Supertrend Targets [ChartPrime]The Supertrend Targets indicator combines the concepts of trend-following with dynamic volatility-based target levels. It takes core simple and classical concepts and provides actionable insights. The core of this indicator revolves around the "Supertrend" algorithm, which essentially uses the Average True Range (ATR) and a multiplier to determine if the price of a financial instrument is in an uptrend or downtrend. The indicator generates various plot points on the trading chart, which traders can use to make informed trading decisions.
Users can set several input parameters such as the source price, custom levels, multiplier scale, length of the average true range, and the window length. Traders can also opt to enable a table that shows numeric target data by percentiles, risk ratio, take profit and stop loss points.
The generated plots and fills on the chart represent various levels of potential gains and drawdowns, acting as potential targets for taking profit or stopping losses. These include the 25th, 50th, 75th, 90th, and 100th percentiles, which are adjustable by scale. There are also plots for average gain and drawdown levels, enhanced by standard deviation curves if enabled.
The Supertrend line indicators are color-coded for ease of understanding: blue for bullish performance and orange for bearish performance. The "Center Line" represents the point at which traders might consider entering a position.
Lastly, the script presents a summary table (when enabled) at the right side of the chart displaying numeric data of the plotted targets. This data provides additional insights on the risk-reward balance for each percentile, helping traders to execute their strategies more effectively.
Here's a comprehensive breakdown of its functionalities and features:
Inputs:
Source: Determines the price series type (e.g., Close, Open, High, Low, etc.).
Show Trailing Stop: Option to display the trailing stop on the chart.
Levels: Sets the number of target levels you want to display. Can range from -5 to 5.
Scale: A scaling factor for adjusting targets, can be between 1 to 100.
Window Length: Length for the target computation, determines how many bars will be considered.
Unique: Ensures every data point used in calculations is unique.
Multiplier: Multiplier for the ATR (Average True Range) to compute the SuperTrend.
ATR Length: Period for the ATR computation.
Custom Level: Allows users to set their own levels using various statistics like Average, Average + STDEV, Percentile, or can be disabled.
Percent Rank: Determines the percentile rank for targeting.
Enable Table: Enables or disables a table display.
Methods:
Flag: Identifies bullish and bearish trend reversals.
Target Percent: Determines the expected price movement (both gains and drawdowns) based on historical trend reversals.
Value Percent: Computes the percentage difference between the current price and the entry price during trend reversals.
Plots:
Multiple target lines are plotted on the chart to visualize potential gain and drawdown levels. These levels are adjusted based on user settings. Additionally, the main Supertrend line is plotted to indicate the prevailing trend direction.
Gain Levels: Target levels which show potential upside from the current price.
Drawdown Levels: Target levels which represent potential downside from the current price.
SuperTrend Line: A line that adjusts based on price volatility and trend direction, acting as a dynamic support or resistance.
In conclusion, the "Supertrend Targets " indicator is a powerful tool that combines the principle of trend-following with dynamic targets, providing traders with insights into potential future price movements. The range of customization options allows traders to adapt the indicator to different trading strategies and market conditions.
Search in scripts for "TAKE"
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul.
DEDICATION:
This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development.
I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion.
Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight.
If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face.
INTRODUCTION:
Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike."
Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity...
This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance.
What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later...
I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand.
The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer.
I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you.
Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects.
PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS:
This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead.
PERIODOGRAM INTERPRETATION:
The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side.
The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity.
SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED:
Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram.
Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters.
Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox.
Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values.
DOMINANT CYCLE VALUE SELECTIONS:
External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it.
AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED:
In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later.
In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients.
Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use.
The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers.
The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram.
POST SCRIPT:
Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
Supply Demand Profiles [LuxAlgo]The Supply Demand Profiles is a charting tool that measures the traded volume at all price levels on the market over a specified time period and highlights the relationship between the price of a given asset and the willingness of traders to either buy or sell it, in other words, highlights key concepts as significant supply & demand zones, the distribution of the traded volume, and market sentiment at specific price levels within a specified time period, allowing traders to reveal dominant and/or significant price levels and to analyze the trading activity of a particular user-selected range.
In other words, this tool highlights key concepts as significant supply & demand zones, the distribution of the traded volume, and market sentiment at specific price levels within a specified time period, allowing traders to reveal dominant and/or significant price levels and to analyze the trading activity of a particular user-selected range.
Besides having the tool as a combo tool, the uniqueness of this version of the tool compared to its early versions is its ability to benefit from different volume data sources and its ability to use a variety of different polarity methods, where polarity is a measure used to divide the total volume into either up volume (trades that moved the price up) or down volume (trades that moved the price down).
🔶 USAGE
Supply & demand zones are presented as horizontal zones across the selected range, hence adding the ability to visualize the price interaction with them
By default, the right side of the profile is the volume profile which highlights the distribution of the traded activity at different price levels, emphasizing the value area, the range of price levels in which the specified percentage of all volume was traded during the time period, and levels of significance, such as developing point of control line, value area high/low lines, and profile high/low labels
The left side of the profile is the sentiment profile which highlights the market sentiment at specific price levels
🔶 DETAILS
🔹 Volume data sources
The users have the option to select volume data sources as either 'volume' (regular volume) or 'volume delta', where volume represents all the recorded trades that occur at a given bar and volume delta is the difference between the buying and the selling volume, that is, the net demand at a given bar
🔹 Polarity methods
The users are able to choose the methods of how the tool to take into consideration the polarity of the bar (the direction of a bar, green (bullish) or red (bearish) bar) among a variety of different options, such as 'bar polarity', 'bar buying/selling pressure', 'intrabar (chart bars at a lower timeframe than the chart's) polarity', 'intrabar buying/selling pressure', and 'heikin ashi bar polarity'.
Finally, the interactive mode of the tool is activated, as such users can easily modify the intervals of their interest just by selecting the indicator and moving the points on the chart
🔶 SETTINGS
The script takes into account user-defined parameters and plots the profiles and zones
🔹 Calculation Settings
Volume Data Source and Polarity: This option is to set the desired volume data source and polarity method
Lower Timeframe Precision: This option is applicable in case any of the 'Intrabar (LTF)' options are selected, please check the tooltip for further details
Value Area Volume %: Specifies the percentage for the value area calculation
🔹 Presentation Settings
Supply & Demand Zones: Toggles the visibility of the supply & demand zones
Volume Profile: Toggles the visibility of the volume profile
Sentiment Profile: Toggles the visibility of the sentiment profile
🔹 Presentation, Others
Value Area High (VAH): Toggles the visibility of the VAH line and color customization option
Point of Control (POC): Toggles the visibility of the developing POC line and color customization option
Value Area Low (VAL): Toggles the visibility of the VAL line and color customization option
🔹 Supply & Demand, Others
Supply & Demand Threshold %: This option is used to set the threshold value to determine supply & demand zones
Supply/Demand Zones: Color customization option
🔹 Volume Profile, Others
Profile, Up/Down Volume: Color customization option
Value Area, Up/Down Volume: Color customization option
🔹 Sentiment Profile, Others
Sentiment, Bullish/Bearish: Color customization option
Value Area, Bullish/Bearish: Color customization option
🔹 Others
Number of Rows: Specify how many rows the profile will have
Placment: Specify where to display the profile
Profile Width %: Alters the width of the rows in the profile, relative to the profile range
Profile Price Levels: Toggles the visibility of the profile price levels
Profile Background, Color: Fills the background of the profile range
Value Area Background, Color: Fills the background of the value area range
Start Calculation/End Calculation: The tool is interactive, where the user may modify the range by selecting the indicator and moving the points on the chart or can set the start/end time using these options
🔶 RELATED SCRIPTS
Volume-Profile
Volume-Profile-Maps
Volume-Delta
Alxuse MACD for tutorialAll abilities of MACD, moreover :
Drawing upper band and lower band & the ability to change values, change colors, turn on/off show.
Crossing MACD line and SIGNAL line in multi timeframe & there are symbols (Circles) with green color (Buy) and red color (Sell) & the ability to change colors, turn on/off show.
