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EMA multi-crossover with EMA 10/20 cross with signalThis indicator plots five key Exponential Moving Averages (EMAs) on your chart—EMA10, EMA21, EMA50, EMA100, and EMA200—to help you analyze market trends over various timeframes. Additionally, it provides clear visual signals when short-term momentum shifts occur: • Bullish Signal: A green upward arrow appears below the price bar when the EMA21 crosses above the EMA10, indicating potential upward momentum. • Bearish Signal: A red downward arrow appears above the price bar when the EMA21 crosses below the EMA10, indicating potential downward momentum. Features: • Comprehensive Trend Analysis: By displaying multiple EMAs, you can observe both short-term and long-term market trends. • Clear Entry and Exit Signals: The crossover arrows help identify potential buying or selling opportunities based on EMA crossovers. • Customization: Colors, line widths, and arrow sizes can be adjusted to fit your personal trading style and chart preferences. How to Use: 1. Trend Identification: Use the EMA lines to determine the overall market trend. o If shorter-term EMAs (like EMA10 and EMA21) are above longer-term EMAs (like EMA200), the market may be in an uptrend. o Conversely, if shorter-term EMAs are below longer-term EMAs, the market may be in a downtrend. 2. Signal Confirmation: Look for the green or red arrows as potential signals for entering or exiting trades. o Green Arrow: Consider looking for buying opportunities. o Red Arrow: Consider looking for selling opportunities or tightening stop losses. 3. Combine with Other Indicators: For improved accuracy, use this indicator alongside other technical analysis tools like RSI, MACD, or support and resistance levels. Disclaimer: This indicator is a tool to assist in your trading decisions and should not be used as a standalone signal. Always perform comprehensive analysis and consider the risks before entering any trade.
Pine Script™指標
由benntrade413提供
RS Theory IndicatorHow to Use: Customize the Reference Symbol: In the settings of the indicator, you can change the referenceSymbol to the benchmark or asset you want to compare against. RS Interpretation: RS > 1: The current asset is outperforming the reference symbol. RS < 1: The current asset is underperforming the reference symbol. RS = 1: The current asset and the reference symbol are performing equally. Alerts: You can enable alerts for when the RS crosses certain levels (e.g., when RS > 1 or RS < 1). How It Works: Reference Symbol: The user inputs the benchmark asset or symbol (e.g., "SPY" for an S&P 500 ETF). This will be used as the comparison symbol. RS Calculation: The RS Value is calculated by dividing the current asset's close price by the reference asset's close price: RS = Close Price of Current Asset Close Price of Reference Asset RS= Close Price of Reference Asset Close Price of Current Asset ​ Plotting: The RS value is plotted on the chart as a line. A horizontal line at RS = 1 is drawn for easy comparison, representing parity (when the asset and reference symbol have the same price). Background Coloring: The background is colored: Green when RS > 1 (indicating the asset is outperforming the benchmark). Red when RS < 1 (indicating the asset is underperforming the benchmark). Alerts: Alerts are triggered when the RS value is above or below 1, indicating outperformance or underperformance relative to the benchmark.
Pine Script™指標
由aliashif提供
RSI on Price Indicator Advanced Multi-Level RSIRSI on Price Indicator | Advanced Multi-Level RSI with Customizable Levels & Background Fill (Free Pine Script for TradingView) Unlock the full potential of your TradingView charts with the 'RSI on Price NEW' indicator. This free Pine Script offers multi-level RSI bands, customizable overbought/oversold levels, and eye-catching background fills. Perfect for intraday, daily, weekly, or monthly analysis. Enhance your trading strategy today! Take your trading analysis to the next level with the 'RSI on Price NEW' indicator for TradingView. This powerful and free Pine Script overlay brings the RSI directly onto your price chart, combining multiple levels of RSI calculations for detailed insights. With fully customizable settings for RSI periods, overbought/oversold thresholds, and dynamic color-coded background fills, this script is perfect for traders who want precision and clarity. Whether you're trading intraday, daily, weekly, or monthly charts, this script offers unparalleled versatility. Optimize your trading strategy today with this innovative RSI tool! Black Line 50 RSI in center above that 3 line is 60, 70, 80 below black line is 40, 30, 20 RSI
Pine Script™指標
由jairuvi12提供
Top-Down Analysis previous dayTop-Down Analysis 2nd Candle with Enhanced Features This powerful TradingView script is designed for traders looking for a comprehensive and customizable top-down analysis tool. The indicator plots horizontal lines based on significant price levels from multiple timeframes (Daily, 4-Hour, 1-Hour, and Weekly), offering clear reference points for technical analysis. Each timeframe is associated with high and low levels from the previous candle, and these levels are represented with customizable line styles, colors, and widths. Key Features: Multi-Timeframe Support: Displays high and low levels from the previous candle for the Daily, 4-Hour, 1-Hour, and Weekly timeframes. Customize which timeframes to show. Customizable Line Appearance: Choose the line color, style (solid, dotted, dashed), and width for each timeframe. This allows for a personalized chart appearance to suit your trading strategy. Text Labels: Add custom text labels to each line, and move them dynamically to the right, keeping them visible as the candles progress. The labels can be customized with user-defined text for each timeframe’s high and low levels. Toggle Line Visibility: Easily control the visibility of the horizontal lines and their labels for each timeframe, allowing you to focus on the levels that matter most. Price Alerts: Set price alerts when the price crosses any of the plotted levels, including the Daily, 4-Hour, 1-Hour, and Weekly levels. Receive notifications when significant price interactions occur. User Control: With inputs for changing timeframes, colors, labels, and more, this indicator is fully customizable to fit your trading style. This indicator is ideal for day traders, swing traders, and anyone utilizing multi-timeframe analysis for more informed decision-making.
