ETF-Benchmark AnalyzerHave you ever wondered which ETF performs the best? Which one is the most volatile, or which one has the smallest drawdown?
This Pine Script™ "ETF-Benchmark Analyzer" compares the performance of an ETF (such as SPY, the S&P 500 ETF) against a benchmark, which can also be adjusted by the user. It provides several key financial metrics, such as:
Performance (%): Displays the total return over a specified lookback period (e.g., 1 year). It compares the performance of the ETF against the benchmark and shows the difference.
Alpha (%): Measures the excess return of the ETF over the expected return, which is calculated using the benchmark’s return. Positive alpha indicates that the ETF has outperformed the benchmark, while negative alpha suggests underperformance. This metric is important because it isolates performance that cannot be explained by exposure to the benchmark's movements.
Sharpe Ratio: A risk-adjusted measure of return. It is calculated by dividing the excess return of the ETF (above the risk-free rate) by its standard deviation (volatility). A higher Sharpe ratio indicates better risk-adjusted returns. The Sharpe ratio is calculated for both the ETF and the benchmark, and their difference is displayed as well.
Drawdown: The percentage decrease from the highest price to the lowest price over the lookback period. This is a critical measure of risk, as it shows the largest potential loss an investor might face during a specific period.
Beta: Measures the ETF’s sensitivity to movements in the benchmark. A beta of 1 means the ETF moves in line with the benchmark; greater than 1 means it is more volatile, while less than 1 means it is less volatile.
These metrics provide a holistic view of the ETF’s performance compared to the benchmark, allowing traders to assess the risk and return profile more effectively.
Scientific Sources
Sharpe Ratio: Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49-58. This paper defines and develops the Sharpe ratio as a measure of risk-adjusted return.
Alpha and Beta: Jensen, M. C. (1968). The Performance of Mutual Funds in the Period 1945–1964. The Journal of Finance, 23(2), 389-416. This paper discusses the concepts of alpha and beta in the context of mutual fund performance.
在腳本中搜尋"摩根标普500指数基金的收益如何"
M2 Money Shift for Bitcoin [SAKANE]M2 Money Shift for Bitcoin was developed to visualize the impact of M2 Money, a macroeconomic indicator, on the Bitcoin market and to support trade analysis.
Bitcoin price fluctuations have a certain correlation with cycles in M2 money supply.In particular, it has been noted that changes in M2 supply can affect the bitcoin price 70 days in advance.Very high correlations have been observed in recent years in particular, making it useful as a supplemental analytical tool for trading.
Support for M2 data from multiple countries
M2 supply data from the U.S., Europe, China, Japan, the U.K., Canada, Australia, and India are integrated and all are displayed in U.S. dollar equivalents.
Slide function
Using the "Slide Days Forward" setting, M2 data can be slid up to 500 days, allowing for flexible analysis that takes into account the time difference from the bitcoin price.
Plotting Total Liquidity
Plot total liquidity (in trillions of dollars) by summing the M2 supply of multiple countries.
How to use
After applying the indicator to the chart, activate the M2 data for the required country from the settings screen. 2.
2. adjust "Slide Days Forward" to analyze the relationship between changes in M2 supply and bitcoin price
3. refer to the Gross Liquidity plot to build a trading strategy that takes into account macroeconomic influences.
Notes.
This indicator is an auxiliary tool for trade analysis and does not guarantee future price trends.
The relationship between M2 supply and bitcoin price depends on many factors and should be used in conjunction with other analysis methods.
Ultra Liquidity HeatmapThe Ultra Liquditiy Heatmap is a unique visualization tool designed to map out areas of high liquidity on the chart using a dynamic heatmap, helping traders identify significant price zones effectively.
Introduction
The Ultra Liquidity Heatmap is an advanced indicator for visualizing key liquidity areas on your chart. Whether you're a scalper, swing trader, or long-term investor, understanding liquidity dynamics can offer a powerful edge in market analysis. This tool provides a straightforward visual representation of these zones directly on your chart.
Detailed Description
The Ultra Liquidity Heatmap identifies high and low liquidity zones by dynamically marking price ranges with heatmap-like boxes.
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Dynamic Zone Creation
For low liquidity zones, the script draws boxes extending from the low to the high of the bar. If the price breaks below a previously defined zone, that box is removed.
Similarly, for high liquidity zones, the script tracks and highlights price ranges above the current high, removing boxes if the price exceeds the zone.
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Customizable Visuals
Users can adjust the transparency and color of the heatmap, tailoring the visualization to their preference.
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Real-Time Updates
The indicator constantly updates as new price data comes in, ensuring that the heatmap reflects the most current liquidity zones.
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Efficiency and Scalability
The script uses optimized arrays and a maximum box limit of 500 to ensure smooth performance even on higher timeframes or during high-volatility periods.
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The Ultra Liquidity Heatmap bridges the gap between raw price data and actionable market insight. Add it to your toolbox and elevate your trading strategy today!
COT Report Indicator with Selectable Data TypeOverview
The COT Report Indicator with Selectable Data Types is a powerful tool for traders who want to gain deeper insights into market sentiment using the Commitment of Traders (COT) data. This indicator allows you to visualize the net positions of different participant categories—Commercial, Noncommercial, and Nonreportable—directly on your chart.
