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Stochastic Adaptive %D [LuxAlgo]The Stochastic Adaptive %D Difference Oscillator indicator provides a sophisticated alternative to classic momentum oscillators, prioritizing a balance between high-grade smoothing and adaptive reactivity. By calculating the divergence between a pre-smoothed Stochastic %D and a specialized Adaptive %D signal line, this tool highlights momentum shifts with significantly reduced noise while maintaining the ability to react quickly to trend accelerations. 🔶 USAGE This indicator is designed for traders who require the clarity of a smooth oscillator without the lag typically associated with heavy filtering. The "Difference Oscillator" component serves as the primary visual guide, representing the spread between momentum and its adaptive average. 🔹 Signal Generation The indicator features three main visual components: Standard %D Line: A dual-smoothed stochastic calculation that acts as the core momentum measure, plotted as a dotted line. Adaptive %D Line: A reactive signal line that adjusts its smoothing alpha based on market intensity, plotted as a dashed line. Difference Oscillator: A histogram-style fill centered at the 50 midline. This represents the momentum "delta"—when price velocity accelerates away from the adaptive baseline, the oscillator expands, providing earlier warning of trend strength or exhaustion. When the Standard %D leads the Adaptive %D, the oscillator fills green, suggesting bullish momentum. When it lags, it fills red, suggesting bearish momentum. The expansion and contraction of this fill help identify whether a trend is gaining or losing "torque" relative to its adaptive mean. 🔶 DETAILS The script achieves its unique balance through a specialized architectural approach that focuses on conserving smoothness while remaining reactive to volatile shifts. 🔹 Smoothness Conservation To eliminate the "jaggedness" often found in standard Stochastics, the indicator applies a pre-smoothing filter (SMA) to the High, Low, and Close sources. This ensures that the foundation of the calculation is filtered for noise before the Stochastic formula is even applied, resulting in much cleaner oscillations. 🔹 Adaptive Reactivity The Adaptive %D signal line employs a variable alpha smoothing mechanism. The "speed" of the signal line is dynamically linked to the position of the %D relative to the 50 midline. Trend Extremes: As momentum reaches overbought (80) or oversold (20) zones, the alpha increases. This allows the signal line to track the %D more aggressively, capturing the peak of the move. Mean Reversion/Ranging: Near the 50 midline, the alpha decreases, making the signal line more "stubborn" and less prone to whipsaws during low-conviction market phases. 🔶 SETTINGS 🔹 Stochastic Settings Stochastic Length: The lookback period used for the raw stochastic range calculation. %K Smoothing: Determines the internal smoothing applied to produce the standard %D line. Price Pre-Smoothing: The length of the SMA applied to price sources before the oscillator is calculated to ensure foundational smoothness. 🔹 Adaptive Smoothing Settings Attenuation Factor: A sensitivity multiplier that controls the reactivity of the Adaptive %D. Higher values increase the "inertia" of the adaptive calculation, making the signal line more conservative. 🔹 Colors Standard %D Color: Sets the color for the core momentum dotted line. Adaptive %D Color: Sets the color for the reactive signal dashed line. Bullish/Bearish Color: Defines the colors used for the Difference Oscillator's gradient fill.
Индикатор Pine Script®
от LuxAlgo
Auto Trend Drawing [LuxAlgo]The Auto Trend Drawing indicator summarizes price action into a smooth, continuous curve that highlights the primary market direction and structure. It is mostly a fun and silly experiment in reproducing users attempts at drawing trends on the chart using the brush tool. This indicator is subject to repainting and is displayed retrospectively based on the most recent price data. 🔶 USAGE The Auto Trend Drawing tool provides a clean, visual representation of the current market "flow" by stripping away minor price noise and focusing on significant pivot points. Traders can use this indicator to quickly identify the prevailing trend, potential trend reversals, and the overall rhythm of the market. Unlike standard trendlines that connect two specific points, this indicator creates a dynamic curve that adapts to the most significant price movements within a user-defined window. 🔹 Trend Identification Bullish Flow: When the curve is sloping upward and the price generally stays above or near the curve's trajectory. Bearish Flow: When the curve is sloping downward and the price remains below or near the curve's trajectory. Consolidation: When the curve flattens out, indicating a lack of clear directional momentum. 🔶 DETAILS The script employs a multi-step process to generate the trend curve: Normalization: Price data is normalized using the Average True Range (ATR). This ensures that the simplification process remains consistent across different assets and timeframes regardless of their volatility. Ramer-Douglas-Peucker (RDP) Algorithm: This algorithm identifies the most "important" anchor points in the price series by recursively simplifying the path. It removes points that deviate less than a certain threshold (set by the Simplification Multiplier) from a straight line. Catmull-Rom Splines: Once the key anchor points are identified, the script uses Catmull-Rom spline interpolation to connect them. This creates a smooth, aesthetic curve that passes through every identified anchor point, providing a more "organic" look than jagged lines. 🔶 SETTINGS Window Size: Determines the number of recent bars the indicator analyzes to build the curve. Simplification Multiplier: Controls the sensitivity of the trend detection. Higher values filter out more noise, resulting in a smoother, more "macro" curve. Lower values allow the curve to follow price more closely. ATR Length: The period used for the ATR normalization process. Curve Tension: Adjusts how tightly the spline curve follows the anchor points. A value of 0.5 is centripetal, while higher or lower values change the curvature between points. Curve Resolution: Sets the number of sub-points calculated between each anchor. Higher values result in a smoother-looking line. Line Color: Changes the color of the trend curve. Line Width: Adjusts the thickness of the displayed curve.
Индикатор Pine Script®
от LuxAlgo
Phase Regression Oscillator [LuxAlgo]The Phase Regression Oscillator indicator is a cycle analysis tool that uses an analytical linear least squares fit to extract the instantaneous phase of a specified frequency relative to price action. By solving a system of normal equations on every bar, the script determines the "best fit" sine wave for a rolling window, providing a high-resolution view of the market's cyclical state. 🔶 USAGE The indicator is designed to identify the current stage of a market cycle by fitting a theoretical sine wave to the most recent price data. Unlike traditional oscillators that rely on simple averages or price extremes, this tool uses regression to find the mathematical phase of the trend. 🔹 Interpreting the Components Sine Oscillator (Solid Line): This is the primary signal. When the line is positive and green, the cycle is in its ascending or peaking phase. When negative and red, the cycle is in its descending or troughing phase. Cosine Component (Dotted Line): This represents the "quadrature" or in-phase component. In cycle analysis, the lead/lag relationship between the sine and cosine lines can help traders identify when a cycle is losing momentum or reaching a turning point. Overbought/Oversold Levels: Dashed horizontal lines indicate levels where the cycle fit has reached extreme mathematical synchronization, often preceding a phase shift or reversal. 🔶 DETAILS The script employs a Linear Least Squares approach to model price as: y = a·sin(ωx) + b·cos(ωx) + c Instead of using a computationally expensive "grid search" to guess the phase, the indicator uses a 3x3 matrix solver (Cramer's Rule) to analytically find the coefficients a , b , and c that minimize the squared error. The phase (φ) is then extracted using the arctangent of the coefficients. By defining the period as a percentage of the window size, the tool ensures that the regression always looks for a cycle length that is proportional to the observed lookback, making it adaptable across different timeframes and asset classes. 🔶 SETTINGS 🔹 Main Settings Window Size (N): The rolling lookback period (in bars) used to calculate the regression fit. Period % of Window: Defines the fixed period (P) of the sine wave as a percentage of the Window Size. A value of 100% means the regression looks for a cycle equal to the window length. 🔹 Levels Overbought Level: The upper threshold used to identify cycle peaks. Oversold Level: The lower threshold used to identify cycle troughs. 🔹 Style Bullish Color: Color for the oscillator when positive, the oversold level, and the upward gradient fill. Bearish Color: Color for the oscillator when negative, the overbought level, and the downward gradient fill. Cosine Color: The color of the dotted quadrature reference line. Zero Line Color: The color of the center baseline.
