Volumetric Support and Resistance [BackQuant]Volumetric Support and Resistance
What this is
This Overlay locates price levels where both structure and participation have been meaningful. It combines classical swing points with a volume filter, then manages those levels on the chart as price evolves. Each level carries:
• A reference price (support or resistance)
• An estimate of the volume that traded around that price
• A touch counter that updates when price retests it
• A visual box whose thickness is scaled by volatility
The result is a concise map of candidate support and resistance that is informed by both price location and how much trading occurred there.
How levels are built
Find structural pivots uses ta.pivothigh and ta.pivotlow with a user set sensitivity. Larger sensitivity looks for broader swings. Smaller sensitivity captures tighter turns.
Require meaningful volume computes an average volume over a lookback period and forms a volume ratio for the current bar. A pivot only becomes a level when the ratio is at least the volume significance multiplier.
Avoid clustering checks a minimum level distance (as a percent of price). If a candidate is too close to an existing level, it is skipped to keep the map readable.
Attach a volume strength to the level estimates volume strength by averaging the volume of recent bars whose high to low range spans that price. Levels with unusually high strength are flagged as high volume.
Store and draw levels are kept in an array with fields for price, type, volume, touches, creation bar, and a box handle. On the last bar, each level is drawn as a horizontal box centered at the price with a vertical thickness scaled by ATR. Borders are thicker when the level is marked high volume. Boxes can extend into the future.
How levels evolve over time
• Aging and pruning : levels are removed if they are too old relative to the lookback or if you exceed the maximum active levels.
• Break detection : a level can be removed when price closes through it by more than a break threshold set as a fraction of ATR. Toggle with Remove Broken Levels.
• Touches : when price approaches within the break threshold, the level’s touch counter increments.
Visual encoding
• Boxes : support boxes are green, resistance boxes are red. Box height uses an ATR based thickness so tolerance scales with volatility. Transparency is fixed in this version. Borders are thicker on high volume levels.
• Volume annotation : show the estimated volume inside the box or as a label at the right. If a level has more than one touch, a suffix like “(2x)” is appended.
• Extension : boxes can extend a fixed number of bars into the future and can be set to extend right.
• High volume bar tint : bars with volume above average × multiplier are tinted green if up and red if down.
Inputs at a glance
Core Settings
• Level Detection Sensitivity — pivot window for swing detection
• Volume Significance Multiplier — minimum volume ratio to accept a pivot
• Lookback Period — window for average volume and maintenance rules
Level Management
• Maximum Active Levels — cap on concurrently drawn levels
• Minimum Level Distance (%) — required spacing between level prices
Visual Settings
• Remove Broken Levels — drop a level once price closes decisively through it
• Show Volume Information on Levels — annotate volume and touches
• Extend Levels to Right — carry boxes forward
Enhanced Visual Settings
• Show Volume Text Inside Box — text placement option
• Volume Based Transparency and Volume Based Border Thickness — helper logic provided; current draw block fixes transparency and increases border width on high volume levels
Colors
• Separate colors for support, resistance, and their high volume variants
How it can be used
• Trade planning : use the most recent support and resistance as reference zones for entries, profit taking, or stop placement. ATR scaled thickness provides a practical buffer.
• Context for patterns : combine with breakouts, pullbacks, or candle patterns. A breakout through a high volume resistance carries more informational weight than one through a thin level.
• Prioritization : when multiple levels are nearby, prefer high volume or higher touch counts.
• Regime adaptation : widen sensitivity and increase minimum distance in fast regimes to avoid clutter. Tighten them in calm regimes to capture more granularity.
Why volume support and resistance is used in trading
Support and resistance relate to willingness to transact at certain prices. Volume measures participation. When many contracts change hands near a price:
• More market players hold inventory there, often creating responsive behavior on retests
• Order flow can concentrate again to defend or to exit
• Breaks can be cleaner as trapped inventory rebalances
Conditioning level detection on above average activity focuses attention on prices that mattered to more participants.
Alerts
• New Support Level Created
• New Resistance Level Created
• Level Touch Alert
• Level Break Alert
Strengths
• Dual filter of structure and participation, reducing trivial swing points
• Self cleaning map that retires old or invalid levels
• Volatility aware presentation using ATR based thickness
• Touch counting for persistence assessment
• Tunable inputs for instrument and timeframe
Limitations and caveats
• Volume strength is an approximation based on bars spanning the price, not true per price volume
• Pivots confirm after the sensitivity window completes, so new levels appear with a delay
• Narrow ranges can still cluster levels unless minimum distance is increased
• Large gaps may jump past levels and immediately trigger break conditions
Practical tuning guide
• If the chart is crowded: increase sensitivity, increase minimum level distance, or reduce maximum active levels
• If useful levels are missed: reduce volume multiplier or sensitivity
• If you want stricter break removal: increase the ATR based break threshold in code
• For instruments with session patterns: tailor the lookback period to a representative window
Interpreting touches and breaks
• First touch after creation is a validation test
• Multiple shallow touches suggest absorption; a later break may then travel farther
• Breaks on high current volume merit extra attention
Multi timeframe usage
Levels are computed on the active chart timeframe. A common workflow is to keep a higher timeframe instance for structure and a lower timeframe instance for execution. Align trades with higher timeframe levels where possible.
Final Thoughts
This indicator builds a lightweight, self updating map of support and resistance grounded in swings and participation. It is not a full market profile, but it captures much of the practical benefit with modest complexity. Treat levels as context and decision zones, not guarantees. Combine with your entry logic and risk controls.
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Offset Strike LinesOffset Strike Lines (OSL) is a tool designed to plot strike-based grid levels by offsetting one symbol against another. It compares two instruments (for example, futures vs. index) and projects evenly spaced horizontal lines above and below a calculated reference price. Each line is annotated with the adjusted counter-symbol price, making it easy to visualize relative levels across markets. Customization options include interval size, number of lines, text size, line and text colors — giving traders a clear, flexible framework for mapping out strike zones and price relationships.
DMI MTF Color Table v5DMI Multi-Timeframe Color Table v5
A comprehensive DMI (Directional Movement Index) table that displays trend direction and strength across multiple timeframes simultaneously. This indicator helps traders quickly assess market conditions and identify confluence across different time horizons.
Features:
Multi-timeframe analysis (7 configurable timeframes)
Color-coded cells based on trend strength and direction
Real-time current market condition display
Customizable strength thresholds and color schemes
Multiple display modes (All, DI+ Only, DI- Only, ADX Only)
Text-based strength classifications (STRONG/MEDIUM/WEAK)
Directional bias indicators (BULL/BEAR)
How It Works:
The table shows DI+, DI-, and ADX values across your chosen timeframes with intelligent color coding:
Green shades indicate bullish momentum (DI+ > DI-)
Red shades indicate bearish momentum (DI- > DI+)
Color intensity reflects trend strength based on ADX values
Current market condition appears in top-right corner
Display Options:
Toggle numerical values, strength text, and timeframe labels
Adjustable table size and transparency
Customizable color schemes for all conditions
Optional current timeframe DMI plot overlay
Educational Use:
This tool is designed for educational purposes to help understand multi-timeframe analysis and DMI interpretation. All trading decisions should be based on your own analysis and risk management.
Credits:
Original concept and development by Profitgang. If you use or modify this script, please provide appropriate credit to the original author.
Note: This indicator is for analysis purposes only. Past performance does not guarantee future results. Always conduct your own research and consider your risk tolerance before making trading decisions.
Smarter Money Concepts - Wyckoff Springs & Upthrusts [PhenLabs]📊Smarter Money Concepts - Wyckoff Springs & Upthrusts
Version: PineScript™v6
📌Description
Discover institutional manipulation in real-time with this advanced Wyckoff indicator that detects Springs (accumulation phases) and Upthrusts (distribution phases). It identifies when price tests support or resistance on high volume, followed by a strong recovery, signaling potential reversals where smart money accumulates or distributes positions. This tool solves the common problem of missing these subtle phase transitions, helping traders anticipate trend changes and avoid traps in volatile markets.
By combining volume spike detection, ATR-normalized recovery strength, and a sigmoid probability model, it filters out weak signals and highlights only high-confidence setups. Whether you’re swing trading or day trading, this indicator provides clear visual cues to align with institutional flows, improving entry timing and risk management.
