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Ehlers Super Passband Filter [Kodeus]The Ehlers Super Passband Filter (ESPF) is based on the digital signal processing techniques introduced by John F. Ehlers. This filter aims to isolate cyclic market components by leveraging a passband design allowing signals within a specified frequency range (defined by fast and slow lengths) to pass while attenuating others. Unlike traditional moving averages or trend-following tools, ESPF provides a more responsive yet smoother signal, which can help traders better identify cycles, turning points, and overbought/oversold conditions with minimal lag. ♦️ Features 📉 Adaptive Filtering: Designed to respond to different market conditions by emphasizing mid-frequency price movements and filtering out noise. 🎛️ Customizable Lengths: Input parameters allow users to fine-tune the fast and slow lengths, tailoring the passband to different asset behaviors and timeframes. 📊 Dynamic RMS Thresholds: Includes a Root Mean Square (RMS) calculation to assess the volatility of the ESPF line, aiding in visual confirmation of signal strength and potential reversals. 🌈 Color-Coded Signal Line: ESPF line changes color based on its position relative to the RMS bounds, visually enhancing breakout and reversion signals. 🧭 Zero Line Reference: A horizontal zero line is plotted to help interpret ESPF directional bias and divergence more clearly. ♦️ Calculations The Ehlers Super Passband Filter is derived from digital signal processing (DSP) principles, specifically the use of a second-order recursive filter to isolate desired frequency components. 1. Filter Coefficients Two alpha parameters (a1 and a2) are calculated as follows: a1 = 5 / Fast Length a2 = 5 / Slow Length These values define the frequency range (or “passband”) the filter will respond to, by mimicking a band-pass filter behavior in signal processing. 2. Recursive Filter Equation The ESPF value is computed using a recursive difference equation, which combines the current and previous inputs (price data) and outputs: ESPF = (a1 - a2) * price + (a2 * (1 - a1) - a1 * (1 - a2)) * price + ((1 - a1) + (1 - a2)) * ESPF - (1 - a1) * (1 - a2) * ESPF This formula smooths the data while allowing oscillations within the defined frequency range to pass through—effectively creating a band-pass filter on price data. 3. Root Mean Square (RMS) Envelope To visualize the volatility or strength of the ESPF signal, the script computes a 50-period Root Mean Square (RMS) of the ESPF values: RMS = sqrt(sum(ESPF², 50) / 50) This RMS band acts as a dynamic envelope. When ESPF crosses above or below this threshold, it may signal strong directional moves or potential turning points. 🔶 Disclaimer Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals. Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator. Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies. Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses. No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
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Kodeus tarafından
Multi-Currency Desk Screener + XAU & US30Stay ahead in the market with our Multi-Currency Desk Screener, designed for Traders and Analysts. This TradingView indicator monitors multiple currency pairs, XAU/USD (Gold), and US30 (Dow Jones Index) simultaneously, providing clear Buy, Sell, or Neutral signals based on Stochastic Oscillator, Moving Average, and Breakout strategy. Features include: Real-time multi-symbol scanning for Forex, XAU, and US30. Visual table displaying signal status for each symbol: Buy (green), Sell (red), Neutral (yellow). Uses Stochastic crossovers with overbought/oversold levels combined with trend filter (MA) for higher probability trades. Breakout confirmation ensures signals align with market momentum. Lightweight and fully compatible with TradingView charts. Perfect for traders who want a quick overview of multiple markets and efficient decision-making without switching charts. Maximize your trading edge with a desk-style visual screener that highlights trading opportunities instantly. Keywords: Multi-Currency Screener, Forex Trading Indicator, XAUUSD Signals, US30 Signals, Stochastic MA Breakout, TradingView Indicator, Multi-Symbol Scanner Want to get access to this powerful indicator and stay ahead of the market? ⚡ Contact me now for details!Join Telegram for get Trading bot, Indicators Free:t.me/forexmillionsdollar
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takeutawayfromme2022 tarafından
Marcius Studio® - Trend Detector™Trend Detector™ — is an advanced trend detection indicator that combines statistical Z-Score analysis with a simplified ADF stationarity test . It is designed to help traders identify strong directional moves while filtering out noise and false signals. Unlike traditional moving average crossovers or momentum oscillators, this tool evaluates both trend direction and trend strength , giving you a clear visual overview of market conditions. Important! This indicator is intended for educational and informational purposes . It does not guarantee future performance and should be used together with proper risk management. Idea Markets spend 70–80% of the time in consolidation and only 20–30% in trending phases . The key to profitable trading is spotting when a major trend shift begins. Trend Detector™ was built exactly for this purpose — to filter noise and highlight true trend reversals. How It Works Calculates the Z-Score of price to detect extreme deviations from the mean. Applies a simplified ADF t-Statistic test to confirm trend validity. Uses an ATR-based ribbon for clean visualization of bullish/bearish phases. Generates Buy/Sell signals when trend switches are confirmed. Displays an Info Panel with real-time metrics: Z-Score, ADF t-Stat, Trend Strength (0–100), ATR % of price. Features Trend Ribbon : visually highlights bullish, bearish, or neutral phases. Confirmation Filter : avoids false flips by requiring multiple bars of validation. Strength Score : quantifies how powerful the current trend is. Signal Markers : “BUY” and “SELL” alerts appear directly on the chart. Customizable Alerts : get notified when new uptrends or downtrends are detected. Recommendations Works well on swing trading timeframes (1H, 4H, Daily). Use in combination with support/resistance zones or volume profile tools for higher accuracy. The higher the Trend Strength Score , the more reliable the trend continuation. Indicator Settings Analysis Period : number of bars for Z-Score & ADF test. ATR Length : used for ribbon visualization. Min Bars to Confirm Trend : filters false trend flips. Show/Hide options for Ribbon, Signals, and Info Panel. Example Settings Timeframe : 1H or 4H Analysis Period : 20 ATR Length : 14 Min Confirmation Bars : 2–3 Disclaimer Trading and investing involve risk — always do your own research (DYOR) and seek professional advice. We are not responsible for any financial losses.
