MTF Custom Synthetic IndexMTF Custom Synthetic Index - Ultimate Index Creation Tool
🎯 What is this indicator?
The MTF Custom Synthetic Index is a powerful, fully customizable indicator that allows you to create your own synthetic index using up to 6 different instruments of your choice. Unlike traditional indices, this tool gives you complete control over instrument selection, weightings, and calculation methodology.
⭐ Key Features
🔧 Complete Customization
Choose ANY instruments: Forex pairs, stocks, commodities, indices, cryptocurrencies, bonds, etc.
Manual weight control: Set exact percentage weights for each instrument (must total 100%).
Flexible instrument direction: Ability to invert enabled instruments that move opposite to your desired index direction (i.e. you can use instruments that are negatively correlated).
📊 Multi-Timeframe Analysis
Simultaneous monitoring: View index strength across up to 3 additional timeframes.
Strength rating system: Automatic classification (Very Strong, Strong, Neutral, Weak, Very Weak).
Normalization options: Z-Score, Min-Max, or Percentage methods for timeframe comparison.
Visual summary table: Real-time strength ratings for all timeframes.
🎨 Professional Visualization
Clean chart display: Smooth index strength line with customizable styling.
Dynamic labelling: Real-time value display with strength ratings.
Color-coded indicators: Visual strength representation with intuitive colour schemes.
💡 Use Cases
🌍 Currency Strength Analysis
USD Index: Combine EURUSD (inverted), USDJPY, AUDUSD (inverted), etc.
EUR Index: Combine EURUSD, EURGBP, EURJPY, etc.
Multi-currency baskets: Track regional currency performance.
📈 Sector/Industry Tracking
Technology sector: Combine AAPL, MSFT, GOOGL with custom weights.
Energy sector: Combine oil, gas, and energy stocks.
Precious metals: Combine gold, silver, platinum with custom allocations.
🏛️ Macro Economic Indices
Interest rate sensitivity: Combine bonds, currency pairs, and rate-sensitive stocks.
Inflation hedges: Combine commodities, TIPS, and inflation-sensitive assets.
Risk appetite: Combine safe havens vs. risk assets.
💰 Portfolio Replication
Custom benchmarks: Create indices that match your specific portfolio allocation.
Strategy testing: Build theoretical indices to test investment strategies.
🔥 Key Benefits
✅ Precision Control
Exact weight specifications with mandatory 100% total.
Choose instruments that matter to your trading strategy.
Advanced ADX/DI calculation methodology with configurable parameters.
✅ Versatile Application
Works with any asset class available on TradingView.
Suitable for scalping, day trading, swing trading, and long-term analysis.
Perfect for both retail and institutional approaches.
✅ Multi-Timeframe Insights
Quickly and easily pot divergences between timeframes.
Confirm trends across multiple time horizons.
Make better-informed trading decisions.
⚙️ Technical Specifications
Calculation Method
Base algorithm: Advanced ADX (Average Directional Index) with Directional Indicators.
Bias calculation: Normalized or raw DI difference with ADX weighting.
Smoothing options: Configurable periods for DI calculation and ADX smoothing.
Validation & Safety
Weight validation: Must total exactly 100% - prevents calculation errors.
Data integrity: Handles missing data and invalid symbols gracefully.
Timeframe validation: Prevents duplicate or invalid timeframe selections.
🚀 Perfect For
Currency traders seeking custom dollar/euro/yen/etc strength indices.
Commodity traders seeking custom precious metal/energy/etc strength indices.
Portfolio managers needing custom benchmark creation.
Macro traders building economic strength indicators.
Systematic traders requiring precise, repeatable index calculations.
📋 Quick Start
Add the indicator to your chart
Configure instruments: Select your desired symbols and weights (must total 100%).
Set timeframes: Choose additional timeframes for multi-timeframe analysis.
Customize display: Adjust colors, labels, and table settings to your preference.
Start trading: Use the index strength readings to guide your trading decisions.
⚠️ Important Notes
Weights must total exactly 100%: The indicator will show an error if weights don't add up correctly.
Data requirements: All selected instruments must have available data for the calculation to work.
Timeframe selection: Multi-timeframe analysis requires different timeframes from your main/selected chart.
Transform your trading with the power of custom index creation. Take control of your analysis and build indices that truly matter to your trading strategy.