The indicator is fully customizable, allowing you to select the type of data to display, sync with your chart's timeframe, or choose a custom timeframe. Whether you're analyzing gold, crude oil, indices, or forex pairs, this indicator adapts seamlessly to your trading needs.
Features
Dynamic Data Selection:
Choose between Commercial, Noncommercial, or Nonreportable data types.
Analyze the net positions of market participants for more informed decision-making.
Flexible Timeframes:
Sync with the chart's timeframe for quick analysis.
Select a custom timeframe to view COT data at your preferred granularity.
Wide Asset Coverage:
Supports various assets, including gold, silver, crude oil, indices, and forex pairs.
Automatically adjusts to the ticker you're analyzing.
Clear Visual Representation:
Displays Net Long, Net Short, and Net Difference (Long - Short) positions with distinct colors for easy interpretation.
Error Handling:
Alerts you if the symbol is unsupported, ensuring you know when COT data isn't available for a specific asset.
How to Use
Add the Indicator:
Click "Indicators" in TradingView and search for "COT Report Indicator with Selectable Data Types."
Add it to your chart.
Customize the Settings:
Data Type: Choose between Commercial, Noncommercial, or Nonreportable positions.
Data Source: Select "Futures Only" or "Futures and Options."
Timeframe: Sync with the chart's timeframe or specify a custom one (e.g., weekly, monthly).
Interpret the Data:
Green Line: Net Long Positions.
Red Line: Net Short Positions.
Black Line: Net Difference (Long - Short).
Supported Symbols:
Gold, Silver, Crude Oil, Natural Gas, Forex Pairs, S&P 500, US30, NAS100, and more.
Who Can Benefit
Trend Followers: Identify the buying/selling trends of Commercial and Noncommercial participants.
Sentiment Analysts: Understand shifts in sentiment among major market players.
Long-Term Traders: Use COT data to confirm or contradict your fundamental analysis.
Example Use Case
For example, if you're trading gold (XAUUSD) and select Noncommercial Positions, you’ll see the long and short positions of speculators. An increase in net long positions may signal bullish sentiment, while an increase in net short positions may indicate bearish sentiment.
If you switch to Commercial Positions, you'll get insights into how hedgers and institutions are positioning themselves, helping you confirm or counterbalance your current trading strategy.
Limitations
The indicator only works with supported symbols (COT data availability is limited to specific assets).
The COT data is updated weekly, so it is not suitable for short-term intraday trading.
RS Cycles [QuantVue]The RS Cycles indicator is a technical analysis tool that expands upon traditional relative strength (RS) by incorporating Beta-based adjustments to provide deeper insights into a stock's performance relative to a benchmark index. It identifies and visualizes positive and negative performance cycles, helping traders analyze trends and make informed decisions.
Key Concepts:
Traditional Relative Strength (RS):
Definition: A popular method to compare the performance of a stock against a benchmark index (e.g., S&P 500).
Calculation: The traditional RS line is derived as the ratio of the stock's closing price to the benchmark's closing price.
RS=Stock Price/Benchmark Price
Usage: This straightforward comparison helps traders spot periods of outperformance or underperformance relative to the market or a specific sector.
Beta-Adjusted Relative Strength (Beta RS):
Concept: Traditional RS assumes equal volatility between the stock and benchmark, but Beta RS accounts for the stock's sensitivity to market movements.
Calculation:
Beta measures the stock's return relative to the benchmark's return, adjusted by their respective volatilities.
Alpha is then computed to reflect the stock's performance above or below what Beta predicts:
Alpha=Stock Return−(Benchmark Return×β)
Significance: Beta RS highlights whether a stock outperforms the benchmark beyond what its Beta would suggest, providing a more nuanced view of relative strength.
RS Cycles:
The indicator identifies positive cycles when conditions suggest sustained outperformance:
Short-term EMA (3) > Mid-term EMA (10) > Long-term EMA (50).
The EMAs are rising, indicating positive momentum.
RS line shows upward movement over a 3-period window.
EMA(21) > 0 confirms a broader uptrend.
Negative cycles are marked when the opposite conditions are met:
Short-term EMA (3) < Mid-term EMA (10) < Long-term EMA (50).
The EMAs are falling, indicating negative momentum.
RS line shows downward movement over a 3-period window.
EMA(21) < 0 confirms a broader downtrend.
This indicator combines the simplicity of traditional RS with the analytical depth of Beta RS, making highlighting true relative strength and weakness cycles.
IV Rank/Percentile with Williams VIX FixDisplay IV Rank / IV Percentile
This indicator is based on William's VixFix, which replicates the VIX—a measure of the implied volatility of the S&P 500 Index (SPX). The key advantage of the VixFix is that it can be applied to any security, not just the SPX.
IV Rank is calculated by identifying the highest and lowest implied volatility (IV) values over a selected number of past periods. It then determines where the current IV lies as a percentage between these two extremes. For example, if over the past five periods the highest IV was 30%, the lowest was 10%, and the current IV is 20%, the IV Rank would be 50%, since 20% is halfway between 10% and 30%.
IV Percentile, on the other hand, considers all past IV values—not just the highest and lowest—and calculates the percentage of these values that are below the current IV. For instance, if the past five IV values were 30%, 10%, 11%, 15%, and 17%, and the current IV is 20%, the IV Rank remains at 50%. However, the IV Percentile is 80% because 4 out of the 5 past values (80%) are below the current IV of 20%.