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marcius-studio tarafından
Marcius Studio® - Cross-Asset Correlator™Cross-Asset Correlator™ — a pair-trading strategy that identifies correlation breakdowns between two assets and captures profit opportunities from market inefficiencies. The strategy enters trades when the correlation drops below a set threshold and closes positions once correlation recovers. The main concept is to exploit temporary divergence between two assets by going long the stronger one and short the weaker one, aiming to profit when their correlation reverts. Important : This script illustrates asset correlation concepts for educational purposes only. It's not for live trading—requires adjustments and offers no performance guarantees. Always apply risk management. TradingView Limitation By default, TradingView’s built-in Strategy interface does not support backtesting with two different assets . To overcome this, the script is implemented as an indicator with a fully custom backtesting engine that calculates PnL, trades, and performance statistics directly on the chart. Idea Markets move in clusters : altcoins follow BTC, memecoins track Solana, L2 projects mirror Ethereum. But correlations aren’t perfect—temporary divergences create pricing inefficiencies. The logic: When an asset lags or overshoots its usual correlation, it’s a mispricing opportunity. Trade the reversion: buy undervalued divergence, sell overextended convergence. The market eventually corrects, but the inefficiency window allows profit before realignment. OKX Signal Bot Integration This script includes a built-in interface for OKX Signal Bot . It can generate structured JSON alerts (ENTER / EXIT, long / short) and directly manage trades on OKX exchange . This allows seamless automation of correlation-based strategies without manual order execution. Note : The OKX Signal Bot (for demo use only) assists with alerts & trade management but does not ensure profits. You are fully responsible for your trades—always apply risk management. Strategy Parameters Symbol 1 / Symbol 2 : trading instruments to be analyzed. SMA Period : smoothing period for price averages. Correlation Period : number of bars used to calculate correlation coefficient. Upper Correlation Threshold : level above which trades are closed. Lower Correlation Threshold : level below which new trades are opened. percentage_investment (%) : allocation per entry signal (used for OKX integration). Example Settings OKX:FARTCOINUSDT.P / OKX:PENGUUSDT.P Timeframe : 1H SMA Period : 60 Correlation Period : 25 Upper Threshold : 0.9 Lower Threshold : 0.1 percentage_investment : 10% How the Code Works Retrieves closing prices of two selected assets. Calculates correlation coefficient and moving averages. When correlation breaks below the lower threshold, the script opens a pair trade (long/short depending on SMA relation). When correlation recovers above the upper threshold, all open trades are closed. Real-time alerts are generated in JSON format for OKX bots (ENTER/EXIT signals). Built-in backtesting engine tracks PnL, trades, and statistics (7d / 30d / total). Visual labels mark entries, exits, and PnL results directly on the chart. Disclaimer Trading involves risk — always do your own research (DYOR) and seek professional financial advice. We are not responsible for any potential financial losses.
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marcius-studio tarafından
Cheat CodeWhy Monday & Friday Monday evening (NY): frequently seeds the weekly expansion. Its DR/IDR often acts as a weekly “starter envelope,” useful for breakout continuation or fade back into the box plays as liquidity builds. Friday evening (NY): often exposes end-of-week traps (run on stops into the close) and sets expectation boundaries into the following week. Carry these levels forward to catch Monday’s reaction to Friday’s closing structure. Typical use-cases Breakout & retest: Price closes outside the Monday DR/IDR → look for retests of the band edge for continuation. Liquidity sweep (“trap”) recognition: Friday session wicks briefly beyond Friday DR/IDR then closes back inside → watch for mean reversion early next week. Bias filter: Above both Monday DR midline and Friday DR midline → bias long until proven otherwise; the inverse for shorts. Session open confluence: Reactions at the open line frequently mark decision points for momentum vs. fade setups. (This is a levels framework, not a signals engine. Combine with your execution model: orderflow, S/R, session timing, or higher-TF bias.) Inputs & styling (quick reference) Display toggles (per day): Show DR / IDR / Middle DR / Middle IDR Show Opening Line Show DR/IDR Box (choose DR or IDR as box source) Show Price Labels Style controls (per day): Line width (1–4), style (Solid/Dashed/Dotted) Independent colors for DR, IDR, midlines, open line Box background opacity Timezone: Default America/New_York (changeable). Optional on-chart warning if your chart TZ differs. Practical notes Works on intraday charts; levels are anchored using weekly timestamps for accuracy on any symbol. Live updating: During the Mon/Fri calc windows, DR/IDR highs/lows and midlines keep updating until the session ends. Clean drawings: Lines, box, and labels are created once per session and then extended/updated—efficient on resources even with long display windows. Max elements: Script reserves ample line/box/label capacity for stability across weeks.
