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HoLo (Highest Open Lowest Open)HoLo (Highest Open Lowest Open) Method Overview HoLo stands for "Highest Open Lowest Open" – a forex trading strategy. Core Concept Definition of HoLo: Highest Open (HO): The highest opening price among all H1 candles of the current trading day Lowest Open (LO): The lowest opening price among all H1 candles of the current trading day Trading Day: Starts at Asia Open Session Strategy Setup Step 1: Mark Key Levels Current day's High/Low Highest Open and Lowest Open (from H1 candles) Step 2: Define the Area of Interest Sell Zone: Between the Highest Open and the current day's High Buy Zone: Between the Lowest Open and the current day's Low Trade Entry Rules Sell Trade: Price goes above the Highest Open Trigger candle (M5, M15, or M30) closes above the Highest Open Enter a sell when price revisits the Highest Open level (Sell Stop Order) Buy Trade: Price drops below the Lowest Open Trigger candle closes below the Lowest Open Enter a buy when price revisits the Lowest Open level (Buy Stop Order) Trigger Timeframe: Choose M1, M5, or M15 based on: Your screen time availability Personal trading style Risk and Profit Management Stop Loss: For sell: Set SL at the day’s High + spread For buy: Set SL at the day’s Low + spread Take Profit (TP) Basic Rule: You should open 2 positions: When profit reaches 1R: Take partial profit + move SL to BE (Break Even) Let the remaining position run using partial TP or trailing stop Money Management: Never risk more than 1% per trade Recommended: 0.5% risk due to multiple opportunities daily Prioritize major pairs. The Indicator How to read data For Day Traders Monitor the sell zone (red area) for potential short entries near resistance Watch the buy zone (blue area) for potential long entries near support Use cross signals for entry/exit points Pay attention to timing markers for key market hours Alert HO (Highest Open) level changes LO (Lowest Close) level changes Price crossing key levels Timing notifications
Indicador Pine Script®
por dangtunglam14
SPX500 Quick Drop & Rise AlertsSimple script thats been adjusted for 1 minute trading on spx500. It will show you and signal to you: dropThreshold: how much the price must rise or fall (in percent) to trigger a signal. Default is 0.05 → 5%. lookbackBars: how many bars back to compare against. Default is 1 (i.e., compare the current close to the previous bar’s close). Theirs a few ways to use this, you might want to use your MA 238 as a reference point. Use it as a target or a level to bounce or reject from. Then use this indicator to help show you where the market energy is flowing. Do some backtesting and see what you see. Only use it for New York open times would probably be best. Youll have to change your mentality depending on if the market is trending / ranging ect of course.
Indicador Pine Script®
por PaperChains
Heikin-Ashi Mean Reversion Oscillator [Alpha Extract]The Heikin-Ashi Mean Reversion Oscillator combines the smoothing characteristics of Heikin-Ashi candlesticks with mean reversion analysis to create a powerful momentum oscillator. This indicator applies Heikin-Ashi transformation twice - first to price data and then to the oscillator itself - resulting in smoother signals while maintaining sensitivity to trend changes and potential reversal points. 🔶 CALCULATION Heikin-Ashi Transformation: Converts regular OHLC data to smoothed Heikin-Ashi values Component Analysis: Calculates trend strength, body deviation, and price deviation from mean Oscillator Construction: Combines components with weighted formula (40% trend strength, 30% body deviation, 30% price deviation) Double Smoothing: Applies EMA smoothing and second Heikin-Ashi transformation to oscillator values Signal Generation: Identifies trend changes and crossover points with overbought/oversold levels Formula: HA Close = (Open + High + Low + Close) / 4 HA Open = (Previous HA Open + Previous HA Close) / 2 Trend Strength = Normalized consecutive HA candle direction Body Deviation = (HA Body - Mean Body) / Mean Body * 100 Price Deviation = ((HA Close - Price Mean) / Price Mean * 100) / Standard Deviation * 25 Raw Oscillator = (Trend Strength * 0.4) + (Body Deviation * 0.3) + (Price Deviation * 0.3) Final Oscillator = 50 + (EMA(Raw Oscillator) / 2) 🔶 DETAILS Visual Features: Heikin-Ashi Candlesticks: Smoothed oscillator representation using HA transformation with vibrant teal/red coloring Overbought/Oversold Zones: Horizontal lines at customizable levels (default 70/30) with background highlighting in extreme zones Moving Averages: Optional fast and slow EMA overlays for additional trend confirmation Signal Dashboard: Real-time table showing current oscillator status (Overbought/Oversold/Bullish/Bearish) and buy/sell signals Reference Lines: Middle line at 50 (neutral), with 0 and 100 boundaries for range visualization Interpretation: Above 70: Overbought conditions, potential selling opportunity Below 30: Oversold conditions, potential buying opportunity Bullish HA Candles: Green/teal candles indicate upward momentum Bearish HA Candles: Red candles indicate downward momentum MA Crossovers: Fast EMA above slow EMA suggests bullish momentum, below suggests bearish momentum Zone Exits: Price moving out of extreme zones (above 70 or below 30) often signals trend continuation 🔶 EXAMPLES Mean Reversion Signals: When the oscillator reaches extreme levels (above 70 or below 30), it identifies potential reversal points where price may revert to the mean. Example: Oscillator reaching 80+ levels during strong uptrends often precedes short-term pullbacks, providing profit-taking opportunities. Trend Change Detection: The double Heikin-Ashi smoothing helps identify genuine trend changes while filtering out market noise. Example: When oscillator HA candles change from red to teal after oversold readings, this confirms potential trend reversal from bearish to bullish. Moving Average Confirmation: Fast and slow EMA crossovers on the oscillator provide additional confirmation of momentum shifts. Example: Fast EMA crossing above slow EMA while oscillator is rising from oversold levels provides strong bullish confirmation signal. Dashboard Signal Integration: The real-time dashboard combines oscillator status with directional signals for quick decision-making. Example: Dashboard showing "Oversold" status with "BUY" signal when HA candles turn bullish provides clear entry timing. 🔶 SETTINGS Customization Options: Calculation: Oscillator period (default 14), smoothing factor (1-50, default 2) Levels: Overbought threshold (50-100, default 70), oversold threshold (0-50, default 30) Moving Averages: Toggle display, fast EMA length (default 9), slow EMA length (default 21) Visual Enhancements: Show/hide signal dashboard, customizable table position Alert Conditions: Oversold bounce, overbought reversal, bullish/bearish MA crossovers The Heikin-Ashi Mean Reversion Oscillator provides traders with a sophisticated momentum tool that combines the smoothing benefits of Heikin-Ashi analysis with mean reversion principles. The double transformation process creates cleaner signals while the integrated dashboard and multiple confirmation methods help traders identify high-probability entry and exit points during both trending and ranging market conditions.
