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.
Search in scripts for "TAKE"
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.
VWAP Open Session Anchored by HampehThe VWAP Open Session Anchored indicator differs from traditional VWAP indicators by automatically anchoring the Volume Weighted Average Price calculation to three market session starts Morning, Evening, and Night. Each session represents a distinct time period within the trading day, offering traders and investors a more comprehensive view of the volume-weighted average price within specific sessions.
What Is the Volume-Weighted Average Price (VWAP)?
The volume-weighted average price (VWAP) is a technical analysis indicator used on intraday charts that resets at the start of every new trading session.
VWAP is important because it provides traders with pricing insight into both the trend and value of a security.
KEY TAKEAWAYS
1. The volume-weighted average price (VWAP) is a single line on intraday charts.
2. It looks similar to a moving average line but smoother.
3. VWAP represents a view of price action throughout a single day's trading session.
4. Retail and professional traders may use the VWAP to help them determine intraday price trends.
5. VWAP typically is most useful to short-term traders.
VWAP is calculated by totaling the dollars traded for every transaction (price multiplied by the volume) and then dividing by the total shares traded.
VWAP = Cumulative Typical Price x Volume/Cumulative Volume
Where Typical Price = High price + Low price + Closing Price/3
Cumulative = total since the trading session opened.
How Is VWAP Used?
VWAP is used in different ways by traders. Traders may use VWAP as a trend confirmation tool and build trading rules around it. For instance, they may consider stocks with prices below VWAP as undervalued and those with prices above it, as overvalued. If prices below VWAP move above it, traders may go long on the stock. If prices above VWAP move below it, they may sell their positions or initiate short positions.
Institutional buyers including mutual funds use VWAP to help move into or out of stocks with as small of a market impact as possible. Therefore, when they can, institutions will try to buy below the VWAP or sell above it. This way their actions push the price back toward the average, instead of away from it.
Source: www.investopedia.com
Nonlinear Regression, Zero-lag Moving Average [Loxx]Nonlinear Regression and Zero-lag Moving Average
Technical indicators are widely used in financial markets to analyze price data and make informed trading decisions. This indicator presents an implementation of two popular indicators: Nonlinear Regression and Zero-lag Moving Average (ZLMA). Let's explore the functioning of these indicators and discuss their significance in technical analysis.
Nonlinear Regression
The Nonlinear Regression indicator aims to fit a nonlinear curve to a given set of data points. It calculates the best-fit curve by minimizing the sum of squared errors between the actual data points and the predicted values on the curve. The curve is determined by solving a system of equations derived from the data points.
We define a function "nonLinearRegression" that takes two parameters: "src" (the input data series) and "per" (the period over which the regression is calculated). It calculates the coefficients of the nonlinear curve using the least squares method and returns the predicted value for the current period. The nonlinear regression curve provides insights into the overall trend and potential reversals in the price data.
Zero-lag Moving Average (ZLMA)
Moving averages are widely used to smoothen price data and identify trend directions. However, traditional moving averages introduce a lag due to the inclusion of past data. The Zero-lag Moving Average (ZLMA) overcomes this lag by dynamically adjusting the weights of past values, resulting in a more responsive moving average.
We create a function named "zlma" that calculates the ZLMA. It takes two parameters: "src" (the input data series) and "per" (the period over which the ZLMA is calculated). The ZLMA is computed by first calculating a weighted moving average (LWMA) using a linearly decreasing weight scheme. The LWMA is then used to calculate the ZLMA by applying the same weight scheme again. The ZLMA provides a smoother representation of the price data while reducing lag.
Combining Nonlinear Regression and ZLMA
The ZLMA is applied to the input data series using the function "zlma(src, zlmaper)". The ZLMA values are then passed as input to the "nonLinearRegression" function, along with the specified period for nonlinear regression. The output of the nonlinear regression is stored in the variable "out".
To enhance the visual representation of the indicator, colors are assigned based on the relationship between the nonlinear regression value and a signal value (sig) calculated from the previous period's nonlinear regression value. If the current "out" value is greater than the previous "sig" value, the color is set to green; otherwise, it is set to red.
The indicator also includes optional features such as coloring the bars based on the indicator's values and displaying signals for potential long and short positions. The signals are generated based on the crossover and crossunder of the "out" and "sig" values.