基本面分析
Gold Z-Score Dashboard - 100-Bar Label Cleanup📌 Indicator Name:
Gold Z-Score Dashboard — 100-Bar Label Cleanup
🧾 Description:
This indicator leverages a statistical approach to detect overbought and oversold conditions using the Z-Score, a measure of price deviation from its moving average. It intelligently combines trend, volume, and volatility filters to reduce false signals and improve trading precision.
✅ Key Features:
Z-Score Logic: Highlights extreme price moves by comparing current price to its recent average, normalized by standard deviation.
Trend Filter (Optional): Uses a higher-timeframe EMA to confirm signals only in the trend direction.
Volume Filter (Optional): Confirms signals only when current volume exceeds its average, avoiding low-activity noise.
ATR Filter (Optional): Ensures signals occur during sufficient market volatility.
Label Cleanup: Each signal label is automatically removed after 100 bars to keep your chart organized.
Built-In Alerts: Get notified instantly when the market enters overbought or oversold zones.
🧠 How It Works:
The Z-Score is calculated as:
(Price−EMA)/StandardDeviation
When the Z-Score crosses below -threshold, an oversold (long) signal is generated.
When it crosses above +threshold, an overbought (short) signal is triggered.
Signals are filtered based on user settings:
✅ Trend must be aligned with higher timeframe EMA
✅ Volume must be above its moving average
✅ ATR must indicate adequate market movement
📈 Best Used For:
Spotting mean reversion opportunities
Avoiding false reversals with smart filters
Cleaner signal visualization via automatic label expiry
Auto Timeframe Period Separators v2
This script automatically plots vertical separator lines for multiple key timeframes — including 5-minute, 15-minute, 1-hour, 4-hour, daily, and weekly — to help you visually identify period boundaries on your charts.
Features:
Customizable enable/disable options for each timeframe separator
Adjustable line color, style (solid, dashed, dotted), and width per timeframe
Dynamic plotting based on the current chart timeframe to reduce clutter
Visibility controls allowing you to define the minimum and maximum chart timeframes where each separator is displayed
Use Cases:
Easily distinguish trading sessions, days, and weeks for better chart analysis
Quickly identify time period breaks across multiple scales
Enhance chart readability without manual adjustments
How to Use:
Enable or disable any timeframe separator according to your preference
Customize colors and styles to suit your chart theme
Adjust visibility ranges to control when separators appear, depending on your current chart timeframe
BTCUSD Multi TP Trade Signal📘 Indicator Description: BTCUSD Multi TP Trade Signal
This indicator is designed to generate high-quality Buy/Sell signals on BTCUSD, using a simple yet effective EMA crossover strategy. It visually plots all associated Take-Profit (TP) and Stop-Loss (SL) levels, allowing traders to plan and manage their trades with precision.
🔑 Key Features
✅ Trade Direction Control: Select to trade Long, Short, or Both directions
✅ Signal Generation: Uses EMA 20/50 crossover logic for trend confirmation
✅ Visual Trade Levels: Plots 4 customizable TP levels and a fixed SL on the chart
✅ Trend Filter Option: Align signals with higher timeframe (HTF) market direction
✅ User-Controlled Settings: Adjustable profit/stop targets and filtering logic
✅ Non-executing tool: Ideal for manual, visual, or alert-based trading
⚙️ Input Settings
Parameter Function
Strategy Direction Filters signals by direction (all, long, short)
Length of Filter Period for trend filter (SMA) on HTF
Candle Time Resolution for time-based conditions
Length of ATR ATR period for potential future enhancements
HTF Higher Time Frame (e.g., Weekly) for trend alignment
Use Filter Toggle the HTF filter ON/OFF
Stop Loss Fixed SL in USD
Take Profit 1–4 TP levels in USD from entry price
📊 How It Works
A Buy signal is plotted when EMA 20 crosses above EMA 50 and other conditions are met.
A Sell signal is plotted when EMA 20 crosses below EMA 50.
Each trade signal includes clearly marked TP1, TP2, TP3, TP4, and SL levels.
Optional HTF trend filter ensures signals align with the broader market trend.
