Filtered Volume Profile [ChartPrime]The "Filtered Volume Profile" is a powerful tool that offers insights into market activity. It's a technical analysis tool used to understand the behavior of financial markets. It uses a fixed range volume profile to provide a histogram representing how much volume occurred at distinct price levels.
Profile in action with various significant levels displayed
How to Use
The script is designed to analyze cumulative trading volumes in different price bins over a certain period, also known as `'lookback'`. This lookback period can be defined by the user and it represents the number of bars to look back for calculating levels of support and resistance.
The `'Smoothing'` input determines the degree to which the output is smoothed. Higher values lead to smoother results but may impede the responsiveness of the indicator to rapid changes in volatility.
The `'Peak Sensitivity'` input is used to adjust the sensitivity of the script's peak detection algorithm. Setting this to a lower value makes the algorithm more sensitive to local changes in trading volume and may result in "noisier" outputs.
The `'Peak Threshold'` input specifies the number of bins that the peak detection mechanism should account for. Larger numbers imply that more volume bins are taken into account, and the resultant peaks are based on wider intervals.
The `'Mean Score Length'` input is used for scaling the mean score range. This is particularly important in defining the length of lookback bars that will be used to calculate the average close price.
Sinc Filter
The application of the sinc-filter to the Filtered Volume Profile reduces the risk of viewing artefacts that may misrepresent the underlying market behavior. Sinc filtering is a high-quality and sharp filter that doesn't manifest any ringing effects, making it an optimal choice for such volume profiling.
Histogram
On the histogram, the volume profile is colored based on the balance of bullish to bearish volume. If a particular bar is more intense in color, it represents a larger than usual volume during a single price bar. This is a clear signal of a strong buying or selling pressure at a particular price level.
Threshold for Peaks
The `peak_thresh` input determines the number of bins the algorithm takes in account for the peak detection feature. The 'peak' represents the level where a significant amount of volume trading has occurred, and usually is of interest as an indicative of support or resistance level.
By increasing the `peak_thresh`, you're raising the bar for what the algorithm perceives as a peak. This could result in fewer, but more significant peaks being identified.
History of Volume Profiles and Evolution into Sinc Filtering
Volume profiling has a rich history in market analysis, dating back to the 1950s when Richard D. Wyckoff, a legendary trader, introduced the concept of volume studies. He understood the critical significance of volume and its relationship with market price movement. The core of Wyckoff's technical analysis suite was the relationship between prices and volume, often termed as "Effort vs Results".
Moving forward, in the early 1800s, the esteemed mathematician J. R. Carson made key improvements to the sinc function, which formed the basis for sinc filtering application in time series data. Following these contributions, trading studies continued to create and integrate more advanced statistical measures into market analysis.
This culminated in the 1980s with J. Peter Steidlmayer’s introduction of Market Profile. He suggested that markets were a function of continuous two-way auction processes thus introducing the concept of viewing markets in price/time continuum and price distribution forms. Steidlmayer's Market Profile was the first wide-scale operation of organized volume and price data.
However, despite the introduction of such features, challenges in the analysis persisted, especially due to noise that could misinform trading decisions. This gap has given rise to the need for smoothing functions to help eliminate the noise and better interpret the data. Among such techniques, the sinc filter has become widely recognized within the trading community.
The sinc filter, because of its properties of constructing a smooth passing through all data points precisely and its ability to eliminate high-frequency noise, has been considered a natural transition in the evolution of volume profile strategies. The superior ability of the sinc filter to reduce noise and shield against over-fitting makes it an ideal choice for smoothing purposes in trading scripts, particularly where volume profiling forms the crux of the market analysis strategy, such as in Filtered Volume Profile.
Moving ahead, the use of volume-based studies seems likely to remain a core part of technical analysis. As long as markets operate based on supply and demand principles, understanding volume will remain key to discerning the intent behind price movements. And with the incorporation of advanced methods like sinc filtering, the accuracy and insight provided by these methodologies will only improve.
Mean Score
The mean score in the Filtered Volume Profile script plays an important role in probabilistic inferences regarding future price direction. This score essentially characterizes the statistical likelihood of price trends based on historical data.
The mean score is calculated over a configurable `'Mean Score Length'`. This variable sets the window or the timeframe for calculation of the mean score of the closing prices.
Statistically, this score takes advantage of the concept of z-scores and probabilities associated with the t-distribution (a type of probability distribution that is symmetric and bell-shaped, just like the standard normal distribution, but has heavier tails).
The z-score represents how many standard deviations an element is from the mean. In this case, the "element" is the price level (Point of Control).
The mean score section of the script calculates standard errors for the root mean squared error (RMSE) and addresses the uncertainty in the prediction of the future value of a random variable.
The RMSE of a model prediction concerning observed values is used to measure the differences between values predicted by a model and the values observed.
The lower the RMSE, the better the model is able to predict. A zero RMSE means a perfect fit to the data. In essence, it's a measure of how concentrated the data is around the line of best fit.
Through the mean score, the script effectively predicts the likelihood of the future close price being above or below our identified price level.
Summary
Filtered Volume Profile is a comprehensive trading view indicator which utilizes volume profiling, peak detection, mean score computations, and sinc-filter smoothing, altogether providing the finer details of market behavior.
It offers a customizable look back period, smoothing options, and peak sensitivity setting along with a uniquely set peak threshold. The application of the Sinc Filter ensures a high level of accuracy and noise reduction in volume profiling, making this script a reliable tool for gaining market insights.
Furthermore, the use of mean score calculations provides probabilistic insights into price movements, thus providing traders with a statistically sound foundation for their trading decisions. As trading markets advance, the use of such methodologies plays a pivotal role in formulating effective trading strategies and the Filtered Volume Profile is a successful embodiment of such advancements in the field of market analysis.
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RibboNN Machine Learning [ChartPrime]The RibboNN ML indicator is a powerful tool designed to predict the direction of the market and display it through a ribbon-like visual representation, with colors changing based on the prediction outcome from a conditional class. The primary focus of this indicator is to assist traders in trend following trading strategies.
The RibboNN ML in action
Prediction Process:
Conditional Class: The indicator's predictive model relies on a conditional class, which combines information from both longcon (long condition) and short condition. These conditions are determined using specific rules and criteria, taking into account various market factors and indicators.
Direction Prediction: The conditional class provides the basis for predicting the direction of the market move. When the prediction value is greater than 0, it indicates an upward trend, while a value less than 0 suggests a downward trend.
Nearest Neighbor (NN): To attempt to enhance the accuracy of predictions, the RibboNN ML indicator incorporates a Nearest Neighbor algorithm. This algorithm analyzes historical data from the Ribbon ML's predictive model (RMF) and identifies patterns that closely resemble the current conditional prediction class, thereby offering more robust trend forecasts.
