Cross-Correlation Lead/Lag AnalyzerCross-Correlation Lead/Lag Analyzer (XCorr)
Discover which instrument moves first with advanced cross-correlation analysis.
This indicator analyzes the lead/lag relationship between any two financial instruments using rolling cross-correlation at multiple time offsets. Perfect for pairs trading, market timing, and understanding inter-market relationships.
Key Features:
Universal compatibility - Works with any two symbols (stocks, futures, forex, crypto, commodities)
Multi-timeframe analysis - Automatically adjusts lag periods based on your chart timeframe
Real-time correlation table - Shows current correlation values for all lag scenarios
Visual lead/lag detection - Color-coded plots make it easy to spot which instrument leads
Smart "Best" indicator - Automatically identifies the strongest relationship
How to Use:
Set your symbols in the indicator settings (default: NQ1! vs RTY1!)
Adjust correlation length (default: 20 periods for smooth but responsive analysis)
Watch the colored lines:
• Red/Orange: Symbol 2 leads Symbol 1 by 1-2 periods
• Blue: Instruments move simultaneously
• Green/Purple: Symbol 1 leads Symbol 2 by 1-2 periods
Check the table for exact correlation values and the "Best" relationship
Interpreting Results:
Correlation > 0.7: Strong positive relationship
Correlation 0.3-0.7: Moderate relationship
Correlation < 0.3: Weak/no relationship
Highest line indicates the optimal timing relationship
Popular Use Cases:
Index Futures : NQ vs ES, RTY vs IWM
Sector Rotation : XLF vs XLK, QQQ vs SPY
Commodities : GC vs SI, CL vs NG
Currency Pairs : EURUSD vs GBPUSD
Crypto : BTC vs ETH correlation analysis
Technical Notes:
Cross-correlation measures linear relationships between two time series at different time lags. This implementation uses Pearson correlation with adjustable periods, calculating correlations from -2 to +2 period offsets to detect leading/lagging behavior.
Perfect for quantitative analysts, pairs traders, and anyone studying inter-market relationships.
Statistics
Trinity Multi Time Frame Trend Dashboard and SignalsUpdated the logic behind the buy and sell signals to them for more responsiveness and also included an ADX filter.
3D Surface Modeling [PhenLabs]📊 3D Surface Modeling
Version: PineScript™ v6
📌 Description
The 3D Surface Modeling indicator revolutionizes technical analysis by generating three-dimensional visualizations of multiple technical indicators across various timeframes. This advanced analytical tool processes and renders complex indicator data through a sophisticated matrix-based calculation system, creating an intuitive 3D surface representation of market dynamics.
The indicator employs array-based computations to simultaneously analyze multiple instances of selected technical indicators, mapping their behavior patterns across different temporal dimensions. This unique approach enables traders to identify complex market patterns and relationships that may be invisible in traditional 2D charts.
🚀 Points of Innovation
Matrix-Based Computation Engine: Processes up to 500 concurrent indicator calculations in real-time
Dynamic 3D Rendering System: Creates depth perception through sophisticated line arrays and color gradients
Multi-Indicator Integration: Seamlessly combines VWAP, Hurst, RSI, Stochastic, CCI, MFI, and Fractal Dimension analyses
Adaptive Scaling Algorithm: Automatically adjusts visualization parameters based on indicator type and market conditions
🔧 Core Components
Indicator Processing Module: Handles real-time calculation of multiple technical indicators using array-based mathematics
3D Visualization Engine: Converts indicator data into three-dimensional surfaces using line arrays and color mapping
Dynamic Scaling System: Implements custom normalization algorithms for different indicator types
Color Gradient Generator: Creates depth perception through programmatic color transitions
🔥 Key Features
Multi-Indicator Support: Comprehensive analysis across seven different technical indicators
Customizable Visualization: User-defined color schemes and line width parameters
Real-time Processing: Continuous calculation and rendering of 3D surfaces
Cross-Timeframe Analysis: Simultaneous visualization of indicator behavior across multiple periods
🎨 Visualization
Surface Plot: Three-dimensional representation using up to 500 lines with dynamic color gradients
Depth Indicators: Color intensity variations showing indicator value magnitude
Pattern Recognition: Visual identification of market structures across multiple timeframes
📖 Usage Guidelines
Indicator Selection
Type: VWAP, Hurst, RSI, Stochastic, CCI, MFI, Fractal Dimension
Default: VWAP
Starting Length: Minimum 5 periods
Default: 10
Step Size: Interval between calculations
Range: 1-10
Visualization Parameters
Color Scheme: Green, Red, Blue options
Line Width: 1-5 pixels
Surface Resolution: Up to 500 lines
✅ Best Use Cases
Multi-timeframe market analysis
Pattern recognition across different technical indicators
Trend strength assessment through 3D visualization
Market behavior study across multiple periods
⚠️ Limitations
High computational resource requirements
Maximum 500 line restriction
Requires substantial historical data
Complex visualization learning curve
🔬 How It Works
1. Data Processing:
Calculates selected indicator values across multiple timeframes
Stores results in multi-dimensional arrays
Applies custom scaling algorithms
2. Visualization Generation:
Creates line arrays for 3D surface representation
Applies color gradients based on value magnitude
Renders real-time updates to surface plot
3. Display Integration:
Synchronizes with chart timeframe
Updates surface plot dynamically
Maintains visual consistency across updates
🌟 Credits:
Inspired by LonesomeTheBlue (modified for multiple indicator types with scaling fixes and additional unique mappings)
💡 Note:
Optimal performance requires sufficient computing resources and historical data. Users should start with default settings and gradually adjust parameters based on their analysis requirements and system capabilities.