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shariff09z tarafından
ELITE RSIRSI = Relative Strength Index It’s a momentum indicator. Basically, it tells you how strong a price move is. It moves between 0 and 100. How to read it: Above 70 → Overbought Price has gone up too fast. Could fall soon. Below 30 → Oversold Price has dropped too fast. Could bounce back soon. Between 30-70 → Normal zone Price is moving normally. No extreme signals. Why traders use it: To spot possible reversals. To confirm trends. To avoid buying at the top or selling at the bottom. Think of it like a speedometer for price: too high → slow down (sell), too low → speed up (buy). If you want, I can also make a super tiny example chart showing RSI signals in a way that even a beginner can understand at a glance.
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sharemanreport tarafından
5EMA Touch/Break EMA Touch/Break Monitor (5 Lines) — Overview Purpose: Track downside touches and breakdowns against key EMAs (default 20/60/100/200/300) to judge pullbacks and risk control. Direction considered: From above to below only. Upside touches/breakouts are not counted. Signals: Downside Touch (T): Previous bar at/above EMA, current low ≤ EMA → draw an upward triangle below the bar in the EMA’s color. Downside Break (B): Previous bar at/above EMA, current low ≤ EMA × (1 − threshold) (default 2%) → draw a gold downward triangle above the bar. Priority: If both occur on the same bar, Break overrides Touch. First-only: Within a continuous run, only the first bar is marked; condition must clear before re-marking. Scope: Signals are produced only for EMAs with length ≥ 60 (adjustable). Display: The status line shows EMA prices only; a top-right table shows EMA name / price / color. Inputs: Adjustable EMA lengths; break threshold (default 2%); optional date filter (default 2024-02-14 → 2025-12-30). Alerts: Global first-only alerts for downside touch/break, plus per-EMA alerts. 用途：跟踪价格对关键 EMA（默认 20/60/100/200/300）的下行触及与下破，便于回踩/风控判断。 仅计算方向：从上向下。向上的触及/突破不计。 信号含义： 下触及（T）：上一根在 EMA 上方，本根 低点 ≤ EMA → K线下方画与该 EMA 同色向上三角形。 下破位（B）：上一根在 EMA 上方，本根 低点 ≤ EMA × (1 − 阈值)（默认 2%） → K线上方画金色向下三角形。 优先级：同根同时满足时，破位优先于触及。 首次原则：连续区间内只标第一根；需先离开条件，才会再次标记。 范围限制：仅对 长度 ≥60 的 EMA 标记信号（阈值可改）。 显示：状态行只显示 5 条 EMA 的价格；右上角表格展示每条 EMA 的名称/价格/颜色。 参数：EMA 长度可改；破位阈值默认 2%；可启用日期过滤（默认 2024-02-14 → 2025-12-30）。 提醒：提供“向下触及/向下破位（首次）”总提醒与每条 EMA 独立提醒。
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Trader_ZENG tarafından
Smart Money Concept Killer Version 1.0.2Smart Money Concept Killer All-in-one SMC tool for fast structure reads on low timeframes (1–5m) with higher-timeframe context. It combines Market Structure (CHoCH/BOS), an EMA ribbon, Liquidity levels, optional Entry highlights, and Take-Profit targets—so you can go from narrative → execution on one chart. Features Main Setting Swing Sensitivity – Controls how “coarse” swings are detected (higher = fewer, cleaner swings; lower = more detail). Sensitivity – Volatility/noise filter for swing detection; tune per symbol & timeframe. Previous Bar – Lookback window for calculations and drawings. EMA Setting Show EMA – Displays an EMA ribbon (EMA 1–25). Bull Color – Ribbon color when price is above EMA25. Bear Color – Ribbon color when below EMA25. Toggles Market Structure – Plots CHoCH and BOS based on recent swings. Liquidity – Highlights likely liquidity pools (e.g., prior swing highs/lows and session levels) to use as targets. Show The Entry – Educational highlight of example entries after structure shift/confirmation (not an auto-signal). Show Take Profit – Suggests TP targets at nearby liquidity/structural levels to help plan RR. Input in Status Line – Shows current swing/structure direction in TradingView’s status line. Quick Workflow Context with EMA Ribbon: Green bias → look for longs; Red bias → look for shorts. Wait for CHoCH → BOS: Use Market Structure to spot the flip (CHoCH) then trend confirmation (BOS). Entry (optional helper): Prefer pullbacks into zones/confluence while ribbon bias stays aligned. Risk & Targets: SL behind the confirming swing; TP at the next liquidity pool or daily/weekly/monthly levels. Tune Sensitivity: More volatile pairs → increase Sensitivity / lower Swing Sensitivity. Higher timeframes → raise Swing Sensitivity to reduce noise. Tips & Limitations Swing/zone visuals can repaint as new swings form—use them for context, not blind signals. If the chart gets heavy, reduce Previous Bar or disable non-essential layers. Always pair with sound risk management. Best For SMC/ICT traders who track liquidity sweep → CHoCH → pullback → BOS on lower timeframes. Intraday/scalpers who like an EMA ribbon filter and liquidity-based targets.
