Mulai

Educational

Fibonacci Auto Retracement & HTF candles ReferenceAdvanced Higher Timeframe (HTF) Candle & Fibonacci Viewer Overview: The Advanced HTF Candle & Fibonacci Viewer is a professional Trading View indicator designed to help traders overlay higher timeframe price structures onto lower timeframe charts. By combining daily candle analysis with precise Fibonacci retracement levels, this tool allows traders to identify critical support and resistance zones, potential breakouts, and retracement opportunities without switching charts. Special Thanks: This script includes a small part of coding inspired by Zeiierman, whose work on HTF analysis provided the foundation for visualizing higher timeframe structures. Full credit to Zeiierman for their invaluable contribution to the Trading View community. Key Features: 1. Multi-Day HTF Range Display Automatically displays high and low of 1–7 previous days. Highlights candle bodies and wicks for clear structure visualization. Ideal for spotting daily ranges and breakout levels. 2. Dynamic Fibonacci Levels Standard levels: 0%, 11.8%, 23.6%, 38.2%, 50%, 61.8%, 76.4%, 88.2%, 100%. Optional mid-level lines for intraday support/resistance identification. Levels adjust automatically to reflect price action direction. 3. Customizable Labels & Colors Adjustable text size, color, transparency, and offset. Fully customizable candle and Fibonacci colors. Mid-level lines can be shown or hidden for a cleaner look. 4. Persistent Levels Levels remain until the next trading session or breakout, helping track trends and retracements consistently. 5. Multi-Timeframe Optimization Works on any chart timeframe, from 1-minute to weekly charts. Provides higher timeframe insight while trading on lower timeframes. Why Traders Love This Indicator: View higher timeframe action without switching charts. Identify high-probability entry and exit zones. Combine with other indicators for complete market analysis. Useful for swing traders, day traders, and scalpers alike. Customization Options: Number of previous days (1–7) Show/hide mid-level lines Show/hide labels Customize label size, color, and offset Customize Fibonacci and candle colors Ideal Use Cases: Swing Trading: Identify daily key levels for entry, exit, and stop-loss. Day Trading: Use HTF ranges on intraday charts to spot breakouts and reversals. Fibonacci Analysis: Locate retracement zones efficiently. Trend Confirmation: Validate trades with higher timeframe structure. Summary: The Advanced HTF Candle & Fibonacci Viewer is a powerful tool for traders seeking clarity, structure, and precision. With higher timeframe insight overlaid on active charts and proper credit to Zeiierman for their HTF coding contribution, this indicator helps traders make informed, confident decisions in any market.
Indikator Pine Script®
oleh Chandannk
580TL — NovaSenseNovaSense by 580TradingLab combines multi-EMA structure, price actions, momentum confirmation, and volatility logic to detect trend strength and early reversals with high accuracy. It filters out market noise, identifies "location zones" for optional entries, and sends timely Buy/Sell alerts when institutional momentum shifts. Designed for traders who value clarity, discipline, and precision. Trade with clarity. Sense the trend before it flips.
Indikator Pine Script®
oleh jlwt225
3D Institutional Battlefield [SurgeGuru]Professional Presentation: 3D Institutional Flow Terrain Indicator Overview The 3D Institutional Flow Terrain is an advanced trading visualization tool that transforms complex market structure into an intuitive 3D landscape. This indicator synthesizes multiple institutional data points—volume profiles, order blocks, liquidity zones, and voids—into a single comprehensive view, helping you identify high-probability trading opportunities. Key Features 🎥 Camera & Projection Controls Yaw & Pitch: Adjust viewing angles (0-90°) for optimal perspective Scale Controls: Fine-tune X (width), Y (depth), and Z (height) dimensions Pro Tip: Increase Z-scale to amplify terrain features for better visibility 🌐 Grid & Surface Configuration Resolution: Adjust X (16-64) and Y (12-48) grid density Visual Elements: Toggle surface fill, wireframe, and node markers Optimization: Higher resolution provides more detail but requires more processing power 📊 Data Integration Lookback Period: 50-500 bars of historical analysis Multi-Source Data: Combine volume profile, order blocks, liquidity zones, and voids Weighted Analysis: Each data source contributes proportionally to the terrain height How to Use the Frontend 💛 Price Line Tracking (Your Primary Focus) The yellow price line is your most important guide: Monitor Price Movement: Track how the yellow line interacts with the 3D terrain Identify Key Levels: Watch for these critical interactions: Order Blocks (Green/Red Zones): When yellow price line enters green zones = Bullish order block When yellow price line enters red zones = Bearish order block These represent institutional accumulation/distribution areas Liquidity Voids (Yellow Zones): When yellow price line enters yellow void areas = Potential acceleration zones Voids indicate price gaps where minimal trading occurred Price often moves rapidly through voids toward next liquidity pool Terrain Reading: High Terrain Peaks: High volume/interest areas (support/resistance) Low Terrain Valleys: Low volume areas (potential breakout zones) Color Coding: Green terrain = Bullish volume dominance Red terrain = Bearish volume dominance Purple = Neutral/transition areas 📈 Volume Profile Integration POC (Point of Control): Automatically marks highest volume level Volume Bins: Adjust granularity (10-50 bins) Height Weight: Control how much volume affects terrain elevation 🏛️ Order Block Detection Detection Length: 5-50 bar lookback for block identification Strength Weighting: Recent blocks have greater impact on terrain Candle Body Option: Use full candles or body-only for block definition 💧 Liquidity Zone Tracking Multiple Levels: Track 3-10 key liquidity zones Buy/Sell Side: Different colors for bid/ask