התחל

Smoothing

Iteratively Reweighted Least Squares (IRLS) [Jamallo]Author's Note: This script is published as a unique mathematical contribution to TradingView's open-source public library. It is intended to introduce a novel application of a robust statistical method for the community and developers to study, adapt, and build upon, rather than to serve as a standalone, out-of-the-box trading strategy. Introduction Almost all moving averages and smoothing filters in technical analysis treat historical price data equally or apply a fixed mathematical decay (like an EMA). The problem? A massive, anomalous wick or a sudden volatility spike will inevitably drag the average away from the true underlying market consensus. Enter Iteratively Reweighted Least Squares (IRLS) . IRLS is a robust statistical method that calculates a "consensus" price by actively identifying and down-weighting outliers. Instead of letting a large wick distort the line, the algorithm assigns less weight to prices that deviate furthest from the current estimate. The result is a filter that cuts through noise, ignores price-distant spikes, and naturally locks onto the dominant, high-density price levels. How It Works The indicator uses the Hardy weight function to determine how heavily each historical candle influences the current estimate. On every bar, the algorithm checks the distance of each sample from the current consensus and iteratively refines the line until it converges on a robust mean. Epsilon — the outlier rejection scale — is derived dynamically from the average High–Low range, keeping the filter dimensionless and consistent across all instruments and timeframes. Parameters Window Size (N) : The rolling lookback window of historical samples the kernel considers. Larger values produce a smoother, slower-responding line. Sparsity (s/N) : The core behavioral control. Dictates the fraction of the window allowed to "vote" on the estimate. Low Sparsity (e.g., 0.1) : Only the 10% of samples closest to the current estimate participate. Produces a snappy, selective line that locks tightly onto the most dominant price cluster. High Sparsity (e.g., 1.0) : All samples participate, resulting in a smoother, more conventional robust mean. Gamma (ε scale) : Controls the strength of outlier rejection. Lower values enforce harsh, median-like rejection. Higher values soften the rejection toward a standard weighted mean. Iterations : The number of reweighting convergence passes per bar. 2–3 is sufficient for practical convergence. Potential Applications The Hardy IRLS filter provides a unique lens into market structure by shifting the focus from simple time-averaged prices to spatial price consensus. Because it rejects price-distant wicks and noise spikes by design, it can serve as a foundation for: Custom trailing stops Dynamic support and resistance trackers Baseline trend or regime filters Feel free to inspect the open-source code, experiment with extreme sparsity and gamma settings, and integrate the IRLS core into your own quantitative projects. References Li Shuang, "Sparse Representation of Hardy Function by Iteratively Reweighted Least Squares," 2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), IEEE, 2020. DOI: 10.1109/ISCEIC51027.2020.00020
אינדיקטור Pine Script®
מאת ‎Jamallo22‎
Iterative Rational Quadratic ChannelThe Iterative Rational Quadratic Channel is a kernel-based smoothing and state estimation framework that applies a Rational Quadratic kernel regression to price data, combined with a rolling standard deviation envelope to construct adaptive dynamic channel boundaries. Unlike exponential kernel methods that prioritize recent data at the expense of historical context, the rational quadratic kernel introduces a heavy-tailed weighting structure that preserves multi-scale memory in price dynamics. This enables the channel to reflect not only short-term fluctuations, but also broader structural regime context. The resulting channel is less reactive to micro-noise and more representative of persistent market structure, making it particularly effective for trend continuity analysis, regime modeling, and reducing sensitivity to false reversals. Its primary utility is as a state estimation and regime-filtering tool for price behavior, rather than a pure high-frequency signal isolation tool. TRADING USES The Rational Quadratic Channel is best interpreted as a regime-aware structural filter rather than a purely reactive trading band. Trend Continuity The channel basis line (RQ smoothed price) provides a stable representation of underlying market direction. Sustained movement above or below the basis reflects trend persistence rather than short-lived fluctuations, making it useful for maintaining directional bias. Regime Persistence Due to the heavy-tailed memory of the rational quadratic kernel, historical price structure continues to influence current valuation. This produces smoother transitions between market phases and reduces sensitivity to short-term reversals, improving regime stability. False Reversal Filtering Compared to exponentially weighted kernels, the RQ channel reduces overreaction to transient volatility spikes. This helps filter out low-quality reversals driven by noise rather than structural change. State Estimation The channel functions as a continuous estimator of market state: - The basis represents the inferred latent price state - The envelope represents dynamic volatility dispersion around that state This makes it well-suited for manual, semi-automated, and automated trading systems requiring a stable structural representation of price rather than raw responsiveness. Gradual shifts in the basis line and channel position can also serve as a framework for monitoring changes in trend direction and regime transitions over time. Volatility & Risk Context The rolling standard deviation envelope expands and contracts based on realized volatility, providing a contextual risk framework. Wider channels indicate increased uncertainty and dispersion, while tighter channels indicate compression and lower variance conditions. THEORY The rational quadratic kernel is a member of the scale-mixture family of Gaussian kernels and can be interpreted as a superposition of Gaussian processes operating at multiple length scales. This allows it to capture both local and global structure in time series data. It is defined as: k(i)=(1+i22αℓ2)−αk(i) = \left(1 + \frac{i^2}{2\alpha \ell^2}\right)^{-\alpha}k(i)=(1+2αℓ2i2​)−α Where: ---> α\alphaα controls tail heaviness (relativeWeight) ---> ℓ\ellℓ defines the characteristic scale (lookback) Unlike Gaussian kernels, which enforce exponential decay and emphasize locality, the rational quadratic kernel follows a power-law decay. This allows older observations to retain influence over the estimator for longer periods, producing a smoothing effect that is inherently multi-scale and well-suited for modeling persistent structural behavior. The rolling standard deviation complements this by measuring dispersion around the estimated state, forming a volatility-adaptive envelope. Rather than acting as a strict statistical confidence interval, it provides a dynamic representation of market expansion and contraction. The iterative implementation processes data sequentially (bar-by-bar), ensuring computational efficiency and making the indicator suitable for real-time use without repainting. CALIBRATION Calibration determines the balance between responsiveness, structural memory, and regime stability. Length (Lookback) Lower (50–100): More responsive, increased sensitivity to short-term structure Medium (150–250): Balanced for swing trading and intermediate regimes Higher (300+): Strong regime persistence, reduced sensitivity to noise Relative Weight (Tail Sensitivity) Controls how quickly historical influence decays: Lower values (≈ 0.5 – 1.0): - Behavior approaches Gaussian - More responsive to recent price action - Faster detection of trend changes - Slightly more sensitive to noise Higher values (≈ 2.0+): - Stronger heavy-tail behavior - Increased influence of older price data - Smoother output and stronger regime anchoring - Improved false reversal filtering Start At Bar (Lag / Structural Anchoring) Controls how much recent price data is excluded from the kernel calculation: Lower values (0–10): - Uses most recent data - Faster reaction to price changes - More sensitive to short-term volatility Moderate values (10–30): - Balanced responsiveness and stability - Reduces noise without excessive lag - Suitable for most trading environments Higher values (30+): - Strong structural anchoring - Significantly reduced sensitivity to recent fluctuations - Enhanced regime persistence - Slower response to turning points This parameter effectively introduces a controlled lag, allowing users to tune the tradeoff between responsiveness and regime stability. MARKET USAGE Stock, Forex, Crypto, Commodities, and Indices.