Volume Delta Pressure Tracker ⚡ by GSK-VIZAG-AP-INDIA📢 Title:
Volume Delta Pressure Tracker ⚡ by GSK-VIZAG-AP-INDIA
📝 Short Description (for script title box):
Real-time volume pressure tracker with estimated Buy/Sell volumes and Delta visualization in an Indian-friendly format (K, L, Cr).
📃 Full Description
🔍 Overview:
This indicator estimates buy and sell volumes using candle structure (OHLC) and displays a real-time delta table for the last N candles. It provides traders with a quick view of volume imbalance (pressure) — often indicating strength behind price moves.
📊 Features:
📈 Buy/Sell Volume Estimation using the candle’s OHLC and Volume.
⚖️ Delta Calculation (Buy Vol - Sell Vol) to detect pressure zones.
📅 Time-stamped Table displaying:
Time (HH:MM)
Buy Volume (Green)
Sell Volume (Red)
Delta (Color-coded)
🔢 Indian Number Format (K = Thousands, L = Lakhs, Cr = Crores).
🧠 Fully auto-calculated — no need for tick-by-tick bid/ask feed.
📍 Neatly placed bottom-right table, customizable number of rows.
🛠️ Inputs:
Show Table: Toggle the table on/off
Number of Bars to Show: Choose how many recent candles to include (5–50)
🎯 Use Cases:
Identify hidden buyer/seller strength
Detect volume absorption or exhaustion
✅ Compatibility:
Works on any timeframe
Ideal for intraday instruments like NIFTY, BANKNIFTY, etc.
Ideal for volume-based strategy confirmation.
🖋️ Developed by:
GSK-VIZAG-AP-INDIA
Zone Levels (Final 888)📌 Zone Levels Indicator – Buy & Sell Zones with Alerts
This script plots clearly defined buy and sell zones on the chart, with custom top/bottom price inputs for each zone. Ideal for traders who want to visually track high-probability reversal or entry areas.
✅ Key Features:
🔧 Fully customizable zones via settings
📏 Extends zones 240 bars to the left and 40 bars to the right
🏷️ Auto-labeled zones with proper price formatting (e.g. 3385–3390)
🔔 Built-in alerts when price enters any zone
🎯 Mid-zone line for key reference level
🟢 Buy Zones:
Buy Z1: 3415–3412
Buy Z2: 3405–3402
Buy Z3: 3400–3397
Buy Z4: 3390–3385
🔴 Sell Zones:
Sell Z1: 3430–3431
Sell Z2: 3434–3436
Sell Z3: 3439–3441
Sell Z4: 3445–3450
This indicator helps discretionary traders who rely on clean visual zones and precise price levels to act confidently without clutter.
Feel free to modify zone values in the settings to match your own strategy or market conditions.
Remark: This script created by AI
Log Return DistributionThis indicator calculates the statistical distribution of logarithmic returns over a user-defined lookback period and visualizes it as a horizontal profile anchored to the most recent opening price.
Lookback Length: The number of recent bars to include in the distribution analysis. A larger value (e.g., 252) provides a long-term statistical view, while a smaller value (e.g., 20) focuses on recent, short-term volatility.
Bins Count: The number of price levels to divide the distribution into. An odd number is recommended (e.g., 31, 51) to ensure a dedicated central line for the 0% return.
Max Line Length: The horizontal length (in bars) of the line representing the most frequent return bin (the mode). This setting scales the entire profile, allowing you to make differences in frequency more or less pronounced visually.
Fibonacci Sequence Moving Average [BackQuant]Fibonacci Sequence Moving Average with Adaptive Oscillator
1. Overview
The Fibonacci Sequence Moving Average indicator is a two‑part trading framework that combines a custom moving average built from the famous Fibonacci number set with a fully featured oscillator, normalisation engine and divergence suite. The moving average half delivers an adaptive trend line that respects natural market rhythms, while the oscillator half translates that trend information into a bounded momentum stream that is easy to read, easy to compare across assets and rich in confluence signals. Everything from weighting logic to colour palettes can be customised, so the tool comfortably fits scalpers zooming into one‑minute candles as well as position traders running multi‑month trend following campaigns.
