RS Triple MA Confluence Signal (Lower Pane)This indicator outputs a binary signal (1 or 0) based on triple moving average confluence of an asset’s relative strength vs a benchmark (e.g., SPY, BTC, etc).
✅ A value of 1 indicates full confluence, where the asset's relative strength is above three customizable moving averages (short, medium, and long).
❌ A value of 0 indicates confluence is off.
This version is designed to be used in a lower pane for:
Quick visual scanning
Dashboard-style layouts
Systematic filtering or alerting
Pairs perfectly with the main overlay tool:
👉 Relative Strength Triple MA Confluence
Use that version for candle coloring and price-level signals, and this version for clean signal tracking and screening support.
Cerca negli script per "spy"
Relative Strength Triple MA ConfluenceThis tool highlights moments of strong outperformance based on three customizable moving averages of an asset's relative strength vs a benchmark (SPY, BTC, etc).
✅ Green candles + triangle-up icon appear when relative strength is above all 3 MAs (short, medium, long)
❌ Red triangle-down appears when full confluence is lost
🔧 Fully customizable MA types (EMA or SMA), lengths, and benchmark
Ideal for traders seeking high-conviction confirmation based on stacked RS strength.
Relative Strength MA ConfluenceThis indicator highlights price candles when two custom moving averages of relative strength vs a benchmark (e.g., SPY or BTC) are both trending positively.
Full confluence: Occurs when the asset's relative strength is above both a short- and long-term MA (default: 21 & 50).
Green candles and a triangle-up icon mark when full confluence begins.
Red triangle-down marks when confluence is lost.
🔧 All settings — including MA type (SMA or EMA), lengths, benchmark symbol, and visual toggles — are fully customizable.
Ideal for swing traders seeking strong trend confirmation based on outperformance relative to a benchmark.
Options Betting Range - FixedOptions Betting Range
Options Betting Range is a powerful TradingView indicator designed to streamline options trading by visualizing high-probability price ranges for key symbols. With automated trendlines and clear labels, it empowers traders to make precise, data-driven decisions based on customizable prediction and execution dates.
## Key Features
Broad S&P 500 Coverage: Supports most S&P 500 stock symbols, excluding those with insufficient options volume for reliable data, alongside major ETFs and indices like SPY, IWM, QQQ, DIA, TLT, ^GSPC, ^IXIC, ^RUT, ^NDX, and ^SOX.
Automated Trendlines: Plots dashed and solid trendlines to mark high/low price boundaries, triggered only on specified prediction dates for clean, uncluttered charts.
Customizable Inputs: Configure prediction and execution dates to align with your trading strategy.
Clear Visuals: Color-coded labels (green for highs, purple for lows) display price ranges and percentage spreads for rapid decision-making.
Single-Execution Logic: Draws trendlines once per prediction date, ensuring chart clarity and efficiency.
## How It Works
Based on the latest daily open interest data, the indicator calculates swing ranges for different strike dates, drawing trendlines and labels to visualize potential price boundaries for options trading.
## Why Use It?
Streamlined Analysis: Automates range visualization, saving time and reducing manual charting.
Strategic Clarity: Objective price levels minimize emotional bias and enhance trade planning.
Versatile Application: Ideal for day traders, swing traders, and options strategists across multiple markets.
## Tips for Best Use
Regular Updates: To maintain the accuracy of options betting ranges, periodically update the indicator. On the view page, hover over the indicator name and click the blue whirlwind icon to complete the update.
## Get Started
Add Options Betting Range to your TradingView chart, select a supported symbol, and customize your prediction/execution dates. Leverage the visualized price ranges to execute precise options trading strategies with confidence.
BBS – Bond Breadth Signal"When bonds scream, breadth collapses, and fear spikes — BBS listens."
🧠 BBS – Bond Breadth Signal
A reversal timing tool built on macro conviction, not price noise.
The Bond Breadth Signal (BBS) was developed to identify major market inflection points by combining four key market stress indicators:
1) 10-Year Yield ROC – Measures sharp moves in the bond market
2) Z-Score of the 10Y – Captures statistical extremes
3) NSHF (Net Highs–Lows) – Signals internal market strength or weakness
4) TLT ROC + VIX – Confirmations of flight to safety and volatility-driven fear
When all conditions align, BBS marks either a For-Sure Buy or For-Sure Sell — these are rare, high-confidence signals designed to cut through noise and focus on true market dislocations.