Stock vs Sector Comparison with HighlightsThis graph is meant as a support to select a stock that is expected to perform better than the sector.
The graph is based on weekly chart. So this is a medium / long term strategy.
How is expected to be used: when the stock has under performed the sector for some time, there is a natural tendence that it will catch up with the sector again. So, for example, if the color change from green to red, you should consider find another stock in the sector. If the stock looses the green color, but is not red yet, you should wait. And vice versa if you start with red. However, life is not that simple, as you can get fake signal. To mitigate this problem, you can adjust the threshold in the input setting, so just go for the signal after x weeks over/underperforming. You also need remember to select the sector in the settings, as the sector is not give automatically when you select the stock.
Below the sectors used:
Sector Name Ticker
S&P 500 (Market Index) SPY
Technology XLK
Financials XLF
Consumer Discretionary XLY
Industrials XLI
Health Care XLV
Consumer Staples XLP
Energy XLE
Utilities XLU
Communication Services XLC
Real Estate XLRE
Materials XLB
Marcel's Dynamic Profit / Loss Calculator for GoldOverview
This Dynamic Risk / Reward Tool for Gold is designed to help traders efficiently plan and manage their trades in the volatile gold market. This script provides a clear visualisation of trade levels (Entry, Stop Loss, Take Profit) while dynamically calculating potential profit and loss. It ensures gold traders can assess their positions with precision, saving time and improving risk management.
Key Features
1. Trade Level Visualisation:
Plots Entry (Blue), Stop Loss (Red), and Take Profit (Green) lines directly on the chart.
Helps you visualise and confirm trade setups quickly which is good for scalping and day trades.
2. Dynamic Risk and Reward Calculations:
Calculates potential profit and loss in real time based on user-defined inputs such as position size, leverage, and account equity.
Displays a summary panel showing risk/reward metrics directly on the chart.
3. Customisable Settings:
Allows you to adjust key parameters like account equity, position size, leverage, and specific price levels for Entry, Stop Loss, and Take Profit.
Defaults are dynamically generated for convenience but remain fully adjustable for flexibility.
How It Works
The script uses gold-specific conventions (e.g., 1 lot = 100 ounces, 1 pip = 0.01 price change) to calculate accurate risk and reward metrics.
It dynamically positions Stop Loss and Take Profit levels relative to the entry price, based on user-defined or default offsets.
A real-time summary panel is displayed in the bottom-right corner of the chart, showing:
Potential Profit: The monetary value if the Take Profit is hit.
Potential Lo
ss: The monetary value if the Stop Loss is hit.
How to Use It
1. Add the script to your chart on a gold trading pair (e.g., XAUUSD).
2. Input your:
Account equity.
Leverage.
Position size (in lots).
Desired En
try Price (default: current close price).
3. Adjust the Stop Loss and Take Profit levels to your strategy, or let the script use default offsets of:
500 pips below the Entry for Stop Loss.
1000 pips above the Entry for Take Profit.
4. Review the plotted levels and the summary panel to confirm your trade aligns with your risk/reward goals.
Why Use This Tool?
Clarity and Precision:
Provides clear trade visuals and financial metrics for confident decision-making.
Time-Saving:
Automates the calculations needed to evaluate trade risk and reward.
Improved Risk Management:
Ensures you never trade without knowing your exact potential loss and gain.
This script is particularly useful for both novice and experienced traders looking to enhance their risk management and trading discipline in the Gold market. Enjoy clearer trades at speed.
PSP Indicator [Elbaz]Precision Swing Point or PSP is a unique technical analysis tool designed to compare the price action of three tickers that are in sync.
It highlights moments when the price structure diverges between the markets, identifying ideal entry points for trades - We would like to enter a trade when we found PSP and one of the tickers took the wick while others didn't.
This strategy provides an edge by focusing on periods of desynchronization between the indices, where one index may be showing strength while another is lagging. The idea is to find the moments where the candle colors (bullish or bearish) differ across the markets, then wait for one of the tickers to "take" the wick of the PSP while other didn't and enter a trade.
Once a divergence is detected, the indicator plots an arrow on the chart, signaling a potential trade entry. To minimize risk, a good place to put stop loss will at the end of the wick of the PSP — the high or low wick of the candle where the divergence occurs.
The PSP Indicator allows for several custom inputs:
- Tickers: Customize the tickers to compare. The default values are S&P 500 E-mini, NASDAQ E-mini, and Dow Jones E-mini, if you trade Crypto you might want to use BTC, ETH, TOTAL3.
- Lookback Period: The lookback input defines how far back the indicator should evaluate to calculate the price structure point.
- Highlight Bar Times: Users can specify particular times during the trading day to highlight, such as the market open or significant news events. This helps traders focus on key trading windows.
RS Theory IndicatorHow to Use:
Customize the Reference Symbol: In the settings of the indicator, you can change the referenceSymbol to the benchmark or asset you want to compare against.
RS Interpretation:
RS > 1: The current asset is outperforming the reference symbol.
RS < 1: The current asset is underperforming the reference symbol.
RS = 1: The current asset and the reference symbol are performing equally.
Alerts: You can enable alerts for when the RS crosses certain levels (e.g., when RS > 1 or RS < 1).
How It Works:
Reference Symbol: The user inputs the benchmark asset or symbol (e.g., "SPY" for an S&P 500 ETF). This will be used as the comparison symbol.