Pine Script™指標
由cesar7olvera提供
Max Volatility Calculation (MAD) in PercentageMax Volatility Calculation (MAD) in Percentage This Pine Script indicator calculates and visualizes the maximum volatility of a financial instrument using the Mean Absolute Deviation (MAD) expressed as a percentage of the mean price. Input Parameter: Users can set the length of the calculation period in bars. Volatility Calculation: It computes the Mean Absolute Deviation from the mean price over the specified number of bars, providing a measure of price volatility. Maximum Volatility Tracking: The script updates the current volatility at every specified length of bars and tracks the maximum volatility observed. Percentage Representation: The maximum volatility is converted to a percentage of the mean price, facilitating easier interpretation. Chart Visualization: The calculated maximum volatility percentage is plotted on the chart for quick reference.
Pine Script™指標
由IANCURTISS提供
Multi-Timeframe Volume-Weighted RSIA multiple timeframe volume-weighted RSI. Blue Line = Current Time Frame Orange Line = Select your desired Time Frame e.g. Blue = Daily, Orange = Weekly 1. Incorporates Market Commitment Value: By factoring in volume, the volume-weighted RSI captures the intensity of trading activity behind price movements. Why it’s useful: Regular RSI measures price momentum but does not differentiate between moves with high or low trading activity. A volume-weighted RSI assigns greater importance to price changes occurring on high volume, reflecting stronger market conviction. 2. Improved Signal Reliability Value: Signals generated by a volume-weighted RSI (e.g., overbought or oversold conditions) may be more reliable because they account for the level of trader participation. Why it’s useful: Low-volume price movements often result in false signals or "noise." A volume-weighted RSI helps filter out such noise, reducing the likelihood of false breakouts or fake reversals. 3. Better Divergence Detection Value: Divergences between price action and the RSI (bullish or bearish divergences) are more meaningful when confirmed by volume. Why it’s useful: Regular RSI might show divergence in price momentum, but this divergence might lack substance if the underlying volume is weak. A volume-weighted RSI ensures that divergence signals align with periods of significant market participation. 4. Enhanced Trend Analysis Value: Trends supported by strong volume are given more weight, helping traders better identify and follow trends. Why it’s useful: Regular RSI might show overbought or oversold signals prematurely during strong trends. Volume-weighted RSI considers whether trends are backed by significant market activity, helping avoid early exits. 5. More Meaningful Overbought/Oversold Levels Value: Levels like 70 (overbought) and 30 (oversold) are more credible when supported by volume. Why it’s useful: In a regular RSI, overbought or oversold levels might occur on light trading, leading to false reversals. A volume-weighted RSI ensures these levels are triggered by substantial market participation, increasing their reliability. Practical Applications: Trend Confirmation: Use the volume-weighted RSI to confirm whether momentum in a trend is supported by strong participation. Divergence Spotting: Identify divergences with more confidence by prioritizing those with volume support. Filtering False Breakouts: Avoid entering trades during weak volume phases by focusing on volume-weighted RSI signals. Limitations: Market Type Dependency: Its usefulness may diminish in low-volume assets or markets where volume data is unavailable (e.g., forex).