Indicador Pine Script®
por AlphaExtract
OpenAI Signal Generator - Enhanced Accuracy# AI-Powered Trading Signal Generator Guide ## Overview This is an advanced trading signal generator that combines multiple technical indicators using AI-enhanced logic to generate high-accuracy trading signals. The indicator uses a sophisticated combination of RSI, MACD, Bollinger Bands, EMAs, ADX, and volume analysis to provide reliable buy/sell signals with comprehensive market analysis. ## Key Features ### 1. Multi-Indicator Analysis - **RSI (Relative Strength Index)** - Length: 14 periods (default) - Overbought: 70 (default) - Oversold: 30 (default) - Used for identifying overbought/oversold conditions - **MACD (Moving Average Convergence Divergence)** - Fast Length: 12 (default) - Slow Length: 26 (default) - Signal Length: 9 (default) - Identifies trend direction and momentum - **Bollinger Bands** - Length: 20 periods (default) - Multiplier: 2.0 (default) - Measures volatility and potential reversal points - **EMAs (Exponential Moving Averages)** - Fast EMA: 9 periods (default) - Slow EMA: 21 periods (default) - Used for trend confirmation - **ADX (Average Directional Index)** - Length: 14 periods (default) - Threshold: 25 (default) - Measures trend strength - **Volume Analysis** - MA Length: 20 periods (default) - Threshold: 1.5x average (default) - Confirms signal strength ### 2. Advanced Features - **Customizable Signal Frequency** - Daily - Weekly - 4-Hour - Hourly - On Every Close - **Enhanced Filtering** - EMA crossover confirmation - ADX trend strength filter - Volume confirmation - ATR-based volatility filter - **Comprehensive Alert System** - JSON-formatted alerts - Detailed technical analysis - Multiple timeframe analysis - Customizable alert frequency ## How to Use ### 1. Initial Setup 1. Open TradingView and create a new chart 2. Select your preferred trading pair 3. Choose an appropriate timeframe 4. Apply the indicator to your chart ### 2. Configuration #### Basic Settings - **Signal Frequency**: Choose how often signals are generated - Daily: Signals at the start of each day - Weekly: Signals at the start of each week - 4-Hour: Signals every 4 hours - Hourly: Signals every hour - On Every Close: Signals on every candle close - **Enable Signals**: Toggle signal generation on/off - **Include Volume**: Toggle volume analysis on/off #### Technical Parameters ##### RSI Settings - Adjust `rsi_length` (default: 14) - Modify `rsi_overbought` (default: 70) - Modify `rsi_oversold` (default: 30) ##### EMA Settings - Fast EMA Length (default: 9) - Slow EMA Length (default: 21) ##### MACD Settings - Fast Length (default: 12) - Slow Length (default: 26) - Signal Length (default: 9) ##### Bollinger Bands - Length (default: 20) - Multiplier (default: 2.0) ##### Enhanced Filters - ADX Length (default: 14) - ADX Threshold (default: 25) - Volume MA Length (default: 20) - Volume Threshold (default: 1.5) - ATR Length (default: 14) - ATR Multiplier (default: 1.5) ### 3. Signal Interpretation #### Buy Signal Requirements 1. RSI crosses above oversold level (30) 2. Price below lower Bollinger Band 3. MACD histogram increasing 4. Fast EMA above Slow EMA 5. ADX above threshold (25) 6. Volume above threshold (if enabled) 7. Market volatility check (if enabled) #### Sell Signal Requirements 1. RSI crosses below overbought level (70) 2. Price above upper Bollinger Band 3. MACD histogram decreasing 4. Fast EMA below Slow EMA 5. ADX above threshold (25) 6. Volume above threshold (if enabled) 7. Market volatility check (if enabled) ### 4. Visual Indicators #### Chart Elements - **Moving Averages** - SMA (Blue line) - Fast EMA (Yellow line) - Slow EMA (Purple line) - **Bollinger Bands** - Upper Band (Green line) - Middle Band (Orange line) - Lower Band (Green line) - **Signal Markers** - Buy Signals: Green triangles below bars - Sell Signals: Red triangles above bars - **Background Colors** - Light green: Buy signal period - Light red: Sell signal period ### 5. Alert System #### Alert Types 1. **Signal Alerts** - Generated when buy/sell conditions are met - Includes comprehensive technical analysis - JSON-formatted for easy integration 2. **Frequency-Based Alerts** - Daily/Weekly/4-Hour/Hourly/Every Close - Includes current market conditions - Technical indicator values #### Alert Message Format ```json { "symbol": "TICKER", "side": "BUY/SELL/NONE", "rsi": "value", "macd": "value", "signal": "value", "adx": "value", "bb_upper": "value", "bb_middle": "value", "bb_lower": "value", "ema_fast": "value", "ema_slow": "value", "volume": "value", "vol_ma": "value", "atr": "value", "leverage": 10, "stop_loss_percent": 2, "take_profit_percent": 5 } ``` ## Best Practices ### 1. Signal Confirmation - Wait for multiple confirmations - Consider market conditions - Check volume confirmation - Verify trend strength with ADX ### 2. Risk Management - Use appropriate position sizing - Implement stop losses (default 2%) - Set take profit levels (default 5%) - Monitor market volatility ### 3. Optimization - Adjust parameters based on: - Trading pair volatility - Market conditions - Timeframe - Trading style ### 4. Common Mistakes to Avoid 1. Trading without volume confirmation 2. Ignoring ADX trend strength 3. Trading against the trend 4. Not considering market volatility 5. Overtrading on weak signals ## Performance Monitoring Regularly review: 1. Signal accuracy 2. Win rate 3. Average profit per trade 4. False signal frequency 5. Performance in different market conditions ## Disclaimer This indicator is for educational purposes only. Past performance is not indicative of future results. Always use proper risk management and trade responsibly. Trading involves significant risk of loss and is not suitable for all investors.