אינדיקטור Pine Script®
מאת ‎SensitiveSuit‎
1
Moving Average Weighted PlusWMA+ is a weighted moving average with an optional smoothing layer, using the same smoothing options found in TradingView’s built-in Moving Average scripts, such as their provided Simple Moving Average (SMA). I have often wondered why this smoothing option is not included in all of the TradingView provided moving averages. WMA+ now brings that same smoothing menu and flexible Bollinger Band option to a WMA. The main plot is a standard Weighted Moving Average (WMA) of the selected source (Close by default). When Smoothing is enabled, a secondary moving average is applied to the WMA output (not to price), which can reduce noise and make the baseline easier to interpret. An optional Bollinger Bands overlay can also be applied to the smoothing line, providing a volatility envelope around the smoothed WMA. As displayed on the chart above. How it works WMA: Calculates a weighted moving average of the selected Source using the chosen Length. Smoothing (optional): Applies a second MA to the WMA output. Available types: - SMA - EMA - SMMA (RMA) - WMA - VWMA - SMA + Bollinger Bands (bands built from the smoothing length and standard deviation) Bollinger Bands (optional): When "SMA + Bollinger Bands" is selected, upper/lower bands are plotted around the smoothing line using BB StdDev. Inputs: Length: WMA length. Source: Price source used for the WMA. Offset: Shifts the WMA plot left/right. Smoothing → Type: Enables/disables smoothing and selects the smoothing MA type. Smoothing → Length: Length for the smoothing MA. Smoothing → BB StdDev: Standard deviation multiplier for Bollinger Bands (only when SMA + Bollinger Bands is selected). Notes: Smoothing is computed from the WMA result (WMA → smoothing MA), which preserves the character of the WMA while allowing an additional layer of filtering. The smoothing UI/logic is adapted from TradingView’s built-in Moving Average scripts for consistency and familiarity. Attribution: Smoothing input structure and smoothing/Bollinger code are adapted from TradingView’s built-in Moving Average scripts and applied to the WMA. Disclaimer: This script is provided for educational and informational purposes only and is not financial advice. If you would like to read TradingView's information on the WMA you can find that here: www.tradingview.com
אינדיקטור Pine Script®
מאת ‎DigitalLRS‎
LOWESS Channel & Extrapolation [LuxAlgo]The LOWESS Channel & Extrapolation indicator calculates a Locally Weighted Scatterplot Smoothing (LOWESS) curve to define a non-linear trend and projects it into future bars using local regression slopes. It provides a dynamic channel based on the standard deviation of residuals, helping traders identify overextended price levels and potential mean-reversion points. The LOWESS Channel & Extrapolation indicator is subject to repainting and displayed retrospectively. 🔶 USAGE This tool is primarily designed for trend analysis and identifying exhaustion points. Because the LOWESS algorithm recalculates based on the most recent data window, the entire historical curve can adjust, making it a powerful tool for backtesting and analyzing past market structures rather than for real-time signal generation without confirmation. 🔹 Trend Identification The central fit line represents the smoothed local trend. When the curve is sloping upward, the local market sentiment is considered bullish; conversely, a downward slope indicates bearish sentiment. 🔹 Overbought/Oversold Conditions The dashed outer channels represent a volatility-adjusted boundary. When price moves outside these boundaries, it is statistically overextended relative to the local trend, often preceding a move back toward the mid-line. 🔹 Extrapolation The indicator extends the most recent local regression slope into the future. This provides a "path of least resistance" projection based on the current momentum of the smoothed curve. 🔶 DETAILS The LOWESS (Locally Weighted Scatterplot Smoothing) algorithm works by performing a separate weighted linear regression for every data point in the window. It uses a "tricube" weighting function, which ensures that data points closer to the focal point have a higher influence on the fit than points further away. This results in a curve that is much more flexible than a simple moving average and can adapt to complex price cycles without the lag associated with traditional filters. The channel width is determined by calculating the Standard Deviation of the residuals (the difference between the actual price and the LOWESS fit). This ensures the channel expands during high volatility and contracts during consolidation. 🔶 SETTINGS Length : Determines the number of historical observations used to fit the LOWESS curve. Larger values result in a smoother, more macro trend. Span : The fraction of data points used for each local regression. A higher span (closer to 1.0) creates a smoother line, while a lower span allows the curve to follow price more tightly. Channel Multiplier : Multiplier applied to the standard deviation of residuals to define the distance of the upper and lower bands from the mid-line. Extrapolation Bars : The number of bars to project the current trend into the future. Fit Color : Sets the color and transparency of the central LOWESS line. Channel Color : Sets the color of the dashed outer bands and the background fill. Line Width : Adjusts the thickness of the central fit line.
אינדיקטור Pine Script®
מאת ‎LuxAlgo‎
Adaptive Centric Moving Average [LuxAlgo]The Adaptive Centric Moving Average indicator provides a dynamic smoothing tool that adjusts its reactivity based on where the price sits relative to its recent trading range midpoint. 🔶 USAGE The Adaptive Centric Moving Average (AMA) is designed to filter out noise during periods of consolidation while remaining highly responsive during trending moves. When the price is near the center of its recent high-low range, the indicator becomes flatter and less prone to "whipsaws." As price moves toward the extremes of its range, the indicator accelerates to catch the emerging trend. Users can utilize the AMA for trend identification and trailing stop-loss levels. The visual gradient fill between the source price and the AMA line helps traders quickly identify the current trend strength and the distance between price and the smoothed average. 🔶 DETAILS The core logic of the script relies on a normalized relative position (similar to a Stochastic calculation) to determine how far the price is from its range midpoint. 🔹 Adaptive Smoothing Logic The indicator calculates a smoothing factor (alpha) based on the absolute distance from the 50% level of the range. When price is at the midpoint (50%), the alpha is zero, causing the moving average to stay flat. As price moves toward the upper or lower boundaries (0% or 100%), the alpha increases, making the average more reactive. 🔹 The Centric Calculation Unlike standard moving averages that track the source price directly, this indicator centers its target around the range midpoint. The Attenuation Factor scales the distance between the source and the midpoint, while the Power Factor applies an exponent to the smoothing factor, allowing for non-linear reactivity. 🔶 SETTINGS 🔹 Price Settings Source: The price series used for calculations (default is Close). Length: The window size used for pre-smoothing the source and determining the highest highs and lowest lows for the range. 🔹 Adaptive Settings Attenuation Factor: Controls the intensity of the price input relative to the midpoint. Lower values increase reactivity, while higher values provide a more stable, base smoothing speed. Power Factor: Exponents the smoothing factor. Higher values make the moving average significantly flatter when the price is near the range midpoint, requiring stronger moves to trigger a reaction. 🔹 Colors AMA Color: The color of the main Adaptive Centric Moving Average line. Bullish Fill: The color used for the gradient fill when the price is above the AMA. Bearish Fill: The color used for the gradient fill when the price is below the AMA.
אינדיקטור Pine Script®
מאת ‎LuxAlgo‎
1
Gaussian Volume Profile [LuxAlgo]The Gaussian Volume Profile indicator is a sophisticated volume analysis tool that uses the Levenberg-Marquardt optimization algorithm to fit a Sum of Gaussians model to historical volume distribution. This approach transcends traditional discrete volume profiles by providing a continuous, noise-reduced representation of liquidity clusters, allowing for the precise identification of high-volume nodes and their respective price boundaries. 🔶 USAGE The indicator projects a lateral volume density map to the right of the current price action. Users can utilize this tool to identify "fair value" zones where the Gaussian peaks are most concentrated. Unlike standard profiles that show jagged horizontal bars, this tool provides a smooth "fit" line that highlights the true center of gravity for volume at specific price levels. 🔹 Identifying High-Volume Nodes The script automatically detects local maxima (peaks) within the fitted Gaussian model. These peaks represent the most significant price levels where the highest density of trading occurred. Horizontal dashed lines are drawn at these apexes, color-coded to match the specific Gaussian component that is most dominant at that price. 🔹 Zone Width and Volatility By observing the width (standard deviation) of the individual Gaussian components (the dotted curves), traders can gauge the "breadth" of a value area. A narrow, sharp peak suggests a very specific price level of agreement, while a wide, shallow curve indicates a broad range where volume was distributed less precisely. 🔶 DETAILS This tool represents a scientific advancement over regular Volume Profiles by applying a Gaussian Density model to market data: Noise Reduction: Discrete profiles are often "noisy," with small volume gaps between price ticks. The Sum of Gaussians model acts as a sophisticated filter, smoothing out insignificant variances to reveal the underlying structural liquidity. Levenberg-Marquardt Optimization: The script utilizes the LM algorithm, a standard in non-linear least squares problems, to iteratively refine the fit of multiple Gaussian pulses. This ensures the model converges on the most mathematically accurate representation of the volume data. Precise Liquidity Centers: While a standard profile bin might be several ticks wide, the Gaussian apex provides a mathematically derived "center" ($\mu$) for liquidity, often offering more precise support and resistance levels. Continuous Distribution: Because it models volume as a continuous function, it can estimate volume density between discrete price bins, providing a more fluid view of market interest. The visual output combines a lateral histogram with a bold Gaussian density curve, color-coded components, and auto-detected peak levels for a comprehensive view of institutional interest. 🔶 SETTINGS 🔹 Profile Settings Lookback Window: The number of historical bars used to calculate the volume profile distribution. Number of Bins: Determines the vertical resolution of the profile. More bins provide more detail but require more computation. 🔹 Gaussian Settings Max Potential Peaks: The maximum number of Gaussian components (nodes) the algorithm will attempt to fit to the data. Max Iterations: Controls how many times the LM optimizer refines the fit. Higher values improve accuracy but may impact performance. Initial Lambda: The damping factor for the optimization algorithm, affecting the early steps of the fitting process. 🔹 Visuals Histogram Resolution: The maximum horizontal length of the projected histogram and fit line, measured in bar widths. Highlight Window Range: Toggles a visual background box covering the historical lookback area for context. Highlight Detected Peaks: Detects local maxima in the final fit and draws horizontal dashed levels at those price points. Fit Color: Sets the static color for the main density curve. Auto: When enabled, the fit color automatically adapts to your chart's foreground color (e.g., white on dark backgrounds).
