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NIG Probability TableNormal-Inverse Gaussian Probability Table This indicator implements the Normal-Inverse Gaussian (NIG) distribution to estimate the likelihood of future price based on recent market behavior. 📊 Key Features: - Estimates the parameters (α: tail heaviness, β: skewness, δ: scale, μ: location) of the NIG distribution using a sliding window over log returns. - Uses a numerically approximated version of the modified Bessel function (K₁) to calculate the NIG probability density function (PDF). - Normalizes the total probability across all bins to ensure the values are interpretable. - Displays a dynamic probability table showing the chance of future returns falling into each bin. ⚠️ Notes: - This is a real-time approximation. The Bessel function and posterior inference are simplified. - Tail probabilities and shape parameters are sensitive to the window size and input settings. - Useful for risk analysis, option overlays, and strategy filters.
Penunjuk Pine Script®
oleh nasu_is_gaji
4
Log-Normal Price ForecastLog-Normal Price Forecast This Pine Script creates a log-normal forecast model of future price movements on a TradingView chart, based on historical log returns. It plots expected price trajectories and bands representing different levels of statistical deviation. Parameters Model Length – Number of bars used to calculate average and standard deviation of log returns (default: 100). Forecast Length – Number of bars into the future for which the forecast is projected (default: 100, max: 500). Volatility SMA Length – The smoothing length for the standard deviation (default: 20). Confidence Intervals – Confidence intervals for price bands (default: 95%, 99%, 99.9%).
Penunjuk Pine Script®
oleh nasu_is_gaji
2
Market Sessions & Viewer Panel [By MUQWISHI]▋ INTRODUCTION : The “Market Sessions & Viewer Panel” is a clean and intuitive visual indicator tool that highlights up to four trading sessions directly on the chart. Each session is fully customizable with its name, session time, and color. It also generates a panel that provides a quick-glance summary of each session’s candle/bar shape, helping traders gain insight into the volatility across all trading sessions. _______________________ ▋ OVERVIEW: _______________________ ▋ CREDIT: This indicator utilizes the “ Timezone — Library ”. A huge thanks to @n00btraders for effort and well-organized work. _______________________ ▋ SESSION PANEL: The Session Panel allows traders to visually compare session volatility using a candlestick/bar pattern. Each bar represents the price action during a session and includes the session status, session name, closing price, change(%) from open, and a tooltip that reveals detailed OHLC and volume when hovered over. Chart Type: It offers two styles Bar or Candle to display based on traders’ preference Sorting: Allowing to arrange session candles/bars based on…   —Left to Right: The most recently opened on the left, moving backward in time to the right.   —Right to Left: The most recently opened on the right, moving backward in time to the left.   —Default: Arrange sessions in the user-defined input order. _______________________ ▋ CHART VISUALIZATION: The chart visualization highlights each trading session using color-coded backgrounds in two selectable drawing styles that span their respective active timeframes. Each session block provides session’s name, close price, and change from open. Chart Type: Candle Chart Type: Box Extra Drawing Feature: This feature may not exist in other indicators within the same category, it extends the session block drawing to the projected end of the session. This's done through estimation based on historical data; however, it doesn’t function fully on seconds-based timeframes due to drawing limitations. _______________________ ▋ INDICATOR SETTINGS: Section(1): Sessions (1) Universal Timezone. (2) Each Session: Enable/Disable, Name, Color, and Time. Section(2): Session Panel (1) Show/Hide Session Panel. (2) Chart Type: Candle/Bar. (3) Bar’s Up/Down color. (4) Width and Height of the bar. (5) Location of Session Panel on chat. (6) Sort: Left to Right (most recent session is placed on the left), Right to Left (most recent session is placed on the right), and Default (as input arrangement). Section(3): Chart Visualization (1) Show/Hide Chart Block Visualization. (2) Draw Shape: Box/Candle. (3) Border Style and Size. (4) Label Styling includes location, size, and some essential selectable infos. Please let me know if you have any questions
Penunjuk Pine Script®
oleh MUQWISHI
2
Elastic Volume-Weighted Student-T TensionOverview The Elastic Volume-Weighted Student-T Tension Bands indicator dynamically adapts to market conditions using an advanced statistical model based on the Student-T distribution. Unlike traditional Bollinger Bands or Keltner Channels, this indicator leverages elastic volume-weighted averaging to compute real-time dispersion and location parameters, making it highly responsive to volatility changes while maintaining robustness against price fluctuations. This methodology is inspired by incremental calculation techniques for weighted mean and variance, as outlined in the paper by Tony Finch: 📄 "Incremental Calculation of Weighted Mean and Variance" . Key Features ✅ Adaptive Volatility Estimation – Uses an exponentially weighted Student-T model to dynamically adjust band width. ✅ Volume-Weighted Mean & Dispersion – Incorporates real-time volume weighting, ensuring a more accurate representation of market sentiment. ✅ High-Timeframe Volume Normalization – Provides an option to smooth volume impact by referencing a higher timeframe’s cumulative volume, reducing noise from high-variability bars. ✅ Customizable Tension Parameters – Configurable standard deviation multipliers (σ) allow for fine-tuned volatility sensitivity. ✅ %B-Like Oscillator for Relative Price Positioning – The main indicator is in form of a dedicated oscillator pane that normalizes price position within the sigma ranges, helping identify overbought/oversold conditions and potential momentum shifts. ✅ Robust Statistical Foundation – Utilizes kurtosis-based degree-of-freedom estimation, enhancing responsiveness across different market conditions. How It Works Volume-Weighted Elastic Mean (eμ) – Computes a dynamic mean price using an elastic weighted moving average approach, influenced by trade volume, if not volume detected in series, study takes true range as replacement. Dispersion (eσ) via Student-T Distribution – Instead of assuming a fixed normal distribution, the bands adapt to heavy-tailed distributions using kurtosis-driven degrees of freedom. Incremental Calculation of Variance – The indicator applies Tony Finch’s incremental method for computing weighted variance instead of arithmetic sum's of fixed bar window or arrays, improving efficiency and numerical stability. Tension Calculation – There are 2 dispersion custom "zones" that are computed based on the weighted mean and dynamically adjusted standard student-t deviation. %B-Like Oscillator Calculation – The oscillator normalizes the price within the band structure, with values between 0 and 1: * 0.00 → Price is at the lower band (-2σ). * 0.50 → Price is at the volume-weighted mean (eμ). * 1.00 → Price is at the upper band (+2σ). * Readings above 1.00 or below 0.00 suggest extreme movements or possible breakouts. Recommended Usage For scalping in lower timeframes, it is recommended to use the fixed α Decay Factor, it is in raw format for better control, but you can easily make a like of transformation to N-bar size window like in EMA-1 bar dividing 2 / decayFactor or like an RMA dividing 1 / decayFactor. The HTF selector catch quite well Higher Time Frame analysis, for example using a Daily chart and using as HTF the 200-day timeframe, weekly or monthly. Suitable for trend confirmation, breakout detection, and mean reversion plays. The %B-like oscillator helps gauge momentum strength and detect divergences in price action if user prefer a clean chart without bands, this thanks to pineScript v6 force overlay feature. Ideal for markets with volume-driven momentum shifts (e.g., futures, forex, crypto). Customization Parameters Fixed α Decay Factor – Controls the rate of volume weighting influence for an approximation EWMA approach instead of using sum of series or arrays, making the code lightweight & computing fast O(1). HTF Volume Smoothing – Instead of a fixed denominator for computing α , a volume sum of the last 2 higher timeframe closed candles are used as denominator for our α weight factor. This is useful to review mayor trends like in daily, weekly, monthly. Tension Multipliers (±σ) – Adjusts sensitivity to dispersion sigma parameter (volatility). Oscillator Zone Fills – Visual cues for price positioning within the cloud range. Posible Interpretations As market within indicators relay on each individual edge, this are just some key ideas to glimpse how the indicator could be interpreted by the user: 📌 Price inside bands – Market is considered somehow "stable"; price is like resting from tension or "charging batteries" for volume spike moves. 📌 Price breaking outer bands – Potential breakout or extreme movement; watch for reversals or continuation from strong moves. Market is already in tension or generating it. 📌 Narrowing Bands – Decreasing volatility; expect contraction before expansion. 📌 Widening Bands – Increased volatility; prepare for high probability pull-back moves, specially to the center location of the bands (the mean) or the other side of them. 📌 Oscillator is just the interpretation of the price normalized across the Student-T distribution fitting "curve" using the location parameter, our Elastic Volume weighted mean (eμ) fixed at 0.5 value. Final Thoughts The Elastic Volume-Weighted Student-T Tension indicator provides a powerful, volume-sensitive alternative to traditional volatility bands. By integrating real-time volume analysis with an adaptive statistical model, incremental variance computation, in a relative price oscillator that can be overlayed in the chart as bands, it offers traders an edge in identifying momentum shifts, trend strength, and breakout potential. Think of the distribution as a relative "tension" rubber band in which price never leave so far alone. DISCLAIMER: The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets. The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is, following TradingView's regulations. Use of indicator and their code are published for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s). WARNING NOTICE! THE INCLUDED FUNCTION MUST BE CONSIDERED FOR TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries, compiler version, or any other externality.
