PE Rating by The Noiseless TraderPE Rating by The Noiseless Trader
This script analyzes a symbol’s Price-to-Earnings (P/E) ratio, using Diluted EPS (TTM) fundamentals directly from TradingView.
The script calculates the Price-to-Earnings ratio (P/E) using Diluted EPS (TTM) fundamentals. It then identifies:
PE High → the highest valuation point over a 3-year historical range.
PE Low → the lowest valuation point over a 3-year historical range.
PE Median → the midpoint between the two extremes, offering a fair-value benchmark.
PE (Int) → an additional intermediate low to track more recent undervaluation points. This is calculated based on lowest valuation point over a 1-year historical range
These levels are plotted directly on the chart as horizontal references, with markers showing the exact bars/dates when the extremes occurred. Candles corresponding to those days are also highlighted for context.
Bars corresponding to these extremes are highlighted (red = PE High, green = PE Low).
How it helps
Provides a historical valuation framework that complements technical analysis. We look for long opportunity or base formation near the PE Low and be cautious when stocks tends to trade near High PE.
We do not short the stock at High PE infact be cautious with long trades.
Helps identify whether current price action is happening near overvalued or undervalued zones.
Adds a long-term perspective to support swing trading and investing decisions. If a stock is coming from Low PE to Median PE and along with that if we get entry based on Classical strategies like Darvas Box, or HH-HL based on Dow Theory.
Offers a simple visual map of how far the market has moved from “cheap” to “expensive.”
This tool is best suited for long-term investors and swing traders who want to merge fundamentals with technical setups.
This indicator is designed as an educational tool to illustrate how valuation metrics (like earnings multiples) can be viewed alongside price action, helping traders connect fundamental context with technical execution in real market conditions.
Ratio
Daily Volume Ratio Bands (20MA)
Daily Volume Ratio Bands (20MA) — by CryptoDaily
This indicator normalizes daily trading volume against the recent 20-day moving average (20MA) and plots it as a volume ratio.
It allows traders to quickly identify whether current volume is strong, weak, or within a normal range compared to historical averages.
Key Features
Normalized volume ratio with 20-day average = baseline (1.0)
Clear bands for easy interpretation (1.0 ~ 1.3 = normal, above = overheated, below = weak)
Intuitive color coding:
🟨 Yellow: Normal range (1.0 ~ 1.3)
🔵 Blue: Above 1.3× average (high/strong volume, breakout confirmation)
⚪️ Gray: Below average (low volume)
🔴 Red: At or below 0.7× (extremely low volume / lack of interest)
How to Use
Breakouts with strong volume (Blue) → higher confidence in trend continuation
Gray/Red during consolidation → signal of weak momentum or sideways phase
Quickly assess whether the market is in overheated or low-activity conditions
Notes
Designed for Daily timeframe (1D) only. It will not function properly on intraday charts.
For educational purposes only. This is not financial advice.
Author
CryptoDaily (YouTube & TradingView)
YouTube channel: cryptodaily_tv
NYSE Advancing Issues & Volume RatiosOverview
This comprehensive market breadth indicator tracks two essential NYSE ratios that provide deep insights into market sentiment and internal strength:
NYSE Advancing Issues Ratio
NYSE Advancing Volume Ratio
Dual Ratio Analysis
Issues Ratio: Measures the percentage of NYSE stocks advancing vs. total issues
Volume Ratio: Measures the percentage of NYSE volume flowing into advancing stocks
Both ratios displayed as easy-to-read percentages (0-100%)
Customizable Display Options
Toggle each ratio on/off independently
Choose from multiple moving average types (SMA, EMA, WMA)
Adjustable moving average periods
Custom color schemes for better visualization
Reference Levels
50% Line: Market neutral point (gray dashed)
10% Line: Extremely bearish breadth (red dotted)
90% Line: Extremely bullish breadth (green dotted)
Optional background highlighting for extreme readings
Smart Alerts
Cross above/below 50% (neutral) for both ratios
Extreme readings: Above 90% (strong bullish) and below 10% (strong bearish)
Real-time notifications for key market breadth shifts
📈 How to Interpret
Bullish Signals
Above 50%: More stocks/volume advancing than declining
Above 90%: Extremely strong market breadth (rare occurrence)
Divergence: Price making new highs while breadth weakens (potential warning)
Market Timing
Extreme readings (10%/90%) often coincide with market turning points
Breadth thrusts from extreme levels can signal powerful moves
Use with other technical indicators for enhanced timing
Tick Ratio Simulator - Advanced Market Sentiment IndicatorOverview
The Tick Ratio Simulator is a sophisticated market sentiment indicator that provides real-time insights into buying and selling pressure dynamics. This proprietary indicator transforms complex market microstructure data into actionable trading signals.
Key Features
Real-Time Sentiment Analysis: Captures instantaneous shifts in market momentum
Multi-Timeframe Adaptability: Customizable calculation periods for any trading style
Visual Clarity: Color-coded histogram with dynamic zone highlighting
Integrated Alert System: Pre-configured alerts for key market transitions
Performance Dashboard: Live metrics display for informed decision-making
Trading Applications
✓ Trend Confirmation: Validate existing trends with momentum analysis
✓ Reversal Detection: Identify potential turning points at extreme readings
✓ Entry/Exit Timing: Optimize trade execution with overbought/oversold zones
✓ Risk Management: Clear visual boundaries for position sizing decisions
Signal Interpretation
Extreme Zones (±75): High probability reversal areas
Standard Thresholds (±50): Traditional overbought/oversold levels
Zero Line Crossings: Momentum shift confirmations
Histogram Expansion/Contraction: Strength of directional bias
Customization Options
Adjustable calculation and smoothing periods
Fully customizable color schemes
Toggle histogram and reference lines
Real-time information table positioning
Alert Conditions
Four pre-built alert templates for automated notifications:
Momentum threshold breaches
Directional changes
Extreme zone entries
Custom level crossovers
Best Practices
Works exceptionally well when combined with:
Volume analysis
Support/resistance levels
Price action patterns
Other momentum oscillators
Note: This indicator uses proprietary calculations to simulate institutional-grade tick analysis without requiring actual tick data feeds. Results are optimized for liquid markets with consistent volume profiles.
For optimal results, adjust parameters based on your specific instrument and timeframe. Past performance does not guarantee future results.
Forward P/E CalculatorI could not find a forward P/E indicator that gave me proper results. So here is mine.
