felci The first row shows HIGH values of NIFTY.
The second row shows LOW values of NIFTY.
Some values are negative (like -2058, -300, -486)—these could indicate changes or differences rather than absolute index values.
The table seems color-coded in the image: green, orange, and light colors—probably to highlight ranges or thresholds.
Educational
Options Max Pain Calculator [BackQuant]Options Max Pain Calculator
A visualization tool that models option expiry dynamics by calculating "max pain" levels, displaying synthetic open interest curves, gamma exposure profiles, and pin-risk zones to help identify where market makers have the least payout exposure.
What is Max Pain?
Max Pain is the theoretical expiration price where the total dollar value of outstanding options would be minimized. At this price level, option holders collectively experience maximum losses while option writers (typically market makers) have minimal payout obligations. This creates a natural gravitational pull as expiration approaches.
Core Features
Visual Analysis Components:
Max Pain Line: Horizontal line showing the calculated minimum pain level
Strike Level Grid: Major support and resistance levels at key option strikes
Pin Zone: Highlighted area around max pain where price may gravitate
Pain Heatmap: Color-coded visualization showing pain distribution across prices
Gamma Exposure Profile: Bar chart displaying net gamma at each strike level
Real-time Dashboard: Summary statistics and risk metrics
Synthetic Market Modeling**
Since Pine Script cannot access live options data, the indicator creates realistic synthetic open interest distributions based on configurable market parameters including volume patterns, put/call ratios, and market maker positioning.
How It Works
Strike Generation:
The tool creates a grid of option strikes centered around the current price. You can control the range, density, and whether strikes snap to realistic market increments.
Open Interest Modeling:
Using your inputs for average volume, put/call ratios, and market maker behavior, the indicator generates synthetic open interest that mirrors real market dynamics:
Higher volume at-the-money with decay as strikes move further out
Adjustable put/call bias to reflect current market sentiment
Market maker inventory effects and typical short-gamma positioning
Weekly options boost for near-term expirations
Pain Calculation:
For each potential expiry price, the tool calculates total option payouts:
Call options contribute pain when finishing in-the-money
Put options contribute pain when finishing in-the-money
The strike with minimum total pain becomes the Max Pain level
Gamma Analysis:
Net gamma exposure is calculated at each strike using standard option pricing models, showing where hedging flows may be most intense. Positive gamma creates price support while negative gamma can amplify moves.
Key Settings
Basic Configuration:
Number of Strikes: Controls grid density (recommended: 15-25)
Days to Expiration: Time until option expiry
Strike Range: Price range around current level (recommended: 8-15%)
Strike Increment: Spacing between strikes
Market Parameters:
Average Daily Volume: Baseline for synthetic open interest
Put/Call Volume Ratio: Market sentiment bias (>1.0 = bearish, <1.0 = bullish) It does not work if set to 1.0
Implied Volatility: Current option volatility estimate
Market Maker Factors: Dealer positioning and hedging intensity
Display Options:
Model Complexity: Simple (line only), Standard (+ zones), Advanced (+ heatmap/gamma)
Visual Elements: Toggle individual components on/off
Theme: Dark/Light mode
Update Frequency: Real-time or daily calculation
Reading the Display
Dashboard Table (Top Right):
Current Price vs Max Pain Level
Distance to Pain: Percentage gap (smaller = higher pin risk)
Pin Risk Assessment: HIGH/MEDIUM/LOW based on proximity and time
Days to Expiry and Strike Count
Model complexity level
Visual Elements:
Red Line: Max Pain level where payout is minimized
Colored Zone: Pin risk area around max pain
Dotted Lines: Major strike levels (green = support, orange = resistance)
Color Bar: Pain heatmap (blue = high pain, red = low pain/max pain zones)
Horizontal Bars: Gamma exposure (green = positive, red = negative)
Yellow Dotted Line: Gamma flip level where hedging behavior changes
Trading Applications
Expiration Pinning:
When price is near max pain with limited time remaining, there's increased probability of gravitating toward that level as market makers hedge their positions.
Support and Resistance:
High open interest strikes often act as magnets, with max pain representing the strongest gravitational pull.
Volatility Expectations:
Above gamma flip: Expect dampened volatility (long gamma environment)
Below gamma flip: Expect amplified moves (short gamma environment)
Risk Assessment:
The pin risk indicator helps gauge likelihood of price manipulation near expiry, with HIGH risk suggesting potential range-bound action.
Best Practices
Setup Recommendations
Start with Model Complexity set to "Standard"
Use realistic strike ranges (8-12% for most assets)
Set put/call ratio based on current market sentiment
Adjust implied volatility to match current levels
Interpretation Guidelines:
Small distance to pain + short time = high pin probability
Large gamma bars indicate key hedging levels to monitor
Heatmap intensity shows strength of pain concentration
Multiple nearby strikes can create wider pin zones
Update Strategy:
Use "Daily" updates for cleaner visuals during trading hours
Switch to "Every Bar" for real-time analysis near expiration
Monitor changes in max pain level as new options activity emerges
Important Disclaimers
This is a modeling tool using synthetic data, not live market information. While the calculations are mathematically sound and the modeling realistic, actual market dynamics involve numerous factors not captured in any single indicator.
Max pain represents theoretical minimum payout levels and suggests where natural market forces may create gravitational pull, but it does not guarantee price movement or predict exact expiration levels. Market gaps, news events, and changing volatility can override these dynamics.
Use this tool as additional context for your analysis, not as a standalone trading signal. The synthetic nature of the data makes it most valuable for understanding market structure and potential zones of interest rather than precise price prediction.
Technical Notes
The indicator uses established option pricing principles with simplified implementations optimized for Pine Script performance. Gamma calculations use standard financial models while pain calculations follow the industry-standard definition of minimized option payouts.