Индикатор Pine Script®
от LuxAlgo
Harmonic Resonance Oscillator [LuxAlgo]The Harmonic Resonance Oscillator indicator provides a specialized oscillator that decomposes price action into multiple harmonic cycles to identify confluence in market rotations. By isolating short, medium, and long-term frequencies, the tool aims to pinpoint exhausted price movements and potential reversal zones through the concept of cyclic resonance. 🔶 USAGE The Harmonic Resonance Oscillator can be used to identify market turning points by observing when the aggregate cycle resonance reaches extreme levels. Unlike standard oscillators that rely on a single lookback period, this tool aggregates multiple filtered cycles to provide a more robust view of market momentum and exhaustion. When the oscillator enters the dynamic overbought (upper) or oversold (lower) zones, it indicates that the various price cycles are aligning at an extreme, often preceding a corrective move or a trend reversal. 🔹 Harmonic Multipliers The script uses a Reference Period combined with three multipliers to define the cycles: The Short Multiplier captures fast, intraday-style fluctuations. The Medium Multiplier focuses on the primary trend rhythm. The Long Multiplier tracks broader market cycles. When all three cycles reach peak or trough levels simultaneously, the oscillator displays a "resonance" peak, which is highlighted by background coloring if the signal exceeds the dynamic thresholds. 🔶 DETAILS The indicator is built upon three primary technical pillars: 🔹 Ehlers' Bandpass Filter At its core, the indicator uses John Ehlers' Cycle decomposition method. The bandpass filter is designed to pass only price components within a specific frequency range while attenuating everything else. This allows the script to "tune in" to specific market rhythms without the lag typically associated with moving averages. 🔹 Normalization & Resonance Each isolated cycle is normalized onto a scale of 0 to 100 using a specific lookback length. The final "Harmonic Resonance" signal is the arithmetic mean of these three normalized cycles. A value of 50 represents a neutral state, while values approaching 0 or 100 represent extreme harmonic alignment. 🔹 Dynamic Volatility-Adjusted Zones The Overbought and Oversold thresholds are not static. They adjust dynamically based on the standard deviation of the resonance signal. During periods of high cyclic volatility, the bands expand to require stronger confluence for a signal; during low volatility, the bands contract to stay sensitive to smaller market rotations. 🔶 SETTINGS 🔹 Harmonic Settings Reference Period: The base period used to calculate the harmonic cycles. Short Multiplier: Multiplier applied to the reference period for the short-term cycle. Medium Multiplier: Multiplier applied to the reference period for the medium-term cycle. Long Multiplier: Multiplier applied to the reference period for the long-term cycle. Bandwidth: Controls the "tightness" of the bandpass filter. Lower values isolate specific cycles more precisely. 🔹 Normalization Settings Normalization Lookback: The window used to scale the cycles and calculate the volatility of the resonance signal. 🔹 Overbought / Oversold Control Overbought Threshold: The base level for the upper dynamic zone (default 80). Oversold Threshold: The base level for the lower dynamic zone (default 20). 🔹 Style Bullish Color: Color of the oscillator when above the 50 midpoint. Bearish Color: Color of the oscillator when below the 50 midpoint. Overbought Color: Color of the upper dynamic threshold. Oversold Color: Color of the lower dynamic threshold. Show Background Highlighting: Toggles the background coloring when resonance reaches extreme levels.
Индикатор Pine Script®
от LuxAlgo
3
Filter Volume1. Indicator Name Filter Volume 2. One-line Introduction A regression-based trend filter that quantifies and visualizes market direction and strength using price behavior. 3. Overall Description Filter Volume+ is a trend-detection indicator that uses linear regression to evaluate the dominant direction of price movement over a given period. It compares historical regression values to determine whether the market is in a bullish, bearish, or neutral state. The indicator applies a percentage threshold to filter out weak or indecisive trends, highlighting only significant movements. Each trend state is visualized through distinct colors: bullish (greenish), bearish (reddish), and neutral (gray), with intensity reflecting trend strength. To reduce noise and create smooth visual signals, a three-step smoothing process is applied to the raw trend intensity. Users can customize the regression source, lookback period, and sensitivity, allowing the indicator to adapt to various assets and timeframes. This tool is especially useful in filtering entry signals based on clear directional bias, making it suitable for trend-following or confirmation strategies. 4. Key Benefits (Title + Description) ✅ Quantified Trend Strength Only displays trend signals when a statistically significant direction is detected using linear regression comparisons. ✅ Visual Clarity with Color Coding Each market state (bullish, bearish, neutral) is represented with distinct colors and transparency, enabling fast interpretation. ✅ Custom Regression Source Users can define the data input (e.g., close, open, indicator output) for regression calculation, increasing strategic flexibility. ✅ Multi-Level Smoothing Applies three layers of smoothing (via moving averages) to eliminate noise and produce a stable, flowing trend curve. ✅ Area Fill Visualization Plots a colored band between the trend value and zero-line, helping users quickly gauge the market's dominant force. ✅ Adjustable Sensitivity Settings Includes tolerance and lookback controls, allowing traders to fine-tune how reactive or conservative the trend detection should be. 5. Indicator User Guide 📌 Basic Concept Filter Volume+ assesses the direction of price by comparing regression values over a selected period. If the percentage of upward comparisons exceeds a threshold, a bullish state is shown; if downward comparisons dominate, it shows a bearish state. ⚙️ Settings Overview Lookback Period (n): The number of bars to compare for trend analysis Range Tolerance (%): Minimum threshold for declaring a strong trend Regression Source: The data used for regression (e.g., close, open) Linear Regression Length: Number of bars used to compute each regression value Bull/Bear Color: Custom colors for bullish and bearish trends 📈 Example Timing When the trend line stays above zero and the green color intensity increases → trend gaining strength After a neutral phase (gray), the color shifts quickly to greenish → early trend reversal 📉 Example Timing When the trend line stays below zero with deepening red color → strong bearish continuation Sudden change from bullish to bearish color with rising intensity 🧪 Recommended Use Use as a trend confirmation filter alongside entry/exit strategies Ideal for swing or position trades in trending markets Combine with oscillators like RSI or MACD for improved signal validation 🔒 Cautions In ranging (sideways) markets, the color may change frequently – avoid relying solely on this indicator in those zones. Low-intensity colors (faded) suggest weak trends – better to stay on the sidelines. A short lookback period may cause over-sensitivity and false signals. When using non-price regression sources, expect the indicator to behave differently – test before deploying. +++
Индикатор Pine Script®
от c9indicator
