Multi-Timeframe Session HighlighterWhat is the Multi-Timeframe Session Highlighter?
It’s a simple Pine Script indicator that paints two special candles on your chart, no matter what timeframe you’re looking at. Think of it as a highlighter pen for session starts and ends—can be used for session-based strategies or just keeping an eye on key turning points.
How it works:
Green Bar (Session Open): Marks the exact bar when your chosen higher-timeframe session kicks off. If you select “4H,” on the indicator, you’ll see green on every 4-hour open, even if you’re staring at a 15-minute chart.
Red Bar (Session Close): Highlights the very last lower-timeframe candle immediately before that session wraps up. So on a 1H chart with “Daily” selected, you’ll get a red band on the 23:00 hour before the new daily bar at midnight.
Customizable: Pick your own colors and transparency level to match your chart theme.
Getting started:
Add the indicator to your chart.
In the inputs, select the session timeframe (for example, “240” for 4H or “D” for daily).
Choose your favorite green and red shades.
That’s it.
Educational
Normalized DXY+Custom USD Index (DXY+) – Normalized Dollar Strength with Bitcoin, Gold, and Yuan.
This custom USD strength index replicates the structure of the official U.S. Dollar Index (DXY), while expanding it to include modern financial assets such as Bitcoin (BTC), Ethereum (ETH), gold (XAU), and the Chinese yuan (CNY).
Weights for the core fiat currencies (EUR, JPY, GBP, CAD, SEK, CHF) follow the official ICE DXY methodology. Additional components are weighted proportionally based on their estimated global economic influence.
The index is normalized from its initial valid data point, meaning it starts at 100 on the first day all asset inputs are available. From that point forward, it tracks the relative strength of the U.S. dollar against this expanded basket.
This provides a more comprehensive and modernized view of the dollar's strength—not only against traditional fiat currencies, but also in the context of rising decentralized assets and non-Western trade power.
RSI - PRIMARIO -mauricioofsousa
MGO Primary – Matriz Gráficos ON
The Blockchain of Trading applied to price behavior
The MGO Primary is the foundation of Matriz Gráficos ON — an advanced graphical methodology that transforms market movement into a logical, predictable, and objective sequence, inspired by blockchain architecture and periodic oscillatory phenomena.
This indicator replaces emotional candlestick reading with a mathematical interpretation of price blocks, cycles, and frequency. Its mission is to eliminate noise, anticipate reversals, and clearly show where capital is entering or exiting the market.
What MGO Primary detects:
Oscillatory phenomena that reveal the true behavior of orders in the book:
RPA – Breakout of Bullish Pivot
RPB – Breakout of Bearish Pivot
RBA – Sharp Bullish Breakout
RBB – Sharp Bearish Breakout
Rhythmic patterns that repeat in medium timeframes (especially on 12H and 4H)
Wave and block frequency, highlighting critical entry and exit zones
Validation through Primary and Secondary RSI, measuring the real strength behind movements
Who is this indicator for:
Traders seeking statistical clarity and visual logic
Operators who want to escape the subjectivity of candlesticks
Anyone who values technical precision with operational discipline
Recommended use:
Ideal timeframes: 12H (high precision) and 4H (moderate intensity)
Recommended assets: indices (e.g., NASDAQ), liquid stocks, and futures
Combine with: structured risk management and macro context analysis
Real-world performance:
The MGO12H achieved a 92% accuracy rate in 2025 on the NASDAQ, outperforming the average performance of major global quantitative strategies, with a net score of over 6,200 points for the year.
H2-25 cuts (bp)This custom TradingView indicator tracks and visualizes the implied pricing of Federal Reserve rate cuts in the market, specifically for the second half of 2025. It does so by comparing the price differences between two specific Fed funds futures contracts: one for June 2025 and one for December 2025. These contracts are traded on the Chicago Board of Trade (CBOT) and are a widely-used market gauge of the expected path of U.S. interest rates.
The indicator calculates the difference between the implied rates for June and December 2025, and then multiplies the result by 100 to express it in basis points (bps). Each 0.01 change in the spread corresponds to a 1-basis point change in expectations for future rate cuts. A positive value indicates that the market is pricing in a higher likelihood of one or more rate cuts in 2025, while a negative value suggests that the market expects the Fed to hold rates steady or even raise them.
The plot represents the difference in implied rate cuts (in basis points) between the two contracts:
June 2025 (ZQM2025): A contract representing the implied Fed funds rate for June 2025.
December 2025 (ZQZ2025): A contract representing the implied Fed funds rate for December 2025.
FXC Candle strategyFxc candle strategy for Gold scalping.
Scalping is a fast-paced trading strategy focusing on capturing small, frequent price movements for incremental profits. High market liquidity and tight spreads are needed for scalping, minimizing execution risks. Scalpers should trade during peak liquidity to avoid slippage
Custom USD IndexThis is a modernized, expanded version of the U.S. Dollar Index (DXY), designed to provide a more accurate representation of the dollar’s global strength in today’s diversified economy.
Unlike the traditional DXY, which excludes major players like China and entirely omits real-world stores of value, this custom index (DXY+) includes:
Fiat Currencies (78.3% total weight):
EUR, JPY, GBP, CAD, AUD, CHF, and CNY — equally weighted to reflect the global currency landscape.
Gold (17.5%):
Gold (XAUUSD) is included as a traditional reserve asset and inflation hedge, acknowledging its continued monetary relevance.
Cryptocurrencies (2.8% total weight):
Bitcoin (BTC) and Ethereum (ETH) represent the emerging digital monetary layer.
The index rises when the U.S. dollar strengthens relative to this blended basket, and falls when the dollar weakens against it. This is ideal for traders, economists, and macro analysts seeking a more inclusive and up-to-date measure of dollar performance.
Rollover Candles 23:00-00:00 UTC+1This indicator highlights the Forex Market Rollover candles during which the spreads get very high and some 'fake price action' occurs. By marking them orange you always know you are dealing with a rollover candle and these wicks/candles usually get taken out later on because there are no orders in these candles.
Optimal settings: The rollover takes only 1 hour, so put the visibility of the indicator on the 1 hour time frame and below (or just the 1h).
