Rozpocznij

Wyszukaj w skryptach "pattern"

Engulfing CandlesThis script serves as the "Engulfing Candles" indicator in TradingView. Here's what it does: - It identifies bullish candlestick patterns where the current candle's high is lower than the previous candle's high, the current candle's low is higher than the previous candle's low, the current candle's close is higher than the previous candle's close, and the current candle's open is higher than the previous candle's open. It also identifies bearish candlestick patterns where the conditions are reversed. - The indicator colors bullish candles in a specific color (Yellow Green) to visually highlight the bullish pattern, and colors bearish candles in another color (Purple pink) to visually highlight the bearish pattern. - Additionally, it triggers an alert when either the bullish or bearish triangle shape appears, notifying traders with the message "A Southern Star Shadows pattern has appeared!"
Wskaźnik Pine Script®
od anodrr2
2
Heikin Ashi RSI + OTT [Erebor]Relative Strength Index (RSI) The Relative Strength Index (RSI) is a popular momentum oscillator used in technical analysis to measure the speed and change of price movements. Developed by J. Welles Wilder, the RSI is calculated using the average gains and losses over a specified period, typically 14 days. Here's how it works: Description and Calculation: 1. Average Gain and Average Loss Calculation: - Calculate the average gain and average loss over the chosen period (e.g., 14 days). - The average gain is the sum of gains divided by the period, and the average loss is the sum of losses divided by the period. 2. Relative Strength (RS) Calculation: - The relative strength is the ratio of average gain to average loss. The RSI oscillates between 0 and 100. Traditionally, an RSI above 70 indicates overbought conditions, suggesting a potential sell signal, while an RSI below 30 suggests oversold conditions, indicating a potential buy signal. Pros of RSI: - Identifying Overbought and Oversold Conditions: RSI helps traders identify potential reversal points in the market due to overbought or oversold conditions. - Confirmation Tool: RSI can be used in conjunction with other technical indicators or chart patterns to confirm signals, enhancing the reliability of trading decisions. - Versatility: RSI can be applied to various timeframes, from intraday to long-term charts, making it adaptable to different trading styles. Cons of RSI: - Whipsaws: In ranging markets, RSI can generate false signals, leading to whipsaws (rapid price movements followed by a reversal). - Not Always Accurate: RSI may give false signals, especially in strongly trending markets where overbought or oversold conditions persist for extended periods. - Subjectivity: Interpretation of RSI levels (e.g., 70 for overbought, 30 for oversold) is somewhat subjective and can vary depending on market conditions and individual preferences. Checking RSIs in Different Periods: Traders often use multiple timeframes to analyze RSI for a more comprehensive view: - Fast RSI (e.g., 8-period): Provides more sensitive signals, suitable for short-term trading and quick decision-making. - Slow RSI (e.g., 32-period): Offers a smoother representation of price movements, useful for identifying longer-term trends and reducing noise. By comparing RSI readings across different periods, traders can gain insights into the momentum and strength of price movements over various timeframes, helping them make more informed trading decisions. Additionally, divergence between fast and slow RSI readings may signal potential trend reversals or continuation patterns. Heikin Ashi Candles Let's consider a modification to the traditional “Heikin Ashi Candles” where we introduce a new parameter: the period of calculation. The traditional HA candles are derived from the open 01, high 00 low 00, and close 00 prices of the underlying asset. Now, let's introduce a new parameter, period, which will determine how many periods are considered in the calculation of the HA candles. This period parameter will affect the smoothing and responsiveness of the resulting candles. In this modification, instead of considering just the current period, we're averaging or aggregating the prices over a specified number of periods . This will result in candles that reflect a longer-term trend or sentiment, depending on the chosen period value. For example, if period is set to 1, it would essentially be the same as traditional Heikin Ashi candles. However, if period is set to a higher value, say 5, each candle will represent the average price movement over the last 5 periods, providing a smoother representation of the trend but potentially with delayed signals compared to lower period values. Traders can adjust the period parameter based on their trading style, the timeframe they're analyzing, and the level of smoothing or responsiveness they prefer in their candlestick patterns. Optimized Trend Tracker The "Optimized Trend Tracker" is a proprietary trading indicator developed by TradingView user ANIL ÖZEKŞİ. It is designed to identify and track trends in financial markets efficiently. The indicator attempts to smooth out price fluctuations and provide clear signals for trend direction. The Optimized Trend Tracker uses a combination of moving averages and adaptive filters to detect trends. It aims to reduce lag and noise typically associated with traditional moving averages, thereby providing more timely and accurate signals. Some of the key features and applications of the OTT include: • Trend Identification: The indicator helps traders identify the direction of the prevailing trend in a market. It distinguishes between uptrends, downtrends, and sideways consolidations. • Entry and Exit Signals: The OTT generates buy and sell signals based on crossovers and direction changes of the trend. Traders can use these signals to time their entries and exits in the market. • Trend Strength: It also provides insights into the strength of the trend by analyzing the slope and momentum of price movements. This information can help traders assess the conviction behind the trend and adjust their trading strategies accordingly. • Filter Noise: By employing adaptive filters, the indicator aims to filter out market noise and false signals, thereby enhancing the reliability of trend identification. • Customization: Traders can customize the parameters of the OTT to suit their specific trading preferences and market conditions. This flexibility allows for adaptation to different timeframes and asset classes. Overall, the OTT can be a valuable tool for traders seeking to capitalize on trending market conditions while minimizing false signals and noise. However, like any trading indicator, it is essential to combine its signals with other forms of analysis and risk management strategies for optimal results. Additionally, traders should thoroughly back-test the indicator and practice using it in a demo environment before applying it to live trading. The following types of moving average have been included: "SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA", "HMA", "KAMA", "LSMA", "TRAMA", "VAR", "DEMA", "ZLEMA", "TSF", "WWMA". Thanks to the authors. Thank you for your indicator “Optimized Trend Tracker”. © kivancozbilgic Thank you for your programming language, indicators and strategies. © TradingView Kind regards. © Erebor_GIT
Wskaźnik Pine Script®
od Erebor_GIT
6
Price Action Fractal Forecasts [AlgoAlpha]🔮 Price Action Fractal Forecasts - Unleash the Power of Historical Patterns! 🌌✨ Dive into the future with AlgoAlpha's Price Action Fractal Forecasts ! This innovative indicator utilizes the mesmerizing complexity of fractals to predict future price movements, offering traders a unique edge in the market. By analyzing historical price action and identifying repeating patterns, this tool forecasts future price trends, providing visually engaging and actionable insights. Key Features: 🔄 Flexible Data Series Selection: Choose your preferred data series for precise analysis. 🕰 Flexible Training and Reference Data Windows: Customize the length of training data and reference periods to match your trading style. 📈 Custom Forecast Length: Adjust the forecast horizon to suit your strategic objectives. 🌈 Customizable Visual Elements: Tailor the colors of forecast deviation cones, data reference areas, and more for optimal chart readability. 🔄 Anticipatory and Repetitive Forecast Modes: Select between anticipating future trends or identifying repetitive patterns for forecasts. 🔎 Enhanced Similarity Search: Leverages correlation metrics to find the most similar historical data segments. 📊 Forecast Deviation Cone: Visualize potential price range deviations with adjustable multipliers. 🚀 Quick Guide to Maximizing Your Trading with Price Action Fractal Forecasts: 🛠 Add the Indicator: Search for "Price Action Fractal Forecasts" in TradingView's Indicators & Strategies. Customize settings according to your trading strategy. 📊 Strategic Forecasting: Monitor the forecast deviation cone and forecast directional changes for insights into potential future price movements. 🔔 Alerts for Swift Action: Set up notifications based on forecast changes to stay ahead of market movements without constant monitoring. Behind the Magic: How It Works The core of the Price Action Fractal Forecasts lies in its ability to compare current market behavior with historical data to unearth similar patterns. It first establishes a training data window to analyze historical prices. Within this window, it then defines a reference length to identify the most recent price action that will serve as the basis for comparison. The indicator searches through the historical data within the training window to find segments that closely match the recent price action in the reference period. Depending on whether you choose the anticipatory or repetitive forecast mode, the indicator either looks ahead to predict future prices based on past outcomes following similar patterns or focuses on the repeating patterns within the reference period itself for forecasts. The forecast's direction can be configured to reflect the mean average of forecasted prices or the end-point relative to the start-point of the forecast, offering flexibility in how forecasts are interpreted. To enhance the comprehensiveness and visualization, the indicator features a forecast deviation cone. This cone represents the potential range of price movements, providing a visual cue for volatility and uncertainty in the forecasted prices. The intensity of this cone can be adjusted to suit individual preferences, offering a visual guide to the level of risk and uncertainty associated with the forecasted price path. Embrace the fractal magic of markets with AlgoAlpha's Price Action Fractal Forecasts and transform your trading today! 🌟🚀
Wskaźnik Pine Script®
od AlgoAlpha
28
Heikin Ashi and Optimized Trend Tracker and PVSRA [Erebor]Heikin Ashi Candles Let's consider a modification to the traditional “Heikin Ashi Candles” where we introduce a new parameter: the period of calculation. The traditional HA candles are derived from the open , high low , and close prices of the underlying asset. Now, let's introduce a new parameter, period, which will determine how many periods are considered in the calculation of the HA candles. This period parameter will affect the smoothing and responsiveness of the resulting candles. In this modification, instead of considering just the current period, we're averaging or aggregating the prices over a specified number of periods . This will result in candles that reflect a longer-term trend or sentiment, depending on the chosen period value. For example, if period is set to 1, it would essentially be the same as traditional Heikin Ashi candles. However, if period is set to a higher value, say 5, each candle will represent the average price movement over the last 5 periods, providing a smoother representation of the trend but potentially with delayed signals compared to lower period values. Traders can adjust the period parameter based on their trading style, the timeframe they're analyzing, and the level of smoothing or responsiveness they prefer in their candlestick patterns. Optimized Trend Tracker The "Optimized Trend Tracker" is a proprietary trading indicator developed by TradingView user ANIL ÖZEKŞİ. It is designed to identify and track trends in financial markets efficiently. The indicator attempts to smooth out price fluctuations and provide clear signals for trend direction. The Optimized Trend Tracker uses a combination of moving averages and adaptive filters to detect trends. It aims to reduce lag and noise typically associated with traditional moving averages, thereby providing more timely and accurate signals. Some of the key features and applications of the OTT include: • Trend Identification: The indicator helps traders identify the direction of the prevailing trend in a market. It distinguishes between uptrends, downtrends, and sideways consolidations. • Entry and Exit Signals: The OTT generates buy and sell signals based on crossovers and direction changes of the trend. Traders can use these signals to time their entries and exits in the market. • Trend Strength: It also provides insights into the strength of the trend by analyzing the slope and momentum of price movements. This information can help traders assess the conviction behind the trend and adjust their trading strategies accordingly. • Filter Noise: By employing adaptive filters, the indicator aims to filter out market noise and false signals, thereby enhancing the reliability of trend identification. • Customization: Traders can customize the parameters of the OTT to suit their specific trading preferences and market conditions. This flexibility allows for adaptation to different timeframes and asset classes. Overall, the OTT can be a valuable tool for traders seeking to capitalize on trending market conditions while minimizing false signals and noise. However, like any trading indicator, it is essential to combine its signals with other forms of analysis and risk management strategies for optimal results. Additionally, traders should thoroughly back-test the indicator and practice using it in a demo environment before applying it to live trading. PVSRA (Price, Volume, S&R Analysis) “PVSRA” (Price, Volume, S&R Analysis) is a trading methodology and indicator that combines the analysis of price action, volume, and support/resistance levels to identify potential trading opportunities in financial markets. It is based on the idea that price movements are influenced by the interplay between supply and demand, and analyzing these factors together can provide valuable insights into market dynamics. Here's a breakdown of the components of PVSRA: • Price Action Analysis: PVSRA focuses on analyzing price movements and patterns on price charts, such as candlestick patterns, trendlines, chart patterns (like head and shoulders, triangles, etc.), and other price-based indicators. Traders using PVSRA pay close attention to how price behaves at key support and resistance levels and look for patterns that indicate potential shifts in market sentiment. • Volume Analysis: Volume is an essential component of PVSRA. Traders monitor changes in trading volume to gauge the strength or weakness of price movements. An increase in volume during a price move suggests strong participation and conviction from market participants, reinforcing the validity of the price action. Conversely, low volume during price moves may indicate lack of conviction and potential reversals. • Support and Resistance (S&R) Analysis: PVSRA incorporates the identification and analysis of support and resistance levels on price charts. Support levels represent areas where buying interest is expected to be strong enough to prevent further price declines, while resistance levels represent areas where selling interest may prevent further price advances. These levels are often identified using historical price data, trendlines, moving averages, pivot points, and other technical analysis tools. The PVSRA methodology combines these three elements to generate trading signals and make trading decisions. Traders using PVSRA typically look for confluence between price action, volume, and support/resistance levels to confirm trade entries and exits. For example, a bullish reversal signal may be considered stronger if it occurs at a significant support level with increasing volume. It's important to note that PVSRA is more of a trading approach or methodology rather than a specific indicator with predefined rules. Traders may customize their analysis based on their preferences and trading style, incorporating additional technical indicators or filters as needed. As with any trading strategy, risk management and proper trade execution are essential components of successful trading with PVSRA. The following types of moving average have been included: "SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA", "HMA", "KAMA", "LSMA", "TRAMA", "VAR", "DEMA", "ZLEMA", "TSF", "WWMA". Thanks to the authors. Thank you for your indicator “Optimized Trend Tracker”. © kivancozbilgic Thank you for your indicator “PVSRA Volume Suite”. © creengrack Thank you for your programming language, indicators and strategies. © TradingView Kind regards. © Erebor_GIT
