Simple Volatility MomentumOverview:
The Simple Volatility Momentum indicator calculates the mean and standard deviation of the changes of price (returns) using various types of moving averages (Incremental, Rolling, and Exponential). With quantifying the dispersion of price data around the mean, statistical insights are provided on the volatility and the movements of price and returns. The indicator also ranks the mean absolute value of the changes of price over a specified time period which helps you assess the strength of the "trend" and "momentum" regardless of the direction of returns.
Simple Volatility Momentum
This indicator can be used for mean reversion strategies and "momentum" or trend based strategies.
The indicator calculates the average return as the momentum metric and then gets the moving average of the average return and standard deviations from average return average. On the options you can determine if you want to use 1 or 2 standard deviation bands or have both of them enabled.
Settings:
Source: By default it's at close.
M Length: This is the length of the "momentum".
Rank Length: This is the length of the rank calculation of absolute value of the average return
MA Type: This is the different type of calculations for the mean and standard deviation. By default its at incremental.
Smoothing factor: (Only used if you choose the exponential MA type.)
The absolute value of the average return helps you see the strength of the "momentum" and trend. If there is a low ranking of the absolute value of the average return then you can eventually expect it to increase which means that the average return is trending, leading to trending price moves. If the Mean ABS rank value is at or near the maximum value 100 and the average return is at -2 standard deviation from the mean, you can see it as the negative momentum or trend being "finished". Similarly, if the Mean ABS value is near or at the maximum value 100 and the average return is at +2 standard deviation from the mean, you can view the uptrend, as "finished" and the Mean ABS rank can't really go higher than 100.
Moving Average Calculations type:
Incremental: Incremental moving averages use an incremental approach to update the moving average by adding the newest data point and subtracting the oldest one.
Exponential: The exponential moving average gives more weight to recent data points while still considering older ones. This is achieved by applying a smooth factor to the previous EMA value and the current data point. EMA's react more quickly to recent changes in the data compared to simple moving averages, making them useful for short term trends and momentum in financial markets.
Rolling: The moving average is calculated by taking the average of a fixed number of data points within a defined window. As new data becomes available, the window moves forward and the average is recalculated. Rolling Moving Averages are useful for smoothing out short-fluctuations and identifying trends over time.
Important thing to note about indicators involving bands and "momentum" or "trend" or prices:
For the explanation we will assume that stock returns follow a normal distribution and price follows a log normal distribution. Please note that in the live market this assumption isn't always true. Many people incorrectly use standard deviations on prices and trade them as mean reversion strategies or overbought or oversold levels which is not what standard deviations are meant for. Assuming you have applied the log transformation on the standard deviation bands (if your input is raw price then you should use a log transformation to remove the skewness of price), and you have a range of 2 standard deviations from the mean, under the empirical rule with enough occurrences 95% of the values will be within the 2 standard deviation range. This doesn't mean that if price falls to the bottom of the 2 standard deviation bound, there is a 95% chance it will revert back to mean, this is incorrect and not how standard deviations or mean reversion works.
"MOMENTUM"
In finance "momentum" refers to the rate of change of a time series data point. It shows the persistence or tendency for a data series to continue moving in its current direction. In finance, "momentum" based strategies capitalize on the observed tendency of assets that have performed well (or poorly) in the recent past to continue performing well (or poorly) in the near future. This persistence is often observed in various financial instruments including stocks, currencies and commodities.
"Momentum" is commonly calculated with the average return, and relies on the assumption that assets with positive "momentum" or a positive average return will likely continue to perform well in the short to medium term, while assets with a negative average return are expected to continue underperforming. This average return or expected value is derived from historical observations and statistical analysis of previous price movements. However, real markets are subject to levels of efficiencies, market fluctuations, randomness, and may not always produce consistent returns over time involving momentum based strategies.
Mean Reversion:
In finance, the average return is an important parameter in mean reversion strategies. Using statistical methodologies, mean reversion strategies aim to exploit the deviations from the historical average return by identifying instances where current prices and their changes diverge from their expected levels based on past performance. This approach involves statistical analysis and predictive modelling techniques to check where and when the average rate of change is likely to revert towards the mean. It's important to know that mean reversion is a temporary state and will not always be present in a specific timeseries.
Using the average return over price offers several advantages in finance and trading since it is less sensitive to extreme price movements or outliers compared to raw price data. Price itself contains a distribution that is usually positively-skewed and has no upper bound. Mean reversion typically occurs in distributions where extreme values are followed by a tendency for the variable to return towards its mean over time, however the probability distribution of price has no tendency for values to revert towards any specific level. Instead, values may continue to increase without a bound. Returns themself contain more stationary behavior than price levels. Mean reversion strategies rely on the assumption that deviations from the mean will eventually revert back to the mean. Returns, being more likely to exhibit stationary, are better suited for mean reversion based strategies.
The distribution of returns are often more symmetrically distributed around their mean compared to price distributions. This symmetry makes it easier to identify deviations from the mean and assess the likelihood of mean reversion occurrence. Returns are also less sensitive to trends and long-term price movements compared to price levels. Mean reversion strategies aim to exploit deviations from mean, which can be obscured when analyzing raw price data since raw price is almost always trending. Returns can filter out the trend component of price movements, making it easier to identify opportunities.
Stationary Process: Implication that properties like mean and variance remain relatively constant over time.
Oscillatori Momentum
CSC_Macchiato Price Trend Oscillator by CoffeeShopCryptoDescription:
Introducing "The Macchiato" – your go-to indicator in the realm of trading, meticulously crafted by CoffeeShopCrypto. Much like the complex flavors of a well-made Macchiato, this tool offers a robust suite of functionalities designed to enhance your trading experience across Forex, stocks, and cryptocurrencies.
The Macchiato stands out for its distinct ability to confirm market trends, identify building momentum, and navigate through periods of market stalls. Drawing cues from the RSI (Relative Strength Index), its color scheme dynamically reflects the shifts in market forces, providing traders with real-time insights into price action dynamics.
Gone are the days of navigating through market uncertainties blindly. With The Macchiato at your disposal, you gain a more clear understanding of market movements, helping you to make informed decisions with confidence. Whether you're a seasoned trader or just starting out, this indicator adds more confluence to your trading strategy, ensuring that you stay ahead of the curve in today's fast-paced trading environment.
So, say goodbye to guesswork and hello to precision trading with The Macchiato – where real-time data analysis meets actionable insights, all in the quest for trading success.
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The Histogram:
The Macchiato Histogram gives you a representation of current change in closing values against the closing values within a "Range" of the lookback period. It calculates your price ranging extremes of the highest high vs lowest low according to your lookback setting. Then it shows you whether the momentum of these closing prices calculated against the highs or the lows is either rising or falling. It takes into account the difference in the highs, lows, open and closing prices as a whole. This helps to determine the actual trend of your market structure.
Histogram Rising / Falling:
The Histogram of the Macchiato doesn't care about being above or below zero. Since we are simply calculating momentum rising or falling, then we only care that the histogram is rising or falling. Especially when it's doing this in line with our Macchiato Average.
Macchiato Average:
Included is a smoothing line called "the Macchiato Average"
Using the average is just like using a moving average against price action, however here we are using one against momentum to determine whether price is moving against, with, better, or worse than the current average of momentum.
The RSI:
The base purpose of the RSI in this tool is mainly to color the columns of the Histogram.
I gave you the benefit of exposing the RSI so you can see where it lies in the oscillator for any additional strategies you may use.
How the RSI Colors the Histogram:
The Histogram has 4 momentum colors and 1 non-momentum color.
They are described below but here we will understand why they exist the way they do against the RSI positioning.
The 4 momentum colors:
You can have the RSI in 4 different positions against its moving average.
Trend Trading Colors and setups:
Strong Long - RSI above zero and above its average is a strong long trend.
Strong Short - RSI below zero and below its average is a strong short trend.
These can also be seen as breakout indications.
Counter Trend Trading and Setups:
Weak Long - RSI above zero and below its average is a weak long trend.
