Mulai

Filter

Intraday Context Authority (Liquidity & Time Filter)📌 Intraday Context Authority – Liquidity & Time Filter Most intraday losses do not come from bad indicators — they come from bad context. This indicator is a Tier-1 context engine designed to sit above your existing indicators and help answer one critical question: Is this a good place and time to trust intraday signals? 🔍 What this indicator does This tool does NOT generate buy or sell signals. Instead, it evaluates: Liquidity structure (range interaction, stop-hunts) Time structure (session phase, compression duration) And outputs one of three context states: ALLOW – Normal trading conditions RESTRICT – Elevated risk, caution advised HIGH RISK – Avoid trusting signals 🧠 Why use it Intraday markets are driven by: Liquidity collection before direction Time-dependent behavior False breakouts late in sessions This indicator helps: Avoid chasing late moves Identify stop-hunt environments Filter false breakouts Reduce low-quality trades ⚙️ How to use it Apply this indicator before momentum, trend, or volume tools Use it as a filter, not a trigger Best suited for intraday timeframes 🚫 What this indicator is NOT Not a strategy Not a signal generator Not a replacement for risk management
Indikator Pine Script®
oleh Market_Logic_India
Laguerre Filter [BackQuant]Laguerre Filter Overview The Laguerre Filter is a powerful trend-following tool designed to smooth price action while maintaining responsiveness to market changes. It is based on the Laguerre recursive filter, which is a type of signal processing filter that adapts to both the current price dynamics and the underlying trend. The Laguerre Filter can be seen as a method to reduce market noise, enabling traders to more easily identify the strength and direction of trends while minimizing lag. The Laguerre Filter is well-suited for markets with varying volatility levels, offering a smoother representation of price action without the delay associated with traditional moving averages. By dynamically adjusting to price movements, the Laguerre Filter provides a more adaptive and reliable signal compared to simpler smoothing techniques. What is the Laguerre Filter? The Laguerre Filter is derived from the Laguerre polynomial, which is used in signal processing for smooth filtering of data. The Laguerre filter is a recursive filter, meaning that each new value is calculated based on both the current price data and previous values, with a weighting system that allows it to adapt to market conditions. This recursive nature helps reduce the impact of short-term fluctuations, enabling the filter to focus on the underlying trend. The Laguerre filter uses a feedback mechanism, where the input signal (price data) is smoothed iteratively. This iterative process helps avoid the lag that is typically associated with traditional moving averages while still capturing the overall trend direction. The filter is designed to have: Adaptive behavior: It reacts quickly to significant price changes while ignoring minor fluctuations. Reduced noise: By filtering out random short-term price movements, it provides a clearer view of the underlying trend. Customizability: Traders can adjust the filter’s sensitivity through user inputs, making it adaptable to different market conditions. Core Calculation Methodology The core of the Laguerre Filter lies in its recursive calculation: Each new value is calculated using the previous value along with the current price input. The recursive formula is governed by two key parameters: the damping factor (gamma) and the order of the filter (number of Laguerre elements). The damping factor controls how responsive the filter is to changes in price. A higher gamma value makes the filter smoother but introduces more lag, while a lower gamma value makes it more reactive to price changes but can introduce more noise. The order defines how many Laguerre elements are used in the calculation. A higher order results in a smoother output but with more delay, while a lower order provides a faster response but less smoothing. The filter works by weighting previous values with a binomial weighting system, which assigns more weight to recent values and less weight to older values. This creates a dynamic smoothing effect that adapts to price volatility, ensuring that the filter is neither too slow nor too noisy. Signal Logic and Trend Detection The Laguerre Filter continuously evaluates the strength and direction of the trend by comparing the current smoothed value to the previous value: If the current value is greater than the previous value, the trend is considered bullish, and the filter will signal a long condition. If the current value is less than the previous value, the trend is considered bearish, and the filter will signal a short condition. The trend detection logic is based on the recursive nature of the filter, which smooths price movements over time. This allows the filter to capture the broader trend while minimizing the influence of short-term price fluctuations. The trend state is also visually represented by color-coding: Green color represents an uptrend (bullish condition). Red color represents a downtrend (bearish condition). Neutral (white) indicates no clear trend direction. This color-coding helps traders easily identify the prevailing trend and decide whether to enter or exit trades based on the trend's strength. Laguerre Filter Behavior and Performance The performance of the Laguerre Filter can be influenced by several factors: Gamma (Damping Factor): A higher gamma value results in a smoother filter but increases lag. A lower gamma value allows for a faster response but may introduce more noise, making it more reactive to smaller price changes. Filter Order: The order determines how many Laguerre elements are used in the filter calculation. A higher order provides more smoothing but increases lag, while a lower order results in a quicker response but less smoothing. The sweet spot for gamma is typically between 0.7 and 0.85, where the filter offers a good balance between smoothness and responsiveness. The filter order is usually set to 4 for classic Laguerre filtering, but higher orders can be used for more smoothing if needed. The Laguerre Filter’s performance shines in markets with sustained trends, where the filter can effectively capture and represent the underlying direction without excessive lag. It is particularly useful in volatile markets, as it helps smooth out noise while providing a clear picture of the trend. Visual Presentation The Laguerre Filter provides a dynamic, color-coded line that follows the trend direction. This line can be displayed alongside price data to visually highlight the market trend. In addition to the main Laguerre line, several visual enhancements can be applied: Gradient fill between the price and the Laguerre Filter line, providing a visual cue for bullish or bearish market conditions. Candle coloring to reflect the current trend, making it easier to spot trend reversals or confirmations directly on the chart. Background shading to visually highlight areas of strong trend or consolidation. Edge glow effect that highlights trend boundaries, making it easy to spot key levels of support or resistance. These visual elements enhance the usability of the Laguerre Filter, allowing traders to quickly assess the market trend and make informed decisions. Practical Use Cases 1) Trend Following The Laguerre Filter is ideal for trend-following strategies. By using the filter to identify the prevailing trend, traders can: Enter long positions when the Laguerre Filter turns bullish (green). Enter short positions when the Laguerre Filter turns bearish (red). By aligning trades with the dominant trend, traders can improve their chances of success. 2) Trend Strength Assessment The Laguerre Filter can also be used to assess the strength of the trend: A rising Laguerre value indicates a strengthening uptrend. A falling Laguerre value indicates a strengthening downtrend. A flattening Laguerre value signals weakening momentum or consolidation. This information can be used to adjust position sizing or to decide when to enter or exit a trade. 3) Trade Management The Laguerre Filter can also assist in trade management: Use the Laguerre line as a trailing stop for long positions in an uptrend. Scale out of positions as the Laguerre value begins to flatten or reverse. Use the Laguerre Filter to avoid trades when the market is in consolidation or lacks a clear trend. Tuning Guidelines The Laguerre Filter can be adjusted for different market conditions using the following parameters: Gamma (Damping Factor): Adjust for the desired level of responsiveness versus smoothness. Typical values range from 0.7 to 0.85. Filter Order: Adjust to control the level of smoothing. The default value of 4 is a good starting point, but higher orders can be used for smoother filters. Summary The Laguerre Filter is a versatile and adaptive trend-following indicator that smooths price data and reduces noise, making it easier to identify and follow trends. By using recursive smoothing techniques and adjustable parameters, the Laguerre Filter provides an accurate representation of market conditions with minimal lag. It is especially useful in volatile markets where traditional moving averages may fail to capture the underlying trend. With its color-coded trend detection, gradient fills, and customizable settings, the Laguerre Filter is a powerful tool for traders looking to stay aligned with the prevailing market direction.