All visual elements use fixed positioning to prevent movement when scrolling charts, and the tool includes performance optimizations to handle real-time calculation without timeout errors.
Universal Gann Square & Cube LevelsUniversal Gann Square & Cube Levels - Dynamic Support/Resistance
Description:
📊 UNIVERSAL GANN LEVELS INDICATOR
This powerful indicator automatically plots Gann Square and Cube levels around the current stock price, providing dynamic support and resistance levels based on W.D. Gann's mathematical theories.
🎯 KEY FEATURES:
✅ Auto-Adaptive: Works for ANY stock price (₹20 to ₹100,000+)
✅ Real-time Detection: Uses current close price automatically
✅ Dual Level System: Square levels (black) + Cube levels (red)
✅ Customizable Range: Adjust percentage range (5% to 50%)
✅ Clean Display: Toggle square/cube lines independently
✅ Universal Compatibility: Works on all timeframes and instruments
📈 HOW IT WORKS:
Square Levels (Black Lines): Based on perfect squares (n²) around current price
Cube Levels (Red Lines): Based on perfect cubes (n³) around current price
Smart Range: Automatically calculates relevant levels within your specified percentage range
Info Display: Shows current price and level counts
⚙️ SETTINGS:
Price Range %: Control how many levels appear (default: 15%)
Show Square Levels: Toggle black square lines on/off
Show Cube Levels: Toggle red cube lines on/off
🔥 PERFECT FOR:
Day traders seeking precise entry/exit points
Swing traders identifying key support/resistance zones
Gann theory practitioners and students
Multi-timeframe analysis across all instruments
💡 USAGE TIPS:
Use 10-20% range for active day trading
Use 30-50% range for swing trading analysis
Watch for price reactions at square/cube intersections
Combine with volume analysis for confirmation
🌟 WHY THIS INDICATOR?
Unlike fixed Gann calculators, this indicator dynamically adapts to ANY price level, making it truly universal for Indian stocks, crypto, forex, and commodities.
⚠️ DISCLAIMER:
This indicator is for educational and informational purposes only. It is not financial advice and should not be considered as a recommendation to buy or sell any security. Trading involves significant risk of loss and may not be suitable for all investors. Past performance does not guarantee future results. Always conduct your own research and consult with a qualified financial advisor before making any investment decisions. The developer assumes no responsibility for any trading losses incurred through the use of this indicator.
📋 COMPATIBILITY:
All TradingView plans
All timeframes (1m to 1M)
Stocks, Crypto, Forex, Commodities
Mobile and desktop platforms
MTF-RISK [Module+]Description
MTF-RISK is a futures risk management tool that calculates standardized position sizing across multiple CME micro contracts, anchored to higher-timeframe structure. By combining multi-timeframe reference levels with a contract-based dollar-per-point model, it allows traders to maintain consistent risk across different futures markets.
Example:
User has selected the 1H timeframe for the risk table. Once an hourly candle closes, the high and low of that completed hour are locked as reference boundaries.
Lower timeframe candles (e.g., 1m, 5m, 15m) reference these established 1H boundaries to calculate:
Distance in points from the current close to the HTF high or low.
Corresponding dollar risk based on the user-defined Max Risk per Trade ($) setting.
The risk table updates in real-time, showing the current stop distance, calculated contract size, and resulting risk in dollars for both upward and downward directions.
Benefit: Traders always maintain a fixed dollar risk, regardless of intraday price movement, while using HTF structure as the anchor for accurate and consistent position sizing.
1. Higher Timeframe Anchor
Always uses the last fully closed candle from the selected higher timeframe (default: 60m).
Captures the prior HTF high and low as reference boundaries.
Lower timeframe closers (e.g., 1m, 5m, 15m bars) reference these established HTF boundaries to measure stop distances and calculate risk.
Use: Ensures all position sizing is tied to completed HTF structure, providing a consistent framework for intraday trades.
2. Risk Model Engine
Traders define maximum dollar risk per trade.
The system calculates allowable micro contracts based on stop distance (current close → HTF high/low).
Supported contracts and their point values:
MNQ (Micro Nasdaq 100): $2.00 per point
MES (Micro S&P 500): $5.00 per point
MYM (Micro Dow Jones): $0.50 per point
MGC (Micro Gold): $10.00 per point
Formula:
Contracts = Max Risk ÷ (Stop Distance × TSE:VALUE per Point)
Risk ↑: Based on distance to HTF high.
Risk ↓: Based on distance to HTF low.
Use: Provides consistent dollar risk sizing across different futures contracts and multiple intraday timeframes.
3. Risk Table Overlay
Compact, real-time on-chart table with customizable styling.
Columns:
OP: Operation time (adjusted by user’s timezone offset).
Points ↑ / ↓: Stop distances in points relative to HTF boundaries.
Risk ↑ / ↓ ($): Dollar exposure at those stops.
Micros ↑ / ↓: Allowable contract count.
Asset: Displays selected futures contract in the header.
Custom features:
Independent text/background colors per column.
Highlighted latest row for clarity.
Adjustable outline, row colors, and text size.
Use: Gives traders immediate insight into position sizing without leaving the chart.
Intended Use:
This is a risk visualization module, not a trade signal generator. Traders can use it to:
Standardize risk sizing across multiple CME micro futures.
Quickly evaluate trade setups relative to HTF structure.
Measure stop distances from lower timeframe closes while referencing HTF boundaries.
Maintain consistency in risk management regardless of the instrument traded.
Limitations & Disclaimers:
Calculations assume standard CME tick values for MNQ, MES, MYM, and MGC.
Other markets may not align with these dollar-per-point values.
This indicator does not predict direction, generate entries, or guarantee outcomes.
For educational and informational purposes only.
Trading involves risk; always use proper risk management.
