War IndexIntroduction
Welcome to the War Index! This project aims to provide traders, investors, and analysts with a specialized financial indicator that tracks the performance of key defense and aerospace companies. By aggregating the percentage changes of selected stocks, the War Index offers insights into the defense sector's dynamics and its relationship with the broader market.
What is the War Index?
The War Index is a custom financial indicator designed to approximate the collective performance of major defense and aerospace companies. It aggregates the daily percentage changes of selected stocks within the defense sector to provide a singular metric that reflects the overall health and trends of this industry. Additionally, the index is compared against the S&P 500 (SPX) to contextualize its performance relative to the broader market.
Index Components
The War Index comprises the following 16 stocks, each representing a significant player in the defense and aerospace industries:
Lockheed Martin Corporation ( NYSE:LMT )
Northrop Grumman Corporation ( NYSE:NOC )
Boeing Company ( NYSE:BA )
Raytheon Technologies Corporation ( NYSE:RTX )
General Dynamics Corporation ( NYSE:GD )
BAE Systems plc ( OTC:BAESY )
L3Harris Technologies, Inc. ( NYSE:LHX )
Textron Inc. ( NYSE:TXT )
Huntington Ingalls Industries, Inc. ( NYSE:HII )
Oshkosh Corporation ( NYSE:OSK )
Leidos Holdings, Inc. ( NYSE:LDOS )
Kratos Defense & Security Solutions, Inc. ( NASDAQ:KTOS )
Spirit AeroSystems Holdings, Inc. ( NYSE:SPR )
Parsons Corporation ( NYSE:PSN )
CACI International Inc ( NYSE:CACI )
ViaSat, Inc. ( NASDAQ:VSAT )
Purpose of the War Index
The War Index serves several key purposes:
Sector Performance Tracking : By aggregating the performance of major defense and aerospace companies, the index provides a clear picture of the sector's overall health.
Investment Analysis : Investors can use the index to identify trends, evaluate sector strength, and make informed decisions regarding their portfolios.
Comparative Benchmarking : Comparing the War Index with broader market indices like the S&P 500 helps in understanding how the defense sector performs relative to the general market.
Disclaimer: This War Index is an approximated indicator intended for informational purposes only. It should not be construed as investment advice. Always conduct your own research or consult with a financial advisor before making investment decisions.
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SMI Ergodic Indicator/Oscillator of Money Flow Index▮ Introduction
The Stochastic Momentum Index Ergodic (SMII) indicator is a technical analysis tool designed to predict trend reversals in the price of an asset.
It functions as a momentum oscillator, measuring the ratio of the smoothed price change to the smoothed absolute price change over a given number of previous periods.
The Ergodic SMI is based on the True Strength Index (TSI) and integrates a signal line, which is an exponential moving average (EMA) of the SMI indicator itself.
The Ergodic SMI oscillator provides a clearer picture of market trends than the traditional stochastic oscillator by incorporating the concept of 'ergodicity', which helps remove market noise.
On ther other hand, MFI (Money Flow Index) is a technical analysis indicator used to measure the inflow of money into an asset and thus help identify buying and selling pressure in a given financial instrument.
When these two indicators are combined, they can provide a more comprehensive view of price direction and market strength.
▮ Motivation: why another indicator?
By combining SMII with MFI, we can gain even more insights into the market.
One way to do this is to use the MFI as an input to the SMII, rather than just using price.
This means we are measuring momentum based on buying and selling pressure rather than just price.
Furthermore, there is the possibility of making several fine adjustments to both the calculation and visualization parameters that are not present in other indicators.
▮ What to look for
When using the SMII MFI indicator, there are a few things to look out for.
First, look at the SMII signal line.
When the line crosses above -40, it is considered a buy signal, while the crossing below +40 is considered a sell signal.
Also, pay attention to divergences between the SMII and the price.
If price is rising but the SMII is showing negative divergence, it could indicate that momentum is waning and a reversal could be in the offing.
Likewise, if price is falling but the SMII is showing positive divergence, this could indicate that momentum is building and a reversal could also be in the offing.
Divergences can be considered in both indicator and/or histogram.
Examples:
▮ Notes
The indicator presented here offers both the 'SMII' and the 'SMIO', that is, the 'Stochastic Momentum Index Ergodic Indicator' together with the 'Stochastic Momentum Index Ergodic Oscillator' (histogram), as per the documentation described in reference links.
So it is important to highlight the differences in relation to my other indicator, the 'Stochastic Momentum Index (SMI) of Money Flow Index (MFI)':
This last one is purely based on the SMI , which is implemented using SMA smoothing for the relative range and the high/low range.
Although they may seem the same in some situations, the calculation is actually different. The TSI tends to be more responsive at the expense of being noisier, while the SMI tends to be smoother. Which of these two indicators is best depends on the situation, the context, and the analyst's personal preference.
Please refer to reference links to more info.
▮ References
SMI documentation
SMII documentation
SMIO documentation
MFI documentation
Rational Qaudratic Kernel Elder Force Index Introduction:
The Rational Quadratic Kernel Elder Force Index is a versatile and mathematically sophisticated technical indicator that enhances the traditional Elder Force Index (EFI) by applying a rational quadratic kernel smoothing technique. This advanced regression method is designed to provide traders with a more adaptive and accurate tool for measuring the strength behind price movements, incorporating the influence of volume. This indicator does not predict future price movements, but it offers a clearer view of market dynamics through its advanced smoothing mechanism.
Key Features:
Elder Force Index Foundation:
The core of this indicator is built on the Elder Force Index, a popular tool developed by Dr. Alexander Elder. The EFI is a momentum indicator that calculates the strength of price movements by combining both price change and volume.
Rational Quadratic Kernel Smoothing: The indicator incorporates a rational quadratic kernel for smoothing, providing traders with a more refined view of price action trends. This kernel is highly adjustable based on user inputs, allowing for flexible tuning to suit individual strategies.
Adaptive Time Frame Weighting:
Through the adjustable parameter Relative Weighting (r), traders can modify the influence of different time frames. Lower values give more weight to longer time frames, while higher values make the behavior resemble that of a Gaussian kernel.
Dynamic Visualization:
The indicator visually displays the smoothed force index as a color-coded line. It dynamically changes color based on market conditions—green when the smoothed force index is positive and red when it is negative—providing a clear and easy-to-read signal for traders.
How It Works:
The Elder Force Index is calculated by multiplying the price change between two bars with the volume of the current bar.
A rational quadratic kernel regression is applied to this raw EFI data. The smoothing process provides a more stable and reliable signal by reducing noise, particularly in volatile markets.
The user-defined parameters, such as the length of the smoothing window and the relative weighting factor, allow traders to customize the indicator to suit their specific trading style or asset class.
User Inputs:
Length:
Sets the smoothing window for the kernel regression. A longer length results in more significant smoothing.
Relative Weighting (r):
Controls the influence of different time frames in the smoothing process. A smaller value emphasizes longer-term data, while a higher value makes it behave more like a traditional Gaussian kernel.
Source:
Select the price source (default is the closing price) for the calculations.
Uptrick: Crypto Volatility Index** Crypto Volatility Index(VIX) **
Overview
The Crypto Volatility Index (VIX) is a specialized technical indicator designed to measure the volatility of cryptocurrency prices. Leveraging advanced statistical methods, including logarithmic returns and variance, the Crypto VIX offers a refined measure of market fluctuations. This approach makes it particularly useful for traders in the highly volatile cryptocurrency market, providing insights that traditional volatility indicators may not capture as effectively.
Purpose
The Crypto VIX aims to deliver a nuanced understanding of market volatility, tailored specifically for the cryptocurrency space. Unlike other volatility measures, the Crypto VIX employs sophisticated statistical methods to reflect the unique characteristics of cryptocurrency price movements. This makes it especially valuable for cryptocurrency traders, helping them navigate the inherent volatility of digital assets and manage their trading strategies and risk exposure more effectively.
