התחל

Benchmark

stock-vs-industry using NQUSB benchmark idexesOriginal idea from Stock versus Industry by Tr33man . ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ PRIMARY IMPROVEMENT: NQUSB Hierarchical Index Benchmarks ═══ The KEY improvement: Multi-Level Industry Granularity with Drill-Down/Drill-Up Navigation From: Simple ETF Comparison (1 Level) Stock → Industry ETF (e.g., "SOXX" for all semiconductors) To: NQUSB Hierarchical Comparison (4 Levels) Level 4 (Primary): NQUSB10102010 → Semiconductors (most specific) Level 3 (Secondary): NQUSB101020 → Technology Hardware and Equipment Level 2 (Tertiary): NQUSB101010 → Software and Computer Services Level 1 (Quaternary): NQUSB10 → Technology (broadest sector) Users can now drill up and down the industry hierarchy to see how their stock performs against different levels of industry classification! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ WHY THIS MATTERS ═══ Original Limitations: Single comparison level - ETF only No drill-down capability - Can't zoom in to more specific industries No drill-up capability - Can't zoom out to broader sectors ETF limitations - Not all industries have dedicated ETFs Arbitrary mappings - Manual ETF selection may not represent true industry Improved Capabilities: 4-level hierarchical navigation - Drill-down and drill-up through industry classifications 361 NQUSB official indices - NASDAQ US Benchmark Index structure Official NASDAQ classification - Industry-standard taxonomy Large Mid Cap (LM) option - Focus on larger companies when needed Enhanced UI - Clear level indicators and full index descriptions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ EXAMPLE: ANALYZING NVDA (Semiconductors) ═══ Level 4 - Primary (Most Specific): NQUSB10102010 - Semiconductors → NVDA vs. AMD, AVGO, QCOM, TXN, etc. (direct competitors) Level 3 - Secondary (Broader): NQUSB101020 - Tech Hardware & Equipment → NVDA vs. AAPL, CSCO + semiconductors Level 2 - Tertiary (Even Broader): NQUSB101010 - Software and Computer Services → NVDA vs. all tech hardware Level 1 - Quaternary (Broadest): NQUSB10 - Technology Sector → NVDA vs. entire technology sector You can now zoom in to see direct competitors or zoom out to understand macro sector trends - all in one indicator! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ COMPARISON SUMMARY ═══ Original Version: Comparison System: Industry ETFs Industry Levels: 1 (flat ETF mapping) Total Classifications: ~140 industries Hierarchy Navigation: ❌ No Data Source: Manual ETF curation Improved Version: Comparison System: NQUSB Official Indices Industry Levels: 4 (hierarchical drill-down/up) Total Classifications: 361 NQUSB indices Hierarchy Navigation: ✅ 4-level drill navigation Data Source: NASDAQ official taxonomy Large/Mid Cap Option: ✅ LM variant toggle Level Indicator: ✅ to labels ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ ADDITIONAL FEATURES ═══ Dual Comparison System - Toggle between ETF mode (original) and Index Benchmark mode (NQUSB hierarchy) Better Fallback Logic - Manual Override > NQUSB Index > ETF > SPY default Enhanced Display - 4-row information table with full NQUSB index description Backward Compatible - All original ETF mappings still work, existing charts won't break Large Mid Cap Toggle - Optional "LM" suffix for focusing on larger companies only ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ For complete documentation, data files, technical details, and the full NQUSB hierarchy structure, visit the GitHub repository. The result: More accurate, more flexible, and more comprehensive industry strength analysis - enabling traders to understand exactly where their stock's performance comes from by drilling through multiple levels of industry classification.
אינדיקטור Pine Script®
מאת ‎ama0000‎
Rolling Correlation vs Another Symbol (SPY Default)This indicator visualizes the rolling correlation between the current chart symbol and another selected asset, helping traders understand how closely the two move together over time. It calculates the Pearson correlation coefficient over a user-defined period (default 22 bars) and plots it as a color-coded line: • Green line → positive correlation (move in the same direction) • Red line → negative correlation (move in opposite directions) • A gray dashed line marks the zero level (no correlation). The background highlights periods of strong relationship: • Light green when correlation > +0.7 (strong positive) • Light red when correlation < –0.7 (strong negative) Use this tool to quickly spot diversification opportunities, confirm hedges, or understand how assets interact during different market regimes.