Indicador Pine Script®
por Aigentswarmtrade
ETH Master Institutional IndicatorETH Master Institutional Indicator (1H) Summary: This strategy is a high-precision, professional-grade trading indicator for Ethereum (ETH), optimized specifically for the 1-hour timeframe. It is built to mirror the decision logic of institutional traders by combining multiple forms of market confirmation to filter out weak or false signals. How It Works: 1. **Trend Confirmation**: - Uses three Exponential Moving Averages (9, 21, 50) to confirm trend direction. - Buy signals require price to be above all three EMAs (strong uptrend), sell signals below all three (strong downtrend). 2. **Momentum Confirmation**: - MACD Line must be above Signal Line for buy signals (bullish momentum). - MACD Line must be below Signal Line for sell signals (bearish momentum). - Histogram must be positive for buys, negative for sells. 3. **RSI Filter**: - Buy signals require RSI > 55 (indicating upward strength). - Sell signals require RSI < 45 (indicating downward pressure). 4. **Volume Confirmation**: - Requires volume to be at least 10% greater than the 20-bar average, signaling institutional activity. 5. **Price Breakout/Breakdown**: - Buy signals only occur when price breaks above recent resistance. - Sell signals only occur when price breaks below recent support. 6. **Visuals**: - Smart Buy and Smart Sell markers are plotted on the chart when all conditions align. - EMA trend guides are also plotted (9 in yellow, 21 in orange, 50 in blue). 7. **Alerts**: - Alerts trigger when a qualified Smart Buy or Smart Sell signal appears, giving traders automated notifications. This strategy is designed for clarity, professional use, and adaptability, with a strong emphasis on confluence across multiple indicators before acting.
Indicador Pine Script®
por metermanx
Wick Theory ChecklistA simple check list for Powell's Wick Theory with the following: 🔹 1. Liquidity Sweep Has price taken out recent highs/lows (previous liquidity)? Is the wick longer than usual, indicating a stop hunt? Did the sweep occur during a key session (e.g., London, New York open)? 🔹 2. Imbalance Is there a clear fair value gap (FVG) or imbalanced candle near the wick area? Is price rejecting from the imbalance, not just sitting inside it? Is the imbalance aligned with higher timeframe structure? 🔹 3. Rejection Block Has a strong candle body rejected from the wick zone? Is the rejection block respected on a retest? Are there multiple rejections or confirmations at this block? 🔹 4. Engineered Liquidity Was liquidity built up intentionally (e.g., equal highs/lows, tight consolidation)? Did price fake out in one direction first before sweeping the opposite side? Are retail traps evident (e.g., false breakouts, trendlines)? 🔹 5. SMT Divergence (Optional) Are correlated instruments showing divergent liquidity grabs? Is one instrument making a higher high while another makes a lower high? Does this divergence happen at or near key zones (POI, FVG, OB)?
Indicador Pine Script®
por op4l
MATIC Institutional Buy/Sell Zones📈 Purpose To identify areas on the chart where institutional-level buying (accumulation) or selling (distribution) may be occurring — based on key technical and volume-based filters — and to help reduce false signals using smart logic. ✅ Smart Buy Signal (Accumulation Zone) Triggered when: RSI < 65 – Price is not overbought; leaves room to rise. MACD line > Signal line – Momentum is positive. Price is above both EMA 50 and BB midline – Price structure is bullish. EMA 10 is below EMA 50 – Early stage of a trend shift. Volume spike above 1.3x average – Sign of strong buyer interest. 📍 Visual Output: Green background highlights zone. Green “Smart Buy” label below bar. ❌ Smart Sell Signal (Distribution Zone) Triggered when: RSI > 55 – Price is mildly overbought, vulnerable to reversal. MACD line < Signal line – Momentum turning bearish. Price is below EMA 50 or BB midline – Weakening trend. EMA 10 is above EMA 50 – Potential early shift downward. Volume spike above 1.3x average – Distribution volume present. 📍 Visual Output: Red background highlights zone. Red “Smart Sell” label above bar. 🧠 Key Features Designed for professional-level clarity. Filters out most retail-level noise by requiring volume confirmation and trend confluence. Combines momentum, structure, and volume into a multi-factor signal system. 🔔 Alerts You can set TradingView alerts for: When a Smart Buy or Smart Sell signal appears — ideal for non-screen time entry/exit alerts.