Crossing MACD line and SIGNAL line in multi timeframe according to the values of upper band and lower band & there are symbols (Triangles) with green color (Long) and red color (Short) & the ability to change colors, turn on/off show.
The ability used in the alert section and create customized alerts.
To receive valid alerts the replay section , the timeframe of the chart must be the same as the timeframe of the indicator.
MACD (Moving Average Convergence/Divergence)
Definition
MACD is an extremely popular indicator used in technical analysis. MACD can be used to identify aspects of a security's overall trend. Most notably these aspects are momentum, as well as trend direction and duration. What makes MACD so informative is that it is actually the combination of two different types of indicators. First, MACD employs two Moving Averages of varying lengths (which are lagging indicators) to identify trend direction and duration. Then, MACD takes the difference in values between those two Moving Averages (MACD Line) and an EMA of those Moving Averages (Signal Line) and plots that difference between the two lines as a histogram which oscillates above and below a center Zero Line. The histogram is used as a good indication of a security's momentum.
MACD Line is a result of taking a longer term EMA and subtracting it from a shorter term EMA.The most commonly used values are 26 days for the longer term EMA and 12 days for the shorter term EMA, but it is the trader's choice.
The Signal Line.
The Signal Line is an EMA of the MACD Line described in Component 1. The trader can choose what period length EMA to use for the Signal Line however 9 is the most common.
The MACD Histogram.
As time advances, the difference between the MACD Line and Signal Line will continually differ. The MACD histogram takes that difference and plots it into an easily readable histogram. The difference between the two lines oscillates around a Zero Line.
A general interpretation of MACD is that when MACD is positive and the histogram value is increasing, then upside momentum is increasing. When MACD is negative and the histogram value is decreasing, then downside momentum is increasing.
What to look for
The MACD indicator is typically good for identifying three types of basic signals; Signal Line Crossovers, Zero Line Crossovers, and Divergence.
SIGNAL LINE CROSSOVERS
A Signal Line Crossover is the most common signal produced by the MACD. First one must consider that the Signal Line is essentially an indicator of an indicator. The Signal Line is calculating the Moving Average of the MACD Line. Therefore the Signal Line lags behind the MACD line. That being said, on the occasions where the MACD Line crosses above or below the Signal Line, that can signify a potentially strong move.
The strength of the move is what determines the duration of Signal Line Crossover. Understanding and being able to analyze move strength, as well as being able to recognize false signals, is a skill that comes with experience.
The first type of Signal Line Crossover to examine is the Bullish Signal Line Crossover. Bullish Signal Line Crossovers occur when the MACD Line crosses above the Signal Line.
The second type of Signal Line Crossover to examine is the Bearish Signal Line Crossover. Bearish Signal Line Crossovers occur when the MACD Line crosses below the Signal Line.
Zero line crossovers
Zero Line Crossovers have a very similar premise to Signal Line Crossovers. Instead of crossing the Signal Line, Zero Line Crossovers occur when the MACD Line crossed the Zero Line and either becomes positive (above 0) or negative (below 0).
The first type of Zero Line Crossover to examine is the Bullish Zero Line Crossover. Bullish Zero Line Crossovers occur when the MACD Line crosses above the Zero Line and go from negative to positive.
The second type of Zero Line Crossover to examine is the Bearish Zero Line Crossover. Bearish Zero Line Crossovers occur when the MACD Line crosses below the Zero Line and go from positive to negative.
Divergence
Divergence is another signal created by the MACD. Simply put, divergence is when the MACD and actual price are not in agreement.
For example, Bullish Divergence occurs when price records a lower low, but the MACD records a higher low. The movement of price can provide evidence of the current trend, however changes in momentum as evidenced by the MACD can sometimes precede a significant reversal.
Bearish Divergence is, of course, the opposite. Bearish Divergence occurs when price records a higher high while the MACD records a lower high.
Summary
What makes the MACD such a valuable tool for technical analysis is that it is almost like two indicators in one. It can help to identify not just trends, but it can measure momentum as well. It takes two separate lagging indicators and adds the aspect of momentum which is much more active or predictive That kind of versatility is why it has been and is used by trader's and analysts across the entire spectrum of finance.
Despite MACD's obvious attributes, just like with any indicator, the trader or analyst needs to exercise caution. There are just some things that MACD doesn't do well which may tempt a trader regardless. Most notably, traders may be tempted into using MACD as a way to find overbought or oversold conditions. This is not a good idea. Remember, MACD is not bound to a range, so what is considered to be highly positive or negative for one instrument may not translate well to a different instrument.
With sufficient time and experience, almost anybody who wants to analyze chart data should be able to make good use out of the MACD.
The added features to the indicator are made for training, it is advisable to use it with caution in tradings.
W and M Pattern Indicator- SwaGThis is a TradingView indicator script that identifies potential buy and sell signals based on ‘W’ and ‘M’ patterns in the Relative Strength Index (RSI). It provides visual alerts and draws horizontal lines to indicate potential trade entry points.
User Manual:
Inputs: The script takes two inputs - an upper limit and a lower limit. The default values are 70 and 40, respectively.
RSI Calculation: The script calculates the RSI based on the closing prices of the last 14 periods.
Pattern Identification: It identifies ‘W’ patterns when the RSI makes a higher low within the lower limit, and ‘M’ patterns when the RSI makes a lower high within the upper limit.
Visual Alerts: The script plots these patterns on the chart. ‘W’ patterns are marked with small green triangles below the bars, and ‘M’ patterns are marked with small red triangles above the bars.
Trade Entry Points: A horizontal line is drawn at the high or low of the candle to represent potential trade entry points. The line starts from one bar to the left and extends 10 bars to the right.
Trading Strategy:
For investing, use a weekly timeframe.
For swing trading, use a daily timeframe.
For intraday trading, use a 5 or 15-minute timeframe. Only consider sell-side signals for intraday trading.
Take a buy position if the high breaks above the green line or sell if the low breaks below the red line.
Use recent signals only and avoid signals that are too old.
Swing highs or lows will be your stop-loss level.
Always think about your stop-loss before entering a trade, not your target.
Avoid trades with a large stop-loss.
Remember, this script is a tool to aid in your trading decisions. Always test your strategies thoroughly before live trading. Happy trading! 😊
Trade Warehouse (SPOT trades)Hello there!
Let's imagine You are trading SPOT, buy more and more every new dump, but bear market is not going to stop... and your first trade was 3 YEARS AGO!!!
Can't believe it is true.
The problem is - exchanges allow You to see only new trades last 6 months(Binance). But I want to see all of them! How do I know AVG Price?
This script is my solution. Just use it to track and store your trade, so You can see AVG without uploading old trades everytime and using calculator.
Script description:
Here You can see the "Trade" type of variable. Python script using Pandas converts trades from .csv file into string type that You can input as trade(price, pair, amount, date..). After it uppends to the trades_array and pushed into the loop.
If trade date is more than current cundle - it pushes new trade to other arrays such a "pair", "avg_tot" etc. to comput it later.
If trade was buy - it increase invested capital and owned amount, opposite for sell and recomputs AVG price.
Since script has at least 1 trade it starts to plot AVG price.
There are 2 AVG price:
1. For total invested counting(You can get negative value if traded successful)
2. Current AVG price since last 0 currency amount(there is dust value to set how many usd we take as dust)
Table represents all assets statistics
Just upload your trades only 1 time, use script to convert it into pine code, and use as indicator. This script allow You to see ALL trades from oldest to the newest.
github.com/Arivadis/...w_Tradings_warehouse
If this script helped You - press Star (on GitHub) Like (on TradingView)
Warning -
Does not include free/earn/withdraw/deposit counting. Only Buy and Sell =>
This script has no idea about your side currency deposits, so if You got Your BTC or EUR or .. from another wallet and sold later - it can break your statistical data. Add this transfer manually(see examples inside script).
Use my github manual to get this script workin.
Installing takes around 3 minutes and contains 3-5 steps
Support and Resistance Signals MTF [LuxAlgo]The Support and Resistance Signals MTF indicator aims to identify undoubtedly one of the key concepts of technical analysis Support and Resistance Levels and more importantly, the script aims to capture and highlight major price action movements, such as Breakouts , Tests of the Zones , Retests of the Zones , and Rejections .