Weak Short - RSI below zero and above its average is a weak short trend.
(Only trade weak momentum to known internal support and resistance areas.)
You'll notice that when the RSI is set up against its RSI Moving average under these conditions, the Histogram is colored accordingly to tell you whether you have strong or weak momentum.
For these colors to appear accordingly, the RSI and the momentum of the histogram must be moving in the same direction as each other.
The 5th color of the Histogram:
(GRAY)
This is where there is a divergence / disagreement between your RSI setup and the momentum being observed. If momentum is moving one way and the force of the RSI is not matching the overall momentum, you have a divergence. This commonly means that higher timeframe momentum is in disagreement to lower or current timeframe closing momentum changes.
Conditions of a Bullish Trending Market:
The color scheme has specific implications when all columns are above zero.
As noted before, the Macchiato doesn't care that it is above or below zero. It simply needs to be rising or falling. The color scheme is depicted by what the RSI is doing in relation to ZERO and where it lies against its average.
This image helps to differentiate what is happening with momentum when in a strong bullish market.
The color scheme is always the same. You always have 4 conditions of momentum.
According to the default settings:
Strong Long = Dark Green
Weak Long = Light Green
Strong Short = Dark Red
Weak short = Light Red
The final color is GRAY which is standard for a NON directional market.
You can alter the colors as you choose for your chart background and color preference.
Macchiato Bias Line Conditions
The "Bias Line" helps you understand whether your current momentum is traveling into opposing forces or if your momentum is going WITH the bias.
Going against the bias would be like momentum and price action converging against each other.
Going in the same direction would offer much larger movements on price.
Using the Bias to trade
Trading Internal Support and Resistance
Noting previous price action, pivots, and price swings is important in its use.
When you see momentum coming to a halt or changing, note the previous price action that you are approaching. You should see the histogram change its color, telling you where momentum is ending in its current direction.
EXAMPLE BULLISH MARKET FLIP
There is the exception however that when the market flips, from bearish to bullish, you can see the following:
BIAS LINE switches to a Bullish Bias
Histogram is showing Strong Long Color
Histogram stays ahead of the BIAS line
Price should break previous swing highs to create new highs.
Strategy you can use in observance. (short pause, to pullback, to continued short)
If the momentum is rising while RSI is falling you'll have gray columns until the RSI is in the correct positions against its average.
For your trend to continue downward you are looking for dark red or "Strong Short" columns.
When there is a disagreement, your columns will be gray and then switch to weak long. (Light Green)
This is because your RSI is above its average while the RSI is bearish.
This light green area can be observed as the pullback area.
If you only want to trade short, you would wait for the pullback to be complete.
This occurs when the weak long changes back to gray and then strong short, or skipping gray into strong short.
Dynamic Price Oscillator (Zeiierman)█ Overview
The Dynamic Price Oscillator (DPO) by Zeiierman is designed to gauge the momentum and volatility of asset prices in trading markets. By integrating elements of traditional oscillators with volatility adjustments and Bollinger Bands, the DPO offers a unique approach to understanding market dynamics. This indicator is particularly useful for identifying overbought and oversold conditions, capturing price trends, and detecting potential reversal points.
█ How It Works
The DPO operates by calculating the difference between the current closing price and a moving average of the closing price, adjusted for volatility using the True Range method. This difference is then smoothed over a user-defined period to create the oscillator. Additionally, Bollinger Bands are applied to the oscillator itself, providing visual cues for volatility and potential breakout signals.
█ How to Use
⚪ Trend Confirmation
The DPO can serve as a confirmation tool for existing trends. Traders might look for the oscillator to maintain above or below its mean line to confirm bullish or bearish trends, respectively. A consistent direction in the oscillator's movement alongside price trend can provide additional confidence in the strength and sustainability of the trend.
⚪ Overbought/Oversold Conditions
With the application of Bollinger Bands directly on the oscillator, the DPO can highlight overbought or oversold conditions in a unique manner. When the oscillator moves outside the Bollinger Bands, it signifies an extreme condition.
⚪ Volatility Breakouts
The width of the Bollinger Bands on the oscillator reflects market volatility. Sudden expansions in the bands can indicate a breakout from a consolidation phase, which traders can use to enter trades in the direction of the breakout. Conversely, a contraction suggests a quieter market, which might be a signal for traders to wait or to look for range-bound strategies.
⚪ Momentum Trading
Momentum traders can use the DPO to spot moments when the market momentum is picking up. A sharp move of the oscillator towards either direction, especially when crossing the Bollinger Bands, can indicate the start of a strong price movement.
⚪ Mean Reversion
The DPO is also useful for mean reversion strategies, especially considering its volatility adjustment feature. When the oscillator touches or breaches the Bollinger Bands, it indicates a deviation from the normal price range. Traders might look for opportunities to enter trades anticipating a reversion to the mean.
⚪ Divergence Trading
Divergences between the oscillator and price action can be a powerful signal for reversals. For instance, if the price makes a new high but the oscillator fails to make a corresponding high, it may indicate weakening momentum and a potential reversal. Traders can use these divergence signals to initiate counter-trend moves.
█ Settings
Length: Determines the lookback period for the oscillator and Bollinger Bands calculation. Increasing this value smooths the oscillator and widens the Bollinger Bands, leading to fewer, more significant signals. Decreasing this value makes the oscillator more sensitive to recent price changes, offering more frequent signals but with increased noise.
Smoothing Factor: Adjusts the degree of smoothing applied to the oscillator's calculation. A higher smoothing factor reduces noise, offering clearer trend identification at the cost of signal timeliness. Conversely, a lower smoothing factor increases the oscillator's responsiveness to price movements, which may be useful for short-term trading but at the risk of false signals.
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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!
Trend Tide Oscillator [UAlgo]🔶 Description:
The "Trend Tide Oscillator " is a technical analysis tool designed to identify potential trend reversals and overbought/oversold conditions in the market. It calculates an oscillator based on the Commodity Channel Index (CCI) and then applies smoothing techniques to provide a clearer view of market momentum.
🔶 Key Features:
Oscillator Calculation : The indicator calculates an oscillator based on the Commodity Channel Index (CCI), which is a momentum-based oscillator used to identify overbought and oversold conditions.
Smoothing : Smoothing techniques are applied to the oscillator to reduce noise and provide a clearer view of market momentum. This helps traders in identifying trends more effectively.
Support and Resistance Zones : The indicator plots support and resistance zones based on the highest and lowest values of the oscillator over a specified lookback (default 50) period. These zones can help traders identify potential areas of price reversal. The indicator considers volatility when plotting the support and resistance zones. This aims to create more adaptable levels that account for fluctuating market conditions.
Visualization : The indicator visually represents overbought and oversold conditions with shapes (⚠️), aiding traders in quickly identifying potential entry or exit points.
Customization : Users can adjust parameters such as oscillator length, smoothing, and overbought/oversold levels, support and resistance lookbacks according to their trading preferences.
🔶 Disclaimer :
This indicator is provided for informational and educational purposes only and should not be considered as financial advice. Trading in the financial markets involves risk, and users should conduct their own research and analysis before making any investment decisions.
Momentum Concepts [AlgoAlpha]🚀 Introducing the Momentum Concepts™ , a robust multi-layered momentum analysis tool developed by AlgoAlpha . This All-in-One indicator offers a comprehensive approach to understanding market momentum, empowering traders with hyper customizable features to tailor their analysis to their specific trading strategies.
Designed with efficiency and compactness in mind, the script shows momentum regimes on three time horizons: The short-term ( Fast Oscillator ), medium-term ( Scalper's Momentum ) and long-term ( Momentum Impulse Oscillator and Hidden Liquidity Flow ). Additionally, the script also includes reversal signals for traders who prefer to trade contrarian/mean-reversion strategies. By utilizing a blend of advanced algorithms and customizable parameters, Momentum Concepts™ provides traders with a vast array of trading strategies ranging from high frequency scalping to timing better entries on long-term swing and investing positions.