Closed-source (Protected): Logic is visible on charts, but source code is hidden.
Simple Turnover (Enhanced v2)📊 Simple Turnover (Enhanced)
🔹 Overview
The Simple Turnover Indicator calculates a stock’s turnover by combining both price and volume, and then compares it against quarterly highs. This helps traders quickly gauge whether market participation in a move is strong enough to confirm a breakout, or weak and likely to be false.
Unlike volume alone, turnover considers both traded volume and price level, giving a truer reflection of capital flow in/out of a stock.
________________________________________
🔹 Formulae Used
1. Average Price (SMA)
AvgPrice=SMA(Close,n)
2. Average Volume (SMA)
AvgVol=SMA(Volume,n)
3. Turnover (Raw)
Turnover raw=AvgPrice × AvgVol
4. Unit Adjustment
• If Millions → Turnover = Turnover raw × 10^−6
• If Crores → Turnover = Turnover raw × 10^−7
• If Raw → Turnover = Turnover raw
5. Quarterly High Turnover (qHigh)
Within each calendar quarter (Jan–Mar, Apr–Jun, Jul–Sep, Oct–Dec), we track the maximum turnover seen:
qHigh=max (Turnover within current quarter)
________________________________________
🔹 Visualization
• Bars → Color follows price candle:
o Green if Close ≥ Open
o Red if Close < Open
• Blue Line → Rolling Quarterly High Turnover (qHigh)
________________________________________
🔹 Strategy Use Case
The Simple Turnover Indicator is most effective for confirming true vs false breakouts.
• A true breakout should be supported by increasing turnover, showing real capital backing the move.
• A false breakout often occurs with weak or declining turnover, suggesting lack of conviction.
📌 Example Strategy (3H timeframe):
1. Identify a demand zone using your preferred supply-demand indicator.
2. From this demand zone, monitor turnover bars.
3. A potential long entry is validated when:
o The current turnover bar is at least 20% higher than the previous one or two bars.
o Example setting: SMA length = 5 (i.e., turnover = 5-bar average close × 5-bar average volume).
4. This confirms strong participation in the move, increasing probability of a sustained breakout.
________________________________________
🔹 Disclaimer
⚠️ This indicator/strategy does not guarantee 100% accurate results.
It is intended to improve the probability of identifying true breakouts.
The actual success of the strategy will depend on price action, market momentum, and prevailing market conditions.
Always use this as a supporting tool along with broader trading analysis and risk management.
RMA EMA Crossover | MisinkoMasterThe RMA EMA Crossover (REMAC) is a trend-following overlay indicator designed to detect shifts in market momentum using the interaction between a smoothed RMA (Relative Moving Average) and its EMA (Exponential Moving Average) counterpart.
This combination provides fast, adaptive signals while reducing noise, making it suitable for a wide range of markets and timeframes.
🔎 Methodology
RMA Calculation
The Relative Moving Average (RMA) is calculated over the user-defined length.
RMA is a type of smoothed moving average that reacts more gradually than a standard EMA, providing a stable baseline.
EMA of RMA
An Exponential Moving Average (EMA) is then applied to the RMA, creating a dual-layer moving average system.
This combination amplifies trend signals while reducing false crossovers.
Trend Detection (Crossover Logic)
Bullish Signal (Trend Up) → When RMA crosses above EMA.
Bearish Signal (Trend Down) → When EMA crosses above RMA.
This simple crossover system identifies the direction of momentum shifts efficiently.
📈 Visualization
RMA and EMA are plotted directly on the chart.
Colors adapt dynamically to the current trend:
Cyan / Green hues → RMA above EMA (bullish momentum).
Magenta / Red hues → EMA above RMA (bearish momentum).
Filled areas between the two lines highlight zones of trend alignment or divergence, making it easier to spot reversals at a glance.
⚡ Features
Adjustable length parameter for RMA and EMA.
Overlay format allows for direct integration with price charts.
Visual trend scoring via color and fill for rapid assessment.
Works well across all asset classes: crypto, forex, stocks, indices.
✅ Use Cases
Trend Following → Stay on the right side of the market by following momentum shifts.
Reversal Detection → Crossovers highlight early trend changes.
Filter for Trading Systems → Use as a confirmation overlay for other indicators or strategies.
Visual Market Insight → Filled zones provide immediate context for trend strength.
Beta SignalsThe Beta Buy/Sell Signal Indicator provides visual cues for potential trade setups by combining multiple technical conditions, including RSI, MACD, SMA, volume filters, and price action. It highlights buy and sell signals when these conditions align, helping traders observe potential short-term opportunities across various market conditions.
Key Features:
Buy/Sell Signals – Signals appear as markers on your chart indicating potential entry points.
RSI Bounce Alerts – Identifies RSI crossing key thresholds (35 for bullish, 65 for bearish) in combination with other technical conditions.
SMA & MACD Filters – Confirms trade setups using trend (SMA) and momentum (MACD) indicators.
Volume & Price Action Filters – Optional volume filter and price movement checks ensure signals are only shown under specific market conditions.
Higher Timeframe RSI Filter – Optional filter for confirming trend strength from a higher timeframe.
Configurable Inputs – Users can adjust RSI length, MACD parameters, SMA period, and other filters to match their preferred trading style.
Usage:
Suitable for short-term trading or as a confirmation tool alongside other strategies.
Signals are designed for observation and strategy testing; they do not guarantee results.
Alerts can be set up for buy and sell bounce signals to assist in monitoring potential setups in real-time.
Skywalker Strong Signals The Skywalker Scanner is a technical analysis tool designed to help traders evaluate market conditions by combining multiple signals into a single system.
Key Features:
EMA Trend Tracking – Fast and slow EMAs visually highlight bullish and bearish market zones.