אינדיקטור Pine Script®
מאת ‎LuxAlgo‎
1
TP-Trend中文 【原理概要】 本指标跳出了传统“仅依赖收盘价”的局限，采用典型价格 $HLC/3$ 作为底层数据源。通过对均线进行二阶平滑处理（EMA of EMA），能比传统双均线更早地发现趋势转折，同时有效过滤震荡市的假突破。 【核心功能】 双重平滑逻辑：$X_2$为典型价格的 6 周期平滑， $X_3$为其二阶 5 周期平滑，捕捉动能加速度。 视觉增强系统： 自动为 K 线着色。红色/棕色代表多头波段，绿色代表空头波段。 信号确认： 亮黄色（Buy）与深蓝色（Sell）柱体精准标记趋势引爆点。 零未来函数： 算法完全基于时序历史数据，信号不漂移，可直接用于实盘参考。 【使用建议】建议配合大周期趋势或成交量指标使用。在日线级别捕捉波段主升浪效果最佳。 English This script utilizes Typical Price ($HLC/3$) instead of standard Close prices to provide a more holistic view of market value. By applying a dual-smoothing EMA process, it identifies trend shifts with reduced lag while maintaining high sensitivity. Trend Coloring: Bars change color based on momentum (Red for Bullish, Green for Bearish). Actionable Signals: Distinct Yellow (Buy) and Blue (Sell) highlights on crossover bars. No Repaint: Purely mathematical approach based on historical data.
אינדיקטור Pine Script®
מאת ‎william_wq‎
Alpha Hunter Integrated MACD & Oscillator [wjdtks255]Indicator Title: Alpha Hunter Integrated MACD & Oscillator Pro Short Description A high-precision hybrid oscillator that integrates MACD dynamics with a secondary-smoothed histogram to eliminate market noise and capture trend reversals with minimal lag. Detailed Description Overview The Alpha Hunter Integrated MACD & Oscillator is designed to overcome the inherent lag of standard MACD indicators. By applying an exponential moving average (EMA) filter to the histogram itself and incorporating a momentum direction check, this tool identifies high-probability entry points while filtering out "whipsaws" commonly found in choppy markets. Key Technical Pillars Dual-Smoothed Histogram: Unlike standard oscillators, this script smooths the raw histogram values using a secondary filtering period. This reveals the true underlying momentum before price action fully shifts. Momentum Directional Filter: Entry signals are only triggered when the MACD line’s slope aligns with the crossover, ensuring you don't enter against a stalling trend. Dynamic Trend Clouds: The visual fill between the MACD and Signal lines acts as a "Trend Cloud," providing immediate visual feedback on the strength and duration of the current trend. The Winning Trading Strategy (How to Use) To maximize win rates, it is highly recommended to use this indicator as a Confirmation Oscillator alongside a Long-term Trend Filter (like a 200 EMA) on your main chart. 1. Long Setup (Buy) Context: Price must be trading above the 200 EMA on the main chart. Signal: A green "BUY" triangle and label appear on the oscillator. Confirmation: The Histogram should be green and rising. Exit: Exit at a pre-defined Take Profit (TP) box or when a bearish MACD crossunder occurs. 2. Short Setup (Sell) Context: Price must be trading below the 200 EMA on the main chart. Signal: A red "SELL" triangle and label appear on the oscillator. Confirmation: The Histogram should be red and falling. Exit: Exit at the designated Stop Loss (SL) or when a bullish MACD crossover occurs. Input Parameters & Optimization Fast/Slow/Signal: Default 12, 26, 9. (Standard for most markets). Signal Smoothing: Set to 5 for a balance of speed and reliability. Increase to 8+ for swing trading on higher timeframes. Recommended Timeframes: 15m, 1h, and 4h for the best signal-to-noise ratio. Author's Note This indicator is a "No-Repaint" script. Signals are confirmed at the close of the candle to ensure reliability during live trading. Always use proper risk management.
אינדיקטור Pine Script®
מאת ‎wjdtks255‎
5
Universal Moving Average🙏🏻 UMA (Universal Moving Average) represents the most natural and prolly ‘the’ final general universal entity for calculating rolling typical value for any type of time-series. Simply via different weighting schemes applied together, it encodes: Location of each datapoint in corresponding fields (price, time, volume) Informational relevance of each datapoint via using windowing functions that are fundamental in nature and go beyond DSP inventions & approximations Innovation in state space (in our case = volatility) The real beauty of this development: being simply a weighting scheme that can be applied to anything: be it weighted median , weighted quantile regression, or weighted KDE , or a simple weighted mean (like in this script). As long as a method accepts weights, you can harness the power of this entity. It means that final algorithmic complexity will match your initial tool. As a moving ‘average’ it beats ALMA, KAMA, MAMA, VIDYA and all others because it is a simple and general entity, and all it does is encoding ‘all’ available information. I think that post might anger a lot of people, because lotta things will be realized as legacy and many paywalls gonna be ignored, specially for the followers of DSP cult, the ones who yet don’t understand that aggregated tick data is not a signal omg, it’s a completely different type of time series where your methods simply don’t fit even closely. I am also sorry to inform y’all, that spectral analysis is much closer to state-space methods in spirit than to DSP. But in fact DSP is cool and I love it, well for actual signals xD ... Weights explained & how to use them: as I already said, the whole thing is based on combining different set of weights, and you can turn them on/off in script settings. Btw I've set em up defaults so you can use the thing on price data out of the box right away. Price, Time, Volume weights: encode location of every datapoint in Price & TIme & Volume field Howtouse: u have to disable one weight that corresponds to the field you apply UMA to. E.g if you apply UMA to prices, you turn off price weighting And turn on time and volume weighting. Or if you apply UMA to volume delta, you turn off volume weighting And turn on price and time weighting. Higher prices are more important, this asymmetry is confirmed and even proved by the fact that prices can’t be negative (don’t even mention that incorrect rollover on CL contract in 2k20...). Signal weights: encode actuality/importance/relevance of datapoints. Howtouse: in DSP terms, it provides smoothing, but also compensates for the lag it introduces. This smoothness is useful if you use slope reversals for signal generation aka watching peaks and valleys in a moving average shape. It's also better to perturb smoothed outputs with this , this way you inject high freq content back, But in controlled way! Signal = information. The fundamental universal entity behind so-called “smoothing” in DSP has nothing to do with signals and goes eons beyond DSP. This is simply about measuring the relevance of data in time. First, new datapoints need some time to be “embedded” into the timeline, you can think of it as time proof, kinda stuff needs time to be proved, accepted; while earliest datapoints lose relevance in time. Second, along with the first notion, at the same time there’s the counter notion that simply weights new data more, acting as a counterweight from the down-weighting of the latest datapoints introduced by the first notion. The first part can be represented as PDF of beta(2, 2) window (a set of weights in our case). It’s actually well known as the Welch window, that lives in between so called statistical and DSP worlds, emerges in multiple contexts. Mainstream DSP users tho mostly don’t use this one, they use primitive legacy windowing function, you can find all kinds on this wiki page. Now the second part, where DSP adepts usually stop, is to introduce the second compensating windowing function. Instead they try to reduce window size, or introduce other kinds of volatility weights, do some tricks, but it ain’t provides obviously. The natural step here is to simply use the integral of the initial window; if the initial window is beta(2, 2) then what we simply need is CDF of beta(2, 2), in fact the vertically inverted shape of it aka survival function . That’s it bros. Simply as that. When both of these are applied you have smth magical, your output becomes smooth and yet not lagging. No arbitrary windowing functions, tricks with data modification etc Why beta(2, 2)? It naturally arises in many contexts, it’s based on one of the most fundamental functions in the universe: x^2. It has finite support. I can talk more bout it on request, but I am absolutely sure this is it. ^^ impulse response of the resulting weighs together (green) compared with uniform weights aka boxcar (red). Made with this script . Weighing by state: encodes state-space innovation of each datapoint, basically magnitude of changes, strength of these changes, aka volatility. Howtouse: this makes your moving average volatility aware in proper math ways. The influence of datapoints will be stronger when changes are stronger. This is weighting by innovations, or weighting by volatility by using squared returns. Why squared returns? They encode state‑space innovations properly because the innovation of any continuous‑time semimartingale is about its quadratic variation, and quadratic variation is built from squared increments, not absolute increments. Adaptive length is not the right way to introduce adaptivity by volatility xD. When you weight datapoints by squared returns you’re already dynamically varying ‘effective’ data size, you don’t need anything else. ... It’s all good, progress happens, that’s how the Universe works, that's how Universal Moving Average works. Time to evolve. I might update other scripts with this complete weighting scheme, either by my own desire or your request. ... ∞
אינדיקטור Pine Script®
מאת ‎gorx1‎
5