RS Calculation: The RS Value is calculated by dividing the current asset's close price by the reference asset's close price:
RS
=
Close Price of Current Asset
Close Price of Reference Asset
RS=
Close Price of Reference Asset
Close Price of Current Asset
Plotting:
The RS value is plotted on the chart as a line.
A horizontal line at RS = 1 is drawn for easy comparison, representing parity (when the asset and reference symbol have the same price).
Background Coloring: The background is colored:
Green when RS > 1 (indicating the asset is outperforming the benchmark).
Red when RS < 1 (indicating the asset is underperforming the benchmark).
Alerts: Alerts are triggered when the RS value is above or below 1, indicating outperformance or underperformance relative to the benchmark.
ICT Candle Block (fadi)ICT Candle Block
When trading using ICT concepts, it is often beneficial to treat consecutive candles of the same color as a single entity. This approach helps traders identify Order Blocks, liquidity voids, and other key trading signals more effectively.
However, in situations where the market becomes choppy or moves slowly, recognizing continuous price movement can be challenging.
The ICT Candle Block indicator addresses these challenges by combining consecutive candles of the same color into a single entity. It redraws the resulting candles, making price visualization much easier and helping traders quickly identify key trading signals.
FVGs and Blocks
In the above snapshot, FVGs/Liquidity Voids, Order Blocks, and Breaker Blocks are easily identified. By analyzing the combined candles, traders can quickly determine the draw on liquidity and potential price targets using ICT concepts.
Unlike traditional higher timeframes that rigidly combine lower timeframe candles based on specific start and stop times, this indicator operates as a "mixed timeframe." It combines all buying and all selling activities into a single candle, regardless of when the transactions started and ended.
Limitations
There are currently TradingView limitations that affect the functionality of this indicator:
TradingView does not have a Candle object; therefore, this indicator relies on using boxes and lines to mimic the candles. This results in wider candles than expected, leading to misalignment with the time axis below (plotcandle is not the answer).
There is a limit on the number of objects that can be drawn on a chart. A maximum of 500 candles has been set.
A rendering issue may cause a sideways box to appear across the chart. This is a display bug in TradingView; scroll to the left until it clears.
Buy When There's Blood in the Streets StrategyStatistical Analysis of Drawdowns in Stock Markets
Drawdowns, defined as the decline from a peak to a trough in asset prices, are an essential measure of risk and market dynamics. Their statistical properties provide insights into market behavior during extreme stress periods.
Distribution of Drawdowns: Research suggests that drawdowns follow a power-law distribution, implying that large drawdowns, while rare, are more frequent than expected under normal distributions (Sornette et al., 2003).
Impacts of Extreme Drawdowns: During significant drawdowns (e.g., financial crises), the average recovery time is significantly longer, highlighting market inefficiencies and behavioral biases. For example, the 2008 financial crisis led to a 57% drawdown in the S&P 500, requiring years to recover (Cont, 2001).
Using Standard Deviations: Drawdowns exceeding two or three standard deviations from their historical mean are often indicative of market overreaction or capitulation, creating contrarian investment opportunities (Taleb, 2007).
Behavioral Finance Perspective: Investors often exhibit panic-selling during drawdowns, leading to oversold conditions that can be exploited using statistical thresholds like standard deviations (Kahneman, 2011).
Practical Implications: Studies on mean reversion show that extreme drawdowns are frequently followed by periods of recovery, especially in equity markets. This underpins strategies that "buy the dip" under specific, statistically derived conditions (Jegadeesh & Titman, 1993).
References:
Sornette, D., & Johansen, A. (2003). Stock market crashes and endogenous dynamics.
Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance.
Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable.
Kahneman, D. (2011). Thinking, Fast and Slow.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.
SPX Open vs SMA AlertThis indicator is specifically designed to identify the first market-relevant candle of the S&P 500 (SPX) after the market opens. The opening price of the trading day is compared to a customizable simple moving average (SMA) period. A visual marker and an alert are triggered when the opening price is above the SMA. Perfect for traders seeking early market trends or integrating automated trading strategies.
Features:
Market Open: The indicator uses the New York market open time (09:30 ET), accounting for time zones and daylight saving time changes.
Flexible Time Offset: Users can set a time offset to trigger alerts after the market opens.
Customizable SMA: The SMA period is adjustable, with a default value of 10.
Visual Representation: A step-line SMA is plotted directly on the chart with subtle transparency and clean markers.
Alert Functionality: Alerts are triggered when conditions are met (opening price > SMA).
Usage:
This indicator is ideal for identifying relevant trading signals early in the session.
Alerts can also serve as triggers for automated trading, e.g., in conjunction with the Trading Automation Toolbox.
Supports both intraday and daily charts.
Alarm Settings:
Select the appropriate symbol (e.g., SPX) and the alert condition "SPX Open > SMA10".
Trigger Settings:
Choose "Once Per Bar Close" to ensure the condition is evaluated at the end of each candle.
If you prefer to evaluate the condition immediately when it becomes true, choose "Once Per Minute".
Duration:
Set the alarm to "Open-ended" if you want it to remain active indefinitely.
Alternatively, set a specific expiration date for the alarm.
RHR_CANDLELibrary "RHR_CANDLE"
Library for Expansion Contraction Indicator, a zero-lag dual perspective indicator based on Jake Bernstein’s principles of Moving Average Channel system.
calc(shortLookback, longLookback)
Calculates Expansion Contraction values.