MTF PO3 Big Candle[RanaAlgo]The MTF PO3 Big Candle indicator displays a synthetic higher-timeframe candle (e.g., 1D or 4H) directly on your current chart for easier multi-timeframe analysis. It fetches OHLC data from the selected timeframe and plots a large, customizable candle with adjustable body thickness, optional wicks, and clear price labels. Dotted guide lines extend the high and low prices backward for reference, while an optional countdown timer shows the remaining time until the candle closes. The candle updates in real-time without repainting, helping traders track key levels from higher timeframes without switching charts. Colors, positioning, and visibility of elements can be fully customized.
Statistical Pairs Trading IndicatorZ-Score Stat Trading — Statistical Pairs Trading Indicator
📊🔗
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What is it?
Z-Score Stat Trading is a powerful indicator for statistical pairs trading and quantitative analysis of two correlated assets.
It calculates the Z-Score of the log-price spread between any two symbols you choose, providing both long-term and short-term Z-Score signals.
You’ll also see real-time correlation, volatility, spread, and the number of long/short signals in a handy on-chart table!
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How to Use 🛠️
1. Add the indicator to your chart.
2. Select two assets (symbols) to analyze in the settings.
3. Watch the Z-Score plots (blue and orange lines) and threshold levels (+2, -2 by default).
4. Check the info table for:
- Correlation
- Volatility
- Spread
- Number of long (NL) and short (NS) signals in the last 1000 bars
5. Set up alerts for signal generation or threshold crossings if you want to be notified automatically.
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Trading Strategy 💡
- This indicator is designed for statistical arbitrage (mean reversion) strategies.
- Long Signal (🟢):
When both Z-Scores drop below the negative threshold (e.g., -2), a long signal is generated.
→ Buy Symbol A, Sell Symbol B, expecting the spread to revert to the mean.
- Short Signal (🔴):
When both Z-Scores rise above the positive threshold (e.g., +2), a short signal is generated.
→ Sell Symbol A, Buy Symbol B, again expecting mean reversion.
- The info table helps you quickly assess the frequency of signals and the current statistical relationship between your chosen assets.
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Best Practices & Warnings 🚦
- Avoid high leverage! Pairs trading can be risky, especially during periods of divergence. Use conservative position sizing.
- Check for cointegration: Before using this indicator, make sure both assets are cointegrated or have a strong historical relationship. This increases the reliability of mean reversion signals.
- Check correlation: Only use asset pairs with a high correlation (preferably 0.8–0.9 or higher) for best results. The correlation value is shown in the info table.
- Scale in and out gradually: When entering or exiting positions, consider doing so in parts rather than all at once. This helps manage slippage and risk, especially in volatile markets.
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⚠️ Note on Performance:
This indicator may work a bit slowly, especially on large timeframes or long chart histories, because the calculation of NL and NS (number of long/short signals) is computationally intensive.
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Disclaimer ⚠️
This script is provided for educational and informational purposes only .
It is not financial advice or a recommendation to buy or sell any asset.
Use at your own risk. The author assumes no responsibility for any trading decisions or losses.
Auto Price Action SR Levels by Chaitu50cAuto Price Action SR Levels by Chaitu50c:
This is a session-based support and resistance indicator that identifies price levels based on actual candle activity, without relying on traditional indicators. It works by clustering open, high, low, or close values of past candles that frequently occur within a defined price range, making it a reliable price action-based tool for intraday traders.
The indicator calculates these levels at the start of each new trading session (based on NSE 09:15 time) and keeps them static throughout the session. This avoids unnecessary noise or flickering due to live price action, giving traders consistent zones to work with during the day.
FEATURES:
* Automatic detection of support and resistance levels based on candle price hits
* Cluster formation using high/low or open/close logic
* Static levels: calculated once per session and remain unchanged until the next session
* Adjustable settings for:
* Cluster range (in points)
* Number of lookback candles
* Line width
* Line color (default: black)
* Minimalist design for a clean chart experience
HOW IT WORKS:
The indicator looks back over a defined number of candles at the beginning of each session. It clusters prices that fall within a specified range (e.g., 250 points) and counts how many times they appear as open, high, low, or close values. If a price level is hit at least once (default), it is considered significant and a line is plotted.
Because clustering is done once per session, the lines do not shift during the session. This allows traders to base decisions on fixed, stable levels formed by prior market structure.
RECOMMENDED FOR:
* Intraday traders
* Price action traders
* Traders who prefer clean charts with logical SR zones
* Nifty, BankNifty, and stock-based day trading
Created by Chaitu50c for traders who rely on logic and structure, not signals.
Disclaimer:
This indicator is intended for educational and informational purposes only. It does not constitute financial advice or trading recommendations. Use at your own discretion and always manage risk responsibly.
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Let me know if you’d like to include use-case examples or screenshots before publishing.
Anchored VWAP by Time (Math by Thomas)📄 Description
This tool lets you plot an Anchored Volume Weighted Average Price (VWAP) starting from any specific date and time you choose. Unlike standard VWAPs that reset daily or weekly, this version gives you full control to track institutional pricing zones from precise anchor points—such as key swing highs/lows, market open, or news-driven candles.
It’s especially useful for price action and Smart Money Concepts (SMC) traders who track liquidity, fair value gaps (FVGs), and institutional zones.
🇮🇳 For NSE India Traders
You can anchor VWAP to Indian market open (e.g., 9:15 AM IST) or major events like RBI policy, earnings, or breakout candles.
The time input uses UTC by default, so for Indian Standard Time (IST), remember:
9:15 AM IST = 3:45 AM UTC
3:30 PM IST = 10:00 AM UTC
⚙️ How to Use
Add the indicator to your chart.
Open the settings panel.
Under “Anchor Start Time”, choose the date & time to begin the VWAP.
Use UTC format (adjust from IST if needed).
Customize the line color and thickness to suit your chart style.
The VWAP will begin plotting from that time forward.
🔎 Best Use Cases
Track VWAP from intraday range breakouts
Anchor from swing highs/lows to identify mean reversion zones
Combine with your FVGs, Order Blocks, or CHoCHs
Monitor VWAP reactions during key macro events or expiry days
🔧 Clean Design
No labels are used, keeping your chart clean.