Calculation
1. Indicator Declaration
The Crypto VIX is plotted in a separate pane below the main price chart for clarity:
indicator("Crypto Volatility Index (VIX)", overlay=false, shorttitle="Crypto VIX")
2. Input Parameters
Users can adjust the period length for volatility calculations:
length = input.int(14, title="Period Length")
3. Calculating Daily Returns
The daily returns are calculated using logarithmic returns:
returns = math.log(close / close )
- **Logarithmic Returns:** These returns provide a normalized measure of price changes, making it easier to compare returns over different periods and across different assets.
4. Average Return Calculation
The average return over the specified period is computed with a Simple Moving Average (SMA):
avg_return = ta.sma(returns, length)
5. Variance Calculation
Variance measures the dispersion of returns from the average:
variance = ta.sma(math.pow(returns - avg_return, 2), length)
- Variance : This tells us how much the returns deviate from the average, giving insight into how volatile the market is.
6. Standard Deviation (Volatility) Calculation
Volatility is derived as the square root of the variance:
volatility = math.sqrt(variance)
- Standard Deviation : This provides a direct measure of volatility, showing how much the price typically deviates from the mean return.
7. Plotting the Indicator
The volatility and average return are plotted:
plot(volatility, color=#21f34b, title="Volatility Index")
plot(avg_return, color=color.new(color.red, 80), title="Average Return", style=plot.style_columns)
Practical Examples
1. High Volatility Scenario
** Example :** During significant market events, such as major regulatory announcements or geopolitical developments, the Crypto VIX tends to rise sharply. For instance, if the Crypto VIX moves from a baseline level of 0.2 to 0.8, it indicates heightened market volatility. Traders might see this as a signal to adjust their strategies, such as reducing position sizes or setting tighter stop-loss levels to manage increased risk.
2. Low Volatility Scenario
** Example :** In a stable market, where prices fluctuate within a narrow range, the Crypto VIX will show lower values. For example, a drop in the Crypto VIX from 0.4 to 0.2 suggests lower volatility and stable market conditions. Traders might use this information to consider longer-term trades or take advantage of potential consolidation patterns.
Best Practices
1. Combining Indicators
- Moving Averages : Use the Crypto VIX with moving averages to identify trends and potential reversal points.
- Relative Strength Index (RSI): Combine with RSI to assess overbought or oversold conditions for better entry and exit points.
- Bollinger Bands : Pair with Bollinger Bands to understand volatility relative to price movements and spot potential breakouts.
2. Adjusting Parameters
- Short-Term Trading : Use a shorter period length (e.g., 7 days) to capture rapid volatility changes suitable for day trading.
- Long-Term Investing : A longer period length (e.g., 30 days) provides a smoother view of volatility, helping long-term investors navigate market trends.
Backtesting and Performance Insights
While specific backtesting data for the Crypto VIX is not yet available, the indicator is built on established principles of volatility measurement, such as logarithmic returns and standard deviation. These methods are well-regarded in financial analysis for accurately reflecting market volatility. The Crypto VIX is designed to offer insights similar to other effective volatility indicators, tailored specifically for the cryptocurrency markets. Its adaptation to digital assets and ability to provide precise volatility measures underscore its practical value for traders.
Originality and Uniqueness
The Crypto Volatility Index (VIX) distinguishes itself through its specialized approach to measuring volatility in the cryptocurrency markets. While the concepts of logarithmic returns and standard deviation are not new, the Crypto VIX integrates these methods into a unique framework designed specifically for digital assets.
- Tailored Methodology : Unlike generic volatility indicators, the Crypto VIX is adapted to the unique characteristics of cryptocurrencies, providing a more precise measure of price fluctuations that reflects the inherent volatility of digital markets.
- Enhanced Insights : By focusing on cryptocurrency-specific price behavior and incorporating advanced statistical techniques, the Crypto VIX offers insights that traditional volatility indicators might miss. This makes it a valuable tool for traders navigating the complex and fast-moving cryptocurrency landscape.
- Innovative Application : The Crypto VIX combines established financial metrics in a novel way, offering a fresh perspective on market volatility and contributing to more effective risk management and trading strategies in the cryptocurrency space.
Summary
The Crypto Volatility Index (VIX) is a specialized tool for measuring cryptocurrency market volatility. By utilizing advanced statistical methods such as logarithmic returns and standard deviation, it provides a detailed measure of price fluctuations. While not entirely original in its use of these methods, the Crypto VIX stands out through its tailored application to the unique characteristics of the cryptocurrency market. Traders can use the Crypto VIX to gauge market risk, adjust their strategies, and make informed trading decisions, supported by practical examples, best practices, and clear visual aids.
Proxy Financial Stress Index StrategyThis strategy is based on a Proxy Financial Stress Index constructed using several key financial indicators. The strategy goes long when the financial stress index crosses below a user-defined threshold, signaling a potential reduction in market stress. Once a position is opened, it is held for a predetermined number of bars (periods), after which it is automatically closed.
The financial stress index is composed of several normalized indicators, each representing different market aspects:
VIX - Market volatility.
US 10-Year Treasury Yield - Bond market.
Dollar Index (DXY) - Currency market.
S&P 500 Index - Stock market.
EUR/USD - Currency exchange rate.
High-Yield Corporate Bond ETF (HYG) - Corporate bond market.
Each component is normalized using a Z-score (based on the user-defined moving average and standard deviation lengths) and weighted according to user inputs. The aggregated index reflects overall market stress.
The strategy enters a long position when the stress index crosses below a specified threshold from above, indicating reduced financial stress. The position is held for a defined holding period before being closed automatically.
Scientific References:
The concept of a financial stress index is derived from research that combines multiple financial variables to measure systemic risks in the financial markets. Key research includes:
The Financial Stress Index developed by various Federal Reserve banks, including the Cleveland Financial Stress Index (CFSI)
Bank of America Merrill Lynch Option Volatility Estimate (MOVE) Index as a measure of interest rate volatility, which correlates with financial stress
These indices are widely used in economic research to gauge financial instability and help in policy decisions. They track real-time fluctuations in various markets and are often used to anticipate economic downturns or periods of high financial risk.
Trading Channel Index (TCI)Overview:
The Trading Channel Index (TCI) is a technical analysis tool designed to identify cyclical trends in financial markets by smoothing out price movements and reducing volatility compared to traditional oscillators, like the Commodity Channel Index (CCI). The TCI helps traders pinpoint overbought and oversold conditions, as well as gauge the strength and direction of market trends.
Calculation:
The TCI is calculated through a multi-step process:
Typical Price (Xt): The typical price is computed as the average of the high, low, and close prices for each bar:
Xt = (High + Low + Close) / 3
Exponential Average (Et): This step smooths the typical price over a specified number of bars (TCI Channel Length) using an exponential moving average (EMA). The smoothing factor alpha is derived from the channel length:
Et = alpha * Xt + (1 - alpha) * Et
Where alpha = 2 / (TCI Channel Length + 1).
Average Deviation (Dt): The average deviation measures how much the typical price deviates from the exponential average over time. This is also smoothed using the EMA:
Dt = alpha * abs(Et - Xt) + (1 - alpha) * Dt
Channel Index (CI): The Channel Index is calculated by normalizing the difference between the typical price and the exponential average by the average deviation:
CI = (Xt - Et) / (0.15 * Dt)
Trading Channel Index (TCI): Finally, the TCI is generated by applying additional smoothing to the Channel Index using another EMA over the specified number of bars (TCI Average Length). The smoothing factor beta is derived from the average length:
TCI = beta * CI + (1 - beta) * TCI
Indicator Variables:
TCI Channel Length:
- Description: This variable sets the number of historical bars used to calculate the Channel Index (CI). A shorter length results in a more sensitive CI that responds quickly to price changes, while a longer length produces a smoother and less volatile CI.
- Default Value: 21
TCI Average Length:
-Description: This variable determines the number of bars over which the Channel Index (CI) is smoothed to produce the TCI. A shorter length makes the TCI more responsive to recent price changes, whereas a longer length further smooths the TCI, reducing its sensitivity to short-term fluctuations.