🔧 Features:
-Background color and signal arrows on confirmation days
-Signals remain visually active for 3 days for added clarity
-Fully adjustable thresholds and alert toggles
-Plot panel for yield, TLT, NSHF, VIX, and Z-score visuals
This tool isn’t designed to fire every day. It’s meant to wait for those moments when the market truly bends — not just wiggles.
Best used on major indices (SPY, QQQ, IWM) to assess macro turning points.
Volatility Regime Tracker (VIX vs Realized + MOVE)Apply to any chart (preferably SPY, QQQ, or macro assets)
Add alert from the top menu using “Spread Crossed Above” or “Below”
Use the background regime shading to spot when markets move from:
🟢 Calm (green): VIX < Realized
🟡 Neutral
🔴 Panic (VIX premium)
SPX to ES/MES**SPX to ES/MES Level Converter**
This indicator is designed for traders who work with SPX price levels but execute trades on ES or MES futures. It allows you to input SPX-based key levels—such as call walls, put walls, vanna/charm zones, volatility triggers, and profit targets—and automatically converts them into their real-time ES/MES equivalents.
### 📌 Features:
- Manual input of SPX levels (e.g. 5900, 5850, etc.)
- Live conversion to ES or MES levels using a dynamic spot ratio
- Plots include:
- 🟢 Call Wall
- 🔴 Put Wall
- 🟠 Vanna
- 🟣 Charm
- 🟡 Volatility Trigger
- ✅ Long Profit Targets
- ❌ Short Profit Targets
- Smoothing parameter to stabilize visual line display
### 🧠 How it Works:
- The indicator calculates a dynamic ratio between the ES/MES price and your manually input SPX spot.
- This ratio is applied to each SPX level to determine its corresponding ES/MES equivalent.
- It plots each line at its translated futures level so your chart reflects accurate futures-aligned decision points.
> Tip: Adjust the `Current SPX Spot` input daily to match live spot values for maximum precision.
This version does not include text labels on the chart. For a labeled version, check out the updated release with `label.new()` annotations for each level.
**Use case:** Great for traders who generate levels off SPX options flow, but execute on ES/MES contracts intraday.
Created by pogchamp99 | Inspired by SPY → ES conversion by ItsAnders81
Single MA Pullback – Producers TradeHow to Use the “Single MA Pullback – Producers Trade” Indicator
This indicator helps options traders identify high-probability CALL and PUT signals based on price reacting to a single moving average.
⸻
✅ How It Works
• Select your preferred MA type (EMA, SMA, WMA, VWMA) and length.
• A Buy signal (CALL) is generated when price crosses above the MA.
• A Sell signal (PUT) is generated when price crosses below the MA.
• Visual arrows mark each signal, and a label suggests an option contract with strike and expiration.
⸻
🧠 Features
• Strike prices are automatically calculated ~1% out of the money.
• Expiration dates target the next Friday, based on the current day of the week.
• Symbol-specific strike rounding (e.g., 1 for SPY/XSP, 5 for most stocks).
⸻
📆 Expiration Date Notes
• Expiration dates shown in the label are based on a best-estimate to the next Friday.
• Depending on the time of day or day of week, the date may be off by one day.
• Always verify expiration dates on your trading platform before placing a trade.
⸻
📌 Important Tip on Expiration
A further out expiration is almost always a better idea — especially for:
• Avoiding time decay (theta)
• Holding through small pullbacks
• Letting your trade develop with less pressure
Even when the label suggests a short-dated contract, you can manually choose a longer expiration (e.g., 2–3 weeks out) for added safety and flexibility.
⸻
📈 Trading Suggestions
1. Green arrow = CALL setup. Red arrow = PUT setup.
2. Labels include trade type, strike price, and suggested expiration.
3. Confirm the signal with volume, price structure, or catalyst.
4. Manage your risk with proper sizing and optional stop-loss/target planning.
$ADD LevelsThis Pine Script is designed to track and visualize the NYSE Advance-Decline Line (ADD). The Advance-Decline Line is a popular market breadth indicator, showing the difference between advancing and declining stocks on the NYSE. It’s often used to gauge overall market sentiment and strength.
1. //@version=5
This line tells TradingView to use Pine Script v5, the latest and most powerful version of Pine.
2. indicator(" USI:ADD Levels", overlay=false)
• This creates a new indicator called ” USI:ADD Levels”.
• overlay=false means it will appear in a separate pane, not on the main price chart.
3. add = request.security(...)