Let's delve into the key features and functionalities of this versatile indicator:
🎯Key Features (summary):
Customizable Fast Oscillator: Tailor the fast oscillator to your preferences with adjustable settings for type, source, trend identification(signal processing) method, length, and more.
Divergence Detection: Identify potential trend reversals with ease using built-in divergence detection for both bullish and bearish signals.
Momentum Impulse Oscillator: Gain deeper insights into trending/ranging markets and underlying market bias with a dedicated oscillator, featuring adjustable trend impulse thresholds.
Scalper's Momentum: Utilize a specialized momentum indicator designed for scalping strategies, featuring agility in signal detection with noise reduction and customizable smoothing parameters.
Hidden Liquidity Flow Analysis: Assess hidden liquidity flows within the market, highlighting excess liquidity and potential squeeze situations.
Trend Confluence Indicator: Evaluate the overall momentum direction with dynamically colored zones, aggregating signals from Momentum Concepts™ components for a holistic view.
User-Friendly Interface: The indicator is presented in a clear and intuitive manner, making it accessible for traders of all experience levels.
All-Rounded Alerts: The indicator comes with a comprehensive alerts extension in a separate script, allowing you to stay informed of important market movements even when away from your trading platform.
🎯Key Features (in-depth):
The Fast Oscillator within Momentum Concepts™ comprises four components designed to provide insights into short-term momentum dynamics:
🔱Price Volume Swings :
This confirmation component uses our proprietary Price Volume Algorithm to analyze price action and volume to identify buying and selling pressure, aiding traders in spotting short-term swings for potential trading opportunities.
⚜️Price Volume Waves :
This leading component also uses our proprietary Price Volume Algorithm but differs from the Price Volume Swings by capturing dominant wave patterns instead. This indicator breaks down price and volume data into a wave-like plot which enables leading insights into market momentum due to the relatively predicable nature of sine-like waves. Leading components such as this and the Alpha Wave are best used with other confirmation components within the Momentum Concepts™ .
🌊Alpha Wave :
The Alpha Wave is a leading non-volume alternative to the Price Volume Waves . It reflects market momentum by analyzing price action only instead of using volume data, resulting in a normalized wave-like plot similar to that of the Price Volume Waves , offering a leading perspective on potential market momentum shifts. Leading components such as this and the Price Volume Waves are best used with other confirmation components within the Momentum Concepts™ .
🐲Dragon RSI :
The Dragon RSI is a confirmation component that determines market momentum by analyzing the directional movement of the Relative Strength Index (RSI). By doing so, users are able to visually identify the current short term trend of the market as well as identify overbought and oversold conditions.
Reversal Signals :
All the Fast Oscillator components come with reversal signals that are based on the respective components being either oversold or overbought.
Divergences :
All the Fast Oscillator components come with bullish and bearish divergences. Divergences within the Fast Oscillator components of Momentum Concepts ™ offer crucial signals for trend shifts. 🔱 Price Volume Swings and ⚜️ Price Volume Waves detect weakening buying or selling pressure, signalling potential reversals or continuations. 🌊 Alpha Wave and 🐲 Dragon RSI identify divergences between momentum and price, aiding traders in anticipating market movements. Leveraging these divergences enhances analysis, aiding traders in formulating meaningful analysis.
Customizable Signal Processing Methods :
All the Fast Oscillator components come with customizable signal processing methods to identify trends on the Fast Oscillator , they include (but not limited to) methods such as Heiken Ashi, and a vast selection of Moving Averages.
Diminishing Momentum Warning :
All the Fast Oscillator components come with a diminishing momentum warning that represents a reducing momentum on the Fast Oscillator . This can act as a take profit signal or as a precautionary warning that the price is about to change direction soon even though the Fast Oscillator has not detected it yet.
Dynamically Colored Reversal Zones :
Last but not least, the dynamic coloring of the reversal zones for Fast Oscillator can be customised based on either the reversal probability of the Fast Oscillator or based on the overall trend confluence of all the components within the Momentum Concepts™ indicator.
The Momentum Impulse Oscillator in Momentum Concepts™ offers crucial insights into long-term momentum trends, aiding traders in identifying the underlying momentum regime and differentiating between trending and consolidating markets.
Underlying Momentum Bias
By default, the Momentum Impulse Oscillator is set to show the longer term trend of price action, this can be used to set the directional bias for the markets and prevent users from trading against the trend.
Trending/Ranging Detection
The Momentum Impulse Oscillator comes with the option to enable trending thresholds, when the Momentum Impulse Oscillator is beyond these thresholds, it indicates a trending market, when Momentum Impulse Oscillator is within the thresholds, it indicates a consolidating/ranging market.
The Scalper's Momentum within Momentum Concepts™ furnishes traders with nuanced signals ideal for short to medium-term trading strategies. It efficiently displays both the medium-term momentum and any emerging divergences towards the opposing direction.
Medium-Term Momentum
The Scalper's Momentum is designed to fill the analysis gap between the Fast Oscillator and the Momentum Impulse Oscillator . Showing momentum insights over the medium-term.
Momentum Convergence-Divergence
The Scalper's Momentum is also capable of showing momentum convergences and divergences, which can be used as take-profit and/or confirmation signals to other components within Momentum Concepts™ .
The Hidden Liquidity Flow component of Momentum Concepts™ is designed to uncover underlying liquidity dynamics. This feature enables traders to anticipate potential price movements based on changes in liquidity flow, enhancing their ability to make informed trading decisions.
Underlying Liquidity Dynamics
The Hidden Liquidity Flow shows the underlying liquidity flow of the market, a positive liquidity flow indicates that liquidity is entering the market and increasing the probability of bullish price action, the opposite is true for negative liquidity flows.
Excess Liquidity Flow
The Hidden Liquidity Flow also indicates when there is an abnormal amount of liquidity flowing through the market, this can indicate the potential for volatility and explosive price action.
🎯Usage Examples:
Now that we have gone through the components and features of Momentum Concepts™ in detail, we'll walk you through the usage examples and strategies that you can utilise to navigate the markets.
Scalping
Using the Scalper's Momentum and the Fast Oscillator as an example, users can first use the Scalper's Momentum as a directional bias and the Fast Oscillator as a means of timing a more precise entry. Take profits can be based on either the Diminishing Momentum Warnings or the Fast Oscillator flipping signals or the Scalper's Momentum flipping signals.
Buying the Dip/Shorting the Pump
Using the Momentum Impulse Oscillator and the Fast Oscillator as an example, users will need to first determine the underlying trend with the Momentum Impulse Oscillator , after which they can use the Fast Oscillator for entry signals into the trend. Take profits can be based on either the Diminishing Momentum Warnings or the Fast Oscillator flipping signals
Reversal Trading
Using the Momentum Impulse Oscillator on a timeframe roughly 3-4 times greater than the chart's timeframe and the Fast Oscillator as an example, users will need to first ensure that the Momentum Impulse Oscillator signals a ranging market on a higher timeframe, divergence signals from the Fast Oscillator can then be used as entries. Take profits can be based on either the Diminishing Momentum Warnings or the Fast Oscillator flipping signals or the Fast Oscillator reaching the zero line.
(These are just examples for reference, the Momentum Concepts™ offers significantly more possibilities for customisation and fine tuning of your trading strategy.)
🎯Conclusion:
In conclusion, Momentum Concepts™ stands as a versatile and powerful tool for traders seeking to decode the intricacies of market momentum across multiple time horizons. With its comprehensive suite of customizable features, including the Fast Oscillator , Scalper's Momentum , Momentum Impulse Oscillator , and Hidden Liquidity Flow , traders can gain deep insights into market dynamics and make well-informed trading decisions. Whether executing high-frequency scalping strategies or timing entries for longer-term positions, Momentum Concepts™ equips traders with the tools they need to navigate diverse market conditions with confidence. By harnessing the power of momentum analysis, this indicator empowers traders to stay ahead of the curve and capitalize on emerging opportunities in the ever-evolving financial markets.