-Default Value: 10
Usage:
Overbought and Oversold Conditions: The TCI often uses levels such as +100 and -100 to identify potential reversal points. When the TCI crosses above +100, it might indicate an overbought condition, signaling a potential sell. Conversely, when it crosses below -100, it could indicate an oversold condition, suggesting a potential buy.
Trend Identification: Sustained values above 0 typically indicate a bullish trend, while values below 0 suggest a bearish trend. The TCI's smoothness helps traders stay in trends longer by reducing the impact of short-term market noise.
Conclusion:
The Trading Channel Index (TCI) is a versatile and powerful tool for traders who wish to capture cyclical price movements with a reduced level of noise. By adjusting the TCI Channel Length and TCI Average Length, traders can tailor the indicator to suit different market conditions, making it applicable across various timeframes and asset classes.
CNN Fear and Greed Index JD modified from minusminusCNN Fear and Greed Index - www.cnn.com
Modified from minusminus -
See Documentation from CNN's website
CNN's Fear and Greed index is an attempt to quantitatively score the Fear and Greed in the SPX using 7 factors:
Market Momentum- S&P 500 (SPX) and its 125-day moving average
Stock Price Strength -Net new 52-week highs and lows on the NYSE
Stock Price Breadth - McClellan Volume Summation Index
Put and Call options - 5-day average put/call ratio
Market Volatility - VIX and its 50-day moving average
Safe Haven Demand - Difference in 20-day stock and bond returns
Junk Bond Demand - Yield spread: junk bonds vs. investment grade
Each Factor has a weight input for the final calculation initially set to a weight of 1. The final calculation of the index is a weighted average of each factor.
3 Factors have separate functions for calculation : See Code for Clarity
SPX Momentum : difference between the Daily CBOE:SPX index value and it's 125 Day Simple moving average.
Stock Price Strength : Net New 52-week highs and lows on the NYSE.
Function calculates a measure of Net New 52-week highs by:
NYSE 52-week highs (INDEX:MAHN) - all new NYSE Highs (INDEX:HIGH)
measure of Net New 52-week lows by:
NYSE 52-week lows (INDEX:MALN) - all new NYSE Lows (INDEX:LOWN)
Then calculate a ratio of Net New 52-week Highs and Lows over Total Highs and Lows then takes a 5-day moving average of that ratio-See Code
Stock Price Breadth is the McClellan Volume Summation Index :
First Calculate the McClellan Oscillator
Second Calculate the Summation Index
4 Factors are Straight data requests
5 Day Simple Moving Average of the Put-Call Ratio on SPY
50 Day Simple Moving Average of the SPX VIX
Difference between 20 Day Simple Moving Average of SPX Daily Close and 20 Day Simple Moving Average of 10Y Constant Maturity US Treasury Note
Yield Spread between ICE BofA US High Yield Index and ICE BofA US Investment Grade Corporate Yield Index
The Fear and Greed Index is a weighted average of these factors - which is then normalized to scale from 0 to 100 using the past 25 values - length parameter.
3 Zones are Shaded: Red for Extreme Fear, Grey for normal jitters, Green for Extreme Greed.
Disclaimer: This is not financial advice. These are just my ideas, and I am not an investment advisor or investment professional. This code is for informational purposes only and do your own analysis before making any investment decisions. This is an attempt to replicate in spirt an index CNN publishes on their website and in no way shape or form infringes on their content, calculations or proprietary information.
From CNN: www.cnn.com
FEAR & GREED INDEX FAQs
What is the CNN Business Fear & Greed Index?
The Fear & Greed Index is a way to gauge stock market movements and whether stocks are fairly priced. The theory is based on the logic that excessive fear tends to drive down share prices, and too much greed tends to have the opposite effect.
How is Fear & Greed Calculated?
The Fear & Greed Index is a compilation of seven different indicators that measure some aspect of stock market behavior. They are market momentum, stock price strength, stock price breadth, put and call options, junk bond demand, market volatility, and safe haven demand. The index tracks how much these individual indicators deviate from their averages compared to how much they normally diverge. The index gives each indicator equal weighting in calculating a score from 0 to 100, with 100 representing maximum greediness and 0 signaling maximum fear.
How often is the Fear & Greed Index calculated?
Every component and the Index are calculated as soon as new data becomes available.
How to use Fear & Greed Index?
The Fear & Greed Index is used to gauge the mood of the market. Many investors are emotional and reactionary, and fear and greed sentiment indicators can alert investors to their own emotions and biases that can influence their decisions. When combined with fundamentals and other analytical tools, the Index can be a helpful way to assess market sentiment.
CNN Fear and Greed IndexThe “CNN Fear and Greed Index” indicator in this context is designed to gauge market sentiment based on a combination of several fundamental indicators. Here’s a breakdown of how this indicator works and what it represents:
Components of the Indicator:
1. Stock Price Momentum:
• Calculates the momentum of the S&P 500 index relative to its 125-day moving average. Momentum is essentially the rate of acceleration or deceleration of price movements over time.
2. Stock Price Strength:
• Measures the breadth of the market by comparing the number of stocks hitting 52-week highs versus lows. This provides insights into the overall strength or weakness of the market trend.
3. Stock Price Breadth:
• Evaluates the volume of shares trading on the rise versus the falling volume. Higher volume on rising days suggests positive market breadth, while higher volume on declining days indicates negative breadth.
4. Put and Call Options Ratio (Put/Call Ratio):
• This ratio indicates the sentiment of investors in the options market. A higher put/call ratio typically signals increased bearish sentiment (more puts relative to calls) and vice versa.
5. Market Volatility (VIX):
• Also known as the “fear gauge,” the VIX measures the expected volatility in the market over the next 30 days. Higher VIX values indicate higher expected volatility and often correlate with increased fear or uncertainty in the market.
6. Safe Haven Demand:
• Compares the returns of stocks (represented by S&P 500) versus safer investments like 10-year Treasury bonds. Higher returns on bonds relative to stocks suggest a flight to safety or risk aversion.
7. Junk Bond Demand:
• Measures the spread between yields on high-yield (junk) bonds and investment-grade bonds. Widening spreads may indicate increasing risk aversion as investors demand higher yields for riskier bonds.
Normalization and Weighting:
• Normalization: Each component is normalized to a scale of 0 to 100 using a function that adjusts the range based on historical highs and lows of the respective indicator.
• Weighting: The user can adjust the relative importance (weight) of each component using input parameters. This customization allows for different interpretations of market sentiment based on which factors are considered more influential.
Fear and Greed Index Calculation:
• The Fear and Greed Index is calculated as a weighted average of all normalized components. This index provides a single numerical value that summarizes the overall sentiment of the market based on the selected indicators.
Usage:
• Visualization: The indicator plots the Fear and Greed Index and its components on the chart. This allows traders and analysts to visually assess the sentiment trends over time.
• Analysis: Changes in the Fear and Greed Index can signal shifts in market sentiment. For example, a rising index may indicate increasing greed and potential overbought conditions, while a falling index may suggest increasing fear and potential oversold conditions.
• Customization: Traders can customize the indicator by adjusting the weights assigned to each component based on their trading strategies and market insights.
By integrating multiple fundamental indicators into a single index, the “CNN Fear and Greed Index” provides a comprehensive snapshot of market sentiment, helping traders make informed decisions about market entry, exit, and risk management strategies.
Uptrick:Intensity IndexPurpose:
The "Uptrick: Intensity Index" strategy is designed to provide traders with insights into the trend intensity of security by combining multiple moving averages and their relative positions. This versatile tool can be used effectively by both short-term and long-term traders to identify potential buy and sell signals based on specific conditions.
Explanation:
Input Parameters and Customization:
Moving Averages Lengths:
Adjust MA1, MA2, and MA3 lengths to change the calculation periods for the moving averages.