Penunjuk Pine Script®
oleh xel_arjona
11
Dynamic RSI Regression Bands (Zeiierman)█ Overview The Dynamic RSI Regression Bands (Zeiierman) is a regression channel tool that dynamically resets based on RSI overbought and oversold conditions. It adapts to trend shifts in real time, creating a highly responsive regression framework that visualizes market sentiment and directional momentum with every RSI-triggered event. Unlike static regression models, this indicator recalibrates its slope and deviation bands only after the RSI crosses predefined thresholds, helping traders pinpoint new phases of momentum, exhaustion, or reversal. You’re not just measuring the trend — you’re tracking when and where the trend deserves to be re-evaluated. █ The Assumption: "A major momentum shift (RSI crossing OB/OS) signals a potential regime change, and thus, the trend model should be recalibrated from that point." Instead of using a fixed-length regression (which assumes trend relevance over a static window), this script resets the regression calculation every time RSI crosses into extreme territory. The underlying idea is that extreme RSI levels often represent emotional peaks in market behavior and are statistically likely to be followed by a new price structure. █ How It Works ⚪ RSI-Based Channel Reset RSI is monitored continuously If RSI crosses above the Overbought level, the indicator resets and starts a new regression channel If RSI crosses below the Oversold level, the same reset logic applies These events act as “anchor points” for dynamic trend analysis ⚪ Regression Channel Logic A custom linear regression is calculated from the RSI reset point forward The lookback grows with each bar after the reset, up to a user-defined max Regression lines are drawn from the reset point to the current bar ⚪ Standard Deviation Bands Upper and lower bands are plotted around the regression line using the standard deviation These serve as dynamic volatility envelopes, great for spotting breakouts or reversals ⚪ Rejection Markers If price hits the upper/lower band and then closes back inside it, a rejection marker is plotted Helps visualize failed breakouts and areas of absorption or reversal pressure █ How to Use ⚪ Detect Trend Shifts Use the RSI resets to identify when the trend might be starting fresh. ⚪ Watch the Bands for Volatility Extremes Use the outer bands as soft areas of potential reversal or momentum breakout. ⚪ Spot Rejections for Potential Entry Signals If price moves outside a band but then quickly returns inside, it often means the breakout failed, and price may reverse. █ Settings Explained RSI Length – How many bars RSI uses. Shorter = faster. OB / OS Levels – Crossing these triggers a regression reset. Base Regression Length – Max number of bars regression can use post-reset. StdDev Multiplier – Controls band width from the regression line. Min Bars After Reset – Ensures channel doesn’t form immediately; waits for structure. Show Reset Markers – Triangles mark where RSI crossed OB/OS. Show Rejection Markers – Circles mark where the price rejected the channel edge. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Penunjuk Pine Script®
oleh Zeiierman
12
The Mayan CalendarThis indicator displays the current date in the Mayan Calendar, based on real-time UTC time. It calculates and presents: 🌀 Long Count (Baktun.Katun.Tun.Uinal.Kin) – A linear count of days since the Mayan epoch (August 11, 3114 BCE). 🔮 Tzolk'in Date – A 260-day sacred cycle combining a number (1–13) and one of 20 day names (e.g., 4 Ajaw). 🌾 Haab' Date – A 365-day civil cycle divided into 18 months of 20 days + 5 "nameless" days (Wayeb'). The calculations follow Smithsonian standards and align with the Maya Calendar Converter from the National Museum of the American Indian: 👉 maya.nmai.si.edu The results are shown in a table overlay on your chart's top-right corner. This indicator is great for symbolic traders, astro enthusiasts, or anyone interested in ancient timekeeping systems woven into financial timeframes. Enjoy, time travelers! ⌛
Penunjuk Pine Script®
oleh BarefootJoey
4
Machine Learning RSI ║ BullVisionOverview: Introducing the Machine Learning RSI with KNN Adaptation – a cutting-edge momentum indicator that blends the classic Relative Strength Index (RSI) with machine learning principles. By leveraging K-Nearest Neighbors (KNN), this indicator aims at identifying historical patterns that resemble current market behavior and uses this context to refine RSI readings with enhanced sensitivity and responsiveness. Unlike traditional RSI models, which treat every market environment the same, this version adapts in real-time based on how similar past conditions evolved, offering an analytical edge without relying on predictive assumptions. Key Features: 🔁 KNN-Based RSI Refinement This indicator uses a machine learning algorithm (K-Nearest Neighbors) to compare current RSI and price action characteristics to similar historical conditions. The resulting RSI is weighted accordingly, producing a dynamically adjusted value that reflects historical context. 📈 Multi-Feature Similarity Analysis Pattern similarity is calculated using up to five customizable features: RSI level RSI momentum Volatility Linear regression slope Price momentum Users can adjust how many features are used to tailor the behavior of the KNN logic. 🧠 Machine Learning Weight Control The influence of the machine learning model on the final RSI output can be fine-tuned using a simple slider. This lets you blend traditional RSI and machine learning-enhanced RSI to suit your preferred level of adaptation. 🎛️ Adaptive Filtering Additional smoothing options (Kalman Filter, ALMA, Double EMA) can be applied to the RSI, offering better visual clarity and helping to reduce noise in high-frequency environments. 🎨 Visual & Accessibility Settings Custom color palettes, including support for color vision deficiencies, ensure that trend coloring remains readable for all users. A built-in neon mode adds high-contrast visuals to improve RSI visibility across dark or light themes. How It Works: Similarity Matching with KNN: At each candle, the current RSI and optional market characteristics are compared to historical bars using a KNN search. The algorithm selects the closest matches and averages their RSI values, weighted by similarity. The more similar the pattern, the greater its influence. Feature-Based Weighting: Similarity is determined using normalized values of the selected features, which gives a more refined result than RSI alone. You can choose to use only 1 (RSI) or up to all 5 features for deeper analysis. Filtering & Blending: After the machine learning-enhanced RSI is calculated, it can be optionally smoothed using advanced filters to suppress short-term noise or sharp spikes. This makes it easier to evaluate RSI signals in different volatility regimes. Parameters Explained: 📊 RSI Settings: Set the base RSI length and select your preferred smoothing method from 10+ moving average types (e.g., EMA, ALMA, TEMA). 🧠 Machine Learning Controls: Enable or disable the KNN engine Select how many nearest neighbors to compare (K) Choose the number of features used in similarity detection Control how much the machine learning engine affects the RSI calculation 🔍 Filtering Options: Enable one of several advanced smoothing techniques (Kalman Filter, ALMA, Double EMA) to adjust the indicator’s reactivity and stability. 📏 Threshold Levels: Define static overbought/oversold boundaries or reference dynamically adjusted thresholds based on historical context identified by the KNN algorithm. 🎨 Visual Enhancements: Select between trend-following or impulse coloring styles. Customize color palettes to accommodate different types of color blindness. Enable neon-style effects for visual clarity. Use Cases: Swing & Trend Traders Can use the indicator to explore how current RSI readings compare to similar market phases, helping to assess trend strength or potential turning points. Intraday Traders Benefit from adjustable filters and fast-reacting smoothing to reduce noise in shorter timeframes while retaining contextual relevance. Discretionary Analysts Use the adaptive OB/OS thresholds and visual cues to supplement broader confluence zones or market structure analysis. Customization Tips: Higher Volatility Periods: Use more neighbors and enable filtering to reduce noise. Lower Volatility Markets: Use fewer features and disable filtering for quicker RSI adaptation. Deeper Contextual Analysis: Increase KNN lookback and raise the feature count to refine pattern recognition. Accessibility Needs: Switch to Deuteranopia or Monochrome mode for clearer visuals in specific color vision conditions. Final Thoughts: The Machine Learning RSI combines familiar momentum logic with statistical context derived from historical similarity analysis. It does not attempt to predict price action but rather contextualizes RSI behavior with added nuance. This makes it a valuable tool for those looking to elevate traditional RSI workflows with adaptive, research-driven enhancements.
Penunjuk Pine Script®
oleh BullVisionCapital
34
Half Causal EstimatorOverview The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output. Core Concept Traditional moving averages suffer from time lag, which can delay signals and reduce their effectiveness for real-time decision making. The Half Causal Estimator addresses this limitation by using a non-causal filtering method that incorporates recent historical data (the causal component) alongside expected future behavior based on time-of-day patterns (the non-causal component). This dual approach allows the filter to respond more quickly to changing market conditions while maintaining smoothness. The name "Half Causal" refers to this hybrid methodology—half of the data window comes from actual historical observations, while the other half is derived from time-of-day patterns observed over multiple days. By incorporating these "future" values from past patterns, the estimator can reduce the inherent lag present in traditional moving averages. How It Works The indicator operates through several coordinated steps. First, it stores and organizes market data by specific times of day (minutes/hours). Then it builds a profile of typical behavior for each time period. For calculations, it creates a filtering window where half consists of recent actual data and half consists of expected future values based on historical time-of-day patterns. Finally, it applies a kernel-based smoothing function to weight the values in this composite window. This approach is particularly effective because market variables like volume, true range, and price changes tend to follow recognizable intraday patterns (they are positive values without DC components). By leveraging these patterns, the indicator doesn't try to predict future values in the traditional sense, but rather incorporates the average historical behavior at those future times into the current estimate. The benefit of using this "average future data" approach is that it counteracts the lag inherent in traditional moving averages. In a standard moving average, recent price action is underweighted because older data points hold equal influence. By incorporating time-of-day averages for future periods, the Half Causal Estimator essentially shifts the center of the filter window closer to the current bar, resulting in more timely outputs while maintaining smoothing benefits. Understanding Kernel Smoothing At the heart of the Half Causal Estimator is kernel smoothing, a statistical technique that creates weighted averages where points closer to the center receive higher weights. This approach offers several advantages over simple moving averages. Unlike simple moving averages that weight all points equally, kernel smoothing applies a mathematically defined weight distribution. The weighting function helps minimize the impact of outliers and random fluctuations. Additionally, by adjusting the kernel width parameter, users can fine-tune the balance between responsiveness and smoothness. The indicator supports three kernel types. The Gaussian kernel uses a bell-shaped distribution that weights central points heavily while still considering distant points. The Epanechnikov kernel employs a parabolic function that provides efficient noise reduction with a finite support range. The Triangular kernel applies a linear weighting that decreases uniformly from center to edges. These kernel functions provide the mathematical foundation for how the filter processes the combined window of past and "future" data points. Applicable Data Sources The indicator can be applied to three different data sources: volume (the trading volume of the security), true range (expressed as a percentage, measuring volatility), and change (the absolute percentage change from one closing price to the next). Each of these variables shares the characteristic of being consistently positive and exhibiting cyclical intraday patterns, making them ideal candidates for this filtering approach. Practical Applications The Half Causal Estimator excels in scenarios where timely information is crucial. It helps in identifying volume climaxes or diminishing volume trends earlier than conventional indicators. It can detect changes in volatility patterns with reduced lag. The indicator is also useful for recognizing shifts in price momentum before they become obvious in price action, and providing smoother data for algorithmic trading systems that require reduced noise without sacrificing timeliness. When volatility or volume spikes occur, conventional moving averages typically lag behind, potentially causing missed opportunities or delayed responses. The Half Causal Estimator produces signals that align more closely with actual market turns. Technical Implementation The implementation of the Half Causal Estimator involves several technical components working together. Data collection and organization is the first step—the indicator maintains a data structure that organizes market data by specific times of day. This creates a historical record of how volume, true range, or price change typically behaves at each minute/hour of the trading day. For each calculation, the indicator constructs a composite window consisting of recent actual data points from the current session (the causal half) and historical averages for upcoming time periods from previous sessions (the non-causal half). The selected kernel function is then applied to this composite window, creating a weighted average where points closer to the center receive higher weights according to the mathematical properties of the chosen kernel. Finally, the kernel weights are normalized to ensure the output maintains proper scaling regardless of the kernel type or width parameter. This framework enables the indicator to leverage the predictable time-of-day components in market data without trying to predict specific future values. Instead, it uses average historical patterns to reduce lag while maintaining the statistical benefits of smoothing techniques. Configuration Options The indicator provides several customization options. The data period setting determines the number of days of observations to store (0 uses all available data). Filter length controls the number of historical data points for the filter (total window size is length × 2 - 1). Filter width adjusts the width of the kernel function. Users can also select between Gaussian, Epanechnikov, and Triangular kernel functions, and customize visual settings such as colors and line width. These parameters allow for fine-tuning the balance between responsiveness and smoothness based on individual trading preferences and the specific characteristics of the traded instrument. Limitations The indicator requires minute-based intraday timeframes, securities with volume data (when using volume as the source), and sufficient historical data to establish time-of-day patterns. Conclusion The Half Causal Estimator represents an innovative approach to technical analysis that addresses one of the fundamental limitations of traditional indicators: time lag. By incorporating time-of-day patterns into its calculations, it provides a more timely representation of market variables while maintaining the noise-reduction benefits of smoothing. This makes it a valuable tool for traders who need to make decisions based on real-time information about volume, volatility, or price changes.