Parameters:
shortLookback (int) : Integer for the short lookback calculation, defaults to 8
longLookback (int) : Integer for the long lookback calculation, defaults to 32
@return Returns array of Expansion Contraction values
stdevCalc(positiveShort, negativeShort, positiveLong, negativeLong, stdevLookback)
Calculates standard deviation lines based on Expansion Contraction Long and Short values.
Parameters:
positiveShort (float) : Float for the positive short XC value from calculation
negativeShort (float) : Float for the negative short XC value from calculation
positiveLong (float) : Float for the positive long XC value from calculation
negativeLong (float) : Float for the negative long XC value from calculation
stdevLookback (int) : Integer for the standard deviation lookback, defaults to 500
@return Returns array of standard deviation values
trend(positiveShort, negativeShort, positiveLong, negativeLong)
Determines if trend is strong or weak based on Expansion Contraction values.
Parameters:
positiveShort (float) : Float for the positive short XC value from calculation
negativeShort (float) : Float for the negative short XC value from calculation
positiveLong (float) : Float for the positive long XC value from calculation
negativeLong (float) : Float for the negative long XC value from calculation
@return Returns array of boolean values indicating strength or weakness of trend
Nasan Hull-smoothed envelope The Nasan Hull-Smoothed Envelope indicator is a sophisticated overlay designed to track price movement within an adaptive "envelope." It dynamically adjusts to market volatility and trend strength, using a series of smoothing and volatility-correction techniques. Here's a detailed breakdown of its components, from the input settings to the calculated visual elements:
Inputs
look_back_length (500):
Defines the lookback period for calculating intraday volatility (IDV), smoothing it over time. A higher value means the indicator considers a longer historical range for volatility calculations.
sl (50):
Sets the smoothing length for the Hull Moving Average (HMA). The HMA smooths various lines, creating a balance between sensitivity and stability in trend signals.
mp (1.5):
Multiplier for IDV, scaling the volatility impact on the envelope. A higher multiplier widens the envelope to accommodate higher volatility, while a lower one tightens it.
p (0.625):
Weight factor that determines the balance between extremes (highest high and lowest low) and averages (sma of high and sma of low) in the high/low calculations. A higher p gives more weight to extremes, making the envelope more responsive to abrupt market changes.
Volatility Calculation (IDV)
The Intraday Volatility (IDV) metric represents the average volatility per bar as an exponentially smoothed ratio of the high-low range to the close price. This is calculated over the look_back_length period, providing a base volatility value which is then scaled by mp. The IDV enables the envelope to dynamically widen or narrow with market volatility, making it sensitive to current market conditions.
Composite High and Low Bands
The high and low bands define the upper and lower bounds of the envelope.
High Calculation
a_high:
Uses a multi-period approach to capture the highest highs over several intervals (5, 8, 13, 21, and 34 bars). Averaging these highs provides a more stable reference for the high end of the envelope, capturing both immediate and recent peak values.
b_high:
Computes the average of shorter simple moving averages (5, 8, and 13 bars) of the high prices, smoothing out fluctuations in the recent highs. This generates a balanced view of high price trends.
high_c:
Combines a_high and b_high using the weight p. This blend creates a composite high that balances between recent peaks and smoothed averages, making the upper envelope boundary adaptive to short-term price shifts.
Low Calculation
a_low and b_low:
Similar to the high calculation, these capture extreme lows and smooth low values over the same intervals. This approach creates a stable and adaptive lower bound for the envelope.
low_c:
Combines a_low and b_low using the weight p, resulting in a composite low that adjusts to price fluctuations while maintaining a stable trend line.
Volatility-Adjusted Bands
The final composite high (c_high) and composite low (c_low) bands are adjusted using IDV, which accounts for intraday volatility. When volatility is high, the bands expand; when it’s low, they contract, providing a visual representation of volatility-adjusted price bounds.
Basis Line
The basis line is a Hull Moving Average (HMA) of the average of c_high and c_low. The HMA is known for its smoothness and responsiveness, making the basis line a central trend indicator. The color of the basis line changes:
Green when the basis line is increasing.
Red when the basis line is decreasing.
This color-coded basis line serves as a quick visual reference for trend direction.
Short-Term Trend Strength Block
This component analyzes recent price action to assess short-term bullish and bearish momentum.
Conditions (green, red, green1, red1):
These are binary conditions that categorize price movements as bullish or bearish based on the close compared to the open and the close’s relationship with the exponential moving average (EMA). This separation helps capture different types of strength (above/below EMA) and different bullish or bearish patterns.
Composite Trend Strength Values:
Each of the bullish and bearish counts (above and below the EMA) is normalized, resulting in the following values:
green_EMAup_a and red_EMAup_a for bullish and bearish strength above the EMA.
green_EMAdown_a and red_EMAdown_a for bullish and bearish strength below the EMA.
Trend Strength (t_s):
This calculated metric combines the normalized trend strengths with extra weight to conditions above the EMA, giving more relevance to trends that have momentum behind them.
Enhanced Trend Strength
avg_movement:
Calculates the average absolute price movement over the short_term_length, providing a measurement of recent price activity that scales with volatility.
enhanced_t_s:
Multiplies t_s by avg_movement, creating an enhanced trend strength value that reflects both directional strength and the magnitude of recent price movement.
min and max:
Minimum and maximum percentile thresholds, respectively, based on enhanced_t_s for controlling the color gradient in the fill area.