🧠 Best Use Cases
Works best on 15-minute to 1-hour BTCUSD charts
Ideal for trend-following intraday or swing trading
Use with confluence (volume, price action, or key levels) for best results
CGPT Golden Cross / Death Cross AlertThis custom indicator identifies Golden Cross (Gx) and Death Cross (Dx) events using either EMA or SMA moving averages. A Golden Cross occurs when a short-term MA (e.g., 50) crosses above a long-term MA (e.g., 200), signaling potential bullish momentum. A Death Cross signals potential bearish momentum, with the short-term MA crossing below the long-term MA.
It includes:
📈 Customizable MA types (EMA or SMA)
⚙️ Adjustable fast & slow MA lengths
🟢🔴 Chart labels for Gx (green) and Dx (red)
🎯 Background highlights for visual trend shifts
🔔 Built-in alert conditions for real-time notifications
Ideal for crypto, stocks, or forex swing and trend trading
Distrodisco_v1.4What it does:
Defines a “distribution session” (customizable time window) and tracks that session’s high/low to compute its distribution width as a percentage.
Compares the current session’s distribution to historical same-day-of-week distributions to detect when it crosses above the median (i.e., a meaningful breakout in context).
Tags the breakout direction (long or short) based on wick extremes and prior-session pivots.
After a tagged break, tracks the pullback/retrace: how far price reverses back toward the tag (used for SL tuning).
Simultaneously measures how far price extends beyond the break before the retrace begins—this “extension before retrace” can be used to calibrate realistic take-profit targets.
Maintains historical accumulators for both retrace sizes and extensions so you can see distributions over time.
Key metrics shown in the table:
Total Days / Median Hits : Coverage of historical samples and how often distribution crosses its median.
Pullback Rate: Percentage of median breaks that produced a pullback (including live/active ones if the session ends mid-retrace).
Current / Historical Distribution Stats: Current session’s width vs. historical median/average for that weekday.
Reversion Ret (revAbs): The largest pullback after a break (live for the session), used as a de-facto stop-loss gauge.
Hist Median Ret: Median of completed historical retraces (and active ones at session end if not closed).
90%ile Ret: Upper-bound reference for retrace size—what the larger retraces look like.
>= X% PBs: User-defined threshold (e.g., enter 0.05 for 0.05%) showing the percentage of historical retraces that met or exceeded that magnitude.
Extension Median / 90%ile / Last Ext: How far price typically runs past the break before reversing—used for take-profit calibration. (If not yet enabled, these are forthcoming additions.)
Inputs:
Distribution Session / Timezone: Define the intra-day window to consider for distribution measurement.
Max Distribution % to Include: Caps abnormally wide distributions from polluting historical buckets.
Filter Out Abnormally Large Days: Toggle to exclude outliers.
Min Pullback to Count (%): Threshold to count “meaningful” retraces in the historical percentage bucket. Enter e.g. 0.05 to represent 0.05%.
Table styling: Color and positioning for easy visibility.
EPS+Sales+Net Profit+MCap+Sector & Industry📄 Full Description
This script displays a comprehensive financial data panel directly on your TradingView chart, helping long-term investors and swing traders make informed decisions based on fundamental trends. It consolidates key financial metrics and business classification data into a single, visually clear table.
🔍 Key Features:
🧾 Financial Metrics (Auto-Fetched via request.financial):
EPS (Earnings Per Share) – Displayed with trend direction (QoQ or YoY).
Sales / Revenue – In ₹ Crores (for Indian stocks), trend change also included.
Net Profit – Also in ₹ Crores, along with percentage change.
Market Cap – Automatically calculated using outstanding shares × price, shown in ₹ Cr.
Free Float Market Cap – Based on float shares × price, also in ₹ Cr.
🏷️ Sector & Industry Info:
Automatically identifies and displays the Sector and Industry of the stock using syminfo.sector and syminfo.industry.
Displayed inline with metrics, making it easy to know what business the stock belongs to.
📊 Table View:
Compact and responsive table shown on your chart.
Columns: Date | EPS | QoQ | Sales | QoQ | Net Profit | QoQ | Metrics
Metrics column dynamically shows:
Market Cap
Free Float
Sector (Row 4)
Industry (Row 5)
🌗 Appearance:
Supports Dark Mode and Mini Mode toggle.
