HTF Live View - GSK-VIZAG-AP-INDIA📘 HTF Live View — GSK-VIZAG-AP-INDIA
🧩 Overview
The HTF Live View indicator provides a real-time visual representation of higher-timeframe (HTF) candle structures — such as 15min, 30min, 1H, 4H, and Daily — all derived directly from live 1-minute data.
This allows traders to see how higher timeframe candles are forming within the current session — without switching chart timeframes.
⚙️ Core Features
📊 Live Multi-Timeframe OHLC Tracking
Continuously calculates and displays Open, High, Low, and Close values for each key timeframe (15m, 30m, 1H, 4H, and Daily) based on the ongoing session.
⏱ Session-Aware Calculation
Automatically syncs with market hours defined by user-selected start and end times. Works across multiple timezones for global compatibility.
🕹 Visual Candle Representation
Draws mini-candles on the chart for each higher timeframe to represent their current body and wick — updated live.
Green body → bullish development
Red body → bearish development
📅 Informative Table Panel
Displays a summary table showing:
Timeframe label
Period (start–end time)
Live OHLC values
Color-coded close values
🌍 Timezone Support
Fully compatible with common regions such as Asia/Kolkata, New York, London, Tokyo, and Sydney.
🔧 User Inputs
Parameter Description
Market Start Hour/Minute Define session start time (default: 09:15)
Session End Hour/Minute Define market close (default: 15:30)
Timezone Select your preferred timezone for session alignment
💡 How It Works
The indicator uses a rolling OHLC calculation function that dynamically computes candle values based on elapsed session time.
Each timeframe (15m, 30m, 1H, 4H, and Daily) is built from 1-minute data to maintain precision even during intraday updates.
Both a visual representation (candles and wicks) and a data table (numeric summary) are displayed for clarity.
🧠 Use Cases
Monitor how HTF candles are forming live without switching chart intervals.
Understand intraday structure shifts (e.g., when 1H turns from red to green).
Confirm trend alignment across multiple timeframes visually.
Combine with your volume, delta, or liquidity tools for deeper confluence.
🪶 Signature
Developed by GSK-VIZAG-AP-INDIA
© prowelltraders — Educational and analytical use only.
⚠️ Disclaimer
This indicator is for educational and informational purposes only.
It does not provide financial advice or guaranteed trading results.
Always perform your own analysis before making investment decisions.
Statistics
Volume Sampled Supertrend [BackQuant]Volume Sampled Supertrend
A Supertrend that runs on a volume sampled price series instead of fixed time. New synthetic bars are only created after sufficient traded activity, which filters out low participation noise and makes the trend much easier to read and model.
Original Script Link
This indicator is built on top of my volume sampling engine. See the base implementation here:
Why Volume Sampling
Traditional charts print a bar every N minutes regardless of how active the tape is. During quiet periods you accumulate many small, low information bars that add noise and whipsaws to downstream signals.
Volume sampling replaces the clock with participation. A new synthetic bar is created only when a pre-set amount of volume accumulates (or, in Dollar Bars mode, when pricevolume reaches a dollar threshold). The result is a non-uniform time series that stretches in busy regimes and compresses in quiet regimes. This naturally:
filters dead time by skipping low volume chop;
standardizes the information content per bar, improving comparability across regimes;
stabilizes volatility estimates used inside banded indicators;
gives trend and breakout logic cleaner state transitions with fewer micro flips.
What this tool does
It builds a synthetic OHLCV stream from volume based buckets and then applies a Supertrend to that synthetic price. You are effectively running Supertrend on a participation clock rather than a wall clock.
Core Features
Sampling Engine - Choose Volume buckets or Dollar Bars . Thresholds can be dynamic from a rolling mean or median, or fixed by the user.
Synthetic Candles - Plots the volume sampled OHLC candles so you can visually compare against regular time candles.
Supertrend on Synthetic Price - ATR bands and direction are computed on the sampled series, not on time bars.
Adaptive Coloring - Candle colors can reflect side, intensity by volume, or a neutral scheme.
Research Panels - Table shows total samples, current bucket fill, threshold, bars-per-sample, and synthetic return stats.
Alerts - Long and Short triggers on Supertrend direction flips for the synthetic series.
How it works
Sampling
Pick Sampling Method = Volume or Dollar Bars.
Set the dynamic threshold via Rolling Lookback and Filter (Mean or Median), or enable Use Fixed and type a constant.
The script accumulates volume (or pricevolume) each time bar. When the bucket reaches the threshold, it finalizes one or more synthetic candles and resets accumulation.
Each synthetic candle stores its own OHLCV and is appended to the synthetic series used for all downstream logic.
Supertrend on the sampled stream
Choose Supertrend Source (Open, High, Low, Close, HLC3, HL2, OHLC4, HLCC4) derived from the synthetic candle.
Compute ATR over the synthetic series with ATR Period , then form upperBand = src + factorATR and lowerBand = src - factorATR .
Apply classic trailing band and direction rules to produce Supertrend and trend state.
Because bars only come when there is sufficient participation, band touches and flips tend to align with meaningful pushes, not idle prints.
Reading the display
Synthetic Volume Bars - The non-uniform candles that represent equal information buckets. Expect more candles during active sessions and fewer during lulls.
Volume Sampled Supertrend - The main line. Green when Trend is 1, red when Trend is -1.
Markers - Small dots appear when a new synthetic sample is created, useful for aligning activity cycles.
Time Bars Overlay (optional) - Plot regular time candles to compare how the synthetic stream compresses quiet chop.
Settings you will use most
Data Settings
Sampling Method - Volume or Dollar Bars.
Rolling Lookback and Filter - Controls the dynamic threshold. Median is robust to outliers, Mean is smoother.
Use Fixed and Fixed Threshold - Force a constant bucket size for consistent sampling across regimes.
Max Stored Samples - Ring buffer limit for performance.
Indicator Settings
SMA over last N samples - A moving average computed on the synthetic close series. Can be hidden for a cleaner layout.
Supertrend Source - Price field from the synthetic candle.
ATR Period and Factor - Standard Supertrend controls applied on the synthetic series.
Visuals and UI
Show Synthetic Bars - Turn synthetic candles on or off.
Candle Color Mode - Green/Red, Volume Intensity, Neutral, or Adaptive.
Mark new samples - Puts a dot when a bucket closes.
Show Time Bars - Overlay regular candles for comparison.
Paint candles according to Trend - Colors chart candles using current synthetic Supertrend direction.
Line Width , Colors , and Stats Table toggles.
Some workflow notes:
Trend Following
Set Sampling Method = Volume, Filter = Median, and a reasonable Rolling Lookback so busy regimes produce more samples.
Trade in the direction of the Volume Sampled Supertrend. Because flips require real participation, you tend to avoid micro whipsaws seen on time bars.