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BeSight tarafından
FxAccurate LSTWHY IS OUR FXACCCURATE LST THE PROFITABLE ? PROTECT YOUR CAPITAL WITH RISK MANAGEMENT Gives entry, stop and target levels from time to time. It finds Trading opportunities by analyzing what the price is doing during established trends. POWERFUL INDICATOR FOR A RELIABLE STRATEGIES We have made these indicators with a lot of years of hard work. It is made at a very advanced level. Established trends provide dozens of trading opportunities, but most trend indicators completely ignore them! The trend trading indicator represents an average of 10 different trades per trend.
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FXACCURATE tarafından
Chaos Theory : public release What is Chaos Theory? Chaos theory is the study of complex systems that appear random but actually follow deterministic mathematical laws. Discovered by meteorologist Edward Lorenz in the 1960s, it revealed that seemingly chaotic behavior often hides precise mathematical patterns. Key Concepts: The Butterfly Effect The famous principle that tiny changes in initial conditions can lead to vastly different outcomes. In markets, this means a small price movement at a critical juncture can cascade into major trend changes. Named after Lorenz's discovery that a butterfly flapping its wings in Brazil could theoretically cause a tornado in Texas. Sensitive Dependence on Initial Conditions Chaotic systems are extremely sensitive to their starting state. While we cannot predict exact long-term outcomes, we can identify probability zones where the system is likely to evolve. This is why weather forecasts work for days, not months - and why our indicator predicts price destinations, not timing. Strange Attractors In chaos theory, systems tend to evolve toward certain states called attractors. Price doesn't move randomly - it's drawn toward these mathematical attractors that we identify as probability zones. Fractals and Self-Similarity Chaotic systems display similar patterns at different scales. This is why price charts look similar whether viewing 1-minute or daily timeframes - the same mathematical forces operate across all time scales. Deterministic Chaos The paradox at the heart of chaos theory: systems that are completely deterministic (following precise mathematical rules) can produce behavior that appears random. Markets aren't random - they're chaotic, which means they're predictable within probability bounds. Why This Matters for Trading Traditional technical analysis assumes markets are either random (efficient market hypothesis) or follow simple patterns (support/resistance). Chaos theory reveals a third truth: markets are complex dynamical systems that follow mathematical laws we can model and predict - not with certainty, but with probability. This is the foundation of our indicator: applying the same mathematics that predicts weather patterns and planetary orbits to identify where price is mathematically likely to travel next. 🌟 Welcome to the World of Chaos Theory We hope to provide our clients with a program that will define future points to which we believe price will expand to, based on a given probability % of one event occurring rather than another. In this case, the other event = price not expanding to our predicted area and reaching an invalidation state. This entire theory and the work done assumes that price behaves like a complex dynamical system that is highly sensitive to initial conditions. 🔮 Predictive vs. Reactive Systems Pay special attention to the language used. Our belief is that we can provide you a tool that is predictive, not reactive - the latter of which falls into the class of descriptive systems. Although the term of price action study is referred to as time-series forecasting, most if not all of the works done under this umbrella do not forecast anything. They only describe the current or recent past state of affairs using averages, volume, volatility, and other concepts. 📊 Understanding Probability-Based Prediction A predictive system conjured from the world of chaos theory is not a final solution to the mystery of price. In reality, we only can give you probabilities of where price may end up - this would be a point in space, not time, which we believe would be more likely than another, depending on the analysis of the initial conditions. To make the point of the last paragraph crystal clear: while we can tell you, with respect to the probabilities, where price will end up in terms of a price point, we don't know WHEN. That is another part of the mystery that perhaps only clairvoyance can hope to uncover. 📈 Performance Statistics For the question of what the probabilities are, meaning the success of the follow through of price, the answer is given in a stats panel, which measures the success of promises made by the indicator - that price would reach a certain point before being invalidated by moving too far in the opposing direction. It's not helpful to advertise or make false claims, therefore one should take advantage that we offer a free version, and using a pre-defined lookback window, confirm the probability calculations and determine the follow through rate with respect to the specific symbol and timeframe that the user decides to use. ⚠️ What This Is Not What this is not → Descriptive. We have zero interest in describing what price is doing. In fact, the entire industry of price forecasting is dedicated to this task, therefore you can rest assured that any coincidence with an RSI or any type of moving average etc. is simply that - coincidence. We do not use any known pre-made indicators or formulas. It has been our belief that price has an underlying mathematical pattern that can be predicted within probability bounds. If you read that carefully, we are predicting the pattern, not looking to find and describe some sort of underlying structure. 