Works on all timeframes (1min to Daily).
Designed for serious intraday & positional traders.
1M Scalp Setup – 2ndHi/2ndLo Breakout1M Scalp Setup – 2ndHi/2ndLo Breakout
This script is designed for 1-minute chart scalpers seeking high-probability intraday breakout setups based on early session price action. The strategy revolves around identifying the first high and low of the day, and then detecting the second breach (2nd high or 2nd low) to anticipate breakout entries.
🔍 Core Logic:
EMA Filter : A configurable EMA (default 8-period) is plotted for trend context.
1st High/Low Detection : Captures the very first high and low of each trading day.
2nd High/Low Markers : Identifies the second time price breaks the initial high or low, acting as a potential signal zone.
Breakout Signals :
A Buy Signal is triggered when price closes above the 2nd high.
A Sell Signal is triggered when price closes below the 2nd low.
Each signal is only triggered once per day to reduce noise and avoid overtrading.
🖌️ Visual Markers:
1stHi and 1stLo : Early session levels (red and green).
2ndHi and 2ndLo : Key breakout reference points (purple and blue).
B and S Labels : Buy and Sell triggers marked in real-time once breakouts occur.
⚙️ Inputs:
EMA Length (default: 8)
Customizable Colors for Buy/Sell signals and key markers
This tool is best used in fast-moving markets or during high-volume sessions. Combine with volume or higher-timeframe confirmation for improved accuracy.
GOD Complex Analysis Table By QTX Algo SystemsGOD Complex Analysis Table by QTX Algo Systems
Overview
The GOD Complex Analysis Table is a powerful visual companion for traders using the GOD Complex ecosystem. It displays detailed confluence scores for each trade type (Reversal Long, Reversal Short, Continuation Long, Continuation Short), offering a breakdown of required vs. extra signals, as well as multi-timeframe (MTF) scores and bias.
This tool is designed to help discretionary traders better understand how multiple conditions across timeframes align to support high-quality trade setups. It is not a standalone signal generator but rather an advanced diagnostic table that reveals the logic driving the GOD Complex entries.
How It Works
Each row in the table represents a trade type (e.g., Reversal Long), and includes:
Required Score – Based on must-have conditions for that trade type (e.g., oversold levels, statistical extremes, key momentum shifts).
Extra Score – Bonus confluence points from higher timeframe agreement, slope shifts, or secondary confirmation indicators.
Total Score – Combined Required + Extra score (max 200), useful for comparing relative strength across trade types.
Breakdown Columns – Show exactly which conditions are currently satisfied or missing, categorized as Required or Extra.
MTF Scores – Score-based analysis across 5m to 1M timeframes, highlighting how confluence changes with zoomed-out perspectives.
MTF Bias Row – Net bullish vs. bearish confluence per timeframe (positive = green, negative = red).
Indicators Used (All Proprietary QTX Tools)
VBM (Volatility-Based Momentum): Confirms directional trend and volatility environment.
VBSMI (Volatility-Based SMI): Adapts momentum oscillator based on market conditions and tilt logic.
SEA (Statistically Extreme Areas): Identifies when price reaches statistically rare volatility/range zones.
SPB (Statistical Price Bands): Tracks dynamically adjusted support/resistance based on percentile deviation.
COI (Continuation Opportunity Indicator): Detects pullback exhaustion and momentum re-acceleration opportunities.
Each trade type (Reversal or Continuation) is scored based on these tools across local and higher timeframes.
Key Table Features
🔍 Reversal Scoring Logic
Reversal trades must meet key oversold/overbought criteria (e.g., VBSMI extremes, SEA/SPB triggers) and be supported by trend weakness or exhaustion in the COI/VBM logic. High confluence across timeframes boosts the score.
📈 Continuation Scoring Logic
Continuations require strong trend alignment (VBM, COI), confirmation of momentum (VBSMI cross + slope), and lack of statistical extremes (no SEA/SPB hits). HTF agreement increases the score further.
🧠 Multi-Timeframe (MTF) Scoring
MTF scores are generated by evaluating each trade type’s core confluence across timeframes (e.g., 5m, 1H, 1D, etc.). This helps traders gauge how well a setup aligns with the broader market structure.
📊 Bias Coloring
The MTF Bias row shows net directional strength. Green = bullish bias. Red = bearish bias. Gray = neutral.
🔎 Factors Breakdown
View factors for each trade type. These factors explain which required and extra conditions are currently contributing or missing.
Customization Options
Table position (top/bottom, left/right)
Table size (small, medium, large)
Show/hide trade type rows
Enable/disable breakdown details
Toggle MTF Score section
Use Cases
Analyze high-confluence setups for discretionary trade planning
Cross-check live trades to understand setup quality
Confirm MTF alignment before entries
Study historical patterns to build intuition and strategy
Disclaimer
This indicator is for educational purposes only. It does not provide financial advice or trade recommendations. Always backtest and validate strategies before use.
VWAP + Candle-Rating SELL (close, robust)This multi‐timeframe setup first scans the 15-minute chart for strong bearish candles (body position in the bottom 40% of their range, i.e. rating 4 or 5) that close below the session VWAP. When it finds the first such “setup” of a trading period, it pins the low of that 15-minute candle as a trigger level and draws a persistent red line there. On the 5-minute chart, the strategy then waits for a similarly strong bearish candle (rating 4 or 5) to close below that marked low—at which point it emits a one‐time SELL signal. The trigger level remains in place (and additional sell signals are locked out) until the market “rescues” the price: a 15-minute bullish candle (rating 1 or 2) closing back above VWAP clears the old setup and allows the next valid bearish 15-minute candle to form a new trigger. This design ensures you only trade the most significant breakdowns after a clear bearish bias and avoids repeated signals until a genuine bullish reversal resets the system.
Levels & Flow📌 Overview
Levels & Flow is a visual trading tool that combines daily pivot levels with a dynamic EMA ribbon to help traders identify structure, momentum, and key decision zones in the market.
This script is designed for discretionary traders who rely on clean visual cues for intraday and swing trading strategies.