Wskaźnik Pine Script®
od Erebor_GIT
1
Sessions Lite [TradingFinder] New York, London, Asia, NYSE Forex🔵 Introduction A trading session is one of the basic concepts in the financial market that refers to specific time periods. In fact, a session means hours during the day and night, during which traders in a certain part of the world conduct their transactions. Although the "Forex" and "CFDs" market is open 24 hours a day and it is possible to trade in it, but in some hours the activity in this market decreases so much that many traders prefer not to trade and only watch the market. On the other hand, there are specific times when the market is very busy and dynamic, and many traders tend to trade during these hours of the day and night. Trading sessions are usually divided into three main categories, which are "Asian", "European" and "North American" sessions. These trading sessions are also called the "Tokyo", "London" and "New York" sessions, respectively. But they also categorized these sessions in more detailed ways such as "Sydney session", "Shanghai session" or "NYSE session". 🔵 Tokyo trading session (Asian session) After the weekend that happens on Saturday and Sunday, the Forex market starts with the Asian session. In this continent, most of the transactions are done in the Tokyo session, and for this reason, it is usually called the Asian session or the Tokyo session. However, other countries such as Australia, China and Singapore also do a lot of trading in this session. The Tokyo session has a lower volume of transactions compared to the London and New York sessions, and therefore the liquidity is lower. In this session, most of the Forex currency pairs move in a price range. For this reason, different people use the ups and downs with the trading strategy in the range and get profit. The low liquidity of the Tokyo session means that trading spreads are also higher during these hours. Besides, most of the transactions of this session are done in the early hours and at the same time as the planned news release. In the Tokyo or Asia session, the best currency pairs to trade are the "Japanese yen", the "Australian dollar", and the "New Zealand dollar". "Nikkei" index is also a good option for trading. If you trade in the Tokyo session, you should also be aware of the release of economic news and data from Australian, New Zealand and Japanese financial institutions. 🔵 London trading session (European session) After the Asian session, it is time for the European session. In this period of time, transactions are very large and many European markets are involved. However, the European session is usually known as the London session. Because of its specific time zone, London is not only known as the Forex trading center in Europe, but it is also known as the Forex trading center in the world. The London session overlaps with two other major trading sessions in the world, Asia and America. This means that most of the Forex transactions are done in this session. According to the latest statistics, 32% of Forex transactions are related to the London session, which shows that about a third of the activity performed in Forex takes place during this period. This will increase the volume of Forex transactions and increase liquidity. An event that causes the spread of transactions to decrease. Of course, high liquidity also leads to greater volatility, which is desirable for many traders. In the European session, the pound and euro currencies and the "DAX", "FTSE100", and "CAC40" indices are known as the best tradable assets. Also, traders of this session should pay attention to the news and data published by the "European Central Bank" and the "Bank of England". The news of countries like Germany, France and Italy are also very important. 🔵 American trading session (New York session) When the New York session begins, several hours have passed since the end of the Tokyo session, but the European session is in the middle. In this session, they usually affect the financial activities carried out in America, but they also affect other countries such as Canada, Mexico and several South American countries. The "US dollar" and stock indices such as "S&P", "Dow Jones" and "Nasdaq" are the most important assets that are traded in this session. The early hours of the American session have a lot of liquidity and volatility due to the overlap with the European session, but with the end of the European session, the activity in the American session also decreases. You can trade all major Forex currency pairs in the New York trading session. In this session, the "Federal Reserve", as the most important central bank in the world, is the institution that you should pay attention to its news and data. The trading session indicator is an analytical tool in the financial markets that is used to display and analyze specific trading periods during a day. These indicators are generally useful for determining support and resistance levels during any trading session and for detecting different trading patterns. For example, usually these indicators display the open and close price levels, the highest and lowest prices during a trading session. Also, you may notice various price patterns such as price channels, price phase phases and market trend changes during different trading sessions using these indicators. 🔵 cause of construction In particular, the session light indicator version is designed and built for those traders who use many different tools on their chart at the same time. These traders can include "Volume Traders", "ICT traders", "Day Traders" and... These individuals can use "Session Lite" without disturbing the display of their other trading tools such as "Order Blocks", "Liquidity", "Zigzag", "FVG" etc. But in general, there are several reasons for making tools like trading session indicators in financial markets, some of which include the following : 1. Analysis of specific time frames : Some traders and investors like to consider specific time frames for price analysis and review. For example, analyzing price changes during each trading session can help analyze trading patterns and identify trading opportunities. 2. Recognize different price patterns : Different price patterns may be observed during trading sessions. Trading session indicators can help to make better trading decisions by analyzing these patterns and their strengths and weaknesses. 3. Identifying Support and Resistance Levels : These tools may help to identify support and resistance levels during any trading session which can be helpful in deciding whether to enter or exit the market. 🔵 How to use The Session Lite indicator displays 8 sessions by default. Asia session, Sydney session, Tokyo session, Shanghai session, Europe session, London session, New York session and New York Stock Exchange (NYSE) session are the sessions that are displayed. You can activate or deactivate the display of each session by using the tick button next to the name of each session. Two gray vertical dashes are also displayed by default, which indicate the beginning of the European session and the New York session. This feature is available for all sessions, but it is enabled by default only for these two sessions, and you can activate it for the rest of the session. You can enable or disable the display of this line by using the Start Session tick key. Likewise, the information table is displayed by default, which includes the open or closed information of each session and the start and end times of each session. These timings are based on the UTC time zone. Accordingly, the schedule of trading sessions is as follows : Asia session from 23:00 to 06:00 Sydney session from 23:00 to 05:00 Tokyo session from 00:00 to 00:06 Shanghai session from 01:30 to 06:57 European session from 07:00 to 16:30 London session from 08:00 to 16:30 New York session from 13:00 to 22:00 New York Stock Exchange (NYSE) session from 14:30 to T 22:00 Important note : the beginning of the European session coincides with the opening of the Frankfurt market. 🔵 Settings • In the settings section, there are customization capabilities according to the type of use of each user. The settings related to showing or not showing the box of each session, the start indicator of each session, setting the start and end time of the session and choosing the desired color to display each session are among the things that can be set from this section. • At the end of the settings, you will see the "Info Table" option; By disabling this option, the "sessions" clock table displayed on the upper right side will be disabled.
Wskaźnik Pine Script®
od TFlab
1
convergingpatternsLibrary "convergingpatterns" Library having implementation of converging chart patterns getPatternNameByType(patternType)   Returns pattern name based on type   Parameters:      patternType (int) : integer value representing pattern type   Returns: string name of the pattern method find(this, sProperties, dProperties, patterns, ohlcArray)   find converging patterns for given zigzag   Namespace types: zg.Zigzag   Parameters:      this (Zigzag type from Trendoscope/ZigzagLite/2) : Current zigzag Object      sProperties (ScanProperties) : ScanProperties Object      dProperties (DrawingProperties type from Trendoscope/abstractchartpatterns/5) : DrawingProperties Object      patterns (array type from Trendoscope/abstractchartpatterns/5) : array of existing patterns to check for duplicates      ohlcArray (array type from Trendoscope/ohlc/1) : array of OHLC values for historical reference   Returns: string name of the pattern ScanProperties   Object containing properties for pattern scanning   Fields:      baseProperties (ScanProperties type from Trendoscope/abstractchartpatterns/5) : Object of Base Scan Properties      convergingDistanceMultiplier (series float) : when multiplied with pattern size gets the max number of bars within which the pattern should converge
Biblioteka Pine Script®
od Trendoscope
2
Advanced Dynamic Threshold RSI [Elysian_Mind]Advanced Dynamic Threshold RSI Indicator Overview The Advanced Dynamic Threshold RSI Indicator is a powerful tool designed for traders seeking a unique approach to RSI-based signals. This indicator combines traditional RSI analysis with dynamic threshold calculation and optional Bollinger Bands to generate weighted buy and sell signals. Features Dynamic Thresholds: The indicator calculates dynamic thresholds based on market volatility, providing more adaptive signal generation. Performance Analysis: Users can evaluate recent price performance to further refine signals. The script calculates the percentage change over a specified lookback period. Bollinger Bands Integration: Optional integration of Bollinger Bands for additional confirmation and visualization of potential overbought or oversold conditions. Customizable Settings: Traders can easily customize key parameters, including RSI length, SMA length, lookback bars, threshold multiplier, and Bollinger Bands parameters. Weighted Signals: The script introduces a unique weighting mechanism for signals, reducing false positives and improving overall reliability. Underlying Calculations and Methods 1. Dynamic Threshold Calculation: The heart of the Advanced Dynamic Threshold RSI Indicator lies in its ability to dynamically calculate thresholds based on multiple timeframes. Let's delve into the technical details: RSI Calculation: For each specified timeframe (1-hour, 4-hour, 1-day, 1-week), the Relative Strength Index (RSI) is calculated using the standard 14-period formula. SMA of RSI: The Simple Moving Average (SMA) is applied to each RSI, resulting in the smoothing of RSI values. This smoothed RSI becomes the basis for dynamic threshold calculations. Dynamic Adjustment: The dynamically adjusted threshold for each timeframe is computed by adding a constant value (5 in this case) to the respective SMA of RSI. This dynamic adjustment ensures that the threshold reflects changing market conditions. 2. Weighted Signal System: To enhance the precision of buy and sell signals, the script introduces a weighted signal system. Here's how it works technically: Signal Weighting: The script assigns weights to buy and sell signals based on the crossover and crossunder events between RSI and the dynamically adjusted thresholds. If a crossover event occurs, the weight is set to 2; otherwise, it remains at 1. Signal Combination: The weighted buy and sell signals from different timeframes are combined using logical operations. A buy signal is generated if the product of weights from all timeframes is equal to 2, indicating alignment across timeframe. 3. Experimental Enhancements: The Advanced Dynamic Threshold RSI Indicator incorporates experimental features for educational exploration. While not intended as proven strategies, these features aim to offer users a glimpse into unconventional analysis. Some of these features include Performance Calculation, Volatility Calculation, Dynamic Threshold Calculation Using Volatility, Bollinger Bands Module, Weighted Signal System Incorporating New Features. 3.1 Performance Calculation: The script calculates the percentage change in the price over a specified lookback period (variable lookbackBars). This provides a measure of recent performance. pctChange(src, length) => change = src - src pctChange = (change / src ) * 100 recentPerformance1H = pctChange(close, lookbackBars) recentPerformance4H = pctChange(request.security(syminfo.tickerid, "240", close), lookbackBars) recentPerformance1D = pctChange(request.security(syminfo.tickerid, "1D", close), lookbackBars) 3.2 Volatility Calculation: The script computes the standard deviation of the closing price to measure volatility. volatility1H = ta.stdev(close, 20) volatility4H = ta.stdev(request.security(syminfo.tickerid, "240", close), 20) volatility1D = ta.stdev(request.security(syminfo.tickerid, "1D", close), 20) 3.3 Dynamic Threshold Calculation Using Volatility: The dynamic thresholds for RSI are calculated by adding a multiplier of volatility to 50. dynamicThreshold1H = 50 + thresholdMultiplier * volatility1H dynamicThreshold4H = 50 + thresholdMultiplier * volatility4H dynamicThreshold1D = 50 + thresholdMultiplier * volatility1D 3.4 Bollinger Bands Module: An additional module for Bollinger Bands is introduced, providing an option to enable or disable it. // Additional Module: Bollinger Bands bbLength = input(20, title="Bollinger Bands Length") bbMultiplier = input(2.0, title="Bollinger Bands Multiplier") upperBand = ta.sma(close, bbLength) + bbMultiplier * ta.stdev(close, bbLength) lowerBand = ta.sma(close, bbLength) - bbMultiplier * ta.stdev(close, bbLength) 3.5 Weighted Signal System Incorporating New Features: Buy and sell signals are generated based on the dynamic threshold, recent performance, and Bollinger Bands. weightedBuySignal = rsi1H > dynamicThreshold1H and rsi4H > dynamicThreshold4H and rsi1D > dynamicThreshold1D and crossOver1H weightedSellSignal = rsi1H < dynamicThreshold1H and rsi4H < dynamicThreshold4H and rsi1D < dynamicThreshold1D and crossUnder1H These features collectively aim to provide users with a more comprehensive view of market dynamics by incorporating recent performance and volatility considerations into the RSI analysis. Users can experiment with these features to explore their impact on signal accuracy and overall indicator performance. Indicator Placement for Enhanced Visibility Overview The design choice to position the "Advanced Dynamic Threshold RSI" indicator both on the main chart and beneath it has been carefully considered to address specific challenges related to visibility and scaling, providing users with an improved analytical experience. Challenges Faced 1. Differing Scaling of RSI Results: RSI values for different timeframes (1-hour, 4-hour, and 1-day) often exhibit different scales, especially in markets like gold. Attempting to display these RSIs on the same chart can lead to visibility issues, as the scaling differences may cause certain RSI lines to appear compressed or nearly invisible. 