2. Core Calculation
Fibonacci periods – The default length array is 5, 8, 13, 21, 34. A single multiplier input lets you scale the whole family up or down without breaking the golden‑ratio spacing. For example a multiplier of 3 yields 15, 24, 39, 63, 102.
Component averages – Each period is passed through Simple Moving Average logic to produce five baseline curves (ma1 through ma5).
Weighting methods – You decide how those five values are blended:
• Equal weighting treats every curve the same.
• Linear weighting applies factors 1‑to‑5 so the slowest curve counts five times as much as the fastest.
• Exponential weighting doubles each step for a fast‑reacting yet still smooth line.
• Fibonacci weighting multiplies each curve by its own period value, honouring the spirit of ratio mathematics.
Smoothing engine – The blended average is then smoothed a second time with your choice of SMA, EMA, DEMA, TEMA, RMA, WMA or HMA. A short smoothing length keeps the result lively, while longer lengths create institution‑grade glide paths that act like dynamic support and resistance.
3. Oscillator Construction
Once the smoothed Fib MA is in place, the script generates a raw oscillator value in one of three flavours:
• Distance – Percentage distance between price and the average. Great for mean‑reversion.
• Momentum – Percentage change of the average itself. Ideal for trend acceleration studies.
• Relative – Distance divided by Average True Range for volatility‑aware scaling.
That raw series is pushed through a look‑back normaliser that rescales every reading into a fixed −100 to +100 window. The normalisation window defaults to 100 bars but can be tightened for fast markets or expanded to capture long regimes.
4. Visual Layer
The oscillator line is gradient‑coloured from deep red through sky blue into bright green, so you can spot subtle momentum shifts with peripheral vision alone. There are four horizontal guide lines: Extreme Bear at −50, Bear Threshold at −20, Bull Threshold at +20 and Extreme Bull at +50. Soft fills above and below the thresholds reinforce the zones without cluttering the chart.
The smoothed Fib MA can be plotted directly on price for immediate trend context, and each of the five component averages can be revealed for educational or research purposes. Optional bar‑painting mirrors oscillator polarity, tinting candles green when momentum is bullish and red when momentum is bearish.
5. Divergence Detection
The script automatically looks for four classes of divergences between price pivots and oscillator pivots:
Regular Bullish, signalling a possible bottom when price prints a lower low but the oscillator prints a higher low.
Hidden Bullish, often a trend‑continuation cue when price makes a higher low while the oscillator slips to a lower low.
Regular Bearish, marking potential tops when price carves a higher high yet the oscillator steps down.
Hidden Bearish, hinting at ongoing downside when price posts a lower high while the oscillator pushes to a higher high.
Each event is tagged with an ℝ or ℍ label at the oscillator pivot, colour‑coded for clarity. Look‑back distances for left and right pivots are fully adjustable so you can fine‑tune sensitivity.
6. Alerts
Five ready‑to‑use alert conditions are included:
• Bullish when the oscillator crosses above +20.
• Bearish when it crosses below −20.
• Extreme Bullish when it pops above +50.
• Extreme Bearish when it dives below −50.
• Zero Cross for momentum inflection.
Attach any of these to TradingView notifications and stay updated without staring at charts.
7. Practical Applications
Swing trading trend filter – Plot the smoothed Fib MA on daily candles and only trade in its direction. Enter on oscillator retracements to the 0 line.
Intraday reversal scouting – On short‑term charts let Distance mode highlight overshoots beyond ±40, then fade those moves back to mean.
Volatility breakout timing – Use Relative mode during earnings season or crypto news cycles to spot momentum surges that adjust for changing ATR.
Divergence confirmation – Layer the oscillator beneath price structure to validate double bottoms, double tops and head‑and‑shoulders patterns.
8. Input Summary
• Source, Fibonacci multiplier, weighting method, smoothing length and type
• Oscillator calculation mode and normalisation look‑back
• Divergence look‑back settings and signal length
• Show or hide options for every visual element
• Full colour and line width customisation
9. Best Practices
Avoid using tiny multipliers on illiquid assets where the shortest Fibonacci window may drop under three bars. In strong trends reduce divergence sensitivity or you may see false counter‑trend flags. For portfolio scanning set oscillator to Momentum mode, hide thresholds and colour bars only, which turns the indicator into a heat‑map that quickly highlights leaders and laggards.
10. Final Notes
The Fibonacci Sequence Moving Average indicator seeks to fuse the mathematical elegance of the golden ratio with modern signal‑processing techniques. It is not a standalone trading system, rather a multi‑purpose information layer that shines when combined with market structure, volume analysis and disciplined risk management. Always test parameters on historical data, be mindful of slippage and remember that past performance is never a guarantee of future results. Trade wisely and enjoy the harmony of Fibonacci mathematics in your technical toolkit.