The script supports Multi-TimeFrame (MTF) functionality allowing users to analyze and observe the Support and Resistance Levels/Zones and their associated Signals from a higher timeframe perspective.
This script is an extended version of our previously published Support-and-Resistance-Levels-with-Breaks script from 2020.
Identification of key support and resistance levels/zones is an essential ingredient to successful technical analysis.
🔶 USAGE
Support and resistance are key concepts that help traders understand, analyze and act on chart patterns in the financial markets. Support describes a price level where a downtrend pauses due to demand for an asset increasing, while resistance refers to a level where an uptrend reverses as a sell-off happens.
The creation of support and resistance levels comes as a result of an initial imbalance of supply/demand, which forms what we know as a swing high or swing low. This script starts its processing using the swing highs/lows. Swing Highs/Lows are levels that many of the market participants use as a historical reference to place their trading orders (buy, sell, stop loss), as a result, those price levels potentially become and serve as key support and resistance levels.
One of the important features of the script is the signals it provides. The script follows the major price movements and highlights them on the chart.
🔹 Breakouts (non-repaint)
A breakout is a price moving outside a defined support or resistance level, the significance of the breakout can be measured by examining the volume. This script is not filtering them based on volume but provides volume information for the bar where the breakout takes place.
🔹 Retests
Retest is a case where the price action breaches a zone and then revisits the level breached.
🔹 Tests
Test is a case where the price action touches the support or resistance zones.
🔹 Rejections
Rejections are pin bar patterns with high trading volume.
Finally, Multi TimeFrame (MTF) functionality allows users to analyze and observe the Support and Resistance Levels/Zones and their associated Signals from a higher timeframe perspective.
🔶 SETTINGS
The script takes into account user-defined parameters to detect and highlight the zones, levels, and signals.
🔹 Support & Resistance Settings
Detection Timeframe: Set the indicator resolution, the users may examine higher timeframe detection on their chart timeframe.
Detection Length: Swing levels detection length
Check Previous Historical S&R Level: enables the script to check the previous historical levels.
🔹 Signals
Breakouts: Toggles the visibility of the Breakouts, enables customization of the color and the size of the visuals
Tests: Toggles the visibility of the Tests, enables customization of the color and the size of the visuals
Retests: Toggles the visibility of the Retests, enables customization of the color and the size of the visuals
Rejections: Toggles the visibility of the Rejections, enables customization of the color and the size of the visuals
🔹 Others
Sentiment Profile: Toggles the visibility of the Sentiment Profiles
Bullish Nodes: Color option for Bullish Nodes
Bearish Nodes: Color option for Bearish Nodes
🔶 RELATED SCRIPTS
Support-and-Resistance-Levels-with-Breaks
Buyside-Sellside-Liquidity
Liquidity-Levels-Voids
CE - 42MACRO Equity Factor Table This is Part 1 of 2 from the 42MACRO Recreation Series
The CE - 42MACRO Equity Factor Table is a whole toolbox packaged in a single indicator.
It aims to provide a probabilistic insight into the market realized GRID Macro Regime, use a multiplex of important Assets and Indices to form a high probability Implied Correlation expectation and allows to derive extra market insights by showing the most important aggregates and their performance over multiple timeframes... and what that might mean for the whole market direction, as well as the underlying asset.
WARNING
By the nature of the macro regimes, the outcomes are more accurate over longer Chart Timeframes (Week to Months).
However, it is also a valuable tool to form a proper,
market realized, short to medium term bias.
NOTE
This Indicator is intended to be used alongside the 2nd part "CE - 42MACRO Yield and Macro"
for a more wholistic approach and higher accuracy.
Due to coding limitations they can not be merged into one Indicator.
Methodology:
The Equity Factor Table tracks specifically chosen Assets to identify their performance and add the combined performances together to visualize 42MACRO's GRID Equity Model.
For this it uses the below Assets, with more to come:
Dividend Compounders ( AMEX:SPHD )
Mid Caps ( AMEX:VO )
Emerging Markets ( AMEX:EEM )
Small Caps ( AMEX:IWM )
Mega Cap Growth ( NASDAQ:QQQ )
Brazil ( AMEX:EWZ )
United Kingdom ( AMEX:EWU )
Growth ( AMEX:IWF )
United States ( AMEX:SPY )
Japan ( AMEX:DXJ )
Momentum ( AMEX:MTUM )
China ( AMEX:FXI )
Low Beta ( AMEX:SPLV )
International ex-US ( NASDAQ:ACWX )
India ( AMEX:INDA )
Eurozone ( AMEX:EZU )
Quality ( AMEX:QUAL )
Size ( AMEX:OEF )
Functionalities:
1. Correlations
Takes a measure of Cross Market Correlations
2. Implied Trend
Calculates the trend for each Asset and uses the Correlation to obtain the Implied Trend for the underlying Asset
There are multiple functionalities to enhance Signal Speed and precision...
Reading a signal only over a certain threshold, otherwise being colored in gray to signal noise or unclear market behavior
Normalization of Signal
Double Normalization of Signal for more Speed... ideal for the Crypto Market
Using an additional Hull Moving Average to enhance Signal Speed
Additional simple Background coloring to get a Signal from the HMA
Barcoloring based on the Implied Correlation
3. Equity Factor Table
Shows market realized Asset performance
Provides the approximate realized GRID market regimes
Informs about "Risk ON" and "Risk OFF" market states
Now into the juicy stuff...
Visuals:
There is a variety of options to change visual settings of what is plotted and where
+ additional considerations.
Everything that is relevant in the underlying logic which can improve comprehension can be visualized with these options.
More to come
Market Correlation:
The Market Correlation Table takes the Correlation of all the Assets to the Asset on the Chart,
it furthermore uses the Normalized KAMA Oscillator by IkkeOmar to analyse the current trend of every single Asset.
(To enhance the Signal you can apply the mentioned Indicator on the relevant Assets to find your target Asset movements that you intend to capture...
and then change the length of the Indicator in here)
It then Implies a Correlation based on the Trend and the Correlation to give a probabilistically adjusted expectation for the future Chart Asset Movement.
This is strengthened by taking the average of all Implied Trends.
Thus the Correlation Table provides valuable insights about probabilistically likely Movement of the Asset over the defined time duration,
providing alpha for Traders and Investors alike.
Equity Factors:
The table provides valuable information about the current market environment (whether it's risk on or risk off),
the rough GRID models from 42MACRO and the actual market performance.
This allows you to obtain a deeper understanding of how the market works and makes it simple to identify the actual market direction,
makes it possible to derive overall market Health and shows market strength or weakness.
Utility:
The Equity Factor Table is divided in 4 Sections which are the GRID regimes:
Economic Growth:
Goldilocks
Reflation
Economic Contraction:
Inflation
Deflation
Top 5 Equity Factors:
Are the values green for a specific Column?
If so then the market reflects the corresponding GRID behavior.
Bottom 5 Equity Factors:
Are the values red for a specific Column?
If so then the market reflects the corresponding GRID behavior.
So if we have Goldilocks as current regime we would see green values in the Top 5 Goldilocks Cells and red values in the Bottom 5 Goldilocks Cells.
You will find that Reflation will look similar, as it is also a sign of Economic Growth.
Same is the case for the two Contraction regimes.
This whole Indicator, as well as the second part, is based to a majority on 42MACRO's models.
I only brought them into TV and added things on top of it.
If you have questions or need a more in-depth guide DM me.
Will make a guide to all functionalities if necessity becomes apparent.
GM
MACD 3D with Signals [Quantigenics]Quantigenics MACD 3D with Buy Sell Signals is a MACD-based trading indicator that aims to identify market trends and potential turning points, for Buy/Sell opportunities, by leveraging price data and volatility.
Unlike the traditional MACD indicator, the average price is calculated from the high, low, and close prices, from which a specialized MACD value is derived. This MACD value, combined with an average and standard deviation, takes into account volatility, and is used to generate an upper and lower boundary.