Trend Intensity Index SMA Length:
Adjust the length of the SMA applied to the TII.
Plot Colors:
Change the colors of the TII and TII MA plots for better visualization.
Background Colors and Transparency:
Set different colors for positive and negative TII MA values.
Control the transparency of the background color.
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MA1 (Length 10): Short-term moving average, useful for capturing short-term market trends.
MA2 (Length 20): Medium-term moving average, providing a balanced view of market trends.
MA3 (Length 50): Long-term moving average, offering insights into long-term market trends.
The script calculates the relative positions of the closing price to each moving average (rel1, rel2, rel3) to determine how far the current price deviates from each average.
Trend Intensity Index (TII):
The TII is calculated as the average of the relative positions (rel1, rel2, rel3), multiplied by 100 to convert it into a percentage. This index reflects the overall intensity of the trend, considering short-term, medium-term, and long-term perspectives.
The TII is plotted in blue, providing a visual representation of trend intensity.
SMA of TII:
An additional SMA is applied to the TII (matii) to smooth out fluctuations and provide a clearer long-term trend signal.
The SMA of TII is plotted in orange, offering a reference for long-term trend analysis.
Determining Potential Price Movements:
For Short-Term Traders:
When the blue TII line crosses above the orange SMA of TII line, it indicates a potential buy signal.
When the blue TII line crosses below the orange SMA of TII line, it indicates a potential sell signal.
For Long-Term Traders:
When the orange SMA of TII line crosses above the highlighted 0 line, it indicates a potential buy signal.
When the orange SMA of TII line crosses below the highlighted 0 line, it indicates a potential sell signal.
Plotting and Visualization:
The TII and its SMA are plotted with distinct colors for easy identification.
A horizontal line at 0 is plotted in gray to serve as a reference point for long-term trend signals.
The background color changes based on the value of the SMA of TII (matii):
Green background for matii values above 0, indicating bullish conditions.
Red background for matii values below 0, indicating bearish conditions.
Utility and Potential Usage:
The "Uptrick: Intensity Index" indicator is a powerful tool for both short-term and long-term traders, offering clear buy and sell signals based on the crossover of the TII and its SMA, as well as the position of the SMA relative to the zero line.
By consolidating multiple moving averages and their relative positions into a single indicator, traders can gain comprehensive insights into market trends and intensity.
The ability to adjust all inputs and toggle visibility options enhances the flexibility and utility of the indicator, making it suitable for various trading styles and market conditions.
Through its versatile design and advanced features, the "Uptrick: Intensity Index" indicator equips traders with actionable insights into trend intensity and potential price movements. By integrating this robust tool into their trading strategies, traders can navigate the markets with greater precision and confidence, thereby enhancing their trading outcomes.
WHAT SETTINGS TO HAVE FOR THE MOVING AVERAGE:
Short-term traders (day traders) might prefer a shorter SMA length (e.g., 5-20 periods) as they are looking for quick signals and react to price changes more rapidly.
Medium-term traders (swing traders) might opt for a medium SMA length (e.g., 20-50 periods) which can filter out some noise and provide a clearer signal on the trend.
Long-term traders (position traders) might choose a longer SMA length (e.g., 50-200 periods) to get a broader view of the market trend and avoid reacting to short-term fluctuations.
Discovery IndexThe Discovery Index is an original technical indicator which attempts to display directional market pressure and momentum based on accumulated candle-over-candle measurements.
Discovery , in this context, is the act of finding (discovering) New Highs and Lows.
> What is 'Discovery'
Not to be confused with "Price Discovery", the term for setting the spot price of an asset.
The term 'Discovery' in Discovery Index is used based on the literal definition of 'Discovery', such as, the action of finding what was previously unknown.
Given this definition,
Discovery is the difference between highs or lows only when the current high is higher than the previous high or the current low is lower than the previous low.
Below is a visual example of exactly where Discovery is seen from each candle.
Since discovery is only based on points of the candle, and not specifically the direction of the candle; it is possible for discovery to occur in both directions from the same candle.
It is also possible for no discovery to occur from a candle.
> Calculation
The Discovery Index is the Net Total of discovery data over a specified length of bars.
Discovery Index = Sum of Upwards Discovery + Sum of Downwards Discovery
Note: Upwards Discovery is always Positive, and Downwards Discovery is always Negative. By adding both together, their Net Total is produced. This value is the "Discovery Index".
Wick Calculation Example
> Volume Discovery
Using Volume for the Discovery Index Calculation allows for a different dimension to be added to the data for new analysis opportunities.
While volume data is only a single value, by accumulating this data over time, we are able to fabricate a candle body from the data by accounting for the direction of the chart candles.
This allows for the Calculation of the Discovery Index based on volume data.
Volume Example
> Display
The display uses a "Candlestick histogram" display. The bodies and wicks from the display represent the discovery data from the respective points in each candle. (Wick Discovery & Candle Body Discovery).
This style of histogram allows for the display of both data sources, preserving the accuracy and distinction between each type, while also providing a clean display.
> Considerations
Discovery index is not an Oscillator, since there are no upper or lower boundaries to its rotations.
There are not (at this time) any "Over-bought" or "Over-sold" Areas, this is partially due to the previous consideration since any levels for these could potentially change from chart to chart. Additionally, it would generally be better to read the data based on the context of the current market.
Non-directional movements effect the Discovery Index as well. Since Discovery does not occur from every bar, the Index reflects hesitations as well as movements in market direction.
With the option to input a symbol, the Discovery Index Indicator is not constrained to one chart ticker for its calculation and could help to see shifts between different symbols, making it easier to compare different assets.
With the separation of wicks and candle body data, a stronger move may be observed by its full-bodied movements, while a potentially more speculative move may be seen from large wick movements. Since wicks are often interpreted as either, Rejection for reversal OR as Testing for continuation, the interpretation for Wick Discovery generally varies based on context.
Discovery Index ⇾ Divergences! Due to its calculation, price (and/or volume) data is displayed in such a way that makes it useful as a tool for identifying divergence opportunities.
Remember, this indicator is lookback based. An immediate significant change from the data source (if not offset by a similar opposite change) will be represented for multiple bars after its occurrence. Due to this, data is likely to be skewed or biased from these occurrences for a period of time after.
Throughout development, "Discovery" has been shortened to just "Disco", therefore, this indicator is also an attempt to bring Disco Back.
Enjoy!
Mag7 IndexThis is an indicator index based on cumulative market value of the Magnificent 7 (AAPL, MSFT, NVDA, TSLA, META, AMZN, GOOG). Such an indicator for the famous Mag 7, against which your main security can be benchmarked, was missing from the TradingView user library.
The index bar values are calculated by taking the weighted average of the 7 stocks, relative to their market cap. Explicitly, we are multiplying each bar period's total outstanding stock amount by the OHLC of that period for each stock and dividing that value by the combined sum of outstanding stock for the 7 corporations. OHLC is taken for the extended trading session.
The index dynamically adjusts with respect to the chosen main security and the bars/line visible in the chart window; that is, the first close value is normalized to the main security's first close value. It provides recalculation of the performance in that chart window as you scroll (this isn't apparent in the demo chart above this description).
It can be useful for checking market breadth, or benchmarking price performance of the individual stock components that comprise the Magnificent 7. I prefer comparing the indicator to the Nasdaq Composite Index (IXIC) or S&P500 (SPX), but of course you can make comparisons to any security or commodity.
Settings Input Options:
1) Bar vs. Line - view as OHLC colored bars or line chart. Line chart color based on close above or below the previous period close as green or red line respectively.
2) % vs Regular - the final value for the window period as % return for that window or index value
3) Turn on/off - bottom right tile displaying window-period performance
Inspired by the simpler NQ 7 Index script by @RaenonX but with normalization to main security at start of window and additional settings input options.
Please provide feedback for additional features, e.g., if a regular/extended session option is useful.