אינדיקטור Pine Script®
מאת ‎EuerMarit‎
Crypto Index Price# Crypto Index Price - Indicator Description ## 📊 What is this indicator? **Crypto Index Price** is an indicator for creating your own cryptocurrency index based on an equal-weighted portfolio. It allows you to track the overall dynamics of the cryptocurrency market through a composite index of selected assets. ## 🎯 Key Features - **Up to 20 assets in the index** — create an index from any trading pairs - **Equal-weighted methodology** — each asset has the same weight in the index - **Moving average** — optional trend filter for the index - **Flexible visualization settings** — customizable colors and line thickness ## 📈 How to Use The indicator is displayed in a separate pane below the chart and shows: 1. **Blue line** — crypto index value 2. **Orange line** (optional) — moving average of the index ### Trading Applications: - **Identify overall market trend** — if the index is rising, most coins are in an uptrend - **Divergences** — divergence between your asset and the index may signal local opportunities - **Signal confirmation** — use the index to confirm trading decisions on individual coins - **Market condition filter** — trade longs when index is above MA, shorts when below ## ⚙️ Settings ### Assets (Symbols) - **Asset 1-10** — main cryptocurrencies (default: BTC, ETH, BNB, SOL, XRP, ADA, AVAX, LINK, DOGE, TRX) - **Asset 11-20** — additional slots for index expansion ### Visual Parameters - **Index line color** — main line color (default: blue) - **Line width** — from 1 to 5 pixels - **Show moving average** — enable/disable MA - **MA period** — moving average calculation period (default: 20) - **MA color** — moving average line color (default: orange) ## 💡 Recommendations - For a top coins index, use 5-10 largest cryptocurrencies by market cap - For an altcoin index, add medium and small coins from your sector - Use MA to filter false signals and identify the global trend - Compare individual asset behavior with the index to find anomalies ## ⚠️ Important The indicator uses equal-weighted methodology — each coin contributes equally regardless of price or market cap. This differs from cap-weighted indices and may provide a different market perspective. --- *This indicator is intended for analysis and is not trading advice. Always conduct your own analysis before making trading decisions.* ---
אינדיקטור Pine Script®
מאת ‎AlekMel‎
Benchmark Relative Performance BRPBenchmark Relative Performance (BRP) is a comprehensive technical analysis tool that compares any stock's performance against a chosen benchmark (QQQ, SPY, IWM, etc.) to identify outperformance and underperformance patterns. Key Features: Dual-line visualization: Shows both ticker and relative strength performance Dynamic color coding: 5-level color system indicating performance strength Customizable benchmark: Choose from any ticker via TradingView's symbol picker Volume weighting: Optional volume analysis for stronger signal confirmation Performance zones: Visual thresholds for strong/moderate performance levels Compact info table: Real-time performance status and values What It Shows: Benchmark Performance Line (Blue): Shows your chosen benchmark's percentage performance Relative Strength Line (Color-coded): Shows how much the ticker outperforms/underperforms Fill Area: Visual gap between ticker and benchmark performance Performance Zones: Dotted lines marking significant performance thresholds Color System: Green: Strong outperformance (above custom threshold) Lime: Standard outperformance Yellow: Neutral/Equal performance Orange: Standard underperformance Red: Strong underperformance (below custom threshold) Best Used For: Stock selection and rotation strategies Sector/ETF relative strength analysis Identifying momentum shifts vs benchmarks Portfolio performance evaluation Market timing based on relative performance Settings: Customizable lookback period (default: 20) Adjustable strong performance threshold (default: 5%) Optional volume weighting factor Full table customization (position, colors, fonts) Performance display (percentage or decimal) Perfect for traders and investors who want to identify stocks showing relative strength or weakness compared to major market benchmarks.
אינדיקטור Pine Script®
מאת ‎thricegrate‎
TimeSeriesBenchmarkMeasuresLibrary "TimeSeriesBenchmarkMeasures" Time Series Benchmark Metrics. \ Provides a comprehensive set of functions for benchmarking time series data, allowing you to evaluate the accuracy, stability, and risk characteristics of various models or strategies. The functions cover a wide range of statistical measures, including accuracy metrics (MAE, MSE, RMSE, NRMSE, MAPE, SMAPE), autocorrelation analysis (ACF, ADF), and risk measures (Theils Inequality, Sharpness, Resolution, Coverage, and Pinball). ___ Reference: - github.com . - medium.com . - www.salesforce.com . - towardsdatascience.com . - github.com . mae(actual, forecasts)   In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.   Parameters:      actual (array) : List of actual values.      forecasts (array) : List of forecasts values.   Returns: - Mean Absolute Error (MAE). ___ Reference: - en.wikipedia.org . - The Orange Book of Machine Learning - Carl McBride Ellis . mse(actual, forecasts)   The Mean Squared Error (MSE) is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error approaches zero.   Parameters:      actual (array) : List of actual values.      forecasts (array) : List of forecasts values.   Returns: - Mean Squared Error (MSE). ___ Reference: - en.wikipedia.org . rmse(targets, forecasts, order, offset)   Calculates the Root Mean Squared Error (RMSE) between target observations and forecasts. RMSE is a standard measure of the differences between values predicted by a model and the values actually observed.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.      order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.      offset (int) : Forecast offset related to target, `default=0`.   Returns: - RMSE value. nmrse(targets, forecasts, order, offset)   Normalised Root Mean Squared Error.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.      