IIR One-Pole Price Filter [BackQuant]IIR One-Pole Price Filter A lightweight, mathematically grounded smoothing filter derived from signal processing theory, designed to denoise price data while maintaining minimal lag. It provides a refined alternative to the classic Exponential Moving Average (EMA) by directly controlling the filter’s responsiveness through three interchangeable alpha modes: EMA-Length , Half-Life , and Cutoff-Period . Concept overview An IIR (Infinite Impulse Response) filter is a type of recursive filter that blends current and past input values to produce a smooth, continuous output. The "one-pole" version is its simplest form, consisting of a single recursive feedback loop that exponentially decays older price information. This makes it both memory-efficient and responsive , ideal for traders seeking a precise balance between noise reduction and reaction speed. Unlike standard moving averages, the IIR filter can be tuned in physically meaningful terms (such as half-life or cutoff frequency) rather than just arbitrary periods. This allows the trader to think about responsiveness in the same way an engineer or physicist would interpret signal smoothing. Why use it Filters out market noise without introducing heavy lag like higher-order smoothers. Adapts to various trading speeds and time horizons by changing how alpha (responsiveness) is parameterized. Provides consistent and mathematically interpretable control of smoothing, suitable for both discretionary and algorithmic systems. Can serve as the core component in adaptive strategies, volatility normalization, or trend extraction pipelines. Alpha Modes Explained EMA-Length : Classic exponential decay with alpha = 2 / (L + 1). Equivalent to a standard EMA but exposed directly for fine control. Half-Life : Defines the number of bars it takes for the influence of a price input to decay by half. More intuitive for time-domain analysis. Cutoff-Period : Inspired by analog filter theory, defines the cutoff frequency (in bars) beyond which price oscillations are heavily attenuated. Lower periods = faster response. Formula in plain terms Each bar updates as: yₜ = yₜ₋₁ + alpha × (priceₜ − yₜ₋₁) Where alpha is the smoothing coefficient derived from your chosen mode. Smaller alpha → smoother but slower response. Larger alpha → faster but noisier response. Practical application Trend detection : When the filter line rises, momentum is positive; when it falls, momentum is negative. Signal timing : Use the crossover of the filter vs its previous value (or price) as an entry/exit condition. Noise suppression : Apply on volatile assets or lower timeframes to remove flicker from raw price data. Foundation for advanced filters : The one-pole IIR serves as a building block for multi-pole cascades, adaptive smoothers, and spectral filters. Customization options Alpha Scale : Multiplies the final alpha to fine-tune aggressiveness without changing the mode’s core math. Color Painting : Candles can be painted green/red by trend direction for visual clarity. Line Width & Transparency : Adjust the visual intensity to integrate cleanly with your charting style. Interpretation tips A smooth yet reactive line implies optimal tuning — minimal delay with reduced false flips. A sluggish line suggests alpha is too small (increase responsiveness). A noisy, twitchy line means alpha is too large (increase smoothing). Half-life tuning often feels more natural for aligning filter speed with price cycles or bar duration. Summary The IIR One-Pole Price Filter is a signal smoother that merges simplicity with mathematical rigor. Whether you’re filtering for entry signals, generating trend overlays, or constructing larger multi-stage systems, this filter delivers stability, clarity, and precision control over noise versus lag, an essential tool for any quantitative or systematic trading approach.
Индикатор Pine Script®
от BackQuant
Standardized Cumulative Deltas [LuxAlgo]The Standardized Cumulative Deltas tool allows traders to compare the cumulative standardized open-close difference for up to 10 different tickers, allowing them to visualize the general sentiment for all selected tickers. These results allow the construction of two areas showing the average or extreme bullish and bearish cumulative change for all enabled tickers, providing a summarized view of the overall ticker group sentiment. 🔶 USAGE This tool is meant to give a full picture of the individuals and/or overall selected tickers, and unlike classical indicators, the displayed series of values is not meant to be directly interpreted over time. Given the selected lookback period, a majority of observations being above 0 indicate an overall bullish market for the asset. By default, the auto lookback period feature is enabled, allowing the tool to use all the visible bars for its calculations. Traders can also set the lookback period manually. The above chart uses a fixed lookback period of 500. Up to 10 tickers can be used. While major cryptocurrencies are set by default, the users can set a specific basket of assets, such as US equities, forex pairs, commodities, etc. 🔹 Densities The provided areas, here called densities, can be used to get an overall sentiment of the selected tickers. The upper density (bullish) processes positive deltas, while the lower one (bearish) processes negative ones. Interpretation is subject to the selected "Density Mode". Average: Densities track the average bullish/bearish cumulative deltas for the selected tickers. For example, a more prominent bullish density would indicate that, on average, cumulative deltas were positive across the tickers. Envelope: Densities track the extreme values made by bullish/bearish cumulative deltas for the selected tickers. Here, a more prominent density would indicate more volatile bullish/bearish movements, depending on the density. 🔹 Dashboard The tool features a dashboard with active tickers and their respective colors for traders' convenience. 🔶 DETAILS 🔹 Densities Densities are obtained by applying a forward-backward exponential moving average on the average, or the highest/lowest cumulative series, depending on the selected Density Mode. The resulting densities are smoothed by the "Smoothing" parameter located in the Settings panel, with higher values returning smoother envelopes with less variability. Do note that the smoothing method used here is subject to repainting. 🔶 SETTINGS Lookback: Select the lookback period and enable/disable the Auto Lookback feature Tickers: Enable/disable and select up to 10 tickers and their colors Density Mode: Determine how densities are calculated 🔹 Dashboard Show Dashboard: Enable/disable the dashboard Position: Select the dashboard position Size: Select the dashboard size 🔹 Style Density: Enable/disable the density areas Bullish Density: Select the color of the top density area Bearish Density: Select the color of the bottom density area Smoothing: Select the smoothing constant for the EMA calculation
Индикатор Pine Script®
от LuxAlgo
5
Kalman Filter (Smoothed)The Kalman Filter is a recursive statistical algorithm that smooths noisy price data while adapting dynamically to new information. Unlike simple moving averages or EMAs, it minimizes lag by balancing measurement noise (R) and process noise (Q), giving traders a clean, adaptive estimate of true price action. 🔹 Core Features Real-time recursive estimation Adjustable noise parameters (R = sensitivity to price, Q = smoothness vs. responsiveness) Reduces market noise without heavy lag Overlay on chart for direct comparison with raw price 🔹 Trading Applications Smoother trend visualization compared to traditional MAs Spotting true direction during volatile/sideways markets Filtering out market “whipsaws” for cleaner signals Building blocks for advanced quant/trading models ⚠️ Note: The Kalman Filter is a state-space model; it doesn’t predict future price, but smooths past and present data into a more reliable signal.
