CCOMET_Scanner_LibraryLibrary "CCOMET_Scanner_Library"
- A Trader's Edge (ATE)_Library was created to assist in constructing CCOMET Scanners
Loc_tIDs_Col(_string, _firstLocation)
TickerIDs: You must form this single tickerID input string exactly as described in the scripts info panel (little gray 'i' that
is circled at the end of the settings in the settings/input panel that you can hover your cursor over this 'i' to read the
details of that particular input). IF the string is formed correctly then it will break up this single string parameter into
a total of 40 separate strings which will be all of the tickerIDs that the script is using in your CCOMET Scanner.
Locations: This function is used when there's a desire to print an assets ALERT LABELS. A set Location on the scale is assigned to each asset.
This is created so that if a lot of alerts are triggered, they will stay relatively visible and not overlap each other.
If you set your '_firstLocation' parameter as 1, since there are a max of 40 assets that can be scanned, the 1st asset's location
is assigned the value in the '_firstLocation' parameter, the 2nd asset's location is the (1st asset's location+1)...and so on.
Parameters:
_string (simple string) : (string)
A maximum of 40 Tickers (ALL joined as 1 string for the input parameter) that is formulated EXACTLY as described
within the tooltips of the TickerID inputs in my CCOMET Scanner scripts:
assets = input.text_area(tIDset1, title="TickerID (MUST READ TOOLTIP)", tooltip="Accepts 40 TICKERID's for each
copy of the script on the chart. TEXT FORMATTING RULES FOR TICKERID'S:
(1) To exclude the EXCHANGE NAME in the Labels, de-select the next input option.
(2) MUST have a space (' ') AFTER each TickerID.
(3) Capitalization in the Labels will match cap of these TickerID's.
(4) If your asset has a BaseCurrency & QuoteCurrency (ie. ADAUSDT ) BUT you ONLY want Labels
to show BaseCurrency(ie.'ADA'), include a FORWARD SLASH ('/') between the Base & Quote (ie.'ADA/USDT')", display=display.none)
_firstLocation (simple int) : (simple int)
Optional (starts at 1 if no parameter added).
Location that you want the first asset to print its label if is triggered to do so.
ie. loc2=loc1+1, loc3=loc2+1, etc.
Returns: Returns 40 output variables in the tuple (ie. between the ' ') with the TickerIDs, 40 variables for the locations for alert labels, and 40 Colors for labels/plots
TickeridForLabelsAndSecurity(_ticker, _includeExchange)
This function accepts the TickerID Name as its parameter and produces a single string that will be used in all of your labels.
Parameters:
_ticker (simple string) : (string)
For this parameter, input the varible named '_coin' from your 'f_main()' function for this parameter. It is the raw
Ticker ID name that will be processed.
_includeExchange (simple bool) : (bool)
Optional (if parameter not included in function it defaults to false ).
Used to determine if the Exchange name will be included in all labels/triggers/alerts.
Returns: ( )
Returns 2 output variables:
1st ('_securityTickerid') is to be used in the 'request.security()' function as this string will contain everything
TV needs to pull the correct assets data.
2nd ('lblTicker') is to be used in all of the labels in your CCOMET Scanner as it will only contain what you want your labels
to show as determined by how the tickerID is formulated in the CCOMET Scanner's input.
InvalID_LblSz(_barCnt, _close, _securityTickerid, _invalidArray, _tablePosition, _stackVertical, _lblSzRfrnce)
INVALID TICKERIDs: This is to add a table in the middle right of your chart that prints all the TickerID's that were either not formulated
correctly in the '_source' input or that is not a valid symbol and should be changed.
LABEL SIZES: This function sizes your Alert Trigger Labels according to the amount of Printed Bars the chart has printed within
a set time period, while also keeping in mind the smallest relative reference size you input in the 'lblSzRfrnceInput'
parameter of this function. A HIGHER % of Printed Bars(aka...more trades occurring for that asset on the exchange),
the LARGER the Name Label will print, potentially showing you the better opportunities on the exchange to avoid
exchange manipulation liquidations.
*** SHOULD NOT be used as size of labels that are your asset Name Labels next to each asset's Line Plot...
if your CCOMET Scanner includes these as you want these to be the same size for every asset so the larger ones dont cover the
smaller ones if the plots are all close to each other ***
Parameters:
_barCnt (float) : (float)
Get the 1st variable('barCnt') from the Security function's tuple and input it as this functions 1st input
parameter which will directly affect the size of the 2nd output variable ('alertTrigLabel') that is also outputted by this function.
_close (float) : (float)
Put your 'close' variable named '_close' from the security function here.
_securityTickerid (string) : (string)
Throughout the entire charts updates, if a '_close' value is never registered then the logic counts the asset as INVALID.
This will be the 1st TickerID variable (named _securityTickerid) outputted from the tuple of the TickeridForLabels()
function above this one.
_invalidArray (array) : (array string)
Input the array from the original script that houses all of the invalidArray strings.
_tablePosition (simple string) : (string)
Optional (if parameter not included, it defaults to position.middle_right). Location on the chart you want the table printed.
Possible strings include: position.top_center, position.top_left, position.top_right, position.middle_center,
position.middle_left, position.middle_right, position.bottom_center, position.bottom_left, position.bottom_right.
_stackVertical (simple bool) : (bool)
Optional (if parameter not included, it defaults to true). All of the assets that are counted as INVALID will be
created in a list. If you want this list to be prited as a column then input 'true' here, otherwise they will all be in a row.
_lblSzRfrnce (string) : (string)
Optional (if parameter not included, it defaults to size.small). This will be the size of the variable outputted
by this function named 'assetNameLabel' BUT also affects the size of the output variable 'alertTrigLabel' as it uses this parameter's size
as the smallest size for 'alertTrigLabel' then uses the '_barCnt' parameter to determine the next sizes up depending on the "_barCnt" value.
Returns: ( )
Returns 2 variables:
1st output variable ('AssetNameLabel') is assigned to the size of the 'lblSzRfrnceInput' parameter.
2nd output variable('alertTrigLabel') can be of variying sizes depending on the 'barCnt' parameter...BUT the smallest
size possible for the 2nd output variable ('alertTrigLabel') will be the size set in the 'lblSzRfrnceInput' parameter.
PrintedBarCount(_time, _barCntLength, _barCntPercentMin)
The Printed BarCount Filter looks back a User Defined amount of minutes and calculates the % of bars that have printed
out of the TOTAL amount of bars that COULD HAVE been printed within the same amount of time.
Parameters:
_time (int) : (int)
The time associated with the chart of the particular asset that is being screened at that point.
_barCntLength (int) : (int)
The amount of time (IN MINUTES) that you want the logic to look back at to calculate the % of bars that have actually
printed in the span of time you input into this parameter.
_barCntPercentMin (int) : (int)
The minimum % of Printed Bars of the asset being screened has to be GREATER than the value set in this parameter
for the output variable 'bc_gtg' to be true.
Returns: ( )
Returns 2 outputs:
1st is the % of Printed Bars that have printed within the within the span of time you input in the '_barCntLength' parameter.
2nd is true/false according to if the Printed BarCount % is above the threshold that you input into the '_barCntPercentMin' parameter.
Recherche dans les scripts pour "泰国一寺庙被曝藏有40多具尸体"
Advanced Speedometer Gauge [PhenLabs]Advanced Speedometer Gauge
Version: PineScript™v6
📌 Description
The Advanced Speedometer Gauge is a revolutionary multi-metric visualization tool that consolidates 13 distinct trading indicators into a single, intuitive speedometer display. Instead of cluttering your workspace with multiple oscillators and panels, this gauge provides a unified interface where you can switch between different metrics while maintaining consistent visual interpretation.
Built on PineScript™ v6, the indicator transforms complex technical calculations into an easy-to-read semi-circular gauge with color-coded zones and a precision needle indicator. Each of the 13 available metrics has been carefully normalized to a 0-100 scale, ensuring that whether you’re analyzing RSI, volume trends, or volatility extremes, the visual interpretation remains consistent and intuitive.
The gauge is designed for traders who value efficiency and clarity. By consolidating multiple analytical perspectives into one compact display, you can quickly assess market conditions without the visual noise of traditional multi-indicator setups. All metrics are non-overlapping, meaning each provides unique insights into different aspects of market behavior.
🚀 Points of Innovation
13 selectable metrics covering momentum, volume, volatility, trend, and statistical analysis, all accessible through a single dropdown menu
Universal 0-100 normalization system that standardizes different indicator scales for consistent visual interpretation across all metrics
Semi-circular gauge design with 21 arc segments providing smooth precision and clear visual feedback through color-coded zones
Non-redundant metric selection ensuring each indicator provides unique market insights without analytical overlap
Advanced metrics including MFI (volume-weighted momentum), CCI (statistical deviation), Volatility Rank (extended lookback), Trend Strength (ADX-style), Choppiness Index, Volume Trend, and Price Distance from MA
Flexible positioning system with 5 chart locations, 3 size options, and fully customizable color schemes for optimal workspace integration
🔧 Core Components
Metric Selection Engine: Dropdown interface allowing instant switching between 13 different technical indicators, each with independent parameter controls
Normalization System: All metrics converted to 0-100 scale using indicator-specific algorithms that preserve the statistical significance of each measurement
Semi-Circular Gauge: Visual display using 21 arc segments arranged in curved formation with two-row thickness for enhanced visibility
Color Zone System: Three distinct zones (0-40 green, 40-70 yellow, 70-100 red) providing instant visual feedback on metric extremes
Needle Indicator: Dynamic pointer that positions across the gauge arc based on precise current metric value
Table Implementation: Professional table structure ensuring consistent positioning and rendering across different chart configurations
🔥 Key Features
RSI (Relative Strength Index): Classic momentum oscillator measuring overbought/oversold conditions with adjustable period length (default 14)
Stochastic Oscillator: Compares closing price to price range over specified period with smoothing, ideal for identifying momentum shifts
MFI (Money Flow Index): Volume-weighted RSI that combines price movement with volume to measure buying and selling pressure intensity
CCI (Commodity Channel Index): Measures statistical deviation from average price, normalized from typical -200 to +200 range to 0-100 scale
Williams %R: Alternative overbought/oversold indicator using high-low range analysis, inverted to match 0-100 scale conventions
Volume %: Current volume relative to moving average expressed as percentage, capped at 100 for extreme spikes
Volume Trend: Cumulative directional volume flow showing whether volume is flowing into up moves or down moves over specified period
ATR Percentile: Current Average True Range position within historical range using specified lookback period (default 100 bars)
Volatility Rank: Close-to-close volatility measured against extended historical range (default 252 days), differs from ATR in calculation method
Momentum: Rate of change calculation showing price movement speed, centered at 50 and normalized to 0-100 range
Trend Strength: ADX-style calculation using directional movement to quantify trend intensity regardless of direction
Choppiness Index: Measures market choppiness versus trending behavior, where high values indicate ranging markets and low values indicate strong trends
Price Distance from MA: Measures current price over-extension from moving average using standard deviation calculations
🎨 Visualization
Semi-Circular Arc Display: Curved gauge spanning from 0 (left) to 100 (right) with smooth progression and two-row thickness for visibility
Color-Coded Zones: Green zone (0-40) for low/oversold conditions, yellow zone (40-70) for neutral readings, red zone (70-100) for high/overbought conditions
Needle Indicator: Downward-pointing triangle (▼) positioned precisely at current metric value along the gauge arc
Scale Markers: Vertical line markers at 0, 25, 50, 75, and 100 positions with corresponding numerical labels below
Title Display: Merged cell showing “𓄀 PhenLabs” branding plus currently selected metric name in monospace font
Large Value Display: Current metric value shown with two decimal precision in large text directly below title
Table Structure: Professional table with customizable background color, text color, and transparency for minimal chart obstruction
📖 Usage Guidelines
Metric Selection
Select Metric: Default: RSI | Options: RSI, Stochastic, Volume %, ATR Percentile, Momentum, MFI (Money Flow), CCI (Commodity Channel), Williams %R, Volatility Rank, Trend Strength, Choppiness Index, Volume Trend, Price Distance | Choose the technical indicator you want to display on the gauge based on your current analytical needs
RSI Settings