This fetches real-time data from the symbol USI:ADD (Advance-Decline Line) using a 1-minute timeframe. You can change the timeframe if needed.
add_symbol = input.symbol(" USI:ADD ", "Market Breadth Symbol")
add = request.security(add_symbol, "1", close)
4. Key Thresholds
These define the market sentiment zones:
Zone. Value. Meaning
Overbought +1500 Extremely bullish
Bullish +1000 Generally bullish trend
Neutral ±500 Choppy, unclear market
Bearish -1000 Generally bearish trend
Oversold -1500 Extremely bearish
5. Plot the ADD Line hline(...)
Draws static lines at +1500, +1000, +500, -500, -1000, -1500 for reference so you can visually assess where ADD stands.
6. Horizontal Threshold Lines bgcolor(...)
• Green background if ADD > +1500 → extremely bullish.
• Red background if ADD < -1500 → extremely bearish.
7. Background Highlights alertcondition(...)
• Green background if ADD > +1500 → extremely bullish.
• Red background if ADD < -1500 → extremely bearish.
8. Alert Conditions. alertcondition(...)
Lets you create automatic alerts for:
• USI:ADD being very high or low.
• Crosses above +1000 (bullish trigger).
• Crosses below -1000 (bearish trigger).
You can use these to trigger trades or monitor sentiment shifts.
Summary: When to Use It
• Use this script in a market breadth dashboard.
• Combine it with price action and volume analysis.
• Monitor for ADD crosses to signal potential market reversals or momentum.
RRC Sniper SetupRRC Sniper Setup, this looks at candles this way:
Go to Market Scanner
Create New Scan → "RRC Sniper Setup"
Add filters listed below with timeframe logic (e.g. 1m/5m)
Run scan on:
Your Watchlist
SPY 500
QQQ 100
AI/Momentum names
1. Reclaim Filter
Find price breaking back above a key level (VWAP or EMA113)
Last 1m Close > EMA 113 (1m)
OR
Last 5m Close > VWAP
2. Retrace Filter
Price pulls back into the zone and holds within a tight range
Current Price < VWAP * 1.0025
AND
Current Price > VWAP * 0.9975
AND
Volume (Current Candle) < Volume (Previous Candle)
✅ 3. Confirm Filter
Price begins moving back up with confirmation candle and volume
Last Candle Close > Last Candle Open
AND
Volume (Current Candle) > Volume (Previous Candle)
Multi-Session ORBThe Multi-Session ORB Indicator is a customizable Pine Script (version 6) tool designed for TradingView to plot Opening Range Breakout (ORB) levels across four major trading sessions: Sydney, Tokyo, London, and New York. It allows traders to define specific ORB durations and session times in Central Daylight Time (CDT), making it adaptable to various trading strategies.
Key Features:
1. Customizable ORB Duration: Users can set the ORB duration (default: 15 minutes) via the inputMax parameter, determining the time window for calculating the high and low of each session’s opening range.
2. Flexible Session Times: The indicator supports user-defined session and ORB times for:
◦ Sydney: Default ORB (17:00–17:15 CDT), Session (17:00–01:00 CDT)
◦ Tokyo: Default ORB (19:00–19:15 CDT), Session (19:00–04:00 CDT)
◦ London: Default ORB (02:00–02:15 CDT), Session (02:00–11:00 CDT)
◦ New York: Default ORB (08:30–08:45 CDT), Session (08:30–16:00 CDT)
3. Session-Specific ORB Levels: For each session, the indicator calculates and tracks the high and low prices during the specified ORB period. These levels are updated dynamically if new highs or lows occur within the ORB timeframe.
4. Visual Representation:
◦ ORB high and low lines are plotted only during their respective session times, ensuring clarity.
◦ Each session’s lines are color-coded for easy identification:
▪ Sydney: Light Yellow (high), Dark Yellow (low)
▪ Tokyo: Light Pink (high), Dark Pink (low)
▪ London: Light Blue (high), Dark Blue (low)
▪ New York: Light Purple (high), Dark Purple (low)
◦ Lines are drawn with a linewidth of 2 and disappear when the session ends or if the timeframe is not intraday (or exceeds the ORB duration).
5. Intraday Compatibility: The indicator is optimized for intraday timeframes (e.g., 1-minute to 15-minute charts) and only displays when the chart’s timeframe multiplier is less than or equal to the ORB duration.
How It Works:
• Session Detection: The script uses the time() function to check if the current bar falls within the user-defined ORB or session time windows, accounting for all days of the week.