Indikator Pine Script®
oleh BackQuant
1
Universal Adaptive Tracking🙏🏻 Behold, this is UAT (Universal Adaptive Tracker) , with less words imma proceed how it compares with alternatives: ^^ comparison with non-adaptive quadratic regression (purple line), that has higher overshoots, less precision ^^ comparison with JMA and its adaptive gain. JMA’s gain is heavily limited, while UAT’s negative and positive gains are soft-saturated with p-order Möbius transform This drop is inspired by, dedicated to, and made will all love towards Jurik Research , who retired in October 2k21. When some1 steps out, some1 has to step in, and that time it’s me (again xd). But there’s some history u gotta know: Some history u gotta know: In ~2008 dudes from forexfactory reverse engineered Jurik Moving Average In late 1990s dudes from Jurik Research approximated the best possible adaptive tracking filter for evolution of prices via engineering miracles Today in 2k26, me I'm gonna present to you the real mathematical objects/entities behind JMA top-edge engineered approximates. You will prolly be even more happy now then all the dem together back then. Why all this? When we talk about object tracking stuff, e.g. air defense, drones, missiles, projectiles, prices, etc, it all comes down to adaptive control and (Position & Velocity & Acceleration) aka PVA state space models (the real stuff many of you count as DSP ). Why? Cuz while position (P) : (mean), or position & velocity (PV) : (linear regression) are stable enough in dem own ways, Position & Velocity & Acceleration (PVA) : (quadratic regression+) require adaptivity do be stable. And real world stuff needs PVA, due to non-linearity for starters. So that’s why. If your goal is Really smoothing and no lag, u gotta go there. I see a lot of folks are crazy with it and want it, so here is it, for y’all. And good news, this is perfect for your favorite Moving Windows. How to use it The upper study: The final filter (main state): just as you use other fast smoothers, MAs, etc, you know better than me here You can also turn in volatility bands in script’s style settings, these do not require any adjustments Finally, you can turn on, in the same place, separate trackers each based on negative and positive volatility exclusively. When both are almost equal, that indicates stability & persistence in markets. May sound like it’s nothing important, but I've never seen anything like it before. Also, if you'd allow your our inner mental gym hero gloriously arise, you can argue that these 2 separate trackers represent 2 fair prices (one for sellers, one for buyers). All better then 1 imaginary fair price for both (forget about it) The lower study: The lower study: you can analyze streams of upward of downward volatilities separately. This is incredibly powerful You can also turn these off and turn on neg & pos intensities, and use them as trend detector, when each or both cross 1.5 (naturally neutral) threshold. ^^ Upper study with expected typical and maximum volatility bands turned On ... The method explained What you got in the end is non-linear, adaptive, lighting fast when needed and slow when required price tracking. All built upon real math entities/objects, not a brilliantly engineered approximation of them. No parameters to optimize, data tells it all. ... It all starts from a process model, in our cause this is... MFPM (Mechanical Feedback Price Model) Doesn’t make gaussian assumptions like most quant mainstream tech, accepts that innovations are Laplace “at best”, relies in L inf and L0 spaces. I created this model neither trynna fit non-fitting ARMA / variants, nor trynna be silly assuming that price state evolution and markets are random. Theory behind it: if no new volume comes, then price evolution would be simply guided by the feedback based on previous trading activity, pushing prices towards the midrange between 2 latest datapoints, being the main force behind so called “pullbacks” and reason why most pullbacks end just a bit past 50% of a move. This is the Real mechanical feedback based mean reversion, that is always there in the markets no matter what, think of it as a background process that is always there, and fresh new volume deviates prices away from it. Btw, this can also be expressed as AR2 with both phis = 0.5 . Then I separate positive and negative innovations from this model and process them separately, reflecting the asymmetry between buy and sell forces, smth that most forget. Both of these follow exponential distribution . Each stream has its own memory so here we use recursive operators . We track maximum innovations (differences between real and expected datapoints) with exponentially decaying damping factor, and keep tracking typical innovation, with the same factor. Then we calculate what’s called in lovely audio engineering as “ crest factor ”, the difference is we don’t do RMS and stuff. But hey again we work with laplace innovations, so we keep things in L0 and L inf spirit. Then we go a couple of steps further, making this crest factor truly relative (resolution agnostic), and then, most importantly, we apply a natural saturation on it based on p-order Möbius transform, but not with arbitrary p and L, but guided by informational limits of the data. These final "intensity" parameters are what we need next to make our object tracking adaptive. Extended Beta(2, 2) Window This is imo the main part of this. Looking at tapering windows in DSP and how wavelets are made from derivatives of PDF functions of probability distributions, I figured that why use just one derivative? That made me come up with Universal Moving Average , that combines PDF and CDF of Beta(2, 2) distribution . And that is fine for P (position) tracking model. Here we need PVA (position & velocity & acceleration). We can realize that everything starts from PDF, and by adding derivatives and anti-derivatives of it as factors of final window weights, we can create smth truly unique, a weightset that is non-arbitrary and naturally provides response alike quadratic regression does, But, naturally smoothed. Why do I consider this a discovery, a primordial math object? Because x^2 itself and Beta(2, 2) based on it are the only primitives, esp out of all these dozens of DSP tapering windows, that provide you a finite amount of derivatives. You can keep differentiating Hann window until the kingdom f come, while Welch window aka Beta(2, 2) has a natural stopping point, because the 3rd derivative is 0, so we can’t use it. Symmetrically, we do 2 steps up from PDF, getting 1st and second anti-derivatives. What’s lovely, symmetrically, 3rd antiderivative even tho exist, it stops making any sense. 2nd one still makes sense, it’s smth like “potential” of probability distribution, not really discussed in mainstream open access sources. Finally, the last part is to introduce adaptivity using these intensity exponents we’ve calculated with MFPM. We do 2 separate trackers, one using the negative intensity exponent, another one uses positive intensity exponent. And at the end, even tho using both together is cool, the final state estimate is calculated simply as the state which intensity has higher. ^^ impulse response of our final kernel with fixed (non adaptive) intensity exponents: 1 (blue) and 2 (red). You see it's all about phase … And that’s all folks. … Actually no … Last, not least, is the ability to add additional innovation weight to the kernel: ^^ Weighting by innovations “On”. Provides incredible tracking precision, paid with smoothness. I think this screenshot, showing what happened after the gap, and how the tracker managed to react, explains it all. ... Live Long and Prosper, all good TradingView ∞
Indikator Pine Script®
oleh gorx1
5
StO Price Action - Luminous Daily RoadmapShort Summary - Luminous Daily Roadmap (LDR) are special trading days - Marks entire trading days using background coloring - Creates a clear daily roadmap directly on the chart - Designed to stay minimal and non-intrusive Full Description Overview - LDR is a proprietary forex trading schedule - Dates of major trend reversals or significant market continuations - Highlights full trading days using background colors - Improves visual structure and day-to-day orientation - Focuses purely on time segmentation, not price signals - Suitable for all markets and timeframes Daily Marking Logic - Each trading day is visually marked across all its bars - Background coloring spans the full session of the day - Works consistently across intraday and higher timeframes Year Look Back (YLB) - YLB defines the starting year for day marking - Markings are only applied from the selected year onward - Allows focused analysis on recent or specific years Visualization - Background color is fully customizable - Uses high transparency to avoid hiding price action Usage - Useful for session-based and daily analysis - Supports routine-based trading and journaling - Enhances visual rhythm of the chart Notes - This indicator is purely visual and non-predictive - No alerts or signals are generated - Best used as a structural overlay for orientation - Can be combined with any price action or indicator-based workflow
Indikator Pine Script®
oleh sto_svc
EMA Buy/Sell & Smart Zones(5Min TF only)### **Indicator Title:** **EMA Buy/Sell & Smart Zones** --- ### **Description:** **EMA Buy/Sell & Smart Zones** is a specialized intraday trading tool designed to combine trend analysis with precise market structure zones. This script utilizes a custom tracking algorithm to identify the **specific candle** that formed the previous session's high or low, allowing it to plot accurate Supply and Demand zones for the current trading day. This indicator has been rigorously tested on the **Nifty Index** and is optimized for use on the **5-minute timeframe**. ### **Key Features** **1. Smart Session Wick Zones ("True Wick" Logic)** The indicator automatically scans every candle of the previous session to locate the exact price action that formed the day's extremes. * **Smart High Zone:** Identifies the specific candle that made yesterday's High and plots a zone from that High down to that candle's Open or Close (based on body direction). * **Smart Low Zone:** Identifies the specific candle that made yesterday's Low and plots a zone from that Low up to that candle's Open or Close. * **Close Range:** Highlights the High-Low range of the very last candle of the previous session to show the closing sentiment. *All zones automatically stop extending at the end of the current session, ensuring the chart remains clean and historically accurate.* **2. EMA Trend System** The script plots three key Exponential Moving Averages to define market direction: * **EMA 21:** Captures short-term momentum. * **EMA 63:** Defines the medium-term trend. * **EMA 1575:** Establishes the long-term baseline. **3. Buy/Sell Signals** Clear signals are generated on the chart based on specific criteria: * **BUY Signal:** Generated when a green candle closes above the EMA 21 and EMA 63. * **SELL Signal:** Generated when a red candle closes below the EMA 21 and EMA 63. * *Note: The logic includes a filter to alternate signals (Buy -> Sell -> Buy), preventing clutter during choppy markets.* ### **How to Use** * **Recommended Timeframe:** **5 Minutes**. * **Recommended Markets:** Indices (Nifty, Bank Nifty) and high-volume stocks. * **Workflow:** * Use the **Smart Zones** (Red/Green boxes) to identify potential rejection areas or breakout targets. * Use the **Buy/Sell Labels** as confirmation triggers when price is reacting near these zones or trending strongly above/below the EMAs. ### **Settings & Customization** * **Visibility Control:** Toggle each box type (High, Low, Close) and text labels on or off individually. * **Color Customization:** Fully adjustable colors for all EMAs, Zone Backgrounds, Borders, and Text Labels to suit your chart theme. * **Label Size:** Adjust the text size of the zone labels directly from the settings menu. --- **Disclaimer:** This tool is for educational purposes and should be used to assist your analysis. Always manage your risk appropriately.