Indicador Pine Script®
por metermanx
time NYThis TradingView Pine Script plots vertical lines at specific key times throughout the trading day based on the New York timezone (Eastern Time), which aligns with Colombian time during Daylight Saving Time (UTC-4). It also highlights the opening price of the 00:00 candle and shades a specific time range on the chart. Key Features: Timezone Configuration: Uses "America/New_York" to define the trading session times. Opening Price at 00:00: Captures and stores the opening price of the candle at exactly 00:00. Optionally stores the bar index (although it's not used visually here). Vertical Lines at Key Times: Draws vertical dashed lines at these specific times each day: 00:00 07:30 08:30 09:45 10:00 10:15 10:30 10:45 11:00 13:30 16:30 These lines extend above and below the chart range, from the highest to the lowest price over the last 500 bars. Background Highlight: Shades the time interval between 11:00 and 13:30 with a semi-transparent gray background, but only for the current day.
Indicador Pine Script®
por dragonthegood
Previous Two Days HL + Asia H/L + 4H Vertical Lines📊 Indicator Overview This custom TradingView indicator visually marks key market structure levels and session data on your chart using lines, labels, boxes, and vertical guides. It is designed for traders who analyze intraday and multi-session behavior — especially around the New York and Asia sessions — with a focus on 4-hour price ranges. 🔍 What the Indicator Tracks 1. Previous Two Days' Ranges (6PM–5PM NY Time) PDH/PDL (Day 1 & Day 2): Draws horizontal lines marking the previous two trading days’ highs and lows. Midlines: Calculates and displays the midpoint between each day’s high and low. Color-Coded: Uses strong colors for Day 1 and more transparent versions for Day 2, to help differentiate them. 2. Asia Session High/Low (6 PM – 2 AM NY Time) Automatically tracks the high and low during the Asia session. Extends these levels until the following day’s NY close (4 PM). Shows a midline of the Asia session (optional dotted line). Highlights the Asia session background in gray. Labels Asia High and Low on the chart for easy reference. 3. Last Closed 4-Hour Candle Range At the start of every new 4H candle, it: Draws a box from the last closed 4H candle. Box spans horizontally across a set number of bars (adjustable). Top and bottom lines indicate the high and low of that 4H candle. Midline, 25% (Q1) and 75% (Q3) levels are also drawn inside the box using dotted lines. Helps traders identify premium/discount zones within the previous 4H range. 4. Vertical 4H Time Markers Draws vertical dashed lines to mark the start and end of the last 4H candle range. Based on the standard 4H bar timing in NY (e.g. 5:00, 9:00, 13:00, 17:00). ⚙️ Inputs & Options Line thickness, color customization for all levels. Option to place labels on the right or left side of the chart. Toggle for enabling/disabling the 4H box. Adjustable box extension length (how far to extend the range visually). ✅ Ideal Use Cases Identifying reaction zones from prior highs/lows. Spotting reversals during Asia or NY session opens. Trading intraday setups based on 4H structure. Anchoring scalping or swing entries off major session levels.
Indicador Pine Script®
por barton_evan
Higher Timeframe Market StructureHTF Market Structure – ZigZag, Break of Structure & Supply/Demand This powerful indicator is designed to identify higher-timeframe market structure using a combination of ZigZag patterns, Break of Structure (BOS) signals, and Supply/Demand zones. Key Features: Automatic detection of Higher Highs (HH), Higher Lows (HL), Lower Lows (LL), and Lower Highs (LH) Internal structure shifts based on Open or High/Low logic Supply and Demand zones plotted on the chart Break of Structure (BOS) lines with optional alerts Mitigation logic to mark or delete invalidated order blocks Customizable aggregation factor to view higher time frame structure on lower time frames How to Use: Focus on market structure and BOS to understand the current trend. Watch for internal shifts as early signals of potential reversals. Use ZigZag lines to connect swing highs and lows to visualize market rhythm. Supply zones (red) and Demand zones (green) are automatically drawn after structure breaks: Use Demand Zones in Bullish Markets for the highest probability entries. Use Supply Zones in Bearish Markets to align with the prevailing trend. Best Practices: Only use Demand Zones in Bullish markets and Supply Zones in Bearish markets for optimal results. Look for price action or reversal signals within these zones to refine your entries. Enable alerts to get notified on: New order blocks Internal shifts BOS events HH, HL, LL, LH formations Liquidity sweeps Customization Options: Aggregation Factor: Control how many candles are grouped for structure analysis. Zone Duration: Define how length of plotted zones. Mitigation Settings: Automatically delete or fade zones after mitigation. Colors: Choose custom colors for bullish and bearish zones and structure markers. This tool is ideal for traders who rely on price action, structure, and smart money concepts. Combine it with your own S&D strategy or integrate it with other confluence tools for even better precision.
Indicador Pine Script®
por The_Forex_Steward
2
UTC Day SeparatorsGlobally consistent back-tests: When you anchor indicators (VWAP, ADR, supply/demand boxes) to daily boundaries, basing them on UTC avoids daylight-saving mismatches between exchanges. Quick regime inspection: You can eyeball overnight gaps or Asia/Europe/US session overlaps by seeing how price behaves relative to successive UTC days. Chart cleanliness: Because the line is dotted and low-contrast, it gives a subtle reference grid without overwhelming candles or other plots.