Penunjuk Pine Script®
oleh The_Peaceful_Lizard
8
BIN Based Support and Resistance [SS]This indicator presents a version of an alternative way to determine support and resistance, using a method called "Bins". Bins provide for a flexible and interesting way to determine support and resistance levels. First off, let's discuss BINS: Bins are ranges or containers into which your data points can be sorted. For example, if you're grouping ages, you might have bins like 0–18, 19–35, 36–50, and 51+. Any data point within these intervals gets placed in the corresponding bin. Binning simplifies complex data sets by grouping values into categories. This is useful for such things as Visualizing data in histograms or bar charts. Reducing noise and highlighting trends. This indicator groups the price action into 10 separate bins. It determines the Support / Resistance level by averaging the values in the Bins to find an iteration of the "central tendency" or average reoccurring value. Pros and Cons Since this is a different approach to support and resistance, I think its important to highlight some of the pros and advantages, but also be open about the cons. First off the PROS Bin Based Support and Resistance Levels dynamically adjust to ranges as opposed to hard / fast peaks and valleys. This makes them better at analyzing price action vs simply drawing lines at random peaks and valleys. Because Bins are analyzing ALL PA within a period's max and min range, Bin Support and Resistance can actually be used similar to Volume profile, where you are able to identify a pseudo-POC, or areas where price tends to consolidate. Take a look at this example on SPY: You can see these 2 SR lines are close together. This represents that this general price range is an area where price likes to accumulate/consolidate. You can see the SPY ended up coming back to this range and consolidating there for a bit. This is a strength of using a BIN based approach to calculating support and resistance, because as indicated before, it looks at price action vs peaks and valleys. As a tip, these areas are areas you want to wait for a break in one direction or the other. The indicator provides for backtest results of the support and resistance lines, to see how many times certain areas acted as resistance or support. Because this is analyzing and distributing PA evenly throughout the period's max and min, the indicator can tell you which areas tend to have higher rejection zones and which have higher support zones.   Now the CONS Because bin based SR take an average approach, the SR lines can sometimes be slightly broken before the ticker finds rejection: To combat this, make sure there is confirmed support. How the indicator actually backtests these lines is by waiting to see if the ticker has 3 consecutive closes above the support line or below the resistance line. So these are things to be mindful of. It doesn't consider pivots. Most support and resistance indicators either identify max and min peaks and valleys or use pivot points. Pivot points are a great way to identify peaks and valleys and thus by extension support and resistance. However, this is also somewhat of a strength, as using BINS forces the indicator to consider ALL price action and not just the extremes (highs and lows). Can be slightly skewed in highly volatile environments. Any time there is a massive drop or rally, it can skew the indicator to give extreme ranges to both ends. For example, the Tariff news collapse on ES1!: Owning to limitations in lookback length, sometimes the min and max range can be exceeded and other traditional areas of support / resistance is where a ticker will find support. Using the indicator Here are some basic use/functionalities of the indicator: Selecting display of backtest results: You can select to have the backtest results shown in a table: Or directly on the lines: Inversely, you can toggle them off completely: You can modify the lookback length. The suggested lookback length is between 250 to 500 candles on smaller timeframes. I also suggest 252 on daily timeframes (which represents 1 trading year). And that's the indicator! It is very easy to use, so you should pick it up in no time! Enjoy and as always, 🚀🚀 safe trades! 🚀🚀
Penunjuk Pine Script®
oleh Steversteves
14
Probability Grid [LuxAlgo]The Probability Grid tool allows traders to see the probability of where and when the next reversal would occur, it displays a 10x10 grid and/or dashboard with the probability of the next reversal occurring beyond each cell or within each cell. 🔶 USAGE By default, the tool displays deciles (percentiles from 0 to 90), users can enable, disable and modify each percentile, but two of them must always be enabled or the tool will display an error message alerting of it. The use of the tool is quite simple, as shown in the chart above, the further the price moves on the grid, the higher the probability of a reversal. In this case, the reversal took place on the cell with a probability of 9%, which means that there is a probability of 91% within the square defined by the last reversal and this cell. 🔹 Grid vs Dashboard The tool can display a grid starting from the last reversal and/or a dashboard at three predefined locations, as shown in the chart above. 🔶 DETAILS 🔹 Raw Data vs Normalized Data By default the tool displays the normalized data, this means that instead of using the raw data (price delta between reversals) it uses the returns between each reversal, this is useful to make an apples to apples comparison of all the data in the dataset. This can be seen in the left side of the chart above (BTCUSD Daily chart) where normalize data is disabled, the percentiles from 0 to 40 overlap and are indistinguishable from each other because the tool uses the raw price delta over the entire bitcoin history, with normalize data enabled as we can see in the right side of the chart we can have a fair comparison of the data over the entire history. 🔹 Probability Beyond or Within Each Cell Two different probability modes are available, the default mode is Probability Beyond Each Cell, the number displayed in each cell is the probability of the next reversal to be located in the area beyond the cell, for example, if the cell displays 20%, it means that in the area formed by the square starting from the last reversal and ending at the cell, there is an 80% probability and outside that square there is a 20% probability for the location of the next reversal. The second probability mode is the probability within each cell, this outlines the chance that the next reversal will be within the cell, as we can see on the right chart above, when using deciles as percentiles (default settings), each cell has the same 1% probability for the 10x10 grid. 🔶 SETTINGS Swing Length: The maximum length in bars used to identify a swing Maximum Reversals: Maximum number of reversals included in calculations Normalize Data: Use returns between swings instead of raw price Probability: Choose between two different probability modes: beyond and inside each cell Percentiles: Enable/disable each of the ten percentiles and select the percentile number and line style 🔹 Dashboard Show Dashboard: Enable or disable the dashboard Position: Choose dashboard location Size: Choose dashboard size 🔹 Style Show Grid: Enable or disable the grid Size: Choose grid text size Colors: Choose grid background colors Show Marks: Enable/disable reversal markers
Penunjuk Pine Script®
oleh LuxAlgo
11
Correlation Heatmap█ OVERVIEW This indicator creates a correlation matrix for a user-specified list of symbols based on their time-aligned weekly or monthly price returns. It calculates the Pearson correlation coefficient for each possible symbol pair, and it displays the results in a symmetric table with heatmap-colored cells. This format provides an intuitive view of the linear relationships between various symbols' price movements over a specific time range. █ CONCEPTS Correlation Correlation typically refers to an observable statistical relationship between two datasets. In a financial time series context, it usually represents the extent to which sampled values from a pair of datasets, such as two series of price returns, vary jointly over time. More specifically, in this context, correlation describes the strength and direction of the relationship between the samples from both series. If two separate time series tend to rise and fall together proportionally, they might be highly correlated. Likewise, if the series often vary in opposite directions, they might have a strong anticorrelation . If the two series do not exhibit a clear relationship, they might be uncorrelated . Traders frequently analyze asset correlations to help optimize portfolios, assess market behaviors, identify potential risks, and support trading decisions. For instance, correlation often plays a key role in diversification . When two instruments exhibit a strong correlation in their returns, it might indicate that buying or selling both carries elevated unsystematic risk . Therefore, traders often aim to create balanced portfolios of relatively uncorrelated or anticorrelated assets to help promote investment diversity and potentially offset some of the risks. When using correlation analysis to support investment decisions, it is crucial to understand the following caveats:  • Correlation does not imply causation . Two assets might vary jointly over an analyzed range, resulting in high correlation or anticorrelation in their returns, but that does not indicate that either instrument directly influences the other. Joint variability between assets might occur because of shared sensitivities to external factors, such as interest rates or global sentiment, or it might be entirely coincidental. In other words, correlation does not provide sufficient information to identify cause-and-effect relationships.  • Correlation does not predict the future relationship between two assets. It only reflects the estimated strength and direction of the relationship between the current analyzed samples. Financial time series are ever-changing. A strong trend between two assets can weaken or reverse in the future. Correlation coefficient A correlation coefficient is a numeric measure of correlation. Several coefficients exist, each quantifying different types of relationships between two datasets. The most common and widely known measure is the Pearson product-moment correlation coefficient , also known as the Pearson correlation coefficient or Pearson's r . Usually, when the term "correlation coefficient" is used without context, it refers to this correlation measure. The Pearson correlation coefficient quantifies the strength and direction of the linear relationship between two variables. In other words, it indicates how consistently variables' values move together or in opposite directions in a proportional, linear manner. Its formula is as follows: 𝑟(𝑥, 𝑦) = cov(𝑥, 𝑦) / (𝜎𝑥 * 𝜎𝑦) Where:  • 𝑥 is the first variable, and 𝑦 is the second variable.  • cov(𝑥, 𝑦) is the covariance between 𝑥 and 𝑦.  • 𝜎𝑥 is the standard deviation of 𝑥.  • 𝜎𝑦 is the standard deviation of 𝑦. In essence, the correlation coefficient measures the covariance between two variables, normalized by the product of their standard deviations. The coefficient's value ranges from -1 to 1, allowing a more straightforward interpretation of the relationship between two datasets than what covariance alone provides:  • A value of 1 indicates a perfect positive correlation over the analyzed sample. As one variable's value changes, the other variable's value changes proportionally in the same direction .  • A value of -1 indicates a perfect negative correlation (anticorrelation). As one variable's value increases, the other variable's value decreases proportionally.  • A value of 0 indicates no linear relationship between the variables over the analyzed sample. Aligning returns across instruments In a financial time series, each data point (i.e., bar) in a sample represents information collected in periodic intervals. For instance, on a "1D" chart, bars form at specific times as successive days elapse. However, the times of the data points for a symbol's standard dataset depend on its active sessions , and sessions vary across instrument types. For example, the daily session for NYSE stocks is 09:30 - 16:00 UTC-4/-5 on weekdays, Forex instruments have 24-hour sessions that span from 17:00 UTC-4/-5 on one weekday to 17:00 on the next, and new daily sessions for cryptocurrencies start at 00:00 UTC every day because crypto markets are consistently open. Therefore, comparing the standard datasets for different asset types to identify correlations presents a challenge. If two symbols' datasets have bars that form at unaligned times, their correlation coefficient does not accurately describe their relationship. When calculating correlations between the returns for two assets, both datasets must maintain consistent time alignment in their values and cover identical ranges for meaningful results. To address the issue of time alignment across instruments, this indicator requests confirmed weekly or monthly data from spread tickers constructed from the chart's ticker and another specified ticker. The datasets for spreads are derived from lower-timeframe data to ensure the values from all symbols come from aligned points in time, allowing a fair comparison between different instrument types. Additionally, each spread ticker ID includes necessary modifiers, such as extended hours and adjustments. In this indicator, we use the following process to retrieve time-aligned returns for correlation calculations:  1. Request the current and previous prices from a spread representing the sum of the chart symbol and another symbol ( "chartSymbol + anotherSymbol" ).  2. Request the prices from another spread representing the difference between the two symbols ( "chartSymbol - anotherSymbol" ).  3. Calculate half of the difference between the values from both spreads ( 0.5 * (requestedSum - requestedDifference) ). The results represent the symbol's prices at times aligned with the sample points on the current chart.  4. Calculate the arithmetic return of the retrieved prices: (currentPrice - previousPrice) / previousPrice  5. Repeat steps 1-4 for each symbol requiring analysis. It's crucial to note that because this process retrieves prices for a symbol at times consistent with periodic points on the current chart, the values can represent prices from before or after the closing time of the symbol's usual session. Additionally, note that the maximum number of weeks or months in the correlation calculations depends on the chart's range and the largest time range common to all the requested symbols. To maximize the amount of data available for the calculations, we recommend setting the chart to use a daily or higher timeframe and specifying a chart symbol that covers a sufficient time range for your needs. █ FEATURES This indicator analyzes the correlations between several pairs of user-specified symbols to provide a structured, intuitive view of the relationships in their returns. Below are the indicator's key features: Requesting a list of securities The "Symbol list" text box in the indicator's "Settings/Inputs" tab accepts a comma-separated list of symbols or ticker identifiers with optional spaces (e.g., "XOM, MSFT, BITSTAMP:BTCUSD"). The indicator dynamically requests returns for each symbol in the list, then calculates the correlation between each pair of return series for its heatmap display. Each item in the list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script raises a runtime error. To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data. Note that the number of symbols allowed in the list depends on the user's plan. Users with non-professional plans can compare up to 20 symbols with this indicator, and users with professional plans can compare up to 32 symbols. Timeframe and data length selection The "Returns timeframe" input specifies whether the indicator uses weekly or monthly returns in its calculations. By default, its value is "1M", meaning the indicator analyzes monthly returns. Note that this script requires a chart timeframe lower than or equal to "1M". If the chart uses a higher timeframe, it causes a runtime error. To customize the length of the data used in the correlation calculations, use the "Max periods" input. When enabled, the indicator limits the calculation window to the number of periods specified in the input field. Otherwise, it uses the chart's time range as the limit. The top-left corner of the table shows the number of confirmed weeks or months used in the calculations. It's important to note that the number of confirmed periods in the correlation calculations is limited to the largest time range common to all the requested datasets, because a meaningful correlation matrix requires analyzing each symbol's returns under the same market conditions. Therefore, the correlation matrix can show different results for the same symbol pair if another listed symbol restricts the aligned data to a shorter time range. Heatmap display This indicator displays the correlations for each symbol pair in a heatmap-styled table representing a symmetric correlation matrix. Each row and column corresponds to a specific symbol, and the cells at their intersections correspond to symbol pairs . For example, the cell at the "AAPL" row and "MSFT" column shows the weekly or monthly correlation between those two symbols' returns. Likewise, the cell at the "MSFT" row and "AAPL" column shows the same value. Note that the main diagonal cells in the display, where the row and column refer to the same symbol, all show a value of 1 because any series of non-na data is always perfectly correlated with itself. The background of each correlation cell uses a gradient color based on the correlation value. By default, the gradient uses blue hues for positive correlation, orange hues for negative correlation, and white for no correlation. The intensity of each blue or orange hue corresponds to the strength of the measured correlation or anticorrelation. Users can customize the gradient's base colors using the inputs in the "Color gradient" section of the "Settings/Inputs" tab. █ FOR Pine Script® CODERS  • This script uses the `getArrayFromString()` function from our ValueAtTime library to process the input list of symbols. The function splits the "string" value by its commas, then constructs an array of non-empty strings without leading or trailing whitespaces. Additionally, it uses the str.upper() function to convert each symbol's characters to uppercase.  • The script's `getAlignedReturns()` function requests time-aligned prices with two request.security() calls that use spread tickers based on the chart's symbol and another symbol. Then, it calculates the arithmetic return using the `changePercent()` function from the ta library. The `collectReturns()` function uses `getAlignedReturns()` within a loop and stores the data from each call within a matrix . The script calls the `arrayCorrelation()` function on pairs of rows from the returned matrix to calculate the correlation values.  • For consistency, the `getAlignedReturns()` function includes extended hours and dividend adjustment modifiers in its data requests. Additionally, it includes other settings inherited from the chart's context, such as "settlement-as-close" preferences. • A Pine script can execute up to 40 or 64 unique `request.*()` function calls, depending on the user's plan. The maximum number of symbols this script compares is half the plan's limit, because `getAlignedReturns()` uses two request.security() calls.  • This script can use the request.security() function within a loop because all scripts in Pine v6 enable dynamic requests by default. Refer to the Dynamic requests section of the Other timeframes and data page to learn more about this feature, and see our v6 migration guide to learn what's new in Pine v6.  • The script's table uses two distinct color.from_gradient() calls in a switch structure to determine the cell colors for positive and negative correlation values. One call calculates the color for values from -1 to 0 based on the first and second input colors, and the other calculates the colors for values from 0 to 1 based on the second and third input colors. Look first. Then leap.
