Kalman Filtered RSI Oscillator [BackQuant]Kalman Filtered RSI Oscillator
The Kalman Filtered RSI Oscillator is BackQuants new free indicator designed for traders seeking an advanced, empirical approach to trend detection and momentum analysis. By integrating the robustness of a Kalman filter with the adaptability of the Relative Strength Index (RSI), this tool offers a sophisticated method to capture market dynamics. This indicator is crafted to provide a clearer, more responsive insight into price trends and momentum shifts, enabling traders to make informed decisions in fast-moving markets.
Core Principles
Kalman Filter Dynamics:
At its core, the Kalman Filtered RSI Oscillator leverages the Kalman filter, renowned for its efficiency in predicting the state of linear dynamic systems amidst uncertainties. By applying it to the RSI calculation, the tool adeptly filters out market noise, offering a smoothed price source that forms the basis for more accurate momentum analysis. The inclusion of customizable parameters like process noise, measurement noise, and filter order allows traders to fine-tune the filter’s sensitivity to market changes, making it a versatile tool for various trading environments.
RSI Adaptation:
The RSI is a widely used momentum oscillator that measures the speed and change of price movements. By integrating the RSI with the Kalman filter, the oscillator not only identifies the prevailing trend but also provides a smoothed representation of momentum. This synergy enhances the indicator's ability to signal potential reversals and trend continuations with a higher degree of reliability.
Advanced Smoothing Techniques:
The indicator further offers an optional smoothing feature for the RSI, employing a selection of moving averages (HMA, THMA, EHMA, SMA, EMA, WMA, TEMA, VWMA) for traders seeking to reduce volatility and refine signal clarity. This advanced smoothing mechanism is pivotal for traders looking to mitigate the effects of short-term price fluctuations on the RSI's accuracy.
Empirical Significance:
Empirically, the Kalman Filtered RSI Oscillator stands out for its dynamic adjustment to market conditions. Unlike static indicators, the Kalman filter continuously updates its estimates based on incoming price data, making it inherently more responsive to new market information. This dynamic adaptation, combined with the RSI's momentum analysis, offers a powerful approach to understanding market trends and momentum with a depth not available in traditional indicators.
Trend Identification and Momentum Analysis:
Traders can use the Kalman Filtered RSI Oscillator to identify strong trends and momentum shifts. The color-coded RSI columns provide immediate visual cues on the market's direction and strength, aiding in quick decision-making.
Optimal for Various Market Conditions:
The flexibility in tuning the Kalman filter parameters makes this indicator suitable for a wide range of assets and market conditions, from volatile to stable markets. Traders can adjust the settings based on empirical testing to find the optimal configuration for their trading strategy.
Complementary to Other Analytical Tools:
While powerful on its own, the Kalman Filtered RSI Oscillator is best used in conjunction with other analytical tools and indicators. Combining it with volume analysis, price action patterns, or other trend-following indicators can provide a comprehensive view of the market, allowing for more nuanced and informed trading decisions.
The Kalman Filtered RSI Oscillator is a groundbreaking tool that marries empirical precision with advanced trend analysis techniques. Its innovative use of the Kalman filter to enhance the RSI's performance offers traders an unparalleled ability to navigate the complexities of modern financial markets. Whether you're a novice looking to refine your trading approach or a seasoned professional seeking advanced analytical tools, the Kalman Filtered RSI Oscillator represents a significant step forward in technical analysis capabilities.
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
移動平均線
Cauchy Distribution Trend AnalysisThis custom Pine Script indicator is designed to analyze assets, including cryptocurrencies, through a lens inspired by the Cauchy distribution's characteristics. It focuses on identifying potential long and short opportunities by evaluating the asset's price position relative to a dynamically calculated median price and a scale parameter. Here's a breakdown of its components and how to use it:
Components
Median Length: The period over which the median price is calculated. The median price acts as a proxy for the Cauchy distribution's location parameter, representing a central value around which the market price fluctuates.
MA Length: The length for calculating the moving average, which is used to determine the scale parameter. The scale parameter estimates the average volatility around the median price, adjusted for the selected averaging method.
Moving Average Type: Offers a choice between HMA (Hull Moving Average), SMA (Simple Moving Average), and EMA (Exponential Moving Average) to calculate the scale parameter. This flexibility allows users to tailor the sensitivity of the scale parameter to the asset's price volatility.
Median Price Calculation: Uses the close price (by default) to calculate the median price over the specified period.
Scale Parameter Calculation: A function that calculates the scale parameter based on the chosen average source. This parameter is used to identify the threshold for long and short conditions.
Strategy Logic
Long Condition: Triggered when the asset's close price is greater than the sum of the median price and the scale parameter. This indicates that the asset's price has moved significantly above the median price, suggesting bullish momentum.
Short Condition: Triggered when the asset's close price is less than the difference between the median price and the scale parameter. This indicates that the asset's price has moved significantly below the median price, suggesting bearish momentum.
EHRHART Algo Premium (V.2)EHRHART Algo Premium is a indicator designed to help traders analyze market flow. It work with multiple EMA for identifying the sentiment of market. It's very simple calculation but it's a good help for people who use price action. I think the visual of the chart is very important and and I wanted to create an indicator very visual. I'm price action lover like lots of people and I personally think it's very important to identify the flow of market because buying when the flow of market is up give you better chance to win your trade. It's not BUY and SELL signal, this indicator don't tell u when u need buy or when u need sell, it's principally here for helping the visual of trading chart (have a good clear chart). I decided to post this indicator because people were asking me how it worked and were curious about these colors, so here we go !
This indicator show:
The main flow ( green candle=buy pressure /red candle=seller pressure ), it's based on two EMA cross over, this two EMA are editable so u can take the combination you want depending on your trading strategy. When the first EMA is above the second EMA candle becoming green and when the second EMA is above the first EMA candle becoming red.
The trend of two EMA crossover (blue=bullish and violet=bearish), it's based on two EMA (two different than main flow) cross over, this two EMA are editable so u can take the combination you want depending on your trading strategy. When the first EMA is above the second EMA the trend becoming blue and when the second EMA is above the first EMA the trend becoming violet.
Potential trend reversals (violet candle), it's calculate with the two EMA of the main flow, when these two EMA becoming closer, the candle becoming violet. It meaning that the trend may reversals. I added sensitivity parameter, so u can adjust it depending on your trading strategy, the more sensitive it is, the more candle will be colored violet.
A system of RSI print on the chart, when the RSI becoming overbought (more than 75) a red triangle will pop up on the chart, and when the RSI becoming oversold (less than 25) a green triangle will pop up on the chart. U can show or hidden these setting.
Bullish candles are represented by hollow candles.
Bearish candles are represented by full candles.
You can use this indicator with multiple strategy, I personally use it with price action (support/resistance) and I made it for that (but it's your choice).
This is an example of how I'll use it:
Here we can see that the price is coming testing our weakly support, however the main flow is bullish (red candle), so I'm waiting my first signal (violet candle). When the first candle passed violet I decided to enter the trade because violet candle after red candle means that the two EMA start closed to themselves meaning that's the flow may turn green. My second signal will be candle passed green, because it meaning the two EMA start deviate from themselves, buyer are taking advantage. In this situation a green triangle on the support will be my third signal.
