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Fibonacci Trend - Aynet1. Inputs lookbackPeriod: Defines the number of bars to consider for calculating swing highs and lows. Default is 20. fibLevel1 to fibLevel5: Fibonacci retracement levels to calculate price levels (23.6%, 38.2%, 50%, 61.8%, 78.6%). useTime: Enables or disables time-based Fibonacci projections. riskPercent: Defines the percentage of risk for trading purposes (currently not used in calculations). 2. Functions isSwingHigh(index): Identifies a swing high at the given index, where the high of that candle is higher than both its previous and subsequent candles. isSwingLow(index): Identifies a swing low at the given index, where the low of that candle is lower than both its previous and subsequent candles. 3. Variables swingHigh and swingLow: Store the most recent swing high and swing low prices. swingHighTime and swingLowTime: Store the timestamps of the swing high and swing low. fib1 to fib5: Fibonacci levels based on the difference between swingHigh and swingLow. 4. Swing Point Detection The script checks if the last bar is a swing high or swing low using the isSwingHigh() and isSwingLow() functions. If a swing high is detected: The high price is stored in swingHigh. The timestamp of the swing high is stored in swingHighTime. If a swing low is detected: The low price is stored in swingLow. The timestamp of the swing low is stored in swingLowTime. 5. Fibonacci Levels Calculation If both swingHigh and swingLow are defined, the script calculates the Fibonacci retracement levels (fib1 to fib5) based on the price difference (priceDiff = swingHigh - swingLow). 6. Plotting Fibonacci Levels Fibonacci levels (fib1 to fib5) are plotted as horizontal lines using the line.new() function. Labels (e.g., "23.6%") are added near the lines to indicate the level. Lines and labels are color-coded: 23.6% → Blue 38.2% → Green 50.0% → Yellow 61.8% → Orange 78.6% → Red 7. Filling Between Fibonacci Levels The plot() function creates lines for each Fibonacci level. The fill() function is used to fill the space between two levels with semi-transparent colors: Blue → Between fib1 and fib2 Green → Between fib2 and fib3 Yellow → Between fib3 and fib4 Orange → Between fib4 and fib5 8. Time-Based Fibonacci Projections If useTime is enabled: The time difference (timeDiff) between the swing high and swing low is calculated. Fibonacci time projections are added based on multiples of 23.6%. If the current time reaches a projected time, a label (e.g., "T1", "T2") is displayed near the high price. 9. Trading Logic Two placeholder variables are defined for trading logic: longCondition: Tracks whether a condition for a long trade is met (currently not implemented). shortCondition: Tracks whether a condition for a short trade is met (currently not implemented). These variables can be extended to define entry/exit signals based on Fibonacci levels. How It Works Detect Swing Points: It identifies recent swing high and swing low points on the chart. Calculate Fibonacci Levels: Based on the swing points, it computes retracement levels. Visualize Levels: Plots the levels on the chart with labels and fills between them. Time Projections: Optionally calculates time-based projections for future price movements. Trading Opportunities: The framework provides tools for detecting potential reversal or breakout zones using Fibonacci levels.
Стратегия Pine Script™
от Aynet-London
Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role. 1. Hammer: • Typically occurs after a downtrend. • Signals a potential trend reversal to the upside. • A Hammer has: • A small body (close and open are close to each other). • A long lower shadow, at least twice as long as the body. • No or a very short upper shadow. 2. Hanging Man: • Typically occurs after an uptrend. • Signals a potential reversal to the downside. • A Hanging Man has: • A small body, similar to the Hammer. • A long lower shadow, at least twice as long as the body. • A small or no upper shadow. These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control. Relevance of Chart Patterns in Forex In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants. Scientific Literature on the Relevance of Candlestick Patterns 1. Behavioral Finance and Candlestick Patterns: Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends. 2. Statistical Validation of Chart Patterns: Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well. 3. Market Efficiency and Candlestick Patterns: The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading. Testing the Strategy in Forex Using the Provided Script The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes. 1. Testing on Different Timeframes: • The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness. 2. Exit Strategies: • The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior. Conclusion The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness. For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades. References: • Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770. • Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731-1764. • Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91. This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
Стратегия Pine Script™
от EdgeTools
Renko Chart EmulationRenko charts are a popular tool in technical analysis, known for their ability to filter out market noise and focus purely on price movements. Unlike traditional candlestick or bar charts, Renko charts are not time-based but are constructed using bricks that represent a fixed price movement. This makes them particularly useful for identifying trends and key levels of support and resistance. While Renko charts are commonly found on platforms with specialized charting capabilities, they can also be emulated in Pine Script as a line indicator. The Renko emulation indicator in Pine Script calculates the movement of price based on a user-defined brick size. Whenever the price moves up or down by an amount equal to or greater than the brick size, a new level is plotted, indicating a shift in price direction. This approach helps traders visualize significant price moves without the distractions of smaller fluctuations. By plotting the Renko levels as a continuous line and coloring it based on direction, this indicator provides a clean and straightforward representation of market trends. Traders can use this Renko emulation line to identify potential entry and exit points, as well as to confirm ongoing trends. The simplicity of Renko charts makes them a favorite among those who prefer a minimalist approach to technical analysis. However, it is essential to choose an appropriate brick size that aligns with the volatility of the trading instrument. A smaller brick size may result in frequent signals, while a larger one can smooth out the chart, focusing only on the most substantial price movements. This script offers a flexible solution for incorporating Renko-style analysis into any trading strategy.