Penunjuk Pine Script®
oleh TradingView
10
Tetris with Auto-PlayThis indicator is implemented in Pine Script™ v6 and serves as a demonstration of TradingView's capabilities. The core concept is to simulate a classic Tetris game by creating a grid-based environment and managing game state entirely within Pine Script. Key Technical Aspects: Grid Representation: The script defines a custom grid structure using a user-defined type that holds the grid’s dimensions and a one-dimensional array to simulate a two-dimensional board. This structure is used to track occupied cells, clear full rows, and determine stack height. Piece Management: A second custom type is used to represent the state of a tetromino piece, including its type, rotation, and position. The code includes functions to calculate the block offsets for each tetromino based on its rotation state. Collision Detection and Piece Locking: Dedicated functions check for collisions against the grid borders and existing blocks. When a collision is detected during a downward move, the piece is locked into the grid, and any complete lines are cleared. AIgo-Driven Placement: The script incorporates a simple heuristic to determine the best placement for the next tetromino. It simulates different rotations and horizontal positions, evaluating each based on aggregated column height, cleared lines, holes, and bumpiness. This decision-making process is encapsulated in an AI-like function that returns the optimal rotation and placement. Rendering Using Tables: The visual representation is managed via TradingView’s table objects. The game board is rendered with a bordered layout, while a separate preview table displays the next piece and the current score. Each cell is updated with text and background colors that correspond to the state of the game. Execution Flow and Timing: The main execution loop handles real-time updates by dropping pieces at set intervals and checking for game-over conditions. The code leverages persistent variables and time comparisons to control game speed and manage transitions between piece drops. Executing: Add the indicator to the chart It starts playing itself till game over There are no parameters to change in this version but the grid in the code directly p.s. Sadly we have no interactive buttons in the current pinescript versions to play ourself, but its about the possibilitys what we could do ;-) Maybe in a future version there is more possible, if i find time to enhance and expand the idea Have fun :-)
Penunjuk Pine Script®
oleh djmad
13
Log Regression Oscillator Channel [BigBeluga] This unique overlay tool blends logarithmic trend analysis with dynamic oscillator behavior. It projects RSI, MFI, or Stochastic lines directly into a log regression channel on the price chart — offering an intuitive way to detect overbought/oversold momentum within the broader price structure. 🔵Key Features: Logarithmic Regression Channel: ➣ Draws a trend-based channel using logarithmic regression, adapting to price growth curvature over time. ➣ Features upper, lower, and optional midline boundaries to visualize trend flow and range extremes. Oscillator Overlay (RSI / MFI / Stochastic): ➣ Projects your chosen oscillator inside the channel using dynamic polylines. ➣ Allows switching between RSI, Money Flow Index, or Stochastic for versatile momentum insight. Threshold-Based Scaling: ➣ The top and bottom of the channel represent traditional oscillator thresholds (e.g., RSI 70/30). ➣ Users can modify the scale in settings to customize what "overbought" or "oversold" means visually. Signal Line Integration: ➣ Adds a yellow moving average (signal line) for smoother confirmation of oscillator turns. ➣ Helps identify divergence, momentum shifts, and fakeouts with better clarity. Live Oscillator Readout: ➣ Displays the real-time oscillator value at the right edge of the chart. ➣ Ensures traders stay aware of current momentum levels without switching panels. 🔵Usage: Momentum Context: ➣ When the oscillator touches the upper regression band, it may signal local overbought pressure. ➣ Touching the lower band may indicate oversold conditions within the current log trend. Divergence Detection: ➣ Use the oscillator’s behavior relative to the channel slope to spot divergence from price. ➣ For example, RSI rising inside a falling channel can flag early trend shifts. Trend-Sensitive Entries: ➣ Combine oscillator signals with log channel direction to filter trades in trend alignment. ➣ Signal line crossovers inside the channel act as early warning for momentum turns. The Log Regression Oscillator Channel transforms how traders view classic momentum tools. By embedding oscillators into a logarithmic trend structure, it offers unmatched clarity on momentum positioning relative to price expansion. Ideal for swing traders, mean-reverters, or trend followers looking to sharpen entries and exits with style.
Penunjuk Pine Script®
oleh BigBeluga
33
Multitimeframe Fair Value Gap – FVG (Zeiierman)█ Overview The Multitimeframe Fair Value Gap – FVG (Zeiierman) indicator provides a dynamic and customizable visualization of institutional imbalances (Fair Value Gaps) across multiple timeframes. Built for traders who seek to analyze price inefficiencies, this tool helps highlight potential entry points, unmitigated gaps, and directional bias using smart volume logic and adaptive visual elements. A Fair Value Gap (FVG) forms when there's a three-candle sequence in which a market imbalance leaves a "gap" between the wicks of candle 1 and candle 3. These areas are often considered footprints of institutional activity, and this indicator gives you the tools to track them with surgical precision across any timeframe you choose—regardless of the one you're viewing. This indicator also includes a trend filter powered by a low-pass Butterworth filter, enabling traders to distinguish between countertrend vs. trend-aligned FVGs for more intelligent decision-making. On top of that, it features a dynamic FVG table for live tracking and bull/bear volume power visualization inside each gap, adding powerful clarity to market intent. █ How It Works The indicator analyzes the open, high, low, close, and volume of candles from a user-selected timeframe. It identifies Fair Value Gaps based on wick logic and only confirms those that meet customizable strength criteria. Once detected, the indicator visualizes each FVG with dynamically extending boxes, optional buy/sell volume bars, and a real-time mitigation check. ⚪ Multitimeframe Logic Users can analyze FVGs from a higher or lower timeframe regardless of their current chart. This is achieved using request.security() to fetch OHLCV data from the chosen timeframe. ⚪ Wick Sensitivity & Impulse Filter The script measures the wick size of potential FVG candles and compares them to a running average. Only FVGs with wick sizes above a certain sensitivity threshold (user-controlled) are plotted. This ensures only meaningful price dislocations (e.g., strong impulsive moves) are shown, reducing noise. ⚪ Midpoint Mitigation Logic FVGs are marked as "mitigated" when the price revisits the gap area. Traders can choose whether full gap closure or just a midpoint touch is required. This allows faster reactivity in real-time trading environments. ⚪ Bull & Bear Power – Volume-Weighted Visualization Every Fair Value Gap box includes sub-bars representing the estimated buy and sell effort that created the gap. These are calculated using the candle's close in relation to its high/low range and volume: Buy Volume % ≈ effort from low to close Sell Volume % ≈ effort from high to close Each sub-bar inside the FVG: Is color-coded (UpCol for bullish, DnCol for bearish) Is drawn proportionally to the strength of buyers or sellers Visually displays who was in control during the imbalance ⚪ FVG Table – Dynamic On-Chart Overview The indicator includes an optional on-chart table that displays all currently active (unmitigated) FVGs in a side panel format: Automatic updates as gaps are formed and mitigated Color-coded rows to show bullish vs. bearish FVGs Timestamps to know precisely when the gap formed User-controlled position via Table Left and Table Right This is a gap watchlist overlay, giving traders a concise view of current inefficiencies without manually scanning the chart. ⚪ FVG Trend Filter (Butterworth Smoother) Using a two-pole Butterworth low-pass filter, the indicator computes a trendline based on average FVG values, offering a smooth but responsive directional signal. Passband Ripple (dB): Controls sensitivity and overshoot tolerance Cutoff Frequency (0–0.5): Sets how quickly the trendline reacts The trendline helps categorize each FVG: Trend up → favor bullish FVGs Trend down → favor bearish FVGs It adds an extra dimension to FVG entries, helping distinguish between trend-aligned and countertrend signals. █ How to Use ⚪ Identify Institutional Gaps Use this tool to identify areas where institutions may have left imbalances behind quickly. These areas often become: Strong support/resistance zones Areas where price might react sharply Targets for liquidity sweeps or retracements ⚪ React to Trend or Countertrend The built-in trendline helps categorize each FVG: Trend up → Bullish FVGs have higher validity Trend down → Bearish FVGs have higher validity ⚪ Volume Context via Bull/Bear Power Each Fair Value Gap is more than just a price imbalance — it’s a story of effort and intent. The Bull/Bear Power feature visualizes the buy and sell pressure behind each FVG, helping you understand how the gap was formed and who was in control. A bullish FVG with a strong buy effort suggests continuation potential — buyers dominated the move. A bullish FVG with a dominant sell effort could signal a trap or reversal — sellers may have overwhelmed the breakout. These insights allow you to confirm imbalance strength, spot traps early, and add confidence to entries based on dominant volume profiles. Instead of viewing gaps as static zones, this feature turns each into a live volume map — a visual breakdown of who moved the market and whether that move had conviction. ⚪ Plan with the FVG Table The FVG Table acts as your on-chart control center for tracking active imbalances. When enabled, it provides a clear summary of all unmitigated Fair Value Gaps, helping you stay organized and focused during fast-moving sessions. Track live and historical gaps: See exactly when and where each FVG formed. Monitor older, still-valid zones: Gaps off-screen but not mitigated remain in play — perfect for anticipating future reactions. Gauge market bias at a glance: The balance of bullish vs. bearish FVGs helps you understand overall directional pressure. Plan entries confidently: Use the table to reference all zones for risk management, confluence stacking, or layered execution strategies. Instead of manually scanning your chart, the FVG Table offers a clean, at-a-glance overview of the market’s inefficiencies — giving you the structure needed to act with precision. █ Settings FVG Timeframe Select any timeframe to source FVGs independent of your current chart. Sensitivity Filter FVGs by how impulsive the move is — it helps you eliminate weak gaps. Mitigated on Mid Control whether gaps are removed at midpoint touch or full fill. Table Settings Control the table position and width. Cleanly view all active FVGs. FVG Style Customize gap box colors, length, and bullish/bearish overlays. Trend Filter Enable or disable the smoothed FVG-based trendline with customizable smoothing controls. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Penunjuk Pine Script®
oleh Zeiierman
33
Relative Crypto Dominance Polar Chart [LuxAlgo]The Relative Crypto Dominance Polar Chart tool allows traders to compare the relative dominance of up to ten different tickers in the form of a polar area chart, we define relative dominance as a combination between traded dollar volume and volatility, making it very easy to compare them at a glance. 🔶 USAGE The use is quite simple, traders just have to load the indicator on the chart, and the graph showing the relative dominance will appear. The 10 tickers loaded by default are the major cryptocurrencies by market cap, but traders can select any ticker in the settings panel. Each area represents dominance as volatility (radius) by dollar volume (arc length); a larger area means greater dominance on that ticker. 🔹 Choosing Period The tool supports up to five different periods Hourly Daily Weekly Monthly Yearly By default, the tool period is set on auto mode, which means that the tool will choose the period depending on the chart timeframe timeframes up to 2m: Hourly timeframes up to 15m: Daily timeframes up to 1H: Weekly timeframes up to 4H: Monthly larger timeframes: Yearly 🔹 Sorting & Sizing Traders can sort the graph areas by volatility (radius of each area) in ascending or descending order; by default, the tickers are sorted as they are in the settings panel. The tool also allows you to adjust the width of the chart on a percentage basis, i.e., at 100% size, all the available width is used; if the graph is too wide, just decrease the graph size parameter in the settings panel. 🔹 Set your own style The tool allows great customization from the settings panel, traders can enable/disable most of the components, and add a very nice touch with curved lines enabled for displaying the areas with a petal-like effect. 🔶 SETTINGS Period: Select up to 5 different time periods from Hourly, Daily, Weekly, Monthly and Yearly. Enable/disable Auto mode. Tickers: Enable/disable and select tickers and colors 🔹 Style Graph Order: Select sort order Graph Size: Select percentage of width used Labels Size: Select size for ticker labels Show Percent: Show dominance in % under each ticker Curved Lines: Enable/disable petal-like effect for each area Show Title: Enable/disable graph title Show Mean: Enable/disable volatility average and select color
Penunjuk Pine Script®
oleh LuxAlgo
11