Индикатор Pine Script®
от JEDSBN
1
Adaptive Momentum Oscillator [LuxAlgo]The Adaptive Momentum Oscillator tool allows traders to measure the current relative momentum over a given period using the maximum delta in price. It features a histogram with gradient color, divergences, and an adaptive moving average that allows traders to clearly see the smoothed trend direction. 🔶 USAGE This unbounded oscillator has positive momentum when values are above 0 and negative momentum when values are below 0. The adaptive moving average is used as a minimum lag smoothing tool over the momentum histogram. 🔹 Signal Line There are two main uses for the signal line drawn on the chart above. Momentum crosses above or below the signal line: acceleration in momentum. Signal line crosses the 0 value: positive or negative momentum. 🔹 Data Length On the chart above, we can compare different length sizes and how the tool values change, allowing traders to get a shorter or longer-term view of current market strength. 🔹 Smoothing Length In the previous figure, we can compare how different Smoothing Length values affect the oscillator output. 🔹 Divergences The divergence detector is disabled by default. Traders can enable it and adjust the divergence length from the settings panel. As we can see in the chart above, by changing the length of the divergences, traders can fine-tune their detection, a small number will detect smaller divergences, and use a larger number for larger divergences. 🔶 SETTINGS Data: Select data source, close price by default Data Length: Select the length for data gathering Smoothing Length: Select the length for data smoothing Divergences: Enable/Disable divergences detection and length
Индикатор Pine Script®
от LuxAlgo
12
Forward-Backward Exponential Oscillator [LuxAlgo]The Forward-Backward Exponential Oscillator is a normalized oscillator able to estimate directional shifts by making use of a unique "Forward-Backward Filtering" calculation method for Exponential Moving Averages (EMAs). This unique method provides a smooth normalized representation of the price with reduced lag. 🔶 USAGE The oscillator consists of 2 series of values derived from normalizing the sum of each EMA's change across the selected user lookback window (length), one less reactive computed forward (in grey), and the other re-calculated backward for each bar (in blue). Given this "Forward-Backwards" calculation method, we are able to produce a more reactive oscillator compared to the same operation done on a simple double-smoothed EMA. The interaction between these 2 values (Forward Value and Backward Value) can highlight shifts in market momentum over time. When the Forward Value is above the Backward Value, the price is seen moving up, and likewise, when the Forward EMA is below, the Backward EMA price is seen moving down. The indicator specifically displays the difference between values through a histogram located at the 50 mark on the oscillator. 🔹 Projection We project the approximated future values of the forward value in front of the current line. This helps show the data that is being used for the creation of the Forward Value. 🔹 Length & Smoothing The Smoothing Input controls the length of the EMAs which are analyzed. The Length Input controls the lookback for the sum of changes from the EMAs. Displayed below is a comparison of varying input sizes and their results. As seen above: A larger length input will result in slower, gradual movement by the oscillator since the summed values are from a larger lookback. A higher smoothing setting will result in smoother EMAs, leading to a smoother oscillator output that is less contaminated by noisy variations. Note: The length of the projection is tied to the "length" input, to get a longer projection, a larger length is required. 🔶 DETAILS Forward-backward filtering is a method applied to LTI (linear time-invariant) filters to provide a filter response with zero-phase shift, this has the visible effect of shifting a regular causal filter response to the right, making it appear has have effectively 0 lag. The name of this operation indicates that the filter is first calculated forward over a series of values (like regular moving averages), then calculated backward, using the previous output as input for the filter, effectively applying the filter twice. While this operation effectively allows us to obtain a zero-lag response when applied to an EMA, it is subject to repainting, as this indicator only returns the normalized sum of changes of the forward-backward EMA, which does not introduce any repainting behaviors in the final output of the oscillator. 🔶 SETTINGS Length: Change the calculation lookback length for the oscillator. Smoothing: Alter the smoothness of the back-end EMA calculations. Source: Change the source input used for the indicator.
Индикатор Pine Script®
от LuxAlgo
5
Adaptive Fisherized Z-scoreHello Fellas, It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator. Today, I chose the Z-score, also called standard score, as indicator of interest. Special Features Advanced Smoothing: JMA, T3, Hann Window and Super Smoother Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform Inverse Fisher Transform (IFT) Signals: Enter Long, Enter Short, Exit Long and Exit Short Bar Coloring: Presents the trade state as bar colors Band Levels: Changes the band levels Decision Making When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization. Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them. Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with. Usage The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades. Interpretation The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form. Signals B -> Buy -> Z-score crosses above lower band S -> Short -> Z-score crosses below upper band BE -> Buy Exit -> Z-score crosses above 0 SE -> Sell Exit -> Z-score crosses below 0 If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about. T3 The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science. The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA. To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works: Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.' Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.' Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.' The formula for the T3 moving average is as follows: T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3) By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action. JMA The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data. JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility. In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action. Inverse Fisher Transform The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators. The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals. Hann Window The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing. Super Smoother The Super Smoother uses a special mathematical process for the smoothing of data points. The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average. Adaptive Length Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm. Happy Trading! Best regards, simwai --- Credits to @cheatcountry @everget @loxx @DasanC @blackcat1402
Индикатор Pine Script®
от simwai
4
Machine Learning: Anchored Gaussian Process Regression [LuxAlgo]Machine Learning: Anchored Gaussian Process Regression is an anchored version of Machine Learning: Gaussian Process Regression . It implements Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them. Users can set a Training Window by choosing 2 points. GPR will be calculated for the data between these 2 points. Do remember that forecasting trends in the market is challenging, do not use this tool as a standalone for your trading decisions. 🔶 USAGE When adding the indicator to the chart, users will be prompted to select a starting and ending point for the calculations, click on your chart to select those points. Start & end point are named 'Anchor 1' & 'Anchor 2', the Training Window is located between these 2 points. Once both points are positioned, the Training Window is set, whereafter the Gaussian Process Regression (GPR) is calculated using data between both Anchors . The blue line is the GPR fit, the red line is the GPR prediction, derived from data between the Training Window . Two user settings controlling the trend estimate are available, Smooth and Sigma. Smooth determines the smoothness of our estimate, with higher values returning smoother results suitable for longer-term trend estimates. Sigma controls the amplitude of the forecast, with values closer to 0 returning results with a higher amplitude. One of the advantages of the anchoring process is the ability for the user to evaluate the accuracy of forecasts and further understand how settings affect their accuracy. The publication also shows the mean average (faint silver line), which indicates the average of the prices within the calculation window (between the anchors). This can be used as a reference point for the forecast, seeing how it deviates from the training window average. 🔶 DETAILS 🔹 Limited Training Window The Training Window is limited due to matrix.new() limitations in size. When the 2 points are too far from each other (as in the latter example), the line will end at the maximum limit, without giving a size error. The red forecasted line is always given priority. 🔹 Positioning Anchors Typically Anchor 1 is located further in history than Anchor 2 , however, placing Anchor 2 before Anchor 1 is perfectly possibly, and won't give issues. 🔶 SETTINGS Anchor 1 / Anchor 2: both points will form the Training Window . Forecasting Length: Forecasting horizon, determines how many bars in the 'future' are forecasted. Smooth: Controls the degree of smoothness of the model fit. Sigma: Noise variance. Controls the amplitude of the forecast, lower values will make it more sensitive to outliers.
Индикатор Pine Script®
от LuxAlgo
12
Machine Learning: Gaussian Process Regression [LuxAlgo]We provide an implementation of the Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them. While this implementation is adapted to real-time usage, do remember that forecasting trends in the market is challenging, do not use this tool as a standalone for your trading decisions. 🔶 USAGE The main goal of our implementation of GPR is to forecast trends. The method is applied to a subset of the most recent prices, with the Training Window determining the size of this subset. Two user settings controlling the trend estimate are available, Smooth and Sigma . Smooth determines the smoothness of our estimate, with higher values returning smoother results suitable for longer-term trend estimates. Sigma controls the amplitude of the forecast, with values closer to 0 returning results with a higher amplitude. Do note that due to the calculation of the method, lower values of sigma can return errors with higher values of the training window. 🔹 Updating Mechanisms The script includes three methods to update a forecast. By default a forecast will not update for new bars (Lock Forecast). The forecast can be re-estimated once the price reaches the end of the forecasting window when using the "Update Once Reached" method. Finally "Continuously Update" will update the whole forecast on any new bar. 🔹 Estimating Trends Gaussian Process Regression can be used to estimate past underlying local trends in the price, allowing for a noise-free interpretation of trends. This can be useful for performing descriptive analysis, such as highlighting patterns more easily. 🔶 SETTINGS Training Window: Number of most recent price observations used to fit the model Forecasting Length: Forecasting horizon, determines how many bars in the future are forecasted. Smooth: Controls the degree of smoothness of the model fit. Sigma: Noise variance. Controls the amplitude of the forecast, lower values will make it more sensitive to outliers. Update: Determines when the forecast is updated, by default the forecast is not updated for new bars.