2. Candlestick Visibility vs. RSI Scaling: Balancing the visibility of candlestick patterns with that of RSI values posed a unique challenge. A single pane for both candlesticks and RSIs may compromise the clarity of either, particularly when dealing with assets that exhibit distinct volatility patterns. Design Solution Placing the buy/sell signals above/below the candles helps to maintain a clear association between the signals and price movements. By allocating RSIs beneath the main chart, users can better distinguish and analyze the RSI values without interference from candlestick scaling. Doubling the scaling of the 1-hour RSI (displayed in blue) addresses visibility concerns and ensures that it remains discernible even when compared to the other two RSIs: 4-hour RSI (orange) and 1-day RSI (green). Bollinger Bands Module is optional, but is turned on as default. When the module is turned on, the users can see the upper Bollinger Band (green) and lower Bollinger Band (red) on the main chart to gain more insight into price actions of the candles. User Flexibility This dual-placement approach offers users the flexibility to choose their preferred visualization: The main chart provides a comprehensive view of buy/sell signals in relation to candlestick patterns. The area beneath the chart accommodates a detailed examination of RSI values, each in its own timeframe, without compromising visibility. The chosen design optimizes visibility and usability, addressing the unique challenges posed by differing RSI scales and ensuring users can make informed decisions based on both price action and RSI dynamics. Usage Installation To ensure you receive updates and enhancements seamlessly, follow these steps: Open the TradingView platform. Navigate to the "Indicators" tab in the top menu. Click on "Community Scripts" and search for "Advanced Dynamic Threshold RSI Indicator." Select the indicator from the search results and click on it to add to your chart. This ensures that any future updates to the indicator can be easily applied, keeping you up-to-date with the latest features and improvements. Review Code Open TradingView and navigate to the Pine Editor. Copy the provided script. Paste the script into the Pine Editor. Click "Add to Chart." Configuration The indicator offers several customizable settings: RSI Length: Defines the length of the RSI calculation. SMA Length: Sets the length of the SMA applied to the RSI. Lookback Bars: Determines the number of bars used for recent performance analysis. Threshold Multiplier: Adjusts the multiplier for dynamic threshold calculation. Enable Bollinger Bands: Allows users to enable or disable Bollinger Bands integration. Interpreting Signals Buy Signal: Generated when RSI values are above dynamic thresholds and a crossover occurs. Sell Signal: Generated when RSI values are below dynamic thresholds and a crossunder occurs. Additional Information The indicator plots scaled RSI lines for 1-hour, 4-hour, and 1-day timeframes. Users can experiment with additional modules, such as machine-learning simulation, dynamic real-life improvements, or experimental signal filtering, depending on personal preferences. Conclusion The Advanced Dynamic Threshold RSI Indicator provides traders with a sophisticated tool for RSI-based analysis, offering a unique combination of dynamic thresholds, performance analysis, and optional Bollinger Bands integration. Traders can customize settings and experiment with additional modules to tailor the indicator to their trading strategy. Disclaimer: Use of the Advanced Dynamic Threshold RSI Indicator The Advanced Dynamic Threshold RSI Indicator is provided for educational and experimental purposes only. The indicator is not intended to be used as financial or investment advice. Trading and investing in financial markets involve risk, and past performance is not indicative of future results. The creator of this indicator is not a financial advisor, and the use of this indicator does not guarantee profitability or specific trading outcomes. Users are encouraged to conduct their own research and analysis and, if necessary, consult with a qualified financial professional before making any investment decisions. It is important to recognize that all trading involves risk, and users should only trade with capital that they can afford to lose. The Advanced Dynamic Threshold RSI Indicator is an experimental tool that may not be suitable for all individuals, and its effectiveness may vary under different market conditions. By using this indicator, you acknowledge that you are doing so at your own risk and discretion. The creator of this indicator shall not be held responsible for any financial losses or damages incurred as a result of using the indicator. Kind regards, Ely
Wskaźnik Pine Script®
od Elysian_Mind
2
Pro Bollinger Bands CalculatorThe "Pro Bollinger Bands Calculator" indicator joins our suite of custom trading tools, which includes the "Pro Supertrend Calculator", the "Pro RSI Calculator" and the "Pro Momentum Calculator." Expanding on this series, the "Pro Bollinger Bands Calculator" is tailored to offer traders deeper insights into market dynamics by harnessing the power of the Bollinger Bands indicator. Its core mission remains unchanged: to scrutinize historical price data and provide informed predictions about future price movements, with a specific focus on detecting potential bullish (green) or bearish (red) candlestick patterns. 1. Bollinger Bands Calculation: The indicator kicks off by computing the Bollinger Bands, a well-known volatility indicator. It calculates two pivotal Bollinger Bands parameters: - Bollinger Bands Length: This parameter sets the lookback period for Bollinger Bands calculations. - Bollinger Bands Deviation: It determines the deviation multiplier for the upper and lower bands, typically set at 2.0. 2. Visualizing Bollinger Bands: The Bollinger Bands derived from the calculations are skillfully plotted on the price chart: - Red Line: Represents the upper Bollinger Band during bearish trends, suggesting potential price declines. - Teal Line: Represents the lower Bollinger Band in bullish market conditions, signaling the possibility of price increases. 3.Analyzing Consecutive Candlesticks: The indicator's core functionality revolves around tracking consecutive candlestick patterns based on their relationship with the Bollinger Bands lines. To be considered for analysis, a candlestick must consistently close either above (green candles) or below (red candles) the Bollinger Bands lines for multiple consecutive periods. 4. Labeling and Enumeration: To convey the count of consecutive candles displaying consistent trend behavior, the indicator meticulously assigns labels to the price chart. The position of these labels varies depending on the direction of the trend, appearing either below (for bullish patterns) or above (for bearish patterns) the candlesticks. The label colors match the candle colors: green labels for bullish candles and red labels for bearish ones. 5. Tabular Data Presentation: The indicator complements its graphical analysis with a customizable table that prominently displays comprehensive statistical insights. Key data points within the table encompass: - Consecutive Candles: The count of consecutive candles displaying consistent trend characteristics. - Candles Above Upper BB: The number of candles closing above the upper Bollinger Band during the consecutive period. - Candles Below Lower BB: The number of candles closing below the lower Bollinger Band during the consecutive period. - Upcoming Green Candle: An estimated probability of the next candlestick being bullish, derived from historical data. - Upcoming Red Candle: An estimated probability of the next candlestick being bearish, also based on historical data. 6. Custom Configuration: To cater to diverse trading strategies and preferences, the indicator offers extensive customization options. Traders can fine-tune parameters such as Bollinger Bands length, upper and lower band deviations, label and table placement, and table size to align with their unique trading approaches.
Wskaźnik Pine Script®
od Julien_Exe
1
Pro RSI CalculatorThe "Pro RSI Calculator" indicator is the latest addition to a series of custom trading tools that includes the "Pro Supertrend Calculator" and the "Pro Momentum Calculator." Building upon this series, the "Pro RSI Calculator" is designed to provide traders with further insights into market trends by leveraging the Relative Strength Index (RSI) indicator. Its primary objective remains consistent: to analyze historical price data and make informed predictions about future price movements, with a specific focus on identifying potential bullish (green) or bearish (red) candlestick patterns. 1. RSI Calculation: The indicator begins by computing the RSI, a widely used momentum oscillator. It calculates two crucial RSI parameters: RSI Length: This parameter determines the lookback period for RSI calculations. RSI Upper and Lower Bands: These thresholds define overbought and oversold conditions, typically set at 70 and 30, respectively. 2. RSI Bands Visualization: The RSI values obtained from the calculation are skillfully plotted on the price chart, appearing as two distinct lines: Red Line: Represents the RSI when indicating a bearish trend, anticipating potential price declines. Teal Line: Represents the RSI in bullish market conditions, signaling the possibility of price increases. 3. Consecutive Candlestick Analysis: The indicator's core functionality revolves around tracking consecutive candlestick patterns based on their relationship with the RSI lines. To be included in the analysis, a candlestick must consistently close either above (green candles) or below (red candles) the RSI lines for multiple consecutive periods. 4. Labeling and Enumeration: To communicate the count of consecutive candles displaying consistent trend behavior, the indicator meticulously assigns labels to the price chart. Label positioning varies depending on the trend's direction, appearing either below (for bullish patterns) or above (for bearish patterns) the candlesticks. The color scheme aligns with the candle colors: green labels for bullish candles and red labels for bearish ones. 5. Tabular Data Presentation: The indicator enhances its graphical analysis with a customizable table that prominently displays comprehensive statistical insights. Key data points in the table include: - Consecutive Candles: The count of consecutive candles displaying consistent trend characteristics. - Candles Above Upper RSI: The number of candles closing above the upper RSI threshold during the consecutive period. - Candles Below Lower RSI: The number of candles closing below the lower RSI threshold during the consecutive period. - Upcoming Green Candle: An estimated probability of the next candlestick being bullish, derived from historical data. - Upcoming Red Candle: An estimated probability of the next candlestick being bearish, also based on historical data. 6. Custom Configuration: To cater to various trading strategies and preferences, the indicator offers extensive customization options. Traders can fine-tune parameters like RSI length, upper, and lower bands, label and table placement, and table size to align with their unique trading approaches.
Wskaźnik Pine Script®
od Julien_Exe
HILOCLOP AnalysisThe "HILOCLOP Analysis" indicator is designed to analyze price data based on different conditions and provide insights into market trends and patterns. Let's break down its features and understand its potential usefulness in trading: Sample Length: The indicator allows the user to specify the sample length, which determines the number of bars or periods considered for the analysis. This parameter can be adjusted to capture short-term or long-term trends and patterns in the market. Raw Up/Down Analysis: The indicator calculates the number of occurrences where the current price values (high, low, open, close) are higher or lower than their previous values. It provides separate counts for each price component. By visualizing these counts on the chart, traders can identify periods of upward or downward movement in the price data. HICLOP Analysis: The indicator offers a color scheme option called "HICLOP," which determines the color of the plotted results. If the HICLOP analysis is enabled, the plots representing raw up/down counts will have different colors based on whether the current count is higher or lower than the previous count. This color coding helps traders quickly identify changes in price trends. Unchecking this Box will Show the general trend. Raw HICLOP Color Scheme Trend Color Scheme Analysis Up vs. Down: The indicator provides an option to analyze instances where all four price components (high, low, open, close) are higher or lower than their respective previous values. This analysis helps traders identify periods of strong upward or downward movement in the market. Analysis High vs. Low: The indicator compares the number of occurrences where the current high is higher than the previous high and the current low is higher than the previous low. It provides insights into whether the market is experiencing higher highs or higher lows, which can help traders determine the strength of an upward or downward trend. Analysis Open vs. Close: The indicator compares the number of occurrences where the current close is higher than the previous close and the current open is higher than the previous open. This analysis helps traders assess the relationship between opening and closing prices, providing insights into the strength of buying or selling pressure in the market. The usefulness of the "HILOCLOP Analysis" indicator in trading depends on the specific trading strategy and the trader's preferences. Here are a few potential use cases: Trend Identification: By analyzing the raw up/down counts and the HICLOP color scheme, traders can identify trends and changes in price momentum. Increasing raw up counts and corresponding color changes to positive values may indicate an upward trend, while increasing raw down counts and negative color changes may suggest a downward trend. Confirmation of Breakouts: Traders often look for confirmation of breakouts from key levels or chart patterns. The "Analysis Up V Dn" feature can help identify instances where all four price components simultaneously confirm a breakout, indicating a potentially significant move in the market. Trend Reversals: The "Analysis High V Low" and "Analysis Open V Close" features can provide insights into potential trend reversals. For example, if there are more higher highs than higher lows, it may indicate a weakening trend, potentially signaling a reversal or a correction.