Earnings X-Ray and Fundamentals Data:VSMarketTrendThis indicator calculates essential financial metrics for stocks using TradingView's built-in functions and custom algorithms. The values are derived from fundamental data sources available on TradingView.
Key Output Metrics(YOY Basic Quaterly DATA)
MC (Market Cap): Company’s total market value (Price × Total Shares).
TS (Total Shares Outstanding): All shares (float + restricted) in circulation.
Sales: Annual revenue (TTM or latest fiscal year).
NETIn: Net income
P/E (Price-to-Earnings): Valuation ratio (Market Cap / Net Income or Price / EPS).
EPS (Earnings Per Share): Net income per share (Net Income / TS).
OPM (Operating Margin %): Core profitability (Operating Income / Revenue × 100).
Quick Ratio: Short-term liquidity ((Current Assets – Inventory) / Current Liabilities).
BVPS (Book Value Per Share): Equity per share (Shareholders’ Equity / TS).
PS (Price-to-Sales): Revenue-based valuation (Market Cap / Annual Revenue).
FCF (Free Cash Flow Per Share): Post-CapEx cash ((Operating Cash Flow – CapEx) / TS).
Data Sources & Methods
Uses TradingView’s request.financial() for income/balance sheet data (Revenue, EBITDA, etc.).
Fetches real-time metrics via request.security() (e.g., Shares Outstanding).
Normalizes data across timeframes (quarterly/annual).
Disclaimer
Not financial advice. Verify with official filings before trading.
RATIO TPI SOLETH | JeffreyTimmermansSOLETH Ratio Trend Probability Indicator
Medium-Term Trend Assessment | Dominant Major Detector: The SOLETH Ratio TPI is a medium-term trend-following tool designed to measure the performance relationship between Solana and Ethereum — two of the leading smart contract platforms in the crypto market. By tracking the SOLETH ratio, this indicator determines which of the two is acting as the dominant major in the current market environment.
Rather than focusing on absolute price movements, the SOLETH Ratio TPI isolates relative strength. An upward-trending ratio means Solana is outperforming Ethereum, while a downward trend means Ethereum is taking the lead.
Key Features
Dominant Major Identification:
The indicator’s primary function is to determine leadership between Solana and Ethereum:
SOL Dominant: SOLETH ratio trending up
ETH Dominant: SOLETH ratio trending down
Neutral: No clear leader
8 Trend-Following Inputs:
Integrates 8 carefully selected medium-term trend-following signals into a composite score for clarity and accuracy in dominance detection.
Score-Based Regime Classification:
Score > 0.1 → SOL in relative uptrend → Dominant Major: SOL
Score < -0.1 → ETH in relative uptrend → Dominant Major: ETH
Between -0.1 and 0.1 → Neutral → No clear dominance
Dynamic Visual Interface:
Background colors change according to the dominant asset.
Bottom dashboard displays the status of all inputs, the composite score, and the determined dominance label.
Use Cases:
Smart Contract Sector Rotation: Identify leadership shifts between Solana and Ethereum to guide allocation within the L1 ecosystem.
Sector Sentiment Insight: Dominance changes often precede broader capital flows into or out of each ecosystem.
Multi-Timeframe Confirmation: Combine with broader market LTPI and MTPI tools to reinforce conviction in rotation-based strategies.
Conclusion
The SOLETH Ratio TPI condenses the competition between two of crypto’s top smart contract platforms into one clear, actionable view. By aggregating 8 powerful medium-term trend-following inputs, it delivers a precise assessment of which chain currently leads the market.
RATIO TPI SOLBTC | JeffreyTimmermansSOLBTC Ratio Trend Probability Indicator
Medium-Term Trend Assessment | Dominant Major Detector: The SOLBTC Ratio TPI is a medium-term trend-following indicator designed to measure the relative strength between Solana and Bitcoin — two of the most influential assets in the crypto market. By analyzing the SOLBTC ratio, this tool identifies which of the two is currently the dominant major in the market cycle.
Unlike standard price-based analysis, this indicator focuses on relative dominance. When Solana outperforms Bitcoin, the ratio trends upward, signaling SOL dominance. When Bitcoin outperforms Solana, the ratio trends downward, signaling BTC dominance.
Key Features
Dominant Major Identification:
The primary goal of this TPI is to determine whether Solana or Bitcoin is leading the market:
SOL Dominant: SOLBTC is trending up
BTC Dominant: SOLBTC is trending down
Neutral: No clear leader in the current cycle
8 Trend-Following Inputs:
Combines 8 carefully selected medium-term trend-following indicators into a single composite score for clear and actionable dominance detection.
Score-Based Regime Classification:
Score > 0.1 → SOL in relative uptrend → Dominant Major: SOL
Score < -0.1 → BTC in relative uptrend → Dominant Major: BTC
Between -0.1 and 0.1 → Neutral → No clear dominance
Dynamic Visuals:
Background colors shift to match the dominant asset
Bottom dashboard displays the state of each input, the composite score, and the resulting dominance label
Use Cases:
Rotation Strategies: Identify when capital is rotating between Solana and Bitcoin to optimize positioning.
Market Leadership Signals: Use dominance changes as a leading indicator for broader altcoin cycles and sentiment shifts.
Multi-Timeframe Confirmation: Pair with LTPI and STPI for higher conviction in directional bias.
Conclusion
The SOLBTC Ratio TPI distills the relationship between Solana and Bitcoin into one simple question: Who is leading right now? By combining 8 powerful trend-following inputs into a clear dominance score, it provides traders and investors with a precise, medium-term view of market leadership.
RATIO TPI ETHBTC | JeffreyTimmermansETHBTC Ratio Trend Probability Indicator
Medium-Term Trend Assessment | Dominant Major Detector: The ETHBTC Ratio TPI is a medium-term trend-following indicator designed to measure the relative strength between Ethereum and Bitcoin — the two most dominant assets in crypto. By analyzing the ETHBTC ratio, this tool provides insights into which of the two is currently leading the market trend.
Unlike absolute price indicators, this tool tracks relative dominance. When Ethereum outperforms Bitcoin, the ratio trends upward, signaling ETH dominance. When Bitcoin outperforms Ethereum, the ratio trends downward, signaling BTC dominance.
Key Features
Dominant Major Identification:
The core purpose of this TPI is to determine which asset — Ethereum or Bitcoin — is the dominant major in the current crypto cycle.
ETH Dominant: ETHBTC is trending up
BTC Dominant: ETHBTC is trending down
Neutral: No clear directional edge
8 Trend-Following Inputs:
The indicator aggregates 8 hand-picked, medium-term trend-following metrics into a single score that simplifies the ETHBTC trend assessment.