Ribbon Visualization:
The Ribbon ML indicator visually represents its predictions through a ribbon-like display. The ribbon changes colors based on the direction predicted by the conditional class. An upward trend is represented by a green color, while a downward trend is depicted by a red color, allowing traders to quickly identify potential market directions.
The introduction of the Nearest Neighbor algorithm provides the Ribbon ML indicator with unique and adaptive behaviors. By dynamically analyzing historical patterns and incorporating them into predictions, the indicator can adapt to changing market conditions and offer more reliable signals for trend following trading strategies.
Manipulation of the NN Settings:
Smaller Value of Neighbours Count:
When the value of "Neighbours Count" is small, the algorithm considers only a few nearest neighbors for making predictions.
A smaller value of "Neighbours Count" leads to more flexible decision boundaries, which can result in a more granular and sensitive model.
However, using a very small value might lead to overfitting, especially if the training data contains noise or outliers.
Larger Value of "Neighbours Count":
When the value of "Neighbours Count" is large, the algorithm considers a larger number of nearest neighbors for making predictions.
A larger value of "Neighbours Count" leads to smoother decision boundaries and helps capture the global patterns in the data.
However, setting a very large value might result in a loss of local patterns and make the model less sensitive to changes in the data.
MTF Fusion - High Volume Expansion Channel [TradingIndicators]Exceptionally high volume and rapid price expansion are key markers of powerful moves, especially when they occur during a breakout or breakdown. The High Volume Expansion Channel (HVEC) uses our multi-timeframe fusion and price compression/expansion algorithms to look for high volume and rapid expansion from multiple higher timeframes at once. It uses this info to determine a high volume and expansion 'grade', and then encodes this result into a colored channel. This channel coloring varies in intensity based on how exceptionally high volume is and how rapidly price is expanding in either direction.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates a 'high volume and expansion grade' (let's call it HVEG), as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful data and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the HVEG value is calculated by determining the HVEG for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the HVEG value from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused HVEG' as (HigherTF_HVEG_1 + HigherTF_HVEG_2 + HigherTF_HVEG_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the high volume and price expansion grade calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Included Features
MTF Fusion high volume and expansion coloring
MTF Fusion ATR-based channel for visual effect
Channel width customization and explanatory labels
Pre-built color stylings
Options
Show Channel Lines: Show/hide the upper and lower lines of the channel
Fill Channel: Fill the channel with coloring depicting the current degree of high volume and rapid price expansion
Channel Width Multiplier: Sets the width of the ATR-based channel
Explanatory Labels: Show/hide explanatory labels describing the visuals
Lookback: Select how you want the degree of high volume expansion to be calculated (longer = long-term high volume and expansion, shorter = short-term high volume and expansion)
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Price & Volume Profile (Expo)█ Overview
The Price & Volume Profile provides a holistic perspective on market dynamics by simultaneously tracking price action and trading volume across a range of price levels. So it is not only a volume-based indicator but also a price-based one. In addition to illustrating volume distribution, it quantifies how frequently the price has fallen within a particular range, thus offering a holistic perspective on market dynamics.
This unique and comprehensive approach to market analysis by considering both price action and trading volume, two crucial dimensions of market activity. Its distinctive methodology offers several advantages:
Holistic Market View: By simultaneously tracking the frequency of specific price ranges (Price Profile) and the volume traded at those ranges (Volume Profile), this indicator provides a more complete picture of market behavior. It shows not only where the market is trading but also how much it's trading, reflecting both price acceptance levels and market participation intensity.
Point of Control (POC): The POC, as highlighted by this indicator, serves as a significant reference point for traders. It identifies the price level with the highest trading activity, thus indicating a strong consensus among market participants about the asset's fair value. Observing how price interacts with the POC can offer valuable insights into market sentiment and potential trend reversals.
Support and Resistance Levels: Price levels with high trading activity often act as support or resistance in future price movements. The indicator visually represents these levels, enabling traders to anticipate potential price reactions.
Price Profile
Price and Volume Profile
█ Calculations
The algorithm analyzes both trade frequency and volume across different price levels. It identifies these levels within the visible chart range, then examines each bar to determine if the selected price falls within these levels. If so, it increases a counter and adds the trading volume. This process repeats across the visible range and is visualized as a horizontal histogram, each bar representing a price level and the bar length reflecting trade frequency and volume. Additionally, it calculates the Point of Control (POC), signifying the price level with the highest activity.
In summary: The histogram presents a dual perspective - not only the traded volume at each price level but also the frequency of the price hitting each range. The longer the bar, the more times the price has frequented that specific range, revealing key insights into price behavior and acceptance levels. These frequently visited areas often emerge as strong support or resistance zones, helping traders navigate market movements.
Please note that the indicator adjusts to the visible price range, making it adaptable to changing market conditions. This dynamic analysis can provide more relevant and timely information than static indicators.
█ How to use
This indicator is beneficial for traders as it offers insights into the distribution of trading activity across different price levels. It helps identify key areas of support and resistance and gives a visual representation of market sentiment and liquidity.
The point of control (POC) , which is the price level with the highest traded volume or frequency count, becomes even more crucial in this context. It marks the price at which the most trading activity occurred, signaling a strong consensus among market participants about the asset's fair value. If the market price deviates significantly from the POC, it could suggest an overbought or oversold condition, potentially leading to a price reversion.
Fair Price Areas/gaps are specific price levels or zones where an asset has spent limited time in the past. These areas are considered interesting or significant because they may have an impact on future price action.
Similar to the concept of fair value gaps, which refers to discrepancies between an asset's market price and its estimated intrinsic value, Fair Price Areas/gaps focus on price levels that have been relatively underutilized in terms of trading activity. When an asset's price reaches a Fair Price Area/gap, traders and investors pay attention because they expect the price to react in some way. The rationale behind this concept is that price tends to gravitate towards areas where it has spent less time in the past, as the market perceives them as significant levels.
█ Settings
The indicator is customizable, allowing users to define the number of price levels (rows), the offset, the data source, and whether to display volume or frequency count. It also adjusts dynamically to the visible price range on the chart, ensuring that the analysis remains relevant and timely with changing market conditions.
Source: The price to use for the calculation. Typically, this is the closing price. By considering the user-selected Source (typically the closing price), the indicator determines the frequency with which the price lands within each designated price level (row) over the selected period. In essence, the indicator provides a count of bars where the Source price falls within each range, essentially creating a "Price Profile."
Row Size: The number of price levels (rows) to divide the visible price range into.
Display: Choose whether to display the number of bars ("Counter") or the total volume ("Volume") for each price level.
Offset: The distance of the histogram from the price chart.
Point of Control (POC): If enabled, the indicator will highlight the price level with the most activity.
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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!
Volume Orderbook (Expo)█ Overview
The Volume Orderbook indicator is a volume analysis tool that visually resembles an order book. It's used for displaying trading volume data in a way that may be easier to interpret or more intuitive for certain traders, especially those familiar with order book analysis.