Momentum Velocity [BackQuant]Momentum Velocity
Main Features:
- Momentum Based Oscillator
- Divergences
- Overbought and Oversold Conditions based off a VZO
- Alert Conditions
- Ability to make Adaptive
- Big User input menu for customisation
The Momentum Velocity indicator is based on the principle of momentum , which is a measure of the rate of change or the speed at which prices move over a specified time period. The underlying assumption of momentum trading is that assets that have performed well in the recent past will continue to perform well in the near future, and conversely, assets that have performed poorly will continue to perform poorly. This concept is widely accepted and empirically supported in financial literature, making the Momentum Velocity indicator empirically sound for several reasons:
Empirical Evidence on Momentum
Academic Research: A foundational piece of research that supports the momentum strategy is Jegadeesh and Titman's study, "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," published in the Journal of Finance in 1993. The authors find that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly generate significantly higher than expected returns over 3- to 12-month holding periods. This study is one of many that empirically validate the momentum effect in stock returns.
Behavioural Finance Theories:
Behavioural finance provides explanations for the momentum effect that go beyond the efficient market hypothesis. Theories such as investor herding, overreaction and under reaction to news, and the disposition effect can cause price trends to continue. The momentum strategy exploits these behavioural biases by assuming that prices will continue to move in their current direction for some time.
Global Evidence:
The momentum effect is not limited to specific markets or asset classes. Studies have documented momentum profits across various countries, markets, and asset types (stocks, bonds, commodities, and currencies). For instance, Asness, Moskowitz, and Pedersen in their paper, "Value and Momentum Everywhere," published in the Journal of Finance in 2013, show that momentum strategies can yield positive returns in different international markets.
Risk Factors:
Some researchers argue that the returns to momentum strategies are compensation for bearing certain risks. However, the empirical evidence suggests that momentum returns are difficult to explain by traditional risk factors alone, adding to the strategy’s attractiveness. The factor model of Carhart (1997), which adds a momentum factor to the Fama and French three-factor model, highlights the importance of momentum as a distinct source of returns.
Empirical Evidence Application
The Momentum Velocity indicator applies these empirical insights by quantitatively measuring the speed and direction of price movements over a given period, adjusting for recent market conditions through adaptive filtering, and normalizing the results to identify potential trading signals. By doing so, it provides traders with a tool that not only captures the essence of the momentum anomaly but also enhances it with modern technical analysis techniques for real-time market application.
Trading Application
Due to the robustness of momentum, traders are able to use this as a confluence metric into their system on any timeframe. Providing robust signals, that by extention are adaptive to the market. This is also further enabled by using adaptive filtering.
Conclusion
In summary, the empirical soundness of the Momentum Velocity indicator is grounded in the well-documented momentum effect observed in financial markets. By leveraging historical price data to predict future price movements, it aligns with both academic research and observed market behavior, making it a potentially valuable tool for traders seeking to exploit momentum-based trading opportunities.
User Inputs:
Calculation Source: Choose the price component (e.g., close) to base calculations on.
Lookback Period: Define the period over which momentum and normalization are calculated.
Use Adaptive Filtering?: Toggle the use of DEMA for more responsive momentum calculation.
Adaptive Lookback Period: Set the period for the adaptive filter when enabled.
Show Momentum Moving Average?: Option to display a moving average of the plotosc for trend smoothing.
MA Period: Specify the period for the momentum moving average.
Show Static High and Low Levels: Display predefined levels indicating extreme momentum thresholds.
Color Bars According to Trend?: Color price bars based on the momentum direction for quick visual reference.
Show Overbought and Oversold Signals: Highlight extreme volume conditions as potential buy/sell signals.
Signal Calculation Period: Set the period for calculating volume-based signals.
Show Detected Divergences?: Enable or disable the visualization of bullish and bearish divergences.
How it can be used in the context of a Trading System
Momentum and momentum divergences are pivotal concepts in trading systems, offering traders insights into the strength and potential reversal points of market trends. Momentum, a measure of the rate of price changes, helps traders identify the velocity of market movements, allowing them to ride the wave of prevailing trends for profits. When momentum divergences occur—where price movement and momentum indicators move in opposite directions—they signal a weakening of the current trend and potential for reversal. Traders can use these signals to adjust their positions, entering or exiting trades based on the anticipation of trend changes. Incorporating momentum and its divergences into a trading system provides a dynamic strategy that leverages the market's natural cycles of trend strength and exhaustion, aiming to capitalize on both continuation and reversal opportunities for enhanced trading outcomes.
We have also added a volume based component for traders to use as a point of confluence. It is shown on the chart giving background hues for overbought and oversold signals.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Squeeze Momentum DeluxeThe Squeeze Momentum Deluxe is a comprehensive trading toolkit built with features of momentum, volatility, and price action. This script offers a suite for both mean reversion and trend-following analysis. Developed based on the original TTM Squeeze implementation by @LazyBear, this indicator introduces several innovative components to enhance your trading insights.
🔲 Components and Features
Momentum Oscillator - as rooted in the TTM Squeeze, quantifies the relationship between price and its extremes over a defined period. By normalizing the calculation, the values become comparable throughout time and across securities, allowing for a nuanced assessment of Bullish and Bearish momentum. Furthermore, by presenting it as a ribbon with a signal line we gain additional information about the direction of price swings.
Squeeze Bars - The original squeeze concept is based on the relationship between the Bollinger Bands and Keltner Channel , once the BB resides inside the KC a squeeze occurs. By understanding their fundamentals a new form of calculation can be inferred.
method bb(float src, simple int len, simple float mult) => method kc(float src, simple int len, simple float mult) =>
float basis = ta.sma (src, len) float basis = ta.sma (src, len)
float dev = ta.stdev(src, len) float rng = ta.atr ( len)
float upper = basis + dev * mult float upper = basis + rng * mult
float lower = basis - dev * mult float lower = basis - rng * mult
Both BB and KC are constructed upon a moving average with the addition of Standard Deviation and Average True Range respectively. Therefore, the calculation can be transformed to when the Stdev is lower than the ATR a squeeze occurs.
method sqz(float src, simple int len) =>
float dev = ta.stdev(src, len)
float atr = ta.atr ( len)
dev < atr ? true : false
This indicator uses three different thresholds for the ATR to gain three levels of price "Squeeze" for further analysis.
Directional Flux- This component measures the overall direction of price volatility, offering insights into trend sentiment. Presented as waves in the background, it includes an OverFlux feature to signal extreme market bias in a particular direction which can signal either exhaustion or vital continuation. Additionally, the user can choose if to base the calculation on Heikin-Ashi Candles to bias the tool toward trend assessment.
Confluence Gauges - Placed at the top and bottom of the indicator, these gauges measure confluence in the relationship between the Momentum Oscillator and Directional Flux. They provide traders with an easily interpretable visual aid for detecting market sentiment. Reversal doritos displayed alongside them contribute to mean reversion analysis.
Divergences (Real-Time) - Equipped with a custom algorithm, the indicator detects real-time divergences between price and the oscillator. This dynamic feature enhances your ability to spot potential trend reversals as they occur.
🔲 Settings
Directional Flux Length - Adjusts the period of which the background volatility waves operate on.
Trend Bias - Bases the calculation of the Flux to HA candles to bias its behavior toward the trend of price action.
Squeeze Momentum Length - Calibrates the length of the main oscillator ribbon as well as the period for the squeeze algorithm.
Signal - Controls the width of the ribbon. Lower values result in faster responsiveness at the cost of premature positives.
Divergence Sensitivity - Adjusts a threshold to limit the amount of divergences detected based on strength. Higher values result in less detections, stronger structure.
🔲 Alerts
Sell Signal
Buy Signal
Bullish Momentum
Bearish Momentum
Bullish Flux
Bearish Flux
Bullish Swing
Bearish Swing
Strong Bull Gauge
Strong Bear Gauge
Weak Bull Gauge
Weak Bear Gauge
High Squeeze
Normal Squeeze
Low Squeeze
Bullish Divergence
Bearish Divergence
As well as the option to trigger 'any alert' call.