Индикатор Pine Script®
от LuxAlgo
31
Savitzky-Golay Filtered Chande Momentum OscillatorThe Savitzky-Golay Filtered Chande Momentum Oscillator (SGCMO) is a modified version of the Chande Momentum Oscillator that functions as a powerful analytical tool, capable of detecting trends and mean reversals. By applying a Savitzky-Golay filter to the price data, the oscillator provides enhanced visualization and smoother readings. (credit to © anieri for the Savitzky-Golay filter code: www.tradingview.com) Chande Momentum Oscillator The Chande Momentum Oscillator (CMO) is a technical indicator developed by Tushar Chande. It measures the momentum of an asset's price movement and provides insights into the overbought or oversold conditions of the market. The CMO calculates the difference between the sum of positive price changes and the sum of negative price changes over a specified period, and then normalizes it to a scale between -100 and +100. Traders and investors use the CMO to identify potential trend reversals, confirm the strength of a current trend, and generate buy or sell signals. Smoothing The Savitzky-Golay filter is a digital filter commonly employed for smoothing and noise reduction in time-series data. In the context of the SGCMO, the aim is to effectively smooth the CMO values, reducing the impact of short-term fluctuations and providing clearer insights into underlying trends. Additionally, an exponential moving average (EMA) filter is applied to further reduce noise and enhance trend visibility. This filtered CMO indicator may provide traders and investors with a clearer and more refined representation of momentum changes in the underlying asset, helping them make more informed trading decisions. Application The SGCMO serves as both a trend-following and mean-reversion tool. Traders can track the current trend using bullish white lines or bearish orange lines in trending markets. Alternatively, they can utilize green and red vertical lines, which indicate price retracement and help capture pullbacks and reversals. Green vertical lines appear when the trend reverses upwards in an oversold zone (-50 to -80), while red vertical lines indicate negative trend reversals in an overbought zone (50 to 80). Opening long positions when green and white lines appear, or short positions when red and orange lines are visible, can be considered. However, it is advisable to combine this indicator with other complementary technical analysis tools and incorporate it into a comprehensive trading strategy to maximize its effectiveness.
Индикатор Pine Script®
от profitprotrading
2
Kalman Filtered ROC & Stochastic with MA SmoothingThe "Smooth ROC & Stochastic with Kalman Filter" indicator is a trend following tool designed to identify trends in the price movement. It combines the Rate of Change (ROC) and Stochastic indicators into a single oscillator, the combination of ROC and Stochastic indicators aims to offer complementary information: ROC measures the speed of price change, while Stochastic identifies overbought and oversold conditions, allowing for a more robust assessment of market trends and potential reversals. The indicator plots green "B" labels to indicate buy signals and blue "S" labels to represent sell signals. Additionally, it displays a white line that reflects the overall trend for buy signals and a blue line for sell signals. The aim of the indicator is to incorporate Kalman and Moving Average (MA) smoothing techniques to reduce noise and enhance the clarity of the signals. Rationale for using Kalman Filter: The Kalman Filter is chosen as a smoothing tool in the indicator because it effectively reduces noise and fluctuations. The Kalman Filter is a mathematical algorithm used for estimating and predicting the state of a system based on noisy and incomplete measurements. It combines information from previous states and current measurements to generate an optimal estimate of the true state, while simultaneously minimizing the effects of noise and uncertainty. In the context of the indicator, the Kalman Filter is applied to smooth the input data, which is the source for the Rate of Change (ROC) calculation. By considering the previous smoothed state and the difference between the current measurement and the predicted value, the Kalman Filter dynamically adjusts its estimation to reduce the impact of outliers. Calculation: The indicator utilizes a combination of the ROC and the Stochastic indicator. The ROC is smoothed using a Kalman Filter (credit to © Loxx: ), which helps eliminate unwanted fluctuations and improve the signal quality. The Stochastic indicator is calculated with customizable parameters for %K length, %K smoothing, and %D smoothing. The smoothed ROC and Stochastic values are then averaged using the formula ((roc + d) / 2) to create the blended oscillator. MA smoothing is applied to the combined oscillator aiming to further reduce fluctuations and enhance trend visibility. Traders are free to choose their own preferred MA type from 'EMA', 'DEMA', 'TEMA', 'WMA', 'VWMA', 'SMA', 'SMMA', 'HMA', 'LSMA', and 'PEMA' (credit to: © traderharikrishna for this code: ). Application: The indicator's buy signals (represented by green "B" labels) indicate potential entry points for buying assets, suggesting a bullish trend. The white line visually represents the trend, helping traders identify and follow the upward momentum. Conversely, the sell signals (blue "S" labels) highlight possible exit points or opportunities for short selling, indicating a bearish trend. The blue line illustrates the bearish movement, aiding in the identification of downward momentum. The "Smoothed ROC & Stochastic" indicator offers traders a comprehensive view of market trends by combining two powerful oscillators. By incorporating the ROC and Stochastic indicators into a single oscillator, it provides a more holistic perspective on the market's momentum. The use of a Kalman Filter for smoothing helps reduce noise and enhance the accuracy of the signals. Additionally, the indicator allows customization of the smoothing technique through various moving average types. Traders can also utilize the overbought and oversold zones for additional analysis, providing insights into potential market reversals or extreme price conditions. Please note that future performance of any trading strategy is fundamentally unknowable, and past results do not guarantee future performance.
Индикатор Pine Script®
от profitprotrading
20
ALMA Smoothed Gaussian Moving AverageThis indicator is an altered version of the Gaussian Moving Average (GMA) (Credit to author: © LeafAlgo ). The GMA applies weights to the prices, giving more importance to the values closer to the current period and gradually diminishing the significance of older prices. The ALMA Smoothed Gaussian Moving Average (ASGMA) applies an ALMA smoothing to its price data to minimize lag and provide a more accurate representation of the underlying trend by dynamically adapting to changing market conditions. The Arnaud Legoux Moving Average (ALMA) is a specialized smoothing technique that adjusts the weights of the moving average based on market volatility. Its calculation uses Wavelet Transform techniques which enables this type of smoothing to capture both high-frequency and low-frequency components of a signal or data. The rationale for this mashup between ALMA and Gaussian filtering is to smooth the moving average line over the smoothed price data and produce stronger trend signals. ASGMA serves as a trend-following indicator, identifying both bullish and bearish trends. It provides buy and sell signals indicated by "B" and "S" labels plotted alongside the price data. Additionally, the ASGMA's Exponential Moving Average (EMA) line alternates between green and red, indicating bullish and bearish momentum, respectively. The ASGMA also incorporates two popular momentum indicators, the Relative Strength Index (RSI) and the Chande Momentum Oscillator (CMO). The inclusion of these indicators aims to enhance trend identification and reversal signals. For a strong buy signal, all three indicators (RSI, CMO, and ASGMA) must indicate bullish conditions, resulting in a vertical green line. Conversely, a vertical red line is plotted when all indicators indicate bearish conditions, representing a strong sell signal. The ASGMA, with its unique combination of smoothing techniques and indicator amalgamation, provides traders and investors with powerful analytical tools. It can be applied in trend-following strategies using the regular buy and sell signals generated by labels and the EMA line. Alternatively, the vertical lines offer stronger buy and sell signals. These features aid in identifying potential entry and exit points, thereby enhancing trading decisions and market analysis. However, it is important to remember that the future performance of any trading strategy is fundamentally unknowable, and past results do not guarantee future performance.