This indicator aggregate and display the total trading volume at different price levels over the entire range of data available on the chart, similar to how an order book displays current buy and sell orders at different price levels. However, unlike a real-time order book, it only considers historical trading data, not current bid and ask orders. This provides a 'historical order book' of sorts, indicating where most trading activities have taken place.
Summary
This is a volume-based indicator that shows the volume traded at specific price levels, highlighting areas of high and low activity.
█ Calculations
The algorithm operates by calculating the cumulative volume traded in each specific price zone within the range of data displayed on the chart. The length of each horizontal bar corresponds to the total volume of trades that occurred within that particular price zone.
In essence, when the price is in a specific zone, the volume is added to the bar representing that zone. A thicker bar implies a larger price zone, meaning that more volume is accumulated within that bar. Therefore, the thickness of the bar visually indicates the amount of trading activity that took place within the associated price zone.
█ How to use
The Volume Orderbook indicator serves as a beneficial tool for traders by identifying key price levels with a significant amount of trading activity. These high-volume areas could represent potential support or resistance levels due to the large number of orders situated there. The indicator's ability to spotlight these zones might be particularly advantageous in pinpointing breakouts or breakdowns when prices move beyond these high-volume regions. Moreover, the indicator could also assist traders in recognizing anomalies, such as when an unusually large volume of trades occurs at unconventional price levels.
Identify Key Price Levels: The indicator highlights high-volume areas where a significant number of trades have occurred, which could act as potential support or resistance levels. This is based on the notion that many traders have established positions at these prices, so these levels may serve as significant areas for market activity in the future.
Volume Nodes: These are the peaks (high-volume areas) and troughs (low-volume areas) seen on the indicator. High-volume nodes represent price levels at which a large amount of volume has been traded, typically areas of strong support or resistance. Conversely, low-volume nodes, where very little volume has been traded, indicate price levels that traders have shown little interest in the past and could potentially act as barriers to price. It's important to note that while high trading volume can imply significant market interest, it doesn't always mean the price will stop or reverse at these levels. Sometimes, prices can quickly move through high-volume areas if there are no current orders (demand) to match with the new orders (supply).
Analyze Market Psychology: The distribution of volume across different price levels can provide insights into the market's psychology, revealing the balance of power between buyers and sellers.
Highlight Potential Reversal Points: The indicator can help identify price levels with high traded volume where the market might be more likely to reverse since these levels have previously attracted significant interest from traders.
Validate Breakouts or Breakdowns: If the price moves convincingly past a high-volume node, it could indicate a strong trend, suggesting a potential breakout or breakdown. Conversely, if the price struggles to move past a high-volume node, it could suggest that the trend is weak and might potentially reverse.
Trade Reversals: High-volume areas could also indicate potential turning points in the market. If the price reaches these levels and then starts to move away, it might suggest a possible price reversal.
Confirm Other Signals: As with all technical indicators, the "Volume Orderbook" should ideally be used in conjunction with other forms of technical and fundamental analysis to confirm signals and increase the odds of successful trades.
Summary
The Volume Orderbook indicator allows traders to identify key price levels, analyze market psychology, highlight potential reversal points, validate breakouts or breakdowns, confirm other trading signals, and anticipate possible trade reversals, thereby serving as a robust tool for trading analysis.
█ Settings
Source: The user can select the source, the default of which is "close." This implies that volume is added to the volume order book when the closing price falls within a specific zone. Users can modify this to any indicator present on their chart. For example, if it's set to an SMA (Simple Moving Average) of 20, the volume will be added to the volume order book when the SMA 20 falls within the specific zone.
Rows and width: These settings allow users to adjust the representation of volume order book zones. "ROWS" pertains to the number of volume order book zones displayed, while "WIDTH" refers to the breadth of each zone.
Table and Grid: These settings allow traders to customize the Volume order-book's position and appearance. By adjusting the "left" parameter, users can shift the position of the Volume order book on the chart; a higher value pushes the order book further to the right. Additionally, users can enable "Table Border" and "Table Grid" options to add gridlines or borders to the Volume order book for easier viewing and interpretation.
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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!
Algorganic Buy / Sell / X-Exit Signal [UOI]The " Algorganic Buy / Sell / X-Exit Signal " indicator is an Algorithmic Machine Learning-based superpack indicator that generates buy and sell signals for trading in financial markets. It is packed with conditional statemnets and filters to avoid false signals and utilizes Nearest Neighbors Model (NNM) algorithm with a distance metric to determine the direction of the price movement and make predictions according to the next past 12 bars for the next 4 to 8 bars in whatever chart frame the trader is using. Ideal time frames are 2, 3, 5 and 15 minutes for option traders and scalpers can use it on the 1 minute chart.
The indicator takes into account various technical indicators such as Relative Strength Index (RSI), Average Directional Index (ADX), CCI, Stochastic, ATR and major EMAs and has two optimizer for confirmation. These indicators are used as features to train the Machine Learning model and at the same time to provide better buy and sell signals with multiple "if" conditions.
The NNM algorithm calculates the distance between the current data point and historical data points. It works like a mixture of ATR and ADX. By considering the nearest neighbors, the model predicts the direction of future price movement. The predictions are filtered using additional criteria, including volatility, trend detection, and, ATR and ADX values.
The indicator provides visual signals on the chart, indicating when to enter a long (buy) or short (sell) position but traders should also be mindful of support and resistance levels and oversold and overbought conditions and the higher timeframe signal. It also offers options for dynamic exits based on specific conditions or fixed exits after a predefined number of bars.
Additionally, the indicator includes filters based on EMA (Exponential Moving Average), SMA (Simple Moving Average), and a kernel regression technique. These filters help to refine the signals and reduce noise in the predictions.
The indicator also includes alert functionalities to notify traders of entry and exit points.
The Algorganic is a versatile trading indicator that provides buy and sell signals based on the analysis of various popular technical indicators in combination with Machine Learning techniques with technical analysis and support and resistance levels to generate trading signals, helping traders make informed decisions. This powerful tool overlays on your price chart and can be used across different markets and timeframes.
Key Features:
1. Dynamic EMA Support and Resistance Levels: You can define the top and bottom lines as either 'Support' or 'Resistance'. These levels are calculated using an Exponential Moving Average (EMA) and Average True Range (ATR) inputs.
2. Exponential Moving Average (EMA): The EMA is calculated based on the EMA length input provided by the user, with a default setting of 21 periods.
3. Average True Range (ATR): The ATR is calculated with a default length of 14 periods and is used in determining the support and resistance levels.
4. Buy/Sell Signals: The indicator provides buy and sell signals when the price hits the defined support or resistance levels. These signals are represented by X-shapes plotted on the chart, with green indicating a hit on support (buy signal), and red indicating a hit on resistance (sell signal).
5. Trend Strength Analysis: It uses a unique combination of technical indicators like MACD, RSI, Velocity, CCI, Stochastic, and a custom trend strength indicator. The settings for each of these indicators can be customized according to user preference.