liquidity Strength Decay: Older zones have diminishing terrain impact 🌊 Liquidity Void Identification Threshold Multiplier: Adjust sensitivity (0.5-2.0) Height Amplification: Voids create significant terrain depressions Acceleration Zones: Price typically moves quickly through void areas Practical Trading Application Bullish Scenario: Yellow price line approaches green order block terrain Price finds support in elevated bullish volume areas Terrain shows consistent elevation through key levels Bearish Scenario: Yellow price line struggles at red order block resistance Price falls through liquidity voids toward lower terrain Bearish volume peaks dominate the landscape Breakout Setup: Yellow price line consolidates in flat terrain Minimal resistance (low terrain) in projected direction Clear path toward distant liquidity zones Pro Tips Start Simple: Begin with default settings, then gradually customize Focus on Yellow Line: Your primary indicator of current price position Combine Timeframes: Use the same terrain across multiple timeframes for confluence Volume Confirmation: Ensure terrain peaks align with actual volume spikes Void Anticipation: When price enters voids, prepare for potential rapid movement Order Blocks & Voids Architecture Order Blocks Calculation Trigger: Price breaks fractal swing points Bullish OB: When close > swing high → find lowest low in lookback period Bearish OB: When close < swing low → find highest high in lookback period Strength: Based on price distance from block extremes Storage: Global array maintains last 50 blocks with FIFO management Liquidity Voids Detection Trigger: Price gaps exceeding ATR threshold Bull Void: Low - high > (ATR200 × multiplier) Bear Void: Low - high > (ATR200 × multiplier) Validation: Close confirms gap direction Storage: Global array maintains last 30 voids Key Design Features Real-time Updates: Calculated every bar, not just on last bar Global Persistence: Arrays maintain state across executions FIFO Management: Automatic cleanup of oldest entries Configurable Sensitivity: Adjustable lookback periods and thresholds Scientific Testing Framework Hypothesis Testing Primary Hypothesis: 3D terrain visualization improves detection of institutional order flow vs traditional 2D charts Testable Metrics: Prediction Accuracy: Does terrain structure predict future support/resistance? Reaction Time: Faster identification of key levels vs conventional methods False Positive Reduction: Lower rate of failed breakouts/breakdowns Control Variables Market Regime: Trending vs ranging conditions Asset Classes: Forex, equities, cryptocurrencies Timeframes: M5 to H4 for intraday, D1 for swing Volume Conditions: High vs low volume environments Data Collection Protocol Terrain Features to Quantify: Slope gradient changes at price inflection points Volume peak clustering density Order block terrain elevation vs subsequent price action Void depth correlation with momentum acceleration Control Group: Traditional support/resistance + volume profile Experimental Group: 3D Institutional Flow Terrain Statistical Measures Signal-to-Noise Ratio: Terrain features vs random price movements Lead Time: Terrain formation ahead of price confirmation Effect Size: Performance difference between groups (Cohen's d) Statistical Power: Sample size requirements for significance Validation Methodology Blind Testing: Remove price labels from terrain screenshots Have traders identify key levels from terrain alone Measure accuracy vs actual price action Backtesting Framework: Automated terrain feature extraction Correlation with future price reversals/breakouts Monte Carlo simulation for significance testing Expected Outcomes If hypothesis valid: Significant improvement in level prediction accuracy (p < 0.05) Reduced latency in institutional level identification Higher risk-reward ratios on terrain-confirmed trades Research Questions: Does terrain elevation reliably indicate institutional interest zones? Are liquidity voids statistically significant momentum predictors? Does multi-timeframe terrain analysis improve signal quality? How does terrain persistence correlate with level strength? LuxAlgo BigBeluga hapharmonic
Indikator Pine Script®
oleh SurgeGuru
2
Dual FUT/Spot price with next monthly expiryThis Pine Script dashboard indicator is specifically designed for pair trading strategies in Indian futures markets (NSE). Let me break down how it facilitates pair trading: Core Pair Trading Concept The script monitors two correlated stocks simultaneously (Symbol A and Symbol B), comparing their: Spot prices vs Futures prices Current month futures vs Next month futures Premium/discount relationships Key Pair Trading Features 1. Dual Symbol Monitoring symbolA = "NSE:TCS" (Default) symbolB = "NSE:INFY" (Default) Allows traders to watch two stocks in the same sector (like TCS and Infosys in IT) to identify relative value opportunities. 2. Basis Analysis for Each Stock The indicator calculates the basis (difference between futures and spot): Price Difference: FUT - SPOT Premium/Discount %: ((FUT - SPOT) / SPOT) × 100 This helps identify when one stock's futures are relatively more expensive than the other's. 3. Multi-Expiry View Near Month Futures (1!): Current active contract Next Month Futures (2!): Upcoming contract This enables calendar spread analysis within each stock and helps anticipate rollover effects. 