🚀Points of Innovation
Sigmoid-based probability threshold for signal filtering, ensuring only statistically significant Wyckoff patterns trigger alerts
ATR-normalized recovery measurement that adapts to market volatility, unlike static recovery checks in traditional indicators
Customizable volume spike multiplier to distinguish institutional volume from retail noise
Integrated dashboard legend with position and size options for personalized chart visualization
Hidden probability plots for advanced users to analyze underlying math without chart clutter
🔧Core Components
Support/Resistance Calculator: Scans a user-defined lookback period to establish dynamic levels for Spring and Upthrust detection
Volume Spike Detector: Compares current volume to a 10-period SMA, multiplied by a configurable factor to identify significant surges
Recovery Strength Analyzer: Uses ATR to measure price recovery after breaks, normalizing for different market conditions
Probability Model: Applies sigmoid function to combine volume and recovery data, generating a confidence score for each potential signal
🔥Key Features
Spring Detection: Spots accumulation when price dips below support but recovers strongly, helping traders enter longs at potential bottoms
Upthrust Detection: Identifies distribution when price spikes above resistance but falls back, alerting to possible short opportunities at tops
Customizable Inputs: Adjust lookback, volume multiplier, ATR period, and probability threshold to match your trading style and market
Visual Signals: Clear + (green) and - (red) labels on charts for instant recognition of accumulation and distribution phases
Alert System: Triggers notifications for signals and probability thresholds, keeping you informed without constant monitoring
🎨Visualization
Spring Signal: Green upward label (+) below the bar, indicating strong recovery after support break for accumulation
Upthrust Signal: Red downward label (-) above the bar, showing failed breakout above resistance for distribution
Dashboard Legend: Customizable table explaining signals, positioned anywhere on the chart for quick reference
📖Usage Guidelines
Core Settings
Support/Resistance Lookback
Default: 20
Range: 5-50
Description: Sets bars back for S/R levels; lower for recent sensitivity, higher for stable long-term zones – ideal for spotting Wyckoff phases
Volume Spike Multiplier
Default: 1.5
Range: 1.0-3.0
Description: Multiplies 10-period volume SMA; higher values filter to significant spikes, confirming institutional involvement in patterns
ATR for Recovery Measurement
Default: 5
Range: 2-20
Description: ATR period for recovery strength; shorter for volatile markets, longer for smoother analysis of post-break recoveries
Phase Transition Probability Threshold
Default: 0.9
Range: 0.5-0.99
Description: Minimum sigmoid probability for signals; higher for strict filtering, ensuring only high-confidence Wyckoff setups
Display Settings
Dashboard Position
Default: Top Right
Range: Various positions
Description: Places legend table on chart; choose based on layout to avoid overlapping price action
Dashboard Text Size
Default: Normal
Range: Auto to Huge
Description: Adjusts legend text; larger for visibility, smaller for minimal space use
✅Best Use Cases
Swing Trading: Identify Springs for long entries in downtrends turning to accumulation
Day Trading: Catch Upthrusts for short scalps during intraday distribution at resistance
Trend Reversal Confirmation: Use in conjunction with other indicators to validate phase shifts in ranging markets
Volatility Plays: Spot signals in high-volume environments like news events for quick reversals
⚠️Limitations
May produce false signals in low-volume or sideways markets where volume spikes are unreliable
Depends on historical data, so performance varies in unprecedented market conditions or gaps
Probability model is statistical, not predictive, and cannot account for external factors like news
💡What Makes This Unique
Probability-Driven Filtering: Sigmoid model combines multiple factors for superior signal quality over basic Wyckoff detectors
Adaptive Recovery: ATR normalization ensures reliability across assets and timeframes, unlike fixed-threshold tools
User-Centric Design: Tooltips, customizable dashboard, and alerts make it accessible yet powerful for all trader levels
🔬How It Works
Calculate S/R Levels:
Uses the highest high and the lowest low over the lookback period to set dynamic zones
Establishes baseline for detecting breaks in Wyckoff patterns
Detect Breaks and Recovery:
Checks for price breaking support/resistance, then recovering on volume
Measures recovery strength via ATR for volatility adjustment
Apply Probability Model:
Combines volume spike and recovery into a sigmoid function for confidence score
Triggers signal only if above threshold, plotting visuals and alerts
💡Note:
For optimal results, combine with price action analysis and test settings on historical charts. Remember, Wyckoff patterns are most effective in trending markets – use lower probability thresholds for practice, then increase for live trading to focus on high-quality setups.
Key Levels & Session Highs/Lows by OdegosProfessional multi-timeframe support and resistance level indicator that automatically tracks and displays key price levels across different trading sessions and timeframes.
🎯 What it shows:
Session Open - Daily market open reference line
Asia & London Sessions - High/low levels from major trading sessions
Previous Day - Yesterday's actual high and low levels
Weekly & Monthly - Higher timeframe support/resistance levels
⚡ Smart Features:
Auto-combines overlapping levels with merged labels
Break detection - Lines stop when price breaks through (optional)
Timezone support - Works with any global timezone
Universal colors - Optimized for both light and dark chart themes
Clean interface - Organized settings with intuitive dropdowns
🛠️ Fully Customizable:
Individual show/hide toggles for each level type
Custom colors, line styles, and widths
Adjustable label text and positioning
Global text color override option
Perfect for day traders, swing traders, and anyone who relies on key support/resistance levels for market analysis.
Correlation HeatMap Matrix Data [TradingFinder]🔵 Introduction
Correlation is a statistical measure that shows the degree and direction of a linear relationship between two assets.
Its value ranges from -1 to +1 : +1 means perfect positive correlation, 0 means no linear relationship, and -1 means perfect negative correlation.
In financial markets, correlation is used for portfolio diversification, risk management, pairs trading, intermarket analysis, and identifying divergences.
Correlation HeatMap Matrix Data TradingFinder is a Pine Script v6 library that calculates and returns raw correlation matrix data between up to 20 symbols. It only provides the data – it does not draw or render the heatmap – making it ideal for use in other scripts that handle visualization or further analysis. The library uses ta.correlation for fast and accurate calculations.
It also includes two helper functions for visual styling :
CorrelationColor(corr) : takes the correlation value as input and generates a smooth gradient color, ranging from strong negative to strong positive correlation.
CorrelationTextColor(corr) : takes the correlation value as input and returns a text color that ensures optimal contrast over the background color.
Library
"Correlation_HeatMap_Matrix_Data_TradingFinder"
CorrelationColor(corr)
Parameters:
corr (float)
CorrelationTextColor(corr)
Parameters:
corr (float)
Data_Matrix(Corr_Period, Sym_1, Sym_2, Sym_3, Sym_4, Sym_5, Sym_6, Sym_7, Sym_8, Sym_9, Sym_10, Sym_11, Sym_12, Sym_13, Sym_14, Sym_15, Sym_16, Sym_17, Sym_18, Sym_19, Sym_20)
Parameters:
Corr_Period (int)
Sym_1 (string)
Sym_2 (string)
Sym_3 (string)
Sym_4 (string)
Sym_5 (string)
Sym_6 (string)
Sym_7 (string)
Sym_8 (string)
Sym_9 (string)
Sym_10 (string)
Sym_11 (string)
Sym_12 (string)
Sym_13 (string)
Sym_14 (string)
Sym_15 (string)
Sym_16 (string)
Sym_17 (string)
Sym_18 (string)
Sym_19 (string)
Sym_20 (string)
🔵 How to use
Import the library into your Pine Script using the import keyword and its full namespace.
Decide how many symbols you want to include in your correlation matrix (up to 20). Each symbol must be provided as a string, for example FX:EURUSD .
Choose the correlation period (Corr\_Period) in bars. This is the lookback window used for the calculation, such as 20, 50, or 100 bars.
Call Data_Matrix(Corr_Period, Sym_1, ..., Sym_20) with your selected parameters. The function will return an array containing the correlation values for every symbol pair (upper triangle of the matrix plus diagonal).