You can also customize:
Number of data points (last 4+ quarters or years)
Table position and size
🎯 Use Case:
This script is ideal for:
Fundamental-focused traders who use EPS/Sales trends to identify momentum.
Swing traders who combine price action with fundamental tailwinds.
Portfolio builders who want to see sector/industry alignment quickly.
It works best with fundamentally sound stocks where earnings and profitability are a major factor in price movements.
✅ Important Notes:
Script uses request.financial which only works with supported symbols (mostly stocks).
Market Cap and Free Float are calculated in ₹ Crores.
All financial values are rounded and formatted for readability (e.g., 1,234 Cr).
🙏 Credits:
Developed and published by Sameer Thorappa
Built with a clean, minimalist approach for high readability and functionality.
High/Low Premarket & Previous Day This scripts adds lines for previous day and premarket high/low with labels that you can toggle on and off. The lines extend through current premarket and trading session
TotM - BTC Price Momentum (30-day)🇬🇧 ENGLISH VERSION
A simple and effective 30-day momentum indicator for Bitcoin.
This indicator calculates the 30-day price momentum of Bitcoin, expressed as a percentage change from the closing price 30 bars ago. It's a lightweight and visual tool to assess short-term strength or overheating of price movements.
🟦 Blue = positive momentum
🔴 Red = overheated (> +40%)
⚫ Gray = negative momentum
Reference lines at 0% and 40% mark equilibrium and overbought zones.
Feel free to customize it for other assets or timeframes.
For educational use only – not financial advice.
Recession Warning Model [BackQuant]Recession Warning Model
Overview
The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments.
Purpose
The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly.
Core Features
- Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress.
- Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries.
- Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix).
- Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads.
- Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability).
- Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals.
- Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals.
- Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R.
Model Complexity Levels
Users can select from four tiers to balance simplicity and depth:
1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring.
2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule.
3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth.
4. Research : Unlocks all indicators plus experimental settings for advanced users.
Key Indicators
Below is a summary of the 13 indicators, their data sources, and economic significance:
- Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress.
- Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs.
- Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions.
- Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator.
- Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending.
- PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction.
- VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress.
- S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption.
- M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction.
- NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions.
- Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress.
- JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns.
- Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating.
Data Processing
- Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period).
- Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback).
- Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise.
- Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations.
Probability Calculation
1. Each indicator’s binary signal is weighted according to user settings or dynamic performance.
2. Weights are normalized to sum to 100% across active indicators.
3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix.
4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes.
5. Optional smoothing ensures stable outputs.
Display and Visualization
- Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands.
- Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards.
- Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence.
- Ensemble Mode : Averages traditional and split probabilities for a balanced view.
- Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis).
- Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses.
Practical Applications
- Asset Allocation : Adjust equity or bond exposures based on sustained probability increases.
- Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis.
- Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%.
- Trading Filters : Disable short-term strategies during high-risk regimes.
- Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated.
Configuration Guidelines
- Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred.
- Use dynamic weighting with at least one economic cycle of data for optimal performance.
- Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals.
- Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization.
- Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading.
Limitations
- Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading.
- Historical data availability may constrain dynamic weighting on shorter timeframes.
- Model accuracy depends on the quality and timeliness of economic data feeds.
Final Note
The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions.
Simple Trading ChecklistCustomisable Simple Trading Checklist
This script overlays a fully customizable trading checklist directly onto your chart, providing an at-a-glance reminder of key trading steps and conditions before entering a position.
It is especially useful for discretionary or rule-based traders who want a consistent on-screen process to follow.
Seasonal Extreme ZonesTrue Seasonal Overlay Chart with Historical Bias
📊 Overview
This innovative Pine Script indicator combines true seasonal overlay visualization with historical bias analysis to provide traders with powerful seasonal trading insights. Unlike traditional seasonal charts that display years chronologically, this indicator overlays multiple years on the same seasonal axis (January-December) for direct pattern comparison.