Score-Based Regime Classification:
Score > 0.1 → ETH is in relative uptrend → Dominant Major: ETH
Score < -0.1 → BTC is in relative uptrend → Dominant Major: BTC
Between -0.1 and 0.1 → Neutral trend → No clear dominance
Dynamic Visuals:
Background color adapts to the dominant asset
Score, trend state per input, and composite result are shown in a clean dashboard
Use Cases:
Rotation Strategy Insight: Understand whether capital is flowing into Ethereum or Bitcoin to adjust your portfolio positioning accordingly.
Dominance-Based Macro Timing: Use the dominance shift as a leading signal for broader altcoin cycles.
Multi-Timeframe Confirmation: Combine with LTPI (Long-Term) and STPI (Short-Term) to build directional conviction.
Conclusion
The ETHBTC Ratio TPI is a highly focused tool that simplifies the complex relationship between Ethereum and Bitcoin into one clear output: who is currently leading the crypto market. With 8 inputs driving a composite trend score and a dynamic dominance label, this indicator is essential for anyone looking to time ETH vs BTC rotations with precision.
Calc win-LoserHow to Use the Calc win-Loser Indicator
The indicator calculates the profit or loss of the operation, showing how much you gained or lost on the invested amount, without adding the initial capital, displaying only the profit or loss separately.
Use a period (.) to separate decimal numbers, without thousand separators (e.g., 1000 for one thousand, 1000.50 for one thousand and fifty cents).
Price Definition for Calculation
Long Position (buy):
Low Price: entry price (lower)
High Price: exit price (higher)
Example: enter at 1 and exit at 3
Short Position (sell):
High Price: entry price (higher)
Low Price: exit price (lower)
Example: enter at 3 and exit at 1
Main Parameters
Parameter Description Example
Low Price Base price for calculation (Long: entry; Short: exit) 1
High Price Base price for calculation (Long: exit; Short: entry) 3
Leverage Operation multiplier (leverage) 2.0
Universal Amount Total amount invested 1000
Broker Fee (%) Percentage fee charged by broker 0.1
Currency Currency symbol for value display USD
Practical Example
Long: entry at 1, exit at 3, 2x leverage, $1000 investment, 0.1% fee.
Short: entry at 3, exit at 1, 2x leverage, $1000 investment, 0.1% fee.
The indicator will show the expected profit or loss based on the percentage difference adjusted by leverage and subtracting the broker fee.
Notes
Adjust prices according to the type of operation (Long or Short).
Use a period for decimals and do not use thousand separators.
This indicator is a simulation tool and does not execute automatic trades.
Original indicator by Canhoto-Medium — protected to maintain order and respect, prevent copying and plagiarism.
Enhanced Zones with Volume StrengthEnhanced Zones with Volume Strength
Your reliable visual guide to market zones — now with Multi-Timeframe (MTF) power!
What you get:
Clear visual zones on your chart — color-coded boxes that highlight important price areas.
Blue Boxes for neutral zones — easy to spot areas of indecision or balance.
Gray Boxes to show normal volume conditions, giving you context without clutter.
Green Boxes highlighting bullish zones where strength is showing.
Red Boxes marking bearish zones where weakness might be in play.
Multi-Timeframe Support:
Seamlessly visualize these zones from higher timeframes directly on your current chart for a bigger-picture view, helping you make smarter trading decisions.
How to use it:
Adjust the box width (in bars) to fit your trading style and timeframe.
Customize colors and opacity to suit your chart theme.
Toggle neutral blue and gray volume boxes on/off to focus on what matters most to you.
Set the maximum number of boxes to keep your chart clean and performant.
Why you’ll love it:
This indicator cuts through the noise by visually marking zones where volume and price action matter the most — without overwhelming your chart. The MTF feature means you’re always aligned with higher timeframe trends without switching views.
Pro tip:
Use these boxes as dynamic support/resistance areas or to confirm trade setups alongside your favorite indicators.
No complicated formulas here, just crisp, actionable visuals designed for clarity and confidence.
Metatrader CalculatorThe “ Metatrader Calculator ” indicator calculates the position size, risk, and potential gain of a trade, taking into account the account balance, risk percentage, entry price, stop loss price, and risk/reward ratio. It supports the XAUUSD, XAGUSD, and BTCUSD pairs, automatically calculating the position size (in lots) based on these parameters. The calculation is displayed in a table on the chart, showing the lot size, loss in dollars, and potential gain based on the defined risk.
Stablecoin Ratio with TPI ScoreThe script measures the stablecoin ratio (total stablecoin market cap divided by total crypto market cap, times 100) and its weekly change. Stablecoins (e.g., USDT, USDC) are a key gateway for capital entering or exiting the crypto ecosystem.
A rising ratio suggests more capital is parked in stablecoins (potential buying power), while a falling ratio indicates capital leaving (selling or withdrawal).
In a macro analysis, this is critical—it reflects the availability of liquid funds that could fuel price movements.
In macroeconomics, liquidity is a driver of asset prices.
In crypto, stablecoins represent sidelined capital ready to deploy.
How does it work?
Stablecoin Ratio:
Formula: (total_stablecoin_mcap / total_crypto_mcap) * 100.
Example: If stablecoins = $235B and total market cap = $2.5T, ratio = 9.4%.
Plotted as a red line in the oscillator pane, showing the percentage of the market held in stablecoins.
Weekly Change:
Calculates the percentage change in the ratio from the previous week:
(current_ratio - previous_ratio) / previous_ratio * 100.
Example: Ratio goes from 9% to 10% = +11.11% change.
TPI Score Assignment:
+1 (Bullish): If the ratio increases by more than 5% week-over-week.
-1 (Bearish): If the ratio decreases by more than 5% week-over-week.
0 (Neutral): If the change is between -5% and +5%.
Plotted as orange step line bars in the oscillator pane, snapping to +1, 0, or -1.
Put/Call RatioPut/Call Ratio Indicator
This indicator visualizes the Put/Call Ratio for various market symbols, helping traders assess market sentiment and potential reversals. It offers a dropdown menu to select from a range of Put/Call Ratios, including broad equities (CBOE), major indices (SPX, QQQ, IWM, VIX), and individual stocks (TSLA, GOOG, META, AMZN, MSFT, INTC).
The indicator plots the Put/Call Ratio with adjustable moving averages and standard deviation bands to highlight overbought or oversold conditions. A short-term moving average (default: 10 periods) is displayed with trend-based coloring, while longer-term moving averages (defaults: 30 and 200 periods) are calculated but hidden by default. Bands at 1, 1.5, and 2 standard deviations provide context for extreme readings.