Weighted Multi-Mode Oscillator [BackQuant]Weighted Multi‑Mode Oscillator
1. What Is It?
The Weighted Multi‑Mode Oscillator (WMMO) is a next‑generation momentum tool that turns a dynamically‑weighted moving average into a 0‑100 bounded oscillator.
It lets you decide how each bar is weighted (by volume, volatility, momentum or a hybrid blend) and how the result is normalised (Percentile, Z‑Score or Min‑Max).
The outcome is a self‑adapting gauge that delivers crystal‑clear overbought / oversold zones, divergence clues and regime shifts on any market or timeframe.
2. How It Works
• Dynamic Weight Engine
▪ Volume – emphasises bars with exceptional participation.
▪ Volatility – inverse ATR weighting filters noisy spikes.
▪ Momentum – amplifies strong directional ROC bursts.
▪ Hybrid – equal‑weight blend of the three dimensions.
• Multi‑Mode Smoothing
Choose from 8 MA types (EMA, DEMA, HMA, LINREG, TEMA, RMA, SMA, WMA) plus a secondary smoothing factor to fine‑tune lag vs. responsiveness.
• Normalization Suite
▪ Percentile – rank vs. recent history (context aware).
▪ Z‑Score – standard deviations from mean (statistical extremes).
▪ Min‑Max – scale between rolling high/low (trend friendly).
3. Reading the Oscillator
Zone Default Level Interpretation
Bull > 80 Acceleration; momentum buyers in control
Neutral 20 – 80 Consolidation / no edge
Bear < 20 Exhaustion; sellers dominate
Gradient line/area automatically shades from bright green (strong bull) to deep red (strong bear).
Optional bar‑painting colours price bars the same way for rapid chart scanning.
4. Typical Use‑Cases
Trend Confirmation – Set Weight = Hybrid, Smoothing = EMA. Enter pullbacks only when WMMO > 50 and rising.
Mean Reversion – Weight = Volatility, reduce upper / lower bands to 70 / 30 and fade extremes.
Volume Pulse – Intraday futures: Weight = Volume to catch participation surges before breakout candles.
Divergence Spotting – Compare price highs/lows to WMMO peaks for early reversal clues.
5. Inputs & Styling
Calculation: Source, MA Length, MA Type, Smoothing
Weighting: Volume period & factor, Volatility length, Momentum period
Normalisation: Method, Look‑back, Upper / Lower thresholds
Display: Gradient fills, Threshold lines, Bar‑colouring toggle, Line width & colours
All thresholds, colours and fills are fully customisable inside the settings panel.
6. Built‑In Alerts
WMMO Long – oscillator crosses up through upper threshold.
WMMO Short – oscillator crosses down through lower threshold.
Attach them once and receive push / e‑mail notifications the moment momentum flips.
7. Best Practices
Percentile mode is self‑adaptive and works well across assets; Z‑Score excels in ranges; Min‑Max shines in persistent trends.
Very short MA lengths (< 10) may produce jitter; compensate with higher “Smoothing” or longer look‑backs.
Pair WMMO with structure‑based tools (S/R, trend lines) for higher‑probability trade confluence.
Disclaimer
This script is provided for educational purposes only. It is not financial advice. Always back‑test thoroughly and manage risk before trading live capital.
MTF Trend Heatmap (EMA + MACD) by PierreKMTF Trend Heatmap (EMA + MACD) by PierreK
This indicator offers a compact and clear heatmap that visualizes the current trend status of an instrument across multiple key timeframes. Based on the combination of the EMA 50 and EMA 200, as well as the MACD indicator, the heatmap shows whether the trend is bullish (green dot) or bearish (red dot) on the 1-minute, 5-minute, 15-minute, 1-hour, 4-hour, and 1-day timeframes.
Highlights:
• Multi-timeframe analysis at a glance - perfect for traders who want to keep an eye on multiple timeframes.
• Clearly defined trend logic: EMA 50 > EMA 200 and MACD above signal and zero line.
‣ Simple color scheme for quick trend decisions.
Optimized for all markets and assets (forex, stocks, cryptocurrencies, etc.).
• Chart overload - heatmap as a separate, clearly positioned table.
Ideal for day traders, swing traders, and anyone who needs quick trend overviews for their trading.
FAQ
Q. Which markets is the indicator suitable for?
A: The indicator works universally on forex, stocks, cryptocurrencies, and other TradingView symbols.
Q: Can I add additional timeframes?
A: Currently, 1m, 5m, 15m, 1h, 4h, and 1d are preset. Adjustments are possible in the code if you are familiar with Pine Script.
Q: How accurate is the trend determination?
A: The trend is determined very reliably using the combination of the EMA 50/200 and MACD (via the signal line and the zero line).
Q: Can I use alerts with this indicator?
A: Alerts are not included in the standard script, but can be added individually.