Global Liquidity Index (Candles)The Global Liquidity Index (Candles) provides a comprehensive overview of major central bank balance sheets worldwide, presenting values converted to USD for consistency and comparability, following relevant forex rates. This indicator, based on the code developed by user ingeforberg , incorporates essential US accounts including the Treasury General Account (TGA) and Reverse Repurchase Agreements (RRP), subtracted from the Federal Reserve's balance sheet to offer a nuanced perspective on US liquidity. Users can tailor their analysis by selectively enabling or disabling specific central banks and special accounts according to their preferences. The index exclusively includes central banks abstaining from currency pegging and with reliable data accessible since late 2007, ensuring a robust aggregated liquidity model.
The calculation of the Global Liquidity Index involves subtracting the Treasury General Account (TGA) and Reverse Repurchase Agreements (RRP) from the Federal Reserve System (FED) and adding the balance sheets of major central banks worldwide: the European Central Bank (ECB), the People's Bank of China (PBC), the Bank of Japan (BOJ), the Bank of England (BOE), the Bank of Canada (BOC), the Reserve Bank of Australia (RBA), the Reserve Bank of India (RBI), the Swiss National Bank (SNB), the Central Bank of the Russian Federation (CBR), the Central Bank of Brazil (BCB), the Bank of Korea (BOK), the Reserve Bank of New Zealand (RBNZ), Sweden's Central Bank (Riksbank), and the Central Bank of Malaysia (BNM).
This tool proves invaluable for individuals seeking a consolidated perspective on global liquidity to interpret macroeconomic trends. Analyzing these balance sheets enables users to discern policy trajectories and assess the global economic landscape, providing insights into asset pricing and assisting investors in making well-informed capital allocation decisions. Historically, assets perceived as riskier, such as small caps and cryptocurrencies, have tended to perform favorably during periods of escalating liquidity. Thus, investors may exercise caution regarding additional risk exposure unless a sustained upward trend in global liquidity is evident.
Main differences between the original and updated indicators:
The "Global Liquidity Index (Candles)" script, compared to the original "Global Liquidity Index" script, offers a more detailed and visually rich representation of liquidity data.
"Global Liquidity Index (Candles)" employs candlestick visualization to represent liquidity data. Each candlestick encapsulates open, high, low, and close prices over a given period. This format provides granular insights into liquidity fluctuations, facilitating a more nuanced analysis.
By using candlesticks, the script offers traders detailed information about liquidity dynamics. They can analyze the patterns formed by candlesticks to discern trends, reversals, and market sentiment shifts, aiding in making informed trading decisions.
RSI/MFI Selling Sentiment IndexPsychological Sales Index (Psychological Sales Index)
Fundamental Indicators of Market Sentiment: The Importance of MFI and RSI
The two fundamental indicators that best reflect market sentiment are Money Flow Index (MFI) and Relative Strength Index (RSI). MFI is an indicator of the flow of funds in a market by combining price and volume, which is used to determine whether a stock is over-bought or over-selling. RSI is an indicator of the overheating of the market by measuring the rise and fall of prices, which is applied to the analysis of the relative strength of stock prices. These two indicators allow a quantitative assessment of the market's buying and selling pressure, which provides important information to understand the psychological state of market participants.
Using timing and fundamental metrics
In order to grasp the effective timing of the sale, in-depth consideration was needed on how to use basic indicators. MFI and RSI represent the buying and selling pressures of the market, respectively, but there is a limit to reflecting the overall trend of the market alone. As a result, a study on how to capture more accurate selling points was conducted by comprehensively considering technical analysis along with psychological factors of the market.
The importance of ADX integration and weighting
The "Average Regional Index (ADX)" was missing in the early version. ADX is an indicator of the strength of a trend, and has experienced a problem of less accuracy in selling sentiment indicators, especially in the upward trend. To address this, we incorporated ADX and adopted a method of adjusting the weights of MFI and RSI according to the values of ADX. A high ADX value implies the existence of a strong trend, in which case it is appropriate to reduce the influence of MFI and RSI to give more importance to the strength of the trend. Conversely, a low ADX value increases the influence of MFI and RSI, putting more weight on the psychological elements of the market.
How to use and interpret
The user can adjust several parameters. Key inputs include 'Length', 'Overbought Threshold', 'DI Length', and 'ADX Smoothing'. These parameters are used to set the calculation period, overselling threshold, DI length, and ADX smoothing period of the indicator, respectively. The script calculates the psychological selling index based on MFI, RSI, and ADX. The calculated index is normalized to values between 0 and 100 and is displayed in the graph. Values above 'Overbought Threshold' indicate an overselling state, which can be interpreted as a potential selling signal. This index allows investors to comprehensively evaluate the psychological state of the market and the strength of trends, which can be used to make more accurate selling decisions.
Fear & Greed Index (Zeiierman)█ Overview
The Fear & Greed Index is an indicator that provides a comprehensive view of market sentiment. By analyzing various market factors such as market momentum, stock price strength, stock price breadth, put and call options, junk bond demand, market volatility, and safe haven demand, the Index can depict the overall emotions driving market behavior, categorizing them into two main sentiments: Fear and Greed.
Fear: Indicates a market scenario where investors are scared, possibly leading to a sell-off or a stagnant market. In such conditions, the indicator helps in identifying potential buying opportunities as assets may be undervalued.
Greed: Represents a state where investors are overly confident and buying aggressively, which can lead to inflated asset prices. The indicator in such cases can signal overbought conditions, advising caution or potential short opportunities.
█ How It Works
The Fear & Greed Index is an aggregate of seven distinct indicators, each gauging a specific dimension of stock market activity. These indicators include market momentum, stock price strength, stock price breadth, put and call options, junk bond demand, market volatility, and safe haven demand. The Index assesses the deviation of each individual indicator from its average, in relation to its typical fluctuations. In compiling the final score, which ranges from 0 to 100, the Index assigns equal weight to each indicator. A score of 100 denotes the highest level of Greed, while a score of 0 represents the utmost level of fear.
S&P 500's Momentum: The Index monitors the S&P 500's position relative to its 125-day moving average. Positive momentum (price above the average) signals growing confidence among investors (Greed), while negative momentum (price below the average) indicates rising fear.
Stock Price Strength: By comparing the number of stocks hitting 52-week highs to those at 52-week lows on the NYSE, the Index gauges market breadth. An extreme number of highs indicates Greed, whereas an extreme number of lows suggests Fear.
Stock Price Breadth (Market Volume): Using the McClellan Volume Summation Index, which considers the volume of advancing versus declining stocks, the Index assesses whether the market is broadly participating in a trend, or if a smaller subset of stocks is driving it.
Put and Call Options: The put/call ratio helps gauge investor sentiment. A rising ratio, particularly above 1, indicates increasing fear, as more investors are buying puts to protect against a decline. A falling ratio suggests growing confidence.
Market Volatility (VIX): The VIX measures expected market volatility. Higher values generally indicate Fear, while lower values point to Greed. The Fear & Greed Index compares the VIX to its 50-day moving average to understand its trend.
Safe Haven Demand: The performance of stocks versus bonds over a 20-day period helps understand where investors are putting their money. Bonds outperforming stocks is a sign of Fear, while the opposite suggests Greed.
Junk Bond Demand: By comparing the yields on junk bonds to safer investment-grade bonds, the Index gauges risk appetite. A narrower yield spread suggests Greed (investors are taking more risk), while a wider spread indicates Fear.
The Fear & Greed Index combines these components, scales, and averages them to produce a single value between 0 (Extreme Fear) and 100 (Extreme Greed).
█ How to Use
The Fear & Greed Index serves as a tool to evaluate the prevailing sentiments in the market. Investors, often driven by emotions, can react impulsively, and sentiment indicators like the Fear & Greed Index aim to highlight these emotional states, helping investors recognize personal biases that might impact their investment choices. When integrated with fundamental analysis and additional analytical instruments, the Index becomes a valuable resource for understanding and interpreting market moods and tendencies.