Wskaźnik Pine Script®
od Kill_Streak
Moving Average Contrarian IndicatorThis indicator is designed to identify potential turning points in the market. By measuring the distance between the price and a moving average, and normalizing it, the MACI provides valuable insights into market sentiment and potential reversals. In this article, we will explore the calculation, interpretation, and practical applications of the MACI, along with its potential limitations. The MACI is calculated in several steps. First, a moving average is computed using a user-defined length, representing the average price over the specified period. The distance between the current price and the moving average is then determined. This distance is normalized using the highest and lowest distances observed within the chosen length, resulting in a value between 0 and 100. Higher MACI values indicate that the price is relatively far from the moving average, potentially signaling an overextension, while lower values suggest price consolidation or convergence with the moving average. Altering the parameters of the Moving Average Contrarian Indicator can provide traders with additional flexibility and adaptability to suit different market conditions and trading styles. By adjusting the length parameter, traders can customize the sensitivity of the indicator to price movements. A shorter length may result in more frequent and responsive signals, which can be useful for short-term traders aiming to capture quick price reversals. On the other hand, a longer length may provide smoother signals, suited for traders who prefer to focus on longer-term trends and are less concerned with minor fluctuations. Experimenting with different parameter values allows traders to fine-tune the indicator to align with their preferred trading timeframes and risk tolerance. However, it is essential to strike a balance and avoid excessive parameter adjustments that may lead to over-optimization or curve fitting. Regular evaluation and optimization based on historical data and real-time market observations can help identify the most suitable parameter values for optimal performance. The coloration of the Moving Average Contrarian Indicator provides visual cues that assist traders in interpreting its signals. The background color, set based on the indicator's values, adds an additional layer of context to the chart. When the indicator is indicating bullish conditions, the background color is set to lime, suggesting a favorable environment for long positions. Conversely, when the indicator signals bearish conditions, the background color is set to fuchsia, indicating a potential advantage for short positions. In neutral or transitional periods, the background color is set to yellow, indicating caution and the absence of a clear bias. The bar color complements the histogram and provides additional visual clarity. When the MACI value is greater than the MACI SMA value and exceeds the threshold of 30, the bars are colored lime, signaling potential bullish conditions. Conversely, when the MACI value is below the MACI SMA value and falls below the threshold of 70, the bars are colored fuchsia, indicating potential bearish conditions. For values that fall between these thresholds, the bars are colored yellow, highlighting a neutral or transitional state. Practical Uses and Strategies: The MACI offers traders and analysts valuable insights into market dynamics and potential reversal points. When the MACI is above its moving average and above a predefined threshold (e.g., 30), it suggests that prices have deviated significantly from the average and may be overbought. This could serve as an early indication for potential short-selling opportunities or taking profits on existing long positions. Conversely, when the MACI is below its moving average and below a predefined threshold (e.g., 70), it suggests oversold conditions, potentially signaling a buying opportunity. Traders can combine MACI with other technical indicators or price patterns to further refine their trading strategies. The MACI can be a powerful tool for identifying potential market reversals. When the MACI reaches extreme levels, such as above 70 or below 30, it indicates overbought or oversold conditions, respectively. Traders can use these signals to anticipate price reversals and adjust their trading strategies accordingly. For example, when the MACI enters the overbought zone, traders may consider initiating short positions or tightening stop-loss levels on existing long positions. Conversely, when the MACI enters the oversold zone, it may indicate a buying opportunity, prompting traders to consider initiating long positions or loosening stop-loss levels. The MACI can also be used in conjunction with price action to identify potential divergence patterns. Divergence occurs when the MACI and price move in opposite directions. For instance, if the price is making higher highs while the MACI is making lower highs, it suggests a bearish divergence, indicating a potential trend reversal. Conversely, if the price is making lower lows while the MACI is making higher lows, it suggests a bullish divergence, signaling a potential trend reversal to the upside. Traders can use these divergence patterns as additional confirmation signals when making trading decisions. Limitations: -- Sideways and Choppy Markets : The MACI performs best in trending markets where price movements are more pronounced. In sideways or choppy markets with limited directional bias, the MACI may generate false signals or provide less reliable indications. Traders should exercise caution when relying solely on the MACI in such market conditions and consider incorporating additional analysis techniques or filters to confirm potential signals. -- Lagging Indicator : The MACI is a lagging indicator, as it relies on moving averages and historical price data. It may not provide timely signals for very short-term trading or capturing rapid price movements. Traders should be aware that there may be a delay between the occurrence of a signal and its confirmation by the MACI. -- False Signals : Like any technical indicator, the MACI is not immune to false signals. It is essential to use the MACI in conjunction with other technical indicators, chart patterns, or fundamental analysis to increase the probability of accurate predictions. Combining multiple confirmation signals can help filter out false signals and enhance the overall reliability of trading decisions. -- Market Conditions : It's important to consider that the effectiveness of the MACI may vary across different markets and asset classes. Each market has its own characteristics, and what works well in one market may not work as effectively in another. Traders should evaluate the performance of the MACI within their specific trading environment and adapt their strategies accordingly. This indicator can be a valuable addition to a trader's toolkit, offering insights into potential entry and exit points. However, it should be used in conjunction with other analysis techniques and should not be relied upon as a standalone trading signal. Understanding its calculation, interpreting its values, and considering its limitations will empower traders to make more informed decisions in their pursuit of trading success.
Wskaźnik Pine Script®
od LeafAlgo
4
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading. *** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed*** █ Brief Overview of the Goertzel Cycle Composite Wave The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions. The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes. The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here: Goertzel Browser The primary purpose of this indicator is to: 1. Detect and analyze the dominant cycles present in the price data. 2. Reconstruct and visualize the composite wave based on the detected cycles. To achieve this, the indicator performs several tasks: 1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components. 2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength. 3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength. 4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing. This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions. █ What is the Goertzel Algorithm? The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics. The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as: 1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed. 2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments. 3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear. 4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues. The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT: 1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases. 2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing. 3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest. 4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential. The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics. █ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis. Unveiling Hidden Market Cycles: Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies. Developing Quantitative Trading Strategies: The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets. For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction. Enhancing Risk Management: The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies. By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy. Expanding Quantitative Toolkits: Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately. Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals. The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies. █ Indicator Inputs src: This is the source data for the analysis, typically the closing price of the financial instrument. detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components. The available options are: hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average. zlagsmthdt: Detrend using zero-lag moving average centered moving average. logZlagRegression: Detrend using logarithmic zero-lag linear regression. hpsmth: Detrend using Hodrick-Prescott filter. zlagsmth: Detrend using zero-lag moving average. DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt. DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt. DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression. HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth. ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth. MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles. squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm. useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles. useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves. UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude. WindowSizePast: These inputs define the window size for the composite wave. FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles. BartNoCycles: This input sets the number of cycles to be used in Bartel's test. BartSmoothPer: This input sets the period for the moving average used in Bartel's test. BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant. SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results. StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles. UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input. SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles. █ Exploring Auxiliary Functions The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool. Zero-Lag Moving Average: The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator. The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator. Bartels Probability: The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets. The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies. Detrend Logarithmic Zero-Lag Regression: The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics. The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding. Bartels Cycle Significance Test: The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding. The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis. Hodrick-Prescott Filter: The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends. The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive. Detrending Options: Detrend Centered Moving Average: The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences. The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants. The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average, █ In-Depth Analysis of the Goertzel Cycle Composite Wave Code The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose. Function signature and input parameters: The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer). Initializing variables and arrays: The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm. Preprocessing input data: The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample. Iterative calculation of Goertzel coefficients: The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure. Cycle strength computation: The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values. Phase calculation: The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients. Peak detection and cycle extraction: The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively. Sorting cycles by amplitude or cycle strength: The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data. Bartels cycle significance test: If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values. Waveform calculation: The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted. Storing waveforms in a matrix: The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle. Returning the number of cycles: The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles. The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data. █ Generating and Visualizing Composite Waveform The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process: Updating WindowSizePast: The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period). Initializing matrices and arrays: The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information. Preparing the source data (srcVal) array: The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth). Goertzel function call: The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles. Initializing arrays for waveforms: The goertzel array is initialized to store the endpoint Goertzel. Calculating composite waveform (goertzel array): The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component. Drawing composite waveform (pvlines): The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward). To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines. █ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading. Enhancements for Financial Modeling Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results. Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data. Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models. Enhancements for General and Advanced Trading Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies. Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size. Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions. Enhancements for High-Frequency Finance Trading Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity. Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations. Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies. While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike. █ Understanding the Limitations of the Goertzel Algorithm While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are: Lagging nature: As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses. Parameter sensitivity: The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance. Complexity: The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions. Overfitting risk: As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance. Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity. While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions. █ Interpreting Results The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data. The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color. █ Conclusion The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development. █ Footnotes What is the Bartels Test for Cycle Significance? The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century. The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series. The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges. If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component. The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles. Deep-dive into the Hodrick-Prescott Fitler The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world. The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend. The HP filter works by minimizing the following objective function: Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences) Where: 1. The first term represents the deviation of the data from the trend. 2. The second term represents the smoothness of the trend. 3. λ is a smoothing parameter that determines the degree of smoothness of the trend. The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend. The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency. However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series. Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables. The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Wskaźnik Pine Script®
od loxx
3