4. Comparative Table The detailed table displays side-by-side: Symbol Spot Price Near Future Near Diff (%)Next Monthly Next Diff (%)Lot SizeTCS₹3,500₹3,520+20 (+0.57%)₹3,535+35 (+1.00%)125INFY₹1,450₹1,455+5 (+0.34%)₹1,460+10 (+0.69%)600 5. Lot Size Integration Critical for position sizing in pair trades - the indicator fetches actual contract lot sizes, enabling proper hedge ratio calculations. Pair Trading Strategies Enabled Strategy 1: Basis Divergence Trading When TCS futures trade at +0.8% premium and INFY at +0.2% Trade: Short TCS futures, Long INFY futures (betting on convergence) The indicator highlights these differences with color-coded cells Strategy 2: Calendar Spread Arbitrage Compare near month vs next month premium for each stock If TCS shows wider calendar spread than INFY, potential arbitrage exists Trade the relative calendar spread difference Strategy 3: Premium/Discount Reversal Monitor which stock moves from premium to discount (or vice versa) Color indicators (green/red) make this immediately visible Enter pairs when relative premium relationships normalize Strategy 4: Lot-Adjusted Pair Trading Use lot size data to create market-neutral positions Example: If TCS lot = 125 and INFY lot = 600 Ratio = 600/125 = 4.8:1 for rupee-neutral positioning Visual Trading Cues Green cells: Futures at premium (contango) Red cells: Futures at discount (backwardation) Purple values: Next month contracts Yellow highlights: Spot prices Practical Pair Trading Example Scenario: Both stocks in same sector, historically correlated Normal state: Both show +0.5% premium Divergence: TCS jumps to +1.2%, INFY stays at +0.5% Trade Signal: Short TCS futures (expensive) Long INFY futures (relatively cheap) Exit: When premiums converge back to similar levels Hedge ratio: Use lot sizes to maintain proper exposure balance Advantages for Pair Traders ✓ Single-screen monitoring of both legs ✓ Real-time basis calculations eliminate manual math ✓ Multi-timeframe view (near + next month) ✓ Automatic lot size fetching for position sizing ✓ Visual alerts through color coding ✓ Percentage normalization for easy comparison This indicator essentially transforms raw price data into actionable pair trading intelligence by highlighting relative value discrepancies between correlated assets in the futures market. Enjoy!!
Indikator Pine Script®
oleh kingshukghosh71
Scientific Correlation Testing FrameworkScientific Correlation Testing Framework - Comprehensive Guide Introduction to Correlation Analysis What is Correlation? Correlation is a statistical measure that describes the degree to which two assets move in relation to each other. Think of it like measuring how closely two dancers move together on a dance floor. Perfect Positive Correlation (+1.0): Both dancers move in perfect sync, same direction, same speed Perfect Negative Correlation (-1.0): Both dancers move in perfect sync but in opposite directions Zero Correlation (0): The dancers move completely independently of each other In financial markets, correlation helps us understand relationships between different assets, which is crucial for: Portfolio diversification Risk management Pairs trading strategies Hedging positions Market analysis Why This Script is Special This script goes beyond simple correlation calculations by providing: Two different correlation methods (Pearson and Spearman) Statistical significance testing to ensure results are meaningful Rolling correlation analysis to track how relationships change over time Visual representation for easy interpretation Comprehensive statistics table with detailed metrics Deep Dive into the Script's Components 1. Input Parameters Explained- Symbol Selection: This allows you to select the second asset to compare with the chart's primary asset Default is Apple (NASDAQ:AAPL), but you can change this to any symbol Example: If you're viewing a Bitcoin chart, you might set this to "NASDAQ:TSLA" to see if Bitcoin and Tesla are correlated Correlation Window (60): This is the number of periods used to calculate the main correlation Larger values (e.g., 100-500) provide more stable, long-term correlation measures Smaller values (e.g., 10-50) are more responsive to recent price movements 60 is a good balance for most daily charts (about 3 months of trading days) Rolling Correlation Window (20): A shorter window to detect recent changes in correlation This helps identify when the relationship between assets is strengthening or weakening Default of 20 is roughly one month of trading days Return Type: This determines how price changes are calculated Simple Returns: (Today's Price - Yesterday's Price) / Yesterday's Price Easy to understand: "The asset went up 2% today" Log Returns: Natural logarithm of (Today's Price / Yesterday's Price) More mathematically elegant for statistical analysis Better for time-additive properties (returns over multiple periods) Less sensitive to extreme values. Confidence Level (95%): This determines how certain we want to be about our results 95% confidence means we accept a 5% chance of being wrong (false positive) Higher confidence (e.g., 99%) makes the test more strict Lower confidence (e.g., 90%) makes the test more lenient 95% is the standard in most scientific research Show Statistical Significance: When enabled, the script will test if the correlation is statistically significant or just due to random chance. Display options control what you see on the chart: Show Pearson/Spearman/Rolling Correlation: Toggle each correlation type on/off Show Scatter Plot: Displays a scatter plot of returns (limited to recent points to avoid performance issues) Show Statistical Tests: Enables the detailed statistics table Table Text Size: Adjusts the size of text in the statistics table 2.Functions explained- calcReturns(): This function calculates price returns based on your selected method: Log Returns: Formula: ln(Price_t / Price_t-1) Example: If a stock goes from $100 to $101, the log return is ln(101/100) = ln(1.01) ≈ 0.00995 or 0.995% Benefits: More symmetric, time-additive, and better for statistical modeling Simple Returns: Formula: (Price_t - Price_t-1) / Price_t-1 Example: If a stock goes from $100 to $101, the simple return is (101-100)/100 = 0.01 or 1% Benefits: More intuitive and easier to understand rankArray(): This function calculates the rank of each value in an array, which is used for Spearman correlation: How ranking works: The smallest value gets rank 1 The second smallest gets rank 2, and so on For ties (equal values), they get the average of their ranks Example: For values Sorted: Ranks: (the two 2s tie for ranks 1 and 2, so they both get 1.5) Why this matters: Spearman correlation uses ranks instead of actual values, making it less sensitive to outliers and non-linear