CAPACE MARKETThis custom indicator combines the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI) into a single trading tool. It calculates the MACD and RSI values, then averages these two indicators to create a composite line. This average line is intended to capture the momentum and relative strength of the market simultaneously, potentially offering a more nuanced view of market conditions.
Key features of the indicator include:
Visualization of MACD and RSI Lines: It plots the MACD and RSI values as separate lines on the chart, allowing traders to see the behavior of each indicator clearly.
Average Line: A line representing the average of the MACD and RSI indicators is plotted, providing a synthesized view of both momentum and strength.
Entry Points Indication: The indicator uses red dots to mark the points where the average line crosses over or under the MACD or RSI lines. These intersections are meant to signal potential entry points for traders.
Market Condition Highlighting: The background color changes based on whether the average line is above or below zero. A green background suggests a positive market condition (bullish), while a red background indicates a negative market condition (bearish).
This tool aims to offer traders an integrated perspective by combining the insights of both MACD and RSI, potentially aiding in the identification of entry and exit points as well as the overall market sentiment.
Adaptive Moving Average (AMA) Signals (Zeiierman)█ Overview
The Adaptive Moving Average (AMA) Signals indicator, enhances the classic concept of moving averages by making them adaptive to the market's volatility. This adaptability makes the AMA particularly useful in identifying market trends with varying degrees of volatility.
The core of the AMA's adaptability lies in its Efficiency Ratio (ER), which measures the directionality of the market over a given period. The ER is calculated by dividing the absolute change in price over a period by the sum of the absolute differences in daily prices over the same period.
⚪ Why It's Useful
The AMA Signals indicator is particularly useful because of its adaptability to changing market conditions. Unlike static moving averages, it dynamically adjusts, providing more relevant signals that can help traders capture trends earlier or identify reversals with greater accuracy. Its configurability makes it suitable for various trading strategies and timeframes, from day trading to swing trading.
█ How It Works
The AMA Signals indicator operates on the principle of adapting to market efficiency through the calculation of the Efficiency Ratio (ER), which measures the directionality of the market over a specified period. By comparing the net price change to total price movements, the AMA adjusts its sensitivity, becoming faster during trending markets and slower during sideways markets. This adaptability is enhanced by a gamma parameter that filters signals for either trend continuation or reversal, making it versatile across different market conditions.
change = math.abs(close - close )
volatility = math.sum(math.abs(close - close ), n)
ER = change / volatility
Efficiency Ratio (ER) Calculation: The AMA begins with the computation of the Efficiency Ratio (ER), which measures the market's directionality over a specified period. The ER is a ratio of the net price change to the total price movements, serving as a measure of the efficiency of price movements.
Adaptive Smoothing: Based on the ER, the indicator calculates the smoothing constants for the fastest and slowest Exponential Moving Averages (EMAs). These constants are then used to compute a Scaled Smoothing Coefficient (SC) that adapts the moving average to the market's efficiency, making it faster during trending periods and slower in sideways markets.
Signal Generation: The AMA applies a filter, adjusted by a "gamma" parameter, to identify trading signals. This gamma influences the sensitivity towards trend or reversal signals, with options to adjust for focusing on either trend-following or counter-trend signals.
█ How to Use
Trend Identification: Use the AMA to identify the direction of the trend. An upward moving AMA indicates a bullish trend, while a downward moving AMA suggests a bearish trend.
Trend Trading: Look for buy signals when the AMA is trending upwards and sell signals during a downward trend. Adjust the fast and slow EMA lengths to match the desired sensitivity and timeframe.
Reversal Trading: Set the gamma to a positive value to focus on reversal signals, identifying potential market turnarounds.
█ Settings
Period for ER calculation: Defines the lookback period for calculating the Efficiency Ratio, affecting how quickly the AMA responds to changes in market efficiency.
Fast EMA Length and Slow EMA Length: Determine the responsiveness of the AMA to recent price changes, allowing traders to fine-tune the indicator to their trading style.
Signal Gamma: Adjusts the sensitivity of the filter applied to the AMA, with the ability to focus on trend signals or reversal signals based on its value.
AMA Candles: An innovative feature that plots candles based on the AMA calculation, providing visual cues about the market trend and potential reversals.
█ Alerts
The AMA Signals indicator includes configurable alerts for buy and sell signals, as well as positive and negative trend changes.
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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!
DEMA Adjusted Average True Range [BackQuant]The use of the Double Exponential Moving Average (DEMA) within your Adjusted Average True Range (ATR) calculation serves as a cornerstone for enhancing the indicator's responsiveness to market changes. To delve deeper into why DEMA is employed specifically in the context of your ATR calculation, let's explore the inherent qualities of DEMA and its impact on the ATR's performance.
DEMA and Its Advantages
As previously mentioned, DEMA was designed to offer a more responsive alternative to the traditional Exponential Moving Average (EMA). By giving more weight to recent price data, DEMA reduces the lag typically associated with moving averages. This reduction in lag is especially beneficial for short-term traders looking to capitalize on trend reversals and other market movements as swiftly as possible.
The calculation of DEMA involves the following steps:
Calculate EMA1: This is the Exponential Moving Average of the price.
Calculate EMA2: This is the Exponential Moving Average of EMA1, thus it is a smoothing of a smoothing, leading to a greater lag.
Formulate DEMA: The formula
EMA1 = EMA of price
EMA2 = EMA of EMA1
DEMA = (2 x EMA1) - EMA2
effectively doubles the weighting of the most recent data points by subtracting the lagged, double-smoothed EMA2 from twice the single-smoothed EMA1.
This process enhances the moving average's sensitivity to recent price movements, allowing the DEMA to adhere more closely to the price bars than either EMA1 or EMA2 alone.
Integration with ATR
In the context of your ATR calculation, the integration of DEMA plays a crucial role in defining the indicator's core functionality. Here's a detailed explanation of how DEMA affects the ATR calculation:
Initial Determination of DEMA : By applying the DEMA formula to the chosen source data (which can be adjusted to use Heikin Ashi candle close prices for an even smoother analysis), you set a foundation for a more reactive trend-following mechanism within the ATR framework.
Application to ATR Bands : The calculated DEMA serves as the central line from which the ATR bands are derived. The ATR value, multiplied by a user-defined factor, is added to and subtracted from the DEMA to form the upper and lower bands, respectively. This dynamic adjustment not only reflects the volatility based on the ATR but does so in a way that is closely aligned with the most recent price action, thanks to the utilization of DEMA.
Enhanced Signal Quality : The responsiveness of DEMA ensures that the ATR bands adjust more promptly to changes in market conditions. This quality is vital for traders who rely on the ATR bands to identify potential entry and exit points, trend reversals, or to assess market volatility.
By employing DEMA as the core component in calculating the Adjusted Average True Range, your indicator leverages DEMA's reduced lag and increased weight on recent data to provide a more timely and accurate measure of market volatility. This innovative approach enhances the utility of the ATR by making it not only a tool for assessing volatility but also a more reactive indicator for trend analysis and trading signal generation.
The main concept of combining these is to reduce lag, get a more robust signal and still capture clear trends over medium time horizons.
For me, this is best used in confluence with other indicators, it can be made faster in order to get fasters response time, or slower. This is all depending on the needs of you as a trader.