Fill Area
The fill area between plot_c_high and plot_c_low is color-coded based on the enhanced trend strength (enhanced_t_s):
Gradient color transitions from blue to green based on the strength level, with blue representing weaker trends and green indicating stronger trends.
This visual fill provides an at-a-glance assessment of trend strength across the envelope, with color shifts highlighting momentum shifts.
Summary
The indicator’s purpose is to offer an adaptive price envelope that reflects real-time market volatility and trend strength. Here’s what each component contributes:
Basis Line: A trend-following line in the center that adjusts color based on trend direction.
Envelope (c_high, c_low): Adapts to volatility by expanding and contracting based on IDV, giving traders a responsive view of expected price bounds.
Fill Area: A color-gradient region representing trend strength within the envelope, helping traders easily identify momentum changes.
Overall, this tool helps to identify trend direction, market volatility, and strength of price movements, allowing for more informed decisions based on visual cues around price boundaries and trend momentum.
Stablecoin Dominance Oscillator
The SDO is a normalized oscillator that tracks the relationship between stablecoin market capitalization (USDT + USDC + DAI) and total crypto market capitalization. It helps identify periods where stablecoins represent an unusually high or low portion of the total crypto market value.
Key components:
Main Signal (Blue Line):
Shows the normalized deviation of stablecoin dominance from its trend. Higher values indicate higher stablecoin dominance relative to history (which often corresponds with market bottoms/fear), while lower values indicate lower stablecoin dominance (often seen during strong bull markets/greed).
Dynamic Bands (Gray):
These adapt to market volatility, expanding during volatile periods and contracting during stable periods
Generally suggest temporary boundaries for the oscillator
Volatility Reference (Purple Line):
Shows the ratio between short-term and long-term volatility
Higher values indicate more volatile market conditions
Helps contextualize the reliability of the current signal
The indicator uses a 500-period lookback for baseline calculations and a 15-period Hull Moving Average for smoothing, making it responsive while filtering out noise. The final signal is normalized and volatility-adjusted to maintain consistent readings across different market regimes.
Dynamic Open Levels# Dynamic Open Levels Indicator v1.0
Release Date: November 5, 2024
Introducing the Dynamic Open Levels indicator on TradingView! This tool helps traders visualize and analyze key opening price levels across multiple timeframes, making your market analysis more effective.
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### Key Features
- Multiple Timeframes : Yearly, Quarterly, Monthly, Weekly, Daily, 4H, and 1H levels available.
- Visibility Controls : Easily toggle visibility for each timeframe to suit your trading style.
- Line Customization : Set custom thickness and colors for lines, making charts easy to interpret.
- Monthly: Purple
- Weekly: Blue
- Daily: Green
- 4H: Red
- 1H: Orange
- Dynamic Coloring : Lines adjust color based on market conditions—teal for bullish (`rgb(34, 171, 148)`) and coral for bearish (`rgb(247, 82, 95)`).
### Labels & Customization
- Real-Time Labels : Each level is labeled for easy identification (e.g., Y for Yearly, Q for Quarterly).
- Label Settings : Customize opacity, text color, size, and position for clarity without cluttering your chart.
- Sizes : Choose from tiny, small, normal, large, to huge.
- Offset : Set labels from 1 to 10 to position them precisely.
- Color Management : Organize all colors under a dedicated Line Colors group for easy adjustments.
### Advanced Plotting & Performance
- Real-Time Updates : Levels are updated dynamically with the latest open prices.
- Extended Lines : Lines extend to the right, offering a consistent reference for future price movement.
- Optimized Performance : Handles up to 500 lines efficiently to maintain smooth performance.
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### Installation Instructions
1. Add to Chart :
- Go to the Indicators section in TradingView.
- Search for Dynamic Open Levels and add it to your chart.
2. Customize Settings :
- Line Thickness : Adjust to suit your preference.
- Visibility : Toggle timeframes like Yearly, Monthly, Weekly, etc., as needed.
- Labels : Configure opacity, text color, size, and offset under the Label Settings group.
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### Documentation & Support
For guidance on using the Dynamic Open Levels indicator, visit our Documentation (#). If you need assistance, check out our Support Channel (#).
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Thank you for choosing Dynamic Open Levels . Stay tuned for future updates that will continue to improve your trading experience!
H A Z E D
XRP Comparative Price Action Indicator - Final VersionXRP Comparative Price Action Indicator - Final Version
The XRP Comparative Price Action Indicator provides a comprehensive visual analysis of XRP’s price movements relative to key cryptocurrencies and market indices. This indicator normalises price data across various assets, allowing traders and investors to assess XRP’s performance against its peers and major market influences at a glance.
Key Features:
• Normalised Price Data: Prices are scaled between 0.00 and 1.00,
enabling straightforward comparisons between different assets.
• Key Comparisons: Includes normalised prices for:
• XRP/USD (Bitstamp)
• XRP Dominance (CryptoCap)
• XRP/BTC (Bitstamp)
• BTC/USD (Bitstamp)
• BTC Dominance (CryptoCap)
• USDT Dominance (CryptoCap)
• S&P 500 (SPY)
• DXY (Dollar Index)
• ETH/USD (Bitstamp)
• ETH Dominance (CryptoCap)
• XRP/ETH (Binance)
• Visual Clarity: Each asset is plotted with distinct colors for easy identification,
with thicker lines enhancing visibility on the chart.