Indicador Pine Script®
por LordFahmi99
New York Midnight Day SeparatorThis Pine Script indicator draws vertical separator lines on the chart at midnight in the New York timezone (Eastern Time). The lines mark the start of each new trading day from Monday to Friday, helping traders visually distinguish daily sessions based on New York market time. The separator lines are rendered as slightly transparent gray lines spanning the full price range of each midnight candle, providing a clean and unobtrusive visual aid for session tracking.
Indicador Pine Script®
por sirin343435
Liquidity mark-out indicator(by Lumiere)This indicator marks out every High that has a bullish candle followed by a bearish one, vice versa for lows. Once the price reaches the marked-out liquidity, the line is removed automatically. This indicator only shows the current liquidity of the time frame you are at. (To get it look like the picture just chance the length to 30-50) Key Features of the Liquidity Mark-Out Indicator: 🔹 Identifies Liquidity Zones – Marks highs and lows based on candlestick patterns. 🔹 Customizable Settings – Toggle highs/lows visibility 🎚️, adjust line colors 🎨, and set line length (bars) 📏. 🔹 Smart Clean-Up – Automatically removes swept levels (when price breaks through) for a clean chart 🧹. 🔹 Pattern-Based Detection – Highs: Detects two-candle reversal patterns (🟢 bullish close → 🔴 bearish close). Lows: Detects two-candle reversal patterns (🔴 bearish close → 🟢 bullish close). 🔹 Dynamic Lines – Projects liquidity levels forward (adjustable length) to track key zones 📈. Perfect For Traders Looking To: ✅ Spot potential liquidity grabs 🎯 ✅ Identify key support/resistance levels 🛑 ✅ Clean up their chart from outdated levels 🖥️
Indicador Pine Script®
por Lumiere-Stocks
1
Session Status Table📌 Session Status Table Session Status Table is an indicator that displays the real-time status of the four major trading sessions: * 🇯🇵 Asia (Tokyo) * 🇬🇧 London * 🇺🇸 New York AM * 🇺🇸 New York PM It shows which sessions are currently open, how much time remains until they open or close, and optionally sends alerts in advance. 🧩 Features: * Real-time session table — shows the status of each session on the chart. * Color-coded statuses: * 🟢 Green – Session is open * 🔴 Red – Session is closed * ⚪ Gray – Weekend * Countdown timers until session open or close. * User alerts — receive a notification a custom number of minutes before a session starts. ⚙️ Customization: * Table position — fully configurable. * Session colors — customizable for open, closed, and weekend states. * Session labels — customizable with icons. * Notifications: * Enabled through TradingView's Alerts panel. * User-defined lead time before session opens. 🕒 Time Zones: All times are calculated in UTC to ensure consistency across different markets and regions, avoiding discrepancies from time zones and daylight saving time. 🚨 How to enable alerts: 1. Open the "Alerts" panel in TradingView. 2. Click "Create Alert". 3. In the condition dropdown, choose "Session Status Table". 4. Set to any alert() trigger. 5. Save — you'll be notified a set number of minutes before each session begins. ℹ️ Technical Notes: * Built with Pine Script version 6. * Logically divided into clear sections: inputs, session calculations, table rendering, and alerts. * Optimized for performance and reliability on all timeframes. Ideal for traders who use session activity in their strategies — especially in Forex, crypto, and futures markets.
Indicador Pine Script®
por Gleb_Cryptox
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment ## 1. Introduction Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985). The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014). ## 2. Theoretical Foundation ### 2.1 Momentum Theory and Market Anomalies The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015). ### 2.2 Drawdown Analysis and Risk Management Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators. ### 2.3 Adaptive Smoothing and Market Regimes The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets. ## 3. Methodology ### 3.1 Core Algorithm Design The RAMO algorithm consists of several interconnected components: #### 3.1.1 Risk-Adjusted Momentum Calculation The fundamental innovation of RAMO lies in its risk adjustment mechanism: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor) Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05) This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952). #### 3.1.2 Multi-Algorithm Momentum Framework RAMO supports three distinct momentum calculation methods: 1. Rate of Change: Traditional percentage-based momentum (Pring, 2002) 2. Price Momentum: Absolute price differences 3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997) This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013). ### 3.2 Leading Indicator Components #### 3.2.1 Momentum Acceleration Analysis The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes: Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n) This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969). #### 3.2.2 Linear Regression Prediction RAMO incorporates linear regression-based prediction to project momentum values forward: Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset) This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes. #### 3.2.3 Volume-Based Exhaustion Detection The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns: Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20) This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989). ### 3.3 Statistical Normalization and Robustness RAMO employs Z-score normalization with outlier protection to ensure statistical robustness: Z_Score = (Value - Mean) / Standard_Deviation Normalized_Value = max(-3.5, min(3.5, Z_Score)) This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions. ### 3.4 Adaptive Threshold Calculation Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992): Upper_Threshold = Mean + (Multiplier × Standard_Deviation) Lower_Threshold = Mean - (Multiplier × Standard_Deviation) This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992). ## 4. Implementation Details ### 4.1 Adaptive Smoothing Algorithm The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility: Volatility_Percentile = Percentrank(Volatility, 100) Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100) This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995). ### 4.2 Risk Environment Classification