RSI Length: Default: 14 | Range: 1+ | Controls the lookback period for RSI calculation, shorter periods increase sensitivity to recent price changes
Stochastic Settings
Stochastic Length: Default: 14 | Range: 1+ | Lookback period for stochastic calculation comparing close to high-low range
Stochastic Smooth: Default: 3 | Range: 1+ | Smoothing period applied to raw stochastic value to reduce noise and false signals
Volume Settings
Volume MA Length: Default: 20 | Range: 1+ | Moving average period used to calculate average volume for comparison with current volume
Volume Trend Length: Default: 20 | Range: 5+ | Period for calculating cumulative directional volume flow trend
ATR and Volatility Settings
ATR Length: Default: 14 | Range: 1+ | Period for Average True Range calculation used in ATR Percentile metric
ATR Percentile Lookback: Default: 100 | Range: 20+ | Historical range used to determine current ATR position as percentile
Volatility Rank Lookback (Days): Default: 252 | Range: 50+ | Extended lookback period for Volatility Rank metric using close-to-close volatility
Momentum and Trend Settings
Momentum Length: Default: 10 | Range: 1+ | Lookback period for rate of change calculation in Momentum metric
Trend Strength Length: Default: 20 | Range: 5+ | Period for directional movement calculations in ADX-style Trend Strength metric
Advanced Metric Settings
MFI Length: Default: 14 | Range: 1+ | Lookback period for Money Flow Index calculation combining price and volume
CCI Length: Default: 20 | Range: 1+ | Period for Commodity Channel Index statistical deviation calculation
Williams %R Length: Default: 14 | Range: 1+ | Lookback period for Williams %R high-low range analysis
Choppiness Index Length: Default: 14 | Range: 5+ | Period for calculating market choppiness versus trending behavior
Price Distance MA Length: Default: 50 | Range: 10+ | Moving average period used for Price Distance standard deviation calculation
Visual Customization
Position: Default: Top Right | Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Right | Controls gauge placement on chart for optimal workspace organization
Size: Default: Normal | Options: Small, Normal, Large | Adjusts overall gauge dimensions and text size for different monitor resolutions and preferences
Low Zone Color (0-40): Default: Green (#00FF00) | Customize color for low/oversold zone of gauge arc
Medium Zone Color (40-70): Default: Yellow (#FFFF00) | Customize color for neutral/medium zone of gauge arc
High Zone Color (70-100): Default: Red (#FF0000) | Customize color for high/overbought zone of gauge arc
Background Color: Default: Semi-transparent dark gray | Customize gauge background for contrast and chart integration
Text Color: Default: White (#FFFFFF) | Customize all text elements including title, value, and scale labels
✅ Best Use Cases
Quick visual assessment of market conditions when you need instant feedback on whether an asset is in extreme territory across multiple analytical dimensions
Workspace organization for traders who monitor multiple indicators but want to reduce chart clutter and visual complexity
Metric comparison by switching between different indicators while maintaining consistent visual interpretation through the 0-100 normalization
Overbought/oversold identification using RSI, Stochastic, Williams %R, or MFI depending on whether you prefer price-only or volume-weighted analysis
Volume analysis through Volume %, Volume Trend, or MFI to confirm price movements with corresponding volume characteristics
Volatility monitoring using ATR Percentile or Volatility Rank to identify expansion/contraction cycles and adjust position sizing
Trend vs range identification by comparing Trend Strength (high values = trending) against Choppiness Index (high values = ranging)
Statistical over-extension detection using CCI or Price Distance to identify when price has deviated significantly from normal behavior
Multi-timeframe analysis by duplicating the gauge on different timeframe charts to compare metric readings across time horizons
Educational purposes for new traders learning to interpret technical indicators through consistent visual representation
⚠️ Limitations
The gauge displays only one metric at a time, requiring manual switching to compare different indicators rather than simultaneous multi-metric viewing
The 0-100 normalization, while providing consistency, may obscure the raw values and specific nuances of each underlying indicator
Table-based visualization cannot be exported or saved as an image separately from the full chart screenshot
Optimal parameter settings vary by asset type, timeframe, and market conditions, requiring user experimentation for best results
💡 What Makes This Unique
Unified Multi-Metric Interface: The only gauge-style indicator offering 13 distinct metrics through a single interface, eliminating the need for multiple oscillator panels
Non-Overlapping Analytics: Each metric provides genuinely unique insights—MFI combines volume with price, CCI measures statistical deviation, Volatility Rank uses extended lookback, Trend Strength quantifies directional movement, and Choppiness Index measures ranging behavior
Universal Normalization System: All metrics standardized to 0-100 scale using indicator-appropriate algorithms that preserve statistical meaning while enabling consistent visual interpretation
Professional Visual Design: Semi-circular gauge with 21 arc segments, precision needle positioning, color-coded zones, and clean table implementation that maintains clarity across all chart configurations
Extensive Customization: Independent parameter controls for each metric, five position options, three size presets, and full color customization for seamless workspace integration
🔬 How It Works
1. Metric Calculation Phase:
All 13 metrics are calculated simultaneously on every bar using their respective algorithms with user-defined parameters
Each metric applies its own specific calculation method—RSI uses average gains vs losses, Stochastic compares close to high-low range, MFI incorporates typical price and volume, CCI measures deviation from statistical mean, ATR calculates true range, directional indicators measure up/down movement, and statistical metrics analyze price relationships
2. Normalization Process:
Each calculated metric is converted to a standardized 0-100 scale using indicator-appropriate transformations
Some metrics are naturally 0-100 (RSI, Stochastic, MFI, Williams %R), while others require scaling—CCI transforms from ±200 range, Momentum centers around 50, Volume ratio caps at 2x for 100, ATR and Volatility Rank calculate percentile positions, and Price Distance scales by standard deviations
3. Gauge Rendering:
The selected metric’s normalized value determines the needle position across 21 arc segments spanning 0-100
Each arc segment receives its color based on position—segments 0-8 are green zone, segments 9-14 are yellow zone, segments 15-20 are red zone
The needle indicator (▼) appears in row 5 at the column corresponding to the current metric value, providing precise visual feedback
4. Table Construction:
The gauge uses TradingView’s table system with merged cells for title and value display, ensuring consistent positioning regardless of chart configuration
Rows are allocated as follows: Row 0 merged for title, Row 1 merged for large value display, Row 2 for spacing, Rows 3-4 for the semi-circular arc with curved shaping, Row 5 for needle indicator, Row 6 for scale markers, Row 7 for numerical labels at 0/25/50/75/100
All visual elements update on every bar when barstate.islast is true, ensuring real-time accuracy without performance impact
💡 Note:
This indicator is designed for visual analysis and market condition assessment, not as a standalone trading system. For best results, combine gauge readings with price action analysis, support and resistance levels, and broader market context. Parameter optimization is recommended based on your specific trading timeframe and asset class. The gauge works on all timeframes but may require different parameter settings for intraday versus daily/weekly analysis. Consider using multiple instances of the gauge set to different metrics for comprehensive market analysis without switching between settings.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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Trend/Range Composite (Single-Line) v1.4🔹 Step 1: Add it to your chart
Copy the whole script.
In TradingView → Pine Editor → paste it.
Click Add to chart.
It will show a white line in a subwindow, plus thresholds at 40 and 60, and a colored background.
Optional: You’ll see a status box (top-right of chart) with details like ADX, ATR, slope, etc.
🔹 Step 2: Understand the Score
The indicator compresses all signals into a 0–100 “Trend Strength Score”:
≥ 60 = TREND (teal background)
→ Market is trending, consider trend strategies like vertical spreads, runners, breakouts.
≤ 40 = RANGE (orange background)
→ Market is choppy/sideways, consider range strategies like butterflies, condors, mean-reversion fades.
40–60 = MIXED (gray background)
→ Indecision / chop. Best to reduce size or wait for clarity.
🔹 Step 3: Use with Your Trading Plan
Intraday (5m, 15m, 30m)
Score < 40 → play support/resistance bounces, fade extremes.
Score > 60 → play momentum breakouts or pullback continuations.
Daily chart
Good for swing context (is this month trending or just chopping?).
🔹 Step 4: Alerts
You can set TradingView alerts:
Cross above 60 → market entering trend mode.
Cross below 40 → market entering range mode.
Useful if you don’t want to watch constantly.
🔹 Step 5: Confirm with Price Levels
The score tells you “trend vs range”, but you still need levels:
If score < 40 → mark PDH / PDL (previous day high/low), VAH/VAL, VWAP. Expect rejections/fades.
If score > 60 → watch for breakouts beyond PDH/PDL or supply/demand zones.
Breadth-Driven Swing StrategyWhat it does
This script trades the S&P 500 purely on market breadth extremes:
• Data source : INDEX:S5TH = % of S&P 500 stocks above their own 200-day SMA (range 0–100).
• Buy when breadth is washed-out.
• Sell when breadth is overheated.
It is long-only by design; shorting and ATR trailing stops have been removed to keep the logic minimal and transparent.
⸻
Signals in plain English
1. Long entry
A. A 200-EMA trough in breadth is printed and the trough value is ≤ 40 %.
or
B. A 5-EMA trough appears, its prominence passes the user threshold, and the lowest breadth reading in the last 20 bars is ≤ 20 %.
(Toggle this secondary trigger on/off with “ Enter also on 5-EMA trough ”.)
2. Exit (close long)
First 200-EMA peak whose breadth value is ≥ 70 %.
3. Risk control
A fixed stop-loss (% of entry price, default 8 %) is attached to every long trade.
⸻
Key parameters (defaults shown)
• Long EMA length 200 • Short EMA length 5
• Peak prominence 0.5 pct-pts • Trough prominence 3 pct-pts
• Peak level 70 % • Trough level 40 % • 5-EMA trough level 20 %
• Fixed stop-loss 8 %
• “Enter also on 5-EMA trough” = true (allows additional entries on extreme momentum reversals)
Feel free to tighten or relax any of these thresholds to match your risk profile or account for different market regimes.
⸻
How to use it
1. Load the script on a daily SPX / SPY chart.
(The price chart drives order execution; the breadth series is pulled internally and does not need to be on the chart.)
2. Verify the breadth feed.
INDEX:S5TH is updated after each session; your broker must provide it.
3. Back-test across several cycles.
Two decades of daily data is recommended to see how the rules behave in bear markets, range markets, and bull trends.
4. Adjust position sizing in the Properties tab.
The default is “100 % of equity”; change it if you prefer smaller allocations or pyramiding caps.
⸻
Why it can help
• Breadth signals often lead price, allowing entries before index-level momentum turns.
• Simple, rule-based exits prevent “waiting for confirmation” paralysis.
• Only one input series—easy to audit, no black-box math.
Trade-offs
• Relies on a single breadth metric; other internals (advance/decline, equal-weight returns, etc.) are ignored.
• May sit in cash during shallow pullbacks that never push breadth ≤ 40 %.
• Signals arrive at the end of the session (breadth is EoD data).
⸻
Disclaimer
This script is provided for educational purposes only and is not financial advice. Markets are risky; test thoroughly and use your own judgment before trading real money.
ストラテジー概要
本スクリプトは S&P500 のマーケットブレッド（内部需給） だけを手がかりに、指数をスイングトレードします。
• ブレッドデータ : INDEX:S5TH
（S&P500 採用銘柄のうち、それぞれの 200 日移動平均線を上回っている銘柄比率。0–100 %）
• 買い : ブレッドが極端に売られたタイミング。
• 売り : ブレッドが過熱状態に達したタイミング。
余計な機能を削り、ロングオンリー & 固定ストップ のシンプル設計にしています。
⸻
シグナルの流れ
1. ロングエントリー
• 条件 A : 200-EMA がトラフを付け、その値が 40 % 以下
• 条件 B : 5-EMA がトラフを付け、
・プロミネンス条件を満たし
・直近 20 本のブレッドス最小値が 20 % 以下
• B 条件は「5-EMA トラフでもエントリー」を ON にすると有効
2. ロング決済
最初に出現した 200-EMA ピーク で、かつ値が 70 % 以上 のバーで手仕舞い。
3. リスク管理
各トレードに 固定ストップ（初期価格から 8 %）を設定。
⸻
主なパラメータ（デフォルト値）
• 長期 EMA 長さ : 200 • 短期 EMA 長さ : 5
• ピーク判定プロミネンス : 0.5 %pt • トラフ判定プロミネンス : 3 %pt
• ピーク水準 : 70 % • トラフ水準 : 40 % • 5-EMA トラフ水準 : 20 %
• 固定ストップ : 8 %
• 「5-EMA トラフでもエントリー」 : ON
相場環境やリスク許容度に合わせて閾値を調整してください。
⸻
使い方
1. 日足の SPX / SPY チャート にスクリプトを適用。
2. ブレッドデータの供給 (INDEX:S5TH) がブローカーで利用可能か確認。
3. 20 年以上の期間でバックテスト し、強気相場・弱気相場・レンジ局面での挙動を確認。
4. 資金配分 は プロパティ → 戦略実行 で調整可能（初期値は「資金の 100 %」）。
⸻
強み
• ブレッドは 価格より先行 することが多く、天底を早期に捉えやすい。
• ルールベースの出口で「もう少し待とう」と迷わずに済む。
• 入力 series は 1 本のみ、ブラックボックス要素なし。
注意点・弱み
• 単一指標に依存。他の内部需給（A/D ライン等）は考慮しない。
• 40 % を割らない浅い押し目では機会損失が起こる。
• ブレッドは終値ベースの更新。ザラ場中の変化は捉えられない。
⸻
免責事項
本スクリプトは 学習目的 で提供しています。投資助言ではありません。
実取引の前に必ず自己責任で十分な検証とリスク管理を行ってください。
EMA Crossover with RSI and DistanceEMA Crossover with RSI and Distance Strategy
This strategy combines Exponential Moving Averages (EMA) with Relative Strength Index (RSI) and distance-based conditions to generate buy, sell, and neutral signals. It is designed to help traders identify entry and exit points based on multiple technical indicators.