Key Overbought/Oversold Signals:
Short-Term Extremes: The 10-day moving average moves beyond 1 standard deviation from the 200-day moving average, signaling potential overbought (above) or oversold (below) conditions. This will be highlighted by red or green background color.
Ratio Extremes: The Put/Call Ratio line itself crosses outside 2 standard deviations from the 200-day moving average, indicating stronger overbought or oversold zones.
Conditional coloring of the ratio line reflects its position relative to the bands, and background shading highlights when the short-term moving average crosses key levels.
Key Features:
Selectable Put/Call Ratio symbols.
Trend-colored moving averages.
Standard deviation bands for volatility analysis.
Dynamic line and background coloring for quick insights.
Usage:
Use this indicator to gauge market sentiment—high ratios may suggest bearish sentiment or oversold conditions, while low ratios may indicate bullish sentiment or overbought conditions. Combine with price action or other tools for confirmation.
Sma Indicator with Ratio (pr)SMA Indicator with Ratio (PR) is a technical analysis tool designed to provide insights into the relationship between multiple Simple Moving Averages (SMAs) across different time frames. This indicator combines three key SMAs: the 111-period SMA, 730-period SMA, and 1400-period SMA. Additionally, it introduces a ratio-based approach, where the 730-period SMA is multiplied by factors of 2, 3, 4, and 5, allowing users to analyze potential market trends and price movements in relation to different SMA levels.
What Does This Indicator Do?
The primary function of this indicator is to track the movement of prices in relation to several SMAs with varying periods. By visualizing these SMAs, users can quickly identify:
Short-term trends (111-period SMA)
Medium-term trends (730-period SMA)
Long-term trends (1400-period SMA)
Additionally, the multiplied versions of the 730-period SMA provide deeper insights into potential price reactions at different levels of market volatility.
How Does It Work?
The 111-period SMA tracks the shorter-term price trend and can be used for identifying quick market movements.
The 730-period SMA represents a longer-term trend, helping users gauge overall market sentiment and direction.
The 1400-period SMA acts as a very long-term trend line, giving users a broad perspective on the market’s movement.
The ratio-based SMAs (2x, 3x, 4x, 5x of the 730-period SMA) allow for an enhanced understanding of how the price reacts to higher or lower volatility levels. These ratios are useful for identifying key support and resistance zones in a dynamic market environment.
Why Use This Indicator?
This indicator is useful for traders and analysts who want to track the interaction of price with different moving averages, enabling them to make more informed decisions about potential trend reversals or continuations. The added ratio-based values enhance the ability to predict how the market might react at different levels.
How to Use It?
Trend Confirmation: Traders can use the indicator to confirm the direction of the market. If the price is above the 111, 730, or 1400-period SMA, it may indicate an uptrend, and if below, a downtrend.
Support/Resistance Levels: The multiplied versions of the 730-period SMA (2x, 3x, 4x, 5x) can be used as dynamic support or resistance levels. When the price approaches or crosses these levels, it might indicate a change in the trend.
Volatility Insights: By observing how the price behaves relative to these SMAs, traders can gauge market volatility. Higher multiples of the 730-period SMA can signal more volatile periods where price movements are more pronounced.
Zanger Volume Ratio (ZVR)Zanger Volume Ratio (ZVR)
Credits:
Most of the underlying code and logic in this script have been adapted from the work originally published by The_Peaceful_Lizard
Overview
The Zanger Volume Ratio (ZVR) is a powerful indicator designed to reveal market dynamics by comparing current cumulative volume to an average determined over a historical look-back period. It uses the concept of relative volume to not only highlight unusual volume spikes, but also uses color to illustrate how today's trading compares to typical levels. This unique method of volume analysis was popularized by Dan Zanger - a trader known for turning $10,775 into $18,000,000 in less than two years - by identifying key shifts in market interest and volume behavior.
Key Features
Volume Pacing Analysis:
The script calculates a volume delta by comparing the cumulative volume at any given moment to an average derived over a user-defined lookback period (Default 20-day). The resulting percentage difference offers a clear visualization and insight into unusual volume activity.
Dynamic Visual Representation:
Choose between either “Columns” or “Area” plot styles to display the percent difference. Additionally, you have the option to switch between a standard plot or a background color display, with customizable transparency, ensuring the indicator fits seamlessly with your chart’s aesthetics.
Dashboard Integration:
A simple dashboard table is displayed on the chart, showcasing the current ZVR value in real-time. With user-configurable position, text size, alignment, and color options, this feature ensures that the key metric is always visible and easy to interpret.
Why Use the Zanger Volume Ratio?
The ZVR is more than just a volume indicator. It acts as a window into market sentiment by highlighting days when trading interest intensifies. Many traders believe that an unusually high volume ratio may confirm trend strength or signal a reversal, making the indicator a valuable tool when used in conjunction with other technical analysis methods.
Whether you’re monitoring stocks, commodities, or forex markets, the Zanger Volume Ratio offers an accessible yet sophisticated method to decode volume dynamics. Its practical design and real-time visual feedback provide traders of all experience levels with critical data to spot high-potential setups.
Chart Description
First Pane: normal Volume Indicator on the foreground, ZVR as Background colors
Second Pane: ZVR Indicator with Column Style (default)
First panel: normal volume indicator in foreground, ZVR as background colors
Second panel: ZVR indicator with column style (default)
Note: This indicator is intended for use on intraday charts only!
Simple Time-Based Strategy(Price Action Hypothesis)Core Theory: Trend Continuation Pattern Recognition**
1. **Price Action Hypothesis**
The strategy is built on the assumption that consecutive price movements (3-bar patterns) indicate momentum continuation:
- *Long Pattern*: Three consecutive higher closes combined with ascending highs
- *Short Pattern*: Three consecutive lower closes combined with descending lows
This reflects a belief that sustained directional price movement creates self-reinforcing trends that can be captured through simple pattern recognition.
2. **Time-Based Risk Management**
Implements a dynamic exit mechanism:
- *Training Phase*: 5-bar holding period (quick turnover)
- *Testing Phase*: 10-bar holding period (extended exposure)
This dual timeframe approach suggests the hypothesis that market conditions may require different holding durations in different market eras.
3. **Adaptive Market Hypothesis**
The structure incorporates two distinct phases:
- *Training Period (11 years)*: Pattern recognition without stop losses
- *Testing Period*: Pattern recognition with stop losses
This assumes markets may change character over time, requiring different risk parameters in different epochs.