🧩 Understanding Market Complexity It should be understood that price is a complex system, even if our initial assessment of the conditions are correct. We have to remember that price is a fractal structure - there are always different initial conditions clashing, as well as forming. This is without taking into account the manipulation of the system, as well as external intervention in the natural progression of the system by news or other significant events. --- 📋 To Summarize: 🔬 1. Chaos Theory Application to Markets - Novel Concept: Treating price as a chaotic particle rather than random movement - What This Means: Chaotic systems have underlying mathematical patterns that can be predicted within probability bounds - Your Benefit: Access to predictive mathematics previously used only in physics and meteorology 🧮 2. Complex Systems Mathematics - Novel Concept: Applying non-linear dynamical systems theory to financial markets - What This Means: Markets behave like complex adaptive systems with emergent properties - Your Benefit: Understanding market behavior at a fundamental mathematical level 🎯 3. Probability Field Mapping - Novel Concept: Creating mathematical probability fields for future price locations - What This Means: Each zone represents a calculated probability destination, not arbitrary support/resistance - Your Benefit: Trade toward mathematically-derived targets instead of guessing --- 💡 Why This is Fundamentally Different from All Other Indicators 📉 Traditional Indicators: - Use historical price data to create lagging signals - Based on statistical averages and linear mathematics - Assume markets are random or follow simple patterns - React to what already happened 🚀 This Chaos Theory Approach: - Uses mathematical modeling to predict future probability zones - Based on non-linear complex systems mathematics - Treats markets as chaotic but predictable systems - Proactively identifies where price is likely to go No Curve Fitting: Unlike indicators optimized for specific timeframes or instruments, chaos theory principles are universal mathematical laws that apply consistently across all markets. --- 🎁 Concrete Benefits You Receive 💫 1. Predictive Intelligence - Know probable price destinations before they're reached - Eliminate guesswork in setting profit targets - Make informed decisions about trade direction 🎯 2. Mathematical Precision - Every zone placement has mathematical justification - No subjective interpretation required - Consistent application across all market conditions 🌍 3. Universal Market Application - Works identically on forex, stocks, crypto, commodities - No need to adjust parameters for different instruments - Mathematical principles transcend market types 🏆 4. Professional-Grade Analysis - Access to institutional-level mathematical modeling - Same complexity as quantitative hedge fund systems - Simplified visual output for practical trading ✅ 5. Real-Time Performance Validation - Built-in statistics track actual prediction accuracy - Transparent performance measurement - Data-driven confidence in signal quality 🛡️ 6. Risk Management Precision - Mathematically-defined probable targets of desired and undesired price locations - Systematic approach eliminates emotional decisions ⏱️ 7. Multi-Timeframe Consistency - Zones maintain mathematical validity across timeframes - Higher timeframe bias with lower timeframe precision - Coherent analysis from scalping to position trading --- 🌟 Novel Trading Advantages Probability-Based Targeting: Instead of hoping price reaches your target, you're trading toward mathematically-calculated probability zones. Chaos Pattern Recognition: Probability-based predictions of the underlying chaotic patterns that govern price movement gives you an edge other traders don't possess. Dynamic Adaptation: Unlike static indicators, this system continuously recalculates based on evolving market mathematics. --- 🔄 Why This Represents a Trading Evolution From Reactive to Predictive: Traditional analysis tells you what happened. Chaos theory mathematics tells you what's likely to happen. From Subjective to Objective: No more debating support and resistance levels. Mathematics determines probable price destinations. From Curve-Fitted to Universal: Based on fundamental mathematical principles that work consistently across all markets and timeframes. From Emotional to Systematic: Clear mathematical signals eliminate the psychological challenges that destroy most traders. This indicator doesn't just give you another way to analyze markets - it gives you access to an entirely different mathematical framework for understanding price behavior. You're not getting a variation of existing concepts; you're getting a completely novel approach based on advanced mathematical principles that treat markets as the complex systems they actually are. --- 📚 How to Use the Indicator 🎨 Zone Mechanics • Orange Zones: Target areas for price expansion • Activation Trigger: Price must close outside any zone (full candle body, not just wicks) • Primary Rule: Price travels to the next zone before closing back behind the originating zone border 🔴 Understanding the Red Dots • Red dots on chart: Represent areas where we had valid zone sets available for trading • Empty spaces indicate: Areas where price closed past the highest/lowest zone or where zone invalidation occurred • Important note: We cannot always identify zones. Simply wait or switch timeframe/symbol
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jacesabr_real tarafından
Session Volatility MonitorOverview Session Volatility Monitor is a versatile volatility indicator tailored for intraday and session-based trading. It computes the average maximum price deviation (either up or down) from the session's opening price over a user-specified number of prior days, providing insights into expected "room to move" in the current session. This helps traders gauge potential exhaustion points, set realistic targets or stops, and identify when a directional move has reached historical norms (flagged as "REACHED" with the exact price level). Displayed via a customisable table and optional horizontal target lines, it's ideal for markets like forex, crypto, futures, or stocks where session volatility matters. The indicator supports custom sessions with timezone adjustments, making it adaptable to global trading hours (e.g., London, NY, or Asia kill zones). For assets with small tick sizes (e.g., forex pairs at 0.0001), a multiplier scales values for readability (e.g., showing pips as 67.0 instead of 0.00670). Key Features Session-Based Calculations: Defines sessions via presets (e.g., "NY Kill Zone: 07:00-10:00") or custom HHMM-HHMM inputs. (please note that preset sessions are mainly for futures e.g. "Full Day18:01-17:00", but also can be useful for forex and crypto) Adjustable UTC offset (e.g., -5 for ET) to align with your asset's timezone—ensures accurate detection regardless of TradingView's UTC internal clock. Tracks the max one-sided move (high - open or open - low) per session, averaging over 1–N previous days (default: 14). Table Display: Avg Max Move: Historical