Wrapping Up
This indicator combines two important concepts: Nonlinear Regression and Zero-lag Moving Average indicators, which are valuable tools for technical analysis in financial markets. These indicators help traders identify trends, potential reversals, and generate trading signals. By combining the nonlinear regression curve with the zero-lag moving average, this indicator provides a comprehensive view of the price dynamics. Traders can customize the indicator's settings and use it in conjunction with other analysis techniques to make well-informed trading decisions.
Purple CloudThe above lines calculate several intermediate values used in the indicator's calculations. Here's a breakdown of each variable:
a1: Represents a modified Exponential Moving Average (EMA) of the high price series, subtracted by a Simple Moving Average (SMA) of the low price series.
a2: Takes the square root of the lowest value between the highest close price over the last 200 bars and the current close price, multiplied by a1.
b1: Represents a modified EMA of the low price series, subtracted by an SMA of the high price series.
b2: Takes the square root of the highest value between the lowest close price over the last 200 bars and the current close price, multiplied by b1.
c1: Represents the square root of a2 multiplied by b2.
These lines create multiple plots using the plot function. Each plot represents a displaced version of c1 by a certain multiple of the Average True Range (ATR) multiplied by a constant factor (0.1, 0.2, 0.3, etc.). The transparency (transp) is set to 100 for all plots.
Pattern Forecast (Expo)█ Overview
The Pattern Forecast indicator is a technical analysis tool that scans historical price data to identify common chart patterns and then analyzes the price movements that followed these patterns. It takes this information and projects it into the future to provide traders with potential price actions that may occur if the same pattern is identified in real-time market data. This projection helps traders to understand the possible outcomes based on the previous occurrences of the pattern, thereby offering a clearer perspective of the market scenario. By analyzing the historical data and understanding the subsequent price movements following the appearance of a specific pattern, the indicator can provide valuable insights into potential future market behavior.
█ Calculations
The indicator works by scanning historical price data for various candlestick patterns. It includes all in-built TradingView patterns, credit to TradingView that has coded them.
Essentially, the indicator takes the historical price moves that followed the pattern to forecast what might happen next.
█ Example
In this example, the algorithm is set to search for the Inverted Hammer Bullish candlestick pattern. If the pattern is found, the historical outcome is then projected into the future. This helps traders to understand how the past pattern evolved over time.
█ How to use
Providing traders with a comprehensive understanding of historical patterns and their implications for future price action allows them to assess the likelihood of specific market scenarios objectively. For example, suppose the pattern forecast indicator suggests that a particular pattern is likely to lead to a bullish move in the market. A trader might consider going long if the same pattern is identified in the real-time market. Similarly, a trader might consider shorting the asset if the indicator suggests a bearish move is likely, if the same pattern is identified in the real-time market.
█ Settings
Pattern
Select the pattern that the indicator should scan for. All inbuilt TradingView patterns can be selected.
Forecast Candles
Number of candles to project into the future.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Chandelier Exit ZLSMA StrategyIntroducing a Powerful Trading Indicator: Chandelier Exit with ZLSMA
If you're a trader, you know the importance of having the right tools and indicators to make informed decisions. That's why we're excited to introduce a powerful new trading indicator that combines the Chandelier Exit and ZLSMA: two widely-used and effective indicators for technical analysis.
The Chandelier Exit (CE) is a popular trailing stop-loss indicator developed by Chuck LeBeau. It's designed to follow the price trend of a security and provide an exit signal when the price crosses below the CE line. The CE line is based on the Average True Range (ATR), which is a measure of volatility. This means that the CE line adjusts to the volatility of the security, making it a reliable indicator for trailing stop-losses.
The ZLEMA (Zero Lag Exponential Moving Average) is a type of exponential moving average that's designed to reduce lag and improve signal accuracy. The ZLSMA takes into account not only the current price but also past prices, using a weighted formula to calculate the moving average. This makes it a smoother indicator than traditional moving averages, and less prone to giving false signals.
When combined, the CE and ZLSMA create a powerful indicator that can help traders identify trend changes and make more informed trading decisions. The CE provides the trailing stop-loss signal, while the ZLSMA provides a smoother trend line to help identify potential entry and exit points.