4. **Asymmetric Risk Control**
Implements stop-losses only in the testing phase:
- Fixed 500-pip (point) stop distance
- Activated post-training period
This reflects a belief that historical patterns might need different risk constraints than real-time trading.
5. **Dual-Path Validation**
The split between training/testing phases suggests:
- Pattern validity should first be confirmed without protective stops
- Real-world implementation requires added risk constraints
6. **Market Efficiency Paradox**
The simultaneous use of both long/short entries assumes:
- Markets exhibit persistent inefficiencies
- These inefficiencies manifest differently in bullish/bearish conditions
- A symmetric approach can capture opportunities in both directions
7. **Behavioral Finance Elements**
The 3-bar pattern recognition potentially exploits:
- Herd mentality in trend formation
- Delayed reaction to price momentum
- Cognitive bias in trend confirmation
8. **Quantitative Time Segmentation**
The annual-based period division (training vs testing) implies:
- Market cycles operate on multi-year timeframes
- Strategy robustness requires validation across different market regimes
- Parameter sensitivity needs temporal validation
This strategy combines elements of technical pattern recognition, temporal adaptability, and phased risk management to create a systematic approach to trend exploitation. The theoretical framework suggests markets exhibit persistent but evolving patterns that can be systematically captured through rule-based execution.
MATA GOLD RATIOMata Gold Instrument: User Guide
The Instrument to Gold Oscillator is a technical analysis tool that normalizes the ratio of an instrument's price (e.g., BTC/USD) to the price of gold (XAU/USD) into a 0-100 scale. This provides a clear and intuitive way to evaluate the relative performance of an instrument compared to gold over a specified period.
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How It Works
1. Calculation of the Ratio:
The ratio is calculated as:
\text{Ratio} = \frac{\text{Instrument Price}}{\text{Gold Price}}
2. Normalization:
The ratio is normalized using the highest and lowest values over a user-defined period (length), typically 14 periods:
\text{Normalized Ratio} = \frac{\text{Ratio} - \text{Min(Ratio)}}{\text{Max(Ratio)} - \text{Min(Ratio)}} \times 100
3. Overbought/Oversold Levels:
Above 80: The instrument is relatively expensive compared to gold (overbought).
Below 20: The instrument is relatively cheap compared to gold (oversold).
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How to Use the Oscillator
1. Identify Overbought and Oversold Levels:
If the oscillator rises above 80, the instrument may be overvalued relative to gold. This could signal a potential reversal or correction.
If the oscillator falls below 20, the instrument may be undervalued relative to gold. This could signal a buying opportunity.
2. Track Trends:
Rising oscillator values indicate the instrument is gaining value relative to gold.
Falling oscillator values indicate the instrument is losing value relative to gold.
3. Crossing the Midline (50):
When the oscillator crosses above 50, the instrument's value is gaining strength relative to gold.
When it crosses below 50, the instrument is weakening relative to gold.
4. Combine with Other Indicators:
Use this oscillator alongside other technical indicators (e.g., RSI, MACD, STOCH) for more robust decision-making.
Confirm signals from the oscillator with price action or volume analysis.
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Example Scenarios
1. Trading Cryptocurrencies Against Gold:
If BTC/USD's oscillator value is above 80, Bitcoin may be overvalued relative to gold. Consider reducing exposure or looking for short opportunities.
If BTC/USD's oscillator value is below 20, Bitcoin may be undervalued relative to gold. This could be a good time to accumulate.
2. Commodities vs. Gold:
Analyze the relative strength of commodities (e.g., oil, silver) against gold using the oscillator to identify periods of overperformance or underperformance.
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Advantages of the Oscillator
Relative Performance Insight: Tracks the performance of an instrument relative to gold, providing a macro perspective.
Clear Visual Representation: The 0-100 scale makes it easy to identify overbought/oversold conditions and trend shifts.
Customizable Periods: The user-defined length allows flexibility in analyzing short- or long-term trends.
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Limitations
Dependence on Gold: As the oscillator is based on gold prices, any external shocks to gold (e.g., geopolitical events) can influence its signals.
No Absolute Buy/Sell Signals: The oscillator should not be used in isolation but as part of a broader analysis strategy.
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By using the Instrument to Gold Oscillator effectively, traders and investors can gain valuable insights into the relative valuation and performance of assets compared to gold, enabling more informed trading and investment decisions.
Dynamic Risk-Adjusted Performance Ratios with TableWith this indicator, you have everything you need to monitor and compare the Sharpe ratio, Sortino ratio, and Omega ratio across multiple assets—all in one place. This tool is designed to help save time and improve efficiency by letting you track up to 15 assets simultaneously in a fully customizable table. You can adjust the lookback period to fit your trading strategy and get a clearer picture of how your assets perform over time. Instead of switching between charts, this indicator puts all the critical information you need at your fingertips.
Sharpe Ratio -
Helps evaluate the overall efficiency of investments by comparing the average return to the total risk (measured by the standard deviation of all returns). Essentially, it tells you how much excess return you’re getting for each unit of risk you’re taking. A higher Sharpe ratio means you’re getting better risk-adjusted performance—something you’ll want to aim for in your portfolio.
Sortino Ratio -
Goes a step further by focusing only on downside risk—because let’s face it, no one worries about positive volatility. This ratio is calculated by dividing the average return by the standard deviation of only the negative returns. Perfect for those concerned about avoiding losses rather than chasing extreme gains. It gives you a sharper view of how well your assets are performing relative to the risks you’re trying to avoid.
Omega Ratio -
Offers a unique perspective by comparing the sum of positive returns to the absolute sum of negative returns. It’s a straightforward way to see if your wins outweigh your losses. A higher Omega ratio means your positive returns significantly exceed the downside, which is exactly what you want when building a strong, reliable portfolio.
This indicator is perfect for traders who want to streamline their decision-making process and gain an edge. Bringing together these three critical ratios into a single user-defined table makes it easy to compare and rank assets at a glance. Whether optimizing a portfolio or looking for the best opportunities, this tool helps you stay ahead by focusing on risk-adjusted returns. The customizable lookback period lets you tailor the analysis to fit your unique trading approach, giving you insights that align with your goals. If you’re serious about making data-driven decisions and improving your trading outcomes, this indicator is a game-changer for your toolkit.