For example :
var string Sym_1 = '' , var string Sym_2 = '' , var string Sym_3 = '' , var string Sym_4 = '' , var string Sym_5 = '' , var string Sym_6 = '' , var string Sym_7 = '' , var string Sym_8 = '' , var string Sym_9 = '' , var string Sym_10 = ''
var string Sym_11 = '', var string Sym_12 = '', var string Sym_13 = '', var string Sym_14 = '', var string Sym_15 = '', var string Sym_16 = '', var string Sym_17 = '', var string Sym_18 = '', var string Sym_19 = '', var string Sym_20 = ''
switch Market
'Forex' => Sym_1 := 'EURUSD' , Sym_2 := 'GBPUSD' , Sym_3 := 'USDJPY' , Sym_4 := 'USDCHF' , Sym_5 := 'USDCAD' , Sym_6 := 'AUDUSD' , Sym_7 := 'NZDUSD' , Sym_8 := 'EURJPY' , Sym_9 := 'EURGBP' , Sym_10 := 'GBPJPY'
,Sym_11 := 'AUDJPY', Sym_12 := 'EURCHF', Sym_13 := 'EURCAD', Sym_14 := 'GBPCAD', Sym_15 := 'CADJPY', Sym_16 := 'CHFJPY', Sym_17 := 'NZDJPY', Sym_18 := 'AUDNZD', Sym_19 := 'USDSEK' , Sym_20 := 'USDNOK'
'Stock' => Sym_1 := 'NVDA' , Sym_2 := 'AAPL' , Sym_3 := 'GOOGL' , Sym_4 := 'GOOG' , Sym_5 := 'META' , Sym_6 := 'MSFT' , Sym_7 := 'AMZN' , Sym_8 := 'AVGO' , Sym_9 := 'TSLA' , Sym_10 := 'BRK.B'
,Sym_11 := 'UNH' , Sym_12 := 'V' , Sym_13 := 'JPM' , Sym_14 := 'WMT' , Sym_15 := 'LLY' , Sym_16 := 'ORCL', Sym_17 := 'HD' , Sym_18 := 'JNJ' , Sym_19 := 'MA' , Sym_20 := 'COST'
'Crypto' => Sym_1 := 'BTCUSD' , Sym_2 := 'ETHUSD' , Sym_3 := 'BNBUSD' , Sym_4 := 'XRPUSD' , Sym_5 := 'SOLUSD' , Sym_6 := 'ADAUSD' , Sym_7 := 'DOGEUSD' , Sym_8 := 'AVAXUSD' , Sym_9 := 'DOTUSD' , Sym_10 := 'TRXUSD'
,Sym_11 := 'LTCUSD' , Sym_12 := 'LINKUSD', Sym_13 := 'UNIUSD', Sym_14 := 'ATOMUSD', Sym_15 := 'ICPUSD', Sym_16 := 'ARBUSD', Sym_17 := 'APTUSD', Sym_18 := 'FILUSD', Sym_19 := 'OPUSD' , Sym_20 := 'USDT.D'
'Custom' => Sym_1 := Sym_1_C , Sym_2 := Sym_2_C , Sym_3 := Sym_3_C , Sym_4 := Sym_4_C , Sym_5 := Sym_5_C , Sym_6 := Sym_6_C , Sym_7 := Sym_7_C , Sym_8 := Sym_8_C , Sym_9 := Sym_9_C , Sym_10 := Sym_10_C
,Sym_11 := Sym_11_C, Sym_12 := Sym_12_C, Sym_13 := Sym_13_C, Sym_14 := Sym_14_C, Sym_15 := Sym_15_C, Sym_16 := Sym_16_C, Sym_17 := Sym_17_C, Sym_18 := Sym_18_C, Sym_19 := Sym_19_C , Sym_20 := Sym_20_C
= Corr.Data_Matrix(Corr_period, Sym_1 ,Sym_2 ,Sym_3 ,Sym_4 ,Sym_5 ,Sym_6 ,Sym_7 ,Sym_8 ,Sym_9 ,Sym_10,Sym_11,Sym_12,Sym_13,Sym_14,Sym_15,Sym_16,Sym_17,Sym_18,Sym_19,Sym_20)
Loop through or index into this array to retrieve each correlation value for your custom layout or logic.
Pass each correlation value to CorrelationColor() to get the corresponding gradient background color, which reflects the correlation’s strength and direction (negative to positive).
For example :
Corr.CorrelationColor(SYM_3_10)
Pass the same correlation value to CorrelationTextColor() to get the correct text color for readability against that background.
For example :
Corr.CorrelationTextColor(SYM_1_1)
Use these colors in a table or label to render your own heatmap or any other visualization you need.
Lot Size + Margin InfoThis indicator is designed to give Futures & Options traders instant access to lot size and estimated margin requirements for the instrument they are viewing — directly on their TradingView chart. It combines real-time symbol detection with a built-in, regularly updated margin lookup table (sourced from Kotak Securities’ published margin requirements), while also handling fallback logic for unknown or unsupported symbols.
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### What It Does
* Automatically Detects the Instrument Type
Identifies whether the current chart’s symbol is a futures contract, option, or a cash/spot instrument.
* Shows Accurate Lot Size
For supported F\&O symbols, it fetches the correct lot size directly from exchange data.
For options, it retrieves the lot size from the option’s point value.
For cash/spot symbols with linked futures, it uses the futures lot size.
* Calculates Estimated Margin
* For futures: `Lot Size × Current Price × Margin%` (Margin% sourced from the internal lookup table).
* For options: `Lot Size × Current Price` (simple multiplication, as options margin ≈ premium cost).
* For unsupported or non-FnO symbols: Displays "No FnO".
* Fallback Margin Logic
If a symbol is missing from the margin lookup table, the script applies a user-defined default margin percentage and highlights the data in orange to indicate it’s using fallback values.
* Debug Mode for Transparency
A toggle to display the exact symbol string used for fetching lot size and margin, so traders can verify the data source.
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### How It Works
1. Symbol Normalization
The script standardizes symbol names to match the margin table format (e.g., converting `"NIFTY1!"` to `"NIFTY"`).
2. Type-Based Handling
* Futures – Uses point value for lot size, applies specific margin % from the table.
* Options – Uses option point value for lot size, margin is simply premium × lot size.
* Cash Symbols with Linked Futures – Attempts to find and use the associated futures contract for lot/margin data.
* Unsupported Symbols – Displays `"No FnO"`.
3. Margin Table Integration
The margin % table is manually updated from a reliable broker’s margin sheet (Kotak Securities) — ensuring alignment with real trading conditions.
4. Customizable Display
* Position (Top Right / Bottom Left / Bottom Right)
* Table background color, text color, font size, border width
* Editable label text for lot size and margin display
* Toggleable lot size and margin sections
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### How to Use
1. Add the Indicator to Your Chart – Works on any NSE Futures, Options, or Cash symbol with linked F\&O.
2. Configure Display Settings – Choose whether to show lot size, margin, or both, and place the info table where you prefer.
3. Adjust Fallback Margin % – If you trade less common contracts, set your default margin % to reflect your broker’s requirement.
4. Enable Debug Mode (Optional) – To see the exact symbol source the script is using.
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### Best For
* Intraday & Positional F\&O Traders who need instant clarity on lot size and margin before entering trades.
* Options Sellers & Buyers who want quick cost estimates.
* Traders Switching Symbols Quickly — saves time by removing the need to check the broker’s margin sheet manually.
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💡 Pro Tip: Since margin requirements can change, keep the script updated whenever your broker revises margin data. This version’s margin table is updated as of 13-08-2025.
Market Structure (DeadCat)🌟 Market Structure (DeadCat) - Indicator Overview 🌟
The Market Structure (DeadCat) indicator plots swing highs and lows (HH, HL, LH, LL) using pivot points, helping you spot uptrends, downtrends, and potential reversals. Perfect for traders who use market structure.
🌟 Key Features 🌟
🔹 Swing Point Labels
HH (Higher High): Signals uptrend strength.
HL (Higher Low): Marks bullish support.
LH (Lower High): Hints at weakening uptrend or reversal.
LL (Lower Low): Confirms downtrend momentum.
🔹 Trend Detection
Uptrend: Tracks HH/HL for bullish momentum.
Downtrend: Tracks LH/LL for bearish momentum.
Waits for breaks of prior HH/HL or LH/LL to confirm new swing points, ensuring reliable signals. 🔄
🔹 Customizable Labels
Adjust label text color (default: black) to suit your chart. Supports up to 500 labels for a clean, focused view. 🖌️
🌟 Indicator Settings 🌟
Swing Length: Fixed at 20 bars (left) and 2 bars (right) for pivot detection.
Label Color: Customize text color for better visibility.
Fibs Has Lied 🌟 Fibs Has Lied - Indicator Overview 🌟
Designed for indices like US30, NQ, and SPX, this indicator highlights setups where price interacts with key EMA levels during specific trading sessions (default: 6:30–11:30 AM EST).
🌟 Key Features & Levels 🌟
🔹EMA Crossover Setups
The indicator uses the 100-period and 200-period EMAs to identify bullish and bearish setups:
- Bullish Setup: Triggers when the 100 EMA crosses above the 200 EMA, followed by two consecutive candles opening above the 100 EMA, with the low within a specified point distance (e.g., 20 points for US30).
- Bearish Setup: Triggers when the 100 EMA crosses below the 200 EMA, followed by two consecutive candles opening below the 100 EMA, with the high within the point distance.