Use the synthetic SMA as a bias rail and trailing reference for partials or re-entries.
Breakout and Continuation
Watch for rapid clustering of new sample markers and a clean flip of the synthetic Supertrend.
The compression of quiet time and expansion in busy bursts often makes breakouts more legible than on uniform time charts.
Mean Reversion
In instruments that oscillate, faded moves against the synthetic Supertrend are easier to time when the bucket cadence slows and Supertrend flattens.
Combine with the synthetic SMA and return statistics in the table for sizing and expectation setting.
Stats table (top right)
Method and Total Samples - Sampling regime and current synthetic history length.
Current Vol or Dollar and Threshold - Live bucket fill versus the trigger.
Bars in Bucket and Avg Bars per Sample - How much time data each synthetic bar tends to compress.
Avg Return and Return StdDev - Simple research metrics over synthetic close-to-close changes.
Why this reduces noise
Time based bars treat a 5 minute print with 1 percent of average participation the same as one with 300 percent. Volume sampling equalizes bar information content. By advancing the bar only when sufficient activity occurs, you skip low quality intervals that add variance but little signal. For banded systems like Supertrend, this often means fewer false flips and cleaner runs.
Notes and tips
Use Dollar Bars on assets where nominal price varies widely over time or across symbols.
Median filter can resist single burst outliers when setting dynamic thresholds.
If you need a stable research baseline, set Use Fixed and keep the threshold constant across tests.
Enable Show Time Bars occasionally to sanity check what the synthetic stream is compressing or stretching.
Link again for reference
Original Volume Based Sampling engine:
Bottom line
When you let participation set the clock, your Supertrend reacts to meaningful flow instead of idle prints. The result is a cleaner state machine, fewer micro whipsaws, and a trend read that respects when the market is actually trading.
Extreme Candle Pattern Visualizer🟠 OVERVIEW
This indicator compares the current candle's percentage change against historical data, then highlights past candles with equal or bigger magnitude of movement. Also, for all the highlighted past candles, it tracks how far price extends before recovering to its starting point. It also provides statistical context through percentile rankings.
IN SHORT: Quickly spot similar price movements in the past and understand how unusual the current candle is using percentile rankings.
🟠 CORE CONCEPT
The indicator operates on two fundamental principles:
1. Statistical Rarity Detection
The script calculates the percentage change (open to close) of every candle within a user-defined lookback period and determines where the current candle ranks in this distribution. A candle closing at -9% might fall in the bottom 5th percentile, indicating it's more extreme than 95% of recent candles. This percentile ranking helps traders identify statistically unusual moves that often precede reversals or extended trends.
2. Recovery Path Mapping
Once extreme candles are identified (those matching or exceeding the current candle's magnitude), the indicator tracks their subsequent price action. For bearish candles, it measures how far price dropped before recovering back to the candle's opening price. For bullish candles, it tracks how high price climbed before returning to the open. This reveals whether extreme moves typically extend further or reverse quickly.
🟠 PRACTICAL APPLICATIONS
Mean Reversion Trading:
Candles in extreme percentiles (below 10% or above 90%) often signal oversold/overbought conditions. The recovery lines show typical extension distances, helping traders set profit targets for counter-trend entries.
Momentum Continuation:
When extreme candles show small recovery percentages before price reverses back, it suggests strong directional momentum that may continue.
Stop Loss Placement:
Historical recovery data reveals typical extension ranges after extreme moves, informing more precise stop loss positioning beyond noise but before major reversals.
Pattern Recognition:
By visualizing how similar historical extremes resolved, traders gain context for current price action rather than trading in isolation.
🟠 VISUAL ELEMENTS
Orange Circles: Mark historical candles with similar or greater magnitude to current candle
Red Lines: Track downward extensions after bearish extreme candles
Green Lines: Track upward extensions after bullish extreme candles
Percentage Labels: Show exact extension distance from candle close to extreme point
Percentile Label: Color-coded box displaying current candle's statistical ranking
Hollow Candles: Background rendering for clean chart presentation
🟠 ORIGINALITY
This indicator uniquely combines statistical percentile analysis with forward-looking recovery tracking. While many indicators identify extreme moves, few show what happened next across multiple historical instances simultaneously. The dual approach provides both the "how rare is this?" question (percentile) and "what typically happens after?" answer (recovery paths) in a single visual framework.
Michal D. Lagless Moving Average | MisinkoMasterThe 𝕸𝖎𝖈𝖍𝖆𝖑 𝕯. 𝕷𝖆𝖌𝖑𝖊𝖘𝖘 𝕸𝖔𝖛𝖎𝖓𝖌 𝕬𝖛𝖊𝖗𝖆𝖌𝖊 is my latest creation of a trend following tool, which is a bit different from the rest. By trying to de-lag the classical moving average, it gives you fast signals on changes in trend as fast as possible, keeping traders & investors always in check for potential risks they might want to avoid.
How does it work?
First we need to calculate lengths. The lengths are calcuted using a user defined input called the "Length Multiplier" and we of course need as well the length input too.
The indicator uses 10 lengths, 5 for an average price, 5 for median price.
The length for the average is the following:
length_2_avg = length_1_avg * length_multiplier
length_3_avg = length_2_avg * length_multiplier
...
and for the median lengths:
length_1_median = length_2_avg
length_2_median = length_3_avg
Here applies this rule
length_x_median < length_x_avg
This is intentional, and it is because the average is a little more reactive, while the median is a bit slower. To make up for the "slowness" of the median, we simple reduce the length of it a bit more than the average.
Now that we have our length we are ready to calculate averages and medians over their respective period. This is the a normal average from elementary school, nothing too fancy.
Now that we have all of them we match the pairs using another user defined input called "Median Weight" like so:
(Average_x * (2-median_weight) + Median_x * median_weight)/2
This gives more weight to the average (also due to the max value limit set to avoid breaking the fundational logic behind it).
After doing it to all the pairs we now average those pairs using another input called "Exponential Weight Multiplier".
The Exponential Weight Multiplier is used for weights which I will cover soon:
weight1 = weight
weight2 = weight * weight
weight3 = weight * weight * weight....
This is done until we have all the weights calculated
This gives exponentially more weight to the less lagging indicators, which is how we delag the indicator.
Then we sum all the pairs like so:
sum = pair1 * weight1 + pair2 * weight2 + pair3 * weight3 + pair4 * weight4 + pair5 * weight5
Then the sum is divided by the sum of weights, this results in us getting the final value.
Methodology & What is the actual point & how was it made?
I want to cover this one a bit deeper:
The methodology behind this was creating an indicator that would not be lagging, and would be able to avoid lag while not producing signals too often.
In many attempts in the first part, I tried using EMA, RMA, DEMA, TEMA, HMA, SMA and so on, but they were too noisy (except for SMA & RMA, but those had their flaws), so I tried the classical average taught in elementary school. This one worked better, but the noise was too high still after all this time. This made me include the median, which helped the noise, but made it far too lagging.