User Inputs:
The script offers several user-configurable inputs, such as the period lengths for DEMA and ATR calculations, the multiplication factor for the ATR, and options to use Heikin Ashi candles or standard price data. Additionally, it allows for the toggling of visual features, like the plotting of the DEMA ATR and its moving average, and the application of color-coded trends on price bars.
Additional Features:
Moving Average Confluence: Traders can opt to display a moving average of the DEMA ATR, choosing from various types (e.g., SMA, EMA, HMA). This feature provides a layer of confluence, aiding in the identification of trend direction and strength.
Trend Identification :
The script employs logical conditions to ascertain the trend direction based on the movement of the DEMA ATR. It assigns colors to represent bullish or bearish trends, which are reflected in the plotted lines and the coloring of price bars.
Alerts :
Customizable alert conditions for trend reversals enhance the utility of the indicator for active trading, notifying users of significant changes in trend direction.
1D Backtests
We include these backtests as a general proxy for how they work.
Please do your own calibrating to suit it to your own needs and backtest.
Past results don't = future results but they can help you understand how it functions.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Volume Based S/R with EMA Crossover SignalsThis Pine Script indicator, titled "Volume Based S/R with EMA Crossover Signals," is designed for use on the TradingView platform and overlays on price charts to help traders identify potential buy and sell opportunities based on volume changes and EMA (Exponential Moving Average) crossovers. Let's break down its components for a detailed understanding:
Inputs
length: The number of bars used to calculate the standard deviation of the volume change. This parameter helps in identifying significant changes in volume over a specified period.
threshold: A multiplier applied to the standard deviation of volume change to determine significant spikes in volume, which are then used to identify support and resistance levels.
smoothLength: The length of the EMA used to smooth the price data, providing a clearer view of the overall price trend and helping to confirm trade signals.
fastEMALength and slowEMALength: The lengths of the fast and slow EMAs, respectively. These are used to generate crossover signals, where the crossing of the fast EMA over the slow EMA may indicate a potential entry or exit point.
Calculations
Volume Change and Standard Deviation: The script calculates the percentage change in volume from one bar to the next and then computes the standard deviation of these changes over the specified length. This process helps identify unusual volume activity, which can precede significant price movements.
Signal Generation Based on Volume: When the absolute value of the volume change divided by its standard deviation exceeds the threshold, it signals significant volume activity, potentially indicating strong support or resistance levels at previous highs or lows.
Smoothed Price: An EMA applied to the closing prices over smoothLength bars helps to confirm the trend direction and filter out noise.
EMA Crossover Signals: The script calculates two EMAs based on the fastEMALength and slowEMALength inputs. A crossover of these two averages generates potential buy or sell signals.
Logic for Buy/Sell Signals
Buy Signal: Generated when the price is above the identified support level (determined by significant volume activity), the fast EMA crosses above the slow EMA, and the price is also above the smoothed price. This confluence of conditions suggests upward momentum and potential buying opportunity.
Sell Signal: The opposite conditions generate a sell signal — when the price is below the identified resistance level, the fast EMA crosses below the slow EMA, and the price is below the smoothed price, indicating downward momentum and a potential selling opportunity.
Plotting
Support and Resistance Levels: Plotted as circles on the chart, with resistance levels in red and support levels in green, based on significant volume activity.
Smoothed Price and EMAs: The smoothed price line and both EMAs are plotted on the chart to help visually assess the trend and the crossover signals.
Buy and Sell Signals: Represented by shapes plotted on the chart, indicating the recommended trading action (buy or sell) based on the combined indicator logic.
Filling Between Support and Resistance: For visual clarity, the area between the identified support and resistance levels is filled, highlighting the range within which the price is expected to fluctuate.
This indicator offers a multi-faceted approach to trading, combining volume analysis with trend following via EMA crossovers. By identifying significant volume-based support and resistance levels and confirming trend direction with EMA crossovers and smoothed price trends, traders can make more informed decisions regarding entry and exit points. However, it's important to use this indicator as part of a comprehensive trading strategy, considering other factors such as market conditions, news, and technical analysis from other indicators.
Hull Trend and CompareThis Pine Script is a TradingView indicator called "Hull Trend and Compare." Its main purpose is to provide a visual representation of price trends and a comparative analysis between the selected symbol and another symbol chosen for comparison.
The key components and functionalities:
Price Trend Visualization:
1.Mode Selection:
Offers three modes: "Normal," "Linear," and "Heikin-Ashi."
Allows users to choose between a standard chart, linear regression, or Heikin-Ashi candlesticks.
2.Hull Moving Average (HullMA):
Calculates the HullMA for the selected mode and length.
Plots the HullMA on the chart.
Colors the background based on the relationship between HullMA and the closing price.
Generates buy and sell signals when the price crosses over or under the HullMA.
Symbol Comparison:
1.Comparison with Another Symbol:
Allows users to compare the selected symbol with another symbol (specified in the sym input).
Provides options to choose the method of calculation for the compared symbol (open, high, low, close).
Users can choose whether to use a different method of calculation (usem), adjust the length (len), and enable or disable comparison (usecmp).
Table Display:
1.Table for Technical Indicators:
Optionally displays a table showing technical indicators for both symbols.
Includes Stochastic Momentum, RSI (Relative Strength Index), and MACD (Moving Average Convergence Divergence).
Colors the table cells based on the direction of the indicators.
Users can customize the table's position, text size, and visibility (shwtbl).
Technical Indicators:
1.Stochastic Momentum (StochMoM):
Calculates %K and %D using the Stochastic formula.
Displays StochMoM values and colors cells based on bullish or bearish conditions.
2.Relative Strength Index (RSI):
Computes the RSI values and colors cells based on the direction of the trend.
3.MACD (Moving Average Convergence Divergence):
Calculates MACD and Signal line values.
Displays MACD values and colors cells based on bullish or bearish conditions.
Summary:
This script provides traders with a versatile tool for analyzing price trends, comparing symbols, and viewing key technical indicators. The combination of visual elements on the chart and a detailed table enhances the ability to make informed trading decisions.
This script is provided for educational purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
F2X IndexThis script is designed to analyze financial market data, particularly focusing on trends and volatility. It allows users to input parameters such as index length and signal length. The script calculates moving averages and differences between the source data and the moving averages. It also optionally adjusts for volatility using the Average True Range (ATR) and can color the signal based on trend direction. The output includes plots for the index and signal, with customizable colors based on trend and volatility. The script provides a visual representation of market dynamics to aid in decision-making for traders and investors.
Multi Time Frame Exponential Moving Average and dasboardThis Pine script, titled "Multi Time Frame Exponential Moving Average (MTF EMA)," provides an innovative approach for traders who wish to track trends across multiple timeframes without having to switch between different charts. It combines two main features: an indicator displaying exponential moving averages (EMA) on five different time periods, as well as a compact dashboard that synthesizes this information on a single chart window.
The originality of this script lies in its ability to provide a comprehensive analysis of EMA trends across different time intervals, allowing traders to quickly and clearly understand the market dynamics without having to navigate between multiple charts. Rather than switching from one chart to another to observe trends on different time scales, traders can now consult a single dashboard to obtain all the necessary information.