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading. █ Brief Overview of the Goertzel Browser The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions. The primary purpose of this indicator is to: 1. Detect and analyze the dominant cycles present in the price data. 2. Reconstruct and visualize the composite wave based on the detected cycles. 3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements. To achieve this, the indicator performs several tasks: 1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components. 2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength. 3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength. 4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing. 5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase. This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions. █ What is the Goertzel Algorithm? The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics. The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as: 1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed. 2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments. 3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear. 4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues. The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT: 1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases. 2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing. 3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest. 4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential. The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics. █ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis. Unveiling Hidden Market Cycles: Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies. Developing Quantitative Trading Strategies: The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets. For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction. Enhancing Risk Management: The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies. By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy. Expanding Quantitative Toolkits: Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately. Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals. The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies. █ Indicator Inputs src: This is the source data for the analysis, typically the closing price of the financial instrument. detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components. The available options are: hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average. zlagsmthdt: Detrend using zero-lag moving average centered moving average. logZlagRegression: Detrend using logarithmic zero-lag linear regression. hpsmth: Detrend using Hodrick-Prescott filter. zlagsmth: Detrend using zero-lag moving average. DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt. DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt. DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression. HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth. ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth. MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles. squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm. useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles. useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves. UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude. WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave. FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles. BartNoCycles: This input sets the number of cycles to be used in Bartel's test. BartSmoothPer: This input sets the period for the moving average used in Bartel's test. BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant. SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results. UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used. Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave. StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles. UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input. SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles. █ Exploring Auxiliary Functions The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool. Zero-Lag Moving Average: The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator. The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator. Bartels Probability: The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets. The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies. Detrend Logarithmic Zero-Lag Regression: The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics. The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding. Bartels Cycle Significance Test: The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding. The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis. Hodrick-Prescott Filter: The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends. The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive. Detrending Options: Detrend Centered Moving Average: The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences. The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants. The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average, █ In-Depth Analysis of the Goertzel Browser Code The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose. Function signature and input parameters: The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer). Initializing variables and arrays: The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm. Preprocessing input data: The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample. Iterative calculation of Goertzel coefficients: The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure. Cycle strength computation: The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values. Phase calculation: The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients. Peak detection and cycle extraction: The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively. Sorting cycles by amplitude or cycle strength: The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data. Bartels cycle significance test: If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values. Waveform calculation: The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted. Storing waveforms in matrices: The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle. Returning the number of cycles: The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles. The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data. █ Generating and Visualizing Composite Waveform The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process: Updating WindowSizePast and WindowSizeFuture: The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period). Initializing matrices and arrays: Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information. Preparing the source data (srcVal) array: The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth). Goertzel function call: The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles. Initializing arrays for past and future waveforms: Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively. Calculating composite waveform for past bars (goertzel array): The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component. Calculating composite waveform for future bars (goertzelFuture array): The future composite waveform is calculated in a similar way as the past composite waveform. Drawing past composite waveform (pvlines): The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward). Drawing future composite waveform (fvlines): The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward). Displaying cycle information in a table (table3): A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle. Filling the table with cycle information: The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places. To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle. █ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading. Enhancements for Financial Modeling Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results. Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data. Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models. Enhancements for General and Advanced Trading Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies. Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size. Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions. Enhancements for High-Frequency Finance Trading Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity. Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations. Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies. While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike. █ Understanding the Limitations of the Goertzel Algorithm While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are: Lagging nature: As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses. Parameter sensitivity: The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance. Complexity: The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions. Overfitting risk: As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance. No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions. Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity. While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions. █ Interpreting Results The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period. The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend. The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series. Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions. This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time. The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals. █ Conclusion The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development. █ Footnotes What is the Bartels Test for Cycle Significance? The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century. The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series. The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges. If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component. The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles. Deep-dive into the Hodrick-Prescott Fitler The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world. The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend. The HP filter works by minimizing the following objective function: Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences) Where: The first term represents the deviation of the data from the trend. The second term represents the smoothness of the trend. λ is a smoothing parameter that determines the degree of smoothness of the trend. The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend. The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency. However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series. Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables. The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Wskaźnik Pine Script®
od loxx
11
Pipe Bottoms Indicator Based on Thomas Bulkowski's TheoriesPipe bottom is a great indicator of short-term bullish reversal trading. Pipe bottoms appear as 2 "spikes" on the chart after a 2 weeks price drop downtrend, which look like 2 parallel lines on the chart. Based on Bulkowski and the definition on his Patternsite, those 2 "spikes" candles can be any shape, whether doji or any other kinds, thus, wicks length or candle bodies are NOT required in this type of trends. However, a wide price range for these 2 spikes compared to other candles that appeared in 2 weeks is required, so I made sure these 2 pipe bottoms have true ranges (candle length) bigger than the average true ranges in the past 10 trading days (14 calendar days). Then, a large price overlap must be between these 2 pipe bottoms, so I made sure that the high of either candle of the 2 pipe bottoms must be bigger than 40% of the true range added from the low of another candle, which in short, means that the high of the lower candle will at least be around the middle of the higher candle, and that will ensure price overlap. You can make this 50%, 60% or even 70% of the true range to ensure even greater price overlap, but you will find out that the signal amounts will decrease quite a lot after adjusting. Bulkowski also suggested 1% variation of the bottom price (low) of those 2 candles, I put it 5% (0.05) personally to get more signals since I do not solely rely on this indicator, and I want more pipe bottoms to show up on my chart for screening purposes. So, feel free to adjust that number for the identification of this pattern. Finally, the way I ensure it has been a downtrend or price drop is by checking if the price today is below the average of the last 10 trading days (14 calendar days), the price of the pipe bottoms must be below the moving averages of the past 10 trading days. I use hl2 here, feel free to change it to close or open as fit your strategy. Also, you can do this by making sure the candle close prices are decreasing in the past 10 trading days, or any other ways to ensure it's been in a downtrend, but for me, this works fine since again, this is just one of the many patterns I use, so I want more indicators to show up. So, as you can see on the chart. Whenever the pattern fits the indicator, a small white cross will show up under the candle with "Pipe" text next to it. But do NOT rush to buy yet, because in order to satisfy the pipe bottom pattern, the current close price must be higher than the high of the pipe bottom. And you will see green arrowups when a pipe bottom occurred the last candle, and the current close price is above the high of the pipe bottoms, that would be the entry point of this indicator. Overall it's a good pattern, especially for a trading bullish reversal after a huge price drop. You can adjust many numbers as I mentioned above to fit your needs, but make sure to add this with other indicators for your strategy, using only 1 indicator or pattern along is never a wise thing, especially in trading. Thanks guys, please like and share.
Wskaźnik Pine Script®
od BoilderRoomColdCaller
2
Support and Resistance linesThis indicator allows you to set 5 SR lines for 8 currency pairs. The SR line variables need to be set in the pine script. It is easy to add more currency pairs or change the number of SR lines. The indicator works in the following time frames: 1D, 12H, 8H, 6H, 4H In the active time frame green and red vertical lines indiate a potential reversal. Higher time frame occurrences are indicated by e.g. 8H, 12H labels above or below the price line. So, e.g. in the 12H window you can see D labels. And in the 8H window you can see 12H and D labels. The indicator recognizes 4 long and 4 short patterns. These patterns are very simple. See the section "Conditions for PriceAction patterns" in the pine script. If you desire please go ahead and change the patterns or add new ones. This is the most difficult part. If you come up with good ones please post them here.
Wskaźnik Pine Script®
od DS24
4
Volume Based Price ActionThis indicator is uses volume and common candlestick patterns to suggest potential trend reversals. It is not quite meant to be used as a buy/sell signal. Instead, it should be used to evaluate market psychology based on when the buyers or sellers fully dominated the price action. In the current version, the indicators looks for engulfing and pin-bar patterns. Optionally, it also considers rising volume as further confirmation to the signal. In future, I plan on allowing it to track more candlestick patterns. Please let me know if you have any suggestions, concerns, or comments!
Wskaźnik Pine Script®
od Zeenobit
5
High/Low Price Tracker w/L20-Table by Deepsage V1This indicator/strategy was planned, designed, and coded with guidance from Deepsage AI Mentors, but reflects my own ideas and strategies. It identifies the highest and lowest price of each candle and includes a table showing high/low price information for up to 20 candles back, providing a clear overview for analysis. It is connected to SageMaster for risk management and smart trade execution, including entering, exiting, starting, or closing positions based on TradingView alerts. Deepsage AI Mentors provide guidance only and do not offer financial advice. They are trained on public trading data from major exchanges, insights from SageMaster users, and educational content from the World of Wisdom platform, providing support and insights for strategy development. SageMaster is a trading ecosystem, not a broker or trading platform. Users retain full control of any automation, and we do not access or manage your funds. Created by Andreas, a Deepsage and SageMaster user. Disclaimer: This is not an official Deepsage, or SageMaster product. Planned Upgrades We are planning V2 and V3 of this indicator with Deepsage: V2: Adds a second table with more price-focused data points: OHLC, Range (high–low), Body Size (|close–open|), Price Change (%), Candle Type/Pattern, Volume, Relative Candle Size. Practical uses include: Trend Continuations (Potential Trade Idea): large body + above-average relative candle size + high volume + bullish/bearish pattern. Potential Reversals (Potential Trade Idea): hammer, doji, engulfing patterns confirmed by volume and relative candle size. Exhaustion Detection: long wicks, large range, small body candles. Other strategies: Sudden volatility, volatility-based stops, pattern-based stops, volume confirmation, breakout trading, swing trading, scalping. V3: Focus on total price movement (sum of all price changes within the candle, not just high–low). Provides advanced volatility analysis beyond traditional high–low measures. Designed to give additional insights while remaining practical for retail traders.
Wskaźnik Pine Script®
od andreasapelseth