Multi-Anchored Linear Regression Channels [TANHEF]█ Overview: The 'Multi-Anchored Linear Regression Channels ' plots multiple dynamic regression channels (or bands) with unique selectable calculation types for both regression and deviation. It leverages a variety of techniques, customizable anchor sources to determine regression lengths, and user-defined criteria to highlight potential opportunities. Before getting started, it's worth exploring all sections, but make sure to review the Setup & Configuration section in particular. It covers key parameters like anchor type, regression length, bias, and signal criteria—essential for aligning the tool with your trading strategy. █ Key Features: ⯁ Multi-Regression Capability: Plot up to three distinct regression channels and/or bands simultaneously, each with customizable anchor types to define their length. ⯁ Regression & Deviation Methods: Regressions Types: Standard: Uses ordinary least squares to compute a simple linear trend by averaging the data and deriving a slope and endpoints over the lookback period. Ridge: Introduces L2 regularization to stabilize the slope by penalizing large coefficients, which helps mitigate multicollinearity in the data. Lasso: Uses L1 regularization through soft-thresholding to shrink less important coefficients, yielding a simpler model that highlights key trends. Elastic Net: Combines L1 and L2 penalties to balance coefficient shrinkage and selection, producing a robust weighted slope that handles redundant predictors. Huber: Implements the Huber loss with iteratively reweighted least squares (IRLS) and EMA-style weights to reduce the impact of outliers while estimating the slope. Least Absolute Deviations (LAD): Reduces absolute errors using iteratively reweighted least squares (IRLS), yielding a slope less sensitive to outliers than squared-error methods. Bayesian Linear: Merges prior beliefs with weighted data through Bayesian updating, balancing the prior slope with data evidence to derive a probabilistic trend. Deviation Types: Regressive Linear (Reverse): In reverse order (recent to oldest), compute weighted squared differences between the data and a line defined by a starting value and slope. Progressive Linear (Forward): In forward order (oldest to recent), compute weighted squared differences between the data and a line defined by a starting value and slope. Balanced Linear: In forward order (oldest to newest), compute regression, then pair to source data in reverse order (newest to oldest) to compute weighted squared differences. Mean Absolute: Compute weighted absolute differences between each data point and its regression line value, then aggregate them to yield an average deviation. Median Absolute: Determine the weighted median of the absolute differences between each data point and its regression line value to capture the central tendency of deviations. Percent: Compute deviation as a percentage of a base value by multiplying that base by the specified percentage, yielding symmetric positive and negative deviations. Fitted: Compare a regression line with high and low series values by computing weighted differences to determine the maximum upward and downward deviations. Average True Range: Iteratively compute the weighted average of absolute differences between the data and its regression line to yield an ATR-style deviation measure. Bias: Bias: Applies EMA or inverse-EMA style weighting to both Regression and/or Deviation, emphasizing either recent or older data. ⯁ Customizable Regression Length via Anchors: Anchor Types: Fixed: Length. Bar-Based: Bar Highest/Lowest, Volume Highest/Lowest, Spread Highest/Lowest. Correlation: R Zero, R Highest, R Lowest, R Absolute. Slope: Slope Zero, Slope Highest, Slope Lowest, Slope Absolute. Indicator-Based: Indicators Highest/Lowest (ADX, ATR, BBW, CCI, MACD, RSI, Stoch). Time-Based: Time (Day, Week, Month, Quarter, Year, Decade, Custom). Session-Based: Session (Tokyo, London, New York, Sydney, Custom). Event-Based: Earnings, Dividends, Splits. External: Input Source Highest/Lowest. Length Selection: Maximum: The highest allowed regression length (also fixed value of “Length” anchor). Minimum: The shortest allowed length, ensuring enough bars for a valid regression. Step: The sampling interval (e.g., 1 checks every bar, 2 checks every other bar, etc.). Increasing the step reduces the loading time, most applicable to “Slope” and “R” anchors. Adaptive lookback: Adaptive Lookback: Enable to display regression regardless of too few historical bars. ⯁ Selecting Bias: Bias applies separately to regression and deviation. Positive values emphasize recent data (EMA-style), negative invert, and near-zero maintains balance. (e.g., a length 100, bias +1 gives the newest price ~7× more weight than the oldest). It's best to apply bias to both (regression and deviation) or just the deviation. Biasing only regression may distort deviation visually, while biasing both keeps their relationship intuitive. Using bias only for deviation scales it without altering regression, offering unique analysis. ⯁ Scale Awareness: Supports linear and logarithmic price scaling, the regression and deviations adjust accordingly. ⯁ Signal Generation & Alerts: Customizable entry/exit signals and alerts, detailed in the dedicated section below. ⯁ Visual Enhancements & Real-World Examples: Optional on-chart table display summarizing regression input criteria (display type, anchor type, source, regression type, regression bias, deviation type, deviation bias, deviation multiplier) and key calculated metrics (regression length, slope, Pearson’s R, percentage position within deviations, etc.) for quick reference. █ Understanding R (Pearson Correlation Coefficient): Pearson’s R gauges data alignment to a straight-line trend within the regression length: Range: R varies between –1 and +1. R = +1 → Perfect positive correlation (strong uptrend). R = 0 → No linear relationship detected. R = –1 → Perfect negative correlation (strong downtrend). This script uses Pearson’s R as an anchor, adjusting regression length to target specific R traits. Strong R (±1) follows the regression channel, while weak R (0) shows inconsistency. █ Understanding the Slope: The slope is the direction and rate at which the regression line rises or falls per bar: Positive Slope (>0): Uptrend – Steeper means faster increase. Negative Slope (<0): Downtrend – Steeper means sharper drop. Zero or Near-Zero Slope: Sideways – Indicating range-bound conditions. This script uses highest and lowest slope as an anchor, where extremes highlight strong moves and trend lines, while values near zero indicate sideways action and possible support/resistance. █ Setup & Configuration: Whether you’re new to this script or want to quickly adjust all critical parameters, the panel below shows the main settings available. You can customize everything from the anchor type and maximum length to the bias, signal conditions, and more. Scale (select Log Scale for logarithmic, otherwise linear scale). Display (regression channel and/or bands). Anchor (how regression length is determined). Length (control bars analyzed): • Max – Upper limit. • Min – Prevents regression from becoming too short. • Step – Controls scanning precision; increasing Step reduces load time. Regression: • Type – Calculation method. • Bias – EMA-style emphasis (>0=new bars weighted more; <0=old bars weighted more). Deviation: • Type – Calculation method. • Bias – EMA-style emphasis (>0=new bars weighted more; <0=old bars weighted more). • Multiplier - Adjusts Upper and Lower Deviation. Signal Criteria: • % (Price vs Deviation) – (0% = lower deviation, 50% = regression, 100% = upper deviation). • R – (0 = no correlation, ±1 = perfect correlation; >0 = +slope, <0 = -slope). Table (analyze table of input settings, calculated results, and signal criteria). Adaptive Lookback (display regression while too few historical bars). Multiple Regressions (steps 2 to 7 apply to #1, #2, and #3 regressions). █ Signal Generation & Alerts: The script offers customizable entry and exit signals with flexible criteria and visual cues (background color, dots, or triangles). Alerts can also be triggered for these opportunities. Percent Direction Criteria: (0% = lower deviation, 50% = regression line, 100% = upper deviation) Above %: Triggers if price is above a specified percent of the deviation channel. Below %: Triggers if price is below a specified percent of the deviation channel. (Blank): Ignores the percent‐based condition. Pearson's R (Correlation) Direction Criteria: (0 = no correlation, ±1 = perfect correlation; >0 = positive slope, <0 = negative slope) Above R / Below R: Compares the correlation to a threshold. Above│R│ / Below│R│: Uses absolute correlation to focus on strength, ignoring direction. Zero to R: Checks if R is in the 0-to-threshold range. (Blank): Ignores correlation-based conditions. █ User Tips & Best Practices: Choose an anchor type that suits your strategy, “Bar Highest/Lowest” automatically spots commonly used regression zones, while “│R│ Highest” targets strong linear trends. Consider enabling or disabling the Adaptive Lookback feature to ensure you always have a plotted regression if your chart doesn’t meet the maximum-length requirement. Use a small Step size (1) unless relying on R-correlation or slope-based anchors as the are time-consuming to calculate. Larger steps speed up calculations but reduce precision. Fine-tune settings such as lookback periods, regression bias, and deviation multipliers, or trend strength. Small adjustments can significantly affect how channels and signals behave. To reduce loading time , show only channels (not bands) and disable signals, this limits calculations to the last bar and supports more extreme criteria. Use the table display to monitor anchor type, calculated length, slope, R value, and percent location at a glance—especially if you have multiple regressions visible simultaneously. █ Conclusion: With its blend of advanced regression techniques, flexible deviation options, and a wide range of anchor types, this indicator offers a highly adaptable linear regression channeling system. Whether you're anchoring to time, price extremes, correlation, slope, or external events, the tool can be shaped to fit a variety of strategies. Combined with customizable signals and alerts, it may help highlight areas of confluence and support a more structured approach to identifying potential opportunities.
Penunjuk Pine Script®
oleh TanHef
11
TASC 2025.04 The Ultimate Oscillator█ OVERVIEW This script implements an alternative, refined version of the Ultimate Oscillator (UO) designed to reduce lag and enhance responsiveness in momentum indicators, as introduced by John F. Ehlers in his article "Less Lag In Momentum Indicators, The Ultimate Oscillator" from the April 2025 edition of TASC's Traders' Tips . █ CONCEPTS In his article, Ehlers states that indicators are essentially filters that remove unwanted noise (i.e., unnecessary information) from market data. Simply put, they process a series of data to place focus on specific information, providing a different perspective on price dynamics. Various filter types attenuate different periodic signals within the data. For instance, a lowpass filter allows only low-frequency signals, a highpass filter allows only high-frequency signals, and a bandpass filter allows signals within a specific frequency range . Ehlers explains that the key to removing indicator lag is to combine filters of different types in such a way that the result preserves necessary, useful signals while minimizing delay (lag). His proposed UltimateOscillator aims to maintain responsiveness to a specific frequency range by measuring the difference between two highpass filters' outputs. The oscillator uses the following formula: UO = (HP1 - HP2) / RMS Where: HP1 is the first highpass filter. HP2 is another highpass filter that allows only shorter wavelengths than the critical period of HP1. RMS is the root mean square of the highpass filter difference, used as a scaling factor to standardize the output. The resulting oscillator is similar to a bandpass filter , because it emphasizes wavelengths between the critical periods of the two highpass filters. Ehlers' UO responds quickly to value changes in a series, providing a responsive view of momentum with little to no lag. █ USAGE Ehlers' UltimateOscillator sets the critical periods of its highpass filters using two parameters: BandEdge and Bandwidth : The BandEdge sets the critical period of the second highpass filter, which determines the shortest wavelengths in the response. The Bandwidth is a multiple of the BandEdge used for the critical period of the first highpass filter, which determines the longest wavelengths in the response. Ehlers suggests that a Bandwidth value of 2 works well for most applications. However, traders can use any value above or equal to 1.4. Users can customize these parameters with the "Bandwidth" and "BandEdge" inputs in the "Settings/Inputs" tab. The script plots the UO calculated for the specified "Source" series in a separate pane, with a color based on the chart's foreground color. Positive UO values indicate upward momentum or trends, and negative UO values indicate the opposite. Additionally, this indicator provides the option to display a "cloud" from 10 additional UO series with different settings for an aggregate view of momentum. The "Cloud" input offers four display choices: "Bandwidth", "BandEdge", "Bandwidth + BandEdge", or "None". The "Bandwidth" option calculates oscillators with different Bandwidth values based on the main oscillator's setting. Likewise, the "BandEdge" option calculates oscillators with varying BandEdge values. The "Bandwidth + BandEdge" option calculates the extra oscillators with different values for both parameters. When a user selects any of these options, the script plots the maximum and minimum oscillator values and fills their space with a color gradient. The fill color corresponds to the net sum of each UO's sign , indicating whether most of the UOs reflect positive or negative momentum. Green hues mean most oscillators are above zero, signifying stronger upward momentum. Red hues mean most are below zero, indicating stronger downward momentum.