The Fear & Greed Index operates on the principle that excessive fear can result in stocks trading well below their intrinsic values,
while uncontrolled Greed can push prices above what they should be.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
[blackcat] L1 Reverse Choppiness IndexThe Choppiness Index is a technical indicator that is used to measure market volatility and trendiness. It is designed to help traders identify when the market is trending and when it is choppy, meaning that it is moving sideways with no clear direction. The Choppiness Index was first introduced by Australian commodity trader E.W. Dreiss in the late 1990s, and it has since become a popular tool among traders.
Today, I created a reverse version of choppiness index indicator, which uses upward direction as indicating strong trend rather than a traditional downward direction. Also, it max values are exceeding 100 compared to a traditional one. I use red color to indicate a strong trend, while yellow as sideways. Fuchsia zone are also incorporated as an indicator of sideways. One thing that you need to know: different time frames may need optimize parameters of this indicator. Finally, I'd be happy to explain more about this piece of code.
The code begins by defining two input variables: `len` and `atrLen`. `len` sets the length of the lookback period for the highest high and lowest low, while `atrLen` sets the length of the lookback period for the ATR calculation.
The `atr()` function is then used to calculate the ATR, which is a measure of volatility based on the range of price movement over a certain period of time. The `highest()` and `lowest()` functions are used to calculate the highest high and lowest low over the lookback period specified by `len`.
The `range`, `up`, and `down` variables are then calculated based on the highest high, lowest low, and closing price. The `sum()` function is used to calculate the sum of ranges over the lookback period.
Finally, the Choppiness Index is calculated using the ATR and the sum of ranges over the lookback period. The `log10()` function is used to take the logarithm of the sum divided by the lookback period, and the result is multiplied by 100 to get a percentage. The Choppiness Index is then plotted on the chart using the `plot()` function.
This code can be used directly in TradingView to plot the Choppiness Index on a chart. It can also be incorporated into custom trading strategies to help traders make more informed decisions based on market volatility and trendiness.
I hope this explanation helps! Let me know if you have any further questions.
Short Term IndeXThe Short-Term Index (STIX) is a simple market indicator designed to assess short-term overbought or oversold conditions in the stock market. Leveraging a combination of advancing and declining issues, STIX provides valuable insights into market sentiment and potential reversals. To enhance its interpretability and reveal the underlying trend with greater clarity, STIX has been refined through a Heiken-Ashi transformation, ensuring a smoother representation of market dynamics.
Calculation and Methodology:
stix = ta.ema(adv / (adv + dec) * 100, len)
STIX is calculated by dividing the difference between the sum of advancing issues (ADV) by the total number of issues traded (ADV + DEC). This quotient is multiplied by 100 to express the result as a percentage. The STIX index ranges from 0 to 100, where extreme values indicate potential overbought (mainly above 60) or oversold (mainly below 40) market conditions.
Heiken-Ashi Transformation:
By applying a Heiken-Ashi transformation to STIX, the indicator gains improved visual clarity and noise reduction. This transformation enhances the ability to identify trend shifts and potential reversal points, making it an even more valuable tool for traders and investors.
Utility and Use Cases:
-The Short-Term Index (STIX) offers a range of practical applications-
1. Overbought/Oversold Conditions: STIX provides a clear indication of short-term overbought or oversold conditions, helping traders anticipate potential market reversals.
2. Reversal Points: STIX can help pinpoint potential reversal points in short-term market trends, providing traders with opportunities to enter or exit positions.
3. Trend Analysis: By observing STIX values over time, traders can assess the strength and sustainability of short-term trends, aiding in trend-following strategies.
The Short-Term Index (STIX), enhanced by its Heiken-Ashi transformation, equips traders and investors with a tool for assessing short-term market conditions, confirming price movements, and identifying potential reversal points. Its robust methodology and refined presentation contribute to a more comprehensive understanding of short-term market dynamics, enabling traders to make well-informed trading decisions.
See Also:
- Other Market Breadth Indicators-
Bolton-Tremblay IndexThe Bolton-Tremblay Index (BOLTR) is a dynamic cumulative advance-decline indicator which incorporates the count of unchanged issues as a fundamental element. This index serves as a valuable tool for identifying shifts in market trends and gauging the overall strength or weakness of the market. To enhance its effectiveness and reveal underlying trends, BOLTR has been refined through a Heiken-Ashi transformation, resulting in a smoother and more insightful representation.
Calculation and Methodology:
r = (adv - dec) / unch
var float bt = na
bt := r > 0 ? nz(bt ) + math.sqrt(math.abs(r)) : nz(bt ) - math.sqrt(math.abs(r))
The BOLTR index is derived from a calculation involving three essential components: advancing issues (ADV), declining issues (DEC), and securities with unchanged closing prices (UNC). By formulating the ratio (ADV - DEC) / UNC, BOLTR captures the relationship between market movements and unchanged securities. This ratio then dictates whether the BOLTR index increases or decreases in the following period. If the ratio is positive, the index advances, and if negative, it retreats. This iterative process yields a cumulative index that reflects the evolving dynamics of market trends.
Heiken-Ashi Transformation:
The addition of a Heiken-Ashi transformation imparts a smoothing effect to the BOLTR index, revealing the underlying trend with greater clarity. This transformation diminishes noise and fluctuations, making it easier to identify meaningful shifts in market sentiment and overall market health.
Utility and Use Cases:
-The Bolton-Tremblay Index offers a range of applications that contribute to informed decision-making-
1. Trend Analysis: BOLTR provides insights into the changing trends of the market, helping traders and investors identify potential shifts in market sentiment.
2. Market Strength Assessment: By considering advancing, declining, and unchanged issues, BOLTR offers a comprehensive assessment of market strength and potential weaknesses.
3. Divergences: Traders can use BOLTR to detect divergences between price movements and the cumulative advance-decline dynamics, potentially signaling shifts in market direction.
The Bolton-Tremblay Index offers a versatile toolset for interpreting market trends, evaluating market health, and making better informed trading decisions.
See Also:
- Other Market Breadth Indicators-
Disparity IndexThe Disparity Index is a technical momentum indicator that measures the relative position of the most recent closing price to a selected moving average. It calculates the percentage difference between the closing price and the moving average, providing insights into price momentum and potential reversals.
The formula for the Disparity Index is: * 100, where Close is the most recent closing price and n-period MA is the chosen moving average over n periods.
The Disparity Index can be used in various ways:
Trend Identification: The Disparity Index helps identify the relationship between the price and a chosen moving average. A positive value indicates that the price is above the moving average, suggesting bullish momentum, while a negative value suggests bearish momentum.
Overbought and Oversold Conditions: The Disparity Index can be used to identify potential overbought and oversold conditions. When the index reaches an extremely high value, it may indicate an overbought condition, implying a possible price correction. Conversely, an extremely low value can signal an oversold condition, indicating a potential price rebound.
Divergence: Traders can use the Disparity Index to identify divergence between the price and the indicator. Divergence occurs when the price and the Disparity Index move in opposite directions, potentially signaling an upcoming price reversal.
Personal Strategy: When the Disparity Index generates a green background, it suggests a potential bullish signal. This occurs when the Disparity Index crosses above the oversold threshold or exhibits a bullish reversal pattern. The green background signifies an area where buyers may have gained control, indicating a favorable environment for initiating long positions. This approach allows you to capitalize on potential upward price movements and join the uptrend.
On the other hand, when the Disparity Index generates a red background, it implies a potential bearish signal. This occurs when the Disparity Index crosses below the overbought threshold or exhibits a bearish reversal pattern. The red background highlights a zone where sellers might dominate, indicating a higher likelihood of downward price movements. By considering selling opportunities in these zones, you can position yourself to profit from potential downside moves and align with the prevailing downtrend.
The Disparity Index can be customized by using different types of moving averages such as simple moving averages (SMAs), exponential moving averages (EMAs), or weighted moving averages (WMAs). Additionally, it can be smoothed using another moving average to reduce noise and generate smoother signals, improving trend identification.