order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.      offset (int) : Forecast offset related to target, `default=0`.   Returns: - NRMSE value. rmse_interval(targets, forecasts)   Root Mean Squared Error for a set of interval windows. Computes RMSE by converting interval forecasts (with min/max bounds) into point forecasts using the mean of the interval bounds, then compares against actual target values.   Parameters:      targets (array) : List of target observations.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - RMSE value for the combined interval list. mape(targets, forecasts)   Mean Average Percentual Error.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.   Returns: - MAPE value. smape(targets, forecasts, mode)   Symmetric Mean Average Percentual Error. Calculates the Mean Absolute Percentage Error (MAPE) between actual targets and forecasts. MAPE is a common metric for evaluating forecast accuracy, expressed as a percentage, lower values indicate a better forecast accuracy.   Parameters:      targets (array) : List of target observations.      forecasts (array) : List of forecasts.      mode (int) : Type of method: default=0:`sum(abs(Fi-Ti)) / sum(Fi+Ti)` , 1:`mean(abs(Fi-Ti) / ((Fi + Ti) / 2))` , 2:`mean(abs(Fi-Ti) / (abs(Fi) + abs(Ti))) * 100`   Returns: - SMAPE value. mape_interval(targets, forecasts)   Mean Average Percentual Error for a set of interval windows.   Parameters:      targets (array) : List of target observations.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - MAPE value for the combined interval list. acf(data, k)   Autocorrelation Function (ACF) for a time series at a specified lag.   Parameters:      data (array) : Sample data of the observations.      k (int) : The lag period for which to calculate the autocorrelation. Must be a non-negative integer.   Returns: - The autocorrelation value at the specified lag, ranging from -1 to 1. ___ The autocorrelation function measures the linear dependence between observations in a time series at different time lags. It quantifies how well the series correlates with itself at different time intervals, which is useful for identifying patterns, seasonality, and the appropriate lag structure for time series models. ACF values close to 1 indicate strong positive correlation, values close to -1 indicate strong negative correlation, and values near 0 indicate no linear correlation. ___ Reference: - statisticsbyjim.com acf_multiple(data, k)   Autocorrelation function (ACF) for a time series at a set of specified lags.   Parameters:      data (array) : Sample data of the observations.      k (array) : List of lag periods for which to calculate the autocorrelation. Must be a non-negative integer.   Returns: - List of ACF values for provided lags. ___ The autocorrelation function measures the linear dependence between observations in a time series at different time lags. It quantifies how well the series correlates with itself at different time intervals, which is useful for identifying patterns, seasonality, and the appropriate lag structure for time series models. ACF values close to 1 indicate strong positive correlation, values close to -1 indicate strong negative correlation, and values near 0 indicate no linear correlation. ___ Reference: - statisticsbyjim.com adfuller(data, n_lag, conf)   : Augmented Dickey-Fuller test for stationarity.   Parameters:      data (array) : Data series.      n_lag (int) : Maximum lag.      conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).   Returns: - `adf` The test statistic. - `crit` Critical value for the test statistic at the 10 % levels. - `nobs` Number of observations used for the ADF regression and calculation of the critical values. ___ The Augmented Dickey-Fuller test is used to determine whether a time series is stationary or contains a unit root (non-stationary). The null hypothesis is that the series has a unit root (is non-stationary), while the alternative hypothesis is that the series is stationary. A stationary time series has statistical properties that do not change over time, making it suitable for many time series forecasting models. If the test statistic is less than the critical value, we reject the null hypothesis and conclude the series is stationary. ___ Reference: - www.jstor.org - en.wikipedia.org theils_inequality(targets, forecasts)   Calculates Theil's Inequality Coefficient, a measure of forecast accuracy that quantifies the relative difference between actual and predicted values.   Parameters:      targets (array) : List of target observations.      forecasts (array) : Matrix with list of forecasts, ordered column wise.   Returns: - Theil's Inequality Coefficient value, value closer to 0 is better. ___ Theil's Inequality Coefficient is calculated as: `sqrt(Sum((y_i - f_i)^2)) / (sqrt(Sum(y_i^2)) + sqrt(Sum(f_i^2)))` where `y_i` represents actual values and `f_i` represents forecast values. This metric ranges from 0 to infinity, with 0 indicating perfect forecast accuracy. ___ Reference: - en.wikipedia.org sharpness(forecasts)   The average width of the forecast intervals across all observations, representing the sharpness or precision of the predictive intervals.   Parameters:      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - Sharpness The sharpness level, which is the average width of all prediction intervals across the forecast horizon. ___ Sharpness is an important metric for evaluating forecast quality. It measures how narrow or wide the prediction intervals are. Higher sharpness (narrower intervals) indicates greater precision in the forecast intervals, while lower sharpness (wider intervals) suggests less precision. The sharpness metric is calculated as the mean of the interval widths across all observations, where each interval width is the difference between the upper and lower bounds of the prediction interval. Note: This function assumes that the forecasts matrix has at least 2 columns, with the first column representing the lower bounds and the second column representing the upper bounds of prediction intervals. ___ Reference: - Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. otexts.com resolution(forecasts)   Calculates the resolution of forecast intervals, measuring the average absolute difference between individual forecast interval widths and the overall sharpness measure.   