⚙️ Key Features
Daily Pivot, Support, and Resistance Lines
Automatically plots the daily pivot level based on the previous day’s OHLC data, along with calculated support and resistance levels.
Fibonacci Retracement Levels
Two dashed lines above and below the pivot represent the retracement of the pivot-resistance and pivot-support range, forming the boundaries of the “no-trade zone.”
No-Trade Zone (Shaded Box)
A gray shaded box between the two Fibonacci levels to visually mark a high-chop/low-conviction zone.
Trend-Based Candle Coloring (Current Day Only)
Candles are colored green if the close is above the pivot, red if below (only on the current trading day).
Bullish/Bearish Trend Label
A small table in the bottom-right corner displays “Bullish” or “Bearish” depending on whether price is above or below the pivot.
20-EMA Gradient Ribbon
A stack of 20 EMAs, each smoothed and color-coded from blue to green to reflect short- to long-term trend alignment.
Cumulative EMA with Adaptive Weighting
An intelligent moving average line that adjusts weight distribution among the 20 EMAs based on recent predictive accuracy using a learning rate and lookback period.
🧠 How It Works
📍 Levels
The script calculates daily pivot, resistance, and support levels using standard formulas:
Pivot = (High + Low + Close) / 3
Resistance = (2 × Pivot) – Low
Support = (2 × Pivot) – High
These levels update each day and extend 143 bars to the right.
📏 Fib Lines
Fib Up = Pivot + (Resistance – Pivot) × 0.382
Fib Down = Pivot – (Pivot – Support) × 0.382
These lines form the “no-trade zone” box.
📈 EMA Ribbon
20 EMAs starting from the user-defined Base Length, each incremented by 1
Each EMA is smoothed using the Smoothing Period
Color-coded from blue to green for intuitive visual flow
Filled between EMAs to visualize trend strength and alignment
🧠 Cumulative EMA Learning
Each EMA’s historical error is calculated over a Lookback Period
Lower-error EMAs receive higher weight; weights are normalized to sum to 1
The result is a cumulative EMA that adapts based on historical predictive power
🔧 User Inputs
Input
Base EMA Length: Sets the period for the shortest EMA (default: 20)
Smoothing Period: Smooths all EMAs and the cumulative EMA
Lookback for Learning: Number of bars to evaluate EMA prediction accuracy
Learning Rate: Adjusts how quickly weights shift in favor of more accurate EMAs
✅ How to Use It
Use the pivot level to define directional bias.
Watch for price breakouts above resistance or breakdowns below support to consider entry.
Avoid trading inside the shaded zone, where direction is less reliable.
Use the EMA ribbon gradient to confirm short/long alignment.
The cumulative EMA helps define trend with noise reduction.
🧪 Best For
Intraday traders who want to blend structure with flow
Swing traders needing clean daily levels with dynamic confirmation
Anyone looking to avoid choppy zones and improve visual clarity
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a trading recommendation. Always test scripts in simulation or on demo accounts before live use. Use at your own risk.
Crypto Portfolio vs BTC – Custom Blend TrackerThis tool tracks the performance of a custom-weighted crypto portfolio (SUI, BTC, SOL, DEEP, DOGE, LOFI, and Other) against BTC. Simply input your start date to anchor performance and compare your basket’s relative strength over time. Ideal for portfolio benchmarking, alt-season tracking, or macro trend validation.
Supports all timeframes. Based on BTC-relative returns (not USD). Open-source and customizable.
Market Map – AK_Trades📌 Market Map – AK_Trades
A real-time context engine designed to enhance your entries, exits, and overall trade confidence.
Built to complement any scalping or breakout strategy — or function as a reliable standalone guide.
🧠 What It Does:
📊 Detects market structure shifts
📍 Draws clean Support/Resistance zones (non-repainting)
🟥 Displays trend background shading + trend label
🚨 Flags breakouts, reversals, and invalidations
📈 Adds a real-time confidence ribbon for quick decision-making
🧭 LEGEND
Element Description
🟩🟥 Background Color Trend direction based on 21/50 EMA (green = uptrend, red = downtrend)
🟥🟩 Dashed Lines Dynamic support (green) and resistance (red) from pivot highs/lows
🔼 BREAKOUT ↑ Triggered only if price breaks key level + 0.25 ATR and volume confirms
🔽 BREAKDOWN ↓ Triggered only on valid breakdown with volume and trend alignment
🟡 Triangle (Up/Down) Reversal Warning – candle closes against current trend & EMAs
❌ Orange X Invalidation Marker – price reversed after breakout within 2 bars
📉 Confidence Strip (Green/Red) Shows strength/weakness of each bar based on trend and EMA proximity
🔤 UPTREND / DOWNTREND Trend label shown top-right of chart
⚠️ Notes:
Use this for bias confirmation, clean visual structure, and exit management.
Best paired with a high-conviction entry signal.
❗Disclaimer:
This script is for educational purposes only. It is not financial advice. Use at your own risk. The author assumes no responsibility for any trading losses incurred.
MFI Candle Trend🎯 Purpose:
The MFI Candle Trend is a custom TradingView indicator that transforms the Money Flow Index (MFI) into candle-style visuals using various smoothing and transformation techniques. Rather than displaying MFI as a line, this script generates synthetic candles from MFI values, helping traders visualize money flow trends, strength, and potential reversals with more clarity.
📌 Trend strength can be analyzed based on buying and selling pressures in the trend direction.
🧩 How It Works:
Calculates MFI values for open, high, low, and close prices.
Applies optional smoothing using the user-selected moving average (EMA, SMA, WMA, etc.).
Transforms the smoothed MFI data into synthetic candles using a selected method:
Normal: Uses raw MFI data
Heikin-Ashi: Applies HA transformation to MFI
Linear: Uses linear regression on MFI values
Rational Quadratic: Applies advanced rational quadratic filtering via an external kernel library
Colors candles based on MFI momentum:
Cyan: Strong positive MFI movement
Red: Strong negative MFI movement
⚙️ Key Inputs:
Method:
The type of smoothing method to apply to MFI
Options: None, EMA, SMA, SMMA (RMA), WMA, VWMA, HMA, Mode
Length:
Period for both the MFI and smoothing calculation
Candle:
Selects the transformation mode for generating synthetic candles
Options: Normal, Heikin-Ashi, Linear, Rational Quadratic
Rational Quadratic:
Adjusts the depth of smoothing for the Rational Quadratic filter (applies only if selected)
📊 Outputs:
Synthetic MFI Candlesticks:
Plotted using the smoothed and transformed MFI values.