relationships. pearsonCorr(): This function calculates the Pearson correlation coefficient: Mathematical Formula: r = (nΣxy - ΣxΣy) / √ Where x and y are the two variables, and n is the sample size What it measures: The strength and direction of the linear relationship between two variables Values range from -1 (perfect negative linear relationship) to +1 (perfect positive linear relationship) 0 indicates no linear relationship Example: If two stocks have a Pearson correlation of 0.8, they have a strong positive linear relationship When one stock goes up, the other tends to go up in a fairly consistent proportion spearmanCorr(): This function calculates the Spearman rank correlation: How it works: Convert each value in both datasets to its rank Calculate the Pearson correlation on the ranks instead of the original values What it measures: The strength and direction of the monotonic relationship between two variables A monotonic relationship is one where as one variable increases, the other either consistently increases or decreases It doesn't require the relationship to be linear When to use it instead of Pearson: When the relationship is monotonic but not linear When there are significant outliers in the data When the data is ordinal (ranked) rather than interval/ratio Example: If two stocks have a Spearman correlation of 0.7, they have a strong positive monotonic relationship When one stock goes up, the other tends to go up, but not necessarily in a straight-line relationship tStatistic(): This function calculates the t-statistic for correlation: Mathematical Formula: t = r × √((n-2)/(1-r²)) Where r is the correlation coefficient and n is the sample size What it measures: How many standard errors the correlation is away from zero Used to test the null hypothesis that the true correlation is zero Interpretation: Larger absolute t-values indicate stronger evidence against the null hypothesis Generally, a t-value greater than 2 (in absolute terms) is considered statistically significant at the 95% confidence level criticalT() and pValue(): These functions provide approximations for statistical significance testing: criticalT(): Returns the critical t-value for a given degrees of freedom (df) and significance level The critical value is the threshold that the t-statistic must exceed to be considered statistically significant Uses approximations since Pine Script doesn't have built-in statistical distribution functions pValue(): Estimates the p-value for a given t-statistic and degrees of freedom The p-value is the probability of observing a correlation as strong as the one calculated, assuming the true correlation is zero Smaller p-values indicate stronger evidence against the null hypothesis Standard interpretation: p < 0.01: Very strong evidence (marked with **) p < 0.05: Strong evidence (marked with *) p ≥ 0.05: Weak evidence, not statistically significant stdev(): This function calculates the standard deviation of a dataset: Mathematical Formula: σ = √(Σ(x-μ)²/(n-1)) Where x is each value, μ is the mean, and n is the sample size What it measures: The amount of variation or dispersion in a set of values A low standard deviation indicates that the values tend to be close to the mean A high standard deviation indicates that the values are spread out over a wider range Why it matters for correlation: Standard deviation is used in calculating the correlation coefficient It also provides information about the volatility of each asset's returns Comparing standard deviations helps understand the relative riskiness of the two assets. 3.Getting Price Data- price1: The closing price of the primary asset (the chart you're viewing) price2: The closing price of the secondary asset (the one you selected in the input parameters) Returns are used instead of raw prices because: Returns are typically stationary (mean and variance stay constant over time) Returns normalize for price levels, allowing comparison between assets of different values Returns represent what investors actually care about: percentage changes in value 4.Information Table- Creates a table to display statistics Only shows on the last bar to avoid performance issues Positioned in the top right of the chart Has 2 columns and 15 rows Populating the Table The script then populates the table with various statistics: Header Row: "Metric" and "Value" Sample Information: Sample size and return type Pearson Correlation: Value, t-statistic, p-value, and significance Spearman Correlation: Value, t-statistic, p-value, and significance Rolling Correlation: Current value Standard Deviations: For both assets Interpretation: Text description of the correlation strength The table uses color coding to highlight important information: Green for significant positive results Red for significant negative results Yellow for borderline significance Color-coded headers for each section => Practical Applications and Interpretation How to Interpret the Results Correlation Strength 0.0 to 0.3 (or 0.0 to -0.3): Weak or no correlation The assets move mostly independently of each other Good for diversification purposes 0.3 to 0.7 (or -0.3 to -0.7): Moderate correlation The assets show some tendency to move together (or in opposite directions) May be useful for certain trading strategies but not extremely reliable 0.7 to 1.0 (or -0.7 to -1.0): Strong correlation The assets show a strong tendency to move together (or in opposite directions) Can be useful for pairs trading, hedging, or as a market indicator Statistical Significance p < 0.01: Very strong evidence that the correlation is real Marked with ** in the table Very unlikely to be due to random chance p < 0.05: Strong evidence that the correlation is real Marked with * in the table Unlikely to be due to random chance p ≥ 0.05: Weak evidence that the correlation is real Not marked in the table Could easily be due to random chance Rolling Correlation The rolling correlation shows how the relationship between assets changes over time If the rolling correlation is much different from the long-term correlation, it suggests the relationship is