The indicator color-codes market trends: aqua indicates upward trends (signifying increased buying pressure), red suggests downward trends (increased selling pressure). When the MACD value crosses above the upper boundary or falls below the lower boundary, the color changes to yellow indicating a possible reversal point and "Momentum Crossover Signals" can be plotted at this point. "Standard Signal" arrows can also plotted when the MACD 3D changes from auqa to red and vice-versa.
A trendline is drawn at the median value, providing a baseline for comparison. A differential value, which measures the distance between the MACD value and the median line, provides additional insight into the price's deviation from this baseline (divergences from the underlying price can be spotted using this data as well). The differential is color-coded: green when MACD is above the median, and red when it's below, with darker shades representing a decreasing gap.
Alerts can be set to trigger with the "Standard Signal" arrows appearing after MACD 3D changes from auqa to red and vice-versa and when the "Momentum Crossover Signal" arrows appear when the MACD value crosses above the upper boundary or falls below the lower boundary indicating a potential reversal. Providing immediate notifications which can be especially helpful in larger time frames where it may take time for a trade setup to develop.
CME_MINI:NQ1!
OANDA:XAUUSD
Enjoy the MACD 3D indicator. Happy Trading!
Support & Resistance AI (K means/median) [ThinkLogicAI]█ OVERVIEW
K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach.
Support and resistance levels are price levels in financial markets where the price tends to react or reverse. Support is a level where the price tends to stop falling and might start to rise, while resistance is a level where the price tends to stop rising and might start to fall. Traders and analysts often look for these levels as they can provide insights into potential price movements and trading opportunities.
█ BACKGROUND
The K-means algorithm has been around since the late 1950s, making it more than six decades old. The algorithm was introduced by Stuart Lloyd in his 1957 research paper "Least squares quantization in PCM" for telecommunications applications. However, it wasn't widely known or recognized until James MacQueen's 1967 paper "Some Methods for Classification and Analysis of Multivariate Observations," where he formalized the algorithm and referred to it as the "K-means" clustering method.
So, while K-means has been around for a considerable amount of time, it continues to be a widely used and influential algorithm in the fields of machine learning, data analysis, and pattern recognition due to its simplicity and effectiveness in clustering tasks.
█ COMPARE AND CONTRAST SUPPORT AND RESISTANCE METHODS
1) K-means Approach:
Cluster Formation: After applying the K-means algorithm to historical price change data and visualizing the resulting clusters, traders can identify distinct regions on the price chart where clusters are formed. Each cluster represents a group of similar price change patterns.
Cluster Analysis: Analyze the clusters to identify areas where clusters tend to form. These areas might correspond to regions of price behavior that repeat over time and could be indicative of support and resistance levels.
Potential Support and Resistance Levels: Based on the identified areas of cluster formation, traders can consider these regions as potential support and resistance levels. A cluster forming at a specific price level could suggest that this level has been historically significant, causing similar price behavior in the past.
Cluster Standard Deviation: In addition to looking at the means (centroids) of the clusters, traders can also calculate the standard deviation of price changes within each cluster. Standard deviation is a measure of the dispersion or volatility of data points around the mean. A higher standard deviation indicates greater price volatility within a cluster.
Low Standard Deviation: If a cluster has a low standard deviation, it suggests that prices within that cluster are relatively stable and less likely to exhibit sudden and large price movements. Traders might consider placing tighter stop-loss orders for trades within these clusters.
High Standard Deviation: Conversely, if a cluster has a high standard deviation, it indicates greater price volatility within that cluster. Traders might opt for wider stop-loss orders to allow for potential price fluctuations without getting stopped out prematurely.
Cluster Density: Each data point is assigned to a cluster so a cluster that is more dense will act more like gravity and
2) Traditional Approach:
Trendlines: Draw trendlines connecting significant highs or lows on a price chart to identify potential support and resistance levels.
Chart Patterns: Identify chart patterns like double tops, double bottoms, head and shoulders, and triangles that often indicate potential reversal points.
Moving Averages: Use moving averages to identify levels where the price might find support or resistance based on the average price over a specific period.
Psychological Levels: Identify round numbers or levels that traders often pay attention to, which can act as support and resistance.
Previous Highs and Lows: Identify significant previous price highs and lows that might act as support or resistance.
The key difference lies in the approach and the foundation of these methods. Traditional methods are based on well-established principles of technical analysis and market psychology, while the K-means approach involves clustering price behavior without necessarily incorporating market sentiment or specific price patterns.
It's important to note that while the K-means approach might provide an interesting way to analyze price data, it should be used cautiously and in conjunction with other traditional methods. Financial markets are influenced by a wide range of factors beyond just price behavior, and the effectiveness of any method for identifying support and resistance levels should be thoroughly tested and validated. Additionally, developments in trading strategies and analysis techniques could have occurred since my last update.
█ K MEANS ALGORITHM
The algorithm for K means is as follows:
Initialize cluster centers
assign data to clusters based on minimum distance
calculate cluster center by taking the average or median of the clusters
repeat steps 1-3 until cluster centers stop moving
█ LIMITATIONS OF K MEANS
There are 3 main limitations of this algorithm:
Sensitive to Initializations: K-means is sensitive to the initial placement of centroids. Different initializations can lead to different cluster assignments and final results.
Assumption of Equal Sizes and Variances: K-means assumes that clusters have roughly equal sizes and spherical shapes. This may not hold true for all types of data. It can struggle with identifying clusters with uneven densities, sizes, or shapes.
Impact of Outliers: K-means is sensitive to outliers, as a single outlier can significantly affect the position of cluster centroids. Outliers can lead to the creation of spurious clusters or distortion of the true cluster structure.
█ LIMITATIONS IN APPLICATION OF K MEANS IN TRADING
Trading data often exhibits characteristics that can pose challenges when applying indicators and analysis techniques. Here's how the limitations of outliers, varying scales, and unequal variance can impact the use of indicators in trading:
Outliers are data points that significantly deviate from the rest of the dataset. In trading, outliers can represent extreme price movements caused by rare events, news, or market anomalies. Outliers can have a significant impact on trading indicators and analyses:
Indicator Distortion: Outliers can skew the calculations of indicators, leading to misleading signals. For instance, a single extreme price spike could cause indicators like moving averages or RSI (Relative Strength Index) to give false signals.
Risk Management: Outliers can lead to overly aggressive trading decisions if not properly accounted for. Ignoring outliers might result in unexpected losses or missed opportunities to adjust trading strategies.
Different Scales: Trading data often includes multiple indicators with varying units and scales. For example, prices are typically in dollars, volume in units traded, and oscillators have their own scale. Mixing indicators with different scales can complicate analysis:
Normalization: Indicators on different scales need to be normalized or standardized to ensure they contribute equally to the analysis. Failure to do so can lead to one indicator dominating the analysis due to its larger magnitude.
Comparability: Without normalization, it's challenging to directly compare the significance of indicators. Some indicators might have a larger numerical range and could overshadow others.
Unequal Variance: Unequal variance in trading data refers to the fact that some indicators might exhibit higher volatility than others. This can impact the interpretation of signals and the performance of trading strategies:
Volatility Adjustment: When combining indicators with varying volatility, it's essential to adjust for their relative volatilities. Failure to do so might lead to overemphasizing or underestimating the importance of certain indicators in the trading strategy.
Risk Assessment: Unequal variance can impact risk assessment. Indicators with higher volatility might lead to riskier trading decisions if not properly taken into account.
█ APPLICATION OF THIS INDICATOR
This indicator can be used in 2 ways:
1) Make a directional trade:
If a trader thinks price will go higher or lower and price is within a cluster zone, The trader can take a position and place a stop on the 1 sd band around the cluster. As one can see below, the trader can go long the green arrow and place a stop on the one standard deviation mark for that cluster below it at the red arrow. using this we can calculate a risk to reward ratio.
Calculating risk to reward: targeting a risk reward ratio of 2:1, the trader could clearly make that given that the next resistance area above that in the orange cluster exceeds this risk reward ratio.