Dynamic Coloring:
Cyan when MFI momentum is increasing
Red when MFI momentum is decreasing
Horizontal Lines:
80: Overbought zone
20: Oversold zone
🧠 Why Use This Indicator?
Unlike traditional MFI indicators that use a line plot, this tool gives traders:
A candle-based visualization of money flow momentum
Enhanced trend and reversal detection using color-coded MFI candles
A choice of smoothing filters and transformations for noise reduction
A powerful combination of momentum and structure-based analysis
To combine volume and price strength into a single chart element
❗Important Note:
This indicator is for educational and analytical purposes only. It does not constitute financial advice. Always use proper risk management and validate with additional tools or analysis.
JPMorgan G7 Volatility IndexThe JPMorgan G7 Volatility Index: Scientific Analysis and Professional Applications
Introduction
The JPMorgan G7 Volatility Index (G7VOL) represents a sophisticated metric for monitoring currency market volatility across major developed economies. This indicator functions as an approximation of JPMorgan's proprietary volatility indices, providing traders and investors with a normalized measurement of cross-currency volatility conditions (Clark, 2019).
Theoretical Foundation
Currency volatility is fundamentally defined as "the statistical measure of the dispersion of returns for a given security or market index" (Hull, 2018, p.127). In the context of G7 currencies, this volatility measurement becomes particularly significant due to the economic importance of these nations, which collectively represent more than 50% of global nominal GDP (IMF, 2022).
According to Menkhoff et al. (2012, p.685), "currency volatility serves as a global risk factor that affects expected returns across different asset classes." This finding underscores the importance of monitoring G7 currency volatility as a proxy for global financial conditions.
Methodology
The G7VOL indicator employs a multi-step calculation process:
Individual volatility calculation for seven major currency pairs using standard deviation normalized by price (Lo, 2002)
- Weighted-average combination of these volatilities to form a composite index
- Normalization against historical bands to create a standardized scale
- Visual representation through dynamic coloring that reflects current market conditions
The mathematical foundation follows the volatility calculation methodology proposed by Bollerslev et al. (2018):
Volatility = σ(returns) / price × 100
Where σ represents standard deviation calculated over a specified timeframe, typically 20 periods as recommended by the Bank for International Settlements (BIS, 2020).
Professional Applications
Professional traders and institutional investors employ the G7VOL indicator in several key ways:
1. Risk Management Signaling
According to research by Adrian and Brunnermeier (2016), elevated currency volatility often precedes broader market stress. When the G7VOL breaches its high volatility threshold (typically 1.5 times the 100-period average), portfolio managers frequently reduce risk exposure across asset classes. As noted by Borio (2019, p.17), "currency volatility spikes have historically preceded equity market corrections by 2-7 trading days."
2. Counter-Cyclical Investment Strategy
Low G7 volatility periods (readings below the lower band) tend to coincide with what Shin (2017) describes as "risk-on" environments. Professional investors often use these signals to increase allocations to higher-beta assets and emerging markets. Campbell et al. (2021) found that G7 volatility in the lowest quintile historically preceded emerging market outperformance by an average of 3.7% over subsequent quarters.
3. Regime Identification
The normalized volatility framework enables identification of distinct market regimes:
- Readings above 1.0: Crisis/high volatility regime
- Readings between -0.5 and 0.5: Normal volatility regime
- Readings below -1.0: Unusually calm markets
According to Rey (2015), these regimes have significant implications for global monetary policy transmission mechanisms and cross-border capital flows.
Interpretation and Trading Applications
G7 currency volatility serves as a barometer for global financial conditions due to these currencies' centrality in international trade and reserve status. As noted by Gagnon and Ihrig (2021, p.423), "G7 currency volatility captures both trade-related uncertainty and broader financial market risk appetites."
Professional traders apply this indicator in multiple contexts:
- Leading indicator: Research from the Federal Reserve Board (Powell, 2020) suggests G7 volatility often leads VIX movements by 1-3 days, providing advance warning of broader market volatility.
- Correlation shifts: During periods of elevated G7 volatility, cross-asset correlations typically increase what Brunnermeier and Pedersen (2009) term "correlation breakdown during stress periods." This phenomenon informs portfolio diversification strategies.
- Carry trade timing: Currency carry strategies perform best during low volatility regimes as documented by Lustig et al. (2011). The G7VOL indicator provides objective thresholds for initiating or exiting such positions.
References
Adrian, T. and Brunnermeier, M.K. (2016) 'CoVaR', American Economic Review, 106(7), pp.1705-1741.
Bank for International Settlements (2020) Monitoring Volatility in Foreign Exchange Markets. BIS Quarterly Review, December 2020.
Bollerslev, T., Patton, A.J. and Quaedvlieg, R. (2018) 'Modeling and forecasting (un)reliable realized volatilities', Journal of Econometrics, 204(1), pp.112-130.
Borio, C. (2019) 'Monetary policy in the grip of a pincer movement', BIS Working Papers, No. 706.
Brunnermeier, M.K. and Pedersen, L.H. (2009) 'Market liquidity and funding liquidity', Review of Financial Studies, 22(6), pp.2201-2238.
Campbell, J.Y., Sunderam, A. and Viceira, L.M. (2021) 'Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds', Critical Finance Review, 10(2), pp.303-336.
Clark, J. (2019) 'Currency Volatility and Macro Fundamentals', JPMorgan Global FX Research Quarterly, Fall 2019.
Gagnon, J.E. and Ihrig, J. (2021) 'What drives foreign exchange markets?', International Finance, 24(3), pp.414-428.
Hull, J.C. (2018) Options, Futures, and Other Derivatives. 10th edn. London: Pearson.
International Monetary Fund (2022) World Economic Outlook Database. Washington, DC: IMF.