RSI Alerts – Provides warnings when RSI reaches overbought or oversold levels to help identify potential momentum shifts.
Volume Filter – Signals are confirmed only when volume exceeds a moving average threshold.
Buy & Sell Conditions – Alerts trigger when EMA crossovers align with RSI thresholds, MACD momentum, and candle confirmation.
How It Works:
Instead of relying on a single indicator, the Skywalker Scanner filters setups so that buy or sell signals only appear when multiple conditions agree. This aims to reduce false positives and provide traders with clearer potential trade opportunities.
Usage:
Suitable across multiple timeframes, from scalping to swing trading.
Can be used standalone or as a confirmation tool alongside other strategies.
Does not guarantee results; intended for educational purposes only.
Cumulative Outperformance | viResearchCumulative Outperformance | viResearch
Conceptual Foundation and Innovation
The "Cumulative Outperformance" indicator by viResearch is a relative strength analysis tool designed to measure an asset’s cumulative performance against a chosen benchmark over a user-defined period. Rooted in comparative return analysis, this indicator allows traders and analysts to assess whether an asset is outperforming or underperforming a broader market or sector, offering insights into trend strength and leadership.
Unlike traditional relative strength indicators that may rely on static ratio comparisons, this script uses cumulative return differentials to provide a more contextual understanding of long-term performance trends. A clean visual representation and dynamic text summary are provided to highlight not only the degree of outperformance but also the directional status — making it accessible to both novice and advanced users.
Technical Composition and Calculation
The indicator compares the cumulative returns of the selected asset and a benchmark symbol over a specified lookback period (length). Returns are calculated as the percent change from the current price to the price length bars ago.
This differential is plotted and color-coded, with a baseline zero line to make outperformance and underperformance visually distinct. A dynamic table in the bottom-right corner displays real-time values for the benchmark symbol, the current outperformance percentage, and a status label (e.g., "Outperforming", "Underperforming", or "Even").
Additionally, a floating label is plotted directly on the chart to make the latest outperformance value immediately visible.
Features and User Inputs
The script includes the following customizable inputs:
Start Date: Defines the point from which to begin tracking outperformance data.
Length: The period over which cumulative returns are measured.
Benchmark Symbol: Select any market index, stock, or crypto as the benchmark (e.g., INDEX:BTCUSD, SPX, etc.).
Practical Applications
This indicator is especially effective in:
Identifying Market Leaders: Compare sectors, stocks, or altcoins against a leading benchmark to identify outperformers.
Sector Rotation Strategies: Monitor when certain assets begin to outperform or lag behind the broader market.
Cross-Market Analysis: Compare crypto pairs, equities, or commodities to their sector benchmarks to find relative strength opportunities.
Visual Aids and Alerts
A purple outperformance line highlights the degree of cumulative difference.
A horizontal dotted white line marks the baseline (zero performance difference).
Real-time table overlay updates the benchmark name, performance delta, and relative status.
Alerts are built-in to notify users when assets begin to outperform or underperform, helping you stay ahead of major shifts.
Advantages and Strategic Value
Benchmark Flexibility: Analyze any asset class against any benchmark of your choice.
Visual Clarity: Dynamic labels and tables make performance tracking intuitive and immediate.
No Repainting: Calculations are based on closed bar data for consistent backtesting and real-time use.
Summary and Usage Tips
The "Cumulative Outperformance | viResearch" script offers a clean and effective way to visualize relative strength between any asset and its benchmark. By focusing on cumulative returns over time, it filters out short-term noise and gives a strategic view of long-term strength or weakness. Use this tool in combination with other momentum or trend-following indicators to refine your market entries and asset selection.
Note: Backtests are based on past results and are not indicative of future performance.
Denys_MVT (Sessions Boxes)Denys_MVT (Sessions Boxes)
This indicator highlights the main trading sessions — Asia, Frankfurt, London, and New York — directly on the chart.
It helps traders visually separate market activity during different times of the day and quickly understand which session is currently active.
🔹 How it works
You can choose between Box Mode (draws a box around the session’s high and low) or Fill Mode (background color for the session).
Each session has its own customizable time range and color.
Labels can be placed automatically at the beginning of each session.
The script uses the time() function with your selected UTC offset to precisely map session times.
🔹 Features
Displays Asia, Frankfurt, London, and New York sessions.
Option to toggle between boxes and background shading.
Adjustable transparency and session colors.
Session labels for easier visual reference.
Works on any symbol and timeframe.
🔹 How to use
Add the indicator to your chart.
Set your local UTC offset in the settings (default: UTC+2).
Enable/disable sessions, change colors, or switch between Box/Fill mode.
Use the session highlights to better understand when volatility typically increases and how different sessions interact.
KCP Twine 2 [Dr.K.C.Prakash]KCP Twine 2
The indicator is a trend-following, range-filtered signal system.
It combines two smoothed volatility filters (fast & slow) and adds conditions for trend confirmation, momentum, and signal strength before showing BUY and SELL labels on the chart.
📊 Best Use Cases
Intraday trading: Works well on 5m, 15m, 1h timeframes to filter noise.
Swing trading: On 4h / Daily charts, helps spot clean trend reversals.
Trend confirmation tool: Can be used alongside other systems (like VWAP, Supertrend, or price action setups) to confirm trend bias.
⚠️ Limitations
Fewer signals (since filters are strict).
Might lag slightly in fast reversals (due to confirmation bars).
Works best in trending conditions, may chop in sideways markets.