2) Take a reversal Trade:
We can use cluster centers (support and resistance levels) to go in the opposite direction that price is currently moving in hopes of price forming a pivot and reversing off this level.
Similar to the directional trade, we can use the standard deviation of the cluster to place a stop just in case we are wrong.
In this example below we can see that shorting on the red arrow and placing a stop at the one standard deviation above this cluster would give us a profitable trade with minimal risk.
Using the cluster density table in the upper right informs the trader just how dense the cluster is. Higher density clusters will give a higher likelihood of a pivot forming at these levels and price being rejected and switching direction with a larger move.
█ FEATURES & SETTINGS
General Settings:
Number of clusters: The user can select from 3 to five clusters. A good rule of thumb is that if you are trading intraday, less is more (Think 3 rather than 5). For daily 4 to 5 clusters is good.
Cluster Method: To get around the outlier limitation of k means clustering, The median was added. This gives the user the ability to choose either k means or k median clustering. K means is the preferred method if the user things there are no large outliers, and if there appears to be large outliers or it is assumed there are then K medians is preferred.
Bars back To train on: This will be the amount of bars to include in the clustering. This number is important so that the user includes bars that are recent but not so far back that they are out of the scope of where price can be. For example the last 2 years we have been in a range on the sp500 so 505 days in this setting would be more relevant than say looking back 5 years ago because price would have to move far to get there.
Show SD Bands: Select this to show the 1 standard deviation bands around the support and resistance level or unselect this to just show the support and resistance level by itself.
Features:
Besides the support and resistance levels and standard deviation bands, this indicator gives a table in the upper right hand corner to show the density of each cluster (support and resistance level) and is color coded to the cluster line on the chart. Higher density clusters mean price has been there previously more than lower density clusters and could mean a higher likelihood of a reversal when price reaches these areas.
█ WORKS CITED
Victor Sim, "Using K-means Clustering to Create Support and Resistance", 2020, towardsdatascience.com
Chris Piech, "K means", stanford.edu
█ ACKNOLWEDGMENTS
@jdehorty- Thanks for the publish template. It made organizing my thoughts and work alot easier.
MACD Bands - Multi Timeframe [TradeMaster Lite]We present a customizable MACD indicator, with the following features:
Multi-timeframe
Deviation bands to spot unusual volatility
9 Moving Average types
Conditional coloring and line crossings
👉 What is MACD?
MACD is a classic, trend-following indicator that uses moving averages to identify changes in momentum. It can be used to identify trend changes, overbought and oversold conditions, and potential reversals.
👉 Multi-timeframe:
This feature allows to analyze the same market data on multiple time frames, which can be in help to identify trends and patterns that would not be visible on a single time frame. When using the multi-timeframe feature, it is important to start with the higher time frame and then look for confirmation on the lower time frames. This will help you to avoid false signals. Please note that only timeframes higher than the chart timeframe is supported currently with this feature enabled. Might get updated in the future.
👉 Deviation bands to spot unusual volatility:
Deviation bands are plotted around the Signal line that can be in help to identify periods of unusual volatility. When the MACD line crosses outside of the deviation bands, it suggests that the market is becoming more volatile and a strong trend may form in that direction.
👉 9 Moving Average types can be used in the script. Each type of moving average offers a unique perspective and can be used in different scenarios to identify market trends.
SMA (Simple Moving Average): This calculates the average of a selected range of values, by the number of periods in that range.
SMMA (Smoothed Moving Average): This takes into account all data available and assigns equal weighting to the values.
EMA (Exponential Moving Average): This places a greater weight and significance on the most recent data points.
DEMA (Double Exponential Moving Average): This is a faster-moving average that uses a proprietary calculation to reduce the lag in data points.
TEMA (Triple Exponential Moving Average): This is even quicker than the DEMA, helping traders respond more quickly to changes in trend.
LSMA (Least Squares Moving Average): This moving average applies least squares regression method to determine the future direction of the trend.
HMA (Hull Moving Average): This moving average is designed to reduce lag and improve smoothness, providing quicker signals for short-term market movements.
VWMA (Volume Weighted Moving Average): This assigns more weight to candles with a high volume, reflecting the true average values more accurately in high volume periods.
WMA (Weighted Moving Average): This assigns more weight to the latest data, but not as much as the EMA.
👉 Conditional coloring :
This feature colors the MACD line line based on it's direction and fills the area between the MACD line and Deviation band edges to highlight the potential volatility and the strength of the momentum. This can be useful to identify when the market is trending strongly and when it is in a more neutral or choppy state.
👉 MACD Line - Signal Line crossings:
This is a classic MACD trading signal that occurs when the MACD line crosses above or below the signal line. Crossovers can be used to identify potential trend reversals. This can be a bullish or bearish signal, depending on the direction of the crossover.
👉 General advice
Confirming Signals with other indicators:
As with all technical indicators, it is important to confirm potential signals with other analytical tools, such as support and resistance levels, as well as indicators like RSI, MACD, and volume. This helps increase the probability of a successful trade.
Use proper risk management:
When using this or any other indicator, it is crucial to have proper risk management in place. Consider implementing stop-loss levels and thoughtful position sizing.
Combining with other technical indicators:
The indicator can be effectively used alongside other technical indicators to create a comprehensive trading strategy and provide additional confirmation.
Keep in Mind:
Thorough research and backtesting are essential before making any trading decisions. Furthermore, it's crucial to have a solid understanding of the indicator and its behavior. Additionally, incorporating fundamental analysis and considering market sentiment can be vital factors to take into account in your trading approach.
Limitations:
This is a lagging indicator. Please note that the indicator is using moving averages, which are lagging indicators.
The indicators within the TradeMaster Lite package aim for simplicity and efficiency, while retaining their original purpose and value. Some settings, functions or visuals may be simpler than expected.
⭐ Conclusion
We hold the view that the true path to success is the synergy between the trader and the tool, contrary to the common belief that the tool itself is the sole determinant of profitability. The actual scenario is more nuanced than such an oversimplification. Our aim is to offer useful features that meet the needs of the 21st century and that we actually use.
🛑 Risk Notice:
Everything provided by trademasterindicator – from scripts, tools, and articles to educational materials – is intended solely for educational and informational purposes. Past performance does not assure future returns.
Liquidity Levels/Voids (VP) [LuxAlgo]The Liquidity Levels/Voids (VP) is a script designed to detect liquidity voids & levels by measuring traded volume at all price levels on the market between two swing points and highlighting the distribution of the liquidity voids & levels at specific price levels.
🔶 USAGE
Liquidity is a fundamental market force that shapes the trajectory of assets.
The creation of a liquidity level comes as a result of an initial imbalance of supply/demand, which forms what we know as a swing high or swing low. As more players take positions in the market, these are levels that market participants will use as a historical reference to place their stops. When the levels are then re-tested, a decision will be made. The binary outcome here can be a breakout of the level or a reversal back to the mean.
Liquidity voids are sudden price changes that occur in the market when the price jumps from one level to another with little trading activity (low volume), creating an imbalance in price. The price tends to fill or retest the liquidity voids area, and traders understand at which price level institutional players have been active.
Liquidity voids are a valuable concept in trading, as they provide insights about where many orders were injected, creating this inefficiency in the market. The price tends to restore the balance.
🔶 SETTINGS
The script takes into account user-defined parameters and detects the liquidity voids based on them, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Levels / Voids
Liquidity Levels/Voids: Color customization option for Unfilled Liquidity Levels/Voids.
Detection Length: Lookback period used for the calculation of Swing Levels.
Threshold %: Threshold used for the calculation of the Liquidity Levels & Voids.
Sensitivity: Adjusts the number of levels between two swing points, as a result, the height of a level is determined, and then based on the above-given threshold the level is checked if it matches the liquidity level/void conditions.
Filled Liquidity Levels/Voids: Toggles the visibility of the Filled Liquidity Levels/Voids and color customization option for Filled Liquidity Levels/Voids.
🔹 Other Features
Swing Highs/Lows: Toggles the visibility of the Swing Levels, where tooltips present statistical information, such as price, price change, and cumulative volume between the two swing levels detected based on the detection length specified above, Coloring options to customize swing low and swing high label colors, and Size option to adjust the size of the labels.