Here came the idea of making the median length lower and adding weights to counter the lag of the median, but it was still too lagging. This made me make the weights for lengths more exponential, while previously they were calculated using a little bit amplified sums that were alright, but nowhere near my desired result.
Using the new weights I got further, and after a bit of testing I was sattisfied with the results.
The logic for the trend was a big part in my development part, there were many I could think of, but not enough time to try them, so I stuck to the usual one, and I leave it up to YOU to beat my trend logic and get even better results.
Use Cases:
- Price/MA Crossovers
Simple, effective, useful
- Source for other indicators
This I tried myself, and it worked in a cool way, making the signals of for example RSI much smoother, so definitely try it out if you know how to code, or just simply put it in the source of the RSI.
- ROC
This trend logic stuck with me, I think you could find a way to make it good, but mainly for the people that can code in pine, trying out to combine the trend logic with ROC could work very well, do not sleep on it!
- Education
This concept is not really that complex, so for people looking for new ideas, inspiration, or just watching how trend following tools behave in general this is something that could benefit anyone, as the concept can be applied to ANYTHING, even the classical RSI, MACD, you could try even the Parabolic SAR, maybe STC or VZO, there is no limit to imagination.
- Strategy creation
Filtering this indicator with "and" conditions, or maybe even "or" or anything really could be very useful in a strategy that desires fast signals.
- Price Distance from bands
I noticed this while looking at past performance:
The stronger the trend the higher the distance from the Moving Average.
Final Notes
Watch out for mean reverting markets, as this is trend following you could get easily screwed in them.
Play around with this if it fits your desired outcome, you might find something I did not.
Hope you find it useful,
See you next time!
Stochastic %K Colored by VolumeDescription:
"Stochastic %K Colored by Volume is a technical indicator that combines the traditional Stochastic %K oscillator with volume-based coloring. It highlights periods of high, low, and neutral trading volume by changing the color of the %K line. Additionally, it identifies bullish and bearish divergences between price and the %K oscillator, helping traders spot potential reversals and trend changes. The indicator also includes key levels for overbought, oversold, and extreme zones to guide trading decisions."
Opening Range Fibonacci Extensions (ATR Adjusted)this script displays daily, weekly, or monthly range extensions as a function of ATR in a Fibonacci retracement
SJA WINFUT B3-10
INDICATOR FOR WINFUT B3 – 5-minute chart.
This indicator was designed to trade the Bovespa index futures contract (WINFUT) on the 5-minute chart.
It integrates technical analysis and macroeconomic context elements.
It combines several indicators in which the system calculates a score weighted by color and intensity for each indicator, generating a metric called “STRENGTH %,” which reflects the dominance of buyers (green), sellers (red), or sideways movement (orange) at the moment.
The calculation is adapted to market hours:
Between 9:00 a.m. and 9:59 a.m., it considers only the available indicators; after 10:00 a.m., it uses all data.
The panel displays real-time information, including divergences between strength and price, providing robust decision support for short-term operations on the mini index.
Buying trend.
The more green indicators (at the top of the panel) and dark blue indicators (at the bottom of the panel) and the higher the strength percentage, the greater the probability of buying.
Selling trend.
The more red indicators (at the top of the panel) and dark blue indicators (at the bottom of the panel) and the higher the strength percentage, the greater the probability of selling.
Translated with DeepL.com (free version)
Swing Data - SimplifiedThe swing data indicator by jfsrev but simplified. Thank you jfsrev for your work!
Aladin Pair Trading System v1Aladin Pair Trading System v1
What is This Indicator?
The Aladin Pair Trading System is a sophisticated tool designed to help traders identify profitable opportunities by comparing two related stocks that historically move together. Think of it as finding when one twin is running ahead or lagging behind the other - these moments often present trading opportunities as they tend to return to moving together.
Who Should Use This?
Beginners: Learn about statistical arbitrage and pair trading
Intermediate Traders: Execute mean-reversion strategies with confidence
Advanced Traders: Fine-tune parameters for optimal pair relationships
Portfolio Managers: Implement market-neutral strategies
💡 What is Pair Trading?
Imagine two ice cream shops next to each other. They usually have similar customer traffic because they're in the same area. If one day Shop A is packed while Shop B is empty, you might expect this imbalance to correct itself soon.
Pair trading works the same way:
You find two stocks that normally move together (like TCS and Infosys)
When one stock moves too far from the other, you trade expecting them to realign
You buy the lagging stock and sell the leading stock
When they come back together, you profit from both sides
Key Features
1. Z-Score Analysis
What it is: A statistical measure showing how far the price relationship has deviated from normal
What it means:
Z-Score near 0 = Normal relationship
Z-Score at +2 = Stock A is expensive relative to Stock B (Sell A, Buy B)
Z-Score at -2 = Stock A is cheap relative to Stock B (Buy A, Sell B)
2. Multiple Timeframe Analysis
Long-term Z-Score (300 bars): Shows the big picture trend
Short-term Z-Score (100 bars): Shows recent movements
Signal Z-Score (20 bars): Generates quick trading signals
3. Statistical Validation
The indicator checks if the pair is suitable for trading:
Correlation (must be > 0.7): Confirms the stocks move together
1.0 = Perfect positive correlation
0.7 = Strong correlation
Below 0.7 = Warning: pair may not be reliable
ADF P-Value (should be < 0.05): Tests if the relationship is stable
Low value = Good for pair trading
High value = Relationship may be random
Cointegration: Confirms long-term equilibrium relationship
YES = Pair tends to revert to mean
NO = Pair may drift apart permanently
Visual Elements Explained
Chart Zones (Color-Coded Areas)
Yellow Zone (-1.5 to +1.5)
Normal Zone: Relationship is stable
Action: Wait for better opportunities
Blue Zone (±1.5 to ±2.0)
Entry Zone: Deviation is significant
Action: Prepare for potential trades
Green/Red Zone (±2.0 to ±3.0)
Opportunity Zone: Strong deviation
Action: High-probability trade setups
Beyond ±3.0
Risk Limit: Extreme deviation
Action: Either maximum opportunity or structural break
Signal Arrows
Green Arrow Up (Buy A + Sell B):
Stock A is undervalued relative to B
Buy Stock A, Short Stock B
Red Arrow Down (Sell A + Buy B):
Stock A is overvalued relative to B
Sell Stock A, Buy Stock B
Settings Guide
Symbol Inputs
Pair Symbol (Symbol B): Choose the second stock to compare
Default: NSE:INFY (Infosys)
Example pairs: TCS/INFY, HDFCBANK/ICICIBANK, RELIANCE/ONGC
Z-Score Parameters
Long Z-Score Period (300): Historical context
Short Z-Score Period (100): Recent trend
Signal Period (20): Trading signals
Z-Score Threshold (2.0): Entry trigger level
Higher = Fewer but stronger signals
Lower = More frequent signals
Statistical Parameters
Correlation Period (240): How many bars to check correlation
Hurst Exponent Period (50): Measures mean-reversion tendency
Probability Lookback (100): Historical probability calculations
Trading Parameters
Entry Threshold (0.0): Minimum Z-score for entry
Risk Threshold (1.5): Warning level
Risk Limit (3.0): Maximum deviation to trade
How to Use (Step-by-Step)
Step 1: Choose Your Pair
Add the indicator to your chart (this becomes Stock A)
In settings, select Stock B (the comparison stock)
Choose stocks from the same sector for best results
Step 2: Verify Pair Quality
Check the Statistics Table (top-right corner):
✅ Correlation > 0.70 (Green = Good)
✅ ADF P-value < 0.05 (Green = Good)
✅ Cointegrated = YES (Green = Good)
If all three are green, the pair is suitable for trading!