Bull sailor intraday SR BY RahulSpecial indicator for intraday support and resistance with his acurrcy
Transaction Value Alert (4Cr+)Transactions with a value of INR 4 crore or above on a one-minute candle indicate FII or DII activity and confirms momentum and is an excellent indicator for the intraday trading
MTF MomentumUniqueness:
MTF Momentum is designed to provide true multiple-timeframe information at once on a single screen with as little clutter as possible. What makes MTF Momentum unique is the way it condenses the perspectives of our other internal models into a single bullish or bearish slope near the current candle, then automatically draws the same bullish or bearish momentum slopes of the next higher timeframes. The structure is engineered to highlight shifts in momentum as they happen on the current candle (angled lines), marking potential reversal points as they build (red and green diamonds), and provides a numerical Q-Score that draws a horizontal marker for elevated Q-Score exhaustion. The design avoids telling you when to buy or sell. Instead, it structures the raw inputs in a way that makes interpretation easier. That makes it useful whether you’re trading actively or simply learning to recognize how momentum flows across layers.
Usefulness:
This indicator is designed to work across multiple timeframes. Instead of juggling the same indicator on 3 different screens, you can see a unified picture that captures both the local momentum and higher timeframes that provide time-dimensional context. When short-term and higher-timeframe angles point in the same direction, MTF Momentum makes that visible in a straightforward way and may help highlight when momentum is consistent across multiple timeframes. When short-term layers push against a stronger higher timeframe, it signals that momentum may be shifting or exhausting. This indicator provides an efficient workflow and helps reduce clutter.
How It Works:
At its core, MTF Momentum is a blend of momentum readings from multiple sources — RSI slopes, EMA stacks, Gaussian smoothing, Fisher-style transforms, and MACD widening analysis built from the same shared core mathematical engines as our other indicators. The uniqueness of this indicator is not tied to any single formula as each component is well-known, but it is in the way they are layered, smoothed, and consolidated that entirely new readings are created.
The process begins with multiple RSI calculations, offset and averaged to reduce jitter. These are smoothed through EMA stacks of varying lengths, then run through Gaussian-style filters that emphasize directional change while filtering noise. The slope differences across these layers form the foundation of the momentum calculation. This momentum reading is then checked against MACD widening conditions. MACD gap expansion is treated as a momentum confirmation — widening gaps with price in agreement add weight, while narrowing gaps or misaligned candles reduce confidence. Additional derivative logic, including Fisher-style transforms, is applied to normalize the outputs and make them more stable across different assets.
Multi-timeframe integration comes from using request.security to pull higher timeframe versions of the same structures that are on the base chart. For example, you can see a one-minute chart overlaid with five-minute and fifteen-minute context. The blending is seamless — higher timeframe momentum is displayed alongside lower timeframe signals that help the user see where current timeframe momentum is in relation to higher timeframes.
How to Use the MTF Momentum Indicator:
Applying the MTF Momentum indicator is straightforward, but interpretation depends on your process.
To use, load the indicator on your preferred base timeframe. Use this general guideline to setup your indicators:
Base timeframe -> 1st HTF -> 2nd HTF
1min -> 5min -> 15min
5min -> 15min -> 1hr
15min -> 1hr -> 4hr
1hr -> 4hr -> 1day
4hr -> 1day -> Weekly
1day -> Weekly -> Monthly
Weekly -> Monthly -> Yearly
When used at base timeframes at 1 hour or lower, higher timeframe lines ARE drawn automatically.
When using a base timeframe above 1 hour (e.g., 4h, Daily), higher-timeframe slopes are NOT drawn automatically. To view them, switch to the higher-timeframe chart itself (for example, Daily or Weekly) and draw an arrow along the slope using TradingView’s drawing tools. Once placed, the arrow will remain visible when you return to your lower base timeframe chart, giving you the higher-timeframe context alongside your current view. This step is optional, purely for visual reference, and does not affect the indicator’s calculations.
These are your higher timeframe momentum angles that can help provide context to the automatically drawn angle on your current timeframe. You can even practice drawing these lines on the lower timeframes such as using a 5min base and 15min and 1hr HTF charts. You can compare your manually drawn angles with the automatic HTF lines by enabling them in the INPUTS tab of the MTF Momentum settings menu.
Q-SCORE:
The Q-Score label presents two values ranging from 0 to 100. These values are a numerical translation of the same momentum conditions our other indicators display visually. Higher values indicate stronger readings of exhaustion within the current trend model, while lower values indicate less. You can think of this as similar to a distribution curve, where some states occur less frequently at the extreme ends of the range and more frequently near the middle. Q-Score values are provided as contextual information only and do not predict reversals or guarantee outcomes.
Blue Dotted & Solid Horizontal line:
The aqua blue horizontal line is a visual representation of the Q-Score values. When one or both numerical values is below 85 the line stays dotted -- it is only when both numerical values exceed 85 that the line changes from dotted to solid.
Green & Red Diamonds:
Diamonds mark areas where the underlying model detects counter-trend behavior. They may flicker on the current candle during intrabar calculations but are locked in at candle close and never get altered or repainted.
Red diamonds highlight points where the model detects counter-trend pressure during a bullish phase. Green diamonds highlight counter-trend pressure during a bearish phase. These markers reflect where momentum conditions have shifted relative to the prevailing trend. They appear where short-term dynamics differ from the broader trend. Traders can interpret these areas in their own context; the diamonds themselves do not predict reversals or guarantee outcomes.
Example ways to use the MTF Momentum indicator:
Look for agreement -- when both your base timeframe and higher timeframe momentums are pointing in the same direction, it reflects stronger alignment. This may help identify areas of trend continuation.
Watch for divergence -- if your short-term momentum pushes opposite to the higher timeframe, it flags a potential transition.
Disclaimer:
This tool does not generate buy or sell signals. It is a framework for visualizing momentum across layers, allowing you to incorporate that information into your own decision-making. How you apply it depends entirely on your goals, timeframe, and risk tolerance. This indicator is provided for educational and informational purposes only. It does not constitute financial advice, trading advice, or investment recommendations. Trading involves risk, and you may lose some or all of your capital. Past performance is not a guarantee of future results. You are solely responsible for any decisions you make — always trade to the best of your own abilities and within your own risk tolerance.