Parameters:      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - The average absolute difference between individual forecast interval widths and the overall sharpness measure, representing the resolution of the forecasts. ___ Resolution is a key metric for evaluating forecast quality that measures the consistency of prediction interval widths. It quantifies how much the individual forecast intervals vary from the average interval width (sharpness). High resolution indicates that the forecast intervals are relatively consistent across observations, while low resolution suggests significant variation in interval widths. The resolution is calculated as the mean absolute deviation of individual interval widths from the overall sharpness value. This provides insight into the uniformity of the forecast uncertainty estimates across the forecast horizon. Note: This function requires the forecasts matrix to have at least 2 columns (min, max) representing the lower and upper bounds of prediction intervals. ___ Reference: - (sites.stat.washington.edu) - (www.jstor.org) coverage(targets, forecasts)   Calculates the coverage probability, which is the percentage of target values that fall within the corresponding forecasted prediction intervals.   Parameters:      targets (array) : List of target values.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - Percent of target values that fall within their corresponding forecast intervals, expressed as a decimal value between 0 and 1 (or 0% and 100%). ___ Coverage probability is a crucial metric for evaluating the reliability of prediction intervals. It measures how well the forecast intervals capture the actual observed values. An ideal forecast should have a coverage probability close to the nominal confidence level (e.g., 90%, 95%, or 99%). For example, if a 95% prediction interval is used, we expect approximately 95% of the actual target values to fall within those intervals. If the coverage is significantly lower than the nominal level, the intervals may be too narrow; if it's significantly higher, the intervals may be too wide. Note: This function requires the targets array and forecasts matrix to have the same number of observations, and the forecasts matrix must have at least 2 columns (min, max) representing the lower and upper bounds of prediction intervals. ___ Reference: - (www.jstor.org) pinball(tau, target, forecast)   Pinball loss function, measures the asymmetric loss for quantile forecasts.   Parameters:      tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.      target (float) : The actual observed value to compare against.      forecast (float) : The forecasted value.   Returns: - The Pinball loss value, which quantifies the distance between the forecast and target relative to the specified quantile level. ___ The Pinball loss function is specifically designed for evaluating quantile forecasts. It is asymmetric, meaning it penalizes underestimates and overestimates differently depending on the quantile level being evaluated. For a given quantile τ, the loss function is defined as: - If target >= forecast: (target - forecast) * τ - If target < forecast: (forecast - target) * (1 - τ) This loss function is commonly used in quantile regression and probabilistic forecasting to evaluate how well forecasts capture specific quantiles of the target distribution. ___ Reference: - (www.otexts.com) pinball_mean(tau, targets, forecasts)   Calculates the mean pinball loss for quantile regression.   Parameters:      tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.      targets (array) : The actual observed values to compare against.      forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).   Returns: - The mean pinball loss value across all observations. ___ The pinball_mean() function computes the average Pinball loss across multiple observations, making it suitable for evaluating overall forecast performance in quantile regression tasks. This function leverages the asymmetric Pinball loss function to evaluate how well forecasts capture specific quantiles of the target distribution. The choice of which column from the forecasts matrix to use depends on the quantile level: - For τ ≤ 0.5: Uses the first column (min) of forecasts - For τ > 0.5: Uses the second column (max) of forecasts This loss function is commonly used in quantile regression and probabilistic forecasting to evaluate how well forecasts capture specific quantiles of the target distribution. ___ Reference: - (www.otexts.com)
ספריית Pine Script®
מאת ‎RicardoSantos‎
4
CRS by TQCRS by TradeQUO Comparative Relative Strength Indicator What it does Computes the percentage difference in performance between your current chart symbol (e.g. GC1!) and a chosen benchmark (e.g. ES1!, DXY, NQ1!). Smooths that “CRS” line with a configurable moving average (SMA, EMA, WMA) — default length is 63 bars. Key Plots CRS (%) (aqua) — how your symbol has held up relative to the benchmark since the chart loaded. CRS MA (%) (fuchsia) — smoothed trend of that relative strength. How to Interpret Trend-Following Long when CRS crosses above its MA and is above 0 → your symbol is outperforming. Short (or reverse) when CRS crosses below its MA and is below 0 → underperformance. Regime Filter CRS > 0 → Risk-On environment for your symbol vs. benchmark. CRS < 0 → Risk-Off relative to the benchmark. Divergence Signals Bearish Divergence: Price makes a higher high, but CRS makes a lower high → potential sell-off incoming. Bullish Divergence: Price makes a lower low, but CRS makes a higher low → look for rebound opportunity. Pairs Trading Outperformance: Long your symbol + Short benchmark when CRS > MA. Underperformance: Short your symbol + Long benchmark when CRS < MA. Why it matters Reveals relative market leadership early, not just absolute price moves. Helps you allocate risk dynamically, spot rotations and catch reversals before they show in price alone.