Key Components:
Exponential Moving Averages (EMA):
The strategy uses four EMAs: EMA 5, EMA 13, EMA 40, and EMA 55.
A buy signal (long) is triggered when EMA 5 crosses above EMA 13 and EMA 40 crosses above EMA 55.
A sell signal (short) is generated when EMA 55 crosses above EMA 40.
The distance between EMAs (5 and 13) is also important. If the current distance between EMA 5 and EMA 13 is smaller than the average distance over the last 5 candles, a neutral condition is triggered, preventing a signal even if all other conditions are met.
Relative Strength Index (RSI):
The 14-period RSI is used to determine market strength and direction.
The strategy requires RSI to be above 50 and greater than the average RSI (over the past 14 periods) for a buy signal.
If the RSI is above 60, a green signal is given, indicating a strong bullish condition, even if the EMA conditions are not fully met.
If the RSI is below 40, a red signal is given, indicating a strong bearish condition, regardless of the EMA crossover.
Distance Conditions:
The strategy calculates the distance between EMA 5 and EMA 13 on each candle and compares it to the average distance of the last 5 candles.
If the current distance between EMA 5 and EMA 13 is lower than the average of the last 5 candles, a neutral signal is triggered. This helps avoid entering a trade when the market is losing momentum.
Additionally, if the distance between EMA 40 and EMA 13 is greater than the previous distance, the previous signal is kept intact, ensuring that the trend is still strong enough for the signal to remain valid.
Signal Persistence:
Once a buy (green) or sell (red) signal is triggered, it remains intact as long as the price is closing above EMA 5 for long trades or below EMA 55 for short trades.
If the price moves below EMA 5 for long trades or above EMA 55 for short trades, the signal is recalculated based on the most recent conditions.
Signal Display:
Green Signals: Represent a strong buy signal and are shown below the candle when the RSI is above 60.
Red Signals: Represent a strong sell signal and are shown above the candle when the RSI is below 40.
Neutral Signals: Displayed when the conditions for entry are not met, specifically when the EMA distance condition is violated.
Long and Short Signals: Additional signals are shown based on the EMA crossovers and RSI conditions. These signals are plotted below the candle for long positions and above the candle for short positions.
Trade Logic:
Long Entry: Enter a long trade when EMA 5 crosses above EMA 13, EMA 40 crosses above EMA 55, and the RSI is above 50 and greater than the average RSI. Additionally, the current distance between EMA 5 and EMA 13 should be larger than the average distance of the last 5 candles.
Short Entry: Enter a short trade when EMA 55 crosses above EMA 40 and the RSI is below 40.
Neutral Condition: If the distance between EMA 5 and EMA 13 is smaller than the average distance over the last 5 candles, the strategy will not trigger a signal, even if other conditions are met.
Dual Timeframe Stochastic Momentum Index w/buy sell signalsThis indicator combines momentum analysis across two timeframes to identify high-probability trading opportunities. It plots the Stochastic Momentum Index (SMI) for both the chart timeframe and a higher timeframe (default 10 minutes) to help traders align with the broader market trend.
Key Features
Displays SMI and its EMA for both timeframes
Background shading indicates favorable trading conditions
Signal dots mark potential entry points
Customizable parameters for fine-tuning
Signals Explained
Bullish Signals (Green Dots)
Appear when the chart timeframe SMI crosses above its EMA
Only trigger during periods when the higher timeframe shows:
SMI is above its EMA (increasing momentum)
SMI is between -40 and +40 (not overbought/oversold)
Bearish Signals (Red Dots)
Appear when the chart timeframe SMI crosses below its EMA
Only trigger during periods when the higher timeframe shows:
SMI is below its EMA (decreasing momentum)
SMI is between -40 and +40 (not overbought/oversold)
Settings
%K Length: Lookback period for SMI calculation (default: 10)
%D Length: Smoothing period for primary calculation (default: 3)
EMA Length: Smoothing period for signal line (default: 3)
Alternative Timeframe: Higher timeframe for trend analysis (default: 10 minutes)
Best Practices
Use higher timeframe signals to determine market bias
Wait for signal dots in the chart timeframe for entry timing
Avoid trades when higher timeframe SMI is in extreme zones (above 40 or below -40)
Consider additional confirmation from price action or other indicators
Note: This indicator combines trend and momentum analysis but should be used as part of a complete trading strategy that includes proper risk management.
[imba]lance algo🟩 INTRODUCTION
Hello, everyone!
Please take the time to review this description and source code to utilize this script to its fullest potential.
🟩 CONCEPTS
This is a trend indicator. The trend is the 0.5 fibonacci level for a certain period of time.
A trend change occurs when at least one candle closes above the level of 0.236 (for long) or below 0.786 (for short). Also it has massive amout of settings and features more about this below.
With good settings, the indicator works great on any market and any time frame!
A distinctive feature of this indicator is its backtest panel. With which you can dynamically view the results of setting up a strategy such as profit, what the deposit size is, etc.
Please note that the profit is indicated as a percentage of the initial deposit. It is also worth considering that all profit calculations are based on the risk % setting.
🟩 FEATURES
First, I want to show you what you see on the chart. And I’ll show you everything closer and in more detail.
1. Position
2. Statistic panel
3. Backtest panel
Indicator settings:
Let's go in order:
1. Strategies
This setting is responsible for loading saved strategies. There are only two preset settings, MANUAL and UNIVERSAL. If you choose any strategy other than MANUAL, then changing the settings for take profits, stop loss, sensitivity will not bring any results.
You can also save your customized strategies, this is discussed in a separate paragraph “🟩HOW TO SAVE A STRATEGY”
2. Sensitive
Responsible for the time period in bars to create Fibonacci levels
3. Start calculating date
This is the time to start backtesting strategies
4. Position group
Show checkbox - is responsible for displaying positions
Fill checkbox - is responsible for filling positions with background
Risk % - is responsible for what percentage of the deposit you are willing to lose if there is a stop loss
BE target - here you can choose when you reach which take profit you need to move your stop loss to breakeven
Initial deposit- starting deposit for profit calculation
5. Stoploss group
Fixed stoploss % checkbox - If choosed: stoploss will be calculated manually depending on the setting below( formula: entry_price * (1 - stoploss percent)) If NOT choosed: stoploss will be ( formula: fibonacci level(0.786/0.236) * (1 + stoploss percent))
6. Take profit group
This group of settings is responsible for how far from the entry point take profits will be and what % of the position to fix
7. RSI
Responsible for configuring the built-in RSI. Suitable bars will be highlighted with crosses above or below, depending on overbought/oversold
8. Infopanels group
Here I think everything is clear, you can hide or show information panels
9. Developer mode
If enabled, all events that occur will be shown, for example, reaching a take profit or stop loss with detailed information about the unfixed balance of the position
🟩 HOW TO USE
Very simple. All you need is to wait for the trend to change to long or short, you will immediately see a stop loss and four take profits, and you will also see prices. Like in this picture:
🟩 ALERTS
There are 3 types of alerts:
1. Long signal
2. Short signal
3. Any alert() function call - will be send to you json with these fields
{
"side": "LONG",
"entry": "64.454",
"tp1": "65.099",
"tp2": "65.743",
"tp3": "66.388",
"tp4": "67.032",
"winrate": "35.42%",
"strategy": "MANUAL",
"beTargetTrigger": "1",
"stop": "64.44"
}
🟩 HOW TO SAVE A STRATEGY
First, you need to make sure that the “MANUAL” strategy is selected in the strategy settings.
After this, you can start selecting parameters that will show the largest profit in the statistics panel.
I have highlighted what you need to pay attention to when choosing a strategy
Let's assume you have set up a strategy. The main question is how to preserve it?
Let’s say the strategy turned out with the following parameters:
Next we need to find this section of code:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
// "NEW STRATEGY" =>
// strategy_settings.sensitivity := 20
// strategy_settings.risk_percent := 1
// strategy_settings.break_even_target := "1"
// strategy_settings.tp1_percent := 1
// strategy_settings.tp1_percent_fix := 40
// strategy_settings.tp2_percent := 2
// strategy_settings.tp2_percent_fix := 30
// strategy_settings.tp3_percent := 3
// strategy_settings.tp3_percent_fix := 20
// strategy_settings.tp4_percent := 4
// strategy_settings.tp4_percent_fix := 10
// strategy_settings.fixed_stop := false
// strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Let's uncomment on the latest strategy called "NEW STRATEGY" rename it to "SOL 5m" and change the sensitivity:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"SOL 5m" =>
strategy_settings.sensitivity := 15
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Now let's find this code:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
And let's add our new strategy there, it turned out like this:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
That's all. Our new strategy is now saved! It's simple! Now we can select it in the list of strategies:
Financial Radar Chart by zdmreRadar chart is often used when you want to display data across several unique dimensions. Although there are exceptions, these dimensions are usually quantitative, and typically range from zero to a maximum value. Each dimension’s range is normalized to one another, so that when we draw our spider chart, the length of a line from zero to a dimension’s maximum value will be the similar for every dimension.
This Charts are useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.
How is the score formed?
Debt Paying Ability
if Debt_to_Equity < %10 : 100
elif < 20% : 90
elif < 30% : 80
elif < 40% : 70
elif < 50% : 60
elif < 60% : 50
elif < 70% : 40
elif < 80% : 30
elif < 90% : 20
elif < 100% : 10
else: 0
ROIC
if Return_on_Invested_Capital > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
ROE
if Return_on_Equity > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
Operating Ability
if Operating_Margin > %50 : 100
elif > 30% : 90
elif > 20% : 80
elif > 15% : 60
elif > 10% : 40
elif > 0 : 20
else: 0
EV/EBITDA
if Enterprise_Value_to_EBITDA < 3 : 100
elif < 5 : 80
elif < 7 : 70
elif < 8 : 60
elif < 10 : 40
elif < 12 : 20
else: 0
FREE CASH Ability
if Price_to_Free_Cash_Flow < 5 : 100
elif < 7 : 90
elif < 10 : 80
elif < 16 : 60
elif < 18 : 50
elif < 20 : 40
elif < 22 : 30
elif < 30 : 20
elif < 40 : 15
elif < 50 : 10
elif < 60 : 5
else: 0
GROWTH Ability
if Revenue_One_Year_Growth > %20 : 100
elif > 16% : 90
elif > 14% : 80
elif > 12% : 70
elif > 10% : 50
elif > 7% : 40
elif > 4% : 30
elif > 2% : 20
elif > 0 : 10
else: 0
RSI 30 CROSSScript will give the RSI 30 40 and 70 level for present price of the stock , when the price cross the green line RSI value will be 70 , blue line RSI value will be 40 and red line RSI value will be 30 . Helps to put entry and exit based on RSI strategy.
RED line give price for RSI 30
BLUE line give price for RSI 40
GREEN line give price for RSI 70
BLACK line give SMA 200
Strategy
Stock price should above 200 MA
price should touch RSI 30 RED line and bounce back.
Entry will be the high of candle lies on RSI 40 BLUE line.
Stop loss will be the RSI 30 price(RED line ) during entry.
Target will be the RSI 70 price ( GREEEN line) during entry.
You can take half profit at RSI 70 and trail stop loss on RSI 70 till it cross.
This will help you to find the Price for stock, when it cross RSI value 30 , 40 and 70 to place entry exit and target based on the trade strategy will follow RSI.
If you want to entry, when stock cross RSI 30 or 40 from below . You can place a stop loss limit buy order at price range .
If you want to exit, When stock cross RSI 70 . you place stock loss at green line price.
Reverse Stochastic Momentum Index On ChartIntroducing the Reverse Stochastic Momentum Index "On Chart" version
According to Investopedia :
“The Stochastic Momentum Index (SMI) is a more refined version of the stochastic oscillator, employing a wider range of values and having a higher sensitivity to closing prices.”
The SMI is considered a refinement of the stochastic oscillator developed by William Blau and introduced in 1993 in an attempt to provide a more reliable indicator, less subject to false swings.