Release Notes:
v1.0 (Initial Release)
MSFusion- MultiScoreFusionThis Pine Script strategy, MSFusion - MultiScoreFusion, combines Ichimoku components and Hull Moving Average (HMA) signals to generate a composite score for each bar.
It evaluates several conditions—such as price crossing above HMA55, Tenkan and Kijun lines, and price position relative to the Ichimoku cloud—and assigns scores to each.
The script displays a label with the total score and a tooltip listing the contributing conditions when a strong bullish signal is detected. This approach helps traders quickly assess market momentum and trend strength using multiple technical criteria.
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility.
1. Introduction and Theoretical Foundation
The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024).
The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance.
The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing.
The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation.
Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes.
2. Model Architecture and Component Structure
The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis.
2.1 Small Cap Relative Performance Component
The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects.
The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is:
Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1
This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment.
2.2 Credit Market Conditions Component
Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment.
The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses.
The mathematical specification combines these elements as:
Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t
where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding.
2.3 Labor Market Dynamics Component
The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment.
The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions.
The component is calculated as:
Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52)
This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures.
2.4 Consumer Demand Strength Component
Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations.
The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is:
Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t
where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure.
2.5 Input Cost Pressures Component
Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation.
The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility:
Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1)
The negative weighting reflects the inverse relationship between input costs and business conditions.
2.6 Monetary Policy Impact Component
Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy.
The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations:
Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t)
2.7 Currency Valuation Effects Component
Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations.
The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations:
Currency_Impact = -1 × DXY_Volatility_t
2.8 Regional Banking Health Component
The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions.
This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions:
Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1
3. Statistical Methodology and Advanced Analytics
The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework.
3.1 Variance-Weighted Aggregation
The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator.
The weight for component i is calculated as:
w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²)
where σᵢ² represents the variance of component i over the lookback period.
3.2 Higher-Order Moment Analysis
Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes.
Skewness is calculated as:
Skewness = E / σ³
Kurtosis is calculated as:
Kurtosis = E / σ⁴ - 3
where μ represents the mean and σ represents the standard deviation of the distribution.
3.3 Regime-Switching Detection
The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds:
- Regime 3 (Very High): Percentile rank > 80
- Regime 2 (High): Percentile rank 60-80
- Regime 1 (Moderate High): Percentile rank 50-60
- Regime 0 (Neutral): Percentile rank 40-50
- Regime -1 (Moderate Low): Percentile rank 30-40
- Regime -2 (Low): Percentile rank 20-30
- Regime -3 (Very Low): Percentile rank < 20
3.4 Information Theory Applications
The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution:
H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ)
Higher entropy values indicate greater unpredictability and information content in the sentiment series.
3.5 Long-Term Memory Analysis
The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic:
H = log(R/S) / log(T)
where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior.
3.6 Structural Break Detection
The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods:
Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k
where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters.
4. Implementation Parameters and Configuration
4.1 Language Selection Parameters
The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations.
4.2 Model Configuration Parameters
Calculation Method: Users can select from four aggregation methodologies:
- Equal-Weighted: All components receive identical weights
- Variance-Weighted: Components weighted inversely to their historical variance
- Principal Component: Weights determined through principal component analysis
- Dynamic: Adaptive weighting based on recent performance
Sector Specification: The model allows for sector-specific calibration:
- General: Broad-based small business assessment
- Retail: Emphasis on consumer demand and seasonal factors
- Manufacturing: Enhanced weighting of input costs and currency effects
- Services: Focus on labor market dynamics and consumer demand
- Construction: Emphasis on credit conditions and monetary policy
Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research.
Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity.
4.3 Statistical Threshold Parameters
Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification.
Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification.
Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default.
4.4 Display and Visualization Parameters
Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements:
- Gold: Traditional financial industry colors
- EdgeTools: Professional blue-gray scheme
- Behavioral: Psychology-based color mapping
- Quant: Value-based quantitative color scheme
- Ocean: Blue-green maritime theme
- Fire: Warm red-orange theme
- Matrix: Green-black technology theme
- Arctic: Cool blue-white theme
Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions.
Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility.
Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction.
4.5 Alert System Configuration
Alert Frequency Options: Three frequency settings to match different trading styles:
- Once Per Bar: Single alert per bar formation
- Once Per Bar Close: Alert only on confirmed bar close
- All: Continuous alerts for real-time monitoring
Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576).
Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes.
5. Practical Application and Interpretation Guidelines
5.1 Index Interpretation Framework
The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity.
The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes.
5.2 Regime Classification System
The model employs a seven-level regime classification system based on percentile rankings:
Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability.
High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment.
Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends.
Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles.
Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand.
Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion.
Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning.
5.3 Component Analysis and Diagnostics
Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy.
Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures.
Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins.
Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment.
5.4 Temporal Analysis and Trend Identification
The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends.
Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss.
5.5 Statistical Confidence and Uncertainty Quantification
The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels.
Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions.
6. Risk Management and Limitations
6.1 Model Limitations and Assumptions
The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts.
The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions.
The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable.
6.2 Data Quality and Reliability Considerations
The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays.
Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility.
6.3 Interpretation Caveats and Best Practices
The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making.
The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications.
7. Academic References and Bibliography
Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.
Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.
Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25.
Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605.
Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C.
Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ.
Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.
Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ.
U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C.
8. Technical Implementation Notes
The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization.
The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals.
When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold.
The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices.
The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making.
The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
Momentum Moving Averages | MisinkoMasterThe Momentum Moving Averages (MMA) indicator blends multiple moving averages into a single momentum-scoring framework, helping traders identify whether market conditions are favoring upside momentum or downside momentum.