אינדיקטור Pine Script®
מאת ‎TradeQUO‎
6
Market Strength Buy Sell Indicator [TradeDots]A specialized tool designed to assist traders in evaluating market conditions through a multifaceted analysis of relative performance, beta-adjusted returns, momentum, and volume—allowing you to identify optimal points for long or short trades. By integrating multiple benchmarks (default S&P 500) and percentile-based thresholds, the script provides clear, actionable insights suitable for both day trading and higher-level timeframe assessments. 📝 HOW IT WORKS 1. Multi-Factor Composite Score Relative Performance (RS Ratio): Compares your asset’s performance to a chosen benchmark (default: SPY). Values above 1.0 indicate outperformance, while below 1.0 suggest underperformance. Beta-Adjusted Returns: Checks the ticker’s excess movement relative to expected market-related moves. This helps distinguish pure “alpha” from broad market effects. Volume & Correlation: Volume spikes often confirm the momentum behind a move, while correlation measures how closely the asset tracks or diverges from its benchmark. These components merge into a 0–100 composite score. Scores above 50 frequently imply bullish strength; drops below 50 often point to underperformance—potentially flagging short opportunities. 2. Intraday & Day Trading Focus Monitoring Below 50: During the trading day, the script calculates live data against the benchmark, offering an intraday-sensitive composite score. A dip under 50 may indicate a short bias for that session, especially when accompanied by high volume or momentum shifts. 3. Higher Timeframe Monitoring Daily Strategies: On daily or weekly charts, the script reveals overall relative strength or weakness compared to the S&P 500. This higher-level perspective helps form broader trading biases—crucial for swing or position trades spanning multiple days. Long/Short Thresholds: Persistent readings above 50 on a daily chart typically reinforce a long bias, while consistent dips below 50 can sustain a short or cautious outlook. 4. Pair Trading Applications Custom Benchmark Selection: By setting a specific ticker pair as your benchmark instead of the default S&P 500, you can identify spread trading opportunities between two correlated assets. This allows you to go long the outperforming asset while shorting the underperforming one when the spread reaches extreme levels. 4. Color-Coded Signals & Alerts Visual Zones (25–75): Color-coded bands highlight strong outperformance (above 75) or pronounced underperformance (below 25). Alerts on Strong Shifts: Automatic alerts can notify you of sudden entries or exits from bullish or bearish zones, so you can potentially act on new market information without delay. ⚙️ HOW TO USE 1. Select Your Timeframe: For scalping or day trading, lower intervals (e.g., 5-minute) offer immediate data resets at the session’s start. For multi-day insight, daily or weekly charts reveal broader performance trends.
 2. Watch Key Levels Around 50: Intraday dips under 50 may be a cue to consider short trades, while bounces above 50 can confirm renewed strength. 3. Assess Benchmark Relationships: Compare your asset’s score and signals to the broader market. A stock falling below its pair’s relative strength line might lag overall market momentum. 4. Combine Tools & Validate: This script excels when integrated with other technical analysis methods (e.g., support/resistance, chart patterns) and fundamental factors for a holistic market view. ❗ LIMITATIONS No Direction Guarantee: The indicator identifies relative strength but does not guarantee directional price moves. Delayed Updates: Since calculations update after each bar close, sudden intrabar changes may not immediately reflect. Market-Specific Behaviors: Some assets or unusual market conditions may deviate from typical benchmarks, weakening signal reliability. Past ≠ Future: High or low relative strength in the past may not predict continued performance. RISK DISCLAIMER All forms of trading and investing involve risk, including the possible loss of principal. This indicator analyzes relative performance but cannot assure profits or eliminate losses. Past performance of any strategy does not guarantee future results. Always combine analysis with proper risk management and your broader trading plan. Consult a licensed financial advisor if you are unsure of your individual risk tolerance or investment objectives.