Indikator Pine Script®
oleh chaitu50c
Kalman Absorption/Distribution Tracker 3-State EKFQuant-Grade Institutional Flow: 3-State EKF Absorption Tracker SUMMARY An advanced, open-source implementation of a 3-State Extended Kalman Filter (EKF) designed to track institutional Order Flow. By analyzing 1-second intrabar microstructure data, this script estimates the true Position, Velocity, and Volatility of the Cumulative Volume Delta (CVD), revealing hidden Absorption and Distribution events in real-time. INTRODUCTION: THE SIGNAL AMIDST THE NOISE In the world of technical analysis, noise is the enemy. Traditional indicators rely on Moving Averages (SMA, EMA) to smooth out price and volume data. The problem is the "Lag vs. Noise" paradox: to get a smooth signal, you must accept lag; to get a fast signal, you must accept noise. This indicator solves that paradox by introducing aerospace-grade mathematics to the TradingView community: The 3-State Extended Kalman Filter (EKF). Unlike moving averages that blindly average past data, a Kalman Filter is a probabilistic state-space model. It constantly predicts where the order flow "should" be, compares it to the actual measurement, and updates its internal model based on the calculated uncertainty of the market. This script is not just another volume oscillator. It is a full microstructure analysis engine that digests intrabar data (down to 1-second resolution) to track the true intent of "Smart Money" while filtering out the noise of retail chop. THE INNOVATION: WHY 3 STATES? Most Kalman Filters found in public libraries are "1-State" (tracking price only) or occasionally "2-State" (tracking price and velocity). This script introduces a highly advanced 3-State EKF. The filter tracks three distinct variables simultaneously in a feedback loop: State 1: Position (The True CVD) This is the noise-filtered estimate of the Cumulative Volume Delta. It represents the actual inventory accumulation of aggressive buyers versus sellers, stripped of random noise. State 2: Velocity (The Momentum) This tracks the rate of change of the order flow. Is buying accelerating? Is selling pressure fading even as price drops? This provides a leading signal before the cumulative value even turns. State 3: Volatility (The Adaptive Regime) This is the game-changer. The filter estimates the current volatility of the order flow (Log-Volatility). In high-volatility environments (like news events), the filter automatically widens its uncertainty bands (Covariance) and reacts faster. In low-volatility environments (chop), it tightens up and ignores minor fluctuations. THE LOGIC: DETECTING ABSORPTION AND DISTRIBUTION The core philosophy of this indicator is based on Wyckoff Logic: Effort vs. Result. -- Effort: Represented by the CVD (Buying/Selling pressure). -- Result: Represented by Price Movement. When these two diverge, we have an actionable signal. The script uses the EKF Velocity state to detect these moments: Absorption (Bullish) This occurs when the EKF detects high negative Velocity (aggressive selling), but Price refuses to drop. The "Smart Money" is absorbing the sell orders via limit buys. The indicator highlights this as a Blue Event in the dashboard. Distribution (Bearish) This occurs when the EKF detects high positive Velocity (aggressive buying), but Price refuses to rise. Limit sellers are capping the market. The indicator highlights this as an Orange Event. TECHNICAL DEEP DIVE: UNDER THE HOOD For the developers and quants, here is how the Pine Script is architected using the "type" and "method" features of Pine Script v5. 1. Data Ingestion (Microstructure) The script uses "request.security_lower_tf" to pull intrabar data regardless of your chart timeframe. This allows the script to see "inside" the bar. A 5-minute candle might look green, but the microstructure might reveal that 80% of the volume was selling absorption at the wick. This script sees that. 2. Tick Classification Standard CVD assumes that if Price Close is greater than Price Open, all volume is buying. This is often flawed. This script offers three modes of tick handling, including a "High-Low Distribution" method that statistically apportions volume based on where the tick closed relative to its high and low. 3. The EKF Mathematics The script implements the standard Extended Kalman Filter equations manually. It calculates the Jacobian matrix to handle the non-linear relationship between volatility and price. The "Process Noise Matrix" (Q) is dynamically scaled by the Volatility State. This means the mathematics of the indicator literally "breathe" with the market conditions—expanding during expansion and contracting during consolidation. THE DASHBOARD & VISUALS: The indicator features a professional-grade HUD (Heads Up Display) located on the chart table. -- EKF State Vector: Displays the real-time Position, Velocity, and Volatility values derived from the matrix. -- Ease of Movement (Wyckoff): Calculates how much price moves per 1,000 contracts of CVD. For example, if Price moves +5 points per 1k Buy CVD, but only -2 points per 1k Sell CVD, the "Path of Least Resistance" is clearly UP. -- Session State: Tracks cumulative confirmed Bullish vs. Bearish events for Today, Yesterday, and the Day Before (3-Day Profile). -- Bias Summary: An algorithmic conclusion telling you if the day is "Confirmed Bullish," "Accumulating," or "Neutral." HOW TO TRADE THIS INDICATOR Strategy A: The Reversal (Absorption Play) Look for price making a Lower Low. Look for the EKF Velocity (Histogram) to be Deep Red (High Selling Pressure). Watch the Dashboard "Absorption" count increase. SIGNAL: When EKF Velocity crosses back toward zero and turns grey/green, the absorption is complete. This indicates sellers are exhausted and limit buyers have control. Strategy B: The Trend Continuation (Ease of Movement) Check the Dashboard "Ease of Movement" section. If "Price per +1K CVD" is significantly higher than "Price per -1K CVD", buyers are efficient. Wait for a pullback where EKF Velocity hits the "Neutral Zone" (Gray). SIGNAL: Enter Long when Velocity ticks positive again, aligning with the dominant Ease of Movement stats. CONFIGURATION GUIDE: Because this is a quant-grade tool, the settings allow for fine-tuning the physics of the filter. -- Velocity Decay: Controls how fast momentum resets to zero. Set high (0.98) for trending markets, or lower (0.85) for mean-reverting chop. -- Volatility Persistence: Controls how "sticky" volatility regimes are. -- Process Noise: Increase this if the filter feels too laggy; decrease it if the filter feels too jittery (noisy). -- Measurement Noise: Increase this to trust the Mathematical Model more than the Price Data (smoother output). WHY OPEN SOURCE? Complex statistical filtering is often sold behind closed doors in expensive "Black Box" algorithms. By releasing this 3-State EKF open source, the goal is to raise the standard of development on TradingView. I encourage the community to inspect the code, specifically the "ekf_update_3state" function, to understand how matrix operations can be simulated in Pine Script to create adaptive, self-correcting indicators. And also update me for improvements. DISCLAIMER: This tool analyzes microstructure volume data. It requires a subscription plan that supports Intrabar inspection (Premium/Pro recommended for best results). Past performance of the Kalman Filter logic does not guarantee future results. Volume analysis is subjective and should be used as part of a comprehensive strategy. SUGGESTED SETTINGS -- Timeframe: Works best on 1m, 3m, or 5m charts (Intrabar data is fetched from 1S). -- Asset Class: Highly effective on Futures (ES, NQ, BTC) and high-volume Forex/Crypto pairs where volume data is reliable. -- Background: Dark mode recommended for Dashboard visibility. WHAT IS A KALMAN FILTER? Imagine driving a car into a tunnel where your GPS signal is lost. Prediction: Your car knows its last speed (Velocity) and position. It predicts where you are every second inside the tunnel. Update: When you exit the tunnel, the GPS connects again. The system compares where it thought you were versus where the satellite says you are. Correction: It corrects your position and updates its estimate of your speed. Now apply this to trading: -- The Tunnel: Market Noise, wicks, and Fake-outs. -- The Car: The True Market Trend. -- This Indicator: The navigation system that tells you where the market actually is, ignoring the noise of the tunnel. Enjoy the indicator and trade safe! Dr. Jay Desai (Investment Management & Derivatives Area, Gujarat University)
Indikator Pine Script®
oleh jaydesaigu
EDUVEST QQE Grade System - S/A/B/C Signal ClassificationEDUVEST QQE Grade System - S/A/B/C Signal Classification ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ ORIGINALITY This indicator introduces a unique grading system (S/A/B/C) for QQE signals, combining traditional QQE analysis with SMC (Smart Money Concepts) price zones and trading session filters. Unlike standard QQE indicators that show all signals equally, this version classifies signals by quality to help traders focus on the highest probability setups. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ WHAT IT DOES - Generates BUY/SELL signals with S/A/B/C grade classification - Automatically detects asset type and applies optimized QQE factors - Integrates SMC price zones (support/resistance) for grade enhancement - Filters signals by trading session time - Displays real-time session and market status Grade Hierarchy: - S (Gold/Orange): Signal near SMC zone + active trading hours - Highest quality - A (Green/Red): Score 70+ during trading hours - High quality - B (Darker): Score 50-69 during trading hours - Medium quality - C (Gray, small): Outside trading hours or weak signal - Low quality ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ HOW IT WORKS 【QQE Core Calculation】 The QQE (Quantitative Qualitative Estimation) is calculated as: 1. RSI with configurable period (default: 14) 2. EMA smoothing of RSI (Smoothing Factor: 5) 3. Dynamic bands using Wilder's smoothing: RSI ± (ATR of RSI × QQE Factor) QQE Factor is auto-adjusted per asset: - USD/JPY: 4.238 - EUR/USD: 3.8 - Gold (XAU/USD): 8.0 - NASDAQ/US100: 9.0 【Signal Generation】 - BUY: QQE line crosses above its trailing stop (QQExlong == 1) - SELL: QQE line crosses below its trailing stop (QQExshort == 1) 【Internal Scoring System】 Score components (0-100): - Signal Base: +25 points when signal occurs - QQE Strength: +10 to +20 based on RSI distance from 50 - Volatility: +15 (optimal ATR ratio 1.1-2.0), -10 (low volatility) - Volume Confirmation: +10 (high volume), -5 (low volume) - Session Bonus: +5 during London/NY sessions - Base: +20 points 【Grade Assignment】 - Grade S: Signal near user-defined SMC price zone (within tolerance %) AND during trading hours - Grade A: Internal score >= 70 AND during trading hours - Grade B: Internal score >= 50 AND during trading hours - Grade C: Outside trading hours OR score < 50 【SMC Price Zone Integration】 Users can set support/resistance levels for each asset. When price is within the tolerance percentage of these levels, signals are upgraded to S-grade, indicating confluence with institutional price levels. 【Trading Session Filter】 Configurable active trading hours (JST timezone): - Default: 15:00 - 01:00 JST (London + NY overlap) - Signals outside this window receive C-grade ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ HOW TO USE 【Recommended Settings】 - Timeframe: 15M, 1H, 4H - Best on: USD/JPY, EUR/USD, Gold, NASDAQ - Focus on: S and A grade signals 【Trading Strategy】 - S-Grade (Gold/Orange): Highest conviction - consider larger position - A-Grade (Green/Red): Strong signal - standard position - B-Grade: Valid but use additional confirmation - C-Grade: Avoid or use minimal size 【Setting Up SMC Zones】 1. Identify key support/resistance on higher timeframe 2. Input prices in SMC Price Settings 3. Adjust tolerance % (default: 0.15%) 4. S-grade appears when signal occurs near these levels 【Info Panel】 Top-right panel shows: - Asset name and detection mode (Auto/Manual) - Current session (Tokyo/London/NY) - Trading hours status - SMC zone proximity 【Alert Setup】 1. Enable alerts in settings 2. Create alert with "Any alert() function call" 3. Alerts include grade, price, and session info ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ SETTINGS Basic Settings: - Enable Alerts: Turn on/off notifications - Time Filter: Activate trading hour filter - Start/End Hour: Define active trading window (JST) QQE Settings: - RSI Period: RSI calculation period - RSI Smoothing: EMA smoothing factor - Auto QQE Factor: Auto-detect optimal factor per asset - Manual QQE Factor: Override when auto is disabled SMC Price Settings: - Support/Resistance levels for each asset - Tolerance %: How close to SMC line for S-grade Display Settings: - Grade Only: Hide QQE lines, show only signals - Show SMC Lines: Display support/resistance on chart - Show Debug: Display asset detection info ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ CREDITS QQE concept originally developed by John Ehlers. SMC (Smart Money Concepts) integration and grading system by EduVest. License: Mozilla Public License 2.0
Indikator Pine Script®
oleh EduVest_doro
Filtered TEMA CrossoverFiltered Dual TEMA Crossover This indicator is a trend-following tool based on the classic Dual Triple Exponential Moving Average (TEMA) Crossover strategy, enhanced with two robust filters: the Chop Index and the Average Directional Index (ADX). The TEMA is known for its low lag and high responsiveness, making the crossover an effective signal for trend reversals. However, trading TEMA crossovers during sideways, choppy markets often leads to false signals. This is where the filters come in. Key Features ▪️Dual TEMA Crossover: Plots two customizable TEMA lines (Fast and Slow) for clear visualization of the primary trend direction. ▪️Intelligent Signal Filtering: Buy and Sell signals are generated only when the market confirms it is in a trending state, thanks to two integrated filters: ➖Chop Index Filter: Blocks signals when the market is detected as sideways or consolidating (Chop Index reading above a user-defined threshold). ➖ADX Filter: Ensures signals are only taken when the trend strength is sufficient (ADX reading above a user-defined minimum threshold). ▪️Customizable Signals: Full control over the signal shapes (Arrows, Triangles, etc.), colors, text, and size. How to Use It Use the Filtered Dual TEMA Crossover to enter positions on trend continuation or reversal while dramatically reducing exposure to low-quality, whipsawing signals common in non-trending environments. Before the filters: After the filters: Minimize Noise. Maximize Clarity. Trade the Trend.
