AVGO Advanced Day Trading Strategy📈 Overview
The AVGO Advanced Day Trading Strategy is a comprehensive, multi-timeframe trading system designed for active day traders seeking consistent performance with robust risk management. Originally optimized for AVGO (Broadcom), this strategy adapts well to other liquid stocks and can be customized for various trading styles.
🎯 Key Features
Multiple Entry Methods
EMA Crossover: Classic trend-following signals using fast (9) and medium (16) EMAs
MACD + RSI Confluence: Momentum-based entries combining MACD crossovers with RSI positioning
Price Momentum: Consecutive price action patterns with EMA and RSI confirmation
Hybrid System: Advanced multi-trigger approach combining all methodologies
Advanced Technical Arsenal
When enabled, the strategy analyzes 8+ additional indicators for confluence:
Volume Price Trend (VPT): Measures volume-weighted price momentum
On-Balance Volume (OBV): Tracks cumulative volume flow
Accumulation/Distribution Line: Identifies institutional money flow
Williams %R: Momentum oscillator for entry timing
Rate of Change Suite: Multi-timeframe momentum analysis (5, 14, 18 periods)
Commodity Channel Index (CCI): Cyclical turning points
Average Directional Index (ADX): Trend strength measurement
Parabolic SAR: Dynamic support/resistance levels
🛡️ Risk Management System
Position Sizing
Risk-based position sizing (default 1% per trade)
Maximum position limits (default 25% of equity)
Daily loss limits with automatic position closure
Multiple Profit Targets
Target 1: 1.5% gain (50% position exit)
Target 2: 2.5% gain (30% position exit)
Target 3: 3.6% gain (20% position exit)
Configurable exit percentages and target levels
Stop Loss Protection
ATR-based or percentage-based stop losses
Optional trailing stops
Dynamic stop adjustment based on market volatility
📊 Technical Specifications
Primary Indicators
EMAs: 9 (Fast), 16 (Medium), 50 (Long)
VWAP: Volume-weighted average price filter
RSI: 6-period momentum oscillator
MACD: 8/13/5 configuration for faster signals
Volume Confirmation
Volume filter requiring 1.6x average volume
19-period volume moving average baseline
Optional volume confirmation bypass
Market Structure Analysis
Bollinger Bands (20-period, 2.0 multiplier)
Squeeze detection for breakout opportunities
Fractal and pivot point analysis
⏰ Trading Hours & Filters
Time Management
Configurable trading hours (default: 9:30 AM - 3:30 PM EST)
Weekend and holiday filtering
Session-based trade management
Market Condition Filters
Trend alignment requirements
VWAP positioning filters
Volatility-based entry conditions
📱 Visual Features
Information Dashboard
Real-time display of:
Current entry method and signals
Bullish/bearish signal counts
RSI and MACD status
Trend direction and strength
Position status and P&L
Volume and time filter status
Chart Visualization
EMA plots with customizable colors
Entry signal markers
Target and stop level lines
Background color coding for trends
Optional Bollinger Bands and SAR display
🔔 Alert System
Entry Alerts
Customizable alerts for long and short entries
Method-specific alert messages
Signal confluence notifications
Advanced Alerts
Strong confluence threshold alerts
Custom alert messages with signal counts
Risk management alerts
⚙️ Customization Options
Strategy Parameters
Enable/disable long or short trades
Adjustable risk parameters
Multiple entry method selection
Advanced indicator on/off toggle
Visual Customization
Color schemes for all indicators
Dashboard position and size options
Show/hide various chart elements
Background color preferences
📋 Default Settings
Initial Capital: $100,000
Commission: 0.1%
Default Position Size: 10% of equity
Risk Per Trade: 1.0%
RSI Length: 6 periods
MACD: 8/13/5 configuration
Stop Loss: 1.1% or ATR-based
🎯 Best Use Cases
Day Trading: Designed for intraday opportunities
Swing Trading: Adaptable for longer-term positions
Momentum Trading: Excellent for trending markets
Risk-Conscious Trading: Built-in risk management protocols
⚠️ Important Notes
Paper Trading Recommended: Test thoroughly before live trading
Market Conditions: Performance varies with market volatility
Customization: Adjust parameters based on your risk tolerance
Educational Purpose: Use as a learning tool and customize for your needs
🏆 Performance Features
Detailed performance metrics
Trade-by-trade analysis capability
Customizable risk/reward ratios
Comprehensive backtesting support
This strategy is for educational purposes. Past performance does not guarantee future results. Always practice proper risk management and consider your financial situation before trading.