RAMO classifies market conditions into three risk environments: - Low Risk: Current_DD < 30% × Max_DD - Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD - High Risk: Current_DD ≥ 70% × Max_DD This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014). ## 5. Signal Generation and Interpretation ### 5.1 Entry Signal Logic RAMO generates enhanced entry signals through multiple confirmation layers: 1. Primary Signal: Crossover between indicator and signal line 2. Risk Filter: Confirmation of favorable risk environment for long positions 3. Leading Component: Early warning signals via acceleration analysis 4. Exhaustion Filter: Volume-based reversal detection This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992). ### 5.2 Divergence Analysis RAMO incorporates both traditional and leading divergence detection: - Traditional Divergence: Price and indicator divergence over 3-5 periods - Slope Divergence: Momentum slope versus price direction - Acceleration Divergence: Changes in momentum acceleration This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999). ## 6. Empirical Advantages and Applications ### 6.1 Risk-Adjusted Performance The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980). ### 6.2 Regime Awareness RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments. ### 6.3 Institutional Applications The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013). ## 7. Limitations and Future Research ### 7.1 Parameter Sensitivity Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions. ### 7.2 Market Microstructure Considerations RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance. ### 7.3 Transaction Cost Integration Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004). ## References Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons. Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press. Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985. Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764. Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84. Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall. Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291. Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill. Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082. Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91. Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance. Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library. Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill. Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790. Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314. Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239. Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Indicador Pine Script®
por EdgeTools
AWR R & LR Oscillator with plots & tableHello trading viewers ! I'm glad to share with you one of my favorite indicator. It's the aggregate of many things. It is partly based on an indicator designed by gentleman goat. Many thanks to him. 1. Oscillator and Correlation Calculations Overview and Functionality: This part of the indicator computes up to 10 Pearson correlation coefficients between a chosen source (typically the close price, though this is user-configurable) and the bar index over various periods. Starting with an initial period defined by the startPeriod parameter and increasing by a set increment (periodIncrement), each correlation coefficient is calculated using the built-in ta.correlation function over successive ranges. These coefficients are stored in an array, and the indicator calculates their average (avgPR) to provide a complete view of the market trend strength. Display Features: Each individual coefficient, as well as the overall average, is plotted on the chart using a specific color. Horizontal lines (both dashed and solid) are drawn at levels 0, ±0.8, and ±1, serving as visual thresholds. Additionally, conditional fills in red or blue highlight when values exceed these thresholds, helping the user quickly identify potential extreme conditions (such as overbought or oversold situations). 2. Visual Signals and Automated Alerts Graphical Signal Enhancements: To reinforce the analysis, the indicator uses graphical elements like emojis and shape markers. For example: If all 10 curves drop below -0.79, a 🌋 emoji appears at the bottom of the chart; When curves 2 through 10 are below -0.79, a ⛰️ emoji is displayed below the bar, potentially serving as a buy signal accompanied by an alert condition; Likewise, symmetrical conditions for correlations exceeding 0.79 produce corresponding emojis (🤿 and 🏖️) at the top or bottom of the chart. Alerts and Notifications: Using these visual triggers, several alertcondition statements are defined within the script. This allows users to set up TradingView alerts and receive real-time notifications whenever the market reaches these predefined critical zones identified by the multi-period analysis. 3. Regression Channel Analysis Principles and Calculations: In addition to the oscillator, the indicator implements an analysis of regression channels. For each of the 8 configurable channels, the user can set a range of periods (for example, min1 to max1, etc.). The function calc_regression_channel iterates through the defined period range to find the optimal period that maximizes a statistical measure derived from a regression parameter calculated by the function r(p). Once this optimal period is identified, the indicator computes two key points (A and B) which define the main regression line, and then creates a channel based on the calculated deviation (an RMSE multiplied by a user-defined factor). The regression channels are not displayed on the chart but are used to plot shapes & fullfilled a table. Blue shapes are plotted when 6th channel or 7th channel are lower than 3 deviations Yellow shapes are plotted when 6th channel or 7th channel are higher than 3 deviations 4. Scores, Conditions, and the Summary Table Scoring System: The indicator goes further by assigning scores across multiple analytical categories, such as: 1. BigPear Score What It Represents: This score is based on a longer-term moving average of the Pearson correlation values (SMA 100 of the average of the 10 curves of correlation of Pearson). The BigPear category is designed to capture where this longer-term average falls within specific ranges. Conditions: The script defines nine boolean conditions (labeled BigPear1up through BigPear9up for