The script uses exponential moving averages (EMA) to identify trends over five time periods: 5 minutes, 15 minutes, 1 hour, 4 hours, and 1 day. The values of the EMAs are calculated based on the closing prices of candles. Bullish or bearish trends are indicated by upward or downward arrows respectively, making it easy to interpret the information on the dashboard.
To use this script, traders can simply add it to their chart on the TradingView platform. They can customize the parameters of the exponential moving averages according to their preferences and choose between a dark or light theme for the dashboard. Then, they can observe trends on different time scales directly on the dashboard, enabling them to make informed trading decisions.
In summary, this script offers a practical and innovative solution for tracking trends across multiple timeframes, combining the efficiency of exponential moving averages with the convenience of a dashboard centralized on a single chart. This allows traders to save time and stay informed about market movements effectively and efficiently.
Volatility Adjusted EMA - by CrunchsterApplies recent volatility adjustment to the exponential moving average, where the smoothing factor is 2/(N + 1) - N being the lookback period or span
Volatility of recent 30 days returns is calculated using standard deviation with a thirty day lookback.
Increased smoothing compared to a standard EMA, which also adjusts to market conditions, as first described by Chande in 1991.
Composite Trend Oscillator [ChartPrime]CODE DUELLO:
Have you ever stopped to wonder what the underlying filters contained within complex algorithms are actually providing for you? Wouldn't it be nice to actually visually inspect for that? Those would require some kind of wild west styled quick draw duel or some comparison method as a proper 'code duello'. Then it can be determined which filter can 'draw' the quickest from it's computational holster with the least amount of lag and smoothness.
In Pine we can do so, discovering how beneficial that would be. This can be accomplished by quickly switching from one filter to another by input() back and forth, requiring visual memory. A better way could be done by placing two indicators added to the chart and then eventually placed into one indicator pane on top of each other.
By adding a filter() helper function that calls other moving average functions chosen for comparison, it can put to the test which moving average is the best drawing filter suited to our expected needs. PhiSmoother was formerly debuted and now it is utilized in a more complex environment in a multitude of ways along side other commonly utilized filters. Now, you the reader, get to judge for yourself...
FILTER VERSATILITY:
Having the capability to adjust between various smoothing methods such as PhiSmoother, TEMA, DEMA, WMA, EMA, and SMA on historical market data within the code provides an advantage. Each of these filter methods offers distinct advantages and hinderances. PhiSmoother stands out often by having superb noise rejection, while also being able to manipulate the fine-tuning of the phase or lag of the indicator, enhancing responsiveness to price movements.
The following are more well-known classic filters. TEMA (Triple Exponential Moving Average) and DEMA (Double Exponential Moving Average) offer reduced transient response times to price changes fluctuations. WMA (Weighted Moving Average) assigns more weight to recent data points, making it particularly useful for reduced lag. EMA (Exponential Moving Average) strikes a balance between responsiveness and computational efficiency, making it a popular choice. SMA (Simple Moving Average) provides a straightforward calculation based on the arithmetic mean of the data. VWMA and RMA have both been excluded for varying reasons, both being unworthy of having explanation here.
By allowing for adjustment refinements between these filter methods, traders may garner the flexibility to adapt their analysis to different market dynamics, optimizing their algorithms for improved decision-making and performance on demand.
INDICATOR INTRODUCTION:
ChartPrime's Composite Trend Oscillator operates as an oscillator based on the concept of a moving average ribbon. It utilizes up to 32 filters with progressively longer periods to assess trend direction and strength. Embedded within this indicator is an alternative view that utilizes the separation of the ribbon filaments to assess volatility. Both versions are excellent candidates for trend and momentum, both offering visualization of polarity, directional coloring, and filter crossings. Anyone who has former experience using RSI or stochastics may have ease of understanding applying this to their chart.
COMPOSITE CLUSTER MODES EXPLAINED:
In Trend Strength mode, the oscillator behavior signifies market direction and movement strength. When the oscillator is rising and above zero, the market is within a bullish phase, and visa versa. If the signal filter crosses the composite trend, this indicates a potential dynamic shift signaling a possible reversal. When the oscillator is teetering on its extremities, the market is more inclined to reverse later.
With Volatility mode, the oscillator undergoes a transformation, displaying an unbounded oscillator driven by market volatility. While it still employs the same scoring mechanism, it is now scaled according to the strength of the market move. This can aid with identification of ranging scenarios. However, one side effect is that the oscillator no longer has minimum or maximum boundaries. This can still be advantageous when considering divergences.
NOTEWORTHY SETTINGS FEATURES:
The following input settings described offer comprehensive control over the indicator's behavior and visualization.
Common Controls:
Price Source Selection - The indicator offers flexibility in choosing the price source for analysis. Traders can select from multiple options.
Composite Cluster Mode - Choose between "Trend Strength" and "Volatility" modes, providing insights into trend directionality or volatility weighting.
Cluster Filter and Length - Selects a filter for the cluster composition. This includes a length parameter adjustment.
Cluster Options:
Cluster Dispersion - Users can adjust the separation between moving averages in the cluster, influencing the sensitivity of the analysis.
Cluster Trimming - By modifying upper and lower trim parameters, traders can adjust the sensitivity of the moving averages within the cluster, enhancing its adaptability.
PostSmooth Filter and Length - Choose a filter to refine the composite cluster's post-smoothing with a length parameter adjustment.
Signal Filter and Length - Users can select a filter for the lagging signal plot, also having a length parameter adjustment.
Transition Easing - Sensitivity adjustment to influence the transition between bullish and bearish colors.
Enjoy
Exponentially Weighted Moving Average Oscillator [BackQuant]Exponentially Weighted Moving Average (EWMA)
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
Applications of the EWMA
The EWMA is widely used in technical analysis. It may not be used directly, but it is used in conjunction with other indicators to generate trading signals. A well-known example is the Negative Volume Index (NVI), which is used in conjunction with its EWMA.
Why is it different from the In-Built TradingView EWMA
Adaptive Algorithms: If your strategy requires the alpha parameter to change adaptively based on certain conditions (for example, based on market volatility), a for loop can be used to adjust the weights dynamically within the loop as opposed to the fixed decay rate in the standard EWMA.
Customization: A for loop allows for more complex and nuanced calculations that may not be directly supported by built-in functions. For example, you might want to adjust the weights in a non-standard way that the typical EWMA calculation doesn't allow for.
Use of the Oscillator
This mainly comes from 3 main premises, this is something I like to do personally since it is easier to work with them in the context of my system. E.g. Using them to spot clear trends without noise on longer timeframes.
Clarity: Plotting the EWMA as an oscillator provides a clear visual representation of the momentum or trend strength. It allows traders to see overbought or oversold conditions relative to a normalized range.
Comparison: An oscillator can make it easier to compare different securities or timeframes on a similar scale, especially when normalized. This is because the oscillator values are typically bounded within a range (like -1 to 1 or 0 to 100), whereas the actual price series can vary significantly.
Focus on Change: When plotted as an oscillator, the focus is on the rate of change or the relative movement of the EWMA, not on the absolute price levels. This can help traders spot divergences or convergences that may not be as apparent when the EWMA is plotted directly on the price chart. This is also one reason there is a conditional plotting on the chart.
Trend Strength: When normalized, the distance of the oscillator from its midpoint can be interpreted as the strength of the trend, providing a quantitative measure that can be used to make systematic trading decisions.