🎯 Key Features
📈 True Seasonal Overlay
Multi-year Performance Lines: Display 2022, 2023, and 2024 performance on the same seasonal timeline
Year-to-Date Calculation: Each year starts at 0% on January 1st, showing cumulative performance
Real Seasonal Comparison: All years aligned to the same calendar position for accurate pattern recognition
Customizable Display: Toggle individual years on/off as needed
🔍 Historical Bias System
Configurable Timeframe: Analyze 5-25 years of seasonal data
Market-Specific Data: Realistic seasonality patterns for each asset class
Smart Bias Calculation: Adjusts extreme values based on historical depth
Automatic Inversion: Handles inverse pairs (JPY, CHF, CAD, DXY) automatically
💹 Multi-Asset Support
Forex Pairs: EURUSD, GBPUSD, USDJPY, USDCHF, AUDUSD, USDCAD, NZDUSD, DXY
Commodities: Gold, Silver, Crude Oil, Natural Gas, Copper, Agricultural products
Indices: DE40 (DAX), SPX500, NASDAQ100, US30, UK100, Nikkei, ASX200, EUROSTOXX50
🛠️ How It Works
Data Collection
Price Tracking: Captures January 1st starting prices for each year
Performance Calculation: Calculates year-to-date percentage performance
Seasonal Mapping: Maps each data point to its corresponding seasonal day
Array Storage: Stores performance data in organized arrays by year
Seasonal Overlay Logic
Interpolation: Finds nearest seasonal performance for current calendar position
True Overlay: Displays all years simultaneously on the same seasonal axis
Pattern Recognition: Enables direct visual comparison of seasonal behaviors
Historical Bias Engine
Asset-Specific Data: Uses realistic seasonal probabilities for each market
Years Adjustment: Moderates extreme values based on historical timeframe
Bias Calculation: Generates percentage-based seasonal bias (0-100%)
Signal Generation: Creates Strong Bull/Bear signals based on thresholds
📊 Visual Elements
Plot Lines
Blue Line (2024): Current year performance - thick line, most prominent
Red Line (2023): Previous year comparison - medium thickness
Purple Line (2022): Two years ago reference - medium thickness
Orange Line (Historical Bias): Long-term seasonal bias - thick, distinct
Threshold Levels
Strong Bull Line: Configurable bullish threshold (default: 70%)
Strong Bear Line: Configurable bearish threshold (default: 30%)
Neutral Line: 50% reference level
Zero Line: 0% performance reference
Background Colors
Green Background: Strong Long Bias periods
Red Background: Strong Short Bias periods
Transparent: Neutral periods
🎯 Trading Applications
Pattern Recognition
Seasonal Consistency: Identify repeating seasonal patterns across years
Divergence Analysis: Spot when current year deviates from historical norms
Trend Confirmation: Use seasonal bias to confirm directional trades
Bias-Based Trading
Strong Long Bias (>70%): Favor long setups, avoid shorts
Strong Short Bias (<30%): Favor short setups, avoid longs
Neutral Zones (30-70%): Focus on technical analysis over seasonal bias
Risk Management
Seasonal Headwinds: Reduce position sizes during unfavorable seasons
Seasonal Tailwinds: Consider larger positions during favorable periods
Entry Timing: Use seasonal overlay to time entries within trend direction
⚙️ Configuration Options
Display Settings
Year Selection: Toggle 2022, 2023, 2024 displays individually
Historical Bias Years: Configure 5-25 years for bias calculation
Threshold Levels: Customize Strong Bull/Bear threshold percentages
Visual Elements: Toggle table, backgrounds, and bias line
Asset Selection
Forex Group: Select from major currency pairs
Commodity Group: Choose from metals, energy, and agricultural products
Index Group: Pick from major global stock indices
Color Customization
Year Colors: Customize colors for each year line
Bias Color: Set historical bias line color
Background Colors: Configure Strong Bull/Bear background colors
📋 Information Panel
The indicator includes a comprehensive information table showing:
Current Asset: Selected instrument with inversion status
Monthly Status: Current month with bias direction and strength
Bias Percentage: Numerical historical bias value
Configuration: Active years and threshold settings
Seasonality Note: Indicates if data is inverted for certain pairs
🚀 Unique Advantages
True Seasonal Comparison: Unlike traditional charts, enables direct year-over-year seasonal comparison
Market-Specific Data: Uses realistic seasonal patterns based on actual market drivers
Automated Handling: Manages complex calculations and data interpolation automatically
Flexible Timeframes: Adapts historical bias calculation to user preferences
Professional Visualization: Clean, intuitive display suitable for all experience levels
💡 Best Practices
Combine with Technical Analysis: Use seasonal bias to filter trade direction, not as standalone signals
Consider Market Regime: Factor in current market conditions and volatility
Multiple Timeframe Analysis: Confirm seasonal bias on different chart timeframes
Risk Management: Always use appropriate position sizing regardless of seasonal bias
This indicator transforms seasonal analysis from static historical data into dynamic, actionable trading intelligence through innovative visualization and robust bias calculation methodology.