Pine Script™指標
由SSHN提供
cup//@version=5 indicator("Cup and Handle Finder", overlay=true) // Settings length = 50 handleLength = 10 minCupSize = 30 breakoutPercentage = 1.02 targetMultiplier = 1.05 stopLossMultiplier = 0.98 // Calculate highest and lowest over the length for cup detection var float highMax = na var float lowMin = na // Detect cup and handle formation var int cupStartTime = na var int cupEndTime = na var bool inCup = false var float cupStartPrice = na var float cupEndPrice = na var float breakoutPrice = na var float targetPrice = na var float stopLossPrice = na // Initialize variables for handle checks var float handleHigh = na var float handleLow = na for i = 0 to length - 1 if not inCup and close <= highMax and close >= (highMax + lowMin) / 2 cupStartPrice := close cupStartTime := time inCup := true cupEndPrice := close cupEndTime := time else inCup := false highMax := ta.highest(high, length) lowMin := ta.lowest(low, length) if inCup and i >= length - handleLength handleHigh := ta.highest(high, handleLength) handleLow := ta.lowest(low, handleLength) if handleHigh > handleLow * breakoutPercentage breakoutPrice := handleHigh targetPrice := breakoutPrice * targetMultiplier stopLossPrice := breakoutPrice * stopLossMultiplier // Draw the cup and handle on the chart if not na(cupStartPrice) line.new(x1=bar_index , y1=cupStartPrice, x2=bar_index , y2=cupEndPrice, color=color.blue, width=2, style=line.style_dashed) if not na(breakoutPrice) line.new(x1=bar_index , y1=breakoutPrice, x2=bar_index, y2=breakoutPrice, color=color.green, width=2, style=line.style_solid) label.new(x=bar_index, y=breakoutPrice, text="Breakout", color=color.green, style=label.style_label_down, size=size.large) label.new(x=bar_index, y=targetPrice, text="Target: " + str.tostring(targetPrice), color=color.green, style=label.style_label_up, size=size.small) label.new(x=bar_index, y=stopLossPrice, text="Stop Loss: " + str.tostring(stopLossPrice), color=color.red, style=label.style_label_down, size=size.small) // Signals for entry and exit based on price movements and strategy conditions plotshape(series=close > breakoutPrice and close < targetPrice, location=location.belowbar, color=color.green, style=shape.labelup, text="Buy", size=size.small) plotshape(series=close < stopLossPrice, location=location.abovebar, color=color.red, style=shape.labeldown, text="Sell", size=size.small)
Pine Script™指標
由TrPlay06提供
QuantifyPS - 1Library "QuantifyPS" normdist(z)   Parameters:      z (float) : (float): The z-score for which the CDF is to be calculated.   Returns: (float): The cumulative probability corresponding to the input z-score. Notes: - Uses an approximation method for the normal distribution CDF, which is computationally efficient. - The result is accurate for most practical purposes but may have minor deviations for extreme values of `z`. Formula: - Based on the approximation formula: `Φ(z) ≈ 1 - f(z) * P(t)` if `z > 0`, otherwise `Φ(z) ≈ f(z) * P(t)`, where: `f(z) = 0.3989423 * exp(-z^2 / 2)` (PDF of standard normal distribution) `P(t) = Σ [c * t^i]` with constants `c` and `t = 1 / (1 + 0.2316419 * |z|)`. Implementation details: - The approximation uses five coefficients for the polynomial part of the CDF. - Handles both positive and negative values of `z` symmetrically. Constants: - The coefficients and scaling factors are chosen to minimize approximation errors. gamma(x)   Parameters:      x (float) : (float): The input value for which the Gamma function is to be calculated. Must be greater than 0. For x <= 0, the function returns `na` as it is undefined.   Returns: (float): Approximation of the Gamma function for the input `x`. Notes: - The Lanczos approximation provides a numerically stable and efficient method to compute the Gamma function. - The function is not defined for `x <= 0` and will return `na` in such cases. - Uses precomputed Lanczos coefficients for accuracy. - Includes handling for small numerical inaccuracies. Formula: - The Gamma function is approximated as: `Γ(x) ≈ sqrt(2π) * t^(x + 0.5) * e^(-t) * Σ(p / (x + k))` where `t = x + g + 0.5` and `p` is the array of Lanczos coefficients. Implementation details: - Lanczos coefficients (`p`) are precomputed and stored in an array. - The summation iterates over these coefficients to compute the final result. - The constant `g` controls the precision of the approximation (commonly `g = 7`). t_cdf(t, df)   Parameters:      t (float) : (float): The t-statistic for which the CDF value is to be calculated.      df (int) : (int): Degrees of freedom of the t-distribution.   