Penunjuk Pine Script®
oleh PineCodersTASC
5
Dynamic Volume Profile Oscillator | AlphaAlgosDynamic Volume Profile Oscillator | AlphaAlgos Overview The Dynamic Volume Profile Oscillator is an advanced technical analysis tool that transforms traditional volume analysis into a responsive oscillator. By creating a dynamic volume profile and measuring price deviation from volume-weighted equilibrium levels, this indicator provides traders with powerful insights into market momentum and potential reversals. Key Features • Volume-weighted price deviation analysis • Adaptive midline that adjusts to changing market conditions • Beautiful gradient visualization with 10-level intensity zones • Fast and slow signal lines for trend confirmation • Mean reversion mode that identifies price extremes relative to volume • Fully customizable sensitivity and smoothing parameters Technical Components 1. Volume Profile Analysis The indicator builds a dynamic volume profile by: • Collecting recent price and volume data within a specified lookback period • Calculating a volume-weighted mean price (similar to VWAP) • Measuring how far current price has deviated from this weighted average • Adjusting this deviation based on historical volatility 2. Oscillator Calculation The oscillator offers two calculation methods: • Mean Reversion Mode (default): Measures deviation from volume-weighted mean price, normalized to reflect potential overbought/oversold conditions • Standard Mode : Normalizes volume activity to identify unusual volume patterns 3. Adaptive Zones The indicator features dynamic zones that: • Center around an adaptive midline that reflects the average oscillator value • Expand and contract based on recent volatility (standard deviation) • Visually represent intensity through multi-level gradient coloring • Provide clear visualization of bullish/bearish extremes 4. Signal Generation Trading signals are generated through: • Main oscillator line position relative to the adaptive midline • Crossovers between fast (5-period) and slow (15-period) signal lines • Color changes that instantly identify trend direction • Distance from the midline indicating trend strength Configuration Options Volume Analysis Settings: • Price Source - Select which price data to analyze • Volume Source - Define volume data source • Lookback Period - Number of bars for main calculations • Profile Calculation Periods - Frequency of profile recalculation Oscillator Settings: • Smoothing Length - Controls oscillator smoothness • Sensitivity - Adjusts responsiveness to price/volume changes • Mean Reversion Mode - Toggles calculation methodology Threshold Settings: • Adaptive Midline - Uses dynamic midline based on historical values • Midline Period - Lookback period for midline calculation • Zone Width Multiplier - Controls width of bullish/bearish zones Display Settings: • Color Bars - Option to color price bars based on trend direction Trading Strategies Trend Following: • Enter long positions when the oscillator crosses above the adaptive midline • Enter short positions when the oscillator crosses below the adaptive midline • Use signal line crossovers for entry timing • Monitor gradient intensity to gauge trend strength Mean Reversion Trading: • Look for oscillator extremes shown by intense gradient colors • Prepare for potential reversals when the oscillator reaches upper/lower zones • Use divergences between price and oscillator for confirmation • Consider scaling positions based on gradient intensity Volume Analysis: • Use Standard Mode to identify unusual volume patterns • Confirm breakouts when accompanied by strong oscillator readings • Watch for divergences between price and volume-based readings • Use extended periods in extreme zones as trend confirmation Best Practices • Adjust sensitivity based on the asset's typical volatility • Use longer smoothing for swing trading, shorter for day trading • Combine with support/resistance levels for optimal entry/exit points • Consider multiple timeframe analysis for comprehensive market view • Test different profile calculation periods to match your trading style This indicator is provided for informational purposes only. Always use proper risk management when trading based on any technical indicator. Not financial advise.
Penunjuk Pine Script®
oleh AlphaNatt
4
real_time_candlesIntroduction The Real-Time Candles Library provides comprehensive tools for creating, manipulating, and visualizing custom timeframe candles in Pine Script. Unlike standard indicators that only update at bar close, this library enables real-time visualization of price action and indicators within the current bar, offering traders unprecedented insight into market dynamics as they unfold. This library addresses a fundamental limitation in traditional technical analysis: the inability to see how indicators evolve between bar closes. By implementing sophisticated real-time data processing techniques, traders can now observe indicator movements, divergences, and trend changes as they develop, potentially identifying trading opportunities much earlier than with conventional approaches. Key Features The library supports two primary candle generation approaches: Chart-Time Candles: Generate real-time OHLC data for any variable (like RSI, MACD, etc.) while maintaining synchronization with chart bars. Custom Timeframe (CTF) Candles: Create candles with custom time intervals or tick counts completely independent of the chart's native timeframe. Both approaches support traditional candlestick and Heikin-Ashi visualization styles, with options for moving average overlays to smooth the data. Configuration Requirements For optimal performance with this library: Set max_bars_back = 5000 in your script settings When using CTF drawing functions, set max_lines_count = 500, max_boxes_count = 500, and max_labels_count = 500 These settings ensure that you will be able to draw correctly and will avoid any runtime errors. Usage Examples Basic Chart-Time Candle Visualization // Create real-time candles for RSI float rsi = ta.rsi(close, 14) Candle rsi_candle = candle_series(rsi, CandleType.candlestick) // Plot the candles using Pine's built-in function plotcandle(rsi_candle.Open, rsi_candle.High, rsi_candle.Low, rsi_candle.Close, "RSI Candles", rsi_candle.candle_color, rsi_candle.candle_color) Multiple Access Patterns The library provides three ways to access candle data, accommodating different programming styles: // 1. Array-based access for collection operations Candle candles = candle_array(source) // 2. Object-oriented access for single entity manipulation Candle candle = candle_series(source) float value = candle.source(Source.HLC3) // 3. Tuple-based access for functional programming styles = candle_tuple(source) Custom Timeframe Examples // Create 20-second candles with EMA overlay plot_ctf_candles( source = close, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 20, timezone = -5, tied_open = true, ema_period = 9, enable_ema = true ) // Create tick-based candles (new candle every 15 ticks) plot_ctf_tick_candles( source = close, candle_type = CandleType.heikin_ashi, number_of_ticks = 15, timezone = -5, tied_open = true ) Advanced Usage with Custom Visualization // Get custom timeframe candles without automatic plotting CandleCTF my_candles = ctf_candles_array( source = close, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 30 ) // Apply custom logic to the candles float ema_values = my_candles.ctf_ema(14) // Draw candles and EMA using time-based coordinates my_candles.draw_ctf_candles_time() ema_values.draw_ctf_line_time(line_color = #FF6D00) Library Components Data Types Candle: Structure representing chart-time candles with OHLC, polarity, and visualization properties CandleCTF: Extended candle structure with additional time metadata for custom timeframes TickData: Structure for individual price updates with time deltas Enumerations CandleType: Specifies visualization style (candlestick or Heikin-Ashi) Source: Defines price components for calculations (Open, High, Low, Close, HL2, etc.) SampleType: Sets sampling method (Time-based or Tick-based) Core Functions get_tick(): Captures current price as a tick data point candle_array(): Creates an array of candles from price updates candle_series(): Provides a single candle based on latest data candle_tuple(): Returns OHLC values as a tuple ctf_candles_array(): Creates custom timeframe candles without rendering Visualization Functions source(): Extracts specific price components from candles candle_ctf_to_float(): Converts candle data to float arrays ctf_ema(): Calculates exponential moving averages for candle arrays draw_ctf_candles_time(): Renders candles using time coordinates draw_ctf_candles_index(): Renders candles using bar index coordinates draw_ctf_line_time(): Renders lines using time coordinates draw_ctf_line_index(): Renders lines using bar index coordinates Technical Implementation Notes This library leverages Pine Script's varip variables for state management, creating a sophisticated real-time data processing system. The implementation includes: Efficient tick capturing: Samples price at every execution, maintaining temporal tracking with time deltas Smart state management: Uses a hybrid approach with mutable updates at index 0 and historical preservation at index 1+ Temporal synchronization: Manages two time domains (chart time and custom timeframe) The tooltip implementation provides crucial temporal context for custom timeframe visualizations, allowing users to understand exactly when each candle formed regardless of chart timeframe. Limitations Custom timeframe candles cannot be backtested due to Pine Script's limitations with historical tick data Real-time visualization is only available during live chart updates Maximum history is constrained by Pine Script's array size limits Applications Indicator visualization: See how RSI, MACD, or other indicators evolve in real-time Volume analysis: Create custom volume profiles independent of chart timeframe Scalping strategies: Identify short-term patterns with precisely defined time windows Volatility measurement: Track price movement characteristics within bars Custom signal generation: Create entry/exit signals based on custom timeframe patterns Conclusion The Real-Time Candles Library bridges the gap between traditional technical analysis (based on discrete OHLC bars) and the continuous nature of market movement. By making indicators more responsive to real-time price action, it gives traders a significant edge in timing and decision-making, particularly in fast-moving markets where waiting for bar close could mean missing important opportunities. Whether you're building custom indicators, researching price patterns, or developing trading strategies, this library provides the foundation for sophisticated real-time analysis in Pine Script. Implementation Details & Advanced Guide Core Implementation Concepts The Real-Time Candles Library implements a sophisticated event-driven architecture within Pine Script's constraints. At its heart, the library creates what's essentially a reactive programming framework handling continuous data streams. Tick Processing System The foundation of the library is the get_tick() function, which captures price updates as they occur: export get_tick(series float source = close, series float na_replace = na)=> varip float price = na varip int series_index = -1 varip int old_time = 0 varip int new_time = na varip float time_delta = 0 // ... This function: Samples the current price Calculates time elapsed since last update Maintains a sequential index to track updates The resulting TickData structure serves as the fundamental building block for all candle generation. State Management Architecture The library employs a sophisticated state management system using varip variables, which persist across executions within the same bar. This creates a hybrid programming paradigm that's different from standard Pine Script's bar-by-bar model. For chart-time candles, the core state transition logic is: // Real-time update of current candle candle_data := Candle.new(Open, High, Low, Close, polarity, series_index, candle_color) candles.set(0, candle_data) // When a new bar starts, preserve the previous candle if clear_state candles.insert(1, candle_data) price.clear() // Reset state for new candle Open := Close price.push(Open) series_index += 1 This pattern of updating index 0 in real-time while inserting completed candles at index 1 creates an elegant solution for maintaining both current state and historical data. Custom Timeframe Implementation The custom timeframe system manages its own time boundaries independent of chart bars: bool clear_state = switch settings.sample_type SampleType.Ticks => cumulative_series_idx >= settings.number_of_ticks SampleType.Time => cumulative_time_delta >= settings.number_of_seconds This dual-clock system synchronizes two time domains: Pine's execution clock (bar-by-bar processing) The custom timeframe clock (tick or time-based) The library carefully handles temporal discontinuities, ensuring candle formation remains accurate despite irregular tick arrival or market gaps. Advanced Usage Techniques 1. Creating Custom Indicators with Real-Time Candles To develop indicators that process real-time data within the current bar: // Get real-time candles for your data Candle rsi_candles = candle_array(ta.rsi(close, 14)) // Calculate indicator values based on candle properties float signal = ta.ema(rsi_candles.first().source(Source.Close), 9) // Detect patterns that occur within the bar bool divergence = close > close and rsi_candles.first().Close < rsi_candles.get(1).Close 2. Working with Custom Timeframes and Plotting For maximum flexibility when visualizing custom timeframe data: // Create custom timeframe candles CandleCTF volume_candles = ctf_candles_array( source = volume, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 60 ) // Convert specific candle properties to float arrays float volume_closes = volume_candles.candle_ctf_to_float(Source.Close) // Calculate derived values float volume_ema = volume_candles.ctf_ema(14) // Create custom visualization volume_candles.draw_ctf_candles_time() volume_ema.draw_ctf_line_time(line_color = color.orange) 3. Creating Hybrid Timeframe Analysis One powerful application is comparing indicators across multiple timeframes: // Standard chart timeframe RSI float chart_rsi = ta.rsi(close, 14) // Custom 5-second timeframe RSI CandleCTF ctf_candles = ctf_candles_array( source = close, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 5 ) float fast_rsi_array = ctf_candles.candle_ctf_to_float(Source.Close) float fast_rsi = fast_rsi_array.first() // Generate signals based on divergence between timeframes bool entry_signal = chart_rsi < 30 and fast_rsi > fast_rsi_array.get(1) Final Notes This library represents an advanced implementation of real-time data processing within Pine Script's constraints. By creating a reactive programming framework for handling continuous data streams, it enables sophisticated analysis typically only available in dedicated trading platforms. The design principles employed—including state management, temporal processing, and object-oriented architecture—can serve as patterns for other advanced Pine