• Reference Lines: Optional horizontal lines indicate the minimum (0) and maximum (1) normalised values, providing clear reference points for analysis.
This indicator is ideal for traders looking to understand XRP’s relative performance, gauge market sentiment, and make informed trading decisions based on comparative price action.
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
Market Phases [OmegaTools]The Market Phases indicator utilizes the Detrended Price Oscillator (DPO) to assess various asset classes, bonds, or stock sectors across different market phases. It offers users the ability to monitor and compare trends in multiple markets through a normalized DPO approach, providing insights into relative overbought or oversold conditions. The indicator supports three distinct modes: "Asset Classes," "Bonds," and "Stock Sectors," allowing flexibility in market analysis based on user preference.
Key Features:
Detrended Price Oscillator (DPO) Calculation: The DPO is computed to remove longer-term trends and focus on shorter-term cyclical behavior. The indicator applies normalization using linear interpolation to smooth out the values for better comparison across different markets.
Three Analysis Modes:
Asset Classes: Compares the DPO for major asset classes, including stocks (S&P 500), bonds (US 10-Year), commodities (Gold), and the US Dollar Index (DXY).
Bonds: Analyzes the DPO across various bond categories such as investment-grade bonds (LQD), high-yield bonds (HYG), emerging market bonds (EMB), and corporate bonds.
Stock Sectors: Provides insight into key stock sectors, including Technology (XLK), Utilities (XLU), Financials (XLF), and Healthcare (XLV).
Real-Time Plotting:
The indicator plots the DPO values of the selected assets, bonds, or sectors on the chart. It provides a visual representation of the market phases, helping to identify potential market reversals or trends. Each plot is color-coded for clarity:
Blue: Asset/Sector 1
Red: Asset/Sector 2
Green: Asset/Sector 3
Orange: Asset/Sector 4
Table Display:
A dynamic table is displayed on the chart, showing the DPO values for the selected mode's assets or sectors. This allows quick comparison and evaluation of market trends.
Inputs:
DPO Length: Defines the lookback period for DPO calculation, adjustable between 10 and 500.
Normalization Length: Sets the length for normalizing the DPO values, with options ranging from 100 to 2000.
Mode: Choose between "Asset Classes," "Bonds," or "Stock Sectors" for tailored market analysis.
This tool is perfect for traders seeking to identify cyclical market phases, compare different asset classes, or monitor sector rotation dynamics. Use it to align your trading strategies with broader market trends and uncover potential trading opportunities across multiple markets.
SMT Divergences [OutOfOptions]Smart Money Technique (SMT) Divergence is designed to identify discrepancies between correlated assets within the same timeframe. It occurs when two related assets exhibit opposing signals, such as one forming a higher low while the other forms a lower low. This technique is particularly useful for anticipating market shifts or reversals before they become evident through other Premium Discount (PD) Arrays.
This indicator works by identifying the highs and lows that have formed for an asset on the current chart and the correlated symbol defined in the settings. Once a pivot on either asset is formed, it checks if the pivot has taken liquidity as identified by the previous pivot in the same direction (i.e., a new high taking out a previous high). If this is the case and the corresponding asset has not taken a similar pivot, the condition is determined to be a potential valid divergence. The indicator will then filter out SMTs formed by adjacent candles, requiring at least one candle difference between the candles forming the SMT.
If the “Candle Direction Validation” setting is enabled, the indicator will further check both assets to ensure that for bullish SMTs, the last high on both assets was formed by down candle, and for bearish SMTs, the low was formed by an up candle. This check can often eliminate low-probability SMTs that are frequently broken.
The referenced chart shows divergence between Nasdaq (NQ) and S&P 500 (ES) futures, which are normally closely correlated assets that move in the same direction. The lines shown represent bullish and bearish divergences between the two when they are formed. As you can see from the chart, SMT Divergences may not always indicate a reversal, or a reversal might be just a short-term retrace. Therefore, SMT Divergences should not be used independently. However, in conjunction with other PD arrays, they can provide strong confirmation of a change in market direction.
Configurability:
Pivot strength - Indicates how many bars to the left/right of a high for pivot to be considered, recommended to keep at 1 for maximum detection speed
Candle Direction Validation - Additional SMT validation to filter out weak/low-probability SMTs be examining candle direction
Line Styling for Bullish/Bearish SMTs - Ability to customize line style, color & width for bullish/bearish SMTs
Label Control - Whether or not to show SMT label and if shown what font size & color should be used
What makes this indicator different:
Unlike other SMT indicators, this indicators has additional built-in controls to remove low-probability SMTs
Financial Crisis Predictor - Doomsday ClockThe **Financial Crisis Predictor - Doomsday Clock** is a composite indicator that evaluates multiple market conditions to determine financial risk levels. It combines four key metrics: market volatility (via VIX), yield curve spread, stock market momentum, and credit risk (via high-yield spread). Each metric contributes to a weighted "risk score," scaled between 0 and 100, which helps gauge the probability of a financial crisis. Here's a breakdown of how it works:
### 1. **Market Volatility (VIX)**
- **How it's measured:**
- Uses the VIX index, which represents expected market volatility.