changing This can indicate: A shift in market regime Changing fundamentals of one or both assets Temporary market dislocations that might present trading opportunities Trading Applications 1. Portfolio Diversification Goal: Reduce overall portfolio risk by combining assets that don't move together Strategy: Look for assets with low or negative correlations Example: If you hold tech stocks, you might add some utilities or bonds that have low correlation with tech 2. Pairs Trading Goal: Profit from the relative price movements of two correlated assets Strategy: Find two assets with strong historical correlation When their prices diverge (one goes up while the other goes down) Buy the underperforming asset and short the outperforming asset Close the positions when they converge back to their normal relationship Example: If Coca-Cola and Pepsi are highly correlated but Coca-Cola drops while Pepsi rises, you might buy Coca-Cola and short Pepsi 3. Hedging Goal: Reduce risk by taking an offsetting position in a negatively correlated asset Strategy: Find assets that tend to move in opposite directions Example: If you hold a portfolio of stocks, you might buy some gold or government bonds that tend to rise when stocks fall 4. Market Analysis Goal: Understand market dynamics and interrelationships Strategy: Analyze correlations between different sectors or asset classes Example: If tech stocks and semiconductor stocks are highly correlated, movements in one might predict movements in the other If the correlation between stocks and bonds changes, it might signal a shift in market expectations 5. Risk Management Goal: Understand and manage portfolio risk Strategy: Monitor correlations to identify when diversification benefits might be breaking down Example: During market crises, many assets that normally have low correlations can become highly correlated (correlation convergence), reducing diversification benefits Advanced Interpretation and Caveats Correlation vs. Causation Important Note: Correlation does not imply causation Example: Ice cream sales and drowning incidents are correlated (both increase in summer), but one doesn't cause the other Implication: Just because two assets move together doesn't mean one causes the other to move Solution: Look for fundamental economic reasons why assets might be correlated Non-Stationary Correlations Problem: Correlations between assets can change over time Causes: Changing market conditions Shifts in monetary policy Structural changes in the economy Changes in the underlying businesses Solution: Use rolling correlations to monitor how relationships change over time Outliers and Extreme Events Problem: Extreme market events can distort correlation measurements Example: During a market crash, many assets may move in the same direction regardless of their normal relationship Solution: Use Spearman correlation, which is less sensitive to outliers Be cautious when interpreting correlations during extreme market conditions Sample Size Considerations Problem: Small sample sizes can produce unreliable correlation estimates Rule of Thumb: Use at least 30 data points for a rough estimate, 60+ for more reliable results Solution: Use the default correlation length of 60 or higher Be skeptical of correlations calculated with small samples Timeframe Considerations Problem: Correlations can vary across different timeframes Example: Two assets might be positively correlated on a daily basis but negatively correlated on a weekly basis Solution: Test correlations on multiple timeframes Use the timeframe that matches your trading horizon Look-Ahead Bias Problem: Using information that wouldn't have been available at the time of trading Example: Calculating correlation using future data Solution: This script avoids look-ahead bias by using only historical data Best Practices for Using This Script 1. Appropriate Parameter Selection Correlation Window: For short-term trading: 20-50 periods For medium-term analysis: 50-100 periods For long-term analysis: 100-500 periods Rolling Window: Should be shorter than the main correlation window Typically 1/3 to 1/2 of the main window Return Type: For most applications: Log Returns (better statistical properties) For simplicity: Simple Returns (easier to interpret) 2. Validation and Testing Out-of-Sample Testing: Calculate correlations on one time period Test if they hold in a different time period Multiple Timeframes: Check if correlations are consistent across different timeframes Economic Rationale: Ensure there's a logical reason why assets should be correlated 3. Monitoring and Maintenance Regular Review: Correlations can change, so review them regularly Alerts: Set up alerts for significant correlation changes Documentation: Keep notes on why certain assets are correlated and what might change that relationship 4. Integration with Other Analysis Fundamental Analysis: Combine correlation analysis with fundamental factors Technical Analysis: Use correlation analysis alongside technical indicators Market Context: Consider how market conditions might affect correlations Conclusion This Scientific Correlation Testing Framework provides a comprehensive tool for analyzing relationships between financial assets. By offering both Pearson and Spearman correlation methods, statistical significance testing, and rolling correlation analysis, it goes beyond simple correlation measures to provide deeper insights. For beginners, this script might seem complex, but it's built on fundamental statistical concepts that become clearer with use. Start with the default settings and focus on interpreting the main correlation lines and the statistics table. As you become more comfortable, you can adjust the parameters and explore more advanced applications. Remember that correlation analysis is just one tool in a trader's toolkit. It should be used in conjunction with other forms of analysis and with a clear understanding of its limitations. When used properly, it can provide valuable insights for portfolio construction, risk management, and pair trading strategy development.