It calculates the distance of the current closing price as it relates to the median of the high/low range of price.
The SMI has a normal range of values between +100 and -100.
When the present closing price is higher than the median, or midpoint value of the high/low range, the resulting value is positive.
When the current closing price is lower than that of the midpoint of the high/low range, the SMI has a negative value.
Here I have reverse engineered the SMI formula to derive 2 functions.
One function calculates the chart price at which the SMI will reach a particular SMI scale value.
The second function calculates the chart price at which the SMI will crossover its signal line.
I have employed those functions here to give the "crossover" price levels for :
Upper alert level ( default 40, color : aqua blue )
Mid-Line ( default value 0, color : white )
Lower alert level ( default -40, color : purple )
Signal line ( default 13, colors : bright red & lime green )
And also to give the SMI eq price ( colors : red & green )
The midline, upper and lower alert levels return the closing price which would make SMI equal to their respective values
The user can infer from this that.....
Closing above these prices will cause the Stochastic Momentum Index to cross above the associated levels
Closing below these prices will cause the Stochastic Momentum Index to cross below the associated levels
Signal line returns the closing price where Stochastic Momentum Index is equal to its signal line
The user can infer from this that.....
Closing above this price will cause the Stochastic Momentum Index to cross above the signal line
Closing below this price will cause the Stochastic Momentum Index to cross below the signal line
SMI eq price returns the closing price which would make the SMI equal to its previous value
The user can infer from this that.....
Closing above this price will cause the Stochastic Momentum Index to increase
Closing below this price will cause the Stochastic Momentum Index to decrease
Note : all returned prices have a returned value filter to replace any values below zero with zero to help prevent auto focus issues.
These levels are displayed as plotted lines on the chart and also as an optional infobox with choice of displayed info.
This allows the user to see directly on the chart the interplay between the various crossover levels and price action and to precisely plan entries, exits and stops for their SMI based trades.
Traditionally traders and analysts will consider:
Positives values above 40 indicate a bullish trend
Negative values below -40 indicate a bearish trend .
Common traditional ways to derive signals from the SMI :
When the SMI crosses below -40 and then moves back above it, a buy signal is generated.
When the SMI crosses above +40 and then moves back below it, a sell signal is generated.
When the SMI line crosses above the signal line. A signal to buy is generated
When the SMI line crosses below the signal line signal to sell is generated.
When the SMI crosses above the zeroline, signal line and the SMI eq level many interpret that as a full bullish bias signal and take trades only in that direction, vice versa for bearish bias.
Traders also look for divergences between the SMI and price action.
The SMI is often used in conjunction with the Chande Momentum Oscillator or R squared indicator to determine overall market trendiness where the SMI is used to determine the direction of the trend, and also with volume indicators to show if the momentum carries significant selling or buying pressure.
CT Reverse Stochastic Momentum IndexIntroducing the Caretakers Reverse Stochastic Momentum Index .
According to Investopedia :
“The Stochastic Momentum Index (SMI) is a more refined version of the stochastic oscillator, employing a wider range of values and having a higher sensitivity to closing prices.”
The SMI was developed by William Blau and introduced in 1993 in an attempt to provide a more reliable indicator, less subject to false swings.
It calculates the distance of the current closing price as it relates to the median of the high/low range of price.
The SMI has a normal range of values between +100 and -100.
When the present closing price is higher than the median, or midpoint value of the high/low range, the resulting value is positive.
When the current closing price is lower than that of the midpoint of the high/low range, the SMI has a negative value.
I have reverse engineered the SMI formula to derive 2 functions.
One function calculates the chart price at which the SMI will reach a particular SMI scale value.
The second function calculates the chart price at which the SMI will crossover its signal line.
I have employed those functions here to give the price level where the SMI will equal :
Upper alert level ( default 40 )
Zero-Line
Lower alert level ( default -40 )
Signal line
The user can infer from these values that when closing prices cross the levels shown, the SMI will cross the indicated level or signal line.
If the price value is less than zero the value will show "impossible".
The advantage of knowing the exact prices that this will happen should give the user an additional edge and precision in risk management.
These crossover levels are displayed via an optional infobox with choice of user selected info.
There is an option to change the decimal places shown.
For easy and intuitive reading of the indicator when ….
SMI is above the signal line both the SMI and Signal line and the space between them is Green.
SMI is below the signal line both the SMI and Signal line and the space between them is Red.
SMI is above the Zeroline the space between them is Green.
SMI is below the Zeroline the space between them is Red.
Traditionally traders and analysts will consider:
Positives values above 40 indicate a bullish trend
Negative values below -40 indicate a bearish trend .
Common traditional ways to derive signals from the SMI :
When the SMI crosses above the zeroline, a buy signal is generated.
When the SMI crosses below the zeroline, a sell signal is generated.
When the SMI crosses below -40 and then moves back above it, a buy signal is generated.
When the SMI crosses above +40 and then moves back below it, a sell signal is generated.
When the SMI line crosses above the signal line. A signal to buy / take profit is generated
When the SMI line crosses below the signal line. A signal to sell / take profit is generated.
Traders also look for divergences between the SMI itself or the SMI histogram and price action.
The SMI is often used in conjunction with the Chande Momentum Oscillator or R squared indicator to determine overall market trendiness where the SMI is used to determine the direction of the trend, and also with volume indicators to show if the momentum carries significant selling or buying pressure.
FlowFusion Money Flow — FP + VWAP Drift + PVT (−100..+100)Title (ASCII only)
FlowFusion Money Flow — Flow Pressure + Rolling VWAP Drift + PVT (Normalized −100..+100)
Short Description
Original money-flow oscillator combining Flow Pressure, Rolling VWAP Drift, and PVT Momentum into one normalized score (−100..+100) with a signal line, thresholds, optional component plots, and ready-made alerts.
Full Description (meets “originality & usefulness”)
What’s original
FlowFusion Money Flow is not a generic mashup. It builds a single score from three complementary, volume-aware components that target different facets of order flow:
Flow Pressure (FP) — In-bar directional drive scaled by relative volume.
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.
Why it belongs: distinguishes real pushes (big body and big volume) from noise.
Rolling VWAP Drift — Direction of VWAP itself over a rolling window, normalized by ATR.
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Why it belongs: persistent VWAP movement signals sustained accumulation/distribution.
PVT Momentum — Price-Volume Trend standardized (z-score) and squashed.
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Composite score:
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with a Signal = SMA(Score, sigLen). Thresholds mark strong accumulation/distribution zones.
How it works (step-by-step)
Compute FP, VWAP Drift, PVT Momentum.
Normalize each to the same
scale.
Weighted average → FlowFusion Score.
Smooth with a Signal line to reduce whipsaw.
Optional background shading when Score exceeds thresholds.
How to use
Direction filter:
Score > 0 favors longs; Score < 0 favors shorts.
Momentum turns:
Score crosses above Signal → setup for long; below → setup for short.
Strength zones:
Above Upper Threshold (default +40) = strong buy pressure; below Lower (−40) = strong sell pressure.
Confluence:
Best near S/R, trendlines, or HTF bias. For scalping on 1–5m, consider sigLen 9–13 and thresholds ±40 to ±50.
Alerts included: zero cross, zone entries, and Score/Signal crossovers.
Inputs (key)
fpLen (20): relative-volume lookback for Flow Pressure.
vwapLen (34): rolling VWAP window.
pvtLen (50): PVT z-score window.
sigLen (9): Signal smoothing.
Weights: wFP, wVWAP, wPVT to bias the blend.
Thresholds: upperBand / lowerBand (defaults +40/−40).
Display: toggle component plots and background shading.
Best practices
Trending markets: increase wVWAP (VWAP Drift) or widen thresholds.
Ranging markets: increase wFP and wPVT; take quicker profits.
News: wait for bar close confirmation or reduce size.
Data quality: use consistent volume feeds (especially in crypto).
Limitations
Oscillators can stay extreme in strong trends; use structure/trend filters.
Volume anomalies (illiquid pairs, API glitches) can distort signals—sanity-check with another venue when possible.
Disclaimer
This indicator is for educational purposes only and is not financial advice. Trading involves risk; past performance does not guarantee future results. Always paper-trade first and use appropriate risk controls.
Briese CoT Movement IndexThis Briese CoT (Commitments of Traders) Movement Index histogram indicator was built based on the formula by Stephen Briese in his book "The Commitments of Traders Bible":
"...difference between the COT Index and its reading of one or several weeks prior. I use six." —Chapter 7, page 75.
The code is a bit of a remix of the "ICT Commitment of Traders°" indicator by toodegrees and is meant for use in a new pane below a Weekly Chart .
The upper and lower thresholds are +40/-40. Some context: "A ± 40 point surge in the COT Index within a six-week period frequently marks the end of a counter-trend price reaction"
40 Point CoT Surge Rules (Commercials) from page 76
"During a correction from a prevailing uptrend, a +40 point movement in the CoT Index within a six-week period often marks the end of a corrective pullback, and the resumption of the major uptrend."
"During a reaction in a prevailing downtrend, a -40 point movement in the CoT Index within a six-week period frequently marks the end of a price reaction, and the resumption of the established downtrend."
"The failure of a ± point CoT Movement Index signal to restart the prevailing trend is a tip-off to a major trend change"
I'd recommend reading Briese's book for examples on how to properly interpret this indictor.
This indicator can be used in conjunction with another one I've published called the "Williams x Briese Hybrid CoT Index" which can be found on my scripts page.
Crypto Breadth | AlphaNatt\ Crypto Breadth | AlphaNatt\
A dynamic, visually modern market breadth indicator designed to track the strength of the top 40 cryptocurrencies by measuring how many are trading above their respective 50-day moving averages. Built with precision, branding consistency, and UI enhancements for fast interpretation.
\ 📊 What This Script Does\
* Aggregates the performance of \ 40 major cryptocurrencies\ on Binance
* Calculates a \ breadth score (0.00–1.00)\ based on how many tokens are above their moving averages
* Smooths the breadth with optional averaging
* Displays the result as a \ dynamic, color-coded line\ with aesthetic glow and gradient fill
* Provides automatic \ background zones\ for extreme bullish/bearish conditions
* Includes \ alerts\ for key threshold crossovers
* Highlights current values in an \ information panel\
\ 🧠 How It Works\
* Pulls real-time `close` prices for 40 coins (e.g., XRP, BNB, SOL, DOGE, PEPE, RENDER, etc.)
* Compares each coin's price to its 50-day SMA (adjustable)
* Assigns a binary score:
• 1 if the coin is above its MA
• 0 if it’s below
* Aggregates all results and divides by 40 to produce a normalized \ breadth percentage\
\ 🎨 Visual Design Features\
* Smooth blue-to-pink \ color gradient\ matching the AlphaNatt brand
* Soft \ glow effects\ on the main line for enhanced legibility
* Beautiful \ multi-stop fill gradient\ with 16 transition zones
* Optional \ background shading\ when extreme sentiment is detected:
• Bullish zone if breadth > 80%
• Bearish zone if breadth < 20%
\ ⚙️ User Inputs\
* \ Moving Average Length\ – Number of periods to calculate each coin’s SMA
* \ Smoothing Length\ – Smooths the final breadth value
* \ Show Background Zones\ – Toggle extreme sentiment overlays
* \ Show Gradient Fill\ – Toggle the modern multicolor area fill
\ 🛠️ Utility Table (Top Right)\
* Displays live breadth percentage
* Shows how many coins (e.g., 27/40) are currently above their MA
\ 🔔 Alerts Included\
* \ Breadth crosses above 50%\ → Bullish signal
* \ Breadth crosses below 50%\ → Bearish signal
* \ Breadth > 80%\ → Strong bullish trend
* \ Breadth < 20%\ → Strong bearish trend
\ 📈 Best Used For\
* Gauging overall market strength or weakness
* Timing trend transitions in the crypto market
* Confirming trend-based strategies with broad market support
* Visual dashboard in macro dashboards or strategy overlays
\ ✅ Designed For\
* Swing traders
* Quantitative investors
* Market structure analysts
* Anyone seeking a macro view of crypto performance
Note: Not financial advise
Market Zone Analyzer[BullByte]Understanding the Market Zone Analyzer
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1. Purpose of the Indicator
The Market Zone Analyzer is a Pine Script™ (version 6) indicator designed to streamline market analysis on TradingView. Rather than scanning multiple separate tools, it unifies four core dimensions—trend strength, momentum, price action, and market activity—into a single, consolidated view. By doing so, it helps traders:
• Save time by avoiding manual cross-referencing of disparate signals.
• Reduce decision-making errors that can arise from juggling multiple indicators.
• Gain a clear, reliable read on whether the market is in a bullish, bearish, or sideways phase, so they can more confidently decide to enter, exit, or hold a position.