Script development beyond this specific application. ------------------------ Library "real_time_candles" A comprehensive library for creating real-time candles with customizable timeframes and sampling methods. Supports both chart-time and custom-time candles with options for candlestick and Heikin-Ashi visualization. Allows for tick-based or time-based sampling with moving average overlay capabilities. get_tick(source, na_replace)   Captures the current price as a tick data point   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      na_replace (float) : Optional - Value to use when source is na   Returns: TickData structure containing price, time since last update, and sequential index candle_array(source, candle_type, sync_start, bullish_color, bearish_color)   Creates an array of candles based on price updates   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)      sync_start (simple bool) : Optional - Whether to synchronize with the start of a new bar      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: Array of Candle objects ordered with most recent at index 0 candle_series(source, candle_type, wait_for_sync, bullish_color, bearish_color)   Provides a single candle based on the latest price data   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)      wait_for_sync (simple bool) : Optional - Whether to wait for a new bar before starting      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: A single Candle object representing the current state candle_tuple(source, candle_type, wait_for_sync, bullish_color, bearish_color)   Provides candle data as a tuple of OHLC values   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)      wait_for_sync (simple bool) : Optional - Whether to wait for a new bar before starting      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: Tuple representing current candle values method source(self, source, na_replace)   Extracts a specific price component from a Candle   Namespace types: Candle   Parameters:      self (Candle)      source (series Source) : Type of price data to extract (Open, High, Low, Close, or composite values)      na_replace (float) : Optional - Value to use when source value is na   Returns: The requested price value from the candle method source(self, source)   Extracts a specific price component from a CandleCTF   Namespace types: CandleCTF   Parameters:      self (CandleCTF)      source (simple Source) : Type of price data to extract (Open, High, Low, Close, or composite values)   Returns: The requested price value from the candle as a varip method candle_ctf_to_float(self, source)   Converts a specific price component from each CandleCTF to a float array   Namespace types: array   Parameters:      self (array)      source (simple Source) : Optional - Type of price data to extract (defaults to Close)   Returns: Array of float values extracted from the candles, ordered with most recent at index 0 method ctf_ema(self, ema_period)   Calculates an Exponential Moving Average for a CandleCTF array   Namespace types: array   Parameters:      self (array)      ema_period (simple float) : Period for the EMA calculation   Returns: Array of float values representing the EMA of the candle data, ordered with most recent at index 0 method draw_ctf_candles_time(self, sample_type, number_of_ticks, number_of_seconds, timezone)   Renders custom timeframe candles using bar time coordinates   Namespace types: array   Parameters:      self (array)      sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks), used for tooltips      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks), used for tooltips      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time), used for tooltips      timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12), used for tooltips   Returns: void - Renders candles on the chart using time-based x-coordinates method draw_ctf_candles_index(self, sample_type, number_of_ticks, number_of_seconds, timezone)   Renders custom timeframe candles using bar index coordinates   Namespace types: array   Parameters:      self (array)      sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks), used for tooltips      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks), used for tooltips      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time), used for tooltips      timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12), used for tooltips   Returns: void - Renders candles on the chart using index-based x-coordinates method draw_ctf_line_time(self, source, line_size, line_color)   Renders a line representing a price component from the candles using time coordinates   Namespace types: array   Parameters:      self (array)      source (simple Source) : Optional - Type of price data to extract (defaults to Close)      line_size (simple int) : Optional - Width of the line      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using time-based x-coordinates method draw_ctf_line_time(self, line_size, line_color)   Renders a line from a varip float array using time coordinates   Namespace types: array   Parameters:      self (array)      line_size (simple int) : Optional - Width of the line, defaults to 2      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using time-based x-coordinates method draw_ctf_line_index(self, source, line_size, line_color)   Renders a line representing a price component from the candles using index coordinates   Namespace types: array   Parameters:      self (array)      source (simple Source) : Optional - Type of price data to extract (defaults to Close)      line_size (simple int) : Optional - Width of the line      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using index-based x-coordinates method draw_ctf_line_index(self, line_size, line_color)   Renders a line from a varip float array using index coordinates   Namespace types: array   Parameters:      self (array)      line_size (simple int) : Optional - Width of the line, defaults to 2      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using index-based x-coordinates plot_ctf_tick_candles(source, candle_type, number_of_ticks, timezone, tied_open, ema_period, bullish_color, bearish_color, line_width, ema_color, use_time_indexing)   Plots tick-based candles with moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_ticks (simple int) : Number of ticks per candle      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      ema_period (simple float) : Period for the exponential moving average      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      line_width (simple int) : Optional - Width of the moving average line, defaults to 2      ema_color (color) : Optional - Color of the moving average line      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart with EMA overlay plot_ctf_tick_candles(source, candle_type, number_of_ticks, timezone, tied_open, bullish_color, bearish_color, use_time_indexing)   Plots tick-based candles without moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_ticks (simple int) : Number of ticks per candle      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart without moving average plot_ctf_time_candles(source, candle_type, number_of_seconds, timezone, tied_open, ema_period, bullish_color, bearish_color, line_width, ema_color, use_time_indexing)   Plots time-based candles with moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_seconds (simple float) : Time duration per candle in seconds      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      ema_period (simple float) : Period for the exponential moving average      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      line_width (simple int) : Optional - Width of the moving average line, defaults to 2      ema_color (color) : Optional - Color of the moving average line      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart with EMA overlay plot_ctf_time_candles(source, candle_type, number_of_seconds, timezone, tied_open, bullish_color, bearish_color, use_time_indexing)   Plots time-based candles without moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_seconds (simple float) : Time duration per candle in seconds      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart without moving average plot_ctf_candles(source, candle_type, sample_type, number_of_ticks, number_of_seconds, timezone, tied_open, ema_period, bullish_color, bearish_color, enable_ema, line_width, ema_color, use_time_indexing)   Unified function for plotting candles with comprehensive options   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Optional - Type of candle chart to display      sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks)      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks)      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time)      timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Optional - Whether to tie open price to close of previous candle      ema_period (simple float) : Optional - Period for the exponential moving average      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      enable_ema (bool) : Optional - Whether to display the EMA overlay      line_width (simple int) : Optional - Width of the moving average line, defaults to 2      ema_color (color) : Optional - Color of the moving average line      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart with optional EMA overlay ctf_candles_array(source, candle_type, sample_type, number_of_ticks, number_of_seconds, tied_open, bullish_color, bearish_color)   Creates an array of custom timeframe candles without rendering them   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to create (candlestick or Heikin-Ashi)      sample_type (simple SampleType) : Method for sampling data (Time or Ticks)      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks)      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time)      tied_open (simple bool) : Optional - Whether to tie open price to close of previous candle      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: Array of CandleCTF objects ordered with most recent at index 0 Candle   Structure representing a complete candle with price data and display properties   Fields:      Open (series float) : Opening price of the candle      High (series float) : Highest price of the candle      Low (series float) : Lowest price of the candle      Close (series float) : Closing price of the candle      polarity (series bool) : Boolean indicating if candle is bullish (true) or bearish (false)      series_index (series int) : Sequential index identifying the candle in the series      candle_color (series color) : Color to use when rendering the candle      ready (series bool) : Boolean indicating if candle data is valid and ready for use TickData   Structure for storing individual price updates   Fields:      price (series float) : The price value at this tick      time_delta (series float) : Time elapsed since the previous tick in milliseconds      series_index (series int) : Sequential index identifying this tick CandleCTF   Structure representing a custom timeframe candle with additional time metadata   Fields:      Open (series float) : Opening price of the candle      High (series float) : Highest price of the candle      Low (series float) : Lowest price of the candle      Close (series float) : Closing price of the candle      polarity (series bool) : Boolean indicating if candle is bullish (true) or bearish (false)      series_index (series int) : Sequential index identifying the candle in the series      open_time (series int) : Timestamp marking when the candle was opened (in Unix time)      time_delta (series float) : Duration of the candle in milliseconds      candle_color (series color) : Color to use when rendering the candle
Perpustakaan Pine Script®
oleh The_Peaceful_Lizard
26
The Investment Clock Orbital GraphThe Investment Clock Orbital Graph is an advanced visualization tool designed to help traders and investors track economic cycles using a dynamic scatter plot of GDP growth vs. CPI inflation rates. This indicator is a fusion of two powerful TradingView indicators: LuxAlgo ’s Relative Strength Scatter Plot – A robust scatter plot for tracking relative strength. The Investment Clock Indicator – A cycle-based approach to market rotation. This indicator contains more information regarding The Investment Clock. By combining these approaches, the Investment Clock Orbital Graph enables traders to visualize economic momentum and inflationary trends in a unique, orbital-style scatter plot. Key Features & Improvements Orbital Graph Representation – Displays GDP growth and CPI inflation as a dynamic, evolving scatter plot, showing how the economy moves through different phases. Quadrant-Based Market Regimes – Identifies four key economic phases: 1)🔥 Overheating (High Growth, High Inflation) 2)📉 Stagflation (Low Growth, High Inflation) 3)🤒 Recovery (High Growth, Low Inflation) 4)🎈 Reflation (Low Growth, Low Inflation) Data-Driven Analysis – Utilizes FRED (Federal Reserve Economic Data) for accurate real-world GDP & CPI data. Trailing Path of Economic Evolution – Tracks historical economic cycles over time to show momentum and cyclical movements. Customizable Parameters – Set sustainable GDP growth and inflation thresholds, adjust trail length, and fine-tune scatter plot resolution. Auto-Labeled Quadrants & Revised Accurate Market Guidance – Each quadrant includes newly updated tooltips and annotations (like ETF suggestions) to help traders make informed decisions. Live Macro Forecasting Tool – Helps traders anticipate future market conditions, rate hikes/cuts, and sector rotations. How to Use for Trading Decisions The Investment Clock Orbital Graph helps traders and macro investors by identifying market phases and providing insights into asset class performance during different economic conditions. 📌 Step 1: Identify the Current Quadrant Locate the most recent point on the orbital graph to see if the economy is in Overheating, Stagflation, Recovery, or Reflation. 📌 Step 2: Forecast Market Trends The trajectory of the points can predict upcoming economic shifts: Overheating → Stagflation ➡️ Expect economic slowdowns, bearish stock markets. Stagflation → Reflation ➡️ Interest rate cuts likely, bonds and defensive stocks perform well. Reflation → Recovery ➡️ Risk-on rally, technology and cyclicals perform best. Recovery → Overheating ➡️ Commodities surge, inflation rises, and central banks intervene. 📌 Step 3: Align Trading & Investing Strategies 🔥 Overheating – Favor commodities & energy (Oil, Industrial Stocks, Materials). 📉 Stagflation – Favor defensive assets (Cash, Utilities, Healthcare). 🤒 Recovery – Favor growth stocks (Technology, Consumer Discretionary). 🎈 Reflation – Favor bonds, value stocks, and financials. 📌 Step 4: Monitor Trends Over Time The indicator visualizes economic movement over multiple months, allowing traders to confirm long-term trends vs. short-term noise. The Investment Clock Orbital Graph is an essential macro trading tool, providing a real-time visualization of economic conditions. By tracking GDP growth vs. CPI inflation, traders and investors can align their portfolios with major macroeconomic shifts, predict sector rotations, and anticipate central bank policy changes.