Indikator Pine Script®
oleh ZAUTEC
TASC 2025.12 The One Euro Filter█ OVERVIEW This script implements the One Euro filter, developed by Georges Casiez, Nicolas Roussel, and Daniel Vogel, and adapted by John F. Ehlers in his article "Low-Latency Smoothing" from the December 2025 edition of the TASC Traders' Tips . The original creators gave the filter its name to suggest that it is cheap and efficient, like something one might purchase for a single Euro. █ CONCEPTS The One Euro filter is an EMA-based low-pass filter that adapts its smoothing factor (alpha) based on the absolute values of smoothed rates of change in the source series. It was designed to filter noisy, high-frequency signals in real time with low latency. Ehlers simplifies the filter for market analysis by calculating alpha in terms of bar periods rather than time and frequency, because periods are naturally intuitive for a discrete financial time series. In his article, Ehlers demonstrates how traders can apply the adaptive One Euro filter to a price series for simple low-latency smoothing. Additionally, he explains that traders can use the filter as a smoothed oscillator by applying it to a high-pass filter. In essence, similar to other low-pass filters, traders can apply the One Euro filter to any custom source to derive a smoother signal with reduced noise and low lag. This script applies the One Euro filter to a specified source series, and it applies the filter to a two-pole high-pass filter or other oscillator, depending on the selected "Osc type" option. By default, it displays the filtered source series on the main chart pane, and it shows the oscillator and its filtered series in a separate pane. █ INPUTS Source: The source series for the first filter and the selected oscillator. Min period: The minimum cutoff period for the smoothing calculation. Beta: Controls the responsiveness of the filter. The filter adds the product of this value and the smoothed source change to the minimum period to determine the filter's smoothing factor. Larger values cause more significant changes in the maximum cutoff period, resulting in a smoother response. Osc type: The type of oscillator to calculate for the pane display. By default, the indicator calculates a high-pass filter. If the selected type is "None", the indicator displays the "Source" series and its filtered result in a separate pane rather than showing the filter on the main chart. With this setting, users can pass plotted values from another indicator and view the filtered result in the pane. Period: The length for the selected oscillator's calculation.
Indikator Pine Script®
oleh PineCodersTASC
1
ADX Trend Strength Filter + TRAMA [DotGain]Summary Are you tired of trading trend signals, only to get stopped out in volatile, sideways chop? The ADX Trend Strength Filter (ADX TSF) is designed to solve this exact problem. It is a comprehensive trend-following system that only generates signals when a trend not only has the right direction and momentum, but also sufficient strength. This indicator filters out weak or indecisive market phases (the "chop") and will only color the bars Green or Red when all conditions for a strong, confirmed trend are met. ⚙️ Core Components and Logic The ADX TSF relies on a triple-filter logic to generate a clear trade signal: Trend Filter (TRAMA): A TRAMA (Trending Adaptive Moving Average) is used as the main trendline. This adaptive average automatically adjusts to market volatility, acting as a dynamic support/resistance level. Price > TRAMA = Bullish Price < TRAMA = Bearish Momentum Filter (RSI Crossover): Momentum is measured by a crossover of two moving averages of the RSI (a fast EMA and a slow SMA). This confirms whether the momentum is pointing in the same direction as the trend. Strength Filter (ADX): This is the most important filter. A signal is only considered valid if the ADX (Average Directional Index) is above a defined threshold (Default: 30). This ensures the trend has sufficient strength. 🚦 How to Read the Indicator The indicator has three states, displayed directly as bar colors on your chart: 🟩 GREEN BARS (Strong Uptrend) All three conditions are met: Price is above the TRAMA. RSI momentum is bullish (Fast MA > Slow MA). ADX is above 30 (Strong trend is present). 🟥 RED BARS (Strong Downtrend) All three conditions are met: Price is below the TRAMA. RSI momentum is bearish (Fast MA < Slow MA). ADX is above 30 (Strong trend is present). 🟧 ORANGE BARS (Neutral / Caution) This state appears if any of the following conditions are true: Weak Trend: The ADX is below 30. The market is in consolidation or a sideways phase. (This is the primary filter!) Indecision: The price is caught in the "Neutral Zone" between the TRAMA and the 200 SMA. Visual Elements Bar Colors: (Green/Red/Orange) Show the current trend status. TRAMA (Orange Line): Your primary adaptive trendline. 200 SMA (White Line): Serves as a reference for the long-term trend. Orange Background (Fill): Fills the area between the TRAMA and SMA to visually highlight the "Neutral Zone." Key Benefit The goal of the ADX TSF is to keep traders out of weak, unpredictable markets and help them participate only in strong, momentum-confirmed trends. Have fun :) Disclaimer This "Buy The F*cking Dip" (BTFD) indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice. The signals generated by this tool (both "Buy" and "Sell") are the result of a specific set of algorithmic conditions. They are not a direct recommendation to buy or sell any asset. All trading and investing in financial markets involves substantial risk of loss. You can lose all of your invested capital. Past performance is not indicative of future results. The signals generated may produce false or losing trades. The creator (© DotGain) assumes no liability for any financial losses or damages you may incur as a result of using this indicator. You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR) and consider your personal risk tolerance before making any trades.
Indikator Pine Script®
oleh DotGain
1
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution. If you would like to see my original Kalman Filter, please find it here: Concept overview The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory: Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price). VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred. By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters. How it works Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement. Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state. Noise parameters : Process Noise controls agility — higher values make the filter more responsive but also more volatile. Measurement Noise controls smoothness — higher values make it steadier but slower to adapt. Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes. Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow. Why this matters Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves. Use cases Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions. Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow. Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness. Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value. Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices. Parameter guidance Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping. Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking. VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics. Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines. Interpretation When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value. When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value. Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance. Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control. Summary The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
Indikator Pine Script®
oleh BackQuant
8
Renko BandsThis is renko without the candles, just the endpoint plotted as a line with bands around it that represent the brick size. The idea came from thinking about what renko actually gives you once you strip away the visual brick format. At its core, renko is a filtered price series that only updates when price moves a fixed amount, which means it's inherently a trend-following mechanism with built-in noise reduction. By plotting just the renko price level and surrounding it with bands at the brick threshold distances, you get something that works like regular volatility bands while still behaving as a trend indicator. The center line is the current renko price, which trails actual price based on whichever brick sizing method you've selected. When price moves enough to complete a brick in the renko calculation, the center line jumps to the new brick level. The bands sit at plus and minus one brick size from that center line, showing you exactly how far price needs to move before the next brick would form. This makes the bands function as dynamic breakout levels. When price touches or crosses a band, you know a new renko brick is forming and the trend calculation is updating. What makes this cool is the dual-purpose nature. You can use it like traditional volatility bands where the outer edges represent boundaries of normal price movement, and breaks beyond those boundaries signal potential trend continuation or exhaustion. But because the underlying calculation is renko rather than standard deviation or ATR around a moving average, the bands also give you direct insight into trend state. When the center line is rising consistently and price stays near the upper band, you're in a clean uptrend. When it's falling and price hugs the lower band, downtrend. When the center line is flat and price is bouncing between both bands, you're ranging. The three brick sizing methods work the same way as standard renko implementations. Traditional sizing uses a fixed price range, so your bands are always the same absolute distance from the center line. ATR-based sizing calculates brick range from historical volatility, which makes the bands expand and contract based on the ATR measurement you chose at startup. Percentage-based sizing scales the brick size with price level, so the bands naturally widen as price increases and narrow as it decreases. This automatic scaling is particularly useful for instruments that move proportionally rather than in fixed increments. The visual simplicity compared to full renko bricks makes this more practical for overlay use on your main chart. Instead of trying to read brick patterns in a separate pane or cluttering your price chart with boxes and lines, you get a single smoothed line with two bands that convey the same information about trend state and momentum. The center line shows you the filtered trend direction, the bands show you the threshold levels, and the relationship between price and the bands tells you whether the current move has legs or is stalling out. From a trend-following perspective, the renko line naturally stays flat during consolidation and only moves when directional momentum is strong enough to complete bricks. This built-in filter removes a lot of the whipsaw that affects moving averages during choppy periods. Traditional moving averages continue updating with every bar regardless of whether meaningful directional movement is happening, which leads to false signals when price is just oscillating. The renko line only responds to sustained moves that meet the brick size threshold, so it tends to stay quiet when price is going nowhere and only signals when something is actually happening. The bands also serve as natural stop-loss or profit-target references since they represent the distance price needs to move before the trend calculation changes. If you're long and the renko line is rising, you might place stops below the lower band on the theory that if price falls far enough to reverse the renko trend, your thesis is probably invalidated. Conversely, the upper band can mark levels where you'd expect the current brick to complete and potentially see some consolidation or pullback before the next brick forms. What this really highlights is that renko's value isn't just in the brick visualization, it's in the underlying filtering mechanism. By extracting that mechanism and presenting it in a more traditional band format, you get access to renko's trend-following properties without needing to commit to the brick chart aesthetic or deal with the complications of overlaying brick drawings on a time-based chart. It's renko after all, so you get the trend filtering and directional clarity that makes renko useful, but packaged in a way that integrates more naturally with standard technical analysis workflows.