average deviation, labeled with days averaged and session time. Current Move: Real-time displacement from session open (positive for up, negative for down). Room to Go Up/Down: Remaining distance to reach the average, updating live; appends "REACHED (price)" if hit during the session. Customisable: Text color, font size (tiny to huge), position (e.g., bottom_left), and value scaling via multiplier/decimal places. Target Lines: Optional horizontal lines at "Up Target" (open + avg move) and "Down Target" (open - avg move). Lines start at the session open bar and extend only through the session duration (e.g., stops at 12:00 for a 07:00-12:00 session)—no further projection post-session. Fully customisable: Toggle on/off, color, style (solid/dotted/dashed), width, label text/background. Display Adjustments for Forex/Crypto: Multiplier: Scales raw values (e.g., set to 10000 for EURUSD to show pips like 45.0 instead of 0.0045). Decimals: Controls precision (0–5 places) for table values. How to Use Add to Chart: Search for "Session Volatility Monitor" in TradingView's indicators and apply to your symbol (e.g., EURUSD for forex, NQ1! for futures, BTCUSD for crypto). Configure Settings: Select a session preset or custom range; adjust UTC offset if needed (e.g., +0 for UTC symbols like crypto). Set "Number of Previous Days to Average" (e.g., 14 for a two-week look back). For small-tick assets, set Multiplier (e.g., 100 for crypto points, 10000 for forex pips) and Decimals (e.g., 0 for whole numbers). Customise table position/size/color and target lines for visibility. Interpret Outputs: Monitor the table for "room to go"—if Room Up is low/negative, upside might be limited; "REACHED" signals a potential reversal or exhaustion. Use target lines as visual S/R levels; they auto-start at session open and halt at close. Combine with price action, volume, or other indicators for entries (e.g., buy near down target if bullish bias). Example Scenario: Forex (GBPUSD, 1-min): Set session to "London Kill Zone: 02:00-05:00" (UTC+0), multiplier=10000. Table shows pips; lines mark expected highs/lows. Limitations and Tips Historical Data Limits: Averages are capped by TradingView's bar history (e.g., ~14 days on 1-min for free plans). Upgrade for deeper look backs or use higher timeframes. Session Accuracy: Ensure UTC offset matches your chart—test with the "In Session" plot (enable in Style tab, zoom y-axis if columns are tiny). No Alerts/Signals: Purely informational; add custom alerts via TradingView for "REACHED" conditions. Performance: On very low timeframes with long sessions, lines might consume line limits (max ~50)—toggle off if needed. Tips: For crypto/forex, experiment with multiplier to match your preferred units (e.g., points vs. decimals). Hide debug plot in Style tab for clean charts. If "REACHED" doesn't trigger, verify on historical data where moves exceed averages. This tool draws from concepts like Average Daily Range but focuses on directional, session-specific volatility for precise intraday decision-making. Feedback welcome! Disclaimer This indicator is for educational purposes only and does not constitute financial advice. Always consult a professional before trading.
Pine Script® göstergesi
mr-bal tarafından
AL Setup Checklist by Mrinal// ✅ Check 1: Liquidity Filter // Ensures average traded value (Volume × Price) for last 100 and 20 days is greater than ₹100 Cr // ✅ Check 2: Trend Continuity - SMA20 > SMA50 for last 20 bars // Ensures short-term moving average (20-day) has stayed above mid-term average (50-day) consistently // ✅ Check 3: Price Above Moving Averages // Current price should be above SMA50 and also above at least one of SMA100 or SMA200 — sign of strength // ✅ Check 4: Positive Slope of SMA50 // Slope of SMA50 is upward, indicating a rising mid-term trend // ✅ Check 5: Volatility Confirmation using NATR // ✅ Check 6: Spike in Daily ATR compared to multi-day ATRs // ATR(1) is significantly high vs ATR(5, 20, 50) — detects breakout/spike days // ✅ Check 7: Strong Close Candle // Candle closed strongly in the upper portion of its range and is rising compared to previous candle // ✅ Check 8: PGO (Price Growth Oscillator) Moderation // Ensures price isn’t too overextended from its 20 and 50 EMA (< 2.5%) — avoids overbought breakouts // ✅ Check 9: RSI Cooling Period // RSI(7) on previous candle is less than 60 — indicates the stock is not overheated before breakout
Pine Script® göstergesi
mrinaljain1995 tarafından
BitLogic - Kalman CompositeBitLogic Kalman Composite (BL-KC) What it is A momentum/condition oscillator that filters price with a multi-stage Kalman and blends two normalized branches into one composite line with a compact score histogram. Built for cleaner flips and fewer whipsaws. How it works Kalman filter (5-stage) on your chosen price source; selectable output (Stage1/Stage5/Average). Branch A : RSI on Kalman price → normalized to ~ . Branch B (selectable) : - Residual Z: z-score of the Kalman residual (observation − predicted state), squashed for stability (distinct vs classic KSO) - Williams %R on Kalman price (normalized). Gain-weighted blend : the composite weights Branch B by the average Kalman gain (when the filter trusts new info more, residual matters more). Zero-line hysteresis : small band around 0 to reduce flip noise. Score (columns) : quadrant logic → 1, 0.5, −0.5, −1 for quick read of bias + slope. No repainting : updates/alerts on bar close. Inputs you’ll care about Q/R (process/measurement noise) : responsiveness vs smoothness. Blend : base weight + gain weighting. Residual Z : lookback & squash scale (controls sensitivity). Hysteresis band and optional EMA smoothing of the composite. Reading it Line (ci) : above 0 → bullish zone; below 0 → bearish zone. Columns (KC_score) : show strength/weakness inside each zone (green ≥ 0, orange < 0). Alerts : bullish/bearish flip fire on close when the composite crosses the band edges. Tips For faster markets: raise Q, lower smoothing, keep a small hysteresis (e.g., 0.03–0.05). For trend following: use Stage5/Average Kalman output and a slightly wider band (0.06–0.10). Want “classic” feel? Switch Branch B to Williams %R. Credits Inspired by the community idea behind the Kalman Synergy Oscillator (Kalman + RSI + %R). This is an independent, from-scratch implementation with a residual z-score branch and gain-weighted blending for distinct behavior. Disclaimer For educational purposes only. Not financial advice. Past performance does not guarantee future results.