• ORB Logic: At the start of each session’s ORB period, the script initializes the high and low based on the first bar’s prices. It then updates these levels if subsequent bars within the ORB period exceed the current high or fall below the current low.
• Plotting: ORB levels are plotted as horizontal lines during the respective session, with visibility controlled to avoid clutter outside session times or on incompatible timeframes.
Use Case:
Traders can use this indicator to identify key breakout levels for each trading session, facilitating strategies based on price action around the opening range. The flexibility to adjust ORB and session times makes it suitable for various markets (e.g., forex, stocks, or futures) and time zones.
Limitations:
• The indicator is designed for intraday timeframes and may not display on higher timeframes (e.g., daily or weekly) or if the timeframe multiplier exceeds the ORB duration.
• Time inputs are in CDT, requiring users to adjust for their local timezone or market requirements.
• If you need to use this for GC/CL/SPY/QQQ you have to adjust the times by one hour.
This indicator is ideal for traders focusing on session-based breakout strategies, offering clear visualization and customization for global market sessions.
Symbol Seasonality Matrix (w/ BTC Base) Symbol Seasonality Matrix (w/ BTC Base)
Compare monthly performance between Bitcoin and any symbol across time
🧠 Overview
This indicator provides a side-by-side monthly return table of Bitcoin (BTCUSD from Bitfinex) and any selected symbol (e.g., ETH, stocks, etc.). It visualizes seasonality patterns, historical performance shifts, and relative trends in a clean matrix layout with dynamic line overlays.
⚙️ Mechanism
BTC Benchmarking:
BTC monthly returns are always shown as a benchmark against the selected chart symbol.
Monthly ROI Calculation:
For each month, the indicator tracks the open and close price and calculates the monthly return using:
(close_end - close_start) / close_start × 100%
It stores both price and return for BTC and the chart symbol.
Table Structure:
Each year is split into two halves:
2023 (Jan ~ Jun) and 2023 (Jul ~ Dec) for clarity.
Color Coding:
Green for positive months
Red for negative months
Monthly trend lines and labels drawn in consistent colors
Background shading per month helps track seasonality
Plot Modes:
regular: raw price
percent: relative % change from the start of selected period
normalized: base=1 scaling to compare trends
Time Range Selector:
You can define start time and end time for comparison — all logic, including table, plots, and highlights, will focus only on this window.
🧭 How to Use
Set the time range:
Choose a meaningful window such as the past 3 years or 2018–2021 to study behavior.
Compare Symbol vs BTC:
Load BTCUSD in a separate chart for baseline.
Switch to ETHUSD, SPY, or any altcoin/equity to view overlayed performance.
Analyze Seasonality:
Look for months with repeated strong/weak performance (e.g., BTC strong in October).
Compare how your asset aligns with BTC trends or diverges.
Choose View Mode:
Use percent to adjust Y-axis scaling and directly compare relative movements.
Use normalized to detect trend correlation without caring about price level.
🔍 Why It’s Useful
Spot seasonal alpha and align entries with favorable months
See if a symbol outperforms or underperforms BTC consistently
Get price-to-return context visually, not just via numbers
Quickly compare assets in real scale or normalized scale
📌 Tip
Try publishing this to a layout with multiple tickers (ETH, SOL, AAPL) to instantly switch comparisons.
Pair with volume-based or macro indicators to layer signals.
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.
VWAP Adaptive (RelVol-Adjusted)This indicator provides an Adaptive VWAP that adjusts volume weighting using RelVol (Relative Volume at Time), offering a more accurate and context-aware price reference during sessions with irregular volume behavior.
Classic VWAP calculates the average price weighted by raw volume, without considering the time of day. This becomes a serious limitation during major market events such as CPI releases, FOMC announcements, NFP, or large-cap earnings. These events often trigger massive volume spikes within one or two candles. As a result, the classic VWAP gets pulled toward those extreme prices and becomes permanently skewed for the rest of the session.
In such conditions, classic VWAP becomes unreliable. It no longer reflects fair value and often misleads traders relying on it for dynamic support, resistance, or reversion signals.
This Adaptive VWAP improves on that by using RelVol, which compares the current volume to the average volume seen at the same time over previous sessions. It gives more weight to price when volume is typical for that moment, and adjusts the influence when volume is statistically abnormal. This reduces the impact of isolated volume spikes and stabilizes the VWAP path, even in high-volatility environments.