Penunjuk Pine Script®
oleh BarefootJoey
6
TimezoneLibrary with pre-defined timezone enums that can be used to request a timezone input from the user. The library provides a `tostring()` function to convert enum values to a valid string that can be used as a `timezone` parameter in pine script built-in functions. The library also includes a bonus function to get a formatted UTC offset from a UNIX timestamp. The timezone enums in this library were compiled by referencing the available timezone options from TradingView chart settings as well as multiple Wikipedia articles relating to lists of time zones. Some enums from this library are used to retrieve an IANA time zone identifier, while other enums only use UTC/GMT offset notation. It is important to note that the Pine Script User Manual recommends using IANA notation in most cases. HOW TO USE This library is intended to be used by Pine Coders who wish to provide their users with a simple way to input a timezone. Using this library is as easy as 1, 2, 3: Step 1 Import the library into your script. Replace with the latest available version number for this library. //@version=6 indicator("Example") import n00btraders/Timezone/ as tz Step 2 Select one of the available enums from the library and use it as an input. Tip: view the library source code and scroll through the enums at the top to find the best choice for your script. timezoneInput = input.enum(tz.TimezoneID.EXCHANGE, "Timezone") Step 3 Convert the user-selected input into a valid string that can be used in one of the pine script built-in functions that have a `timezone` parameter. string timezone = tz.tostring(timezoneInput) EXPORTED FUNCTIONS There are multiple 𝚝𝚘𝚜𝚝𝚛𝚒𝚗𝚐() functions in this library: one for each timezone enum. The function takes a single parameter: any enum field from one of the available timezone enums that are exported by this library. Depending on the selected enum, the function will return a time zone string in either UTC/GMT notation or IANA notation. Note: to avoid confusion with the built-in `str.tostring()` function, it is recommended to use this library's `tostring()` as a method rather than a function: string timezone = timezoneInput.tostring() offset(timestamp, format, timezone, prefix, colon)   Formats the time offset from a UNIX timestamp represented in a specified timezone.   Namespace types: series OffsetFormat   Parameters:      timestamp (int) : (series int) A UNIX time.      format (series OffsetFormat) : (series OffsetFormat) A time offset format.      timezone (string) : (series string) A UTC/GMT offset or IANA time zone identifier.      prefix (string) : (series string) Optional 'UTC' or 'GMT' prefix for the result.      colon (bool) : (series bool) Optional usage of colon separator.   Returns: Time zone offset using the selected format. The 𝚘𝚏𝚏𝚜𝚎𝚝() function is provided as a convenient alternative to manually using `str.format_time()` and then manipulating the result. The OffsetFormat enum is used to decide the format of the result from the `offset()` function. The library source code contains comments above this enum declaration that describe how each enum field will modify a time offset. Tip: hover over the `offset()` function call in the Pine Editor to display a pop-up containing: Function description Detailed parameter list, including default values Example function calls Example outputs for different OffsetFormat.* enum values NOTES At the time of this publication, Pine cannot be used to access a chart's selected time zone. Therefore, the main benefit of this library is to provide a quick and easy way to create a pine script input for a time zone (most commonly, the same time zone selected in the user's chart settings). At the time of the creation of this library, there are 95 Time Zones made available in the TradingView chart settings. If any changes are made to the time zone settings, this library will be updated to match the new changes. All time zone enums (and their individual fields) in this library were manually verified and tested to ensure correctness. An example usage of this library is included at the bottom of the source code. Credits to HoanGhetti for providing a nice Markdown resource which I referenced to be able to create a formatted informational pop-up for this library's `offset()` function.
Perpustakaan Pine Script®
oleh n00btraders
4
*Auto Backtest & Optimize EngineFull-featured Engine for Automatic Backtesting and parameter optimization. Allows you to test millions of different combinations of stop-loss and take profit parameters, including on any connected indicators. ⭕️ Key Futures Quickly identify the optimal parameters for your strategy. Automatically generate and test thousands of parameter combinations. A simple Genetic Algorithm for result selection. Saves time on manual testing of multiple parameters. Detailed analysis, sorting, filtering and statistics of results. Detailed control panel with many tooltips. Display of key metrics: Profit, Win Rate, etc.. Comprehensive Strategy Score calculation. In-depth analysis of the performance of different types of stop-losses. Possibility to use to calculate the best Stop-Take parameters for your position. Ability to test your own functions and signals. Customizable visualization of results. Flexible Stop-Loss Settings: • Auto ━ Allows you to test all types of Stop Losses at once(listed below). • S.VOLATY ━ Static stop based on volatility (Fixed, ATR, STDEV). • Trailing ━ Classic trailing stop following the price. • Fast Trail ━ Accelerated trailing stop that reacts faster to price movements. • Volatility ━ Dynamic stop based on volatility indicators. • Chandelier ━ Stop based on price extremes. • Activator ━ Dynamic stop based on SAR. • MA ━ Stop based on moving averages (9 different types). • SAR ━ Parabolic SAR (Stop and Reverse). Advanced Take-Profit Options: • R:R: Risk/Reward ━ sets TP based on SL size. • T.VOLATY ━ Calculation based on volatility indicators (Fixed, ATR, STDEV). Testing Modes: • Stops ━ Cyclical stop-loss testing • Pivot Point Example ━ Example of using pivot points • External Example ━ Built-in example how test functions with different parameters • External Signal ━ Using external signals ⭕️ Usage ━ First Steps: When opening, select any point on the chart. It will not affect anything until you turn on Manual Start mode (more on this below). The chart will immediately show the best results of the default Auto mode. You can switch Part's to try to find even better results in the table. Now you can display any result from the table on the chart by entering its ID in the settings. Repeat steps 3-4 until you determine which type of Stop Loss you like best. Then set it in the settings instead of Auto mode. * Example: I flipped through 14 parts before I liked the first result and entered its ID so I could visually evaluate it on the chart. Then select the stop loss type, choose it in place of Auto mode and repeat steps 3-4 or immediately follow the recommendations of the algorithm. Now the Genetic Algorithm at the bottom right will prompt you to enter the Parameters you need to search for and select even better results. Parameters must be entered All at once before they are updated. Enter recommendations strictly in fields with the same names. Repeat steps 5-6 until there are approximately 10 Part's left or as you like. And after that, easily pour through the remaining Parts and select the best parameters. ━ Example of the finished result. ━ Example of use with Takes You can also test at the same time along with Take Profit. In this example, I simply enabled Risk/Reward mode and immediately specified in the TP field Maximum RR, Minimum RR and Step. So in this example I can test (3-1) / 0.1 = 20 Takes of different sizes. There are additional tips in the settings. ━ * Soon you will start to understand how the system works and things will become much easier. * If something doesn't work, just reset the engine settings and start over again. * Use the tips I have left in the settings and on the Panel. ━ Details: Sort ━ Sorting results by Score, Profit, Trades, etc.. Filter ━ Filtring results by Score, Profit, Trades, etc.. Trade Type ━ Ability to disable Long\Short but only from statistics. BackWin ━ Backtest Window Number of Candle the script can test. Manual Start ━ Enabling it will allow you to call a Stop from a selected point. which you selected when you started the engine. * If you have a real open position then this mode can help to save good Stop\Take for it. 1 - 9 Сheckboxs ━ Allow you to disable any stop from Auto mode. Ex Source - Allow you to test Stops/Takes from connected indicators. Connection guide: //@version=6 indicator("My script") rsi = ta.rsi(close, 14) buy = not na(rsi) and ta.crossover (rsi, 40) // OS = 40 sell = not na(rsi) and ta.crossunder(rsi, 60) // OB = 60 Signal = buy ? +1 : sell ? -1 : 0 plot(Signal, "🔌Connector🔌", display = display.none) * Format the signal for your indicator in a similar style and then select it in Ex Source. ⭕️ How it Works Hypothesis of Uniform Distribution of Rare Elements After Mixing. 'This hypothesis states that if an array of N elements contains K valid elements, then after mixing, these valid elements will be approximately uniformly distributed.' 'This means that in a random sample of k elements, the proportion of valid elements should closely match their proportion in the original array, with some random variation.' 'According to the central limit theorem, repeated sampling will result in an average count of valid elements following a normal distribution.' 'This supports the assumption that the valid elements are evenly spread across the array.' 'To test this hypothesis, we can conduct an experiment:' 'Create an array of 1,000,000 elements.' 'Select 1,000 random elements (1%) for validation.' 'Shuffle the array and divide it into groups of 1,000 elements.' 'If the hypothesis holds, each group should contain, on average, 1~ valid element, with minor variations.' * I'd like to attach more details to My hypothesis but it won't be very relevant here. Since this is a whole separate topic, I will leave the minimum part for understanding the engine. Practical Application To apply this hypothesis, I needed a way to generate and thoroughly mix numerous possible combinations. Within Pine, generating over 100,000 combinations presents significant challenges, and storing millions of combinations requires excessive resources. I developed an efficient mechanism that generates combinations in random order to address these limitations. While conventional methods often produce duplicates or require generating a complete list first, my approach guarantees that the first 10% of possible combinations are both unique and well-distributed. Based on my hypothesis, this sampling is sufficient to determine optimal testing parameters. Most generators and randomizers fail to accommodate both my hypothesis and Pine's constraints. My solution utilizes a simple Linear Congruential Generator (LCG) for pseudo-randomization, enhanced with prime numbers to increase entropy during generation. I pre-generate the entire parameter range and then apply systematic mixing. This approach, combined with a hybrid combinatorial array-filling technique with linear distribution, delivers excellent generation quality. My engine can efficiently generate and verify 300 unique combinations per batch. Based on the above, to determine optimal values, only 10-20 Parts need to be manually scrolled through to find the appropriate value or range, eliminating the need for exhaustive testing of millions of parameter combinations. For the Score statistic I applied all the same, generated a range of Weights, distributed them randomly for each type of statistic to avoid manual distribution. Score ━ based on Trade, Profit, WinRate, Profit Factor, Drawdown, Sharpe & Sortino & Omega & Calmar Ratio. ⭕️ Notes For attentive users, a little tricks :) To save time, switch parts every 3 seconds without waiting for it to load. After 10-20 parts, stop and wait for loading. If the pause is correct, you can switch between the rest of the parts without loading, as they will be cached. This used to work without having to wait for a pause, but now it does slower. This will save a lot of time if you are going to do a deeper backtest. Sometimes you'll get the error “The scripts take too long to execute.” For a quick fix you just need to switch the TF or Ticker back and forth and most likely everything will load. The error appears because of problems on the side of the site because the engine is very heavy. It can also appear if you set too long a period for testing in BackWin or use a heavy indicator for testing. Manual Start - Allow you to Start you Result from any point. Which in turn can help you choose a good stop-stick for your real position. * It took me half a year from idea to current realization. This seems to be one of the few ways to build something automatic in backtest format and in this particular Pine environment. There are already better projects in other languages, and they are created much easier and faster because there are no limitations except for personal PC. If you see solutions to improve this system I would be glad if you share the code. At the moment I am tired and will continue him not soon. Also You can use my previosly big Backtest project with more manual settings(updated soon)
Penunjuk Pine Script®
oleh HALDRO
13
HTF Candle Volume Thermometer [ChartPrime]The HTF Candle Volume Thermometer is a powerful volume heatmap tool that visualizes higher timeframe candle volume distributions directly on the chart. It helps traders identify key price levels where liquidity is concentrated, allowing for more informed trading decisions. ⯁ KEY FEATURES Higher Timeframe Volume Mapping Uses higher timeframe (HTF) candles to create a heatmap of volume distribution within each candle. Dynamic Volume Heatmap Colors each HTF candle background green for bullish and red for bearish, with a gradient heat overlay highlighting volume concentration. Max Volume Point Identification Marks the level within each HTF candle where the highest volume was recorded, using red for the most significant volume area. Fully Customizable Display Users can adjust the HTF timeframe, color settings, and resolution to tailor the indicator to their trading preferences. Segmented Volume Distribution Each HTF candle is divided into smaller levels, allowing traders to see volume changes within the range of each candle. Key Level Detection Max volume points often act as key support and resistance levels where price is likely to react, helping traders refine their strategies. ⯁ HOW TO USE Identify Liquidity Zones Use the max volume levels to determine areas where price is likely to find support or resistance. Assess Trend Strength Compare volume distribution between bullish and bearish HTF candles to gauge market momentum. Optimize Trade Entries & Exits Look for price reactions at high-volume areas to refine stop-loss and take-profit levels. Adjust Heatmap Resolution Customize the resolution setting to get a more detailed or broader view of volume segmentation within HTF candles. ⯁ CONCLUSION The HTF Candle Volume Thermometer is a must-have tool for traders who want to integrate volume analysis with higher timeframe structures. By visualizing volume heatmaps within each HTF candle, this indicator helps traders pinpoint critical liquidity zones and key price levels.
Penunjuk Pine Script®
oleh ChartPrime
19