📊 52-Week Price % Distance (Advanced Table)This TradingView Pine Script displays a compact, informative table showing how far the current price is from the 52-week high and low, expressed as percentages.
10kaDum by NAVHere's a comprehensive description for publishing your "10kaDum by NAV" script on TradingView:
---
# 🎯 10kaDum by NAV - Advanced SMA Alignment Trading System
**A comprehensive trading strategy that combines SMA alignment signals with intelligent dip-buying and automated profit-taking mechanisms.**
## 📊 Strategy Overview
The 10kaDum system identifies optimal entry and exit points using Simple Moving Average (SMA) alignment patterns, while providing additional opportunities through systematic dip buying and profit taking.
### Core Signals:
🟢 **MAIN BUY**: Triggers when Close < SMA20 < SMA50 < SMA200 (bearish alignment - potential reversal)
🟡 **10kaDum BUY**: Secondary buy signal when price drops 10% after main buy (dollar-cost averaging)
🟠 **10kaDum SELL**: Exit signal when price rises 10% from 10kaDum buy (quick profit taking)
🔴 **MAIN SELL**: Exit signal when Close > SMA20 > SMA50 > SMA200 (bullish alignment - trend reversal)
## 🔥 Key Features
### Smart Signal Management
- **One-time signals**: Each signal triggers only once until the opposite condition occurs
- **No signal spam**: Clean, actionable entries without repetitive alerts
- **Cycle-based logic**: Complete trading cycles from entry to exit
### Performance Analytics
- **Real-time statistics table** with configurable position and styling
- **Closed Trades**: Total number of completed 10kaDum cycles
- **Average Days**: Mean holding period for 10kaDum positions
- **Minimum Days**: Fastest trade completion time
- **Min Days Date**: When the fastest trade occurred
### Advanced Label System
- **Performance metrics**: Days held, gain percentage, and CAGR on exit signals
- **Calendar days calculation**: Accurate time-based performance (not just bars)
- **Anti-overlap positioning**: Smart label placement to avoid chart clutter
- **Fully customizable**: Colors, sizes, and text styling
### Professional Customization
- **Label colors**: Separate color controls for each signal type
- **Table styling**: Full control over fonts, colors, and positioning
- **Position controls**: Precise offset adjustments for optimal visibility
- **Alert system**: Built-in alerts for all signal types
## 📈 Trading Logic
**Entry Strategy:**
1. Wait for bearish SMA alignment (potential bottom)
2. Enter initial position on MAIN BUY
3. Add to position on 10% dips (10kaDum BUY)
**Exit Strategy:**
1. Take quick profits on 10kaDum positions (+10% gain)
2. Hold main position until bullish SMA alignment
3. Full exit on MAIN SELL signal
## 🎨 Visual Elements
- **SMA Lines**: 20, 50, and 200-period moving averages
- **Colored Labels**: Distinct visual signals for each action
- **Statistics Table**: Live performance tracking
- **Clean Design**: Minimal chart clutter with maximum information
## ⚡ Best Practices
- Use on daily or higher timeframes for best results
- Combine with volume analysis for confirmation
- Monitor the statistics table for strategy performance
- Adjust position sizes based on signal type
- Set alerts for hands-free trading
## 🔧 Customization Options
- **9 table positions** (top, middle, bottom × left, center, right)
- **5 font sizes** (tiny to huge)
- **Full color customization** for all elements
- **Adjustable label spacing** for different chart scales
- **Toggle statistics display** on/off
---
**Disclaimer**: This indicator is for educational purposes. Past performance doesn't guarantee future results. Always practice proper risk management and consider your financial situation before trading.
**Created by**: NAV
**Version**: 1.0
**Category**: Strategy / Trend Analysis
---
This description highlights the key features, provides clear usage instructions, and maintains a professional tone suitable for TradingView's marketplace.
Max Drawdown (Asset-Based Lookback)Max Drawdown (Long-Term Trading)
🟦 Majors BTC, ETH, BNB, LTC 180 – 365
Captures full correction cycles and recovery patterns (6–12 months).
🟩 Altcoins SOL, ADA, DOT, LINK, AVAX 90 – 180
Alts move faster than majors; 3–6 months catches most large swings.
🟥 Meme coins DOGE, SHIB, PEPE, FLOKI 60 – 120
Volatile with quick trend reversals; 2–4 months captures parabolic runs + drawdowns.
📅 Chart Timeframe:
Use Daily (1D) timeframe for all these.
For extra macro insight, try Weekly (1W) with 52 bars (≈ 1 year).
Compare multiple assets using the same period to assess relative risk.
If you're building a long-term portfolio, combine this with:
200-day SMA or EMA for trend context.
Sharpe Ratio or Sortino Ratio if you're looking for risk-adjusted return metrics.
PCR tableOverview
This indicator displays a multi-period table of forward-looking price projections. It combines normalized directional momentum (Positive Change Ratio, PCR) with volatility (ATR) and presents a forecast for upcoming time intervals, adjusted for your local UTC offset.