6. Bull/Bear Tug of War: This feature paints the little triangles green if the majority of the indicators are bullish, and red if the majority are bearish. This is a powerful feature to visualize the overall market sentiment.
7. Buy/Sell Alert: The script generates alerts for potential buy and sell signals. Alerts contain information about the signal type, ticker symbol, and current price.
8. Plot EMA Line: This indicator includes an option to display an additional EMA line on the chart, which can be toggled on or off as per the user's choice.
How to use it:
You basically need to master riding this machine. There are a lot of conditions that have been added to make sure novice traders do not make a mistake. The image below shows how to use the indicator. Pay attention to colors:
Longer time frame you should pay attention to the EMA lines and over bought and oversold levels in the optimizers. here is an example:
And another example on 15 min timeframe:
On top of all the above, this indicator has a built-in advanced support and resistance tool that dynamically identifies pivot points and their corresponding support and resistance zones based on the historical data of a given asset. So what this means is that you should ignore a buy signal very close to a resistance and only enter when the resistance is broken.
Here are the configurable support and resistance parameters:
1. Pivot Period : The period considered for pivot detection. The range is between 4 to 30 days with a default value of 25.
2. Source: The price point to be used as the source for pivot detection. You can choose between 'High/Low' and 'Close/Open'.
3. Maximum Number of Pivot: This defines the maximum number of pivot points that the algorithm will store. This can be anywhere from 5 to 100, with 45 as the default value.
4. Maximum Channel Width % : This sets the maximum width of the support/resistance channel as a percentage. Minimum value is 1, with a default value of 10. Higher numbers capture longer timeframe and lower number shorter timeframes. For scalping use 5 or 8 for swing use 12 or 14.
5. Maximum Number of Lines: This sets the maximum number of support/resistance lines displayed on the chart. It ranges from 1 to 15 with a default of 10.
6. Minimum Strength: This is the minimum strength of the support or resistance line, defined by the number of times price touches it. It ranges from 1 to 10 with a default of 2.
7. Line Style: This option allows the user to choose the line style between 'Solid', 'Dotted', and 'Dashed'.
8. Line Width: This allows users to choose the width of the line ranging from 1 to 4.
9. Resistance Color and Support Color: These define the colors for the resistance and support lines.
The script also includes functions to calculate if the price has crossed over or under a support or resistance line.
The S/R assist uses these inputs to calculate pivot highs and lows, create support and resistance zones, and plot these on the chart. When the price crosses a support or resistance line, the script can identify this as a possible trading signal. The lines' strengths are also calculated, and only those with strengths above the user-defined minimum are drawn on the chart.
MTF Fusion - SuperTrend [TradingIndicators]SuperTrend is undoubtedly one of the most popular and influential indicators ever developed, and by combining it with our MTF Fusion algorithm, we believe we have made it more useful and powerful than ever with MTF Fusion SuperTrend .
Let's start with a brief review of what the original SuperTrend indicator is and how it works.
What is SuperTrend?
The SuperTrend indicator is a popular technical analysis tool used in financial markets to identify the direction of a trend and potential entry and exit points for trading. It was developed by Olivier Seban, a French trader, and first introduced in his book "Tout le monde peut gagner en bourse" ("Everyone Can Win in the Stock Market") published in 2008.
SuperTrend is based on the concept of Average True Range (ATR) and uses two parameters: the multiplier and the period. The ATR measures the volatility of a financial instrument, and the SuperTrend indicator utilizes this information to plot a line above or below the price chart. It is an 'AITM' (Always In The Market) indicator, which, in its original form, is always 'long' or 'short' - and never 'flat'.
Here's a brief overview of how the SuperTrend indicator works:
Calculation of the ATR: The ATR is calculated using historical price data over a specified period. It measures the average range between high and low prices, reflecting the market's volatility.
Calculation of the upward (long/bullish) and downward (short/bearish) SuperTrend lines: The SuperTrend indicator multiplies the ATR by a specified multiplier (typically 2 or 3) and adds/subtracts the result from the current closing price. This calculation determines the upward and downward SuperTrend lines.
Plotting the Indicator: The SuperTrend indicator plots a line above the price chart when the price is trending upwards, and below the price chart when the price is trending downwards. The distance between the price and the indicator line provides insights into the strength of the trend.
Traders commonly use the SuperTrend indicator to identify potential buy or sell signals. For example, a buy signal may be generated when the price crosses above the indicator line, indicating an uptrend. Conversely, a sell signal may be triggered when the price crosses below the indicator line, signaling a downtrend.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates SuperTrend lines, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of SuperTrend lines are calculated by determining the value of the SuperTrend indicator for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a SuperTrend line from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_SuperTrend_1 + HigherTF_SuperTrend_2 + HigherTF_SuperTrend_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the SuperTrend calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Unlike in the original indicator, flat/'No Trend' areas exist in MTF Fusion SuperTrend!
MTF Fusion SuperTrend only shows a Fusion SuperTrend when the majority of SuperTrends from higher timeframes are in agreement and signaling the same trend direction . So, unlike the original SuperTrend indicator, MTF Fusion SuperTrend sometimes shows no SuperTrend line at all - typically in flat or indecisive areas, which we think is beneficial and helps to filter out noise on smaller timeframes.
Included Features
Fusion SuperTrend lines
Dynamic Multi-Timeframe SuperTrends
Filled zones to highlight trends
Full customization of SuperTrend parameters
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion SuperTrends calculated from multiple higher timeframes
MTF View: Show/hide the SuperTrends from multiple higher timeframes used to calculate the Fusion SuperTrends
Fill Trending Zones: Show/hide the fill for 'trending zones' between price and the Fusion SuperTrends
Multiplier: Sets the multiplier for all SuperTrend calculations
ATR Period: Sets the ATR period for all SuperTrend calculations
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Oscillator Toolkit (Expo)█ Overview
The Oscillators Toolkit stands at the forefront of technical trading tools, offering a comprehensive suite of sophisticated, adaptive, and unique oscillators. This toolkit has been thoughtfully designed to cater to all trading styles, ensuring versatility and utility for every trader. The toolkit features our flagship oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and Bellcurves. Furthermore, it offers many great features such as trend recognition, market impulses, and trend changes; all consolidated into a single, easy-to-use indicator.
Access to these high-quality oscillators and tools can elevate your trading strategy, providing you with insightful market analysis and potential trading opportunities. In addition, these tools help traders and investors to identify and interpret various market trends, momentum, and volatility patterns more efficiently.
The Oscillator toolkit works in any market and timeframe for discretionary analysis and includes many oscillators and features:
█ Oscillators
WaveTrend Momentum
The WaveTrend Momentum oscillator is a significant component of the toolkit. It factors in both the direction and the momentum of market trends. The waves within this system are both quick and responsive, operating independently to offer the most pertinent insights at the most opportune moments. Their rapid response time ensures that traders receive timely information, which is essential in the fast-paced, dynamic world of trading.