In our indicator, the CE and ZLSMA are plotted together on the chart, making it easy to see both the trailing stop-loss and the trend line at the same time. The CE line is displayed as a dotted line, while the ZLSMA line is displayed as a solid line.
Using this indicator, traders can set their stop-loss levels based on the CE line, while also using the ZLSMA line to identify potential entry and exit points. The combination of these two indicators can help traders reduce their risk and improve their trading performance.
In conclusion, the Chandelier Exit with ZLSMA is a powerful trading indicator that combines two effective technical analysis tools. By using this indicator, traders can identify trend changes, set stop-loss levels, and make more informed trading decisions. Try it out for yourself and see how it can improve your trading performance.
Warning: The results in the backtest are from a repainting strategy. Don't take them seriously. You need to do a dry live test in order to test it for its useability.
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Here is a description of each input field in the provided source code:
length: An integer input used as the period for the ATR (Average True Range) calculation. Default value is 1.
mult: A float input used as a multiplier for the ATR value. Default value is 2.
showLabels: A boolean input that determines whether to display buy/sell labels on the chart. Default value is false.
isSignalLabelEnabled: A boolean input that determines whether to display signal labels on the chart. Default value is true.
useClose: A boolean input that determines whether to use the close price for extrema calculations. Default value is true.
zcolorchange: A boolean input that determines whether to enable rising/decreasing highlighting for the ZLSMA (Zero-Lag Exponential Moving Average) line. Default value is false.
zlsmaLength: An integer input used as the length for the ZLSMA calculation. Default value is 50.
offset: An integer input used as an offset for the ZLSMA calculation. Default value is 0.
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Ty for checking this out and good luck on your trading journey! Likes and comments are appreciated. 👍
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Credits to:
▪ @everget – Chandelier Exit (CE)
▪ @netweaver2022 – ZLSMA
MVRV Z ScoreIndicator Overview
MVRV Z-Score is a bitcoin chart that uses blockchain analysis to identify periods where Bitcoin is extremely over or undervalued relative to its 'fair value'.
It uses three metrics:
1. Market Value: The current price of Bitcoin multiplied by the number of coins in circulation. This is like market cap in traditional markets i.e. share price multiplied by number of shares.
2. Realised Value: Rather than taking the current price of Bitcoin, Realised Value takes the price of each Bitcoin when it was last moved i.e. the last time it was sent from one wallet to another wallet. It then adds up all those individual prices and takes an average of them. It then multiplies that average price by the total number of coins in circulation.
In doing so, it strips out the short term market sentiment that we have within the Market Value metric. It can therefore be seen as a more 'true' long term measure of Bitcoin value which Market Value moves above and below depending on the market sentiment at the time.
3. Z-score (Orange): A standard deviation test that pulls out the extremes in the data between market value and realised value.
How It Can Be Used
The MVRV Z-score has historically been very effective in identifying periods where market value is moving unusually high above realised value. These periods are highlighted by the z-score (red line) entering the pink box and indicates the top of market cycles. It has been able to pick the market high of each cycle to within two weeks.
It also shows when market value is far below realised value, highlighted by z-score entering the green box. Buying Bitcoin during these periods has historically produced outsized returns.
(Bar colors shows when market is trending down or a top - strong red or trending up and bottom - strong green. Added allerts for values)
Bitcoin Price Prediction Using This Tool
MVRV Z-Score bitcoin chart is useful for predicting Bitcoin price at the extremes of market conditions. It is able to forecast where Bitcoin price may need to pull back when MVRV Z-score enters the upper red band, and also when CRYPTOCAP:BTC price may rally after spending time in the lower green band.
Historically it has picked major Bitcoin price highs to within 2 weeks.
Created By
@aweandwonder who has unfortunately since deleted the original article and his online profile.
He built on the initial work to create MVRV by Murad Mahmudov and David Puell
Boxes_PlotIn the world of data visualization, heatmaps are an invaluable tool for understanding complex datasets. They use color gradients to represent the values of individual data points, allowing users to quickly identify patterns, trends, and outliers in their data. In this post, we will delve into the history of heatmaps, and then discuss how its implemented.