Индикатор Pine Script™
от LuxmiAI
Correlation Coefficient Master TableThe Correlation Coefficient Master Table is a comprehensive tool designed to calculate and visualize the correlation coefficient between a selected base asset and multiple other assets over various time periods. It provides traders and analysts with a clear understanding of the relationships between assets, enabling them to analyze trends, diversification opportunities, and market dynamics. You can define key parameters such as the base asset’s data source (e.g., close price), the assets to compare against (up to six symbols), and multiple lookback periods for granular analysis. The indicator calculates the covariance and normalizes it by the product of the standard deviations. The correlation coefficient ranges from -1 to +1, with +1 indicating a perfect positive relationship, -1 a perfect negative relationship, and 0 no relationship. You can specify the lookback periods (e.g., 15, 30, 90, or 120 bars) to tailor the calculation to their analysis needs. The results are visualized as both a line plot and a table. The line plot shows the correlation over the primary lookback period (the Chart Length), which can be used to inspect a certain length close up, or could be used in conjunction with the table to provide you with five lookback periods at once for the same base asset. The dynamically created table provides a detailed breakdown of correlation values for up to six target assets across the four user-defined lengths. The table’s cells are formatted with rounded values and color-coded for easy interpretation. This indicator is ideal for traders, portfolio managers, and market researchers who need an in-depth understanding of asset interdependencies. By providing both the numerical correlation coefficients and their visual representation, users can easily identify patterns, assess diversification strategies, and monitor correlations across multiple timeframes, making it a valuable tool for decision-making.
Индикатор Pine Script™
от DeVrizii
Center of Candle Trendline### **Center of Candle Trendline** This script dynamically plots a trendline through the center of each candlestick's body. The "center" is calculated as the average of the open and close prices for each candle. The trendline updates in real-time as new candles form, providing a clean and straightforward way to track the market's midline movement. #### **Features:** 1. **Dynamic Trendline:** The trendline connects the center points of consecutive candlestick bodies, giving a clear visual representation of price movements. 2. **Accurate Center Calculation:** The center is determined as `(open + close) / 2`, ensuring the trendline reflects the true midpoint of each candlestick body. 3. **Real-Time Updates:** The trendline updates automatically as new bars form, keeping your chart up to date with the latest price action. 4. **Customization-Ready:** Adjust the line’s color, width, or style easily to fit your chart preferences. #### **How to Use:** - Add this script to your chart to monitor the price movement relative to the center of candlestick bodies. - Use the trendline to identify trends, reversals, or price consolidation zones. #### **Applications:** - **Trend Analysis:** Visualize how the market trends around the center of candlesticks. - **Reversal Identification:** Detect potential reversal zones when the price deviates significantly from the trendline. - **Support and Resistance Zones:** Use the trendline as a dynamic support or resistance reference. This tool is perfect for traders who want a clean and minimalistic approach to tracking price action. Whether you're a beginner or an experienced trader, this script provides valuable insights without overwhelming your chart. #### **Note:** This is not a standalone trading strategy but a visual aid to complement your analysis. Always combine it with other tools and techniques for better trading decisions. --- Feel free to tweak this description based on your preferences or style!