Step 3: Wait for Signals
BUY SIGNAL (Green Arrow Up)
Z-Score crosses above -2.0
Action: Buy Stock A, Sell Stock B
Exit: When Z-Score returns to 0
SELL SIGNAL (Red Arrow Down)
Z-Score crosses below +2.0
Action: Sell Stock A, Buy Stock B
Exit: When Z-Score returns to 0
Step 4: Risk Management
Yellow Zone: Monitor only
Blue Zone: Prepare for entry
Green/Red Zone: Active trading zone
Beyond ±3.0: Maximum risk - use caution
⚠️ Important Warnings
Not All Pairs Work: Always check the statistics table first
Market Conditions Matter: Correlation can break during market stress
Use Stop Losses: Set stops at Z-Score ±3.5 or beyond
Position Sizing: Trade both legs with appropriate hedge ratios
Transaction Costs: Factor in brokerage and slippage for both stocks
Example Trade
Scenario: TCS vs INFOSYS
Correlation: 0.85 ✅
Z-Score: -2.3 (TCS is cheap vs INFY)
Action to be taken:
Buy 1lot of TCS Future
Sell 1lot of INFOSYS Future
Expected Outcome:
As Z-Score moves toward 0, TCS outperforms INFOSYS
Close both positions when Z-Score crosses 0
Profit from the convergence
Best Practices
Test Before Trading: Use paper trading first
Sector Focus: Choose pairs from the same industry
Monitor Statistics: Check correlation daily
Avoid News Events: Don't trade pairs during earnings/major news
Size Appropriately: Start small, scale with experience
Be Patient: Wait for high-quality setups (±2.0 or beyond)
What Makes This Indicator Unique?
Multi-timeframe Z-Score analysis: Three different perspectives
Statistical validation: Built-in correlation and cointegration tests
Visual risk zones: Easy-to-understand color-coded areas
Real-time statistics: Live pair quality monitoring
Beginner-friendly: Clear signals with educational zones
Technical Background
The indicator uses:
Engle-Granger Cointegration Test: Validates pair relationship
ADF (Augmented Dickey-Fuller) Test: Tests stationarity
Pearson Correlation: Measures linear relationship
Z-Score Normalization: Standardizes deviations
Log Returns: Handles price differences properly
Support & Community
For questions, suggestions, or to share your pair trading experiences:
Comment below the indicator
Share your successful pair combinations
Report any issues for quick fixes
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. Pair trading involves risk, including the risk of loss.
Always:
Do your own research
Understand the risks
Trade with money you can afford to lose
Consider consulting a financial advisor
📌 Quick Reference Card
Z-ScoreInterpretationAction-3.0 to -2.0A very cheap vs BStrong Buy A, Sell B-2.0 to -1.5A cheap vs BBuy A, Sell B-1.5 to +1.5Normal rangeHold/Wait+1.5 to +2.0A expensive vs BSell A, Buy B+2.0 to +3.0A very expensive vs BStrong Sell A, Buy B
Good Pair Statistics:
Correlation: > 0.70
ADF P-value: < 0.05
Cointegration: YES
Version: 1.0
Last Updated: 10th October 2025
Compatible: TradingView Pine Script v6
Happy Trading!
FOREXSOM Session Boxes (Local Time) — Asian, London & New YorkFOREXSOM Session Boxes (Local Time) highlights the three major Forex sessions — Asian, London, and New York — using your chart’s local timezone automatically.
This indicator helps traders visualize market structure, liquidity zones, and timing across global trading hours with accuracy and clarity.
Key Features
Automatically adjusts to your chart’s local timezone
Highlights Asian, London, and New York sessions with clean color zones
Works on all timeframes and asset classes
Ideal for Smart Money Concepts (SMC), ICT, and price action strategies
Helps identify range breakouts, session highs/lows, and liquidity grabs
How It Works
Each session box updates in real time to show the current range as the market develops.
The boxes reset at the end of each session, making it easy to compare volatility and liquidity shifts between regions.
Sessions (default times):
Asian: 17:00 – 03:00
London: 02:00 – 11:00
New York: 07:00 – 16:00
How to Use
Add the indicator to your chart.
Ensure your chart timezone matches your local time in chart settings.
Watch session ranges form and look for liquidity sweeps or breakouts between overlaps (London/New York).
Created by FOREXSOM
Empowering traders worldwide with precision-built tools for Smart Money and institutional trading education.
Date Marker📅 Date Marker
Date Marker is a simple, lightweight indicator that draws a single vertical line on a chosen date — ideal for quickly comparing how different charts looked at the same point in time.
Switch between symbols or timeframes, and the line automatically stays fixed at your selected date.
Perfect for studying market reactions to key events, earnings, announcements, or macro shifts.
Multi Brownian Forecast📊 Multi Brownian Forecast (Time-Adaptive, Probabilistic)
This indicator uses a sophisticated Geometric Brownian Motion (GBM) Monte Carlo simulation to project future price paths. It adapts to any chart timeframe and provides quantitative, multi-period probability signals.
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🧠 Core Mathematical Methodology
The model relies on GBM, which is a continuous-time stochastic process that models asset prices.
1. Historical Analysis (Drift & Volatility):
* The script first calculates Logarithmic Returns over a user-defined Historical Lookback (Hours) .
* Drift ($\mu$): Computed as the average of the log returns.
* Volatility ($\sigma$): Computed as the standard deviation of the log returns.
* These values are then time-adapted to an hourly step, compensating for the chart's current timeframe (e.g., 5-minute, 1-hour).
2. Monte Carlo Simulation:
* It runs a specified Number of Simulations (e.g., 1000).
* For each simulation, the price is stepped forward hourly using the GBM formula, which incorporates the calculated drift and a random shock drawn from a normal distribution (generated via the Box-Muller transform ).