In trending markets, the Disparity Index is particularly effective as a trend indicator due to its ability to quickly capture price changes. It can provide early indications of trend strength and potential reversals, allowing traders to enter or exit positions in a timely manner. This advantage over traditional moving averages makes the Disparity Index a valuable tool for trend-following strategies.
Enjoy!
Relative Trend Index (RTI) by Zeiierman█ Overview
The Relative Trend Index (RTI) developed by Zeiierman is an innovative technical analysis tool designed to measure the strength and direction of the market trend. Unlike some traditional indicators, the RTI boasts a distinctive ability to adapt and respond to market volatility, while still minimizing the effects of minor, short-term market fluctuations.
The Relative Trend Index blends trend-following and mean-reverting characteristics, paired with a customizable and intuitive approach to trend strength, and its sensitivity to price action makes this indicator stand out.
█ Benefits of using this RTI instead of RSI
The Relative Strength Index (RSI) and the Relative Trend Index (RTI) are both powerful technical indicators, each with its own unique strengths.
However, there are key differences that make the RTI arguably more sophisticated and precise, especially when it comes to identifying trends and overbought/oversold (OB/OS) areas.
The RSI is a momentum oscillator that measures the speed and change of price movements and is typically used to identify overbought and oversold conditions in a market. However, its primary limitation lies in its tendency to produce false signals during extended trending periods.
On the other hand, the RTI is designed specifically to identify and adapt to market trends. Instead of solely focusing on price changes, the RTI measures the relative positioning of the current closing price within its recent range, providing a more comprehensive view of market conditions.
The RTI's adaptable nature is particularly valuable. The user-adjustable sensitivity percentage allows traders to fine-tune the indicator's responsiveness, making it more resilient to sudden market fluctuations and noise that could otherwise produce false signals. This feature is advantageous in various market conditions, from trending to choppy and sideways-moving markets.
Furthermore, the RTI's unique method of defining OB/OS zones takes into account the prevailing trend, which can provide a more precise reflection of the market's condition.
While the RSI is an invaluable tool in many traders' toolkits, the RTI's unique approach to trend identification, adaptability, and enhanced definition of OB/OS zones can provide traders with a more nuanced understanding of market conditions and potential trading opportunities. This makes the RTI an especially powerful tool for those seeking to ride long-term trends and avoid false signals.
█ Calculations
In summary, while simple enough, the math behind the RTI indicator is quite powerful. It combines the quantification of price volatility with the flexibility to adjust the trend sensitivity. It provides a normalized output that can be interpreted consistently across various trading scenarios.
The math behind the Relative Trend Index (RTI) indicator is rooted in some fundamental statistical concepts: Standard Deviation and Percentiles.
Standard Deviation: The Standard Deviation is a measure of dispersion or variability in a dataset. It quantifies the degree to which each data point deviates from the mean (or average) of the data set. In this script, the standard deviation is computed on the 'close' prices over a specified number of periods. This provides a measure of the volatility in the price over that period. The higher the standard deviation, the more volatile the price has been.
Percentiles: The percentile is a measure used in statistics indicating the value below which a given percentage of observations in a group falls. After calculating the upper and lower trends for the last 'length' periods and sorting these values, the script uses the 'Sensitivity ' parameter to extract percentiles from these sorted arrays. This is a powerful concept because it allows us to adjust the sensitivity of our signals. By choosing different percentiles (controlled through the 'Sensitivity' parameter), we can decide whether we want to react only to extreme events (high percentiles) or be more reactive and consider smaller deviations from the norm as significant (lower percentiles).
Finally, the script calculates the Relative Trend Index value, which is essentially a normalized measure indicating where the current price falls between the upper and lower trend values. This simple ratio is incredibly powerful as it provides a standardized measure that can be used across different securities and market conditions to identify potential trading signals.
Core Components
Trend Data Count: This parameter denotes the number of data points used in the RTI's calculation, determining the trend length. A higher count captures a more extended market view (long-term trend), providing smoother results that are more resistant to sudden market changes. In contrast, a lower count focuses on more recent data (short-term trend), yielding faster responses to market changes, albeit at the cost of increased susceptibility to market noise.
Trend Sensitivity Percentage: This parameter is employed to select the indices within the trend arrays used for upper and lower trend definitions. By adjusting this value, users can affect the sensitivity of the trend, with higher percentages leading to a less sensitive trend.
█ How to use
The RTI plots a line that revolves around a mid-point of 50. When the RTI is above 50, it implies that the market trend is bullish (upward), and when it's below 50, it indicates a bearish (downward) trend. Furthermore, the farther the RTI deviates from the 50 line, the stronger the trend is perceived to be.
Bullish
Bearish
The RTI includes user-defined Overbought and Oversold levels. These thresholds suggest potential trading opportunities when they are crossed, serving as a cue for traders to possibly buy or sell. This gives the RTI an additional use case as a mean-reversion tool, in addition to being a trend-following indicator.
In short
Trend Confirmation and Reversals: If the percentage trend value is consistently closer to the upper level, it can indicate a strong uptrend. Similarly, if it's closer to the lower level, a downtrend may be in play. If the percentage trend line begins to move away from one trend line towards the other, it could suggest a potential trend reversal.
Identifying Overbought and Oversold Conditions: When the percentage trend value reaches the upper trend line (signified by a value of 1), it suggests an overbought condition - i.e., the price has been pushed up, perhaps too far, and could be due for a pullback, or indicating a strong positive trend. Conversely, when the percentage trend value hits the lower trend line (a value of 0), it indicates an oversold condition - the price may have been driven down and could be set to rebound, or indicate a strong negative trend. Traders often use these overbought and oversold signals as contrarian indicators, considering them potential signs to sell (in overbought conditions) or buy (in oversold conditions). If the RTI line remains overbought or oversold for an extended period, it indicates a strong trend in that direction.
█ Settings
One key feature of the RTI is its configurability. It allows users to set the trend data length and trend sensitivity.
The trend data length represents the number of data points used in the trend calculation. A longer trend data length will reflect a more long-term trend, whereas a shorter trend data length will capture short-term movements.
Trend sensitivity refers to the threshold for determining what constitutes a significant trend. High sensitivity levels will deem fewer price movements as significant, hence making the trend less sensitive. Conversely, low sensitivity levels will deem more price movements as significant, hence making the trend more sensitive.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Rich Robin Index, The Crypto Fear & Greed Index with RSI Trend The Relative Strength Index (RSI) is a technical indicator based on price movements that is used to determine whether a particular asset is overbought or oversold. It measures the ratio of rising to falling prices over a certain period of time.
The Fear & Greed Index, on the other hand, is a composite index that tracks the sentiment of the crypto market. It is based on seven indicators, each of which measures a different aspect of market behavior. These indicators are: Safe Haven Demand, Stock Price Breadth, Market Momentum, Stock Price Strength, Put and Call Options, Junk Bond Demand, and Market Volatility.
The combination of the RSI and the Fear & Greed Index can provide valuable insights for crypto traders. The RSI can help identify overbought and oversold conditions, while the Fear & Greed Index can give an overall sense of the sentiment in the market. Together, they can provide a more complete picture of the market conditions. For example, if the RSI is indicating that an asset is overbought, but the Fear & Greed Index is showing that the market is still in a state of fear, it may be a good time to sell. On the other hand, if the RSI is indicating that an asset is oversold, but the Fear & Greed Index is showing that the market is in a state of greed, it may be a good time to buy.
Overall, the combination of the RSI and the Fear & Greed Index can provide useful information for traders to make more informed decisions, by giving a sense of the market conditions, and providing a way to identify overbought and oversold conditions.
UFO + Realtime Divergences (UO x MFI)UFO + Realtime Divergences (UO x MFI) + Alerts
The UFO is a hybrid of two powerful oscillators - the Ultimate Oscillator (UO) and the Money Flow Index (MFI)
Features of the UFO include:
- Optional divergence lines drawn directly onto the oscillator in realtime.