Returns: (float): Approximate CDF value for the given t-statistic. Notes: - This function computes a one-tailed p-value. - Relies on an approximation formula using gamma functions and standard t-distribution properties. - May not be as accurate as specialized statistical libraries for extreme values or very high degrees of freedom. Formula: - Let `x = df / (t^2 + df)`. - The approximation formula is derived using: `CDF(t, df) ≈ 1 - * x^((df + 1) / 2) / 2`, where Γ represents the gamma function. Implementation details: - Computes the gamma ratio for normalization. - Applies the t-distribution formula for one-tailed probabilities. tStatForPValue(p, df)   Parameters:      p (float) : (float): P-value for which the t-statistic needs to be calculated. Must be in the interval (0, 1).      df (int) : (int): Degrees of freedom of the t-distribution.   Returns: (float): The t-statistic corresponding to the given p-value. Notes: - If `p` is outside the interval (0, 1), the function returns `na` as an error. - The function uses binary search with a fixed number of iterations and a defined tolerance. - The result is accurate to within the specified tolerance (default: 0.0001). - Relies on the cumulative density function (CDF) `t_cdf` for the t-distribution. Formula: - Uses the cumulative density function (CDF) of the t-distribution to iteratively find the t-statistic. Implementation details: - `low` and `high` define the search interval for the t-statistic. - The midpoint (`mid`) is iteratively refined until the difference between the cumulative probability and the target p-value is smaller than the tolerance. jarqueBera(n, s, k)   Parameters:      n (float) : (series float): Number of observations in the dataset.      s (float) : (series float): Skewness of the dataset.      k (float) : (series float): Kurtosis of the dataset.   Returns: (float): The Jarque-Bera test statistic. Formula: JB = n * Notes: - A higher JB value suggests that the data deviates more from a normal distribution. - The test is asymptotically distributed as a chi-squared distribution with 2 degrees of freedom. - Use this value to calculate a p-value to determine the significance of the result. skewness(data)   Parameters:      data (float) : (series float): Input data series.   Returns: (float): The skewness value. Notes: - Handles missing values (`na`) by ignoring invalid points. - Includes error handling for zero variance to avoid division-by-zero scenarios. - Skewness is calculated as the normalized third central moment of the data. kurtosis(data)   Parameters:      data (float) : (series float): Input data series.   Returns: (float): The kurtosis value. Notes: - Handles missing values (`na`) by ignoring invalid points. - Includes error handling for zero variance to avoid division-by-zero scenarios. - Kurtosis is calculated as the normalized fourth central moment of the data. regression(y, x, lag)   Parameters:      y (float) : (series float): Dependent series (observed values).      x (float) : (series float): Independent series (explanatory variable).      lag (int) : (int): Number of lags applied to the independent series (x).   Returns: (tuple): Returns a tuple containing the following values: - n: Number of valid observations. - alpha: Intercept of the regression line. - beta: Slope of the regression line. - t_stat: T-statistic for the beta coefficient. - p_value: Two-tailed p-value for the beta coefficient. - r_squared: Coefficient of determination (R²) indicating goodness of fit. - skew: Skewness of the residuals. - kurt: Kurtosis of the residuals. Notes: - Handles missing data (`na`) by ignoring invalid points. - Includes basic error handling for zero variance and division-by-zero scenarios. - Computes residual-based statistics (skewness and kurtosis) for model diagnostics.
Pine Script™腳本庫
由Sigaud提供
OutofOptionsHelperLibraryLibrary "OutofOptionsHelperLibrary" Helper library for my indicators/strategies isUp(i)   is Up candle   Parameters:      i (int)   Returns: bool isDown(i)   is Down candle   Parameters:      i (int)   Returns: bool TF(t)   format time into date/time string   Parameters:      t (int)   Returns: string S(s)   format data to string   Parameters:      s (float)   Returns: string S(s)   format data to string   Parameters:      s (int)   Returns: string S(s)   format data to string   Parameters:      s (bool)   Returns: string barClose(price, up, strict)   Determine if candle closed above/below price   Parameters:      price (float)      up (bool)      strict (bool) : bool if close over is required or if close at the price is good enough   Returns: bool processSweep(L, price, up, leftB)   Determine how many liquidity sweeps were made   Parameters:      L (array)      price (float)      up (bool)      leftB (int)   Returns: int liquidity   Fields:      price (series float)      time (series int)      oprice (series float)      otime (series int)      sweeps (series int)      bars_swept (series int)
Pine Script™腳本庫
由outofoptions提供