Lo, A.W. (2002) 'The statistics of Sharpe ratios', Financial Analysts Journal, 58(4), pp.36-52.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011) 'Common risk factors in currency markets', Review of Financial Studies, 24(11), pp.3731-3777.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012) 'Carry trades and global foreign exchange volatility', Journal of Finance, 67(2), pp.681-718.
Powell, J. (2020) Monetary Policy and Price Stability. Speech at Jackson Hole Economic Symposium, August 27, 2020.
Rey, H. (2015) 'Dilemma not trilemma: The global financial cycle and monetary policy independence', NBER Working Paper No. 21162.
Shin, H.S. (2017) 'The bank/capital markets nexus goes global', Bank for International Settlements Speech, January 15, 2017.
Support & Resistance ZonesAdvanced Support & Resistance Detection Algorithm
This indicator identifies meaningful price levels by analyzing market structure using a proprietary statistical approach. Unlike traditional methods that rely on simple swing highs/lows or moving averages, this system dynamically detects zones where price has shown consistent interaction, revealing true areas of supply and demand.
Core Methodology
Price Data Aggregation
Collects highs and lows over a configurable lookback period.
Normalizes price data to account for volatility, ensuring levels remain relevant across different market conditions.
Statistical Significance Filtering
Rejection of random noise: Eliminates insignificant price fluctuations using adaptive thresholds.
Volume-weighted analysis (implied): Stronger reactions at certain price levels are given higher priority, even if volume data is unavailable.
Dynamic Level Extraction
Density-based S/R Zones: Instead of fixed swing points, the algorithm identifies zones where price has repeatedly consolidated.
Time decay adjustment: Recent price action has more influence, ensuring levels adapt to evolving market structure.
Strength Quantification
Each level is assigned a confidence score based on:
Touch frequency: How often price revisited the zone.
Reaction intensity: The magnitude of bounces/rejections.
Time relevance: Whether the level remains active or has been broken decisively.
Adaptive Level Merging & Pruning
Proximity-based merging: If two levels are too close (within a volatility-adjusted threshold), they combine into one stronger zone.
Decay mechanism: Old, untested levels fade away if price no longer respects them.
Why This Approach Works Better Than Traditional Methods
✅ No subjective drawing required – Levels are generated mathematically, removing human bias.
✅ Self-adjusting sensitivity – Works equally well on slow and fast-moving markets.
✅ Focuses on statistically meaningful zones – Avoids false signals from random noise.
✅ Non-repainting & real-time – Levels only update when new data confirms their validity.
How Traders Can Use These Levels
Support/Resistance Trading: Fade bounces off strong levels or trade breakouts with confirmation.
Confluence with Other Indicators: Combine with RSI, MACD, or volume profiles for higher-probability entries.
Stop Placement: Place stops just beyond key levels to avoid premature exits.
Technical Notes (For Advanced Users)
The algorithm avoids overfitting by dynamically adjusting zones sensitivity based on market conditions.
Unlike fixed pivot points, these levels adapt to trends, making them useful in both ranging and trending markets.
The strength percentage helps filter out weak levels—only trade those with a high score for better accuracy.
Note: Script takes some time to load.
Institutional Volume Toolkit & Automatic Wick @ MaxMaserati 2.0Institutional Volume Toolkit with Automatic Wick Analyzer @ MaxMaserati 2.0
Overview
This advanced technical indicator combines institutional volume analysis with precise wick detection to reveal hidden market dynamics driving price action. Designed for serious traders who recognize that volume is the fuel behind meaningful price movements, this toolkit identifies key support/resistance zones created by significant institutional activity while simultaneously analyzing candlestick wicks to detect buyer/seller dominance.
Key Features
Volume Defense Levels
Institutional Support/Resistance Detection: Automatically identifies price levels where significant volume occurred during swing points
Dynamic Strength Visualization: Defense level thickness varies based on relative volume importance
Volume % Labels: Clearly shows the strength of each defense level relative to average volume
Adaptive Coloring: Green for buyer defense (support), red for seller defense (resistance)
Wick Analysis System
Smart Wick Detection: Analyzes upper and lower wicks to identify real-time buying and selling pressure
Automatic Dominance Calculation: Determines whether buyers or sellers are dominant at key price points
Multi-Line Analysis: Displays 3-line sets at critical wick areas for precise entry/exit signals
Volume-Enhanced Signals: Combines wick analysis with volume comparison for stronger signals
Trading Applications
Support/Resistance Trading: Identify high-probability bounce or reversal zones created by institutional volume
Break & Retest Strategies: Trade with confidence when price retests broken volume levels
Divergence Detection: Spot divergences between price action and institutional commitment
Swing Trading Setup: Perfect for identifying optimal swing trade entries with favorable risk/reward
Trend Confirmation: Validate trend strength by assessing institutional participation
Setup Guidelines
Volume Defense Levels: Customize volume lookback and threshold to match your timeframe
Wick Analysis: Set filters (wick-to-body ratio, volume percentage, dominance) to eliminate noise
Visual Settings: Adjust colors and label sizes to match your chart preferences
Combine Timeframes: Use on higher timeframes for strategic levels, lower timeframes for entries
This indicator combines sophisticated volume analysis with candlestick psychology, providing a complete toolkit for identifying institutional activity where smart money is actively defending price levels. By revealing the hidden forces driving market movements, it offers a significant edge for traders seeking to align their positions with institutional flow.
Bloomberg Financial Conditions Index (Proxy)The Bloomberg Financial Conditions Index (BFCI): A Proxy Implementation
Financial conditions indices (FCIs) have become essential tools for economists, policymakers, and market participants seeking to quantify and monitor the overall state of financial markets. Among these measures, the Bloomberg Financial Conditions Index (BFCI) has emerged as a particularly influential metric. Originally developed by Bloomberg L.P., the BFCI provides a comprehensive assessment of stress or ease in financial markets by aggregating various market-based indicators into a single, standardized value (Hatzius et al., 2010).