COT-Wallstreetstory OANDA Edition🔥 COT Wallstreetstory OANDA Edition - Professional COT Analysis Tool
This indicator provides comprehensive Commitment of Traders (COT) analysis across multiple asset classes with advanced signal generation for both long-term and intraday trading strategies.
🌟 KEY FEATURES:
✅ Multi-Asset Support:
- Forex: EUR, GBP, JPY, CHF, AUD, CAD, NZD, MXN
- Commodities: Gold, Silver, Crude Oil, Natural Gas, Copper, Grains
- Indices: S&P 500, Nasdaq, Dow Jones, Russell 2000, VIX
- Custom: Enter any CFTC code manually
✅ Smart Currency Inversion:
- Automatic data inversion for JPY, CHF, CAD, MXN pairs
- Shows "ORIGINAL" vs "INVERTED" display mode
- No more confusion with inverse correlations
✅ Dual Signal System:
- Long-term Signals: For W1/D1 swing trading
- Intraday Bias: For H4 setup → M15 entry strategies
- Visual backgrounds indicate signal strength
✅ Extreme Zones:
- Horizontal extreme zones with market-specific recommendations
- Customizable thresholds for each asset class
- Visual alerts when COT data reaches extreme levels
✅ Professional Visualization:
- Clean, emoji-free interface for serious traders
- Sensitivity arrows: ↑↑↑ Conservative, ↑↑ Normal, ↓ Aggressive
- Color-coded display modes and signal status
🎯 TRADING APPLICATIONS:
📈 Long-term Strategy:
Monitor when Commercials reach extreme positions and Non-Commercials follow. Perfect for identifying major trend reversals on weekly/daily charts.
⚡ Intraday Strategy:
Use Non-Commercial and Retail positioning relative to recent weeks to determine directional bias for H4 liquidity sweeps and M15 entries.
🔧 CUSTOMIZATION:
- Adjustable extreme thresholds for each market
- Three sensitivity levels for signal generation
- Customizable colors and line styles
- Optional info table with current market status
📊 TECHNICAL DETAILS:
- Uses TradingView's official COT Library
- Weekly COT data from CFTC reports
- Supports all major OANDA trading pairs
- Compatible with any timeframe (recommended: M15-D1)
⚠️ IMPORTANT NOTE:
This indicator displays COT data from CME futures markets. While trading OANDA spot markets, you're analyzing the underlying futures sentiment which drives institutional positioning.
Perfect for professional traders who understan
EMA Trend Confirmation with Alerts此脚本是基于EMA 200周期 50周期 20周期加以合并并进行改进的一个脚本指标，主要作用是用于观察趋势走向，其中有上升下降和震荡趋势，经过多数测试，此指标适用于短线交易，推荐周期为20或15，大周期和长线交易详见RSI+EMA结合指标
This script is an improved script indicator based on the EMA 200 period, 50 period, and 20 period. Its main function is to observe the trend direction, including up, down, and oscillating trends. After many tests, this indicator is suitable for short-term trading, and the recommended period is 20 or 15. For large-cycle and long-term trading, please refer to the RSI+EMA combination indicator.
FEDFUNDS Rate Divergence Oscillator [BackQuant]FEDFUNDS Rate Divergence Oscillator
1. Concept and Rationale
The United States Federal Funds Rate is the anchor around which global dollar liquidity and risk-free yield expectations revolve. When the Fed hikes, borrowing costs rise, liquidity tightens and most risk assets encounter head-winds. When it cuts, liquidity expands, speculative appetite often recovers. Bitcoin, a 24-hour permissionless asset sometimes described as “digital gold with venture-capital-like convexity,” is particularly sensitive to macro-liquidity swings.