Example of how to use the WaveTrend Momentum Oscialltor
The WaveTrend Momentum is proficient at identifying trend reversals and pullbacks, allowing traders to enter or exit trades at optimal moments.
Leading RSI
The Leading Relative Strength Index (RSI) is a type of momentum oscillator that is commonly used in technical analysis to predict price movements. As the name suggests, it is an advanced form of the traditional Relative Strength Index (RSI), and it provides traders with more timely signals for market entries and exits.
The Leading RSI works on similar principles but is designed to provide signals ahead of the traditional RSI. This is achieved through more advanced mathematical modeling and calculations, which aim to identify shifts in market momentum before they happen. It takes into account not only the current price action but also considers historical data in a way that can foresee changes in trend directions.
Example of how to use the Leading RSI
The Leading RSI is an enhanced version of the traditional Relative Strength Index, offering more timely indications of divergences and overbought or oversold market conditions.
Momentum Oscillator
This oscillator measures the amount that a security's price has changed over a given time span. It is an excellent tool for understanding the strength of a trend and its potential endurance. When the momentum oscillator rises, it suggests that the price is moving upwards and vice versa.
The Momentum Oscillator is an advanced technical analysis tool that helps traders identify the rate of change or the momentum of the market. It is typically used to determine the strength or speed at which the price of an asset increases or decreases for a set of returns. This oscillator is considered 'fast-moving' and 'sensitive' because it responds quickly to changes in price momentum. The fast-moving nature of this oscillator helps traders to get early signals for potential market entry or exit points.
The Momentum Oscillator analyzes the current price compared to the previous price and adds two additional layers of analysis: 'Buy & Sell moves' and 'Extremes.'
Buy & Sell Moves: This layer of the oscillator helps identify the buying and selling pressure in the market. This can provide traders with valuable information about the possible direction of future price moves. When there is high buying pressure (demand), the price tends to rise, and when there is high selling pressure (supply), the price tends to fall.
Extremes: This layer helps to identify extreme overbought or oversold conditions. When the oscillator enters the overbought territory, it could indicate that the price is at a high and could potentially reverse. Conversely, if the oscillator enters the oversold territory, it could suggest that the price is at a low and could potentially rebound.
Example of how to use the Momentum Oscillator
The Momentum Oscillator is a sensitive and fast-moving oscillator that adapts quickly to price changes while keeping track of the long-term momentum, making it easier to spot buying or selling opportunities in trends.
Bellcurves
The Bellcurves indicator is a powerful tool for traders that uses statistical analysis to help identify potential market reversals and key support and resistance levels by leveraging the principles of statistical analysis to measure market impulses. The concept behind this tool is the normal distribution, also known as the bell curve, which is a fundamental statistical concept signifying that data tends to cluster around the average or mean value. The "impulses" in the market context refer to significant price movements driven by a high volume of trading activity. These are typically sharp and swift moves either upwards (bullish impulse) or downwards (bearish impulse). These impulses often signify a strong sentiment in the market and can result at the beginning of a new trend or the continuation of an existing one.
In effect, the Bellcurve indicator is designed to filter out minor price fluctuations or 'noise,' allowing traders to focus solely on significant market impulses. This makes it easier for traders to identify key market movements.
Example of how to use the Bellcurve
The Bellcurves uses the principles of statistical analysis to identify significant market impulses and potential market reversals.
█ Why is this Oscillator Toolkit Needed?
The Oscillator Toolkit is a vital asset for traders for several reasons:
Insight into Market Trends: The Oscillator Toolkit provides valuable insight into current market trends. This includes understanding whether the market is bullish (rising) or bearish (falling), as well as identifying potential future price movements.
Identification of Overbought or Oversold Conditions: Oscillators like those in the toolkit can help traders identify when an asset is overbought (potentially overvalued) or oversold (potentially undervalued). This can signal potential market reversals.
Confirmation of Price Patterns: The oscillators in the toolkit can confirm price patterns and trends. For example, if a price pattern suggests a bullish trend, an oscillator can help confirm this by showing rising momentum.
Versatility Across Markets and Timeframes: The Oscillator Toolkit is designed to work across a variety of markets, including stocks, forex, commodities, and cryptocurrencies. It's also effective across different timeframes, from short-term day trading to longer-term investment strategies.
Timely Trade Signals: By providing real-time insights into market conditions and price momentum, the Oscillator Toolkit offers timely signals for trade entries and exits.
Enhancing Trading Strategy: Every trader has a unique approach to the market. The Oscillator Toolkit, with its suite of different oscillators, provides a robust set of tools that can be customized to enhance any trading strategy, whether it's a trend following, swing trading, scalping, or any other approach.
█ Any Alert Function Call
This function allows traders to combine any feature and create customized alerts. These alerts can be set for various conditions and customized according to the trader's strategy or preferences.
█ How are the Oscillators calculated? - Overview
The Toolkit combines many of our existing premium indicators and new technical analysis algorithms to analyze the market. This overview covers how the main features are calculated.
WaveTrend Momentum
The WaveTrend Momentum oscillator operates at its core by comparing the current price to previous prices. If the current price is higher than the previous price, the oscillator value will rise, indicating an uptrend. Conversely, if the current price is lower than the previous price, the oscillator value will fall, indicating a downtrend. To make it unique and useful normalized weighting functions are added.
Leading RSI
The Leading RSI is based on the traditional Relative Strength Index, with an added exploration function that takes into account historical price movements.
Momentum Oscillator
The Momentum oscillator measures how quickly the price is changing, on average, over a certain period, relative to the variability of the price over that same period. It gives higher values when the price is changing rapidly in one direction and lower values when the price is fluctuating or changing more slowly. In addition, other functions, such as market extremes and buying/selling pressure, are factored in.
Bellcurves
The Bellcurves assume that some common historical price data is normally distributed, and once these patterns or moves are found the in the price data, a Bellcurve is formed.
█ In conclusion , the Oscillator Toolkit is an advanced, versatile, and indispensable asset for traders across various markets and timeframes. This innovative collection includes different oscillators, including the WaveTrend Momentum, Leading RSI, Momentum Oscillator, and the Bellcurves Indicator, each serving a unique function in providing valuable insights into the market's behavior.
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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!
MTF Fusion - PSAR [TradingIndicators]MTF Fusion PSAR intelligently adapts to whatever timeframe you're trading - dynamically calculating Parabolic SAR (Stop and Reverse) levels combined from four appropriate higher timeframes to give you a much broader view of the market and an edge in your trading decisions. It is the third indicator in our MTF Fusion series, and leverages our MTF Fusion algorithm - only this time to visualize J. Welles Wilder Jr.'s famous Parabolic SAR indicator.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates PSAR levels, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of PSAR levels are calculated by determining the value of the PSAR indicator for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a PSAR level from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_PSAR_Level_1 + HigherTF_PSAR_Level_2 + HigherTF_PSAR_Level_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the PSAR calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
What is the PSAR indicator?