For example, on SPY 1-minute or 5-minute charts during a CPI release, a massive spike in volume and price can occur within a single candle. Classic VWAP will immediately anchor itself to that spike. Adaptive VWAP using RelVol softens that effect and maintains a more realistic trajectory.
Key features:
- Adaptive VWAP weighted by time-adjusted Relative Volume (RelVol)
- Designed to maintain VWAP reliability during macroeconomic events
- Flexible anchoring: Session, Week, Month, Quarter, Earnings, etc.
- Optional display of Classic VWAP for comparison
- Up to 3 customizable deviation bands (standard deviation or percentage)
This tool is ideal for intraday traders who need a VWAP that remains usable and unbiased, even in volatile sessions. It adds robustness to VWAP-based strategies by incorporating time-sensitive volume normalization.
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
SMT - JimmyTrades🔧 SMT – JimmyTrades: Publication Rules and User Guide
📌 What This Script Does
This script detects Smart Money Traps (SMT) Divergences between the instrument on your chart and a comparative symbol (default: ES). It automatically plots both confirmed and unconfirmed bullish and bearish SMT setups across multiple timeframes.
These SMT divergences can help traders:
Identify potential reversal points
Confirm high-probability entries in line with smart money behavior
Enhance bias when confluence aligns with other market structure or liquidity factors
⚙️ Important Settings
Please make sure you correctly configure the following inputs:
Symbol: The comparative asset to check divergence against. Common examples: ES, NQ, SPX.
Session Type: Ensure this matches your chart’s session setting: Extended or Regular.
Adjustment Type: Match this to your chart (None, Dividends, or Splits) under TradingView’s chart settings (bottom-right corner).
Pivot Lookback: Controls the sensitivity of divergence detection (default is 15). Higher values reduce signal frequency.
Timeframes: You can enable up to six timeframes independently for SMT scanning.
🟢 Bullish SMT Signals
Bullish SMTs are identified when price on your chart makes a lower low, but the comparative symbol (e.g., ES) does not, suggesting potential accumulation or trap liquidity.
🔴 Bearish SMT Signals
Bearish SMTs are flagged when your chart makes a higher high, while the comparative symbol fails to do so, hinting at distribution or a stop run setup.
📈 How to Use This Script
Add the indicator to your chart.
Set the correct comparative symbol (e.g., ES for NQ, SPX for SPY, etc.).
Choose your preferred timeframes.
Watch for unconfirmed SMTs (dotted lines) as potential early warnings.
Look for confirmed SMTs (solid lines) once price respects the divergence zone for several bars.
Combine with structure, liquidity sweeps, killzones, and high-impact news for higher confluence.
🧠 Best Practices
Use SMT signals as part of a broader trade plan—not standalone entries.
Focus on SMTs forming after liquidity sweeps or during session opens (London/NY).
Combine with your higher-timeframe bias, breaker blocks, or Pegasus/Unicorn entry models.
⚠️ Limitations
Historical backtest may show perfect SMTs—real-time confirmation requires patience.
SMTs may not play out without proper context—avoid blindly entering based on signal alone.
This script is not financial advice—use at your own discretion and always manage risk.
Multi-Index Gap Confluence Indicator by ATALLAOverview of the Multi-Index Gap Confluence Indicator
This indicator is designed to identify and highlight price gaps across multiple market indices and their related ETFs/futures. It specifically looks for:
True gaps (where there's no overlap between the current and previous bar's range)
Negative gaps (where only the candle bodies have no overlap, but wicks might)
The indicator has the capability to:
Visualize gaps on charts using colored rectangles
Compare gaps across up to 6 different symbols (3 ETFs and 3 futures)
Generate confluence signals when multiple symbols show gaps simultaneously
Customize appearance and detection parameters
Key Components
Gap Detection
The script distinguishes between:
True gaps: No overlap at all between current and previous bars
Negative gaps: Only the candle bodies have no overlap
Multi-Asset Comparison
The indicator can monitor gaps across six major market indices:
ETFs: QQQ (Nasdaq-100), SPY (S&P 500), and DIA (Dow Jones)
Futures: NQ1! (Nasdaq-100), ES1! (S&P 500), and YM1! (Dow Jones)
Confluence Detection
The script identifies when multiple assets display gaps simultaneously, with:
Configurable minimum threshold (default is 5 out of 6 assets)
Option to require both ETF and futures representation
A strong confluence signal when 5-6 assets show gaps
Customization Options
The indicator offers many parameters for customization:
Gap colors and opacity
Symbol selection and enablement
Confluence thresholds
Display options
Visual Elements
The indicator displays:
Colored rectangles highlighting gap areas
Optional up/down triangles for gap direction
A flag symbol for strong confluence signals (when 5-6 assets show gaps)
Labels listing which specific assets have gaps
Practical Use
This indicator appears designed for traders looking to identify potentially significant market moves by spotting when multiple major indices show price gaps simultaneously. The emphasis on "strong confluence" (5-6 assets showing gaps) suggests these are considered particularly noteworthy signals.