- Signals are marked with green (buy) or red (sell) triangles and text, ensuring you don’t miss a setup. 📈
🔹 Reset Conditions for Re-Entries
After an initial setup, the indicator watches for “reset” opportunities:
- Buy Reset: If price moves below the 200 EMA after a bullish crossover, then returns with two consecutive candles where lows are above the 100 EMA (within point distance), a new buy signal is plotted.
- Sell Reset: If price moves above the 200 EMA after a bearish crossover, then returns with two consecutive candles where highs are below the 100 EMA (within point distance), a new sell signal is plotted.
This feature captures additional entries after liquidity grabs or fakeouts, aligning with ICT’s manipulation concepts. 🔄
🔹 Session-Based Filtering
Focus your trades during high-liquidity windows! The default session (6:30–11:30 AM EST, New York timezone) targets the London/NY overlap, where price often seeks liquidity or sets up for reversals. Toggle the time filter off for 24/7 signals if desired. 🕒
🔹Symbol-Specific Point Distance
Customizable entry zones based on your chosen index:
- US30: 20 points from the 100 EMA.
- NQ: 3 points from the 100 EMA.
- SPX: 2.5 points from the 100 EMA.
This ensures setups are tailored to the volatility of your market, maximizing relevance. 🎯
🔹 Market Structure Markers (Optional)
Visualize swing points with pivot-based labels:
- HH (Higher High): Signals uptrend continuation.
- HL (Higher Low): Indicates potential bullish support.
- LH (Lower High): Suggests weakening uptrend or reversal.
- LL (Lower Low): Points to downtrend continuation.
- Toggle these on/off to keep your chart clean while analyzing trend direction. 📊
🔹 EMA Visualization
Optionally plot the 100 EMA (blue) and 200 EMA (red) to see key levels where price reacts. These act as dynamic support/resistance, perfect for spotting liquidity pools or ICT’s Power of 3 setups. ⚖️
🌟 Customization Options 🌟
- Symbol Selection: Choose US30, NQ, or SPX to adjust point distance for entries.
- Time Filter: Enable/disable the 6:30–11:30 AM EST session to focus on high-liquidity periods.
- EMA Display: Toggle 100/200 EMAs on/off to reduce chart clutter.
- Market Structure: Show/hide HH/HL/LH/LL labels for cleaner analysis.
- Signal Markers: Green (buy) and red (sell) triangles with text are auto-plotted for easy identification.
🌟 Usage Tips 🌟
- Best Timeframes: Use on 3m for intraday scalping and 30m for swing trades.
- Combine with ICT Tools: Pair with order blocks, fair value gaps, or kill zones for stronger setups.
- Focus on Session: The default 6:30–11:30 AM EST session captures London/NY volatility—perfect for liquidity-driven moves.
- Avoid Overcrowding: Disable market structure or EMAs if you only want setup signals.
Enhanced 4H Candle Countdown & High/Low IndicatorBy profitgang
This Pine Script indicator provides real-time tracking of 4-hour timeframe levels with an integrated countdown timer, designed to help traders monitor key support and resistance zones.
Key Features
📊 Visual Elements
4H High/Low Lines: Clear visualization of previous 4-hour candle high and low levels
Range Fill: Subtle background fill between high and low for better context
Mid-Level Line: Shows the middle point of the 4H range
Position Indicator: Visual cue showing current price position within the range
⏰ Countdown Timer
Real-time countdown to next 4H candle close
Customizable table position (9 different locations)
Adjustable text size (6 size options from Tiny to Huge)
Distance calculations showing percentage distance from key levels
🎯 Signal Generation
Long signals when price crosses above 4H low
Short signals when price crosses below 4H high
RSI confluence filter to reduce false signals
Background highlighting for active signals
TradingView alerts compatible
⚙️ Customization Options
Toggle all features on/off independently
Custom colors for all elements
Table positioning (top/middle/bottom + left/center/right)
Text size selection for optimal readability
Alert notifications for level breaks and updates
How It Works
The indicator fetches the previous 4-hour candle's high and low values and displays them as horizontal lines on your current timeframe chart. It continuously calculates the time remaining until the current 4H candle closes and presents this information in a clean, customizable table.
Use Cases
Swing Trading: Identify key 4H support and resistance levels
Intraday Trading: Monitor when new 4H levels will be established
Risk Management: Calculate distance from key levels for position sizing
Multi-timeframe Analysis: Combine with lower timeframe setups
Educational Purpose
This indicator is designed for educational and analytical purposes to help traders understand price action relative to higher timeframe levels. It provides clear visual feedback about market structure and timing.
Settings Groups
Display Settings: Toggle features, positioning, and sizing
Colors: Customize all visual elements
Signal Settings: Configure alert conditions and confluence filters
Compatibility
Works on all timeframes (recommended for 1m to 1H charts)
Compatible with all instruments
Includes proper alert functionality for automated notifications
Optimized for both light and dark themes
This indicator does not provide financial advice. Always conduct your own research and risk management before making trading decisions.
Adaptive Candle Signals█ OVERVIEW
The Adaptive Candle Signals indicator is a Pine Script® tool designed to identify key candlestick patterns on the chart, such as Bullish Engulfing, Bearish Engulfing, Piercing Line, Dark Cloud Cover, Morning Star, Evening Star, Three White Soldiers, Three Black Crows, and Three Inside Up/Down. The indicator allows customization of settings, including a Moving Average (MA) filter, candle size control, and maximum wick percentage, enabling precise adaptation to various trading strategies. Signals are displayed as labels on the chart, and each pattern can trigger alerts for user convenience.
█ CONCEPTS
The indicator is designed with flexibility and readability in mind. Its main features include:
Features
Signal Filtering: Enables the use of a Moving Average (MA) filter to confirm signals based on trend direction. Bullish signals are generated when the price is above the MA, and bearish signals when below.
Pattern Customization: Users can enable or disable individual candlestick patterns and adjust their parameters, such as maximum wick percentage or candle size multiplier. The candle size multiplier applies to the largest candle in the pattern and determines its minimum size relative to the average candle body size over a specified volatility period.
Labels and Colors: Signals are displayed as clear labels with customizable colors for bullish and bearish patterns.
Alerts: Each pattern has a dedicated alert function, facilitating integration with automated trading strategies.
List of Patterns
The indicator recognizes the following candlestick patterns (labels displayed in parentheses):
Bullish Engulfing (BE): Signals a potential upward reversal after a downtrend.
Bearish Engulfing (BE): Indicates a possible downward reversal after an uptrend.
Piercing Line (PL): A bullish pattern suggesting a bounce from support.
Dark Cloud Cover (DC): A bearish pattern indicating a potential downward reversal.
Morning Star (MS): A three-candle bullish pattern signaling an upward reversal.
Evening Star (ES): A three-candle bearish pattern indicating a downward reversal.
Three White Soldiers (3WS): A strong bullish signal based on three large bullish candles.
Three Black Crows (3BC): A strong bearish signal based on three large bearish candles.
Three Inside Up/Down (3Up/3Dn): Patterns indicating trend reversal based on an inside bar structure.
Settings
Settings are organized as follows:
MA Filter: Allows enabling a Moving Average (SMA, EMA, WMA) to filter signals based on trend direction.
Pattern Parameters: Each pattern has its own settings, such as volatility period, candle size multiplier, and maximum wick percentage. The size of the largest candle in the pattern is compared to the average candle body size over the specified volatility period.
Colors and Labels: Users can customize label colors and their distance from candles to improve readability.
█ SETTINGS
Detailed description of the indicator’s settings:
MA Filter:
Use MA Filter: Enables/disables the Moving Average filter.
MA Length: Specifies the period of the Moving Average (default: 50).
MA Type: Choose between SMA, EMA, or WMA.
MA Source: Select the data source (default: close price).
Pattern Settings:
Enable Pattern: Checkbox for each pattern (e.g., Bullish Engulfing, Morning Star).
Maximum Wick Percentage: Defines the maximum allowable wick size as a percentage of the candle body.
Big Candle Filter: Enables/disables checking if the largest candle in the pattern is larger than the average over the specified volatility period.
Volatility Period: Sets the period for calculating the average candle body size.
Candle Multiplier: Multiplier determining the minimum size of the largest candle in the pattern relative to the average candle body size over the specified volatility period.
Appearance:
Signal Text Color: Color of the label text (default: white).
Bullish Label Color: Color for bullish signals (default: green).
Bearish Label Color: Color for bearish signals (default: red).
Label Offset Factor: Controls the distance of labels from candles (from 0.0 to 1.0).
█ HOW TO USE
Add the indicator to your TradingView chart.
Configure the settings in the indicator’s dialog box:
Enable desired candlestick patterns.