Indikator Pine Script®
oleh kingshukghosh71
Advanced Time TechniqueAdvanced Time Technique (ATT) The Advanced Time Technique (ATT) identifies mathematically significant price levels based on candle count sequences within higher timeframes. The indicator tracks specific numerical patterns to project potential reversal zones. Calculation Methodology: - Monitors candle cycles in user-selected higher timeframes (1H, 2H, 3H) - Identifies key candle counts: 3, 11, 17, 29, 41, 47, 53, 59 - Projects these counts as visual markers on the current chart - Uses pure price action without lagging indicators Key Features: - HTF Candle Boxes: Displays higher timeframe candle ranges as colored boxes - ATT Circles: Places circular markers at specified candle counts - Multi-timeframe Analysis: References 1-hour, 2-hour, or 3-hour timeframes - Prediction Labels: Shows upcoming ATT levels within user-defined range - Historical Display: Optional viewing of past ATT markers Visual Components: - Colored boxes representing HTF candle ranges (bullish/bearish) - Circle markers positioned above/below bars based on candle color - Optional numerical display on ATT circles - Customizable colors and transparency settings Trading Applications: - Identifies potential reversal zones at mathematically significant intervals - Highlights liquidity concentration areas - Useful for intraday and scalp trading strategies - Complements price action and market structure analysis The indicator works by counting candles within the selected higher timeframe and marking specific numerical sequences where price reactions commonly occur.
Indikator Pine Script®
oleh TradeSymmetry
1
ka66: Symbol InformationThis shows a table of all current (Pine v6) `syminfo.` values. Please note this is primarily of use to Pine Developers, or the curious trader. There are a few of these around on TradingView, but many seem to focus on the use case they have. This script just dumps all values, in alphabetical order of properties. You can use this to inspect the details of the symbol, which in turn, can be fed into various scripts covering tasks such as: Position Sizing calculation (which requires things like tick, pointvalue, and currency details) Recommendation engines (which use the recommendation_* properties) Fundamentals on stocks (which may use share count information, and possibly employee information) Note that not all table values are populated, as they depend on the instrument being introspected. For example, a share ticker will have some different details to a Forex pair. The `NaN` values (the "Not A Number" special value in programming parlance) are not a bug, they are simply Pine reporting that no value is set for it. I have opted to dump out values as-is as the focus is developers. My motivation to create it was to write a position sizing tool. Additionally, the output of this script is cleanly formatted, with monospace fonts and conventional alignment for tables/forms with key and values. It also allows customising the table position. Ideally this feature is made part of the default TradingView customisation, but at this time, it is not, and tables don't auto-adjust their positions.
Indikator Pine Script®
oleh ka66
Smart Money Volume Tools | Lyro RSSmart Money Volume Tools | Lyro RS Overview The Smart Money Volume Tools (SMVT) is a multi-dimensional volume-based analysis suite designed to visualize the interplay between price action, moving averages, and smart money behavior. By integrating dynamic moving averages, volume normalization, and multi-timeframe intelligence, SMVT helps traders identify when institutional (smart money) or retail participants are influencing price movements — all in a single, adaptive display. Unlike traditional oscillators or trend tools, SMVT dynamically adjusts its sensitivity and thresholds based on volume z-scores and normalized momentum, revealing true intent behind price shifts rather than reacting to them. 🔹 Key Features 4 Core Analytical Modes: Trail Mode – Identifies directional bias using dynamic volume-weighted trails based on adaptive ATR multipliers. Volume Mode – Displays normalized volume strength vs. price trend, highlighting volume-driven expansions. Smart Money Volume Mode – Detects institutional buying/selling spikes from lower timeframes using volume z-score outliers. Retail Money Volume Mode – Contrasts retail-driven impulses to visualize crowd behavior and exhaustion points. Dynamic Volume Normalization: Converts volume impulses into a 0–100 range using a sigmoid function for smoother interpretation. Multi-Timeframe Intelligence: Automatically reads lower timeframe volume data to distinguish smart vs. retail activity. Adaptive Color Systems: Multiple palette modes ( Classic , Mystic , Accented , Royal ) or full custom color control. Signal Table Overlay: Built-in real-time module summary showing status for Trail , Volume , Smart Money , and Retail Money — right on your chart. 