Concepts & Calculations
Positive Change Ratio (PCR):
((total positive change)/(total change)-0.5)*2, producing a value between –100 and +100.
Synthetic ATR: Calculates average true range over the same lookbacks to capture volatility.
PCR × ATR: Forms a volatility-weighted directional forecast, indicating expected move magnitude.
Future Price Projection: Adds PCR × ATR value to current close to estimate future price at each lookahead interval.
Table Layout
There are 12 forecast horizons—1× to 12× the chart timeframe (e.g., minutes, hours, days). Each row displays:
1. Future Time: Timestamp of each projection (adjustable via UTC offset)
2. PCR: Directional bias per period (–1 to +1)
3. PCR × ATR: E xpected move magnitude
4. Future Price: Close + (PCR × ATR)
High and low PCR×ATR rows are highlighted green for minimum value in the price forecast (buy signal) or red for maximum value in the price forecast (sell signal).
How to Use
1. Set UTC offset to your time zone for accurate future timestamps.
2. View PCR to assess bullish (positive) or bearish (negative) momentum.
3. Use PCR × ATR to estimate move strength and direction.
4. Reference Future Price for potential levels over upcoming intervals, and for buy and sell signals.
Limitations & Disclaimers
* This model uses linear extrapolation based on recent price behavior. It does not guarantee future prices.
* It uses only current bar data and no lookahead logic—compliant with Pine Script rules.
* Designed for analytical insight, not as an automated signal or trade executor.
* Best used on standard bar/candle charts (avoid non-standard types like Heikin‑Ashi or Renko).
8 AM & 9 AM NY Candle HighlighterThis indicator helps me to know when the 9am NY candle has closed above or below the previous candle.
Orthogonal Projections to Latent Structures (O-PLS)Version 0.1
Orthogonal Projections to Latent Structures (O-PLS) Indicator for TradingView
This indicator, named "Orthogonal Projections to Latent Structures (O-PLS)", is designed to help traders understand the relevance or predictive power of various market variables on the future close price of the asset it's applied to. Unlike standard correlation coefficients that show a simple linear relationship, O-PLS aims to separate variables into "predictive" (relevant to Y) and "orthogonal" (irrelevant noise) components. This Pine Script indicator provides a simplified proxy of the relevance score derived from O-PLS principles.
Purpose of the Indicator
The primary purpose of this indicator is to identify which technical factors (such as price, volume, and other indicators) have the strongest relationship with the future price movement of the current trading instrument. By providing a "relevance score" for each input variable, it helps traders focus on the most influential data points, potentially leading to more informed trading decisions.
Inputs
The indicator offers the following user-definable inputs:
* **Lookback Period:** This integer input (default: 100, min: 10, max: 500) determines the number of past bars used to calculate the relevance scores for each variable. A longer lookback period considers more historical data, which can lead to smoother, less reactive scores but might miss recent shifts in variable importance.
* **External Asset Symbol:** This symbol input (default: `BINANCE:BTCUSDT`) allows you to specify an external asset (e.g., `BINANCE:ETHUSDT`, `NASDAQ:TSLA`) whose close price will be included in the analysis as an additional variable. This is useful for cross-market analysis to see how other assets influence the current chart.
* **Plot Visibility Checkboxes (e.g., "Plot: Open Price Relevance", "Plot: Volume Relevance", etc.):** These boolean checkboxes allow you to toggle the visibility of individual relevance score plots on the chart, helping to declutter the display and focus on specific variables.
Outputs
The indicator provides two main types of output:
Relevance Score Plots: These are lines plotted in a separate pane below the main price chart. Each line corresponds to a specific market variable (Open Price, Close Price, High Price, Low Price, Volume, various RSIs, SMAs, MFI, and the External Asset Close). The value of each line represents the calculated "relevance score" for that variable, typically scaled between 0 and 10. A higher score indicates a stronger predictive relationship with the future close price.
Sorted Relevance Table : A table displayed in the top-right corner of the chart provides a clear, sorted list of all analyzed variables and their corresponding relevance scores. The table is sorted in descending order of relevance, making it easy to identify the most influential factors at a glance. Each variable name in the table is colored according to its plot color, and the external asset's name is dynamically displayed without the "BINANCE:" prefix.
How to Use the Indicator
1. **Add to Chart:** Apply the "Orthogonal Projections to Latent Structures (O-PLS)" indicator to your desired trading chart (e.g., ETH/USDT).
2. **Adjust Inputs:**
* **Lookback Period:** Experiment with different lookback periods to see how the relevance scores change. A shorter period might highlight recent correlations, while a longer one might show more fundamental relationships.
* **External Asset Symbol:** If you trade BTC/USDT, you might add ETH/USDT or SPX as an external asset to see its influence.
3. **Analyze Relevance Scores:**
* **Plots:** Observe the individual relevance score plots over time. Are certain variables consistently high? Do scores change before significant price moves?
