess short-term holder market behavior and to help identify buying or selling opportunities based on extremes indicated by the Z-Score. Combining this tool with other analysis can improve timing decisions in Bitcoin trading.
Pine Script® göstergesi
simonweisser_business tarafından
Simplified Hashrate Oscillator + Z-ScoreIndicator Description for TradingView Simplified Hashrate Oscillator + Z-Score (SHO + Z) This indicator analyzes the Bitcoin network's mining hashrate data by comparing short-term and long-term moving averages of the hashrate to create an oscillator that reflects changes in mining activity. How it works: The indicator calculates two Simple Moving Averages (SMAs) of the Bitcoin network hashrate — a short-term SMA (default 21 days) and a long-term SMA (default 105 days). The difference between these two averages is normalized and expressed as a percentage, forming the Hashrate Oscillator line. Two user-defined threshold lines (default ±7%) are plotted as upper and lower reference levels on the oscillator. When the oscillator approaches these levels, it indicates potential extremes in mining activity. Z-Score Explanation: The Z-Score is a normalized measure that translates the oscillator's current value into a standardized scale roughly ranging from -2 to +2. It shows how far the current hashrate oscillator value deviates from the user-defined thresholds. A Z-Score near +2 means the oscillator is close to or above the upper threshold (possible overbought conditions). A Z-Score near -2 means the oscillator is near or below the lower threshold (possible oversold conditions). This helps users assess the relative strength or weakness of the mining hashrate movement in a normalized context. Data Source: The hashrate data is sourced daily from the Bitcoin network hashrate dataset provided by Quandl (QUANDL:BCHAIN/HRATE), a reliable blockchain data provider. The indicator requests daily hashrate values and calculates SMAs accordingly. How to use: Watch the Hashrate Oscillator line for movements towards or beyond the threshold lines as signals of miner capitulation or recovery phases. Use the Z-Score displayed in the table to quickly gauge how extreme the current reading is relative to set thresholds. Adjust the short and long SMA periods and threshold lines to suit your preferred sensitivity and trading timeframe.
Pine Script® göstergesi
simonweisser_business tarafından
SP 500 PE Ratio (Loose Date Match)📈 **S&P 500 PE Ratio (from Excel Data)** This custom indicator visualizes the historical S&P 500 Price-to-Earnings (PE) Ratio loaded from Excel. Each data point represents a snapshot of the market valuation at a specific time, typically on an annual or quarterly basis. 🔹 **What it does:** - Plots the PE ratio values on the chart aligned with historical dates - Uses stepwise or linear rendering to account for missing trading days - Helps identify valuation cycles and extremes (e.g., overvalued vs undervalued) 🔍 **Use case:** - Long-term market analysis - Compare PE trends with price performance - Spot long-term entry/exit zones based on valuation 🛠️ Future plans: - Add value zone highlighting (e.g., PE > 30 = red, PE < 15 = green) - Support for dynamic datasets (via Google Sheets or Notion) Category: `Breadth indicators`, `Cycles` 💡 Source: Manually imported data (can be replaced with any custom macro data series)
Pine Script® göstergesi
tctctc999 tarafından
Granger Causality Flow IndicatorGranger Causality Flow Indicator (GC Flow) █ OVERVIEW The Granger Causality Flow Indicator (GC Flow) attempts to quantify the potential predictive relationship between two user-selected financial instruments (Symbol X and Symbol Y). In essence, it explores whether the past values of one series (e.g., Symbol X) can help explain the current value of another series (e.g., Symbol Y) better than Y's own past values alone. This indicator provides a "Granger Causality Score" (GC Score) for both directions (X → Y and Y → X). A higher score suggests a stronger statistical linkage where one series may lead or influence the other. The indicator visualizes this "flow" of potential influence through background colors and on-chart text. Important Note: "Granger Causality" does not imply true economic or fundamental causation. It is a statistical concept indicating predictive power or information flow. This implementation also involves simplifications (notably, using AR(1) models) due to the complexities of full Vector Autoregression (VAR) models in Pine Script®. █ HOW IT WORKS The indicator's methodology is based on comparing the performance of Autoregressive (AR) models: 1. Data Preprocessing: Fetches historical close prices for two user-defined symbols (X and Y). Optionally applies first-order differencing (`price - price `) to the series. Differencing is a common technique to achieve a proxy for stationarity, which is an underlying assumption for Granger Causality tests. Non-stationary series can lead to spurious correlations. 2. Autoregressive (AR) Models (Simplified to AR(1)): Due to Pine Script's current limitations for complex multivariate time series models, this indicator uses simplified AR(1) models (where the current value is predicted by its immediately preceding value). Restricted Model (for Y → Y): Predicts the target series (e.g., Y) using only its own past value (Y ). `Y = c_R + a_R * Y + residuals_R` The variance of `residuals_R` (Var_R) is calculated. Unrestricted Model (Proxy for X → Y): To test if X Granger-causes Y, the indicator examines if the past values of X (X ) can explain the residuals from the restricted model of Y. `residuals_R = c_UR' + b_UR * X + residuals_UR` The variance of these final `residuals_UR` (Var_UR) is calculated. The same process is repeated to test if Y Granger-causes X. 3. Granger Causality (GC) Score Calculation: The GC Score quantifies the improvement in prediction from adding the other series' past values. It's calculated as: `GC Score = 1 - (Var_UR / Var_R)` A score closer to 1 suggests that the "causing" series significantly reduces the unexplained variance of the "target" series (i.e., Var_UR is much smaller than Var_R), indicating stronger Granger causality. A score near 0 (or capped at 0 if Var_UR >= Var_R) suggests little to no improvement in prediction. The score is calculated over a rolling `Calculation Window`. Pine Script® Snippet (Conceptual GC Score Logic): // Conceptual representation of GC Score calculation // var_R: Variance of residuals when Y is predicted by Y // var_UR: Variance of residuals when Y's AR(1) residuals are predicted by X score = 0.0 if var_R > 1e-9 // Avoid division by zero score := 1.0 - (var_UR / var_R) score := score < 0 ? 