The Squeeze Momentum Deluxe is a comprehensive tool that goes beyond traditional momentum indicators, offering a rich set of features to elevate your trading strategy. I recommend using toolkit alongside other indicators to have a wide variety of confluence to therefore gain higher probabilistic and better informed decisions.
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
---
Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
ML - Momentum Index (Pivots)Building upon the innovative foundations laid by Zeiierman's Machine Learning Momentum Index (MLMI), this variation introduces a series of refinements and new features aimed at bolstering the model's predictive accuracy and responsiveness. Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), my adaptation seeks to enhance the original by offering a more nuanced approach to momentum-based trading.
Key Features :
Pivot-Based Analysis: Shifting focus from trend crosses to pivot points, this version employs pivot bars to offer a distinct perspective on market momentum, aiding in the identification of critical reversal points.
Extended Parameter Set: By integrating additional parameters for making predictions, the model gains improved adaptability, allowing for finer tuning to match market conditions.
Dataset Size Limitation: To ensure efficiency and mitigate the risk of calculation timeouts, a cap on the dataset size has been implemented, balancing between comprehensive historical analysis and computational agility.
Enhanced Price Source Flexibility: Users can select between closing prices or (suggested) OHLC4 as the basis for calculations, tailoring the indicator to different analysis preferences and strategies.
This adaptation not only inherits the robust framework of the original MLMI but also introduces innovations to enhance its utility in diverse trading scenarios. Whether you're looking to refine your short-term trading tactics or seeking stable indicators for long-term strategies, the ML - Momentum Index (Pivots) offers a versatile tool to navigate the complexities of the market.
For a deeper understanding of the modifications and to leverage the full potential of this indicator, users are encouraged to explore the tooltips and documentation provided within the script.
The Momentum Indicator calculations have been transitioned to the MLMomentumIndex library, simplifying the process of integration. Users can now seamlessly incorporate the momentumIndexPivots function into their scripts to conduct detailed momentum analysis with ease.
Composite Bull-Bear Dominance IndexNote: CREDITS: This is based on the Up Down Volume Indicator (published in Trading View) and Elder Ray Index (Bull Bear Power).
The Composite Bull Bear Dominance Index (CBBDI) is a indicator that combines up down volume analysis with Bull and Bear Power to provide a comprehensive view of market dynamics. It calculates Z-scores for up down volume delta and bull bear power measures, averages them, and then smoothes the result using Weighted Moving Average (WMA) for Bull and Bear Power and Volume Weighted Moving Average (VWMA) for Up and Down Volume Delta. The advantages include responsiveness to short-term trends, noise reduction through weighting, incorporation of volume information, and the ability to identify significant changes in buying and selling pressure. The indicator aims to offer clear signals for traders seeking insights into overall market dominance and indicate if the bulls or the bears have the upper hand.
Volume Analysis (Up/Down Volume Delta):
Up/Down Volume Delta reflects the net difference between buying and selling volume, providing insights into the prevailing market sentiment.
Positive Delta: Indicates potential bullish dominance due to higher buying volume.
Negative Delta: Suggests potential bearish dominance as selling volume surpasses buying volume.
Price Analysis (Bull and Bear Power):
Bull and Bear Power measure the strength of buying and selling forces based on price movements and the Exponential Moving Average (EMA) of the closing price.
Positive Bull Power: Reflects bullish dominance, indicating potential upward momentum.
Positive Bear Power: Suggests bearish dominance, indicating potential downward momentum.
Composite Bull Bear Dominance Index (CBBDI):
CBBDI combines the standardized Z-scores of Up/Down Volume Delta and Bull Bear Power, providing an average measure of both volume and price-related dominance.
Positive CBBDI: Indicates an overall bullish dominance in both volume and price dynamics.
Negative CBBDI: Suggests an overall bearish dominance in both volume and price dynamics.
Smoothing Techniques:
The use of Weighted Moving Average (WMA) for smoothing Bull and Bear Power Z-scores, and Volume Weighted Moving Average (VWMA) for smoothing Up/Down Volume Delta, reduces noise and provides a clearer trend signal.
Smoothing helps filter out short-term fluctuations and emphasizes more significant trends in both volume and price movements.
Color Coding:
CBBDI values are color-coded based on their direction, visually representing the prevailing market sentiment.
Green Colors: Positive values indicate potential bullish dominance.
Red Colors: Negative values suggest potential bearish dominance.
Median Proximity Percentile [AlgoAlpha]📊🚀 Introducing the "Median Proximity Percentile" by AlgoAlpha, a dynamic and sophisticated trading indicator designed to enhance your market analysis! This tool efficiently tracks median price proximity over a specified lookback period and finds it's percentile between 2 dynamic standard deviation bands, offering valuable insights for traders looking to make informed decisions.
🌟 Key Features:
Color-Coded Visuals: Easily interpret market trends with color-coded plots indicating bullish or bearish signals.
Flexibility: Customize the indicator with your preferred price source and lookback lengths to suit your trading strategy.
Advanced Alert System: Stay ahead with customizable alerts for key trend shifts and market conditions.
🔍 Deep Dive into the Code:
Choose your preferred price data source and define lookback lengths for median and EMA calculations. priceSource = input.source(close, "Source") and lookbackLength = input.int(21, minval = 1, title = "Lookback Length")
Calculate median value, price deviation, and normalized value to analyze market position relative to the median. medianValue = ta.median(priceSource, lookbackLength)
Determine upper and lower boundaries based on standard deviation and EMA. upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier
lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
Compute the percentile value to track market position within these boundaries. percentileValue = 100 * (normalizedValue - lowerBoundary)/(upperBoundary - lowerBoundary) - 50
Enhance your analysis with Hull Moving Average (HMA) for smoother trend identification. emaValue = ta.hma(percentileValue, emaLookbackLength)
Visualize trends with color-coded plots and characters for easy interpretation. plotColor = percentileValue > 0 ? colorUp : percentileValue < 0 ? colorDown : na
Set up advanced alerts to stay informed about significant market movements. // Alerts
alertcondition(ta.crossover(emaValue, 0), "Bullish Trend Shift", "Median Proximity Percentile Crossover Zero Line")
alertcondition(ta.crossunder(emaValue, 0), "Bearish Trend Shift", "Median Proximity Percentile Crossunder Zero Line")
alertcondition(ta.crossunder(emaValue,emaValue ) and emaValue > 90, "Bearish Reversal", "Median Proximity Percentile Bearish Reversal")
alertcondition(ta.crossunder(emaValue ,emaValue) and emaValue < -90, "Bullish Reversal", "Median Proximity Percentile Bullish Reversal")
🚨 Remember, the "Median Proximity Percentile " is a tool to aid your analysis. It’s essential to combine it with other analysis techniques and market understanding for best results. Happy trading! 📈📉
Momentum Bias Index [AlgoAlpha]Description:
The Momentum Bias Index by AlgoAlpha is designed to provide traders with a powerful tool for assessing market momentum bias. The indicator calculates the positive and negative bias of momentum to gauge which one is greater to determine the trend.
Key Features:
Comprehensive Momentum Analysis: The script aims to detect momentum-trend bias, typically when in an uptrend, the momentum oscillator will oscillate around the zero line but will have stronger positive values than negative values, similarly for a downtrend the momentum will have stronger negative values. This script aims to quantify this phenomenon.
Overlay Mode: Traders can choose to overlay the indicator on the price chart for a clear visual representation of market momentum.
Take-profit Signals: The indicator includes signals to lock in profits, they appear as labels in overlay mode and as crosses when overlay mode is off.
Impulse Boundary: The script includes an impulse boundary, the impulse boundary is a threshold to visualize significant spikes in momentum.
Standard Deviation Multiplier: Users can adjust the standard deviation multiplier to increase the noise tolerance of the impulse boundary.
Bias Length Control: Traders can customize the length for evaluating bias, enabling them to fine-tune the indicator according to their trading preferences. A higher length will give a longer-term bias in trend.