The "Boxes_Plot" library is a powerful and versatile tool for visualizing multiple indicators on a trading chart using colored boxes, commonly known as heatmaps. These heatmaps provide a user-friendly and efficient method for analyzing the performance and trends of various indicators simultaneously. The library can be customized to display multiple charts, adjust the number of rows, and set the appropriate offset for proper spacing. This allows traders to gain insights into the market and make informed decisions.
Heatmaps with cells are interesting and useful for several reasons. Firstly, they allow for the visualization of large datasets in a compact and organized manner. This is especially beneficial when working with multiple indicators, as it enables traders to easily compare and contrast their performance. Secondly, heatmaps provide a clear and intuitive representation of the data, making it easier for traders to identify trends and patterns. Finally, heatmaps offer a visually appealing way to present complex information, which can help to engage and maintain the interest of traders.
History of Heatmaps
The concept of heatmaps can be traced back to the 19th century when French cartographer and sociologist Charles Joseph Minard used color gradients to visualize statistical data. He is well-known for his 1869 map, which depicted Napoleon's disastrous Russian campaign of 1812 using a color gradient to represent the dwindling size of Napoleon's army.
In the 20th century, heatmaps gained popularity in the fields of biology and genetics, where they were used to visualize gene expression data. In the early 2000s, heatmaps found their way into the world of finance, where they are now used to display stock market data, such as price, volume, and performance.
The boxes_plot function in the library expects a normalized value from 0 to 100 as input. Normalizing the data ensures that all values are on a consistent scale, making it easier to compare different indicators. The function also allows for easy customization, enabling users to adjust the number of rows displayed, the size of the boxes, and the offset for proper spacing.
One of the key features of the library is its ability to automatically scale the chart to the screen. This ensures that the heatmap remains clear and visible, regardless of the size or resolution of the user's monitor. This functionality is essential for traders who may be using various devices and screen sizes, as it enables them to easily access and interpret the heatmap without needing to make manual adjustments.
In order to create a heatmap using the boxes_plot function, users need to supply several parameters:
1. Source: An array of floating-point values representing the indicator values to display.
2. Name: An array of strings representing the names of the indicators.
3. Boxes_per_row: The number of boxes to display per row.
4. Offset (optional): An integer to offset the boxes horizontally (default: 0).
5. Scale (optional): A floating-point value to scale the size of the boxes (default: 1).
The library also includes a gradient function (grad) that is used to generate the colors for the heatmap. This function is responsible for determining the appropriate color based on the value of the indicator, with higher values typically represented by warmer colors such as red and lower values by cooler colors such as blue.
Implementing Heatmaps as a Pine Script Library
In this section, we'll explore how to create a Pine Script library that can be used to generate heatmaps for various indicators on the TradingView platform. The library utilizes colored boxes to represent the values of multiple indicators, making it simple to visualize complex data.
We'll now go over the key components of the code:
grad(src) function: This function takes an integer input 'src' and returns a color based on a predefined color gradient. The gradient ranges from dark blue (#1500FF) for low values to dark red (#FF0000) for high values.
boxes_plot() function: This is the main function of the library, and it takes the following parameters:
source: an array of floating-point values representing the indicator values to display
name: an array of strings representing the names of the indicators
boxes_per_row: the number of boxes to display per row
offset (optional): an integer to offset the boxes horizontally (default: 0)
scale (optional): a floating-point value to scale the size of the boxes (default: 1)
The function first calculates the screen size and unit size based on the visible chart area. Then, it creates an array of box objects representing each data point. Each box is assigned a color based on the value of the data point using the grad() function. The boxes are then plotted on the chart using the box.new() function.
Example Usage:
In the example provided in the source code, we use the Relative Strength Index (RSI) and the Stochastic Oscillator as the input data for the heatmap. We create two arrays, 'data_1' containing the RSI and Stochastic Oscillator values, and 'data_names_1' containing the names of the indicators. We then call the 'boxes_plot()' function with these arrays, specifying the desired number of boxes per row, offset, and scale.
Conclusion
Heatmaps are a versatile and powerful data visualization tool with a rich history, spanning multiple fields of study. By implementing a heatmap library in Pine Script, we can enhance the capabilities of the TradingView platform, making it easier for users to visualize and understand complex financial data. The provided library can be easily customized and extended to suit various use cases and can be a valuable addition to any trader's toolbox.