Precision Pivot Confluence Engine [JOAT]Precision Pivot Confluence Engine Introduction The Precision Pivot Confluence Engine is an open-source technical indicator that combines Central Pivot Range (CPR) analysis with Smart Money Concepts (SMC), multi-oscillator divergence detection, and institutional order flow patterns. This mashup integrates multiple proven methodologies into a unified confluence system designed to identify high-probability trading zones where institutional and retail liquidity intersect. The indicator is built for traders who understand that no single signal provides consistent edge, but multiple confirming factors working together can significantly improve trade selection. By synthesizing CPR levels, Fair Value Gaps, Order Blocks, liquidity sweeps, and divergence patterns, this tool helps identify structural market inflection points. Chart showing CPR levels, FVG zones, Order Blocks, and divergence signals on 4H timeframe Why This Mashup Exists This indicator combines five distinct analytical frameworks that complement each other: CPR Analysis: Identifies key pivot levels where institutional algorithms and retail traders make decisions Smart Money Concepts: Tracks Fair Value Gaps, Order Blocks, and Breaker Blocks showing institutional positioning Divergence Detection: Uses RSI, MACD, and Stochastic RSI to identify momentum exhaustion Liquidity Analysis: Detects liquidity sweeps where stop hunts occur before reversals Volume Confirmation: Validates moves with volume analysis and delta calculations Each component addresses a different aspect of market structure. CPR provides static reference levels, SMC reveals dynamic institutional behavior, divergences show momentum shifts, liquidity sweeps identify stop hunts, and volume confirms genuine moves versus noise. Together, they create a multi-dimensional view of market conditions. Core Components Explained 1. Enhanced CPR System The Central Pivot Range system calculates Daily and Weekly pivot levels using the formula: Pivot = (High + Low + Close) / 3 BC (Bottom Central) = (High + Low) / 2 TC (Top Central) = (Pivot - BC) + Pivot The indicator analyzes CPR width to determine market regime: Narrow CPR (width < 0.5%): Indicates compression and potential breakout conditions Wide CPR (width > 1.5%): Suggests ranging market with less directional conviction Price position relative to CPR: Above both Daily and Weekly pivots = bullish structure, below = bearish structure CPR levels act as magnetic zones where price tends to react. The indicator tracks distance from pivots to identify overextension and mean reversion opportunities. 2. Smart Money Concepts Integration Fair Value Gaps (FVG): Bullish FVG occurs when current low > high from 2 bars ago, leaving an unfilled gap Bearish FVG occurs when current high < low from 2 bars ago The indicator calculates FVG size as percentage of price and filters for significant gaps (> 0.3%) to avoid noise. FVGs represent inefficient price delivery where institutions moved price quickly, often returning to fill these gaps later. Order Blocks (OB): Bullish OB: Two consecutive bearish candles followed by strong bullish candle with high volume Bearish OB: Two consecutive bullish candles followed by strong bearish candle with high volume Order Blocks mark the last opposite-direction move before a strong impulse, indicating where institutions accumulated or distributed positions. Breaker Blocks: Failed Order Blocks that get violated become Breaker Blocks, signaling potential trend reversal. The indicator tracks the last bullish and bearish OB levels and alerts when price breaks through them. Liquidity Sweeps: The indicator identifies when price briefly exceeds recent highs/lows (20-bar lookback) but closes back inside the range. These "stop hunts" often precede reversals as institutions trigger retail stops before moving price in the intended direction. Example showing FVG zones, Order Blocks, and liquidity sweep markers 3. Multi-Oscillator Divergence System The indicator simultaneously tracks divergences across three oscillators: RSI Divergence: Bullish: Price makes lower low, RSI makes higher low (momentum improving despite price weakness) Bearish: Price makes higher high, RSI makes lower high (momentum deteriorating despite price strength) MACD Divergence: Tracks histogram divergences using the same pivot-based logic Stochastic RSI Divergence: More sensitive than RSI, catches early momentum shifts The indicator uses a 5-bar pivot lookback to identify swing highs/lows and compares current pivots with previous pivots to detect divergences. When multiple oscillators show divergence simultaneously, it signals strong momentum exhaustion. 4. Volume Analysis Engine Volume MA Comparison: Identifies high volume (> 1.5x MA) and climax volume (> 3x MA) Volume Delta: Cumulative difference between buying volume (green candles) and selling volume (red candles) Delta Trend: Compares current delta to 20-period MA to identify institutional accumulation or distribution Volume Confirmation: Validates bullish moves with high volume + rising delta, bearish moves with high volume + falling delta 5. Confluence Scoring System The indicator calculates a real-time confluence score (0-100) by weighting each component: Confluence Score Components: - CPR Position: Up to 15 points (bullish above pivots, bearish below) - SMC Signals: Up to 10 points (FVG + OB + Breaker + Liquidity Sweeps) - Divergence: Up to 10 points (single oscillator = 5, multiple = 10) - Volume: Up to 10 points (confirmed volume = 7, climax = additional 3) - Trend Alignment: Up to 5 points (price vs key MAs) Scores above 70 indicate strong confluence for potential trades. The dashboard displays individual component scores for transparency. Visual Elements CPR Lines: Daily Pivot (yellow), TC/BC (yellow transparent), Weekly Pivot (yellow circles) FVG Boxes: Green boxes for bullish FVGs, red boxes for bearish FVGs Order Block Boxes: Solid green/red boxes marking institutional zones Breaker Block Labels: "BB" markers when Order Blocks fail Liquidity Sweep Labels: "LIQ" and "STRONG LIQ" positioned at sweep tips Divergence Labels: "D" markers at divergence pivot points Dashboard: Top-right table showing confluence score and component breakdown How Components Work Together The mashup creates a layered analysis approach: Layer 1 - Structure: CPR levels define key zones where reactions are likely Layer 2 - Institutional Behavior: SMC concepts show where smart money is positioned Layer 3 - Momentum: Divergences indicate when current trend is losing steam Layer 4 - Confirmation: Volume validates whether moves are genuine or false Layer 5 - Synthesis: Confluence score combines all factors into actionable signal Example scenario: Price approaches Daily Pivot (Layer 1) where a bullish Order Block exists (Layer 2), RSI shows bullish divergence (Layer 3), and volume delta is rising (Layer 4). The confluence score jumps to 85 (Layer 5), signaling high-probability long setup. Input Parameters CPR Settings: Show Daily CPR: Toggle daily pivot levels (default: enabled) Show Weekly CPR: Toggle weekly pivot levels (default: enabled) CPR Width Threshold: Defines narrow vs wide CPR (default: 0.5% / 1.5%) Smart Money Concepts: Show FVG: Display Fair Value Gap boxes (default: enabled) Show Order Blocks: Display Order Block boxes (default: enabled) Show Breaker Blocks: Display Breaker Block labels (default: enabled) Show Liquidity Sweeps: Display liquidity sweep markers (default: enabled) FVG Min Size: Minimum gap size to display (default: 0.3%) Lookback Bars: Bars to scan for liquidity levels (default: 20) Divergence Detection: Show Divergences: Toggle divergence labels (default: enabled) RSI Length: Period for RSI calculation (default: 14) Pivot Lookback: Bars for pivot detection (default: 5) Volume Analysis: Show Volume Analysis: Toggle volume indicators (default: enabled) Volume MA Length: Period for volume moving average (default: 20) High Volume Multiplier: Threshold for high volume (default: 1.5x) Climax Volume Multiplier: Threshold for climax volume (default: 3.0x) Display Options: Show Dashboard: Toggle confluence score table (default: enabled) Max FVG Boxes: Limit displayed FVG boxes (default: 20) Max OB Boxes: Limit displayed Order Block boxes (default: 15) Label Spacing: Minimum bars between labels to prevent overlap (default: 10-15) How to Use This Indicator Step 1: Identify Market Structure Check CPR position and width. Narrow CPR suggests breakout potential, wide CPR suggests range-bound conditions. Step 2: Look for SMC Confluence Identify FVGs, Order Blocks, and recent liquidity sweeps. These zones often provide high-probability entry areas. Step 3: Check for Divergences Look for divergence labels at swing points. Multiple oscillator divergences increase signal strength. Step 4: Confirm with Volume Ensure volume supports the move. Rising delta + high volume confirms bullish moves, falling delta + high volume confirms bearish moves. Step 5: Review Confluence Score Check the dashboard. Scores above 70 indicate strong confluence. Individual component scores show which factors are contributing. Step 6: Wait for Price Action Confirmation The indicator identifies zones and conditions, but wait for price action confirmation (candlestick patterns, breakouts, etc.) before entering trades. Best Practices Use on 15-minute to 4-hour timeframes for optimal signal quality Combine with proper risk management - indicator shows zones, not exact entries Pay attention to confluence score - higher scores generally indicate better setups Watch for FVG fills and Order Block retests as entry triggers Liquidity sweeps followed by reversal often provide excellent risk:reward entries Divergences work best when combined with SMC zones or CPR levels Volume confirmation is critical - avoid low-volume signals Indicator Limitations Does not provide exact entry/exit signals - requires trader interpretation Can generate false signals in choppy, low-volume conditions Multiple visual elements may clutter chart - adjust display settings as needed Divergences can persist longer than expected - price can continue trending despite divergence FVGs and Order Blocks don't always get retested - not every zone provides entry opportunity Confluence score is a guide, not a guarantee - high scores can still result in losing trades Requires understanding of SMC concepts and CPR analysis for effective use Performance varies across different markets and timeframes Technical Implementation Built with Pine Script v6 using: Custom CPR calculations with width analysis Box and label management with anti-overlap logic Persistent variables for tracking Order Blocks and Breaker Blocks Pivot-based divergence detection across multiple oscillators Volume delta calculation with cumulative tracking Real-time confluence scoring system Dynamic dashboard with component breakdown The code is fully open-source and can be modified to suit individual trading styles and preferences. Originality Statement This indicator is original in its integration approach. While individual components (CPR, FVG, Order Blocks, RSI divergence, volume analysis) are established concepts, this mashup is justified because: It synthesizes five distinct methodologies that address different market aspects The confluence scoring system provides quantitative measurement of setup quality Anti-overlap logic and timeframe-adaptive filtering reduce visual clutter Component integration creates layered analysis not available in individual indicators The combination helps identify zones where multiple institutional and technical factors align Each component contributes unique information: CPR provides static structure, SMC reveals dynamic institutional behavior, divergences show momentum shifts, liquidity analysis identifies stop hunts, and volume confirms genuine moves. The mashup's value lies in presenting these complementary perspectives simultaneously with a unified scoring system. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors. Technical indicators are tools for analysis, not crystal balls. Past performance and backtested results do not guarantee future performance. Market conditions change, and strategies that worked historically may not work in the future. The confluence score is a mathematical calculation based on current market data, not a prediction of future price movement. High confluence scores do not guarantee profitable trades. Users must conduct their own analysis and risk assessment before making trading decisions. Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool. -Made with passion by officialjackofalltrades
Wskaźnik Pine Script®
od officialjackofalltrades
Triple MTF MA Painter - Directional TogglesThis Pine Script is a **multi-filter trend-following indicator**. It acts as a "gatekeeper," only allowing a signal to fire when price action, momentum, and volume (if selected) all align in the same direction across up to three different moving averages and timeframes. Here is a breakdown of how the logic flows from the code to your chart: ### 1. The Triple-Filter Architecture The code uses three independent Moving Average (MA) engines. You can configure each one to be a different type: * **SMA/EMA:** For standard price smoothing. * **VWMA:** To ensure the trend is backed by high trading volume. * **ZLEMA:** To catch price reversals with almost zero lag. Each MA also has a **Multi-Timeframe (MTF)** capability. This means you can be on a 5-minute chart but only see signals when the price is above the **Daily** or **4-Hour** trend line. ### 2. The Logic Filter (The "Gate") The script calculates whether the current price is "cleared" for a signal: * **Bullish Condition:** Price must be above MA 1 **AND** MA 2 **AND** MA 3. * **Bearish Condition:** Price must be below MA 1 **AND** MA 2 **AND** MA 3. > **Note:** If you toggle an MA "OFF" in the settings, the code intelligently ignores it so it doesn't block your signals. ### 3. Price Action Trigger Even if the price is above all three MAs, the code won't fire a signal until it sees a specific candle pattern: * **Bullish Trigger:** The current bar's High and Low must both be higher than the previous bar's High and Low. * **Bearish Trigger:** The current bar's High and Low must both be lower than the previous bar's High and Low. ### 4. The State Machine (The "Memory") To prevent the chart from becoming cluttered with 20 labels in a row during a long trend, I implemented a **State Machine**. * Once a **Bullish** signal triggers, the script "remembers" it. * It will refuse to show another Bullish signal until a **Bearish** signal occurs to reset the cycle. * This ensures you only see the **first** valid entry point of a new trend. ### 5. UI and Customization The code is designed to be user-friendly without needing to look at the script: * **Directional Toggles:** You can hide all bearish signals if you only want to go long. * **Visual Toggles:** You can turn off the bar coloring if it interferes with your other indicators, keeping only the labels. * **Source Selection:** You can choose to calculate the MAs based on `Close`, `Open`, or even the `HL2` (midpoint of the bar). --- ### Summary of Workflow 1. **Check MAs:** Is price above/below all active MAs? 2. **Check Timeframe:** Are the higher timeframe trends in agreement? 3. **Check Pattern:** Did the current candle break the previous candle's range? 4. **Check History:** Is this a new trend direction, or are we already in one? 5. **Output:** If all checks pass, paint the bar and drop a label. Would you like me to add a **Stop Loss / Take Profit** calculation to the labels so you know exactly where to exit the trade?
Wskaźnik Pine Script®
od Trading_Andrea
Daily NATR Percentile (Regime + Alerts)Daily NATR Percentile — Volatility Regime Filter for Swing Traders This indicator is designed to help traders objectively identify market volatility regimes and adjust risk exposure accordingly. Rather than using ATR as a stop or entry signal, this tool treats volatility as an environmental variable — answering the question: Is the current market condition supportive of swing trading, or is capital preservation the priority? It calculates Daily NATR (Normalized ATR) and ranks today’s value against the previous N days using a percentile approach. This removes price-level bias and focuses on relative volatility expansion or contraction. The result is a normalized 0–100 volatility score: • Low values = quiet, trend-friendly environment • High values = unstable, distribution / forced-selling regime ⸻ Core Components 1. Daily NATR Percentile • ATR is calculated on Daily timeframe • Normalized by Daily Close (ATR / Close × 100) • Current value is ranked against a configurable Daily lookback window (default: 20 days) This answers: How volatile is today compared to recent history? ⸻ 2. Volatility Regime Levels (Customizable) Default thresholds: • Low Vol: 20% • Mid: 50% • High Vol: 80% These levels define three behavioral modes: 🟢 Low Regime (Quiet Market) • Trend continuation favored • Breakouts and pullbacks work best • Full position sizing allowed • Pyramid / add-ons acceptable 🟡 Mid Regime (Mixed Participation) • Selective entries only • Reduced position sizing • Faster profit taking • Fewer simultaneous positions 🔴 High Regime (Distribution / Forced Selling) • Correlation spikes • Fake breakouts increase • Intraday whipsaws common Risk management mode: • Minimal exposure • No pyramiding • Only tight support entries (if any) • Cash becomes a position Background shading visually highlights High and Low regimes. ⸻ 3. Percentile Slope (Volatility Acceleration) The slope measures how fast volatility is changing. This is critical for detecting regime transitions: • Slope > 0 → volatility expanding • Slope < 0 → volatility contracting Key patterns: Percentile Slope Interpretation Low Down Ideal swing environment Low Up Early warning of instability High Up Active volatility expansion High Down Possible stabilization after panic The slope is not an entry signal — it is an early feedback mechanism for adjusting exposure. ⸻ How I Personally Use This Indicator This tool governs position sizing and portfolio exposure, not trade selection. My workflow: 1. Identify market regime using Daily NATR Percentile 2. Observe slope for acceleration or deceleration 3. Adjust exposure progressively: Example logic: • Percentile < 30 AND slope < 0 → Full risk allowed • Percentile rising from Low + slope turning positive → Reduce new entries, tighten stops • Percentile > 70 AND slope > 0 → Defensive mode (minimal risk, capital preservation) This creates a feedback loop: Market environment → risk allocation → performance feedback Exposure increases only when conditions improve, and contracts automatically when volatility expands. ⸻ Philosophy Most drawdowns don’t come from bad stock selection — they come from trading aggressively in hostile environments. This indicator enforces a simple principle: Trade the market you have, not the market you want. ⸻ Suggested Defaults • ATR Length: 14 • Percentile Lookback: 20 • High: 80 • Low: 20 • Slope Length: 5 Designed for swing traders, but adaptable to any timeframe via Daily regime filtering.