Dynamic Laguerre Filter Bands | OttoThis indicator combines trend-following and volatility analysis by enhancing the traditional Laguerre filter with a dynamic, volatility-adjusted band system. Instead of using fixed thresholds, the bands adapt in real-time to changing market conditions by applying smoothed standard deviation calculations. This design keeps the indicator responsive to significant price movements while effectively filtering out short-term market noise, resulting in more accurate trend identification and breakout signals. Core Concept The indicator is built around the following key components: Laguerre Filter: The Laguerre filter is designed to smooth out price data by reducing market noise while still being quick enough to detect real changes in price direction. Its goal is to create a clear, smooth trend line that helps traders/investors focus on the overall market trend without getting distracted by small, random price swings. It uses a parameter called gamma to control how it balances smoothness and responsiveness: A lower gamma gives more weight to recent price data, making the filter react faster to new price changes. This means the trend line is more sensitive but may also be less smooth and more prone to small fluctuations. A higher gamma gives more weight to past price data, making the filter smoother and less sensitive to quick changes. This helps reduce noise and produces a steadier trend line, but it also introduces more lag, meaning the filter reacts slower to new price moves. By adjusting gamma, the Laguerre filter lets you choose the balance between following price changes quickly and having a stable, noise-free trend signal. Standard Deviation: shows how much price varies from the mean. In this indicator, it’s used to measure market volatility. Volatility Bands: The upper and lower bands are based on an EMA-smoothed standard deviation of price. The EMA reduces sudden jumps in volatility, creating smoother and more stable bands that still respond to changing market conditions. These bands are plotted around the Laguerre filter line, expanding and contracting in a controlled way to stay aligned with real market movement while avoiding short-term noise. Signal Logic: A long signal is triggered when the close price crosses above the upper band. A short signal occurs when the close price falls below the lower band. ⚙️ Inputs Source: Price source used in calculations Gamma: Adjusts how much the Laguerre filter responds to price changes. Lower gamma values make the filter react more to recent prices, while higher values give more influence to older data, making the line smoother but slower to respond. Volatility Length: Period used to calculate standard deviation Volatility Smoothing Length: EMA smoothing length for standard deviation Multiplier: Scales the width of the bands based on volatility 📈 Visual Output Laguerre Filter Line: Plots the laguerre filter line, colored dynamically based on signal direction (green for bullish, purple for bearish) Upper & Lower Bands: Volatility-based bands that adjust with market conditions. (green for bullish, purple for bearish) Glow Effect: Optional glow layer to enhance visibility of the laguerre filter trend line (green for bullish, purple for bearish) Bar Coloring: Candlesticks and bar colors reflect the active signal state for fast visual interpretation (green for bullish, purple for bearish) How to Use Apply the indicator to your chart and monitor for signal events: Long Signal: When price closes above the upper band Short Signal: When price closes below the lower band 🔔 Alerts This indicator supports optional alert conditions you can enable for: Long Signal: Close price crossing above the upper band Short Signal: Close price crossing below the lower band ⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
אינדיקטור Pine Script®
מאת ‎oquant‎
2
FVG Trailing Stop [LuxAlgo]The FVG Trailing Stop indicator tracks unmitigated Fair Value Gaps (FVG) data to produce a Trailing Stop indicator able to determine if the market is uptrending or downtrending easily. 🔶 USAGE The FVG Trailing Stop is intended to identify trend directions through its position relative to the closing price: Bullish: Price is located above the Trailing Stop, indicating that all Bearish FVGs have been mitigated and the trend is anticipated to continue upwards. Bearish State: Price is located below the Trailing Stop, indicating that all Bullish FVGs have been mitigated and the trend is anticipated to continue downwards. The Trailing Stop originates from two extremities obtained from the average of respective unmitigated FVGs. The specific directional average is also displayed as a more transparent secondary line, however, the trailing stop is derived from this value and a new trend will not be detected until the opposite directional average is crossed. Price reaching the Trailing Stop is caused by retracements and can lead to the following scenarios: Outcome 1: The directional average is crossed next, indicating a new trend direction. Outcome 2: The directional average is held as support or resistance, leading to a new impulse and a continuation of the trend. 🔹 Reset on Cross While price crossing the Trailing Stop should be considered as a sign of an upcoming trend change; it is possible for the price to still evolve outside it. As a solution, we have included the "Reset on Cross" feature, which (as the name suggests) hides and resets the Trailing Stop each time it is crossed, leading to a "Neutral" state. This opens the opportunity for the Trailing Stop to be displayed again once the price moves again in the direction of the pre-established trend. A trader might use this to accumulate positions within a specific trend. 🔶 DETAILS The script uses a typical identification method for FVGs. Once identified, the script collects the point of the FVG farthest from the current price when formed. For Upwards FVGs this is the bottom of the FVG. For Downwards FVGs this is the top of the FVG. The data is managed only to use the last input lookback of FVGs. If an FVG is mitigated, it frees up a spot in the memory for a new FVG, however, if the lookback is full, the oldest will be deleted. From there, it uses a "trailing" logic only to move the Trailing Stop in one direction until the trailing stop resets or the direction flips. The extremities used to calculate the Trailing Stop are created from 2 calculation steps, the first step involves taking the raw average of the FVG mitigation levels, and the second step applies a simple moving average (SMA) smoothing of the precedent-obtained averages. 🔶 SETTINGS Unmitigated FVG Lookback: Sets the maximum number of Unmitigated FVGs that the script will use. Smoothing Length: Sets the smoothing length for the Trailing Stop to reduce erratic results. Reset on Cross: When enabled, hide and reset the Trailing Stop until the price starts moving in the pre-established trend direction again.
אינדיקטור Pine Script®
מאת ‎LuxAlgo‎
17
Candle Breakout Oscillator [LuxAlgo]The Candle Breakout Oscillator tool allows traders to identify the strength and weakness of the three main market states: bullish, bearish, and choppy. Know who controls the market at any given moment with an oscillator display with values ranging from 0 to 100 for the three main plots and upper and lower thresholds of 80 and 20 by default. 🔶 USAGE The Candle Breakout Oscillator represents the three main market states, with values ranging from 0 to 100. By default, the upper and lower thresholds are set at 80 and 20, and when a value exceeds these thresholds, a colored area is displayed for the trader's convenience. This tool is based on pure price action breakouts. In this context, we understand a breakout as a close above the last candle's high or low, which is representative of market strength. All other close positions in relation to the last candle's limits are considered weakness. So, when the bullish plot (in green) is at the top of the oscillator (values above 80), it means that the bullish breakouts (close below the last candle low) are at their maximum value over the calculation window, indicating an uptrend. The same interpretation can be made for the bearish plot (in red), indicating a downtrend when high. On the other hand, weakness is indicated when values are below the lower threshold (20), indicating that breakouts are at their minimum over the last 100 candles. Below are some examples of the possible main interpretations: There are three main things to look for in this oscillator: Value reaches extreme Value leaves extreme Bullish/Bearish crossovers As we can see on the chart, before the first crossover happens the bears come out of strength (top) and the bulls come out of weakness (bottom), then after the crossover the bulls reach strength (top) and the bears weakness (bottom), this process is repeated in reverse for the second crossover. The other main feature of the oscillator is its ability to identify periods of sideways trends when the sideways values have upper readings above 80, and trending behavior when the sideways values have lower readings below 20. As we just saw in the case of bullish vs. bearish, sideways values signal a change in behavior when reaching or leaving the extremes of the oscillator. 🔶 DETAILS 🔹 Data Smoothing The tool offers up to 10 different smoothing methods. In the chart above, we can see the raw data (smoothing: None) and the RMA, TEMA, or Hull moving averages. 🔹 Data Weighting Users can add different weighting methods to the data. As we can see in the image above, users can choose between None, Volume, or Price (as in Price Delta for each breakout). 🔶 SETTINGS Window: Execution window, 100 candles by default 🔹 Data Smoothing Method: Choose between none or ten moving averages Smoothing Length: Length for the moving average Weighting Method: Choose between None, Volume, or Price 🔹 Thresholds Top: 80 by default Bottom: 20 by default
אינדיקטור Pine Script®
מאת ‎LuxAlgo‎
6
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.