Library "Boxes_Plot"
boxes_plot(source, name, boxes_per_row, offset, scale)
Parameters:
source (float ) : - an array of floating-point values representing the indicator values to display
name (string ) : - an array of strings representing the names of the indicators
boxes_per_row (int) : - the number of boxes to display per row
offset (int) : - an optional integer to offset the boxes horizontally (default: 0)
scale (float) : - an optional floating-point value to scale the size of the boxes (default: 1)
Price Extrapolator with Std DeviationPrice Extrapolator with Deviation Cones - A Powerful Tool for Predicting Future Prices
Subtitle: Discover how this custom indicator can help you forecast potential price movements with greater accuracy, using historical data.
Introduction
Predicting future price movements is always a challenge for traders and investors. However, by using historical data and statistical analysis, it is possible to make educated guesses about the likelihood of certain outcomes. One such tool for predicting future prices is the Price Extrapolator with Standard Deviation Cones. This custom indicator, can help you visualize potential price movements and their associated risks.
In this post, we will explain how the Price Extrapolator with Deviation Cones works, how to adjust its settings to suit your needs, and how to interpret its output. By the end of this article, you should have a better understanding of how this powerful tool can help you make more informed decisions when trading or investing in financial markets.
Understanding the Price Extrapolator with Deviation Cones
The Price Extrapolator with Deviation Cones is a custom indicator that uses historical price data to calculate the average log return and standard deviation of log returns over a specified period. It then uses this information to extrapolate a series of future price points, as well as upper and lower standard deviation bands that form the "deviation cones."
The average log return represents the expected price change, while the standard deviation of log returns provides a measure of the uncertainty or risk associated with the prediction. The deviation cones can help you visualize the range of potential price movements and assess the likelihood of different outcomes.
Configuring the Indicator
To use the Price Extrapolator with Deviation Cones, you will need to configure several input settings:
1. Length: This setting determines the number of historical data points used to calculate the average log return and standard deviation of log returns. A higher value will produce a smoother, less sensitive indicator, while a lower value will make the indicator more responsive to recent price changes.
2. Number of Future Price Points: This setting controls the number of future price points to extrapolate. Increasing this value will extend the deviation cones further into the future.
3. Multiplier: This setting adjusts the tightness of the deviation cones by controlling the standard deviation multiplier. A higher value will result in wider cones, indicating greater uncertainty, while a lower value will produce narrower cones, suggesting more confidence in the prediction.
Interpreting the Output
After configuring the indicator, you will see the following output on your chart:
1. Green Line: This line represents the extrapolated future price points based on the average log return. It provides a central estimate of potential price movements.
2. Red Lines: These lines form the upper and lower bounds of the deviation cones. They represent the range of potential price movements, taking into account the uncertainty associated with the prediction.
When using the Price Extrapolator with Deviation Cones, it is essential to remember that the output is only a prediction based on historical data and should not be taken as a guarantee of future price movements. However, by providing a visual representation of potential price movements and their associated risks, this indicator can help you make more informed decisions when trading or investing in financial markets.
The Extreme Limitations of the Price Extrapolator with Deviation Cones
While the Price Extrapolator with Deviation Cones can be a valuable addition to your trading toolbox, it is essential to recognize its limitations. As with any forecasting tool, it is not infallible and should be used in conjunction with other forms of analysis. In this section, we will discuss the extreme limitations of this indicator and provide insight into how to use it effectively despite these constraints.
1. Reliance on Historical Data
The Price Extrapolator with Deviation Cones relies heavily on historical price data to make its predictions. While this can provide valuable insights into past trends and patterns, it may not accurately predict future price movements in a constantly changing market.
Market conditions can change rapidly, and historical data may not be a reliable indicator of future performance. Economic events, geopolitical tensions, and changes in market sentiment can all influence price movements in ways that may not be captured by historical data alone.
2. Assumption of Lognormal Distribution
The indicator assumes that price returns follow a lognormal distribution, which may not always be the case. Financial markets can exhibit skewness and kurtosis, resulting in distributions that are not symmetrical or normally distributed. This can lead to inaccurate predictions and a false sense of security when relying on the deviation cones.
3. No Consideration of Fundamental Factors
The Price Extrapolator with Deviation Cones is a purely technical analysis tool, meaning it does not take into account fundamental factors that can influence price movements. Changes in company earnings, interest rates, or economic data can significantly impact asset prices and may not be factored into the indicator's predictions.