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2. Why a Trader Should Use It
• Unified View: Combines all essential market dimensions into one easy-to-read score and dashboard, eliminating the need to piece together signals manually.
• Adaptability: Automatically adjusts its internal weighting for trend, momentum, and price action based on current volatility. Whether markets are choppy or calm, the indicator remains relevant.
• Ease of Interpretation: Outputs a simple “BULLISH,” “BEARISH,” or “SIDEWAYS” label, supplemented by an intuitive on-chart dashboard and an oscillator plot that visually highlights market direction.
• Reliability Features: Built-in smoothing of the net score and hysteresis logic (requiring consecutive confirmations before flips) minimize false signals during noisy or range-bound phases.
---
3. Why These Specific Indicators?
This script relies on a curated set of well-established technical tools, each chosen for its particular strength in measuring one of the four core dimensions:
1. Trend Strength:
• ADX/DMI (Average Directional Index / Directional Movement Index): Measures how strong a trend is, and whether the +DI line is above the –DI line (bullish) or vice versa (bearish).
• Moving Average Slope (Fast MA vs. Slow MA): Compares a shorter-period SMA to a longer-period SMA; if the fast MA sits above the slow MA, it confirms an uptrend, and vice versa for a downtrend.
• Ichimoku Cloud Differential (Senkou A vs. Senkou B): Provides a forward-looking view of trend direction; Senkou A above Senkou B signals bullishness, and the opposite signals bearishness.
2. Momentum:
• Relative Strength Index (RSI): Identifies overbought (above its dynamically calculated upper bound) or oversold (below its lower bound) conditions; changes in RSI often precede price reversals.
• Stochastic %K: Highlights shifts in short-term momentum by comparing closing price to the recent high/low range; values above its upper band signal bullish momentum, below its lower band signal bearish momentum.
• MACD Histogram: Measures the difference between the MACD line and its signal line; a positive histogram indicates upward momentum, a negative histogram indicates downward momentum.
3. Price Action:
• Highest High / Lowest Low (HH/LL) Range: Over a defined lookback period, this captures breakout or breakdown levels. A closing price near the recent highs (with a positive MA slope) yields a bullish score, and near the lows (with a negative MA slope) yields a bearish score.
• Heikin-Ashi Doji Detection: Uses Heikin-Ashi candles to identify indecision or continuation patterns. A small Heikin-Ashi body (doji) relative to recent volatility is scored as neutral; a larger body in the direction of the MA slope is scored bullish or bearish.
• Candle Range Measurement: Compares each candle’s high-low range against its own dynamic band (average range ± standard deviation). Large candles aligning with the prevailing trend score bullish or bearish accordingly; unusually small candles can indicate exhaustion or consolidation.
4. Market Activity:
• Bollinger Bands Width (BBW): Measures the distance between BB upper and lower bands; wide bands indicate high volatility, narrow bands indicate low volatility.
• Average True Range (ATR): Quantifies average price movement (volatility). A sudden spike in ATR suggests a volatile environment, while a contraction suggests calm.
• Keltner Channels Width (KCW): Similar to BBW but uses ATR around an EMA. Provides a second layer of volatility context, confirming or contrasting BBW readings.
• Volume (with Moving Average): Compares current volume to its moving average ± standard deviation. High volume validates strong moves; low volume signals potential lack of conviction.
By combining these tools, the indicator captures trend direction, momentum strength, price-action nuances, and overall market energy, yielding a more balanced and comprehensive assessment than any single tool alone.
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4. What Makes This Indicator Stand Out
• Multi-Dimensional Analysis: Rather than relying on a lone oscillator or moving average crossover, it simultaneously evaluates trend, momentum, price action, and activity.
• Dynamic Weighting: The relative importance of trend, momentum, and price action adjusts automatically based on real-time volatility (Market Activity State). For example, in highly volatile conditions, trend and momentum signals carry more weight; in calm markets, price action signals are prioritized.
• Stability Mechanisms:
• Smoothing: The net score is passed through a short moving average, filtering out noise, especially on lower timeframes.
• Hysteresis: Both Market Activity State and the final bullish/bearish/sideways zone require two consecutive confirmations before flipping, reducing whipsaw.
• Visual Interpretation: A fully customizable on-chart dashboard displays each sub-indicator’s value, regime, score, and comment, all color-coded. The oscillator plot changes color to reflect the current market zone (green for bullish, red for bearish, gray for sideways) and shows horizontal threshold lines at +2, 0, and –2.
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5. Recommended Timeframes
• Short-Term (5 min, 15 min): Day traders and scalpers can benefit from rapid signals, but should enable smoothing (and possibly disable hysteresis) to reduce false whipsaws.
• Medium-Term (1 h, 4 h): Swing traders find a balance between responsiveness and reliability. Less smoothing is required here, and the default parameters (e.g., ADX length = 14, RSI length = 14) perform well.
• Long-Term (Daily, Weekly): Position traders tracking major trends can disable smoothing for immediate raw readings, since higher-timeframe noise is minimal. Adjust lookback lengths (e.g., increase adxLength, rsiLength) if desired for slower signals.
Tip: If you keep smoothing off, stick to timeframes of 1 h or higher to avoid excessive signal “chatter.”
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6. How Scoring Works
A. Individual Indicator Scores
Each sub-indicator is assigned one of three discrete scores:
• +1 if it indicates a bullish condition (e.g., RSI above its dynamically calculated upper bound).
• 0 if it is neutral (e.g., RSI between upper and lower bounds).
• –1 if it indicates a bearish condition (e.g., RSI below its dynamically calculated lower bound).
Examples of individual score assignments:
• ADX/DMI:
• +1 if ADX ≥ adxThreshold and +DI > –DI (strong bullish trend)
• –1 if ADX ≥ adxThreshold and –DI > +DI (strong bearish trend)
• 0 if ADX < adxThreshold (trend strength below threshold)
• RSI:
• +1 if RSI > RSI_upperBound
• –1 if RSI < RSI_lowerBound
• 0 otherwise
• ATR (as part of Market Activity):
• +1 if ATR > (ATR_MA + stdev(ATR))
• –1 if ATR < (ATR_MA – stdev(ATR))
• 0 otherwise
Each of the four main categories shares this same +1/0/–1 logic across their sub-components.
B. Category Scores
Once each sub-indicator reports +1, 0, or –1, these are summed within their categories as follows:
• Trend Score = (ADX score) + (MA slope score) + (Ichimoku differential score)
• Momentum Score = (RSI score) + (Stochastic %K score) + (MACD histogram score)
• Price Action Score = (Highest-High/Lowest-Low score) + (Heikin-Ashi doji score) + (Candle range score)
• Market Activity Raw Score = (BBW score) + (ATR score) + (KC width score) + (Volume score)
Each category’s summed value can range between –3 and +3 (for Trend, Momentum, and Price Action), and between –4 and +4 for Market Activity raw.
C. Market Activity State and Dynamic Weight Adjustments
Rather than contributing directly to the netScore like the other three categories, Market Activity determines how much weight to assign to Trend, Momentum, and Price Action:
1. Compute Market Activity Raw Score by summing BBW, ATR, KCW, and Volume individual scores (each +1/0/–1).
2. Bucket into High, Medium, or Low Activity:
• High if raw Score ≥ 2 (volatile market).
• Low if raw Score ≤ –2 (calm market).
• Medium otherwise.
3. Apply Hysteresis (if enabled): The state only flips after two consecutive bars register the same high/low/medium label.
4. Set Category Weights:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use the trader’s base weight inputs (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 % by default).
D. Calculating the Net Score
5. Normalize Base Weights (so that the sum of Trend + Momentum + Price Action always equals 100 %).
6. Determine Current Weights based on the Market Activity State (High/Medium/Low).
7. Compute Each Category’s Contribution: Multiply (categoryScore) × (currentWeight).
8. Sum Contributions to get the raw netScore (a floating-point value that can exceed ±3 when scores are strong).
9. Smooth the netScore over two bars (if smoothing is enabled) to reduce noise.
10. Apply Hysteresis to the Final Zone:
• If the smoothed netScore ≥ +2, the bar is classified as “Bullish.”
• If the smoothed netScore ≤ –2, the bar is classified as “Bearish.”
• Otherwise, it is “Sideways.”
• To prevent rapid flips, the script requires two consecutive bars in the new zone before officially changing the displayed zone (if hysteresis is on).
E. Thresholds for Zone Classification
• BULLISH: netScore ≥ +2
• BEARISH: netScore ≤ –2
• SIDEWAYS: –2 < netScore < +2
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7. Role of Volatility (Market Activity State) in Scoring
Volatility acts as a dynamic switch that shifts which category carries the most influence:
1. High Activity (Volatile):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal +1.
• The script sets Trend weight = 50 % and Momentum weight = 35 %. Price Action weight is minimized at 15 %.
• Rationale: In volatile markets, strong trending moves and momentum surges dominate, so those signals are more reliable than nuanced candle patterns.
2. Low Activity (Calm):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal –1.
• The script sets Price Action weight = 55 %, Trend = 25 %, and Momentum = 20 %.
• Rationale: In quiet, sideways markets, subtle price-action signals (breakouts, doji patterns, small-range candles) are often the best early indicators of a new move.
3. Medium Activity (Balanced):
• Raw Score between –1 and +1 from the four volatility metrics.
• Uses whatever base weights the trader has specified (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
Because volatility can fluctuate rapidly, the script employs hysteresis on Market Activity State: a new High or Low state must occur on two consecutive bars before weights actually shift. This avoids constant back-and-forth weight changes and provides more stability.
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8. Scoring Example (Hypothetical Scenario)
• Symbol: Bitcoin on a 1-hour chart.
• Market Activity: Raw volatility sub-scores show BBW (+1), ATR (+1), KCW (0), Volume (+1) → Total raw Score = +3 → High Activity.
• Weights Selected: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Signals:
• ADX strong and +DI > –DI → +1
• Fast MA above Slow MA → +1
• Ichimoku Senkou A > Senkou B → +1
→ Trend Score = +3
• Momentum Signals:
• RSI above upper bound → +1
• MACD histogram positive → +1
• Stochastic %K within neutral zone → 0
→ Momentum Score = +2
• Price Action Signals:
• Highest High/Lowest Low check yields 0 (close not near extremes)
• Heikin-Ashi doji reading is neutral → 0
• Candle range slightly above upper bound but trend is strong, so → +1
→ Price Action Score = +1
• Compute Net Score (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 1 × 0.15 = 0.15
• Raw netScore = 1.50 + 0.70 + 0.15 = 2.35
• Since 2.35 ≥ +2 and hysteresis is met, the final zone is “Bullish.”
Although the netScore lands at 2.35 (Bullish), smoothing might bring it slightly below 2.00 on the first bar (e.g., 1.90), in which case the script would wait for a second consecutive reading above +2 before officially classifying the zone as Bullish (if hysteresis is enabled).
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9. Correlation Between Categories
The four categories—Trend Strength, Momentum, Price Action, and Market Activity—often reinforce or offset one another. The script takes advantage of these natural correlations:
• Bullish Alignment: If ADX is strong and pointed upward, fast MA is above slow MA, and Ichimoku is positive, that usually coincides with RSI climbing above its upper bound and the MACD histogram turning positive. In such cases, both Trend and Momentum categories generate +1 or +2. Because the Market Activity State is likely High (given the accompanying volatility), Trend and Momentum weights are at their peak, so the netScore quickly crosses into Bullish territory.
• Sideways/Consolidation: During a low-volatility, sideways phase, ADX may fall below its threshold, MAs may flatten, and RSI might hover in the neutral band. However, subtle price-action signals (like a small breakout candle or a Heikin-Ashi candle with a slight bias) can still produce a +1 in the Price Action category. If Market Activity is Low, Price Action’s weight (55 %) can carry enough influence—even if Trend and Momentum are neutral—to push the netScore out of “Sideways” into a mild bullish or bearish bias.
• Opposing Signals: When Trend is bullish but Momentum turns negative (for example, price continues up but RSI rolls over), the two scores can partially cancel. Market Activity may remain Medium, in which case the netScore lingers near zero (Sideways). The trader can then wait for either a clearer momentum shift or a fresh price-action breakout before committing.
By dynamically recognizing these correlations and adjusting weights, the indicator ensures that:
• When Trend and Momentum align (and volatility supports it), the netScore leaps strongly into Bullish or Bearish.
• When Trend is neutral but Price Action shows an early move in a low-volatility environment, Price Action’s extra weight in the Low Activity State can still produce actionable signals.