אינדיקטור Pine Script®
מאת ‎The_Peaceful_Lizard‎
14
[GYTS] FiltersToolkit LibraryFiltersToolkit Library 🌸 Part of GoemonYae Trading System (GYTS) 🌸 🌸 --------- 1. INTRODUCTION --------- 🌸 💮 What Does This Library Contain? This library is a curated collection of high-performance digital signal processing (DSP) filters and auxiliary functions designed specifically for financial time series analysis. It includes a shortlist of our favourite and best performing filters — each rigorously tested and selected for their responsiveness, minimal lag and robustness in diverse market conditions. These tools form an integral part of the GoemonYae Trading System (GYTS), chosen for their unique characteristics in handling market data. The library contains two main categories: 1. Smoothing filters (low-pass filters and moving averages) for e.g. denoising, trend following 2. Detrending tools (high-pass and band-pass filters, known as "oscillators") for e.g. mean reversion This collection is finely tuned for practical trading applications and is therefore not meant to be exhaustive. However, will continue to expand as we discover and validate new filtering techniques. I welcome collaboration and suggestions for novel approaches. 🌸 ——— 2. ADDED VALUE ——— 🌸 💮 Unified syntax and comprehensive documentation The FiltersToolkit Library brings together a wide array of valuable filters under a unified, intuitive syntax. Each function is thoroughly documented, with clear explanations and academic sources that underline the mathematical rigour behind the methods. This level of documentation not only facilitates integration into trading strategies but also helps underlying the underlying concepts and rationale. 💮 Optimised performance and readability The code prioritizes computational efficiency while maintaining readability. Key optimizations include: - Minimizing redundant calculations in recursive filters - Smart coefficient caching - Efficient state management - Vectorized operations where applicable 💮 Enhanced functionality and flexibility Some filters in this library introduce extended functionality beyond the original publications. For instance, the MESA Adaptive Moving Average (MAMA) and Ehlers’ Combined Bandpass Filter incorporate multiple variations found in the literature, thereby providing traders with flexible tools that can be fine-tuned to different market conditions. 🌸 ——— 3. THE FILTERS ——— 🌸 💮 Hilbert Transform Function This function implements the Hilbert Transform as utilised by John Ehlers. It converts a real-valued time series into its analytic signal, enabling the extraction of instantaneous phase and frequency information—an essential step in adaptive filtering. Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004) 💮 Homodyne Discriminator By leveraging the Hilbert Transform, this function computes the dominant cycle period through a Homodyne Discriminator. It extracts the in-phase and quadrature components of the signal, facilitating a robust estimation of the underlying cycle characteristics. Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004) 💮 MESA Adaptive Moving Average (MAMA) An advanced dual-stage adaptive moving average, this function outputs both the MAMA and its companion FAMA. It combines adaptive alpha computation with elements from Kaufman’s Adaptive Moving Average (KAMA) to provide a responsive and reliable trend indicator. Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004) 💮 BiQuad Filters A family of second-order recursive filters offering exceptional control over frequency response: - High-pass filter for detrending - Low-pass filter for smooth trend following - Band-pass filter for cycle isolation The quality factor (Q) parameter allows fine-tuning of the resonance characteristics, making these filters highly adaptable to different market conditions. Source: Robert Bristow-Johnson's Audio EQ Cookbook, implemented by @The_Peaceful_Lizard 💮 Relative Vigor Index (RVI) This filter evaluates the strength of a trend by comparing the closing price to the trading range. Operating similarly to a band-pass filter, the RVI provides insights into market momentum and potential reversals. Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004) 💮 Cyber Cycle The Cyber Cycle filter emphasises market cycles by smoothing out noise and highlighting the dominant cyclical behaviour. It is particularly useful for detecting trend reversals and cyclical patterns in the price data. Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004) 💮 Butterworth High Pass Filter Inspired by the classical Butterworth design, this filter achieves a maximally flat magnitude response in the passband while effectively removing low-frequency trends. Its design minimises phase distortion, which is vital for accurate signal interpretation. Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004) 💮 2-Pole SuperSmoother Employing a two-pole design, the SuperSmoother filter reduces high-frequency noise with minimal lag. It is engineered to preserve trend integrity while offering a smooth output even in noisy market conditions. Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004) 💮 3-Pole SuperSmoother An extension of the 2-pole design, the 3-pole SuperSmoother further attenuates high-frequency noise. Its additional pole delivers enhanced smoothing at the cost of slightly increased lag. Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004) 💮 Adaptive Directional Volatility Moving Average (ADXVma) This adaptive moving average adjusts its smoothing factor based on directional volatility. By combining true range and directional movement measurements, it remains exceptionally flat during ranging markets and responsive during directional moves. Source: Various implementations across platforms, unified and optimized 💮 Ehlers Combined Bandpass Filter with Automated Gain Control (AGC) This sophisticated filter merges a highpass pre-processing stage with a bandpass filter. An integrated Automated Gain Control normalises the output to a consistent range, while offering both regular and truncated recursive formulations to manage lag. Source: John F. Ehlers – “Truncated Indicators” (2020), “Cycle Analytics for Traders” (2013) 💮 Voss Predictive Filter A forward-looking filter that predicts future values of a band-limited signal in real time. By utilising multiple time-delayed feedback terms, it provides anticipatory coupling and delivers a short-term predictive signal. Source: John Ehlers - "A Peek Into The Future" (TASC 2019-08) 💮 Adaptive Autonomous Recursive Moving Average (A2RMA) This filter dynamically adjusts its smoothing through an adaptive mechanism based on an efficiency ratio and a dynamic threshold. A double application of an adaptive moving average ensures both responsiveness and stability in volatile and ranging markets alike. Very flat response when properly tuned. Source: @alexgrover (2019) 💮 Ultimate Smoother (2-Pole) The Ultimate Smoother filter is engineered to achieve near-zero lag in its passband by subtracting a high-pass response from an all-pass response. This creates a filter that maintains signal fidelity at low frequencies while effectively filtering higher frequencies at the expense of slight overshooting. Source: John Ehlers - TASC 2024-04 "The Ultimate Smoother" Note: This library is actively maintained and enhanced. Suggestions for additional filters or improvements are welcome through the usual channels. The source code contains a list of tested filters that did not make it into the curated collection.
ספריית Pine Script®
מאת ‎GoemonYae‎
14
Ehlers Maclaurin Ultimate Smoother [CT]Ehlers Maclaurin Ultimate Smoother Introduction The Ehlers Maclaurin Ultimate Smoother is an innovative enhancement of the classic Ehlers SuperSmoother. By leveraging advanced Maclaurin series approximations, this indicator offers superior market analysis and signal generation. The indicator combines Ehlers' Ultimate Smoother with Maclaurin series approximations to create a more efficient and accurate smoothing mechanism: Input price data passes through the initial smoothing phase Maclaurin series approximates trigonometric functions Enhanced high-pass filter removes market noise Final smoothing phase produces the output signal Why the Maclaurin Approach? The Maclaurin series is a special form of the Taylor series, centered around 0. It provides an efficient way to approximate complex functions using polynomial terms. In this indicator, we use the Maclaurin approach to improve the sine and cosine functions, resulting in: Faster Calculations: By using polynomial approximations, we significantly reduce computational complexity. Improved Stability: The approximation provides a more stable numerical basis for calculations. Preservation of Precision: Despite the approximation, we maintain the precision needed for price smoothing. Calculations The indicator employs several key mathematical components: Maclaurin Series Approximation: sin(x) ≈ x - x³/3! + x⁵/5! - x⁷/7! + x⁹/9! cos(x) ≈ 1 - x²/2! + x⁴/4! - x⁶/6! + x⁸/8! Smoothing Algorithm: Uses exponential smoothing with optimized coefficients Implements high-pass filtering for noise reduction Applies dynamic weighting based on market conditions Mathematical Foundation Utilizes Maclaurin series for trigonometric approximation Implements Ehlers' smoothing principles Incorporates advanced filtering techniques Technical Advantages Signal Processing: Lag Reduction: Faster signal detection with less delay. Noise Filtration: Effective elimination of high-frequency noise. Precision Enhancement: Preservation of critical price movements. Adaptive Processing: Dynamic response to market volatility. Visual Enhancements: Smart color intensity mapping. Real-time visualization of trend strength. Adaptive opacity based on movement significance. Implementation Core Configuration: Plot Type: Choose between the original and the Maclaurin enhanced version. Length: Default set to 30, optimal for daily timeframes. hpLength: Default set to 10 for enhanced noise reduction. Advanced Parameters: The indicator offers advanced control with: Dual processing modes (Original/Maclaurin). Dynamic color intensity system. Customizable smoothing parameters. Professional Analysis Tools: Accurate trend reversal identification. Advanced support/resistance detection. Superior performance in volatile markets. Technical Specifications Maclaurin Series Implementation: The indicator employs a 5-term Maclaurin series approximation for both sine and cosine, ensuring efficient and accurate computation. Performance Metrics Improved processing efficiency. Reduced memory utilization. Increased signal accuracy. Licensing & Attribution © 2024 Mupsje aka CasaTropical Professional Credits Original Ultimate and SuperSmoother concept: John F. Ehlers Maclaurin enhancement: Casa Tropical (CT) www.mathsisfun.com
אינדיקטור Pine Script®
מאת ‎Mupsje‎
4
Catalyst TrendCatalyst Trend – A Comprehensive Trend and Regime Analyzer The Catalyst Trend indicator was designed to dynamically and intuitively merge various classic analytical techniques. The goal is to filter out short-term market noise and reveal reliable trend phases or potential turning points. Below is a detailed explanation of its core elements and practical usage. 1. Concept and Idea Multidimensional Trend Detection This indicator goes beyond a simple momentum or volatility focus. It factors in multiple measurements to provide a more well-rounded market perspective. Versatile Indicator Fusion Linear Regression (LinReg): Multiple LinReg calculations are combined to smooth out price fluctuations and produce a robust trendline—known here as the “Cycle Reduced Line.” ADX (Average Directional Index): Measures trend strength. RSI (Relative Strength Index): Flags potential overbought or oversold conditions, in both the current timeframe and a higher timeframe. ATR (Average True Range): Assesses volatility; used to dynamically adjust calculation lengths. By weaving these elements together, the indicator adds value beyond simply stacking multiple indicators. It adapts to real-time market conditions, aiming to highlight genuine trends and reduce false signals. 2. Key Functions and Calculations Dynamic Length & Smoothing A blend of volatility (ATR), ADX values, and RSI inputs determines how many candles are used in the LinReg calculations and how heavily the data is smoothed. This allows the indicator to respond promptly during periods of high volatility, while automatically adjusting to filter out unnecessary noise in quieter phases.c Cycle Reduced Line The script averages several offset LinReg calculations to produce a cleaner overall signal. Random outliers are thus minimized, making the trend path more visually consistent. An additional EMA smoothing (“Final Smoothing”) further stabilizes this trendline, reducing the impact of minor price fluctuations. Channel Bands (Optional) These bands are derived from the standard deviation of the price residual (the difference between the smoothed price and the trendline). They highlight potential over-extension zones: the upper band can mark short-term overbought areas, while the lower band might indicate oversold conditions. Trend and Sideways Determination Slope Calculation: The slope of the trendline (comparing the current bar to the previous one) helps identify short-term directional shifts. DX Threshold: Once the ADX surpasses a user-defined threshold and the slope is positive, it may indicate a developing uptrend. Similarly, if the slope is negative and ADX > threshold, it could signal a potential downtrend. Multi-Level Color Coding Original Mode: Interpolated colors reflect uptrends, downtrends, and sideways phases, factoring in metrics like ADX and RSI. Single Color: For a neutral look, the indicator can be displayed in one uniform color. HTF RSI: This mode uses the higher-timeframe RSI to color the trendline (Long/Short/Neutral), offering a quick gauge of overarching market pressure. 3. Use Cases and Interpretation Timeframes & Markets The indicator is versatile and adapts well to different intervals, from 5-minute charts to weekly views. It can be applied to various markets—crypto, forex, stocks—since volatility and trend strength are universal concepts. Signal Recognition Color Swings into a more pronounced upward hue (e.g., green) may signal mounting strength. Neutral or mixed tones often point to sideways phases, which breakout traders might watch for potential price surges. A shift to downward colors (e.g., red) may indicate a growing bearish trend. Channel Bands & Volatility When the bands spread widely, it’s wise to proceed with caution: abrupt spikes above the upper band or below the lower band can flag rapid short-term extremes. These bands are more of a reference for potential overextension than a strict buy or sell trigger. Additional Confirmations Not a standalone panacea: The Catalyst Trend indicator is an analytical tool, best used alongside other methods such as volume analysis or price action (candlestick patterns, support/resistance levels) to bolster confidence in trading decisions. 4. Practical Tips Parameter Adjustments Depending on the market—crypto vs. traditional currency pairs—different ADX, RSI, or smoothing periods may be more effective. Experiment with the settings to tailor the indicator to your preferred timeframe. Strategic Integration Trailing Stops: For those riding a trend, the trendline or the channel bands may serve as a reference to trail stop-loss orders. Trend Confirmation: Using RSI and ADX filters can help traders avoid sideways markets or stay the course when the trend is strong. 5. Important Final Notes No Guarantee of Profits No indicator can predict the future. Markets are inherently volatile and often unpredictable. Responsible Risk Management Test the indicator in a demo environment or with smaller positions before committing to large trades.