Индикатор Pine Script™
от Jay_R_Dhanki
WT SETUP WITH LINES AND LABELS//@version=5 indicator("WT SETUP WITH LINES AND LABELS", "WT SETUP", overlay=true, max_lines_count=500, max_labels_count=500) // Timeframe selection tf = input.string("D", "Timeframe", options= ) // Level selection level_selection = input.string("Yesterday", "Level Calculation", options= ) // Color input options color pivotColor = input.color(color.white, "Pivot Color", group="Colors") color highColor = input.color(color.red, "High Color", group="Colors") color lowColor = input.color(color.green, "Low Color", group="Colors") color closeColor = input.color(color.white, "Close Color", group="Colors") color r1Color = input.color(color.red, "R1 Color", group="Colors") color r2Color = input.color(color.red, "R2 Color", group="Colors") color r3Color = input.color(color.red, "R3 Color", group="Colors") color r4Color = input.color(color.red, "R4 Color", group="Colors") color s1Color = input.color(color.green, "S1 Color", group="Colors") color s2Color = input.color(color.green, "S2 Color", group="Colors") color s3Color = input.color(color.green, "S3 Color", group="Colors") color s4Color = input.color(color.green, "S4 Color", group="Colors") offset = level_selection == "Yesterday" ? 1 : 0 float lowPrev = request.security(syminfo.tickerid, tf, low ) float highPrev = request.security(syminfo.tickerid, tf, high ) float closePrev = request.security(syminfo.tickerid, tf, close ) float pivot = (lowPrev + highPrev + closePrev) / 3 float s1 = (2 * pivot) - highPrev float r1 = (2 * pivot) - lowPrev float rangeValue = highPrev - lowPrev float s2 = pivot - rangeValue float r2 = pivot + rangeValue float s3 = lowPrev - 2 * (highPrev - pivot) float r3 = highPrev + 2 * (pivot - lowPrev) float s4 = pivot * 3 - (3 * highPrev - lowPrev) float r4 = pivot * 3 + (highPrev - 3 * lowPrev) var line pivotLine = na var line r1Line = na var line r2Line = na var line r3Line = na var line r4Line = na var line s1Line = na var line s2Line = na var line s3Line = na var line s4Line = na var line highLine = na var line lowLine = na var line closeLine = na var label pivotLabel = na var label r1Label = na var label r2Label = na var label r3Label = na var label r4Label = na var label s1Label = na var label s2Label = na var label s3Label = na var label s4Label = na var label highLabel = na var label lowLabel = na var label closeLabel = na if barstate.islast pivotLine := line.new(bar_index, pivot, bar_index + 1, pivot, extend=extend.right, color=pivotColor, width=1) r1Line := line.new(bar_index, r1, bar_index + 1, r1, extend=extend.right, color=r1Color, width=1) r2Line := line.new(bar_index, r2, bar_index + 1, r2, extend=extend.right, color=r2Color, width=1) r3Line := line.new(bar_index, r3, bar_index + 1, r3, extend=extend.right, color=r3Color, width=1) r4Line := line.new(bar_index, r4, bar_index + 1, r4, extend=extend.right, color=r4Color, width=1) s1Line := line.new(bar_index, s1, bar_index + 1, s1, extend=extend.right, color=s1Color, width=1) s2Line := line.new(bar_index, s2, bar_index + 1, s2, extend=extend.right, color=s2Color, width=1) s3Line := line.new(bar_index, s3, bar_index + 1, s3, extend=extend.right, color=s3Color, width=1) s4Line := line.new(bar_index, s4, bar_index + 1, s4, extend=extend.right, color=s4Color, width=1) highLine := line.new(bar_index, highPrev, bar_index + 1, highPrev, extend=extend.right, color=highColor, width=1) lowLine := line.new(bar_index, lowPrev, bar_index + 1, lowPrev, extend=extend.right, color=lowColor, width=1) closeLine := line.new(bar_index, closePrev, bar_index + 1, closePrev, extend=extend.right, color=closeColor, width=1) tfString = tf == "D" ? "Daily" : (tf == "W" ? "Weekly" : "Monthly") levelString = level_selection == "Yesterday" ? "Yesterday's" : "Latest" pivotLabel := label.new(bar_index, pivot, tfString + " " + levelString + " Pivot " + str.tostring(pivot, "(#.##)"), color=color.gray, style=label.style_label_down, textcolor=color.white) r1Label := label.new(bar_index, r1, "R1 " + str.tostring(r1, "(#.##)"), color=r1Color, style=label.style_label_down, textcolor=color.white) r2Label := label.new(bar_index, r2, "R2 " + str.tostring(r2, "(#.##)"), color=r2Color, style=label.style_label_down, textcolor=color.white) r3Label := label.new(bar_index, r3, "R3 " + str.tostring(r3, "(#.##)"), color=r3Color, style=label.style_label_down, textcolor=color.white) r4Label := label.new(bar_index, r4, "R4 " + str.tostring(r4, "(#.##)"), color=r4Color, style=label.style_label_down, textcolor=color.white) s1Label := label.new(bar_index, s1, "S1 " + str.tostring(s1, "(#.