The Parabolic SAR (Stop and Reverse) indicator is a technical analysis tool that helps identify potential trend reversals in price movements. It was developed by J. Welles Wilder Jr. and is widely used by traders to determine entry and exit points in the market. It consists of levels that are plotted above or below current price. The position of these plots relative to the price provides valuable information about the prevailing trend and potential reversal points.
Here's how the original PSAR indicator works:
Upward Trend: When the Parabolic SAR level is plotted below the price, it indicates an upward trend in the market. The level generally moves closer to the price as the trend progresses. This creates a parabolic curve that rises with time. Traders typically interpret this as a bullish signal, suggesting that it may be a good time to buy or hold positions.
Downward Trend: Conversely, when the Parabolic SAR level is plotted above the price, it indicates a downward trend in the market. The plot generally moves closer to the price as the trend continues, forming a parabolic curve that declines with time. This is considered a bearish signal, suggesting that it may be a suitable time to sell or avoid taking long positions.
Reversal Points: The primary purpose of the Parabolic SAR indicator is to identify potential trend reversals. When the price crosses above or below the Parabolic SAR level, it indicates a possible reversal in the trend.
The Parabolic SAR indicator is versatile and can be used in various market conditions and timeframes. It is particularly useful in trending markets, where it helps traders ride the trend and capture potential profits. However, it's important to note that the Parabolic SAR may generate false signals or provide delayed indications in sideways or choppy markets.
Included Features
Fusion PSAR levels
Filled zones to highlight trends
Full customization of PSAR parameters
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion PSAR levels calculated from multiple higher timeframes
Fill Trending Zones: Show/hide the fill for 'trending zones' between price and the Fusion PSAR levels
Start: Defines the rate at which the PSAR levels move closer to the price during the initial stages of a trend (higher = faster convergence, lower = slower convergence)
Increment: Controls the rate at which the acceleration factor increases or decreases as the trend continues (higher = faster convergence, lower = slower convergence)
Max: Sets a limit on the maximum value that the acceleration factor can reach
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
MTF Fusion - S/R Levels [TradingIndicators]MTF Fusion S/R Levels intelligently adapt to whatever timeframe you're trading - dynamically calculating pivot-based support and resistance levels combined from four appropriate higher timeframes to give you a much broader view of the market and an edge in your trading decisions. It is the second indicator in our MTF Fusion series, and leverages our MTF Fusion algorithm - only this time to visualize pivot-based S/R levels and zones.
These levels are not programmed to repaint - so you can use them in real-time just as they appeared historically.
What is MTF Fusion?
Multi-Timeframe (MTF) Fusion is the process of combining calculations from multiple timeframes higher than the chart's into one 'fused' value or indicator. It is based on the idea that integrating data from higher timeframes can help us to better identify short-term trading opportunities within the context of long-term market trends.
How does it work?
Let's use the context of this indicator, which calculates S/R Levels based on pivot points, as an example to explain how MTF Fusion works and how you can perform it yourself.
Step 1: Selecting Higher Timeframes
The first step is to determine the appropriate higher timeframes to use for the fusion calculation. These timeframes should typically be chosen based on their ability to provide meaningful price levels and action which actively affect the price action of the smaller timeframe you're focused on. For example, if you are trading the 5 minute chart, you might select the 15 minute, 30 minute, and hourly timeframe as the higher timeframes you want to fuse in order to give you a more holistic view of the trends and action affecting you on the 5 minute. In this indicator, four higher timeframes are automatically selected depending on the timeframe of the chart it is applied to.
Step 2: Gathering Data and Calculations
Once the higher timeframes are identified, the next step is to calculate the data from these higher timeframes that will be used to calculate your fused values. In this indicator, for example, the values of support and resistance levels are calculated by determining pivot points for all four higher timeframes.
Step 3: Fusing the Values From Higher Timeframes
The next step is to actually combine the values from these higher timeframes to obtain your 'fused' indicator values. The simplest approach to this is to simply average them. If you have calculated the value of a support level from three higher timeframes, you can, for example, calculate your 'multi-timeframe fused level' as (HigherTF_Support_Level_1 + HigherTF_Support_Level_2 + HigherTF_Support_Level_3) / 3.0.
Step 4: Visualization and Interpretation
Once the calculations are complete, the resulting fused indicator values are plotted on the chart. These values reflect the fusion of data from the multiple higher timeframes, giving a broader perspective on the market's behavior and potentially valuable insights without the need to manually consider values from each higher timeframe yourself.
What makes this script unique? Why is it closed source?
While the process described above is fairly unique and sounds simple, the truly important key lies in determining which higher timeframes to fuse together, and how to weight their values when calculating the fused end result in such a way that best leverages their relationship for useful TA.
This MTF Fusion indicator employs a smart, adaptive algorithm which automatically selects appropriate higher timeframes to use in fusion calculations depending on the timeframe of the chart it is applied to. It also uses a dynamic algorithm to adjust and weight the lookbacks used for pivot and S/R level calculations depending on each higher timeframe's relationship to the chart timeframe. These algorithms are based on extensive testing and are the reason behind this script's closed source status.
Included Features
Fusion Support and Resistance Levels
Dynamic Multi-Timeframe S/R Levels
Breakaway Zone fills to highlight breakouts and breakdowns from the Fusion S/R Levels
Customizable lookback approach
Pre-built color stylings
Options
Fusion View: Show/hide the Fusion S/R Levels calculated from multiple higher timeframes
MTF View: Show/hide the S?R levels from multiple higher timeframes used to calculate the Fusion S/R Levels
Breakaway Zones: Show/hide the fill for zones where price breaks away from the Fusion S/R Levels
Lookback: Select how you want your S/R Levels to be calculated (longer = long-term levels, shorter = short-term levels)
Pre-Built Color Styles: Use a pre-built color styling (uncheck to use your own colors)
Manual Color Styles: When pre-built color styles are disabled, use these color inputs to define your own
Complete Discrete Fourier Transform ToolkitThis is an expansion from my Discrete Fourier Transform Overlay indicator which offers various features that may be useful for traders wishing to apply frequency analysis or integral transform to their trading. For those unfamiliar with the concept, the discrete Fourier transform decomposes wave or wave-like data into functions depending on frequency. This can be helpful in demonstrating or interpreting trends and periodic frequencies in time-series price data, or oscillating indicators.
This toolkit has the following features:
Fourier bands (deviation cloud): The deviation cloud expresses the uncertainty in the DFT algorithm, as well as the relative change in frequency of the curve.
Fourier supertrend: The supertrend is applied as a product of the DFT algorithm, instead of onto the price data itself. This filters the supertrend from infrequent periodicities. For trading, this means that the supertrend will not be affected by false breakouts or breakdowns. See the image below for an example:
Future updates may include:
Projection of the probabilistic uncertainty principle. In a nutshell, the concept can be used to project uncertainties forwards through price data to forecast the path of least resistance, or, the most probable frequency.