Indikator Pine Script®
oleh The_Peaceful_Lizard
37
IIR One-Pole Price Filter [BackQuant]IIR One-Pole Price Filter A lightweight, mathematically grounded smoothing filter derived from signal processing theory, designed to denoise price data while maintaining minimal lag. It provides a refined alternative to the classic Exponential Moving Average (EMA) by directly controlling the filter’s responsiveness through three interchangeable alpha modes: EMA-Length , Half-Life , and Cutoff-Period . Concept overview An IIR (Infinite Impulse Response) filter is a type of recursive filter that blends current and past input values to produce a smooth, continuous output. The "one-pole" version is its simplest form, consisting of a single recursive feedback loop that exponentially decays older price information. This makes it both memory-efficient and responsive , ideal for traders seeking a precise balance between noise reduction and reaction speed. Unlike standard moving averages, the IIR filter can be tuned in physically meaningful terms (such as half-life or cutoff frequency) rather than just arbitrary periods. This allows the trader to think about responsiveness in the same way an engineer or physicist would interpret signal smoothing. Why use it Filters out market noise without introducing heavy lag like higher-order smoothers. Adapts to various trading speeds and time horizons by changing how alpha (responsiveness) is parameterized. Provides consistent and mathematically interpretable control of smoothing, suitable for both discretionary and algorithmic systems. Can serve as the core component in adaptive strategies, volatility normalization, or trend extraction pipelines. Alpha Modes Explained EMA-Length : Classic exponential decay with alpha = 2 / (L + 1). Equivalent to a standard EMA but exposed directly for fine control. Half-Life : Defines the number of bars it takes for the influence of a price input to decay by half. More intuitive for time-domain analysis. Cutoff-Period : Inspired by analog filter theory, defines the cutoff frequency (in bars) beyond which price oscillations are heavily attenuated. Lower periods = faster response. Formula in plain terms Each bar updates as: yₜ = yₜ₋₁ + alpha × (priceₜ − yₜ₋₁) Where alpha is the smoothing coefficient derived from your chosen mode. Smaller alpha → smoother but slower response. Larger alpha → faster but noisier response. Practical application Trend detection : When the filter line rises, momentum is positive; when it falls, momentum is negative. Signal timing : Use the crossover of the filter vs its previous value (or price) as an entry/exit condition. Noise suppression : Apply on volatile assets or lower timeframes to remove flicker from raw price data. Foundation for advanced filters : The one-pole IIR serves as a building block for multi-pole cascades, adaptive smoothers, and spectral filters. Customization options Alpha Scale : Multiplies the final alpha to fine-tune aggressiveness without changing the mode’s core math. Color Painting : Candles can be painted green/red by trend direction for visual clarity. Line Width & Transparency : Adjust the visual intensity to integrate cleanly with your charting style. Interpretation tips A smooth yet reactive line implies optimal tuning — minimal delay with reduced false flips. A sluggish line suggests alpha is too small (increase responsiveness). A noisy, twitchy line means alpha is too large (increase smoothing). Half-life tuning often feels more natural for aligning filter speed with price cycles or bar duration. Summary The IIR One-Pole Price Filter is a signal smoother that merges simplicity with mathematical rigor. Whether you’re filtering for entry signals, generating trend overlays, or constructing larger multi-stage systems, this filter delivers stability, clarity, and precision control over noise versus lag, an essential tool for any quantitative or systematic trading approach.
Indikator Pine Script®
oleh BackQuant
Volume Based Sampling [BackQuant]Volume Based Sampling What this does This indicator converts the usual time-based stream of candles into an event-based stream of “synthetic” bars that are created only when enough trading activity has occurred . You choose the activity definition: Volume bars : create a new synthetic bar whenever the cumulative number of shares/contracts traded reaches a threshold. Dollar bars : create a new synthetic bar whenever the cumulative traded dollar value (price × volume) reaches a threshold. The script then keeps an internal ledger of these synthetic opens, highs, lows, closes, and volumes, and can display them as candles, plot a moving average calculated over the synthetic closes, mark each time a new sample is formed, and optionally overlay the native time-bars for comparison. Why event-based sampling matters Markets do not release information on a clock: activity clusters during news, opens/closes, and liquidity shocks. Event-based bars normalize for that heteroskedastic arrival of information: during active periods you get more bars (finer resolution); during quiet periods you get fewer bars (coarser resolution). Research shows this can reduce microstructure pathologies and produce series that are closer to i.i.d. and more suitable for statistical modeling and ML. In particular: Volume and dollar bars are a common event-time alternative to time bars in quantitative research and are discussed extensively in Advances in Financial Machine Learning (AFML). These bars aim to homogenize information flow by sampling on traded size or value rather than elapsed seconds. The Volume Clock perspective models market activity in “volume time,” showing that many intraday phenomena (volatility, liquidity shocks) are better explained when time is measured by traded volume instead of seconds. Related market microstructure work on flow toxicity and liquidity highlights that the risk dealers face is tied to information intensity of order flow, again arguing for activity-based clocks. How the indicator works (plain English) Choose your bucket type Volume : accumulate volume until it meets a threshold. Dollar Bars : accumulate close × volume until it meets a dollar threshold. Pick the threshold rule Dynamic threshold : by default, the script computes a rolling statistic (mean or median) of recent activity to set the next bucket size. This adapts bar size to changing conditions (e.g., busier sessions produce more frequent synthetic bars). Fixed threshold : optionally override with a constant target (e.g., exactly 100,000 contracts per synthetic bar, or $5,000,000 per dollar bar). Build the synthetic bar While a bucket fills, the script tracks: o_s: first price of the bucket (synthetic open) h_s: running maximum price (synthetic high) l_s: running minimum price (synthetic low) c_s: last price seen (synthetic close) v_s: cumulative native volume inside the bucket d_samples: number of native bars consumed to complete the bucket (a proxy for “how fast” the threshold filled) Emit a new sample Once the bucket meets/exceeds the threshold, a new synthetic bar is finalized and stored. If overflow occurs (e.g., a single native bar pushes you past the threshold by a lot), the code will emit multiple synthetic samples to account for the extra activity. Maintain a rolling history efficiently A ring buffer can overwrite the oldest samples when you hit your Max Stored Samples cap, keeping memory usage stable. Compute synthetic-space statistics The script computes an SMA over the last N synthetic closes and basic descriptors like average bars per synthetic sample, mean and standard deviation of synthetic returns, and more. These are all in event time , not clock time. Inputs and options you will actually use Data Settings Sampling Method : Volume or Dollar Bars. Rolling Lookback : window used to estimate the dynamic threshold from recent activity. Filter : Mean or Median for the dynamic threshold. Median is more robust to spikes. Use Fixed? / Fixed Threshold : override dynamic sizing with a constant target. Max Stored Samples : cap on synthetic history to keep performance snappy. Use Ring Buffer : turn on to recycle storage when at capacity. Indicator Settings SMA over last N samples : moving average in synthetic space . Because its index is sample count, not minutes, it adapts naturally: more updates in busy regimes, fewer in quiet regimes. Visuals Show Synthetic Bars : plot the synthetic OHLC candles. Candle Color Mode : Green/Red: directional close vs open Volume Intensity: opacity scales with synthetic size Neutral: single color Adaptive: graded by how large the bucket was relative to threshold Mark new samples : drop a small marker whenever a new synthetic bar prints. Comparison & Research Show Time Bars : overlay the native time-based candles to visually compare how the two sampling schemes differ. How to read it, step by step Turn on “Synthetic Bars” and optionally overlay “Time Bars.” You will see that during high-activity bursts, synthetic bars print much faster than time bars. Watch the synthetic SMA . Crosses in synthetic space can be more meaningful because each update represents a roughly comparable amount of traded information. Use the “Avg Bars per Sample” in the info table as a regime signal. Falling average bars per sample means activity is clustering, often coincident with higher realized volatility. Try Dollar Bars when price varies a lot but share count does not; they normalize by dollar risk taken in each sample. Volume Bars are ideal when share count is a better proxy for information flow in your instrument. Quant finance background and citations Event time vs. clock time : Easley, López de Prado, and O’Hara advocate measuring intraday phenomena on a volume clock to better align sampling with information arrival. This framing helps explain volatility bursts and liquidity droughts and motivates volume-based bars. Flow toxicity and dealer risk : The same authors show how adverse selection risk changes with the intensity and informativeness of order flow, further supporting activity-based clocks for modeling and risk management. AFML framework : In Advances in Financial Machine Learning , event-driven bars such as volume, dollar, and imbalance bars are presented as superior sampling units for many ML tasks, yielding more stationary features and fewer microstructure distortions than fixed time bars. ( Alpaca ) Practical use cases 1) Regime-aware moving averages The synthetic SMA in event time is not fooled by quiet periods: if nothing of consequence trades, it barely updates. This can make trend filters less sensitive to calendar drift and more sensitive to true participation. 2) Breakout logic on “equal-information” samples The script exposes simple alerts such as breakout above/below the synthetic SMA . Because each bar approximates a constant amount of activity, breakouts are conditioned on comparable informational mass, not arbitrary time buckets. 3) Volatility-adaptive backtests If you use synthetic bars as your base data stream, most signal rules become self-paced : entry and exit opportunities accelerate in fast markets and slow down in quiet regimes, which often improves the realism of slippage and fill modeling in research pipelines (pair this indicator with strategy code downstream). 