Market Warning Dashboard Enhanced📊 Market Warning Dashboard Enhanced
A powerful macro risk dashboard that tracks and visualizes early signs of market instability across multiple key indicators—presented in a clean, professional layout with a real-time thermometer-style danger gauge.
🔍 Included Macro Signals:
Yield Curve Inversion: 10Y-2Y and 10Y-3M spreads
Credit Spreads: High-yield (HYG) vs Investment Grade (LQD)
Volatility Structure: VIX/VXV ratio
Breadth Estimate: SPY vs 50-day MA (as a proxy)
🔥 Features:
Real-time Danger Score: 0 (Safe) to 100 (Extreme Risk)
Descriptive warnings for each signal
Color-coded thermometer gauge
Alert conditions for each macro risk
Background shifts on rising systemic risk
⚠️ This dashboard can save your portfolio by alerting you to macro trouble before it hits the headlines—ideal for swing traders, long-term investors, and anyone who doesn’t want to get blindsided by systemic risk.
Global ETF Capital FlowsThe Global ETF Capital Flows indicator is designed as a research and monitoring tool for identifying capital allocation trends across major global exchange-traded funds (ETFs). It provides standardized fund flow data for regional equity markets (including the United States, Europe, Asia, and Emerging Markets), as well as alternative asset classes such as bonds and gold.
Fund flows into and out of ETFs are increasingly recognized as a leading indicator of investor behavior, particularly in the context of tactical asset allocation and risk appetite (Ben-David et al., 2017). By tracking aggregated ETF flows, the script enables the user to detect shifts in global investment preferences, which may precede price action and influence broader macro trends (Bank of International Settlements, 2018). For example, consistent inflows into U.S. large-cap ETFs such as SPY or QQQ may signal heightened investor confidence in domestic equities, whereas rising flows into bond ETFs such as TLT may suggest a flight to safety or expectations of declining interest rates (Israeli et al., 2017).
The visualization aspect of the script uses standardized z-scores to represent cumulative flows over a specified period. This normalization allows users to compare fund flows across regions and asset classes on a relative basis, filtering out scale differences and allowing for more effective cross-market analysis. According to Coates and Herbert (2008), normalization techniques such as z-scores are crucial in behavioral finance research, as they help detect anomalies and emotional extremes in investor activity.
Practically, this indicator is suited for top-down macro analysis, sector rotation strategies, and confirmation of technical signals. For instance, significant positive deviations in the standardized flow data for European ETFs may support a bullish bias on regional equities, especially if corroborated by technical breakouts or improving economic indicators. Conversely, elevated inflows into gold ETFs may be interpreted as hedging behavior against geopolitical uncertainty or inflationary pressure, consistent with historical patterns of gold’s role as a safe haven (Baur and Lucey, 2010).
Additionally, the tool allows for visual alerts when flow anomalies exceed a user-defined threshold, thereby supporting more responsive and data-driven decision-making. This feature aligns with findings from the CFA Institute (2019), which emphasize the growing importance of alternative data and automated alert systems in modern portfolio management.
From a research perspective, the indicator facilitates empirical study into capital mobility, intermarket relationships, and ETF investor psychology. It offers real-time monitoring of region-specific investment flows, thus serving as a proxy for investor conviction, liquidity trends, and cross-border risk-on/risk-off sentiment. Several recent studies have demonstrated the predictive power of ETF flows on future returns and volatility, particularly during periods of market stress or structural dislocations (Madhavan, 2016; Pan and Zeng, 2019).
References
• Baur, D.G. and Lucey, B.M., 2010. Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), pp.217-229.
• Ben-David, I., Franzoni, F. and Moussawi, R., 2017. Exchange-traded funds (ETFs). Annual Review of Financial Economics, 9, pp.169–189.
• Bank of International Settlements (BIS), 2018. ETFs – growing popularity, growing risks? BIS Quarterly Review, March 2018.