Machine learning capabilities. Justin Doherty has done the Pine Script community a great service in introducing kNN algorithms with Lorentzian distance calculations; however, this is only the start of relativistic mechanics that can be applied to time series data. The DFT algorithm essentially filters data into its periodicities; this data can be inserted into a relativistic kNN algorithm - Lorenz or otherwise - to possibly improve accuracy.
Pattern Forecast (Expo)█ Overview
The Pattern Forecast indicator is a technical analysis tool that scans historical price data to identify common chart patterns and then analyzes the price movements that followed these patterns. It takes this information and projects it into the future to provide traders with potential price actions that may occur if the same pattern is identified in real-time market data. This projection helps traders to understand the possible outcomes based on the previous occurrences of the pattern, thereby offering a clearer perspective of the market scenario. By analyzing the historical data and understanding the subsequent price movements following the appearance of a specific pattern, the indicator can provide valuable insights into potential future market behavior.
█ Calculations
The indicator works by scanning historical price data for various candlestick patterns. It includes all in-built TradingView patterns, credit to TradingView that has coded them.
Essentially, the indicator takes the historical price moves that followed the pattern to forecast what might happen next.
█ Example
In this example, the algorithm is set to search for the Inverted Hammer Bullish candlestick pattern. If the pattern is found, the historical outcome is then projected into the future. This helps traders to understand how the past pattern evolved over time.
█ How to use
Providing traders with a comprehensive understanding of historical patterns and their implications for future price action allows them to assess the likelihood of specific market scenarios objectively. For example, suppose the pattern forecast indicator suggests that a particular pattern is likely to lead to a bullish move in the market. A trader might consider going long if the same pattern is identified in the real-time market. Similarly, a trader might consider shorting the asset if the indicator suggests a bearish move is likely, if the same pattern is identified in the real-time market.
█ Settings
Pattern
Select the pattern that the indicator should scan for. All inbuilt TradingView patterns can be selected.
Forecast Candles
Number of candles to project into the future.
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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!
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
Goertzel Cycle Composite Wave
Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
Fourier Extrapolator of 'Caterpillar' SSA of Price
Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
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XPrecisionSwing (XPS)* XPrecisionSwing (XPS) Indicator *
Is a visual representation of the Forces of Supply / Demand in the markets in the form of UP and DOWN waves. The Supply / Demand (denoted by a number on top or below the wave line) is computed using the *MBox Precision Supply / Demand* algorithm. These numbers diligently show the forces of Supply and Demand moving price in the markets. The algorithm for computing the numbers on the top and bottom of the wave lines measures the strength of the Supply / Demand. It is this algorithm that makes this indicator unique as it gives an accurate representation of the forces pulling the market up and down. When forces oppose each other, meaning when the direction of price does not agree with the direction of the Supply or Demand it creates a divergence and an opportunity in the markets. These situations are called BUY / SELL Imbalances. Explanation about this below.
* WHAT THE SCRIPT DOES *
The XPrecisionSwing indicator draws swing waves lines going up and down. These waves lines are representative of Supply and Demand. Waves going up are Demand, while waves going down are Supply. The strength of the Supply / Demand corresponds to the number drawn either on top of the wave line or below it. The numbers drawn on the chart are powered by the *MBox Precision Supply / Demand* algorithm, which are representative of the Forces of Supply / Demand in the markets. This is not just volume added up like in a regular zig zag indicator, since volume alone does not show Supply / Demand, and regular volume will not show BUY / SELL Imbalances as depicted by XPrecisionSwing. Volume summated will not show both positive and negative numbers on the chart. Having Supply / Demand split into both positive and negative numbers allows us to see BUY / SELL Imbalances, which can be a very powerful divergence. Information on how these numbers are computed are in the "HOW IT WORKS" section.
The numbers drawn on the chart can be either negative or positive. Positive relates to Demand, while negative relates to Supply. In this manner the strength of Supply and Demand can be gauged in each wave. If the price goes up but the number is negative (More Supply) it is a divergence and called a SELL Imbalance. This means there was more Supply even though price went up. It is important to pay attention to these scenarios, as often it can be indicative of NO DEMAND. Conversely. if the price goes down but the number is positive (No Demand) it is a divergence and is called a BUY Imbalance. This means there was more Demand even though price went down. This is indicative of NO SUPPLY. As such, it now becomes possible to know when there is a sign of Supply, Demand, No Supply, No Demand, Supply Exhaustion, and Demand exhaustion. Supply occurs when the negative numbers on the charts begin to increase (more negatively). Demand occurs when the positive numbers on the chart begin to increase (more positively). A Supply Exhaustion pattern happens when the price is starting to move down more slowly, while Supply is decreasing, and Demand is increasing. This means that the behavior of the market is changing and also a signal to look to reverse positions. A Demand Exhaustion pattern happens when the price is starting to move up more slowly, while Demand is decreasing, and Supply is increasing. The behavior of the market here is also changing.
* HOW IT WORKS *
- Technical Details for the Numbers on the Swing -
The numbers on the chart represent Supply / Demand. Supply or Demand is determined by analyzing the movement of price and quantity of volume.
When price goes up and is combined with an increase in volume it is Expansion of Demand.
(Positive Numbers get larger)
However if price goes up and is combined with a decrease in volume it is Contraction of Demand.
(Positive Numbers get smaller)
When price goes down and is combined with an increase in volume it is Expansion of Supply.
(Negative Numbers get larger)
However if price goes down and is combined with a decrease in volume it is Contraction of Supply.
(Negative Numbers get smaller)
- Technical Details for the Swing -
The way XPrecisionSwing draws the swings is fractal in nature, which make it very convenient and easier to use over the traditional zig zag indicator. The traditional zig zag indicator uses a tick reversal which needs to be adjusted every time you change time frames. However, with XPrecisionSwing you do not have to change any settings every time you load a different time frame since it will adjust to any time frame you are loading. How the swing is drawn is explained below.
XPrecisionSwing uses 3 bars (by default) to define a swing
This parameter can be adjusted. Can be 1, 2, 4 bars, etc...
Swings are always drawn using High / Low of the bar
- Rules -
To start upswing, bar high needs to be higher than previous 3 candle highs
To start downswing, bar low needs to be lower than previous 3 candle lows
If in upswing, a higher high will continue the upswing
if in downswing, a lower low will continue the downswing
- Exceptions -
If outside bar (both high and low exceeds previous 3 bars) swing will continue in current direction
- Swing Confirmation -
Swing wave line in progress (unconfirmed) is denoted by a brown box around the swing number
Once the brown box disappears, that swing wave and number is confirmed
* HOW TO USE IT *
As the numbers on the down waves increase (negatively), this shows that the bears have taken control of the markets. Conversely, as the numbers on the up waves increase (positively), this shows the bulls have taken control of the markets. Whoever is in control is the direction you generally want to place your trades in. When you see an increase in Supply (numbers on down wave) accompanied with a decrease in Demand (numbers on up wave) this shows a Supply + Demand Exhaustion Pattern. This is stronger than if you only see an increase in Supply without a decrease in Demand.