##)"), color=s1Color, style=label.style_label_up, textcolor=color.white) s2Label := label.new(bar_index, s2, "S2 " + str.tostring(s2, "(#.##)"), color=s2Color, style=label.style_label_up, textcolor=color.white) s3Label := label.new(bar_index, s3, "S3 " + str.tostring(s3, "(#.##)"), color=s3Color, style=label.style_label_up, textcolor=color.white) s4Label := label.new(bar_index, s4, "S4 " + str.tostring(s4, "(#.##)"), color=s4Color, style=label.style_label_up, textcolor=color.white) highLabel := label.new(bar_index, highPrev, "High " + str.tostring(highPrev, "(#.##)"), color=highColor, style=label.style_label_down, textcolor=color.white) lowLabel := label.new(bar_index, lowPrev, "Low " + str.tostring(lowPrev, "(#.##)"), color=lowColor, style=label.style_label_up, textcolor=color.white) closeLabel := label.new(bar_index, closePrev, "Close " + str.tostring(closePrev, "(#.##)"), color=color.gray, style=label.style_label_down, textcolor=color.white) else line.delete(pivotLine) line.delete(r1Line) line.delete(r2Line) line.delete(r3Line) line.delete(r4Line) line.delete(s1Line) line.delete(s2Line) line.delete(s3Line) line.delete(s4Line) line.delete(highLine) line.delete(lowLine) line.delete(closeLine) label.delete(pivotLabel) label.delete(r1Label) label.delete(r2Label) label.delete(r3Label) label.delete(r4Label) label.delete(s1Label) label.delete(s2Label) label.delete(s3Label) label.delete(s4Label) label.delete(highLabel) label.delete(lowLabel) label.delete(closeLabel)
Индикатор Pine Script™
от bcomingtrader
Dynamic Volatility Differential Model (DVDM)The Dynamic Volatility Differential Model (DVDM) is a quantitative trading strategy designed to exploit the spread between implied volatility (IV) and historical (realized) volatility (HV). This strategy identifies trading opportunities by dynamically adjusting thresholds based on the standard deviation of the volatility spread. The DVDM is versatile and applicable across various markets, including equity indices, commodities, and derivatives such as the FDAX (DAX Futures). Key Components of the DVDM: 1. Implied Volatility (IV): The IV is derived from options markets and reflects the market’s expectation of future price volatility. For instance, the strategy uses volatility indices such as the VIX (S&P 500), VXN (Nasdaq 100), or RVX (Russell 2000), depending on the target market. These indices serve as proxies for market sentiment and risk perception (Whaley, 2000). 2. Historical Volatility (HV): The HV is computed from the log returns of the underlying asset’s price. It represents the actual volatility observed in the market over a defined lookback period, adjusted to annualized levels using a multiplier of \sqrt{252} for daily data (Hull, 2012). 3. Volatility Spread: The difference between IV and HV forms the volatility spread, which is a measure of divergence between market expectations and actual market behavior. 4. Dynamic Thresholds: Unlike static thresholds, the DVDM employs dynamic thresholds derived from the standard deviation of the volatility spread. The thresholds are scaled by a user-defined multiplier, ensuring adaptability to market conditions and volatility regimes (Christoffersen & Jacobs, 2004). Trading Logic: 1. Long Entry: A long position is initiated when the volatility spread exceeds the upper dynamic threshold, signaling that implied volatility is significantly higher than realized volatility. This condition suggests potential mean reversion, as markets may correct inflated risk premiums. 2. Short Entry: A short position is initiated when the volatility spread falls below the lower dynamic threshold, indicating that implied volatility is significantly undervalued relative to realized volatility. This signals the possibility of increased market uncertainty. 3. Exit Conditions: Positions are closed when the volatility spread crosses the zero line, signifying a normalization of the divergence. Advantages of the DVDM: 1. Adaptability: Dynamic thresholds allow the strategy to adjust to changing market conditions, making it suitable for both low-volatility and high-volatility environments. 2. Quantitative Precision: The use of standard deviation-based thresholds enhances statistical reliability and reduces subjectivity in decision-making. 