Here are the backtests on the 1D Timeframe for
BITSTAMP:BTCUSD
BITSTAMP:ETHUSD
COINBASE:SOLUSD
When using this script the user is able to define a source and period, which by extension calculates the alpha. An option to colour the bars accord to trend.
This makes it super easy to use in a system.
I recommend using this as above the midline (0) for a positive trend and below the midline for negative trend.
Hence why I put a label on the last bar to ensure it is easier for traders to read.
Lastly, The decreasing colour relative to RoC, this also helps traders to understand the strength of the indicator and gain insight into when to potentially reduce position size.
This indicator is best used in the medium timeframe.
Dynamic Gradient Filter
Sigmoid Functions:
History and Mathematical Basis:
Sigmoid functions have a rich history in mathematics and are widely used in various fields, including statistics, machine learning, and signal processing.
The term "sigmoid" originates from the Greek words "sigma" (meaning "S-shaped") and "eidos" (meaning "form" or "type").
The sigmoid curve is characterized by its smooth S-shaped appearance, which allows it to map any real-valued input to a bounded output range, typically between 0 and 1.
The most common form of the sigmoid function is the logistic function:
Logistic Function (σ):
Defined as σ(x) = 1 / (1 + e^(-x)), where:
'x' is the input value,
'e' is Euler's number (approximately 2.71828).
This function was first introduced by Belgian mathematician Pierre François Verhulst in the 1830s to model population growth with limiting factors.
It gained popularity in the early 20th century when statisticians like Ronald Fisher began using it in regression analysis.
Specific Sigmoid Functions Used in the Indicator:
sig(val):
The 'sig' function in this indicator is a modified version of the logistic function, clamping a value between 0 and 1 on the sigmoid curve.
siga(val):
The 'siga' function adjusts values between -1 and 1 on the sigmoid curve, offering a centered variation of the sigmoid effect.
sigmoid(val):
The 'sigmoid' function provides a standard implementation of the logistic function, calculating the sigmoid value of the input data.
Adaptive Smoothing Factor:
The ' adaptiveSmoothingFactor(gradient, k)' function computes a dynamic smoothing factor for the filter based on the gradient of the price data and the user-defined sensitivity parameter 'k' .
Gradient:
The gradient represents the rate of change in price, calculated as the absolute difference between the current and previous close prices.
Sensitivity (k):
The 'k' parameter adjusts how quickly the filter reacts to changes in the gradient. Higher values of 'k' lead to a more responsive filter, while lower values result in smoother outputs.
Usage in the Indicator:
The "close" value refers to the closing price of each period in the chart's time frame
The indicator calculates the gradient by measuring the absolute difference between the current "close" price and the previous "close" price.
This gradient represents the strength or magnitude of the price movement within the chosen time frame.
The "close" value plays a pivotal role in determining the dynamic behavior of the "Dynamic Gradient Filter," as it directly influences the smoothing factor.
What Makes This Special:
The "Dynamic Gradient Filter" indicator stands out due to its adaptive nature and responsiveness to changing market conditions.
Dynamic Smoothing Factor:
The indicator's dynamic smoothing factor adjusts in real-time based on the rate of change in price (gradient) and the user-defined sensitivity '(k)' parameter.
This adaptability allows the filter to respond promptly to both minor fluctuations and significant price movements.
Smoothed Price Action:
The final output of the filter is a smoothed representation of the price action, aiding traders in identifying trends and potential reversals.
Customizable Sensitivity:
Traders can adjust the 'Sensitivity' parameter '(k)' to suit their preferred trading style, making the indicator versatile for various strategies.
Visual Clarity:
The plotted "Dynamic Gradient Filter" line on the chart provides a clear visual guide, enhancing the understanding of market dynamics.
Usage:
Traders and analysts can utilize the "Dynamic Gradient Filter" to:
Identify trends and reversals in price movements.
Filter out noise and highlight significant price changes.
Fine-tune trading strategies by adjusting the sensitivity parameter.
Enhance visual analysis with a dynamically adjusting filter line on the chart.
Literature:
en.wikipedia.org
medium.com
en.wikipedia.org
LMACD - Logarithmic MACD Weekly BTC Index [Logue]Logarithmic Moving Average Convergence Divergence (LMACD) Weekly Indicator - The LMACD is a momentum indicator that measures the strength of a trend using 12-period and 26-period moving averages. The weekly LMACD for this indicator is calculated by determining the difference between the log (base 10) of the 12-week and 26-week exponential moving averages. Larger positive numbers indicate a larger positive momentum.
For tops: The default setting for tops is based on decreasing "strength" of BTC tops. A decreasing linear function (trigger = slope * time + intercept) was fit to past cycle tops for this indicator and is used as the default to signal macro tops. The user can change the slope and intercept of the line by changing the slope and/or intercept factor. The user also has the option to indicate tops based on a horizontal line via a settings selection. This line default value is 0.125. This indicator is triggered for a top when the LMACD is above the trigger value.
For bottoms: Bottoms are displayed based on a horizontal line with a default setting of -0.07. The indicator is triggered for a bottom when the LMACD is below the bottom trigger value.
MA / Connectable [Azullian]Streamline trend analysis with the Moving Average indicator. Filter out market noise, aiding in the clear identification of market directions for dynamic strategy development.
This connectable moving average indicator is part of an indicator system designed to help test, visualize and build strategy configurations without coding. Like all connectable indicators , it interacts through the TradingView input source, which serves as a signal connector to link indicators to each other. All connectable indicators send signal weight to the next node in the system until it reaches either a connectable signal monitor, signal filter and/or strategy.
█ UNIFORM SETTINGS AND A WAY OF WORK
Although connectable indicators may have specific weight scoring conditions, they all aim to follow a standardized general approach to weight scoring settings, as outlined below.
■ Connectable indicators - Settings
• 🗲 Energy: Energy applies an ATR multiplier to the plotted shapes on the chart. A higher value plots shapes farther away from the candle, enhancing visibility.
• ☼ Brightness: Brightness determines the opacity of the shape plotted on the chart, aiding visibility. Indicator weight also influences opacity.
• → Input: Use the input setting to specify a data source for the indicator. Here you can connect the indicator to other indicators.
• ⌥ Flow: Determine where you want to receive signals from:
○ Both: Weights from this indicator and the connected indicator will apply
○ Indicator only: Only weights from this indicator will apply
○ Input only: Only weights from the connected indicator will apply
• ⥅ Weight multiplier: Multiply all weights in the entire indicator by a given factor, useful for quickly testing different indicators in a granular setup.
• ⥇ Threshold: Set a threshold to indicate the minimum amount of weight it should receive to pass it through to the next indicator.
• ⥱ Limiter: Set a hard limit to the maximum amount of weight that can be fed through the indicator.
■ Connectable indicators - Weight scoring settings
▢ Weight scoring conditions
• SM – Signal mode: Enable specific conditions for weight scoring
○ Start: A new trend starting will score
○ End: A trend ending will score
○ Zone: Continuous scoring for each candle between the start and the end.
• SP – Signal period: Defines a range of candles within which a signal can score.
• SC - Signal count: Specifies the number of bars to retrospectively examine and score.