The FED Divergence Oscillator quantifies the behavioural gap between short-term monetary policy (proxied by the effective Fed Funds Rate) and Bitcoin’s own percentage price change. By converting each series into identical rate-of-change units, subtracting them, then optionally smoothing the result, the script produces a single bounded-yet-dynamic line that tells you, at a glance, whether Bitcoin is outperforming or underperforming the policy backdrop—and by how much.
2. Data Pipeline
• Fed Funds Rate – Pulled directly from the FRED database via the ticker “FRED:FEDFUNDS,” sampled at daily frequency to synchronise with crypto closes.
• Bitcoin Price – By default the script forces a daily timeframe so that both series share time alignment, although you can disable that and plot the oscillator on intraday charts if you prefer.
• User Source Flexibility – The BTC series is not hard-wired; you can select any exchange-specific symbol or even swap BTC for another crypto or risk asset whose interaction with the Fed rate you wish to study.
3. Math under the Hood
(1) Rate of Change (ROC) – Both the Fed rate and BTC close are converted to percent return over a user-chosen lookback (default 30 bars). This means a cut from 5.25 percent to 5.00 percent feeds in as –4.76 percent, while a climb from 25 000 to 30 000 USD in BTC over the same window converts to +20 percent.
(2) Divergence Construction – The script subtracts the Fed ROC from the BTC ROC. Positive values show BTC appreciating faster than policy is tightening (or falling slower than the rate is cutting); negative values show the opposite.
(3) Optional Smoothing – Macro series are noisy. Toggle “Apply Smoothing” to calm the line with your preferred moving-average flavour: SMA, EMA, DEMA, TEMA, RMA, WMA or Hull. The default EMA-25 removes day-to-day whips while keeping turning points alive.
(4) Dynamic Colour Mapping – Rather than using a single hue, the oscillator line employs a gradient where deep greens represent strong bullish divergence and dark reds flag sharp bearish divergence. This heat-map approach lets you gauge intensity without squinting at numbers.
(5) Threshold Grid – Five horizontal guides create a structured regime map:
• Lower Extreme (–50 pct) and Upper Extreme (+50 pct) identify panic capitulations and euphoria blow-offs.
• Oversold (–20 pct) and Overbought (+20 pct) act as early warning alarms.
• Zero Line demarcates neutral alignment.
4. Chart Furniture and User Interface
• Oscillator fill with a secondary DEMA-30 “shader” offers depth perception: fat ribbons often precede high-volatility macro shifts.
• Optional bar-colouring paints candles green when the oscillator is above zero and red below, handy for visual correlation.
• Background tints when the line breaches extreme zones, making macro inflection weeks pop out in the replay bar.
• Everything—line width, thresholds, colours—can be customised so the indicator blends into any template.
5. Interpretation Guide
Macro Liquidity Pulse
• When the oscillator spends weeks above +20 while the Fed is still raising rates, Bitcoin is signalling liquidity tolerance or an anticipatory pivot view. That condition often marks the embryonic phase of major bull cycles (e.g., March 2020 rebound).
• Sustained prints below –20 while the Fed is already dovish indicate risk aversion or idiosyncratic crypto stress—think exchange scandals or broad flight to safety.
Regime Transition Signals
• Bullish cross through zero after a long sub-zero stint shows Bitcoin regaining upward escape velocity versus policy.
• Bearish cross under zero during a hiking cycle tells you monetary tightening has finally started to bite.
Momentum Exhaustion and Mean-Reversion
• Touches of +50 (or –50) come rarely; they are statistically stretched events. Fade strategies either taking profits or hedging have historically enjoyed positive expectancy.
• Inside-bar candlestick patterns or lower-timeframe bearish engulfings simultaneously with an extreme overbought print make high-probability short scalp setups, especially near weekly resistance. The same logic mirrors for oversold.
Pair Trading / Relative Value
• Combine the oscillator with spreads like BTC versus Nasdaq 100. When both the FED Divergence oscillator and the BTC–NDQ relative-strength line roll south together, the cross-asset confirmation amplifies conviction in a mean-reversion short.
• Swap BTC for miners, altcoins or high-beta equities to test who is the divergence leader.
Event-Driven Tactics
• FOMC days: plot the oscillator on an hourly chart (disable ‘Force Daily TF’). Watch for micro-structural spikes that resolve in the first hour after the statement; rapid flips across zero can front-run post-FOMC swings.