By comparing faster, more adaptive moving averages (DEMA, TEMA, ALMA, HMA) against a baseline EMA, the MMA produces a cumulative score that reflects the prevailing strength and direction of the trend.
🔎 Methodology
Moving Averages Used
EMA (Exponential Moving Average) → Baseline reference.
DEMA (Double Exponential Moving Average) → Reacts faster than EMA.
TEMA (Triple Exponential Moving Average) → Even faster, reduces lag further.
ALMA (Arnaud Legoux Moving Average) → Smooth but adaptive, with adjustable σ and offset.
HMA (Hull Moving Average) → Very responsive, reduces lag, ideal for momentum shifts.
Scoring System
Each comparison is made against the EMA baseline:
If another MA is above EMA → +1 point.
If another MA is below EMA → -1 point.
The total score reflects overall momentum:
Positive score → Bullish bias.
Negative score → Bearish bias.
Trend Logic
Bullish Signal → When the score crosses above 0.1.
Bearish Signal → When the score crosses below -0.1.
Neutral or sideways trends are identified when the score remains between thresholds.
📈 Visualization
All five moving averages are plotted on the chart.
Colors adapt to the current score:
Cyan (Bullish bias) → Positive momentum.
Magenta (Bearish bias) → Negative momentum.
Overlapping fills between MAs highlight zones of convergence/divergence, making momentum shifts visually clear.
⚡ Features
Adjustable length parameter for all MAs.
Adjustable ALMA parameters (sigma and offset).
Cumulative momentum score system to filter false signals.
Works across all markets (crypto, forex, stocks, indices).
Overlay design for direct chart integration.
✅ Use Cases
Trend Confirmation → Ensure alignment with market momentum.
Momentum Shifts → Spot when faster MAs consistently outperform the baseline EMA.
Entry & Exit Filter → Avoid trades when the score is neutral or indecisive.
Divergence Visualizer → Filled zones make it easier to see when MAs begin separating or converging.
Low History Required → Unlike most For Loops, this script does not require that much history, making it less lagging and more responsive
⚠️ Limitations
Works best in trending conditions; performance decreases in sideways/choppy ranges.
Sensitivity of signals depends on chosen length and ALMA settings.
Should not be used as a standalone buy/sell system—combine with volume, structure, or higher timeframe analysis.
Scenario Screener — Consolidation → Bullish SetupThe script combines multiple indicators to filter out false signals and only highlight strong conditions:
Consolidation Check
Uses ATR % of price → filters out stocks in tight ranges.
Uses Choppiness Index → confirms sideways/non-trending behavior.
Momentum Shift (Bullish Bias)
MACD Histogram > 0 → bullish momentum starting.
RSI between 55–70 → strength without being overbought.
Stochastic %K & %D > 70 → confirms strong momentum.
Volume & Accumulation
Chaikin Money Flow (CMF > 0) → buying pressure.
Chaikin Oscillator > 0 (debug only) → accumulation phase.
Trend Direction
+DI > -DI (from DMI) → buyers stronger than sellers.
ADX between 18–40 → healthy trend strength (not too weak, not overheated).
Breakout Filter (Optional)
If enabled, requires price to cross above 20 SMA before signal confirmation.
📈 Outputs
✅ Green label (“MATCH”) below the bar when all bullish conditions align.
✅ Background highlight (light green) when signal appears.
✅ Info Table (top-right) summarizing key values:
Signal = True/False
MACD, CMF, Chaikin values
DynamoSent DynamoSent Pro+ — Professional Listing (Preview)
— Adaptive Macro Sentiment (v6)
— Export, Adaptive Lookback, Confidence, Boxes, Heatmap + Dynamic OB/OS
Preview / Experimental build. I’m actively refining this tool—your feedback is gold.
If you spot edge cases, want new presets, or have market-specific ideas, please comment or DM me on TradingView.
⸻
What it is
DynamoSent Pro+ is an adaptive, non-repainting macro sentiment engine that compresses VIX, DXY and a price-based activity proxy (e.g., SPX/sector ETF/your symbol) into a 0–100 sentiment line. It scales context by volatility (ATR%) and can self-calibrate with rolling quantile OB/OS. On top of that, it adds confidence scoring, a plain-English Context Coach, MTF agreement, exportable sentiment for other indicators, and a clean Light/Dark UI.
Why it’s different
• Adaptive lookback tracks regime changes: when volatility rises, we lengthen context; when it falls, we shorten—less whipsaw, more relevance.
• Dynamic OB/OS (quantiles) self-calibrates to each instrument’s distribution—no arbitrary 30/70 lines.
• MTF agreement + Confidence gate reduce false positives by highlighting alignment across timeframes.
• Exportable output: hidden plot “DynamoSent Export” can be selected as input.source in your other Pine scripts.
• Non-repainting rigor: all request.security() calls use lookahead_off + gaps_on; signals wait for bar close.
Key visuals
• Sentiment line (0–100), OB/OS zones (static or dynamic), optional TF1/TF2 overlays.
• Regime boxes (Overbought / Oversold / Neutral) that update live without repaint.
• Info Panel with confidence heat, regime, trend arrow, MTF readout, and Coach sentence.
• Session heat (Asia/EU/US) to match intraday behavior.
• Light/Dark theme switch in Inputs (auto-contrasted labels & headers).
⸻
How to use (examples & recipes)
1) EURUSD (swing / intraday blend)
• Preset: EURUSD 1H Swing
• Chart: 1H; TF1=1H, TF2=4H (default).
• Proxies: Defaults work (VIX=D, DXY=60, Proxy=D).
• Dynamic OB/OS: ON at 20/80; Confidence ≥ 55–60.
• Playbook:
• When sentiment crosses above 50 + margin with Δ ≥ signalK and MTF agreement ≥ 0.5, treat as trend breakout.