The original Bloomberg Financial Conditions Index synthesizes approximately 50 different financial market variables, including money market indicators, bond market spreads, equity market valuations, and volatility measures. These variables are normalized using a Z-score methodology, weighted according to their relative importance to overall financial conditions, and then aggregated to produce a composite index (Carlson et al., 2014). The resulting measure is centered around zero, with positive values indicating accommodative financial conditions and negative values representing tighter conditions relative to historical norms.
As Angelopoulou et al. (2014) note, financial conditions indices like the BFCI serve as forward-looking indicators that can signal potential economic developments before they manifest in traditional macroeconomic data. Research by Adrian et al. (2019) demonstrates that deteriorating financial conditions, as measured by indices such as the BFCI, often precede economic downturns by several months, making these indices valuable tools for predicting changes in economic activity.
Proxy Implementation Approach
The implementation presented in this Pine Script indicator represents a proxy of the original Bloomberg Financial Conditions Index, attempting to capture its essential features while acknowledging several significant constraints. Most critically, while the original BFCI incorporates approximately 50 financial variables, this proxy version utilizes only six key market components due to data accessibility limitations within the TradingView platform.
These components include:
Equity market performance (using SPY as a proxy for S&P 500)
Bond market yields (using TLT as a proxy for 20+ year Treasury yields)
Credit spreads (using the ratio between LQD and HYG as a proxy for investment-grade to high-yield spreads)
Market volatility (using VIX directly)
Short-term liquidity conditions (using SHY relative to equity prices as a proxy)
Each component is transformed into a Z-score based on log returns, weighted according to approximated importance (with weights derived from literature on financial conditions indices by Brave and Butters, 2011), and aggregated into a composite measure.
Differences from the Original BFCI
The methodology employed in this proxy differs from the original BFCI in several important ways. First, the variable selection is necessarily limited compared to Bloomberg's comprehensive approach. Second, the proxy relies on ETFs and publicly available indices rather than direct market rates and spreads used in the original. Third, the weighting scheme, while informed by academic literature, is simplified compared to Bloomberg's proprietary methodology, which may employ more sophisticated statistical techniques such as principal component analysis (Kliesen et al., 2012).
These differences mean that while the proxy BFCI captures the general direction and magnitude of financial conditions, it may not perfectly replicate the precision or sensitivity of the original index. As Aramonte et al. (2013) suggest, simplified proxies of financial conditions indices typically capture broad movements in financial conditions but may miss nuanced shifts in specific market segments that more comprehensive indices detect.
Practical Applications and Limitations
Despite these limitations, research by Arregui et al. (2018) indicates that even simplified financial conditions indices constructed from a limited set of variables can provide valuable signals about market stress and future economic activity. The proxy BFCI implemented here still offers significant insight into the relative ease or tightness of financial conditions, particularly during periods of market stress when correlations among financial variables tend to increase (Rey, 2015).
In practical applications, users should interpret this proxy BFCI as a directional indicator rather than an exact replication of Bloomberg's proprietary index. When the index moves substantially into negative territory, it suggests deteriorating financial conditions that may precede economic weakness. Conversely, strongly positive readings indicate unusually accommodative financial conditions that might support economic expansion but potentially also signal excessive risk-taking behavior in markets (López-Salido et al., 2017).
The visual implementation employs a color gradient system that enhances interpretation, with blue representing neutral conditions, green indicating accommodative conditions, and red signaling tightening conditions—a design choice informed by research on optimal data visualization in financial contexts (Few, 2009).
References
Adrian, T., Boyarchenko, N. and Giannone, D. (2019) 'Vulnerable Growth', American Economic Review, 109(4), pp. 1263-1289.
Angelopoulou, E., Balfoussia, H. and Gibson, H. (2014) 'Building a financial conditions index for the euro area and selected euro area countries: what does it tell us about the crisis?', Economic Modelling, 38, pp. 392-403.
Aramonte, S., Rosen, S. and Schindler, J. (2013) 'Assessing and Combining Financial Conditions Indexes', Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C.
Arregui, N., Elekdag, S., Gelos, G., Lafarguette, R. and Seneviratne, D. (2018) 'Can Countries Manage Their Financial Conditions Amid Globalization?', IMF Working Paper No. 18/15.
Brave, S. and Butters, R. (2011) 'Monitoring financial stability: A financial conditions index approach', Economic Perspectives, Federal Reserve Bank of Chicago, 35(1), pp. 22-43.
Carlson, M., Lewis, K. and Nelson, W. (2014) 'Using policy intervention to identify financial stress', International Journal of Finance & Economics, 19(1), pp. 59-72.
Few, S. (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, Oakland, CA.
Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K. and Watson, M. (2010) 'Financial Conditions Indexes: A Fresh Look after the Financial Crisis', NBER Working Paper No. 16150.
Kliesen, K., Owyang, M. and Vermann, E. (2012) 'Disentangling Diverse Measures: A Survey of Financial Stress Indexes', Federal Reserve Bank of St. Louis Review, 94(5), pp. 369-397.
López-Salido, D., Stein, J. and Zakrajšek, E. (2017) 'Credit-Market Sentiment and the Business Cycle', The Quarterly Journal of Economics, 132(3), pp. 1373-1426.
Rey, H. (2015) 'Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence', NBER Working Paper No. 21162.
BTC/ETH Lot Size for Dexin - V1.0
█ Overview - This tool is specifically tailored for Delta Exchange India’s users.
I use this interactive tool before taking a position in the BTC’s futures perpetual market . With only 3 mouse clicks, I see all the necessary information, whether a Long or Short position.
A visual of Liquidation Price Level, Stop Loss Price Level, Entry Price Level, Break-even Price Level, and Take Profit Price Level can be immediately seen.
On the top right corner of the chart, which Leverage is to be used, No. of Lots to be taken, expected Profit amount, Loss amount, Brokerage Fees, Risk to Reward Ratios, and Return on Investment are shown, excluding brokerage travel. To get the correct answer in the table that suits your account and risk-taking appetite, the user needs to enter the account balance and Risk per trade.
It also does live tracking of the position, and alerts can be configured too.
█ How to Use
Load the indicator on an active chart.
In the Trading View, ensure that the Magnets is enabled (on the left panel). This will precisely select the price levels you want to choose from OHLC for a candle.