0 : score // Ensure score is not negative 4. Determining Causal Flow: The calculated GC Scores for X → Y and Y → X are compared against a user-defined `Significance Threshold for GC Score`. If GC_X→Y > threshold AND GC_Y→X > threshold: Bidirectional flow. If GC_X→Y > threshold only: X → Y flow. If GC_Y→X > threshold only: Y → X flow. Otherwise: No significant flow. █ HOW TO USE IT Interpreting the Visuals: Background Color: Green: Indicates X → Y (Symbol 1 potentially leads Symbol 2). Orange: Indicates Y → X (Symbol 2 potentially leads Symbol 1). Blue: Indicates Bidirectional influence. Gray: No significant Granger causality detected based on the threshold. Data Window Plots: The actual GC Scores for X → Y (blue) and Y → X (red) are plotted and visible in TradingView's Data Window. A dashed gray line shows your `Significance Threshold`. On-Chart Table (Last Bar): Displays the currently detected causal direction text (e.g., "BTCUSDT → QQQ"). Potential Applications: Intermarket Analysis: Explore potential lead-lag relationships between different asset classes (e.g., commodities and equities, bonds and currencies). Pair Trading Components: Identify if one component of a potential pair tends to lead the other. Confirmation Tool: Use alongside other analyses to see if a move in one asset might foreshadow a move in another. Considerations: Symbol Choice: Select symbols that have a plausible economic or market relationship. Stationarity: Granger Causality tests ideally require stationary time series. The `Use Differencing` option is a simple proxy. True stationarity testing is complex. Non-stationary data can yield misleading results. Lag Order (p): This indicator is fixed at p=1 due to Pine Script® limitations. In rigorous analysis, selecting the optimal lag order is crucial. Calculation Window: Shorter windows are more responsive but may be noisier. Longer windows provide smoother scores but lag more. Significance Threshold: Adjust this based on your desired sensitivity for detecting causal links. There's no universally "correct" threshold; it depends on the context and noise level of the series. █ INPUTS Symbol 1 (X): The first symbol in the analysis. Symbol 2 (Y): The second symbol (considered the target when testing X → Y). Use Differencing: If true, applies first-order differencing to both series as a proxy for stationarity. Calculation Window (N): Lookback period for AR model coefficient estimation and variance calculations. Lag Order (p): Currently fixed at 1. This defines the lag used (e.g., X , Y ) in the AR models. Significance Threshold for GC Score: A value between 0.01 and 0.99. The calculated GC Score must exceed this to be considered significant. █ VISUALIZATION Background Color: Dynamically changes based on the detected Granger causal flow (Green for X → Y, Orange for Y → X, Blue for Bidirectional, Gray for None). GC Scores (Data Window): Blue Plot: GC Score for X → Y. Red Plot: GC Score for Y → X. Significance Threshold Line: A dashed gray horizontal line plotted at the level of your input threshold. On-Chart Table: Displayed on the top-right (on the last bar), showing the current causal direction text. █ ALERTS The indicator can generate alerts for: Emergence of X → Y causality. Emergence of Y → X causality. General change or cessation of a previously detected causal relationship. █ IMPORTANT DISCLAIMERS & LIMITATIONS Correlation vs. Causation: Granger causality measures predictive power, not true underlying economic causation. A strong GC Score doesn't prove one asset *causes* another to move, only that its past values improve predictions. Stationarity Assumption: While differencing is offered, it's a simplified approach. Non-stationary data can lead to spurious (false) Granger causality detection. Model Simplification (AR(1)): This script uses AR(1) models for simplicity. Real-world relationships can involve more complex dynamics and higher lag orders. The fixed lag of p=1 is a significant constraint. Sensitivity to Parameters: Results can be sensitive to the chosen symbols, calculation window, differencing option, and significance threshold. No Statistical Significance Testing (p-values): This indicator uses a direct threshold on the GC Score itself, not a formal statistical test (like an F-test producing p-values) typically found in econometric software. Use this indicator as an exploratory tool within a broader analytical framework. Do not rely on it as a standalone basis for trading decisions. █ CREDITS & LICENSE Author: mastertop ( Twitter: x.com ) Version: 1.0 (Released: 2025-05-08) This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org © mastertop, 2025
Pine Script® göstergesi
MASTERTOP_ASTRAY tarafından
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® göstergesi
PineCodersTASC tarafından
5
Yield Curve Approximation A yield curve is a graph that plots the yields (interest rates) of bonds with the same credit quality but different maturity dates. It helps investors understand the relationship between short-term and long-term interest rates. 🔹 Types of Yield Curves 1️⃣ Normal Yield Curve – Upward-sloping, indicating economic expansion. 2️⃣ Inverted Yield Curve – Downward-sloping, often a recession warning. 3️⃣ Flat Yield Curve – Suggests economic uncertainty or transition. The yield curve is widely used to predict economic conditions and interest rate movements. You can learn more about it here. Would you like insights on how traders use the yield curve for investment decisions? How to Trade Using This? ✅ If the yield curve is steepening (green) → Favor growth stocks, commodities, and high-risk assets. ✅ If the yield curve is flattening or inverting (red) → Consider bonds, defensive sectors, or hedging strategies. ✅ Pair with economic news and interest rate decisions to refine predictions.
Pine Script® göstergesi
GTR511 tarafından