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10. Market Activity State & Its Role (Detailed)
The Market Activity State is not a direct category score—it is an overarching context setter for how heavily to trust Trend, Momentum, or Price Action. Here’s how it is derived and applied:
1. Calculate Four Volatility Sub-Scores:
• BBW: Compare the current band width to its own moving average ± standard deviation. If BBW > (BBW_MA + stdev), assign +1 (high volatility); if BBW < (BBW_MA × 0.5), assign –1 (low volatility); else 0.
• ATR: Compare ATR to its moving average ± standard deviation. A spike above the upper threshold is +1; a contraction below the lower threshold is –1; otherwise 0.
• KCW: Same logic as ATR but around the KCW mean.
• Volume: Compare current volume to its volume MA ± standard deviation. Above the upper threshold is +1; below the lower threshold is –1; else 0.
2. Sum Sub-Scores → Raw Market Activity Score: Range between –4 and +4.
3. Assign Market Activity State:
• High Activity: Raw Score ≥ +2 (at least two volatility metrics are strongly spiking).
• Low Activity: Raw Score ≤ –2 (at least two metrics signal unusually low volatility or thin volume).
• Medium Activity: Raw Score is between –1 and +1 inclusive.
4. Hysteresis for Stability:
• If hysteresis is enabled, a new state only takes hold after two consecutive bars confirm the same High, Medium, or Low label.
• This prevents the Market Activity State from bouncing around when volatility is on the fence.
5. Set Category Weights Based on Activity State:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use trader’s base weights (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
6. Impact on netScore: Because category scores (–3 to +3) multiply by these weights, High Activity amplifies the effect of strong Trend and Momentum scores; Low Activity amplifies the effect of Price Action.
7. Market Context Tooltip: The dashboard includes a tooltip summarizing the current state—e.g., “High activity, trend and momentum prioritized,” “Low activity, price action prioritized,” or “Balanced market, all categories considered.”
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11. Category Weights: Base vs. Dynamic
Traders begin by specifying base weights for Trend Strength, Momentum, and Price Action that sum to 100 %. These apply only when volatility is in the Medium band. Once volatility shifts:
• High Volatility Overrides:
• Trend jumps from its base (e.g., 40 %) to 50 %.
• Momentum jumps from its base (e.g., 30 %) to 35 %.
• Price Action is reduced to 15 %.
Example: If base weights were Trend = 40 %, Momentum = 30 %, Price Action = 30 %, then in High Activity they become 50/35/15. A Trend score of +3 now contributes 3 × 0.50 = +1.50 to netScore; a Momentum +2 contributes 2 × 0.35 = +0.70. In total, Trend + Momentum can easily push netScore above the +2 threshold on its own.
• Low Volatility Overrides:
• Price Action leaps from its base (30 %) to 55 %.
• Trend falls to 25 %, Momentum falls to 20 %.
Why? When markets are quiet, subtle candle breakouts, doji patterns, and small-range expansions tend to foreshadow the next swing more effectively than raw trend readings. A Price Action score of +3 in this state contributes 3 × 0.55 = +1.65, which can carry the netScore toward +2—even if Trend and Momentum are neutral or only mildly positive.
Because these weight shifts happen only after two consecutive bars confirm a High or Low state (if hysteresis is on), the indicator avoids constantly flipping its emphasis during borderline volatility phases.
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12. Dominant Category Explained
Within the dashboard, a label such as “Trend Dominant,” “Momentum Dominant,” or “Price Action Dominant” appears when one category’s absolute weighted contribution to netScore is the largest. Concretely:
• Compute each category’s weighted contribution = (raw category score) × (current weight).
• Compare the absolute values of those three contributions.
• The category with the highest absolute value is flagged as Dominant for that bar.
Why It Matters:
• Momentum Dominant: Indicates that the combined force of RSI, Stochastic, and MACD (after weighting) is pushing netScore farther than either Trend or Price Action. In practice, it means that short-term sentiment and speed of change are the primary drivers right now, so traders should watch for continued momentum signals before committing to a trade.
• Trend Dominant: Means ADX, MA slope, and Ichimoku (once weighted) outweigh the other categories. This suggests a strong directional move is in place; trend-following entries or confirming pullbacks are likely to succeed.
• Price Action Dominant: Occurs when breakout/breakdown patterns, Heikin-Ashi candle readings, and range expansions (after weighting) are the most influential. This often happens in calmer markets, where subtle shifts in candle structure can foreshadow bigger moves.
By explicitly calling out which category is carrying the most weight at any moment, the dashboard gives traders immediate insight into why the netScore is tilting toward bullish, bearish, or sideways.
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13. Oscillator Plot: How to Read It
The “Net Score” oscillator sits below the dashboard and visually displays the smoothed netScore as a line graph. Key features:
1. Value Range: In normal conditions it oscillates roughly between –3 and +3, but extreme confluences can push it outside that range.
2. Horizontal Threshold Lines:
• +2 Line (Bullish threshold)
• 0 Line (Neutral midline)
• –2 Line (Bearish threshold)
3. Zone Coloring:
• Green Background (Bullish Zone): When netScore ≥ +2.
• Red Background (Bearish Zone): When netScore ≤ –2.
• Gray Background (Sideways Zone): When –2 < netScore < +2.
4. Dynamic Line Color:
• The plotted netScore line itself is colored green in a Bullish Zone, red in a Bearish Zone, or gray in a Sideways Zone, creating an immediate visual cue.
Interpretation Tips:
• Crossing Above +2: Signals a strong enough combined trend/momentum/price-action reading to classify as Bullish. Many traders wait for a clear crossing plus a confirmation candle before entering a long position.
• Crossing Below –2: Indicates a strong Bearish signal. Traders may consider short or exit strategies.
• Rising Slope, Even Below +2: If netScore climbs steadily from neutral toward +2, it demonstrates building bullish momentum.
• Divergence: If price makes a higher high but the oscillator fails to reach a new high, it can warn of weakening momentum and a potential reversal.
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14. Comments and Their Necessity
Every sub-indicator (ADX, MA slope, Ichimoku, RSI, Stochastic, MACD, HH/LL, Heikin-Ashi, Candle Range, BBW, ATR, KCW, Volume) generates a short comment that appears in the detailed dashboard. Examples:
• “Strong bullish trend” or “Strong bearish trend” for ADX/DMI
• “Fast MA above slow MA” or “Fast MA below slow MA” for MA slope
• “RSI above dynamic threshold” or “RSI below dynamic threshold” for RSI
• “MACD histogram positive” or “MACD histogram negative” for MACD Hist
• “Price near highs” or “Price near lows” for HH/LL checks
• “Bullish Heikin Ashi” or “Bearish Heikin Ashi” for HA Doji scoring
• “Large range, trend confirmed” or “Small range, trend contradicted” for Candle Range
Additionally, the top-row comment for each category is:
• Trend: “Highly Bullish,” “Highly Bearish,” or “Neutral Trend.”
• Momentum: “Strong Momentum,” “Weak Momentum,” or “Neutral Momentum.”
• Price Action: “Bullish Action,” “Bearish Action,” or “Neutral Action.”
• Market Activity: “Volatile Market,” “Calm Market,” or “Stable Market.”
Reasons for These Comments:
• Transparency: Shows exactly how each sub-indicator contributed to its category score.
• Education: Helps traders learn why a category is labeled bullish, bearish, or neutral, building intuition over time.
• Customization: If, for example, the RSI comment says “RSI neutral” despite an impending trend shift, a trader might choose to adjust RSI length or thresholds.
In the detailed dashboard, hovering over each comment cell also reveals a tooltip with additional context (e.g., “Fast MA above slow MA” or “Senkou A above Senkou B”), helping traders understand the precise rule behind that +1, 0, or –1 assignment.
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15. Real-Life Example (Consolidated)
• Instrument & Timeframe: Bitcoin (BTCUSD), 1-hour chart.
• Current Market Activity: BBW and ATR both spike (+1 each), KCW is moderately high (+1), but volume is only neutral (0) → Raw Market Activity Score = +2 → State = High Activity (after two bars, if hysteresis is on).
• Category Weights Applied: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Sub-Scores:
1. ADX = 25 (above threshold 20) with +DI > –DI → +1.
2. Fast MA (20-period) sits above Slow MA (50-period) → +1.
3. Ichimoku: Senkou A > Senkou B → +1.
→ Trend Score = +3.
• Momentum Sub-Scores:
4. RSI = 75 (above its moving average +1 stdev) → +1.
5. MACD histogram = +0.15 → +1.
6. Stochastic %K = 50 (mid-range) → 0.
→ Momentum Score = +2.
• Price Action Sub-Scores:
7. Price is not within 1 % of the 20-period high/low and slope = positive → 0.
8. Heikin-Ashi body is slightly larger than stdev over last 5 bars with haClose > haOpen → +1.
9. Candle range is just above its dynamic upper bound but trend is already captured, so → +1.
→ Price Action Score = +2.
• Calculate netScore (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 2 × 0.15 = 0.30
• Raw netScore = 1.50 + 0.70 + 0.30 = 2.50 → Immediately classified as Bullish.
• Oscillator & Dashboard Output:
• The oscillator line crosses above +2 and turns green.
• Dashboard displays:
• Trend Regime “BULLISH,” Trend Score = 3, Comment = “Highly Bullish.”
• Momentum Regime “BULLISH,” Momentum Score = 2, Comment = “Strong Momentum.”
• Price Action Regime “BULLISH,” Price Action Score = 2, Comment = “Bullish Action.”
• Market Activity State “High,” Comment = “Volatile Market.”
• Weights: Trend 50 %, Momentum 35 %, Price Action 15 %.
• Dominant Category: Trend (because 1.50 > 0.70 > 0.30).
• Overall Score: 2.50, posCount = (three +1s in Trend) + (two +1s in Momentum) + (two +1s in Price Action) = 7 bullish signals, negCount = 0.
• Final Zone = “BULLISH.”
• The trader sees that both Trend and Momentum are reinforcing each other under high volatility. They might wait one more candle for confirmation but already have strong evidence to consider a long.
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• .
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Disclaimer
This indicator is strictly a technical analysis tool and does not constitute financial advice. All trading involves risk, including potential loss of capital. Past performance is not indicative of future results. Traders should:
• Always backtest the “Market Zone Analyzer ” on their chosen symbols and timeframes before committing real capital.
• Combine this tool with sound risk management, position sizing, and, if possible, fundamental analysis.
• Understand that no indicator is foolproof; always be prepared for unexpected market moves.
Goodluck
-BullByte!
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RSI3M3+ v.1.8RSI3M3+ v.1.8 Indicator
This script is an advanced trading indicator based on Walter J. Bressert's cycle analysis methodology, combined with an RSI (Relative Strength Index) variation. Let me break it down and explain how it works.