Pine Script® göstergesi
BitLogic_X tarafından
Correlation Heatmap Matrix [TradingFinder] 20 Assets Variable🔵 Introduction Correlation is one of the most important statistical and analytical metrics in financial markets, data mining, and data science. It measures the strength and direction of the relationship between two variables. The correlation coefficient always ranges between +1 and -1 : a perfect positive correlation (+1) means that two assets or currency pairs move together in the same direction and at a constant ratio, a correlation of zero (0) indicates no clear linear relationship, and a perfect negative correlation (-1) means they move in exactly opposite directions. While the Pearson Correlation Coefficient is the most common method for calculation, other statistical methods like Spearman and Kendall are also used depending on the context. In financial market analysis, correlation is a key tool for Forex, the Stock Market, and the Cryptocurrency Market because it allows traders to assess the price relationship between currency pairs, stocks, or coins. For example, in Forex, EUR/USD and GBP/USD often have a high positive correlation; in stocks, companies from the same sector such as Apple and Microsoft tend to move similarly; and in crypto, most altcoins show a strong positive correlation with Bitcoin. Using a Correlation Heatmap in these markets visually displays the strength and direction of these relationships, helping traders make more accurate decisions for risk management and strategy optimization. 🟣 Correlation in Financial Markets In finance, correlation refers to measuring how closely two assets move together over time. These assets can be stocks, currency pairs, commodities, indices, or cryptocurrencies. The main goal of correlation analysis in trading is to understand these movement patterns and use them for risk management, trend forecasting, and developing trading strategies. 🟣 Correlation Heatmap A correlation heatmap is a visual tool that presents the correlation between multiple assets in a color-coded table. Each cell shows the correlation coefficient between two assets, with colors indicating its strength and direction. Warm colors (such as red or orange) represent strong negative correlation, cool colors (such as blue or cyan) represent strong positive correlation, and mid-range tones (such as yellow or green) indicate correlations that are close to neutral. 🟣 Practical Applications in Markets Forex : Identify currency pairs that move together or in opposite directions, avoid overexposure to similar trades, and spot unusual divergences. Crypto : Examine the dependency of altcoins on Bitcoin and find independent movers for portfolio diversification. Stocks : Detect relationships between stocks in the same industry or find outliers that move differently from their sector. 🟣 Key Uses of Correlation in Trading Risk management and diversification: Select assets with low or negative correlation to reduce portfolio volatility. Avoiding overexposure: Prevent opening multiple positions on highly correlated assets. Pairs trading: Exploit temporary deviations between historically correlated assets for arbitrage opportunities. Intermarket analysis: Study the relationships between different markets like stocks, currencies, commodities, and bonds. Divergence detection: Spot when two typically correlated assets move apart as a possible trend change signal. Market forecasting: Use correlated asset movements to anticipate others’ behavior. Event reaction analysis: Evaluate how groups of assets respond to economic or political events. ❗ Important Note It’s important to note that correlation does not imply causation — it only reflects co-movement between assets. Correlation is also dynamic and can change over time, which is why analyzing it across multiple timeframes provides a more accurate picture. Combining correlation heatmaps with other analytical tools can significantly improve the precision of trading decisions. 🔵 How to Use The Correlation Heatmap Matrix indicator is designed to analyze and manage the relationships between multiple assets at once. After adding the tool to your chart, start by selecting the assets you want to compare (up to 20). Then, choose the Correlation Period that fits your trading strategy. Shorter periods (e.g., 20 bars) are more sensitive to recent price movements, making them suitable for short-term trading, while longer periods (e.g., 100 or 200 bars) provide a broader view of correlation trends over time. The indicator outputs a color-coded matrix where each cell represents the correlation between two assets. Warm colors like red and orange signal strong negative correlation, while cool colors like blue and cyan indicate strong positive correlation. Mid-range tones such as yellow or green suggest correlations that are close to neutral. This visual representation makes it easy to spot market patterns at a glance. One of the most valuable uses of this tool is in portfolio risk management. Portfolios with highly correlated assets are more vulnerable to market swings. By using the heatmap, traders can find assets with low or negative correlation to reduce overall risk. Another key benefit is preventing overexposure. For example, if EUR/USD and GBP/USD have a high positive correlation, opening trades on both is almost like doubling the position size on one asset, increasing risk unnecessarily. The heatmap makes such relationships clear, helping you avoid them. The indicator is also useful for pairs trading, where a trader identifies assets that are usually correlated but have temporarily diverged — a potential arbitrage or mean-reversion opportunity. Additionally, the tool supports intermarket analysis, allowing traders to see how movements in one market (e.g., crude oil) may impact others (e.g., the Canadian dollar). Divergence detection is another advantage: if two typically aligned assets suddenly move in opposite directions, it could signal a major trend shift or a news-driven move. Overall, the Correlation Heatmap Matrix is not just an analytical indicator but also a fast, visual alert system for monitoring multiple markets at once. This is particularly valuable for traders in fast-moving environments like Forex and crypto. 🔵 Settings 🟣 Logic Correlation Period : Number of bars used to calculate correlation between assets. 🟣 Display Table on Chart : Enable/disable displaying the heatmap directly on the chart. Table Size : Choose the table size (from very small to very large). Table Position : Set the table location on the chart (top, middle, or bottom in various alignments). 🟣 Symbol Custom Select Market : Choose the market type (Forex, Stocks, Crypto, or Custom). Symbol 1 to Symbol 20: In custom mode, you can define up to 20 assets for correlation calculation. 🔵 Conclusion The Correlation Heatmap Matrix is a powerful tool for analyzing correlations across multiple assets in Forex, crypto, and stock markets. By displaying a color-coded table, it visually conveys both the strength and direction of correlations — warm colors for strong negative correlation, cool colors for strong positive correlation, and mid-range tones such as yellow or green for near-zero or neutral correlation. This helps traders select assets with low or negative correlation for diversification, avoid overexposure to similar trades, identify arbitrage and pairs trading opportunities, and detect unusual divergences between typically aligned assets. With support for custom mode and up to 20 symbols, it offers high flexibility for different trading strategies, making it a valuable complement to technical analysis and risk management.