אינדיקטור Pine Script®
מאת ‎jonathanalbrecht_trader‎
Hybrid Triple Exponential Smoothing🙏🏻 TV, I present you HTES aka Hybrid Triple Exponential Smoothing, designed by Holt & Winters in the US, assembled by me in Saint P. I apply exponential smoothing individually to the data itself, then to residuals from the fitted values, and lastly to one-point forecast (OPF) errors, hence 'hybrid'. At the same time, the method is a closed-form solution and purely online, no need to make any recalculations & optimize anything, so the method is O(1). ^^ historical OPFs and one-point forecasting interval plotted instead of fitted values and prediction interval Before the How-to, first let me tell you some non-obvious things about Triple Exponential smoothing (and about Exponential Smoothing in general) that not many catch. Expo smoothing seems very straightforward and obvious, but if you look deeper... 1) The whole point of exponential smoothing is its incremental/online nature, and its O(1) algorithm complexity, making it dope for high-frequency streaming data that is also univariate and has no weights. Consequently: - Any hybrid models that involve expo smoothing and any type of ML models like gradient boosting applied to residuals rarely make much sense business-wise: if you have resources to boost the residuals, you prolly have resources to use something instead of expo smoothing; - It also concerns the fashion of using optimizers to pick smoothing parameters; honestly, if you use this approach, you have to retrain on each datapoint, which is crazy in a streaming context. If you're not in a streaming context, why expo smoothing? What makes more sense is either picking smoothing parameters once, guided by exogenous info, or using dynamic ones calculated in a minimalistic and elegant way (more on that in further drops). 2) No matter how 'right' you choose the smoothing parameters, all the resulting components (level, trend, seasonal) are not pure; each of them contains a bit of info from the other components, this is just how non-sequential expo smoothing works. You gotta know this if you wanna use expo smoothing to decompose your time series into separate components. The only pure component there, lol, is the residuals; 3) Given what I've just said, treating the level (that does contain trend and seasonal components partially) as the resulting fit is a mistake. The resulting fit is level (l) + trend (b) + seasonal (s). And from this fit, you calculate residuals; 4) The residuals component is not some kind of bad thing; it is simply the component that contains info you consciously decide not to include in your model for whatever reason; 5) Forecasting Errors and Residuals from fitted values are 2 different things. The former are deltas between the forecasts you've made and actual values you've observed, the latter are simply differences between actual datapoints and in-sample fitted values; 6) Residuals are used for in-sample prediction intervals, errors for out-of-sample forecasting intervals; 7) Choosing between single, double, or triple expo smoothing should not be based exclusively on the nature of your data, but on what you need to do as well. For example: - If you have trending seasonal data and you wanna do forecasting exclusively within the expo smoothing framework, then yes, you need Triple Exponential Smoothing; - If you wanna use prediction intervals for generating trend-trading signals and you disregard seasonality, then you need single (simple) expo smoothing, even on trending data. Otherwise, the trend component will be included in your model's fitted values → prediction intervals. 8) Kind of not non-obvious, but when you put one smoothing parameter to zero, you basically disregard this component. E.g., in triple expo smoothing, when you put gamma and beta to zero, you basically end up with single exponential smoothing. ^^ data smoothing, beta and gamma zeroed out, forecasting steps = 0 About the implementation * I use a simple power transform that results in a log transform with lambda = 0 instead of the mainstream-used transformers (if you put lambda on 2 in Box-Cox, you won't get a power of 2 transform) * Separate set of smoothing parameters for data, residuals, and errors smoothing * Separate band multipliers for residuals and errors * Both typical error and typical residuals get multiplied by math.sqrt(math.pi / 2) in order to approach standard deviation so you can ~use Z values and get more or less corresponding probabilities * In script settings → style, you can switch on/off plotting of many things that get calculated internally: - You can visualize separate components (just remember they are not pure); - You can switch off fit and switch on OPF plotting; - You can plot residuals and their exponentially smoothed typical value to pick the smoothing parameters for both data and residuals; - Or you might plot errors and play with data smoothing parameters to minimize them (consult SAE aka Sum of Absolute Errors plot); ^^ nuff said More ideas on how to use the thing 1) Use Double Exponential Smoothing (data gamma = 0) to detrend your time series for further processing (Fourier likes at least weakly stationary data); 2) Put single expo smoothing on your strategy/subaccount equity chart (data alpha = data beta = 0), set prediction interval deviation multiplier to 1, run your strat live on simulator, start executing on real market when equity on simulator hits upper deviation (prediction interval), stop trading if equity hits lower deviation on simulator. Basically, let the strat always run on simulator, but send real orders to a real market when the strat is successful on your simulator; 3) Set up the model to minimize one-point forecasting errors, put error forecasting steps to 1, now you're doing nowcasting; 4) Forecast noisy trending sine waves for fun. ^^ nuff said 2 All Good TV ∞
אינדיקטור Pine Script®
מאת ‎gorx1‎
5
Normalized Linear Regression (LSMA) OscillatorNormalized Linear Regression (LSMA) Oscillator By Nathan Farmer The Normalized LSMA Oscillator is a trend-following indicator that enhances the classic Linear Regression (LSMA) by applying a range of normalization techniques. This indicator allows traders to smooth out and normalize LSMA signals for better trend detection and dynamic market adaptation. Key Features: Configurable Normalization Methods: This indicator offers several normalization techniques, such as Z-Score, Min-Max, Mean Normalization, Robust Scaler, Logistic Function, and Quantile Transformation. Each method helps in refining LSMA outputs to improve clarity in both trending and ranging market conditions. Smoothing Options: Smoothing can be applied after normalization, helping to reduce noise in the signals, thus making trend-following strategies that use this indicator more effective. Recommended Settings: Logistic Function Normalization: Recommended length of around 12, based on my preferred signal frequency. Z-Score Normalization: Medium period (close to the default of 50), based on my preferred signal frequency. Min-Max Normalization: Medium period, based on my preferred signal frequency. Mean Normalization: Medium period, based on my preferred signal frequency. Robust Scaler: Medium period, based on my preferred signal frequency. Quantile Transformation: Medium period, based on my preferred signal frequency. Usage: Designed primarily for trend-following strategies, this indicator adapts well to varying market conditions. Traders can experiment with the various normalization and smoothing settings to match the indicator to their specific needs and market preferences. Recommendation before usage: Always backtest the indicator for yourself with respect to how you intend to use it. Modify the parameters to suit your needs, over your preferred time frame, on your preferred asset. My preferences are for the assets I happened to be looking at when I made this indicator. Odds are, you're looking at something else, over a different time frame, in a different market environment than what my settings are tailored for.