🔹 Display Options
Mode: Controls the lookback length of detection and visualization.
# Bars: Lookback length customization, in case Mode is set to Present.
🔶 RELATED SCRIPTS
Liquidity-Voids-FVG
Buyside-Sellside-Liquidity
Swing-Volume-Profiles
Fibo Levels with Volume Profile and Targets [ChartPrime]The Fib Levels With Volume Profile and Targets (FIVP) is a trading tool designed to provide traders with a unique understanding of price movement and trading volume through the lens of Fibonacci levels. This dynamic indicator merges the concepts of Fibonacci retracement levels with trading volume analytics to offer predictive insights into potential price trajectories.
Features:
1. Fibonacci Levels: The FPI showcases three prominent Fibonacci levels on both sides of the current price, offering an intricate picture of potential support and resistance levels.
2. Support and Resistance Recognition: Harnessing the power of Fibonacci levels, the FPI provides traders with potential areas of support and resistance, aiding in informed decision-making for entries, exits, and stop placements.
3. Customizable Timeframe Settings: In order to cater to different trading strategies and styles, users can manually select their preferred timeframe for the Fibonacci calculations, ensuring optimal relevance and accuracy for their trading approach.
4. Volume Analytics: One of the standout features of the FIVP is its ability to calculate trading volume for every bar that is sandwiched between the top and lower Fibonacci levels. This ensures traders have a clear vision of where the majority of trading activity is occurring, lending weight to the credibility of the displayed support and resistance zones.
5. Volume-Derived Price Targeting: The Possible Target Arrow function is an innovative feature. By analyzing and comparing the trading volume in the bearish and bullish zones, it provides an arrow indicating the potential direction the market might take. If the bull volume surpasses the bear volume, the market is likely skewing bullish and vice versa.
Usage
Ideal for both novice and seasoned traders, the FPI offers a rich tapestry of information. It allows for refined technical analysis, more precise entries and exits, and a holistic view of the interplay between price and trading volume. Whether you're scalping, day trading, or swing trading, the Fibonacci Profile Indicator is designed to enhance your trading strategy, providing a comprehensive perspective of the market's potential movements.
Normal Distribution CurveThis Normal Distribution Curve is designed to overlay a simple normal distribution curve on top of any TradingView indicator. This curve represents a probability distribution for a given dataset and can be used to gain insights into the likelihood of various data levels occurring within a specified range, providing traders and investors with a clear visualization of the distribution of values within a specific dataset. With the only inputs being the variable source and plot colour, I think this is by far the simplest and most intuitive iteration of any statistical analysis based indicator I've seen here!
Traders can quickly assess how data clusters around the mean in a bell curve and easily see the percentile frequency of the data; or perhaps with both and upper and lower peaks identify likely periods of upcoming volatility or mean reversion. Facilitating the identification of outliers was my main purpose when creating this tool, I believed fixed values for upper/lower bounds within most indicators are too static and do not dynamically fit the vastly different movements of all assets and timeframes - and being able to easily understand the spread of information simplifies the process of identifying key regions to take action.
The curve's tails, representing the extreme percentiles, can help identify outliers and potential areas of price reversal or trend acceleration. For example using the RSI which typically has static levels of 70 and 30, which will be breached considerably more on a less liquid or more volatile asset and therefore reduce the actionable effectiveness of the indicator, likewise for an asset with little to no directional volatility failing to ever reach this overbought/oversold areas. It makes considerably more sense to look for the top/bottom 5% or 10% levels of outlying data which are automatically calculated with this indicator, and may be a noticeable distance from the 70 and 30 values, as regions to be observing for your investing.
This normal distribution curve employs percentile linear interpolation to calculate the distribution. This interpolation technique considers the nearest data points and calculates the price values between them. This process ensures a smooth curve that accurately represents the probability distribution, even for percentiles not directly present in the original dataset; and applicable to any asset regardless of timeframe. The lookback period is set to a value of 5000 which should ensure ample data is taken into calculation and consideration without surpassing any TradingView constraints and limitations, for datasets smaller than this the indicator will adjust the length to just include all data. The labels providing the percentile and average levels can also be removed in the style tab if preferred.
Additionally, as an unplanned benefit is its applicability to the underlying price data as well as any derived indicators. Turning it into something comparable to a volume profile indicator but based on the time an assets price was within a specific range as opposed to the volume. This can therefore be used as a tool for identifying potential support and resistance zones, as well as areas that mark market inefficiencies as price rapidly accelerated through. This may then give a cleaner outlook as it eliminates the potential drawbacks of volume based profiles that maybe don't collate all exchange data or are misrepresented due to large unforeseen increases/decreases underlying capital inflows/outflows.
Thanks to @ALifeToMake, @Bjorgum, vgladkov on stackoverflow (and possibly some chatGPT!) for all the assistance in bringing this indicator to life. I really hope every user can find some use from this and help bring a unique and data driven perspective to their decision making. And make sure to please share any original implementaions of this tool too! If you've managed to apply this to the average price change once you've entered your position to better manage your trade management, or maybe overlaying on an implied volatility indicator to identify potential options arbitrage opportunities; let me know! And of course if anyone has any issues, questions, queries or requests please feel free to reach out! Thanks and enjoy.
Volume Delta Methods (Chart) [LuxAlgo]The Volume Delta Methods (Chart) aims at highlighting the relationship between Buying or Selling Pressure and Price by presenting Volume Delta , and multiple derivatives of volume delta such as Cumulative Volume Delta (CVD) , Buy/Sell Volume , Total Volume , etc on top of the Main Price Chart .
The script uses two different intrabar (chart bars at a lower timeframe than the chart's) analyses to achieve the most approximate calculation of the volume delta and offers fully customizable visualization features using various types of charts such as line, area, baseline, candles, and histograms.
The script allows traders to see "within" the price bar, provides more transparency over a traditional volume histogram, and also allows users to monitor price and volume activity together.
🔶 USAGE
Volume delta is the difference between the buying volume and the selling volume, in other words, it is the net demand at a given bar allowing traders a more detailed insight when analyzing the market sentiment. A volume delta greater than 0 indicates more buying than selling pressure, whereas a volume delta less than 0 indicates more selling than buying pressure.
Volume delta plus total volume (regular volume) adds additional insight, where the total volume represents all the recorded trades for security that occurs in a given time interval. It is a measurement of the participation, enthusiasm, and interest in a given security.
Divergences occur when the polarity of the volume delta does not match the polarity of the price bar.
The users can enable the display of the numerical values of the volume delta.
Cumulative Volume Delta (CVD) is a way of using Volume Delta to measure an asset’s mid-to-long-term buy and sell pressure. It compares buying and selling volume over time and offers insights into market behavior at specific price points. Cumulative Volume Delta is effectively a continuation of the principles of Volume Delta but involves longer time periods and offers different trading signals.
Like the Volume Delta, the Cumulative Volume Delta (CVD) indicator measures the relationship between buy and sell pressure but does not focus on one specific candle in particular. Rather, the Cumulative Volume Delta takes the relative differences and combines them all over an extended time period.
Users have the ability Cumulative Volume Delta in various types of charts along with an optional smoothing line.
Placed above price bars options.
Interacting with price bar options helps to better identify CVD Divergences.
CVD Divergences
CVD reveals buying and selling trends that may or may not complement the price trend of the asset itself. Sometimes, price trends can run in contrast to trading behavior — sell volume can be dominant while the spot price is rising, and vice versa.
🔶 DETAILS
Theoretically, volume delta is calculated by taking the difference between the volume that traded at the ask price and the volume that traded at the bid price. The most precise calculation method uses tick data but requires huge amounts of data on historical bars, which usually limits the historical depth of charts. This indicator uses two different intrabar analysis methods for the volume delta calculation, where intrabars are chart bars at a lower timeframe than the chart's timeframe:
The logic used to assign intrabar volume to the "up" or "down".