• CFA Institute, 2019. Investment Professional of the Future. Available at: www.cfainstitute.org .
• Coates, J.M. and Herbert, J., 2008. Endogenous steroids and financial risk taking on a London trading floor. Proceedings of the National Academy of Sciences, 105(16), pp.6167–6172.
• Israeli, D., Lee, C.M. and Sridharan, S.A., 2017. Is there a dark side to ETF trading? Evidence from corporate bond ETFs. SSRN Working Paper. Available at SSRN: ssrn.com
• Madhavan, A., 2016. Exchange-Traded Funds and the New Dynamics of Investing. Oxford University Press.
• Pan, K. and Zeng, Y., 2019. ETF Arbitrage Under Liquidity Mismatch. Journal of Finance, 74(6), pp.2731–2783.
Anchored Bollinger Band Range [SS]This is the anchored Bollinger band indicator.
What it does?
The anchored BB indicator:
Takes a user defined range and calculates the Standard Deviation of the entire selected range for the high and low values.
Computes a moving average of the high and low during the selected period (which later becomes the breakout range average)
Anchors to the last high and last low of the period range to add up to 4 standard deviations to the upside and downside, giving you 4 high and low targets.
How can you use it?
The anchored BB indicator has many applicable uses, including
Identifying daily ranges based on premarket trading activity ( see below ):
Finding breakout ranges for intraday pattern setups ( see below ):
Identified pattern of interest:
Applying Anchored BB:
Identifying daily or pattern biases based on the position to the opening breakout range average (blue line). See the examples with explanations:
ex#1:
ex#2:
The Opening Breakout Average
As you saw in the examples above, the blue line represents the opening breakout range average.
This is the average high of the period of interest and the average low of the period of interest.
Price action above this line would be considered Bullish, and Bearish if below.
This also acts as a retracement zone in non-trending markets. For example:
Best Use Cases
Identify breakout ranges for patterns on larger timeframes. For example
This pattern on SPY, if we overlay the Anchored BB:
You want to see it actually breakout from this range and hold to confirm a breakout. Failure to exceed the BB range, means that it is just ranging with no real breakout momentum.
Identify conservative ranges for a specific period in time, for example QQQ:
Worst Use Cases
Using it as a hard and fast support and resistance indicator. This is not what it is for and ranges can be exceeded with momentum. The key is looking for whether ranges are exceeded (i.e. high momentum, thus breakout play) or they are not (thus low volume, rangy).
Using it for longer term outlooks. This is not ideal for long term ranges, as with any Bollinger/standard deviation based approach, it is only responsive to CURRENT PA and cannot forecast FUTURE PA.
User Inputs
The indicator is really straight forward. There are 2 optional inputs and 1 required input.
Period Selection: Required. Selects the period for the indicator to perform the analysis on. You just select it with your mouse on the chart.
Visible MA: Optional. You can choose to have the breakout range moving average visible or not.
Fills: Optional. You can choose to have the fills plotted or not.
And that is the indicator! Very easy to use and hope you enjoy and find it helpful!
As always, safe trades everyone! 🚀
Horizontal Price TableOverview:
This script displays a dynamic price table on your chart, showing real-time prices and daily percentage changes for up to 7 user-defined tickers. You can customize both which tickers are shown and how many are visible, all through the settings panel.
How it works (Step-by-Step):
User-Defined Tickers:
The script provides input fields for up to 7 tickers using input.symbol(). You can track stocks, indexes, ETFs, crypto, or futures — anything supported by TradingView.
Choose How Many to Display:
An additional dropdown lets you choose how many of the 7 tickers to actually display (between 1 and 7). This gives you control over screen space and focus.
Market Data Fetching:
For each displayed ticker, the script fetches:
The current day’s closing price (close)
The previous day’s closing price (close )
This data is pulled using request.security() on the daily timeframe (1D).
% Change Calculation:
The script calculates the daily percentage change using:
(Current Price−Previous Close)/Previous Close×100(Current Price−Previous Close)/Previous Close×100
Cleaned Ticker Names:
Ticker symbols often include an exchange prefix like NASDAQ:AAPL. The script automatically removes anything before the colon (:), so only the clean symbol (e.g., AAPL) is shown in the table.
Table Display:
A visual table appears at the top-center of your chart, showing:
Row 1: Ticker symbol (cleaned)
Row 2: Current price (rounded to 2 decimals)
Row 3: Daily % change (green for gains, red for losses)
Customization:
You can choose the background color of the table.