Индикатор Pine Script®
от profitprotrading
41
MTF Evolving Weighted Composite Value Area🧾 Description: This indicator calculates evolving value areas across 3 different timeframes/periods and combines them into one composite, multi-timeframe evolving value area - with each of the underlying timeframes' VAs assigned their own weighting/importance in the final calculation. Layered with extra smoothing options, this creates an informative and useful 'rolling value area' effect that can give you a better perspective on the value area across multiple periods at once as it develops - without total calculation resets at the onset of every new period. Let's start with a simplified primer on value areas and then jump in to the new ideas this indicator introduces. 🤔 What is a value area? Value areas are a tool used in market profile analysis to determine the range of prices that represents where most trading activity occurred during a specific time period, typically within a single 'bar' of a certain higher timeframe, such as the 4-hour, daily, or weekly. It helps traders understand the levels where the market finds value. To calculate the value area, we look at the distribution of prices and trading volume. We determine a percentage, usually 70% or 80%, that represents the significant portion of trading volume. Then, we identify the price range that contains this percentage of trading volume, which becomes the value area. Value areas are useful because they provide insights into market dynamics and potential support and resistance levels. They show where traders have been most active and where they find value, and traders can use this information to make better-informed decisions. For example, if price is trading within the value area, it suggests that it's within a range where traders see value and are actively participating, which could indicate a balanced market. If the price moves above or below the value area, it may signal a potential shift in market sentiment or a breakout/breakdown from the established range. By understanding the value area, traders can identify potential areas of supply and demand, determine levels of interest for buyers and sellers, and make decisions based on the market's perception of value. 📑 Limitations of traditional value areas Static representation: Value areas are usually represented as static zones calculated after the fact. For example, after a daily period is completed, a typical 1D VA indicator will display the value area for the past period with static horizontal lines. This approach doesn't give you the power to see how the value area evolved, or developed, during the time period, as it is only displayed retroactively. It also doesn't give you the ability to view it as it evolves in real-time. This is why we chose to use an evolving value area representation, specifically borrowed from @sourcey's Value Area POC/VAH/VAL script function for calculating evolving VAs. Rollover resets - no memory of past periods!: The traditional value area is calculated over a static period - it is calculated from the beginning of the period, for example a 1 day period, to the end, and that's the end of it. When the next daily period begins, the calculation resets, and has no memory of the preceding period. This limits the usefulness of the value area visual when viewed near the beginning of a new period before price and volume have been given ample time to define an area. Hard to absorb all of that information: Value areas aren't generally meant to be a hardline representation of something extremely exact - they're based on a percentage of the area where traders appeared to find value over a certain time period. Most traders use them as a guide for support and resistance levels or finding an expected range. Traders typically overlay multiple VAs - sometimes requiring several instances of the same indicator to be applied - to represent the VA across multiple timeframes such as the 4H, 1D, or 1W. The chart quickly gets cluttered and it's not necessarily easy to understand the relationship between these multiple periods' VAs at a glance. 🧪 New concepts introduced in this indicator With the evolving weighted composite value area we tried to address these limitations, and we think the result can be useful and intuitive for traders who want more dynamic and practical VAs for their everyday technical analysis. ⚖️ 1. A composite, weighted multi-timeframe VA This indicator's value areas represent a combination or composite of the value areas calculated across multiple timeframes. The VAs calculated across each timeframe are then given a weighting percentage, which determines their contribution to the final 'weighted composite value area'. Pictured below: a 4H/1D/1W MTF evolving weighted composite VA on the BTCUSDT Perpetual Futures (Binance) 5 minute chart: Traditionally, when traders wanted to get a view of where the majority of trading activity occurred over the past four hours, day, and week, they would need to apply three value area indicators (or sometimes one if it allows multiple custom timeframes), each set to a different period (4H, 1D, 1W). The chart gets cluttered quickly and the information is hard to absorb in one shot. Addressing this problem was the main impetus for creating this weighted composite process. 〰️ 2. Rolling and smoothed evolving VAs Because the composite VA is calculated based on multiple period VAs, there is no one single point where the area calculation resets (unless all 3 selected timeframes happen to rollover on the same bar). This creates a 'rolling' effect that gives a sense of the progression of the VA as price transitions through the different underlying time periods, without the traditional 'jump' in calculations between periods. Pictured below: a 1D/1W/1M MTF evolving weighted composite VA on the NQ futures 1H chart: To help give even more of a sense of perspective and 'progression' of the VA, there are also smoothing options to even out the 'jumps' at period-rollover points. ✔️ What's it good for? Smoothed, rolling, and evolving multi-timeframe VAs that give you a better real-time perspective of where traders are finding value across multiple time periods at once. 📎 References 1. @sourcey's Value Area POC/VAH/VAL script by adapting its f_poc(tf) function. 💠 Features: A MTF evolving weighted composite value area based on 3 underlying VAs calculated across customizable timeframes Aesthetic and flexible coloring and color theme styling options Period-roller labels and options for ease-of-use and legibility ⚙️ Settings: Calculation Decimal Resolution: This setting essentially determines how 'granular' the value area calculating process is. This value should be set to some multiple of the tick size/smallest decimal of the symbol's price chart. Eg. On BTCUSDT, the tick size/decimal is usually 0.1. So, you might use 0.5. On TSLA, the tick size is 0.01. You might use 0.05 or 0.25. Beware: if the resolution is too small, calculation will take too long and the script may timeout. Show Me Suggested Resolutions: If enabled, a label will display in the bottom right of the chart with some suggested resolutions for the current chart. Area Percentage: Set the displayed percentage of the calculated composite value area. Igor method = 70%; Daniel method: 68%. Use a Color Theme: When this setting is enabled, all manual 'Bullish and Bearish Colors' are overridden. All plots will use the colors from your selected Color Theme - excepting those plots set to use the 'Single Color' coloring method. Color Theme: When 'Use a Color Theme' is enabled, this setting allows you to select the color theme you wish to use. Resistance Color: When 'Use a Color Theme' is disabled, this will set the 'resistance color' for the composite VA. Support Color: When 'Use a Color Theme' is disabled, this will set the 'support color' for the composite VA. Show Period Rollover Labels: When enabled, a label will show above or below the composite VA marking any underlying period rollovers with the label 'New __' (eg. 'New 4H', 'New 1D', 'New 1W'). Size: Sets the font size of the period rollover labels. Show Period Rollover Lines: When enabled, a translucent vertical dashed line will be drawn across the composite VA when one of the underlying periods rolls over. Fill Composite Value Area: When enabled, the composite VA will be filled with a gradient coloring from the support line to the resistance line using their respective colors. Smooth: When enabled, a smoothing moving average will be applied to the composite value area. Smoothing Period: Set the lookback period for the smoothing average. Smoothing Type: Set the calculation type for the smoothing average. Options include: Exponential, Simple, Weighted, Volume-Weighted, and Hull. Enable: Include/exclude a timeframe's VA in the composite VA calculation. Timeframe: Set the timeframe for this specific underlying VA. Weighting %: Set the weighting percentage or 'importance' of this timeframe's value area in calculating the composite VA. Beware! The sum of the weighting percentages across all enabled timeframes must ALWAYS add up to 100 in order for this indicator to work as designed.