○ Single: Score for a single occurrence
○ All occurrences: Score for all occurrences
○ Single + Threshold: Score for single occurrences within the signal period (SP)
○ Every + Threshold: Score for all occurrences within the signal period (SP)
▢ Weight scoring direction
• ES: Enter Short weight
• XL: Exit long weight
• EL: Enter Long weight
• XS: Exit Short weight
▢ Weight scoring values
• Weights can hold either positive or negative scores. Positive weights enhance a particular trading direction, while negative weights diminish it.
█ MA - INDICATOR SETTINGS
■ Main settings
• Enable/Disable Indicator: Toggle the entire indicator on or off.
• T - Type: Choose a type of moving average. (ALMA, EMA, HMA, RMA, SMA, SWMA, VWMA, WMA)
• L - Length: Set a period on which the moving average is calculated.
• F - Filter: Set a conditional filter for scoring:
○ Line direction: Score bullish when the trend line is going up, score bearish when the trendline is going down.
○ Line candle position: Score bullish when the candles are above the current trendline, score bearish when the candles are below the current trendline
○ Any: Score if any of the previously mentioned conditions are true
○ All: Score if all of the previously mentioned conditions are true
• S - Source: Choose an alternative data source for the Moving average calculation.
• T - Timeframe: Select an alternative timeframe for the Moving average calculation.
• C - Candletype: Choose a candletype for the alternative source.
■ Scoring functionality
• For each moving average you'll be able to score Bullish, Bearish or Neutral for each of the conditions as mentioned in the filter above.
█ PLOTTING
• Standard: Symbols (EL, XS, ES, XL) Moving average lines are plotted with bearish, bullish and neutral zones, in the visuals section you can enable plotting by weight which will only show the parts of the moving average line to which weight is addressed.
• Conditional Settings: A larger icon appears if global conditions are met. For instance, with a Threshold(⥇) of 12, Signal Period (SP) of 3, and Scoring Condition (SC) set to "EVERY", a moving average signaling over two times in 3 candles (scoring 6 each) triggers a larger icon.
█ USAGE OF CONNECTABLE INDICATORS
■ Connectable chaining mechanism
Connectable indicators can be connected directly to the signal monitor, signal filter or strategy , or they can be daisy chained to each other while the last indicator in the chain connects to the signal monitor, signal filter or strategy. When using a signal filter you can chain the filter to the strategy input to make your chain complete.
• Direct chaining: Connect an indicator directly to the signal monitor, signal filter or strategy through the provided inputs (→).
• Daisy chaining: Connect indicators using the indicator input (→). The first in a daisy chain should have a flow (⌥) set to 'Indicator only'. Subsequent indicators use 'Both' to pass the previous weight. The final indicator connects to the signal monitor, signal filter, or strategy.
■ Set up this indicator with a signal filter and strategy
The indicator provides visual cues based on signal conditions. However, its weight system is best utilized when paired with a connectable signal filter, signal monitor, or strategy .
Let's connect the MA to a connectable signal filter and a strategy :
1. Load all relevant indicators
• Load MA / Connectable
• Load Signal filter / Connectable
• Load Strategy / Connectable
2. Signal Filter: Connect the MA to the Signal Filter
• Open the signal filter settings
• Choose one of the three input dropdowns (1→, 2→, 3→) and choose : MA / Connectable: Signal Connector
• Toggle the enable box before the connected input to enable the incoming signal
3. Signal Filter: Update the filter signals settings if needed
• The default settings of the filter enable EL (Enter Long), XL (Exit Long), ES (Enter Short) and XS (Exit Short).
4. Signal Filter: Update the weight threshold settings if needed
• All connectable indicators load by default with a score of 6 for each direction (EL, XL, ES, XS)
• By default, weight threshold (TH) is set at 5. This allows each occurrence to score, as the default score in each connectable indicator is 1 point above the threshold. Adjust to your liking.
5. Strategy: Connect the strategy to the signal filter in the strategy settings
• Select a strategy input → and select the Signal filter: Signal connector
6. Strategy: Enable filter compatible directions
• Set the signal mode of the strategy to a compatible direction with the signal filter.
Now that everything is connected, you'll notice green spikes in the signal filter representing long signals, and red spikes indicating short signals. Trades will also appear on the chart, complemented by a performance overview. Your journey is just beginning: delve into different scoring mechanisms, merge diverse connectable indicators, and craft unique chains. Instantly test your results and discover the potential of your configurations. Dive deep and enjoy the process!
█ BENEFITS
• Adaptable Modular Design: Arrange indicators in diverse structures via direct or daisy chaining, allowing tailored configurations to align with your analysis approach.
• Streamlined Backtesting: Simplify the iterative process of testing and adjusting combinations, facilitating a smoother exploration of potential setups.
• Intuitive Interface: Navigate TradingView with added ease. Integrate desired indicators, adjust settings, and establish alerts without delving into complex code.
• Signal Weight Precision: Leverage granular weight allocation among signals, offering a deeper layer of customization in strategy formulation.
• Advanced Signal Filtering: Define entry and exit conditions with more clarity, granting an added layer of strategy precision.
• Clear Visual Feedback: Distinct visual signals and cues enhance the readability of charts, promoting informed decision-making.
• Standardized Defaults: Indicators are equipped with universally recognized preset settings, ensuring consistency in initial setups across different types like momentum or volatility.
• Reliability: Our indicators are meticulously developed to prevent repainting. We strictly adhere to TradingView's coding conventions, ensuring our code is both performant and clean.
█ COMPATIBLE INDICATORS
Each indicator that incorporates our open-source 'azLibConnector' library and adheres to our conventions can be effortlessly integrated and used as detailed above.
For clarity and recognition within the TradingView platform, we append the suffix ' / Connectable' to every compatible indicator.
█ COMMON MISTAKES, CLARIFICATIONS AND TIPS
• Removing an indicator from a chain: Deleting a linked indicator and confirming the "remove study tree" alert will also remove all underlying indicators in the object tree. Before removing one, disconnect the adjacent indicators and move it to the object stack's bottom.
• Point systems: The azLibConnector provides 500 points for each direction (EL: Enter long, XL: Exit long, ES: Enter short, XS: Exit short) Remember this cap when devising a point structure.
• Flow misconfiguration: In daisy chains the first indicator should always have a flow (⌥) setting of 'indicator only' while other indicator should have a flow (⌥) setting of 'both'.
• Hide attributes: As connectable indicators send through quite some information you'll notice all the arguments are taking up some screenwidth and cause some visual clutter. You can disable arguments in Chart Settings / Status line.
• Layout and abbreviations: To maintain a consistent structure, we use abbreviations for each input. While this may initially seem complex, you'll quickly become familiar with them. Each abbreviation is also explained in the inline tooltips.
• Inputs: Connecting a connectable indicator directly to the strategy delivers the raw signal without a weight threshold, meaning every signal will trigger a trade.
█ A NOTE OF GRATITUDE
Through years of exploring TradingView and Pine Script, we've drawn immense inspiration from the community's knowledge and innovation. Thank you for being a constant source of motivation and insight.
█ RISK DISCLAIMER
Azullian's content, tools, scripts, articles, and educational offerings are presented purely for educational and informational uses. Please be aware that past performance should not be considered a predictor of future results.