Ticker names appear in white text with a gray background.
% change is color-coded: green for positive, red for negative.
Why Use This Script?
Track multiple tickers at once without leaving your chart.
Clean, customizable layout.
Useful for monitoring watchlists, portfolios, or related markets.
Tips:
Combine this with your favorite indicators for a personalized dashboard.
Works great on any chart or timeframe.
Ensure the tickers entered are valid on TradingView (e.g., SPY, BTCUSD, NQ1!, etc.).
Teddy LiteOverview
"Teddy" overlays key price levels—Daily Open (DO), Average Daily Range (ADR), and ADR Extensions (ADE)—on intraday charts. Designed for traders, it provides a clear framework to align with market ranges, avoid choppy price action, and stay out of overbought/oversold conditions, enhancing decision-making in dynamic markets.
Originality and Usefulness
"Teddy" uniquely combines DO, ADR High/Low, and ADE High/Low with dynamic percentage labels, while offering a concise view of price boundaries for daily Highs and Lows.
What It Does
Plots DO, ADR High/Low, and ADE High/Low as levels on the chart.
Labels each level with percentage distances from the current price (e.g., "ADRH (2.34%)").
Customizes visuals for clarity (colors, line styles, label sizes).
How It Works
Data Sources: Retrieves daily open and historical high/low data to compute ranges.
Calculations:
Daily Open (DO): Marks the session’s opening price.
ADR: Estimates typical daily range from past data, centered on DO to set High/Low bounds.
ADE: Extends ADR by a fixed percentage for outer limits.
Visualization: Updates lines and labels live, with user-defined colors, styles, and sizes.
How It Helps Traders
"Teddy" guides traders to avoid chasing markets in extended conditions:
Respecting the range: ADR High/Low define range-friendly zones—price above DO nearing ADR High signals bullish momentum is peaking, while below DO near ADR Low supports bearish momentum peaking.
Avoiding Choppy Conditions: Price lingering near DO often indicates indecision; "Teddy" highlights this level, helping you define balanced market conditions that favor choppy conditions.
Steering Clear of Overbought/Oversold: ADE High/Low mark extended levels where reversals are extremely—price hitting ADE Highlights the trend strength on the day but warns price is extremely over extended.
This structured approach keeps trades aligned with the markets average range, so traders can avoid extremes favorable levels for choppiness.
How to Use It
Apply to an intraday chart (e.g., SPY 5m).
Customize via inputs:
"Appearance Settings": Colors, line styles (Solid, Dotted, Dashed), widths (1-6), label visibility, and sizes (Tiny to Huge).
Watch levels: Consider reducing risk as the market approaches our ADRH/L levels. Trades can also play breakouts/failed breakouts at ADR High/Low or at ADE High/Low. Additionally remaining patient while the auction remains in balance near Day Open is an option as well.
Underlying Concepts
Range Dynamics: ADR reflects average daily volatility, DO anchors context, and ADE flags extensions.
Price Action: Levels highlight Volatility/Range (ADR) versus consolidation (DO) or expansive exhaustion (ADE).
Limitations
Optimized for day traders during live sessions; less effective in low-volatility periods.
Requires sufficient historical data for accurate ADR/ADE.
Levels are contextual and where I expect reactive price action to occur.. They are not guaranteed signals.
Compare Strength with SLOPE Description
This indicator compares the relative strength between the current asset and a benchmark (e.g., BTC vs. ETH or AAPL vs. SPY) using a linear regression slope of their ratio over time.
The ratio is calculated as: close / benchmark
A linear regression slope is computed over a user-defined window
The slope represents trend strength: if it’s rising, the current asset is outperforming the benchmark
Plots
Gray Line: The raw ratio between the asset and benchmark
Orange Line: The slope of the ratio (shows momentum)
Background Color :
Green: The asset is significantly stronger than the benchmark
Red: The asset is significantly weaker than the benchmark
No color: No clear trend
Settings
Slope Window Length: Number of candles used in the regression (default = 10)
Slope Threshold: Sensitivity of trend detection. Smaller values detect weaker trends.
Example Use Cases
Style Rotation Strategy: Use the slope to determine whether "Growth" or "Value" style is leading.
Pair Trading / Relative Performance: Track which asset is leading in a pair (e.g., BTC vs ETH).
Factor Timing: Serve as a timing model to allocate between different sectors or factors.
Happy trading!