אינדיקטור Pine Script®
מאת ‎tradedots‎
Custom Index CompositeCustom Index Composite calculates an unweighted composite index by averaging the daily returns of multiple stock tickers. Instead of using price-level weighting, it focuses solely on percentage change, allowing you to compare diverse market themes side by side on a common basis. Why Use a Custom Index Composite? Unlike traditional indices that often lean on market capitalization or price-level data, a custom composite based solely on returns strips out the bias inherent to high-priced stocks. This provides several benefits: Objective Cross-Comparison: When stocks or market themes trade at very different price levels, it can be difficult to assess performance objectively. Using percentage returns, the composite creates an even playing field, enabling a clear comparison between different assets or themes. Tailored Benchmarking: By selecting and combining specific tickers, you can create benchmarks that better represent the segments or strategies you’re interested in. This is particularly useful when standard indices do not capture the nuances of your investment approach. Performance Normalization: Converting raw price data into daily percentage returns minimizes distortions that arise from price differences. This normalization helps in understanding true performance trends across the chosen tickers, making the composite index a more reliable gauge of relative market movement. Custom Analysis Framework: The indicator offers flexibility to adjust the lookback period (defaulting to about 3 months) so you can fine-tune the sensitivity of the index to recent market behavior. This enables you to either smooth out volatility or capture a more immediate trend, depending on your analytical needs. Key Features: Configurable Appearance: You can easily configure the line color, line width, index name, and index name color via the options panel. Ticker Configuration: By default, you can enter up to 15 different tickers into the composite index. Technically, the indicator supports up to 40 tickers (these additional inputs are commented out by default to maintain performance), and you may enable them individually if required. Calculated Bars Length: The indicator uses a “Calculated bars length” setting, which is set by default to 63 days (approximately 3 months). This value can be adjusted, and it is recommended to use the greatest common denominator for consistent analysis. How To Configure Your Chart: Add the Indicator: Place the Custom Index Composite on your chart. Disable Main Symbol Visibility: Hide the primary symbol’s plot and set its scale to “None” to prevent interference with the composite display. Pin to Right Scale: Set the scale of the first composite indicator to “Pinned to right scale.” This helps maintain consistency across different composite indicators. Add Multiple Composites: You can add additional composite indicators and set their scales to “Pinned to right scale” (or alternatively to “A”) for convenient comparison. Limitations: If a ticker symbol is set once in the options, it cannot be cleared to an empty value later. As a result, the symbol will continue to appear in the indicator’s title on the chart. The only way to remove an unwanted symbol is to completely reset the settings and re-enter your desired tickers.
אינדיקטור Pine Script®
מאת ‎fyntrade‎
Relative Performance Indicator by ComLucro - 2025_V01The "Relative Performance Indicator by ComLucro - 2025_V01" is a powerful tool designed to analyze an asset's performance relative to a benchmark index over multiple timeframes. This indicator provides traders with a clear view of how their chosen asset compares to a market index in short, medium, and long-term periods. Key Features: Customizable Lookback Periods: Analyze performance across three adjustable periods (default: 20, 50, and 200 bars). Relative Performance Analysis: Calculate and visualize the difference in percentage performance between the asset and the benchmark index. Dynamic Summary Label: Displays a detailed breakdown of the asset's and index's performance for the latest bar. User-Friendly Interface: Includes customizable colors and display options for clear visualization. How It Works: The script fetches closing prices of both the asset and a benchmark index. It calculates percentage changes over the selected lookback periods. The indicator then computes the relative performance difference between the asset and the index, plotting it on the chart for easy trend analysis. Who Is This For?: Traders and investors who want to compare an asset’s performance against a benchmark index. Those looking to identify trends and deviations between an asset and the broader market. Disclaimer: This tool is for educational purposes only and does not constitute financial or trading advice. Always use it alongside proper risk management strategies and backtest thoroughly before applying it to live trading. Chart Recommendation: Use this script on clean charts for better clarity. Combine it with other technical indicators like moving averages or trendlines to enhance your analysis. Ensure you adjust the lookback periods to match your trading style and the timeframe of your analysis. Additional Notes: For optimal performance, ensure the benchmark index's data is available on your TradingView subscription. The script uses fallback mechanisms to avoid interruptions when index data is unavailable. Always validate the settings and test them to suit your trading strategy.
אינדיקטור Pine Script®
מאת ‎comlucro‎
Normalized Performance ComparisonThis script visualizes the relative performance of a primary asset against a benchmark composed of three reference assets. Here's how it works: User Inputs: - Users specify ticker symbols for three reference assets (default: Platinum, Palladium, Rhodium). Data Retrieval: - Fetches closing prices for the primary asset (the one the script is applied to) and the three reference assets. Normalization: - Each asset's price is normalized by dividing its current price by its initial price at the start of the chart. This allows for performance comparison on a common scale. Benchmark Creation: - The normalized prices of the three reference assets are combined to create a composite benchmark. Ratio Calculation: - Computes the ratio of the normalized primary asset price to the combined normalized benchmark price, highlighting relative performance. Plotting: - Plots this ratio as a blue line on the chart, showing the primary asset's performance relative to the benchmark over time. This script helps users quickly assess how well the primary asset is performing compared to a set of reference assets.