Core Concepts
The RSI3M3+ indicator combines:
A short-term RSI (3-period)
A 3-period moving average to smooth the RSI
Bressert's cycle analysis principles to identify optimal trading points
RSI3M3+ Indicator VisualizationImage Walter J. Bressert's Cycle Analysis Concepts
Walter Bressert was a pioneer in cycle analysis trading who believed markets move in cyclical patterns that can be measured and predicted. His key principles integrated into this indicator include:
Trading Cycles: Markets move in cycles with measurable time spans from low to low
Timing Bands: Projected periods when the next cyclical low or high is anticipated
Oscillator Use: Using oscillators like RSI to confirm cycle position
Entry/Exit Rules: Specific rules for trade entry and exit based on cycle position
Key Parameters in the Script
Basic RSI Parameters
Required bars: Minimum number of bars needed (default: 20)
Overbought region: RSI level considered overbought (default: 70)
Oversold region: RSI level considered oversold (default: 30)
Bressert-Specific Parameters
Cycle Detection Length: Lookback period for cycle identification (default: 30)
Minimum/Maximum Cycle Length: Expected cycle duration in days (default: 15-30)
Buy Line: Lower threshold for buy signals (default: 40)
Sell Line: Upper threshold for sell signals (default: 60)
How the Indicator Works
RSI3M3 Calculation:
Calculates a 3-period RSI (sRSI)
Smooths it with a 3-period moving average (sMA)
Cycle Detection:
Identifies bottoms: When the RSI is below the buy line (40) and starting to turn up
Identifies tops: When the RSI is above the sell line (60) and starting to turn down
Records these points to calculate cycle lengths
Timing Bands:
Projects when the next cycle bottom or top should occur
Creates visual bands on the chart showing these expected time windows
Signal Generation:
Buy signals occur when the RSI turns up from below the oversold level (30)
Sell signals occur when the RSI turns down from above the overbought level (70)
Enhanced by Bressert's specific timing rules
Bressert's Five Trading Rules (Implemented in the Script)
Cycle Timing: The low must be 15-30 market days from the previous Trading Cycle bottom
Prior Top Validation: A Trading Cycle high must have occurred with the oscillator above 60
Oscillator Behavior: The oscillator must drop below 40 and turn up
Entry Trigger: Entry is triggered by a rise above the price high of the upturn day
Protective Stop: Place stop slightly below the Trading Cycle low (implemented as 99% of bottom price)
How to Use the Indicator
Reading the Chart
Main Plot Area:
Green line: 3-period RSI
Red line: 3-period moving average of the RSI
Horizontal bands: Oversold (30) and Overbought (70) regions
Dotted lines: Buy line (40) and Sell line (60)
Yellow vertical bands: Projected timing windows for next cycle bottom
Signals:
Green up arrows: Buy signals
Red down arrows: Sell signals
Trading Strategy
For Buy Signals:
Wait for the RSI to drop below the buy line (40)
Look for an upturn in the RSI from below this level
Enter the trade when price rises above the high of the upturn day
Place a protective stop at 99% of the Trading Cycle low
For Sell Signals:
Wait for the RSI to rise above the sell line (60)
Look for a downturn in the RSI from above this level
Consider exiting or taking profits when a sell signal appears
Alternative exit: When price moves below the low of the downturn day
Cycle Timing Enhancement:
Pay attention to the yellow timing bands
Signals occurring within these bands have higher probability of success
Signals outside these bands may be less reliable
Practical Tips for Using RSI3M3+
Timeframe Selection:
The indicator works best on daily charts for intermediate-term trading
Can be used on weekly charts for longer-term position trading
On intraday charts, adjust cycle lengths accordingly
Market Applicability:
Works well in trending markets with clear cyclical behavior
Less effective in choppy, non-trending markets
Consider additional indicators for trend confirmation
Parameter Adjustment:
Different markets may have different natural cycle lengths
You may need to adjust the min/max cycle length parameters
Higher volatility markets may need wider overbought/oversold levels
Trade Management:
Enter trades when all Bressert's conditions are met
Use the protective stop as defined (99% of cycle low)
Consider taking partial profits at the projected cycle high timing
Advanced Techniques
Multiple Timeframe Analysis:
Confirm signals with the same indicator on higher timeframes
Enter in the direction of the larger cycle when smaller and larger cycles align
Divergence Detection:
Look for price making new lows while RSI makes higher lows (bullish)
Look for price making new highs while RSI makes lower highs (bearish)
Confluence with Price Action:
Combine with support/resistance levels
Use with candlestick patterns for confirmation
Consider volume confirmation of cycle turns
This RSI3M3+ indicator combines the responsiveness of a short-term RSI with the predictive power of Bressert's cycle analysis, offering traders a sophisticated tool for identifying high-probability trading opportunities based on market cycles and momentum shifts.
THANK YOU FOR PREVIOUS CODER THAT EFFORT TO CREATE THE EARLIER VERSION THAT MAKE WALTER J BRESSERT CONCEPT IN TRADINGVIEW @ADutchTourist
AlphaEdge Crypto Tracker [CHE]AlphaEdge Crypto Tracker
Efficiently Identify Top Performers and Underperformers Among 40 Crypto Assets at a Glance
In the fast-paced world of cryptocurrency trading, staying ahead requires the ability to quickly assess the performance of multiple assets simultaneously. AlphaEdge Crypto Tracker is an advanced Pine Script™ indicator designed for TradingView that empowers traders to effortlessly monitor and evaluate 40 different crypto assets in real-time.
This tool is my Christmas gift to all traders. I wish you all a Merry Christmas and successful trades in the coming year!
Why It’s Important to Identify Winners and Losers Among 40 Assets at a Glance:
1. Time Efficiency: Managing a diverse portfolio can be overwhelming. With AlphaEdge Crypto Tracker, traders can swiftly identify which assets are performing exceptionally well (winners) and which are underperforming (losers) without the need to analyze each asset individually.
2. Informed Decision-Making: By having a clear overview of top gainers and losers, traders can make strategic decisions such as reallocating investments, taking profits, or cutting losses, thereby optimizing their trading strategies.
3. Risk Management: Quickly spotting underperforming assets helps in mitigating potential losses and adjusting positions to maintain a balanced and profitable portfolio.
4. Opportunity Identification: Recognizing top-performing assets allows traders to capitalize on emerging trends and maximize their returns by focusing on the most promising opportunities.
Key Features of AlphaEdge Crypto Tracker :
- Comprehensive Asset Tracking: Monitors 40 crypto assets simultaneously, providing a broad view of the market landscape.
- Max Gain and Adjusted Max Loss Calculations: Utilizes a 14-bar (configurable) period to calculate the highest gains and the adjusted maximum losses for each asset, offering insights into potential profitability and risk.
- Dynamic Ranking: Automatically sorts and ranks assets based on their performance, highlighting the top 10 gainers and top 10 losers for easy comparison.
- Customizable Display:
- Table Settings: Adjust the size, position, and colors of the performance table to fit your chart layout.
- Interactive Tooltips: Hover over asset names to view detailed tooltips, enhancing usability and information accessibility.
- Visual Alerts: Changes in asset performance are visually indicated through background color updates, allowing for immediate recognition of significant shifts.
- User-Friendly Interface: Intuitive table layout with clear headers and organized data presentation, making it easy for traders of all levels to interpret the information.
How It Works:
1. Data Calculation: For each of the 40 tracked assets, AlphaEdge Crypto Tracker calculates the maximum gain and adjusted maximum loss over the defined trading period.
2. Sorting and Ranking: The assets are sorted based on their maximum gains and adjusted maximum losses, automatically updating to reflect the latest market movements.
3. Real-Time Display: The top 10 gainers and losers are displayed in a neatly organized table directly on your TradingView chart, providing immediate visual insights.
4. Customization: Users can tailor the tracking period, select specific assets to monitor, and adjust the table’s appearance to match their trading style and preferences.
Conclusion:
AlphaEdge Crypto Tracker is an essential tool for cryptocurrency traders seeking to enhance their market analysis and decision-making processes. By providing a comprehensive and customizable overview of multiple assets, it enables traders to efficiently identify profitable opportunities and manage risks effectively. Whether you’re a seasoned trader or just starting, AlphaEdge Crypto Tracker equips you with the insights needed to navigate the dynamic crypto market with confidence.
Get Started Today:
Integrate AlphaEdge Crypto Tracker into your TradingView setup and take control of your crypto trading strategy with unparalleled clarity and precision.
Disclaimer:
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
License Information:
This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0. You can view the full license (mozilla.org).
© chervolino
Dema Percentile Standard DeviationDema Percentile Standard Deviation
The Dema Percentile Standard Deviation indicator is a robust tool designed to identify and follow trends in financial markets.
How it works?
This code is straightforward and simple:
The price is smoothed using a DEMA (Double Exponential Moving Average).
Percentiles are then calculated on that DEMA.
When the closing price is below the lower percentile, it signals a potential short.
When the closing price is above the upper percentile and the Standard Deviation of the lower percentile, it signals a potential long.
Settings
Dema/Percentile/SD/EMA Length's: Defines the period over which calculations are made.
Dema Source: The source of the price data used in calculations.
Percentiles: Selects the type of percentile used in calculations (options include 60/40, 60/45, 55/40, 55/45). In these settings, 60 and 55 determine percentile for long signals, while 45 and 40 determine percentile for short signals.
Features
Fully Customizable
Fully Customizable: Customize colors to display for long/short signals.
Display Options: Choose to show long/short signals as a background color, as a line on price action, or as trend momentum in a separate window.
EMA for Confluence: An EMA can be used for early entries/exits for added signal confirmation, but it may introduce noise—use with caution!
Built-in Alerts.
Indicator on Diffrent Assets
INDEX:BTCUSD 1D Chart (6 high 56 27 60/45 14)
CRYPTO:SOLUSD 1D Chart (24 open 31 20 60/40 14)
CRYPTO:RUNEUSD 1D Chart (10 close 56 14 60/40 14)
Remember no indicator would on all assets with default setting so FAFO with setting to get your desired signal.
RSI 15/60 and ADX PlotIn this script, the buy and sell criteria are based on the Relative Strength Index (RSI) values calculated for two different timeframes: the 15-minute RSI and the hourly RSI. These timeframes are used together to check signals when certain thresholds are crossed, providing confirmation across both short-term and longer-term momentum.
Buy Criteria:
Condition 1:
Hourly RSI > 60: This means the longer-term momentum shows strength.
15-minute RSI crosses above 60: This shows that the shorter-term momentum is catching up and confirms increasing strength.
Condition 2:
15-minute RSI > 60: This indicates that the short-term trend is already strong.
Hourly RSI crosses above 60: This confirms that the longer-term trend is also gaining strength.
Both conditions aim to capture the moments when the market shows increasing strength across both short and long timeframes, signaling a potential buy opportunity.
Sell Criteria:
Condition 1:
Hourly RSI < 40: This indicates that the longer-term trend is weakening.
15-minute RSI crosses below 40: The short-term momentum is also turning down, confirming the weakening trend.
Condition 2:
15-minute RSI < 40: The short-term trend is already weak.
Hourly RSI crosses below 40: The longer-term trend is now confirming the weakness, indicating a potential sell.
These conditions work to identify when the market is showing weakness in both short-term and long-term timeframes, signaling a potential sell opportunity.
ADX Confirmation :
The Average Directional Index (ADX) is a key tool for measuring the strength of a trend. It can be used alongside the RSI to confirm whether a buy or sell signal is occurring in a strong trend or during market consolidation. Here's how ADX can be integrated:
ADX > 25: This indicates a strong trend. Using this threshold, you can confirm buy or sell signals when there is a strong upward or downward movement in the market.
Buy Example: If a buy signal (RSI > 60) is triggered and the ADX is above 25, this confirms that the market is in a strong uptrend, making the buy signal more reliable.
Sell Example: If a sell signal (RSI < 40) is triggered and the ADX is above 25, it confirms a strong downtrend, validating the sell signal.
ADX < 25: This suggests a weak or non-existent trend. In this case, RSI signals might be less reliable since the market could be moving sideways.
Final Approach:
The RSI criteria help identify potential overbought and oversold conditions in both short and long timeframes.
The ADX confirmation ensures that the signals generated are happening during strong trends, increasing the likelihood of successful trades by filtering out weak or choppy market conditions.
This combination of RSI and ADX can help traders make more informed decisions by ensuring both momentum and trend strength align before entering or exiting trades.
Trend and RSI Bias FusionTrend and RSI Bias Fusion Indicator
This is my first ever indicator. I created this indicator for myself. I was inspired by the indicators created by Bjorgum, Duyck and QuantTherapy and decided to create multiple indicators that either work well combined with their indicators or something new that applies some of their indicator concepts. I decided to share this because I believe in learning and earing together as a community. I will later share the rest of the indicators I have created. This is my first time ever sharing any indicator so if you guys have any questions or suggestions write them.
Overview
The "Trend and RSI Bias Fusion" indicator is a versatile tool designed to help traders identify key market trends, potential reversals, momentum shifts, and RSI-based pullbacks. This indicator fuses trend analysis and RSI bias into a single, comprehensive visual, making it easier to make informed trading decisions across various timeframes and market conditions.
Features
Dual Timeframe Analysis: Combines trend analysis on a higher timeframe (e.g., Daily) with RSI analysis on a lower timeframe (e.g., 4-Hour), providing a more granular view of market conditions. You can, however, choose any timeframe you want for instance 12hr with trend and 2hr RSI analysis.
Trend and Momentum Visualization: The indicator uses Exponential Moving Averages (EMAs) to determine trend direction and colors the chart background to reflect bullish or bearish trends, along with momentum strength.
RSI Bias Detection: Automatically identifies overbought and oversold conditions using the RSI, providing a clear indication of potential market reversals or continuations.
Color-Coded Bars: Optionally color codes bars based on either trend direction or RSI bias, giving you a quick visual cue of the market's state.
Reversal Markers: Displays trend reversal markers on the chart when the short-term EMA crosses over or under the long-term EMA.
Calculation Details
Exponential Moving Averages (EMAs): The indicator calculates short-term and long-term EMAs using the closing prices.
The crossover between these EMAs is used to determine the trend direction:
Short-Term EMA: Typically a 14-period EMA.
Long-Term EMA: Typically a 50-period EMA.
Momentum: Calculated using the RSI and then centered around zero by subtracting 50. This allows the indicator to distinguish between positive and negative momentum.
RSI Bias: The RSI is calculated on a lower timeframe to detect overbought (above 60) and oversold (below 40) conditions, which are used to determine the bias:
RSI Above 60: Indicates potential overbought conditions (bearish bias).
RSI Below 40: Indicates potential oversold conditions (bullish bias).
How to Use the Indicator
Select Your Timeframes: Choose your preferred trend timeframe (e.g., Daily) and RSI timeframe (e.g., 4-2 Hour) in the indicator settings. These should match your trading strategy and the asset class you're analyzing.