the “up” direction). Here's the rules : BigPear1up = (bigsma_avgPR <= 0.5 and bigsma_avgPR > 0.25) BigPear2up = (bigsma_avgPR <= 0.25 and bigsma_avgPR > 0) BigPear3up = (bigsma_avgPR <= 0 and bigsma_avgPR > -0.25) BigPear4up = (bigsma_avgPR <= -0.25 and bigsma_avgPR > -0.5) BigPear5up = (bigsma_avgPR <= -0.5 and bigsma_avgPR > -0.65) BigPear6up = (bigsma_avgPR <= -0.65 and bigsma_avgPR > -0.7) BigPear7up = (bigsma_avgPR <= -0.7 and bigsma_avgPR > -0.75) BigPear8up = (bigsma_avgPR <= -0.75 and bigsma_avgPR > -0.8) BigPear9up = (bigsma_avgPR <= -0.8) Conditions: The script defines nine boolean conditions (labeled BigPear1down through BigPear9down for the “down” direction). BigPear1down = (bigsma_avgPR >= -0.5 and bigsma_avgPR < -0.25) BigPear2down = (bigsma_avgPR >= -0.25 and bigsma_avgPR < 0) BigPear3down = (bigsma_avgPR >= 0 and bigsma_avgPR < 0.25) BigPear4down = (bigsma_avgPR >= 0.25 and bigsma_avgPR < 0.5) BigPear5down = (bigsma_avgPR >= 0.5 and bigsma_avgPR < 0.65) BigPear6down = (bigsma_avgPR >= 0.65 and bigsma_avgPR < 0.7) BigPear7down = (bigsma_avgPR >= 0.7 and bigsma_avgPR < 0.75) BigPear8down = (bigsma_avgPR >= 0.75 and bigsma_avgPR < 0.8) BigPear9down = (bigsma_avgPR >= 0.8) Weighting: If BigPear1up is true, 1 point is added; if BigPear2up is true, 2 points are added; and so on up to 9 points from BigPear9up. Total Score: The positive score (posScoreBigPear) is the sum of these weighted conditions. Similarly, there is a negative score (negScoreBigPear) that is calculated using a mirrored set of conditions (named BigPear1down to BigPear9down), each contributing a negative weight (from -1 to -9). In essence, the BigPear score tells you—in a weighted cumulative way—where the longer-term correlation average falls relative to predefined thresholds. 2. Pear Score What It Represents: This category uses the immediate average of the Pearson correlations (avgPR) rather than a longer-term smoothed version. It reflects a more current picture of the market’s correlation behavior. How It’s Calculated: Conditions: There are nine conditions defined for the “up” scenario (named Pear1up through Pear9up), which partition the range of avgPR into intervals. For instance: Pear1up = (avgPR > -0.2 and avgPR <= 0) Pear2up = (avgPR > -0.4 and avgPR <= -0.2) Pear3up = (avgPR > -0.5 and avgPR <= -0.4) Pear4up = (avgPR > -0.6 and avgPR <= -0.5) Pear5up = (avgPR > -0.65 and avgPR <= -0.6) Pear6up = (avgPR > -0.7 and avgPR <= -0.65) Pear7up = (avgPR > -0.75 and avgPR <= -0.7) Pear8up = (avgPR > -0.8 and avgPR <= -0.75) Pear9up = (avgPR > -1 and avgPR <= -0.8) There are nine conditions defined for the “down” scenario (named Pear1down through Pear9down), which partition the range of avgPR into intervals. For instance: Pear1down = (avgPR >= 0 and avgPR < 0.2) Pear2down = (avgPR >= 0.2 and avgPR < 0.4) Pear3down = (avgPR >= 0.4 and avgPR < 0.5) Pear4down = (avgPR >= 0.5 and avgPR < 0.6) Pear5down = (avgPR >= 0.6 and avgPR < 0.65) Pear6down = (avgPR >= 0.65 and avgPR < 0.7) Pear7down = (avgPR >= 0.7 and avgPR < 0.75) Pear8down = (avgPR >= 0.75 and avgPR < 0.8) Pear9down = (avgPR >= 0.8 and avgPR <= 1) Weighting: Each condition has an associated weight, such as 0.9 for Pear1up, 1.9 for Pear2up, and so on, up to 9 for Pear9up. Sum up : Pear1up = 0.9 Pear2up = 1.9 Pear3up = 2.9 Pear4up = 3.9 Pear5up = 4.99 Pear6up = 6 Pear7up = 7 Pear8up = 8 Pear9up = 9 Total Score: The positive score (posScorePear) is the sum of these values for each condition that returns true. A corresponding negative score (negScorePear) is calculated using conditions for when avgPR falls on the positive side, with similar weights in the negative direction. This score quantifies the current correlation reading by translating its relative level into a numeric score through a weighted sum. 3. Trendpear Score What It Represents: The Trendpear score is more dynamic as it compares the current avgPR with its short-term moving average (sma_avgPR / 14 periods ) and also considers its relationship with an even longer moving average (bigsma_avgPR / 100 periods). It is meant to capture the trend or momentum in the correlation behavior. How It’s Calculated: Conditions: Nine conditions (from Trendpear1up to Trendpear9up) are defined to check: Whether avgPR is below, equal to, or above sma_avgPR by different margins; Whether it is trending upward (i.e., it is higher than its previous value). Here are the rules Trendpear1up = (avgPR <= sma_avgPR -0.2) and (avgPR >= avgPR ) Trendpear2up = (avgPR > sma_avgPR -0.2) and (avgPR <= sma_avgPR -0.07) and (avgPR >= avgPR ) Trendpear3up = (avgPR > sma_avgPR -0.07) and (avgPR <= sma_avgPR -0.03) and (avgPR >= avgPR ) Trendpear4up = (avgPR > sma_avgPR -0.03) and (avgPR <= sma_avgPR -0.02) and (avgPR >= avgPR ) Trendpear5up = (avgPR > sma_avgPR -0.02) and (avgPR <= sma_avgPR -0.01) and (avgPR >= avgPR ) Trendpear6up = (avgPR > sma_avgPR -0.01) and (avgPR <= sma_avgPR -0.001) and (avgPR >= avgPR ) Trendpear7up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR <= bigsma_avgPR) Trendpear8up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR -0.03) Trendpear9up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR) Weighting: The weights here are not linear. For example, the lightest condition may add 0.1 point, whereas the most extreme condition (e.g., when avgPR is not only above the moving average but also reaches a high proportion relative to bigsma_avgPR) might add as much as 90 points. Trendpear1up = 0.1 Trendpear2up = 0.2 Trendpear3up = 0.3 Trendpear4up = 0.4 Trendpear5up = 0.5 Trendpear6up = 0.69 Trendpear7up = 7 Trendpear8up = 8.9 Trendpear9up = 90 Total Score: The positive score (posScoreTrendpear) is the sum of the weights from all conditions that are satisfied. A negative counterpart (negScoreTrendpear) exists similarly for when the trend indicates a downward bias. Trendpear integrates both the level and the direction of change in the correlations, giving a strong numeric indication when the market starts to diverge from its short-term average. 