אינדיקטור Pine Script®
מאת ‎nathanfarmer‎
1
Kalman PSaR [BackQuant]Kalman PSaR Overview and Innovation The Kalman PSaR combines the well-known Parabolic SAR (PSaR) with the advanced smoothing capabilities of the Kalman Filter . This innovative tool aims to enhance the traditional PSaR by integrating Kalman filtering, which reduces noise and improves trend detection. The Kalman PSaR adapts dynamically to price movements, making it a highly effective indicator for spotting trend shifts while minimizing the impact of false signals caused by market volatility. Please Find the Basic Kalman Here: Kalman Filter Dynamics The Kalman Filter is a powerful algorithm for estimating the true value of a system amidst noisy data. In the Kalman PSaR, this filter is applied to the high, low, and closing prices, resulting in a smoother and more accurate representation of price action. The filter’s parameters—process noise and measurement noise—are customizable, allowing traders to fine-tune the sensitivity of the indicator to market conditions. By reducing the impact of noise, the Kalman-filtered PSaR offers clearer signals for identifying trend reversals and continuations. Enhanced PSaR Calculation The traditional Parabolic SAR is a popular trend-following indicator that highlights potential entry and exit points based on price acceleration. In the Kalman PSaR, this calculation is enhanced by the Kalman-filtered prices, providing a smoother and more reliable signal. The indicator continuously updates based on the acceleration factor and max step values, while the Kalman filter ensures that sudden price spikes or market noise do not trigger false signals. Min Step and Max Step: These settings control the sensitivity of the PSaR. The Min Step sets the initial acceleration factor, while the Max Step limits how fast the PSaR adapts to price changes, helping traders fine-tune the indicator’s responsiveness. Optional Smoothing Techniques To further enhance the signal clarity, the Kalman PSaR includes an optional smoothing feature. Traders can choose from various smoothing methods, such as SMA, Hull, EMA, WMA, TEMA, and more, to reduce short-term fluctuations and emphasize the underlying trend. The smoothing period is customizable, allowing traders to adjust the indicator’s behavior according to their preferred trading style and timeframe. Color-Coded Candle Painting The Kalman PSaR features color-coded candles that change according to the trend direction. When the price is above the PSaR, candles are painted green to indicate a long trend, and when the price is below the PSaR, candles are painted red to signal a short trend. This visual representation makes it easy to interpret market sentiment at a glance, improving decision-making speed during fast-moving markets. Key Features and Customization Kalman Filter Customization: The process noise and measurement noise parameters allow traders to adjust how aggressively the filter adapts to price changes, making it suitable for both volatile and stable markets. Smoothing Options: A variety of moving average types, such as SMA, Hull, EMA, and more, can be applied to smooth the PSaR values, ensuring that the signal remains clear even in choppy markets. Dynamic Trend Detection: The Kalman PSaR dynamically updates based on price movements, helping traders spot trend reversals early while filtering out false signals caused by short-term volatility. Bar Coloring and PSaR Plotting: Traders can choose to color candles based on trend direction or plot the PSaR directly on the chart for additional visual clarity. Practical Applications Trend-Following Strategies: The Kalman PSaR excels in trend-following strategies by providing timely signals of trend changes. The dynamic nature of the indicator allows traders to capture significant price movements while avoiding market noise. Reversal Identification: The indicator’s ability to filter out noise and provide smoother signals makes it ideal for identifying reversals in volatile markets. Risk Management: By plotting clear stop levels based on the PSaR, traders can use this indicator to effectively manage risk, placing stop-loss orders at key points based on the trend direction. Conclusion The Kalman PSaR is a fusion of the classic Parabolic SAR and the Kalman filter, offering enhanced trend detection with reduced noise. Its customizable filtering and smoothing options, combined with dynamic trend-following capabilities, make it a versatile tool for traders seeking to improve their timing and signal accuracy. The adaptive nature of the Kalman filter, combined with the robust PSaR logic, helps traders stay on the right side of the market and manage risk more effectively.
אינדיקטור Pine Script®
מאת ‎BackQuant‎
14
ARIMA Indicator with Optional SmoothingOverview The ARIMA (AutoRegressive Integrated Moving Average) Indicator is a powerful tool used to forecast future price movements by combining differencing, autoregressive, and moving average components. This indicator is designed to help traders identify trends and potential reversal points by analyzing the historical price data. Key Features AutoRegressive Component (AR): Utilizes past values to predict future prices. Moving Average Component (MA): Averages past price differences to smooth out noise. Differencing: Reduces non-stationarity in the time series data. Optional Smoothing: Applies EMA to the ARIMA output for a smoother signal. Customizable Parameters: Allows users to adjust AR and MA orders, differencing periods, and smoothing lengths. Concepts Underlying the Calculations Differencing: Subtracts previous prices from current prices to remove trends and seasonality, making the data stationary. AutoRegressive Component (AR): Predicts future prices based on a linear combination of past values. Moving Average Component (MA): Uses past forecast errors to refine future predictions. Exponential Moving Average (EMA): Applies more weight to recent prices, providing a smoother and more responsive signal. How It Works The ARIMA Indicator first calculates the differenced series to achieve stationarity. Then, it computes the simple moving average (SMA) of this differenced series. The indicator uses the AR and MA components to adjust the SMA, creating an approximation of the ARIMA model. Finally, an optional smoothing step using EMA can be applied to the ARIMA approximation to produce a smoother signal. How Traders Can Use It Traders can use the ARIMA Indicator to: Identify Trends: Detect emerging trends by observing the direction of the ARIMA line. Spot Reversals: Look for divergences between the ARIMA line and the price to identify potential reversal points. Generate Trading Signals: Use crossovers between the ARIMA line and the price to generate buy or sell signals. Filter Noise: Enable the optional smoothing to filter out market noise and focus on significant price movements. Example Usage Instructions Add the ARIMA Indicator to your chart. Adjust the input parameters to suit your trading strategy: Set the SMA Length (e.g., 14). Choose the Differencing Period (e.g., 1). Define the AR Order (p) and MA Order (q) (e.g., 1). Configure the Smoothing Length if smoothing is desired (e.g., 5). Enable or disable smoothing as needed. Observe the ARIMA line (blue) and compare it to the price chart. Use the ARIMA line to identify trends and potential reversals. Implement trading decisions based on the ARIMA line’s behavior relative to the price.
אינדיקטור Pine Script®
מאת ‎PuzzlerTrades‎
6
mathLibrary "math" It's a library of discrete aproximations of a price or Series float it uses Fourier Discrete transform, Laplace Discrete Original and Modified transform and Euler's Theoreum for Homogenus White noice operations. Calling functions without source value it automatically take close as the default source value. Here is a picture of Laplace and Fourier approximated close prices from this library: Copy this indicator and try it yourself: import AutomatedTradingAlgorithms/math/1 as math //@version=5 indicator("Close Price with Aproximations", shorttitle="Close and Aproximations", overlay=false) // Sample input data (replace this with your own data) inputData = close // Plot Close Price plot(inputData, color=color.blue, title="Close Price") ltf32_result = math.LTF32(a=0.01) plot(ltf32_result, color=color.green, title="LTF32 Aproximation") fft_result = math.FFT() plot(fft_result, color=color.red, title="Fourier Aproximation") wavelet_result = math.Wavelet() plot(wavelet_result, color=color.orange, title="Wavelet Aproximation") wavelet_std_result = math.Wavelet_std() plot(wavelet_std_result, color=color.yellow, title="Wavelet_std Aproximation") DFT3(xval, _dir) Discrete Fourier Transform with last 3 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter   Returns: Aproxiated source value DFT2(xval, _dir) Discrete Fourier Transform with last 2 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter   Returns: Aproxiated source value FFT(xval) Fast Fourier Transform once. It aproximates usig last 3 points.   Parameters:      xval (float) : Source series   Returns: Aproxiated source value DFT32(xval) Combined Discrete Fourier Transforms of DFT3 and DTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series   Returns: Aproxiated source value DTF32(xval) Combined Discrete Fourier Transforms of DFT3 and DTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series   Returns: Aproxiated source value LFT3(xval, _dir, a) Discrete Laplace Transform with last 3 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter      a (float) : laplace coeficient   Returns: Aproxiated source value LFT2(xval, _dir, a) Discrete Laplace Transform with last 2 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter      a (float) : laplace coeficient   Returns: Aproxiated source value LFT(xval, a) Fast Laplace Transform once. It aproximates usig last 3 points.   Parameters:      xval (float) : Source series      a (float) : laplace coeficient   Returns: Aproxiated source value LFT32(xval, a) Combined Discrete Laplace Transforms of LFT3 and LTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series      a (float) : laplace coeficient   Returns: Aproxiated source value LTF32(xval, a) Combined Discrete Laplace Transforms of LFT3 and LTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series      a (float) : laplace coeficient   Returns: Aproxiated source value whitenoise(indic_, _devided, minEmaLength, maxEmaLength, src) Ehler's Universal Oscillator with White Noise, without extra aproximated src. It uses dinamic EMA to aproximate indicator and thus reducing noise.   Parameters:      indic_ (float) : Input series for the indicator values to be smoothed      _devided (int) : Divisor for oscillator calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed indicator value whitenoise(indic_, dft1, _devided, minEmaLength, maxEmaLength, src) Ehler's Universal Oscillator with White Noise and DFT1. It uses src and sproxiated src (dft1) to clearly define white noice. It uses dinamic EMA to aproximate indicator and thus reducing noise.   