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✨ Key Features
Probabilistic Quartile Forecast: Plots a dynamic "cone" of probability on the chart. It shows key price percentiles (Q1, Q2/Median, Q3, and Q4/Outer Bound) at the forecast's expiration, visualizing the expected range of price outcomes based on the simulations.
Multi-Period Probability Signals: This is the core signal feature. Users can define multiple, independent forecast periods (e.g., 4h, 16h, 48h) in a comma-separated list.
* For each period, a Probability Up and Probability Down is calculated based on hitting a custom Target Price Change (%) (e.g., 2%) at a certain confidence level given a simulation over the historical backlook.
* The probabilities are displayed in a chart table. The cell text turns white if the calculated probability exceeds the user-defined Signal Confidence (%) .
Conditional Fibonacci Retracement: Optionally displays a Fibonacci Retracement on the chart. This feature is only activated when one of the multi-period signals reaches its minimum confidence threshold, providing a contextual technical level when a probabilistic edge is found.
Smart Money Volume Activity [AlgoAlpha]🟠 OVERVIEW
This tool visualizes how Smart Money and Retail participants behave through lower-timeframe volume analysis. It detects volume spikes far beyond normal activity, classifies them as institutional or retail, and projects those zones as reactive levels. The script updates dynamically with each bar, showing when large players enter while tracking whether those events remain profitable. Each event is drawn as a horizontal line with bubble markers and summarized in a live P/L table comparing Smart Money versus Retail.
🟠 CONCEPTS
The core logic uses Z-score normalization on lower-timeframe volumes (like 5m inside a 1h chart). This lets the script detect statistically extreme bursts of buying or selling activity. It classifies each detected event as:
Smart Money — volume inside the candle body (suggesting hidden accumulation or distribution)
Retail — volume closing at bar extremes (suggesting chase entries or panic exits)
When new events appear, the script plots them as horizontal levels that persist until price interacts again. Each level acts as a potential reaction zone or liquidity footprint. The integrated P/L table then measures which class (Retail or Smart Money) is currently “winning” — comparing cumulative profitable versus losing volume.
🟠 FEATURES
Classifies flows into Smart Money or Retail based on candle-body context.
Displays live P/L comparison table for Smart vs Retail performance.
Alerts for each detected Smart or Retail buy/sell event.
🟠 USAGE
Setup : Add the script to any chart. Set Lower Timeframe Value (e.g., “5” for 5m) smaller than your main chart timeframe. The Period input controls how many bars are analyzed for the Z-score baseline. The Threshold (|Z|) decides how extreme a volume must be to plot a level.
Read the chart : Horizontal lines mark where heavy Smart or Retail volume occurred. Bright bubbles show the strongest events — their size reflects Z-score intensity. The on-chart table updates live: green cells show profitable flows, red cells show losing flows. A dominant green Smart Money row suggests institutions are currently controlling price.
See what others are doing :
Settings that matter : Raising Threshold (|Z|) filters noise, showing only large players. Increasing Period smooths results but reacts slower to new bursts. Use Show = “Both” for full comparison or isolate “Smart Money” / “Retail” to focus on one class.
PnL PortfolioThis indicator provides a comprehensive, real-time overview of your open trading portfolio directly on the chart. It allows you to track up to 20 different trading pairs simultaneously.
For each asset, simply input the Pair Symbol, Average Entry Price, and Position Quantity. The script securely fetches the current market price and dynamically calculates and displays a customizable table showing:
Real-Time Profit/Loss ($)
Percentage PnL (%)
Entry Price and Position Quantity
The table uses color coding to clearly highlight profitable (green) or losing (red) positions, and its location on the chart (top/bottom, left/right) is fully adjustable.
PnL TrackerThis script allows you to manually input the details for up to 64 unique positions in the settings, each requiring a Symbol, Average Cost, and Quantity (Qty).
Key Features:
Average Cost Line: Plots a horizontal line on the chart corresponding to your recorded Average Cost for the security currently being viewed.
Real-Time PnL Label: A dynamic label attached to the Average Cost line provides an instant summary of your PnL in both percentage and currency for the last visible bar.
Detailed PnL Box: Displays a consolidated, easy-to-read table in the bottom-right corner of the chart, clearly showing:
The Symbol and Quantity of your position.
Your Average Cost.
The current PnL in percentage (%) and base currency (e.g., USD, EUR).
Visibility Controls: Toggles in the settings allow you to show or hide the Average Cost line and the PnL summary box independently.
This tool is perfect for actively managing and visualizing your multi-asset portfolio positions without leaving your main trading chart. Simply enter your positions in the indicator's settings, and the script will automatically track the PnL for the symbol matching the current chart.
PnL PortfolioThis script allows you to input the details for up to 20 active positions across various trading pairs or markets. Stop manually calculating your trades—get instant, real-time feedback on your performance.
Key Features:
Multi-Pair Tracking: Monitor up to 20 unique symbols simultaneously.
Required Inputs: Easily define the Symbol, Entry Price, and Position Quantity (size) for each trade in the indicator settings.
Real-Time PnL: Instantly calculates and displays two critical metrics based on the current market price:
% PnL (Percentage Profit/Loss)
Absolute Profit/Loss (in currency)
Color-Coded Feedback: The PnL columns are color-coded (green/teal for profit, red/maroon for loss) for immediate visual confirmation of your trade health.
Customizable Layout: Choose where the dashboard table appears on your chart (top-left, top-right, bottom-left, or bottom-right) to keep your trading view clean.
This is an essential overlay for any trader managing multiple active positions and needing a consolidated, easy-to-read overview.
Delta Volume Heatmap🔥 Delta Volume Heatmap
The Delta Volume Heatmap visualizes the real-time strength of per-bar delta volume — highlighting the imbalance between buying and selling pressure.
Each column’s color intensity reflects how strong the delta volume deviates from its moving average and standard deviation.
🟩 Green tones = Buy-dominant activity (bullish imbalance)
🟥 Red tones = Sell-dominant activity (bearish imbalance)
This tool helps traders quickly identify:
Abnormal volume spikes
Absorption or exhaustion zones
Potential reversal or continuation signals
First Passage Time - Distribution AnalysisThe First Passage Time (FPT) Distribution Analysis indicator is a sophisticated probabilistic tool that answers one of the most critical questions in trading: "How long will it take for price to reach my target, and what are the odds of getting there first?"
Unlike traditional technical indicators that focus on what might happen, this indicator tells you when it's likely to happen.
Mathematical Foundation: First Passage Time Theory
What is First Passage Time?