4. Deviation Score What It Represents: The “Écart” score quantifies how far the asset’s price deviates from the boundaries defined by the regression channels. This metric can indicate if the price is excessively deviating—which might signal an eventual reversion—or confirming a breakout. How It’s Calculated: Conditions: For each channel (with at least seven channels contributing to the scoring from the provided code), there are three levels of deviation: First tier (EcartXup): Checks if the price is below the upper boundary but above a second boundary. Second tier (EcartXup2): Checks if the price has dropped further, between a lower and a more extreme boundary. Third tier (EcartXup3): Checks if the price is below the most extreme limit. Weighting: Each tier within a channel has a very small weight for the lowest severities (for example, 0.0001 for the first tier, 0.0002 for the second, 0.0003 for the third) with weights increasing with the channel index. First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) Total Score: The overall positive score (posScoreEcart) is the sum of all the weights for conditions met among the first, second, and third tiers. The corresponding negative score (negScoreEcart) is calculated similarly (using conditions when the price is above the channel boundaries), with the weights being the same in magnitude but negative in sign. This layered scoring method allows the indicator to reflect both minor and major deviations in a gradated and cumulative manner. Example : Score + = 321.0001 Score - = -0.111 The asset price is really overextended in long term view, not for mid term & short term expect the in the very short term. Score + = 0.0033 Score - = -1.11 The asset price is really extended in short term view, not for mid term (even a bit underextended) & long term is neutral 5. Slope Score What It Represents: The Slope score captures the trend direction and steepness of the regression channels. It reflects whether the regression line (and hence the underlying trend) is sloping upward or downward. How It’s Calculated: Conditions: if the slope has a uptrend = 1 if the slope has a downtrend = -1 Weighting: First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) The positive slope conditions incrementally add weights from 0.0001 for the smallest positive slopes to 100 for the largest among the seven checks. And negative for the downward slopes. The positive score (posScoreSlope) is the sum of all the weights from the upward slope conditions that are met. The negative score (negScoreSlope) sums the negative weights when downward conditions are met. Example : Score + = 111 Score - = -0.1111 Trend is up for longterm & down for mid & short term The slope score therefore emphasizes both the magnitude and the direction of the trend as indicated by the regression channels, with an intentional asymmetry that flags strong downtrends more aggressively. Summary For each category—BigPear, Pear, Trendpear, Écart, and Slope—the indicator evaluates a defined set of conditions. Each condition is a binary test (true/false) based on different thresholds or comparisons (for example, comparing the current value to a moving average or a channel boundary). When a condition is true, its assigned weight is added to the cumulative score for that category. These individual scores, both positive and negative, are then displayed in a table, making it easy for the trader to see at a glance where the market stands according to each analytical dimension. This comprehensive, weighted approach allows the indicator to encapsulate several layers of market information into a single set of scores, aiding in the identification of potential trading opportunities or market reversals. 5. Practical Use and Application How to Use the Indicator: Interpreting the Signals: On your chart, observe the following components: The individual correlation curves and their average, plotted with visual thresholds; Visual markers (such as emojis and shape markers) that signal potential oversold or overbought conditions The summary table that aggregates the scores from each category, offering a quick glance at the market’s state. Trading Alerts and Decisions: Set your TradingView alerts through the alertcondition functions provided by the indicator. This way, you receive immediate notifications when critical conditions are met, allowing you to react as soon as the market reaches key levels. This tool is especially beneficial for advanced traders who want to combine multiple technical dimensions to optimize entry and exit points with a confluence of signals. Conclusion and Additional Insights In summary, this advanced indicator innovatively combines multi-scale Pearson correlation analysis (via multiple linear regressions) with robust regression channel analysis. It offers a deep and nuanced view of market dynamics by delivering clear visual signals and a comprehensive numerical summary through a built-in score table. Combine this indicator with other tools (e.g., oscillators, moving averages, volume indicators) to enhance overall strategy robustness.
Indicador Pine Script®
por AlwwAl
Watchlist AlertThis “Watchlist Alert” indicator is to help traders monitor multiple symbols and notify them whenever a specified target price is reached. Upon loading the script, you can define up to ten ticker symbols along with their individual price targets. The script stores these pairs in a persistent map so that, on each new bar, it retrieves the previous and current close prices for every symbol in your watchlist. If a symbol’s price crosses above or below its target, the script sends an alert (using your chosen alert frequency) and records the timestamp of that event. Visually, the indicator displays a small table at the top center of your chart. For each watched symbol, it shows four columns: the symbol name, its latest close price (in the chart’s timeframe), the target price you set, and the last time an alert was emitted (formatted as MM.dd HH:mm:ss). By comparing the previous close to the current close and checking against the stored “lastAlertTime,” the script ensures that you receive exactly one alert per crossing event per bar. In short, the key features are: Input up to ten symbols with their corresponding float price targets. Automatically check each symbol’s previous and current close values every bar. Trigger a single alert when price crosses a target—either upward or downward. Maintain a map of last alert timestamps to prevent duplicate notifications. Display a real-time table listing each symbol’s current price, target, and last alert time. Whenever you need to keep tabs on multiple instruments across different timeframes without manually tracking price levels, simply add this indicator to your chart. It runs in the background and pushes alerts as soon as any watched symbol touches its defined threshold.
Indicador Pine Script®
por genlius