When you first load the tool on the bottom of the chart, you will see a blue box with text in white color guiding you on what you need to do.
Before the first click, the box shall prompt “On the signal candle, set the entry level, where the position would be executed”.
Once the entry price level is selected, the next prompt in the blue box shall be “Set the stop loss level where the position would be exited”. Thus, you need to click the stop loss price level.
Now that the two clicks of Entry and Stop Loss are already done, the last remaining is for the take profit. The last prompt shall be “Set the profit level where the position would be exited”. Therefore, you need to select your take-profit level
Finally, when all three points are selected, the tool shall draw trade zones.
The tool automatically determines whether it is a Long Position or Short Position from the Stop loss and take-profit price levels concerning the entry price level
If the take profit level is above the entry price, the stop must be below, and vice versa; otherwise, an error occurs.
You can change levels by dragging the handles that appear when you select the indicator, or by entering new values in the settings.
Once the position tool is on a chart, it will appear at the same levels on all symbols you use.
If you select the position tool on your chart and delete it, this will also delete the indicator from the chart. You will need to re-add it if you want to draw another position tool. You can add multiple instances of the indicator if you need a position tool on more than one of your charts.
█ Features
Display
The tool displays the following information as graphical visuals
The Liquidation to Stop Loss, Stop Loss to Entry, Entry to Break-even, and Entry to Take Profit zones shall be initiated from the entry candle point.
If you want to be from the candle that crossed the level at a different time from the entry candle, you may go to the settings and adjust the time accordingly. Please note that the time interval is 15 minutes, so at times you may not be able to see the graphical display; however, once the 15-minute time interval is over, you will see the graphical display on the chart.
The tool displays the following information in a tabulated manner
The first row indicates the Leverage that is best suited. The leverage selection by default is greater than or equal to the risk distance.
The second row indicates the number of lots that is computed in relation to the account balance, Risk appetite, Entry price, and Stop Loss price.
The third row indicates estimated profit considering taker's fees and is computed in relation to the number of Lots, Entry price, and Take-Profit price.
The fourth row indicates estimated loss considering taker's fees and is computed in relation to the number of Lots, Entry price, and Stop Loss price.
The fifth row indicates the actual Risk to Reward Ratio, ignoring the travel that pertains to fees.
The sixth row indicates actual Return on Investment, ignoring the travel that pertains to fees.
The intent is to allow the user to make an informed decision prior to taking a position by seeing “$/Rs.” or “% of R O I” or “R : R”.
In case the user wants to know beforehand what the expected charges are that need to be borne before taking a position, that too is made available in the seventh and eighth rows. Both sides' charges are made available for ready reference, irrespective of the outcome of the trade, the user knows the consequences beforehand.
█ Settings
'Trade Sizing'
The tool's input menu is divided into various parts. The first part is 'Trade Sizing'. The user needs to key in the exact number that appears in the Delta Exchange India account against 'Account Balance ($)'. The second thing the user needs to do is key in the 'Risk per Trade'. By default,t it is set to 0.25 and has a default stop change of 0.25. Alternatively, the user can key in any number (Whole number or Rational number) within 100 if that suits their risk management criterion.
'Trade Levels'
Allows users to manually set the Entry, Time, Stop Loss, and Take Profit Price Levels.
'Aggressive Mode Selection'
As the Liquidation zone is shown on the chart, if the user feels that the liquidation price level is too far from the stop loss, this option of 'Use Aggressive Leverage?' allows to increase the leverage, thus reducing the investment amount and in return increasing the Return on Investment %.
The second option in this category is 'Compute Lots based on invested Margin?' itself is self-explanatory, and thus the tabulated data shall be populating the data based on the number entered by the user against 'Margin to be invested ($)'. It is for the user to ensure that the estimated outcomes are within their risk management criterion.
'Conversion & Charges'
If the user wants to see the Profit, Loss, and Fees amount in 'Rs.', all that needs to be done is simply enable the 'Show P&L in Rs.?' The conversion shall take place considering 1 USD = 85 Rs. Same as that carried out by Delta Exchange India.
If the user wants to see the Brokerage Fees, all that needs to be done is simply enable the 'Show Brokerage Fees?'. On enabling this, the table shall show Profitable Trade's (PT) Fees and Lost Trade's (LT) Fees irrespective of the outcome of the trade. The intent is to allow the user to make informed decisions to avoid regrets or surprises at the end of the trade.
'Table'
The division of the input section is related to table position, font size and colors for text and background.
█ Alerts
Alerts can be configured by clicking 'More' (the three dots that appear when you place the cursor on the indicator title that appears on the top left corner of the chart). Alternatively, one can configure alerts by right-clicking on either of the two price levels - Stop Loss price level or Take Profit Price level. Upon right clicking, a window shall appear and the topmost line on that window shall display 'Add alert on ……….' The user can thus put alerts on either of the key levels, such as Stop Loss, Take Profit, and Break Even, or on all of them one by one.
RSI - SECUNDARIO - mauricioofsousaSecondary RSI – MGO
Reading the rhythm behind the price action
The Secondary RSI is a specialized oscillator developed as part of the MGO (Matriz Gráficos ON) methodology. It works as a refined strength filter, designed to complement traditional RSI readings by isolating the true internal rhythm of price action and reducing the influence of market noise.
While the standard RSI measures price momentum, the Secondary RSI focuses on identifying breaks in oscillatory balance—the moments when the market shifts from accumulation to distribution or from compression to expansion.
🎯 What the Secondary RSI highlights:
Internal imbalances in energy between buyers and sellers
Micro-divergences not visible on standard RSI
Areas of price fatigue or overextension that often precede reversals
Confirmation zones for MGO oscillatory events (RPA, RPB, RBA, RBB)
📊 Recommended use:
Combine with the Primary RSI for dual-layer validation
Use as a noise-reduction tool before entering trends
Ideal in medium timeframes (12H / 4H) where oscillatory patterns form clearly
🧠 How it works:
The Secondary RSI recalculates the momentum signal using a block-based interpretation (aligned with the MGO structure) instead of simply following raw candle data. It adapts to the periodic nature of price behavior and provides the trader with a more stable and reliable measure of true market strength.