🔹 How It Works Volume Strength Calculation: Calculates relative volume strength using a moving average baseline, then normalizes the result via a sigmoid function — mapping activity into a clean 0–100 range. Smart Money Detection: Scans lower timeframe data for extreme volume z-scores ( z > 2 ) to pinpoint institutional accumulation or distribution zones. Trail Logic: Uses adaptive upper and lower trails based on ATR and volume intensity to track volatility-adjusted trend direction. Color Logic: Trail, candle, and fill colors change dynamically according to the active signal type and selected palette — making directional bias instantly visible. 🔹 Practical Use Swing Confirmation (Trail Mode): Confirms sustained bullish or bearish momentum supported by volume, ideal for trailing positions and managing exits. Volume Expansion (Volume Mode): Highlights key moments when institutional liquidity pushes price before visible breakout confirmation. Smart vs. Retail Divergence: Identify conflicts between retail activity and smart money to detect exhaustion or reversal points early. Table Overlay Utility: Instantly see all active signals across modules in one compact, on-chart interface. 🔹 Customization Custom color palettes or manual bullish/bearish color selection. Adjustable EMA lengths and Volume SMA period . Selectable lower timeframe source for Smart Money analysis. Flexible table position & size controls — choose between Top, Middle, Bottom and Tiny to Huge. Switch freely between Trail , Volume , Smart Money , and Retail Money modes. Credits Thank you to @AlgoAlpha for the smart money and retail activity source code. ⚠️Disclaimer This indicator is a tool for technical analysis and does not provide guaranteed results. It should be used in conjunction with other analysis methods and proper risk management practices. The creators of this indicator are not responsible for any financial decisions made based on its signals.
Indikator Pine Script®
oleh LyroRS
4
Price Above PDH - Complete Multi-Confirmation Alert🎯 COMPLETE FEATURES: $jmoskyhigh cashapp 1. Comprehensive Input Settings ✅ All visual customization options ✅ Color pickers for every element ✅ Toggle for each confirmation requirement ✅ Adjustable thresholds and timeframes ✅ Multiple alert options per day ✅ Customizable panel position 2. Full Confirmation System ✅ Volume: Must exceed customizable multiplier of average ✅ Moving Averages: Fast MA must be above Slow MA ✅ VWAP: Price must be above VWAP ✅ All confirmations must remain valid for ENTIRE hold period ✅ Any confirmation failure = Complete reset
Indikator Pine Script®
oleh jmslimus
Trade Journal ProTrade Journal Pro A powerful, visual trading journal that enforces discipline with real-time feedback, reflective prompts, and strict risk limits — all in one clean overlay box. Jesus is King — trade with wisdom, not emotion. FEATURES • AUTO-CALCULATED DAILY TRADES → `Trades Today = Wins + Losses + Breakevens` (no manual input needed) • 4 ENFORCED RISK LIMITS 1. Max Trades Per Day 2. Max Risk Rule Violations 3. Max Consecutive Losses (tilt protection) 4. Max Total Losses Allowed (lifetime/session cap) • SMART VISUAL FEEDBACK • GREEN BOX = You hit a limit exactly → “WELL DONE!” • RED BOX = Breached any limit → “STOP & REFLECT” + ALERT • Dark = Normal (under all limits) • REFLECTIVE PROMPTS (Customizable) 1. Why this setup? 2. What was my emotional state? 3. Did I follow my plan? • LIVE ADVICE ENGINE → Win: “Great execution! Log what worked.” → Loss: “Loss = tuition. What did you learn?” → Breakeven: “Review entry/exit precision.” • DAILY REMINDER → Always visible: “Trade the plan, not the emotion.” • FULLY CUSTOMIZABLE • Font size (Tiny → Huge) • Box position (bars to the right) • Toggle: Metrics / Prompts / Advice • Custom colors, messages, limits • ALERTS • Breach any limit → Immediate alert • Hit limit exactly → Discipline win notification HOW TO USE 1. After each closed trade: → Update Wins, Losses, or Breakevens → Update Consecutive Losses (reset to 0 on win/BE) → Increment Risk Violations if you broke a rule 2. Answer the 3 prompts in your journal 3. Let the box guide your behavior: • GREEN = Celebrate discipline • RED = STOP TRADING. Reflect. Reset. Perfect for day traders, swing traders, or anyone building a professional edge through journaling and risk control. No strategy entries. No repainting. Pure accountability. “The market is a mirror. This journal is the polish.” Developed with integrity. Built to protect your capital — and your peace.