WMA Killer Ratio Analysis | JeffreyTimmermansWMA Killer Ratio Analysis
The WMA Killer Ratio Analysis is a highly responsive trend-following indicator designed to deliver quick and actionable insights on the ETHBTC ratio. By utilizing advanced smoothing methods and normalized thresholds, this tool efficiently identifies market trends. Let’s dive into the details:
Core Mechanics
1. Smoothing with Standard Deviations
The WMA Killer Ratio Analysis begins by smoothing source price data using standard deviations, which measure the typical variance in price movements. This creates dynamic deviation levels:
Upper Deviation: Marks the high boundary, indicating potential overbought conditions.
Lower Deviation: Marks the low boundary, signaling potential oversold conditions.
These levels are integrated with the Weighted Moving Average (WMA), filtering out market noise and honing in on significant price shifts.
2. Weighted WMA Bands
The WMA is further refined with dynamic weighting:
Upper Weight: Expands the WMA, creating an Upper Band to capture extreme price highs.
Lower Weight: Compresses the WMA, forming a Lower Band to reflect price lows.
This adaptive dual-weighting system highlights potential areas for trend reversals or continuations with precision.
3. Normalized WMA (NWMA) Analysis
The Normalized WMA adds a deeper layer of trend evaluation: It calculates the percentage change between the source price and its smoothed average. Positive NWMA values suggest overbought conditions, while negative NWMA values point to oversold conditions.
Traders can customize long (buy) and short (sell) thresholds to align signal sensitivity with their strategy and market conditions.
Signal Logic
Buy (Long) Signals: Triggered when the price remains above the lower deviation level and the NWMA crosses above the long threshold. Indicates a bullish trend and potential upward momentum.
Sell (Short) Signals: Triggered when the price dips below the upper deviation level and the NWMA falls beneath the short threshold. Suggests bearish momentum and a potential downward trend.
Note: The WMA Killer Ratio Analysis is most effective when paired with other forms of analysis, such as volume, higher time-frame trends, or fundamental data.
Visual Enhancements
The WMA Killer Ratio Analysis emphasizes usability with clear and dynamic plotting features:
1. Color-Coded Trend Indicators: The indicator changes color dynamically to represent trend direction. Users can customize colors to suit specific trading pairs (e.g., ETHBTC, SOLBTC).
2. Threshold Markers: Dashed horizontal lines represent long and short thresholds, giving traders a visual reference for signal levels.
3. Deviation Bands with Fill Areas: Upper and Lower Bands are plotted around the WMA. Shaded regions highlight deviation zones, making trend boundaries easier to spot.
4. Signal Arrows and Bar Coloring: Arrows or triangles appear on the chart to mark potential buy (upward) or sell (downward) points. Candlesticks are color-coded based on the prevailing trend, allowing traders to interpret the market direction at a glance.
Customization Options
Adjustable Thresholds: Tailor the sensitivity of long and short signals to your strategy.
Dynamic Weighting: Modify upper and lower band weights to adapt the WMA to varying market conditions.
Source Selection: Choose the preferred input for price data smoothing, such as closing price or an average (hl2).
The WMA Killer Ratio Analysis combines rigorous mathematical analysis with intuitive visual features, providing traders with a reliable way to identify trends and make data-driven decisions. While it excels at detecting key market shifts, its effectiveness increases when integrated into a broader trading strategy.
-Jeffrey
Quick scan for signal🙏🏻 Hey TV, this is QSFS, following:
^^ Quick scan for drift (QSFD)
^^ Quick scan for cycles (QSFC)
As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy behavior, that can be later confirmed or denied by more sophisticated tools. This metric is O(n) in windowed mode and O(1) if calculated incrementally on each data update, so you can scan Ks of datasets w/o worrying about melting da ice.
^^ windowed mode
Now the post will be divided into several sections, and a couple of things I guess you’ve never seen or thought about in your life:
1) About Efficiency Ratios posted there on TV;
Some of you might say this is the Efficiency Ratio you’ve seen in Perry's book. Firstly, I can assure you that neither me nor Perry, just as X amount of quants all over the world and who knows who else, would say smth like, "I invented it," lol. This is just a thing you R&D when you need it. Secondly, I invite you (and mods & admin as well) to take a lil glimpse at the following screenshot:
^^ not cool...
So basically, all the Efficiency Ratios that were copypasted to our platform suffer the same bug: dudes don’t know how indexing works in Pine Script. I mean, it’s ok, I been doing the same mistakes as well, but loxx, cmon bro, you... If you guys ever read it, the lines 20 and 22 in da code are dedicated to you xD
2) About the metric;
This supports both moving window mode when Length > 0 and all-data expanding window mode when Length < 1, calculating incrementally from the very first data point in the series: O(n) on history, O(1) on live updates.
Now, why do I SQRT transform the result? This is a natural action since the metric (being a ratio in essence) is bounded between 0 and 1, so it can be modeled with a beta distribution. When you SQRT transform it, it still stays beta (think what happens when you apply a square root to 0.01 or 0.99), but it becomes symmetric around its typical value and starts to follow a bell-shaped curve. This can be easily checked with a normality test or by applying a set of percentiles and seeing the distances between them are almost equal.
Then I noticed that on different moving window sizes, the typical value of the metric seems to slide: higher window sizes lead to lower typical values across the moving windows. Turned out this can be modeled the same way confidence intervals are made. Lines 34 and 35 explain it all, I guess. You can see smth alike on an autocorrelogram. These two match the mean & mean + 1 stdev applied to the metric. This way, we’ve just magically received data to estimate alpha and beta parameters of the beta distribution using the method of moments. Having alpha and beta, we can now estimate everything further. Btw, there’s an alternative parameterization for beta distributions based on data length.
Now what you’ll see next is... u guys actually have no idea how deep and unrealistically minimalistic the underlying math principles are here.
I’m sure I’m not the only one in the universe who figured it out, but the thing is, it’s nowhere online or offline. By calculating higher-order moments & combining them, you can find natural adaptive thresholds that can later be used for anomaly detection/control applications for any data. No hardcoded thresholds, purely data-driven. Imma come back to this in one of the next drops, but the truest ones can already see it in this code. This way we get dem thresholds.
Your main thresholds are: basis, upper, and lower deviations. You can follow the common logic I’ve described in my previous scripts on how to use them. You just register an event when the metric goes higher/lower than a certain threshold based on what you’re looking for. Then you take the time series and confirm a certain behavior you were looking for by using an appropriate stat test. Or just run a certain strategy.
To avoid numerous triggers when the metric jitters around a threshold, you can follow this logic: forget about one threshold if touched, until another threshold is touched.