• CPI and NFP prints: extremes reached into the release often mean positioning is one-sided. A reversion toward neutral in the first 24 hours is common.
6. Alerts Suite
Pre-bundled conditions let you automate workflows:
• Bullish / Bearish zero crosses – queue spot or futures entries.
• Standard OB / OS – notify for first contact with actionable zones.
• Extreme OB / OS – prime time to review hedges, take profits or build contrarian swing positions.
7. Parameter Playground
• Shorten ROC Lookback to 14 for tactical traders; lengthen to 90 for macro investors.
• Raise extreme thresholds (for example ±80) when plotting on altcoins that exhibit higher volatility than BTC.
• Try HMA smoothing for responsive yet smooth curves on intraday charts.
• Colour-blind users can easily swap bull and bear palette selections for preferred contrasts.
8. Limitations and Best Practices
• The Fed Funds series is step-wise; it only changes on meeting days. Rapid BTC oscillations in between may dominate the calculation. Keep that perspective when interpreting very high-frequency signals.
• Divergence does not equal causation. Crypto-native catalysts (ETF approvals, hack headlines) can overwhelm macro links temporarily.
• Use in conjunction with classical confirmation tools—order-flow footprints, market-profile ledges, or simple price action to avoid “pure-indicator” traps.
9. Final Thoughts
The FEDFUNDS Rate Divergence Oscillator distills an entire macro narrative monetary policy versus risk sentiment into a single colourful heartbeat. It will not magically predict every pivot, yet it excels at framing market context, spotting stretches and timing regime changes. Treat it as a strategic compass rather than a tactical sniper scope, combine it with sound risk management and multi-factor confirmation, and you will possess a robust edge anchored in the world’s most influential interest-rate benchmark.
Trade consciously, stay adaptive, and let the policy-price tension guide your roadmap.
Mereks Wick Theory🚨 Wick Precision Zones – Top & Bottom Sniper
📈 Timeframes: 1H & 4H | 🔍 Powered by Smart Wick Logic + FVG Detection
Mark the exact 50% of key wicks where smart money reacts.
This advanced indicator auto-detects Fair Value Gaps (FVGs), Order Blocks, and liquidity zones, then highlights precision reversal levels with color-coded lines (🔴 short / 🟢 long).
✅ Alerts before and when price hits the zone
✅ Works even with just a strong wick inside FVG
✅ Filters in real-time to increase signal accuracy
✅ Built for top- and bottom-ticking entries
Normalized Dist from 4H MA200 + Chart HighlightsNormalized Distance from 4H EMA200 + Highlighting Extremes
This indicator measures the distance between the current price and the 4-hour EMA200, normalized into a z-score to detect statistically significant deviations.
🔹 The lower pane shows the normalized z-score.
🔹 Green background = price far below EMA200 (z < -2).
🔹 Red background = price far above EMA200 (z > 3.1).
🔹 These thresholds are user-configurable.
🔹 On the main chart:
🟥 Red candles indicate overheated prices (z > upper threshold)
🟩 Green candles signal oversold conditions (z < lower threshold)
The EMA200 is always taken from a fixed 4H timeframe, regardless of your current chart resolution.
Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND
The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns.
The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets.
The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks.
Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991).
MATHEMATICAL METHODOLOGY
The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t.
Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns.
Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively.
ADVANCED RISK METRICS
The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification.
The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions.
The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced.
Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture.
To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds.
The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns.
COLOR SCHEMES AND VISUALIZATION
The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools.
The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments.
Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations.
PRACTICAL APPLICATIONS
Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles.
The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions.
Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable.
Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements.
Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals.
STATISTICAL SIGNIFICANCE AND VALIDATION
The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns.
Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns.
Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference.
IMPLEMENTATION BEST PRACTICES
Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment.
Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives.
Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification.
ALERT SYSTEM AND AUTOMATION
The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations.
New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken.
TRADING STRATEGIES
Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods.
Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles.
Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions.
LIMITATIONS AND CONSIDERATIONS
Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates.
Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities.
Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical.
Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging.
TECHNICAL IMPLEMENTATION
Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible.
Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis.
The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations.
Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences.
CONCLUSION
The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions.
The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases.
Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes.
BIBLIOGRAPHY
Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228.
Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58.
Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152.
Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill.
Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91.
Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138.
Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64.
Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