- Applies two exponential moving averages (EMAs) to smooth out the data—one fast and one slow.
- Triggers a signal if the fast EMA crosses above the slow EMA and VIX exceeds a defined threshold (default is 30).
- **Weighting:**
- Contributes up to 35% of the total risk score when active.
### 2. **Yield Curve Spread**
- **How it's measured:**
- Takes the difference between the yields of 10-year and 2-year U.S. Treasury bonds (inversion indicates recession risk).
- If the spread drops below a certain threshold (default is 0.2), it signals a potential recession.
- **Weighting:**
- Contributes up to 25% of the risk score.
### 3. **Stock Market Momentum**
- **How it's measured:**
- Analyzes the S&P 500 (SPY) using a 20-day EMA for price momentum.
- Checks for a cross under the 20-day EMA and if the 5-day rate of change (ROC) is less than -2.
- This combination signals bearish market momentum.
- **Weighting:**
- Contributes up to 20% of the risk score.
### 4. **Credit Risk (High Yield Spread)**
- **How it's measured:**
- Assesses high-yield corporate bond spreads using EMAs, similar to the VIX logic.
- A crossover of the fast EMA above the slow EMA combined with spreads exceeding a defined threshold (default is 5.0) indicates increased credit risk.
- **Weighting:**
- Contributes up to 20% of the total risk score.
### 5. **Risk Score Calculation**
- The final **risk score** ranges from 0 to 100 and is calculated using the weighted sum of the four indicators.
- The score is smoothed to minimize false signals and maintain stability.
### 6. **Risk Zones**
- **Extreme Risk:** If the risk score is ≥ 75, indicating a severe crisis warning.
- **High Risk:** If the risk score is between 15 and 75, signaling heightened risk.
- **Moderate Risk:** If the risk score is between 10 and 15, representing potential concerns.
- **Low Risk:** If the risk score is < 10, suggesting stable conditions.
### 7. **Visual & Alerts**
- The indicator plots the risk score on a chart with color-coded backgrounds to indicate risk levels: green (low), yellow (moderate), orange (high), and red (extreme).
- Alert conditions are set for each risk zone, notifying users when the risk level transitions into a higher zone.
This indicator aims to quickly detect potential financial crises by aggregating signals from key market factors, making it a versatile tool for traders, analysts, and risk managers.
Williams %R StrategyThe Williams %R Strategy implemented in Pine Script™ is a trading system based on the Williams %R momentum oscillator. The Williams %R indicator, developed by Larry Williams in 1973, is designed to identify overbought and oversold conditions in a market, helping traders time their entries and exits effectively (Williams, 1979). This particular strategy aims to capitalize on short-term price reversals in the S&P 500 (SPY) by identifying extreme values in the Williams %R indicator and using them as trading signals.
Strategy Rules:
Entry Signal:
A long position is entered when the Williams %R value falls below -90, indicating an oversold condition. This threshold suggests that the market may be near a short-term bottom, and prices are likely to reverse or rebound in the short term (Murphy, 1999).
Exit Signal:
The long position is exited when:
The current close price is higher than the previous day’s high, or
The Williams %R indicator rises above -30, indicating that the market is no longer oversold and may be approaching an overbought condition (Wilder, 1978).
Technical Analysis and Rationale:
The Williams %R is a momentum oscillator that measures the level of the close relative to the high-low range over a specific period, providing insight into whether an asset is trading near its highs or lows. The indicator values range from -100 (most oversold) to 0 (most overbought). When the value falls below -90, it indicates an oversold condition where a reversal is likely (Achelis, 2000). This strategy uses this oversold threshold as a signal to initiate long positions, betting on mean reversion—an established principle in financial markets where prices tend to revert to their historical averages (Jegadeesh & Titman, 1993).
Optimization and Performance:
The strategy allows for an adjustable lookback period (between 2 and 25 days) to determine the range used in the Williams %R calculation. Empirical tests show that shorter lookback periods (e.g., 2 days) yield the most favorable outcomes, with profit factors exceeding 2. This finding aligns with studies suggesting that shorter timeframes can effectively capture short-term momentum reversals (Fama, 1970; Jegadeesh & Titman, 1993).
Scientific Context:
Mean Reversion Theory: The strategy’s core relies on mean reversion, which suggests that prices fluctuate around a mean or average value. Research shows that such strategies, particularly those using oscillators like Williams %R, can exploit these temporary deviations (Poterba & Summers, 1988).
Behavioral Finance: The overbought and oversold conditions identified by Williams %R align with psychological factors influencing trading behavior, such as herding and panic selling, which often create opportunities for price reversals (Shiller, 2003).
Conclusion:
This Williams %R-based strategy utilizes a well-established momentum oscillator to time entries and exits in the S&P 500. By targeting extreme oversold conditions and exiting when these conditions revert or exceed historical ranges, the strategy aims to capture short-term gains. Scientific evidence supports the effectiveness of short-term mean reversion strategies, particularly when using indicators sensitive to momentum shifts.
References:
Achelis, S. B. (2000). Technical Analysis from A to Z. McGraw Hill.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Williams, L. (1979). How I Made One Million Dollars… Last Year… Trading Commodities. Windsor Books.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
This explanation provides a scientific and evidence-based perspective on the Williams %R trading strategy, aligning it with fundamental principles in technical analysis and behavioral finance.