In general, when the metric gets higher than certain thresholds, like upper deviation, it means the signal is stronger than noise. You confirm it with a more sophisticated tool & run momentum strategies if drift is in place, or volatility strategies if there’s no drift in place. Otherwise, you confirm & run ~ mean-reverting strategies, regardless of whether there’s drift or not. Just don’t operate against the trend—hedge otherwise.
3) Flex;
Extension and limit thresholds based on distribution moments gonna be discussed properly later, but now you can see this:
^^ magic
Look at the thresholds—adaptive and dynamic. Do you see any optimizations? No ML, no DL, closed-form solution, but how? Just a formula based on a couple of variables? Maybe it’s just how the Universe works, but how can you know if you don’t understand how fundamentally numbers 3 and 15 are related to the normal distribution? Hm, why do they always say 3 sigmas but can’t say why? Maybe you can be different and say why?
This is the primordial power of statistical modeling.
4) Thanks;
I really wanna dedicate this to Charlotte de Witte & Marion Di Napoli, and their new track "Sanctum." It really gets you connected to the Source—I had it in my soul when I was doing all this ∞
Daily Ratio OCHL Averager by Munif ShaikhThe "Daily Ratio OCHL Averager" indicator, is designed for use in financial charts. It calculates an average value based on the daily open, close, high, and low prices, and visualizes this average on the chart.
Ratio Calculation:
The script calculates a ratio representing the normalized difference as a percentage. This ratio helps determine if the current price is above or below the calculated average.
Plotting the Average Line:
The average value (dDaily) is plotted on the chart with a dynamic color indicating whether the current price is above (green) or below (red) the average.
Traders can use this indicator to visually analyze how the current price compares to the daily average. The color-coded average line helps quickly identify bullish or bearish conditions. The ratio percentage provides an additional quantitative measure of this relationship.
This indicator can be particularly useful in identifying trends and potential reversal points by showing how prices behave relative to their daily average, aiding in making informed trading decisions.
RiskMetrics█ OVERVIEW
This library is a tool for Pine programmers that provides functions for calculating risk-adjusted performance metrics on periodic price returns. The calculations used by this library's functions closely mirror those the Broker Emulator uses to calculate strategy performance metrics (e.g., Sharpe and Sortino ratios) without depending on strategy-specific functionality.
█ CONCEPTS
Returns, risk, and volatility
The return on an investment is the relative gain or loss over a period, often expressed as a percentage. Investment returns can originate from several sources, including capital gains, dividends, and interest income. Many investors seek the highest returns possible in the quest for profit. However, prudent investing and trading entails evaluating such returns against the associated risks (i.e., the uncertainty of returns and the potential for financial losses) for a clearer perspective on overall performance and sustainability.
One way investors and analysts assess the risk of an investment is by analyzing its volatility , i.e., the statistical dispersion of historical returns. Investors often use volatility in risk estimation because it provides a quantifiable way to gauge the expected extent of fluctuation in returns. Elevated volatility implies heightened uncertainty in the market, which suggests higher expected risk. Conversely, low volatility implies relatively stable returns with relatively minimal fluctuations, thus suggesting lower expected risk. Several risk-adjusted performance metrics utilize volatility in their calculations for this reason.
Risk-free rate
The risk-free rate represents the rate of return on a hypothetical investment carrying no risk of financial loss. This theoretical rate provides a benchmark for comparing the returns on a risky investment and evaluating whether its excess returns justify the risks. If an investment's returns are at or below the theoretical risk-free rate or the risk premium is below a desired amount, it may suggest that the returns do not compensate for the extra risk, which might be a call to reassess the investment.
Since the risk-free rate is a theoretical concept, investors often utilize proxies for the rate in practice, such as Treasury bills and other government bonds. Conventionally, analysts consider such instruments "risk-free" for a domestic holder, as they are a form of government obligation with a low perceived likelihood of default.
The average yield on short-term Treasury bills, influenced by economic conditions, monetary policies, and inflation expectations, has historically hovered around 2-3% over the long term. This range also aligns with central banks' inflation targets. As such, one may interpret a value within this range as a minimum proxy for the risk-free rate, as it may correspond to the minimum rate required to maintain purchasing power over time.
The built-in Sharpe and Sortino ratios that strategies calculate and display in the Performance Summary tab use a default risk-free rate of 2%, and the metrics in this library's example code use the same default rate. Users can adjust this value to fit their analysis needs.
Risk-adjusted performance
Risk-adjusted performance metrics gauge the effectiveness of an investment by considering its returns relative to the perceived risk. They aim to provide a more well-rounded picture of performance by factoring in the level of risk taken to achieve returns. Investors can utilize such metrics to help determine whether the returns from an investment justify the risks and make informed decisions.
The two most commonly used risk-adjusted performance metrics are the Sharpe ratio and the Sortino ratio.
1. Sharpe ratio
The Sharpe ratio , developed by Nobel laureate William F. Sharpe, measures the performance of an investment compared to a theoretically risk-free asset, adjusted for the investment risk. The ratio uses the following formula:
Sharpe Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑎
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑎 = Standard deviation of the investment's returns (volatility)
A higher Sharpe ratio indicates a more favorable risk-adjusted return, as it signifies that the investment produced higher excess returns per unit of increase in total perceived risk.
2. Sortino ratio
The Sortino ratio is a modified form of the Sharpe ratio that only considers downside volatility , i.e., the volatility of returns below the theoretical risk-free benchmark. Although it shares close similarities with the Sharpe ratio, it can produce very different values, especially when the returns do not have a symmetrical distribution, since it does not penalize upside and downside volatility equally. The ratio uses the following formula:
Sortino Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑑
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑑 = Downside deviation (standard deviation of negative excess returns, or downside volatility)
The Sortino ratio offers an alternative perspective on an investment's return-generating efficiency since it does not consider upside volatility in its calculation. A higher Sortino ratio signifies that the investment produced higher excess returns per unit of increase in perceived downside risk.
█ CALCULATIONS
Return period detection
Calculating risk-adjusted performance metrics requires collecting returns across several periods of a given size. Analysts may use different period sizes based on the context and their preferences. However, two widely used standards are monthly or daily periods, depending on the available data and the investment's duration. The built-in ratios displayed in the Strategy Tester utilize returns from either monthly or daily periods in their calculations based on the following logic:
• Use monthly returns if the history of closed trades spans at least two months.
• Use daily returns if the trades span at least two days but less than two months.
• Do not calculate the ratios if the trade data spans fewer than two days.