4) Regime diagnostics Avg Bars per Sample trending down: activity is dense; expect larger realized ranges. Return StdDev (synthetic) rising: noise or trend acceleration in event time; re-tune risk. Interpreting the info panel Method : your sampling choice and current threshold. Total Samples : how many synthetic bars have been formed. Current Vol/Dollar : how much of the next bucket is already filled. Bars in Bucket : native bars consumed so far in the current bucket. Avg Bars/Sample : lower means higher trading intensity. Avg Return / Return StdDev : return stats computed over synthetic closes . Research directions you can build from here Imbalance and run bars Extend beyond pure volume or dollar thresholds to imbalance bars that trigger on directional order flow imbalance (e.g., buy volume minus sell volume), as discussed in the AFML ecosystem. These often further homogenize distributional properties used in ML. alpaca.markets Volume-time indicators Re-compute classical indicators (RSI, MACD, Bollinger) on the synthetic stream. The premise is that signals are updated by traded information , not seconds, which may stabilize indicator behavior in heteroskedastic regimes. Liquidity and toxicity overlays Combine synthetic bars with proxies of flow toxicity to anticipate spread widening or volatility clustering. For instance, tag synthetic bars that surpass multiples of the threshold and test whether subsequent realized volatility is elevated. Dollar-risk parity sampling for portfolios Use dollar bars to align samples across assets by notional risk, enabling cleaner cross-asset features and comparability in multi-asset models (e.g., correlation studies, regime clustering). AFML discusses the benefits of event-driven sampling for cross-sectional ML feature engineering. Microstructure feature set Compute duration in native bars per synthetic sample , range per sample , and volume multiple of threshold as inputs to state classifiers or regime HMMs . These features are inherently activity-aware and often predictive of short-horizon volatility and trend persistence per the event-time literature. ( Alpaca ) Tips for clean usage Start with dynamic thresholds using Median over a sensible lookback to avoid outlier distortion, then move to Fixed thresholds when you know your instrument’s typical activity scale. Compare time bars vs synthetic bars side by side to develop intuition for how your market “breathes” in activity time. Keep Max Stored Samples reasonable for performance; the ring buffer avoids memory creep while preserving a rolling window of research-grade data.
Indikator Pine Script®
oleh BackQuant
PolyFilter [BackQuant]PolyFilter A flexible, low-lag trend filter with three smoothing engines—optimized for clean bias, fewer whipsaws, and clear alerting. What it does PolyFilter draws a single “intelligent” baseline that adapts to price while suppressing noise. You choose the engine— Fractional MA , Ehlers 2-Pole Super Smoother , or a Multi-Kernel blend . The line can color itself by slope (trend) or by position vs price (above/below), and you get four ready-made alerts for flips and crosses. What it plots PolyFilter line — your smoothed trend baseline (width set by “Line Width”). Optional candle & background coloring — choose: color by trend slope or by whether price is above/below the filter. Signal markers — Arrows with L/S when the slope flips or when price crosses the line (if you enable shapes/alerts). How the three engines differ Fractional MA (experimental) — A power-law weighting of past bars (heavier focus on the most recent samples without throwing away history). The Adaptation Speed acts like the “fraction” exponent (default 0.618). Lower values lean more on recent bars; higher values spread weight further back. Ehlers 2-Pole Super Smoother — Classic low-lag IIR smoother that aggressively reduces high-frequency noise while preserving turns. Great default when you want a steady, responsive baseline with minimal parameter fuss. Multi-Kernel — A 70/30 blend of a Gaussian window and an exponential kernel. The Gaussian contributes smooth structure; the exponential adds a hint of responsiveness. Useful for assets that oscillate but still trend. Reading the colors Trend mode (default) — Line & candles turn green while the filter is rising (signal > signal ) and red while it’s falling. Above/Below mode — Line & candles reflect price’s position relative to the filter: green when price > filter, red when price < filter. This is handy if you treat the filter like a dynamic “fair value” or bias line. Inputs you’ll actually use Calculation Settings Price Source — Default HLC/3. Switch to Close for stricter trend, or HLC3/HL2 to soften single-print spikes. Filter Length — Window/period for all engines. Shorter = snappier turns; longer = smoother line. Adaptation Speed — Only affects Fractional MA . Lower it for faster, more local weighting; raise it for smoother, more global weighting. Filter Type — Pick one of: Fractional MA, Ehlers 2-Pole, Multi-Kernel. UI & Plotting Color based off… — Choose Trend (slope) or > or < Close (position vs price). Long/Short Colors — Customize bull/bear hues to your theme. Show Filter Line / Paint candles / Color background — Visual toggles for the line, bars, and backdrop. Line Width — Make the filter stand out (2–3 works well on most charts). Signals & Alerts PolyFilter Trend Up — Slope flips upward (the filter crosses above its prior value). Good for early continuation entries or stop-tightening on shorts. PolyFilter Trend Down — Slope flips downward. Often used to scale out longs or rotate bias. PolyFilter Above Price — The filter line crosses up through price (filter > price). This can confirm that mean has “caught up” after a pullback. PolyFilter Below Price — The filter line crosses down through price (filter < price). Useful to confirm momentum loss on bounces. Quick starts (suggested presets) Intraday (5–15m, crypto or indices) — Ehlers 2-Pole, Length 55–80. Trend coloring ON, candle paint ON. Look for pullbacks to a rising filter; avoid fading a falling one. Swing (1H–4H) — Multi-Kernel, Length 80–120. Background color OFF (cleaner), candle paint ON. Add a higher-TF confirmation (e.g., 4H filter rising when you trade 1H). Range-prone FX — Fractional MA, Length 70–100, Adaptation ~0.55–0.70. Consider Above/Below mode to trade mean reversion to the line with a strict risk cap. How to use it in practice Bias line — Trade in the direction of the filter slope; stand aside when it flattens and color chops back and forth. Dynamic support/resistance — Treat the line as a moving value area. In trends, entries often appear on shallow tags of the line with structure confluence. Regime switch — When the filter flips and holds color for several bars, tighten stops on the opposing side and look for first pullback in the new color. Stacking filters — Many users run PolyFilter on the active chart and a slower instance (longer length) on a higher timeframe as a “macro bias” guardrail. Tuning tips If you see too many flips, lengthen the filter or switch to Multi-Kernel. If turns feel late, shorten the filter or try Ehlers 2-Pole for lower lag. On thin or very noisy symbols, prefer HLC3 as the source and longer lengths. Performance note: very large lengths increase computation time for the Multi-Kernel and Fractional engines. Start moderate and scale up only if needed. Summary PolyFilter gives you a single, trustworthy baseline that you can read at a glance—either as a pure trend line (slope coloring) or as a dynamic “above/below fair value” reference. Pick the engine that matches your market’s personality, set a sensible length, and let the color and alerts guide bias, entries on pullbacks, and risk on reversals.
Indikator Pine Script®
oleh BackQuant
2
Algorithmic Kalman Filter [CRYPTIK1]Price action is chaos. Markets are driven by high-frequency algorithms, emotional reactions, and raw speculation, creating a constant stream of noise that obscures the true underlying trend. A simple moving average is too slow, too primitive to navigate this environment effectively. It lags, it gets chopped up, and it fails when you need it most. This script implements an Algorithmic Kalman Filter (AKF), a sophisticated signal processing algorithm adapted from aerospace and robotic guidance systems. Its purpose is singular: to strip away market noise and provide a hyper-adaptive, self-correcting estimate of an asset's true trajectory. The Concept: An Adaptive Intelligence Unlike a moving average that mindlessly averages past data, the Kalman Filter operates on a two-step principle: Predict and Update. Predict: On each new bar, the filter makes a prediction of the true price based on its previous state. Update: It then measures the error between its prediction and the actual closing price. It uses this error to intelligently correct its estimate, learning from its mistakes in real-time. The result is a flawlessly smooth line that adapts to volatility. It remains stable during chop and reacts swiftly to new trends, giving you a crystal-clear view of the market's real intention. How to Wield the Filter: The Core Settings The power of the AKF lies in its two tuning parameters, which allow you to calibrate the filter's "brain" to any asset or timeframe. Process Noise (Q) - Responsiveness: This controls how much you expect the true trend to change. A higher Q value makes the filter more sensitive and responsive to recent price action. Use this for highly volatile assets or lower timeframes. A lower Q value makes the filter smoother and more stable, trusting that the underlying trend is slow-moving. Use this for higher timeframes or ranging markets. Measurement Noise (R) - Smoothness: This controls how much you trust the incoming price data. A higher R value tells the filter that the price is extremely noisy and to be more skeptical. This results in a much smoother, slower-moving line. A lower R value tells the filter to trust the price data more, resulting in a line that tracks price more closely. The interaction between Q and R is what gives the filter its power. The default settings provide a solid baseline, but a true operator will fine-tune these to perfectly match the rhythm of their chosen market. Tactical Application The AKF is not just a line; it's a complete framework for viewing the market. Trend Identification: The primary signal. The filter's color code provides an unambiguous definition of the trend. Teal for an uptrend, Pink for a downtrend. No more guesswork. Dynamic Support & Resistance: The filter itself acts as a dynamic level. Watch for price to pull back and find support on a rising (Teal) filter in an uptrend, or to be rejected by a falling (Pink) filter in a downtrend. A Higher-Order Filter: Use the AKF's trend state to filter signals from your primary strategy. For example, only take long signals when the AKF is Teal. This single rule can dramatically reduce noise and eliminate low-probability trades. This is a professional-grade tool for traders who are serious about gaining a statistical edge. Ditch the lagging averages. Extract the signal from the noise.