* **Table:** Refer to the sorted table on the latest confirmed bar to quickly identify the top-ranked variables.
4. **Incorporate into Strategy:** Use the insights from the relevance scores to:
* Prioritize certain indicators or price actions in your trading strategy. For example, if "Volume" has a high relevance score, it suggests volume confirmation is critical for future price moves.
* Understand the influence of inter-market relationships (via the External Asset Close).
How the Indicator Works
The indicator works by performing the following steps on each bar:
1. **Data Fetching:** It gathers historical data for various price components (open, high, low, close), volume, and calculated technical indicators (SMA, RSI, MFI) for the specified `lookback` period. It also fetches the close price of an `External Asset Symbol` .
2. **Standardization (Z-scoring):** All collected raw data series are standardized by converting them into Z-scores. This involves subtracting the mean of each series and dividing by its standard deviation . Standardization is crucial because it brings all variables to a common scale, preventing variables with larger absolute values from disproportionately influencing the correlation calculations.
3. **Correlation Calculation (Proxy for O-PLS Relevance):** The indicator then calculates a simplified form of correlation between each standardized input variable and the standardized future close price (Y variable) . This correlation is a proxy for the relevance that O-PLS would identify. A high absolute correlation indicates a strong linear relationship.
4. **Relevance Scaling:** The calculated correlation values are then scaled to a range of 0 to 10 to provide an easily interpretable "relevance score" .
5. **Output Display:** The relevance scores are presented both as time-series plots (allowing observation of changes over time) and in a real-time sorted table (for quick identification of top factors on the current bar) .
How it Differs from Full O-PLS
This indicator provides a *simplified proxy* of O-PLS principles rather than a full, mathematically rigorous O-PLS model. Here's why and how it differs:
* **Dimensionality Reduction:** A full O-PLS model would involve complex matrix factorization techniques to decompose the independent variables (X) into components that are predictive of Y and components that are orthogonal (unrelated) to Y but still describe X's variance. Pine Script's array capabilities and computational limits make direct implementation of these matrix operations challenging.
* **Orthogonal Components:** A true O-PLS model explicitly identifies and removes orthogonal components (noise) from the X data that are unrelated to Y. This indicator, in its simplified form, primarily focuses on the direct correlation (relevance) between each X variable and Y after standardization, without explicitly modeling and separating these orthogonal variations.
* **Predictive Model:** A full O-PLS model is ultimately a predictive model that can be used for regression (predicting Y). This indicator, however, focuses solely on **identifying the relevance/correlation of inputs to Y**, rather than building a predictive model for Y itself. It's more of an analytical tool for feature importance than a direct prediction engine.
* **Computational Intensity:** Full O-PLS involves Singular Value Decomposition (SVD) or Partial Least Squares (PLS) algorithms, which are computationally intensive. The indicator uses simpler statistical measures (mean, standard deviation, and direct correlation calculation over a lookback window) that are feasible within Pine Script's execution limits.
In essence, this Pine Script indicator serves as a practical tool for gaining insights into variable relevance, inspired by the spirit of O-PLS, but adapted for the constraints and common use cases of a TradingView environment.
Entry Signal Paint (RSI + DMI + Stoch + MACD)RSI above 60
Stoch - cross overslod
DMI - Ungli
// === RSI Condition ===
rsi = ta.rsi(rsiSource, rsiPeriod)
rsiCondition = rsi > 60
// === ADX and DI Condition ===
adx = ta.adx(adxPeriod)
plusDI = ta.plus_di(adxPeriod)
minusDI = ta.minus_di(adxPeriod)
adxCondition = adx > 15 and plusDI > minusDI
// === Stochastic Condition ===
k = ta.stoch(close, high, low, stochK)
d = ta.sma(k, stochD)
stochOversoldCross = ta.crossover(k, d) and k < 20
ZScore Plot with Ranked TableVersion 0.1
ZScore Plot with Ranked Table — Overview
This indicator visualizes the rolling ZScores of up to 10 crypto assets, giving traders a normalized view of log return deviations over time. It's designed for volatility analysis, anomaly detection, and clustering of asset behavior.
🎯 Purpose
• Show how each asset's performance deviates from its historical mean
• Identify potential overbought/oversold conditions across assets
• Provide a ranked leaderboard to compare asset behavior instantly
⚙️ Inputs
• Lookback: Number of bars to calculate mean and standard deviation
• Asset 1–10: Choose up to 10 symbols (e.g. BTCUSDT, ETHUSDT)
📈 Outputs
• ZScore Lines: Each asset plotted on a normalized scale (mean = 0, SD = 1)
• End-of-Line Labels: Asset names displayed at latest bar
• Leaderboard Table: Ranked list (top-right) showing:
◦ Asset name (color-matched)
◦ Final ZScore (rounded to 3 decimals)
🧠 Use Cases
• Quantitative traders seeking cross-asset momentum snapshots
• Signal engineers tracking volatility clusters
• Risk managers monitoring outliers and systemic shifts
Z-Score Multi-Model ClusteringA price/volume clustering framework combining three market behavior models into a single indicator. Designed to help identify emerging trend strength, turning points, and volatility-driven entries or exits.