Индикатор Pine Script®
от DarkWaveAlgo
9
T3 JMA KAMA VWMAEnhancing Trading Performance with T3 JMA KAMA VWMA Indicator Introduction In the dynamic world of trading, staying ahead of market trends and capitalizing on volume-driven opportunities can greatly influence trading performance. To address this, we have developed the T3 JMA KAMA VWMA Indicator, an innovative tool that modifies the traditional Volume Weighted Moving Average (VWMA) formula to increase responsiveness and exploit high-volume market conditions for optimal position entry. This article delves into the idea behind this modification and how it can benefit traders seeking to gain an edge in the market. The Idea Behind the Modification The core concept behind modifying the VWMA formula is to leverage more responsive moving averages (MAs) that align with high-volume market activity. Traditional VWMA utilizes the Simple Moving Average (SMA) as the basis for calculating the weighted average. While the SMA is effective in providing a smoothed perspective of price movements, it may lack the desired responsiveness to capitalize on short-term volume-driven opportunities. To address this limitation, our T3 JMA KAMA VWMA Indicator incorporates three advanced moving averages: T3, JMA, and KAMA. These MAs offer enhanced responsiveness, allowing traders to react swiftly to changing market conditions influenced by volume. T3 (T3 New and T3 Normal): The T3 moving average, one of the components of our indicator, applies a proprietary algorithm that provides smoother and more responsive trend signals. By utilizing T3, we ensure that the VWMA calculation aligns with the dynamic nature of high-volume markets, enabling traders to capture price movements accurately. JMA (Jurik Moving Average): The JMA component further enhances the indicator's responsiveness by incorporating phase shifting and power adjustment. This adaptive approach ensures that the moving average remains sensitive to changes in volume and price dynamics. As a result, traders can identify turning points and anticipate potential trend reversals, precisely timing their position entries. KAMA (Kaufman's Adaptive Moving Average): KAMA is an adaptive moving average designed to dynamically adjust its sensitivity based on market conditions. By incorporating KAMA into our VWMA modification, we ensure that the moving average adapts to varying volume levels and captures the essence of volume-driven price movements. Traders can confidently enter positions during periods of high trading volume, aligning their strategies with market activity. Benefits and Usage The modified T3 JMA KAMA VWMA Indicator offers several advantages to traders looking to exploit high-volume market conditions for position entry: Increased Responsiveness: By incorporating more responsive moving averages, the indicator enables traders to react quickly to changes in volume and capture short-term opportunities more effectively. Enhanced Entry Timing: The modified VWMA aligns with high-volume periods, allowing traders to enter positions precisely during price movements influenced by significant trading activity. Improved Accuracy: The combination of T3, JMA, and KAMA within the VWMA formula enhances the accuracy of trend identification, reversals, and overall market analysis. Comprehensive Market Insights: The T3 JMA KAMA VWMA Indicator provides a holistic view of market conditions by considering both price and volume dynamics. This comprehensive perspective helps traders make informed decisions. Analysis and Interpretation The modified VWMA formula with T3, JMA, and KAMA offers traders a valuable tool for analyzing volume-driven market conditions. By incorporating these advanced moving averages into the VWMA calculation, the indicator becomes more responsive to changes in volume, potentially providing deeper insights into price movements. When analyzing the modified VWMA, it is essential to consider the following points: Identifying High-Volume Periods: The modified VWMA is designed to capture price movements during high-volume periods. Traders can use this indicator to identify potential market trends and determine whether significant trading activity is driving price action. By focusing on these periods, traders may gain a better understanding of the market sentiment and adjust their strategies accordingly. Confirmation of Trend Strength: The modified VWMA can serve as a confirmation tool for assessing the strength of a trend. When the VWMA line aligns with the overall trend direction, it suggests that the current price movement is supported by volume. This confirmation can provide traders with additional confidence in their analysis and help them make more informed trading decisions. Potential Entry and Exit Points: One of the primary purposes of the modified VWMA is to assist traders in identifying potential entry and exit points. By capturing volume-driven price movements, the indicator can highlight areas where market participants are actively participating, indicating potential opportunities for opening or closing positions. Traders can use this information in conjunction with other technical analysis tools to develop comprehensive trading strategies. Interpretation of Angle and Gradient: The modified VWMA incorporates an angle calculation and color gradient to further enhance interpretation. The angle of the VWMA line represents the slope of the indicator, providing insights into the momentum of price movements. A steep angle indicates strong momentum, while a shallow angle suggests a slowdown. The color gradient helps visualize this angle, with green indicating bullish momentum and purple indicating bearish momentum. Conclusion By modifying the VWMA formula to incorporate the T3, JMA, and KAMA moving averages, the T3 JMA KAMA VWMA Indicator offers traders an innovative tool to exploit high-volume market conditions for optimal position entry. This modification enhances responsiveness, improves timing, and provides comprehensive market insights. Enjoy checking it out! --- Credits to: ◾ @cheatcountry – Hann Window Smoothing ◾ @loxx – T3 ◾ @everget – JMA
Индикатор Pine Script®
от simwai
2
MathEasingFunctionsLibrary "MathEasingFunctions" A collection of Easing functions. Easing functions are commonly used for smoothing actions over time, They are used to smooth out the sharp edges of a function and make it more pleasing to the eye, like for example the motion of a object through time. Easing functions can be used in a variety of applications, including animation, video games, and scientific simulations. They are a powerful tool for creating realistic visual effects and can help to make your work more engaging and enjoyable to the eye. --- Includes functions for ease in, ease out, and, ease in and out, for the following constructs: sine, quadratic, cubic, quartic, quintic, exponential, elastic, circle, back, bounce. --- Reference: easings.net learn.microsoft.com ease_in_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. select(v, formula, effect, bounded)   Parameters:      v (float)      formula (string)      effect (string)      bounded (bool)
Библиотека Pine Script®
от RicardoSantos
6
Adaptive Fusion ADX VortexIntroduction The Adaptive Fusion ADX DI Vortex Indicator is a powerful tool designed to help traders identify trend strength and potential trend reversals in the market. This indicator uses a combination of technical analysis (TA) and mathematical concepts to provide accurate and reliable signals. Features The Adaptive Fusion ADX DI Vortex Indicator has several features that make it a powerful tool for traders. The Fusion Mode combines the Vortex Indicator and the ADX DI indicator to provide a more accurate picture of the market. The Hurst Exponent Filter helps to filter out choppy markets (inspired by balipour). Additionally, the indicator can be customized with various inputs and settings to suit individual trading strategies. Signals The enterLong signal is generated when the algorithm detects that it's a good time to buy a stock or other asset. This signal is based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value is above a certain threshold and there is a crossover between the plus and minus lines of the ADX indicator, then the algorithm will generate an enterLong signal. Similarly, the enterShort signal is generated when the algorithm detects that it's a good time to sell a stock or other asset. This signal is also based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value is above a certain threshold and there is a crossunder between the plus and minus lines of the ADX indicator, then the algorithm will generate an enterShort signal. The exitLong and exitShort signals are generated when the algorithm detects that it's a good time to close a long or short position, respectively. These signals are also based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value crosses above a certain threshold