First Passage Time (FPT) is a concept in stochastic processes that measures the time it takes for a random process to reach a specific threshold for the first time. Originally developed in physics and mathematics, FPT has applications in:
Quantitative Finance: Option pricing, risk management, and algorithmic trading
Neuroscience: Modeling neural firing patterns
Biology: Population dynamics and disease spread
Engineering: Reliability analysis and failure prediction
The Mathematics Behind It
This indicator uses Geometric Brownian Motion (GBM), the same stochastic model used in the Black-Scholes option pricing formula:
dS = μS dt + σS dW
Where:
S = Asset price
μ = Drift (trend component)
σ = Volatility (uncertainty component)
dW = Wiener process (random walk)
Through Monte Carlo simulation, the indicator runs 1,000+ price path simulations to statistically determine:
When each threshold (+X% or -X%) is likely to be hit
Which threshold is hit first (directional bias)
How often each scenario occurs (probability distribution)
🎯 How This Indicator Works
Core Algorithm Workflow:
Calculate Historical Statistics
Measures recent price volatility (standard deviation of log returns)
Calculates drift (average directional movement)
Annualizes these metrics for meaningful comparison
Run Monte Carlo Simulations
Generates 1,000+ random price paths based on historical behavior
Tracks when each path hits the upside (+X%) or downside (-X%) threshold
Records which threshold was hit first in each simulation
Aggregate Statistical Results
Calculates percentile distributions (10th, 25th, 50th, 75th, 90th)
Computes "first hit" probabilities (upside vs downside)
Determines average and median time-to-target
Visual Representation
Displays thresholds as horizontal lines
Shows gradient risk zones (purple-to-blue)
Provides comprehensive statistics table
📈 Use Cases
1. Options Trading
Selling Options: Determine if your strike price is likely to be hit before expiration
Buying Options: Estimate probability of reaching profit targets within your time window
Time Decay Management: Compare expected time-to-target vs theta decay
Example: You're considering selling a 30-day call option 5% out of the money. The indicator shows there's a 72% chance price hits +5% within 12 days. This tells you the trade has high assignment risk.
2. Swing Trading
Entry Timing: Wait for higher probability setups when directional bias is strong
Target Setting: Use median time-to-target to set realistic profit expectations
Stop Loss Placement: Understand probability of hitting your stop before target
Example: The indicator shows 85% upside probability with median time of 3.2 days. You can confidently enter long positions with appropriate position sizing.
3. Risk Management
Position Sizing: Larger positions when probability heavily favors one direction
Portfolio Allocation: Reduce exposure when probabilities are near 50/50 (high uncertainty)
Hedge Timing: Know when to add protective positions based on downside probability
Example: Indicator shows 55% upside vs 45% downside—nearly neutral. This signals high uncertainty, suggesting reduced position size or wait for better setup.
4. Market Regime Detection
Trending Markets: High directional bias (70%+ one direction)
Range-bound Markets: Balanced probabilities (45-55% both directions)
Volatility Regimes: Compare actual vs theoretical minimum time
Example: Consistent 90%+ bullish bias across multiple timeframes confirms strong uptrend—stay long and avoid counter-trend trades.
First Hit Rate (Most Important!)
Shows which threshold is likely to be hit FIRST:
Upside %: Probability of hitting upside target before downside
Downside %: Probability of hitting downside target before upside
These always sum to 100%
⚠️ Warning: If you see "Low Hit Rate" warning, increase this parameter!
Advanced Parameters
Drift Mode
Allows you to explore different scenarios:
Historical: Uses actual recent trend (default—most realistic)
Zero (Neutral): Assumes no trend, only volatility (symmetric probabilities)
50% Reduced: Dampens trend effect (conservative scenario)
Use Case: Switch to "Zero (Neutral)" to see what happens in a pure volatility environment, useful for range-bound markets.
Distribution Type
Percentile: Shows 10%, 25%, 50%, 75%, 90% levels (recommended for most users)
Sigma: Shows standard deviation levels (1σ, 2σ)—useful for statistical analysis
⚠️ Important Limitations & Best Practices
Limitations
Assumes GBM: Real markets have fat tails, jumps, and regime changes not captured by GBM
Historical Parameters: Uses recent volatility/drift—may not predict regime shifts
No Fundamental Events: Cannot predict earnings, news, or macro shocks
Computational: Runs only on last bar—doesn't give historical signals
Remember: Probabilities are not certainties. Use this indicator as part of a comprehensive trading plan with proper risk management.
Created by: Henrique Centieiro. feedback is more than welcome!
VWAP Deviation Oscillator [BackQuant]VWAP Deviation Oscillator
Introduction
The VWAP Deviation Oscillator turns VWAP context into a clean, tradeable oscillator that works across assets and sessions. It adapts to your workflow with four VWAP regimes plus two rolling modes, and three deviation metrics: Percent, Absolute, and Z-Score. Colored zones, optional standard deviation rails, and flexible plot styles make it fast to read for both trend following and mean reversion.
What it does
This tool measures how far price is from a chosen VWAP and expresses that gap as an oscillator. You can view the deviation as raw price units, percent, or standardized Z-Score. The plot can be a histogram or a line with optional fills and sigma bands, so you can quickly spot polarity shifts, overbought and oversold conditions, and strength of extension.
VWAP modes track a session VWAP that resets (4H, Daily, Weekly) or a rolling VWAP that updates continuously over a fixed number of bars or days.
Deviation modes let you choose the lens: Percent, Absolute, or Z-Score. Each highlights different aspects of stretch and mean pressure.
Visual encoding uses a 10-zone color palette to grade the magnitude of deviation on both sides of zero.
Volatility guards compute mode-specific sigma so thresholds are stable even when volatility compresses.
Why this works
VWAP is a high signal anchor used by institutions to gauge fair participation. Deviations around VWAP cluster in regimes: mild oscillations within a band, decisive pushes that signal imbalance, and standardized extremes that often precede either continuation or snapback. Expressing that distance as a single time series adds clarity: bias is the oscillator’s sign, risk context is its magnitude, and regime is the way it behaves around sigma lines.
How to use it
Trend following
Favor the side of the zero line. Bullish when the oscillator is above zero and making higher swing highs. Bearish when below zero and making lower swing lows. Use +1 sigma and +2 sigma in your mode as strength tiers. Pullbacks that hold above zero in uptrends, or below zero in downtrends, are often continuation entries.
Mean reversion
Fade stretched readings when structure supports it. Look for tests of +2 sigma to +3 sigma that fail to progress and roll back toward zero, or the mirror on the downside. Z-Score mode is best when you want standardized gates across assets. Percent mode is intuitive for intraday scalps where a given percent stretch tends to mean revert.
Session playbook
Use Daily or Weekly VWAP for intraday or swing context. Rolling modes help when the asset lacks clean session boundaries or when you want a continuous anchor that adapts to liquidity shifts.
Key settings
VWAP computation
VWAP Mode = 4 Hours, Daily, Weekly, Rolling (Bars), Rolling (Days). Session modes reset the VWAP when a new session begins. Rolling modes compute VWAP over a fixed trailing window.
Rolling (Lookback: Bars) controls the trailing bar count when using Rolling (Bars).