Interpret Trend and Momentum
Background Color: The background color reflects the current trend direction:
Green/Lime: Uptrend, with lime indicating positive momentum.
Red/Maroon: Downtrend, with maroon indicating positive momentum within a downtrend.
Momentum Histogram: The histogram plot shows momentum, color-coded by the trend. A histogram above zero with green/lime indicates bullish momentum, while below zero with red/maroon indicates bearish momentum.
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Read RSI Bias:
The RSI bias line helps identify the current market state relative to overbought or oversold levels. The RSI value is plotted on the chart, with lines at 60 and 40 to mark these levels.
When the RSI crosses above 60, it suggests a bearish bias; crossing below 40 suggests a bullish bias.
Use Reversal Markers: The indicator places small circles on the chart at points where the short-term EMA crosses the long-term EMA, signaling potential trend reversals.
Bar Color Customization:
You can choose to color the bars based on either the trend or the RSI bias in the indicator settings. In the Images below I have changed the colors to fit my personal style , Blue for uptrend and Pink for downtrend:
Trend-Based: Bars will reflect the trend direction (green for uptrend or in this case blue, red for downtrend or in this case pink).
RSI-Based: Bars will reflect RSI conditions (yellow for overbought, maroon for oversold).
Image above: RSI is set to 4hr and Trend is set to daily, uses RSI bar color
Image above: RSI is set to 4hr and Trend is set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, uses RSI bar color
Image above: Both RSI and Trend are set to daily, uses Trend bar color
Image above: Both RSI and Trend are set to daily, without bar color
Image above: Both RSI and Trend are set to daily, how it looks on a clean chart
Example Use Case Swing Traders:
For instance, if you're trading a 4-hour chart of USDCHF:
Set the trend timeframe to Daily and the RSI timeframe to 4-Hour.
Watch for background color shifts and reversal markers to determine trend direction.
Use RSI bias to time your entries and exits, especially around overbought/oversold levels.
Enable bar coloring to quickly see when conditions favor either trend continuation or reversal.
This indicator is particularly effective for swing traders and those who want to align their trades with higher timeframe trends while using momentum and RSI for entry and exit signals.
For Day Traders
Timeframe Selection:
Trend Timeframe: Set to a higher intraday timeframe such as the 1 or 2 Hour chart.
RSI Timeframe: Set to a shorter timeframe like 15-10 Minutes or 5-Minutes to capture finer details of intraday momentum shifts.
Using the Indicator:
Trend Identification: Day traders can use the background color to quickly identify whether the market is in a bullish or bearish trend on the 1-Hour chart. A green background suggests looking for long opportunities, while a red background suggests short opportunities.
Momentum Analysis: The histogram can help day traders gauge the strength of the current trend. For example, if the histogram is green and above zero, the trader may consider buying pullbacks within the trend.
RSI Bias: Monitor RSI levels on the lower timeframe (e.g., 15-Minutes). If the RSI crosses below 40, it indicates an oversold condition, potentially signaling a buying opportunity, especially if it aligns with a bullish trend on the higher timeframe.
Trade Execution:
Look for entries when the RSI shows a reversal or pullback in the direction of the higher timeframe trend.
Use the trend reversal markers to confirm potential intraday reversals, adding extra confidence to trade setups.
For Scalpers
Timeframe Selection:
Trend Timeframe: Set to a short intraday timeframe like 15-Minutes or 5-Minutes.
RSI Timeframe: Use an even shorter timeframe, such as 1-Minute, to capture rapid price movements.
Final Notes:
The "Trend and RSI Bias Fusion" indicator is a powerful tool that combines trend analysis, momentum assessment, and RSI insights into one cohesive package. By integrating these different aspects, the indicator helps traders navigate complex market environments with greater clarity and confidence. Customize the settings to fit your specific trading style and market and use it to stay ahead of market trends and potential reversals.
My Scripts/Indicators/Ideas /Systems that I share are only for educational purposes!
Bullish Divergence Short-term Long Trade FinderThis script is a Bullish divergence trade finder built to find small periods where Bitcoin will likely rise from. It looks for bullish divergence followed by a higher low as long as the hour RSI value is below the 40 mark, if then it will enter an long. It marks out Buy signals on the RSI if the value dips below 'RSI Bull Condition Minimum' (Default 40) on the current time frame in view. It also marks out Sell signals found when the RSI is above the 'RSI Bearish Condition Minimum' (Default 50). The sell signals are bearish divergence that has occurred recently on the RSI. When a long is in play it will sell if it finds bearish divergence or the time frame in view reaches RSI value higher than the 'RSI Sell Value'(Default 75). You can set your stop loss value with the 'Stop loss Percentage' (default 5).
Available inputs:
RSI Period: relative strength measurement length(Typically 14)
RSI Oversold Level: the bottom bar of the RSI (Typically 30)
RSI Overbought Level: the top bar of the RSI (Typically 70)
RSI Bearish Condition Minimum: The minimum value the script will use to look for a pivot high that starts the Bearish condition to Sell (Default 50)
RSI Bearish Condition Sell Min: the minimum value the script will accept a bearish condition (Default 60)
RSI Bull Condition Minimum: the minimum value it will consider a pivot low value in the RSI to find a divergence buy (Default 40)
Look Back this many candles: the amount of candles thee script will look back to find a low value in the RSI (Default 25)
RSI Sell Value: The RSI value of the exit condition for a long when value is reached (Default 75)
Stop loss Percentage: Percentage value for amount to lose (Default 5)
The formula to enter a long is stated below:
If price finds a lower low and there is a higher low found following a lower low and price has just made another dip and price closes lower than the last divergence and Relative strength index hour value is less than 40 enter a long.
The formula to exit a long is stated below:
If the value drops below the stop loss percentage OR (the RSI value is greater than the value of the parameter 'RSI Sell Value' or bearish divergence is found greater than the parameter 'RSI Bearish Condition Minimum' )
This script was built from much strategy testing on BTC but works with alts (occasionally) also. It is most successful to my knowledge using the 15 min and 7 min time frames with default values. Hope it helps! Follow for further possible updates to this script or other entry or exit strategies.
snapshot:
I only have a Pro trading view account so I cannot share a larger data set about this script because the buy signals happen pretty rarely. The most amount that I could find within a view for me was 40 trades within a viewable time. The suggested/default parameters that I have do not occur very often so it limits the data set. Adjustments can be made to the parameters so that trades can be entered more often. The scripts success is dependent on the values of the parameters set by the user. This script was written to be used for BTC/USD or BTC/USDT trading. I am unable to share a larger dataset without putting out results that are intended to fail or having a premium account so reaching the 100 trade minimum is not possible with my account.
5EMA BollingerBand Nifty Stock Scanner
What ?
We all heard about (well: over-heard) 5-EMA strategy. Which falls into the broader category of mean reversal type of trading setup.
What is mean reversal?
Price (or any time series, in fact) tries to follow a mean . Whenever price diverges from the mean it tries to meet it back.
It is empirically observed by some traders (I honestly don't know who first time observed it) that in Indian context specially, 5 Exponential Moving Average (5-EMA) works pretty good as that mean.
So whenever price moves away from that 5-EMA, it ultimately comes back and attain total nirvana :) Means: if price moved way higher than the 5EMA without touching it, then price will correct to meet it's 5-EMA and if price moved way lower, it will be uplifted to meet it's 5-EMA. Funny - but it works !
Now there are already enough social media coverage on this 5-EMA strategy/setup. Even TradingView has some excellent work done on these setups. Kudos to all those great souls.
So when we came to know about this, we were thinking what we should do for the community. Because it is well cover topic (specially in Indian context). Also, there are public indicators.
Then we thought why not come up with a scanner which will scan all the Nifty-50 constituent stocks and find out on the fly, real-time which all stocks are matching this 5-EMA setup and causing a Buy/Sell trade recommendation.
Hence here we are with the first version of our first scanner on the 5EMA setup (well it has some more masala than merely a 5-EMA setup).
Why?
Parts of why is already covered up.
Now instead of blindly following 5-EMA setup, we added the Bollinger band as well. Again: it's also not new. There are enough coverage in social media about the 5-EMA+BB strategy/setup. We mercilessly borrowed from all of these.
Suppose you have an indicator.
Now you apply the indicator in your chart. And then you need to (rock) and roll through your watchlist of Nifty-50 stocks (note: TradingView has no default watchlist of Nifty-50 stock by default - you have to create one custom watchlist to list all manually) to find out which all are matching the setup, need to take a note about the trade recomendations (entry, SL, target) and other stuffs like VWAP, Volume, volatility (Bollinger Band Width).
Not any more.
This scanner will track all the Nifty-50 stocks (technically: 40 stocks other than Banking stocks) and provide which one to Buy or Sell (if any), what's the entry, SL, target, where is the VWAP of the day, what's the picture in volume (high, low, rising, falling) and the implied volatility (using Bolling band width). Also it has a naive alerting mechanism as well.
In fact the code is there to monitor the (Future) OI also and all the OI drama (OI vs price and all the 4 stuffs like long build up, long unwinding, short covering, short buildup). But unfortunately, due to some limitations of the TradingView (that one can not monitor more than 40 `ta.security` call) we have to comment out the code. If you wish you can monitor only 20 stocks and enable the OI monitoring also (20 for stocks + 20 for their OI monitoring .. total 40 `ta.security` call).
How?
To know the divergence from 5-EMA we just check if the high of the candle (on closing) is below the 5-EMA. Then we check if the closing is inside the Bollinger Band (BB). That's a Buy signal. SL: low of the candle, T: middle and higher BB.
Just opposite for selling. 5-EMA low should be above 5-EMA and closing should be inside BB (lesser than BB higher level). That's a Sell signal. SL: high of the candle, T: middle and lower BB.
Along with we compare the current bar's volume with the last-20 bar VWMA (volume weighted moving average) to determine if the volume is high or low.
Present bar's volume is compared with the previous bar's volume to know if it's rising or falling.
VWAP is also determined using `ta.vwap` built-in support of TradingView.
The Bolling Band width is also notified, along with whether it is rising or falling (comparing with previous candle).
Simple, but effective.
Customization
As usual the EMA setup (5 default), the BB setup (20 SMA with 1.5 standard deviation), we provided option wherther to include or exclude BB role in the 5-EMA setup (as we found out there are two schools of thought .. some people use BB some don't. Lets make all happy :))
We also provide options to choose other symbols using Settings if they wish so. We have the default 40 non banking Nifty stocks (why non-banking? - Bank Nifty is in ATH :) .. enough :)). But if user wishes can monitor others too (provided the symbol is there in TradingView).
Although we strongly recommend the timeframe as 30 minutes , you can choose what's fit you most.
The output of the scanner is a table. By default the table is placed in the right-bottom (as we are most comfortable with that). However you can change per your wish. We have the option to choose that.
What is unique in it ?
This is more of an indicator. This is a scanner (of Nifty-50 stocks). So you can apply (our recommendation is in 30m timeframe) it to any chart (does not matter which chart it is) and it will show every 30 mins (which is also configurable) which all stocks (along with trade levels) to Buy and Sell according to the setup.
It will ease your trading activity.
You can concentrate only on the execution, the filtering you can leave it to this one.
Limitations
There is a build in limitation of the TradingView platform is that one can call only upto 40 securities API. Not beyond that. So naturally we are constraint by that. Otherwise we could monitor 190 Nifty F&O stocks itself.
30m is the recommended timeframe. In very lower (say 5m) this script tends to go out of heap (out of memory). Please note that also.
How to trade using this?
Put any chart in 30m (recommended) timeframe.
Apply this screener from Indicators (shortcut to launch indicators is just type / in your keyboard).
This will provide the Buy (shown in green color) or Sell (shown in red color) recommendations in a table, at every 30m candle closing.
Note the volume and BB width as well.
Wait for at least 2 5-minutes candles to close above/below the recommended level .
Take the trade with the SL and target mentioned.
Mentions
@QuantNomad. The whole implementation concept we mercilessly borrowed from him, even some of his code snippet we took it (after asking him through one of his videos comment section and seeking explicit permission which he readily granted within an hour). Thank You sir @QuantNomad. Indebted to you.
Monika (Rawat) ji: for reviewing, correcting, providing real time examples during live market hours, often compromising her own trading activities, about the effectiveness and usefulness of this setup. Thank You madam ji. Indebted to you.
There are innumerable contents in social media about this. Don't even know whom all we checked. Thanks to all of them.
Happy Trading (in stocks - isn't enough of Indices already?)
Disclaimer
This piece of software does not come up with any warrantee or any rights of not changing it over the future course of time.
We are not responsible for any trading/investment decision you are taking out of the outcome of this indicator.