Wskaźnik Pine Script®
od soso6079
Market Structure Dashboard | Flux ChartsGENERAL OVERVIEW Market Structure Dashboard is a multi-timeframe market structure analysis indicator. It combines EMA trend detection, swing high/low tracking, market structure labels, Order Block detection, Fair Value Gap detection, liquidity sweep detection, volume analysis, volatility analysis, trading sessions, ICT killzones, a weighted trend bias system, and HTF levels into one unified dashboard. Each component is calculated independently across up to 7 configurable timeframes and displayed together in a single organized view. (Screenshot: Full dashboard overview - all sections visible) (Screenshot: Dashboard on a busy chart showing OB/FVG boxes, swing labels, HTF lines) WHAT IS THE THEORY BEHIND THIS INDICATOR? The core idea is that a trade setup becomes more reliable when multiple timeframes agree on direction. A bullish signal on a 5-minute chart carries more weight when the 15-minute, 1-hour, and daily timeframes also show bullish conditions. Analyzing each timeframe separately is both time-consuming and prone to error. The Market Structure Dashboard automates this process by calculating key metrics across all enabled timeframes and presenting them side by side. The indicator draws from two established trading methodologies. Smart Money Concepts (SMC) focuses on identifying institutional footprints in price action through patterns like Order Blocks, Fair Value Gaps, and liquidity sweeps. Inner Circle Trader (ICT) methodology emphasizes time-based analysis through specific trading windows called killzones and the importance of previous day, week, and month highs and lows. Rather than treating these concepts in isolation, the dashboard organizes them into a layered framework. Structure shows where the market has been. Zones show where it may react. Sessions and killzones show when activity tends to increase. The trend bias system combines all factors into a single weighted score, giving traders a quick read on overall market sentiment across timeframes. The purpose of the Market Structure Dashboard is to present the current market activity across multiple timeframes and how these conditions relate to earlier market structure, volume, and timing. (Screenshot: Multi-timeframe confluence example - all TFs showing bearish alignment) (Screenshot: Multi-timeframe disagreement example - mixed signals across TFs) MARKET STRUCTURE DASHBOARD FEATURES The Market Structure Dashboard indicator includes 14 main features: EMA Trend Detection Swing High/Low Tracking Market Structure Labels (HH/HL/LH/LL) Order Block Detection Fair Value Gap Detection Liquidity Sweep & Reclaim Detection Volume Analysis Volatility Analysis Trading Sessions ICT Killzones Trend Bias System HTF Levels (PDH/L, PWH/L, PMH/L) Visual Overlays Dashboard Customization Each component operates independently while sharing the same underlying market structure logic. All features are calculated across up to 7 user-configurable timeframes and displayed in a unified dashboard. Detailed explanations for each component are provided in the sections that follow. EMA TREND DETECTION 🔹 What is an EMA? An Exponential Moving Average (EMA) is a type of moving average that gives more weight to recent price data. Unlike a Simple Moving Average that weights all prices equally, the EMA responds faster to recent price changes while still considering historical data. Traders use EMAs to identify trend direction and dynamic support/resistance levels. When price trades above the EMA, the short-term trend is considered bullish. When price trades below the EMA, the short-term trend is considered bearish. The distance between price and EMA can indicate trend strength, with larger distances suggesting stronger momentum. 🔹 How the Indicator Uses EMA The dashboard calculates a 9-period EMA (configurable) for each enabled timeframe. The EMA Trend column displays both direction and distance. ◇ Direction is shown with an up arrow (↑) when price is above EMA, or a down arrow (↓) when price is below EMA. ◇ Distance is displayed as percentage, price, or pips based on the Distance Display setting. For example, "+0.45% ↑" means price is 0.45% above the EMA on that timeframe. ◇ Color coding shows green when price is above EMA (bullish) and red when price is below EMA (bearish). The EMA can optionally be plotted as a visual overlay on the chart. It can also be included as a factor in the Trend Bias calculation, where each timeframe's EMA direction contributes to the overall bias score. (Screenshot: EMA column showing bearish readings - red, ↓) SWING HIGH/LOW TRACKING 🔹 What are Swing Highs and Lows? A swing high is a price peak where a candle's high is higher than the highs of surrounding candles. A swing low is a price trough where a candle's low is lower than the lows of surrounding candles. These points represent short-term reversals and define the boundaries of price movement. Swing points are foundational to market structure analysis. Breaking a swing high suggests bullish momentum. Breaking a swing low suggests bearish momentum. The sequence of swing points creates market structure patterns that reveal trend direction. 🔹 How the Indicator Tracks Swing Highs/Lows? The indicator detects swing points using a configurable Swing Length parameter (default: 5). A swing high is confirmed when a candle's high is higher than the specified number of candles on both sides. A swing low is confirmed when a candle's low is lower than the specified number of candles on both sides. This confirmation requirement means swing points are identified with a delay, ensuring they are valid pivots rather than temporary spikes. This same Swing Length setting is also used by Order Block detection and Market Structure labels, so adjusting it affects all three features. ◇ The Swing H/L column displays a visual position indicator showing where price sits within the current swing range. A dot moves along a bar between L (swing low) and H (swing high) to show exact position. ◇ When price breaks outside the range, arrows indicate the direction. An up arrow (↑) appears when price breaks above the swing high. A swing high break indicates that buyers have pushed price beyond the previous peak, suggesting bullish momentum and a potential continuation higher. (Screenshot: Price above Swing High) A down arrow (↓) appears when price breaks below the swing low. A swing low break indicates that sellers have pushed price beyond the previous trough, suggesting bearish momentum and a potential continuation lower (Screenshot: Price breaks Swing Low) When a liquidity sweep occurs (price breaks a level then reclaims it), special arrows appear: ⤴ for a swept and reclaimed low, ⤵ for a swept and reclaimed high. A swept and reclaimed swing means price broke beyond the level, likely triggering stop-loss orders resting beyond it, but then reversed back inside the range. This suggests the breakout was a false move and the opposite direction may follow. Liquidity sweeps are explained in detail in the Liquidity Sweep & Reclaim Detection section below. ◇ Color coding shows green when price is in the lower half of the range or breaks above the swing high, and red when price is in the upper half or breaks below the swing low. (Screenshot) ◇ Tooltips provide additional context when hovering over any Swing H/L cell, such as "Price is nearing swing low on 15M" or "Price above swing high on 1H - swing high broken." MARKET STRUCTURE LABELS (HH/HL/LH/LL) 🔹 What is Market Structure? Market structure refers to the pattern of swing highs and swing lows that price creates over time. By comparing consecutive swing points, each new swing can be classified into one of four types. ◇ HH (Higher High): A swing high that is higher than the previous swing high, indicating bullish momentum. ◇ HL (Higher Low): A swing low that is higher than the previous swing low, indicating bullish momentum. ◇ LH (Lower High): A swing high that is lower than the previous swing high, indicating bearish momentum. ◇ LL (Lower Low): A swing low that is lower than the previous swing low, indicating bearish momentum. (Screenshot: Bullish and Bearish Swing Points) Bullish structure consists of HH and HL patterns, where price makes higher highs and higher lows. Bearish structure consists of LH and LL patterns, where price makes lower highs and lower lows. Mixed structure contains conflicting patterns and indicates consolidation or potential trend change. 🔹 How the Indicator Displays Market Structure The Structure column shows the last three structure labels in sequence along with an overall bias arrow. ◇ "LL-LH-HL →" indicates mixed structure with no clear direction. ◇ "HH-HL-HH ↑" indicates bullish structure with higher highs and higher lows. ◇ "LH-LL-LH ↓" indicates bearish structure with lower highs and lower lows. (Screenshot: Dashboard showing neutral, bearish and bullish indication across different timeframes) The indicator tracks each new swing point as it forms, compares it to the previous swing of the same type, and assigns the appropriate label. Market Structure labels use the same Swing Length setting as Swing High/Low tracking, so both features stay synchronized. Structure bias is determined by the most recent high type and low type combined. If the last swing high was HH and the last swing low was HL, bias is bullish. If the last swing high was LH and the last swing low was LL, bias is bearish. Any other combination shows neutral. Color coding shows green for bullish structure, red for bearish structure, and gray for mixed or neutral structure. ORDER BLOCK DETECTION 🔹 What is an Order Block? An Order Block is a concept from Smart Money analysis representing a candle or consolidation area where institutional orders may have been placed. In SMC methodology, Order Blocks are identified as the last opposing candle before a significant price move that breaks market structure. ◇ A Bullish Order Block is the last bearish candle before a rally that breaks a swing high. When price returns to this zone, it may find support. ◇ A Bearish Order Block is the last bullish candle before a drop that breaks a swing low. When price returns to this zone, it may find resistance. Order Blocks are considered "mitigated" when price trades completely through them, suggesting the institutional orders have been filled. 🔹 How the Indicator Detects Order Blocks The detection algorithm follows a specific sequence to identify valid Order Blocks. ◇ Step 1: The indicator tracks swing highs and swing lows using the configured Swing Length setting (shared with Swing High/Low tracking and Market Structure labels). ◇ Step 2: When price breaks above a swing high, the indicator identifies a bullish breakout. When price breaks below a swing low, it identifies a bearish breakout. ◇ Step 3: For a bullish Order Block, the indicator finds the candle with the lowest low between the broken swing high and the current bar. For a bearish Order Block, it finds the candle with the highest high between the broken swing low and the current bar. ◇ Step 4: The Order Block zone is created spanning from that candle's low to its high. ◇ Step 5: Mitigation is applied when price closes through the Order Block. Bullish OBs are mitigated when price closes below the zone. Bearish OBs are mitigated when price closes above the zone. The Order Block column shows the nearest unmitigated Order Block for each timeframe. "IN BULL OB ↑" means price is currently inside a bullish Order Block. "BULL OB (5.4%) ↑" means the nearest OB is bullish and 5.5% away. "NONE" means no unmitigated Order Blocks exist on that timeframe. (Screenshot: Nearest order block is Bull OB) (Screenshot: Price in Bear OB) FAIR VALUE GAP DETECTION 🔹What is a Fair Value Gap? A Fair Value Gap (FVG), also called an imbalance, is a three-candle pattern where a gap exists between the first and third candle that the middle candle did not fill. This gap represents an area where price moved quickly, creating an imbalance in the market. ◇ A Bullish FVG forms when the first candle's high is lower than the third candle's low, creating an upward gap. When price returns to this gap, it may find support. ◇ A Bearish FVG forms when the first candle's low is higher than the third candle's high, creating a downward gap. When price returns to this gap, it may find resistance. FVGs are considered mitigated when price wicks into the gap, filling the inefficiency. 🔹 How the Indicator Detects FVGs The detection logic checks for the three-candle gap pattern with specific conditions. ◇ For a Bullish FVG, the current candle's low must be above the candle from three bars ago's high (gap exists), and the middle candle must be bullish (displacement candle). ◇ For a Bearish FVG, the current candle's high must be below the candle from three bars ago's low (gap exists), and the middle candle must be bearish (displacement candle). ◇ The FVG zone spans from the gap's bottom to its top. ◇ Mitigation occurs when price wicks below the gap bottom for bullish FVGs, or above the gap top for bearish FVGs. Note that FVG mitigation is more sensitive than Order Block mitigation. FVGs only need a wick to touch them, while Order Blocks require a close through them. The FVG column displays similarly to Order Blocks. "IN BULL FVG ↑" means price is inside a bullish Fair Value Gap. "BULL FVG (0.2%) ↑" means the nearest FVG is bullish and 0.2% away. "NONE" means no unmitigated FVGs exist on that timeframe. (Screenshot: Price in Bull FVG) (Screenshot: Bear FVG +3.4% away) LIQUIDITY SWEEP & RECLAIM DETECTION 🔹 What is a Liquidity Sweep? Liquidity refers to resting orders in the market, particularly stop-loss orders. Traders commonly place stops just beyond swing highs and swing lows, creating pools of liquidity at these levels. A liquidity sweep occurs when price breaks beyond a swing point, potentially triggering stops, but then reverses and closes back inside the range. ◇ A Bullish Liquidity Sweep occurs when price breaks below a swing low, then reverses and closes back above it. This pattern suggests potential buying interest after weak hands have been stopped out. ◇ A Bearish Liquidity Sweep occurs when price breaks above a swing high, then reverses and closes back below it. This pattern suggests potential selling interest after weak hands have been stopped out. 🔹 How the Indicator Detects Liquidity Sweeps The indicator tracks whether each swing level has been broken and then reclaimed. ◇ A swing low is marked as broken when price trades below it. A swing high is marked as broken when price trades above it. ◇ A reclaim is detected when price closes back above a broken swing low (bullish) or back below a broken swing high (bearish). ◇ The break and reclaim flags reset when a new swing point forms, ensuring fresh detection for each level. When a liquidity sweep is detected, the Swing H/L column displays special indicators. The ⤴ symbol indicates a bullish liquidity sweep where price swept the low and reclaimed. The ⤵ symbol indicates a bearish liquidity sweep where price swept the high and reclaimed. Tooltips provide additional context such as "Liquidity sweep - price swept swing low and reclaimed on 15M." (Screenshot: Swing High Swept) (Screenshot: Previous Month Low Swept) VOLUME ANALYSIS 🔹 What is Volume Analysis? Volume represents the number of shares, contracts, or units traded during a given period. High volume suggests strong interest and participation behind a price move. Low volume suggests weak interest and moves may lack follow-through. Comparing current volume to average volume helps identify unusual activity. 