Pine Script® göstergesi
TFlab tarafından
1
Live NY Session Movement (points)//@version=5 indicator("Live NY Session Movement (points)", overlay=true) // --- Inputs --- nySession = input.session("0830-1700", "NY Session (local NY time)") nyTimezone = input.string("America/New_York", "Session Timezone") showShade = input.bool(true, "Shade NY Session") // --- In-session detection (per-bar) --- inNy = not na(time(timeframe.period, nySession, nyTimezone)) // --- Track session H/L and live movement --- var float sessHigh = na var float sessLow = na var label liveLab = na var bool wasIn = false // session edge flags justStarted = inNy and not wasIn justEnded = not inNy and wasIn wasIn := inNy if justStarted sessHigh := high sessLow := low if inNy sessHigh := na(sessHigh) ? high : math.max(sessHigh, high) sessLow := na(sessLow) ? low : math.min(sessLow, low) movePts = sessHigh - sessLow // create once, then update in place each bar if na(liveLab) liveLab := label.new(bar_index, high, "NY Move: " + str.tostring(movePts, format.mintick), style=label.style_label_down, textcolor=color.white, color=color.new(color.blue, 0), size=size.small) label.set_x(liveLab, bar_index) label.set_y(liveLab, high) label.set_text(liveLab, "NY Move: " + str.tostring(movePts, format.mintick)) else // clean up at end of session sessHigh := na sessLow := na if not na(liveLab) label.delete(liveLab) liveLab := na // Optional: shade the session so you can see it clearly bgcolor(showShade and inNy ? color.new(color.blue, 92) : na)
Pine Script® göstergesi
DM_Empires tarafından
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster Plain-English overview This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism. What Monte Carlo is and why quants rely on it • Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve. • Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon. • Core strengths in quant finance . – Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?” – Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic. – Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess. • Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.” How this indicator builds its probability cone 1) Seasonal pattern discovery The script builds two day-of-year maps as new data arrives: • A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information. • A volatility map that tracks the typical absolute return for the same calendar day. It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day. 2) Choice of randomness engine You can pick how the future shocks are generated: • Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock. • Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution. Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands. 3) Volatility scaling to current conditions Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa. 4) Many futures, summarized by percentiles The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles: • 5th and 95th → approximate 95% band (outer cone). • 16th and 84th → approximate 68% band (inner cone). • 50th → the median or “expected path.” These are drawn as polylines so you can immediately see central tendency and dispersion. 5) A historical overlay (optional) Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward. Inputs you control and how to think about them Monte Carlo Simulation • Price Series for Calculation . The source series, typically close. • Enable Probability Forecasts . Master switch for simulation and drawing. • Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon. • Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty. • Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice). • Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history. • Volatility Scaling . On by default so the cone respects today’s range context. Plotting & UI • Probability Cone . Plots the outer and inner percentile envelopes. • Expected Path . Plots the median line through the cone. • Historical Overlay . Dotted seasonal-only projection for context. • Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts. What appears on your chart • A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band. • A median path (default blue) running through the center of the cone. • An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent. • Optional historical seasonal path drawn as dotted segments for the next 30 bars. How to use it in trading 1) Position sizing and stop logic The cone translates “volatility plus seasonality” into distances. • Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play. • Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable. • If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality. 2) Entry timing with seasonal bias When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system. 3) Target selection Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive. 4) Scenario planning & “what-ifs” Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through. 5) Options and vol tactics • When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate. • When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics. Reading the probability cone like a pro • Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite. • Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained. • Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone. • Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst. Methodological notes (what the code actually does) • Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price. • Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives. • Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable. • Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation. • Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better. • Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones. • Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive. • Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history. Strengths and limitations • Strengths : – Probabilistic thinking replaces single-point guessing. – Seasonality adds a small but meaningful directional bias that many markets exhibit. – Volatility scaling adapts to the current regime so the cone stays realistic. • Limitations : – Seasonality can break around structural changes, policy shifts, or one-off events. – The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision. – The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts. – Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories. Tuning guide • Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets. • Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes. • Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong. • Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence. Workflow examples • Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band. • Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated. • Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range. Good habits • Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality. • Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt. • Validate seasonality for your symbol and timeframe; not every market has strong calendar effects. Summary The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
Pine Script® göstergesi
BackQuant tarafından
2