• In Oversold with rising Coach & TF agreement, take fade longs back toward mid-range.
• Alerts: Enable Breakout Long/Short and Fade; keep cooldown 8–12 bars.
2) SPY (daytrading)
• Preset: SPY 15m Daytrade; Chart: 15m.
• VIX (D) matters more; preset weights already favor it.
• Start with static 30/70; later try dynamic 25/75 for adaptive thresholds.
• Use Coach: in US session, when it says “Overbought + MTF agree → sell rallies / chase breakouts”, lean momentum-continuation after pullbacks.
3) BTCUSD (crypto, 24/7)
• Preset: BTCUSD 1H; Chart: 1H.
• DXY and BTC.D inform macro tone; keep Carry-forward ON to bridge sparse ticks.
• Prefer Dynamic OB/OS (15/85) for wider swings.
• Fade signals on weekend chop; Breakout when Confidence > 60 and MTF ≥ 1.0.
4) XAUUSD (gold, macro blend)
• Preset: XAUUSD 4H; Chart: 4H.
• Weights tilt to DXY and US10Y (handled by preset).
• Coach + MTF helps separate trend legs from news pops.
⸻
Best practices
• Theme: Switch Light/Dark in Inputs; the panel adapts contrast automatically.
• Export: In another script → Source → DynamoSent Pro+ → DynamoSent Export. Build your own filters/strategies atop the same sentiment.
• Dynamic vs Static OB/OS:
• Static 30/70: fast, universal baseline.
• Dynamic (quantiles): instrument-aware; use 20/80 (default) or 15/85 for choppy markets.
• Confidence gate: Start at 50–60% to filter noise; raise when you want only A-grade setups.
• Adaptive Lookback: Keep ON. For ultra-liquid indices, you can switch it OFF and set a fixed lookback.
⸻
Non-repainting & safety notes
• All request.security() calls use lookahead=barmerge.lookahead_off and gaps=barmerge.gaps_on.
• No forward references; signals & regime flips are confirmed on bar close.
• History-dependent funcs (ta.change, ta.percentile_linear_interpolation, etc.) are computed each bar (not conditionally).
• Adaptive lookback is clamped ≥ 1 to avoid lowest/highest errors.
• Missing-data warning triggers only when all proxies are NA for a streak; carry-forward can bridge small gaps without repaint.
⸻
Known limits & tips
• If a proxy symbol isn’t available on your plan/exchange, you’ll see the NA warning: choose a different symbol via Symbol Search, or keep Carry-forward ON (it defaults to neutral where needed).
• Intraday VIX is sparse—using Daily is intentional.
• Dynamic OB/OS needs enough history (see dynLenFloor). On short histories it gracefully falls back to static levels.
Thanks for trying the preview. Your comments drive the roadmap—presets, new proxies, extra alerts, and integrations.
Trend Compass (Manual)## Trend Compass (Manual) - A Discretionary Trader's Dashboard
### Summary
Trend Compass is a simple yet powerful dashboard designed for discretionary traders who want a constant, visual reminder of their market analysis directly on their chart. Instead of relying on automated indicators, this tool gives you **full manual control** to define the market state across different timeframes or conditions.
It helps you stay aligned with your higher-level analysis (e.g., HTF bias, current market structure) and avoid making impulsive decisions that go against your plan.
### Key Features
- **Fully Manual Control:** You decide the trend. No lagging indicators, no confusing signals. Just your own analysis, displayed clearly.
- **Multiple Market States:** Define each row as an `Uptrend`, `Downtrend`, `Pullback`, or `Neutral` market.
- **Customizable Rows:** Display up to 8 rows. You can label each one however you like (e.g., "D1", "H4", "Market Structure", "Liquidity Bias").
- **Flexible Panel:** Change all colors, text sizes, and place the panel in any of the 9 positions on your chart.
- **Clean & Minimalist:** Designed to provide essential information at a glance without cluttering your chart.
### How to Use
1. **Add to Chart:** Add the indicator to your chart.
2. **Open Settings:** Go into the indicator settings.
3. **Configure Rows:**
- In the "Rows (Manual Control)" section, set the "Number of rows" you want to display.
- For each row, give it a custom **Label** (e.g., "m15").
- Select its current state from the dropdown menu (`Uptrend`, `Downtrend`, etc.).
- To remove a row, simply set its state to `Hidden`.
4. **Customize Style:**
- In the "Panel & Visual Style" section, adjust colors, text sizes, and the panel's position to match your chart's theme.
This tool is perfect for price action traders, ICT/SMC traders, or anyone who values a clean chart and a disciplined approach to their analysis.
Ichimoku + MTF Dashboard (Confidence + Row Shading)Name: Ichimoku + Multi-Timeframe (MTF) Dashboard
Purpose
This indicator is designed to give a complete trend, momentum, and alignment picture of a stock across multiple timeframes (hourly, daily, weekly) using the Ichimoku Kinko Hyo system. It combines:
Classic Ichimoku signals: Tenkan/Kijun crossovers, cloud position (Kumo), Chikou span, and cloud twists.
MTF Dashboard: Aggregates hourly, daily, and weekly Ichimoku conditions into a clean visual table.
Dynamic coloring: Each signal is represented with green/red fills, and rows are shaded for full alignment. Aggregate column highlights mixed signals in yellow.
Entry Signals (Long/Short)The indicator visualizes precise entry signals for long and short setups directly on the price chart. Long is marked with a green triangle-up, short with a red triangle-down. To contextualize trend structure, the Fast EMA (5) is plotted in black and the Slow EMA (20) in blue (line width 1). Signals print only at bar close for reproducible execution. Applicable across all timeframes—ideal for top-down analysis from the 195-minute chart through daily to weekly.