- Buying/Selling pressure of the intrabar option (default)
(close - low) > (high - close) => UP
(close - low) < (high - close) => DOWN
(close - low) = (high - close) => close - previous close is used
- Polarity of the intrabar option
close > open => UP
close < open => DOWN
close = open => close - previous close is used
🔶 SETTINGS
The script takes into account user-defined parameters and performs calculations and presentations based on them, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Calculation Settings
Calculation Method: Calculation method selection, available options 'Intrabar Buying/Selling Pressure' or 'Intrabar Polarity'.
Lower Timeframe Precision: Sets indicator precision, default option is 'Auto'.
🔹 Presentation Settings
Volume Delta: Toggles the visibility of the Volume Delta
Cumulative Volume Delta: Toggles the visibility of the Cumulative Volume Delta
Volume Delta/Price Bar Divergences: Toggles the visibility of the Volume Delta Divergences
Volume Delta Numerical Values: Toggles the visibility of the Volume Delta Numerical Values
🔹 Other Features
Volume MA: Toggles the visibility of the Volume Moving Average
CVD Smoothing: Toggles the visibility of the Cumulative Volume Delta's Smoothing Line
🔹 Volume Delta, Others
Volume Delta: Positive, Negative: Volume Delta color customization options
Volume Histogram: Growing, Falling: Volume Histogram color customization options
Display Length: Length of the visual objects presented with this indicator
Volume Delta Height: Volume delta height customization options
Volume Histogram Height: Volume histogram height customization options
Vertical Offset: Volume delta and histogram vertical positioning customization options
🔹 Cumulative Volume Delta, Others
CVD Line, Width, and Color: Cumulative Volume Delta - Line Width and Color customization options
CVD Area/Baseline, Gradient Coloring: Cumulative Volume Delta - Area and Baseline background gradient coloring customization options
CVD Candles Color, Positive, and Negative: Cumulative Volume Delta - Candles coloring customization options
CVD/Smoothing Background: Highlights and adjusts the transparency of the area between the Cumulative Volume Delta Line and it's Smoothing Line
🔶 RELATED SCRIPTS
Liquidity-Sentiment-Profile
EquiVolume
Volume-Footprint
Trend Correlation Oscillator [SS]Hello,
Publishing this simple indicator.
What is it?
The Trend Correlation Oscillator takes the concept of my autocorrelation oscillator but applies it simply to time instead of autocorrelation.
It performs a correlation assessment to time. The theory behind it is the stronger the correlation, the more "exhausted" the trend and the more likely the trend will reverse. It is kind of building off of random walk theory in which the market should be random and efficient.
Does it work?
If you follow me on my indicator side, you will know that my indicators are all based on my own research and findings and stuff that I personally find that works. All of this comes from years of losing money trying to use conventional systems and finally developing my own stuff that I find works well. This is such an invention. It does work extremely well but its best applied for day traders. If you want to use this as a swing trader, play around with the lookback length. I don't have general recommendations to swing traders wanting to use this because this isn't an indicator I personally would use for swing trading (I would use the autocorrelation oscillator for that).
How to use it:
The default setting is to a 14 candle lookback. This works the best. It also should really be used on the 5 minute chart and not the 1 minute chart, as from my experience this works much better.
When a trend is approaching "exhaustion" to the upside, the indicator will turn red to let you know we are approaching a trend exhaustion. Once the exhaustion is at its peak and beginning to reverse, the indicator will place a cross symbol on where your entry should be. See the image below for an example:
It also works well if you combine it with my PTCR Correlation Indicator:
Closing thoughts
That is basically the indicator. Its one of my more simple ones, but many times simple is better and most effective!
Hopefully you find it helpful.
As always let me know your questions, comments and feedback/recommendations for improvements below.
Please know I do read and make note of all recommendations for indicators and improvements, however as it is just me managing them, it takes time for implementation and review :-).
Safe trades!
Liquidity Voids (FVG) [LuxAlgo]The Liquidity Voids (FVG) indicator is designed to detect liquidity voids/imbalances derived from the fair value gaps and highlight the distribution of the liquidity voids at specific price levels.
Fair value gaps and liquidity voids are both indicators of sell-side and buy-side imbalance in trading. The only difference is how they are represented in the trading chart. Liquidity voids occur when the price moves sharply in one direction forming long-range candles that have little trading activity, whilst a fair value is a gap in price.
🔶 USAGE
Liquidity can help you to determine where the price is likely to head next. In conjunction with higher timeframe market structure, and supply and demand, liquidity can give you insights into potential price movement. It's essential to practice using liquidity alongside trend analysis and supply and demand to read market conditions effectively.
The peculiar thing about liquidity voids is that they almost always fill up. And by “filling”, we mean the price returns to the origin of the gap. The reason for this is that during the gap, an imbalance is created in the asset that has to be made up for. The erasure of this gap is what we call the filling of the void. And while some voids waste no time in filling, some others take multiple periods before they get filled.
🔶 SETTINGS
The script takes into account user-defined parameters and detects the liquidity voids based on them, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Detection
Liquidity Voids Threshold: Act as a filter while detecting the Liquidity Voids. When set to 0 basically means no filtering is applied, increasing the value causes the script to check the width of the void compared to a fixed-length ATR value
Bullish: Color customization option for Bullish Liquidity Voids
Bearish: Color customization option for Bearish Liquidity Voids
Labels: Toggles the visibility of the Liquidity Void label
Filled Liquidity Voids: Toggles the visibility of the Filled Liquidity Voids
🔹 Display Options
Mode: Controls the lookback length of detection and visualization
# Bars: Lookback length customization, in case Mode is set to Present
🔶 RELATED SCRIPTS
Buyside-Sellside-Liquidity
Fair-Value-Gaps
Bar Dependent Moving AverageImagine using an exercising strategy from a gym coach who retired 10 years ago as there dozens of other trainers who constantly try to adapt and find a better approach. You would be missing out.
It's the same with using fixed periods, you just look back.
How about an exercising strategy from a gym coach who is simultaneously trying to adapt himself?
How could you figure out a strategy that adapts with the market as the market is indirectly finding a better approach to take you out?
Relative Moving Averages simply speaking count the total amount of bars and divide it by the period for which you want to use the moving average. Instead of looking at the last X bars, it divides the total amount of bars / X as the moving average period.
It takes all bars into account, the entire chart history essentially turning it into an adaptive moving average, in the example mentioned above into an adaptive exercising strategy.
The Bar Dependent Moving Average Periods are 2,3,5,8 etc., i.e. the entire fibonacci sequence up to 4181.
The white line is the relative fibonacci moving average, which is calculated by adding all Fibonacci Bar Dependent Moving Averages with one another / by the total amount of fibonacci numbers used i.e. 18.
The bar-dependent fibonacci moving average is upon further investigation (on shorter timeframes <30m) a useful support/resistance indicator since the price tends to wick/consolidate or fully break through the fibonoacci bar dependent moving average.
If there are more than 5000 bars of Candlestick data enable the lock in the settings menu to limit the maximum last X bars back to 5000 to make it possible for Tradingview to run the script since Tradingview does not allow it to look more than 5000 bars back.
Custom Fib by Dr. MauryaThis indicator is based on purely Fibonacci levels.
How it works:
Let's first understand the Fibonacci levels.
The main Fibonacci numbers are 0, 0.236, 0.392, 0.5, 0.618, 0.764, 1 whereas 0 equal to low and 1 equal to high.
As the market is moving in any direction, new lows or new highs are developing and hence Fibonacci levels are also changing throughout the time.
Sometime market retraces from various levels like 0.5, 0.618/0.382(mirror value), 0.762/0.236 (mirror value).
Retracement : The three mid-level 0.382, 0.5 and 0.618 are act as a retracement or like pivot levels for market. These levels are filled with green and red colors to attention the buyers and sellers to take a trade either side if any candlestick pattern are observed at these levels.
one direction trend takes support of 0.236/0.786(mirror value) (blue line in chart)
Sometime buy/sell on dip levels are happen at 0.762/0.392 (mirror value).
Targets: Target could be Fibonacci extension level lowest targets (1, 1.18, 1.23,), medium targets (1.39, 1.5, 1.61) and large target (2.0, 2.5.2.61, 3.0) as depended on your study volume levels and trend strength.