Indikator Pine Script®
oleh CRYPTIK1
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts. What this is A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks. Why Theil-Sen Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns. What it plots Filtered baseline that advances by a fraction of the robust slope each bar. Optional candle coloring by baseline slope sign for quick trend read. Alerts when the baseline slope turns up or down. How it behaves (high level) Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes. Takes the median of those slopes to get a robust estimate for the bar. Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line. Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive. Inputs and what they do Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother. Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier. Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves. Slope Cap Mode — optional guardrail on each bar’s slope: None — no cap. ATR — cap scales with recent true range. Percent — cap scales with price level. Points — fixed absolute cap in price points. ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size. UI Settings — show or hide the line, paint candles by slope, choose long and short colors. How to read it Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction. Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise. Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets. Use cases Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups. Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure. Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles. Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars. Tuning guidance Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves. Position trading — longer window, moderate response. ATR cap tends to scale well across cycles. Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities. Alerts included Theil-Sen Up Slope — baseline’s one-bar change crosses above zero. Theil-Sen Down Slope — baseline’s one-bar change crosses below zero. Strengths Robust to outliers through median-based slope estimation. Continuously advances with price rather than re-anchoring, which reduces lag at turns. User-selectable slope caps to tame shock bars without over-smoothing everything. Minimal visuals with optional candle painting for fast regime recognition. Notes This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
Indikator Pine Script®
oleh BackQuant
Deadband Hysteresis Filter [BackQuant]Deadband Hysteresis Filter What this is This tool builds a “debounced” price baseline that ignores small fluctuations and only reacts when price meaningfully departs from its recent path. It uses a deadband to define how much deviation matters and a hysteresis scheme to avoid rapid flip-flops around the decision boundary. The baseline’s slope provides a simple trend cue, used to color candles and to trigger up and down alerts. Why deadband and hysteresis help They filter micro noise so the baseline does not react to every tiny tick. They stabilize state changes. Hysteresis means the rule to start moving is stricter than the rule to keep holding, which reduces whipsaw. They produce a stepped, readable path that advances during sustained moves and stays flat during chop. How it works (conceptual) At each bar the script maintains a running baseline dbhf and compares it to the input price p . Compute a base threshold baseTau using the selected mode (ATR, Percent, Ticks, or Points). Build an enter band tauEnter = baseTau × Enter Mult and an exit band tauExit = baseTau × Exit Mult where typically Exit Mult < Enter Mult . Let diff = p − dbhf . If diff > +tauEnter , raise the baseline by response × (diff − tauEnter) . If diff < −tauEnter , lower the baseline by response × (diff + tauEnter) . Otherwise, hold the prior value. Trend state is derived from slope: dbhf > dbhf → up trend, dbhf < dbhf → down trend. Inputs and what they control Threshold mode ATR — baseTau = ATR(atrLen) × atrMult . Adapts to volatility. Useful when regimes change. Percent — baseTau = |price| × pctThresh% . Scale-free across symbols of different prices. Ticks — baseTau = syminfo.mintick × tickThresh . Good for futures where tick size matters. Points — baseTau = ptsThresh . Fixed distance in price units. Band multipliers and response Enter Mult — outer band. Price must travel at least this far from the baseline before an update occurs. Larger values reject more noise but increase lag. Exit Mult — inner band for hysteresis. Keep this smaller than Enter Mult to create a hold zone that resists small re-entries. Response — step size when outside the enter band. Higher response tracks faster; lower response is smoother. UI settings Show Filtered Price — plots the baseline on price. Paint candles — colors bars by the filtered slope using your long/short colors. How it can be used Trend qualifier — take entries only in the direction of the baseline slope and skip trades against it. Debounced crossovers — use the baseline as a stabilized surrogate for price in moving-average or channel crossover rules. Trailing logic — trail stops a small distance beyond the baseline so small pullbacks do not eject the trade. Session aware filtering — widen Enter Mult or switch to ATR mode for volatile sessions; tighten in quiet sessions. Parameter interactions and tuning Enter Mult vs Response — both govern sensitivity. If you see too many flips, increase Enter Mult or reduce Response. If turns feel late, do the opposite. Exit Mult — widening the gap between Enter and Exit expands the hold zone and reduces oscillation around the threshold. Mode choice — ATR adapts automatically; Percent keeps behavior consistent across instruments; Ticks or Points are useful when you think in fixed increments. Timeframe coupling — on higher timeframes you can often lower Enter Mult or raise Response because raw noise is already reduced. Concrete starter recipes General purpose — ATR mode, atrLen=14 , atrMult=1.0–1.5 , Enter=1.0 , Exit=0.5 , Response=0.20 . Balanced noise rejection and lag. Choppy range filter — ATR mode, increase atrMult to 2.0, keep Response≈0.15 . Stronger suppression of micro-moves. Fast intraday — Percent mode, pctThresh=0.1–0.3 , Enter=1.0 , Exit=0.4–0.6 , Response=0.30–0.40 . Quicker turns for scalping. Futures ticks — Ticks mode, set tickThresh to a few spreads beyond typical noise; start with Enter=1.0 , Exit=0.5 , Response=0.25 . Strengths Clear, explainable logic with an explicit noise budget. Multiple threshold modes so the same tool fits equities, futures, and crypto. Built-in hysteresis that reduces flip-flop near the boundary. Slope-based coloring and alerts that make state changes obvious in real time. Limitations and notes All filters add lag. Larger thresholds and smaller response trade faster reaction for fewer false turns. Fixed Points or Ticks can under- or over-filter when volatility regime shifts. ATR adapts, but will also expand bands during spikes. On extremely choppy symbols, even a well tuned band will step frequently. Widen Enter Mult or reduce Response if needed. This is a chart study. It does not include commissions, slippage, funding, or gap risks. Alerts DBHF Up Slope — baseline turns from down to up on the latest bar. DBHF Down Slope — baseline turns from up to down on the latest bar. Implementation details worth knowing Initialization sets the baseline to the first observed price to avoid a cold-start jump. Slope is evaluated bar-to-bar. The up and down alerts check for a change of slope rather than raw price crossings. Candle colors and the baseline plot share the same long/short palette with transparency applied to the line. Practical workflow Pick a mode that matches how you think about distance. ATR for volatility aware, Percent for scale-free, Ticks or Points for fixed increments. Tune Enter Mult until the number of flips feels appropriate for your timeframe. Set Exit Mult clearly below Enter Mult to create a real hold zone. Adjust Response last to control “how fast” the baseline chases price once it decides to move. Final thoughts Deadband plus hysteresis gives you a principled way to “only care when it matters.” With a sensible threshold and response, the filter yields a stable, low-chop trend cue you can use directly for bias or plug into your own entries, exits, and risk rules.
Indikator Pine Script®
oleh BackQuant
10
Kalman Filter (Smoothed)The Kalman Filter is a recursive statistical algorithm that smooths noisy price data while adapting dynamically to new information. Unlike simple moving averages or EMAs, it minimizes lag by balancing measurement noise (R) and process noise (Q), giving traders a clean, adaptive estimate of true price action. 🔹 Core Features Real-time recursive estimation Adjustable noise parameters (R = sensitivity to price, Q = smoothness vs. responsiveness) Reduces market noise without heavy lag Overlay on chart for direct comparison with raw price 🔹 Trading Applications Smoother trend visualization compared to traditional MAs Spotting true direction during volatile/sideways markets Filtering out market “whipsaws” for cleaner signals Building blocks for advanced quant/trading models ⚠️ Note: The Kalman Filter is a state-space model; it doesn’t predict future price, but smooths past and present data into a more reliable signal.