Ultimate Momentum"Ultimate Momentum" – Elevating Your Momentum Analysis
Experience a refined approach to momentum analysis with "Ultimate Momentum," a sophisticated indicator seamlessly combining the strengths of RSI and CCI. This tool offers a nuanced understanding of market dynamics with the following features:
1. Harmonious Fusion: Witness the dynamic interplay between RSI and CCI, providing a comprehensive understanding of market nuances.
2. Optimized CCI Dynamics: Delve confidently into market intricacies with optimized CCI parameters, enhancing synergy with RSI for a nuanced perspective on trends.
3. Standardized Readings: "Ultimate Momentum" standardizes RSI and CCI, ensuring consistency and reliability in readings for refined signals.
4. Native TradingView Integration: Immerse yourself in the reliability of native TradingView codes for RSI and CCI, ensuring stability and compatibility.
How RSI and CCI Work Together:
RSI (Relative Strength Index): Captures price momentum with precision, measuring the speed and change of price movements.
CCI (Commodity Channel Index): Strategically integrated to complement RSI, offering a unique perspective on price fluctuations and potential trend reversals.
Why "Ultimate Momentum"?
In a crowded landscape, "Ultimate Momentum" stands out, redefining how traders interpret momentum. Gain a profound understanding of market dynamics, spot trend reversals, and make informed decisions.
Your Insights Matter:
Share your suggestions to enhance "Ultimate Momentum" in the comments. Your feedback is crucial as we strive to deliver an unparalleled momentum analysis tool.
Machine Learning: STDEV Oscillator [YinYangAlgorithms]This Indicator aims to fill a gap within traditional Standard Deviation Analysis. Rather than its usual applications, this Indicator focuses on applying Standard Deviation within an Oscillator and likewise applying a Machine Learning approach to it. By doing so, we may hope to achieve an Adaptive Oscillator which can help display when the price is deviating from its standard movement. This Indicator may help display both when the price is Overbought or Underbought, and likewise, where the price may face Support and Resistance. The reason for this is that rather than simply plotting a Machine Learning Standard Deviation (STDEV), we instead create a High and a Low variant of STDEV, and then use its Highest and Lowest values calculated within another Deviation to create Deviation Zones. These zones may help to display these Support and Resistance locations; and likewise may help to show if the price is Overbought or Oversold based on its placement within these zones. This Oscillator may also help display Momentum when the High and/or Low STDEV crosses the midline (0). Lastly, this Oscillator may also be useful for seeing the spacing between the High and Low of the STDEV; large spacing may represent volatility within the STDEV which may be helpful for seeing when there is Momentum in the form of volatility.
Tutorial:
Above is an example of how this Indicator looks on BTC/USDT 1 Day. As you may see, when the price has parabolic movement, so does the STDEV. This is due to this price movement deviating from the mean of the data. Therefore when these parabolic movements occur, we create the Deviation Zones accordingly, in hopes that it may help to project future Support and Resistance locations as well as helping to display when the price is Overbought and Oversold.
If we zoom in a little bit, you may notice that the Support Zone (Blue) is smaller than the Resistance Zone (Orange). This is simply because during the last Bull Market there was more parabolic price deviation than there was during the Bear Market. You may see this if you refer to their values; the Resistance Zone goes to ~18k whereas the Support Zone is ~10.5k. This is completely normal and the way it is supposed to work. Due to the nature of how STDEV works, this Oscillator doesn’t use a 1:1 ratio and instead can develop and expand as exponential price action occurs.
The Neutral (0) line may also act as a Support and Resistance location. In the example above we can see how when the STDEV is below it, it acts as Resistance; and when it’s above it, it acts as Support.
This Neutral line may also provide us with insight as towards the momentum within the market and when it has shifted. When the STDEV is below the Neutral line, the market may be considered Bearish. When the STDEV is above the Neutral line, the market may be considered Bullish.
The Red Line represents the STDEV’s High and the Green Line represents the STDEV’s Low. When the STDEV’s High and Low get tight and close together, this may represent there is currently Low Volatility in the market. Low Volatility may cause consolidation to occur, however it also leaves room for expansion.
However, when the STDEV’s High and Low are quite spaced apart, this may represent High levels of Volatility in the market. This may mean the market is more prone to parabolic movements and expansion.
We will conclude our Tutorial here. Hopefully this has given you some insight into how applying Machine Learning to a High and Low STDEV then creating Deviation Zones based on it may help project when the Momentum of the Market is Bullish or Bearish; likewise when the price is Overbought or Oversold; and lastly where the price may face Support and Resistance in the form of STDEV.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Nasan Rate of Change (ROC)**NOTE: FOR COMPARISON TRADITIONAL ROC IS PLOTTED WITH THE SAME ROC LENGTH OF 9. IT IS NOT PART OF THE INDICATOR"
The Nasan ROC indicator is smoothed version of the of the traditional ROC indicator. The Nasna ROC uses a triple pass moving average differencing strategy. A cumulative sum of the deviations obtained from the moving average differencing provides a smooth "noise free" trend and this cumulative sum of deviations is used for calculating ROC.
Let's break down the components and understand the indicator we discussed earlier:
Sequential Triple Pass Filter:
Three filters with lengths specified by length1, length2, and length3 are applied to the closing prices (close).
The filters involve calculating the cumulative sum of the differences between the closing prices and their respective moving averages.
The idea is to detrend the data and accumulate the deviations from the average over time, emphasizing longer-term trends.
Calculation of Rate of Change (ROC) of Cumulative Sum:
The Rate of Change (ROC) of the cumulative sum (rocCumulativeSum) is calculated using the ta.roc function with a specified length (rocLength).
ROC measures the percentage change in the cumulative sum over a specified period.
The ROC histogram provides insights into the momentum of the detrended series. Positive values suggest increasing momentum, while negative values suggest decreasing momentum.
Pay attention to the color of the histogram bars.
The histogram bars are colored green if the current ROC value is greater than or equal to the previous ROC value, and red otherwise.
This coloring is based on the concept that a positive ROC suggests upward momentum, while a negative ROC suggests downward momentum.
Volatility - Volume Impact:
The Average True Range (ATR) is calculated with a period of 14.
Volume strength is calculated as a factor (VCF) that considers the ratio of the simple moving average (SMA) of the current volume to the SMA of the volume over a longer period (144).
This volume factor (VCF) is then multiplied by ATR, creating a synergy with volatility and volume.
Visualization with Background Color Gradient:
A background color gradient is applied to the chart based on the calculated volume strength (f1).
The gradient color ranges from black (indicating low ATR and volume strength) to purple (indicating high ATR and volume strength). A low value indicates a ranging market with no significant price movements and it is safter to avoid signals generated from ROC histogram in these region.
Synergy of ROC and Volume Strength:
Observe how the ROC signals align with the background color gradient. For example, confirm whether positive ROC aligns with periods of high ATR and volume strength.
This synergy can provide confirmation or divergence signals, adding another layer of analysis.
Market Forecast w/ Signals [QuantVue]The Market Forecast With Signals Indicator is an upgraded version of the popular ThinkorSwim platforms Market Forecast. This upgraded version utilizes stochastic oscillators, moving averages, and momentum calculations to find potential buying and selling opportunities.
Stochastic Oscillator
The indicator calculates three variations of the Fast Stochastic Oscillator for different time periods:
🔹Intermediate: Calculated over a medium-term period (default 31 bars).
🔹Momentum: Calculated over a short-term period (default 5 bars).
🔹Near Term: Calculated over a very short-term period (default 3 bars).
These calculations involve finding the highest and lowest values within their respective periods and comparing the current close to this range.
Moving Average Smoothing
The results of the Fast Stochastic Oscillator for the Intermediate and Near Term are then smoothed using a Simple Moving Average (SMA):
🔹Intermediate: 5-period SMA of the Intermediate Stochastic Oscillator.
🔹Near Term: 2-period SMA of the Near Term Stochastic Oscillator.
Momentum Indicator
A custom momentum calculation is performed, using the recent high and low prices over four periods.