- Configurable alerts to notify you when divergences occur, as well as centerline crossovers.
- Configurable lookback periods to fine tune the divergences drawn in order to suit different trading styles and timeframes.
- Background colouring option to indicate when the oscillator has crossed its centerline.
- Alternate timeframe feature allows you to configure the oscillator to use data from a different timeframe than the chart it is loaded on.
- 2x MTF triple-timeframe Stochastic RSI overbought and oversold confluence signals painted at the top of the panel for use as a confluence for reversal entry trades.
The core calculations of the UFO+ combine the factory settings of the Ultimate Oscillator and Money Flow Index, taking an average of their combined values for its output eg:
UO_Value + MFI_Value / 2
The result is a powerful oscillator capable of detecting high quality divergences, including on very low timeframes and highly volatile markets, it benefits from the higher weighting of the most recent price action provided by the Ultimate Oscillators calculations, as well as the calculation of the MFI, which incorporates volume data. The UFO and its incorporated 2x triple-timeframe MTF Stoch RSI overbought and oversold signals makes it well adapted for low timeframe scalping and regular divergence trades in particular.
The Ultimate Oscillator (UO)
Tradingview describes the Ultimate Oscillator as follows:
“The Ultimate Oscillator indicator (UO) is a technical analysis tool used to measure momentum across three varying timeframes. The problem with many momentum oscillators is that after a rapid advance or decline in price, they can form false divergence trading signals. For example, after a rapid rise in price, a bearish divergence signal may present itself, however price continues to rise. The Ultimate Oscillator attempts to correct this by using multiple timeframes in its calculation as opposed to just one timeframe which is what is used in most other momentum oscillators.”
You can read more about the UO and its calculations here
The Money Flow Index ( MFI )
Investopedia describes the True Strength Indicator as follows:
“The Money Flow Index ( MFI ) is a technical oscillator that uses price and volume data for identifying overbought or oversold signals in an asset. It can also be used to spot divergences which warn of a trend change in price. The oscillator moves between 0 and 100. Unlike conventional oscillators such as the Relative Strength Index ( RSI ), the Money Flow Index incorporates both price and volume data, as opposed to just price. For this reason, some analysts call MFI the volume-weighted RSI .”
You can read more about the MFI and its calculations here
The Stochastic RSI (relating to the built-in MTF Stoch RSI feature)
The popular oscillator has been described as follows:
“The Stochastic RSI is an indicator used in technical analysis that ranges between zero and one (or zero and 100 on some charting platforms) and is created by applying the Stochastic oscillator formula to a set of relative strength index ( RSI ) values rather than to standard price data. Using RSI values within the Stochastic formula gives traders an idea of whether the current RSI value is overbought or oversold. The Stochastic RSI oscillator was developed to take advantage of both momentum indicators in order to create a more sensitive indicator that is attuned to a specific security's historical performance rather than a generalized analysis of price change.”
You can read more about the Stochastic RSI and its calculations here
How do traders use overbought and oversold levels in their trading?
The oversold level, that is when the Stochastic RSI is above the 80 level is typically interpreted as being 'overbought', and below the 20 level is typically considered 'oversold'. Traders will often use the Stochastic RSI at an overbought level as a confluence for entry into a short position, and the Stochastic RSI at an oversold level as a confluence for an entry into a long position. These levels do not mean that price will necessarily reverse at those levels in a reliable way, however. This is why this version of the Stoch RSI employs the triple timeframe overbought and oversold confluence, in an attempt to add a more confluence and reliability to this usage of the Stoch RSI .
What are divergences?
Divergence is when the price of an asset is moving in the opposite direction of a technical indicator, such as an oscillator, or is moving contrary to other data. Divergence warns that the current price trend may be weakening, and in some cases may lead to the price changing direction.
There are 4 main types of divergence, which are split into 2 categories;
regular divergences and hidden divergences. Regular divergences indicate possible trend reversals, and hidden divergences indicate possible trend continuation.
Regular bullish divergence: An indication of a potential trend reversal, from the current downtrend, to an uptrend.
Regular bearish divergence: An indication of a potential trend reversal, from the current uptrend, to a downtrend.
Hidden bullish divergence: An indication of a potential uptrend continuation.
Hidden bearish divergence: An indication of a potential downtrend continuation.
How do traders use divergences in their trading?
A divergence is considered a leading indicator in technical analysis , meaning it has the ability to indicate a potential price move in the short term future.
Hidden bullish and hidden bearish divergences, which indicate a potential continuation of the current trend are sometimes considered a good place for traders to begin, since trend continuation occurs more frequently than reversals, or trend changes.
When trading regular bullish divergences and regular bearish divergences, which are indications of a trend reversal, the probability of it doing so may increase when these occur at a strong support or resistance level . A common mistake new traders make is to get into a regular divergence trade too early, assuming it will immediately reverse, but these can continue to form for some time before the trend eventually changes, by using forms of support or resistance as an added confluence, such as when price reaches a moving average, the success rate when trading these patterns may increase.
Typically, traders will manually draw lines across the swing highs and swing lows of both the price chart and the oscillator to see whether they appear to present a divergence, this indicator will draw them for you, quickly and clearly, and can notify you when they occur.
Setting alerts.
With this indicator you can set alerts to notify you when any/all of the above types of divergences occur, on any chart timeframe you choose.
Configurable pivot period.
You can adjust the default pivot lookback values to suit your prefered trading style and timeframe. If you like to trade a shorter time frame, lowering the default lookback values will make the divergences drawn more sensitive to short term price action.
Disclaimer: This script includes code from the stock UO and MFI by Tradingview as well as the Divergence for Many Indicators v4 by LonesomeTheBlue.
Dollar Index (DXY) Candles [Loxx]Dollar Index (DXY) Candles is an educational/experimental indicator that attempts to recreate the Dollar Index DXY: TVC:DXY .This is useful so others traders can see how the DXY is calculated.
The U.S. Dollar Index (USDX, DXY, DX) is an index (or measure) of the value of the United States dollar relative to a basket of foreign currencies, often referred to as a basket of U.S. trade partners' currencies. The Index goes up when the U.S. dollar gains "strength" (value) when compared to other currencies.
The index is maintained and published by ICE (Intercontinental Exchange, Inc.), with the name "U.S. Dollar Index" a registered trademark.
It is a weighted geometric mean of the dollar's value relative to following select currencies:
Euro (EUR), 57.6% weight.
Japanese yen (JPY) 13.6% weight.
Pound sterling (GBP), 11.9% weight.
Canadian dollar (CAD), 9.1% weight.
Swedish krona (SEK), 4.2% weight.
Swiss franc (CHF) 3.6% weight.
USDX = 50.14348112 × EURUSD^-0.576 × USDJPY^0.136 × GBPUSD^-0.119 × USDCAD^0.091 × USDSEK^0.042 × USDCHF^0.036
These candles won't match the ticker DXY dollar for dollar, but it comes very close. Either way, the general trend and volatility of this synthetic recreation is the same as the DXY.
Read more here: en.wikipedia.org
T3 Volatility Quality Index (VQI) w/ DSL & Pips Filtering [Loxx]T3 Volatility Quality Index (VQI) w/ DSL & Pips Filtering is a VQI indicator that uses T3 smoothing and discontinued signal lines to determine breakouts and breakdowns. This also allows filtering by pips.***
What is the Volatility Quality Index ( VQI )?
The idea behind the volatility quality index is to point out the difference between bad and good volatility in order to identify better trade opportunities in the market. This forex indicator works using the True Range algorithm in combination with the open, close, high and low prices.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included
Signals
Alerts
Related indicators
Zero-line Volatility Quality Index (VQI)
Volatility Quality Index w/ Pips Filtering
Variety Moving Average Waddah Attar Explosion (WAE)
***This indicator is tuned to Forex. If you want to make it useful for other tickers, you must change the pip filtering value to match the asset. This means that for BTC, for example, you likely need to use a value of 10,000 or more for pips filter.