Parameters:      indic_ (float) : Input series for the indicator values to be smoothed      dft1 (float) : Aproximated src value for white noice calculation      _devided (int) : Divisor for oscillator calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed indicator value smooth(dft1, indic__, _devided, minEmaLength, maxEmaLength, src) Smoothing source value with help of indicator series and aproximated source value It uses src and sproxiated src (dft1) to clearly define white noice. It uses dinamic EMA to aproximate src and thus reducing noise.   Parameters:      dft1 (float) : Value to be smoothed.      indic__ (float) : Optional input for indicator to help smooth dft1 (default is FFT)      _devided (int) : Divisor for smoothing calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed source (src) series smooth(indic__, _devided, minEmaLength, maxEmaLength, src) Smoothing source value with help of indicator series It uses dinamic EMA to aproximate src and thus reducing noise.   Parameters:      indic__ (float) : Optional input for indicator to help smooth dft1 (default is FFT)      _devided (int) : Divisor for smoothing calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed src series vzo_ema(src, len) Volume Zone Oscillator with EMA smoothing   Parameters:      src (float) : Source series      len (simple int) : Length parameter for EMA   Returns: VZO value vzo_sma(src, len) Volume Zone Oscillator with SMA smoothing   Parameters:      src (float) : Source series      len (int) : Length parameter for SMA   Returns: VZO value vzo_wma(src, len) Volume Zone Oscillator with WMA smoothing   Parameters:      src (float) : Source series      len (int) : Length parameter for WMA   Returns: VZO value alma2(series, windowsize, offset, sigma) Arnaud Legoux Moving Average 2 accepts sigma as series float   Parameters:      series (float) : Input series      windowsize (int) : Size of the moving average window      offset (float) : Offset parameter      sigma (float) : Sigma parameter   Returns: ALMA value Wavelet(src, len, offset, sigma) Aproxiates srt using Discrete wavelet transform.   Parameters:      src (float) : Source series      len (int) : Length parameter for ALMA      offset (simple float)      sigma (simple float)   Returns: Wavelet-transformed series Wavelet_std(src, len, offset, mag) Aproxiates srt using Discrete wavelet transform with standard deviation as a magnitude.   Parameters:      src (float) : Source series      len (int) : Length parameter for ALMA      offset (float) : Offset parameter for ALMA      mag (int) : Magnitude parameter for standard deviation   Returns: Wavelet-transformed series LaplaceTransform(xval, N, a) Original Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient   Returns: Aproxiated source value NLaplaceTransform(xval, N, a, repeat) Y repetirions on Original Laplace Transform over N set of close prices, each time N-k set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value LaplaceTransformsum(xval, N, a, b) Sum of 2 exponent coeficient of Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient   Returns: Aproxiated source value NLaplaceTransformdiff(xval, N, a, b, repeat) Difference of 2 exponent coeficient of Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value N_divLaplaceTransformdiff(xval, N, a, b, repeat) N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, with dynamic rotation   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value LaplaceTransformdiff(xval, N, a, b) Difference of 2 exponent coeficient of Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient   Returns: Aproxiated source value NLaplaceTransformdiffFrom2(xval, N, a, b, repeat) N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value N_divLaplaceTransformdiffFrom2(xval, N, a, b, repeat) N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor, dynamic rotation   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value LaplaceTransformdiffFrom2(xval, N, a, b) Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient   Returns: Aproxiated source value
ספריית Pine Script®
מאת ‎AutomatedTradingAlgorithms‎
1
Fine-tune Inputs: Fourier Smoothed Volume zone oscillator WFSVZ0Use this Strategy to Fine-tune inputs for the (W&)FSVZ0 Indicator. Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data. I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame. MEANINGFUL DESCRIPTION: The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction. The Wavelet & Fourier Smoothed Volume Zone Oscillator (W&)FSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the Discrete Fourier Transform . Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends. By combining the Wavalet and Fourier aproximation with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions. HOW TO USE THE INDICATOR: The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well) The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in a natural trend. HOW TO USE THE STRATEGY: Here you fine-tune the inputs until you find a combination that works well on all Timeframes you will use when creating your Automated Trade Algorithmic Strategy. I suggest 4h, 12h, 1D, 2D, 3D, 4D, 5D, 6D, W and M. When I ndicator/Strategy returns 0 or natural trend , Strategy Closes All it's positions. ORIGINALITY & USFULLNESS: Personal combination of Fourier and Wavalet aproximation of a price which results in less noise Volume Zone Oscillator. The Wavelet Transform is a powerful mathematical tool for signal analysis, particularly effective in analyzing signals with varying frequency or non-stationary characteristics. It dissects a signal into wavelets, small waves with varying frequency and limited duration, providing a multi-resolution analysis. This approach captures both frequency and location information, making it especially useful for detecting changes or anomalies in complex signals. The Discrete Fourier Transform (DFT) is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element. This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding. DETAILED DESCRIPTION: My detailed description of the indicator and use cases which I find very valuable. What is oscillator? Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets. What is volume zone oscillator? Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price. Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average. What does this mean? If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely. If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely. Rising means that VZO on close is higher than the previous day. Falling means that VZO on close is lower than the previous day. What if VZO is falling above 0 line? It means we have a high probability of a bearish trend. Thus the indicator returns 0 and Strategy closes all it's positions when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend. In the next Image you can see that trend is negative on 4h, negative on 12h and positive on 1D. That means trend is negative. I am sorry, the chart is a bit messy. The idea is to use the indicator over more than 1 Timeframe. What is approximation and smoothing? They are mathematical concepts for making a discrete set of numbers a continuous curved line. Fourier and Wavelet approximation of a close price are taken from aprox library. Key Features: You can tailor the Indicator/Strategy to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length. Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification. Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics. Choose between the traditional Fast Fourier Transform (FFT) , the innovative Double Discrete Fourier Transform (DTF32) and Wavelet soothed Fourier soothed price series to suit your analytical needs. Image of Wavelet transform with FAST settings, Double Fourier transform with FAST settings. Improved noice reduction with SLOW settings, and standard FSVZO with SLOW settings: Fast setting are setting by default: VZO length = 2 NoiceR max Length = 2 Slow settings are: VZO length = 5 or 7 NoiceR max Length = 8 As you can see fast setting are more volatile. I suggest averaging fast setting on 4h 12h 1d 2d 3d 4d W and M Timeframe to get a clear view on market trend. What if I want long only when VZO is rising and above 15 not 0? You have set Setting VzoDifference to 15. That reduces the number of trend changes. Example of W&FSVZO with VzoDifference 15 than 0: VZO crossed 0 line but not 15 line and that's why Indicator returns 0 in one case an 1 in another. What is Smooth length setting? A way of calculating Bullish or Bearish (W&)FSVZO . If smooth length is 2 the trend is rising if: rising = VZO > ta.ema(VZO, 2) Meaning that we check if VZO is higher that exponential average of the last 2 elements. If smooth length is 1 the trend is rising if: rising = VZO_ > VZO_ Use this Strategy to fine-tune inputs for the (W&)FSVZO Indicator. (Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data) I suggest using " Close all " input False when fine-tuning Inputs for 1 TimeFrame . When you export data to Excel/Numbers/GSheets I suggest using " Close all " input as True , except for the lowest TimeFrame . I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
אסטרטגיית Pine Script®
מאת ‎AutomatedTradingAlgorithms‎
TASC 2024.04 The Ultimate Smoother█ OVERVIEW This script presents an implementation of the digital smoothing filter introduced by John Ehlers in his article "The Ultimate Smoother" from the April 2024 edition of TASC's Traders' Tips . █ CONCEPTS The UltimateSmoother preserves low-frequency swings in the input time series while attenuating high-frequency variations and noise. The defining input parameter of the UltimateSmoother is the critical period , which represents the minimum wavelength (highest frequency) in the filter's pass band. In other words, the filter attenuates or removes the amplitudes of oscillations at shorter periods than the critical period. According to Ehlers, one primary advantage of the UltimateSmoother is that it maintains zero lag in its pass band and minimal lag in its transition band, distinguishing it from other conventional digital filters (e.g., moving averages ). One can apply this smoother to various input data series, including other indicators. █ CALCULATIONS Ehlers derived the UltimateSmoother using inspiration from the design principles he learned from his experience with analog filters , as described in the original publication. On a technical level, the UltimateSmoother's unique response involves subtracting a high-pass response from an all-pass response . At very low frequencies (lengthy periods), where the high-pass filter response has virtually no amplitude, the subtraction yields a frequency and phase response practically equivalent to the input data. At other frequencies, the subtraction achieves filtration through cancellation due to the close similarities in response between the high-pass filter and the input data.
אינדיקטור Pine Script®
מאת ‎PineCodersTASC‎
8