Display
The indicator plots the smoothed Intermediate, Near Term, and custom Momentum calculations as separate lines on the chart.
Trading Signals
While the original indicator plots the lines mentioned above, the Market Forecast w/ Signals goes a step further by identifying key moments when nuanced signals fire. The built in alerts and visual aids make spotting these trading opportunities a breeze.
Clusters - Bullish and Bearish clusters are identified based on the convergence of all three lines (Intermediate, Near, and Momentum) above 80 (Bearish) or below 20 (Bullish).
The background color of the chart changes to indicate these clusters, aiding in quick identification of market extremes.
Trend Reversals - Marked with labels on the chart, this is based on the direction of the cluster (bullish or bearish) and the subsequent price movement crossing a threshold determined during the cluster formation.
Divergences - Divergences between the Near Term line and price highs/lows are detected using pivot points. These divergences are then plotted as lines on the chart, highlighting potential discrepancies between price action and momentum, which can signal reversals.
Indicator Features:
🔹Custom Colors
🔹Show/Hide Signals
🔹Alerts
Give this indicator a BOOST and COMMENT your thoughts!
We hope you enjoy.
Cheers!
Dynamic Volume-Volatility Adjusted MomentumThis Indicator in a refinement of my earlier script PC*VC Moving average Old with easier to follow color codes, overbought and oversold zones. This script has converted the previous script into a standardized measure by converting it into Z-scores and also incorporated a volatility based dynamic length option. Below is a detailed Explanation.
The "Dynamic Volume-Volatility Adjusted Momentum" or "Nasan Momentum Oscillator" is designed to capture market momentum while accounting for volume and volatility fluctuations. It leverages the Typical Price (TP), calculated as the average of high, low, and close prices, and introduces the Price Coefficient (PC) based on deviations from the simple moving average (SMA) across various time frames. Additionally, the Volume Coefficient (VC) compares current volume to SMA, and calculates Intraday Volatility (IDV) which gauges the daily price range relative to the close. Then intraday volatility ratio is calculated ( IDV Ratio) as the ratio of current Intraday Volatility (IDV) to the average of IDV for three different length periods, which provides a relative measure of current intraday volatility compared to its recent historical average. An inter-day ATR based Relative Volatility (RV) is calculated to adjusts for changing market volatility based on which the dynamic length adjustment adapts the moving average (standard length is 14). The PC *VC/IDV Ratio integrates price, volume, and volatility information which provides a volume and volatility adjusted momentum. This volume and volatility adjusted momentum is converted into a standardized Z-Score. The Z-Score measures deviations from the mean. Color-coded plots visually represent momentum, and thresholds aid in identifying overbought or oversold conditions.
The indicator incorporates a nuanced approach to emphasize the joint impact of price and volume while considering the stabilizing effect of lower intraday volatility. Placing the volume ratio (VC) in the numerator means that higher volume positively contributes to the overall ratio, aligning with the observation that increased volumes often accompany robust price movements. Simultaneously, the decision to include the inverse of intraday volatility (1/IDV) in the denominator acts as a dampener, reducing the impact of extreme intraday volatility on the momentum indicator. This design choice aims to filter out noise, giving more weight to significant price changes supported by substantial trading activity. In essence, the indicator's design seeks to provide a more robust momentum measure that balances the influence of price, volume, and volatility in the analysis of market dynamics.
3x MTF MACD v3.0MACD's on 3 different Time Frames
Indicator Information
- Each Time Frame shows start of Trend and end of trend of the MACD vs the Signal Cross
- They are labled 1,2,3 with respective up or down triangle for possible direction.
User Inputs
- configure the indicator by specifying various inputs. These inputs include colors for bullish
and bearish conditions, the time frame to use, whether to show a Simple Moving Average
(SMA) line, and other parameters.
- Users can choose time frames for analysis (like 30 minutes, 1 hour, etc.)
but they must be in mintues.
- The code also allows users to customize how the indicator looks on the chart by providing
options for position and color.
Main Calculations
- The script calculates the Simple Moving Average (SMA) based on the user-defined time
frame.
- It then determines the color of the plot (line) based on certain conditions, such as whether
the SMA is rising or falling. These conditions help users quickly identify market trends.
Label Creation
- The code creates labels that can be displayed on the chart.
These labels indicate whether there's a bullish or bearish signal.
Level Detection
- The script determines and labels key levels or points of interest in the chart based on
certain conditions.
- It can show labels like "①" and "▲" for bullish conditions and "▼" for bearish conditions.
Table Display
- There's an option to show a table on the chart that displays information about the MACD
indicator Chosen and the NUmber Bubble assocated with that time frame
- The table can include information like which time frame is being analyzed, whether the SMA
line is shown, and other relevant data.
Plotting on the Chart
- The script plots the Simple Moving Average (SMA) on the chart. The color of this line
changes based on the calculated trend conditions.
ATR (Average True Range)
- The script also plots the Average True Range (ATR) on the chart. ATR is used to measure
market volatility.
"In essence, this script is a highly customizable MACD and SMA indicator for traders. It assists traders in comprehending market trends, offering insights into different MACD cycles concerning various timeframes.
Users can configure it to match their trading strategies, and it presents information in a user-friendly manner with colors, labels, and tables.
This simplifies market analysis, allowing traders to make more informed decisions without the distraction of multiple indicators."
Worm *Public*This Pine Script code is designed to create a custom technical indicator called "Worm" that helps identify trends in the market based on momentum. Let's break down the code and its settings:
Indicator Title and Overlay:
The indicator is named "Worm (Clean)" and is set to be overlaid on the price chart.
Input Settings:
The code defines various input settings, which can be customized by the user. These settings include:
Indicator Settings (e.g., Alpha, Gap)
Backtest Settings (e.g., HighlightCrossovers, ApplyNorm)
Color Settings (e.g., Buy Color, Sell Color, Wait Color)
Location Settings for displaying the indicator above, below, or at the price.
Toggleable Inputs:
These settings allow you to choose whether the momentum indicator should be displayed above, below, or at the price chart. You can also specify the colors for buy, sell, and wait signals.
Indicator Calculations:
The code calculates momentum using various formulas involving the source price data (e.g., open, high, low, close). Momentum values are stored in variables L0, L1, L2, L3, and lrsi.
It also calculates the Color values for the indicator based on certain conditions and user-defined settings.
Bcolor and Scolor are used to determine the color of the plotted indicator based on buy and sell conditions.
Bollinger Bands (BB) and Keltner Channels (KC) Calculation:
The code calculates Bollinger Bands (UpperBB and LowerBB) and Keltner Channels (UpperKC and LowerKC) using the source price data.
It also determines whether the market is in a squeeze (SqzOn) or not (NoSqz) based on the relationship between BB and KC.
Signal Generation:
Buy and sell signals are generated based on various conditions, including momentum values and the squeeze state.
The color of the indicator line is determined based on the buy and sell signals.
LagF Calculation:
The LagF variable is calculated based on certain formulas involving the L0Line, L1Line, L2Line, and L3Line values.
Control Color:
The Color variable is used to control the color of the LagF indicator line based on certain conditions.
Plotting:
The momentum indicator (Val) is plotted on the chart with the specified colors and style.
The LagF indicator (Worm) is also plotted with a dynamic color based on market conditions.
Alerts are triggered when buy or sell signals are generated.
Experimental Section:
This section appears to be left for experimentation and may contain additional code or features.
Overall, this Pine Script code calculates and displays a custom momentum-based indicator called "Worm" on a price chart. It generates buy and sell signals based on momentum and squeeze conditions and allows users to customize various settings, including indicator location and colors. The code is designed for technical analysis and trend identification in financial markets.
Triple Ehlers Market StateClear trend identification is an important aspect of finding the right side to trade, another is getting the best buying/selling price on a pullback, retracement or reversal. Triple Ehlers Market State can do both.
Three is always better
Ehlers’ original formulation produces bullish, bearish and trendless signals. The indicator presented here gate stages three correlation cycles of adjustable lengths and degree thresholds, displaying a more refined view of bullish, bearish and trendless markets, in a compact and novel way.