Stoploss: You can choose any preceding lines for stoploss: e.g. if you enter long on 0.618 or 0.5 levels you can set SL on 0.762
Previous day three mid-point 0.382, 0.5, 0.618 (filled with red and green color) as well as high and low could also act as resistance or support levels for current day market.
Lets understand the Input section of indicator
The first input section allowed to choose where you want to start developing Fibonacci : select session for intraday then weekly, monthly and yearly options are available.
Now you can set any Fibonacci levels (as you wish) you can set upto 20 levels.
By default, total 7 Fibonacci levels are plotted (0, 0.236, 0.392, 0.618, 0.762 and 1.
Further you can set Fibonacci extension level for long side (1.18, 1.23, 1.39, 1.5, 1.61, 2 etc).
You must be careful when you enter Fibonacci extension level lower side (short side). You need to enter value -0.5 (equal to 1.5 for long). -0.618 (equal to 1.618 for long), -1(equal to 2.0 for long).
You can fill color between any two adjacent lines from style sections.
You can also select labels from input tab if you want to see Fibonacci numbers on chart as labels.
You can also shift the labels from current bar to desired offset bar by changing the value in input section.
Conclusion
This indicator is highly customisable developing Fibonacci levels because everyone and different scripts works on different fib levels.
This indicator keeps the Fibonacci levels at a particular time and it plots only new lines when new low or high established without affecting the previous Fibonacci levels. Overall, as the market moves, you will find the trending plot goes which side.
Selective Moving Average: DemoThis indicator produces a conditional moving average based off of your chosen inputs. For example, you can create an EMA that only takes into account closing prices when the 14 period RSI is greater than 50, or a VWMA that tracks hl2 values when the hl2 value is within one standard deviation from the mean. The possibilities are highly configurable to your liking. Please comment below additional conditions you might like me to add to the moving average and I will try my best to get to your feedback.
The following parameters are configurable:
--> Source: This is the source of the moving average that you want to create. You can use external sources if you have another indicator on your chart.
--> Condition: This is the condition that you want to take into account when the moving average is calculating itself. For instance, I have the following conditions pre-built (more to come): Source within 1 standard deviation of the mean (of the source), Source within 2 standard deviations of the mean (of the source), Positive volume, Negative volume, RSI greater than 50, RSI less than 50, Candlestick length greater than body.
--> Length: The length of the selective moving average. For conditions that occur infrequently, a larger length may be necessary to improve accuracy.
--> Average type: The type of moving average (SMA, EMA, RMA, etc.) that you wish to create
--> Condition length: An optional parameter if you are using a condition that depends on a length itself, i.e. the RSI - here you can change the RSI length. The RSI source will be the moving average source, but future updates may separate the two.
Weekly Range Support & Resistance Levels [QuantVue]Weekly Range Support & Resistance Levels
Description:
The Weekly Range Support & Resistance Levels analyzes weekly ranges and takes the average range of the last 30 weeks (default setting).
It also takes the average +/- a standard deviation, and creates support & resistance levels/zones based on the weekly opening price.
The levels will update each week, and previous weekly levels can be toggled on or off.
Settings:
🔹Averaging Period
🔹Standard Deviation Multiplier
🔹Toggle Support & Resistance Prices
🔹Show Weekly Open Line
🔹Show Previous Levels
Don't hesitate to reach out with any questions or concerns. We hope you enjoy!
Cheers.
Script TimerWanna know how long your script takes to execute.
Just put this function at the end of your code and it will tell you how much time it takes to run your algo from start to end.
Data will show in the data window panel measured in seconds
Machine Learning : Cosine Similarity & Euclidean DistanceIntroduction:
This script implements a comprehensive trading strategy that adheres to the established rules and guidelines of housing trading. It leverages advanced machine learning techniques and incorporates customised moving averages, including the Conceptive Price Moving Average (CPMA), to provide accurate signals for informed trading decisions in the housing market. Additionally, signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation are utilised to enhance the signal quality and improve trading accuracy.
Features:
Market Analysis: The script utilizes advanced machine learning methods such as Lorentzian, Euclidean distance, and Cosine similarity to analyse market conditions. These techniques measure the similarity and distance between data points, enabling more precise signal identification and enhancing trading decisions.
Cosine similarity:
Cosine similarity is a measure used to determine the similarity between two vectors, typically in a high-dimensional space. It calculates the cosine of the angle between the vectors, indicating the degree of similarity or dissimilarity.
In the context of trading or signal processing, cosine similarity can be employed to compare the similarity between different data points or signals. The vectors in this case represent the numerical representations of the data points or signals.
Cosine similarity ranges from -1 to 1, with 1 indicating perfect similarity, 0 indicating no similarity, and -1 indicating perfect dissimilarity. A higher cosine similarity value suggests a closer match between the vectors, implying that the signals or data points share similar characteristics.
Lorentzian Classification:
Lorentzian classification is a machine learning algorithm used for classification tasks. It is based on the Lorentzian distance metric, which measures the similarity or dissimilarity between two data points. The Lorentzian distance takes into account the shape of the data distribution and can handle outliers better than other distance metrics.
Euclidean Distance:
Euclidean distance is a distance metric widely used in mathematics and machine learning. It calculates the straight-line distance between two points in Euclidean space. In two-dimensional space, the Euclidean distance between two points (x1, y1) and (x2, y2) is calculated using the formula sqrt((x2 - x1)^2 + (y2 - y1)^2).
Dynamic Time Windows: The script incorporates a dynamic time window function that allows users to define specific time ranges for trading. It checks if the current time falls within the specified window to execute the relevant trading signals.
Custom Moving Averages: The script includes the CPMA, a powerful moving average calculation. Unlike traditional moving averages, the CPMA provides improved support and resistance levels by considering multiple price types and employing a combination of Exponential Moving Averages (EMAs) and Simple Moving Averages (SMAs). Its adaptive nature ensures responsiveness to changes in price trends.
Signal Processing Techniques: The script applies signal processing techniques such as Know sure thing, Rational Quadratic, and sigmoid transformation to enhance the quality of the generated signals. These techniques improve the accuracy and reliability of the trading signals, aiding in making well-informed trading decisions.
Trade Statistics and Metrics: The script provides comprehensive trade statistics and metrics, including total wins, losses, win rate, win-loss ratio, and early signal flips. These metrics offer valuable insights into the performance and effectiveness of the trading strategy.
Usage:
Configuring Time Windows: Users can customize the time windows by specifying the start and finish time ranges according to their trading preferences and local market conditions.
Signal Interpretation: The script generates long and short signals based on the analysis, custom moving averages, and signal processing techniques. Users should pay attention to these signals and take appropriate action, such as entering or exiting trades, depending on their trading strategies.
Trade Statistics: The script continuously tracks and updates trade statistics, providing users with a clear overview of their trading performance. These statistics help users assess the effectiveness of the strategy and make informed decisions.
Conclusion:
With its adherence to housing trading rules, advanced machine learning methods, customized moving averages like the CPMA, and signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation, this script offers users a powerful tool for housing market analysis and trading. By leveraging the provided signals, time windows, and trade statistics, users can enhance their trading strategies and improve their overall trading performance.
Disclaimer:
Please note that while this script incorporates established tradingview housing rules, advanced machine learning techniques, customized moving averages, and signal processing techniques, it should be used for informational purposes only. Users are advised to conduct their own analysis and exercise caution when making trading decisions. The script's performance may vary based on market conditions, user settings, and the accuracy of the machine learning methods and signal processing techniques. The trading platform and developers are not responsible for any financial losses incurred while using this script.
By publishing this script on the platform, traders can benefit from its professional presentation, clear instructions, and the utilisation of advanced machine learning techniques, customised moving averages, and signal processing techniques for enhanced trading signals and accuracy.
I extend my gratitude to TradingView, LUX ALGO, and JDEHORTY for their invaluable contributions to the trading community. Their innovative scripts, meticulous coding patterns, and insightful ideas have profoundly enriched traders' strategies, including my own.