4. Limited Time Horizon
The indicator only provides predictions for a limited number of future price points, which may not be sufficient for long-term investors or traders with longer holding periods. Additionally, the accuracy of the predictions may decrease as the time horizon extends, due to the compounding effects of uncertainty and the limitations of historical data.
5. Potential for Overfitting
When adjusting the settings of the Price Extrapolator with Deviation Cones, there is a risk of overfitting the model to the historical data. This can result in an indicator that appears to have excellent predictive power on past data but performs poorly on unseen, future data. It is crucial to be cautious when optimizing the settings and use out-of-sample testing to validate the indicator's performance.
Using the Price Extrapolator with Deviation Cones Effectively
Despite these limitations, the Price Extrapolator with Deviation Cones can still be a valuable tool when used correctly. To use this indicator effectively, consider the following tips:
1. Supplement with Other Forms of Analysis: Use the Price Extrapolator with Deviation Cones alongside other technical and fundamental analysis methods to gain a more comprehensive understanding of potential price movements.
2. Diversify your Trading Strategies: Do not rely solely on the Price Extrapolator with Deviation Cones for your trading decisions. Instead, diversify your strategies and consider multiple indicators and methods to reduce the risk of overreliance on a single tool.
3. Be Cautious with Optimized Settings: When adjusting the indicator's settings, be mindful of the risk of overfitting and validate the performance with out-of-sample testing.
4. Keep an Eye on Market Conditions: Stay informed about current market conditions, economic events, and news that may impact your trading decisions. This will help you make more informed decisions when using the Price Extrapolator with Deviation Cones.
In conclusion, the Price Extrapolator with Deviation Cones is a powerful and versatile tool that can aid traders and investors in predicting potential future price movements. However, it is crucial to remember that this indicator has its limitations, which stem from its reliance on historical data, the assumption of lognormal distribution, its disregard for fundamental factors, limited time horizons, and the potential for overfitting. Despite these constraints, when used correctly and in conjunction with other forms of analysis, the Price Extrapolator with Deviation Cones can provide valuable insights and assist in making more informed trading and investing decisions.
By understanding the underlying mechanics of the indicator, adjusting its settings according to your needs, and being aware of its limitations, you can incorporate the Price Extrapolator with Deviation Cones into your trading arsenal effectively. Always remember that no single tool or indicator is infallible, and it is essential to use a diverse range of analysis methods and strategies to navigate the ever-changing financial markets successfully. Happy trading!
4 Pole ButterworthTitle: 4 Pole Butterworth Filter: A Smooth Filtering Technique for Technical Analysis
Introduction:
In technical analysis, filtering techniques are employed to remove noise from time-series data, helping traders to identify trends and make better-informed decisions. One such filtering technique is the 4 Pole Butterworth Filter. In this post, we will delve into the 4 Pole Butterworth Filter, explore its properties, and discuss its implementation in Pine Script for TradingView.
4 Pole Butterworth Filter:
The Butterworth filter is a type of infinite impulse response (IIR) filter that is widely used in signal processing applications. Named after the British engineer Stephen Butterworth, this filter is designed to have a maximally flat frequency response in the passband, meaning it does not introduce any distortions or ripples in the filtered signal.
The 4 Pole Butterworth Filter is a specific type of Butterworth filter that utilizes four poles in its transfer function. This design provides a steeper roll-off between the passband and the stopband, allowing for better noise reduction without significantly affecting the underlying data.
Why Choose the 4 Pole Butterworth Filter for Smoothing?
The 4 Pole Butterworth Filter is an excellent choice for smoothing in technical analysis due to its maximally flat frequency response in the passband. This property ensures that the filtered signal remains as close as possible to the original data, without introducing any distortions or ripples. Additionally, the 4 Pole Butterworth Filter provides a steeper roll-off between the passband and the stopband, enabling better noise reduction while preserving the essential features of the data.
Implementing the 4 Pole Butterworth Filter:
In Pine Script, we can implement the 4 Pole Butterworth Filter using a custom function called `fourpolebutter`. The function takes two input parameters: the source data (src) and the filter length (len). The filter length determines the cutoff frequency of the filter, which in turn affects the amount of smoothing applied to the data.