- The Buy / Sell Imbalances -
If you see a positive blue number on the bottom of a DOWN Wave, this means that there was more buying than selling even though price moved down.
If you see a negative red number on the top of an UP Wave, this means that there was more selling than buying even though price moved up.
Both of these cases signify and imbalance and a divergence.
* EXAMPLE AND USE CASES *
- Sell Imbalance Example -
If you see a large negative number with a lower low on a down wave, and then the next up wave is a lower high also with a negative number it shows that there is only Supply flooding the market and no sign of Demand. This is a very powerful combo.
- Buy Imbalance Example -
If you see a large positive number with a higher high on an up wave, and then the next down wave is a higher low also with a positive number it shows that there is only Demand flooding the market and no sign of Supply. This is a very powerful combo.
- Supply Exhaustion example -
If you see price movement struggling to make newer lows and the Supply numbers on the down waves are decreasing, while the Demand numbers on the up waves are increasing this is indicative of a *Change of Behavior*, and that the market is showing signs of reversal.
- Break out on Demand example -
If you see price has been ranging and now the numbers on the UP waves begin to increase while breaking out of a previous area of resistance, it is a good sign that the movement is backed by the strength coming from the Demand.
* BUY / SELL IMBALANCE ALERTS *
The Green / Red crosses on the chart show exactly where the Buy / Sell Imbalance Alerts trigger.
These will NEVER repaint! The crosses can be hidden in Styles if you wish to.
Alerts can be set very easily with the instructions below.
1. Right Click Chart -> Add Alert...
(Ignore Caution Warning. These alerts will *ONLY* trigger on Confirmed BUY / SELL Imbalances and will NOT repaint)
2. Select Condition to be "XPrecisionSwing"
3. Select "Buy Imbalance" or "Sell Imbalance"
4. Select "Greater Than" with Value = 0
5. Options set "Once Per Bar"
6. Customize Any other Alert Options you want
* WHAT MAKES IT ORIGINAL *
XPrecisionSwing gives an inside look into the markets by showing price movements as a series of waves going up and down with their corresponding Supply / Demand numbers associated with each wave. Reading the numbers shows the strength of Supply / Demand. The bigger the number the stronger the Supply / Demand is. The smaller the number the weaker the Supply / Demand is. It becomes possible to see where Supply / Demand comes in, along with Exhaustion of Supply / Demand to spot opportunities to place trades. The Buy / Sell Imbalances show imbalances where price movement and the direction of the Supply / Demand diverge to create potential opportunities as well.
* AUTHOR *
This script is published by MBoxWave LLC
Smoothing R-Squared ComparisonIntroduction
Heyo guys, here I made a comparison between my favorised smoothing algorithms.
I chose the R-Squared value as rating factor to accomplish the comparison.
The indicator is non-repainting.
Description
In technical analysis, traders often use moving averages to smooth out the noise in price data and identify trends. While moving averages are a useful tool, they can also obscure important information about the underlying relationship between the price and the smoothed price.
One way to evaluate this relationship is by calculating the R-squared value, which represents the proportion of the variance in the price that can be explained by the smoothed price in a linear regression model.
This PineScript code implements a smoothing R-squared comparison indicator.
It provides a comparison of different smoothing techniques such as Kalman filter, T3, JMA, EMA, SMA, Super Smoother and some special combinations of them.
The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement.
The input parameters for the Kalman filter include the process noise covariance and the measurement noise covariance, which help to adjust the sensitivity of the filter to changes in the input data.
The T3 smoothing technique is a popular method used in technical analysis to remove noise from a signal.
The input parameters for the T3 smoothing method include the length of the window used for smoothing, the type of smoothing used (Normal or New), and the smoothing factor used to adjust the sensitivity to changes in the input data.
The JMA smoothing technique is another popular method used in technical analysis to remove noise from a signal.
The input parameters for the JMA smoothing method include the length of the window used for smoothing, the phase used to shift the input data before applying the smoothing algorithm, and the power used to adjust the sensitivity of the JMA to changes in the input data.
The EMA and SMA techniques are also popular methods used in technical analysis to remove noise from a signal.
The input parameters for the EMA and SMA techniques include the length of the window used for smoothing.
The indicator displays a comparison of the R-squared values for each smoothing technique, which provides an indication of how well the technique is fitting the data.
Higher R-squared values indicate a better fit. By adjusting the input parameters for each smoothing technique, the user can compare the effectiveness of different techniques in removing noise from the input data.
Usage
You can use it to find the best fitting smoothing method for the timeframe you usually use.
Just apply it on your preferred timeframe and look for the highlighted table cell.
Conclusion
It seems like the T3 works best on timeframes under 4H.
There's where I am active, so I will use this one more in the future.
Thank you for checking this out. Enjoy your day and leave me a like or comment. 🧙♂️
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Credits to:
▪@loxx – T3
▪@balipour – Super Smoother
▪ChatGPT – Wrote 80 % of this article and helped with the research
Spectral Gating (SG)The Spectral Gating (SG) Indicator is a technical analysis tool inspired by music production techniques. It aims to help traders reduce noise in their charts by focusing on the significant frequency components of the data, providing a clearer view of market trends.
By incorporating complex number operations and Fast Fourier Transform (FFT) algorithms, the SG Indicator efficiently processes market data. The indicator transforms input data into the frequency domain and applies a threshold to the power spectrum, filtering out noise and retaining only the frequency components that exceed the threshold.
Key aspects of the Spectral Gating Indicator include:
Adjustable Window Size: Customize the window size (ranging from 2 to 6) to control the amount of data considered during the analysis, giving you the flexibility to adapt the indicator to your trading strategy.
Complex Number Arithmetic: The indicator uses complex number addition, subtraction, and multiplication, as well as radius calculations for accurate data processing.
Iterative FFT and IFFT: The SG Indicator features iterative FFT and Inverse Fast Fourier Transform (IFFT) algorithms for rapid data analysis. The FFT algorithm converts input data into the frequency domain, while the IFFT algorithm restores the filtered data back to the time domain.
Spectral Gating: At the heart of the indicator, the spectral gating function applies a threshold to the power spectrum, suppressing frequency components below the threshold. This process helps to enhance the clarity of the data by reducing noise and focusing on the more significant frequency components.
Visualization: The indicator plots the filtered data on the chart with a simple blue line, providing a clean and easily interpretable representation of the results.
Although the Spectral Gating Indicator may not be a one-size-fits-all solution for all trading scenarios, it serves as a valuable tool for traders looking to reduce noise and concentrate on relevant market trends. By incorporating this indicator into your analysis toolkit, you can potentially make more informed trading decisions.