Rolling (Lookback: Days) converts days to bars at runtime and uses that trailing span.
Use Close instead of HLC3 switches the price reference. HLC3 is smoother. Close makes the anchor track settlement more tightly.
Deviation measurement
Deviation Mode
Percent : 100 * (Price / VWAP - 1). Good for uniform scaling across instruments.
Absolute : Price - VWAP. Good when price units themselves matter.
Z-Score : Standardizes the absolute residual by its own mean and standard deviation over Z/Std Window . Ideal for cross-asset comparability and regime studies.
Z/Std Window sets the mean and standard deviation window for Z-Score mode.
Volatility controls
Percent Mode Volatility Lookback estimates sigma for percent deviations.
Absolute Mode Volatility Lookback estimates sigma for absolute deviations.
Minimum Sigma Guard (pct pts) prevents the percent sigma from collapsing to near zero in extremely quiet markets.
Visualization
Plot Type = Histogram or Line. Histogram emphasizes impulse and polarity changes. Line emphasizes trend waves and divergences.
Positive Color / Negative Color define the palette for line mode. Histogram uses a 10-bucket gradient automatically.
Show Standard Deviations plots symmetric rails at ±1, ±2, ±3 sigma in the current mode’s units.
Fill Line Oscillator and Fill Opacity add a soft bias band around zero for line mode.
Line Width affects both the oscillator and the sigma rails.
Reading the zones
The oscillator’s color and height map deviation to nine graded buckets on each side of zero, with deeper greens above and deeper reds below. In Percent and Absolute modes, those buckets are scaled by their mode-specific sigma. In Z-Score mode the bucket edges are fixed at 0.5, 1.0, 2.0, and 2.8.
0 to +1 sigma weak positive bias, usually rotational.
+1 to +2 sigma constructive impulse. Pullbacks that hold above zero often continue.
+2 to +3 sigma strong expansion. Watch for either trend continuation or exhaustion tells.
Beyond +3 sigma statistical extreme. Requires structure to avoid fading too soon.
Mirror logic applies on the negative side.
Suggested workflows
Trend continuation checklist
Pick a session VWAP that matches your timeframe, for example Daily for intraday or Weekly for position trades.
Wait for the oscillator to hold the correct side of zero and for a sequence of higher swing lows in the oscillator (uptrend) or lower swing highs (downtrend).
Buy pullbacks that stabilize between zero and +1 sigma in an uptrend. Sell rallies that stabilize between zero and -1 sigma in a downtrend.
Use the next sigma band or a prior price swing as your target reference.
Mean reversion checklist
Switch to Z-Score mode for standardized thresholds.
Identify tests of ±2 sigma to ±3 sigma that fail to extend while price meets support or resistance.
Enter on a polarity change through the prior histogram bar or a small hook in line mode.
Fade back to zero or to the opposite inner band, then reassess.
Notes on the three modes
Percent is easy to reason about when you care about proportional stretch. It is well suited to intraday and multi-asset dashboards.
Absolute tracks cash distance from VWAP. This is useful when instruments have tight ticks and you plan risk in price units.
Z-Score standardizes the residual and is best for quant studies, cross-asset comparisons, and threshold research that must be scale invariant.
What the alerts can tell you
Polarity changes at zero can mark the start or end of a leg.
Crosses of ±1 sigma identify overbought or oversold in the current mode’s units.
Zone changes signal an upgrade or downgrade in deviation strength.
Troubleshooting and edge cases
If your instrument has long flat periods, keep Minimum Sigma Guard above zero in Percent mode so the rails do not vanish.
In Rolling modes, very short windows will respond quickly but can whip around. Session modes smooth this by resetting at well known boundaries.
If Z-Score looks erratic, increase Z/Std Window to stabilize the estimate of mean and sigma for the residual.
Final thoughts
VWAP is the anchor. The deviation oscillator is the narrative. By separating bias, magnitude, and regime into a simple stream you can execute faster and review cleaner. Pick the VWAP mode that matches your horizon, choose the deviation lens that matches your risk framework, and let the color graded zones guide your decisions.
Performance-based Asset Weighting(MTF)**Performance-Based Asset Weighting (MTF/Symbol Free Setting)**
#### Overview
This indicator is a tool that visualizes the relative strength of performance (price change rate) as “weight (allocation ratio)” for **four user-defined stocks**.
By setting any specified past point in time as the baseline (where all symbols are equally weighted at 25%), it aims to provide an intuitive understanding of which symbols outperformed others and attracted capital, or underperformed and saw capital outflows.
**【Default Settings and Application Scenario: Pension Fund Rebalancing Analysis】**
The default settings reference the basic portfolio of Japan's Government Pension Investment Fund (GPIF), configuring four major asset classes: domestic equities, foreign equities, domestic bonds, and foreign bonds. It is known that when market fluctuations cause deviations from this equal-weighted ratio, rebalancing occurs to restore the original ratio (selling assets whose weight has increased and buying assets whose weight has decreased).
Analyzing using this default setting can serve as a reference point for considering **“whether rebalancing sales (or purchases) by pension funds and similar entities are likely to occur in the future.”**
**【Important: Usage Notes】**
The weights shown by this indicator are **theoretical reference values** calculated solely based on performance from the specified start date. Even if large investors conduct significant rebalancing (asset buying/selling) during the period, those transactions themselves are not reflected in this chart's calculations.
Therefore, please understand that the actual portfolio ratios may differ. **Use this solely as a rough guideline. **
#### Key Features
* **Freely configure the 4 assets for analysis:** You can freely set any 4 assets (stocks, indices, currencies, cryptocurrencies, etc.) you wish to compare via the settings screen.
* **Performance-based weight calculation:** Rather than simple price composition ratios, it calculates each asset's price change since the specified start date as a “performance index” and displays each asset's proportion of the total sum.
* **Freely set analysis start date:** You can set any desired starting point for analysis, such as “after the XX shock” or “after earnings announcements,” using the calendar.
* **Multi-Timeframe (MTF) Support:** Independently of the timeframe displayed on the chart, you can freely select the timeframe (e.g., 1-hour, 4-hour, daily) used by the indicator for calculations.
#### Calculation Principle
This indicator calculates weights in the following three steps:
1. **Obtaining the Base Price**
Obtain the closing price for each of the four stocks on the user-set “Start Date for Weight Calculation.” This becomes the **base price** for analysis.
2. **Calculating the Performance Index**
Divide the current price of each stock by the **base price** obtained in Step 1 to calculate the “Performance Index”.
`Performance Index = Current Price ÷ Base Date Price`
This quantifies how many times the current performance has increased compared to the base date performance, which is set to “1”.
3. **Calculating Weights**
Sum the “Performance Indexes” of the four stocks. Then, calculate the percentage contribution of each stock's Performance Index to this total sum and plot it on the chart.