Indikator Pine Script®
oleh CreativeAdvance
Sunmool's NY Lunch Model BacktestingICT NY Lunch Model Backtesting (12:00–13:00 NY) 🗽🍔 This research indicator tests an ICT narrative using the New York lunch window (12:00–13:00 America/New_York). It records that hour’s high/low and measures, during the post-lunch session (default 13:00–16:00), how often: ⬆️ If the afternoon trends up, the Lunch Low gets swept first. ⬇️ If the afternoon trends down, the Lunch High gets swept first. It reports these as conditional probabilities, not trade signals. 📈 👀 What it shows 🟦 Lunch Range box (toggle): high/low from 12:00–13:00 NY 🔻🔺 Sweep signals (bar-anchored) Low sweep: triangle below bar + optional “L” High sweep: triangle above bar + optional “H” 🧱 Optional small box wrapping the swept candle 📊 Stats table (top-right) P(L-swept | Up) — % of Up-days where Lunch Low was swept P(H-swept | Down) — % of Down-days where Lunch High was swept 🔁 Contradictions + sample sizes (Up-days / Down-days) 🎯 Direction logic (Up/Down) Anchor: 13:00 open (pmOpen) ⏰ Threshold: ATR × multiple or % from 13:00 Close ≥ pmOpen + threshold → Up-day Close ≤ pmOpen − threshold → Down-day Tiny moves under the threshold are ignored to reduce noise 🧹 ⚙️ Inputs 🌐 Timezone: America/New_York (DST handled) 🍽️ Lunch window: 1200–1300 🕓 Post-lunch window: default 1300–1600 (try 17:00/20:00 for sensitivity) 📐 Trend threshold: ATR / Percent (with length/multiple or % level) 📅 Weekdays-only toggle (FX/Equities style) 👁️ Display toggles: Lunch box / sweep arrows / sweep text / sweep candle box / stats table 🔔 TF hint when chart TF > 15m 🧭 How to use Use 5–15m charts for accurate lunch range capture. Scroll ~1 year for meaningful samples. Run sensitivity checks: vary ATR/% thresholds and the post-lunch end time. For crypto, compare with vs without weekends. 🚀 🧠 Reading the results High P(L-swept | Up) with a solid Up-day count ⇒ on up afternoons, lunch low is often swept. High P(H-swept | Down) ⇒ on down afternoons, lunch high is often swept. Lower Contradictions = cleaner tendency. Remember: this is a probabilistic tendency, not a rule. 🎲 📝 Notes & limits All markers (arrows, text, sweep boxes) are bar-anchored; the lunch range box is a research overlay you can toggle. Real-time vs historical bar building can differ—interpret on bar close. 🔒
Indikator Pine Script®
oleh sunmool_
MoneyPlant-Auto Support Resistance V2.0 🧭 Overview MoneyPlant – Auto Support Resistance is a professional-grade indicator designed to automatically detect dynamic Support and Resistance levels using real-time market structure. It combines trend confirmation, structure analysis, and momentum logic to identify high-probability trading zones in all market conditions. ⚙️ Core Concept This indicator uses a unique combination of classic and proprietary logic to filter only the most relevant S/R levels: • Dynamic Support/Resistance Mapping: Detects strong reaction levels based on price structure, candle rejection points, and breakout validation. • EMA & WMA Trend Filter: Uses a triple-moving-average model (default EMA 18, EMA 25, and WMA 7) to confirm current market bias. • MACD Momentum Filter: Confirms trend strength and helps avoid false breakouts. • Smart Alignment Logic: Generates signals only when structure, trend, and momentum all align in the same direction. 🧠 How It Works 1. Buy Setup:  When price breaks above a resistance level with bullish EMA/WMA alignment and positive MACD momentum → Buy Signal triggers. 2. Sell Setup:  When price breaks below a support level with bearish EMA/WMA alignment and negative MACD momentum → Sell Signal triggers. 3. Auto-Refreshing Zones:  Support and Resistance zones update dynamically as market structure evolves. 🎯 Best Use Cases • Works effectively on Stocks, Indices, Forex, and Commodities (e.g., XAUUSD, NIFTY, BANKNIFTY ). • Ideal for Intraday & Swing Trading (15 min – 1 hour timeframes). • Fully compatible with TradingView alerts and automation tools. 💡 Key Features ✅ Automatic Support/Resistance detection ✅ Adaptive EMA + WMA + MACD trend logic ✅ Real-time Buy/Sell alerts ✅ Multi-timeframe compatibility ✅ Optimized for clean chart visuals ⚖️ Recommended Settings • EMA Fast: 18 • EMA Slow: 25 • WMA Filter: 7 • MACD: Default parameters (Users may adjust EMA/WMA settings according to their own trading style.) 🔒 How to Get Access To get access to this invite-only script, please send me a private message on TradingView or use the link in my profile. Once your username is added via Manage Access, you’ll be able to use the indicator. 🧾 Notes for Traders This tool does not repaint, and it’s meant for educational and analytical purposes only. Each license is valid for one TradingView username — no resale or redistribution is permitted. Developed by MoneyPlant Smart Automation for Professional Traders
Strategi Pine Script®
oleh saandeeppavaar
Gold THB per Baht (XAU -> Thai baht gold)What it does This indicator converts international gold prices (XAU) into Thai retail “baht gold” price (THB per 1 baht gold weight) in real time. It multiplies the XAU price (per troy ounce) by USD/THB and converts ounces to Thai baht-weight using the exact gram ratios. Formula THB per baht gold = XAU (USD/oz) × USDTHB × (15.244 / 31.1035) × (1 + Adjustment%) + FlatFeeTHB 1 troy ounce = 31.1035 g 1 Thai baht gold = 15.244 g Conversion factor ≈ 0.490103
Indikator Pine Script®
oleh golfcu