Adjust the MA filter parameters to restrict signals to the trend.
Set colors and label offset for better readability.
Enable alerts for selected patterns to receive real-time notifications.
Monitor the labels on the chart and use them alongside other technical analysis tools.
█ LIMITATIONS
The indicator relies on historical price data and may produce false signals in volatile market conditions.
The big candle filter may be less effective on charts with low volatility.
The indicator performs best when combined with other analysis methods, such as support and resistance levels.
IU Indicators DashboardDESCRIPTION
The IU Indicators Dashboard is a comprehensive multi-stock monitoring tool that provides real-time technical analysis for up to 10 different stocks simultaneously. This powerful indicator creates a customizable table overlay that displays the trend status of multiple technical indicators across your selected stocks, giving you an instant overview of market conditions without switching between charts.
Perfect for portfolio monitoring, sector analysis, and quick market screening, this dashboard consolidates critical technical data into one easy-to-read interface with color-coded trend signals.
USER INPUTS
Stock Selection (10 Configurable Stocks):
- Stock 1-10: Customize any symbols (Default: NSE:CDSL, NSE:RELIANCE, NSE:VEDL, NSE:TCS, NSE:BEL, NSE:BHEL, NSE:TATAPOWER, NSE:TATASTEEL, NSE:ITC, NSE:LT)
Technical Indicator Parameters:
- EMA 1 Length: First Exponential Moving Average period (Default: 20)
- EMA 2 Length: Second Exponential Moving Average period (Default: 50)
- EMA 3 Length: Third Exponential Moving Average period (Default: 200)
- RSI Length: Relative Strength Index calculation period (Default: 14)
- SuperTrend Length: SuperTrend indicator period (Default: 10)
- SuperTrend Factor: SuperTrend multiplier factor (Default: 3.0)
Visual Customization:
- Table Size: Choose from Normal, Tiny, Small, or Large
- Table Background Color: Customize dashboard background
- Table Frame Color: Set frame border color
- Table Border Color: Configure border styling
- Text Color: Set text display color
- Bullish Color: Color for positive/bullish signals (Default: Green)
- Bearish Color: Color for negative/bearish signals (Default: Red)
LOGIC OF THE INDICATOR
The dashboard employs a multi-timeframe analysis approach using five key technical indicators:
1. Triple EMA Analysis
- Compares current price against three different EMA periods (20, 50, 200)
- Bullish Signal: Price above EMA level
- Bearish Signal: Price below EMA level
- Provides short-term, medium-term, and long-term trend perspective
2. RSI Momentum Analysis
- Uses 14-period RSI with 50-level threshold
- Bullish Signal: RSI > 50 (upward momentum)
- Bearish Signal: RSI < 50 (downward momentum)
- Identifies momentum strength and potential reversals
3. SuperTrend Direction
- Utilizes SuperTrend with configurable length and factor
- Bullish Signal: SuperTrend direction = -1 (uptrend)
- Bearish Signal: SuperTrend direction = 1 (downtrend)
- Provides clear trend direction with volatility-adjusted signals
4. MACD Histogram Analysis
- Uses standard MACD (12, 26, 9) histogram values
- Bullish Signal: Histogram > 0 (bullish momentum)
- Bearish Signal: Histogram < 0 (bearish momentum)
- Identifies momentum shifts and trend confirmations
5. Real-time Data Processing
- Implements request.security() for multi-symbol data retrieval
- Uses barstate.isrealtime logic for accurate live data
- Processes data only on the last bar for optimal performance
WHY IT IS UNIQUE
Multi-Stock Monitoring
- Monitor up to 10 different stocks simultaneously on a single chart
- No need to switch between multiple charts or timeframes
Highly Customizable Interface
- Full color customization for personalized visual experience
- Adjustable table size and positioning
- Clean, professional dashboard design
Real-time Analysis
- Live data processing with proper real-time handling
- Instant visual feedback through color-coded signals
- Optimized performance with smart data retrieval
Comprehensive Technical Coverage
- Combines trend-following, momentum, and volatility indicators
- Multiple timeframe perspective through different EMA periods
- Balanced approach using both lagging and leading indicators
Flexible Configuration
- Easy symbol switching for different markets (NSE, BSE, NYSE, NASDAQ)
- Adjustable indicator parameters for different trading styles
- Suitable for both swing trading and position trading
HOW USERS CAN BENEFIT FROM IT
Portfolio Management
- Quick Portfolio Health Check: Instantly assess the technical status of your entire stock portfolio
- Diversification Analysis: Monitor stocks across different sectors to ensure balanced exposure
- Risk Management: Identify which positions are showing bearish signals for potential exit strategies
- Rebalancing Decisions: Spot strongest performers for potential position increases
Market Screening and Analysis
- Sector Rotation: Compare different sector stocks to identify rotation opportunities
- Relative Strength Analysis: Quickly identify which stocks are outperforming or underperforming
- Market Breadth Assessment: Gauge overall market sentiment by monitoring diverse stock selections
- Trend Confirmation: Validate market trends by observing multiple stock behaviors
Time-Efficient Trading
- Single-Glance Analysis: Get complete technical overview without chart-hopping
- Pre-Market Preparation: Quickly assess overnight changes across multiple positions
- Intraday Monitoring: Track multiple opportunities simultaneously during trading hours
- End-of-Day Review: Efficiently review all watched stocks for next-day planning
Strategic Decision Making
- Entry Point Identification: Spot stocks showing bullish alignment across multiple indicators
- Exit Signal Recognition: Identify positions showing deteriorating technical conditions
- Swing Trading Opportunities: Find stocks with favorable technical setups for swing trades
- Long-term Investment Guidance: Use 200 EMA signals for long-term position decisions
Educational Benefits
- Pattern Recognition: Learn how different indicators behave across various market conditions
- Correlation Analysis: Understand how stocks move relative to each other
- Technical Analysis Learning: Observe multiple indicator interactions in real-time
- Market Sentiment Understanding: Develop better market timing skills through multi-stock observation
Workflow Optimization
- Reduced Chart Clutter: Keep your main chart clean while monitoring multiple stocks
- Faster Analysis: Complete technical analysis of 10 stocks in seconds instead of minutes
- Consistent Methodology: Apply the same technical criteria across all monitored stocks
- Alert Integration: Easy visual identification of stocks requiring immediate attention
This indicator is designed for traders and investors who want to maximize their market awareness while minimizing analysis time. Whether you're managing a portfolio, screening for opportunities, or learning technical analysis, the IU Indicators Dashboard provides the comprehensive overview you need for better trading decisions.
DISCLAIMER :
This indicator is not financial advice, it's for educational purposes only highlighting the power of coding( pine script) in TradingView, I am not a SEBI-registered advisor. Trading and investing involve risk, and you should consult with a qualified financial advisor before making any trading decisions. I do not guarantee profits or take responsibility for any losses you may incur.
Mutanabby_AI | Algo Pro Strategy# Mutanabby_AI | Algo Pro Strategy: Advanced Candlestick Pattern Trading System
## Strategy Overview
The Mutanabby_AI Algo Pro Strategy represents a systematic approach to automated trading based on advanced candlestick pattern recognition and multi-layered technical filtering. This strategy transforms traditional engulfing pattern analysis into a comprehensive trading system with sophisticated risk management and flexible position sizing capabilities.
The strategy operates on a long-only basis, entering positions when bullish engulfing patterns meet specific technical criteria and exiting when bearish engulfing patterns indicate potential trend reversals. The system incorporates multiple confirmation layers to enhance signal reliability while providing comprehensive customization options for different trading approaches and risk management preferences.
## Core Algorithm Architecture
The strategy foundation relies on bullish and bearish engulfing candlestick pattern recognition enhanced through technical analysis filtering mechanisms. Entry signals require simultaneous satisfaction of four distinct criteria: confirmed bullish engulfing pattern formation, candle stability analysis indicating decisive price action, RSI momentum confirmation below specified thresholds, and price decline verification over adjustable lookback periods.
The candle stability index measures the ratio between candlestick body size and total range including wicks, ensuring only well-formed patterns with clear directional conviction generate trading signals. This filtering mechanism eliminates indecisive market conditions where pattern reliability diminishes significantly.
RSI integration provides momentum confirmation by requiring oversold conditions before entry signal generation, ensuring alignment between pattern formation and underlying momentum characteristics. The RSI threshold remains fully adjustable to accommodate different market conditions and volatility environments.
Price decline verification examines whether current prices have decreased over a specified period, confirming that bullish engulfing patterns occur after meaningful downward movement rather than during sideways consolidation phases. This requirement enhances the probability of successful reversal pattern completion.