`Weight (%) = (Individual Performance Index ÷ Total Performance Index of 4 Stocks) × 100`
Using this logic, on the analysis start date, all stocks' performance indices are set to “1”, so the weights start equally at 25%.
#### Usage
* **Application Example 1: Market Sentiment Analysis (Using Default Settings)**
Analyze using the default asset classes. By observing the relative strength between “Equities” and “Bonds”, you can assess whether the market is risk-on or risk-off.
* **Application Example 2: Sector/Theme Strength Analysis**
Configure settings for groups like “Top 4 semiconductor stocks” or “4 GAFAM stocks.” Setting the start date to the beginning of the year or earnings season allows you to instantly compare which stocks within the same sector are performing best.
* **Application Example 3: Cryptocurrency Power Map Analysis**
By setting major cryptocurrencies like “BTC, ETH, SOL, ADA,” you can analyze which currencies are attracting market capital.
**【About Legend Display】**
Due to Pine Script specification constraints, the legend on the chart will display fixed names: **“Stock 1” to “Stock 4”. **
Please note that the symbol you entered for “Symbol 1” in the settings corresponds to the “Symbol 1” line on the chart.
#### Settings
* **Symbol 1 to Symbol 4:** Set the four symbols you wish to analyze.
* **Timeframe for Calculation:** Select the timeframe the indicator references when calculating weights.
* **Start Date for Weight Calculation:** This serves as the base date for comparing performance.
#### Disclaimer
This script is solely a tool to assist with market analysis and does not recommend buying or selling any specific financial instruments. Please make all final investment decisions at your own discretion.
-------------------------------------------------------------------------------------------------------------------
**Performance-based Asset Weighting（MTF・シンボル自由設定）**
#### 概要
このインジケーターは、**ユーザーが自由に設定した4つの銘柄**について、パフォーマンス（騰落率）の相対的な強さを「ウェイト（構成比率）」として可視化するツールです。
指定した過去の任意の時点を基準（全銘柄が均等な25%）として、そこからどの銘柄のパフォーマンスが他の銘柄を上回り、資金が向かっているのか、あるいは下回っているのかを直感的に把握することを目的としています。
**【デフォルト設定と活用シナリオ：年金基金のリバランス考察】**
デフォルト設定では、日本の年金積立金管理運用独立行政法人（GPIF）の基本ポートフォリオを参考に、主要4資産クラス（国内株式, 外国株式, 国内債券, 外国債券）が設定されています。市場の変動によってこの均等な比率に乖離が生じると、元の比率に戻すためのリバランス（比率が増えた資産を売り、減った資産を買う）が行われることが知られています。
このデフォルト設定で分析することで、**「今後、年金基金などによるリバランスの売り（買い）が発生する可能性があるか」を考察するための、一つの目安として利用できます。**
**【重要：利用上の注意点】**
このインジケーターが示すウェイトは、あくまで指定した開始日からのパフォーマンスのみを基に算出した**理論上の参考値**です。実際に大口投資家などが途中で大規模なリバランス（資産の売買）を行ったとしても、その取引自体はこのチャートの計算には反映されません。
そのため、実際のポートフォリオ比率とは異なる可能性があることをご理解の上、**あくまで大まかな目安としてご活用ください。**
#### 主な特徴
* **分析対象の4銘柄を自由に設定可能:** 設定画面から、比較したい4つの銘柄（株式、指数、為替、仮想通貨など）を自由に設定できます。
* **パフォーマンス基準のウェイト計算:** 単純な価格の構成比ではなく、指定した開始日からの各銘柄の騰落を「パフォーマンス指数」として算出し、その合計に占める各銘柄の割合を表示します。
* **分析開始日の自由な設定:** 「〇〇ショック後」「決算発表後」など、分析したい任意の時点をカレンダーから設定できます。
* **マルチタイムフレーム（MTF）対応:** チャートに表示している時間足とは別に、インジケーターが計算に使う時間足（1時間足、4時間足、日足など）を自由に選択できます。
#### 計算の原理
このインジケーターは、以下の3ステップでウェイトを算出しています。
1. **基準価格の取得**
ユーザーが設定した「ウェイト計算の開始日」における、4つの各銘柄の終値を取得し、これを分析の**基準価格**とします。
2. **パフォーマンス指数の算出**
現在の各銘柄の価格を、ステップ1で取得した**基準価格**で割ることで、「パフォーマンス指数」を算出します。
`パフォーマンス指数 = 現在の価格 ÷ 基準日の価格`
これにより、基準日のパフォーマンスを「1」とした場合、現在のパフォーマンスが何倍になっているかが数値化されます。
3. **ウェイトの算出**
4つの銘柄の「パフォーマンス指数」の合計値を算出します。そして、合計値に占める各銘柄のパフォーマンス指数の割合（%）を計算し、チャートに描画します。
`ウェイト (%) = (個別のパフォーマンス指数 ÷ 4銘柄のパフォーマンス指数の合計) × 100`
このロジックにより、分析開始日には全銘柄のパフォーマンス指数が「1」となるため、ウェイトは均等に25%からスタートします。
#### 使用方法
* **応用例1：市場のセンチメント分析（デフォルト設定利用）**
デフォルト設定の資産クラスで分析し、「株式」と「債券」の力関係を見ることで、市場がリスクオンなのかリスクオフなのかを判断する材料になります。
* **応用例2：セクター・テーマ別の強弱分析**
設定画面で、例えば「半導体関連の主要4銘柄」や「GAFAMの4銘柄」などを設定します。開始日を年初や決算時期に設定することで、同セクター内でどの銘柄が最もパフォーマンスが良いかを一目で比較できます。
* **応用例3：仮想通貨の勢力図分析**
「BTC, ETH, SOL, ADA」など、主要な仮想通貨を設定することで、市場の資金がどの通貨に向かっているのかを分析できます。
**【凡例の表示について】**
Pine Scriptの仕様上の制約により、チャート上の凡例は**「銘柄1」〜「銘柄4」という固定名で表示されます。**
お手数ですが、設定画面でご自身が「銘柄1」に入力したシンボルが、チャート上の「銘柄1」のラインに対応する、という形でご覧ください。
#### 設定項目
* **銘柄1〜銘柄4:** 分析したい4つのシンボルをそれぞれ設定します。
* **計算に使う時間足:** インジケーターがウェイトを計算する際に参照する時間足を選択します。
* **ウェイト計算の開始日:** パフォーマンスを比較する上での基準日となります。
#### 免責事項
このスクリプトはあくまで市場分析を補助するためのツールであり、特定の金融商品の売買を推奨するものではありません。投資の最終的な判断は、ご自身の責任において行ってください。
ATT Numbers Header (Movable)For anybody that trades with ATT (Advanced Time Technique) And can't remember the numbers and want's to have them on their chart at all time with full customizability as well this indicator is for you.