🔹 How the Indicator Analyzes Volume The dashboard calculates current volume as a percentage of its 20-period simple moving average. ◇ The Volume column displays a visual bar using filled and empty blocks to represent volume level relative to average. ◇ Volume states are classified as EXTREME (over 200% of average), HIGH (over 120%), NORMAL (over 80%), LOW (over 50%), or VERY LOW (50% or less). (Screenshot: Extreme Volume) ◇ Color coding shows yellow for extreme volume, orange for high volume, and gray for normal, low, and very low. ◇ Tooltips show the exact percentage, such as "Volume is currently at 145% of average." VOLATILITY ANALYSIS 🔹 What is Volatility? Volatility measures how much price fluctuates over a given period. High volatility means large price swings. Low volatility means small price movements. The Average True Range (ATR) is a common volatility measure that calculates the average of true ranges over a period. 🔹 How the Indicator Measures Volatility The dashboard calculates a 14-period ATR and compares it to its own 20-period average (configurable). ◇ The Volatility column displays the current state as HIGH (ATR over 130% of average), NORMAL (ATR between 70-130% of average), or LOW (ATR under 70% of average). ◇ Color coding shows red for high volatility, gray for normal, and green for low volatility. ◇ Tooltips provide context such as "Volatility is currently high" or "Volatility is currently low." Low volatility often precedes significant moves, making it a useful setup indicator when combined with price at key levels. (Screenshot: High Volatility) TRADING SESSIONS 🔹 What are Trading Sessions? Financial markets have varying activity levels throughout the day. Trading is typically divided into three major sessions based on which financial centers are open. ◇ Asian Session runs from 7:00 PM to 3:00 AM EST. It is characterized by generally lower volatility and ranging price action ◇ London Session runs from 3:00 AM to 12:00 PM EST. It is characterized by higher volatility and trending moves ◇ New York Session runs from 8:00 AM to 5:00 PM EST. It has high volatility especially during the London overlap from 8:00 AM to 12:00 PM EST, affecting USD pairs and all majors. 🔹 How the Indicator Displays Sessions The Session column shows the current session name in the first row as ASIAN, LONDON, NEW YORK, or OFF HOURS (between sessions from 5:00 PM to 7:00 PM EST). ◇ The second row shows a progress bar that fills as the session advances, with each block representing approximately one hour. ◇ Sessions are color-coded as blue for Asian, green for London, orange for New York, and gray for off hours. These colors can be customized in the settings ◇ The indicator uses New York (EST) timezone for all session calculations and includes replay mode support. (Asian Session and Killzone) ICT KILLZONES 🔹 What are Killzones? Killzones are specific time windows within each trading session when market activity tends to be higher. These windows are derived from ICT (Inner Circle Trader) methodology and represent times when significant moves are more likely to occur. ◇ Asian Killzone runs from 8:00 PM to 12:00 AM EST and often sets the initial range for the day. ◇ London Killzone runs from 2:00 AM to 5:00 AM EST and covers the London open when major moves are common. ◇ New York AM Killzone runs from 9:30 AM to 11:00 AM EST and covers the NYSE open, a high volume period. ◇ New York Lunch runs from 12:00 PM to 1:00 PM EST and typically has lower activity and consolidation. ◇ New York PM Killzone runs from 1:30 PM to 4:00 PM EST when afternoon continuation moves occur. 🔹 How the Indicator Displays Killzones The Killzone column shows the current killzone in the first row as ASIAN KZ, LONDON KZ, NY AM KZ, NY LUNCH, NY PM KZ, or NO KILLZONE when outside all killzones. ◇ When outside a killzone, the second row shows a countdown to the next killzone, such as "NY AM KZ in 2h:15m." ◇ Killzones are color-coded as blue for Asian, green for London, orange for NY AM, gray for NY Lunch, and purple for NY PM. These colors can be customized in the settings TREND BIAS SYSTEM 🔹 What is Trend Bias? The Trend Bias System aggregates multiple factors across all enabled timeframes to produce a single directional bias score. Instead of analyzing each factor and timeframe separately, this system provides a weighted summary of overall market sentiment. 🔹 How the Indicator Calculates Trend Bias The calculation involves three components working together. (Screenshot: BTC Bearish Trend) ◇ Factors determine what contributes to bias. Users can enable or disable Structure (market structure bias), Order Block (direction of nearest OB), FVG (direction of nearest FVG), EMA Trend (price position relative to EMA), and Swing Position (where price sits in the swing range). Each enabled factor contributes +1 for bullish, -1 for bearish, or 0 for neutral per timeframe. ◇ Weights determine how much each timeframe matters. Each timeframe has a configurable weight from 0 to 10. Default weights are 1 for 1M and 5M, 2 for 15M, 1H, and 4H, 3 for Daily, and 4 for Weekly. Higher weights mean that timeframe contributes more to the final score. (Screenshot: Gold Bullish Trend) ◇ Score Calculation combines factors and weights. For each active timeframe, the sum of factor scores is multiplied by the timeframe's weight. The total score is the sum of all timeframe scores. The maximum possible score is the sum of each weight multiplied by the number of enabled factors. The bias percentage equals the total score divided by the maximum possible score, multiplied by 100. ◇ Bias Labels are assigned based on percentage. Over 50% shows BULLISH ↑. Between 20% and 50% shows LEAN BULL ↑. Between -20% and 20% shows NEUTRAL →. Between -50% and -20% shows LEAN BEAR ↓. Below -50% shows BEARISH ↓. The Trend Bias column displays the bias label in the first row and the raw score in the second row, such as "+22/60" meaning 22 points out of 60 possible. HTF LEVELS (PDH/L, PWH/L, PMH/L) 🔹 What are HTF Levels? Higher Timeframe (HTF) Levels are significant price points from previous completed periods. These levels represent clear, objective reference points that many traders watch. ◇ PDH/PDL (Previous Day High/Low) are the high and low of the previous completed trading day and act as intraday support and resistance. ◇ PWH/PWL (Previous Week High/Low) are the high and low of the previous completed week and are significant levels for swing trading. ◇ PMH/PML (Previous Month High/Low) are the high and low of the previous completed month and are major levels for position trading. 🔹 How the Indicator Displays HTF Levels The HTF Levels Dashboard section (optional) shows a swing-style position bar for each enabled level, displaying where price sits within the previous day, week, or month range. ◇ The same liquidity sweep detection applies to HTF levels. If price sweeps PDL and reclaims, the ⤴ indicator appears. (Screenshot: Previous Week Low Swept) ◇ Visual overlays can plot HTF level lines on the chart with customizable colors and line styles. ◇ When multiple levels are close together, labels automatically combine. For example, "PDH/PWH" appears when both levels are at similar prices, or "PDL/PWL/PML" when all three lows align. (Screenshot: PWH/PMH labels combined when Previous Week Low and Previous Month Low align) VISUAL OVERLAYS Beyond the dashboard, the indicator offers optional visual overlays that plot directly on the price chart. 🔹 Order Block Zones When enabled, Order Blocks appear as semi-transparent rectangular boxes. Green boxes represent bullish Order Blocks and red boxes represent bearish Order Blocks. Boxes span from the OB candle's low to its high and extend forward based on the Extend setting. Optional labels show "OB ↑" or "OB ↓" inside the zones. 🔹 FVG Zones Fair Value Gaps appear as boxes with dashed borders to distinguish them from Order Blocks. Green dashed boxes represent bullish FVGs and red dashed boxes represent bearish FVGs. They share the same extend and label options as Order Blocks. (Order Blocks & Fair Value Gaps) 🔹 Swing Labels HH, HL, LH, and LL labels can be plotted directly at each swing point on the chart. Labels appear above swing highs and below swing lows. Green labels indicate bullish structure (HH, HL) and red labels indicate bearish structure (LH, LL). The Show Last setting controls how many labels appear. 🔹 Swing Lines Horizontal lines can be drawn at the current swing high and swing low. A red line appears at the swing high and a green line at the swing low. Line styles are customizable as solid, dashed, or dotted. (Swing Labels & Swing Lines) 🔹 HTF Level Lines Horizontal lines can be plotted at Previous Day, Week, and Month highs and lows. Each level has a separate enable toggle with customizable colors and line styles. Labels auto-combine when levels are close together. 🔹 EMA Line A standard EMA line can be plotted on the chart using the same EMA Length setting as the dashboard with customizable color. DASHBOARD CUSTOMIZATION: The dashboard is highly customizable to fit different trading styles and screen setups. 🔹Dashboard Position Choose from 9 dashboard positions including top left, top center, top right, middle left, middle center, middle right, bottom left, bottom center, and bottom right. 🔹Dashboard Colors Two color themes are available. Dark Mode has dark backgrounds with light text and is the default. Light Mode has light backgrounds with dark text. 🔹Column Toggles Enable or disable individual columns in each dashboard section to show only the information needed. The Market Structure Dashboard section can toggle EMA Trend, Swing H/L, Structure, Order Block, and FVG columns. The Current Timeframe Dashboard section can toggle Volume, Swing H/L, and Volatility columns. The Market Context Dashboard section can toggle Session, Killzone, and Trend Bias columns. The HTF Levels Dashboard section can toggle PDH/L, PWH/L, and PMH/L levels. 🔹Color Settings Customize colors for trend colors (bull, bear, neutral), session colors (Asian, London, NY), and killzone colors (Asian KZ, London KZ, NY AM, Lunch, PM). 🔹Distance Display Choose how distances are shown. Percent shows values like "0.45%" and is the default. Price shows raw values like "45.50". Pips shows values like "45 pips" and is useful for forex. SETTINGS: 🔹 Timeframes Configure which timeframes are analyzed in the dashboard. Enable toggles turn each of the 7 timeframes on or off. Timeframe selection sets the specific timeframe for each slot (1M, 5M, 15M, 1H, 4H, D, W, M, or custom). Trend weight controls how much each timeframe contributes to the overall bias calculation (0-10), with higher values giving that timeframe more influence. 🔹 Market Structure Dashboard Controls the main multi-timeframe dashboard section. The enable toggle turns the entire section on or off. Column toggles allow you to show or hide individual columns: EMA Trend, Swing H/L, Structure, Order Block, and FVG. Disabling columns you don't need reduces visual clutter and focuses the dashboard on the information most relevant to your trading style. 🔹 Current Timeframe Dashboard Controls the current chart timeframe section that displays volume, swing position, and volatility data. The enable toggle turns the entire section on or off. Column toggles allow you to show or hide individual columns: Volume, Swing H/L, and Volatility. 🔹 Market Context Dashboard Controls the market context section that displays session, killzone, and trend bias information. The enable toggle turns the entire section on or off. Column toggles allow you to show or hide individual columns: Session, Killzone, and Trend Bias. 🔹 HTF Levels Dashboard Controls the higher timeframe levels section that displays previous day, week, and month high/low data. The enable toggle turns the entire section on or off. Level toggles allow you to show or hide individual levels: PDH/L, PWH/L, and PMH/L. 🔹 Trend Bias Settings Controls which factors contribute to the trend bias calculation. Factor toggles allow you to include or exclude Structure, Order Block, FVG, EMA Trend, and Swing H/L from the bias score. Disabling factors you don't find relevant customizes how the overall bias is determined. 🔹 Visual Overlays Controls what is plotted directly on the price chart. Order Blocks and FVGs each have an enable toggle, bull/bear colors, show last count (how many zones to display), extend bars (how far zones project forward), and labels toggle. Swing Labels have an enable toggle, bull/bear colors, and show last count. Swing Lines have an enable toggle, high/low colors, line style (solid, dashed, dotted), and extend bars. HTF Level Lines for Previous Day, Week, and Month highs/lows each have an enable toggle, colors, and line style, with a shared extend setting for all HTF lines. EMA has an enable toggle and color setting. 🔹 General Settings Core indicator parameters. EMA Length sets the period for EMA calculation (default 9). Swing Length sets how many bars are required to confirm a pivot and is used for Swing Point detection, Order Block detection, and Market Structure labels (default 5). Volatility Lookback sets the period for ATR averaging (default 20). Distance Display controls how distances are shown: Percent, Price, or Pips. Dashboard Position sets where the dashboard appears on the chart (9 options). Dashboard Theme switches between Dark Mode and Light Mode. Color settings allow customization of trend colors (bull, bear, neutral), session colors (Asian, London, NY), and killzone colors (Asian KZ, London KZ, NY AM, Lunch, PM). (Full Dashboard) (Customized Display) UNIQUENESS: The Market Structure Dashboard focuses on multi-timeframe confluence by calculating and displaying the same analytical components across up to 7 timeframes simultaneously. Unlike indicators that show one timeframe at a time, each row in the dashboard represents a complete analysis of that timeframe's structure, zones, and trend state. This allows traders to observe alignment, disagreement, and transitions across timeframes within a single view. The weighted Trend Bias System combines structure, zones, EMA, and swing position into a single score that accounts for timeframe importance. Higher timeframes can be weighted more heavily, reflecting their greater significance in establishing overall market direction. The dashboard also integrates time-based context through session and killzone tracking, helping traders identify when market conditions align with historically active trading windows. All components coexist without overriding each other, providing a comprehensive framework for multi-timeframe market structure analysis.
Wskaźnik Pine Script®
od fluxchart
15