## Advanced Position Management System
The strategy incorporates dual position sizing methodologies to accommodate different account sizes and risk management approaches. Percentage-based position sizing calculates trade quantities as equity percentages, enabling consistent risk exposure across varying account balances and market conditions. This approach proves particularly valuable for systematic trading approaches and portfolio management applications.
Fixed quantity sizing provides precise control over trade sizes independent of account equity fluctuations, offering predictable position management for specific trading strategies or when implementing precise risk allocation models. The system enables seamless switching between sizing methods through simple configuration adjustments.
Position quantity calculations integrate seamlessly with TradingView's strategy testing framework, ensuring accurate backtesting results and realistic performance evaluation across different market conditions and time periods. The implementation maintains consistency between historical testing and live trading applications.
## Comprehensive Risk Management Framework
The strategy features dual stop loss methodologies addressing different risk management philosophies and market analysis approaches. Entry price-based stop losses calculate stop levels as fixed percentages below entry prices, providing predictable risk exposure and consistent risk-reward ratio maintenance across all trades.
The percentage-based stop loss system enables precise risk control by limiting maximum loss per trade to predetermined levels regardless of market volatility or entry timing. This approach proves essential for systematic trading strategies requiring consistent risk parameters and capital preservation during adverse market conditions.
Lowest low-based stop losses identify recent price support levels by analyzing minimum prices over adjustable lookback periods, placing stops below these technical levels with additional buffer percentages. This methodology aligns stop placement with market structure rather than arbitrary percentage calculations, potentially improving stop loss effectiveness during normal market fluctuations.
The lookback period adjustment enables optimization for different timeframes and market characteristics, with shorter periods providing tighter stops for active trading and longer periods offering broader stops suitable for position trading approaches. Buffer percentage additions ensure stops remain below obvious support levels where other market participants might place similar orders.
## Visual Customization and Interface Design
The strategy provides comprehensive visual customization through eight predefined color schemes designed for different chart backgrounds and personal preferences. Color scheme options include Classic bright green and red combinations, Ocean themes featuring blue and orange contrasts, Sunset combinations using gold and crimson, and Neon schemes providing high visibility through bright color selections.
Professional color schemes such as Forest, Royal, and Fire themes offer sophisticated alternatives suitable for business presentations and professional trading environments. The Custom color scheme enables precise color selection through individual color picker controls, maintaining maximum flexibility for specific visual requirements.
Label styling options accommodate different chart analysis preferences through text bubble, triangle, and arrow display formats. Size adjustments range from tiny through huge settings, ensuring appropriate visual scaling across different screen resolutions and chart configurations. Text color customization maintains readability across various chart themes and background selections.
## Signal Quality Enhancement Features
The strategy incorporates signal filtering mechanisms designed to eliminate repetitive signal generation during choppy market conditions. The disable repeating signals option prevents consecutive identical signals until opposing conditions occur, reducing overtrading during consolidation phases and improving overall signal quality.
Signal confirmation requirements ensure all technical criteria align before trade execution, reducing false signal occurrence while maintaining reasonable trading frequency for active strategies. The multi-layered approach balances signal quality against opportunity frequency through adjustable parameter optimization.
Entry and exit visualization provides clear trade identification through customizable labels positioned at relevant price levels. Stop loss visualization displays active risk levels through colored line plots, ensuring complete transparency regarding current risk management parameters during live trading operations.
## Implementation Guidelines and Optimization
The strategy performs effectively across multiple timeframes with optimal results typically occurring on intermediate timeframes ranging from fifteen minutes through four hours. Higher timeframes provide more reliable pattern formation and reduced false signal occurrence, while lower timeframes increase trading frequency at the expense of some signal reliability.
Parameter optimization should focus on RSI threshold adjustments based on market volatility characteristics and candlestick pattern timeframe analysis. Higher RSI thresholds generate fewer but potentially higher quality signals, while lower thresholds increase signal frequency with corresponding reliability considerations.
Stop loss method selection depends on trading style preferences and market analysis philosophy. Entry price-based stops suit systematic approaches requiring consistent risk parameters, while lowest low-based stops align with technical analysis methodologies emphasizing market structure recognition.
## Performance Considerations and Risk Disclosure
The strategy operates exclusively on long positions, making it unsuitable for bear market conditions or extended downtrend periods. Users should consider market environment analysis and broader trend assessment before implementing the strategy during adverse market conditions.
Candlestick pattern reliability varies significantly across different market conditions, with higher reliability typically occurring during trending markets compared to ranging or volatile conditions. Strategy performance may deteriorate during periods of reduced pattern effectiveness or increased market noise.
Risk management through stop loss implementation remains essential for capital preservation during adverse market movements. The strategy does not guarantee profitable outcomes and requires proper position sizing and risk management to prevent significant capital loss during unfavorable trading periods.
## Technical Specifications
The strategy utilizes standard TradingView Pine Script functions ensuring compatibility across all supported instruments and timeframes. Default configuration employs 14-period RSI calculations, adjustable candle stability thresholds, and customizable price decline verification periods optimized for general market conditions.
Initial capital settings default to $10,000 with percentage-based equity allocation, though users can adjust these parameters based on account size and risk tolerance requirements. The strategy maintains detailed trade logs and performance metrics through TradingView's integrated backtesting framework.
Alert integration enables real-time notification of entry and exit signals, stop loss executions, and other significant trading events. The comprehensive alert system supports automated trading applications and manual trade management approaches through detailed signal information provision.
## Conclusion
The Mutanabby_AI Algo Pro Strategy provides a systematic framework for candlestick pattern trading with comprehensive risk management and position sizing flexibility. The strategy's strength lies in its multi-layered confirmation approach and sophisticated customization options, enabling adaptation to various trading styles and market conditions.
Successful implementation requires understanding of candlestick pattern analysis principles and appropriate parameter optimization for specific market characteristics. The strategy serves traders seeking automated execution of proven technical analysis techniques while maintaining comprehensive control over risk management and position sizing methodologies.
Gelişmiş Mum Ters StratejiAdvanced Candle Reversal Strategy Overview
This TradingView PineScript indicator detects potential reversal signals in candlestick patterns, focusing on a sequence of directional candles followed by a wick-based reversal candle. Here's a step-by-step breakdown:
User Inputs:
candleCount (default: 6): Number of consecutive candles required (2–20).
wickRatio (default: 1.5): Minimum wick-to-body ratio for reversal (1.0–5.0).
Options to show background colors and an info table.
Candle Calculations:
Computes body size (|close - open|), upper wick (high - max(close, open)), and lower wick (min(close, open) - low).
Identifies bullish (close > open) or bearish (close < open) candles.
Checks for long upper wick (≥ body * wickRatio) for short signals or long lower wick for long signals.
Sequence Check:
Verifies if the last candleCount candles are all bearish (for long signal) or all bullish (for short signal), including the current candle.
Signal Conditions:
Long Signal: candleCount bearish candles + current candle has long lower wick (plotted as green upward triangle below bar with "LONG" text).
Short Signal: candleCount bullish candles + current candle has long upper wick (plotted as red downward triangle above bar with "SHORT" text).
Additional Features:
Alerts for signals with custom messages.
Optional translucent background color (green for long, red for short).
Plots tiny crosses for long wicks not triggering full signals (yellow above for upper, orange below for lower).
Info table (top-right): Displays strategy summary, candle count, and signal explanations.
Debug label: On signals, shows wick/body ratio above the bar.
The strategy aims for reversals after trends (e.g., after 6 red candles, a red candle with long lower wick signals buy). Customize via inputs; backtest for effectiveness. Not financial advice.
Position Size 📐 DT/ST (Today's Open)💡 Purpose:
This indicator automatically calculates intraday (DT) and swing trading (ST) position sizes based on your account capital, risk per trade, and stop-loss percentage, using today’s daily open price as the entry price reference.
⚙️ Main Functionalities:
Dynamic Position Sizing
Calculates Full size position based on the maximum risk you allow per trade.
Breaks it down into ¼ Size, ⅓ Size, and ½ Size positions for flexible scaling.
Two Distinct Trading Styles:
DT (Day Trading) – Uses your specified intraday stop-loss % (default: 2%).
ST (Swing Trading) – Uses your specified swing stop-loss % (default: 10%).
Lot Size Rounding
Automatically rounds quantities to a chosen lot size (e.g., 1 for cash equity or futures lot size for derivatives).
Customizable Table Position
Display the table anywhere on your chart: Top Right, Top Left, Bottom Right, or Bottom Left.
Optimized for Dark or Light Themes
Yellow header with black text for visibility.