or there is a crossover between the minus and plus lines of the ADX indicator, then the algorithm will generate an exitLong signal. Usage Traders can use this indicator in a variety of ways, depending on their trading strategy and style. Short-term traders may use it to identify short-term trends and potential trade opportunities, while long-term traders may use it to identify long-term trends and potential investment opportunities. The indicator can also be used to confirm other technical indicators or trading signals. Personally, I prefer to use it for short-term trades. Strengths One of the strengths of the Adaptive Fusion ADX DI Vortex Indicator is its accuracy and reliability. The indicator uses a combination of TA and mathematical concepts to provide accurate and reliable signals, helping traders make informed trading decisions. It is also versatile and can be used in a variety of trading strategies. Weaknesses While this indicator has many strengths, it also has some weaknesses. One of the weaknesses is that it can generate false signals in choppy or sideways markets. Additionally, the indicator may lag behind the market, making it less effective in fast-moving markets. That's a reason why I included the Hurst Exponent Filter and special smoothing. Concepts The Adaptive ADX DI Vortex Indicator with Fusion Mode and Hurst Filter is based on several key concepts. The Average Directional Index (ADX) is used to measure trend strength, while the Vortex Indicator is used to identify trend reversals. The Hurst Exponent is used to filter out noise and provide a more accurate picture of the market. In conclusion, the Adaptive Fusion ADX DI Vortex Indicator is a versatile and powerful tool for traders. By combining technical analysis and mathematical concepts, this indicator provides accurate and reliable signals for identifying trend strength and potential trend reversals. While it has some weaknesses, its many strengths and features make it a valuable addition to any trader's toolbox. --- Credits to: ▪️@cheatcountry – Hann Window Smoohing ▪️@loxx – VHF and T3 ▪️@balipour – Hurst Exponent Filter
Индикатор Pine Script®
от simwai
5
Ehlers Data Sampling Relative Strength Indicator [CC]The Data Sampling Indicator was created by John Ehlers (Stocks and Commodities Mar 2023) and this is a genius method to reduce noise in the market data but also doesn't introduce any lag while doing so. The way this works is because traditionally, people have always relied on the close price as the default input for many indicators such as the RSI or MACD as examples. Since the open is usually virtually identical to the previous close, it has been ignored by most people but Ehlers discovered that if you do a simple average of open and close for the input on any indicator, you can remove much of the noise without any added lag. I have used the RSI as he did in his example and plotted both to show the difference between the traditional RSI and using Ehlers' process as the new Data Sampling RSI. You can clearly see that this new RSI follows the price fluctuations much closer and is much smoother than the traditional RSI. As usual, I have included different colors to show the strength of the buy or sell signals so darker colors mean it is a very strong signal and lighter colors means it is a normal signal. Buy when the line turns green and sell when it turns red. Feel free to try out this method to replace the input for any indicator and let me know how this works for you! And of course let me know if you would like me to publish any indicator script.
Индикатор Pine Script®
от cheatcountry
2
VHF Adaptive Linear Regression KAMAIntroduction Heyo, in this indicator I decided to add VHF adaptivness, linear regression and smoothing to a KAMA in order to squeeze all out of it. KAMA: Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements. VHF: Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length. Linear Regression Curve: A line that best fits the prices specified over a user-defined time period. This is very good to eliminate bad crosses of KAMA and the pric. Usage You can use this indicator on every timeframe I think. I mostly tested it on 1 min, 5 min and 15 min. Signals Enter Long -> crossover(close, kama) and crossover(kama, kama ) Enter Short -> crossunder(close, kama) and crossunder(kama, kama ) Thanks for checking this out! -- Credits to ▪️@cheatcountry – Hann Window Smoohing ▪️@loxx – VHF and T3 ▪️@LucF – Gradient
Индикатор Pine Script®
от simwai
Multi SMI Ergodic OscillatorThe Multi SMI Ergodic Oscillator (Multi SMIEO) indicator can be used to identify potential buy and sell signals based on the relationship between the TSI and EMA lines. The script is creating an indicator that plots multiple (3) sets of Time Series Indicator (TSI-Indicator) and Exponential Moving Average (EMA-Signal) lines as a single indicator. The TSI is a momentum oscillator that helps identify overbought and oversold conditions. It is calculated using the close prices of an asset, a short-term moving average, and a long-term moving average. The script uses three different pairs of input values for the short-term and long-term periods, which can be adjusted by the user. The EMA is a type of moving average that gives more weight to recent prices. It is calculated by applying a weighting factor to the most recent price, and then adding that weighted value to the previous EMA value. The script uses three different input values for the length of the EMA, which can also be adjusted by the user. After calculating the TSI and EMA for each set, the script plots them on the same graph, with different colors and widths to differentiate them. The three sets of TSI and EMA lines are plotted to allow the user to compare the results of different periods. The script also plots a horizontal line at zero, which is used as a reference point for the oscillations of the indicator lines. One way to use this indicator is to look for crossovers between the TSI and the EMA lines. A bullish crossover occurs when the TSI crosses above the EMA. This suggests that the buying pressure is increasing and a potential buy signal is generated. A bearish crossover occurs when the TSI crosses below the EMA. This suggests that the selling pressure is increasing and a potential sell signal is generated. Some other ways that the indicator can be used include: 1. Identifying trends: The TSI and EMA lines can be used to identify the direction of the trend. An uptrend is present when the TSI and EMA lines are both trending upwards, while a downtrend is present when the TSI and EMA lines are both trending downwards. 2. Overbought and oversold conditions: The TSI can be used to identify overbought and oversold conditions. When the TSI is above the upper limit of the range, the asset is considered overbought and may be due for a price correction. Conversely, when the TSI is below the lower limit of the range, the asset is considered oversold and may be due for a price rebound. 3. Confirming price action: The Multi SMIEO indicator can be used to confirm price action. If a bullish divergence is present, it confirms a potential bullish reversal. If a bearish divergence is present, it confirms a potential bearish reversal. 4. Multiple time frame analysis: By using different periods for the TSI and EMA lines, the indicator can be used to analyze the asset on multiple time frames. It can be useful to compare the results of different periods to get a better understanding of the asset's price movements. 5. Risk management: This indicator can be used as an element of risk management strategy, it can help traders to identify overbought and oversold conditions to set stop loss or take profit levels. The Multi SMI Ergodic Oscillator (Multi SMIEO) is a versatile indicator that can be used in a number of ways to analyze the price movements of an asset. It can be used to identify potential buy and sell signals, trends, overbought and oversold conditions, and to confirm price action. By using different periods for the TSI and EMA lines, the indicator can also be used to analyze the asset on multiple time frames. However, it is important to remember that indicators are based on historical data, and past performance does not guarantee future results. It is important to use the indicator as part of a comprehensive trading strategy that includes risk management and other analysis techniques, such as fundamental and technical analysis. It is also important to keep in mind that indicators are not a standalone solution for trading, they should be used in conjunction with other market analysis and research techniques to generate better results. Lastly, it is important to keep in mind that trading in financial markets comes with a certain level of risk and it is crucial to always have a proper risk management plan in place. Never invest more than you can afford to lose.
Индикатор Pine Script®
от Jake_from_State_Farm
2