Indikator Pine Script®
oleh JEDSBN
1
[Pandora][Swarm] Rapid Exponential Moving AverageENVISIONING POSSIBILITY What is the theoretical pinnacle of possibility? The current state of algorithmic affairs falls far short of my aspirations for achievable feasibility. I'm lifting the lid off of Pandora's box once again, very publicly this time, as a brute force challenge to conventional 'wisdom'. The unfolding series of time mandates a transcendental systemic alteration... THE MOVING AVERAGE ZOO: The realm of digital signal processing for trading is filled with familiar antiquated filtering tools. Two families of filtration, being 'infinite impulse response' (EMA, RMA, etc.) and 'finite impulse response' (WMA, SMA, etc.), are prevalently employed without question. These filter types are the mules and donkeys of data analysis, broadly accepted for use in finance. At first glance, they appear sufficient for most tasks, offering a basic straightforward way to reduce noise and highlight trends. Yet, beneath their simplistic facade lies a constellation of limitations and impediments, each having its own finicky quirks. Upon closer inspection, identifiable drawbacks render them far from ideal for many real-world applications in today's volatile markets. KNOWN FUNDAMENTAL FLAWS: Despite commonplace moving average (MA) popularity, these conventional filters suffer from an assortment of fundamental flaws. Most of them don't genuinely address core challenges of how to preserve the true dynamics of a signal while suppressing noise and retaining cutoff frequency compliance. Their simple cookie cutter structures make them ill-suited in actuality for dynamic market environments. In reality, they often trade one problem for another dilemma, forsaking analytics to choose between distortion and delay. A deeper seeded issue remains within frequency compliance, how adequately a filter respects (or disrespects) the underlying signal’s spectral properties according to it's assigned periodic parameter. Traditional MAs habitually distort phase relationships, causing delayed reactions with surplus lag or exaggerations with excessive undershoot/overshoot. For applications requiring timely resilience, such as algorithmic trading, these shortcomings are often functionally unacceptable. What’s needed is vigorous filters that can more accurately retain signal behaviors while minimizing lag without sacrificing smoothness and uniformity. Until then, the public MA zoo remains as a collection of corny compromises, rather than a favorable toolbelt of solutions. P.S.: In PSv7+, in my opinion, many of these geriatric MAs deserve no future with ease of access for the naive, simply not knowing these filters are most likely creating bigger problems than solving any. R.E.M.A. What is this? I prefer to think of it as the "radical EMA", definitely along my lines of a retire everything morte algorithm. This isn't your run of the mill average from the petting zoo. I would categorize it as a paradigm shifting rampant economic masochistic annihilator, sufficiently good enough to begin ruthlessly executing moving averages left and right. Um, yeah... that kind of moving average destructor as you may soon recognize with a few 'Filters+' settings adjustments, realizing ordinary EMA has been doing us an injustice all this time. Does it possess the capability to relentlessly exterminate most averaging filters in existence? Well, it's about time we find out, by uncaging it on the loose into the greater economic wilderness. Only then can we truly find out if it is indeed a radical exponential market accelerant whose time has come. If it is, then it may eventually become a reality erasing monolithic anomaly destined for greatness, ultimately changing the entire landscape of trading in perpetuity. UNLEASHING NEXT-GEN: This lone next generation exoweapon algorithm is intended to initiate the transformative beginning stages of mass filtration deprecation. However, it won't be the only one, just the first arrival of it's alien kind from me. Welcome to notion #1 of my future filtration frontier, on this episode of the algorithmic twilight zone. Where reality takes a twisting turn one dimension beyond practical logic, after persistent models of mindset disintegrate into insignificance, followed by illusory perception confronted into cognitive dissonance. An evolutionary path to genuine advancement resides outside the prison of preconceptions, manifesting only after divergence from persistent binding restrictions of dogmatic doctrines. Such a genesis in transformative thinking will catalyze unbounded cognitive potential, plowing the way for the cultivation of total redesigns of thought. Futuristic innovative breakthroughs demand the surrender of legacy and outmoded understandings. Now that the world's largest assembly of investors has been ensembled, there are additional tasks left to perform. I'm compelled to deploy this mathematical-weapon of mass financial creation into it's rightful destined hands, to "WE THE PEOPLE" of TV. SCRIPT INTENTION: Deprecate anything and everything as any non-commercial member sees desirably fit. This includes your existing code formulations already in working functional modes of operation AND/OR future projects in the works. Swapping is nearly as simple as copying and pasting with meager modifications, after you have identified comparable likeness in this indicators settings with a visual assessment. Results may become eye opening, but only if you dare to look and test. Where you may suspect a ta.filter() is lacking sufficient luster or may be flat out majorly deficient, employing rema, drema, trema, or qrema configurations may be a more suitable replacement. That's up to you to discern. My code satire already identifies likely bottom of the barrel suspects that either belong in the extinction record or have already been marked for deprecation. They are ordered more towards the bottom by rank where they belong. SuperSmoother is a masterpiece here to stay, being my original go-to reference filter. Everything you see here is already deprecated, including REMA... REMA CHARACTERISTICS - VERY low lag - No overshoot - Frequency compliant - Proper initialization at bar_index==0 - Period parameter accepts poitive floating point numerics (AND integers!) - Infinite impulse response (IIR) filter - Compact code footprint - Minimized computational overhead
Indikator Pine Script®
oleh ImmortalFreedom
10
Combo 2/20 EMA & Bandpass Filter by TamarokDescription: This strategy combines a 2/20 exponential moving average (EMA) crossover with a custom bandpass filter to generate buy and sell signals. Use the Fast EMA and Slow EMA inputs to adjust trend sensitivity, and the Bandpass Filter Length, Delta, and Zones to fine-tune momentum turns. Signals occur when both EMA and BPF agree in direction, with optional reversal and time filters. How to use: 1. Add the script to your chart in TradingView. 2. Adjust the EMA and BP Filter parameters to match your asset’s volatility. 3. Enable ‘Reverse Signals’ to trade counter-trend, or use the time filter to limit sessions. 4. Set alerts on Long Alert and Short Alert for automated notifications. Inspiration: Based on HPotter’s original combo strategy (Stocks & Commodities Mar 2010). Updated to Pine Script v6 with streamlined code and alerts. WARNING: For purpose educate only
Strategi Pine Script®
oleh tamarok
Kent Directional Filter🧭 Kent Directional Filter Author: GabrielAmadeusLau Type: Filter 📖 What It Is The Kent Directional Filter is a directionality-sensitive smoothing tool inspired by the Kent distribution, a probability model used to describe directional and elliptical shapes on a sphere. In this context, it's repurposed for analyzing the angular trajectory of price movements and smoothing them for actionable insights. It’s ideal for: Detecting directional bias with probabilistic weighting Enhancing momentum or trend-following systems Filtering non-linear price action 🔬 How It Works Price Angle Estimation: Computes a rough angular shift in price using atan(src - src ) to estimate direction. Kent Distribution Weighting: κ (kappa) controls concentration strength (how sharply it prefers a direction). β (beta) controls ellipticity (bias toward curved vs. linear moves). These parameters influence how strongly the indicator favors movements at ~45° angles, simulating a directional “lens.” Smoothing: A Simple Moving Average (SMA) is applied over the raw directional probabilities to reduce noise and highlight the underlying trend signal. ⚙️ Inputs Source: Price series used for angle calculation (default: close) Smoothing Length: Window size for the moving average Pi Divisor: Pi / 4 would be 45 degrees, you can change the 4 to 3, 2, etc. Kappa (κ): Controls how focused the directionality is (higher = sharper filter) Beta (β): Adds curvature sensitivity; higher values accentuate asymmetrical moves 🧠 Tips for Best Results Use κ = 1–2 for moderate directional filtering, and β = 0.3–0.7 for smooth elliptical bias. Combine with volume-based indicators to confirm breakout strength. Works best in higher timeframes (1h–1D) to capture macro directional structure. I might revisit this.
Indikator Pine Script®
oleh GabrielAmadeusLau
1
TASC 2025.07 Laguerre Filters█ OVERVIEW This script implements the Laguerre filter and oscillator described by John F. Ehlers in the article "A Tool For Trend Trading, Laguerre Filters" from the July 2025 edition of TASC's Traders' Tips . The new Laguerre filter utilizes the UltimateSmoother filter in place of an exponential moving average (EMA) in its calculation, offering improved responsiveness and reduced lag. █ CONCEPTS As Ehlers explains in his article, the Laguerre filter is a form of transversal filter . A transversal filter calculates an output signal using a tapped delay line . It creates multiple delayed versions of an input signal, applies weight to each delay, and then calculates their sum to generate the filtered result. The Laguerre filter's structure relies on Laguerre polynomials — solutions to a differential equation solved by Edmond Laguerre in the 1800s. When Ehlers analyzed the formula for these polynomials on discrete systems (e.g., financial time series), he found that the first term's expression corresponds to an EMA response, and all subsequent terms correspond to an all-pass response. In contrast to other filter types, an all-pass filter produces phase shift (i.e., delay) in an input signal's components without affecting its amplitude. Ehlers observed that these characteristics of Laguerre polynomials make them suitable for use in a transversal filter structure, and thus the Laguerre filter was born. However, he notes that EMAs are not great filters in general. As such, to improve on the Laguerre filter's design, Ehlers modified it by replacing the EMA term with his UltimateSmoother filter. The resulting Laguerre filter has significantly reduced lag, achieving a tighter response to market fluctuations while maintaining smoothness. Ehlers suggests that traders can analyze crossings between the UltimateSmoother and this Laguerre filter, or those between two Laguerre filters of different order, for helpful buy and sell signals. In addition to the Laguerre filter, Ehlers derived a smooth, low-lag oscillator based on the difference between the first and second terms in the modified filter structure, scaled by the root mean square (RMS). The resulting oscillator provides an alternative filtered representation of market data, which can help traders identify swing and mean-reversion signals. █ USAGE This indicator calculates both the Laguerre filter and the Laguerre oscillator described in Ehlers' article. It displays the Laguerre filter on the main chart pane and the oscillator in a separate pane. Users can control the behavior of the filter and oscillator with the inputs in the "Settings/Inputs" tab: The "Period" input defines the critical period of the UltimateSmoother used in the Laguerre filter and oscillator calculations. Its default value is 30. The "Gamma" input determines the weighting behavior of the Laguerre filter and oscillator. It accepts a positive value between 0 and 1. Use a lower value for quicker responsiveness to market changes, and a higher value for trends. The default value is 0.5. The "RMS length" input determines the length of the RMS calculation for oscillator normalization. The default value is 100 bars.
Indikator Pine Script®
oleh PineCodersTASC
13