Blue row labels for strategy type.
Grey background with white text for calculated values.
Live Market Adaptation
All values update in real-time as today’s daily open price changes (on new daily candles).
Works for any symbol, asset class, or time frame.
🧮 Formula:
Position Size (Full) = Max Risk ₹ / (Price × StopLoss%)
¼, ⅓, and ½ Sizes = Scaled from Full size
📌 Ideal For:
Traders who want quick, ready-to-use position sizes right on their chart.
Those who follow fixed risk-per-trade and need fast decision-making without manual calculations.
Taiyoz Gaps1. Purpose
Tyoz Gaps highlights “gaps” between yesterday’s close and today’s open directly on your chart. A gap occurs when the opening price is significantly above or below the prior bar’s close. By drawing persistent boxes around each gap, you can instantly see where price left a void and monitor when (or if) that void gets completely filled.
2. Gap Detection Logic
Threshold: A gap is only detected if the open-to-previous-close difference exceeds a user-defined “Minimal Deviation” (percentage of the 14-bar average high-low range).
Direction:
Gap Up: today’s open > yesterday’s close
Gap Down: today’s open < yesterday’s close
3. Box Drawing
For each detected gap, the script draws a rectangular box spanning from yesterday’s close level to today’s open level.
Border & Fill Colors are configurable separately for up-gaps and down-gaps.
Boxes extend to the right as new bars form.
4. Display & Filtering Options
Show Gap Up / Show Gap Down toggles let you hide bullish or bearish gaps independently.
Max Number of Gaps: Limits how many boxes remain on-screen; oldest boxes are removed when the limit is exceeded.
Limit Max Gap Trail Length: Optionally force-close any gap box after a given number of bars, even if unfilled.
5. Closing Logic
Full-Fill Only: A gap box stays visible until price fully “fills” it—i.e., for an up-gap, price must exceed the top edge (yesterday’s close); for a down-gap, price must cross below the bottom edge.
Once filled, the box is removed and a “Gap Closed” alert flag is set.
6. Labels & Alerts
Each active gap can optionally show a label at the gap’s lower edge containing:
Absolute size (in price points) and percentage of the gap
Bar count since the gap formed
Label Text Color and Label Text Size are both user-configurable.
Two built-in alertcondition()s fire when a new gap appears or when a gap closes.
MTF Dashboard 9 Timeframes + Signals# MTF Dashboard Pro - Multi-Timeframe Confluence Analysis System
## WHAT THIS SCRIPT DOES
This script creates a comprehensive dashboard that simultaneously analyzes market conditions across 9 different timeframes (1m, 5m, 15m, 30m, 1H, 4H, Daily, Weekly, Monthly) using a proprietary confluence scoring methodology. Unlike simple multi-timeframe displays that show individual indicators separately, this script combines trend analysis, momentum, volatility signals, and volume analysis into unified confluence scores for each timeframe.
## WHY THIS COMBINATION IS ORIGINAL AND USEFUL
**The Problem Solved:** Most traders manually check multiple timeframes and struggle to quickly assess overall market bias when different timeframes show conflicting signals. Existing MTF scripts typically display individual indicators without synthesizing them into actionable intelligence.
**The Solution:** This script implements a mathematical confluence algorithm that:
- Weights each indicator's signal strength (trend direction, RSI momentum, MACD volatility, volume analysis)
- Calculates normalized scores across all active timeframes
- Determines overall market bias with statistical confidence levels
- Provides instant visual feedback through color-coded symbols and star ratings
**Unique Features:**
1. **Confluence Scoring Algorithm**: Mathematically combines multiple indicator signals into a single confidence rating per timeframe
2. **Market Bias Engine**: Automatically calculates overall directional bias with percentage strength across all selected timeframes
3. **Dynamic Display System**: Real-time updates with customizable layouts, color schemes, and selective timeframe activation
4. **Statistical Analysis**: Provides bullish/bearish vote counts and overall confluence percentages
## HOW THE SCRIPT WORKS TECHNICALLY
### Core Calculation Methodology:
**1. Trend Analysis (EMA-based):**
- Fast EMA (default: 9) vs Slow EMA (default: 21) crossover analysis
- Returns values: +1 (bullish), -1 (bearish), 0 (neutral)
**2. Momentum Analysis (RSI-based):**
- RSI levels: >70 (strong bullish +2), >50 (bullish +1), <30 (strong bearish -2), <50 (bearish -1)
- Provides overbought/oversold context for trend confirmation
**3. Volatility Analysis (MACD-based):**
- MACD line vs Signal line positioning
- Histogram strength comparison with previous bar
- Combined score considering both direction and momentum strength
**4. Volume Analysis:**
- Current volume vs 20-period moving average
- Thresholds: >150% MA (strong +2), >100% MA (bullish +1), <50% MA (weak -2)
**5. Confluence Calculation:**
```
Confluence Score = (Trend + RSI + MACD + Volume) / 4.0
```
**6. Market Bias Determination:**
- Counts bullish vs bearish signals across all active timeframes
- Calculates bias strength percentage: |Bullish Count - Bearish Count| / Total Active TFs * 100
- Determines overall market direction: BULLISH, BEARISH, or NEUTRAL
### Multi-Timeframe Implementation:
Uses `request.security()` calls to fetch data from each timeframe, ensuring all calculations are performed on the respective timeframe's data rather than current chart timeframe, providing accurate multi-timeframe analysis.
## HOW TO USE THIS SCRIPT
### Initial Setup:
1. **Timeframe Selection**: Enable/disable specific timeframes in "Timeframe Selection" group based on your trading style
2. **Indicator Configuration**: Adjust EMA periods (Fast: 9, Slow: 21), RSI length (14), and MACD settings (12/26/9) to match your analysis preferences
3. **Display Options**: Choose table position, text size, and color scheme for optimal visibility
### Reading the Dashboard:
**Symbol Interpretation:**
- ⬆⬆ = Strong bullish signal (score ≥ 2)
- ⬆ = Bullish signal (score > 0)
- ➡ = Neutral signal (score = 0)
- ⬇ = Bearish signal (score < 0)
- ⬇⬇ = Strong bearish signal (score ≤ -2)
**Confluence Stars:**
- ★★★★★ = Very high confidence (score > 0.75)
- ★★★★☆ = High confidence (score > 0.5)
- ★★★☆☆ = Medium confidence (score > 0.25)
- ★★☆☆☆ = Low confidence (score > 0)
- ★☆☆☆☆ = Very low confidence (score > -0.25)
**Market Bias Section:**
- Shows overall market direction across all active timeframes
- Strength percentage indicates conviction level
- Overall confluence score represents average agreement across timeframes
### Trading Applications:
**Entry Signals:**
- Look for high confluence (4-5 stars) across multiple timeframes in same direction
- Higher timeframe alignment provides stronger signal validation
- Use confluence percentage >75% for high-probability setups
**Risk Management:**
- Lower timeframe conflicts may indicate choppy conditions
- Neutral bias suggests ranging market - adjust position sizing
- Strong bias with high confluence supports larger position sizes
**Timeframe Harmony:**
- Short-term trades: Focus on 1m-1H alignment
- Swing trades: Emphasize 1H-Daily alignment
- Position trades: Prioritize Daily-Monthly confluence
## SCRIPT SETTINGS EXPLANATION
### Dashboard Settings:
- **Table Position**: Choose optimal location (Top Right recommended for most layouts)
- **Text Size**: Adjust based on screen resolution and preferences
- **Color Scheme**: Professional (default), Classic, Vibrant, or Dark themes
- **Background Color/Transparency**: Customize table appearance
### Timeframe Selection:
All timeframes optional - activate based on trading timeframe preference:
- **Lower Timeframes (1m-30m)**: Scalping and day trading
- **Medium Timeframes (1H-4H)**: Swing trading
- **Higher Timeframes (D-M)**: Position trading and long-term bias
### Indicator Parameters:
- **Fast EMA (Default: 9)**: Shorter period for trend sensitivity
- **Slow EMA (Default: 21)**: Longer period for trend confirmation
- **RSI Length (Default: 14)**: Standard momentum calculation period
- **MACD Settings (12/26/9)**: Standard MACD configuration for volatility analysis
### Alert Configuration:
- **Strong Signals**: Alerts when confluence >75% with clear directional bias
- **High Confluence**: Alerts when multiple timeframes strongly agree
- All alerts use `alert.freq_once_per_bar` to prevent spam
## VISUAL FEATURES
### Chart Elements:
- **Background Coloring**: Subtle background tint reflects overall market bias
- **Signal Labels**: Strong buy/sell labels appear on chart during high-confluence signals
- **Clean Presentation**: Dashboard overlays chart without interfering with price action
### Color Coding:
- **Green/Bullish**: Various green shades for positive signals
- **Red/Bearish**: Various red shades for negative signals
- **Gray/Neutral**: Neutral color for conflicting or weak signals
- **Transparency**: Configurable transparency maintains chart readability
## IMPORTANT USAGE NOTES
**Realistic Expectations:**
- This tool provides analysis framework, not trading signals
- Always combine with proper risk management
- Past performance does not guarantee future results
- Market conditions can change rapidly - use appropriate position sizing
**Best Practices:**
- Verify signals with additional analysis methods
- Consider fundamental factors affecting the instrument
- Use appropriate timeframes for your trading style
- Regular parameter optimization may be beneficial for different market conditions
**Limitations:**
- Effectiveness may vary across different instruments and market conditions
- Confluence scoring is mathematical model - not predictive guarantee
- Requires understanding of underlying indicators for optimal use
This script serves as a comprehensive analysis tool for traders who need quick, organized access to multi-timeframe market information with statistical confidence levels.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
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US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427.
Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press.
Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408.
Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180.
Cochrane, J. H. (2005). Asset pricing. Princeton University Press.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.
Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205.
Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119.
Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120.
Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.
Fisher, I. (1930). The theory of interest. Macmillan.
Foley, J. D., van Dam, A., Feiner, S. K., & Hughes, J. F. (1995). Computer graphics: Principles and practice. Addison-Wesley.
Gordon, M. J. (1962). The investment, financing, and valuation of the corporation. Richard D. Irwin.
Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press.
Koenig, E. F. (2002). Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), 1-14.
Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson.
Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29-50.
Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. National Bureau of Economic Research.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Mishkin, F. S. (2012). The economics of money, banking, and financial markets. Pearson.
Nielsen, J. (1993). Usability engineering. Academic Press.
Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.
OECD (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing.
Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-time signal processing. Prentice Hall.
Sahm, C. (2019). Direct stimulus payments to individuals. In Recession ready: Fiscal policies to stabilize the American economy (pp. 67-92). The Hamilton Project, Brookings Institution.
Siegel, J. J. (2002). Stocks for the long run: The definitive guide to financial market returns and long-term investment strategies. McGraw-Hill.
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Adam Mancini ES Game Plan LevelsThis script plots Support & Resistance levels from Adam Mancini's newsletter.
You can copy and paste levels from Adam's Newsletter to Indicator settings.
You can also add custom text after the support level. For e.g 6550 : Your custom text
ADR Tracker Version 2Description
The **ADR Tracker** plots a customizable panel on your chart that monitors the Average Daily Range (ADR) and shows how today’s price action compares to that average. It calculates the daily high–low range for each of the past 14 days (can be adjusted) and then takes a simple moving average of those ranges to determine the ADR.
**Features:**
* **Current ADR value:** Shows the 14‑day ADR in price units.
* **ADR status:** Indicates whether today’s range has reached or exceeded the ADR.
* **Ticks remaining:** Calculates how many minimum price ticks remain before the ADR would be met.
* **Real‑time tracking:** Monitors the intraday high and low to update the range continuously.
* **Customizable panel:** Uses TradingView’s table object to display the information. You can set the table’s horizontal and vertical position (top/middle/bottom and left/centre/right) with inputs. The script also lets you change the text and background colours, as well as the width and height of each row. Table cells use explicit width and height percentages, which Pine supports in v6. Each call to `table.cell()` defines the text, colours and dimensions for its cell, so the panel resizes automatically based on your settings.
**Usage:**
Apply the indicator to any chart. For the most accurate real‑time tracking, use it on intraday timeframes (e.g. 5‑min or 1‑hour) so the current day’s range updates as new bars arrive. Adjust the inputs in the settings panel to reposition the list or change its appearance.
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This description explains what the indicator does and highlights its customizable table display, referencing the Pine Script table features used.
MVRV Altcoins📌 Technical Description of Indicator: MVRV Altcoins
This advanced script calculates the Market Value to Realized Value (MVRV) ratio across multiple cryptocurrencies simultaneously. It offers two analytical modes: Normal and Z-Score, optimized for visual comparison and real-time monitoring of up to 13 predefined assets. If a user applies the indicator to a symbol that is not among the 13 programmed assets, the default behavior displays the Bitcoin chart as a fallback reference.
🔍 What Is MVRV and Why Is It Important?
MVRV is an on-chain metric designed to assess whether a cryptocurrency is overvalued or undervalued by comparing its market capitalization to its realized capitalization.
- Market Cap: The total circulating supply multiplied by the current market price.
- Realized Cap: The sum value of all coins based on the price at the time they last moved on-chain, offering a time-weighted valuation.
Normal Calculation:
MVRV_Normal = Market Cap / Realized Cap
This version reflects investor profitability and identifies potential accumulation or distribution zones.
📊 Z-Score Calculation:
MVRV_ZScore = (Market Cap − Realized Cap) / Standard Deviation of Market Cap
This formula evaluates how extreme the current market conditions are compared to historical norms. It normalizes the difference using statistical dispersion, turning it into a volatility-aware metric that better reflects valuation extremes.
🔎 How Market Cap Is Computed
Unlike conventional indicators relying on consolidated feeds, this script uses modular components from CoinMetrics to construct the active capitalization more accurately, especially for altcoins. Here's the breakdown:
Active Capitalization = MARKETCAPFF + MARKETCAPACTSPLY
Realized Capitalization = MARKETCAPREAL
Component Definitions:
- MARKETCAPFF: Market Cap Free Float — total valuation based only on truly circulating coins.
- MARKETCAPACTSPLY: Capitalization from actively circulating supply — filters dormant or locked coins.
- MARKETCAPREAL: Realized Cap — historical valuation weighted by the last on-chain movement of each coin.
This method offers enhanced precision and compatibility across assets that may lack comprehensive data from centralized providers.
⚙️ User-Configurable Parameters
- MVRV Mode: Choose between Normal and Z-Score.
- Percentage Scale View: If enabled, visual output is scaled using predefined divisors (100 / 3.5 or 100 / 6).
- Thresholds for Analysis:
- Normal mode: Define overbought and oversold levels (default 1.0 and 3.5).
- Z-Score mode: Configure statistical boundaries (default 0.0 and 6.0).
- Table Controls:
- Adjustable position on screen (9 options).
- Font size customization: tiny, small, normal, large.
- Color scheme personalization:
- Header: text and background
- Body: text and background
- Central column separator color
📊 Multicrypto Table Architecture
The indicator renders a high-performance visual table displaying data from up to 13 assets simultaneously. Each asset is represented as a vertical column featuring eigth historical data points plus the most recent value.
- Assets are displayed in two blocks separated by a decorative column.
- Each value is rounded to one decimal place for clarity.
- Cells are styled dynamically based on user settings.
🎨 Decorative Column Separator
Since the entire table is built as a unified structure, a color-configurable empty column is inserted mid-table to act as a visual divider. This approach improves readability and aesthetic balance without duplicating code or splitting table logic.
🔁 Default Behavior on Unsupported Assets
If the active chart is not one of the 13 predefined assets, the indicator will automatically display Bitcoin’s data. This ensures the chart remains functional and informative even outside the target asset group.
🎯 Color Interpretation by Condition
The MVRV value for each asset is highlighted using a traffic light system:
- Green: Undervalued (below oversold threshold)
- Red: Overvalued (above overbought threshold)
- Yellow: Neutral zone
This coding simplifies decision-making and visual scanning across assets.
Final Notes
This indicator is modular and fully adaptable, with well-commented sections designed for efficient customization. Its multiactive architecture makes it a valuable tool for crypto analysts tracking diversified portfolios beyond Bitcoin and Ethereum.
It supports visual storytelling across assets, comparative historical evaluation, and identification of strategic zones — whether for accumulation, distribution, or monitoring on-chain sentiment.
ATR Dynamic Stop (Table + Plot + ATR %)📊 This script displays dynamic stop levels based on ATR, designed for active traders.
Features:
- Shows long and short stop levels (price ± ATR × multiplier).
- Displays values as a floating table on the top-right corner.
- Optional plot lines directly on the chart.
- Option to calculate based on realtime price or last close.
- Displays the ATR value both in price units and as a percentage of the selected price.
- Fully customizable table: text size, text color, background color.
Inputs:
- ATR Multiplier and Length.
- Show/hide stop lines on the chart.
- Select price source (realtime or last close).
- Table appearance options.
Ideal for:
- Traders who want a clear visual stop guide.
- Combining volatility with risk management.