PUELL - PUELL Top and Bottom Indicator for BTC [Logue]Puell Multiple Indicator (PUELL) - The Puell multiple is the ratio between the daily coin issuance in USD and its 365-day moving average. This multiple helps to measure miner profitability. The PUELL indicator smooths the Puell multiple using a 14-day simple moving average. When the PUELL goes to high values relative to historical values, it indicates the profitability of the miners is high and a top may be near. When the PUELL is low relative to historical values, it indicates the profitability of the minors is low and a bottom may be near. The default trigger values are PUELL values above 3.0 for a "top" and below 0.5 for a "bottom".
Urika Trend StrengthThe Urika Directional Strength (UDS) indicator calculates and visualizes the strength of the directional trend in the price data. It helps traders see the strength and direction of the trend and allows them to make informed trading decisions based on trend changes.
Calculation:
The Simple Moving Average is used to determine the upper and lower directional bands by adding and subtracting the product of the standard deviation of the price data and the multiplier of the moving average.
Direction: The upward directional trend and downward directional trend are calculated by taking the absolute value of the difference between the price data and the upper and lower directional bands, divided by the product of the standard deviation and the multiplier.
Strength: It is calculated by taking the absolute value of the difference between the price data and the moving average, divided by the product of the standard deviation and the multiplier.
Interpretation:
Direction: The position of the long and short lines at the top indicates the direction of the ticker. Long line for long position and Short line for short position.
Strength: When the Strength line is below the directional lines, it is a weak trend or consolidating. If it stays in between the two directional lines, it is a strong trend.
Divergence Signal [TradingFinder] RSI & MACD Reversal On Swing🔵 Introduction
Sometimes in analyzing price charts using indicators, you may observe a discrepancy. For instance, while the price of stocks, currencies, or commodities is increasing, the indicator shows a decrease. Such a phenomenon in technical analysis is termed "divergence." Divergences are categorized into three types based on their formation and the prediction they make about the continuation of the price trend: "Regular Divergence," "Hidden Divergence," and "Time Divergence."
🟣 Important :
• This indicator exclusively identifies regular divergences since its primary function is to detect reversal points.
• This indicator identifies divergences using three indicators: "Moving Average Convergence Divergence" (MACD), "Relative Strength Index" (RSI), and "Awesome Oscillator" (AO). The user can choose each of these indicators in the settings using the "Divergence Detection Method" dropdown menu for identifying divergences. These settings are by default set to the MACD mode.
🔵Types of Divergence
Divergences, as mentioned, offer different predictions about the continuation of price trends. Hence, they have various types. We will focus on explaining regular divergences based on this indicator.
🟣 Regular Divergence(RD) :
Regular divergence is a situation arising from contradictory behavior between the indicator and the price chart at the end of a trend. By identifying regular divergences, we anticipate a change in trend direction resembling a reversal pattern.
Regular divergence has two types based on the trend and prediction:
Negative Regular Divergence (RD-) :
This type occurs between two price peaks at the end of an uptrend. Despite forming a new high, the indicator fails to recognize it, indicating a negative regular divergence. The likelihood of a subsequent downtrend is high. Negative divergence suggests strong selling pressure and weak buying power, portraying an unfavorable future for the stock.
Positive Regular Divergence (RD+) :
In contrast, positive regular divergence happens at the end of a downtrend and between two price troughs. As depicted in the chart, although the price forms a new low, the indicator doesn't acknowledge it. Positive regular divergence indicates robust buying pressure and weak selling power. Upon identifying positive divergence in the chart, we expect a price increase for the stock under review
🔵 How to Use
Information from the indicator is displayed in two ways: Table and Label.
🟣 Table : The table displays information about the latest divergence. This includes the type of divergence, existence or absence of divergence, consecutive divergences, divergence quality, and change in indicator phase.
Type Divergence : Indicates the type of divergence, which can be either "Bullish Divergence" or "Bearish Divergence."
Exist : Indicates the presence of divergence with a "+" sign and absence with a "-" sign. A green color is used for bullish divergence and red for bearish divergence.
Consecutive : Shows the number of consecutive divergences. For example, if there are 3 consecutive divergences, the number 3 is displayed.
Divergence Quality : Displays the quality of the divergence based on the number of consecutive divergences. If there is 1 divergence, the quality is "Normal"; for 2 divergences, it's "Good"; and for 3 or more divergences, it's "Strong."
Change Phase Indicator : Indicates whether a phase change in the indicator has occurred with "+" for yes and "-" for no.
🟣 Label : Unlike the table, which only shows information about the latest divergence, labels display information about each divergence at the point where it occurs. The information includes the type of divergence, detection method, divergence quality, consecutive divergences, and change in phase indicator. The selected method of detection is also displayed. For example, if the chosen method is the "AO" indicator, the label will show "Method: AO."
🔵 Settings
Fractal Period : Determines the period of swings. The minimum and default value is 2.
Divergence Detect Method : Selects the indicator (MACD, RSI, or AO) used for detecting divergences. The default indicator is MACD.
Show Fractal : Chooses whether to display fractals or not. The default is "No."
Show Table : Determines whether to display the table or not. The default is "Yes."
Show Label : Chooses whether to display labels or not. The default is "Yes."
Label Size : Adjusts the size of the labels from "Tiny" to "Large."
PhiSmoother Moving Average Ribbon [ChartPrime]DSP FILTRATION PRIMER:
DSP (Digital Signal Processing) filtration plays a critical role with financial indication analysis, involving the application of digital filters to extract actionable insights from data. Its primary trading purpose is to distinguish and isolate relevant signals separate from market noise, allowing traders to enhance focus on underlying trends and patterns. By smoothing out price data, DSP filters aid with trend detection, facilitating the formulation of more effective trading techniques.
Additionally, DSP filtration can play an impactful role with detecting support and resistance levels within financial movements. By filtering out noise and emphasizing significant price movements, identifying key levels for entry and exit points become more apparent. Furthermore, DSP methods are instrumental in measuring market volatility, enabling traders to assess volatility levels with improved accuracy.
In summary, DSP filtration techniques are versatile tools for traders and analysts, enhancing decision-making processes in financial markets. By mitigating noise and highlighting relevant signals, DSP filtration improves the overall quality of trading analysis, ultimately leading to better conclusions for market participants.
APPLYING FIR FILTERS:
FIR (Finite Impulse Response) filters are indispensable tools in the realm of financial analysis, particularly for trend identification and characterization within market data. These filters effectively smooth out price fluctuations and noise, enabling traders to discern underlying trends with greater fidelity. By applying FIR filters to price data, robust trading strategies can be developed with grounded trend-following principles, enhancing their ability to capitalize on market movements.
Moreover, FIR filter applications extend into wide-ranging utility within various fields, one being vital for informed decision-making in analysis. These filters help identify critical price levels where assets may tend to stall or reverse direction, providing traders with valuable insights to aid with identification of optimal entry and exit points within their indicator arsenal. FIRs are undoubtedly a cornerstone to modern trading innovation.
Additionally, FIR filters aid in volatility measurement and analysis, allowing traders to gauge market volatility accurately and adjust their risk management approaches accordingly. By incorporating FIR filters into their analytical arsenal, traders can improve the quality of their decision-making processes and achieve better trading outcomes when contending with highly dynamic market conditions.