🔍 How It Works
This indicator classifies market states by comparing normalized price/volume behavior (via Z-Score) to different types of statistical or geometric "cluster centers." You can choose from three clustering approaches:
🧠 Clustering Models
1. Percentile (Z+CVD) – Trend Momentum Bias
Uses volume Z-Score + Cumulative Volume Delta (CVD).
Detects institutional pressure by clustering volume surges with directional delta.
Best for: Breakouts, momentum trades, volume-led reversals.
Cluster Colors:
🔹 Green triangle = Strong bullish confluence
🔻 Red triangle = Bearish divergence (bull trap risk)
⚪ Gray = Neutral/low conviction
2. Euclidean (Z+Slope) – Swing Mean-Reversion
Measures the angle of recent Z-score slope and compares it to directional cluster centers.
Helps detect early directional shifts or exhaustion.
Best for: Swing entries, pullback setups, exit timing
3. Hilbert Phase – Turn Detection via Signal Phase
Applies Hilbert Transform to the Z-Score, measuring the phase difference between trend and oscillator components.
Ideal for anticipating turns or detecting cyclical inflection points.
Useful for: Scalping, top/bottom spotting, volatility fades
✅ Features
Auto-updating cluster logic based on current data
Tooltips and clean user interface
Optional cluster bar coloring (can be toggled off)
Signal-only plotting keeps candlesticks readable
Clear entry/exit logic with triangle markers
Supports trend, swing, and oscillation-based systems
🛠️ Suggested Use Cases
Combine with VWAP, Session High/Low, or Liquidity Zones to confirm entry conditions.
Use Cluster 2 (strong bullish) on pullbacks to trend structure for add-on entries.
Use Cluster 1 in strong trends to watch for potential traps or exits.
Toggle models based on your strategy: e.g., Hilbert for scalping, Percentile for macro trend breaks.
🧪 Best Timeframes
Works across all markets and timeframes
For Percentile (Z+CVD), use intraday TF with 1m–5m CVD source
Hilbert and Euclidean preferred on 5m–1h for accurate slope/phase signals
⚠️ Notes
Clusters do not generate trade signals alone; use them in context with structure, VWAP, or trend filters.
Marker signals are filtered with a magnitude threshold to reduce noise.
Multi-Crypto Principal Component AnalysisVersion 0.2
## 📌 Multi-Crypto Principal Component Analysis (PCA) — Indicator Summary
### 🎯 Purpose
This indicator identifies **cryptocurrency assets that are behaving differently** from the rest of the market, using a simplified approach inspired by Principal Component Analysis (PCA). It’s designed to help traders spot **cross-market divergences**, detect outliers, and improve asset selection and correlation-based strategies.
### ⚙️ How It Works
The indicator analyzes the **log returns** of up to 7 user-defined assets over a configurable lookback period (default: 100 bars). It computes the **z-score** (standardized deviation) for each asset’s return series and compares it against the average behavior of the group.
If an asset’s behavior deviates significantly (beyond a threshold of 1.5 standard deviations), it’s flagged as an **outlier**.
- Each outlier is plotted as a **colored dot horizontally spaced** above the price bar
- Up to **3 dots per bar** are shown for visual clarity
This PCA-style detection works in real time, directly on the chart, and gives you a quick overview of which assets are breaking correlation.
### 🔧 Inputs
- 🕒 **Lookback Period**: Number of bars to analyze (default: 100)
- 🔢 **Assets 1–7**: Choose any 7 crypto symbols from any exchange
- 🎨 **Colors**: Predefined per asset (e.g. BTCUSDT = red, ETHUSDT = yellow)
- 📈 **Threshold**: Internal (1.5 std dev); adjustable in code if needed
### 📊 Outputs
- 🟢 Dots above candles representing assets that are acting as outliers
- 🧠 Real-time clustering insight based on statistical deviation
- 🧭 Spatially spaced dots to avoid visual overlap when multiple outliers appear
### ⚠️ Limitations
- This is a **PCA-inspired approximation**, not true matrix-based PCA
- It does **not compute principal components or eigenvectors**
- Sensitivity may vary with asset volatility or sparse trading data
- Real PCA requires external tools like Python or R for full dimensional analysis
This tool is ideal for traders who want real-time crypto correlation insights without needing external data science platforms. It’s lightweight, fast, and highly visual — and gives you a powerful lens into market dislocations across multiple assets.
Multi-TF Z-Score IndicatorIndicator to find the Z score for the daily 4h, 1h, 15m and 5 min time frames with 20 previous samples.