אינדיקטור Pine Script®
מאת ‎wiggium‎
benchLibrary "bench" A simple banchmark library to analyse script performance and bottlenecks. Very useful if you are developing an overly complex application in Pine Script, or trying to optimise a library / function / algorithm... Supports artificial looping benchmarks (of fast functions) Supports integrated linear benchmarks (of expensive scripts) One important thing to note is that the Pine Script compiler will completely ignore any calculations that do not eventually produce chart output. Therefore, if you are performing an artificial benchmark you will need to use the bench.reference(value) function to ensure the calculations are executed. Please check the examples towards the bottom of the script. Quick Reference (Be warned this uses non-standard space characters to get the line indentation to work in the description!) ``` // Looping benchmark style benchmark = bench.new(samples = 500, loops = 5000) data = array.new_int() if bench.start(benchmark)   while bench.loop(benchmark)     array.unshift(data, timenow)   bench.mark(benchmark)   while bench.loop(benchmark)     array.unshift(data, timenow)   bench.mark(benchmark)   while bench.loop(benchmark)     array.unshift(data, timenow)   bench.stop(benchmark)   bench.reference(array.get(data, 0)) bench.report(benchmark, '1x array.unshift()') // Linear benchmark style benchmark = bench.new() data = array.new_int() bench.start(benchmark) for i = 0 to 1000   array.unshift(data, timenow) bench.mark(benchmark) for i = 0 to 1000   array.unshift(data, timenow) bench.stop(benchmark) bench.reference(array.get(data, 0)) bench.report(benchmark,'1000x array.unshift()') ``` Detailed Interface new(samples, loops) Initialises a new benchmark array   Parameters:      samples : int, the number of bars in which to collect samples      loops : int, the number of loops to execute within each sample   Returns: int , the benchmark array active(benchmark) Determing if the benchmarks state is active   Parameters:      benchmark : int , the benchmark array   Returns: bool, true only if the state is active start(benchmark) Start recording a benchmark from this point   Parameters:      benchmark : int , the benchmark array   Returns: bool, true only if the benchmark is unfinished loop(benchmark) Returns true until call count exceeds bench.new(loop) variable   Parameters:      benchmark : int , the benchmark array   Returns: bool, true while looping reference(number, string) Add a compiler reference to the chart so the calculations don't get optimised away   Parameters:      number : float, a numeric value to reference      string : string, a string value to reference mark(benchmark, number, string) Marks the end of one recorded interval and the start of the next   Parameters:      benchmark : int , the benchmark array      number : float, a numeric value to reference      string : string, a string value to reference stop(benchmark, number, string) Stop the benchmark, ending the final interval   Parameters:      benchmark : int , the benchmark array      number : float, a numeric value to reference      string : string, a string value to reference report(Prints, benchmark, title, text_size, position)   Parameters:      Prints : the benchmarks results to the screen      benchmark : int , the benchmark array      title : string, add a custom title to the report      text_size : string, the text size of the log console (global size vars)      position : string, the position of the log console (global position vars) unittest_bench(case) Cache module unit tests, for inclusion in parent script test suite. Usage: bench.unittest_bench(__ASSERTS)   Parameters:      case : string , the current test case and array of previous unit tests (__ASSERTS) unittest(verbose) Run the bench module unit tests as a stand alone. Usage: bench.unittest()   Parameters:      verbose : bool, optionally disable the full report to only display failures
ספריית Pine Script®
מאת ‎GeoffHammond‎
13
Does your trading pass the seasonality test?Are you consistently beating the standard "Buy and Hold" benchmark? Check your performance against the next most common strategy: Buy at the start of the holiday season and close in the new year. By default, the strategy starts with $100,000 and risks 25% of the account on each swing trade. Commission is set to be 0.5% of each trade. You can tweak the direction of trades by adjusting the first setting. Tweak the holding period by changing the Entry and Exit months. Red plot: Balance of account Orange plot: Equity of account
אסטרטגיית Pine Script®
מאת ‎DasanC‎
1
BETA (against any benchmark index - defaulted to NSE:NIFTY)Beta value of a stock relative to benchmark index. Thanks to Ricardo Santos for the original script. This script is adapted from it. To understand beta, refer Investopedia link: www.investopedia.com A beta value of 1 means the stock is directly correlated to benchmark index - volatility would be same as overall market. Beta value less than 1 and greater than 0 means the stock is less volatile than the market. Beta value more than 1 would mean the stock is more volatile than the market. A beta value of 1.2 would roughly translate to the stock being 20% more volatile than the overall market. A negative beta value indicates the stock is inversely correlated to market. In the example chart, you can see the Beta value change in NSE:RELIANCE with respect to NSE:NIFTY.