Within the `fourpolebutter` function, we first calculate the filter coefficients based on the filter length. These coefficients are essential for calculating the output of the filter at each data point. Next, we compute the filtered output using a recursive formula that involves the current and previous data points as well as the filter coefficients.
Finally, we create a script that takes user inputs for the source data and filter length and plots the 4 Pole Butterworth Filter on a TradingView chart.
By adjusting the input parameters, users can configure the 4 Pole Butterworth Filter to suit their specific requirements and improve the readability of their charts.
Conclusion:
The 4 Pole Butterworth Filter is a powerful smoothing technique that can be used in technical analysis to effectively reduce noise in time-series data. Its maximally flat frequency response in the passband ensures that the filtered signal remains as close as possible to the original data, while its steeper roll-off between the passband and the stopband provides better noise reduction. By implementing this filter in Pine Script, traders can easily integrate it into their trading strategies and enhance the clarity of their charts.
Chebyshev type I and II FilterTitle: Chebyshev Type I and II Filters: Smoothing Techniques for Technical Analysis
Introduction:
In technical analysis, smoothing techniques are used to remove noise from a time series data. They help to identify trends and improve the readability of charts. One such powerful smoothing technique is the Chebyshev Type I and II Filters. In this post, we will dive deep into the Chebyshev filters, discuss their significance, and explain the differences between Type I and Type II filters.
Chebyshev Filters:
Chebyshev filters are a class of infinite impulse response (IIR) filters that are widely used in signal processing applications. They are known for their ability to provide a sharper cutoff between the passband and the stopband compared to other filter types, such as Butterworth filters. The Chebyshev filters are named after the Russian mathematician Pafnuty Chebyshev, who created the Chebyshev polynomials that form the basis for these filters.
The two main types of Chebyshev filters are:
1. Chebyshev Type I filters: These filters have an equiripple passband, which means they have equal and constant ripple within the passband. The advantage of Type I filters is that they usually provide a faster roll-off rate between the passband and the stopband compared to other filter types. However, the trade-off is that they may have larger ripples in the passband, resulting in a less smooth output.
2. Chebyshev Type II filters: These filters have an equiripple stopband, which means they have equal and constant ripple within the stopband. The advantage of Type II filters is that they provide a more controlled output by minimizing the ripple in the passband. However, this comes at the cost of a slower roll-off rate between the passband and the stopband compared to Type I filters.
Why Choose Chebyshev Filters for Smoothing?
Chebyshev filters are an excellent choice for smoothing in technical analysis due to their ability to provide a sharper transition between the passband and the stopband. This sharper transition helps in preserving the essential features of the underlying data while effectively removing noise. The two types of Chebyshev filters offer different trade-offs between the smoothness of the output and the roll-off rate, allowing users to choose the one that best suits their requirements.
Implementing Chebyshev Filters:
In the Pine Script language, we can implement the Chebyshev Type I and II filters using custom functions. We first define the custom hyperbolic functions cosh, acosh, sinh, and asinh, as well as the inverse tangent function atan. These functions are essential for calculating the filter coefficients.
Next, we create two separate functions for the Chebyshev Type I and II filters, named chebyshevI and chebyshevII, respectively. Each function takes three input parameters: the source data (src), the filter length (len), and the ripple value (ripple). The ripple value determines the amount of ripple in the passband for Type I filters and in the stopband for Type II filters. A higher ripple value results in a faster roll-off rate but may lead to a less smooth output.
Finally, we create a main function called chebyshev, which takes an additional boolean input parameter named style. If the style parameter is set to false, the function calculates the Chebyshev Type I filter using the chebyshevI function. If the style parameter is set to true, the function calculates the Chebyshev Type II filter using the chebyshevII function.
By adjusting the input parameters, users can choose the type of Chebyshev filter and configure its characteristics to suit their needs.
Conclusion:
The Chebyshev Type I and II filters are powerful smoothing techniques that can be used in technical analysis to remove noise from time series data. They offer a sharper transition between the passband and the stopband compared to other filter types, which helps in preserving the essential features of the data while effectively reducing noise. By implementing these filters in Pine Script, traders can easily integrate them into their trading strategies and improve the readability of their charts.