Educational
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.
Alpha SignalsThis strategy is designed to highlight potential short-term market setups using a fast and slow EMA crossover system on a 5-minute chart. It provides visual signals directly on the chart to help traders observe trend changes and potential entry points.
Key Features:
EMA Crossover Entries – The strategy enters long trades when the fast EMA crosses above the slow EMA and short trades when the fast EMA crosses below the slow EMA.
Time-Based Exits – Trades are automatically closed after a configurable number of bars to manage exposure.
Visual Alerts – Buy and sell signals are displayed as labels directly on the chart for easy interpretation.
Configurable Settings – Users can adjust fast and slow EMA lengths as well as the exit bar count to suit their trading preferences.
Usage:
Suitable for short-term traders focusing on the NQ1 futures contract or other instruments with similar volatility.
Can be used for observation, back testing, or as a confirmation tool alongside other strategies.
Does not guarantee profitability; intended for educational purposes and strategy testing only.
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.
🚀 ETH Price LinesThis Pine Script strategy ("🚀 ETH Price Lines") does:
Trend detection with short & long SMAs
Noise reduction using Kalman filters
Signal confirmation from ADX (trend strength) + volume
Entry/exit:
Buy when short-term crosses above long-term
Sell when it crosses below
Risk management: optional stop-loss (default 3%)
Visuals: plots SMAs, Kalman lines, buy/sell markers, and triggers alerts
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.
Trend Magic EMA RMI Trend Sniper📌 Indicator Name:
Trend Magic + EMA + MA Smoothing + RMI Trend Sniper
📝 Description:
This is a multi-functional trend and momentum indicator that combines four powerful tools into a single overlay:
Trend Magic – Plots a dynamic support/resistance line based on CCI and ATR.
Helps identify trend direction (green = bullish, red = bearish).
Acts as a trailing stop or dynamic level for trade entries/exits.
Exponential Moving Average (EMA) – Smooths price data to highlight the underlying trend.
Customizable length, source, and offset.
Serves as a trend filter or moving support/resistance.
MA Smoothing + Bollinger Bands (Optional) – Adds a secondary smoothing filter based on your choice of SMA, EMA, WMA, VWMA, or SMMA.
Optional Bollinger Bands visualize volatility expansion/contraction.
Great for spotting consolidations and breakout opportunities.
RMI Trend Sniper – A momentum-based system combining RSI and MFI.
Highlights bullish (green) or bearish (red) conditions.
Plots a Range-Weighted Moving Average (RWMA) channel to gauge price positioning.
Provides visual BUY/SELL labels and optional bar coloring for fast decision-making.
📊 Uses & Trading Applications:
✅ Trend Identification: Spot the dominant market direction quickly with Trend Magic & EMA.
✅ Momentum Confirmation: RMI Sniper helps confirm whether the market has strong bullish or bearish pressure.
✅ Dynamic Support/Resistance: Trend Magic & EMA act as adaptive levels for stop-loss or trailing positions.
✅ Volatility Analysis: Optional Bollinger Bands show squeezes and potential breakout setups.
✅ Entry/Exit Signals: BUY/SELL alerts and color-coded candles make spotting trade opportunities simple.
💡 Best Use Cases:
Swing Trading: Follow Trend Magic + EMA alignment for higher probability trades.
Scalping/Intraday: Use RMI signals with bar coloring for quick momentum entries.
Trend Following Strategies: Ride trends until Trend Magic flips direction.
Breakout Trading: Watch for price closing outside the Bollinger Bands with RMI confirmation.