INTRODUCTORY DEBUT:
ChartPrime's " PhiSmoother Moving Average Ribbon " indicator aims to mark a significant advancement in technical analysis methodology by removing unwanted fluctuations and disturbances while minimizing phase disturbance and lag. This indicator introduces PhiSmoother, a powerful FIR filter in it's own right comparable to Ehlers' SuperSmoother.
PhiSmoother leverages a custom tailored FIR filter to smooth out price fluctuations by mitigating aliasing noise problematic to identification of underlying trends with accuracy. With adjustable parameters such as phase control, traders can fine-tune the indicator to suit their specific analytical needs, providing a flexible and customizable solution.
Mathemagically, PhiSmoother incorporates various color coding preferences, enabling traders to visualize trends more effectively on a volatile landscape. Whether utilizing progression, chameleon, or binary color schemes, you can more fluidly interpret market dynamics and make informed visual decisions regarding entry and exit points based on color-coded plotting.
The indicator's alert system further enhances its utility by providing notifications of specifically chosen filter crossings. Traders can customize alert modes and messages while ensuring they stay informed about potential opportunities aligned with their trading style.
Overall, the "PhiSmoother Moving Average Ribbon" visually stands out as a revolutionary mechanism for technical analysis, offering traders a comprehensive solution for trend identification, visualization, and alerting within financial markets to achieve advantageous outcomes.
NOTEWORTHY SETTINGS FEATURES:
Price Source Selection - The indicator offers flexibility in choosing the price source for analysis. Traders can select from multiple options.
Phase Control Parameter - One of the notable standout features of this indicator is the phase control parameter. Traders can fine-tune the phase or lag of the indicator to adapt it to different market conditions or timeframes. This feature enables optimization of the indicator's responsiveness to price movements and align it with their specific trading tactics.
Coloring Preferences - Another magical setting is the coloring features, one being "Chameleon Color Magic". Traders can customize the color scheme of the indicator based on their visual preferences or to improve interpretation. The indicator offers options such as progression, chameleon, or binary color schemes, all having versatility to dynamically visualize market trends and patterns. Two colors may be specifically chosen to reduce overlay indicator interference while also contrasting for your visual acuity.
Alert Controls - The indicator provides diverse alert controls to manage alerts for specific market events, depending on their trading preferences.
Alertable Crossings: Receive an alert based on selectable predefined crossovers between moving average neighbors
Customizable Alert Messages: Traders can personalize alert messages with preferred information details
Alert Frequency Control: The frequency of alerts is adjustable for maximum control of timely notifications
Moving Average PropertiesThis indicator calculates and visualizes the Relative Smoothness (RS) and Relative Lag (RL) or call it accuracy of a selected moving average (MA) in comparison to the SMA of length 2 (the lowest possible length for a moving average and also the one closest to the price).
Median RS (Relative Smoothness):
Interpretation: The median RS represents the median value of the Relative Smoothness calculated for the selected moving average across a specified look-back period (max bar lookback is set at 3000).
Significance: A more negative (larger) median RS suggests that the chosen moving average has exhibited smoother price behavior compared to a simple moving average over the analyzed period. A less negative value indicates a relatively choppier price movement.
Median RL (Relative Lag):
Interpretation: The median RL represents the median value of the Relative Lag calculated for the selected moving average compared to a simple moving average of length 2.
Significance: A higher median RL indicates that the chosen moving average tends to lag more compared to a simple moving average. Conversely, lower values suggest less lag in the selected moving average.
Ratio of Median RS to Median RL:
Interpretation: This ratio is calculated by dividing the median RS by the median RL.
Significance: Traders might use this ratio to assess the balance between smoothness and lag in the chosen moving average. This a measure of for every % of lag what is the smoothness achieved. This can be used a benchmark to decide what length to choose for a MA to get an equivalent value between two stocks. For example a TESLA stock on a 15 minute time frame with a length of 12 has a value (ratio of RS/RL) of -150 , where as APPLE stock of length 35 on a 15 minute chart also has a value (ratio of RS/RL) of -150.
I imply that a MA of length 12 working on TESLA stock is equivalent to MA of length 35 on a APPLE stock. (THIS IS A EXAMPLE).
My assumption is that finding the right moving average length for a stock isn't a one-size-fits-all situation. It's not just about using a fixed length; it's about adapting to the unique characteristics of each stock. I believe that what works for one stock might not work for another because they have different levels of smoothness or lag in their price movements. So, instead of applying the same length to all stocks, I suggest adjusting the length of the moving average to match the values that we know work best for achieving the desired smoothness or lag or its ratio (RS/RL). This way, we're customizing the indicator for each stock, tailoring it to their individual behaviors rather than sticking to a one-size-fits-all approach.
Users can choose from various types of moving averages (EMA, SMA, WMA, VWMA, HMA) and customize the length of the moving average. RS measures the smoothness of the MA, while RL measures its lag compared to a simple moving average. The script plots the median RS and RL values, the selected MA, and the ratio of median RS to median RL on the price chart. Traders can use this information to assess the performance of different moving averages and potentially inform their trading decisions.
Bitcoin Pi Cycle Top Indicator - Daily Timeframe Only1 Day Timeframe Only
The Bitcoin Pi Cycle Top Indicator has garnered attention for its historical effectiveness in identifying the timing of Bitcoin's market cycle peaks with remarkable precision, typically within a margin of 3 days.
It utilizes a specific combination of moving averages—the 111-day moving average and a 2x multiple of the 350-day moving average—to signal potential tops in the Bitcoin market.
The 111-day moving average (MA): This shorter-term MA is chosen to reflect more recent price action and trends within the Bitcoin market.
The 350-day moving average (MA) multiplied by 2: This longer-term MA is adjusted to capture broader market trends and cycles over an extended period.
The key premise behind the Bitcoin Pi Cycle Top Indicator is that a potential market top for Bitcoin can be signaled when the 111-day MA crosses above the 350-day MA (which has been doubled). Historically, this crossover event has shown a remarkable correlation with the peaks of Bitcoin's price cycles, making it a tool of interest for traders and investors aiming to anticipate significant market shifts.
#Bitcoin
Leading T3Hello Fellas,
Here, I applied a special technique of John F. Ehlers to make lagging indicators leading. The T3 itself is usually not realling the classic lagging indicator, so it is not really needed, but I still publish this indicator to demonstrate this technique of Ehlers applied on a simple indicator.
The indicator does not repaint.
In the following picture you can see a comparison of normal T3 (purple) compared to a 2-bar "leading" T3 (gradient):
The range of the gradient is:
Bottom Value: the lowest slope of the last 100 bars -> green
Top Value: the highest slope of the last 100 bars -> purple
Ehlers Special Technique
John Ehlers did develop methods to make lagging indicators leading or predictive. One of these methods is the Predictive Moving Average, which he introduced in his book “Rocket Science for Traders”. The concept is to take a difference of a lagging line from the original function to produce a leading function.
The idea is to extend this concept to moving averages. If you take a 7-bar Weighted Moving Average (WMA) of prices, that average lags the prices by 2 bars. If you take a 7-bar WMA of the first average, this second average is delayed another 2 bars. If you take the difference between the two averages and add that difference to the first average, the result should be a smoothed line of the original price function with no lag.
T3
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.
Thanks for checking this out and give a boost, if you enjoyed the content.
Best regards,
simwai
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Credits to @loxx