3. Market Versatility: The strategy’s reliance on volatility metrics makes it universally applicable across asset classes and markets, ensuring robust performance. Scientific Relevance: The strategy builds on empirical research into the predictive power of implied volatility over realized volatility (Poon & Granger, 2003). By leveraging the divergence between these measures, the DVDM aligns with findings that IV often overestimates future volatility, creating opportunities for mean-reversion trades. Furthermore, the inclusion of dynamic thresholds aligns with risk management best practices by adapting to volatility clustering, a well-documented phenomenon in financial markets (Engle, 1982). References: 1. Christoffersen, P., & Jacobs, K. (2004). The importance of the volatility risk premium for volatility forecasting. Journal of Financial and Quantitative Analysis, 39(2), 375-397. 2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. 3. Hull, J. C. (2012). Options, Futures, and Other Derivatives. Pearson Education. 4. Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539. 5. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17. This strategy leverages quantitative techniques and statistical rigor to provide a systematic approach to volatility trading, making it a valuable tool for professional traders and quantitative analysts.
Стратегия Pine Script™
от EdgeTools
Market Regime DetectorMarket Regime Detector The Market Regime Detector is a tool designed to help traders identify and adapt to the prevailing market environment by analyzing price action in relation to key macro timeframe levels. This indicator categorizes the market into distinct regimes—Bullish, Bearish, or Reverting—providing actionable insights to set trading expectations, manage volatility, and align strategies with broader market conditions. What is a Market Regime? A market regime refers to the overarching state or condition of the market at a given time. Understanding the market regime is critical for traders as it determines the most effective trading approach. The three main regimes are: Bullish Regime: Characterized by upward momentum where prices are consistently trending higher. Trading strategies often focus on buying opportunities and trend-following setups. Bearish Regime: Defined by downward price pressure and declining trends. Traders typically look for selling opportunities or adopt risk-off strategies. Reverting Regime: Represents a consolidation phase where prices move within a defined range. Ideal for mean-reversion strategies or range-bound trading setups. Key Features of the Market Regime Detector: Dynamic Market Regime Detection: Identifies the market regime based on macro timeframe high and low levels (e.g., weekly or monthly). Provides clear and actionable insights for each regime to align trading strategies. Visual Context for Price Levels: Plots the macro high and low levels on the chart, allowing traders to visualize critical support and resistance zones. Enhances understanding of volatility and trend boundaries. Regime Transition Alerts: Sends alerts only when the market transitions into a new regime, ensuring traders are notified of meaningful changes without redundant signals. Alert messages include clear regime descriptions, such as "Market entered a Bullish Regime: Price is above the macro high." Customizable Visualization: Background colors dynamically adjust to the current regime: Blue for Reverting. Aqua for Bullish. Fuchsia for Bearish. Option to toggle high/low line plotting and background highlights for a tailored experience. Volatility and Expectation Management: Offers insights into market volatility by showing when price action approaches, exceeds, or reverts within macro timeframe levels. Helps traders set realistic expectations and adjust their strategies accordingly. Use Cases: Trend Traders: Identify bullish or bearish regimes to capture sustained price movements. Range Traders: Leverage reverting regimes to trade between defined support and resistance zones. Risk Managers: Use macro high and low levels as dynamic stop-loss or take-profit zones to optimize trade management. The Market Regime Detector equips traders with a deeper understanding of the market environment, making it an essential tool for informed decision-making and strategic planning. Whether you're trading trends, ranges, or managing risk, this indicator provides the clarity and insights needed to navigate any market condition.
Индикатор Pine Script™
от RWCS_LTD
Price Down from Monthly High (%)//@version=5 indicator("Price Down from Monthly High (%)", overlay=true) // Get the highest price of the current month monthlyHigh = request.security(syminfo.tickerid, "M", high) // Calculate the percentage price down from the monthly high percentageDown = ((monthlyHigh - close) / monthlyHigh) * 100 // Plot the percentage down from the monthly high plot(percentageDown, color=color.red, title="Percentage Down from Monthly High", linewidth=2)
Индикатор Pine Script™
от RATHIK96