SMR - Simple Market Recap📊 Simple Market Recap (SMR)
🎯 A comprehensive market overview tool displaying price changes, percentage movements, and status indicators for multiple financial instruments across customizable timeframes with intelligent data synchronization.
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📋 OVERVIEW
The Simple Market Recap indicator provides a professional market analysis dashboard that displays key performance metrics for major financial instruments. This educational tool features intelligent asset selection, automatic dark mode detection, comprehensive period analysis with bilingual support, and advanced data synchronization ensuring accurate price data regardless of the current chart symbol.
Perfect for:
Market overview analysis and educational study
Multi-asset performance comparison and research
Weekly, daily, and monthly market recap visualization
Educational purposes and market trend analysis
━━━━━━━━━━━━━━
🚀 KEY FEATURES & ENHANCEMENTS
🌙 Intelligent Dark Mode Detection
Automatic chart background color analysis and adaptation
Dynamic color scheme adjustment for optimal visibility
Enhanced contrast ratios for both light and dark themes
Professional appearance across all chart backgrounds
📊 Comprehensive Asset Coverage
Major Forex Pairs: EURUSD, GBPUSD, AUDUSD, NZDUSD, USDCHF, USDJPY, USDCAD
Indices & Dollar: DXY (US Dollar Index), SPX (S&P 500)
Commodities: XAUUSD (Gold), USOIL (Crude Oil)
Bonds: US10Y (10-Year Treasury)
Cryptocurrencies: BTCUSDT, ETHUSDT
Selective asset display with individual on/off controls
Fixed asset order: DXY, EURUSD, GBPUSD, AUDUSD, NZDUSD, USDCHF, USDCAD, USDJPY, XAUUSD, USOIL, SPX, US10Y, ETHUSDT, BTCUSDT
⏰ Flexible Timeframe Analysis
Multiple Periods: Daily (1D), Weekly (1W), Monthly (1M)
Time Selection: Current Period or Previous Period analysis
Dynamic Titles: Automatic report naming with dates and periods
Historical Comparison: Compare current vs previous period performance
📈 Enhanced Data Visualization
Professional table with adaptive row count based on selected assets
Color-coded price movements: Enhanced green for positive, bright red for negative
Status emojis: ↗️ Up, ↘️ Down, ↔️ Sideways, ❓ No data
Smart price formatting based on asset type and price level
Improved contrast colors for better visibility in all lighting conditions
🔄 Advanced Data Synchronization
Symbol-Independent Accuracy: Correct data display regardless of current chart symbol
Real-Time Security Requests: Direct data fetching from specific instrument sources
Cross-Asset Reliability: Accurate price data for all monitored assets simultaneously
Data Integrity: No cross-contamination between different financial instruments
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🎨 PROFESSIONAL TABLE LAYOUT
Adaptive Design Features:
Automatic dark mode detection and color adaptation
Enhanced contrast ratios for better readability
Professional color scheme with clear data separation
Responsive design for all screen sizes and themes
Comprehensive Data Display:
Dynamic Title Row: Period-specific report titles with dates
Asset Column: Selected financial instruments
Open/Close Prices: Period opening and closing values
Change Percentage: Color-coded performance indicators
Pips Movement: Precise pip calculations for each asset
Status Indicators: Visual emoji representations of trend direction
Visual Design Features:
Merged title cells for clean header presentation
Asset-specific price formatting for optimal readability
Color-coded positive/negative movements
Professional table borders and spacing
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⚙️ ADVANCED CUSTOMIZATION
Timeframe Controls:
Report Period selection: Daily, Weekly, or Monthly analysis
Time Selection toggle: Current vs Previous period comparison
Dynamic row count based on active asset selection
Automatic title generation with period-specific formatting
Asset Selection:
Individual toggle controls for each supported asset
Major forex pairs with complete coverage
Cryptocurrency and precious metals options
Index and commodity instrument support
Display Options:
9 table positioning options across the entire chart
5 text size levels from Tiny to Huge for optimal visibility
Language selection between English and Vietnamese
Automatic theme adaptation for all chart backgrounds
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⚠️ EDUCATIONAL & ANALYTICAL PURPOSE
This indicator is designed exclusively for educational market analysis and research purposes .