This library's `detectPeriod()` function applies related logic to available chart data rather than trade data to determine which period is appropriate:
• It returns true if the chart's data spans at least two months, indicating that it's sufficient to use monthly periods.
• It returns false if the chart's data spans at least two days but not two months, suggesting the use of daily periods.
• It returns na if the length of the chart's data covers less than two days, signifying that the data is insufficient for meaningful ratio calculations.
It's important to note that programmers should only call `detectPeriod()` from a script's global scope or within the outermost scope of a function called from the global scope, as it requires the time value from the first bar to accurately measure the amount of time covered by the chart's data.
Collecting periodic returns
This library's `getPeriodicReturns()` function tracks price return data within monthly or daily periods and stores the periodic values in an array . It uses a `detectPeriod()` call as the condition to determine whether each element in the array represents the return over a monthly or daily period.
The `getPeriodicReturns()` function has two overloads. The first overload requires two arguments and outputs an array of monthly or daily returns for use in the `sharpe()` and `sortino()` methods. To calculate these returns:
1. The `percentChange` argument should be a series that represents percentage gains or losses. The values can be bar-to-bar return percentages on the chart timeframe or percentages requested from a higher timeframe.
2. The function compounds all non-na `percentChange` values within each monthly or daily period to calculate the period's total return percentage. When the `percentChange` represents returns from a higher timeframe, ensure the requested data includes gaps to avoid compounding redundant values.
3. After a period ends, the function queues the compounded return into the array , removing the oldest element from the array when its size exceeds the `maxPeriods` argument.
The resulting array represents the sequence of closed returns over up to `maxPeriods` months or days, depending on the available data.
The second overload of the function includes an additional `benchmark` parameter. Unlike the first overload, this version tracks and collects differences between the `percentChange` and the specified `benchmark` values. The resulting array represents the sequence of excess returns over up to `maxPeriods` months or days. Passing this array to the `sharpe()` and `sortino()` methods calculates generalized Information ratios , which represent the risk-adjustment performance of a sequence of returns compared to a risky benchmark instead of a risk-free rate. For consistency, ensure the non-na times of the `benchmark` values align with the times of the `percentChange` values.
Ratio methods
This library's `sharpe()` and `sortino()` methods respectively calculate the Sharpe and Sortino ratios based on an array of returns compared to a specified annual benchmark. Both methods adjust the annual benchmark based on the number of periods per year to suit the frequency of the returns:
• If the method call does not include a `periodsPerYear` argument, it uses `detectPeriod()` to determine whether the returns represent monthly or daily values based on the chart's history. If monthly, the method divides the `annualBenchmark` value by 12. If daily, it divides the value by 365.
• If the method call does specify a `periodsPerYear` argument, the argument's value supersedes the automatic calculation, facilitating custom benchmark adjustments, such as dividing by 252 when analyzing collected daily stock returns.
When the array passed to these methods represents a sequence of excess returns , such as the result from the second overload of `getPeriodicReturns()`, use an `annualBenchmark` value of 0 to avoid comparing those excess returns to a separate rate.
By default, these methods only calculate the ratios on the last available bar to minimize their resource usage. Users can override this behavior with the `forceCalc` parameter. When the value is true , the method calculates the ratio on each call if sufficient data is available, regardless of the bar index.
Look first. Then leap.
█ FUNCTIONS & METHODS
This library contains the following functions:
detectPeriod()
Determines whether the chart data has sufficient coverage to use monthly or daily returns
for risk metric calculations.
Returns: (bool) `true` if the period spans more than two months, `false` if it otherwise spans more
than two days, and `na` if the data is insufficient.
getPeriodicReturns(percentChange, maxPeriods)
(Overload 1 of 2) Tracks periodic return percentages and queues them into an array for ratio
calculations. The span of the chart's historical data determines whether the function uses
daily or monthly periods in its calculations. If the chart spans more than two months,
it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D"
periods. If the chart covers less than two days, it does not store changes.
Parameters:
percentChange (float) : (series float) The change percentage. The function compounds non-na values from each
chart bar within monthly or daily periods to calculate the periodic changes.
maxPeriods (simple int) : (simple int) The maximum number of periodic returns to store in the returned array.
Returns: (array) An array containing the overall percentage changes for each period, limited
to the maximum specified by `maxPeriods`.
getPeriodicReturns(percentChange, benchmark, maxPeriods)
(Overload 2 of 2) Tracks periodic excess return percentages and queues the values into an
array. The span of the chart's historical data determines whether the function uses
daily or monthly periods in its calculations. If the chart spans more than two months,
it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D"
periods. If the chart covers less than two days, it does not store changes.
Parameters:
percentChange (float) : (series float) The change percentage. The function compounds non-na values from each
chart bar within monthly or daily periods to calculate the periodic changes.
benchmark (float) : (series float) The benchmark percentage to compare against `percentChange` values.
The function compounds non-na values from each bar within monthly or
daily periods and subtracts the results from the compounded `percentChange` values to
calculate the excess returns. For consistency, ensure this series has a similar history
length to the `percentChange` with aligned non-na value times.
maxPeriods (simple int) : (simple int) The maximum number of periodic excess returns to store in the returned array.
Returns: (array) An array containing monthly or daily excess returns, limited
to the maximum specified by `maxPeriods`.
method sharpeRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)
Calculates the Sharpe ratio for an array of periodic returns.
Callable as a method or a function.
Namespace types: array
Parameters:
returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or
daily periods.
annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When
`periodsPerYear` is `na`, the function divides this value by 12 to calculate a
monthly benchmark if the chart's data spans at least two months or 365 for a daily
benchmark if the data otherwise spans at least two days. If `periodsPerYear`
has a specified value, the function divides the rate by that value instead.
forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation
only occurs on the last available bar. Optional. The default is `false`.
periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value
determined by the time span of the chart's data.
Returns: (float) The Sharpe ratio, which estimates the excess return per unit of total volatility.
method sortinoRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)
Calculates the Sortino ratio for an array of periodic returns.
Callable as a method or a function.
Namespace types: array
Parameters:
returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or
daily periods.
annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When
`periodsPerYear` is `na`, the function divides this value by 12 to calculate a
monthly benchmark if the chart's data spans at least two months or 365 for a daily
benchmark if the data otherwise spans at least two days. If `periodsPerYear`
has a specified value, the function divides the rate by that value instead.
forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation
only occurs on the last available bar. Optional. The default is `false`.
periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value
determined by the time span of the chart's data.
Returns: (float) The Sortino ratio, which estimates the excess return per unit of downside
volatility.