Stick with the default settings, or experiment with the cycle period and threshold angle of each cycle, then choose whether ‘Recent trend weighting’ is included in candle colouring.
John Ehlers is a highly respected trading maths head who may need no introduction here. His idea for Market State was published in TASC June 2020 Traders Tips. The awesome interpretation of Ehlers’ work on which Triple Ehlers Market State’s correlation cycle calculations are based can be found at:
DISCLAIMER: None of this is financial advice.
Weighted Oscillator Convergence DivergenceThe Weighted Oscillator Convergence Divergence (WOCD) aims to help traders identify potential trend reversals or momentum shifts in financial markets by calculating and visualizing the difference between a smoothed oscillator (WMA) value and its exponential moving average (EMA) and simple moving average (SMA) counterparts. This indicator is particularly useful for traders who want an alternative perspective on price momentum and divergence.
Key Features:
Inputs:
Length: The user can specify the number of bars to consider for calculations (default is 9).
Smoothing 1: Defines the smoothing factor for the first smoothed value (default is 5).
Smoothing 2: Specifies the smoothing factor for the second smoothed value (default is 7).
Ma Type: There are three types of moving averages you can choose (Wilder, non-lag, Weighted is by default).
Color Settings: Users can customize the indicator's colors for various elements, such as length, smoothing values, and different sections of the histogram.
Calculation:
WOCD calculates the raw oscillator value by subtracting the close price from a 3-period High, Low, Close (HLC3) moving average.
It then applies smoothing to this raw oscillator value using two different methods: exponential moving average (EMA) and simple moving average (SMA) with user-defined smoothing periods.
Histogram Plot:
The indicator plots a histogram based on the difference between the smoothed oscillator and the first smoothed value.
When the histogram is above zero and rising, it is colored according to the "Above Grow" color setting. When it's above zero and falling, it uses the "Fall" color for visualization.
Similarly, when the histogram is below zero and rising, it is colored according to the "Below Grow" color setting, and when it's below zero and falling, it uses the "Fall" color.
Oscillator and Smoothed Values:
The indicator also plots the smoothed oscillator, smoothed value 1 (EMA-based), and smoothed value 2 (SMA-based) on the chart.
Zero Line:
A horizontal line at zero is drawn on the chart for reference.
How to Use the WOCD Indicator:
Trend Identification: Observe the histogram's direction and color. A rising histogram above zero may indicate bullish momentum, while a falling histogram below zero could signal bearish momentum.
Divergence: Look for divergences between price action and the histogram. When the histogram and price move in opposite directions, it can be a potential reversal signal.
Crossovers: Pay attention to crossovers between the smoothed oscillator and its smoothed counterparts (EMA and SMA). These crossovers can indicate changes in trend strength or direction.
Zero Line: The zero line can act as a reference point. Positive histogram values suggest bullish sentiment, while negative values indicate bearish sentiment.
Comparison to MACD Indicator:
The WOCD indicator shares some similarities with the Moving Average Convergence Divergence (MACD) indicator but also has distinct differences:
Similarities:
Both WOCD and MACD are momentum oscillators designed to identify potential trend reversals and divergences.
They use moving averages (EMA in the case of MACD) to smooth the raw oscillator values.
Both indicators provide histogram representations of the difference between the oscillator and its smoothed counterpart.
Differences:
WOCD uses a 3-period High, Low, Close (HLC3) moving average to calculate the raw oscillator value, whereas MACD uses the difference between two exponential moving averages (usually 12-period and 26-period EMAs).
The smoothing in WOCD employs both EMA and SMA, while MACD exclusively uses EMA.
WOCD allows users to customize colors for various elements, enhancing visual clarity.
Momentum ChannelbandsThe "Momentum Channelbands" is indicator that measures and displays an asset's momentum. It includes options to calculate Bollinger Bands and Donchian Channels around the momentum. Users can customize settings for a comprehensive view of momentum-related insights. This tool helps assess trend strength, identify overbought/oversold conditions, and pinpoint highs/lows. It should be used alongside other indicators due to potential lag and false signals.
Composite Momentum IndicatorComposite Momentum Indicator" combines the signals from several oscillators, including Stochastic, RSI, Ultimate Oscillator, and Commodity Channel Index (CCI) by averaging the standardized values (Z-Scores). Since it is a Z-Score based indicators the values will be typically be bound between +3 and -3 oscillating around 0. Here's a summary of the code:
Input Parameters: Users can customize the look-back period and set threshold values for overbought and oversold conditions. They can also choose which oscillators to include in the composite calculation.
Oscillator Calculations: The code calculates four separate oscillators - Stochastic, RSI, Ultimate Oscillator, and CCI - each measuring different aspects of market momentum.
Z-Scores Calculation: For each oscillator, the code calculates a Z-Score, which normalizes the oscillator's values based on its historical standard deviation and mean. This allows for a consistent comparison of oscillator values across different timeframes.
Composite Z-Score: The code aggregates the Z-Scores from the selected oscillators, taking into account user preferences (whether to include each oscillator). It then calculates an average Z-Score to create the "Composite Momentum Oscillator."
Conditional Color Coding: The composite oscillator is color-coded based on its average Z-Score value. It turns green when it's above the overbought threshold, red when it's below the oversold threshold, and blue when it's within the specified range.
Horizontal Lines: The code plots horizontal lines at key levels, including 0, ±3, ±2, and ±1, to help users identify important momentum levels.
Gradient Fills: It adds gradient fills above the overbought threshold and below the oversold threshold to visually highlight extreme momentum conditions.
Combining the Stochastic, RSI, Ultimate Oscillator, and Commodity Channel Index (CCI) into one composite indicator offers several advantages for traders and technical analysts:
Comprehensive Insight: Each of these oscillators measures different aspects of market momentum and price action. Combining them into one indicator provides a more comprehensive view of the market's behavior, as it takes into account various dimensions of momentum simultaneously.
Reduced Noise: Standalone oscillators can generate conflicting signals and produce noisy readings, especially during choppy market conditions. A composite indicator smoothes out these discrepancies by averaging the signals from multiple indicators, potentially reducing false signals.
Confirmation and Divergence: By combining multiple oscillators, traders can seek confirmation or divergence signals. When multiple oscillators align in the same direction, it can strengthen a trading signal. Conversely, divergence between the oscillators can warn of potential reversals or weakening trends.
Customization: Traders can tailor the composite indicator to their specific trading strategies and preferences. They have the flexibility to include or exclude specific oscillators, adjust look-back periods, and set threshold levels. This adaptability allows for a more personalized approach to technical analysis.
Clarity and Efficiency: Rather than cluttering the chart with multiple individual oscillators, a composite indicator condenses the information into a single plot. This enhances the clarity of the chart and makes it easier for traders to quickly interpret market conditions.
Overbought/Oversold Identification: Combining these oscillators can improve the identification of overbought and oversold conditions. It reduces the likelihood of false signals since multiple indicators must align to trigger these extreme conditions.
Educational Tool: For novice traders and analysts, a composite indicator can serve as an educational tool by demonstrating how different oscillators interact and influence each other's signals. It allows users to learn about multiple technical indicators in one glance.
Efficient Use of Screen Space: A single composite indicator occupies less screen space compared to multiple separate indicators. This is especially beneficial when analyzing multiple markets or timeframes simultaneously.
Holistic Approach: Instead of relying on a single indicator, a composite approach encourages a more holistic assessment of market conditions. Traders can consider a broader range of factors before making trading decisions.
Increased Confidence: A composite indicator can boost traders' confidence in their decisions. When multiple reliable indicators align, it can provide a stronger basis for taking action in the market.
In summary, combining the Stochastic, RSI, Ultimate Oscillator, and CCI into one composite indicator enhances the depth and reliability of technical analysis. It simplifies the decision-making process, reduces noise, and offers a more complete picture of market momentum, ultimately helping traders make more informed and well-rounded trading decisions.
* Feel free to compare against individual oscillatiors*