📚 Educational Applications:
Understanding multi-asset market performance correlation
Studying period-based price movements and trends
Analyzing market volatility across different timeframes
Learning to read and interpret market recap data
📊 Analysis Capabilities:
Market overview visualization for educational study
Multi-timeframe performance comparison research
Historical period analysis and trend identification
Cross-asset correlation studies and market research
🚨 Important Disclaimer: This tool provides educational market data visualization only and does NOT generate trading signals or investment advice. All data is for learning and analysis purposes. Users must conduct independent research and consult financial professionals before making any investment decisions.
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🛠️ SETUP & CONFIGURATION
Quick Start Guide:
Add the indicator to your chart from the indicators library
Select your preferred language (English or Vietnamese)
Choose your desired reporting timeframe (Daily, Weekly, or Monthly)
Select Current Period or Previous Period for analysis
Toggle on/off specific assets you want to monitor
Adjust table position and text size for optimal viewing
Advanced Configuration:
Customize asset selection based on your analysis needs
Configure timeframe settings for different market studies
Set up language preferences for your region
Fine-tune display options for your screen setup
Optimize table positioning for your chart layout
Theme Optimization:
Indicator automatically detects your chart theme
Colors adapt automatically for optimal contrast and readability
No manual adjustments required for theme changes
Professional appearance maintained across all backgrounds
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🔧 TECHNICAL SPECIFICATIONS
Performance & Reliability:
Pine Script v6 with optimized data retrieval
Real-time updates with minimal CPU and memory usage
No repainting or lookahead bias in calculations
Stable performance across all timeframes and instruments
Universal Compatibility:
Works with all TradingView chart types and instruments
Compatible with mobile and desktop platforms
Supports all timeframes with period-specific analysis
Cross-platform functionality with consistent behavior
Data Precision:
High-precision floating-point calculations
Asset-specific formatting and pip calculations
Real-time price data from multiple exchanges
Accurate percentage and movement calculations
Advanced Features:
Automatic chart background detection and color adaptation
Dynamic table sizing based on active asset selection
Intelligent price formatting for different asset classes
Professional status indicators with emoji visualization
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📋 VERSION HISTORY
v1.7 - Enhanced Data Synchronization & Color Improvements
Fixed critical data synchronization issue - accurate data regardless of current chart symbol
Enhanced data retrieval system with symbol-specific security requests
Improved color scheme: brighter red for negative values, enhanced contrast
Fixed asset order: DXY, EURUSD, GBPUSD, AUDUSD, NZDUSD, USDCHF, USDCAD, USDJPY, XAUUSD, USOIL, SPX, US10Y, ETHUSDT, BTCUSDT
Optimized price formatting with proper decimal display and leading zeros
Enhanced calendar-based time calculations for accurate period reporting
Improved pip calculations for different asset classes
Professional color coding with adaptive contrast for all themes
Previous Versions:
v1.6 - Data accuracy improvements and bug fixes
v1.5 - Enhanced market analysis with flexible timeframes
v1.4 - Professional table layout and bilingual support
Earlier versions - Core market data display functionality development
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Author: tohaitrieu
Version: 1.7
Category: Market Analysis / Educational Overview
Language Support: English, Vietnamese
License: Educational Use Only
This indicator is provided exclusively for educational and analytical purposes to help users understand market overview concepts and multi-asset analysis. It features automatic theme adaptation, flexible timeframe analysis, enhanced data synchronization, and comprehensive market data visualization for the most accurate and informative educational experience. It does not provide trading signals or investment advice. Always conduct thorough research and consider professional guidance before making financial decisions.
Quarterly Theory Markers[DarkKnightDrako]
Quarterly Theory Zones
Grayscale Chart Color ( Background colors can be changed)
12PM - 12PM with Asia, London, NewYork AM, NewYork PM lines.