אינדיקטור Pine Script®
מאת ‎piram.manickam‎
11
Godmode 4.0.2 [Supply/Demand]First off, a huge thank you to the following people: LEGION: LazyBear: www.tradingview.com xSilas: www.tradingview.com Ni6HTH4awK: www.tradingview.com sco77m4r7and: SNOW_CITY: www.tradingview.com oh92: www.tradingview.com alexgrover: www.tradingview.com cI8DH: www.tradingview.com DonovanWall: www.tradingview.com shtcoinr: www.tradingview.com This is the third iteration of Godmode. This time I borrowed the method used by shtcoinr to render supply/demand, resistance and support zones. The idea here is to input the appropriate benchmark tickerid to the asset class you're trading and to paint zones according to the price activity of the selected tickerid. This works very well trying to paint meaningful zones against noisy stocks, currencies, commodities etc. Use a correlation coefficient to determine the best benchmark for your asset class. Want to Learn? If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources. Suggestions or Questions? Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
אינדיקטור Pine Script®
מאת ‎chrysopoetics‎
63
Godmode 4.0.1 [Correlator]First off, a huge thank you to the following people: @LEGION: @LazyBear: www.tradingview.com @xSilas: www.tradingview.com @Ni6HTH4awK: www.tradingview.com @sco77m4r7and: @SNOW_CITY: www.tradingview.com @oh92: www.tradingview.com @alexgrover: www.tradingview.com @cI8DH: www.tradingview.com @DonovanWall: www.tradingview.com This is my second iteration of Godmode. This time I allowed the possibility to correlate two benchmarks against one another, thereby giving you twice the signals (once there's a strong correlation between the two, inverse or otherwise). That aside, there are no changes to this indicator that the first iteration doesn't have: There are still more iterations planned, but if you guys have any ideas or wishes regarding what direction I go, then please let me know. Want to Learn? If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources as well as any other scripts I publish. Suggestions or Questions? Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
אינדיקטור Pine Script®
מאת ‎chrysopoetics‎
2
Godmode 4.0.0 [Oscillator]First off, a huge thank you to the following people: LEGION: LazyBear: www.tradingview.com xSilas: www.tradingview.com Ni6HTH4awK: www.tradingview.com sco77m4r7and: SNOW_CITY: www.tradingview.com oh92: www.tradingview.com alexgrover: www.tradingview.com cI8DH: www.tradingview.com DonovanWall: www.tradingview.com Since I've been on TradingView I've become somewhat enthralled by Godmode and the collective work that goes in to it, so I decided to publish my own iteration, building off the ideas already present. (This is a great way to get familiar with Pine by the way, just in case there are any beginners reading this) Changes The first change I made was to allow the user to select whatever tickerid they wanted as a benchmark. If trading XBTUSD on BitMEX for example, the indicator will react to exchange-specific activity, which means it will respond to all the little whipsaws, whipsaws that can be especially present on a futures exchange. By typing CRYPTOCAP:BTC or CRYPTOCAP:TOTAL we endeavor to remove noise. It can also signal earlier. Less noise and less lag. Another idea would be to choose a benchmark that has a strong inverse relationship with the asset you're trading: try CRYPTOCAP:USDT as the benchmark against BTC to see what I mean. I also added the ability to smooth the plot, yet again removing noise but adding considerable lag. The linear regression of the wave-trend is calculated in place of the EMA. This is plotted as columns with the midline (50) as the base. This is just calculating the slope of the wave-trend and can signal a weakening trend before a reversal takes place. Using cI8DH's True RSI script () as inspiration, I added a function for calculating the True TSI in an attempt to remove any bullish bias. Funnily enough, when I tried to do the same with the RSI I had some problems. I'll try to resolve this in the coming weeks. Made slight changes to the aesthetics. Tried to bring the two main plots alive by making their bold, opaque colors stand off the subtle tones in the background. To Do List 1. I would like to sort out the issue with the True RSI. 2. When the plots are smoothed, there's an issue with the green 'Caution!' dots appearing in the lower half of the indicator. 3. I'd like to adjust the code so that if the 'Benchmark' box is empty, that it will automatically register the current tickerid as the 'Benchmark'. If anyone has any suggestions on other fixes or how to apply the fixes mentioned by me, please don't hesitate to reach out to me here or through other media platforms. Want to Learn? If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources. Suggestions or Questions? Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open. BITMEX:XBTUSD CRYPTOCAP:BTC CRYPTOCAP:TOTAL CRYPTOCAP:USDT.D
אינדיקטור Pine Script®
מאת ‎chrysopoetics‎
6